Anthropomorphism is a defining feature of AI voice assistants, with humanlike voice, interactivity, and empathy cues shaping user perceptions. While prior research has examined the benefits and drawbacks of anthropomorphism, its relationship with ambivalence, i.e. the coexistence of positive and negative evaluations, remains underexplored. Drawing on Attitudinal Ambivalence Theory, this study investigates how anthropomorphic cues influence ambivalence and how ambivalence, in turn, shapes attitude and continuance intention. Survey data from users of AI voice assistants (e.g. Alexa, Siri, Google Assistant) show that voice and interactivity cues directly reduce ambivalence, whereas empathy exerts an indirect effect through these pathways. Ambivalence also mediates the effects of these cues on attitude. Moreover, ambivalence exhibits a dual role: it directly reduces attitude while simultaneously strengthening the relationship between attitude and continuance intention. These findings advance IS research by disentangling anthropomorphic design into cue-level mechanisms and by reconceptualizing ambivalence as a context-dependent moderator that both constrains and amplifies user responses. Practically, the study offers design insights for reducing evaluative conflict and fostering more coherent human-AI interactions.

Traditional post-adoption models of system continuance and discontinuance typically conceptualize satisfaction as a bipolar construct, varying along a continuum from negative to neutral to positive. However, users often experience mixed feelings, particularly when deliberating whether to switch from a current system to an alternative that offers both benefits and drawbacks. Recent research in the Information Systems (IS) field has begun to acknowledge the prevalence of such ambivalent evaluations, advocating for a unipolar approach that treats satisfaction and dissatisfaction as distinct constructs, each ranging independently from low (neutral) to high. We propose and empirically test an Approach-Avoidance Model to explain the psychological mechanisms underlying these mixed responses. The model posits that users separately appraise system benefits (approach factors) and drawbacks (avoidance factors), forming two parallel affective evaluations: unipolar satisfaction and unipolar dissatisfaction. These evaluations are then jointly considered when making post-adoption decisions regarding system continuance, discontinuance, or switching. Empirical evidence from a U.S. user sample supports the proposed model, demonstrating its explanatory, predictive, and diagnostic utility. Specifically, the Approach-Avoidance Model outperforms the traditional bipolar framework in capturing avoidance-related processes and the influence of dissatisfaction on discontinuance intention. Notably, unipolar dissatisfaction exhibits a stronger predictive effect on discontinuance than satisfaction does, highlighting its role as an early warning signal of potential user abandonment. A post-hoc analysis using a German sample was conducted to assess cross-cultural generalizability. While the overall model structure remained stable, significant cultural differences emerged: perceived security risk had a weaker effect on increasing dissatisfaction, whereas enjoyment had a stronger effect on reducing dissatisfaction among German users. These exploratory findings suggest that emotional and risk-related appraisals may vary across cultural contexts, even as the relative influences of satisfaction and dissatisfaction remain robust.

The online travel industry, particularly hotel booking platforms, operates in a highly competitive environment where user retention is crucial for reducing acquisition costs and ensuring long-term profitability. While ambivalence toward technology has been linked to unfavorable outcomes, its antecedents and associations with user retention in the context of online hotel booking platforms remain underexplored. This study addresses this gap by examining how key platform features—perceived security risk, effort, usefulness, and enjoyment—are associated with user ambivalence and retention. Using structural equation modeling with survey data from 387 participants, the findings reveal that perceived usefulness and enjoyment are negatively associated with ambivalence, whereas perceived effort and security risk are positively associated with it. Ambivalence, in turn, is negatively associated with user retention. Additionally, perceived enjoyment moderates this relationship by weakening the negative association between ambivalence and retention. Mediation analyses suggest that ambivalence partially mediates the relationships between perceived usefulness and enjoyment with retention, while fully mediating the associations between perceived effort and security risk with retention. These findings highlight the importance of addressing user ambivalence and provide actionable insights for enhancing user retention. Strategies such as improving enjoyable features and addressing perceived effort and security concerns may contribute to increased engagement and competitiveness in online hotel booking platforms.

Purpose In this current review, we aimed to understand technology addiction interventions and provide guidelines for IS scholars to use IT to prevent or attenuate technology addiction. Design/methodology/approach We systematically reviewed articles associated with technology and substance addiction interventions. These articles included review articles, peer-reviewed articles, conference proceedings, and online articles. Findings We propose a roadmap for technology addiction intervention development and testing based on the review. Next, we summarize the similarities and differences between substance addiction and technology addiction in terms of antecedents, negative consequences, and neurobiological mechanisms. Based on this, two types of potential interventions for substance addiction were reviewed to explore how they can be used for technology addiction. To conclude, IT-mediated interventions were summarized, and promising avenues for future research were highlighted. Originality/value Technology addiction has a broad range of adverse impacts on mental health and well-being. With the knowledge and insight from this review, the Information Systems community can become part of the solution to technology addiction.

User satisfaction is a longstanding determinant of information systems (IS) post-adoption continuance intention. Traditionally, user satisfaction has been conceptualized as a bipolar continuum, stretching from high dissatisfaction to high satisfaction, with neutrality as a midpoint. This view may limit the effectiveness of user satisfaction measurements as it implies that users cannot experience mixed feelings of satisfaction and dissatisfaction. However, users often have simultaneous mixed feelings. By considering user satisfaction and dissatisfaction as separate unipolar dimensions, each ranging from neutral to high, we address the limitation of the bipolar approach. Data collected from an online survey supports this unipolar perspective, enabling the representation of mixed feelings and mitigating the limitations of the bipolar view. Furthermore, both user satisfaction and dissatisfaction uniquely contribute to continuance intention, broadening our understanding of user post-adoption behavior. This study paves the way for future research on the antecedents and consequences of user satisfaction and dissatisfaction.

The chapter delves into Fred's retrospective account of developing the Technology Acceptance Model (TAM), shedding light on the model's conceptualization process. In the 1980s, the prevalent challenge of high rejection rates for new systems led to the belief that predicting user acceptance might be an unsolvable problem. TAM challenged this notion, asserting that consistent prediction, explanation and improvement of user acceptance are indeed achievable. The model's success was attributed to advancements in theory and measurement. To enhance contemporary attitude theory, the centralization of attitude toward using a target system was crucial. Attitude, causally connected to intention and behaviour, played a key role in predicting usage. However, for the model to explain why individuals develop positive or negative attitudes toward system use, identifying pertinent beliefs or perceptions was necessary. TAM identified two key overlooked drivers of user acceptance – perceived usefulness and perceived ease of use. These beliefs act as determinants of attitude, creating links in the causal chain connecting system design features to user acceptance. They form the core of the original model and remain at its heart. The resulting TAM model proved remarkably effective, initiating extensive subsequent research supporting its predictive and explanatory capabilities. TAM stands as the leading model for predicting and explaining user acceptance.

Achieving the promised benefits of a new technology is closely tied to its sustained use. The dominant approach has been to predict use based on behavioral intention. Central to this approach is the assumption that use comes from conscious decision making, resulting from “thinking”, “reflecting”, and “cognition”. However, related work has shown that, over time and with increasing experience, use becomes habitual (routine, automatic) and when this happens, it is not subject to conscious decision making. This paper extends the technology adoption and use literature by testing the relative effects of intention and habit as determinants of use. We conducted a longitudinal field study over the period of one year, with 4 points of measurement in 7 organizations among 1235 users, to examine the effects of intention and habit from the formative stage of experience with a new technology through to an established, stable stage. The results provided strong support for habit as a predictor of use, especially over time, as use became well-rehearsed, and habit was a stronger determinant of use than intention was. In fact, habit dominates intention as a predictor of use, as experience increases. We discuss key implications for research and practice.

Employees’ nonwork use of information technology (IT), or cyberslacking, is of growing concern due to its erosion of job performance and other negative organizational consequences. Research on cyberslacking antecedents has drawn on diverse theoretical perspectives, resulting in the lack of a cohesive explanation of cyberslacking. Further, prior studies have generally overlooked IT-specific variables. To address cyberslacking problems in organizations, as well as research gaps in the literature, we used a combination of a literature-based approach and a qualitative inquiry to develop a model of cyberslacking that includes a 2×2 typology of antecedents. The proposed model was tested and supported in a three-wave field study of 395 employees in a U.S. Fortune-100 organization. This study organizes antecedents from diverse research streams and validates their relative impact on cyberslacking, thus providing a cohesive theoretical explanation of cyberslacking. This study also incorporates contextualization (i.e., IT-specific factors) into theory development and enriches the IS literature by examining the nonwork aspects of IT use and their negative consequences to organizations. In addition, the results provide practitioners with insights into the nonwork use of IT in organizations, particularly regarding how they can take organizational action to mitigate cyberslacking and maintain employee productivity.

In the original version of the book, the title of Chapter 12 includes a small error in the chapter title. It should be changed from “Resolving the Paradoxical Effect of Human-Like Tying Errors by Conversational Agents” to “Resolving the Paradoxical Effect of Human-Like Typing Errors by Conversational Agents”. In the original version of the book, the following belated corrections have been incorporated in chapter “Measurement of Heart Rate and Heart Rate Variability: A Review of NeuroIS Research with a Focus on Applied Methods”. The correction chapters and the book have been updated with the changes.

Drawing from the knowledge management literature, we developed and tested a nomological network related to knowledge sharing – i.e., knowledge seeking and knowledge providing using knowledge management systems. We investigated the effect of cultural contingencies on the prediction of both knowledge seeking and knowledge providing. In addition, we examined the effect of knowledge sharing using a KMS on employee job performance. We conducted a study using a field survey of 224 employees in an organization in the People’s Republic of China and United States. We found that sensitivity to image and sensitivity to organizational incentives influenced both knowledge seeking and knowledge providing, and the effect was varied across individuals with different cultural values. For example, our findings suggested that the negative relationship between sensitivity to image and knowledge seeking was stronger for individuals with collectivistic values than for those with individualistic values. We also found that both knowledge seeking and knowledge providing led to better job performance.

Technology adoption is one of the most important research streams in the field of IS. However, it has largely been studied with endpoint models rather than process models. In this research, we attempt to develop a process model for technology adoption using fMRI (functional Magnetic Resonance Imaging) techniques. fMRI techniques allow us to directly investigate the cognitive process of technology adoption instead of using mental representations such as perceived usefulness and perceived ease of use. We measure the brain activities of 22 students from a major western US university while they are making the decision to download or not download apps using a novel temporal neural correlate analysis. The research finds that the download decision is a complex process that includes the visual regions of the brain, the anterior cingulate cortex, the middle temporal gyrus and many other regions. The practical implication is that there are many points of intervention to influence the download decision. In particular, the visual features of app presentation seem to be a very important consideration.

In this article, we try to explore and understand the neurodynamics of the decision-making process for mobile application downloading. We begin the model development in a rather unorthodox fashion. Patterns of brain activation regions are identified, across participants, at different time instance of the decision-making process. Region-wise activation knowledge from previous studies is used to put together the entire process model like a cognitive jigsaw puzzle. We find that there are indeed a common dynamic set of activation patterns that are consistent across people and apps. That is to say that not only are there consistent patterns of activation there is a consistent change from one pattern to another across time as people make the app adoption decision. Moreover, this pattern is clearly different for decisions that end in adoption than for decisions that end with no adoption.

Information systems (IS) are complex and effortful, placing ever-greater demands on humans??? executive functions. Executive functions, general-purpose control processes that regulate one???s thoughts and behaviors, are the subject of growing investigation in cognitive psychology. The present research examines the relationship between individuals??? executive functions and IS learning. Using neuropsychological methods from cognitive psychology, we measured three key dimensions of executive functions: working memory, shifting, and inhibition. Two empirical studies were conducted. Study 1 tested the relationship between executive functions and IS learning in a self-paced offline learning environment. Study 2 replicated Study 1 and extended it to include a comparison of two self-paced online learning methods: behavior modeling and text-based learning. Both studies found significant effects of executive functions on IS learning after controlling for known IS learning determinants. Study 2 also showed that declarative knowledge was higher for behavior modeling than for text-based learning. Overall, our research highlights the influence of executive functions on IS learning. This research advances knowledge about determinants of IS learning and opens important research avenues for gaining deeper insights into cognitive mechanisms underlying effective IS learning.

. Several research questions from information systems (IS) are based on textual data, such as product reviews and fake news. In this paper, we investigate in which areas NeuroIS is best suited to better understand human processing of text and subsequent human behavior or decision making. To evaluate this question, we propose a taxonomy to distinguish these research questions dep ending on how users’ corresponding response is formed. We first review all publications about textual data in the IS basket journals from 2010 – 2020. Then, we distinguish text-based research questions along two dimensions, namely, if a user’s response is in fluenced by subjectivity and if additional information is required to make an objective assessment. We find that NeuroIS research on textual data is still in its infancy. Existing NeuroIS studies focus on texts, where users’ responses are subject to a high er need for additional data, which is not part of the text.

Purpose In the past decade, smartphone adoption has reached almost 100% in industrialized countries, which is predominantly due to advancements in capabilities. Given the increasing number of people who are addicted to the smartphone and the significant growth of people who consume music via the smartphone, the purpose of the study is to explore the underlying mechanisms through which musical consumption affects smartphone addiction. Design/methodology/approach Based on dual-systems theory, a research model was developed to determine the impact of System 1 (emotion related to music) and System 2 (self-control) on smartphone addiction. A partial-least-squares approach was used to test the model with 294 survey participants. Findings The empirical data confirmed the research model. Regarding System 1, musical emotion positively influenced smartphone addiction through musical consumption and musical response. Moreover, musical preference significantly affected musical response. Regarding System 2, self-control negatively predicted smartphone addiction. Research limitations/implications The study is limited, as the participants were college students who are not representative of all populations. Originality/value The study extends the literature on the dark side of information technology use and complements a research agenda by Gefen and Riedl (2018) on consideration of music in information systems (IS) research.

. Insider threat represents a significant source of violations of information security. Our previous research using event-related potentials (ERPs) has revealed patterns of neural activity that distinguish ethical decision making from decisions that do not involve an ethical component. In the current study, we sought to gain insight into the locus of the effect of ethical decision making on the posterior N2 component of the ERPs. The ERP data revealed that the N2 was greater in amplitude for control trials relative to ethical violation trials, and time-frequency analyses revealed that this resulted from a reduction in phase-locked activity across trials rather than a decrease in EEG power. These findings may indicate that ethical decision making related to information security is associated with a greater inward focus of attention than is the case for decision making on control trials.

Interaction with technology involves not only externally directed cognition, but also internally directed cognition. Although the information systems (IS) field has made a significant progress toward understanding of how individuals use technology, more emphasis has been given to goal-directed external activity that requires focused external attention and less or no emphasis on goal-directed internal activity called mind wandering. Drawing upon the emerging cognitive neuroscience literature, the current research investigates the relationships between self-regulation, mind wandering, and cognitive absorption. Specifically, we hypothesize there is a U-shape relationship between mind wandering and cognitive absorption. Based on a cross-sectional study of 323 individuals, the results reveal that the relationship between mind wandering and cognitive absorption is curve-linear. As mind wandering increases, cognitive absorption decreases to a certain point, after which, cognitive absorption increases as mind wandering increases. The results also show self-regulation has a significant effect on mind wandering and cognitive absorption.

NeuroIS is a field in Information Systems (IS) that makes use of neuroscience and neurophysiological tools and knowledge to better understand the development, adoption, and impact of information and communication technologies. The fact that NeuroIS now exists for more than a decade motivated us to comprehensively review the academic literature. Investigation of the field's development provides insights into the status of NeuroIS, thereby contributing to identity development in the NeuroIS field. Based on a review of N=200 papers published in 55 journals and 13 conference proceedings in the period 2008-2017, we addressed the following four research questions: Which NeuroIS topics were investigated? What kind of NeuroIS research was published? How was the empirical NeuroIS research conducted? Who published NeuroIS research? Based on a discussion of the findings and their implications for future research, which considers results of a recent NeuroIS survey (N=60 NeuroIS scholars), we conclude that today NeuroIS can be considered an established research field in the IS discipline. However, our review also indicates that further efforts are necessary to advance the field, both from a theoretical and methodological perspective.

Blue of all colors seems to be generally preferred by humans and animals. Consequently, the use of this color in ecommerce context has several positive effects such as increased trustworthiness and aesthetic ratings. These effects are, in this study, hypothesized to be caused by specific neural processes in the prefrontal cortex of human decision makers. Consequently, this study tackles the research question whether there is a distinct neural activation pattern for blue websites that helps to explain why blue is often most favored. To investigate this, one website is designed and manipulated in color to which user reactions are measured by employing functional near-infrared spectroscopy (fNIRS). The results of this study show that blue colored websites seem to require generally less processing power related to cognitive processing while revealing increases in brain structures related to processing pleasant and aesthetic stimuli.