Iris Reinhartz-Berger , Alan Hartman , Doron Kliger
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Adoption of IT solutions: A data-driven analysis approach
Many IT departments provide solutions that satisfy a variety of needs to deliver services and reach business goals. These IT solutions may fall short of addressing all the requirements of the relevant business units and hence are only partially adopted by some of them. The objective of this research is to develop an analysis method that supports the selection of solutions whose prospects of adoption are high. The method is data-driven and considers the preferences of the business units, as service providers, customers or owners. Under the assumption that the generation of value to all stakeholders will increase adoption, the approach encompasses the variation in business unit characteristics, as expressed by feature preferences and performance indicators, as well as the value gained from adopting the solutions by the business units in their different roles with respect to a given service. Besides providing a systematic tool for analyzing IT alternatives, the method supports the identification of design gaps and obstacles for adoption. These gaps and obstacles can be addressed by service design techniques and behavioral economics practices depending on the organizational leadership style (autocracy or democracy). To shed light on the feasibility of the approach, we report on the design and results of a case study of a vehicle fleet management service in a real setting.
期刊介绍:
Information systems are the software and hardware systems that support data-intensive applications. The journal Information Systems publishes articles concerning the design and implementation of languages, data models, process models, algorithms, software and hardware for information systems.
Subject areas include data management issues as presented in the principal international database conferences (e.g., ACM SIGMOD/PODS, VLDB, ICDE and ICDT/EDBT) as well as data-related issues from the fields of data mining/machine learning, information retrieval coordinated with structured data, internet and cloud data management, business process management, web semantics, visual and audio information systems, scientific computing, and data science. Implementation papers having to do with massively parallel data management, fault tolerance in practice, and special purpose hardware for data-intensive systems are also welcome. Manuscripts from application domains, such as urban informatics, social and natural science, and Internet of Things, are also welcome. All papers should highlight innovative solutions to data management problems such as new data models, performance enhancements, and show how those innovations contribute to the goals of the application.