Dynamics Of Trust Research Articles

Do Humans Trust AI in HRM? Why Do? Why Not? — Insights from a Decade of Research

This literature review investigates the complex issue of trust in the context of Artificial Intelligence (AI) applications in Human Resource Management (HRM). Adopting the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we systematically reviewed 43 research articles published between 2013 and 2023 to examine the attitudes towards AI as a disruptive technology in HRM practices, the ethical and legal challenges that influence trust, and the strategies for building a trustworthy future. The findings reveal a mixed picture of trust, with growing adoption of AI in various HRM practices, such as recruitment, selection, performance management, and employee development, accompanied by significant concerns related to bias, transparency, privacy, and job displacement. The review identifies key factors that affect trust in AI, including perceived usefulness, ease of use, and fairness, as well as the ethical and legal challenges that erode trust, such as data privacy, algorithmic bias, and lack of explainability. Furthermore, it discusses the implications for HRM professionals and proposes strategies for enhancing trust, such as ensuring human oversight, promoting transparency, and developing AI literacy skills. This review contributes to the research on AI in HRM by providing a nuanced understanding of the trust dynamics, challenges, and opportunities associated with this disruptive technology, offering valuable insights for both researchers and practitioners.

How to Be Certain: Using Known Relations and Trust Discount to Determine Confidence About the Degree of Uncertainty

Ensuring certainty in the rapidly evolving digital twin environments amidst inherent uncertainty is paramount in decision-making for critical engineering systems. This paper presents a novel approach for quantifying and tracking confidence levels within a network of sensors and structural models, specifically addressing uncertainty arising from faulty sensors. By leveraging known relations and implementing a trust discount mechanism, our methodology offers a fundamental framework for navigating in the presence of uncertainties. A quasi-real-time case study on a cantilever beam model is simulated to demonstrate the efficacy of the proposed approach. We showcase the ability of our method to accurately assess and adapt to varying levels of uncertainty introduced by faulty sensors. Our findings highlight the importance of incorporating trust dynamics and established relationships within digital twin environments to achieve improved certainty despite imperfect data. This research contributes to the theoretical underpinnings of digital twin technology and offers practical insights for its application across diverse domains.

Analyzing Trust Dynamics in Human–Robot Collaboration through Psychophysiological Responses in an Immersive Virtual Construction Environment

Analyzing Trust Dynamics in Human–Robot Collaboration through Psychophysiological Responses in an Immersive Virtual Construction Environment

Organizational Climate and Performance in Higher Education: A Bibliometric Analyses Using Dimensions Database

This study conducts a comprehensive bibliometric analysis of research on organizational climate and performance in higher education. The analysis identifies a significant trend in publication patterns, noting a paucity of relevant studies from the early 1980s to 2016. However, from 2016 to 2023, there has been a consistent increase in the number of articles published, reflecting a growing recognition of the significance of organizational climate and performance in higher education. Most publications focus on trade, management, tourism, and services, with substantial contributions from education policy, sociology, philosophy, and related fields. Indonesia leads in publication output, followed by the USA, Malaysia, and Australia. The top-cited articles explore various topics, including the relationship between organizational culture, climate, and employee turnover, servant leadership approaches, university departmental climate, research excellence assessment, and trust dynamics. Although bibliometric analysis may not capture all research activities, this study provides valuable insights into the current research landscape. Future research directions may include investigating specific mechanisms linking organizational climate, work environment, satisfaction, and engagement across different sectors, examining the role of organizational behavior in employee productivity, and assessing and enhancing performance measurement systems.

Exploring the Impact of Financial Statement Manipulations on Stakeholders

This article delves into the multifaceted effects of financial statement manipulations on various stakeholders including shareholders, investors, employees, creditors, and regulatory bodies. The research is driven by the growing concern over the credibility of financial reporting and its cascading effects on stakeholder trust and decision-making processes. This comprehensive study synthesizes findings from recent scholarly articles, focusing on diverse sectors and geographical regions to provide a global perspective on the issue. The purpose of this article is to critically evaluate how financial manipulations impact different stakeholder groups, with a particular emphasis on the trust dynamics and financial decision-making processes. It aims to bridge the gap in existing literature by offering a holistic view of the consequences of financial statement manipulations, drawing on empirical data and theoretical frameworks such as the fraud diamond theory and activity-based accounting. The content of the article is organized into several key sections. Initially, it provides a detailed literature review, highlighting historical and current perspectives on financial statement manipulations. Subsequent sections delve into the methodology employed for data collection and analysis, followed by a comprehensive discussion on the effects of these manipulations on each stakeholder group. The article employs a mix of qualitative and quantitative research methods, analyzing data from various case studies and empirical research to draw its conclusions. Finally, this article reveals that financial statement manipulations significantly impact stakeholder trust and decision-making, albeit with varying degrees depending on stakeholder sophistication and regulatory frameworks. It offers insights into the roles of corporate governance and auditor vigilance in mitigating these impacts. The article concludes with recommendations for enhancing transparency and stakeholder confidence in financial reporting, alongside suggestions for future research directions in this domain. Keywords: Financial Statement Manipulation, Stakeholder Trust, Corporate Governance, Audit Vigilance, Decision-Making Processes, Earnings Management, Regulatory Frameworks.

Exemplary Self-Discipline, Leniency towards Others: Competitive Contexts Amplify the "Black Sheep Effect" in Restoring Ingroup Trust.

Intergroup interaction, a pivotal aspect of social interaction, encompasses both cooperation and competition. Group identity significantly impacts individual behaviors and decision-making processes. This influence manifests in two contrasting ways when addressing rule-breaking by interaction partners: in-group favoritism, where individuals are more lenient towards infractions committed by in-group members, and the black sheep effect, where in-group members are penalized for their rule-breaking. Although trust is crucial in intergroup interactions, the precise impact of group identity on trust restoration and the potential moderating role of intergroup interaction types remain to be elucidated. This study presents two experiments designed to explore these dynamics. In Study 1, the manipulation of group identity through a point estimation task was utilized to evaluate its impact on intergroup trust restoration via a series of repeated trust games. Study 2 aimed to explore the moderating role of intergroup interaction on intergroup trust restoration by contrasting cooperation and competition situations. The results uncovered a "black sheep effect", where participants demonstrated a greater propensity for trust restoration with out-group members than with in-group members. This effect, however, was only evident in competitive contexts. Conversely, in cooperative contexts, the individual's trust in the in-group and out-group members is effectively repaired. These findings contribute to a deeper comprehension of trust dynamics in intergroup interactions, promoting trust establishment and repair between diverse groups, thereby boosting team collaboration efficiency and mitigating conflicts.

Trusting the trust machine: How does blockchain-enabled governance configuration enhance financing credibility?

This study highlights the role of blockchain technology in addressing the trust crisis in supply chain operations and financial activities. While previous research has acknowledged blockchain as a response to trust issues, limited studies have explored its specific contributions and its impact on accelerating cash flow in supply chains. Drawing on governance theory, this study investigates the relationship between blockchain technology and supply chain finance (SCF) through multiple case studies. The central argument is that blockchain technology alone does not automatically generate trust. Instead, trust is established through the configuration of various governance mechanisms. We propose that trust in blockchain applications is derived from two main factors: the requirement of codifiability and verifiability. Through a cross-case analysis, we found that in situations where both the requirement of codifiability and verifiability are high, blockchain governance tends to dominate. When only one factor is high, a configuration of blockchain governance and relational governance emerges. When both factors are low, contractual governance proves to be efficient. This study underscores the need for a nuanced understanding of trust dynamics in blockchain applications and highlights the importance of considering contextual factors in trust research related to blockchain technology.

Investigating the Dynamics of Organizational Commitment and Trust Among Public Employees in a Philippine Local Government Unit (LGU)

This descriptive-correlational study explored the interplay between organizational commitment and trust among public employees within a Local Government Unit (LGU) in Surigao City, Philippines. Data from 72 employees across four departments were analyzed using Spearman's rank correlations to examine associations between trust-building factors (HR practices, performance evaluations, delegation) and commitment levels. Additionally, Kruskal-Wallis H-tests and Mann-Whitney U-tests were employed to investigate potential demographic influences (age, sex, educational attainment) on both commitment and trust perceptions. While public employees exhibited moderate levels of organizational commitment, trust in HR policies, performance evaluations, and delegation practices remained high. Interestingly, trust in managers' follow-through and decision-making involvement displayed a potential U-shaped relationship with age. Educational attainment also emerged as a significant factor influencing trust perceptions across various organizational aspects. These findings suggest that fostering trust through transparent leadership, fair practices, and employee empowerment can be a valuable strategy for LGUs to cultivate a more committed workforce. Furthermore, the study highlights the potential need for tailored approaches to building trust based on employee demographic characteristics.

Catalysing cooperation: the power of collective beliefs in structured populations

Collective beliefs can catalyse cooperation in a population of selfish individuals. We study this transformative power of collective beliefs, an effect that intriguingly persists even when beliefs lack moralising components. Besides the process itself, we consider the structure of human populations explicitly. We incorporate the intricate structure of human populations into our model, acknowledging the bias brought by social and cultural identities in interaction networks. Hence, we develop our model by assuming a heterogeneous group size and structured population. We recognise that beliefs, typically complex story systems, might not spontaneously emerge in society, resulting in different spreading rates for actions and beliefs within populations. As the degree of connectedness can vary among individuals perpetuating a belief, we examine the speed of trust build-up in networks with different connection densities. We then scrutinise the timing, speed and dynamics of trust and belief spread across specific network structures, including random Erdös-Rényi networks, scale-free Barabási-Albert networks, and small-world Newman-Watts-Strogatz networks. By comparing these characteristics across various network topologies, we disentangle the effects of structure, group size diversity, and evolutionary dynamics on the evolution of trust and belief.

The role of stable coins in mitigating volatility in cryptocurrency markets

This study aims to analyze the link between Perceived Volatility Reduction (PVR), Risk Perception, Stablecoin Usage Frequency, Market Confidence, and Stablecoin Adoption (SA). The primary goal is to determine if and to what degree these variables impact stablecoin adoption. We created a questionnaire to gather information from 198 Malaysians. To analyze the research model and test the hypotheses, the Structural Equation Modeling-Partial Least Squares (SEM-PLS) method was utilized. According to the findings, there is a strong and positive association between Perceived Volatility Reduction (PVR) and Stablecoin Adoption (SA). Market players are more likely to adopt stablecoins if they perceive them as useful instruments for mitigating the severe price volatility inherent in traditional cryptocurrencies. This finding emphasizes the importance of risk perception and market stability in driving market behavior. Trust in stablecoin systems, transparency, and regulatory, compliance influenced PVR and SA. The study's findings underscore the significance of perceived volatility reduction (PVR) in driving stablecoin adoption (SA), highlighting the importance of risk perception and market stability. Trust in stablecoin systems, transparency, and regulatory compliance emerge as crucial factors influencing PVR and SA. These insights offer valuable guidance for investors navigating the cryptocurrency market, governments managing stablecoin supply, and scholars studying trust dynamics in the cryptocurrency ecosystem.

Trusting: Alone and together

ABSTRACT We study the problem of an agent continuously faced with the decision of placing or not placing trust in an institution. The agent makes use of Bayesian learning in order to estimate the institution’s true trustworthiness and makes the decision to place trust based on myopic rationality. Using elements from random walk theory, we explicitly derive the probability that such an agent ceases placing trust at some point in the relationship, as well as the expected time spent placing trust conditioned on their discontinuation thereof. We then continue by modeling two truster agents, each in their own relationship to the institution. We consider two natural models of communication between them. In the first (“observable rewards”) agents disclose their experiences with the institution with one another, while in the second (“observable actions”) agents merely witness the actions of their neighbor, i.e. placing or not placing trust. Under the same assumptions as in the single agent case, we describe the evolution of the beliefs of agents under these two different communication models. Both the probability of ceasing to place trust and the expected time in the system elude explicit expressions, despite there being only two agents. We therefore conduct a simulation study in order to compare the effect of the different kinds of communication on the trust dynamics. We find that a pair of agents in both communication models has a greater chance of learning the true trustworthiness of an institution than a single agent. Communication between agents promotes the formation of long-term trust with a trustworthy institution as well as the timely exit from a trust relationship with an untrustworthy institution. Contrary to what one might expect, we find that having less information (observing each other’s actions instead of experiences) can sometimes be beneficial to the agents.

VIRTSI: A novel trust dynamics model enhancing Artificial Intelligence collaboration with human users – Insights from a ChatGPT evaluation study

The rapid integration of intelligent processes and methods into information systems in the Artificial Intelligence (AI) era has led to a substantial shift towards autonomous software decision-making. This evolution necessitates robust human oversight, especially in critical domains like Healthcare, Education, and Energy. Human trust in AI plays a vital role in influencing decision-making processes of users interacting with AI.This paper presents VIRTSI (Variability and Impact of Reciprocal Trust States towards Intelligent systems), a novel rigorous computational model for human-AI Interaction. VIRTSI simulates human trust states, spanning from overtrust to distrust, through user modelling. It comprises: 1. A trust dynamics representational model based on Deterministic Finite State Automata (DFAs), illustrating transitions among cognitive trust states in response to AI-generated replies. 2. A trust evaluation model based on Confusion Matrices, originating from machine learning and Accuracy Metrics, providing a quantitative framework for analysing human trust dynamics. As a result, this is the first time that trust dynamics have been thoroughly traced in a representational model and a method has been developed to assess the impact of possibly harmful states like overtrust and distrust.An empirical study on the recently launched Large Language Model of generative AI, ChatGPT (version 3.5), provides a radical underexplored AI-generated platform for evaluating the human-AI interaction through VIRTSI. The study involved 1200 interactions of real users as well as AI experts together with experts in two very different domains of evaluation, namely software engineering and poetry. This study traces trust dynamics and the emerging human-AI interaction, in concrete examples of real user synergies with generative AI. The research reveals the vital role of maintaining normal trust states for optimal human-AI interaction and that both AI and human users need further steps towards this goal. The real-world implications of this research can guide the creation and evaluation of user interfaces with AI and the incorporation of functionalities in the development of generative AI chatbots in terms of trust by providing a new rigorous DFA representational method of trust dynamics and a corresponding new perspective of confusion matrix evaluation method of the dynamics’ impact in the efficiency of human-AI dialogues.

Exploring Trust Dynamics in Online Social Networks: A Social Network Analysis Perspective

This study explores trust dynamics within online social networks, blending social science theories with advanced machine-learning (ML) techniques. We examine trust’s multifaceted nature—definitions, types, and mechanisms for its establishment and maintenance—and analyze social network structures through graph theory. Employing a diverse array of ML models (e.g., KNN, SVM, Naive Bayes, Gradient Boosting, and Neural Networks), we predict connection strengths on Facebook, focusing on model performance metrics such as accuracy, precision, recall, and F1-score. Our methodology, executed in Python using the Anaconda distribution, unveils insights into trust formation and sustainability on social media, highlighting the potent application of ML in understanding these dynamics. Challenges, including the complexity of modeling social behaviors and ethical data use concerns, are discussed, emphasizing the need for continued innovation. Our findings contribute to the discourse on trust in social networks and suggest future research directions, including the application of our methodologies to other platforms and the study of online trust over time. This work not only advances the academic understanding of digital social interactions but also offers practical implications for developers, policymakers, and online communities.

Consumer Confidence in Digital Finance: Trust Dynamics in Online Banking

Consumer Confidence in Digital Finance: Trust Dynamics in Online Banking

Psychological Trust Dynamics in Climate Change Adaptation Decision-Making Processes: A Literature Review

There has been a growth in interest among academics and professionals in psychological trust dynamics during climate change adaptation. This literature review aimed to examine the research concerning trust dynamics in climate change adaptation from different levels of analysis, encompassing the different phases of adaptation and considering the importance of trust in climate change decision-making. The method consisted of systematically reviewing researches on this topic published in scientific articles, by using appropriate and relevant search keywords (e.g., trust, community, natural hazard, climate change adaptation, decision-making) in academic databases. A total of 25 studies met inclusion criteria. All the articles focused on the latter phases of the climate change adaptation cycle, specifically implementation and monitoring/evaluation, with limited attention devoted to decision-making related to earlier phases of preparation, assessment of risks, and identification and selection of adaptation options. The reviews also indicates that psychological trust is related to different adaptive actions (e.g., adoption of renewable energy technologies), and low- and high-impact mitigation behaviors (e.g., acceptance of paying taxes for actions that mitigate climate change). Therefore, this review underscores the significant role of psychological trust dynamics in shaping individuals’ decision-making processes concerning climate change adaptation, thereby yielding immediate and direct implications for climate systems. Therefore, it is essential to actively promote the culture of trust within the context of climate change adaptation.

Exploring Motivators for Trust in the Dichotomy of Human—AI Trust Dynamics

This study analyses the dimensions of trust in artificial intelligence (AI), focusing on why a significant portion of the UK population demonstrates a higher level of trust in AI compared to humans. Conducted through a mixed-methods approach, this study gathered 894 responses, with 451 meeting the criteria for analysis. It utilised a combination of a six-step Likert-scale survey and open-ended questions to explore the psychological, sociocultural, and technological facets of trust. The analysis was underpinned by structural equation modelling (SEM) and correlation techniques. The results unveil a strong predilection for trusting AI, mainly due to its perceived impartiality and accuracy, which participants likened to conventional computing systems. This preference starkly contrasts with the scepticism towards human reliability, which is influenced by the perception of inherent self-interest and dishonesty in humans, further exacerbated by a general distrust in media narratives. Additionally, this study highlights a significant correlation between distrust in AI and an unwavering confidence in human judgment, illustrating a dichotomy in trust orientations. This investigation illuminates the complex dynamics of trust in the era of digital technology, making a significant contribution to the ongoing discourse on AI’s societal integration and underscoring vital considerations for future AI development and policymaking.

The Impact of Trust in Platform Providers on User Participation and Economic Benefits in the Sharing Economy in Indonesia

This research investigates the effect of trust in platform providers on user participation and economic benefits in the sharing economy within the context of Indonesia. Utilizing a quantitative approach, Structural Equation Modeling (SEM) with Partial Least Squares (PLS) path analysis was employed to analyze data collected from a sample of 150 participants. The measurement model assessment confirmed the reliability and validity of the latent constructs, while the structural model estimation revealed significant positive relationships between trust in platform providers and user participation, trust in platform providers and economic benefits, as well as user participation and economic benefits. Mediation analysis further demonstrated the mediating role of user participation in the relationship between trust in platform providers and economic benefits. The moderation analysis did not reveal any significant moderation effects. Overall, the findings contribute to understanding the dynamics of trust, user behavior, and economic outcomes in the Indonesian sharing economy context, offering insights for platform providers, policymakers, and researchers.

Consumers’ Trust in Sharing Economy Peer-To-Peer Platforms: A Case Study of Online Food Delivery in Hanoi, Vietnam

Purpose: The purpose of this study is to the influence of trust on consumers’ transactional intention within a P2P sharing platform focusing on consumers who used OFD in Hanoi, Vietnam. Design/methodology/approach: A structured questionnaire is developed in a Google Form and distributed mainly through web-based and telephone-based platforms. In total, 240 valid responses were collected from Hanoi. To ensure the study's reliability and representativeness while minimizing bias, a convenience sampling method is employed, considering the demographic distribution of consumers across different age groups, genders, occupations, income levels, and various online food delivery platforms, income, frequency of consumption, and average expense. Analytical techniques included descriptive statistics, Cronbach’s Alpha reliability testing, and multiple regression analysis. Results and discussions: Findings revealed that four of the seven factors examined exerted a significant positive impact on Consumers’ Intention to Transact: "Consumer’s Trust in supplying Peer’s Integrity”, "Consumer's Trust in Products Ability”, "Consumer’s Trust in supplying Peer’s Benevolence”, and "Consumer’s Trust in Platform’s Integrity”. Conversely, while “Consumer’s Trust in supplying Peer’s Ability”, “Consumer’s Trust in Platforms Ability”, and “Consumer’s Trust in Platform’s Benevolence” do influence ‘Customers’ Intention to Transact’, their effect lacks statistical significance. Research Implications: This research sheds light on Vietnam's Sharing Economy, showcasing its transformative impact and governmental support. It focuses on the developing OFD sector, highlighting its rapid growth and significance. By adopting a trust-based research model, the study offers insights into consumer behavior within OFD platforms, contributing to the understanding of trust dynamics. Originality/Value: The research provides a theoretical foundation for understanding trust's role in shaping consumer behavior and transactional intentions, laying the groundwork for empirical research and practical applications. Practically, the study offers actionable insights for policymakers and industry players. It emphasizes the importance of integrity, reliability, and effective communication in building consumer trust.

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.

I want to be safe: understanding the main drivers behind vaccination choice throughout the pandemic

BackgroundDespite being a major advancement in modern medicine, vaccines face widespread hesitancy and refusal, posing challenges to immunization campaigns. The COVID-19 pandemic accentuated vaccine hesitancy, emphasizing the pivotal role of beliefs in efficacy and safety on vaccine acceptance rates. This study explores the influence of efficacy and safety perceptions on vaccine uptake in Italy during the pandemic.MethodsWe administered a 70-item questionnaire to a representative sample of 600 Italian speakers. Participants were tasked with assessing the perceived effectiveness and safety of each vaccine dose, along with providing reasons influencing their vaccination choices. Additionally, we conducted an experimental manipulation, exploring the effects of four framing messages that emphasized safety and/or efficacy on participants’ willingness to receive a hypothetical fourth vaccine dose. Furthermore, participants were asked about their level of trust in the scientific community and public authorities, as well as their use of different information channels for obtaining COVID-19-related information.ResultsOur study reveals a dynamic shift in vaccine efficacy and safety perceptions throughout the COVID-19 pandemic, potentially influencing vaccination compliance. Initially perceived as more effective than safe, this assessment reversed by the time of the third dose. Beliefs regarding safety, rather than efficacy, played a significant role in anticipating future vaccinations (e.g., the booster dose). Safety-focused messages positively affected vaccination intent, while efficacy-focused messages showed limited impact. We also observed a changing trend in reasons for vaccination, with a decline in infection-related reasons and an increase in social related ones. Furthermore, trust dynamics evolved differently for public authorities and the scientific community.ConclusionsVaccine perception is a dynamic process shaped by evolving factors like efficacy and safety perceptions, trust levels, and individual motivations. Our study sheds light on the complex dynamics that underlie the perception of vaccine safety and efficacy, and their impact on willingness to vaccinate. We discuss these results in light of bounded rationality, loss aversion and classic utility theory.