Trust Model Research Articles

A systematic survey on security and privacy issues of medicine supply chain: Taxonomy, framework, and research challenges

AbstractSeveral decades ago, the medicine supply chain (MSC) transferred the medicines from the manufacturer to the end‐consumer and kept all records in a manual register. The manual intermediary management of MSC and medicine data often leads to issues like unauthorized third parties participating in the process and illegally tempering medicine data. As a result of this medicine temperament, end users get counterfeit medicine that poses severe consequences for patients' health. Over time, manual data management and intermediaries transform into digital platforms that track, manage, and exchange data in real‐time. Real‐time data exchange opens attackers up to target MSCs, access medicine data illegally, and modify medicine conditions, locations, and specifications. With the objective of this, the proposed survey identifies security and privacy issues and discusses security solutions. This security solution involves various data security and privacy frameworks such as micro‐segmentation, zero trust model, and many other software‐based security solutions. Moreover, The proposed survey explores radio frequency identification for medicine tracking in which each intermediary transforms the medicine location over the internet. In contrast, the Internet of Things is used to exchange medicine temperature conditions in real‐time. Furthermore, cybersecurity‐based solutions help protect against malicious threats, and blockchain keeps data private and temper‐proof. Artificial intelligence provides machine learning and deep learning models for analyzing large amounts of data to generate insights from the MSC data. Therefore, this survey addresses the various security and privacy issues and provides solutions that help researchers carry out work in this domain.

An extended trust and distrust network-based dual fuzzy recommendation model and its application based on user-generated content

The social network-based recommendation algorithm can not only effectively solve the information overload problem, but also fully exploit the personalized preferences of users. However, current studies fail to consider the distrust feature and propagation characteristic of social networks. In addition, methods for quantifying user preferences and social relationships are deficient and inconsistent with reality. Therefore, an extended trust and distrust network-based dual fuzzy recommendation model (ETD-DFR) is proposed. First, the trust and distrust network is constructed, and then the network is extended based on the social balance theory; secondly, the weighted PageRank algorithm is utilized to calculate the trust and distrust between users; Further, a dual fuzzy framework is proposed to represent the fuzzy features of user preferences as well as trust and distrust; In addition, Louvain's algorithm is employed to partition the network into subgroups to quickly find trusted users for recommendation. An empirical analysis is conducted based on the dataset of consumer review websites, and the experimental results show that the performance of the proposed ETD-DFR model is excellent. Finally, comparative and sensitivity analyses are constructed to further verify the proposed ETD-DFR model is efficient and robust.

Employees' taking charge behavior and empowering leadership: the role of leader trust in employees and risk propensity

Purpose Previous research has mainly focused on the outcomes of empowering leadership, and empirical evidence on how to encourage leaders to display empowering behaviors has been overlooked, particularly from an interpersonal perspective.Design/methodology/approach Based on the integrative model of organizational trust, we investigate the relationship between employees' taking charge behaviors and empowering leadership considering the mediating role of leader trust in employees and the moderating role of risk propensity.Findings The results indicate that taking charge is positively associated with leader trust in employees, which influences the development of empowering leadership. Additionally, leaders' risk propensity moderates these relationships, that is, a higher level of risk propensity can enhance the promoting effect of leader trust on empowering behaviors.Originality/value This study explores the antecedents and mechanisms influencing empowering leadership and proposes the moderating effect of risk propensity. The findings not only clarify how and why employees' taking charge behaviors can stimulate empowering leadership but also offer a more comprehensive understanding of the antecedents of empowering leadership.

Adaptive Trust Model for Multi-Agent Teaming Based on Reinforcement-Learning-Based Fusion

Adaptive Trust Model for Multi-Agent Teaming Based on Reinforcement-Learning-Based Fusion

On Implementing Social Community Clouds Based on Markov Models

Social networks reflect, to a wide extent, the real-world relationships that allow users to connect and share information. The number of people that interact in social networks keeps increasing, and the devices used are equipped with more and more computational capacities. This gives rise to the formulation of social clouds, which refer to resource-sharing infrastructures that enable friends to share their resources within the social network. As modern applications become more and more sophisticated, users should be able to share their own services and computing resources through social networks. This poses many challenges for the design options of a computing system composed of a set of trusted friends. The spotlight turns on the design of a proper trust model that considers the suitability of the trusted users to execute an application’s tasks and on the fair distribution of these tasks among these users. Therefore, social networks and their trust-based applications in a distributed environment have seen increasing attention in the research community. In this regard, we present a social community cloud implementation model, where friendly relationships determine resource provisioning. The issues of fairness and allocation of time are of great importance and they are thoroughly investigated. We use extensive simulations to illustrate that the communities can be employed to construct cloud infrastructures, such that the shared resources can be utilized fairly and efficiently. Our experiments have shown that our model achieves a higher allocation rate (percentage of tasks successfully allocated and completed) than competitive models and reduces the average response time and the total execution time. Finally, our work does not overutilize the resources.

Research on Vehicle Task Management Based on TBMADDPG

<p>Recently, the traditional cloud computing network of vehicle networking has some problems to be solved: 1) the security trust between the vehicle and the subgrade unit; 2) The vehicle may be attacked by potentially malicious edge servers during task unloading. In this paper, in order to solve the above problems, aiming at the security problem of the edge computing network in the vehicle task unloading and resource allocation problems of multi-vehicle and multi-roadbed units in the urban intersection scene, the vehicle task unloading and resource management optimization algorithm and trust model based on the vehicle edge computing network are constructed, and the approximate optimal simulation is carried out for the urban intersection scene. Simulation results show that the proposed algorithm can effectively improve the overall efficiency of the system.</p> <p> </p>

The Role of Brand Trust, Brand Image, Brand Equity on Repurchase Intention

In the rapidly growing business world, companies are faced with intense competition due to the emergence of new companies. This research analyzes the role of brand trust, brand image and brand equity on repurchase intention of Wardah brand cosmetics. The aim of this research is to determine brand trust, brand image and brand equity on repurchase intentions. This research uses quantitative analysis with purposive sampling technique. The population of this study were FEB students at Muhammadiyah University, Surkarta who had purchased Wardah cosmetics with a sample of 192 respondents. The data collection technique uses a questionnaire with a standardized Likert scale using SPSS version 26. The analytical method used in this research is multiple linear regression analysis using several variables, namely brand trust, brand image, brand equity and repurchase intention. The results of this research show that brand trust has a significant effect on repurchase intentions, brand image has a positive and significant effect on repurchase intentions, brand equity has a significant effect on repurchase intentions. This is proven by F sig. 0.000 is smaller than 0.05. The R Square value of 0.982 indicates that the ability of the brand trust model (X1), brand image (X2), brand equity (X3) explains the variation in the repurchase intention variable (Y) by 98.2% and the remainder is influenced by other independent variables by 1.8 %.

ASSOCIATE: A simulator for assessing soft security in the Cognitive Internet of Things

By augmenting the Internet of Things (IoT) with Artificial Intelligence (AI), the Cognitive Internet of Things (CIoT) has emerged, where intelligent objects autonomously sense, perceive, make decisions, and collaborate with minimal initial knowledge. Ensuring trust and security in such a cognitive society poses significant challenges. Soft security approaches, which leverage behavioral norms, have arisen as a promising solution to address these challenges. In this paper, we introduce ASSOCIATE, a modular and multi-layered tool designed for simulating and evaluating soft security models in the CIoT domain. ASSOCIATE applies a trust model to CIoT simulations and assesses the society’s soft security. The evaluation process encompasses society preparation, generation, trust model preparation, execution, and report generation. Input configuration files in the JSON format define simulation parameters, allowing the simulator to generate the requested society and conduct simulations. Evaluation results are presented through charts and graphs and saved in image and CSV formats. We demonstrate the tool’s functionality by enhancing the Santander dataset using the society generation module and analyzing various trust profiles. The proposed tool provides a valuable means of assessing the efficacy of soft security models in CIoT simulations, facilitating a comprehensive analysis of trust and security in CIoT systems.

Does training improve users' mental models about adaptive cruise control?

While Advanced Driver Assistance Systems (ADAS) promise safety benefits to drivers, there is evidence to suggest that drivers are unaware or uninformed about their vehicles’ systems and thus have poor mental models about the systems. Previous studies suggest that training improves drivers’ mental models, although some studies report limited impacts. This study investigated the relationship between training and drivers’ mental models about Adaptive Cruise Control (ACC), compared the impact of two different training approaches on drivers’ mental models, and examined the relationship between driver knowledge and trust regarding ADAS technologies. This study was conducted online, and participants were randomly and equally assigned to one of three training groups – owner’s manual (text-based); state diagram visualization; or sham (control). Surveys measured their trust and mental models about ACC before and after training. The results found that the text-based group outperformed the visualization group and the control group in terms of post-training overall mental model scores, but these differences were not statistically significant. No correlation between post-training mental model scores and overall trust scores was found. This study provides evidence that training improves users’ mental models about technology and finds that different training platforms or paradigms may affect learning differently.

A Trust Model for Electrical Stove Technology Acceptance of Indonesian Stereotype

The surge in LPG consumption within Indonesian households, coupled with stagnant LPG production, has compelled the government to import 75% of LPG by 2022. Despite initial plans for a transition to electric stoves in 2023, the program was halted due to widespread public resistance to electric stove technology. The rejection was attributed, in part, to low trust in this emerging technology, emphasizing the pivotal role of trust in influencing technological acceptance. In response, this research endeavors to construct a robust and reliable model capturing trust in the acceptance of electric stove technology. Utilizing the PLS Structural Equation Model method for data analysis, the focus centers on two trust indicators: propensity trust and human-machine trust. In the quest for a valid model, the statistical modeling technique considers five initial hypotheses developed with 32 trust-related items. Upon analysis, one hypothesis is rejected, while four are accepted. Notably, the trust human-machine (THM) variable emerges as a significant influencer of trust for electric stove technology (TTK), substantiated by a path coefficient of 0.917 and p = <0.001. Thus, the resulting model takes on a reflective form, offering insights into the intricate dynamics of trust in the acceptance of electric stove technology.

A robust and machine learning-driven identification scheme for malicious nodes in UASNs

Underwater Acoustic Sensor Networks (UASNs) have garnered widespread adoption across maritime domains and national security sectors. Their deployment, often in remote and challenging conditions, necessitates robust trust models to assess node integrity and to pinpoint compromised units, particularly under adversarial assault. Prevailing trust models, however, predominantly focus on nodes’ communicative actions and energy consumption, neglecting the influence of ambient aquatic noise on trust assessments. Moreover, these models typically falter amidst the UASN-specific impediments of high packet collision rates and elevated error probabilities inherent to underwater signal transmission, leading to consequential inaccuracies in node evaluation. To address these limitations, this paper proposes a robust and machine learning-driven malicious node identification scheme (RMIS) for UASNs. The proposed scheme first models and quantifies the impact of packet collisions and the underwater environment on node communication, utilizing existing environment models and MAC protocols. Second, communication traffic is exploited as effective and reliable evidence of trust. The traffic evidence can reflect multiple types of attacks by analyzing changes in traffic in a UASN. Third, the evaluation models are trained using support vector machine and K-means++ algorithms. Finally, two types of trust update mechanisms are proposed to handle the dynamic underwater environment and on–off attack. The simulation results indicate that compared to the other three identification schemes, RMIS is more effective in identifying malicious nodes. The effectiveness of the RMIS becomes particularly noticeable as the ratio of malicious nodes increases. Furthermore, the trust update mechanisms effectively limit the impact of the on–off attack and improve the robustness of the RMIS.

Facets of Trust and Distrust in Collaborative Robots at the Workplace: Towards a Multidimensional and Relational Conceptualisation

The relevance of trust on the road to successful human-robot interaction is widely acknowledged. Thereby, trust is commonly understood as a monolithic concept characterising dyadic relations between a human and a robot. However, this conceptualisation seems oversimplified and neglects the specific interaction context. In a multidisciplinary approach, this conceptual analysis synthesizes sociological notions of trust and distrust, psychological trust models, and ideas of philosophers of technology in order to pave the way for a multidimensional, relational and context-sensitive conceptualisation of human-robot trust and distrust. In this vein, trust is characterised functionally as a mechanism to cope with environmental complexity when dealing with ambiguously perceived hybrid robots such as collaborative robots, which enable human-robot interactions without physical separation in the workplace context. Common definitions of trust in the HRI context emphasise that trust is based on concrete expectations regarding individual goals. Therefore, I propose a three-dimensional notion of trust that binds trust to a reference object and accounts for various coexisting goals at the workplace. Furthermore, the assumption that robots represent trustees in a narrower sense is challenged by unfolding influential relational networks of trust within the organisational context. In terms of practical implications, trust is distinguished from acceptance and actual technology usage, which may be promoted by trust, but are strongly influenced by contextual moderating factors. In addition, theoretical arguments for considering distrust not only as the opposite of trust, but as an alternative and coexisting complexity reduction mechanism are outlined. Finally, the article presents key conclusions and future research avenues.

Do procedural fairness and conflict perception affect the acceptance of the temporary nuclear spent fuel storage facility? Evidence from South Korea

This study uses data collected from the residents near South Korean nuclear power plants to explore the complex psychological factors that may influence the local acceptance of the expansion of temporary nuclear spent fuel (NSF) storage facilities in South Korea. Employing structural equation modeling, we examine the roles of perceived procedural fairness and perceived local conflicts in the traditional model of trust, knowledge, and perceptions of risks and benefits. Our findings demonstrate that perceived procedural fairness significantly influences trust, which further mediates the relationship between perceived local conflicts and benefit and risk perceptions, ultimately shaping local acceptance of temporary NSF storage facilities. Our findings underscore the importance of perceived procedural fairness and trust in shaping local acceptance and highlight perceived local conflicts’ role in amplifying the perceptions of benefits and risks. These findings inform new policy implications for the local acceptance of expanding temporal NSF storage facilities.

EEG-based assessment of driver trust in automated vehicles

Effective collaboration between automated vehicles (AVs) and human drivers relies on maintaining an appropriate level of trust. However, real-time assessment of human trust remains a significant challenge. While initial efforts have delved into the potential use of physiological signals, such as skin conductance and heart rate, to evaluate trust, limited attention has been given to the feasibility of assessing trust through electroencephalogram (EEG) signals. This study aimed to address this issue by using EEG signals to objectively assess driver trust towards AVs. A simulated driving experiment was conducted, where driver trust was manipulated by introducing different types of AV malfunctions. Self-reported trust ratings were collected and used to classify driver trust into three levels: low, medium, and high. A total of 420 time- and frequency-domain EEG features were extracted, and nine machine learning algorithms were applied to construct driver trust assessment models. Additionally, to explore the potential of developing cost-effective models with reduced feature inputs, this study developed trust models using features solely from single brain regions: frontal, parietal, occipital, or temporal. The results showed that the best-performing model, utilizing features from the whole brain and employing the Light Gradient Boosting Machine (LightGBM) algorithm, achieved an accuracy of 88.44% and an F1-score of 78.31%. In comparison, models based on single brain regions did not achieve comparable performance to the comprehensive model. However, the frontal and parietal regions showed important potentials for developing cost-effective trust assessment models. This study also performed feature analysis on the best-performing model to identify features highly responsive to changes in trust. The results showed that an increased power of beta waves tended to indicate a lower level of trust in AVs. These findings contribute to our understanding of the neural correlates of trust in AVs and hold practical implications for the development of trust-aware AV technologies capable of adapting and responding to driver's trust levels effectively.

Human-AI Collaboration: Understanding User Trust in ChatGPT Conversations

This research paper delves into the critical dimension of Human-AI Collaboration, with a specific focus on unraveling the intricacies of user trust in ChatGPT conversations. In an era marked by increasing AI integration into various aspects of human life, understanding and fostering user trust in conversational AI systems like ChatGPT is essential for effective collaboration. The study employs a comprehensive approach, investigating metrics for trust measurement, analyzing user experiences, and exploring the factors that influence trust. By examining the evolving impact of trust on collaboration and conducting comparative analyses with other conversational AI models, the research aims to provide valuable insights. Ultimately, the paper not only contributes to a nuanced understanding of user trust in ChatGPT conversations but also offers practical recommendations for developers and stakeholders to enhance the collaborative potential of AI systems in real-world applications. Keywords: Human-AI Collaboration, ChatGPT Conversations, Conversational AI, Trust Metrics, User Trust.

Predicting Public Cooperation with Face Covering at the Early Phases of COVID-19: Building Public Trust, Confidence, Knowledge Through Governmental Two-Way Symmetrical Communication

ABSTRACT Despite the agreement on the importance of two-way communication during governmental response to pandemics, few studies provided empirical evidence to support the effectiveness of such communication strategy in times of a public health crisis. Integrating the concept of two-way symmetrical communication and the Trust, Confidence, and Cooperation model (the TCC model), this study investigates how strategic communication practices, in particular two-way symmetrical communication, is related to public’s perceived trust and confidence in governments (i.e., CDC in this study), and how these perceptions may affect decisions to comply with the preventive behaviors (i.e., face covering in this study), while taking the moderating role of knowledge into consideration. An online survey was conducted with 502 participants who were living in the United States in early April 2020. The findings showed that two-way symmetrical communication may increase public trust and confidence in governments, and improve individuals’ knowledge about the pandemic, which all subsequently leads to compliance with recommended behaviors. The results contribute to the theoretical understanding of pandemic communication and provide implications for practitioners in pandemic management, suggesting that health institutions should adopt two-way symmetrical communication practices in respond to public health emergencies.

Modeling AI Trust for 2050: perspectives from media and info-communication experts

AbstractThe study explores the future of AI-driven media and info-communication as envisioned by experts from all world regions, defining relevant terminology and expectations for 2050. Participants engaged in a 4-week series of surveys, questioning their definitions and projections about AI for the field of media and communication. Their expectations predict universal access to democratically available, automated, personalized and unbiased information determined by trusted narratives, recolonization of information technology and the demystification of the media process. These experts, as technology ambassadors, advocate AI-to-AI solutions to mitigate technology-driven misuse and misinformation. The optimistic scenarios shift responsibility to future generations, relying on AI-driven solutions and finding inspiration in nature. Their present-based forecasts could be construed as being indicative of professional near-sightedness and cognitive dissonance. Visualizing our findings into a Glasses Model of AI Trust, the study contributes to key debates regarding AI policy, developmental trajectories, and academic research in media and info-communication fields.

Enhancing trust transfer in supply chain finance: a blockchain-based transitive trust model

AbstractArtificial intelligence and blockchain technology have become indispensable in the era of the digital economy, particularly in the field of financial financing. However, when it comes to supply chain finance (SCF), existing models primarily focus on risk identification and credit evaluation, neglecting the critical aspects of trust transfer continuity and reliability within the chain. To address this issue, this paper proposes a blockchain-based transitive trust model for SCF, which ensures seamless trust transfer from core enterprises to bottom suppliers during financing enterprise credit evaluation. The model utilizes multi-layer metrics to calculate the comprehensive trust value of underlying suppliers, serving as the basis for credit delivery. Additionally, the model stores transitive signature receivable warrants on the blockchain and utilizes splittable delivery of warrants to underlying suppliers. The model’s rationality and correctness are verified through experimental analysis, with results demonstrating that the transitive trust model enhances Small and Medium-Sized Enterprises’ (SMEs) trust at the bottom of the supply chain, thus alleviating financing difficulties for SMEs.

Виклики зміцнення довіри в галузі е-комерції

The aim of the article is to allocate the components of the phenomenon of trust in e-commerce, identify models of trust and generalize the factors that ambiguously affect its strengthening. The search for ways to restore the Ukrainian economy actualizes the study of e-commerce, which has already become a catalyst for economic growth in many countries around the world. In today’s environment, building trusting relationships in the field of e-commerce is critical for the success of online businesses. It is proved that trust is a basic condition for the interaction of participants in online commercial activities. The fundamental structure of trust in e-commerce includes interpersonal, institutional, and digital constructs. In the current trust structure, one of the components that determines the overall trust of the online consumer is a priority. Different models of trust in e-commerce, presented today in different business systems, are a reflection of changes in the fundamental structure and are embodied in the current structure of trust. The challenges of developing trust in e-commerce are driven by cultural values, generational attitudes, and the level of digital maturity of the online consumer, so that building a trajectory for building trust in e-commerce depends on their awareness. In the current environment, the use of new technologies such as the Internet of Things, artificial intelligence, and machine learning in e-commerce creates new challenges for strengthening digital trust, as the reliability and accuracy of information obtained by a machine is questioned without taking into account factors that are subjective and non-obvious. Today, a transitional model of trust is relevant for Ukrainian e-commerce, due to the transformation of economic institutions and deformations of the socio-demographic structure. The development of e-commerce in Ukraine is constrained by a low level of institutional trust, cybersecurity problems, restrictions related to the quantitative and qualitative composition of potential online consumers, etc.

Optimizing QoS and security in agriculture IoT deployments: A bioinspired Q-learning model with customized shards

Agriculture Internet of Things (AIoTs) deployments require design of high-efficiency Quality of Service (QoS) & security models that can provide stable network performance even under large-scale communication requests. Existing security models that use blockchains are either highly complex or require large delays & have higher energy consumption for larger networks. Moreover, the efficiency of these models depends directly on consensus-efficiency & miner-efficiency, which restricts their scalability under real-time scenarios. To overcome these limitations, this study proposes the design of an efficient Q-Learning bioinspired model for enhancing QoS of AIoT deployments via customized shards. The model initially collects temporal information about the deployed AIoT Nodes, and continuously updates individual recurring trust metrics. These trust metrics are used by a Q-Learning process for identification of miners that can participate in the block-addition process. The blocks are added via a novel Proof-of-Performance (PoP) based consensus model, which uses a dynamic consensus function that is based on temporal performance of miner nodes. The PoP consensus is facilitated via customized shards, wherein each shard is deployed based on its context of deployment, that decides the shard-length, hashing model used for the shard, and encryption technique used by these shards. This is facilitated by a Mayfly Optimization (MO) Model that uses PoP scores for selecting shard configurations. These shards are further segregated into smaller shards via a Bacterial Foraging Optimization (BFO) Model, which assists in identification of optimal shard length for underlying deployment contexts. Due to these optimizations, the model is able to improve the speed of mining by 4.5%, while reducing energy needed for mining by 10.4%, improving the throughput during AIoT communications by 8.3%, and improving the packet delivery consistency by 2.5% when compared with existing blockchain-based AIoT deployment models under similar scenarios. This performance was observed to be consistent even under large-scale attacks.