An Integrated TPB-TAM Framework for Sustained Online Game Consumption: Psychological Drivers and Social Constraints

An Integrated TPB-TAM Framework for Sustained Online Game Consumption: Psychological Drivers and Social Constraints | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article An Integrated TPB-TAM Framework for Sustained Online Game Consumption: Psychological Drivers and Social Constraints Lin SUN This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9343976/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract This study examines the factors shaping online game consumption behavior in the context of the gaming industry’s rapid expansion and associated concerns about impulsive spending. Integrating the theory of planned behavior and the technology acceptance model, the research constructs a comprehensive framework to analyze user decision-making processes. Using survey data and regression analysis, the findings demonstrate that behavioral attitude, subjective norms, and perceived behavioral control are key drivers of sustained consumption, with emotional experience and team relationships being particularly influential. Additionally, social pressures were found to undermine users’ ability to exercise control over their spending. This study contributes to the literature by extending behavioral theories to online game consumption and offers valuable insights for both policymakers and industry practitioners in promoting healthier consumption practices. Online Game Consumption Behavior Theory of Planned Behavior TPB-TAM༛Psychological Drivers and Social Constraints Figures Figure 1 1 Introduction With the continuous advancement of mobile Internet technology, online games have become one of the most significant forms of entertainment in people’s daily lives. Stimulated by the integrated development of social media, the online gaming industry has also experienced rapid growth, evolving into a massive market. However, the immersive design of online games consumes a substantial number of users’ fragmented time, increasing their dependency on games and the frequency of irrational spending. At the same time, the overly rapid development of the gaming market has led to uneven quality in online game content, affecting users’ ideological values. Online game consumption behavior not only pertains to the development of the gaming industry but also involves multiple aspects such as consumer behavior, psychology, and social influence. Therefore, conducting an in-depth study of online game consumption behavior can help us better understand the behavioral characteristics and patterns of consumers within online games, providing theoretical support and practical guidance for the development of the gaming industry. This research aims to explore the influencing factors, motivations, and characteristics of online game consumption behavior, with the hope of offering valuable references and suggestions for research and practice in related fields [ 1 ][ 2 ][ 3 ] . An analysis of existing research findings reveals that domestic studies on this topic date back to the 1990s. The relevant research outcomes can be divided into four parts: characteristics of online game consumption behavior, consumption motivation, influencing factors of consumption behavior, and consequences of consumption behavior. (1) Online Game Consumption Behavior Most scholars define online game consumption behavior as the actions of users spending money during their participation in online games by purchasing the game itself, virtual items, game services, and so on. However, with advancements in game technology and operational methods, online game consumption behavior now encompasses not only transactions involving real currency but also virtual currency transactions, such as the purchase and use of in-game gold coins, diamonds, and other virtual currencies. Additionally, some online games offer subscription systems or paid items, allowing users to access more game content or privileges by paying. The characteristics of online game consumption behavior lie in its virtuality and digitalization; consumers conduct transactions over the internet to obtain services and items in a virtual world, which primarily satisfies psychological needs. Representative consumption behaviors mainly include purchasing game items, recharging game accounts, and trading virtual game currencies. (2) Online Game Consumption Motivation Based on the types of consumption needs, motivations can be categorized into four types: social needs, leisure needs, subconscious release, and self-actualization. Users exhibit different types and levels of needs at different stages, showing a parabolic pattern that peaks during the “immersion” stage; the longer they spend in the game, the more pronounced these needs become. When exploring online game consumption motivations, the technology acceptance model is often employed to analyze adolescents’ online behavior, investigating the composition of behavioral motivation from the perspective of perceived usefulness [ 4 ] . Simultaneously, some scholars examine online game users’ consumption attitudes and behavioral intentions from the perspectives of product attributes and consumption experience, concluding that product attributes have a greater impact on users’ consumption intentions than consumption experience. Moreover, users’ consumption attitudes negatively affect consumption intentions; that is, prolonged immersion in a game is not truly due to a fondness for the game itself but is a compelled behavior to obtain a certain experience [ 5 ] . This indirectly confirms the “addictive” nature of online game consumption behavior; many interviewed users have revealed that they have no affection for a particular game but continue to use it due to certain special tendencies or reasons [ 6 ] . (3) Influencing Factors of Online Game Consumption Behavior Based on the analysis of consumption motivation, it can be found that users’ personal characteristics, game characteristics, social characteristics, game perception, and game experience are the main factors influencing online game consumption. Some scholars hold a positive view of online consumption behavior, arguing that online games are an inevitable aspect of individual and societal life, and it is crucial to adapt to and accept them. Online games are not exclusive to adolescents; they are considered a beneficial means of entertainment and relaxation and even a way of career positioning or employment [ 7 ] . Furthermore, from the perspective of game companies’ profitability, game consumption is the main avenue supporting the healthy development of the gaming industry [ 8 ] . Existing studies have also found that online game addiction, game loyalty, and users’ consumption willingness are closely related; online game consumption enhances users’ game loyalty, where loyalty acts as a mediating factor between game addiction and purchase intention, exerting a strong indirect effect. Therefore, some scholars believe that users perceived behavioral control, external environmental stimuli, performance expectations, and consumption habits are also primary influencing factors of online game consumption behavior [ 9 ][ 10 ] . (4) Consequences of Online Game Consumption Behavior Currently, online games and social networks are undergoing deep integration, with online games gradually occupying a larger proportion of users’ entertainment time. Particularly in the online game consumption behavior of minors, due to their immature psychological development, phenomena such as game addiction and impulsive consumption occur frequently [ 11 ][ 12 ] . Moreover, users’ excessive reliance on online games may lead to estrangement from family and friends in real life, causing issues like social isolation and social barriers, potentially resulting in psychological health problems such as anxiety, depression, and addiction. In severe cases, it can affect users’ academic performance, career, family life, and societal well-being [ 13 ][ 14 ][ 15 ] . Existing research outcomes typically address online game consumption behavior constraints through psychological, social, economic, familial, and legal interventions. Analyzing existing research findings, it becomes evident that studying online game consumption behavior is closely related to users’ psychological health and social development. However, while constructing models of influencing factors through consumption experience and psychology can explore the relationships among the individual, the game, and society, they cannot explain the dynamics of intervention factors. Moreover, current online game consumption demands and behaviors are becoming increasingly diversified, and users’ consumption psychology has undergone significant changes. Especially under the influence of factors such as users’ internal control and external interventions, online consumption behavior is gradually becoming more rational. Given that the theory of reasoned action and the theory of planned behavior have been widely applied in social and consumer psychology research, this study employs these theories to delineate the pathways of online game consumption behavior, exploring the impact relationships of internal and external factors on online game consumption behavior. 2 Theoretical Framework and Hypotheses Development With the advancement of technology, both the form of online games and the characteristics of users have undergone significant changes. Users’ attitudes toward participating in games and their awareness of consumption within games have shifted dramatically, with some even considering it as a career path. Therefore, when studying online game consumption behavior, it is essential to revisit and restructure the relevant consumption content and theoretical foundations. From the user’s perspective, online game consumption aims to acquire in-game virtual items, services, or other game-related content to enhance the gaming experience or gain in-game advantages. From the operators’ standpoint, online game consumption serves to strengthen users’ identification with the game, thereby increasing customer loyalty. Moreover, users’ consumption behavior relies on payment systems provided by gaming platforms or third-party payment platforms. Therefore, when analyzing online consumption behavior, it’s necessary to consider the acceptance level of information technology. This study will analyze the composition of online game consumption behavior through social cognitive theory, user experience theory, and consumer behavior theory. 2.1 Theoretical Foundation (1) Social Cognitive Theory Social cognitive theory emphasizes the impact of individual characteristics, social interactions, and information transmission on individual cognition [ 16 ][ 17 ] . Firstly, when choosing online games, users consider factors such as personal interests, gaming skills, gaming experience, and gaming objectives. However, under the subconscious influence of different cultural backgrounds, users’ attitudes toward games, preferences for game content, and consumption habits may vary, thus affecting their cognition of online game types and content. Secondly, social environmental factors also influence online game consumption. For example, recommendations from friends, family, or other users on social networks, as well as industry regulations, can affect users’ decisions. Particularly under varying policy contexts, inconsistencies in age restriction conditions require continuous attention and regulation from game operators and supervisory bodies. Lastly, channels through which users obtain game information—such as official game sources and review websites—impact their understanding and perception of games, thereby influencing their cognition. (2) User Experience Theory User experience theory highlights users’ perceptions, emotions, attitudes, and behaviors when using a product or service. Firstly, online games require users to invest a certain amount of time and maintain continuous attention and engagement [ 18 ] . Therefore, ensuring a positive user experience in interface design, operational smoothness, and graphic quality is essential. Secondly, emotional experience factors represented by game narratives, character designs, and musical scores affect users’ satisfaction and loyalty [ 19 ] . Lastly, in online games, the challenge level and timely feedback can enhance users’ gaming experience and sense of fulfillment [ 20 ] . Thus, reward mechanisms, incentive systems, and payment models in games can elevate users’ consumption awareness. (3) Consumer Behavior Theory Consumer behavior theory refers to the behavioral patterns and psychological processes exhibited by consumers when purchasing goods or services. Similarly, this theory applies to the realm of online games [ 21 ][ 22 ][ 23 ] . Firstly, when users purchase virtual items or game services, they undergo a series of cognitive processes, including need recognition, information search, evaluation and comparison, and decision-making. These processes are influenced by game content, social impacts, personal preferences, and other factors, thereby affecting the users’ final consumption behavior. Secondly, users may experience positive or negative emotions due to social relationships within the game, gaming experiences, and competitive pressures, which in turn influence their consumption behavior [ 24 ][ 25 ][ 26 ][ 27 ] . Finally, factors such as the design of the gaming platform, promotional activities, and behaviors of other users can also impact users’ consumption behaviors. In summary, the online game consumption process is a complex integration of cognition, deliberation, and action. In the cognition stage, users develop consumption intentions influenced by game content, social factors, and personal preferences. During the deliberation stage, users consider economic, technological, and constraint factors to decide whether to consume. In the action stage, users assess their satisfaction with the experience, which affects their ongoing engagement. Understanding these influencing factors is crucial for game developers and operators, as it helps them better grasp the patterns of users’ consumption behaviors, enabling them to devise more effective marketing strategies and game designs. For users, this understanding can assist them in making more rational consumption decisions, avoiding impulsive behaviors driven by emotions or environmental factors. 2.2 Integration of Theoretical Foundations The Theory of Planned Behavior posits that an individual's behavioral intention is jointly determined by three core factors: behavioral attitude, subjective norms, and perceived behavioral control, which in turn influence actual behavior. This theory has demonstrated robust explanatory power in predicting various intentional behaviors. The Technology Acceptance Model emphasizes that users’ willingness to adopt and use a technology depends primarily on its perceived usefulness and perceived ease of use. Online game consumption possesses the dual attributes of being both a “planned behavior” and “the use of an interactive technology.” On the one hand, consumption decisions are influenced by personal attitudes toward spending, social pressure, and self-control capabilities. On the other hand, as a technological product, the convenience and perceived value of the consumption process in games are crucial. Therefore, integrating TPB and TAM allows for capturing both the psychological and social drivers of consumption behavior and incorporating how the characteristics of the technological platform shape the consumption process. This integration provides a more comprehensive lens through which to understand online game consumption. Beyond regular consumption, online game consumption behavior is subject to mandatory constraints due to its addictive nature, including industry regulations and technological limitations. Therefore, building upon theoretical behavioral analysis and integrating the theory of planned behavior with the technology acceptance model, this study categorizes the influencing factors of online game consumption behavior into five components: cognition, deliberation, intention, action, and sustained action, as illustrated in Fig. 1 . 2.3 Definition of Research Constructs Based on the integrated framework, this study involves the following core constructs: (1) Behavioral Attitude: This refers to an individual's overall positive or negative evaluation of conducting consumption behavior within online games. In this study, it consists of two dimensions: Game Experience Satisfaction: The user's level of satisfaction with the technical and design aspects of the game, such as its interface, operational smoothness, and graphic quality. Emotional Experience Satisfaction: The emotional fulfillment and pleasure derived by the user from game narratives, character design, music, team-based social interactions, and similar elements. (2) Subjective Norms: This refers to the social pressure perceived by an individual when deciding whether to engage in game consumption—specifically, the extent to which significant others or groups believe he/she should perform the behavior. It includes: Individual Characteristics: The influence of intrinsic factors such as cultural background, personal interests, gaming skills, and experience on game choice. Social Relationships: Social pressure originating from friends, family, game teammates, etc. Information Channels: The influence of external information sources such as official information, review websites, and social media. (3) Perceived Behavioral Control: This refers to the perceived ease or difficulty of executing game consumption behavior, reflecting an individual's perception of control over factors that facilitate or hinder consumption. It encompasses: Perceived Value: The benefits a user believes consumption can bring, such as personal capability enhancement, social influence, and team contribution, along with the perceived usefulness and perceived ease of use of the consumption process itself. External Control: Perceived external constraints stemming from platform rules, cultural norms, policies and regulations, family restrictions, and security concerns. Internal Control: Perceived internal limitations related to one's own economic conditions, technical capabilities, and understanding of the platform's operational model. (4) Behavioral Intention: This refers to the subjective probability and strength of an individual's plan to consume within online games. (5) Actual Behavior and Sustained Behavior: Actual Behavior: The game consumption behavior (e.g., purchasing items, recharging) actually performed by an individual within a specific period. Sustained Behavior: The behavioral pattern of repeated and stable engagement in game consumption over time, which is the outcome variable of primary interest in this study. 2.4 Research Hypotheses Based on the theoretical framework outlined above, we propose the following research hypotheses: H1: Behavioral attitude has a significant positive effect on sustained game consumption behavior. H2: Subjective norms have a significant positive effect on sustained game consumption behavior. H3: Perceived behavioral control has a significant positive effect on sustained game consumption behavior. H4: Behavioral intention plays a mediating role between actual behavior and sustained behavior. H5: Behavioral attitude indirectly influences sustained consumption behavior by positively affecting perceived behavioral control and behavioral intention. H6: Subjective norms may have a negative effect on perceived behavioral control, meaning social pressure may weaken the user's sense of control. H7: Within behavioral attitude, satisfaction with emotional experience (particularly team relationships) has a stronger influence on consumption intention than satisfaction with game experience. 3 Materials and Methods As the online gaming industry has developed, game types and operational methods have been continuously changing and innovating. From the initial time-based fee models to the current mix of free-to-play games with in-game purchases of items and equipment, various consumption methods coexist. The consumption behavior of users in online games can be divided into four stages according to its developmental trajectory, as shown in Table 1 . Table 1 Methods of Online Game Consumption Time Game Type Consumption Patterns 1980–1990 Text-Based Multiplayer Online Games Graphical Multiplayer Online Games Charging for game services (hourly rates or subscription packages) 1991–2000 Large-Scale Online Games Massively Multiplayer Online Role-Playing Games Purchasing the game client (one-time purchase) Renting/Purchasing game equipment terminals 2001–2010 Large-Scale Online Games Social Games Mobile Games Advertising/User-free game models Purchasing in-game items Virtual currency transactions 2011–Present E-Sports Games Virtual Reality Games Casual Games Motion-Sensing Games Subscription systems Game peripheral products Advertising revenue Interactive promotions (likes, shares) Therefore, online game consumption behavior refers to the payment actions users undertake during their participation in online games to acquire virtual items, services, or other game-related content within the game. These payments are predominantly made using real currency but can also include other forms such as virtual currencies, reward points, or vouchers. Such consumption behaviors encompass purchasing game clients, virtual items, in-game services, peripheral products, viewing advertisements, subscriptions, and more. 3.1 Questionnaire Design Online game consumption behavior is closely tied to the operational strategies of gaming platforms. From the perspective of consumption methods, interactive behaviors such as liking, commenting, sharing, leaving messages, and posting belong to the actual action phase. Payment behaviors like purchasing equipment and in-game items are part of the sustained action phase. This behavior is influenced by a multitude of factors, including personal attributes, environmental conditions, technology, and institutional policies, all of which interact in complex ways. The specific classifications are as follows: (1) Behavioral Attitude: The attitude toward online game consumption behavior is composed of two main factors: gaming experience and emotional factors [ 28 ][ 29 ] . Users’ perception of gaming experience is determined by their satisfaction with the games or platform’s interface design, operational smoothness, and graphic quality. Emotional experience is influenced by elements such as the game’s storyline, characters, music, and the user’s loyalty to the game [ 30 ][ 31 ][ 32 ] . (2) Subjective Norms: Users’ subjective norms regarding games are shaped by three factors: individual characteristics, social relationships, and information channels [ 33 ][ 34 ][ 35 ] . Individual characteristics include cultural background, personal interests, skill level, gaming experience, and gaming objectives. Social relationships are affected by social pressures from friends, family, and other users on social networks. Information channels consist of official game sources, review websites, social media platforms, and other outlets. (3) Perceived Behavioral Control: Consumption in online games is a form of virtual spending, and during the consumption control process, it is influenced by perceived value, external control, and internal control [ 36 ] . Perceived value encompasses performance expectancy, social expectancy, organizational expectancy, perceived usefulness, and perceived ease of use. External control includes platform constraints, cultural norms, regulatory policies, family restrictions, and security concerns. Internal control involves factors related to economics, technology, and platform operational methods [ 37 ][ 38 ] . The questionnaire consisted of four parts: (1) basic demographic information and gaming behavior; (2) the core scales based on the integrated TPB-TAM framework; (3) behavioral intention and consumption habits; (4) open-ended questions (optional). The core scales all employed a 5-point Likert scale, ranging from "1 = strongly disagree" to "5 = strongly agree." The measurement items for all constructs were adapted from established mature scales in the literature, tailored to the specific context of online game consumption. The measurement dimensions for the core constructs are outlined as follows: Behavioral Attitude: Adapted from Ajzen's measurement suggestions for TPB and Davis's TAM scales, it consisted of two sub-dimensions: game experience satisfaction and emotional experience satisfaction. Subjective Norms: Drawing on references from TPB-related research, it encompassed the influence of individual characteristics, social relationship pressure, and information channels. Perceived Behavioral Control: Integrating perceived control from TPB and perceived usefulness/ease of use from TAM, it included perceived value, perceived external control, and perceived internal control. Behavioral Intention: This directly measured users' future consumption willingness. Actual and Sustained Behavior: This was measured through self-reports of consumption frequency, amount spent, and consumption stability over the past six months. To examine the relationships among the factors influencing online game consumption behavior, this study employed a social survey method to collect basic user data. Building upon the theoretical foundations and the analysis of influencing factors, we designed a questionnaire using a five-point Likert scale. The questionnaire covers personal demographic information, game consumption behavior, game preferences, and other related factors. To ensure the authenticity and validity of the data, a preliminary survey was conducted through social media promotions, gaming communities, and offline venues. A total of 30 questionnaires were collected during this pilot phase, which led to the inclusion of additional questions related to behavioral intentions and game consumption behavior. The final questionnaire is presented in Appendix 1. 3.2 Participants and Recruitment This study adopted a cross-sectional survey design, collecting data through an anonymous online questionnaire. The questionnaire was distributed and promoted via mainstream Chinese social media platforms, popular online gaming community forums, and game-related social media groups. Participants were required to meet all of the following criteria: Be 14 years of age or older. Have played at least one online game in the past six months. Have engaged in consumption behavior (including topping up accounts, purchasing virtual items, subscribing to services, etc.) in at least one online game within the past six months. Voluntarily participate and complete all questionnaire items. Questionnaires meeting any of the following conditions were considered invalid and excluded: The completion time was too short (less than 50% of the estimated reasonable reading time). Responses to all scale items exhibited obvious patterns (e.g., selecting the same option for every item). Key information (e.g., age, amount spent) was missing or clearly illogical. To ensure the diversity and representativeness of the sample, we implemented an incentive mechanism during the questionnaire distribution phase to encourage participants to complete the survey. Additionally, we enhanced monitoring and management of the data collection process to ensure data completeness and accuracy. Over a two-month period, a total of 511 valid responses were collected. 3.3 Ethical Approval and Informed Consent This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. Ethical approval was obtained from the Institutional Review Board (IRB) of Dalian Neusoft University of Information prior to data collection. All participants provided informed consent before participating. For participants under 18 years of age, informed consent was obtained from their parents or legal guardians, and assent was obtained from the minors. The consent form clearly stated the purpose of the study, the voluntary nature of participation, the confidentiality of responses, and the right to withdraw at any time. The questionnaire was anonymous, and no personally identifiable information was collected. 3.4 Data Examination and Processing In this survey sample, the proportion of female users slightly exceeds that of male users. The distributions of age, average monthly income, and average monthly expenditure approximately conform to a normal distribution. Additionally, students account for 53.03% of the total respondents, which closely aligns with the characteristics of the online game consumer demographic. The specific distributions are shown in Table 2 . Table 2 Distribution of Basic Sample Characteristics Respondent Attributes No. % Gender Male 240 46.97% Female 271 53.03% Age Under 14 years old 47 9.20% 14 ~ 18 131 25.64% 19 ~ 25 156 56.16% 26 ~ 30 90 17.61% 31 ~ 35 55 10.76% Over 35 32 6.26% Occupation Student 271 53.03% Professional User 32 6.26% Management Personnel 21 4.11% Technical Staff 33 6.46% Sales Personnel 41 8.02% Service Personnel 25 4.89% Freelancer 32 6.26% Agricultural Worker 12 2.35% Production Worker 15 2.94% Others 29 5.68% Average Monthly Income Below 1,500 21 4.11% 1,500 ~ 3,000 148 28.96% 3,001 ~ 5,000 168 32.88% 5,001 ~ 7,000 125 24.46% Above 7,000 49 9.59% Average Monthly Expenditure Below 500 35 6.85% 500 ~ 1,000 168 32.88% 1,001 ~ 2,000 188 36.79% 2,001 ~ 5000 71 13.89% Above 5,000 49 9.59% In this survey sample, the proportion of female users slightly exceeds that of male users. The distributions of age, average monthly income, and average monthly expenditure approximately conform to a normal distribution. Additionally, students account for 53.03% of the total respondents, which aligns well with the characteristics of the online game consumer demographic. The specific distributions are shown in Table 3 . Table 3 Statistics of Sample Game Experience Game Experience Category No. % Years of Gaming Within 1 year 108 21.14% 1 ~ 2 years 91 17.81% 2 ~ 3 years 126 24.66% 4 years and above 186 36.4% Game Type Massively Multiplayer Online Role-Playing Games (MMORPGs) 143 27.98% Multiplayer Online Battle Arena (MOBA) Games 188 36.79% First-Person Shooter (FPS) Games 147 28.77% Side-Scrolling Action Games 82 16.05% Card Games 141 27.59% Simulation Management Games 85 16.53% Puzzle Adventure Games 62 12.13% Average Online Duration Within 1 hour 72 14.09% 1 ~ 2 hours 171 33.46% 2 ~ 3 hours 144 28.18% 3 ~ 4 hours 84 16.44% Over 5 hours 40 7.83% Login Frequency Occasionally 92 18.00% Weekly 111 21.72% Every 3 days 134 26.22% Every 2 days 84 16.44% Daily 90 17.61% Amount Spent Below 100 yuan 293 57.34% 100 ~ 500 yuan 123 24.07% 501 ~ 1,000 yuan 59 11.55% 1,001 ~ 2,000 yuan 16 3.13% Above 2000 yuan 20 3.91% Consumption Frequency Once every six months 21 4.11% Once a month 93 18.20% Once a week 103 20.16% Once every three days 82 16.05% Once or more per day 212 41.49% Subscription and Sharing Frequency Occasionally 26 5.09% Once every two weeks 103 20.16% Once a week 165 32.29% Once every three days 112 21.92% Once or more per day 105 20.55% 4 Results 4.1Reliability and Validity Testing of the Sample Cronbach’s α coefficient was used to assess the internal consistency of the scales, while the KMO and Bartlett’s tests were employed to examine structural validity. The test results are presented in Table 4 . The data analysis indicates that, except for Behavioral Attitude and Actual Action, the alpha coefficients of the other scales exceed 0.7, suggesting a high level of internal consistency within the sample data. Additionally, in the structural validity tests, except for Behavioral Attitude and Subjective Norms, which fall within the 0.6–0.7 range, the KMO values of the other scales are above 0.7. This implies that principal component analysis can be utilized for latent variable transformation. Table 4 Reliability and Validity Tests of Each Scale Variable KMO Bartlett α Attitude Toward Behavior .699 0.000 .616 Subjective Norms .601 0.000 .723 Perceived Behavioral Control .700 0.000 .718 Behavioral Intention .756 0.000 .771 Actual Behavior .701 0.000 .623 Sustained Behavior .896 0.000 .853 During the validation phase, we utilized AMOS software to map the relationships among the influencing factors of online game consumption behavior. Linear regression was employed to test the path relationships between observed variables. The model fit index value of the sample data is 0.527, and the chi-square to degrees of freedom ratio is 1.9, indicating that the model has a certain degree of explanatory power. 4.2 Model Testing Regression analysis revealed that sustained consumption behavior in this sample is primarily influenced by actual actions. Additionally, users’ attitude toward behavior and subjective norms positively enhance the frequency of their sustained behaviors. Users’ perceived behavioral control is mainly affected by attitude toward behavior, while subjective norms negatively impact perceived behavioral control. In terms of behavioral intention, subjective norms have a negative but minimal effect. However, subjective norms positively influence users’ actual actions. The specific coefficients are shown in Table 5 . Table 5 Model Test Coefficients Independent Variable Dependent Variable Coefficient R 2 P Attitude Toward Behavior Sustained Behavior .131 .354 .000 Actual Behavior .246 .000 Subjective Norms .198 .733 Attitude Toward Behavior Perceived Behavioral Control .183 .173 .000 Subjective Norms − .001 .000 Perceived Behavioral Control Behavioral Intention .263 .304 .000 Subjective Norms − .073 .000 Attitude Toward Behavior .358 .000 Attitude Toward Behavior Actual Behavior .220 .542 .000 Subjective Norms .236 .000 Behavioral Intention .419 .070 5 Discussion To specifically illustrate the relationships among the influencing factors, we validated the variables of behavioral attitude, perceived behavioral control, and subjective norms. (1) Explanation of Behavioral Attitude In this survey sample, behavioral attitude is primarily composed of emotional experience satisfaction, while game experience satisfaction is comparatively lower. The specific influence coefficients are shown in Table 6 . Within emotional experience satisfaction, team relationships have the most significant impact on users’ emotional satisfaction, followed by character design and game storyline. Satisfaction levels related to game loyalty and music have relatively lower impacts. Table 6 Regression Coefficients of Attitude Toward Behavior Measurement Variable Coefficient Latent Variable Coefficient Game/Platform Interface Design .083 Game Experience Satisfaction .219 Operational Smoothness .101 Graphics Quality .196 Game Storyline . 110 Emotional Experience Satisfaction .379 Character Design .116 Music Design .090 Team Relationships . 298 Loyalty .062 (2) Explanation of Subjective Norms Users’ subjective norms are mainly influenced by individual characteristics, with cultural background, proficiency level, and gaming experience showing significant impacts, as illustrated in Table 7 . While personal interests and gaming objectives have some influence, their effects are relatively minor. In terms of social relationships, team pressure and peer pressure are the primary factors; although family pressure is positive, its impact is minimal. Lastly, regarding information channels, official sources and social media platforms have a greater influence, whereas review websites and other platforms have less impact. Table 7 Regression Coefficients of Subjective Norms Observed Variable Coefficient Latent Variable Coefficient Cultural Background .183 Individual Characteristics .583 Personal Interests .101 Proficiency Level .179 Gaming Experience .169 Gaming Objectives .105 Peer Pressure .100 Social Relationships .200 Family Pressure .002 Team Pressure .205 Official Channels .217 Information Channels .092 Review Websites .090 Social Media Platforms .262 Other Platforms .105 (3) Explanation of Perceived Behavioral Control Users perceived behavioral control is primarily influenced by perceived value, with significant impacts from perceived usefulness, performance expectancy, and perceived ease of use. Secondly, internal control affects users perceived behavioral control, mainly influenced by the platform’s operational methods and economic factors. Lastly, among external control factors, family constraints have a notable impact, while platform constraints, cultural constraints, and security constraints have less significant effects. The specific path coefficients are shown in Table 8 . Table 8 Path Coefficients of Perceived Behavioral Control Observed Variable Coefficient Latent Variable Coefficient Platform Operational Strategies .342 Internal Control .242 Economy .137 Technology .095 Performance Expectancy .107 Perceived Value .307 Social Expectancy .014 Organizational Expectancy .092 Perceived Usefulness .169 Perceived Ease of Use .104 Platform Constraints .054 External Control .151 Cultural Constraints .094 Policy Constraints .110 Family Constraints .419 Security Constraints .020 Combining the above path analysis and explanations, we can observe that once users engage in online game consumption behavior, they tend to continue consuming. The initiation of consumption actions is mainly influenced by behavioral intention, which is primarily determined by behavioral attitude. Within users’ attitude toward behavior, satisfaction with the game’s emotional experience has a significant impact, especially the factor of team relationships. Moreover, the survey sample lacks perceived behavioral control regarding cultural constraints, policy constraints, and security constraints. Additionally, users’ subjective norms can directly and negatively affect behavioral intention and perceived behavioral control, indicating that individual characteristics, social relationships, and information channels can reduce users perceived behavioral control. 6 Conclusion This study investigated the drivers of online game consumption behavior by integrating the theory of planned behavior (TPB) and the technology acceptance model (TAM). Through a social survey and regression analysis, we identified a behavioral pathway encompassing cognition, deliberation, intention, and sustained action. The results reveal that once users begin spending in online games, they are likely to continue. Furthermore, users demonstrated limited awareness of cultural, institutional, and security-related constraints, while their social networks, personal traits, and information sources collectively impeded the development of rational consumption habits. Despite these insights, this research has several limitations. The study relied primarily on a TPB-informed framework, and the measurement scales were relatively narrow in scope. The sample size was modest and covered a limited range of game genres, which may affect the generalizability of the findings. Thus, this work represents an initial exploration into the factors shaping in-game purchase behavior, and further studies—employing longitudinal designs, broader samples, and more varied game contexts—are needed to deepen our understanding. Declarations Data Availability Statement ： The data that support the findings of this study are available from the corresponding author upon reasonable request. Author Contributions Statement: Funding Statement: No funding was received for this study. Competing interests ： The author(s) declare no competing interests. Ethical statements ： This study was conducted in accordance with relevant ethical guidelines for research involving human participants. All participants were informed of the study’s purpose and provided explicit consent prior to participation through a statement displayed at the beginning of the questionnaire. The research did not involve the collection of personally identifiable information, and all data were anonymized and analyzed in aggregate form. No ethical approval was required for this type of survey-based study, as confirmed by the relevant institutional guidelines. Clinical trial number: not applicable. References Zhou Jingfan. Research on Juveniles' Large-Amount Game Recharge Behavior [J]. Legal Syst Econ, 2019(4):3. Xuan X, Shiwei S. Re-exploration of the Burden of Proof for the Validity of Juveniles' Online Tipping [J]. J China Three Gorges Univ (Humanities Social Sciences). 2022;44(02):95–100. Wang S. From Gamification of Society to Societal Gamification: The Advent of a Gamified Society in the Internet Age [J]. Explor Contention, 2019(10): 148–56. Liu Yang Y, Xuecheng. Analysis of the Consumption Behavior of Chinese Online Game Players and Its Influencing Factors [J]. J Graduate School Chin Acad Social Sci, 2010(03): 84–9. Dai Junjie Z, Yuxiang M, Jianjun. 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Dilemmas and Solutions to Refunds for Online Game Recharges by Minors [J]. J Shandong Adm Inst, 2021(06): 21–30. Li Jinyue. Research on Revenue Recognition Issues in Online Game Companies [J]. Sci Technol Economic Market, 2021(04): 42–3. Liu Yana G, Yingtong. An Empirical Study on Adolescents' Addiction to Online Games and Its Triggering of Crime, as well as Corresponding Response Mechanisms [J]. J Shandong Univ (Philosophy Social Sci Edition), 2020, (03): 9–21. Qu Guohua L, Xue Q, Weihua, et al. Evolutionary Game Analysis of Government and Online Game Firms' Development Strategies with Public Participation [J]. Chin J Manage Sci. 2020;28(04):207–19. Zhang Rui X. Yugui. Anti-Addiction Systems and Online Game Operations [J]. Sci Technol Inform (Academic Research), 2007, (27): 520–1. Liu Jiantao. How Should We View Online Games Correctly?—An Interview with Professor Shen Qiyun from Beijing Normal University [J]. Publishing Res, 2006, (02): 13–5. Shen, Mingwei. Kong Qingfeng. The Mechanism of Online Game Policies [J]. Journal of Shandong Normal University (Humanities and Social Sciences Edition), 2005, (06): 79–82. Wu Yuehua Z, Xinyue L. The Impact of College Students' Online Game Behavior on Academic Misconduct [J]. Libr Forum. 2021;41(05):89–98. Wu Yuehua. Research on the Mechanism of the Impact of Online Games on Adolescents' Morality [J]. J Shanghai Jiao Tong Univ (Philosophy Social Sci Edition). 2020;28(04):71–84. Chen Jieshou. Analysis of User Experience in Online Games in the Audience-Centered Era [J]. Popular Literature Art, 2022, (01): 188–90. Tian Yuqi. Research on Interactive Design of Online Game Live Broadcasting Platforms Based on User Experience [J]. Digit Technol Application. 2021;39(06):132–4. Wang Chengyu. Research on Game Design Strategies from the Perspective of User Experience [J]. Cult Horizons, 2012, (05): 161. Huang Chao. Analysis of Consumer Behavior of Online Game Players—Taking Mobile Games as an Example [J]. Journalism Knowl, 2020, (03): 80–3. Huang Yexin H, Zenghui HY. Analysis of College Students' Online Game Consumption Behavior from the Perspective of Game Developers—Taking Shanghai Jianqiao University as an Example [J]. Value Eng. 2020;39(01):53–4. Zhang Dingkun. Analysis of Consumer Behavior in Online Games [J]. Mark Circ, 2019, (32): 200–1. Yang Jianxia C. Self-Control, Addiction, and Vertical Differentiated Competition [J]. J Manage Sci China. 2018;21(07):11–34. Yin Xuehan. A Brief Analysis of Online Game Consumption Behavior [J]. Knowledge Economy. 2017, (04): 62 + 64. Liu Xiaoshi. Research on Irrational Consumption Behavior of Online Game Players [J]. J Heze Univ. 2014;36(03):130–3. Wang Danyang S. Liqun. Analysis of the Development Trend of the Mobile Game Market Based on Consumer Behavior Analysis [J]. Mod Mark (Academic Edition), 2011, (04): 101. Jiang Lei S, Jiacheng X, Shaolong. Analysis of the Inducing Mechanism and Coping Strategies of Online Game Consumption from the Marketing Perspective [J]. China Bus Rev, 2022, (18): 79–82. Meng Jia'an. Research on the Impact of the Internet Economy on the Consumption Behavior of Student Groups—Based on the Perspective of High School Students [J]. Mod Bus, 2020, (08): 11–2. Meng Yujie. Investigation and Analysis of Game Consumption Among College Students—Taking a University in Hefei as an Example [J]. Mod Communication, 2019, (22): 147–8. Zhao Rongxiu. Thoughts on Online Game Consumption Behavior [J]. Mark Circ, 2019, (24): 107–8. Wen Lu Z, Haowen Z. Analysis of the Influencing Factors of College Students' Online Game Consumption Behavior—Based on Empirical Research in Nanjing Universities [J]. New Media Res. 2018;4(17):5–8. Pan Liming. Research on the Online Consumption Behavior of College Students from the Perspective of Symbols [J]. J Chongqing Univ Sci Technol (Social Sci Edition), 2014, (06): 74–6. Lv, Yi. Liang Shuang. Characteristics of Virtual Consumption and Regulatory Considerations—Based on Network Analysis [J]. Bus Cult (Academic Edition), 2010, (06): 152–4. Qu Lisong. The Relationship Between Types of Online Consumption and Personality Traits of College Students [J]. Psychol Technol Application, 2015, (12): 32–7. Zhang Chunhua W, Lu. An Empirical Study on Online Game Consumption Behavior and Its Influencing Factors—Based on the Differential Analysis of Gender and Education of College Students [J]. Jiangsu Social Sci, 2018, (06): 50–8. Hao Lujie L, Qinghua, Jiang Qingquan. Research on the Influence Path of Avatar Identification on the Consumption of Virtual Products in Online Games [J]. J Beijing Univ Posts Telecommunications (Social Sci Edition). 2018;20(05):1–7. Peng Y, Chun H. Inductive Analysis of Influencing Factors of Online Game Consumption Behavior [J]. J Beijing Univ Posts Telecommunications (Social Sci Edition), 2007, (06): 5–9. Guo Guoqing W, Yuxi YH. Analysis of User Engagement in the Freemium Business Model—An Empirical Study Based on Online Games [J]. Manage Rev. 2019;31(07):199–209. Dong Jianrong L, Xiaoping, Tang Liping. Research on Product Attributes and Consumption Behavior Based on Online Games—Taking College Students' Game Addiction as an Example [J]. Journal of South-Central University for Nationalities (Humanities and Social Sciences Edition); 2007. pp. 83–5. S1. Additional Declarations No competing interests reported. Supplementary Files Appendix1.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 30 Apr, 2026 Editor invited by journal 10 Apr, 2026 Editor assigned by journal 09 Apr, 2026 Submission checks completed at journal 09 Apr, 2026 First submitted to journal 07 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9343976","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":633514740,"identity":"66c6562f-0b70-4826-8d51-f3b4699ceb5f","order_by":0,"name":"Lin SUN","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYHACxgMJDDYQZgWIOECEHqCWNAjrTAKxWhgYDpOgRX5G7oEDD3ecz+OXSH/44OAPBjm+GwmMnwvwaDG4kZdwIPHM7WLJGTnGBkAnGkveSGCWnoFPi0SOwYHEttuJG27ksEl/SGAAMhLYmHnwOgys5Vzi/hvpzySAttQT1MJwA6zlQOIGiQQzkJYEA0JaDM68AWlJTpxx5g3QL2kShjPPPGyWxuuw9hzDhz/b7BL724EhdsDGRp7vePLBz3gdhgYkgJixgQQNo2AUjIJRMAqwAQAvBFSjuKJYigAAAABJRU5ErkJggg==","orcid":"","institution":"Liaoning Normal University","correspondingAuthor":true,"prefix":"","firstName":"Lin","middleName":"","lastName":"SUN","suffix":""}],"badges":[],"createdAt":"2026-04-07 11:08:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9343976/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9343976/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108813020,"identity":"8b012b7a-bea0-490b-861f-f12753789119","added_by":"auto","created_at":"2026-05-08 16:13:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":56362,"visible":true,"origin":"","legend":"\u003cp\u003eOnline Game Consumption Behavior Path Based on the Theory of Planned Behavior\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9343976/v1/1fdd13ef707a990a00e7742c.png"},{"id":108976835,"identity":"c3a3611a-6043-495f-b740-b4a915647eeb","added_by":"auto","created_at":"2026-05-11 11:29:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":549938,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9343976/v1/f4e7a897-de76-4d0b-86a3-31775980ef1a.pdf"},{"id":108812994,"identity":"017e12fd-34ee-4f1a-a619-2462b2dd84ba","added_by":"auto","created_at":"2026-05-08 16:13:20","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18235,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9343976/v1/6d56547b1bc6f832223d4b2c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"An Integrated TPB-TAM Framework for Sustained Online Game Consumption: Psychological Drivers and Social Constraints","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eWith the continuous advancement of mobile Internet technology, online games have become one of the most significant forms of entertainment in people\u0026rsquo;s daily lives. Stimulated by the integrated development of social media, the online gaming industry has also experienced rapid growth, evolving into a massive market. However, the immersive design of online games consumes a substantial number of users\u0026rsquo; fragmented time, increasing their dependency on games and the frequency of irrational spending. At the same time, the overly rapid development of the gaming market has led to uneven quality in online game content, affecting users\u0026rsquo; ideological values. Online game consumption behavior not only pertains to the development of the gaming industry but also involves multiple aspects such as consumer behavior, psychology, and social influence. Therefore, conducting an in-depth study of online game consumption behavior can help us better understand the behavioral characteristics and patterns of consumers within online games, providing theoretical support and practical guidance for the development of the gaming industry. This research aims to explore the influencing factors, motivations, and characteristics of online game consumption behavior, with the hope of offering valuable references and suggestions for research and practice in related fields \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e][\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e][\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAn analysis of existing research findings reveals that domestic studies on this topic date back to the 1990s. The relevant research outcomes can be divided into four parts: characteristics of online game consumption behavior, consumption motivation, influencing factors of consumption behavior, and consequences of consumption behavior.\u003c/p\u003e \u003cp\u003e \u003cb\u003e(1) Online Game Consumption Behavior\u003c/b\u003e \u003c/p\u003e \u003cp\u003eMost scholars define online game consumption behavior as the actions of users spending money during their participation in online games by purchasing the game itself, virtual items, game services, and so on. However, with advancements in game technology and operational methods, online game consumption behavior now encompasses not only transactions involving real currency but also virtual currency transactions, such as the purchase and use of in-game gold coins, diamonds, and other virtual currencies. Additionally, some online games offer subscription systems or paid items, allowing users to access more game content or privileges by paying. The characteristics of online game consumption behavior lie in its virtuality and digitalization; consumers conduct transactions over the internet to obtain services and items in a virtual world, which primarily satisfies psychological needs. Representative consumption behaviors mainly include purchasing game items, recharging game accounts, and trading virtual game currencies.\u003c/p\u003e \u003cp\u003e \u003cb\u003e(2) Online Game Consumption Motivation\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBased on the types of consumption needs, motivations can be categorized into four types: social needs, leisure needs, subconscious release, and self-actualization. Users exhibit different types and levels of needs at different stages, showing a parabolic pattern that peaks during the \u0026ldquo;immersion\u0026rdquo; stage; the longer they spend in the game, the more pronounced these needs become. When exploring online game consumption motivations, the technology acceptance model is often employed to analyze adolescents\u0026rsquo; online behavior, investigating the composition of behavioral motivation from the perspective of perceived usefulness \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Simultaneously, some scholars examine online game users\u0026rsquo; consumption attitudes and behavioral intentions from the perspectives of product attributes and consumption experience, concluding that product attributes have a greater impact on users\u0026rsquo; consumption intentions than consumption experience. Moreover, users\u0026rsquo; consumption attitudes negatively affect consumption intentions; that is, prolonged immersion in a game is not truly due to a fondness for the game itself but is a compelled behavior to obtain a certain experience \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. This indirectly confirms the \u0026ldquo;addictive\u0026rdquo; nature of online game consumption behavior; many interviewed users have revealed that they have no affection for a particular game but continue to use it due to certain special tendencies or reasons \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003e(3) Influencing Factors of Online Game Consumption Behavior\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBased on the analysis of consumption motivation, it can be found that users\u0026rsquo; personal characteristics, game characteristics, social characteristics, game perception, and game experience are the main factors influencing online game consumption. Some scholars hold a positive view of online consumption behavior, arguing that online games are an inevitable aspect of individual and societal life, and it is crucial to adapt to and accept them. Online games are not exclusive to adolescents; they are considered a beneficial means of entertainment and relaxation and even a way of career positioning or employment \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Furthermore, from the perspective of game companies\u0026rsquo; profitability, game consumption is the main avenue supporting the healthy development of the gaming industry \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Existing studies have also found that online game addiction, game loyalty, and users\u0026rsquo; consumption willingness are closely related; online game consumption enhances users\u0026rsquo; game loyalty, where loyalty acts as a mediating factor between game addiction and purchase intention, exerting a strong indirect effect. Therefore, some scholars believe that users perceived behavioral control, external environmental stimuli, performance expectations, and consumption habits are also primary influencing factors of online game consumption behavior \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e9\u003c/span\u003e][\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003e(4) Consequences of Online Game Consumption Behavior\u003c/b\u003e \u003c/p\u003e \u003cp\u003eCurrently, online games and social networks are undergoing deep integration, with online games gradually occupying a larger proportion of users\u0026rsquo; entertainment time. Particularly in the online game consumption behavior of minors, due to their immature psychological development, phenomena such as game addiction and impulsive consumption occur frequently \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e11\u003c/span\u003e][\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Moreover, users\u0026rsquo; excessive reliance on online games may lead to estrangement from family and friends in real life, causing issues like social isolation and social barriers, potentially resulting in psychological health problems such as anxiety, depression, and addiction. In severe cases, it can affect users\u0026rsquo; academic performance, career, family life, and societal well-being \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e13\u003c/span\u003e][\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e14\u003c/span\u003e][\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Existing research outcomes typically address online game consumption behavior constraints through psychological, social, economic, familial, and legal interventions.\u003c/p\u003e \u003cp\u003eAnalyzing existing research findings, it becomes evident that studying online game consumption behavior is closely related to users\u0026rsquo; psychological health and social development. However, while constructing models of influencing factors through consumption experience and psychology can explore the relationships among the individual, the game, and society, they cannot explain the dynamics of intervention factors. Moreover, current online game consumption demands and behaviors are becoming increasingly diversified, and users\u0026rsquo; consumption psychology has undergone significant changes. Especially under the influence of factors such as users\u0026rsquo; internal control and external interventions, online consumption behavior is gradually becoming more rational. Given that the theory of reasoned action and the theory of planned behavior have been widely applied in social and consumer psychology research, this study employs these theories to delineate the pathways of online game consumption behavior, exploring the impact relationships of internal and external factors on online game consumption behavior.\u003c/p\u003e"},{"header":"2 Theoretical Framework and Hypotheses Development","content":"\u003cp\u003eWith the advancement of technology, both the form of online games and the characteristics of users have undergone significant changes. Users\u0026rsquo; attitudes toward participating in games and their awareness of consumption within games have shifted dramatically, with some even considering it as a career path. Therefore, when studying online game consumption behavior, it is essential to revisit and restructure the relevant consumption content and theoretical foundations.\u003c/p\u003e \u003cp\u003eFrom the user\u0026rsquo;s perspective, online game consumption aims to acquire in-game virtual items, services, or other game-related content to enhance the gaming experience or gain in-game advantages. From the operators\u0026rsquo; standpoint, online game consumption serves to strengthen users\u0026rsquo; identification with the game, thereby increasing customer loyalty. Moreover, users\u0026rsquo; consumption behavior relies on payment systems provided by gaming platforms or third-party payment platforms. Therefore, when analyzing online consumption behavior, it\u0026rsquo;s necessary to consider the acceptance level of information technology. This study will analyze the composition of online game consumption behavior through social cognitive theory, user experience theory, and consumer behavior theory.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Theoretical Foundation\u003c/h2\u003e \u003cp\u003e \u003cb\u003e(1) Social Cognitive Theory\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSocial cognitive theory emphasizes the impact of individual characteristics, social interactions, and information transmission on individual cognition \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e16\u003c/span\u003e][\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Firstly, when choosing online games, users consider factors such as personal interests, gaming skills, gaming experience, and gaming objectives. However, under the subconscious influence of different cultural backgrounds, users\u0026rsquo; attitudes toward games, preferences for game content, and consumption habits may vary, thus affecting their cognition of online game types and content. Secondly, social environmental factors also influence online game consumption. For example, recommendations from friends, family, or other users on social networks, as well as industry regulations, can affect users\u0026rsquo; decisions. Particularly under varying policy contexts, inconsistencies in age restriction conditions require continuous attention and regulation from game operators and supervisory bodies. Lastly, channels through which users obtain game information\u0026mdash;such as official game sources and review websites\u0026mdash;impact their understanding and perception of games, thereby influencing their cognition.\u003c/p\u003e \u003cp\u003e \u003cb\u003e(2) User Experience Theory\u003c/b\u003e \u003c/p\u003e \u003cp\u003eUser experience theory highlights users\u0026rsquo; perceptions, emotions, attitudes, and behaviors when using a product or service. Firstly, online games require users to invest a certain amount of time and maintain continuous attention and engagement \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Therefore, ensuring a positive user experience in interface design, operational smoothness, and graphic quality is essential. Secondly, emotional experience factors represented by game narratives, character designs, and musical scores affect users\u0026rsquo; satisfaction and loyalty \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Lastly, in online games, the challenge level and timely feedback can enhance users\u0026rsquo; gaming experience and sense of fulfillment \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Thus, reward mechanisms, incentive systems, and payment models in games can elevate users\u0026rsquo; consumption awareness.\u003c/p\u003e \u003cp\u003e \u003cb\u003e(3) Consumer Behavior Theory\u003c/b\u003e \u003c/p\u003e \u003cp\u003eConsumer behavior theory refers to the behavioral patterns and psychological processes exhibited by consumers when purchasing goods or services. Similarly, this theory applies to the realm of online games \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e21\u003c/span\u003e][\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e22\u003c/span\u003e][\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Firstly, when users purchase virtual items or game services, they undergo a series of cognitive processes, including need recognition, information search, evaluation and comparison, and decision-making. These processes are influenced by game content, social impacts, personal preferences, and other factors, thereby affecting the users\u0026rsquo; final consumption behavior. Secondly, users may experience positive or negative emotions due to social relationships within the game, gaming experiences, and competitive pressures, which in turn influence their consumption behavior \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e24\u003c/span\u003e][\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e25\u003c/span\u003e][\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e26\u003c/span\u003e][\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Finally, factors such as the design of the gaming platform, promotional activities, and behaviors of other users can also impact users\u0026rsquo; consumption behaviors.\u003c/p\u003e \u003cp\u003eIn summary, the online game consumption process is a complex integration of cognition, deliberation, and action. In the cognition stage, users develop consumption intentions influenced by game content, social factors, and personal preferences. During the deliberation stage, users consider economic, technological, and constraint factors to decide whether to consume. In the action stage, users assess their satisfaction with the experience, which affects their ongoing engagement. Understanding these influencing factors is crucial for game developers and operators, as it helps them better grasp the patterns of users\u0026rsquo; consumption behaviors, enabling them to devise more effective marketing strategies and game designs. For users, this understanding can assist them in making more rational consumption decisions, avoiding impulsive behaviors driven by emotions or environmental factors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Integration of Theoretical Foundations\u003c/h2\u003e \u003cp\u003eThe Theory of Planned Behavior posits that an individual's behavioral intention is jointly determined by three core factors: behavioral attitude, subjective norms, and perceived behavioral control, which in turn influence actual behavior. This theory has demonstrated robust explanatory power in predicting various intentional behaviors. The Technology Acceptance Model emphasizes that users\u0026rsquo; willingness to adopt and use a technology depends primarily on its perceived usefulness and perceived ease of use.\u003c/p\u003e \u003cp\u003eOnline game consumption possesses the dual attributes of being both a \u0026ldquo;planned behavior\u0026rdquo; and \u0026ldquo;the use of an interactive technology.\u0026rdquo; On the one hand, consumption decisions are influenced by personal attitudes toward spending, social pressure, and self-control capabilities. On the other hand, as a technological product, the convenience and perceived value of the consumption process in games are crucial. Therefore, integrating TPB and TAM allows for capturing both the psychological and social drivers of consumption behavior and incorporating how the characteristics of the technological platform shape the consumption process. This integration provides a more comprehensive lens through which to understand online game consumption.\u003c/p\u003e \u003cp\u003eBeyond regular consumption, online game consumption behavior is subject to mandatory constraints due to its addictive nature, including industry regulations and technological limitations. Therefore, building upon theoretical behavioral analysis and integrating the theory of planned behavior with the technology acceptance model, this study categorizes the influencing factors of online game consumption behavior into five components: cognition, deliberation, intention, action, and sustained action, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Definition of Research Constructs\u003c/h2\u003e \u003cp\u003eBased on the integrated framework, this study involves the following core constructs:\u003c/p\u003e \u003cp\u003e(1) Behavioral Attitude: This refers to an individual's overall positive or negative evaluation of conducting consumption behavior within online games. In this study, it consists of two dimensions:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eGame Experience Satisfaction: The user's level of satisfaction with the technical and design aspects of the game, such as its interface, operational smoothness, and graphic quality.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eEmotional Experience Satisfaction: The emotional fulfillment and pleasure derived by the user from game narratives, character design, music, team-based social interactions, and similar elements.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e(2) Subjective Norms: This refers to the social pressure perceived by an individual when deciding whether to engage in game consumption\u0026mdash;specifically, the extent to which significant others or groups believe he/she should perform the behavior. It includes:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eIndividual Characteristics: The influence of intrinsic factors such as cultural background, personal interests, gaming skills, and experience on game choice.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSocial Relationships: Social pressure originating from friends, family, game teammates, etc.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eInformation Channels: The influence of external information sources such as official information, review websites, and social media.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e(3) Perceived Behavioral Control: This refers to the perceived ease or difficulty of executing game consumption behavior, reflecting an individual's perception of control over factors that facilitate or hinder consumption. It encompasses:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003ePerceived Value: The benefits a user believes consumption can bring, such as personal capability enhancement, social influence, and team contribution, along with the perceived usefulness and perceived ease of use of the consumption process itself.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eExternal Control: Perceived external constraints stemming from platform rules, cultural norms, policies and regulations, family restrictions, and security concerns.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eInternal Control: Perceived internal limitations related to one's own economic conditions, technical capabilities, and understanding of the platform's operational model.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e(4) Behavioral Intention: This refers to the subjective probability and strength of an individual's plan to consume within online games.\u003c/p\u003e \u003cp\u003e(5) Actual Behavior and Sustained Behavior:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eActual Behavior: The game consumption behavior (e.g., purchasing items, recharging) actually performed by an individual within a specific period.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSustained Behavior: The behavioral pattern of repeated and stable engagement in game consumption over time, which is the outcome variable of primary interest in this study.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Research Hypotheses\u003c/h2\u003e \u003cp\u003eBased on the theoretical framework outlined above, we propose the following research hypotheses:\u003c/p\u003e \u003cp\u003eH1: Behavioral attitude has a significant positive effect on sustained game consumption behavior.\u003c/p\u003e \u003cp\u003eH2: Subjective norms have a significant positive effect on sustained game consumption behavior.\u003c/p\u003e \u003cp\u003eH3: Perceived behavioral control has a significant positive effect on sustained game consumption behavior.\u003c/p\u003e \u003cp\u003eH4: Behavioral intention plays a mediating role between actual behavior and sustained behavior.\u003c/p\u003e \u003cp\u003eH5: Behavioral attitude indirectly influences sustained consumption behavior by positively affecting perceived behavioral control and behavioral intention.\u003c/p\u003e \u003cp\u003eH6: Subjective norms may have a negative effect on perceived behavioral control, meaning social pressure may weaken the user's sense of control.\u003c/p\u003e \u003cp\u003eH7: Within behavioral attitude, satisfaction with emotional experience (particularly team relationships) has a stronger influence on consumption intention than satisfaction with game experience.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Materials and Methods","content":"\u003cp\u003eAs the online gaming industry has developed, game types and operational methods have been continuously changing and innovating. From the initial time-based fee models to the current mix of free-to-play games with in-game purchases of items and equipment, various consumption methods coexist. The consumption behavior of users in online games can be divided into four stages according to its developmental trajectory, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMethods of Online Game Consumption\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGame Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConsumption Patterns\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1980\u0026ndash;1990\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eText-Based Multiplayer Online Games\u003c/p\u003e \u003cp\u003eGraphical Multiplayer Online Games\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCharging for game services (hourly rates or subscription packages)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1991\u0026ndash;2000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLarge-Scale Online Games\u003c/p\u003e \u003cp\u003eMassively Multiplayer Online Role-Playing Games\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePurchasing the game client (one-time purchase)\u003c/p\u003e \u003cp\u003eRenting/Purchasing game equipment terminals\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2001\u0026ndash;2010\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLarge-Scale Online Games\u003c/p\u003e \u003cp\u003eSocial Games\u003c/p\u003e \u003cp\u003eMobile Games\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdvertising/User-free game models\u003c/p\u003e \u003cp\u003ePurchasing in-game items\u003c/p\u003e \u003cp\u003eVirtual currency transactions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2011\u0026ndash;Present\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE-Sports Games\u003c/p\u003e \u003cp\u003eVirtual Reality Games\u003c/p\u003e \u003cp\u003eCasual Games\u003c/p\u003e \u003cp\u003eMotion-Sensing Games\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSubscription systems\u003c/p\u003e \u003cp\u003eGame peripheral products\u003c/p\u003e \u003cp\u003eAdvertising revenue\u003c/p\u003e \u003cp\u003eInteractive promotions (likes, shares)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTherefore, online game consumption behavior refers to the payment actions users undertake during their participation in online games to acquire virtual items, services, or other game-related content within the game. These payments are predominantly made using real currency but can also include other forms such as virtual currencies, reward points, or vouchers. Such consumption behaviors encompass purchasing game clients, virtual items, in-game services, peripheral products, viewing advertisements, subscriptions, and more.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Questionnaire Design\u003c/h2\u003e \u003cp\u003eOnline game consumption behavior is closely tied to the operational strategies of gaming platforms. From the perspective of consumption methods, interactive behaviors such as liking, commenting, sharing, leaving messages, and posting belong to the actual action phase. Payment behaviors like purchasing equipment and in-game items are part of the sustained action phase. This behavior is influenced by a multitude of factors, including personal attributes, environmental conditions, technology, and institutional policies, all of which interact in complex ways. The specific classifications are as follows:\u003c/p\u003e \u003cp\u003e(1) Behavioral Attitude: The attitude toward online game consumption behavior is composed of two main factors: gaming experience and emotional factors \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e28\u003c/span\u003e][\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Users\u0026rsquo; perception of gaming experience is determined by their satisfaction with the games or platform\u0026rsquo;s interface design, operational smoothness, and graphic quality. Emotional experience is influenced by elements such as the game\u0026rsquo;s storyline, characters, music, and the user\u0026rsquo;s loyalty to the game \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e30\u003c/span\u003e][\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e31\u003c/span\u003e][\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e(2) Subjective Norms: Users\u0026rsquo; subjective norms regarding games are shaped by three factors: individual characteristics, social relationships, and information channels \u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e33\u003c/span\u003e][\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e34\u003c/span\u003e][\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. Individual characteristics include cultural background, personal interests, skill level, gaming experience, and gaming objectives. Social relationships are affected by social pressures from friends, family, and other users on social networks. Information channels consist of official game sources, review websites, social media platforms, and other outlets.\u003c/p\u003e \u003cp\u003e(3) Perceived Behavioral Control: Consumption in online games is a form of virtual spending, and during the consumption control process, it is influenced by perceived value, external control, and internal control \u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. Perceived value encompasses performance expectancy, social expectancy, organizational expectancy, perceived usefulness, and perceived ease of use. External control includes platform constraints, cultural norms, regulatory policies, family restrictions, and security concerns. Internal control involves factors related to economics, technology, and platform operational methods \u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e37\u003c/span\u003e][\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe questionnaire consisted of four parts: (1) basic demographic information and gaming behavior; (2) the core scales based on the integrated TPB-TAM framework; (3) behavioral intention and consumption habits; (4) open-ended questions (optional). The core scales all employed a 5-point Likert scale, ranging from \"1\u0026thinsp;=\u0026thinsp;strongly disagree\" to \"5\u0026thinsp;=\u0026thinsp;strongly agree.\"\u003c/p\u003e \u003cp\u003eThe measurement items for all constructs were adapted from established mature scales in the literature, tailored to the specific context of online game consumption. The measurement dimensions for the core constructs are outlined as follows:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eBehavioral Attitude: Adapted from Ajzen's measurement suggestions for TPB and Davis's TAM scales, it consisted of two sub-dimensions: game experience satisfaction and emotional experience satisfaction.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSubjective Norms: Drawing on references from TPB-related research, it encompassed the influence of individual characteristics, social relationship pressure, and information channels.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePerceived Behavioral Control: Integrating perceived control from TPB and perceived usefulness/ease of use from TAM, it included perceived value, perceived external control, and perceived internal control.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eBehavioral Intention: This directly measured users' future consumption willingness.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eActual and Sustained Behavior: This was measured through self-reports of consumption frequency, amount spent, and consumption stability over the past six months.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eTo examine the relationships among the factors influencing online game consumption behavior, this study employed a social survey method to collect basic user data. Building upon the theoretical foundations and the analysis of influencing factors, we designed a questionnaire using a five-point Likert scale. The questionnaire covers personal demographic information, game consumption behavior, game preferences, and other related factors. To ensure the authenticity and validity of the data, a preliminary survey was conducted through social media promotions, gaming communities, and offline venues. A total of 30 questionnaires were collected during this pilot phase, which led to the inclusion of additional questions related to behavioral intentions and game consumption behavior. The final questionnaire is presented in Appendix 1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Participants and Recruitment\u003c/h2\u003e \u003cp\u003eThis study adopted a cross-sectional survey design, collecting data through an anonymous online questionnaire. The questionnaire was distributed and promoted via mainstream Chinese social media platforms, popular online gaming community forums, and game-related social media groups. Participants were required to meet all of the following criteria:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eBe 14 years of age or older.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHave played at least one online game in the past six months.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHave engaged in consumption behavior (including topping up accounts, purchasing virtual items, subscribing to services, etc.) in at least one online game within the past six months.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eVoluntarily participate and complete all questionnaire items.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eQuestionnaires meeting any of the following conditions were considered invalid and excluded:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eThe completion time was too short (less than 50% of the estimated reasonable reading time).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eResponses to all scale items exhibited obvious patterns (e.g., selecting the same option for every item).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eKey information (e.g., age, amount spent) was missing or clearly illogical.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eTo ensure the diversity and representativeness of the sample, we implemented an incentive mechanism during the questionnaire distribution phase to encourage participants to complete the survey. Additionally, we enhanced monitoring and management of the data collection process to ensure data completeness and accuracy. Over a two-month period, a total of 511 valid responses were collected.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003e3.3 Ethical Approval and Informed Consent\u003c/strong\u003e\u003c/p\u003e \u003c/p\u003e \u003cp\u003e This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. Ethical approval was obtained from the Institutional Review Board (IRB) of Dalian Neusoft University of Information prior to data collection.\u003c/p\u003e \u003cp\u003e All participants provided informed consent before participating. For participants under 18 years of age, informed consent was obtained from their parents or legal guardians, and assent was obtained from the minors. The consent form clearly stated the purpose of the study, the voluntary nature of participation, the confidentiality of responses, and the right to withdraw at any time. The questionnaire was anonymous, and no personally identifiable information was collected.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Data Examination and Processing\u003c/h2\u003e \u003cp\u003eIn this survey sample, the proportion of female users slightly exceeds that of male users. The distributions of age, average monthly income, and average monthly expenditure approximately conform to a normal distribution. Additionally, students account for 53.03% of the total respondents, which closely aligns with the characteristics of the online game consumer demographic. The specific distributions are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of Basic Sample Characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eRespondent Attributes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46.97%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53.03%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnder 14 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.20%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u0026thinsp;~\u0026thinsp;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.64%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u0026thinsp;~\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56.16%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u0026thinsp;~\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.61%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u0026thinsp;~\u0026thinsp;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.76%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOver 35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.26%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003eOccupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53.03%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProfessional User\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.26%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eManagement Personnel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.11%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTechnical Staff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.46%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSales Personnel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.02%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eService Personnel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.89%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFreelancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.26%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgricultural Worker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.35%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProduction Worker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.94%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.68%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eAverage Monthly Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBelow 1,500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.11%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,500\u0026thinsp;~\u0026thinsp;3,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.96%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,001\u0026thinsp;~\u0026thinsp;5,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.88%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,001\u0026thinsp;~\u0026thinsp;7,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.46%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbove 7,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.59%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eAverage Monthly Expenditure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBelow 500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.85%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e500\u0026thinsp;~\u0026thinsp;1,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.88%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,001\u0026thinsp;~\u0026thinsp;2,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.79%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,001\u0026thinsp;~\u0026thinsp;5000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.89%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbove 5,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.59%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn this survey sample, the proportion of female users slightly exceeds that of male users. The distributions of age, average monthly income, and average monthly expenditure approximately conform to a normal distribution. Additionally, students account for 53.03% of the total respondents, which aligns well with the characteristics of the online game consumer demographic. The specific distributions are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistics of Sample Game Experience\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGame Experience\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eYears of Gaming\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWithin 1 year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.14%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;~\u0026thinsp;2 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.81%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u0026thinsp;~\u0026thinsp;3 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.66%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 years and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eGame Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMassively Multiplayer Online Role-Playing Games (MMORPGs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.98%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMultiplayer Online Battle Arena (MOBA) Games\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.79%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFirst-Person Shooter (FPS) Games\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.77%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSide-Scrolling Action Games\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.05%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCard Games\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.59%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSimulation Management Games\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.53%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePuzzle Adventure Games\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.13%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eAverage Online Duration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWithin 1 hour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.09%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;~\u0026thinsp;2 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.46%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u0026thinsp;~\u0026thinsp;3 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.18%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u0026thinsp;~\u0026thinsp;4 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.44%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOver 5 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.83%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eLogin Frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOccasionally\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.00%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeekly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.72%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEvery 3 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.22%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEvery 2 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.44%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDaily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.61%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eAmount Spent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBelow 100 yuan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57.34%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u0026thinsp;~\u0026thinsp;500 yuan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.07%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e501\u0026thinsp;~\u0026thinsp;1,000 yuan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.55%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,001\u0026thinsp;~\u0026thinsp;2,000 yuan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.13%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbove 2000 yuan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.91%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eConsumption Frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOnce every six months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.11%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOnce a month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.20%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOnce a week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.16%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOnce every three days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.05%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOnce or more per day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41.49%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eSubscription and Sharing Frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOccasionally\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.09%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOnce every two weeks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.16%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOnce a week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.29%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOnce every three days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.92%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOnce or more per day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.55%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.1Reliability and Validity Testing of the Sample\u003c/h2\u003e \u003cp\u003eCronbach\u0026rsquo;s α coefficient was used to assess the internal consistency of the scales, while the KMO and Bartlett\u0026rsquo;s tests were employed to examine structural validity. The test results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The data analysis indicates that, except for Behavioral Attitude and Actual Action, the alpha coefficients of the other scales exceed 0.7, suggesting a high level of internal consistency within the sample data. Additionally, in the structural validity tests, except for Behavioral Attitude and Subjective Norms, which fall within the 0.6\u0026ndash;0.7 range, the KMO values of the other scales are above 0.7. This implies that principal component analysis can be utilized for latent variable transformation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eReliability and Validity Tests of Each Scale\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKMO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBartlett\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eα\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttitude Toward Behavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.616\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubjective Norms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.723\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Behavioral Control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.718\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBehavioral Intention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.771\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActual Behavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.623\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSustained Behavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.853\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDuring the validation phase, we utilized AMOS software to map the relationships among the influencing factors of online game consumption behavior. Linear regression was employed to test the path relationships between observed variables. The model fit index value of the sample data is 0.527, and the chi-square to degrees of freedom ratio is 1.9, indicating that the model has a certain degree of explanatory power.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Model Testing\u003c/h2\u003e \u003cp\u003eRegression analysis revealed that sustained consumption behavior in this sample is primarily influenced by actual actions. Additionally, users\u0026rsquo; attitude toward behavior and subjective norms positively enhance the frequency of their sustained behaviors. Users\u0026rsquo; perceived behavioral control is mainly affected by attitude toward behavior, while subjective norms negatively impact perceived behavioral control. In terms of behavioral intention, subjective norms have a negative but minimal effect. However, subjective norms positively influence users\u0026rsquo; actual actions. The specific coefficients are shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel Test Coefficients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndependent Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDependent Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttitude Toward Behavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSustained Behavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e.354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActual Behavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubjective Norms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.733\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttitude Toward Behavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePerceived Behavioral Control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubjective Norms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Behavioral Control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eBehavioral Intention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e.304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubjective Norms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttitude Toward Behavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttitude Toward Behavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eActual Behavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e.542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubjective Norms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBehavioral Intention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.070\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5 Discussion","content":"\u003cp\u003eTo specifically illustrate the relationships among the influencing factors, we validated the variables of behavioral attitude, perceived behavioral control, and subjective norms.\u003c/p\u003e \u003cp\u003e \u003cb\u003e(1) Explanation of Behavioral Attitude\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn this survey sample, behavioral attitude is primarily composed of emotional experience satisfaction, while game experience satisfaction is comparatively lower. The specific influence coefficients are shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Within emotional experience satisfaction, team relationships have the most significant impact on users\u0026rsquo; emotional satisfaction, followed by character design and game storyline. Satisfaction levels related to game loyalty and music have relatively lower impacts.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression Coefficients of Attitude Toward Behavior\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasurement Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLatent Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGame/Platform Interface Design\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eGame Experience Satisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e.219\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOperational Smoothness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGraphics Quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.196\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGame Storyline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e. 110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eEmotional Experience Satisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e.379\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacter Design\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.116\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMusic Design\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.090\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTeam Relationships\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e. 298\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLoyalty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u003cb\u003e(2) Explanation of Subjective Norms\u003c/b\u003e\u003c/p\u003e\u003cp\u003eUsers\u0026rsquo; subjective norms are mainly influenced by individual characteristics, with cultural background, proficiency level, and gaming experience showing significant impacts, as illustrated in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. While personal interests and gaming objectives have some influence, their effects are relatively minor. In terms of social relationships, team pressure and peer pressure are the primary factors; although family pressure is positive, its impact is minimal. Lastly, regarding information channels, official sources and social media platforms have a greater influence, whereas review websites and other platforms have less impact.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression Coefficients of Subjective Norms\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObserved Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLatent Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCultural Background\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eIndividual Characteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e.583\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePersonal Interests\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProficiency Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.179\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGaming Experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGaming Objectives\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeer Pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSocial Relationships\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e.200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily Pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTeam Pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.205\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOfficial Channels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eInformation Channels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e.092\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReview Websites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.090\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial Media Platforms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.262\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Platforms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \n\u003cp\u003e\u003cb\u003e(3) Explanation of Perceived Behavioral Control\u003c/b\u003e\u003c/p\u003e \n\u003cp\u003eUsers perceived behavioral control is primarily influenced by perceived value, with significant impacts from perceived usefulness, performance expectancy, and perceived ease of use. Secondly, internal control affects users perceived behavioral control, mainly influenced by the platform\u0026rsquo;s operational methods and economic factors. Lastly, among external control factors, family constraints have a notable impact, while platform constraints, cultural constraints, and security constraints have less significant effects. The specific path coefficients are shown in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePath Coefficients of Perceived Behavioral Control\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObserved Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLatent Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatform Operational Strategies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eInternal Control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e.242\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEconomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.137\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechnology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.095\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerformance Expectancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003ePerceived Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e.307\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial Expectancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrganizational Expectancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.092\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Usefulness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Ease of Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatform Constraints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eExternal Control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e.151\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCultural Constraints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.094\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolicy Constraints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily Constraints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.419\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecurity Constraints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCombining the above path analysis and explanations, we can observe that once users engage in online game consumption behavior, they tend to continue consuming. The initiation of consumption actions is mainly influenced by behavioral intention, which is primarily determined by behavioral attitude. Within users\u0026rsquo; attitude toward behavior, satisfaction with the game\u0026rsquo;s emotional experience has a significant impact, especially the factor of team relationships.\u003c/p\u003e \u003cp\u003eMoreover, the survey sample lacks perceived behavioral control regarding cultural constraints, policy constraints, and security constraints. Additionally, users\u0026rsquo; subjective norms can directly and negatively affect behavioral intention and perceived behavioral control, indicating that individual characteristics, social relationships, and information channels can reduce users perceived behavioral control.\u003c/p\u003e"},{"header":"6 Conclusion","content":"\u003cp\u003eThis study investigated the drivers of online game consumption behavior by integrating the theory of planned behavior (TPB) and the technology acceptance model (TAM). Through a social survey and regression analysis, we identified a behavioral pathway encompassing cognition, deliberation, intention, and sustained action. The results reveal that once users begin spending in online games, they are likely to continue. Furthermore, users demonstrated limited awareness of cultural, institutional, and security-related constraints, while their social networks, personal traits, and information sources collectively impeded the development of rational consumption habits.\u003c/p\u003e \u003cp\u003eDespite these insights, this research has several limitations. The study relied primarily on a TPB-informed framework, and the measurement scales were relatively narrow in scope. The sample size was modest and covered a limited range of game genres, which may affect the generalizability of the findings. Thus, this work represents an initial exploration into the factors shaping in-game purchase behavior, and further studies\u0026mdash;employing longitudinal designs, broader samples, and more varied game contexts\u0026mdash;are needed to deepen our understanding.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions Statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical statements\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with relevant ethical guidelines for research involving human participants. All participants were informed of the study\u0026rsquo;s purpose and provided explicit consent prior to participation through a statement displayed at the beginning of the questionnaire. The research did not involve the collection of personally identifiable information, and all data were anonymized and analyzed in aggregate form. No ethical approval was required for this type of survey-based study, as confirmed by the relevant institutional guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003enot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZhou Jingfan. Research on Juveniles' Large-Amount Game Recharge Behavior [J]. Legal Syst Econ, 2019(4):3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXuan X, Shiwei S. Re-exploration of the Burden of Proof for the Validity of Juveniles' Online Tipping [J]. 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S1.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"discpsy","sideBox":"Learn more about [Discover Psychology](https://www.springer.com/44202)","snPcode":"","submissionUrl":"","title":"Discover Psychology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Online Game Consumption Behavior, Theory of Planned Behavior, TPB-TAM༛Psychological Drivers and Social Constraints","lastPublishedDoi":"10.21203/rs.3.rs-9343976/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9343976/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines the factors shaping online game consumption behavior in the context of the gaming industry\u0026rsquo;s rapid expansion and associated concerns about impulsive spending. Integrating the theory of planned behavior and the technology acceptance model, the research constructs a comprehensive framework to analyze user decision-making processes. Using survey data and regression analysis, the findings demonstrate that behavioral attitude, subjective norms, and perceived behavioral control are key drivers of sustained consumption, with emotional experience and team relationships being particularly influential. Additionally, social pressures were found to undermine users\u0026rsquo; ability to exercise control over their spending. 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