On the Value of Mobile Business Processes: Evidence from Sweden and the Netherlands

Identifying and assessing the benefits of mobile technology in a business context is often problematic. In this paper we start from the position that the benefits of mobile technology are hard to quantify in isolation, and that the unit of analysis to identify value should be the business process. An exploratory case study approach is used to identify the benefits of mobile technology at the level of the business process. We describe two cases from Sweden (vehicle dispatching and timber supply chain management) and one case from the Netherlands (mobile parking). We then illustrate how benefits of mobile technology are contingent to the difficulty of coordinating mobile actors. Next, the value of mobility is contingent to the costs of not being able to coordinate during the period that the actors are difficult to reach. Finally, we assert that it is also related to the costs of available substitutes for mobile technology in a business process.Mobile Technology; Information Systems; Technology Benefits; Exploratory Study; Business Processes

Analysis of Mobile Business Processes for the Design of Mobile Information Systems

The adoption of mobile technologies into companies frequently follows a technology-driven approach without precise knowledge about the potential benefits that may be realised. Especially in larger organisations with complex business processes, a systematic procedure is required if a verifiable economic benefit is to be created by the use of mobile technologies. Therefore, the term “mobile business process”, as well as requirements for information systems applied in such processes, are defined in this paper. Subsequently, we introduce a procedure for the systematical analysis of the distributed structure of a business process model in order to identify mobile sub-processes. For that purpose, the method Mobile Process Landscaping is used to decompose a process model into different levels of detail. The method aims to manage the complexity and limit the process analysis to the potentially mobile sub-processes from the beginning. The result of the analysis can be used on the one hand as a foundation for the redesign of the business processes and on the other hand for the requirements engineering of mobile information systems. An application of this method is shown by the example of business processes in the insurance industry

The Effect Of Mobile BI On Organisational Managerial Decision-Making

Managerial decision-making has always involved the use of numerous distinct information resources. Modern managerial decision-making processes require a wealth of information that is enhanced and transformed into knowledge in order to take effective action. Mobility in business is increasingly exercising influence on core business processes of organisations. Recent advances in wireless technologies coupled with the rapid growth of mobile devices in business have led to a new era in business computing. Mobile Business Intelligence (Mobile BI) is a system that has been conceived to assist, accelerate and to enhance the managerial decision-making processes. Drawing from an array of previous studies that attempted to measure the value of Business Intelligence (BI) and other IT systems in organisations, this study develops a new kind of measure which is based on an understanding of the distinct properties of Mobile BI systems in an organisational-oriented context

Effects of Mobile Business Processes on the Software Process

The adoption of mobile technologies into companies frequently follows a technology-driven approach without precise knowledge about the potential benefits that may be realised. Especially in larger organisations with complex business processes, a systematic procedure is required if a verifiable economic benefit is to be created by the use of mobile technologies. Therefore, the term “mobile business process” is defined in this paper. Subsequently, we introduce a procedure for the systematical analysis of the distributed structure of a business process model in order to identify requirements for software engineering in mobile sub-processes. For that purpose, the method Mobile Process Landscaping is used to decompose a process model into different levels of detail. The method aims to manage the complexity and limit the process analysis to the potentially mobile sub-processes from the beginning. The result of the analysis can be used on the one hand as a foundation for the redesign of the business processes and on the other hand for the requirements engineering of mobile information systems

Collaboration Support Through Mobile Processes and Entailment Constraints

The computational capability of smart mobile devices increasingly fosters their prevalence in many business domains. Along this trend, process management technology is going to be enhanced with mobile task support. However, tasks executed stationarily so far cannot be simply transfered to mobile devices. For the latter purpose, we developed an approach within the MARPLE project enabling mobile and robust task execution in the context of business processes. In particular, this approach provides self-healing techniques that relieve mobile users from manually handling errors (e.g., lost connections) during mobile task execution. In this paper, we extend the collaboration facilities of our approach by adding entailment constraints to mobile task management. In the context of a business process, for example, two tasks may have to be executed by the same (mobile) user. Related research on integrating such constraints with business processes has received growing attention recently. However, realizing entailment constraints in the context of mobile processes and tasks raises additional issues, which must be probably integrated with the mentioned error handling techniques. We present fundamental entailment constraints supported by our approach and discuss how they can be realized in a robust and flexible manner. In particular, this will significantly enhance mobile task and process support in next generation information systems

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

Quality Measurement for Mobile M-ERP Applications

Mobile business boosting the popularization of the M-ERP(Mobile ERP) in the enterprise: The high penetration of mobile phones enables each employee to become an effective information carrier of the enterprise. The information application of the enterprise becomes more clear-cut. The mobile M-ERP pivots on the frequently changing key indexes of the enterprise operation, and takes the personalized and format-based data collection, analysis and processing platform as tool. It consolidates and unifies quickly diverse people, internal data, and external applications of the enterprise into an integrated whole, and provides real-time, key, and overall data reports to the management personnel. The recent wave of enterprise resource planning (M-ERP) systems adoption is a significant commitment of resource and may affect almost all business processes. M-ERP systems are integrated systems in that they promote cooperation among groups, teamwork, and process expertise and business knowledge. Firm that successfully implements an M-ERP system should raise revenues or decrease costs. The main purpose of this paper is to investigate the relationship among M-ERP system internal control, quality and performance in Korean firms. To investigate the relationship, the questionnaires were collected from 131 M-ERP – adopting Korean manufacturing companies. The following results were determined by verifying six hypotheses using LISREL. Internal control support perceived quality and quality support perceived performance. We expect that the results of the research can be used as the guidance of the implementation strategy of M-ERP systems. And these results provide important insights that complement extant research findings and also raise future research issues

Modeling and Analysis of Complex Technology Adoption Decisions: An Investigation in the Domain of Mobile ICT

Mobile information and communication technologies (ICT) promise to significantly transform enterprises, their business processes and services, improve employee productivity, effectiveness, and efficiency, and create new competitive advantages and business agility. Despite the plethora of potential benefits, however, widespread enterprise adoption of mobile ICT has not been as extensive as initially anticipated. Drawing on the extant information systems, technology management, and organizational innovation literature, this dissertation investigates the salient drivers and inhibitors of emerging ICT adoption, in general, and mobile ICT in particular, and develops an integrative ICT adoption decision framework. From this synthesis we identify four broad elements that influence an enterprise s decision to adopt mobile ICT: (1) business value, (2) costs and economics, (3) strategic alignment, and (4) enterprise readiness. The latter decision element has received only little theoretical and practical attention. In order to fill this gap, this dissertation explored the concept of enterprise readiness in further detail and identified eight key dimensions and their associated assessment indicators. Using a two-stage expert study and experimental design approach, we empirically validated these dimensions and determined their relative importance. Results indicated that leadership readiness followed by technology, data and information, and resource readiness, contributed the most to enterprise readiness for mobile ICT. The results are implemented into a web-based readiness diagnostic tool (RDT) that enables decision makers to assess an enterprise s readiness for mobile ICT. The benefits of the RDT are multifold: first, it navigates the decision maker through the complex readiness assessment space; second, it identifies potential organizational deficiencies and provides a means to assess potential sources of risks associated with the adoption and implementation of mobile ICT; and third, it enables decision makers to benchmark their level of readiness against other organizations. The dissertation concludes by highlighting both theoretical and practical implications for emerging and mobile ICT adoption management and suggesting directions for future research.Ph.D.Committee Chair: Rouse, William; Committee Member: Cross, Steve; Committee Member: Cummins, Michael; Committee Member: DeMillo, Richard; Committee Member: Vengazhiyil, Rosha

Approaching the potential of cyber-physical systems to tourism projects

We are witnessing the need for a quick and intelligent reaction from organizations to the level and speed of change in business processes. This is often associated with the emerging of new information systems and technologies, bursting problems like the persistence of wrong information, systems not fully used and slow response. This requires two main actions: synchronizing people’s visions and strategies within the organization and selecting the information which is relevant for the strategic goals. The main challenge of the proposed approach is to choose the information systems’ portfolio management aligned with the enterprise architecture. This integration leads to modelling the process architecture of the company, which in turn serves as a reference for knowledge-base management to cope with business prospects. This kind of flexible framework can contribute to managing the potential adherence to new systems such as the mobile, cloud, big-data or IoT-based services that tend to proliferate especially in such areas as tourism and health