Advertisement

Electronic Markets

, Volume 29, Issue 1, pp 37–53 | Cite as

User preferences and willingness to pay for in-vehicle assistance

  • A. Cristina Mihale-WilsonEmail author
  • Jan Zibuschka
  • Oliver Hinz
Research Paper
Part of the following topical collections:
  1. Smart Services: The move to customer

Abstract

As consumers’ demand for interconnectivity and infotainment grows continuously, car manufacturers face the challenge of developing more sophisticated, user appealing and economically viable in-vehicle infotainment assistants while staying within the boundaries of their limited resources. Based on the results extracted from an empirical study with 278 participants from Germany, this contribution supports car manufacturers to tackle this challenge by providing concrete guidance on optimal feature design, pricing, as well as initial market segmentation. Regarding the optimal feature design, we note that delivering continuously available and flawless systems with a speech input interface should be the top priority when developing such vehicular assistance. Further, we suggest that the in-vehicle infotainment assistants should be either reactive (i.e., react only to driver’s instruction) or independently proactive (i.e., exert full control without engaging the driver in decisions), but not semi-automatic (i.e., assistant issues recommendations and then follows the driver’s instructions).

Keywords

User preferences WTP In-vehicle intelligent personal assistant Choice-based conjoint analysis 

JEL classification

D12 M39 

Notes

References

  1. Alt, F., Kern, D., Schulte, F., Pfleging, B., Shirazi, A.S., & Schmidt, A. (2010). Enabling micro-entertainment in vehicles based on context information. In Proceedings of the 2nd international conference on automotive user interfaces and interactive vehicular applications. ACM, 117–124.Google Scholar
  2. Bruner, G. C. (2009). Marketing Scales Handbook: A compilation of multi-item measures for consumer behavior & advertising research. (Vol. 5). GCBII Productions.Google Scholar
  3. Cellario, M. (2001). Human-centered intelligent vehicles: Toward multimodal interface integration. IEEE Intelligent Systems, 16, 78–81.CrossRefGoogle Scholar
  4. Chapman, C. N., Love, E., & Alford, J. L. (2008). Quantitative early-phase user research methods: Hard data for initial product design. In Hawaii international conference on system sciences. Proceedings of the 41st Annual. IEEE, 37–37.Google Scholar
  5. Coppola, R., & Morisio, M. (2016). Connected car: Technologies, issues, future trends. ACM Comput. Surv. CSUR, 49, 46.Google Scholar
  6. Cowan, B.R., Pantidi, N., Coyle, D., Morrissey, K., Clarke, P., Al-Shehri, S., Earley, D., & Bandeira, N. (2017). What can i help you with?: Infrequent users’ experiences of intelligent personal assistants. In Proceedings of the 19th international conference on human-computer interaction with Mobile devices and services. ACM, 43.Google Scholar
  7. Eichhorn, M., Pfannenstein, M., Muhra, D., & Steinbach, E. (2010). A SOA-based middleware concept for in-vehicle service discovery and device integration. In Intelligent vehicles symposium (IV). 2010 IEEE. IEEE, 663–669.Google Scholar
  8. Gaffar, A., & Kouchak, S. M. (2017). Minimalist design: An optimized solution for intelligent interactive infotainment systems. In Intelligent systems conference (IntelliSys). 2017.IEEE, 553–557.Google Scholar
  9. Gensler, S., Hinz, O., Skiera, B., & Theysohn, S. (2012). Willingness-to-pay estimation with choice-based conjoint analysis: Addressing extreme response behavior with individually adapted designs. European Journal of Operational Research, 219, 368–378.CrossRefGoogle Scholar
  10. Green, P. E., Krieger, A. M., & Wind, Y. (2001). Thirty years of conjoint analysis: Reflections and prospects. Interfaces, 31, 56–73.CrossRefGoogle Scholar
  11. Green, P. E., & Srinivasan, V. (1990). Conjoint analysis in marketing: New developments with implications for research and practice. Journal of Marketing, 3–19.Google Scholar
  12. Howe, K. (2009). Anthropomorphic systems: An approach for categorization. In International conference on internationalization, design and global development 173–179.Google Scholar
  13. Hüger, F. (2011). User interface transfer for driver information systems: A survey and an improved approach. In Proceedings of the 3rd international conference on automotive user interfaces and interactive vehicular applications. ACM, 113–120.Google Scholar
  14. Jackson, D. N. (1976). Jackson personality inventory JPI: Manual. Research Psychologists Press.Google Scholar
  15. Kalish, S., & Nelson, P. (1991). A comparison of ranking, rating and reservation price measurement in conjoint analysis. Marketing Letters, 2, 327–335.CrossRefGoogle Scholar
  16. Kelley, K., & Maxwell, S. E. (2003). Sample size for multiple regression: Obtaining regression coefficients that are accurate, not simply significant. Psychological Methods, 8, 305–321.CrossRefGoogle Scholar
  17. Kohli, R., & Mahajan, V. (1991). A reservation-price model for optimal pricing of multiattribute products in conjoint analysis. Journal of Marketing Research, 28, 347–354.CrossRefGoogle Scholar
  18. Kumaraguru, P., & Cranor, L. F. (2005). Privacy indexes: a survey of Westin’s studies. http://repository.cmu.edu/isr/856/. Accessed 12 July 2018
  19. Large, D. R., Clark, L., Quandt, A., Burnett, G., & Skrypchuk, L. (2017). Steering the conversation: A linguistic exploration of natural language interactions with a digital assistant during simulated driving. Applied Ergonomics, 63, 53–61.CrossRefGoogle Scholar
  20. Luger, E., & Sellen, A. (2016). Like having a really bad PA: the gulf between user expectation and experience of conversational agents. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM, 5286–5297.Google Scholar
  21. Macario, G., Torchiano, M., & Violante, M. (2009). An in-vehicle infotainment software architecture based on google android. In Industrial embedded systems, 2009. SIES’09. IEEE international symposium on. IEEE, 257–260.Google Scholar
  22. Meuter, M. L., Bitner, M. J., Ostrom, A. L., & Brown, S. W. (2005). Choosing among alternative service delivery modes: An investigation of customer trial of self-service technologies. Journal of Marketing, 69, 61–83.CrossRefGoogle Scholar
  23. Moniri, M. M., Feld, M., & Müller, C. (2012). Personalized in-vehicle information systems: Building an application infrastructure for smart cars in smart spaces. In Intelligent environments (IE). 2012 8th International Conference On. IEEE, 379–382.Google Scholar
  24. Müller, C., & Weinberg, G. (2011). Multimodal input in the car, today and tomorrow. IEEE Multimed., 18, 98–103.CrossRefGoogle Scholar
  25. Olaverri-Monreal, C., Lehsing, C., Trübswetter, N., Schepp, C. A., & Bengler, K. (2013). In-vehicle displays: Driving information prioritization and visualization. In Intelligent vehicles symposium (IV). 2013 IEEE. IEEE, 660–665.Google Scholar
  26. Parada-Loira, F., González-Agulla, E., & Alba-Castro, J. L. (2014). Hand gestures to control infotainment equipment in cars. In Intelligent vehicles symposium proceedings. 2014 IEEE. IEEE, 1–6.Google Scholar
  27. Pfeuffer, N., Benlian, A., Gimpel, H., & Hinz, O. (2018). Catchword “anthropomorphic information systems.” bus. Inf. Systems Engineering forthcoming.Google Scholar
  28. Ram, P., Markkula, J., Friman, V., & Raz, A. (2018). Security and privacy concerns in connected cars: A systematic mapping study. In 2018 44th Euromicro conference on software engineering and advanced applications (SEAA). IEEE, 124–131.Google Scholar
  29. Rhiu, I., Kwon, S., Bahn, S., Yun, M. H., & Yu, W. (2015). Research issues in smart vehicles and elderly drivers: A literature review. Int. J. Hum.-Comput. Interact., 31, 635–666.CrossRefGoogle Scholar
  30. Rosario, B., Lyons, K., & Healey, J. (2011). A dynamic content summarization system for opportunistic driver infotainment. In Proceedings of the 3rd international conference on automotive user interfaces and interactive vehicular applications. ACM, 95–98.Google Scholar
  31. Schlereth, C., & Skiera, B. (2012). DISE: Dynamic intelligent survey engine. In Quantitative marketing and marketing management. Springer, 225–243.Google Scholar
  32. Spiekermann, S., & Pallas, F. (2007). Technologiepaternalismus—Soziale Auswirkungen des Ubiquitous Computing jenseits von Privatsphäre. In Die Informatisierung Des Alltags. Springer, 311–325.Google Scholar
  33. Spiekermann, S., & Ziekow, H. (2006). RFID: A systematic analysis of privacy threats and a 7-point plan to adress them. J. Inf. Syst. Secur., 1, 2–17.Google Scholar
  34. Steenkamp, J. E., & Baumgartner , H. (1995). Development and Cross- Cultural Validation of a Short form of CSI as a Measure of Optimum Stimulation Level. International Journal of Research in Marketing, 97–104.Google Scholar
  35. Steenkamp, J.-B. E., & Gielens, K. (2003). Consumer and market drivers of the trial probability of new consumer packaged goods. Journal of Consumer Research, 30, 368–384.CrossRefGoogle Scholar
  36. Stratistics MRC (2017). In-Car Entertainment - Global Market Outlook (2016–2022) [WWW Document]. URL https://www.strategymrc.com/report/in-car-entertainment-market. Accessed 6 Dec 2018.
  37. Strayer, D. L., Cooper, J. M., Turrill, J., Coleman, J. R., & Hopman, R. J. (2017). The smartphone and the driver’s cognitive workload: A comparison of apple, Google, and Microsoft’s intelligent personal assistants. Can. J. Exp. Psychol. Can. Psychol. Expérimentale, 71, 93–110.CrossRefGoogle Scholar
  38. Strayer, D. L., Turrill, J., Coleman, J. R., Ortiz, E. V., & Cooper, J. M. (2014). Measuring cognitive distraction in the automobile II: Assessing in-vehicle voice-based. Accident; Analysis and Prevention, 372, 379.Google Scholar
  39. Street, D. J., & Burgess, L. (2007). The construction of optimal stated choice experiments: Theory and methods. John Wiley & Sons.Google Scholar
  40. Vermeulen, B., Goos, P., & Vandebroek, M. (2008). Models and optimal designs for conjoint choice experiments including a no-choice option. International Journal of Research in Marketing, 25, 94–103.CrossRefGoogle Scholar
  41. Viereckl, R., Ahlemann, D., Koster, A., & Jusch, S. (2015). Connected Car study 2015: Racing ahead with autonomous cars and digital innovation. Strategy&pwc.Google Scholar
  42. Wee, D., Kässer, M., Bertoncello, M., Heineke, K., Eckhard, G., Hölz, J., Saupe, F., & Müller, T. (2015). Competing for the connected customer—Perspectives on the opportunities created by car connectivity and automation. McKinsey Co..Google Scholar
  43. Wertenbroch, K., & Skiera, B. (2002). Measuring consumers’ willingness to pay at the point of purchase. Journal of Marketing Research, 39, 228–241.CrossRefGoogle Scholar
  44. Williams, K.J., Peters, J.C., & Breazeal, C.L. (2013). Towards leveraging the driver’s mobile device for an intelligent, sociable in-car robotic assistant. In Intelligent vehicles symposium (IV), 2013 IEEE. IEEE, 369–376.Google Scholar
  45. Wulf, L., Garschall, M., Himmelsbach, J., & Tscheligi, M. (2014). Hands free-care free: elderly people taking advantage of speech-only interaction. In Proceedings of the 8th Nordic Conference on Human-Computer Interaction: Fun, Fast, Foundational. ACM, 203–206.Google Scholar

Copyright information

© Institute of Applied Informatics at University of Leipzig 2019

Authors and Affiliations

  • A. Cristina Mihale-Wilson
    • 1
    Email author
  • Jan Zibuschka
    • 2
  • Oliver Hinz
    • 1
  1. 1.Department of Information Systems and Information ManagementGoethe University FrankfurtFrankfurt am MainGermany
  2. 2.Robert Bosch GmbHRenningenGermany

Personalised recommendations