Can’t Stop, Won’t Stop Online In-Game Purchase on Mobile Phone: The Role of Flow Experience and Gaming Addiction (2024)



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Submitted May 13, 2024
Published Sep 14, 2024
10.24018/ejbmr.2024.9.5.2401

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This study investigates factors influencing compulsive in-game purchases among Indonesian internet users, particularly Generation Z, and their societal impacts. It explores competitive behavior and flow experience in online gaming addiction, compulsive in-game purchases, and the link between them. Using quota sampling, 206 participants completed tailored questionnaires assessing online game addiction, flow, compulsive buying, and competitiveness. Structural equation modeling (PLS-SEM) was employed to analyze relationships between these constructs, revealing significant links between gaming addiction, flow experiences, and compulsive online purchasing. Competitive gaming may moderate the relationship between flow experience and compulsive buying.

Keywords: Compulsive buying behavior Flow experience Online gaming addiction

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Introduction

Indonesian internet users have grown rapidly since 2017. An impressive 90% of internet users between the ages of 16 and 64 in Indonesia engaged in online purchases of products and Services. Payment ability and habit have a positive effect on purchase intention for online purchases, which leads to compulsive buying (Akbaret al., 2018). This phenomenon has affected many Indonesians, some of whom are showing compulsive buying behavior. Many researchers have looked at this topic of compulsive buying. For example, Zhenget al. (2020) studied how stress relates to compulsive buying. Other researchers, like Kukar-Kinneyet al. (2016) and Duroyet al. (2014), focused on the psychological side of compulsive buying. However, only a few studies have explored how this issue connects to online games. Even though this scenario also applies when the consumer purchases online goods in online games. Many studies focus on the factors contributing to compulsive buying, either from psychological or marketing viewpoints (Shafeeet al., 2023; Luonget al., 2023; Aquino & Lins, 2023). However, when it comes to the online game context, researchers haven’t really linked in-game purchases to compulsive buying, especially in mobile games. This gap provides researchers with a chance to understand the reasons behind the compulsive in-game buying behavior of online games. Therefore, the objective of this study is to understand compulsive in-game purchases, especially in mobile games. To this end, this study aims to describe the role of flow experience and gaming addiction towards compulsive in-game purchases.

Literature Review

Online Mobile Phone Game

A mobile game is a type of video game commonly played on a mobile phone; this includes online games (Yong Jin, 2016). An online game refers to a video game played to some extent or primarily over the Internet or another accessible computer network (Adams & Rollings, 2006). Online games are found everywhere on today’s gaming devices, such as computers, game consoles, and mobile phones.

Flow Experience

The state of “flow” is experienced by humans when they engage fully with external triggers and focus on a task with complete involvement (Csikszentmihalyi, 1988). Those who attain this mental state are engrossed in pursuing their interests and become oblivious to their surroundings (Csikszentmihalyi & LeFevre, 1989). In fact, this is associated with a shopper’s emotional standpoint when contemplating an impromptu online purchase (Wuet al., 2020).

Online Game Addiction

Online game addiction is described as a type of Internet addiction and refers to an unhealthy psychological reliance on a particular group of technological tools, specifically online games (Xuet al., 2012). The collection of research on online game addiction can be divided into two main groups. One set of studies looks at what leads to or causes online game addiction (Changet al., 2018; Kusset al., 2012), while another set of research investigates the results or outcomes of being addicted to online games (Katja Wiemer-Hastings & Xu, 2005). We extend the literature of the first category by considering the influence of flow experiences on online games by individuals. Based on the study conducted by Kiatsakared and Chen (2022) shows that the increased intensity of flow experience among players of online games correlates with a heightened potential for the development of addiction to online gaming. Therefore, this study proposes hypothesis 1:

  • H1: Flow Experiences have a positive impact on Online Game Addiction.

Becker (1962) states that consumers can exhibit irrational behavior. Addictive consumption is characterized by an uncontrollable desire to make purchases and may involve compulsive buying or excessive spending beyond available payment capacity. In purchasing virtual items in online games, addicted players do not care about the negative consequences of their purchases. Therefore, this study proposes hypothesis 2: H2: Gaming Addiction has a positive impact on compulsive in-game buying.

Compulsive In-Game Purchase

Compulsive buying refers to an addictive shopping behavior where individuals find it challenging to exercise significant moderation in their purchases (O’Guinn & Faber, 1989). According to Ozkaraet al. (2017), the elements of “enjoyment,” “perceived control,” and “merging of action and awareness” exert positive and significant influences on the intention to make online purchases. The state of flow experienced during online mobile games has the potential to contribute to the development of compulsive behavior in individuals. Therefore, this study proposes hypothesis 3:

  • H3: Flow Experiences has a positive impact on Compulsive Online Game Purchase.

Competitive Gaming

Competitive play of commercial games is now a prevalent cultural trend (Nielsen & Karhulahti, 2017). To excel in esports, individuals typically must commit about six hours of deliberate daily practice over several years (Kariet al., 2019). While imbalanced work-life dynamics can harm individuals, there is a key difference between managing substance use and Managing time devoted to a cherished activity or job. Loss of control is seen as the central aspect of addiction (Nielsen & Karhulahti, 2017). Situational Competitiveness increases the probability of achieving Flow by impacting the game’s challenge level and evoking arousal or excitement during gameplay (Sepehr & Head, 2018). The state of online game user competitiveness has shown effect on both flow experience and gaming addiction. Therefore, this study proposes hypothesis 4:

  • H4: Online mobile game user competitiveness moderates the relationship between flow experience and gaming addiction.

Changes in compulsive internet use were primarily associated with changes in online gaming (Van Rooijet al., 2010). The state of online game user competitiveness has shown the effect on both flow experience and gaming addiction. Therefore, this study proposes hypothesis 5 and 6:

  • H5: Online mobile game user competitiveness moderates the relationship between flow experience and compulsive in-game purchase.
  • H6: Online mobile game user competitiveness moderates the relationship between gaming addiction and compulsive in-game purchase.

Methodology

Participants

This cross-sectional study included 258 participants with prior experience in mobile online gaming. The recruitment process involved quota sampling in two Indonesian provinces, namely Kalimantan Selatan and Central Java. The inclusion criteria encompassed individuals of any gender, race, ethnicity, or socioeconomic status who engaged in recreational or competitive mobile online gaming (excluding exclusive Mobile Legends players) and could provide informed consent. Exclusion criteria were applied to those who had never purchased in-game items from online games, resulting in a final sample of 206 participants. This comprised 173 male respondents (84%) and 33 female respondents (16%). Most participants fell within the 17–25 age group, constituting 120 respondents (58.2%). The subsequent age group, 26–30 years old, comprised 42 participants (20.4%), followed by the 31–40 age group with a total of 19 respondents (9.2%). The remaining participants did not fit into these specified age groups.

Measures

Quota sampling is used because not all individuals have an equal opportunity to participate in this study. To determine online game addiction, five items of the questionnaire adopted the questions from the scale developed by Japet al. (2013), and for the five items of the questionnaire to determine flow, we adopted the questionnaire from the scale developed by Choi and Kim (2004), five items for compulsive buying by Eroğlu and Kocatürk (2020). To determine the competitiveness of the respondents, four items of the questionnaire as the measurement criteria developed by Demetrovicset al. (2011) were adopted. This method, commonly known as the Likert scale, provides responses to statements consisting of five categories, from strongly disagree at point 1 to strongly agree at point 5. Most of the participants had an income of < Rp 5,000,000 (136 participants). The measurement was carried out in the context of structural equation modelling (SEM).

Results

In this study, the authors investigated the relationship between flow experience, game addiction, in-game compulsive buying, and the moderating influence of competitiveness of online mobile game users. We used Fornell and Larcker’s (1981) criteria for convergent validity. We looked at (1) how well each item related to its construct, (2) the composite reliability (CR) for each construct, and (3) the average variance extracted (AVE). According to the standard set by Fornell and Larcker (1981), each item should have a load of at least 0.7 for the data to be considered valid. However, our analysis showed that one item related to gaming addiction, one item related to competitive gaming, and one item related to compulsive buying did not meet this criterion. Therefore, we excluded these specific items from the respective scales. The Average Variance Extracted (AVE) value should be at least 0.5, and we confirmed this from the data we collected. From Table I, we determined that the data showed a satisfactory level of convergent validity among the reflective constructs. After that, we evaluated the discriminant validity of the data. We looked at the correlation matrix in Table II along with the square root of the Average Variance Extracted (AVE) value displayed diagonally. The table indicates a consistent pattern where the square roots of the AVEs consistently surpass the correlation values found off the diagonal.

Construct validity and reliability Cronbach’s alpha Composite reliability (rho_a) Composite reliability (rho_c) Average variance extracted (AVE)
Compulsive Buying 0.791 0.800 0.864 0.615
Competitive Gaming 0.646 0.675 0.806 0.582
Flow experience 0.840 0.977 0.874 0.583
Gaming Addiction 0.735 0.741 0.833 0.556
Table I. Construct Reliability and Convergent Validity
Discriminant validity Compulsive Buying Competitive Gaming Flow Experience Gaming Addiction Competitive Gaming × Flow Experience Competitive Gaming × Gaming Addiction
Compulsive Buying
Competitive Gaming 0.134
Flow Experience 0.162 0.618
Gaming Addiction 0.656 0.202 0.247
Competitive Gaming × Flow Experience 0.022 0.158 0.054 0.131
Competitive Gaming × Gaming Addiction 0.204 0.260 0.157 0.065 0.407
Table II. Discriminant Validity

After ensuring sufficient validity and reliability of the data, we tested the proposed research model. The results from Table III show that Flow Experience (beta: 0.212, p < 0.05) had a positive and statistically significant effect on Gaming Addiction, supporting H1. Additionally, H2 was supported as Gaming Addiction had a positive and statistically significant effect on compulsive buying (beta: 0.506, p < 0.001). However, on the other hand, Flow Experience (beta: −0.075, p > 0.05) did not have a statistically significant effect on Compulsive Buying. Therefore, H3 was not supported. The examination of the moderating effect of competitive gaming on H4–H6 was carried out. The results indicate that H5 is supported, indicating a negative and statistically significant moderating impact of competitive gaming on the relationship between Flow Experience and Compulsive Buying (beta: −0.138, p < 0.05). Conversely, H4 did not receive support, indicating the absence of a significant moderating impact of competitive gaming on the relationship between flow experiences and gaming addiction (beta:−0.078, p > 0.05). Similarly, H6 did not find support, indicating no significant moderating impact of competitive gaming on the relationship between gaming addiction and compulsive buying (beta: 0.007, p > 0.05).

Path coefficient Original sample (O) Sample mean (M) Standard deviation (STDEV) T statistics (|O/STDEV|) P values
FE -> CB −0.075 −0.089 0.116 0.648 0.258**
FE -> GA 0.212 0.193 0.128 1.659 0.049*
GA -> CB 0.506 0.512 0.060 8.454 0.000*
CG × GA -> CB 0.007 −0.002 0.063 0.115 0.454**
CG × FE -> CB −0.138 −0.122 0.081 1.694 0.045*
CG × FE -> GA −0.078 −0.065 0.090 0.866 0.193**
Table III. Hypothesis Result and Structural Relationship

Conclusion

This study aimed to explore the connections among Gaming addiction, flow experiences, and online compulsive buying as shown in Fig. 1. In essence, our research provides empirical evidence suggesting a potential relationship between gaming addiction, flow experience, and online compulsive buying. In this discussion, we emphasize the key findings. To begin, in H1, there exists a positive correlation between flow experience and gaming addiction. The results indicate that when an individual immerses themselves in a flow state, it may lead to gaming addiction. This finding is supported by previous research, including a study by Kiatsakared and Chen (2022), revealing that heightened flow experience among online game players correlates with an increased potential for developing an addiction to online gaming. Additionally, in H2, a positive correlation is observed between gaming addiction and compulsive gaming.

Fig. 1. Research model.

The results suggest that higher levels of gaming addiction contribute to increased compulsive buying behavior. Graneroet al.’s study (2016) indicates a strong correlation between compulsive buying behavior and other addictive behaviors, emphasizing a deficit in self-control capacity. Furthermore, our results indicate that gaming addiction mediates the connection between flow experiences and compulsive buying, aligning with prior findings from Niu and Chang (2014). For the third hypothesis or H3, our findings did not support this hypothesis. According to research by Sanjamsai and Phukao (2018), the construct of flow experiences can be categorized into two types: cognitive flow and emotional flow. A study from Sanjamsai and Phukao (2018) also notes that cognitive flow directly influences the recognition of the advantages of playing online games, while emotional flow directly affects the recognition of the mental and physical impact of game playing or game addiction behavior. Our construct for flow experiences aligns with the first category, cognitive flow experiences. Continuing with this topic, the findings for H5. Competitive gaming was observed to have a negative moderating influence on flow experiences and compulsive buying.

A fundamental aspect of game competitiveness lies in the cognitive flow construct of challenge-based skills. This forms the core foundation of game competitiveness. A study by Sepehr and Head (2018) supported the significant impact of competition on experiencing flow and deriving satisfaction from the video gameplay experience. This implies that competition in video games can lead individuals to experience a state of cognitive flow, as mentioned earlier, potentially leading to positive outcomes associated with playing online games. The results of the hypothesis test (H4) show an insignificant P value. Flow experience can be felt by someone when online mobile games can balance the difficulty level with the challenges offered (Chen, 2018). If the level of difficulty offered by a game is too low, players will easily get bored, but if it is too complicated, players will find it difficult to play. In addition, players will be confused if there are too many challenges, and if there are too few, players will easily get bored. In the end, these two things can trigger the flow experience to be felt or not (Baron, 2012; Ramos-Diazet al. 2018; Chen, 2018) and also affect gaming addiction. If it is related to the results of this study, there is a distortion that makes players unable to feel the flow experience presented. This distortion can take the form of the difficulty level offered being too low, the challenges offered being too few, or vice versa. Thus, players of online mobile games have difficulty feeling the flow of experience offered. The hypothesis test (H6) results show an insignificant P value. The connection between competitive gaming and gaming addiction has been described before. In addition, Jinet al. (2017) research shows that user engagement significantly impacts purchase intention. If this is related to the results of this study, there is a distortion that makes players not have enough user engagement to convert it into their intention to purchase. Other than that, competitive gaming also promotes cognitive flow for the players, which encourages the advantages of playing online games (Sanjamsai & Phukao, 2018).

The study results indicate a significant relationship between gaming addiction, gaming experience, and compulsive online purchases. Specifically, the study found a positive correlation between flow experience and game addiction. Additionally, higher levels of gaming addiction are associated with increased compulsive buying behavior. Research has shown a strong correlation between compulsive buying and other addictive behaviors, indicating deficits in self-control. Additionally, competitive gaming was found to have a negative moderating effect on flow experience and compulsive buying. The results shed light on the complex relationship between gaming addiction, flow experiences, and online compulsive buying. These findings can inform future research and interventions aimed at promoting healthy gaming behavior and reducing the risks associated with gaming addiction and compulsive buying. It is recommended to manage time and emotions while playing online games to prevent addiction. This study has some limitations. Firstly, it is important to note that the data was collected from a limited sample, specifically online game players who use mobile phones, and only from two provinces, namely South Kalimantan and Central Java. Therefore, it may be necessary to conduct further research using a more diverse data source to confirm whether the findings can be generalized to both mobile phone and PC users. Additionally, it is worth considering that purchase behavior may vary depending on game characteristics, income, and generation. To further enhance the reliability of the results, it may be worth considering the inclusion of specific game types, income levels, or generations in future research.

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