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This paper takes Chinese A-share listed companies from 2007 to 2021 as research samples to investigate the influence of supplier concentration on debt maturity structure and its mechanism. It further analyzes whether the relationship between the two is different in the case of different monetary policies, collateral assets, and total debt. The research conclusion is of practical significance for enterprises to construct a balanced debt maturity structure and prevent financial risks.

This paper adopts the empirical research method. The data came from the CSMAR database, which eliminated ST and *ST and companies with missing data, resulting in a sample of 20,328. Stata16 was used for statistical analysis.

There is an inverted U-shaped relationship between supplier concentration and debt maturity structure, and market position and trade credit play an intermediary role. In the case of tight monetary policy, fewer collateral assets, and higher total debt, the inverse U-shaped relationship is more significant.

This paper examines the relationship between supplier concentration and debt maturity structure from a non-linear perspective for the first time, providing theoretical support for enterprises to form a reasonable debt structure, and deepening the theoretical cognition of the relationship between supplier concentration and corporate debt maturity structure.

Contract Manufacturers (CM, factory) can cultivate factory brand products by imitating Original Equipment Manufacturers' (OEM, brand owner) National Brand products, and compete with OEM through the online retailer, that is, factory encroachment. In practice, few consumers can identify the quality of those two products in the online market. Implementing blockchain technology (BTI) can help all consumers identify product quality but may change the operation decisions and incur implementation costs. This study aims to explore how will the BTI strategies affect participants' operation performance under the factory encroachment and delve into the decisions regarding NB product quality and CM encroachment.

This study constructs a three-level outsourcing supply chain comprising one contract manufacturer (CM, factory), one original equipment manufacturer (OEM) and one online retailer. By utilizing the Stackelberg game, the authors first compared the results between two strategic decisions of BTI and no-BTI by online retailers under the factory encroachment scenario. Then, the NB product quality decision and the CM's encroachment decision are also investigated.

BTI strategy can benefit all participants (triple win), which both occurs in exogenous and endogenous quality cases, and the triple win area will expand (shrink) as the BTI cost decreases (increases). In addition, the OEM will improve product quality to confront competition from the CM, and the OEM may not always benefit from the BTI, it depends on the maturity of the market. Interestingly, BTI could improve the consumer surplus when the proportion of novice consumers is low. Finally, this study also investigates the extended case that CM always encroaches into the market whether the online retailer choose BTI or not, which hurts OEM's profit and decreases the product quality.

This study sheds light on the strategic decisions of online retailers' BTI regarding supply chain members' profits, consumer surplus and social welfare under factory encroachment. It also demonstrates that the BTI strategy, under different quality decisions (endogenous and exogenous), can be more profitable for chain members and consumers.

This paper aims to empirically investigate time–frequency linkages between Euramerican mature and Asian emerging crude oil futures markets in terms of correlation and risk spillovers.

With daily data, the authors first undertake the MODWT method to decompose yield series into four different timescales, and then use the R-Vine Copula-CoVaR to analyze correlation and risk spillovers between Euramerican mature and Asian emerging crude oil futures markets.

The empirical results are as follows: (a) short-term trading is the primary driver of price volatility in crude oil futures markets. (b) The crude oil futures markets exhibit certain regional aggregation characteristics, with the Indian crude oil futures market playing an important role in connecting Euramerican mature and Asian emerging crude oil futures markets. What’s more, Oman crude oil serves as a bridge to link Asian emerging crude oil futures markets. (c) There are significant tail correlations among different futures markets, making them susceptible to “same fall but different rise” scenarios. The volatility behavior of the Indian and Euramerican markets is highly correlated in extreme incidents. (d) Those markets exhibit asymmetric bidirectional risk spillovers. Specifically, the Euramerican mature crude oil futures markets demonstrate significant risk spillovers in the extreme short term, with a relatively larger spillover effect observed on the Indian crude oil futures market. Compared with India and Japan in Asian emerging crude oil futures markets, China's crude oil futures market places more emphasis on changes in market fundamentals and prefers to hold long-term positions rather than short-term technical factors.

The MODWT model is utilized to capture the multiscale coordinated motion characteristics of the data in the time–frequency perspective. What’s more, compared to traditional methods, the R-Vine Copula model exhibits greater flexibility and higher measurement accuracy, enabling it to more accurately capture correlation structures among multiple markets. The proposed methodology can provide evidence for whether crude oil futures markets exhibit integration characteristics and can deepen our understanding of connections among crude oil futures prices.

This study is designed to examine the dynamic interrelationships between four cryptocurrencies (Bitcoin, Ethereum, Dogecoin and Cardano) and the Indian equity market. Additionally, the study seeks to investigate the potential safe haven, hedge and diversification uses of these digital currencies within the Indian equity market.

This study employs the wavelet approach to examine the time-varying volatility of the studied assets and the lead-lag relationship between stocks and cryptocurrencies. The authors execute the entire analysis using daily data from 1st October 2017 to 30th September 2023.

The result of the study shows that financial distress due to the pandemic and the Russian invasion of Ukraine have a negative effect on the Indian equities and cryptocurrency markets, escalating their price volatility. Also, the connectedness between the returns of stock and digital currency exhibits a strong positive relationship during periods of financial distress. Additionally, cryptocurrencies serve as a tool of diversification or hedging in the Indian equities markets during normal financial circumstances, but they do not serve as a diversifier or safe haven during periods of financial turmoil.

This study contributes to understanding the relationship between the Indian equity market and four cryptocurrencies using wavelet techniques in the time and frequency domains, considering both normal and crisis times. This can offer valuable insights into the potential of cryptocurrencies inside the Indian equities markets, mainly with respect to varying financial conditions and investment horizons.

This paper aims to explore the quantile-specific short- and long-term effects of economic policy uncertainty (EPU) on the efficiency of the green bond market.

This study examines the long-term cointegration relationship and the short-term fluctuation relationship of EPU, WTI crude oil price (WTI) and European Union Allowances price (EUA) with the green bond market efficiency (GBE) using the quantile autoregressive distributed lag method. Additionally, the authors analyze the differences before and after the Covid-19 pandemic.

EPU has a significant positive impact on the GBE before the outbreak. However, during the crisis period, the impact of EPU and WTI was greatly weakened, whereas the impact of EUA was strengthened.

This paper demonstrates the dynamics of GBE and its influencing factors under different periods. The findings provide insights for market participants and policymakers to gain a clearer understanding of the green bond market.

This paper extends the study of green bonds by quantifying the GBE and elucidating the nonlinear relationship between efficiency and independent variables at different quantiles over different periods.

This paper investigates the impact of financial integration on the capital structure of the firms operating in mainland China, examining the firm-level and country-level integrating variables for 2,878 listed Chinese firms over the period of 1991–2016 in regard to the firms' capital structures. Finally, the study revisits the associations for the state-owned and multinational firms in the context of China.

A large sample of unbalanced data from firms were used to explore the relationship firm-level and country-level integrating variables has with firm leverage and maturity; this is accomplished using the fixed effect model. For robustness, a system-generalised method of moments was used.

The results indicate that internationalisation positively impacts the leverage and debt maturity of all listed Chinese firms and multinational firms and that state-owned firms are financed mainly by the state. For country-level integration, the authors find that credit and equity markets are negatively related to a firm's leverage. A negative relation with credit markets suggests that Chinese firms have much cheaper financing options than the benefits that arise from credit market integration. Moreover, the effect of equity market integration is more pronounced on Chinese firms' capital structure and debt maturity than credit market integration.

The results provide valuable implications of financial integration for policymakers as well as capital structure decision-making for managers in China.

Few studies have examined the impact of integration on firms' capital structures in developing countries. After controlling for unobserved heterogeneity and endogeneity, this study adds new multilevel integration evidence on the capital structure of Chinese firms.

This study has two main objectives. First, to examine the indirect effects of digital platform capability and digital resilience on digital transformation (DT) outcomes for small- and medium-sized enterprises (SMEs), and second, to investigate how digital business model maturity influences these indirect effects.

The study adopts a quantitative design and collects data through a self-reporting survey from individuals in the technological industries. The Partial Least Squares-Structural Equation Modelling (PLS-SEM) and PLS multi-group analysis examine the measurement and structural models and the significance of differences in indirect paths based on the digital business model maturity level, serving as a moderator.

The findings of this study provide valuable insights into the internationalisation of digital SMEs. They indicate that digital platform capability and resilience fully mediate, connecting digital resources to SME growth. The study also confirms the digital business model maturity’s positive and significant moderating effect on these indirect relationships.

This research contributes to the existing literature by focusing on the international outcomes of platform ecosystems in developing markets. It explores how digital platform capability and resilience support the digital transformation of SMEs, considering their vulnerability due to their small size. The study also fills a research gap by investigating the relationship between big data, digital leadership and the international growth of digital platforms. Lastly, it explores the role of digital maturity in the relationships between antecedents, determinants and outcomes of digitalisation.

This study aims to develop a modular and prescriptive digital transformation maturity model whose constituent elements have conceptual integrity as well as reveal the priority weights of maturity model components.

A literature review with a concept-centric analysis enlightens the characteristics of constituent parts and reveals the gaps for each component. Therefore, the interdependency network among model dimensions and priority weights are identified using decision-making trial and evaluation laboratory (DEMATEL)-based analytic network process (ANP) method, including 19 industrial experts, and the results are robustly validated with three different analyses. Finally, the applicability of the developed maturity model and the constituent elements are validated in the context of the manufacturing industry with two case applications through a strict protocol.

Results obtained from DEMATEL-based ANP suggest that smart processes with a priority weight of 17.91% are the most important subdimension for reaching higher digital maturity. Customer integration and value, with a priority weight of 17.30%, is the second most important subdimension and talented employee, with 16.24%, is the third most important subdimension.

The developed maturity model enables companies to make factual assessments with specially designed measurement instrument including incrementally evolved questions, prioritize action fields and investment strategies according to maturity index calculations and adapt to the dynamic change in the environment with spiral maturity level identification.

A novel spiral maturity level identification is proposed with conceptual consistency for evolutionary progress to adapt to dynamic change. A measurement instrument that is incrementally structured with 234 statements and a measurement method that is based on the priority weights and leads to calculating the maturity index are designed to assess digital maturity, create an improvement roadmap to reach higher maturity levels and prioritize actions and investments without any external support and assistance.

This study aims to develop theory on the process toward digital service innovation (DSI) and to generate insights into how companies deal with the rising complexity associated with DSI, both inside and outside of the organization, through organizational learning and alignment.

After purposeful sampling, in-depth, longitudinal case studies of three manufacturers are presented as illustration. Per case, multiple semi-structured interviews are conducted, and insights are validated through rich additional data gathering. Each company's DSI pathway is reconstructed with critical incident technique. Next, using systematic combining, a middle-range theory is developed by proposing a theoretical frame concerning the relations between DSI maturity, learning and alignment.

The authors posit that, as companies gradually develop and progress toward DSI maturity, they deal with a rising degree of complexity, fueling their learning needs. Companies that are apt to learn, pass through multiple cycles of learning and alignment to overcome specific complexities associated with different DSI stages, with each cycle unlocking new DSI opportunities and challenges.

The study applies a stage-based view on DSI combined with complexity management and organizational learning and alignment theory. It offers a theoretical frame and propositions to be used by researchers for future DSI studies and by managers to evaluate alternative DSI strategies and implementation steps.

In theory, the impact of debt liquidity risk (DLR) on the firm's future growth is ambiguous. This study aims to examine the empirical relationship between the DLR and firms' growth rate using annual data for USA companies from 1976 to 2020.

Given the longitudinal nature of the data, the author uses OLS (ordinary least squares) regression methodology with fixed effects to control for unobserved characteristics that might affect the dependent variable. Instrument variable regression is also used to address the potential endogeneity problem.

The results show that firms having higher DLR, as proxied by more short-term debt, experience lower growth rate. An increase in firms' short-term debt decreases the firms' future growth rate as evidenced by lower assets, revenue and employee growth rate. Moreover, the authors' results show that small firms or firms with more investment opportunities grow fast if the firms take higher DLR. Finally, cyclical firms with higher DLR exhibit lower growth rate during the credit tighten period. The authors' results hold for both the pre-zero lower bound (ZLB) era and ZLB period.

To the authors' best knowledge, this is one of the earliest studies to carefully examine the effects of DLR on firms' growth rate. While prior research finds that firms with higher growth potential, measured by market-to-book (MTB) ratio, use more short-term debt, the authors' research directly addresses whether DLR affects firms' future growth rate. The authors’ findings also help explain why firms with high growth potential use more short-term debt.