Information & Management最新文献

Governments realize a significant gap exists between existing efforts in customer experience and citizen expectations for implementing a digital platform in e-government (DPEG), hindering citizens’ continuance intention. This study proposes a novel initiative called gamification-enabled customer experience. Based on the motivational lens, we develop and test a conceptual research model. We find that gamification-enabled customer experience positively impacts intrinsic and extrinsic motivations and continuance intention towards DPEG. Our results enrich IS research on digital platforms, customer experience, types of motivation, and behavior. We also offer practical guidelines for implementing a DPEG to execute gamified customer experience strategies.

Contemporary digital work practices increasingly necessitate prompt responses by employees to work-related communication. The impact of employees’ response behavior to work communication (RBWC) on collaborative performance remains underexplored. To address this gap, we propose a theoretical model integrating the theories of person-environment fit, social support, and conservation of resources. RBWC is conceptualized as the degree of communication response (in)congruence, representing the (mis)fit between rapid response expectations and the employee's ability to respond rapidly. We investigate the diverse effects of various states of response congruence on collaborative performance, mediated by the social connectivity paradox manifested as social support and social overload, while grounding this work on social support theory. The results from surveying 268 professionals and employing polynomial regression and surface response analysis demonstrate that different response congruence states significantly influence the social connectivity paradox and subsequently impact collaborative performance. Social support mediates the relationship between response congruence and collaborative performance, while social overload mediates the linear association between response ability and collaborative performance. Theoretical and practical implications are discussed.

The risk of malicious deep synthesis lurks in hedonic applications, yet people tend to be ethically tolerant, leaving governance in a quandary. This study explores the reason for this from the perspective of moral judgment and finds that hedonic applications reduce people's sensitivity to ethical norms. We then explore the mechanism of the governance measure labels, using moral sensitivity as a moderating variable, and find that people with low moral sensitivity are immune. Finally, we propose and validate the inclusion of message framing as an enhancement. This study explains the governance dilemma from a moral-psychological perspective and provides practical governance improvements.

Data governance is recognised as a new capability for organisations to maximize the value of data. Process mining is essential for the resilient growth of businesses, making process data a strategic asset for organisations. Even though the availability of reliable process data is vital for obtaining dependable insights into process mining techniques, there exists no framework that explains how to govern process data holistically. We address this gap by presenting the first data governance framework for process mining that was derived from a Delphi study conducted with a panel of academics and practitioners from around the world. The framework provides multiple avenues for future research.

This paper investigates the association between retail investors’ online activity and the pricing of initial public offerings (IPOs). We utilize data from Google Trends and StockTwits to analyze price revision for 901 U.S. IPOs, and find that the online search count, social media post count, and post sentiment are positively associated with IPO pricing. One-standard-deviation increases in these variables correspond to price revision increases of 9.02 %, 50.73 %, and 70.22 %, respectively. Additionally, online search plays a more significant role in influencing IPO price revision when social media discussions about a specific IPO exhibit higher sentiment inequality among participants.

While Twitter remains one of the most popular social media networks within the information security community, threat actors continue to abuse the platform to create, share, and spread malicious contents. In this study, we focus on whether Twitter- and vulnerability-related features can help predict vulnerabilities known to be actively exploited. Using a sample of 6004 tweets, results show that Twitter features (tweets and quote tweets) that combine the benefits of both content creation and sharing can predict active exploitation of vulnerabilities. Furthermore, findings show that certain technical and vulnerability-related features are also capable of predicting active exploitations.

This systematic literature review synthesizes the conceptualizations of ethical principles in AI auditing literature and the knowledge contributions to the stakeholders of AI auditing. We explain how the literature discusses fairness, transparency, non-maleficence, responsibility, privacy, trust, beneficence, and freedom/autonomy. Conceptualizations vary along social/technical- and process/outcome-oriented dimensions. The main stakeholders of ethics-based AI auditing are system developers and deployers, the wider public, researchers, auditors, AI system users, and regulators. AI auditing provides three types of knowledge contributions to stakeholders: 1) guidance; 2) methods, tools, and frameworks; and 3) awareness and empowerment.

Companies increasingly rely on individuals' skills, enthusiasm, and creativity, all the while striving to fully embrace digitalization as an integral aspect of the business. Technological mediation has altered the design and functionality of work, although the Information Systems (IS) literature about human resource management (HRM) provides little empirical evidence of the synergy between the two fields. To shed light on how these interlinked systems generate performance gains, we draw on the sociotechnical perspective to propose the concept of digital HR strategy as an explicit recognition of the fusion and integration of HR processes and digital technologies. This paper also theorizes and empirically demonstrates that a digital HR strategy improves organizational performance. The model is empirically tested using archival data from a sample of 351 firms.

Smart Service Innovations (SmartSIs) are crucial for future competitiveness, but established organizations often struggle with the complexity of generating SmartSI ideas. Thus, we propose the SmartSI Compass, a method for systematically generating SmartSI ideas drawing from current smart service research and being theoretically anchored in technological, value creation, and systems perspectives. We developed the method in collaboration with practitioners according to design science research. With the SmartSI Compass, we make a prescriptive contribution to the body of knowledge on the initiation phase of digital innovation and support practitioners in generating SmartSI ideas.

Online reviews exert a considerable influence on consumer purchase behavior, yet there remains ambiguity about the most effective managerial response strategies for positive and negative reviews. Addressing this gap, our study introduces a Strategy-Aware, Deep Learning-Based Natural Language Processing (Sa-DLNLP) model designed to optimize firm responses. The proposed model underwent rigorous evaluation through a human-coded study and was subsequently validated by a separate user response study. Our findings reveal that active-constructive responses significantly enhance the impact of positive reviews, whereas passive-constructive strategies are more effective in mitigating the damage from negative reviews. Additionally, the study underscores the importance of concise, personalized, and prompt responses across the board. Interestingly, responses that are overly explanatory, excessively empathetic, or challenge customers were found to be counterproductive when dealing with negative reviews. This study not only demystifies the art of managing online reviews but also offers an advanced deep learning methodology that can directly benefit the disciplines of Information Systems and Management.