BIBLIOMETRIC ANALYSIS OF FLOW THEORY FROM PAST TO PRESENT WITH VISUAL MAPPING TECHNIQUE: A MARKETING-SIDED APPROACH

Year 2022, Volume: 17 Issue: 57, 243 - 267, 30.01.2022

Zübeyir Çelik , Aypar Uslu

https://doi.org/10.14783/maruoneri.990480

Cited By: 6

Abstract

The aim of this study is to present a general literature typology of flow theory where a history of roughly 47 years (1975-present) exists. YÖK (Council of Higher Education) Thesis Center and Google Academic databases were used for this paper and flow and flow experience concepts have been examined through these sources. YÖK Thesis Center is a website within higher education institution in Turkey, where publication of master’s and doctoral thesis. A number of studies published in the time period from 1975 to the present had been obtained and these studies were reviewed. Subsequently, frequency analyses were made for the research and the bibliographic mapping of the data was done using VOSviewer software. As a result of the analysis, a bibliography of 110 selected studies is presented. Flow experience, which is mainly subject to physical activities, is evaluated in the areas of technology acceptance and consumer behavior in computer-mediated environments. Flow theory is mostly integrated with the technology acceptance model. Flow theory experience is characterized by the dimensions of concentration, enjoyment, and control, respectively. This research provides clear explanations for bibliographic analysis of studies on flow, models/theories with which flow theory is most integrated, and dimensions of flow experience.

Keywords

Flow Theory, Bibliography, Models/Theories, Dimensions, Marketing

GÖRSEL HARİTALAMA TEKNİĞİYLE GEÇMİŞTEN GÜNÜMÜZE AKIŞ TEORİSİNİN BİBLİYOMETRİK ANALİZİ: PAZARLAMA YÖNLÜ BİR YAKLAŞIM

Abstract

Bu çalışmanın amacı, yaklaşık 47 yıllık (1975-günümüz) bir geçmişi olan akış teorisinin genel bir literatür tipolojisini sunmaktır. Bu çalışma için YÖK Tez Merkezi ve Google Akademik veri tabanları kullanılmış ve bu kaynaklar üzerinden akış ve akış deneyimi kavramları incelenmiştir. YÖK Tez Merkezi, Türkiye’de yükseköğretim kurumları bünyesinde yüksek lisans ve doktora tezlerinin yayımlandığı bir web sitesidir. 1975 yılından günümüze kadar geçen zaman diliminde yayımlanmış çok sayıda çalışma elde edilmiş ve bu çalışmalar gözden geçirilmiştir. Daha sonra araştırma için frekans analizleri yapılmış ve VOSviewer yazılımı kullanılarak verilerin bibliyografik haritalaması yapılmıştır. Analiz sonucunda seçilen 110 çalışmanın bibliyografyası sunulmuştur. Esas olarak fiziksel aktivitelere tabi olan akış deneyimi, bilgisayar aracılı ortamlarda teknoloji kabulü ve tüketici davranışlarında değerlendirilmektedir. Diğer taraftan araştırma sonuçlarına göre akış teorisi, teknoloji kabul modeli ile en çok entegre olmaktadır. Dahası, akış deneyimi, sırasıyla konsantrasyon, zevk ve kontrol boyutu ile fazla karakterizedir. Bu araştırma, akış üzerine yapılan çalışmaların bibliyografik analizi, akış teorisinin en çok entegre edildiği modeller/teoriler ve akış deneyiminin boyutları için net açıklamalar sağlamaktadır.

Keywords

Akış Teorisi, Bibliyografya, Model/Teoriler, Boyutlar, Pazarlama

References

• Aaker, J. L., & Lee, A. Y. (2006). Understanding regulatory fit, Journal of Marketing Research, 43(1), 15-19.
• Agarwal, R., & Karahanna, E. (2000). Time flies when you're having fun: Cognitive absorption and beliefs about ınformation technology usage. MIS quarterly, 665-694.
• Ahmad, N., & Abdulkarim, H. (2019). The impact of flow experience and personality type on the intention to use virtual world. International Journal of Human–Computer Interaction, 35(12), 1074-1085.
• Ajzen, I. (1985). From intentions to actions: A Theory of planned behavior, In Action Control. Springer, Berlin, Heidelberg, 11-39.
• Ajzen, I. (1987). Attitudes, traits, and actions: Dispositional prediction of behavior in personality and social psychology, In Advances in Experimental Social Psychology, 20, 1-63. Academic Press.
• Ajzen, I. (1991), The theory of planned behavior, Organizational Behavior and Human Decision Processes, 50(2), 179-211.
• Akbari, M., Rezvani, A., Shahriari, E., Zúñiga, M. A., & Pouladian, H. (2020). Acceptance of 5 G technology: Mediation role of trust and concentration. Journal of Engineering and Technology Management, 57, 101585.
• Alwahaishi, S., & Snásel, V. (2013). Acceptance and use of information and communications technology: A UTAUT and flow based theoretical model. Journal Of Technology Management & Innovation, 8(2), 61-73.
• An, S., Choi, Y., & Lee, C. K. (2021). Virtual travel experience and destination marketing: Effects of sense and information quality on flow and visit intention. Journal of Destination Marketing & Management, 19, 100492.
• Andrews, J. C. (1986). Motivation, ability and opportunity to process information: Conceptual and experimental manipulation issues, Advances in Consumer Research, 15, 219-225.
• Barhorst, J. B., McLean, G., Shah, E., & Mack, R. (2021). Blending the Real World and the Virtual World: Exploring the Role of Flow in Augmented Reality Experiences. Journal of Business Research, 122, 423-436.
• Bauer, R. A. (1960). Consumer Behavior As Risk Taking. In Hancock, R. S. (Ed.), Dynamic Marketing for a Changing World (389-398). Chicago: American Marketing Association.
• Baytar, U., & Yükselen, C. (2018). The Effect of customers’ flow experience in online shopping channels on satisfaction and purchasing decisions, the roles of information quality and channel quality. Beykent University Journal of Social Sciences, 11(2), 19-35.
• Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 351-370.
• Bilgihan, A., Nusair, K., Okumus, F., & Cobanoglu, C. (2015). Applying flow theory to booking experiences: An integrated model in an online service context. Information & Management, 52(6), 668-678.
• Cacioppo, J. T., & Petty, R. E. (1984). The elaboration likelihood model of persuasion. Advances in Consumer Research, 11, 673–675.
• Calvo-Porral, C., Faíña-Medín, A., & Nieto-Mengotti, M. (2017). Exploring technology satisfaction: An approach through the flow experience. Computers in Human Behavior, 66, 400-408.
• Case, D. O. (2002). Looking for ınformation: A survey of research on information seeking, needs, and behavior. San Diego, Academic Press (Library and Information Science).
• Chen, Y. M., Hsu, T. H., & Lu, Y. J. (2018), Impact of flow on mobile shopping intention. Journal of Retailing and Consumer Services, 41, 281-287.
• Chen, C. C., & Lin, Y. C. (2018). What drives live-stream usage intention? The perspectives of flow, entertainment, social interaction, and endorsement. Telematics and Informatics, 35(1), 293-303.
• Chang, H. H., & Wang, I. C. (2008), An investigation of user communication behavior in computer mediated environments. Computers in Human Behavior, 24(5), 2336-2356.
• Chen, H., Wigand, R. T., & Nilan, M. S. (1999). Optimal experience of web activities. Computers in Human Behavior, 15(5), 585-608.
• Chen, H., Wigand, R. T., & Nilan, M. (2000). Exploring web users’ optimal flow experiences. Information Technology & People, 13(4), 263-281.
• Choi, D. H., Kim, J., & Kim, S. H. (2007). ERP training with a web-based electronic learning system: The flow theory perspective, International Journal of Human-Computer Studies, 65(3), 223-243.
• Chung, J., & Tan, F. B. (2004), Antecedents of perceived playfulness: An exploratory study on user acceptance of general information-searching websites. Information & Management, 41(7), 869-881.
• Clarke, S. G., & Haworth, J. T. (1994). ‘Flow’experience in the daily lives of sixth‐form college students. British Journal of Psychology, 85(4), 511-523.
• Cruz-Cárdenas, J., Zabelina, E., Guadalupe-Lanas, J., Palacio-Fierro, A., & Ramos-Galarza, C. (2021). Covid-19, consumer behavior, technology, and society: A literature review and bibliometric analysis. Technological Forecasting and Social Change, 173, 121179.
• Csikszentmihalyi, M. (1975a). Play and intrinsic rewards. Journal of Humanistic Psychology.
• Csikszentmihalyi, M. (1975b). Beyond boredom and anxiety: Experiencing flow in work and play. San Francisco/Washington/London.
• Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. New York: Harper & Row.
• Csikszentmihalyi, M., & Csikszentmihalyi, I. S. (1988). Optimal experience: Psychological studies of flow in consciousness. Cambridge University Press, New York, NY.
• Csikszentmihalyi, M., & Figurski, T. J. (1982). Self‐awareness and aversive experience in everyday life. Journal of Personality, 50(1), 15-19.
• Csikszentmihalyi, M., & Larson, R. (1987). Validity and reliability of the experience-sampling method. The Journal of Nervous and Mental Disease. 175, 526-536.
• Csikszentmihalyi, M., & LeFevre, J. (1989). Optimal experience in work and leisure. Journal of Personality and Social Psychology, 56(5), 815.
• Csikszentmihalyi, M., Larson, R., & Prescott, S. (1977). The ecology of adolescent activity and experience. Journal of Youth and Adolescence, 6(3), 281-294.
• Csikszentmihalyi, M., & J. Nakamura. (1989). The dynamics of intrinsic motivation: A study of adolescents. In C. Ames & R. Ames (Eds.), Research on Motivation in Education.: Goals And Cognitions (Vol. 3, pp. 45-71). New York: Academic Press.
• Çabuk, S., & Kuş, A. S. (2019). The effect of flow experience in e-retail sites on consumer purchase intention - an investigation on brands in the clothing and shoe sector. Business & Management Studies: An International Journal. 7(3), 257-279.
• Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user ınformation systems: Theory and results (Doctoral Dissertation, Massachusetts Institute Of Technology).
• Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace 1, Journal of Applied Social Psychology, 22(14), 1111-1132.
• Deci, E. L., & Ryan, R. M. (1985a). Intrinsic motivation and self-determination in human behavior. New York: Plenum Press.
• Deci, E. L., & Ryan, R. M. (1985b). The general causality orientations scale: Self-determination in personality. Journal of Research in Personality, 19(2), 109–134.
• Deci, E. L., & Ryan, R. M. (1987). The support of autonomy and the control of behavior. Journal of Personality and Social Psychology, 53(6), 1024-1037.
• DeLone, W. H., & McLean, E. R. (1992). Information systems success: The quest for the dependent variable. Information Systems Research, 3(1), 60-95.
• Deng, L., Turner, D. E., Gehling, R., & Prince, B. (2010). User experience, satisfaction, and continual usage intention of IT. European Journal of Information Systems, 19(1), 60-75.
• Easterbrook, J. A. (1959). The effect of emotion on cue utilization and the organization of behavior. Psychological Review, 66(3), 183.
• Ellis, G. D., Voelkl, J. E., & Morris, C. (1994). Measurement and analysis issues with explanation of variance in daily experience using the flow model. Journal of Leisure Research, 26(4), 337-356.
• Engeser, S., & Rheinberg, F. (2008). Flow, performance and moderators of challenge-skill balance. Motivation and Emotion, 32(3), 158-172.
• Ettis, S. A. (2017). Examining the relationships between online store atmospheric color, flow experience and consumer behavior. Journal of Retailing and Consumer Services, 37, 43-55.
• Finneran, C. M., & Zhang, P. (2003). A person–artefact–task (PAT) model of flow antecedents in computer-mediated environments. International Journal of Human-Computer Studies, 59(4), 475-496.
• Finneran, C. M., & Zhang, P. (2005). Flow in computer-mediated environments: promises and challenges. Communications of the Association For Information Systems, 15(1), 4.
• Fishbein, M., & I. Ajzen, (1975). Belief, attitude, İntention, and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.
• Gao, L., & Bai, X. (2014). Online consumer behaviour and its relationship to website atmospheric induced flow: Insights into online travel agencies in China. Journal of Retailing and Consumer Services, 21(4), 653-665.
• Ghani, J. A. (1995). Flow in human computer interactions: Test of a model. Human Factors in Information Systems: Emerging Theoretical Bases, 291-311.
• Ghani, J. A., & Deshpande, S. P. (1994). Task characteristics and the experience of optimal flow in human—computer interaction. The Journal of Psychology, 128(4), 381-391.
• Ghani, J. A., Supnick, R., & Rooney, P. (1991). The experience of flow in computer-mediated and in face-to-face groups. In ICIS (Vol. 91, No. 6, pp. 229-237).
• Guo, Y. M., & Poole, M. S. (2009). Antecedents of flow in online shopping: A test of alternative models. Information Systems Journal, 19(4), 369-390.
• Hausman, A. V., & Siekpe, J. S. (2009). The effect of web interface features on consumer online purchase ıntentions, Journal of Business Research, 62(1), 5-13.
• Heidegger, M. (1927/1996). Being and time: A translation of sein und zeit (J. Stambaugh, Trans.), Albany, NY: SUNY Press.
• Ho, L. A., & Kuo, T. H. (2010). How can one amplify the effect of e-learning? An examination of high-tech employees’ computer attitude and flow experience. Computers in Human Behavior, 26(1), 23-31.
• Hoffman, D. L., & Novak, T. P. (1996). Marketing in hypermedia computer-mediated environments: Conceptual foundations. Journal of marketing, 60(3), 50-68.
• Hoffman, D. L., & Novak, T. P. (2009). Flow online: Lessons learned and future prospects. Journal of interactive marketing, 23(1), 23-34.
• Horton, D., & Wohl, R. (1956). Mass communication and para-social interaction: Observations on intimacy at a distance. Psychiatry, 19, 215–229.
• Hsu, C. L. (2020). How vloggers embrace their viewers: Focusing on the roles of para-social interactions and flow experience. Telematics and Informatics, 49, 101364.
• Hsu, C. L., Chang, K. C., & Chen, M. C. (2012). The impact of website quality on customer satisfaction and purchase intention: Perceived playfulness and perceived flow as mediators. Information Systems and e-Business Management, 10(4), 549-570.
• Hsu, C. L., & Lu, H. P. (2004). Why do people play on-line games? An extended tam with social influences and flow experience. Information & Management, 41(7), 853-868.
• Hsu, C. L., Wu, C. C., & Chen, M. C. (2013). An empirical analysis of the antecedents of e-satisfaction and e-loyalty: Focusing on the role of flow and its antecedents. Information Systems and e-Business Management, 11(2), 287-311.
• Huang, M. H. (2006). Flow, enduring, and situational involvement in the web environment: A tripartite second‐order examination. Psychology & Marketing, 23(5), 383-411.
• Hyun, H., Thavisay, T., & Lee, S. H. (2021). Enhancing the role of flow experience in social media usage and its impact on shopping. Journal of Retailing and Consumer Services, 102492.
• Jackson, S. A., & Marsh, H. W. (1996). Development and validation of a scale to measure optimal experience: The flow state scale. Journal of Sport and Exercise Psychology, 18(1), 17-35.
• Jung, Y., Perez-Mira, B., & Wiley-Patton, S. (2009). Consumer adoption of mobile TV: Examining psychological flow and media content. Computers in Human Behavior, 25(1), 123-129.
• Kaur, P., Dhir, A., & Rajala, R. (2016). Assessing flow experience in social networking site based brand communities. Computers in Human Behavior, 64, 217-225.
• Kim, C., Oh, E., & Shin, N. (2010). An empirical investigation of digital content characteristics, value, and flow. Journal of Computer Information Systems, 50(4), 79-87.
• Kim, B., Yoo, M., & Yang, W. (2020). Online engagement among restaurant customers: The importance of enhancing flow for social media users. Journal of Hospitality & Tourism Research, 44(2), 252-277.
• Korzaan, M. L. (2003). Going with the flow: Predicting online purchase intentions. Journal of Computer Information Systems, 43(4), 25-31.
• Koufaris, M. (2002). Applying the technology acceptance model and flow theory to online consumer behavior. Information Systems Research, 13(2), 205-223.
• Larson, R., & Csikszentmihalyi, M. (1983). The experience sampling method. New Directions for Methodology of Social & Behavioral Science, 15, 41–56.
• Lee, M. C. (2010). Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation–confirmation model. Computers & Education, 54(2), 506-516.
• Lee, S. M., & Chen, L. (2010). The impact of flow on online consumer behavior. Journal of Computer Information Systems, 50(4), 1-10.
• Lee, C. H., & Wu, J. J. (2017). Consumer online flow experience: The relationship between utilitarian and hedonic value, satisfaction and unplanned purchase. Industrial Management & Data Systems, 117(10), 2452-2467.
• Li, D., & Browne, G. J. (2006). The role of need for cognition and mood in online flow experience. Journal of Computer Information Systems, 46(3), 11-17.
• Liu, H., Chu, H., Huang, Q., & Chen, X. (2016). Enhancing the flow experience of consumers in china through ınterpersonal interaction in social commerce. Computers in Human Behavior, 58, 306-314.
• Lu, Y., Zhou, T., & Wang, B. (2009). Exploring Chinese users’ acceptance of instant messaging using the theory of planned behavior, the technology acceptance model, and the flow theory. Computers in Human Behavior, 25(1), 29-39.
• Mahfouz, A. Y., Joonas, K., & Opara, E. U. (2020). An overview of and factor analytic approach to flow theory in online contexts. Technology in Society, 61, 101228.
• Mahnke, R. (2014). Designing flow experience on the web: a grounded theory of online shopping flow. In 2014 47th Hawaii International Conference on System Sciences (pp. 3015-3024). IEEE.
• Mahnke, R., Benlian, A., & Hess, T. (2015). A grounded theory of online shopping flow. International Journal of Electronic Commerce, 19(3), 54-89.
• Mahnke, R., Wagner, T. M., & Benlian, A. (2012), Flow experience on the web: Measurement validation and mixed method survey of flow activities. In ECIS (p. 33).
• Maslow, A. H. (1968). Toward a pyschology of being. New York: Van Nostrand Reinhold.
• Maslow, A.H. (1970). Motivation and personality (2nd ed.). NewYork: Harper and Row.
• Maslow, A. H. (1971). The farther reaches of human natura. New York: Viking Press.
• Massimini, F., & M. Carli. (1988). The systematic assessment of flow in daily experience. In Csikszentmihalyi, M. & Csikszentmihalyi, I. S. (Eds.), Optimal Experience: Psychological Studies of Flow in Consciousness, New York: Cambridge University Press, 266-287.
• Mathwick, C., & Rigdon, E. (2004). Play, flow, and the online search experience. Journal of Consumer Research, 31(2), 324-332.
• Mehrabian, A., & Russell, J. A. (1974). An approach to environmental psychology. The MIT Press.
• Mennecke, B. E., Triplett, J. L., Hassall, L. M., Conde, Z. J., & Heer, R. (2011). An examination of a theory of embodied social presence in virtual worlds. Decision Sciences, 42(2), 413-450.
• Moon, J. W., & Kim, Y. G. (2001). Extending the TAM for a world-wide-web context. Information & Management, 38(4), 217-230.
• Moneta, G. B., & Csikszentmihalyi, M. (1996). The effect of perceived challenges and skills on the quality of subjective experience. Journal of Personality, 64(2), 275-310.
• Myers, I. B. (1962). The myers-briggs type indicator. Palo Alto, CA: Consulting Psychologists Press.
• Nel, D., van Niekerk, R., Berthon, J. P., & Davies, T. (1999). Going with the flow: Websites and customer involvement. Internet Research, 9(2), 109-116.
• Novak, T. P., & Hoffman, D. L. (1997). Measuring the flow experience among web users. Interval Research Corporation, 31(1), 1-35.
• Novak, T. P., Hoffman, D. L., & Yung, Y. F. (1998). Modeling the structure of the flow experience among web users. In INFORMS Marketing Science and the Internet Mini-Conference.
• Novak, T. P., Hoffman, D. L., & Yung, Y. F. (2000). Measuring the customer experience in online environments: A structural modeling approach. Marketing Science, 19(1), 22-42.
• Obadă, D. R. (2014). Online flow experience and perceived quality of a brand website: InPascani.ro case study. Procedia-Social and Behavioral Sciences, 149, 673-679.
• Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17(4), 460-469.
• Ozkara, B. Y., Ozmen, M., & Kim, J. W. (2017). Examining the effect of flow experience on online purchase: A novel approach to the flow theory based on hedonic and utilitarian value. Journal of Retailing and Consumer Services, 37, 119-131.
• Özkara, B. Y. (2015). Investigation of the effect of flow experience on information satisfaction in the context of consumers’ online ınformation search. ESOGU, Institute of Social Sciences, Unpublished Doctoral Thesis.
• Pace, S. (2004). A grounded theory of the flow experiences of web users. International Journal of Human-Computer Studies, 60(3), 327-363.
• Park, E. (2020). User acceptance of smart wearable devices: An expectation-confirmation model approach. Telematics and Informatics, 47, 101318.
• Pearce, J. M., Ainley, M., & Howard, S. (2005). The ebb and flow of online learning. Computers in Human Behavior, 21(5), 745-771.
• Pelet, J. É., Ettis, S., & Cowart, K. (2017). Optimal experience of flow enhanced by telepresence: Evidence from social media use. Information & Management, 54(1), 115-128.
• Pilke, E. M. (2004). Flow experiences in information technology use. International Journal of Human-Computer Studies, 61(3), 347-357.
• Rettie, R. (2001). An exploration of flow during internet use. Internet research, 11(2), 103-113.
• Rha, I., Williams, M. D., & Heo, G. (2005). Optimal flow experience in web-based instruction. Asia Pacific Education Review, 6(1), 50-58.
• Richard, M. O., & Chebat, J. C. (2016). Modeling online consumer behavior: Preeminence of emotions and moderating influences of need for cognition and optimal stimulation level. Journal of Business Research, 69(2), 541-553.
• Rissler, R., Nadj, M., & Adam, M. (2017). Flow in information systems research: Review, integrative theoretical framework, and future directions.
• Rodríguez-Ardura, I., & Meseguer-Artola, A. (2016). What leads people to keep on e-learning? An empirical analysis of users' experiences and their effects on continuance intention. Interactive Learning Environments, 24(6), 1030-1053.
• Rodriguez-Sanchez, A. M., Schaufeli, W. B., Salanova, M., & Cifre, E. (2008). Flow experience among information and communication technology users. Psychological Reports, 102(1), 29-39.
• Rogers, E.M. (2003). Diffusion of innovations, 5th edn. Free Press, New York, NY.
• Sampat, B., & Krishnamoorthy, B. (2016). Motivations for social network site (Sns) gaming: A uses and gratification & flow perspective. Journal of International Technology and Information Management, 25(3), 75-98.
• Sharafi, P., Hedman, L., & Montgomery, H. (2006). Using information technology: Engagement modes, flow experience, and personality orientations. Computers in Human Behavior, 22(5), 899-916.
• Sharkey, U., Acton, T., & Conboy, K. (2012). Optimal experience in online shopping: The influence of flow.
• Shin, N. (2006). Online learner’s ‘flow experience: An empirical study. British Journal of Educational Technology, 37(5), 705-720.
• Shin, D. H., & Kim, W. Y. (2008). Applying the technology acceptance model and flow theory to cyworld user behavior: Implication of the web2. 0 user acceptance. CyberPsychology & Behavior, 11(3), 378-382.
• Skadberg, Y. X., & Kimmel, J. R. (2004). Visitors’ flow experience while browsing a web site: Its measurement, contributing factors and consequences, Computers in Human Behavior, 20(3), 403-422.
• Smith, D. N., & Sivakumar, K. (2004). Flow and internet shopping behavior: A conceptual model and research propositions. Journal of Business Research, 57(10), 1199-1208.
• Taylor, S., & Todd, P. (1995). Assessing IT usage: The role of prior experience. MIS Quarterly, 561-570.
• Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: Toward a conceptual model of utilization. MIS Quarterly, 125-143.
• Thompson, R. L., Higgins, C. A., & Howell, J. M. (1994). Influence of experience on personal computer utilization: Testing a conceptual model. Journal of Management Information Systems, 11(1), 167-187.
• Trevino, L. K., & Webster, J. (1992). Flow in computer-mediated communication: Electronic mail and voice mail evaluation and impacts. Communication Research, 19(5), 539-573.
• Van Noort, G., Voorveld, H. A., & Van Reijmersdal, E. A. (2012). Interactivity in brand web sites: Cognitive, affective, and behavioral responses explained by consumers' online flow experience. Journal of Interactive Marketing, 26(4), 223-234.
• Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204.
• Venkatesh, V., & Morris, M. G. (2000). Why don't men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS Quarterly, 115-139.
• Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 425-478.
• Voelkl, J. E., & Ellis, G. D. (1998), Measuring flow experiences in daily life: An examination of the items used to measure challenge and skill. Journal of Leisure Research, 30(3), 380-389.
• Webster, J., Trevino, L. K., & Ryan, L. (1993), The dimensionality and correlates of flow in human-computer interactions. Computers in Human Behavior, 9(4), 411-426.
• Wong, M., & Csikszentmihalyi, M. (1991a). Affiliation motivation and daily experience: Some issues on gender differences. Journal of Personality and Social Psychology, 60(1), 154-164.
• Wong, M. M., & Csikszentmihalyi, M. (1991b). Motivation and academic achievement: The effects of personality traits and the duality of experience. Journal of Personality, 59(3), 539-574.
• Wu, L., Chiu, M. L., & Chen, K. W. (2020). Defining the determinants of online impulse buying through a shopping process of integrating perceived risk, expectation-confirmation model, and flow theory issues. International Journal of Information Management, 52, 102099.
• Xin Ding, D., Hu, P. J. H., Verma, R., & Wardell, D. G. (2010). The impact of service system design and flow experience on customer satisfaction in online financial services. Journal of Service Research, 13(1), 96-110.
• Yang, H., & Lee, H. (2018). Exploring user acceptance of streaming media devices: An extended perspective of flow theory. Information Systems and e-Business Management, 16(1), 1-27.
• Yang, K. C., & Shih, P. H. (2020). Cognitive age in technology acceptance: At what age are people ready to adopt and continuously use fashionable products?. Telematics and Informatics, 51, 101400.
• Yanık, A. (2014). The effect of flow experience in new media usage on risk perception and online touristic purchase intention. Adnan Menderes University, Institute of Social Sciences, Unpublished Doctoral Thesis.
• Zhao, H. (2019). Information quality or entities’ interactivity? Understanding the determinants of social network-based brand community participation. Future Internet, 11(4), 87.
• Zhou, T. (2011). Understanding mobile internet continuance usage from the perspectives of utaut and flow. Information Development, 27(3), 207-218.
• Zhou, T. (2013). The effect of flow experience on user adoption of mobile TV. Behaviour & Information Technology, 32(3), 263-272.
• Zhou, T., Li, H., & Liu, Y. (2010). The effect of flow experience on mobile sns users' loyalty. Industrial Management & Data Systems, 110(6), 930-946.
• Zhou, T., & Lu, Y. (2011). Examining mobile instant messaging user loyalty from the perspectives of network externalities and flow experience. Computers in Human Behavior, 27(2), 883-889.