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Artificial Intelligence and Cognitive Computing in Companies in Portugal: An Outcome of Partial Least Squares—Structural Equations Modeling

Author

Listed:
  • Renato Lopes da Costa

    (Business Research Unit, ISCTE Business School, Instituto Universitario de Lisboa, 1649-026 Lisbon, Portugal)

  • Varun Gupta

    (Multidisciplinary Research Centre for Innovations in SMEs (MrciS), GISMA University of Applied Sciences, 14469 Potsdam, Germany
    Business School, GISMA University of Applied Sciences, 14469 Potsdam, Germany)

  • Rui Gonçalves

    (Business Research Unit, ISCTE Business School, Instituto Universitario de Lisboa, 1649-026 Lisbon, Portugal
    LabEST, Instituto Piaget, 2805-059 Almada, Portugal)

  • Álvaro Dias

    (Business Research Unit, ISCTE Business School, Instituto Universitario de Lisboa, 1649-026 Lisbon, Portugal
    ECEO/TRIE, Universidade Lusófona de Humanidades e Tecnologias, 1749-024 Lisbon, Portugal)

  • Leandro Pereira

    (Business Research Unit, ISCTE Business School, Instituto Universitario de Lisboa, 1649-026 Lisbon, Portugal)

  • Chetna Gupta

    (Department of Computer Science& Engineering and Information Technology, Jaypee Institute of Information Technology (JIIT), Noida 201306, India)

Abstract

Artificial intelligence (AI) and cognitive computing (CC) are different, which is why each technology has its advantages and disadvantages, depending on the task/operation that a business wants to optimize. Nowadays, it is easy to confuse both by simply associating CC with the widespread theme of AI. This way, companies that want to implement AI know that what they want, in most cases, are the features provided by CC. It is important in these situations to know how to differentiate them, so that it is possible to identify in which circumstance one is more suitable than another, to get more out of the benefits that each has to offer. This project focuses on highlighting the capabilities of both technologies, more specifically in business contexts in which the implementation of intelligent systems and the interest of companies in them is favourable. It also identifies which aspects of these technologies are most interesting for companies. Based on this information, it is evaluated whether these aspects are relevant in decision making. Data analysis is carried out by employing partial least squares structural equations modelling (PLS-SEM) and descriptive statistical techniques.

Suggested Citation

  • Renato Lopes da Costa & Varun Gupta & Rui Gonçalves & Álvaro Dias & Leandro Pereira & Chetna Gupta, 2022. "Artificial Intelligence and Cognitive Computing in Companies in Portugal: An Outcome of Partial Least Squares—Structural Equations Modeling," Mathematics, MDPI, vol. 10(22), pages 1-24, November.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:22:p:4358-:d:978218
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    References listed on IDEAS

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    1. Piotr Tarka, 2018. "An overview of structural equation modeling: its beginnings, historical development, usefulness and controversies in the social sciences," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(1), pages 313-354, January.
    2. Amlan Haque & Mario Fernando & Peter Caputi, 2019. "The Relationship Between Responsible Leadership and Organisational Commitment and the Mediating Effect of Employee Turnover Intentions: An Empirical Study with Australian Employees," Journal of Business Ethics, Springer, vol. 156(3), pages 759-774, May.
    3. Ajay Agrawal & Joshua Gans & Avi Goldfarb, 2019. "Economic Policy for Artificial Intelligence," Innovation Policy and the Economy, University of Chicago Press, vol. 19(1), pages 139-159.
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    Cited by:

    1. Winata, Robbie Kurniawan & Soekarno, Subiakto, 2024. "Literature Review on Digitalization and Financial Performance," OSF Preprints my9fk, Center for Open Science.

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