The influence of personality traits on the relationship between gamification mechanisms and willingness to buy

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Master in Business Administration

Specialisation: Digital Business

The influence of personality traits on the relationship between

gamification mechanisms and willingness to buy

Puck Blom 10819614

Supervisor: Sara Valentini Amsterdam, Augustus 6th 2018

Keywords: gamification, effectiveness, intrinsic motivation, extrinsic motivation, Bartle's gamer types, Yee’s taxonomy, willingness to buy, sharing information, wishlist

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Statement of Originality

This document is written by student Puck Blom who declares to take full responsibility for the contents of this document.

I declare that the text and the work presented in this document are original and that no sources other than those mentioned in the text and its references have been used in creating it.

The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.

Abstract

More and more companies introduce gamification elements in their everyday businesses, as there is currently a broad consensus that gamification is an effective tool to motivate, encourage, engage, explore and promote. However more and more research is yielding contradiction results, leaving the reasons and motivations behind gamification unclear. In this paper, the influence of two

gamification mechanisms on willingness to buy is tested and specifically, how different gamer personality traits influence this relationship. Data was collected through a survey with a

convenience sample (n=248). This data was analysed by linear regressions and segmented by a k-means cluster. Only one of out six hypotheses is supported, but this study yields interesting results as a foundation for future research.

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After spending 15 years of research on establishing a protein that may eliminate HIV, researchers at the University of Washington developed Foldit, an online video puzzle game. After only 10 days, the 46.000 game participants had solved the extremely difficult problem with the protein in question ('Foldit", n.d.). The participants did not receive any form of reward. They simply participated for the sake of contributing to science, while enjoying themselves. Foldit is not alone in this trend. Car2go introduced their EcoScore to promote eco-friendly driving in May 2012. This EcoScore keeps track of driving behavior, such as acceleration, braking and cruising with displaying a number between 1 and 100. If the driver manages to achieve an EcoScore of 100 points, a rainbow appears on the screen ("Blog Car2Go", 2012). More and more companies are introducing elements of gaming in their everyday business. This trend is called gamification. Kapp (2012) describes this trend as incorporating game elements in a non-game environment to encourage behavior, promote learning, engage people and even solve problems. Not only consumers (as Car2Go) but also employees can be motivated by gamification. Think about an employee of the month contest or handing out badges for excellent teamwork.

Since gamification is a relatively new field for both practitioners and academia, the existing literature is lacking extensive knowledge regarding this subject. At this moment, most studies consider gamification to have a positive effect on motivation and willingness to participate and engage (Hofacker, de Ruyter, Lurie, Manchanda & Donaldson, 2016). However, more and more academic research is yielding contradicting findings, suggesting that the effect of gamification is not always that successful (Hanus & Fox, 2015; Orji, Mandryk, Vassileva & Gerling, 2013). Currently there are two large limitations of gamification research. Firstly, most persuasive games seem to be based on intuition, rather than actual models or theories and secondly, most persuasive games tend to take a one-size-fits-all approach despite growing proof that treating participants as a

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monopolistic group is not a good research design (Orji et al., 2013; Yee, Duchenaut & Nelson, 2012).

Despite this absence of knowledge and experience, there is currently a broad consensus that gamification is an effective tool to motivate, encourage, engage, explore and promote. However, the reasons, motivations and moderators remain unclear. The purpose of this study is to clarify some of these motivations and influences by looking at possible differences in personality. Personality will be measured by gamer types. This leads to the research question; How do gamer type personality

traits influence the relationship between gamification mechanisms and willingness to buy? This

study will not only be useful for the academic world but also provide managerial relevance. As this study contributes new insights to the existing literature regarding gamification, the academic relevance is presented. By providing new findings, a stronger foundation will be built for future research on gamification. Another contribution of this study is providing new insights to the business world regarding gamification, its effectiveness and application. For example, online retailers may be able to offer consumers tailored incentives to correspond to their individual needs and preferences and increase their conversion rate.

This paper is structured as follows; firstly the theory behind gamification and its

motivations, willingness to buy and the gamer types are elaborated. After formulating the research design and proposing a theoretical model, the results from this study are described and discussed, along with the practical implications. Finally, the limitations and suggestions for further research are reviewed.

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2. Theoretical framework

In this section, the literature regarding gamification will be reviewed, and specifically the two mechanisms 1.adding a product to a wishlist and 2.sharing this wishlist onculine, as well as willingness to buy and different personality traits of gamers. Moreover, the hypotheses will be drawn. The final section contains an overview of all variables and hypotheses.

2.1 Gamification

Gamification is defined by Hamari & Koivisto (2015) as different technologies that aspire to promote intrinsic rewards towards various activities, commonly by employing design characteristics to games. This entails changing the behavior of consumers with a focus on intrinsic motivation. However, gamification does not only motivate intrinsic motivation, but also extrinsic, for example by giving the employee of the month a monetary bonus (Birk, Mandryk & Atkins, 2016). A more extensive definition is given by Kapp (2012), who describes gamification as incorporating game elements in a non-game environment. Gamification can adopt a variety of forms, ranging from badges to loyalty programs (Hanus & Fox, 2015). More examples of gamification are reward programs, social competition, leaderboards and even free products (Orji et al., 2013). Gamification can also contribute to entertaining consumers, to accelerate repurchases, increase willingness to purchase, to retaining consumers and to in-store engagement (Hofacker et al., 2016).

Gamification can have different target audiences. For example Hanus and Fox (2015) focused their research towards students, whereas Birk et al. discussed the use of gamification in job training activities (2016). In general these target audiences can be classified in two categories: inside and outside agents, whereas employees are inside agents and consumers, clients and students are outside agents. In this paper, the target audience is outside agents, thus consumers. Gamification can also have different goals, such as improving user experience, encourage user engagement or

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shape user behavior (Deterding, Björk, Nacke, Dixon & Lawley, 2013). Kapp (2012) extends the potential goals of gamification even further by stating that gamification can not only motivate a certain behavior and engage people but also promote learning and even solve problems, as Foldit mentioned in the introduction of this paper.

However, gamification can also create a negative impact on job performance. Suboptimal performance despite high incentives is known as choking under pressure (Dohmen, 2008). This causes paradoxical performance effects when the pressure is perceived as too high (Cox, 2011; Dohmen, 2008; Hanus & Fox, 2015). Leaderboards and on-the-job competition are examples of gamification mechanisms where choking under pressure is fairly easy to occur due to the social component (Hanus & Fox, 2015). Competition elements should be constructive and destructive competition should be prevented. Gamification mechanisms can not only have a negative impact on job performance, but also on consumer decisions.

2.2 Willingness to buy and the consumer decision process

As Jahangir, Parvez and Bhattacharjee (2009) explained, willingness to buy is no more than the behavioural intention of a consumer to purchase products. It refers to the probability that a certain consumer will purchase a product from a specific brand within a product category

(Pourahmadi, Delafrooz, 2014). If the product is evaluated and purchased at the same time, this is called an unitary purchase decision (Popovich & Hamilton, 2017). However, the purchase intention and the actual purchase do not always happen at the same time. Often consumers purchase

decisions involve an initial evaluation and an ensuing reevaluation (Popovich & Hamilton, 2017). The time that evolves between these two moments is called purchase delay (Greenleaf & Lehmann, 1995) or consumer procrastination (Darpy, 2000) This time can be split in two parts; active decision time and passive decision time. Active decision time is when a consumer is exploring alternatives,

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comparing prices and gathering information, while passive decision time is spent on non-related activities (Greenleaf & Lehmann, 1995). After the decision time, active and passive, the purchase decision is reevaluated. Reevaluating a purchase decision, and thus postponing, can lead to

decreased purchase intent (willingness to buy) and lower willingness to pay (Popovich & Hamilton, 2017).

The consumer decision process often results in a staged evaluation process (Popovich & Hamilton, 2017). A consumer, let’s call her Ann, might leaf through a paper catalogue, highlighting attractive products. Then several days later, Ann looks through the catalogue, crossing out some products after a second evaluation. She looks up the products that are left in the consideration set online for reviews, adds a few products to her wishlist and makes a final purchase decision after sleeping a night on it. Her decision process involved at least four consideration stages, both active and passive, and all four delayed her purchase decision.

2.3 Gamification mechanisms

2.3a Add to wishlist

The current technology has enabled the online consumer to postpone their purchase

decisions in many ways (Popovich & Hamilton, 2017). For example, a consumer can add a product to their shopping cart, wish list or even keep the productpage open in a browser tab. These are all examples that postpone the purchase decision, as the consumer is returning to the website at a later time to continue their decision process and thus decrease the likelihood that the consumer will actually purchase the product. Following this line of reasoning, if there is no possibility to put an item on a wishlist, the consumer has fewer ways to postpone the purchase decision.

However, another factor that may influence the purchase decision is the effect of reward timing. Immediacy, the proximity between an activity and its reward, strengthens the association, so

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the goal is more fused with the activity and will increase intrinsic motivation. Rewarding before the activity is completed, so called early rewards, strengthens this association and thus intrinsic

motivation (Woolley & Fishbach, 2018). For example, Ann added a few products on her wishlist. Some online retailers reward this action with points in their loyalty programs. Ann would receive points for adding the product on her wishlist, but would not make the actual purchase decision until the next day or maybe even week. This early reward, even with a deferred purchase decision could increase her intrinsic motivation and thus her intention to purchase. However, this effect is only plausible if Ann was intrinsically motivated before the (early) reward. In the current academic literature, the evidence for a negative effect of delaying purchase decisions is substantial. However this has not been tested in a gaming context, which may change the influence of purchase decision delays. However, assuming the gaming context does not alter this influence, this leads to the following hypothesis:

H1a [H1a] - Asking gamers to add a product on their wishlist in order to receive rewards has a

negative effect on the likelihood of purchase.

2.3a Share wishlist online

In recent years, consumers have drastically increased their social media presence (Cabosky, 2016). This change drastically transformed the approach of academics to strive for more insights into online consumer opinion-sharing behavior (Kim & Ahmad, 2012). An example of a new industry measurement is WOM, word of mouth (Cabosky, 2016). One of the biggest complications of social media metrics concerns the non-sharers. It is very difficult to measure the influence of WOM if the total population is unknown. Another issue is the demographics of the respondents. There is a clear distinction in social media presence, when comparing millennials (18-24) to older respondents, which gives a disturbed picture of the present metrics (Cabosky, 2016). Moreover, positive WOM is shared more than negative (Jansen, Zhang, Sobel & Chowdury, 2009). This may

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be due to the fact that self-presentation is driven by the need to display the best possible impression to other (Nadkarni & Hofmann, 2012). Yet, when information is shared, social pressure arises to also act as described in the shared information (Osatuyi, 2013). This pressure converges the

behavior and the shared information of the social media user, which indicates that when a consumer shares their wishlist on social media, they may feel the urge to actually purchase the items on the wishlist. This has not been tested specifically in a gamification context. However, assuming that this does not alter this influence, it leads to the following hypothesis:

H1b [H1b] - Asking gamers to share their wishlist online has a positive effect on the likelihood of

purchase. 2.4 Gamification participants personality traits

This social pressure, among other factors, does not yield the same results for everyone. For example, mobile consumers have very different gamification needs than console gamers. The latter desires a fully immersive gaming experience, while mobile consumers engage with mobile

gamification to provide transient benefits, such as relieving boredom while waiting on the train (Hofacker et al., 2016). This entails that the gamification aspects to be developed for mobile

consumers, should have a low barrier-to-entry and use only a limited amount of cognitive resources (Hofacker et al., 2016). For gamification in the business world, console gamers itself are not very relevant, but the underlying implication is. As Yee et al (2012) stated, different people play games for various reasons and an incentive (extrinsic or intrinsic) has different effects on different people (Chen, Ford & Farris, 1999; Ryan & Deci, 2000). Motivation simply means 'to be moved to do something' (Ryan & Deci, 2000). An intrinsic activity is described by as an activity that is experienced as an end in itself, for its inherent satisfaction (Ryan & Deci, 2000; Woolley & Fishbach, 2018). Extrinsically motivated means completing an activity to obtain external benefits, separated outcomes that result from completing the activity (Sansone & Harackiewicz, 1996).

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Introduction of extrinsic rewards can decrease the intrinsic motivation by diluting the association between the original goal and the activity itself (Woolley & Fishbach, 2018). Extrinsic motivation refers to performing an activity because of a separable outcome (Ryan & Deci, 2000). This extrinsic motivation can be bifurcated into two separate motives; active (coerced) and passive (self-endorsed with a sense of value or utility).

Hanus & Fox (2012), support the statement of Chen et al. (2012) by explaining when people are intrinsically motivated and an extrinsic reward is offered, it may backfire and cause them to lose interest. Think about people that work voluntarily at the church. If money is offered for this same task, the purpose gets lost and they do not want to work at the church anymore. This is also known as the 'over-justification effect’(Birk et al., 2016; Lepper, 1981). Orji et al. (2013) support this tailoring of gamification by developing a personalised approach in their research for different users to increase healthy behavior. To create an individual approach to tailor different needs, those different needs have to be established. The four gamer types of Barlett; Killers, Achievers,

Socialites and Explorers lay the foundation for the needs and wants of different gamer types (Orji et al., 2013; Yee et al., 2012).

2.4a Killer

The first type, the Killer, is mostly focused on winning, obtaining a high rank and is enthusiastic about direct peer-to-peer competition (Bartle, 1990). They work to win, need

challenges and clearness. Killers make quick decisions and are fixated on their goals. For the Killer, leaderboards, badges and ranking are the way to go. As a Killer decides immediately, they would probably not postpone the purchase decision by for example adding a product on their wishlist. Moreover, as the Killer will make quick decisions and brag about their accomplishments, they would probably not share a wishlist online as a wishlist is not an accomplishment, while actually buying the newest high-end smartwatch is. This leads to the following hypotheses:

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H2a [H2a] - Asking a 'Killer’ to add a product to their wishlist will have a negative effect on

willingness to buy.

H2b [H2b] - Asking a 'Killer’ to share their wishlist online will have a negative effect on willingness

to buy.

2.4b Achiever

The second type, the Achiever, is mostly focused on attaining status and to quickly accomplish preset goals (Bartle, 1990). They tend to have a strong need for improvement and recognition. They work to master a specific task or action. The Achiever thrives with hierarchy and ranks. The Achiever is engaged by setting pre-determined achievements and rewards per

progression. As the Achiever aims at rapidly accomplishing targets, they would probably not postpone the purchase decision by for example adding a product on their wishlist. However, with sharing their wishlist an Achiever could aim for recognition and reach for status. This leads to the following hypotheses:

H3a [H3a] - Asking an "Achiever" to add a product to their wishlist will have a negative effect on

H3b [H3b] - Asking an "Achiever" to share their wishlist online will have a positive effect on

2.4c Socialite

The third type, the Socialite, is mostly focused on socialising and developing a network of friends and contacts (Bartle, 1990). The Socialite is avid of friends lists and social chats. They learn in groups through collaboration and they need others to network, form ideas and create a sense of likeableness. The Socialite works for social standing and they prosper with collaboration, sharing

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gifts and charity work. As the Socialite is very likely to share information and consults close acquaintances, they are very likely to put a product on their wishlist and share this with their online friends. This leads to the following hypotheses:

H4b [H4a] - Asking a 'Socialites’ to add a product on their wishlist will have a positive effect on

H4b [H4b] - Asking a 'Socialites’ to share their wishlist online will have a positive effect on

2.4c Explorer

The fourth and last type, the Explorer, is mostly focused on discovering the unknown. The explorer is avid of baffled goals (Bartle, 1990). The Explorer learns by failing and is in need of freedom to fail. They love experimenting and trying out new things. The Explorer works to shape and discover. Explorers flourish with obfuscated achievements and investigate without a clear and absolute goal. As the Explorer will not make a quick purchase decision but will search and examine other alternatives, they are more likely to use the wishlist option and actually buy the product after exploring and possibly discussing it with close acquaintances. This leads to the following

hypotheses:

H5a [H5a] - Asking an 'Explorer’ to add a product to their wishlist will have a positive effect on

H5b [H5b] - Asking an 'Explorer’ to share their wishlist online will have a positive effect on

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3. Methodology

In this section, the measures that were used in this research are discussed and the experimental design will be elaborated. Moreover the procedure to collect respondents will be discussed. Furthermore, the sample will be described and the design and measures of the survey and its constructs will be elaborated. Finally, the segmentation technique to identify the four gamer personality gamer types will be explained.

3.1 Measures and experimental design

The survey used an experimental design, by recreating an online shopping experience where the 'add on wishlist' and 'add on your wishlist and share with your friends’ option is tested. This means there is one control group and two experimental designs. Each respondent read the same introduction of the fictional website Jupiter.com. The respondent is asked to imagine wanting to buy a smartwatch and a friend told them about Jupiter. Jupiter.com is described as a new online retailer in electronics, with an interesting reward program. The following page explained their reward program, such as how many points a consumer receives for specific actions and the possible applications of those points. The respondents were randomly assigned to one of the three groups. All scenarios comprised multiple reward options, such as signing up for the newsletter. However, the second and third scenario had extra options. The second scenario included an 'add on wishlist' option, where the third scenario included an 'add on wishlist' and a 'share your wishlist with your friends' option. All these options were rewarded with a certain amount of points, as described in the survey. In the second scenario, a 5-item Likert-scale question was included regarding how likely it is that the respondent will include the smartwatch on their wishlist. For the third scenario was also a 5-item Likert-scale question included, regarding how likely it is that the respondent will add the product on their wishlist and share it with their friends. After these scenarios, all respondents were

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asked to fill out the same questions about willingness to purchase and whether they would recommend the reward program to a friend. To measure willingness to purchase, the 4-item construct of Lim, Sia and Lee was used, as found in Lim et al. (2006). This construct was reduced to 2 questions, to prevent the survey from becoming too lengthy. (E.g. 5-item Likert scale - I would seriously think about purchasing the smartwatch on Jupiter.com.). Furthermore, to measure the gamer types, the 30-item scale of Bartle was used, as found in Bartle, 1990. In this survey the construct was reduced to 12 questions to prevent the survey from becoming too lengthy. Each question has 2 possible answers. (e.g. Would you rather have; a) a spell to damage other players or b) a spell that increases the rate at which you gain experience points?). Finally, the survey ended with a general part regarding demographics, such as age ( variable), gender (nominal variable), nationality (ordinal variable) and living situation (nominal variable) and as control variable average hours of gaming per week (interval variable). To control for potential influence of the company used in the survey, all respondents have read the same background information before continuing the survey, to prevent nescience. To prevent brand familiarity from influencing the consumer’s purchase decision, a fictional online retailer was used. See Appendix for an entire overview of all survey questions.

A survey with three groups was designed, with two proposed gamification mechanisms; add a product on the wishlist with a reward and add a product on the wishlist and share it online. The first group is the control group (without a wishlist). Before distributing the actual survey, a pre-test was done, to ensure the differences between the three conditions were noticed. The participants were randomly, but evenly, divided in one of the three conditions. The results were obtained in two weeks.

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3.2 Data collection procedure

In this paper a quantitive study was used to answer the research question. Data were

collected by means of a self-report survey, with an experimental design. The survey was constructed with Qualtrics, due to its convenience and extensive design options. The questionnaire was created in English and translated into Dutch to ensure a larger response rate. The participants were

approached through the social network of the researcher, namely Facebook, LinkedIn, direct email and word-of-mouth. Both online sharing and direct targeting were combined to increase the

response rate. A survey with three conditions was designed, with the three proposed gamification mechanisms; add on wishlist without reward, add on wishlist privately with reward, add on wishlist publicly with reward. Before distributing the actual survey, a pre-test was done, to ensure the differences between the three conditions were noticed. The participants were randomly, but evenly, divided in one of the three conditions. The results were obtained in two weeks.

3.3 Sample

The population of this study is internet users. As this population is substantial and the sampling frame is unknown, this research is conducted through a non-probability convenience sample. Of the 410 respondents that started the survey, 248 actually finished (n=248). The sample consists mostly of Dutch online consumers (96,4%), with the remaining 5,6% divided fairly equal among Russian, Spanish, English, Bulgarian and American online consumers. Over half of the respondents were female (62,9%). By far, most of the respondents were employed (73%), whereas 17% was a student and the remaining 10% was fairly even divided among the other options. The average age of the respondents was 36,39 years with 78 as the oldest respondent and 16 as the youngest age. 70% (n=173) played on average 0 to 2 hours online or video-games, while only 2,5% (n=6) played for more than 10 hours. The control group (no wishlist) consisted of 82 respondents,

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while Condition 2 (add to wishlist-option) was filled in by 81 respondents and Condition 3 (add to wishlist & share option) by 85 respondents.

3.4 Segmentation by k-means clustering

After the general analysis, the sample was divided in four groups by a k-means cluster. As the number of groups was known and the gamer types were identified beforehand (Killer, Achiever, Explorer, Socialites), the sample could be segmented with a k-means cluster. For each question, the respondents must choose 1 out of 2 answers. Each answer is typical for 1 of the gamer types. The most common and dominant gamer type was identified per cluster after performing a frequency analysis. Per question each gamer type received a valid percentage per cluster. These numbers were added up per cluster and gamer type and compared. As seen in Table 2, the first cluster was

identified as Explorers (Escore = 405,7). The second cluster was identified as Socialites (Sscore = 342,2). The third cluster was identified as Killers (Kscore = 456,3). The fourth cluster was

identified as Achievers (Ascore = 296,2). A more complete overview of these scores can be found in Appendix A.

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4. Results

In this section, the results as established from the analysis will be elaborated. Firstly, the general hypotheses will be tested. Moreover, the four specific hypotheses will be tested for each gamer type and both conditions. Finally, an overview will be drawn of all variables, betas and p-values.

4.1 General analysis

All analyses were performed in IBM SPSS Statistics 23 and Microsoft Excel. First, a reliability analysis was performed on the scale to measure willingness to buy. The Cronbach’s Alpha of 0,772 is reliable enough. The corrected total item correlation is 0,629. No questions were removed, as the construct only consisted of two questions, which is the minimal for a construct. Three respondents had missing data considering their nationality and six respondents had missing value for their intention to buy, whereas one respondent had a missing value on their age. The results of these respondents were still used as they are considered to not significantly modify the results. Moreover, the occupation Student and Employee were heavily correlated, as can be found in the correlations table in Appendix B. For each analysis, the occupation with the highest percentage was chosen, while the other was left out of the analysis. This was done to keep the group to be analysed as representative as possible.

4.1.1 Testing hypothesis 1 and 2

As seen in Table 3, in model 1 are only the control variables included, whereas model 2 includes also the independent variables. The results in Table 3 shows that the overall variance explained for willingness to buy is 10%. This is a relatively large increase compared to predicting willingness to buy by only the control variables (7,3%). A significant predictor of willingness to buy is age (β= -0,191, p<0,05). This means that older people are less willing to buy by 0,191 units. The

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first prediction was that adding a product on your wishlist in exchange for earning a reward, a purchase decision delay, would have a negative effect on the likelihood of purchase (H1a). This hypothesis was tested by performing a linear regression, with intention to buy as dependent variable (probability between 0 and 100) and group 2 - add a product to the wishlist - as independent

variable. Table 2 shows that adding a product on your wishlist actually significantly increases the intention to buy, contrary to what hypothesis 1a described (β = 9,941, p<.05). This indicates that a deferred purchase decisions actually increases the intrinsic motivation and thus willingness to buy, as Woolley & Feshbach (2018) described.

The second prediction was that sharing information online, such as a wishlist, in exchange for earning a reward would have a positive effect on the likelihood of purchase. This hypothesis was also tested by performing a linear regression with intention to buy as dependent variable, but with condition 3 - add on wishlist & share - as independent variable. Table 2 shows that adding a product on your wishlist and sharing it indeed increases the intention to buy, which is in line with what hypothesis 1b stated. However, this increase is not significant (β = 0,354, p>.05). The first model has F(248) = 2,548, p = 0,014. The second model has F(248) = 2,823, p = 0,004.

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4.2 Testing moderating effects of gamer types

In this paragraph, the data analysis for the four different gamer personalities is discussed. All four models use the same independent variables, but the hypotheses differ per personality type. Firstly the Killer-type is analysed. Secondly, the Achiever-type is analysed. Thirdly, the Socialites-type is analysed and lastly the Explorer-Socialites-type is analysed.

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4.2.1 Regression analysis Killer

The second regression was performed to analyse the results of the Killer-gamer type. A linear regression with as independent variable the second and third condition (add product to wishlist // add a product to wishlist and share) and dependent variable intention to buy. The second model shows the predicted main effect of the gamification mechanisms on willingness to buy, specifically for the Killer. Willingness to buy is measured by intention to buy. The prediction H2a was that adding a product to the wishlist will have a negative effect on the intention to buy. Contrary to the expectation, Table 4 shows a positive, but insignificant effect (β = 0,212, ns). The prediction H2b was that adding a product to your wishlist and sharing it online will have a negative effect on the intention to buy. Contrary to the expectation, Table 4 shows a positive, but

insignificant effect (β = 0,025, ns). This means that both hypotheses are not supported. The variance in willingness to buy explained by the model is 33,5%, whereas the controls only explained 30,2%. This is relatively not a large increase, but 33,5% of the variance explained is rather high. The first model has F(25) = 1,369, p = 0,277. The second model has F(25) = 1,073, p = 0,426.

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4.2.2 Regression analysis Achiever

The third regression was performed to analyse the results of the Achiever-gamer type. A linear regression with as independent variables the second and third condition (add a product to wishlist // add a product to wishlist and share it online) and dependent variable intention to buy. The third model shows the predicted main effect of the gamification mechanisms on willingness to buy, specifically for the Achiever. The prediction H3a was that adding a product to the wishlist will have a negative effect on the intention to buy. In line with the expectation, Table 5 shows a negative, but insignificant effect (β = -0,012, ns). The prediction H3b was that adding a product to the wishlist will have a positive effect on the intention to buy. Contrary to the expectation, Table 5 shows a negative, but insignificant effect (β = -0,086, ns). The total variance of willingness to buy that this model explained is 21,3%, which is only 0,5% more variance that only the controls explain. Surprisingly, gender has a significant effect on intention to buy. This means that for the Achievers, females are less willing to buy by 0,320 units (p<0,05). The first model has F(64) = 2,302, p = 0,048. The second model has F (64) = 1,506, p = 0,172.

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4.2.3 Regression analysis Socialites

The fourth regression was performed to analyse the results of the Socialites-gamer type. A linear regression with as independent variable the second and third condition (add a product to the wishlist // add a product to the wishlist & share online) and dependent variable intention to buy. The fourth model shows the predicted main effect of the gamification mechanism on willingness to buy, specifically for the Socialites. The prediction H4a was that adding a product to the wishlist will have a positive effect on the intention to buy. In line with the expectation, Table 6 shows a positive, but insignificant effect (β = 0,162, ns). The prediction H4b was that adding a product to the wishlist and sharing it online will have a positive effect on the intention to buy. In line with the expectation, Table 6 shows a positive, but insignificant effect (β = 0,048, ns). Surprisingly, age has a significant negative effect on intention to buy by -0,324 (p<0,05). This means that for the Socialites, if the age increases by one year, the intention to buy decreases by 0,324 units. This model explains 18,5% of the variance in intention to buy, which is only 1,9% less than only the controls explain. The first model has F (89) = 2,254, p = 0,038. The second model has F (89) = 1,936, p = 0,059.

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4.2.4 Regression analysis Explorer

The fifth regression was performed to analyse the results of the Explorer-gamer type. A linear regression with as independent variable the second and third condition (add a product to the wishlist // add a product to the wishlist and share it online) and dependent variable intention to buy. The fifth model shows the predicted main effect of the gamification mechanism on willingness to buy, specifically for the Explorer. The prediction H5a was that adding a product to the wishlist will have a positive effect on the intention to buy. In line with the expectation, Table 7 shows a positive and significant effect (β = 0,396 p<.05). The prediction H5b was that adding a product to the wishlist and sharing it online will have a positive effect on the intention to buy. In line with the expectation, Table 7 shows a positive but insignificant effect (β = 0,245 p>.05). This model explain 22,7% of the variance in intention to buy, which is a large increase of the 11,4% that the controls explain. The first model has F (67) = 1,261, p = 0,289. The second model has F (67) = 2,092, p = 0,051.

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All five models are summarised in Figure 2 to give a clear overview of the relationships between the variables, betas and p-values. Moreover, all R² are below 0,1, but Model 5 explain the most variance.

Figure 2: Complete overview

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5. Discussion

In this section, the results as established from the analysis will be elaborated. The

hypotheses of this study will be concluded and further discussed with theory found in the literature. Moreover, the limitations will be described and suggestions for further research will be stated. Furthermore, the academic and managerial implications will be stated and limitations will be described. The final section will draw a short conclusion of the paper.

5.1 Discussion of findings

This study aimed to contribute to the literature concerning gamification, and in particular the effect of gamification elements on willingness to buy. More specifically, by looking at personality differences, partially the variances in willingness to buy were tried to be explained. Gamification was measured by encouraging consumers to add a product to their wishlist by offering them a reward and to share their wishlist online. A modified version of the Gamer type construct by Bartle (1990) was used to measure personality. This resulted in four personality groups; Killer, Achiever, Socialites and Explorer. The first hypothesis (H1a) predicted that purchase decision delays, such as adding a product on your wishlist, has a negative effect on the likelihood of purchase. This

hypothesis was not supported. The analysis actually suggested a positive significant relationship. In other words, adding a product on your wishlist will increase the willingness to buy. This is contrary to what the literature suggests. Popovich and Hamilton suggest that reevaluating a purchase

decision will lower the probability the consumer actually buys the product (2017). A possible explanation for this may be the effect of reward timing. Immediate rewards may increase intrinsic motivation and therefore increase the intention to purchase. Another possible explanation is this significant positive relationship is driven by the cluster of Explorer, as solely this group has a

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significant positive relationship between add a product to the wishlist to receive rewards and willingness to buy.

The second hypothesis (H1b) predicted that sharing information online, such as wishlist, has a positive effect on the likelihood of purchase. This hypothesis was not supported. The analysis did suggest a positive relationship but this result was not significant. In other words, it is not proven that sharing information online has an effect on the likelihood of purchase. The social pressure, as explained by Osatuyi (2013) to converge behavior and the shared information is evidently not strong enough. Another explanation may be that the current metrics of social media is not advanced enough and does not provide information that is representative enough of online sharing behavior.

The third hypothesis (H2a) predicted that for the personality type Killer adding a product to the wishlist decreases the intention to buy. This hypothesis was not supported. The analysis showed a positive, but insignificant effect. A possible explanation for this may be that the Killer is so

encouraged by the immediate reward that the effect of postponing the purchase decision is nullified. Hypothesis H2b predicted that for the personality type Killer sharing their wishlist online decreases the intention to buy. This hypothesis was not supported. The analysis showed a positive, but

insignificant effect. This could mean that the Killer perceives sharing their wishlist online as a goal and see their wishlist also as an accomplishment.

The fourth hypothesis (H3a) predicted that for the personality type Achiever adding a product to the wishlist decreases the intention to buy. This hypothesis was not supported. The analysis showed a negative, but insignificant effect. Hypothesis 3b predicted that adding a product to the wishlist and sharing it online has a positive effect on the intention to buy. This hypothesis was not supported. The analysis showed a negative but insignificant effect. A possible explanation for this may be that the Achiever is fond of rewards per progression. Adding a product on your wishlist may not be seen as an isolated progression, but as part of a complete reward program and

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strengthen the negative effect of postponing the purchase. Moreover, gender had a significant effect on intention to buy such that females are less willing to buy a product.

The fifth hypothesis (H4a) predicted that adding a product to the wishlist will increase the intention to buy. This hypothesis was not supported. The analysis showed a positive but

insignificant effect. Hypothesis H4b predicted that for the personality type Socialites adding a product to the wishlist and sharing it online increases the intention to buy. This hypothesis was not supported. The analysis showed a positive, but insignificant effect. A possible explanation for this is that a Socialites is already so intrinsically motivated that the extrinsic reward that is offered, such as points from a reward program, does not influence their overall motivation. For this personality type, the over-justification effect is not present (Birk et al., 2016; Lepper, 1981). Moreover, age has a significant negative effect on intention to buy. So older Socialites are less willing to buy a product.

The sixth hypothesis (H5a) predicted that for the personality type Explorer adding a product to your wishlist increases the intention to buy. This hypothesis was supported. In other words, if an Explorer adds a product to their wishlist, it enhances their opportunity to examine and investigate other alternatives. This is in line with the personality description of Bartle (1990). Hypothesis 5b predicted that adding a product to the wishlist and sharing it online increases the intention to buy. This hypothesis was not supported. The analysis showed a positive, but insignificant effect.

In conclusion, only one out of six hypotheses was supported showing that Explorers increase their intention to buy after adding a product on their wishlist. This means the remaining five hypotheses are not proven with this paper.

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5.2 Implications - practical & academic

Since only one out of six hypotheses was supported, the practical implication of this research is not as large as desirable. However, it still showed different effects for different personality types. This is useful for the business world, as they can consider personalised reward programs, tailored to individuals. This research showed that people responded differently to the same stimuli. So they should carefully determine how and based on what characteristics they would classify different groups of consumers. If they do this correctly, they could increase their turnover, and thus profit, by enhancing the consumers’ willingness to buy.

However, this paper is not only useful for the business world. It is also useful for academics. Although not all the hypotheses of this paper are supported, they lay an exploratory basis that can form the foundation for future research. It also provides a possible explanation for the variation in effectiveness of gamification strategies.

5.3 Limitations & future research

During this study, several drawbacks were encountered. Future research should focus on preventing these flaws. One of the main limitations is the unsupported hypotheses. The amount of respondents is unsubstantial, since the complete sample was divided equally among the three conditions. Moreover, each respondent was assigned to one of four clusters, which made the sample per hypothesis very small. This was especially true for the killers (n=26). Furthermore, the

occupations Student en Employed were heavily correlated. This led to excluding 1 of these 2 options for every analysis. The group with the lowest percentage was excluded, to keep that segment as representative as possible. This may partially explain the unsupported hypotheses. Future research should increase the sample size substantially.

Another limitation is the testing of the effectiveness of gamification. Adding a product to your wishlist is very specific, as well as sharing your wishlist online, and other forms of

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gamification strategies could yield a very different result. This should be explored in future

research. Moreover, the cross-sectional survey was self-reported. This could lead to several biases, such as social desirability bias. This was prevented as much as possible, by emphasising in the survey there are no right or wrong answers and all results will be processed anonymously.

Moreover, social media research is not very progressed yet. This led to the use of theory from other disciplines to form hypotheses. In addition, the metrics for analysing social media give a disturbed image. It is very difficult to measure correctly influence since there is little to no data regarding non-sharers. The metrics and literature for social media have to progress to create a better understanding of social media patterns and behaviors.

Lastly, the construct to test for personality in terms of gamer type, has not been tested as used in this research. The number of questions was reduced from 36 tot 12, keeping the same gamer type ratio in the answer. Also a pre-test was performed to check if the three conditions for the gamification mechanism were indeed perceived as different. These constructs should be tested more thoroughly in future research.

5.4 Conclusion

While more and more companies incorporate gamification elements in their every day business, there is an ongoing academic debate regarding the effectiveness of gamification. This study aimed to contribute to this debate by researching the effect of adding a product to a wishlist and sharing that wishlist online, both in exchange for a reward, on willingness to buy. As a moderator, four gamer personality traits were analysed to try and explain the contradiction in previous research results. The analyses led to one supported hypothesis and a few interesting outcomes that should be researched further. These insights can be useful for the business world, through creating a more effective reward program and academics in future research. In conclusion, there are some individual differences in terms of personality, age and gender when it comes to effectiveness of gamification.

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7. Appendix

Appendix A

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Appendix B

S

urvey outline - English

Figure 4: Correlations table

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Page 3

Page 4 - control group

Page 5 Page 4 - group 3 add product to wishlist & share it

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Page 6

Page 7 Page 5.1 - group 2 add product to wishlist

Page 5.1 - group 3 add product to wishlist & share online

Page 5.2 - group 2 add product to wishlist

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Page 9

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Construct for gamer types

retrieved from: Bartle, R. (1996). Hearts, clubs, diamonds, spades: Players who suit MUDs. Journal of MUD research, 1(1), 19.

1. Would you rather 

- Become a hero faster than your friends (a)  - Know more secrets than your friends? (e) 2. Which is more enjoyable to you? 

- Killing a big monster (a) 

- Bragging about it to your friends (s) 3. Would you rather be ..

- Popular? (s)  - Wealthy? (a)

4. Would you rather win - Atriviacontest?(e)  - An arena battle? (k) 5. Would you rather  - Defeat an enemy? (k)  - Explore a new area? (e) 6. What’s worse? 

- To be without power (k)  - To be without friends? (s)

7. When playing a video game, is it more fun to - Have the highest score on the list (a) 

- Beat your best friend one-on-one? (k)

8. You are being chased by a monster in an online game. Do you - Ask a friend for help in killing it (s) 

- Hide somewhere you know the monster won't follow? (e) 9. Which would you enjoy more as an online game player? - Running your own tavern (s) 

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10. If you're alone in an area, do you think - It's safe to explore (e) 

- You'll have to look elsewhere for prey (k) 11. Which do you enjoy more in quests? - Getting involved in the storyline (s)  - Getting the rewards at the end (a) 12. Would you rather have 

- A spell to damage other players (k) 

- A spell that increases the rate at which you gain experience points? (a) a=achiever

k=killer e=explorer s=socialites

The influence of personality traits on the relationship between gamification mechanisms and willingness to buy

Master in Business Administration

Specialisation: Digital Business