Measures Of Trust Research Articles

Supplemental Material for The Epistemic Trust Assessment—An Experimental Measure of Epistemic Trust

Supplemental Material for The Epistemic Trust Assessment—An Experimental Measure of Epistemic Trust

Trust Predicts Compliance with Covid-19 Containment Policies: Evidence from Ten Countries Using Big Data

Previous evidence indicates that trust is an important correlate of compliance with Covid-19 containment policies. However, this conclusion hinges on two crucial assumptions: first, that compliance does not change over time, and second, that mobility and self-reported measures are good proxies for compliance. We demonstrate that compliance changes over the period March 2020 to January 2021, in ten mostly European countries, and that increasing (decreasing) trust in others predicts increasing (decreasing) compliance. We develop the first time-varying measure of compliance, which is calculated as the association between containment policies and people’s mobility behavior using data from Oxford Policy Tracker and Google. We also develop new measures of both trust in others and national institutions by applying sentiment analysis to Twitter data. We test the predictive role of trust using a variety of dynamic panel regression techniques. This evidence indicates compliance should not be taken for granted and confirms the importance of cultivating social trust.

Validating the potential measures of e-brand trust and e-brand loyalty: an examination using SEM

Validating the potential measures of e-brand trust and e-brand loyalty: an examination using SEM

Community cohesion and violence against women in Ghana, Pakistan, and South Sudan: A secondary data analysis.

Background:Much knowledge has been accumulated on individual-level risks and protective factors of violence against women. However, the influence of factors operating at the community level, such as community cohesion, remains unclear, especially in low- and middle-income countries. This study examined whether community cohesion, a combined measure of mutual trust and tolerance, affects women’s likelihood of experiencing intimate partner violence, violence perpetrated by a family member, and violence occurring in public spaces.Methods:Data on 4785 women aged between 18 and 60 years in Ghana, Pakistan, and South Sudan, initially collected by the ‘What Works to Prevent Violence Against Women and Girls’ Research and Innovation Programme, were used for this study. Binary logistic regression analyses were used to assess the association between a composite measure of community cohesion and recent experience of physical, sexual, and psychological violence while controlling for different individual-, community-, and state-level variables.Results:Multivariate analyses revealed that community cohesion was associated with lower risks of public spaces violence (adjusted odds ratio = 0.396, 95% confidence interval = 0.312–0.503, P < 0.001) and family member violence (adjusted odds ratio = 0.839, 95% confidence interval = 0.754–0.934, P < 0.001). There was no statistically significant association between intimate partner violence and community cohesion, but adjusted models showed that women with more developed social networks were at higher risks of experiencing intimate partner violence (adjusted odds ratio = 1.104, 95% confidence interval = 1.062–1.148, P < 0.001).Conclusion:Our findings suggest that community cohesion may have a protective effect against the most visible forms of violence against women. However, this effect may be attenuated or even eliminated by other individual- or household-level mechanisms in the case of violence between intimates or family members.

A Comparison of Single-Item and Multi-Item Measures of Trust: An Empirical Study

A Comparison of Single-Item and Multi-Item Measures of Trust: An Empirical Study

Beliefs, Learning, and Personality in the Indefinitely Repeated Prisoner's Dilemma

The indefinitely repeated prisoner’s dilemma (IRPD) captures the trade-off between the short-term payoff from exploiting economic partners and the long-term gain from building successful relationships. We aim to understand more about how people form and use beliefs about others in the IRPD. To do so, we elicit beliefs about the supergame strategies chosen by others. We find that initial beliefs match behavior quite well and that most subjects choose strategies that perform well given their beliefs. Motivated by belief clustering, we use beliefs to estimate a level-k model of boundedly rational thinking. We analyze how beliefs and behavior evolve with experience: beliefs become more accurate over time, and we use beliefs to provide new evidence about the mechanism that underlies learning from experience. Finally, we find that a survey measure of trust predicts cooperative behavior and optimism about others’ cooperation, which helps to explain how trust underpins successful economic exchanges.

Reliability and Validity of the Japanese Version of an Affect- and Cognition-based Trust Measure in the Workplace

Reliability and Validity of the Japanese Version of an Affect- and Cognition-based Trust Measure in the Workplace

A fuzzy trust measurement method considering patients' trust opinions in Internet plus Healthcare

With the outbreak of COVID-19, Internet plus Healthcare has developed rapidly with a number of Internet plus Healthcare platforms emerging. The problem of doctor-patient trust is a key issue restricting the development of the Internet plus Healthcare, which has aroused extensive attention of scholars. The patient's perceived trust on the Internet plus Healthcare platform has the characteristics of subjectivity, ambiguity, and high perceived risk. Therefore, existing trust calculation method becomes inapplicable because these characteristics have not been considered. In order to solve this problem, this study extracts influencing factors of patient trust on the Internet plus Healthcare platform, gives a trust calculation method based on intuitionistic fuzzy set theory, and added a risk preference coefficient in order to integrate the characteristics of patients' high perceived risk into the proposed method. This method is conducive to the platform to provide patients with more accurate doctor recommendations

Self-reported &amp; revealed trust: Experimental evidence

I study the relationship between self-declared trust attitudes – using a well-recognised and established personality questionnaire — and trust choices in an induced infinitely repeated trust game. I find that self-reported trust measures are significantly related with trust choices as long as trust is part of equilibrium strategies. I find that questions regarding others’ intentions is a missing component in previous work that studies self-reports of trust. An important aspect of the design is that first movers are not privy to the choices made by their partners. This design feature, coupled with an uncertainty element introduced in determining the first mover’s final payoff, allows me to analyse how first movers react to bad outcomes. Trusting individuals are more likely to give the benefit of doubt to others. Analysis of the incentivised subjective beliefs reveals that the effect of personality traits on trust choices is not through the formation of beliefs.

CBiLSTM: A Hybrid Deep Learning Model for Efficient Reputation Assessment of Cloud Services

The cloud market is characterized by fierce rivalry among cloud service providers. The availability of various services with identical functionalities on the market complicates the selection decision for service requesters. Although objective trust measurements can be used to evaluate the trustworthiness of services, they are not always available and are static in nature. Subjective approaches are not always viable because they often require repeated service invocations to collect client feedback. To overcome these limitations, we propose, in this paper, a reputation-based trust assessment approach that combines the Net Brand Reputation (NBR) measure with a deep learning-based sentiment analysis model using online user reviews. CBiLSTM is the name of the proposed deep learning model that hybridizes the Convolutional Neural Networks (CNN) and the Bidirectional Long Short-Term Memory (BiLSTM) layers. The CNN layers deal with text inputs’ high dimensionality, while the BiLSTM layer explores the context of the extracted features in both forward and backward directions. CBiLSTM was trained on a new dataset named CLOSER-DREAM, containing more than 13,000 reviews relating to several emerging cloud services to classify these reviews and assess the overall reputation of the cloud services providers. The results of the series of experiments that were conducted have shown that CBiLSTM outperforms the classic deep learning models with 98% of precision, 99% of recall, 98% as an F1-score, and 99.7% of accuracy. Also, CBiLSTM offered a reasonable training time of about 519ms with the CLOSER-DREAM dataset. The classification obtained by applying CBiLSTM was proven to be an effective method to calculate the NBR measure used for the reputation assessment of cloud service providers. The proposed technique yielded an NBR score of 98.3% for Google cloud services, which is close to the real/actual NBR of 96.25%.

A Bifactor Model of Trait Trust, Distrust, and Suspicion

Abstract. We explored competing models using bifactor item response theory (IRT) analyses to determine the relationship between trait measures of trust, distrust, and suspicion. The model with a general factor for all three scales fits the data best. We explored the relationship of the emergent general factor by correlating it with two latent traits: Agreeableness and the Trust facet of Agreeableness. The exploratory findings showed evidence that the general factor from the best-fitting model was practically identical to the Trust facet of Agreeableness. We concluded that trait trust, distrust, and suspicion reside on a continuum represented by the general factor, which is dispositional trust.

Measuring Political Trust in a Modern Democracy

Systematic review of articles on the problem of measuring political trust published in such authoritative scientific journals as “European Political Science Review”, “British Journal of Politics and International Relations”, “Parliamentary Affairs”, “Journal of Public Policy”, “Political Science Quarterly”, “Perspectives on Politics” and “International Journal of Public Opinion Research”, demonstrates considerable attention of scientists to the problem of political trust in a modern democracy and pursues as its goal the development of a methodological basis for political trust research. The methodology of the review on the problem of measuring political trust involves a comparative analysis of the results of studies in the field of assessing political trust. Among the main tasks of the article are: generalization of scientific approaches to political trust, development of methods for political trust measurements and its result interpretation, characterization of the crisis of trust in a modern democracy. The objectives of the study include identifying conceptual scientific works of Western scientists for the period 2011–2021, which allow tracing the modernization of ideas about the object of political trust, characterize the methods of measuring the level of political trust used in modern socio-political science, and reveal differences in the formation of moral and strategic trust. The differences in moral political trust, where trusting relationships are formed on the basis of the experience and personal interaction of subjects, and strategic trust, where political culture is formed and certain expectations that political leaders will make correct, rational decisions are based on fundamental ideas about the political an order where constant change forms personal and institutional ties. Measuring political trust, which is the basis of interaction between citizens and the state, is an issue of fundamental importance for characterizing the quality of democracy, and the rule of law is impossible without a high level of political trust.

Measurement of interpersonal trust in virtual software teams: A systematic literature review

Measurement of interpersonal trust in virtual software teams: A systematic literature review

Trust in automated vehicles

<p id="p00005">Automated driving (AD) is one of the key directions in the intelligent vehicles field. Before full automated driving, we are at the stage of human-machine cooperative driving: Drivers share the driving control with the automated vehicles. Trust in automated vehicles plays a pivotal role in traffic safety and the efficiency of human-machine collaboration. It is vital for drivers to keep an appropriate trust level to avoid accidents. We proposed a dynamic trust framework to elaborate the development of trust and the underlying factors affecting trust. The dynamic trust framework divides the development of trust into four stages: dispositional, initial, ongoing, and post-task trust. Based on the operator characteristics (human), system characteristics (automated driving system), and situation characteristics (environment), the framework identifies potential key factors at each stage and the relation between them. According to the framework, trust calibration can be improved from three approaches: trust monitoring, driver training, and optimizing HMI design. Future research should pay attention to the following four perspectives: the influence of driver and HMI characteristics on trust, the real-time measurement and functional specificity of trust, the mutual trust mechanism between drivers and AD systems, and ways in improving the external validity of trust studies.

FSM based Intrusion Detection of Packet Dropping Attack using Trustworthy Watchdog Nodes

Introduction: * The proposed TWIST model aims to achieve a secure MANET by detecting and mitigating packet dropping attack using finite state machine based IDS model. * To determine the trust values of the nodes using context-aware trust calculation * To select the trustworthy nodes as watchdog nodes for performing intrusion detection on the network * To detect and isolate the packet dropping attackers from routing activities, the scheme uses FSM based IDS for differen-tiating the packet dropping attacks from genuine nodes in the MANET. Method: In this methodology, instead of launching an intrusion detection system (IDS) in all nodes, an FSM based IDS is placed in the trustworthy watchdog nodes for detecting packet dropping attacker nodes in the network. The proposed FSM based intrusion detection scheme has three steps. The three main steps in the proposed scheme are context- aware trust calculation, watchdog node selection, and FSM based intrusion detection. In the first process, the trust calculation for each node is based on specific parameters that are different for malicious nodes and normal nodes. The second step is the watchdog node selection based on context-aware trust value calculation for ensuring that the trust-worthy network monitors are used for detecting attacker nodes in the network. The final process is FSM based intrusion detection, where the nodes acquire each state based on their behavior during the data routing. Based on the node behavior, the state transition occurs, and the nodes that drop data packets exceeding the defined threshold are moved to the malicious state and restricted to involve in further routing and services in the network Result: The performance of the proposed (TWIST) mechanism is assessed using the Network Simulator 2 (NS2). The proposed TWIST model is implemented by modifying the Ad-Hoc On-Demand Distance Vector (AODV) protocol files in NS2. Moreover, the proposed scheme is compared with Detection and Defense against Packet Drop attack in the MANET (DDPD) scheme. A performance analysis is done for the proposed TWIST model using performance metrics such as detection accuracy, false-positive rate, and overhead and the performance result is compared with that of the DDPD scheme. After the compare result we have analyzed that the proposed TWIST model exhibits better performance in terms of detection accuracy, false positive rate, energy consumption, and overhead compared to the existing DDPD scheme. Conclusion: In the TWIST model, an efficient packet dropping detection scheme based on the FSM model is proposed that efficiently detects the packet dropping attackers in the MANET. The trust is evaluated for each node in the network, and the nodes with the highest trust value are selected as watchdog nodes. The trust calculation based on parameters such as residual energy, the interaction between nodes and the neighbor count is considered for determining watchdog node selec-tion. Thus, the malicious nodes that drop data packets during data forwarding cannot be selected as watchdog nodes. The FSM based intrusion detection is applied in the watchdog nodes for detecting attackers accurately by monitoring the neigh-bor nodes for malicious behavior. The performance analysis is performed between the proposed TWIST mechanism and existing DDPD scheme. The proposed TWIST model exhibits better performance in terms of detection accuracy, false positive rate, energy consumption, and overhead compared to the existing DDPD scheme Discussion: This work may extend the conventional trust measurement of MANET routing, which adopts only routing behavior observation to cope with malicious activity. In addition, performance evaluation of proposed work under packet dropping attack has not been performed for varying the mobility of nodes in terms of speed. Furthermore, various perfor-mance metric parameters like route discovery latency and malicious discovery ratio which can be added for evaluate the performance of protocol in presence of malicious nodes. This may be considered in future work for extension of protocol for better and efficient results. Furthermore, In the future, the scheme will focus on providing proactive detection of packet dropping attacker nodes in MANET using a suitable and efficient statistical method.

An Integrity Measurement Scheme for Containerized Virtual Network Function

The deployment of virtual network function (VNF) in the container can realize the 5G service-based architecture (SBA) with high flexibility. The container carrying the VNF has poor isolation and low protection capabilities, and there is a security risk of being tampered and replaced. Current security protection technologies such as access control, intrusion detection, and virus detection cannot ensure that the container is not illegally modified. In order to fundamentally protect the integrity of containerized VNFs, this paper proposes a containerized VNF trust measurement scheme container integrity measurement (CIM). The scheme extends the chain of trust to bare metal containers and virtual machine containers, and experiments are carried out in a containerized VNF communication environment. The results show that the integrity measurement protection scheme is effective. Compared with ordinary containers, the average CPU usage of trusted containers has increased by 26%, and the average memory usage growth rate is less than 1%, the performance overhead caused by CIM is acceptable.

Conceptualization and Measurement of Trust in Home–School Contexts: A Scoping Review

Objective and Method: This review unravels the complexity of trust in home–school contexts across the globe by drawing on 79 peer-reviewed quantitative empirical studies spanning over two decades (2000–2020). The goal is to refocus attention on how trust has been defined and operationalized in recent scholarship.Findings: The findings reveal four essential pillars in the conceptualization of trust: the trustor’s propensity to trust, shared goals, the trustor–trustee relationship, and the trustee’s trustworthiness. However, the operationalization of trust in existing measures does not fully capture these essential pillars, as it is mainly based on trustee characteristics of benevolence, reliability, openness, competence, and honesty rather than on the trustor’s actual trust behavior.Conclusion: Most “trust studies” are essentially measuring trustworthiness and not the purported trust. Therefore, a shift in the conceptualization and measurement of trust is proposed. The review contributes to the understanding and assessment of home–school and workplace relationships.

A profile of the health information seeker in Chile: Introducing the Chilean health information environments (EIS) survey

AbstractIn recent years, many countries have sought to assess health information acquisition among their populations. The Health Information Environments (Entornos de Información de Salud; EIS) survey is the first systematic and representative study that addresses access and use of health information in Chile. Modeled after the National Cancer Institute's Health Information National Trends Survey (HINTS), the survey included measures of general health information acquisition, source trust, and health behaviors, with a special focus on physical activity, fruit and vegetable consumption, and smoking cessation. The EIS survey was applied face‐to‐face in Santiago, the capital of Chile, between December 2020 and February 2021 in a sample of 1411 individuals older than 24 years. In this paper, we present the main sections of the EIS questionnaire, the application procedure, response rates, and sample characteristics. Additionally, we used EIS data to present a profile of health information seekers in Chile.

The cultural origins of institutional trust: The case of the European Central Bank

Does culture have a causal effect on institutional trust? We answer this question by assessing the non-economic determinants of public trust in the European Central Bank. To analyse institutional trust, we employ a novel dataset of citizen perceptions and knowledge about the European Central Bank. Cultural traits are measured by indicators of social trust at the level of Euro area sub-national regions. We show that individuals living in regions with lower social trust systematically exhibit less trust in the European Central Bank. An instrumental variable approach based on historical variables of education and political institutions supports a causal interpretation of our findings. These results are robust across different model specifications and measures of trust.

Communicating the diagnosis of cancer or depression: Results of a randomized controlled online study using video vignettes.

BackgroundCommunicating a diagnosis is highly important, yet complex, especially in the context of cancer and mental disorders. The aim was to explore the communication style of an oncologist vs. psychotherapist in an online study.MethodsPatients (N = 136: 65 cancer, 71 depression) were randomly assigned to watch a standardized video vignette with one of two communication styles (empathic vs. unempathic). Outcome measures of affectivity, information recall, communication skills, empathy and trust were applied.ResultsRegardless of diagnosis, empathic communication was associated with the perception of a significantly more empathic (p < 0.001, ηpartial2 = 0.08) and trustworthy practitioner (p = 0.014, ηpartial2 = 0.04) with better communication skills (p = 0.013, ηpartial2 = 0.05). Cancer patients reported a larger decrease in positive affect (p < 0.001, ηpartial2 = 0.15) and a larger increase in negative affect (p < 0.001, ηpartial2 = 0.14) from pre‐ to post‐video than depressive patients. Highly relevant information was recalled better in both groups (p < 0.001, d = 0.61–1.06).ConclusionsThe results highlight the importance of empathy while communicating both a diagnosis of cancer and a mental disorder. Further research should focus on the communication of a mental disorder in association with cancer.