Timely Quality Problem Resolution in Peer-Production Systems: Impacts of Contributor Experience and Control Mechanisms

Although online peer-production systems have proven to be effective in producing high-quality content, their open call for participation makes them susceptible to ongoing quality problems. A key concern is that the problems should be addressed quickly to prevent low-quality content from remaining in place for extended periods. We examine the impacts of two control mechanisms, bots and policy citations, and the number of contributors, with and without prior experience in editing an article, on the cleanup time of 4,473 quality problem events in Wikipedia. We define cleanup time as the time it takes to resolve a quality problem once it has been detected in an article. Using an accelerated failure time model, we find that the number of bots editing an article during a quality problem event has no effect on cleanup time; that citing policies to justify edits during the event is associated with a longer cleanup time; and that more contributors, with or without prior experience in editing the article, are associated with a shorter cleanup time. We also find important interactions between each of the two control mechanisms and the number of different types of contributors. There is a marginal increase in cleanup time that is larger when an increase in the number of contributors is accompanied by fewer bots editing the article during a quality problem event. This interaction effect is more pronounced when increasing the number of contributors without prior experience in editing the article. Further, there is a marginal decrease in cleanup time that is larger when an increase in the number of contributors, with or without prior experience in editing the article, is accompanied by fewer policy citations. Taken together, our results show that the use of bots and policy citations as control mechanisms must be considered in conjunction with the number of contributors with and without prior experience in editing an article. Accordingly, the number of contributors and their experience alone may not explain important outcomes in peer production; it is also important to find an appropriate mix of different control mechanisms and types of contributors to address quality problems quickly.

Digital platforms facilitate the flow of information and the execution of transactions. This study investigates the impact of signals from platform-provided online information regarding search and experience attributes of products on the prices of their offline transactions. We situate our theorizing and empirical work in the context of digital real estate platforms. Our results suggest that online information pertaining to properties’ experience attributes has a significant influence on the prices of offline property transactions. The amount of online information relating to experience attributes—specifically, length of textual property description and the number of photos—positively influences the sale price of a property. In contrast, the amount of online property information related to search attributes—specifically, facts and features—has no significant influence on the property’s sale price. In addition, online property information on experience attributes has a significant impact on the sale price of uncommon properties (those valued significantly above or below their neighborhood averages), whereas its impact on the price of common properties (those valued close to their neighborhood averages) is insignificant. The findings are robust to various model specifications and across property transactions in different years, seasons, and geographical regions. They are also neither subject to confounding effect of real estate agents’ service quality nor driven by unobserved property heterogeneities. The findings shed light on how signals from online property information are used by home buyers and sellers for different types of properties. The insights have implications for how real estate professionals can better utilize digital platforms to convey signals regarding properties and facilitate property transactions and for how the platforms can be designed to support the exchange of information that provides signals on the quality of offline goods that are highly risky and experiential.

Match formation is challenging in online matching platforms where suppliers are subject to dynamic capacity constraints. We provide a theoretical foundation for understanding how online matching platforms support the transmission and triangulation of multisource information for consumers to infer provider service quality and dynamic capacity states, and achieve desirable matching outcomes. Situating this study in the context of an online health consultation community (OHCC) and drawing upon signaling theory, we theorize how physicians’ owned and earned signals influence physicians’ voluntary online consultations with new patients they have not consulted with previously. Importantly, we articulate how these signaling effects are contingent upon physicians’ dynamic capacity in OHCC. We collected longitudinal data from a large OHCC in China and used a hidden Markov model (HMM) to characterize the dynamic physician capacity in the OHCC and test the hypotheses. Our findings reveal that service professionals’ owned and earned signals interactively work together to balance supply and demand dynamically, and thereby facilitating matchmaking. In OHCCs, where physicians provide voluntary service beyond their primary jobs at hospitals, we find that owned and earned signals increase patient consultations in different patterns contingent upon physicians’ capacity states. In addition, we discover the complementary and substitute relationships between owned signals and earned signals change when physicians are in different capacity states. The findings have significant implications for our understanding of online match formation under dynamic capacity constraints and the design of OHCCs.

We set out to explore the unintended effects of human-machine moderation in mitigating harassment within online communities. We examine communities that use a block-list type bot to prevent harassment from the source of harassment. Drawing from social categorization and selective exposure theories, we theorize that employing a machine alongside humans for community moderation will create unintended adverse effects. Specifically, within the moderated focal community, we hypothesize an emboldening effect characterized by an increase in harassment among community members directed at their outgroup members. Additionally, we expect a disengaging effect, that is, a downward trend in the focal community’s membership. Finally, in neighboring communities that share the same topic of discussion, we expect a spillover effect, that is, an increase in harassment. Employing Detoxify, a Bidirectional Encoder Representations from Transformers (BERT)-based model, we evaluate harassment scores in the focal community by analyzing 4 million Reddit comments across various communities. These scores serve as inputs for Bayesian Structural Time Series analysis, revealing evidence for both disengaging and spillover effects. For the emboldening effect, we use community-specific keywords in a predefined computer-assisted document classification approach, Keyword Assisted Topic Model (keyATM), to identify the target of harassment. We use mean comparison and regression discontinuity to assess the change in the level of harassment targeting outgroup members before and after the human-machine moderation implementation.

Subscription-based crowdfunding (SBC) is an emerging platform for creators of digital content (e.g., music, comics, stories, and videos) to build their person brands and garner support from fans. A unique feature that sets SBC apart from other crowdfunding models is the creator-centered freemium model. That is, creators can offer free content to attract fans, who can then subscribe to financially support a creator; in return, the backers gain access to the creator’s premium content and exclusive perks. In doing so, creators receive regular (e.g., monthly) subscription payments from their backers to sustain their ongoing creative activities. The SBC context engenders unique creator-centric dynamics and mechanisms that are ill-understood in crowdfunding literature. In this study, we investigate the effects of SBC creators’ two information control strategies: (1) earnings concealment and (2) private postings. From a brand management perspective, we theorize that earnings concealment improves brand authenticity, whereas private postings foster brand differentiation, co-creation, and attachment. These will lead to a positive impact on the financial (backer base) and nonfinancial (fan participation) outcomes of an SBC campaign. Furthermore, we propose a reinforcing dynamic between information control and SBC performance whereby better SBC outcomes will increase the creators’ tendencies to engage in information control. To test our hypotheses, we obtained panel data from a large SBC platform that contains monthly observations of 92,850 creators from August 2016 to December 2017. The results of our empirical analyses provide evidence for the benefits of the two information control strategies in SBC and demonstrate the reinforcing relationships between information control and SBC outcomes for creators. We discuss the theoretical and practical implications of our findings.

Social trading platforms have, over the last decade or so, been gaining a strong foothold in individual investment markets. Users on these platforms can observe (“view”) traders’ detailed transactions over time. They can also ‘‘follow’’ anyone of those traders, just like with other social media platforms, investing their money in accordance with the strategies of their trader of choice. We study whether and how the disposition effect bias of individual traders is affected by two social features of the platform, “Views” and “Followers.” We find a differentiated impact on this bias from those two social features, which is conditional on the level of market turbulence. We attribute this to how traders assess the signal originating from Views and Followers in relation to how committal it is.

We illustrate the emergent spectrum of human-AI hybrids in digital platforms and discuss some implications for IS research by using one class of digital platforms: digital labor platforms. Recognizing the service orientation and the expanding role of AI in digital platforms, we define digital labor platforms as online environments where digital services are sourced and delivered in exchange for compensation, with constituent tasks for the services determined, executed, and coordinated by human and AI agents. Work done on these platforms is, by definition, digital and can thus be modularized into tasks which require a range of cognitive skills for execution and coordination, providing a rich context to illustrate human-AI hybrids and some key issues for next-generation digital platforms.

Digital platforms can use application programming interfaces (APIs) to support third-party development of new apps and achieve growth at an unprecedented scale. However, there is also a dilemma between original new development and copycatting by third-party suppliers. Motivated by this tension, we examined how APIs provided by digital platforms may influence two types of third-party new app development: original apps and app copycatting. We also investigated how these influences are dependent on app market conditions. We empirically tested our theoretical conjectures using data on a leading web browser platform, and applying analytics techniques on app source code to identify original apps and copycat apps. Based on a difference-in-differences identification strategy, our findings suggest that the provision of platform APIs enhance the original new development of apps. While platform APIs may facilitate app copycatting as well, our findings suggest that platform APIs can enhance app suppliers’ relative attractiveness to original new development in comparison to copycatting. The enhancing effect of platform APIs on original new development is strengthened by app market potential and high market-level app complexity. The enhancing effect of platform APIs on app copycatting is strengthened by app market potential and high market concentration. Our study has important theoretical and practical implications.

In the context of software platforms, we examine how cross-side network effects (CNEs) on different platform sides (app-side and user-side) are temporally asymmetric, and how these CNEs are influenced by the platform’s governance policies. Informed by a perspective of value creation and capture, we theorize how the app-side and the user-side react to each other with distinct value creation/capture processes, and how these processes are influenced by the platform’s governance policies on app review and platform updates. We use a time-series analysis to empirically investigate the platform ecosystem of a leading web browser. Our findings suggest that while the growth in platform usage results in long-term growth in both the number and variety of apps, the growth in the number of apps and the variety of apps only leads to short-term growth in platform usage. We also find that long app review time weakens the long-term CNE of the user-side on the app-side, but not the short-term CNE of the app-side on the user-side. Moreover, we find that frequent platform updates weaken the CNEs of both the user-side and the app-side on each other. These findings generate important implications regarding how a software platform may better govern its ecosystem with different participants.

As organizations increasingly use digital platforms to facilitate innovation, researchers are seeking to understand how platforms shape business practices. Although extant literature offers important insights into platform management from a platform-owner perspective, we know little about how organizations manage industry platforms provided by external parties to generate opportunities and overcome challenges in relation to their infrastructure and work processes. As part of larger ecosystems, these digital platforms offer organizations bundles of digital options that they can selectively invest in over time. At the same time, organizations’ previous investments in digital infrastructure and work processes produce a legacy of digital debt that conditions how they manage their digital platforms over time. Against this backdrop, we investigate how digital options and digital debt were implicated in a large Scandinavian media organization’s management of a news production platform over nearly 17 years. Drawing on extant literature and the findings from this case, we theorize the progression of and interactions between digital options and digital debt during an organization’s digital platform management in relation to its infrastructure and work processes. The theory reveals the complex choices that organizations face in such efforts: While they may have to resolve digital debt to make a platform’s digital options actionable, hesitancy to plant digital debt may equally well prevent them from realizing otherwise attractive digital options. Similarly, while identified digital options may offer organizations new opportunities to resolve digital debt, eagerness to realize digital options may just as easily lead to unwise planting of digital debt.