Abstract
Many previous studies have identified that flow is a constructive construct for explaining consumer behaviors in the context of computer-mediated environments. Success in websites is dependent on their ability to create opportunities for consumers to experience flow. However, relatively little research has been conducted to understand how that flow forms (i.e., antecedents of flow) and impacts subsequently (i.e., consequences of flow). Thus, this research contributes in integrating the technology acceptance model (TAM), flow theory, and regulatory fit theory to investigate the unique role of flow for understanding the antecedents and consequences of flow. The results showed that perceived ease of use, perceived usefulness and regulatory fit exhibited significant positive effects on flow, and additionally the results indicated that regulatory fit has the strongest effect on flow. The results also confirmed that flow, perceived ease of use and perceived usefulness significantly affect e-satisfaction, which in turn affects e-loyalty, and additionally the results indicated that flow is the strongest determinant of e-loyalty. Important implications of these findings are discussed and directions for future research are also provided.
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References
Aaker JL, Lee AY (2006) Understanding regulatory fit. J Mark Res 43(1):15–19
Adams DA, Nelson RR, Todd PA (1992) Perceived usefulness, ease of use, and usage of information technology: a replication. MIS Q 16(2):227–247
Ajzen I, Fishbein M (1980) Understanding attitudes and predicting social behavior. Prentice-Hall, Englewood Cliffs
Anderson RE, Srinivasan SS (2003) E-satisfaction and e-loyalty: a contingency framework. Psychol Market 20(2):123–138
Anderson EW, Sullivan MW (1993) The antecedents and consequences of customer satisfaction for firms. Mark Sci 12(2):125–143
Arbaugh JB (2000) Virtual classroom characteristics and student satisfaction with Internet-based MBA courses. J Manag Educ 24(1):32–54
Avnet T, Higgins ET (2006) How regulatory fit affects value in consumer choices and opinions. J Mark Res 43(1):1–10
Bhattacherjee A (2001) Understanding information systems continuance: an expectation-confirmation model. MIS Q 25(3):351–370
Bigne-Alcaniz E, Ruiz-Mafe C, Aldas-Mznzano J, Sanz-Blas S (2008) Influence of online shopping information dependency and innovativeness on internet shopping adoption. Online Inf Rev 32(5):648–667
Cesario J, Grant H, Higgins ET (2004) Regulatory fit and persuasion: transfer from “feeling right”. J Pers Soc Psychol 86(3):388–404
Chang HH, Wang IC (2008) An investigation of user communication behavior in computer mediated environments. Comput Hum Behav 24(5):2336–2356
Chang YP, Zhu DH (2012) The role of perceived social capital and flow experience in building users’ continuance intention to social networking sites in China. Comput Hum Behav 28(3):995–1001
Chang HH, Wang YH, Yang WY (2009) The impact of e-service quality, customer satisfaction and loyalty on e-marketing: moderating effect of perceived value. Total Q Manag Bus Excell 20(4):423–443
Chen H, Wigand RT, Nilan MS (1999) Optimal experience of web activities. Comput Hum Behav 15(5):585–608
Chen WK, Huang HC, Chou SCT (2008) Understanding consumer recommendation behavior in a mobile phone service context. European conference on information systems (ECIS), Galway, Ireland
Chiu CM, Lin HY, Sun SY, Hsu MH (2009) Understanding customers’ loyalty intentions towards online shopping: an integration of technology acceptance model and fairness theory. Behav Inform Technol 28(4):347–360
Choi D, Kim J (2004) Why people continue to play online games: in search of critical design factors to increase customers loyalty to online contents. Cyber Psychol Behav 7(1):11–24
Chou TJ, Ting CC (2003) The role of flow experience in cyber-game addiction. Cyber Psychol Behav 6(6):663–675
Csikszentmihalyi M (1975) Beyond boredom and anxiety. Jossey-Bass, San Francisco
Csikszentmihalyi M (1990) Flow: the psychology of optimal experience. Harper & Row, New York
Csikszentmihalyi M (1993) The evolving self. Harper & Row, New York
Csikszentmihalyi M, Csikszentmihalyi IS (1988) Introduction to part IV. In: Csikszentmihalyi M, Csikszentmihalyi IS (eds) Optimal experience: psychological studies of flow in consciousness. Cambridge University Press, Cambridge, pp 251–265
Cyr D, Bonanni C, Bowes J, Ilsever J (2005) Beyond trust: web site design preferences across cultures. J Global Inform Manag 13(4):25–54
Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 13(3):319–340
Davis FD, Bagozzi RP, Warshaw PR (1989) User acceptance of computer technology: a comparison of two theoretical models. Manage Sci 35(8):982–1003
Doolin B, Dillon S, Thompson F, Corner JL (2005) Perceived risk, the Internet shopping experience and online purchasing behavior: a New Zealand perspective. J Global Inform Manag 13(2):66–88
Evanschitzky H, Iyer GR, Hesse J, Ahlert D (2004) E-satisfaction: a re-examination. J Retail 80(3):239–247
Fornell C, Larcker DF (1981) Evaluating structural equation models with unobservable variables and measurement error. J Mark Res 18(1):39–50
Forster J, Higgins ET, Idson LC (1998) Approach and avoidance strength during goal attainment: regulatory focus and the ‘‘goal looms larger’’ effect. J Pers Soc Psychol 75(5):1115–1131
Frank O, Snijders T (1994) Estimating the size of hidden populations using snowball sampling. J Off Stat 10(1):53–67
Freitas AL, Higgins ET (2002) Enjoying goal-directed action: the role of regulatory fit. Psychol Sci 13(1):1–6
Gremler DD (1995) The effect of satisfaction, switching costs, and interpersonal bonds on service loyalty. Unpublished doctoral dissertation, Arizona State University, Tucson, Arizona
Hair JF, Anderson RE, Tatham RL, Black WC (1995) Multivariate data analysis with readings. Prentice-Hall, Englewood Cliffs, NJ
Heckathorn D (1997) Respondent-driven sampling: a new approach to the study of hidden populations. Soc Probl 44(22):174–199
Heskett J, Sasser W, Schlesinger L (1997) Service profit chain: how leading companies link profit and growth to loyalty, satisfaction, and value. Free Press, New York
Higgins ET (1997) Beyond pleasure and pain. Am Psychol 52(12):1280–1300
Hoffman DL, Novak T (1996) Marketing in hypermedia computer-mediated environments: conceptual foundations. J Mark 60(3):50–68
Hsu CL, Lu HP (2004) Why do people play on-line games? An extended TAM with social influences and flow experience. Inform Manage 41(7):853–868
Hsu MH, Yen CH, Chiu CM, Chang CM (2006) A longitudinal investigation of continued online shopping behavior: an extension of the theory of planned behavior. Int J Human Comput Stud 64(9):889–904
Hsu CL, Chang KC, Chen MU (2011) The impact of website quality on customer satisfaction and purchase intention: perceived playfulness and perceived flow as mediators. Inform Syst E-Business Manag. doi:10.1007/s10257-011-0181-5
Hsu CL, Wu CC, Chen MU, Chang KC (2012) Formation of e-satisfaction and e-loyalty: an extension of technology acceptance model with perceived quality and flow experience. J O 19(1):61–83
Igbaria M, Parasuraman S, Baroudi JJ (1996) A motivational model of microcomputer usage. J Manage Inform Syst 13(1):127–143
Jackson SA, Marsh HW (1996) Development and validation of a scale to measure optimal experience: the flow state scale. J Sport Exerc Psychol 18(1):17–35
Jacoby J (1971) Brand loyalty: a conceptual definition. In: Proceedings of 79th annual convention of American Psychological Association, pp. 655–656
Jarvenpaa SL, Todd PA (1997) Consumer reactions to electronic shopping on the world wide web. Int J Electron Comm 1(2):59–88
Kabadayi S, Gupta R (2005) Web site loyalty: an empirical investigation of its antecedents. Int J Int Mark Advert 2(4):321–345
Kamis A, Stern T, Ladik DM (2010) A flow-based model of web site intentions when users customize products in business-to-consumer electronic commerce. Informat Syst Frontier 12(2):157–168
Koufaris M (2002) Applying the technology acceptance model and flow theory to online consumer behavior. Inform Syst Res 13(2):205–223
Kuo YF, Yen SN (2009) Towards an understanding of the behavioral intention to use 3G mobile value-added services. Comput Hum Behav 25(1):103–110
Lai F, Griffin M, Babin BJ (2009) How quality, value, image, and satisfaction create loyalty at a Chinese telecom. J Bus Res 62(10):980–986
Lee AY, Aaker JL (2004) Bringing the frame into focus: the Influence of regulatory fit on processing fluency and persuasion. J Pers Soc Psychol 86(2):205–218
Lee MC, Tsai TR (2010) What drives people to continue to play online games? An extension of technology model and theory of planned behavior. Int J Human Comput Interact 26(6):601–620
Lee H, Choi SY, Kang YS (2009) Formation of e-satisfaction and repurchase intention: moderating roles of computer self-efficacy and computer anxiety. Expert Syst Appl 36(4):7848–7859
Lin HF (2008) Antecedents of virtual community satisfaction and loyalty: an empirical test of competing theories. Cyber Psychol Behav 11(2):138–144
Lin JSC, Chang HC (2011) The role of technology readiness in self-service technology acceptance. Manag Serv Q 21(4):424–444
Lin CP, Ding CG (2005) Opening the black box: assessing the mediating mechanism of relationship quality and the moderating effects of prior experience in ISP service. Int J Service Industry Manage 16(1):55–80
Lin CP, Ding CG (2006) Evaluating the group differences in gender during the formation of relationship quality and loyalty in ISP service. J Organ End User Comput 18(2):38–62
Lin GTR, Sun CC (2009) Factors influencing satisfaction and loyalty in online shopping: an integrated model. Online Inf Rev 33(3):458–475
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. Comput Hum Behav 25(1):29–39
Mathwick C, Rigdon E (2004) Play, flow, and the online search experience. J Consumer Res 31(2):324–332
Novak TP, Hoffman D, Yung YF (2000) Measuring the customer experience in online environments: a structural modeling approach. Marking Sci 19(1):22–42
Nunnally JC (1978) Psychometric theory. McGraw-Hill, New York
O’Cass A, Carlson J (2010) Examining the effects of website-induced flow in professional sporting team websites. Internet Res 20(2):115–134
Oinas-Kukkonen H (2000) Balancing the vendor and consumer requirements for electronic shopping systems. Inf Technol Manage 1(1–2):73–84
Oliver RL (1980) A cognitive model of the antecedents and consequences of satisfaction decisions. J Mark Res 17(4):460–469
Oliver RL (1999) Whence consumer loyalty? J Mark 63:33–44
Plessner H, Unkelbach C, Memmert D, Baltes A, Kolb A (2009) Regulatory fit as a determinant of sport performance: how to succeed in a soccer penalty-shooting. Psychol Sport Exerc 10(1):108–115
Reichheld FF, Schefter P (2000) E-loyalty: your secret weapon on the web. Harvard Bus Rev 78(4):105–113
Shah J, Higgins ET, Friedman RS (1998) Performance incentives and means: how regulatory focus influences goal attainment. J Pers Soc Psychol 74(2):285–293
Shih H (2004) An empirical study on predicting user acceptance of e-shopping on the web. Inform Manage 41(3):351–368
Shin N (2006) Online learner’s ‘flow’ experience: an empirical study. Brit J Educ Technol 37(5):705–720
Siekpe JS (2005) An examination of the multidimensionality of the flow construct in a computer-mediated environment. J Electron Comm Res 6(1):31–43
Skadberg YX, Kimmel JR (2004) Visitors’ flow experience while browsing a web site: its measurement, contributing factors and consequences. Comput Hum Behav 20(3):403–422
Srinivasan SS, Anderson R, Ponnavolu K (2002) Customer loyalty in e-commerce: an exploration of its antecedents and consequences. J Retail 78(1):41–50
Taylor S, Todd PA (1995) Understanding information technology usage: a test of competing models. Inform Syst Res 6(2):144–176
Tong DYK (2009) A study of e-recruitment technology adoption in Malaysia. Indust Manage Data Syst 109(2):281–300
Trevino LK, Webster J (1992) Flow in computer-mediated communication: electronic mail and voice mail evaluation and impacts. Commun Res 19(5):539–573
Tsai HT, Huang HC, Jaw YT, Chen WK (2006) Why on-line customers remain with a particular e-retailer: an integrative model and empirical evidence. Psychol Market 23(5):447–464
Venkatesh V, Davis FD (2000) A theoretical extension of the technology acceptance model: four longitudinal field studies. Manage Sci 46(2):186–204
Webster J, Trevino LK, Ryan L (1993) The dimensionality and correlates of flow in human–computer interactions. Comput Hum Behav 9(4):411–426
Westbrook RA (1981) Sources of consumer satisfaction with retail outlets. J Retail 57(3):68–85
Wolfinbarger M, Gilly MC (2001) Shopping online for freedom, control, and fun. Calif Manage Rev 43(2):34–55
Wu JJ, Chang YS (2005) Towards understanding members’ interactivity, trust, and flow in online community. Indust Manage Data Syst 105(7):937–954
Zeithaml VA, Berry LL, Parasuraman A (1996) The behavioral consequences of service quality. J Mark 60(2):31–46
Zhou T (2012) Examining mobile banking user adoption from the perspectives of trust and flow experience. Inf Technol Manage 13(1):27–37
Zhou T, Lu YB (2011) Examining mobile instant messaging user loyalty from the perspectives of network externalities and flow experience. Comput Hum Behav 27(2):883–889
Zhou L, Dai L, Zhang D (2007) Online shopping acceptance model–critical survey of consumer factors in online shopping. J Electron Comm Res 8(1):41–62
Zhou T, Li H, Liu Y (2010) The effect of flow experience on mobile SNS users’ loyalty. Indust Manage Data Syst 110(6):930–946
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Hsu, CL., Wu, CC. & Chen, MC. An empirical analysis of the antecedents of e-satisfaction and e-loyalty: focusing on the role of flow and its antecedents. Inf Syst E-Bus Manage 11, 287–311 (2013). https://doi.org/10.1007/s10257-012-0194-8
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DOI: https://doi.org/10.1007/s10257-012-0194-8