IDEAS home Printed from https://ideas.repec.org/a/eee/techno/v106y2021ics0166497221000936.html
   My bibliography  Save this article

Understanding managers’ attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making

Author

Listed:
  • Cao, Guangming
  • Duan, Yanqing
  • Edwards, John S.
  • Dwivedi, Yogesh K.

Abstract

While using artificial intelligence (AI) could improve organizational decision-making, it also creates challenges associated with the “dark side” of AI. However, there is a lack of research on managers' attitudes and intentions to use AI for decision making. To address this gap, we develop an integrated AI acceptance-avoidance model (IAAAM) to consider both the positive and negative factors that collectively influence managers' attitudes and behavioral intentions towards using AI. The research model is tested through a large-scale questionnaire survey of 269 UK business managers. Our findings suggest that IAAAM provides a more comprehensive model for explaining and predicting managers' attitudes and behavioral intentions towards using AI. Our research contributes conceptually and empirically to the emerging literature on using AI for organizational decision-making. Further, regarding the practical implications of using AI for organizational decision-making, we highlight the importance of developing favorable facilitating conditions, having an effective mechanism to alleviate managers’ personal concerns, and having a balanced consideration of both the benefits and the dark side associated with using AI.

Suggested Citation

  • Cao, Guangming & Duan, Yanqing & Edwards, John S. & Dwivedi, Yogesh K., 2021. "Understanding managers’ attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making," Technovation, Elsevier, vol. 106(C).
    • Handle: RePEc:eee:techno:v:106:y:2021:i:c:s0166497221000936
    DOI: 10.1016/j.technovation.2021.102312
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0166497221000936
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.technovation.2021.102312?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. Armstrong, J. Scott & Overton, Terry S., 1977. "Estimating Nonresponse Bias in Mail Surveys," MPRA Paper 81694, University Library of Munich, Germany.
    2. Jarrahi, Mohammad Hossein, 2018. "Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making," Business Horizons, Elsevier, vol. 61(4), pages 577-586.
    3. Gudergan, Siegfried P. & Ringle, Christian M. & Wende, Sven & Will, Alexander, 2008. "Confirmatory tetrad analysis in PLS path modeling," Journal of Business Research, Elsevier, vol. 61(12), pages 1238-1249, December.
    4. Yueh, Hsiu-Ping & Lu, Ming-Hsin & Lin, Weijane, 2016. "Employees' acceptance of mobile technology in a workplace: An empirical study using SEM and fsQCA," Journal of Business Research, Elsevier, vol. 69(6), pages 2318-2324.
    5. Ilias O. Pappas & Patrick Mikalef & Michail N. Giannakos & John Krogstie & George Lekakos, 2018. "Big data and business analytics ecosystems: paving the way towards digital transformation and sustainable societies," Information Systems and e-Business Management, Springer, vol. 16(3), pages 479-491, August.
    6. Wannasiri Bhuasiri & Hangjung Zo & Hwansoo Lee & Andrew P. Ciganek, 2016. "User Acceptance of e-government Services: Examining an e-tax Filing and Payment System in Thailand," Information Technology for Development, Taylor & Francis Journals, vol. 22(4), pages 672-695, October.
    7. Kieran Mathieson, 1991. "Predicting User Intentions: Comparing the Technology Acceptance Model with the Theory of Planned Behavior," Information Systems Research, INFORMS, vol. 2(3), pages 173-191, September.
    8. Brougham, David & Haar, Jarrod, 2018. "Smart Technology, Artificial Intelligence, Robotics, and Algorithms (STARA): Employees’ perceptions of our future workplace," Journal of Management & Organization, Cambridge University Press, vol. 24(2), pages 239-257, March.
    9. Nripendra P. Rana & Yogesh K. Dwivedi & Banita Lal & Michael D. Williams & Marc Clement, 2017. "Citizens’ adoption of an electronic government system: towards a unified view," Information Systems Frontiers, Springer, vol. 19(3), pages 549-568, June.
    10. Kraus, Mathias & Feuerriegel, Stefan & Oztekin, Asil, 2020. "Deep learning in business analytics and operations research: Models, applications and managerial implications," European Journal of Operational Research, Elsevier, vol. 281(3), pages 628-641.
    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. Syam, Sid S. & Courtney, James F., 1994. "The case for research in decision support systems," European Journal of Operational Research, Elsevier, vol. 73(3), pages 450-457, March.
    13. Pomerol, Jean-Charles, 1997. "Artificial intelligence and human decision making," European Journal of Operational Research, Elsevier, vol. 99(1), pages 3-25, May.
    14. Michael Breward & Khaled Hassanein & Milena Head, 2017. "Understanding Consumers’ Attitudes Toward Controversial Information Technologies: A Contextualization Approach," Information Systems Research, INFORMS, vol. 28(4), pages 760-774, December.
    15. Yogesh K. Dwivedi & Nripendra P. Rana & Anand Jeyaraj & Marc Clement & Michael D. Williams, 2019. "Re-examining the Unified Theory of Acceptance and Use of Technology (UTAUT): Towards a Revised Theoretical Model," Information Systems Frontiers, Springer, vol. 21(3), pages 719-734, June.
    16. Thomas Davenport & Abhijit Guha & Dhruv Grewal & Timna Bressgott, 2020. "How artificial intelligence will change the future of marketing," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 24-42, January.
    17. van Esch, Patrick & Black, J. Stewart, 2019. "Factors that influence new generation candidates to engage with and complete digital, AI-enabled recruiting," Business Horizons, Elsevier, vol. 62(6), pages 729-739.
    18. Dastani, Mehdi & Hulstijn, Joris & van der Torre, Leendert, 2005. "How to decide what to do?," European Journal of Operational Research, Elsevier, vol. 160(3), pages 762-784, February.
    19. Jinxin Pan & Shuai Ding & Desheng Wu & Shanlin Yang & Jun Yang, 2019. "Exploring behavioural intentions toward smart healthcare services among medical practitioners: a technology transfer perspective," International Journal of Production Research, Taylor & Francis Journals, vol. 57(18), pages 5801-5820, September.
    20. L. G. Pee & Shan L. Pan & Lili Cui, 2019. "Artificial intelligence in healthcare robots: A social informatics study of knowledge embodiment," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 70(4), pages 351-369, April.
    21. Nripendra P. Rana & Yogesh K. Dwivedi & Banita Lal & Michael D. Williams & Marc Clement, 0. "Citizens’ adoption of an electronic government system: towards a unified view," Information Systems Frontiers, Springer, vol. 0, pages 1-20.
    22. Kamalaldin, Anmar & Sjödin, David & Hullova, Dusana & Parida, Vinit, 2021. "Configuring ecosystem strategies for digitally enabled process innovation: A framework for equipment suppliers in the process industries," Technovation, Elsevier, vol. 105(C).
    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. 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.
    2. Athota, Vidya S. & Pereira, Vijay & Hasan, Zahid & Vaz, Daicy & Laker, Benjamin & Reppas, Dimitrios, 2023. "Overcoming financial planners’ cognitive biases through digitalization: A qualitative study," Journal of Business Research, Elsevier, vol. 154(C).
    3. Mahmud, Hasan & Islam, A.K.M. Najmul & Ahmed, Syed Ishtiaque & Smolander, Kari, 2022. "What influences algorithmic decision-making? A systematic literature review on algorithm aversion," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    4. Mahmud, Hasan & Islam, A.K.M. Najmul & Mitra, Ranjan Kumar, 2023. "What drives managers towards algorithm aversion and how to overcome it? Mitigating the impact of innovation resistance through technology readiness," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    5. Chakraborty, Debarun & Polisetty, Aruna & G, Sowmya & Rana, Nripendra P. & Khorana, Sangeeta, 2024. "Unlocking the potential of AI: Enhancing consumer engagement in the beauty and cosmetic product purchases," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).
    6. Won, Jongho & Lee, Daeho & Lee, Junmin, 2023. "Understanding experiences of food-delivery-platform workers under algorithmic management using topic modeling," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
    7. Akter, Shahriar & Hossain, Md Afnan & Sajib, Shahriar & Sultana, Saida & Rahman, Mahfuzur & Vrontis, Demetris & McCarthy, Grace, 2023. "A framework for AI-powered service innovation capability: Review and agenda for future research," Technovation, Elsevier, vol. 125(C).
    8. Hack-Polay, Dieu & Mahmoud, Ali B. & Ikafa, Irene & Rahman, Mahfuzur & Kordowicz, Maria & Verde, Juan Manuel, 2023. "Steering resilience in nursing practice: Examining the impact of digital innovations and enhanced emotional training on nurse competencies," Technovation, Elsevier, vol. 120(C).
    9. Philippe Funk, 2022. "Artificial Intelligence And Cybersecurity Implications For Business Management," Economy & Business Journal, International Scientific Publications, Bulgaria, vol. 16(1), pages 252-261.
    10. Dicuonzo, Grazia & Donofrio, Francesca & Fusco, Antonio & Shini, Matilda, 2023. "Healthcare system: Moving forward with artificial intelligence," Technovation, Elsevier, vol. 120(C).
    11. Ying, Ying & Wang, Shixiang & Liu, Yang, 2022. "Make bricks without straw: Eco-innovation for resource-constrained firms in emerging markets," Technovation, Elsevier, vol. 114(C).
    12. Opazo-Basáez, Marco & Monroy-Osorio, Juan Carlos & Marić, Josip, 2024. "Evaluating the effect of green technological innovations on organizational and environmental performance: A treble innovation approach," Technovation, Elsevier, vol. 129(C).
    13. Chowdhury, Soumyadeb & Budhwar, Pawan & Dey, Prasanta Kumar & Joel-Edgar, Sian & Abadie, Amelie, 2022. "AI-employee collaboration and business performance: Integrating knowledge-based view, socio-technical systems and organisational socialisation framework," Journal of Business Research, Elsevier, vol. 144(C), pages 31-49.
    14. Jung-Chieh Lee & Yuyin Tang & SiQi Jiang, 2023. "Understanding continuance intention of artificial intelligence (AI)-enabled mobile banking applications: an extension of AI characteristics to an expectation confirmation model," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-12, December.
    15. Simona‐Vasilica Oprea & Ionut Nica & Adela Bâra & Irina‐Alexandra Georgescu, 2024. "Are skepticism and moderation dominating attitudes toward AI‐based technologies?," American Journal of Economics and Sociology, Wiley Blackwell, vol. 83(3), pages 567-607, May.
    16. Badghish, Saeed & Shaik, Aqueeb Sohail & Sahore, Nidhi & Srivastava, Shalini & Masood, Ayesha, 2024. "Can transactional use of AI-controlled voice assistants for service delivery pickup pace in the near future? A social learning theory (SLT) perspective," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    17. Stroh, Tim & Mention, Anne-Laure & Duff, Cameron, 2023. "The impact of evolved psychological mechanisms on innovation and adoption: A systematic literature review," Technovation, Elsevier, vol. 125(C).
    18. Hao Dong & Yang Zhang & Tianqing Chen, 2023. "A Study on Farmers’ Participation in Environmental Protection in the Context of Rural Revitalization: The Moderating Role of Policy Environment," IJERPH, MDPI, vol. 20(3), pages 1-13, January.
    19. David Mortimore, 2024. "Moving beyond human-centric organizational designs," Journal of Organization Design, Springer;Organizational Design Community, vol. 13(2), pages 65-75, June.
    20. Justy, Théo & Pellegrin-Boucher, Estelle & Lescop, Denis & Granata, Julien & Gupta, Shivam, 2023. "On the edge of Big Data: Drivers and barriers to data analytics adoption in SMEs," Technovation, Elsevier, vol. 127(C).
    21. Zhiyuan Yu & Doudou Jin, 2021. "Determinants of Users’ Attitude and Intention to Intelligent Connected Vehicle Infotainment in the 5G-V2X Mobile Ecosystem," IJERPH, MDPI, vol. 18(19), pages 1-19, September.
    22. Pervaiz Akhtar & Arsalan Mujahid Ghouri & Haseeb Ur Rehman Khan & Mirza Amin ul Haq & Usama Awan & Nadia Zahoor & Zaheer Khan & Aniqa Ashraf, 2023. "Detecting fake news and disinformation using artificial intelligence and machine learning to avoid supply chain disruptions," Annals of Operations Research, Springer, vol. 327(2), pages 633-657, August.

    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. Queiroz, Maciel M. & Fosso Wamba, Samuel, 2019. "Blockchain adoption challenges in supply chain: An empirical investigation of the main drivers in India and the USA," International Journal of Information Management, Elsevier, vol. 46(C), pages 70-82.
    2. Arpan Kumar Kar, 2021. "What Affects Usage Satisfaction in Mobile Payments? Modelling User Generated Content to Develop the “Digital Service Usage Satisfaction Model”," Information Systems Frontiers, Springer, vol. 23(5), pages 1341-1361, September.
    3. Urvashi Tandon & Amit Mittal & Sridhar Manohar, 2021. "Examining the impact of intangible product features and e-commerce institutional mechanics on consumer trust and repurchase intention," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(4), pages 945-964, December.
    4. Yogesh K. Dwivedi & Nripendra P. Rana & Anand Jeyaraj & Marc Clement & Michael D. Williams, 2019. "Re-examining the Unified Theory of Acceptance and Use of Technology (UTAUT): Towards a Revised Theoretical Model," Information Systems Frontiers, Springer, vol. 21(3), pages 719-734, June.
    5. Arpan Kumar Kar, 0. "What Affects Usage Satisfaction in Mobile Payments? Modelling User Generated Content to Develop the “Digital Service Usage Satisfaction Model”," Information Systems Frontiers, Springer, vol. 0, pages 1-21.
    6. Cristopher Siegfried Kopplin, 2021. "Two heads are better than one: matchmaking tools in coworking spaces," Review of Managerial Science, Springer, vol. 15(4), pages 1045-1069, May.
    7. Baillette, Paméla & Barlette, Yves & Leclercq-Vandelannoitte, Aurélie, 2018. "Bring your own device in organizations: Extending the reversed IT adoption logic to security paradoxes for CEOs and end users," International Journal of Information Management, Elsevier, vol. 43(C), pages 76-84.
    8. Ya-na Wang & Lifu Jin & Hanping Mao, 2019. "Farmer Cooperatives’ Intention to Adopt Agricultural Information Technology—Mediating Effects of Attitude," Information Systems Frontiers, Springer, vol. 21(3), pages 565-580, June.
    9. Pillai, Rajasshrie & Sivathanu, Brijesh & Dwivedi, Yogesh K., 2020. "Shopping intention at AI-powered automated retail stores (AIPARS)," Journal of Retailing and Consumer Services, Elsevier, vol. 57(C).
    10. Isaac Kofi Mensah, 2019. "Factors Influencing the Intention of University Students to Adopt and Use E-Government Services: An Empirical Evidence in China," SAGE Open, , vol. 9(2), pages 21582440198, June.
    11. Mina Nasiri & Minna Saunila & Juhani Ukko & Tero Rantala & Hannu Rantanen, 2023. "Shaping Digital Innovation Via Digital-related Capabilities," Information Systems Frontiers, Springer, vol. 25(3), pages 1063-1080, June.
    12. Zeleke Siraye Asnakew, 2020. "Customers’ Continuance Intention to Use Mobile Banking: Development and Testing of an Integrated Model," The Review of Socionetwork Strategies, Springer, vol. 14(1), pages 123-146, April.
    13. Kuttimani Tamilmani & Nripendra P. Rana & Yogesh K. Dwivedi, 2021. "Consumer Acceptance and Use of Information Technology: A Meta-Analytic Evaluation of UTAUT2," Information Systems Frontiers, Springer, vol. 23(4), pages 987-1005, August.
    14. Maduku, Daniel K. & Mpinganjira, Mercy & Rana, Nripendra P. & Thusi, Philile & Ledikwe, Aobakwe & Mkhize, Njabulo Happy-boy, 2023. "Assessing customer passion, commitment, and word-of-mouth intentions in digital assistant usage: The moderating role of technology anxiety," Journal of Retailing and Consumer Services, Elsevier, vol. 71(C).
    15. Makarius, Erin E. & Mukherjee, Debmalya & Fox, Joseph D. & Fox, Alexa K., 2020. "Rising with the machines: A sociotechnical framework for bringing artificial intelligence into the organization," Journal of Business Research, Elsevier, vol. 120(C), pages 262-273.
    16. Konstantinos Dendrinos & George Spais, 2024. "An investigation of selected UTAUT constructs and consumption values of Gen Z and Gen X for mobile banking services and behavioral intentions to facilitate the adoption of mobile apps," Journal of Marketing Analytics, Palgrave Macmillan, vol. 12(3), pages 492-522, September.
    17. Yuzong Zhao & Hui Wang & Zhen Guo & Mingli Huang & Yongtao Pan & Yongrui Guo, 2022. "Online Reservation Intention of Tourist Attractions in the COVID-19 Context: An Extended Technology Acceptance Model," Sustainability, MDPI, vol. 14(16), pages 1-17, August.
    18. Cai, Lanhui & Yuen, Kum Fai & Xie, Diancen & Fang, Mingjie & Wang, Xueqin, 2021. "Consumer's usage of logistics technologies: Integration of habit into the unified theory of acceptance and use of technology," Technology in Society, Elsevier, vol. 67(C).
    19. Shin-Cheng Yeh & Ai-Wei Wu & Hui-Ching Yu & Homer C. Wu & Yi-Ping Kuo & Pei-Xuan Chen, 2021. "Public Perception of Artificial Intelligence and Its Connections to the Sustainable Development Goals," Sustainability, MDPI, vol. 13(16), pages 1-34, August.
    20. 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.

    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:techno:v:106:y:2021:i:c:s0166497221000936. 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: http://www.sciencedirect.com/science/journal/01664972 .

    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.