Feel, Don\u27t Think Review of the Application of Neuroscience Methods for Conversational Agent Research

Conversational agents (CAs) equipped with human-like features (e.g., name, avatar) have been reported to induce the perception of humanness and social presence in users, which can also increase other aspects of users’ affection, cognition, and behavior. However, current research is primarily based on self-reported measurements, leaving the door open for errors related to the self-serving bias, socially desired responding, negativity bias and others. In this context, applying neuroscience methods (e.g., EEG or MRI) could provide a means to supplement current research. However, it is unclear to what extent such methods have already been applied and what future directions for their application might be. Against this background, we conducted a comprehensive and transdisciplinary review. Based on our sample of 37 articles, we find an increased interest in the topic after 2017, with neural signal and trust/decision-making as upcoming areas of research and five separate research clusters, describing current research trends

Design Science Research in Green IS - Analyzing the Past to Guide Future Research

The field of Green IS research addresses the environmental challenges of our rapidly growing economy. IS-based solutions are valuable tools for emissions reduction and waste mitigation. The development of novel sustainable IS artifacts falls in the domain of Design Science Re-search. Applying the Design Science Research paradigm helps to design, develop, improve and implement IS related artifacts to solve prevailing problems or challenges. Hence, regarding sustainability, it acts as an aid to the goals of Green IS. Thus, the development of novel sustain-able artifacts falls in the intersection of Green IS and Design Science Research. We analyze the relationship between Design Science Research and Green IS by providing a deeper understanding of current research knowledge and opportunities at the intersection of these two important IS topics. Based on these findings, we present five directions for future re-search that aim to facilitate further contributions to theoretical knowledge, Design Science Re-search, and sustainable solution development in the field of Green IS

Toward User-Based Relocation Information Systems in Station-Based One-Way Car Sharing

Car sharing is an important mobility service to approach urban and suburban mobility problems. It is a sustainable means of transport because it lowers the emissions of each car sharing customer by substituting privately owned vehicles by joint used ones. Nevertheless, car sharing still has to fully unfold its potential and has to overcome certain obstacles. For instance, it is necessary to substitute operator-based relocation by user-based relocation, which is more sustainable and cost-efficient. In this paper, we propose a framework and algorithms for implementing user-based relocation in the context of station-based one-way car sharing. Through a simulated car sharing system with 34.418 rental data we were able to demonstrate, that user-based relocation has the potential to increase the number of accepted rentals. Implementing the proposed system would increase service quality, providers’ profits and the positive environmental impact of car sharing

On Conversational Agents in Information Systems Research: Analyzing the Past to Guide Future Work

Conversational agents (CA), i.e. software that interacts with its users through natural language, are becoming increasingly prevalent in everyday life as technological advances continue to significantly drive their capabilities. CA exhibit the potential to support and collaborate with humans in a multitude of tasks and can be used for innovation and automation across a variety of business functions, such as customer service or marketing and sales. Parallel to the increasing popularity in practice, IS researchers have engaged in studying a variety of aspects related to CA in the last few years, applying different research methods and producing different types of theories. In this paper, we review 36studies to assess the status quo of CA research in IS, identify gaps regarding both the studied aspects as well as applied methods and theoretical approaches, and propose directions for future work in this research area

Generating Rental Data for Car Sharing Relocation Simulations on the Example of Station-Based One-Way Car Sharing

Developing sophisticated car sharing simulations is a major task to improve car sharing as a sustainable means of transportation, because new \ algorithms for enhancing car sharing efficiency are formulated using them. \ \ Simulations rely on input data, which is often gathered in car sharing systems or artificially generated. Real-world data is often incomplete and biased while artificial data is mostly generated based on initial assumptions. Therefore, developing new ways for generating testing data is an important task for future research. \ \ In this paper, we propose a new approach for generating car sharing data for relocation simulations by utilizing machine learning. Based on real-world data, we could show that a combined methods approach consisting of a Gaussian Mixture Model and two classification trees can generate appropriate artificial testing data

Towards a Taxonomy of Platforms for Conversational Agent Design

Software that interacts with its users through natural language, so-called conversational agents (CAs), is permeating our lives with improving capabilities driven by advances in machine learning and natural language processing. For organizations, CAs have the potential to innovate and automate a variety of tasks and processes, for example in customer service or marketing and sales, yet successful design remains a major challenge. Over the last few years, a variety of platforms that offer different approaches and functionality for designing CAs have emerged. In this paper, we analyze 51 CA platforms to develop a taxonomy and empirically identify archetypes of platforms by means of a cluster analysis. Based on our analysis, we propose an extended taxonomy with eleven dimensions and three archetypes that contribute to existing work on CA design and can guide practitioners in the design of CA for their organizations

Designing Anthropomorphic Enterprise Conversational Agents

The increasing capabilities of conversationalagents (CAs) offer manifold opportunities to assist users ina variety of tasks. In an organizational context, particularlytheir potential to simulate a human-like interaction vianatural language currently attracts attention both at thecustomer interface as well as for internal purposes, often inthe form of chatbots. Emerging experimental studies onCAs look into the impact of anthropomorphic design ele-ments, so-called social cues, on user perception. However,while these studies provide valuable prescriptive knowl-edge of selected social cues, they neglect the potentialdetrimental influence of the limited responsiveness ofpresent-day conversational agents. In practice, many CAsfail to continuously provide meaningful responses in aconversation due to the open nature of natural languageinteraction, which negatively influences user perceptionand often led to CAs being discontinued in the past. Thus,designing a CA that provides a human-like interactionexperience while minimizing the risks associated withlimited conversational capabilities represents a substantialdesign problem. This study addresses the aforementionedproblem by proposing and evaluating a design for a CAthat offers a human-like interaction experience while mit-igating negative effects due to limited responsiveness.Through the presentation of the artifact and the synthesis ofprescriptive knowledge in the form of a nascent designtheory for anthropomorphic enterprise CAs, this researchadds to the growing knowledge base for designing human-like assistants and supports practitioners seeking to intro-duce them into their organizations

Replication Research of Moody, Siponen, and Pahnila’s Unified Model of Information Security Policy Compliance

Information security compliance behavior research has produced several theoretical models derived from different disciplines to explain or predict violations of information security policies (ISP) or related employee intentions. The application of these theories to ISP violations has led to an increasing number of information security behavioral models. Based on this observation, Moody et al. (2018) reviewed and empirically compared 11 theories that predict information system security behavior using a Finnish sample. Drawing on these findings, they derived and tested a unified model of ISP compliance (UMISPC). This study is a conceptual replication of the refined UMISPC by Moody et al (2018). For the replication, we considered the general tendency to violate policy rather than respondents considering specific behaviors according to the scenario approach that Moody et al. (2018) used to test the refined UMISPC. Further, in contrast to Moody et al. (2018), we tested the refined UMISPC with respondents from Germany. In our data, we found empirical evidence for seven of the eight proposed relationships of the refined UMISPC. Only the relationship between fear and reactance remained insignificant in our estimation. Although more research is necessary to confirm our results, we interpret them as further support for the model’s generalizability

DESIGN FOR FAST REQUEST FULFILLMENT OR NATURAL INTERACTION? INSIGHTS FROM AN EXPERIMENT WITH A CONVERSATIONAL AGENT

Conversational agents continue to permeate our lives in different forms, such as virtual assistants on mobile devices or chatbots on websites and social media. The interaction with users through natural language offers various aspects for researchers to study as well as application domains for practitioners to explore. In particular their design represents an interesting phenomenon to investigate as humans show social responses to these agents and successful design remains a challenge in practice. Compared to digital human-to-human communication, text-based conversational agents can provide complementary, preset answer options with which users can conveniently and quickly respond in the interaction. However, their use might also decrease the perceived humanness and social presence of the agent as the user does not respond naturally by thinking of and formulating a reply. In this study, we conducted an experiment with N=80 participants in a customer service context to explore the impact of such elements on agent anthropomorphism and user satisfaction. The results show that their use reduces perceived humanness and social presence yet does not significantly increase service satisfaction. On the contrary, our findings indicate that preset answer options might even be detrimental to service satisfaction as they diminish the natural feel of human-CA interaction

Promoting Sustainable Mobility Beliefs with Persuasive and Anthropomorphic Design: Insights from an Experiment with a Conversational Agent

Sustainable mobility behavior is increasingly relevant due to the vast environmental impact of current transportation systems. With the growing variety of transportation modes, individual decisions for or against specific mobility options become more and more important and salient beliefs regarding the environmental impact of different modes influence this decision process. While information systems have been recognized for their potential to shape individual beliefs and behavior, design-oriented studies that explore their impact, in particular on environmental beliefs, remain scarce. In this study, we contribute to closing this research gap by designing and evaluating a new type of artifact, a persuasive and human-like conversational agent, in a 2x2 experiment with 225 participants. Drawing on the Theory of Planned Behavior and Social Response Theory, we find empirical support for the influence of persuasive design elements on individual environmental beliefs and discover that anthropomorphic design can contribute to increasing the persuasiveness of artifacts