Can Fact-Checking Influence User Beliefs About Misinformation Claims: An Examination of Contingent Effects

2023 | Management Information Systems Quarterly | Citations: 0

Authors: Bhattacherjee, Anol

Abstract: Prior research has suggested that corrective fact-checking has inconsistent eff ... Expand

Abstract: Prior research has suggested that corrective fact-checking has inconsistent effects on beliefs about online misinformation claims. This study attempts to explain this inconsistency using three contingent factorsclaim-source credibility, fact-checker credibility, and attitude strength-which respectively relate to three key parties in the fact-checking process: the source of a misleading claim, the fact-checker, and the user evaluating the fact-check. I hypothesize the interplay between these factors, which is tested using two online experiments on COVID-19-related misinformation with over 900 participants. Multilevel analysis of pretest-posttest, repeated measures data supports the hypothesized moderating effects and offers additional insights about how these effects vary between earlier versus later phases of misinformation cycles. The paper concludes with a discussion of contributions to research and practice. Collapse

Topics: misinformation pandemic BuzzFeed covid 19 user attitude

Methods: experiment regression analysis method longitudinal research statistical power analysis theoretical contribution

Rising Above vs. Falling Below: When and Why Status Change Affects Interpersonal Helping in Workgroups

2023 | Organization Science | Citations: 0

Authors: Doyle, Sarah P.; Lount, Robert B.

Abstract: The current research sheds new light on how and why status hierarchies impact in ... Expand

Abstract: The current research sheds new light on how and why status hierarchies impact interpersonal helping by examining people’s reactions to recently experienced status change. Specifically, we incorporate findings from research on the self-serving attributional bias to theorize about how the direction of status change (i.e., a gain or a loss) can shape the extent to which people accept or deflect personal responsibility for their change in status, which we argue will then impact other-concern and, thus, their willingness to help. Further, we identify status change legitimacy as a key contingency that will strengthen or weaken the psychological and behavioral effects of status change. Among firefighter teams (Study 1), participants in the laboratory (Studies 2 and 3), and student teams (Study 4), we show that (1) status change impacts interpersonal helping through its impact on changes in other-concern and (2) status change legitimacy moderates the effect of status change on both other-concern and interpersonal helping. Additionally, we document an asymmetry with regards to the effects of status change on both other-concern and helping behavior (i.e., with the negative impact of a status loss being stronger than the positive impact of a status gain). Theoretical and practical implications are discussed. Collapse

Topics: team productivity missing data accounting

Methods: survey polynomial regression parametric test survey design two-way ANOVA

Developing a Reference Model for Artificial Intelligence Management

2023 | European Conference On Information Systems | Citations: 0

Authors: Fukas, Philipp; Thomas, Oliver

Abstract: The adoption and diffusion of Artificial Intelligence (AI) in organizations are ... Expand

Abstract: The adoption and diffusion of Artificial Intelligence (AI) in organizations are significantly influenced by many sociotechnical factors such as people, processes, or regulations. However, previous research has mostly focused on a reactive description of individual influencing factors and lacks an overarching perspective that enables active management of an organization's various AI capabilities. Therefore, this paper provides a first overarching perspective by identifying all relevant activities for the management of AI in literature and grouping them into eight different fields of action. These fields of action are then evaluated by practitioners and combined into a cross-industry reference model for AI Management. While this reference model is the first of its kind and is already making a valuable contribution to the emerging field of AI Management in Information Systems Research, further insights are expected from the future refinement and application of the model. Collapse

Topics: artificial intelligence database system knowledge base

Methods: survey one sample t-test reference modelling literature sample descriptive statistic

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

2023 | International Conference on Information Systems | Citations: 0

Authors: Hildebrandt, Fabian; Lichtenberg, Sascha; Brendel, Benedikt; Landmann, Elisa

Abstract: The perception of humanness in a conversational agent (CA) has been shown to s ... Expand

Abstract: 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' intentions towards negative WoM through the cognitive pathway: users' 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. Collapse

Topics: electronic mail Google+

Methods: experiment chatbot theory development survey partial least squares regression

Theories: social response theory

How to attract low prosocial funders in crowdfunding? Matching among funders, project descriptions, and platform types

2023 | Information & Management | Citations: 0

Authors: Li, Yuanqing; Cabano, Frank; Li, Pingshu

Abstract: The amount of crowdfunding research that investigates funding success factors ha ... Expand

Abstract: The amount of crowdfunding research that investigates funding success factors has been increasing. The existing research shows inconsistent evidence regarding how a prosocial project description affects funding success and largely ignores the issue of alignment/misalignment among different factors in affecting funding success. We suggest that funders’ prosocial motivation can be an important factor for this inconsistent evidence. We integrate the elaboration likelihood model and language expectancy theory and demonstrate distinct decision-making patterns from high and low prosocial motivation funders. Through three experiments, we provide evidence for alignment/misalignment effects among funders’ prosocial motivation, prosocial project descriptions, and platform types (donation-based vs. reward-based). While there are no differences for participants with high prosocial motivation across conditions, we find that participants with low prosocial motivation are more willing to contribute to a project that has alignment between the different factors, namely to a project that has a high prosocial description on a donation-based platform, or to a project that has a low prosocial description on a reward-based platform. This research sheds light on the crowdfunding and prosocial motivation literature. Collapse

Topics: crowdfunding crowdsourcing decision making cost benefit analysis cross cultural research

Methods: experiment parametric test descriptive statistic ANOVA one-way ANOVA

Theories: expectancy theory of motivation elaboration likelihood model organizational behavior theory

Product information and green consumption: An integrated perspective of regulatory focus, self-construal, and temporal distance

2023 | Information & Management | Citations: 0

Authors: Luan, Jing; Filieri, Raffaele; Xiao, Jie; Han, Qingqing; Zhu, Bing; Wang, Tao

Abstract: Understanding consumer information processing is fundamental. Previous studies i ... Expand

Abstract: Understanding consumer information processing is fundamental. Previous studies investigated the effect of message framing and self-construal in green consumption using self-reported measures and obtaining contrasting results. This study used the eye-tracking method across two studies to examine the effect of promotion- vs. prevention-framed messages on green consumption. Study 1 confirms that promotion (vs. prevention)-framed information attracts more attention from independent (vs. interdependent) individuals. Moreover, temporal distance strengthens this matching effect (Study 2). Overall, this study uncovers consumers’ processing of messages with different regulatory frames from a visual attention perspective and offers valuable insights about the three-way interaction to promote green consumption. Collapse

Topics: social media WeChat marketing management consumer behavior

Methods: oculometry experiment parametric test repeated measures ANOVA Student's t-test

Theories: self construal theory regulatory focus theory regulatory fit theory

HUMAN-AI COLLABORATION IN CONCEPTUALIZING DESIGN SCIENCE RESEARCH STUDIES: PERCEIVED HELPFULNESS OF GENERATIVE LANGUAGE MODEL'S SUGGESTIONS

2023 | European Conference On Information Systems | Citations: 0

Authors: Memmert, Lucas; Cvetkovic, Izabel; Bittner, Eva

Abstract: Solving complex problems was named a new challenge for research on human-AI col ... Expand

Abstract: Solving complex problems was named a new challenge for research on human-AI collaboration. In our study we focus on a particular means to solving complex problems: design science research (DSR). We investigate whether AI, more specifically, generative language models (GLM), can support an individual in conceptualizing DSR studies by making helpful suggestions. To do so we use extracts of a published DSR study and have GPT-3, a GLM provide suggestions for aspects of this study. These suggestions are then evaluated in a survey (n=33) regarding their helpfulness. Results show that GLM suggestions are perceived to be helpful, with some variation depending on expertise. Reported interest in using such a tool in the future was high. Describing how GLMs can offer helpful suggestions we contribute toward a DSR tool support ecosystem and, more generally, towards knowledge on how humans and (generative) AI systems can team up to solve complex problems. Collapse

Topics: artificial intelligence human computer interaction human AI collaboration generative AI system support

Methods: survey design requirement design principle correlation analysis descriptive statistic

Driven to Distraction: The Unintended Consequences of Organizational Learning from Failure Caused by Human Error

2023 | Organization Science | Citations: 0

Authors: Park, Brian; Lehman, David W.; Ramanujam, Rangaraj

Abstract: Research to date offers inconclusive and even conflicting evidence regarding whe ... Expand

Abstract: Research to date offers inconclusive and even conflicting evidence regarding whether organizations learn from failure. The present study sheds new light on this debate by highlighting a previously overlooked factor: whether the failure was caused by human error. In attempts to learn from failure, organizational members tend to focus on simplified representations of experiences and, in doing so, distinguish between failures attributed to human errors versus other causes. Our core thesis is that failures resulting from human error attract significant amounts of attentional resources, thereby depleting the limited stock of organizational attention otherwise directed at managing the risk of failures resulting from other causes. We hypothesize that this disproportionate allocation of attention simultaneously is associated with both positive learning outcomes and negative side effects, specifically, a subsequent decrease in failures resulting from human error and also an increase in failures resulting from other causes. We also hypothesize that failures resulting from causes other than human error attract less organizational attention and, thus, lead to weaker learning outcomes. The proposed hypotheses were tested and supported using data from accident reports filed by natural gas pipeline operators with the U.S. Pipeline Hazardous Materials Safety Administration from 2002 to 2012. Additional analyses, including text analysis of accident reports, a series of simulations, and a supplementary study, point to organizational attention as the mechanism at play. Taken together, these findings suggest that organizational learning from failure caused by human error produces not only benefits, but also unintended consequences. Collapse

Topics: organization learning unintended consequence anonymity database system organizational memory

Methods: simulation archival research regression analysis method qualitative content analysis robustness check

Towards Effective Human-AI Decision-Making: The Role of Human Learning in Appropriate Reliance on AI Advice

2023 | International Conference on Information Systems | Citations: 0

Authors: Schemmer, Max; Bartos, Andrea; Spitzer, Philipp; Hemmer, Patrick; Kühl, Niklas; Liebschner, Jonas; Satzger, Gerhard

Abstract: The true potential of human-AI collaboration lies in exploiting the complementa ... Expand

Abstract: The true potential of human-AI collaboration lies in exploiting the complementary capabilities of humans and AI to achieve a joint performance superior to that of the individual AI or human, i.e., to achieve complementary team performance (CTP). To realize this complementarity potential, humans need to exercise discretion in following AI's advice, i.e., appropriately relying on the AI's advice. While previous work has focused on building a mental model of the AI to assess AI recommendations, recent research has shown that the mental model alone cannot explain appropriate reliance. We hypothesize that, in addition to the mental model, human learning is a key mediator of appropriate reliance and, thus, CTP. In this study, we demonstrate the relationship between learning and appropriate reliance in an experiment with 100 participants. This work provides fundamental concepts for analyzing reliance and derives implications for the effective design of human-AI decision-making. Collapse

Topics: artificial intelligence decision making explainable artificial intelligence missing data organization learning

Methods: experiment structural equation modeling parametric test Student's t-test descriptive statistic

IMPACT OF DATA COLLECTION ON ML MODELS: ANALYZING DIFFERENCES OF BIASES BETWEEN LOW- VS. HIGH-SKILLED ANNOTATORS

2023 | International Conference on Business Informatics | Citations: 0

Authors: Schneider, Johannes; Eisenhardt, Daniel; Utama, Christian; Meske, Christian

Abstract: ... Expand

Abstract: Collapse

Topics: Twitter crowdsourcing

Methods: machine learning Kolmogorov-Smirnov test natural language processing experiment parametric test

Theories: information theory