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UNIVERSITY  OF  GRONINGEN  

The  Influence  of  Personality  on  Hedonic  

and  Utilitarian  Shopping  Motives  and  

the  Intention  to  Shop  Online

 

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The Influence of Personality on Hedonic and Utilitarian

Shopping Motives and the Intention to Shop Online

Does our personality determine how we shop?

University of Groningen

Faculty of Business and Economics Msc Marketing Management Master Thesis

July 2014

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Management summery

This research is aimed at gaining more insights about peoples shopping motives and finding out what drives them to shop online and offline in regular stores. This is important for retailers to know, because this is the age of upcoming e-commerce, and the retail environment is changing. Retailers are presented with new online opportunities, but this also means that traditional offline retailers are facing some challenges: how do they keep people coming to their stores? In order for online and offline retailers to be as successful as possible, they must target the right people, know what kind of products to sell and know whether they should sell through an online or offline store. This research will therefore take a deeper dive into consumers’ shopping motivations, thereby differentiating between hedonic shopping (providing the shopper with enjoyment and fun) and utilitarian shopping (related to successfully accomplishing the shopper’s goal) motivations to see how these motivations relate to online and offline shopping intentions. Previous research has shown that personalities may account for differences in shopping motivations. Also, our personalities may influence our intentions to shop online and in traditional stores. This paper therefore also researches the effect of the Big Five (Extroversion, Agreeableness, Openness to experience, Conscientiousness and Neuroticism) on shopping motives and the intention to shop online. The following research question has been formulated:

What is the effect of hedonic and utilitarian shopping motives on the intention to shop online, and to what extent are these motives and the intention to shop online affected by the Big Five personality dimensions?

The research was conducted by gathering data from 242 respondents that filled out a survey. Hedonic and utilitarian shopping motivations were measured with a questionnaire disgned by Cardoso and Pinto (2010). Intentions to shop online were measured by assessing respondents’ preference towards an online or offline store when they were asked to imagine buying a selection of 16 products (8 hedonic and u utilitarian). Personality was measured with the Big Five Inventory (BFI) consisting of 44 self-report items.

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especially for utilitarian products. Regular stores should therefore namely focus on selling hedonic products and target females high on hedonic shopping motives, in order to gain competitive advantage over online stores. Online stores should focus on selling utilitarian products and come up with a campaign to target more males. When hedonic and utilitarian shopping motives are split up into multiple underlying shopping motives, this research finds role shopping shows a negative, but idea shopping shows a positive relation with intention to shop online. For retailers this means that online retailers should provide consumers with inspiration, latest trends, and new ideas and offline retailers should target consumers who enjoy shopping for others.

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Preface

In September 2009, I started as a psychology student at the Rijksuniversiteit. Although I was very interested in the field of psychology, I soon knew that this was not the direction I had in mind for my future career. During my bachelor, I grew towards social psychology with a special interest towards consumer behavior. I therefore decided to take two marketing courses in my minor and I was immediately hooked. I decided to transfer to the faculty of Economics and Business and start the master Marketing Management in September 2013. I knew I had made the right decision when I started my first three courses and I have developed a passion for marketing ever since. Of course I enjoyed some courses more than others. Probably due to my psychology background, I noticed that I was most drawn to B2C marketing and still enjoyed courses involving consumer behavior most.

When you start your bachelor or even you master, writing a master thesis always seems so far away and frankly, a little bit scary. Time absolutely flew by this year and before I knew it I was filling out my preference form for a thesis topic. I was very happy to be given the topic Retail Marketing, because this was also a course that I had followed and enjoyed a lot. Under the supervision of prof. dr. Laurens Sloot I found a way to integrate psychology and marketing and came to this very interesting research. With mixed feelings I am now laying the last hand on my master thesis, because every sentence that I write that will bring me closer to finishing this paper, will also mean that I am closer to finishing my time as a student in Groningen. As much as I have loved my time in Groningen, all good things must come to end. But where one door closes, another one opens. In August I will start an internship at Unilever which I am very excited about.

I would like to take this opportunity to thank some people that have helped me writing my thesis. First of all, I would like to thank prof. dr. Laurens Sloot for his guidance and feedback that was always just one phone call away. Thanks to my fellow thesis group members for the mental and practical support. Finally, a warm thanks to all my friends and family for filling out my endless questionnaire and helping me along the road.

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Table of content

Management summery………...2

Preface………4

1. Introduction………..7

2. Literature review and hypotheses……….9

2.1 The changing retail environment………9

2.2 Hedonic and utilitarian shopping motives………...11

2.2.1 Motives for online shopping……….12

2.2.2 Motives for offline shopping………12

2.3 Hedonic and utilitarian products………...13

2.4 Fundamental model………..13

2.5 The role of the Big Five personality dimensions……….15

2.6 Extended model………18

3. Methodology………...…24

3.1 Research design………24

3.2 Participants……….………..24

3.3 Measurements………...………25

3.3.1 Measuring hedonic and utilitarian shopping motives……….……….25

3.3.2 Measuring the Big Five personality dimensions………...…...26

3.3.3 Measuring the intention to shop online………27

3.4 Control variables……….….30

3.5 Plan of analysis………...31

3.5.1. Analyses of the demographics………31

3.5.2 Basis analysis and pre-insights………..………..32

3.5.3 Testing hypotheses………..……….32

3.5.4. Extended analyses………..……….33

4. Results……….34

4.1 Descriptive statistics……….………34

4.1.1 Online behavior and online shopping frequencies………..………..36

4.2 Basic analysis and pre-insights………...………..38

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4.2.2 Hedonic and utilitarian shopping motives……….…....……40

4.2.3 Intention to shop online……….40

4.2.4 Test of normality………42 4.2.5 Correlations………44 4.3 Testing hypotheses………46 4.3.1 Fundamental model………46 4.3.2 Extended model……….49 4.4 Extended analysis……….…51

4.4.1 Underlying hedonic and utilitarian constructs………..………51

4.4.2 The effect of the underlying shopping constructs on the intention to shop online………...…….53

4.4.3 The effect of the big five on the underlying shopping constructs………54

4.5 Mediation effects……….……….57

5. Discussion………..…….58

5.1 The effect of shopping motives on the intention to shop online………..……59

5.2 The effect of the big five personality traits on shopping motives…………...…….61

5.3 The effect of the big five on the intention to shop online………...………..64

5.4 The effect of the underlying shopping constructs on the intention to shop online……….……….66

5.5 The effect of the big five on underlying HSM and USM constructs………….….66

6. Managerial implications………...…..70

6.1 Main findings………70

6.2 Implications for offline retailers……….………….71

6.3 Implications for online retailers………..……….75

7. Limitations and further research……….…………78

References……….80

Appendix 1. Original Big Five Inventory………...………..86

Appendix 2. Original shopping motives questionnaire………87

Appendix 3. Online questionnaire………...………….89

Appendix 4. Basic analysis: Skewness, Kurtosis and correlations……….…...…..95

Appendix 5. Testing hypotheses: Regression analyses output ………97

Appendix 6: Extended analysis: Factor analyses………103

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1. Introduction

In the age of upcoming e-commerce, retailers are presented with new opportunities for online retailing, but also with new challenges. Online sales are growing fast and online retailers are improving their websites and services rapidly in order to gain more market share. In order to enhance customer satisfaction and loyalty, online retailers can provide numerous advantages such as convenience, a well-designed user interface, customized information, a variety of product information, and competitive pricing (Verhoef & Langerak, 2001; Park & Kim, 2003). Lin (2010) also stresses the importance of the extent to which a customer can participate in an interactive multimedia-based environment and the extent to which a customer believes that paying online is secure.

Whilst online sales are flourishing, traditional offline stores are confronted with decreasing store-traffic and declining sales, leading to increasing shop vacancies (Retail Gazette, 2013). In order to maintain consumers coming to the offline stores, more and more retailers are changing their store environment to provide consumers with superior customer experience in the store. In The Netherlands, the Bijenkorf has implemented a premium experience strategy, continuing only with their seven bestselling flagship stores in which they have invested in service, brands and their look and feel, in order to enhance the shopping experience. But who must they target? What do consumers value most in their store and online shopping experience? An understanding of why consumers select particular channels for their shopping is of greatest importance for online as well as offline retailers (Black et al. 2002).

This research will therefore take a deeper dive into consumers’ shopping motivations, thereby differentiating between hedonic and utilitarian shopping motivations. Utilitarian motives are related to acquiring a product and successfully accomplishing the shopper’s goal. Hedonic motives are about providing the shopper with enjoyment and fun, and relate to the multi-sensory, fantasy and emotive aspects of one’s shopping experience (Westbrook and Black, 1985).

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Furthermore, our personalities may also influence our intentions to shop online and in traditional stores. This research will use the Big Five taxonomy of personality, according to which, human personality can be described by five main dimensions which are Extroversion (the preference for social interaction); Agreeableness (the orientation toward compassion and caring about others); Openness to Experience (the tolerance of new ideas and new ways of doing things); Conscientiousness (the preference for goal-oriented activity); and Neuroticism (the inability to cope effectively with negative emotions) (Digman, 1990). What the role of these personality dimensions exactly is and how personality, shopping motives and intentions to shop online/offline relate to each other is still unresolved and this research is conducted to find these answers. Does our personality determine how we shop? More specifically, the following research question has been formulated:

What is the effect of hedonic and utilitarian shopping motives on the intention to shop online, and to what extent are these motives and the intention to shop online affected by the Big Five personality dimensions?

In order to understand these relations in more detail, this research is divided into three main variables that may have effects on each other: hedonic and utilitarian shopping motives, the intention to shop online and the Big Five. The following three effects will be researched:  

1. The effect of hedonic and utilitarian shopping motivations on the intention to shop online. A distinction is made between shopping for hedonic or utilitarian products. 2. The effect of the Big Five personality dimensions on hedonic and utilitarian

shopping motivations.

3. The effect of the Big Five personality dimensions on the intention to shop online.

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2. Literature review and hypotheses

This literature review will give a good theoretical understanding of the concepts involved in this research and will substantiate the formulated hypotheses. To start, the changing retail environment will be discussed. Then, the concepts hedonic and utilitarian shopping motives will be discussed in depth and their effect on the intention to shop online will be hypothesized. The fundamental model will be proposed. Next, this literature review will discuss the Big Five personality dimensions in detail and propose the extended model, with its corresponding hypotheses.

2.1 The changing retail environment

Ecommerce numbers are booming. According to forecasts by Forrester Research, online sales which generated $231 billion for U.S. retailers last year are expected to increase 13% to $262 billion in 2014. Online sales growth is expected to outpace sales growth at traditional offline stores over the next five years, reaching $370 billion in sales by 2017. Forrester Research expects that online sales in Western Europe will grow at an even faster rate than the U.S. in the next five years, from €112 billion in 2012 to €191 billion by 2017 which is an annual growth rate of 11%.

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obtain more freedom and control trough convenience, selection, availability of information and lack of sociality. Additionally, other benefits that online retailers are able to provide to consumers are personalization through user input and interactivity trough chat, consumer reviews and email. Furthermore, increased online product selections and online tools such as search engines and order tracking are praised (Wolfinbarger and Gilly, 2001). They also found that online shoppers obtain more freedom and control trough convenience, selection, availability of information and lack of sociality. These online benefits and the increased

competition have led to volume trickling from the offline stores. In the Netherlands a research

by Locatus (2013) showed an increase in shop vacancy from 6,4% to 6,9% compared to the year before. This has forced traditional retailers to cut labor, reduce costs and sacrifice service. Because of this, they are less able to differentiate their offline store, which leads to customers focusing increasingly on price and convenience. This only strengthens the advantage of the online retailers (Rigby, 2011).

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2.2 Hedonic and Utilitarian shopping motives

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predictors of hedonic motivation. In contrast to Arnold and Reynolds, they found no support for value, social and idea shopping as hedonic motives.

2.2.1 Motives for online shopping

Although results from previous literature are not all congruent, it seems that overall, utilitarian motives can be served best by online retailing and hedonic motives can be served best by offline retailing. However, research also speaks of dual motivations, which implies that both utilitarian and hedonic motivations can be present at the same time (Childers et al. 2001). Wolfinbarger and Gilly (2001) acknowledged that consumers shop differently, depending on whether their motivations are mostly experiential or goal-orientated. They found that online shopping was more goal-orientated than experiential, and concluded that 66 up to 80 percent of the online purchases was goal oriented. Alba et al (1997) state that online retailing offers many opportunities that appeal to more utilitarian motivations such as the availability of product information, enabling direct multi-attribute comparisons, and reducing buyer search costs. Rigby (2011) also identified these opportunities, as well as convenient and fast check-out, and the customers’ ability to get anything, anytime, anywhere. Again, these are more utilitarian values. Chen & Lee (2008) found that consumers who perceive higher utilitarian values in websites will have a higher level of trust in online shopping. This was not true for higher hedonic values. Online shopping tends to be less hedonic, as the online shopping experience is still far less exiting than its offline competition (Wolfinbarger and Gilly, 2001). Contrastingly, Childers et al. (2001) found that while utilitarian motives are important predictors of online attitudes, the more immersive, hedonic aspects play at least an equal role. They argue that online retailers now have powerful new media at their disposal that can fulfil hedonic needs such as, images, videos, humor, sounds, animation, games and more interactive multimedia that can make online shopping an enjoyable experience.

2.2.1 Motives for offline shopping

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environment (Wakefield & Baker, 1998). Rigby also argues that offline stores have the advantage of making shopping an exciting experience, have the ability to let consumers test, try and experience the products, and can provide personal face-to-face service. These advantages relate to Arnold and Reynolds’ adventure shopping and social shopping. According to Babin et al. (1994), seeking hedonic experiences is often far more significant than the acquisition of products. Of course, it must be kept in mind that the shopping experience is integrative (utilitarian and hedonic) of nature, and a retailer must be able to fulfill both hedonic and utilitarian needs (Baker et al. 2002).

2.3 Hedonic and utilitarian products

We cannot only identify hedonic and utilitarian shopping motives, but products themselves can also be hedonic or utilitarian in nature. The hedonic component in products is related to

sensory attributes, and focuses on the affective gratification of the consumer (Batra and Ahtola, 1991). Hedonic products can provide pleasure, fun and excitement to the consumer (Hirschman and Holbrook 1982). Examples of hedonic products can be sports cars, luxury watches,

perfume and designer clothes. On the other hand, the utilitarian component in products relates to non-sensory and functional attributes and focuses on the instrumental expectations a

consumer has about the product (Batra and Ahtola, 1991). Utilitarian products are goods such as microwaves, personal computers and detergent. It must be noted that although we can categorize products as being overall hedonic or utilitarian, it is the consumers’ perception of the product that defines them as such. A consumer buying a new car for example may care more about hedonic attributes (e.g. design) than about utilitarian attributes (e.g. gas mileage) leading to this car being hedonic to one consumer, and maybe utilitarian to another consumer.

2.4 Fundamental model

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fundamental model, a distinction is made between the intention to shop online for hedonic products and the intention to shop online for utilitarian products. Consequently, a negative effect on the intention to shop online is a positive effect on the intention to shop offline in a regular store. The fundamental model is shown below in figure 1.

Figure 1 Fundamental model

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Online shopping: hedonic products

Online shopping: utilitarian products

Utilitarian shopping motives (USM) 0 +

Hedonic shopping motives (HSM) - 0

Table 1 Expected effects of HSM and USM on intention to shop online.

Based on this, the following has been hypothesized:

H1a: HSM are negatively related to the intention to shop online for hedonic products. H1b: There is no relation between HSM and the intention to shop online for utilitarian products.

H2a: USM are positively related to the intention to shop online for utilitarian products. H2b: There is no relation between USM and the intention to shop online for hedonic products.

2.5 The role of the Big Five personality dimensions

For the above it seems that consumers’ motives could be important indicators of their choice between buying products online or offline. But how are these motives formed? This research examines if personality traits influence these motives. The fundamental model in figure 1 can be extended adding the effect of personality traits on these motives and on the intention to shop online.

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dimensions and developed the 16 Personality Factor Questionnaire to measure them (Catell, 1973). Although this questionnaire is still used today, many researchers found it still overly complex and high correlations were found between factors. Today, with computer technology, it is easier to perform large scale factor analyses, and over the past 35 years, researchers have come to a broad but not universal consensus that most individual differences in human personality can be classified in the Big-Five framework with five broad dimensions, namely Neuroticism, Extraversion, Openness to Experience, Agreeableness, and Conscientiousness (Goldberg, 1981; John, 1990). The Big Five does not imply that personality differences can be reduced to only five traits. Rather, these five dimensions represent personality at the broadest level of abstraction, and each dimension summarizes a large number of distinct, more specific personality characteristics (John & Shrivastiva, 1999).

Each Big Five dimension is a bipolar factor with two opposite sides to the spectrum. Neuroticism can be described as a vulnerability to emotional upset, and correlates with terms such as anxious, depressed, self-consciousness, impulsive and vulnerable. Neurotic individuals find comfort in their own environment and dislike engaging in settings beyond their control (Eysenck & Eysenck, 1985). Opposite to Neuroticism is Emotional Stability. The dimension extroversion has introversion opposite to it, and relates to concepts such as being outgoing, thrill-seeking, talkative and energetic. Introversion is manifested in more reserved and solitary behavior. Extraverts are inclined to favor social activities and intense personal interactions (Bakker et al., 2006). Extroverts are people that are open to new experiences and are eager to interact with the outer world and introverts are known to be shy and feel less comfortable around strangers (Myers-Briggs et al., 1998). Individuals that score high on Openness to Experience are more creative, intellectual, open-minded, independent, and fantasy full. Opposite to Openness to Experience is Non-Openness. Agreeableness relates to concepts such as trusting, altruistic, compliant, and kindness, and is the opposite of Antagonism. Finally, individuals that score high on Conscientiousness are orderly, responsible and dependable and strive for achievement (John & Shrivastiva, 1999; Gray, 2007).

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versions of the NEO-PI have been developed (Gray, 2007). The NEO-PI-R (Costa & McCrae, 1992) has brought the questionnaire down to 60 items but is still quite long. The Big Five Inventory (BFI) was constructed in the late 1980s (John, Donahue, & Kentle,

1991) and contains 44 short-phrase items. Gosling et al. (2003) developed an extremely brief 10 item-questionnaire to measure the Big-Five named the BFI-10. Although somewhat inferior to the NEO-PI, their instrument reached adequate levels of convergence with other multi-item questionnaires, high test-retest reliability and high correlation between self and observer ratings. However, it must be noted that there were substantial losses in comparison to the full-scale BFI and therefore the BFI-10 cannot be seen as substitute but rather as an alternative when time is limited.

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There is very limited research done on the direct role of the Big Five on the choice between online and offline retailers. Different personality types have different values and this research expects that this will affect their desire to shop online or offline. Landers and Lounsbury (2004) looked at the relationship between internet usage and the big five, and found that internet usage was negatively related to Agreeableness, conscientiousness and extraversion. Goby (2006) suggests that the lack of physical closeness to strangers in an online environment may mask introverts’ insecurity and make communication easier. In the same research it was found that extraverts agreed more strongly that introverts that they were able to express themselves better in an offline environment.

  Hedonic motivations   Utilitarian motivations  

Guido (2006)   Openness to experience agreeableness extraversion  

Emotional stability conscientiousness Karl, Peluchette and Harland (2007)   extraversion

openness to experience emotional stability  

Conscientiousness and agreeableness  

Chen & Lee (2008)   extraversion

openness to experience emotional stability  

Conscientiousness and agreeableness  

Huang and Yuang (2010)   Extraversion emotional stability openness to experience  

Conscientiousness  

Kelly and Johnson (2005)     Conscientiousness

 

Table 2 A summary of previous research on the effect of the big five on HSM and USM

2.6 Extended model

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hypothesized. The model also implies a direct effect of the big five personality traits on the intention to shop online.

 

Figure 2. Extended model.

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H3a: The personality trait Extraversion is positively related to HSM. H3b: The personality trait Extraversion is not related to USM.

Individuals that have high openness to experience, value excitement and entertainment because of their creativity, open-mindedness and fantasy fullness. They have more imagination and curiosity and like variability (Costa and McCare, 1985). Furthermore, the more open to experience a person is, the more he is willing to consider various opinions and viewpoints, accept new experiences, and seek out opportunities to learn new things. Fantasy is related to hedonic shopping motives (Westbrook and Black, 1985). This research expects openness to experience individuals’ fantasy to relate to Arnold and Reynolds’ adventure shopping and therefore also have more hedonic shopping motives. Matzler et al.(2006) pointed out that consumers with more openness to experience showed stronger reactions to emotional stimuli and that it would therefore be attractive for such consumers to seek out hedonic values and gain gratification via shopping. Creativity and open-mindedness makes these individuals curious for new products, ideas and trends. Moordian and Olver (1996) found that openness to experience was correlated to sensory stimulation and learning about new trends. These are both hedonic motivations. Learning about new trends is related to Arnold and Reynolds’ idea

shopping and Westbrook and Black (1985) relate multi-sensory aspects of the shopping experience to hedonic consumption. This research also expects openness to experience

individuals to score high on Arnold and Reynolds’ adventure shopping and therefore also have more hedonic motives. The following is hypothesized:

H4a: The personality trait Openness to experience is positively related to HSM. H4b: The personality trait Openness to experience not related to USM.

Literature has come to the consensus that people who score high on conscientiousness have more utilitarian values (Guido, 2006; Karl, Peluchette and Harland, 2007; Chen & Lee, 2008; Huang and Yuang, 2010; Kelly and Johnson 2005). Conscientious individuals are orderly, responsible, cautious and strive for achievement (Pervin et al., 2004). This type of people tends to be less sensitive in interpersonal relationships, so they are likely to to seek satisfaction in achievement by being conscientious about their work. Consumers with higher degrees of conscientiousness tend to learn things from shopping such as information processing,

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utilize their knowledge to evaluate whether products are appropriate for their social status by assessing alternative products and making product selections. Moordian and Olver (1996) found Conscientiousness was inversely related to self-gratification which is hedonic. This has to do with the strive for self-control of these individuals. Therefore, it is expected that

conscientious consumers tend to have utilitarian shopping motives and not so much hedonic shopping motivations. The following is hypothesized:

H5a: The personality trait Conscientiousness is positively related to USM. H5b: The personality trait Conscientiousness not related to HSM.

The outcomes of research on emotional stability are divided. Most research however, has found emotional stability to relate positively to hedonic shopping motives (Karl, Peluchette and Harland, 2007; Chen & Lee, 2008; Huang and Yuang, 2010). Consequently, the opposite of emotional stability, neuroticism, will be negatively related to hedonic motives. People who are more neurotic are more self-conscious and more vulnerable to emotional hurt or inflictions and they have stronger negative emotional reactions, keep their word, and tend to display negative emotional responses (Costa and McCrae, 1985). It can be argued that because of these neurotic traits, neuroticism relates negatively to hedonic shopping motivations. There self-consciousness shy’s them away from social interaction and and adventure and excitement only frightens them. Therefore the following is hypothesized:

H6a: The personality trait Neuroticism is negatively related to HSM. H6b: The personality trait Neuroticism not related to USM.

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were beneficial to reach their goals. Based on this previous research, this study therefore proposes that consumers who are more agreeable also tend to have utilitarian shopping motivations. This leads to the following hypotheses:

H7a: The personality trait Agreeableness is positively related to HSM. H7b: The personality trait Agreeableness is positively related to USM.

This research also expects to find direct effects of the Big Five personality traits on the intention to shop online. Extraversion and openness to experience are expected to have a negative direct effect on online shopping. Extraverts are likely to value the excitement and sociality that an offline experience can offer and will therefore prefer a physical store over shopping online. Mooradian and Olver(1996) examined the effect of personality on shopping motives and found a positive relation between extraversion and social experiences outside the home which supports the expectations. Furthermore, extraverts are attracted to sensory stimulating environments (Morover, Stelmack, 1990). Even though online web shops can stimulate sight and hearing, stimulation is much less than in an offline store, where one can also feel, smell and possibly even taste products. Based on this the hypothesis below is formulated:

H8: Extraversion is negatively related to the intention to shop online.

People who are open to experience are creative and fantasy full. They are found to have a positive relation to sensory stimulation and learning about new trends (Mooradian and Olver, 1996). Based on this, shopping offline seems to fit these people better because of the more creative environment and the inspiration and new ideas they can gather in shopping windows and in-store displays.

H9: Openness to experience is negatively related to the intention to shop online.

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shopping online. (Mooradian and Olver, 1996) found a positive relation between conscientiousness and bargains. Online web shops can have competitive pricing because they don’t have to invest in employee service or a shop. Online shopping also has the advantage to easily compare the different prices in different stores without having to go from shop to shop. Conscientious individuals will probably value this, and like to get the best price. Therefore the following is hypothesized:

H10: Conscientiousness is positively related to the intention to shop online.

Neurotics are expected to value the control they have whilst shopping online and they can shop in their own, safe environment. Also, Huang and Yang (2010) found that neuroticism is positively related to online shopping due to a lack of sociality. Tsao and Chang (2010) found that consumers with high degrees of neuroticism tend to utility motivated to shop online.

H11: Neuroticism is positively related to the intention to shop online.

Individuals that are high on agreeableness are trusting so they will not really perceive the risks of online shopping, but also like offline shopping. They will probably let their choice depend on their environment and because they are compliant and altruistic, they are easily influenced by others around them. Overall, they do not particularly prefer online or offline shopping. From these expectations the hypothesis below has been formulated:

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3. Methodology

 

The previous chapter has summarized previous literature and come up with several hypotheses and shown these in a conceptual model. To test this conceptual model, an empirical research is performed. This chapter will specify the methodology of this research. First, the research design and the method of data collection will be elaborated on, followed by the questionnaire design and the measurement of the different variables.

3.1 Research design

This research investigates the influence of personality and shopping motives on the intention to shop online. In able to test the hypotheses, the conceptual model will be empirically tested through quantitative research. Data will be obtained through an online questionnaire with self-report measures. In the questionnaire, participants’ personality will be assessed and they will be assigned a score on each of the big five personality traits. The questionnaire will also ask participants about their hedonic and utilitarian shopping motives. Finally, participants will be presented with 20 products and they need to choose whether they prefer to buy these products online or offline. This will measure the intention to shop online. The  questionnaire  ends  with   a  number  of  questions  concerning  control  variables.  

The questionnaire will be distributed online among people living in the Netherlands aged 18 to 80 years old. This group is expected to be confronted regularly with the choice between buying online or offline and can make independent decisions. An online questionnaire is chosen because the distribution is fast and flexible. Another benefit is that the collected data will be digital with is more efficient to process results (Malhotra, 2006). Furthermore, the online link to the questionnaire can be distributed through multiple channels such as e-mail, Facebook and snowballing (ask direct contacts to forward the link to their friends and family).

3.2 Participants

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(48.8%). The dataset mainly contains data from higher educated individuals because namely students filled out the questionnaire.

 

3.3  Measurements  

 

The questionnaire can be accessed via an online link. First, the participants will be told that this is a study about shopping motivations and they will be thanked for their participation. They can then move on to the questionnaire which consists of five parts. In the first part, the participant’s hedonic and utilitarian shopping motives are measured. In the second part their personality will be assessed. In the third part, participants are presented with a number of hedonic and utilitarian products of which they have to indicate whether they prefer to buy them online or offline. The fourth part is a manipulation check to check whether the chosen products are in fact seen as hedonic or utilitarian and what the participants’ level of involvement is with the products used in this research. Finally, participants will be asked to answer a number of control questions regarding their age, gender, income, education and number of hours per day they spend online and how frequently they shop online and in regular stores. Each participant will be provided with exactly the same questionnaire. Because individual differences are measured and this research is interested if these individual differences (personality and shopping motives) influence the percentage online shopping, no manipulation is needed or used. The full questionnaire can be found in appendix 3.

3.3.1 Measuring hedonic and utilitarian shopping motives

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Arnold and Reynolds’ (2003) six hedonic shopping motivations. Cardoso and Pinto (2010) studied measurement scales from 1985 up to 2007 and developed their own scale, incorporating most recent research. They used a questionnaire composed of two scales: one form Arnold and Reynolds (2003), and another from Kim (2006) with two utilitarian dimensions. The two scales resulted in 24 items, 18 hedonic and 6 utilitarian, measured on a seven-point Likert scale with 1 = disagree strongly and 7 = agree strongly. This research will use the questionnaire by Cardoso and Pinto (2010) because it is up to date and measures shopping motivations instead of values. This original questionnaire can be found in appendix 2. The hedonic constructs measured are: pleasure and gratification shopping, idea shopping, social shopping, role shopping and value shopping. The utilitarian constructs are achievement and efficiency. For this research, the original items were translated in Dutch and the items were put in random order so the participants don’t know exactly what is being measured.

3.3.2 Measuring the Big Five personality dimensions

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Traits Relating items Extraversion 1, 6R 11, 16, 21R, 26, 31R, 36 Agreeableness 2R, 7, 12R, 17, 22, 27R, 32, 37R, 42 Conscientiousness 3, 8R, 13, 18R, 23R, 28, 33, 38, 43R Neuroticism 4, 9R, 14, 19, 24R, 29, 34R, 39 Openness 5, 10, 15, 20, 25, 30, 35R, 40, 41R, 44

Table 3 Classification of items in the Big Five Inventory.

 

3.3.3 Measuring intention to shop online

In order to measure the participants’ online shopping intentions, they are presented with 20 products. The name of the product is shown. These products are available online and offline in the Netherlands. In the past, online retailing was primarily focused on selling books and movies, but now practically everything can be purchased online. The participants will be asked to imagine buying the following products. When viewing the product, the participants will be presented with the following:

‘A number of products will follow. Imagine that you want to purchase these products. Please indicate whether you prefer to buy these products in an online web shop or in a regular store. You can assume that there is no difference between the products in the online web shop and the regular store regarding price and quality.’

The participants could then indicate where they preferred to buy the products on a 7-point Likert scale (1= definitely in a regular store, 2= preference for a regular store, 3= small preference for a regular store, 4= neutral, 5= small preference for an online store, 6= preference for an online store, 7= definitely in an online store). The higher the value for this question, the higher the intention to shop online will be.

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product (Batra and Ahtola, 1991). Examples of hedonic products are luxury watches, perfume and chocolate. Water boilers, laptops and health insurance are examples of utilitarian products. This research wants to control for this and therefore half of the products are hedonic, and half of the products are utilitarian in nature. Furthermore, although not discussed in the literature review, this research also will control for product involvement. Participants will be higher involved for one product than for the other and we need an equal number of high and low involvement products in order to make sure that this doesn’t influence the results. Involvement refers to the products’ personal relevance to the participant and the extent to which the participant is knowledgeable about the product. Although involvement is personal, products can generally be categorized in high and low involvement. Again, half products will be high involvement products, and half will be low involvement products. High/low involvement product and hedonic/utilitarian product will be crossed, resulting in an equal number of products per cell. To identify a number of products that are representative for each of the cells, a pre-test was conducted. A pre-selection of 32 products was rated on two dimensions (hedonic value and level of involvement) by 7 Dutch respondents. Means for both dimensions were calculated, see table 4 below.

Table 4 Mean scores on involvement and hedonic value for the pre-selected products.

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Three products were removed from the selection because they showed gender differences in involvement and/or hedonic value: Lingerie and a sewing machine showed differences in involvement but not in hedonic value and nail polish showed differences on both dimensions. The remaining 29 products are presented below in a perceptual map. The perceptual map visualizes how the products are perceived and based on this, 16 products were selected for the main research and are shown in table 5.

Figure 3 perceptual map.

Table 5 Final product classification for main research.

 

  Hedonic products   Utilitarian products  

Low involvement   Flowers Game

Toy (stuffed animal) DVD   Batteries Water boiler Organizer Ink cartridge   High involvement   Evening dress / tuxedo

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In order to be sure that the final chosen products are generally seen as hedonic/utilitarian and low/high involvement, this research also includes a manipulation check. Participants are asked to rate the hedonic value and the involvement of all 16 products on two 7-point Likert scales with 1=totally disagree and 7= totally agree. The following questions were asked:

• Hedonic value: The purchase of this product provides me with pleasure and enjoyment • Involvement: The purchase of this product is personally relevant to me and I would like

to learn more about this product.

3.4 Control variables

Several control variables are incorporated in the research. These variables can describe the participants in the research and give insights in the representativeness of the sample compared to the population in the Netherlands. They can also give additional information about the effects in the conceptual model. These control questions were asked at the end of the questionnaire. The following questions were asked:

• What is your gender?

This is a nominal variable with the answer possibilities Male (1) and Female (2). • What is your age?

An open question resulting in interval data. • What is your yearly income?

This is a nominal variable with a 7-point scale. The answer possibilities are: no income, < 10.000, 10.000-20.000, 20.000-30.000, 30.000-40.000, 40.000-50.000 and >50.000. • What is your level of education?

This is a nominal variable with a 8-point scale. Answer possibilities are: basic

education, Mbo 2/3, Mbo 4, Havo/Vwo, Hbo/Wo bachelor, Wo master/Doctor, other.

Besides asking demographic questions, several questions were asked regarding the respondents time spent online, online shopping behavior and offline shopping behavior. The following questions were asked:

• How many hours a day do you spend online?

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• How often do you buy something online?

This is a nominal variable on a 5-point scale. Answer possibilities are: never, less than once a month, 1-2 times a month, about once a week, about twice a week or more. • How often do you buy something offline/ in a regular store (not counting supermarket

visits)

This is a nominal variable on a 5-point scale. Answer possibilities are: never, less than once a month, 1-2 times a month, about once a week, about twice a week or more.

3.5 Plan of analysis

The plan of analysis consists of four parts. First the demographic analysis will be conducted. This consists of describing the sample by using the control variables, and checking to what extent the sample corresponds with the population in the Netherlands. The second part will focus on getting some basic insights in the data, checking reliability of the constructs, assessing the normality of the distributions and looking at correlations between the constructs. In the third part, the hypotheses in the fundamental en extended model are tested. Finally, an extended analysis will provide extra insights.

3.5.1. Analyses of the demographics

The first step in analyzing the results is describing the demographics by looking at the control variables. The respondents that participated in the survey will be described by looking at their gender, age, income and education. Gender, income and education are nominal variables and insights in these characteristics can be gained by looking at the frequencies. Age was in interval question, so these respondents will first have to be categorized into three different age groups: 18-39, 40-59, and 60+. The sample will be compared to the Central Bureau of Statistics of the Netherlands in order to see if the respondents are representative for the intended target group, in this case the Dutch population. Based on this, a decision will be made on whether or not the data needs to be weighted. Insights are also gained on the control variables regarding the average the respondents spend online per day and their online and store shopping frequencies.

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3.5.2.  Basis  analysis  and  pre-­‐insights    

Basic insights are gained in the three main concepts: Hedonic and utilitarian motivations, intention to shop online and the Big Five. Multiple items were used to measure hedonic and utilitarian shopping motives and the big five personality dimensions. The average of these questions can be used to create a single new variable. 18 items form the construct hedonic shopping motive, and 6 items form the construct utilitarian shopping motive. In the personality questionnaire, the items specifically measure the five dimensions. However, in order to create a single new variable with multiple items, the internal consistency between the items needs to be sufficient. A reliability analysis can measure this with Cronbach’s Alpha. Cronbach’s Alpha should be at least 0.7 in order to combine the items. Means and standard deviations for the concepts can then be calculated.

Before testing the hypotheses, the dataset will be tested for normality. By looking at the skewness and kurtosis of the distribution, it can be concluded whether the data is normally distributed. Also, to gain some pre-insights about the relations between the concepts, correlations will be looked at.

 

3.5.3 Testing  hypotheses  

 

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3.5.4 Extended analyses

An extended analysis will provide more detail on hedonic and utilitarian shopping motives. By using factor analysis, underlying dimensions will be identified. These dimensions can be regressed on the intention to shop online for both types of products to gain more specific insights about which hedonic, and which utilitarian motives are actually causing the effects. The Big Five can also be regressed on each of these underlying dimensions in order to gain extra insights about which personality dimensions are related to these shopping motives.

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4. Results

In this chapter, the data gathered by the quantitative research are analyzed. The dataset descriptives are summarized in order to see if the dataset is representative for the Dutch

population. Reliability is checked in order to test the internal consistency of the constructs. The hypotheses are tested by using linear regression analysis.

4.1 Descriptive statistics

The original dataset contained 308 surveys. However, 58 surveys were unfinished or incomplete so these were removed from the data set. One respondent was only 16 so these answers were also not taken into account in the results. Seven of the surveys were not filled out seriously and contained errors extreme answers or giving the same score on every single question. These were also removed from the data set leaving 242 usable answers from respondents.

In this ‘clean’ dataset, 40.9% are male and 59.1% are female. However, according to the Central Bureau of Statistics (CBS), the division of males and females should be 49.5 and 50.5 respectively. This is displayed in table 6. Their income ranges from no income to €50.000 or more. The frequencies are displayed in the figure 4 below.

Figure 4 Income of respondents.

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overrepresented whilst lower educated people are underrepresented. This can be explained by the large number of Hbo and WO students that filled in the questionnaire. This results however in the limitation that the conclusions of this research are only applicable to higher educated individuals in the Netherlands.

The age of the respondents was also asked. The youngest respondent is 18 and the oldest is 76 years old. The respondents were categorized in three age groups: 18-39, 40-59, and 60+. The age groups are not representative for the Dutch population according to the CBS, so the dataset was weighted. The first group is overrepresented: it accounts for 55.8% while is should only account for 33% according to CBS. This group therefore receives a weight of 33 / 55.8 = 0.59. The second group was slightly underrepresented and receives a weight of 38 / 29.8 = 1.28. The oldest group was also underrepresented and receives a weight of 29 / 14.5 = 2.0. The rest of the analyses are conducted with this weighting. These descriptive frequencies and the frequencies of the sample after weighting are displayed in table 6.

Table 6 Descriptive statistics.

Demographic variable

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11,10%   26,50%   30,80%   20,30%   11,30%  

hours  per  day  spent  online  

Hours  online  per  day  

<  1   hour   1-­‐2   hours   2-­‐4   hours   4-­‐6   hours   >  6   hours  

4.1.1 Online behavior and online shopping frequencies

In order to gain some general insights about the respondents online shopping behavior, they were asked how many hours they spend online per day, how often they buy something online, and how often they buy something in a regular store. The largest group, 30.8%, spends 2-4 hours online daily. Figure 5 shows the results.

Respondents indicate to buy more in regular stores than online. The largest group, 47%, buys something online once or twice a month. 3% buys online twice a week or more and 7.9% never buys something online. For shopping in regular stores, the largest group (38.6%) also shops once or twice a week. However, 20.1% buys something twice a week or more, and only 0.5% never buys something in a store. Figure 5 shows the results.

Figure 5 Hours spent online per day and online and offline purchase frequencies.

7,9   0,5   35,5   9,3   47   38,6   6,1   31,4   3   20,1   Online  purchase  

frequency   offline  purchase  frequency  

Online  and  offline  purchase  

frequencies  

never  

<  once  a  month   1-­‐2  Nmes  a   month   about  once  a   week  

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Interestingly, there seems to be a gender difference in hours per day that are spent online. Men spend more time online, with the highest percentage of the men spending 2-4 hours online. The largest group of females spends 1-2 hours per day online. This is displayed in figure 6. Looking at gender differences in purchase frequency, we see that men buy online more frequently than women. Men may value the efficient way of shopping and value the other benefits of

online shopping, whereas females may miss the shopping experience when they buy online. Females buy slightly more often in regular stores than males. These differences are displayed in figure 7.

Figure 7 Gender differences in online and store purchase frequency.

Looking at age differences, not surprisingly the youngest age group spends the most time online, followed by the group 40-59 and the oldest age group 60+ spends the least time online. When it comes to the frequency that products are bought online, the difference between the age groups is less clear. All age groups mostly buy something online once or twice a month.

Surprisingly, when it comes to purchasing something online twice a week or more, the 60+ group has the highest frequency.

0   5   10   15   20   25   30   35   <  1  

hour   hours  1-­‐2   hours  2-­‐4   hours  4-­‐6   hours  >  6  

Gender  differences  in  hours  per  day   spent  online  (%)     Male   Female   0   10   20   30   40   50   60  

never   <  once  a  

month   Nmes  a  1-­‐2   month   about   once  a   week   twice  a   week  or   more  

Online  purchase  frequency  (%)  

0   5   10   15   20   25   30   35   40   45   never   <  once   a   month   1-­‐2   Nmes  a   month   about   once  a   week   twice  a   week  or   more  

Store  purchase  frequency  (%)  

Male   Female  

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 Figure 8 Age differences in online and store purchase frequency.

4.2 Basic analyses and pre-insights

Prior to testing the hypotheses, some basic analysis and organization of the constructs must be done. The big five inventory items must be recoded and scores on the separate constructs must be calculated. Furthermore, the items to measure hedonic and utilitarian shopping motives will be separated into the two constructs. Intention to shop online is calculated and this research will distinguish between intention to shop online for hedonic products and intention to shop online for utilitarian products. Reliability analyses will be executed on all constructs to check the internal consistency of the items forming these single variables. The items can be combined when Cronbach’s Alpha is at least 0.7.

4.2.1 Big Five personality traits

Respondents’ scores on the big five personality traits were calculated. First the reversed questions are recoded (1=7, 2=6 etc.). The score on the trait extraversion consists of 8 items with a Cronbach’s Alpha of 0.816, meaning that the scale is internally consistent. Deleting items does not improve the Cronbach’s alpha. In order to be able to categorize the respondents as extraverts, an average score on the 8 items is calculated (M = 4.89, sd = 0.88). The cut score is an average of 5 on all 8 items, so every respondent that scores above this average is defined as an extravert. In this dataset, 49.8% of the respondents are extravert.

0   10   20   30   40   50   60   never   <  once   a   month   1-­‐2   Nmes  a   month   about   once  a   week   twice  a   week  or   more  

Age  differences  in  online  purchase  frequency  (%)  

18-­‐39   40-­‐59   60+   0   5   10   15   20   25   30   35   40   <  1  

hour   hours  1-­‐2   hours  2-­‐4   hours  4-­‐6   hours  >  6  

Age  differences  in  h/day  spent  online  (%)  

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There are 9 items that correspond to the trait agreeableness with a Cronbach’s Alpha of 0.727. The scale is internally consistent and deleting items does not give a higher Cronbach’s Alpha. The mean score for agreeableness is computed and has M = 5.37, sd = 0.71. Individuals are categorized as agreeable when they have an average score of 5 or higher on the 9 items. In this research, 78.4% of the people are agreeable.

Conscientiousness has 9 corresponding items with a Cronbach’s Alpha of 0.77. The scale is internally consistent. The mean score was computed with M = 4.99, sd = 0.86. Individuals with an average score of 5 or higher are classified as conscientious. The percentage of conscientious individuals is 56.5%.

To test the internal consistency of the 8 items that measure Neuroticism, Cronbach’s alpha was computed and gave α = 0.795. Deleting item 29 gives a slightly higher Cronbach’s Alpha (α = 0.803) but because this difference is so small and the internal consistency was already good, the item was left in. Again, the mean score was computed (M = 3.22 and sd = 0.93). Because of the low mean, we slightly adjust the cut score to an average of 4 or higher on the 8 items. This is a slightly different cut score than for the other personality constructs, adapted to the mean of the sample population. 21.3% of the respondents are neurotics.

There are 10 items measuring openness to experience and these items have an internal

consistency of α = 0.771. Deleting items does not increase Cronbach’s Alpha. The total score is computed and has M = 4.71, sd = 0.79. A mean score of 5 or higher classifies as being open to experience, in this case that is 37.7% of the respondents.

Personality trait Number of items Cronbach’ s Alpha % in dataset (N=243) Mean St.dev Extraversion 8 0.816 49.8% 4.89 0.88 Agreeableness 9 0.727 78.4% 5.37 0.71 Conscientiousness 9 0.770 56.5% 4.99 0.86 Neuroticism 8 0.795 21.3% 3.22 0.93 Openness to experience 10 0.771 37.7% 4.71 0.79

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4.2.2. Hedonic and utilitarian shopping motives

In the survey respondents’ hedonic and utilitarian shopping motives were measured on a 7-point Likert scale. 18 of the items correspond to hedonic shopping motivations (HSM). Cronbach’s Alpha was computed (α = 0.89) which indicates strong internal consistency. A average score is computed (M = 3.80, sd = 0.99) and individuals are classified as having HSM at a score of 4.5 on average on the 18 items. 29.7% of the respondents have hedonic shopping motives. There were 6 items corresponding to the construct utilitarian shopping motives (USM). Cronbach’s Alpha is α = 0.62, which is slightly low. Deleting items does not improve the alpha so this research has decided to continue with this construct keeping in mind that internal consistency is low. Also here, an average score is computed (M = 4.89, sd = 0.89). Respondents are classified as having USM at a score of 4.5 on average or higher leading to 73.7% respondents with USM. There is a difference between males and females regarding shopping motivations. Males and females have about equal utilitarian shopping motives, but females have higher hedonic shopping motives (figure 9).

Figure 9 Gender differences in hedonic and utilitarian shopping motivations.

4.2.2. Intention to shop online

Online purchase intention was measured by asking respondents where they would prefer to buy a number of hedonic and utilitarian products. All products together form a total online

purchase intention without distinguishing between products, but the constructs are also measured separately. 1   2   3   4   5   6   7   HSM   USM  

Gender  differences  in  shopping  mo@ves  

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In the questionnaire intention to shop online for hedonic products and intention to shop online for utilitarian products were measured by using 8 hedonic and 8 utilitarian products. These products were selected with a pre-test, but in order to check if the products that were used in the questionnaire were actually perceived as being hedonic or utilitarian, a manipulation check was placed in the questionnaire. This consisted of 16 questions asking the respondents to which extent the purchase of the product gives them a feeling of pleasure and enjoyment (hedonic value). Cronbach’s Alpha for the 8 hedonic

products was α = 0.78. All items positively contributed to the internal consistency of the hedonic value. Cronbach’s Alpha for the utilitarian products was α = 0.92. The hedonic products (M= 5.36, st = 0.84) scored higher than the utilitarian products (M= 3.39, st = 1.15) which means the hedonic products were actually perceived as more hedonic than the utilitarian products and that the manipulation was therefore successful.

For intention to shop online, Cronbach’s Alpha’s are above 0.7 for all three constructs so they have good internal consistency. The mean for intention to shop online for hedonic products (M=2.41, sd= 0.75) is lower than the mean for intention to shop online for utilitarian products (M=3.74, sd = 1.28). This can be seen in table 8. When males and females are distinguished from each other, results show that males have higher intentions to buy products online. This is the case for hedonic and also utilitarian products. This is in line with the previous finding that males spend more time online and have higher online purchase frequencies. This finding is depicted in figure 11. 1   2   3   4   5   6   7  

Hedonic  products   UNlitarian  products  

hedonic  value  

Figure 10 Hedonic value for hedonic

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 Table 8 Reliability analyses and means for shopping motivations and intention to shop online.

Figure 11 Intention to shop online for hedonic and utilitarian products

 

4.2.3 Test of normality

To gain better insights in the distribution of the data, normality is tested by looking at skewness and kurtosis. The skewness statistic indicates whether the data is asymmetrical and therefore deviates from a normal distribution. The kurtosis statistic indicates if the data is flattened or peaked. Statistics can be found in table 9 and in the appendix 4. Starting with the big five personality variables, openness to experience is approximately symmetric. Neuroticism has a Skewness > 0, which means the distribution is moderately skewed to the right. This means that most values are concentrated on the left side of the mean, with extreme values on the right. Conscientiousness, agreeableness and extraversion are moderately skewed to the left because Skewness < 0. Most values are concentrated on the right of the mean, with

Construct Number of items Cronbach’s Alpha Mean St.dev. Shopping motives Hedonic 18 0.89 3.80 0.99 Utilitarian 6 0.62 4.89 0.89 Online shopping intention Hedonic products 8 0.72 2.41 0.75 Utilitarian products 8 0.88 3.74 1.28 Total 16 0.89 3.08 0.94 1   2   3   4   5   6   7   Hedonic  

products   UNlitarian  products   total  

Inten@on  to  shop  online  

1   2   3   4   5   6   7   IntenNon  to   shop  online  for  

UNlitarian   products  

IntenNon  to   shop  online  for  

Hedonic   products  

IntenNon  to   shop  online  

total  

Gender  differences  in  inten@on  to  shop  online  

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extreme values to the left. For the personality variables conscientiousness, neuroticism and openness to experience, the Kurtosis < 0, which means the distribution is platykurtic: flatter than a normal distribution with a wider peak. The probability for extreme values is less than for a normal distribution, and the values are wider spread around the mean. The kurtosis for extraversion is approximately zero so the distributions can be seen as normal. Agreeableness has a kurtosis > 0 which is called a leptokurtic distribution, sharper than a normal distribution, with values concentrated around the mean. There are no extreme deviations from the normal distribution, so for further analysis normality is assumed. Moderate skeweness and moderately flattened and peaked data should be kept in mind.

Hedonic and utilitarian shopping motives are moderately skewed to the left. Kurtosis shows that hedonic shopping motives has a slightly platykurtic, flatter distribution whilst utilitarian shopping motives has a leptokurtic, peaked distribution. This means there is a high probability for extreme values. This must be kept in mind.

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Table 9 Skewness and Kurtosis statistics.

4.2.4. Correlations

Before the hypotheses are tested, per-insights are gained in the relationships between the constructs in the fundamental and extended model by looking at Pearson’s correlations. Starting with the fundamental model, where hedonic and utilitarian shopping motives are expected to have a relation with the intention to shop online for hedonic and utilitarian products, there is a negative correlation between HSM and intention to shop online for hedonic as well as utilitarian products. There is no significant correlation between USM and intention to shop online for hedonic and utilitarian products. Table 10 displays these values and scatterplots in figure 12 show the relations.

Table 10 Pearson correlations for the fundamental model

Variable Skewness St. error

Skewness Kurtosis St. error Kurtosis Extraversion -0.353 0.157 0.075 V0.312 Agreeableness -0.554 0.157 0.792 V0.312 Conscientiousness -0.396 0.157 -0.191 V0.312 Neuroticism 0.343 0.157 -0.338 V0.312 Openness to experience 0.010 0.157 -0.335 V0.312 HSM -0.225 0.157 -0.525 0.312 USM -0.721 0.157 1.732 V0.312

Intention to shop online

for hedonic products 0.449 0.157 0.188 V0.312 Intention to shop online

for utilitarian products

-0.099 0.157 -0.203 V0.312

Intention to shop online

total -0.075 0.157 -0.123 V0.312

  Intention to shop online hed.

prod.   Intention to shop online ut. prod.  

HSM     R Sig. R = -0.19 p< 0.01***   R = -0.17 p< 0.01***   USM     R   Sig.     R = 0.02 p = 0.79   R = 0.04 p = 0.49   ***  Significant  at  1%  level  

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Figure 12 Correlations between HSM, USM and intention to shop online for hedonic and

utilitarian products

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