This paper explores the creation of value through the interactions of consumer and professional stakeholders in digital innovation ecosystems. We examine this by applying the methodological approach of Social Media Analytics (SMA) which is an interdisciplinary approach that seeks to combine, extend and adapt methods for analysing social media data. By utilising the SMA framework to track user-generated contents published on social media platforms, we assess how consumer and professional stakeholders associate value to Storytel, a new entrant in the Swedish publishing industry that is offering digital subscription service for streaming audiobooks. Drawing from a dataset of 2,633 user-generated contents, our findings illustrate the value-creating practices in which stakeholders in Storytel's ecosystems associate value to Storytel's digital innovation. Our findings further highlight that the value-creating practices arising from the interactions of consumer and professional stakeholders in social media give rise to the hybridisation of value, where multiple forms of value categories merge in the studied case. This study contributes to extant literature on management of innovation and information systems by (i) shedding light on how value is created by examining valuecreating practices as a result of the interactions between stakeholders and (ii) examining the resulting merging of value categories within digital innovation ecosystems and thus exploring the hybridisation of value.
The process of convergence, from science and technology convergence to that of markets as well as entire industries can be witnessed in a range of different high technology environments such as IT and NanoBiotech. Although this phenomenon has been subject of analysis in an increasing number of studies, the notion of industry convergence-the final step of a full convergence process-still lacks a common definition. The missing conceptual definition of what industry convergence really is and how it can be assessed impedes both analyses and monitoring-let alone its anticipation. To address the missing conceptual definition of the final step in convergence, this paper seeks to develop a framework based on novel indicators that enable identifying and monitoring trends of industry convergence in high technology environments. Building on indicators in the domain of collaboration, a framework, which distinguishes different stages and types of industry convergence is developed. Subsequently, the newly developed framework is empirically illustrated in the area of stationary energy storage based on publicly available data. To this end, the full text database Nexis is used to conduct a search in news reports on collaborations in the domain of stationary energy storage. The study contributes to the growing body of convergence literature by providing a novel framework allowing the identification of not only industry convergence as the final step of the convergence process but also the classification of its type. Practical implications include an orientation for companies in converging environments on when and how to close the resulting technology and market competence gaps.
Although action research offers great advantages of connecting academia and practice, it is surprisingly underutilised in innovation management. This paper, therefore, focuses on how innovation management research and researchers can more effectively and efficiently apply action research to their domain. The analysis commences with the rationale for aligning action research and innovation management before assessing the strengths and limitations of existing interdisciplinary action research approaches from an innovation management perspective. Combining and enhancing the strengths of these approaches, a new Action Innovation Management Research (AIM-R) framework is developed to assist in resolving the increasing demand for action-orientation in innovation management. AIM-R offers a structured research process for systematically applying action research as a way of encouraging rigorous research processes, while also importantly stimulating relevant practical outcomes. AIM-R specifically considers different change levels (individual, team, organisational) and objects (e.g. outcome, process, capability) critical for the multi-faceted character of innovation management. A real-world example towards the end of the article illustrates how AIM-R has been applied to a complex problem-solution space. This example adds important insights for readers wanting to apply this more engaged, but currently underutilised, innovation management research technique. Starkey et al., 2009;Flyvbjerg and Sampson, 2011;Mårtensson et al., 2016). Therefore, calls for more problemoriented, human centred and interdisciplinary approaches in modern
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