LICENSE TO CHEAT DOES FARMERS MARKETS PATRONAGE … · 4.5 Hypothesis testing ... fresh food. In the US, farmers’ markets have grown from 1,000 to over 8,700 locations – contributing

LICENSE TO CHEAT: DOES FARMERS’

MARKETS PATRONAGE INFLUENCE NON-

PROSOCIAL BEHAVIOUR?

Emma Joenpolvi

GradCertMBus / BA

Submitted in fulfilment of the requirements for the degree of

Master of Philosophy (Research)

School of Advertising, Marketing, and Public Relations

QUT Business School

Queensland University of Technology

2019

License to cheat: Does farmers’ markets patronage influence non-prosocial behaviour? i

Keywords

Deviant behaviour, farmers’ markets, licensing, local food, regulatory focus

License to cheat: Does farmers’ markets patronage influence non-prosocial behaviour? ii

Abstract

Farmers’ markets are a growing industry with increased economic importance in the

Western world. Consumers shop at farmers’ markets for prosocial reasons such as

supporting the local economy, community, and local business. But what if there are

unintended consequences to this prosocial behaviour? Licensing explains how

person’s prosocial behaviour gives a temporary boost to their positive self-image and

this elevated sense of themselves as a moral person subsequently gives them a

“license” to act in a deviant manner, since they already asserted themselves as a

“good person”. Arguably, the effect of licensing also depends on individual

differences such as regulatory focus of promotion and prevention, which impacts a

person’s motivation to pursue a goal in line with their desired end state – either

focused on hopes and ideals (promotion) or maintaining the security of status quo

(prevention). This thesis studies the effect of licensing within an in-group context of

local community and economy, utilising individual differences in promotion and

prevention focus to ascertain whether some consumers are more prone to deviant

behaviour. As indicated by the results from this study, licensing does take place in an

in-group of local community that can form within a farmers’ markets, but only for

consumers low-medium in promotion focus. Individuals with a high promotion focus

and prevention focus do not engage in licensing in this framework. This research

assists in identifying the unintended outcomes for the local economy through

licensing in and provides suggestions on how to assist in diminishing this behaviour.

With gaining more insight into consumer behaviour in this context, this research

further supports creation of new farmers’ market places and in turn, local economies.

License to cheat: Does farmers’ markets patronage influence non-prosocial behaviour? iii

Table of Contents

Keywords .................................................................................................................................. i

Abstract .................................................................................................................................... ii

List of Figures ........................................................................................................................... v

List of Tables .......................................................................................................................... vi

List of Abbreviations ............................................................................................................. vii

Statement of Original Authorship ......................................................................................... viii

Acknowledgements ................................................................................................................. ix

Chapter 1: Introduction ...................................................................................... 1

1.1 Introduction .................................................................................................................... 1

1.2 Research objectives, gaps and questions ........................................................................ 6

1.3 Theoretical frameworks .................................................................................................. 8

1.4 Research method ............................................................................................................. 8

1.5 Contributions of research ................................................................................................ 9

1.6 Outline of the thesis ...................................................................................................... 10

Chapter 2: Literature Review ........................................................................... 13

2.1 Introduction .................................................................................................................. 13

2.2 Farmers’ markets .......................................................................................................... 14

2.3 Licensing ...................................................................................................................... 22

2.4 Regulatory focus theory: Promotion and prevention .................................................... 29

2.5 Research questions and hypothesis development ......................................................... 33

2.6 Summary and implications ........................................................................................... 36

Chapter 3: Research Design .............................................................................. 37

3.1 Introduction .................................................................................................................. 37

3.2 Philosophical approach ................................................................................................. 37

3.3 Quantitative and qualitative methods ........................................................................... 39

3.4 Research design ............................................................................................................ 41

3.5 Data collection technique ............................................................................................. 43

3.6 Sampling approach ....................................................................................................... 45

3.7 Data collection procedure ............................................................................................. 49

3.8 Data analysis plan ......................................................................................................... 53

3.9 Ethical considerations ................................................................................................... 56

3.10 Summary ....................................................................................................................... 57

Chapter 4: Data Analysis .................................................................................. 59

4.1 Introduction .................................................................................................................. 59

License to cheat: Does farmers’ markets patronage influence non-prosocial behaviour? iv

4.2 Data collection .............................................................................................................. 59

4.3 Treatment of data .......................................................................................................... 60

4.4 Sample characteristics .................................................................................................. 63

4.5 Hypothesis testing ......................................................................................................... 66

4.6 Conclusion .................................................................................................................... 73

Chapter 5: Discussion and Conclusion ............................................................ 75

5.1 Introduction .................................................................................................................. 75

5.2 Summary of findings .................................................................................................... 75

5.3 Implications for theory ................................................................................................. 77

5.4 Implications for practise ............................................................................................... 82

5.5 Limitations .................................................................................................................... 83

5.6 Recommendations for future research .......................................................................... 85

5.7 Conclusion .................................................................................................................... 87

References ................................................................................................................. 89

Appendices ................................................................................................................ 97

Appendix A Full survey deployed on MTurk ......................................................................... 97

License to cheat: Does farmers’ markets patronage influence non-prosocial behaviour? v

List of Figures

Figure 1.1 Literature gaps in farmers’ markets patronage outcomes ........................... 6

Figure 4.1– Ranking of reasons for attending farmers’ markets with 1 being

most important and 5 least important reason to attend. ............................... 68

Figure 4.2 – Path effect sizes (β) for mediation model testing licensing (H1) .......... 69

Figure 4.3 – Frequency of attendance predicting positive self-image with

promotion as a moderator indicating effect weakening at high

promotion levels. .......................................................................................... 72

Figure 4.4 – Path effect sizes (β) for mediation model testing regulatory focus

of promotion (H2) ........................................................................................ 72

License to cheat: Does farmers’ markets patronage influence non-prosocial behaviour? vi

List of Tables

Table 3.1 ..................................................................................................................... 39

Table 4.1 ..................................................................................................................... 65

Table 4.2 ..................................................................................................................... 67

Table 5.1 ..................................................................................................................... 77

License to cheat: Does farmers’ markets patronage influence non-prosocial behaviour? vii

List of Abbreviations

AFN = Alternative food network

ANES = Current Population Survey and the American National Election Studies

ANEPS = American National Election 2008–2009 Panel Study

IV = Independent variable

DV = Dependent variable

MTurk = Amazon’s Mechanical Turk

License to cheat: Does farmers’ markets patronage influence non-prosocial behaviour? viii

Statement of Original Authorship

The work contained in this thesis has not been previously submitted to meet

requirements for an award at this or any other higher education institution. To the

best of my knowledge and belief, the thesis contains no material previously

published or written by another person except where due reference is made.

Signature:

Date: June 2019

QUT Verified Signature

License to cheat: Does farmers’ markets patronage influence non-prosocial behaviour? ix

Acknowledgements

I count myself as one of the most fortunate research students out there. I was able to

learn and grow with the help of two incredible minds, Associate Professor Gary Mortimer

and Dr Frank Mathmann. I am so grateful for the unrelenting support, time, and knowledge

you have given me. This insurmountable mission became entirely achievable because of

your dedication, teaching, and patience. Thank you for guiding me through this mountainous

terrain of writing a thesis.

I would not be who I am without the most inspirational, most accomplished, most

fiercely strong person I know, my mum, äiti, Riitta Aarniokoski. Team of two, me and you, I

know I can achieve anything I set my sight on because you are there to lead my way. Thank

you for letting me become the person that I am – never judging, always loving, and

sometimes giving me the much-needed hypothetical kick up the backside. I am so grateful to

be your daughter. And my stepdad Barry, thank you for looking after us.

My family in Finland. I wouldn’t have this strength without your help forming me into

the person I am today. Thank you to my dad, Mikko whom I got my quiet poise from, my

aunties – Anja, Seija, Raija & Kusti, Iitu, Sari & Tepa, and cousins – Sanna, Kimmo, Kaisa

and our newbie, Stella, Harjavallan hirmut. To Hilja ja Pauli Aarniokoski in light.

Alana, who gave me the motivation I needed to start this journey, your friendship,

wisdom, reassurance, and feedback has given me the courage to strive for more. For your

unwavering support, patience and understanding with many of the plans I’ve had to miss or

cancel: Alex, Genna, Sanna, Harri, Leo, Lu, Andie, Mike, Colette, Taz, Giselle, Laura, Dean

& mini-alpaca to come. I am ready to schedule those dinners now. And for letting me out of

the office to become an officially proven master, thank you for your persistent support and

care: Jenni, Tony, Anne, Lisa, and Billie.

For being an integral part of my research journey. For always questioning, for always

answering, and for all the bad influence and churros – I am forever grateful Toby. And my

other peers, thank you for the insights and late-night submission help.

Sally, Quentin, Harry, Tom. Always believing I can do anything I put my mind to.

And finally, thank you James – for this crazy journey of the past 12 years. I wouldn’t

be who I am without your love. Thank you for being my encouragement, my rock, my hot air

balloon man, fuelling me with fire to rise.

For all this, I am one lucky lady.

Introduction 1

Chapter 1: Introduction

1.1 INTRODUCTION

Farmers’ markets are gaining popularity in developed countries as an

alternative retail channel for farmers to sell, and consumers to access, fresh food. In

the US, farmers’ markets have grown from 1,000 to over 8,700 locations –

contributing over $1.2 billion to the economy (Abelló, Palma, Waller, & Anderson,

2014; Garner, 2015; Madigan, 2017; USDA, 2018b). In the UK, it is predicted that

over 500 farmers’ markets contribute more than £500 million to a farmer’s revenue

while directly serving millions of consumers (Pearson et al., 2011). In Australia, over

150 markets are registered through Australian Farmers’ Markets Association – up

from 80 markets in 2006 (AFMA, 2019) and in Germany, it is estimated that 30,000-

40,000 farms across the country are selling their products direct to consumers

(Bavorova, Unay-Gailhard, & Lehberger, 2016). Farmers’ markets provide an

opportunity for small to medium size farms to sell their goods directly to consumers,

thus removing intermediaries that might otherwise facilitate the sale of their products

for lesser gross return to the farmer (Sadler, Clark, & Gilliland, 2013). Yet, despite

the growth of farmers’ markets, academic research has provided few insights on the

effect of farmers’ markets patronage on consumer behaviour.

To date, research has largely been limited to drivers for the growth in

farmers’ markets. This problem has been investigated from two angles; consumers

and farmers. From the consumers’ perspective, researchers have identified a desire

for local food, perceived product quality, freshness, and healthiness, authenticity

Introduction 2

seeking, support for local community, and social links and traceability as drivers to

the growth of farmers’ markets (Autio, Collins, Wahlen, & Anttila, 2013; Bianchi &

Mortimer, 2015; McEachern, Warnaby, Carrigan, & Szmigin, 2010; Murphy, 2011).

From the farmers’ perspective, producers are turning to farmers’ markets to receive a

direct feedback relationship with customers. This enables farmers to better respond

to demands with increased control over their production and marketing, consequently

contributing to the growth of the industry (Hunt, 2007).

Desire for local food is an integral part of farmers’ market shopping

behaviour and authentic local food is accessible at farmers’ markets (Bianchi &

Mortimer, 2015). The produce at farmers’ markets is perceived to be fresher, of

higher quality and healthier (Murphy, 2011). Consumers tend to seek traceability and

human interaction, with farmers’ markets allowing consumers to connect with local

producers, consequently creating an authentic experience (Autio et al., 2013;

Spielmann & Bernelin, 2015). Consumers who shop at farmers’ markets are also

driven by their desire for helping the local community and McEachern et al. (2010)

discovered that support for local community was a major driving factor for

consumers to shop at farmers’ markets. Farmers’ markets contribute to the local

economy financially, but also through increase in people visiting the area, which

assists in creating a strong local community and identity (McEachern et al., 2010).

Furthermore, consumers are attending markets regularly to contribute to the viability

of local farms (Carson, Hamel, Giarrocco, Baylor, & Mathews, 2016) and to promote

self-reliance to reduce food miles and dependence on the global food chain (Seyfang,

2007). However, despite researchers having turned their attention to examining

antecedents and drivers of farmers’ markets patronage, the current literature remains

Introduction 3

mostly silent on how frequenting farmers’ markets impacts subsequent consumer

behaviour.

Communities can form around a consumption ideal as much as around brands

and services thus shopping via farmers’ markets assists in forming an in-group based

around mutual community goals (Spielmann & Bernelin, 2015; Zepeda & Deal,

2009). As previous research attests, consumers attend farmers’ markets for prosocial

reasons, such as supporting the local economy and jobs, helping small business and

giving back to their community (Alkon, 2008; Otto & Varner, 2005). Licensing

explains whether persons’ prosocial and pro-environmental behaviours lead to a

boost in moral self-image, thus allowing for future non-prosocial behaviour without

negatively affecting the perception of their moral self (Khan & Dhar, 2006).

Therefore, the same customers who attend farmers’ markets for prosocial reasons to

support their in-group, which is local economy, may engage in non-prosocial

behaviour that may neglect their in-groups as a result, like cheating as demonstrated

by Mazar and Zhong (2010). However, extant research has not yet considered

farmers’ markets in the licensing literature whatsoever, despite their growing

economic and social importance (Simbrunner & Schlegelmilch, 2017; Tey, Arsil,

Brindal, Teoh, & Lim, 2017). Furthermore, the consumers’ group association has

been established to affect their moral credentials that induce licensing. In a previous

study by Kouchaki (2011), moral credentials were vicariously gained via the in-

group’s previous moral actions, which licensed the participants to engage in

discriminatory behaviour despite not completing the previous moral actions

themselves. However, what remains unknown is whether people specifically take

part in behaviour to benefit their in-group, subsequently licensing them to act in a

deviant manner against their own in-group. For example, whether consumers in an

Introduction 4

alternative retail context, such as farmers’ markets, are likely to allow their in-group

association with prosocial consumption to first benefit their local economy and then

license their subsequent decisions to negatively affect their in-group.

It is also important to note that there have been inconsistencies with regards

to the size of licensing effect in the literature. Meta-analysis investigating the impact

of licensing in previous literature found there to be a publication bias with published

studies having a larger licensing effect present than unpublished studies (Blanken,

van de Ven, & Zeelenberg, 2015). Arguably, part of this variance might be due to

individual differences among participants. This argument is further supported by

calls for future research on individual differences as moderators of the licensing

effect (Joosten, van Dijke, Van Hiel, & De Cremer, 2014). The present study

addresses these calls by investigating the moderating effects of promotion and

prevention focus.

Consumers attend farmers’ markets to support the local community

(McEachern et al., 2010; Spielmann & Bernelin, 2015). However, there are

individual differences that may affect farmers’ market patronage, for example, a

focus on gains or losses in their motivational goal pursuit. These individual

differences can be examined from the perspective of regulatory focus theory which

introduces two pathways of goal pursuit: promotion and prevention (Higgins, 2002;

Higgins & Cornwell, 2016; Higgins, Shah, & Friedman, 1997). Regulatory focus can

be a chronic trait or it can be induced by situational primes (Higgins 1997; 1998).

Promotion focus refers to focusing on hopes, ideals, aspirations, and gains, whereas

prevention focus is concerned with safety, security, and maintaining the status quo

(Higgins, 2002). In a farmers’ markets context, supporting the local community may

lead to consumers with a high promotion focus to be motivated by their positive

Introduction 5

ideals and gains towards the local community and therefore engage in less licensing

behaviour. Consumers with a prevention focus may be overall more prone to

maintain their status quo and thus not experience a boost in moral self-image as a

result of farmers’ markets patronage, also weakening the licensing effect. By gaining

knowledge on the motivational goals that could allow for a weakening of the

licensing effect in a farmers’ markets context, the impact of licensing related deviant

behaviour could be diminished and thus further assist in supporting local economies.

However, regulatory focus theory has not been applied in prosocial shopping context

of farmers’ markets, so little is known about how these elements play out.

Farmers’ markets continue to grow and are an important element in local

economy. Still, research is scarce in considering behavioural outcomes of

frequenting farmers’ markets. Support for the local community is a major driver

contributing to farmers’ markets patronage (McEachern et al., 2010) but this act of

prosocial behaviour may contribute to subsequent non-prosocial behaviour as

explained through licensing however, research is yet to consider an in-group scenario

centred around prosocial consumption (in-group vs. in-group) or farmers’ markets in

relation to licensing. Additionally, previous literature on licensing has also suggested

for future research to include investigation into individual differences (Chen, 2016;

Joosten et al., 2014). By considering the role of regulatory focus in licensing,

individual’s regulatory focus of promotion and prevention may become evident in

helping to explain the differences in licensing within a farmers’ markets context.

This chapter proceeds to considering the research objectives, questions and gaps, and

to developing the research questions and methodology.

Introduction 6

Figure 1.1 Literature gaps in farmers’ markets patronage outcomes

1.2 RESEARCH OBJECTIVES, GAPS AND QUESTIONS

Overall, this current study seeks to address three research gaps: (1) consumer

behaviour outcomes of frequenting farmers’ markets, (2) investigating the licensing

effect in an in-group specific context such as a farmers’ markets (3) if individual

differences, such as regulatory focus (promotion and prevention), explain variance in

the licensing effect. A summary of these gaps is presented in Figure 1.1.

Despite the growing number of farmers’ markets and patrons frequenting them

to support the local economy (McEachern et al., 2010), research on behavioural

outcomes for consumers is at its infancy. Farmers’ markets as alternative retail

spaces have been investigated in relation to drivers for patronage (Murphy, 2011),

but for the industry to continue to grow and develop, it is important to develop an

understanding of the behavioural outcomes for consumers shopping at farmers’

Introduction 7

markets specifically, instead of supermarkets or other traditional retail channels.

Additionally, licensing behaviour has been investigated in many prosocial and pro-

environmental contexts such as green consumption (Geng, Cheng, Tang, Zhou, &

Ye, 2016; Mazar & Zhong, 2010; Meijers, Verlegh, Noordewier, & Smit, 2013),

charitable donations (Gneezy, Imas, & Madarász, 2014; Meijers, Verlegh,

Noordewier, & Smit, 2015), racial bias (Effron, Miller, & Monin, 2012; Monin &

Miller, 2001), and food choices (Chen, 2016; Witt Huberts, Evers, & De Ridder,

2012), however, it has not been applied in farmers’ markets context despite the

opportunity for instantiation of in-group specific deviance, specifically whether the

members of the in-group will act in a deviant manner against their in-group due to

their positive moral self-image. To answer these concerns, this thesis discusses the

following research question:

Research Question 1: Does farmers’ markets patronage lead to unintended

behavioural and attitudinal outcomes in licensing in-group members to cheat from

other in-group members?

Furthermore, little is known about the impact of individual differences with

regards to licensing. People’s individual regulatory focus differences in promotion

and prevention may influence the relationship between frequenting farmers’ markets

and deviant behaviours when the consumers evaluate their prosocial actions, such as

attending farmers’ markets. People with a promotion focus concentrate on ideals and

aspirations whereas individuals with a prevention focus are more likely to maintain

status quo (Higgins, 2002). Therefore, consumers with a high promotion focus may

be driven by their in-group association to benefit the local community and thus

weaken the impact of licensing. Consumers with a prevention focus may be likely to

Introduction 8

maintain status quo and thus also engage in less licensing behaviour, however,

regulatory focus is not yet investigated in relation to licensing in a farmers’ markets

context. So, the final research question asks:

Research Question 2: Do individual differences in promotion and prevention

have an impact on licensing behaviour in a farmers’ markets context?

1.3 THEORETICAL FRAMEWORKS

To address the research questions, this research is guided by licensing theory

(Monin & Miller, 2001). Licensing will be used to understand the behavioural

outcomes of consumers attending farmers’ markets and whether licensing allows for

in-group members supporting local economy to act in a deviant manner against their

own in-group (RQ1). Additionally, licensing is investigated through the moderating

factors of promotion and prevention to enhance our understanding on how individual

differences impact the effect on licensing in farmers’ markets context (RQ2).

1.4 RESEARCH METHOD

This study aims to add to the knowledge in farmers’ markets patronage

outcomes, licensing, and regulatory focus literatures by an investigation of this

moderated mediation model and its gaps as presented in Figure 1.1. To do this, this

study will employ several scales measuring relevant constructs, such as self-image,

regulatory focus, and operationalisation of licensing.

In line with a positivist descriptive approach this study will measure cheating

behaviour within an in-group setting of farmers’ markets with a cross-sectional

Introduction 9

online survey that was conducted utilising Amazon Mechanical Turk research panel

participants in the United States. An online survey was chosen as the method of data

collection due to ease of implication and manageable costs (Babbie, 2015).

Additionally, since this is the first study to investigate regulatory focus in the realm

of farmers’ markets licensing behaviour, it was important to choose a data collection

method that allows for inspection of the point of change in the individual differences

of the participants, and this can be achieved with an online survey measuring

regulatory focus.

1.5 CONTRIBUTIONS OF RESEARCH

This research makes several theoretical and practical contributions to farmers’

markets and licensing literature. It will provide new insights into behavioural

outcomes of frequenting farmers’ markets, licensing in an in-group vs. in-group

context, and for recognising individual differences in promotion and prevention

focus that may diminish the impact of a licensing effect. Extant research in the

licensing literature has focused on contributions on whether licensing is evident in

differing settings (Simbrunner & Schlegelmilch, 2017) but is yet to assess how

consumers’ prosocial and subsequently deviant behaviour works within an in-group

framework. This study will fill that gap in the literature, as well as the gap in

outcomes of frequenting farmers’ markets that is not yet well represented in the

current body of research. Instead, the literature on farmers’ markets has so far

focused on the drivers that influence the behaviour as identified through the

introduction. In another theoretical contribution, it has been acknowledged by

Blanken et al. (2015) that a publication bias in current licensing literature is present.

Introduction 10

This is arguably due to variance in consumers’ individual differences, and regulatory

focus of promotion and prevention will be investigated as potential moderators to

weaken the impact of licensing after frequenting farmers’ markets. Individual

differences have not been thoroughly investigated in licensing and this study adds to

the existing body of research by introducing promotion and prevention into this new

context (Blanken et al., 2015).

Managerially, farmers’ markets operators will benefit from this research

through an enhanced understanding of their customers and the behavioural outcomes

that could influence their frequency of visitation. By increasing their knowledge on

individual differences that may impact the licensing effect, farmers’ markets

operators can start to develop a strategy to better serve their consumers based on

their chronic promotion and prevention focus or priming this via marketing messages

to diminish its impact. Further to this, awareness of licensing effect within a farmers’

markets context is the first step to allow the operators to understand how their

customers may act in a deviant manner against their in-group after their prosocial

purchases. This will potentially help to create strategies on how to minimise the

effect in the future and induce continuing support for the local economy.

1.6 OUTLINE OF THE THESIS

This thesis consists of five chapters. From the Introduction (Chapter One),

continuing to Chapter Two: Literature Review which will discuss the current body of

research in farmers’ markets, licensing and regulatory focus, including a

development of the hypotheses guiding the study. Chapter Three: Research Design

provides an overview on the survey methodology used in testing the relationships of

Introduction 11

this study with the theoretical underpinnings, validity and reliability of this method.

This thesis then continues to a discussion on sampling, survey design and

measurement, and analysis of the data. Chapter Four: Data Analysis includes the

results of the study and hypothesis testing and finally, Chapter Five: Results and

Conclusion concludes the thesis by addressing the research questions, theoretical

contributions and practical applications of this thesis, also including limitations and

directions for future research.

Literature Review 13

Chapter 2: Literature Review

2.1 INTRODUCTION

The outcomes of farmers’ markets patronage are not yet fully understood in the

literature. For the future growth of the industry, it is integral to investigate the

potential deviant behaviours that could be a consequence of attending farmers’

markets as explained by licensing. Licensing takes place after a boost in moral self-

image that is gained from prosocial behaviour (Dunning, 2007), such as attending

farmers’ markets. Additionally, farmers’ markets is an industry inherently supportive

of its in-group. Licensing has not yet been investigated in an industry that provides

an opportunity for the instantiation of an in-group specific deviance and this

distinction is important to make to assist explaining the impact of licensing within

consumption groups. This research goes to provide knowledge on the question

whether there could be unintended consequences for attending farmers’ markets and

whether individual differences play a role through regulatory focus of promotion and

prevention. There have been calls in the literature to investigate the impact of

individual differences in licensing (Blanken et al., 2015; Joosten et al., 2014;

Simbrunner & Schlegelmilch, 2017) and these motivational orientations in goal-

pursuit could have an impact on consumers’ potential deviant or prosocial behaviour.

This chapter will first discuss the existing literature on farmers’ markets, its

economic importance, and antecedents of patronage – identifying gaps in the current

literature on patronage outcomes. Licensing literature is then critically reviewed to

explain the effect in other prosocial, group-specific, and consumer behaviour fields.


The inconsistencies in the current licensing literature are reviewed to minimise the

impact on this research. This will demonstrate the considerations taken to ensure

validity of results of this study to confidently add new knowledge to the literature in

the field of farmers’ markets outcomes as explained with licensing. One of the

criticisms identified is the lack of knowledge about individual differences in the

licensing condition. Thus, to answer those calls in current literature, the individual

differences in regulatory focus of promotion and prevention are considered and this

chapter follows on to review the theory of regulatory focus and discuss the potential

implications for licensing in a farmers’ markets context. The chapter will conclude

with the presentation of research questions and development of hypotheses.

2.2 FARMERS’ MARKETS

Farmers’ markets is an alternative retail channel where farmers and local food

producers sell their products directly to the consumer (La Trobe, 2001). To be

defined as a farmers’ market, the produce sold must come from a farm or associated

operators within a certain radius to the market and sold direct to the consumer.

Farmers’ markets are often temporary retail environments that are build and

dismantled for operation one day a week (Murphy, 2011). They function as an

alternative channel for farmers to sell their produce and for consumers to access local

fresh foods, thus supporting the local economy and community (Hughes, Brown,

Miller, & McConnell, 2008). For small to medium sized farms, farmers’ markets

provide an opportunity to create revenue without losing income to intermediaries or

wholesalers that might otherwise facilitate the sale. The products may also receive a

premium price at the markets while connecting directly with the final consumer


(Hughes et al., 2008; Sadler et al., 2013). For consumers, shopping at farmers’

markets offers them an experience with the local food producers, allows them to ask

questions directly from the grower, and facilitates social relationships while also

supporting the local farmer, community, and economy (Smithers, Lamarche, &

Joseph, 2008).

2.2.1 Economic impact of farmers’ markets

Farmers’ markets in developed countries such as USA, UK, and Australia are

becoming more popular. This comes after a decline in patronage and sales in the

1950s with the advent of supermarkets, refrigeration, and increased globalisation

allowing consumers more choice and convenience (Klimek, Bingen, & Freyer,

2018). However, over the past two decades, the number of farmers’ markets in the

USA has increased steadily again from 1,000 locations in the 1990s to over 8,700

markets in 2018, contributing over $1.2 billion to the economy (Abelló et al., 2014;

Garner, 2015; USDA, 2018b). According to a 2012 Census of Agriculture in USA, it

was found that direct-to-consumer sales by farmers had increased by 60% and

research has found this to be the result of prosocial reasons such as supporting the

local economy (Shi & Hodges, 2016). In the UK, it is predicted that over 500

farmers’ markets contribute more than £500 million to a farmer’s revenue while

directly serving millions of consumers (Pearson et al., 2011). Moreover, it is

estimated that there are 30,000-40,000 German farms involved in selling their

produce directly to consumers, with 80% of consumers purchasing local products on

a monthly basis (Bavorova, Traikova, & Doms, 2018; Bavorova et al., 2016). In

Australia, there are over 150 registered farmers’ markets, an increase from 60

markets in 2006. This is inserting over $2 million to the Victorian economy weekly,


a trend replicated nationwide (AFMA, 2011; AFMA, 2019). Therefore, the economic

importance of farmers’ markets is increasing.

2.2.2 Antecedents of farmers’ markets patronage

Extant research has investigated antecedents of farmer’s markets patronage,

finding that the growth of farmers’ markets can be attributed to drivers such as

support for local community and economy (Leiper & Clarke-Sather, 2017;

McEachern et al., 2010; Pearson et al., 2011), emergence in desire for local food

(Bianchi & Mortimer, 2015; Kumar & Smith, 2018), quality and freshness of

produce sold at farmers’ markets (Dodds et al., 2014; Murphy, 2011), as well as

increased traceability and authenticity seeking behaviour (Autio et al., 2013;

Spielmann & Bernelin, 2015). The extant literature considers these antecedents to be

driving the growth of farmers’ markets and the impact of each antecedent is

discussed next.

2.2.3 Desire for local foods

Local foods are an important component of a farmers’ market and it is one of

the antecedents that drive patronage. Most research concerning local foods has been

completed in developed countries, such as the USA, UK, Australia, and New

Zealand (Bianchi & Mortimer, 2015; Memery, Angell, Megicks, & Lindgreen, 2015;

Mirosa & Lawson, 2012). Local food has been defined with different criteria

depending on the country but consensus in the existing literature is that local food

must be produced and retailed within a certain distance from the consumers’ location

(Mirosa & Lawson, 2012). Bianchi and Mortimer (2015) found that positive attitude


towards local foods facilitates farmers’ markets patronage, since they are an easy and

more authentic outlet for purchasing local foods. Local food is perceived to be

healthier, environmentally friendly, and supportive of the local economy (Kumar &

Smith, 2018). It incites more trust and connectedness between consumers and

producers by facilitating social networks in communities. Additionally, local food

contributes to diversifying the food system and reducing reliance on the global

network of food production (Pearson et al., 2011). Research has concluded that those

who prefer local foods, tend to purchase them through non-traditional retail channels,

such as farmers’ markets rather than supermarkets, to establish traceability and social

links (Spielmann & Bernelin, 2015).

2.2.4 Traceability and authenticity

Traceability is an important motivation for consumers interested in local food

consumption, thus facilitating farmers’ markets patronage. Consumers value the

social links developed at farmers’ markets, where friendly, personalised service is the

standard and consumers can come to contact with the farmer to find out more

information about the food directly from the producer (Szmigin, Maddock, &

Carrigan, 2003). Moore’s (2006) research supports this theory, he found personal

connection to be of paramount importance when shopping at farmers’ markets.

Consumers who engage in conversation around the produce they purchase develop a

personal contact between the producer and consumer. This contact also increases

traceability which is improved when shopping through non-traditional food channels,

such as farmers’ markets (Moore, 2006). Knowledge about where the food came

from, who made it, how it was produced and who is selling the produce are of major

interest for consumers at farmers’ markets, since this knowledge is often unavailable


when purchasing via traditional channels, such as supermarkets (Spielmann &

Bernelin, 2015). Traceability can also assist in increasing consumer confidence

through improving accountability when purchasing directly from the producer (La

Trobe, 2001) and this can assist in strengthening trust, which is a major factor in

increasing loyalty and future patronage (Giampietri, Koemle, Yu, & Finco, 2016).

Additionally, farmers’ markets as specialist retail outlets are considered a more

authentic experience than supermarkets (Kuznesof, Tregear, & Moxey, 1997). The

authenticity of the shopping experience is especially important for consumers who

seek to purchase local food products, rather than industrially mass-produced foods.

Some markets have guidelines to encourage authenticity, where a certain percentage

of products must be local produce to be considered authentic (Murphy, 2011). In

fact, previous literature has discussed the authenticity concerns of markets where in

some cases, non-local food products and resellers are present when participation is

not monitored strictly, which has diluted the value of authentic food claims at some

markets, encouraging rules on producer attendance (Smithers & Joseph, 2010).

However, overall farmers’ markets allow consumers to experience the authenticity of

traditional food production and reconnect with their local produce and producers

(Autio et al., 2013) through produce perceived as healthy, fresh, and good quality.

2.2.5 Product quality and freshness

Product quality, freshness, and healthiness are considered important drivers to

shop at farmers’ markets (Abelló et al., 2014; Dodds et al., 2014; Murphy, 2011). In

previous research, approximately 80% of the respondent’s perceived food purchased

directly from the farmer to be fresher, and 69% perceived the taste to be better. The


increase in perceived product quality contributes to buying frequency, with

consumers who perceive the quality to be better more likely to visit the markets more

often (Bavorova et al., 2016). In addition to perceived product quality, consumers

attend farmers’ markets for prosocial reasons, such as support for local community

and perceived sustainability.

2.2.6 Support for local community and economy

Communities can form around a consumption ideal, like supporting the local

economy via shopping at farmers’ markets, as much as around brands or services.

Consumers who have similar purchase habits, are a part of the same community and

in-group (Spielmann & Bernelin, 2015; Zepeda & Deal, 2009). Belonging to a group

that is centred on a similar consumption ideal of supporting the local economy gives

the consumers a sense of belonging and supports their values of locality within the

community, determining their identity. Support for the local economy and

community is also driven by the consumers’ altruism rather than purposes for self-

interest (Memery et al., 2015) – attributing farmers’ markets as a part of prosocial

consumer behaviour. Farmers’ markets foster local communities through connecting

producers and consumers in their common ideals of local food consumption,

indicating a sense of moral economy based on “principles such as fairness, justice,

and reciprocity” in farmers’ markets participation (Leiper & Clarke-Sather, 2017, p.

841).

Consumers are motivated to purchase local foods due to being able to witness

the direct social and economic impact they make in their communities (Hu, Batte,

Woods, & Ernst, 2012; Memery et al., 2015). Supporting the local economy is a


major driver for attending farmers’ markets, with previous research identifying that

89% of consumers attend to strengthen the local economy and positively impact the

viability of local farms (Carson et al., 2016). Farmers’ markets provide multiple

benefits to the local economy through regeneration of rural services and

infrastructure that allows for greater diversification of produce. Additionally,

farmers’ markets and local food networks encourage visitors to the area due to the

recreational shopping opportunities provided. The revenue created in local

communities often stays within the community, also adding to an increase in local

employment and other community services (Guthrie, Guthrie, Lawson, & Cameron,

2006; Pearson et al., 2011). Therefore, supporting the local community and economy

acts as an important motivational goal to patronage of farmers’ markets, contributing

to social, economic, and environmental sustainability as a part of prosocial

consumption ideals.

Local community ties in with the idea of perceived sustainability. This is

typically seen to encompass dimensions such as economic, social, and environmental

sustainability as a balanced entity (Forssell & Lankoski, 2015). Perceived

sustainability in relation to alternative food networks such as farmers’ markets is

well-researched and provides an altruistic motivation for consumers to attend

farmers’ markets and further support their local communities. Sustainability is

considered an altruistic act with past research identifying environmental factors as a

motivation to purchase local foods, however, the literature does not agree whether an

environmental impact overall has a major influence on sustainable behaviours (Birch,

Memery, & De Silva Kanakaratne, 2018). Nevertheless, there is a consensus that

ethical consumption values are aligned with supporting the local community and

consumption of local food, which is accessible through farmers’ markets


(McEachern et al., 2010) and ethical self-identity that can be achieved through

prosocial consumption is a motivation that drives farmers’ markets patronage (Birch

et al., 2018).

2.2.7 Farmers’ markets literature gap

Current literature agrees that the antecedents of support for local community

and economy is an important antecedent to farmers’ markets patronage (Leiper &

Clarke-Sather, 2017; McEachern et al., 2010; Pearson et al., 2011). Additionally,

desire for local food (Bianchi & Mortimer, 2015; Kumar & Smith, 2018), quality and

freshness of produce sold at farmers’ markets (Dodds et al., 2014; Murphy, 2011),

and increased traceability and authenticity seeking behaviour (Autio et al., 2013;

Spielmann & Bernelin, 2015) contribute to farmers’ markets patronage. However, it

is important to consider the behavioural and attitudinal outcomes to further facilitate

the growth of the industry. Despite the existing research considering antecedents and

motivations for farmers’ markets patronage, the literature is yet to address how

frequenting farmers’ markets impacts subsequent consumer behaviour and whether

there are unintended consequences for farmers’ markets patronage. Consumers are

attending farmers’ markets for prosocial reasons such as supporting the local

community, but it is unknown how this prosocial behaviour of supporting local

community subsequently affects consumer behaviour and whether there are

unintended consequences as explained through licensing.


2.3 LICENSING

Every day, consumer choices are made based on a variety of aspects, like

whether the purchase supports their self-image and social values, or whether it goes

against their moral credentials (Mazar & Zhong, 2010). Licensing describes a

behaviour where a person acts in a prosocial manner, which helps them to assert an

altruistic self-image, but leads to deviant subsequent behaviour because they gain a

“license” to act in a non-prosocial manner through their previous moral actions

(Dunning, 2007). In consumer decisions, licensing takes effect when a decision with

a positive moral consequence is paired with a subsequent negative moral

consequence – resulting in a deviant outcome (Simbrunner & Schlegelmilch, 2017).

Licensing, also known as moral licensing in psychology literature, has been

investigated in a wide variety of applications ranging from self-control (Khan &

Dhar, 2007) to racial bias (Monin & Miller, 2001) and health goals (Chen, 2016).

Within a marketing context, licensing can have an impact on consumers’ willingness

to purchase hedonistic luxury products after a prior virtuous act (Khan & Dhar,

2006). Licensing has also been shown to contribute to an increase in self-indulgent

behaviour after receiving prosocial marketing messages (Kouchaki & Jami, 2018).

This research proposes that licensing may also take place after prosocial acts such as

shopping via farmers’ markets to support the local economy. For example, shopping

at farmers’ markets may lead to the consumer identifying with members of the local

community that support prosocial shopping behaviour, thus forming an in-group

based on these moral credentials and elevating the self-image they gain via their

consumer shopping decisions. This elevated sense of self-image as a “good person”

may then lead to subsequent non-prosocial behaviour that goes against the in-group’s

basic value of supporting the local community, like engaging in cheating their own


in-group. Thus, licensing may impact consumer choices within an in-group

framework, but, current literature has not yet investigated licensing that appears with

in-groups acting in a deviant manner against their own in-group, in this case, local

economy and community. Extant literature does provide a framework for licensing

and its effect on both individual and group behaviour.

2.3.1 Licensing in the literature: In-group vs. out-group

Licensing was first investigated in the context of sexist statements and racial

bias in job selection. Monin & Miller (2001) discovered that people who were first

given an opportunity to disagree with sexist statements were more likely to

recommend a male for a job that would be traditionally considered suitable for

males. They argued that by establishing their moral credentials with an egalitarian

act, it gave the participants a license to subsequently express discriminatory

statements. They also tested whether participants would feel licensed to express

racially biased views after first selecting an African-American job applicant and

discovered that given an opportunity to first make a choice to show non-racist views,

the participants were more likely to express prejudice in a future hiring decision

(Monin & Miller, 2001). Since then, licensing has been discovered to have an effect

in various studies considering prejudice. One such study considered endorsement of

African-American Presidential candidate Barack Obama giving participants a license

for racially biased views. This resulted in choosing a white candidate for a job over

an African-American candidate (Effron, Cameron, & Monin, 2009). Prejudiced

views were also expressed in a study in which participants were more likely to write

about a Hispanic friend rather than an acquaintance when they were aware that

subsequently they were required to write about a negative experience with Hispanics,


rather than a positive one – indicating that participants were more keen to establish a

stronger group association with that racial group to indicate non-prejudiced moral

credentials (Bradley-Geist, King, Skorinko, Hebl, & McKenna, 2010). Additionally,

in another study considering racial prejudice, when White participants were given an

opportunity to exhibit a non-racist view, they were more likely to subsequently be

less sensitive to racial bias, and even allocate more funding favouring White

organisations instead of Black organisations (Effron et al., 2012). This indicates that

White participants preferred their in-group, at the expense of the out-group of Black

participants.

Licensing effect has been previously investigated specifically in in-group vs.

out-group context when it comes to prejudices. A study completed by Krumm &

Corning (2008) discovered that participants agreed with more discriminatory

statements when they identified with the executor’s in-group, in this case sexual

orientation. The in-group participants were much less likely to see the executor’s

behaviour as discriminatory when the executor displayed their moral credentials of

non-judgmental views, compared to when he or she did not portray moral

credentials. Out-group members though were not as swayed by the moral credentials

displayed by the executor as the in-group members (Krumm & Corning, 2008).

Therefore, due to their group identification in-group members were licensed to

behave in a deviant manner after addressing previous positive behaviours more so

than out-group members. This was echoed in behavioural outcomes of a study testing

whether a causal relationship was found to exist with perceived progress on

egalitarian goals and increased racial bias. It was discovered by Mann & Kawakami

(2012) that after receiving feedback on their progress on egalitarian goals, the non-

Black participants were not only more likely to sit further away from Black


participants, but also sit closer to White participants. Additionally, these participants

showed more implicit prejudice (Mann & Kawakami, 2012). This indicates a bias

against the out-group, potentially increasing support for the in-group. Therefore, a

member of an in-group is likely to act in a deviant manner against the out-group

when licensed to do so via gaining moral credentials.

2.3.2 Licensing in an in-group setting of farmers’ markets

A review of the existing licensing literature presents a major gap in

investigating licensing within an in-group context. The literature gives evidence that

licensing effect holds in an in-group vs. out-group context, however, the extant

literature has not investigated licensing within in-group vs. in-group scenario,

specifically whether a prosocial in-group membership based on mutual consumption

ideals of attending farmers’ markets would first provide an elevated positive self-

image and subsequently lead to deviant behaviour against their own in-group.

Consumers often form a community based on their similar consumption ideals and

thus can either advance or disadvantage their own in-group (Zepeda & Deal, 2009).

In a farmers’ markets context, the consumers’ group behaviour may have an

impact on the effect of licensing. Attending farmers’ markets supports the local

economy, helps local communities, and enables consumers to purchase their food

products with this prosocial behaviour in mind. This helps form communities around

the consumption ideal that supports that behaviour, thus creating an in-group aligned

with social integration via all members of the community from producers to

consumers (Spielmann & Bernelin, 2015). A critical review of the alternative food

network (AFN) literature by Tregear (2011) found that current literature applies


assumptions that consumers who attend AFNs inherently have certain moral

credentials towards a just, equal, and sustainable goals along with a heightened sense

of altruism (Tregear, 2011). Therefore, by looking at farmers’ markets in the context

of licensing may provide new viewpoints on the subsequent consumer behaviour

outcomes.

2.3.3 Licensing in consumer behaviour: Prosocial and pro-environmental

contexts

In the field of consumer behaviour and marketing, individual behaviour in

licensing has been investigated in many prosocial and pro-environmental contexts

such as green consumption leading to cheating and stealing (Geng et al., 2016; Mazar

& Zhong, 2010), antecedents and outcomes of charitable donations (Gneezy et al.,

2014; Meijers et al., 2015), consumer choice after a virtuous act (Mazar & Zhong,

2010), and even outside the domain of altruistic behaviour with unhealthy food

choices as a consequence of completing health goals and self-control (Chen, 2016;

Witt Huberts et al., 2012).

Pro-social and pro-environmental consumption is increasingly important in

today’s global marketplace. Extant research on licensing in a green consumption

context has begun to understand how licensing may have an effect on a consumers’

pro-environmental consumption and behaviour in a subsequent deviant manner

through previous studies. Licensing within a pro-environmental shopping domain has

been demonstrated in a study by Mazar & Zhong (2010) who concluded that

consumers have a higher likelihood of stealing and cheating after the purchase of

green products, instead of conventional products. This was tested through an


experiment in which participants were assigned to two different virtual shopping

groups, one with conventional products and another with perceived green products.

After making their virtual purchases, the participants were given an opportunity to

cheat on a game in which the correct answer was easy to identify but they were

encouraged to report on the wrong answer by manipulation in increasing their reward

when reporting consistently on only one of the two options whether it was correct or

not. Afterward, the participants were asked to independently pay themselves, thus

giving them an opportunity to not only cheat in the game but also to steal.

Participants who made purchases in the green condition were found to be

significantly more likely to firstly, cheat in the game, and secondly, to steal more

money than participants in the conventional shop condition (Mazar & Zhong, 2010).

The results from this study indicate that green consumption may lead to unintended

deviant behaviours in unrelated domains of green consumer choices leading to

cheating and stealing (Mazar & Zhong, 2010). The research is indicative of

preliminary results for the impact of licensing in green consumption context and is

echoed in a study completed by Geng et al. (2016) in which participants behaved in a

less environmentally friendly manner after purchasing green, rather than

conventional products. This was tested with a water consumption task after being

exposed to either green or conventional shopping list scenario. The participants were

also surveyed on the probability of their future pro-environmental behaviours. It was

discovered that participants exposed to the green shopping list were less concerned

with their water consumption and future environmental behaviours, thus indicating a

licensing effect in related pro-environmental domains (Geng et al., 2016).

In the context of charitable donations, environmentally friendly behaviour has

been seen to diminish because of previous charitable donation (Meijers et al., 2015).


A study conducted in The Netherlands discovered that people who donated to a local

charity subsequently expressed lower environmental intentions than participants who

had not donated to a charity. The study demonstrates that licensing takes place even

when people independently choose to behave in a moral way in the first instance,

since they were measured on their previous donation behaviour, rather than through a

prime condition (Meijers et al., 2015).

2.3.4 Inconsistencies in licensing

There has been some criticism of the impact of licensing and the effect of

positive self-image to subsequent behaviour due to small sample sizes and lack of

consideration of individual differences (Blanken et al., 2015; Simbrunner &

Schlegelmilch, 2017). The criticism is also evident in psychology literature since the

effect of licensing goes against existing psychological theories indicating that a

person’s self-image should stay consistent and prosocial behaviour only increases

their self-perception as a moral person (Simbrunner & Schlegelmilch, 2017).

However, it has been suggested that licensing is one element of person’s moral self-

image congruity and comparatively deviant behaviour still allows a person to feel

moral despite a lapse in prosocial behaviour (Sachdeva, Iliev, & Medin, 2009;

Simbrunner & Schlegelmilch, 2017).

Two meta-analyses (Blanken et al., 2015; Simbrunner & Schlegelmilch,

2017) have been completed to test the impact of licensing and neither of them

discount the effect, but they offer suggestions for future research to utilise larger

sample sizes and consider individual differences. Blanken et al. (2015)

systematically conducted a meta-analysis of 91 published and un-published studies


from 22 published and 8 unpublished articles within moral licensing. The analysis

discovered that the sample sizes in previous studies were not sufficient to make

overarching predictions on the validity of the phenomenon, therefore, for future

studies it is of uttermost importance to seek a larger sample (minimum 165

participants per condition). However, the analysis excluded the measurement of

whether the participants were high or low in continuous personality variables such as

racism scale and only utilised the main effect size, therefore the overall impact of

individual differences in licensing remains unknown (Blanken et al., 2015). But

individual differences are considered in the meta-analysis by Simbrunner &

Schlegelmilch (2017). They discovered that cultural background with an analysis on

the type of comparison explains almost a quarter of variance in the effect, with even

a bigger proportion remaining unexplained. Therefore, it is recommended for future

research to investigate individual differences and personality-related characteristics

to fill this gap in the literature and contribute more fine-tuned results on the impact of

licensing. To respond to these recommendations, it is suggested in this thesis that the

effect of licensing is moderated by a person’s promotion and prevention focus.

2.4 REGULATORY FOCUS THEORY: PROMOTION AND PREVENTION

Regulatory focus theory developed by Higgins et al. (1997; 1998; 2002;

2016) is concerned with goal attainment strategies via two distinctive pathways:

promotion and prevention focus. These are motivational strategies for pursuing

goals, and they differ in ways that individuals with either promotion or prevention

focus (or both) approach tasks and choice scenarios presented to them (Higgins,

1998). Promotion focus as a motivational strategy emphasises gaining positive


outcomes and avoiding states where there is a lack of positive outcomes. It is

concerned with strong ideals, hopes, aspirations and pursuing accomplishments and

improvement to a better state. Promotion focus indicates desired goals as gains and

non-gains and is motivated by a want to find an ideal, positive end state, or

protection from the absence of that state (Higgins, 2002; Higgins et al., 1997). For

example, a person with a promotion focus may be motivated by the positive aspects

of going to a farmers’ markets, such as supporting the local community to gain a

positive outcome for their local economy. Or, they may attempt to avoid a lack of a

positive outcome, such as supporting the local markets and farmers to avoid losing

their community ties.

In contrast, prevention focus is related to an absence and avoidance of

negative outcomes. It is concerned with upholding responsibilities and duties, and

safeguarding security to minimise possible losses. Prevention goals are thus viewed

as losses and non-losses and the motivation to approach prevention focused goals are

by attempting to maintain status quo to avoid slipping into a worse state (Arnold &

Reynolds, 2009; Higgins, 2002; Higgins et al., 1997). In contrast to a promotion

focus, a consumer with a prevention focus may attend farmers’ markets due to social

responsibility and by supporting farmers’ markets, they may be motivated to avoid a

loss of their values in their duty to support the local economy.

Promotion and prevention focus can be an enduring chronic state that an

individual has learnt growing up (Higgins, 1998) or it can be induced temporarily

through priming. Both motivational systems can co-exist in the same person

(Higgins, 1997). Previous research attests that when pursuing goals, individuals with

promotion focus (either chronic or induced with situational primes), will approach

goals with eagerness and individuals with prevention focus will use vigilance


(Higgins et al., 2001). Higgins et al. (2001) found that individuals high in promotion

possess an eagerness goal and are less likely to make an ‘error of omission’ which

could lead to severing opportunities. On the contrary, the vigilance goal related to a

prevention focus indicated that individuals were less likely to make an ‘error of

commission’ so attempting to evade errors. Therefore, individuals with a promotion

focus are more likely to strategise to goal attainment that allows for advancement to

an ideal state and individuals with a prevention focus are more likely to avoid

strategies that obstruct goals (Higgins et al., 2001). Regulatory focus has been

researched in the realm of attitudes and motivations, but these orientations also have

implications for consumer behaviour (Das, 2016; Werth & Foerster, 2007).

2.4.1 Regulatory focus in a consumption context

Regulatory focus theory has been researched in consumption contexts such as

hedonic vs. utilitarian consumption (Chernev, 2004; Das, 2016; Roy & Ng, 2012).

The results from these studies provide evidence for the consideration of promotion

and prevention focus as an important component to consumers’ purchasing

behaviour, especially when looking at individual differences in a shopping context.

In a farmers’ markets setting, previous research has shown that farmers’

markets offer a hedonic consumption motivation due to the community-based values

that a local food shopping experience through farmers’ markets can provide

(Cicatiello, Pancino, Pascucci, & Franco, 2015; Wang, 2014). For a promotion-

focused individual, this may be seen through a gain vs. non-gain lens where the gain

of supporting the local economy is an important motivational principle. However,

local food purchases at a farmers’ markets are also linked to a utilitarian


consumption principle due to social responsibility and value ties (Spielmann &

Bernelin, 2015). In the case of a prevention focus, an individual may be motivated to

see their social responsibility in a loss vs. non-loss situation, where they attempt to

uphold their duties. Thus, farmers’ markets as a shopping channel can satisfy both

dimensions – consumers who favour gains vs. non-gains (promotion) and consumers

with loss vs. non-loss (prevention) motivation.

Therefore, it is possible that for consumers with a promotion focus, the value

they get from farmers’ markets leads to more local community gain-focused decision

making and with a prevention focus, consumers are more likely to see farmers’

markets patronage as a non-loss, thus maintaining status quo. Consequently,

promotion focus related to prosocial consumption to make gains for the local

community potentially has a weakening impact on the licensing effect by reducing

deviant behaviour as a result, and prevention focus linked to non-loss could also

weaken the licensing effect within a farmers’ markets context.

2.4.2 Regulatory focus and prosocial behaviour

Regulatory focus can have an impact on a person’s prosocial and deviant

behaviour. When making a purchase decision, an individual weighs up the expected

costs and benefits that would incur as a result of deviant behaviour which will

influence their decision to either act in a prosocial or unethical manner. Extant

research has called for future studies to investigate the connection between

regulatory focus and prosocial behaviour (Gino & Margolis, 2011). This study

provides further evidence for the impact of promotion and prevention focus on


prosocial behaviour but goes even deeper by introducing regulatory focus in a

licensing context.

Consumers with a high promotion focus may evaluate what they and their local

community gain from a farmers’ markets, seeing their prosocial consumption values

and ideals as their motivational pursuit and thus, they may engage in less non-

prosocial behaviour as a result. In an application to licensing theory, this indicates

that promotion focus is likely to have a weakening impact on the licensing effect on

farmers’ markets patronage.

Individuals with a prevention focus are driven to ensure that status quo

remains, protecting themselves from a risk of moving to a worse state (Higgins &

Cornwell, 2016). Therefore, prevention focused individuals maintain the status quo

and are therefore less likely to engage in non-prosocial behaviour to avoid risking a

potential loss. In the realm of licensing, this indicates that it is likely that individuals

with a prevention focus are less likely to act in a deviant manner as a result of

farmers’ markets patronage and thus, prevention focus also weakens the licensing

effect.

2.5 RESEARCH QUESTIONS AND HYPOTHESIS DEVELOPMENT

Despite the growing number of farmers’ markets and patrons frequenting

them to support the local economy (McEachern et al., 2010), research on unintended

behavioural outcomes remains scarce. For the industry to continue to grow and

develop, it is important to develop an understanding of the potential outcomes for

farmers’ markets and local economies. Additionally, licensing behaviour has been

investigated in many prosocial settings, however, it has not been applied in farmers’


markets context despite the opportunity for operationalisation of in-group specific

deviance. Specifically, whether the members of the in-group will act in a deviant

manner against their own in-group due to a boost in positive moral self-image.

Therefore, this research addresses the following question:

Research Question 1: Does farmers’ markets patronage lead to unintended

behavioural and attitudinal outcomes in licensing in-group members to cheat other

in-group members?

Furthermore, little is known about the impact of individual differences with

regards to licensing. People’s individual regulatory focus differences in promotion

and prevention may influence the relationship between frequenting farmers’ markets

and deviant behaviours when the consumers evaluate their prosocial actions.

Consumers with a promotion focus are motivated by positive gains and consumers

with prevention focus want to avoid losses (Arnold & Reynolds, 2009; Higgins,

2002; Higgins et al., 1997). Therefore, promotion-focused consumers may focus on

what they gain from farmers’ markets and engage in less non-prosocial behaviour as

a result of an in-group specific operationalisation, and consumers with a prevention

focus are likely to maintain status quo and thus also diminish the licensing effect. So,

the second research question asks:

Research Question 2: Do individual differences in promotion and prevention

have an impact on licensing behaviour in a farmers’ markets context?

Overall, it is suggested that licensing may take place after a prosocial act of

shopping via farmers’ markets to support the local economy in an in-group context.

For example, shopping at farmers’ markets may lead to the consumer identifying


with members of the local community that support prosocial shopping behaviour,

thus forming an in-group. Through their shopping, they gain a boost in their self-

image as a “good person”, which may then lead to subsequent non-prosocial

behaviour that goes against the in-group’s basic value of supporting the local

community, like engaging in subsequent cheating as indicated by Mazar & Zhong

(2010). Thus, licensing may impact consumer choices within an in-group framework,

but, current literature has not yet investigated licensing appearing with in-groups

acting in a deviant manner against their own in-group, in this case, local economy

and community. Therefore, the following hypotheses is proposed:

Hypothesis 1 (H1): Supporting the community and local economy through

farmers’ markets patronage leads to unintended behavioural outcomes through

licensing, with consumers cheating their in-group members.

Additionally, individual differences should be accounted for in licensing. It is

hypothesised that a prosocial act such as supporting the local community by

shopping at farmers’ markets directs consumers with a high promotion focus to

consider gains for their local community and this may diminish the impact of

licensing. Individuals with a prevention focus are likely to focus on maintaining the

status quo of prosocial behaviour thus are less likely to engage in licensing overall.

Thus a person’s individual differences in regulatory focus may diminish their

subsequent licensing behaviour. Second and third hypotheses are related to the

diminishing impact of regulatory focus in licensing and they state:

Hypothesis 2 (H2): Individual differences in promotion focus weakens the

effect of licensing within a farmers’ markets context.


Hypothesis 3 (H3): Individual differences in prevention focus weakens the

effect of licensing within a farmers’ markets context.

2.6 SUMMARY AND IMPLICATIONS

This chapter has discussed the extant literature on the fields of farmers’

markets, licensing, and regulatory focus. It is suggested that through farmer’s

markets patronage, consumers gain a positive moral self-image which then leads to

deviant behaviour as explained with licensing. Additionally, it is indicated that the

inherent in-group setting of farmers’ markets community may be influential in the

effect of licensing. Individual differences in regulatory focus of promotion and

prevention are also discussed as potentially having an impact on weakening this

behaviour. This thesis will explore licensing in an in-group scenario of farmers’

markets and proposes that individual differences in promotion and prevention may

diminish the effect of licensing.

Research Design 37

Chapter 3: Research Design

3.1 INTRODUCTION

The aim of the study is to explore outcomes of farmers’ markets shopping

behaviour and whether patronage leads to an elevated self-image and subsequent

deviant behaviour of cheating as explained with licensing. This chapter delves into

the methodology that underpins this research. It will discuss the philosophical

approach providing the orientation to this research, then continuing with a discussion

on quantitative and qualitative research methods. Then, the research design is

outlined whereas the following section provides an overview of the data collection

technique. The overall sampling technique is revealed with data collection procedure

overview provided, then moving into a preliminary data analysis plan with ethical

considerations deliberated.

3.2 PHILOSOPHICAL APPROACH

Research paradigms in social sciences are pinned with philosophical roots that

provide a basic orientation for theory and research (Neuman, 2014). Social sciences

often investigate people in the context of their lives to answer epistemological

questions about the nature of truth and knowledge, and ontological questions about

‘being’ in relation to human judgments and actions (Somekh, 2005). Every research

design must consider the epistemological, ontological, and methodological

expectations of the study since the research paradigms are underlined by the

philosophical assumptions of the researcher (Crotty, 1998). Research paradigms are

Research Design 38

the basis of any research design and inform the strategy, method, and data collection

(Strang, 2015). In social sciences, most widely used research paradigms include

positivist, interpretive, and critical social science (Neuman, 2014). All three

paradigms are outlined in Table 3.1.

Pinpointing the research paradigm of the research assists in designing a

suitable methodology to answer the research questions (Neuman, 2014). This thesis

adopts a positivist epistemological approach that emphasises empirical observations

through rigorous scientific methods, and is value-free. The positivist approach is

theory-driven and is concerned with predicting future patterns with human actions

(Neuman, 2014). In line with the epistemological positivist approach, a realist

ontology is applied with the assumption that the world exists independently without

direct observation of it, but if a phenomenon is in existence, it can be discovered with

its observational laws that naturally exist (Barker, 2016; Neuman, 2014). A positivist

approach is a foundation for quantitative, descriptive research methodology that is

utilised in this study with an online survey design which aims to test two hypotheses

and examine relationships between variables using quantitative methods (Barker,

2016).

Research Design 39

Table 3.1

Research paradigms in social sciences

Positivist social science Interpretive social

science

Critical social science

Ontology:

Nature of

being and

understanding

what

constitutes

reality.

One logic of science;

Value-free;

Stable;

Reality exists in a

pattern and exhibits in a

natural order which is to

be discovered.

Nominalist ontology;

Social reality is a

perception of people

as they experience and

assign meaning to it;

Reality is fragile and

ever-changing;

Social world is a

social construction.

Reality consists of

multiple layers and is

socially constructed and

not permanent;

Reality is constructed from

cultural beliefs, social

interactions, and

subjective experiences.

Epistemology:

Nature of

truth and

knowledge.

Objective;

Causal laws;

Empirical facts;

Intersubjectivity.

Subjective;

Voluntarism;

Natural attitude;

Common sense.

Value-mediated;

Changing Structures;

Internal contradictions.

Methodology:

How to find

the

knowledge.

Quantitative approach;

Hypothesis verification;

Surveys and experiments

used for data collection.

Hermeneutical;

Qualitative;

In-depth interviews,

focus groups, and

observation used for

data collection.

Dialectic;

Utilises action research

where researcher works to

fix injustices uncovered by

the research project.

Sources: Babbie, 2015; Neuman, 2014; Somekh, 2005; Walter, 2018

3.3 QUANTITATIVE AND QUALITATIVE METHODS

In social sciences, there are two fundamental research approaches –

quantitative and qualitative enquiry. The debate between these methods has been

evident since the rise of qualitative approaches in the 1960s, with quantitative

researchers defending their position much like the researchers championing for

Research Design 40

qualitative methods (Punch, 2014). Qualitative and quantitative research approaches

are different, but also complimentary to one another with qualitative enquire used in

exploratory research to assist in creating constructs for quantitative analysis in a

mixed methods research design (Neuman, 2014).

Qualitative data is usually produced by exploratory research. It is commonly

evidence in words, pictures, symbols, or a narrative form to assist in understanding

the participants own, genuine experiences. Qualitative data can be used to increase

the knowledge of a new or under-researched phenomenon, people, groups, or settings

(Ruane, 2005). Exploratory research can be conducted with one-on-one interviews,

focus groups, observation, case studies, and fieldwork. The sample sizes are often

small and allow for the researcher to get first-hand information from their

participants (Ruane, 2005). Qualitative research is often in the interpretive or critical

social science enquiry, whereas quantitative research adopts a positivist approach

(Neuman, 2014).

Unlike qualitative data, quantitative data deals with precise measurement of

variables to test hypotheses, often linking causal connections (Neuman, 2014).

Quantitative methods are frequently aligned with descriptive research that seeks to

find detailed information about a setting, group, or social phenomenon. It seeks to be

an accurate description of the phenomenon under investigation and utilises exact

strategies in measurement and sampling to drive the research design. Quantitative

methods often produce numerical results and categories to summarise much larger

amounts of data than qualitative enquiry. Quantitative data formed from descriptive

research often answers the question of what and who, and describes the size of the

phenomenon (Ruane, 2005).

Research Design 41

In line with descriptive quantitative research, this thesis seeks to find

descriptive link of licensing as a phenomenon within the realm of farmers’ markets,

attempts to identify what the consequences of patronage are, who is likely to engage

in the behaviour, and what diminishes its impact. Therefore, a quantitative

descriptive approach was adopted in completion of this study.

3.4 RESEARCH DESIGN

A research design is a plan that guides the study, on all its layers including the

philosophical approach, strategy, method, and data collection and analysis techniques

(Strang, 2015). For rigorous research, it is of uttermost importance to consider the

details of the study from beginning to end. This allows the researcher to organise a

study in a way that maximises the possibility of compiling the right evidence to

answer the research questions under examination (Gorard, 2013). The philosophical

approach describes the researcher’s worldview and way of thinking, whereas strategy

includes the formulation of goals, research questions, and hypotheses along with unit

and level of analysis. This includes decisions on deductive or inductive research

goals which in turn inform generalisability of results with quantitative or qualitative

data measurement strategies (Strang, 2015). The research questions and hypotheses

for this study were formulated from existing theory and therefore employed a

deductive approach. With the philosophical approach being positivist and strategy

deductive, a quantitative data measurement strategy was used to test and describe

observations.

The method of research is chosen appropriately after considering philosophical

approach and strategy and aligning method accordingly. It is related to the unit of

Research Design 42

analysis – specifying relationships of interest, group association, types of data, and

research questions and hypotheses (Strang, 2015). As a positivist, descriptive study

examining within-group variables, this study collected cross-sectional data with an

online survey to test hypotheses formulated from theory. Data collection and

analysis, including sampling technique, descriptive statistics, and inferential statistics

required must also be planned as a part of the research design to ensure rigorous

results (Strang, 2015).

This study employed a non-probability convenience sampling strategy with

self-selection utilising a research panel (Walter, 2013). The sample was collected

with Amazon’s Mechanical Turk (MTurk) research data panel in the US, which

became the sampling frame of the study. Within the panel, all US-based members

had access to the listing, and they could choose to enrol in the study. It was important

to employ a sampling strategy that allowed for investigation of US-based farmers’

markets patrons due to the size of their industry which is larger than in other Western

countries, and therefore a research panel was deemed as the most effective and

economic option to access large amounts of data to investigate farmers’ markets

patronage outcomes and licensing. Some studies suggest that internet-based surveys

are accurate compared to other sampling methods, but a selection bias can be a

problem for reliability of results because of non-random sampling. In research

panels, participants choose to participate in a study rather than being a part of a

random selection process, thus making the sampling a non-probability method based

on convenience sampling and self-selection (Yang, Donovan, Young, Greenblatt, &

Desroches, 2015). However, with MTurk, to determine the reliability and validity of

MTurk data in the US, multiple studies have compared the data against existing valid

panels such as online American National Election 2008–2009 Panel Study (ANEPS)

Research Design 43

and the Current Population Survey and the American National Election Studies

(ANES). The demographic data of MTurk in comparison to other survey platforms

was slightly skewed towards more females and single people who rent rather than

own, but from an attitudinal point of view, the ANES sample responded similarly to

MTurk participants (Sheehan, 2018).

Finally, the data was analysed utilising a regression-based analysis (Hayes

Process Model; Hayes, 2018) which is discussed further in Chapter 4: Data Analysis.

3.5 DATA COLLECTION TECHNIQUE

Due to the descriptive quantitative nature of this research, an online survey was

chosen as the preferred method for its ability to answer the research questions.

Descriptive research allows for the investigation of numerical features of a

population and online surveys are used to collect large samples to effectively

describe the characteristics of a large population (Babbie, 2015). An online survey

was deemed as the most suitable method for data collection of a large sample, thus

increasing reliability of the results.

To complete the study, an online survey methodology was chosen due to its

ease and availability of access to a representative sample of participants in the US

where farmers’ markets are experiencing the most growth. It is integral to utilise a

survey method that allows for a large sample to be obtained without relative

complications. Surveys overall are very valuable when describing characteristics of a

big population. It is important to obtain large numbers of participants when utilising

a descriptive research methodology where several variables are present to be

analysed concurrently (Babbie, 2015). Utilising a survey methodology distributed

Research Design 44

online allows for easy distribution and a quick response time. Also, with the rise of

smartphone technology, participants can be reached at any location at any time,

which increases convenience for partaking in research (Evans & Mathur, 2018). It is

also possible to remove non-response error in the questionnaire by coding online

surveys in a way that does not allow access to following sections without fully

completing all previous questions. Additionally, for data analysis purposes, an online

survey method allows for the data to be stored and analysed without input of data to

a separate program, thus reducing the possibility of errors when data is transferred

from one format to another, such as from paper to statistical program (Babbie, 2015;

Evans & Mathur, 2018).

Another possible method for data collection includes an experimental design.

There are different experimental designs, including pre-experimental design, true

experimental design, and quasi-experimental design. Overall benefits of this method

include ability to make generalisable causal claims and rigour of the method itself.

Experimental designs often include a control group which allows for the researcher

to establish whether the intervention is truly causing the change (Babbie, 2015).

However, given that for this descriptive research design it is integral to discover how

licensing changes across regulatory focus (promotion / prevention) levels, utilising

existing scales of promotion and prevention and applying them through a survey

allows for investigation of point of change. With experimental research, the

manipulation would allow to discover differences in high or low regulatory focus

levels, but it would not reveal the point of change. Therefore, for the first study to

investigate licensing in a farmers’ markets setting, surveying participants utilising

scales was decided upon to access detailed information about levels of promotion or

prevention required to detect a change in behaviour.

Research Design 45

Alternatively, other survey methods would include in-person interviews,

telephone interviews, or focus groups (Babbie, 2015). These options were abandoned

in favour of an online survey due to the large quantity of participants required and

personal interviews or focus groups would have been too time consuming and costly

to conduct.

3.6 SAMPLING APPROACH

A sample is a smaller subset of a population of interest. A sample is utilised to

investigate a larger population of interest which would be difficult to research in its

entirety (Ruane, 2005). Therefore, a smaller number of participants can be drawn

from the population and representative samples can be used to convey precise

information about the entire population of interest, thus generalising the results

(Babbie, 2015; Ruane, 2005). The sampling approach defines the process of which

the sampling is conducted with, starting with a unit of analysis, target population,

sampling frame, size, and finally, the sampling technique.

3.6.1 Unit of analysis

In social research, the unit of analysis is often identified on an individual

level, as opposed to a group-level. The individual description of the unit of analysis

is used to form a composite picture of the group representation of the individual

(Babbie, 2015). The unit of analysis of this study was individual farmers’ markets

patrons. The participants’ minimum age requirement was 18-years of age and they

Research Design 46

were required to have attended farmers’ markets within the past 12 months to

appropriately be able to reflect the experience.

3.6.2 Target population

Target population is the specifically defined population of interest on to which

the findings are going to be generalised (Babbie, 2015; Neuman, 2014). Based on

previous research on farmers’ markets patrons in the US, the consumers more likely

to attend farmers’ markets are female (64% - 77% depending on study), in their 30s

or 40s, with an average household income of over $25,000 / year (Byker, Shanks,

Misyak, & Serrano, 2012). The target population of this study was US respondents

who have attended farmers’ markets in the past 12 months. This definition rejected

participants who were aware of but had not attended farmers’ markets as recently as

within 12 months, but the distinction was important for two reasons. Firstly, with the

research questions enquiring specifically about licensing in the context of farmers’

markets as opposed to any other prosocial shopping behaviour, it was important to

ensure the individuals who took part in the survey had attended farmers’ markets.

This warranted that they had prior knowledge on the topic but also that they were

able to recall their prosocial behaviour through personal experience. Secondly, when

being prompted to write reviews on their most recent farmers’ markets experiences,

it is important that the participants had a recent memory of their patronage, so that

they can accurately choose to describe or not describe the event for the licensing

condition to operationalise if applicable.

Research Design 47

3.6.3 Sampling frame and recruitment method

The sampling frame for the study was the Amazon Mechanical Turk (M-Turk)

database members in the USA who were over 18-years of age who had visited

farmers’ markets in the past 12 months. The crowdsourcing platform identified the

participants who received an opportunity to take part in the survey based on their

location. All M-Turk participants are required to be over 18-years of age, so this

criterion was automatically met when using the program. Compared to student

samples, M-Turk samples are considered more diverse (Hulland & Miller, 2018) and

therefore non-probability sampling was employed with the study being distributed in

the program to their US based workers.

3.6.4 Sample size

An existing meta-analysis on licensing by Blanken et al. (2015) has noted that

sample sizes in some studies are not large enough to provide conclusive evidence on

the effect. To respond to calls in the licensing literature to ensure a sufficient sample

sizes, it is of uttermost importance to utilise a survey tool that allows for a collection

of a large sample. A recommended a sample size of at least 165 participants per cell

for a significant effect, thus a larger sample size (minimum N=200) was sought to

ensure that the rigour was held up for analysis of adequate results (Blanken et al.,

2015).

3.6.5 Sampling technique

The two main sampling methods are probability sampling and non-probability

sampling. Probability sampling allows for representativeness of the results due to

Research Design 48

sample being drawn at random with each member of the population having an equal

opportunity to participate (Babbie, 2015). Probability sample is utilised to draw

inferences from a population with using relatively small samples due to their

representativeness of the population. It also diminishes researcher bias by removing

any influence that the researcher might consciously or subconsciously have when

collecting their sample, since the sample is drawn at random. For a probability

sample, a complete list of all elements in the population of interest is drawn into a

sampling frame. Then participants are randomly selected from the sampling frame

and every person in the population has an equal chance of being selected (Walter,

2013). For the purposes of this thesis, there are no complete lists of every farmers’

markets patron in the US and creating one would have outside the scope of this

research project, therefore, a non-probability convenience sampling technique was

employed through MTurk research panel.

Non-probability sampling is appropriate to use in survey research when it is

impossible to obtain a probability sample (Bernard, 2013). There are different types

of non-probability sampling, including purposive, snowball, quota, and convenience

sampling. Purposive sampling includes choosing the sample systematically based on

what is known about the target population whereas snowball sampling asks

participants to suggest other possible respondents to the study and is often used with

groups that are not readily accessible. Quota sampling uses quotas to fill in order to

draw a representative sample that satisfies the characteristics of the target population.

Convenience sampling is used to approach people that are easily accessible, such as

friends, relatives, classmates, or research panels and is a low-cost sampling method

for gathering participants, however, there are validity questions with using this

sampling method as it does not necessarily lead to a representative sample (Walter,

Research Design 49

2013). Therefore, it is important to diligently design the data collection procedure to

ensure validity of the results.

3.7 DATA COLLECTION PROCEDURE

In deciding the location of the data collection, the size of the industry across

Western countries was considered in order to best investigate the effect in a country

where farmers’ markets were well established. Farmers’ markets are a growing

industry across the Western world, yet, the industry in the United States of America

is the largest by economic contribution and market numbers, therefore, the research

was completed within the USA (Abelló, Palma, Waller, & Anderson, 2014; Garner,

2015; USDA, 2018b). Online surveys can be administered across countries, and due

to the specific characteristics of this research, with the USA chosen as the destination

for data collection, an online survey will ease the data collection (Babbie, 2015).

Following ethical clearance, to answer the research questions, a cross-sectional

online survey utilising Amazon Mechanical Turk (MTurk) research panel members

in the USA was distributed. The survey was built using Qualtrics software (Qualtrics,

2018) and MTurk was chosen as the preferred data collection method due to its

potential to access large samples economically. MTurk allows for a greater

participant diversity than some other samples, such as university undergraduates, and

this may assist in improving internal and external validity of the results (Hulland &

Miller, 2018). Additionally, MTurk panel members are rated according to their

performances by researchers after a task has been completed, therefore the members

are often seen as conscientious and willing to complete tasks assigned (Hulland &

Miller, 2018). However, the inherent danger in utilising MTurk is participant

Research Design 50

misrepresentation for financial gain, and thus participants may be dishonest about

their qualification to the study and in their responses (Hulland & Miller, 2018). To

minimise this possibility, the participant information sheet outlined the topic of the

research and a screening question was employed to assist in only a relevant sample

of farmers’ markets patrons being studied. The screening question specifically asked

potential participants whether they had visited farmers’ markets in the past 12

months, to ensure that only patrons with knowledge on the topic were invited into the

study. For additional completion safeguards and to remove non-response error,

questions (except the licensing condition analysing cheating behaviour) were locked

so that all questions had to be answered before being able to move into the next

section (Babbie, 2015).

3.7.1 Survey distribution

There are several online survey tools available for creating and distributing an

online survey. Different software programs offer different advantages but overall, all

programs allow for customisability of the survey and functions that improve the

validity of the results compared to pen and paper research methods. This is due to

online survey functions that enable to request complete responses, randomise

response fields, and uniform formatting (Fielding, 2017). The online survey tools

create a unique URL to the study that can be distributed to participants. The program

then tracks the responses and stores them securely on its server. They often offer

basic analysis techniques as well, but the data can be downloaded directly to a data

analysis program in the file format required, thus reducing data input errors (Babbie,

2015).

Research Design 51

The online survey for this study was built and hosted with Qualtrics software

(Qualtrics, 2018). A specific URL to the survey was distributed online to MTurk

research panel members in November 2018 and the results were exported directly to

SPSS 23.0 for analysis.

3.7.2 Survey design

To begin the survey, the participants were first presented with a short definition

of farmers’ markets:

“According to USDA, a farmers’ market is a place where two or more

farmer-producers sell their own agricultural products directly to the general

public at a fixed location. These products include fruits and vegetables, meat,

fish, poultry, dairy products, and grains” (USDA, 2018a).”

This was followed with a screening question: “Have you made a purchase at a

farmers’ markets in the past 12 months?” Participants who answered “No” were

screened out of the survey. The survey included questions about the participants’

farmers’ markets patronage, such as frequency of visit, amount spent and ranking

reasons for attending, such as, “support local community and economy”, “purchase

produce that is fresher and healthier”, “socialise”, “meet the farmers producing the

food”, and “save money on produce” (Leiper & Clarke-Sather, 2017; McEachern et

al., 2010; Tey, et al., 2017). These response options were randomised to ensure

participants did not receive the same ranking order to improve validity of responses.

The survey then continued to self-image measurements by utilising a scale

based on a compassion and self-image scale developed by Crocker & Canevello

Research Design 52

(2008). The survey items measured compassion and self-image and the original

survey was amended to better fit the purposes of this scope of the study. Original

survey specified its use to the area of friendships and this was amended to reflect the

area of purchases at a farmers’ markets to apply it to this specific context. The

participants were asked to review statements that started with this sentence:

“Through the purchases that I made at a farmers' market I tried to...”. Questions

included items such as: “…make a positive difference in someone else’s life” and

“…avoid the possibility of being wrong” (full survey and scales can be found in

Appendix A) which were reviewed on a 5-item scale (not at all – always). Utilising

the self-image scale assists in determining the participants’ level of positive self-

image and altruistic behaviour allowing to test this against any subsequent deviant

behaviour of cheating that might be due to farmers’ markets attendance.

To test for the impact of licensing and answer research question 1 (RQ 1: Does

farmers’ markets patronage lead to unintended behavioural and attitudinal outcomes

in licensing in-group members to cheat from other in-group members?) the

participants were requested to write six reviews of their most recent farmers’ markets

experiences with no less than three full sentences. They were advised that their

responses would be anonymous and randomised so the researcher could not connect

responses to any specific participants. Instructions advised them that for every

review they complete in full, they receive an extra $0.15. After submitting their

reviews, they had no option to return to the previous window.

Following the submission of their reviews, the participants received an

instruction advising that due to the reviews being anonymous, they need to indicate

how many reviews they completed in full so that they can be accurately

compensated. Participants were advised that their honesty is important since should

Research Design 53

they give an inaccurate answer, another participant would not receive their reward.

This instruction tested their willingness to cheat in-group members after being made

aware of the consequences of any dishonesty. The data from this section of the

survey is investigated in relation to any potential cheating within an in-group that

occurs as per the licensing condition. This will assist in answering research questions

1 and either accepting or rejecting hypothesis 1.

Following this, to address research question 2 (RQ2: Do individual differences

in promotion and prevention have an impact on licensing behaviour in a farmers’

markets context?), scales measuring regulatory focus were employed to test for

individual differences. For additional potential mediators and covariates, data from

involvement (situational) (Zaichkowsky, 1985), and sense of entitlement (Campbell,

Bonacci, Shelton, Exline, & Bushman, 2004) scales are collected. The short social

desirability scale (Vésteinsdóttir, Reips, Joinson, & Thorsdottir, 2017) was employed

to validate self-report measures before the final section of demographic questions.

The full survey can be found in Appendix A.

3.8 DATA ANALYSIS PLAN

To draw from relationships between variables, the data analysis plan for this

study was a mediated regression analysis with a moderator utilising Hayes Process

Model Macro with SPSS 23.0 (Hayes, 2018). A regression analysis in its basic form

is a data analysis method that assists in defining specific function of the dependent

variable (IV) relating to the independent variable (DV) (Babbie, 2015). With a

mediated regression, the goal is to confirm how the IV contributes to the DV with an

intervening variable located between the IV and the DV mediating the relationship

Research Design 54

(Hayes, 2018). In this case, how the frequency of farmers’ markets patronage (IV) as

a prosocial deed then influenced their positive self-image (mediator) and in turn lead

to higher cheating behaviour (DV). Additionally, the impact of the moderating

variables of promotion and prevention focus were tested for individual differences in

cheating. A moderator impacts the extent of a causal effect between the IV and the

DV along with the effect of the mediator and this mediated-moderation is described

as conditional process analysis (Hayes, 2018). It is concerned with looking at the

“estimation and interpretation of the conditional nature (the moderation component)

of the indirect and/or direct effects (the mediation component) of X on Y in a causal

system” (Hayes, 2018, p. 11). The model for this study included both a mediation

and moderation effect and therefore the conditional process analysis was deemed as

the most effective and appropriate method for analysing the research questions and

testing the hypotheses.

3.8.1 Reliability and validity

To maintain robust research practises, it is integral to include a well designed

and developed survey representative of validity and reliability. There are two types

of validity to consider, internal and external validity. Internal validity is the

confidence in the claims made with the research and is improved with robust

research practises and methodical control of the variables, sampling, and research

design overall (Crano, 2015). External validity in turn refers to how generalisable the

results are in different settings, time, and people (Crano, 2015). Overall, validity

establishes whether the way measurement was completed accurately reflects its

intended measures (Walter, 2013). Reliability then refers to the consistency of the

Research Design 55

results and whether repeating the same study would lead to same results (Walter,

2013).

For this study, scales and data collection techniques were used to increase the

validity and reliability of the results. All scales used in this study were already

established and high in internal validity. Additionally, to increase a likelihood of true

responses and thus the internal validity of the study, a short version of the Marlowe-

Crowne social desirability scale was utilised to authenticate self-report measures

(Vésteinsdóttir, et al., 2017). Social desirability is a serious threat to survey research

due to responders occasionally reporting responses that paints them in a different,

more socially desirable light especially when participating in questions around being

a good citizen or fulfilling moral responsibilities. In these instances, their responses

may be inflated to appear more in line of what they believe they should do, rather

than what they actually do (Brace, 2013). Social desirability can be managed with

measuring its impact through a social desirability scale that allows for inspection of

the survey results to investigate whether this could become an issue with internal

validity of the results.

Reliability can be achieved with more clear-cut guidelines than validity due to

survey research presenting all responders with the same information, and same

questions. This allows for elimination of unreliability better than many other

methods (such as observation, or qualitative methods of interviews and focus groups)

that may be subject to researcher bias (Babbie, 2015). To control for reliability in the

survey, all participants received the same set of questions in the same order.

Additionally, the survey software, Qualtrics was set up to prevent ballot box stuffing

which prevents the same participant taking the survey multiple times. The location of

the respondents was controlled through M-Turk, where it was specified that the

Research Design 56

participants must be from the US. This ensured reliability of the location-based

responses to ascertain only data from the target country was included.

3.9 ETHICAL CONSIDERATIONS

Ethics are an important component to consider for any research project.

Research should hold up to rigorous ethical standards and obligations and prior to

collecting data, it is important to consider the potential ethical issues of the study to

ensure that high ethical standards are held to minimise harm to participants while

also maximising the benefits of the research (Walter, 2013). Prior to commencing

data collection, an ethics approval was obtained from University Human Research

Ethics Committee, QUT Ethics Approval Number 1800001044. Before beginning the

survey, the participants received a “participant information sheet” that outlined the

basic purpose of the study (“to investigate outcomes of attending a farmers’

market”), expected benefits, information about the survey and types of questions it

entailed, as well as an assurance of confidentiality. The research team had no access

to the contact details of the individual participants and the responses remain

anonymous. The contact details for the research team were included for any

questions and concerns and the participants were advised that they were able to

withdraw at any time. Additionally, the information for US based suicide telephone

and text message line were included due to the sensitive nature of some of the

questions in the survey.

To investigate the effect of licensing, the true purpose of the study instructing

the participants on the use of the theory was not disclosed at the beginning of the

survey. However, at the very end of the survey, they were debriefed: “This study is

Research Design 57

investigating farmers' markets, specifically testing the effect of licensing theory on

a prosocial shopping context”, outlining the theory and advising that it was important

to not disclose this information at the beginning to accurately measure the impact of

licensing. The participants were then given an opportunity to withdraw from the

study before submitting their responses. Only fully completed data was used as a part

of this study.

3.10 SUMMARY

This chapter provided a detailed overview of the research methodology

employed in this study utilising a positivist paradigm choice and a descriptive

quantitative methodology with a cross-sectional online survey as an instrument of

data collection. This chapter outlined the online survey research design and the

MTurk data collection method including the sampling technique that was based on a

non-probability convenience sample with self-selection. Considerations of data

collection were discussed and the regression-based data collection procedure of

Hayes Process Model Macro using SPSS 23.0 was introduced along with

confirmation of robust ethical practises employed as a part of this study. The

following chapter 4 will dive into the data analysis of the study.

Data Analysis 59

Chapter 4: Data Analysis

4.1 INTRODUCTION

This chapter discusses the data analysis process and results from the online

survey, to address the developed hypotheses. This chapter illustrates the treatment

and preparation of data, in order to conduct the necessary analyses.

4.2 DATA COLLECTION

The data was collected via an online survey completed with Qualtrics, and with

M-Turk participants in the USA in November 2018. Participants received a reward of

US$1 for completion of the survey, with opportunities to earn an extra US$0.15 per

completed review (up to 6 reviews). Participants were first subjected to a screening

question to confirm that they indeed had visited farmers’ markets in the past 12

months. Collection was completed in three different batches and each round of data

collection was completed within 24 hours. The benefits of choosing an online survey

over other options include quick data collection, immediate feedback, ease of access

to data via Qualtrics, and location-independence (Babbie, 2015).

4.2.1 Data security

All the data gathered was anonymous so individual participants could not be

identified. Data was downloaded from Qualtrics to SPSS 23.0 and securely stored on

a private server located within the university. The access to Qualtrics and the server

Data Analysis 60

where the SPSS files resided were password-protected. Data was only accessible by

three people, the student and the supervisory team.

4.3 TREATMENT OF DATA

The complete dataset was downloaded from Qualtrics and imported directly to

SPSS 23.0. The responses were therefore already loaded at appropriate columns and

organised per question. This reduced data input error since the data needed only

minimal preparations after responses were gathered. The completed data output was

visually inspected for non-response and all responses were deemed valid due to

forced responses as a part of the survey design (Babbie, 2015). The full sample

collected for the study was N = 266. Some cases from the full dataset had to be

removed to ensure validity and final sample size of N = 218 was obtained. Deleted

cases are discussed below.

4.3.1 Duplicate IP-Addresses

The IP-Address and location data was inspected to check for duplicates.

Precautions to avoid duplicate responses were taken. These included a Qualtrics

command to prevent ballot box stuffing (Qualtrics, 2018) and instruction advising

participants not to partake more than once. However, due to the survey being

distributed at different times, there were 34 duplicate IP addresses which were

reviewed carefully. Upon inspection it became evident that many of the responses

were similar and therefore they could be either two or three entries by the same

person. In order to ensure validity, it was decided that all the 34 cases should be

Data Analysis 61

removed. This is a criterion often used when working with an MTurk sample

(Pauszek, Sztybel, & Gibson, 2017; Savani & King, 2015; Walters, Christakis,

Wright, & Alamian, 2018).

4.3.2 Outliers

Outliers can skew the data and detecting them becomes important to ensure this

does not happen (Wilcox, 2017). After removing duplicate IP-addresses from the

dataset, the response for average spend per visit to a farmers’ market ranged from

US$2 to US$80,000. The mean for an average spend per visit to a farmers’ markets

was US$444.34 (SD = US$5250.51). However, extant literature indicates that

consumers spend on average US$30 per visit (Morckel, 2018; Sadler et al., 2013).

Additional descriptive statistics revealed that spend per visit was non-normally

distributed, with skewness of 15.19 (SE = .16) and kurtosis of 231.14 (SE = .32). For

a normal distribution, skewness numbers between -3 and +3 and kurtosis value of 3

are typical (Young, 2017). With the light of these statistics, a mean absolute

deviation was calculated to detect possible outliers. The mean absolute deviation

score of -.88 was obtained. The cases above this threshold indicated spend per visit

of over US$500 and were therefore deemed as outliers and removed. With this,

skewness of 2.56 was achieved, with kurtosis also reduced to 6.92. Final sample size

is N = 218.

4.3.3 Data preparation

Once data was cleaned as discussed above (N=218), the individual reviews

were inspected and coded appropriately. Every review was visually checked against

Data Analysis 62

the response on how many reviews the participants claimed to have fully completed.

The coding for cheating behaviour was 0 = No for not cheating and 1 = Yes for

cheating. Cheating behaviour was determined on the basis of completion of three full

sentences in each review as per the instruction. A sentence was defined with Oxford

English Dictionary definition of the word “sentence, noun” which defines it as:

“A series of words in connected speech or writing, forming the grammatically

complete expression of a single thought; in popular use often (…) such a portion of a

composition or utterance as extends from one full stop to another. In Grammar, the

verbal expression of a proposition, question, command, or request, containing

normally a subject and a predicate (though either of these may be omitted by

ellipsis). In grammatical use, though not in popular language, a ‘sentence’ may

consist of a single word, as in Latin algeo ‘I am cold’, where the subject (= I) is

expressed by the ending of the verb” (Oxford English Dictionary, 2018).

To ensure participants were clearly aware of the requirements of the task, the

following instruction was presented, “Please write six reviews of your most recent

visits to your local farmers’ market. Each review must be no less than three full

sentences”. Any review that did not meet the requirements of a sentence was

categorized as cheating behaviour and coded 1. Additionally, some responses for the

reviews were copied from online sources. This was evident from the type of

language used in these responses and was confirmed with an online search. The

online search was conducted by entering the sentence into Google search engine in

parentheses to see the source. Overall, 13 cases were discovered to be from various

websites either discussing farmers’ markets or other websites. These were also

deemed as cheating and coded 1.

Data Analysis 63

Additionally, some manipulation of the data was conducted due to survey items

including reverse-coded questions. These were items in regulatory focus scale and

frequency of visitation. For accuracy of analysis, the items in regulatory focus scale

were reverse-coded where necessary. Also, the frequency of visitation scores were

reverse-coded from 1 = Once a week, 5 = Less often than monthly to 1 = Less often

than monthly, 5 = Once a week. Once this was completed, composite scores for each

of the scales required were created for data analysis.

4.4 SAMPLE CHARACTERISTICS

Valid respondents were those identified as over 18-years old, who had visited

farmers’ markets in the past 12 months (N = 218, 89 female / 129 male, SD = .493).

Overall, the sample consisted of 40.8% females and 59.2% males. Majority of the

sample were between 25-35 years of age with 58.7% included in this category.

Furthermore, 16.1% were between 36-45 years, 11% 46-55 years, 7.8% 18-24 years,

5.5% 56-65 years, and 0.5% either 66-75 years or over 76-years of age. 25.2% of

participants indicated attending farmers’ markets weekly, with 24.8% attending

every two weeks, 10.6% every three, and 17.9% every four weeks. 21.2% indicated

attending less often than monthly. The average spend in one visit to the farmers’

market was $52.07 (SD = $56.66) with an average annual household income of

$30,001-$50,000. The majority of the responders were married with 51.8%

indicating so, and 33.9% were single. Additionally, 34.9% of responders were living

with their partner / spouse and children, and 29.4% with their partner or spouse.

Political orientation was measured on a scale from 1-10 between left-wing (1) and

right-wing (10) orientations was at a mean of 5.21 (SD = 2.86) (Napier & Jost,

Data Analysis 64

2008). Scale measuring political orientations between -5 to +5, extremely liberal (-5)

to extremely conservative (+5) presented a mean result of -1.78 (SD = 3.26)

(Amodio, Jost, Master, & Yee, 2007). Summary of sample characteristics can be

found from Table 4.1.

Data Analysis 65

Table 4.1

Sample Characteristics Characteristic (N=218) N (SD) %

Gender

Female

Male

Other

89

129

0

40.8

59.2

0

Age

18-24 years

25-35 years

36-45 years

46-55 years

56-65 years

66 – 75 years

76+ years

17

128

35

24

12

1

1

7.8

58.7

16.1

11.0

5.5

.5

.5

Annual income

Less than $30,000

$30,001 - $50,000

$50,001 - $70,000

$70,001 - $90,000

$90,001 - $110,000

Over $110,001

Prefer not to answer

36

59

47

34

20

20

2

16.5

27.1

21.6

15.6

9.2

9.2

.9

Marital status

Single

Married

Cohabiting

In a relationship

Other

74

113

17

13

1

33.9

51.8

7.8

6.0

.5

Household status

Live alone

Live with partner/spouse

Live with partner/spouse

and children

Live with children

Live with others

48

64

76

12

18

22.0

29.4

34.9

5.5

8.3

Average spend at farmers’ markets per visit $52.07 ($56.66) -

Frequency of attending farmers’ markets

Less often than monthly

Every four weeks / monthly

Every three weeks

Every two weeks

Once a week

47

39

23

54

55

21.6

17.9

10.6

24.8

25.2

Political orientation scale of 1 to 10 (Napier & Jost, 2008) 5.21 (2.86) -

Political orientation scale -5 to 5 (Amodio et al., 2007) -1.78 (3.26) -

Data Analysis 66

4.5 HYPOTHESIS TESTING

After inspecting, cleaning, and recoding the data, the responses were subjected

to logistic regression analyses using the PROCESS Macro for SPSS (Model 4 & 8;

Hayes, 2018). Regression analysis assists in explaining the relationship of dependent

variable (DV) in terms of variation in the independent variable (IV) and whether DV

follows on from the impact of IV (Babbie, 2015). Licensing effect was investigated

separately first with Model 4, and individual differences in regulatory focus

orientations – promotion and prevention – were analysed after with Model 8.

Descriptive statistics are discussed next, before data analysis of hypotheses 1, 2 and

3.

4.5.1 Descriptive statistics

Cronbach’s alpha and correlations

Cronbach’s alpha is a measurement that indicates the internal consistency of

the scale items used when collecting data. The measurement assists in determining

the reliability of the scales used. A Cronbach’s alpha was determined for variables

using a scale measurement and self-image and prevention coefficients are over the

very stringently advised cut-off point of α > .7 (Cronbach, 1951) and are therefore

high in reliability. Promotion score of α > .6 is at the lower limit of the acceptable

threshold as recommended by Hair (2010) so it is still deemed as a reliable

measurement of its construct. The correlation coefficients presented in Table 4.2

below range from .33 to -.42. The strongest positive correlation is between

promotion and prevention at a moderate level of .33 (ns). This is to be expected with

the promotion and prevention scales both measuring goal pursuit. The strongest

Data Analysis 67

negative correlation between promotion and self-image at a level of -.42 indicates

another moderate correlation at a significance level of p < .001. This indicates that it

is possible to make predictions about a person’s self-image based on their promotion

focus. With the correlations at moderate levels, multicollinearity was not a

substantial risk to the analysis (Hair, 2010; Tabachnick & Fidell, 2013). Exact

measurements and correlations can be seen in Table 4.2 below.

Table 4.2

Descriptive data for focal variables in logistic regression

Variables Mean

(SD) Cronbach’s Alpha 1 2 3

1 Self-image 2.35 (1.21) .93 .00

2 Promotion 3.48 (.70) .62 -.42** .00

3 Prevention 3.15 (.92) .81 -.31* .33 .00

Note. Cronbach’s (1951) alpha reliability coefficients: *p < .05; **p < .01, ***p < .001

Reasons for attending farmers’ markets

Reasons for attending farmers’ markets were ranked and in the order of

importance: 121 participants ranked “To purchase produce that is fresher and

healthier”, followed by 81 responses for “To support my local community and

economy” as the second and 59 responses as the third most important reason. Figure

4.1 below shows the full ranking of reasons to attend farmers’ markets with 1 being

the most important and 5 being the least important reason to attend.

Data Analysis 68

Figure 4.1– Ranking of reasons for attending farmers’ markets with 1 being most

important and 5 least important reason to attend.

4.5.2 Regression analyses

Hypothesis 1: Licensing effect

Licensing effect occurs when a person’s prosocial actions elevate their self-

image and allow for deviant behaviour as a result. It was expected that participants

who attended farmers’ markets would indicate a higher positive self-image and

consequently engage in cheating behaviour due to this elevated self-image. It was

hypothesised that this would be true even after receiving a message about potentially

damaging their own in-group as a result of deviant behaviour. To investigate

12

1

48

24

15

10

41

81

59

23

14

34

54

57

43

30

8

21

54

92

43

14

14

24

45

12

1

1 2 3 4 5

RANKING OF REASONS FOR ATTENDING

FARMERS' MARKETS

Fresher and healthier produce Support for local economy Save money on produce

To meet the farmers To socialise

Data Analysis 69

hypothesis 1 a logistic regression analysis with dichotomous outcome variable

(cheating: 1 = yes, 0 = no) was employed utilising PROCESS Macro for SPSS,

Model 4 (Hayes, 2018). The effect of farmers’ markets patronage frequency (IV) on

cheating behaviour (DV) was analysed through positive self-image gained (mediator)

to test for a licensing effect.

Results from the analysis indicated a significant licensing effect. Firstly,

frequency of visitation to farmers’ market was a significant predictor of self-image, β

= .29; t(216) = 5.77, p = .00. R2 score of .13 indicates that self-image explains 13%

of the variation in frequency of attending. Secondly, self-image was a positive

significant predictor of cheating behaviour β = .66; SE = .14, p = .00. For the

analysis of the full model, a bootstrapping analysis with PROCESS Macro indicated

that when farmers’ markets frequency was mediated with positive self-image,

cheating as a result was significant (β = .19; SE = .06, p < .01). 95% bootstrapped

CIs to test the indirect effect of the mediator for IV and DV did not include a 0 and

were therefore significant with 95% CIs [.0954, .3234]. Thus, hypothesis 1 is

supported. Figure 4.2 below presents the path effect sizes within the mediation

model.

Figure 4.2 – Path effect sizes (β) for mediation model testing licensing (H1)

Data Analysis 70

Hypothesis 2: Individual differences

Regulatory focus of promotion and prevention impact whether a person attains

goals through positive or negative outcomes, or in other words, through maximising

gains (promotion) or minimizing loss (prevention) (Higgins et al., 1997; Zivnuska,

Kacmar, & Valle, 2017). It is expected that promotion (H2) and prevention (H3)

focus moderate the relationship between farmers’ markets patronage frequency and

positive self-image in turn leading to a weakened effect on licensing. Testing of

hypotheses 2 & 3 were completed with PROCESS Model 8 (Hayes, 2018). The two

hypotheses were investigated separately, and results are presented below.

H2 – Promotion as moderator: The model was tested through a logistic

regression with PROCESS Model 8 that included the independent variable of

farmers’ markets patronage frequency (X), dependent variable of cheating (Y; 1 =

yes, 0 = no), and mediator of positive self-image (M). Regulatory focus of promotion

was used as the moderator (W) variable to first test for individual differences. The

results were analysed with a component approach that is the most effective technique

of analysis to reduce Type I errors when compared to only observing a single index

of moderated mediation, for example a bias corrected bootstrap (Yzerbyt, Muller,

Batailler, & Judd, 2018). Thus, results from all relevant paths are included along with

the bootstrap index of moderated mediation outcomes.

First, the results of path a, X (farmers’ markets patronage frequency) to M

(positive self-image) with moderator W (promotion) showed a significant interaction

effect, β = -.13; SE = .06, p < .05. This indicates that promotion does moderate the

relationship between farmers’ markets patronage frequency and positive self-image.

Inspection of path b, M (self-image) to Y (cheating) also reveals a significant result

Data Analysis 71

at, β = .53; SE = .15, p < .001. This confirms that both paths of the moderated

mediation model are significant. Additionally, the 95% bootstrapped CIs do not

include a 0 at 95% CIs [-.1567, -.0116] which further supports the results that the

moderated mediation relationship with promotion focus as the moderator to a

licensing model is in fact significant.

To understand the intricacies of promotion orientation better, the Johnson-

Neyman technique was used and path a was inspected (Hayes, 2018). Licensing is

significant up to a promotion value of 4.44 (β = .15; SE = .08, p = .05). Figure 4.3

reveals the Johnson-Neyman output. Therefore, hypothesis 2 is supported with

licensing only significant for low to medium promotion focused participants likely to

cheat, with the effect weakening with a high promotion focus participants who do not

engage in deviant behaviour as a result of farmers’ markets patronage. Figure 4.4

presents the path effect sizes of within the mediated moderation model.

Data Analysis 72

Figure 4.3 – Frequency of attendance predicting positive self-image with promotion as

a moderator indicating effect weakening at high promotion levels.

Figure 4.4 – Path effect sizes (β) for mediation model testing regulatory focus of

promotion (H2)

Data Analysis 73

H3 – Prevention: As with promotion orientation, hypothesis 3 was tested

through PROCESS Model 8 with logistic regression and included IV (X = farmers’

markets patronage frequency), DV (Y = cheating), mediator (M = positive self-

image), and in turn, prevention as the moderator variable (W). The data was

inspected with the same component approach. Path a of X (farmers’ markets

patronage frequency) to M (positive self-image) with moderator W (prevention)

yielded a non-significant interaction result at β = -.10; SE = .05, p = .052. Therefore,

despite path b of M (self-image) to Y (cheating) being significant at β = .63; SE =

.15, p = .00, prevention as a moderator in this model is non-significant. Additionally,

cross checking the 95% bootstrapped CIs do include a 0 at 95% CIs [-.1442, .0027]

and H3 is rejected.

Other constructs measured as a part of the survey including regulatory mode,

involvement, sense of entitlement, and social desirability were deemed non-

significant, and were accordingly not included in further analysis.

With the analysis of the data, it is evident that H1 and H2 are supported with

H3 rejected. The implications of this evidence presented will follow in chapter 5.

4.6 CONCLUSION

After collection, the data was subjected to rigorous inspection, cleaning, and

removal process to ensure validity of the responses. These included inspections if IP-

addresses and removing cases that indicated a spend per visit with mean absolute

deviation score of -.88 and above. The final sample size was N = 218. Logistic

regression with Hayes PROCESS Model Macro for SPSS was conducted to test

hypothesis 1 (Model 4; Hayes, 2018) and hypotheses 2 and 3 (Model 8; Hayes,

Data Analysis 74

2018). Support for hypotheses 1 and 2 were found and they were thus confirmed.

This implies that licensing effect occurs with farmers’ markets patronage in an in-

group setting and promotion is a significant moderator with the effect of licensing

weakening with a high promotion focus. Hypothesis 3 was rejected as prevention

focus does not appear to act as a moderator. Chapter 5 will include the discussion,

limitations, and future research of this analysis.

Discussion and Conclusion 75

Chapter 5: Discussion and Conclusion

5.1 INTRODUCTION

This chapter discusses the theoretical and practical contributions of this

research with its limitations and recommendations for future research. First, a

summary of findings is presented to show that this research has answered the

research questions and hypotheses presented. This is followed by implications for

theory discussing the farmers’ markets patronage outcomes of licensing, in-group

behaviour, and promotion focus as a moderator. Practical implications will be

reviewed, and this chapter concludes with an evaluation of its limitations and

recommendations for future research.

5.2 SUMMARY OF FINDINGS

This study investigated whether frequenting farmers’ markets has an impact on

subsequent deviant behaviour within an in-group setting as explained by licensing.

Additionally, individual differences in promotion and prevention regulatory focus

were examined as possible moderators to this behaviour. The final sample size of the

study was N = 218 (89 female / 129 male). The ranking of reasons to attend farmers’

markets was inspected with majority of respondents indicating that they attend

farmer’s markets “To purchase produce that is fresher and healthier”, with “To

support my local community and economy” as the second and third most important

reason for patronage. This indicates the activation of an in-group specific deviance

that allows for inspection of the data in the framework of in-group deviant behaviour.


Results from the analysis indicated a significant licensing effect. Participants

frequenting farmers’ markets gained a positive self-image which gave them a license

to cheat from their own in-group members as a result. The mediation model tested

sheds light to how prosocial behaviour at farmers’ markets can lead to unintended

deviant outcomes. As a consequence of farmers’ markets patronage, the participants

can think of themselves as moral people, and further transgressions are allowed due

to them already gaining a self-image as a “good person”. Additionally, their in-group

membership as a farmers’ markets patron did not stop the participants from cheating,

even when they knew that it can negatively impact other members of their in-group.

These results support H1 and the implications from this effect are discussed in

theoretical and practical contributions below. In addition, the impact of licensing can

vary based on individual differences and therefore, data was analysed with the

moderating variables of promotion and prevention (regulatory focus) to test whether

differences in these goal pursuit tendencies weaken the effect of licensing.

Investigation of regulatory focus of promotion (H2) and prevention (H3) as

moderators for licensing within a farmers’ markets context revealed support for H2

but not for H3. A participant with a ‘promotion’ focus is focused on ideals and

aspirations whereas a ‘prevention’ focus is dependent on duties and responsibilities

(Higgins, et al., 1997). It is evident that promotion focus has a significant

relationship as a moderator between farmers’ markets patronage and positive self-

image that then leads to weakening of deviant outcomes in terms of cheating. The

Johnson-Neyman score revealed details about the levels of promotion significance

and it was discovered that only participants that have a low to medium chronic

promotion focus exhibit a licensing behaviour with the effect becoming non-

significant for individuals with a high promotion focus. The theoretical and practical


implications of these results are discussed further below, and Table 5.1 summarises

the outcomes in relation to the hypotheses.

Table 5.1

Summary of outcomes

Research

Question

Hypothesis Outcome Implications

RQ1 H1: Supporting the

community and local

economy through

farmers’ markets

patronage leads to

unintended behavioural

outcomes through

licensing, with consumers

cheating their own in-

group members.

Supported Results indicate that licensing

is an outcome of farmers’

markets patronage. Attending

farmers’ markets gives

consumer a license to act in a

deviant manner due to

gaining a moral self-image

that allows for future

transgressions.

RQ2 H2: Individual differences

in promotion focus

weakens the effect of

licensing within a

farmers’ markets context.

Supported The impact of licensing

weakens with a higher

promotion focus. Low to

medium promotion focus

leads to deviant behaviour

with high promotion focus

weakening the effect.

RQ2 H3: Individual differences

in prevention focus

weakens the effect of

licensing within a

farmers’ markets context.

Rejected Chronic prevention focus

does not have an impact on

licensing in a farmers’

markets in-group context.

5.3 IMPLICATIONS FOR THEORY

The findings from this study contain theoretical implications to farmers’

markets in-group behaviour, licensing, and regulatory focus literature.


5.3.1 Farmers’ markets and licensing

The antecedents of farmers’ markets patronage are well investigated in the

existing literature, however, outcomes that derive from consumers shopping via

farmers’ markets are relatively unknown. Farmers’ markets are a unique prosocial

shopping channel that brings together local food producers and consumers. Support

for local food is driven by the consumers’ altruism instead of self-interest (Memery

et al., 2015). Good intentions gained from this altruism can contribute to a positive

self-image (Tonin & Vlassopoulos, 2013), further leading to deviant behaviour due

to this elevated self-image as explained through licensing.

Theoretically the first finding of this research contributes to the literature on

farmers’ markets by introducing licensing as an outcome. The extant literature is yet

to consider non-prosocial behaviour as a potential consequence for frequenting

farmers’ markets and this study is the first to test for its effect. The results from the

study indicate that attending farmers’ markets does lead to a boost in positive self-

image that subsequently is followed by deviant behaviour in terms of cheating. In

other words, attending farmers’ markets gives consumer a license to act in a deviant

manner due to gaining a moral self-image that allows for future transgressions. This

is a significant finding for the farmers’ markets literature to better understand

patronage outcomes and consumer behaviour within this prosocial shopping channel.

The results suggest that licensing can have negative implications to farmers’ markets,

making it an important variable to investigate within the farmers’ markets literature

to understand its effect.

Licensing effect has typically been investigated in related circumstances, such

as pro-environmental actions reducing environmental protection behaviours (Geng et


al., 2016). However, this study considers licensing within two seemingly unrelated

domains of farmers’ markets attendance and cheating. This distinction is important to

gain further knowledge about the wide-ranging implications of licensing within a

farmers’ markets context. The results from this study indicate that within a farmers’

markets setting, licensing can impact the consumers in other areas of their behaviour

by increasing their overall non-prosocial actions as a result of attending farmers’

markets. Therefore, the results from this study are far-reaching, since the spill over

effects from attending farmers’ markets can reach to licensing deviant behaviours in

unrelated domains. For the local economy in which a farmers’ markets takes place,

the cheating could have real monetary implications if it spills over into behaviour

such as helping themselves to produce on display, not weighing their purchases

accurately, or even adding more produce to their bag after payment has been

completed. Additionally, due to the unique characteristics of a farmers’ markets

context with its prosocial support for local community and economy, this study took

into account the in-group behaviour of farmers’ markets patrons.

5.3.2 In-group and licensing

This study is the first to consider the unintended consequences of attending

farmers’ markets and the impact of licensing effect that takes places within an in-

group setting of farmers’ markets consumers. Consumption ideals that form around a

prosocial shopping channel such as a farmers’ markets can give its in-group a sense

of belonging and support for their values of local community and economy,

integrating not only the consumers but also the producers in the community

(Spielmann & Bernelin, 2015).


Investigating licensing within farmers’ markets literature offers a unique

context to consider a prosocial shopping channel that is based on support of local

community and economy which forms an in-group that includes consumers and

producers and is based on mutual prosocial values of supporting the in-group.

Accordingly, this study also considers these characteristics by investigating the

impact of this group in a prosocial shopping context. The consumers identify support

for local community and economy as the second and third most important reason to

attend farmers’ markets. The results from this study thus indicate that this support for

the local community assist them in developing a positive self-image from their

prosocial behaviour that subsequently leads to cheating. This cheating is possibly at

the expense of their in-group.

Therefore, arguably, licensing takes place even within communities of in-

groups and this is an important theoretical distinction to make to the current body of

licensing literature that has so far investigated in-group behaviour only within a

supportive context or in-group vs. out-group scenarios. However, individual

differences also have an impact on licensing in an in-group setting with promotion

focus being a significant moderator for this behaviour.

5.3.3 Licensing and regulatory focus: Promotion and prevention

The impact of licensing as an outcome of farmers’ markets patronage is

moderated by the individual differences in regulatory focus of promotion. For

individuals with a promotion focus, ideals and aspirations are important and they are

enabled to access their goals through positive outcomes. Individuals with a

prevention focus approach goals with avoidance to negative outcomes and focusing


on responsibilities and duties (Higgins & Cornwell, 2016; Higgins et al., 1997; Park

& Ryu, 2018).

Results from the study provide support for chronic promotion focus to weaken

licensing, however, chronic prevention focus was deemed not to have an effect. It is

important to note that the effect of frequency of attending farmers’ markets on self-

image was significant for participants low to medium in promotion focus, but not for

the participants highest in this tendency (licensing is significant up to a promotion

value of 4.43). In other words, participants with the highest focus on aspirations and

ideals did not subject to licensing as a result of attending farmers’ markets thus

weakening the effect of promotion on licensing. They may be concerned with

pursuing gains for the community overall, rather than themselves whereas

participants with a low to medium promotion focus may experience a lack of ideals.

Goal pursuit requires mental and emotional resources (Touré-Tillery &

Fishbach, 2011) and if a lack of ideals in a chronic low promotion focus is taking

place, this could lead to an overall depletion of positive self-image due to the energy

required to maintain this goal. Thus, it is possible that for the individuals low to

medium in promotion focus, the prosocial act of going to farmers’ markets could

subject the individual to being more sensitive to a temporary boost in positive self-

image gained. Additionally, the differences in promotion focus within licensing

behaviour could be derived from the influence of their in-group, where individuals

high in promotion are concerned with making gains for the community, rather than

focusing on individual gains.

This research assists in understanding the differences between levels of

promotion focus in licensing within a prosocial shopping context of farmers’

markets, an industry specifically supportive of its own in-group. Previous literature


has attested that promotion focus increases positive behavioural reactions to in-

groups in an out-group setting (Shah, Brazy, & Higgins, 2004). This study expands

those findings by providing evidence in an in-group scenario that only low-medium

promoters are likely to cheat their own in-group, with high promoters still

maintaining support for their in-group. This is an important distinction from a

theoretical point of view since it assists in explaining deviant behaviour within

consumption communities, not only in scenarios where an in-group is observed in

relation to an out-group. Therefore, this study provides evidence that overall, the

effect of licensing is weakened by a high promotion focus but for a subset of

individuals, those being low to medium in promotion focus, prosocial shopping

experience such as farmers’ markets leads to deviant behaviours against their own in-

group.

5.4 IMPLICATIONS FOR PRACTISE

There are two implications from this study that are of practical importance.

Firstly, it is evident that consumers attending farmers’ markets who are high in

promotion focus do not engage in licensing behaviour. Therefore, for this consumer

segment, farmers’ markets should maintain or increase prosocial behaviour.

Secondly, licensing in a farmers’ market in-group setting takes place for individuals

low to medium in promotion focus. This information provides a guideline in the

identification of individuals who are prone to deviant behaviour as a result of

attending farmers’ markets and can therefore assist in preventing the licensing effect.

This research directly benefits farmers’ markets operators by enabling them to

develop a communication strategy that curbs potential non-prosocial behaviour of


individuals with low to medium promotion focus by introducing marketing material

to induce a high promotion focus.

Regulatory focus is not only chronic but can also be primed by situational

effects (Higgins et al., 2001). Therefore, by priming a promotion focus to increase it

for consumers with situational messages placed at a farmers’ markets, it could be

possible to reduce licensing. The marketing messages that are related to a promotion

focus should have a message connected to positive and optimistic outcomes and

ideals, such as information about how shopping at a farmers’ markets supports local

farmers and community. This is important since the implications of licensing can

extend to deviant behaviours in other contexts with consequences not only to

farmers’ markets but also to the broader community. By targeting the communication

to specific individuals, the marketing appeals should be successful in discouraging

licensing by these patrons.

This study provides evidence that outcomes of farmers’ markets patronage can

include deviant behaviour and in order for the industry to continue its growth, it is

important to enable all members of the in-group that provide support for farmers’

markets to continue their support with future moral decisions as opposed to

exhibiting licensing behaviour that can impact the in-group and the wider community

in negative ways.

5.5 LIMITATIONS

Every research has its limitations and it is important to consider consequences

of that to the findings. For the research design of this study, a cross-sectional online

survey was utilised as a tool for data collection. The participants completed the study

independently and this can increase common method bias. Additionally, common


method bias can become a problem with questions that are ambiguous, complex or

abstract, especially for participants low in motivation, however, uttermost care was

taken to ensure questions were carefully designed for comprehension to increase

engagement (Mackenzie & Podsakoff, 2012).

An online survey tool does not allow for making causal claims. This is a

limitation of the study since the results can only provide a guideline for behaviour at

one point in time, rather than conclusive evidence about behaviour change across

different conditions (Babbie, 2015). However, the results from this study give an

indication of behavioural intentions and can be used as a baseline for future

experimental designs that can provide more irrefutable evidence about the impact of

licensing in a farmers’ markets in-group setting.

Using an MTurk sample is an accepted, but somewhat contested, practice in the

literature. It is evident that using a sample from MTurk is more representative than

using a sample that consists of undergraduate students (Hulland & Miller, 2018),

however, a sample from MTurk is a non-probability sample and therefore not

representative of the population. Using a non-probability convenience sample is a

limitation to the study and the results can be generalisable only to the sample under

examination in the context of farmers’ markets. Additionally, there are concerns

about participant misrepresentation in order to qualify for the study for financial gain

within MTurk participants (Hulland & Miller, 2018). For this study, a screening

question was employed but some participants may have misrepresented their

farmers’ markets shopping behaviour to qualify for the study and this becomes a

limitation. However, careful inspection and removal of outliers in the dataset

increases confidence in the validity of the sample.


For any study, a threat to the validity of the results is impulsiveness of the

participants to respond to questions quickly, without reading the instructions or

reflecting on their responses (Mackenzie & Podsakoff, 2012). Some participants may

race through the study without properly understanding the instructions given, but to

minimise the possibility for this, the instructions were kept as short and succinct as

possible with highlighting the important information either in bolded letters or by

providing it upfront. Additionally, the final data included the completion times for

participants, and they were deemed satisfactory for time taken to complete the study.

Limitations also exist in in-group vs. out-group behaviour. The participants

were instructed that their cheating behaviour would impact their own in-group

members negatively, however, direct group association was not measured. Therefore,

in-group association is based on theoretical investigation of existing literature that

includes strong evidence that farmers’ markets as a consumption ideal may indeed

translate into an in-group vs. out-group scenario due to its inherent prosocial

consumption ideals (Spielmann & Bernelin, 2015). It is possible that some

participants in the study were focusing their dishonesty towards the research itself,

rather than considering their in-group so this is a limitation of the study.

5.6 RECOMMENDATIONS FOR FUTURE RESEARCH

This research contributes knowledge as a preliminary step to gaining an

understanding of farmers’ markets patronage outcomes with regards to licensing,

however, substantial scope for future research remains. This study has limitations as

discussed above and those limitations should be addressed as a first approach to

future studies.


This study should be replicated in an experimental setting to include a

manipulation across licensing, in-group, and regulatory focus conditions in order to

measure behavioural change within the constructs. The results from this research

provide preliminary evidence of significance in the patronage outcomes of farmers’

markets. It would be important to test those results for causal effect to make

conclusions on a population level. Licensing has previously been measured in

experimental settings under different prosocial consumption conditions like green

products leading to cheating and stealing (Mazar & Zhong, 2010) and pro-

environmental products leading to non-pro-environmental actions (Geng et al.,

2016), so replicating this study as an experiment would add to causal licensing

literature.

Licensing effect can take place in unrelated fields, but it is possible that it

could also take place across unrelated times. This research included one study that

was completed in one sitting, however, it would be an important avenue for future

research to look at longitudinal aspects of licensing effect in a farmers’ markets

setting and whether the impact can be traced to different points in time. Should

licensing travel across different tasks and scenarios at longer timeframes, it would

become evident that it has a greater impact than previously thought. A longitudinal

study could provide interesting insights to the underlying mechanism that controls

positive self-image and its impact on subsequent immoral behaviour.

Regulatory focus is a well-researched area but the results from this study

indicate that for a chronic low to medium promotion focus, the licensing effect is

stronger due to a boost in positive self-image from farmers’ markets patronage which

then leads to subsequent cheating behaviour. Future research should look at how

chronic promotion (and prevention) focus impacts self-image and whether low


promotion focus leads to a depletion of positive self-image, thus being in danger of

licensing as a result. This study provides a framework model to consider this as a

possibility, so contributing to future research in a positive self-image area of

regulatory focus would give more evidence to how low promotion focus can lead to

licensing as a result.

Finally, future research should continue to investigate patronage outcomes of

farmers’ markets in more detail to ensure that the industry has knowledge about what

consequences (positive or negative) consumer behaviour has for it to continue to

grow and develop.

5.7 CONCLUSION

The research presented in this thesis investigated farmers’ markets patronage

outcomes and whether this leads to subsequent deviant behaviours as explained by

licensing. Additionally, individual differences in regulatory focus were considered as

moderators weakening this behaviour. Firstly, it was expected that shopping through

farmers’ markets, a shopping channel that is inherently supportive of its own in-

group, would lead to licensing behaviour due to an elevated positive self-image

gained via consumers’ prosocial actions (H1). Secondly, individual differences in

regulatory focus were considered as possible moderators for the licensing effect.

Promotion focus was expected to weaken the effect of licensing due to positive gain

motivation for their local community (H2) and prevention focus was also expected to

have a weakening impact by maintaining status quo (H3). This research found

support for hypotheses 1 and 2, but hypothesis 3 was rejected. The results from this

study indicate that licensing takes place in an in-group setting of farmers’ markets

and is an outcome of farmers’ markets patronage. However, this is only true for


participants low to medium in promotion focus. Participants high in promotion focus

were not impacted by licensing and thus promotion weakened the effect.

Theoretically this is an important contribution to the licensing and regulatory focus

literature due to evidence that licensing varies across promotion levels when

investigated in an in-group setting. From a practical point of view, these results can

assist in curbing future licensing behaviour with marketing communications designed

to prime consumers low to medium in promotion focus to a high promotion focus.

This will assist in diminishing licensing behaviour that can have far-reaching bearing

on the industry of farmers’ markets.

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Appendices 97

Appendices

Appendix A

Full survey deployed on MTurk

Farmers’ markets demographics

Web

Page

#

Question # Question Text Answer Instructions & Text

0 0 Participant Information and Informed Consent CONSENT TO PARTICIPATE

1 1 According to USDA, a farmers’ market is a place

where two or more farmer-producers sell their own

agricultural products directly to the general public at

a fixed location. These products include fruits and

vegetables, meat, fish, poultry, dairy products, and

grains.

Have you made a purchase at a farmers’ market in

the past 12 months?

1 – Yes

2 – No (exit)

2 2 How often do you visit farmers’ markets? Please write down how frequently you visit farmers’ markets:

Appendices 98

1 – Once a week

2 – Every two weeks

3 – Every three weeks

4 – Every four weeks / monthly

5 – Less often than monthly

3 3 On average how much money do you usually spend

at farmers’ markets on one visit?

Please write down average amount of money spent per visit:

$ X

Reasons for attending farmers’ markets

Web

Page

#

Question Text

4 4 In the order of importance, please rank the following statements from 1-5, with number 1 being the most important

reason to attend farmers’ markets and number 5 the least important reason

I attend farmers’ markets to support my local community and economy.

I attend farmers’ markets to purchase produce that is fresher and healthier.

Appendices 99

I attend farmers’ markets to socialise.

I attend farmers’ markets to meet the farmers producing the food I eat.

I attend farmers’ markets to save money on produce.

Compassion and self-image scale

Web

Page

#

Question Text

5

5

Compassion

and self-

image scale

(Crocker &

Canevello,

2008)

Read each of the following statements and decide how much you agree with each according to your beliefs and

experiences.

Please respond according to the following scale:

-----------------------------------------------------------------------------------

1 = not at all

2 = rarely

3 = sometimes

4 = often

5 = always

-----------------------------------------------------------------------------------

Please review the statements continuing from this sentence:

Through the purchases that I made at a farmers' market I tried to...

Question Item Scale

Appendices 100

1 …make a positive difference in someone else’s life. 1 = not at all

2 = rarely

3 = sometimes

4 = often

5 = always

2 …be supportive of others.

3 …to have compassion for others.

4 …avoid being selfish or self-centered.

5 …avoid doing things that aren’t helpful to me or others.

6 …get others to recognize or acknowledge your positive qualities.

7 …convince others that you are right.

8 …avoid the possibility of being wrong.

9 …avoid being rejected by others.

10 …avoid taking risks or making mistakes.

Appendices 101

Operationalisation of licensing condition in an in-group setting

Web

Page #

Question

#

Question Text Answer Instructions & Text

7 7 Please write four reviews of your most recent visits to your

local farmers’ market. Each review must be no less than

three full sentences.

Your responses will be anonymous and randomised,

therefore we have no way of connecting you directly to

what you have written in your reviews.

For every review you complete in full, you will receive an

extra $0.25.

Please review up to four of your latest farmers’ markets

experiences with no less than three full sentences.

Review 1

Review 2

Review 3

Review 4

Click “Submit”.

(moves to next page)

8 8 Thank you for your reviews!

As your reviews are anonymous, please confirm how many

reviews you fully completed, so we can compensate you for

your time.

Importantly, your honesty is greatly appreciated. Our

research budget is limited, hence, for every incomplete

review you are compensated for, another participant will not

receive their reward.

Please click “next” to proceed.

Click “next”.

No option to go back to previous window.

Appendices 102

9 9 Please confirm the number of reviews you fully completed. Answer options:

0 for extra $0

1 for extra $0.25

2 for extra $0.50

3 for extra $0.75

4 for extra $1

Regulatory mode scale

10 10

Assessment

and

locomotion

orientations

(Kruglanski

et al., 2000)

Read each of the following statements and decide how much you agree with each according

to your beliefs and experiences. Please respond according to the following scale:

---------------------------------------------------------------------------------

1 = strongly disagree

2 = moderately disagree

3 = slightly disagree

4 = slightly agree

5 = moderately agree

6 = strongly agree

-----------------------------------------------------------------------

Question Item Scale

1 I don’t mind doing things even if they involve extra effort. 1 = strongly disagree

2 = moderately disagree

3 = slightly disagree

4 = slightly agree

5 = moderately agree

6 = strongly agree

Appendices 103

2 I never evaluate my social interactions with others after they occur.

3 I am a “workaholic.”

4 I feel excited just before I am about to reach a goal.

5 I enjoy actively doing things, more than just watching and observing.

6 I spend a great deal of time taking inventory of my positive and negative

characteristics.

7 I like evaluating other people’s plans.

8 I am a “doer.”

9 I often compare myself with other people.

10 I don’t spend much time thinking about ways others could improve

themselves.

11 I often critique work done by myself and others.

12 I believe one should never engage in leisure activities.

13 When I finish one project, I often wait awhile before getting started on a new

one.

14 I have never been late for work or for an appointment.

15 I often feel that I am being evaluated by others.

16 When I decide to do something, I can’t wait to get started.

17 I always make the right decision.

Appendices 104

18 I never find faults with someone I like.

19 I am a critical person.

20 I am very self-critical and self-conscious about what I am saying.

21 By the time I accomplish a task, I already have the next one in mind.

22 I often think that other people’s choices and decisions are wrong

23 I have never hurt another person’s feelings.

24 I am a “low energy” person.

25 Most of the time my thoughts are occupied with the task that I wish to

accomplish.

26 I feel that there is no such thing as an honest mistake.

27 I rarely analyze the conversations I have had with others after they occur.

28 When I get started on something, I usually persevere until I finish.

29 I am a “go-getter.”

30 When I meet a new person I usually evaluate how well he or she is doing on

various dimensions (e.g., looks, achievements, social status, clothes).

Appendices 105

Regulatory focus scale

11 11

Self-

regulatory

focus:

prevention

& promotion

(Higgins et

al., 2001)

Read each of the following statements and decide how much you agree with each according

to your beliefs and experiences. Please respond according to the following scale:

-----------------------------------------------------------------------

1 = never or seldom

2 = *BLANK*

3 = sometimes

4 = *BLANK*

5 = very often

-----------------------------------------------------------------------

Question Item Scale

1 Compared to most people, are you typically unable to get what you want out

of life?

1 = never or seldom

2 = *BLANK*

3 = sometimes

4 = *BLANK*

5 = very often

2 Growing up, would you ever “cross the line” by doing things that your

parents would

not tolerate?

3 How often have you accomplished things that got you “psyched” to work

even harder?

4 Did you get on your parents' nerves often when you were growing up?

5 How often did you obey rules and regulations that were established by your

parents?

Appendices 106

6 Growing up, did you ever act in ways that your parents thought were

objectionable?

7 Do you often do well at different things that you try?

8 Not being careful enough has gotten me into trouble at times.

9 When it comes to achieving things that are important to me, I find that I don't

perform as well as I ideally would like to do.

10 I feel like I have made progress toward being successful in my life.

11 I have found very few hobbies or activities in my life that capture my interest

or motivate me to put effort into them.

Involvement (situational) scale

12 12

Involvement

(situational)

(Zaichkowsky,

1985)

The purpose of this study is to measure a person's involvement or interest in farmers’ markets shopping. To take this

measure, we need you to judge farmers’ markets shopping against a series of descriptive scales according to how

YOU perceive the experience.

Please mark your response on the sliding scale.

Question Item Scale

1 unimportant / important Sliding scale

2 of no concern / of concern to me

Appendices 107

3 irrelevant / relevant

4 means nothing to me / means a lot to me

5 worthless / valuable

6 not beneficial / beneficial

7 doesn’t matter / matters to me

8 boring / interesting

9 unexciting / exciting

10 unappealing / appealing

11 nonessential / essential

12 insignificant / significant to me

13 undesirable / desirable

14 mundane / fascinating

15 uninvolving / involving

16 not needed / needed

17 useless / useful

Appendices 108

Sense of entitlement scale

13 13

Sense of

entitlement

(Campbell,

Bonacci,

Shelton,

Exline, &

Bushman,

2004)

Please respond to the following items using the number that best reflects your own beliefs. Please use the following 7-

point scale:

-------------------------------------------------------------------------

1 = strong disagreement.

2 = moderate disagreement.

3 = slight disagreement.

4 = neither agreement nor disagreement.

5 = slight agreement.

6 = moderate agreement.

7 = strong agreement

-----------------------------------------------------------------------

Question Item Scale

1 I honestly feel I’m just more deserving than others.

1 = strong disagreement.

2 = moderate

disagreement.

3 = slight disagreement.

4 = neither agreement nor

disagreement.

5 = slight agreement.

6 = moderate agreement.

7 = strong agreement

2 Great things should come to me.

3 If I were on the Titanic, I would deserve to be on the first lifeboat!

Appendices 109

4 I demand the best because I’m worth it.

5 I do not necessarily deserve special treatment.

6 I deserve more things in my life.

7 People like me deserve an extra break now and then.

8 Things should go my way.

9 I feel entitled to more of everything.

Social desirability scale

14 14

Social

desirability

scale

(Vésteinsdóttir,

Reips, Joinson,

& Thorsdottir,

2017).

Please respond to the following statements with “True” or “False”.

-----------------------------------------------------------------------

1 = True

2 = False

-----------------------------------------------------------------------

Question Item Scale

1 I have never intensely disliked anyone. 1 = True

2 = False

2 I sometimes feel resentful when I don't get my way.

3 No matter who I'm talking to, I'm always a good listener.

Appendices 110

4 There have been occasions when I took advantage of someone.

5 I'm always willing to admit it when I make a mistake.

6 I sometimes try to get even, rather than forgive and forget.

7 There have been occasions when I felt like smashing things.

8 There have been times when I was quite jealous of the good fortune of

others.

9 I have never felt that I was punished without cause.

10 I have never deliberately said something that hurt someone's feelings.

Demographic questions

What is your gender? 1 – Female

2 – Male

3 – Other

What is your age group? 1 – 18-24 years

2 – 25-35 years

3 – 36-45 years

4 – 46-55 years

5 – 56-65 years

6 – 66 – 75 years

7 – 76+ years

Appendices 111

Annual Household income before tax? 1 – Less than $30,000

2 – $30,001 - $50,000

3 – $50,001 - $70,000

4 – $70,001 - $90,000

5 – $90,001 - $110,000

6 – Over $110,001

7 – Prefer not to answer

Please indicate your actual annual income?

1 – X dollars / year

OR

2 – Prefer not to say

What is your marital status?

1 – Single

2 – Married

3 – Cohabiting

4 – In a relationship

5 – Other

What is your household status?

1 – Live alone

2 – Live with partner / spouse

3 – Live with partner / spouse and children

4 – Live with children

5 – Live with others

How many people are a part of your

household?

1 – 1 person

2 – 2 people

3 – 3 people

4 – 4 people

5 – 5 people

Appendices 112

6 – 6 or more people

Please indicate your political orientation on the

following sliding scale.

Sliding scale with opposite ends of:

1 – Left-wing

10 – Right-wing

(Napier & Jost, 2008)

Please indicate your political orientation on the

following sliding scale.

Sliding scale with opposite ends of:

-5 – Extremely Liberal

+5 – Extremely Conservative

(Amodio, Jost, Master, & Yee, 2007)

Documents

LICENSE TO CHEAT DOES FARMERS MARKETS PATRONAGE … · 4.5 Hypothesis testing ... fresh food. In the US, farmers’ markets have grown from 1,000 to over 8,700 locations – contributing