IDEAS home Printed from https://ideas.repec.org/a/eee/teinso/v65y2021ics0160791x21000105.html
   My bibliography  Save this article

A new acceptance model for artificial intelligence with extensions to UTAUT2: An empirical study in three segments of application

Author

Listed:
  • Gansser, Oliver Alexander
  • Reich, Christina Stefanie

Abstract

More and more products in everyday life are using artificial intelligence (AI). The purpose of this research is to investigate influence factors in an acceptance model on behavioral intention and use behavior for products containing AI in an everyday life environment. Using PLS-Analysis, this study analyzes additional influence factors to the UTAUT2 model in the three application segments mobility, household, and health, using a sample of 21,841 respondents. Except for safety security, all additional factors to the UTAUT2 model play a relevant role in explaining behavioral intention and use behavior of products containing AI. This study answers the applicability of an established acceptance model for products that incorporate AI, extended by five additional influencing factors.

Suggested Citation

  • Gansser, Oliver Alexander & Reich, Christina Stefanie, 2021. "A new acceptance model for artificial intelligence with extensions to UTAUT2: An empirical study in three segments of application," Technology in Society, Elsevier, vol. 65(C).
  • Handle: RePEc:eee:teinso:v:65:y:2021:i:c:s0160791x21000105
    DOI: 10.1016/j.techsoc.2021.101535
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0160791X21000105
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.techsoc.2021.101535?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Chiara Longoni & Andrea Bonezzi & Carey K Morewedge, 2019. "Resistance to Medical Artificial Intelligence," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 46(4), pages 629-650.
    2. Shuhaiber, Ahmed & Mashal, Ibrahim, 2019. "Understanding users’ acceptance of smart homes," Technology in Society, Elsevier, vol. 58(C).
    3. Ritu Agarwal & Jayesh Prasad, 1998. "A Conceptual and Operational Definition of Personal Innovativeness in the Domain of Information Technology," Information Systems Research, INFORMS, vol. 9(2), pages 204-215, June.
    4. Ha, Sejin & Stoel, Leslie, 2009. "Consumer e-shopping acceptance: Antecedents in a technology acceptance model," Journal of Business Research, Elsevier, vol. 62(5), pages 565-571, May.
    5. Ajzen, Icek, 1991. "The theory of planned behavior," Organizational Behavior and Human Decision Processes, Elsevier, vol. 50(2), pages 179-211, December.
    6. Sheppard, Blair H & Hartwick, Jon & Warshaw, Paul R, 1988. "The Theory of Reasoned Action: A Meta-analysis of Past Research with Recommendations for Modifications and Future Research," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 15(3), pages 325-343, December.
    7. Gary C. Moore & Izak Benbasat, 1991. "Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation," Information Systems Research, INFORMS, vol. 2(3), pages 192-222, September.
    8. Donna L Hoffman & Thomas P Novak & Eileen FischerEditor & Robert KozinetsAssociate Editor, 2018. "Consumer and Object Experience in the Internet of Things: An Assemblage Theory Approach," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 44(6), pages 1178-1204.
    9. Sung S. Kim & Naresh K. Malhotra, 2005. "A Longitudinal Model of Continued IS Use: An Integrative View of Four Mechanisms Underlying Postadoption Phenomena," Management Science, INFORMS, vol. 51(5), pages 741-755, May.
    10. Adnan, Nadia & Md Nordin, Shahrina & bin Bahruddin, Mohamad Ariff & Ali, Murad, 2018. "How trust can drive forward the user acceptance to the technology? In-vehicle technology for autonomous vehicle," Transportation Research Part A: Policy and Practice, Elsevier, vol. 118(C), pages 819-836.
    11. Viswanath Venkatesh & Fred D. Davis, 2000. "A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies," Management Science, INFORMS, vol. 46(2), pages 186-204, February.
    12. Chao, Shih-Liang & Lin, Pei-Shan, 2009. "Critical factors affecting the adoption of container security service: The shippers' perspective," International Journal of Production Economics, Elsevier, vol. 122(1), pages 67-77, November.
    13. Eung-Suk Park & ByungYong Hwang & Kyungwan Ko & Daecheol Kim, 2017. "Consumer Acceptance Analysis of the Home Energy Management System," Sustainability, MDPI, vol. 9(12), pages 1-15, December.
    14. Aguirre, Elizabeth & Mahr, Dominik & Grewal, Dhruv & de Ruyter, Ko & Wetzels, Martin, 2015. "Unraveling the Personalization Paradox: The Effect of Information Collection and Trust-Building Strategies on Online Advertisement Effectiveness," Journal of Retailing, Elsevier, vol. 91(1), pages 34-49.
    15. Peter, J Paul & Tarpey, Lawrence X, Sr, 1975. "A Comparative Analysis of Three Consumer Decision Strategies," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 2(1), pages 29-37, June.
    16. repec:ucp:bkecon:9780226316529 is not listed on IDEAS
    17. Lee, Sang Yup & Lee, Keeheon, 2018. "Factors that influence an individual's intention to adopt a wearable healthcare device: The case of a wearable fitness tracker," Technological Forecasting and Social Change, Elsevier, vol. 129(C), pages 154-163.
    18. Reinartz, Werner & Haenlein, Michael & Henseler, Jörg, 2009. "An empirical comparison of the efficacy of covariance-based and variance-based SEM," International Journal of Research in Marketing, Elsevier, vol. 26(4), pages 332-344.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Khan, Ikram Ullah, 2022. "How does culture influence digital banking? A comparative study based on the unified model," Technology in Society, Elsevier, vol. 68(C).
    2. Ayman Batisha, 2023. "A lighthouse to future opportunities for sustainable water provided by intelligent water hackathons in the Arabsphere," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-13, December.
    3. Jameel, Alaa S. & Harjan, Sinan Abdullah & Ahmad, Abd Rahman, 2023. "Behavioral Intentions to use Artificial Intelligence Among Managers in Small and Medium Enterprises," OSF Preprints w69yh, Center for Open Science.
    4. Roemer, Ellen & Henseler, Jörg, 2022. "The dynamics of electric vehicle acceptance in corporate fleets: Evidence from Germany," Technology in Society, Elsevier, vol. 68(C).
    5. Arpaci, Ibrahim & Karatas, Kasim & Kusci, Ismail & Al-Emran, Mostafa, 2022. "Understanding the social sustainability of the Metaverse by integrating UTAUT2 and big five personality traits: A hybrid SEM-ANN approach," Technology in Society, Elsevier, vol. 71(C).
    6. Liu, Yun & Wang, Xingyuan & Wang, Shuyang, 2022. "Research on service robot adoption under different service scenarios," Technology in Society, Elsevier, vol. 68(C).
    7. Schwambach, Gislene Cássia S. & López, Óscar Hernández & Sott, Michele Kremer & Carvalho Tedesco, Leonel Pablo & Molz, Rolf Fredi, 2022. "Acceptance and perception of wearable technologies: A survey on Brazilian and European companies," Technology in Society, Elsevier, vol. 68(C).
    8. Haili Yang & Yueyue Luo & Yunhua Qiu & Jiantao Zou & Mohammad Masukujjaman & Abdullah Mohammed Ibrahim, 2023. "Modeling the Enablers of Consumers’ E-Shopping Behavior: A Multi-Analytic Approach," Sustainability, MDPI, vol. 15(8), pages 1-28, April.
    9. Tzu-Hsin Chu & Cheng-Min Chao & Hsieh-Hsi Liu & Der-Fa Chen, 2022. "Developing an Extended Theory of UTAUT 2 Model to Explore Factors Influencing Taiwanese Consumer Adoption of Intelligent Elevators," SAGE Open, , vol. 12(4), pages 21582440221, December.
    10. Zhang, Weidong & Zuo, Na & He, Wu & Li, Songtao & Yu, Lu, 2021. "Factors influencing the use of artificial intelligence in government: Evidence from China," Technology in Society, Elsevier, vol. 66(C).
    11. Rongbin Yang & Santoso Wibowo, 2022. "User trust in artificial intelligence: A comprehensive conceptual framework," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2053-2077, December.
    12. Dhiman, Neeraj & Jamwal, Mohit & Kumar, Ajay, 2023. "Enhancing value in customer journey by considering the (ad)option of artificial intelligence tools," Journal of Business Research, Elsevier, vol. 167(C).
    13. Rodney Duffett & Rodica Milena Zaharia & Tudor Edu & Raluca Constantinescu & Costel Negricea, 2024. "Exploring the Antecedents of Artificial Intelligence Products’ Usage. The Case of Business Students," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 26(65), pages 106-106, February.
    14. Yan Shi & Abu Bakkar Siddik & Mohammad Masukujjaman & Guangwen Zheng & Muhammad Hamayun & Abdullah Mohammed Ibrahim, 2022. "The Antecedents of Willingness to Adopt and Pay for the IoT in the Agricultural Industry: An Application of the UTAUT 2 Theory," Sustainability, MDPI, vol. 14(11), pages 1-23, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Attié, Elodie & Meyer-Waarden, Lars, 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 ca," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    2. Gao, Tao (Tony) & Rohm, Andrew J. & Sultan, Fareena & Pagani, Margherita, 2013. "Consumers un-tethered: A three-market empirical study of consumers' mobile marketing acceptance," Journal of Business Research, Elsevier, vol. 66(12), pages 2536-2544.
    3. John D'Ambra & Concepción S. Wilson & Shahriar Akter, 2013. "Application of the task-technology fit model to structure and evaluate the adoption of E-books by Academics," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(1), pages 48-64, January.
    4. Christopher R. Plouffe & John S. Hulland & Mark Vandenbosch, 2001. "Research Report: Richness Versus Parsimony in Modeling Technology Adoption Decisions—Understanding Merchant Adoption of a Smart Card-Based Payment System," Information Systems Research, INFORMS, vol. 12(2), pages 208-222, June.
    5. Nastjuk, Ilja & Herrenkind, Bernd & Marrone, Mauricio & Brendel, Alfred Benedikt & Kolbe, Lutz M., 2020. "What drives the acceptance of autonomous driving? An investigation of acceptance factors from an end-user's perspective," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    6. Małecka, Agnieszka & Mitręga, Maciej & Mróz-Gorgoń, Barbara & Pfajfar, Gregor, 2022. "Adoption of collaborative consumption as sustainable social innovation: Sociability and novelty seeking perspective," Journal of Business Research, Elsevier, vol. 144(C), pages 163-179.
    7. Paul Juinn Bing Tan, 2013. "Applying the UTAUT to Understand Factors Affecting the Use of English E-Learning Websites in Taiwan," SAGE Open, , vol. 3(4), pages 21582440135, October.
    8. Riffat Ara Zannat Tama & Md Mahmudul Hoque & Ying Liu & Mohammad Jahangir Alam & Mark Yu, 2023. "An Application of Partial Least Squares Structural Equation Modeling (PLS-SEM) to Examining Farmers’ Behavioral Attitude and Intention towards Conservation Agriculture in Bangladesh," Agriculture, MDPI, vol. 13(2), pages 1-22, February.
    9. Xinlu Wen & Marios Sotiriadis & Shiwei Shen, 2023. "Determining the Key Drivers for the Acceptance and Usage of AR and VR in Cultural Heritage Monuments," Sustainability, MDPI, vol. 15(5), pages 1-24, February.
    10. Viswanath Venkatesh, 2000. "Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model," Information Systems Research, INFORMS, vol. 11(4), pages 342-365, December.
    11. Natarajan, Thamaraiselvan & Balasubramanian, Senthil Arasu & Kasilingam, Dharun Lingam, 2017. "Understanding the intention to use mobile shopping applications and its influence on price sensitivity," Journal of Retailing and Consumer Services, Elsevier, vol. 37(C), pages 8-22.
    12. Hossain, Akram & Quaresma, Rui & Rahman, Habibur, 2019. "Investigating factors influencing the physicians’ adoption of electronic health record (EHR) in healthcare system of Bangladesh: An empirical study," International Journal of Information Management, Elsevier, vol. 44(C), pages 76-87.
    13. Kasilingam, Dharun Lingam, 2020. "Understanding the attitude and intention to use smartphone chatbots for shopping," Technology in Society, Elsevier, vol. 62(C).
    14. Donglin Han & Huiying (Cynthia) Hou & Hao Wu & Joseph H. K. Lai, 2021. "Modelling Tourists’ Acceptance of Hotel Experience-Enhancement Smart Technologies," Sustainability, MDPI, vol. 13(8), pages 1-19, April.
    15. Hoffmann, Stefan & Lasarov, Wassili & Reimers, Hanna, 2022. "Carbon footprint tracking apps. What drives consumers' adoption intention?," Technology in Society, Elsevier, vol. 69(C).
    16. Iviane Ramos-de-Luna & Francisco Montoro-Ríos & Francisco Liébana-Cabanillas, 2016. "Determinants of the intention to use NFC technology as a payment system: an acceptance model approach," Information Systems and e-Business Management, Springer, vol. 14(2), pages 293-314, May.
    17. repec:dau:papers:123456789/13000 is not listed on IDEAS
    18. Mäntymäki, Matti & Riemer, Kai, 2014. "Digital natives in social virtual worlds: A multi-method study of gratifications and social influences in Habbo Hotel," International Journal of Information Management, Elsevier, vol. 34(2), pages 210-220.
    19. Ilyoo Barry Hong, 2018. "Social and Personal Dimensions as Predictors of Sustainable Intention to Use Facebook in Korea: An Empirical Analysis," Sustainability, MDPI, vol. 10(8), pages 1-16, August.
    20. Kulviwat, Songpol & Bruner II, Gordon C. & Al-Shuridah, Obaid, 2009. "The role of social influence on adoption of high tech innovations: The moderating effect of public/private consumption," Journal of Business Research, Elsevier, vol. 62(7), pages 706-712, July.
    21. Sung S. Kim & Naresh K. Malhotra, 2005. "A Longitudinal Model of Continued IS Use: An Integrative View of Four Mechanisms Underlying Postadoption Phenomena," Management Science, INFORMS, vol. 51(5), pages 741-755, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:teinso:v:65:y:2021:i:c:s0160791x21000105. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/technology-in-society .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.