Technology acceptance theories and factors influencing artificial Intelligence-based intelligent products

Telematics and Informatics - Tập 47 - Trang 101324 - 2020
Kwonsang Sohn1, Ohbyung Kwon1
1School of Management, Kyung Hee University, Kyungheedae-ro 26, Dongdaemun-gu, Seoul, South Korea

Tài liệu tham khảo

Adams, 1992, Perceived usefulness, ease of use, and usage of information technology: a replication, MIS Q, 16, 227, 10.2307/249577 Adapa, 2018, Factors influencing the adoption of smart wearable devices, Int. J. Hum. Comput. Interact., 34, 399, 10.1080/10447318.2017.1357902 Adomian, 1988, A review of the decomposition method in applied mathematics, J. Math. Anal. Appl., 135, 501, 10.1016/0022-247X(88)90170-9 Agarwal, 2000, Time flies when you're having fun: cognitive absorption and beliefs about information technology usage, MIS Q., 24, 665, 10.2307/3250951 Aguilar, 2018, An adaptive intelligent management system of advertising for social networks: A case study of Facebook, IEEE Trans. Comput. Soc. Syst., 5, 20, 10.1109/TCSS.2017.2759188 Ajzen, 1985, 11 Ajzen, 1991, The theory of planned behavior, Organ. Behav. Hum. Decis. Process., 50, 179, 10.1016/0749-5978(91)90020-T Ajzen, 1973, Attitudinal and normative variables as predictors of specific behavior, J. Pers. Soc. Psychol., 27, 41, 10.1037/h0034440 André, 2018, Consumer choice and autonomy in the age of artificial intelligence and big data, Cust. Need Solut., 5, 28, 10.1007/s40547-017-0085-8 Ang, 2004, Decomposition analysis for policymaking in energy: which is the preferred method?, Energy Policy, 32, 1131, 10.1016/S0301-4215(03)00076-4 Basoglu, 2017, What will it take to adopt smart glasses: a consumer choice based review?, Technol. Soc., 50, 50, 10.1016/j.techsoc.2017.04.005 Berggren, 2008, Trust and growth: a shaky relationship, Empir. Econ., 35, 251, 10.1007/s00181-007-0158-x Business wire, 2017. Global Smart Speaker Vendor & OS Shipment and Installed Base Market Share by Region: Q4 2017. Available from: https://www.businesswire.com/news/home/20180227006077/en/Strategy-Analytics-Explosive-Growth-Smart-Speakers-Continues. Cabada, 2018, An affective and web 3.0-based learning environment for a programming language, Telemat. Inform., 35, 611, 10.1016/j.tele.2017.03.005 Changchit, 2003, An investigation into the feasibility of using an Internet-based intelligent system to facilitate knowledge transfer, J. Comput. Inf. Syst., 43, 91 Chen, 2017, EHR: A sensing technology readiness model for lifestyle changes, Mobile Netw. Appl., 22, 478, 10.1007/s11036-017-0871-4 Chuah, 2016, Wearable technologies: the role of usefulness and visibility in smartwatch adoption, Comput. Hum. Behav., 65, 276, 10.1016/j.chb.2016.07.047 Davis, 1985 Davis, 1989, Perceived usefulness, perceived ease of use, and user acceptance of information technology, MIS Q., 13, 319, 10.2307/249008 Davis, 1989, User acceptance of computer technology: A comparison of two theoretical models, Manage. Sci., 35, 982, 10.1287/mnsc.35.8.982 Fan, 2018, Investigating the impacting factors for the healthcare professionals to adopt artificial intelligence-based medical diagnosis support system(AIMDSS), Ann. Oper. Res., 1 Faraz, 2009, Hotelling’s T 2 control chart with two adaptive sample sizes, Qual. Quant., 43, 903, 10.1007/s11135-008-9167-x Fernández-Llamas, 2018, May I teach you? Students' behavior when lectured by robotic vs. human teachers, Comput. Hum. Behav., 80, 460, 10.1016/j.chb.2017.09.028 Gao, 2015, An empirical study of wearable technology acceptance in healthcare, Ind. Manage. Data Syst., 115, 1704, 10.1108/IMDS-03-2015-0087 Gartner, 2017. Gartner’s 2017 Hype Cycle for Artificial Intelligence. Available from: https://www.gartner.com/doc/3770467/hype-cycle-artificial-intelligence. González García, 2017, A review about smart objects, sensors, and actuators, Int. J. Interact Multimedia Artif Intell. Groß, 2015, Mobile shopping: A classification framework and literature review, Int. J. Retail Distrib. Manag., 43, 221, 10.1108/IJRDM-06-2013-0119 Gu, 2016, An empirical study on factors influencing consumers' initial trust in wearable commerce, J. Comput. Inf. Syst., 56, 79 Hamari, J., Koivisto, J., 2013, June. Social motivations to use gamification: An empirical study of gamifying exercise. In ECIS (Vol. 105). Hendrickson, 1993, On the test-retest reliability of perceived usefulness and perceived ease of use scales, MIS Q., 17, 227, 10.2307/249803 Hou, 2016, Have we solved the idiosyncratic volatility puzzle?, J. Financ. Econ., 121, 167, 10.1016/j.jfineco.2016.02.013 Hsieh, 2015, Healthcare professionals’ use of health clouds: integrating technology acceptance and status quo bias perspectives, Int. J. Med. Inform., 84, 512, 10.1016/j.ijmedinf.2015.03.004 Hsieh, 2016, An empirical investigation of patients’ acceptance and resistance toward the health cloud: The dual factor perspective, Comput. Hum. Behav., 63, 959, 10.1016/j.chb.2016.06.029 Larue, 2015, Assessing driver acceptance of Intelligent Transport Systems in the context of railway level crossings, Transp. Res. Pt. F-Traffic Psychol. Behav., 30, 1, 10.1016/j.trf.2015.02.003 Lee, 2003, The technology acceptance model: Past, present, and future, Commun. Assoc. Inf. Syst., 12, 752 Li, 2008, Why do we trust new technology? A study of initial trust formation with organizational information systems, J. Strateg. Inf. Syst., 17, 39, 10.1016/j.jsis.2008.01.001 Liang, 2017, Fear of autonomous robots and artificial intelligence: Evidence from national representative data with probability sampling, Int. J. Soc. Robot., 9, 379, 10.1007/s12369-017-0401-3 Lim, 2003, A conceptual framework on the adoption of negotiation support systems, Inf. Softw. Technol., 45, 469, 10.1016/S0950-5849(03)00027-2 Lin, 2012, The integration of value-based adoption and expectation–confirmation models: An example of IPTV continuance intention, Decis. Support Syst., 54, 63, 10.1016/j.dss.2012.04.004 Lunney, 2016, Wearable fitness technology: a structural investigation into acceptance and perceived fitness outcomes, Comput. Hum. Behav., 65, 114, 10.1016/j.chb.2016.08.007 Mallat, 2007, Exploring consumer adoption of mobile payments–A qualitative study, J. Strateg. Inf. Syst., 16, 413, 10.1016/j.jsis.2007.08.001 Marangunić, 2015, Technology acceptance model: a literature review from 1986 to 2013, Univers. Access Inf. Soc., 14, 81, 10.1007/s10209-014-0348-1 Mathieson, 1991, Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior, Inf. Syst. Res., 2, 173, 10.1287/isre.2.3.173 Meyer, 2009, Intelligent products: a survey, Comput. Ind., 60, 137, 10.1016/j.compind.2008.12.005 Karahanna, 1999, The psychological origins of perceived usefulness and ease-of-use, Inf. Manage., 35, 237, 10.1016/S0378-7206(98)00096-2 Kim, 2015, An acceptance model for smart watches: Implications for the adoption of future wearable technology, Internet Res., 25, 527, 10.1108/IntR-05-2014-0126 Kim, 2007, Value-based adoption of mobile Internet: An empirical investigation, Decis. Support Syst., 43, 111, 10.1016/j.dss.2005.05.009 Kim, 2017, A study on the adoption of IoT smart home service: Using value-based adoption model, Total Qual. Manag. Bus., 28, 1149, 10.1080/14783363.2017.1310708 Oechslein, O., Fleischmann, M., Hess, T., 2014, January. An application of UTAUT2 on social recommender systems: Incorporating social information for performance expectancy. In System Sciences (HICSS), 2014 47th Hawaii International Conference on (pp. 3297–3306). IEEE. Ooi, 2016, Mobile technology acceptance model: An investigation using mobile users to explore smartphone credit card, Expert Syst. Appl., 59, 33, 10.1016/j.eswa.2016.04.015 Perlovsky, L.I., Bonniot-Cabanac, M.C., Cabanac, M., 2010, July. Curiosity and pleasure. In Neural Networks (IJCNN), The 2010 International Joint Conference on (pp. 1–3). IEEE. Podsakoff, 1986, Self-reports in organizational research: Problems and prospects, J. Manag., 12, 531 Rahman, 2017, Assessing the utility of TAM, TPB, and UTAUT for advanced driver assistance systems, Accid. Anal. Prev., 108, 361, 10.1016/j.aap.2017.09.011 Ram, 1987, A model of innovation resistance, Adv. Consumer Res., 14, 208 Renko, 2014, Perceived usefulness of innovative technology in retailing: Consumers׳ and retailers׳ point of view, J. Retail. Consumer Serv., 21, 836, 10.1016/j.jretconser.2014.02.015 Rogers, 2003 Roll, 2018, Learning at scale, Int. J. Artif. Intell. Educ., 1 Segars, 1993, Re-examining perceived ease of use and usefulness: a confirmatory factor analysis, MIS Q., 17, 517, 10.2307/249590 Setiawan, 2019, A virtual reality teaching simulation for exercise during pregnancy, Int. J. Emerg. Technol. Learn., 14, 34, 10.3991/ijet.v14i01.8944 Sirdeshmukh, 2002, Consumer trust, value, and loyalty in relational exchanges, J. Mark., 66, 15, 10.1509/jmkg.66.1.15.18449 Subramanian, 1994, A replication of perceived usefulness and perceived ease of use measurement, Decis. Sci., 25, 863, 10.1111/j.1540-5915.1994.tb01873.x Taylor, 1995, Understanding information technology usage: A test of competing models, Inf. Syst. Res., 6, 144, 10.1287/isre.6.2.144 Thorhauge, 2016, Accounting for the Theory of Planned Behaviour in departure time choice, Transp. Res. Pt. F-Traffic Psychol. Behav., 38, 94, 10.1016/j.trf.2016.01.009 Tseng, 2013, Designing an intelligent health monitoring system and exploring user acceptance for the elderly, J. Med. Syst., 37, 9967, 10.1007/s10916-013-9967-y Ukpabi, 2017, Consumers’ acceptance of information and communications technology in tourism: a review, Telemat. Inform., 34, 618, 10.1016/j.tele.2016.12.002 Venkatesh, 2000, A theoretical extension of the technology acceptance model: four longitudinal field studies, Manage. Sci., 46, 186, 10.1287/mnsc.46.2.186.11926 Venkatesh, 2003, User acceptance of information technology: Toward a unified view, Manage. Sci., 27, 425 Voss, 1998, The roles of price, performance, and expectations in determining satisfaction in service exchanges, J. Mark., 62, 46, 10.1177/002224299806200404 Wallace, 2014, The adoption of software measures: a technology acceptance model (TAM) perspective, Inf. Manage., 51, 249, 10.1016/j.im.2013.12.003 Wang, 2016, A novel approach to conduct the importance-satisfaction analysis for acquiring typical user groups in business-intelligence systems, Comput. Hum. Behav., 54, 673, 10.1016/j.chb.2015.08.014 Wang, 2015, Understanding the moderating roles of types of recommender systems and products on customer behavioral intention to use recommender systems, Inf. Syst. E-Bus. Manag., 13, 769, 10.1007/s10257-014-0269-9 Wigfield, 2000, Expectancy–value theory of achievement motivation, Contemp. Educ. Psychol., 25, 68, 10.1006/ceps.1999.1015 Williams, 2006, Distribution of Hotelling's T 2 statistic based on the successive differences estimator, J. Qual. Technol., 38, 217, 10.1080/00224065.2006.11918611 Williams, 2015, The unified theory of acceptance and use of technology (UTAUT): a literature review, J. Enterp. Inf. Manage., 28, 443, 10.1108/JEIM-09-2014-0088 Wu, 2017, Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model, Comput. Hum. Behav., 67, 221, 10.1016/j.chb.2016.10.028 Yang, 2017, User acceptance of smart home services: an extension of the theory of planned behavior, Ind. Manage. Data Syst., 117, 68, 10.1108/IMDS-01-2016-0017 Yang, 2016, User acceptance of wearable devices: an extended perspective of perceived value, Telemat. Inform., 33, 256, 10.1016/j.tele.2015.08.007 Yang, 2009, The effects of consumer perceived value and subjective norm on mobile data service adoption between American and Korean consumers, J. Retail. Consumer Serv., 16, 502, 10.1016/j.jretconser.2009.08.005 Zhang, 2007, Agent-based simulation of consumer purchase decision-making and the decoy effect, J. Bus. Res., 60, 912, 10.1016/j.jbusres.2007.02.006