參考文獻 |
Abdolrahmani, A., Kuber, R., & Branham, S. M. (2018). " Siri Talks at You" An Empirical
Investigation of Voice-Activated Personal Assistant (VAPA) Usage by Individuals Who Are Blind. In Proceedings of the 20th International ACM SIGACCESS Conference on Computers and Accessibility (p. 249-258).
Aguirre-Urreta, M., & Marakas, G. (2010). Is it really gender? An empirical investigation
into gender effects in technology adoption through the examination of individual differences. Human Technology, 6(2), 155-190.
Alalwan, A.A., Dwivedi, Y.K., Rana, N.P., Lal, B. and Williams, M.D. (2015). Consumer
adoption of internet banking in Jordan: examining the role of hedonic motivation, habit, self-efficacy and trust. Journal of Financial Services Marketing, 20(2), 145-157.
Almahamid, S., & Rub, F. A. (2011). Factors that determine continuance intention to use e-
learning system: An empirical investigation. In Proceedings of the International Conference on Telecommunication Technology and Applications (p. 242-246).
Ammari, T., Kaye, J., Tsai, J. Y., & Bentley, F. (2019). Music, Search, and IoT: How
People Use Voice Assistants. ACM Trans. Comput. Hum. Interact., 26(3), 1-28.
Anderson, E. W., & Sullivan, M. W. (1993). The antecedents and consequences of customer
satisfaction for firms. Marketing science, 12(2), 125-143.
Armstrong, J. S., & Overton, T. S. (1977). Estimating nonresponse bias in mail
surveys. Journal of marketing research, 14(3), 396-402.
Attié, E., & Meyer-Waarden, L. (2022). The acceptance and usage of smart connected objects
according to adoption stages: an enhanced technology acceptance model integrating the diffusion of innovation, uses and gratification and privacy calculus theories. Technological Forecasting and Social Change, 176, 121485. doi: 10.1016/j.techfore.2022.121485
Ayanso, A., Herath, T. C., & O′Brien, N. (2015). Understanding continuance intentions of
physicians with electronic medical records (EMR): An expectancy-confirmation perspective. Decision Support Systems, 77, 112-122.
Bandura, A. (1986), The explanatory and predictive scope of self-efficacy theory. Journal
of Social and Clinical Psychology, 4(3), 359-373.
Bandura, A. (1991). Social cognitive theory of self-regulation. Organizational Behavior and
Human Decision Processes. 50(2), 248-287.
Benlian, A., Klumpe, J., & Hinz, O. (2020). Mitigating the intrusive effects of smart home
assistants by using anthropomorphic design features: A multimethod investigation. Information Systems Journal, 30(6), 1010-1042.
Bhattacherjee, A. (2001a). Understanding information systems continuance: An
expectation– confirmation model. MIS Quarterly, 25(3), 351-370.
Bhattacherjee, A., Perols, J. and Sanford, C. (2008). Information technology continuance:
a theoretic extension and empirical test. Journal of Computer Information Systems, 49(1), 17-26.
Brown, S.A., Venkatesh, V. and Goyal, S. (2014). Expectation confirmation in information
systems research: a test of six competing models. MIS Quarterly, 38(3), 729-756.
Bryman, A. and Bell, E. (2007). Business Research Methods (2nd ed.). Oxford: Oxford
University Press.
Buteau, E., & Lee, J. (2021). Hey Alexa, why do we use voice assistants? The driving factors
of voice assistant technology use. Communication Research Reports, 38(5), 336-345.
Cao, G., & Wang, P. (2022). Revealing or concealing: privacy information disclosure in
intelligent voice assistant usage-a configurational approach. Industrial Management & Data Systems. doi: 10.1108/IMDS-08-2021-0485
Carrington, M. J., Neville, B. A., & Whitwell, G. J. (2014). Lost in translation: Exploring
the ethical consumer intention–behavior gap. Journal of Business Research, 67(1), 2759-2767.
Cassar, G. and Friedman, H. (2009). Does self-efficacy affect entrepreneurial investment?.
Strategic Entrepreneurship Journal, 3(3), 241-260.
Chakraborty, I. (2016). Patanjali: The Story of a Yoga Brand. Ushus Journal of Business
Management, 15(4), 55-67.
Cham, T. H., Cheah, J. H., Cheng, B. L., & Lim, X. J. (2021). I Am too old for this! Barriers
contributing to the non-adoption of mobile payment. International Journal of Bank Marketing. doi: 10.1108/IJBM-06-2021-0283
Chen, Y.-Y., Huang, H.-L., Hsu, Y.-C., Tseng, H.-C. and Lee, Y.-C. (2010). Confirmation
of expectations and satisfaction with the Internet shopping: the role of Internet self-efficacy. Computer and Information Science, 3(3), 14-22.
Chen, K., Chen, J.V. and Yen, D.C. (2011). Dimensions of self-efficacy in the study of smart
phone acceptance. Computer Standards and Interfaces, 33(4), 422-431.
Chen, H. T., & Chen, W. (2015). Couldn′t or wouldn′t? The influence of privacy concerns
and self-efficacy in privacy management on privacy protection. Cyberpsychology, Behavior, and Social Networking, 18(1), 13-19.
Chen, L., Zheng, W., Yang, B., & Bai, S. (2016). Transformational leadership, social capital
and organizational innovation. Leadership & Organization Development Journal, 37(7), 843-859.
Chen, H. T. (2018). Revisiting the privacy paradox on social media with an extended privacy
calculus model: The effect of privacy concerns, privacy self-efficacy, and social capital on privacy management. American behavioral scientist, 62(10), 1392-1412.
Cheng, Y.-M. (2014). Extending the expectation-confirmation model with quality and flow
to explore nurses’ continued blended e-learning intention. Information Technology and People, 27(3), 230-258.
Cheng, Y. M. (2020). Investigating medical professionals′ continuance intention of the cloud-
based e-learning system: an extension of expectation–confirmation model with flow theory. Journal of Enterprise Information Management, 34(4), 1169-1202.
Chiang, H.-S. (2013). Continuous usage of social networking sites: the effect of innovation
and gratification attributes. Online Information Review, 37(6), 851-871.
Chiang, H. S., & Hsiao, K. L. (2015). YouTube stickiness: the needs, personal, and
environmental perspective. Internet Research, 25(1), 85-106.
Choi, T. R., & Drumwright, M. E. (2021). “OK, Google, why do I use you?” Motivations,
post-consumption evaluations, and perceptions of voice AI assistants. Telematics and Informatics, 62, 101628. doi: 10.1016/j.tele.2021.101628
Chow, W. S., & Shi, S. (2014). Investigating students’ satisfaction and continuance intention
toward e-learning: An Extension of the expectation–confirmation model. Procedia-Social and Behavioral Sciences, 141, 1145-1149.
Chung, H., & Lee, S. (2018). Intelligent virtual assistant knows your life. arXiv:
1803.00466. http://arxiv.org/abs/1803.00466
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). New
Jersey: Lawrence Erlbaum.
Compeau, D.R. and Higgins, C.A. (1995). Computer self-efficacy: development of a
measure and initial test. MIS Quarterly, 19(2), 189-211.
Dabholkar, P. A., Shepherd, C. D., et al. (2000). A comprehensive framework for service
quality: An investigation of critical conceptual and measurement issues through a longitudinal study. Journal of Retailing, 76(2), 139-173.
Daneji, A. A., Ayub, A. F. M., & Khambari, M. N. M. (2019). The effects of perceived
usefulness, confirmation and satisfaction on continuance intention in using massive open online course (MOOC). Knowledge Management & E-Learning, 11(2), 201-214.
Davis, F.D., (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of
Information Technology. MIS Quarterly,13(3), 319-340.
Dehghani, M. (2018). Exploring the motivational factors on continuous usage intention of
smartwatches among actual users. Behaviour & Information Technology, 37 (2), 145-158.
Devaraj, S., Fan, M. and Kohli, R. (2002). Antecedents of B2C channel satisfaction and
preference: validating e-commerce metrics. Information Systems Research, 13(3), 316-333.
Dienlin, T., & Metzger, M. J. (2016). An extended privacy calculus model for SNSs:
Analyzing self-disclosure and self-withdrawal in a representative US sample. Journal of Computer-Mediated Communication, 21(5), 368-383.
Dubiel, M., Halvey, M., & Azzopardi, L. (2018). A survey investigating the usage of virtual
personal assistants. arXiv: 1807.04606. http://arxiv.org/abs/1807.04606
Dwivedi, Y.K., Lal, B. and Williams, M.D. (2009). Managing consumer adoption of
broadband: examining drivers and barriers. Industrial Management and Data Systems, 109(3), 357-369.
Eagly, A. H. (1990). On the advantages of reporting sex comparisons. American Psychologists,
45, 560–562.
Eastin, M.S and LaRose, R. (2000). Internet Self-Efficacy and the Psychology of the Digital
Divide. Journal of Computer- Mediated Communication ,6(1).
http://www.ascusc.org/jcmc/vol6/issue1/eastin.html
Eeuwen, M. (2017). Mobile conversational commerce: Messenger chatbots as the next
interface between businesses and consumers (Master′s thesis). University of Twente. http://essay.utwente.nl/71706/1/van%20Eeuwen_MA_BMS.pdf
El Baz, J., & Ruel, S. (2021). Can supply chain risk management practices mitigate the
disruption impacts on supply chains’ resilience and robustness? Evidence from an empirical survey in a COVID-19 outbreak era. International Journal of Production Economics, 233, 107972. doi: 10.1016/j.ijpe.2020.107972
ElHaffar, G., Durif, F., & Dubé, L. (2020). Towards closing the attitude-intention-behavior
gap in green consumption: A narrative review of the literature and an overview of future research directions. Journal of Cleaner Production, 275, 122556.
doi: 10.1016/j.jclepro.2020.122556
Elliot, S., Li, G., & Choi, C. (2013). Understanding service quality in a virtual travel
community environment. Journal of Business Research, 66, 1153-1160.
Fagan, M.H., Neill, S. and Wooldridge, B.R. (2004). An empirical investigation into the
relationship between computer self-efficacy, anxiety, experience, support and usage. Journal of Computer Information Systems, 44(2), 95-104.
Farah, M. F., Hasni, M. J. S., & Abbas, A. K. (2018). Mobile-banking adoption: Empirical
evidence from the banking sector in Pakistan. International Journal of Bank Marketing, 36(7), 1386-1413.
Fernandes, T., & Oliveira, E. (2021). Understanding consumers’ acceptance of automated
technologies in service encounters: Drivers of digital voice assistants’ adoption. Journal of Business Research, 122, 180-191.
Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables
and measurement error: Algebra and statistics. Journal of Marketing Research, 18(3), 382-388.
Gao, L., Bai, X., & Park, A. (2017). Understanding sustained participation in virtual travel
communities from the perspectives of is success model and flow theory. Journal of Hospitality & Tourism Research, 41(4), 475-509.
Gao, L., & Waechter, K. A. (2017). Examining the role of initial trust in user adoption of
mobile payment services: an empirical investigation. Information Systems Frontiers, 19(3), 525-548.
Geeng, C., & Roesner, F. (2019). Who′s in control? Interactions in multi-user smart homes.
In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (p. 1-13).
Gupta, A., Yousaf, A. and Mishra, A. (2020). How pre-adoption expectancies shape post-
adoption continuance intentions: an extended expectation-confirmation model. International Journal of Information Management, 52, 102094.
doi: 10.1016/j.ijinfomgt.2020.102094
Gupta, A., Dhiman, N., Yousaf, A., & Arora, N. (2021). Social comparison and continuance
intention of smart fitness wearables: An extended expectation confirmation theory perspective. Behaviour & Information Technology, 40(13), 1341-1354.
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal
of Marketing Theory and Practice, 19(2), 139-152.
Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An assessment of the use of
partial least squares structural equation modeling in marketing research. Journal of the academy of marketing science, 40(3), 414-433.
Hair Jr, J. F., Sarstedt, M., Matthews, L. M., & Ringle, C. M. (2016). Identifying and treating
unobserved heterogeneity with FIMIX-PLS: part I-method. European Business Review, 28(1), 63-76.
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019a). When to use and how to
report the results of PLS-SEM. European business review, 31(1), 2-24.
Halilovic, S., & Cicic, M. (2013). Antecedents of information systems user behaviour–
extended expectation-confirmation model. Behaviour & Information Technology, 32(4), 359-370.
Hamilton, K.A., Lee, S.Y., Chung, U.C., Liu, W. and Duff, B.R. (2020). Putting the ‘Me’
in endorsement: understanding and conceptualizing dimensions of self-endorsement using intelligent personal assistants. New Media and Society, 23(6), 1506-1526.
Han, S., & Yang, H. (2018). Understanding the adoption of intelligent personal assistants:
A parasocial relationship perspective. Industrial Management and Data Systems, 118 (3), 618-636.
Hanafizadeh, P., Keating, B.W. and Khedmatgozar, H.R. (2014a). A systematic review of
internet banking adoption. Telematics and Informatics, 31(3), 492-510.
Haque, M. Z., Qian, A., Amin, M., & Islam, T. (2020). An Empirical Study on Geotagging
Technology Adoption Among the Social Networking Sites (SNSs) Users: The Moderating Effect of Geotagg’s Use Frequency. Journal of Information & Knowledge Management, 19(3), 2050018. doi: 10.1142/S0219649220500185
Hasan, B. (2006). Delineating the effects of general and system-specific computer self-
efficacy beliefs on IS acceptance. Information and Management, 43(5), 565-571.
Hassan, L. M., Shiu, E., & Shaw, D. (2016). Who says there is an intention–behaviour gap?
Assessing the empirical evidence of an intention–behaviour gap in ethical consumption. Journal of Business Ethics, 136(2), 219-236.
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path
modeling in international marketing. Advances in International Marketing, 20, 277-320.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant
validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115-135.
Hernandez, B., Jimenez, J. and Martin, M.J. (2011). Age, gender and income: do they
really moderate online shopping behaviour?. Online Information Review, 35(1), 113-133.
Ho, S. Y. (2006). The attraction of Internet personalization to Web users. Electronic Markets,
16(1), 41-50.
Ho, C. H. (2010). Continuance intention of e-learning platform: Toward an integrated
model. International Journal of Electronic Business Management, 8(3), 206-215.
Holden, H., & Rada, R. (2011). Understanding the influence of perceived usability and
technology self-efficacy on teachers’ technology acceptance. Journal of Research on Technology in Education, 43(4), 343-367.
Hong, S. J., Thong, J., & Tam, K. Y. (2005). Understanding continued IT usage: An
extension to the expectation-confirmation model in IT domain. In Proceedings of the Pacific Asia Conference on Information Systems (p. 1267-1280).
Hong, J.-C., Hwang, M.-Y., Tai, K.-H. and Chen, Y.-L. (2014). Using calibration to enhance
students’ self-confidence in English vocabulary learning relevant to their judgment of over-confidence and predicted by smartphone self-efficacy and English learning anxiety. Computers and Education, 72, 313-322.
Hossain, M. A., & Quaddus, M. (2012). Information systems theory. New York: Springer.
Hsu, C. L., & Lin, J. C. C. (2016). Effect of perceived value and social influences on mobile
app stickiness and in-app purchase intention. Technological Forecasting and Social Change, 108, 42-53.
Hsu, C. L., & Lin, J. C. C. (2021). Factors affecting customers’ intention to voice shopping
over smart speaker. The Service Industries Journal, 41, 1-21.
Hu, Q., Lu, Y., Pan, Z., Gong, Y. and Yang, Z. (2021). Can AI artifacts influence human
cognition? The effects of artificial autonomy in intelligent personal assistants. International Journal of Information Management, 56, 102250.
doi: 10.1016/j.ijinfomgt.2020.102250
Huang, C.-C., Lin, T.-C. and Wang, J.-W. (2008). Understanding knowledge management
system usage antecedents: an integration of social cognitive theory and task technology fit. Information and Management, 45(6), 410-417.
Jain, S., Basu, S., Dwivedi, Y. K., & Kaur, S. (2022). Interactive voice assistants–Does brand
credibility assuage privacy risks?. Journal of Business Research, 139, 701-717.
Joo, Y. J., So, H. J., & Kim, N. H. (2018). Examination of relationships among students′
self-determination, technology acceptance, satisfaction, and continuance intention to use K-MOOCs. Computers & Education, 122, 260-272.
Jumaan, I. A., Hashim, N. H., & Al-Ghazali, B. M. (2020). The role of cognitive absorption
in predicting mobile internet users’ continuance intention: An extension of the expectation-confirmation model. Technology in Society, 63, 101355.
doi: 10.1016/j.techsoc.2020.101355
Kang, J. W., & Namkung, Y. (2019b). The role of personalization on continuance intention
in food service mobile apps: A privacy calculus perspective. International Journal of Contemporary Hospitality Management, 31(2), 734-752.
Kang, H., & Oh, J. (2021). Communication privacy management for smart speaker use:
Integrating the role of privacy self-efficacy and the multidimensional view. New Media & Society, 23, 1-23.
Kim, S. J., Wang, R. J. H., & Malthouse, E. C. (2015). The effects of adopting and using a
brand′s mobile application on customers′ subsequent purchase behavior. Journal of Interactive Marketing, 31, 28-41.
Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment
approach. International Journal of e-Collaboration (ijec), 11(4), 1-10.
Kock, N. (2017). Partial least squares path modeling. Cham: Springer.
Kowalczuk, P. (2018). Consumer acceptance of smart speakers: a mixed methods approach.
Journal of Research in Interactive Marketing, 12(4), 418-431.
LaRose R, Rifon N. J. (2007). Promoting i-Safety: Effects of privacy warnings and privacy
seals on risk assessment and online privacy behavior. Journal of Consumer Affairs, 41(1), 127–149.
Laufer, R. S., & Wolfe, M. (1977). Privacy as a concept and a social issue: A
multidimensional developmental theory. Journal of social Issues, 33(3), 22-42.
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, N. and Kwon, O. (2013). Para–social relationships and continuous use of mobile devices.
International Journal of Mobile Communications, 11(5), 465-484.
Li, Y. (2012). Theories in online information privacy research: A critical review and an
integrated framework. Decision support systems, 54(1), 471-481.
Li, J., Liu, X., Ma, L., & Zhang, W. (2019). Users’ intention to continue using social fitness-
tracking apps: expectation confirmation theory and social comparison theory perspective. Informatics for Health and Social Care, 44(3), 298-312.
Liao, Y., Vitak, J., Kumar, P., Zimmer, M., & Kritikos, K. (2019). Understanding the role
of privacy and trust in intelligent personal assistant adoption. In International Conference on Information (p. 102-113).
Limayem, M., Hirt, S.G. and Christy, M.K.C. (2007). How habit limits the predictive power
of intention: the case of information systems continuance. MIS Quarterly, 31(4), 705-737.
Lin, J. C. C. (2007). Online stickiness: its antecedents and effect on purchasing
intention. Behaviour & information technology, 26(6), 507-516.
Lin, C. S., Tzeng, G. H., Chin, Y. C., & Chang, C. C. (2010). Recommendation sources on
the intention to use e‐books in academic digital libraries. The Electronic Library, 28(6), 844-857.
Lin, X., Featherman, M., & Sarker, S. (2017). Understanding factors affecting users’ social
networking site continuance: A gender difference perspective. Information & Management, 54(3), 383-395.
Loureiro, S. M. C., Japutra, A., Molinillo, S., & Bilro, R. G. (2021). Stand by me: analyzing
the tourist–intelligent voice assistant relationship quality. International Journal of Contemporary Hospitality Management, 33(11), 3840-3859.
Luqman, A., Razak, R. C., Ismail, M., & Alwi, M. A. M. (2016). The influence of individual
characteristics in predicting mobile commerce usage activities’ continuance intention. Journal of Entrepreneurship and Business, 4(2), 54-69.
McLean, G., & Osei-Frimpong, K. (2019). Hey Alexa examine the variables influencing the
use of artificial intelligent in-home voice assistants. Computers in Human Behavior, 99, 28-37.
Miller, J. B. (1976). Toward a new psychology of women. Boston: Beacon Press.
Moorthy, A. E., & Vu, K.-P. (2015). Privacy concerns for use of voice-activated personal
assistant in the public space. International Journal of Human-Computer Interaction, 31 (4), 307-335.
Morris, M. G., & Venkatesh, V. (2000). Age differences in technology adoption decisions:
Implications for a changing work force. Personnel psychology, 53(2), 375-403.
Moussawi, S., Koufaris, M., & Benbunan-Fich, R. (2022). The role of user perceptions of
intelligence, anthropomorphism, and self-extension on continuance of use of personal intelligent agents. European Journal of Information Systems, 32, 1-22.
Nascimento, B., Oliveira, T. and Tam, C. (2018). Wearable technology: what explains
continuance intention in smartwatches?. Journal of Retailing and Consumer Services, 43, 157-169.
Nasirian, F., Ahmadian, M., & Lee, O. K. D. (2017). AI-based voice assistant systems:
Evaluating from the interaction and trust perspectives. In Proceedings of the Twenty-third Americas Conference on Information Systems.
Nguyen, H. V., Nguyen, C. H., & Hoang, T. T. B. (2019). Green consumption: Closing
the intention‐behavior gap. Sustainable Development, 27(1), 118-129.
Nguyen, Q. N., Ta, A., & Prybutok, V. (2019). An integrated model of voice-user interface
continuance intention: The gender effect. International Journal of Human–Computer Interaction, 35(15), 1362-1377.
Okuonghae, O., Igbinovia, M. O., & Adebayo, J. O. (2022). Technological readiness and
computer self-efficacy as predictors of E-learning adoption by LIS students in Nigeria. Libri, 72(1), 13-25.
Oliver, R. L. (1980). A cognitive model for the antecedents and consequences of satisfaction
decisions. Journal of Marketing Research, 17, 460-469.
Oliver, R. (1999). Whence consumer loyalty? The Journal of Marketing, 63, 33–44.
Orji, R. O. (2010). Impact of gender and nationality on acceptance of a digital library: an
empirical validation of nationality based UTAUT using SEM. Journal of Emerging Trends in Computing and Information Sciences, 1(2), 68-79.
Pal, D., Babakerkhell, M. D., & Zhang, X. (2021). Exploring the Determinants of Users’
Continuance Usage Intention of Smart Voice Assistants. IEEE Access, 9, 162259-162275.
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.
Rahi, S., Mansour, M. M. O., Alharafsheh, M., & Alghizzawi, M. (2021). The post-adoption
behavior of internet banking users through the eyes of self-determination theory and expectation confirmation model. Journal of Enterprise Information Management, 34(6). doi: 10.1108/jeim-04-2020-0156
Rifon, N. J., LaRose, R., & Choi, S. M. (2005). Your privacy is sealed: Effects of web
privacy seals on trust and personal disclosures. Journal of consumer affairs, 39(2), 339-362.
Rogers, E. M. (1995). Diffusion of innovations (4th ed.). New York: Free Press.
Saffarizadeh, K., Boodraj, M., & Alashoor, T. M. (2017). Conversational Assistants:
Investigating Privacy Concerns, Trust, and Self-Disclosure. In Proceedings of the International Conference on Information Systems (p. 1-12).
Salloum, S. A., AlAhbabi, N. M. N., Habes, M., Aburayya, A., & Akour, I. (2021). Predicting
the intention to use social media sites: A hybrid SEM-machine learning approach. In International Conference on Advanced Machine Learning Technologies and Applications (p. 324-334).
Sarstedt, M., Hair, J. F., Pick, M., Liengaard, B. D., Radomir, L., & Ringle, C. M. (2022).
Progress in partial least squares structural equation modeling use in marketing research in the last decade. Psychology & Marketing, 39(5), 1035-1064.
Schnall, R., Higgins, T., Brown, W., Carballo-Dieguez, A., & Bakken, S. (2015). Trust,
perceived risk, perceived ease of use and perceived usefulness as factors related to mHealth technology use. Studies in health technology and informatics, 216, 467-471.
Schneider, W. and Chein, J.M. (2003). Controlled and automatic processing: behavior,
theory, and biological mechanisms. Cognitive Science, 27(3), 525-559.
Seman, N. A. A., Govindan, K., Mardani, A., Zakuan, N., Saman, M. Z. M., Hooker, R. E., &
Ozkul, S. (2019). The mediating effect of green innovation on the relationship between green supply chain management and environmental performance. Journal of cleaner production, 229, 115-127.
Shao, C., & Kwon, K. H. (2021). Hello Alexa! Exploring effects of motivational factors and
social presence on satisfaction with artificial intelligence‐enabled gadgets. Human Behavior and Emerging Technologies, 3(5), 978-988.
Sharma, S., Singh, G., Sharma, R., Jones, P., Kraus, S., & Dwivedi, Y. K. (2020). Digital
health innovation: exploring adoption of COVID-19 digital contact tracing apps. IEEE Transactions on Engineering Management. doi: 10.1109/TEM.2020.3019033
Sheeran, P., & Webb, T. L. (2016). The intention–behavior gap. Social and personality
psychology compass, 10(9), 503-518.
Shezan, F. H., Hu, H., Wang, J., Wang, G., & Tian, Y. (2020). Read between the lines:
An empirical measurement of sensitive applications of voice personal assistant systems. In Proceedings of The Web Conference 2020 (p. 1006-1017).
Shiau, W. L., Yuan, Y., Pu, X., Ray, S., & Chen, C. C. (2020). Understanding fintech
continuance: perspectives from self-efficacy and ECT-IS theories. Industrial Management & Data Systems, 120, 1659-1689.
Shmueli, G., & Koppius, O. R. (2011). Predictive analytics in information systems
research. MIS quarterly, 35(3), 553-572.
Singh, S. K., Chen, J., Del Giudice, M., & El-Kassar, A. N. (2019). Environmental ethics,
environmental performance, and competitive advantage: Role of environmental training. Technological Forecasting and Social Change, 146, 203-211.
Söllner, M., Bitzer, P., Janson, A., & Leimeister, J. M. (2018). Process is king: Evaluating
the performance of technology-mediated learning in vocational software training. Journal of Information Technology, 33(3), 233-253.
Stone, R. W., & Baker-Eveleth, L. (2013). Students’ expectation, confirmation, and
continuance intention to use electronic textbooks. Computers in Human Behavior, 29(3), 984-990.
Sun, Y., Li, S., & Yu, L. (2022). The dark sides of AI personal assistant: effects of service
failure on user continuance intention. Electronic Markets, 32(1), 17-39.
Sundar, S. S., & Marathe, S. S. (2010). Personalization versus customization: The
importance of agency, privacy, and power usage. Human Communication Research, 36(3), 298-322.
Susanto, A., Chang, Y. and Ha, Y. (2016). “Determinants of continuance intention to use
the smartphone banking services: an extension to the expectation-confirmation model”. Industrial Management and Data Systems, 116(3), 508-525.
Taylor, S. and Todd, P.A. (1995). Decomposition and crossover effects in the theory of
planned behavior: a study of consumer adoption intentions. International Journal of Research in Marketing, 12(2), 137-155.
Thong, J.Y.L., Hong, S.-J. and Tam, K.Y. (2006). The effects of post-adoption beliefs on
the expectation-confirmation model for information technology continuance. International Journal of Human-Computer Studies, 64(9), 799-810.
Trajkova, M., & Martin-Hammond, A. (2020). "Alexa is a Toy": Exploring Older Adults′
Reasons for Using, Limiting, and Abandoning Echo. In Proceedings of the 2020 CHI conference on human factors in computing systems (p. 1-13).
Tsai, H.-T., & Bagozzi, R. P. (2014). Contribution behavior in virtual communities:
Cognitive, emotional and social influences. MIS Quarterly, 38, 143-163.
Vailshery, L. S. (2021). Number of digital voice assistants in use worldwide from 2019 to
2024. https://www.statista.com/statistics/973815/worldwide-digitalvoice-assistant-in-use/
Valdez-Juárez, L. E., Gallardo-Vázquez, D., & Ramos-Escobar, E. A. (2021). Online buyers
and open innovation: Security, experience, and satisfaction. Journal of Open Innovation: Technology, Market, and Complexity, 7(1), 37. doi: 10.3390/joitmc7010037
Vimalkumar, M., Sharma, S. K., Singh, J. B., & Dwivedi, Y. K. (2021). Okay Google, what
about my privacy?: User’s privacy perceptions and acceptance of voice based digital assistants. Computers in Human Behavior, 120, 106763. doi: 10.1016/j.chb.2021.106763
Wang, E. S. T., & Lin, R. L. (2017). Perceived quality factors of location-based apps on
trust, perceived privacy risk, and continuous usage intention. Behaviour & Information Technology, 36(1), 2-10.
Wang, M. M., & Wang, J. J. (2019). Understanding solvers′ continuance intention in
crowdsourcing contest platform: An extension of expectation-confirmation model. Journal of theoretical and applied electronic commerce research, 14(3), 17-33.
Wilkowska, W., Offermann-van Heek, J., & Ziefle, M. (2021). User Acceptance of
Lifelogging Technologies: The Power of Experience and Technological Self-Efficacy. In Proceedings of the 7th International Conference on Information and Communication Technologies for Ageing Well and e-Health, Prague, Czech Republic (p. 24-26).
Wold, H. (1985). Encyclopedia of statistical sciences. New York: Wiley.
Wong, K. K. K. (2013). Partial least squares structural equation modeling (PLS-SEM)
techniques using SmartPLS. Marketing Bulletin, 24(1), 1-32.
Wood, R. and Bandura, A. (1989a), Impact of conceptions of ability on self-regulatory
mechanisms and complex decision making, Journal of Personality and Social Psychology, 56(3), 407-415.
Wood, R. and Bandura, A. (1989b). Social cognitive theory of organizational management.
Academy of Management Review, 14(3), 361-384.
Wu, J. H., Wang, S. C., & Tsai, H. H. (2010). Falling in love with online games: The uses
and gratifications perspective. Computers in Human Behavior, 26(6), 1862-1871.
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. doi: 10.1016/j.ijinfomgt.2020.102099
Yang, Q., Gong, X., Zhang, K.Z.K., Liu, H. and Lee, M.K.O. (2020), Self-disclosure in
mobile payment applications: common and differential effects of personal and proxy control enhancing mechanisms, International Journal of Information Management, 52, 102065. doi: 10.1016/j.ijinfomgt.2019.102065
Yao, M. Z., Rice, R. E., & Wallis, K. (2007). Predicting user concerns about online
privacy. Journal of the American Society for Information Science and Technology, 58(5), 710-722.
Yi, M.Y. and Hwang, Y. (2003), Predicting the use of web-based information systems:
self-efficacy, enjoyment, learning goal orientation, and the technology acceptance model, International Journal of Human-Computer Studies, 59(4), 431-449.
Youn, S. (2009). Determinants of online privacy concern and its influence on privacy
protection behaviors among young adolescents. Journal of Consumer affairs, 43(3), 389-418.
Yu, T. K., Lin, M. L., & Liao, Y. K. (2017). Understanding factors influencing information
communication technology adoption behavior: The moderators of information literacy and digital skills. Computers in Human Behavior, 71, 196-208.
Yuan, S., Liu, Y., Yao, R. and Liu, J. (2016). An investigation of users’ continuance intention
towards mobile banking in China. Information Development, 32(1), 20-34.
Zhang, H., Lu, Y., Gupta, S., & Gao, P. (2015). Understanding group-buying websites
continuance: an extension of expectation confirmation model. Internet Research, 25(5), 767-793.
Zhao, X., Lynch Jr, J. G., & Chen, Q. (2010). Reconsidering Baron and Kenny: Myths
and truths about mediation analysis. Journal of consumer research, 37(2), 197-206.
Zheng, Y., Zhao, K. and Stylianou, A. (2013). The impacts of information quality and system
quality on users’ continuance intention in information-exchange virtual communities: an empirical investigation. Decision Support Systems, 56(1), 513-524.
Zhou, T. (2012). Examining Location-based Services Usage from the Perspectives of Unified
Theory of Acceptance and Use of Technology and Privacy Risk. Journal of Electronic Commerce Research, 13(2), 135-144.
Zhou, T. (2013). An empirical examination of continuance intention of mobile payment
services. Decision Support Systems, 54(2), 1085-1091.
Zhou, W., Tsiga, Z., Li, B., Zheng, S. and Jiang, S. (2018), What influence users’ e-finance
continuance intention? The moderating role of trust, Industrial Management and Data Systems, 118(8), 1647-1670.
Zimmeck, S., Story, P., Smullen, D., Ravichander, A., Wang, Z., Reidenberg, J., Russell, N. C.,
& Sadeh, N. (2019). Maps: Scaling privacy compliance analysis to a million apps. Proceedings on Privacy Enhancing Technologies 2019, 3, 66-86. |