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This study aims to explore the repost features of microblogs acting to promote the information diffusion of government-generated content on social media.

This study proposes a topic−sentiment analysis using a mixed social media analytics framework to analyse the microblogs collected from the Sina Weibo accounts of 30 Chinese provincial police departments. On the basis of this analysis, this study presents the distribution of reposted microblogs and reveals the reposting characteristics of police-generated microblogs (PGMs).

The experimental results indicate that children’s safety and crime-related PGMs with a positive sentiment can achieve a high level of online information diffusion.

This study is novel, as it reveals the reposting features of PGMs from both a topic and sentiment perspectives, and provides new findings that can inspire users’ reposting behaviour.

In public firms, the largest shareholder can make decisions on cash dividends in favor of its own interests at the expense of other investors. While the second largest shareholder can actively participate in corporate governance and protect the interests of investors, its impact has not been fully understood. This research investigates how shareholding ratio and ownership type of the second largest shareholder moderate the relationship between controlling shareholder's shareholding ratio and cash dividends.

The authors conducted econometrics analysis based on a panel data of China's A-share listed companies from 2007 to 2017.

The authors find that the controlling shareholder's shareholding ratio has a significant negative impact on cash dividends. However, this influence is conditional on the shareholding ratio of the second largest shareholder. The negative impact is weakened when the second largest shareholder holds a large proportion of shares or when the shareholding gap between the second largest and the controlling shareholder is small.

This research extends the existing literature by highlighting the nuanced moderating effect of the second largest shareholder on the relationship between the controlling shareholder and cash dividends, thus making a unique contribution to the understanding of corporate governances in the emerging financial market in China.

Predicting highly cited papers can enable an evaluation of the potential of papers and the early detection and determination of academic achievement value. However, most highly cited paper prediction studies consider early citation information, so predicting highly cited papers by publication is challenging. Therefore, the authors propose a method for predicting early highly cited papers based on their own features.

This research analyzed academic papers published in the Journal of the Association for Computing Machinery (ACM) from 2000 to 2013. Five types of features were extracted: paper features, journal features, author features, reference features and semantic features. Subsequently, the authors applied a deep neural network (DNN), support vector machine (SVM), decision tree (DT) and logistic regression (LGR), and they predicted highly cited papers 1–3 years after publication.

Experimental results showed that early highly cited academic papers are predictable when they are first published. The authors’ prediction models showed considerable performance. This study further confirmed that the features of references and authors play an important role in predicting early highly cited papers. In addition, the proportion of high-quality journal references has a more significant impact on prediction.

Based on the available information at the time of publication, this study proposed an effective early highly cited paper prediction model. This study facilitates the early discovery and realization of the value of scientific and technological achievements.

Building on the intergroup relations perspective on the social identity theory, the authors examine whether firms' environmental, social and governance (ESG) varies when local firms have non-local CEOs. Additionally, the authors examine which contextual factors may strengthen or weaken the effectiveness of ESG in helping non-local CEOs garner trust, legitimacy and resources support from local stakeholders.

Pooled OLS regressions, based on unbalanced panel data and controlling for year and industry fixed effects, were estimated using a sample composed of 836 Chinese A-share listed firms that have Bloomberg ESG disclosure scores data from 2006 to 2019.

Results suggest that companies with non-local CEOs, who are perceived as outgroup members by the local stakeholders, would lead to a higher level of ESG performance to overcome the intergroup bias they face. In addition, results also show that companies with a lower level of previous ESG decoupling and having more slack will mitigate the relationship between non-local CEOs and ESG performance. Conversely, firm visibility at a high level will promote the positive relationship between non-local CEOs and ESG performance.

This study offers theoretical insights that extend the focus of intragroup relation to outgroup identity, by introducing an intergroup relations lens to explore how outgroup (or nonprototypical) leaders utilize ESG to counter intergroup bias they suffer. Moreover, this study also extends current literature focusing on non-local CEOs and ESG performance.

In this study, the authors investigate whether supply chain (SC) strategies (lean or agile) improve or hinder the supply chain transparency (SCT) and what factors affect this relation.

The authors measure the level of SC strategy using natural language processing based on the annual financial reports of listed firms. Secondary data analysis is conducted on various databases encompassing 1,241 listed firms in China from 2011 to 2020. Additional tests are performed to assess the robustness of the results, and alternative explanations are duly considered.

The authors find that firms with an advanced level of SC strategy perform better on SCT. Furthermore, the authors observe that Agile SC strategy and Lean SC strategy have different effects on SCT over a firm’s life cycle. Agile SC strategy (the ratio of the proportion of Agile SC strategy word frequency divided by the proportion of Lean SC strategy word frequency greater than 1) has a significantly positive effect on SCT in the maturity stage; Lean SC strategy (the ratio less than 1) has a positive effect on SCT in the growth and decline stages. An increase in online media coverage negatively moderates the impact of the SC strategy (frequency of Lean and Agile SC strategy-related keywords) on SCT in the maturity stage. An increase in government environmental subsidies positively moderates the impact of SC strategy on SCT in the maturity and decline stages. Additionally, an increase in industrial competition intensity positively moderates the impact of the SC strategy on SCT in the decline stage.

The authors' study contributes to the Operations and Supply Chain Management (OSCM) literature by revealing the positive impact of SC strategy on SCT with objective secondary data. Additionally, the authors examine the moderating effects of moderators over the lifecycle of a firm on this relationship in an emerging market context. The authors' findings offer valuable guidance to companies operating in diverse market environments, providing actionable insights to strengthen their SC strategies and enhance SCT.

This paper aims to identify the impact of the new ambient air quality standards (AAQS) on firm innovation.

Taking listed companies from 2009 to 2014 as the research object, the authors focus on the quasinatural experiment of the pilot policy of the new AAQS and apply the time-varying difference-in-differences (DID) method to conduct the empirical test.

The authors find that the new AAQS has a negative effect on firm innovation, and this negative impact may be caused by the increased environmental expenditures following the implementation of the new AAQS. Furthermore, the authors find that firm profitability and state ownership weaken this negative effect, but the effect of the degree of industrial pollution is statistically insignificant.

The study is an initial effort to explore the causal effect of the new AAQS on firm innovation. This study enriches the literature on the impact of environmental regulations on firm innovation and provides some reference value for the formulation of environmental regulation policies in developing countries.

Green supplier selection (GSS) is acknowledged as important governance in green supply chain management (GSCM). However, this paper argues that GSS is not a stand-alone GSCM governance mode that determines manufacturers' environmental performance but rather one that needs to be aligned with contractual governance, particularly contractual control and adaptation, to promote environmental performance effects. This paper adopts GSS as ex ante governance and introduces behavior and outcome controls as ex post contractual control and adaptation, respectively. Thus, this paper addresses how GSS affects environmental performance directly and indirectly through behavior and outcome controls within transaction cost economics (TCE) theory.

This research model was tested on 300 Chinese manufacturing firms, and multiple regression analysis was used to validate our hypotheses.

A direct relationship was observed between GSS and environmental performance. This direct relationship is positively mediated by behavior and outcome controls.

This paper develops and elucidates an integrative green supply chain process proceeding from the implementation of ex ante GSS and ex post contractual governance to the realization of environmental performance. Furthermore, this paper considers two different forms of contractual governance, specifically contractual control and adaptation, and explains how they can be implemented using behavior and outcome controls from the perspective of TCE theory.

As various different and even contradictory concepts are proposed to depict a firm's capabilities related to big data, and extant relevant research is fragmented and scattered in several disciplines, there is currently a lack of holistic and comprehensive understanding of how big data alters value creation by facilitating firm capabilities. To narrow this gap, this study aims to synthesize current knowledge on the firm capabilities and transformation of value creation facilitated by big data.

The authors adopt an inductive and rigorous approach to conduct a systematic review of 185 works, following the “Grounded Theory Literature-Review Method”.

The authors introduce and develop the concept of big data competency, present an inductive framework to open the black box of big data competency following the logic of virtual value chain, provide a structure of big data competency that consists of two dimensions, namely, big data capitalization and big data exploitation, and further explain the evolution of value creation structure from value chain to value network by connecting the attributes of big data competency (i.e. connectivity and complementarity) with the transformation of value creation (i.e. optimizing and pioneering).

The big data competency, an inclusive concept of firm capabilities to deal with big data, is proposed. Based on this concept, the authors highlight the significant contributions that extant research has made toward our understanding of how big data alters value creation by facilitating firm capabilities. Besides, the authors provide a future research agenda that academics can rely on to study the strategic management of big data.

The purpose of this paper is to propose a subcontractor selection model to fully consider the impact of construction enterprise demands on subcontractor selection. The objectives are to understand the translating process of specific enterprise demands to the evaluating criteria and the weight calculation process.

A three-stage model of subcontractor selection was designed based on quality function deployment (QFD), analytic hierarchy process (AHP) and improved grey correlation analysis (IGCA). In the proposed model, specific enterprise demands are translated by the QFD method, and the weights of the criteria are determined by the IGCA. The AHP is used to quantify the exporters' experience and construct the judgment matrix, which is used as inputting of the grey correlation analysis.

The proposed model provides a feasible process for subcontractor selection by fully considering the actual requirements of the project. By combining the company requirements and project requirement to put forward the requirements of the target subcontractor, the selection process ensures that the selected subcontractor and the project have a higher “fit”

Few researches on construction subcontractor selection have taken into account the “voice” of the company's stakeholders. Therefore, this paper designs a three-stage construction subcontractor selection model by introducing QFD to achieve the transmission of “voice” in the subcontractor selection process, so as to take full consideration of the project objectives and the needs of the company's stakeholders. Meanwhile, in order to decrease the subjective of weight calculation, this paper designs an AHP-IGCA allocation method to determine the weight of relevant indicators. By integrating the proposed weight calculation method and QFD method, the subcontractor selection results become more reasonable and objective.

One of the significant dimensions in organizations is the human resource discussion and its related procedures. Human beings have a special place and particular importance in modern management to focus on organizational behavior and human resource management. The cloud-based human resource (CBHR) can be converted into human capital and impressive contributions to health, education and moral valence. Also, one of the most robust operational paradigms is the cloud-based supply chain management (CBSCM) for developing the competitive pros of service and manufacturing organizations to give the same attention to those who manage the supply chain or those operating in it. The present investigation's target is to check out whether CBHR and CBSCM enhance the performance of multinational organizations.

Multinational organizations consist of numerous integrated applications, including manufacturing, logistics, marketing, accounting, distribution, human resources, finance, supply chain, etc. This paper proposes using CBHR and CBSCM to improve financial, marketing and collaborative performances. We focus on the applications in multinational organizations. This study has utilized the SEM to estimate the measurement model's validity and reliability and assess the causal model. The offered model and the questionnaires were analyzed using SPSS and LISREL.

The research results showed that CBSCM influences the companies' performance. Additionally, the outcomes showed that CBHRM affects the performance of companies. The results support the proposition that CBSCM and CBHRM are both necessary and good for financial performance, marketing performance and collaborative performance.

Using a questionnaire to mentally measure some of the variable dimensions of a firm's financial performance and market performance for which objective data are available can be helpful. However, since such information is considered confidential in companies, it is not possible to access it.

Our innovation is the primary attempt of applying CBHR and CBSCM to elevate performance in multinational organizations.