Impact of gaming habits on motivation to attain gaming goals, perceived price fairness, and online gamer loyalty: Perspective of consistency principle

Telematics and Informatics 49 (2020) 101367

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Impact of gaming habits on motivation to attain gaming goals, perceived price fairness, and online gamer loyalty: Perspective of consistency principle

T

Gen-Yih Liaoa,b,1, Fan-Chen Tsengc, T.C.E. Chengd, Ching-I Tenge,f,g,

⁎

a

Department of Information Management, Chang Gung University, Taiwan Department of Nursing, Chang Gung Memorial Hospital, Taoyuan Branch, Taiwan c Department of Multimedia and M-Commerce, Kainan University, No.1 Kainan Road, Taoyuan City 33857, Taiwan d Fung Yiu King – Wing Hang Bank Professor in Business Administration, Chair Professor of Management, and Dean of Faculty of Business, Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Hong Kong e Graduate Institute of Business and Management, Chang Gung University, Taiwan f Department of Rehabilitation, Chang Gung Memorial Hospital, Linkou, Taiwan g Department of Business and Management, Ming Chi University of Technology, Taiwan, 259, Wenhua 1st Rd, Gueishan, Taoyuan 333, Taiwan b

ARTICLE INFO

ABSTRACT

Keywords: Online game Loyalty Habit Perceived price fairness Survey Structural equation modelling

Online games are popular electronic commerce applications that have a business model of selling gaming items to gamers. Such a business model helps gamers attain gaming goals while cultivating their gaming habits. Gaming habits can lead gamers to play games automatically, indicating their impact on gamers. However, little is known about how gaming habits affect gamers’ perceptions of the prices of the gaming items, goal-attaining motivation, and online gamer loyalty. Grounded in the consistency principle, we construct a framework to explain how gaming habits impact motivation to attain gaming goals, perceived price fairness, and online gamer loyalty. We collected 5,144 responses from online gamers and used structural equation modelling to test the research model. We found that gaming habits are positively related to motivation to attain gaming goals and perceived price fairness, which are further positively related to online gamer loyalty. Ours is the first study using the perspective of the consistency principle to examine the mechanism underlying the impact of gaming habits on online gamer loyalty. Our findings provide novel insights for electronic commerce managers that they could focus on enhancing perceived price fairness and motivation to attain gaming goals, thus establishing a loyal user base. Such findings could also apply to interactive hedonic systems, indicating their potential academic impact.

1. Introduction Online games are popular electronic commerce applications worldwide, generating huge revenues for online game providers. The global game market had more than 2.3 billion active gamers in 2018, of whom 1.1 billion spent money on games (NewZoo, 2018). In particular, a single online game, i.e., Diablo III, sold more than 3.5 million copies on its inaugural release day (Statista, 2019), Corresponding author at: Graduate Institute of Business and Management, Chang Gung University, Taiwan. E-mail addresses: [email protected] (G.-Y. Liao), [email protected] (F.-C. Tseng), [email protected] (T.C.E. Cheng), [email protected], [email protected] (C.-I. Teng). 1 0000-0002-6712-1400. ⁎

https://doi.org/10.1016/j.tele.2020.101367 Received 8 September 2019; Received in revised form 17 December 2019; Accepted 6 February 2020 Available online 11 February 2020 0736-5853/ © 2020 Elsevier Ltd. All rights reserved.

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revealing the huge potential of popular online games in creating profits. The promising gaming market has attracted numerous providers. For example, in the U.K., the number of active game providers had reached 2,261 in June 2018 (UKie, 2019). Such strong competition indicates the importance of research into online gamer loyalty, i.e., gamers’ intention to play the same game repeatedly. The literature has proposed various antecedents to online gamer loyalty, including purchase motivations (Hamari et al., 2017a, 2017b), sense of community (Tseng et al., 2015) consumption values (Teng, 2018), ease of use and attitude (Hamari and Koivisto, 2015), enjoyment (Dindar, 2018), avatar identification and flow (Liao et al., 2019), avatar ownership and social identity (Moon et al., 2013), and social presence (Fang et al., 2018; Johnson et al., 2018). Such antecedents are cognitively evaluated, i.e., they can explain conscious continued gaming. Such intentional gaming behaviour occurs repeatedly, leading to continued gaming without conscious intention (De Guinea and Markus, 2009), i.e., automatic play. Automatic gaming behaviour without conscious intention indicates habits (Limayem et al., 2007). Habits are important determinants of actual usage of information systems (Limayem et al., 2007), so exclusion of habits would neglect or underestimate the power of automatic play in strengthening the intention to repeatedly engage in gaming behaviour. However, little is known about whether and how gaming habits fuel online gamer loyalty, i.e., the intention to repeatedly play an online game, indicating a research gap. Research filling this gap could offer game providers useful insights into the effect of gaming habits, enabling them to build a loyal gamer base that yields a competitive advantage. Moreover, such research helps clarify the mechanism through which gaming habits affect gamer loyalty. Hence, research filling this gap could provide research opportunities for identifying and examining gaming habits as an additional novel antecedent to online gamer loyalty. To seek theories to guide our work to fill this gap, we began by considering the features of online games. Online games frequently offer gamification mechanisms, e.g., a ranking system and badges to indicate the master gamers (Hamari, 2017). Such records are normally shared by gamers, and the advanced gaming behaviours that resulted in this ranking are public to all the gamers in the game. The impact of previous public behaviour on subsequent behaviour has been elucidated by the consistency principle (CP), which posits that people tend to align their subsequent behaviour with their previous public behaviour (Garnefeld et al., 2011). Gaming behaviour can be publicly observed, e.g., via avatars’ activities that are visible to all other gamers in the game. Hence, gaming behaviour is a public behaviour, indicating the suitability of using the CP as the theoretical lens for our study. To build the mechanism underlying the impact of gaming habits on gamer loyalty, we used the gaming features to identify the concepts for inclusion in our model. Gamers highly value achievements (Dindar, 2018). Therefore, they would value the attainment of gaming goals. Hence, we chose to include motivation to attain gaming goals. CP indicates the importance of previous public behaviour in affecting individuals’ subsequent behaviour (Garnefeld et al., 2011). Frequent engagement in public behaviour associated with games, i.e., frequent gaming behaviour, can develop gaming habits, motivating us to include gaming habits in our model. Moreover, habits can inform goals, (Wood and Neal, 2007). Therefore, we also include motivation to attain gaming goals in our model. Moreover, in gaming contexts, game providers frequently update or upgrade their gaming content, avatars, and gaming items. These important in-game assets provide affective values to gamers (Bae et al., 2019a). Purchase of gaming items offers a means to display social responsibility (Bae et al., 2019b) and generates an enormous amount of economic value (Xu et al., 2017). When gamers buy new gaming items, perceived price fairness should be critical in their purchase decisions, as this is known as influential on purchase decisions, i.e., perceived price fairness determines the intention to seek products or services from the same provider (Ding and Lii, 2016; Luo et al., 2012). That is, perceived price fairness likely predicts loyalty, motivating us to include perceived price fairness. Following Hong et al. (2014), we contextualize the two salient features of CP, i.e., past public behaviour and alignment in subsequent behaviour, into the online gaming context. First, we contextualize past public behaviour into gaming habits. Second, we contextualize alignment with public behaviour in subsequent behaviour into gamer loyalty. Such contextualized concepts extend CP into the gaming context, providing unique opportunities within this context to use this theory. In addition to identifying and filling an existing research gap, this study engages in the problematization process (Alvesson and Sandberg, 2011). Specifically, we identify that CP has an implicit assumption that past public behaviour directly influences alignment in subsequent behaviour. However, we challenge this assumption and provide an alternative assumption, i.e., past public behaviour should indirectly influence alignment in subsequent behaviour through other factors, e.g., motivation to attain gaming goals and perceived price fairness. Therefore, the purpose of this study is to examine how gaming habits influence gamers’ motivation to attain gaming goals, their perception of price fairness of the gaming items, and their loyalty to online games. The overall contribution of this study is to expand the theoretical scope of CP by adopting two mediating factors, i.e., motivation to attain gaming goals and perceived price fairness. Moreover, by examining the effect of gaming habits on gamer loyalty, game providers will be able to utilize gaming behaviour to predict future engagement and use the mediators to achieve such an effect. In this study, we make several specific contributions to the literature. First, Dindar and De Gortari (2017) examined the relation between video game players’ individual gaming habits and their after-gaming perceptions, thoughts, and behaviours. In concordance with their focus on gaming habits, the present study is distinct in introducing two mediators, i.e., perceived price fairness and motivation to attain gaming goals, to explain the effect of gaming habits on gamer loyalty. Consequently, our study bridges the gap between gaming habits and gamer loyalty, and could thus contribute to game providers fostering gamer loyalty. Second, Manero et al. (2016) examined the role of gaming habits in building a typology of gamers. In line with their study examining the importance of gaming habits, our work is new in investigating the automatic aspect of habitual gaming and examining its role in cultivating gamer loyalty. The present study therefore can shed light on the influence of an automatic mechanism on continuance intention, motivating future studies to explore the essence of gaming habits and their impact on gamers. Third, Hamari and Keronen (2017) examined how gaming enjoyment is related to the intention to repeatedly play online games. 2

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Consistent with their examination of the importance of such an intention, our study is new in examining how gaming habits influence playing intention. We contribute to the continuance intention literature by providing a novel mechanism that begins with gaming habits, which then exert their effect through two mediators, i.e., motivation to attain gaming goals and perceived price fairness. Fourth, Cheung et al. (2015) examined the link between engagement and online sales of gaming items. In line with their research on the effect of gaming item sales on gaming behaviour, our research is novel in including perceived price fairness, a perception of gaming items, to explain gaming behaviour. Our study should encourage future research to explore how perceptions of gaming items could affect gamers’ behaviour. Section 2 reviews the theory and the prior research, thus developing the framework and the hypotheses, while Section 3 details how this study is implemented. Section 4 describes the sample profile and displays the testing results, Section 5 discusses the findings and their implications, and, finally, Section 6 draws the conclusions. 2. Theoretical background and hypothesis development 2.1. Consistency principle (CP) CP posits that people tend to align their subsequent behaviour with their previous public behaviour (Cialdini et al., 1995). Public behaviour is a physical expression of statements and public commitments. Validated effects of public commitments include voters’ actual balloting behaviour (Greenwald et al., 1987), and customers’ actual purchases of recommended items (Garnefeld et al., 2013). This array of public behaviour in varying contexts justifies our selection of CP in examining individuals’ public behaviour, i.e., online gaming behaviour. In the online gaming context, gaming habits refer to automatic gaming behaviour, which is public and observable by other gamers. Hence, automatic gaming behaviour generates a record of gaming frequency that are publicly observable, and thus implies a public commitment to playing games. This connection between gaming habits and public commitment also supports the applicability of CP in the online gaming context. CP requires measuring a specific behaviour. This behaviour in our study is playing online games. Habits and loyalty are related to the same behaviour: playing online games. Habits and loyalty slightly differ in that habits refers to the current automatic play, while loyalty refers to the intention to play the same game repeatedly in the future. In this sense, we argue that CP can be applied to our study. CP has been applied to guide research into social media, in which message sharing is a major feature (Choi et al., 2017). Social media users can share messages with friends and even the whole Internet. Publicly sharing messages is captured in CP as public commitment that will then impact subsequent behaviour. In the context of social media, an example of such behaviour is volunteering for an issue-related cause after publicly sharing social cause videos (Lane and Dal Cin, 2018). In the case of online games, a gamer’s behaviour is publicly observable by all the other gamers. Such publicly observable behaviour could induce future gaming behaviour, as posited by CP. That is, when gamers revisit the gaming environment, the environment in which they have made public commitments provides an external stimulus that drives subsequent behaviour. Such links between external stimuli and behaviour define habits. Accordingly, we review literature pertinent to habit. Fig. 1 illustrates the research framework grounded in CP. The model is new in examining the role of perceived price fairness in the link between gaming habits and online gamer loyalty. Moreover, to increase the analytical rigour, we included in the model various control variables that are related to gaming behaviour (De Gortari, 2018; Lee et al., 2018). According to CP, the subsequent behaviour should be conscious behaviour. Therefore, we use “loyalty”, which is a conscious

Fig. 1. Research framework. Note. All paths are hypothesized as having positive coefficients. Dotted lines represent the links that are not hypothesized. 3

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intention to engage in the gameplay, thus fitting CP. Gaming habits can also be automatic (Limayem et al., 2007), or not conscious. However, gaming habits are the antecedent, but not the subsequent behaviour in our model, thus not contradicting CP. Fig. 1 illustrates the research framework, which contains the six study hypotheses. These hypotheses predict the relations among the four key study constructs, i.e., gaming habits, motivation to attain gaming goals, perceived price fairness, and gamer loyalty. Next, we review the pertinent literature, connect the constructs, and develop the hypotheses. 2.2. Gaming habits Habit refers to the learned sequences of acts that become automatic responses to specific situations (Limayem et al., 2007). Research on IT continuance intention posits that users who frequently use IT devices can render their behaviour habitual or automatic (Limayem et al., 2007). Such automatic behaviour has been validated as influential in the context of location-based gaming (e.g., De Gortari, 2018) and mobile services (Yen and Wu, 2016). Such automatic behaviour may be repeated without conscious intention (De Guinea and Markus, 2009). Accordingly, we define gaming habits as the extent to which gamers automatically access online games without thinking or reasoning (Hsiao et al., 2016). Gaming habits are useful for building a typology of gamers (Manero et al., 2016). Specifically, gamers comprise four types, i.e., full gamers (who play all kinds of games with a high frequency), hardcore gamers (who play sports games or first-person shooter games frequently), casual gamers (who play musical, social, or thinking games for a moderate amount of time), and non-gamers (Manero et al., 2016). Gaming habits represent automatic gaming behaviour (Limayem et al., 2007). Such behaviour would lead gamers to repeatedly play the game and gradually approach their goals, thus motivating them to engage in attaining their gaming goals (Teng, 2017), linking gaming habits to motivation (i.e., motivation to attain gaming goals). Moreover, gaming habits would make gamers accustomed to the price set by the game providers. Therefore, they would see the price as fair, or have their perception of price fairness enhanced, linking gaming habits to perceived price fairness. Accordingly, we developed H1 and H2 to predict these two links. Habits indicate learned responses to perform automatic behaviour without deliberate thinking (Amoroso and Lim, 2017). Such automatic behaviour provides gamers with opportunities to infer personal goals from their habits. Such inference tends to value particular response outcomes (Wood and Neal, 2007). Valued outcomes in gaming include game achievements, increased friends, and escaping stress (Dindar, 2018). These outcomes are considered by gamers as gaming goals to attain, i.e., motivation to attain gaming goals. Moreover, gaming habits are automatic behaviours that frequently expose avatars (gamers’ representations) to other gamers, thus publicly displaying their behaviour in games. According to CP (Garnefeld et al., 2011), this public behaviour would align individuals’ subsequent behaviours to their previous ones. Frequent gaming behaviour would gradually lead gamers toward their gaming goals, i.e., goal proximity (Locke and Latham, 1990), which further strengthens the intention to attain gaming goals (Teng, 2017). Hence, we hypothesize: H1: Gaming habits are positively related to motivation to attain gaming goals. Habits induce automatic behaviour that inhibits deliberate thinking (Amoroso and Lim, 2017). Deliberate thinking increases recall of reference points in past experiences, e.g., past product prices. The reference points are typically used to evaluate the encountered prices (Heath et al., 1999), increasing the possibility that gamers may consider gaming items as not priced fairly, i.e., decreasing the perceived price fairness. On the contrary, gaming habits inhibit deliberative thinking, and thus can help gamers feel accustomed to the prices of the gaming items, maintaining their perception of price fairness, building the link as hypothesized in H2. Moreover, CP posits that individuals would align their future behaviour to be consistent with their previous public behaviour (Garnefeld et al., 2011). Gaming habits generate frequent avatar activities that are publicly exposed to other gamers. This public exposure increases the perceived value of gaming items if gamers were to make the purchase. The heightened evaluation increases the perceived price fairness. Hence, we hypothesize: H2: Gaming habits are positively related to perceived price fairness. 2.3. Motivation to attain gaming goals Motivation to attain gaming goals shows that gamers highly value the gameplay activity. Such a high value would induce gamers to perceive the gaming items as highly valuable, because such items are useful for reaching goals during play. That is, such gamers would regard the gaming items as offering good value for money (perceived price fairness), linking motivation to perceived price fairness. Moreover, motivation to attain gaming goals could effectively strengthen gamers’ intention to repeatedly play the game, i.e., enhance their loyalty (Teng, 2017), linking motivation to loyalty. Accordingly, we develop H3 and H4 to predict these two links. Purchase of gaming items can be a means of achieving goals, e.g., participating in game providers’ social responsibility initiatives (Bae et al., 2019b). According to the goal gradient perspective, motivation to attain gaming goals indicates gamers’ intention to pursue the means to engage in games (Teng, 2017). Such pursuit reflects the value of these means expected by gamers. Expected value would enhance the perception of good value for money and make the price more appropriate, connecting to the definition of perceived price fairness. That is, enhanced expected value exerts strong and positive impacts on a customer’s perceived price fairness (Bolton and Lemon, 1999). Thus, gamers who place a high value on gaming items will perceive their prices as fair, building the positive link as hypothesized in H3. Hence, we hypothesize: H3: Motivation to attain gaming goals is positively related to perceived price fairness. Online gamers are motivated to obtain achievements (Teng, 2017). Even though the achievements can only take place in the future, such motivation will still encourage gamers to continue to play and offer positive recommendations (Huang et al., 2017a, 4

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2017b; Liao and Teng, 2017). Empirical studies have suggested various goals (e.g., achievement, immersion, and socialization) in gaming motivation can foster gamer loyalty directly or indirectly (Dindar, 2018). Moreover, motivation to use specific means to achieve gaming goals, e.g., using in-game accessories, increases purchase intention (Shukla and Drennan, 2018). Hence, we hypothesize: H4: Motivation to attain gaming goals is positively related to online gamer loyalty. 2.4. Perceived price fairness and online gamer loyalty Online service studies have indicated online loyalty as the degree to which a service user possesses a positive attitudinal disposition toward the provider and considers buying again from this provider when a need for the service arises (Gummerus et al., 2004). Online user loyalty has been examined in various online contexts, e.g., B2C services (Valvi and West, 2013), B2B services (Rauyruen et al., 2009), online travel information services (Garnefeld et al., 2011), and online gaming (Hamari et al., 2017a, 2017b; Liao et al., 2019). These studies suggest that online service users exhibit different forms of loyalty to services or service providers, including positive word-of-mouth and increased purchases. Prices are a highly influential element in customer purchases (Xia et al., 2004) and very important in forming user loyalty (Valvi and West, 2013), linking price perception to loyalty and thus motivating us to develop H5 to predict this link. The concept of fairness refers to a judgment of whether an outcome and/or the process leading to such an outcome are reasonable, acceptable, or just (e.g., Bolton et al., 2003). In the context of consumer behaviour, perceived price fairness involves a comparison of a price or procedure with a pertinent standard, reference, or norm to determine whether the price is fair (Xia et al., 2004), and perceived price fairness is a determinant of purchase decisions (Luo et al., 2012). Therefore, it would motivate user adoption behaviour in the online context. Perceived price fairness refers to customers’ perceptions of prices when the services’ quality exceeds the reference points, e.g., service performance, price expectation, and competitors’ prices (Kwak et al., 2015). Perceiving prices as fair promotes purchase intention (Homburg et al., 2014). Moreover, fairness perception fuels users’ purchase decisions (Luo et al., 2012). Purchasing more in-game items leads to a strong intention to remain in the same game (Hamari et al., 2017a, 2017b), representing gamer loyalty. Hence, we hypothesize the following: H5: Perceived price fairness is positively related to gamer loyalty. Habit is a learned response automatically triggered by stimulus cues (De Guinea and Markus, 2009) without deliberate thinking to seek better alternatives (Amoroso and Lim, 2017). Based on CP, people tend to align their subsequent behaviour with their previous public behaviour (Garnefeld et al., 2011). That is, when gaming habits are formed, gamers stick to particular games and continuously play those games. Previous literature has also identified habits as important in determining customer loyalty or repurchase intention (Rauyruen et al., 2009). IS studies have suggested the role of habits in non-conscious continuance intention in IT use (De Guinea and Markus, 2009), and supported the habit–loyalty link with empirical evidence in the context of using health apps (Yuan et al., 2015) and mobile financial services (Yen and Wu, 2016). Therefore, it can be inferred that gaming habits would lead gamers to keep playing the games they used to play. Hence, we hypothesize the following: H6: Gaming habits are positively related to gamer loyalty. It may be noted that loyalty refers to future habits, so this hypothesis (H6) becomes tautologous. However, loyalty refers to the present intention to repeatedly play a game, not ensuring future habits, i.e., automatic gaming behaviour in the future. Moreover, present habits are not equivalent to future habits, further conceptually distinguishing habit and loyalty. 3. Methods 3.1. Sample and data collection process We solicited potential respondents to join our study by posting the link to the online survey on websites that online gamers frequent, e.g., www.gamer.com.tw, the most popular local gaming website hosting forums for various games. The various forums represent various sources, thus minimizing the bias owing to a single source or pertaining to a single game. Interested potential respondents could click on the invitation link, which led them to the online form containing the items assessing the study constructs and fields for the respondents’ demographic information. The online form stated that the study was interested in understanding respondents’ perceptions of online games. The respondents could enter a lottery issuing a total of US$180 in gift certificates. Such modest incentives are common in local web surveys and should not create significant self-selection bias. We consulted Steelman et al. (2014) and set several criteria for evaluating whether or not a response is valid as follows: A valid response must contain existent game names, existent in-game avatars, a playing history longer than zero years, a playing frequency greater than zero hours per week, and an email address not duplicating any other address in the same dataset. We found 318 responses to be invalid and discarded them, leaving a sample of 5,144 valid and useable responses. We ask the respondents to identify their favourite games. The most frequently mentioned games were League of Legends (23.6%), Hearthstone (21.5%), and MapleStory (19.7%). These all have in-game items (e.g., artifacts, equipment, and decorations) for sale. Hence, participants could assess their perception of price fairness of such gaming items, indicating their suitability for this study. Asking the participants to nominate their favourite games is an approach acceptable in online gaming literature (e.g., Teng, 2018). The reason is that assessing their favourite games maximizes respondents’ successful recall. Moreover, such an approach may increase the absolute scores for loyalty. However, our study tests the hypotheses by using correlations, not the absolute scores, thus unlikely biasing the test results. 5

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3.2. Measurement We use three items to measure each study construct. We borrowed all the items from the related literature and slightly modified them to fit our research context. We measured gaming habits using the scale from Bhattacherjee et al. (2012). We measured perceived price fairness using the items from Herrmann et al. (2007). We measured loyalty based on the items from Hong et al. (2015). The items measuring motivation to attain gaming goals were adapted from Hollenbeck et al. (1989). To enhance analytical rigour, we collected the following information on the control variables and used them in the subsequent analyses: respondent’s gender (male coded as 1; female coded as 2), age, education, income, gaming history (years spent), and gaming frequency (weekly hours). We measured all the items using a five-point scale, ranging from 1 (strongly disagree) to 5 (strongly agree). A higher score indicates a higher level of the measured construct. We placed the items measuring the study constructs at the beginning of the online form, and the items collecting the demographic information at the end. Such an approach helped reduce the risk of participants answering the study items based on their stereotypes inferred from the demographic information, reducing potential bias. 3.3. Psychometric properties We conducted an exploratory factor analysis (EFA) to verify the factor structure. Table 1 summarizes the EFA results. Such results indicate that all the items adequately load on the factors that are theoretically assumed. To evaluate the measurement quality of the study items, we applied a widely used technique, i.e., confirmatory factor analysis (CFA). All the Cronbach’s α values were ≥0.83 and had lower bounds of the 95% confidence intervals for the Cronbach’s α values ≥0.82, demonstrating confident reliability. Moreover, all the composite reliability (CR) values were ≥0.87 and the average variance extracted (AVE) values ≥0.70, showing adequate reliability (Bagozzi and Yi, 1988). That is, the p value for the condition that the Cronbach’s α value < 0.70 is very low, i.e., < 0.001, conforming to the suggestion of using statistics as inferential rather than merely cut-off points (Guide and Ketokivi, 2015). All of the indicator loadings were ≥0.82, supporting convergent validity (Hair et al., 1998). Eventually, the maximum squared correlation of 0.38 is smaller than the minimum AVE value of 0.70), reflecting discriminant validity (Fornell and Larcker, 1981). In sum, the above statistics indicate that the reliability and validity of the study items are acceptable. Table 2 reports the CFA results. We provide the complete questionnaire and the study items in the Appendix. The model fit the data adequately, in terms of the performance of the fit indices, i.e., CFI = 0.99, NNFI = 0.98, IFI = 0.99, AGFI = 0.95, and RMSEA = 0.051. Moreover, we followed the suggestion in the methodological literature (e.g., Guide and Ketokivi, 2015) by testing the fit statistics. Specifically, the RMSEA has a confidence interval of [0.049, 0.051]. The p value for RMSEA < 0.08 is smaller than 0.05, further supporting that the model fit is adequate. Table 3 lists the correlations among the study constructs. These range from 0.28 to 0.62, indicating a low likelihood of common method variance (CMV). To formally test CMV, we adopted the suggestion of the methodological literature (e.g., Podsakoff et al., 2003) by adding a construct named CMV to the measurement model. The model containing CMV had a significantly inferior fit with the data than the original model. Specifically, the model containing CMV had an incremental χ2 value of (1,307.19–1,260.94 = ) 46.25 and a difference in (61–59 = ) 2 degrees of freedom. The incremental χ2 value (46.25) exceeded the threshold value, i.e., χ2(α = 0.05, df = 2) = 5.99. In sum, the issue of CMV should be minimal in the measurement.

Table 1 Summary of exploratory factor analysis.

GH1 GH2 GH3 PF1 PF2 PF3 LOY1 LOY2 LOY3 MAG1 MAG2 MAG3 MAG4

1

2

3

4

0.26 0.26 0.27 0.12 0.13 0.15 0.16 0.32 0.24 0.82 0.85 0.83 0.79

0.11 0.10 0.12 0.93 0.93 0.88 0.12 0.11 0.14 0.13 0.08 0.16 0.17

0.78 0.86 0.86 0.09 0.10 0.10 0.26 0.23 0.27 0.19 0.19 0.24 0.25

0.29 0.26 0.24 0.09 0.10 0.14 0.81 0.75 0.78 0.18 0.19 0.22 0.23

Note. GH denotes gaming habits; PF denotes perceived price fairness; LOY denotes loyalty; MAG denotes motivation to attain gaming goals.

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Table 2 Summary of confirmatory factor analyses. Construct-Item Gaming Habits GH1 GH2 GH3 Perceived Price Fairness PF1 PF2 PF3 Loyalty LOY1 LOY2 LOY3 Motivation to Attain Gaming Goals MAG1 MAG2 MAG3 MAG4

M

SD

λ

3.86 3.69 3.81

0.84 0.93 0.89

0.85 0.95 0.95

3.14 3.07 3.03

1.09 1.08 1.09

0.92 0.94 0.90

3.74 3.40 3.74

0.87 1.02 0.91

0.82 0.82 0.86

3.45 3.57 3.49 3.43

0.93 0.99 0.95 0.97

0.82 0.86 0.92 0.89

α

C.I. of α

CR

AVE

0.91

[0.91, 0.91]

0.94

0.84

0.93

[0.92, 0.93]

0.94

0.84

0.83

[0.82, 0.84]

0.87

0.70

0.91

[0.90, 0.91]

0.93

0.76

Note. λ denotes indicator loading; α denotes Cronbach’s α values; C.I. denotes 95% confidence interval of the Cronbach’s α values; CR denotes composite reliability; AVE denotes average variance extracted. Table 3 Correlations among the study constructs.

1. 2. 3. 4.

Gaming Habits Perceived Price Fairness Loyalty Motivation to Attain Gaming Goals

1

2

3

4

0.92 0.28 0.62 0.57

0.92 0.32 0.34

0.84 0.56

0.87

Note. All the correlations have a p value < 0.01. Italicized numbers on the diagonal are the squared average variance extracted (AVE) values.

4. Results 4.1. Sample profile Table 4 illustrates the profile of the 5,144 participants. Among the participants, 4,736 provided complete demographic information. Therefore, the total numbers in Table 4 should be 4,736 or more. The profile indicated that the sample mainly comprised well-educated (63.8% had attended college or university), young (94.4% were 29 years old or younger) males (89.6%). The mean age of the participants was 21.55 years. Moreover, a significant proportion (33.0%) of the sample had a monthly income exceeding US $401. Such features were consistent with the features of the local population. The sample had a gender composition consistent with the literature. Specifically, the gender composition, i.e., 89.6% were male, was consistent with that reported in gaming literature (e.g., 81.2% in Liao et al., 2017; 97% in both Dindar, 2018; Dindar and De Gortari, 2017). Moreover, the age profile, i.e., 94.4% were ≤29 years old, was comparable to that in gaming studies (e.g., 91.1% were ≤29 years old in Merhi, 2016; 97.1% were ≤29 years old in Zhao et al., 2018). The participants had played the nominated game for an average of 2.34 years with a standard deviation of 3.04 years. They had played the nominated game for an average of 14.92 h per week with a standard deviation of 18.20 h. Table 4 Sample profile. Variable

Category

Frequency

%

Gender

Male Female ≤24 years old 25–29 years old ≥30 years old High school or below University/college Graduate institute ≤ US$ 400.00 US$ 401.00–800.00 ≥ US$ 801.00

4259 495 3783 687 266 1720 2743 291 3184 574 996

89.6 10.4 79.9 14.5 5.6 36.2 57.7 6.1 67.0 12.1 20.9

Age Education Monthly Income

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Fig. 2. Analytical results. Note. Solid lines represent the paths that carry significant coefficients, i.e., p < .05; dotted lines represent the paths that carry insignificant coefficients.

4.2. Hypothesis testing We adopted the structural equation modelling (SEM) technique to test the research hypotheses. SEM is known for its ability to deal with psychological measurement issues, appropriate to the needs of our study. The use of SEM for data analysis and hypothesis testing is common in the pertinent electronic commerce literature (Cheung et al., 2015; Moon et al., 2013). The findings supported all of the study hypotheses, justifying our examination of the hypothesized links. Specifically, gaming habits were positively related to motivation to attain gaming goals and perceived price fairness (path coefficient, hereafter p.c. = 0.64 and 0.12, t = 44.59 and 6.72, p < .001), supporting H1 and H2. Motivation to attain gaming goals was positively related to perceived price fairness (p.c. = 0.30, t = 16.06, p < .001), supporting H3. Moreover, motivation to attain gaming goals, perceived price fairness, and gaming habits were all positively related to online gamer loyalty (p.c. = 0.32, 0.12, and 0.46, t = 20.60, 10.10, and 29.33, p < .001), supporting H4, H5, and H6. Fig. 2 illustrates the analytical findings. The structural model fit the data adequately (CFI = 0.98, IFI = 0.98, NNFI = 0.98, AGFI = 0.94, RMSEA = 0.053, p (RMSEA < 0.08) < 0.001). The χ2 value and the χ2/df were known for their positive association with sample size (Hair et al., 1998; Marsh et al., 1988). Therefore, the large sample herein justified that these two indices should not be regarded as critical. The model explained 62% (equivalent to R2) of the variance of gamer loyalty. Such an explained proportion was equivalent to a very large effect size, according to the methodological literature (e.g., Cohen, 1992). Such a large effect size revealed the practical relevance of the study findings herein. 4.3. Additional analyses To further exploit the study findings, this study discussed the path coefficient associated with the control variables. This study additionally found that when compared with male gamers, female gamers were more loyal to online games (p.c. = 0.08, t = 7.07, p < .001). The reason may be that female gamers are on average more patient than males in cultivating gaming avatars. Such patience boosts female gamers’ loyalty to games. Income was found to be positively related to online gamer loyalty (p.c. = 0.03, t = 2.58, p < .05). The reason may be that a high income enables a gamer to pay more to play a game and spend more on gaming items, enhancing their intention to repeatedly play the game. Moreover, both gaming history and gaming frequency were positively related to online gamer loyalty (p.c. = 0.07 and 0.03, t = 6.15 and 2.43, p < .05). The reason may be the sunk costs that have accumulated during the gaming history, which stand as barriers to switching to other games. We additionally conducted sensitivity analyses. Specifically, we removed the control variable and found the coefficients changed to a minor degree, i.e., ≤ 0.03. Moreover, all test results remained the same. In short, the removal of control variables did not change the analytical results, supporting the robustness of our study findings. We conducted Sobel tests on all potential mediations and found that all Sobel’s z values ≥ 4.92 (p < .001). That shows strong support for all possible mediations in our model. We additionally compared the three direct impacts on gamer loyalty. We found that the impacts of gaming habits and motivation to attain gaming goals are stronger than the impact of perceived price fairness. Such findings supported the impact as discovered by Teng (2017a, 2017b), but indicated that the impact of gaming habits could be further explained by future studies. Following Guide and Ketokivi’s (2015) suggestions to check the possibility of reverse causality, we conducted the endogeneity test, i.e., reversing the impact of gaming habits on perceived price fairness and motivation to attain gaming goals, to turn gaming habits from an exogenous construct to an endogenous construct. Such a reversal significantly deteriorated the model fit, i.e., Δχ2 = 2,002.27–1,956.42 =) 45.85 without any change in degrees of freedom. Specifically, the χ2 value represented the lack of 8

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model fit. Therefore, an increased χ2 value indicated a worse model fit. The incremental χ2 value (45.85) exceeded the threshold value, i.e., χ2(α = 0.05, df = 1) = 3.84. The threshold value displayed the statistical cut-off point. If the Δχ2 value exceeded this threshold, this value exhibited statistical significance. That is, our model passed the endogeneity test. It may be questioned whether motivation (rather than habits) may be the antecedent. Hence, we tested this likelihood, by reversing the path from habits to motivation. This reversal significantly deteriorated the model fit, i.e., Δχ2 = 45.85, but did not change the degrees of freedom. This increase in χ2 value (45.85) is statistically significant. That is, our structural model explained the data significantly better than the model implementing this reversal, supporting that motivation is not the antecedent. Among the three impacts on online gamer loyalty, the impact of perceived price fairness was the smallest. Therefore, it may be questioned whether this impact is excessive. Removal of that impact built a rival model. We found that the rival model displayed a significantly worse fit with the data, i.e., Δχ2 = 2,057.78–1,956.42 = 101.36 with a change of one degree of freedom. The incremental χ2 value (101.36) exceeded the threshold value, i.e., χ2(α = 0.05, df = 1) = 3.84. That is, the removal of that impact was not adequate, supporting our present findings. One may assume that gaming habits, weekly hours, and years spent are likely highly correlated. We computed their correlations and they ranged from 0.11 to 0.15, not exhibiting high correlations. Future studies examining the same set of variables should also compute their correlations and examine the issue of multicollinearity. 5. Discussion 5.1. Main findings and contributions The analytical results of this study indicate that gaming habits are positively related to motivation to attain gaming goals and perceived price fairness, both of which are further positively related to online gamer loyalty. That is, gaming habits strengthen gamers’ motivation to attain gaming goals and facilitate their perception that the price charged by the game is fair. Both the motivation and the perception further cultivate gamers’ loyalty, while the goal-oriented motivation can foster the perceived price fairness. Moreover, gaming habits are directly related to online gamer loyalty, showing the importance of cultivating gaming habits. This study is the first to examine the impact of gaming habits on perceived price fairness and online gamer loyalty. This examination is new in alerting users to the fact that their gaming habits may make them accustomed to the prices of the gaming items, leading to enhanced perceived price fairness. On the other hand, this examination also indicates the value of habitual gamers to the game providers. That is, this finding is useful for both parties in practice. This study contributes to CP by introducing the important and novel consequence of habit. That is, after individuals align their subsequent behaviour with their previous public behaviour, they may form a habit. This theoretical extension broadens CP from explaining ongoing behaviour to explaining long-lasting behaviour. 5.2. Theoretical implications Our study makes a theoretical contribution to CP. Specifically, our study discloses two pathways leading to current public behaviours (i.e., habits) to the intention to engage in future behaviour (i.e., loyalty). The two pathways are the goal-gradient pathway (i.e., motivation to attain gaming goals) and the deliberative thinking hindrance pathway (i.e., perceived price fairness). The first pathway indicates that current public behaviour could help the individuals approach their goals, thus further strengthening the motivation to engage in the future behaviour. The second pathway indicates that current frequent public behaviour hinders deliberative thinking, i.e., unlikely re-evaluating behaviour, leading directly to engaging in the future behaviour. These two pathways deepen our understanding of the theoretical mechanism underlying CP. This study contributes to online gaming literature in several aspects. First, Hamari et al., 2017a, 2017b) reported that service quality could influence the intention to purchase online gaming items. The present study is concordance in theirs in investigating the effect of service features on user behaviour. However, our study identifies a novel predictor of user behaviour, i.e., perceived price fairness, to understand the role of habit on loyalty. Future studies could incorporate how perceived price fairness interacts with service quality to influence the intention to purchase online gaming items, thus offering further insights for game providers. Second, Manero et al. (2016) developed a framework to cluster gamers according to their gaming preferences and habits, and indicated that gaming habits can influence the effectiveness of educational video games. Based on this framework, Manero et al. (2017) investigated the impacts of demographic variables (gender and age) and gaming habits on the effectiveness of educational video games, and found that gaming habits play a dominant role. In line with their studies on gaming habits, our study extends previous research to reveal the effect of gaming habits on perceived price fairness and loyalty. Therefore, we provide a new construct (or factor), i.e., gaming habits, for research on perceived price fairness and loyalty in online games. Third, Merhi (2016) proposed a framework to identify the antecedents to intention to play online games. Consistent with his work on examining the antecedents to intention to play online games, our study goes a step further by verifying two novel antecedents, i.e., gaming habits and perceived price fairness. These novel antecedents help establish a more comprehensive framework to explain the intention to play online games. Online gaming studies have examined the antecedents to online gamer loyalty, including avatar identification, participation in gaming communities, social presence (Teng, 2017), expectancy for growth (Liao and Teng, 2017), temperament and character dimensions (Huang et al., 2017a, 2017b), gaming habits (Teng, 2018), and relationship characteristics (length, depth, and breadth), goal proximity, and motivation to attain gaming goals (Teng, 2017). However, these studies have not covered perceived price fairness, 9

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the core innovation of this research, showing the uniqueness of the present study. 5.3. Managerial implications We invited the participants to nominate the games they played. Therefore, various games were nominated, raising the generalizability of our findings to most online games. Particularly for online games, our results indicate that gaming habits are positively related to perceived price fairness and loyalty. Therefore, we suggest that online game providers develop strategies to foster gamers’ gaming habits. For example, some online game providers offer properties that enable avatars to revive themselves after death, so gamers can continue playing the game without suspension. Moreover, game providers could incorporate a scoring system and leaderboard to improve user engagement (Cechetti et al., 2019). That is, gamers could have easy access to their performance in quantified terms. These means may help cultivate gamers’ habits. Furthermore, habits can be cultivated by perceived enjoyment and social ties. Therefore, game providers could intentionally take steps to enhance users’ perception of enjoyment, such as boosting fun, curiosity, exploration, and the flow experience in designing online games. For example, online game providers could offer constant content increments such as adding new maps or new gaming missions to arouse gamers’ curiosity and motivation to explore new frontiers. In addition, online game providers could increase gamers’ social ties by encouraging the social interaction capabilities in games to enable gamers to organize gamer communities such as guilds. Specifically, online game providers could also offer incentives to online game community organizers to hold periodic events or contests to increase gamers’ perceived strength of the social relationship with other gamers (Hsiao et al., 2016). Furthermore, online game providers could utilize both social ties and perceived enjoyment by requiring gamers to form teams to jointly explore new maps or try new avatars, hence cultivating gaming habits. 5.4. Research limitations and future research directions We adopted a cross-sectional design, which is commonly used in the related literature (e.g., Teng, 2018). The reason is its adequacy in examining the links between user psychological variables. However, such a design is known for its limited ability to directly examine causality. We suggest that future studies replicate our work and adopt experimental and/or longitudinal designs. Future studies may recruit a panel and survey the same set of variables, enabling further analyses such as cross-lagged panel analyses. Moreover, subsequent studies may cooperate with gaming providers to obtain behavioural data on gaming expenditure. By cooperating with gaming providers, researchers could also collect behavioural data, such as purchasing frequency and total purchase amount, enabling computation of customer lifetime values. We did not restrict ourselves to recruiting gamers playing the same game. One gamer could nominate one game, adding up to the number of online games in our study. This approach enables us to claim external validity for our findings. Such an approach is common in the pertinent literature (Liao et al., 2019; Teng, 2018). An alternative approach is to recruit gamers that play a single game, which could enable the researchers to include the features of a specific game genre. Moreover, habits may come from previous use behaviour, which should stem from stable and continuous satisfaction, triggering an intriguing question: Would habits lead to gamer loyalty or vice versa? This study’s view is that present habits seed present loyalty. However, we do not exclude the possibility that present loyalty seeds future habits. Hence, future studies should adopt a longitudinal design to address the interwoven relations among present habits and loyalty and future habits and loyalty. Similarly, our study did not measure past habits, limiting us from examining the link between past habits and current habits. Both past and current habits may best fit CP and therefore they are recommended for future studies to explore. One excellent means is to ask participants how many hours they played in the past and how many hours they play now. Furthermore, CP may or may not explain how the current habits were formed. How to form the current habits is an interesting topic, while it is not the focus of our study. Thus, future studies may explore how to use CP to explore means for cultivating current habits. Enjoyment is a well-known predictor of playing online games (Ghazali et al., in press; Hamari and Keronen, 2017). We do not aim to replicate most of the known predictors. Hence, we chose to exclude enjoyment from our framework. However, future studies could build a comprehensive framework to explain the detailed mechanism underlying the formation of playing online games. Such an aim would certainly motivate the inclusion of already-known predictors, including enjoyment. Nature of the game, e.g., game speed, could enhance the persuasion effect on online gamers. Game speed plays an important role in inducing flow and enhancing attention and enjoyment (Vashisht, 2017). Future research may investigate the effect of game speed in perceptual or behavioural measurement, which could offer game providers specific and useful guidelines in game designs. Our study includes the concept of perceived price fairness. Therefore, our finding may not be generalizable to games where there is not a purchasing option. 6. Conclusions We examined the impacts of gaming habits on perceived price fairness of gaming items and on loyalty to a game. We found that gaming habits have a positive effect on perceived price fairness, which in turn is positively related to gamer loyalty. Furthermore, gaming habits also directly influence gamer loyalty. Our work is novel in examining these relationships, and provides new insights to both gamers and online game providers. Overall, our study highlights the importance of gaming habits in cultivating online gamers’ behavioural patterns. Such a finding enables gamers to understand themselves, and online game providers to find ways to keep their 10

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customer bases. Our findings provide insights for electronic commerce managers to utilize games to generate untapped revenues. Future studies can draw on our findings to extend the research to similar scenarios, e.g., online shopping or online content consumption. Acknowledgements This work was supported by the Ministry of Science and Technology, Taiwan [MOST 103-2410-H-182-011-MY3] and Chang Gung Memorial Hospital [BMRP644]. Cheng was also supported in part by The Hong Kong Polytechnic University under the Fung Yiu King - Wing Hang Bank Endowed Professorship in Business Administration. Appendix:. Scale item, source, and anchors Please assess whether you agree with the following items, as they describe your feelings when playing the game you nominated. Construct

Item

Source

Gaming Habits

Playing this online game is one of my habits. Playing this online game is quite automatic for me. Playing this online game is natural to me. Perceived Price Fairness The price of playing this online game is appropriate when compared to its performance. The price of the items in this online game meets my expectation. The price of the items in this online game is good value for money when compared to those in other games. Loyalty When I want to play an online game, I will choose this online game. I will continue to play this online game as much as possible. This online game is my favorite choice of online games, and I will continue to use it. Motivation to Attain Gam- I treat seriously the gaming goals I set in this online game. ing Goals I want to achieve the gaming goals I set in this online game. I think the gaming goals I set in this online game are worth aiming for. I am willing to expend a great deal more effort—beyond what I’d normally expend—on achieving the gaming goals I set in this online game.

Bhattacherjee et al. (2012) Herrmann et al. (2007) Hong et al. (2015) Teng (2017)

Note. All the anchors are: 1 denotes “strongly disagree”, 2 denotes “disagree”, 3 denotes “neutral”, 4 denotes “agree”, and 5 denotes “strongly agree”.

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