JP Casaletto - Dissertation - MBA (Henley Business School)

The antecedents of consumer trust in user-generated
product recommendations of fast-moving consumer
goods on Facebook.
By
John P. Casaletto
Student Number: 18900387 (IAMT)
Word count: 17,587
October 2012
Management Challenge submitted in partial fulfilment of the requirements
for the degree of Master of Business Administration

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Executive Summary
Trust is important to the study of advertising, as consumers must have trust in ad-
conveyed information in order for advertising to perform effectively as an
information source. However, six decades of survey data consistently indicate that
about 70% of consumers think that advertising is often untruthful. Meanwhile, user-
generated content has emerged as one of the most widespread and trusted forms of
advertising, while the level of trust has dropped significantly for producer-generated
content such as branded websites and television adverts.
Facebook is now the largest social network in the world, with more than a billion
users. However, despite the huge number of consumers on Facebook, advertisers are
struggling to communicate their brand messages to these consumers. This study
takes up this challenge from the perspective of a Maltese distributor of fast-moving
consumer goods that is currently evaluating whether (and how) it should invest in
Facebook as a medium of advertising.
The investigation focuses on the measurement of trust in a very specific form of
user-generated content: user-generated product recommendations of fast-moving
consumer goods on Facebook. This was achieved through a review of academic and
practitioner literature that served to clarify the concept of consumer trust, and how
this could be applied to user-generated product recommendations, as opposed to
traditional producer-generated forms of advertising.
A theoretical model was developed and subsequently tested through a quantitative
survey that was administered to Facebook users in Malta. The results showed that
consumer trust in user-generated product recommendations on Facebook is relatively
high. A significant model emerged that explained 31% of the variance in consumer
trust in user-generated product recommendations, with the following emerging as
significant predictors of this construct: benevolence, consumer conformity,
advertising scepticism, trust in Facebook (as a medium), gender and daily active use
of Facebook.

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Based on the review of current thinking and the results of the quantitative survey, a
number of recommendations for further action were made.
Facebook should be included as part of an organisation’s marketing mix, as the
medium has a high penetration rate in Malta, and registered a high level of daily
active use amongst the respondents surveyed in this study. However, television
should not be abandoned as an advertising medium, as it still enjoys a high level of
daily active use amongst respondents despite the entry of Facebook as a competing
medium.
Advertisers should aim to stimulate user-generated product recommendations for
their brands, as opposed to simply buying paid advertising on Facebook. This form
of activity on Facebook will provide marketers with an opportunity to reconnect with
consumers that are highly sceptical of traditional producer-generated advertising.
Keywords: trust, UGC, Facebook, FMCG, advertising

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Acknowledgement
This Management Challenge is the crowning achievement of what has proven to be a
life-changing journey of learning and discovery. I would like to take this opportunity
to express my immense gratitude to everyone that offered their support and
assistance throughout this project.
I owe a debt of gratitude to my academic supervisor, Dr. George Christodoulides
who offered pearls of wisdom, encouragement and constructive criticism throughout
this project.
I would also like to thank my employer, Dr. Alec Mizzi, who encouraged me to
pursue this MBA in the first place, and who gave me the push that I needed to finally
choose the management problem that formed the basis of this Management
Challenge.
A special mention goes out to Lorenzo Mulè Stagno, Christine Caruana, Francis
Farrugia and Dr. Liberato Camilleri for the excellent and timely local support that
they provided.
I have to acknowledge the Facebook community in Malta, who rallied around my
data collection initiative and shared and re-shared the link to my survey. They
collectively gave me a glimpse of Facebook’s immense potential for spreading a
message.
Special thanks is due to my dear wife Sue, who provided the constant care, support
and encouragement that allowed me to devote the best part of 2012 to this
Management Challenge.
Finally, I would like to thank my children Alex and Hannah, for accepting that their
father had to disappear into the study for hours at a time, on weekends and
throughout their holidays. I owe them many, many hours of outings and quality time
in order to repay this debt.

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Table of Contents
1 Introduction.......................................................................................................... 1
1.1 Background ................................................................................................... 1
1.1.1 User-generated product recommendations............................................. 2
1.2 Business context............................................................................................ 2
1.2.1 The sponsoring organisation .................................................................. 3
1.2.2 Fast-moving consumer goods ................................................................ 3
1.2.3 Television viewership in Malta.............................................................. 4
1.2.4 Facebook penetration in Malta............................................................... 4
1.3 The management problem............................................................................. 6
1.4 The professional significance of the study.................................................... 7
1.4.1 The marketer’s perspective .................................................................... 7
1.4.2 The academic’s perspective ................................................................... 7
1.5 Meeting the terms of reference...................................................................... 7
1.5.1 Henley’s objectives................................................................................ 7
1.5.2 The sponsor’s objectives........................................................................ 8
1.5.3 Personal objectives................................................................................. 8
1.6 Structure and contents of each section .......................................................... 9
2 Review of current thinking................................................................................. 11
2.1 User-generated content................................................................................ 11
2.1.1 The importance of UGC to marketers.................................................. 12
2.2 The medium of communication................................................................... 12
2.2.1 Web 2.0 ................................................................................................ 13
2.2.2 Social media......................................................................................... 13
2.2.3 Social networks.................................................................................... 14
2.2.4 Social media marketing........................................................................ 14
2.2.5 Facebook .............................................................................................. 14
2.2.6 The shift from Facebook web to mobile devices ................................. 15
2.3 The dissemination of UGC.......................................................................... 16
2.3.1 Word of mouth..................................................................................... 16
2.3.2 Electronic word of mouth..................................................................... 17
2.3.3 The role of WOM in mass persuasion ................................................. 17
2.3.4 The importance of WOM to marketers ................................................ 18

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2.4 Trust............................................................................................................. 18
2.4.1 The theoretical foundations of trust ..................................................... 18
2.4.1.1 The personality theorist’s perspective .......................................... 19
2.4.1.2 The sociologist’s perspective........................................................ 19
2.4.1.3 The social psychologist’s perspective........................................... 20
2.4.2 The relationship between trust and credibility..................................... 21
2.4.2.1 Source credibility.......................................................................... 22
2.4.2.2 Advertising credibility/scepticism................................................ 22
2.4.2.3 Ad content credibility ................................................................... 23
2.4.2.4 Website credibility........................................................................ 23
2.4.3 Consumer trust in advertising .............................................................. 23
2.4.3.1 Reliability...................................................................................... 24
2.4.3.2 Usefulness..................................................................................... 24
2.4.3.3 Affect ............................................................................................ 25
2.4.3.4 Willingness to rely on................................................................... 25
2.5 The potential antecedents of trust in UGPR................................................ 26
2.5.1 Advertising scepticism......................................................................... 26
2.5.2 Trust in Facebook as a medium ........................................................... 26
2.5.3 Familiarity and experience with a website........................................... 27
2.5.4 Propensity to trust ................................................................................ 27
2.5.5 Consumer conformity........................................................................... 28
2.5.6 Self-esteem........................................................................................... 28
2.5.7 Benevolence......................................................................................... 28
2.5.8 Age....................................................................................................... 29
2.5.9 Level of education................................................................................ 29
2.5.10 Gender.................................................................................................. 30
2.6 Summary ..................................................................................................... 31
3 The investigation................................................................................................ 32
3.1 Objectives of the investigation.................................................................... 32
3.1.1 Research questions............................................................................... 32
3.1.2 Theoretical model and research hypotheses......................................... 32
3.2 Investigation design..................................................................................... 35
3.2.1 Research strategy ................................................................................. 35

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3.2.2 Population and sample ......................................................................... 35
3.2.3 Survey administration mode ................................................................ 35
3.2.4 Instrumentation .................................................................................... 36
3.2.4.1 Questionnaire Design.................................................................... 36
3.2.4.2 Variable identification and measurement ..................................... 37
3.2.4.3 Modification of scales................................................................... 37
3.2.4.4 Response format ........................................................................... 38
3.2.4.5 Specific focus on low-involvement FMCG.................................. 38
3.2.5 Pilot study ............................................................................................ 39
3.2.6 Data collection ..................................................................................... 41
3.2.7 Preparation and cleansing of data ........................................................ 41
3.2.8 Analysis................................................................................................ 42
3.3 Ethical considerations.................................................................................. 42
3.4 Delimitations of the research....................................................................... 43
4 Findings and analysis......................................................................................... 45
4.1 Sample demographics.................................................................................. 45
4.1.1 Daily active use by medium................................................................. 46
4.1.2 Daily active Facebook use by device type........................................... 47
4.2 Reliability of scales ..................................................................................... 48
4.3 Descriptive statistics.................................................................................... 49
4.4 Assumption of normality for dependent variable........................................ 51
4.5 Correlation analysis..................................................................................... 53
4.6 Regression analysis ..................................................................................... 54
4.6.1 Testing the theoretical model............................................................... 54
4.6.2 Diagnostic analysis............................................................................... 55
4.7 Summary ..................................................................................................... 57
5 Conclusions and recommendations.................................................................... 58
5.1 Conclusions ................................................................................................. 58
5.1.1 The extent of consumer trust in UGPR on Facebook .......................... 58
5.1.2 The antecedents of consumer trust in UGPR on Facebook ................. 59
5.1.2.1 Advertising scepticism.................................................................. 60
5.1.2.2 Trust in Facebook as a medium.................................................... 61
5.1.2.3 Daily active use/Facebook experience.......................................... 61

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5.1.2.4 Propensity to trust......................................................................... 62
5.1.2.5 Consumer conformity ................................................................... 62
5.1.2.6 Self-esteem.................................................................................... 62
5.1.2.7 Benevolence.................................................................................. 63
5.1.2.8 Demographic variables ................................................................. 63
5.1.3 Evaluation of fit with research objectives............................................ 64
5.2 Managerial implications and recommendations.......................................... 64
5.2.1 Include Facebook in marketing campaigns.......................................... 64
5.2.2 Expect Facebook to increase in importance......................................... 65
5.2.3 Encourage consumers to post UGPR on Facebook.............................. 65
5.2.4 Reconnect with consumers that are sceptical of PGC.......................... 65
5.2.5 Implications for target marketing......................................................... 66
5.2.6 Leverage consumer benevolence ......................................................... 66
5.2.7 Do not abandon television advertising................................................. 66
5.2.8 Cater for a mobile audience ................................................................. 67
5.3 Recommendations for further research ....................................................... 67
6 Reflection........................................................................................................... 70
6.1 Evaluation of findings ................................................................................. 70
6.1.1 Relevance and value of the research .................................................... 70
6.1.2 Limitations of research......................................................................... 70
6.1.3 Fit with current thinking ...................................................................... 71
6.1.4 Advancement of knowledge and understanding .................................. 72
6.1.5 Influence of philosophical stance......................................................... 72
6.2 My experience of the research process........................................................ 73
6.3 Achievement of personal development objectives...................................... 74
6.3.1 My development as a marketer ............................................................ 74
6.3.2 My development as an academic ......................................................... 74
6.3.3 My development as an individual ........................................................ 75
7 References.......................................................................................................... 76
8 Appendices......................................................................................................... 87
8.1 Appendix A: Questionnaire used for data collection ................................. 87
8.2 Appendix B: Extracts from research diary................................................. 92

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Table of Figures
Figure 1 - Consumer trust in various forms of advertising (Nielsen, 2012) ................ 1
Figure 2 - Example of UGPR on Facebook ................................................................. 2
Figure 3 - Television reach in Malta (Broadcasting Authority, 2012)......................... 4
Figure 4 - Facebook users in Malta (Socialbakers.com, 2012).................................... 5
Figure 5 - Age profile of Maltese Facebook users (Socialbakers.com, 2012)............. 5
Figure 6 - The dissemination of UGPR ..................................................................... 13
Figure 7 - Online tools used for product recommendations (Zuberance, 2012)........ 15
Figure 8 - Consumer response to ads by medium (eMarketer.com, 2012)................ 16
Figure 9 - Two-step flow theory (Communicationtheory.org, 2012) ........................ 17
Figure 10 - The sociologist’s perspective of trust...................................................... 20
Figure 11 - Model of trust (Mayer et al., 1995) ......................................................... 21
Figure 12 - Model of trust in advertising (Soh, 2006) ............................................... 24
Figure 13 - Theoretical model and hypotheses .......................................................... 33
Figure 14 - The seven-point Likert scale used in this study ...................................... 38
Figure 15 - Sample UGPR used in questionnaire ...................................................... 39
Figure 16 - Histogram of Trust in UGPR .................................................................. 51
Figure 17 - Normal Q-Q Plot of Trust in UGPR........................................................ 52
Figure 18 - Normal P-P Plot of Trust in UGPR......................................................... 52
Figure 19 - Residual plot for Trust in UGPR............................................................. 56
Figure 20 - Tested model of antecedents of consumer trust in UGPR....................... 59

Table of Tables
Table 1 - Structure of questionnaire........................................................................... 36
Table 2 - List of variables .......................................................................................... 37
Table 3 - Cronbach’s alpha (pilot study).................................................................... 40
Table 4 - Demographic profile of sample .................................................................. 45
Table 5 - Daily active use by medium ....................................................................... 46
Table 6 - Device ownership and Facebook access by device type ............................ 47
Table 7 - Daily active Facebook use by device type.................................................. 48
Table 8 - Cronbach’s alpha (full study) ..................................................................... 49

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Table 9 - Descriptive statistics for key variables ....................................................... 50
Table 10 - Correlation matrix..................................................................................... 53
Table 11 - Coefficients for dependent variable: Trust in UGPR ............................... 54
Table 12 - Summary of hypothesis testing results ..................................................... 55
Table 13 - Collinearity statistics for regression model .............................................. 56
Table 14 - Comparison of Trust in UGPR and PGC between studies ....................... 58
Table 15 - Relationship between daily active use and Facebook experience ............ 61
Abbreviations
eWOM Electronic word-of-mouth
FMCG Fast-moving consumer goods
PGC Producer-generated content
UGC User-generated content
UGPR User-generated product recommendations
UK United Kingdom
US United States of America
WOM Word-of-mouth

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1 Introduction
This chapter will serve to provide the background and context of this study, and to
introduce the sponsoring organisation, along with the management problem that
inspired this project.
1.1 Background
Trust is important to the study of advertising, as consumers must have trust in ad-
conveyed information in order for advertising to perform effectively as an
information source (Soh et al, 2009). However, six decades of survey data
consistently indicate that about 70% of consumers think that advertising is often
untruthful (Calfee and Ringold, 1994).
Meanwhile, user-generated content has emerged as one of the most widespread and
trusted forms of advertising. In fact, a global consumer survey revealed that
“recommendations from people I know” and “consumer opinions posted online”
were the two most trusted sources of brand information among respondents.
Conversely, the level of trust dropped significantly for producer-generated content
such as branded websites and television adverts, as outlined in Figure 1 (Nielsen,
2012).
Figure 1 - Consumer trust in various forms of advertising (Nielsen, 2012)

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Despite the huge number of consumers on Facebook, advertisers are struggling to
communicate their brand message to them. According to a May 2012 poll, 83% of
Facebook users in the US “hardly ever or never clicked” on online ads or sponsored
content when using Facebook (Greenlightdigital.com, 2012). This statistic highlights
the problem that inspired this research project, and indicates that this is a current
issue for brand owners worldwide.
1.1.1 User-generated product recommendations
This study will be focusing on a specific type of user-generated content which will
be referred to as user-generated product recommendations (UGPR). This refers to
product recommendations that are posted on Facebook by consumers. These
comments typically appear on the Facebook wall of the individual that posted the
comment, or may be viewed on the Facebook news feed, as illustrated in Figure 2.
Figure 2 - Example of UGPR on Facebook
1.2 Business context
This section will introduce the sponsoring organisation and the industry within which
it competes. A brief overview of television reach and Facebook penetration in Malta
will then be provided, since this information will aid the understanding of the
Management Problem that will be outlined in Section 1.3.

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1.2.1 The sponsoring organisation
Alf Mizzi & Sons (Marketing) Group is Malta’s largest distributor of fast-moving
consumer goods to the retail supermarket and grocery sector. The organisation
employs over 300 people and operates out of state-of-the-art custom-built premises.
Until the early 1980s, the organisation mainly handled imported, essential
commodities. However, in the early 1990s the organisation upgraded its chilled and
frozen infrastructure and ventured into temperature-controlled foods. Following an
intensive business development effort and the consequent acquisition of several top
international brands, the organisation rapidly rose to a dominant position in the
branded foods sector.
It is now the sole distributor for a large portfolio of local and international brands,
which include amongst others: McCain, Kerrygold, Muller and Cadbury. Its focus
is on marketing branded products and it is by far the single largest advertiser on
Maltese television.
1.2.2 Fast-moving consumer goods
The fast-moving consumer goods (FMCG) industry includes everyday consumer
products such as food (e.g. bread, snacks) and non-food (e.g. laundry detergent,
shampoo). They tend to be low-involvement, utilitarian goods due to their relatively
low prices and frequency of consumption. Consequently, these products are
typically purchased as the outcome of a small-scale consumer decision, and are often
heavily supported in terms of advertising and promotions.
A study by Çelebi (2007) identified that the main considerations of consumers when
shopping for FMCG were price (24%), quality (24%), experimentation (14%),
organisational trust (14%) and word-of-mouth (7%). However, a survey by BlogHer
(2012) suggests that 56% of mothers based a food purchase decision upon
information that they had read online. Furthermore, a study by Socialbakers.com
(2011) identified FMCG as the largest industry on Facebook amongst the countries
with the largest Facebook populations in the world.

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1.2.3 Television viewership in Malta
Survey data covering the past five years showed that a relatively consistent 66% of
the population claim to be television viewers as outlined in Figure 3 (Broadcasting
Authority, 2012).
Figure 3 - Television reach in Malta (Broadcasting Authority, 2012)
However, only 38% to 42% of the respondents watch Maltese television stations,
with the balance of viewers opting to watch foreign stations instead. The national
average of television viewing stood at 1.62 hours per day in the 2nd
quarter of 2012,
and this had increased by 7% vs. the previous year (Broadcasting Authority, 2012).
These statistics indicate that the consumption of television as a medium is relatively
stable amongst the Maltese population, although foreign television stations are
gradually eroding the viewership of the Maltese stations. If this trend persists, the
reach and effectiveness of the organisation’s advertising on this medium will
inevitably decline over time.
1.2.4 Facebook penetration in Malta
Facebook penetration in Malta amounts to 53% of the country's total population and
89% of the country’s Internet users. The total number of Facebook users in Malta

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currently amount to 213,880 and have grown by more than 13,220 in the last six
months as illustrated in Figure 4 (Socialbakers.com, 2012).
Figure 4 - Facebook users in Malta (Socialbakers.com, 2012)
The current age profile of Maltese Facebook users is shown in Figure 5. The largest
age group is currently the 25-34 year olds, with a total of 59,886 users, followed by
the 18-24 year olds. The split by gender is fairly even, with 51% male users and
49% female users (Socialbakers.com, 2012).
Figure 5 - Age profile of Maltese Facebook users (Socialbakers.com, 2012)

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These statistics indicate that Facebook already has a significant portion of the
Maltese population as registered users, and that this user base is growing rapidly.
This outlines the importance of this medium to marketers, such as the sponsoring
organisation.
1.3 The management problem
The sponsoring organisation has a long track record of repeated and sustained
success in terms of sales growth and market share of its product portfolio. This is
largely due to its consistent investment in advertising, in order build its brands and to
generate consumer pull. Television has been the predominant above-the-line
advertising medium used by the company over the past two decades. However, it
has periodically invested in billboard campaigns, radio advertising, newspaper
advertising and magazine advertising.
Management are now concerned about the apparent shift in consumer focus away
from traditional offline media, in favour of the Internet, and in particular towards
Facebook. While the company has a lot of experience and expertise in the effective
use of traditional media, it has limited experience with online advertising and social
media.
Within this context, the management problem can be stated as follows:
“Facebook appears to be gaining popularity amongst our target consumers. This
appears to be shifting their entertainment habits away from television, which is
currently our primary means of communicating our brand messages to them. We
need to evaluate whether we should be extending our marketing efforts to include
consumer-driven marketing on Facebook. Moreover, since Facebook is driven by
comments that are created and posted by the consumers themselves, we need to
understand the extent to which consumers trust the product recommendations that
they see on this medium, as well as the antecedents of this trust.”

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1.4 The professional significance of the study
1.4.1 The marketer’s perspective
This study aims to improve the knowledge relating to consumer trust in user-
generated product recommendations that are posted on the world’s most popular
social networking site: Facebook. An understanding of the extent to which
consumers trust their peers, as well as the factors that lead to this trust, could help
practitioners take more informed decisions when developing their online marketing
strategies for this relatively new medium.
1.4.2 The academic’s perspective
This study will seek to develop a model of the antecedents of consumer trust in user-
generated product recommendations. Furthermore, this study will extend the use of a
valid and reliable scale: ADTRUST (Soh, 2006). This instrument was originally
developed and applied to the measurement of producer-generated content, while this
study will adapt this instrument to allow the measurement of user-generated content.
1.5 Meeting the terms of reference
This study was designed to satisfy Henley Business School’s terms of reference for
the Management Challenge, and seeks to satisfy the objectives of the three key
stakeholders.
1.5.1 Henley’s objectives
I will seek to satisfy Henley’s objectives by building upon the knowledge gained
during the Masters programme, and applying it to the management problem.
This will be achieved by conducting a review of current thinking in order to gain an
understanding of the key concepts surrounding consumer trust, user-generated
content and the specificities of the chosen medium: Facebook.

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A theoretical model will be constructed, based on the potential antecedents of
consumer trust in product recommendations that are identified through the literature.
This model will then be tested through a quantitative study that will collect primary
data directly from the consumers of the sponsoring organisation’s products.
This data will be subjected to statistical analysis in order to test the hypotheses and
the theoretical model produced. The results will then be used to develop logical,
appropriate and actionable practitioner recommendations for the sponsor.
A log detailing the process of completing the Management Challenge will be
maintained throughout the research project, and this will be used to compile the
personal reflection that will be included in the final section of this report.
1.5.2 The sponsor’s objectives
The sponsoring organisation is in the process of devising its long-term advertising
strategy which includes the decision of whether or not to feature Facebook marketing
in its strategy for 2013 and beyond. Despite the narrow focus of this study, the
insights provided by this research could potentially have significant and long-term
implications on the organisation’s advertising strategy.
No deliverables have been requested by the sponsoring organisation, other than
access to the Management Challenge document upon completion.
1.5.3 Personal objectives
In my current role, I am aiming to develop a high-level awareness and appreciation
of the wide selection of digital and social media marketing opportunities. This is
proving to be a crucial skill in my career, and the sponsoring organisation is
expecting me to guide them into this new area, irrespective of my academic
endeavours.
As a marketer, I would also like to develop a deeper understanding of consumer
attitudes towards advertising in general. However, since I need to be very specific

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for the purposes of this study, I will be focusing on consumer trust. Nonetheless, I
will strive to read around the subject in order to improve my knowledge and
understanding in this area.
As an academic, I would love to contribute towards the current body of knowledge in
what is still a relatively young subject. While advertising has been the focus of a
multitude of academic research over the years, this study promises to cover new
ground by exploring the extent to which consumers trust product information that
they obtain from their peers in a relatively new environment: Facebook.
The skills gained through undertaking a project of this magnitude will have a positive
and lasting impact on my performance as a manager, and as a student as I continue to
further my studies in the future. The entire process will inevitably fine-tune my time
management, project management and stakeholder management skills as I will be
juggling the demands of my full-time job and simultaneously working on this
Management Challenge.
1.6 Structure and contents of each section
This chapter introduced the management problem that the author will seek to address
throughout this study. The rest of the report will be structured as follows:
Chapter 2 will review the current thinking relating to consumer trust and attempt to
identify potential antecedents of this trust. This will culminate in the generation of a
theoretical model, along with a set of hypotheses that will be empirically tested
through this research.
Chapter 3 will clearly articulate the specific research questions and hypotheses that
will be addressed by the quantitative survey. A detailed description of the research
strategy, design and implementation will be provided.

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Chapter 4 will present the results of the survey and outline the key statistical
analysis that was applied to the data in order the test the theoretical model and
hypotheses.
Chapter 5 will answer the research questions that were formulated in Chapter 3, and
draw conclusions based on the analysis presented in Chapter 4. Managerial
implications and recommendations will be presented, based on the conclusions of
this study.
Chapter 6 will conclude this report with a review of the personal development
achieved by the author throughout the Management Challenge process.

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2 Review of current thinking
The first part of this chapter will serve to unpack the key terms, technologies and
mechanisms by which the product recommendations being discussed in this study
will be created and disseminated. The rest of the chapter will delve into the notion of
consumer trust, and will identify a number of potential antecedents of this trust that
are suggested by the literature.
2.1 User-generated content
The growing Internet population and the relative ease with which consumers can
publish content on this medium has empowered all kinds of consumers to express
their views publicly. It is estimated that 75% of information on the Internet is
generated by individuals, and that this information is doubling every two years
(Gantz and Reinsel, 2011).
The term that is used to describe the content being generated by these consumers is
user-generated content (UGC). However, it is also referred to as consumer-generated
media (Grannell, 2009) and user-created content (OECD, 2007). Conversely,
content generated by the producers, or marketers of products is referred to as
producer-generated content (PGC) and includes content such as television
commercials and brand-owned websites.
UGC has been defined in the literature as the creation of content by consumers that:
• is made available through publicly-accessible transmission media such as the
Internet;
• reflects some degree of creative effort; and
• is created for free outside professional routines and practices (Christodoulides et
al., 2012; OECD, 2007).
Prime examples of UGC include consumer reviews on websites such as Amazon,
videos uploaded to YouTube, Wikipedia articles, Twitter messages and comments
posted on Facebook.

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2.1.1 The importance of UGC to marketers
UGC is a rapidly growing vehicle for brand conversations and consumer insights
(Christodoulides et al., 2012). Research among UK adults indicates that the weekly
consumption of UGC is comparable with traditional media such as commercial radio
and regional newspapers, with 60% of respondents claiming to have accessed UGC
in the past week (Luetjens and Stansforth, 2007).
Influence has been shifting from PGC towards key opinion leaders in the customer-
base, thus shifting from the conventional publisher-centric media model to a more
user-centric model (Daugherty et al., 2008). In fact, a significant amount of UGC
concerns brand-related material, with one study citing that 77% of YouTube,
Facebook and Twitter listings that appeared for brand-related searches were not
controlled by the marketer (360i, 2009). In this context, user-generated brand
messages are regarded as brand touch points next to corporate communication
efforts, affecting a consumer’s brand experience and brand expectations (Burmann,
2010; Krishnamurthy and Dou, 2008).
A recent study (Bazaarvoice, 2012) highlighted that most consumers, regardless of
their age, conduct Internet research prior to purchasing. Most of them look for UGC
to help them buy, with 51% of the consumers surveyed saying that they trust
information obtained from UGC, versus just 16% that trust the information found on
a company’s website.
2.2 The medium of communication
The product recommendations being discussed in this study will be communicated
over the Facebook news feed. Facebook is a social networking website that runs on
Web 2.0 technology. This section will serve to briefly introduce these key terms and
technologies, which have been represented diagrammatically in Figure 6.

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Figure 6 - The dissemination of UGPR
2.2.1 Web 2.0
Web 2.0 is “a collection of open-source, interactive and user-controlled online
applications that expand the experiences, knowledge and market power of the users
as participants in business and social processes” (Constantinides and Fountain,
2008).
2.2.2 Social media
While the term Web 2.0 refers to a wider group of online applications, Social Media
refers to the social aspects of Web 2.0 technologies. In fact, Kaplan and Haenlein
(2010) define Social Media as "a group of Internet-based applications that build on
the ideological and technological foundations of Web 2.0 and that allow the creation
and exchange of user-generated content."

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2.2.3 Social networks
While the Web is largely organised around content, social networks are organised
around users. Typically, users join one or more social networks, publish a profile
page and establish links with other users. The resulting social network provides a
basis for maintaining these social relationships, and for sharing content that has been
published or endorsed by other users (Mislove et al, 2007). Consumers are
increasingly using social networking services as trusted sources of information and
opinions (Jansen et al., 2009; Mislove et al, 2007).
2.2.4 Social media marketing
Traditional marketing is producer-generated, with one-way messages being pushed
onto consumers, typically interrupting their activities. Conversely, social media
marketing has emerged as a new set of activities designed to take advantage of Web
2.0 technology and the increasing tendency for consumers to create and share UGC.
Social media marketing depends on user participation, and involves multidirectional
dialogs: where brands talk to consumers, consumers talk to brands, and consumers
talk to one another. Most of the content and connections in the social community are
created by the consumers, and not by the brand (Akar and Topçu, 2011). Thus,
marketers are becoming increasingly aware of the fact that they are losing control
over the conversations that consumers are having about their brands (Berthon et al.,
2008).
2.2.5 Facebook
Facebook is the largest social networking website in the world, and is already
reported to have one billion people using it every month (Lee, 2012). Research by
Gartner (2012) revealed that 70% of online consumers use Facebook at least once a
week, and that 20% of these consumers have made a purchase after receiving a
marketing message on Facebook. This shows the potential that this medium has to
directly affect a brand’s market performance based on the brand-related information
that consumers are exposed to (Gartner, 2012; Lipsman et al., 2012).

15
Around 3.2 billion comments are posted by Facebook users every day. This is a
massive amount of UGC, some of which will inevitably refer to brands. In fact, a
recent survey of brand advocates (Zuberance, 2012) found that 35% of respondents
used Facebook to post consumer recommendations online, as illustrated by Figure 7.
Figure 7 - Online tools used for product recommendations (Zuberance, 2012)
2.2.6 The shift from Facebook web to mobile devices
More than half of Facebook’s users access the website on a mobile device (Sengupta,
2012). This relatively new method of accessing the Internet does not appear to be a
fad, and the number of browser-enabled phones is expected to surpass the number of
personal computers on the market by 2013 (Gartner, 2010).
Research has shown that consumers are less likely to see, spend time on and recall
the ads on Facebook when using a smartphone, as outlined in Figure 8
(EyeTrackShop, 2012; eMarketer.com, 2012).
E-mail
57%
Facebook
35%
Blog
1%
eCommerce &
third-party sites
5%
Twitter
1%
LinkedIn
1%

16
Figure 8 - Consumer response to ads by medium (eMarketer.com, 2012)
This suggests that marketers will need to work harder to get their brand messages
across to consumers that are increasingly shifting their consumption of Facebook
content onto smartphones. This decline in ad effectiveness on the mobile phone may
increase the relative importance of UGC for marketers.
2.3 The dissemination of UGC
UGC is often confused with electronic word-of-mouth. However, the two differ
depending on whether the content is generated by users or conveyed by users. This
distinction is important to make, as UGC will have little impact as an information
source unless it is disseminated amongst user groups through the process of
electronic word-of-mouth (Morrison and Cheong, 2008).
2.3.1 Word of mouth
Word of mouth (WOM) has been defined as “oral, person-to-person communication
between a receiver and a communicator whom the receiver perceives as non-
commercial, concerning a brand, product, or a service.” (Arndt, 1967). Thus,
WOM consists of three essential parts: interpersonal communication, commercial
content and non-commercially motivated communicators (Nyilasi, 2006).

17
2.3.2 Electronic word of mouth
With the advent of Internet technologies, traditional WOM communication has been
extended to electronic media, such as online discussion forums, blogs, review sites,
and social networking sites. In this context, electronic word of mouth (eWOM) has
been defined as “any positive or negative statement made by potential, actual, or
former customers about a product or company, which is made available to a
multitude of people and institutions via the Internet” (Hennig-Thurau et al., 2004).

2.3.3 The role of WOM in mass persuasion
The “two-step” flow theory of communication (illustrated in Figure 9) originally
suggested that a transfer of information occurs from the mass media to opinion
leaders, and influence then spreads from opinion leaders to their followers
(Lazarsfield et al., 1994). This implies that WOM is an intermediate step in the mass
persuasion process (Morrison and Cheong, 2008).
Figure 9 - Two-step flow theory (Communicationtheory.org, 2012)
While this “two-step” model has been widely criticised over the years, adaptations
have been suggested which allow for multiple bi-directional flows between the
producers, opinion leaders and followers, made possible by the Internet and the
social media technology (Weimann, 2011).

18
2.3.4 The importance of WOM to marketers
The importance of WOM in the consumer marketplace is not a new phenomenon,
and it had already been described as “one of the most important sources of
information for the consumer” several decades ago (Arndt, 1967). WOM has now
become one of the most important and effective communication channels for
marketers. In fact, a nationwide survey revealed that the average American
consumer participates in 121 WOM conversations over the course of a typical week,
during which specific brand names are mentioned 92 times (Keller, 2007).
Nowadays, as the credibility of “official” marketing messages is waning, the power
of one consumer recommending a product to others is increasing (Keller, 2007).
Moreover, research has shown that strong consumer advocacy on behalf of a brand
or company is one of the best predictors of sales growth (Hu et al., 2006; Marsden et
al., 2005; Reichheld, 2003) and have been found to have an effect on purchase
decisions (Freedman, 2008; Graham and Havlena, 2007).
2.4 Trust
Despite the apparently high level of trust that consumers place in recommendations
made by their peers, few studies have examined which factors influence consumer
trust in user-generated product recommendations (Hsaio et al., 2010). Thus, the aim
of this section is to review the current thinking relating to consumer trust, with a
view to applying this construct to trust in UGPR on Facebook.
2.4.1 The theoretical foundations of trust
Trust is a complex notion, whose definition varies across subject domains and
disciplines. This section will present a cross-section of differing views that have
been broadly categorised into the three main theoretical perspectives on trust
(Cheung and Lee, 2006; Soh, 2006) in order to provide a foundation for the
understanding of this construct.

19
2.4.1.1 The personality theorist’s perspective
Personality theorists view trust as a belief, expectancy or feeling that is deeply rooted
in the personality (Cheung and Lee, 2006). This perspective explores how
individuals with different developmental experience, personality types and cultural
backgrounds vary in their propensity to trust other people in general, as opposed to
trusting or distrusting a specific individual. This propensity to trust is considered to
be a personality trait that is stable over time and across situations (Glanville and
Paxton, 2007). From this perspective, trust is often defined as a generalised
expectancy that the words or behaviours of others can be relied upon (Rotter, 1967).
2.4.1.2 The sociologist’s perspective
From a sociological perspective, trust is considered to be a social good that is
necessary for all levels of social relationships. Unlike the personality theorist’s
perspective, it is considered to apply to relationships between groups of people, as
opposed to each participating individual’s psychological state (Soh, 2006).
Trust is considered to be a tool for the reduction of modern society’s complexity and
unpredictability. Since people do not have the ability to rationally predict all
potential future events, they have no choice but to place their trust in others (Shapiro,
1987). Thus, sociologists explore the role that institutions such as legal frameworks
and industry associations play in the reduction of uncertainty between relative
strangers (Cheung and Lee, 2006).
In this perspective, trust has been viewed as the expectations that social actors have
of one another in social relationships and social systems, such as the adherence to
moral social obligations, competence and integrity (Barber, 1983). However, Lewis
and Weigert (1985) argue that limiting the conceptualisation of trust to expectations
does not reflect trust in its entirety. Instead, they suggest that trust is a mixture of
feelings and rational thinking. The exclusion of either of these factors from the
analysis of trust may incorrectly view trust as blind faith (without any cognitive base)
or a rationally calculated prediction (without any emotional base). Thus, these
authors conclude that trust is multi-faceted with distinct cognitive, emotional and

20
behavioural dimensions that are merged into a unitary social experience as illustrated
in Figure 10.
Firstly, trust is based on a cognitive process that discriminates between people and
institutions that are to be trusted and distrusted, based on evidence of trustworthiness.
Secondly, the emotional base consists of the emotional bond among all those that
participate in the relationship. The authors argue that a betrayal of trust strikes a
deadly blow to the foundation of the relationship itself, and not merely at the specific
content of the betrayal. Thirdly, the behavioural base involves undertaking a risky
course of action on the confident expectation that all persons involved will act
competently and dutifully (Lewis and Weigert, 1985).
2.4.1.3 The social psychologist’s perspective
Social psychologists study trust at the interpersonal and group levels, specifically
focusing on the transactions between individuals (Cheung and Lee, 2006; Doney and
Cannon, 1997). They view trust as a state of mind that is closely related to
situational factors of trust. Thus, in this context, is important to identify the
characteristics of the trustworthy party, and the situational elements that constitute
trust in interpersonal relationships (Soh, 2006).
Mayer et al. (1995) identified the three most frequently cited antecedents of trust in
the literature: ability, benevolence and integrity. They proposed the model shown in
Figure 11 to explain the factors concerning the trustor and the trustee that lead to
trust.

TRUST BETWEEN GROUPS
COGNITIVE
(evidence based)
BEHAVIOURAL
(taking risk)
AFFECTIVE
(emotional bond)
Figure 10 - The sociologist’s perspective of trust

21
Figure 11 - Model of trust (Mayer et al., 1995)
Ability refers to a group of skills, competencies and characteristics that enable a
party to have influence within a specific domain (Mayer et al., 1995). This could
arguably be linked to the notion of usefulness in the context of consumers relaying
product information (Soh, 2006).
Benevolence refers to the extent to which a trustee is believed to want to help the
trustor, even though the trustee is not required to be helpful and thus there is no
extrinsic reward for doing so (Mayer et al., 1995).
Integrity has been described as the consistency of the trustee’s past actions and
credible communications (Cheung and Lee, 2006). However, in order for integrity to
lead to trust, the trustor must perceive that the trustee adheres to a set of principles
that the trustor finds acceptable (Mayer et al., 1995). In terms of this study, a
recommender’s integrity would be deemed weak if they are seen to be profit seeking,
or if they are known to be associated with a brand in some way.
2.4.2 The relationship between trust and credibility
The relationship between trust and credibility has been portrayed differently in the
literature, ranging from the consideration that trust is just one dimension of

22
credibility (Ohanian, 1990) to the belief that trust should be treated as a separate and
independent construct to credibility (Soh et al., 2007).
At a high level, advertising credibility has been defined as a consumer’s perception
of the truthfulness and believability of advertising in general (Obermiller and
Spangenberg, 1998; MacKenzie and Lutz, 1989). However, there are several
perspectives of advertising credibility discussed in the literature.
2.4.2.1 Source credibility
The credibility of the source of product information has been an important concern
among advertising researchers (Metzger and Flanagin, 2000) and has been studied in
two main categories: endorser credibility and advertiser credibility (Soh, 2006). The
latter is more related to PGC, so endorser credibility is more relevant to this study.
For the purposes of this study, the endorser is the individual that posts the UGPR on
Facebook.
Ohanian (1991) describes source credibility as the message sender's positive
characteristics that influence the receiver's acceptance of the message communicated,
such as: expertise, trustworthiness and physical attractiveness. While these factors
were actually identified in the context of celebrity endorsements in advertising, they
could arguably hold true for Facebook posts, as these tend to appear next to the real
name (and sometimes a picture) of the individual posting the comment on Facebook.
2.4.2.2 Advertising credibility/scepticism
Advertising credibility represents a consumer’s perceptions of the truthfulness and
believability of advertising in general, not simply the particular ad in question
(MacKenzie and Lutz, 1989). A similar (but opposite) construct was proposed by
Obermiller and Spangenberg (1998) called consumer scepticism towards advertising,
which they defined as the tendency towards disbelief of advertising claims. The
authors elaborate that scepticism is not just limited to the literal truth of ad claims,
but could also apply to the motives of the advertiser or the value of the information
being transmitted (Obermiller and Spangenberg, 1998). Boush et al. (1993) take a

23
more positive approach, arguing that a sceptical audience may question everything,
but will at least pay attention to a message.
2.4.2.3 Ad content credibility
This has been defined as the extent to which the consumer perceives claims made
about the brand in an advertisement to be truthful and believable (MacKenzie and
Lutz, 1989). Believability is an important indicator of advertising effectiveness, with
research showing that a message’s effectiveness is restricted if it is not deemed
believable by the recipient (Beltramini, 2006).
2.4.2.4 Website credibility
Given the frequency with which consumers tend to engage in brand-related WOM
conversations (Keller, 2007) it is important to understand the impact that website
credibility will have on a consumer’s trust in the messages that they read on the
Facebook news feed.
While the Internet has become an important source of information, there is no
overarching quality control or editing process (Choi and Rifon, 2002). Trust has
been identified as a powerful filter to help consumers sift through the huge amounts
of information that they are exposed to on the Internet. In fact, 84% of respondents
in a recent survey said that being trustworthy is a requirement before interacting with
an information source (About.com, 2012).
2.4.3 Consumer trust in advertising
Soh (2006) developed a thorough model of trust in advertising, drawing on a wealth
of literature on the study of trust. This produced a valid and reliable instrument (the
ADTRUST scale) that was specific to the measurement of consumer trust in
advertising. Further studies applied this scale to various advertising media (Soh et al,
2007; Soh et al, 2009).

24
The model proposed that trust is a multi-dimensional construct that should be
operationalized as the combination of: a consumer’s perception of the reliability and
usefulness of advertising; a consumer’s emotional response to advertising; and a
consumer’s willingness to rely on the information transmitted in the advertising
message. These factors reflect the cognitive evaluation, emotional response and
behavioural intent proposed in the sociologist’s viewpoint (Lewis and Weigert,
1985).
Soh (2006) proposed that trust in advertising consists of four components as
illustrated in Figure 12. The twenty-item ADTRUST scale will be used to measure
trust in UGPR for this study. Thus, these four components are being considered an
integral part of trust, and will not be considered as antecedents.
Figure 12 - Model of trust in advertising (Soh, 2006)
2.4.3.1 Reliability
Reliability reflects a consumer’s evaluation of the ethical principles of advertising,
including honesty and reliability. Importance is also given to the information quality
of advertising and hence its informational value. In fact, a study by Rieh (2002)
found that 63% of the consumers surveyed mentioned that the trustworthiness of
information is the most important facet when judging information quality and
cognitive authority on the Internet. These principles should be transferable to the
individual that is posting the UGPR on Facebook.
2.4.3.2 Usefulness
This reflects a consumer’s feeling of how useful advertising is for purchase decision
making. From a consumer’s perspective, the primary function of advertising is to
provide them with product information that will allow them to choose between
Reliability Usefulness
Affect Willingness to rely on

25
alternatives (Soh, 2006). Thus, advertising needs to be a good source of product
information in order for it to be deemed useful.
This concept also applies to UGPR, in the sense that consumers will judge a
recommendation posted on their Facebook news feed to be useful if it is a good
source of product information.
2.4.3.3 Affect
Affect reflects a consumer’s emotional response to the advertising message, such as
its likeability and how enjoyable it is. This could arguably be linked to the notion of
attractiveness discussed in source credibility (Ohanian, 1991).
One of the major problems that marketers are facing when advertising on Facebook
is that they are trying to advertise to an audience that is looking to be entertained,
and not informed. In fact, 83% of Facebook users in the US “hardly ever clicked” or
“never clicked” on online ads or sponsored content when using Facebook
(Greenlightdigital.com, 2012). UGPR on Facebook will arguably overcome this
obstacle by at least gaining a consumer’s attention. However, in order to gain a
consumer’s trust, the UGPR must trigger an emotional response with the recipient by
being likeable, enjoyable and stimulating positive affection (Soh, 2006).
2.4.3.4 Willingness to rely on
This reflects a consumer’s behavioural intent to act on the basis of the information
conveyed in the advertising message. Soh (2006) cites the willingness to recommend
products to friends or family, and the willingness to take purchase-related decisions
as examples of behavioural intent. Both of these examples are applicable to the
product recommendations posted by consumers on Facebook. In fact, research
conducted by BlogHer (2012) on mothers in the USA, highlighted that information
obtained from social media helped respondents make decisions for their families. In
fact, 56% of the mothers in the general population had purchased a food product
based on advice that they had read online, suggesting a willingness to rely on the
advice found on this medium.

26
2.5 The potential antecedents of trust in UGPR
This section will serve to identify a number of potential antecedents of consumer
trust in UGPR that have emerged from this review of current thinking. The resultant
hypotheses will be used to develop a theoretical model in Chapter 3.
2.5.1 Advertising scepticism
Obermiller and Spangenberg (1998) argue that as consumers become more aware of
persuasion techniques and marketing tactics, they become more sceptical of ad
claims. Thus, consumers with very high advertising scepticism may be impossible to
persuade by means of information or argument, because they would not believe any
stated claims. However, they may be persuaded by other means such as non-
information appeals.
Many consumers categorically label advertising as untrustworthy, but nonetheless
still rely on it. However, there are others who are so unbelieving that they reject
advertising completely. Thus, these highly sceptical individuals need to obtain
product information to guide their decision making process from other sources, such
as their peers. This leads to the following hypothesis:
H1: A consumer’s level of advertising scepticism will be positively related to their
level of trust in UGPR on Facebook.
2.5.2 Trust in Facebook as a medium
Consumer judgements of information credibility are more a function of the website
provider’s credibility, which is viewed as the source of the information, than by their
perceptions of the actual author or creator of the content (Rieh, 2002). This
phenomenon was first described as the vehicle source effect by Aaker and Brown
(1972) who showed through an empirical study that the same content exposed on
high-status vehicles are generally more effective.
This suggests that a consumer’s perception of whether or not to trust information on
the Facebook news feed could be affected by their perception of Facebook’s

27
credibility as a website provider. In terms of this study, Hsiao et al. (2010) found
empirical evidence that a consumer’s trust in a website could enhance their trust in
the product recommendations found on that website. This leads to the following
hypothesis:
H2: A consumer’s trust in Facebook (as a medium) will be positively related to
their level of trust in UGPR on Facebook.

2.5.3 Familiarity and experience with a website
The literature suggests that experienced Internet users are more likely to view the
Internet as a credible source of information. Specifically, familiarity is a
precondition of trust (Gefen, 2000) and this familiarity is gained through
accumulated use and experience of the website in question (Kim et al., 2008;
Metzger and Flanagin, 2000).
This suggests that a consumer’s familiarity and experience with Facebook, will lead
to higher levels of trust in the UGPR that are read on this website. This leads to the
following hypotheses, which seek to measure a respondent’s accumulated use and
experience with Facebook as a medium:
H3a: A consumer’s daily active use of Facebook will be positively related to their
H3b: A consumer’s Facebook experience will be positively related to their level of
trust in UGPR on Facebook.
2.5.4 Propensity to trust
This characteristic had already been identified in the personality theorist’s
perspective of trust. People with different developmental experiences, personality
types and cultural backgrounds vary in their propensity to trust (Hofstede, 1980)
which will result in a range of propensities ranging from blind trust to a complete
unwillingness to trust. Mayer et al. (1995) suggests that a trustor’s propensity to

28
trust will influence the amount of trust one has for a trustee even before anything is
known about that person. This leads to the following hypothesis:
H4: A consumer’s propensity to trust will be positively related to their level of
2.5.5 Consumer conformity
Consumer conformity refers to the tendency of consumers to be influenced by others
in decisions and behaviours involving consumer products. Since consumer
conformity represents susceptibility to some kind of social influence, those that
conform are less trusting of their own judgement, and consequently look to the
judgement of others (Boush et al., 1993). Thus, conformists should be more likely to
trust their friend’s recommendations than non-conformists. This leads to the
following hypothesis:
H5: A consumer’s level of consumer conformity will be positively related to their
2.5.6 Self-esteem
A high self-esteem has been linked to a decreased need to yield to others, and an
increased confidence in one’s own position (Obermiller and Spangenberg, 1998).
This suggests that consumers with a high self-esteem would be less trusting of
others, which leads to the following hypothesis:
H6: A consumer’s self-esteem will be negatively related to their level of trust in
UGPR on Facebook.
2.5.7 Benevolence
Benevolence did not specifically emerge as a component of trust in advertising in the
ADTRUST scale (Soh, 2006). This could be explained by the fact that the original

29
instrument was developed to measure trust in producer-generated advertising which
is not generally perceived by consumers as being altruistic.
In terms of this study, it would be logical to assume that consumers would judge peer
recommendations as being altruistic, and thus, worthy of their trust. Consequently,
benevolence could potentially emerge as an antecedent of trust when applied to
UGPR. This leads to the following hypothesis:
H7: A consumer’s perception of their Facebook friend’s benevolence will be
positively related to their level of trust in UGPR on Facebook.
2.5.8 Age
A nationally representative survey of North American consumers (Bazaarvoice,
2012) suggests that “Millennials” (born between 1977 and 1995) trust UGC more
than PGC. While 84% of “Millennials” report that UGC has at least some influence
on what they buy, this figure drops to 70% for “Baby Boomers”. Of particular
relevance to this study is the finding that “Millennials” are three times as likely (22%
vs. 7%) as “Baby Boomers” to turn to social channels when looking for opinions
about products to buy. Furthermore, a study by Bousch et al. (1993) produced
empirical evidence that suggested that the older respondents in their study tended to
exhibit lower levels of trust in non-business sources of product information.
Both of these studies suggest a negative relationship between age and trust in UGPR,
which leads to the following hypothesis:
H8: A consumer’s age will be negatively related to their level of trust in UGPR
on Facebook.
2.5.9 Level of education
Boush et al. (1993) specifically compared trust in advertising and trust in consumer
reports in their study. While there was a negative relationship between a

30
respondent’s level of education and their level of trust in producer-generated
advertising, a positive relationship was observed for trust in consumer reports.
This suggests that a consumer’s level of education will be positively related to their
trust in the information imparted by fellow consumers. This leads to the following
hypothesis:
H9: A consumer’s level of education will be positively related to their level of
2.5.10 Gender
The literature suggests differences in the ways that both genders respond to product
information on websites, which could potentially have an impact on the levels of
trust shown towards this information (Lin and Bui, 2011; Sun et al. 2010; Awad and
Ragowsky, 2008; Cyr et al., 2007; Boush et al., 1993).
Men and women have different reactions and expectations of information posted on a
website. Men tend to dominate conversations, post longer communications and post
messages that are more informative. Women tend to resent attempts at individuals to
dominate a conversation, and place greater emphasis to the responsiveness of other
consumers to their contributions (Awad and Ragowsky, 2008). According to social
presence theory, women will be more likely to trust on a website where their peers
can easily respond to their posts, such as Facebook with its high user-base and
familiar interface. On the other hand, men prefer a more task-oriented interface
when looking for product information.
This leads to the following hypothesis:
H10: Female consumers will exhibit higher levels of trust in UGPR on Facebook
when compared to their male counterparts.

31
2.6 Summary
This chapter explored the notion of consumer trust in detail, and sought to apply the
concept of trust in advertising to UGPR. A number of potential antecedents of
consumer trust in UGPR were identified through this review of current thinking, and
these will be used to construct a theoretical model that will be presented in the next
chapter.

32
3 The investigation
The first part of this chapter will serve to define the specific research questions that
this investigation is seeking to answer. The theoretical model that was derived
through the review of current thinking will then be presented, along with a set of
hypothesised antecedents of trust in UGPR.
The rest of the chapter will outline the research strategy that was employed, and
provide details of the specific techniques that were used in order to achieve the
objectives of this investigation.
3.1 Objectives of the investigation
3.1.1 Research questions
This investigation is intended to answer the following research questions:
RQ1: To what extent do consumers trust UGPR on Facebook?
RQ2: What are the antecedents of consumer trust in UGPR on Facebook?
3.1.2 Theoretical model and research hypotheses
This study will aim to measure the predictive strength of the theoretical model
presented in Figure 13, and to identify the individual antecedents of trust that emerge
as statistically significant predictors of this construct.
It is being recognised that a number of inter-relationships may exist between the
various antecedents being proposed in this model. However, this is beyond the scope
of this research project, and thus these inter-relationships will not be explored in this
study.

33
Daily active use
Facebook
experience
Propensity to trust
Consumer
conformity
Benevolence
Trust in Facebook
Trust in UGPR
Self-esteem
Gender
Age
Level of education
Advertising
scepticism
H9 (+)
H10
(Female > Male)
H8 (-)
H7 (+)
H6 (-)
H5 (+)
H4 (+)
H3b (+)
H3a (+)
H2 (+)
H1 (+)
Figure 13 - Theoretical model and hypotheses
The relationships between the potential antecedents that were identified during the
review of current thinking in Section 2, and trust in UGPR are stated in the
following hypotheses:
H1: A consumer’s level of advertising scepticism will be positively related to
H2: A consumer’s trust in Facebook (as a medium) will be positively related to

34
H3a: A consumer’s daily active use of Facebook will be positively related to their
H3b: A consumer’s Facebook experience will be positively related to their level
of trust in UGPR on Facebook.
H4: A consumer’s propensity to trust will be positively related to their level of
H5: A consumer’s level of consumer conformity will be positively related to
H6: A consumer’s self-esteem will be negatively related to their level of trust in
UGPR on Facebook.
H7: A consumer’s perception of their Facebook friend’s benevolence will be
positively related to their level of trust in UGPR on Facebook.
H8: A consumer’s age will be negatively related to their level of trust in UGPR
on Facebook.
H9: A consumer’s level of education will be positively related to their level of
H10: Female consumers will exhibit higher levels of trust in UGPR on Facebook
when compared to their male counterparts.

35
3.2 Investigation design
3.2.1 Research strategy
A deductive approach was adopted for this study. This was a cross-sectional study
where data was collected over a period of one week, and the unit of analysis was the
individual consumer. The research followed a fixed design, where quantitative data
relating to consumer trust in UGPR and its hypothesised antecedents were collected
using a set of established survey instruments that were identified in the literature.
The theoretical model that was developed through the review of current thinking was
subsequently tested through the application of statistical techniques, in order to
determine whether the hypotheses were supported by the data collected.
3.2.2 Population and sample
This study focused on Facebook users that were listed as resident in Malta, and were
at least eighteen years old at the time the survey took place. Thus, the target
population consisted of 183,080 individuals (Facebook, 2012).
A sample size of 384 was required in order to achieve a confidence level of 95%
with a confidence interval of 5 (Bartlett et al., 2001; Krejcie and Morgan, 1970). The
sampling method used for this research was referral sampling, as this was deemed to
be the most effective way to reach as wide a selection of respondents as possible
within the time and budgetary restraints of the project.
3.2.3 Survey administration mode
The survey was administered on-line using http://www.surveymonkey.com for the
following reasons:
The target population for this research project were all Facebook users, and thus
would all have access to the Internet. This made it a cost-effective way for the
author to collect a larger number of responses to this survey, given the time and cost
limitations of this study.

36
The use of an online survey allowed the author to employ a referral sampling
strategy, which relies on respondents forwarding the link to the survey to their
contacts, thus propagating the survey beyond the author’s network of contacts.
From a data quality perspective, the electronic collection of data eliminated the
process of manual data entry, and thus eliminated the risk of human error that could
be introduced by this process (Hair et al., 2007).
From an ethical perspective, the use of an independent website effectively separated
the researcher from the respondents, thus preserving the anonymity of the responses
collected.
3.2.4 Instrumentation
This section describes the development of the instrument that was used to gather the
data required for this study.
3.2.4.1 Questionnaire Design
The questionnaire was designed online using professional survey-design software
(http://www.surveymonkey.com) and was structured as outlined in Table 1. The
final version of the survey may be seen in Appendix A.
Table 1 - Structure of questionnaire
Page Purpose Variables
1 Introduction and informed consent -
2 Screening questions Age, Facebook experience, Country of residence
3 Consumer trust measurement
questions
Trust in UGPR
4 Hypothesised antecedent
measurement questions
Advertising scepticism, Trust in Facebook, Propensity to
trust, Consumer conformity, Self-Esteem, Benevolence
5 Media usage and demographic
questions
Daily active use, Gender, Level of education

37
3.2.4.2 Variable identification and measurement
The variables required to test the theoretical model proposed by this study have been
listed in Table 2.
Table 2 - List of variables
Variable Items Source of instrument
Trust in UGPR 20 Soh (2006)
Reliability 9
Usefulness 4
Affect 3
Willingness to rely on 4
Advertising scepticism 9 Obermiller and Spangenberg (1998)
Trust in Facebook 3 Gefen (2000)
Daily active use - -
Facebook experience - -
Propensity to trust 8 McShane and Glinow (2008); Mayer and Davis (1999)
Consumer conformity 3 Boush et al. (1993)
Self-esteem 10 Rosenberg (1965)
Benevolence 5 Mayer and Davis (1999)
Age - -
Level of education - -
Gender - -
3.2.4.3 Modification of scales
Existing instruments were used to measure all variables in this study in order to
ensure the validity and reliability of the data collected (Hair et al., 2007). However,
minor amendments had to be made to some of the scales in order to adapt them to the
context of this study.
The first sixteen questions of the ADTRUST scale were reproduced verbatim from
the original source article (Soh, 2006). However, the last four questions were
modified by replacing the phrases “ad-conveyed information” and “ads” with
“product recommendations by my Facebook friends”.

38
The questions in the Benevolence scale were reproduced from the source article
(Mayer and Davis, 1999) but the phase “the trustee” was replaced with “My
Facebook friends”.
3.2.4.4 Response format
A balanced, seven-point Likert scale was used for all the continuous scale items.
Verbal labels were used throughout as illustrated in Figure 14, in order to help
respondents give more precise answers (Hair et al., 2007).
Strongly
disagree
Moderately
disagree
Slightly
disagree
Neither disagree
nor agree
Slightly
agree
Moderately
agree
Strongly
agree
Figure 14 - The seven-point Likert scale used in this study
Demographic and media usage questions were presented in the format used in similar
studies (National Statistics Office, 2012; Ball et al., 2009) in order to present a
familiar format to respondents.
All questions were set as “compulsory” at design stage of the questionnaire, in order
to avoid the accidental omission of data. The questionnaire was configured to warn
the respondent that a question had been omitted, before allowing the respondent to
proceed to the next set of questions.
3.2.4.5 Specific focus on low-involvement FMCG
The time and effort involved in the decision-making process prior to purchasing a
product, could potentially result in different levels of consumer trust in the source of
product information (Chung and Darke, 2006; Obermiller and Spangenberg, 1998).
Thus, a respondent’s replies to the various trust items in the questionnaire could be
considerably different when responding about a high-involvement product, a low-
involvement product, or worse still when the consumers are not sure which type of
product they are being asked about.

39
The author catered for this concern by specifically focusing the respondents’
attention to a fictitious consumer recommendation (Figure 15) that mentioned frozen
chips as an example of a typical low-involvement and utilitarian FMCG.
Figure 15 - Sample UGPR used in questionnaire
Frozen chips are one of the largest FMCG categories in Malta (based on internal data
from the sponsoring organisation) and thus represented a FMCG that the vast
majority of respondents could relate to. The brand name was purposely omitted from
the recommendation and replaced by “Brand X” in order to eliminate any emotional
affinity towards a particular brand.
3.2.5 Pilot study
The pilot study consisted of 30 individuals that were asked to complete the survey
between the 6th
and 7th
of September 2012. These individuals were purposely
selected by the author in order to obtain a mixture of gender, ages and fluency of the
English language.
This pilot study served to test the functionality and wording of the questionnaire.
Detailed feedback was obtained from the individuals that participated in this pilot
study, which served to identify minor errors in the wording of the Facebook
experience and Daily active use questions.
A full data cleansing and encoding exercise was carried out on the data generated by
the pilot study in order to identify any possible issues with the data prior to launching

40
the full version of the survey. No major issues were identified throughout this
process.
The scales used produced a reasonably wide range of responses in the pilot study
data. The narrowest range of responses was for the dependent variable Trust in
UPGR, which ranged between a minimum of 3.3 and a maximum of 6.2. While, the
widest range of responses was registered on the Benevolence scale, with responses
ranging from a minimum of 1.2 to a maximum of 6.4.
Finally, the reliability of the pilot study data was evaluated by computing Cronbach’s
alpha, as outlined in Table 3.
Table 3 - Cronbach’s alpha (pilot study)
Construct α
Dependent variable
Trust in UGPR .94
- Reliability .91
- Usefulness .92
- Affect .86
- Willingness to rely on .76
Independent variables
Advertising scepticism .96
Trust in Facebook .94
Propensity to trust .75
Consumer conformity .66
Self-esteem .84
Benevolence .95
Almost all of the constructs registered Cronbach’s alpha coefficients that were above
the generally accepted minimum level of α=.70 (Hair et al., 2007). The exception
was Consumer conformity (α=.66) which fell just below the .70 threshold. However,
this was above the recommended minimum Cronbach’s alpha of .60 for exploratory
studies, which is .60 (Nunnally, 1978; Robinson, Shaver, & Wrightsman, 1991).
Thus, based on this analysis of the pilot study data, the decision was taken to proceed
with the full study.

41
3.2.6 Data collection
Data collection spanned a period of seven days between the 10th
and 16th
of
September 2012 and 538 respondents completed the survey.
The link to the on-line survey was initially distributed to the author’s friends, family,
academic and business contacts. This link was subsequently re-distributed by a
number of individuals who either forwarded the e-mail or shared the link on their
Facebook wall. This process helped propagate the link to the survey pretty rapidly,
and helped achieve a wide sample of respondents.
It was not possible to calculate a response rate to this survey, since the number of
individuals contacted to take the survey could not be determined due to the viral
nature by which the link to the survey was shared.
3.2.7 Preparation and cleansing of data
The raw data was downloaded from http://www.surveymonkey.com and imported
into Microsoft Excel where the initial editing was performed.
The 538 responses were filtered down to 445 complete responses by eliminating any
respondents that fell outside the target population of this study. This was a simple
process since the first three questions of the survey were screening questions, thus
responses were deleted if the following had been selected: “Under 18” for Age, “I
do not use Facebook” for Facebook experience, or “Other” for Country of residence.
There were no incomplete responses amongst these 445 responses, due to the fact
that the survey had not allowed respondents to proceed through the survey unless all
options had been filled in.
These 445 responses were then imported into SPSS, and were encoded in order to
convert them into a form that would allow them to be put through the statistical tests
required for this study. The scale items of the various constructs being measured
were tested for reliability and then converted into summated average scores for

42
further analysis. The variables: Age, Level of education, Daily active use and
Facebook experience were encoded into increasing numerical scales to reflect the
ordinal nature of each variable. Gender was encoded as a dummy variable (with
Male = 0 and Female = 1) in order for it to be entered as an independent variable in
the multiple linear regression model (Hair et al., 2007).
3.2.8 Analysis
The data was analysed using SPSS version 19. Descriptive data was computed for
all variables, and the assumption of normality was tested for the dependent variable.
A series of one-way ANOVA tests were used to compare the means across the
various categorical variables, while an independent samples t-test was used to
compare the means across the gender variable.
The Pearson product-moment correlation coefficient was then computed to assess the
relationship between the continuous variable in order to determine the degree of
covariation between each set of variables.
Finally, the theoretical model was tested through multiple linear regression in order
to determine its ability to predict Trust in UGPR.
3.3 Ethical considerations
Ethical approval for this research was initially sought through the Management
Challenge Proposal process, specifically through the Ethics Form. Following a
review of the criteria set out in the Management Challenge Guide (Henley Business
School, 2011) the following ethical considerations were made:
• The first page of the questionnaire included an informed consent section
based on the template provided by Henley Business School.

43
• No personal data was collected, and no attempt was made to link any
respondent to the data collected. A third-party website
(http://www.surveymonkey.com) was used to collect the data, in order to
separate the author from the data collection process and to preserve the
respondent’s anonymity.
• Consumers under the age of eighteen were excluded from the study by means
of skip-logic built into the survey: the survey was designed to terminate
automatically in the event that a respondent chose the “Under 18” option in
the first question.
• The dataset collected was fully anonymised, and was stored on the author’s
password-protected personal computer throughout the study. The dataset will
be destroyed two years after the end of this project.
3.4 Delimitations of the research
The following are the factors that will affect the study, over which the researcher has
some degree of control:
• The decision to focus the respondents’ attention on just one type of product
will limit the application of this study to low-involvement and utilitarian type
goods. However, it was deemed the lesser of two evils to focus on one
specific product type, rather than risk getting unfocused data.
• While the study will focus on the measurement of consumer trust in UGPR
on the Facebook news feed, it will not seek to measure consumer trust in
alternative forms of advertising on the same medium, such as Facebook paid
ads or sponsored stories.

44
• This investigation is seeking to measure an overall perception of consumer
trust in their Facebook friends in general, as opposed to differentiating
between close friends, family and acquaintances.
• Only Facebook users that are resident in Malta will be targeted by this study,
thus the findings will not be generalizable to the entire population of Malta.
• Since a fixed-design approach was adopted, this investigation will only serve
to measure and test the antecedents that were extracted through the review of
current thinking, and will not seek to identify other antecedents directly from
the consumers.
• While a number of inter-relationships may exist between the various
antecedents being proposed in this theoretical model, these will not be
explored in this study.

45
4 Findings and analysis
This chapter will present and analyse the results obtained from the investigation that
was described in Chapter 3. The sample demographics will be presented, along with
an analysis of the respondents’ daily active use of various media. The chapter will
conclude with the testing of the theoretical model that was proposed in Chapter 3.
4.1 Sample demographics
The study yielded 445 usable responses, and the demographic profile of the sample is
show in Table 4.
Table 4 - Demographic profile of sample
Measure Items N %
Gender Male 195 44
Female 250 56
Age 18-24 32 7
25-34 185 42
35-44 127 29
45-54 67 15
55-64 26 6
65 and over 8 2
Level of education Secondary School 36 8
Sixth-Form 51 12
Diploma 99 22
Bachelors Degree 142 32
Masters Degree 99 22
Doctorate Degree 18 4
Facebook experience Less than 1 year 15 3
1-2 years 45 10
2-3 years 92 21
3-4 years 112 25
More than 4 years 181 41
Daily active use Less than 1 hour 172 39
(Facebook) 1-2 hours 147 33
2-3 hours 58 13
3-4 hours 19 4
More than 4 hours 49 11

46
The study resulted in a good response from both genders, although a higher
proportion (56%) of respondents were female. Just over 70% of respondents were
between the age of 25 and 44, while a further 15% of respondents fell into the “45-
54” age group.
The three largest groups in terms of level of education were those that had completed
a Diploma, Bachelor’s degree or a Master’s degree, and collectively accounted for
just over 76% of respondents.
Experience with Facebook as a medium was high, with 87% of the sample having
used Facebook for more than two years, while a substantial 41% of respondents had
been using it for more than four years.
Daily active use of Facebook was high, with 61% of respondents claiming to actively
use this medium for more than one hour per day, while 11% claimed to use it for
more than four hours on a typical day.
4.1.1 Daily active use by medium
Table 5 outlines the amount of time (in hours) that respondents spent actively using
each medium on a typical day. Since the sample was extracted from a population of
active Facebook users, this data does include a bias towards Facebook use among the
media that were surveyed. In fact, the Internet and Facebook are the only two media
that registered no responses in the “None” response.
Table 5 - Daily active use by medium
Percentage of TV Radio Newspaper Magazine Internet Facebook
respondents % % % % % %
None 11 27 47 59 - -
Less than 1 hour 36 56 45 37 10 39
1 to 2 hours 31 9 7 3 25 33
2 to 3 hours 14 3 - - 21 13
3 to 4 hours 6 2 - - 16 4
More than 4 hours 2 4 - - 28 11
Total 100 100 100 100 100 100

47
Internet use is high, with varying levels of daily active use amongst respondents, and
28% claiming that they actively use this medium for more than four hours on a
typical day.
Facebook use is also high, but daily active use is markedly lower than the Internet,
with 61% of respondents actively using this medium for more than one hour each
day, and 11% that actively use this medium for more than four hours each day.
Television is actively viewed by 89% of respondents on a typical day. The largest
group (36%) watch less than one hour per day, but 31% of respondents watch
between one and two hours of television per day.
Radio is actively used by 73% of respondents on a typical day. However, the
majority of respondents that do use this medium listen to less than one hour of radio
each day.
The least used media on a typical day were magazines and newspapers, with 59%
and 47% of respondents respectively choosing the “None” responses for these media.
Furthermore, the majority of respondents that do use these media only use them for
less than one hour per day.
4.1.2 Daily active Facebook use by device type
Table 6 highlights the respondents that own each type of device, and that access
Facebook on a typical day on each type of device.
Table 6 - Device ownership and Facebook access by device type
Percentage of PC/Laptop Smartphone Tablet/iPad
respondents % % %
Own this type of device 99 82 42
Access Facebook on this device 94 63 30
Given that a high number of respondents selected the “I do not own this type of
device” selection for the Smartphone and tablet/iPad questions, additional statistics

48
were computed in order to gauge the daily active Facebook use among the
respondents that actually owned each type of device (Table 7).
Table 7 - Daily active Facebook use by device type
Percentage of PC/Laptop Smartphone Tablet/iPad
respondents (N=440) (N=363) (N=186)
% % %
None 5 23 29
Less than 1 hour 43 52 44
1 to 2 hours 29 14 20
2 to 3 hours 8 4 3
3 to 4 hours 5 1 2
More than 4 hours 11 6 3
Total 100 100 100
A high percentage of smartphone owners (23%) and Tablet/iPad owners (29%) do
not access Facebook from these devices, suggesting that they only access Facebook
from their PC/laptop.
The bulk of respondents within each device type access Facebook for less than an
hour on a typical day, with 43% of PC/laptop owners, 52% of smartphone owners
and 44% of Tablet/iPad owners selecting this option. However, there were a
substantial number of respondents that access Facebook for longer periods of time.
Notably, 29% of PC/laptop users and 20% of tablet/iPad users access Facebook
between one and two hours per day. Moreover, 11% of respondents access Facebook
for more than four hours per day on their PC/laptop.
4.2 Reliability of scales
The reliability of the scales used in this study were tested for internal consistency by
computing Cronbach’s alpha. The resulting coefficients for each construct have been
listed in Table 8.

49
Table 8 - Cronbach’s alpha (full study)
Construct α
Dependent variable
Trust in UGPR: .95
- Reliability .93
- Usefulness .89
- Affect .90
- Willingness to rely on .88
Advertising scepticism .95
Trust in Facebook .92
Propensity to trust .62
Consumer conformity .59
Self-esteem .82
Benevolence .88
While most of the scales exhibited high levels of reliability, Propensity to Trust (α =
.62) and Consumer Conformity (α = .59) failed to achieve the generally accepted
minimum reliability coefficient of .70 (Hair et al., 2007). However, they both met
the recommended minimum Cronbach’s alpha for exploratory studies, which is .60
(Nunnally, 1978; Robinson, Shaver, & Wrightsman, 1991). A decision was thus
made to include these variables in subsequent analysis.
4.3 Descriptive statistics
Average summated scores were computed for all of these scales using SPSS, and
these new variables were used as the inputs to subsequent analysis. Table 9 shows
the descriptive statistics for these new variables.
The mean score of 4.62 for the dependent variable Trust in UGPR, was significantly
above the neutral score of 4.00 (t = 12.60, p < .0005). This implies that there was a
positive overall feeling of Trust in UGPR amongst respondents.

50
Table 9 - Descriptive statistics for key variables
Mean Standard
Deviation
Skewness Kurtosis
Dependent variable
Trust in UGPR 4.62 1.04 -.60 .39
Advertising scepticism 3.56 1.38 .06 -.71
Trust in Facebook 4.04 1.38 -.24 -.48
Propensity to trust 3.28 .75 -.15 .25
Consumer conformity 3.93 1.24 -.05 -.47
Self-esteem 5.77 .88 -.83 .31
Benevolence 3.85 1.25 -.21 -.25
Respondents exhibited relatively high levels of Self-esteem (M = 5.77, SD = .88)
which was significantly higher than the neutral score of 4.00 (t = 42.19, p < .0005).
Advertising scepticism (M = 3.56, SD = 1.38) was significantly below the neutral
score of 4.00 (t = -6.75, p < .0005). This suggests that on average, respondents were
not very sceptical of advertising.
Propensity to trust registered the lowest mean score (M = 3.28, SD = .75) amongst
all the scale variables that were measured in this study, and was significantly below
the neutral score of 4.00 (t = -20.28, p < .0005).
The respondents’ mean score for Benevolence (M = 3.85, SD = 1.25) was
significantly below the neutral score of 4.00 (t = -2.58, p = .010). This suggests that
respondents had a negative overall perception of the benevolence of their Facebook
friends.
Consumer conformity (t = -1.18, p = .237) and Trust in Facebook (t = .63, p =
.528) did not score significantly differently to the neutral score of 4.00 which
suggests that overall, respondents exhibited a neutral feeling towards these
hypothesised antecedents.

51
4.4 Assumption of normality for dependent variable
The assumption that the dependent variable follows a normal distribution was key to
the subsequent analysis in this section, as a number of the statistical tests used in this
analysis depend on this assumption (Hair et al., 2007).
Figure 16 - Histogram of Trust in UGPR
The histogram (Figure 16) shows that the distribution of Trust in UGPR scores is
slightly skewed to the left, but still closely follows the normal curve. Moreover, the
P-P plot (Figure 17) and Q-Q plot (Figure 18) show that most of the observations are
close to the 45° line with the exception of a few observations which are close to the
lower end of the scale. Finally, the coefficient of Skewness (-.60) which assesses the
symmetry of the distribution, and the coefficient of Kurtosis (.39) which assesses the
peakedness of the distribution are both close to zero (Table 9). All of these
observations indicate that the assumption of normality is satisfied (Hair et al.,2007;
Myers, 2000).

52
Figure 17 - Normal Q-Q Plot of Trust in UGPR
Figure 18 - Normal P-P Plot of Trust in UGPR

53
4.5 Correlation analysis
The Pearson product-moment correlation coefficient was computed to assess the
relationship between Trust in UGPR and the hypothesised antecedents in order to
determine the degree of covariation between each set of variables (Hair et al., 2007).
The results were summarised in a correlation matrix (Table 10).
Table 10 - Correlation matrix
1 2 3 4 5 6 7
1 Trust in UGPR
2 Advertising Scepticism .40**
3 Trust in Facebook .39** .45**
4 Propensity to trust .20** .19** .27**
5 Consumer conformity .36** .38** .37** .20**
6 Self-esteem -.06 -.01 -.06 -.02 -.13**
7 Benevolence .40** .43** .40** .33** .25** -.00
**
Correlation is significant at the .01 level (2-tailed).
There was a moderate, positive correlation between Trust in UGPR and Benevolence
(r = .402, p < .0005). This suggests that as a consumer’s perception of the
benevolence of their Facebook friends increases, their trust in UGPR will also
increase.
Advertising scepticism also exhibited a moderate, positive correlation with Trust in
UGPR (r = .400, p < .0005). This suggests that as a consumer’s advertising
scepticism increases, their trust in UGPR will also increase.
Three variables registered small but definite, positive correlations with Trust in
UGPR: Trust in Facebook as a medium (r = .393, p < .0005), Consumer conformity
(r = .360, p < .0005) and Propensity to trust (r = .202, p < .0005).
There was no statistically significant correlation between Trust in UGPR and Self-
esteem (r = -.062, p = .193).

54
4.6 Regression analysis
The major limitation of the Pearson correlation is that it only investigates the
relationship between a dependent variable and a single predictor variable. The
advantage of using regression analysis is that it not only identifies significant
predictors, but it also ranks them by their contribution in explaining variations in the
responses (Myers, 2000). Thus, multiple linear regression was used to test the
predictive strength of the theoretical model that was proposed in this study. The
dependent variable was Trust in UGPR, while all the hypothesised antecedents were
set as independent variables.
4.6.1 Testing the theoretical model
A significant model emerged that explained 31.0% of the variance in consumer Trust
in UGPR (F11,433 = 17.66, p < .0005, R2
= .310). This suggests that there are other
factors, which were not included in this research model, that influence a consumer’s
level of Trust in UGPR. The standardised beta coefficients shown in Table 11
indicate the relative predictive strength of each variable that emerged as significant
predictors (p < .05) in this model.
Table 11 - Coefficients for dependent variable: Trust in UGPR
Beta Std. Error Standardised
Beta
t p
(Constant) 2.275 .472 4.820 .000
Advertising scepticism .129 .037 .170 3.468 .001
Trust in Facebook .106 .037 .140 2.900 .004
Daily Active Use .075 .035 .092 2.136 .033
Facebook experience .011 .039 .012 .278 .781
Propensity to trust .059 .060 .042 .974 .331
Consumer conformity .138 .038 .165 3.619 .000
Self-esteem .000 .049 .000 -.002 .999
Benevolence .181 .040 .216 4.502 .000
Age -.056 .041 -.059 -1.378 .169
Level of education -.034 .035 -.042 -.973 .331
Gender .188 .086 .090 2.183 .030

JP Casaletto - Dissertation - MBA (Henley Business School)

JP Casaletto - Dissertation - MBA (Henley Business School)

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