TY - JOUR A1 - Schachner, Theresa A1 - Gross, Christoph A1 - Hasl, Andrea A1 - Wangenheim, Florian von A1 - Kowatsch, Tobias T1 - Deliberative and paternalistic interaction styles for conversational agents in digital health BT - procedure and validation through a web-based experiment JF - Journal of medical internet research : international scientific journal for medical research, information and communication on the internet ; JMIR N2 - Background: Recent years have witnessed a constant increase in the number of people with chronic conditions requiring ongoing medical support in their everyday lives. However, global health systems are not adequately equipped for this extraordinarily time-consuming and cost-intensive development. Here, conversational agents (CAs) can offer easily scalable and ubiquitous support. Moreover, different aspects of CAs have not yet been sufficiently investigated to fully exploit their potential. One such trait is the interaction style between patients and CAs. In human-to-human settings, the interaction style is an imperative part of the interaction between patients and physicians. Patient-physician interaction is recognized as a critical success factor for patient satisfaction, treatment adherence, and subsequent treatment outcomes. However, so far, it remains effectively unknown how different interaction styles can be implemented into CA interactions and whether these styles are recognizable by users. Objective: The objective of this study was to develop an approach to reproducibly induce 2 specific interaction styles into CA-patient dialogs and subsequently test and validate them in a chronic health care context. Methods: On the basis of the Roter Interaction Analysis System and iterative evaluations by scientific experts and medical health care professionals, we identified 10 communication components that characterize the 2 developed interaction styles: deliberative and paternalistic interaction styles. These communication components were used to develop 2 CA variations, each representing one of the 2 interaction styles. We assessed them in a web-based between-subject experiment. The participants were asked to put themselves in the position of a patient with chronic obstructive pulmonary disease. These participants were randomly assigned to interact with one of the 2 CAs and subsequently asked to identify the respective interaction style. Chi-square test was used to assess the correct identification of the CA-patient interaction style. Results: A total of 88 individuals (42/88, 48% female; mean age 31.5 years, SD 10.1 years) fulfilled the inclusion criteria and participated in the web-based experiment. The participants in both the paternalistic and deliberative conditions correctly identified the underlying interaction styles of the CAs in more than 80% of the assessments (X-1(,8)8(2)=38.2; P<.001; phi coefficient r(phi)=0.68). The validation of the procedure was hence successful. Conclusions: We developed an approach that is tailored for a medical context to induce a paternalistic and deliberative interaction style into a written interaction between a patient and a CA. We successfully tested and validated the procedure in a web-based experiment involving 88 participants. Future research should implement and test this approach among actual patients with chronic diseases and compare the results in different medical conditions. This approach can further be used as a starting point to develop dynamic CAs that adapt their interaction styles to their users. KW - conversational agents KW - chatbots KW - human-computer interaction KW - physician-patient relationship KW - interaction styles KW - deliberative KW - interaction KW - paternalistic interaction KW - digital health KW - chronic KW - conditions KW - COPD Y1 - 2021 U6 - https://doi.org/10.2196/22919 SN - 1438-8871 VL - 23 IS - 1 PB - Healthcare World CY - Richmond, Va. ER - TY - CHAP A1 - Kocur, Alexander A1 - Clausen, Sünje A1 - Hofeditz, Lennart A1 - Brünker, Felix A1 - Fromm, Jennifer A1 - Stieglitz, Stefan T1 - Fighting false information BT - designing a conversational agent for public sector organizations T2 - ECIS 2023 research-in-progress papers N2 - The digital transformation poses challenges for public sector organizations (PSOs) such as the dissemination of false information in social media which can cause uncertainty among citizens and decrease trust in the public sector. Some PSOs already successfully deploy conversational agents (CAs) to communicate with citizens and support digital service delivery. In this paper, we used design science research (DSR) to examine how CAs could be designed to assist PSOs in fighting false information online. We conducted a workshop with the municipality of Kristiansand, Norway to define objectives that a CA would have to meet for addressing the identified false information challenges. A prototypical CA was developed and evaluated in two iterations with the municipality and students from Norway. This research-in-progress paper presents findings and next steps of the DSR process. This research contributes to advancing the digital transformation of the public sector in combating false information problems. KW - false information KW - conversational agents KW - crisis communication KW - media literacy Y1 - 2023 UR - https://aisel.aisnet.org/ecis2023_rip/65 PB - AIS Electronic Library (AISeL) CY - [Erscheinungsort nicht ermittelbar] ER - TY - JOUR A1 - Hofeditz, Lennart A1 - Mirbabaie, Milad A1 - Ortmann, Mara T1 - Ethical challenges for human–agent interaction in virtual collaboration at work JF - International journal of human computer interaction N2 - In virtual collaboration at the workplace, a growing number of teams apply supportive conversational agents (CAs). They take on different work-related tasks for teams and single users such as scheduling meetings or stimulating creativity. Previous research merely focused on these positive aspects of introducing CAs at the workplace, omitting ethical challenges faced by teams using these often artificial intelligence (AI)-enabled technologies. Thus, on the one hand, CAs can present themselves as benevolent teammates, but on the other hand, they can collect user data, reduce worker autonomy, or foster social isolation by their service. In this work, we conducted 15 expert interviews with senior researchers from the fields of ethics, collaboration, and computer science in order to derive ethical guidelines for introducing CAs in virtual team collaboration. We derived 14 guidelines and seven research questions to pave the way for future research on the dark sides of human–agent interaction in organizations. KW - conversational agents KW - human–computer interaction KW - virtual collaboration KW - ethics KW - virtual teams KW - trust Y1 - 2023 U6 - https://doi.org/10.1080/10447318.2023.2279400 SN - 1044-7318 SN - 1532-7590 PB - Taylor & Francis CY - New York, NY ER - TY - CHAP A1 - Rieskamp, Jonas A1 - Mirbabaie, Milad A1 - Hofeditz, Lennart A1 - Vischedyk, Justin T1 - Conversational agents and their influence on the well-being of cliniciansclinicians T2 - ACIS 2023 proceedings N2 - An increasing number of clinicians (i.e., nurses and physicians) suffer from mental health-related issues like depression and burnout. These, in turn, stress communication, collaboration, and decision- making—areas in which Conversational Agents (CAs) have shown to be useful. Thus, in this work, we followed a mixed-method approach and systematically analysed the literature on factors affecting the well-being of clinicians and CAs’ potential to improve said well-being by relieving support in communication, collaboration, and decision-making in hospitals. In this respect, we are guided by Brigham et al. (2018)’s model of factors influencing well-being. Based on an initial number of 840 articles, we further analysed 52 papers in more detail and identified the influences of CAs’ fields of application on external and individual factors affecting clinicians’ well-being. As our second method, we will conduct interviews with clinicians and experts on CAs to verify and extend these influencing factors. KW - conversational agents KW - well-being KW - mental health KW - hospitals KW - clinicians Y1 - 2023 UR - https://aisel.aisnet.org/acis2023/66 PB - Australasian Association for Information Systems CY - Wellington ER -