How mobile technologies support business models: Case study-based empirical analysis

[Otros] Les technologies mobiles ont poussé la connectivité des systèmes informatiques à la limite, permettant aux personnes et aux objets de se connecter les uns aux autres à tout moment. La quantité d'informations dont disposent les entreprises a augmenté de façon exponentielle, en grande partie grâce à la géolocalisation et à la vaste gamme de capteurs intégrés dans les appareils mobiles. Ces informations peuvent être utilisées pour améliorer les activités et les processus métier, mais également pour créer de nouveaux modèles d'affaires. En nous concentrant sur les modèles d'affaires, nous analysons les technologies mobiles comme catalyseurs des changements d'activité. Nous examinons les caractéristiques distinctives des technologies mobiles et examinons comment celles¿ci peuvent supporter différentes fonctions de l'entreprise. Une étude basée sur une analyse qualitative comparée d'ensemble floue (fsQCA) de 30 cas, de différents secteurs, a permis d'identifier les facteurs de succès de la technologie mobile pour différentes activités du cœur de métier des firmes. Les résultats montrent que plusieurs combinaisons de technologie mobile procurent un avantage concurrentiel lorsqu'elles correspondent au modèle d'affaire.[EN] Mobile technologies have pushed the connectivity of IT systems to the limit, enabling people and things to connect to one another at all times. The amount of information companies have at their disposal has increased exponentially, thanks largely to geolocation and to the vast array of sensors that have been integrated into mobile devices. This information can be used to enhance business activities and processes, but it can also be used to create new business models. Focusing on business models, we analyze mobile technologies as enablers of activity changes. We consider the differentiating characteristics of mobile technologies and examine how these can support different business functions. A study based on fuzzy-set qualitative comparative analysis (fsQCA) of 30 cases across different industries allows us to identify mobile technology success factors for different core activities. The results show that several combinations of mobile technology initiatives provide a competitive advantage when these initiatives match the business model.Peris-Ortiz, M.; Devece Carañana, CA.; Hikkerova, L. (2020). How mobile technologies support business models: Case study-based empirical analysis. Canadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l Administration. 37(1):95-105. https://doi.org/10.1002/cjas.1550S95105371Al-Debei, M. M., & Avison, D. (2010). Developing a unified framework of the business model concept. European Journal of Information Systems, 19(3), 359-376. doi:10.1057/ejis.2010.21Arlotto, J., Sahut, J.-M., & Teulon, F. (2011). Le concept de Business Model au travers de la littérature. Gestion 2000, 28(4), 33. doi:10.3917/g2000.284.0033Clemons, E. K. (2009). Business Models for Monetizing Internet Applications and Web Sites: Experience, Theory, and Predictions. Journal of Management Information Systems, 26(2), 15-41. doi:10.2753/mis0742-1222260202Comberg, C., & Velamuri, V. K. (2017). The introduction of a competing business model: the case of eBay. International Journal of Technology Management, 73(1/2/3), 39. doi:10.1504/ijtm.2017.082356Coursaris C. Hassanein H. &Head M. (2006).Mobile technologies and the value chain: Participants activities and value creation(p. 8) sInternational Conference on Mobile Business Copenhagen Denmark.Ehrenhard, M., Wijnhoven, F., van den Broek, T., & Zinck Stagno, M. (2017). Unlocking how start-ups create business value with mobile applications: Development of an App-enabled Business Innovation Cycle. Technological Forecasting and Social Change, 115, 26-36. doi:10.1016/j.techfore.2016.09.011European Parliament(2015).The Internet of things: Opportunities and challenges. Retrieved fromwww.europarl.europa.eu/RegData/etudes/BRIE/2015/557012/EPRS_BRI(2015)557012_EN.pdfGurrin, C., Smeaton, A. F., & Doherty, A. R. (2014). LifeLogging: Personal Big Data. Foundations and Trends® in Information Retrieval, 8(1), 1-125. doi:10.1561/1500000033Hübner, A. H., Kuhn, H., & Wollenburg, J. (2016). Last mile fulfilment and distribution in omni-channel grocery retailing: a strategic planning framework. International Journal of Retail & Distribution Management, 44(3). doi:10.1108/ijrdm-11-2014-0154Kauffman, R. J., & Wang, B. (2008). Tuning into the digital channel: evaluating business model characteristics for Internet firm survival. Information Technology and Management, 9(3), 215-232. doi:10.1007/s10799-008-0040-3Liang, T., Huang, C., Yeh, Y., & Lin, B. (2007). Adoption of mobile technology in business: a fit‐viability model. Industrial Management & Data Systems, 107(8), 1154-1169. doi:10.1108/02635570710822796Martinez-Simarro, D., Devece, C., & Llopis-Albert, C. (2015). How information systems strategy moderates the relationship between business strategy and performance. Journal of Business Research, 68(7), 1592-1594. doi:10.1016/j.jbusres.2015.01.057Mello P.A.(2012).A critical review of applications in QCA and fuzzy‐set analysis and a ‘toolbox' of proven solutions to frequently encountered problems APSA Annual Meeting Paper. Retrieved fromhttps://ssrn.com/abstract=2105539Melville, Kraemer, & Gurbaxani. (2004). Review: Information Technology and Organizational Performance: An Integrative Model of IT Business Value. MIS Quarterly, 28(2), 283. doi:10.2307/25148636Ngai, E. W. T., & Gunasekaran, A. (2007). Mobile commerce: Strategies, technologies, and applications. Decision Support Systems, 43(1), 1-2. doi:10.1016/j.dss.2005.05.002Palattella, M. R., Dohler, M., Grieco, A., Rizzo, G., Torsner, J., Engel, T., & Ladid, L. (2016). Internet of Things in the 5G Era: Enablers, Architecture, and Business Models. IEEE Journal on Selected Areas in Communications, 34(3), 510-527. doi:10.1109/jsac.2016.2525418Pateli, A. G., & Giaglis, G. M. (2005). Technology innovation‐induced business model change: a contingency approach. Journal of Organizational Change Management, 18(2), 167-183. doi:10.1108/09534810510589589Piccoli, & Ives. (2005). Review: IT-Dependent Strategic Initiatives and Sustained Competitive Advantage: A Review and Synthesis of the Literature. MIS Quarterly, 29(4), 747. doi:10.2307/25148708Porter M. E.(2001).Strategy and the Internet. Harvard Business Review March 63–78.Ragin C. C.(2008).User's Guide to Fuzzy‐Set/Qualitative Comparative Analysis. Working Paper University of Arizona Arizona.Ray, G., Barney, J. B., & Muhanna, W. A. (2003). Capabilities, business processes, and competitive advantage: choosing the dependent variable in empirical tests of the resource-based view. Strategic Management Journal, 25(1), 23-37. doi:10.1002/smj.366Richter, C., Kraus, S., & Syrjä, P. (2015). The shareconomy as a precursor for digital entrepreneurship business models. International Journal of Entrepreneurship and Small Business, 25(1), 18. doi:10.1504/ijesb.2015.068773Schneider, M. R., Schulze-Bentrop, C., & Paunescu, M. (2009). Mapping the institutional capital of high-tech firms: A fuzzy-set analysis of capitalist variety and export performance. Journal of International Business Studies, 41(2), 246-266. doi:10.1057/jibs.2009.36Sheng, H., Nah, F. F.-H., & Siau, K. (2005). Strategic implications of mobile technology: A case study using Value-Focused Thinking. The Journal of Strategic Information Systems, 14(3), 269-290. doi:10.1016/j.jsis.2005.07.004Sorescu, A. (2017). Data-Driven Business Model Innovation. Journal of Product Innovation Management, 34(5), 691-696. doi:10.1111/jpim.12398Tallon, P. P. (2007). A Process-Oriented Perspective on the Alignment of Information Technology and Business Strategy. Journal of Management Information Systems, 24(3), 227-268. doi:10.2753/mis0742-1222240308Tjaden, G. S. (1996). Measuring the information age business. Technology Analysis & Strategic Management, 8(3), 233-246. doi:10.1080/09537329608524248Vilmos A. Kovacs K. &Kutor L. (2007).NFC applications and business model of the ecosystem(pp.1469–1473) 16th IST Mobile and Wireless Communications Summit Budapest Hungary. doi:https://doi.org/10.1109/ISTMWC.2007.4299324.Wirtz, B. W., Schilke, O., & Ullrich, S. (2010). Strategic Development of Business Models. Long Range Planning, 43(2-3), 272-290. doi:10.1016/j.lrp.2010.01.005Woodbridge R.(2010).9 mobile business models that you can use right now to generate revenue. Tether. Retrieved February 2 2019 fromhttp://untether.tv/2010/8‐mobile‐business‐models‐that‐you‐can‐use‐right‐now‐to‐generate‐revenue/Woodside, A. G., & Zhang, M. (2011). Identifying X-Consumers Using Causal Recipes: «Whales» and «Jumbo Shrimps» Casino Gamblers. Journal of Gambling Studies, 28(1), 13-26. doi:10.1007/s10899-011-9241-5Woodside, A. G. (2013). Moving beyond multiple regression analysis to algorithms: Calling for adoption of a paradigm shift from symmetric to asymmetric thinking in data analysis and crafting theory. Journal of Business Research, 66(4), 463-472. doi:10.1016/j.jbusres.2012.12.02

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