Dynamics Of Trust Research Articles

The Dual‐Edged State Paradox: Fighting for Justice When the State is Unreliable

Sociological theories of collective resistance offer insights into how communities mobilize against oppression but often overlook the complexities when the state, an entity relied upon for justice, also acts as an agent of betrayal. Based on 8 years of ethnographic research in Palermo (2016–2024), this study examines the relationship between Sicilian activists and the state, shaped by the dual forces of mafia oppression and state complicity. The present research explores how activists navigate the complexities of collective trauma rooted in state complicity, while simultaneously relying on parts of the state as indispensable allies in their fight for justice. The study introduces the “Dual‐Edged State Paradox,” as a novel framework that captures this contradictory relationship. To navigate the paradox, activists adopt two key strategies: ‘fragmented trust dynamics’ and ‘layered collective resistance’. These strategies illustrate how activists continually recalibrate trust and engage in multi‐layered resistance, adjusting their approach to balance public protest with collaboration based on the positioning of different state actors. This research contributes to our sociological knowledge on collective action, particularly in settings where activists grapple with both external violence of non‐state actors and internal betrayal from the state apparatus.

Mathematical Modeling of the Dynamics of Trust in Automation

Trust in automation (TiA) is crucial for human-automation interactions, but traditionally measured statically via discrete survey data points. We developed a mathematical model capturing the dynamic nature of TiA, incorporating measurable components across different timescales, and validated it through experimental data. Our findings show that: (1) trust timescales are important for predicting automation usage, emphasizing trust’s key role in decision-making; (2) short-timescale trust significantly impacts overall trust dynamics, highlighting the importance of recent interactions; (3) trust levels are generally higher in highly reliable automation scenarios, though this pattern emerges after an initial evaluation period; and (4) automation type, implying varying workloads, does not significantly impact trust dynamics in highly reliable systems but fosters increased trust in lower-reliability systems. This study advances our understanding of trust dynamics in automation, contributing to more intuitive and trustworthy technology development.

Comparative analysis of moral decision-making and trust dynamics: human reasoning vs. ChatGPT-3 narratives

Comparative analysis of moral decision-making and trust dynamics: human reasoning vs. ChatGPT-3 narratives

Trust Dynamics in Financial Decision Making: Behavioral Responses to AI and Human Expert Advice Following Structural Breaks.

This study explores the trust dynamics in financial forecasting by comparing how individuals perceive the credibility of AI and human experts during significant structural market changes. We specifically examine the impact of two types of structural breaks on trust: Additive Outliers, which represent a single yet significant anomaly, and Level Shifts, which indicate a sustained change in data patterns. Grounded in theoretical frameworks such as attribution theory, algorithm aversion, and the Technology Acceptance Model (TAM), this research investigates psychological responses to AI and human advice under uncertainty. This experiment involved 157 participants, recruited via Amazon Mechanical Turk (MTurk), who were asked to forecast stock prices under different structural break scenarios. Participants were randomly assigned to either the AI or human expert treatment group, and the experiment was conducted online. Through this controlled experiment, we find that, while initial trust levels in AI and human experts are comparable, the credibility of advice is more severely compromised following a structural break in the Level Shift condition, compared to the Additive Outlier condition. Moreover, the decline in trust is more pronounced for human experts than for AI. These findings highlight the psychological factors influencing decision making under uncertainty and offer insights into the behavioral responses to AI and human expert systems during structural market changes.

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.

Have people ‘had enough of experts’? The impact of populism and pandemic misinformation on institutional trust in comparative perspective

ABSTRACT Public trust in institutions is a key prerequisite for effective crisis management. However, the rise of populism and misinformation in recent years made it increasingly difficult to maintain institutional trust. Despite this recognition, we still lack a systematic understanding of how exposure to misinformation and populist political orientation affect people’s trust in institutions. This paper fills this gap by adopting an original approach to trust, focusing on prospective trust rather than trust in the present, and by comparing four countries led by populist leaders during the pandemic – Brazil, Poland, Serbia, and the United States. The comparative design allows us to consider not only the role of individual-level factors (populist attitudes and misinformation exposure) but also the role of different approaches to the COVID-19 pandemic adopted in the four countries. The study utilizes data from a cross-sectional survey, carried out between November and December 2022 (N = 5000). Our findings show that populist attitudes are the most significant predictor of distrust in political institutions in all four countries. Believing in false information related to COVID-19, on the other hand, has a stronger impact on distrust in expert institutions – public health authorities, scientists, and medical professionals. The data also highlight the importance of local context and different approaches to handling the pandemic in the dynamics of trust. In Poland and Serbia, populist voters have more trust in both healthcare authorities as well as in political institutions; however, in Brazil and the United States, populist voters were more likely to distrust expert institutions.

Exploring Dynamic Influences on Trust in Human-Autonomy Teaming: An Entropy and Event Layer Analysis

This research aims to study trust in Human-Autonomy Teaming (HAT). A multiple regression model was employed to explore potential predictors in tasks that might dynamically influence trust levels. By computing entropy across different system layers of a remotely piloted aerial vehicle system, we measured the influence of these dynamic predictors on trust. The findings suggest the presence of potential trust predictors in human-autonomy interactions as measured using entropy. Some layers had to be combined due to high multicollinearity, addressed through Principal Component Analysis (PCA). Further research is required for a more in-depth understanding of these components and their implications for trust dynamics.

Strengthening Donor Trust via Enhanced Performance and Transparency in Zakat Management

General Background: Lazismu, an extension of Lembaga Amil Zakat, Infaq Sadaqoh Muhammadiyah, plays a crucial role in facilitating charitable donations and maintaining donor trust in the humanitarian sector. Specific Background: Despite its importance, Lazismu faces challenges regarding the management of zakat funds and transparency in financial reporting, which has led to diminished donor trust. Previous studies have inadequately explored the relationship between performance recognition and donor trust in zakat institutions. Knowledge Gap: This research aims to fill this gap by quantitatively examining how various independent variables—human resource performance enhancement, financial report transparency, and interpersonal communication within the fundraising division—impact donor trust at Lazismu in Mojokerto. Aims: The study specifically investigates the influence of these independent variables on donor trust in Lazismu, with a focus on enhancing organizational performance to regain donor confidence. Results: The findings reveal that enhanced human resource performance, transparent financial reporting, and improved interpersonal communication significantly contribute to increasing donor trust. Novelty: This research introduces a structured approach to evaluating zakat management performance through quantitative measures, offering insights into the dynamics of donor engagement and trust. Implications: The results underscore the necessity for zakat institutions to prioritize financial transparency and effective communication strategies to foster stronger relationships with donors, ultimately leading to increased zakat contributions and enhanced community support. Highlights: Trust Boost: Performance and transparency enhance donor confidence significantly. Engagement: Strong communication improves donor relationships and trust. Methodology: Quantitative research analyzes zakat institution performance effects. Keywords: Lazismu, donor trust, zakat management, financial transparency, interpersonal communication

Fostering digital trust in manufacturing companies: Exploring the impact of industry 4.0 technologies

This study examines the multifaceted impacts of digital innovations on organisational structures and stakeholders ‘commitment. The integration of digital technologies, including information technologies, IoT, AI, AR/VR, blockchain, robotics, and automation, underscores the indispensable role of trust in contemporary business operations. Focusing on Italian manufacturing firms at the forefront of Industry 4.0 implementation, this research seeks to unravel the nuanced factors contributing to digital trust. A comprehensive framework, derived from an integrative literature review, categorises two distinct groups of factors influencing a firm's decision to establish digital trust. Employing a configurational approach, specifically Qualitative Comparative Analysis (QCA), the joint impacts of multiple factors on digital trust levels are scrutinized, offering insights into how different elements synergise to trigger digital trust. This study aims to bridge existing gaps in understanding the intricate dynamics of trust within organisations undergoing digital transformation.

Determining Trust in Information: Initial Literature Review

ABSTRACTPew Research Center's findings reveal a notable trend: U.S. adults under the age of thirty exhibit a level of trust in information on social media that is comparable to their trust in traditional news outlets (Liedke & Gottfriend, 2022). This is particularly significant given the growing concern over misinformation, with a staggering 95% of Americans acknowledging its prevalence in society (Seitz & Fingerhut, 2021). To explore the factors influencing individuals' trust in information, researchers conducted a preliminary review of the literature, focusing on information science. This preliminary review identified seven major themes (health, politics & business, information sources, social factors, age groups, education & language, and privacy & security) that will guide a more extensive systematic review in the future. These themes provide valuable insights into the complexities of trust in information and should serve as a foundation for further investigation. Insights derived from this preliminary review are invaluable for scholars and practitioners seeking a deeper understanding of information trust dynamics. By illuminating these complex factors, this research lays the groundwork for future studies and interventions aimed at enhancing information trust among the general public.

Building Trust to Study Trust: Paving the Way for Applied Trust Research in Social and Resocializing Institutions

Trust plays a pivotal role when aiding individuals in vulnerable positions. However, understanding the practical dynamics of trust remains limited. Empirical research on trust, particularly in collaborative and applied settings, is scarce. Trust in communications training is often treated as a nebulous concept, complicating its operationalization. This article delineates the initial stages of an applied research project on trust building in encounters between practitioners and citizens in vulnerable positions, outlines our project’s design, and details our experiences with cultivating trust with our participants. Employing an applied conversation analytic approach, we use video- and audio-ethnography to capture everyday interactions between employees and residents in social and re-socializing institutions. Combining a trust-building approach to get access to the field with rigorous analysis, we eventually aim to identify indicators of trust and distrust. Our distinctive approach, combining design, execution, and collaboration with inspiration from participant-oriented research, ensures relevance, accessibility, quality, and feasibility of our research will contribute to the conceptualization of trust as a social interactional phenomenon. It fosters co-ownership with practitioners, enhancing the impact of our findings, and allows us to rewrite the teaching material for future generations of social workers.

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.

A Conceptual Review of Trust and Knowledge-Sharing Behavior Among Academics in Public Universities

sharing behavior among academicians in public universities. Trust is recognized as a fundamental element underpinning effective knowledge sharing in organizations, crucial for skill development, enhancing competencies, and sustaining competitive advantages. In academic settings, knowledge dissemination occurs through various channels such as teaching, publications, training, and research. Despite the assumption that academicians are actively engaged in knowledge creation and sharing, real-world practices may not always reflect this. This paper reviews existing literature to elucidate how trust impacts knowledge-sharing behavior among academicians, with a focus on Malaysian public universities. The findings aim to offer insights into the dynamics of trust in knowledge sharing and propose strategies to foster a knowledge-sharing culture in academia.

In ChatGPT We Trust? Unveiling the Dynamics of Reuse Intention and Trust Towards Generative AI Chatbots among Iranians

In ChatGPT We Trust? Unveiling the Dynamics of Reuse Intention and Trust Towards Generative AI Chatbots among Iranians

Meta-Analyzing the Trust-Performance Link in Collaboration: Moderating Effects of Conceptual and Contextual Factors

Trust is vital for effective collaboration, but its impact on collaboration performance has produced inconsistent findings. This study examines the trust-collaboration performance link by testing the moderating effects of diverse conceptual and contextual factors. Through a comprehensive meta-analysis of 31 empirical studies, we synthesize siloed findings and offer a systematic and evidence-based examination of the trust-collaboration performance link. Our findings show that the trust-collaboration performance relationship is positive and stronger when trust is cognition-based rather than affect-based, when performance content focuses on process and outcomes, and when performance is subjectively measured. In addition, trust is more important in low generalized trust countries and in cross-sectoral or nonprofit collaboration. These moderation effects manifest differently in different collaboration forms, with trust playing a less salient role in contracting. These findings unravel the nuanced dynamics of trust within collaboration and have substantial implications for scholars and practitioners in collaborative governance and network management.

Abstract B112: Empowering cancer advocates: Evaluating the impact of a comprehensive training program

Abstract The Cancer Advocacy Training Program aimed to enhance participants' knowledge, skills, and confidence in cancer advocacy, particularly addressing disparities in cancer health outcomes. This evaluation assesses the program's impact through pre- and post-training survey comparisons. The program included 25 participants from various states and focused on comprehensive training. Surveys measured changes in knowledge, attitudes, self-perceived skills, and confidence. Results showed that accurate identification of cancer's main categories increased from 83% to 96%, and awareness of the NCI's mammogram recommendations rose from 70% to 96%. Recognition of the root causes of health disparities remained at 100%, and belief in the inclusivity of cancer advocacy roles was consistently high. Understanding of trust dynamics in advocacy increased by 50%, and recognition of essential political advocacy components improved by 14%. Specific learning outcomes indicated consistent identification of research advocates, a 23% increase in knowledge of strategic funding sources, a 31% improvement in understanding health communication audiences, a 19% rise in awareness of patient advocacy benefits, and a 13% increase in comprehension of informed consent. Confidence in advocacy skills also saw significant improvements. "Extremely confident" participants increased to 33% in problem-solving, adaptability, and communication with stakeholders. Confidence in fundraising and partnership building rose to 19%, evaluation of advocacy outcomes to 24%, identifying key issues to 29%, presentation skills to 33%, and using digital tools to 29%. Self-efficacy results demonstrated notable gains: confidence in solving difficult problems related to cancer advocacy increased with participants reporting "extremely confident" rising from 11% to 33%. Confidence in adaptability under stress improved significantly, with "extremely confident" participants rising from 11% to 33%. Effective communication with stakeholders saw a significant increase, with "extremely confident" participants rising from 17% to 33%. Fundraising and partnership-building confidence increased from 6% to 19%, and confidence in evaluating advocacy outcomes improved, with "extremely confident" participants rising from 6% to 24%. The program effectively enhanced participants' knowledge, attitudes, and advocacy skills, integrating theoretical and practical learning. Gains in confidence across problem-solving, adaptability, and communication skills underline the program's success in preparing participants for real-world advocacy. Participant feedback confirmed the program's relevance and efficacy, suggesting future iterations should emphasize diversity in advocacy and deepen content on key topics. This program serves as a valuable model for empowering cancer advocates and addresses the critical need for targeted training to reduce cancer health disparities. Citation Format: Manisha Salinas, Monica Albertie. Empowering cancer advocates: Evaluating the impact of a comprehensive training program [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr B112.

The Impact of Cybersecurity Attacks on Human Trust in Autonomous Vehicle Operations.

This study examines the extent to which cybersecurity attacks on autonomous vehicles (AVs) affect human trust dynamics and driver behavior. Human trust is critical for the adoption and continued use of AVs. A pressing concern in this context is the persistent threat of cyberattacks, which pose a formidable threat to the secure operations of AVs and consequently, human trust. A driving simulator experiment was conducted with 40 participants who were randomly assigned to one of two groups: (1) Experience and Feedback and (2) Experience-Only. All participants experienced three drives: Baseline, Attack, and Post-Attack Drive. The Attack Drive prevented participants from properly operating the vehicle in multiple incidences. Only the "Experience and Feedback" group received a security update in the Post-Attack drive, which was related to the mitigation of the vehicle's vulnerability. Trust and foot positions were recorded for each drive. Findings suggest that attacks on AVs significantly degrade human trust, and remains degraded even after an error-less drive. Providing an update about the mitigation of the vulnerability did not significantly affect trust repair. Trust toward AVs should be analyzed as an emergent and dynamic construct that requires autonomous systems capable of calibrating trust after malicious attacks through appropriate experience and interaction design. The results of this study can be applied when building driver and situation-adaptive AI systems within AVs.

Predicting Trust Dynamics With Personal Characteristics

Previous research into trust dynamics in human-autonomy interaction has demonstrated that individuals exhibit specific patterns of trust when interacting repeatedly with automated systems. Moreover, people with different types of trust dynamics have been shown to differ across seven personal characteristic dimensions: masculinity, positive affect, extraversion, neuroticism, intellect, performance expectancy, and high expectations. In this study, we develop classification models aimed at predicting an individual’s trust dynamics type–categorized as Bayesian decision-maker, disbeliever, or oscillator–based on these key dimensions. We employed multiple classification algorithms including the random forest classifier, multinomial logistic regression, Support Vector Machine, XGBoost, and Naive Bayes, and conducted a comparative evaluation of their performance. The results indicate that personal characteristics can effectively predict the type of trust dynamics, achieving an accuracy rate of 73.1%, and a weighted average F1 score of 0.64. This study underscores the predictive power of personal traits in the context of human-autonomy interaction.

Implementing Autonomous Control in the Digital-Twins-Based Internet of Robotic Things for Remote Patient Monitoring.

Conventional patient monitoring methods require skin-to-skin contact, continuous observation, and long working shifts, causing physical and mental stress for medical professionals. Remote patient monitoring (RPM) assists healthcare workers in monitoring patients distantly using various wearable sensors, reducing stress and infection risk. RPM can be enabled by using the Digital Twins (DTs)-based Internet of Robotic Things (IoRT) that merges robotics with the Internet of Things (IoT) and creates a virtual twin (VT) that acquires sensor data from the physical twin (PT) during operation to reflect its behavior. However, manual navigation of PT causes cognitive fatigue for the operator, affecting trust dynamics, satisfaction, and task performance. Also, operating manual systems requires proper training and long-term experience. This research implements autonomous control in the DTs-based IoRT to remotely monitor patients with chronic or contagious diseases. This work extends our previous paper that required the user to manually operate the PT using its VT to collect patient data for medical inspection. The proposed decision-making algorithm enables the PT to autonomously navigate towards the patient's room, collect and transmit health data, and return to the base station while avoiding various obstacles. Rather than manually navigating, the medical personnel direct the PT to a specific target position using the Menu buttons. The medical staff can monitor the PT and the received sensor information in the pre-built virtual environment (VE). Based on the operator's preference, manual control of the PT is also achievable. The experimental outcomes and comparative analysis verify the efficiency of the proposed system.

Trust, trustworthiness and AI governance

An emerging issue in AI alignment is the use of artificial intelligence (AI) by public authorities, and specifically the integration of algorithmic decision-making (ADM) into core state functions. In this context, the alignment of AI with the values related to the notions of trust and trustworthiness constitutes a particularly sensitive problem from a theoretical, empirical, and normative perspective. In this paper, we offer an interdisciplinary overview of the scholarship on trust in sociology, political science, and computer science anchored in artificial intelligence. On this basis, we argue that only a coherent and comprehensive interdisciplinary approach making sense of the different properties attributed to trust and trustworthiness can convey a proper understanding of complex watchful trust dynamics in a socio-technical context. Ensuring the trustworthiness of AI-Governance ultimately requires an understanding of how to combine trust-related values while addressing machines, humans and institutions at the same time. We offer a road-map of the steps that could be taken to address the challenges identified.