Trust Model Research Articles

Trusting as an Emergent: Implications for Design

The quest for an adequate model of trust in AI systems has led to rosters of influencing factors and proposals for how trust can be “designed-in.” An alternative is to consider trusting as an emergent. While progressive maturation of user trust may be desired, it is the exception. Trust can arise rapidly, but it can diminish in a heartbeat. It can be orthogonal to reliance. Trusting and relying are context-contingent. Work-centered design principles can make trustability achievable, but it can only be realized through user experience. And principles for “designing-in” trust do not span the creativity gap.

The pivotal role of positive emotions in maintaining nurses’ trust and preventing pandemic fatigue

Abstract Background Nursing is a challenging profession that demands quick adaptation to changing conditions. The COVID-19 pandemic has placed unprecedented pressure on nursing teams, leading to high-stress levels and uncertainty. This study examines how nurses’ emotional response at the start of the pandemic predicts their trust in the healthcare system and pandemic fatigue after three morbidity waves. Methods This study included collecting self-reported data among Israeli nurses in three time periods: 346 nurses during March-May 2020 (T1), 260 nurses during June-July 2020 (T2), and 91 nurses during February 2022 (T3). Change in the study variables was assessed using repeated-measure ANOVA. Two multiple linear regression models were calculated for trust and pandemic fatigue at T3, with the emotions at T1 and professional seniority as predictors. Results A significant increase in negative emotions was observed between T1 and T2 (M = 2.65, SD = 1.12 and M = 3.20, SD = 1.29 respectively). Furthermore, there was a significant decrease in trust levels between T1 and T3 (M = 4.68, SD = 1.14 and M = 4.07, SD = 1.08 respectively). The regression model for trust was significant, with 20% of the variance explained in it, revealing that higher positive emotions at T1 predicted higher trust at T3. Similarly, the regression model for pandemic fatigue was significant (R2=22%), showing that lower professional seniority and lower positive emotions at T1 were predictors of higher pandemic fatigue at T3. Conclusions During a crisis, nurses may experience intense negative emotions like fear and anxiety. However, positive emotions, such as pride and a sense of mission, are crucial in maintaining their trust and preventing the onset of pandemic fatigue. Healthcare organizations should promote emotional support and provide emotional resources so that nurses can continue to deliver high-quality care even during challenging times. Key messages • During times of crisis, it’s crucial to foster positivity and encourage a sense of pride and accomplishment in nursing staff. • Healthcare organizations must provide emotional support to nursing teams in routine and crisis situations to prevent burnout and maintain trust and long-term functioning.

Trusting robots: a relational trust definition based on human intentionality

As robots are increasingly found in various aspects of human life, understanding how humans relate to and interact with robots is becoming more relevant. One important factor affecting the quality of human-robot interactions is trust, and in recent years there has been an increase in scientific interest in the nature of trust between humans and robots. However, it can be observed that a clear distinction between trust and its related constructs is not always made when examining trust in robots. To overcome these limitations, we propose a definition of trust in robots that clearly separates trust from its related constructs. To this end, we extend an established trust model for interpersonal trust, which we adapt to describe trust in HRI. The new definition describes trust as a particular way in which human trustor relates to a robot. By characterizing trust as an asymmetric relation with certain properties, the proposed definition adopts a perspective that focuses on the human trustor in describing trust. Furthermore, we argue that the proposed definition of trust is suitable for describing trust in all types of robots and for any form of human-robot interaction.

Blockchain technology and consumers’ organic food consumption: a moderated mediation model of blockchain-based trust and perceived blockchain-related information transparency

Purpose While the application of blockchain technology in the organic food supply chain has been increasingly recognized, the extant knowledge of how blockchain-driven traceability influences consumer perceptions and purchase intentions remains underexplored. Grounded in the stimulus-organism-response theory, this study aims to construct a moderated mediation model to examine blockchain-enabled traceability’s direct and indirect impacts on organic food purchase intention through perceived blockchain-related information transparency, considering the moderating role of blockchain-based trust. Design/methodology/approach A purposive sample of 5,326 Vietnamese consumers was surveyed using the PROCESS macro to test the proposed hypotheses. Findings The findings indicate that blockchain-enabled traceability significantly enhances perceived blockchain-related information transparency, which positively influences organic food purchase intention. Furthermore, blockchain-based trust was found to positively moderate both the direct effect of transparency on purchase intention and the indirect impact of traceability on purchase intention through transparency. Practical implications Practical and managerial insights for stakeholders in the organic food sector are also discussed. Originality/value These results contribute to the literature by extending the stimulus-organism-response model to the context of blockchain technology in supply chains and highlighting the critical role of trust in moderating the effectiveness of technological innovations.

Application of zero trust model in preventing medical errors

Medical errors can occur in many areas of healthcare, including hospitals, clinics, and surgery centers. They can result in negative consequences for patients and their loved ones. Over the years, different methods have been used to reduce medical errors. Zero Trust is an information security model that denies access to applications and data by default. Other industries have successfully used Zero Trust Model (ZTM), and it has been shown to improve outcomes. This editorial analyzes how the ZTM can be introduced to prevent medical errors in healthcare settings. ZTM application in healthcare could potentially revolutionize patient safety by tightly controlling and monitoring access to sensitive patient data and critical systems. By enhancing security measures, the ZTM could address the paramount concerns of patient data privacy and safety in healthcare. The zero-trust approach offers a potential solution by identifying consistent causes of errors and providing viable solutions to prevent their recurrence. In the era of worsening ransomware attacks on healthcare systems, the ZTM could also have enormous implications in other cybersecurity aspects. With this manuscript, the authors advocate for the broader application of ZTM across other facets of healthcare cybersecurity.

Agent-Based Trust and Reputation Model in Smart IoT Environments

The Internet of Things (IoT) enables smart devices to connect, share and exchange data with each other through the internet. Since an IoT environment is open and dynamic, IoT participants may need to collaborate with unknown entities with no proven track record. To ensure successful collaboration among these entities, it is important to establish a mechanism that ensures all entities operate in a trustworthy manner. We present a trust and reputation model that can be used to select the best service provider in an IoT environment. Our proposed model, IoT-CADM (Comprehensive Agent-based Decision-making Model for IoT) is an agent-based decentralised trust and reputation model that can be used to select the best service provider for a particular service based on multi-context quality of service. IoT-CADM is developed using a smart multi-agent IoT environment where information about entities is collected and evaluated using a trust and reputation algorithm. We evaluated the performance of the proposed model against some other well-known models in a simulated smart factory supply chain system. Our experimental results showed that the proposed IoT-CADM achieved the best performance.

Elements of trust and trust-building in gerontological social work. The social worker’s perspective

ABSTRACT Although trust between social workers and their clients emerges as a critical factor in social work, it has often been taken for granted and rarely placed under detailed scrutiny in social work research. This study explores the elements on which clients’ trust is built, and social workers’ trust-building practices in gerontological social work (GSW) in Finland. We analysed seven focus group discussions of gerontological social workers abductively, using Russell Hardin’s model of trust as an analytical tool. This study identifies six elements of clients’ trust: (1) trust in the individual social worker and in the institution, (2) trust as mutually recognised goals, (3) trust in the professional’s competence, (4) trust as a risk, (5) trust as dynamic, (6) trust as cognitive. A social worker builds trust by listening carefully to clients, considering their interests when choosing their own goals and actions, clearly articulating the goals and premises of social work, and demonstrating that the social worker can contribute meaningfully to the client’s life. In GSW, clients’ distrust and refusal of services are common. Consequently, trust-building often requires plenty of time, creativity and perseverance from the social worker. This is challenged by the fact that there is not always enough time to build trust, trust’s essential nature is not often recognised in the service system, and social workers may lack competence and understanding of the elements of trust and trust-building. In order to meet older adults’ complex needs, it is essential to meet these challenges and place trust at the centre of GSW.

Decentering governance models: education policy and school accountability in Post-Devolution Wales

ABSTRACT This paper investigates education policy reform in post-Devolution Wales with a focus on school accountability. Specifically, it provides a historicist analysis of Wales’ decision to abandon publicly mandated school league tables and subsequent accountability reforms. The paper uses the literature on four governance models to investigate successive accountability regimes in Wales through time. These have changed from a Trust and Altruism model, via a Transparent Public Rankings model, to a new, emerging model that relies on self-assessment and collaborative accountability standards. Notably, the Organization for Economic Cooperation and Development (OECD) has played an increasingly important role in shaping this process. Central to our analysis is Mark Bevir and Roderick Rhodes’ decentered theory. The paper argues that governance models need to be understood with reference to the dynamic interplay of historical constraints, power struggles and new belief systems that respond to policy dilemmas. As Wales has embarked on several substantial education policy reform initiatives, the paper demonstrates the importance of a historically informed approach to policy design and implementation which is responsive to competing traditions and beliefs.

Enhancing University English Teaching through Mobile Interactive Applications from an Educational Technology Perspective

In the context of globalization and rapid advancements in information technology (IT), the application of educational technology in higher education has become increasingly prevalent. Particularly in university English teaching, mobile interactive applications have emerged as a significant teaching model. These applications transcend the constraints of time and space, providing students with a flexible learning environment that promotes interaction between teachers and students, thereby enhancing learning outcomes. However, existing research has primarily focused on the technological development and effectiveness evaluation of mobile interactive applications, neglecting the impact of student interaction and trust on learning outcomes. Additionally, there is a lack of dynamism and personalization in the recommendation of educational resources and study partners. This study constructs global and local dynamic trust models for mobile interactions, calculates trustworthiness based on mobile interactive behaviors, and integrates mobile interaction trust relationships and behaviors with a dynamic English recommendation algorithm, thereby providing a new theoretical framework and a practical approach for university English teaching, aiming to enhance the teaching effectiveness and improve students’ learning experiences.

The Dynamics of Trust and Trustworthiness Perceptions in a Government Agency: A Longitudinal Perspective

Although trust is widely recognized as key to effective public administration, little is known about trust dynamics, especially in typical governance relationships. We conducted an intensive longitudinal experience sampling study via a weekly survey of Florida residents’ ( N = 97) trust in and perceptions of the trustworthiness of the (US) National Hurricane Center over a 25-week hurricane season. We estimated two-level time series models to examine reciprocal relationships among four constructs: trust, ability, benevolence, and integrity. All four exhibited significant intraindividual variability such that participants demonstrated change in their ratings of the agency over time. Ability perceptions showed the greatest variability over the study duration. In line with classic models of trust, previous-week trustworthiness perceptions were significant predictors of subsequent trust. Our results also provide some support for self-reinforcing accounts of trust by demonstrating that previous-week trust predicted subsequent trustworthiness. Several of the one-week lagged relationships weakened substantially when tested with two-week lags, suggesting that trustworthiness is the stronger driver of trust. Overall, our results demonstrate that trust is not static, even in business-as-usual governance relationships. Results position perceptions of ability, benevolence, and integrity as partially responsible for changes in trust over time. Further, an exploratory analysis suggested that people who reported higher average integrity perceptions tended to have more stable trust. Thus, all the dimensions of trustworthiness serve as short-term levers for administrators seeking to positively manage relationships with the public, and integrity may be most critical for supporting steady levels of trust.

Synthesizing a three Layer Architectural Framework for Adaptive Intelligent User Interfaces for the Disabled Community

Knowledge acquisition has been a bottleneck in the design for efficient User Interfaces for Disabled Community. The best suited interfaces have been oriented towards adaptability that is the User Interface needs to adapt and configure itself according to the needs and likes of the user. Such user interfaces can arouse interest in the mind of the disabled and motivate them to become an asset to the nation, and use their intellect to contribute to the society. This study pioneers an innovative approach, aiming to develop User Interfaces (UIs) for the Disabled Community. The research introduces a comprehensive three-layer architectural framework employing Learning, Trust, and Knowledge Models. Unlike conventional approaches, this Architectural Framework is designed to learn not only from the Disabled but also from their Assistants/Guides, employing principles of knowledge engineering. While the former offers static context, the latter contributes dynamic insights by observing behaviors, responses, and actions of the Disabled. This symbiotic relationship forms the foundational basis for developing intelligent adaptive UI’s that not only accommodate but actively engage and motivate the Disabled Community. The proposed research methodology involves a meticulous gathering of data through extensive user studies and interactions. By using the Human Computer Interface technology, the study aims to develop a robust framework capable of comprehensively understanding the preferences, needs, and nuances of disabled users. The expected outcomes include the development of a prototype adaptive UI, validated through rigorous testing and feedback from the disabled community. Additionally, the study anticipates contributing a comprehensive set of guidelines and best practices for designing intelligent adaptive UIs, ensuring comfort and ease for impaired users, boosting their confidence and encouraging greater involvement in socio-development activities.

A context-aware zero trust-based hybrid approach to IoT-based self-driving vehicles security

With the speedy progression and adoption of IoT devices in modern self-driving vehicles (SDVs), autonomous driving industry is gradually reforming its capabilities to provide better transportation services. However, this domain faces enormous security and privacy challenges and thus has become an attractive target for attackers due to its rapid growth and market worth. Furthermore, the rapid transformation in technological tools in transport industry and speedy evolution of cyber-attacks paved the way for designing efficient IDSs. Motivated by these challenges, we put forward a new secure and efficient IDS approach for the security of SDVs. The propose approach utilizes an emerging strategy to mitigate security vulnerabilities and cyber attacks detection using zero trust (ZT) model. Through this work, we put forward a context-aware zero trust security framework for IoT-based SDVs. The proposed framework utilizes a context-aware design to evaluate the trustworthiness of the devices using multi-source trust and reputation model. Then, to make the framework more effective and reliable, we introduce crawler system into the context of the IoT-devices in SDVs to make the system unbiased. Additionally, an observer module is developed that employs state-of-the-art machine learning algorithm to detect malicious actions. Empirical results on two standard benchmark datasets (i.e., Car Hacking and ToN_IoT) validate the practicality and robustness of propose framework in real-world transport systems with enhanced security and trust management against evolving cyber-threats. Detection results demonstrate that the proposed framework secured the best performance by achieving 99.43% and 99.52% accuracy for Car Hacking and ToN_IoT, respectively. The findings of this study will help the security professionals and researchers to comprehend the importance of ZT architecture in developing effective and robust security solutions for modern IoT-based SDVs.

Trust Assessment Skills Among Close Friends in a Ubiquitous Social Media Environment

For ages, individuals have relied on trust assessment skills to evaluate spoken words and accompanying body language social signals (BLSS) in friendships. This interactive process seeks to avoid harmful associations and unreliable information. However, with widespread social media use, assessment of BLSS is difficult, and users commonly report harm from defective estimations of interactions. This study evaluates how individuals adjust their trust assessment skills for close social media friendships, given the importance they place on BLSS in its role as a moderator. The research utilizes PLS-SEM, ANOVA, and cluster analysis to evaluate 247 U.S. online survey participants aged 18 to 67. The study adapts Mayer et al.’s trust model using social learning and cognitive dissonance theories. The independent variables, ability, benevolence, and integrity, strongly predict the dependent trust belief. In line with cognitive dissonance theory, BLSS has a negative and significant moderating effect on ability. BLSS reflects a small effect and widely insignificant moderating relationship with benevolence; analysis shows that a moderating relationship should not apply to benevolence. Counter to the hypothesized direction, BLSS produced a positive moderating effect on integrity, which was insignificant by a narrow margin. The analysis explains the basis for the positive relationship. Lastly, statistical analysis reveals an age-based shift in the relevancy of BLSS; younger users place less importance on BLSS, whereas older users place more importance on BLSS. This position for younger users aligns with their exposure and acclimation to social environments with widespread mobile communication and social media use since childhood.

Blockchain-based food traceability system and pro-environmental consumption: A moderated mediation model of technology anxiety and trust in organic food product

Blockchain technology is increasingly recognized as a transformative tool for enhancing clarity and responsibility in the organic food supply chain, where concerns about product authenticity and safety are paramount. By offering provable and translucent information about the origination, production processes, and administration of organic items, blockchain helps to reduce information asymmetry and builds consumer trust, which is critical for purchase decisions in the organic food market. Drawing on insights from the trust theory and the stimulus-organism-response framework, this study uses a moderated mediation model to explore how technology anxiety negatively moderates the direct and indirect influences of blockchain-based food traceability systems on trust in organic food products and organic food purchase intention. Using a targeted sample of 5326 consumers in Vietnam, the PROCESS macro approach (models 8 and 4) is utilized to examine the formulated hypotheses. The results demonstrate that blockchain-based food traceability systems notably enhance consumer trust in organic food products and their intention to buy these products. Trust in organic food products serves as a mediator in the connection between blockchain-based traceability and purchase intention, while technology anxiety weakens the positive impacts of blockchain technology on trust and purchase intention when anxiety levels are high. This research makes crucial contributions to the literature by offering a mechanism-based explanation of how blockchain technology enhances consumer trust in organic food products and integrates technological anxiety as a moderator, providing a deeper understanding of how technological innovations shape consumer behavior. In light of the findings, several critical recommendations have been proposed for practitioners and policymakers.

Causal evidence for social group sizes from Wikipedia editing data.

Human communities have self-organizing properties in which specific Dunbar Numbers may be invoked to explain group attachments. By analysing Wikipedia editing histories across a wide range of subject pages, we show that there is an emergent coherence in the size of transient groups formed to edit the content of subject texts, with two peaks averaging at around for the size corresponding to maximal contention, and at around as a regular team. These values are consistent with the observed sizes of conversational groups, as well as the hierarchical structuring of Dunbar graphs. We use a model of bipartite trust to derive a scaling law that fits the data and may apply to all group size distributions when these are based on attraction to a seeded group process. In addition to providing further evidence that even spontaneous communities of strangers are self-organizing, the results have important implications for the governance of the Wikipedia commons and for the security of all online social platforms and associations.

Success Factors for Conceptual Digital Voting Model

With the advancement of technology in the digital age, blockchain technology has evolved into a technology critical to delivering secure and reliable decentralized applications. An application that has brought blockchain technology is elections to close the gap in traditional elections for transparency and credibility However, in COVID-19, bringing this technology to change elections allows access to all citizens to be able to vote. This research uses a structural equation model (SEM) questionnaire to explore the success factors of election implementation using blockchain technology was analysed from 400 voters who responded to the questionnaire using Mplus Version 7. This research has prepared a conceptual model supporting the effecting factors in implementing an election system using blockchain technology with voters The researcher has created an electoral system using blockchain technology that is readily available. Technology acceptance factor and credibility were utilized from the voters’ point of view in 9 Factors. Those interested in applying the model to improve elections using blockchain technology can study to improve elections. In addition, Conceptual model ideas were used to develop a model for acceptance and trust in elections using blockchain technology.

Transitioning to Human‐Centered AI: A Systematic Review of Theories, Scenarios, and Hypotheses in Human‐AI Interactions

ABSTRACTThis study conducted a systematic review of human‐AI interaction (HAI)over the past decade for the implemented theories and scenarios, and the tested hypotheses to discover the changes in the current transition to human‐centered AI (HCAI). Moving from acceptance theories, Computers are social actors (CASA), anthropomorphism, and the integrative trust model are the most frequent theories. Augmentation scenarios of decision‐making, teamwork, and human‐AI collaborations are common in the latest HAI studies. Users' trust, acceptance, and intention to use an AI system are the main research targets in HAI studies. These trends show a clear transition toward HCAI. This paper also discusses opportunities tied to HAI studies based on the interconnections between the various theories, scenarios, and hypotheses.

The Effects of E-Commerce Recommendation System Transparency on Consumer Trust: Exploring Parallel Multiple Mediators and a Moderator

Recommendation systems are used in various fields of e-commerce and can bring many benefits to consumers but consumers’ trust in recommendation systems (CTRS) is lacking. Recommendation system transparency (RST) is an important factor that affects CTRS. Applying a three-layered trust model, this paper discusses the influence of RST on CTRS in the e-commerce domain, demonstrating the mediating role of perceived effectiveness and discomfort and the moderating role of consumers’ domain knowledge. We recruited 500 participants for an online hypothetical scenario experiment. The results show that consumers’ perceived effectiveness and discomfort can mediate the relationship between RST and CTRS. Specifically, RST (vs. non-transparency) leads to higher perceived effectiveness ( promoting CTRS) and lower levels of discomfort (which inhibits CTRS), in turn increasing CTRS. Domain knowledge positively moderates the positive impact of RST on perceived effectiveness, while negatively moderating the negative impact of RST on discomfort. Further, gender has a negative impact on CTRS when consumers are purchasing experience products but there is no effect when purchasing search products.

MSLTChain: A Trust Model Based on the Multi-Dimensional Subjective Logic for Tree Sharding Blockchain System

MSLTChain: A Trust Model Based on the Multi-Dimensional Subjective Logic for Tree Sharding Blockchain System

TIP: A trust inference and propagation model in multi-human multi-robot teams

Trust is a crucial factor for effective human–robot teaming. Existing literature on trust modeling predominantly focuses on dyadic human-autonomy teams where one human agent interacts with one robot. There is little, if not no, research on trust modeling in teams consisting of multiple human and robotic agents. To fill this important research gap, we present the Trust Inference and Propagation (TIP) model to model and estimate human trust in multi-human multi-robot teams. In a multi-human multi-robot team, we postulate that there exist two types of experiences that a human agent has with a robot: direct and indirect experiences. The TIP model presents a novel mathematical framework that explicitly accounts for both types of experiences. To evaluate the model, we conducted a human-subject experiment with 15 pairs of participants (N=30\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N=30$$\\end{document}). Each pair performed a search and detection task with two drones. Results show that our TIP model successfully captured the underlying trust dynamics and significantly outperformed a baseline model. To the best of our knowledge, the TIP model is the first mathematical framework for computational trust modeling in multi-human multi-robot teams.