Motivating change in commuters’ mobility behaviour: Digital nudging for public transportation use

References

• Abrahamse, W., Steg, L., Vlek, C., & Rothengatter, T. (2007). The effect of tailored information, goal setting, and tailored feedback on household energy use, energy-related behaviors, and behavioral antecedents. Journal of Environmental Psychology, 27(4), 265–276. https://doi.org/10.1016/j.jenvp.2007.08.002
   Web of Science ®Google Scholar
• Adomavicius, G., Bockstedt, J.C., Curley, S.P., & Zhang, J. (2013). Do recommender systems manipulate consumer preferences? A study of anchoring effects. Information Systems Research, 24(4), 956–975. https://doi.org/10.1287/isre.2013.0497
   Web of Science ®Google Scholar
• Anagnostopoulou, E., Urbančič, J., Bothos, E., Magoutas, B., Bradesko, L., Schrammel, J., & Mentzas, G. (2020). From mobility patterns to behavioural change: Leveraging travel behaviour and personality profiles to nudge for sustainable transportation. Journal of Intelligent Information Systems, 54(1), 157–178. https://doi.org/10.1007/s10844-018-0528-1
   Web of Science ®Google Scholar
• Anenberg, S., Miller, J., Henze, D., & Minjares, R. (2019). A global snapshot of the air pollution-related health impacts of transportation sector emissions in 2010 and 2015. International Council on Clean Transportation.
   Google Scholar
• Arentze, T.A., & Molin, E.J. (2013). Travelers’ preferences in multimodal networks: Design and results of a comprehensive series of choice experiments. Transportation Research Part A: Policy and Practice, 58, 15–28.
   Web of Science ®Google Scholar
• Backhaus, K., Erichson, B., & Weiber, R. (2015). Fortgeschrittene multivariate Analysemethoden: eine anwendungsorientierte Einführung. Springer-Verlag.
   Google Scholar
• Baltar, F., & Brunet, I. (2012). Social research 2.0: Virtual snowball sampling method using Facebook. Internet Research, 22(1), 57–74. https://doi.org/10.1108/10662241211199960
   Web of Science ®Google Scholar
• Barton, A., & Grüne-Yanoff, T. (2015). From libertarian paternalism to nudging—and beyond. Review of Philosophy and Psychology, 6(3), 341–359. https://doi.org/10.1007/s13164-015-0268-x
   Web of Science ®Google Scholar
• Beermann, V., Rieder, A., & Uebernickel, F. (2022). Green nudges: how to induce. Pro-Environmental Behavior Using Technology.
   Google Scholar
• Berger, B., Matt, C., Steininger, D.M., & Hess, T. (2015). It is not just about competition with “free”: Differences between content formats in consumer preferences and willingness to pay. Journal of Management Information Systems, 32(3), 105–128. https://doi.org/10.1080/07421222.2015.1095038
   Web of Science ®Google Scholar
• Bothos, E., Apostolou, D., & Mentzas, G. (2016). A recommender for persuasive messages in route planning applications. 2016 7th International Conference on Information, Intelligence, Systems & Applications (IISA), Chalkidiki, Greece.
   Google Scholar
• Buchanan, K., & Russo, R. (2019). Money doesn’t matter! Householders’ intentions to reduce standby power are unaffected by personalised pecuniary feedback. PLoS One, 14(10), e0223727. https://doi.org/10.1371/journal.pone.0223727
   PubMed Web of Science ®Google Scholar
• Bucher, D., Mangili, F., Cellina, F., Bonesana, C., Jonietz, D., & Raubal, M. (2019). From location tracking to personalized eco-feedback: A framework for geographic information collection, processing and visualization to promote sustainable mobility behaviors. Travel Behaviour and Society, 14, 43–56. https://doi.org/10.1016/j.tbs.2018.09.005
   Web of Science ®Google Scholar
• Byerly, H., Balmford, A., Ferraro, P.J., Hammond Wagner, C., Palchak, E., Polasky, S., Ricketts, T.H., Schwartz, A.J., & Fisher, B. (2018). Nudging pro‐environmental behavior: Evidence and opportunities. Frontiers in Ecology and the Environment, 16(3), 159–168. https://doi.org/10.1002/fee.1777
   Web of Science ®Google Scholar
• Cellina, F., Bucher, D., Mangili, F., Veiga Simão, J., Rudel, R., & Raubal, M. (2019). A large scale, app-based behaviour change experiment persuading sustainable mobility patterns: Methods, results and lessons learnt. Sustainability, 11(9), 2674. https://doi.org/10.3390/su11092674
   Web of Science ®Google Scholar
• Chalak, A., Al-Naghi, H., Irani, A., & Abou-Zeid, M. (2016). Commuters’ behavior towards upgraded bus services in Greater Beirut: Implications for greenhouse gas emissions, social welfare and transport policy. Transportation Research Part A: Policy and Practice, 88, 265–285. https://doi.org/10.1016/j.tra.2016.04.001
   Web of Science ®Google Scholar
• Chang, H., Zhang, L., & Xie, G.-X. (2015). Message framing in green advertising: The effect of construal level and consumer environmental concern. International Journal of Advertising, 34(1), 158–176. https://doi.org/10.1080/02650487.2014.994731
   Web of Science ®Google Scholar
• Danaf, M., Abou-Zeid, M., & Kaysi, I. (2014). Modeling travel choices of students at a private, urban university: Insights and policy implications. Case Studies on Transport Policy, 2(3), 142–152. https://doi.org/10.1016/j.cstp.2014.08.006
   Google Scholar
• Demarque, C., Charalambides, L., Hilton, D.J., & Waroquier, L. (2015). Nudging sustainable consumption: The use of descriptive norms to promote a minority behavior in a realistic online shopping environment. Journal of Environmental Psychology, 43, 166–174. https://doi.org/10.1016/j.jenvp.2015.06.008
   Web of Science ®Google Scholar
• De Vos, J. (2020). The effect of COVID-19 and subsequent social distancing on travel behavior. Transportation Research Interdisciplinary Perspectives, 5, 100121. https://doi.org/10.1016/j.trip.2020.100121
   PubMedGoogle Scholar
• Dogruel, L., Joeckel, S., & Vitak, J. (2017). The valuation of privacy premium features for smartphone apps: The influence of defaults and expert recommendations. Computers in Human Behavior, 77, 230–239. https://doi.org/10.1016/j.chb.2017.08.035
   Web of Science ®Google Scholar
• Doran, R., Hanss, D., & Øgaard, T. (2017). Can social comparison feedback affect indicators of eco-friendly travel choices? Insights from two online experiments. Sustainability, 9(2), 196. https://doi.org/10.3390/su9020196
   Web of Science ®Google Scholar
• Esposito, G., Hernández, P., van Bavel, R., & Vila, J. (2017). Nudging to prevent the purchase of incompatible digital products online: An experimental study. PLoS One, 12(3), e0173333. https://doi.org/10.1371/journal.pone.0173333
   PubMed Web of Science ®Google Scholar
• Franssens, S., Botchway, E., De Swart, W., & Dewitte, S. (2021). Nudging commuters to increase public transport use: A field experiment in Rotterdam. Frontiers in Psychology, 12, 633865. https://doi.org/10.3389/fpsyg.2021.633865
   PubMed Web of Science ®Google Scholar
• Fyhri, A., Karlsen, K., & Sundfør, H.B. (2021). Paint it red-a multimethod study of the nudging effect of coloured cycle lanes. Frontiers in Psychology, 12, 1995. https://doi.org/10.3389/fpsyg.2021.662679
   Web of Science ®Google Scholar
• Gao, Q., Trajcevski, G., Zhou, F., Zhang, K., Zhong, T., & Zhang, F. (2019). DeepTrip: Adversarially understanding human mobility for trip recommendation. Proceedings of the 27th ACM SIGSPATIAL international conference on advances in geographic information systems, Chicago, IL, USA.
   Google Scholar
• German Federal Environmental Agency. (2019). Veränderung im Mobilitätsverhalten zur Förderung einer nachhaltigen Mobilität. Retrieved 6.March.2021 from https://www.umweltbundesamt.de/sites/default/files/medien/1410/publikationen/2019-08-29-texte_101-2019_mobilitaetsverhalten.pdf
   Google Scholar
• German Federal Environmental Agency. (2021). Energiebedingte Emissionen. Retrieved 10.June.2021 from https://www.umweltbundesamt.de/daten/energie/energiebedingte-emissionen#energiebedingte-treibhausgas-emissionen
   Google Scholar
• German Federal Statistical Office. (2020). Durchschnittsalter der Bevölkerung in Deutschland von 2011 bis 2019. Retrieved 22.April.2021 from https://de.statista.com/statistik/daten/studie/1084430/umfrage/durchschnittsalter-der-bevoelkerung-in-deutschland/
   Google Scholar
• German Federal Statistical Office. (2022). Höhe des durchschnittlichen Nettolohns/Nettogehalts im Monat je Arbeitnehmer in Deutschland von 1991 bis 2021. Statista. Retrieved 20.October.2022 from https://de.statista.com/statistik/daten/studie/370558/umfrage/monatliche-nettoloehne-und-gehaelter-je-arbeitnehmer-in-deutschland/
   Google Scholar
• Ghose, A., Li, B., & Liu, S. (2019). Mobile targeting using customer trajectory patterns. Management Science, 65(11), 5027–5049. https://doi.org/10.1287/mnsc.2018.3188
   Web of Science ®Google Scholar
• Graham, J., Koo, M., & Wilson, T.D. (2011). Conserving energy by inducing people to drive less. Journal of Applied Social Psychology, 41(1), 106–118. https://doi.org/10.1111/j.1559-1816.2010.00704.x
   Web of Science ®Google Scholar
• Gramsch, B., Guevara, C.A., Munizaga, M., Schwartz, D., & Tirachini, A. (2022). The effect of dynamic lockdowns on public transport demand in times of COVID-19: Evidence from smartcard data. Transport Policy, 126, 136–150. https://doi.org/10.1016/j.tranpol.2022.06.012
   PubMed Web of Science ®Google Scholar
• Gravert, C., & Collentine, L.O. (2021). When nudges aren’t enough: Norms, incentives and habit formation in public transport usage. Journal of Economic Behavior & Organization, 190, 1–14. https://doi.org/10.1016/j.jebo.2021.07.012
   Web of Science ®Google Scholar
• Grinstein, A., & Riefler, P. (2015). Citizens of the (green) world? Cosmopolitan orientation and sustainability. Journal of International Business Studies, 46(6), 694–714. https://doi.org/10.1057/jibs.2015.1
   Web of Science ®Google Scholar
• Grison, E., Burkhardt, J.-M., & Gyselinck, V. (2017). How do users choose their routes in public transport? The effect of individual profile and contextual factors. Transportation Research Part F, Traffic Psychology and Behaviour, 51, 24–37. https://doi.org/10.1016/j.trf.2017.08.011
   Web of Science ®Google Scholar
• Grison, E., Gyselinck, V., & Burkhardt, J.-M. (2016). Exploring factors related to users’ experience of public transport route choice: Influence of context and users profiles. Cognition, Technology & Work, 18(2), 287–301. https://doi.org/10.1007/s10111-015-0359-6
   Web of Science ®Google Scholar
• Hair, J., Black, W., Babin, B., & Anderson, R. (2014). Multivariate data analysis (new int. ed.). Pearson Education.
   Google Scholar
• Hauslbauer, A.L., Schade, J., Drexler, C.E., & Petzoldt, T. (2022). Extending the theory of planned behavior to predict and nudge toward the subscription to a public transport ticket. European Transport Research Review, 14(1), 1–14. https://doi.org/10.1186/s12544-022-00528-3
   Web of Science ®Google Scholar
• Haustein, S., & Hunecke, M. (2013). Identifying target groups for environmentally sustainable transport: assessment of different segmentation approaches. Current Opinion in Environmental Sustainability, 5(2), 197–204. https://doi.org/10.1016/j.cosust.2013.04.009
   Web of Science ®Google Scholar
• Henkel, C., & Kranz, J. (2018). Pro-environmental behavior and green information systems research-review, synthesis and directions for future research 39th International Conference on Information Systems (ICIS), San Francisco, USA.
   Google Scholar
• Henkel, C., Seidler, A.-R., Kranz, J., & Fiedler, M. (2019). How to nudge pro-environmental behaviour: An experimental study 27th European Conference on Information Systems (ECIS), Stockholm and Uppsala, Sweden.
   Google Scholar
• Hensher, D.A. (2006). Towards a practical method to establish comparable values of travel time savings from stated choice experiments with differing design dimensions. Transportation Research Part A: Policy and Practice, 40(10), 829–840.
   Web of Science ®Google Scholar
• Hinkeldein, D., Schoenduwe, R., Graff, A., & Hoffmann, C. (2015). Who would use integrated sustainable mobility services – and why? Sustainable Urban Transport, 7, 177–203.
   Google Scholar
• Hong, W., Chan, F.K., Thong, J.Y., Chasalow, L.C., & Dhillon, G. (2014). A framework and guidelines for context-specific theorizing in information systems research. Information Systems Research, 25(1), 111–136. https://doi.org/10.1287/isre.2013.0501
   Web of Science ®Google Scholar
• Hunecke, M., Haustein, S., Böhler, S., & Grischkat, S. (2010). Attitude-based target groups to reduce the ecological impact of daily mobility behavior. Environment and Behavior, 42(1), 3–43. https://doi.org/10.1177/0013916508319587
   Web of Science ®Google Scholar
• Hurling, R., Catt, M., De Boni, M., Fairley, B., Hurst, T., Murray, P., Richardson, A., & Sodhi, J. (2007). Using internet and mobile phone technology to deliver an automated physical activity program: Randomized controlled trial. Journal of Medical Internet Research, 9(2), e7. https://doi.org/10.2196/jmir.9.2.e7
   PubMed Web of Science ®Google Scholar
• Isensee, C., Teuteberg, F., & Griese, K.M. (2022). Exploring the use of mobile apps for fostering sustainability-oriented corporate culture: A qualitative analysis. Sustainability, 14(12), 7380. https://doi.org/10.3390/su14127380
   Web of Science ®Google Scholar
• Jariyasunant, J., Abou-Zeid, M., Carrel, A., Ekambaram, V., Gaker, D., Sengupta, R., & Walker, J.L. (2015). Quantified traveler: Travel feedback meets the cloud to change behavior. Journal of Intelligent Transportation Systems, 19(2), 109–124. https://doi.org/10.1080/15472450.2013.856714
   Web of Science ®Google Scholar
• Kanuri, V.K., Thorson, E., & Mantrala, M.K. (2014). Using reader preferences to optimize news content: A method and a case study. The International Journal on Media Management, 16(2), 55–75. https://doi.org/10.1080/14241277.2014.943898
   Google Scholar
• Keller, S. (2022). Entwicklung des Modal Split im Personenverkehr in Deutschland in den Jahren 2013 bis 2025*. Statista. Retrieved 22 September 2022 from https://de.statista.com/statistik/daten/studie/168397/umfrage/modal-split-im-personenverkehr-in-deutschland/
   Google Scholar
• Khan, N.A., Habib, M.A., & Jamal, S. (2020). Effects of smartphone application usage on mobility choices. Transportation Research Part A: Policy and Practice, 132, 932–947. https://doi.org/10.1016/j.tra.2019.12.024
   Web of Science ®Google Scholar
• Kim, H.B., Iwamatsu, T., Nishio, K.-I., Komatsu, H., Mukai, T., Odate, Y., & Sasaki, M. (2020). Field experiment of smartphone-based energy efficiency services for households: Impact of advice through push notifications. Energy and Buildings, 223, 110151. https://doi.org/10.1016/j.enbuild.2020.110151
   Web of Science ®Google Scholar
• Kollmuss, A., & Agyeman, J. (2002). Mind the gap: Why do people act environmentally and what are the barriers to pro-environmental behavior? Environmental Education Research, 8(3), 239–260. https://doi.org/10.1080/13504620220145401
   Google Scholar
• Kosinski, M., Matz, S.C., Gosling, S.D., Popov, V., & Stillwell, D. (2015). Facebook as a research tool for the social sciences: Opportunities, challenges, ethical considerations, and practical guidelines. The American Psychologist, 70(6), 543. https://doi.org/10.1037/a0039210
   PubMed Web of Science ®Google Scholar
• Kretzer, M., & Maedche, A. (2018). Designing social nudges for enterprise recommendation agents: An investigation in the business intelligence systems context. Journal of the Association for Information Systems, 19(12), 4.
   Web of Science ®Google Scholar
• Kronrod, A., Grinstein, A., & Wathieu, L. (2012). Go green! Should environmental messages be so assertive? Journal of Marketing, 76(1), 95–102. https://doi.org/10.1509/jm.10.0416
   Web of Science ®Google Scholar
• Kuhnimhof, T., Armoogum, J., Buehler, R., Dargay, J., Denstadli, J.M., & Yamamoto, T. (2012). Men shape a downward trend in car use among young adults—evidence from six industrialized countries. Transport Reviews, 32(6), 761–779. https://doi.org/10.1080/01441647.2012.736426
   Web of Science ®Google Scholar
• Lieberoth, A., Holm, N., & Bredahl, T. (2018). Selective psychological effects of nudging, gamification and rational information in converting commuters from cars to buses: A controlled field experiment. Transportation Research Part F, Traffic Psychology and Behaviour, 55, 246–261. https://doi.org/10.1016/j.trf.2018.02.016
   Web of Science ®Google Scholar
• Luce, R.D., & Tukey, J.W. (1964). Simultaneous conjoint measurement: A new type of fundamental measurement. Journal of Mathematical Psychology, 1(1), 1–27. https://doi.org/10.1016/0022-2496(64)90015-X
   Web of Science ®Google Scholar
• Mahmassani, H.S., & Chang, G.-L. (1986). Experiments with departure time choice dynamics of urban commuters. Transportation Research Part B: Methodological, 20(4), 297–320. https://doi.org/10.1016/0191-2615(86)90045-7
   Web of Science ®Google Scholar
• Meske, C., & Amojo, I. (2019). Status quo, critical reflection and road ahead of digital nudging in information systems research - a discussion with Markus Weinmann and Alexey Voinov. arXiv preprint arXiv:1911.08202.
   Google Scholar
• Mihale-Wilson, A.C., Zibuschka, J., & Hinz, O. (2019). User preferences and willingness to pay for in-vehicle assistance. Electronic Markets, 29(1), 37–53. https://doi.org/10.1007/s12525-019-00330-5
   Web of Science ®Google Scholar
• Möser, G., & Bamberg, S. (2008). The effectiveness of soft transport policy measures: A critical assessment and meta-analysis of empirical evidence. Journal of Environmental Psychology, 28(1), 10–26. https://doi.org/10.1016/j.jenvp.2007.09.001
   Web of Science ®Google Scholar
• Mulley, C. (2017). Mobility as a Services (MaaS)–does it have critical mass? Transport Reviews, 37(3), 247–251. https://doi.org/10.1080/01441647.2017.1280932
   Web of Science ®Google Scholar
• Naous, D., & Legner, C. (2017). Leveraging market research techniques in IS–a review of conjoint analysis in is research Proceedings of the 38th International Conference on Information Systems, Seoul, South Korea.
   Google Scholar
• Nobis, C., Kuhnimhof, T., & German Federal Ministry of Transport and Digital Infrastructure. (2018). Mobilitaet in Deutschland- MiD: Ergebnisbericht.
   Google Scholar
• Peer, S., & Börjesson, M. (2018). Temporal framing of stated preference experiments: Does it affect valuations? Transportation Research Part A: Policy and Practice, 117, 319–333. https://doi.org/10.1016/j.tra.2018.08.027
   Web of Science ®Google Scholar
• Pihlajamaa, O., Heino, I., & Kuisma, S. (2019). Nudging towards sustainable mobility behaviour in nature destinations: Parkkihaukka mobile information service. ITS European Congress.
   Google Scholar
• Pojani, E., Van Acker, V., & Pojani, D. (2018). Cars as a status symbol: Youth attitudes toward sustainable transport in a post-socialist city. Transportation Research Part F, Traffic Psychology and Behaviour, 58, 210–227. https://doi.org/10.1016/j.trf.2018.06.003
   Web of Science ®Google Scholar
• QuestionPro. (2021). Conjoint analysis-D-Optimal design. Retrieved 16.November.2021 from https://www.questionpro.com/help/conjoint-analysis-d-optimal-design.html
   Google Scholar
• Romaniuk, J., & Sharp, B. (2016). How brands grow. part 2: including emerging markets, services and durables, new brands and luxury brands. Oxford University Press.
   Google Scholar
• Sanguinetti, A., & Amenta, N. (2022). Nudging consumers toward greener air travel by adding carbon to the equation in online flight search. Transportation Research Record, 2676(2), 788–799. https://doi.org/10.1177/03611981211046924
   Web of Science ®Google Scholar
• Sattler, H., & Hartmann, A. (2008). Commercial use of conjoint analysis. In M. Höck & K. l. Voigt (Eds.), Operations management in theorie und praxis (pp. 103–119). Springer.
   Google Scholar
• Schubert, C. (2017). Green nudges: Do they work? Are they ethical? Ecological Economics, 132, 329–342. https://doi.org/10.1016/j.ecolecon.2016.11.009
   Web of Science ®Google Scholar
• Schulz, T., Böhm, M., Gewald, H., & Krcmar, H. (2021). Smart mobility–an analysis of potential customers’ preference structures. Electronic Markets, 31(1), 105–124. https://doi.org/10.1007/s12525-020-00446-z
   Web of Science ®Google Scholar
• Semanjski, I., & Gautama, S. (2016). Crowdsourcing mobility insights–reflection of attitude based segments on high resolution mobility behaviour data. Transportation Research Part C: Emerging Technologies, 71, 434–446. https://doi.org/10.1016/j.trc.2016.08.016
   Web of Science ®Google Scholar
• Steg, L. (2005). Car use: Lust and must. Instrumental, symbolic and affective motives for car use. Transportation Research Part A: Policy and Practice, 39(2–3), 147–162.
   Web of Science ®Google Scholar
• Sullivan, A.N., & Lachman, M.E. (2017). Behavior change with fitness technology in sedentary adults: A review of the evidence for increasing physical activity. Frontiers in Public Health, 4, 289. https://doi.org/10.3389/fpubh.2016.00289
   PubMed Web of Science ®Google Scholar
• Székely, N., Weinmann, M., & Vom Brocke, J. (2016). Nudging People to Pay CO2 offsets-the effect of anchors in flight booking processes. ECIS.
   Google Scholar
• Tang, P., Aghaabbasi, M., Ali, M., Jan, A., Mohamed, A.M., & Mohamed, A. (2022). How sustainable is people’s travel to reach public transit stations to go to work? A machine learning approach to reveal complex relationships. Sustainability, 14(7), 3989. https://doi.org/10.3390/su14073989
   Web of Science ®Google Scholar
• Taube, O., & Vetter, M. (2019). How green defaults promote environmentally friendly decisions: Attitude‐conditional default acceptance but attitude‐unconditional effects on actual choices. Journal of Applied Social Psychology, 49(11), 721–732. https://doi.org/10.1111/jasp.12629
   Web of Science ®Google Scholar
• Thaler, R.H., & Sunstein, C.R. (2009). Nudge: Improving decisions about health, wealth, and happiness. Penguin.
   Google Scholar
• Thaler, R.H., Sunstein, C.R., & Balz, J.P. (2013). The behavioral foundations of public policy. In E. Shafir (Ed.), Choice architecture (pp. 428–439). Princeton University Press.
   Google Scholar
• Tiefenbeck, V., Wörner, A., Schöb, S., Fleisch, E., & Staake, T. (2019). Real-time feedback promotes energy conservation in the absence of volunteer selection bias and monetary incentives. Nature Energy, 4(1), 35–41. https://doi.org/10.1038/s41560-018-0282-1
   Web of Science ®Google Scholar
• Tussyadiah, I., & Miller, G. (2019). Nudged by a robot: Responses to agency and feedback. Annals of Tourism Research, 78, 102752. https://doi.org/10.1016/j.annals.2019.102752
   Web of Science ®Google Scholar
• van Lierop, D., & Bahamonde Birke, F.J. (2021). Commuting to the future: Assessing the relationship between individuals’ usage of information and communications technology, personal attitudes, characteristics and mode choice. Networks and Spatial Economics, 1–19. https://doi.org/10.1007/s11067-021-09534-9
   Web of Science ®Google Scholar
• Wang, C., Zhang, X., & Hann, I.-H. (2018). Socially nudged: A quasi-experimental study of friends’ social influence in online product ratings. Information Systems Research, 29(3), 641–655. https://doi.org/10.1287/isre.2017.0741
   Web of Science ®Google Scholar
• Weinmann, M., Schneider, C., & Vom Brocke, J. (2016). Digital nudging. Business & Information Systems Engineering, 58(6), 433–436. https://doi.org/10.1007/s12599-016-0453-1
   Web of Science ®Google Scholar
• Willing, C., Brandt, T., & Neumann, D. (2017). Intermodal mobility. Business & Information Systems Engineering, 59(3), 173–179. https://doi.org/10.1007/s12599-017-0471-7
   Web of Science ®Google Scholar
• Witchayaphong, P., Pravinvongvuth, S., Kanitpong, K., Sano, K., & Horpibulsuk, S. (2020). Influential factors affecting travelers’ mode choice behavior on mass transit in Bangkok, Thailand. Sustainability, 12(22), 9522. https://doi.org/10.3390/su12229522
   Web of Science ®Google Scholar
• Wlömert, N., & Eggers, F. (2016). Predicting new service adoption with conjoint analysis: External validity of BDM-based incentive-aligned and dual-response choice designs. Marketing Letters, 27(1), 195–210. https://doi.org/10.1007/s11002-014-9326-x
   Web of Science ®Google Scholar
• Zeiske, N., van der Werff, E., & Steg, L. (2021). The effects of a financial incentive on motives and intentions to commute to work with public transport in the short and long term. Journal of Environmental Psychology, 78, 101718. https://doi.org/10.1016/j.jenvp.2021.101718
   Web of Science ®Google Scholar
• Zhu, K., Zhang, L., & Pattavina, A. (2017). Learning geographical and mobility factors for mobile application recommendation. IEEE Intelligent Systems, 32(3), 36–44. https://doi.org/10.1109/MIS.2017.52
   Web of Science ®Google Scholar
• Zimmermann, S., Hein, A., Schulz, T., Gewald, H., & Krcmar, H. (2021). Digital nudging toward pro-environmental behavior: A literature review. Pacific Asia Conference on Information Systems (PACIS), Dubai, UAE.
   Google Scholar
• Zimmermann, S., Schulz, T., & Gewald, H. (2020). Salient attributes of mobility apps: what does really matter for the citizen? Pacific Asia Conference on Information Systems (PACIS), Dubai, UAE.
   Google Scholar