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This study analyzes the role of regional cooperative banks in identifying and financing small and medium-sized enterprises (SMEs) from a proximity perspective. Access to finance is a major challenge for SMEs. Regional cooperative banks can remove this barrier based on cooperative bank's characteristics and geographic proximity to SMEs. Understanding the interplay between these financial actors and firms can contribute to a better support of SMEs development.

The results are based on a case study of eight SMEs located in southwestern France. Interviews were conducted with two regional cooperative funds and eight SMEs. The interview guide included questions related to the company, the projects financed and how financing was accessed.

Results reveal that a combination of three forms of proximity allows regional cooperative banks and SMEs to establish effective financing operations. They show that regional cooperative banks are key players in the existing financing mechanisms for SMEs. Such financing is often used to gain access to larger players at a later stage. The findings suggest the need for public policies that promote the integration of financing actors in regional ecosystems to advance SMEs' development.

This article examines how SMEs access financing, with a focus on regional cooperative banks, which have received little attention in the literature. Moreover, the relationships between these actors are studied through the lens of proximity. Regional cooperative banks are able to finance projects that may have been overlooked by traditional banks due to trust-building local dynamics.

Energy conservation and environmental protection industry (ECEPI) is a strategic choice to promote energy conservation and emission reduction, develop green economy and circular economy. However, China’s ECEPI is still in the stage of rapid development and the overall scale is relatively small, what development periods have the ECEPI experienced? This study aims to contribute to a better understanding of collaborative innovation evolution based on social network analysis from the perspective of multi-dimensional proximity.

Methodologically, this study uses social network analysis method to explore the co-evolution of multidimensional collaboration networks. It divides China’s ECEPI into four periods based on national policies from 2001 to 2020. This contribution constructs collaborative innovation networks from geographical, technological and organizational proximity.

The results show that the collaborative innovation network was initially formed in the central region of China, gradually expanded to neighboring cities and the core positions of Beijing, Jiangsu and Guangdong have been continuously consolidated. C02F has been the core of the collaboration networks, and the research focus has gradually shifted from the treatment of wastewater, sewage or sludge to the separation field. Enterprises always occupy a dominant position in the collaboration networks.

This research investigates the dynamic evolution process of collaborative innovation network in China’s ECEPI from the perspective of multidimensional proximity, explores the community structure, important nodes and multidimensional proximity features in the network, expands the research perspective on evolution characteristics of innovative network and the research field of social network analysis. Theoretically, this study enriches collaborative innovation theory, social network theory and multi-dimensional proximity theory.

This study aims to investigate the structural characteristics, spatial evolution paths and internal driving mechanisms of the knowledge transfer (KT) network in China’s patent-intensive industries (PIIs). The authors' goal is to provide valuable insights to inform policy-making that fosters the development of relevant industries. The authors also aim to offer a fresh perspective for future spatiotemporal studies on industrial KT and innovation networks.

In this study, the authors analyze the patent transfer (PT) data of listed companies in China’s information and communication technology (ICT) industry, spanning from 2010 to 2021. The authors use social network analysis and the quadratic assignment procedure (QAP) method to explore the problem of China’s PIIs KT from the perspectives of technical characteristics evolution, network and spatial evolution and internal driving mechanisms.

The results indicate that the knowledge fields involved in the PT of China’s ICT industry primarily focus on digital information transmission technology. From 2010 to 2021, the scale of the ICT industry’s KT network expanded rapidly. However, the polarization of industrial knowledge distribution is becoming more serious. QAP regression analysis shows that economic proximity and geographical proximity do not affect KT activities. The similarity of knowledge application capacity, innovation capacity and technology demand categories in various regions has a certain degree of impact on KT in the ICT industry.

The current research on PIIs mainly focuses on measuring economic contributions and innovation efficiency, but less on KT in PIIs. This study explores KT in PIIs from the perspectives of technological characteristics, network and spatial evolution. The authors propose a theoretical framework to understand the internal driving mechanisms of industrial KT networks.

The objective of this study is to examine how the heterogeneity of the institutional environments within a single country influences International Financial Reporting Standards (IFRS) convergence and earnings quality based on a meso- and multi-level approach.

Using hierarchical linear modeling (HLM) to capture the between-group heteroskedasticity and within-cluster interdependence, this study investigates the simultaneous effect by incorporating institutional factors residing at different hierarchical levels and the interaction effects of factors within the same level on IFRS convergence and earnings quality in the largest IFRS adopter, China.

The results show that after IFRS convergence (i.e. 2007–2015), earnings quality decreases in terms of conservatism. However, the further analysis indicates that the strong institutional environment could mitigate the negative impact of IFRS on conservatism.

Consistent with the emphasis of heterogeneity within a country by Terracciano et al. (Science, 2005, 310 (5745)), this study indicates that the heterogeneity in the institutional environments and the simultaneous effect of the multilevel institutional environments within a single country cannot be ignored. This study also indicates that, equally important, research methodology plays a substantial role in investigating the outcomes of IFRS convergence. Finally, this study, based on an integrated theory, adopts a meso-paradigm linking macro- and micro-level institutions to provide comprehensive insights into IFRS convergence and conservatism.

One increasingly popular financing option for entrepreneurial ventures is to attract corporate venture capital (CVC) investments. Prior research tends to take a CVC-centric perspective assessing the benefits and contingencies for incumbent firms or corporate investors to engage with entrepreneurial ventures. Few studies have taken the opposite perspective of investigating factors that entrepreneurial ventures need to take into account when engaging with CVC investments. As such, this study aims to investigate pre- and post-IPO entrepreneurial venture performance that partners with CVC providers or corporate investors, as well as to assess organizational and environmental contingencies.

This study draws on a sample of 631 entrepreneurial ventures from the CSMAR database ranging from 2009 to 2019, along with CVC financing data from the CVSource database and financial data in entrepreneurial ventures’ annual reports from the Juchao Network. This study applies multiple linear regression modelling and fixed effect panel data analyses to test the proposed hypotheses.

The results show that CVC investment contributes to entrepreneurial ventures’ financial performance, both pre- and post-IPO. However, while research and development (R&D) intensity and geographic proximity strengthen the positive relationship between CVC investment and entrepreneurial ventures’ performance pre-IPO, R&D intensity has a negative moderating effect on the relationship between CVC investment and entrepreneurial ventures’ performance post-IPO.

First, in emerging economies, adopting a CVC financing strategy is an important strategic choice for entrepreneurial ventures that have a great demand for external capital, resources and technology support. Second, leveraging the relationship between external financing and internal R&D investment is essential for them to maintain their core competitiveness and sustainable growth. Moreover, entrepreneurial ventures should deal with the coopetitive relationship with incumbent companies and manage their dependency on other market participants in the external environment.

This study focuses on the performance implications for entrepreneurial ventures engaging with CVC investments pre- and post-IPO. First, this study broadens and expands prior research on the mechanism of the relationship between CVC and entrepreneurial ventures’ financial performance. Second, the research conducts a comparative study of the moderating effects of different timings. Third, this study applies learning theory to the field of CVC in emerging economies.

The purpose of this paper is to investigate the most recurrent variables characterizing the collaborative relationships of industrial symbiosis (IS) (hereinafter also referred to as “anatomic” variables) established in the attempt to adopt circular economy (CE) by collecting evidence from a rich empirical set of implementation cases in Italy.

The current literature on IS was reviewed, and a content analysis was performed to identify and define the “anatomic” variables affecting its adoption in the circular economy. We followed a multiple-case study methodology investigating 50 cases of IS in Italy and performed a content analysis of the “anatomic” variables characterizing each case.

This research proposes the “anatomic” variables (i.e. industrial sectors involved, public actors involvement, governmental support, facilitator involvement and geographical proximity) explaining the cases of IS in the circular economy. Each “anatomic” variable is discussed at length based on the empirical evidence collected, with a particular reference to the impact on the different development strategies (i.e. “bottom-up” and “top-down”) in the cases observed.

Current literature on IS focuses on a sub-set of variables characterizing collaboration in IS. This research builds on extant literature to define a new framework of five purposeful “anatomic” variables defining IS in the circular economy. Moreover, we also collect and discuss a broad variety of empirical evidence in what is a still under-investigated context (i.e. Italy).

From the perspective of local government tax administration, the impact of geographic dispersion on the corporate tax burden is investigated in this paper.

Using unbalanced panel data with a sample of listed companies from 2003 to 2020 in China, this paper focuses on the effect of geographic dispersion on corporate tax burden and the mechanisms.

It is found that corporate tax burden is positively related to geographic dispersion. It is also found that geographic dispersion affects the corporate tax burden by increasing the effort of local government tax administration. In addition, the relation between geographic dispersion and corporate tax burden is more pronounced for local SOEs prior to the implementation of Golden Tax Project III and in cases where local governments face stronger financial pressure to obtain revenue.

This study has important implications for the promotion of the coordinated development of the regional economy, as well as the legalization, modernization and informatization of tax administration.

Using a sample of manufacturing firms listed in China between 2007 and 2019, first, this paper aims to examine whether peer firms influence corporate trade credit supply. Next, the authors examine the channels through which peer firms influence corporate trade credit supply by testing the predictions of rivalry and information theories. Furthermore, the authors examine the heterogeneity of the industry peer effect on corporate trade credit supply. Finally, the authors examine the economic consequences of the industry peer effect on corporate trade credit supply.

The sample includes all manufacturing firms listed on both the Shanghai and Shenzhen securities exchanges for the sample period from 2007 to 2019, and the data come from the China Stock Market & Accounting Research database. The authors use the fixed effects method to examine the industry peer effect on trade credit supply. The results are robust to a series of robustness tests. To address the potential endogeneity problem, the authors adopt appropriate instruments by estimating instrumental variable models (two-stage least square). The authors use Heckman’s two-stage model to mitigate the sample selection bias.

The authors provide strong empirical evidence showing that the industry peer effect on trade credit supply exists in the manufacturing sector. It is also found that both competitive rivalry-based and information-based theories can provide explanations of the industry peer effect on trade credit supply. This process is both active imitation and passive reaction. Additional analysis suggests that the industry peer effect on trade credit supply is more pronounced for state-owned firms, firms with low customer concentration and firms with high geographical proximity. The amplification effect and spillover effect are the economic consequences of the industry peer effect on trade credit supply. In other words, the trade credit supply based on peer effect will not only increase the liquidity risk of the firm per se but also induce and increase the liquidity risk of the industry.

The study makes some important contributions. First, the authors find robust evidence that peer firms’ trade credit supply is an important factor in explaining corporate trade credit supply, which extends the literature by connecting the firm’s trade credit supply with the peer effect. Second, the study provides a new micro-perspective for understanding that firms use trade credit supply as a tool of competition, which proves the importance of rivals’ decision-making as a determinant of corporate decisions. Third, the authors examine the industry peer effect on trade credit supply, which not only helps to guide firms to pay more attention to the potential risk and spillover effects of the trade credit supply decision-making relevance but also helps to clarify the industry interaction phenomenon of corporate decision-making behavior. It is an important practical significance to play a role as a bridge between the microlevel of the firm and the meso-level of the industry. Finally, the study provides inspiration for the formulation of industry norms and policies.

Informal learning networks are critical to response to calls for practitioners to reskill and upskill in off-site construction projects. With the transition to the coronavirus disease 2019 (COVID-19) pandemic, social media-enabled online knowledge communities play an increasingly important role in acquiring and disseminating off-site construction knowledge. Proximity has been identified as a key factor in facilitating interactive learning, yet which type of proximity is effective in promoting online and offline knowledge exchange remains unclear. This study takes a relational view to explore the proximity-related antecedents of online and offline learning networks in off-site construction projects, while also examining the subtle differences in the networks' structural patterns.

Five types of proximity (physical, organizational, social, cognitive and personal) between projects members are conceptualized in the theoretical model. Drawing on social foci theory and homophily theory, the research hypotheses are proposed. To test these hypotheses, empirical case studies were conducted on two off-site construction projects during the COVID-19 pandemic. Valid relational data provided by 99 and 145 project members were collected using semi-structured interviews and sociometric questionnaires. Subsequently, multivariate exponential random graph models were developed.

The results show a discrepancy arise in the structural patterns between online and offline learning networks. Offline learning is found to be more strongly influenced by proximity factors than online learning. Specifically, physical, organizational and social proximity are found to be significant predictors of offline knowledge exchange. Cognitive proximity has a negative relationship with offline knowledge exchange but is positively related to online knowledge exchange. Regarding personal proximity, the study found that the homophily effect of hierarchical status merely emerges in offline learning networks. Online knowledge communities amplify the receiver effect of tenure. Furthermore, there appears to be a complementary relationship between online and offline learning networks.

Proximity offers a novel relational perspective for understanding the formation of knowledge exchange connections. This study enriches the literature on informal learning within project teams by revealing how different types of proximity shape learning networks across different channels in off-site construction projects.

This paper aims to use text mining methods to explore the similarities and differences between countries’ national digital transformation (DT) and Industry 4.0 (I4.0) policies. The study examines the applicability of text mining as an alternative for comprehensive clustering of national I4.0 and DT strategies, encouraging policy researchers toward data science that can offer rapid policy analysis and benchmarking.

With an exploratory research approach, topic modeling, principal component analysis and unsupervised machine learning algorithms (k-means and hierarchical clustering) are used for clustering national I4.0 and DT strategies. This paper uses a corpus of policy documents and related scientific publications from several countries and integrate their science and technology performance. The paper also presents the positioning of Türkiye’s I4.0 and DT national policy as a case from a developing country context.

Text mining provides meaningful clustering results on similarities and differences between countries regarding their national I4.0 and DT policies, aligned with their geographic, economic and political circumstances. Findings also shed light on the DT strategic landscape and the key themes spanning various policy dimensions. Drawing from the Turkish case, political options are discussed in the context of developing (follower) countries’ I4.0 and DT.

The paper reveals meaningful clustering results on similarities and differences between countries regarding their national I4.0 and DT policies, reflecting political proximities aligned with their geographic, economic and political circumstances. This can help policymakers to comparatively understand national DT and I4.0 policies and use this knowledge to reflect collaborative and competitive measures to their policies.

This paper provides a unique combined methodology for text mining-based policy analysis in the DT context, which has not been adopted. In an era where computational social science and machine learning have gained importance and adaptability to political and social science fields, and in the technology and innovation management discipline, clustering applications showed similar and different policy patterns in a timely and unbiased manner.