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Although manufacturer-initiated rewards are widely used to secure distributors’ compliance, the spillover effect on unrewarded distributors (i.e. observers) in the same distribution channel is under-researched. Using insights from social learning theory, this paper aims to investigate how manufacturer-initiated rewards affect observers’ expectation of reward and shape observers’ compliance toward the manufacturer. Furthermore, this paper explores how such effects are contingent upon distributor relationship features.

To test the hypotheses, hierarchical multiple regression and bootstrapping analyses were performed using survey data from 280 Chinese distributors.

The magnitude of a manufacturer-initiated reward to a distributor stimulates expectation of reward among observers, which enhances compliance; observers’ expectation of reward mediates the impact of reward magnitude on compliance. Moreover, network centrality (of the rewarded peer) negatively moderates the positive impact of reward magnitude on observers’ expectation of reward, whereas observers’ dependence (on the manufacturer) positively moderates this dynamic.

Manufacturers should pay attention to the spillover effects of rewards. Overall, they should use rewards of appropriate magnitude to show willingness to recognize outstanding distributors. This will inspire unrewarded distributors, which will then be more compliant. Furthermore, manufacturers should know that specific types of distributor relationship features may significantly vary the spillover effects.

This study illuminates the spillover effects of manufacturer-initiated reward by opening the “black box” of the link between reward magnitude and observers’ compliance and by specifying the effects’ boundary conditions.

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.

This paper aims to disentangle Chinese-English-rich resources linguistic and speaker timbre features, achieving cross-lingual speaker transfer for Cambodian.

This study introduces a novel approach: the construction of a cross-lingual feature disentangler coupled with the integration of time-frequency attention adaptive normalization to proficiently convert Cambodian speaker timbre into Chinese-English without altering the underlying Cambodian speech content.

Considering the limited availability of multi-speaker corpora in Cambodia, conventional methods have demonstrated subpar performance in Cambodian speaker voice transfer.

The originality of this study lies in the effectiveness of the disentanglement process and precise control over speaker timbre feature transfer.

The effect of bolt stripping failure on the ductility of steel end plate beam-column connections has received relatively little investigation to date. The objective with the present work is to establish a validated numerical model of end plate connections at elevated temperatures, which predicts the mechanical behaviour and failure modes observed in the experimental tests including the bolt stripping failure. Furthermore, the validated FE model was used to investigate the effect of stripping failure on both the rotational and load-bearing capacity of end plate connection.

The analysis was conducted on a validated numerical model of end plate connections at elevated temperatures, which predicts the mechanical behaviour and failure modes observed in the experimental tests including the bolt stripping failure. The material was modelled considering ductile damage initiation and evolution featured in ABAQUS/Standard.

This study demonstrates that thick end plates can prevent stripping failure which significantly improves the rotational capacity of the connection. This failure mode can develop readily with thin end plates; however the effect is often unrealistically mitigated through idealised experimental tests. The rotational capacity of a connection can be 5.0 times higher if stripping failure is avoided, particularly at elevated temperatures. Eurocode 3 part 1.8 does not consider the possibility of stripping failure when discussing the requirements for plastic analysis. It is concluded in the present study that by allowing for the possibility of bolt stripping, the mode of failure can often shift from end plate failure to bolt stripping, this in turn significantly reduces the connection rotational capacity.

The effect of bolt stripping failure on the ductility of steel end plate beam-column connections has received relatively little investigation to date.

This study aims to explore the formation mechanism of aerodynamic noise of a high-speed maglev train and understand the characteristics of dipole and quadrupole sound sources of the maglev train at different speed levels.

Based on large eddy simulation (LES) method and Kirchhoff–Ffowcs Williams and Hawkings (K-FWH) equations, the characteristics of dipole and quadrupole sound sources of maglev trains at different speed levels were simulated and analyzed by constructing reasonable penetrable integral surface.

The spatial disturbance resulting from the separation of the boundary layer in the streamlined area of the tail car is the source of aerodynamic sound of the maglev train. The dipole sources of the train are mainly distributed around the radio terminals of the head and tail cars of the maglev train, the bottom of the arms of the streamlined parts of the head and tail cars and the nose tip area of the streamlined part of the tail car, and the quadrupole sources are mainly distributed in the wake area. When the train runs at three speed levels of 400, 500 and 600 km·h⁻¹, respectively, the radiated energy of quadrupole source is 62.4%, 63.3% and 71.7%, respectively, which exceeds that of dipole sources.

This study can help understand the aerodynamic noise characteristics generated by the high-speed maglev train and provide a reference for the optimization design of its aerodynamic shape.

With the worldwide open government data (OGD) movement and frequent public health emergencies in recent years, academic research on OGD for public health emergencies has been growing. However, it is not fully understood how to promote OGD on public health emergencies. Therefore, this paper aims to explore the factors that influence OGD on public health emergencies.

The technology–organization–environment framework is applied to explore factors that influence OGD during COVID-19. It is argued that the effects of four key factors – technical capacity, organizational readiness, social attention and top-down pressure – are contingent on the severity of the pandemic. A unique data set was created by combining multiple data sources which include archival government data, a survey of 1,034 Chinese respondents during the COVID-19 outbreak and official COVID-19 reports.

The data analysis indicates that the four factors positively affect OGD, and pandemic severity strengthens the effects of technical capacity, organizational readiness and social attention on OGD.

This study provides theoretical insights regarding how to improve OGD during public health emergencies, which can guide government efforts in sharing data with the public when dealing with outbreak in the future.

In 2015, Bursa Malaysia Berhad (BMB) issued the first edition of the Sustainability Reporting Guide (SRG 1.0) to aid Malaysian public listed companies (PLCs) in preparing corporate social responsibility reporting (CSRR). After receiving users' commentaries, BMB issued the second edition of SRG (SRG 2.0) in 2018. Given the major amendments in CSRR regulatory guidelines, there is a need to analyze the readability of CSRR in light of the new guide and to investigate the combined effects of SRG 2.0 and the assurance of CSR information on the readability of CSRR.

This study employs two readability indices to compare the readability of CSRR ex-ante and ex-post the implementation of SRG 2.0 across a sample of Malaysian PLCs that maintained their market capitalization among the top 100 companies.

The practical findings of the multivariate regression revealed that the readability of CSRR is reduced after the introduction of SRG 2.0. Meanwhile, the readability of CSRR is positively influenced by combining the effect of SRG 2.0 and CSRR assurance.

This study provides empirical evidence that the amendment to CSRR has made CSR reports more challenging to read and thus reduces their communicative value. Therefore, in their quest to mandate more CSRR information from companies, regulators might need to consider advocating that such data is reported in a readable manner. This study also shows the influential role of CSR information assurance in enhancing the readability of CSRR.

This study helps assess the readability of CSRR among Malaysian companies after the adoption of SRG 2.0. It also contributes to the literature on CSRR, as the readability of such reporting within the context of Malaysia has not been widely examined in previous studies.

The 2019 coronavirus disease (COVID-19) pandemic has profoundly affected family and school life. Evidence demonstrates how pandemic-induced online learning and home confinement can influence family dynamics and, consequently, students’ mental health and quality of life. This chapter extends the literature by building upon the perspective of family systems theory and focusing the analysis on graduate students who are underrepresented in COVID-19 research. Drawing from an online survey among 337 graduate students enrolled in a state university in the Philippines during the second year of the pandemic, this study examines the three family relationship domains (cohesion, expressiveness, and conflict), their predictive relationships with life satisfaction, and the mediating role of mental well-being on these relationships. Findings indicate favorable levels of cohesion, expressiveness, and conflict in the family. Respondents’ age, sex assigned at birth, and marital status were significantly correlated with at least one domain of family relationship. Cohesion and expressiveness yielded significant positive predictive relationships on mental well-being and life satisfaction. Furthermore, findings indicate the partial mediation of mental well-being on the relationship between cohesion and life satisfaction and full mediation on expressiveness and life satisfaction.

This paper aims to develop a dynamic two-sided stable matching method based on preference information of the matching objects in uncertain environments, so as to solve the matching problem of cloud manufacturing tasks and services with load balancing.

For dynamic two-sided matching, due to the complexity of social environment and the limitation of human cognition, hesitation and fuzziness always exist in the process of multi-criteria assessment. First, in order to obtain the accurate preference information of each matching object, uncertain linguistic variables, uncertain preference ordinal and incomplete complementary matrices are used to evaluate multi-criteria preference information. This process is undertaken by considering the probability of each possible matching pair. Second, the preference information at different times is integrated by using the time-series weight to obtain the comprehensive satisfaction degree matrices of the matching objects. Further, the load adjustment parameter is used to increase the satisfaction degree of the matching objects. Afterward, a dynamic two-sided stable matching optimization model is constructed by considering stable matching conditions. The model aims to maximize the satisfaction degree and minimizes the difference in the satisfaction degree of matching objects. The optimal stable matching results can be obtained by solving the optimization model. Finally, a numerical example and comparative analysis are presented to demonstrate the characteristics of the proposed method.

Uncertain linguistic variables, uncertain preference orders and incomplete complementary matrices are used to describe multi-criteria preference information of the matching objects in uncertain environments. A dynamic two-sided stable matching method is proposed, based on which a DTSMDM (dynamic two-sided matching decision-making) model of cloud manufacturing with load balancing can be constructed. The study proved that the authors can use the proposed method to obtain stable matching pairs and higher matching objective value through comparative analysis and the sensitivity analysis.

A new method for the two-sided matching decision-making problem of cloud manufacturing with load balancing is proposed in this paper, which allows the matching objects to elicit language evaluation under uncertain environment more flexibly to implement dynamic two-sided matching based on preference information at different times. This method is suitable for dealing with a variety of TSMDM (two-sided matching decision-making) problems.