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Whilst the relationship between corporate social responsibility (CSR) and corporate financial performance has been well documented, CSR has rarely been studied from the perspective of corporate poverty alleviation. This study aims to test whether participation in targeted poverty alleviation (TPA) affects firms' market value and to explore how the magnitudes of market value vary in different CSR environments.

Based on recent Chinese TPA initiatives and on 108 TPA announcements of Chinese-listed firms from 2016 to 2020, this study adopts an event study method to investigate the impact of firm's TPA announcements on the firm's market value. Then, the authors construct a cross-sectional regression to analyse different CSR factors that may affect market reactions.

The results demonstrate that TPA announcements can increase a firm's overall market value. Additionally, the results show that TPA way and firm ownership significantly moderate the market reaction, namely the positive reaction is more significant when the TPA announcements involve charity poverty alleviation rather than industrial poverty alleviation and for privately owned firms rather than state-owned firms.

The empirical results help TPA practitioners obtain a nuanced understanding of whether and when to participate in poverty alleviation is worthwhile. This study also provides a reference for poverty alleviation work in countries with similar backgrounds.

This study not only provides empirical evidence for the consequences of poverty alleviation behaviour of firms in developing countries, but also complements the field of CSR research in developed countries.

The purpose of this research is to investigate the R&D investment (RDI) of manufacturing firms from the perspective of supply chain network, especially the effect of firms' supply chain network structures (network power and network cohesion) on its RDI, and further to explore the contingency conditions of this effect within the context of Chinese manufacturing supply chains.

The authors collect a large sample of Chinese manufacturing firms over the period 2014–2019 and construct a large-scale supply chain network, and finally obtain 2,390 firms from 20,483 observations. Ordinary least squares regression was adopted to analyse how supply chain network structures affect RDI in manufacturing firms.

It is surprising that firm's supply chain network structures have a negative effect on RDI. In addition, knowledge and technology intensity (KTI) positively moderate the relationship between network cohesion and RDI.

This study contributes to the innovation stream from the perspectives of supply chain network, and provides the empirical findings that the negative role of a firm's supply chain network structure on its RDI for the first time. The rationale for these negative effects is straightforward according to the social capital theory that manufacturing firms with a high level of social capital that are possibly to accept established patterns of thinking and behaviour, causing them to decrease the enthusiasm of RDI.

The purpose of this study is to forecast the development performance of important economies in a smart city using mixed-frequency data.

This study introduces reverse unrestricted mixed-data sampling (RUMIDAS) to support vector regression (SVR) to develop a novel RUMIDAS-SVR model. The RUMIDAS-SVR model was estimated using a quadratic programming problem. The authors then use the novel RUMIDAS-SVR model to forecast the development performance of all high-tech listed companies, an important sector of the economy reflecting the potential and dynamism of urban economic development in Shanghai using the mixed-frequency consumer price index (CPI) producer price index (PPI), and consumer confidence index (CCI) as predictors.

The empirical results show that the established RUMIDAS-SVR is superior to the competing models with regard to mean absolute error (MAE) and root-mean-squared error (RMSE) and multi-source macroeconomic predictors contribute to the development performance forecast of important economies.

Smart city policy makers should create a favourable macroeconomic environment, such as controlling inflation or stabilising prices for companies within the city, and companies within the important city economic sectors should take initiative to shoulder their responsibility to support the construction of the smart city.

This study contributes to smart city monitoring by proposing and developing a new model, RUMIDAS-SVR, to help the construction of smart cities. It also empirically provides strategic insights for smart city stakeholders.

The purpose of this study is to bridge the gap in the literature on supply chain finance (SCF) by exploring the relationship between network capabilities and corporate financial performance (CFP) in financial supply chains (FSCs).

The authors collect panel data and adopt regression analysis to analyse the joint investment activities among 1359 manufacturing firms and 289 financial service providers in China to explore how network capabilities, both network power and network centrality, improve CFP in the FSCs.

Under the FSCs environments, network centrality (i.e. eigenvector centrality, closeness centrality and betweenness centrality) raises CFP (ROA, ROE and Tobin's Q) and network power (node degree, clustering coefficient) also improves CFP. However, node strength from the network power stream has a negative effect on Tobin's Q, indicating that when the partner of a firm has an extremely strong influence in FSCs; this weakens the bargaining ability and flexibility of the focal firm, thus reducing its long-term financial performance.

The joint investment activities among supply chain partners and financial service providers help managers understand the advanced financing solutions generated by internal and external network organisations as well as be aware of network capabilities' impact on CFP in FSCs.

This study answers the call for more empirical research on SCF to provide a broader sample to examine financial supply chain management. This is one of the earliest studies to shed light on a new perspective – how network capabilities improve CFP in the FSCs.

This study aims to explore the dependence structure among Chinese firms across the emerging 5G industry at different stages and to provide some strategic insights for market participants.

This study adopt macroeconomic fundamentals and the log-returns of 45 listed firms in the Chinese 5G industry to construct the weighted adjacency matrix by measuring the correlation parameters and then use the triangulated maximally filtered graph (TMFG) algorithm to construct the dependence network. It analyses the topological structure of the constructed networks to obtain the dependence characteristics for each firm in the whole industrial supply chain at different levels.

The empirical results provide a comprehensive and concise snapshot of the industrial structure, across the whole 5G industry at different levels, rather than just a “one-to-one” pattern. Specifically, the dependence characteristics of different firms are heterogeneous, and most firms are highly connected with partners in the whole industrial supply chain, whereas a few firms that are weakly connected. Those closely connected firms are usually in the midstream. In addition, compared with firms at different levels, downstream firms usually have closer dependencies and stronger influence capabilities.

Regulators not only should promote stability development for those firms most intensely connected with whole industry chain but also protect those firms with weak link in the whole industry chain. Investors should better understand the embedded ties among different firms to obtain effective market information and can select multiple firms with fewer connections as backup to conduct joint investment for risk mitigation. Mangers should give priority to the central players/firms in the whole industrial supply chain and establish the alliances with closely connected firms.

This study contributes to both the information system and operation management literature by constructing a new network method, Copula-TMFG, to capture the dependence structure among Chinese firms in 5G industry, empirically providing some strategic insights for 5G industry stakeholders, such as regulators, investors and managers.

Corporate social responsibility (CSR) decoupling indicates a misalignment between how firms report CSR and what firms actually practice with respect to CSR. The purpose of this paper is to examine the relationship between CSR decoupling and financial performance and the factors affecting this relationship.

This paper collects and combines secondary panel data from multiple sources of Chinese listed firms from 2008 to 2020 to test the direct impact of CSR decoupling on firms’ financial performance and the moderating role of customer structure and operational slack.

This paper finds that CSR decoupling is negatively associated with firms’ financial performance. These findings further suggest that the negative relationship can be suppressed by customer stability and operational slack, but amplified by customer concentration. These conclusions remain robust to alternate measures of independent and dependent variables and narrower samples.

In the literature, the effect of CSR on firms’ financial performance is inconclusive. This is the first study to examine the impact of CSR decoupling on firms’ financial performance and the factors affecting this relationship. This paper contributes to the CSR decoupling literature from an operations and supply chain management perspective.