Digital humans (agents controlled by artificial intelligence with highly realistic faces and voices) are beginning to appear in place of text-based chatbots in routine customer service roles, such as soliciting feedback and reviews. There are two fundamental ways digital humans may transform the process of providing online reviews. First, with highly human-like visual appearances, digital humans can significantly enhance the perceived humanness of agents collecting reviews compared to traditional text-based chatbots. Second, by enabling spoken rather than typed communication, the interaction may take on a more casual, conversational nature, making the process feel like an informal conversation. We conducted an online experiment to investigate how users respond to digital humans and text-based chatbots when providing online reviews of restaurants. Our findings suggest that digital humans enhance the perceived humanness of the agent, making the review process feel more like a casual conversation. This perceived humanness and casual conversational nature mediate the impact of digital humans on outcomes, increasing perceived effectiveness, efficiency, satisfaction, and usage intention. Our study demonstrates that replacing a text-based chatbot with a verbally interactive digital human has medium to large effects on improving key outcome measures, suggesting that deploying digital humans can help firms increase the volume of online product and service reviews.

Among modern information technologies, robots help reduce the effort employees expend on tasks that are repetitive and physically demanding. When helping employees, robots may be required to display enhanced efficiency, but such a design can also increase employees' effort required for operational troubleshooting. It is not yet known whether effort saving (i.e., increasing nurses' time and energy saved) or reduced troubleshooting effort (i.e., reducing nurses' time and energy costs) is more important for enhancing users' perception that the robot is performing optimally (user-perceived robot performance) and positive workplace outcomes. This hinders robot providers from making optimal decisions on robot design. In a healthcare context, nurses comprise the largest workforce and thus we examined autonomous mobile robots that help nurses carry heavy equipment and materials to and from operating rooms to meet the demand of surgical operations. Hence, this study examined the relative influence of increased effort saving versus reduced troubleshooting effort on perceived robot performance, patient care, and nurse health. We collected responses from 331 operating room nurses through two waves of surveys. Compared with reduced troubleshooting effort, effort saving effectively increased nurseperceived robot performance, patient care and nurse health, from 39 % to 77 %. Nurses' greater professional experience reduced the negative influence of troubleshooting effort on perceived robot performance. These findings showed that designing information technologies for high efficiency is more important than designing for ease of troubleshooting. This research contributes to decision-making of robot makers and hospitals by indicating that the effects of benefits and costs may depend on the features of users.

We develop a construct called information security apathy, which we define as the extent to which individuals lack interest in information security. In Study 1, we develop and refine a scale to measure information security apathy, assess its content and its convergent, discriminant, and predictive validity, and show that it is distinct from and more stable over time than seven security motivation and attitude constructs used in prior research. In Study 2, we examine the relative effects of security apathy and security knowledge on security decisions by presenting users with a series of security situations and asking what security actions they would be likely to take. We also investigate the personality factors that influence security apathy. In Study 3, we again examine the relative effects of security apathy (and security knowledge) and its personality correlates, but this time when job responsibilities pose strong competing priorities to security compliance, a situation in which apathy should be particularly important. Studies 2 and 3 show that security apathy has a medium to large effect on security decisions—a noticeably larger effect than security knowledge. Our measure of security apathy offers researchers a better ability to predict security compliance and organizations a better way of assessing where to focus their security efforts (reducing apathy versus providing training).

AI-controlled digital humans that look and sound highly human-realistic are beginning to appear in place of text-based chatbots for a variety of routine customer service tasks. We used an online experiment to examine how people respond to digital humans and chatbots when providing online reviews. The results show that participants perceived the digital human interface to be more effective, efficient, and satisfying. Participants found the digital human to be more human-like, which elicited a stronger emotional response and led to higher satisfaction. Participants also used more casual and friendly language when interacting with the digital human. Overall, users preferred the digital human interface over the chatbot. These findings suggest that using highly realistic digital humans in consumer service contexts instead of chatbots could be beneficial.

AI technology has been introduced on e-commerce live-streaming as a substitute for human presenters who are expensive and constrained by work time. However, the cartoon avatar AI presenters that are commonly used today are less effective in driving sales. We propose that increasing human likeness is a practical method to boost sales, particularly for products with hedonic values and rich sensory attributes. The results show that a digital human presenter (an AI agent with a highly human realistic face and voice) sold better compared to a cartoon avatar presenter across a range of different types of products, and that this effect is driven both by affect (by increasing positive emotions) and cognition (by improving product quality evaluations). We extend current research on AI agent design to the live-streaming context and show that the highly realistic appearance of digital humans, as a powerful design, can enhance positive consumer responses to AI agents through two parallel theoretical routes (emotional and cognitive). These results can help e-commerce platforms and brands improve their AI technology and obtain expected business benefits.

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.

In an attempt to combat fake news, policymakers in many countries are considering mandating the disclosure of artificial intelligence (AI) recommendations of social media news articles. We used two randomized controlled experiments to investigate the effects of labeling social media news stories as recommended by AI. Our results show that an AI recommendation reduced belief in true news articles and had no material effect on belief in fake news. In contrast, a recommendation by an expert increased belief in true news articles, but had no effect for fake news articles. A friend recommendation had no effect for fake articles and inconsistent effects for true articles. Belief that an article was true led to news engagement (liking, commenting, sharing), but an AI recommendation weakened this relationship, making confirmation bias the primary factor influencing engagement. The trustworthiness of the recommender only partially explained these effects, which suggests that there are other theoretical factors at work. This study reveals that the explicit labeling of AI curation of social media news stories does not help combat fake news, but instead is likely to backfire and have unintended negative effects by decreasing the belief of and engagement with true news articles.

Purpose I adapt the Integrated Model of Workplace Safety (Christian et al. , 2009) to information security and highlight the need to understand additional factors that influence security compliance and additional security outcomes that need to be studied (i.e. security participation). Research limitations/implications This model argues that distal factors in four major categories (employee characteristics, job characteristics, workgroup characteristics and organizational characteristics) influence two proximal factors (security motivation and security knowledge) and the security event itself, which together influence two important outcomes (security compliance and security participation). Practical implications Safety is a systems design issue, not an employee compliance issue. When employees make poor safety decisions, it is not the employee who is at fault; instead, the system is at fault because it induced the employee to make a poor decision and enabled the decision to have negative consequences. Social implications Security compliance is as much a workgroup issue as an individual issue. Originality/value I believe that by reframing information security from a compliance issue to a systems design issue, we can dramatically improve security.

Online games are one of the most popular information systems in the world, with over 2 billion users worldwide. Most users follow the social norms and standards of behavior in online games, but some violate social norms, negatively affecting others’ gameplay experiences and weakening loyalty to the game. The social norms in games and other online environments often differ from the social norms in the real world, so are users who violate norms doing so deliberately or are they unaware of the norms? Our results suggest that many users violate social norms because they do not understand them—about a third of the 1,093 participants in our study reported being unsure of the social norms in an online game they played regularly. Greater experience with a game did not significantly increase social norm awareness. Instead, perceptions of social presence, and learning routines (i.e., community learning and instructional support) influenced social norm awareness, which in turn influenced norm compliance. Thus, a technology’s social affordances, social presence, and learning routines are more important than experience using a technology in helping users become aware of and comply with social norms.

Artificial Intelligence (AI) can infer one's personality from online behavior, which offers an interesting alternative to traditional, self-reported personality assessments. Recent studies comparing AI-inferred personality to personality derived from traditional assessments have found noticeable differences between the two (meta-analyses have found mean correlations of 0.3 between AI-inferred personality and personality from surveys). One important but unanswered question is how users perceive their personality derived from both methods. Which do users perceive to be more accurate, and more satisfying to use? To answer this question, we used both methods to conduct personality assessments of 595 participants and then asked users how well the two sets of results fit them, as well as their satisfaction and intention to use them. Participants reported that both results fit them equally well, even though the two methods reported different personality scores. Users were equally satisfied with both methods but were more likely to use the survey, likely because it took less time. Our findings imply that both methods measure different aspects of user personality, and both may be useful. We discuss the pros and cons of AI-inferred versus traditional, self-reported personality and indicate future research directions of AI-inferred personality assessment and the implications of their use for real-world applications.

Online games are popular computer applications around the globe. Games are frequently designed to require extensive in-game knowledge to attain in-game goals, so it may be central to continued gameplay. Little is known about how players seek knowledge, internalize knowledge, and subsequently use it to attain in-game goals. We used theories of flow and learning to build a theoretical framework and examined it by using responses from more than four thousand players. We found that encouraging players to seek and internalize in-game knowledge is an effective strategy to increase gameplay. Interestingly, learning satisfaction was more important than knowledge internalization in predicting goal progress, showing a novel insight for game providers to nudge their players in their knowledge searching. We concluded that asking players to search and internalize in-game knowledge may be a more effective strategy than creating their focused immersion to encourage repeated gameplay.

Avatar-mediated communication (AMC), commonly used in online environments such as games and the emerging metaverse, is different from traditional computer-mediated communication in that it is a human-object-object-human relationship mediated by the individual's avatar and the avatar of the person with whom they are communicating. We conceptualize AMC by using three key concepts: user-avatar identification (i.e., how a user perceives their avatar as themselves), avatar-avatar identification (i.e., how a user perceives their avatar as part of a community of avatars), and social presence (i.e., how a user perceives the other avatar as a representation of the other person). We tested this model using 778 individuals who responded to three waves of data collection. The results show that the three factors of AMC influence users' social identification with their community and strengthen its impact on loyalty. From a theoretical perspective, our research adds two novel constructs-user-avatar identification and avatar-avatar identification-that play key roles in AMC in addition to the well-known effects of social presence. From a practical perspective, our research helps developers better design online games and virtual worlds such as the metaverse.

Information security is a multilevel phenomenon with employee security decisions being influenced by macrolevel factors (e.g., organizational policies), mesolevel factors (e.g., one's immediate workgroup-IW), and microlevel factors (e.g., individual personalities). We argue that an employee's local IW (i.e., immediate supervisor and coworkers) has a strong effect on security. This paper focuses on the effects of these mesolevel factors in the presence of macro-and microlevel factors. Drawing on the social structure and social learning framework as well as workgroup research, we hypothesize that the security behavior of an employee's IW supervisor and coworkers moderated by the nature of these relationships influences information security decisions. Our research, based on a sample of 217 full-time employees, reveals that the IW significantly affects security decisions, over and above the micro-and macrolevel factors. These effects are moderated by the nature of the relationship between employees and their IW supervisor (leader-member exchange) and coworkers (team-member exchange). A post hoc analysis shows that the mesolevel factors alone had the same explanatory power as the micro-and macrolevels combined. Our research suggests that future theory and research should include the IW and that organizations should share security responsibilities with line managers and help them understand their substantial impact on information security. Security training programs should ask employees about the behaviors of their IW supervisor and coworkers and, where needed, deliver anti-neutralization training to mitigate the effects of the IW's noncompliance behaviors.