While the cybersecurity literature on behavioral factors has expanded, current countermeasures often overlook employee-specific behavioral differences, leading to generic solutions. This study addresses this gap by introducing a typology of knowledge worker cybersecurity behaviors through cluster analysis. Based on online survey data (n=264), we identify six main dimensions of cybersecurity attitude and behavior and, based on these, four behavioral cybersecurity types with distinct characteristics and demographic profiles: Naïve Greenhorns, Traditional Examiners, Flexible Mavericks, and Reliable Troupers. Contrary to common beliefs, the study found that older employees demonstrate high cybersecurity resilience, while younger ones pose a higher risk. These findings underscore the importance of tailored, human-centered cybersecurity approaches.

Fake news undermines individuals’ ability to make informed decisions. However, the theoretical understanding of how users assess online news as real or fake has thus far remained incomplete. In particular, previous research cannot explain why users fall for fake news inadvertently and despite careful thinking. In this work, we study the role of affect when users assess online news as real or fake. We employ NeuroIS measurements as a complementary approach beyond self-reports, which allows us to capture affective responses in situ, i.e., directly in the moment they occur. We draw upon cognitive dissonance theory, which suggests that users experiencing affective responses avoid unpleasant information to reduce psychological discomfort. In our NeuroIS experiment, we measured affective responses based on electrocardiography and eye tracking. We find that lower heart rate variability and shorter mean fixation duration are associated with greater perceived fakeness and a higher probability of incorrect assessments, thus providing evidence of affective information processing. These findings imply that users may fall for fake news automatically and without even noticing. This has direct implications for information systems (IS) research and practice as effective countermeasures against fake news must account for affective information processing.

The expansion of technostress research in the organisational and private IS usage contexts has generated substantial theoretical and empirical insights into the relationship between technostress creators and psychological and behavioural outcomes. However, we observe empirical inconsistencies in terms of effect sizes and conceptual inconsistencies regarding the aggregated and disaggregated treatment of technostress creators. Against this background, we argue that a fine-grained estimation and comparison of effect size strengths of technostress creators on outcomes can provide clarity on these essential matters. Using the Hunter and Schmidt method, we integrated and synthesised empirical data from 102 articles, encompassing 113 independent studies with a total of 49,955 observations. Our analysis offers four important contributions to the technostress literature. First, it confirms that technostress is meaningful in terms of its detrimental impact on both psychological and behavioural outcomes. Second, the results provide accurate effect size estimates for technostress creators on different outcomes in organisational and private usage contexts. Third, the results reveal that psychological outcomes are more immediate than behavioural outcomes. Fourth, the findings suggest that in certain contexts, a disaggregated account of technostress creators can reveal meaningful empirical information.

Fake news on social media has large, negative implications for society. However, little is known about what linguistic cues make people fall for fake news and, hence, how to design effective countermeasures for social media. In this study, we seek to understand which linguistic cues make people fall for fake news. Linguistic cues (e.g., adverbs, personal pronouns, positive emotion words, negative emotion words) are important characteristics of any text and also affect how people process real vs. fake news. Specifically, we compare the role of linguistic cues across both cognitive processing (related to careful thinking) and affective processing (related to unconscious automatic evaluations). To this end, we performed a within-subject experiment where we collected neurophysiological measurements of 42 subjects while these read a sample of 40 real and fake news articles. During our experiment, we measured cognitive processing through eye fixations, and affective processing in situ through heart rate variability. We find that users engage more in cognitive processing for longer fake news articles, while affective processing is more pronounced for fake news written in analytic words. To the best of our knowledge, this is the first work studying the role of linguistic cues in fake news processing. Altogether, our findings have important implications for designing online platforms that encourage users to engage in careful thinking and thus prevent them from falling for fake news.

Time pressure, a common phenomenon in everyday workplace environments, is an important driver for non-secure cybersecurity behaviour in organisations. Under time pressure, users are more likely to rely upon fast, affect-driven decision making, increasing their susceptibility to make mistakes and justify non-secure workarounds. This contributes to the role of human error in cybersecurity and counteracts cybersecurity measures (CSMs) designed to protect organisations from threats and vulnerabilities. In this study, we report results from an online survey (N = 207), investigating how users perceive the effectiveness of CSMs for facilitating secure behaviour under time pressure. Understanding how users perceive the effectiveness of CSMs is important to inform the design and implementation of such measures in practice. We find that perceived CSM effectiveness differs greatly across measures. Thereby, users’ appreciation of incident severity and the general level of time pressure in their daily lives emerge as important antecedents. We discuss theoretical and practical implications for the design and implementation of CSMs.

Purpose A growing body of research has identified time pressure as a key driver of cybersecurity (CS) risks and vulnerabilities. To strengthen CS, organizations use CS documents (e.g. best practices, guidelines and policies) intended to strengthen CS. The purpose of this paper is to provide an overview of how specifically time pressure is addressed by CS documents. Design/methodology/approach The authors conducted a systematic search for CS documents followed by a content analysis of the identified documents. First, the authors carried out a systematic Web search and identified 92 formal and informal CS documents (e.g. security policies, procedures, guidelines, manuals and best practices). Second, they systematically analyzed the resulting documents (n = 92), using a structured approach of data familiarization and low-/high-level coding for the identification and interpretation of themes. Based on this analysis, the authors formulated a conceptual framework that captures the sources and effects of time pressure along the themes of industry, operations and users. Findings The authors developed a conceptual framework that outlines the role of time pressure for the CS industry, threats and operations. This provides a shared frame of reference for researchers and practitioners to understand the antecedents and consequences of time pressure in the organizational CS context. Research limitations/implications While the analyzed documents acknowledge time pressure as an important factor for CS, the documents provide limited information on how to respond to these concerns. Future research could, hence, consult with CS experts and policymakers to inform the development of effective guidelines and policies on how to address time pressure in the identified areas. A dedicated analysis within each area will allow to investigate the corresponding aspects of time pressure in-depth along with a consideration for targeted guidelines and policies. Last, note that a differentiation between CS document types (e.g. formal vs informal and global vs regional) was beyond the scope of this paper and may be investigated by future work. Originality/value This study makes three main contributions to the CS literature. First, this study broadens the understanding of the role of time pressure in CS to consider the organizational perspective along the themes of industry, threats and operations. Second, this study provides the first comprehensive assessment of how organizations address time pressure through CS documents, and how this compares to existing research in academic literature. Third, by developing a conceptual framework, this study provides a shared frame of reference for researchers and practitioners to further develop CS documents that consider time pressure’s role in secure behavior.

User interfaces often utilise imagery of pristine natural environments, even if the system’s purpose and context are unrelated to nature. In this paper, we build on evolutionary psychology to develop a theoretical model for the influence of nature imagery on user perceptions of trust, visual aesthetics, and purchase intentions in a corporate sales setting. We evaluate our model by means of an online experiment (n = 408) using a website with different configurations of nature imagery. The results provide support for our theoretical model and hence confirm a positive influence of nature presence, that is, the extent to which the website allows a user to experience the natural environment as being present, on trust, visual aesthetics, and purchase intentions. Thereby, user perceptions of nature presence are specifically linked to nature imagery depicting water as well as vegetation. This study furthers our understanding of how the environmental context of on-site imagery can have subtle information processing benefits for users. For practitioners this study offers insight to the types of imagery that could be utilised more effectively in corporate interface designs.

While previous research established that culture plays an important role in technology adoption, there is only limited work on the role of cultural appropriateness in user interface design for users from a specific background. In this study, we focus on the case of avatar design as a user interface element for facilitating positive user experience. Building on the theoretical lenses of social response theory and the “Computers Are Social Actors” paradigm, we develop a research model to investigate how cultural appropriateness of avatar design is a vital driver for users’ trust. We evaluate our research model by means of an online experiment ( n = 313) in the context of online health advice for users from Saudi Arabia. The avatars differed in appearance (Arab, non-Arab), gender (male, female), and clothing (athletic, medical, everyday). Our results show that Arab avatars exhibited significantly higher cultural appropriateness than non-Arab avatars. Furthermore, participants were more inclined to select an Arab avatar (88.2%) that matched their gender (77.3%). Confirming the critical role of cultural appropriateness, our study demonstrates the importance of carefully considering the target audience in designing user interfaces.

Automatic human affect recognition is a key step towards more natural human-computer interaction. Recent trends include recognition in the wild using a fusion of audiovisual and physiological sensors, a challenging setting for conventional machine learning algorithms. Since 2010, novel deep learning algorithms have been applied increasingly in this field. In this paper, we review the literature on human affect recognition between 2010 and 2017, with a special focus on approaches using deep neural networks. By classifying a total of 950 studies according to their usage of shallow or deep architectures, we are able to show a trend towards deep learning. Reviewing a subset of 233 studies that employ deep neural networks, we comprehensively quantify their applications in this field. We find that deep learning is used for learning of (i) spatial feature representations, (ii) temporal feature representations, and (iii) joint feature representations for multimodal sensor data. Exemplary state-of-the-art architectures illustrate the progress. Our findings show the role deep architectures will play in human affect recognition, and can serve as a reference point for researchers working on related applications.

Accurate detection of individual intake gestures is a key step towards automatic dietary monitoring. Both inertial sensor data of wrist movements and video data depicting the upper body have been used for this purpose. The most advanced approaches to date use a two-stage approach, in which (i) frame-level intake probabilities are learned from the sensor data using a deep neural network, and then (ii) sparse intake events are detected by finding the maxima of the frame-level probabilities. In this study, we propose a single-stage approach which directly decodes the probabilities learned from sensor data into sparse intake detections. This is achieved by weakly supervised training using Connectionist Temporal Classification (CTC) loss, and decoding using a novel extended prefix beam search decoding algorithm. Benefits of this approach include (i) end-to-end training for detections, (ii) simplified timing requirements for intake gesture labels, and (iii) improved detection performance compared to existing approaches. Across two separate datasets, we achieve relative F1score improvements between 1.9% and 6.2% over the two-stage approach for intake detection and eating/drinking detection tasks, for both video and inertial sensors.

Automatic detection of individual intake gestures during eating occasions has the potential to improve dietary monitoring and support dietary recommendations. Existing studies typically make use of on-body solutions such as inertial and audio sensors, while video is used as ground truth. Intake gesture detection directly based on video has rarely been attempted. In this study, we address this gap and show that deep learning architectures can successfully be applied to the problem of video-based detection of intake gestures. For this purpose, we collect and label video data of eating occasions using 360-degree video of 102 participants. Applying state-of-the-art approaches from video action recognition, our results show that (1) the best model achieves an F1score of 0.858, (2) appearance features contribute more than motion features, and (3) temporal context in form of multiple video frames is essential for top model performance.

In today's fast-paced society, users of information technology increasingly operate under high time pressure. Loaded with multiple tasks and racing against deadlines, users experience considerable cognitive load and stress which can detrimentally impact their behaviour. As a result, scholars have shown that the human firewall in cybersecurity is often compromised, with potentially catastrophic consequences for users, the organisations they represent, and their clients. However, despite concerns about the impact of time pressure on human cybersecurity (HCS) behaviour, research on this matter is scant and there is no literature review available that may inform researchers and practitioners about the current body of knowledge. To address this gap, we conducted a systematic literature review of 21 studies in leading outlets. Synthesising the findings of the extant literature, we present an integrative theoretical framework that conceptualises the impact of time pressure on HCS behaviour along the contexts, psychological constructs, consequences, and moderating factors of the phenomenon. For researchers, this framework can serve as a ‘route map’ to conceptualise the role of time pressure in the HCS context and to identify directions for further research. Practitioners can use the framework as a guide for devising effective countermeasures and for designing and provisioning systems.

In this paper, we present an evolutionary trust game to investigate the formation of trust in the so-called sharing economy from a population perspective. To the best of our knowledge, this is the first attempt to model trust in the sharing economy using the evolutionary game theory framework. Our sharing economy trust model consists of four types of players: a trustworthy provider, an untrustworthy provider, a trustworthy consumer, and an untrustworthy consumer. Through systematic simulation experiments, five different scenarios with varying proportions and types of providers and consumers were considered. Our results show that each type of players influences the existence and survival of other types of players, and untrustworthy players do not necessarily dominate the population even when the temptation to defect (i.e., to be untrustworthy) is high. Our findings may have important implications for understanding the emergence of trust in the context of sharing economy transactions.

Emotion elicitation and classification have been performed on standardized stimuli sets, such as international affective picture systems and international affective digital sound. However, the literature which elicits and classifies emotions in a financial decision making context is scarce. In this paper, we present an evaluation to detect emotions of private investors through a controlled trading experiment. Subjects reported their level of rejoice and regret based on trading outcomes, and physiological measurements of skin conductance response and heart rate were obtained. To detect emotions, three labeling methods, namely binary, tri-, and tetrastate blended models were compared by means of C4.5, CART, and random forest algorithms, across different window lengths for heart rate. Taking moving window lengths of 2.5s prior to and 0.3s postevent (parasympathetic phase) led to the highest accuracies. Comparing labeling methods, accuracies were 67% for binary rejoice, 44% for a tristate, and 45% for a tetrastate blended emotion models. The CART yielded the highest accuracies.

The objective of this paper is to examine the possibilities and limitations of heart rate variability (HRV) as an indicator of emotional arousal for mobile applications which require online biofeedback. In contrast to offline classification, feature extraction for online applications sets other requirements to the window size in which data is analyzed as the delay between a change of a person's arousal level and the reaction of an application should be as short as possible. For this purpose we compare various HRV features in order to evaluate how far window size can be decreased to enable online arousal recognition. Using data from a study where high and low arousal were induced in a game scenario, HRV features are analyzed for their discriminatory power depending on the window size using Fisher's discriminant analysis. Moreover, we use these features to train an SVM based classifier. Results indicate that for some features it is possible to use ultra-short term window sizes, i.e. window sizes shorter than the 5 minute window which has traditionally been used for short term HRV analysis.