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Anticipating acceptance of emerging technologies using twitter: the case of self-driving cars

Author

Listed:
  • Christopher Kohl

    (Technical University of Munich)

  • Marlene Knigge

    (Technical University of Munich)

  • Galina Baader

    (Technical University of Munich)

  • Markus Böhm

    (Technical University of Munich)

  • Helmut Krcmar

    (Technical University of Munich)

Abstract

In an early stage of developing emerging technologies, there is often great uncertainty regarding their future success. Companies can reduce this uncertainty by listening to the voice of customers as the customer eventually decides to accept an emerging technology or not. We show that risk and benefit perceptions are central determinants of acceptance of emerging technologies. We present an analysis of risk and benefit perception of self-driving cars from March 2015 until October 2016. In this period, we analyzed 1,963,905 tweets using supervised machine learning for text classification. Furthermore, we developed two new metrics, risk rate (RR) and benefit rate (BR), which allow analyzing risk and benefit perceptions on social media quantitatively. With our results, we provide impetus for further research on acceptance of self-driving cars and a methodological contribution to acceptance of emerging technologies research. Furthermore, we identify crucial issues in the public perception of self-driving cars and provide guidance for the management of emerging technologies to increase the likelihood of their acceptance.

Suggested Citation

  • Christopher Kohl & Marlene Knigge & Galina Baader & Markus Böhm & Helmut Krcmar, 2018. "Anticipating acceptance of emerging technologies using twitter: the case of self-driving cars," Journal of Business Economics, Springer, vol. 88(5), pages 617-642, July.
    • Handle: RePEc:spr:jbecon:v:88:y:2018:i:5:d:10.1007_s11573-018-0897-5
    DOI: 10.1007/s11573-018-0897-5
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    Cited by:

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    2. Franziska Schlichte & Sebastian Junge & Jan Mammen, 2019. "Being at the right place at the right time: does the timing within technology waves determine new venture success?," Journal of Business Economics, Springer, vol. 89(8), pages 995-1021, December.
    3. Chiarello, Filippo & Giordano, Vito & Spada, Irene & Barandoni, Simone & Fantoni, Gualtiero, 2024. "Future applications of generative large language models: A data-driven case study on ChatGPT," Technovation, Elsevier, vol. 133(C).
    4. Gogoll, Jan & Uhl, Matthias, 2018. "Rage against the machine: Automation in the moral domain," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 74(C), pages 97-103.
    5. McLeay, Fraser & Olya, Hossein & Liu, Hongfei & Jayawardhena, Chanaka & Dennis, Charles, 2022. "A multi-analytical approach to studying customers motivations to use innovative totally autonomous vehicles," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    6. Giordano, Vito & Spada, Irene & Chiarello, Filippo & Fantoni, Gualtiero, 2024. "The impact of ChatGPT on human skills: A quantitative study on twitter data," Technological Forecasting and Social Change, Elsevier, vol. 203(C).

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    More about this item

    Keywords

    Acceptance; Benefit perception; Risk perception; Self-driving cars; Text mining; Voice of customer;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • M30 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - General
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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