The effect of recommendations on purchase intention

CAN RECOMMENDATIONS IN ADVERTISEMENTS

BACKFIRE?

Eliza Komen, August 2012

THE EFFECT OF

RECOMMENDATIONS ON

PURCHASE INTENTION

2

The effect of

recommendations on

purchase intention

Can recommendations in advertisements backfire?

by

Eliza Komen

University of Groningen

Faculty of Economics and Business

Master thesis, MSc Marketing

August, 2012

Supervisor 1: Dr. Jia Liu

Supervisor 2: Stefanie Salmon

Bataviastraat 46a

9715 KP Groningen

0625494975/0508538580

[email protected]

S1816527

3

Management summary

In this research the influence of recommendations on purchase intention is investigated. The general

findings in literature say that recommendations enhance purchase intention because it lowers search

costs and reduces information asymmetry. But nowadays, consumers have a greater urge to

differentiate themselves and they want the freedom to choose for themselves what is good or not.

This is called the need for uniqueness (Hoyer& MacInnis, 2008). Could therefore recommendations

lower purchase intention? Existing research on this topics stem from Leibenstein (1950), he indicated

the snob-effect which means that recommendations do lower purchase intention. The author limited

his findings to scarcity. Therefore the aim of this study was to investigate if purchase intention is

indeed enhanced by recommendations, and whether this main relationship could become negative -

counter effect - due to the moderating influence of NFU and personal relevance of the product.

The research was performed via a questionnaire with in total 159 participants. The results support

the literature findings on the positive influence of recommendations on purchase intention.

Furthermore, recommendations have a negative effect on purchase intention due to NFU, which is in

line with literature. However, the results did not show a significant influence of personal relevance of

the product on the relation between recommendation and purchase intention.

The main implications for this study are that although it seems like a good idea at first to incorporate

recommendations in your advertisements, marketers should be aware of the possible counter effect.

The results show that a high NFU is a common personality trait among consumers, hence,

recommendations could therefore lower the purchase intention. Leibenstein (1950) stated that for

most commodities the motivation for exclusiveness is not that great. Therefore, marketers should

thoroughly investigate whether the product they want to advertise could be used by consumers to

differentiate themselves. Because when the need for exclusiveness is low, marketers could include

recommendation to enhance purchase intention. Whereas for certain products marketers should

leave the recommendation out, or make it less visible to diminish the counter effect of

recommendations on purchase intention. The main academic implication is that this research

contributes to current literature findings because it investigated the moderating effect of NFU and

the personal relevance of the product on the main relation between recommendations and purchase

intention.

4

Preface

After studying for four years in Utrecht at TIO, university of applied science, I felt the urge to develop

myself more on an academic level. Therefore I decided to continue studying, and I have chosen

Business administration at the University of Groningen. Since I have come in contact with marketing

in my second year at TIO, I have always been very interested in the topic. Therefore it was clear to

me that I was going to specialize myself in marketing.

After 3,5 years of studying in Groningen, it was time for me to start writing my master thesis. I did

not had a quite clear idea about the research topic I was interested in. But then I heard of the

possibility to write your thesis in a small group and I became interested. When I was starting writing

my thesis, I was well aware of the challenge I was putting myself into. Writing a master thesis is a

time consuming and intensive process, accompanied with ups and downs. Because I wanted to finish

in this academic year, I put a lot of pressure on myself and my supervisor, dr. Jia Liu. There were

times when my motivation hit rock bottom, especially due to the fact that I still had three courses to

finish besides my thesis, and I had a time consuming job next to being a student. But looking back on

the past six months, I could say that I have learned a lot while writing this thesis. Actually, I also

enjoyed the process a lot. This is mainly because it was very motivating to write the thesis in a small

group. I have received a lot of constructive feedback from miss Liu, my second supervisor miss

Salmon and my fellow students Suzanne Legtenberg and Victorine Marchesini. Furthermore, Suzanne

and Victorine were very helpful in certain phases like analyzing the data. With each other’s help, we

managed to finish our thesis in time and keep the process of writing a thesis fun.

I should furthermore thank all the people who took the effort to fill in my questionnaire. Especially

the people who took the effort of distributing my questionnaire further to their friends and family.

Without them I would have not managed to acquire enough response.

Now that I have finished my master, I am going to travel upcoming February. After eight years of

studying it is now time to do absolutely nothing for a few months. Needless to say that I am very

much looking forward to that. When I return I hope to get a marketing related job soon, so I can put

my obtained knowledge into practice.

Groningen, 27 July 2012

Eliza Komen

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Table of contents

MANAGEMENT SUMMARY ........................................................................................................... 3

PREFACE ...................................................................................................................................... 4

TABLE OF CONTENTS .................................................................................................................... 5

LIST OF TABLES ............................................................................................................................. 7

LIST OF FIGURES ........................................................................................................................... 7

LIST OF ABBREVIATIONS ............................................................................................................... 7

1. INTRODUCTION ........................................................................................................................ 8

2. LITERATURE REVIEW ............................................................................................................... 10

2.1 RECOMMENDATIONS ........................................................................................................................ 10

2.1.1 INTERPERSONAL INFLUENCE ............................................................................................... 10 2.1.1.1 WORD OF MOUTH ........................................................................................................ 10 2.1.1.2 SALESPERSONS ............................................................................................................. 12 2.1.1.3 CREDIBILITY .................................................................................................................. 12

2.1.2 AUTOMATIC RECOMMENDATION SYSTEMS ....................................................................... 13 2.1.2.1 INFLUENCE OF AUTOMATIC RECOMMENDATION SYSTEMS ....................................... 14

2.1.3 INDEPENDENT WEBSITES ..................................................................................................... 14 2.1.3.1 CREDIBILITY .................................................................................................................. 15

2.2 PURCHASE INTENTION ...................................................................................................................... 16

2.2.1 ACTUAL AND IDEAL STATE ................................................................................................... 16

2.3 INFLUENCE OF RECOMMENDATIONS ON PURCHASE INTENTION ................................................................ 17 2.4 NEED FOR UNIQUENESS..................................................................................................................... 17

2.4.1 CONSEQUENCES OF NEED FOR UNIQUENESS ..................................................................... 18 2.4.2 TOPICS RELATED TO NEED FOR UNIQUENESS ..................................................................... 18

2.5 PERSONAL RELEVANCE OF THE PRODUCT. ............................................................................................. 20

2.5.1 ELABORATION LIKELIHOOD MODEL AND HEURISTIC SYSTEMATIC PROCESSING MODEL .. 21

2.6 CONCEPTUAL MODEL ........................................................................................................................ 23

3. METHODOLOGY ...................................................................................................................... 25

3.1 RESEARCH DESIGN ............................................................................................................................ 25

3.1.1 CHOICE OF RESEARCH .......................................................................................................... 25 3.1.2 CHOICE OF SAMPLE ............................................................................................................. 26

3.2 MEASURES OF THE MAJOR VARIABLES.................................................................................................. 26

3.2.1 RECOMMENDATION – INDEPENDENT VARIABLE ................................................................ 26 3.2.2 PERSONAL RELEVANCE SCENARIO – MODERATING VARIABLE ........................................... 27 3.2.3 NEED FOR UNIQUENESS – MODERATING VARIABLE ........................................................... 27 3.2.4 PURCHASE INTENTION – DEPENDENT VARIABLE ................................................................ 28

3.3 MANIPULATION CHECK AND CONTROL VARIABLES .................................................................................. 28

3.3.1 RANDOM ASSIGNMENT ....................................................................................................... 28 3.3.2 RECOMMENDATION ............................................................................................................ 28 3.3.2 PERSONAL RELEVANCE ........................................................................................................ 28

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3.3.3 WINE CONSUMPTION .......................................................................................................... 28

4. RESULTS ................................................................................................................................. 30

4.1 SAMPLE CHARACTERISTICS ................................................................................................................. 30

4.1.1 DEFINITIVE NUMBER OF PARTICIPANTS .............................................................................. 30 4.1.2 DESCRIPTION OF THE SAMPLE ............................................................................................. 30 4.1.3 REPRESENTATIVENESS ......................................................................................................... 31

4.2 MANIPULATION CHECKS .................................................................................................................... 32 4.3 RELIABILITY ..................................................................................................................................... 32 4.4 MAIN RESULTS ................................................................................................................................ 33

4.4.1 PURCHASE INTENTION AMONG THE FOUR CONDITIONS ................................................... 33 4.4.2 HYPOTHESES TESTING ......................................................................................................... 33

4.4.2.1 HYPOTHESIS 1 ............................................................................................................... 35 4.4.2.2 HYPOTHESIS 2 ............................................................................................................... 35 4.4.2.3 HYPOTHESIS 3 ............................................................................................................... 36

4.5 FURTHER RESULTS ............................................................................................................................ 37 4.6 SUMMARY MAJOR FINDINGS .............................................................................................................. 37

5. CONCLUSION AND RECOMMENDATIONS ................................................................................ 39

5.1 SUMMARY AND CONCLUSIONS ........................................................................................................... 39 5.2 MANAGERIAL AND ACADEMIC IMPLICATIONS ........................................................................................ 40 5.3 LIMITATIONS AND FURTHER RESEARCH ................................................................................................. 41

REFERENCES ............................................................................................................................... 44

APPENDIX 1: QUESTIONNAIRE .................................................................................................... 51

APPENDIX 2: DESCRIPTIVES TOTAL SAMPLE................................................................................. 61

APPENDIX 3: REPRESENTATIVENESS OF THE SAMPLE ................................................................... 63

APPENDIX 4: DESCRIPTIVES SAMPLE PER CONDITION IN CHARTS ................................................. 64

APPENDIX 4: DESCRIPTIVES SAMPLE PER CONDITION IN CHARTS ................................................. 64

APPENDIX 5: ANOVA FOR MANIPULATION CHECK ....................................................................... 68

APPENDIX 6: CRONBACH’S ALPHA ............................................................................................... 69

APPENDIX 7: ANOVA FOR TESTING PURCHASE INTENTION AMONG THE FOUR CONDITIONS ........ 72

APPENDIX 8: LINEAR REGRESSION ANALYSIS FOR TESTING HYPOTHESES...................................... 73

APPENDIX 9: MEAN NFU ............................................................................................................. 76

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List of tables Table Page number

Table 1: Research design 26 Table 2: Sample description 31 Table 3: Familiarity with Supermarket Wine handbook and Omfietswijn-logo 31 Table 4: Sample description per condition 32 Table 5: Cronbach’s alpha scores 33 Table 6: Significant figures of regression 35

List of figures Figure Page number

1: Conceptual model 25 2: Omfietswijn-logo 27

List of abbreviations The following abbreviations were used throughout this report (in alphabetical order):

e.g. For example

et al. And others

etc. Etcetera

H Hypothesis

i.e. That means

NFU Need for uniqueness

SPSS Statistical Package for the Social Sciences

WOM Word of mouth

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1. Introduction

Recommendations play a big role in marketing nowadays. Consumers can easily obtain information

of products on the Internet. Therefore they could compare and choose from a lot of different

products in their purchase decision. The general conclusion is that recommendations enhance the

purchase intention of consumers, because it lowers search costs and reduces information

asymmetry, which exist when there is a lot of different information that contradict each other (Laffey

& Gandy, 2009). However, consumers also have the need to differentiate themselves. Having status

is getting more important. This can be obtained by acquiring unique products to impress others.

Furthermore, consumers want to have the freedom to choose for themselves what is good or not

instead of letting others tell you that, this is called the need for uniqueness (Hoyer & MacInnis,

2008). Could recommendations therefore have a counter effect on purchase intention? To answer

this question, this research answers the following problem statement;

“How does included recommendations in advertisements influence purchase intention, and

could this influence be turned due to the moderating effect of personal relevance of the product and

need for uniqueness on this relation?”

The following research questions are answered; how does included recommendations influence

purchase intention? How does personal relevance of the product affects the relation between

recommendations and purchase intention? How does the need for uniqueness affects the relation

between recommendations and purchase intention? To answer the problem statement I undertook a

quantitative research - questionnaire - among 159 people.

Based on existing discussion and publication we may conclude that recommendations influence

purchase intention because research shows that authority sells (Jones, 2011), that people are

influenced by word of mouth in their purchase decisions (Chen, Wang & Xie, 2011), and that the

demand for a commodity is increased due to the fact that others are also consuming the same

commodity - bandwagon effect - (Leibenstein, 1950). Therefore it could be concluded that products

which are recommended will enhance consumers’ purchase intention.

However, a lot of research is focused on how purchase intention is enhanced by positive word of

mouth, authority and the bandwagon effect. Little research is focused on the topic if

recommendations could have a counter effect whereby consumers’ purchase intention will decrease

due to recommendations. The existing research is done by Leibenstein (1950) and he came up with

9

the snob-effect which means that recommendation lowers purchase intention. However, prior

research concerning the snob-effect is mostly based on scarcity (Van Herpen, Pieters & Zeelenberg,

2005). Furthermore, Leibenstein (1950) stated in his research that for most commodities and most

buyers the motivation for exclusiveness is not that great. Is this also true for experience goods? With

these goods it is hard to assess the quality before the purchase. This implies that consumers turn to

various sources of information for experience goods (Nelson, 1970). Therefore, recommendations

could have an effect on purchase intention for experience goods because it can be seen as an

information source consumers use in their purchase decisions.

This study builds on the research of Leibenstein (1950). My main contributions are that I investigate

whether recommendations negatively influence purchase intention due to the personal relevance of

the product. When a product is highly relevant, it is consistent with people’s values, needs, goals and

emotions and will have a higher outcome risk (Hoyer & MacInnis, 2008). When people perceive

something as highly relevant the product influences people in their purchase decision due to risk of

the outcome and the route of persuasion taken. If a product is highly relevant to you, people are

persuaded via central processing, therefore recommendations could lower purchase intention due to

counterarguments. Whenever a product has low relevance on consumers life, the accompanied

outcome risk is lower and consumers are less involved in acquiring information on beforehand which

implies that they could be easier influenced by simple heuristics like recommendations (peripheral

route). Second, I include NFU to check whether the lowered purchase intention is owing to a high

NFU. Third, it helps marketers decide whether to advertise with statements like “recommended by x

people”, or “best tested according to Consumentenbond, January 2012”, this depends on the fact

whether the product is of high or low relevance to the consumer. Because at a first glance, including

recommendations in advertisements seems like a good idea. People could use the rule of thumb that

if a lot of people have the product, or it is been recommended by either friends or an authority

figure, it must be good. However, for some goods people want the freedom to judge for themselves

whether the product is good or not (Hoyer & MacInnis, 2008). Furthermore, consumers want to

differentiate themselves from others to give them some sort of status. Therefore it could be that you

should, as a marketer, do not include recommendations in advertisements for certain products due

to a possible counter effect.

I organize this paper in five chapters. I will continue with the literature review, which will be

investigated in chapter two. The research design and plan of analysis are stated in chapter three. The

results will be presented and discussed in chapter four. Finally, chapter five consist out of conclusion,

implications, limitations of the research and directions for future research.

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2. Literature review

In this chapter the findings of the current literature are discussed. It provides a theoretical

background from which the hypothesis are drawn up. This chapter starts with the independent

variable - recommendations -, than the dependent variable - purchase intention - will be explained

and the relationship between recommendations and purchase intention is described. Lastly, personal

relevance of the product and NFU are introduced as two possible moderators that influence the main

effect.

2.1 Recommendations

Recommendations occur when a customer will refer a seller or product positively to another

potential customer (Palmatier, Dant, Grewal & Evans, 2006). It reduces search efforts and could

increase sales because recommendations deliver relevant information to consumers, but this

depends on how trustworthy the source is.

There are several types of recommendations. Below is an overview.

2.1.1 Interpersonal influence

Interpersonal influence has two main types; informative and normative influence (Deutsch &

Gerrard, 1955). Informative influence refers to the tendency to accept information from others as

evidence of reality. For example, opinion leaders directly influence other consumers by giving them

advice and verbal directions about their search for, purchase of, and use of a product (Flynn,

Goldsmith & Eastman, 1994). Normative influence on the other hand entails the tendency to

conform to the expectations of others (Burnkrant & Cousineau, 1975). Hence, normative opinion

leaders exert social pressure and social support and thereby influence decision making processes of

the influenced consumer (Glock & Nicosia, 1964). Since people aim to create and maintain

meaningful social relationships, they often engage in behaviors approved by others such as adopting

a product to appeal to other product adopters (Cialdini & Goldstein, 2004). Grewal, Mehta & Kardes

(2000) say that the product and situation determine which type of influence is more important.

Privately consumed goods prioritize the informative influence, whereas for publicly consumer goods

both types of influences are critical. Two forms of interpersonal influences are WOM and

salespersons, they are described in the subparagraphs below.

2.1.1.1 Word of mouth

Word of mouth (hereafter WOM) occurs when consumers inform one another about (un)favorable

product characteristics. It is defined as informal communication between consumers about the

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ownership, usage, or characteristics of products, services, and sellers (Westbrook, 1987). In addition

to exchanging information, consumers may also influence others simply by visibly using a product

(Gilbert, Jager, Deffuant & Adjali, 2007). Hoyer & MacInnis (2008) define WOM as a nonmarketing

source that is delivered personally. Several authors like Silverman (1997) suggest that WOM has the

most important influence in the consumer decision making process. However, high mass media

usage is necessary to increase the speed of information and therefore make consumers become

aware of the product. WOM effectively encourages people to start using a product (Herr, Kardes &

Kim, 1991), and it is more likely to activate people to act upon received advice than mass media

(Gelb & Johnson, 1995).

Research shows that positive WOM is more common than negative WOM (East, Hammond & Wright,

2007) and it is more persuasive than written information (Herr et al., 1991). Online forums, blogs,

websites and e-mail can potentially magnify the effect of WOM (Hoyer & MacInnis, 2008). This is

because review sites on the Internet have also become more influential. Furthermore, producers

discovered blogs as a possible channel to stimulate the social diffusion of their products. By sending

free samples to popular bloggers, they hope to get positive product reviews that generate WOM

recommendations (Gilbert et al., 2007). An example of this is Nivea which uses readers of a certain

magazine that fits the target group as consumers that give recommendations to other consumers.

They send samples of new product to readers, after that they stated in the magazine that “86% of

the readers is convinced and would recommend product X to their friends”.

WOM can come from a person’s reference group (in-group) or from opinion leaders like market

mavens which is a consumer on whom others rely for information about the marketplace in general.

A market maven seems to know all about the best products, good sales and the best stores (Hoyer &

MacInnis, 2008).

Not every person has the same amount of influence on other consumers. This depends on the status

that people have. People of high status may have a disproportionate influence on other consumers,

which is one of the reasons why many producers use famous people to endorse their products

(Gilbert et al., 2007). Different types of influential consumers possess varying characteristics, which

implies their varying influence on the consumer around them. van Eck, Jager & Leeflang (2011) made

a typology of influential consumers;

Innovators/early adopters (Engel, Kegerreis & Blackwell, 1969) which are consumers who

influence other consumers through their innovative behavior and knowledge about a specific

product category.

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Market mavens (Feick & Price, 1987) are consumers who may not have knowledge about a

specific product category, but rather about markets in general.

Opinion leaders (Katz & Lazarsfeld, 1955) who are consumers that represent a combination

of innovative behavior and market knowledge.

Both opinion leaders and early adopters reveal similar characteristic, which makes it likely that many

opinion leaders are early adopters and vice versa (van Eck et al., 2011). By sharing their expert

evaluations, opinion leaders ‘translate’ marketing messages into WOM, which recipients perceive as

more reliable than an advertisement (Nielsen, 2007).

With taking all of the above into account, it seems that providing WOM may be attractive for several

reasons. However, consumers who promote a product through WOM may decrease the uniqueness

of their possessions. Thus, positive WOM may hurt consumers who have a high NFU (Cheema &

Kaikati 2010). The NFU is later in this report described.

2.1.1.2 Salespersons

Recommendations could also stem from salespersons at the point of purchase. Zeng and Reinartz

(2003) stated that consumers rely on salespersons for successfully choosing among product

alternatives. This is because it requires certain levels of consumer expertise to choose successfully,

which is the understanding of the attributes in a product and knowledge about how various

alternatives stack up on these attributes. Clearly, information does not mean expertise.

Hoyer & MacInnis (2008) define salespersons as a marketing source delivered personally. Consumers

may use the knowledge and assistance of salespersons to further their personal goals. Salespeople

are sometimes questioned for their credibility because they work for the company and could

therefore be not objective.

2.1.1.3 Credibility

Recommendations stem from different sources. But which one is the best for consumers? This

depends on how credible consumers perceive the recommendation. Consumers tend to perceive

information delivered through marketing sources as being less credible, more biased, and

manipulative. In contrast, nonmarketing sources appear more credible because we do not believe

that they have a personal stake in our purchase, consumption, or disposition decisions (Hoyer &

MacInnis, 2008).

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Because nonmarketing sources are credible, they tend to have more influence on consumer

decisions than marketing sources do (Lazarsfeld, Berelson & Gaudet, 1948). We tend to believe

information that we hear from people with whom we have close relationships, in part because their

similarity to us (and our values and preferences) makes their opinions credible (Duhan, Johnson,

Wilcox & Harrell, 1997). Certain people are also regarded as more credible than others because they

are experts or are generally recognized as having unbiased opinions. Similarly, certain media have

higher credibility because they base their opinions on carefully acquired and trustworthy information

(Hoyer & MacInnis, 2008).

2.1.2 Automatic recommendation systems

Bodapati (2008) distinguish two kinds of recommendation systems based on the underlying

technology. Recommender systems can be broadly categorized as content-based and collaborative.

The main difference between the two systems are that content-based systems match customer

interests with information about the products, while collaborative systems utilize preference ratings

from the other customers (Cheung, Kwok, Law & Tsui, 2003).

Content-based systems provide recommendations to a customer by automatically matching his

interests with product contents, and have been reported in the literature on recommending web

pages, newsgroup messages and news items. Notice that recommendations are made without

relying on information provided by the other customers. Typically, they use an intelligent engine to

mine the customer’s ratings records and then create predictive user models for product

recommendation (Bodapati, 2008). These customer ratings may either be acquired explicitly by form-

filling or implicitly via an intelligent agent (Cooley, Mobasher, Srivastava, 1999). Purchase records can

also be good indicators of customer preferences (Bodapati, 2008). This is in line with a cornerstone

idea in customer relationship management whereby a firm should emphasize selling more products

to existing consumers rather than merely acquiring more consumers. To achieve add-on selling,

many firms use automatic recommendation systems (Bodapati, 2008). The author further discussed

that companies should not only base their recommendation decisions on the raw purchase

probability but also on the purchase probability conditional of the recommendation; thus, it is

important to consider the sensitivity of the purchase to the recommendation.

It has been intuitively assumed that recommendations increase sales by providing high-quality,

useful information to customers. However, customers may not trust recommendations whose

rationale is not properly explained (Wang & Benbasat, 2007).

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Cheung, Kwok, Law & Tsui (2003) call automatic recommendations a form of personalized direct

marketing, whereby direct marketing is defined as a promotion process which motivates customers

to place orders through various channels (McDonald, 1998). Due to the increasing popularity of

Internet commerce, a wealth of information about the customers can now be readily acquired on-

line.

One consequence of the giant growth of Internet is that a tremendous amount of product

information can now be made available to the consumers at a very low cost. However, consumers,

who were used to having only a limited range of product choices due to physical and/or time

constraints, are now facing the problem of information overload. An effective way to increase

customer satisfaction and consequently customer loyalty should be one that helps the customers

identify products according to their interests. This calls for the provision of personalized product

recommendations (Cheung et al., 2003).

However, Pathak, Garfinkel, Gopal, Venkatesan & Yin (2010) argue that recommendations itself add

to information overload and could therefore become lost in the clutter. This is because a typical

product web page of an online retailer includes information such as product features, images, expert

reviews, customer reviews, and ratings. Adding recommendations could enhance the information

overload.

2.1.2.1 Influence of automatic recommendation systems

Automatic recommendation systems attempt to analyze a customer’s purchase history and identify

products the customer may buy if the firm were to bring these products to the customer’s attention

(Bodapati, 2008). An example of this is YouTube, amazon.com (“recommended because you

purchased…”/“Consumers who bought this item also bought….”) or hm.com whereby the site

automatically come up with the same kind of clothes that you might also like when you are shopping

online. The aim of recommender systems is to help users navigate through an universe of products in

an online store by providing products that the users may like (Choi, Lee & Kim, 2011). Automatic

recommendation systems contribute to better customer experience and enhance success in meeting

customer needs (Liang, Lai & Ku, 2006). These systems reduce search effort exertion by customers

and increase cross-selling by providing relevant product recommendations (Choi et al., 2011).

2.1.3 Independent websites

Lastly, another kind of recommendations are those which stem from independent comparison

websites like consumentenbond.nl, kiesbeter.nl or independer.com. Comparison websites enable

15

consumers to evaluate and choose products from a range of providers (Laffey & Gandy, 2009).

Moreover, the authors stated that comparison websites lower search costs and reduce information

asymmetry by offering extensive product information to support consumers in making a product

choice. Zeng and Reinartz (2003) call these website expertise providers. Their mission is to facilitate

the development of consumer expertise and help consumers make better decisions. They generate

their own proprietary information that is very valuable for consumer decision making. Most

comparison sites are based on expensive products whereby the risk of purchasing a product is more

present than for convenience goods. However, there are also comparison sites for convenience

goods. An example is omfietswijn.nl. This site ranks wine that different supermarkets are selling.

It can be argued that independent website are some kind of WOM. However, the difference between

WOM and independent website is that the latter contains information and recommendations which

stem from comparing and evaluating different brands. The purpose is to help consumers in their

purchase decision by recommending the best products in certain product categories by comparing

and evaluating different products. An intensive research has been set up to compare the different

products. In the case of WOM, this stems normally from family and friends who purchase a certain

product and are satisfied without an extensive investigating beforehand or trying different products.

Furthermore we can say that websites like kiesbeter.nl and consumentebond.nl are more

independent - the sites do not gain anything by their recommendations - and more knowledgeable

than regular WOM.

These independent comparison sites are becoming more popular. Furthermore, most product

providers cannot simply ignore the impact of comparison websites; they are a too important

distribution channel and are encouraging customers to regularly switch (Laffey & Gandy, 2009).

2.1.3.1 Credibility

Zeng and Reinartz (2003) stated that independence and trust are key ingredients for success in this

domain. Independence is necessary to guarantee that the advice that consumers are going to get is

unbiased. Consumers also need to build up trust towards the expertise providers, trust in its

capabilities of providing deep and sound advice and trust in its independence. Because these sources

are independent - a nonmarketing source - they are viewed by consumers as high in credibility. Hoyer

& MacInnis (2008) say that many consumers choose movies based on films critics’ recommendations,

make dining decisions based on restaurant reviews, make buying decisions based on Consumer

Reports articles, and choose hotels based on the American Automobile Association’s rating. This

16

shows that people are influenced in their purchase decision through an entity outside a marketing

organization.

In short we could say that independent websites influence consumers because they are seen as a

trustworthy - nonmarketing - source to obtain information. They will enhance the purchase intention

because people are influenced in their purchase decision through an entity outside a marketing

organization. Therefore I focus on independent website in this research because I do not want the

answers to be biased by credibility issues. This means that recommendations that stem from

marketing sources (e.g. salespersons) will not be investigated. Besides, Internet is a very popular

source to obtain information. About 95% of Dutch people who is active on Internet use it to obtain

information (cbs.nl).

2.2 Purchase intention

Purchase intention is the dependent variable. This research investigates whether recommendations

(the independent variable) has influence on purchase intention (dependent variable) and how this

relationship is influenced by two moderators.

Chintagunta & Lee (2012) say that the intention to purchase a particular product precedes the actual

purchase. It reflect consumers’ likelihood of purchasing a product.

2.2.1 Actual and ideal state

Normally someone buys a product because they experience a gap between their ideal state and their

actual state. Hoyer & MacInnis (2008) define an ideal state as a way that consumers would like a

situation to be. We form our ideal state by relying on simple expectations, usually based on past

experience. Furthermore, it can be a function of our future goals or aspirations. Both expectations

and aspirations are often stimulated by our own personal motivations - what we want to be, based

on our self-image - and by aspects of our own culture. Reference groups also play a critical role

because we strive to be accepted by others and because reference groups serve as a guide to our

behavior. Finally, major changes in personal circumstances can instigate new ideal states (Hoyer &

MacInnis, 2008).

The actual state is the real situation as consumers perceive it now. It is mostly influenced by simple

physical factors like malfunction of running out of a product. Needs also play a critical role, if your

friends make fun of your clothes, your actual state would not be acceptable. Problem recognition

occurs if consumers become aware of a discrepancy between the actual state and the ideal state

(Hoyer & MacInnis, 2008). If this occurs, a consumer get involved with the consumer decision

17

process. This is not for every situation and consumer true, whether a consumer is likely to act

depends on the amount of discrepancy, the level of motivation, ability, and opportunity. The higher

these levels, the more likely that the consumer will act (Hoyer & MacInnis, 2008). If problem

recognition is stimulated, consumers will start the decision process to solve this problem. This starts

with internal search, which is the process of recalling stored information from memory (Hoyer &

MacInnis, 2008). If the consumer’s decision cannot be entirely based on information from memory,

they engage in external search, the process of collecting information from outside sources like

magazines, dealers and advertisements (Hoyer & MacInnis, 2008).

2.3 Influence of recommendations on purchase intention

In short we could say that WOM has the most important influence in the consumer decision making

process (Silverman, 1997). Furthermore, Zeng and Reinartz (2003) say that consumers rely on

salespersons for successfully choosing among product alternatives. Automatic recommendation

systems contribute to better customer experience and enhance success in meeting customer needs

(Liang et al., 2006) which will enhance the purchase intention. Lastly, independent websites lower

search costs and reduce information asymmetry (Laffey & Gandy, 2009).

Therefore we could say that recommendation have an influence on purchase intention because they

assist in consumers’ purchase decision by reducing search efforts and they could increase sales

because recommendations deliver relevant information to consumers, but this depends on how

trustworthy the source is. Furthermore, based on existing discussion and publication we may

conclude that recommendations influence purchase intention because research shows that authority

sells (Jones, 2011), that people are influenced by WOM in their purchase decisions (Chen, Wang &

Xie, 2011), and that the demand for a commodity is increased due to the fact that others are also

consuming the same commodity - bandwagon effect - (Leibenstein, 1950). Therefore it could be

concluded that products which are recommended will enhance consumers’ purchase intention.

Therefore I come up with the following hypothesis:

H1: Recommendations, compared with no recommendations,

will increase purchase intention.

2.4 Need for uniqueness

NFU is defined as a characteristic trait of consumers who pursue novelty through the purchase, use

and disposition of goods and services (Tepper Tian, Bearden & Hunter, 2001). It covers three

18

behavioral dimensions: creative choice counter conformity, unpopular choice counter conformity

and avoidance of similarity. In the case of creative choice counter conformity the consumer’s choice

reflects social distinctiveness, yet the choice is one that others will approve of. Unpopular choice

counter conformity means choosing products and brands that do not conform to establish

distinctiveness despite possible social disapproval. Avoidance of similarity reflects losing interest in

possessions that become commonplace to avoid the norm and hence re-establish distinctiveness

(Hoyer & MacInnis, 2008).

2.4.1 Consequences of need for uniqueness

People who score high on the personality trait NFU may dispose clothing that has become too

popular in favor of emerging fashion trends, seek out handcrafted or personalized items, and

customize products to their own specifications (Hoyer & MacInnis, 2008).

Furthermore, they may show more reactance to recommendations (Hoyer & MacInnis, 2008). This

because they do not want to be said what is good and what not. They are doing the opposite of what

the individual or groups wants them to do (Hoyer & MacInnis, 2008). This is because people feel that

their freedom is being threatened and therefore engage in reactance (Hoyer & MacInnis, 2008).

Another consequence of a high NFU is the desire to possess unique products (Simonson & Nowlis,

2000), which provide differentiation from other people. For example, high uniqueness consumers are

likely to prefer distinct product designs (Bloch, 1995) with attributes that “define the person as

different from members of his or her reference group” (Snyder, 1992). High uniqueness consumers

are more drawn to scarce products than low uniqueness consumers, exert more effort to own

innovative products (Lynn 1992; Snyder 1992), and are more likely to choose options that others do

not choose (Worchel, Lee, & Adewole, 1975).

2.4.2 Topics related to need for uniqueness

NFU is related to self-monitoring behavior whereby people look to others for cues on how to behave.

If you score low on self-monitoring behavior you are guided more by your own preferences and

desires and you are less influenced by normative expectations (Becherer & Richard, 1978). Normative

influence represent how others influence our behavior through social pressure, it is the collective

decisions about what constitutes appropriate behavior. Normative influence can also affect

conformity, the tendency for an individual to behave as the group behaves (Hoyer & MacInnis, 2008).

Stafford (1966) say that conformity is related to brand-choice congruence because you might

conform buying the same brands as others in your group do.

19

Besides, NFU is related to the personality trait competitiveness which is de desire to outdo others

through conspicuous consumption of material items like gadgets (Hoyer & MacInnis, 2008). People

who express NFU are somewhat rebellious. This means that they are doing the opposite of what an

individual or group wants you to do. Algesheimer, Dholakia and Hermann (2005) found that when a

member feels too much pressure to perform certain rituals or assume certain roles, the desire to

participate in the community or buy the brand in the future may be lowered.

Furthermore, NFU is somewhat the same as the snob effect. This is the extent to which the demand

for a consumer good is decreased owing to the fact that others are also consuming the same

commodity (or that others are increasing their consumption of that commodity). This represents the

desire of people to be exclusive; to be different; to dissociate themselves from the "common herd"

(Leibenstein, 1950). Therefore, including recommendations could have a counter effect because if a

product is recommended, the overall demand could increase. This implies a decreasing demand for

people who show the snob-effect because they want to be exclusive. This is in line with this research

because the NFU has similarities with the snob-effect. The author further said that for most

commodities and most buyers, the motivation for exclusiveness is not that great.

In short we could say that NFU influence consumers because they are guided by the behavior of

others. If a lot of people show the same behavior, high NFU consumers react by showing a devious

behavior simply to differentiate themselves. This will moderate the effect of recommendations on

purchase intention because if you score high on NFU, recommendations could lower your purchase

intention because you want to differentiate yourself from others by not buying the product, and

because you want to determine for yourself what is good or not. For people who score low on NFU, it

is not likely that recommendations will decrease their purchase intention. Those consumers do not

have a strong urge to differentiate themselves, furthermore, they look at other consumers for

guidelines on how to behave. Which implies that recommendations could be seen as an advice on

what is (dis)approved of by other consumers.


H2: Recommendations, compared with no recommendations, will decrease purchase

intention for people who score high on need for uniqueness, compared with people who score

low on need for uniqueness.

20

2.5 Personal relevance of the product.

Hoyer & MacInnis (2008) define personal relevance as something that has a direct bearing on the self

and has potentially significant consequences or implications for our lives. Normally, products which

will have a higher risk in terms of outcomes - e.g. loss of money or damage for your image when the

quality is bad -, will have a higher personal relevance. The authors stated that people perceive

something as personally relevant when it is consistent with their values, needs, goals and emotions.

Trampe, Stapel, Siero & Mulder (2010) stated that personal relevance is regarded as the extent to

which an advocacy has intrinsic importance, or personal meaning. Consumers often buy products and

brands for what they mean, rather than solely for what they can do (Belk 1988; Berger & Heath

2007). Furthermore, the consumers’ perception of the personal relevance of a product itself is being

influenced by product attributes (Zhu, Wang, Yan & Wu, 2009).

Zeng and Reinartz (2003) made a distinction between products and services which are bought

primarily for their physical performance, and products and services which are bought for their social

image or for their sensory enjoyment. The former are called functional goods (for example,

detergents, insurance, and appliances), the latter are called value-expressive goods (for example,

clothing, jewelry, and office location). If a product is dominated by functional concerns, it will often

be evaluated along a sober list of various product attributes, which makes comparisons quite easy. In

contrast, value expressive goods are not chosen on attribute information but based on judgments

that are holistic and difficult to articulate. Issues such as the relation of the product to one's self,

nonverbal cues, and emotional experiences become overriding aspects of the purchase.

Moreover, there is the emblematic function of products whereby products are being used to

symbolize membership in social groups. We consciously or unconsciously use brands and products to

symbolize the groups to which we belong or want to belong (Edson Escalas & Bettman, 2003). A

watch or a car may symbolize our social status. This is especially true for products whereby the

consumer is highly involved and when the products are consumed in public. Therefore we can

roughly divide products into ones that are used in a private matter and products which are used

publicly. When a product is used to symbolize our social status we want to purchase something that

a certain group of consumers have (Edson Escalas & Bettman, 2003). Therefore, recommendation

may lower the purchase intention because if a lot of people recommend/purchase the product, and

we want to distance ourselves from certain groups, we will not buy the recommended product.

21

2.5.1 Elaboration Likelihood Model and Heuristic Systematic Processing Model

In marketing it is widely known that when under conditions of high personal relevance people chose

to spend more time thinking about the advertisement than individuals exposed to the same

advertisement under low personal relevance conditions (Haugtvedt & Strathman, 1990). This is

linked to two dual process theories, namely; the concept of the Elaboration Likelihood Model (Petty

& Cacioppo, 1984) and the Heuristic Systematic Processing Model (Chaiken, 1980). Since there is a

great deal of overlap between these two theories, they will be integrated into one theoretical

framework.

Fennis & Stroebe (2010) say that dual process theories distinguish two routes to persuasion, which

form the endpoints of a continuum of processing intensity. The dual process theories of persuasion

consider two modes of information processing, systematic and non-systematic (i.e. peripheral or

heuristic processing). Modes differ in the extent to which individuals engage in message-relevant

thought in order to decide on whether to accept message arguments. The mode used depends on

processing ability and processing motivation. Processing motivation is important because unless an

issue is relevant to recipients, they will not expend much effort in thinking about arguments for or

against the issue. Personal relevance (i.e. the importance of an outcome for the individual) is the

major variable affecting processing motivation. Processing ability is important because in order to

judge the validity of the arguments contained in a communication a person needs knowledge, time

and peace of mind - i.e. absence of distraction - (Fennis & Stroebe, 2010).

The central route to persuasion is taken when recipients carefully and thoughtfully consider the

arguments presented in support of a position, - systematic processing -. Consumers are influenced

via the quality of the arguments (Petty & Cacioppo, 1984). The second route reflects the fact that

people often change their attitudes without thinking about the arguments contained in a

communication, for example, because an expert or a trusted friend has made a recommendation or

because the issue is unimportant. Petty & Cacioppo (1986) called this mode of attitude change the

peripheral route to persuasion. Consumers are then influenced via rules of thumbs (Petty &

Cacioppo, 1984).

Originally it was assumed that peripheral (heuristic)- and systematic processing modes are

compensatory: the more individuals relied on systematic processing, the less they would use

heuristic processing (Petty & Cacioppo, 1986). More recently it has been suggested that the two

modes of processing can co-occur if systematic processing of arguments does not allow one to arrive

22

at a clear-cut conclusion, for example, because the arguments contained in a communication are

ambiguous (Bohner, Moskowitz, Chaiken, 1995; Chaiken & Maheswaran, 1994).

Therefore we can say that when products are highly relevant to the consumer, the advertisement

with recommendation will be more evaluated through central-route processing because people are

more motivated to elaborate the advertisement thoroughly. This means that consumers may come

up with counterarguments because they analyze the true merits or central issues of the message

carefully and effortful (Hoyer & MacInnis, 2008). Petty & Cacioppo (1984) stated that under central

processing cognitive responses occur that deeply scrutinize the depicted claims in advertisements.

This could result in agreeing with the claims - support arguments -, or questioning them and come up

with counterarguments. However, when people are taking the peripheral route because the product

is of low personal relevance, a recommendation can function as a heuristic. This is a simple decision

rule to help consumers making purchase decisions (Hoyer & MacInnis, 2008). Therefore, both routes

of persuasion are applicable for this research. For example (Trampe, 2012), if a product advertise

with the claim “sold more than 11.000.000 times this year” consumers in the central processing

could come up with the counter argument “Do I like everything that the majority of consumers

like?”. Consumers in the peripheral route could be thinking “11.000.000 people cannot be wrong”.

This indicates that via the central route a recommendation could not have the intended effect

because people scrutinize the message more and could question the depicted recommendation.

They could feel that it is not trustworthy enough, or they like to determine for themselves what is

good or not.

This research focuses on experience goods, which are goods whereby it is difficult to assess the

quality prior to purchase and usage, for example wine. Nelson (1970) stated that when making

purchasing decisions for experience goods, consumers usually turn to various sources of quality

information on the product in the absence of any pre-purchase quality assessment.

When the personal relevance of the product is high, people engage more in acquiring information on

beforehand. Zeng and Reinartz (2003) stated that when information search is an important factor in

consumer decision making, Internet has several advantages for information search because it has

greatly improved the effectiveness of information search. Furthermore, they stated that it depends

on the perceived risk, frequency of purchase and functionality versus value expressive whether

Internet is being used in the information search.

23

In short we could say that personal relevance of the product influence people in their purchase

decision due to risk of the outcome, the emblematic function of products and the route to

persuasion taken. This will moderate the effect of recommendations on purchase intention because

if a product is highly relevant to you, recommendations could lower your purchase intention. Under

central processing consumers come up with counterarguments whereby the recommendations lower

the purchase intention. Whenever a product has low relevance on consumers life, the accompanied

outcome risk is lower. Hence, consumers are less involved in acquiring information on beforehand.

This implies that they could be easier influenced by simple heuristics like recommendations

(peripheral route). Therefore, recommendations will not decrease purchase intention for low

personal relevant products.


H3: Recommendations, compared with no recommendations, will decrease purchase

intention for high personal relevant products, compared with low personal relevant products.

2.6 Conceptual model

The conceptual model gives an overview of all of the above mentioned hypotheses. For the main

effect I expect a positive relationship, it is logical that the purchase intention will increase due to

recommendations. People could use the rule of thumb that if a lot of people have the product, or it is

been recommended by either friends or an authority figure, it must be good. However, could this

main effect be turned due to the need for uniqueness and personal relevance of the product.

The conceptual model for this research is depicted below.

24

Figure 1: Conceptual model

H2

H3

H1 Purchase intention

Recommendation

Need for

uniqueness

Personal relevance

of the product

25

3. Methodology

In this chapter the used methodology is described to investigate the research question and the

formulated hypotheses. Furthermore, it explains how the independent variable, the two moderating

variables and the dependent variable were measured.

3.1 Research design

To answer the problem statement;

“How does included recommendations in advertisements influence purchase intention,

and could this influence be turned due to the moderating effect of personal

relevance of the product and need for uniqueness on this relation?”

I used a 2 (recommendation versus no recommendation) × 2 (low versus high personal relevance) ×

2 (low versus high NFU) design. The design is depicted below in table 1.

Low personal relevance High personal relevance

Low NFU High NFU Low NFU High NFU

Recommendation in

advertisement

Condition 1:

Wine, situation 1

Condition 2:

Wine, situation 1

Condition 3: Wine,

situation 2

Condition 4: Wine,

situation 2

No recommendation

in advertisement

Condition 5: Wine,

situation 1

Condition 6:

Wine, situation 1

Condition 7: Wine,

situation 2

Condition 8: Wine,

situation 2

I designed four different advertisements (please see appendix 1). To gain representative measure

results, the minimum number of respondents was set at 160, 20 for each condition.

3.1.1 Choice of research

I used a questionnaire to investigate the problem statement. Participants were randomly assigned to

one of the four advertisements. I distributed the questionnaire via internet (Qualtrics) to accelerate

the coding of data and to minimize possible faults by respondents (like not filling in a question, I

forced response for the questions). The link to the questionnaire was posted on Facebook and

emailed to friends and family. In both cases I requested to further share or email the link to the

recipients’ friends and family, to create a snowball sampling. After 1,5 week, I still did not had

enough respondents. I emailed Liane Voerman to ask whether she will post my link on the Master

Table 1: Research design

26

Marketing community on Nestor. Furthermore, I printed out my questionnaire and put it at my work

- Julia’s at Groningen railway station -. After 2,5 week I had collected the number of required

respondents.

In the questionnaire I mostly used 7-point Likert scales, except for the manipulation check questions.

For a complete overview of the questionnaire, please see appendix 1.

3.1.2 Choice of sample

This research had no restrictions whatsoever when it comes to participation. Everyone is allowed to

fill in the questionnaire which creates a broader sample. This is because the main effect is applicable

for the two sexes, all age groups and all education levels. This could enhance the generalizability of

the results. The only thing that could bias the data is the nationality of the respondent, because the

used recommendation - see 3.2.1 - is applicable for the Netherlands. However, this recommendation

is being explained in the questionnaire, therefore people understand the recommendation and could

be influenced by it.

3.2 Measures of the major variables

This part describes how the major variables - independent variable, moderating variables and the

dependent variable - were measured.

3.2.1 Recommendation – independent variable

I wanted to use an existing recommendation expression to make the recommendation more

persuasive and credible. Because I have chosen for wine, the recommendation

“Omfietswijn” was used, which is related to the Supermarket wine handbook.

This handbook is well known in the Netherlands. It is a guide for good

supermarket wines. The Omfietswijn concept is given to wines in the guide that

have a high value for money. The Omfietswijn-logo has also an own app where

the wines are ranked. Because it is not a marketing source, we can see this as

an independent review site - the kind of recommendation I focus on in this

study -.

Two advertisements include this recommendation, the other two are without the recommendation.

Because this logo is only used in the Netherlands, I asked at the end of the survey if people are

Dutch. If not, this could explain the non familiarity with the logo of certain participants. However, I

also included a brief description of the meaning of the logo stated; “Recommended by Supermarket

Figure 2: Omfietswijn-logo

27

wine handbook 2012 as ‘Omfietswijn’. Supermarkets could have such special and delicious wine that

you are happy to bike to the other side of the city for it. You can recognize real good supermarket

wines by the Omfietswijn-logo” to make sure that people who are not familiar with it, still

understand its purpose. This should control for the unfamiliarity with the logo.

3.2.2 Personal relevance scenario – moderating variable

I have chosen for the product wine whereby I manipulate the using occasions to create a low or high

personal relevance of the product situation. One occasion is whereby you need to pick a bottle of

wine for the weekly dinner with your friends (situation 1). The other occasion is a dinner whereby

you will meet your parents in law for the first time. To make a good impression you bring along a

bottle of wine (situation 2). Please see appendix 1 for a complete overview of the manipulated

occasions.

In situation 2 the outcome is more risky, e.g. you could make a bad impression on your parents in law

- high personal relevance -. But bringing a bad wine to your weekly dinner with friends will not harm

you that much - low personal relevance -.

People should base their purchase intention on the described scenario – hence, personal relevance -

and not on a particular wine brand. Therefore I used a non existing brand to exclude the possible

brand attitude bias.

In my theoretical framework I indicated that I want to focus on experience products whereby it is

hard to assess the quality before purchasing, so you probably acquire more information on

beforehand, or you will be more influenced by rules of thumb like recommendations. This condition

is applicable for wine because you could not easily determine the quality of the wine before using it.

3.2.3 Need for uniqueness – moderating variable

According to previous research, NFU can be measured with the scale of Snyder & Fromkin (1977).

The authors developed a scale that consist out of 32 statements. In this research I used the 10

statements which were most applicable. Participants were asked to indicate how much they agreed

with each statement.

28

3.2.4 Purchase intention – dependent variable

To measure the dependent variable, purchase intention, I asked the likeliness of purchasing and

whether people are willing to buy. These questions were based on Dodds, Monroe & Grewal, 1991

and Bone & Ellen, 1992.

3.3 Manipulation check and control variables

This part describes how the manipulations that were performed are checked. Furthermore, the

controlling variables which could influence the results of this research are explained.

3.3.1 Random assignment

As said before, I used manipulation to assign the participants to one of the four conditions. To check

whether participants were roughly equally divided between the four conditions, I performed a

frequency test.

3.3.2 Recommendation

For the independent variable, I checked whether people saw the recommendation, hence, if the

recommendation manipulation worked out correctly. People whereby the manipulation failed would

be excluded for further analysis by removing them from the data set. Furthermore, I wanted to check

whether people were familiar with the Omfietswijn-logo. Because if this is not the case, than the

recommendation could be less persuasive.

3.3.2 Personal relevance

I instructed participants that they were buying wine for the weekly dinner with their friends, or for a

dinner whereby they meet their parents in law for the first time. The first one is the low personal

relevance scenario, the latter is the high personal relevance scenario. To check whether this

manipulation worked out correctly I asked whether the product was important and meaningful to

them (based on Mano & Oliver, 1993; Zaichkowsky, 1985).

3.3.3 Wine consumption

Even though participants were asked to imagine they are shopping for wine, and the scenarios did

not explicitly stated that the wine was for own consumption, I still wanted to check whether the

purchase intention could have been influenced by liking wine in general. Therefore I asked if people

like red wine, if they buy it often and how knowledgeable they are. I control for these three variables

in the regression. The reasons why these variables have an possible influence on purchase intention

could be that if you like red wine, you are more willing to buy wine anyway. If you like wine you have

29

a positive attitude towards the product and you are therefore more likely to buy it. Moreover, if you

buy red wine often, you probably have a preference for certain brands of wine. The wine in the

advertisement was of a fictitious brand, indicating that you rather go for your familiar brand instead

of this unfamiliar brand. Lastly, people who are knowledgeable are less influenced by the

advertisement. They determine for themselves, based on their knowledge, whether it is a good wine.

An advertisement with or without recommendation has little influence on this decision.

30

4. Results

In this chapter are the most relevant results of this research described. First of all the sample

characteristics and the manipulation checks are explained. After that, the reliability analysis is

depicted and the main results are explained. Lastly, further results and a summary of the major

findings are depicted.

4.1 Sample characteristics

This part gives an overview of the descriptives of the sample.

4.1.1 Definitive number of participants

A total of 187 people filled in the questionnaire. However, 26 of these respondents did not

completed the questionnaire. All of them stopped immediately at question one. This could indicate

that people were deterred of the level of English. I deleted all 26 incomplete questionnaires because

they did not give me any answers whatsoever. This means that 161 questionnaires remained. After

that, I checked whether the independent variable manipulation worked our correctly. Only two

participants indicated the wrong answer, stating that the wine was recommended when it was not.

Because only two respondents did not indicate the correct answer, I excluded them from the data set

and continued the analysis with 159 participants in total.

Because there were four different advertisement, a largely equally number of respondents per

condition will give the most reliable results. The distributions of the respondents per advertisement

were 39 for advertisement 1, 40 for advertisement 2, 40 for advertisement 3, and 40 for

advertisement 4.

4.1.2 Description of the sample

A description of the total sample (n = 159) is depicted below in table 2:

Gender Age Nationality Education level

Female 100 (62.9%) <20 10 (6.3%) Dutch 155 (97.5%) Secondary school 1 (0.6%)

Male 59 (37.1%) 21-40 131 (82.4%) Other EC 3 (1.9%) MBO 12 (7.5%)

41-60 13 (8.2%) Other 1 (0.6%) HBO 60 (37.7%)

>60 5 (3.1 %) University 86 (54.1%)

Table 2: Sample description

31

As can be retrieved from the table, the questionnaire was mostly filled in by Dutch women in the age

of 21-40 years with an Academic education level.

Even though almost 98% of the participants were Dutch, their familiarity with the Supermarket Wine

handbook and the Omfietswijn-logo is lower than expected, see table 3.

Familiar Frequency Percentage

Yes 91 57,2%

No 68 42,8%

Please see appendix 2 for the associated graphs.

4.1.3 Representativeness

Of the 159 respondents, 25.8% indicated that they do not like red wine. This could bias the results of

purchase intention. Nevertheless, both personal relevance scenarios stated not specifically that the

wine was for own consumption. So even though people did not like red wine, they could still buy it as

a gift (scenario 2 - parents in law -). Therefore the answers could still be representative, and are

included in the sample. However, I used it as a control variable in the regression.

Furthermore, I wanted to check whether there is no significant difference between age and gender

of the participants in the four conditions. I performed an Univariate ANOVA on age to check for

significance, this was not possible for gender because this is a dummy variable. Age (F (3, 87) = .173,

P >.05, Sig. = .915) is not significant, indicating that there is no significant age difference between the

participants groups of the four conditions and I do not need to control for this. Besides, I checked

whether NFU is significantly different between the four conditions. Even though this personality trait

is not possible to manipulate, I still wanted to check for validity reasons if there is no significant

difference among respondents in the conditions. There is no significant difference (F (47, 87) = 1.317,

p >.05, Sig.= .134) between the four conditions. Please see appendix 3 for the all the significant

figures.

Because gender could not be checked on significance with Univariate ANOVA, I also calculated

frequencies per condition. An overview of the sample characteristics per condition can be found

below in table 4. Furthermore, appendix 4 depict an graphical overview of the sample characteristics

per condition. As can be retrieved from table 4, the distribution is somewhat skew. Therefore I

control for gender in the regression.

Table 3: Familiarity with Supermarket Wine handbook and Omfietswijn-logo

32

Gender Percentage

Condition 1 Male

Female

46%

54%

Condition 2 Male

Female

23%

77%

Condition 3 Male

Female

35%

65%

Condition 4 Male

Female

45%

55%

4.2 Manipulation checks

To check whether the manipulation for the independent variable recommendations worked out

correctly I checked the data set on failed manipulations. If a participants indicated the wrong answer,

they were excluded from the data set - as stated in 4.1.1 -. To check whether the manipulation for

personal relevance worked out correctly, I performed an ANOVA. The outcome of ANOVA was

significant (p <.05, Sig. = .000) which indicate that respondents who were assigned to the low

condition, showed a low level of personal relevance of the product - based on a 5 point semantic

differential scale - (M = 2.21, SD = 1.22) whereas respondents who were assigned to the high

condition showed a high level of personal relevance of the product (M = 4.25, SD = .96). Please see

appendix 5 for the associated SPSS output.

4.3 Reliability

An internal consistency reliability was performed to assess the reliability of the summated scales. As

stated in chapter 3, the Cronbach’s alpha needs to be >.60 to take the questions together and

transforming them into a new variable. There were four constructs with summated scales, namely

NFU, purchase intention, the usage of recommendations and personal relevance of the product. For

all the summated scales I used the same scale (hence, 7 point Likert scale or 5 point semantic

differential scale) therefore recoding is not necessary before performing a reliability analysis. Table 5

shows the Cronbach’s alpha for the four constructs.

Table 4: Sample description per condition

33

Name of summated scale Number of items Cronbach’s alpha

NFU 10 .931

Purchase intention 2 .958

Using of recommendations 2 .790

Personal relevance 2 .961

As can be derived from table 5, all four construct have a Cronbach’s alpha >.60, which means that

they can be transformed and compute into four new variables. The ten questions regarding NFU are

transformed into the variable Sum_NFU. The two questions for purchase intention are transformed

into the variable Sum_PI. The two questions for the usage of recommendations are transformed into

the variable Sum_UsingRec. Lastly, the two questions for personal relevance of the product are

transformed into the variable Sum_PR. Please see appendix 6 for an overview of the Cronbach’s

alpha and the related questions.

4.4 Main results

This section describes the main results of this research. First, the results of purchase intention among

the four conditions are explained. Then the results of the hypothesis, which were formulated in

chapter 2, are explained. A linear regression analysis was used to test the three hypotheses.

4.4.1 Purchase intention among the four conditions

First of all, I wanted to check whether de dependent variable purchase intention varies among the

four condition. I performed an univariate ANOVA whereby sum_PI is the dependent variable and

options is the independent variable. As can be concluded from the output - please see appendix 7 -

the means for the conditions with a recommendation (condition 2 and 4) are higher (M = 4.86, SD =

1.56 and M = 4.89, SD = 1.64) than the means for the conditions (1 and 3) without recommendation

(M = 4.12, SD = 1.57 and M = 4.15, SD = 1.50). Furthermore, there is a significant difference for

purchase intention between the four conditions (F (3, 155) = 2.984, p <.05, Sig. = .033) hence,

purchase intention is influenced by the condition participants are in.

4.4.2 Hypotheses testing

To test the three hypothesis I used a linear regression. The confidence interval level was set at 95%

which means that the hypotheses are significant when p <.05. The regression equation for this

research is;

Table 5: Cronbach’s alpha scores

34

: Purchase intention

: Intercept

: Recommendation

: NFU

: Personal relevance of the product

: Interaction recommendation and NFU

: Interaction recommendation and personal relevance of the product

: Gender

: Liking red wine

: Buying red wine

: Being wine knowledgeable

: Error term

i: Respondent i

Not all of the above variables are significant as is depicted in table 6:

B t Sig.

Constant 4.707 7.652 .000

Recommended -.657 -2.041 .043

NFU .533 4.451 .000

Personal relevance .249 .753 .453

Interaction recommendation and NFU -.392 -2.179 .031

Interaction recommendation and personal relevance -.420 -.889 .375

Gender .295 1.150 .252

Liking red wine .277 2.188 .030

Buying red wine -.247 -2.207 .029

Wine knowledgeable -.198 -2.069 .040

As can be retrieved from table 6, the independent variable recommendation (p <.05, Sig. = .043) and

the interaction between recommendation and NFU (p <.05, Sig. = .031) are significant. Furthermore

the controlling variables liking (p <.05, Sig. = .030), buying red wine (p <.05, Sig. = .029) and being

wine knowledgeable (p <.05, Sig. = .040) are significant. These are further described in chapter 4.5.

The conclusions per hypothesis are described in the below depicted subparagraphs.

Table 6: Significant figures of regression

35

4.4.2.1 Hypothesis 1

Hypothesis 1 concerns the positive influence of recommendation on purchase intention. The

dependent variable is Sum_pi and the independent variable is recommended. Whether there was a

recommendation used in the condition was recoded to 0 = yes and 1 = no. The goodness of fit is

Adjusted R2 = .177 indicating that about 18% of the original variation is explained by the regression

model and 82% is residual variability. Moreover, ANOVA is significant (F (9) = 4.771, p <.05, Sig. =

.000) and the independent variable recommended is also significant (p <.05, Sig. = .043) hence,

purchase intention is influenced by recommendations. It can be concluded based on the value and

the direction of the coefficients that compared with recommended advertisements (dummy level 0),

the purchase intention for not recommended advertisements decrease with .675 unit.

Therefore it can be concluded that H1: “Recommendations, compared with no recommendations,

will increase purchase intention” is supported.

Please see appendix 8 for a total overview of the associated SPSS output.


Hypothesis 2 concerns the negative effect of recommendations on purchase intention for people

who score high on NFU. The dependent variable is Sum_pi and the independent variables are

recommended, nfu_centered and inter_recNFU. To perform a regression, some prerequisites need to

be done. First of all I computed a new variable namely nfu_centered via data, aggregate, to diminish

the possibility for multicollinearity. Furthermore, I created an interaction variable of recommended

and sum_nfu.

All the three variables - recommended (p <.05, Sig. = .043), nfu_centered (p <.05, Sig. = .000) and

inter_recNFU (p <.05, Sig. = .031) - are significant. Hence, purchase intention is influenced by

recommendations and NFU. It can be concluded based on the value and the direction of the

coefficients that, compared with recommended advertisements (dummy level 0), the purchase

intention for not recommended advertisements decreases with .675 unit. However, because the

interaction variable has a negative value we may conclude that people who score high on NFU show

a lower purchase intention in the case of recommendations.

Therefore it can be concluded that H2: “Recommendations, compared with no recommendations, will

decrease purchase intention for people who score high on need for uniqueness” is supported.

36



Hypothesis 3 concerns the negative effect of recommendations on purchase intention for high

personal relevant products. The dependent variable is Sum_pi and the independent variables are

recommended, personal relevance, and inter_recPR. As can be seen, I created an interaction variable

of recommended and personal relevance. Because both variables are dummies, I did not need to

center a certain variable.

Only recommended is significant (P <.05, Sig. = .043) hence, recommendations influence purchase

intention. It can be concluded based on the value and the direction of the coefficients that,

compared with recommended advertisements (dummy level 0), the purchase intention for not

recommended advertisements decrease with .675 unit. Because none of the other variables are

significant, we cannot confirm that purchase intention is decreased by recommendations for high

personal relevant products.

Therefore it can be concluded that H3: “Recommendations, compared with no recommendations, will

decrease purchase intention for high personal relevant products” is not supported.

Even though hypothesis 3 is not supported, I still wanted to check whether personal relevance itself

has a significant influence on purchase intention. The dependent variable is Sum_pi and the

independent variable is personal relevance.

The model indicated that personal relevance is also not significant (P >.05, Sig. = .921) hence,

personal relevance has no influence on purchase intention.


Because hypothesis 3 is not supported, I checked whether the manipulation worked out correctly. As

stated in chapter 4.2, the outcome of ANOVA was significant (p <.05, Sig. = .000) which indicate that

respondents who were assigned to the low condition, showed a low level of personal relevance of

the product (M = 2.21, SD = 1.22) whereas respondents who were assigned to the high condition

showed a high level of personal relevance of the product (M = 4.25, SD = .96). Moreover, participants

were randomly assigned to one of the four conditions. There were no significant differences between

the four samples of the conditions, except for gender but I controlled for that. Besides, there were no

37

measures taken with regards to assigning participants. These two factors could therefore not explain

the rejection of hypothesis 3.

4.5 Further results

Even though these results are not necessary to test the hypothesis, they could have an influence on

the purchase intention. Therefore further research is performed to get a more thorough

understanding. In the questionnaire I asked (based on a 7 point Likert scale) whether people like red

wine, if they buy red wine often, and whether they are knowledgeable with regards to wine. These

results could influence the purchase intention, even though a certain scenario was outlined.

Therefore I controlled for these variables in the regression.

First I checked the influence of liking red wine on purchase intention. The mean of liking wine is M =

4.89, SD = 1.91, based on a 7 point Likert scale we may conclude that most participants like red wine.

Furthermore, the results did indicated a significant difference (p <.05, Sig. = .03), hence liking red

wine has a significant influence on purchase intention. Because the B-value is positive, the influence

on purchase intention is positive, i.e. liking red wine influences purchase intention positively. Second,

I checked the influence of buying red wine often on purchase intention. The mean of buying wine is

M = 4.26, SD = 2.07, based on a 7 point Likert scale we may conclude that most participants buy red

wine often. Furthermore, the results did indicated a significant difference (p <.05, Sig. = .029), hence

buying red win often has a significant influence on purchase intention. Because the B-value is

negative, the influence on purchase intention is negative, i.e. buying red wine often influences

purchase intention negatively. Lastly, I checked whether being knowledgeable influences the

purchase intention. The mean of being knowledgeable is M = 3.23, SD = 1.70, based on a 7 point

Likert scale we may conclude that most participants are not very knowledgeable with regards to

wine. The results showed a significant difference (P <.05, Sig. = .04), hence being knowledgeable

does influence purchase intention. Because the B-value is negative, the influence on purchase

intention is negative, i.e. being knowledge influences purchase intention negatively.

4.6 Summary major findings

As can be concluded from chapter 4, hypothesis 1 and 2 are confirmed whereas hypothesis 3 is

rejected. Based on literature review and the questionnaire it is logical than hypothesis 1 and 2 are

supported. Literature indicate that recommendations influences purchase intention positively.

Because hypothesis 2 is not manipulated - NFU is not possible to manipulate - this could enhance the

validity of the hypothesis. Hypothesis 3 is not supported. The possible explanation for not supporting

hypothesis 3 is the manipulation of personal relevance of the product. Even though the scenario

38

indicated that the wine was either important or not that important to participants, the product is still

just wine. The implications of wine on the personal life of people is low, indicating that the personal

relevance is also low (Hoyer & MacInnis, 2008). Furthermore, wine is not really a product people use

to differentiate themselves. With certain products you want to be unique to create status, this

enhance the personal relevance of the product. It is possible that hypothesis 3 could be confirmed if

a different product was chosen which has more consequences on the life of participants.

Therefore I can conclude the following;

1) Recommendations, compared with no recommendations, influences purchase intention

positively

2) Recommendations, compared with no recommendations, will decrease purchase intention

for people who score high on NFU, compared with people who score low on NFU.

I cannot conclude whether recommendations, compared with no recommendations, will decrease

the purchase intention for high personal relevant products, compared with low personal relevant

products.

39

5. Conclusion and recommendations

In this chapter a summary of the results of chapter 4 are discussed. Furthermore the research

question will be answered.

5.1 Summary and conclusions

Recommendations play a big role in marketing nowadays. Consumers can easily obtain product

information on the internet, and recommendations assist them in the search for good products. An

example is the Consumentenbond which has a powerful influence on Dutch consumers in their

purchase decision. The general conclusion is that recommendations influence purchase intention

positively. However people also have a higher need to differentiate themselves, and want the

freedom to choose for themselves what is good or not (Hoyer & MacInnis, 2008). Could therefore

recommendations harm purchase intention? The purpose of this research was to answer the

research question how recommendations influence purchase intention, and if NFU and personal

relevance of the product could have a counter effect on the main relation.

This research confirmed the previous findings in literature for the influence of recommendations on

purchase intention. Consistent with literature I found a positive influence of recommendations on

purchase intention. The reason for this is that recommendation lower search costs which triggers

consumers to use the rule of thumb of ‘if others have it, it must be good’. Moreover,

recommendations influence purchase intention because authority sells (Jones, 2011), people are

influenced by WOM in their purchase decisions (Chen, Wang & Xie, 2011), and the demand for a

commodity is increased due to the fact that others are also consuming the same commodity -

bandwagon effect - (Leibenstein, 1950). It is interesting, however, to see the strong effect that

recommendations have on purchase intention.

Furthermore, this research also confirms the previous literature findings of the moderating role of

NFU on the relation between recommendations and purchase intention. Consistent with literature I

found a negative influence of recommendations on purchase intention for people who score high on

NFU. The reason for this effect is that people who score high on NFU could show reactance to

recommendation because they do not want to be said what is good and what not. They are doing the

opposite of what the individual or groups wants them to do (Hoyer & MacInnis, 2008). The

moderating effect of NFU on the relation between recommendation and purchase intention is quite

strong.

40

Moreover, this research showed no significant moderating influence of personal relevance of the

product on the relation between recommendations and purchase intention. This is not consistent

with the current findings from literature. Literature indicates that personal relevance of the product

influence people in their purchase decision due to risk of the outcome (Hoyer & MacInnis, 2008),

counter arguments (Petty & Cacioppo, 1984) and the emblematic function of products (Edson Escalas

& Bettman, 2003). This will moderate the effect of recommendations on purchase intention because

if a product is highly relevant to you recommendations could lower your purchase intention. As is

explained in chapter 4, this research did not found any significant moderating effect. The possible

reason why hypothesis 3 is not supported is that even though I manipulated the using occasion, the

product itself is still just wine. It could be that a lot of people see wine as a product with a low risk

which has low consequences on their life i.e. a low personal relevance. Furthermore, personal

relevance could be increased for products we use to show our membership in social groups, the so

called emblematic function of products (Edson Escalas & Bettman, 2003). This is especially true for

product which we use to differentiate ourselves. With products which you use to differentiate

yourself you could enhance your status by having exclusive items like a watch of car. Wine is not

really a product you use to differentiate yourself, even if you use it in public - dinner with friends or

with your parents in law -, it will not enhance your status and people could probably not care if more

people has bought that particular wine. Besides, participants needed to imagine that they were

having a dinner with friends, or a dinner with their parents in law for the first time. More than 11% of

the participants were older than 40 years old. This could influence the scenario with the parents in

law because the older you are, the less likelier it is that you will have a dinner whereby you meet

your parents in law. Most likely, those participants have a partner for a long time, indicating that

meeting parents in law is not going to happen in the near feature. This could harm their imagination

for this scenario.

In short we can conclude that recommendations influences purchase intention positively, however

there is a counter effect for people who score high on NFU. There was no significant result found for

the moderating effect of personal relevance of the product on purchase intention.

5.2 Managerial and academic implications

This research is useful for marketers because it helps them decide whether to advertise with

statements like “recommended by x people”, or “best tested according to Consumentenbond, January

2012”. Because at a first glance, including recommendations in advertisements seems like a good

idea. People could use the rule of thumb that if a lot of people have the product, or it is been

recommended by either friends or an authority figure, it must be good. However, for some goods

41

people want the freedom to judge for themselves whether the product is good or not (Hoyer &

MacInnis, 2008). Furthermore, consumers want to differentiate themselves from others to acquire

some sort of status. Therefore recommendation could have a counter effect on purchase intention.

This research indicates that especially NFU moderates the effect of recommendations on purchase

intention. Even though the rule of thumb is triggered with recommendations, a possible underlying

mechanism might be that consumers want the freedom to choose for themselves and not been told

what is good and what not. As can be derived from appendix 9, the average participant in this

research showed a high NFU, indicating that it is a common personality trait among consumers,

hence, recommendations could therefore lower the purchase intention. However, this is dependent

on the kind of product marketers want to advertise. For some products, consumers could simply not

care whether more people have it. Leibenstein (1950) indicated this also in his research, he did

notice that certain people have the so called ‘snob effect’. This is the extent to which the demand for

a consumer good is decreased owing to the fact that others are also consuming the same

commodity. It represents the desire of people to be exclusive; to be different; to dissociate

themselves from the ‘common herd’ (Leibenstein, 1950). However, the author said that for most

commodities the motivation for exclusiveness is not that great. Therefore, marketers should

thoroughly investigate whether the product they want to advertise could be used by consumers to

differentiate themselves, and to acquire some sort of status. For most products the need for

exclusiveness is not that great, hence for most products marketers could include recommendations

to enhance purchase intention, whereas for certain products marketers should leave the

recommendation out or make it less visible to diminish the counter effect of recommendations on

purchase intention.

The academic implications are the contributions to the current literature findings. First of all, there

were little findings with regard to the counter effect of recommendations. The existing research was

done by Leibenstein (1950) and the author focused on scarcity with regards to the counter effect of

recommendations. This current research has left these two facts out of consideration, but focused

instead on the moderating effect of NFU and personal relevance of the product on the main relation.

The results showed that NFU indeed lowers the purchase intention. Personal relevance, on the other

hand, did not had a significant effect on purchase intention.

5.3 Limitations and further research

Although this research was performed with respect to research rules and ethics, there are still some

limitations worth mentioning which should be addresses in further research.

42

This research consist out of 159 participants in total, mostly students and the majority consisted out

of females. Future research should study the effect of recommendations on purchase intention and

the moderating effects of NFU and personal relevance of the product in a more diverse sample to

enhance external validity.

Besides, I have chosen to manipulate the using occasion instead of the product to create either a low

or a high personal relevance of the product. As mentioned before, wine is probably not the most

applicable product to choose for this aim due to the low consequences of the product on the lives of

participants. Furthermore, wine is normally not used to differentiate yourself. Hence, even though

participants indicated correctly that the wine was (un)important to them, in the back of their mind

they could have thought otherwise, which subconsciously could influence the purchase intention.

Future research should account for this limitation with choosing products that are more clear with

regards to being relevant for people or not. Moreover, the described scenarios might not be

applicable for certain participants, especially the scenario with the parents in law dinner. This is

somewhat hard to imagine if you are 60 years old. This could mean that there is a possibility that

participants were not able to project their behavior in line with the described scenario. Future

research should account for this limitation by using a scenario which is more applicable for all

participants, regardless their age or any other factor.

Furthermore, the advertisements were designed very basically. It was described that the

advertisements were depicted in the leaflet of a supermarket, however, I did not made a real leaflet

with more advertisements besides the wine. This to make sure participants were focused on the

wine. This is however not very realistic, normal leaflets contain a lot more pictures and

advertisement of others products or other brands of wine. This could influence the results because

participants could be more distracted by the other products and pay lower attention to the intended

advertisement. This could harm the manipulation because people may not remember whether the

product was recommended or not. However, the realness of the leaflet will be enhanced which also

enhance the validity because you measure what you intended to measure. The managerial

implications indicated that recommendations in advertisements may backfire for certain consumers.

To measure this accurately, you should also make the advertisement as real as possible.

Furthermore, participants only saw the advertisement once, whether in reality they are multiple

times confronted with the advertisement (in the leaflet, in the supermarket itself etcetera). The

mere-exposure effect stated that the more people are confronted with things, the more positive they

evaluate it. Future research should address for this limitation by making it more realistic, hence use

multiple confrontation moments.

43

Lastly, in literature there is an ongoing discussion how accurately purchase intention really predicts

purchase behavior. As a marketer you are interested in the real behavior, not only the intention. De

Canniere, Pelsmacker & Geuens (2009) say that the predictive power of intentions on real behaviour

is low due to elapsed time between the measurement of intentions and the scanning of behaviour.

Within this timeframe, changes to the customer's general and purchase specific context are bound to

happen, which might negatively impact the predictive power of intentions on behaviour. However,

Chandon, Morwitz & Reinartz (2005) say that intention do indeed predict actual behaviour. Besides,

asking consumers to their purchase intention enhances purchase intention. The study showed that

the correlation between the intentions and purchase behaviour is 58% greater among surveyed

consumers than it is among similar non-surveyed consumers. However, due to the ongoing

discussion in literature, it is still arguable how accurately the purchase intention indicated in this

study really predicts the actual purchase behaviour.

With taken all of the above into account, we may conclude that purchase intention is enhanced by

recommendations. However, NFU provides a counter effect. Based on literature, personal relevance

moderates the main effect of recommendations on purchase intention, however, there was no

evidence found for this effect in this research. It is wise for marketers to consider including

recommendations in their advertisements because at first glance it seems like a good idea, but - as

this research shows - it could backfire.

44

References

Algesheimer, R., Dholakia, U.M. & Hermann, A. (2005). The Social Influence of Brand Community:

Evidence from European Car Clubs. Journal of Marketing. July: 19-34.

Becherer, R.C. & Richard, L.C. (1978). Self-Monitoring as a Moderating Variable in Consumer

Behavior. Journal of Consumer Research. December: 159-162.

Belk, R.W. (1988). Possessions and the Extended Self. Journal of Consumer Research. Vol. 15: 139-

167.

Berger, J. & Heath, C. (2007). Where Consumers Diverge from Others: Identity Signaling and Product

Domains. Journal of Consumer Research. Vol. 34 (2): 121-134.

Bloch, P.H. (1995). Seeking the Ideal Form: Product Design and Consumer Response. Journal of

Marketing. July, 59: 16–29.

Bodapati, A.V. (2008). Recommendation Systems with Purchase Data. Journal of Marketing

Research. Vol. 45 (1): 77-93.

Bohner, G., Moskowitz, G. & Chaiken, S. (1995). The interplay of heursitic and systematic

processing of social information. In W. Stroebe & M. Hewstone (Eds.) European

Review of Social Psychology. Vol. 6: 33-68.

Bone, P.F. & Ellen, P.S. (1992). The Generation and Consequences of Communication-evoked

Imagery. Journal of Consumer Research. Vol. 19 (1): 93-104.

Burnkrant, R.E. & Cousineau, A. (1975). Informational and normative social influence in buyer

behavior. Journal of Consumer Research. Vol. 2 (3): 206-215.

Canniere, De, M.H., De Pelsmacker, P & Geuens. M. (2009). Relationship Quality and the Theory of

Planned Behavior models of behavioral intentions and purchase behavior. Journal of Business

Research. Vol. 62: 82-92.

http://web.ebscohost.com/ehost/viewarticle?data=dGJyMPPp44rp2%2fdV0%2bnjisfk5Ie46PN%2frOPfgeyk44Dt6fJYr6itSbOlsEivp55RuKqxUrConlnLnPKK3%2bTxeeHq54fs3%2bJVr6%2b1UbWotU6zqaSE3%2bTlVePkpHzgs%2bCN5pzyeeWzv2ak1%2bxVsauzSLOprkmk3O2K69fyVeTr6oTy2%2faM&hid=13

http://web.ebscohost.com/ehost/viewarticle?data=dGJyMPPp44rp2%2fdV0%2bnjisfk5Ie46PN%2frOPfgeyk44Dt6fJYr6itSbOlsEivp55RuKqxUrConlnLnPKK3%2bTxeeHq54fs3%2bJVr6%2b1UbWotU6zqaSE3%2bTlVePkpHzgs%2bCN5pzyeeWzv2ak1%2bxVsauzSLOprkmk3O2K69fyVeTr6oTy2%2faM&hid=13

45

Chaiken, S. (1980). Heuristic versus systematic information processing and the use of source versus

message cues in persuasion. Journal of Personality and Social Psychology. Vol. 39: 752-766.

Chaiken, S. & Maheswaran, D. (1994). Heuristic processing can bias systematic processing:

Effects of source credibility, argument ambiguity, and task importance on attitude

judgement. Journal of Personality and Social Psychology. Vol. 66: 460-473.

Chandon, P, Morwitz, V.G and Reinartz, W.J. (2005). Do Intentions Really Predict Behavior? Self-

Generated Validity Effects in Survey Research. Journal of Marketing. Vol. 69 (2): 1-14.

Cheema, A. & Kaikati, A.M. (2010). The Effect of Need for Uniqueness on Word of Mouth.

Journal of Marketing Research. Vol. 47 (3): 553-563.

Chen, Y., Wang, Q. & Xie, J. (2011). Online social interactions: A natural experiment on word of

mouth versus observational learning. Journal of Marketing Research, Vol. 48 (2): 238-254.

Cheung, K., Kwok, J.T., Law, M.H. & Tsui, K. (2003). Mining customer product rating for

personalized marketing. Decision Support Systems. Vol. 35 (2): 231-243

Chintagunta, P. & Lee, J. (2012). A pre-diffusion growth model of intentions and purchase.

Journal of the Academy of Marketing Science. Vol. 40 (1): 137-154

Choi, J., Lee, H.J. & Kim, Y.C. (2011). The Influence of Social Presence on Customer Intention to Reuse

Online Recommender Systems: The Roles of Personalization and Product Type. International

Journal of Electronic Commerce. Vol. 16 (1): 129-154.

Cialdini, R.B. & Goldstein, N.J. (2004). Social influence: Compliance and conformity. Annual

Review of Psychology. Vol. 55 (1): 591-621.

Coelho, P.R.P. & McClure, J.E. (1993). Toward an economic theory of fashion. Economic

Inquiry. Vol. 31 (4): 595-608

Cooley, R., Mobasher, B. & Srivastava, J. (1999). Data preparation for mining world wide web

browsing patterns. Knowledge and Information Systems. Vol. 1 (1): 5 – 32.

javascript:__doLinkPostBack('','mdb%7E%7Ebuh%7C%7Cjdb%7E%7Ebuhjnh%7C%7Css%7E%7EJN%20%22Journal%20of%20Marketing%22%7C%7Csl%7E%7Ejh','');

46

Deutsch, M. & Gerrard, H.B. (1955). A study of normative and informational social influences upon

individual judgment. Journal of Abnormal Social Psychology. Vol. 51 (3): 629-636.

Dodds, W.B., Monroe, K.B. & Grewal, D. (1991). Effects of Price, Brand, and Store Information on

Buyers’ Product Evaluations. Journal of Marketing Research. Vol. 28 (3): 307-319

Duhan, D., Johnson, S., Wilcox, J. & Harrell, G. (1997). Influences of Consumer Use of Word-of-Mouth

Recommendation Sources. Journal of the Academy of Marketing Science. Vol. 25: 283-295.

East, R., Hammond, K. & Wright, M. (2007). The Relative Incidence of Positive and Negative Word of

Mouth: A Multi-Category Study. International Journal of Research in Marketing. June: 175-

184.

Eck, P.S. van, Jager, W. & Leeflang, P.S.H. (2011). Opinion Leaders’ Role in Innovation Diffusion: A

Simulation Study. Journal of Product Innovation Management. Vol. 28 (2): 187-203.

Edson Escalas, J. & Bettman, J.R. (2003). You Are What They Eat: The Influence of Reference Groups

on Consumers’ Connections to Brands. Journal of Consumer Psychology. Vol. 13 (3): 339-348.

Engel, J.F., Kegerreis, R.J. & Blackwell, R.D. (1969). Word-of-mouth communication by the

innovator. Journal of Marketing. Vol. 33 (3): 15-19.

Feick, L. & Price, L.L. (1987). The market maven: A diffuser of marketplace information.

Journal of Marketing. Vol. 51 (1): 83-97.

Fennis, B.M. & Stroebe, W. (2010). The Psychology of Advertising. Hove, UK: Psychology Press.

Flynn, L.R., Goldsmith, R.E. & Eastman, J.K. (1994). The King and Summers opinion leadership scale:

Revision and refinement. Journal of Business Research. Vol. 31 (1): 55-64.

Gelb, B. & Johnson, M. (1995). Word-of-mouth communication: Causes and consequences.

Journal of Health Care Marketing. Vol. 15 (3): 54-58.

Gilbert, N., Jager. W., Deffuant, G. & Adjali, I. (2007). Complexities in markets: Introduction to the

Special Issue. Journal of Business Research. Vol. 60 (8): 813-815.

47

Glock, C.Y. & Nicosia, F.M. (1964). Uses of sociology in studying “consumption” behavior.

Journal of Marketing. Vol. 28 (3): 51-54.

Grewal, R., Mehta, R. & Kardes, F.R. (2000). The role of the social-identity function of attitudes in

consumer innovativeness and opinion leadership. Journal of Economic Psychology. Vol. 21

(3): 233-252.

Haugtvedt, C. & Strathman, A.J. (1990). Situational Product Relevance and Attitude Persistence.

Advances in Consumer Research. Vol. 17 (1): 766-769.

Herpen, E. van, Pieters, R. & Zeelenberg, M. (2005). How Product Scarcity Impacts on Choice: Snob

and Bandwagon Effects. Advances in Consumer Research, Vol. 32 (1): 623-624.

Herr, Kardes & Kim. Effects of Word-of-Mouth and Product-Attribute Information on Persuasion.

Huizingh, E. (2007). Applied Statistics with SPSS. London, UK: SAGE Publications Ltd.

Jones, G.S. (2011). Six tips for: Building brand authority. Multichannel Merchant. Vol. 7 (7): 22-23

Katz, E. & Lazarsfeld, P.F. (1955). Personal influence. Glencoe, IL, USA: The Free Press.

Lazarsfeld, P.F., Berelson, B. & Gaudet, H. (1948). The People’s Choice: How the Voter Makes Up His

Mind in a Presidential Campaign. New York, USA: Columbia University Press.

Laffey, D. & Gandy, A. (2009). Comparison website in UK retail financial services. Journal of

Financial Services Marketing. Vol. 14 (2): 173-186

Leibenstein, H. (1950). Bandwagon, Snob, and Veblen Effects in the Theory of Consumers’

Demand. Quarterly Journal of Economics. Vol. 64 (2): 183-207.

Liang, T., Lai, H. & Ku, Y. (2006). Personalized content recommendation and user satisfaction:

Theoretical synthesis and empirical findings. Journal of management Information

Systems. Vol. 23 (3): 45–70.

48

Lynn, M. (1992). The Psychology of Unavailability: Explaining Scarcity and Cost Effects on Value. Basic

and Applied Social Psychology. Vol. 31 (1): 3–7.

Malhotra, N.K. (2010). Marketing Research. An applied orientation. Sixth Edition. New Jersey, USA:

Pearson.

Mano, H. & Oliver, R.L. (1993). Assessing the dimensionality and structure of the Consumption

Experience: Evaluation, feeling and satisfaction. Journal of Consumer Research. Vol. 20 (3):

451-466.

McDonald, W.J. (1998). Direct Marketing. Singapore: McGraw-Hill.

Nelson, P. (1970). Information and consumer behavior. Journal of Political Economy. Vol. 78 (2): 311–

329.

Nielsen. (2007). Word-of-mouth the most powerful selling tool: Nielsen global survey.

Palmatier, R.W., Dant, R.P., Grewal, D. & Evans, K.R. (2006). Factors Influencing the Effectiveness of

Relationship Marketing: A Meta-Analysis. Journal of Marketing. Vol. 70: 136-153.

Pathak, B., Garfinkel, R., Gopal, R.D., Venkatesan, R. & Yin, F. (2010). Empirical Analysis of the Impact

of Recommender Systems on Sales. Journal of Management Information Systems. Vol. 27 (2):

159-188

Petty, R.E. & Cacioppo, J.T. (1984). The effects of involvement on responses to argument

quantity and quality: Central and peripheral routes to persuasion. Journal of

Personality and Social Psychology. Vol. 46 (1): 69-81.

Petty, R.E. & Cacioppo, J.T. (1986). Communication and Persuasion: Central and Peripheral

Routes to Attitude Change. New York, USA: Springer.

Rodriguez, A. & Locay, L. (2002). Two Models of Intertemporal Price Discrimination. Journal of

Economics. Vol. 76 (3): 261-278.

49

Sawyer, A.G. & Howard, D.J. (1991). Effects of omitting conclusions in advertisements to involved and

uninvolved audiences. Journal of Marketing Research. Vol. 28 (4): 467-474.

Simonson, I. & Nowlis, S. (2000). The Role of Explanations and Need for Uniqueness in Consumer

Decision Making: Unconventional Choices Based on Reasons. Journal of Consumer Research.

Vol. 27 (1): 49–68.

Snyder, C.R. (1992) Product Scarcity by Need for Uniqueness Interaction: A Consumer Catch-22

Carousel? Basic & Applied Social Psychology. Vol. 13 (1): 9–24.

Snyder, C.R. & Fromkin, H.L. (1977). Abnormality as a positive characteristic: The development and

validation of a scale measuring need for uniqueness. Journal of Abnormal Psychology. Vol. 86

(5): 518-552

Stafford, J.E. (1966). Effects of Group Influence on Consumer Brand Preferences. Journal of

Marketing Research. February: 68-75.

Stroebe, W. (2008). Strategies of attitude and behaviour change. In M. Hewstone et al. (eds.).

Introduction to social psychology: A European perspective. Oxford, UK: Blackwell.

Tepper Tian, K., Bearden, W.O. & Hunter, G.L. (2001). Consumers’ Need for Uniqueness: Scale

Development and Validation. Journal of Consumer Research. Vol. 28, June: 50- 66.

Trampe, D., Stapel, D.A., Siero, F.W. & Mulder, H. (2010). Beauty as a tool: The effect of model

attractiveness, product relevance, and elaboration likelihood on advertising effectiveness.

Psychology & Marketing. Vol. 27 (12): 1101-1121.

Wang, W. & Benbasat, I. (2007). Recommendation agents for electronic commerce: Effects

of explanation facilities on trusting beliefs. Journal of Management Information

Systems. Vol. 23 (4): 217–246.

Westbrook, R.A. (1987). Product/consumption-based affective responses and postpurchase

processes. Journal of Marketing Research. Vol. 24 (3): 258–270

50

Worchel, S., Lee, J. & Adewole, A. (1975). Effects of Supply and Demand on Ratings of Object Value.

Journal of Personality and Social Psychology. Vol. 32 (5): 906–914.

Zaichkowsky, J.L. (1985). Measuring the involvement construct. Journal of Consumer Research. Vol.

12 (3): 341-352.

Zeng, M. & Reinartz, W. (2003). Beyond Online Search: THE ROAD TO PROFITABILITY. California

Management Review. Vol. 45 (2): 107-130

Zhu, H., Wang, Q, Yan, L. & Wu, G. (2009). Are consumers what they consume? – Linking

lifestyle segmentation to product attributes: an exploratory study of the Chinese

mobile phone market. Journal of Marketing Management. Vol. 25 (3/4): 295-314.

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Appendix 1: Questionnaire

Dear participant,

As a final part of my Master’s degree in Marketing I am currently writing my thesis on placebo effects

in marketing. This survey is part of the research I am doing. With filling in this survey you will help me

graduate and besides, you deliver a contribution to science. Therefore I hope you will take the effort

to fill in the survey for me. It will take approximately 5-10 minutes and your answers will be

anonymous. Please read the questions carefully, and answer them truthfully.

Your help is sincerely appreciated. Thank you.

Kind regards,

Eliza Komen

52

1. The following statements concern your perceptions about yourself in a variety of situations.

Please indicate how much you agree with each statement. There are no right or wrong answers,

please select the number that most closely reflects you on each statement.

a. When I am in a group of strangers, I am reluctant to express my opinion publicly.

Strongly Strongly disagree agree 1 2 3 4 5 6 7

b. I like wearing a uniform because it makes me proud to be a member of the organization it

represents.


c. Other’s disagreement makes me uncomfortable.


d. It bothers me if people think I am being too unconventional.


e. I always try to follow the rules.


f. Feeling “different” in a crowd of people makes me feel uncomfortable.


g. It is better to always agree with the opinions of others than to be considered a disagreeable

person.


53

h. I do not like to say unusual things to people.


i. I do not like to go my own way.


j. When I am with a group of people I agree with their ideas so that no arguments will arise.


After that, the participants are assigned to one of the four different conditions (no recommendation

and low personal relevance, recommendation and low personal relevance, no recommendation and

high personal relevance, recommendation and high personal relevance). The four different

advertisements are shown below;

54

1. Advertisement for low personal relevance and without recommendation

Please look carefully at the wine shown below. Now imagine you are shopping for wine and answer

the following questions.

While you are in the supermarket, you saw this advertisement in their leaflet. Tonight you are having

your weekly dinner with your friends. As always, you are supposed to bring the wine. Your friends

like wine but they are not very knowledgeable. Therefore buying a wine that seems to have a good

quality is not that important to you.

Château Libre cabernet sauvignon

- The Château Libre cabernet sauvignon 2008 has a dark purple color with

ruby red tones. The nose is full of ripe red fruits, sweet spice and a hint

of tobacco. The mouth feel shows red currant and cassis fruit layered

with notes of cedar and espresso beans. The finish is soft and sweet.

- 13,5 % vol.

55

2. Advertisement for low personal relevance and with recommendation




your weekly dinner with your friends. As always, you are supposed to bring the wine. Your friends

like wine but they are not very knowledgeable. Therefore buying a wine that seems to have a good

quality is not that important to you.






- 13,5 % vol.

- Recommended by Supermarket wine handbook

2012 as ‘omfietswijn’. Supermarkets could have

such special and delicious wine that you are happy

to bike to the other side of the city for it. You can

recognize real good supermarket wines by the

Omfietswijn-logo.

56

3. Advertisement for high personal relevance and without recommendation




a dinner whereby you will meet your parents in law for the first time. To make a good impression you

want to bring along a bottle of wine. You know that your parents in law are wine enthusiasts and

have moderate wine knowledge. Therefore buying a wine that seems to have a good quality is

important to you.






- 13,5 % vol.

57

4. Advertisement for high personal relevance and with recommendation




a dinner whereby you will meet your parents in law for the first time. To make a good impression you

want to bring along a bottle of wine. You know that your parents in law are wine enthusiasts and

have moderate wine knowledge. Therefore buying a wine that seems to have a good quality is

important to you.






- 13,5 % vol.

- Recommended by Supermarket wine handbook

2012 as ‘Omfietswijn’. Supermarkets could have

such special and delicious wine that you are happy

to bike to the other side of the city for it. You can

recognize real good supermarket wines by the

Omfietswijn-logo.

58

2. Please indicate how much you agree with the following statements

a. The likelihood that I will buy Château Libre wine is

Very unlikely Very likely

1 2 3 4 5 6 7

b. I am willing to buy Château Libre wine Strongly Strongly disagree agree 1 2 3 4 5 6 7


a. I like red wine

Strongly Strongly disagree agree 1 2 3 4 5 6 7 b. I buy red wine often (at least once a month)

Strongly Strongly disagree agree 1 2 3 4 5 6 7 c. Compared to an average person, I know a lot about wine

Strongly Strongly

disagree agree

1 2 3 4 5 6 7


a. I often read recommendations before buying a product


b. Recommendations of independent comparison websites (for example consumentenbond.nl or

independer.nl) are a source of information I use when I decide to buy something


59

5. Based on the described scenario (hence, the purpose to buy wine) the product is….

a. Unimportant to me 1 2 3 4 5 Important to me

b. Not meaningful to me 1 2 3 4 5 Meaningful to me

6. The depicted wine in the advertisement was

a. Recommended

b. Not recommended

c. Not sure

7. I am familiar with the “Supermarket wine handbook” and “Omfietswijn” concept

a. Yes

b. No

8. To which of the following age groups do you belong?

a. <20 years

b. 21-40 years

c. 41-60 years

d. >60 years

9. What is your gender?

a. Male

b. Female

10. What is your nationality?

a. Dutch

b. Other European country

c. Other

60

11. What is your level of education?

a. Primary school

b. Secondary school

c. MBO

d. HBO

e. University

f. Postdoctoral

g. Other

61

Appendix 2: Descriptives total sample

Gender Age Education level

Condition 1 Male

Female

46%

54%

<20

21-40

41-60

>60

8%

77%

13%

2%

Sec. school

MBO

HBO

University

0%

12%

44%

44%

Condition 2 Male

Female

23%

77%

<20

21-40

41-60

>60

3%

83%

9%

5%

Sec. school

MBO

HBO

University

0%

5%

35%

60%

Condition 3 Male

Female

35%

65%

<20

21-40

41-60

>60

10%

83%

5%

2%

Sec. school

MBO

HBO

University

3%

9%

43%

45%

Condition 4 Male:

Female:

45%

55%

<20

21-40

41-60

>60

5%

87%

6%

2%

Sec. school

MBO

HBO

University

0%

3%

30%

67%

62

62,9

37,1

0

10

20

30

40

50

60

70

Female Male

Gender

6,3

82,4

8,2 3,1

0

20

40

60

80

<20 21-40 41-60 >60

Age

97,5

1,9 0,6 0

10

20

30

40

50

60

70

80

90

100

Dutch Other EC Other

Nationality

0,6

7,5

37,7

54,1

0

10

20

30

40

50

60

Secondary MBO HBO University

Education level

63

Appendix 3: Representativeness of the sample

Tests of Between-Subjects Effects

Dependent Variable: Sum_pi

Source Type III Sum of

Squares

df Mean Square F Sig.

Corrected Model 205,385a 71 2,893 1,283 ,133

Intercept 692,536 1 692,536 307,231 ,000

Age 1,167 3 ,389 ,173 ,915

Sum_nfu 139,497 47 2,968 1,317 ,134

Age * Sum_nfu 67,272 21 3,203 1,421 ,131

Error 196,108 87 2,254

Total 3630,250 159

Corrected Total 401,494 158

a. R Squared = ,512 (Adjusted R Squared = ,113)

64

Appendix 4: Descriptives sample per condition in charts

Condition 1

54

46

0

10

20

30

40

50

60

Female Male

Gender

8

77

13

2

0

10

20

30

40

50

60

70

80

<20 21-40 41-60 >60

Age

0

12

44 44

0 5

10 15 20 25 30 35 40 45 50

Education level

65

Condition 2

77

23

0

10

20

30

40

50

60

70

80

Female Male

Gender

3

83

9 5

0

20

40

60

80

<20 21-40 41-60 >60

Age

0 5

35

60

0

10

20

30

40

50

60

70

Education level

66

Condition 3

65

35

0

10

20

30

40

50

60

70

Female Male

Gender

10

83

5 2 0

20

40

60

80

<20 21-40 41-60 >60

Age

3

9

43 45

0

5

10

15

20

25

30

35

40

45

50


Education level

67

Condition 4

55

45

0

10

20

30

40

50

60

70

Female Male

Gender

5

87

6 2 0

20

40

60

80

<20 21-40 41-60 >60

Age

0 3

30

67

0

10

20

30

40

50

60

70


Education level

68

Appendix 5: ANOVA for manipulation check

Moderator personal relevance of the product

Descriptives

Sum_impor

N Mean Std. Deviation Std. Error

95% Confidence Interval for Mean

Minimum Maximum Lower Bound Upper Bound

low 79 2,2089 1,21847 ,13709 1,9359 2,4818 1,00 5,00

high 80 4,2500 ,95798 ,10711 4,0368 4,4632 1,00 5,00

Total 159 3,2358 1,49663 ,11869 3,0014 3,4703 1,00 5,00

ANOVA

Sum_impor

Sum of Squares df Mean Square F Sig.

Between Groups 165,602 1 165,602 138,072 ,000

Within Groups 188,304 157 1,199

Total 353,906 158

69

Appendix 6: Cronbach’s alpha

Name of construct Questions Cronbach’s alpha

NFU 1) When I am in a group of strangers, I am reluctant to

express my opinion publicly.

2) I like wearing a uniform because it makes me proud to be a

member of the organization it represents.

3) Other’s disagreement makes me uncomfortable.

4) It bothers me if people think I am being too

unconventional.

5) I always try to follow the rules.

6) Feeling “different” in a crowd of people makes me feel

uncomfortable.

7) It is better to always agree with the opinions of others than

to be considered a disagreeable person.

8) I do not like to say unusual things to people.

9) I do not like to go my own way.

10) When I am with a group of people I agree with their ideas

so that no arguments will arise.

.931

Purchase Intention 1) The likelihood that I will buy Château Libre wine is

2) I am willing to buy Château Libre wine

.958

Usage of

recommendations

1) I often read recommendations before buying a product

2) Recommendations of independent comparison websites

(for example consumentenbond.nl or independer.nl) are a

source of information I use when I decide to buy something

.790

Personal relevance

of product

1) Based on the described scenario (hence, the purpose to

buy wine) the product is….

a. Unimportant to me - Important to me

b. Not meaningful to me - Meaningful to me

.961

70

Scale: NFU

Case Processing Summary

N %

Cases Valid 159 100,0

Excludeda 0 ,0

Total 159 100,0

a. Listwise deletion based on all variables in the procedure.

Reliability Statistics

Cronbach's Alpha

Cronbach's Alpha

Based on

Standardized Items N of Items

,929 ,931 10

Scale: Purchase intention


N %


Excludeda 0 ,0

Total 159 100,0



Cronbach's Alpha

Cronbach's Alpha

Based on


,957 ,958 2

Scale: Using recommendations


N %


Excludeda 0 ,0

Total 159 100,0



71

Cronbach's Alpha

Cronbach's Alpha

Based on


,790 ,790 2

Scale: Personal relevance scenario


N %


Excludeda 0 ,0

Total 159 100,0



Cronbach's Alpha

Cronbach's Alpha

Based on


,961 ,961 2

72

Appendix 7: ANOVA for testing purchase intention among the four conditions

Between-Subjects Factors

N

Opties

1,00 39

2,00 40

3,00 40

4,00 40

Descriptive Statistics


Opties Mean Std. Deviation N

1,00 4,1154 1,56635 39

2,00 4,8625 1,55657 40

3,00 4,1500 1,49872 40

4,00 4,8875 1,63491 40

Total 4,5063 1,59408 159

Tests of Between-Subjects Effects


Source Type III Sum of

Squares

df Mean Square F Sig.

Corrected Model 21,925a 3 7,308 2,984 ,033

Intercept 3224,869 1 3224,869 1316,903 ,000

Opties 21,925 3 7,308 2,984 ,033

Error 379,568 155 2,449

Total 3630,250 159

Corrected Total 401,494 158

a. R Squared = ,055 (Adjusted R Squared = ,036)

73

Appendix 8: Linear regression analysis for testing hypotheses


Mean Std. Deviation N

Sum_pi 4,5063 1,59408 159

Recommended ,50 ,502 159

nfu_centered ,0000 1,30769 159

Personal relevance ,50 ,502 159

inter_recNFU ,0749 ,87044 159

inter_recPR ,2516 ,43529 159

What is your gender? 1,63 ,485 159

I like red wine. 4,89 1,906 159

I buy red wine often (at least

once a month). 4,26 2,066 159

Compared to an average

person, I know a lot about wine. 3,23 1,703 159

Model Summary

Model R R Square Adjusted R Square Std. Error of the

Estimate

1 ,473a ,224 ,177 1,44631

a. Predictors: (Constant), Compared to an average person, I know a lot about

wine., Recommended, Personal relevance, inter_recNFU, What is your gender?, I

buy red wine often (at least once a month)., nfu_centered, inter_recPR, I like red

wine.

ANOVAa

Model Sum of Squares df Mean Square F Sig.

1

Regression 89,816 9 9,980 4,771 ,000b

Residual 311,678 149 2,092

Total 401,494 158

a. Dependent Variable: Sum_pi

b. Predictors: (Constant), Compared to an average person, I know a lot about wine., Recommended, Personal

relevance, inter_recNFU, What is your gender?, I buy red wine often (at least once a month)., nfu_centered,

inter_recPR, I like red wine.

74

Coefficientsa

Model Unstandardized Coefficients Standardized

Coefficients

t Sig.

B Std. Error Beta

1

(Constant) 4,707 ,615 7,652 ,000

Recommended -,675 ,331 -,212 -2,041 ,043

nfu_centered ,533 ,120 ,437 4,451 ,000

Personal relevance ,249 ,331 ,078 ,753 ,453

inter_recNFU -,392 ,180 -,214 -2,179 ,031

inter_recPR -,420 ,473 -,115 -,889 ,375

What is your gender? ,295 ,257 ,090 1,150 ,252

I like red wine. ,277 ,127 ,331 2,188 ,030

I buy red wine often (at least

once a month). -,247 ,112 -,320 -2,207 ,029

Compared to an average person,

I know a lot about wine. -,198 ,096 -,211 -2,069 ,040


75

Direct influence of personal relevance on purchase intention


Mean Std. Deviation N

Sum_pi 4,5063 1,59408 159

Personal relevance ,50 ,502 159

Model Summary

Model R R Square Adjusted R Square Std. Error of the

Estimate

1 ,008a ,000 -,006 1,59910

a. Predictors: (Constant), Personal relevance

ANOVAa

Model Sum of Squares df Mean Square F Sig.

1

Regression ,025 1 ,025 ,010 ,921b

Residual 401,469 157 2,557

Total 401,494 158


b. Predictors: (Constant), Personal relevance

Coefficientsa

Model Unstandardized Coefficients Standardized

Coefficients

t Sig.

B Std. Error Beta

1 (Constant) 4,494 ,180 24,977 ,000

Personal relevance ,025 ,254 ,008 ,099 ,921


76

Appendix 9: Mean NFU


N Minimum Maximum Mean Std. Deviation

Sum_nfu 159 1,20 6,70 3,4289 1,30769

Valid N (listwise) 159

The effect of recommendations on purchase intention

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