Governance Mechanisms in Digital Platform Ecosystems: Addressing the Generativity-Control Tension

Digital platform owners repeatedly face paradoxical design decisions with regard to their platforms’ generativity and control, requiring them to facilitate co-innovation whilst simultaneously retaining control over third-party complementors. To address this challenge, platform owners deploy a variety of governance mechanisms. However, researchers and practitioners currently lack a coherent understanding of what major governance mechanisms platform owners rely on to simultaneously foster generativity and control. Conducting a structured literature review, we connect the fragmented academic discourse on governance mechanisms with each aspect of the generativity-control tension. Next to providing avenues for prospective digital platform research, we elaborate on the double-sidedness of governance mechanisms in fostering both generativity and control

Governance Mechanisms in Digital Platform Ecosystems: Addressing the Generativity-Control Tension

Digital platform owners repeatedly face paradoxical design decisions with regard to their platforms’ generativity and control, requiring them to facilitate co-innovation whilst simultaneously retaining control over third-party complementors. To address this challenge, platform owners deploy a variety of governance mechanisms. However, researchers and practitioners currently lack a coherent understanding of what major governance mechanisms platform owners rely on to simultaneously foster generativity and control. Conducting a structured literature review, we connect the fragmented academic discourse on governance mechanisms with each aspect of the generativity-control tension. Next to providing avenues for prospective digital platform research, we elaborate on the double-sidedness of governance mechanisms in fostering both generativity and control

Digital Rule of Thumb: A Natural Experiment on Autocomplete in Search Engines

Search engines are an essential part of our lives. However, we do not fully understand what affects users\u27 search inputs. One of the most notable features affecting search inputs is autocomplete, an intelligent agent suggesting queries while typing. Understanding the impact of autocomplete helps eCommerce companies retain customers; examining its impact is difficult since all search engines have adopted it, and experiments are risky for firms. We overcome the challenges by leveraging a novel natural experiment of an eCommerce company. Our preliminary results suggest that the deactivation of autocomplete for the incorrect keyword led to a substantial drop in website visits in the PC channel compared to the mobile channel. In addition, website visits substantially shifted from the incorrect keyword to the correct keyword in the mobile channel but not in the PC environment. This short paper is expected to shed new light on our understanding of autocomplete\u27s impact

Two-sided Adverse Selection and Bilateral Reviews in the Sharing Economy

Online peer-to-peer platforms match service providers with consumers. Both providers and consumers derive heterogeneous payoffs depending on whom they are matched with. To ensure that providers and consumers identify the most valuable matches, many of these platforms elicit relevant information from and also disclose the information to the market participants by adopting bilateral review schemes. Although the bilateral review scheme has its own merits in reducing information asymmetry and possibly enabling better matches, its impact on the various stakeholders in online peer-to-peer platforms remains unexplored. We show that, in equilibrium, the bilateral review scheme intensifies price competition among service providers to attract low-cost consumers and consequently reduces the platform\u27s profit. Interestingly, service providers may be better off with more intense price competition and lower prices when the proportion of low-cost consumers is sufficiently high. More importantly, we find that social welfare is not always higher under the bilateral review scheme compared to either the unilateral review scheme or no reviews. Our findings demonstrate that even though the bilateral review scheme eliminates the information asymmetry and adverse selection on both sides of the market, it does not necessarily enhance market efficiency when competing providers strategically respond to reviews by adjusting their prices

Unveiling the Cloak: Kernel Theory Use in Design Science Research

Theory is an essential part of design research and helps us to explain what we see or guide what we design. In the paper, we shed light on how kernel theories are used in developing design principles in Design Science Research (DSR). We do this by reporting on a systematic literature review, from which we have extracted a set of six mechanisms to operationalize kernel theory. Each mechanism consists of an activity (e.g., “transform to” or “derive from”) and an application point (e.g., meta-requirements or design principles) representing wherein the chain of concepts the kernel theory was used. The paper reflects on what we have learned about the use of kernel theories and translates this into recommendations and issues for further research. We provide researchers with guidance to use kernel theories more efficiently and give a big picture of the possibilities of kernel theory operationalization

Towards a Framework for Evaluation of Blockchain Implementations

Organizations appear to implement blockchain solutions based on fear of missing out instead of a clear understanding of blockchain usefulness. Actually, ninety percent of current blockchain projects do not need a blockchain to meet their requirements. Therefore, we employ a Design Science Research approach to develop a framework for evaluation of blockchain implementations. The framework incorporates common factors of blockchain decisions, including blockchain innovation, blockchain design, inter-organizational integration, and implementation environment. We contribute to the scientific literature by structuring previous research efforts in a four-step framework, which provides a fruitful ground for future conceptual and empirical studies. For practitioners, the framework is useful to identify blockchain projects that facilitate purposeful blockchain adoption

NOT ALL TASKS ARE ALIKE: EXPLORING THE EFFECT OF TASK REPRESENTATION ON USER ENGAGEMENT IN CROWD-BASED IDEA EVALUATION

Crowdsourcing has experienced increasing popularity in recent years. While performance-based issues, such as the quantity or quality of output produced by the crowd, have been in the focus of research, users’ experience, which unfolds through interaction with the crowdsourcing platform and ultimately creates engagement, has been largely neglected. However, user engagement does not only determine the scope of effort users put into the crowdsourcing task, but is considered a determinant for future participation. This paper focusses on the role of task representation–manifested in mechanisms for crowd-based idea evaluation–as potential stimuli for user engagement. Therefore, we conduct a web-based experiment with 198 participants to investigate how different task representations translate into differences in users’ experience and their engagement. In particular, we analyze two distinctive task representations: sequential judgement tasks in form of multi-criteria rating scales and simultaneous choice tasks in the form of enterprise crowdfunding. We find differences in task representation to influence user engagement while mediated by a user’s perceived cognitive load. Moreover, our findings indicate that user engagement is determined by a user’s perceived meaningfulness of a task. These results enhance our understanding of user engagement in crowdsourcing and contribute to theory building in this emerging field

Algorithms as a Manager: A Critical Literature Review of Algorithm Management

We review the literature on algorithmic management to help future researchers acquire a comprehensive recap of past research with detailed discussions on the main findings and develop a taxonomy as a tool of summarization that assists researchers in reflecting critically on their systems and identifying potential gaps. We determine five critical areas of algorithmic management: the mechanisms of algorithmic management, effects of algorithmic management, second party\u27s response to algorithmic management, concerns around algorithmic management, design of algorithmic management, and policy implications. These topics are analyzed and discussed