Measures Of Trust Research Articles

A tale of government spending efficiency and trust in the state

This paper empirically links the efficiency and performance assessment of the general government, proxied by efficiency scores, to the trust in government. Government spending efficiency scores are first computed via data envelopment analysis (DEA). Then, relying on panel data and instrumental variable approaches, we estimate the effect of public sector efficiency on citizens trust on national governments. The sample covers 36 OECD countries between 2007 and 2019. We find that the more efficient countries in terms of government spending are Australia, Chile, Ireland, New Zealand, South Korea, Switzerland. Secondly, our main finding is that better public sector spending efficiency is positively associated with citizens’ higher trust in governments. In general, political economy variables and the existence of fiscal rules do not seem to significantly affect our measure of trust. The results hold using alternative proxies for public sector efficiency, alternative measures for trust, specifications with different control variables and different empirical approaches (instrumental variables).

Development and validation of the Trust in Multidimensional Healthcare Systems Scale (TIMHSS)

ContextThe COVID-19 pandemic has reignited a commitment from the health policy and health services research communities to rebuilding trust in healthcare and created a renewed appetite for measures of trust for system monitoring and evaluation. The aim of the present paper was to develop a multidimensional measure of trust in healthcare that: (1) Is responsive to the conceptual and methodological limitations of existing measures; (2) Can be used to identify systemic explanations for lower levels of trust in equity-deserving populations; (3) Can be used to design and evaluate interventions aiming to (re)build trust.MethodsWe conducted a 2021 review of existing measures of trust in healthcare, 72 qualitative interviews (Aug-Dec 2021; oversampling for equity-deserving populations), an expert review consensus process (Oct 2021), and factor analyses and validation testing based on two waves of survey data (Nov 2021, n = 694; Jan-Feb 2022, n = 740 respectively).FindingsWe present the Trust in Multidimensional Healthcare Systems Scale (TIMHSS); a 38-item correlated three-factor measure of trust in doctors, policies, and the system. Measurement of invariance tests suggest that the TIMHSS can also be reliably administered to diverse populations.ConclusionsThis global measure of trust in healthcare can be used to measure trust over time at a population level, or used within specific subpopulations, to inform interventions to (re)build trust. It can also be used within a clinical setting to provide a stronger evidence base for associations between trust and therapeutic outcomes.

TwI-FTM: Two-way IoT-FoG trust management scheme for task offloading in IoT-FoG networks

Based on trust management, the service establishment between IoT/User devices and fog networks reduces the impact of malicious data and nodes. Hence, building a trusted environment in the IoT and fog layers is very important. There is no assurance that the generated data or task is standard data; it may also have abnormal data to modify its originality. Similarly, the nodes in the fog environment also have malicious nodes during task offloading. For this, the novel Two-way IoT data & Fog Trust management (TwI-FTM) algorithm is proposed to identify the abnormal data with the help of the Trusted Data Measurement (TDM) and Fog Access Trust Evaluation (FATE) method for identifying the malicious fog access nodes in fog network. Identifying these abnormal data and malicious fog access nodes is done by continuously monitoring fog control nodes with the threshold level. Once it is identified, they will automatically be removed from the IoT-FoG network. The obtained trust measurements are analyzed and compared with other existing methods; it is identified that the proposed TwI-FTM outperforms and has a higher trust degree of 0.68 compared with others. It achieved higher detection accuracy of malicious fog access nodes in fog networks at 21 %, 16 %, and 14 % compared with OTM, TTM, and SLA-Trust, respectively. Finally, the task completion time was also reduced, and it was achieved at 4.5 ms, 5 ms, and 13 ms compared with TTM, OTM, and SLA-Trust, respectively.

Ethics Perspective of Quality Accounting Information and Future Certainty: A Pattern of Rationally Perceived Decision

Purpose: This research tested the distinctive capability of detecting misleading information when real earnings have positively stimulated the smooth movement of the market price. Methodology/approach: This causal research used the primary data from the investor community in 2022, which amounted to 3.242 respondents for running SEM and endogeneity testing; FGD has been the fittest validation process for capturing the rationality models. Findings: High consistency of integrity-characteristic played a crucial role in designing a prudent accounting treatment which indicates the ethics code related to highly sustainable business during an uncertain future global economy. Inevitably, a measurement of trust in high earning quality pointed out that this quality measurement is to protect this investment decision in a highly secured, safe area. Practical implications: One centralized platform of financial reporting services should support the attractiveness of obedience and compliance as an incomparable advantage of positive earnings management. High-prospected firms should be assessed by high-quality accounting information; the delisting procedure has been prioritized firmly for low-prospect ones. Originality/value: This research explores the pattern of predictive simulation modeling as the existence of game theory, adapted from simplex-linear programming; this rationality model has strengthened as a fundamental requirement to illustrate high future certainty.

Low-rank human-like agents are trusted more and blamed less in human-autonomy teaming.

If humans are to team with artificial teammates, factors that influence trust and shared accountability must be considered when designing agents. This study investigates the influence of anthropomorphism, rank, decision cost, and task difficulty on trust in human-autonomous teams (HAT) and how blame is apportioned if shared tasks fail. Participants (N = 31) completed repeated trials with an artificial teammate using a low-fidelity variation of an air-traffic control game. We manipulated anthropomorphism (human-like or machine-like), military rank of artificial teammates using three-star (superiors), two-star (peers), or one-star (subordinate) agents, the perceived payload of vehicles with people or supplies onboard, and task difficulty with easy or hard missions using a within-subject design. A behavioural measure of trust was inferred when participants accepted agent recommendations, and a measure of no trust when recommendations were rejected or ignored. We analysed the data for trust using binomial logistic regression. After each trial, blame was apportioned using a 2-item scale and analysed using a one-way repeated measures ANOVA. A post-experiment questionnaire obtained participants' power distance orientation using a seven-item scale. Possible power-related effects on trust and blame apportioning are discussed. Our findings suggest that artificial agents with higher levels of anthropomorphism and lower levels of rank increased trust and shared accountability, with human team members accepting more blame for team failures.

On the Evaluation Framework of Comprehensive Trust for Data Interaction in Intermodal Transport

Due to the necessity to realize “building a strong transportation nation”, the construction of intermodal transportation is based on the information resource integration of diverse transport systems. To ensure the data security during the interaction between different transport modes, as well as the effect of data application, the status of entities and data flow in the network should be supervised throughout. Therefore, an evaluation framework of comprehensive trust is proposed in this paper. With feature analysis of transportation big data, a quality assessment is conducted by three-dimensional metric sets, which is considered as a significant factor of trust measurement. Furthermore, a hierarchical trust structure is put forward to assess the trust of entities in different levels, in terms of the static and dynamic evidence. Furthermore, the visualization of a dynamic global information security state is discussed, based on temporal knowledge graphs. As shown in practical application and simulation analysis, this framework can meet the requirements of data security supervision and lay the foundation of further intelligent management. This research is of great significance to improve the data security level in intermodal transport, and to promote the utilization and sharing of public information resources.

Slave trade, trust, and corruption

ABSTRACT Theory predicts that societies with higher social capital have lower levels of government corruption. This hypothesis has been difficult to evaluate empirically because of widespread reliance on aggregated, country-level measures of trust and corruption, and reverse causality. We utilize micro-level international data from 34 African nations and two alternative measures of both trust and corruption to re-evaluate the evidence of this theorized relationship. To account for reverse causality, we use historical transatlantic slave exportation data as an instrumental variable. Slave trade has historically lowered interpersonal trust in Africa, which has had a long lasting effect on past and present levels of corruption. Our findings indicate that a higher level of personalized trust leads to more corruption, whereas a higher level of generalized trust leads to less corruption. This result is robust to alternative specifications of regression models, including different measures of trust and experienced versus perceived types of corruption.

Psychometric evaluation of the Trust in Science and Scientists Scale.

Reliable and valid measurement of trust in science and scientists is important. Assessing levels of such trust is important in determining attitudes and predicting behaviours in response to medical and scientific interventions targeted at managing public crises. However, trust is a complex phenomenon that has to be understood in relation to both distrust and mistrust. The Trust in Science and Scientists Scale has been adopted with increasing frequency in large-scale public health research. Detailed psychometric evaluation of the scale is overdue and makes meaningful comparisons between studies that use the scale difficult. Here, we examine the scale's dimensionality across five separate samples. We find that two factors emerge that are divided by their item polarity. Implications for scale use and trust in science measurement are discussed.

How Valid Are Trust Survey Measures? New Insights From Open-Ended Probing Data and Supervised Machine Learning

Trust is a foundational concept of contemporary sociological theory. Still, empirical research on trust relies on a relatively small set of measures. These are increasingly debated, potentially undermining large swathes of empirical evidence. Drawing on a combination of open-ended probing data, supervised machine learning, and a U.S. representative quota sample, our study compares the validity of standard measures of generalized social trust with more recent, situation-specific measures of trust. We find that survey measures that refer to “strangers” in their question wording best reflect the concept of generalized trust, also known as trust in unknown others. While situation-specific measures should have the desirable property of further reducing variation in associations, that is, producing more similar frames of reference across respondents, they also seem to increase associations with known others, which is undesirable. In addition, we explore to what extent trust survey questions may evoke negative associations. We find that there is indeed variation across measures, which calls for more research.

Information about changes in platform economy taxation diminishes optimism regarding future use

Some economic interactions are based on trust, others on monetary incentives or monitoring. In the tax compliance context, the monitoring approach creates compliance based on audits and fines (enforced compliance), in contrast to the trust-based (voluntary compliance) approach, which is based on taxpayers’ willingness to comply. Here, we examine how changes in taxation regarding platform economy revenues affect intended labor supply on such platforms. New EU legislation, effective from 2023, will mandate data sharing between platforms and tax authorities across Europe, thus resulting in increased monitoring. We investigate how this upcoming shift in monitoring power affects the intended use of platforms and how it may interact with users’ trust. We use a survey among platform workers (N = 626) in the Netherlands to examine views of the proposed regulation change, corrected for the proportion of platform income and several measures of trust. We experimentally manipulate information by either informing participants about the upcoming monitoring change or not. Results show that informing respondents about the change negatively affects expected supply of labor, and this effect is independent of respondents’ trust. We discuss the policy implications of these results.

Consumer trust in local food system – An empirical research in Hungary

AbstractConsumer trust is essential to any market but particularly relevant to the food sector. Sustainable food systems require integration of small-scale food producers, for them consumer trust is pivotal. However, comprehensive measurement of consumer trust regarding local food is a less explored area. Based on the adjusted version of Benson's trust toolkit, local food trust was measured on four levels. This approach, connecting locality/proximity to food-related trust, was tested with a representative quantitative consumer survey (n = 1,001) in Hungary. Interpersonal trust, general organisation trust, local food chain trust, and local food product trust were measured on a 5-point Likert scale. Correlation and boxplot analysis conducted revealed that trust levels correlate significantly but remain independent from demographic characteristics, indicating that trust in local food is not region-, education-, or income-specific amongst Hungarian consumers. A relatively high level of consumer trust was measured for local food products and producers compared to other stakeholders, strengthening the assumption for the proximity-trust relationship regarding food. This is a key factor for small-scale food producers: only shared values with the local community and earned trust can attract customers despite less flexibility with pricing and limited capacity for advertising.

Sustainable Decision-Making Enhancement: Trust and Linguistic-Enhanced Conflict Measurement in Evidence Theory

This research presents an advanced methodology to enhance conflict measurement within the Dempster–Shafer framework, integrating linguistic preferences and trust relationships for improved sustainability decision-making. By developing a unique algorithm, we introduce a novel approach to quantify inter-expert similarity and establish consensus thresholds. Furthermore, our study innovates with a dual-path adjustment mechanism to effectively reconcile discrepancies in expert opinions. These methodological advancements enable a more accurate and nuanced representation of expert judgments, facilitating superior decision support in sustainability-oriented applications. Through rigorous numerical simulations and a detailed case study, we validate our approach’s efficacy in optimizing decision-making processes, underscoring its potential to significantly influence sustainable practices and policy formulation. Our contributions not only advance theoretical understanding but also offer practical tools for incorporating expert insights into the pursuit of sustainability goals, marking a significant leap forward in decision-making research.

Modelling and Measuring Trust in Human–Robot Collaboration

Recognizing trust as a pivotal element for success within Human–Robot Collaboration (HRC) environments, this article examines its nature, exploring the different dimensions of trust, analysing the factors affecting each of them, and proposing alternatives for trust measurement. To do so, we designed an experimental procedure involving 50 participants interacting with a modified ‘Inspector game’ while we monitored their brain, electrodermal, respiratory, and ocular activities. This procedure allowed us to map dispositional (static individual baseline) and learned (dynamic, based on prior interactions) dimensions of trust, considering both demographic and psychophysiological aspects. Our findings challenge traditional assumptions regarding the dispositional dimension of trust and establish clear evidence that the first interactions are critical for the trust-building process and the temporal evolution of trust. By identifying more significant psychophysiological features for trust detection and underscoring the importance of individualized trust assessment, this research contributes to understanding the nature of trust in HRC. Such insights are crucial for enabling more seamless human–robot interaction in collaborative environments.

Program Behavior Dynamic Trust Measurement and Evaluation Based on Data Analysis

Industrial control terminals play an important role in industrial control scenarios. Due to the special nature of industrial control networks, industrial control terminal systems are vulnerable to malicious attacks, which can greatly threaten the stability and security of industrial production environments. Traditional security protection methods for industrial control terminals have coarse detection granularity, and are unable to effectively detect and prevent attacks, lacking real-time responsiveness to attack events. Therefore, this paper proposes a real-time dynamic credibility evaluation mechanism based on program behavior, which integrates the matching and symmetry ideas of credibility evaluation. By conducting a real-time dynamic credibility evaluation of function call sequences and system call sequences during program execution, the credibility of industrial control terminal application program behavior can be judged. To solve the problem that the system calls generated during program execution are unstable and difficult to measure, this paper proposes a partition-based dynamic credibility evaluation method, dividing program behavior during runtime into function call behavior and system call behavior within function intervals. For function call behavior, a sliding window-based function call sequence benchmark library construction method is proposed, which matches and evaluates real-time measurement results based on the benchmark library, thereby achieving symmetry between the benchmark library and the measured data. For system call behavior, a maximum entropy system call model is constructed, which is used to evaluate the credibility of system call sequences. Experiment results demonstrate that our method performs better in both detection success rate and detection speed compared to the existing methods.

Designing for Trust: How Human-Machine Interface Can Shape the Future of Urban Air Mobility

This study investigates the influence of human-machine interface (HMI) design on passenger trust in autonomous electric vertical take-off and landing (eVTOL) vehicles. An immersive simulator-based experiment was conducted with 34 participants, exposing them to four HMI conditions: baseline, movement, hazard detection, and full information condition. As related measures of passenger trust, we collected self-reported measures including trust, perceived safety, perceived reliability, and intention to use. In addition, physiological measures including gaze behavior, electrodermal activity, and heart rate were collected. The results indicated that movement and hazard detection information improved passenger trust, suggesting that HMI design could play a crucial role in enhancing the acceptance of autonomous eVTOLs. In addition, gaze behavior showed a stronger relationship with self-reported trust than other physiological measures. The findings underscore the importance of HMI design in fostering passenger trust, which is critical to the success of urban air mobility.

Does Political Trust Matter? A Meta-analysis on the Consequences of Trust

AbstractPolitical trust has long been seen as fundamental for societal cooperation and democratic legitimacy. However, evidence about its consequences are partial and fragmented, and we do not currently have a systematic understanding of whether political trust warrants such vast attention. This paper conducts a systematic review and meta-analysis of 61 studies reporting 329 coefficients derived from over three and a half million observations globally. After synthesising the conceptual and theoretical frameworks in the extant literature, the meta-analysis results show that trust is weakly to moderately related to outcomes as diverse as voter turnout, vote choice, policy preferences and compliance, but is unrelated to informal participation. These results are robust to a range of considerations such as the measurement of trust, modelling strategy, region of study, publication bias, and design of the study. The review also highlights substantial geographical and methodological gaps, particularly the reliance on cross-sectional designs. Substantively, the results show that trust is importantly and robustly related to what people want from their political systems and how they interact with it. By systematically analysing the extant research, the paper provides a robust, systematic, and empirical foundation to advance research on political trust.

In Financial Statements We Trust: Institutional Investors’ Stockholdings after Restatements

ABSTRACT How prior trust moderates investor responses to restatements is unknown. We examine how societal trust affects the changes in institutional investors’ shareholdings around a restatement. We consider two competing hypotheses based on the erosion of trust and confirmatory bias. We find the change in institutional investors’ shareholdings around a restatement is more negative for investors from high trust areas compared to low trust areas, consistent with an erosion of trust where high trust institutional investors view the restatement as a violation of trust. Further analyses show that our findings vary with the regulatory or economic environment, type of institution, and type of restatement. Our results are also robust to different tests that address endogeneity and use alternative societal trust measures. Overall, we contribute to the literature by examining the role of societal trust in a dynamic setting where investors’ trust-based beliefs about the credibility of accounting information are not realized. Data Availability: GSS Sensitive Data Files are not available from the authors. Persons interested in obtaining these data should contact the GSS at GSS@NORC.org. Other data are available from the public sources cited in the text. JEL Classifications: G11; G23; G41.

Development and Psychometric Validation of the Body Trust Scale.

Interoception (e.g., abilities to recognize/attend to internal sensations) is robustly associated with psychopathology. One form of interoception, body trust, is relevant for the development of disordered eating and suicidal thoughts/behaviors. However, measures of body trust are narrow, despite research suggesting body trust is multifaceted. The aim of this study was to develop a comprehensive measure of body trust: The Body Trust Scale (BTS). 479 U.S. adults completed self-report surveys containing the BTS and psychopathology measures. Exploratory and confirmatory factor analyses revealed a three-factor structure: Comfort with One's Body, Physical Attractiveness, and Comfort with Internal Sensations. Factors showed strong construct, convergent, and divergent validity, as well as moderate predictive validity for suicidal thoughts/non-suicidal self-injury. Furthermore, factors showed strong internal consistency, test-retest reliability, and were invariant across the gender binary. The BTS can be used in research and clinical settings to understand how specific facets of body trust relate to psychopathology.

Quantifying the effects of religious distance on trade misinvoicing

ABSTRACT This study investigates the impact of religious distance on trade misinvoicing, using bilateral data from 87 countries over the period 1990–2018. Trade misinvoicing refers to the combined occurrence of export under-invoicing and import over-invoicing. Religiosity is utilized as a measure of personal trust in God and adherence to religious values and principles. To capture religiosity, a composite index is constructed, considering three components: the number of religious adherents, the significance of religion, and religious service attendance. The empirical findings reveal that countries with greater religious distance exhibit reduced trade misinvoicing. Within the three dimensions of religiosity, belief and the importance of religion act as deterrents to trade misinvoicing, while religious service attendance is found to facilitate such misinvoicing. Furthermore, the effects vary across different types of misinvoicing, time periods, and sectors.

Maritime decision-makers and cyber security: deck officers’ perception of cyber risks towards IT and OT systems

Through a quantitative study of deck officers’ cyber risk perceptions towards information (IT) and operational (OT) systems, this paper contributes to substantiate the importance of considering human behaviour within maritime cyber security. Using survey data from 293 deck officers working on offshore vessels, statistical analyses were conducted to measure and predict the participants cyber risk perceptions towards IT and OT systems. Performing a Wilcoxon signed-rank test revealed a significant discrepancy in the levels of cyber risk perception between the system categories. Hierarchical regression analyses were conducted to develop statistical models, considering multiple independent variables, including perceived benefit, cyber security training, experience with cyber-attacks, and trust towards various stakeholders. Key findings revealed distinct results for IT and OT systems, and the regression models varied in both predictive power and significance of the independent variables. Perceived benefit positively predicts deck officers cyber risk perception for both IT and OT systems, while trust, which included measures of social trust and confidence, was not found to be significant. Cyber security training and experience with cyber-attacks only influence deck officers’ perception of cyber risks related to operational technology. Practical implications of this work provide actionable recommendations for the maritime industry, including tailored risk communication tools, training programs, reporting systems, and holistic policies.