How to cite this paper
Cebeci, U., Ertug, A & Turkcan, H. (2020). Exploring the determinants of intention to use self-checkout systems in super market chain and its application.Management Science Letters , 10(5), 1027-1036.
Refrences
Abdullah, F., Ward, R., & Ahmed, E. (2016). Investigating the influence of the most commonly used external variables of TAM on students’ Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) of e-portfolios. Computers in Human Behavior, 63, 75-90.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.
Akour, I. A., & Dwairi, M. A. (2011). Testing technology acceptance model in developing countries: The case of Jordan. International Journal of Business and Social Science, 2(14).278-284.
Bailey, A. A., Pentina, I., Mishra, A. S., & Ben Mimoun, M. S. (2017). Mobile payments adoption by US consumers: an extended TAM. International Journal of Retail & Distribution Management, 45(6), 626-640.
Brown, I. T. (2002). Individual and technological factors affecting perceived ease of use of web‐based learning technolo-gies in a developing country. The Electronic Journal of Information Systems in Developing Countries, 9(1), 1-15.
Bulmer, S., Elms, J., & Moore, S. (2018). Exploring the adoption of self-service checkouts and the associated social obli-gations of shopping practices. Journal of Retailing and Consumer Services, 42, 107-116.
Chen, L., Gillenson, M. L., & Sherrell, D. L. (2002). Enticing online consumers: A technology acceptance perspective. In-formation & Management, 39(8), 705-719.
Chen, K., Chen, J. V., & Yen, D. C. (2011). Dimensions of self-efficacy in the study of smart phone acceptance. Computer Standards & Interfaces, 33(4), 422-431.
Cheng, Y. M. (2015). Towards an understanding of the factors affecting m-learning acceptance: Roles of technological characteristics and compatibility. Asia Pacific Management Review, 20(3), 109-119.
Chipp, K., Hoenig, S. & Nel, D. (2006). What can industrializing countries do to avoid the need for marketing reform?. In Sheth, J.N. & Sisodia, R.S., Does Marketing Need Reform?: Fresh Perspectives on the Future. New York: M.E. Sharpe.
Chuo, Y. H., Tsai, C. H., Lan, Y. L., & Tsai, C. S. (2011). The effect of organizational support, self efficacy, and computer anxiety on the usage intention of e-learning system in hospital. African Journal of Business Management, 5(14), 5518-5523.
Collier, J. E., Moore, R. S., Horky, A., & Moore, M. L. (2015). Why the little things matter: Exploring situational influ-ences on customers' self-service technology decisions. Journal of Business Research, 68(3), 703-710.
Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quar-terly, 19(2), 189-211.
Curran, J. M., Meuter, M. L., & Surprenant, C. F. (2003). Intentions to use self-service technologies: a confluence of mul-tiple attitudes. Journal of Service Research, 5(3), 209-224.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 13(3), 319-340.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two the-oretical models. Management science, 35(8), 982-1003.
Demoulin, N. T., & Djelassi, S. (2016). An integrated model of self-service technology (SST) usage in a retail context. In-ternational Journal of Retail & Distribution Management, 44(5), 540-559.
Dutot, V. (2015). Factors influencing near field communication (NFC) adoption: An extended TAM approach. The Jour-nal of High Technology Management Research, 26(1), 45-57.
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Read-ing, MA: Addison-Wesley.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–51.
Gelbrich, K., & Sattler, B. (2014). Anxiety, crowding, and time pressure in public self-service technology acceptance. Journal of Services Marketing, 28(1), 82-94.
Groß, M. (2018). Heterogeneity in consumers’ mobile shopping acceptance: A finite mixture partial least squares model-ling approach for exploring and characterising different shopper segments. Journal of Retailing and Consumer Ser-vices, 40, 8-18.
Gu, J. C., Lee, S. C., & Suh, Y. H. (2009). Determinants of behavioral intention to mobile banking. Expert Systems with Applications, 36(9), 11605-11616.
Jaklič, J., Grublješič, T., & Popovič, A. (2018). The role of compatibility in predicting business intelligence and analytics use intentions. International Journal of Information Management, 43, 305-318.
Jin, C. H. (2014). Adoption of e-book among college students: The perspective of an integrated TAM. Computers in Hu-man Behavior, 41, 471-477.
Kallweit, K., Spreer, P., & Toporowski, W. (2014). Why do customers use self-service information technologies in retail? The mediating effect of perceived service quality. Journal of Retailing and Consumer Services, 21(3), 268-276.
Kim, C., Mirusmonov, M., & Lee, I. (2010). An empirical examination of factors influencing the intention to use mobile payment. Computers in Human Behavior, 26(3), 310-322.
Kim, J., & Forsythe, S. (2010). Factors affecting adoption of product virtualization technology for online consumer elec-tronics shopping. International Journal of Retail & Distribution Management, 38(3), 190-204.
Kim, M., & Qu, H. (2014). Travelers' behavioral intention toward hotel self-service kiosks usage. International Journal of Contemporary Hospitality Management, 26(2), 225-245.
Larson, R. B. (2019). Supermarket self-checkout usage in the United States. Services Marketing Quarterly, 40(2), 141-156.
Lee, H. J., & Yang, K. (2013). Interpersonal service quality, self-service technology (SST) service quality, and retail pat-ronage. Journal of Retailing and Consumer Services, 20(1), 51-57.
Lee, H. J., & Lyu, J. (2016). Personal values as determinants of intentions to use self-service technology in retailing. Computers in Human Behavior, 60, 322-332.
Lee, J., Kim, J., & Choi, J. Y. (2019). The adoption of virtual reality devices: The technology acceptance model integrat-ing enjoyment, social interaction, and strength of the social ties. Telematics and Informatics, 39, 37-48.
Leung, L. S. K., & Matanda, M. J. (2013). The impact of basic human needs on the use of retailing self-service technolo-gies: A study of self-determination theory. Journal of Retailing and Consumer Services, 20(6), 549-559.
Liébana-Cabanillas, F., Sánchez-Fernández, J., & Muñoz-Leiva, F. (2014). Antecedents of the adoption of the new mobile payment systems: The moderating effect of age. Computers in Human Behavior, 35, 464-478.
Liu, S. (2012). The impact of forced use on customer adoption of self-service technologies. Computers in Human Behav-ior, 28(4), 1194-1201.
Liu, G. S., & Tai, P. T. (2016). A Study of Factors Affecting the Intention to Use Mobile Payment Services in Vietnam. Economics, 4(6), 249-273.
Lu, H. P., Hsu, C. L., & Hsu, H. Y. (2005). An empirical study of the effect of perceived risk upon intention to use online applications. Information Management & Computer Security, 13(2), 106-120.
Meuter, M. L., Ostrom, A. L., Roundtree, R. I., & Bitner, M. J. (2000). Self-service technologies: understanding customer satisfaction with technology-based service encounters. Journal of Marketing, 64(3), 50-64.
Meuter, M. L., Ostrom, A. L., Bitner, M. J., & Roundtree, R. (2003). The influence of technology anxiety on consumer use and experiences with self-service technologies. Journal of Business Research, 56(11), 899-906.
Natarajan, T., Balasubramanian, S. A., & Kasilingam, D. L. (2018). The moderating role of device type and age of users on the intention to use mobile shopping applications. Technology in Society, 53, 79-90.
Nunnally, J. C. (1978). Psychometric Theory. 2nd ed., McGraw-Hill Series in Psychology), New York, McGraw-Hill.
Oghazi, P., Mostaghel, R., Hultman, M., & Parida, V. (2012). Antecedents of technology-based self-service acceptance: a proposed model. Services Marketing Quarterly, 33(3), 195-210.
Oh, J., & Yoon, S. J. (2014). Validation of haptic enabling technology acceptance model (HE-TAM): Integration of IDT and TAM. Telematics and Informatics, 31(4), 585-596.
Orel, F. D., & Kara, A. (2014). Supermarket self-checkout service quality, customer satisfaction, and loyalty: Empirical evidence from an emerging market. Journal of Retailing and Consumer Services, 21(2), 118-129.
Ozturk, A. B., Bilgihan, A., Nusair, K., & Okumus, F. (2016). What keeps the mobile hotel booking users loyal? Investi-gating the roles of self-efficacy, compatibility, perceived ease of use, and perceived convenience. International Jour-nal of Information Management, 36(6), 1350-1359.
Park, J., Ahn, J., Thavisay, T., & Ren, T. (2019). Examining the role of anxiety and social influence in multi-benefits of mobile payment service. Journal of Retailing and Consumer Services, 47, 140-149
Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879-903.
Sánchez, R. A., & Hueros, A. D. (2010). Motivational factors that influence the acceptance of Moodle using TAM. Com-puters in human behavior, 26(6), 1632-1640.
Sánchez-Prieto, J. C., Olmos-Migueláñez, S., & García-Peñalvo, F. J. (2017). MLearning and pre-service teachers: An as-sessment of the behavioral intention using an expanded TAM model. Computers in Human Behavior, 72, 644-654.
Schliewe, J., & Pezoldt, K. (2010). A cross-cultural comparison of factors influencing self-scan checkout use. Journal of Business & Economics Research, 8(10), 39-47.
Schmidthuber, L., Maresch, D., & Ginner, M. (2018). Disruptive technologies and abundance in the service sector-toward a refined technology acceptance model. Technological Forecasting and Social Change.
Tsai, J. M., Cheng, M. J., Tsai, H. H., Hung, S. W., & Chen, Y. L. (2019). Acceptance and resistance of telehealth: The perspective of dual-factor concepts in technology adoption. International Journal of Information Management, 49, 34-44.
Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342-365.
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., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. Management Information Systems Quarterly, 27(3), 425–478.
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision sci-ences, 39(2), 273-315.
Verma, P., & Sinha, N. (2018). Integrating perceived economic wellbeing to technology acceptance model: The case of mobile based agricultural extension service. Technological Forecasting and Social Change, 126, 207-216.
Wang, C., Harris, J., & Patterson, P. (2013). The roles of habit, self-efficacy, and satisfaction in driving continued use of self-service technologies: a longitudinal study. Journal of Service Research, 16(3), 400-414.
Weijters, B., Rangarajan, D., Falk, T., & Schillewaert, N. (2007). Determinants and outcomes of customers' use of self-service technology in a retail setting. Journal of Service Research, 10(1), 3-21.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.
Akour, I. A., & Dwairi, M. A. (2011). Testing technology acceptance model in developing countries: The case of Jordan. International Journal of Business and Social Science, 2(14).278-284.
Bailey, A. A., Pentina, I., Mishra, A. S., & Ben Mimoun, M. S. (2017). Mobile payments adoption by US consumers: an extended TAM. International Journal of Retail & Distribution Management, 45(6), 626-640.
Brown, I. T. (2002). Individual and technological factors affecting perceived ease of use of web‐based learning technolo-gies in a developing country. The Electronic Journal of Information Systems in Developing Countries, 9(1), 1-15.
Bulmer, S., Elms, J., & Moore, S. (2018). Exploring the adoption of self-service checkouts and the associated social obli-gations of shopping practices. Journal of Retailing and Consumer Services, 42, 107-116.
Chen, L., Gillenson, M. L., & Sherrell, D. L. (2002). Enticing online consumers: A technology acceptance perspective. In-formation & Management, 39(8), 705-719.
Chen, K., Chen, J. V., & Yen, D. C. (2011). Dimensions of self-efficacy in the study of smart phone acceptance. Computer Standards & Interfaces, 33(4), 422-431.
Cheng, Y. M. (2015). Towards an understanding of the factors affecting m-learning acceptance: Roles of technological characteristics and compatibility. Asia Pacific Management Review, 20(3), 109-119.
Chipp, K., Hoenig, S. & Nel, D. (2006). What can industrializing countries do to avoid the need for marketing reform?. In Sheth, J.N. & Sisodia, R.S., Does Marketing Need Reform?: Fresh Perspectives on the Future. New York: M.E. Sharpe.
Chuo, Y. H., Tsai, C. H., Lan, Y. L., & Tsai, C. S. (2011). The effect of organizational support, self efficacy, and computer anxiety on the usage intention of e-learning system in hospital. African Journal of Business Management, 5(14), 5518-5523.
Collier, J. E., Moore, R. S., Horky, A., & Moore, M. L. (2015). Why the little things matter: Exploring situational influ-ences on customers' self-service technology decisions. Journal of Business Research, 68(3), 703-710.
Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quar-terly, 19(2), 189-211.
Curran, J. M., Meuter, M. L., & Surprenant, C. F. (2003). Intentions to use self-service technologies: a confluence of mul-tiple attitudes. Journal of Service Research, 5(3), 209-224.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 13(3), 319-340.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two the-oretical models. Management science, 35(8), 982-1003.
Demoulin, N. T., & Djelassi, S. (2016). An integrated model of self-service technology (SST) usage in a retail context. In-ternational Journal of Retail & Distribution Management, 44(5), 540-559.
Dutot, V. (2015). Factors influencing near field communication (NFC) adoption: An extended TAM approach. The Jour-nal of High Technology Management Research, 26(1), 45-57.
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Read-ing, MA: Addison-Wesley.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–51.
Gelbrich, K., & Sattler, B. (2014). Anxiety, crowding, and time pressure in public self-service technology acceptance. Journal of Services Marketing, 28(1), 82-94.
Groß, M. (2018). Heterogeneity in consumers’ mobile shopping acceptance: A finite mixture partial least squares model-ling approach for exploring and characterising different shopper segments. Journal of Retailing and Consumer Ser-vices, 40, 8-18.
Gu, J. C., Lee, S. C., & Suh, Y. H. (2009). Determinants of behavioral intention to mobile banking. Expert Systems with Applications, 36(9), 11605-11616.
Jaklič, J., Grublješič, T., & Popovič, A. (2018). The role of compatibility in predicting business intelligence and analytics use intentions. International Journal of Information Management, 43, 305-318.
Jin, C. H. (2014). Adoption of e-book among college students: The perspective of an integrated TAM. Computers in Hu-man Behavior, 41, 471-477.
Kallweit, K., Spreer, P., & Toporowski, W. (2014). Why do customers use self-service information technologies in retail? The mediating effect of perceived service quality. Journal of Retailing and Consumer Services, 21(3), 268-276.
Kim, C., Mirusmonov, M., & Lee, I. (2010). An empirical examination of factors influencing the intention to use mobile payment. Computers in Human Behavior, 26(3), 310-322.
Kim, J., & Forsythe, S. (2010). Factors affecting adoption of product virtualization technology for online consumer elec-tronics shopping. International Journal of Retail & Distribution Management, 38(3), 190-204.
Kim, M., & Qu, H. (2014). Travelers' behavioral intention toward hotel self-service kiosks usage. International Journal of Contemporary Hospitality Management, 26(2), 225-245.
Larson, R. B. (2019). Supermarket self-checkout usage in the United States. Services Marketing Quarterly, 40(2), 141-156.
Lee, H. J., & Yang, K. (2013). Interpersonal service quality, self-service technology (SST) service quality, and retail pat-ronage. Journal of Retailing and Consumer Services, 20(1), 51-57.
Lee, H. J., & Lyu, J. (2016). Personal values as determinants of intentions to use self-service technology in retailing. Computers in Human Behavior, 60, 322-332.
Lee, J., Kim, J., & Choi, J. Y. (2019). The adoption of virtual reality devices: The technology acceptance model integrat-ing enjoyment, social interaction, and strength of the social ties. Telematics and Informatics, 39, 37-48.
Leung, L. S. K., & Matanda, M. J. (2013). The impact of basic human needs on the use of retailing self-service technolo-gies: A study of self-determination theory. Journal of Retailing and Consumer Services, 20(6), 549-559.
Liébana-Cabanillas, F., Sánchez-Fernández, J., & Muñoz-Leiva, F. (2014). Antecedents of the adoption of the new mobile payment systems: The moderating effect of age. Computers in Human Behavior, 35, 464-478.
Liu, S. (2012). The impact of forced use on customer adoption of self-service technologies. Computers in Human Behav-ior, 28(4), 1194-1201.
Liu, G. S., & Tai, P. T. (2016). A Study of Factors Affecting the Intention to Use Mobile Payment Services in Vietnam. Economics, 4(6), 249-273.
Lu, H. P., Hsu, C. L., & Hsu, H. Y. (2005). An empirical study of the effect of perceived risk upon intention to use online applications. Information Management & Computer Security, 13(2), 106-120.
Meuter, M. L., Ostrom, A. L., Roundtree, R. I., & Bitner, M. J. (2000). Self-service technologies: understanding customer satisfaction with technology-based service encounters. Journal of Marketing, 64(3), 50-64.
Meuter, M. L., Ostrom, A. L., Bitner, M. J., & Roundtree, R. (2003). The influence of technology anxiety on consumer use and experiences with self-service technologies. Journal of Business Research, 56(11), 899-906.
Natarajan, T., Balasubramanian, S. A., & Kasilingam, D. L. (2018). The moderating role of device type and age of users on the intention to use mobile shopping applications. Technology in Society, 53, 79-90.
Nunnally, J. C. (1978). Psychometric Theory. 2nd ed., McGraw-Hill Series in Psychology), New York, McGraw-Hill.
Oghazi, P., Mostaghel, R., Hultman, M., & Parida, V. (2012). Antecedents of technology-based self-service acceptance: a proposed model. Services Marketing Quarterly, 33(3), 195-210.
Oh, J., & Yoon, S. J. (2014). Validation of haptic enabling technology acceptance model (HE-TAM): Integration of IDT and TAM. Telematics and Informatics, 31(4), 585-596.
Orel, F. D., & Kara, A. (2014). Supermarket self-checkout service quality, customer satisfaction, and loyalty: Empirical evidence from an emerging market. Journal of Retailing and Consumer Services, 21(2), 118-129.
Ozturk, A. B., Bilgihan, A., Nusair, K., & Okumus, F. (2016). What keeps the mobile hotel booking users loyal? Investi-gating the roles of self-efficacy, compatibility, perceived ease of use, and perceived convenience. International Jour-nal of Information Management, 36(6), 1350-1359.
Park, J., Ahn, J., Thavisay, T., & Ren, T. (2019). Examining the role of anxiety and social influence in multi-benefits of mobile payment service. Journal of Retailing and Consumer Services, 47, 140-149
Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879-903.
Sánchez, R. A., & Hueros, A. D. (2010). Motivational factors that influence the acceptance of Moodle using TAM. Com-puters in human behavior, 26(6), 1632-1640.
Sánchez-Prieto, J. C., Olmos-Migueláñez, S., & García-Peñalvo, F. J. (2017). MLearning and pre-service teachers: An as-sessment of the behavioral intention using an expanded TAM model. Computers in Human Behavior, 72, 644-654.
Schliewe, J., & Pezoldt, K. (2010). A cross-cultural comparison of factors influencing self-scan checkout use. Journal of Business & Economics Research, 8(10), 39-47.
Schmidthuber, L., Maresch, D., & Ginner, M. (2018). Disruptive technologies and abundance in the service sector-toward a refined technology acceptance model. Technological Forecasting and Social Change.
Tsai, J. M., Cheng, M. J., Tsai, H. H., Hung, S. W., & Chen, Y. L. (2019). Acceptance and resistance of telehealth: The perspective of dual-factor concepts in technology adoption. International Journal of Information Management, 49, 34-44.
Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342-365.
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., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. Management Information Systems Quarterly, 27(3), 425–478.
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision sci-ences, 39(2), 273-315.
Verma, P., & Sinha, N. (2018). Integrating perceived economic wellbeing to technology acceptance model: The case of mobile based agricultural extension service. Technological Forecasting and Social Change, 126, 207-216.
Wang, C., Harris, J., & Patterson, P. (2013). The roles of habit, self-efficacy, and satisfaction in driving continued use of self-service technologies: a longitudinal study. Journal of Service Research, 16(3), 400-414.
Weijters, B., Rangarajan, D., Falk, T., & Schillewaert, N. (2007). Determinants and outcomes of customers' use of self-service technology in a retail setting. Journal of Service Research, 10(1), 3-21.