International Journal of Information Management最新文献

Firms are increasingly investing in the use of artificial intelligence (AI). Some succeed in creating and appropriating substantial economic value, but many fail. There is no consensus as to how a firm should use AI to create and appropriate economic value. This paper provides an answer to that question. A novel research model is advanced based on the notion of data network effects being realized within a firm’s business model. This research model is tested in a unique and natural industrial setting of two competing firms that simultaneously adopt the use of similar predictive AI. This setting is researched with two distinct empirical studies that employ mixed-methods research. The results shows that one firm fails to convert its AI use into economic value creation and appropriation while the other succeeds. Value is created and appropriated by ensuring that AI users perceive high user value that realize data network effects while being located in the firm’s business model architecture so as to activate business value drivers. These findings confirm the here proposed research model and offer novel theoretical contributions and specific managerial implications.

Imagine a world where chatbots are the first responders to crises, efficiently addressing concerns and providing crucial information. ChatGPT has demonstrated the capability of GenAI (Generative Artificial Intelligence)-powered chatbots when deployed to answer crisis-related questions in a timely and cost-efficient manner, thus replacing humans in crisis communication. However, public reactions to such messages remain unknown. To address this problem, this study recruited participants (N₁ = 399, N₂ = 189, and N₃ = 121) and conducted two online vignette experiments and a qualitative survey. The results suggest that, when organizations fail to handle crisis-related requests, stakeholders exhibit higher satisfaction and lower responsibility attribution to chatbots providing instructing (vs. adjusting) information, as they are perceived to be more competent. However, when organizations satisfy requests, chatbots that provide adjusting (vs. instructing information) lead to higher satisfaction and lower responsibility attribution due to higher perceived competence. The second experiment involving a public emergency crisis scenario reveals that, regardless of the information provided (instructing or adjusting), stakeholders exhibit greater satisfaction and positive attitudes toward high-competence (vs. low-competence) chatbots. The qualitative study further confirms the experimental findings and offers insights to improve crisis chatbots. These findings contribute to the literature by extending situational crisis communication theory to nonhuman touchpoints and providing a deeper understanding of using chatbots in crisis communication through the lens of machine heuristics. The study also offers practical guidance for organizations to strategically integrate chatbots and human agents in crisis management based on context.

We propose a model that explains how e-government service design characteristics influence citizens’ service experience. Drawing on the public value perspective, we define four categories of e-government impacts and identify their corresponding design characteristics: (1) capability—accuracy and completeness; (2) interactions—accessibility and self-service capability; (3) orientation—user support and personalization capability; and (4) value distribution—privacy protection. We propose that citizens’ perceptions of the design characteristics affect four core technology beliefs—i.e., performance expectancy, effort expectancy, facilitating conditions, and trust—that in turn affect two service experience outcomes—i.e., perceived service quality and continuance intention. We tested our model in a two-stage survey of 2623 users of two e-government services—i.e., an e-government portal and an online appointment booking service. The results showed that citizens’ perceptions of the design characteristics predict the core technology beliefs that in turn predict perceived service quality and eventually continuance intention. Further mediation analyses showed that the core technology beliefs significantly mediate the effects of design characteristics on perceived service quality, but with different patterns of full and partial mediation across the two services. These findings contribute to understanding the underlying mechanisms through which service design characteristics influence service experience. We discuss the theoretical and practical implications on e-government service design and delivery.

Organizational information security performance increasingly depends on employees’ extra-role security behaviors (ERBs), which go beyond the scope of formal organizational prescription and control. At the same time, however, the literature suggests ERBs are largely unresponsive to traditional outline-and-control approaches to behavioral security. Instead, this stream finds that ERBs are primarily cultivated through social interactions with other organizational agents, namely the IS department and the direct supervisor. While important progress has been made in explicating the social nature of ERBs and the organizational agents that shape it, little is currently understood about the attributes of these employee-agent relationships that give rise to their influence on ERB enactment. Tied to this void, review of the literature reveals two separate and fundamentally different explanations of relational influence in this context which, according to theory, are associated with different relational attributes. Responding to these gaps, the current study presents a mixed-method examination of the relational antecedents of ERB enactment. We first theoretically develop and quantitatively examine a dual-channel model of socially motivated ERB enactment that highlights two co-existing motivational channels—an exchange-based channel rooted in norms of reciprocity and an identity-based channel rooted in self-verification. Then, applying the findings from quantitative examination of the dual-channel model, we qualitatively examine the specific attributes of these employee-agent relationships that promote ERB enactment. In doing so, this study makes multiple contributions to the literature including unification of prior work in this stream and introduction of detailed profiles of effective employee-agent relationships in this context.

The rise of generative AI has brought with it a surprising paradox: systems that excel at tasks once thought to be uniquely human, like fluent conversation or persuasive writing, while simultaneously failing to meet traditional expectations of computing, in terms of reliability, accuracy, and veracity (e.g., given the various issues with so-called ‘hallucinations’). We argue that, when generative AI is seen through a traditional computing lens, its development focuses on optimizing for traditional computing traits that remain in principle unattainable. This risks backgrounding what is most novel and defining about it. As probabilistic technologies, generative AIs do not store, in any traditional sense, any data or content. Rather, essential features of training data become encoded in deep neural networks as patterns, that become practically available as styles. We discuss what happens when the distinction between objects and their appearance dissolves and all aspects of images or text become understood as styles, accessible for exploration and creative combination and generation. For example, defining visual qualities of entities like ‘chair’ or ‘cat’ become available as ‘chair-ness’ or ‘cat-ness’ for creative image generation. We argue that, when understood as style engines, unique generative AI capabilities become conceptualized as complementing traditional computing ones. This will aid both computing practitioners and information systems researchers in reconciling and integrating generative AI into the traditional IS landscape. Our conceptualization leads us to propose four archetypes of generative AI application and use, and to highlight future avenues for information systems research made visible by this conceptualization, as well as implications for practice and policymaking.

The emergence of crowdsourcing as a new form of work has introduced a paradox among workers who receive small payments for piecemeal microwork yet continue to participate in microwork digital labor platforms (DLPs). To better understand what sustains microworkers’ participation, this study draws upon individual labor supply theory to quantitatively examine the impacts of microworkers’ motivations, perceptions, and preferences on their labor supply and wages. To explore the meaning of monetary rewards for microworkers, a qualitative inquiry explores microworkers’ spending patterns. Based on a survey of 306 microworkers on Amazon Mechanical Turk, our hierarchical regression analysis reveals that while individual motivations for monetary rewards, enjoyment, and microtime structure have some impact on the microwork labor supply and wages, their impact is limited. Our thematic analysis uncovers diverse meanings attached to microwork earnings. The two most noted are meeting subsistence needs and nonessential expenditures, both of which have positive effects on microwork wages. By investigating the elasticity of the microwork labor supply and wages and offering a nuanced understanding of monetary rewards, our study contributes to information management research on DLPs. Moreover, it provides practical insights for various stakeholders, including microworkers, requesters, and DLP operators.

GenAI-driven technologies such as ChatGPT influence activities in all areas of life and are used in private and work contexts. This study uses an individual-centered perspective to explain what motivates users to use ChatGPT continuously. We propose that four motivational factors and two technology characteristics together lead to continuance intention among individual ChatGPT users. Therefore, we use a mixed-methods design to combine findings from a quantitative survey study and a qualitative interview study. In Study 1, we follow a configurational approach to analyze multi-wave data from 279 participants with fsQCA. We identify five configurations that lead to high continuance intention and show that perceived ease of use and perceived novelty are necessary for this outcome. Interestingly, the observed factors together cannot explain low continuance intention. In Study 2, we complement these findings with insights based on 15 semi-structured interviews. We illustrate the configurations by identifying 27 individual use cases in the private and work contexts as well as additional factors that facilitate and hinder individual ChatGPT continuance intention. We draw meta-inferences by combining findings of both studies to develop five propositions. Based on that, we contribute a motivational, individual perspective on GenAI continuance intention, present practical implications as well as valuable future research opportunities.

As one of the essential components of daily life, artificial intelligence (AI) assistants gradually form a relationship with users as either servant or partner. This study employed a mixed-methods approach integrating both quantitative and qualitative methodologies to examine the influence of various user-AI assistant relationship types on subjective well-being. The results indicate that partnerships are more conducive to fostering subjective well-being in users than master-servant relationships. This influence is mediated through social support, which encompasses both informational support and emotional support, and is moderated by the presence of feelings of social loneliness. These findings contribute to the available literature on user-AI relationships and subjective well-being and can inform the market launch of AI assistants.

Organizations use information and communication technologies (ICT) to enable remote work, yet the performance of groups using ICT varies widely. Individual intelligence is a strong predictor of individual job performance, yet research shows that when groups use ICT to support decision making tasks, group performance is not influenced by the combined intelligence of group members. Thus, group performance may be unpredictable because ICT inhibits the ability of intelligent team members to influence team outcomes. We completed two studies that examine whether an ICT-based technique for structuring collaboration to improve information integration and organization can help groups leverage the intelligence of group members. Results show that the performance of groups that used traditional commercial ICT was not related to the average intelligence of group members. In contrast, the performance of groups using our proposed technique was related to the average intelligence of group members. Thus, the more structured approach to ICT use enabled groups to perform at the level of their intelligence.

This research is based on the stimulus-organism-response framework and adopts a temporal perspective to examine a relatively overlooked social media behavior, namely, restricted use. We specifically investigate how changes in social overload and information overload over time act as stimuli, resulting in dissatisfaction as a negative personal state, which in turn leads to the restricted use of social media. We employ a mixed-methods approach to examine this phenomenon. First, we conduct a quantitative study using a three-wave survey of 664 domestic Chinese social media users. Both social overload change and information overload change contribute to user dissatisfaction, consequently influencing restricted use behavior. Moreover, prominence negatively moderates the relationship between social overload change and dissatisfaction but does not affect the relationship between information overload change and dissatisfaction. Second, we triangulate these findings through in-depth interviews with 26 experienced social media users. The qualitative inputs from Study 2 help explain both the supported and unsupported hypotheses in Study 1. Our study offers new insights into the dynamic processes underlying restricted usage behavior within the context of social media.