Embodied Conversational Agent-Based Kiosk for Automated Interviewing

Sorting Insiders From Co-Workers: Remote Synchronous Computer-Mediated Triage for Investigating Insider Attacks

OBJECTIVE

Develop and investigate the potential of a remote, computer-mediated and synchronous text-based triage, which we refer to as InSort, for quickly highlighting persons of interest after an insider attack.

BACKGROUND

Insiders maliciously exploit legitimate access to impair the confidentiality and integrity of organizations. The globalisation of organisations and advancement of information technology means employees are often dispersed across national and international sites, working around the clock, often remotely. Hence, investigating insider attacks is challenging. However, the cognitive demands associated with masking insider activity offer opportunities. Drawing on cognitive approaches to deception and understanding of deception-conveying features in textual responses, we developed InSort, a remote computer-mediated triage.

METHOD

During a 6-hour immersive simulation, participants worked in teams, examining password protected, security sensitive databases and exchanging information during an organized crime investigation. Twenty-five percent were covertly incentivized to act as an 'insider' by providing information to a provocateur.

RESULTS

Responses to InSort questioning revealed insiders took longer to answer investigation relevant questions, provided impoverished responses, and their answers were less consistent with known evidence about their behaviours than co-workers.

CONCLUSION

Findings demonstrate InSort has potential to expedite information gathering and investigative processes following an insider attack.

APPLICATION

InSort is appropriate for application by non-specialist investigators and can be quickly altered as a function of both environment and event. InSort offers a clearly defined, well specified, approach for use across insider incidents, and highlights the potential of technology for supporting complex time critical investigations.

Fostering positive customer attitudes and usage intentions for scheduling services via chatbots

Purpose

This article draws on a reasoned action perspective and the two fundamental dimensions (i.e. warmth and competence) of the Stereotype Content Model (SCM) to analyze customers' chatbot-related attitudes and usage intentions in service retailing. The authors investigate how chatbot, customer, and contextual characteristics, along with perceptions of chatbot warmth and competence, shape customers' chatbot-related attitudes. Furthermore, the authors analyze whether the customer generation or the service context moderates the relationship between chatbot-related attitudes and usage intentions.

Design/methodology/approach

The results are based on two studies (N = 807). Study 1 relies on a 2 (chatbot communication style: high vs low social orientation) × 2 (customer generation: generation X [GenX] vs generation Z [GenZ]) × 2 (service context: restaurant vs medical) between-subjects design. Study 2 relies on a similar number of respondents from GenX and GenZ who answered questions on scheduling a service with either the dentist or the favorite restaurant of the respondents.

Findings

GenZ shows more positive attitudes toward chatbots than GenX, due to higher perceptions of warmth and competence. While GenZ has similar attitudes toward chatbots with a communication style that is high or low in social orientation, GenX perceives chatbots with a high social orientation as warmer and has more favorable attitudes toward chatbots. Furthermore, the positive effect of chatbot-related attitudes on usage intentions is stronger for GenX than GenZ. These effects do not significantly differ between the considered contexts.

Originality/value

This research formulates future directions to stimulate debate on factors that service retailers should consider when employing chatbots.

Roles and Research Trends of Neuroscience on Major Information Systems Journal: A Bibliometric and Content Analysis

Over the past decade, neuroscience has been integrated into information systems as a new methodology and perspective to study and solve related problems. Therefore, NeuroIS has emerged as a new cutting-edge research field. This review aimed to identify, summarize, and classify existing NeuroIS publications through knowledge mapping and bibliometric analysis. To effectively understand the development trend of NeuroIS, this study referred to the journal selection index of the Association of Business Schools in 2021 and journals above three stars in the field of information management as the main selection basis. A total of 99 neuroscience papers and their citation data were included from 19 major information systems journals of SCI/SSCI. This study analyzed bibliometric data from 2010 to 2021 to identify the most productive countries, universities, authors, journals, and prolific publications in NeuroIS. To this end, VOSviewer was used to visualize mapping based on co-citation, bibliographic coupling, and co-occurrence. Keywords with strong citation bursts were also identified in this study. This signifies the evolution of this research field and may reveal potential research directions in the near future. In selecting research methods and analysis tools for NeuroIS, content analysis was used to further conclude and summarize the relevant trends. Moreover, a co-citation network analysis was conducted to help understand how the papers, journals, and authors in the field were connected and related, and to identify the seminal or pioneering major literature. For researchers, network maps visualized mainstream research and provided a structural understanding of NeuroIS. The review concludes by discussing potential research topics in this field.

The Impact of Pedagogical Agents' Gender on Academic Learning: A Systematic Review

Virtual learning environments often use virtual characters to facilitate and improve the learning process. These characters, known as pedagogical agents, can take on different roles, such as tutors or companions. Research has highlighted the importance of various characteristics of virtual agents, including their voice or non-verbal behaviors. Little attention has been paid to the gender-specific design of pedagogical agents, although gender has an important influence on the educational process. In this article, we perform an extensive review of the literature regarding the impact of the gender of pedagogical agents on academic outcomes. Based on a detailed review of 59 articles, we analyze the influence of pedagogical agents' gender on students' academic self-evaluations and achievements to answer the following questions: (1) Do students perceive virtual agents differently depending on their own gender and the gender of the agent? (2) Does the gender of pedagogical agents influence students' academic performance and self-evaluations? (3) Are there tasks or academic situations to which a male virtual agent is better suited than a female virtual agent, and vice versa, according to empirical evidence? (4) How do a virtual agent's pedagogical roles impact these results? (5) How do a virtual agent's appearance and interactive capacities impact these results? (6) Are androgynous virtual agents a potential solution to combatting gender stereotypes? This review provides important insight to researchers on how to approach gender when designing pedagogical agents in virtual learning environments.

Understanding the Design Elements Affecting User Acceptance of Intelligent Agents: Past, Present and Future

Intelligent agents (IAs) are permeating both business and society. However, interacting with IAs poses challenges moving beyond technological limitations towards the human-computer interface. Thus, the knowledgebase related to interaction with IAs has grown exponentially but remains segregated and impedes the advancement of the field. Therefore, we conduct a systematic literature review to integrate empirical knowledge on user interaction with IAs. This is the first paper to examine 107 Information Systems and Human-Computer Interaction papers and identified 389 relationships between design elements and user acceptance of IAs. Along the independent and dependent variables of these relationships, we span a research space model encompassing empirical research on designing for IA user acceptance. Further we contribute to theory, by presenting a research agenda along the dimensions of the research space, which shall be useful to both researchers and practitioners. This complements the past and present knowledge on designing for IA user acceptance with potential pathways into the future of IAs.

Trust me, I'm a bot – repercussions of chatbot disclosure in different service frontline settings

Purpose

Chatbots are increasingly prevalent in the service frontline. Due to advancements in artificial intelligence, chatbots are often indistinguishable from humans. Regarding the question whether firms should disclose their chatbots' nonhuman identity or not, previous studies find negative consumer reactions to chatbot disclosure. By considering the role of trust and service-related context factors, this study explores how negative effects of chatbot disclosure for customer retention can be prevented.

Design/methodology/approach

This paper presents two experimental studies that examine the effect of disclosing the nonhuman identity of chatbots on customer retention. While the first study examines the effect of chatbot disclosure for different levels of service criticality, the second study considers different service outcomes. The authors employ analysis of covariance and mediation analysis to test their hypotheses.

Findings

Chatbot disclosure has a negative indirect effect on customer retention through mitigated trust for services with high criticality. In cases where a chatbot fails to handle the customer's service issue, disclosing the chatbot identity not only lacks negative impact but even elicits a positive effect on retention.

Originality/value

The authors provide evidence that customers will react differently to chatbot disclosure depending on the service frontline setting. They show that chatbot disclosure does not only have undesirable consequences as previous studies suspect but can lead to positive reactions as well. By doing so, the authors draw a more balanced picture on the consequences of chatbot disclosure.

A Decade of NeuroIS Research

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.

Designing Anthropomorphic Enterprise Conversational Agents

The increasing capabilities of conversational agents (CAs) offer manifold opportunities to assist users in a variety of tasks. In an organizational context, particularly their potential to simulate a human-like interaction via natural language currently attracts attention both at the customer interface as well as for internal purposes, often in the form of chatbots. Emerging experimental studies on CAs look into the impact of anthropomorphic design elements, so-called social cues, on user perception. However, while these studies provide valuable prescriptive knowledge of selected social cues, they neglect the potential detrimental influence of the limited responsiveness of present-day conversational agents. In practice, many CAs fail to continuously provide meaningful responses in a conversation due to the open nature of natural language interaction, which negatively influences user perception and often led to CAs being discontinued in the past. Thus, designing a CA that provides a human-like interaction experience while minimizing the risks associated with limited conversational capabilities represents a substantial design problem. This study addresses the aforementioned problem by proposing and evaluating a design for a CA that offers a human-like interaction experience while mitigating negative effects due to limited responsiveness. Through the presentation of the artifact and the synthesis of prescriptive knowledge in the form of a nascent design theory for anthropomorphic enterprise CAs, this research adds to the growing knowledge base for designing human-like assistants and supports practitioners seeking to introduce them into their organizations.

Artificial Intelligence Crime: An Interdisciplinary Analysis of Foreseeable Threats and Solutions

Artificial intelligence (AI) research and regulation seek to balance the benefits of innovation against any potential harms and disruption. However, one unintended consequence of the recent surge in AI research is the potential re-orientation of AI technologies to facilitate criminal acts, term in this article AI-Crime (AIC). AIC is theoretically feasible thanks to published experiments in automating fraud targeted at social media users, as well as demonstrations of AI-driven manipulation of simulated markets. However, because AIC is still a relatively young and inherently interdisciplinary area-spanning socio-legal studies to formal science-there is little certainty of what an AIC future might look like. This article offers the first systematic, interdisciplinary literature analysis of the foreseeable threats of AIC, providing ethicists, policy-makers, and law enforcement organisations with a synthesis of the current problems, and a possible solution space.

Designing emotional BDI agents: good practices and open questions

Intelligent agents built on the basis of the BDI (belief–desire–intention) architecture are known as BDI agents. Currently, due to the increasing importance given to the affective capacities, they have evolved giving way to the BDI emotional agents. These agents are generally characterized by affective states such as emotions, mood or personality but sometimes also by affective capacities such as empathy or emotional regulation. In the paper, a review of the most relevant proposals to include emotional aspects in the design of BDI agents is presented. Both BDI formalizations and BDI architecture extensions are covered. From the review, common findings and good practices modeling affect, empathy and regulatory capacities in BDI agents, are extracted. In spite of the great advance in the area several, open questions remain and are also discussed in the paper.

The Effects of Modality, Device, and Task Differences on Perceived Human Likeness of Voice-Activated Virtual Assistants

Paying attention to the rising popularity of virtual assistants (VAs) that offer unique user experiences through voice-centered interaction, this study examined the effects of modality, device, and task differences on perceived human likeness of, and attitudes toward, voice-activated VAs. To do so, a 2 (modality: voice vs. text) × 2 (device: mobile vs. laptop) × 2 (task type: hedonic vs. utilitarian) mixed factorial experimental design was employed. Findings suggest that voice (vs. text) interaction leads to more positive attitudes toward the VA system mediated by heightened perceived human likeness of the VA, but only with utilitarian (vs. hedonic) tasks. Interestingly, laptop (vs. mobile phone) interaction also enhanced perceived human likeness of the VA. This study offers theoretical and practical implications for VA research by exploring the combinational effects of modality, device, and task differences on user perceptions through human-like interactions.

Trusting Virtual Agents

We present artificial intelligent (AI) agents that act as interviewers to engage with a user in a text-based conversation and automatically infer the user's personality traits. We investigate how the personality of an AI interviewer and the inferred personality of a user influences the user's trust in the AI interviewer from two perspectives: the user's willingness to confide in and listen to an AI interviewer. We have developed two AI interviewers with distinct personalities and deployed them in a series of real-world events. We present findings from four such deployments involving 1,280 users, including 606 actual job applicants. Notably, users are more willing to confide in and listen to an AI interviewer with a serious, assertive personality in a high-stakes job interview. Moreover, users’ personality traits, inferred from their chat text, along with interview context, influence their perception of and their willingness to confide in and listen to an AI interviewer. Finally, we discuss the design implications of our work on building hyper-personalized, intelligent agents.

Measuring Deception: A Look at Antecedents to Deceptive Intent

A Deceptive Belief Inventory scale is developed and validated using 10 first-order factors to represent 3 second-order constructs (deception confidence, duping delight, and guiltless deception). A new theoretical model describing how deception confidence, duping delight, and guiltless deception may influence a person’s intent to deceive others is also tested. Traditional deceptive communication research has focused on situation-specific factors surrounding deception. This study focuses on understanding and assessing a person’s propensity to deceive others. The findings of this study can be used to better understand the factors that may influence a person’s reported propensity to deceive and ultimately be used to improve security procedures designed to protect critical information systems.

Mimicry and expressiveness of an ECA in human-agent interaction: familiarity breeds content!

Background

Two experiments investigated the effect of features of human behaviour on the quality of interaction with an Embodied Conversational Agent (ECA).

Methods

In Experiment 1, visual prominence cues (head nod, eyebrow raise) of the ECA were manipulated to explore the hypothesis that likeability of an ECA increases as a function of interpersonal mimicry. In the context of an error detection task, the ECA either mimicked or did not mimic a head nod or brow raise that humans produced to give emphasis to a word when correcting the ECA’s vocabulary. In Experiment 2, presence versus absence of facial expressions on comprehension accuracy of two computer-driven ECA monologues was investigated.

Results

In Experiment 1, evidence for a positive relationship between ECA mimicry and lifelikeness was obtained. However, a mimicking agent did not elicit more human gestures. In Experiment 2, expressiveness was associated with greater comprehension and higher ratings of humour and engagement.

Conclusion

Influences from mimicry can be explained by visual and motor simulation, and bidirectional links between similarity and liking. Cue redundancy and minimizing cognitive load are potential explanations for expressiveness aiding comprehension.

Electronic supplementary material

The online version of this article (doi:10.1186/s40469-016-0008-2) contains supplementary material, which is available to authorized users.

Interviewing Suspects with Avatars: Avatars Are More Effective When Perceived as Human

It has been consistently demonstrated that deceivers generally can be discriminated from truth tellers by monitoring an increase in their physiological response. But is this still the case when deceivers interact with a virtual avatar? The present research investigated whether the mere “belief” that the virtual avatar is computer or human operated forms a crucial factor for eliciting physiological cues to deception. Participants were interviewed about a transgression they had been seduced to commit, by a human-like virtual avatar. In a between-subject design, participants either deceived or told the truth about this transgression. During the interviews, we measured the physiological responses assessing participants' electrodermal activity (EDA). In line with our hypothesis, EDA differences between deceivers and truth tellers only were significant for participants who believed they interacted with a human operated (compared to a computer operated) avatar. These results have theoretical as well as practical implications which we will discuss.

Comprehension and engagement in survey interviews with virtual agents

This study investigates how an onscreen virtual agent's dialog capability and facial animation affect survey respondents' comprehension and engagement in “face-to-face” interviews, using questions from US government surveys whose results have far-reaching impact on national policies. In the study, 73 laboratory participants were randomly assigned to respond in one of four interviewing conditions, in which the virtual agent had either high or low dialog capability (implemented through Wizard of Oz) and high or low facial animation, based on motion capture from a human interviewer. Respondents, whose faces were visible to the Wizard (and videorecorded) during the interviews, answered 12 questions about housing, employment, and purchases on the basis of fictional scenarios designed to allow measurement of comprehension accuracy, defined as the fit between responses and US government definitions. Respondents answered more accurately with the high-dialog-capability agents, requesting clarification more often particularly for ambiguous scenarios; and they generally treated the high-dialog-capability interviewers more socially, looking at the interviewer more and judging high-dialog-capability agents as more personal and less distant. Greater interviewer facial animation did not affect response accuracy, but it led to more displays of engagement—acknowledgments (verbal and visual) and smiles—and to the virtual interviewer's being rated as less natural. The pattern of results suggests that a virtual agent's dialog capability and facial animation differently affect survey respondents' experience of interviews, behavioral displays, and comprehension, and thus the accuracy of their responses. The pattern of results also suggests design considerations for building survey interviewing agents, which may differ depending on the kinds of survey questions (sensitive or not) that are asked.

A truly human interface: interacting face-to-face with someone whose words are determined by a computer program

We use speech shadowing to create situations wherein people converse in person with a human whose words are determined by a conversational agent computer program. Speech shadowing involves a person (the shadower) repeating vocal stimuli originating from a separate communication source in real-time. Humans shadowing for conversational agent sources (e.g., chat bots) become hybrid agents (“echoborgs”) capable of face-to-face interlocution. We report three studies that investigated people’s experiences interacting with echoborgs and the extent to which echoborgs pass as autonomous humans. First, participants in a Turing Test spoke with a chat bot via either a text interface or an echoborg. Human shadowing did not improve the chat bot’s chance of passing but did increase interrogators’ ratings of how human-like the chat bot seemed. In our second study, participants had to decide whether their interlocutor produced words generated by a chat bot or simply pretended to be one. Compared to those who engaged a text interface, participants who engaged an echoborg were more likely to perceive their interlocutor as pretending to be a chat bot. In our third study, participants were naïve to the fact that their interlocutor produced words generated by a chat bot. Unlike those who engaged a text interface, the vast majority of participants who engaged an echoborg did not sense a robotic interaction. These findings have implications for android science, the Turing Test paradigm, and human–computer interaction. The human body, as the delivery mechanism of communication, fundamentally alters the social psychological dynamics of interactions with machine intelligence.