IDEAS home Printed from https://ideas.repec.org/a/gam/jjrfmx/v14y2021i8p339-d598239.html
   My bibliography  Save this article

Artificial Intelligence: The Attitude of the Public and Representatives of Various Industries

Author

Listed:
  • Tatjana Vasiljeva

    (Faculty of Business and Economics, RISEBA University of Applied Sciences, LV-1048 Riga, Latvia)

  • Ilmars Kreituss

    (Faculty of Business and Economics, RISEBA University of Applied Sciences, LV-1048 Riga, Latvia)

  • Ilze Lulle

    (Faculty of Business and Economics, RISEBA University of Applied Sciences, LV-1048 Riga, Latvia)

Abstract

This paper looks at public and business attitudes towards artificial intelligence, examining the main factors that influence them. The conceptual model is based on the technology–organization–environment (TOE) framework and was tested through analysis of qualitative and quantitative data. Primary data were collected by a public survey with a questionnaire specially developed for the study and by semi-structured interviews with experts in the artificial intelligence field and management representatives from various companies. This study aims to evaluate the current attitudes of the public and employees of various industries towards AI and investigate the factors that affect them. It was discovered that attitude towards AI differs significantly among industries. There is a significant difference in attitude towards AI between employees at organizations with already implemented AI solutions and employees at organizations with no intention to implement them in the near future. The three main factors which have an impact on AI adoption in an organization are top management’s attitude, competition and regulations. After determining the main factors that influence the attitudes of society and companies towards artificial intelligence, recommendations are provided for reducing various negative factors. The authors develop a proposition that justifies the activities needed for successful adoption of innovative technologies.

Suggested Citation

  • Tatjana Vasiljeva & Ilmars Kreituss & Ilze Lulle, 2021. "Artificial Intelligence: The Attitude of the Public and Representatives of Various Industries," JRFM, MDPI, vol. 14(8), pages 1-17, July.
    • Handle: RePEc:gam:jjrfmx:v:14:y:2021:i:8:p:339-:d:598239
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1911-8074/14/8/339/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1911-8074/14/8/339/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Holmlund, Maria & Van Vaerenbergh, Yves & Ciuchita, Robert & Ravald, Annika & Sarantopoulos, Panagiotis & Ordenes, Francisco Villarroel & Zaki, Mohamed, 2020. "Customer experience management in the age of big data analytics: A strategic framework," Journal of Business Research, Elsevier, vol. 116(C), pages 356-365.
    3. Fotis Kitsios & Maria Kamariotou, 2021. "Artificial Intelligence and Business Strategy towards Digital Transformation: A Research Agenda," Sustainability, MDPI, vol. 13(4), pages 1-14, February.
    4. Pumplun, Luisa & Tauchert, Christoph & Heidt, Margareta, 2019. "A New Organizational Chassis for Artificial Intelligence - Exploring Organizational Readiness Factors," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 112582, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    5. Cubric, Marija, 2020. "Drivers, barriers and social considerations for AI adoption in business and management: A tertiary study," Technology in Society, Elsevier, vol. 62(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. Tassisius Muzivi & Dennis Maravanyika & Ranzi M. Rusikee & Judith Mwenje, 2022. "Factors which Influence the Corporate Culture of an Entity- Analysing the Dynamics of Culture Stability and Inevitable Change," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 6(4), pages 389-395, April.
    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. 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.
    4. George Amoako & Paul Omari & Desmond K. Kumi & George Cudjoe Agbemabiase & George Asamoah, 2021. "Conceptual Framework—Artificial Intelligence and Better Entrepreneurial Decision-Making: The Influence of Customer Preference, Industry Benchmark, and Employee Involvement in an Emerging Market," JRFM, MDPI, vol. 14(12), pages 1-20, December.

    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. Cristian-Mihai Vidu & Florina Pinzaru & Andreea Mitan, 2022. "What managers of SMEs in the CEE region should know about challenges of artificial intelligence’s adoption? – an introductive discussion," Nowoczesne Systemy Zarządzania. Modern Management Systems, Military University of Technology, Faculty of Security, Logistics and Management, Institute of Organization and Management, issue 1, pages 63-76.
    2. Jan Jöhnk & Malte Weißert & Katrin Wyrtki, 2021. "Ready or Not, AI Comes— An Interview Study of Organizational AI Readiness Factors," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(1), pages 5-20, February.
    3. Yasheng Chen & Mohammad Islam Biswas, 2021. "Turning Crisis into Opportunities: How a Firm Can Enrich Its Business Operations Using Artificial Intelligence and Big Data during COVID-19," Sustainability, MDPI, vol. 13(22), pages 1-17, November.
    4. Siliang Tong & Nan Jia & Xueming Luo & Zheng Fang, 2021. "The Janus face of artificial intelligence feedback: Deployment versus disclosure effects on employee performance," Strategic Management Journal, Wiley Blackwell, vol. 42(9), pages 1600-1631, September.
    5. 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).
    6. Sudhanshu Joshi & Manu Sharma & Rashmi Prava Das & Joanna Rosak-Szyrocka & Justyna Żywiołek & Kamalakanta Muduli & Mukesh Prasad, 2022. "Modeling Conceptual Framework for Implementing Barriers of AI in Public Healthcare for Improving Operational Excellence: Experiences from Developing Countries," Sustainability, MDPI, vol. 14(18), pages 1-23, September.
    7. Sarker, Moniruzzaman & Mohd-Any, Amrul Asraf & Kamarulzaman, Yusniza, 2021. "Validating a consumer-based service brand equity (CBSBE) model in the airline industry," Journal of Retailing and Consumer Services, Elsevier, vol. 59(C).
    8. Justyna Łapińska & Iwona Escher & Joanna Górka & Agata Sudolska & Paweł Brzustewicz, 2021. "Employees’ Trust in Artificial Intelligence in Companies: The Case of Energy and Chemical Industries in Poland," Energies, MDPI, vol. 14(7), pages 1-20, April.
    9. Cheng-Feng Cheng & Chien-Che Huang & Ming-Chang Lin & Ta-Cheng Chen, 2023. "Exploring Effectiveness of Relationship Marketing on Artificial Intelligence Adopting Intention," SAGE Open, , vol. 13(4), pages 21582440231, December.
    10. Saravanan Periyasami & Aravin Prince Periyasamy, 2022. "Metaverse as Future Promising Platform Business Model: Case Study on Fashion Value Chain," Businesses, MDPI, vol. 2(4), pages 1-21, November.
    11. Fazal Ur Rehman & Basheer M. Al-Ghazali & Adel Ghaleb Haddad & Ehab Abdullatif Qahwash & M. Sadiq Sohail, 2023. "Exploring the Reverse Relationship between Circular Economy Innovation and Digital Sustainability—The Dual Mediation of Government Incentives," Sustainability, MDPI, vol. 15(6), pages 1-21, March.
    12. Cai Li & Sheikh Farhan Ashraf & Saba Amin & Muhammad Nabeel Safdar, 2023. "Consequence of Resistance to Change on AI Readiness: Mediating–Moderating Role of Task-oriented Leadership and High-Performance Work System in the Hospitality Sector," SAGE Open, , vol. 13(4), pages 21582440231, December.
    13. Chen, Yan & Zhang, Lei & Zhao, Yulu & Xu, Bing, 2022. "Implementation of penalized survival models in churn prediction of vehicle insurance," Journal of Business Research, Elsevier, vol. 153(C), pages 162-171.
    14. Christopher Kurzhals & Lorenz Graf‐Vlachy & Andreas König, 2020. "Strategic leadership and technological innovation: A comprehensive review and research agenda," Corporate Governance: An International Review, Wiley Blackwell, vol. 28(6), pages 437-464, November.
    15. Pallant, Jason I. & Pallant, Jessica L. & Sands, Sean J. & Ferraro, Carla R. & Afifi, Eslam, 2022. "When and how consumers are willing to exchange data with retailers: An exploratory segmentation," Journal of Retailing and Consumer Services, Elsevier, vol. 64(C).
    16. Prikshat, Verma & Islam, Mohammad & Patel, Parth & Malik, Ashish & Budhwar, Pawan & Gupta, Suraksha, 2023. "AI-Augmented HRM: Literature review and a proposed multilevel framework for future research," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    17. Gupta, Shivam & Modgil, Sachin & Choi, Tsan-Ming & Kumar, Ajay & Antony, Jiju, 2023. "Influences of artificial intelligence and blockchain technology on financial resilience of supply chains," International Journal of Production Economics, Elsevier, vol. 261(C).
    18. Youmi Suk & Kyung T. Han, 2024. "A Psychometric Framework for Evaluating Fairness in Algorithmic Decision Making: Differential Algorithmic Functioning," Journal of Educational and Behavioral Statistics, , vol. 49(2), pages 151-172, April.
    19. Ksenia V. Ekimova, 2023. "Development of the potential of the digital economy of Russian regions through artificial intelligence humanisation," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-9, December.
    20. Shah, Tushar R., 2022. "Can big data analytics help organisations achieve sustainable competitive advantage? A developmental enquiry," Technology in Society, Elsevier, vol. 68(C).

    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:gam:jjrfmx:v:14:y:2021:i:8:p:339-:d:598239. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.