skip to main content
10.1145/3341162.3350844acmconferencesArticle/Chapter ViewAbstractPublication PagesubicompConference Proceedingsconference-collections
research-article

"K.I.T.T., where are you?": why smart assistance systems in cars enrich people's lives

Published:09 September 2019Publication History

ABSTRACT

Personal smart assistance systems make people's lives easier and enable exceptional convenience, e.g. by supporting users during bothersome tasks. While personal intelligent assistants offer a lot of comfort to their users, there are also worries about data protection and data security since personal data about users is collected, aggregated and analyzed for ubiquitous assistance systems. Smart assistance systems can for example be found in cars. Connected to other internet of things devices, those assistants can help with the search for free parking lots in a crowded city or enable easy refueling in cooperation with intelligent charging stations. As the users' motivation to engage in those smart assistance systems is still undetected we investigate the influence of several potential drivers on the intention to use smart assistance systems in cars. This study uses survey data (N = 150) and structural equation modeling as the analysis method. Our results provide empirical evidence that convenience motives, performance expectancy, personal innovativeness, and perceived risk are drivers for consumers' intention to use smart assistance systems in cars. Moreover, we motivate further research in the field of smart assistance systems. Furthermore, we discuss academic and practical implications.

References

  1. Emeli Adell, András Várhelyi, and Lena Nilsson. 2018. Modelling acceptance of driver assistance systems: application of the unified theory of acceptance and use of technology. In Driver Acceptance of New Technology. CRC Press, 23--34.Google ScholarGoogle Scholar
  2. Ritu Agarwal and Jayesh Prasad. 1998. A conceptual and operational definition of personal innovativeness in the domain of information technology. Information systems research 9, 2 (1998), 204--215. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Noura Aleisa and Karen Renaud. 2017. Privacy of the internet of things: a systematic literature review. In Proceedings of the 50th Hawaii International Conference on System Sciences.Google ScholarGoogle ScholarCross RefCross Ref
  4. Richard P Bagozzi and Youjae Yi. 1988. On the evaluation of structural equation models. Journal of the academy of marketing science 16, 1 (1988), 74--94.Google ScholarGoogle ScholarCross RefCross Ref
  5. Richard P Bagozzi, Youjae Yi, and Lynn W Phillips. 1991. Assessing construct validity in organizational research. Administrative science quarterly (1991), 421--458.Google ScholarGoogle Scholar
  6. Michele Bertoncello, Gianluca Camplone, Paul Gao, Hans-Werner Kaas, Detlev Mohr, T Möller, and Dominik Wee. 2016. Monetizing car data---new service business opportunities to create new customer benefits. McKinsey & Company (2016).Google ScholarGoogle Scholar
  7. Gray E. Burnett and J. Mark Porter. 2001. Ubiquitous computing within cars: designing controls for non-visual use. International Journal of Human-Computer Studies 55, 1 (2001), 521--531. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Lawrence D. Burns. 2013. A vision of our transport future. Nature 497, 1 (2013), 181--182.Google ScholarGoogle ScholarCross RefCross Ref
  9. Nil Goksel Canbek and Mehmet Emin Mutlu. 2016. On the track of artificial intelligence: Learning with intelligent personal assistants. Journal of Human Sciences 13, 1 (2016), 592--601.Google ScholarGoogle ScholarCross RefCross Ref
  10. Yun-Nung Chen. 2015. Unsupervised Learning and Modeling of Knowledge and Intent for Spoken Dialogue Systems. Unpublished doctoral dissertation, Carnegie Mellon University, Pennsylvania, USA. (2015), Retrieved from http://www.cs.cmu.edu/yvchen/doc/dissertation.pdf.Google ScholarGoogle Scholar
  11. Wynne W Chin, Barbara L Marcolin, and Peter R Newsted. 2003. A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Information systems research 14, 2 (2003), 189--217. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Adrian Cho. 2007. Robotic Cars Tackle Crosstown Traffic---and Not One Another. Science 318, 16 (2007).Google ScholarGoogle Scholar
  13. Fred D Davis. 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly (1989), 319--340. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. William B Dodds, Kent B Monroe, and Dhruv Grewal. 1991. Effects of price, brand, and store information on buyers' product evaluations. Journal of marketing research 28, 3 (1991), 307--319.Google ScholarGoogle Scholar
  15. Yulin Fang, Israr Qureshi, Heshan Sun, Patrick McCole, Elaine Ramsey, and Kai H Lim. 2014. Trust, satisfaction, and online repurchase intention: The moderating role of perceived effectiveness of e-commerce institutional mechanisms. Mis Quarterly 38, 2 (2014). Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Sandrine Fischer, Makoto Itoh, and Toshiyuki Inagaki. 2009. A cognitive schema approach to diagnose intuitiveness: An application to onboard computers. In Proceedings of the 1st International Conference on Automotive User Interfaces and Interactive Vehicular Applications. ACM, 35--42. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Martin Fishbein and Icek Ajzen. 1975. Belief, attitude, intention, and behavior: An introduction to theory and research. (1975).Google ScholarGoogle Scholar
  18. Luke Fletcher, Seth Teller, Edwin Olson, David Moore, Yoshiaki Kuwata, Jonathan How, and John Leonard. 2008. The MIT-Cornell Collision and Why It Happened. Journal of Field Robotics 25, 10 (2008), 775--807. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Claes Fornell and David F Larcker. 1981. Structural equation models with unobservable variables and measurement error: Algebra and statistics.Google ScholarGoogle Scholar
  20. Daniel Franzmann, Christoph Mittendorf, and Uwe Ostermann. 2018. Vacuum Cleaning as a Service. Multikonferenz Wirtschaftsinformatik (2018), 1565--1576.Google ScholarGoogle Scholar
  21. David Gefen, Elena Karahanna, and Detmar W. Straub. 2003. Trust and TAM in Online Shopping: An Integrated Model. MIS Quarterly 27, 1 (2003), 51--90. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. David Gefen, Edward Rigdon, and Detmar Straub. 2011. An Update and Extension to SEM Guidelines for Administrative and Social Science Research. Editorial Comment. MIS Quarterly 35 (06 2011), III--XII.Google ScholarGoogle Scholar
  23. Michael H Graham. 2003. Confronting multicollinearity in ecological multiple regression. Ecology 84, 11 (2003), 2809--2815.Google ScholarGoogle ScholarCross RefCross Ref
  24. Rajdeep Grewal, Joseph A Cote, and Hans Baumgartner. 2004. Multicollinearity and measurement error in structural equation models: Implications for theory testing. Marketing science 23, 4 (2004), 519--529. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. JF Hair, William C Black, Barry J Babin, and Rolph E Anderson. 2010. Multivariate Data Analysis. New Jersey, Pearson (2010).Google ScholarGoogle Scholar
  26. Joseph F Hair, G Tomas M Hult, Christian M Ringle, Marko Sarstedt, and Kai Oliver Thiele. 2017. Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods. Journal of the Academy of Marketing Science 45, 5 (2017), 616--632.Google ScholarGoogle ScholarCross RefCross Ref
  27. Sangyeal Han and Heetae Yang. 2018. Understanding adoption of intelligent personal assistants: A parasocial relationship perspective. Industrial Management & Data Systems 118, 3 (2018), 618--636.Google ScholarGoogle ScholarCross RefCross Ref
  28. Ned Kock and Gary Lynn. 2012. Lateral collinearity and misleading results in variance-based SEM: An illustration and recommendations. Journal of the Association for Information Systems 13, 7 (2012).Google ScholarGoogle ScholarCross RefCross Ref
  29. Ponnurangam Kumaraguru and Lorrie Faith Cranor. 2005. Privacy indexes: a survey of Westin's studies. Carnegie Mellon University, School of Computer Science, Institute for ....Google ScholarGoogle Scholar
  30. Marc Langheinrich. 2001. Privacy by design---principles of privacy-aware ubiquitous systems. In International conference on Ubiquitous Computing. Springer, 273--291. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. David R Large, Leigh Clark, Annie Quandt, Gary Burnett, and Lee Skrypchuk. 2017. Steering the conversation: a linguistic exploration of natural language interactions with a digital assistant during simulated driving. Applied ergonomics 63 (2017), 53--61.Google ScholarGoogle Scholar
  32. David R. Large, Leigh Clark, Annie Quandt, Gary Burnett, and Lee Skrypchuk. 2018. Steering the conversation: A Linguistic Exploration of Natural Language Interactions with a Digital Assistant During Simulated Driving. Applied Ergonomics 63, 1 (2018), 53--61.Google ScholarGoogle ScholarCross RefCross Ref
  33. Irene Lopatovska, Mildred Velazquez, Rachel Richardson, and Guida Lai. 2019. User sentiments towards intelligent personal assistants. iConference 2019 Proceedings (2019).Google ScholarGoogle Scholar
  34. Paul Benjamin Lowry and James Gaskin. 2014. Partial least squares (PLS) structural equation modeling (SEM) for building and testing behavioral causal theory: When to choose it and how to use it. IEEE transactions on professional communication 57, 2 (2014), 123--146.Google ScholarGoogle ScholarCross RefCross Ref
  35. D Harrison McKnight, Vivek Choudhury, and Charles Kacmar. 2002. The impact of initial consumer trust on intentions to transact with a web site: a trust building model. The journal of strategic information systems 11, 3--4 (2002), 297--323.Google ScholarGoogle Scholar
  36. Cristina Mihale-Wilson, Jan Zibuschka, and Oliver Hinz. 2017. About user preferences and willingness to pay for a secure and privacy protective ubiquitous personal assistant. In Proceedings of the 25th European Conference on Information Systems (ECIS), Guimarães, Portugal, June 5--10.Google ScholarGoogle Scholar
  37. Cristina Mihale-Wilson, Jan Zibuschka, and Oliver Hinz. 2019. User preferences and willingness to pay for in-vehicle assistance. Electronic Markets (2019), 1--17.Google ScholarGoogle Scholar
  38. Christoph Mittendorf, Nicholas Berente, and Roland Holten. {n.d.}. Trust in sharing encounters among millennials. Information Systems Journal ({n. d.}).Google ScholarGoogle Scholar
  39. Christoph Mittendorf, Daniel Franzmann, and Uwe Ostermann. 2017. Why Would Customers Engage in Drone Deliveries? Twenty-third Americas Conference on Information Systems (2017).Google ScholarGoogle Scholar
  40. Andreas I Nicolaou and D Harrison McKnight. 2006. Perceived information quality in data exchanges: Effects on risk, trust, and intention to use. Information systems research 17, 4 (2006), 332--351. Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. Scott W O'Leary-Kelly and Robert J. Vokurka. 1998. The empirical assessment of construct validity. Journal of operations management 16, 4 (1998), 387--405.Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. Paul Pavlou. 2001. Integrating trust in electronic commerce with the technology acceptance model: model development and validation. Amcis 2001 proceedings (2001), 159.Google ScholarGoogle Scholar
  43. Paul A. Pavlou and David Gefen. 2004. Building Effective Online Marketplaces with Institution- Based Trust. Information Systems Research 15, 1 (2004), 37--59. Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. Konrad Reif. 2010. Fahrstabilisierungssysteme und Fahrerassistenzsysteme. Springer.Google ScholarGoogle Scholar
  45. Christian M. Ringle, Sven Wende, and Jan-Michael Becker. 2015. Smart-PLS 3. http://www.smartpls.com.Google ScholarGoogle Scholar
  46. Shannon C Roberts, Mahtab Ghazizadeh, and John D Lee. 2012. Warn me now or inform me later: Drivers' acceptance of real-time and post-drive distraction mitigation systems. International Journal of Human-Computer Studies 70, 12 (2012), 967--979. Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. M Rogers Everett. 1995. Diffusion of innovations. New York 12 (1995).Google ScholarGoogle Scholar
  48. Joao Santos, Joel J. P. C. Rodrigues, Joao Casal, Kashif Saleem, and Victor Denisov. 2018. Intelligent Personal Assistants Based on Internet of Things Approaches. IEEE Systems Journal 12, 2 (2018), 1793--1802.Google ScholarGoogle ScholarCross RefCross Ref
  49. Emma L Slade, Michael D Williams, and Yogesh Dwivedi. 2013. Extending UTAUT2 To Explore Consumer Adoption Of Mobile Payments. UKAIS 36 (2013).Google ScholarGoogle Scholar
  50. Detmar Straub, Marie-Claude Boudreau, and David Gefen. 2004. Validation guidelines for IS positivist research. Communications of the Association for Information systems 13, 1 (2004), 24.Google ScholarGoogle Scholar
  51. David L Strayer, Joel M Cooper, Jonna Turrill, James R Coleman, and Rachel J Hopman. 2017. The smartphone and the driver's cognitive workload: A comparison of Apple, Google, and Microsoft's intelligent personal assistants. Canadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale 71, 2 (2017), 93.Google ScholarGoogle Scholar
  52. Guozi Sun, Siqi Huang, Wan Bao, Yitao Yang, and Zhiwei Wang. 2014. A privacy protection policy combined with privacy homomorphism in the internet of things. In 2014 23rd International Conference on Computer Communication and Networks (ICCCN). IEEE, 1--6.Google ScholarGoogle ScholarCross RefCross Ref
  53. Deqing Tan and Junhui Bai. 2015. Analysis of the Factor Affected Chinese Audience Choice Behavior between Traditional TV and Network Video in PLS-SEM. Modern Economy 6, 07 (2015), 833.Google ScholarGoogle ScholarCross RefCross Ref
  54. Viswanath Venkatesh, Michael G Morris, Gordon B Davis, and Fred D Davis. 2003. User acceptance of information technology: Toward a unified view. MIS quarterly (2003), 425--478. Google ScholarGoogle ScholarDigital LibraryDigital Library
  55. Viswanath Venkatesh, James YL Thong, and Xin Xu. 2012. Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS quarterly 36, 1 (2012), 157--178. Google ScholarGoogle ScholarDigital LibraryDigital Library
  56. D Wee, M Kässer, M Bertoncello, K Heineke, G Eckhard, J Hölz, F Saupe, and T Müller. 2015. Competing for the connected customer- perspectives on the opportunities created by car connectivity and automation. McKinsey & Company (2015).Google ScholarGoogle Scholar
  57. Michael Wooldridge and Nicholas R Jennings. 1995. Intelligent agents: Theory and practice. The knowledge engineering review 10, 2 (1995), 115--152.Google ScholarGoogle ScholarCross RefCross Ref
  58. Thomasz Zaleskiewicz. 2001. Beyond Risk Seeking and Risk Aversion: Personality and the Dual Nature of Economic Risk Taking. European Journal of Personality 15, 1 (2001), 37--59.Google ScholarGoogle ScholarCross RefCross Ref
  59. Jan Zibuschka, Michael Nofer, and Oliver Hinz. 2016. Zahlungsbereitschaft für Datenschutzfunktionen intelligenter Assistenten. Multikonferenz Wirtschaftsinformatik (MKWI), Ilmenau (2016), 1391--1402.Google ScholarGoogle Scholar

Index Terms

  1. "K.I.T.T., where are you?": why smart assistance systems in cars enrich people's lives

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Conferences
        UbiComp/ISWC '19 Adjunct: Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers
        September 2019
        1234 pages
        ISBN:9781450368698
        DOI:10.1145/3341162

        Copyright © 2019 ACM

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 9 September 2019

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article

        Acceptance Rates

        Overall Acceptance Rate764of2,912submissions,26%

        Upcoming Conference

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader