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The Power of Trust: Designing Trustworthy Machine Learning Systems in Healthcare

2023 | International Conference on Information Systems | Citations: 0
Authors: Fecho, Mariska; Zöll, Anne

Abstract: Machine Learning (ML) systems have an enormous potential to improve medical car ... Expand

Abstract: Machine Learning (ML) systems have an enormous potential to improve medical care, but skepticism about their use persists. Their inscrutability is a major concern which can lead to negative attitudes reducing end users trust and resulting in rejection. Consequently, many ML systems in healthcare suffer from a lack of user-centricity. To overcome these challenges, we designed a user-centered, trustworthy ML system by applying design science research. The design includes meta-requirements and design principles instantiated by mockups. The design is grounded on our kernel theory, the Trustworthy Artificial Intelligence principles. In three design cycles, we refined the design through focus group discussions (N1=8), evaluation of existing applications, and an online survey (N2=40). Finally, an effectiveness test was conducted with end users (N3=80) to assess the perceived trustworthiness of our design. The results demonstrated that the end users did indeed perceive our design as more trustworthy. Collapse

Semantic filters: trust theory

Topics: health information system artificial intelligence information privacy concern privacy collaborative work

Methods: survey design science kernel theory experimental group design artifact

Theories: trust theory
Explanation matters: An experimental study on explainable AI
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2023 | Electronic Markets | Citations: 0
Authors: Hamm, Pascal; Klesel, Michael; Coberger, Patricia; Wittmann, H. Felix

Abstract: Explainable artificial intelligence (XAI) is an important advance in the field o ... Expand

Abstract: Explainable artificial intelligence (XAI) is an important advance in the field of machine learning to shed light on black box algorithms and thus a promising approach to improving artificial intelligence (AI) adoption. While previous literature has already addressed the technological benefits of XAI, there has been little research on XAI from the user’s perspective. Building upon the theory of trust, we propose a model that hypothesizes that post hoc explainability (using Shapley Additive Explanations) has a significant impact on use-related variables in this context. To test our model, we designed an experiment using a randomized controlled trial design where participants compare signatures and detect forged signatures. Surprisingly, our study shows that XAI only has a small but significant impact on perceived explainability. Nevertheless, we demonstrate that a high level of perceived explainability has a strong impact on important constructs including trust and perceived usefulness. A post hoc analysis shows that hedonic factors are significantly related to perceived explainability and require more attention in future research. We conclude with important directions for academia and for organizations. Collapse

Semantic filters: trust theory

Topics: explainable artificial intelligence artificial intelligence perceived ease of use perceived usefulness digital dashboard

Methods: experimental group experiment parametric test experimental task ANOVA

Theories: information systems theory trust theory
Would You Accept Doctor ChatGPT: An Empirical Study Based on the UTAUT Model

2023 | Americas Conference on Information Systems | Citations: 0
Authors: Kong, Deli; Qi, Jiayin; Fu, Xiangling

Abstract: Since its introduction, ChatGPT has generated significant interest in many area ... Expand

Abstract: Since its introduction, ChatGPT has generated significant interest in many areas in just a few months, including healthcare. Recently, researchers have claimed that it has passed the U.S. medical licensure examination (Kung et. al, 2022). However, few empirical studies have investigated whether ChatGPT would be valued and accepted by public in the healthcare context. To understand the public's willingness to accept Artificial Intelligence-Generated Content (AIGC) applications like ChatGPT in the healthcare sector, this study proposes a model of factors affecting the user acceptance of ChatGPT for healthcare purposes integrating the Unified Theory of Acceptance and Use of Technology (UTAUT) with the Trust Theory, the Perceived Risk Theory, and the Perceived Illness Theory. We will analyze the data collected from questionnaires using Structural Equation Modeling (SEM). The findings of this study will provide insights into the factors affecting the user acceptance of ChatGPT for healthcare services. KeywordsChatGPT, UTAUT model, perceived risk, trust theory, perceived illness.ChatGPT is an AI language model developed by OpenAI, a leading AI research organization. It is a generative language model that uses deep learning techniques to produce human-like responses to natural language queries. ChatGPT is based on the GPT (Generative Pre-trained Transformer) architecture, which uses unsupervised learning techniques to pre-train the model on large volumes of text data. The pre-training enables ChatGPT to learn the underlying patterns and structures of natural language, making it highly effective in generating contextually relevant responses (Kirmani, 2022). In healthcare, ChatGPT has the potential to serve as a health counselor, providing personalized advice and recommendations to patients. Researches have shown that AI-enable technologies have the ability to provide accurate and reliable health-related information and can be an effective tool for health communication (Palanica et al., 2019;Jiang et al., 2017).The literature on technology adoption mainly applies traditional behavioral models, including the Technology Acceptance Model (TAM: Davis, 1989), as well as its extension, the Unified Theory of Acceptance and Use of Technology (UTAUT: Venkatesh et al., 2003).Artificial intelligence has the potential to transform the healthcare industry, making diagnosis and treatment more accurate and cost-effective (Liu et al., 2019). However, the acceptance and adoption of Acceptance of Doctor ChatGPT Collapse

Semantic filters: trust theory

Topics: behavioral intention artificial intelligence user acceptance healthcare data social influence

Methods: chatGPT survey structural equation modeling SEM tool

Theories: unified theory of acceptance and use of technology trust theory
Examining the Relationship between National Cybersecurity Commitment, Culture, and Digital Payment Usage: An Institutional Trust Theory Perspective
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2023 | Information Systems Frontiers | Citations: 1
Authors: Krishna, Ben; Krishnan, Satish; Sebastian, M. P.

Abstract: Cyberattacks can be considered one of the fundamental challenges that paralyze t ... Expand

Abstract: Cyberattacks can be considered one of the fundamental challenges that paralyze the progress of digital payment usage (DPU) progress among citizens, as consumers shun away from using digital banking services due to increased concern over information security. National Cybersecurity Commitment (NCSC) has emerged as a preventive cybersecurity mechanism for countries to tackle such cybersecurity threats. Previous studies have shown that a country's NCSC positively impacts the business and economy of the country. This study examines the effect of NCSC on digital payment usage (DPU) across nations by grounding our discussion on the institutional trust theory. As trusting belief in security measures is a culturally embedded characteristic, we also examine the moderating role of national culture through Hofstede’s cultural dimensions. We use multilevel models to analyze publicly available archives of repeated cross-sectional data covering 76 countries to test the proposed relationships. Our findings indicate that NCSC has a positive influence on DPU. Further, our results highlight that the relationship between NCSC and DPU in a country is contingent on cultural dimensions. Overall, the evidence suggests that a competent cybersecurity environment compatible with cultural values can influence the speedy diffusion of digital payments in a country. Implications of our findings for research and practice are also discussed. Collapse

Semantic filters: trust theory

Topics: IT security internet technology national culture electronic finance information system use

Methods: longitudinal research hierarchical linear modeling cross sectional research archival research time series analysis

Theories: trust theory cultural dimensions theory contingency theory theory of economic growth
The Application of the Principles of Responsible AI on Social Media Marketing for Digital Health
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2023 | Information Systems Frontiers | Citations: 1
Authors: Liu, Rui; Gupta, Suraksha; Patel, Parth

Abstract: Social media enables medical professionals and authorities to share, disseminate ... Expand

Abstract: Social media enables medical professionals and authorities to share, disseminate, monitor, and manage health-related information digitally through online communities such as Twitter and Facebook. Simultaneously, artificial intelligence (AI) powered social media offers digital capabilities for organizations to select, screen, detect and predict problems with possible solutions through digital health data. Both the patients and healthcare professionals have benefited from such improvements. However, arising ethical concerns related to the use of AI raised by stakeholders need scrutiny which could help organizations obtain trust, minimize privacy invasion, and eventually facilitate the responsible success of AI-enabled social media operations. This paper examines the impact of responsible AI on businesses using insights from analysis of 25 in-depth interviews of health care professionals. The exploratory analysis conducted revealed that abiding by the responsible AI principles can allow healthcare businesses to better take advantage of the improved effectiveness of their social media marketing initiatives with their users. The analysis is further used to offer research propositions and conclusions, and the contributions and limitations of the study have been discussed. Collapse

Semantic filters: trust theory

Topics: social media responsible artificial intelligence social media marketing artificial intelligence health information system

Methods: qualitative interview personal interview conceptual modelling literature study

Theories: trust theory technology acceptance model theory of information
How does e-government use affect citizens’ trust in government? Empirical evidence from China
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2023 | Information & Management | Citations: 0
Authors: Li, Yan; Shang, Huping

Abstract: Although much literature has discussed the potential of e-government for improvi ... Expand

Abstract: Although much literature has discussed the potential of e-government for improving citizens’ trust in govern ment, the question of how e-government use affects citizens’ trust in government remains underexplored. To investigate the driving mechanism of the effects of different types of e-government use on trust in government, this research develops a conceptual framework based on the institutional-based trust theory, the process-based trust theory, and the expectancy-disconfirmation model. Based on data collected from 1668 citizen users living in five cities in China, the study verifies the proposed mediated moderation model and then tests the robustness of the results using process analysis. The results reveal that citizens’ use of e-government revitalizes their trust in government indirectly, via influence on citizens’ evaluation of government integrity, performance, and responsiveness from the experience of using e-government, while the mediating effects of citizens’ evaluation of government on relationship between e-government use and public trust are diminished by citizens’ expectations for their government. The analysis also offers strong support for the conclusion that the effects of different types of e-government use on public trust are differentially distributed among multiple levels of governments in China. Collapse

Semantic filters: trust theory

Topics: government system website information storage and retrieval system internet technology social capital

Methods: survey descriptive statistic data transformation moderation analysis factor analysis

Theories: trust theory
Emotion-regulatory chatbots for enhancing consumer servicing: An interpersonal emotion management approach
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2023 | Information & Management | Citations: 0
Authors: Luo, Bei; Lau, Raymond Y.K.; Li, Chunping

Abstract: To reduce service staff’s burden in customer servicing and improve the performan ... Expand

Abstract: To reduce service staff’s burden in customer servicing and improve the performance of automatic service re covery, we propose an emotion-regulatory chatbot for service recovery applications drawing on interpersonal emotion management (IEM) theory. In addition, we develop a model to examine the underlying mechanisms through which perceived IEM strategies influence consumers’ emotions and behavioral intentions. Our experi mental results verify the effectiveness of IEM strategies. We find that appraisals and consumers’ post-recovery emotions sequentially mediate the relationship between perceived emotion regulatory strategies and positive word-of-mouth (PWOM). Collapse

Semantic filters: trust theory

Topics: behavioral intention robotic electronic commerce customer service word of mouth

Methods: chatbot experiment experimental group structural equation modeling parametric test

Theories: control theory trust theory affect control theory
Trust and Vulnerability in the Cybersecurity Context

2023 | HICSS | Citations: 0
Authors: Rosalind Searle, Rosalind; Renaud, Karen

Abstract: Cybersecurity attacks offer a pertinent context within which to examine the curr ... Expand

Abstract: Cybersecurity attacks offer a pertinent context within which to examine the currently under-explored dynamics of trust and distrust and their consequences for organisations and their employees. Such attacks wreak havoc in organisations, costly both in financial and reputational terms. We outline the dynamic, multi-level processes following initial attack making salient vulnerability to an unseen exploiter, and then making germane trust and trustworthiness in a number of relationships in an employing organisation. Drawing from events theory, we devise a multi-level conceptual trust model of the interrelations between the emotional, cognitive and social processes an attack can produce, to distinguish two paths revealing their markedly different durations, magnitudes, and level of relational and threat consequences. We explore these as dynamic experiences of trust to elucidate vulnerability and its experience and management for the targeted individual, with key organisational actors. This leads to the formation of an anchoring event that creates enduring changes to multiple relationships within the organisation with consequences for individual, team and organisational resilience and risk. Collapse

Semantic filters: trust theory

Topics: IT security threat IT security social identity

Methods: conceptual modelling

Theories: general systems theory trust theory
What most influences consumers’ intention to use? Different motivation and trust stories for uber, airbnb, and taskrabbit
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2023 | European Journal of Information Systems | Citations: 0
Authors: Tripp, John; McKnight, D. Harrison; Lankton, Nancy

Abstract: While many factors drive the sharing economy’s growth, trust and motivations pla ... Expand

Abstract: While many factors drive the sharing economy’s growth, trust and motivations play critical roles. However, which factors are more important to young consumers? This research compares a trust model with a motivation model for predicting intention to use Airbnb, Uber, and TaskRabbit, comparing within each platform, not between them. The study uses a survey sample of Midwest United States business undergraduates (nearly all Generation Z). Each sample varies in experience levels: relatively high experience Uber respondents, very low experience TaskRabbit respondents, and medium experience for Airbnb. Results show that within Uber, the trust model predicts significantly better than the motivation model. Within Airbnb and TaskRabbit, the motivation model predicts only slightly better. By combining the trust and motivation variables into one model by platform, we find differing degrees of complementarity between the trust and motivation variables, with those for Airbnb being the most complementary in terms of predictive power and those for Uber being the least complementary. Within Airbnb and TaskRabbit, combining the trust and motivation models shows the two most significant variables were enjoyment and trusting intention. Within Uber, trusting intention and trusting beliefs in the provider were the most significant. We provide implications and directions for future research. Collapse

Semantic filters: trust theory

Topics: usage intention Uber TaskRabbit Airbnb mobile application

Methods: survey statistical power analysis partial least squares regression ANOVA partial least squares path modeling

Theories: motivation theory self determination theory trust theory
Heterogeneous Demand Effects of Recommendation Strategies in a Mobile Application: Evidence from Econometric Models and Machine-Learning Instruments

2022 | Management Information Systems Quarterly | Citations: 0
Authors: Adamopoulos, Panagiotis; Ghose, Anindya; Tuzhilin, Alexander

Abstract: In this paper, we examine the effectiveness of various recommendation strategies ... Expand

Abstract: In this paper, we examine the effectiveness of various recommendation strategies in the mobile channel and their impact on consumers’ utility and demand levels for individual products. We find significant differences in effectiveness among various recommendation strategies. Interestingly, recommendation strategies that directly embed social proofs for the recommended alternatives outperform other recommendations. In addition, recommendation strategies combining social proofs with higher levels of induced awareness due to the prescribed temporal diversity have an even stronger effect on the mobile channel. We also examine the heterogeneity of the demand effect across items, users, and contextual settings, further verifying empirically the aforementioned information and persuasion mechanisms and generating rich insights. We also facilitate the estimation of causal effects in the presence of endogeneity using machine-learning methods. Specifically, we develop novel econometric instruments that capture product differentiation (isolation) based on deeplearning models of user-generated reviews. Our empirical findings extend the current knowledge regarding the heterogeneous impact of recommender systems, reconcile contradictory prior results in the related literature, and have significant business implications. Collapse

Semantic filters: trust theory

Topics: mobile system broadband cellular network mobile channel online review mobile application

Methods: econometric modeling instrumental variables estimation machine learning robustness check computational algorithm

Theories: symbolic interaction theory social network theory cognitive theory consumer search theory trust theory

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Found 93 articles

The Power of Trust: Designing Trustworthy Machine Learning Systems in Healthcare

Explanation matters: An experimental study on explainable AI

Would You Accept Doctor ChatGPT: An Empirical Study Based on the UTAUT Model

Examining the Relationship between National Cybersecurity Commitment, Culture, and Digital Payment Usage: An Institutional Trust Theory Perspective

The Application of the Principles of Responsible AI on Social Media Marketing for Digital Health

How does e-government use affect citizens’ trust in government? Empirical evidence from China

Emotion-regulatory chatbots for enhancing consumer servicing: An interpersonal emotion management approach

Trust and Vulnerability in the Cybersecurity Context

What most influences consumers’ intention to use? Different motivation and trust stories for uber, airbnb, and taskrabbit

Heterogeneous Demand Effects of Recommendation Strategies in a Mobile Application: Evidence from Econometric Models and Machine-Learning Instruments