Digital Innovation, Transformation, and Entrepreneurship

Permanent URI for this collectionhttps://hdl.handle.net/10125/107555

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Risks and Benefits of Technologies for Organizational Change Enablement - A Role Theory Perspective
(2024-01-03) Brechtelsbauer, Bastian; Laumer, Sven
As organizational change is omnipresent yet often challenging, organizations increasingly employ information technology (IT) to support and improve their change management. We refer to these technologies as change management mediation technologies (CMMTs). Despite their increasing relevance in practice, little is known about their successful implementation and usage as well as potential risks and benefits they encompass. To this end, we present findings from a multi-case study on two companies that utilize a CMMT to enable their employees for ongoing and future change projects, focusing especially on the digital transformation. We use role theory to describe and explain how CMMT usage can change employees’ roles and how this is connected to different risks and benefits for individuals and organizations. Thereby, we add to the growing literature on CMMTs and showcase a novel application of role theory in IS. Moreover, practical implications and opportunities for further research are discussed.
Initiating and expanding data network effects: A longitudinal case study of generativity in the evolution of an AI platform
(2024-01-03) Kandaurova, Maria; Skog, Daniel A.
This study explores the emergence and expansion of data network effects (DNEs) in AI platforms. Previous research has focused on direct and indirect network effects. However, the rise of AI platforms necessitates understanding DNEs for platforms’ learning and improvement. Through a longitudinal case study of a Conversational AI (CAI) platform's 12-year evolution, the study identifies generative feedback loops as the mechanism for DNEs. These loops are initiated by adding functions that enhance the platform's generative capacity, resulting in more diverse data that improves platform learning. DNEs develop through interactions with different ecosystem actors, including clients and external developers, and rely on various data sources beyond user data to enhance AI platform capabilities. This study contributes to IS literature, specifically digital platform literature, following recent calls to empirically examine DNEs to better understand how AI platforms grow and improve their algorithmic capabilities over time.
Power to All or Few People? An Exploration of Power Dynamics in Holacracy
(2024-01-03) Wurm, Bastian; Mendling, Jan; Minnaar, Reinald; Strauss, Erik
Power is key to all organizing. It allows actors to perform actions, make decisions and assign tasks to others. In bureaucratic organizations power is mainly associated with the position that the actor holds. Because actors compete for power, change their position within an organization or leave an organization, power is dynamically changing. We refer to these changes in power as power dynamics. Many New Forms of Organizing, such as Holacracy, claim that individuals have more decision-making capacity, i.e., that power is more equally distributed within the organization. In this paper, we use a unique dataset from a holacratic organization to empirically examine how power dynamics in Holacracy evolve over time. In particular, we use temporal network analysis to reconstruct and contrast two related networks that capture information on how decisions in Holacracy are made. Our findings indicate that also in Holacracy power is not equally distributed, but that few individuals hold most power.
Data as a Strategic Resource beyond Predictive Analytics
(2024-01-03) Steinberger, Tom; Jung, Ju Yeon; Cho, Lily
Extending IS theories of data and strategy that assume data are ultimately used for predictive analytics, this paper explores how data may be used as a strategic resource beyond the statistical predictions of analytics tools. Our point of view is that a choice exists of which relations in data — abstract statistical relations for predictive analytics, or domain-specific, conceptual relations for understanding — are to be enrolled in knowledge creation. We present evidence from the choice of data variables in 162 scientific papers in a subfield of metagenomics, supplemented by analysis of 231 patents from the same subfield. We discuss how accounting for the strategic use of data beyond analytics has important implications for IS theories regarding the value of domain knowledge and the location of bottlenecks in digital ecosystems.
Offense or Defense? Digital Innovation Strategy to Face Competitive Position Shifting in Mobile App Platform
(2024-01-03) Gao, Yuting; Kang, Lele; Jiang, Qiqi; Chen, Jing
Changes in competitive positions within digital platforms, such as transitioning from a challenger to a leader, are common occurrences. However, how achieved competitive positions can be sustained is a critical yet understudied issue. To address this gap, this study examines the effects of competitive position shift on development strategy change in the context of digital innovation. Using data collected from the Apple App Store for over six months and a PSM-DID design, our study reveals that the likelihood of incremental innovation decreases when a challenger's competitive position rises to a “gradual catch-up” stage, and the probability of radical innovation reduces in the “forging ahead” stage. Additionally, a drop in the competitive position to a “falling behind” stage decreases the possibility of radical innovation. Our study contributes to the literature on competitive dynamics and platform innovation and provides practical guidance to mobile app developers.
Demystifying the Design of Industrial IoT Platform-Based Business Models – Archetypes and Their Strategic Response to Main Challenges
(2024-01-03) Millan, Michael; Lüttgens, Dirk; Brenk, Sebastian; Piller, Frank
Platforms are on the rise in the Industrial Internet of Things (IIoT) as they transform industrial manufacturing and change how companies create and capture value. More and more companies are starting to develop IIoT platform-based business models (PBMs), but often face difficulties. Since these difficulties have mainly been discussed in business-to-consumer settings, we study those PBMs in an IIoT environment due to their differences from business-to-consumer environments. Using a mixed method, we first develop a taxonomy for IIoT PBMs based on a systematic literature review of 400 articles, 21 interviews, and 45 real-world PBMs and, secondly quantitatively analyze those real-world examples to derive five archetypes. By drawing conclusions about how these difficulties are addressed differently by the archetypes, our study contributes to the emerging research area of platforms in industrial business-to-business settings and guide firms to inform the design of new IIoT PBMs, ensuring they consider strategic factors.
All Bark and No Bite? Toward an Understanding of Chief Digital Officers’ Power in Organizations
(2024-01-03) Sciuk, Christian; Hess, Thomas
Organizations are increasingly installing Chief Digital Officers (CDOs) to cope with the challenges of digital transformation (DT). Due to DT’s cross-functional nature and the far-reaching tasks involved, CDOs must wield sufficient influence to manage DT effectively. Thus far, we lack a profound understanding of how CDOs’ power is composed. To address this research gap, we conducted a multiple-case study drawing on 25 interviews across six case companies. We identify several drivers of CDOs’ power, both in terms of formal and informal power types. Particularly, we demonstrate that CDOs’ power depends not only on organizational contingencies but also on the managers’ personal characteristics. We contribute to literature by adding a power notion to discussions on DT in general and CDOs specifically. Further, we sensitize practitioners to establish the CDO role in a way that is endowed with sufficient power and shed light on how CDOs can increase their power base.
Under Pressure: an Ethnographic Report from an Ambidextrous IPaaS Platform Entity
(2024-01-03) Pettersen, Lene
This paper presents the findings of an ethnographic field study in a Scandinavian company (“Magic”) offering its clients a cloud-based semantic integration platform as a Service (iPaaS). The platform seeks to enable the integration of data in new ways and thus assist organizations with their digital transformation. The company was established as a subsidiary unit of its parent IT consultancy, representing a well-known strategy to spark radical change and innovation—termed the ‘ambidextrous solution’ in the literature. The paper examines how the ambidextrous framework fit when the subsidiary is a digital platform. New dimensions that include key differences between the pipeline parent and the platform subsidiary is added to the ambidextrous framework. Some of the findings point to several risks for Magic becoming a mini version of the parent firm. As a result, Magic risks being outperformed by big American platform players, such as Google.
Contests for Predictive Algorithms: Ensembling, Interdependency, and Optimal Rewards Design
(2024-01-03) Ahsen, Eren; Ayvaci, Mehmet; Raghunathan, Srinivasan; Subramanyam, Ramanath
With the increased availability of data and widespread use of AI/ML technologies, firms are increasingly using crowdsourcing contests to acquire predictive algorithms for business use. A unique feature of these contests is that the designer can create an ensemble of submitted algorithms and improve upon the predictive accuracy (or quality) of individual algorithms. Given this departure from conventional contests, we ask how the optimal rewarding schemes should be in the presence of ensembling and interdependent algorithms. To answer this question, we develop a stylistic model of a contest for a predictive algorithm with two participants. As opposed to the single best contribution typically sought out in conventional contests and the winner-takes-all reward scheme, we find that both the winner and the runner-up could receive rewards in contests for algorithms, depending on whether the ensembled algorithms substitute or complement each other as determined by the algorithm interdependency. We show that the degree of substitution or complementarity can fundamentally alter the structure of the optimal reward scheme. We highlight our results using a real-world algorithm contest for predicting breast cancer using digital mammograms, we demonstrate the practical applicability of our framework.
Introduction to the Minitrack on Digital Innovation, Transformation, and Entrepreneurship
(2024-01-03) Lyytinen, Kalle; Berente, Nicholas; Yoo, Youngjin

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