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This study aims to propose a consensus model that considers dynamic trust and the hesitation degree of the expert's evaluation, and the model can provide personalized adjustment advice to inconsistent experts.

The trust degree between experts will be affected by the decision-making environment or the behavior of other experts. Therefore, based on the psychological “similarity-attraction paradigm”, an adjustment method for the trust degree between experts is proposed. In addition, we proposed a method to measure the hesitation degree of the expert's evaluation under the multi-granular probabilistic linguistic environment. Based on the hesitation degree of evaluation and trust degree, a method for determining the importance degree of experts is proposed. In the feedback mechanism, we presented a personalized adjustment mechanism that can provide the personalized adjustment advice for inconsistent experts. The personalized adjustment advice is accepted readily by inconsistent experts and ensures that the collective consensus degree will increase after the adjustment.

The results show that the consensus model in this paper can solve the social network group decision-making problem, in which the trust degree among experts is dynamic changing. An illustrative example demonstrates the feasibility of the proposed model in this paper. Simulation experiments have confirmed the effectiveness of the model in promoting consensus.

The authors presented a novel dynamic trust consensus model based on the expert's hesitation degree and a personalized adjustment mechanism under the multi-granular probabilistic linguistic environment. The model can solve a variety of social network group decision-making problems.

Perceptions of trust have been identified as an important element in the risk communication process. This research is concerned with establishing the degree of trust the general public has in various possible sources of information about the health effects associated with consuming genetically modified food. Participants were asked directly about the degree to which they would trust information about the health effects associated with consuming genetically modified food from a variety of sources, including a fictitious source included as a control. They were also asked about the degree to which they believed each source had a vested interest in misinforming the public about the possible health effects associated with such consumption, and the degree of knowledge they believed each source had about any possible health effects. The results indicate that perceptions of “vested interest” and “degree of knowledge” are important elements in determining levels of trust, although probably not exhaustive. Furthermore, that younger consumers are likely to be the most responsive audience for risk information, but general audience response to risk information is likely to be influenced by preconceptions about the source of the information, preconceptions that can be derived entirely from the name of the information source.

In the final step, the trust model is applied to the on-demand federated multipath distance vector routing protocol (AOMDV) to introduce path trust as a foundation for routing selection in the route discovery phase, construct a trusted path, and implement a path warning mechanism to detect malicious nodes in the route maintenance phase, respectively.

A trust-based on-demand multipath distance vector routing protocol is being developed to address the problem of flying ad-hoc network being subjected to internal attacks and experiencing frequent connection interruptions. Following the construction of the node trust assessment model and the presentation of trust evaluation criteria, the data packet forwarding rate, trusted interaction degree and detection packet receipt rate are discussed. In the next step, the direct trust degree of the adaptive fuzzy trust aggregation network compute node is constructed. After then, rely on the indirect trust degree of neighbouring nodes to calculate the trust degree of the node in the network. Design a trust fluctuation penalty mechanism, as a second step, to defend against the switch attack in the trust model.

When compared to the lightweight trust-enhanced routing protocol (TEAOMDV), it significantly improves the data packet delivery rate and throughput of the network significantly.

Additionally, it reduces the amount of routing overhead and the average end-to-end delay.

Complex cost structures and multiple conflicting objectives make selecting an appropriate cloud service difficult. The purpose of this study is to propose a novel group consensus decision making method for cloud services selection with knowledge deficit by trust functions.

This article proposes a knowledge deficit-based multi-criteria group decision-making (MCGDM) method for cloud-service selection based on trust functions. Firstly, the concept of trust functions and a ranking method is developed to express the decision-making opinions. Secondly, a novel 3D normalized trust degree (NTD) is defined to measure the consensus levels. Thirdly, a knowledge deficit-based interactive consensus model is proposed for the inconsistent experts to modify their decision opinions. Finally, a real case study has been carried out to illustrate the framework and compare it with other methods.

The proposed method is practical and effective which is verified by the real case study. Knowledge deficit is an important concept in cloud service selection which is verified by the comparison of the proposed recommended mechanism based on KDD with the conventional recommended mechanism based on average value. A 3D NTD which considers three values (trust, not trust and knowledge deficit) is defined to measure the consensus levels. A knowledge deficit-based interactive consensus model is proposed to help decision-makers reach group consensus. The proposed group consensus model enables the inconsistent decision-makers to accept the revised opinions of those with less knowledge deficit, rather than accepting the recommended opinions averagely.

The proposed a knowledge deficit-based MCGDM cloud service selection method considers group consensus in cloud service selection. The concept of knowledge deficit is considered in modeling the group consensus measuring and reaching method.

Green technology adoption (GTA) in small and micro enterprises (SMEs) is a complex multi-attribute group decision-making issue. Conflicts of opinions can hamper the achievement of group coherence. The purpose of this paper is to solve the conflict decision-making problem in SMEs.

This paper reconstructs the GTA evaluation index system for SMEs and proposes an intragroup coordination relationship model to mitigate group decision-making conflicts based on the cloud model and social networks (GCS). The numerical characteristics of these GTA indicators are determined using fuzzy theory and cloud model.

It was found that establishing a trust relationship network and a conflict coordination relationship could improve the consistency of group decision-making. The degree of coordination has subtle differences with the trust relationship. Particularly, the coordination relationship has obvious influences on the decision-making results among small groups in SMEs.

The conflict coordination model can significantly improve the consistency of group decision-making in SMEs. At the same time, it discusses the differences between the level of conflict coordination and the trust propagation in the process of group decision-making.

The purpose of this paper is to emphasise the importance of high levels of trust and collaboration for increasing the likelihood of project management (PM) success. However, the link between these three constructs remains unclear.

In this study, the authors use structural equation modelling (SEM) based on the findings from an international survey of 151 project practitioners to demonstrate the significance of project team trust and collaboration for increasing the likelihood of PM success.

The results indicate that PM success becomes more likely as the degree of collaboration improves which, in turn, is influenced by an increase in the level of trust between team members. The two factors of PM success are project performance and knowledge integration and innovation. The six factors of the degree of collaboration that were studied are physical proximity, commitment, conflict, coordination, relationships and incentives. The three factors of the level of trust investigated are expectations, knowledge exchange and imported trust.

The results of the study are expected to provide insight for project practitioners to increase the likelihood of PM success by taking cognisance of the factors that influence collaboration and trust. The results of the study may also provide insight into teaching and learning in tertiary education, in terms of professionalism and integrity issues.

This paper presents a new perspective for investigating PM success. SEM techniques are used to determine the likelihood of PM success by promoting trust and collaboration in the project team. This unique approach highlights the “human factors” that influence perceived PM success which should benefit both researchers and practitioners.

The development of social networks enhances the interaction between people, which brings new challenges to the research of group decision-making (GDM). This study aims at the problem that the synergy and redundancy due to interaction among decision-makers are ignored in the previous GDM, a trust-enhanced consensus reaching model based on interaction among decision-makers with incomplete preferences is proposed.

Firstly, confidence level is introduced to improve the hesitation phenomenon that should be considered when calculating trust degree; Secondly, a new trust propagation operator is developed to deal with indirect trust relationships; Thirdly, trust degree is transformed into interaction index to quantify the synergy and redundancy in decision-making. Fuzzy capacities of decision-makers are used to replace traditional weights, and the final scores of alternatives are obtained through Choquet integral.

The proposed model using fuzzy capacity can reflect the synergy or redundancy among decision-makers and improve the accuracy of final ranking result and reduce the loss of information.

This study proposes a trust-enhanced consensus reaching model, which develops a new trust propagation operator to ensure the continuous attenuation of trust in propagation process. And the proposed model uses fuzzy capacity to improve the enhancement or attenuation on the scores of alternatives.

The objective of this paper is to focus on seniors’ acceptance of the usage of connected healthcare technologies in their homes. The authors integrated into technology acceptance model (TAM) several latent variables such as social presence, trust and degree of intrusiveness perceived with the use of connected health technologies.

The authors distributed the survey by post to 605 seniors. The authors targeted elderly people using connected health technologies (assistive alarm, telecare, sensors, etc.) at home and/or receiving healthcare at home. The authors received 213 questionnaires back. As The authors had several latent variables, the authors used partial least squares (PLS), a variance-based structural equation modeling method.

The results show that the level of trust in these technologies impacts significantly the perception of usefulness and the degree of intrusiveness. In parallel, the degree of usefulness of these technologies impacts positively elderly people’s intention to accept their usage. Finally, one can claim that the perception of the social presence with the use of these technologies impacts positively the degree of perceived usefulness, trust and intrusiveness.

The sample covers a population benefiting from similar connected health technologies. It was difficult to distinguish and interpret the added value of each technology separately. As more and more elderly people use or are least familiarizing themselves with a range of connected technologies it would be interesting to identify which sets of connected technologies contribute the most to a positive feeling of social presence.

These results are particularly relevant to stakeholders in the health industry in their quest to improve their products/services. A better understanding of the relation that the elderly have with connected health technologies is an essential prerequisite to supporting the development of new solutions capable of meeting the specific needs of our seniors.

The authors want to apply the TAM to connected health technologies designed for elderly people and the authors also want to extend it by integrating the social presence, trust and degree of intrusiveness variables to our research model.

Cloud eases information processing, but it holds numerous risks, including hacking and confidentiality problems. It puts businesses at risk in terms of data security and compliance. This paper aims to maximize the covered human resource (HR) vulnerabilities and minimize the security costs in the enterprise cloud using a fuzzy-based method and firefly optimization algorithm.

Cloud computing provides a platform to improve the quality and availability of IT resources. It changes the way people communicate and conduct their businesses. However, some security concerns continue to derail the expansion of cloud-based systems into all parts of human life. Enterprise cloud security is a vital component in ensuring the long-term stability of cloud technology by instilling trust. In this paper, a fuzzy-based method and firefly optimization algorithm are suggested for optimizing HR vulnerabilities while mitigating security expenses in organizational cloud environments. MATLAB is employed as a simulation tool to assess the efficiency of the suggested recommendation algorithm. The suggested approach is based on the firefly algorithm (FA) since it is swift and reduces randomization throughout the lookup for an optimal solution, resulting in improved performance.

The fuzzy-based method and FA unveil better performance than existing met heuristic algorithms. Using a simulation, all the results are verified. The study findings showed that this method could simulate complex and dynamic security problems in cloud services.

The findings may be utilized to assist the cloud provider or tenant of the cloud infrastructure system in taking appropriate risk mitigation steps.

Using a fuzzy-based method and FA to maximize the covered HR vulnerabilities and minimize the security costs in the enterprise cloud is the main novelty of this paper.

By integrating the diffusion of innovation (DOI) theory, technology acceptance model (TAM), and social capital theory, the purpose of this paper is to: develop a model of consumer behavior and trial willingness toward nano-foods from product, consumer, and social perspectives; examine the effects of innovative features, consumer characteristics, and trust in authority on subjective perceptions (perceived trustworthiness and perceived benefit) as well as the social influence on attitudes toward nano-foods and trial willingness; examine the moderating role of product uncertainty on the relationship between these characteristics and subjective perceptions; and examine the effect of perceived benefit on perceived trustworthiness.

The results of the structural equation model (SEM), with nano-food knowledge data collected from 431 respondents, supported the research model and revealed the main effects hypothesized in this study and the moderating effect of product uncertainty. Simple slope analysis was further adopted to test the significant moderating effects.

The SEM results indicated that innovative characteristics (relative advantage, lack of observability, and novelty), consumer characteristics (perceived technology application), and social characteristics (trust in authority) affect perceived trustworthiness or perceived benefit. Social influence also has a direct effect on attitude toward nano-foods and trial willingness. Product uncertainty significantly moderates the relationship between characteristics (relative advantage and perceived technology application) and subjective perceptions (perceived trustworthiness and perceived benefit).

With increasing numbers and kinds of nanotechnology products now being developed and sold, it is important to go further to determine consumer perceptions and attitudes toward these. This study, thus, applied the DOI, TAM, and social capital theory to examine this issue. However, other theories might also be used to carry out research from other perspectives. This study should, thus, be seen as preliminary, and it is hoped that more works will discuss consumer attitudes toward nanotechnology products in the future.

When a new nano-food is introduced, the current study suggests that food manufacturers use the description on the package as a communicative tool. Detailing the advantages of nano-foods on food packages might be a useful way to enhance trial willingness and to reduce the fears and insecurities related to the use of nano-related products. In addition, if food manufacturers could cooperate with organizations or individuals seen as having some authority in this area (e.g. nanotechnology researchers) in order to disseminate accurate information about nanotechnology and related food products, this might be an effective way to increase sales and profits.

This is the first paper integrating the DOI, the social capital theory and the TAM to empirically investigate consumer willingness to try nano-food products.