Level Of Trustworthiness Research Articles

Evolving trust in business relationships – A behavioural experiment

Abstract Using situation-specific and dyadic data, we analyse how trust in inter-organisational relationships evolve over time. Based on a multidisciplinary approach, we define four trust-related concepts, which include both behavioural and perceptual aspects of this multifaceted phenomenon. We also develop the hypothesis that the behavioural consistency of the trustee affects the level of his/her trustworthiness as perceived by the trustor. To test this hypothesis, the paper specifies a finite Dynamic Trust Game that, in a unique way, models longer-term relationships characterised by interdependent actions between partners. In contrast to the simple Repeated Games modelling discrete exchange episodes, this game corresponds to the requirements of the interaction approach of the relationship management, since the iterations of the game are interrelated and embedded in previous ones. Timely development of the behavioural variables in the game reflects an inverse U-shape with an increasing willingness to cooperate until round 8, with a maximum cooperation level of 80% on average. Behaviour seems to affect the perceived level of trustworthiness. However, we need additional experimental data on inconsistent behaviours to get a clear understanding of this effect.

A study of the properties of the tectonic structure of the Kryvyi Rih city based on statistical analysis of seismicity

The article concerns results of studying two significant seismic events — an explosion (23.09.2020 09:00) and an earthquake (30.09.2020 20:00), which occurred in the area of the city of Kryvyi Rih. There were developed algorithms to process records by the Kryvyi Rih seismic station. There was done a comprehensive interpretation of the seismologic and geological-geophysical data. The seismic events’ coordinates were determined, the quality of seismological data processing was evaluated based on the expert method, the level of trustworthiness of the resulting parameters was estimated. The nature of the eathquake’s origin was analyzed based on the shape and the records’. The structure of the mechanism of the earthquake center shows that this was a natural seismic event of the induced type. The article also provides results of geotectonics study of the region of its origin and shows the connection of the geological structures with the epicenter’s parameters. The parameters of the stress-strain state of the crust in the Kryvyi Rih iron-ore basin are calculated, and the parameters of the epicenter of the induced earthquake (2020.09.30 20:00:40.836, UTC) are established. The experimentally calculated depth of the epicenter of local earthquake coincides with the hypothetical depth of 15 km at the lower edge of faults according to the model of the distribution of the depths of the earthquake hypocenters on the Eastern European platform. The proposed methods and algorithms open new opportunities for modern geophysical research in the long-term seismic hazard assessment of Kryvyi Rih. Meanwhile, for a more precise determination of the place, time and nature of local seismic events in the area it is necessary to develop, here and on adjacent territories, a network of seismic stations capable of recording events of comparable and lesser magnitude.

Метод оценки уровня благонадежности сотрудников в системе кадровой безопасности предприятия (на примере предприятий критической информационной инфраструктуры (КИИ))

The article discusses a method for assessing the level of trustworthiness based on the compilation of a competence profile. This method provides a qualitative assessment of employee competencies and has been applied to employees employed at critical information infrastructure enterprises. The article presents the approbation data, shows the consistency of the expert assessment.

TrustFSDV: Framework for Building and Maintaining Trust in Self-Driving Vehicles

The deployment of fully self-sufficient, self-driving vehicles (SDVs) to city roads in a diverse range of benefit-driven concepts is on the horizon where the higher levels of automation with automated driving systems (ADSs) are being installed on vehicles. The acceptance of SDVs as safety-critical systems to successfully substitute for human drivers requires a high level of trustworthiness in numerous socio-technical aspects. By the time SDVs are available to the general public with challenges and opportunities, the trustworthiness of SDVs in rapidly changing, partially observable, multiagent, stochastic, sequential, dynamic, continuous and unknown environments shall be ensured from the perspective of the stakeholders. This paper analyses the dynamics of trust in SDVs from the perspectives of all stakeholders to result in how the “trustworthiness” of SDVs can be ensured with mitigated risks and dangers (i.e., non-maleficence) and maximised benefits (i.e., beneficence) as a holistic view of all the dimensions and dynamics of SDVs. Besides, a framework, namely, TrustFSDV, that designates a transitional roadmap with delegated responsibilities and liabilities along with the desired performance indicators, is proposed for the stakeholders to pursue in ensuring a sufficient level of trust within the concepts of human-vehicle integration and society-vehicle harmonisation — inducing the advancement of SDVs and acceptance of them by the society — while the future is being revolutionised by SDVs. To conclude the key findings in this research, i) although instant trust can be gained through hard work, it can be lost readily without forward-thinking during the life-cycle of SDVs, ii) a high level of trustworthiness cannot only be achieved by manufacturers, it requires the strict collaboration of all the stakeholders with distributed responsibilities and liabilities while moulding the requirements of the stakeholders in a system under the supervision of related disciplines, iii) the manufacturers along with the press shall not ignore the real technical limitations of the SDV technology when communicating about benefits to the potential customers, and iv) drastic steps are needed to be taken concerning ethical and legal perspectives, which is perceived as the primary concern beyond technology to build and maintain a high level of trustworthiness.

Diversity in financial planning: Race, gender, and the likelihood to trust a financial planner

AbstractThis study used an experiment to investigate the association between race, gender, and trust as it relates to working with a financial planner among a sample of Black and White MTurk respondents. Results revealed neither a respondent nor a financial planner's race was related to the level of trustworthiness perceived by a respondent. However, respondents were more likely to trust women financial planners than men financial planners. In addition, women respondents were more likely than men respondents to trust financial planners. Implications of this study are important in understanding the challenges and opportunities faced by underrepresented populations in succeeding as a financial planner.

35.2 Trust and Childhood Maltreatment: Evidence of Bias in Appraisal of Unfamiliar Faces

Child maltreatment is associated with poorer social functioning and increased risk of mental health problems in adolescence and adulthood, but the processes underlying these associations remain unclear. Although crucial for establishing and maintaining relationships, trust judgments have not been experimentally investigated in children who have experienced abuse and neglect. A community-based sample of 75 children and adolescents aged 8 to 16 years with maltreatment documented on the basis of social services records, and a group of 70 peers selected to be of similar age, cognitive ability, socioeconomic status, gender, and ethnicity, took part in a facial trustworthiness judgment task. Children judged 10 computer-generated facial stimuli with each identity at 1 SD increments from –3 to +3 SD from the mean along a computationally modeled trustworthiness dimension. A generalized-linear multilevel model (GLMM) with a probit link function was carried out to estimate parameters at the group level while assigning random effects to the face identities. The bias (corresponding to the level of trustworthiness at which the child was equally likely to respond “trustworthy” or “not trustworthy”) was significantly greater in the group of children with documented histories of maltreatment (95% CI, 0.79-2.36) than in children without maltreatment histories (95% CI, –0.48 to 0.28). This study provides experimental evidence that children and adolescents with documented maltreatment perceive others as less trustworthy than their peers. Given that facial trustworthiness judgments have been linked to approach and avoidance, over time, alterations in trust processing may disrupt the development of social bonds and contribute to “social thinning” (a reduction in the extent and quality of social relationships), leaving children more vulnerable to environmental stressors, increasing the risk of mental health difficulties.

VERCASM-CPS: Vulnerability Analysis and Cyber Risk Assessment for Cyber-Physical Systems

Since Cyber-Physical Systems (CPS) are widely used in critical infrastructures, it is essential to protect their assets from cyber attacks to increase the level of security, safety and trustworthiness, prevent failure developments, and minimize losses. It is necessary to analyze the CPS configuration in an automatic mode to detect the most vulnerable CPS components and reconfigure or replace them promptly. In this paper, we present a methodology to determine the most secure CPS configuration by using a public database of cyber vulnerabilities to identify the most secure CPS components. We also integrate the CPS cyber risk analysis with a Controlled Moving Target Defense, which either replaces the vulnerable CPS components or re-configures the CPS to harden it, while the vulnerable components are being replaced. Our solution helps to design a more secure CPS by updating the configuration of existing CPS to make them more resilient against cyber attacks. In this paper, we will compare cyber risk scores for different CPS configurations and show that the Windows® 10 build 20H2 operating system is more secure than Linux Ubuntu® 20.04, while Red Hat® Enterprise® Linux is the most secure in some system configurations.

Evolution of trust in the sharing economy with fixed provider and consumer roles under different host network structures

We present an evolutionary trust game to investigate the formation of trust in sharing economy situations, where participants have a fixed provider or consumer role, and can only choose between trustworthy or untrustworthy behaviour. There are a variety of sharing economy platforms catering for differing goods and services, the properties of which may affect the degree to which these roles are variable for users. To the best of our knowledge, this is the first time the evolution of trust in sharing economy situations with fixed provider and consumer roles is being studied in the literature. Our trust model comprises four player types: trustworthy consumer, trustworthy provider, untrustworthy consumer, and untrustworthy provider. Five scenarios with varying initial population ratios of these player types under different host network structures are systematically investigated. Our results show that, in contrast to previous work that allowed switching roles between providers and consumers, trust declines monotonically as the reward for trustworthy behaviour is reduced, with a critical transition point for inversion of trustworthy/untrustworthy populations. In addition, the initial population of trustworthy providers is shown to significantly affect the point at which trustworthy behaviour most declines, with a high proportion resulting in the persistence of trustworthiness even when the reward for such behaviour is significantly low. Our results also show that, when different host network structures are considered, the average degree of the underlying network is an important factor in determining the level of trustworthiness in the population. Our findings may be of importance for understanding the emergence and maintenance of trust in sharing economy platforms where user roles are completely or predominantly rigid.

Performability Evaluation of Load Balancing and Fail-over Strategies for Medical Information Systems with Edge/Fog Computing Using Stochastic Reward Nets.

The aggressive waves of ongoing world-wide virus pandemics urge us to conduct further studies on the performability of local computing infrastructures at hospitals/medical centers to provide a high level of assurance and trustworthiness of medical services and treatment to patients, and to help diminish the burden and chaos of medical management and operations. Previous studies contributed tremendous progress on the dependability quantification of existing computing paradigms (e.g., cloud, grid computing) at remote data centers, while a few works investigated the performance of provided medical services under the constraints of operational availability of devices and systems at local medical centers. Therefore, it is critical to rapidly develop appropriate models to quantify the operational metrics of medical services provided and sustained by medical information systems (MIS) even before practical implementation. In this paper, we propose a comprehensive performability SRN model of an edge/fog based MIS for the performability quantification of medical data transaction and services in local hospitals or medical centers. The model elaborates different failure modes of fog nodes and their VMs under the implementation of fail-over mechanisms. Sophisticated behaviors and dependencies between the performance and availability of data transactions are elaborated in a comprehensive manner when adopting three main load-balancing techniques including: (i) probability-based, (ii) random-based and (iii) shortest queue-based approaches for medical data distribution from edge to fog layers along with/without fail-over mechanisms in the cases of component failures at two levels of fog nodes and fog virtual machines (VMs). Different performability metrics of interest are analyzed including (i) recover token rate, (ii) mean response time, (iii) drop probability, (iv) throughput, (v) queue utilization of network devices and fog nodes to assimilate the impact of load-balancing techniques and fail-over mechanisms. Discrete-event simulation results highlight the effectiveness of the combination of these for enhancing the performability of medical services provided by an MIS. Particularly, performability metrics of medical service continuity and quality are improved with fail-over mechanisms in the MIS while load balancing techniques help to enhance system performance metrics. The implementation of both load balancing techniques along with fail-over mechanisms provide better performability metrics compared to the separate cases. The harmony of the integrated strategies eventually provides the trustworthiness of medical services at a high level of performability. This study can help improve the design of MIS systems integrated with different load-balancing techniques and fail-over mechanisms to maintain continuous performance under the availability constraints of medical services with heavy computing workloads in local hospitals/medical centers, to combat with new waves of virus pandemics.

The role of emotions in the selection of family external top management team members in family firms

Family firms often face the challenge of staffing their top management team with people from outside the owner family. This decision-making process can evoke numerous emotions, involves different dynamics and is crucial for the long-term success of the organization. Using a qualitative research design involving 19 Swiss family businesses and interviews with executive search professionals, we investigate how family businesses select non-family top management team (TMT) members. The selection process is organized as a funnel and ends in selecting the candidate with the highest fit and highest level of trustworthiness. In the TMT member selection process, emotions become increasingly important over time. The owner family influences different points in the process, controls it and integrates a family effect, especially by integrating emotions.

Investigating when religious culture increases trade credit: A signaling theory approach

AbstractDrawing on signaling theory, we propose that religious culture leads suppliers to alter their beliefs about their customers’ trustworthiness. Strong religious cultures are interpreted as a signal of fewer agency problems and less information asymmetry, which helps firms gain more trade credit from suppliers. The empirical tests performed in this study show that firms headquartered in areas with strong religious cultures receive more trade credit than other firms. This positive relation is more prominent when such firms are located in areas with lower levels of social trust. A further analysis reveals that the increasing effect of religious culture on trade credit eases financing constraints. Overall, our findings indicate that religious culture can be a broad indicator of firms’ ethical preferences that influences their access to trade credit by signaling their potential for a high level of trustworthiness.

Quantitative palaeobathymetric reconstructions based on foraminiferal proxies: a case study from the Neogene of south‐west Spain

AbstractQuantitative palaeobathymetric reconstructions based on foraminiferal proxies have been used in a wide variety of geological studies on tectonic, climatic and environmental changes in sedimentary basins. However, palaeo‐water‐depth estimates are frequently biased due to taphonomic processes (dissolution, transport) as well as local conditions (primary productivity, oxygen content, and organic matter fluxes). This study evaluated the level of reliability of three commonly used transfer equations based on planktic and benthic foraminiferal proxies that produce different palaeobathymetric results, using Neogene sediments from the Guadalquivir Basin, south‐west Spain. The most trustworthy method involves removing abundant infaunal benthic foraminifera before applying the palaeo‐water‐depth equation based on benthic foraminiferal water‐depth ranges and presence/absence of species. This new approach provides sound palaeobathymetric results that improve estimations of long and short‐term relative sea‐level changes with negligible influence of taphonomic as well as ecological factors. When using one equation based on planktic/benthic ratios (P/B ratios), removing all infaunal benthic foraminiferal species improves the accuracy of P/B ratios, which yields more accurate palaeodepth reconstructions despite the low level of trustworthiness of this equation for both long and short‐term relative sea‐level variations.

The effect of facial features on facial anthropomorphic trustworthiness in social robots

As the nature of human-robot relationships have become increasingly bound to shift from supervisor–machine to friend–companion, people have exhibited an increasing interest in making social judgments toward such anthropomorphic objects, such as trustworthiness. However, the facial features of social robots and their potential effect on anthropomorphic trustworthiness are seldom analyzed and discussed comprehensively. This study examined whether the trustworthiness perception toward a social robot shared similarity with baby schema features on the human face. It also explored the effects of different combinations of baby schema facial features, especially the positions and sizes of the eyes and mouth, on facial anthropomorphic trustworthiness. A 5-way mixed experiment (N = 270) was conducted accordingly. The results indicated that people would experience a high level of facial anthropomorphic trustworthiness toward robots with baby schema features (i.e., large eyes, with medium vertical and horizontal positions of the eyes and mouth). This paper contributes to the literature on facial anthropomorphic trustworthiness in human-robot interaction and provides suggestions for social robot design.

A Multi-Objective Approach to Big Data View Materialization

Big data comprises voluminous and heterogeneous data that has a limited level of trustworthiness. This data is used to generate valuable information that can be used for decision making. However, decision making queries on Big data consume a lot of time for processing resulting in higher response times. For effective and efficient decision making, this response time needs to be reduced. View materialization has been used successfully to reduce the query response time in the context of a data warehouse. Selection of such views is a complex problem vis-à-vis Big data and is the focus of this paper. In this paper, the Big data view selection problem is formulated as a bi-objective optimization problem with the two objectives being the minimization of the query evaluation cost and the minimization of the update processing cost. Accordingly, a Big data view selection algorithm that selects Big data views for a given query workload, using the vector evaluated genetic algorithm, is proposed. The proposed algorithm aims to generate views that are able to reduce the response time of decision-making queries.

Multi-service model for blockchain networks

Multi-service networks aim to efficiently supply distinct goods within the same infrastructure by relying on a (typically centralised) authority to manage and coordinate their differential delivery at specific prices. In turn, final customers constantly seek to lower costs whilst maximising quality and reliability. This paper proposes a decentralised business model for multi-service networks using Ethereum blockchain features – gas, transactions, and smart contracts – to execute multiple services at different prices. By employing the Ethereum cryptocurrency token, Ether, to quantify the quality of service and reliability of distinct private Ethereum networks, our model concurrently processes streams of services at different gas prices while differentially delivering reliability and service quality. This multi-service business model has been extensively tested on five concurrent Ethereum networks with various combinations of gas prices, miners, and regular nodes using a Proof of Authority consensus algorithm and throughput as the evaluation metric. It has exhibited linear scalability, providing increased throughput in high-quality Ethereum networks, i.e., composed of more validator nodes. The results also indicate that different mining prices do not impact the network performance, but networks with more miners had limited scalability and an increased level of trustworthiness and reliability.

A Binary Trust Game for the Internet of Things

The IoT is transforming the ordinary physical objects around us into an ecosystem of information that will enrich our lives. The key to this ecosystem is the cooperation among the devices, where things look for other things to provide composite services for the benefit of human beings. However, cooperation among nodes can only arise when nodes trust the information received by any other peer in the system. Previous efforts on trust were concentrated on proposing models and algorithms to manage the level of trustworthiness. In this paper, we focus on modelling the interaction between trustor and trustee in the IoT and on proposing guidelines to efficiently design trust management models. Simulations show the impacts of the proposed guidelines on a simple trust model.

How can trustworthy influencers be identified in electronic word-of-mouth communities?

Given eWOM's growing importance and the interest of companies in having their products positively rated, it is necessary to analyze the behavior of influencers in online communities and determine the activities that might explain their level of trustworthiness. This paper focuses on identifying the different attributes obtained from online communities’ system-generated profiles to consider trustworthy reviewers. A structural equation model has been developed to measure trustworthiness as a construct, using the peer-nominated approach and a variety of indicators. Findings reveal a range of behavior patterns that can identify influencers based on their trustworthiness. A reviewer's involvement and sociability have strong relationships with the trust that he/she evokes in other users. Actions such posting reviews, scoring other members’ reviews and adding them to his/her trust network have a great relationship with trustworthiness. Also, a reviewer's specialization and experience have significant, although weaker, relationships with his/her level of trustworthiness. This research makes significant managerial contributions in detecting the most trustworthy and influential reviewers, and their characteristic actions to focus their use of viral marketing techniques on this subset of users with the aim of sparking interest in certain products in a faster, more credible and more efficient way in terms of costs.

Neutrosophic-based machine learning context for the trustworthiness of devices in the internet of things

The industrial sector is among the most suited sectors that may considerably advantage from the implementation of the ideas and technology of the Industrial Internet of Things (IIoT), and it is one of the most competitive industries in the world. The increased use of automated processes in manufacturing sectors results in a wide variety of applications based on IIoT. These applications call for the efficient integration of a wide variety of different systems and the execution of smooth operations across all machines. The issue of integration and smooth operation presents IIoT as a new subject of study in smart manufacturing. This carries with it several problems, including those on security, accountability, confidence, and dependability. As part of the Industrial Internet of Things (IIoT), many devices will be linked to one another and interact with one another through wireless and internet infrastructure. When this kind of situation plays out, the reliability of the IIoT devices becomes a key component in the process of preventing injection by hostile machines. As a result, an intelligent computer model is required to effectively cluster and categorize the level of trustworthiness possessed by the IIoT devices. In this article, we describe a trust model for the Internet of Things (IIoT) that is based on the neutrosophic TOPSIS and is utilized by IIoT apps to determine the trust score of IIoT devices. The reliability of devices is evaluated by the model that was constructed using the historical knowledge, chronological knowledge, and network behavior information that is received from IIoT devices. In addition to that, the model suggests KNN, and a Decision tree to categorize the attributes that were collected.

RETRACTED: Optimization of hospital computer network and helicobacter pylori ulcer nursing analysis

Abstract Helicobacter pylori is a widespread bacterium that lives mainly in the gastric mucosa and can cause serious diseases such as peptic ulcer, gastric cancer and gastrointestinal lymphoma. Oral-oral transmission seems to be the main route. The endoscopy is used to diagnose the risk of infection by the nurse and assistants for gastric endoscopy after the problem occurs after the gastroscopy implantation. In health centers, smart wireless devices can reduce the workload of medical staff. These devices feature low power wireless sensors, Personalized Wireless Hubs (PWH), and receivers.. Current solutions to prevent bedsores are the use of various techniques for patient mobility and displacement, which is impossible for some patients or dangerous for others, and it can bring problems to health care providers. On the other hand, the development of information technology in healthcare systems, including Wireless Sensor Network (WSN) applications, has led to convenient and fast service delivery. In this review, we describe the clinical medical field of applications for data some representative challenges, their level of trustworthiness and the need to ensure the privacy and security of wireless sensor network to introduce proposed that duet. Are exacerbated by the lack of resources for these challenges inherent networking site wireless sensor. They are classified by the someone Diagnosis Of Dental Diseases(DDD) in patients using wireless sensor networks that can be used to identify the doctors or medical professionals is based on Helicobacter pylori is detected on ulcer development. The Calculation of Helicobacter pylori is classified by Ada Boost classification algorithm used by ulcer to diagnose diseases. A wireless sensor network algorithm to detect Helicobacter pylori called the accuracy of the results.

A Trustworthy Human–Machine framework for collective decision making in Food–Energy–Water management: The role of trust sensitivity

We propose a hybrid Trustworthy Human–Machine collective decision-making framework to manage Food–Energy–Water (FEW) resources. Decisions for managing such resources impact not only the environment but also influence the economic productivity of FEW sectors and the well-being of society. Therefore, while algorithms can be used to develop optimal solutions under various criteria, it is essential to explain such solutions to the community. More importantly, the community should accept such solutions to be able realistically to apply them. In our collaborative computational framework for decision support, machines and humans interact to converge on the best solutions accepted by the community. In this framework, trust among human actors during decision making is measured and managed using a novel trust management framework. Furthermore, such trust is used to encourage human actors, depending on their trust sensitivity, to choose among the solutions generated by algorithms that satisfy the community’s preferred trade-offs among various objectives. In this paper, we show different scenarios of decision making with continuous and discrete solutions. Then, we propose a game-theory approach where actors maximize their payoff regarding their share and trust weighted by their trust sensitivity. We run simulations for decision-making scenarios with actors having different distributions of trust sensitivities. Results showed that when actors have high trust sensitivity, a consensus is reached 52% faster than scenarios with low trust sensitivity. The utilization of ratings of ratings increased the solution trustworthiness by 50%. Also, the same level of solution trustworthiness is reached 2.7 times faster when ratings of ratings included.