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Feedback, Affect, and Creative Behavior
A Multi-level Model Linking Feedback to Performance
by
Amanda L. Christensen
A Dissertation Presented in Partial Fulfillment
of the Requirements for the Degree
Doctor of Philosophy
Approved April 2014 by the
Graduate Supervisory Committee:
Angelo Kinicki, Chair
Zhen Zhang
Peter Hom
ARIZONA STATE UNIVERSITY
May 2014
i
ABSTRACT
Researchers lament that feedback interventions often fail. Traditional theories
assume a cognitive relationship between the receipt of feedback and its impact on
employee performance. I offer a theoretical model derived from Affective Events and
Broaden and Build Theories to shed new light on the feedback-performance relationship.
I bridge the two primary streams of feedback literature-the passive receipt and active
seeking-to examine how employees' affective responses to feedback drive how they use
feedback to improve performance. I develop and test a model whereby supervisor
developmental feedback and coworker feedback seeking relate to the positivity ratio (the
ratio of positive as compared to negative affect), enabling them to be more creative and
thus improving their performance. I test my model using Experience Sampling
Methodology with a sample of MBA students over a two week working period.
ii
TABLE OF CONTENTS
Page
LIST OF TABLES………………………………………………………….……………..v
LIST OF FIGURES……………………………….…………………….………...……...vi
CHAPTER
1 INTRODUCTION………………………………………………………………...1
Contributions.................................................…………………………..…5
Structure of this Dissertation.........................…………………………..…5
2 LITERATURE REVIEW…………………………………………………………6
Two Views of Feedback and Performance....…………………………..…7
Feedback and Affect......…………………………………………………16
Affect and Creative Behavior.......……………………………………….21
Creative Behavior and Performance....…………………………………..26
Study Overview.........................................................................................27
3 THEORY AND HYPOTHESES………………………………………………...28
Supervisor Developmental Feedback and Positive Affect.....……………29
Coworker Feedback Seeking and Positive Affect.....……………...…….31
iii
CHAPTER Page
The Positivity Ratio and Creative Behavior.…………………………….33
Creative Behavior and Overall Job Performance………………………...34
The Mediating Role of the Positivity Ratio and Creative Behavior in the
Feedback–Performance Relationship...............................……………..…36
4 RESEARCH METHODOLOGY AND DATA ANALYSIS……………..……..38
Sample and Procedure……………………………………………………38
Measures..........…………………………………………………………..42
Analyses.…………………………………………………………………45
5 RESULTS................................................................................…………………..48
Hypothesis Tests........……………………………………………………49
6 DISCUSSION..........................................................................…………………..55
Theoretical Contributions and Future Research…………………………55
Practical Contributions………………………...…………………………62
Limitations…………………………………….…………………………63
Conclusion…………………………………….…………………………66
iv
Page
REFERENCES…………………………………………………………………………..68
APPENDIX
A INSTITUTIONAL REVIEW BOARD APPROVAL FORM..............................84
B SURVEY INSTRUMENTS..................................................................................86
v
LIST OF TABLES
Table Page
1. Measurement Table..............................…………………………………………....…40
2. Descriptive Statistics and Correlations…………………………………………....…49
3. Percentage of Variability for Null Models...........……………………………………49
4. Multilevel Results Predicting Positivity Ratio, Creative Behavior, and Overall
Performance for Supervisor Developmental Feedback.....................................................50
5. Multilevel Results Predicting Positivity Ratio, Creative Behavior, and Overall
Performance for Coworker Feedback Seeking..................................................................51
vi
LIST OF FIGURES
Figure Page
1. Theoretical Model…………………………………………………………………......4
2. Empirical Model...…………………………………………………………………...47
1
CHAPTER 1: INTRODUCTION
The giving and receiving of feedback is ubiquitous in organizations. Feedback
provides information regarding the effectiveness of employees’ work behaviors (Kinicki,
Prussia, Wu, & McKee-Ryan, 2004) and is a key component of performance
management systems used around the world. A survey of nearly 300 corporations across
15 countries revealed that 91% of the studied organizations implemented a formal
performance management system (Cascio, 2006). Unfortunately, research suggests that
such systems may be ineffective. Pulakos (2009), for example, showed that only 30% of
workers reported that their company’s performance management system helped them
improve their performance and less than 40% said their systems provided clear goals or
generated honest feedback. Further, a meta-analysis showed that over one-third of
feedback interventions resulted in subsequent decreases in performance (Kluger &
DeNisi, 1996), leaving Smither, London, and Reilly (2005: 42) to conclude that the
effects of feedback on performance, when not negative, were “very small.” All told,
research reveals that feedback does not tend to consistently spur enhanced performance.
Scholars have pursued two streams of research to address this problem. The first
views the feedback recipient as a passive receiver who may choose to respond to the
feedback message based on unidimensional characteristics such as sign and specificity
(e.g., Northcraft, Schmidt, & Ashford, 2011; Prussia & Kinicki, 1996; Van Dijk &
Kluger, 2011). The second stream regards the recipient as an active seeker of
performance-related information (e.g., Ashford & Cummings, 1983). Both streams
confirm the importance of feedback for learning, motivation, and performance, but
related research is limited in three ways.
2
First, research in these two traditions has developed separately (Kinicki et al.,
2004), resulting in a lack of integration across past studies. Recipients obtain feedback
both passively and actively from multiple sources and feedback in real job situations is
more complex than research on single dimensions reveals. This study is based on the
premise that researchers can gain a better understanding of the link between feedback and
performance by jointly considering elements from both streams. Second, both traditions
assume that feedback translates directly to performance or indirectly through cognitive
mechanisms such as employees' desire or intent to respond (Fedor, 1991; Ilgen, Fisher, &
Taylor, 1979; Kinicki et al., 2004). This study, in contrast, builds from the proposition
that affect influences employees’ responses to feedback. Affect encompasses both
emotions and moods as the felt experiences of individuals in reaction to situations or
events (Mischel & Shoda, 1995), placing demands on attentional resources and
influencing how individuals behave in response to events (Beal, Weiss, Barros, &
MacDermid, 2005; Smith & Lazarus, 1990). Affect can be detrimental to performance,
such as when employees feel depressed or ashamed, or it may enhance performance when
employees experience positive affective states such as excitement or interest (Beal et al.,
2005; Russell, 1980). Understanding the behavioral implications of employees' affective
responses to feedback is important in understanding why certain types of feedback elicit
desirable responses.
Third, creative behavior, defined as the generation of new and useful ideas
concerning processes, procedures, services, and products at work (Amabile, 1988;
Oldham & Cummings, 1996), may be an important missing link in the feedback–
performance process. Feedback, in an instructional capacity, signals to employees that
3
they may need to try a different approach for accomplishing tasks or that they may need
to alter their efforts altogether (Taylor, Fisher, & Ilgen, 1984). Accordingly, employees
need to consider the exhibition of new behaviors, approaching their tasks differently upon
the receipt of performance feedback. This is contrary to past research traditions that
assume that feedback, if accepted, translates directly into performance. While feedback
provides a guide as to how to improve performance, employees must discover for
themselves how to implement that feedback and approach tasks in a manner that is new
or different to them. This underscores the need to consider the role of creative behavior
within the feedback–performance relationship.
The overall goal of this study is to enhance understanding about the relationship
between feedback and performance. I draw on affective events theory (AET: Weiss &
Cropanzano, 1996) and the broaden and build theory of positive emotions (Fredrickson,
1998, 2001) to provide a theoretical foundation for the hypotheses. I integrate the two
dominant streams of past feedback research by adopting the view that employees are not
solely individuals who await information from their supervisors but who also proactively
self-regulate their performance by seeking information from those around them (Ashford,
Blatt, & Walle, 2003). I consider the impact of developmental feedback delivered by the
supervisor and feedback actively sought from coworkers. Supervisor developmental
feedback is information intended to help employees learn, develop, and make
improvements on the job (Li, Harris, Boswell, & Xie, 2011; Zhou, 2003). Employees
may also proactively seek feedback from coworkers to supplement information provided
by supervisors. Both supervisor developmental feedback and coworker feedback seeking
provide employees with important task related information and support that are expected
4
to influence employees' positive affect, which in turn is expected to foster creative
behavior and subsequent task performance. The model shown in Figure 1 represents the
theoretical predictions tested in this study.
Figure 1—Theoretical Model
The relationships in Figure 1 are derived from affective events and broaden and
build theories. Supervisor developmental feedback and coworker feedback seeking
increase employees' experiences of positive as compared to negative affect (i.e., the ratio
of positive to negative affect, termed the positivity ratio; see Fredrickson, 2013). Positive
affect prompts individuals to make diverse mental associations and promotes approach
and exploration behaviors, influencing creative behaviors (Fredrickson, 1998; Isen,
Johnson, Mertz, & Robinson, 1985b). I propose that a greater ratio of positive as
compared to negative affect works as a stimulus to employees’ creative behavior through
broadened mental associations and new perspectives provided by broader information
processing strategies (Sutcliffe & Vogus, 2003). Creative behavior, in turn, enhances
performance by providing employees with new and useful approaches to their work
(Zhang & Bartol, 2010).
Coworker
Feedback
Seeking
Creative
Behavior
Supervisor
Developmental
Feedback
Positivity
Ratio
Overall
Performance
5
Contributions
This study contributes to the feedback literature by integrating the two dominant
streams in the literature by considering how supervisor delivered developmental feedback
and employees' own attempts to seek feedback from coworkers influences performance
and by emphasizing the importance of affective responses to feedback. In addition to
developing a model derived from AET and broaden and build theories, this paper
contributes to the feedback literature by drawing from Fredrickson’s broaden and build
theory (Fredrickson, 1998, 2001) to examine creative behavior as a key mediator in the
feedback–performance process. Time is an important parameter in the relationship
between events, affect, and behaviors as they fluctuate in the workplace (Weiss &
Cropanzano) so I test this model using experience sampling methodology over a 10 day
period (two working weeks) to examine within- and between-person relationships.
Structure of this Dissertation
This dissertation is structured as follows. Chapter 2 reviews the literature relevant
to the theoretical model, including work on feedback and performance, feedback and
affect, affect and creative behavior, and creative behavior and performance. I develop the
hypotheses for the theoretical model in Chapter 3. I detail my study methodology and
results in Chapters 4 and 5 and conclude with a discussion in Chapter 6.
6
CHAPTER 2: LITERATURE REVIEW
The emphasis in this dissertation is to understand the mechanisms through which
feedback influences employee job performance. Feedback is information regarding the
effectiveness of employees' behaviors at work (Ilgen et al., 1979), and performance is the
effectiveness of those behaviors in promoting organizational goals (Campbell, 1990).
Management scholars and organizational psychologists have long believed that feedback
is an important enabler of enhanced performance; indeed, feedback is one of the most
frequently used behavioral modification tools and motivational strategies in organizations
(Ilgen et al., 1979; Zhou, 2008).
Researchers have developed two separate streams of research to understand the
relationship between feedback and performance. The first stream views the feedback
recipient as a passive receiver, and depending on several factors which will be discussed
below, the recipient may accept the feedback and choose to respond to the feedback
message (Fedor, 1991; Ilgen et al., 1979; Kinicki et al., 2004). Alternatively, the recipient
may not accept the feedback, resulting in no changes in subsequent behavior and
performance. The second stream views employees as active seekers of feedback who use
this information in pursuit of the need to feel competent or autonomous (Ryan & Deci,
2000). Empirical investigations, however, reveal that feedback oftentimes has no effect
and even negative effects on performance (Kluger & DeNisi, 1996; Smither et al., 2005).
In this section, I review the conceptualizations and mechanisms of the feedback–
performance relationship in the passive and active literatures. Then I review the small
body of work that has examined the relationship between feedback and affect, followed
7
by work on affect and creative behavior, and finally creative behavior and performance.
These literatures all contribute to the theoretical framing of the model shown in Figure 1.
Two Views of Feedback and Performance
I review two streams of feedback research: the employee as passive recipient and
as active seeker. I begin by reviewing four primary theoretical models and empirical
research in the passive recipient tradition. I then review work done on a less researched
type of feedback—developmental feedback—that is one of the main focuses of this
dissertation, followed by a review of the active seeking literature.
The employee as passive feedback recipient: Theoretical models and
empirical tests. There are four primary theoretical models (Fedor, 1991; Ilgen et al.,
1979; Kluger & DeNisi, 1996; Taylor et al., 1984) outlining processes through which
employees respond to feedback. These models position the feedback message as
originating outside the employee as opposed to being actively sought. Models in this
tradition generally examine employees' desire to respond to feedback based on
antecedents such as feedback acceptance, source, valence, consistency, and specificity.
The mechanisms outlined in these models are largely cognitive and assume that
employees—if they choose to respond to the feedback—apply it directly, resulting in
enhanced performance. The majority of empirical research in this tradition has rarely
examined the proposed mediating mechanisms within these models (Kinicki et al., 2004;
Smither et al., 2005).
Ilgen and colleagues (1979) developed a theoretical process model based on
individuals' perceptions of feedback, acceptance, desire to respond, and intended
8
response. Perceptions of feedback are based on the receivers’ characteristics (such as
expectations, frame of reference, and self-esteem), the source delivering the message, and
the timing, sign, and frequency of feedback. Perceptions of feedback are formed
subjectively. Feedback messages have no value in themselves until they are interpreted.
Thus, the value or quality of the message can only be determined by the recipient's
perception and cognition. From there, individuals judge the accuracy of the feedback
(i.e., acceptance), and if they desire to respond to the message, form an intention to
respond which is then expected to result in performance changes. Kinicki and colleagues
(2004) tested this model longitudinally and found evidence for the mediating chain of
variables.
Taylor and colleagues (1984: 82) noted that, "even when feedback is provided in
a direct, timely manner, it often fails to affect a recipient's behavior in the way intended
by the source." These researchers updated Ilgen et al.'s (1979) model using control theory
(Carver & Scheier, 1981) as an organizing perspective. Taylor and colleagues (1984)
discussed how performance standards are set and the comparison process used to evaluate
feedback in relation to standards. Individuals may increase, decrease, or make no change
in their behavior based on the value of their goals (Campion & Lord, 1982) and their
responses to the feedback. This perspective suggests that behavioral responses to
feedback, when they occur, include changes in effort, intensity, and persistence of
behavior. Research on the control theory perspective suggests that feedback draws
recipients' attention toward aspects of the task on which feedback is provided and
influences goal setting (e.g., DeShon, Kozlowski, Schmidt, Milner, & Wiechmann, 2004;
Kernan & Lord, 1990).
9
Building on these previous models, Fedor (1991) theorized that individuals
respond to feedback messages similarly to how they respond to persuasive messages:
feedback is given with the intention to persuade others of the need to change their
behavior. Individuals respond to messages that have personal significance by perceiving
and developing attitudes toward them (Fishbein & Ajzen, 1975; Petty & Cacioppo,
1979). Feedback recipients react to factors of the message source, such as power and
credibility, and characteristics of the message itself such as timing, valence, and
specificity. Individuals form behavioral intentions based upon their reactions to the
feedback, yet behavioral change only occurs when recipients feel they have volitional
control over the intended changes (Ajzen & Fishbein, 1977). This model is useful in
explaining how recipients react to feedback as a persuasive message in forming
behavioral intentions, yet less is known about how behavioral change occurs to enhance
performance. Fedor and colleagues (2001) found that supervisor power moderates the
relationship between recipients’ self-esteem and performance improvement and Kinicki
and colleagues (2004) found that the source's credibility influenced recipients'
perceptions of feedback accuracy.
Finally, Kluger & DeNisi (1996) conducted a meta-analysis of the feedback
literature and found that over one-third of feedback interventions decreased performance.
This finding led them to put forth their feedback intervention theory, hypothesizing that
feedback interventions that draw attention to the self—such as those that threaten self-
esteem—attenuate performance; and interventions focused on the task increase
performance through motivation and learning. Vancouver and Tischner (2004), in support
of this model, found that feedback sign positively related to task performance when
10
individuals were given an opportunity to reaffirm their self-concepts. Ilies and Judge
(2005) further found that feedback comparing individuals to their peers was less effective
than feedback focused on individuals' own performance.
Researchers agree that higher quality feedback, defined as feedback that draws
attention to the task (e.g., Taylor & Fiske, 1978) and keeps performance goals at the top
of recipients' attention (Shah & Kruglanski, 2003), is more helpful to recipients and
should increase performance more than lower quality feedback (Kinicki et al., 2004;
Larson, Glynn, Fleenor, & Scontrino, 1986; Northcraft et al., 2011). Larson and
colleagues (1986), in support of a move away from single dimensions, noted that factors
such as timeliness, specificity, and frequency were highly related and were more
appropriately conceptualized as an overall quality construct. Kinicki and colleagues
(2004) found evidence of this and found that higher quality feedback environments,
consisting of frequency, specificity, and positivity, related to individuals' perceived
accuracy of feedback. Northcraft, Schmidt, and Ashford (2011) similarly found evidence
that feedback quality, consisting of timing and specificity, was associated with task
salience and motivation to complete tasks.
Higher quality feedback is task-related and focused on learning and development,
notably, task-focused and not person-focused (Kluger & DeNisi, 1996); yet researchers
have rarely investigated recipients' perceptions of feedback beyond specific dimensions
such as timing, specificity, frequency, and valence (Fedor, 1991; Li et al., 2011).
Feedback is perceptual—individuals react to their environments as they perceive them
(Endler & Magnusson, 1976). Feedback may be timely and frequent, but if it invokes
defensiveness or threatens self-esteem, learning may be circumvented (Kluger & DeNisi,
11
1996). The previously mentioned theoretical models suggest that behavioral change
following feedback is most likely to occur when feedback is supportive, nonthreatening,
gives the recipient perceptions of control over the behavioral outcome, and is goal- or
future-oriented (Fedor, 1991; Fishbein & Ajzen, 1975; Kluger & DeNisi, 1996; Taylor et
al., 1984). Specific feedback dimensions (i.e., specificity and frequency) capture only
parts of these components. More research is needed that captures recipients’ perceptions
of the quality of the feedback they receive beyond single dimensions.
The role of developmental feedback. One task-based approach that focuses on
learning, motivation, behavioral control, and future orientation is the study of
developmental feedback. Developmental feedback is defined as the extent to which
individuals perceive that information provided by others is intended to help them learn,
develop, and make improvements on the job (Li et al., 2011; Zhou, 2003). This type of
feedback provides useful, quality information to receivers and is focused on helping
recipients make improvements. The focus on improvement distinguishes this type of
feedback from traditional performance feedback that is either positive or negative and
focused on past outcomes. The influence of developmental feedback is independent of
sign—it may convey both positive and negative information, but it is likely to propel
individuals to improve future performance because of its informational and motivational
orientation. Prior work has found that developmental feedback is positively associated
with helping behaviors and task performance (Li et al., 2011) and with creativity when
combined with the presence of creative coworkers (Zhou, 2003). Future research is
needed to examine the process by which developmental feedback affects performance.
12
Summary of research on the employee as passive feedback recipient. Work in
this research tradition has focused more heavily on unidimensional and specific aspects
of feedback (e.g., timing, frequency, and valence) and less on recipients' perceptions of
the quality of feedback (for exceptions, see Li et al., 2011; Zhou, 2003; Zhou & George,
2001). Research has focused primarily on cognitive variables, such as perceived accuracy
and desire to respond, that influence the relationship between feedback and performance.
Other researchers, in contrast, have suggested that feedback is an affect-driven process;
that is, recipients may not necessarily respond to specific feedback dimensions
cognitively, but rather they respond to the tone of the overall message affectively (Kluger
& DeNisi, 1996; Kluger, Lewinsohn, & Aiello, 1994). Research examining the effect of
feedback on affect has largely studied the effect of feedback sign (i.e., positive or
negative) on positive and negative feedback (e.g., Brett & Atwater, 2001; Ilies & Judge,
2005). Studies rarely examine the influence of feedback on affect beyond this dimension.
Feedback in real job situations, however, is complex: messages are rarely
unidimensional, and recipients obtain feedback through multiple methods (passively and
actively) and from multiple sources such as supervisors and coworkers (Greller &
Herold, 1975). More research is needed that examines employees' affective responses to
supervisor developmental feedback and employees' own efforts to supplement this
feedback with information from coworkers. Scholars have provided several useful
theories regarding the relationship between feedback and performance, yet the role of
affect in the feedback–performance relationship remains developed to a lesser extent. The
purpose of this dissertation is to provide a perspective of the feedback–performance
relationship that more closely approximates the complexity of real job situations by
13
examining developmental feedback passively received from supervisors, employees’
efforts to seek feedback from coworkers, and the influence of affect on feedback.
The next section reviews work done from the perspective that employees are
active feedback seekers, followed by a review of work done on the relationship between
feedback and affect.
The employee as active feedback seeker. Ashford and Cummings (1983)
reacted to the feedback literature's emphasis on annual performance reviews by noting
that employees actively seek feedback from various sources and methods; employees
desire and use feedback in their everyday lives—they do not simply wait for feedback to
be delivered to them. Employee feedback seeking behavior is a means through which
employees gather information about their work-related behaviors on their own schedule
and terms and according to their own needs. The feedback seeking literature has
examined three primary motives that employees engage in seeking: image
defense/enhancement, conformity, and self-regulatory performance improvement. The
first two motives view feedback seeking as a strategy employees use to influence others’
views of them, manage how they appear to others, and fit in with the environment
(Ashford et al., 2003; Lam, Huang, & Snape, 2007; Tsui, Ashford, Clair, & Xin, 1995).
The third motive views feedback seeking as an employee-driven, proactive behavior that
employees use to self-regulate their performance (Ashford & Tsui, 1991; Grant &
Ashford, 2008; Parker & Collins, 2010; Porath & Bateman, 2006). This view is
consistent with Deci and Ryan’s (2000) self-determination theory in that it assumes that
employees seek feedback so that they can improve their performance and therefore
satisfy the innate need for competence and autonomy. I adopt this latter view for the basis
14
of this dissertation because the question of interest is how employees use feedback to
influence performance; the underlying assumption in the theoretical framework of this
study is that employees desire to perform well in organizations and that they work
towards their own standards and goals.
Employees seek feedback to achieve their personal goals—they are not solely
individuals who await information from others and who work merely to meet the
expectations of others (Ashford & Cummings, 1983). Employees take control of their
work-related behaviors and goals in line with self-regulation theory's (Carver & Scheier,
1981; Vohs & Baumeister, 2004) view that employees guide their own goal-directed
activities by establishing their own standards and monitoring progress towards their
goals. It also is consistent with self-regulation theory and the proposition that employees
can experience a sense of competence vis-à-vis the feedback received from others. The
perspective that feedback seekers are self-determined contributors who set their own
goals and standards is in line with Ashford and Cummings' (1983) original
conceptualization of feedback seeking (Ashford et al., 2003).
Seeking enhances employees' ability to perform well by adapting to continuously
changing work environments and responding to changing goals and role expectations
(Morrison & Weldon, 1990; Tsui & Ashford, 1994). Feedback seeking informs
individuals of others' views of their work, and allows individuals to gain differing
perspectives as views of their work change over time in response to shifting conditions—
even if, in extreme cases, recipients seek only from a single source (De Stobbeleir,
Ashford, & Buyens, 2011). Seeking stimulates divergent thinking by providing differing
views and increasing the amount and diversity of information about employees' work,
15
leading to enhanced task and creative performance (Chen, Lam, & Zhong, 2007; De
Stobbeleir et al., 2011). More frequent feedback seeking allows for more views of
employees' work-related behaviors. Empirical research reveals that more frequent seeking
is related to heightened feelings of control (Ashford & Black, 1996) and increased goal
setting (Renn & Fedor, 2001). More research is needed that investigates the mediating
mechanisms through which seeking relates to performance (Ashford et al., 2003).
Ashford and Cummings (1983) proposed two strategies that employees use to
seek feedback: through the direct tactic of inquiry and the indirect tactic of monitoring.
Inquiry involves direct requests to others for information regarding performance.
Employees may ask multiple sources to supplement information from themselves or
others. Monitoring involves individuals' observation of the environment for personally
relevant information, including their own task progress and the actions of others around
them (Ashford & Cummings, 1983; Greller & Herold, 1975). Research has shown that
monitoring may be subject to greater perceptual bias and is less accurate than information
gained through inquiry (Ashford & Tsui, 1991). Reflection of task performance without
direct feedback delivered from others has been found to have no effect on performance
improvement (Anseel, Lievens, & Schollaert, 2009). Direct verbal inquiry provides
employees with a clear picture of how others see their work and behaviors, allowing for
behavioral adjustments and improvements to work behaviors and ideas (De Stobbeleir et
al., 2011). This dissertation looks specifically at seeking through inquiry because this
strategy allows for deeper information from others and is prone to fewer biases.
Summary of research on the employee as active feedback seeker. Research to
date on the passive receipt and active seeking literatures has been developed separately.
16
Research suggests that feedback seeking is a valuable supplement to formally delivered
feedback from supervisors (Ashford & Cummings, 1983), yet supervisors' attempts to
deliver quality feedback and employees' own efforts to gain additional feedback have not
been examined in tandem. This is problematic because it fails to capture the reality of
organizational life. This study attempts to address this by bringing together these two
streams of work and expanding them to include theory on affect. Research in the active
seeking tradition has not examined employees' affective responses to feedback seeking
and research in the passive area has given it little attention despite suggestions that affect
may be an important mediator in predicting performance (Kluger & DeNisi, 1996; Kluger
et al., 1994). The next section reviews work on the relationship between feedback and
affect.
Feedback and Affect
Although feedback has the power to help individuals develop and understand
workplace standards (Ilgen et al., 1979), certain factors determine whether individuals
actually use the feedback they receive (Fedor, 1991). Individuals' affective response to
feedback is one of these factors. I apply affective events theory (AET; Weiss &
Cropanzano, 1996) as a useful framework for studying the role of affect in the feedback–
performance relationship. I begin by explaining the conceptualization of affect used in
this dissertation, then summarizing affective events theory to establish a foundation for
the theoretical model advanced in the hypotheses section, and finally reviewing work on
the relationship between feedback and affect.
17
Affect. Affect includes both emotions and moods. Emotions are generally tied
closely to the appraisal of an event and can be summarized as a feeling of pleasantness or
unpleasantness stemming from an event (Frijda, 1993). Emotions may elicit physiological
changes, such as a body’s action readiness and increased arousal and vigilance (Frijda,
1993). Emotions also accompany cognitive appraisals, such that cognitive appraisals of
the situation may inform or be informed by emotions (Plutchik, 1994). Both emotions
and moods are experiences, that is, they are temporary felt states, in contrast to individual
trait differences in chronic levels of affect. Moods differ, however, from emotions on
three primary features: duration, intensity, and diffuseness. Moods are generally longer
lasting than emotions and are generally less intense. Additionally, moods are more
diffuse than emotions in that the moods may not be as tightly connected to their sources
as are emotions, and as a result, a wider variety of cognitive and behavioral responses
may stem from moods (Frijda, 1993; Morris, 1989).
These distinctions, however, are sometimes arbitrary (Frijda, 1993; Morris, 1989).
Emotions sometimes last longer than moods and may be as diffuse as moods in their
consequences. Emotions may turn into moods when individuals no longer actively focus
on the cause of the emotion, but when reminded of the cause, moods may transform back
into emotions (Clore, 1992). The distinction between moods and emotions is not entirely
clear and can be taken too far (Weiss & Cropanzano, 1996). For this reason, I follow
previous research (Beal et al., 2005; Bledow, Schmitt, Frese, & Kuhnel, 2011; Weiss &
Cropanzano, 1996) and use the term affect to encompass both emotions and moods as the
felt experiences of individuals in reaction to different features of situations or events
(Mischel & Shoda, 1995). It is important to note that while some individuals are more
18
likely to experience chronic levels of affect over time (i.e., trait-like affect characterized
as trait positive affect or trait negative affect; Watson, Clark, & Tellegen, 1988), the
focus here is on affect as states, or affect that is subject to temporal change.
Affective events theory. Affective events theory (AET; Weiss & Cropanzano,
1996) provides an overarching framework that focuses on employees' affective responses
to events at work. The theory suggests that individuals' behaviors may be in reaction to
emotional responses stemming from work occurrences. Events happen in the workplace
and individuals respond affectively—for example, feeling angry, frustrated, inspired, or
joyful. These reactions have behavioral consequences, such as the effort that employees
put towards their work, how they approach tasks, or the manner in which they interact
with their colleagues. The impact of events on affective reactions may vary in part due to
individual differences such as goals, personality, or disposition. AET recognizes the
importance of time as a parameter in the relationship between events, affect, and
behaviors or attitudes. Employees’ affective states fluctuate over time and it is important
to consider those fluctuations when studying how affect influences individuals.
Events represent important happenings in the workplace that bring about a change
in what individuals are currently experiencing in regard to goals or personal desires
(Weiss & Cropanzano, 1996). Events that are important to individuals’ goals evoke
affective reactions (Frijda, 1988, 1993); those that help individuals reach their goals
generally yield positive affective responses and those that threaten individuals goals lead
to negative affective responses (Frijda, 1988). Further, events in the workplace appraised
as opportunities for learning, goal attainment, and growth generate positive affect and
attitudes (Cavanaugh, Boswell, Roehling, & Boudreau, 2000). Lazarus (1991a)
19
supported this view, maintaining that events indicating achievement and progress toward
desired outcomes elicit pleasurable emotions. Empirical studies show that positive affect
is associated with challenge stressors, goal attainment, and task satisfaction (Gabriel,
Diefendorff, & Erickson, 2011; Ilies & Judge, 2005; Locke & Latham, 1990; Rodell &
Judge, 2009) and that negative affect is associated with events hindering growth and goal
attainment, such as those that increase role ambiguity and work constraints (Chen &
Spector, 1991; Rodell & Judge, 2009).
Affect is an important part of individuals’ experiences at work and has
consequences for work-related behaviors and attitudes (Weiss & Cropanzano, 1996).
Affect is driven externally by features or events in the environment, including employees'
own behaviors, and also internally by the relevance of those events to individuals’ goals
and desires. The next section examines the effects of feedback on affect.
The relationship between feedback and affect. Feedback represents one such
event that elicits affective reactions because feedback is closely tied to individuals’ egos
and goals (Kluger & DeNisi, 1996; Kluger et al., 1994). Taylor and colleagues (1984:
111) note that “feedback virtually always provokes an affective response.” Individuals’
affective reactions to feedback stem primarily from the tone and implicit meaning of the
feedback message. Individuals react primarily to the content of the feedback message, for
example, whether the feedback signals progress towards goals or yields opportunities for
learning (Kluger et al., 1994; Swann & Schroeder, 1995). Individuals consider their
perceptions of the feedback situation and how to respond to the feedback in a way to
maximize the potential outcome in reacting to feedback and deciding whether to act or set
higher goals (Swann & Schroeder, 1995). Recipients use the feedback for development of
20
their skills if they believe they have control over the needed behaviors to improve the
situation and have control over the expected outcome (Ajzen, 1991; Ilgen & Davis,
2000). Situations that are supportive of learning and development are expected to
increase individuals’ feeling of control over the situation, providing them with the drive
to apply themselves to improve their skills and the situation (Mathieu & Martineau, 1997;
Maurer, 2001).
Feedback provides individuals with important information regarding workplace
standards and their own performance (Ilgen et al., 1979). Individuals experience positive
or negative affect in response to the overall message provided by the feedback (Swann &
Schroeder, 1995). After receiving feedback, individuals may set higher goals or engage in
corrective actions to reach their previously set goals (Bandura & Locke, 2003; Ilies &
Judge, 2005). Feedback that is delivered in a developmental or supportive manner is
expected to influence individuals by raising their self-efficacy beliefs, thereby
influencing their affective states and ensuing behaviors (Kluger et al., 1994).
Many studies have examined the effect of feedback valence on affect and have
found that, in general, positive feedback elicits positive affect and negative feedback
elicits negative affect (Belschak & Den Hartog, 2009; Brett & Atwater, 2001; Ilies, De
Pater, & Judge, 2007; Kluger et al., 1994) . Researchers recognize that affect is important
to employees' behaviors and attitudes at work, yet researchers have not examined the
impact of developmental feedback on affect. Research supportive of affective events
theory suggests that employees react to events on the basis of how supportive they are of
employees' goals, and not merely the valence of events. Fugate, Harrison, and Kinicki
(2011) demonstrated that employees' negative appraisals of situations were related to
21
negative affect, while Rodell and Judge (2009) found that employees reacted positively to
challenge stressors in their daily lives—events that could be deemed to have a negative
valence but do not deter from employees' goals.
Feedback in real job situations is rarely all positive or all negative; feedback is
more complex and may signal both positive and negative information. Developmental
feedback, unlike performance feedback that has a focus on past outcomes, has a future
orientation and may contain unfavorable information (Zhou, 2003). This type of feedback
focuses on improvement and provides employees with quality information to help them
reach their goals. Research has not examined the impact of developmental feedback on
employees' affective states. This is a striking omission because employees' perceptions of
feedback and their ensuing responses determine whether they use the feedback. Affect is
a natural response to feedback and influences employees' behaviors. The purpose of
feedback is to either motivate employees or to instruct them on how to change their
behaviors and do something new. The use of creative behavior is one method that
employees can use to improve performance. Future research that more closely
approximates feedback in the real world by incorporating the role of affect and creative
behavior within the feedback–performance relationship is clearly needed. This study
attempts to address this void.
Affect and Creative Behavior
The purpose of feedback is to persuade others that they may need to try a new or
different approach to reach their performance goals (Fedor, 1991; Taylor et al., 1984).
Feedback indicates which behaviors are working well and offers suggestions for new
22
behaviors to enhance performance. The failure to examine how employees use feedback
to approach their work in a novel manner by engaging in creative work behaviors is one
plausible explanation of the nonsignificant or negative relationships between feedback
and subsequent performance.
Creative behavior in organizations is the suggestion of novel and useful ideas,
products, or procedures (Amabile, 1988; Oldham & Cummings, 1996). Creative behavior
is desirable and possible in a wide array of jobs and applies to services, products, and
procedures and varies in terms of the degree to which it departs from existing ideas
(Mumford & Gustafson, 1988; Shalley, Zhou, & Oldham, 2004). It may be adaptive or
completely original, or incremental or radical (Madjar, Greenberg, & Chen, 2011;
Oldham & Cummings, 1996). Researchers agree that creative behavior is important for
creative jobs (e.g., Amabile, 1985; Mumford, Scott, Gaddis, & Strange, 2002), yet
individuals enact creative behaviors even in jobs traditionally thought to be non-creative,
such as workers on an assembly line or in a customs agency (Hirst, Van Knippenberg,
Chen, & Sacramento, 2011; Zhou, 2008). This conclusion is supported by both self-
regulation and self-determination theories. These theories are based on the proposition
that employees are motivated to improve existing work conditions, processes, or
behaviors that thwart the accomplishment of meaningful goals or a sense of competence.
As such, creative behavior is possible and desirable from individuals at all levels even
though it may not be central to a role (Amabile, 1988; Oldham & Cummings, 1996;
Woodman, Sawyer, & Griffin, 1993).
According to Fredrickson's broaden and build theory (1998, 2001), positive affect
enhances creative behavior because it broadens individuals' thought and action repertoires
23
and builds personal resources. Positive affect broadens individuals' thought associations
by increasing the cognitive material available for processing and facilitates behavioral
flexibility by allowing more diverse cognitive elements to become associated (Isen,
1999a, b). The positive emotion of interest, for example, produces more accurate
subsequent knowledge than does a negative emotion like boredom (Fredrickson, 2003).
Positivity promotes approach and exploration and opens individuals to experiential
learning opportunities. Isen and Reeve (2005) showed that positive affect fosters intrinsic
motivation and responsible work behavior—study participants in a positive mood
enjoyed novel and challenging tasks more than those in a neutral mood and were more
likely to spend time completing work needing to be done.
Positive affect provides individuals with resources to persist and approach
problems creatively. Individuals in a positive state have increased coping abilities and are
more likely to attend to negative information (Trope & Neter, 1994) and to engage in
more careful processing of information when it is important to their goals (Aspinwall &
Brunhart, 1996). Positive affect influences individuals' ability to assimilate new
information with existing schemas, enhancing flexibility in thinking, decision making,
and behavior (Isen, 2008). Experiences of positive emotions induces individuals to
discard or amend unnecessary behavioral scripts and to pursue novel and creative
thoughts and actions (Fredrickson, 1998). Amabile, Barsade, Mueller, and Staw (2005)
found that positive affect influenced individuals' creative behavior in organizations and
Isen and colleagues found a similar relationship in several studies (Estrada, Isen, &
Young, 1994; Isen, Daubman, & Nowicki, 1987; Isen, Johnson, Mertz, & Robinson,
1985a).
24
Negative affect has also been linked to creative behavior. Scholars argue that
negative mood provides information indicating that something needs to change
(Kaufmann & Vosburg, 1997; Martin & Stoner, 1996; Schwarz, 2002). This occurs
because affect represents an informational function. When negative emotions such as
frustration and anger arise, individuals recognize that something is wrong and they are in
turn motivated to overcome the negative mood by trying new approaches to problems.
George and Zhou (2002) found that individuals in a negative mood who also experienced
their feelings clearly and were in contexts that rewarded creativity were more likely to
demonstrate creative behavior. The conclusion from many studies is that negative affect
influences creativity under certain conditions, such as when individuals are highly
committed to their jobs (Kaufmann, 2003; Zhou & George, 2001).
Researchers agree that both positive and negative affect have important effects on
work outcomes. Positive affect promotes approach behaviors (Cacioppo, Gardner, &
Berntson, 1999; Watson, Wiese, Vaidya, & Tellegen, 1999) and continued action (Carver
& Scheier, 1990; Clore, 1994) and negative affect promotes avoidance (Fazio, Eiser, &
Shook, 2004) and narrow thought processes and behavioral options (Bledow et al., 2011;
Fredrickson, Tugade, Waugh, & Larkin, 2003). Individuals often experience a mix of
positive and negative affect (Bledow et al., 2011; Fong, 2006; George & Zhou, 2007),
suggesting that researchers may benefit by examining both types of affect together. I take
the perspective in this study that certain types of feedback (i.e., supervisor developmental
feedback and coworker feedback seeking) are a positive organizational resource and that,
as described previously, are more likely to arouse positive than negative affect. I look
specifically at the positivity ratio for this reason.
25
Feedback, as an event tied closely to individuals' goals, may signal both positive
and negative information (Li et al., 2011; Zhou, 2003). Feedback characterized by quality
information, focused on improvement, delivered by a supportive source, and sought
under employees' own discretion is expected to increase positive affect, controlling for
negative affect. Greater levels of positive affect as compared to negative affect—termed
the positivity ratio—are expected to increase individuals' behavioral variability and
willingness to apply feedback in novel ways. Losada and Heaphy (2004) found that
higher levels of positivity were linked to greater behavioral flexibility and performance in
business teams and Sutcliffe and Vogus (2003) suggested that greater positivity increased
variability in perspectives and information processing strategies. In support of this
proposition, Rego and colleagues (2012) revealed that the positivity ratio mediated the
relationship between employee optimism and creativity, and Radey and Figley (2007)
showed that higher levels of positive to negative affect predicted greater satisfaction in a
sample of social workers. Further, Galvin, Christensen, Kinicki, and Reina (working
paper) further found that higher positivity ratios in feedback given to CEO’s from their
direct reports were positively associated with top management teams' job satisfaction,
organizational commitment, CEO effectiveness, and firm performance.
The bulk of work in the management field has focused on studying positive and
negative affect as separate constructs, and less work has examined the impact of the ratio
of positive to negative affect, even though individuals may experience both types of
affect throughout their workday. The objective of this study is to examine two types of
feedback and their relation to performance through the positivity ratio and creative
behavior. Studies show that increased positivity in organizations makes employees and
26
managers more successful in the long run (Fredrickson, 2003; Staw & Barsade, 1993;
Staw, Sutton, & Pellod, 1994).
Creative Behavior and Performance
Creative behavior refers to employees' attempts to develop ideas, methods, and
products that are adaptive or original and useful to the organization (Oldham &
Cummings, 1996). Creative behavior is a means of communicating ideas, recognizing
problems, using intuition and guesswork, developing hypotheses, and offering
contradicting viewpoints (Torrance, 1988). Problems change in organizations and
employees need to develop solutions and ideas that are novel or adaptive from what has
been tried in the past. Successful performance at work requires the ability to form novel
and useful ideas (Drazin, Glynn, & Kazanjian, 1999).
Research to date has examined creative behavior primarily as an outcome and has
rarely extended it to examine the impact on overall job performance. Studies have shown
that creative behavior occurs and is desirable in a wide range of jobs, including those in
which creativity is not an explicit requirement. Oldham and Cummings (1996) found a
significant relationship between creative behavior and overall job performance in a
setting of manufacturing engineers and technicians. Gong, Huang, and Farh (2009) found
a significant relationship between creativity and employee sales and job performance in a
sample of insurance agents. A study by Ng and Feldman (2009) reported a significant
correlation between creative behavior and job performance in a sample of employees in
professional jobs.
27
Study Overview
This study aims to make several theoretical contributions. The integration of the
passive receipt and active seeking feedback literatures provides a broader view and new
perspectives of employees' experiences with feedback at work. The study of employees'
affective responses to feedback suggests a means through which employees can thrive at
work through their experience of positive affect and by improving their job performance
through creative behavior. I develop hypotheses for the theoretical model in the next
chapter.
28
CHAPTER 3: THEORY AND HYPOTHESES
I use affective events theory (AET; Weiss & Cropanzano, 1996) and the broaden
and build theory of positive emotions (Fredrickson, 1998, 2001) to develop a theoretical
understanding of how feedback impacts employee performance. This is an important
contribution to the literature because feedback is one of the most frequently used
performance management tools in organizations and research reveals that feedback
oftentimes has little to no effect on performance (see Kluger & DeNisi, 1996; Pulakos,
2009; Smither et al., 2005). Many studies assume that feedback directly translates to
performance or that the relationship is mediated by employees' cognitive responses to
feedback. I take a different perspective by theorizing that employees' affective responses
to feedback and their ensuing creative behaviors are what relates feedback to
performance.
This study focuses on feedback as a positive organizational resource because
positive affect and positive organizational practices have been linked to effectiveness and
optimal functioning at work (Fredrickson & Losada, 2005; Losada & Heaphy, 2004;
Sutcliffe & Vogus, 2003). I contribute to the feedback literature by integrating the
passive approach to studying feedback (i.e., supervisor developmental feedback) with the
active feedback seeking approach into one theoretical model. Specifically, I test a model
that considers the influence of supervisor developmental feedback and coworker
feedback seeking on the ratio of employees' positive as compared to negative affect at
work and how this ratio, termed the positivity ratio, broadens individuals' thought and
behavioral repertoires to enhance creative behavior and thereby overall job performance.
29
Supervisor Developmental Feedback and Positive Affect
Theory suggests that feedback is most effective when it is task- and goal- or
future-oriented, focused on learning and improvement, and provides recipients with
perceptions of behavioral control (Fedor, 1991; Fishbein & Ajzen, 1975; Kluger &
DeNisi, 1996; Taylor et al., 1984). Empirical research on feedback, however, has not
focused on these developmental aspects of feedback and instead has evaluated specific
dimensions of feedback such as valence, frequency, and timing (e.g., Northcraft et al.,
2011; Prussia & Kinicki, 1996; Van Dijk & Kluger, 2011).
Developmental feedback provides recipients with helpful and useful (i.e., quality)
information and is future-oriented in that it directs recipients to make improvements on
the job (Li et al., 2011; Zhou, 2003). Feedback is closely tied to individuals' egos and
goals and thus represents a salient event that elicits affective reactions (Kluger & DeNisi,
1996; Kluger et al., 1994). Recipients respond to the tone and implicit meaning of
feedback messages, determining whether they signal support and yield opportunities for
growth and development in line with individuals' goals (Kluger et al., 1994; Swann &
Schroeder, 1995). Developmental feedback delivered from the supervisor may signal
both positive and negative information, yet because of its informational and motivational
orientation, is likely to evoke more positive than negative affective reactions.
AET posits that employees react to events based on how supportive they are of
their goals and not merely on whether events are positive or negative. Individuals make
appraisals of situations and respond on the basis of the perceived harm or benefit in
relation to their goals (Fugate et al., 2011; Smith & Lazarus, 1990). Appraisals are based
30
on beliefs regarding individuals' constraints, demands, and resources. Employees are
more likely to experience positive as compared to negative affect when they perceive
they have the needed resources to act on the situation. Fugate, Prussia, and Kinicki
(2012), for example, showed that individuals with greater self-efficacy and perceived
behavioral control were less likely to make negative appraisals of change situations.
Rodell and Judge (2009) further found that employees experienced positive affect in
response to challenge stressors in their workday—events that are generally seen as
stressful yet associated with growth and improvement. Positive affect is experienced
when individuals feel progress towards a goal and appraise situations as supportive for
growth and learning (Cavanaugh et al., 2000).
Supervisor developmental feedback focuses on recipients' growth and learning
and is hypothesized to be associated with more positive as compared to negative affect.
Developmental feedback provides recipients with resources and quality information to
help them reach their goals. It emphasizes improvement, signals support, and boosts
recipients' behavioral control even though it may contain both favorable and unfavorable
information. Other studies support the relationship between goal-related events and
positive affect. Rodell and Judge (2009), for instance, found that stressful events
associated with learning and goal attainment were associated with positive affect and Ilies
and Judge (2005) found that feedback supportive of goals was also related to positive
affect. I examine the positivity ratio, as opposed to positive affect on its own, because I
theorize that employees experience both positive and negative affect in response to
feedback, but that they will be more likely to experience positive as compared to negative
affect when receiving developmental feedback, leading to the following hypothesis:
31
Hypothesis 1: Supervisor developmental feedback is positively related to the
positivity ratio.
Coworker Feedback Seeking and Positive Affect
Supervisors are expected to give feedback as part of their role because it is a key
component of the performance management process (Kinicki, Jacobson, Peterson, &
Prussia, 2013). Even so, supervisors may not always be aware of specific information that
employees need or they may not always be available to provide timely feedback. This is
why employees’ feedback seeking is a valuable resource employees use to gain
information on their own schedule and according to their own needs. This in turn allows
them to make timely adjustments and improvements to their work as goals and
expectations change (De Stobbeleir et al., 2011). Feedback seeking in this view is a
proactive, self-regulatory strategy that employees use as they set their own standards and
seek feedback to achieve their goals (Ashford & Tsui, 1991; Porath & Bateman, 2006).
I look at employees' attempts to seek feedback specifically from coworkers
because supervisors may not be aware of particular information that employees need or
they may not always be available to deliver feedback. In the absence of feedback from
one’s manager, employees are likely to seek feedback from coworkers because the costs
of seeking this feedback is lower than trying to get it from their manager. The costs of
feedback seeking include the effort involved in obtaining feedback, the possibility of
"losing face," and feelings of embarrassment (Ashford & Cummings, 1983; Fedor, 1991).
Employees' immediate coworkers mitigate these costs by providing high levels of support
and encouragement and by being more immediately accessible in dealing with work-
32
related issues (Madjar, 2005). Employees actively seek feedback from coworkers because
coworkers have no formal authority to give it and may not readily offer it unless they are
asked (De Stobbeleir et al., 2011). Further, seeking feedback from coworkers
supplements information obtained from supervisors and the task itself. Taking this
approach contributes to the literature because most past research focused on seeking from
supervisors (Chen et al., 2007; Lam et al., 2007) or was mute with respect to the feedback
source (for exceptions, see Callister, Kramer, & Turban, 1999; De Stobbeleir et al.,
2011).
Seeking is an emotionally charged event because it provides employees with
information about themselves (Ashford et al., 2003) and may contain both positive and
negative information. Politeness theory (Brown & Levinson, 1978) suggests that
coworkers are more likely to deliver information with sensitivity and to put the
information in a positive light. Coworkers generally provide support and encouragement
for each other (Madjar, 2005) and are on the same hierarchical level and don't want to
risk losing face in such psychologically and physically close situations (Albrecht & Hall,
1991; Albrecht & Ropp, 1984). Politeness theory further proposes that employees will
soften comments that may damage face (Harrison, Kinicki, Galvin, & Jacobson, working
paper). Employees are more likely to seek from those who put a positive light on the
feedback (Festinger, 1954) and are less likely to actively seek feedback when they expect
the information to be negative (Ashford et al., 2003; Northcraft & Ashford, 1990).
More frequent seeking brings employees closer to others' perspectives of their
work as views shift over time and conditions change (De Stobbeleir et al., 2011).
Feedback seeking, regardless from what source, reduces uncertainty, giving employees a
33
sense of control over their work environment (Ashford & Black, 1996; Callister et al.,
1999; Morrison, 1993) and fostering goal attainment (Ashford et al., 2003). This occurs
because frequent seeking enables employees to respond positively to events that support
their goals, thereby gaining control over situations (Lazarus, 1991b). AET predicts that
these factors—environmental control and goal attainment—are associated with higher
levels of positive affect.
I hypothesize that though employees may experience both positive and negative
affect in response to feedback seeking, more frequent seeking is associated with higher
levels of positive as compared to negative affect because it provides employees with
behavioral control and support and information needed to achieve their goals in
organizations. The above discussion leads to the following hypothesis:
Hypothesis 2: Coworker feedback seeking is positively related to the positivity
ratio.
The Positivity Ratio and Creative Behavior
Fredrickson's broaden and build theory of positive emotions (Fredrickson, 1998,
2001) suggests that the experience of positive affect encourages approach behavior
(Cacioppo et al., 1999; Watson et al., 1999) and continued action (Carver & Scheier,
1990; Clore, 1994). Isen (1999a, b) proposed that positive affect has three main effects on
cognition: first, positive affect leads to the ability to process more complex cognitive
content; second, it increases the cognitive material available for processing, allowing for
broader associations; and third, it enhances cognitive flexibility, increasing the number of
diverse cognitive associations. Positive affect broadens thought–behavior repertoires by
34
prompting individuals to explore ideas, take in new information, and consider a broader
array of cognitions and behaviors, resulting in greater creativity (Fredrickson, 2001;
Kahn & Isen, 1993). Positive affect has been shown to enhance individuals' creative
ability and behaviors across many settings, including those where creative behavior was
not explicitly desired (e.g., Amabile et al., 2005; Isen, 1999a, b; Isen et al., 1987; To,
Fisher, Ashkanasy, & Rowe, 2012).
I look specifically at the ratio of positive to negative affect in predicting creative
behavior because I adopt the view that feedback is a positive organizational resource
(Ashford, 1986). Greater levels of expressed positivity have been shown to enhance
behavioral variability within business teams (Losada & Heaphy, 2004), expand
information processing strategies and broaden perspectives in organizations (Sutcliffe &
Vogus, 2003), and increase individuals' ability to adapt to unexpected situations
(Fredrickson et al., 2003). A study by Galvin, Christensen, Kinicki, and Reina (working
paper) further found that greater levels of positivity were positively associated with top
management teams' aggregated job satisfaction and organizational commitment, CEO
effectiveness, and firm performance. The above discussion results in the following
prediction:
Hypothesis 3: The positivity ratio is positively related to creative behavior.
Creative Behavior and Overall Job Performance
Creativity theorists point to a positive connection between creative behavior and
creative performance (Amabile, 1996; Shalley et al., 2004; Zhou & Shalley, 2003).
Evidence suggests that willingness to try new things, explore new processes, and
35
otherwise engage in new ways of improving work results in creative performance
(Amabile, Conti, Coon, Lazenby, & Herron, 1996; Gilson & Shalley, 2004; Mumford,
Baughman, Threlfall, Supinski, & Costanza, 1996), even in jobs where creative
performance is not typically seen as an essential element of the role, such as in the case of
physicians (Estrada et al., 1994) or employees in a customs bureau (Hirst et al., 2011).
Theory further suggests that creative behavior may be an important element
influencing overall job performance. Regardless of the job, employees frequently face
new challenges and problems that require novel or adaptive solutions. Creativity is a
means through which employees consider new perspectives, develop hypotheses,
generate and communicate ideas, and explore different options to approach their work
(Torrance, 1988). Madjar, Greenberg, and Chen (2011) found a positive relationship
between creativity and routine, noncreative performance and Gong, Huang, and Farh
(2009) found a similar relationship between creative performance and job performance in
a group of insurance agents. Oldham and Cummings (1996) further found a significant
relationship between creative performance and overall job performance in a setting of
manufacturing engineers and technicians. Successful performance in jobs depends on the
incorporation of novel and useful ideas and approaches to work (Drazin et al., 1999),
leading to the following hypothesis:
Hypothesis 4: Creative behavior is positively related to overall job performance.
36
The Mediating Role of the Positivity Ratio and Creative Behavior in the Feedback–
Performance Relationship
I predict that the influence on performance by supervisor developmental feedback
and coworker feedback seeking is partially mediated by the positivity ratio and creative
behavior. I predict partial mediation because other factors such as knowledge transfer or
skills acquisition may also be responsible for affecting employees' performance.
Feedback itself is information that must be processed, interpreted, and applied to increase
performance. This suggests that the relationship between feedback and performance is
indirect. I propose that a combination of AET theory (Weiss & Cropanzano, 1996) and
Fredrickson’s (1998, 2001) broaden and build theory of emotions provides the theoretical
pathways for feedback to influence performance. Consider how this process unfolds.
The affective events theory framework (Weiss & Cropanzano, 1996) portends that
employees' experienced affect in response to events at work influences their cognitions
and behaviors. Feedback represents a salient event in the workplace and provokes
affective reactions by invoking individuals' egos (Ashford et al., 2003; Kluger et al.,
1994). Supervisor developmental feedback signals support to employees and focuses on
learning, motivation, behavioral control, and future orientation (Li et al., 2011; Zhou,
2003), all of which are expected to increase positive as compared to negative affect.
Coworker feedback seeking, as a supplement to supervisor feedback, is a self-regulatory
strategy that employees use to achieve their own goals in the workplace and helps
employees to make behavioral adjustments and improvements to work behaviors and
ideas (Ashford et al., 2003; De Stobbeleir et al., 2011), which is also likely to increase
positive as compared to negative affect. The experience of more positive as compared to
37
negative affect broadens individuals' modes of thinking, enhances flexibility, and
increases the ability to assimilate new information, resulting in creative behavior
(Fredrickson, 1998, 2001; Isen, 2008). Creative behavior, in turn, is expected to enhance
individuals' overall job performance. Employees often face new challenges at work and
need to approach their work in a way that is novel and useful for them. The essence of
feedback is to improve employee job performance by influencing them to change their
work behaviors (Fedor, 1991), making creativity an important component in the
feedback–performance relationship. The following hypotheses are derived from this
discussion:
Hypothesis 5a: The relationship between supervisor developmental feedback and
overall performance is partially mediated by the positivity ratio and creative
behavior.
Hypothesis 5b: The relationship between coworker feedback seeking and overall
performance is partially mediated by the positivity ratio and creative behavior.
38
CHAPTER 4: RESEARCH METHODOLOGY AND DATA ANALYSIS
Sample and Procedure
The sample consisted of 63 students in a full-time MBA program who were all
taking two core courses at a large southwestern university in the United States in the final
term during their first year of the two-year program. The final sample included 51 males
(81%) and 12 females (19%). Participants’ average age was 28.65 years (SD = 4.56), and
they had on average 60.75 months of full-time work experience (SD = 41.17) and 18.76
months of part-time work experience (SD = 24.76). Participants’ ethnicities were 59%
Caucasian, 35% Asian, 3% African American, and 3% Hispanic. Participants were
recruited by the author and the chair of this dissertation, neither of whom were teaching
the participants at the time. Respondents were told about the voluntary nature of the study
and were assured that none of their professors would see their individual data and that
participation would have no effect on their grades. Individuals were directed to an online
survey link where they were able to opt in to the study by completing an initial survey
that assessed demographic characteristics and control variables. Participants received a
$25 gift card at the end of the study.
The sample was chosen to enhance internal validity of the study. All students
worked in the same groups for two core courses and the final grades were associated with
a combination of individual work and group work worth approximately 70% and 30%,
respectively, of their final grades for these two courses . Feedback norms at this
institution were for professors to provide developmental feedback and for students to
receive feedback from their group members regarding their group work. Students were
39
also taught in their first term by one of the authors on how to provide specific, descriptive
feedback. I confirmed the feedback norms by interviewing the two professors teaching
the stimulus core classes about the nature of the feedback normally given to students and
by interviewing several students about the feedback seeking habits of themselves and
their peers several weeks before the study began. I found that feedback seeking frequency
varied among students, but that it was indeed happening.
I used experience sampling methodology (ESM) because several of the
variables—supervisor developmental feedback, coworker feedback seeking, affect, and
creative behavior—were expected to vary day by day. ESM enhances ecological validity
by capturing responses in naturally occurring situations in which I expected to observe
the study variables (Brunswick, 1949) and reduces memory-based bias associated with
reports that ask respondents to recall longer periods of time (Csikszentmihalyi & Larson,
1987; Wheeler & Reis, 1991). Beal and Weiss (2003) noted that ESM allows for an
investigation of between- and within-person variability and can eliminate spurious third
variable explanations and coincidental trends in the data. Altogether, I sent 10 surveys,
including the initial survey of control variables. I sought to strike a balance between
collecting enough information and not overburdening respondents (Uy, Foo, & Aguinis,
2010).
I used an event-contingent protocol requiring participants to respond only when
instances of our independent variables were expected to take place (Uy et al., 2010). I
learned from interviews with the two professors teaching the stimulus courses being used
in the study that three of the assignments across the two classes were related and that
professors were planning to give helpful, written feedback to the students so that students
40
could build on their work for the next assignment. Professors were also available during
class and office hours and by email to give additional feedback. I timed the surveys to
coincide with the dates students were expected to receive developmental feedback on
these projects and when I expected students to seek out feedback from each other.
More specifically, I timed the surveys around the students' three major term
projects in which the professors would be delivering feedback to the students.
Measurements of participants’ ratings of supervisor (in this case, professor)
developmental feedback were collected on the days when professors returned graded
assignments, approximately one week after the assignments were due. Measurements of
coworker feedback seeking were collected up to two days before an assignment was due
because there is typically a flurry of work that occurs right before a project's deadline;
coworker feedback seeking and supervisor developmental feedback were measured on
separate days because the events did not occur on the same days. All told, this process
resulted in collecting supervisor developmental feedback on three days and coworker
feedback seeking on six days. See Table 1 for a timeline of the variables and their
measurement days.
Table 1— Measurement Table
0 1 2 3 4 5 6 7 8 9 +10 days
Demographic control variables X
Supervisor developmental feedback X X X
Coworker feedback seeking X X X X X X
Daily affect X X X X X X X X X
Creative behavior X X X X X X X X X
Final grade X
41
Measurements of affect and creative behavior were collected on all days so that
they could be matched with the respective independent variables. All daily variables were
measured using 5-point Likert-type scales to reduce difficulty for participants to respond
and to provide adequate range without problems associated with scale coarseness
(Aguinis, Pierce, & Culpepper, 2009). I used the students' final course grades as a
measure of overall performance. I asked students for permission to collect grades at the
end of the study. The daily surveys required a considerable amount of effort on the
participants' part, and I wanted to be sensitive to grade privacy during the study so as not
to affect responses or the participation rate. Professors reported students' total raw points
at the end of the study.
An online survey link was emailed to every participant at 4:00 pm on each
measurement day. A reminder email was sent at 8:00 pm to those who had not yet
completed the survey. Participants had until midnight that day to complete the survey—
after midnight, the survey link closed to eliminate problems with late responses. The
survey instructions prompted individuals to reflect upon their day at school or while
working on school related tasks. I obtained 548 observations out of a possible 630, which
equals a response rate of 87%. Participants completed an average of 8.7 surveys each.
Sixty-four individuals volunteered for the study, but one dropped out after completing
only one survey, resulting in a final sample of 63 individuals. This represents 85% of the
class that I attempted to recruit. Both the number of participants and the response rate are
comparable to similar ESM studies. For example, the number of participants in the study
by Huang and Ryan (2011) was 56 and the overall response rate in the study by Bledow,
Rosing, and Frese (2011) was 73%.
42
Measures
Supervisor developmental feedback. Supervisor developmental feedback was
measured using a three-item Likert-scale ranging from 1 ("strongly disagree") to 5
("strongly agree") adapted for the sample from Zhou (2003). To fit the sample, the word
“supervisor” was changed to “core professors.” I asked participants to reflect on their
core professors because I timed the surveys around the assignments in those classes.
Sample items included, “While giving me feedback, my core professors focused on
helping me to learn and improve” and “My core professors provided me with useful
information on how to improve my performance on my school work.” The mean
coefficient alpha (averaged across days) was .72.
Coworker feedback seeking. To assess the frequency which participants sought
feedback each day, participants were asked to indicate on a Likert-scale ranging from 1 to
5, how often they sought feedback from their team members for that day only (1 =
“didn’t ask;” 2 = “once;” 3 = “twice;” 4 = “3 times;” 5 = “more than 3 times”). I asked
participants to reflect on their team member seeking behavior because of the close
contact that participants had with their teams and because they were working on both
individual and team projects during the measurement period. I asked participants to
reflect on their “core” team members because a set of core classes was required for all
students in the program—of which they belonged to one team for those classes—in
addition to their specialty classes (e.g., supply chain, finance, etc.). To fit the sample, I
adapted three items from De Stobbeleier, Ashford, and Buyens (2011) and Callister,
Kramer, and Turban (1999). Items included, “Today, I asked one or more of my core
team members about my performance on our assignments” and “Today, I directly asked
43
one or more of my core team members for feedback about my work in the team.” The
mean coefficient alpha (averaged across days) was .93.
Positivity ratio. I assessed daily levels of affect using items from To, Fisher,
Ashkanasy, and Rowe (2012). To et al. (2012) adapted items from the Positive and
Negative Affect Schedule (PANAS; Watson et al., 1988) and from De Dreu et al. (2008).
I used a Likert-scale ranging from 1 ("very slightly or not at all") to 5 ("extremely") as
used by Watson et al. (1988). The four adjectives used to measure positive affect were,
“inspired,” “enthusiastic,” “interested,” and “excited.” The four adjectives used to
measure negative affect were, “angry,” “upset,” “ashamed,” and “disinterested.” An
exploratory factor analysis of the eight affect items revealed a two-factor solution,
suggesting that the items revealed two distinct factors. The mean coefficient alpha
(averaged across days) for positive affect was .96 and for negative affect was .90.
Following Fredrickson (2013) and Rego et al. (2012), the positivity ratio was derived by
dividing the mean of the positive affect items by the mean of the negative affect items.
Trait-like affect was taken into consideration by separating individuals' mean scores from
their daily scores to account for people's average levels of affect, as discussed in the
following section.
Creative behavior. Group members rated each others’ creative behavior on a daily
basis (excluding their own) because participants worked closely in groups and they were
in a better position to rate each other’s creative behavior than were professors.
Respondents were asked to rate each of their group members’ creative behavior for today
only on a Likert-scale ranging from 1 (“not at all”) to 5 (“very”). I used Oldham and
Cummings’ (1996) three-item scale. Sample items were, “How CREATIVE was this
44
person’s work? Creativity refers to the extent to which the person develops ideas,
methods, or work that are both original and useful to the team” and “How ORIGINAL
and PRACTICAL was this person’s work? Original and practical work refers to
developing ideas, methods, or work that are both totally unique and especially useful to
the team.” The mean coefficient alpha (averaged across days) was .96. Participating
groups had an average of 4.2 individuals (SD = .77); an average of 81% of group
members rated each other each day. The rwg(J) (James, Demaree, & Wolf, 1984) within
groups for each particular individual as rated by peers averaged .75 across days.
Overall performance. I used participants’ final course grades from their two core
classes to measure overall performance. Grades were reported on a scale of 0-100 for
each class.
Control variables. I controlled for intrinsic motivation, conscientiousness, and
learning goal orientation. Prior research has shown that individuals’ intrinsic motivation
may affect their creative performance (Amabile, 1985, 1988). I controlled for intrinsic
motivation using a four-item scale developed by Grant (2008). The question stem for all
items read, “Why are you motivated to do your work?” Participants were asked to
respond to items on a scale of 1 (“strongly disagree”) to 5 (“strongly agree”). Sample
items included, “Because I enjoy the work itself” and “Because I find the work
engaging.” The coefficient alpha was .86.
I controlled for conscientiousness because it has been related to individuals’
reactions to feedback in prior studies (Colquitt & Simmering, 1998) and is related to
performance (Barrick & Mount, 1991a). Respondents were asked to rate how well several
45
statements described themselves as they generally are now and not how they wished to be
seen in the future. Participants responded to three items from Donnellan et al. (2006)
using a scale ranging from 1 (“very inaccurate”) to 5 (“very accurate”). Sample items
included, “I get chores done right away” and “I make a mess of things” (reverse coded).
The coefficient alpha was .69.
I controlled for learning goal orientation (Vandewalle, 1997) because it has been
related to reactions to feedback (Brett & Atwater, 2001) and to feedback seeking
behaviors (Ashford et al., 2003; Morrison, 2002). Participants were asked to rate how
they behave in general using a Likert-scale of 1 (“strongly disagree”) to 5 (“strongly
agree”). The items were adapted slightly to fit the sample. Sample items included, “I
often read materials related to my program of study to improve my ability even if they are
not required” and “I am willing to select a challenging school assignment that I can learn
a lot from.” The coefficient alpha was .77.
Analyses
Data were analyzed using SAS Proc Mixed (Littell, Milliken, Stroup, Wolfinger,
& Schabenberger, 2006), which allowed me to control for three levels of data. Level 1
included the within-person variables that were measured daily (supervisor developmental
feedback, coworker feedback seeking, positivity ratio, and creative behavior). Level 2
included variables that were between individuals (overall performance, intrinsic
motivation, conscientiousness, and learning goal orientation), and Level 3 included
individuals’ group membership. To reduce potential confounding and improve
interpretability of the models, all Level 1 variables were centered at each person’s mean
46
value (Hofmann, Griffin, & Gavin, 2000). Person means were added back at Level 2
(Kreft, de Leeuw, & Aiken, 1995; Zhang, Zyphur, & Preacher, 2009). Level 2 predictors
were centered at the grand mean.
Participants’ age, work experience, and ethnicity were not significantly related to
any of the main study variables and thus were not included in the analyses. Sex was not
significantly related to any of the main study variables except performance. The model
results predicting performance did not change when sex was included in the analysis—
consequently, this variable was not included in the final analyses for simplicity.
The study took place in a natural organizational setting. The two exogenous
variables occurred on separate days and thus were measured on separate days as shown in
Table 1. This is a case of "planned missingness" in the data design (Graham, 2009).
Normal estimation procedures assume that data is missing at random and use a best guess
using data from other daily measurements to estimate missing data points. Supervisor
developmental feedback was not measured on days that I measured coworker feedback
seeking because the two variables did not occur at the same time. To avoid potential
distortion regarding the relationship between supervisor developmental feedback and the
positivity ratio and coworker feedback seeking and the positivity ratio, I analyzed two
parallel models (see Figure 2). To illustrate, the data file for the days measuring
supervisor developmental feedback were kept separate from the data file for the days
measuring coworker feedback seeking. I controlled for the mean of coworker feedback
seeking when analyzing the supervisor feedback models and vice versa. In the models
predicting creative behavior, I used the following day’s creative behavior ratings as the
outcome to minimize bias associated with using ratings on the same day.
47
Figure 2—Empirical Model
Coworker
Feedback
Seeking (T 1, 2, 4, 5, 7, 9)
Creative
Behavior (T4, 7, 9)
Supervisor
Developmental
Feedback(T 3, 6, 8)
Positivity
Ratio(T 3, 6, 8)
Overall
Performance
Positivity
Ratio(T 1, 2, 4, 5, 7, 9)
Creative
Behavior (T2, 3, 5, 6, 8)
Level 1 Level 2
48
CHAPTER 5: RESULTS
Descriptive statistics and correlations are presented in Table 2. Within-person
correlations are reported above the diagonal and between-person correlations are reported
below the diagonal. Between-person correlations were calculated by aggregating within-
person variables (Level 2). Before testing the hypotheses, I estimated the percentage of
variance at the within-, between-, and team levels for each of the main study variables, as
shown in Table 3, providing support for treating each variable at the hypothesized level.
The percentage of within-individual variance for supervisor developmental feedback was
79%, coworker feedback seeking was 76%, positivity ratio was 57%, and creative
behavior was 45%. The percentage of between-individual variance for overall
performance was 70%. These percentages indicate that there was considerable variance to
be explained within and between individuals, and are consistent with other ESM studies
(e.g., Scott & Barnes, 2011; To et al., 2012). For example, the percentage of variance in
the variables at the individual-level in the study by Rodell and Judge (2009) was about
42% and at the between-level was about 41%.
49
Table 2— Descriptive Statistics and Correlations
Variable M SD 1 2 3 4 5 6 7
1. Supervisor developmental feedback 3.46 .55 — -.04 .10 -.05 2. Coworker feedback seeking 1.33 .34 .11 — .13* -.06 3. Positivity ratio 2.40 .90 .11 -.17 — .18** 4. Creative behavior 3.26 .53 -.22 .10 .15 — 5. Overall performance 172.42 8.61 .12 .08 .03 .34** — 6. Learning goal orientation 3.88 .52 .12 -.08 .04 .01 .10 — 7. Conscientiousness 3.35 .82 .28* -.03 -.09 -.25* .04 .13 —
8. Intrinsic motivation 3.59 .78 -.21 -.02 .01 -.06 .18 .43* .21
.78 -.21 -.02 .01 -.06 .15 .43** .21
Note. Correlations above the diagonal are within-person (Level 1; n = 189-378).
aCorrelations below the diagonal are between-person (Level 2; 63).
bBetween-person correlations were calculated on within-person variables (variables 1-5)
by aggregating these variables to the between-person level (Level 2).
c* p .05. ** p .01.
Table 3— Percentage of Variability for Null Models
Variable % variability within-
individual
% variability between-
individual
% variability
team-level
Supervisor developmental
feedback
79% 10% 11%
Coworker feedback seeking 76% 23% 1%
Positivity ratio 57% 43% 0%
Creative behavior 45% 22% 33%
Overall performance NA 70% 30%
Hypothesis Tests
Model 1 in Table 4 shows that supervisor developmental feedback was
significantly related to the positivity ratio (b = .28, p < .05), revealing that perceptions of
supervisor developmental feedback were significantly related to the positivity ratio on a
daily basis. Hypothesis 1 was supported. Similarly, results for Model 1 in Table 5
revealed that coworker feedback seeking was positively associated with the positivity
ratio (b = .27, p < .05), demonstrating that individuals’ feedback seeking behaviors were
50
significantly associated with the positivity ratio on a daily basis. Hypothesis 2 was
supported.
Table 4—Multilevel Results Predicting Positivity Ratio, Creative Behavior, and Overall
Performance for Supervisor Developmental Feedback
Note. Values are unstandardized regression coefficients.
aStandard errors are in parentheses.
bN = 189 nested within 63 individuals.
cLevel 1 variables are centered at each person’s mean value.
dLevel 2 variables are grand-mean centered.
e* p < .05. ** p < .01.
Positivity ratio Creative behavior Overall performance
Estimate
Model 1 Model 2 Model 3
Intercept
Level 1 (within-person)
2.50 (1.39) 3.00 (.82)** 146.22 (7.47)**
Supervisor developmental feedback .28 (.14)* -.16 (.08)
Positivity ratio .14 (.06) *
Level 2 (between-person)
Supervisor developmental feedback .30 (.27) -.17 (.16) 4.26 (1.20)**
Coworker feedback seeking -.69 (.39) .05 (.24) -.02 (1.71)
Learning goal orientation -.10 (.29) .22 (.16) -1.04 (1.15)
Intrinsic motivation .25 (.20) -.04 (.12) 2.76 (.82)**
Conscientiousness -.20 (.18) -.10 (.11) -.21 (.78)
Positivity ratio -.05 (.09) -1.04 (.63)
Creative behavior 5.41 (1.29)**
Pseudo R2 -.02 -.01 .23
OLS-based R2 .05 .10 .24
51
Table 5—Multilevel Results Predicting Positivity Ratio, Creative Behavior, and Overall
Performance for Coworker Feedback Seeking
Note. Values are unstandardized regression coefficients.
aStandard errors are in parentheses.
bN = 378 nested within 63 individuals.
cLevel 1 variables are centered at each person’s mean value.
dLevel 2 variables are grand-mean centered.
e* p < .05. ** p < .01.
In support of Hypothesis 3, Tables 4 and 5 show that creative behavior was
significantly predicted by the positivity ratio—Table 4, Model 2 (b = .14, p< .05)—and
coworker feedback seeking—Table 5, Model 2 (b = .12, p< .05). Individuals’
experiences of higher ratios of positive to negative affect were significantly associated
with observations of their creative behavior on a daily basis.
Positivity ratio Creative behavior Overall performance
Estimate
Model 1 Model 2 Model 3
Intercept
Level 1 (within-person)
2.91 (1.26)* 4.07 (.72)** 148.04 (5.54)**
Coworker feedback seeking .27 (.11)* -.09 (.08)
Positivity ratio .12 (.05)*
Level 2 (between-person)
Supervisor developmental feedback .25 (.24) -.12 (.14) 4.31 (.87)**
Coworker feedback seeking -.47 (.35) -.19 (.20) -.17 (1.23)
Learning goal orientation -.19 (.27) -.06 (.14) -1.00 (.81)
Intrinsic motivation .03 (.18) .06 (.10) 2.64 (.58)**
Conscientiousness -.14 (.16) -.03 (.09) -.25 (.56)
Positivity ratio -.01 (.07) -1.18 (.45)**
Creative behavior 4.90 (.96)**
Pseudo R2 .07 .01 .19
OLS-based R2 .06 .05 .24
52
Hypothesis 4 predicted that creative behavior is positively related to overall
performance. Because final course grades were assessed at the end of the period, creative
behavior was aggregated to Level 2. Each person’s average creative behavior score was
then grand-mean centered. As seen in Table 4, Model 3 (b = 5.41, p< .05) and Table 5,
Model 3 (b = 4.90, p< .05), creative behavior was positively and significantly related to
performance. Thus, between individuals, creative behavior was significantly associated
with overall performance. Hypothesis 4 was supported.
Hypotheses 5a and 5b predicted that the relationship between supervisor
developmental feedback and performance and between coworker feedback seeking and
performance, respectively, are mediated by the positivity ratio and creative behavior.
Following the Monte Carlo Method for Assessing Mediation (MCMAM) as advocated by
Selig and Preacher (2008), I used R statistical software to estimate the confidence interval
around the product of coefficients for the paths with 20,000 Monte Carlo bootstrapped
repetitions. The first three links of my theoretical model are at level 1 and the last link is
at level 2 (so that the overall mediation model is 1-1-1-2). A level 2 link requires that the
entire chain be tested at that level (Zhang et al., 2009). I therefore used the level 2
coefficients and their standard errors to calculate the confidence intervals. This reduces
the total sample size for mediation testing to N = 63. The confidence interval for the
supervisor developmental feedback to performance path as mediated by the positivity
ratio and creative behavior was -.59 to .28 and the confidence interval for the coworker
feedback seeking to performance path as mediated by the positivity ratio and creative
behavior was -.42 to .50. Both of the confidence intervals included zero and thus do not
support my hypotheses.
53
It is possible that the mediation tests were not supported because of low statistical
power (N = 63). Another possibility is that there is not enough variance at level 2 to find
a significant mediated relationship at that level. Only 10% of the variance in supervisor
developmental feedback and 23% of the variance in coworker feedback seeking was at
level 2 (see Table 2). I tested mediation of the day level variables (i.e., supervisor
developmental feedback, coworker feedback seeking, positivity ratio, and creative
behavior) using the method described above (MacKinnon, Lockwood, & Williams, 2004)
to supplement my analysis. Neither of the confidence intervals for these tests included
zero (confidence interval for supervisor developmental feedback to positivity ratio to
creative behavior = .000457 to .1031; confidence interval for coworker feedback seeking
to positivity ratio to creative behavior = .0007628 to .07697), providing support for
mediation of this reduced chain at level 1.
I calculated pseudo-R2s for each model using the approach recommended by
Snijders and Bosker (1999). This was done because the analytical approach of analyzing
relationships in steps did not allow us to test the overall fit of the entire model. I
compared each step's new total variance explained (Level 1 and Level 2 variances) with
the total variance explained by the null model, or the intercept-only model, using the
calculation below:
PseudoR2 = (Varnull model−Varcurrent model)/Varnull model.
The pseudo-R2 represents the proportion of total variance in the dependent
variable accounted for by the addition of the predictor variables (Snijders & Bosker,
1999). A problem with the pseudo-R2
is that adding predictors may increase the amount
54
of unexplained variance, leading to negative pseudo-R2s. The pseudo-R
2s in the models
range from -.02 to .23. I thus supplemented the pseudo-R2s with OLS-based R
2s as
recommended by Hofmann, Morgeson, and Gerras (2003) because these provide an
unbiased assessment of the proportion of variance explained by each model. The OLS-
based R2s range from .05 to .24. All models explain a proportion of variance that is
comparable to other studies in the management field. For example, Zhang, Waldman, and
Wang (2012) reported a pseudo-R2
of .04 and an OLS-based R2
of .06.
55
CHAPTER 6: DISCUSSION
My study clarifies the mechanisms through which feedback associates with future
performance. This is an important pursuit because feedback is ubiquitous in
organizations, it represents an integral component of performance management systems,
and research reveals that the outcome of many feedback interventions is a decrease in
performance (Kluger & DeNisi, 1996; Latham, Almost, Mann, & Moore, 2005). To
address this problem, scholars have pursued two dominant lines of research: the active
seeking and passive receipt of feedback. I integrate these literatures to examine how
employees' perceptions of supervisor delivered developmental feedback and their own
attempts to seek feedback from coworkers influence performance. I use affective events
and broaden and build theories to position the positivity ratio and creative behavior as
two key mechanisms through which feedback affects performance and test my theoretical
model in an experience sampling study over a 10 day period.
Theoretical Contributions and Future Research
The results of my study show that supervisor developmental feedback and
coworker feedback seeking influence individuals' positive as compared to negative
affective states, which, as predicted by broaden and build theory, influence creative
behavior. Creative behavior, in turn, relates to overall job performance. These results
provide support for my first four hypotheses. The mediation tests for hypotheses 5a and
5b, however, were not significant. This could be because of low statistical power or
because of restricted variance at level 2 as explained in the previous chapter. The
mediation tests for the day level variables only (i.e., supervisor developmental feedback,
coworker feedback seeking, positivity ratio, and creative behavior) were supported.
56
Evidence suggests that both supervisor developmental feedback and feedback
seeking are beneficial to performance. Bringing these two research streams together using
affective events and broaden and build theories represents a different approach wherein
the receipt and seeking of feedback are seen as a positive resource within organizations.
Supervisors can deliver feedback with the intention of being developmental, helping
employees to achieve higher levels of performance. At the same time, employees can
supplement supervisory feedback by seeking additional input from coworkers.
Theoretically, these results link work on positive organizational behavior (e.g., Cameron,
Mora, Leutscher, & Calarco, 2011; Cameron & Spreitzer, 2011), perceived
organizational support (Rhoades & Eisenberger, 2002), and self-regulation theory
(Carver & Scheier, 1981) by positing that supervisors support their employees' best
interests and that employees are self-regulatory individuals who take charge of their own
performance in organizations. These findings suggest that future research should consider
how organizations can support the giving and seeking of feedback by making structural
changes that support open communication among peers and supervisors.
This research advances knowledge about leadership by answering calls to include
both the leader and follower in theoretical models (Avolio, Walumbwa, & Weber, 2009).
The results of this study suggest that the specific performance management behaviors that
leaders exhibit affect employee performance via a chain that includes affect and
creativity, variables that have been ignored in past feedback research. This has
implications for performance management leadership (Kinicki et al., 2013) and
transformational leadership theory's (Bass, 1985) focus on individualized consideration.
Leaders can be most effective when they are in tune to their followers' individual self-
57
regulatory behaviors. Employees who more actively seek out feedback from others may
need less feedback from the supervisor or may need less monitoring. Leaders can also
train employees to seek feedback from coworkers as a supplement to leaders' formal
feedback.
Importantly, the empirical results of this study show that over 75% of the variance
in supervisor developmental feedback and coworker feedback seeking are within
individuals and vary day by day (see Table 3). This has implications for leadership
research revealing that these behaviors are not constant within individuals and that future
research would benefit from experience sampling studies to examine the effects of
variability in leaders' and employees' behaviors. Why are leaders not consistent in
delivering developmental feedback? How do employees' daily experiences at work affect
their feedback seeking behaviors? Research is needed that explores the factors that lead
to the oscillating nature of these variables.
These findings also highlight a contribution to research on interactional
psychology (Schneider, 1983). Research shows that individual difference variables such
as conscientiousness, learning goal orientation, and intrinsic motivation are related to
performance (e.g., Barrick & Mount, 1991b; VandeWalle, Brown, Cron, & Slocum Jr,
1999; Zapata-Phelan, Colquitt, Scott, & Livingston, 2009). I thus controlled for these
variables in tests of my theoretical model (e.g., Amabile, 1985, 1988; Ashford et al.,
2003; Brett & Atwater, 2001; Colquitt & Simmering, 1998). Results of my study indicate
that employees' day to day behaviors, affective states, and perceptions of leader behaviors
drive performance over and above oft studied individual differences. This means that
while it is important to continue studying stable differences among employees, it is also
58
worthwhile to consider the situationally-driven daily behaviors they enact to function
well in organizations. Conscientiousness, for example, could influence daily feedback
seeking behaviors. I tested this in a supplemental analysis and found no support for this
idea, though this could be attributable to a lack of statistical power at level 2.
The positivity ratio may be an important construct to consider in other areas of
organizational research as well. Although negative affective states such as anger and
frustration can be functional for individuals because these emotions signal the need for
change (George & Zhou, 2002; Kaufmann, 2003), other scholars (Fredrickson, 2013;
Fredrickson & Losada, 2005) underscore the importance of more positive as compared to
negative affect for optimal functioning in organizations. Positive affect widens the scope
of attention (Fredrickson & Branigan, 2005), strengthens individuals' immune system
functioning (Davidson et al., 2003), and increases psychological growth (Fredrickson et
al., 2003), all desirable functions for working individuals. Negative affect may also be
functional when it motivates employees to tend to the underlying cause of the negativity
(Zhou & George, 2001). That is, negative affect is functional when individuals respond to
it in an effort to remove the negative state (To, Ashkanasy, Fisher, & Rowe, 2010).
Prolonged episodes of negative affect are dysfunctional in organizations, creating stress,
health problems, disengagement, and at worst, violence (Andersson & Pearson, 1999;
Carver & Scheier, 1990; Watson, 1988). The use of the positivity ratio in the
organizational sciences is valuable because it predicts that more positive affect is
associated with optimally functioning individuals while acknowledging that negative
affect may also be instrumental. Future research is needed to determine the “tipping
point” of positive to negative affect and other forms of leader behavior that affect this
59
ratio. For example, it would be valuable to study how other forms of performance
management leader behavior uncovered by Kinicki and colleagues (Kinicki et al., 2013),
such as the process of goal-setting, support and coaching, and establishing/monitoring
performance expectations, impact this affective “tipping point.”
Neuroscience techniques might also be used to examine how various forms of
feedback or types of verbal messages affect the emotional centers of the brain. A
qualitative study, for example, might help researchers understand the emotional
implications of developmental and non-developmental feedback. How does the receipt of
developmental feedback affect coping during stressful job situations? What words do
supervisors use in the delivery of feedback that indicate it is developmental? Is there a
time period that employees need to process the feedback? How do impressions of the
feedback change over time and what is the role of peers in helping each other make sense
of the feedback message? These questions are important in understanding more about the
role of feedback on performance improvement.
My research invites new thinking about the relationship between feedback and
performance by considering the role of creative behavior in theoretical models. Creative
behavior is integral to the application of feedback and past models assume that when
employees accept feedback they directly apply it. Yet feedback originating from others
comes from a different perspective. Models of cognitive processing (e.g., Lord & Foti,
1986) indicate that individuals process information through their own schemas developed
from past experiences and pre-existing knowledge systems. This suggests that employees
process feedback and apply it according to their unique knowledge, perspectives, and
needs. Creative behavior is about approaching tasks differently, about developing
60
modifications, adaptations, or completely new methods. The inclusion of creative
behavior in feedback–performance models adds a behavioral component to the
application of feedback. There must be some behavioral change after employees receive
feedback and that occurs before performance outcomes are reached. This component is
creative behavior.
I contribute to the creativity literature by explicitly drawing a link between
creativity and overall job performance. Creativity research implies that creativity is
generally good for organizations. My findings supply evidence that creative behavior
exhibited on a day to day basis is associated with individuals' overall performance.
Management scholars often opine about the changing nature of problems, workplaces,
and the broader business environment. Many agree that our world is not stagnant; thus,
creativity is germane to enhancing performance. Furthermore, forty-five percent of the
variance in creative behavior was within individuals (see Table 3). This underscores work
suggesting that creativity is not necessarily a trait within employees (Amabile, 1988) but
rather that it is a behavior that anyone can enact to improve performance. Creative
behavior involves letting go of existing behaviors and being open to new approaches.
Research could investigate the effect of mindfulness training on creativity. Mindfulness
enables employees to be more present, thus promoting flexibility and adaptability within
the mind and potentially resulting in more creative behavior.
This study points to other areas for future research. Attention could be given to
the role of negative affect after the receipt of feedback. My results show that
developmental feedback is associated with more positive than negative affect and
research suggests that employees often experience a combination of the two states after
61
receiving feedback. Scholars contend that some negative moods may be functional
because they provide energy and information that something needs to change (e.g.,
moderate levels of anxiety; De Dreu et al., 2008). Scholars could learn more about how
employees cope with negative affect and direct the energy in a positive direction. Bledow
and colleagues (2011) found that negative affect followed by a shift to positive affect was
associated with employees' work engagement. It is possible that employees experienced
more positive as compared to negative affect in their study, but it would be helpful to
know more about how negative affect is shifted to positive states.
This study has implications for research on teams and the social side of
creativity, though I did not test any team-level constructs. Creativity is often the
enmeshment of ideas arising from other people (Perry-Smith & Shalley, 2003)—my
results indicate that 33% of the variance in creative behavior was attributable to the team
level (see Table 3). The individuals in my study were assigned to their groups, meaning
that team-level creativity did not arise out of an attraction–selection process. Groups had
similar goals, requirements, and working conditions. Why were some groups more
creative than others? Future research could consider constellations of team members'
personalities and functional backgrounds, though I was unable to test this idea in my
study because of a small number of groups (N = 14). Scholars may also wish to study the
personal interactions among team members to find what allows for greater creativity. For
example, how do individuals cue that they are open to new ideas from team members,
and how does this manifest at the team-level?
62
Practical Contributions
My research has three main practical implications for managers and employees.
Developmental feedback is perceptual. This implies that managers may intend to give
developmental feedback but employees may construe it differently. Managers may wish
to check back in with employees shortly after delivering feedback to help employees
process it and to respond to questions that may have arisen. Supervisors do not always
have time to give quality feedback, however, so employees should be encouraged to
actively seek it out from their colleagues. Organizations could prepare training programs
aimed at teaching individuals how to give, receive, and seek feedback. Supervisors'
feedback giving could be measured with an instrument similar to the performance
management behavior questionnaire (Kinicki et al., 2013) where employees are asked to
rate their manager on relevant behavioral dimensions.
This study emphasizes the importance of positivity in organizations. Positive
affect eases the path to new relationships, skills, and knowledge (Fredrickson, 2013).
This does not mean, however, that workplaces should be centered on parties and making
people feel good. The adjectives used to measure positivity in this study were inspired,
enthusiastic, interested, and excited. These moods were chosen because they are
functional in the workplace by providing energy and enhanced cognitive capacity (To et
al., 2012). Other moods, such as serene and relaxed, do not propel individuals with the
motivation to persist or to approach their work differently (Feldman Barrett & Russell,
1998; Seo, Barrett, & Bartunek, 2004). This means that, as work on transformational
leadership suggests (Bass, 1985), leaders should seek to inspire and motivate those
around them by communicating confidence and helping followers to achieve ambitious
63
goals. Employees have a dual role. They can proactively manage and regulate their
moods by noticing nonfunctional states and engaging in thoughts or activities to shift
them (To et al., 2010).
Finally, my research has implications for how supervisors engage in performance
management. My findings point to the significance of creative behavior for overall job
performance. Managers are responsible for specific productivity outcomes in
organizations such as level of sales, quality defects, and safety related incidents.
Managers use goal setting as part of the performance management process to set targets
for these responsibilities and others (Kinicki et al., 2013). I propose that the process may
be improved by empowering employees to creatively determine how they reach their
goals. Employees are on the front line and often understand more about their jobs than do
supervisors. Creativity, encompassing novel, adaptive, and useful ideas, allows
employees to take ownership of work processes, potentially reducing the need for
monitoring on management's behalf as employees reach organizational goals. It is
possible that a reciprocal relationship exists between creativity and psychological
ownership and empowerment. Employees may take more ownership of ideas that they
helped create. This, in turn, would provide them with the impetus to persist and engage in
future creative efforts. Allowing employees to be creative in their jobs may also enhance
the four cognitions of empowerment (meaning, competence, self-determination, and
impact; Spreitzer, 1996). These conditions may spur more creativity.
Limitations
My contributions should be qualified in regard to several limitations that lead to
areas for future research. MBA students participated in my study, raising questions about
64
generalizability to full-time working adults. Participants in my study, however, had an
average of about five years of working experience and were approximately 29 years of
age. I believe my sample was similar to working adults in that participants were enrolled
in a full-time professional program, had professors (similar to supervisors) monitoring
their work, had standardized performance goals, and worked on a combination of
individual and team work. Participants received feedback from their professors and
sought feedback from their colleagues. This research setting was not unlike that
experienced by employees in other working situations. In spite of this, future research is
needed to test the generalizability of these results.
Another strength of my study is that it occurred in a natural organizational setting.
Participants received several feedback interventions (i.e., supervisor developmental
feedback and coworker feedback seeking) over the course of the measurement period.
However, similar to many non-experimental studies, this design potentially suffers from
the possibility of demand characteristics where participants knew what I was looking for,
causing them to potentially change their behaviors (Orne, 1962). I asked the participants
not to change any of their behaviors at the outset of the study to reduce the likelihood of
this issue. Further, I led a debrief with the students at the end of the study to find out if
anyone had changed their behaviors. I received no indication that participants changed
their behaviors or survey responses to accommodate the research process. I feel even
more confident that participants were not subject to demand characteristics because the
majority of variance in the daily variables is at the individual level (see Table 3). The
presence of demand characteristics would systematically bias the between-individual
variance upward.
65
Sixty-three individuals participated in my study. This sample size is not large by
typical between-person research design standards. The number of daily observations in
my study, however, was 548. This is sufficient to meet sample size requirements for
multilevel analysis (Scherbaum & Ferreter, 2009). The sample size is comparable to other
experience sampling studies (see Bledow et al., 2011; Rodell & Judge, 2009).
Evidence suggests that too much positivity may be dysfunctional—the problem
with "too much of a good thing" (c.f. Grant & Schwartz, 2011). Indeed, Fredrickson
(2013) points to research indicating that individuals diagnosed with bipolar disorder
experience abnormally high positive moods during manic episodes. Researchers
uncovered an inverted U-shaped relationship between positivity and emotional stability
(Diener, Colvin, Pavot, & Allman, 1991) and creativity (Davis, 2009; George & Zhou,
2007), providing support for the too much of a good thing hypothesis. Yet others found
no evidence that too much positivity was dysfunctional in "mentally normal" adults
(Friedman, Schwartz, & Haaga, 2002). To and colleagues (2012), for example, found a
linear relationship between positive affect and creativity. Notwithstanding, I tested for a
curvilinear relationship in a supplementary analysis and found no support for
curvilinearity. It is worth noting that the mean of the ratio of positive to negative affect in
my study was 2.40 (S. D. = .90). It is possible that participants in my sample did not
experience high enough levels of positivity to make this relationship significant. This
raises questions about how much positivity is best. Fredrickson and Losada (2005) tried
solving this with the use of nonlinear dynamic models. Their use of the models was
called into question (Brown, Sokal, & Friedman, 2013), however, and their claim about
the optimal specific level of positive to negative affect was withdrawn. Importantly,
66
theory and evidence support the notion that more positive as compared to negative affect
is beneficial; the exact point at which it is optimal is unknown.
Other unmeasured variables could influence the relationship between feedback
and performance. Quality feedback is both informational and motivational. It is possible
that motivation and knowledge enhancement are outcomes of feedback. I controlled for
trait-like intrinsic motivation but it is possible that intrinsic motivation is less stable than
it is conventionally treated and may be better modeled on a day by day basis. Some
specific emotions, including several measured in this study, are similar to motivation in
that they provide individuals with energy and physical arousal that influence key
outcomes of work motivation theories such as the amount of effort and persistence
individuals apply to tasks (Feldman Barrett & Russell, 1998; Seo et al., 2004). Future
research could consider the interplay of the affective, motivational, and cognitive effects
of feedback. Finally, it is possible that individual differences could be a moderating or
exogenous influence on the feedback variables. I tested for this in a supplemental analysis
and found no significant relationships. This could be because most of the variance was
within individuals (see Table 3) or because of a low N (N = 63) at the between individual
level.
Conclusion
I examine the feedback–performance relationship using affective events and
broaden and build theories. I integrate the passive receipt and active seeking feedback
literatures and introduce the concept of the positivity ratio as an important mediating
mechanism in the feedback to performance process. My study also reveals creative
67
behavior as an outcome of the positivity ratio and shows that creative behavior is
significantly related to employees' overall job performance. I hope this study serves as a
foundation for integrating feedback literature and for considering the role of affect and
creative behavior in feedback–performance models.
68
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APPENDIX A
INSTITUTIONAL REVIEW BOARD APPROVAL FORM
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APPENDIX B
SURVEY INSTRUMENTS
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Supervisor (professor) developmental feedback (Zhou, 2003). We would like to
ask some questions about the feedback that you have received most recently from your
CORE PROFESSORS IN TRI 3. Using the scale below, please indicate the extent to
which you agree with each item. (1=Strongly disagree; 2=Disagree; 3=Neutral; 4=Agree;
5=Strongly agree)
a. While giving me feedback, my core professors focused on helping me to learn
and improve
b. My core professors never gave me developmental feedback (reverse code)
c. My core professors provided me with useful information on how to improve my
performance on my school work
Coworker feedback inquiry (adapted from Callister et al., 1999; De Stobbeleir
et al., 2011). We would like to ask some questions about feedback that you may have
sought from ONE OR MORE OF YOUR CORE TRI 3 TEAM MEMBERS TODAY
only either in person, by email, phone, or text. Using the scale below, please indicate the
extent to which you sought feedback from one or more of your core team members
TODAY only. (1=Didn’t ask; 2=Once; 3=Twice; 4=3 times; 5=More than 3 times)
a. Today I asked one or more of my core team members about my performance on
our assignments
b. Today, I asked one or more of my core team members if I am meeting my task
requirements in the team
c. Today, I directly asked one or more of my core team members for feedback
about my work in the team
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Positivity Ratio (De Dreu et al., 2008; To et al., 2012; Watson et al., 1988). We
would like to ask some questions about how you felt while working on school related
tasks or while at school TODAY only. Using the scale below, please indicate the extent
to which you experienced feeling each of the following while working on school related
tasks or while at school TODAY only. (1=Very slightly or not at all; 2=A little;
3=Moderately; 4=Quite a bit; 5=Extremely)
a. Inspired
b. Angry
c. Upset
d. Interested
e. Ashamed
f. Enthusiastic
g. Discouraged
h. Excited
Creative Behavior (Oldham & Cummings, 1996). We would like to ask about
your CORE TRI 3 TEAM MEMBERS’ creativity for TODAY only. Begin by listing the
names below of each of your team members. Do not list or rate yourself. (Survey
Monkey piped in each team member’s name to a separate creativity scale for each
member.)
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Using the scale below, please rate [Team member]’s creativity for TODAY only.
Regarding [Team member] TODAY… (1=Not at all; 2=A little; 3=Moderately; 4=Quite
a bit; 5=Very)
a. How CREATIVE was this person’s work? Creativity refers to the extent to
which the person develops ideas, methods, or work that are both original and
useful to the team
b. How ORIGINAL and PRACTICAL was this person’s work? Original and
practical work refers to developing ideas, methods, or work that are both totally
unique and especially useful to the team
c. How ADAPTIVE and PRACTICAL was this person’s work? Adaptive and
practical work refers to using existing information or materials to develop ideas,
methods, or work that are useful to the team
Intrinsic motivation (Grant, 2008). We would like to ask some questions about
why you are motivated to do your work at school. Using the scale below, please indicate
the extent to which you agree with each item. Why are you motivated to do your work?
(1=Strongly disagree; 2=Disagree; 3=Neutral; 4=Agree; 5=Strongly agree)
a. Because I enjoy the work itself
b. Because it’s fun
c. Because I find the work engaging
d. Because I enjoy it
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Conscientiousness (Donnellan et al., 2006). Use the rating scale below to
describe how accurately each statement describes you. Describe yourself as you generally
are now, not as you wish to be in the future. Please describe yourself honestly. (1=Very
inaccurate; 2=Moderately inaccurate; 3=Neither inaccurate nor accurate; 4=Moderately
accurate; 5=Very accurate)
a. I get chores and routine tasks done right away
b. I often forget to put things back in their proper place (reverse code)
c. I like order
d. I make a mess of things (reverse code)
Learning goal orientation (adapted from Vandewalle, 1997). The following
statements are about how you behave in general. Please indicate the extent to which you
agree or disagree with each statement. There are no right or wrong answers. (1=Strongly
disagree; 2=Disagree; 3=Neutral; 4=Agree; 5=Strongly agree)
a. I often read materials related to my program of study to improve my ability
even if they are not required
b. I am willing to select a challenging school assignment that I can learn a lot
from
c. I often look for opportunities to develop new skills and knowledge
d. I enjoy challenging and difficult tasks at school where I’ll learn new skills
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e. For me, development of my work ability is important enough to take risks
g. I prefer to work in situations that require a high level of ability and talent
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