Practice-based IS Research
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ItemTechnological Evolution Agility and Dynamic IT Capabilities: A Delphi Study( 2021-01-05)Robust information technology infrastructures (ITI) are essential for organizations since they are the heart of almost every organization and are considered as key assets that play strategic roles and affect organizational performance. To cope with the effects of technological evolution, IT managers must have an articulated vision of their ITI as well as the ability to acquire, deploy, combine and reconfigure their ITI, i.e. dynamic IT capabilities. However, the underlying organizational actions of dynamic IT capabilities are difficult to identify and to circumscribe. Drawing on a Delphi study involving 29 IT management experts, this study has identified key organizational actions deployed to overcome the challenges related to the constant and rapid technological evolution to be agile. Overall, the experts emphasized the importance of collaboration, competencies, roadmap, standardization and monitoring to overcome the challenges and exploit the opportunities related to the constant and rapid technological evolution while fostering organizational agility.
ItemIaaS, PaaS, or SaaS? The Why of Cloud Computing Delivery Model Selection – Vignettes on the Post-Adoption of Cloud Computing( 2021-01-05)Most large-scale organizations adopted Cloud Computing (CC) on a company level in recent years. Managers now face the challenge to appropriately implement CC "operationally", i.e., for information systems (ISs). We refer to this as post-adoption, addressing the extent of technology usage after adoption. Specifically, managers need to choose among the CC delivery models Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), and Software-as-a-Service (SaaS). We differentiate the determinants of this post-adoption decision for IaaS, PaaS, and SaaS. Based on this analysis, we derive criteria that guide managers' delivery model selection: Adopt 1) IaaS for ISs requiring flexibility and reduced time to market, 2) PaaS to access specialized resources, and 3) SaaS to focus on core competencies. Moreover, we analyze the impact on the CC strategy and postulate them as recommendations: I) acknowledge the interplay between governance and time-to-market, II) realize cost savings on company level, and III) consider strategically important ISs for CC.
ItemGuiding Companies to Reduce Technostress: A Mixed-Methods Study Deriving Practice-Oriented Recommendations( 2021-01-05)Technostress is a major challenge for employees using information technology. Technostress research has revealed the causes, i.e. techno-stressors, and resulting adverse consequences for employees and companies. However, there is a lack of practical insights guiding companies on how to reduce technostress. To offer such practical insights, we follow a mixed-methods approach. The qualitative study bases on eleven expert interviews and reveals seven measures that reduce technostress. We then elaborate on these interview results with a quantitative study of 110 employees. The quantitative results reveal the degree to which the seven measures are useful to reduce specific techno-stressors. Our results show that although there are measures used in practice, none reduces all different techno-stressors. We complement existent theoretical technostress research by offering practice-oriented recommendations on how to reduce technostress. Based on the illustration of which measures are useful for which techno-stressors, practitioners can choose the measures that best fits their needs.
ItemGetting Started with Corporate Open Source Governance: A Case Study Evaluation of Industry Best Practices( 2021-01-05)Open source software usage in companies is on the rise, often resulting in lower development costs, higher quality, and quick availability of code. However, using open source software in products comes with legal, business, and technical risks. Experienced companies prevent and address these risks through corporate open source governance. In our previous work, we studied how top-tier companies got started with corporate open source governance. We proposed a set of industry best practices on the topic, using the practical format of interconnected context-problem-solution patterns. In this study, we put the proposed state-of-the-art practices to the test by evaluating their real-life application in a case study at a Germany-based multibillion-dollar corporation with products in four distinct industries and more than 17000 employees worldwide. In the course of two and a half years, we conducted 35 semi-structured employee interviews and workshops in five divisions of the company to assess the initial situation of open source governance, the process of getting started with governance following our recommendations, and the outcomes. In this paper, we report the results of this longitudinal case study by presenting the artifacts created while getting started with open source governance, as well as the transferability evaluation of the proposed best practices, both individually and collectively.
ItemDeploying a Model for Assessing Cognitive Automation Use Cases: Insights from Action Research with a Leading European Manufacturing Company( 2021-01-05)Cognitive automation moves beyond rule-based automation and thus imposes novel challenges on organizations when assessing the automation potential of use cases. Thus, we present an empirically grounded and conceptually operationalized model for assessing cognitive automation use cases, which consists of four assessment dimensions: data, cognition, relationship, and transparency requirements. We apply the model in a real-world organizational context in the course of an action research project at the customer service department of ManuFact AG, and present unique empirical insights as well as the impact the application of the model had on the organization. The model shall help practitioners to make more informed decisions on selecting use cases for cognitive automation and to plan respective endeavors. For research, the identified factors affecting the suitability of a use case for cognitive automation shall deepen our understanding of cognitive automation in particular, and AI as the driving force behind cognitive automation in general.