IDEAS home Printed from https://ideas.repec.org/a/eee/teinso/v63y2020ics0160791x20302694.html
   My bibliography  Save this article

Deep learning diffusion by infusion into preexisting technologies – Implications for users and society at large

Author

Listed:
  • Engström, Emma
  • Strimling, Pontus

Abstract

Artificial Intelligence (AI) in the form of Deep Learning (DL) technology has diffused in the consumer domain in a unique way as compared to previous general-purpose technologies. DL has often spread by infusion, i.e., by being added to preexisting technologies that are already in use. We find that DL-algorithms for recommendations or ranking have been infused into all the 15 most popular mobile applications (apps) in the U.S. (as of May 2019). DL-infusion enables fast and vast diffusion. For example, when a DL-system was infused into YouTube, it almost immediately reached a third of the world's population. We argue that existing theories of innovation diffusion and adoption have limited relevance for DL-infusion, because it is a process that is driven by enterprises rather than individuals. We also discuss its social and ethical implications. First, consumers have a limited ability to detect and evaluate an infused technology. DL-infusion may thus help to explain why AI's presence in society has not been challenged by many. Second, the DL-providers are likely to face conflicts of interest, since consumer and supplier goals are not always aligned. Third, infusion is likely to be a particularly important diffusion process for DL-technologies as compared to other innovations, because they need large data sets to function well, which can be drawn from preexisting users. Related, it seems that larger technology companies comparatively benefit more from DL-infusion, because they already have many users. This suggests that the value drawn from DL is likely to follow a Matthew Effect of accumulated advantage online: many preexisting users provide a lot of behavioral data, which bring about better DL-driven features, which attract even more users, etc. Such a self-reinforcing process could limit the possibilities for new companies to compete. This way, the notion of DL-infusion may put light on the power shift that comes with the presence of AI in society.

Suggested Citation

  • Engström, Emma & Strimling, Pontus, 2020. "Deep learning diffusion by infusion into preexisting technologies – Implications for users and society at large," Technology in Society, Elsevier, vol. 63(C).
  • Handle: RePEc:eee:teinso:v:63:y:2020:i:c:s0160791x20302694
    DOI: 10.1016/j.techsoc.2020.101396
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0160791X20302694
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.techsoc.2020.101396?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. Natarajan, Thamaraiselvan & Balasubramanian, Senthil Arasu & Kasilingam, Dharun Lingam, 2018. "The moderating role of device type and age of users on the intention to use mobile shopping applications," Technology in Society, Elsevier, vol. 53(C), pages 79-90.
    2. McFadden, Daniel L., 1984. "Econometric analysis of qualitative response models," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 24, pages 1395-1457, Elsevier.
    3. Libai, Barak & Bart, Yakov & Gensler, Sonja & Hofacker, Charles F. & Kaplan, Andreas & Kötterheinrich, Kim & Kroll, Eike Benjamin, 2020. "Brave New World? On AI and the Management of Customer Relationships," Journal of Interactive Marketing, Elsevier, vol. 51(C), pages 44-56.
    4. Naglis, Michael & Bhatiasevi, Veera, 2019. "Why do people use fitness tracking devices in Thailand? An integrated model approach," Technology in Society, Elsevier, vol. 58(C).
    5. Fred D. Davis & Richard P. Bagozzi & Paul R. Warshaw, 1989. "User Acceptance of Computer Technology: A Comparison of Two Theoretical Models," Management Science, INFORMS, vol. 35(8), pages 982-1003, August.
    6. Spatar, Daria & Kok, Orhun & Basoglu, Nuri & Daim, Tugrul, 2019. "Adoption factors of electronic health record systems," Technology in Society, Elsevier, vol. 58(C).
    7. Veugelers, Reinhilde & Pezzoni, Michele, 2019. "How fast is this novel technology going to be a hit?," CEPR Discussion Papers 13447, C.E.P.R. Discussion Papers.
    8. Gruber, Mario, 2020. "An evolutionary perspective on adoption-diffusion theory," Journal of Business Research, Elsevier, vol. 116(C), pages 535-541.
    9. Matemba, Elizabeth D. & Li, Guoxin, 2018. "Consumers' willingness to adopt and use WeChat wallet: An empirical study in South Africa," Technology in Society, Elsevier, vol. 53(C), pages 55-68.
    10. Alam, Mohammad Zahedul & Hu, Wang & Kaium, Md Abdul & Hoque, Md Rakibul & Alam, Mirza Mohammad Didarul, 2020. "Understanding the determinants of mHealth apps adoption in Bangladesh: A SEM-Neural network approach," Technology in Society, Elsevier, vol. 61(C).
    11. Al-Emran, Mostafa & Mezhuyev, Vitaliy & Kamaludin, Adzhar, 2020. "Towards a conceptual model for examining the impact of knowledge management factors on mobile learning acceptance," Technology in Society, Elsevier, vol. 61(C).
    12. John Naughton, 2016. "The evolution of the Internet: from military experiment to General Purpose Technology," Journal of Cyber Policy, Taylor & Francis Journals, vol. 1(1), pages 5-28, January.
    13. 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. Sætra, Henrik Skaug, 2023. "Generative AI: Here to stay, but for good?," Technology in Society, Elsevier, vol. 75(C).
    2. Henrik Skaug Sætra, 2021. "AI in Context and the Sustainable Development Goals: Factoring in the Unsustainability of the Sociotechnical System," Sustainability, MDPI, vol. 13(4), pages 1-19, February.
    3. Rodríguez-López, María Eugenia & Higueras-Castillo, Elena & Rojas-Lamorena, Álvaro J. & Alcántara-Pilar, Juan Miguel, 2024. "The future of TV-shopping: predicting user purchase intention through an extended technology acceptance model," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    4. Skare, Marinko & Soriano, Domingo Riberio, 2021. "Technological and knowledge diffusion link: An international perspective 1870–2019," Technology in Society, Elsevier, vol. 66(C).
    5. Galaz, Victor & Centeno, Miguel A. & Callahan, Peter W. & Causevic, Amar & Patterson, Thayer & Brass, Irina & Baum, Seth & Farber, Darryl & Fischer, Joern & Garcia, David & McPhearson, Timon & Jimenez, 2021. "Artificial intelligence, systemic risks, and sustainability," Technology in Society, Elsevier, vol. 67(C).
    6. Adib, Saif Ahmed & Mahanti, Aniket & Naha, Ranesh Kumar, 2021. "Characterisation and comparative analysis of thematic video portals," Technology in Society, Elsevier, vol. 67(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. Jumaan, Ibrahim A. & Hashim, Noor Hazarina & Al-Ghazali, Basheer M., 2020. "The role of cognitive absorption in predicting mobile internet users’ continuance intention: An extension of the expectation-confirmation model," Technology in Society, Elsevier, vol. 63(C).
    2. Albayati, Hayder & Kim, Suk Kyoung & Rho, Jae Jeung, 2020. "Accepting financial transactions using blockchain technology and cryptocurrency: A customer perspective approach," Technology in Society, Elsevier, vol. 62(C).
    3. Rajak, Manindra & Shaw, Krishnendu, 2021. "An extension of technology acceptance model for mHealth user adoption," Technology in Society, Elsevier, vol. 67(C).
    4. Cliff R. Kikawa & Charity Kiconco & Moses Agaba & Dimas Ntirampeba & Amos Ssematimba & Billy M. Kalema, 2022. "Social Media Marketing for Small and Medium Enterprise Performance in Uganda: A Structural Equation Model," Sustainability, MDPI, vol. 14(21), pages 1-20, November.
    5. Sami S. Binyamin & Md. Rakibul Hoque, 2020. "Understanding the Drivers of Wearable Health Monitoring Technology: An Extension of the Unified Theory of Acceptance and Use of Technology," Sustainability, MDPI, vol. 12(22), pages 1-20, November.
    6. Su-Chen(Cecilia) Lin & Mei-Chen Chuang & Chen-Yuan Huang & Chia-En Liu, 2023. "Nursing Staff’s Behavior Intention to Use Mobile Technology: An Exploratory Study Employing the UTAUT 2 Model," SAGE Open, , vol. 13(4), pages 21582440231, November.
    7. Kala Kamdjoug, Jean Robert & Wamba-Taguimdje, Serge-Lopez & Wamba, Samuel Fosso & Kake, Ingrid Bive'e, 2021. "Determining factors and impacts of the intention to adopt mobile banking app in Cameroon: Case of SARA by afriland First Bank," Journal of Retailing and Consumer Services, Elsevier, vol. 61(C).
    8. Balakrishnan, Vimala & Shuib, Nor Liyana Mohd, 2021. "Drivers and inhibitors for digital payment adoption using the Cashless Society Readiness-Adoption model in Malaysia," Technology in Society, Elsevier, vol. 65(C).
    9. Shah, Sayed Kifayat & Zhongjun, Tang, 2021. "Elaborating on the consumer’s intention–behavior gap regarding 5G technology: The moderating role of the product market-creation ability," Technology in Society, Elsevier, vol. 66(C).
    10. Sharma, Mahak & Antony, Rose & Sehrawat, Rajat & Cruz, Angel Contreras & Daim, Tugrul U., 2022. "Exploring post-adoption behaviors of e-service users: Evidence from the hospitality sector /online travel services," Technology in Society, Elsevier, vol. 68(C).
    11. Simarpreet Kaur & Sangeeta Arora, 2023. "Understanding customers’ usage behavior towards online banking services: an integrated risk–benefit framework," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 28(1), pages 74-98, March.
    12. Trivedi, Shrawan Kumar, 2020. "A study on credit scoring modeling with different feature selection and machine learning approaches," Technology in Society, Elsevier, vol. 63(C).
    13. Miluska Murillo-Zegarra & Carla Ruiz-Mafe & Silvia Sanz-Blas, 2020. "The Effects of Mobile Advertising Alerts and Perceived Value on Continuance Intention for Branded Mobile Apps," Sustainability, MDPI, vol. 12(17), pages 1-20, August.
    14. Khayer, Abul & Talukder, Md. Shamim & Bao, Yukun & Hossain, Md. Nahin, 2020. "Cloud computing adoption and its impact on SMEs’ performance for cloud supported operations: A dual-stage analytical approach," Technology in Society, Elsevier, vol. 60(C).
    15. Al-Momani, Ala'a M. & Ramayah, T. & Al-Sharafi, Mohammed A., 2024. "Exploring the impact of cybersecurity on using electronic health records and their performance among healthcare professionals: A multi-analytical SEM-ANN approach," Technology in Society, Elsevier, vol. 77(C).
    16. Chong, Lee-Lee & Ong, Hway-Boon & Tan, Siow-Hooi, 2021. "Acceptability of mobile stock trading application: A study of young investors in Malaysia," Technology in Society, Elsevier, vol. 64(C).
    17. Zaki Irfan Al Hafizh & Anas Hidayat, 2022. "The role of digital payment benefits toward switching consumer behavior in the case of OVO application," International Journal of Research in Business and Social Science (2147-4478), Center for the Strategic Studies in Business and Finance, vol. 11(7), pages 23-34, October.
    18. Yang, Hui-Yu, 2019. "The effects of visuospatial cueing on EFL learners' science text and picture processing through mobile phones," Technology in Society, Elsevier, vol. 59(C).
    19. Yong Liu & Meng Wang & Danyu Huang & Qiang Huang & Hua Yang & Zhigang Li, 2019. "The impact of mobility, risk, and cost on the users’ intention to adopt mobile payments," Information Systems and e-Business Management, Springer, vol. 17(2), pages 319-342, December.
    20. Christian Arnold & Kai-Ingo Voigt, 2019. "Determinants of Industrial Internet of Things Adoption in German Manufacturing Companies," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 16(06), pages 1-21, October.

    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:63:y:2020:i:c:s0160791x20302694. 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.