Information systems (IS) research usually investigates phenomena at one level of analysis at a time. However, complex IS phenomena may be difficult to address from such a single-level perspective. A multilevel perspective offers an alternative means to examine phenomena by simultaneously accounting for multiple levels of analysis. Although useful guidelines for theory development are widely available, they give little specific attention to developing theory that is conceptualized and analyzed at multiple levels. Multilevel theorizing or developing theory from a multilevel perspective is more complex and involves unique challenges. To promote multilevel theorizing in the IS discipline, we focus on addressing challenges involved in multilevel theorizing and propose a holistic framework for systematically developing theory from a multilevel perspective. Drawing from the organization science and IS literature, the proposed framework harmonizes and synthesizes previous guidelines, providing a practical basis for conceptualizing and studying multilevel phenomena.

A systematic framework for multilevel theorizing in information systems research

Although there have been many AI chatbots in industry service, social media, and e-commerce platforms, research on AI chatbots such as Replika, neglected the effects of human-like traits on users' continuance using intention. This article aims to explore the main effects of human-like traits (perceived warmth vs. perceived competence) of friendship AI chatbots (FAIC) on continuance using intention and customer engagement, and the moderating effects of the need to belong and information sensitivity. Three studies are conducted to collect data ( N total = 1420 ). Our findings of Study 1 demonstrate that perceived warmth and perceived competence can increase the continuance using intention to FAIC and customer engagement, and perceived usefulness plays a mediating role in our conceptual model. Additionally, consumers' need to belong (high vs. low) (Study 2) and information sensitivity (high vs. low) (Study 3) related to chat contents moderate the main effects significantly. This article contributes to the literature on the relationship between FAIC and consumers by presenting the influence of perceived warmth and perceived competence and establishing the underlying process. Analogously, the findings can be beneficial for marketers and firms in designing and developing the coding program of FAIC to promote consumers' continuance using intention and customer engagement.

How do friendship artificial intelligence chatbots (FAIC) benefit the continuance using intention and customer engagement?

Selecting candidates for a specific job or nominating a person for a specific position takes time and effort due to the need to search for the individual’s file. Ultimately, the hiring decision may not be successful. However, artificial intelligence helps organizations or companies choose the right person for the right job. In addition, artificial intelligence contributes to the selection of harmonious working teams capable of achieving an organization’s strategy and goals. This study aimed to contribute to the development of machine-learning models to analyze and cluster personality traits and classify applicants to conduct correct hiring decisions for particular jobs and identify their weaknesses and strengths. Helping applicants to succeed while managing work and training employees with weaknesses is necessary to achieving an organization’s goals. Applying the proposed methodology, we used a publicly available Big-Five-personality-traits-test dataset to conduct the analyses. Preprocessing techniques were adopted to clean the dataset. Moreover, hypothesis testing was performed using Pearson’s correlation approach. Based on the testing results, we concluded that a positive relationship exists between four personality traits (agreeableness, conscientiousness, extraversion, and openness), and a negative correlation occurred between neuroticism traits and the four traits. This dataset was unlabeled. However, we applied the K-mean clustering algorithm to the data-labeling task. Furthermore, various supervised machine-learning models, such as random forest (RF), support vector machine (SVM), K-nearest neighbor (KNN), and AdaBoost, were used for classification purposes. The experimental results revealed that the SVM attained the highest results, with an accuracy of 98%, outperforming the other classification models. This study adds to the current literature and body of knowledge through examining the extent of the application of artificial intelligence in the present and, potentially, the future of human-resource management. Our results may be of significance to companies, organizations and their leaders and human-resource executives, in addition to human-resource professionals.

/pdf/application-of-artificial-intelligence-for-better-investment-hoxcihqz.pdf

Application of Artificial Intelligence for Better Investment in Human Capital

Gaze convergence of multiuser eye movements during simultaneous collaborative use of a shared system interface has been proposed as an important albeit sparsely explored construct in human-computer interaction literature. Here, we propose a novel index for measuring the gaze convergence of user dyads and address its validity through two consecutive eye-tracking studies. Eye-tracking data of user dyads were synchronously recorded while they simultaneously performed tasks on shared system interfaces. Results indicate the validity of the proposed gaze convergence index for measuring the gaze convergence of dyads. Moreover, as expected, our gaze convergence index was positively associated with dyad task performance and negatively associated with dyad cognitive load. These results suggest the utility of (theoretical or practical) applications such as synchronized gaze convergence displays in diverse settings. Further research perspectives, particularly into the construct’s nomological network, are warranted.

/pdf/collaborative-use-of-a-shared-system-interface-the-role-of-2e86egkdqf.pdf

Collaborative Use of a Shared System Interface: The Role of User Gaze—Gaze Convergence Index Based on Synchronous Dual-Eyetracking

Telling lies and faking emotions is quite common in human-human interactions: though there are risks, in many situations such behaviours provide social benefits. In recent years, there have been many social robots and chatbots that fake emotions or behave deceptively with their users. In this paper, I present a few examples of such robots and chatbots, and analyze their ethical aspects. Three scenarios are presented where some kind of lying or deceptive behaviour might be justified. Then five approaches to deceptive behaviours – no deception, blatant deception, tactful deception, nudging, and self deception are discussed and their implications are analyzed. I conclude by arguing that we need to develop localized and culture specific solutions to incorporating deception in social robots and chatbots. 

Ethical Aspects of Faking Emotions in Chatbots and Social Robots*

Drawing from the tension between a company’s desire for customer information to tailor experiences and a consumer’s need for privacy, this study aims to test the effect of two information disclosure nudges on users’ information disclosure behaviors. Whereas previous literature on user-chatbot interaction focused on encouraging and increasing users’ disclosures, this study introduces measures that make users conscious of their disclosure behaviors to low and high-sensitivity questions asked by chatbots. A within-subjects laboratory experiment entailed 19 participants interacting with chatbots, responding to pre-tested questions of varying sensitivity while being presented with different information disclosure nudges. The results suggest that question sensitivity negatively impacts users’ information disclosures to chatbots. Moreover, this study suggests that adding a sensitivity signal—presenting the level of sensitivity of the question asked by the chatbot—influences users’ information disclosure behaviors. Finally, the theoretical contributions and managerial implications of the results are discussed.

/pdf/users-information-disclosure-behaviors-during-interactions-11z20if7.pdf

Users’ Information Disclosure Behaviors during Interactions with Chatbots: The Effect of Information Disclosure Nudges

The phenomenon of multiple users interacting together with a single shared system interface to perform a task (i.e., a multiuser human-computer interaction) is under-investigated in the Human-Computer Interaction (HCI) literature, yet it shows promising avenues for research. For example, little is known about cross-level influences driving collaborative use of a shared system interface, and the literature lacks knowledge about collective adaptation of users to triggers in this setting. The present work contributes to contemporary research on multiuser HCI with system interfaces. As an initial effort, it focusses on the joint use of online shopping platforms by couples. A survey is conducted with 390 respondents in the USA about couples’ habits regarding joint online shopping. Results suggest that joint online shopping is overwhelmingly common among couples and that they engage in such activity in a wide variety of ergonomic layouts. Our findings constitute preliminary evidence and intrinsically call for more researchers’ interest in investigating emotional, cognitive and behavioral dynamics taking place when multiple users jointly use system interfaces. Such research endeavors may ultimately inform and optimize multiuser system designs and corresponding products and services.

Multiuser Human-Computer Interaction Settings: Preliminary Evidence of Online Shopping Platform Use by Couples.

From a user’s perspective, this pilot study investigates the contributors and irritants related to the media content format used by chatbots to assist users in an online setting. In this study, we use automated facial expression analysis (AFEA), which analyses users’ facial expressions and captures the valence of their lived experience. A questionnaire and a single-question interview were also used to measure the users’ perceived experience. All measures taken together allowed us to explore the effects of three media content formats (i.e., an interactive question and answer (Q&A), a video, and a link referring to a webpage) used in chatbots on both the lived and perceived experiences of users. In line with Media Richness Theory (MRT), our results show that an interactive Q&A might be an optimal chatbot design approach in providing users with sought-after information or assistance with transactions. Moreover, important avenues for future research emerge from this study and will be discussed.

Does Media Richness Influence the User Experience of Chatbots: A Pilot Study

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Users’ Information Disclosure Behaviors during Interactions with Chatbots: The Effect of Information Disclosure Nudges

Multiuser Human-Computer Interaction Settings: Preliminary Evidence of Online Shopping Platform Use by Couples.

Does Media Richness Influence the User Experience of Chatbots: A Pilot Study