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Using structural topic modelling to predict users’ sentiment towards intelligent personal agents. An application for Amazon’s echo and Google Home

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  • Sánchez-Franco, Manuel J.
  • Arenas-Márquez, Francisco J.
  • Alonso-Dos-Santos, Manuel

Abstract

Despite growing levels of usage of Intelligent Personal Assistants (hereinafter, IPA), their in-home usage has not been studied in depth by scholars. To increase our understanding of user interactions with IPA, our research created a theoretical framework rooted in technology acceptance models and Uses and Gratification Theory. Our empirical method designs an ambitious analysis of natural and non-structured narratives (user-generated content) on Amazon’s Echo and Google Home. And to identify key aspects that differentially influence the evaluation of IPA our method employs machine-learning algorithms based on text summarisation, structural topic modelling and cluster analysis, sentiment analysis, and XGBoost regression, among other approaches. Our results reveal that (hedonic and utilitarian) benefits gratification, social influence and facilitating conditions have a direct impact on the users’ sentiment for IPA. To sum up, designers and managers should recognise the challenge of increasing the customer satisfaction of current and potential users by adjusting doubtful users’ technical skills and the (hedonic, cognitive, and social) benefits and functionalities of IPA to avoid boredom after a short lapse of time. Finally, the discussion section outlines future lines of research and theoretical and managerial implications.

Suggested Citation

  • Sánchez-Franco, Manuel J. & Arenas-Márquez, Francisco J. & Alonso-Dos-Santos, Manuel, 2021. "Using structural topic modelling to predict users’ sentiment towards intelligent personal agents. An application for Amazon’s echo and Google Home," Journal of Retailing and Consumer Services, Elsevier, vol. 63(C).
    • Handle: RePEc:eee:joreco:v:63:y:2021:i:c:s0969698921002241
    DOI: 10.1016/j.jretconser.2021.102658
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    1. McLean, Graeme & Osei-Frimpong, Kofi, 2019. "Chat now… Examining the variables influencing the use of online live chat," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 55-67.
    2. Marikyan, Davit & Papagiannidis, Savvas & Alamanos, Eleftherios, 2019. "A systematic review of the smart home literature: A user perspective," Technological Forecasting and Social Change, Elsevier, vol. 138(C), pages 139-154.
    3. Climent, Francisco & Momparler, Alexandre & Carmona, Pedro, 2019. "Anticipating bank distress in the Eurozone: An Extreme Gradient Boosting approach," Journal of Business Research, Elsevier, vol. 101(C), pages 885-896.
    4. 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.
    5. Kevin M. Quinn & Burt L. Monroe & Michael Colaresi & Michael H. Crespin & Dragomir R. Radev, 2010. "How to Analyze Political Attention with Minimal Assumptions and Costs," American Journal of Political Science, John Wiley & Sons, vol. 54(1), pages 209-228, January.
    6. Pantano, Eleonora & Pizzi, Gabriele, 2020. "Forecasting artificial intelligence on online customer assistance: Evidence from chatbot patents analysis," Journal of Retailing and Consumer Services, Elsevier, vol. 55(C).
    7. Rauschnabel, Philipp A. & He, Jun & Ro, Young K., 2018. "Antecedents to the adoption of augmented reality smart glasses: A closer look at privacy risks," Journal of Business Research, Elsevier, vol. 92(C), pages 374-384.
    8. Philipp A. Rauschnabel, 2018. "A Conceptual Uses & Gratification Framework on the Use of Augmented Reality Smart Glasses," Progress in IS, in: Timothy Jung & M. Claudia tom Dieck (ed.), Augmented Reality and Virtual Reality, pages 211-227, Springer.
    9. Atanu Sengupta & Sanjoy De, 2020. "Review of Literature," India Studies in Business and Economics, in: Assessing Performance of Banks in India Fifty Years After Nationalization, chapter 0, pages 15-30, Springer.
    10. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    11. Viswanath Venkatesh & Fred D. Davis, 2000. "A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies," Management Science, INFORMS, vol. 46(2), pages 186-204, February.
    12. Heetae Yang & Hwansoo Lee, 2019. "Understanding user behavior of virtual personal assistant devices," Information Systems and e-Business Management, Springer, vol. 17(1), pages 65-87, March.
    13. Shin, Jungwoo & Park, Yuri & Lee, Daeho, 2018. "Who will be smart home users? An analysis of adoption and diffusion of smart homes," Technological Forecasting and Social Change, Elsevier, vol. 134(C), pages 246-253.
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