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Trust is an increasingly important requirement for any business and as a result has become a contemporary design criterion for business processes. However, the literature to date is very much focused on the technical (security) aspects, which are provider centric, as opposed to trust that is customer centric. In this paper, the authors extended an initial meta-model of trust-aware process design by proposing a way to capture trust-intensity for four trust dimensions, i.e. input, people, process and output and an organizational trust position. The authors also investigate the deployment of the extended meta-model in practice.

An extensive literature study is conducted to derive an understanding of the dimension's customer trust when interacting with an organization. Based on the findings of the literature review and a previously developed trust meta-model, the authors propose a way to describe an organizational trust position, i.e. the depiction of how much uncertainty is prevalent in the trust dimensions. Next, the authors conducted an exploratory case study using secondary data to validate the extended meta-model.

The case study demonstrated the applicability of the extended trust meta-model and derived actionable practices. In this case, the Indonesian food delivery company GoFood, the authors identified trust concerns in the input, process, resources and output of their business at the start of their operations. Since then, GoFood took specific actions to reduce their operational, behavioral and perceived uncertainty and these identified trust concerns. To a lesser degree, GoFood has managed vulnerability issues and invested in measures to increase customers' confidence. As a result of reduced uncertainties, GoFood's business has grown and became the number one in food service delivery in Indonesia.

The approach to capture trust (in the trust dimensions) is still a simplified version and a pre-step for a fully developed management tool or method. The use of a secondary data from a single case study also limits the validity and generalizability of the findings.

The extended meta-model proposed in this paper has several implications related to the organization's BPM capabilities. The result also demonstrates how trust measures related to reducing uncertainty, reducing vulnerability and increasing confidence can be applied in practice. Strategies used by the case company presented here such as rating systems to increase confidence can be used by other firms within a similar context.

Having an empirically validated framework for the management of trust, allows organizations to execute an operational model for the development of trusted engagement with the main benefactor being the customer.

Previous trust-related studies focused on conceptual ideas only, relied on fictive examples or were very much focused on the technical (security) aspects of business processes. This study is the first empirical validation of a trust meta-model that serves managers to understand their trust position and to guide trust-building actions.

This paper aims to improve the recommendations performance for cold-start users and controversial items. Collaborative filtering (CF) generates recommendations on the basis of similarity between users. It uses the opinions of similar users to generate the recommendation for an active user. As a similarity model or a neighbor selection function is the key element for effectiveness of CF, many variations of CF are proposed. However, these methods are not very effective, especially for users who provide few ratings (i.e. cold-start users).

A new user similarity model is proposed that focuses on improving recommendations performance for cold-start users and controversial items. To show the validity of the authors’ similarity model, they conducted some experiments and showed the effectiveness of this model in calculating similarity values between users even when only few ratings are available. In addition, the authors applied their user similarity model to a recommender system and analyzed its results.

Experiments on two real-world data sets are implemented and compared with some other CF techniques. The results show that the authors’ approach outperforms previous CF techniques in coverage metric while preserves accuracy for cold-start users and controversial items.

In the proposed approach, the conditions in which CF is unable to generate accurate recommendations are addressed. These conditions affect CF performance adversely, especially in the cold-start users’ condition. The authors show that their similarity model overcomes CF weaknesses effectively and improve its performance even in the cold users’ condition.

In the existing era, the Internet of Things (IoT) can be considered entirely as a system of embedding intelligence. The transverse character of IoT systems and various components associated with the arrangement of IoT systems have confronted impediments in the form of security and trust. There is a requirement to efficiently secure the IoT environment. The present study recommends a framework for impediments to secure and trustworthy IoT environments.

The present study identifies thirteen potential impediments to secure and trustworthy IoT environment. Further, a framework is developed employing Total Interpretive Structural Model (TISM) and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) approach. The integrated approach is employed as TISM organizes inter-relations among the identified impediments, while MICMAC analysis organizes interpretations related to the driving and dependence power of the impediments.

The results from the study represents that security of IoT from arbitrary attacks is the impediment that has attained the highest driving power. The impediments such as “security of IoT from arbitrary attacks”, “profiling” and “trust and prominence structure” are identified at the top level in the analysis.

The previous studies highlight the facilitating contribution of IoT on various devices but neglect the impediments that can contribute towards a safe and trustworthy IoT environment. Also, the present study has its limitations as it depends upon the experts’ recommendations and suggestions.

The existing framework could be beneficial in constructing policies and suggestions to efficiently cater the impediments to a secure and trustworthy IoT environment.

This paper aims to propose and evaluate an intrusion sensitivity (IS)-based approach regarding the detection of pollution attacks in collaborative intrusion detection networks (CIDNs) based on the observation that each intrusion detection system may have different levels of sensitivity in detecting specific types of intrusions.

In this work, the authors first introduce their adopted CIDN framework and a newly designed aggregation component, which aims to collect feedback, aggregate alarms and identify important alarms. The authors then describe the details of trust computation and alarm aggregation.

The evaluation on the simulated pollution attacks indicates that the proposed approach is more effective in detecting malicious nodes and reducing the negative impact on alarm aggregation as compared to similar approaches.

More efforts can be made in improving the mapping of the satisfaction level, enhancing the allocation, evaluation and update of IS and evaluating the trust models in a large-scale network.

This work investigates the effect of the proposed IS-based approach in defending against pollution attacks. The results would be of interest for security specialists in deciding whether to implement such a mechanism for enhancing CIDNs.

The experimental results demonstrate that the proposed approach is more effective in decreasing the trust values of malicious nodes and reducing the impact of pollution attacks on the accuracy of alarm aggregation as compare to similar approaches.

This paper aims to propose a trust-based ant-colony recommender system. It achieves high accuracy and coverage by integrating the importance level of friends. This paper has two main contributions, namely, selecting higher-quality raters and improving the prediction step. From these two contributions, the proposed trust-based ant-colony recommender system could provide more accurate and wider-coverage prediction than existing systems.

To obtain higher-quality raters, the data set was preprocessed, and then, trust values were calculated. The depth of search was increased to obtain higher coverage levels. This work also focuses on the importance level of friends in the system. Because the levels of influence on the active user of all friends are not equal, the importance level of friends is integrated into the system by transposing rater’s rating to the active user’s perspective and then assigning a weight to each rater.

The experimental evaluation clearly demonstrates that the proposed method achieves better results in terms of both accuracy and coverage than existing trust-based recommender systems. It was found that integrating the importance level of friends into the system, which transposes ratings and assigns weight to each user, can increase accuracy and coverage.

Existing trust-based ant-colony recommender systems do not consider the importance level of friends in the prediction step. Most of them only focus on finding raters and then using the rater’s real ratings in the prediction step. A new method is proposed that integrates the importance level of friends into the system by transposing a rater’s rating to match the active user’s perspective and assigning a weight for each rater. The experimental evaluation demonstrates that the proposed method achieves better accuracy and coverage than existing systems.

How the application of multivariate analysis can aid charities in improving their fund raising appeals is demonstrated. Using a major UK charity as an example and analysing the socio‐demographic, awareness and psychographic profiles of nearly 500 respondents, the scope for market segmentation and a priori identification of potential donors is explored.

Security of wireless body area network communication is highly important as it directly impacts human life. This paper aims to focus on battlefield application area of WBAN for implementing security where data must be protected against various possible attacks before delivering over a public network.

Providing a strong security system is still a research challenge due to low computational power of used sensors for protecting transmission data. In this paper, the authors have proposed an optimized security solution for multithreaded wireless body area network (MWBAN) using trust-based distributed group key management technique to overcome the drawbacks of existing elliptical curve cryptography-homomorphism (ECC-Homomorphism) scheme as well as coded cooperative data exchange group key management (CCDE_GKM) scheme.

The proposed optimized security solution is implemented for a particular deployment strategy and test runs are conducted. It is found that when number of attack nodes increased to 25, compared to ECC–Homomorphism and CCDE_GKM for the proposed trust-based distributed group key management technique there is an improvement in performance parameters such as throughput is dropped to only 10.11 Kbps, average delay is just 3.4 s, energy consumption is maximum 29 joules, packet loss is only 12.3 per cent, 90.9 per cent truly can detect attack, only 8.9 per cent false attack detection and 84 per cent true negative detection.

Medical care can be provided to human beings with much ease and flexibility via remote monitoring. The user can be at any place, can do his/her everyday work while remotely being monitored of their health parameters and secured transmission of their data to the health-care center for medical service in need.

This paper presents an optimized security solution for MWBAN using trust-based distributed group key management technique where bilinear pairing theory is used as major cryptographic base. Optimal key is selected based on trust value and also attack nodes are detected based on trust value to control participation in communication.

A cross-domain intelligent software-defined network (SDN) routing method based on a proposed multiagent deep reinforcement learning (MDRL) method is developed.

First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between the root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to acquire global network state information in real time. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a network traffic state prediction mechanism is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time.

Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and open shortest path first (OSPF) routing methods.

Message transmission and message synchronization for multicontroller interdomain routing in SDN have long adaptation times and slow convergence speeds, coupled with the shortcomings of traditional interdomain routing methods, such as cumbersome configuration and inflexible acquisition of network state information. These drawbacks make it difficult to obtain global state information about the network, and the optimal routing decision cannot be made in real time, affecting network performance. This paper proposes a cross-domain intelligent SDN routing method based on a proposed MDRL method. First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to realize the real-time acquisition of global network state information. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a prediction mechanism for the network traffic state is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time. Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and OSPF routing methods.

The explosion of data due to the sophistication of information and communication technology makes it simple for prospective tourists to learn about previous hotel guests' experiences. They prioritize the rating score when selecting a hotel. However, rating scores are less reliable for suggesting a personalized preference for each aspect, especially when they are in a limited number. This study aims to recommend ratings and personalized preference hotels using cross-domain and aspect-based features.

We propose an aspect-based cross-domain personalized recommendation (AsCDPR), a novel framework for rating prediction and personalized customer preference recommendations. We incorporate a cross-domain personalized approach and aspect-based features of items from the review text. We extracted aspect-based feature vectors from two domains using bidirectional long short-term memory and then mapped them by a multilayer perceptron (MLP). The cross-domain recommendation module trains MLP to analyze sentiment and predict item ratings and the polarities of the aspect based on user preferences.

Expanded by its synonyms, aspect-based features significantly improve the performance of sentiment analysis on accuracy and the F1-score matrix. With relatively low mean absolute error and root mean square error values, AsCDPR outperforms matrix factorization, collaborative matrix factorization, EMCDPR and Personalized transfer of user preferences for cross-domain recommendation. These values are 1.3657 and 1.6682, respectively.

This study assists users in recommending hotels based on their priority preferences. Users do not need to read other people's reviews to capture the key aspects of items. This model could enhance system reliability in the hospitality industry by providing personalized recommendations.

This study introduces a new approach that embeds aspect-based features of items in a cross-domain personalized recommendation. AsCDPR predicts ratings and provides recommendations based on priority aspects of each user's preferences.

The purpose of this study is to investigate how book group recommendations can be used as a meaningful way to suggest suitable books to users, given the increasing number of individuals engaging in sharing and discussing books on the web.

The authors propose reviews fine-grained classification (CFGC) and its related models such as CFGC1 for book group recommendation. These models can categorize reviews successively by function and role. Constructing the BERT-BiLSTM model to classify the reviews by function. The frequency characteristics of the reviews are mined by word frequency analysis, and the relationship between reviews and total book score is mined by correlation analysis. Then, the reviews are classified into three roles: celebrity, general and passerby. Finally, the authors can form user groups, mine group features and combine group features with book fine-grained ratings to make book group recommendations.

Overall, the best recommendations are made by Synopsis comments, with the accuracy, recall, F-value and Hellinger distance of 52.9%, 60.0%, 56.3% and 0.163, respectively. The F1 index of the recommendations based on the author and the writing comments is improved by 2.5% and 0.4%, respectively, compared to the Synopsis comments.

Previous studies on book recommendation often recommend relevant books for users by mining the similarity between books, so the set of book recommendations recommended to users, especially to groups, always focuses on the few types. The proposed method can effectively ensure the diversity of recommendations by mining users’ tendency to different review attributes of books and recommending books for the groups. In addition, this study also investigates which types of reviews should be used to make book recommendations when targeting groups with specific tendencies.