Trust In Technology Research Articles

A qualitative systematic review of trust in technology

Although many information systems (IS) scholars have researched the antecedents and consequences of trust in technology, the sheer amount of work published, authors’ use of inconsistent construct terms, and variations in conceptualization and measurement make it difficult to compare and aggregate findings across studies. To provide scholars with an overview of the literature, we performed a systematic qualitative review of the IS literature. The goal of our review was to identify the antecedents and consequences of trust in technology that have received frequent empirical support, as well as those for which the evidence is not yet conclusive. To that end, we curated a glossary of unique construct terms and summarized the results of 241 relationships tested in 62 empirical research articles (both quantitative and qualitative) from the AIS “Basket of Eight” journals. From these, we identified 62 unique relationships: 22 are well-supported, 6 are supported by suggestive evidence, and 34 have received little support. These relationships summarize what is well-known and less well-known within the literature. By taking stock, we prepare a path for future research on the phenomenon of trust in the context of emerging technologies (e.g., artificial intelligence and blockchain). We conclude the paper with opportunities and directions for future research.

From Perception to Prediction: Leveraging Explainable AI in Self-Driving Cars for Enhanced Passenger Trust

Self-driving cars hold immense potential for revolutionizing transportation. However, public acceptance hinges on trust in the car's ability to navigate safely and make critical decisions. This trust deficit stems from the "black box" nature of traditional machine learning models used in self-driving cars. Passengers are left in the dark about the car's perception of the environment and the reasoning behind its actions. This research proposes leveraging Explainable Artificial Intelligence (XAI) techniques to enhance passenger trust in self-driving cars. By incorporating explainability into the perception and prediction modules of the car's decision- making system, we aim to provide passengers with real-time insights into how the car perceives its surroundings and translates those perceptions into driving decisions. This paper explores various XAI methods suitable for self-driving car applications. We discuss the integration of these techniques into the perception and prediction pipelines, enabling the car to explain its reasoning behind lane changes, obstacle avoidance maneuvers, and other critical actions. We evaluate the effectiveness of the proposed approach through user studies, assessing how explainability can improve passenger trust and comfort in self-driving vehicles. The ultimate goal of this research is to foster greater transparency and trust in self-driving car technology, paving the way for wider public adoption and a future of safe and reliable autonomous transportation.

English A cross cultural investigation on Smart Home Technology:

Some individuals may feel threatened by the digital age and suffer from technophobia, whereas technophilic individuals embrace new technologies. Relying on a study of 243 participants aged 18-65, this study explored the roles of technophobia, technophilia, trust in technology, and digital competence, and how they interact within the SHT paradigm. Technophobia positively correlated with age and negatively with ownership and most digital skills. Males outscored females in overall competence, especially in creative digital skills and technophilia. Spanish participants scored higher than English participants on operational digital skills, creative digital skills, and technophilia-dependency. A hierarchical regression model with technophobia as the dependent variable showed that operational, navigational, and mobile digital skills contributed negatively to technophobia. Technophilia-enthusiasm negatively predicted technophobia, whereas technophilia-reputation did so positively. Culture and gender were not significant predictors of technophobia in this context. There is a need to provide digital skills for all sections of society to increase self-efficacy and counterbalance any feelings of anxiety and technophobia that may manifest in individuals who feel left behind in the modern digital world. The onus is for SHT manufacturers to design products that require basic digital skills and are custom-made for different populations cohorts within society.

“It would work for me too”: How Online Communities Shape Software Developers’ Trust in AI-Powered Code Generation Tools

While revolutionary AI-powered code generation tools have been rising rapidly, we know little about how and how to help software developers form appropriate trust in those AI tools. Through a two-phase formative study, we investigate how online communities shape developers’ trust in AI tools and how we can leverage community features to facilitate appropriate user trust. Through interviewing 17 developers, we find that developers collectively make sense of AI tools using the experiences shared by community members and leverage community signals to evaluate AI suggestions. We then surface design opportunities and conduct 11 design probe sessions to explore the design space of using community features to support user trust in AI code generation systems. We synthesize our findings and extend an existing model of user trust in AI technologies with sociotechnical factors. We map out the design considerations for integrating user community into the AI code generation experience.

Trusting Intelligent Automation in Expert Work: Accounting Practitioners’ Experiences and Perceptions

AbstractAI-based applications are increasingly used in knowledge-intensive expert work, which has led to a discussion regarding their trustworthiness, i.e., to which degree these applications are ethical and reliable. While trust in technology is an important aspect of using and accepting novel information systems, little is known about domain experts’ trust in machine learning systems in their work. To provide a real-life, empirical perspective on the topic, this study reports findings from an interview study of accounting practitioners’ (N = 9) trust in intelligent automation in their work. The findings underline the holistic nature of trust, suggesting that contextual and social aspects, such as participatory design practices, shape domain experts’ trust in intelligent automation. For instance, the participants emphasize their contribution to product development and open communication with the system developers. In addition, the findings shed light on the characteristics of domain experts as technology users, such as the necessity of situation-specific expert knowledge when evaluating the systems’ reliability. Thus, our findings suggest that trust in intelligent automation manifests at different levels, both in human-AI interaction and interpersonal communication and collaboration. This research contributes to the existing literature on trust in technology, especially AI-powered applications, by providing insights into trust in intelligent automation in expert work.

Mixed Method Approach towards Continuous Use of Chatbots in Healthcare Apps: An Integration of Humanizing Experience Theory

The unprecedented adoption of apps has transformed consumer interactions. Chatbots are revolutionizing healthcare apps, but more than their integration is needed to guarantee a seamless user experience. This study advances the technology acceptance model by including anthropomorphic elements that build trust in technology for continuous chatbot use in mobile health apps. It leverages the humanizing experience theory to comprehensively examine the boundary conditions that explain the effects of humanizing digital experiences, such as self-representation and intimacy, on utilitarian and hedonic conversational cues. This research contributes to interactive marketing and mobile commerce by offering insights into user behavior for continued usage of health apps.

Exploring STEAM teachers’ trust in AI-based educational technologies: a structural equation modelling approach

In the rapidly evolving landscape of education, Artificial Intelligence (AI) has emerged as a transformative tool with the potential to revolutionize teaching and learning processes. However, the successful integration of AI in education depends on the trust and acceptance of teachers. This study addresses a significant gap in research by investigating the trust dynamics of 677 in-service Science, Technology, Engineering, Arts, and Mathematics (STEAM) teachers in Nigeria towards AI-based educational technologies. Employing structural equation modelling for data analysis, our findings reveal that anxiety, preferred methods to increase trust, and perceived benefits significantly influence teachers' trust in AI-based edtech. Notably, the lack of human characteristics in AI does not impact trust among STEAM teachers. Additionally, our study reports a significant gender moderation effect on STEAM teachers' trust in AI. These insights are valuable for educational policymakers and stakeholders aiming to create an inclusive, AI-enriched instructional environment. The results underscore the importance of continuous professional development programs for STEAM teachers, emphasizing hands-on experiences to build and sustain confidence in integrating AI tools effectively, thus fostering trust in the transformative potentials of AI in STEAM education.

Designing human-centered learning analytics and artificial intelligence in education solutions: a systematic literature review

ABSTRACT The recent advances in educational technology enabled the development of solutions that collect and analyse data from learning scenarios to inform the decision-making processes. Research fields like Learning Analytics (LA) and Artificial Intelligence (AI) aim at supporting teaching and learning by using such solutions. However, their adoption in authentic settings is still limited, among other reasons, derived from ignoring the stakeholders' needs, a lack of pedagogical contextualisation, and a low trust in new technologies. Thus, the research fields of Human-Centered LA (HCLA) and Human-Centered AI (HCAI) recently emerged, aiming to understand the active involvement of stakeholders in the creation of such proposals. This paper presents a systematic literature review of 47 empirical research studies on the topic. The results show that more than two-thirds of the papers involve stakeholders in the design of the solutions, while fewer papers involved them during the ideation and prototyping, and the majority do not report any evaluation. Interestingly, while multiple techniques were used to collect data (mainly interviews, focus groups and workshops), few papers explicitly mentioned the adoption of existing HC design guidelines. Further evidence is needed to show the real impact of HCLA/HCAI approaches (e.g., in terms of user satisfaction and adoption).

Adaptable robots, ethics, and trust: a qualitative and philosophical exploration of the individual experience of trustworthy AI

AbstractMuch has been written about the need for trustworthy artificial intelligence (AI), but the underlying meaning of trust and trustworthiness can vary or be used in confusing ways. It is not always clear whether individuals are speaking of a technology’s trustworthiness, a developer’s trustworthiness, or simply of gaining the trust of users by any means. In sociotechnical circles, trustworthiness is often used as a proxy for ‘the good’, illustrating the moral heights to which technologies and developers ought to aspire, at times with a multitude of diverse requirements; or at other times, no specification at all. In philosophical circles, there is doubt that the concept of trust should be applied at all to technologies rather than their human creators. Nevertheless, people continue to intuitively reason about trust in technologies in their everyday language. This qualitative study employed an empirical ethics methodology to address how developers and users define and construct requirements for trust throughout development and use, through a series of interviews. We found that different accounts of trust (rational, affective, credentialist, norms based, relational) served as the basis for individual granting of trust in technologies and operators. Ultimately, the most significant requirement for user trust and assessment of trustworthiness was the accountability of AI developers for the outputs of AI systems, hinging on the identification of accountable moral agents and perceived value alignment between the user and developer’s interests.

Unlocking the potential of blockchain technology in China’s supply chain: a survey of industry professionals

PurposeThis study aims to identify the essential elements impacting the adoption of blockchain technology (BCT) in supply chain management (SCM) by integrating the technology acceptance and information system success (ISS) models.Design/methodology/approachQuestionnaire-based data was collected from 236 supply chain professionals from Beijing. The proposed research framework was evaluated using structural equation modeling (SEM) by using SPSS 23 and AMOS 24 software.FindingsThe empirical findings specify the positive influence of total quality on perceived usefulness and compatibility. Further, perceived ease of use positively influences perceived usefulness, compatibility and behavioral intention. Moreover, perceived usefulness positively impacts compatibility and behavioral intention. Compatibility positively influences behavioral intention. Finally, technology trust was found to be a significant moderator between perceived usefulness and behavioral intention and between perceived ease of use and adoption intention to use BCT in SCM.Originality/valueThis study empirically develops the second-order construct of total quality, representing the ISS model. Furthermore, this study established how the ISS and technology acceptance models influence behavioral intention through compatibility. Finally, this study confirmed the moderating role of technology trust among perceived ease of use, perceived usefulness and behavioral intention.

AiWATERS: an artificial intelligence framework for the water sector

The presence of Artificial Intelligence (AI) and Machine Learning (ML) applications has led to its widespread adoption across diverse domains. AI is making its way into industry, beyond research and academia. Concurrently, the water sector is undergoing a digital transformation. Water utilities in the United States are at different stages in their journey of digital transformation, and the decision makers in water sector, who are non-expert stakeholders in AI applications, need to better understand this technology to make informed decisions. While AI has numerous benefits to offer, there are also many challenges related to data, model development, knowledge integration and ethical concerns that should be considered before implementing it for real world applications. Civil engineering is a licensed profession where critical decision making is involved. Therefore, trust in any decision support technology is critical for its acceptance in real-world applications. Therefore, this research proposes a framework called aiWATERS (Artificial Intelligence for the Water Sector) which can serve as a guide for the water utilities to successfully implement AI in their system. Based on this framework, we conduct pilot interviews and surveys with various small, medium, and large water utilities in the United States (US) to capture their current state of AI implementation and identify the challenges faced by them. The research findings reveal that most of the water utilities in the United States are at an early stage of implementing AI as they face concerns regarding the black box nature, trustworthiness, and sustainability of AI technology in their system. The aiWATERS framework is intended to help the utilities navigate through these issues in their journey of digital transformation.

Integrating connected autonomous shuttle buses as an alternative for public transport – A simulation-based study

The concept of connected autonomous shuttles is gaining recognition for its potential to revolutionize traditional public transport by offering a safer and more consistent travel experience. Despite technological advancements facilitating their integration into current traffic systems, research in this area is still exploratory, with public acceptance and trust in autonomous technology posing significant challenges. This paper contributes to the field by validating the benefits of deploying connected autonomous shuttles and examining their impact on ridership via an immersive experience. We introduce a VR-enabled co-simulation strategy to analyze the effects of replacing a traditional bus service line with connected shuttles in Melbourne, Australia. Our case study results reveal that while autonomous shuttles can reduce travel time because of optimized vehicle motion, their traffic efficiency is affected by fleet size, with the optimal fleet size identified as four in the study area. Furthermore, we observed a mismatch between participants’ stated intentions and actual boarding behavior, indicating that the attractive appearance of this novel mobility mode may not necessarily enhance ridership. Our work offers an alternative approach to simulation studies in futuristic public transportation and complement existing literature in the field.

From video to vital signs: using personal device cameras to measure pulse rate and predict blood pressure using explainable AI

In this study, we address the need for low-cost, rapid, and reliable methods for blood pressure measurement to facilitate timely diagnosis and treatment of hypertension. We aim to enhance understanding and trust in the novel remote photoplethysmography technology, specifically as deployed by Lifelight®\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^\\circledR$$\\end{document}, for vital sign monitoring. Lifelight®\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^\\circledR$$\\end{document} utilizes remote photoplethysmography to extract physiologically meaningful features from video recordings on personal devices. This study describes the accurate calculation of pulse rates and the extraction of morphological and time series features from the remote photoplethysmography signal for blood pressure prediction. Unlike common approaches, distinct model types are employed for predicting systolic and diastolic blood pressure. Evaluation on an independent clinical laboratory’s dataset demonstrates the efficacy of the proposed method. The pulse rate accuracy, measured as root mean square error, is 1.9 beats per minute. The model for systolic blood pressure prediction shows a mean error of 1.2 mmHg and a standard deviation of 15.9 mmHg. The model for diastolic prediction shows a mean error of -\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$-$$\\end{document}3.2 mmHg and a standard deviation of 13.1 mmHg. This research establishes the validity of remote photoplethysmography technology by showcasing accurate pulse rate calculations and effective feature extraction for blood pressure prediction. The results indicate the potential for widespread adoption of remote photoplethysmography in measuring blood pressure and other vital signs.

Trustworthy Artificial Intelligence for Social Governance

As AI technology continues to evolve, it plays an increasingly significant role in everyday life and social governance. However, the frequent occurrence of issues such as algorithmic bias, privacy breaches, and data leaks has led to a crisis of trust in AI among the public, presenting numerous challenges to social governance. Establishing technical trust in AI, reducing uncertainties in AI development, and enhancing its effectiveness in social governance have become a consensus among policymakers and researchers. By comparing different types of AI, the paper proposes and conceptualizes the idea of trustworthy AI, then discusses its characteristics and its value and impact pathways in social governance. The analysis addresses how mismatches in technological trust can affect social stability and the advancement of AI strategies. The paper highlights the potential of trustworthy AI to improve the efficiency of social governance and solve complex social problems.

Cognitive Reapportionment and the Art of Letting Go: A Theoretical Framework for the Allocation of Decision Rights

ABSTRACT We have entered an era where, in addition to us humans, systems can also think. It is imperative to decide how decision rights and authorities are allocated and distributed across interconnected co-cognitors: things that think. To address this question vital to the redesign of organizations, we reconceptualize cognitive reapportionment as the dynamic reallocation of decision rights and authorities across human and system co-cognitors capable of independent decision making. We articulate the Helix Model of Decision Journeys, wherein decision journeys, comprised of decision elements allocable to human or system co-cognitors, interweave and integrate business processes. The dynamic reapportionment of cognitive responsibilities across co-cognitors is dependent upon the type of scientific reasoning—deduction, induction, or abduction—at each decision element. We further propose two intertwined mechanisms for the art of letting go that facilitate the adoption of the Helix model. First, the nexus of omnipresent system co-cognitors collectively needs to evoke trust. Only when Technology Trust Thresholds are exceeded can decision making be reorganized and control relinquished to system co-cognitors. Second, the world of omnipresent cognitors will compel us to redesign, reprogram, and remix our workflow and business processes. Only through a purposeful effort to Redesign in the Remix Regime can the deduction-induction-abduction nature of decision tasks serve as the basis for their reapportionment.

Blockchain Technology Adoption Through the UTAUT Model

This paper studies the adoption of blockchain technology under the scope of the Unified Theory of Acceptance and Use of Technology (UTAUT). Previous results on Management Information Systems (MIS) research are divergent about the significance of UTAUT variables in explaining the adoption behaviour of blockchain technology. The paper focuses on this specific concern and tries to contribute to existing studies by testing the model in a specific context (Tunisia) and by considering the individual variable “trust in technology” as a mediating one. For this aim, a structural equation approach is adopted among 95 Tunisian professional respondents operating in technology-based sectors. The findings stipulate the importance of facilitating conditions and performance expectancy as drivers of the adoption intention. Additionally, the study reveals that trust in technology is significant in its mediating role in influencing the intention of adoption with the facilitating conditions and the social influence constructs. Moreover, the paper uncovers a direct relationship with the same variable. These findings provide valuable insights for both researchers and practitioners in understanding the factors that influence blockchain technology adoption in the Tunisian context and stress the indirect role of trust in technology with which decision-makers should be concerned.

Quantum Technologies: a Hermeneutic Technology Assessment Approach

This paper develops a hermeneutic technology assessment of quantum technologies. It offers a “vision assessment” of quantum technologies that can eventually lead to socio-ethical analysis. Section 2 describes this methodological approach and in particular the concept of the hermeneutic circle applied to technology. Section 3 gives a generic overview of quantum technologies and their impacts. Sections 4 and 5 apply the hermeneutic technology assessment approach to the study of quantum technologies. Section 5 proposes distinguishing three levels in the analysis of the creation and communication of social meanings to quantum technologies: (a) fictions, (b) popularization, and (c) scientific journalism. Section 6 analyzes the results and defines some lines of action to increase social acceptance and trust in quantum technologies. The aim of this paper is to contribute to the debate on quantum technologies by enhancing the reflection on them and their potential, as well as illustrating the complexity of technological innovation and the need to shape it.

Persona-PhysioSync AV: Personalized Interaction through Personality and Physiology Monitoring in Autonomous Vehicles.

The emergence of autonomous vehicles (AVs) marks a transformative leap in transportation technology. Central to the success of AVs is ensuring user safety, but this endeavor is accompanied by the challenge of establishing trust and acceptance of this novel technology. The traditional "one size fits all" approach to AVs may limit their broader societal, economic, and cultural impact. Here, we introduce the Persona-PhysioSync AV (PPS-AV). It adopts a comprehensive approach by combining personality traits with physiological and emotional indicators to personalize the AV experience to enhance trust and comfort. A significant aspect of the PPS-AV framework is its real-time monitoring of passenger engagement and comfort levels within AVs. It considers a passenger's personality traits and their interaction with physiological and emotional responses. The framework can alert passengers when their engagement drops to critical levels or when they exhibit low situational awareness, ensuring they regain attentiveness promptly, especially during Take-Over Request (TOR) events. This approach fosters a heightened sense of Human-Vehicle Interaction (HVI), thereby building trust in AV technology. While the PPS-AV framework currently provides a foundational level of state diagnosis, future developments are expected to include interaction protocols that utilize interfaces like haptic alerts, visual cues, and auditory signals. In summary, the PPS-AV framework is a pivotal tool for the future of autonomous transportation. By prioritizing safety, comfort, and trust, it aims to make AVs not just a mode of transport but a personalized and trusted experience for passengers, accelerating the adoption and societal integration of autonomous vehicles.

Effects of trust and customer perceived value on the acceptance of urban air mobility as public transportation

Urban air mobility (UAM) is expected to become an innovative mode of public transportation. Although multiple factors may facilitate the adoption of UAM, its implementation may be difficult owing to the lack of customer acceptance and usage intentions. This study proposes a research model to explore the effects of trust and customer perceived value on the acceptance of UAM as public transportation. In this study, empirical data from 573 respondents were collected via an online survey. Additionally, thirteen hypotheses on trust and customer-perceived value were presented, and a relationship model was conceptualized and analyzed using structural equation modeling based on the partial least squares method. The results indicated that all thirteen hypotheses proposed in this study were statistically supported and trust in technology and customer perceived value was found to positively influence the intention to use UAM as public transport. Notably, functional, emotional, and social values were found to considerably affect trust in technology and customer-perceived value; specifically, functional value had the greatest impact in this context. The results of this study validate the importance of trust and customer-perceived value, which should be prioritized in the adoption of UAM as a public transportation service.

Patients' experiences and perspectives regarding the use of digital technology to support exercise-based cardiac rehabilitation: a qualitative interview study.

Despite the well-known benefits of exercise-based cardiac rehabilitation for the secondary prevention of cardiovascular disease, participation in cardiac rehabilitation programmes and adherence to secondary prevention recommendations remain limited. Digital technologies have the potential to address low participation and adherence but attempts at implementing digital health interventions in real-life clinical practice frequently encounter various barriers. Studies about patients' experiences and perspectives regarding the use of digital technology can assist developers, researchers and clinicians in addressing or pre-empting patient-related barriers. This study was therefore conducted to investigate the experiences and perspectives of cardiac rehabilitation patients in Austria with regard to using digital technology for physical activity and exercise. Twenty-five current and former cardiac rehabilitation patients (18 men and 7 women, age range 39 to 83) with various cardiac conditions were recruited from a clinical site in Salzburg, Austria. Semi-structured qualitative interviews were audio-recorded and transcribed verbatim. The analysis followed a descriptive phenomenological approach, applying the framework analysis method. The sample was diverse, including interviewees who readily used digital devices to support their physical activity, exercise and health monitoring, and interviewees who did not. Simplicity, convenience and accessibility were highlighted as important facilitators for the use of digital technology, while annoyance with digital devices, concerns about becoming dependent on them, or simply a preference to not use digital technology were commonly stated reasons for non-use. Interviewees' views on data protection, data sharing and artificial intelligence revealed wide variations in individuals' prior knowledge and experience about these topics, and a need for greater accessibility and transparency of data protection regulation and data sharing arrangements. These findings support the importance that is attributed to user-centred design methodologies in the conceptualisation and design of digital health interventions, and the imperative to develop solutions that are simple, accessible and that can be personalised according to the preferences and capabilities of the individual patient. Regarding data protection, data sharing and artificial intelligence, the findings indicate opportunity for information and education, as well as the need to offer patients transparency and accountability in order to build trust in digital technology and digital health interventions.