本文精选了管理信息系统领域国际顶刊《Journal of Management Information Systems》近期发表的论文，提供管理信息系统领域最新的学术动态。

Hate Speech Detection on Online News Platforms: A Deep-Learning Approach Based on Agenda-Setting Theory

原刊和作者：

Journal of Management Information Systems Volume 42, Issue 3

Seong-Su Kim （LG CNS）

Seongbeom Kim （Yonsei University）

Hee-Woong Kim （Yonsei University）

Abstract

Hate speech on online news platforms has emerged as a critical societal challenge, influencing public discourse and impacting industries. However, existing detection methods often fail to capture its contextual nature and are grounded in weak theoretical foundations. To address this gap, we integrate agenda-setting theory with design science paradigms to develop a deep learning model for detecting hate speech on online news platforms. Leveraging bidirectional encoder representations from transformers (BERT), our model analyzes the interplay between news headlines, texts, and user comments, capturing both media issue salience and emotional agenda-setting effects. Empirical validation demonstrates that our model outperforms traditional baselines in hate speech detection and topic classification tasks. This contextualized approach enhances prediction accuracy, explainability, and domain adaptability, contributing to improved performance and broader applicability. Our study makes theoretical and methodological contributions to Information Systems research and offers practical insights for implementing ethical, real-time hate speech detection strategies.

Link: https://doi.org/10.1080/07421222.2025.2520173

Can Correction Messages Reduce the Spread of Fake News on Social Media? The Impact of Information Updates on the Effectiveness of Corrections

原刊和作者：

Journal of Management Information Systems Volume 42, Issue 3

Kelvin K. King （Syracuse University）

Abstract

Although government agencies and fact-checking organizations issue correction messages to mitigate misinformation dissemination on social media, their efforts have been largely ineffective. Drawing on Information Manipulation Theory (IMT) and information extracted from over 84 million tweets, we examine the impact of correction messages and explore how different information updates affect their effectiveness. Our empirical approach differentiates between subpopulations of correction messages. The results support the existence of two distinct types: correction messages that significantly mitigate the spread of fake news—a 1 percent increase in “effective” messages decreases the spread of fake news by 0.89 percent; and those that amplify the misinformation—a 1 percent increase in “ineffective” messages increases the spread of fake news by 1.37 percent. Despite the higher count of “effective” correction messages, they are less potent than their “ineffective” counterparts. Our study extends the tenets of IMT to correction messages to identify effective platform interventions and highlight the efficacy of information updates in reducing the spread of fake news. Furthermore, it offers significant insights to social media platforms and fact-checking organizations on designing and deploying effective corrections.

Link: https://doi.org/10.1080/07421222.2025.2520174

Understanding the Drivers and Outcomes of Ideologically Charged Social Media Firestorms: The Sociotechnical and Social Learning Perspectives

原刊和作者：

Journal of Management Information Systems Volume 42, Issue 3

Tommy K. H. Chan （the University of Manchester）

Zach W. Y. Lee （the University of Leicester）

Meizhi Pan （Durham University）

Abstract

Social media firestorms, characterized by the rapid spread of negative electronic word of mouth (eWOM), can ignite widespread criticism of a firm’s brand transgressions, service failures, product-harm crises, or ideological conflicts. This study, grounded in the sociotechnical and social learning perspectives, examines the drivers and outcomes of social learning in ideologically charged firestorms, which represent an emerging form. We investigate the effects of social consensus and message persuasiveness, platform content centricity, and individual–firm relationship closeness on the social learning of negative eWOM behavior and purchase behavior. Two studies, comprising longitudinal quantitative and qualitative surveys, were conducted. The results indicate that message persuasiveness is strongly associated with the social learning of negative eWOM behavior. Negative eWOM behavior subsequently influences post-firestorm purchasing behavior. The moderating effects of platform content centricity and individual–firm relationship closeness are discussed. These findings advance the literature and offer practitioners actionable insights into managing digital crises.

Link: https://doi.org/10.1080/07421222.2025.2520170

Boundary Morphing: Causes, Consequences, and Architectural Levers

原刊和作者：

Journal of Management Information Systems Volume 42, Issue 3

Amrit Tiwana （the University of Georgia）

Stephen K. Kim （Iowa State University）

Abstract

We study how app competitiveness evolves through intertemporal shifts between proprietary code and platform-sourced functionality. We introduce the novel mechanism of boundary morphing—apps expanding their boundaries by adding proprietary code or contracting them by increasing platform-sourced functionality—by integrating transaction cost economics (TCE) with platforms theory. Using seven years of data from over 600 AndroidOS apps, we show that platform specificity and market uncertainty drive boundary contraction, while frequent updates counterintuitively catalyze boundary expansion. App modularity—acting as a shift parameter—reshapes this calculus, enabling boundary expansion amid uncertainty and rapid updates. We contribute: (1) boundary morphing as a mechanism to straddle synergy-differentiation tradeoffs, (2) IS-native insights in a TCE scaffold to explain morphing, and (3) modularity’s role in reshaping it. Together, these findings explain how apps morph boundaries as an architectural lever to stay competitive.

Link: https://doi.org/10.1080/07421222.2025.2520179

Algorithmic Stakeholder Governance on Content Platforms: A Lead Role Perspective

Journal of Management Information Systems Volume 42, Issue 3

Mareike Möhlmann （Bentley University）

Robert Gregory （University of Miami）

Ola Henfridsson （University of Miami）

Abstract

Algorithmic governance is not enough on its own to manage and protect the interests of multiple stakeholders. Therefore, platforms are increasingly seeking to involve stakeholders in their algorithmic governance, synthesizing the domains of algorithmic governance and stakeholder governance. We denote this synthesis as algorithmic stakeholder governance. In this paper, we examine algorithmic stakeholder governance in the context of content platforms focusing on the algorithmically mediated interactions among three key stakeholders—creators, consumers, and advertisers. We explore this topic by conducting an in-depth study of the YouTube platform. Using grounded theory techniques, we generate a model that explains the process by which platform stakeholder interactions are algorithmically governed to address key stakeholder conflicts related to free speech, information diversity, and content safety. Our model suggests that algorithmic stakeholder governance provides a forum for resolving these stakeholder conflicts and theorizes the interplay between the platform’s approach to algorithmic stakeholder governance and stakeholder participation. Our research contributes primarily to the literature on platform governance and algorithmic governance on platforms more specifically, by shifting the focus from centralized control and viewing users as objects of governance towards a more decentralized and balanced perspective where platform users are viewed as active stakeholders.

Link: https://doi.org/10.1080/07421222.2025.2520177