Under Pressure? - The Effect of Conversational Agents on Task Pressure and Social Relatedness in Digital Labor

Digital labor platforms (and many other digital workplaces) can be anonymous, isolated, and lacking social interaction. In this context, implementing conversational agents (CAs) to provide social presence and relatedness could be a remedy. However, based on research in the context of human-to-human interaction, two counteracting effects of CAs’ social presence can be derived. First, social presence and social relatedness induce enjoyment. Second, social presence can lead to a perception of task pressure, which reduces enjoyment. We conducted a three-condition online experiment with 269 participants from a commercial digital labor platform to investigate these effects. Our results show that social presence directly leads to social relatedness and enjoyment, and indirectly to task pressure. However, this perceived task pressure does not reduce enjoyment and only positively effects performance. Thus, it appears that introducing CAs as part of digital labor platforms is a win-win situation for users and work

Become a Lifesaver - How to Design Conversational Agents to Increase Users’ Intention to Donate Blood

Donating blood is a selfless act that impacts public welfare, potentially saving human lives. However, blood shortage is a rising worldwide issue due to increased demand. Thus, finding ways to animate and motivate potential donors to donate blood is para-mount. In this context, conversational agents (CAs) offer a promising approach to edu-cating, promoting, and achieving desired behaviors. In this paper, we conducted an online experimental study (N=303) and investigated the effect of a human-like designed CA and fear-inducing communication on users’ intention to donate. Our results show that users’ intention is driven by perceived persuasiveness rather than perceived human-ness and that fear-inducing communication does not significantly affect the intention to donate. Against this background, we provide numerous theoretical and practical impli-cations, contributing to information system literature by enhancing our understanding of how fear-inducing communication is used in CA interactions

Who’s Bad? – The Influence of Perceived Humanness on Users’ Intention to Complain about Conversational Agent Errors to Others

The perception of humanness in a conversational agent (CA) has been shown to strongly impact users’ processing and reaction to it. However, it is largely unclear how this perception of humanness influences users’ processing of errors and subsequent intention for negative word-of-mouth (WoM). In this context, we propose two pathways between perceived humanness and negative WoM: a cognitive pathway and an affective pathway. In a 2x2 online experiment with chatbots, we manipulated both the occurrence of errors and the degree of humanlike design. Our findings indicate that perceived humanness effects users\u27 intentions towards negative WoM through the cognitive pathway: users\u27 confirmation of expectations is increased by perceived humanness, reducing negative WoM intentions. However, it has no effect on users’ anger and frustration and does not interact with the effects of errors. For practice, our results indicate that adding humanlike design elements can be a means to reduce negative WoM

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

Behavioral Design in Online Supermarkets: How Virtual Shopping Cart Functions Impact Sustainable Consumption

In recent years, the negative environmental impact of consumers\u27 dietary habits has become more visible. Accordingly, in-store interventions to promote more sustainable (e.g., organic) food choices have received increased scholarly attention. Thereby, online grocery shopping is gaining momentum as web-applications provide decision support tools such as real-time spending feedback (RSF). Building on budgeting and spending literature, this study provides initial insights on the impact of RSF on consumers’ organic food choices in online supermarkets. Using a free simulation experimental approach, we were able to track participants’ real grocery shopping behavior within a realistic online shopping environment. Within a baseline (no RSF) and an intervention (RSF) condition (between subject design), we show that RSF facilitated participants to stay within their budget and significantly reduced underspending. Somewhat surprisingly in response to the RSF, participants who usually buy fewer organic products purchased significantly more organic food items, both in absolute and relative terms

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

Computing Incentives for User-Based Relocation in Carsharing

Carsharing offers an environmentally friendly alternative to private car ownership. However, carsharing providers face the challenging task of matching shifting vehicle supply with fluctuating customer demand to prevent related operational inefficiencies and ensure customer satisfaction. To date, researchers have improved existing relocation strategies and developed new concepts with the use of information technology tools. Still, current literature lacks research on optimization and implementation of user-based relocation solutions. The most urgent need currently lies in the development of algorithms to compute and implement effective incentives for user-based relocation. We address these needs by utilizing a design science research approach to develop an automated machine learning-based incentive computation solution for incentivizing user-based relocation. We use a survey of 274 participants resulting in 1370 individual data points to train an incentive computation model, which is then applied within a small-scale field test. Results suggest that the algorithm computes appropriate incentives

CASSI: Designing a Simulation Environment for Vehicle Relocation in Carsharing

Simulations offer an efficient solution to comprehensive represent operational services and to track the impact of changing systematic factors and business constraints. Carsharing services provide users with mobility services on demand. Although research has introduced strategies to optimize efforts to set up and operate such a system, they lack reusable and flexible simulation environments. For instance, carsharing research applies simulations to better understand and solve the problem of balancing vehicle supply and demand, which operators need to solve to prevent operational inefficiencies and ensure customer satisfaction. Hence, one cannot feasibly test new balancing mechanisms directly in a real-world environment. As for now, researchers have implemented simulations from scratch, which results in high development efforts and a limited ability to compare results. In this paper, we address this gap by designing a versatile carsharing simulation tool that researchers can easily use and adapt. The tool simplifies the process of modeling a carsharing system and developing operation strategies. Furthermore, we propose various system performance measures to increase the developed solutions’ comparability

Is Making Mistakes Human? On the Perception of Typing Errors in Chatbot Communication

The increasing application of Conversational Agents (CAs) changes the way customers and businesses interact during a service encounter. Research has shown that CA equipped with social cues (e.g., having a name, greeting users) stimulates the user to perceive the interaction as human-like, which can positively influence the overall experience. Specifically, social cues have shown to lead to increased customer satisfaction, perceived service quality, and trustworthiness in service encounters. However, many CAs are discontinued because of their limited conversational ability, which can lead to customer dissatisfaction. Nevertheless, making errors and mistakes can also be seen as a human characteristic (e.g., typing errors). Existing research on human-computer interfaces lacks in the area of CAs producing human-like errors and their perception in a service encounter situation. Therefore, we conducted a 2x2 online experiment with 228 participants on how CAs typing errors and CAs human-like behavior treatments influence user’s perception, including perceived service quality