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As technology evolves into streamlined tasks, Voice Assistant Devices (VADs) have become increasingly valuable for office settings. This study explores the factors influencing employees’ intentions to use VADs at work using a sample of 324 respondents from India. Integrating theories from technology adoption, innovation diffusion, and trust, this study highlights that performance expectancy, compatibility and observability significantly improve attitudes toward VAD usage in professional settings. The quality of VAD outputs notably boosts user trust, emphasizing the need for high-performance devices. Employees’ receptiveness to innovation also heightens their expectations for VADs’ effectiveness. Moreover, the compatibility and visibility of VAD benefits critically shape user attitudes, directly affecting users’ willingness to adopt this technology. Our study can help organizations plan strategies for applying VADs to support workplace tasks.

This study investigated whether the quality of governance, trustworthiness, and confidence impacts bank credit growth. In addition, we examined credit growth cyclicality in 10 members of the Association of Southeast Asian Nations. By employing data concerning 282 banks between 2012 and 2019, this study found that trustworthiness boosted bank credit growth. Overall, the increased quality of governance was found to increase credit growth, except for the specific indicators of voice and accountability and political stability, which were found not to influence bank credit growth. Moreover, similar to prior findings in related fields, the empirical results of this study confirmed the complementary effect of informal and formal institutions on bank credit growth. Lastly, results indicated that banks were pro-cyclical regarding credit growth. Overall, the results of this study highlighted the role of the supervisory powers of governments in boosting credit expansion, mainly during economic upturns.

Globally, the coronavirus disease (COVID-19) pandemic has sparked unexpected and violent outbursts against doctors, nurses, and other health personnel. In the Indian context, studies on violence against doctors and other medical staff largely focus on supply-demand imbalances in health care, overcrowding, drug shortages, negligence of critical care patients, lack of diagnostic and other essential devices (e.g., X-ray and ultrasound equipment and oxygen cylinders), deaths of patients, and bribery and corruption (collusion between doctors and pharmaceutical companies). While these factors explain such violence against medical personnel partly, we argue that it is largely rooted in a lack of trust in doctors and hospitals, which eroded rapidly during the COVID-19 pandemic. We analyze the covariates of trust in public and private health-care providers based on an all-India panel survey and delineate policies to rebuild trust, especially in public health care.

The role of social capital in economic development has been a subject of interest to both academics and practitioners of development for several decades. However, empirical evidence on social capital in the context of developing countries is still relatively scant. This study explores the effects of social capital on economic development in Indonesia, a large and multi-ethnic developing country. Using district-level data for 2006–2019, we find that the relationships between social capital and economic development are complex. There are both favorable and unfavorable effects of social capital on economic development, as well as nonlinear effects. Hence, we cannot draw unequivocal conclusions on the benefits or disadvantages of social capital for economic development. Nevertheless, this study finds that trust among people across different ethnic groups, participation in communal works and social activities, and trust in government are the most important forms of social capital needed to improve people’s welfare.

The introduction of trust-based approaches in social scenarios modeled as multi-agent systems (MAS) has been recognized as a valid solution to improve the effectiveness of these communities. In fact, they make interactions taking place in social scenarios much fruitful as possible, limiting or even avoiding malicious or fraudulent behaviors, including collusion. This is also the case of multi-layered neural networks (NN), which can face limited, incomplete, misleading, controversial or noisy datasets, produced by untrustworthy agents. Many strategies to deal with malicious agents in social networks have been proposed in the literature. One of the most effective is represented by Eigentrust, often adopted as a benchmark. It can be seen as a variation of PageRank, an algorithm for determining result rankings used by search engines like Google. Moreover, Eigentrust can also be viewed as a linear neural network whose architecture is represented by the graph of Web pages. A major drawback of Eigentrust is that it uses some additional information about agents that can be a priori considered particularly trustworthy, rewarding them in terms of reputation, while the non pre-trusted agents are penalized. In this paper, we propose a different strategy to detect malicious agents which does not modify the real reputation values of the honest ones. We introduce a measure of effectiveness when computing reputation in presence of malicious agents. Moreover, we define a metric of error useful to quantitatively determine how much an algorithm for the identification of malicious agents modifies the reputation scores of the honest ones. We have performed an experimental campaign of mathematical simulations on a dynamic multi-agent environment. The obtained results show that our method is more effective than Eigentrust in determining reputation values, presenting an error which is about a thousand times lower than the error produced by Eigentrust on medium-sized social networks.

This paper investigates the evolutionary public goods game on a network and studies the effect of the leaders rewiring mechanism (LRM) on the evolution of cooperation. A trust mechanism is introduced to give information to the leader about the sincerity of the group. The network dynamics is driven by the LRM, allowing leaders to change their game groups if these groups are not trusted anymore. We investigate how the emergence of the network guided by LRM affects the transformation of individuals’ strategies and empowers them to cooperate. We find that LRM plays a crucial role in the emergence of cooperation, by clustering the graph into regions with high clusters of cooperators and small one of defectors. LRM enables cooperators to form compact big clusters, thus reducing exploitation by defectors.

Trust was found to promote entrepreneurship in the US. We investigated whether this was true in a developing country, Indonesia. We failed to replicate this; this failure was true whether trust was estimated at the individual or community level or whether ordinary least squares (OLS) or two stage least squares (2SLS) was employed. We reconciled the difference between our results and those for the US by arguing that the weak enforcement of property rights in developing countries and the consequent hold-up problem make it more efficient for entrepreneurs to produce generic goods than relationship-specific goods—producing generic goods does not depend on trust.

This study seeks to highlight the implications of governance and reporting practices in ensuring accountability and building donors trust in Waqf Institutions (WIs). Data gathered through the survey are analyzed using PLS-SEM technique. The conceptual model was developed based on the critical review of the past literature. Among the three proxies of board attributes, only board ability has a significant positive impact on accountability. Voluntary information disclosure has a significant positive impact on accountability. Accountability has a significant impact on building trust in waqf management. Results provided by the study advocate for the adoption of formal reporting and improved governance mechanisms to enhance donors’ trust in WIs.

A macroeconomic model based on the economic variables (i) assets, (ii) leverage (defined as debt over asset) and (iii) trust (defined as the maximum sustainable leverage) is proposed to investigate the role of credit in the dynamics of economic growth, and how credit may be associated with both economic performance and confidence. Our first notable finding is the mechanism of reward/penalty associated with patience, as quantified by the return on assets. In regular economies where the EBITA/Assets ratio is larger than the cost of debt, starting with a trust higher than leverage results in the highest long-term return on assets (which can be seen as a proxy for economic growth). Therefore, patient economies that first build trust and then increase leverage are positively rewarded. Our second main finding concerns a recommendation for the reaction of a central bank to an external shock that affects negatively the economic growth. We find that late policy intervention in the model economy results in the highest long-term return on assets. However, this comes at the cost of suffering longer from the crisis until the intervention occurs. The phenomenon that late intervention is most effective to attain a high long-term return on assets can be ascribed to the fact that postponing intervention allows trust to increase first, and it is most effective to intervene when trust is high. These results are derived from two fundamental assumptions underlying our model: (a) trust tends to increase when it is above leverage; (b) economic agents learn optimally to adjust debt for a given level of trust and amount of assets. Using a Markov Switching Model for the EBITA/Assets ratio, we have successfully calibrated our model to the empirical data of the return on equity of the EURO STOXX 50 for the time period 2000–2013. We find that dynamics of leverage and trust can be highly nonmonotonous with curved trajectories, as a result of the nonlinear coupling between the variables. This has an important implication for policy makers, suggesting that simple linear forecasting can be deceiving in some regimes and may lead to inappropriate policy decisions.

Traditional discretionary access control (DAC) or mandatory access control (MAC) is not quite effective for remote environment. General role-based access control (RBAC) also has its limitations to control access for applications like remote healthcare due to its static nature. Design of effective access control model is indeed a big challenge for such applications. Fine-grained access control logic capable of adapting changes based on run-time information needs to be considered to meet the requirements of remote healthcare scenarios. In this work, an adaptive access control model is proposed keeping in compliance with state–of-the-art scenario towards ensuring quality of context and trust relationship between owners and users. It focuses on inter-component relationship, where phases are executed either in online or in offline mode to avoid performance bottleneck. Adaptive binding is integrated with application specific workflows. Workflow net models for different scenarios of medical kiosk-based rural healthcare are analyzed to validate proposed access control logic at design time that can reduce the possibility of major faults at run time. Our proposed work supports formal analysis and verification using Workflow Petri Net Designer (WoPeD) tool. Comparative analysis shows how proposed access control model advances the state of art.

Increasing availability of information has furthered the need for recommender systems across a variety of domains. These systems are designed to tailor each user's information space to suit their particular information needs. Collaborative filtering is a successful and popular technique for producing recommendations based on similarities in users' tastes and opinions. Our work focusses on these similarities and the fact that current techniques for defining which users contribute to recommendation are in need of improvement.

In this paper we propose the use of trustworthiness as an improvement to this situation. In particular, we define and empirically test a technique for eliciting trust values for each producer of a recommendation based on that user's history of contributions to recommendations.

We compute a recommendation range to present to a target user. This is done by leveraging under/overestimate errors in users' past contributions in the recommendation process. We present three different models to compute this range. Our evaluation shows how this trust-based technique can be easily incorporated into a standard collaborative filtering algorithm and we define a fair comparison in which our technique outperforms a benchmark algorithm in predictive accuracy.

We aim to show that the presentation of absolute rating predictions to users is more likely to reduce user trust in the recommendation system than presentation of a range of rating predictions. To evaluate the trust benefits resulting from the transparency of our recommendation range techniques, we carry out user-satisfaction trials on BoozerChoozer, a pub recommendation system. Our user-satisfaction results show that the recommendation range techniques perform up to twice as well as the benchmark.

The interactions between parties within large open systems are driven by trust. A major component in building trust is represented by reputation. When the trust value of a target agent, computed by its partner based on previous experience, and the reputation it has been assigned are different, an agreement is reached by negotiation. This paper describes a normative multiagent system and proposes a negotiation model where reputation represents the leverage that ensures norm enforcement. The negotiation framework is comprised of a marketplace using barter exchanges. Based on this model, the paper shows how self-interested agents manage to establish cooperation relationships in order to accomplishing their goals, being aware of the influence that reputation has on the costs of future transactions.

Despite the widespread usage of the evaluation mediums for online services by the clients, there is a requirement for a trust evaluation tool that provides the clients with the degree of trustworthiness of the service providers. Such a tool can provide increased familiarity with unknown third party entities, e.g. service providers, especially when those entities neither project completely trustworthy nor totally untrustworthy behaviour. Indeed, developing some metrics for trust evaluation under uncertainty can come handy, e.g., for customers interested in evaluating the trustworthiness of an unknown service provider throughout queries to other customers of unknown reliability.

In this research, we propose an evaluation metric to estimate the degree of trustworthiness of an unknown agent, say a^D, through the information acquired through a group of agents who have interacted with agent a^D. This group of agents is assumed to have an unknown degree of reliability. In order to tackle the uncertainty associated with the trust of these set of unknown agents, we suggest to use possibility distributions. Later, we introduce a new certainty metric to measure the degree of agreement in the information reported by the group of agents in A on agent a^D. Fusion rules are then used to measure an estimation of the agent a^D’s degree of trustworthiness. To the best of our knowledge, this is the first work that estimates trust, out of empirical data, subject to some uncertainty, in a discrete multi-valued trust domain. Finally, numerical experiments are presented to validate the proposed tools and metrics.

As web-based social network allows anyone to post the content without any restriction, the trustworthiness of the content creator plays an important role before using the content. An effiective way to find the trustworthiness is, by analyzing the web resources related to the content creator. Therefore the trustworthiness is assessed using the provenance based ontological model called W7 model. Since it is a real time data, the computed trust for each reviewer using the ontological model is uncertain and vague. An appropriate way to classify such data is using the fuzzy logic with gradual trust level. As the computed trust data are feature-based and non-symbolic, the classification ambiguity need to be reduced greatly. This is achieved with the fuzzy decision tree approach, which is a fusion of fuzzy sets with decision tree. The truth of the rule is crucial in trustworthy user classification, as highly truthful rules really increase the credibility of the user in their domain. Therefore, in the proposed model, degree of truth is used as a pruning criteria that classifies the users with minimum number of fuzzy evidence or knowledge. This paper proposes a semantic provenance based gradual trust model to classify the trustworthy reviewers in a book-based social networks using fuzzy decision tree approach. Performance analysis of the proposed model in the terms of classifier accuracy, precision, recall, the number of rules generated and its time complexity are discussed. The analysis shows that the proposed learning model outperforms other classification models. This method is also applied to other data sets and the performance of the classifier is assessed.

Case study based literature on relationship development presents in-depth information on contextual factors in relationship development. However, little quantitative evidence is available about key aspects of buyer-supplier relationships in each stage of its development, such as the level of trust/commitment, buyer's and supplier's dependence. The study will try to fill this gap by identifying and quantifying these aspects from the buyer's perspective in each development stage. A comprehensive survey among 238 Dutch purchasing professionals provides evidence on how these characteristics of relationships change when relationships develop over time. The results largely confirm the hypotheses, which stem from the extant literature about organizational dependence and trust/commitment. A notable finding is that the buyer perceives to be dependent on the supplier, even in a desirable relationship. Managerial implications are that: (1) industrial marketers should be aware that professional purchasers feel dominated by them, even in relationships that are positively evaluated and therefore desirable in the view of the buyer; and (2) that purchasers should be aware that dependence implies vulnerability, even when the relationship is still developing in an otherwise desirable way.

This study uses theories on trust and opportunism to investigate the economic behaviour and business practices of enterprises in the Republic of Karelia, one of Russia's northern territories. Three enterprise types that have emerged during transition period in the Republic ("passives", "realists" and "innovators") have been identified on the basis of this analysis. Furthermore, both formal and informal market institutions in the Republic of Karelia were found to be underdeveloped, which creates numerous possibilities for opportunistic behaviour, both by authorities and by business actors. This, in turn, stimulates the creation of informal business networks and the use of particularly cautious business practices. The study is largely based on the results of a standardised survey and in-depth interviews conducted with the heads of 100 enterprises operating in the Republic of Karelia. The interviews were conducted in June and July of 2004 by the Institute of Economics research group in the Karelian Research Centre of the Russian Academy of Sciences.

Culture offers an important setting for entrepreneurship to grow, and trust is critical for entrepreneurship to thrive. In recent years, there has been debate whether Chinese culture facilitates or hinders entrepreneurship; there has also been a call for empirical investigation of trust in entrepreneurship research. Our paper investigates the relationship between Chinese cultural values and two kinds of trust, in two different enterprises as two subcultures in China. The two kinds of trust are dispositional trust and interpersonal trust; and the two enterprises are a joint venture and a state-owned enterprise. We composed questionnaire from established work about trust and cultural values, ran survey research on 226 employees in the two organizations in China, and analyzed the survey data by descriptive statistics, factor analysis, correlations, and MANOVA. We found that dispositional trust and interpersonal trust are different at individual level; Chinese cultural values correlate significantly with both dispositional trust and interpersonal trust, and positively correlate to both kinds of trust. Employees in the state-owned enterprise held higher level of Chinese cultural values but had lower level of interpersonal trust, which suggests potential problems in its management. Our study is one of the recent studies that separately measure dispositional trust and interpersonal trust, and our findings across two different types of organizations have practical implications for entrepreneurship research in China. Our study is also one of the recent studies that find Chinese cultural values may benefit trust in enterprises, although some earlier studies suggested the opposite.

In multi-synchronous collaboration users replicate shared data, modify it and redistribute modified versions of this data without the need of a central authority. However, in this model, no usage restriction mechanism was proposed to control what users can do with the data after it has been released to them. In this paper, we extend the multi-synchronous collaboration model with contracts that express usage restrictions and that are checked a posteriori by users when they receive the modified data. We propose a merging algorithm that deals not only with changes on data but also with contracts. A log auditing protocol is used to detect users who do not respect contracts and to adjust user trust levels. Our contract-based model was implemented and evaluated by using PeerSim simulator.

Providing a creditable basis for access control decision-making is not an easy task for the resource pooling, dynamic, and multi-tenant cloud environment. The trust notation can provide this creditable basis, based on multiple factors that can accurately compute the user’s trust for the granting access entity. In this paper, the formal trust model has been introduced, which presents a novel method to provide the basis for granting access. It is based on three factors and their semantic relations, which investigate important measures for the cloud environment. Also, a new Trust-Based Access Control (TB-AC) model has been proposed. The proposed model supports dynamically changing the user’s assigned permissions based on its trust level. In addition, TB-AC ensures secure resource sharing among potential untrusted tenants. TB-AC has been deployed on a separated VM in our private cloud environment, which is built using OpenStack. The experimental results indicated that TB-AC can evaluate access requests within reasonable and acceptable processing times, which is based on the final trust level calculation and the communication between TB-AC and some of the intended OpenStack services. By considering very rough conditions and huge traffic overhead, the final trust level can be calculated in an average time of 200ms. Furthermore, the communication overhead between TB-AC and each of Keystone, Nova, and Neutron is very light. Finally, TB-AC has been tested under different scenarios and is provable, usable and scalable.

In this paper, the problem of cooperation is formulated in a dynamic framework. The proposed model interrelates the process of a joint production activity involving two partners and the dynamics of a common monitoring activity of their respective contributions. Analysis of the existence of a stationary equilibrium leads to a set of predictions on the long run issue of cooperation from given initial conditions.