Frontiers in Business, Economics and Management ISSN: 2766-824X | Vol. 17, No. 3, 2024 469 Factors Affecting the Purchasing Behaviors of Virtual Commodities for Otome Game Players: Case of Light and Night Xiangfei Wang a, Nutteera Phakdeephirot b, * Rattanakosin International College of Creative Entrepreneurship, Rajamangala University of Technology Rattanakosin, Nakhon Pathom, 73170, Thailand a wang.xiangfei@outlook.rmutr.ac.th, b nutteera.pha@rmutr.ac.th Abstract: The objectives of this research were (1) To examine the impact of game story, character set, dubbing music, and art style on player satisfaction in the otome game “Light and Night”. (2) To examine the impact of the game story, character set, dubbing music, and art style on player flow experience. (3) To examine the relationship between player satisfaction and the flow experience, and their impact on in-game purchase intentions. Major Findings/Results: (1) In terms of the impact of game story, character set, dubbing music, and art style on player satisfaction, the results of the research show that all of these elements significantly increase player satisfaction with the game, thus indicating a direct correlation between game design elements and overall player satisfaction. (2) In terms of the impact of game story, character set, dubbing music, and ar t style on players' immersive experience, it was found that, in particular, the quality of character setting and dubbing music can significantly improve players' immersive experience and enhance their emotional and flow experience of the game. (3) Regarding the relationship between player satisfaction and flow experience and its impact on purchase intentions, the research shows that higher satisfaction and immersive experiences significantly increase the likelihood of players purchasing virtual goods, highlighting a positive feedback loop between these variables and purchase behavior. Keywords: Otome game, Consumer purchase behavior, SOR theory, Flow experience, Satisfaction, Market strategies. 1. Introduction With the rapid development of the mobile Internet and the popularization of smartphones, the Internet continues to penetrate all aspects of people's lives, work, entertainment, and so on, changing the traditional mode of interpersonal communication, as shown in the 53rd CNNIC Statistical Report on the Development of the Internet in China, Role- playing games occupy an important position in the field of video games due to their unique narrative depth, diverse aesthetic presentation and fine character construction. Children grow up playing different roles in play with their friends, and humans explore emotions and self through imitation and play in artistic activities such as music, literature, theater, and film. The earliest role-playing game in the form of a game was the tabletop role-playing game (TRPG), which gradually evolved into the present-day electronic role-playing game after intelligent computers were used as a gaming platform. (Sun Qishun, 2009) Role-playing games emphasize the characteristics of the characters and the content of the game's plot so that players feel that the characters in the game are played by themselves. (Hu, 2008) Against this background, Otome games, a genre developed specifically for female game consumers, have gradually become an important area of the game market and academic research. By deeply studying gamers' consumption behavior in Otome games, this study will help fill the current research gap of gamers' consumption factors in Otome games and provide new theoretical perspectives and empirical research bases for future generations. By digging deeper into the relationship between player satisfaction, heart flow experience, and consumer purchase intention, the research results are expected to provide game developers and marketing practitioners with suggestions for practical marketing strategies and game design. 2. Literature Review Since there are some differences in the definition of willingness to pay by different scholars, this paper will follow Dodds' definition of purchase intention: the likelihood that a consumer will purchase a product. (Doods & Krause, 1991) Ajzen (1980) considers willingness as the subjective likelihood that an individual or group will engage in a particular behavior. Based on this willingness to consume is considered an important indicator of consumer behavior, i.e. the probability that a consumer will consume a particular good. (Ajzen, 1980) Xu Mengxiao (2011) pointed out that the basis and prerequisite of consumption behavior is the willingness to consume, and thus divided the consumption behavior into five stages: knowledge, emotion, intention, behavior, and evaluation, i.e., perception of the goods, consumption emotion, consumption willingness, purchasing behavior, and evaluation after purchase. (Xu Mengxiao, 2011). Tian Xiaoxia's (2014) research points out that consumption willingness is an important indicator for predicting consumers to carry out consumption behavior, and it is also the basic prerequisite for consumers to carry out consumption behavior. (Tian Xiaoxia, 2014) Malone (1981) was the first to propose the theory of intrinsic motivation, attributing challenge, fantasy, and curiosity as the main components of potential motivation for play. (Malone, 1981) the concept of satisfaction was first introduced by Cardozo, whose case study examined the relationship between users' satisfaction with a product and their willingness to make repeat purchases. (Cardozo, 1965) Cardozo's explanation of satisfaction and its impact on repeat purchases was widely recognized by 470 academics. In subsequent studies on repeat purchases or continued use, scholars generally regard satisfaction as an important influencing factor. Li Bin (2016) argued that satisfaction can be defined as the relative relationship between users' expectations before using a product and their actual feelings after using it. When users' actual feelings after using the product exceed their expectations, their satisfaction will be higher. The SOR (Stimulus-Individual Physiological, Psychological-Response) theory originated in environmental psychology, where Skinner (1935) proposed the correlation between environment and behavior through the study of stimulus and response (S-R). (Skinner, 1935) Figure 2-1. Theoretical model of SOR Chia-Lin et al. (2012) explored and analyzed that information quality, system quality, and service quality stimulate consumers' professionalism and indulgence, and have an impact on consumers' satisfaction and purchase intention. (Chia-Lin et al., 2012) According to the grounded theory hypothesis of the SOR theoretical model H1: Game story setting is positively correlated with player satisfaction. H2: Character setting is positively correlated with player satisfaction. H3: Dubbing music is positively correlated with player satisfaction. H4: Art Style is positively correlated with player satisfaction. H5: Game story setting is positively correlated with player flow experience. H6: Character setting is positively correlated with player flow experience. H7: Dubbing music is positively correlated with player flow experience. H8: Art Style is positively correlated with player flow experience. H9: Player satisfaction is positively correlated with player purchase intentions. H10: Player flow experience is positively correlated with player purchase intentions. Figure 2-2. Research Hypothesis Model Diagram 2.1. Literature Research Method This study uses computer software to analyze, descriptive statistics, reliability, validity, exploratory factor analysis, confirmatory factor analysis, and structural equation modeling. 2.2. Population and Sample Size For the sample size, the margin of error was set to decimal (e=0.05), and the number of Light and Night's official microblogging followers was selected to be 2,618,000 followers, according to the sampling formula for the sample size of simple random sampling (Yamane, 1973): 3. The Results 3.1. Basic Information Table 3-1. Statistical data of population in this research Frequency analyzer Question options frequency percentage Cumulative percentage 1. Gender Male 6 1.496% 1.496% Female 395 98.504% 100.000% 2. Age groups: 18-22 years old 15 3.741% 3.741% 23-26 years old 178 44.389% 48.130% 27-30 years old 112 27.930% 100.000% Above 30 years old 96 23.940% 72.070% 3. Monthly income 1000CNY and below 15 3.741% 3.741% 1001 CNY-2500CNY 29 7.232% 10.973% 2501 CNY-4000 CNY 146 36.409% 47.382% 4001 CNY-8000 CNY 101 25.187% 72.569% 8001CNY and above 110 27.431% 100.000% 4. Gaming Frequency Less than or equal to 1 day a week 76 18.953% 18.953% 2 days a week 104 25.935% 44.888% 3 days a week 86 21.446% 66.334% 4 days a week 76 18.953% 85.287% Greater than or equal to 5 days a week 59 14.713% 100.000% 5. Amount of in-game spending 1001 CNY - 5000 CNY 95 3.990% 92.768% 5001 CNY-10000 CNY 170 33.916% 77.057% 10001 CNY-20000 CNY 107 26.683% 37.656% 20001CNY and above 29 7.232% 100.000% 471 Table 3-2. Descriptive statistics of indicators/items. Descriptive statistics variable sample volume minimum value maximum values standard deviation average value A1. Otome games have a complete and clearly explained game story background. 401 1 5 1.188 3.805 A2. Otome games have a main storyline and a rich variety of sub-stories. 401 1 5 1.167 3.853 A3. otome games have a coherent main storyline. 401 1 5 1.142 3.840 B1. otome game has several male characters that can be cheated according to the player's preference. 401 1 5 1.132 3.853 B2. otome game has several male characters with distinctive personalities. 401 1 5 1.158 3.721 B3. Otome game has a variety of card faces for players to choose from. 401 1 5 1.124 3.813 C1. I feel comfortable with the character voices in otome games. 401 1 5 1.167 3.805 C2. The character voices in otome games fit the game story and the characters. 401 1 5 1.186 3.743 C3. I feel comfortable with the music and sound effects in otome games. 401 1 5 1.126 3.903 D1. The art style in the otome game is in line with the story background. 401 1 5 1.212 3.818 D2. The character modeling design of the otome game is in line with the story background. 401 1 5 1.227 3.713 D3. The overall visual effect of the otome game is in line with the story background. 401 1 5 1.147 3.823 E1. I am very satisfied with the otome game. 401 1 5 1.225 3.781 E2. I am very happy after playing an otome game 401 1 5 1.186 3.843 E3. After playing otome games I feel like otome games. 401 1 5 1.137 3.788 FF1. I really enjoy and immerse myself in the interaction with male characters in otome games. 401 1 5 1.145 3.808 FF2. When I play Otome game, I have a high level of mental concentration. 401 1 5 1.134 3.815 FF3. I feel back to the real world when I exit the Otome game story. 401 1 5 1.144 3.646 G1. I enjoy using Otome games and buying virtual goods from Otome games. 401 1 5 1.171 3.733 G2. I will continue to use Otome games and buy virtual goods in Otome games in the future. 401 1 5 1.183 3.776 G3. I will use Otome games as a common pastime. 401 1 5 1.178 3.813 Game Story 401 1 5 1.03 3.833 Character setting 401 1 5 1.001 3.796 Dubbing music 401 1 5 1.013 3.817 Art Style 401 1 5 1.047 3.785 Satisfaction 401 1 5 1.03 3.804 Flow Experience 401 1 5 0.979 3.756 Purchase intentions 401 1 5 1.027 3.774 From Table 3-1, it can be seen that 98.5% of the respondents are female players, 96.259% of the respondents are adults over 18 years old and 89.03% of the respondents have a monthly income of more than 2,500 CNY, which reflects the independence and independent spending power of the player group. d high-level consumer groups have a high level of performance in game profitability. 3.2. Reliability Analysis Table 3-3. Reliability analysis variables Cronbach Reliability analysis Dimension items Sample Volume Cronbac h α Game story 3 401 0.860 0.921 Character setting 3 401 0.853 Dubbing music 3 401 0.844 Art Style 3 401 0.848 Satisfaction 3 401 0.840 Flow Experience 3 401 0.821 Purchase intentions 3 401 0.842 As can be seen in Table 3-3, the Cronbach's alpha value for each dimensional variable is greater than 0.8, which indicates that the reliability of the scale is good, suggesting that the questionnaire data has a high degree of reliability and trustworthiness. Therefore, we can proceed to the next step of analysis. 3.3. Exploratory Factor Analysis Table 3-4. KMO and Bartlett’s Test KMO and Bartlett’s Test Bartlett Sphericity Test KMO 0.904 chi-square distribution 4454.466 df 210 P 0.000 The validity was verified using KMO and Bartlett's test, as can be seen from the table above: the KMO test value of the survey data is 0.904, which is greater than 0.7, indicating that the questionnaire is suitable for factor analysis. 472 Table 3-5. Table of the factor load coefficient after rotation Table of the factor load coefficient after rotation Question item (The following otome games all refer to Light and Night) Factor load coefficient Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Factor 7 A1. Otome games have a complete and clearly explained game story background. 0.071 0.85 0.112 0.105 0.185 0.103 0.176 A2. Otome games have a main storyline and a rich variety of sub- stories. 0.12 0.805 0.172 0.103 0.082 0.191 0.197 A3. Otome games have a coherent main storyline. 0.124 0.797 0.165 0.138 0.117 0.173 0.091 B1. Otome game has several male characters that can be cheated according to the player's preference. 0.758 0.137 0.047 0.177 0.188 0.214 0.178 B2. Otome game has several male characters with distinctive personalities. 0.829 0.047 0.181 0.155 0.096 0.12 0.182 B3. Otome game has a variety of card faces for players to choose from. 0.84 0.138 0.167 0.093 0.139 0.106 0.125 C1. I feel comfortable with the character voices in otome games. 0.168 0.169 0.161 0.148 0.153 0.775 0.164 C2. The character voices in otome games fit the game story and the characters. 0.142 0.191 0.163 0.124 0.146 0.787 0.141 C3. I feel comfortable with the music and sound effects in otome games. 0.128 0.124 0.118 0.148 0.173 0.806 0.166 D1. The art style in the otome game is in line with the story background. 0.136 0.163 0.095 0.104 0.815 0.136 0.137 D2. The character modeling design of the Otome game is in line with the story background. 0.15 0.151 0.192 0.21 0.781 0.159 0.149 D3. The overall visual effect of the otome game is in line with the story background. 0.129 0.08 0.175 0.08 0.815 0.165 0.131 E1. I am very satisfied with the Otome game. 0.116 0.203 0.119 0.178 0.176 0.108 0.801 E2.I am very happy after playing an otome game 0.175 0.152 0.1 0.151 0.136 0.176 0.773 E3. After playing otome games I feel like Otome games. 0.204 0.124 0.105 0.177 0.119 0.189 0.781 FF1. I really enjoy and immerse myself in the interaction with male characters in otome games. 0.174 0.112 0.771 0.174 0.177 0.183 0.09 FF2. When I play Otome game, I have a high level of mental concentration. 0.068 0.173 0.808 0.153 0.105 0.09 0.136 FF3. I feel back to the real world when I exit the otome game story. 0.153 0.151 0.792 0.092 0.162 0.149 0.08 G1. I enjoy using Otome games and buying virtual goods from Otome games. 0.109 0.124 0.131 0.774 0.078 0.145 0.298 G2. I will continue to use Otome games and buy virtual goods in Otome games in the future. 0.113 0.112 0.112 0.816 0.159 0.163 0.14 G3. I will use Otome games as a common pastime. 0.199 0.119 0.191 0.808 0.139 0.105 0.089 Note: Blue indicates that the absolute value of the load factor is greater than 0.5 473 3.4. Confirmatory Factor Analysis Figure 3-1. An exploratory factor path diagram The chi-square degrees of freedom ratio χ2/df value of this model is 1.165 less than 3, which results in an ideal fit; the values of the GFI, IFI, TLI, and CFI indexes are all greater than 0.90, respectively; and the RMSEA index is 0.020, which is less than 0.08, which indicates that the model fit is better. Table 3-6. Confirmatory factor fitting index Commonly used indicators χ2/df RMSEA GFI IFI TLI CFI Criterion for judgment <3 <0.08 >0.9 >0.9 >0.9 >0.9 Numerical value 1.165 0.020 0.957 0.994 0.992 0.994 Table 3-7. Gathering efficiency Subactive variables Item Non-standardized factor loading CR AVE Game Story A1 0.851 0.861 0.674 A2 0.836 A3 0.773 Character setting B1 0.778 0.855 0.663 B2 0.836 B3 0.827 Dubbing music C1 0.810 0.844 0.644 C2 0.800 C3 0.797 Art Style D1 0.778 0.848 0.651 D2 0.858 D3 0.781 Satisfaction EE1 0.813 0.841 0.638 EE2 0.773 EE3 0.809 Flow Experience FF1 0.806 0.821 0.605 FF2 0.754 FF3 0.772 Purchase intentions G1 0.798 0.842 0.641 G2 0.798 G3 0.805 474 As can be seen from the 3-7 aggregation validity table, the standardized path coefficients of each question item on the factor to which it belongs are all above 0.7. Table 3-8. Discrimination validity Game story Character setting Dubbing music Art Style Satisfaction Flow Experience Purchase intentions Game story 0.821 Character setting 0.399 0.814 Dubbing music 0.529 0.510 0.802 Art Style 0.469 0.487 0.544 0.807 Satisfaction 0.532 0.543 0.559 0.515 0.799 Flow Experience 0.498 0.488 0.529 0.537 0.446 0.778 Purchase intentions 0.436 0.494 0.512 0.490 0.577 0.512 0.800 From the discriminant validity table, it can be seen that the absolute value of the correlation coefficient between any two factors is less than the square root of the AVE of the corresponding factor, i.e., there is a certain degree of differentiation between the factors, i.e., it shows that the discriminant validity of the scale is reliable. 3.5. Structural Equation Model Analysis Figure 3-2. The AMOS pathway diagram Table 3-9. Structural equation model fitting index Commonly used indicators χ2/df RMSEA GFI IFI TLI CFI Criterion for judgement <3 <0.08 >0.9 >0.9 >0.9 >0.9 Numerical value 1.203 0.023 0.954 0.992 0.990 0.992 NO. Path Off-standard path coefficient Standardization path coefficient S.E. C.R. P Hypothesis H1 Game story-->Satisfaction 0.238 0.233 0.059 3.945 *** Accepted H2 Character setting- ->satisfaction 0.267 0.301 0.069 4.371 *** Accepted H3 Dubbing music-->Satisfaction 0.218 0.229 0.072 3.192 0.001 Accepted H4 Art Style-->Satisfaction 0.164 0.172 0.067 2.578 0.01 Accepted H5 Game story-->Flow Experience 0.203 0.185 0.057 3.231 0.001 Accepted H6 Character setting-->Flow Experience 0.195 0.205 0.066 3.086 0.002 Accepted H7 Dubbing music-->Flow Experience 0.196 0.191 0.07 2.738 0.006 Accepted H8 Art Style-->Flow Experience 0.248 0.242 0.066 3.682 *** Accepted H9 Satisfaction-->Purchase intentions 0.451 0.428 0.059 7.284 *** Accepted H10 Flow Experience-->Purchase intentions 0.328 0.335 0.062 5.433 *** Accepted 475 3.6. Research Conclusions Therefore, the results of the research hypotheses in this paper are shown in Table 3-10, which presents five hypotheses, all of which are supported by empirical evidence. Table 3-10. hypothesis results NO. Hypothetical content Results H1 Game story setting is positively correlated with player satisfaction; Accepted H2 Character set is positively correlated with player satisfaction Accepted H3 Dubbing music is positively correlated with player satisfaction; Accepted H4 Art Style is positively correlated with player satisfaction; Accepted H5 Game story setting is positively correlated with player flow experience; Accepted H6 Character setting is positively correlated with player flow experience; Accepted H7 Dubbing music is positively correlated with player flow experience; Accepted H8 Art Style is positively correlated with player flow experience; Accepted H9 Player satisfaction is positively correlated with player purchase intentions; Accepted H10 Player flow experience is positively correlated with player purchase intentions. Accepted 4. Conclusion and Discussion 4.1. Discussion of Information Disclosure 4.1.1. Discussion about Satisfaction According to the path analysis validation questionnaire data in structural equation modeling, it can be seen that the game story is positively correlated with player satisfaction (β=0.238, p<0.05); character setting is positively correlated with player satisfaction (β=0.267, p<0.05); dubbing music is positively correlated with player satisfaction (β=0.218, p<0.05); and art style is positively correlated with player satisfaction (β=0.164, p<0.05), and hypotheses H1, H2, H3, and H4 are valid. To summarize the four points mentioned above have a great impact on player satisfaction, the impact of the love story interpreted in the game on players' emotions is consistent with the conclusions of Huicong Jin and Huizhen Xu, and the emotional needs and the experience of using the game affect players' game participation and satisfaction. 4.1.2. Discussion with concern for flow experience According to the path analysis validation questionnaire data in the structural equation modeling, it can be seen that the game story has a positive correlation with the player's heart flow experience (β=0.203, p<0.05); the character setting has a positive correlation with the player's heart flow experience (β=0.195, p<0.05); the dubbing music has a positive correlation with the player's heart flow experience (β=0.196, p<0.05); the art style has a positive correlation with the player's heart flow experience is positively correlated (β = 0.248, p < 0.05), and hypotheses H5, H6, H7, and H8 are valid. In summary, the four points mentioned above are positively correlated to players' mind-flow experience, and immersion experience is positively correlated to gamers' willingness to use, which is consistent with the findings of Wei Ting et al. In particular, visual and auditory sensations (i.e., art styles and dubbing music) enhanced players' mind-flow experience and promoted players' continued willingness to use, which is consistent with the findings of Zhang yaqi et al. and Choi & Kim. 4.1.3. Discussion about willingness to buy According to the path analysis in the structural equation model to validate the questionnaire data, it can be seen that player satisfaction is positively correlated with the willingness to buy (β = 0.451, p < 0.05); player's heart flow experience is positively correlated with the willingness to buy (β = 0.328, p < 0.05), and the hypotheses H9 and H10 are valid. To summarize, the stronger the player satisfaction and heart flow experience, the greater the impact on purchase intention, the players have an emotional attachment to the characters and purchased items in the game, mapping their own emotions to the game characters, which is consistent with the conclusions of Jack & Griffiths, He Yufei et al. 4.2. Conclusion According to the above discussion, this paper verifies the consumer group of the Otome game Light and Night player consumer group's willingness to consume virtual goods in the game, and then elaborates on the research questions about this paper: (1) This study uses questionnaire analysis, descriptive line statistical analysis, reliability and validity test, exploratory factor analysis, validation factor analysis, and structural equation modeling to test the valid data, and ultimately verifies that the game story, art style, character setting, and dubbing music have a positive correlation with the player's satisfaction with the game; (2) This study uses questionnaire analysis, descriptive statistical analysis, reliability, and validity test, exploratory factor analysis, validation factor analysis, and structural equation modeling to test the valid data, and finally verifies that the game story, art style, character set, and dubbing music are positively correlated to players' flow experience; (3) This study uses questionnaire analysis, descriptive statistical analysis, reliability and validity test, exploratory factor analysis, validation factor analysis, and structural equation modeling to test the valid data, and finally verifies that player satisfaction and flow experience are positively correlated with purchase intention. 4.3. Suggestion Players can establish deep emotional connections and interaction with in-game characters by exploring different game story directions, which enhances players' heart flow experience when playing in the game, and also obtains emotional resonance, which is a key part of the game experience. Community activities and exchanges between players provide a rich episodic value for Otome games, and players can share their game experiences through social media or fan communities. Increase game interactions. Inside the game, the emotional connection between players and characters should be the core, strengthening dynamic dialogues, emotional responses and diversified side quests, and reducing mandatory player-player interactions to adapt to the needs of players who tend to prefer private immersion experiences. Outside the game, feedback platforms such as social media and player communities can be used to build a two-way communication channel between developers and players, while encouraging player creation and sharing to enhance community activity and player stickiness. This kind of internal and external interaction design not only satisfies the emotional needs of players but also provides support for 476 the long-term development of the game. References [1] Ajzen, I. (1980). Understanding attitudes and predicting social behavior. Understanding attitudes and predicting social behavior. [2] Bartle, R. (1996). HEARTS, CLUBS, DIAMONDS, SPADES: PLAYERS WHO SUIT MUDS. [3] Cardozo, R. N. (1965). An Experimental Study of Customer Effort, Expectation, and Satisfaction. Journal of Marketing Research, 2(3), 244-249. [4] Chang, C. C. (2013). Examining users′ intention to continue using social network games: A flow experience perspective. Telematics and Informatics, 30(4), 311-321. [5] Changjo, Yoo, and, Jonghee, Park, and, Deborah, J., & MacInnis. (1998). Effects of Store Characteristics and In-Store Emotional Experiences on Store Attitude - ScienceDirect. Journal of Business Research, 42(3), 253-263. [6] Chia-Lin, Hsu, Kuo-Chien, Chang, Mu-Chen, & Chen. (2012). The impact of website quality on customer satisfaction and purchase intention: perceived playfulness and perceived flow as mediators. Information Systems & E Business Management. [7] Choi, D., & Kim, J. (2004). Why people continue to play online games: in search of critical design factors to increase customer loyalty to online contents. CyberPsychology & Behavior, 7(1), p.11-24. [8] Cleghorn, J., & Griffiths, M. D. (2015). Why do gamers buy "virtual assets"? An insight in to the psychology behind purchase behavior. Digital Education Review, 27, 98-117. [9] Csikszentmihalyi, M. (1975). Play and Intrinsic Rewards. Journal of Humanistic Psychology, 15(3), 41-63. [10] Donovan, R. J., & Rossiter, J. R. (1982). Store atmosphere: an environmental psychology approach. [11] Doods, H. N., & Krause, J. (1991). Different neuropeptsde Y receptor subtypes in rat and rabbit vas deferens. European Journal of Pharmacology, 204(1), 101. [12] Eroglu, S. A., Machleit, K. A., & Davis, L. M. (2001). Atmospheric qualities of online retailing: A conceptual model and implications. Journal of Business Research, 54(2), 177-184. [13] Hamari, J., Alha, K., Jarvela, S., Kivikangas, J. M., Koivisto, J., & Paavilainen, J. (2017). Why do players buy in-game content? An empirical study on concrete purchase motivations. Computers in Human Behavior, 68(MAR.), 538-546. [14] Jack, & Griffiths, M. D. (2015). Why Do Gamers Buy "Virtual Assets"? An Insight in to the Psychology behind Purchase Behaviour. Digital Education Review, 85-104. [15] Jeong, S. W., Fiore, A., Niehm, L., & Lorenz, F. O. (2009). The role of experiential value in online shopping: The impacts of product presentation on consumer responses towards an apparel web site. Internet Research: Electronic Networking Applications and Policy. [16] Kaltcheva, V. D., & Weitz, B. A. (2006). When Should a Retailer Create an Exciting Store Environment? Journal of Marketing, 70(1), 107-118. [17] Kim, H. W., Chan, H. C., & Kankanhalli, A. (2012). What Motivates People to Purchase Digital Items on Virtual Community Websites? The Desire for Online Self-Presentation. Information Systems Research, 23(4), 1232-1245. [18] Malone, T. (1981). What makes computer games fun? (abstract only). ACM. [19] Massimini, F., & Carli, M. (1988). The systematic assessment of flow in daily experience. Optimal Experience. [20] Mehrabian, A., & Russell, J. A. (1974). An approach to environmental psychology. [21] Mullet, G. M., & Karson, M. J. (1985). Analysis of Purchase Intent Scales Weighted by Probability of Actual Purchase. Journal of Marketing Research, 22, 93 - 96. [22] Sevgin, A., Eroglu, Karen, A., Machleit, Lenita, M., & Davis. (2003). Empirical testing of a model of online store atmospherics and shopper responses. Psychology & Marketing. [23] Skinner, B. F. (1935). The Generic Nature of the Concepts of Stimulus and Response. Journal of General Psychology, 12, 40- 65. [24] Vieira, V. A. (2013). Stimuli–organism-response framework: A meta-analytic review in the store environment. Journal of Business Research, 66(9), 1420-1426. [25] Woo, Gon, Kim, and, Yun, Ji, & Moon. (2009). Customers' cognitive, emotional, and actionable response to the servicescape: A test of the moderating effect of the restaurant type. International Journal of Hospitality Management. [26] Yee, N. (2006). Motivations for Play in Online Games. Cyberpsychology & behavior: the impact of the Internet, multimedia and virtual reality on behavior and society, 9 6, 772-775. [27] Zhang, Y., & Song, Y. (2022). The Effects of Sensory Cues on Immersive Experiences for Fostering Technology-Assisted Sustainable Behavior: A Systematic Review. Behavioral Sciences, 12(10), 361. https://www.mdpi.com/2076- 328X/12/10/361 https://www.mdpi.com/2076-328X/12/10/361 https://www.mdpi.com/2076-328X/12/10/361