We review the IS strategizing literature and highlight its main strengths and weaknesses. Strengths include an account given to the relevance of tensions between planned and executed strategy, and associated tradeoffs such as rigidity and flexibility, formal and informal strategizing and the exploitation of static resources vis à vis the exploration of novel capabilities. Weaknesses relate to a predominant focus on an organizational level of analysis and a lack of power considerations. In this paper we aim to build on these strengths and to ameliorate these weaknesses by proposing a comprehensive IS strategizing framework that uses extant IS strategizing research as a foundation, rejuvenated by insights from the emerging strategy‐as‐practice literature. The paper extends our understanding of IS strategizing in light of the practice perspective by providing a multilevel account and incorporating power considerations.
The emergence of “big data” offers organizations unprecedented opportunities to gain and maintain competitive advantage. Trying to exploit the strategic business potential embedded in big data, many organizations have started to renovate their business models or develop new ones, giving rise to the phenomenon of big-data business models. Although big-data business model research is still in its infancy, a significant number of studies on the topic have been published since 2014. We thus suggest it is time to perform a critical review and assessment of the literature at the intersection of business models and big data (analytics), thereby responding to recent calls for further research on and sustained analysis of big-data business models. In particular, our review uses three major criteria (big-data business model types, dimensions, and deployment) to assess the state of the big-data business model literature and identify shortcomings in this literature. On this basis, we derive and discuss five central research perspectives (supply chain, stakeholder, ethics, national, and process), providing guidance for future research and theory development in the area. These perspectives also have practical implications on how to address the current big-data business model deployment gap.
Most studies on absorptive capacity (AC) are based on assumptions that are characteristic of viewing knowledge from an epistemology of possession (knowledge is possessed by individuals and is transferrable). However, the literature on managing knowledge (or better knowledge work) acknowledges also an epistemology of practice (knowledge is unpredictable and dynamic and constituted in and through practice). Moreover, the literature on AC is relatively silent on the relationship between knowledge and power. In this paper, the authors argue that the AC construct should be interpreted in light of the possession and the practice perspectives of knowledge and power. The analysis includes a systematic literature review of AC that supports the authors' claims and, based on this, they suggest an interpretation of the construct that takes into account knowledge–power relationships. This review and theorizing contribute to a richer and processual view of AC.
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