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LANGUAGE OF LIFE-GIVING CONNECTION:

THE EMOTIONAL TONE OF LANGUAGE THAT FOSTERS FLOURISHING

CAMPUS SUSTAINABILITY PROGRAMS

by

LINDA ROBSON

Submitted in partial fulfillment of the requirements

for the degree of

Doctor of Philosophy

Department of Organizational Behavior

CASE WESTERN RESERVE UNIVERSITY

May 2015

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CASE WESTERN RESERVE UNIVERSITY

SCHOOL OF GRADUATE STUDIES

We hereby approve the dissertation of

LINDA ROBSON

Candidate for the degree of Doctorate of Philosophy

Committee Chair

Ronald Fry, Ph.D.

Committee Member

Mark Chupp, Ph.D.

Committee Member

David Cooperrider, Ph.D.

Committee Member

Peter Whitehouse, M.D., Ph.D.

We certify that written approval has been obtained

for any proprietary material contained therein.

TABLE OF CONTENTS

List of Tables………………………………………………………………………...…. vii

List of Figures……………………………………………………………………….….. ix

Abstract …….…………………………………………………………………………... x

CHAPTER ONE: INTRODUCTION TO THE STUDY..……………………………... 1

CHAPTER TWO: BACKGROUND..………………………………………………… 10

The Role of Higher Education………………………………………………….. 10

The Language of Fear and Negativity………………………………………….. 13

Sustainability in Higher Education…………………………………………..… 15

CHAPTER THREE: LITERATURE REVIEW………………………………..……... 19

Social Construction…………………………………………………………..… 19

Organizational Change…………………………………………………………. 22

Disconfirmation………………………………………………………….. 26

Anxieties Associated with Learning and Change……………………….. 27

Restructuring…………………………………………………………….. 28

Imitation and Positive or Defensive Identification with a Role Model….. 29

Defensive Identification………………...……………………………….. 31

Scanning: Insight or Trial and Error Learning………………………….. 31

Personal and Relational Re-Freezing (Crystallizing)……………………. 33

Positive Organizational Scholarship..………………………………...………… 36

Positive Organizational Scholarship and Language……………………………. 43

Emotion…………………………………………………………………………. 54

Research Questions and Hypotheses…………………………………………… 59

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CHAPTER FOUR: RESEARCH DESIGN AND METHODOLOGY.……………….. 65

Content Analysis………………………………..…………….………………… 65

Directed Content Analysis……………………………………………………… 67

Theoretical Issues in Content Analysis…………………………………………. 69

Assumptions……………………………………………………………………. 71

Inclusion and Exclusion Criteria……………………………………………...... 71

Confidentiality………………………………………………...………………... 76

Research Design……………………………………………..….……………… 77

Data Collection……………………………………………..…………… 77

Data Analysis…………………………………………………...……….. 83

Reliability ……………………………………………………………...... 90

CHAPTER FIVE: RESULTS……………………………………………………..….… 94

Data Analysis…………………………………………………………..………. 94

Analysis of Emotional Tone Used by Sustainability Programs…………. 94

Analysis of Language by Performance Category……………………...… 96

Analysis of Interview Data by Performance Category………………….. 97

Analysis of Group Meeting Data by Performance Category……………. 99

Analysis of Website Data by Performance Category…………………..... 101

Positive / Negative Ratios by Performance Category………………….... 104

Discourse Families Aggregated by Data Source and Performance

Category …………………………………………………………………

105

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CHAPTER SIX: DISCUSSION…………………………………………………….… 128

Overview............................................................................................................... 128

Support for Hypotheses……………………………………………..…………. 129

Additional Findings…………………………………………………………….. 139

Implications for Future Research ………………………………………………. 147

Implications for Practice………………………………………………………... 150

Contributions of the Study……………………………………………………… 152

Limitations of the Study……………………………………………………...… 153

CHAPTER SEVEN: CONCLUSION…………………………………………………. 155

Appendix A……………………………………………………………………………... 159

References………………………………………………………………………………. 161

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LIST OF TABLES

Table 1. Stages of the Change Process (Schein, 2002)

Table 2. Ohio Colleges and Universities Included in Campus Sustainability Report Card Ranking 2011

Table 3a. Table 3b.

Positive Discourse Categories from Cooperrider et al (2008) Negative Discourse Categories from Cooperrider et al (2008)

Table 4. Positive, Negative, and Neutral Labels Added to Cooperrider et al’s Positive/Negative Discourse Code (2008)

Table 5. Positive, Negative and Neutral Discourse for All Sources of Data by Performance Category

Table 6. Positive, Negative, and Neutral Discourse for Interview Data by Performance Category

Table 7. Positive, Negative, and Neutral Discourse for Group Meeting Data by Performance Category

Table 8. Positive, Negative, and Neutral Discourse for Website Data by Performance Category

Table 9. Positive / Negative Discourse Ratios by Performance Category

Table 10. Number and Frequency of Positive Discourse Labels by Performance Category: All Sources of Data

Table 11. Direct Quotes from Positive Discourse Labels with Highest Frequency: High Performers

Table 12. Direct Quotes from Positive Discourse Labels with Highest Frequency: Moderate Performers

Table 13. Direct Quotes from Positive Discourse Labels with Highest Frequency: Base Performers

Table 14. Number and Frequency of Positive Discourse Labels: Interview Data

Table 15. Number and Frequency of Positive Discourse Labels: Group Meeting Data

Table 16. Number and Frequency of Positive Discourse Labels: Website Data

Table 17. Number and Frequency of Negative Discourse Labels by Performance Category: All Sources of Data

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Table 18. Direct Quotes from Negative Discourse Labels with Highest Frequency: High Performers

Table 19. Direct Quotes from Negative Discourse Labels with Highest Frequency: Moderate Performers

Table 20. Direct Quotes from Negative Discourse Labels with Highest Frequency: Base Performers

Table 21. Number and Frequency of Negative Discourse Labels: Interview Data

Table 22. Number and Frequency of Negative Discourse Labels: Group Meeting Data

Table 23. Number and Frequency of Negative Discourse Labels: Website Data

Table 24. Number and Frequency of Neutral Discourse Labels by Performance Category: All Sources of Data

Table 25. Direct Quotes from Neutral Discourse Labels with Highest Frequency: High Performers

Table 26. Direct Quotes from Neutral Discourse Labels with Highest Frequency: Moderate Performers

Table 27. Direct Quotes from Neutral Discourse Labels with Highest Frequency: Base Performers

Table 28. Number and Frequency of Neutral Discourse Labels: Interview Data

Table 29. Number and Frequency of Neutral Discourse Labels: Group Meeting Data

Table 30. Number and Frequency of Neutral Discourse Labels: Website Data

Table 31. Support for Hypotheses

Table 32. P/N Ratios for High and Base Performance Categories, Comparing Group Meetings and Website Data

Table 33. Chi-square Analysis of All Data Sources

Table 34. Chi-square Analysis of Interview Data

Table 35. Chi-square Analysis of Group Meeting Data

Table 36. Chi-square Analysis of Website Data

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LIST OF FIGURES

Figure 1. Greenpeace 2013 Artic Protection Campaign

Figure 2. Discourse by Performance Category

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ABSTRACT

Insights from Positive Organizational Scholarship (POS) were used as a lens

through which to examine the language used by campus sustainability programs. The use

of positive, negative, and neutral language was explored in ten college and university

sustainability programs and these findings were compared to the performance ranking of

each sustainability program. Directed content analysis was used to analyze one-on-one

interviews, campus sustainability staff meetings, and campus sustainability programs

websites. Taking all sources of data together, high performing sustainability programs

demonstrate a 4:1 positive to negative (P/N) ratio, where moderate performers possessed

a 2:1 P/N ratio and base performers a 1:1 P/N ratio. These findings, associating higher

incidence of positive language with high performing programs joins other scholarship,

which connects higher levels of positive language with higher functioning individuals,

higher team performance, high relational satisfaction, and increased longevity of teams

and dyads. Heretofore, few links between the sustainability domain and POS literature

exist. This study serves as one of the first such bridges.

Keywords: language, Positive Organizational Scholarship, sustainability, flourishing,

performance, love, change, higher education

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CHAPTER ONE: INTRODUCTION TO THE STUDY

In 2009, the American Association of Sustainability in Higher Education

estimated more than 700 institutions were engaged in sustainability efforts, with almost

200 of these institutions employing a full time Sustainability Professional to lead the

initiatives (AASHE, 2009). In less than two years, almost 600 colleges and universities

signed the American College & University Presidents’ Climate Commitment, publically

stating their institution will move toward climate neutrality and committing to actions and

public reporting of progress (AASHE, 2008).

Despite the promising story these numbers suggest, and the leadership exhibited

among several schools, sustainability is a fairly recent development in the higher

education sector (Chronicle of Higher Education, 2006). Each sustainability program

represents a process of subtle demarcation, defining the field.

While technical and operational practices exist, which decrease the

environmental and financial footprints of a higher educational institution, the long-term

goals for sustainability are underpinned by changes in individual and system behavior,

and mindset within the broader campus community (Hart & Milstein, 2003; McKenzie-

Mohr & Smith, 1999). As such, the success of the campus Sustainability Professional,

and his or her program, is predicated on an ability to capture the attention of campus

constituencies and hold their attention long enough to establish new habits and thus long-

term behavior change toward sustainable practices (MacKenzie-Mohr & Smith, 1999). In

this way, sustainability programs, which have been rated as high performers have been

more successful than their counterparts at organizational change within their institutions.

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The success of Sustainability Professionals in higher educational settings is

complicated by the diversity of the incumbents’ occupational backgrounds (AASHE,

2008). The skills and knowledge required is a varied composite, which includes:

teaching and research; business; energy industry knowledge; construction practices,

materials, and processes; transportation; food utilization; waste minimization; and

environmental science (Sharp, 2005; AASHE, 2008, 2010). Embedded within this list of

explicit capabilities is the implicit expectation that Sustainability Professionals will foster

change within the complex systems of universities and colleges. Are campus

sustainability leaders equipped with the skills to transform these systems?

For insight into how to engage campus constituents, the Sustainability

Professional who is unfamiliar with the field of organizational development and change,

will often reference other college and university sustainability programs, benchmarking

what is included under the umbrella term of ‘sustainability’ and collecting data about

how other institutions go about fostering and maintaining sustainable behavior change. It

becomes clear soon after a higher educational sustainability program is launched that

changes in infrastructure alone are not enough to achieve institutional goals or to win the

attention of constituents. To capture attention and motivate campus stakeholders to adopt

sustainable behaviors, campus Sustainability Professionals borrow and mimic techniques

used at other campuses and from environmental non-governmental organizations (NGOs)

(Sharp, 2005). My own experience as a Sustainability Professional suggests that the habit

of looking to colleagues on other campuses as role models for what a sustainability

program should look like- in other words, mimetic isomorphism (DiMaggio & Powell,

1983)- offers researchers in the field of organizational change an opportunity to increase

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the use of effective sustainability communication practices for a wide audience of

campuses if the researchers are able to influence leading programs. This technique has

its shortcomings.

In the literature review contained in Chapter Three of this manuscript, a more

thorough explanation will be presented, but for now it is worth noting that mimicking

role models offers only short term gains, based on Schein’s theory of change (2002).

Schein explains that role models increase the likelihood of rapid new learning through

imitation and identification, but this approach risks that people (or institutions) will learn

things that do not really fit their context or culture. In other words, imitation and

identification provide a quick, but not necessarily lasting, solution.

The adoption and implementation of sustainability principles within an institution

of higher education is an organizational change process and, when viewed through this

lens, a host of methodologies and scholarship becomes available. Organizational change

research has the potential to create a significant and positive impact on sustainability

programs across the nation’s campuses. The central focus of this study is the compelling

discussion advocating the powerful role that language plays in directing our attention and

acting as proxy for our mindset and intentions, which comes from both positive

organizational scholarship (POS) and positive psychology (Schwartz, 1986; Cooperrider,

1997; Fredrikson, 1998; Schmidt, 2005; McKenzie-Mohr & Smith, 1997).

Our language serves as proxy for our thoughts and mindset and our words steer

our vision and that of others, shaping what we will create (Cooperrider, 1997; Gergen,

1994; Ludema et al. 1997). The scholarship of Fredrickson (2003) and Cooperrider and

Sekerka (2003) offer convincing arguments for steering organizational change with

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hopeful and compelling narratives of the system’s desired future. Of interest to this

research is diversity of narratives. The focus is not solely about content, but is equally

about the processes through which sustainability is communicated in higher education

contexts and claims are staked for attention and influence (Meppem, 2000).

The sustainability narrative coming from environmental NGOs regularly utilizes

crisis messaging, and thus draws from and perpetuates emotional responses of fear

(Spence et al, 2011; Feinberg & Willer, 2010; Luntz, 2006; Potter et al, 2006; Hoog et al,

2005; Das et al, 2003; LaTour & Rotfeld, 1997). Many climate change communication

strategies by NGOs and government agencies drew the reasonable conclusion that

because the threat of climate change was perceived as something to worry about in the

future, increasing the fear factor might be a good way of getting people to be more

concerned (Feinberg & Willer, 2010).

For example, on Earth Day 2014, StopGlobalWarming.org sent e-mails

summarizing the recent release of the Intergovernmental Panel on Climate Change

(IPCC) report. The environmental organization’s e-mail subject line was “Climate

Change Report Offers Dire Warning.” The e-mail uses phrasing to describe the report,

such as, “paints a bleak picture” and “dire warning.” When encouraging citizen action,

fear-based language is still used, “the worst is yet to come if no measures are taken,” and

“catastrophic scenarios can still be avoided” (StopGlobalWarming.org April 22, 2014).

In both of these phrases, it is the threat that impacts the reader, not a sense of hope or

possibility for the future.

Another example comes from Green Peace, whose 2013 save-the-Arctic

campaign featured a bedraggled Santa Claus, in a stained undershirt, who threatens to

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cancel Christmas because melting polar ice has decimated his workshop at the North

Pole. In the print ads and short film produced, Santa implores children to get their parents

to sign the petition to express their outrage (Greenpeace, 2013).

Figure 1. 2013 Greenpeace Artic Protection Campaign

(Greenpeace.org.uk, 2015)

An approach built around fear appeals in not completely misguided. Spence, Poortinga,

Butler, and Pidgeon (2011) found that if the ‘psychological distance’ between an

individual and the impacts of climate change is reduced through a first hand experience,

such as experiencing a flooding event which is similar to the sort of impacts climate

change will bring, they are more likely to express concern over climate change and show

a greater willingness to adopt sustainable behaviors, such as reducing energy

consumption or their carbon footprint. Additionally, acknowledging that climate change

is happening, and will cause significant problems for human and natural systems can be a

frightening prospect, however research has shown that deliberate attempts to instill fear

or guilt in people carry a considerable risk of backfiring.

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Studies on fear appeals show the potential for fear to change attitudes or verbal

expressions of concern, but often not actions or behavior (Das et al, 2003). The impact of

fear appeals is specific to both the context and the audience. For those who do not yet

realize the potentially frightening aspects of climate change, people need to first

experience themselves as vulnerable to the risks in some way in order to feel moved or

affected (Das et al, 2003; Hoog et al, 2005; Spence et al, 2011). While fear of a negative

outcome, can be an effective way of promoting behavioral changes, the link between the

threat and the behavior must be personal and direct (Hoog et al, 2005).

Typically, climate change is perceived as neither a direct nor a personal threat,

therefore, scaring or shocking people into recycling is not necessarily the most effective

idea. Fear appeals often fall prey to denial responses (Schmidt, 2005) and are

disempowering, producing feelings of helplessness, remoteness and lack of control

(O’Neill and Nicholson-Cole, 2009). For these reasons, fear is not sufficient to achieve

long-term behavior change in human systems (LaTour & Rotfeld, 1997; Hulme, 2008).

LeDoux (1998) and Goleman and collegues (2001) explain why.

When we are afraid, we are hard-wired to engage in defensive behavior: we run; we

fight; or we freeze. Fear, a response activated and controlled by the amygdala, is in a

different part of the brain than the prefrontal cortex, which supports logical, strategic, and

reasonable decision-making, (LeDoux, 1998; Goleman et al, 2001). The body and brain

react to fear in a “prepackaged” way, shaped by evolution and occurring involuntarily

(LaDoux, 1998, p. 175) and these fear responses take place before the prefrontal cortex

has the chance to start thinking about what to do (LaDoux, 1998; Goleman et al, 2001).

When we are afraid, we dream smaller dreams, we speak less freely, we identify more

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easily with perceptions of an “us,” while growing more distrustful toward anyone we

perceive as one of “them.”

Several researchers have identified the relationship between the positive

emotional affect of communicative gestures and interpersonal interactions links to higher

levels of functioning and performance (Schwartz & Gottman, 1976; Schwartz, 1986;

Gottman, 1994, 1999; Losada & Heaphy, 2004). Fredrikson (1998) echoed the power of

positive affect in fostering systems for higher performance among individuals and teams.

The impact of positive interactions was confirmed by several other scholars who

advocate for emotionally safe or “expansive” and “generative” spaces in support of high

performance, improved levels of satisfaction of organizational members, and better

organizational capacity to deal with increasingly complex environments (Losada &

Heaphy, 2004; Echeverria, 1994; Stacey, 1992, 1996). This brief snapshot of the

scholarship that grounds this study will be expanded upon in later sections of this

dissertation. However, at this point, it is worth highlighting that two decades of research

support the relationship between positive language and higher levels of success in human

systems.

If we apply these rubrics to the sustainability discourse within higher education,

would we find language that leads to higher performance? What is the language of

sustainability currently directing our attention toward? The research described here

addresses these issues by way of answering two questions. First, what is the emotional

tone of sustainability language that is used in higher educational contexts? And second,

how does the emotional tone of campus sustainability narrative relate to the performance

of sustainability programs?

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In this study, language is positioned as the unit of analysis, which is best explored

using descriptive, qualitative methods. Scholars of social issues in management support

this approach and encourage descriptive research in order to bring clarity to ‘complex

social phenomena’ (Rozin, 2001; Margolis & Walsh, 2003). I have explored the language

used by sustainability professionals on ten university and college campuses in the State of

Ohio. The study included three phases. First I conducted a survey of the sustainability

narrative in higher education contexts, collecting data from campus sustainability

websites, interviews with campus sustainability leaders, and sustainability team meetings.

The second phase of the research study included a thematic analysis of the emotional tone

of sustainability language, applying Cooperrider and colleagues’ (2008) code of positive

and negative discourse categories. The code was further developed to capture possible

new categories. In the final phase of this research, I compared results from the thematic

analysis with sustainability program performance rankings, to identify whether emotional

tone of the sustainability discourse impacts the success of higher education sustainability

programs, based on national sustainability program rankings.

Chapter Two provides the context of my inquiry, providing background on the

higher education sector, current debates in the environmental field, and the relationship

between higher education and sustainability. Chapter Three reviews pertinent literature

which represents the groundwork of the theoretical basis from which this research has

emerged, including Organizational Change, Social Construction, Positive Organizational

Scholarship, Organizational Development, and Emotion. Chapter Four details the

research design, including sample, and data collection. In this section the qualitative

methodology employed to analyze data- thematic analysis- is discussed at length as well

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as the code which was used to analyze the data. Chapter Five presents the results of the

analysis, from multiple perspectives. Findings are offered organized by type of data,

performance category, and categorized by the positive / negative discourse code.

Discussion of the findings, implications for future research, contributions to the field, and

limitations of the study make up Chapter Six and final conclusions are offered in Chapter

Seven.

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CHAPTER TWO: BACKGROUND

This chapter provides the context for my study, including factors that created the

need for such a study and the landscape within which my research exists. I begin by

discussing the higher education sector, then the language of fear and negativity, followed

by a review of sustainability in higher education. Throughout this chapter, dynamics,

which are central to this research are highlighted.

The Role of Higher Education

As attention to ecological and social challenges deepen, colleges and universities

are increasingly committed to fostering learning and service for the purpose of

developing solutions for real world problems (Pollack et al, 2009). Calls for profound

changes in higher education are becoming commonplace as both critics and visionaries

lay out a context for education’s role in creating a sustainable future (Bowers, 2001;

Bogotch, 2002; Furman & Gruenewald, 2004; Reid & Petocz, 2006; UNESCO, 2006;

Hammond & Churchman, 2008). Blaze, Corcoran, and Wals (2004) believe higher

education should play the pivotal role in turning society toward a sustainable future. Orr

(2002) agrees, stating that the higher education sector is not only best situated, but also

obliged to lead this transformation of society.

These calls to action land in a sector with structures originated in medieval times

(Hammond & Churchman, 2008). The ‘ancient’ cultures of the academe can

inadvertently stall movement toward sustainable campuses and curricula (Hammond &

Churchman, 2008). From the 1960s to the 1980’s, numerous scholars explored the nature

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of higher education’s culture (for example, Clark, 1963, 1970; Becker, 1963; Chait, 1982;

and Dill, 1982). Dill (1982) found that higher education in the U.S. shared many

essential cultural characteristics with Japanese firms, a popular topic of study for

organizational scholars at the time. He wrote, "Ironically, the organizations in Western

society, which most approximate the essential characteristics of Japanese firms are

academic institutions. They are characterized by lifetime employment, centralized

decision making, individual responsibility, infrequent promotion, and implicit, informal

evaluation" (Dill, 1982).

The holistic, systems-oriented nature of sustainability, and its programs of

change, has often bumped up against the pillars of academic disciplines, which are

reinforced by professional bodies, career structures, and criteria for promotion and

advancement (Dill, 1982; UNESCO, 2002; Patterson, 2007). Since the 1980’s, academe

has been changing, gradually moving away from cultures of inquiry toward more

corporate models, defining managerial techniques based on strategic planning, marketing

and management control in higher education settings (Tierney, 1997,1988). There is an

increasing centralization of decision-making, marginalizing academicians from decisions,

and reducing the multiplicity of institutional views (Tierney, 1997, 1988; Murray &

Dollery, 2005). Multiple, and often contradictory, forces influence academia across

several aspects of these complex systems, pitting ethos and inquiry against resource

management (Sterling, 2004). Universities are increasingly reliant on fee charging,

research performance, and links with industry or government for their survival

(Marginson, 2000; Clugston, 2004), a practice that may impede the creation and

transformation of sustainability practices.

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Understanding the barriers inherent to universities, which inhibit innovation and

whole-system engagement, is important to this study. One example is the increasing lack

of a sense of collective community in an increasingly competitive and isolated

environment (Doyle & Hind, 1998; Churchman, 2004; Hammond & Churchman, 2008).

This relates to the tendency for competing university priorities, which serve to undermine

innovative and systemic programs (Thomas, 2004; Hammond & Churchman, 2008;

Rowe, 2007). While university priorities are often established in a “top-down” manner,

organizational change requires goals to be shared by members of the community and

developed through face-to-face discourse and discussion (Senge, 1990; Meadows, 1996;

Fien, 2002).

Isolated, compartmentalized, or piecemeal sustainability reforms- in other words

“bolt-on” sustainability- will not support an adequate or effective response to the current

ecological, ethical, and social concerns that comprise sustainability (Thomas, 2004; Orr,

2006; Rowe, 2007). Yet we know that campus sustainability programs are rarely

embedded throughout institutional operations and culture in higher education, often

remaining relegated to individual initiatives such as recycling or green buildings (Orr,

2006; Rowe, 2007).

The research described throughout this manuscript was initially inspired by the

frontiers and opportunities I saw in the higher educational sustainability field, such as 1)

What are campus Sustainability Professionals missing; 2) What skills or knowledge must

they acquire; and 3) In what ways must they interact with their institutions for more

comprehensive and systemic change? When I acted as a Sustainability Professional, I

came to the field with a theoretical and practical grounding in organizational change and

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development and could see the benefits of brining this body of knowledge to

sustainability in higher education.

In particular, I was curious about what ways the domain of positive organizational

scholarship (POS) could benefit and inform campus Sustainability Professionals. My

specific interest was in the role language plays as a tool in change.

A favorite quote from Henry David Thoreau is, “All good things are wild, and

free.” Language is as such. Language and the larger narratives it builds, is inherently

democratic in its availability. No matter how vast or limited resources possessed by a

sustainability program are, the Sustainability Professional always has choices about the

language she uses to guide and direct the attention of her audiences, the conversations she

has with other members of the sustainability initiative on her campus, and in crafting the

overall narrative of the sustainability program. Language can draw us closer to one

another, establish a sense of “we” and fuels the vision of what might be. It is a powerful

change technology , evolved over millennia, capable of changing the way people think,

how they act, and what they feel (Pagel, 2011).

The Language of Fear and Negativity

Language is at the heart of my inquiry and has been of growing interest among

others working with environmental organizations, political campaigns, and organizational

change. Since the mid 1990s, environmental insiders and critics alike have questioned the

methods used by large environmental organizations (Tokar, 1997; Schellenburg &

Nordhaus, 2004, Nordhaus & Schellenburg, 2007; Luntz, 2006) and have cautioned

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environmental organizations that unless the divisive approach is adapted to keep up with

its shifting target audience, environmentalism may well make itself irrelevant. (Luntz,

2006; Werbach, 2009; Shellenberger & Nordhaus, 2004; Norhaus & Shellenberger,

2006).

Schmidt (2005) characterizes environmentalism’s mindset as being data-focused,

“us versus them”, dualistic, punitive, regulatory, and fear-based. The emphasis on

compliance, regulation, and policies, while seemingly necessary for corporate polluters

from the 1970’s and 1980’s is characteristic of a deeper mindset which holds people to be

generally untrustworthy, out for themselves, and mal-intentioned (Hart & Milstein,

2003). Using focus groups and polling research methods, Luntz (2006) reports Americans

find the fear-based and us-versus-them messaging as being divisive, which inhibits

adoption of pro-environmental behaviors.

Kegan and Lehay (2001) find virtue in complaining, whining, and other

negatively toned language. While these researchers admit negatively toned language

lacks transformative ability, it allows people to let off steam, and serves a relational,

group dynamic function, allowing people to connect around a common enemy. When

people are disappointed, they feel less alone when they find allies who share their

perspectives, and therefore negative language and negative talk among colleagues can

play an important role in communication (Kegan & Lehay, 2001). Fear-based language

is widespread, complaining exists across contexts, and it possesses something essential:

passion. “Where there is passion, there are also possibilities for transformation. People

don’t complain if they don’t care.” (Kegan & Lehay, 2001, p. 20). Baumeister and his

colleagues (2001) agree, finding negative circumstances take a more significant toll on

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our senses, compared to positive experiences, and therefore our reactions to, and the

impact of, negative life events is stronger.

Fear-based communication strategies remain central in advertising, politics, and

entertainment (Shellenberger & Nordhaus, 2004; Nordhaus & Schellenberger; 2007).

LeDoux (1998) reminds us how powerful fear is, inextricably linked to our perceptions of

survival. In organizational settings, many managers would argue that some degree of fear

is a good thing, motivating employees to follow cultural norms and perform at higher

levels than if they did not feel some insecurity about their positions (McManus, 2006;

Kotter, 1996; 2008; Pechmann & Reibling, 2006,). Marketers use the “fear appeal” to

stimulate our innate drive for status to sell everything from financial services to

moisturizers (Lawrence & Nohria, 2002; McManus, 2006; Potter et al, 2006). The

entertainment industry uses fear appeal as the basis of television programming (Potter et

al, 2006), and fear has been a tool leveraged by politicians for centuries (Machiavelli,

1513; McManus, 2006; Pechmann & Reibling, 2006; Westen, 2008). If fear is effective

at capturing attention, and influencing decision making among individuals, does it also

drive successful systemic change? This study sheds light on this very question, and in

later sections reveals what emotional tones, and in what ratio, are associated with high

performance.

Sustainability in Higher Education

In 1987, the Brundtland Commission Report popularized "ecologically sustainable

development" as a means for simultaneously dealing with economic and ecological

problems (World Commission on Environment and Development, 1987). The 1992 Earth

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Summit in Rio de Janeiro further cemented international commitment to ecologically

sustainable development through treaties for dealing with ozone depletion, global

warming, and declining biodiversity (Stern et al, 1992). Sustainability has been defined

in a variety of complimentary ways, distinct to the sectors engaging in it. Broad

agreement exists that it is “the ability to meet the needs of today, without compromising

the ability of future generations to meet their needs” (World Commission on

Environment and Development. 1987). Elkington (1994) offers a corporate-specific

definition, describing the sustainable firm as one which “contributes to sustainable

development by delivering simultaneously economic, social, and environmental

benefits—the so-called triple bottom line.'' Cortese (1993) provides a definition

particularly tailored to higher education, calling for learning environments which fosters

awareness, knowledge, skills and values to achieve a future where current and future

generations achieve good health, economic security, social fairness and stability while

restoring and sustaining the Earth's life support systems.

A definition I find particular affinity with comes from Ehrenfeld (2008) who

suggests the word ‘sustainability’ itself inspires short term thinking and development of

strategies concerned with reducing harm, but not necessarily creating or developing value

or improvement. Ehrenfeld encourages us to diverge from this ‘be less bad’ mindset and

offers a rather hopeful, future-oriented term: flourishing. Ehrenfeld offers an expanded

and generative definition for sustainability: sustainability is the possibility that humans

and all other life will flourish on Earth for all time (Ehrenfeld, 2008; Hoffman &

Ehrenfeld, 2013; Laszlo & Brown, 2014).

Many in the higher education sector believe we are in the midst of revolutionary

17

change. In just the past few years, the threat of global warming has shifted in the United

States from a distant worry to a present and intense national public conversation.

Business leaders and policy makers are responding with new processes and products and

markets are shifting dramatically. These shifts have been rapid by any measure and they

challenge American higher education to keep pace, re-design, and ultimately to lead in

the realm of the environment and sustainability. American higher education produces 30

percent of the world’s scientists and a remarkably large percentage of the world’s

business, diplomatic and government leaders (Cortese, 1993). But even institutions as

accomplished as U.S. colleges and universities change at different speeds and in different

ways.

In 2008, the Association for the Advancement of Sustainability in Higher

Education (AASHE) reported the changes on educational campuses needed to embrace

the new energy economy began long ago, however, they may actually be lagging in

higher education overall. AASHE’s findings differed for campus leadership and

operations. They found that presidents, administrators and physical plant managers value

sustainability; they speak to it, hire the staff to support it, and the campuses they lead are

steadily becoming “greener”. But at the same time, the educational programs offered to

students have not reflected the institutional values (AASHE, 2008). From 2001 to 2008

comparison of the curricular and academic dimensions of sustainability showed no

significant gains, despite the growing depth of the global warming challenge and what it

means to future professions and their related disciplines (AASHE, 2008; Coyle, 2008).

To facilitate campus cultures, which embrace sustainability embedded throughout

the institutional culture, campus sustainability programs, and the Sustainability

18

Professionals who drive them, must necessarily see the whole rather than simply the parts

of the institutional system. Sustainability efforts on campuses must take into account

additional characteristics of campus life, such as the diversity of services and functions of

a campus, to include the different meanings the campus may attribute for different

stakeholders. Chapter Three presents a review of literature from management and

psychology, which speak to these tasks, and form the theoretical basis of this study.

19

CHAPTER THREE: LITERATURE REVIEW

This chapter provides a review of the literature relevant to this study on the

language of sustainability in higher educational contexts and the impact the emotional

tone of this language has on sustainability program performance. Each section of this

chapter includes an overview of the bodies of literature, which are pertinent to my

research and includes linkages to the issues and research that are most relevant to my

study. The review begins with a discussion of social construction, followed by

organizational change, positive organizational scholarship (POS), POS and language, and

emotion.

Social Construction

Perhaps to a far greater extent than is generally acknowledged, human systems

create their realities through symbolic and mental processes (Schwartz, 1986; Shotter,

1993). Human systems mentally and verbally project expectations and images ahead of

themselves, which can serve to bring the future powerfully into the present as a

mobilizing agent (Cooperrider, 1997). Images are operative, virtually everywhere. Every

system holds conscious and subconscious self-images, images of its own potential and

desired future, as well as the potential of the others. It has been argued (Barrett &

Cooperrider, 2001) that every organization, product, or innovative service began as an

image, the spark of an idea, which was developed and nurtured into fruition. In this

sense, positive images build hope and momentum, and are essential elements in

organizational change, as powerful tools for a new narrative.

20

A central premise of social construction is that knowing is created and takes place

through interactions within and with human systems (Cooperrider et al. 2003). In other

words, human systems are the broad products of agreement of the members of the

system. As such, what can be called the culture of a system-- the patterns of actions,

production, or interactions—is not fixed, but remains a dynamic process of construction

based on dialogue, interpretations, and narratives, in iterative cycles of re-affirmation

(Barrett et al. 1995; Cooperrider et al. 2003). The actions taken within human systems

are done so within an agreed upon context, predicated on the stories, beliefs, and meaning

making of each system. The stories, beliefs, and meaning making are embedded in the

language of the system (Gergen, 1982; Ludema et al. 1997; Cooperrider et al. 2003;

Barrett et al. 1995), thus making language one of the most powerful vehicles

communities have for changing social action (Cooperrider et al. 2003).

Through this constructionist epistemological lens, the development of

vocabularies of hope becomes an essential methodological tool for crafting positive

changes within organizations. In this perspective, words are not pictures, but rather tools

and navigation devices (Barrett et al. 1995), allowing members of systems to coordinate

ongoing relations with one another. Through the combination of cognition and emotion,

generative images and vocabularies of hope become powerful catalysts for change

(Cooperrider, 1997; Ludema et al. 1997). Social construction is a useful theoretical and

methodological lens to explore the role of language in creating positive organizational

change.

21

Constructionism is based on the notion that knowledge is not a product of

empirical observation as more positivist paradigms would suggest, but rather knowledge,

like language, is a social artifact (Ludema et al. 1997). Three tenets of social

construction are particular helpful for this conversation, integrating positive imagery and

positive language (Berger & Luckmann, 1967; Gergen, 1982, 1994; Astley, 1985; Schein,

1985; Unger, 1987; Clegg, 1990; Weick, 1995; Ludema et al. 1997).

1. The pragmatics of language: Words do not gain their “truth value” by

accurately describing the world (picture theory of language); rather they

gain their power by virtue of their function within sets of relationships and

as public activity (Wittgenstein, 1968; Barrett et al. 1995). Thus,

vocabularies that offer positive images and hopeful possibilities for new

ways of relating function are powerful resources for strengthening social

and organizational systems.

2. Language, knowledge, action: Constructionist views a direct and

simultaneous link between language, knowledge, and action. All language

sustains certain kinds of knowledge, to the exclusion of others, and all

knowledge sustains certain patterns of activity, to the exclusion of others.

Thus, the more hopeful the available vocabularies, the more positive will

be the forms of social action and organizing that they support.

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3. Methodology: Social construction encourages a generative theoretical

approach. Because constructionism is based on the belief that “words

create worlds,” methods of inquiry, which highlight and establish future

orientated and optimistic visions of social and organizational life are

sought out. Rather than critiquing “Does this method efficiently eliminate

the opposition?” the question for evaluating good method becomes, “To

what extent does this method stimulate moral dialogue about how we can

and should organize ourselves, and to what extent does it present

compelling new images and possibilities for collective action?”

These three tenets in conjunction with the positioning of dialogue, made possible

by language, is one of the most powerful resources in any system for changing social

order. Therefore, changes in linguistic practices hold profound opportunities for change

in social systems (Cooperrider et al, 2008). Through the application of social

construction, language becomes one of the most democratic and powerful tools of

organizational change, accessible by all, and powerful in the creation of positive change.

Organizational Change

A review of leading organizational development (OD) and change theories is

necessary for any discussion about the success of campus sustainability programs, which

are implicitly and explicitly comprehensive and systemic change initiatives. The

following section reviews three theories, which have informed and shaped this research:

Kotter; Schein; and POS.

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John Kotter and Edgar Schein are two practitioners in the world of organizational

change worth mentioning in any conversation about change in complex systems, due to

their influence on the field, and- specifically relevant to this research- because of their

assessments of the role of anxiety and fear in change processes. We begin with Kotter.

One of the pertinent aspects of Kotter’s work is to eliminate the “existing

mindset,” which uses fear to build a sense of urgency as an unfreezing fulcrum in

organizational change (1996, 2008). Whereas Schein, an organizational development

theorist, proposes that fear is necessary and natural in organizational change (1995,

1999).

The emphasis of Kotter’s work is on overcoming complacency in order to achieve

excellence. As an organizational strategist, he prescribes creating a sense of urgency, not

only as the first step in any change process, but because “without belief that the status

quo is unacceptable, it is difficult to make any progress on a major change effort”

(Kotter, 1996, p.46). Kotter espouses the use of fear to motivate change, like the

examples provided in earlier sections of advertisers and environmental NGOs. Kotter

advises managers to use urgency proactively and goes so far as to suggest leaders create

fictional crises or allow for real crises to emerge, such as allowing a financial loss, to

motivate teams to achieve better performance (Kotter, 1996, 2008; Cooperrider &

Sekerka, 2006). Despite specific examples and suggestions of turning up the heat on an

organizational sense of urgency, Kotter does not describe any differentiation between

creating a sense of urgency and a sense of fear, or how much urgency is effective versus

levels of urgency which lead to anxiety, potentially stalling change efforts.

24

Kotter identifies urgency (rather than passion, learning, engagement, or meaning)

as the antidote to employee complacency. He offers that, in a complacent environment, a

leader should “…relentlessly bombard employees with information about problems”

(p.45), or expose managers to negative data and unsatisfied stakeholders will be the

disconfirming data necessary to instigate new behaviors (1996). Kotter created a guide

(2008) solely dedicated to leveraging urgency, which focused managers’ and leaders’

attentions on their organization’s need to overcome a lack of urgency and an excess of

complacency. Kotter’s view of organizations is not shaped by a belief that “everything is

a mess, but instead, that the world contains great opportunities and great hazards” (2008,

p. 129).

Despite his recurring stance advocating for the power of urgency, Kotter’s

perspective is holistic. Highly successful behavior change happens primarily by speaking

to people’s feelings (Kotter & Cohen, 2002, p. 194). In successful change efforts, people

find ways to help others see the problems or solutions in manners that influence

emotions, not just thoughts. “Fear can produce movement. It can dynamite a cement

wall, but we have yet to see great transformations launched with fear as the primary and

sustaining force.” (Kotter & Cohen, 2002, p. 28).

Organizational theorist Edgar Schein, like Kotter, is also concerned with creating

organizational excellence, however he offers a different approach to achieve this goal.

Schein counsels practitioners (1999; 1987; 1993) about designing and leading

transformational organizational change based on his early work in clinical and social

psychology, which dealt with attitude changes that had occurred in military and civilian

prisoners of the Chinese Communists during the Korean war (Schein 1956, 1961, 1968).

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Schein was inspired by Lewin's three-stage model of change (1947), which

depicted a process of un-freezing, changing, and re-freezing (or crystallizing), and this

model became the theoretical foundation upon which Schein’s own change theory was

built (Schein, 1987, 1993, 1995, 1996, 1999a, 1999b, 2002). Lewin's basic model of

change prompted Schein to develop a range of insights and new concepts that enriched

change theory and made change dynamics more understandable and manageable.

Central to Schein’s approach was to see that human change, whether at the

individual or group level, was a profound psychological process that involved unlearning,

with some degree of loss of ego identity and difficult re-learning as one cognitively

attempted to restructure one's thoughts, perceptions, feelings, and attitudes (Schein, 1993,

1995, 1999a, 1999b, 2002). Schein’s model of change, presented in Table 1, is

comprised of three stages-- un-freezing, changing, and re-freezing-- with sub-stages:

disconfirmation; induction of guilt or survival anxiety; creation of psychological safety or

overcoming learning anxiety; cognitive redefinition; imitation and positive or defensive

re-identification with a role model; scanning, insight, or trial and error learning; and

finally, personal and relational re-freezing (Schein, 1993; 1995; 1999a; 1999b, 2002).

Each of these stages is described in greater detail below Table 1.

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Table 1. Stages of the Change Process (Schein, 2002) Stage 1. Unfreezing: Creating the motivation to change Disconfirmation Creation of survival anxiety or guilt Creation of psychological safety to overcome learning anxiety Stage 2. Changing: Learning new concepts, new meanings, and new standards Imitation of and identification with role models Scanning for solutions and trial-and-error learning Stage 3. Refreezing: Internalizing new concepts, meaning, and standards Incorporating into self-concept and identity Incorporating into ongoing relationships and groups

Note. Adapted from “Models and Tools for Stability and Change in Human Systems,” by Edgar H. Schein, 2010. Reflections, 2, p. 36. Copyright 2002 by the Society for Organizational Learning and the Massachusetts Institute of Technology.

Disconfirmation. Like Kotter (1996, 2008) Schein believes that all forms of

change (a term he uses interchangeably with learning) begin with some form of

dissatisfaction or frustration generated by data that dis-confirm previously held

perceptions of reality, expectations, or hopes (1995; 1999). This may be adaptation to

changing environment, evolving sector regulations, or shifting customer desires.

Disconfirming information can also be genuinely creative and generative learning, a

desire to know more, to be more. No matter the shape it takes, disconfirmation of some

previously held belief is the launch pad and pre-requisite for learning and change to occur

(Schein, 1995; 1999).

While disconfirmation functions as the primary driving force in learning, the

information is confronted with challenges before it is accepted. As information that

challenges our “quasi-stationary equilibria”, it is natural for individuals and large,

complex systems alike to resist disconfirming information (Schein, 1993; 1995; 1999;

1999). The information can be ignored, dismissed as irrelevant, or, as has been the case

27

with sustainability, the validity of the information is questioned or fully denied (Schein,

1999).

In order to continue through the following stages and undergo a complete change

process, the information must be accepted and connected to something the system cares

about. As such, the disconfirmation arouses "survival anxiety" or the feeling that if

change does not occur, the system will fail to meet its needs or fail to achieve its goals or

ideals ("survival guilt") (Schein 1999).

Anxieties Associated with Learning and Change. It is at this stage in the learning

process that Schein’s theory of change diverges from Kotter’s (Kotter, 1996, 2008;

Schein 1968, 1999, 1999). Schein acknowledges the natural, and often subconscious,

grief reaction that results from accepting disconfirming information and leaving a

previously held belief, habit, or mindset. While the grief reaction from disconfirmation

may be subtle, anxieties arise. In light of the disconfirming data, feelings of survival

anxiety or guilt may be present, as the goals or wellbeing of the system may now seem to

have been compromised prior to the new information, practice, or mindset. Additionally,

the sense of defensiveness that can arise when presented with disconfirming information

is learning anxiety, which is the feeling that if we allow ourselves to enter a learning or

change process, if we admit that something was wrong or imperfect, we lose our grip on

effectiveness, self-esteem, and maybe even our identity. Learning anxiety is the

fundamental restraining force, which increases in direct proportion to the amount of

disconfirmation, leading to the maintenance of the equilibrium by defensive avoidance of

the disconfirming information.

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This process can be conceptualized in its own right as creating for the learner

some degree of "psychological safety" (Schein, 1995; 1997). Attention to this stage and

skill in decreasing learning anxiety while increasing psychological safety is the key to

producing change (Schein, 1987; 1993; 1995; 1996; 1999; 1999). Schein argues that

unless sufficient psychological safety is created, the disconfirming information will be

denied or in other ways defended against, no survival anxiety will be felt, and learning

anxiety will work to keep change at bay (Schein 1993; 1995; 1999; 1999). The key to

effective change management, in Schein’s view, becomes the ability to balance the

amount of perceived threat and anxiety produced by disconfirming data with enough

psychological safety to allow the change target to accept the information, feel the survival

anxiety, and begin moving toward change and learning.

Framed in this way, it becomes clear that in Schein’s belief system, change

management involves a delicate approach, balancing various tactics that change agents

employ to create psychological safety. Schein uses examples of breaking down the

learning process in to small steps and creating small groups or teams to address change to

address change together. Providing practice fields in which errors are embraced as

valuable learning rather than failure or the creation of parallel learning systems that allow

some relief from day to day work pressures, and use of a shared positive vision to

encourage the learner are additional ways of increasing learners’ sense of security

(Schein, 1995).

Restructuring. Cognitive restructuring, sometimes referred to as re-framing, is

the process by which we learn something new. This is the phase of learning when new

information is taken in, resulting in one or more of the following modes of redefinition:

29

1) semantic redefinition, which is learning that words can mean something different from

what we had assumed; 2) cognitive broadening is when we learn that a given concept can

be much more broadly interpreted than what we had assumed; and 3) new standards of

judgment or evaluation--we learn that the anchors we used for judgment and comparison

are not absolute, and if we use a different anchor our scale of judgment shifts (Schein,

1995).

According to Schein, the key to sustained change is about how new information is

assimilated by learners. The new information that makes any or all of these processes

possible comes into us by one of two fundamental mechanisms. The first approach—

identification with a role model—offers easier short-term gains in assimilation of the new

information but with questionable long-term results (Schein, 1995; 1999). The second

approach—learning through trial and error, or scanning—while a longer process, results

in more thorough assimilation of the new information (Schein, 1995; 1999; 1999).

Imitation and Positive or Defensive Identification with a Role Model. As

described above, cognitive re-definition occurs when the learner has become unfrozen,

has accepted disconfirming information as is motivated to change, and has, therefore

opened him or herself up to new information. The most basic mechanism of acquiring

new information that leads to cognitive restructuring is to discover in a conversational

process that the interpretation that someone else puts on a concept is different from one's

own. If one is motivated to change, like when the factors described above have been

operating, he or she may be able to "hear" or "see" something from a new perspective.

Schein’s dissertation and early body of work focused on this area of change, in his studies

of what has colloquially been labeled "brainwashing" with POWs held by Chinese

30

communists (Schein 1956, 1961, 1968). The POWs were judged "guilty" yet felt

innocent (disconfirmation). The prisoners were finally able to admit their guilt when they

could identify with their more advanced cell mates sufficiently to realize that the

concepts of "crime" and "guilt" were defined differently by the Chinese communists. One

was guilty because a crime was defined as "any action that could be harmful to the

communists" even if no harm had occurred. A postcard home, could conceivably contain

information that would help the enemy; so sending the postcard was an act of espionage,

no matter what information it contained. The sender had to learn to appreciate and

confess his or her guilt. Moreover, being born into the wrong social class was a crime

because middle class attitudes could be very harmful to the communist cause (Schein

1956; 1995). Semantic redefinition, cognitive broadening and changing standards of

judgment were all present in this process.

Only by recognizing this potential for harm, confessing one's guilt, and

acknowledging the incorrectness of one's social origins could one hope to learn how to be

a good communist or to be released from jail. Once one had accepted the new cognitive

frame of reference and learned the new definitions and standards, one could make rapid

progress in re-education and remove the heavy disconfirming pressure. The key to the

whole process, however, was to identify psychologically with other prisoners (role

models) who had already made the cognitive shift and learning to see the world through

their eyes (Schein, 1995). Outside of this painful context, there are parallels for

organizational change. In many types of organizations, identification takes the form of

mentoring programs. The mentor is often both a source of psychological safety and the

role model to facilitate cognitive redefinition (Schein, 1968; Van Maanen & Schein,

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1979).

Defensive identification. Defensive identification is an arguably less common

process that occurs when the learner is a captive in a hostile environment in which the

most salient role models are the hostile captors, such as prison guards, or in more

common situations, authoritarian bosses. Defensive identification, or identifying with the

aggressor (Bettelheim, 1943) was described in relation to Nazi Concentration Camps

where some prisoners, in the face of survival anxiety, took on the values and beliefs of

the guards and maltreated fellow prisoners. Genuine new learning and change occurred,

but, of course, in a direction deemed undesirable by others (Schein, 1995).

While an extreme and uncomfortable example, this serves to remind practitioners

that unfreezing creates motivation to learn (adaptation, not inquiry), but does not

necessarily control or predict the direction of learning. If the only new information

available is from salient and powerful role models, learning will occur in that direction.

One of the key elements of a managed change process is, therefore, what kind of role

models one makes available to the learners once they are unfrozen. In the event that

there are no positive role models or one wants the learning to be more genuinely creative

and self-identified with by the learner, the conditions for “scanning” must be created

(Schein, 1995; 1999).

Scanning: Insight or Trial and Error Learning. A learner or “change target” can

be highly motivated to learn something, yet have neither appropriate role models nor

initial feelings for where the answer or solution might lie. The learner then searches or

scans his or her environment by reading, looking on line, traveling, talking to people,

hiring consultants, entering therapy, going back to school, to expose him or herself to a

32

variety of new information that might reveal a solution to the problem. Alternatively,

when the learner finally feels psychologically safe, he or she may spontaneously

experience an insight that spells out the solution. Practitioners and change agents count

on such insights because of the assumption that the best and most stable solution will be

one that the learner has invented for him or herself. Once some cognitive redefinition has

taken place, the new mental categories are tested with new behavior which leads to a

period of trial and error, either reinforcing assimilation of the new behaviors, new

information, new mindsets, or starts a new cycle of disconfirmation and search.

Schein notes that in the process of search, if role models are readily available,

they will most likely be used. Identification is thus an efficient and fast process, but it

may lead to solutions that do not stick because they do not fit the learner's total

personality. If one wants to avoid that, one must create learning environments that do not

display role models, thereby forcing the learner to scan and invent his or her own

solutions.

It is this dynamic, to rely on identification with a role model, that explains why so

many consultation processes go awry. The consultant, by design or unwittingly, becomes

a role model and generates solutions and cognitive categories that do not really fit into

the culture of the client organization and will therefore only be adopted temporarily. A

similar result occurs when organizations attempt to check on their own performance by

"benchmarking," or comparing themselves to a reference group of organizations and

attempting to identify "best practices." The speed and simplicity of that process is offset

by two dangers. First, it may be that none of the organizations in the reference set have

scanned for a good solution so the whole set continues to operate sub-optimally, or,

33

second, that the identified best practice works only in certain kinds of organizational

cultures and will fail in the particular organization that is trying to improve itself. In other

words, those who are modeling themselves on others can attempt to learn things that will

not survive because they do not fit the personality or culture of the learning system. For

change to remain more stable it must be re-frozen or crystallized.

Personal and Relational Re-freezing (Crystallizing). Central to the success of

learning new behaviors is the congruence with the rest of the learner’s suite of behaviors

and personality. If a new practice or mindset does not fit the broader and existing

constellation of beliefs and habits, it will launch new rounds of disconfirmation that often

lead to unlearning the very thing one has learned. Schein makes his teaching point clear

to practitioners: for a crystallization or re-freezing to occur it is best to avoid

identification with a role model and encourage scanning so that the learner will pick

solutions that fit him or her. Moreover, when an old behavior has been supported or

enabled by a group, relational re-freezing must occur, thus, it is best to train the entire

group that holds the norms (Schein, 1968; 1995; 1999).

To summarize, both Kotter and Schein have put forth change theories, which can

be characterized as disconfirming. The disconfirmation perspective is deficit-focused,

identifies dis-satisfactions, and focuses on fixing errors. From my vantage point, it is

indiscernible whether sustainability thought-leaders, framing current sustainability

discourse, are intentional in their use of disconfirming change strategies, but it is clear

that by using visual images and stories of planetary crisis, collapse, or threats to national

security in order to motivate society-wide change, we are focusing attention on global

scale, systemic problems that need solving, rather than creating new opportunities or

34

hopeful narratives to lead us forward.

Disconfirming change theories have deep roots in the academic community and

are widely applied by practitioners, however with mixed results. This may be due to the

fact these theories are not transformational, but rather reactions to dysfunction (Sekerka

& Fredrickson, 2008).

Disconfirming theories of change, and the beliefs about human systems

underpinning these approaches to change, are no longer the only game in town. What

would happen to our change practices if we began all of our research or our practical

interventions with the positive presumption—that organizations, as centers of human

relatedness, are alive with infinite constructive capacity (Cooperrider & Whitney, 2001)?

Positive Organizational Scholarship (POS) is trying to answer that question.

POS offers the OD field balance through its affirmative stances. Through the POS

lens, organizational development and change is a purposeful evolution (Sekerka &

Fredrickson, 2008). This confirming, constructionist approach to change, which is

interested in contributing to the best of organizational life, is what the research detailed in

this manuscript is aligned with.

The field of (POS) offers a theory of change and for how we view human systems

and organizations. Underpinning POS is a desire to better understand how to build

contexts that enable human flourishing (Dutton & Heaphy, 2003). POS views

organizations as being alive and hubs of human relatedness (Cooperrider & Sekerka,

2006) and considers organizations as being energized by and drawn toward desired

futures, not just away from dis-satisfaction. The POS orientation focuses on developing

strengths of the organization, including high quality relationships (Dutton & Heaphy,

35

2003) and fostering narratives and vocabularies of hope (Ludema et al, 1997; Barrett,

1990). Through scholarship and practical applications, POS addresses multiple levels of

system and employs strategies focusing on idealized images of the future (Fredrikson,

2003; Cooperrider & Sekerka, 2003).

One particular area where a confirming theory of change differs from

disconfirming counterparts is in the privilege given to fear and anxiety in the change

process. In POS and confirming change, positive emotional affect and psychological

safety are highlighted. An early example comes from Schwartz (1986), who offered an

early paper on the power of positive emotional affect among individuals, arguing a 1.7:1

ratio of positive to negative ratio of internal dialogue or self-talk was found among higher

functioning participants in his sample. Fredrickson (1998) posited that positive emotions

serve to broaden one’s momentary “thought-action repertoires,” which in turn has the

effect of building that individual's physical, intellectual, and social resources, allowing

them to think more creatively, see more options, and read their environment with more

accuracy. In later work, Fredrickson (2001) further developed this idea, advocating for

the development of more emotionally safe or “expansive” and “generative” spaces in

support of higher performance, improved levels of satisfaction of organizational

members, and better organizational capacity to deal with increasingly complex

environments. More discussion of the positive emotional affect is described throughout

the rest of this chapter.

36

Positive Organizational Scholarship (POS) & Positive Psychology

Positive psychology is the scientific study of the strengths and virtues that enable

individuals and communities to thrive (Duckworth et al, 2005; Peterson et al, 2003). POS

seeks to understand what represents the best of the human condition, emphasizing

positive deviance, exploring enablers, motivation, and effects associated with positive

interventions (Cameron et al, 2003). These complimentary fields were founded on the

belief that people want to lead meaningful and fulfilling lives, to cultivate what is best

within themselves, and to enhance their experiences of love, work, and play (Seligman,

2007).

Positive psychology has three central concerns: positive emotions, positive

individual traits, and positive institutions. Understanding positive emotions entails the

study of contentment with the past, happiness in the present, and hope for the future.

Understanding positive individual traits consists of the study of the strengths and virtues,

such as the capacity for love and work, courage, compassion, resilience, creativity,

curiosity, integrity, self-knowledge, moderation, self-control, and wisdom. Understanding

positive institutions entails the study of the strengths that foster better communities, such

as justice, responsibility, civility, parenting, nurturance, work ethic, leadership,

teamwork, purpose, and tolerance (Seligman & Csikszentmihalyi, 2000;

Csikszentmihalyi, 2000).

In an extensive review of existing literature on the influence internal dialogue has

on human systems, Schwartz (1986) suggests higher functioning systems exhibit at least

a 1.7:1 ratio, of positive to negative thoughts, whereas dysfunctional counterparts were

characterized by a 1:1 ratio, balancing positive and negative thoughts. Both Schwartz

37

(1986) and Cooperrider (1997) argue that positively biased organizational

communications and “internal dialogue” will contribute more to heliotropic movement

toward goals and future images than either neutral (characterized by in-attention) or

organizational dialogue and communications underpinned with a negative or restrictive

tone, such as a focus on problems or deficiencies (Cooperrider, 1997).

Cooperrider (1997) explores the thesis that the creation of positive imagery on a

collective basis may well be the most fruitful activity for systems to engage in if their aim

is to help bring to fruition a positive and humanly significant future. In his exploration of

the potential of positive imagery in human systems, Cooperrider (1997) offers a review of

the literature on human systems and positive images. The author stresses the consistency

of findings of research done on the relationship between positive imagery and positive

action across diverse areas of study and across levels of system, illustrating his points by

citing examples using placebo, Pygmalion studies in primary schools, positive emotion,

internal dialogue, cultural vitality, and metacognitive competence (1997). The more an

organization experiments with the affirmative mode, the more its affirmative and

heliotropic competence will grow. Citing William James, Cooperrider’s conclusion is that

when human systems identify or state an image that asserts that the future is worth living

for, it will provoke those actions that help create the fact.

Emphasizing the positive is a position that Hallsmith (2003) counsels systems

starting the process of adopting more sustainable modes of operating. Instead of focusing

on what can be defined as problems, institutions and communities are more likely to

attract participants and support to sustainability efforts when the message and images are

positive. Moreover, she argues an initial collection of the strengths and positive stories of

38

the system build a base of information about the community that can help identify

strategies of improvement (Hallsmith, 2003).

Cooperrider (1997) connected streams of research, inquiry, poetry, and

philosophy for a treatise on the conscious crafting of future images. “Human beings,” he

asserts, “create our own realities through symbolic and mental processes.” Because of

this uniquely human ability- of future-creating mental activism- consciousness evolution

of the future is a human option that should be exercised. The development and use of

positive imagery reveals itself as permeable and emergent, open to the mind’s causal

influence. “[Reality] is conditioned, reconstructed, and often profoundly created through

our anticipatory images, values, plans, intentions, beliefs, and the like.”

Of critical relevance to my research is Cooperrider’s argument that the

relationship between image and action in human systems operates in a heliotropic

fashion, meaning human systems exhibit an observable and largely automatic tendency to

evolve in the direction of positive anticipatory images of the future. Important under-

girders to the research proposed here are drawn from Heidegger (1967, 1971), Schultz

(1967) and Weick’s (1976) work, which collectively suggests that each social action

begins with an image of the future, engaging us in a forward-looking projection of

ourselves into desired future images. As such, our future images become active causal

agents in the present (Cooperrider, 1997).

In the absence of a vision, a system is likely to “muddle along with business as

usual, not satisfied with the way things are, with no clear plan that takes them to another

reality” (Hallsmith, 2003). The use of generative discourse, hopeful images and a

compelling vision of the future serves not only to inspire systems engaged in change, but

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also to liberate these communities from tacit and unconscious beliefs and motivations

which are keeping the status quo in place (Hallsmith, 2003; Coupland et al., 2005). The

value of a shared vision emerges when the community creates a common understanding

regarding its direction and goals and when members co-author their future (Hallsmith,

2003; Shotter, 1993). This shared sense of a desired future, this shared sense of the

benefit they will move toward attaining, encourages, motivates and coordinates actions

that create transformational change in systems (Senge, 2006; Hallsmith, 2003;

Cooperrider, 1997). Using positive images and compelling visions of the future, systems

make explicit and conscious common goals, and thus the actions to achieve these,

through the use of positive images and visions of the future and by focusing on benefit

attainment, rather than simply behavioural change (Goleman, 2000; Goleman et al,

2001).

A frequently cited phenomenon illustrating the power of image is the Pygmalion

(or fulfilled prophesy) dynamic, which can be applied positively and negatively. The

Pygmalion dynamic is the power of positive images creating self-fulfilling realities.

Three phases of the positive Pygmalion dynamic (as modelled by Jussim, 1986) begin

with a positive image of others followed by affirmative thoughts and affirmative actions,

leading to a heliotropic (creating positive, image fulfilling) action (Cooperrider, 1997

citing Jussim, 1986). The initial stage of Jussim’s model describes how positive images

of the other are formed through mechanisms like reputation, projection, stereotypes,

objective measures, early performance, and the like. From this first phase, interactions

occur over time and the positive images begin to materialize, fulfilling prophecies and

expectations. Entering into the second phase, the initial positive expectations and images

40

are enhanced by additional behaviors and responses that were not included in the early

estimation. Combined, these anticipatory positive images stitch together a patchwork of

positive “interpersonal expectancy” mediated by expectancy-consistent cognition and

expectancy-consistent treatment (Cooperrider, 1997). Through positive expectancy, it is

possible that this affirmative capacity to cognitively tune into the most positive aspects of

another person. Positive expectancy refers to an attitude of optimism. It is a state of

confidence expectation and pleasurable anticipation (Rosenthal & Jacobson, 2003),

which can be applied as a cognitive and creative tool in the construction of reality and

future images. We see what our images make us capable of seeing. In the final phase of

Jussim’s model (1986), perception, memory, learning are each cued and shaped by the

positive images projected through expectancies. Through this process, human systems

fall into a cycle of affirmative and prophetic expectancies, seeing “proof” of our images

through, which further endows our positive prospects of the future. As the adage goes,

“seeing is believing,” thus our acts often take on deeper positive tone and character

depending on the strength, vitality, and force of a given image (Cooperrider, 1997).

A more recent conception of heliotropism in organizations is mirror flourishing.

Cooperrider and Fry (2013) and Cooperrider (in Laszlo & Brown, 2014), have observed

that when institutions themselves become vehicles of flourishing through corporate social

responsibility (CSR) and sustainability initiatives, the people within those institutions

begin to experience a similar phenomenon in their own lives, such as increased senses of

connectedness and thriving (Glavas & Piderit, 2009). Cooperrider and Fry call this

phenomenon mirror flourishing (2013).

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The promise of mirror flourishing is one of intimacy and deepening connection to

the relational field surrounding, including, and within organizations. Cooperrider and Fry

define mirror flourishing as “the constant flourishing or growing together that happens

naturally and reciprocally to us when participate in, or witness, the acts that help nature

flourish, others flourish, or the world as a whole to flourish” (2012, p. 8).

The mirroring effect is not a one-way expression, nor is it simply a receiving of

good feelings. It is, writes Cooperrider (in Laszlo & Brown, 2014), a symbiotic flow,

speaking to the unified and integrative relationships between our world, our institutions,

and ourselves, further blurring boundaries and calling into question notions of “in here”

and “out there.” The implications for Sustainability Professionals are noteworthy: mirror

flourishing presents the possibility that when we help the world to flourish, we too may

flourish.

Support for the two-way manifestation of flourishing is supported in Post’s

extensive writing on altruism (2002, 2003, 2005; Post et al, 2002; Post & Neimark,

2008). Post has observed and documented the positive unintended consequences of

altruism, categorizing how when we do good for others, we thrive as a result, with

improved well-being, physical health, social connections, and longevity (Post, 2005; Post

& Neimark, 2008). Post and colleagues (2002) describe altruism as one of the purest acts

of agape or brotherly love.

Love. It is neither a new conceptual model nor is it complicated, but discussion

of love in organizational contexts is like a day on the verge of its dawn. Margaret Atwood

once remarked, “The Eskimo had fifty-two names for snow because it was important to

them. There ought to be as many for love.” It appears increasingly possible that

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management scholars, and POS scholars in particular, have taken on this challenge and

have been exploring the forms love takes in organizational contexts.

Organizational scholars speak plainly and clearly about love. Fry (2003) and Fry

and Kriger (2009) include altruistic love in their dimensions of spiritual leadership. These

authors define love as a sense of wholeness, harmony, and well-being produced through

care, concern, and appreciation for both self and others (Fry, 2003 p. 712).

Fredrickson (2013) produced an entire book on the topic of love, describing how

ubiquitous love is, in organizational contexts and beyond. She provides possibly the

easiest definition of love, in terms of its inclusiveness: love is the micro-moments of

warmth and connection that you share with another living being.

Fredrickson refers to love as the supreme emotion. Rather than being a synonym

for care, connectedness, joy, or gratitude, it overarches these because each can be turned

in to an instance of love if experienced in close connection with another. Love is supreme

in an additional way. Referencing her earlier work (2001) on positive emotions,

Fredrickson writes, while all positive emotions provide benefits, like broadening your

mindset and building your resourcefulness, the benefits of love run far deeper. As the

supreme emotion, love makes us come most fully alive and feel most fully human. It is,

she asserts, perhaps the most essential emotional experience for thriving and health

(2013).

Maturana, a biologist and who extended his writings to organizational contexts,

and Bunnell (1999) define love as the collection of relational behaviors through which

another person, being, or thing arises as a legitimate other in coexistence with oneself (p.

59). Perhaps most aptly for the content of this research, Coombe (2011) elucidates the

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parallels between sustainability definitions and definitions of love, noting that both come

about only after shifts from limited self-interest to a recognition of connectedness,

mutuality, and interest in the well-being of others. Both sustainability, particularly when

defined as flourishing (Laszlo & Brown, 2014) and love are reflections of a

consciousness of interdependence and connectedness. According to Coombe,

sustainability may be one of the clearest expressions of love coming from the

organizational level of system (2011).

Positive Organizational Scholarship (POS) and Language

If positive images lead to positive action, as Cooperrider (1997) asserts, then

developing future images consciously becomes a critical aspect of organizational change.

Guiding images of the future exists deep within the internal dialogue of human systems,

like organizations. The image is therefore part of the “public domain” of the system and

is neither a person-centered or position-centered phenomenon. Guiding images and the

internal dialogue, which contains them, are situational and interactional tapestries in the

organization, property and creation of the whole rather than of any single element or part

(Shotter, 1993; Stacey, 1992). An organization’s guiding image of the future stretches

deeper and is far richer than a single leader’s policy or vision statement. The guiding

image, as part of an organization’s internal dialogue, is part of the complex, cooperative

aspects of organizational life (Shotter, 1993; Schwartz, 1986).

The following section reviews scholarship from and related to the POS domain,

which describes the role of language at both team and organizational levels of system.

Because my research studied the language of the sustainability programs (individuals and

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teams) who are change agents within their institutions, team and group level phenomena

are particularly relevant. The section also includes a review of the arguments of language

in organizational change.

In human systems, like teams, organizations, or societies, the guiding image of the

future does not, even metaphorically, exist within some individual or unified and separate

“mass of brain” (Cooperrider, 1997). The guiding image exists in a very observable and

tangible way in the language, the ongoing dialogue and narrative flowing through every

level of system, expressing itself anew at every moment (Losada & Heaphy, 2004;

Cooperrider, 1997; Schwartz, 1986; Shotter, 1993; Barrett et al. 1995). Much like a

fingerprint, a system’s language is unique and the sole property of that community,

reflecting the beliefs and the spirit of the community (Shotter, 1993; Millroy, 2001;

Hallsmith, 2003; Coupland et al, 2005), be that a married couple, a team, an organization,

or a nation.

The emotional tone of verbal and written communications is, in and of itself, a

social phenomenon, a realm of distinctiveness that, although operating on a linguistic

plane (Irvine, 2001), is proxy for other characteristics (Copland et al, 2005), like team

performance (Echeverria, 1994; Bartle & Saver, 2000, Losada & Heaphy, 2004). A

growing body of organizational and psychological scholarship has established the

importance of emotional processes in groups; the role affect plays among group members

through a number of processes (Bartel & Saavedra, 2000; Losada & Heaphy, 2004;

Barsade, 2002).

Barrett (1990) studied the relational development of a group over 5 years. Over

this period, Barrett found that changes among members’ social relationships and

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language preceded the emergence of new knowledge. In addition to development of the

group, Barrett noted the individual development occurring, supported by a group context

which legitimized talk about possibilities (Barrett, 1990). Barrett says, “People inherently

want to be released from stale scripts and frozen schemas that lock them into perception

of the world” (1990, p. 280).

Supporting the findings of Barrett’s research, Stacey (1996) found that teams in

particular, and organizations more broadly, engage in nonlinear feedback networks,

which employ ongoing processes of positive and negative feedback, constructing

organizational culture and norms (Shotter, 1993; Stacey, 1992; 1996). Underlying these

processes are conscious and unconscious communication between group members

(Stacey, 1996; Losada & Heaphy, 2004).

Teams who employed positive speech patterns and gestures and teams who

employed neutral and negative speech patterns and gestures had noteworthy differences

in team member satisfaction (Echeverria, 1994). Those teams whose language contained

encouraging utterances and positive gestures were found to have higher team member

satisfaction and identified more strategic opportunities. Moreover, teams characterized as

having a positive emotional tone also had generative or expansive emotional spaces,

which facilitated creative thinking and opportunity recognition. Those teams who used

negative or critical speech patterns and gestures showed lower team member satisfaction

and fostered restricted emotional spaces, which have been found to inhibit possibilities

for action (Echeverria, 1994).

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The language we use has emotional impact and is correlated with higher levels of

satisfaction, more successful and enduring relationships, higher team performance,

cohesiveness, and morale as reported by Donnellon (1996), Gottman (1994; 1999), and

Losada and Heaphy (2004) in particular. Donnellon (1996) found more cohesive team

language, which used encouraging statements in conjunction with “us,” “we,” and “our”,

correlated with higher functioning teams, whereas lower functioning teams used “your,”

“my,” and divisive statements.

Because they represent important building blocks to the research I have

conducted, the work of Schwartz (1986), Gottman (1979, 1994, 1999), and Losada and

Heaphy (2004) warrants a more extensive account. What this group has in common is

their exploration of positivity and negativity as it occurs in thoughts, common language

and interactions, and their contributions of positivity / negativity (P/N) ratios associated

with different measures of functioning or performance. Additionally, they raise the issue

of asymmetry between the effects of negative and positive phenomena. We begin with

the work of Schwartz (1986) and Schwartz and Gottman (1976).

In 1986 Schwartz put forth one of the early papers, summarizing the effects of

positive and negative thoughts and discussing P/N ratios. Schwartz traces the theoretical

history of positive and negative self-talk conceptually and considers the asymmetry

between positive and negative cognitions. Research, which offers the example at the

center of Schwartz’s paper is Schwartz and Gottman’s investigation (1976), originally set

out to assess patterns of positive and negative coping cognitions in a task analysis among

assertive and low assertive subjects. Schwartz later reconsidered these data, to assess P/N

ratios, inspired by work done by Hollandsworth and colleagues (1979), who conducted

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studies on text anxiety, finding low-anxious subjects reported approximately two

‘facilitative’ statements for every ‘deliberative’ statement, whereas high-anxiety subjects

reported a one to one ratio of facilitative to deliberative statements.

Calculating Mean Positive Thought Scores / Mean Negative Thought Scores from

1976, Schwartz found functional (‘assertives’) subjects yielding a 1.7:1 P/N ratio and

dysfunctional (‘low assertives’) yielding a P/N ratio of 1:1 (1986). Moreover, the 1.7:1

P/N pattern among functional subjects (based on data collected through self-report) has

been directly replicated in other studies of assertiveness, such as Bruch (1981) and

Heimberg et al. (1983), social anxiety (Glass et al, 1982), test anxiety as mentioned above

(Hollandsworth et al, 1979), and self-esteem (Vasta & Brockner, 1979). Because these

findings have been replicated using diverse methods of assessment, consistency of the

ratio cannot be attributed to an artifact of method (Schwartz, 1986). Thus, writes

Schwartz, across problem areas and methods of cognitive assessment, the functional

groups were characterized by approximately a 1.7: 1 P/N thought ratio and the

dysfunctional groups by a 1:1 P/N thought ratio. Schwartz’s second thesis point is the

asymmetry between negative and positive thoughts.

Each of the studies included in Schwartz’s review demonstrate that negative

cognitions, relative to positive, weigh more heavily in distinguishing functional versus

dysfunctional groups- in other words, bad is stronger than good (Baumeister et al, 2001).

In Schwartz and Gottman's (1976) task analysis study, although high and low assertives

differed in the frequency of both positive and negative self-statements, there was a

stronger relationship on the negative dimension. This asymmetry has been consistently

supported by subsequent studies of non-assertiveness. Bruch (1981), who replicated

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Schwartz and Gottman’s study, found a significant inverse relationship between cognitive

complexity and negative self-statements, whereas complexity and positive self-statements

were not related. Rhyne and colleagues (1983) extended the task analysis to a broader

subject population, finding that negative self-statements added significantly to a multiple

regression equation predicting actual assertive behavior, whereas positive self-statements

failed to increase the predictive power. Klass (1981) examined the relationship of

frequency and impact of two types of positive (criticism of other and self-directed

concerns) and negative (harm and responsibility) self-statements to a measure of guilt

over assertion. Looking at both frequency and impact, the negative self-statements

(relative to positive) were more strongly related to guilt. The asymmetrical effects of

negative thoughts versus positive ones extend further. Schwartz details study after study,

in which the asymmetrical pattern has also been observed, including topics such as

coping (Kendall et al, 1979), social anxiety, test anxiety (Hollandsworth et al, 1979;

Galassi et al, 1981), and coping with stressful medical procedures (Cacioppo et al, 1979).

Schwartz summarizes: it appears that negative events and cognitions are more salient

and make a greater impact than positive ones-that negative thoughts and feelings, relative

to positive, may be more central to adaptation (1986).

The work of Gottman and Gottman and colleagues (Gottman 1979, 1994, 1995,

1999; Gottman & Krokoff, 1989; Gottman & Levenson, 1986) has reported on

longitudinal studies of married couples’ interactions, communication patterns, emotion

affect, and relational satisfaction. To do this, Gottman and his colleagues videotaped

couples having conversations about an array of topics, some mundane and others more

sensitive, such as discussing specific issues in the relationship. The couples’

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conversations and behavior were coded as verbal or non-verbal, positive or negative. As

one might expect, positivity of communication is associated with high relational

satisfaction. These subjects communicate with more positive verbal behaviors (such as

agreement, expressing care, politeness, constructive problem solving) as well as non-

verbal behaviors (such as nodding, concerned tones of voice, smiling) (Gottman, 1979).

In later, related work, Gottman and Levenson (1986; Levenson & Gottman, 1983, 1985)

created the videotapes observing the couples, then shared the taped interactions with the

couples, to collect ongoing ratings of affect. Emerging from these data was the important

role of reciprocity, defined as one person expressing a similar emotion or change in

emotion right after their partner had indicated a feeling.

Like the asymmetries found in Gottman’s earlier work with Schwartz (1976),

reciprocity of negative affect was especially potent and in particular was more influential

than reciprocity of positive affect. As part of the longitudinal nature of this study, couples

were followed up with two years later. Those subject who had initially shown higher

rates of negative affect reciprocity reported greater declines in relationship satisfaction,

whereas reciprocity of positive affect had no significant effect (Levenson & Gottman,

19855). Therefore, relationships are most affects by patterns in which one person

responds negatively to the other’s negative act or feeling. In light of these findings and

the asymmetrical impact of negative and positive encounters, Gottman (1994) proposed

that for relationships to succeed, positive interactions have to outnumber negative ones

by at least five to one.

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Losada and Heaphy’s study (2004) examined the language of teams by observing

and recording meetings of sales teams in a specially designed boardroom, which was a

computer lab designed for team research. All dialogue was recorded and subsequently

coded. The sales teams participating in the study were categorized into three groups

(high performance, mixed success, and low performance) based on their performance on

distinct business indicators of customer satisfaction, profitability, and evaluations by

supervisors, peers, and direct reports.

As each team met in the mock boardroom setting, Losada and Heaphy’s team paid

attention to the manner in which team members communicated with one another (Losada

& Heaphy, 2004). Three dimensions were given particular attention in terms of team

member interactions:

(1) positive or negative utterances;

(2) self-focused or other-focused communications, and

(3) advocacy-based (driving one’s own point of view) or inquiry-based (asking

questions) language.

In addition to these key parameters, the degree to which team members influenced the

behavior of the colleagues, referred to as “connectivity,” was also observed and tracked.

Overlaying the results of the team meeting observations with sales performance, the

results were noteworthy.

Teams with the highest sales were characterized by conversations that had high

positivity ratios, with an average of six positive comments to each negative utterance (a

6:1 positivity to negativity or P/N ratio). Moreover, high performing teams were

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characterized by asking as many, if not more, questions of their colleagues than

defending or advocating for their own point of view. Additionally, individuals in the

high performing teams were characterized as being focused outwardly (on the team as a

whole, on other members of the team, or the larger contexts of the team) as much or more

than they were self-focused and scored highest on the degree to which they were able to

influence one another—the team’s “connectivity” (Losada & Heaphy, 2004; Fredrickson,

2009).

Comparing the 6:1 positivity ratio of the highest performing sales team to the

other groups in the sample, the researchers found the low-performing teams had

remarkably low positivity ratios, well below 1:1. These teams demonstrated low levels

of connectivity, asked almost no questions of one another, and were consistently self-

focused rather than being outwardly focused (Losada & Heaphy, 2004; Fredrickson,

2009). As one might expect, the teams who fell into the “mixed-performance” category,

fell in the middle of each of Losada and Heaphy’s dimensions, with a positivity ratio of

2:1, just above the low performing teams.

Losada applied a nonlinear dynamic model to the data in order to predict to

positivity ratios, which marks the tipping point for human systems. (Losada & Heaphy,

2004; Fredrickson & Losada, 2005; Fredrickson, 2009). This mathematical model was

subsequently shown to be invalid (Brown et al., 2013), although the evidence linking

high positivity ratios with high performance remains valid (Expression of Concern, ABP,

2014).

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At first glance, these data might suggest that interactions that are peppered with

positive comments are all that are required for high functioning human systems,

implicitly suggesting we avoid negativity. But dismissing the value of negative or critical

comments, and only focusing on pleasantries misses the point of this important research,

and in fact, is not supported by Losada and Heaphy’s analysis.

High performance teams are not high performing because they have learned to

sprinkle in a target number of positive expressions. High-performing teams stood apart

from the other groups in this research for their ability, when experiencing pressure, to

stay resilient and to rebound. The higher performing teams were able to leverage group

strengths by asking questions to identify new information, new options, and ideas. They

remained open to one another and flexible due to their underlying high positivity ratio.

The study’s high performing (high positivity ratio) teams avoided getting stuck in critical

thought spirals or self-absorbed advocacy (Losada & Heaphy, 2004; Fredrickson &

Losada, 2005; Fredrickson, 2009). Challenges and critical comments are present,

necessary, and perhaps even natural in groups, however it is how the group (or in this

case the sales team) responds and keeps moving forward that is distinguishing.

Resilience and positivity go hand in hand, without positivity there is no rebound (Tugade

& Fredrickson, 2004). Fredrickson’s (1998, 2001) broaden-and-build theory of positive

emotions offers an overarching theoretical explanation by linking the cumulative

experience of momentary positive emotions to the development of resources for long-

term success and well-being. This, and other similar work, are described later.

Kegan and Lahey (2001) view language as a technology for creating

transformative learning in organizations. The places where we live and work are places

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where certain forms of speech are promoted and other ways of talking are discouraged or

made impossible. Work settings are language communities and all leaders in

organizations are language leaders, possessing an exponentially greater access and

opportunity to shape existing language rules (Kegan & Lahey, 2001). An analogy can be

drawn to each child’s development of language. As an individual’s language develops as

part of the whole of child, it is also indicative of and in accordance with their

membership in a larger society (Millroy, 2001). It is impossible to disassociate facts of

language from the cultural values that saturate them.

Even when looking at groups or team within organizational systems, the role

language plays in system-wide change initiatives has been explored across diverse

contexts and sectors (Barrett, 1990; Donnellon, 1996; Cooperrider, 1997; Gottman, 1999;

Losada & Heaphy, 2004; Schmidt, 2005; Fredrickson & Losada, 2005; Fredrickson,

2009). All language, contends Ludema and colleagues (1997), sustains certain kinds of

knowledge, to the exclusion of other kinds of knowledge. These authors extend the idea,

asserting that all knowledge sustains certain patterns of activity, to the exclusion of

others. Thus, the more hopeful the available vocabularies, the more positive will be the

forms of future image, social action, and organization that they support. Those

vocabularies that offer positive examples of future images and hopeful possibilities will

be more powerful resources for organizational change (Gergen, 1994; Ludema et al.

1997; Cooperrider, 1990, 1997; Hallsmith, 2003). The Pygmalion dynamic is evident

when the positive image of another serves as a powerful cognitive measuring stick, which

appears to trigger in the perceiver an increased capacity to perceive the successes of the

other (Deaux & Ernswiller, 1974), recall more positive than negative aspects of the other

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(Hastie & Kumar, 1979), as well as enabling positive outcomes to ambiguous situations,

rather than expectations of negative or unsatisfactory possibilities (Darley & Gross,

1983).

Emotion

William James (1902), arguably the forefather of positive psychology, wrote,

“Emotional occasions, especially violent ones, are extremely potent in precipitating

mental rearrangements. He described the “sudden and explosive ways” in which negative

emotions such as jealousy, guilt, fear, or anger can “seize upon” each of us (p. 163). He

went on to say, “ Hope, happiness, security, resolve—emotions characteristic of

conversion, however, can be equally explosive. And emotions that come in this explosive

way seldom leave things as they found them” (James, 1902, p. 163-164). A century

later, social scientists are catching up to James’ insights. Central to many existing

theories of emotion is the concept of specific-action tendencies – the idea that emotions

prepare the body both physically and psychologically to act in particular ways

(Fredrickson, 1998).

The broaden-and-build theory of positive emotions (Fredrickson, 1998;

Fredrickson & Cohn, 2008) proposes that positive emotions are evolved adaptations that

function to build lasting resources. Unlike negative emotions, which narrow attention,

cognition, and physiology toward coping with an immediate threat or problem (Carver,

2003; Cosmides & Tooby, 2000; LeDoux, 1998), positive emotions produce unique and

broad-ranging thoughts and actions (Fredrickson, 1998). While these new thought-

action-repertoires are usually not critical to one’s immediate safety or survival, over time

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they aggregate into consequential resources that can change people’s lives (Cohn et al,

2009; Fredrickson, 2001; 2003).

Evidence confirms that positive emotions broaden thought-action repertoires:

induced positive emotions produce wider visual search patterns, novel and creative

thoughts and actions, more inclusive social groups, and more flexible goals and mindsets

(Ashby et al, 1999; Fredrickson & Cohn, 2008). The work of Fazio, Eiser and Shook

(2004) has leant further support to the broaden-and-build theory, arguing that positive

and open mindsets produce psychological safety, allowing for exploratory and

experiential learning, and producing more accurate mental maps of the world (Fazio et al,

2004). Thus, relative to negative or even neutral emotions, positive emotions are more

likely to promote interest and curiosity among groups.

Negativity and neutrality narrow thought-action-repertoires, limiting possible

responses, increasing self-protection (Fazio et al, 2004; Fredrickson, 2009, Schmidt,

2006). LeDoux (1998) explored relationships between brain physiology and experience

of emotions. Much of his work revolves around how brain systems process fear and

related experience, like anxiety. LeDoux asserts that fear or anxiety are closely related,

the difference is that anxiety comes from within, distinct from fear because there is no

external stimulus (1998). LeDoux proposes that fear, unlike other kinds of emotions

(anger or happiness) is a conditioned result of a system in the brain. The system does not

result in the experience of fear, but rather is an evolutionary by product, which detects

danger and produces responses that maximize the probability of surviving a dangerous

situation in the most beneficial way. In other words defensive behaviors represent the

operation of brain systems that have been programmed by evolution to deal with danger

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in routine ways (LeDoux, 1998).

In 1890, William James noted that nothing characterizes the ascendency of human

kind more clearly than the reduction of the conditions under which fear is evoked. James

was undoubtedly referring to man’s ability to establish societies in which we are not in

danger of becoming someone else’s dinner. As a species, we have been successful in

creating ways of living in which the likelihood of encountering predators is greatly

reduced, but of course, not all danger growls. In our quest for dominion over nature we

have created new forms of danger- crime, automobile accidents, financial crises, nuclear

threats, and now, climate change. “We’ve traded in the dangers of a life amongst the

wild things for other dangers that may, in the end, be far more harmful to our species than

any natural predator” (LeDoux, 1998, p.129). The dangers modern humans face are not

fewer or less significant than those of our ancestors, they are simply different.

Baumeister and colleagues (2001) serve as a linkage, highlighting the theme of

asymmetry in the effect of negative phenomena versus positive events or utterances

(Schwartz, 1986; Schwartz & Gottman, 1976; Gottman, 1979, 1995). The Baumeister

team wrote a fascinating paper, exploring the hypothesiss ‘bad is stronger than good.’

Through an extensive, and seemingly exhaustive, survey of psychological literature, the

authors delve into diverse realms of psychology scholarship including, but not limited to,

the science of first impressions, experimental exposure to odors, traumatic life events,

number of negative and positive words in the English language, learning, conceptions of

the self, childhood development, and relationships. Like Schwartz’s thorough survey

(1986) but on a vaster scale, the researchers combed through other scholars’ research,

finding study after study, across a broad range of psychological phenomena, citing over

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and over that circumstances with a negative valence will have a greater impact than equal

events with a positive valence. This greater impact of bad was found in cognition,

motivation, inner and intrapsychic processes, interpersonal interactions, decision making,

memories, emotional responses, information processing and more (Baumeister et al,

2001). So, while there may be notable, yet singular contradictions, the Baumeister team

concluded that negative information and events produce more emotion, have bigger

requirements for adjustment, and have longer lasting effects (2001).

Reflecting on all the literature reviewed above, it is clear that there are multiple

and interrelated research lenses one can look through to explore the impact a positive

perspective, positive action, and positives words can have on supporting human systems

(Fredrickson 2001, 2003, 2009; Losada & Heaphy, 2004; Losada & Fredrickson, 2005;

Cooperrider, 1997; Cooperrider & Sekerka, 2003, Goleman et al, 2001). Our language

directs our attention and therein shapes the results of what we create (Cooperrider, 1997;

Fredrikson, 1998; Schmidt, 2005; McKenzie-Mohr & Smith, 1997). Taken together, the

literature cited point to the importance of language on increased levels of performance in

teams and organizations, improved levels of satisfaction of organizational members, and

better organizational capacity to deal with increasingly complex environments (Schein,

1999; Cooperrider, 1997; Losada & Heaphy,2004; Echeverria, 1994; Stacey,1992, 1996).

I posit that the knowledge resulting from the confirming approaches to change

and theories of positive emotional affect reviewed above is essential for Sustainability

Professionals on university and college campuses to be successful in leading their

institutions into systemic, comprehensive change. Additionally, in light of the asymmetry

of effects that negative information and events have (Schwarz, 1986; Gottman 1994;

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Baumeister et al, 2001), it becomes more clear that negativity in the sustainability

rhetoric, especially when unbalanced by a prevalence of positivity, reduces the

audience’s capacity to respond.

In the preceding sections, relevant studies and literature from management and

psychology were presented to argue that words are more than objective symbols. The

explicit language of a system and the internal dialogue of a system are both creating the

system itself, which is an ongoing and collective process (Ludema et al, 1997; Shotter,

1993; Losada & Heaphy, 2004; Kegan & Lahey, 2001). Language does not gain its

“truth value” by accurately describing the world, but rather by virtue of its function

within relationships (Ludema et al. 1997). Language is internal to the common culture

(Millroy, 2001; Irvine & Gal, 2000; Coupland et al, 2005) and is itself a socio-cultural

phenomenon (Ferguson, 1994). The conscious combination of hopeful vocabularies and

positive narratives makes it possible to vicariously experience that which is held in the

imagination of a system (Ludema et al, 1997; Cooperrider, 1997).

Based on the literature described above, I have a clear bias and theory of change. I

believe that disconfirming events and information have a powerful effect and get our

attention, but does not result in or sustain comprehensive, systemic change. Therefore, I

believe that change occurs as a confirming and evolutionary process, characterized by a

future-oriented stance, proactive in creating opportunities to further develop the system’s

strengths.

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Research Questions and Hypotheses

This research focuses on the emerging field of sustainability in higher education to

explore these assumptions. Through a multi-phased qualitative research design I will

analyze the language itself, across three different contexts: (1) language as it is used to

represent the ‘public face’ of sustainability via campus sustainability websites; (2)

language use at the individual-level derived from interviews with campus sustainability

leaders; and (3) language occurring among sustainability teams on higher education

campuses. Two research questions will be explored in my study:

1. What is the emotional tone of sustainability language used in higher educational

contexts?

2. How does the emotional tone of campus sustainability narratives relate to the

performance of sustainability programs?

These questions generate three hypotheses. Hypotheses 1a and 1b relate to the current

state of higher education sustainability program communications, and highlight two

dynamics: mimetic isomorphism (DiMaggio & Powell, 1983) and the role data-driven

arguments play in sustainability narratives (Schmidt, 2005).

In the introduction of this proposal, it is noted that the majority of sustainability

professionals in higher education come from diverse occupational backgrounds (AASHE,

2008) and while they are charged with transforming campus cultures through education,

research, management and operations, the majority of incumbents do not possess the

skills necessary to implement these changes. Additionally, professional development

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opportunities related to system wide change strategies are scant in the higher education

sustainability domain. Uncertainty is a powerful driver for imitation (DiMaggio &

Powell, 1983), therefore for insight into how to engage campus constituents, the

Sustainability Professional, unfamiliar with organizational development and change—or

unaware that organizational development is indeed what she or he is doing by way of the

sustainability program—turn to exemplars and role models for assistance.

The advantages of imitation in the “economy of human action” are considerable;

when an organization faces a problem with ambiguous causes, or unclear or un-tried

solutions, bench-marking what other institutions are doing and how they do it can yield

viable solutions with little cost in time and money (DiMaggio & Powell, 1983). While

modeling one’s program from the examples of others may serve to establish credibility

for new sustainability program, it also runs into problems. As detailed in in the literature

review, Schein (2002) believed role modeling to be the cruder of change approaches.

While faster than trial and error learning, copying role modeled behavior often does not

fit the target’s culture or context (Schein, 2002). DiMaggio and Powell (1983) also stress

that mimicking results in homogeneity, especially in small domains or sectors, which

applies to a field such as higher educational sustainability programs.

Mimicking the activities, communication, and organizational forms from

successful sustainability programs or environmental organizations helps to legitimate

fledgling sustainability programs. New Sustainability Professionals ascertain what topics

are included in typical sustainability program and what are not, what kinds of information

should be featured on the sustainability program’s website, and how to communicate and

phrase sustainability-related issues (Sharp, 2005). This is evidenced by similar definitions

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for sustainability, wide spread use of certain kinds of data in ‘making the case’ for

sustainability, common phrasing, and using sustainability websites as hubs of information

and instruction.

Hart and Milstein (2003, Schmidt (2005) and Ehrenfeld (2008) use similar

language to describe the mindset of environmental NGOs and orientation of the

sustainability movement: data-focused; regulatory; compliance-oriented; and concerned

with being less bad. In my experience of being a Sustainability Professional, a majority

of the focus was on energy conservation and recycling, both at my institution and with

the other sustainability programs with whom I interacted. As such, the messages being

communicated to campus interest groups, like students, faculty, and leadership, had to do

with sharing data and information about the campus, as well as instructions on how

individuals could support the sustainability initiatives through their behaviors, such as

Greening Your Dorm Room or Greening Your Office initiatives.

Hypothesis 1a and 1b reflect my experiences of mimetic isomorphism, and

assume this still occurs within the communication of sustainability programs, and

influences the emotional tone of sustainability communications on higher education

campuses. These hypotheses relates to the penchant for information sharing and

instruction giving and using “data” to make the case for sustainability, which would

result in narratives that are largely neutral in their emotional tone, characterized by

statistics, data and facts, and instructions. Additionally, these hypotheses served as “null

hypotheses” or default positions, in the event positive and negative emotional tone were

not represented in the data in a significant way.

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H1a: The current state of sustainability language by its proponents in higher

education, including all performance categories, will be dominated by a neutral

emotional tone.

H1b: Instruction and information sharing will comprise the majority of the

neutral discourse.

Hypothesis 2 derives from arguments made by Schwartz (1986), Gottman (1994),

and Losada and Heaphy (2004) regarding P/N ratios associated with differences in

performance and functioning. Each of these has been described in the Literature Review.

Studying intra-psychic self-talk, Schwartz (1986) and Schwartz and Gottman (1976)

found a 1.7:1 P/N ratio in functional subjects versus a 1:1 P/N ratio in dysfunctional

subjects. Gottman’s longitudinal studies of couples found high relational satisfaction in

those couples whose verbal and non-verbal interactions possessed a 5:1 P/N ratio, and

Losada and Heaphy (2004) found high-performing sales teams demonstrated a 6:1 P/N

ratio. In all of these examples, dysfunction and low performance were associated with a

1:1 P/N ratio. When taken together with Ludema and colleagues (1997), Shotter (1993),

Kegan and Lahey (2001) it is both the explicit language of a system and the internal

dialogue of a system that creates the system itself. Therefore, when language in a system

is confirming, collaborative, creation-oriented, and proactive- when the system’s

narrative is generally positive- the system will recognize higher performance. For

Hypothesis 2, I chose a ‘middle ground’ approach for predicting P/N ratios in my study

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population, from the range created by Schwartz (1986), Gottman (1994), and Losada and

Heaphy (2004).

H2: Higher education sustainability programs with at least a 3:1 ratio of positive

to negative language in their personal and public communications (written and

verbal) will be rated higher on a national campus sustainability ranking than

sustainability programs whose communications employ a lower ratio of positive

to negative communication acts.

I have described the concepts and methods of Schein, and Kotter as three

influential figures in organizational change. These thought leaders approach change from

a disconfirmation stance, believing that disconfirmation functions as the primary driver in

all change (Schein, 1996). The disconfirmation theory of change asserts that

dissatisfaction or frustration, generated by data that dis-confirm previously held

perceptions of reality, expectations, or hopes, starts the change (or learning) process

(Schein, 1995, 1996, 1999; Kotter, 1996, 2008). Based on Schein’s own description of

disconfirmation (1995, 1996, 1999) it is a reactive, problem solving orientation to

change—it is the equivalent of “coming in on the back foot.” In contrast, the

confirmation-based scholarship of Fredrickson (2003), Cooperrider and Sekerka (2003)

and Schmidt (2005) offer convincing arguments for steering organizational change with

hopeful and compelling narratives of the system’s desired future, characterizing change

as about being at the front of opportunities, being proactive, and creation-oriented.

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H3: The highest performing campuses will approach change for sustainability

through a confirmation-based approach, characterized by a future-oriented stance,

a propensity for innovation, being proactive and creating opportunities to further

develop their institution’s sustainability program.

In the next chapter details about research design and qualitative methodology of

this study are provided, including description of the study population and coding process.

Following this, results are presented in Chapter Five and the discussion and implications

of the findings are offered in Chapter Six.

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CHAPTER FOUR: RESEARCH DESIGN AND METHOD

In organizational research, the search for research methods that are able to capture

a holistic view of organizational issues has led to greater usage of descriptive and

interpretivist methods (Klein & Meyers, 1999). Describing and evaluating the emotional

tone of sustainability language at institutions of higher education requires a

methodological approach capable of rendering the underlying emotional themes held in

each campus’ patterns of communication (Grant et al., 1998; Mumby & Clair, 1997).

Qualitative methodologies have become one of the more accepted approaches for

studying such complex social phenomena (Margolis & Walsh, 2003; Miles & Huberman,

1994; Tesch, 1990).

Content Analysis

The goal of content analysis is “to provide knowledge and understanding of the

phenomenon under study” (Downe-Wamboldt, 1992, p. 314). In this study, qualitative

content analysis is defined as a research method for the interpretation of the content of

text data through the systematic classification process of coding and identifying themes

or patterns. These themes or patterns can represent either explicit communication or

inferred communication (Hseih & Shannon, 2005).

Content analysis has a long history in research (Kohlbacher, 2006; Hsieh &

Shannon, 2005; Titscher et al, 2000) with a family of methodologies, including both

qualitative and quantitative approaches (Hsieh & Shannon, 2005). Several researchers

trace use of content analysis back to the 18th century in Europe and early 20th century in

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the US (Rosengren, 1981; Hseih & Shannon, 2005; Kohlbacher, 2006; Titscher e al,

2000). Although initially qualitative or quantitative methods were used (Hseih &

Shannon, 2005) later studies, used content analysis primarily as a quantitative research

method, with text data coded into explicit categories and then described using statistics.

For example, in the fields of journalism or linguistics, content analysis is used to track

(count) usage of particular words or phrases in written documents over periods of time,

for example newspapers or annual reports, in order to assess the attention being paid to a

particular concept or phenomenon (Krippendorf, 1980).

Qualitative content analysis is one of numerous research methods used to analyze

text data. Other methods include ethnography, grounded theory, phenomenology, and

historical research (Hsieh & Shannon, 2005). Research using qualitative content analysis

focuses on the characteristics of language as communication with attention to the content

or contextual meaning of the text (Hseih & Shannon, 2005; Tesch, 1990). Text data

might be in verbal, print, or electronic form and might have been obtained from narrative

responses, open-ended survey questions, interviews, focus groups, observations, or print

media such as articles, books, or manuals (Kondracki &Wellman, 2002).

Among the diverse content analysis approaches, Hseih and Shannon (2005) group

current applications of content analysis into three distinct approaches: summative;

conventional; and directed. The three approaches share a common goal of interpreting

meaning from the content of text data. Hseih and Shannon differentiate the approaches

based on origins of codes, coding schemes, and challenges to trustworthiness. A

summative content analysis involves counting and comparisons, usually of keywords or

content, followed by the interpretation of the underlying context. In conventional content

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analysis, coding categories are derived directly from the text data. With a directed

approach, analysis starts with a theory or relevant research findings as guidance for initial

codes. The research methodology here falls into the directed content analytic approach.

Directed Content Analysis

When research is being situated within existing theory, or prior research exists

about a phenomenon that is incomplete or would benefit from further description, the

qualitative researcher might choose to use a directed approach to content analysis. Potter

and Levine-Donnerstein (1999) categorize this as a deductive use of theory based on their

distinctions on the role of theory. The goal of a directed approach to content analysis is to

validate or extend conceptually a theoretical framework or theory. Existing theory or

research can help focus the research question. It can provide predictions about the

variables of interest or about the relationships among variables, thus helping to determine

the initial coding scheme or relationships between codes. Mayring (2000) referred to this

as deductive category application.

Content analysis using a directed approach is guided by a more structured process

than is a conventional approach (Hickey & Kipping, 1996). One strategy that can be used

with directed content analysis is to begin with an existing or pre-determined code. This is

how I performed my analysis, which is detailed below. Data that cannot be coded are

identified immediately because they do not fit into any of the categories of the existing

code and are analyzed later to determine if they represent a new category or a

subcategory of the existing code.

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Coding refers to the creation of categories in relation to data; the grouping

together of different communication acts or utterances under an umbrella term that can

enable them to be regarded as of the same type (Aronson, 1994; Boyatzis, 1998).

Decisions about what counts as a category can originate from different sources, such as

theory, literature, the data itself, or the researcher’s experience. I will analyze my data by

first applying an existing code of Positive and Negative Discourse Categories, developed

by Cooperrider and colleagues (2008) (see Tables 2a & 2b). If phenomena present in my

data are not reflected in the Positive and Negative Discourse Categories, additional

categories or sub-categories will be created in order to capture un-represented themes.

The Positive and Negative Discourse Categories will be described in greater detail below.

The findings from a directed content analysis offer supporting and non-supporting

evidence for a theory. This evidence can be presented by showing codes with exemplars

and by offering descriptive evidence. The theory or prior research used will guide the

discussion of the findings. Newly identified categories could offer a contradictory view

of the phenomenon or could further refine, extend, or enrich the theory (Hsieh &

Shannon, 2005).

The main strength of a directed approach to content analysis is that existing

theory can be supported and extended. In addition, as research in an area grows, a

directed approach makes explicit the reality that researchers are likely to be working from

informed perspectives (Hseih & Shannon, 2005). Additional methodological and

practical benefits have been noted in implementing content analysis (Woodrum, 1984).

First, content analysis is a safe methodology because the coding scheme can be corrected

if flaws are detected as the study proceeds (Tallerico, 1991; Woodrum, 1984). Second,

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when content analysis is done correctly, it entails the specification of category criteria for

reliability and validity checks that fosters the creation of a replicable database (Lissack,

1998; Woodrum, 1984).

The directed approach also presents challenges. Grounding a research study in

existing theory has some inherent limitations in that researchers approach the data with

an informed but, nonetheless, strong bias. Hence, researchers might be more likely to find

evidence that is supportive rather than non-supportive of a theory. Second, researcher’s

bias may direct respondents through the wording of questions, leading or cueing

participants to answer questions in a certain way or agree. Third, an overemphasis on the

theory can blind researchers to contextual aspects of the phenomenon and miss important

findings or insights that do not fit the predetermined categories/codes.

Theoretical Issues in Content Analysis

As with any decision to choose a particular research design, there are

consequences to using content analysis. One of the central positions, associated with all

qualitative research, pertains to the idea of interpretivism or projection (Braun & Clark,

2006). This ideas suggests that I, as the researcher, will project my perspectives and

“truths” on to the data or interpret the data through my understanding and meaning

making of the actions of others, which may not be the meaning the actors intended. I am

of two minds in relation to the challenge.

First, I will be engaging additional analysts (coders of data) to establish a

consistency of coding, labeling, and interpretation (Boyatzis, 1998). See below for further

discussion on reliability. With this said, and with the seriousness that I have for

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conducting an accurate and thorough program of research, I and we inhabit cultural

worlds and engage in cultural practices that are defined by shared interpretations. While

I have done all that I could to alleviate biases, I cannot remove myself from my own

context and the shared contextual assumptions and interpretations that my sample is

embedded in.

Language is a prominent aspect of the ways in which we make sense of and order

our experiences of the world. Wittgenstein’s famous phrase ‘the limits of my language

are the limits of my world’ draws attention to the idea that language forms, in some quite

profound way, a tangible context for our actions. Exploring these contexts – their specific

features and interrelationships – is, in essence, the central project that drives management

and organizational scholarship, and is certainly the central project driving my work.

The key assumption about content analysis is that the analysis of texts lets the

researcher understand other people’s cognitive schemas (Huff, 1990; Gephart, 1993;

Woodrum, 1984). Content analysis assumes that groups of words reveal underlying

themes—both the explicit and the inferred (Hseih & Shannon, 2005)—and that, for

instance, co-occurrences of keywords can be interpreted as reflecting association between

the underlying concepts (Huff, 1990; Weber, 1990). Foremost to organizational research,

qualitative content analysis provides a replicable methodology to access deep individual

or collective structures such as values, intentions, attitudes, and cognitions (Carley, 1997;

Huff, 1990; Kabanoff, 1996). As such, content analysis is applicable to a broad range of

organizational phenomena.

My research will apply directed content analysis to words, phrases, utterances,

full sentences, multiple sentences, and images from public, archival documents, such as

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websites and promotional materials, and transcriptions of one-on-one interviews and staff

meetings. A detailed explanation of inclusion and exclusion criteria, data collection, and

analysis is presented below.

Assumptions

The research project I propose is based on two, interrelated assumptions, and is

founded on the scholarship reviewed in the previous sections:

Human systems create and communicate their realities through symbolic and

mental processes (Schwartz, 1986; Shotter, 1993; Barrett et al, 1995), such as

language and the creation of images of the future, which serve to bring those

expectations powerfully into the present as a mobilizing agent (Cooperrider,

1997).

Generative and positive narratives build hope and momentum, and are essential

elements in confirmation-based change in complex human systems (Barrett &

Cooperrider, 1990; Kegan & Lahey, 2001; Dutton, 2003).

Inclusion and Exclusion Criteria

The sample population for this study was limited to Sustainability Professionals

from institutions of higher education that (a) exist within the State of Ohio, and who are

also (b) evaluated by the Rockefeller Sustainable Endowment Institute, via the College

Sustainability Report Card (see Table 1). Limiting my sample to colleges and

universities within Ohio serves to equalize factors related to energy policies, and state-

level differences in the funding supporting sustainability initiatives.

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The Rockefeller Sustainable Endowment Institute is a nonprofit organization

engaged in research and education to advance sustainability in campus operations and

endowment practices. The College Sustainability Report Card is designed to identify

colleges and universities who are leading by example in their commitment to

sustainability. The aim is to provide accessible information to all higher educational

institutions to learn from one another's experiences, enabling them to establish more

effective sustainability policies (greenreportcard.org, 2011). The report card “grade” each

institution receives is determined by assessing performance across indicators in nine main

categories.

1. Administration: examines sustainability policies and commitments by school

administrators and trustees.

2. Climate Change & Energy: looks at energy efficiency, conservation, commitment

to emissions reductions, and use of renewable energy on campus.

3. Food & Recycling: evaluates dining services policies, including recycling and

composting programs.

4. Green Building: recognizes campus-wide green building guidelines and green

building design for new and existing buildings.

5. Student Involvement: looks at student participation in sustainability initiatives and

support for these activities by school administrators.

6. Transportation: focuses on alternative transportation for students, faculty, and

staff, as well as alternative fuel or hybrid technology for campus fleets.

7. Endowment Transparency: addresses accessibility to endowment investment

information and shareholder proxy voting records.

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8. Investment Priorities: considers prioritization of return on investment, investment

in renewable energy funds, and investment in community development loan funds.

9. Shareholder Engagement: looks at shareholder proxy voting practices, including

opportunities for student, faculty, and alumni participation.

Categories and indicators which comprise the College Sustainability Report Card

were collected based on research of best practices in sustainability in higher education,

specifically concerning campus operations and endowment policies, which speaks to the

comprehensive reach sustainability programs would ideally have. The nine topic

categories are comprised of 48 indicators, which address a broad range of policies and

programs, however exclude teaching, research, or other academic aspects concerning

sustainability.

Category grades are calculated based on the total number of points earned for the

indicators within the category. To receive an "A" in any category, a school needed to

accumulate at least 70 percent of total available points for the indicators in that category.

At least 50 percent of available points were necessary to receive a "B," 30 percent of

available points for a "C," and 10 percent of available points for a "D." No school

received a "D" or “F” in the Investment Priorities category because all schools were

awarded a minimum grade of “C” for aiming to optimize investment return. Only full

letter grades were given for individual categories (i.e., no plus or minus) of A, B, C, D,

and F were used for the individual categories (greenreportcard.org 2011). Overall grades

given to each institution evaluated are averages of the grades received for each of the

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nine categories (greenreportcard.org, 2011), which is calculated at the plus and minus

level.

In the State of Ohio, the Campus Sustainability Report Card evaluates 17 colleges

and universities annually and the results are published in a Campus Sustainability Report

Card (Table 2). Table 2 presents each college and university and the grades given in the

2011 Report Card. The downside of using this particular rubric is that its data is dated,

however there remains is no alternative ranking system, which is both as thorough as the

Report Card and includes as many institutions.

Based on the Sustainability Report Card’s grading system, I have organized the

sustainability programs into three performance categories: high performers, moderate

performers, and base performers. The grade and performance categories will be used in

the final stages of data analysis, explained in detail in the section describing Phase 3 data

analysis.

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Table 2: Ohio Colleges and Universities Included in Campus Sustainability Report Card Ranking 2011

CATEGORY RED:

High Performers

CATEGORY BLUE: Moderate Performers

CATEGORY YELLOW:

Base / Beginner Performers

INSTITUTION GRADE INSTITUTION GRADE INSTITUTION GRADE

Oberlin College

A Ohio University B Ohio Northern University

C

Case Western Reserve University

B+ University of Dayton

B Hocking College C

Denison College B+ University of Toledo

B- Youngstown State University

C-

The Ohio State University

B+ John Carroll University

B- College of Wooster

C-

University of Cincinnati

B+ Ohio Wesleyan University

B- University of Akron

D+

Miami University B-

Kenyon College C+

Invitations were sent to the sustainability leaders at each of the seventeen

institutions listed in Table 2, with the goal of recruiting at least three institutions in each

performance category. Ten of the institutions listed in Table 2 responded: four were from

the high performing sustainability programs; three from the moderately performing

sustainability programs; and three from the base performer category. Eighty percent of

the study sample was composed of universities, while the remaining 20% were colleges.

Institution size, as defined by student populations, ranged from just over 2,000 to almost

58,000. Thirty percent of my study sample was from public higher educational

institutions, 70% were private, and 40% of my sample was comprised of colleges or

universities that have a religious affiliation.

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One of the criteria for participation in this study was that each sustainability team

have at least three members; therefore, team meetings were, by definition, meetings of

three or more people. “Sustainability teams” were defined as any group collectively

working on developing sustainability initiatives on campus, and could include any

member of the campus community, such as students, staff, faculty, or other institutional

stakeholders. In other words, sustainability teams could be formally appointed staff with

sustainability in their title or job description or teams could be made up of committee

members, or students.

Confidentiality

As institutions were recruited for participation they were assigned a number /

letter label for tracking, described below. Through the transcription process, names of

individuals, institutions, and other identifiable characteristics were removed, prior to

coding. While members of the sample cannot remain anonymous due to my study design

and proposed data collection methods, confidentiality of respondents has been

maintained.

Participating institutions were categorized according to Table 2. Once assigned a

performance category color based on the letter grade received from the 2011 Campus

Sustainability Report Card (red = high performers, blue = moderate performers, yellow =

base performers), each institution in my sample was assigned a letter for tracking data

related to their institution (e.g. Red A, Blue A, or Yellow A). For data management

purposes, each phase of data collection was also assigned a number, such as RedA1 for

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website data, RedA2 for the one-on-one interviews with sustainability leaders, and

RedA3 for transcripts of sustainability team meetings.

Each participating sustainability program submitted at least one team meeting

recording. Some submitted two and one university submitted three. Similarly, for the

interviewing process, some institutions had only one person to interview, while others

had up to three sustainability leaders to interview. I will speak to the implications this had

on results in a later section, however the purpose of mentioning these differences now is

to explicate how the data tracking and respondent identifying was designed.

Research Design

Data Collection

My research treats language itself as the unit of analysis. I collected

language data from three sources at each participating institution: (1) one-on-one,

open-ended interviews; (2) sustainability team meetings; and (3) archival data in

the form of sustainability program websites. The interviews and group meetings

were audio recorded and transcribed. During the transcription process, all

identifying characteristics and references were removed to “clean” the data prior

to sharing the data with the other raters involved in coding.

(1) One-on-One Interviews with Sustainability Leaders: A total of 20 interviews

were conducted: nine interviews with high performing sustainability programs in my

sample; five with moderately performing sustainability programs; and six interviews with

individuals representing base performing sustainability programs. Two interviews were

lost to technical, recording issues. When contacted to re-interview, one respondent

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declined because he had left his position. The second respondent was non-responsive

after three attempts at contact. Therefore, 18 interviews were coded.

I conducted conduct one-on-one interviews with at least one representative

from each participating institution, affiliated with the campus’ sustainability

activities. ‘Sustainability leader’ was defined as either someone in a formal

organizational position, such as Sustainability Coordinator or Director of

Sustainability, or, for those intuitions lacking a formal position, chair of the

campus sustainability committee.

My assumption is that in their leadership role, these representatives

influence the formal or public persona of the sustainability program. Therefore,

the interview questions were open-ended and aimed at ascertaining interviewees’

perspectives on sustainability and capturing their language and framing of

sustainability. Interviews were completely voluntary and interviewees completed

an informed consent process and were aware that they may choose not to answer

any question or end the interview at any point, although none chose to do either.

The interviews were audio recorded and transcribed into Word documents,

and were cleaned of any references, which might identify the institution or

individual respondents. The blinded transcriptions were imported into ATLAS.ti

qualitative analysis software for coding (ATLAS.ti Americas, Corvallis, OR),

which will be described later in this section.

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The one-on-one interview consisted of open-ended questions, such as:

§ Is sustainability an important issue for your campus? Whether you

answer yes or no, what makes it so?

§ How and why did sustainability begin here?

§ What do you think motivates people to engage in sustainability?

§ How do you engage people to adopt sustainable behaviors or to support

sustainability on campus?

§ Has your approach to getting the campus “on board” changed since the

beginning? If so, how?

§ If you were to close your eyes, and think of the sustainability program

here, taken in its entirety, what comes to mind? How do you feel?

§ What are the current strengths of your program?

§ What skills or knowledge to you need to be better in your role?

§ What do you need to do to further develop the sustainability program?

§ What kind of developments would you like to see for your campus in

terms of sustainability, over the next five to ten years?

§ If you were to only speak from your heart / gut (no rules, institutional

guidelines, no filter) what would you say to people about sustainability?

§ Tell me about the time when you realized you had a passion for

sustainability. What were you doing, who were you working with, where

were you, what was the ah-hah that clicked for you?

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Additional, more specific, questions were also included.

§ Who is responsible for creating the content on your sustainability website

and for other communications about the sustainability program?

§ As you create the content for the website (e-mail blast, newsletter or

presentation) who is the audience you are communicating with? How do

you shape your messaging to get your audience(s) engaged with

sustainability?

§ Who else would you recommend I speak with here?

§ Does the sustainability program have its own funding?

§ Are there staff positions solely focused on sustainability? Title / position

§ Has your president signed on the ACUPCC?

§ Has your institution created a Climate Action Plan?

(2) Sustainability Team Meetings: The language of sustainability teams at each

participating institution was collected by audio recording for at least one sustainability

team meeting at each participating college or university. Assessing language at the level

of team allows for the most direct application of the language studies of Losada and

Heaphy (2004) and Donnellon (1996).

As stated above, part of the inclusion criteria was that only those colleges or

universities with a team of three or more people would be included in this study. These

teams could be comprised of sustainability specific staff, students, or an appointed

sustainability committee. To obtain a representative sample of language from each

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sustainability program in my sample, I encouraged each respondent to submit two

meeting recordings, if possible.

In order to keep my presence from influencing the content or tone of the team

discussion (Orne, 1962; Fernald et al., 2012) I asked each team to record their meetings

without me being present. This was achieved by using a free conference calling system,

which included a recording function. Using the remote recording technology of a

conference calling system meant the impact of the researcher or the awareness of being

researched was as light as possible (Orne, 1962; Fernald et al., 2012). Teams conducted

regularly scheduled meetings with a telephone (which could be a mobile phone) placed at

the center of the discussants, with the speaker function enabled, thus picking up all voices

in the room. I provided informed consent forms for everyone present at each meeting and

these were mailed back to me with signatures. Once I was aware of a sustainability

team’s meeting schedule, I provided each with a conference call dial-in telephone number

and a participant pass code. When the team dialed in, they used an additional key-stroke

to begin recording of their conversation.

Recorded conversations were automatically saved and were retrieved by me as a

voice file, after the fact. These recordings were downloaded and transcribed, with all

identifying characteristics and references being removed in the transcription process. The

group meeting files were then imported into ATLAS.ti qualitative software and coded

using the categories of Cooperrider and colleagues (2008), which will be described in

detail below (see also Table 2a & 2b).

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Sustainability team meeting recordings submitted for this study typically lasted,

on average, about 60 minutes, no matter what the performance category of the

sustainability program. I should note that there were two meetings in my sample, which

lasted less than 30 minutes and two meetings, which lasted about 80 minutes. The

discrepancies in length of meeting time resulted in varying lengths of meeting transcripts,

differences in amount of codable data and therefore coding frequencies reported in the

results section. I considered devising a rule in which all meetings were coded for the

same amount of content, as was done with website data, described below. However, in

contrast to website data, meeting data contains no clear demarcations. In this data set, the

sustainability team meetings are guided by agendas and conversation is free flowing. My

interest for including team meetings was to collect data similar to that used in by Losada

and Heaphy (2004) and Gottman (1994; 1999) in order to hear how members of the

sustainability teams talk to one another about sustainability.

(3) Website Content: A directed content analysis was performed on the

text of each participating institution’s sustainability website. Text was copied and

pasted into rich text documents and imported into ATLAS.ti for analysis. Other

management researchers have applied similar techniques when using website data

and content analysis. Jose and Lee (2007) used content analysis to analyze content

of online corporate environmental disclosure of Fortune 200 firms. Govers and

Go (2005) analyzed how websites are used to establish and promote identity and

sense of culture in the tourism industry. Kiyatkin and colleagues (in press)

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compared website text of business schools and corporations, estimating which

sector leads awareness and action in terms of social issues.

The sustainability programs in my sample had a wide range of web-based

resources. Some members of my sample had multiple pages of information,

programs, community events, maps of green attributes on campus, or links to

related centers and institutes. Other schools in my sample had far less messaging

via their websites. To assure that I obtained a representative sample of language

and a “like for like” comparison between schools, I created a template from the

school with the briefest web presence, noting what information was given or

topics addressed. I limited myself to only the topics or messaging themes on this

template when gathering data from other participants in my study. For example,

all the webpages in this study defined sustainability, discussed what and where to

recycle, and offered contact information about initiatives open to campus

involvement and key sustainability team contacts. Whether or not users accessed

the sustainability webpage or used search options from the university’s website

did not impact my research. The results obtained were, therefore, not a question of

website traffic or responses to the website content, but rather how the campus

sustainability program discussed sustainability on the website.

Data Analysis

As described in earlier sections of this chapter, data collected from

interviews, group meetings, and websites were given identifier codes that

communicated the performance category and data source, understood only by me.

I recruited three people to act as additional coders for this study. None of these

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individuals lives in NE Ohio, has ties or knowledge of any the institutions who

participated in this study, nor are any of them involved in the field of

sustainability. We applied directed content analysis to create a map capturing the

degree to which differences in emotional tone existed within sustainability

language of higher education across the three performance aggregates and taken

together as a whole. Coding for all data was conducted using Cooperrider and

associates’ (2008) Positive and Negative Discourse Categories featured in the

Appreciative Inquiry Handbook for Leaders of Change (2nd Ed. p.20-22) (see

Tables 3a & 3b).

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Table 3a: Positive Discourse Categories from Cooperrider et al (2008, p. 20)

Positive

Discourse Labels

Label Definition

Positive valuing

Any mention of positive values past or present.

Hope toward future

Any mention of hope, optimism, or positive anticipation toward the future.

Skill or competency

Any mention of skill, competency, action, or positive quality about self or others.

Openness, receptivity, learning

Any mention of receptivity in self or others accompanied by a positive outcome, also, any noticing of one’s, or another’s, learning or interests.

Active connection, effort to include, cooperation, or combination

Any noticing of efforts to include, cooperate, connect, and related that may be accompanied by at least an inferred positive outcome.

Mention of surprise, curiosity, or excitement

Any mention of curiosity, surprise, openness to fresh insights, or excitement in self or others.

Notice of facilitating action or movement toward a positive outcome

Any mention of a facilitating action or movement toward a real or imagined positive outcome or any mention of a facilitating object or circumstance. Also, noticing of any event that enhances another event, an effective state, or a person; noticing facilitative or positive cause and effect.

Effort to reframe in positive terms

Any mention of a negative emotion or action accompanied by the possibility of a positive desired outcome; also, any mention of a change in mood from negative to positive, including any mention of an obstacle that is temporary or getting over a negative static state, or reframing a negative situation into more positive terms.

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Table 3b: Negative Discourse Categories from Cooperrider et al (2008, p. 21)

Negative

Discourse Labels

Label Definitions

Negative valuing

Any mention of negative valuing; for example, fatalism, apathy, or dislike. Any description of person, a group, a circumstance, or an event as a problem or an obstacle.

Concern, worry, preoccupation, doubt

Any mention of concern, worry, or preoccupation without mention of a possible model to alleviate concern or to enhance understanding; any mention of doubt, suspicion, or lack of confidence in future outcomes.

Unfulfilled expectation

Any mention of any event, action, state, or person that does not match intention, wish, desire, goal, or other unfulfilled expectation.

Lack of receptivity, absence of connection

Any mention of a lack of receptivity in self or others, including a lack of collaboration, a lack of understanding, a failure to listen or failure to agree, or any explicit mention of an absence of connection.

Deficiency in self or others

Any mention of a sense that something is missing; for example, a deficiency in self or others or a lack of motivation, appropriate effort, skill, or competence or an absence of resources (such as time or money).

Negative effect Any mention of feelings of dissatisfaction, selfishness, sadness, defensiveness, irritation, or anger without mentioning a possible antidote or relief or effort to understand.

Withdrawal or suppression

Any mention of avoidance, ignoring, withdrawal of energy or surrender, or suppressing of self or others.

Control or domination

Any notice of effort or action to disrupt, dominate, wield control, or halt a mood or an action in self or other.

Wasted effort Any mention of excessive investment of time, resources, or energy without mention of reward or positive outcome.

Prediction, image of a negative future

Any mention of prediction, vision, image, or expectation of a negative future.

Attribution of control by others in combination with self-deprecation

Any notice of effort or action in /others to disrupt, dominate, or wield control in combination with attribution of helplessness to self or self-pity.

Negative cause and effect relation

Any explicit notice of a cause and effect relationship leading to a negative outcome

Reframing a situation in negative terms

Any mention of a positive emotion with the possibility of a negative outcome; mention of experiencing a change in mood from positive to negative or getting into a negative state, focusing on possible obstacles, or reframing a positive situation into more negative terms.

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The Positive and Negative Discourse Category labels were used to build “code families”

in the ATLAS.ti software program and were applied to highlighted portions of text—or

quotations from the data. Each label and its definition were visible for consistency in the

coding process among multiple coders.

When using a code developed by another scholar, and the objective is to replicate,

extend, or challenge their findings, the code must be used exactly as it appears in the

earlier or original research (Boyatzis, 1998). This study is neither an extension, challenge

nor replication of Cooperrider and colleague’s earlier research. During my initial process

of coding, labels were added to the positive and negative discourse families, reflecting

phenomena I was finding in the raw data that was not being accurately represented by the

existing code. These additional labels were meant as complements to the existing code,

adapting it to the different types of raw information contained in my data. This process

was conducted prior to the training of the three other coders. Two examples of this are

the addition of Embedded Sustainability to the Positive Discourse family and Bolt-On

Sustainability to the Negative Discourse family. The terms come from Laszlo and

Zhexembayeva (2011)

Embedded Sustainability implies systemic changes in thinking and practice.

Internal and external boundary lines become blurred, as notions of competitor or

customer shift to ideas of partner. Rather than defensiveness, a spirit of inquiry and

exploration become comfortable modes of operation, seeking new partnerships and

innovation. Sustainability becomes the lens through which institutions can re-discover

and re-design themselves (Laszlo & Zhexembayeva, 2011).

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Bolt-on Sustainability on the other hand, characterizes a superficial or piecemeal

response to sustainability, leaving the mainstream institution un-impacted by its presence.

Bolt-on sustainability efforts produce fragmentary wins on the fringes of an institution’s

activities (Laszlo & Zhexembayeva, 2011; Sterling, 2004). When sustainability efforts

are bolted-on, they lack integration throughout the institution’s operations or policies.

This study is concerned with emotional tone of narratives and the impact of the

emotional tone on change. Embedded Sustainability was added to the positive discourse

family because it complements and vivifies other labels included in this code family, such

as Active Connection, Effort to Include, Cooperation, or Combination and Mention of

Surprise, Curiosity, or Excitement. Bolt-on Sustainability is categorized as a negative

because it relates to labels included in the negative code family, such as Lack of

Receptivity, Absence of Connection; and Withdrawal or Suppression.

An additional discourse family of codes was created to capture neutral discourse

themes. I anticipated these labels prior to beginning the coding process, based on my

experience as a sustainability coordinator in higher education, which suggested neutral or

information-based discourse to be a major component of sustainability communications.

This includes instructions, directions, or sharing of data from the campus or experts in the

field, as indicated in Hypothesis 1a. Table 4 presents the labels added to the code.

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Table 4. Positive, Negative, and Neutral Labels Added to Cooperrider et al’s Positive/Negative Discourse

Code (2008)

Labels Added

Label Definitions

Positive Discourse Labels

Systems Thinking or Interconnectedness

Any mention of or expressed desire for interconnectedness. Includes references to how parts of the whole work together or a systems level perspective striving for better connection within a system toward a positive end.

Positive Cause & Effect Any explicit notice of a cause and effect relationship leading to a positive outcome.

Embedded Sustainability

Any mention of the need for, or presence of, sustainability as being embedded or integrated into processes or thinking, applied to one’s self, others, the institution, or society. References that sustainability is everyone’s concern, job, or opportunity and connections between sustainability and transformational change for the system.

Emphasizes the “We” Any references to being part of a group, the institution, or a larger domain (ie sustainability professionals). Any references that one’s role or department is part of the larger whole of the institution, or mention of achieving things together. Includes mention of working with others across campus toward a positive end and with an inclusive tone or campus pride, such as ‘we at [name of institution] are really good at….” Use of words like: we; our; us; team; together. This is the opposite of an “us vs. them” mindset.

Negative Discourse Labels Bolt-on Sustainability Any mention that sustainability efforts are present for PR (public relations),

marketing, school rankings, or recruitment purposes. Also includes any comments, which suggest sustainability is only pertinent to, or the concern of, one group, one position or one person, or department. This label includes any views of sustainability as transactional, or as being worthwhile in order to save money or reduce energy consumption, as well as compartmentalizing sustainability to be just about the environment or recycling, for example.

Separateness References to a lack of connectedness with other parts of the campus, the institution, other groups or departments on campus. Any emphasis on differences among groups on campus (i.e. emphasizing administration or faculty as though they are a separate class), references to silos. Also, any mention or reference to an "us vs them" perspective. Use of words such as: they; theirs; those guys.

Emphasizes the “I” A focus on one’s self as opposed to seeing one’s self as part of a team or community. Individualism, self-focused, narcissistic comments which communicate the speaker sees the achievements has being due to their efforts, or references that they don’t have to work within the typical rules. Includes language that is predominately about me, mine, my, I. References to others will only be done in a passing way, others play only a small role, big achievements are / have been achieved by me, I’m the special ingredient around here.

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Table 4 cont’d. Positive, Negative, and Neutral Labels Added to Cooperrider et al.’s Positive/Negative

Discourse Code (2008)

Neutral Discourse Labels Behavioral Instruction Any mention of recommended behaviors, which are framed as helping to

create a more sustainable campus, world, life, office, dorm room, department or the like. These are often directions, suggestions, instructions, tools, tips, or lists of behaviors one would ideally be engaging in.

Uses Expert Data Any mention of expert statistics, quotes, or exert data related to sustainability. Includes scientific and other research information. An ‘expert’ can be someone on campus with a position of authority or a particular knowledge / skill set, or someone outside the institution. Also includes quotes from famous people, writers, poets, etc.

Uses Campus Data Any mention of campus statistics or data related to the person’s own campus size, population, energy consumption, purchasing, waste, recycling, behaviors, etc. Also, may include references to increases or decreases in the above (i.e., pre or post sustainability intervention).

Information to Inspire Behavior Change

Any reference that simply sharing information, increasing awareness of statistics, data, facts, opportunities, cost savings, etc. will be effective in changing perspectives, beliefs, behaviors.

How Change Occurs Any references or conversation, which include the speaker’s theories on how behavior or organizational change occurs, such as “sustainability will gain traction once the President / students get behind it,” or “it’s gotta be personal for people or they won’t change,” “it’s gotta hit people in their pocket books before they’ll change their behavior.”

Reliability

Armstrong and colleagues (1997) observed that generally, qualitative

methodologies are not explicit about use of the concept of inter-rater reliability to

establish consistency of findings, however the concept emerges implicitly in descriptions

of procedures for carrying out the analysis. Despite this, the frequent attention paid to

qualitative analyses being better conducted as a group activity suggests results will be

improved if one view is moderated by another (Armstrong et al, 1997). Boyatzis (1998)

is more direct, arguing that the establishment of reliability is critical when applying

qualitative and interpretivist research methods. He defines reliability as a “consistency of

observation, labeling, or interpretation” (Boyatzis, 1998, p. 144). Krippendorf (2004)

generally agrees, defining reliability as the degree to which a coding process is

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reproducible with different coders, elsewhere, over time, and under conditions that should

not affect the results. While there is some disagreement about the efficacy of measures of

inter-rater reliability (see Krippendorf, 2004; Lombard et al, 2003; Pope et al, 2000;

Armstrong et al, 1997) there remains merit in involving more than one analyst in

situations where researcher bias is perceived to be present and an alternative has not yet

been identified.

For the research described here, use of multiple data analysts is essential due to

my relationship with one of the sites in my sample. Though not formally connected at the

time of this study, as one of the founders of one of the sustainability programs

participating in the study, my preferences for confirming approaches to change and

hopeful messaging have surely influenced the current emotional tone of discourse,

despite my having left this position five years ago. Although it is a vastly different

program today than when I left my role, I must be aware of possible personal and

theoretical biases. This led me to pay particular attention to inter-reter reliability in

coding the data.

Based on Boyatzis’ (1998) definition, inter-rater reliability is consistency among

multiple data analysts or viewers. Consistency is achieved when multiple people identify

the same themes in the same data. Inter-rater reliability is the “consistency of judgment

among multiple observers” (Boyatzis, 1998, p. 147). The most common way of

establishing reliability among multiple analysts is through double coding, in which coders

examine the same raw data, making judgments without interacting or seeing the results of

one another’s analysis. Observers then share and compare their results, discussing each

label or category until agreement is reached (Boyatzis, 1998).

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My coding-training protocol included reviewing the code and definitions with my

coders, and thinking of examples from shared experiences or popular culture (movies, TV

shows) that fit each label. In this way, my three coders were able to perform successful

“dry runs” using data outside my sample, which enabled them to gain skill at applying the

Positive, Negative and Neutral Discourse Categories Code (Cooperrider et al, 2008).

These exercises were then followed with short samples from my data set, which I had

already coded so the coders could see how selections from the data (quotes) can

sometimes be longer to capture the entirety of an idea or sentiment and are sometimes

shorter if the quote is more succinct or directly speaking to one of the code labels.

Moreover, providing the coders-in-training examples of coded transcripts allowed them

to see how more than one label can apply to a selected passage of text. In the next step,

each of the three coders-in-training, had to look at short, “clean” selections of text from

my data set and select the same quotations or selections of data as I had in samples from

the study data and apply the same label or labels as I did, as described by Boyatzis

(1998). We did this process together, either in person or via Skype. Through these

training exercises both the other coders and I developed skills and greater consistency in

our coding. I was able to ascertain the degree of similarity and consistency among my

coders before our collective efforts were applied to the sample. This approach also

allowed me to determine general similarities and difference in interpretation of label

names and definitions, and to determine the degree of difficulty in applying the code.

Once the coders expressed their confidence with their understanding of the code

and coding process, we then went on to establish inter-rater reliability. I provided each

coder multiple selections from interviews, group meetings, and websites to code, in order

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to establish inter-rater reliability. Boyatzis (1998) and Morse and colleagues (2002) have

presented multiple ways to establish reliability among coders. Included in both of these

papers is percentage agreement scores, which are calculated as the number of times of

observation that multiple analysts agree, divided by the number of times of total possible

observations. Percentage agreement is most appropriate when the unit of coding and unit

of analysis are the same, like language. Therefore, I employed this method to determine

an inter-rater reliability score for each of the three coders. One of the coders scored 87%,

a second scored 83%, and the third 82%, resulting in an average inter-rater reliability

score of 84%, which is considered to be reliable.

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CHAPTER FIVE: RESULTS

This chapter provides the results of my study on the exploration of the emotional

tone of the language used by sustainability programs in higher education contexts. My

study had two aims: first, to map and describe the emotional tone of language currently

used by college and university-based sustainability programs and secondly, to identify

whether differences in emotional tone correlated with campus sustainability rankings of

high, moderate, and base performance. The descriptive analysis of the data, illustrative

quotes, and interpretation of the research findings are presented in this chapter.

Data Analysis

Analysis of emotional tone used by sustainability programs

Analysis of language tone was determined by identifying discourse as positive,

negative or neutral, as described in Chapter Four. Initial analysis of language was

performed for all participating sustainability programs and results were aggregated by the

high, moderate, and base performance categories. Percentages of quotations from

discourse that were coded as positive, negative, and neutral are shown in Table 5. Across

all sources of data (one-on-one interviews, group meetings, and sustainability program

websites) and including all three of the performance categories, a total of 2,252

quotations were coded. Of the total number of coded quotations from interviews, group

meetings, and websites, 54% (1,224) were from the four high performing sustainability

programs, 22% (504) were from the three moderately performing institutions, and 23%

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(524) of the quotations coded were from the three base performing sustainability

programs.

Table 5. Positive, Negative and Neutral Discourse for All Sources of Data by Performance Category1

High

Performers

Moderate

Performers

Base

Performers

Number of Programs 4 3 3

Positive Discourse Labels 63.8% 50.2% 39.3%

Negative Discourse Labels 15.4% 22.8% 39.3%

Neutral Discourse Labels 20.8% 27% 21.4%

Number of Quotes Coded

(total N = 2,252) 1224 504 524

It should be noted that there were more than twice as many quotations from the

high performing institutions as the moderate and base performers, combined. This may be

due to the greater number of individuals interviewed from high performing sustainability

programs (N = 7 interviews) than from moderate performing programs (N = 5) and base

performing programs (N = 6). Additionally, sustainability programs making up the high

performer category submitted more group meetings, and group meetings which were

often longer in length (often longer than 60 minutes). The high performing programs

submitted a total of 9 group meeting recordings, compared to 4 group meeting recordings

1 A Chi-Square analysis was performed on data from Tables 5 – 8. Results are presented in Appendix A. To summarize, the relationships between performance categories were found to be statistically significant for all sources of data (Table 5), interview data (Table 6), and group meeting data (Table 7). Relationships presented in Table 8 for website data were not found to be significant.

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from moderate performing programs, and 3 for the base performing sustainability

programs.

Analysis of language by performance category

High performing sustainability programs, all data: When all three sources of

data were combined for high performing sustainability programs positive discourse labels

accounted for 63.8% of quotes, while negative discourse labels represented just 15.4% of

quotations. Neutral labels were applied to 20.8% of high performers’ quotes.

Calculation of the positive / negative ratio from this data, resulted in a 4:1 ratio (63.8%:

15.4%) of the top ranking institutions in my sample.

Moderate performing sustainability programs, all data: Among the moderate

performing programs, positive discourse comprised 50.2% of the coded quotations.

Labels categorized as negative discourse were applied to 22.9% of the moderate

performer’s quotes, and 27% of the quotes were coded with neutral labels. This yielded a

2:1 positive / negative ratio (50.2%: 22.8%) for the moderate performers in my sample.

Base performing sustainability programs, all data: When the language for the

base performing sustainability programs was analyzed, 39.3% of the quotes were labeled

positive, 39.4% of quotes were coded with negative discourse labels and neutral labels

were applied to 21.4% of the base performers’ quotes. The sustainability programs

falling into the base performance category were labeled with negative discourse codes 2.5

times more frequently across all sources of data than those programs in the high

performing category and had a 1:1 ratio (39.3%: 39.4%) of positive to negative discourse.

Direct quotes from the data are presented later in this chapter.

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Analysis of interview data by performance category

Thirty-eight percent of these data came from interviews with sustainability leaders

representing high performing sustainability programs (N = 7), 28% of the interview data

(N = 5) represented the moderate performers in my sample, and the remaining 33% of the

interview data (N = 6) resulted from interviews with base performing sustainability

programs. Women made up 39% of my interviews sample (N = 7) and men comprised

the remaining 61% (N = 11), ranging from 23 to 68 years of age.

Table 6 presents percentages of quotations coded as positive, neutral, and

negative discourse collected through one-on-one interviews, aggregated by the high,

moderate, and base performance categories. A total of 1,101 quotes were coded from

interview data representing all three of the performance categories. Four hundred and

ninety of these quotes were from interviews with high performers, 244 quotes from

moderate performers, and 367 quotes came from interviews of participants in base

performing sustainability programs.

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Table 6. Positive, Negative, and Neutral Discourse for Interview Data by Performance Category

High

Performers

Moderate

Performers

Base

Performers

Number of Programs 4 3 3

Positive Discourse Labels 71.2% 40.6% 42.5%

Negative Discourse Labels 21.6% 41.4% 39.2%

Neutral Discourse Labels 7.1% 18% 18.3%

Number of Quotes Coded

(total N = 1,101) 490 244 367

High performing sustainability programs, interviews: Seven, one-on-one

interviews were conducted with people identified as “sustainability leaders” at four

higher educational campuses categorized as high performing by the Sustainability Report

Card. Four hundred and ninety quotes were coded from these seven interviews, with

71.2% of the quotes coded with positive discourse labels. Negative discourse labels were

applied to 21.6% of the high performers’ interview quotes, and 7.1% were labeled as

neutral discourse. Within the interviews, the high performing sustainability programs’

ratio of positive / negative discourse was greater than 3:1.

Moderate performing sustainability programs, interviews: Two hundred and

forty-four quotes were coded from five interviews with higher educational sustainability

programs categorized as moderate performers. Of these, 40.6% were labeled as positive

discourse, 41.4% as negative discourse, and 18% as neutral discourse. Calculations from

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analysis of this interview data resulted in a positive to negative discourse ratio of 1:1 for

the moderate performing sustainability programs.

Base performing sustainability programs, interviews: Six interviews were

conducted with “sustainability leaders” of higher educational sustainability programs

categorized as base performing, resulting in 367 coded quotes. Positive discourse labels

were applied to 42.5% of the quotes, 39.2% of the quotes were labeled as negative

discourse. Neutral discourse labels were applied to the remaining 18.3% of quotes from

interviews in this category. The base performing sustainability programs in my sample

had a positive / negative discourse ratio that was slightly greater than 1:1.

Analysis of group meeting data by performance category

Sustainability team meetings were audio recorded and the transcripts were coded

with the same procedures described for one on one interviews. Fourteen group meetings

were submitted from all of the sustainability programs. One audio recording from the

moderate performer program had such poor sound quality, due to the audio device, that

the recordings could not be transcribed. The total number of meetings analyzed from the

sustainability programs was seven high performing, three moderate performing and three

base performing. From the 13 group meetings transcribed for analysis, 405 quotes were

coded. While this data source is closest to that described by Gottman (1994) and Losada

and Heaphy (2004), it has also been the most complicated, given reliance on technology

for data collection, which was not always dependable.

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Group meetings ranged in length from less than 30 minutes to more than 60

minutes, these differences are illustrated in Table 7 through the range of total number of

words transcribed for each category. All groups had some combination of narrative-rich

and narrative-thin discourse, meaning more strategic versus more operational discussions

respectively. Within the mix of discourse across performance categories, high

performing sustainability teams had more narrative-rich conversations, discussing

dilemmas, management, future visions for their programs, and strategy. Those programs

categorized as base performers demonstrated more narrative-thin conversations,

characterized by agreements about responsibilities or action lists for the day or week.

Table 7 presents these data aggregated by performance category.

Table 7. Positive, Negative, and Neutral Discourse for Group Meeting Data by Performance Category

High

Performers

Moderate

Performers

Base

Performers

Number of Programs 4 3 3

Total Number of Words 28,590 15,013 9,083

Positive Discourse Labels 52.2% 41.9% 14.3%

Negative Discourse Labels 25.7% 29% 61.2%

Neutral Discourse Labels 22.1% 29% 24.5%

Number of Quotes Coded

(total N = 405 ) 276 31 98

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High performing sustainability programs, group meetings: From the seven group

meetings analyzed from high performers, 276 quotes were coded. Labels from the

positive discourse family were applied to 52.5% of the quotes. Negative discourse labels

were applied to 25.7% of the group meeting quotes, with 22.1% of the quotes labeled as

neutral. The positive / negative communication ratio of participants discourse was

approximately 2:1.

Moderate performing sustainability programs, group meetings: From the three

group meetings analyzed from moderate performers in the sample, 31 quotes were coded.

Positive discourse labels were applied to 41.9% of these quotes, with 29% coded with

negative discourse labels and 29% coded with neutral labels. The group conversations of

moderately performing sustainability programs had a 1.4: 1 positive / negative

communication ratio.

Base performing sustainability programs, group meetings: Data analyzed from

the three base performing sustainability program group meetings, 98 quotes were coded.

Of these, 14.3% were positively labeled, whereas 61.2% were labeled as negative

discourse. Neutral discourse labels represented 24.5% of the quotes. The base performing

sustainability programs’ group meeting conversations had a 1:4 positive / negative ratio.

Analysis of website data

Content analysis was performed on the text of each participating institution’s

sustainability website analyzed with ATLAS.ti as described in Chapter Four. Ten of the

sustainability programs had websites, and all of these were analyzed. As stated earlier,

some programs had more extensive websites than others. The majority of university or

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college sustainability program websites were comprised of information about research

institutes and centers, student clubs, certification programs, and other similar areas.

However, some of the sustainability programs included in my sample offered fewer web-

based resources, resulting in less content to examine.

In an attempt to compare like with like, it was necessary to create a consistent

way of analyzing the website data. Therefore, I created a guidelines to determine which

items would be selected from each website for analysis. I reviewed the website of the

sustainability program with the least number of sections in its website, and used those

sections to create a template of sections or pages to be analyzed across all the

sustainability programs in my population. To illustrate, all of the websites contain

definitions of sustainability and why the issue is important to their campus. Additionally,

all the websites include information on recycling as well as presentation of the

institution’s sustainability activities. Once the template was created from the institution

with the least content, I then collected data from the same sections of the rest of the

institutions in my sample. The results of how these “like” sections were coded are shown

in Table 8.

It is interesting to note the similarity across the three performance categories, with

specific regard to the positivity / negativity ratios and the percentages of quotes coded

with neutral discourse labels. These points will be addressed in Chapter Six.

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Table 8. Positive, Negative, and Neutral Discourse for Website Data by Performance Category

High

Performers

Moderate

Performers

Base

Performers

Number of Programs 4 3 3

Positive Discourse Labels 62.9% 61.6% 61%

Negative Discourse Labels 2.4% 2.2% 5.1%

Neutral Discourse Labels 34.7% 36.2% 33.9%

Number of Quotes Coded

(total N = 746 ) 458 229 59

High performing sustainability programs, websites: Among the four high

performing sustainability programs in my sample, 458 quotes were coded from their

websites. Of these quotes, 62.9% were coded with labels from the positive discourse

family. Negative discourse labels were applied to just 2.4% and neutral discourse labels

were applied to 34.7% of the quotes. The websites of high performing sustainability

programs in my population had a positivity / negativity ratio of nearly 26:1.

Moderate performing sustainability programs, websites: Three websites were

analyzed from the moderate performers in my sample, with 229 quotes or passages being

coded. Positive discourse labels were applied to 61.6% of the quotes. Negative

discourse labels were applied in only 2.2% of the quotes with neutral labels being related

to 36.2% of the quotes. The ratio of positivity / negativity for the moderate performers’

websites was 28:1.

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Base performing sustainability programs, websites: Much like the other two

performance category, I found a fairly high percentage of quotes being coded with

positive discourse labels from the three websites of the base performing members of my

sample. Positive discourse comprised 61% of the quotes, 5.1% were negative, and like

the two other categories, neutral discourse labels were applied to 33.9% of the website

quotes and passages. The positivity / negativity ratio for the base performers’ websites

was approximately 12:1.

Positive / negative ratios, by performance category

The ratios of positive to negative (P/N) communication for each data source and

all sources combined, aggregated by performance groups, are shown in Table 9.

Evaluation of all sources of data combined, revealed all forms of communication of high

performing sustainability programs had twice as much positivity as the moderate

programs, and four times as much as the base performing programs. Looking solely at

interview data, high performing programs exhibited three times as much positivity in

their language than both moderate and base performing programs. Group meeting data

revealed the high performers communicated using language that was two-times more

positive as the moderate performing programs. Group meetings among the base

performing sustainability programs had four times more negativity than positivity, and

they communicated with four times more negativity than their moderate or high

performing counterparts in this sample. In contrast to the ratio differences in interview

and group meeting data, website data revealed much higher P/N ratios across all three

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performance categories. Implications of these ratio results will be discussed in further

detail in Chapter Six.

Table 9. Positive / Negative Discourse Ratios by Performance Category

Discourse families, aggregated by data source and performance category

Thus far, I have presented results of the data analysis from the perspective of

performance categories and sources of data and the percentages of these that are positive,

negative, or neutral. These results may help determine whether different performance

categories employ different emotional tone in their sustainability communication.

In the next section of this chapter, I take a more granular look at the data by

unpacking the positive, negative, and neutral discourse code. By evaluating where and

how often each label is applied- across different sources of data and performance

categories, rather than by simply looking at the code families of positive, negative, or

neutral- information about sustainability communication in higher educational contexts

can be expanded. I begin with the positive discourse family of labels.

High

Performers

Moderate

Performers

Base

Performers

Number of Programs 4 3 3

All Data Combined 4:1 2:1 1:1

Interview Data 3:1 1:1 1:1

Group Meeting Data 2:1 1:1 1:4

Website Data 26:1 28:1 12:1

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Positive Discourse: The positive discourse family of labels and the frequency of

each label’s application are shown in Tables 10 – 16. Table 10 provides an overview of

label frequencies for all sources of data combined. Tables 11– 13 present direct quotes

associated with the three labels with the highest frequencies for each performance group.

Tables 14 – 16 present the positive discourse label frequencies, organized by data

sources, beginning with interview data (Table 14), group meeting data (Table 15), and

website data (Table 16).

The frequencies of labels that comprise the positive discourse family, for all

sources of data (interviews, group meetings, and websites) for each performance category

are shown in Table 10. There are two numbers in each column, one indicates the

frequency of coded quotations for each label. The other number, presented in

parentheses, shows the frequency as a percentage of the total label applications within

that performance category for that particular source of data. Within each table, I have

bolded the three most frequently occurring positive discourse labels for each performance

category.

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Table 10. Number and Frequency of Positive Discourse Labels by Performance Category: All Sources of

Data

All Sources of Data High Performers

Moderate Performers

Base Performers

Number of Positive Coded Quotes: 781 253 206 Percent of all Positive Quotes (1,240) 63% 20% 17%

Positive Discourse Family Labels Frequency of Label & Within-Performance

Category Percentage (highest frequencies in bold) Facilitating Action, Movement Toward a Positive Outcome 155 (20%) 64 (25%) 36 (17%)

Active Connection/Effort to Include, Cooperate or Combination 114 (15%) 39 (15%) 24 (12%) Positive Valuing 67 (9%) 26 (10%) 10 (5%) Skill or Competency 56 (7%) 17 (7%) 22 (11%) Systems Thinking or Interconnectedness 57 (7%) 17 (7%) 14 (7%) Openness/Receptivity to Learning 45 (6%) 28 (11%) 14 (7%) Embedded Sustainability 58 (7%) 13 (5%) 9 (4%) Emphasizes the "We" 41 (5%) 9 (4%) 23 (11%) Positive Cause & Effect 47 (6%) 10 (4%) 13 (6%) Hope Toward Future 49 (6%) 10 (4%) 10 (5%) Envisioned Ideal 34 (4%) 11 (4%) 10 (5%) Surprise, Curiosity, Excitement 35 (4%) 1 (0%) 9 (4%) Reframing in Positive Terms 23 (3%) 8 (3%) 12 (6%)

Across all sources of data from high performers, the three most frequently applied

coding labels were: Facilitating Action, Movement Toward a Positive Outcome; Active

Connection/Effort to Include, Cooperate or Combination; and Positive Valuing. Table

11 presents examples of quotes coded with these labels from sustainability programs in

this performance category.

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Table 11. Direct Quotes from Positive Discourse Labels with Highest Frequency: High Performers

Among moderate performers, the most frequently applied labels were also

Facilitating Action, Movement Toward a Positive Outcome; Active Connection/Effort to

Include, Cooperate or Combination; and Openness or Receptivity to Learning. Table 12

presents direct quotes from moderate performer data.

Label

Quote

Facilitating Action, Movement Toward a Positive Outcome

“I needed to have those higher level administrative conversations about what our [academic integration] intentions were and how we can… paint a picture of where we're going academically in sustainability because… we really didn't have an academic, sustainability vision. Those meetings helped us create one.”

Active Connection/Effort to Include, Cooperate or Combination

“So what we have created is a process and a building energy management agreement form,. What I do is… before we initiate a setback, we get signoff from the building customers, from the automation guys, and from our maintenance organization.”

Positive Valuing

“The faculty, I think, comes from that place… I think deep down they really want us to be sustainable because they want us to be great and they want us to be here in a hundred years.”

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Table 12. Direct Quotes from Positive Discourse Labels with Highest Frequency: Moderate Performers

Label

Quote

Facilitating Action, Movement Toward a Positive Outcome

“I think that some process or project aspects of sustainability actually strike sentimental chords with some people and they go, ‘Yeah, yeah, I can do that.’”

Active Connection/Effort to Include, Cooperate or Combination

“The first thing I thought I’d do was, let’s find what our actual educational philosophy is, because we have one that’s explicit on paper, and find that adaptation and change piece of it and start to make that language connect with the goals of the sustainability program.”

Openness or Receptivity to Learning

“I've been doing a lot of soul searching on that, and I've really started trying to what I truly believe. So like I said, [trying to motivate people is] so new to me, and I've been all – in the last year I've really evolved, I think, a lot on that. And I really try to avoid reward-based things, because I don't think that is really what's motivating to people.”

The most frequently applied positive discourse labels for all sources of data for

the sustainability programs making up the base performing category were Facilitating

Action, Movement Toward a Positive Outcome; Active Connection/Effort to Include,

Cooperate or Combination; and Skill or Competency. Table 13 features direct quotes

from this performance category, for these labels.

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Table 13. Direct Quotes from Positive Discourse Labels with Highest Frequency: Base Performers

Label

Quote

Facilitating Action, Movement Toward a Positive Outcome

“I came in about 7 plus years ago, and it wasn’t really big then. It was probably about 6 years ago that we started seeing changes and that’s when the new dean came in. He really wanted to get sustainability, get it out there..”

Active Connection/Effort to Include, Cooperate or Combination

“I think a lot of [our strength] is the cooperation between faculty and staff. I think that’s a big strength we have, there’s a lot of support for ideas about how to be more sustainable. When someone does something about sustainability in our classrooms, that gets passed on to somebody else and it’s like a domino effect.”

Skill or Competency

“Yes, we have newer buildings but [our competitor institution has] this cool vibe going on, and they like to compete with us on recycling… and they beat us!”

The positive discourse labels as applied to interview data across the three

performance categories are shown in Table 14. The three predominant labels from all

interview data, which combines the three performance categories, were: Facilitating

Action, Movement Toward a Positive Outcome; Active Connection/Effort to Include,

Cooperate or Combination; and Skill or Competency. As with all sources of data

described in Table 9, there were similarities across the performance categories, but not

exact duplication.

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Table 14. Number and Frequency of Positive Discourse Labels: Interview Data

Interview Data ALL Interviews

High Performer

Moderate Performer

Base Performer

Number of Positive Coded Quotes 604 349 99 156 Percent Positive Coded Quotes 100% 58% 16% 26%

Positive Discourse Family Labels Frequency of Label & Within-Performance Category

Percentage (highest frequencies in bold) Facilitating Action, Movement Toward a Positive Outcome 85 (14%) 47 (13%) 14 (14%) 24 (15%) Active Connection/Effort to Include, Cooperate or Combination 72 (12%) 46 (13%) 8 (8%) 18 (12%) Positive Valuing 37 (6%) 26 (7%) 6 (6%) 5 (3%) Skill or Competency 72 (12%) 40 (11%) 11 (11%) 21 (13%) Systems Thinking or Interconnectedness 50 (8%) 28 (8%) 10 (10%) 12 (8%) Openness/Receptivity to Learning 75 (12%) 36 (10%) 26 (26%) 13 (8%) Embedded Sustainability 40 (7%) 31 (9%) 4 (4%) 5 (3%) Emphasizes the "We" 34 (6%) 15 (4%) 3 (3%) 16 (10%) Positive Cause & Effect 33 (5%) 19 (5%) 4 (4%) 10 (6%) Hope Toward Future 30 (5%) 20 (6%) 5 (5%) 5 (3%) Envisioned Ideal 23 (4%) 15 (4%) 1 (1%) 7 (4%) Surprise, Curiosity, Excitement 17 (3%) 7 (2%) 1 (1%) 9 (6%) Reframing in Positive Terms 36 (6%) 19 (5%) 6 (6%) 11 (7%)

Labels with the highest frequency, obtained from interview data with high

performing sustainability programs, were Facilitating Action, Movement Toward a

Positive Outcome; Openness or Receptivity to Learning; and Active Connection/Effort to

Include, Cooperate or Combination and Skill or Competency.

The labels most frequently applied to moderate performer interviews were

Facilitating Action, Movement Toward a Positive Outcome; Active Connection/Effort to

Include, Cooperate or Combination; and Skill or Competency.

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The three labels coded most frequently for base performers were the same as for

their high and moderate performing colleagues: Facilitating Action, Movement Toward a

Positive Outcome; Active Connection/Effort to Include, Cooperate or Combination; and

Skill or Competency.

The label frequencies for positive discourse coded data from all group meetings

combined and by performance category are shown in Table 15.

Table 15. Number and Frequency of Positive Discourse Labels: Group Meeting Data

Group Meeting Data ALL Group Meetings

High Performer

Moderate Performer

Base Performer

Number of Positive Coded Quotes 171 144 13 14 Percent Positive Coded Quotes 100% 84% 13% 13%

Positive Discourse Family Labels Frequency of Label & Within-Performance Category

Percentage (highest frequencies in bold) Facilitating Action, Movement Toward a Positive Outcome 41 (24%) 32 (22%) 1 (8%) 8 (57%) Active Connection/Effort to Include, Cooperate or Combination 27 (16%) 20 (14%) 4 (31%) 3 (21%) Positive Valuing 14 (8%) 14 (10%) 0 (0%) 0 (0%) Skill or Competency 5 (3%) 5 (3%) 0 (0%) 0 (0%) Systems Thinking or Interconnectedness 11 (6%) 9 (6%) 2 (15%) 0 (0%) Openness/Receptivity to Learning 6 (4%) 6 (4%) 0 (0%) 0 (0%) Embedded Sustainability 9 (5%) 9 (6%) 0 (0%) 0 (0%) Emphasizes the "We" 11 (6%) 8 (6%) 3 (23%) 0 (0%) Positive Cause & Effect 12 (7%) 8 (6%) 2 (15%) 2 (14%) Hope Toward Future 13 (8%) 11 (8%) 1 (8%) 1 (7%) Envisioned Ideal 3 (2%) 3 (2%) 0 (0%) 0 (0%) Surprise, Curiosity, Excitement 15 (9%) 15 (10%) 0 (0%) 0 (0%) Reframing in Positive Terms 4 (2%) 4 (3%) 0 (0%) 0 (0%)

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For all group meetings, the three most recurrent labels were Facilitating Action,

Movement Toward a Positive Outcome; Active Connection, Effort to Include, Cooperate

or Combination; and Surprise, Curiosity, Excitement. A direct quote, illustrating the

Surprise, Curiosity, Excitement label is:

“When I first heard about it, it was like, ‘Well, you know we're talking about this recycling program… ‘Really? Oh, interesting… ‘We're actually talking about a program?!’ Yeah, we'd like you to develop a program. ‘No kidding?!.. Ground floor?! ... Alright! I'm in!’” The label frequencies for each performance group were also determined. The

same labels that were most frequently applied for all group meetings combined were also

the most frequent for the high performer group meetings (frequency in bold). Among

moderate performers, Active Connection, Effort to Include, Cooperate or Combination

was the most frequently applied label, followed by Emphasizes the “We”. Positive Cause

and Effect and Systems Thinking or Interconnectedness occurred with the same

frequency.

Emphasizes the “We” was applied to this quote: “I think the whole point of

sustainability is the teamwork ethic. This is only going to be successful if we do it

together.”

Positive Cause and Effect was applied to this quotation: “There’s an obvious

connection between [faith] identity and mission and the values central to sustainability,

and that’s the language we’re going to be working on because I feel once we make that

connection, it will kick the snowball down the hill!” A direct quote illustrating Systems

Thinking or Interconnectedness is:

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“I keep saying this, the one thing that sustainability has taught me is about the connectedness of everything-- everything is connected to everything. You know, every action has a reaction. Well, it isn't just that every action has a reaction, every action has an impact…I’m thinking of the student this morning from the other school, it’s totally mystifying why a facilities manager would be opposed to sustainability. The people on every campus, in my mind, and I'm not saying this because I'm doing it, I mean, to me it makes no sense why, but the people.. on campus who should be leading the charge should be the facilities manager because look at what we deal with-- the entire system!”

Results from the base performer group meetings were led by Facilitating Action,

Movement Toward a Positive Outcome followed by Active Connection, Effort to Include,

Cooperate or Combination; and Positive Cause and Effect.

Frequencies of positive discourse was also determined for website data, which are

presented in Table 16. While the labels of Facilitating Action, Movement Toward a

Positive Outcome, Active Connection or Effort to Include or Combination and Positive

Valuing were the most frequent labels for all website data combined and for the high and

moderate performer categories, the base performers’ highest labels were Emphasizes the

We, Positive Valuing, and Embedded Sustainability, Hope Toward Future, and

Facilitating Action, Movement Toward a Positive Outcome.

Embedded Sustainability is shown by this selection: “[The founders] were able to

change a conversation, from [sustainability] not just being about technology and really

giving it some teeth and momentum to the idea that it’s not just about the light bulbs, that

there’s a behavior change, organizational component and that there’s this whole cultural

side of sustainability.” The label Hope Toward Future is described by this line, “I feel

like we’re headed in the right direction. I feel like we have a solid foundation.”

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Table 16. Number and Frequency of Positive Discourse Labels: Website Data

Website Data All

Website Data

High Performer

Moderate Performer

Base Performer

Number of Positive Coded Quotes 465 288 141 36 Percent Positive Coded Quotes 100% 62% 30% 8%

Positive Discourse Family Labels Frequency of Label & Within-Performance Category

Percentage (highest frequencies in bold) Facilitating Action, Movement Toward a Positive Outcome 129 (28%) 76 (26%) 49 (35%) 4 (11%) Active Connection/Effort to Include, Cooperate or Combination 78 (17%) 48 (17%) 27 (19%) 3 (8%) Positive Valuing 52 (11%) 27 (9%) 20 (14%) 5 (14%) Skill or Competency 18 (4%) 11 (4%) 6 (4%) 1 (3%) Systems Thinking or Interconnectedness 27 (6%) 20 (7%) 5 (4%) 2 (6%) Openness/Receptivity to Learning 6 (1%) 3 (1%) 2 (1%) 1 (3%) Embedded Sustainability 31 (7%) 18 (6%) 9 (6%) 4 (11%) Emphasizes the "We" 28 (6%) 18 (6%) 3 (2%) 7 (19%) Positive Cause & Effect 25 (5%) 20 (7%) 4 (3%) 1 (3%) Hope Toward Future 26 (6%) 18 (6%) 4 (3%) 4 (11%) Envisioned Ideal 29 (6%) 16 (6%) 10 (7%) 3 (8%) Surprise, Curiosity, Excitement 13 (3%) 13 (5%) 0 (0%) 0 (0%) Reframing in Positive Terms 3 (1%) 0 (0%) 2 (1%) 1 (3%)

Negative discourse: The negative discourse family of labels and the frequency of

each label’s application are shown in Tables 17 – 23. Table 17 provides an overview of

label frequencies for all sources of data combined. Tables 18 – 20 present direct quotes

associated with the three labels with the highest frequencies for each performance group.

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Table 17. Number and Frequency of Negative Discourse Labels by Performance Category: All Sources of

Data

All Sources of Data High Performers

All Sources of Data Medium Performers

All Sources of Data

Base Performe

rs Number of Negative Coded Quotes: 188 115 206 Percent of all Negative Quotes ( 37% 23% 41%

Negative Discourse Family Labels

Frequency of Label & Within-Performance Category Percentage

(highest frequency in bold) Bolt-on Sustainability 17 (9%) 33 (29%) 57 (28%)

Deficiency in Self or Others 41 (22%) 12 (10%) 14 (7%) Lack of Receptivity/Absence of Connection

18 (10%) 14 (12%) 22 (11%)

Negative Valuing 20 (11%) 7 (6%) 15 (7%) Separateness & Individualism 12 (6%) 11 (10%) 18 (9%) Negative Affect 16 (9%) 8 (7%) 14 (7%) Concern/Worry/Preoccupation/ Doubt

18 (10%) 4 (3%) 8 (4%)

Attribution of Control by Others in Combination w Self Deprecation

8 (4%) 5 (4%) 11 (5%)

Emphasizes the "I" 0 (0%) 4 (3%) 15 (7%) Negative Cause & Effect 7 (4%) 7 (6%) 5 (2%) Unfulfilled Expectation 10 (5%) 3 (3%) 5 (2%) Wasted Effort 8 (4%) 0 (0%) 8 (4%) Reframing in Negative Terms 4 (2%) 4 (3%) 5 (2%) Prediction/Image of a Negative Future

4 (2%) 2 (2%) 4 (2%)

Withdrawal/Ignoring/Avoidance/ Suppression

1 (0%) 1 (1%) 4 (2%)

Control or Domination 4 (2%) 0 (0%) 1 (0%)

Table 17 features all sources of data combined for each performance category.

While negative labels are distributed across the three performance categories fairly

evenly, it is noteworthy to see, within each performance group, differences in frequencies

of each label. For example, for moderate and base performing programs, Bolt-on

Sustainability is applied at least twice as often as the next most applied codes. Among

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high performers the negative discourse label with the highest frequency is Deficiency in

Self or Other, but Bolt-on Sustainability is not included in the three most frequently

applied codes for this performance category.

Looking only at data coming from the high performing category, Table 18

presents direct quotations for each of the highest frequency labels.

Table 18. Direct Quotes from Negative Discourse Labels with Highest Frequency: High Performers

Label

Quote

Deficiency in Self or Other

“I don’t talk about sustainability in any kind of… at a philosophical level or even a business level. I think that’s one of the areas I feel most uncomfortable, you know. You hear people talk about the ‘internal rate of return’ for the sustainability programs being higher and stuff, but I’m not comfortable with any of that, like that level of conversation.”

Negative Valuing

“They did not approach it in a professional manner for one… It was not a positive exchange.”

Concern / Worry / Preoccupation / Doubt

“It is not a great option for us because it costs a lot of money and is a big, expensive process.”

Table 19 features direct quotes representing the moderate performance category, and the

most frequently applied negative discourse labels.

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Table 19. Direct Quotes from Negative Discourse Labels with Highest Frequency: Moderate Performers

Label

Quote

Bolt-on Sustainability

“… if administration could be able to see the potential economic benefit of making sustainability an initiative-then I think they'd be far more interested. But, at this stage of the game, you are looking at a one or two or three million dollars short fall this fiscal year because of enrollment whatever state funding cuts- it's hard in the day to day fight to stay afloat, to argue for sustainability to be a priority.”

Deficiency in Self or Others

“The change resistance comes from middle management.”

Lack of Receptivity / Absence of Connection

“My perception was that they were feeling so stepped on by the administration to do more with less every single year, because of cuts from the state, that any perception of money being spent- no matter how small- they felt like they had to push back on the administration and point out the inefficiencies, or what they didn't perceive as effective use of resources.”

Direct quotations from base performer programs’ negative discourse is presented in Table

20.

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Table 20. Direct Quotes from Negative Discourse Labels with Highest Frequency: Base Performers

Label

Quote

Bolt-on Sustainability

… if administration could be able to see the potential economic benefit of making sustainability an initiative-then I think they'd be far more interested. But, at this stage of the game, you are looking at a one or two or three million dollars short fall this fiscal year because of enrollment whatever state funding cuts- it's hard in the day to day fight to stay afloat, to argue for sustainability to be a priority.

Lack of Receptivity / Absence of Connection

A lot of people just don’t understand and I've gotten this this feedback sometimes where its like, “Well, I'm not gonna go live in a cave and give up my car.” It's like, that’s not what [sustainability] means.

Separateness / Individualism

“Talking about how coordinated the campus’ sustainability program is, uh… piece-meal is a great way to put it.”

Table 21 presents results for negative discourse label frequencies for interview

data. For all performance groups combined, Bolt-on Sustainability and Deficiency in Self

or Others are the labels applied with the highest frequency. Bolt-on Sustainability is

approximately three times more frequent in moderate and base performers than in the

high performing programs.

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As in Table 18, the label with the highest frequency for high performers in

Deficiency in Self or Others. Among high performers, this label occurs twice as often as

it does among moderate sustainability programs and almost four times as often as it does

among the base performing programs.

Table 21. Number and Frequency of Negative Discourse Labels: Interview Data

All Interview

Data

High Performer

Moderate Performer

Base Performer

Number of Negative Coded Quotes: 351 30% (N = 106)

29% (N = 101)

41% (N = 144)

Negative Discourse Family Labels

Frequency of Label & Within-Performance Category Percentage (highest frequency in bold)

Bolt-on Sustainability 87 (25%) 12 (11%) 29 (29%) 46 (32%) Deficiency in Self or Others 48 (14%) 28 (26%) 12 (12%) 8 (6%) Lack of Receptivity/Absence of Connection 37 (11%) 11 (10%) 13 (13%) 13 (9%)

Negative Valuing 26 (7%) 9 (8%) 7 (7%) 10 (7%) Separateness & Individualism 30 (9%) 7 (7%) 10 (10%) 13 (9%) Negative Affect 28 (8%) 12 (11%) 7 (7%) 9 (6%) Concern/Worry/Preoccupation/ Doubt 16 (5%) 5 (5%) 4 (4%) 7 (5%)

Attribution of Control by Others in Combination w Self Deprecation 16 (5%) 6 (6%) 4 (4%) 6 (4%)

Emphasizes the "I" 16 (5%) 0 (0%) 1 (1%) 15 (10%) Negative Cause & Effect 8 (2%) 2 (2%) 5 (5%) 1 (1%) Unfulfilled Expectation 6 (2%) 2 (2%) 2 (2%) 2 (1%) Wasted Effort 11 (3%) 6 (6%) 0 (0%) 5 (3%) Reframing in Negative Terms 10 (3%) 2 (2%) 4 (4%) 4 (3%) Prediction/Image of a Negative Future 5 (1%) 0 (0%) 2 (2%) 3 (2%)

Withdrawal/Ignoring/Avoidance/ Suppression 4 (1%) 1 (1%) 1 (1%) 2 (1%)

Control or Domination 3 (1%) 3 (3%) 0 (0%) 0 (0%)

Table 22 provides the results of negative discourse label frequencies for group

meetings, organized by performance categories. Looking at all three categories it is

interesting to note the spread of frequencies across all the labels. The three most

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frequently applied negative discourse labels for high performing sustainability programs

are Deficiency in Self or Other; Concern Worry, Preoccupation or Doubt; and Negative

Valuing. Base performers’ most frequent label is Bolt-On Sustainability and Lack of

Receptivity / Absence of Connection followed by Deficiency in Self or Others, and Lack

of Receptivity/Absence of Connection; Negative Valuing; Separateness and

Individualism; and Concern/Worry/Preoccupation/Doubt.

Table 22. Number and Frequency of Negative Discourse Labels: Group Meeting Data

All Group Meeting

Data

High Performer

Group Meetings

Moderate Performer

Group Meetings

Base Performer Group Meetings

Number of Negative Coded Quotes: 139 51% (N = 71)

6% (N = 9)

42% (N = 59)

Negative Discourse Family Labels

Frequency of Label & Within-Performance Category Percentage (highest frequency in bold)

Bolt-on Sustainability 14 (10%) 5 (7%) 0 (0%) 9 (15%) Deficiency in Self or Others 17 (12%) 11 (15%) 0 (0%) 6 (10%) Lack of Receptivity/Absence of Connection 17 (12%) 7 (10%) 1 (11%) 9 (15%) Negative Valuing 14 (10%) 9 (13%) 0 (0%) 5 (8%) Separateness & Individualism 11 (8%) 5 (7%) 1 (11%) 5 (8%) Negative Affect 9 (6%) 3 (4%) 1 (11%) 5 (8%) Concern/Worry/Preoccupation/Doubt 13 (9%) 12 (17%) 0 (0%) 1 (2%) Attribution of Control by Others in Combination w Self Deprecation 8 (6%) 2 (3%) 1 (11%) 5 (8%) Emphasizes the "I" 3 (2%) 0 (0%) 3 (33%) 0 Negative Cause & Effect 8 (6%) 3 (4%) 1 (11%) 4 (7%) Unfulfilled Expectation 11 (8%) 7 (10%) 1 (11%) 3 (5%) Wasted Effort 5 (4%) 2 (3%) 0 (0%) 3 (5%) Reframing in Negative Terms 3 (2%) 2 (3%) 0 (0%) 1 (2%) Prediction/Image of a Negative Future 2 (1% 2 (3%) 0 (0%) 0 (0%) Withdrawal/Ignoring/Avoidance/ Suppression 2 (1%) 0 (0%) 0 2 (3%) Control or Domination 2 (1%) 1 (1%) 0 1 (2%)

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Table 23 sorts frequencies for the negative discourse family for website data,

aggregated by performance category. These results stand out compared to interview and

group meeting data for their relatively low incidence of negative label application. Across

all three performance groups, frequencies of negative labels are at their lowest.

Discussion of why this is and why low negativity occurs across the three performance

groups follows in Chapter Six.

Table 23. Number and Frequency of Negative Discourse Labels: Website Data

All Website

Data

High Performer Websites

Moderate Performer Websites

Base Performer Websites

Number of Negative Coded Quotes: 19 58% (N = 11)

26% (N = 5)

18% (N = 3)

Negative Discourse Family Labels

Frequency of Label & Within-Performance Category Percentage (highest frequency in bold)

Bolt-on Sustainability 6 (32%) 0 (0%) 4 (80%) 2 (67%) Deficiency in Self or Others 2 (11%) 2 (18%) 0 (0%) 0 (0%) Lack of Receptivity/Absence of Connection 0 (0%) 0 (0%) 0 (0%) 0 (0%) Negative Valuing 2 (11%) 2 (18%) 0 (0%) 0 (0%) Separateness & Individualism 0 (0%) 0 (0%) 0 (0%) 0 (0%) Negative Affect 1 (5%) 1 (9%) 0 (0%) 0 (0%) Concern/Worry/Preoccupation/Doubt 1 (5%) 1(9%) 0 (0%) 0 (0%) Attribution of Control by Others in Combination w Self Deprecation 0 (0%) 0 (0%) 0 (0%) 0 (0%) Emphasizes the "I" 0 (0%) 0 (0%) 0 (0%) 0 (0%) Negative Cause & Effect 3 (16%) 2 (18%) 1 (20%) 0 (0%) Unfulfilled Expectation 1 (5%) 1 (9%) 0 (0%) 0 (0%) Wasted Effort 0 (0%) 0 (0%) 0 (0%) 0 (0%) Reframing in Negative Terms 0 (0%) 0 (0%) 0 (0%) 0 (0%) Prediction/Image of a Negative Future 3 (16%) 2 (18%) 0 (0%) 1 (35%) Withdrawal/Ignoring/Avoidance/Suppression 0 (0%) 0 (0%) 0 (0%) 0 (0%) Control or Domination 0 (0%) 0 (0%) 0 (0%) 0 (0%)

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Neutral discourse: Tables 24 – 30 present the neutral discourse family of labels

and the frequency of each label’s application (by number of coded quotations),

aggregated by performance categories and sources of data. I begin with Table 24, which

includes all sources of data combined for each of the three performance categories.

Following, are Tables 25 – 27, which provide direct quotes for each of the performance

categories and highest frequency labels. Tables 28 – 30 are organized by the source of

data: interview data; group meeting data; and website data. Within tables 24 and 28 – 30,

I have bolded the two most frequently occurring positive discourse labels for each

performance category due to the lower number of total labels in this code family.

Table 24. Number and Frequency of Neutral Discourse Labels by Performance Category: All Sources of

Data

All Sources of Data

High Performers

All Sources of Data

Moderate Performers

All Sources of Data

Base Performers

Number of Neutral Coded Quotes 51%

(N = 255) 27%

(N = 136) 22%

(N = 112)

Neutral Discourse Family Labels

Frequency of Label & Within-Performance Category Percentage

(highest frequency in bold)

Information to Inspire Behavior Change 79 (31%) 55 (40%) 38 (34%)

How Change Occurs 67 (26%) 42 (31%) 47 (42%)

Uses Campus Data- 76 (30%) 25 (18%) 13 (12%) Behavioral Instruction 30 (12%) 11 (8%) 12 (11%) Uses Expert Data 3 (1%) 3 (2%) 2 (2%)

Table 24 presents frequency results for the sources of data, combining the three

performance groups. It offers a high level snapshot of where the neutral discourse labels

were applied most frequently across sources of data and within each column, we see

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which labels were most regularly used in coding. Table 25 features data from the high

performing data set.

Table 25. Direct Quotes from Neutral Discourse Labels with Highest Frequency: High Performers

Label

Quote

Information to Inspire Behavior Change

“It was when I began to read the books and began to understand the impact of climate change and I realized that sustainability was the one thing that everyone can do personally.”

Uses Campus Data

“When you crunch the numbers to assess how it compares to FY00 or even FY08, it’s like maybe a quarter of the usage!”

Table 26 features direct quotes representing the moderate performance category, and the

most frequently applied neutral discourse labels.

Table 26. Direct Quotes from Negative Discourse Labels with Highest Frequency: Moderate Performers

Label

Quote

Information to Inspire Behavior Change

“In reality, my method is to just make people aware of things. “

How Change Occurs

“Once students got interested in sustainability then the administration had to pay attention. On a campus like ours, student interest carries a lot of weight.”

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Direct quotations from base performer programs’ neutral discourse is presented in Table

27.

Table 27. Direct Quotes from Neutral Discourse Labels with Highest Frequency: Base Performers

Label

Quote

Information to Inspire Behavior Change

“We calculated that one year’s worth of paper consumption on this campus would cover the football field and be 8 reams of paper tall. I use this in my classes all the time, to get people’s attention.”

How Change Occurs

“If we had a president who was in to sustainability then it would be a different story around here, you know… when leadership is behind something, they find a way to make it happen.”

Table 28 presents the frequencies neutral discourse labels were applied to

interviews. In interviews, the communication of respondents from base performing

programs in my sample were coded with neutral labels more than moderate performers

and almost twice as much as interviews with representatives of high performing

sustainability programs. How Change Occurs was the most frequently applied label for

all three of the performance groups.

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Table 28. Number and Frequency of Neutral Discourse Labels: Interview Data

ALL

Interviews

High Performer Interviews

Moderate Performer Interviews

Base Performer Interviews

Number of Neutral Coded Quotes 146 24%

(N = 35) 30%

(N = 44) 46%

(N = 67)

Neutral Discourse Family Labels

Frequency of Label & Within-Performance Category Percentage

(highest frequency in bold)

Information to Inspire Behavior Change 32 (22%) 4 (11%) 8 (18%) 20 (30%)

How Change Occurs 89 (61%) 23 (66%) 31 (70%) 35 (52%)

Uses Campus Data- 20 (14%) 8 (23%) 3 (7%) 9 (13%) Behavioral Instruction 4 (3%) 0 (0%) 1 (2%) 3 (4%) Uses Expert Data 1 (1%) 0 (0%) 1 (2%) 0 (0%)

Table 29 features the frequencies with which neutral labels were applied to group

meeting data. Within all the group meeting data How Change Occurs was the most

frequently applied label. Uses Campus Data represents a higher percentage of the

conversation among high performing sustainability programs, than in the meetings of

moderate and low performing programs.

Table 29. Number and Frequency of Neutral Discourse Labels: Group Meeting Data

All Group

Meetings

High Performer

Group Meetings

Moderate Performer

Group Meetings

Base Performer

Group Meetings

Number of Neutral Coded Quotes 95 64%

(N = 61) 10%

(N = 9) 26%

(N = 25)

Neutral Discourse Family Labels

Frequency of Label & Within-Performance Category Percentage

(highest frequency in bold)

Information to Inspire Behavior Change 13 (14%) 5 (8%) 1 (11%) 7 (28%)

How Change Occurs 34 (36%) 18 (30%) 5 (56%) 11 (44%)

Uses Campus Data- 38 (40%) 33 (54)% 1 (11%) 4 (16%) Behavioral Instruction 6 (6%) 4 (7%) 1 (11%) 1 (4%) Uses Expert Data 4 (4%) 1 (2%) 1 (11%) 2 (8%)

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Table 30 provides frequency results for neutral discourse frequencies for website

data from high, moderate, and base performing sustainability programs in my sample. Of

all the sources of data, websites have the highest number of neutral label applications. It

is via sustainability program websites that definitions, instructions, and various pieces of

campus data (from building energy consumption to course lists for sustainability majors)

are most regularly and most broadly communicated to constituents of the sustainability

programs. Par for this type of data source, Information to Inspire Behavior Change is the

most frequently applied label for each of the performance groups.

Table 30. Number and Frequency of Neutral Discourse Labels: Website Data

All

Websites

High Performer Websites

Moderate Performer Websites

Base Performer Websites

Number of Neutral Coded Quotes 262 61%

(N = 159) 32%

(N = 83) 8%

(N = 20)

Neutral Discourse Family Labels

Frequency of Label & Within-Performance Category Percentage

(highest frequency in bold)

Information to Inspire Behavior Change 127 (48%) 70 (44%) 46 (55%) 11 (55%)

How Change Occurs 33 (13%) 26 (16%) 6 (7%) 1 (5%)

Uses Campus Data- 56 (21%) 35 (22%) 21 (25%) 0 (0%) Behavioral Instruction 43 (16%) 26 (16%) 9 (11%) 8 (40%) Uses Expert Data 3 (1%) 2 (1%) 1 (1%) 0 (0%)

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CHAPTER SIX: DISCUSSION

This chapter begins with a discussion of the study’s primary findings, followed by

additional findings of interest. Later in this section, limitations of the study are presented,

followed by implications for future research and the study’s contributions. I will first

offer an overview of the research questions, hypotheses, and their relationship to

findings.

Overview

This research explored two questions. First, what is the emotional tone of

sustainability language used in higher educational contexts? Second, how does the

emotional tone of campus sustainability narratives relate to the performance of

sustainability programs? Three hypotheses were associated with these questions.

H1a: The current state of sustainability language in higher education, including

all performance categories, will be dominated by a neutral emotional tone.

H1b: Instruction and information sharing will comprise the majority of the

neutral discourse.

H2: Higher education sustainability programs with at least a 3:1 ratio of positive

to negative language in their personal and public communications (written and

verbal) will be rated higher on a national campus sustainability ranking than

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sustainability programs whose communications employ a lower ratio of positive

to negative communication acts.

H3: The highest performing campuses will approach change for sustainability

through a confirmation-based approach, characterized by a future oriented stance,

propensity for innovation, being proactive and creating opportunities to further

develop their institution’s sustainability program.

Support for Hypotheses

Table 31 presents each hypothesis, whether or not it was supported by the

findings, and if there was support, those findings are outlined. Discussion of the findings

follows the table.

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Table 31. Support for Hypotheses

Hypotheses

Supported

Findings

H1a: The current state of sustainability language in higher education, including all performance categories, will be dominated by a neutral emotional tone. H1b: Instruction and information sharing will comprise the majority of the neutral discourse.

No

Yes

H1a: Based on all sources of data (Table 4), neutral discourse does not predominate across performance categories. H1b. Within the neutral discourse family of labels, the most frequently applied label was Information to Inspire Behavior Change (Table 17).

H2: Higher education sustainability programs with at least a 3:1 ratio of positive to negative language in their personal and public communications (written and verbal) will be rated higher on a national campus sustainability ranking than sustainability programs whose communications employ a lower ratio of positive to negative communication acts.

Yes

Sustainability programs in the high performer category had a 4:1 P/N ratio. Moderate performers had at 2:1 P/N ration, and base performers had a 1:1 P/N ratio (Table 8).

H3: The highest performing campuses will approach change for sustainability through a confirmation-based approach, characterized by a future oriented stance, propensity for innovation, being proactive and creating opportunities to further develop their institution’s sustainability program.

Yes

1. The positive discourse labels with the highest frequencies applied in the high performance category were Facilitating Action, Movement Toward a Positive Outcome, Active Connection/Effort to Include, Cooperate or Combination, and Positive Valuing. 2. Direct quotes from high performing interviews, group meetings, and websites, which support this hypothesis are presented in the discussion.

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Hypothesis 1a:

Hypothesis 1a anticipated neutral discourse would play a larger role in the

sustainability narratives than findings support. This expectation was not supported by the

data. As presented in Table 4, when all sources of data are combined, neutral discourse

comprises only 20.8% for high performing sustainability programs (compared to 63.8%

positive discourse), 27% for moderate performers (compared to 50.2% positive

discourse), and 21.4% for base performers (compared to 39.3% positive discourse).

Reviewing just website data (Table 8), where neutral discourse had the highest

frequencies compared to interview and group meeting data, positive discourse again

prevailed. For high performing programs, neutral discourse labels comprised 34.7% of

the total data, whereas positive discourse labels were applied to 62.9% of this category’s

website data. For the moderate performers in this sample, neutral discourse labels were

applied to 36.2% of the website data and labels reflecting the positive discourse family

were applied to 61.6%. Among base performers, website data was labeled as 33.9%

neutral and 61% positive.

Hypothesis 1a served a two-fold purpose. First, it reflected my experiences as a

Sustainability Professional and was based on observations and critiques that

environmental and sustainability rhetoric is data-driven, compliance and regulatory-

focused by Meppem (2000), Schmidt (2005), Hart and Milstein (2003), and Malka and

colleagues (2009). Second, although I was inspired by the work of Schwartz (1986),

Gottman (1994), and especially Losada and Heaphy (2004), I was not convinced that

higher educational sustainability narratives would contain clearly positive emotional tone

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to the extent that P/N ratios would be significant. In this way, Hypothesis 1a served as a

“null hypothesis.”

Hypothesis 1b:

Even though H1a was not supported by the data, there is support for H1b. The

most frequently applied neutral labels did indeed reflect a propensity for information

sharing. In the case of these data, information is shared as a means of motivating change.

Higher educational Sustainability Professionals assume inaction or disinterest in

sustainability is based in a lack of knowledge, underpinned by the belief that “if you

knew what I know, you would do what I do.”

Malka, Krosnick, and Langer (2009) explored whether or not sharing information

about global warming had an impact on Americans’ concern related to the issue. They

found that pre-existing trust in who was doing the communicating- in their study it was

‘scientists’- and pre-existing agreement or disagreement with the content- is global

warming real and is it a threat to human systems- determined levels of concern and

predicted action or inaction.

In a higher education context, the findings of Malka and colleagues indicate that

who is communicating to the campus constituency is as important as what they are

communicating. In a complex human system like a college or university, with multiple

interest groups and stakeholders, information coming from institutional leaders, faculty

members and other kinds of thought leaders each represent diverse aspects of the system.

While this is key to a systemic approach to communicating about sustainability, it is only

part of the story.

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While this argument may send a hopeful message to campus Sustainability

Professionals as they think of recruiting popular professors, student leaders, or beloved

staff members, it is incomplete for one reason. It assumes neutral discourse, like

information sharing, will evoke and sustain behavior change. A data-centric narrative

privileges particular kinds of reasoning, knowledge, and inspiration and excludes others

(Meppem, 2000) and is not an effective counterbalance to negative remarks or

information (Fredrickson, 2013).

At the turn of the 20th Century, William James wrote, “The world we see that

seems so insane is the result of a belief system that is not working. To perceive the world

differently, we must be willing to change our belief system, let the past slip away, expand

our sense of now, and dissolve the fear in our minds” (1897). Language with a positive

emotional tone opens our minds and allows us to appreciate- and see more clearly- what

is, whereas neutrality (and understandably negativity) hide what is from us, by inspiring

us to protect ourselves, pulling back from situations (Fredrickson, 2009).

Hypothesis 2:

The sustainability programs making up the high performance category in this

study are indeed characterized by more positive language than negative, with a 4:1 P/N

ratio. Comparing the high performers’ P/N ration to the other groups in the sample, it is

clear that the base performers had a low positivity ratio at 1:1. As one might expect, the

moderate performers fell into the middle, with a positivity ratio of 2:1. These findings

not only support H2 but they also join and support Gottman’s findings (1994) and those

presented in Losada and Heaphy’s research (2004), which characterize high, moderate,

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and low performing sales teams by high, moderate, and low positivity ratios in their

language.

As discussed in the literature review, Losada applied a nonlinear dynamic model

to the data in order to predict to positivity ratios, which marks the tipping point for

human systems (Losada & Heaphy, 2004; Fredrickson & Losada, 2005; Fredrickson,

2009). This mathematical model was challenged and found to be invalid by Brown and

colleagues (2013), although the evidence linking high positivity ratios with high

performance remains valid (Expression of Concern, ABP, 2014).

Figure 2 presents the findings supporting H2 by showing the downward slope of

positive discourse present in all sources of data from high to moderate to base performers

and the upward slope of the presence of negative discourse, at its lowest with high

performers and increasing for moderates and increasing further for base performing

sustainability programs.

Figure 2. Discourse by Performance Category

64%

50%

39%

15%

23%

39%

21% 27%

21%

0%

10%

20%

30%

40%

50%

60%

70%

High Performers Moderate Performers Base Performers

% o

f cod

es w

ithin

per

form

ance

cat

egor

y

Positive Discourse Labels

Negative Discourse Labels

Neutral Discourse Labels

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Hypothesis 3:

Hypothesis 3 anticipated a spirit of innovation and explicit hope, and proactively

scanning for opportunities. This hypothesis predicted that the high performing

sustainability programs would approach change through a confirmation-based

orientation. Ways in which this hypothesis is supported are described, starting with

excerpts from the raw data. I then discuss the corroboration with confirming stances to

change, and introduce additional literature that may add to clarity of these findings.

The positive discourse labels with the highest frequencies applied across all

performance categories were Facilitating Action or Movement Toward a Positive

Outcome, Active Connection/Effort to Include, Cooperate or Combination, and Positive

Valuing. As presented in the previous chapter, remarks to which these labels were

applied were twice as frequent among high performers than with moderate performers

and four times as frequent as with base performers. Examining just the high performers,

these labels were applied to numerous quotes and passages of data from all three sources

of data. A selection of direct quotes from interview data with representatives from high

performing sustainability programs, are provided below, demonstrating hope, innovation,

and a proactive approach to change.

“[Sustainability professionals'] perspectives are really wide, [whereas] most people in an academic setting’s [view is] really deep. I love the wide and I love seeing where the connections are and where they could be.”

“[The university’s vision plan] is phenomenal, so it's not just master planning. It envisions everything! It’s like our dream. It looks at the arts corridor, it looks at the river corridor, the arts district, the medical center, student life, all aspects of campus, and literally… it's got sustainability principles all through it. No new academic space, period, so we’ll either re-purpose existing space or demolish and build new for need, not for a college. Stuff like that. Pedestrianizing the core, cutting down on parking, so all the stuff you see, is part of the framework.”

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“You know what [sustainability] is for us? It's about graduating global citizens, really. I think the greatest impact we can make… For me it's about, look at how many students who graduated per year, and if each student went out there and tried to make a difference that would be the largest impact we would have in the world.”

“So we can say that we [integrated sustainability into our institutional mission] but that means that we now need to continue to do all the things that we are doing and push further. We need to continue to be in the forefront of all these things. You know, [NAME OF INSTITUTION] has this reputation for being pretty sustainable, but in order to keep that reputation we need to keep growing and exploring, we can’t just continue to be doing all the things that we’ve been doing and expect to stay at our edge.”

It is fair to say the high performing sustainability programs in this study have each

been successful at organizational change. The success of sustainability programs

included in the high performing category is demonstrated by the grade assigned to their

efforts by the Campus Sustainability Report Card and by other factors not evaluated by

the Report Card, such as content and activities discussed in interviews and contained in

website data like integration of sustainability into curriculum, development of new

interdisciplinary streams of research, changes in investment policy and practice, the

development of institutes and centers, and changes in institutional strategy from the top

levels of these campuses. However, the opportunity for organizational scholars studying

sustainability programs is that these are change processes which do not have a clear end,

they are ongoing, ever-deepening and expansive change initiatives with the potential to

impact every aspect of institutional operations, strategy, and culture.

Taken together, Facilitating Action, Movement Toward a Positive Outcome,

Active Connection/Effort to Include, Cooperate or Combination, and Positive Valuing

can be interpreted as a particular way of approaching change, aligned with confirmation-

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based theories from POS. These discourse labels describe a suite of behaviors,

specifically speaking to the way high performers are scanning their organizational

environments (Schein, 1995; 1999). The high performing sustainability programs are

looking for ways to develop, seeking connections and opportunities with a propensity not

found among the other performance categories. They see how events and people can

enhance and embolden movement toward sustainability goals within their institution and

they are active in their search of the environment and experiences for that which is

positive, that which is right, that which is good, that which works well, and what

strengths they and others possess.

I am not suggesting these behaviors (or even these specific positive discourse

labels) are unique to just the high performers in this study. What is unique, however is

the frequency with which high performers are engaging in and speaking to these

dynamics.

Moreover, it is possible that the high performing sustainability programs may be

demonstrating an observable progression from a sustainability mindset to a flourishing

mindset. Sustainability, by its very definition is static and refers to maintenance.

Ehrenfeld (2008) offers a convincing argument that the social and ecological dilemmas

we are facing at a global scale will not be addressed by a maintenance mindset, nor will

they be resolved by encouraging behaviors which are simply less harmful. Laszlo and

others (2012) offer that our language holds us back and that the time has come to shift

from language that encourages less harm toward a discourse focused on prosperity and

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flourishing. Ehrenfeld (2008), Hoffman and Ehrenfeld (2013), Laszlo and colleagues

(2012), and Laszlo and Brown (2014) encourage this shift, evolving to a broader and

deeper orientation of what sustainability could mean, and does mean when we call it

flourishing.

As described by the authors above, flourishing is the active, generative nature of

building connectedness across boundaries, fostering webs of interconnectedness, which

seeds the kind of world in which all species exist harmoniously, for all time. This, in

addition to Cooperrider and Fry’s (2013) and Cooperrider’s (2014) proposition of mirror

flourishing help us see Hypothesis 3 and the high performing members of this sample

with more dimensionality. For example, among the high performers, there exists a

richness or texture in their experiences of leading change at their colleges and universities

that does not exist as vividly with the moderate and base performers in the study. It is

possible that the generative behaviors and mindsets indicated by the most frequently

applied positive discourse labels (Facilitating Action, Movement Toward a Positive

Outcome; Active Connection/Effort to Include, Cooperate or Combination; and Positive

Valuing) and their ubiquity, are leading to far more than successful sustainability

programs measurable by third party ranking surveys or annual reports.

The possibility of mirror flourishing, especially as it blurs the boundaries of “in

here” and “out there” brings the proactivity of high performers’ collaborations and

connection seeking to life. Mirror flourishing brings positive valuing to scale, and adds

color to the high performers’ awareness that interactions and circumstances can be

generative, leading to a ripple effect of good leading to positive outcomes.

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Taking the spirit described in Hypothesis 3 together with the connected nature of

sustainability as flourishing (Ehrenfeld, 2008; Laszlo & Brown, 2014) and the boundary-

blurring phenomena of mirror flourishing (Cooperrider & Fry, 2013; Cooperrider, 2014),

it seems plausible that the high performers in this study are experiencing love in their

organizational contexts. Hypothesis 3 and the data which supports it describes shared

micro-moments of positivity (Fredrickson, 2013); the sense of agape resulting from

altruistic acts (Post et al. 2002); the sense of wholeness, harmony, and well-being

produced through care, concern, and appreciation for both self and others (Fry, 2003 p.

712); relational behaviors through which another person, being, or thing arises as a

legitimate other in coexistence with oneself (Maturana & Bunnell, 1999); and shifts from

limited self-interest to a recognition of connectedness, mutuality, and interest in the well-

being of others (Coombe, 2011).

Additional Findings

Beyond confirmation and disconfirmation of hypotheses, additional findings are

of interest. The first regards the difference in P/N ratios between interviews and group

meeting data. Specifically, interview data from the high performing category possess a

3:1 P/N ratio, however in group meeting data representing the same population the P/N

ratio drops to 2:1. Looking at the other end of the performance spectrum, base

performers’ interview data possesses a 1:1 P/N ratio, but plummets to 1:4 P/N in group

meeting data. This begs the question, what phenomena are present at group meetings,

which are not present during one on one interviews, and vice versa.

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A possible explanation comes from Goffman’s work in sociology (1959), which

uses metaphors of theater to propose that individuals act in life, using either their front

stage or back stage versions of themselves. According to Goffman’s theory, it is likely

that during one-on-one interviews, the Sustainability Professionals being interviewed are

aware they are making an impression on the interviewer (me), that I am collecting

information about them and their institutions, so they are likely to be working hard

(consciously or subconsciously) to create a desired impression. This is the front stage

self. Once in group meeting contexts, with colleagues, it is likely that impression

management is less figural. Participants are engaging in an ‘internal’ discussion, which

is, in comparison to being interviewed and recorded by someone outside the system, can

be experienced as a private place where individuals can be themselves and set aside a

sensed need to manage impressions or play a role.

Considering the apparent power of negative comments, events, and

circumstances, as put forth by Schwartz (1986), Gottman (1994), and Baumeister et al

(2001), I became curious about where negative labels were applied in high performer

group meeting data, and wondered how the high performers ‘made up for’ the

asymmetrical impact of negative utterances or quotes with positive interactions.

Considering the possibility of mirror flourishing in these systems, I was curious if it is

possible to see such a phenomenon come to life.

Looking at all the negative labels applied to high performer group meetings, two

clear patterns emerge. In about half of the instances, a negative label is an aspect within

a generally neutral or positive conversation. For example, the following direct quote from

the data illustrates two negative labels: Lack of Receptivity or Absence of Connection and

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Negative Valuing. This passage also includes Hope Toward Future, Reframing in

Positive Terms, Openness and Receptivity to Learning, and the neutral label Information

to Inspire Behavior Change. In this selection, three staff members of a high performing

sustainability program discuss opportunities to educate people about campus

sustainability, including a walking map of sustainability installations and points of

interest and the need for related campus signage for sustainability features.

Speaker A: The brochure has a green map of the campus so I’m very excited for Parents’ Weekend and things like that. This summer, we’re hosting the American Institute of Architects green building conference, so this will be especially great for them. To my knowledge there isn’t another map or brochure for people who just want to walk through campus. Speaker B: Yeah, the other piece of the location brochure is kind of on top of my priority list, and that is external signage. I got some money authorized to spend on education, so I think it is important to get a few key signs up around campus, telling people what stuff is, or what to look at. Overall, we already have one or two at the wind turbine, one pointing out the solar array, that a lot of people don’t see otherwise. We should have at least one about the pervious paving and snow melt capture. Speaker C: I would like to get a sign up at [NAME OF BUILDING] by the steam vent of the [NAME OF BUILDING], saying “Hey! We’re not wasting energy! This steam vent comes from the steam tunnels on campus to heat other things and melt the ice on these sidewalks! Blah, blah, blah!” People are always telling me we’re wasting energy and they get so worked up about it. It’s like they’re saying, ‘Ah ha!... caught you! You’re not really doing sustainability here!’ Speaker B: Yeah, I think that people have a lot of questions about why that happens. I’ve seen people go walking by it, saying stuff about why we say we’re so sustainable… they see this and think we’re wasteful and green washing. So, especially with the green building project happening right next door at the [NAME OF BUILDING] that’s a high traffic area, which means it’s a good opportunity to tell people what they’re seeing, why it’s good. We gotta get how ‘efficiency’ works— that it’s a relationship— into the conversation. Explaining what they’re seeing is really is necessary.

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In the other half of the instances, a challenging circumstance or issue is being

discussed. While that portion of the meeting agenda may have an overall negative tone,

there are multiple positive labels embedded through the selection, seemingly dissipating

the impact of the negative and quite possibly demonstrating how mirror flourishing

‘shows up.’ An illustration of this, includes one the most frequently applied negative

labels, Deficiency in Self or Other. It occurs with two positive labels: Openness and

Receptivity to Learning and Reframing in Positive Terms. In this passage, a Sustainability

Director speaks to an error that both he and the Sustainability Committee are responsible

for, however he does so reframing the situation as both a positive indication of visibility

and as a learning experience for the group’s work together.

I won’t mention the person’s name, but this is important for us in moving forward with the Sustainability Report that we did… and we can learn from this as it relates to some of the other irons we have in the fire, that we’re trying to get out. The Sustainability Report needed to go out and was ready to go, and we sort of- as a group- said yes. On my end I did not do due diligence of actually going back and making sure that the students actually did have it ready to go. … There were typographical errors and a couple of grammatical errors. …I got a call from someone on the Donor Committee who was not so nice in the feedback that was given to me. I made the changes in the document and sort of fell on the sword for everyone on this committee saying, ‘You know what, it was totally my fault.’ I wanted to raise this issue in our meeting today because I know several of you interact with the Donor Committee and I wanted you to know what the situation was in case it gets brought up, and also we need to be more diligent in reviewing this kind of stuff before it goes out, so maybe that’s a role that could rotate in the future, and not just relying on students for that. But, you know, this also tells us that our documents are getting the attention… are being read, which is cool especially by people on the Donor Committee.

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Another example of the negative topic made buoyant by positive labels is featured here.

This conversation is between the Sustainability Director (SD), the Vice President (VP) he

reports to, occurs within a larger Sustainability Committee meeting. The discussion

concerns a member of the custodial staff who is suspected of taking items, which staff

and faculty members have set out for recycling.

The negative labels applied are Concern, Worry, Preoccupation, or Doubt, and

Negative Valuing. The positive labels also applied to this segment are Reframing in

Positive Terms, Facilitating Action, Movement Toward a Positive Outcome, and Systems

Thinking or Interconnectedness.

VP: You know there were rumors at one point that people, that when they pick up recycling that some people purposefully fill the recycling bags with trash and were stealing. Do you know whether that could have happened? SD: I supposed that could have happened…. There are rumors that a custodian may have taken something from the end of year “Dump & Run” event at the dorms. VP: Yeah? Well, I know that the person who cleans my building and I know they are not inviting that kind of thing. SD: Okay, good! But in another… well, the rumor is that this has occurred in the past, and no one in Facilities will deny that it hasn’t, but I don’t think it’s common place anymore. Some of the carts that the cleaners have, have two big barrels and but because we collect trash one day and recycling the next usually one of the barrels should always be empty… VP: (interjects / cross talk) But that doesn’t mean it isn’t happening now… I know, okay… SD: … it’s possible that she may have taken something, but I don’t know for sure and I don’t know if it’s a pattern or is happening consistently now. They’re not supposed keep anything. When I talk to other custodians, they tell me that they don’t take anything and they don’t know if [NAME OF CUSTODIAN] does, but then, I hear from others that [NAME OF CUSTODIAN] is a good person….so let it go.

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VP: This is one of those small kinds of things that could get her into big trouble unfortunately. SD: Well, depending on how we deal with it. VP: Yeah, exactly. SD: There’s enough of a reason to address it in some way with all the custodians, we’re meeting with them in a couple weeks. We can demonstrate we take this seriously, that taking things—even from recycling—is considered stealing here, but we can address it to the group so we’re not accusing one person in particular. VP: But, honestly, if she’s taking something that was put out for recycling, to take home and use… I mean, maybe we need to re-visit those parts of the rules…. She’s actually re-using things. We can change the clause and name it after her!.. (laughter around the table). SD: Maybe this is another opportunity for us to consider our campus surplus differently. I mean, maybe we should have some kind of an annual thing where members of the campus can come and get stuff that otherwise be sent off campus.

An additional finding concerns website data, and its general uniformity across

performance categories. While there were subtle differences between high, moderate, and

base sustainability programs, data collected from websites follows a clear pattern of

being comprised of positive discourse and neutral discourse.

This finding evokes two further questions. First, why is there such similarity

across all three performance categories? As described in early sections of this thesis,

mimetic isomorphism, or the habit of mimicking what others are doing to increase

perceptions or appearances of a system’s credibility- is common among higher

educational sustainability programs, particularly in relation to website data, which is

easily accessible and replicable. Looking at what esteemed sustainability programs

include on their websites and mimicking this is a common practice and is often suggested

as a way to begin designing a new sustainability program or website.

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The second question these findings stimulate is about the credibility of data

collected from archival sources, such as websites and annual reports. While Goffman’s

work focused on individuals, the theory of a front stage and back stage presentation of

self is an apt metaphor for organizations (1959). In this data set, the websites (being

public documents) are quintessentially ‘front stage,’ managing impressions and crafting

narratives. Group meetings on the other hand are equally ‘back stage,’ featuring behind

the scenes conversations. Table 32 compares the P/N ratio for high performers and base

performers for group meeting and website data, resulting in an extreme snapshot of

narrative emotional-tone.

Table 32. P/N Ratios for High and Base Performance Categories, Comparing Group Meetings and

Website Data

High

Performance

Base

Performance

Group Meeting Data P/N 2:1 1:4

Website Data P/N 26:1 12:1

Related to the above discussion, the importance of multiple sources of data is

important for creating a rich and more accurate picture of the narratives of higher

educational sustainability programs. Collecting only one of the three sources of data

would have resulted in a different and limited sense of what makes up sustainability

discourse and the emotional tone of that discourse.

Despite the ‘front stage’ nature of website data across all ten sustainability

programs in this study, the data did provide evidence not captured by the Campus

Sustainability Report Card grades used to create the performance categories each school

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was categorized in. The assessments of website data presented a challenge, as described

in the methodology discussion, because of the vast differences in amount of content

existing between institutions. To control for these differences, I created a template based

on the content and topic headings addressed by the programs with the least content. The

result is that portions of website data relating to faculty research, interdisciplinary centers

and institutes, curriculum development, and other activities which were present at high

performing schools was not included in the data because these topics were not included

by the schools with the least website content.

Carl Sagan once said, “An absence of evidence is not evidence of absence.”

While that may be true about our knowledge of the universe, or the fossil record, an

absence of website evidence for campus sustainability program is evidence of absence of

sustainability in diverse realms of the institution. Thus, website data provides the most

clear demonstrations of what bolt-on approaches to sustainability look like versus

embedded sustainability. Among base performers, website content addressed recycling,

energy conservation, and student activities, like student clubs or sustainability events, like

end of year ‘dump and run’ donation events for residence halls.

Among high performers’ websites, we also see information on recycling, energy

conservation, and student activities, but this is in addition to published research by

faculty, discussion forums and speaker series, research institutes applying sustainable

design, boundary-spanning partnerships between the institution, government, industry,

and the corporate sector to elevate best practices of sustainability and flourishing, to

name just a few examples.

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Implications for future research

The findings from this research have the potential to germinate several follow up

threads of inquiry. First, future research is needed to test and validate the code, with

specific regard to the positive and negative labels added during the coding process and

the addition of the neutral discourse category. Further development and validation of this

code would enable expansion and critique of this research. Related to data analysis, a

possible follow-up study differentiating specific units of language (words, phrases,

conversations, whole narratives) could reveal the differing impact among them.

Research employing experimental designs can test efficacy of language used in

sustainability communications, such as measuring usage of recycling bins which are

labeled with phrasing using different emotional tones, which would represent an

expansion on the field studies of community-based social marketing (McKenzie-Mohr &

Smith, 1999). Similar experimental designs including environmental NGOs and other

large climate or sustainability organizations could test these findings in fundraising

communication using focus groups and other methodologies, to determine which ratios of

emotional tone result in highest donations.

Further exploration is needed to identify causality and define the role of language

in change processes, specifically exploring the order of what must change first, thoughts,

language, or behavior. Such research, would investigate the sequence of influence,

perhaps through a longitudinal study of interventions which change language and testing

whether and how these interventions result in sustainability program performance over

time. Moreover, with results about the emotional tone of sustainability language,

additional research should explore the impact on targets of these communications.

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Through such a study, one could explore how attitudes are constructed and drawn upon to

be persuasive, thereby illustrating and making explicit the narratives created for each

campus’ community (Potter, 1996).

Related to these ideas, the opportunities that the P/N ratios provide for additional

research are rich. Systems theorists suggest the communication and the essence of the

sustainability programs are holographic, a fractal of the larger system in which they are

embedded, therefore what occurs at one level of system is present at all levels of system

(Perey, 2014). The boundaries of my study were drawn around sustainability programs

within institutions of higher education, so I did not attempt to include assessment of each

institution’s general, or universal, narrative emotional tone. The current study could be

expanded, comparing P/N ratios for intra-organizational sustainability programs and the

organizations within which they are embedded. Specific to my data set, a study exploring

each college and university’s general narrative, sampling broadly from campus data

sources, to compare how the P/N ratios of the sustainability programs relate to the

system’s entire discourse would shed light on nuances such as mutual causality. Research

questions exploring how the emotional tone of the organizational narrative influences the

narrative of the sustainability program, and vice versa, would shed light on how change

in complex systems occurs. Staying with the same study population, an expansive study

could group sustainability programs by the size of their institutions to explore the

emotional tone of narratives of change in small, moderate, and vast systems.

Additional research possibilities exist for both narrower and broader study

populations. A deeper dive into only high performing sustainability programs could

foster more detailed knowledge about narrative and its links to performance. Moreover, a

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longitudinal study following base performers and their development over time would

offer rich insights into how programs develop and change over time, with specific regard

to observing and tracking their language and narratives over time. Such a study could

track both the sustainability program’s narrative and university-wide narrative over time,

identifying whether moving to higher performance in the sustainability program

correlates with a shift to a higher P/N ratio in both narratives.

Follow-up research, using the same population of schools, could be conducted,

looking at a broader group of campus thought leaders, including the role faculty members

play in creating and disseminating the sustainability narrative, student voices in

influencing their campuses with regard to sustainability, and exploration of campus /

student newspaper coverage of sustainability. Additionally, studying a broader segment

of population of systems, beyond just the higher education context could elucidate further

understanding of the influence of narrative emotional tone on change.

Data from the high performance category supports the idea that sustainability is

an activity of connection, looking for and speaking to strengths and assets, and using the

generative momentum of events and relationships to create more flourishing. Future

research taking a deeper look at the human experience inside these kinds of environments

and exploring the development mirror flourishing, and themes of love, is highly

warranted. Models and theory building depicting how mirror flourishing begins and

under what conditions would offer POS an additional avenue for understanding how

context and behaviors work together to create flourishing across boundaries.

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An aspect of organizational change not addressed by this study regards the role of

formal and informal leadership at each of the campuses included in my sample. Future

research could return to each institution and perform in-depth case studies focusing on

the development over time of the sustainability programs, with specific regard to the

actors who initiated and nurtured each program, for clues about how types of leadership

correlate with performance.

Implications for practice

By conducting a survey of the language used by higher educational sustainability

programs, a first map of the landscape has been created. This initial sketch invites

expansion, increased specificity and nuance, and critique. Cooperrider and Fry (2012)

wrote, “Patterns for the future can be found in the texture of the actual,” (p. 6) and

through this study’s categorization and coding of the language for emotional tone, such

patterns have emerged, which should not only inform Sustainability Professionals but

also indicate paths leading forward, leading from sustainability to flourishing.

In a very practical way, this research puts forth the idea that higher performance is

consistently associated with higher frequencies of positivity in discourse. It appears that

high, moderate, and base performers are saying the same things, but the differentiator is

how often. The positive language used most often by high performers also points to the

development of a spirit of the sustainability program, in which connectedness and

positive valuing are at the center.

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We do not yet know if flourishing among members of the sustainability program

begins with high marks from the Campus Sustainability Report Card or if the good

grades are an outcome of flourishing relationships in the institution. What is clear

however is that when our teams and organizations are helping others to thrive, we too

begin to thrive. This gives Sustainability Professionals choices about where to intervene

in the development of their programs: acting “out there” through the programs and

activities of the sustainability program or building connectedness, hope, collaboration,

and expressing positive valuing “in here.”

Based on the findings of this study, universities and colleges with sustainability

programs might consider a review of their formal and informal communications noting

the presence and context of positive and negative remarks, striving for a higher positive

to negative ratio as found in this research (4:1) and others (Losada & Heaphy, 2004;

Gottman,1994; Schwartz, 1986). This means using negative remarks or crisis messages

wisely and purposefully, balancing these with a higher number of positive remarks,

which focus the audiences attention on what is desired in terms of solutions, behaviors, or

a future vision. In contexts when negativity arises, like self-deprecating remarks or

reflection on a lack of connection or engagement in sustainability team meetings, address

it. Negative conversation or perspectives can be an indication support or encouragement

is needed, or that thought partnership is needed to collectively address a challenge.

Sustainability programs characterized as moderate or base performers should

consider increasing the frequency of their positive remarks and increase the overall P/N

ratio of their formal and informal communication. Applying the findings of this study, in

a presentation in which a negative or “crisis” message is necessary, these kinds of

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remarks should be used wisely, with four or five remarks, which are aspirational, solution

or hope oriented, and which bring the audience’s mind to the flourishing future you wish

to create.

Moderate and base performers could benefit from how their choice of language

focuses the attention of the audience and therefore choose to elevate awareness about the

prevalence of cooperation, collaborative relationships, strengths, and connectedness. Ask

questions of the sustainability team, such as: how is this sustainability program here

already building flourishing natural and human systems? In which areas of my life do I

experience flourishing? Where and with who are our collaborative relationships at their

strongest and how can we multiply these bonds? What are the unique strengths of our

program and the people who are part of it? What do I love about my role in the

sustainability program?

Contributions of this study

This research has specifically contributed to a growing field of scholarship and

practice exploring the effects of the emotional tone of language in human systems

(Schwartz, 1986; Gottman, 1994; Losada & Heaphy, 2004; Fredrikson, 1998, 2003,

2009; Fredrikson & Losada, 2005; Fredrikson & Cohn, 2008; Donnellon, 1996; Barrett &

Cooperrider, 1990; Cooperrider, 1997; Barrett et al. 1995). My work has grounded these

theories in a field study, which broadens the context of application, adding breadth to

existing knowledge about the role language plays in human systems. While this research

did not attempt to establish tipping points of P/N ratios in language, it did support the

more general findings of Schwartz (1986), Gottman (1994), and Losada and Heaphy

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(2004), linking positive discourse with high performance. With specific regard to Losada

and Heaphy’s work (2004), it is unfortunate that the controversy over the mathematical

modeling has taken attention away from underpinning idea, which is that positivity, in the

form of language, narratives, and mindset benefit human systems.

Additionally, there has not yet existed much of a connection between the promise

of POS’ scholarship and practice with applied sustainability. This study serves as one of

the first, linking two verdant fields, which have much to gain and contribute from further

exploration and collaboration.

Limitations of this study

There are four main limitations to this study: sample size; vulnerability to

technological issues; single-sector study population; and a limiting research design. I

begin with the study’s sample size.

With performance categories comprised of four (high), three (moderate), and

three (moderate) sustainability programs it is hard to make generalizations beyond the

study population. Related to sample size, the small population meant that the data was

limited and vulnerable to inconsistency, like technological issues. Problems with quality

of audio recordings for example derail such a small sample size and complications in

analysis arise when different study participants provide different amounts of data. The

third limitation is that this study only included organizations in the higher education

sector. While there are likely similarities with other sectors, colleges and universities are

unique in their power structures, which greatly influence change narratives.

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The fourth limitation is one of research design. In its current design this study cannot

engage in conversation attributing causation to either performance or emotional tone of

language. It is not clear whether positive narratives lead to high performance, or if high

performance leads to more positive narratives. Moreover, related to causality, this study

did not control for amount of financial resources available to the sustainability programs

in the sample or for high-level institutional commitment, such as engagement and support

from board of trustees or the president’s office.

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CHAPTER SEVEN: CONCLUSIONS

Evolutionary biologist Mark Pagel describes language as our species’ most

important social technology and the most powerful trait that natural selection devised.

Investigation of narrative offers a general contribution to the literature of organizational

change. What would formerly be regarded as only part of the way to understanding an

issue – language – has been treated as central by this study, a phenomenon worthy of

attention and investigation in and of itself. (Oswick et al, 2000). This study expands the

central premises of social construction theory, drawing from positive psychology and

positive organizational scholarship, by focusing attention on the language needed for

successful sustainability programs.

Language- be it through metaphor, stories, or the longer arc of narrative- is one of

organizational change’s most powerful tools. Language is alchemical. It is through

language that the mundane transforms to meaningful and the illusions of un-questioned

mental models are not only revealed, but can be re-designed to support flourishing human

systems. Every narrative is a story, all of which holds a certain poetry about experience

in human systems. Our language reflects identity, emotion, and boundaries; we can let it

carry us toward new regions of knowing, feeling, and experiencing. Historian and

memoirist Rebecca Solnit wrote, “Stories are both compasses and architecture. We

navigate by our stories, and we build both our sanctuaries and prisons with our stories”

(2014, p. 3). Applying the metaphors of navigation and architecture to human systems

sheds light on the importance of the tone, content, and process of organizational

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discourse. Attention paid to narratives reveals the direction and architecture of our

language, are we steering ourselves toward cages or are we steadily building cathedrals?

What does our language reveal about us? The language themes and patterns of the

high performers in this study tell a story of connectedness, to each other and to positive

things, events, and interactions all around them. The findings of this research are clear.

Positive discourse, and plenty of it, is a key characteristic separating high performance

sustainability programs from sustainability programs at the moderate and base

performance levels. Positive language, when it outnumbers negative language by 4:1, is

essential, not just for positivity’s sake, but because of how a positive emotional tone

counteracts the effects of negative remarks and interactions (Gottman, 1994) and how

positive emotions effect how we think, what and how much information we are able to

take in, and how we act (Fredrickson, 2009).

The kind of positive discourse that has been found in high performing

sustainability programs in this study tells a story of active connection and cooperation,

and the power people and events have on enhancing the momentum toward a positive

goal. Positive discourse sees the good in the system, in individuals, and in what is

possible. Cooperrider and Fry (2013) suggest these dynamics are not at all surprising,

occurring with regularity across sectors in organizations pursuing environmental and

social flourishing. Mirror flourishing (when people on the inside of the institution begin

to flourish when involved or are witness to their institution’s efforts to help others and the

planet flourish) may be the x-factor of the high performing sustainability programs

studied here and the reason why positive discourse makes up more of their conversations

than neutral and negative discourse combined. One characteristic of mirror flourishing is

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that previously held beliefs about division, like ‘out there,’ or ‘them’ no longer seem to

accurately capture the new experiences of connectedness. In this way, there is only

‘here’ and there is only ‘us.’

If there is only an ‘us’ than we have all we need, we are all we need. Language

that reflects and invites connectedness, is language expressing love. Language that

encourages flourishing natural and social systems is language of love. The narratives of

the high performers are all about love, even when it seems like they are not.

When the conversation is about how to handle an issue with a custodian who is

taking things home from recycling bins, the conversation is about love. When the

conversation is about helping people learn and understand more about their

interconnectedness to the planet and their places, the conversation is about love. When

the conversation is about how a team can work better together, the conversation is about

love.

Sustainability on higher educational campuses is devised of policies, practices,

and behaviors, which express care and whose goal is development of flourishing natural

and social systems. Sustainability is one example of what love looks like when expressed

at the organizational level (Coombe, 2011). Sustainability offers us direct access to the

opportunities for expressing and experiencing love prevalent through our organizational

interactions and beyond.

The best way to love anyone, is to love everyone. Adjust your gaze to see the

prevalence of love all around you. Love is evidenced everywhere. We express and

receive love through the smallest of interactions and in our enduring relationships with

colleagues, family, and friends. We express and receive love when we do good on behalf

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of others and the planet. We express and receive love through our inquiries and our

narratives. Love is our language and our language is love.

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APPENDIX A

A Chi-square test was performed to examine the relation between high and base

performance categories and positive and negative discourse categories for: all sources of

data; interview data; group meeting data; and website data. Apart from the website data,

the relationship between these variables was shown to be significant. Tables 33 – 36

present these analyses.

Table 33. Chi-square Analysis for All Sources of Data

Positive Discourse Negative Discourse Marginal Row Totals High Performers 64 (51.83) [2.86] 15 (27.17) [5.45] 79 Base Performers 39 (51.17) [2.9] 39 (26.83) [5.52] 78 Marginal Column Totals 103 54 157 (Grand Total)

The Chi-square statistic is 16.7289. The P value is 4.3E-05. This result is significant at p < 0.01.

Table 34. Chi-square Analysis for Interview Data Positive Discourse Negative Discourse Marginal Row Totals High Performers 71 (60.58) [1.79] 22 (32.42) [3.35] 93 Base Performers 43 (53.42) [2.03] 39 (28.58) [3.8] 82 Marginal Column Totals 114 61 175 (Grand Total) The Chi-square statistic is 10.9668. The P value is 0.000928. This result is significant at p < 0.01. Table 35. Chi-square Analysis for Group Meeting Data Positive Discourse Negative Discourse Marginal Row Totals High Performers 52 (33.65) [10.0] 26 (44.35) [7.59] 78 Base Performers 14 (32.35) [10.41] 61 (42.65) [7.9] 75 Marginal Column Totals 66 87 153 (Grand Total) The Chi-square statistic is 35.9142. The P value is 0. This result is significant at p < 0.01.

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Table 36. Chi-square Analysis for Website Data Positive Discourse Negative Discourse Marginal Row Totals High Performers 63 (61.53) [0.04] 2 (3.47) [0.62] 65 Base Performers 61 (62.47) [0.03] 5 (3.53) [0.62] 66 Marginal Column Totals 124 7 131 (Grand Total) The Chi-square statistic is 1.3104. The P value is 0.25232. This result is not significant at p < 0.01.

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