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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.
Literature Review 14
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
Literature Review 15
(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,
Literature Review 16
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
Literature Review 17
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
Literature Review 18
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
Literature Review 19
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
Literature Review 20
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
Literature Review 21
(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.
Literature Review 22
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
Literature Review 23
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,
Literature Review 24
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
Literature Review 25
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
Literature Review 26
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
Literature Review 27
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).
Literature Review 28
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
Literature Review 29
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
Literature Review 30
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
Literature Review 31
(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
Literature Review 32
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
Literature Review 33
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’
Literature Review 34
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
Literature Review 35
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.
Literature Review 36
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.
Discussion and Conclusion 76
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
Discussion and Conclusion 77
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.
Discussion and Conclusion 78
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
Discussion and Conclusion 79
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).
Discussion and Conclusion 80
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
Discussion and Conclusion 81
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
Discussion and Conclusion 82
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
Discussion and Conclusion 83
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
Discussion and Conclusion 84
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.
Discussion and Conclusion 85
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.
Discussion and Conclusion 86
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
Discussion and Conclusion 87
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
Discussion and Conclusion 88
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)