Search Results (79)

In the context of the explosive growth of the digital economy, how inter-organizational connectivity affects corporate ambidextrous innovation has emerged as a pressing issue in the current digital economy. Based on the perspectives of the innovation ecosystem and digital coupling, this paper explores the inner mechanism of this issue through structural modeling by using the data of China’s high-tech enterprise alliance cooperation from 2015 to 2022. It is found in the empirical study that the local efficiency and reach rate of the digital innovation ecosystem have an inverted U-shaped relationship with exploratory innovation, and the local efficiency and reach rate of the digital innovation ecosystem have a negative effect on firm exploitative innovation. In addition, the level of firms’ digital transformation mediates the relationship between the local efficiency, reach rate, and ambidextrous innovation. The level of market development plays a moderating role in the relationship between the local efficiency, reach rate, and ambidextrous innovation. The findings provide a theoretical basis for the digital innovation ecosystem to realize the role of a “resource pool” through structural connections, which in turn provides important guidance for the digital transformation and innovation development of high-tech enterprises. Full article

With the increase in greenwashing, corporate greenwashing governance has become a crucial component of urban environmental management. Industrial clusters are a key form of urban economic organization, yet the mechanisms through which they affect corporate greenwashing remain unclear. This study examines how different types of industrial agglomeration influence corporate greenwashing using a sample of Chinese A-share listed companies. The key findings include the following: (1) Specialized agglomeration inhibits corporate greenwashing through a “supervision” effect generated by intra-industry competition, while diversified agglomeration exacerbates greenwashing via a “collusion” effect arising from inter-industry cooperation. (2) The inhibitory role of specialized agglomeration is amplified under conditions of low public and strong government environmental concern, while the promotional effect of diversified agglomeration becomes more pronounced in contexts of high public and weak government environmental concern. Government environmental concern can be categorized into “general” and “specific” types, with the former being more effective in governing corporate greenwashing. (3) Specialized agglomeration demonstrates superior efficacy in curbing greenwashing among firms with green innovations related to energy-saving, alternative energy production, waste management, and transportation, while diversified agglomeration intensifies greenwashing tendencies in firms without green innovations. (4) Collusive greenwashing under diversified agglomeration yields short-term firm value gains but incurs hidden costs, including elevated operational risks and declining profit margins. This research provides critical insights for promoting corporate green transition and fostering zero-carbon industrial clusters. Full article

Carbon markets play a pivotal role in achieving carbon peaking targets, with accurate price forecasting being essential for effective policymaking and corporate decision making. This study develops an integrated forecasting framework, combining an overall market model and a market-specific model, to predict carbon price trends in China from 2025 to 2026, while examining inter-market heterogeneity across eight regional markets. The overall market forecast reveals a fluctuating upward trend in the national carbon price over the next two years. Market-specific forecasts highlight significant disparities in price trends, as follows: the Shanghai and Guangzhou markets are projected to experience faster growth and the Beijing market to maintain stable prices, while the Tianjin and Chongqing markets exhibit more moderate increases. These disparities reflect the profound influence of regional economic levels, policy enforcement, and market maturity on carbon market development. By incorporating seasonal fluctuations and stochastic disturbances, we construct a forecasting model aligned with historical data dynamics and achieve differentiated forecasts through the analysis of historical price levels across markets, addressing the limitations of uniform target pricing in prior studies. These findings offer actionable insights for carbon market participants and policymakers, providing a robust foundation for designing differentiated carbon pricing policies to support China’s carbon peaking objectives. Full article

Digital technology has transformed sustainability reporting practices, creating both opportunities and homogenization risks. This study analyzes 9903 sustainability reports from Chinese listed companies (2009–2021) through cosine similarity analysis. It reveals high intercorporate similarity (mean = 0.776). Fixed-effects modeling demonstrates that digital adoption increases report similarity, while analyst scrutiny and innovation capacity significantly mitigate this convergence effect. The findings suggest that digital tools promote isomorphic disclosure patterns through template-driven reporting. However, market monitoring (analyst attention) and R&D investment counterbalance this trend by incentivizing unique environmental, social, and governance (ESG) disclosures. This study offers novel insights into information asymmetry theory and social learning theory. The increased similarity in reporting will lead to standardization among Chinese companies, thereby enhancing their comparability in the international market. This will not only help Chinese companies improve their performance assessments for global investors but also facilitate cross-border investments. Full article

Corporate financial resilience and its integration with institutional reforms play a crucial role in promoting organizational sustainability in the digital economy. Previous research has predominantly focused on internal determinants of corporate financial resilience. However, it has paid limited attention to the role of external institutional factors. This gap is particularly evident in the context of data factor marketization (DFM). We addressed this gap by investigating the impact of DFM on corporate financial resilience, drawing on resource dependence theory (RDT) to highlight the importance of the external policy environment and inter-organizational resource exchange. We employed a double machine learning (DML) framework to assess corporate financial resilience using comprehensive panel data from Chinese listed firms. This approach overcomes the limitations of traditional econometric methods by allowing nonlinear interactions and high-dimensional controls. The results show that DFM significantly enhances corporate financial resilience, with its impact varying across different institutional contexts. Additionally, firm characteristics moderate this relationship. Specifically, ownership structure strengthens or weakens the positive effect of DFM, while industry competition and geographical location have varying effects on resilience outcomes. We offered novel theoretical insights and practical guidance for policymakers seeking to leverage institutional reforms to enhance financial resilience within an increasingly volatile and uncertain business landscape. Full article

This study examines the role of board experience diversity in fostering corporate green technological innovation (CGTI), focusing on the moderating effects of absorptive capacity and director network location. Integrating upper echelons theory with absorptive capacity theory, we explore how board experience diversity enhances strategic decision-making and innovation. We hypothesize that board experience diversity improves CGTI by broadening cognitive perspectives. We also examine the moderating effect of absorptive capacity on the relationship between board experience diversity and innovation. We examine Chinese A-share listing firms, finding that board experience diversity positively affects CGTI, and absorptive capacity strengthens this effect. Additionally, we show that director network location, proxied by centrality in inter-board networks, not only strengthens the association between board experience diversity and innovation but also affects innovation. Furthermore, we conducted heterogeneity and mechanism tests, confirming the robustness of these relationships. These findings contribute to the literature on corporate governance and sustainability by emphasizing the roles of board experience diversity, absorptive capacity, and network position in driving CGTI. Full article

Several studies have investigated the determinants of firms’ capital structure choices. Though an intercorporate loan is an essential source of corporate debt, there are no studies that examine the determinants of firms’ preference to use the intercorporate loan as a source of debt. This study examines the relevance of the conventional capital structure determinants in explaining firms’ tendency to use intercorporate loans. The study is based on a dataset of 53,112 firm-year observations comprising 3739 non-financial listed Indian firms for 21 years from 2002 to 2022. The random effect logistic regression model is used to investigate the objectives. The conventional capital structure determinants are relevant in explaining firms’ decisions to use intercorporate loans. Firm size, asset tangibility, and earnings volatility favorably influence the tendency to use intercorporate loans, whereas profitability, growth, uniqueness, dividend payment, ownership concentration, and foreign promoter holdings adversely affect the same. The results reveal that the influence of firm size, uniqueness, earnings volatility, and ownership concentration are not unidirectional for group-affiliated and standalone firms. The findings are mostly consistent with the arguments of conventional capital structure theories. The results of this study will be pragmatic for financial managers in their capital structure decisions. Full article

As environmental regulations in China have become more stringent, balancing these regulations with employment growth represents a major concern for policymakers and researchers striving for sustainable economic development. Although several studies have investigated the influence of environmental regulations on labor demand, most focus on corporate or regional levels, with limited exploration of industry-specific dynamics. Given the external and interconnected nature of environmental challenges, regulatory policies not only significantly impact the targeted industry but also generate spillover effects across interrelated sectors, thereby shaping broader sustainability transitions. This paper examines the mechanisms through which environmental regulations affect industry employment, incorporating the role of industrial linkages in sustainable labor market adjustments. Using panel data from 34 Chinese industries (2009–2018), this study empirically analyzes how stringent regulations influence employment dynamics, considering variations in technology and pollution intensity. The findings reveal a U-shaped relationship between environmental regulations and employment, accompanied by downward network spillover effects. Furthermore, threshold regressions demonstrate industry-specific effects, wherein clean and high-tech industries experience minimal short-term disruptions but benefit from regulatory intensity in the long run. In contrast, low-tech and pollution-intensive industries face notable direct and spillover effects, with low-tech sectors exhibiting job creation effects and pollution-intensive sectors experiencing displacement effects. To achieve sustainable employment structures while maintaining environmental integrity, policymakers must recognize inter-industry linkages and implement targeted policies that support sectoral transitions toward green and high-tech advancements. Encouraging upstream improvements to enhance downstream sustainability will be essential for balancing regulatory enforcement with long-term employment stability. Full article

The success of a cluster depends on the interplay between intra- and inter-regional collaboration, while empirical research on the mechanisms through which intra- and inter-regional collaborations trigger cluster growth is rarely focused. Based on the local buzz–global pipelines framework and incorporating an evolutionary perspective, we explore how intra- and inter-regional innovation networks can be integrated to drive the growth of emerging industries. We draw on Zhangjiang, China’s most advanced AI industry cluster, using social network analysis and qualitative methods, combining patent data with semi-structured interviews. The results indicate that with the return of multinational corporations and the limitation of Western technology, universities have become the primary source of AI innovation in Zhangjiang. The government has played a pivotal role in propelling the accelerated growth of China’s AI industry, particularly through the backing of pioneering AI private enterprises and the calibration of the potential inefficiencies associated with a state-led model with the dynamism of market forces. The ‘Buzz-and-pipeline’ dynamics in the AI innovation network are shaped by four processes: local networking, market demand, resource integration, and policy synergy. This underscores the intricate interconnections between the national and local scales in AI innovation networks. Full article

The objective of this work is to analyze the relevance of governance (corporate and ownership concentration) for the divisional inter-temporal dynamic efficiency in piped natural gas utilities in Brazil. The main innovative contribution of this work is the application of inter-temporal (dynamic) efficiency analysis with network structure in the first stage, in this case, the ‘Dynamic DEA (Data Envelopment Analysis) with network structure: A slacks-based–DNSBM (Dynamic Network Slacks-Based Measure)’, in which two divisional interactions ${\delta }_{ok}^{\ast }$ (technical–operational and economic–financial division) between ${\tau }_o^{t\ast }$ periods and divisional ${\rho }_{ok}^{t\ast }$ periods are reflected in the overall efficiency scores ${\theta }_o^{\ast }$, representing an approach not yet explored by the previous literature on the sector and subject investigated. The database used corresponds to 21 Brazilian natural gas utilities in the form of a balanced data panel, which were collected for the period 2014–2019. The second stage (explanatory) was estimated through the panel with random effects to identify the relationship between governance and efficiency, considering certain context factors. The results show that the average general efficiency was 74.96%, resulting from the interactions between the average efficiency rates of 72.21% of the technical–operational division and 82.03% of the economic–financial division, and suggest that the corporate governance index and ownership (public or private) are not relevant factors for the efficiency results studied. Full article

Corporations are increasingly looking beyond inter-company collaborations to multidimensional collaborative activities between departments and organizational members within the company to strengthen innovative market competitiveness. High-tech corporations, specifically, are adopting collaborative learning approaches to promote work capability among engineers from the perspective of developing new technologies and increasing productivity. This study aimed to empirically verify the effect of the service quality of collaborative learning programs on the innovative work behavior of engineers in high-tech companies. Job autonomy, self-efficacy, and learning transfer were set as mediating variables and their effects on innovative work behavior were analyzed. The sample comprised 298 high-tech engineers in South Korea. Collaborative learning service quality was found to have a direct positive effect on job autonomy, self-efficacy, and learning transfer. However, job autonomy and self-efficacy did not influence innovative work behavior through learning transfer. On the other hand, collaborative learning quality had a positive effect on innovative work behavior through learning transfer. Thus, improving the service quality of collaborative learning programs in high-tech corporations can enhance learning transfer within the organization, leading to innovative business outcomes. Moreover, to maximize the effectiveness of collaborative learning, the service quality of learning programs can be improved by prioritizing learning transfer rather than job autonomy or self-efficacy. Full article

Automated nucleic acid extractors are useful instruments for the high-throughput processing of bio-samples and are expected to improve research throughput in addition to decreased inter-sample variability inherent to manual processing. We evaluated three commercial nucleic acid extractors Bioer GenePure Pro (Bioer Technology, Hangzhou, China), Maxwell RSC 16 (Promega Corporation, Madison, WI, USA), and KingFisher Apex (ThermoFisher Scientific, Waltham, MA, USA) based on their DNA yield, DNA purity, and 16S rRNA gene amplicon results using both human fecal samples and a mock community (ZymoBIOMICS Microbial Community Standard (Zymo Research Corp., Irvine, CA, USA)). Bead-beating provided incremental yield to effectively lyse and extract DNA from stool samples compared to lysis buffer alone. Differential abundance analysis and comparison of prevalent bacterial species revealed a greater representation of Gram-positive bacteria in samples subjected to mechanical lysis, regardless of sample type. All three commercial extractors had differences in terms of yield, inter-sample variability, and subsequent sequencing readouts, which we subsequently share in the paper and believe are significant considerations for all researchers undertaking human fecal microbiota research. Full article

Objective: The purpose of this study was to evaluate the chromatic adaptability and color stability of two different composite resins, Omnichroma and Estelite Bulk-Fill Flow, in Class V restorations at different times. Materials and methods: Standardized Class V cavities were prepared on the labial surface of 34 extracted intact and noncarious human permanent molars. The dental elements were randomly divided into two groups according to the resin composite material. Group 1 was restored with Omnichroma (Tokuyama Dental Corporation Inc., Tokyo, Japan) and Group 2 was restored with Estelite BulkFill Flow Universal (Tokuyama Dental Corporation Inc., Tokyo, Japan). Color properties were assessed using a spectrophotometer, SpectroShadeTM Micro (MHT Optic Research, Niederhasli, Switzerland), at baseline (T0), immediately after Class V restoration (T1), 24 h after restoration (T2) and after thermocycling (T3) (ISO/TS 11405, 2015 protocol). Color difference (ΔE) was analyzed with a Student’s t-test and a Wilcoxon–Mann–Whitney test to evaluate the differences, in terms of chromatic adaptability, between the two materials (inter-group analysis) while Kruskal–Wallis test e Two-way ANOVA statistical tests were used to evaluate the color stability of each material over time (intra-group analysis). Results: Regarding the inter-group analysis, there were no statistically significant differences between the two materials in all the comparisons: T0–T1 (p = 0.9025), T0–T2 (p = 0.2779), T0–T3 (p = 0.4694). Moreover, both groups showed an average ΔE > 2. In the intra-group analysis, no statistically significant differences were observed in either Group 1 (p = 0.954) or Group 2 (p = 0.8654). Conclusions: The in vitro color matching, assessed by spectrophotometry, of the two tested resin composites does not vary at different time intervals. Furthermore, even though both composites use different mechanisms to produce the color the human eye perceives, they show very similar chromatic adaptability. Full article

Merleau-Ponty’s Phenomenology of Perception (1945), written after his extensive research in psychology, anthropology, and the other social sciences and also after his intensive encounter with the thought of Husserl and Heidegger, is an attempt to leave those malevolent dualisms behind and replace them with a phenomenology that engages with beings as befits their essence and the conditions of their being: a phenomenology that no longer imposes on our experience a morally irresponsible and offensive ontology; a phenomenology that, instead, reminds us of our responsibility as guardians of nature and life and brings to light very new possibilities for ethical life, community, and dwelling on the earth of this planet. Full article

In the context of a new round of scientific and technological revolution and industrial transformation, inter-regional international cooperation is facing fierce competition and sustainable development pressure in domestic, geopolitical, and global industrial chains and that a rational division of labor and coordination of cooperative innovation subjects, key technology nodes, and technology subgroups are of great importance to improve and upgrade the industrial and supply chain cooperation of China–Japan–ROK, as well as to enhance the efficiency of cooperation and innovation. This study uses the patented technical cooperation and innovation dual network structure analysis model and social network analysis (SNA) to analyze the dual network relationship and evolution characteristics of patent technology cooperation and innovation at different stages, based on data from 5912 invention patents applied by China, Japan, and ROK. We find that the China–Japan–ROK patent technology collaboration network is unmatched in size, and the areas of cooperation are expanding on a daily basis. However, the network’s innovation activities have not yet stabilized, and there is still room for collaborative innovation among enterprises to grow and evolve. Multinational corporations in Japan and South Korea have occupied the network’s core position at various times, forming seven key innovation groups with high-tech enterprises such as Samsung Display, Samsung Electronics, Hyundai Motor, NEC, and LEKIN as core nodes. Patents such as H01L, G02F, H04N, H01M, and G02B dominate the key technology nodes and technology subgroups, indicating that high-tech patents such as electronic information technology, semiconductors, displays, and automobile manufacturing are the primary areas of cooperation and innovation between China, Japan, and South Korea. Full article