ASSESSING USER PREFERENCES FOR MOBILE APPLICATIONS IN PUBLIC TRANSPORTATION: A PRE-STUDY USING A CONJOINTBASED RESEARCH METHODOLOGY

Abstract

Developing a successful mobile application requires the involvement of the user in the development process. Features and functionalities have to be carefully cho- sen to meet user expectations and preferences. This paper presents work in pro- gress and proposes a conjoint-based approach for the assessment of user prefer- ences regarding mobile applications in the context of public transportation. A pre- study was conducted based on a computer-aided online survey with two conjoint analysis variants: Choice-based conjoint (CBC) analysis and adaptive choice- based conjoint analysis (ACBC). The paper compares these two different conjoint methods and their implications for the study design and participation. As a result, some preliminary findings on relevant mobile app features and recommendations for the applicable conjoint analysis variant were derived

Keywords

• Mobile Media, Mobile Applications, Conjoint Analysis, CBC, ACBC, User Preferences, Public Transportation

Kaynakça

1. Bauer, Hans H., Anja Schüle and David Toma (2006), “Value-Based Design of
2. Mobile Service Offers in the Automotive Industry“, Proceedings of the IADIS International Conference e-Commerce 2006, pp. 143-150.
3. Bauer, Hans H., Tina Reichardt, Stefanie Exler and Eva Tranka (2007), “Utility- based Design of Mobile Ticketing Applications – A Conjoint-analytical Ap- proach”, Int’l Journal of Mobile Communications, Vol. 5, No. 4, pp. 457-473.
4. Eckjans, Dennis, Jens Fähling, Jan M. Leimeister and Helmut Krcmar (2011),
5. “Mobile Customer Integration: A Smartphone Application Prototype for Conduct- ing Mobile Conjoint Studies”, 10th Int’l Conf. on Mobile Business, pp. 129-135. Giessmann, Andrea and Katarina Stanoevska-Slabeva (2013), “What are Devel- opers’ Preferences on Platform as a Service? An Empirical Investigation“, 46th
6. Hawaii International Conference on System Sciences, pp. 1035-1044.
7. Google (2012), Our Mobile Planet: Understanding the Mobile Consumer, http://services.google.com/fh/files/blogs/our_mobile_planet_germany_en.pdf Accessed 16.04.2013].
8. Green, Paul E. and Srini V. Srinivasan (1990), “Conjoint Analysis in Marketing:

9. New Developments with Implications for Research and Practice”, Journal of Marketing, Vol. 54, Nr. 4, pp. 3-19. Green, Paul E., Abba M. Krieger and Yoram Jerry Wind (2001), “Thirty Years of
10. Conjoint Analysis: Reflections and Prospects”, Interfaces, Vol. 31, No. 3, pp. 56- Jaeger, Sara R., Duncan Hedderley and Halliday J.H. MacFie (2000), “Methodo- logical Issues in Conjoint Analysis: A Case Study“, European Journal of Market- ing, Vol. 35, No. 11, pp. 1217-1239.
11. Kohne, Frank, Carsten Totz and Kai Wehmeyer (2005), “Consumer Preferences for Location-based Service Attributes: A Conjoint Analysis”, International Jour- nal of Management and Decision Making, Vol. 6, No. 1, pp. 16-32.
12. Nikou, Shahrokh, Harry Bouwman and Mark de Reuver (2012), “The Potential of
13. Converged Mobile Telecommunication Services: A Conjoint Analysis”, Info, Vol. , No. 5, pp. 21-35. Sawtooth Software (2008), “CBC Technical Paper. The CBC System for Choice
14. Based Conjoint Analysis“, Sawtooth Technical Paper Series, http://www. sawtoothsoftware.com/download/techpap/cbctech.pdf, [Accessed 18.04.2013].
15. Schaupp, Christian L. and France Bélanger (2005), “A Conjoint Analysis of
16. Online Consumer Satisfaction”, Journal of Electronic Commerce Research, Vol. , No. 2, pp. 95-111.