Challenges and Good Practices in Conversational AI-Driven Service Automation

Conversational AI offers novel opportunities for companies to automate customer interactions. However, many companies grapple with effectively implementing conversational AI. Utilizing an engaged, consortium-based research approach, we examine the unique challenges faced by six companies in the insurance and banking sector while implementing conversational AI solutions and identify best practices to address these challenges. Finally, drawing upon the lessons learned, we offer guidance for developing conversational AI capabilities and fostering conversational AI success stories

NOT ALL TASKS ARE ALIKE: EXPLORING THE EFFECT OF TASK REPRESENTATION ON USER ENGAGEMENT IN CROWD-BASED IDEA EVALUATION

Crowdsourcing has experienced increasing popularity in recent years. While performance-based issues, such as the quantity or quality of output produced by the crowd, have been in the focus of research, users’ experience, which unfolds through interaction with the crowdsourcing platform and ultimately creates engagement, has been largely neglected. However, user engagement does not only determine the scope of effort users put into the crowdsourcing task, but is considered a determinant for future participation. This paper focusses on the role of task representation–manifested in mechanisms for crowd-based idea evaluation–as potential stimuli for user engagement. Therefore, we conduct a web-based experiment with 198 participants to investigate how different task representations translate into differences in users’ experience and their engagement. In particular, we analyze two distinctive task representations: sequential judgement tasks in form of multi-criteria rating scales and simultaneous choice tasks in the form of enterprise crowdfunding. We find differences in task representation to influence user engagement while mediated by a user’s perceived cognitive load. Moreover, our findings indicate that user engagement is determined by a user’s perceived meaningfulness of a task. These results enhance our understanding of user engagement in crowdsourcing and contribute to theory building in this emerging field

“I Will Follow You!” – How Recommendation Modality Impacts Processing Fluency and Purchase Intention

Although conversational agents (CA) are increasingly used for providing purchase recommendations, important design questions remain. Across two experiments we examine with a novel fluency mechanism how recommendation modality (speech vs. text) shapes recommendation evaluation (persuasiveness and risk), the intention to follow the recommendation, and how modality interacts with the style of recommendation explanation (verbal vs. numerical). Findings provide robust evidence that text-based CAs outperform speech-based CAs in terms of processing fluency and consumer responses. They show that numerical explanations increase processing fluency and purchase intention of both recommendation modalities. The results underline the importance of processing fluency for the decision to follow a recommendation and highlight that processing fluency can be actively shaped through design decisions in terms of implementing the right modality and aligning it with the optimal explanation style. For practice, we offer actionable implications on how to make effective sales agents out of CAs

To Rate or to Fund? - The Effect of Idea Evaluation Platform Design on Decision Quality and User Engagement

Given the importance of scalable and real-time idea evaluation for commercial success and survival, organizations increasingly draw on the principle of the wisdom of crowds. This principle is commonly being operationalized in platforms that involve custo

Alexa, are you still there? Understanding the Habitual Use of AI-Based Voice Assistants

Voice assistants are a novel class of information systems that fundamentally change human–computer interaction. Although these assistants are widespread, the utilization of these information systems is oftentimes only considered on a surface level by individuals. In addition, prior research has focused predominantly on initial use instead of looking deeper into post-adoption and habit formation. In consequence, this paper reviews how the notion of habit has been conceptualized in relation to biographical utilization of voice assistants and presents findings based on a qualitative study approach. From a perspective of post-adoption users, the study suggests that existing habits persist, and new habits hardly ever form in the context of voice assistant utilization. This paper outlines four key factors that help explain voice assistant utilization behavior and furthermore provides practical implications that help to ensure continued voice assistant use in the future

Towards Developing Trust-Supporting Design Features for AI-Based Chatbots in Customer Service

Chatbots are predicted to play a key role in customer service based on recent advances in the area of Artificial Intelligence (AI). However, a lack of user trust impedes the wide-spread adaption of AI-based chatbots. Still, there is a lack of systematically derived design knowledge concerning user trust in those agents. In this short paper, we report on the first steps of our design science research project on which design principles are relevant for building trust in chatbots. Based on trust literature and user interviews, we propose preliminary requirements and design principles for trust-enhancing design features for chatbots in customer service. Furthermore, we present a first instantiation of those principles. These insights will support researchers and practitioners to better understand how user trust in chatbots can be systematically built to increase adoption and usage

A Review of the Empirical Literature on Conversational Agents and Future Research Directions

The knowledge base related to user interaction with conversational agents (CAs) has grown dramatically but remains segregated. In this paper, we conduct a systematic literature review to investigate user interaction with CAs. We examined 107 papers published in outlets related to IS and HCI research. Then, we coded for design elements and user interaction outcomes, and isolated 7 significant determinants of these outcomes, as well as 42 themes with inconsistent evidence, providing grounds for future research. Building upon the insights from the analysis, we propose a research agenda to guide future research surrounding user interaction with CAs. Ultimately, we aim to contribute to the body of knowledge of IS and HCI in general and user interaction with CA in particular by indicating how developed a research field is regarding the number and content of the respective contributions. Furthermore, practitioners benefit from a structured overview related to CA design effects

Voice as a Contemporary Frontier of Interaction Design

Voice assistants’ increasingly nuanced and natural communication bears new opportunities for user experiences and task automation while challenging existing patterns of human-computer interaction. A fragmented research field, as well as constant technological advancements, impede a common apprehension of prevalent design features of voice-based interfaces. As part of this study, 86 papers across domains are systematically identified and analysed to arrive at a common understanding of voice assistants. The review highlights perceptual differences to other human-computer interfaces and points out relevant auditory cues. Key findings regarding those cues’ impact on user perception and behaviour are discussed along with the three design strategies 1) personification, 2) individualization, and 3) contextualization. Avenues for future research are lastly deducted. Our results provide relevant opportunities to researchers and designers alike to advance the design and deployment of voice assistants