This paper develops a new trust and personalized individual semantics (PISs) based fusion method for heterogeneous multi-criteria group decision making (HMCGDM) and applies to the selection of live streaming commerce plan. Firstly, a consistency-driven optimization model is built to determine the PIS of linguistic terms in linguistic preference relation of criteria given by the trustor and recommenders. Secondly, this paper defines a harmony degree to reflect the degree of difference in the evaluation information of different recommenders in HMCGDM problem. Then, considering the trust relationship between trustor and recommenders and between the recommenders themselves, two multi-objective mathematical programming models are constructed. One purpose is to produce values to positions that are not assigned, and the other purpose is to fuse the evaluation information of recommenders. In addition, combining the preference information of criteria given by trustor and Shannon entropy based on the collective evaluation information, the subjective and objective weights of criteria can be determined respectively. The comprehensive criteria’s weights are acquired by an optimization model which aims at minimizing the Jenson-Shannon divergence between the subjective and objective weights. Finally, an example of selection of influencer or celebrity in live streaming commerce for promoting new products sales is offered to verify the effectiveness of the proposed method. 

Trust and personalized individual semantics based fusion method for heterogeneous multi-criteria group decision making and application to live streaming commerce

The COVID-19 pandemic has formed a new reality for customers around the world. In response to this situation, users of digital technologies are facing the necessity to adopt and use specific technologies almost overnight. This research aims to examine the determinants of social (s)-commerce acceptance in Saudi female customers in a post-pandemic era. Utilizing the unified theory of acceptance and use of technology (UTUAT2) and social commerce constructs as a theoretical framework under the circumstances of the COVID-19 pandemic, we examine the antecedents of purchase intention and s-commerce use behavior of Saudi female customers. Moreover, using individual differences in the literature, we test the moderating role of customer age on their purchase intention and use behavior. Data were obtained from 475 Saudi women customers. Our results indicate that performance expectancy, facilitating conditions, price value, and habit are significantly predicting customer purchase intention. Furthermore, social commerce constructs are important to improve customer trust, which in turn influences purchase intention. The results also show that the effects of facilitating conditions, price value, and habit vary on purchase intention and use behavior because of the customer age profiles. Finally, this paper represents important insights for researchers, service providers, marketers, and policymakers.

/pdf/social-commerce-acceptance-after-post-covid-19-pandemic-in-2nexicic.pdf

Social Commerce Acceptance after Post COVID-19 Pandemic in Saudi Women Customers: A Multi-Group Analysis of Customer Age


Purpose
This study aims to examine the impact of different subtypes of trust on the willingness of companies to share knowledge. To measure improvement in work performance, three perspectives of interpersonal trust, institution-based trust and their combined effect on fostering trust are examined.


Design/methodology/approach
A comparative analysis and quantitative measurement are used in this study (with a sample size of 147) to determine which knowledge-sharing channels need to be established to increase effectiveness and efficiency.


Findings
This study found that both interpersonal and institutional trust can positively influence employees' willingness to share personal knowledge. Despite this, the combination of these two types of trust cannot outperform the scenarios in which one type of trust can reach its maximum. As a result of institutional trust, trustees are more likely to trust others when they feel protected.


Research limitations/implications
Trust may take on multiple dimensions in different business contexts and industries. In this study, the limited sample size and domain may only reveal some of these aspects of trust, which may not be representative of other contexts.


Originality/value
Few researchers have examined the degree of trust and its impact on knowledge dissemination using relevant parameters. Their focus is solely on the interaction between interpersonal trust and knowledge sharing. As a result of this study, the concept of “trust” was quantified, with more tangible metrics to provide better estimates when assessed in different business contexts.


An investigation of the impact of interpersonal and institutional trust on knowledge sharing in companies: invisible hands for knowledge sharing

Most trust inference algorithms based on 
 
 Q
 
 -learning and learning automata often suffer from the problem of insufficient storage space, as well as some algorithms using the shortest trust path do not fully utilize the trust information. To solve the above problems, a trust inference algorithm (DuelingDQNTrust) based on deep reinforcement learning is proposed. It uses Dueling DQN to compute 
 
 Q
 
 values to reduce the required storage space and does not limit the traversal depth. Moreover, the way to find out the trusted neighbors of a user is crucial because it affects the reliability of the trust path. So three combination strategies are proposed to find users’ trusted neighbors more accurately. One strategy is to define a priority metric by combining node importance and user similarity to find the top 
 
 k
 
 neighbor users of the user as the trusted neighbor set. Another two strategies consider node importance and user similarity in a different order of priority, which first uses a metric to find the top 
 
 k
 
 neighbor users of the user, and then further filters these 
 
 k
 
 users on another metric to determine the trusted neighbor set of the user. The evaluation of the algorithm on the FilmTrust dataset indicates that the algorithm outperforms several existing trust inference algorithms.

/pdf/a-trust-inference-method-employing-combinatorial-strategies-21qpf1hb.pdf

A Trust Inference Method Employing Combinatorial Strategies

Decisions made in areas such as economics, engineering, industry and medical sciences are usually based on finding and interpreting solutions to optimisation problems. When modelling an optimisation problem, it should be clear that people do not make decisions in a vacuum or in isolation from the reality. So, there is always a decision-making context that, in addition to the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">natural constraints of the problem, acts as a filter on the candidate solutions available. If this fact is omitted, optimal but useless solutions to the problem can be obtained. In this paper, we propose a systematic way of modelling contexts based on fuzzy propositions and two approaches ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori and a posteriori ) for solving optimisation problems under their influence. In the proposed a priori approach, the context is explicitly included in the mathematical model of the problem. As this approach may have a limited application due to the increasing number of constraints and their nature, an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a posteriori approach is proposed, in which a set of solutions, obtained by any means (like exact algorithms, simulation or metaheuristics), are checked for their suitability to the context by using a multi-criteria decision-making methodology. A simple fish harvesting problem in a sustainability context and a tourist trip design problem in a pandemic context were solved for illustration purposes. Our results provide researchers and practitioners with a methodology for more effective optimisation and decision-making.

Modelling Contexts as Fuzzy Propositions in Optimisation Problems

Shopping without much experience on target items is not unusual in social commerce (s-commerce). Inexperienced users are often influenced by user reviews when making purchasing decisions, which can be easily distorted due to low review quality. To address this issue, this study integrates two important review dimensions into purchasing decision model, i.e. the trust relationships to experienced users and the reliability of experienced users’ reviews. A purchasing decision model for s-commerce is proposed based on trust computation and item reviews. The trust degree of inexperienced users to experienced ones is computed based on item reviews and propagated using the parameterized Hamacher t-norm operator. Based on the item review information, inexperienced users can make the final purchasing decisions on target items with trust information to experienced users and their reliability on item reviews. A numerical example with a data subset of Epinions.com is provided and the comparative analysis is conducted to verify the effectiveness of the proposed method. This study proposes an inexperienced purchasing decision method considering the risk attitude of users with flexible trust propagation parameters, which can provide a new perspective for the s-commerce recommendations.

A social commerce purchasing decision model with trust network and item review information

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A social commerce purchasing decision model with trust network and item review information