Trust Network Research Articles

Sustainable pavement maintenance and rehabilitation planning using the quantum cognitive trust network

Selecting appropriate pavement maintenance strategies can enhance resource efficiency, reduce environmental pollution, prolong road lifespan, and improve societal sustainability. Currently, most models, which selecting pavement maintenance options, exhibit high subjectivity and low reliability. This study employs an innovative probabilistic linguistic multi-attribute decision-making model basing on the quantum cognitive theory. Therefore, the model focuses on expert decision-making groups for pavement maintenance strategies. It considers the trust networks within the decision-making process, as well as the sequential and interference effects arising from experts' belief biases and behavioral interactions. By doing so, it facilitates a more scientific selection of optimal strategies among multiple maintenance options. The proposed method has been validated through practical case studies of road segments, and its scientific validity and effectiveness have been further corroborated through sensitivity analysis.

A Consensus Reaching Process for Product Design Decision-Making by Integrating Intuitionistic Fuzzy Sets and Trust Network

A Consensus Reaching Process for Product Design Decision-Making by Integrating Intuitionistic Fuzzy Sets and Trust Network

Disaster Management Strategy Through Social Capital Development

Indonesia is one of the countries with the highest level of natural disaster vulnerability in the world due to extreme geographical and climate conditions. In facing this risk, the government has implemented a disaster management policy based on community involvement. One important aspect in this approach is social capital, which includes social participation, interpersonal trust, and community networks, as a crucial component to strengthen community resilience. This study explores the role of the Meteorology, Climatology, and Geophysics Agency (BMKG) in Lampung Province in developing weather literacy and disaster mitigation programs through digital media to build social capital in the community. The results of the study show that although BMKG has succeeded in increasing community participation through digital channels, community involvement is currently still passive and limited. To achieve comprehensive disaster preparedness, it is necessary to increase active community participation, strengthen trust through two-way communication, and expand community networks. With a social capital-based approach, BMKG is expected to be able to create a more responsive and adaptive mitigation program, making the community an active partner in facing the threat of disasters in the future.

Keterlekatan (Embeddedness) Tindakan Ekonomi Antara Tukang Kredit Dengan Client Dalam Peminjaman Uang (Studi pada Masyarakat di Jorong Sungai Tarab, Kecamatan Sungai Tarab, Kabupaten Tanah Datar)

This study aims to explain the form of embeddedness of economic action between “Tukang Kredit” and clients in lending money to the community in Jorong Sungai Tarab, Kec. Sungai Tarab, Kab Tanah Datar. The approach used is a qualitative approach with a descriptive type. The theory used is Granovetter's theory, namely the concept of embeddedness. The technique of withdrawing informants using purposive sampling technique with the number of informants 14 people. Data collection methods with observation, interviews, and document studies. The unit of analysis is individual. Data analysis used was developed by Miles and Huberman, namely data collection, data reduction, data presentation and conclusion drawing. Based on the results of the study, the embeddedness of economic action between loan sharks and clients in lending money is due to social relationships and social interactions based on personal relationships that last a long time from time to time so that from these economic actions there are the same values, such as exchange and reciprocity, trust, kinship, and social networks so that it can be said to be embeddedness in a relational form, this habit occurs when the perpetrator sees an opportunity to carry out economic action in Jorong Sungai Tarab, Kec. Sungai Tarab.

Safeguarding IoT networks against DDoS attacks using deep learning based zero trust network access

Safeguarding IoT networks against DDoS attacks using deep learning based zero trust network access

Influence of social capital on happiness of multicultural family: A cross-sectional study using the 2019 Korea community health survey

This study aims to identify factors related to the impact of social capital on happiness among multicultural families using the 2019 Community Health Survey, which represents the South Korean population. The study utilized data from the 2019 Korea Community Health Survey, and the study participants, aged 20 years or older, included 3524 members of multicultural families from a total of 229,099 adult households. The study found a significant difference in happiness scores across different age groups (t = 57.00, p < 0.01). Based on the median value of happiness, significant relationships were found with the independent variables: Physical Environment of Trust (t = −5.13, p < 0.001), Social Networks (t = −5.51, p < 0.001), and Social Participation (t = −5.47, p < 0.001). Happiness was found to have a positive correlation with the Physical Environment of Trust (r = 0.12, p < .001), Social Participation (r = 0.11, p < 0.001), and Social Network (r = 0.13, p ≤ 0.001). In contrast, Age (r = −0.13, p ≤ 0.001) and Stress (r = −0.14, p ≤ 0.001) showed negative correlations with happiness (r = 0.57, p < 0.001). The analysis identified a positive community physical environment (t = 3.85, p < 0.01), increased social networks (t = 4.27, p < 0.01), and higher social participation (t = 6.88, p < 0.01) as significant predictors of happiness. This model suggests that the explanation power is 15%, which is statistically significant (R2 = 0.15, F = 57.72, p < 0.001). This study highlights the influence of social capital on the happiness of multicultural families living in Korea. Given the increasing number of multicultural families in the country, strategic interventions aimed at enhancing social networks and participation are necessary to promote their happiness.

Predictors of risk reduction behavior: Evidence in last-mile communities

Despite extensive research using Protection Motivation Theory (PMT) to analyze factors influencing protective behaviors during disasters, understanding how last-mile communities in vulnerable contexts—particularly in the Philippines—respond to early warning messages remains limited. This understanding is crucial, as these communities often bear the brunt of extreme weather events. Drawing data from surveys and semi-structured interviews, this study examined the predictors of risk reduction behaviors before and after Super Typhoon Mangkhut in two last-mile communities in Northern Philippines — Cabalitian and Mapita. Regression analysis demonstrated that all threat appraisal variables—perceived vulnerability, perceived severity, and fear—are predictors of risk reduction behaviors before Mangkhut. Coping appraisal variables, specifically response efficacy and self-efficacy, also positively influenced risk reduction action before Mangkhut. Among socio-demographic variables, only gender and age are predictors of risk reduction behaviors, with their influence varying between the two communities. Expanding the application of PMT, prior typhoon experience, trust, and social network strength also positively and significantly influenced risk reduction behaviors before and after Mangkhut. The study identified key infrastructural, institutional, and operational interventions to enhance coping capacity and reduce vulnerability in these communities, alongside policy implications to inform disaster risk reduction strategies and empower local preparedness efforts.

ANALYSIS OF SOCIAL CAPITAL AND ENTREPRENEURIAL INTENTION OF FARMERS IN NIGERIA

This study aimed at analysing social capital and the entrepreneurial intention of farmers in Nigeria. The study specifically investigated the effect of social capital on Attitude towards Behaviour (ATB) of farmers; and Perceived Social Norms (PSN) of farmers. This study employed survey. 120 questionnaires were mailed to respondents, and 158 were physically administered. The study analysed data using both descriptive and inferential analytical technique (regression analysis). Social trust, network density, network size and demographic diversity were used as proxies of social capital, and ATB of farmers and PSN of farmers as entrepreneurial intention. Finding showed that network size, network and social trust have significant positive effects on ATB of farmers in Nigeria, and that demographic diversity has significant negative effects on ATB of farmers in Nigeria. Furthermore, finding showed that network density and social trust have significant negative effects on PSN of farmers in Nigeria. The study concluded that entrepreneurial intention of farmers depends on social capital. The study recommended that network size, network density and social trust within the social capital should be improved and sustained, and that farmers should take advantage of network size and demographic diversity as these contribute to PSN.

Strategic behavior in multi-criteria sorting with trust relationships-based consensus mechanism: Application in supply chain risk management

Strategic behavior is common in multi-criteria sorting, where manipulated alternatives are allocated to the target category to achieve the intended goal. Engaging in such strategic behavior in sorting consensus often comes at a cost, which is closely tied to the degree of preference adjustments and the level of trust relationships among decision makers. This study investigates strategic behavior using piecewise cost-based consensus mechanism in social trust network. Firstly, the trust relationship dependent piecewise cost is formally defined and formulated by considering the impact of trust relationship on the preference adjustments willingness to achieve sorting consensus. Then, based on the trust relationship dependent piecewise cost, a preference strategic manipulation model is proposed to achieve strategic behavior in sorting consensus, which is implemented by minimizing the cost of strategically guiding decision makers in adjusting their preferences. Considering that social trust network affects strategic manipulation, a preference-network collaborative strategic manipulation model is further proposed, which is designed to strategically guide decision makers in adjusting both their preferences and social trust networks. Moreover, the practicality of proposed strategic manipulation models is exemplified through a case study on supply chain risk management and their performance is validated via simulation analysis.

Enhancing Worker Recruitment in Collaborative Mobile Crowdsourcing: A Graph Neural Network Trust Evaluation Approach

Enhancing Worker Recruitment in Collaborative Mobile Crowdsourcing: A Graph Neural Network Trust Evaluation Approach

Collaborating with C-Level Executives to Align Security with Business Goals: How Strategic Security Initiatives were Integrated with Broader Business Objectives

A link between cybersecurity and the organization's objectives is crucial in contemporary business. With the advancement of technology, today's digital transformation has enshrined cybersecurity not as a technical problem but as an essential tool for business competition. This paper aims to understand how cybersecurity professionals work alongside C-level executives to ensure security mirrors organizational goals and objectives. By beginning with business objectives in mind, emphasizing communication, and incorporating security into transformation projects, cybersecurity can be positioned to align with business goals to support business advancement. The paper also identifies Risk management according to business risk appetite, business continuity management, and regulatory compliance as essential framework components. Rapid threat intelligence and innovative tools such as cloud security and Zero Trust Network Access (ZTNA) are key examples of how protection can precede business initiatives. This collaboration shields business operations against cyber risk and advances competitive advantage by strengthening consumers' perceptions and optimizing business functions. Therefore, with the ever-advancing threat environment to its systems, organizations must embrace AI-driven threat sensors, automated compliance solutions, and effective incident response. Finally, cybersecurity must be recognized as one of the main enablers that drive innovation and business growth and allow companies to succeed in safe digital environments. The paper offers practical advice to cybersecurity professionals on communicating with the board and other executives, thereby achieving the organizational security culture necessary to succeed in the long run.

Reliability-based ordinal consensus adjustment model for large scale group decision making

In large scale group decision-making (LSGDM), there are the substantial number of decision makers (DMs) with diverse knowledge, backgrounds, and interests related to the decision-making problem, and it is not possible to assure that all DMs are completely reliable. Thus, in order to enhance the quality of decision-making, it is necessary to analyze the reliabilities of DMs in LSGDM. This paper proposes the method to evaluate the reliabilities of DMs, sorts these DMs according to their degree of reliability, and investigates the consensus reaching process based on categories and an ordinal consensus measure. Considering the DMs' trust network, the uncertainty of a DM's evaluation information represented by a fuzzy preference relation (FPR), the deviation between a DM's FPR and those of the other DMs, and additive consistency of FPRs, the reliability of a DM is assessed using four criteria: PageRank centrality, professional competence, collaborative competence, and additive consistency. Following these reliability assessment criteria, ELECTRE-TRI is employed to sort DMs into three ordered categories according to DMs' different levels of reliability under the four assessment criteria. Furthermore, an improved ordinal consensus measure is designed to consider both the importance weights of positions and the deviation of Borda counts of the same alternative in two rankings. As for the consensus reaching process, due to the varied reliabilities of DMs in different categories, we propose a multiple strategies feedback mechanism for DMs in different categories. Finally, a numerical example is provided to illustrate the rationality and validity of the proposed model.

Risk evaluation of livestream e-commerce platforms based on expert trust networks and CODAS

As an emerging e-commerce model, livestream e-commerce (LEC) has presented novel opportunities for the advancement of the retail industry. While retailers reap the benefits by this platform, they also encounter challenges associated with platform risks. This paper aims to propose a novel hybrid model to address the risks inherent in livestream e-commerce platforms (LECPs) evaluation problems. Firstly, the paper makes use of q-Rung orthopair fuzzy (q-ROF) trust network (TN) to handle the intricate and uncertain evaluation environment of LECPs and utilizes combinative distance-based assessment (CODAS) to rank the alternative platforms. Secondly, considering that experts have different levels of familiarity with different indicator, a q-ROF-TN matrix between experts is established for each indicator to ascertain their respective weights, which adds robustness to the evaluation process. Thirdly, the dual feedback mechanism is designed to enhance group consensus that attempts to ensure that the evaluation process is collaborative and considers diverse expert opinions, leading to more reliable outcomes. Finally, a set of evaluation indicators is constructed covering various aspects like capital flow management capability and public domain traffic support, and the level of risk of the four LECPs is evaluated using the proposed method. Furthermore, the reliability and effectiveness of the proposed evaluation model are validated through sensitivity analysis and comparative study.

Consensus model for probabilistic linguistic multi-attribute group decision-making based on incomplete social trust networks

PurposeExperts may adjust their assessments through communication and mutual influence, and this dynamic evolution relies on the spread of internal trust relationships. Due to differences in educational backgrounds and knowledge experiences, trust relationships among experts are often incomplete. To address such issues and reduce decision biases, this paper proposes a probabilistic linguistic multi-attribute group decision consensus model based on an incomplete social trust network (InSTN).Design/methodology/approachIn this paper, we first define the new trust propagation operators based on the operations of Probability Language Term Set (PLTS) with algebraic t-conorm and t-norm, which are combined with trust aggregation operators to estimate InSTN. The adjustment coefficients are then determined through trust relations to quantify their impact on expert evaluation. Finally, the particle swarm algorithm (PSO) is used to optimize the expert evaluation to meet the consensus threshold.FindingsThis study demonstrates the feasibility of the method through the selection of treatment plans for complex cases. The proposed consensus model exhibits greater robustness and effectiveness compared to traditional methods, mainly due to the effective regulation of trust relations in the decision-making process, which reduces decision bias and inconsistencies.Originality/valueThis paper introduces a novel probabilistic linguistic multi-attribute swarm decision consensus model based on an InSTN. It proposes a redefined trust propagation and aggregation approach to estimate the InSTN. Moreover, the computational efficiency and decision consensus accuracy of the proposed model are enhanced by using PSO optimization.

The fuzzy graph model for conflict resolution considering power asymmetry based on social trust network

Conflicts are widespread in all spheres of society, and power asymmetries between conflicting actors are becoming more pronounced. However, the underlying causes of this asymmetry are often overlooked. To objectively reveal the fundamental reasons for power asymmetry, this paper utilizes Social Trust Networks (STN) to calculate decision-makers’ (DMs) weights. To further investigate the complex phenomenon of power asymmetry and accurately describe the inherent fuzziness in DMs’ subjective judgments, two multiple decision-makers (multi-DMs) power asymmetry models based on fuzzy preferences are constructed: one with a leader, a subordinate leader, and a follower, and the other with a leader and two followers. In response to the issue of neglecting power intensity differences in previous research, this paper provided an in-depth discussion of the effects of different degrees of power on the adjustment of followers’ preferences. On this basis, the stability definitions were extended to fuzzy power asymmetry conditions. Finally, this paper presented a case study on business operation conflicts in commercial banks and drew conclusions based on the analysis.

Digital Marketing Strategies and their Impact on Consumer Behavior

The advent of digital marketing has greatly altered customer behavior by altering the way in which companies communicate with their target audiences. The effects of different digital marketing methods on customer actions are discussed in this article. Content marketing, social media marketing, SEO, email marketing, and influencer marketing are some of the key tactics that are addressed. Research shows that these tactics increase customer involvement, familiarity with the brand, and ultimately, sales. The goal of content marketing is to increase customer trust and loyalty by providing them with high-quality, relevant material. In order to increase brand awareness and customer engagement, social media marketers use sites like Twitter, Instagram, and Facebook to build tailored and interactive campaigns. With search engine optimization (SEO), companies may increase their online presence and more effectively reach their target audience. One of the most effective ways to reach out to customers, build rapport, and increase sales is through email marketing. Influencer marketing leverages preexisting trust networks to sell items in a genuine way by teaming up with people who have large online followings. Not only do these tactics affect the whole buyer experience, from awareness to post-purchase assessment, but they also significantly affect consumers' views and preferences. The study highlights the need of comprehending customer behavior and psychology in order to customize digital marketing campaigns successfully. It also stresses how important it is for companies to keep up with new technology and trends if they want to be competitive. Businesses may improve marketing strategies, get insights into consumer preferences, and boost customer happiness by employing data analytics. The report finds that the most effective way to influence customer behavior and achieve corporate goals is through an integrated approach that combines numerous digital marketing methods.

Eye, Heart, the Brain: The Psychophysiology of Trust in AVs

Automation misuse and acceptance, influenced by trust, environmental conditions, and confidence, have hindered drivers from fully benefiting from partially automated vehicles. This study investigates how driver trust changes with AV reliance, differences in mental and physiological states, and continuous measures’ effectiveness. The takeover drivers reported lower trust than the non-takeover drivers in all scenarios. Nontakeover drivers’ elevated DLPFC activation aligns with trust networks and emotion regulation. The groups also differed in neural activation preand during scenarios with the takeover group showed more PFC, V2V3, and IFC engagement pre-scenario. Gaze revealed the takeover group fixated more on the AV button or dashboard, indicating readiness to take over, while non-takeover drivers focused on the rearview mirror, reflecting situational awareness. HRV responses showed higher physiological arousal in the takeover group pre-scenario. In summary, our multimodal approach reveals takeover behavior is associated with lower trust, cognitive unloading, increased stress, and anticipatory visual attention.

Using attentive temporal GNN for dynamic trust assessment in the presence of malicious entities

Trust networks are built on the premise that there is a network of trust among entities. Precisely evaluating pairwise trust in such networks is essential for enhancing the quality of service in various applications, including online marketing, recommender systems, and IoT-based applications. However, it remains challenging to consider dynamic trust and account for malicious behavior in a scalable and effective manner.Recently, Graph Neural Networks (GNN)-based methods have emerged as powerful and scalable tools for trust estimation. Nevertheless, they lack thorough consideration for the time-dependent nature of trust, especially in the presence of malicious behaviors, leading to inaccurate trust predictions. To address this gap, we propose a novel trust assessment model called MATA, which handles the time dependency of trust and exhibits robustness against malicious behaviors. MATA leverages an extended Graph Convolutional Network (GCN) integrated with Recurrent Neural Networks (RNNs) and an attention mechanism to jointly capture time dependencies and trace entities’ behavior over time in complex networks. Additionally, by clustering the learned trust representations over time, we assign reputation values to entities. This allows us to monitor their dynamic behavior and mitigate potential adverse effects on trust assessment.In our experiments on real-world datasets, MATA outperforms the state-of-the-art methods by up to 8.4 % in accuracy. This improvement is a testament to the effectiveness of the proposed approach. Furthermore, our work enhances the robustness of trust assessment models, as evidenced by consistently higher performance metrics in the face of various trust-related attacks.

“Akili zetu ziwe moja / Let our minds be one”: Emergent strategy for adapting to seafood insecurity in coastal Kenyan fishing communities

Small-scale fisheries provide food sovereignty and capital for millions globally. However, coastal Kenyan communities who harvest reef fishery resources are often still food insecure, especially due to the detrimental effects of climate change and overfishing in their communities. This study aims to understand the environmental perceptions and priorities of fishing families in Kilifi County, Kenya, and how these relate to both food security and sustainable development policy. We rely on qualitative methods including interviews with household caregivers from 42 fishing families to provide insights not only on fishery sustainability but also on the additional aspects of reproductive labor related to fish food security, including the food securing aspects of availability, accessibility, utilization, and stability. Our findings highlight the complex challenges that environmental change places on families in this situation: lack of fish availability, seasonal food inaccessibility, shifting baselines, and experiences of loss in the food system are markedly a prevalent result. Despite these struggles, we see emergent strategies to address food needs unfold in a variety of resilient and opportunistic ways, including adaptation, trust, and multimodal networks of support. Further policy support must be given to communities to practice food sovereignty in their own contexts, especially as climate changes continue and these strategies become salient adaptive practices elsewhere.

HyGate-GCN: Hybrid-Gate-Based Graph Convolutional Networks with dynamical ratings estimation for personalized POI recommendation

The presence of user-generated ratings has dramatically facilitated the development of recommendation systems to aid users in discovering relevant and personalized points of interest (POI). It is worth mentioning that users’ choices and preferences are not static but rather dynamic, reflecting the ever-changing nature of human experiences and influences. Furthermore, the utilization of social influence and geographical proximity of users is still insufficient to capture the homophily effect within networks. In this paper, an interesting Hybrid Gate-based Graph Convolutional Network (HyGate-GCN) combining with feature vectors embedding and interaction, where a modified gated-GCN is proposed for personalized recommendations by adequately employing the behavior of users’ check-ins, temporal properties of users’ decisions, social properties of users, as well as the user/POI profile information data. Specifically, a novel POI graph reflecting the geographical proximity is first established to describe the behavior of users’ check-ins and, at the same time, an improved overlap ratio about POIs is employed to effectively describe temporal properties of users’ decisions. Then, an attention mechanism is developed to encode feature vectors of both the users and POIs, with the objective of assigning higher importance to features that are deemed relevant. Furthermore, a temporal Kalman filter dynamically estimating ratings is developed to exploit the information about the evolving preferences of users over time. Finally, a modified gated-GCN model with merging and refining gates is constructed to effectively acquire the homophily phenomenon in both trust network graphs and spatial adjacency matrix graphs of users and POIs respectively. Experimental results provide evidence of the effectiveness of our approach in improving accuracy and personalization.