IDEAS home Printed from https://ideas.repec.org/a/eee/ininma/v36y2016i6p1248-1259.html
   My bibliography  Save this article

A computational literature review of the technology acceptance model

Author

Listed:
  • Mortenson, Michael J.
  • Vidgen, Richard

Abstract

A literature review is a central part of any research project, allowing the existing research to be mapped and new research questions to be posited. However, due to the limitations of human data processing, the literature review can suffer from an inability to handle large volumes of research articles. The computational literature review (CLR) is proposed here as a complementary part of a wider literature review process. The CLR automates some of the analysis of research articles with analyses of impact (citations), structure (co-authorship networks) and content (topic modeling of abstracts). A contribution of the paper is to demonstrate how the content of abstracts can be analyzed automatically to provide a set of research topics within a literature corpus. The CLR software can be used to support three use cases: (1) analysis of the literature for a research area, (2) analysis and ranking of journals, and (3) analysis and ranking of individual scholars and research teams. The working of the CLR software is illustrated through application to the technology acceptance model (TAM) using a set of 3,386 articles. The CLR is an open source offering, developed in the statistical programming language R, and made freely available to researchers to use and develop further.

Suggested Citation

  • Mortenson, Michael J. & Vidgen, Richard, 2016. "A computational literature review of the technology acceptance model," International Journal of Information Management, Elsevier, vol. 36(6), pages 1248-1259.
    • Handle: RePEc:eee:ininma:v:36:y:2016:i:6:p:1248-1259
    DOI: 10.1016/j.ijinfomgt.2016.07.007
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0268401216300329
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijinfomgt.2016.07.007?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. Mary M. Crossan & Marina Apaydin, 2010. "A Multi‐Dimensional Framework of Organizational Innovation: A Systematic Review of the Literature," Journal of Management Studies, Wiley Blackwell, vol. 47(6), pages 1154-1191, September.
    2. Lutz Bornmann & Rüdiger Mutz, 2015. "Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(11), pages 2215-2222, November.
    3. Margaret E. Roberts & Brandon M. Stewart & Dustin Tingley & Christopher Lucas & Jetson Leder‐Luis & Shana Kushner Gadarian & Bethany Albertson & David G. Rand, 2014. "Structural Topic Models for Open‐Ended Survey Responses," American Journal of Political Science, John Wiley & Sons, vol. 58(4), pages 1064-1082, October.
    4. Jahangirian, Mohsen & Eldabi, Tillal & Garg, Lalit & Jun, Gyuchan T. & Naseer, Aisha & Patel, Brijesh & Stergioulas, Lampros & Young, Terry, 2011. "A rapid review method for extremely large corpora of literature: Applications to the domains of modelling, simulation, and management," International Journal of Information Management, Elsevier, vol. 31(3), pages 234-243.
    5. Hsiao, Chun Hua & Yang, Chyan, 2011. "The intellectual development of the technology acceptance model: A co-citation analysis," International Journal of Information Management, Elsevier, vol. 31(2), pages 128-136.
    6. Scott Deerwester & Susan T. Dumais & George W. Furnas & Thomas K. Landauer & Richard Harshman, 1990. "Indexing by latent semantic analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(6), pages 391-407, September.
    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. Gupta, Ashish & Li, Han & Farnoush, Alireza & Jiang, Wenting, 2022. "Understanding patterns of COVID infodemic: A systematic and pragmatic approach to curb fake news," Journal of Business Research, Elsevier, vol. 140(C), pages 670-683.
    2. Ligorio, Lorenzo & Venturelli, Andrea & Caputo, Fabio, 2022. "Tracing the boundaries between sustainable cities and cities for sustainable development. An LDA analysis of management studies," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    3. Caloffi, Annalisa & Colovic, Ana & Rizzoli, Valentina & Rossi, Federica, 2023. "Innovation intermediaries' types and functions: A computational analysis of the literature," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    4. 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).
    5. 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.
    6. Renata Walczak & Magdalena Kludacz-Alessandri & Liliana Hawrysz, 2022. "Use of Telemedicine Technology among General Practitioners during COVID-19: A Modified Technology Acceptance Model Study in Poland," IJERPH, MDPI, vol. 19(17), pages 1-18, September.
    7. Christian WEISMAYER, 2022. "Applied Research in Quality of Life: A Computational Literature Review," Applied Research in Quality of Life, Springer;International Society for Quality-of-Life Studies, vol. 17(3), pages 1433-1458, June.
    8. Nala Alahmari & Sarah Alswedani & Ahmed Alzahrani & Iyad Katib & Aiiad Albeshri & Rashid Mehmood, 2022. "Musawah: A Data-Driven AI Approach and Tool to Co-Create Healthcare Services with a Case Study on Cancer Disease in Saudi Arabia," Sustainability, MDPI, vol. 14(6), pages 1-41, March.
    9. Shahidi, Niousha & Tossan, Vesselina & Bourliataux-Lajoinie, Stéphane & Cacho-Elizondo, Silvia, 2022. "Behavioural intention to use a contact tracing application: The case of StopCovid in France," Journal of Retailing and Consumer Services, Elsevier, vol. 68(C).
    10. Pera Bożena & Hajdukiewicz Agnieszka & Hodak Danijela Ferjanić, 2022. "Digital Competencies among Higher Education Professors and High-School Teachers: Does Teaching Experience matter?," Business Systems Research, Sciendo, vol. 13(2), pages 72-95, December.
    11. Tai Abdulrahman Alshammari & Khalid Mhasan Alshammary & Fahad Maiyah Alshammari, 2022. "Impact of Perceived Quality of E-Health Services on Patient Behavioral Intention to Use E-Health Services: A Moderating Role of Knowledge of E-Health Management," Journal of Economics and Behavioral Studies, AMH International, vol. 14(1), pages 23-38.
    12. 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.
    13. Balakrishnan, Janarthanan & Abed, Salma S. & Jones, Paul, 2022. "The role of meta-UTAUT factors, perceived anthropomorphism, perceived intelligence, and social self-efficacy in chatbot-based services?," Technological Forecasting and Social Change, Elsevier, vol. 180(C).

    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. Andrej Kastrin & Dimitar Hristovski, 2021. "Scientometric analysis and knowledge mapping of literature-based discovery (1986–2020)," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1415-1451, February.
    2. Andreas Rehs, 2020. "A structural topic model approach to scientific reorientation of economics and chemistry after German reunification," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(2), pages 1229-1251, November.
    3. Beatrice Ferrario & Stefanie Stantcheva, 2022. "Eliciting People's First-Order Concerns: Text Analysis of Open-Ended Survey Questions," AEA Papers and Proceedings, American Economic Association, vol. 112, pages 163-169, May.
    4. Ebadi, Ashkan & Tremblay, Stéphane & Goutte, Cyril & Schiffauerova, Andrea, 2020. "Application of machine learning techniques to assess the trends and alignment of the funded research output," Journal of Informetrics, Elsevier, vol. 14(2).
    5. Imran Ali & Devika Kannan, 2022. "Mapping research on healthcare operations and supply chain management: a topic modelling-based literature review," Annals of Operations Research, Springer, vol. 315(1), pages 29-55, August.
    6. Carlos Olmeda-Gómez & Carlos Romá-Mateo & Maria-Antonia Ovalle-Perandones, 2019. "Overview of trends in global epigenetic research (2009–2017)," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(3), pages 1545-1574, June.
    7. Leonardo Cei & Edi Defrancesco & Gianluca Stefani, 2022. "What topic modelling can show about the development of agricultural economics: evidence from the Journal Citation Report category top journals," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 49(2), pages 289-330.
    8. Karakaya, Emrah & Nuur, Cali, 2018. "Social sciences and the mining sector: Some insights into recent research trends," Resources Policy, Elsevier, vol. 58(C), pages 257-267.
    9. Viergutz, Tim & Schulze-Ehlers, Birgit, 2018. "The use of hybrid scientometric clustering for systematic literature reviews in business and economics," DARE Discussion Papers 1804, Georg-August University of Göttingen, Department of Agricultural Economics and Rural Development (DARE).
    10. Swen Nadkarni & Reinhard Prügl, 2021. "Digital transformation: a review, synthesis and opportunities for future research," Management Review Quarterly, Springer, vol. 71(2), pages 233-341, April.
    11. Irina Wedel & Michael Palk & Stefan Voß, 2022. "A Bilingual Comparison of Sentiment and Topics for a Product Event on Twitter," Information Systems Frontiers, Springer, vol. 24(5), pages 1635-1646, October.
    12. Tal Shahor, 2018. "Is the Marginal Effect of Education on Income Diminishing?," European Journal of Economics and Business Studies Articles, Revistia Research and Publishing, vol. 4, May - Aug.
    13. Piñeiro-Chousa, Juan & López-Cabarcos, M. Ángeles & Romero-Castro, Noelia María & Pérez-Pico, Ada María, 2020. "Innovation, entrepreneurship and knowledge in the business scientific field: Mapping the research front," Journal of Business Research, Elsevier, vol. 115(C), pages 475-485.
    14. Sandra Wankmüller, 2023. "A comparison of approaches for imbalanced classification problems in the context of retrieving relevant documents for an analysis," Journal of Computational Social Science, Springer, vol. 6(1), pages 91-163, April.
    15. Bentivoglio, Deborah & Bucci, Giorgia & Belletti, Matteo & Finco, Adele, 2022. "A theoretical framework on network’s dynamics for precision agriculture technologies adoption," Revista de Economia e Sociologia Rural (RESR), Sociedade Brasileira de Economia e Sociologia Rural, vol. 60(4), January.
    16. Silvia Cosimato & Roberto Vona, 2021. "Digital Innovation for the Sustainability of Reshoring Strategies: A Literature Review," Sustainability, MDPI, vol. 13(14), pages 1-16, July.
    17. Mohammed Salem Binwahlan, 2023. "Polynomial Networks Model for Arabic Text Summarization," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 10(2), pages 74-84, February.
    18. Curci, Ylenia & Mongeau Ospina, Christian A., 2016. "Investigating biofuels through network analysis," Energy Policy, Elsevier, vol. 97(C), pages 60-72.
    19. Minchul Lee & Min Song, 2020. "Incorporating citation impact into analysis of research trends," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(2), pages 1191-1224, August.
    20. van den Broek, Tijs & van Veenstra, Anne Fleur, 2018. "Governance of big data collaborations: How to balance regulatory compliance and disruptive innovation," Technological Forecasting and Social Change, Elsevier, vol. 129(C), pages 330-338.

    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:ininma:v:36:y:2016:i:6:p:1248-1259. 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/international-journal-of-information-management .

    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.