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Corporate social responsibility (CSR) has now been paid notable attention by a large number of firms. The aim of this paper is to investigate a better way to implement CSR in a socially responsible supply chain with different channel leaderships.

Started from measuring CSR by purely taking consumer surplus into account, a stylized centralized channel and two different decentralized channels are first developed and examined for equilibria in the socially responsible chain. Furthermore, this paper extends to a socially irresponsible supply chain and a broader practice of CSR by simultaneously incorporating environmental externality and consumer surplus into CSR.

With the analytical studies, several interesting and important results and managerial insights are clearly revealed. For example, but not limited to, it is found that: (1) Compared with the leader, the follower can effectively implement CSR for a better performance in both pure and socially responsible profits in the decentralized channel; (2) undertaking socially responsible concerns does not always mean reducing the economic profit and conversely being socially irresponsible does not always induce an increase in economic profit for the socially responsible member(s); (3) CSR concern level plays a key role in both the pure and socially responsible profit. An increase in CSR concern level clearly increases the socially responsible profit and poses an ambiguous impact on pure profit depending on different channel leaderships: it decreases the centralized channel in pure profit but increases the pure profit with the decentralized channel under certain conditions; and (4) interestingly, the authors find that decentralization can outperform centralization regarding on both pure and socially responsible channel profits by properly implementing CSR concern levels.

The results derived in this paper provide novel managerial implications to the socially responsible members in terms of pricing decisions, order quantity and CSR practice. In addition, this paper assists the socially responsible supply chain in determining the optimal channel leadership to undertake CSR. That is, decentralization may achieve a better performance than integration under certain market conditions.

To the best of the authors' knowledge, this paper is the first attempt to explore the interactive of the CSR practice and channel leadership in a socially responsible supply chain.

Phishing attacks are the most common cyber threats targeted at users. Digital nudging in the form of framing and priming may reduce user susceptibility to phishing. This research focuses on two types of digital nudging, framing and priming, and examines the impact of framing and priming on users' behavior (i.e. action) in a cybersecurity setting. It draws on prospect theory, instance-based learning theory and dual-process theory to generate the research hypotheses.

A 3 × 2 experimental study was carried out to test the hypotheses. The experiment consisted of three levels for framing (i.e. no framing, negative framing and positive framing) and two levels for priming (i.e. with and without priming).

The findings suggest that priming users to information security risks reduces their risk-taking behavior, whereas positive and negative framing of information security messages regarding potential consequences of the available choices do not change users' behavior. The results also indicate that risk-averse cybersecurity behavior is associated with greater confidence with the action, greater perceived severity of cybersecurity risks, lower perceived susceptibility to cybersecurity risks resulting from the action and lower trust in the download link.

This research shows that digital nudging in the form of priming is an effective way to reduce users' exposure to cybersecurity risks.

This paper aims to build a moderate mediation model to delineate when and how employee with perceived overqualification will exert extra effort and therefore engage in more altruistic helping behavior.

The research hypotheses were empirically tested using multitime and multisource survey data. Given the nested nature of data (i.e. 52 immediate supervisors rated 143 subordinates), multilevel structural equation modeling analyses within Mplus were conducted to test the proposed model.

The results support the proposed moderated mediation effect and indicate that perceived overqualification is positively related to extra effort on a condition that there is either strong desire for higher workplace status or more developmental job opportunities. The extra effort will subsequently lead to more altruistic helping behavior.

Based on the findings of this paper, human resource managers should consider the job applicant’s desire for workplace status and the organizational context the employer can provide when hiring overqualified employees. Second, organizations should carefully conduct job design to improve overqualified employees’ on-the-job developmental experiences. Third, training programs should be conducted to help satisfy needs and improve workplace status of overqualified employees, so that they can exert extra job effort and engage in pro-organizational behaviors.

Drawing on motivation–opportunity–ability theory, this paper extends the limited understanding of important boundary conditions under which perceived overqualification can be beneficial. The findings add to the knowledge on extant literature by identifying altruistic helping behavior as a new outcome of perceived overqualification.

With the development of data mining technology, diverse and broader domain knowledge can be extracted automatically. However, the research on applying knowledge mapping and data visualization techniques to genealogical data is limited. This paper aims to fill this research gap by providing a systematic framework and process guidance for practitioners seeking to uncover hidden knowledge from genealogy.

Based on a literature review of genealogy's current knowledge reasoning research, the authors constructed an integrated framework for knowledge inference and visualization application using a knowledge graph. Additionally, the authors applied this framework in a case study using “Manchu Clan Genealogy” as the data source.

The case study shows that the proposed framework can effectively decompose and reconstruct genealogy. It demonstrates the reasoning, discovery, and web visualization application process of implicit information in genealogy. It enhances the effective utilization of Manchu genealogy resources by highlighting the intricate relationships among people, places, and time entities.

This study proposed a framework for genealogy knowledge reasoning and visual analysis utilizing a knowledge graph, including five dimensions: the target layer, the resource layer, the data layer, the inference layer, and the application layer. It helps to gather the scattered genealogy information and establish a data network with semantic correlations while establishing reasoning rules to enable inference discovery and visualization of hidden relationships.

The purpose of this study is to reveal the friction reduction performance and mechanism of granular flow lubrication during the milling of difficult-to-machining materials and provide a high-performance lubrication method for the precision cutting of nickel-based alloys.

The milling tests for Inconel 718 superalloy under dry cutting, flood lubrication and granular flow lubrication were carried out, and the milling force and machined surface quality were used to evaluate their friction reduction effect. Furthermore, based on the energy dispersive spectrometer (EDS) spectrums and the topographical features of machined surface, the lubrication mechanism of different granular mediums was explored during granular flow lubrication.

Compared with flood lubrication, the granular flow lubrication had a significant force reduction effect, and the maximum milling force was reduced by about 30%. At the same time, the granular flow lubrication was more conducive to reducing the tool trace size, repressing surface damage and thus achieving better surface quality. The soft particles had better friction reduction performance than the hard particles with the same particle size, and the friction reduction performance of nanoscale hard particles was superior to that of microscale hard particles. The friction reduction mechanism of MoS₂ and WS₂ soft particles is the mending effect and adsorption film effect, whereas that of SiO₂ and Al₂O₃ hard particles is mainly manifested as the rolling and polishing effect.

Granular flow lubrication was applied in the precision milling of Inconel 718 superalloy, and a comparative study was conducted on the friction reduction performance of soft particles (MoS₂, WS₂) and hard particles (SiO₂, Al₂O₃). Based on the EDS spectrums and topographical features of machined surface, the friction reduction mechanism of soft and hard particles was explored.

This paper aims to collect and compile the historical data of Guanfu Salt Farm, officially built by the Song Dynasty (960–1279) within modern Hong Kong territories, to reconstruct its history for the reflection of Hong Kong society of the time.

This paper is largely based on identification and analysis of historical documents, including keyword search on electronic databases and verification with the original sources, with reference to archaeological findings when necessary.

This paper reconstructs the history of Guanfu Salt Farm based on documentary sources with reference to archaeological findings. English translation of Chinese sources is also provided when necessary.

There has been an absence of systematic compilation of historical data of Hong Kong during the Song Dynasty, which are limited in quantity and scattered across different sources. This paper seeks to fill the vacuum of knowledge about pre-colonial Hong Kong, with a more comprehensive reconstruction of the history of Guanfu Salt Farm.

The purpose of this study is to empirically examine the cause–effect relationships between workplace friendship and knowledge-sharing behavior, from a static perspective. Furthermore, it investigates the bi-directional relationship between the increase in both workplace friendship and knowledge-sharing behavior over same time periods, and also endeavors to identify whether there is a significant negative lagged effect of the increase in both workplace friendship on knowledge-sharing behavior, and vice versa, across time from a dynamic perspective.

The study conducts a three-wave questionnaire survey to test the research model. A latent change score approach was used to test the direct relationship between changes in workplace friendship and changes in knowledge-sharing behavior.

The findings reveal that knowledge-sharing behavior fosters workplace friendship and workplace friendship promotes the emergence of knowledge-sharing behavior. An increase in workplace friendship promotes an increase in knowledge-sharing behavior over same time periods. However, an increase in workplace friendship will lead to a lagged decrease of knowledge-sharing behavior across time, and vice versa.

The time interval in this study is a little short to capture the full changes in workplace friendship. Some important control factors and mediating mechanisms are not included in the research model.

This study guides managers to focus on various motivators to better strengthen workplace friendship and knowledge-sharing behavior and to consider and effectively respond to the negative side of workplace friendship and knowledge-sharing behavior across time.

This study emphasizes the predictivity of one important interaction patterns, namely, knowledge-sharing behavior on friendship at the workplace, from a static perspective. This study also shows the benefits of an increase in workplace friendship for the development of knowledge-sharing behavior in the same time period. Furthermore, the study presents a counterintuitive finding when taking the lag effect into consideration in exploring the relationship between changes both in workplace friendship and knowledge-sharing behavior, and identifies a negative side of both when viewed over longer periods.

This study aims to investigate the decisions related to business aspects of cloud computing and discuss the research density, models/techniques used and identify opportunities for future work.

In this paper, 155 research articles shortlisted through a systematic review were analyzed and a classification framework was developed. Using this framework, the research density is discussed and a detailed review of four widely researched decision themes is provided.

It was found that current research on business aspects is spread across 23 decision themes. The distribution, however, is skewed with 50 per cent pertaining to just four themes, namely, pricing, markets, sourcing and adoption. Simulation appears to be the preferred modeling approach. Decision themes in consumer behavior, sustainability, auditing and culture offer opportunities for future research.

The classification framework organizes extant research on applied models and allows researchers to identify potential avenues for application, improvement and development of models to support business decisions. The review is limited to academic articles and does not include industry reports.

Practitioners can readily understand various perspectives relevant to a decision theme such as pricing or sourcing, seek and use associated models such as simulation, optimization and game theory to support their decision-making.

Most of the extant review paper deal with cloud computing technology. This study is the first systematic review on the models applied to business aspects of cloud computing. This study provides a classification framework and explicitly lists associated decision themes, models/techniques and opportunities.