Search results

The paper aims to examine the nature of competition within an industry by proposing and examining three separate sources of competitive heterogeneity: the strategies that industry members use, the performance that they obtain, and how effectively the strategies are utilized to obtain such performance results.

To do so, a restricted latent structure finite mixture model is devised that can quantify the contribution of these three potential sources of heterogeneity in the formulation of latent competitive groups within an industry. The paper illustrate this modeling framework with respect to COMPUSTAT strategy and performance data collected for public banks in the USA.

The paper shows how traditional conceptualizations via strategic or performance groups are inadequate to fully represent intra‐industry heterogeneity.

This research paper proposes a new class of restricted finite mixture‐based models, which fit a variety of alternative forms/models of heterogeneity. Information heuristics are developed to indicate “best model.”

The relationship between exports and productivity has always been a hot topic for scholars, but no unified conclusions have been made by theory and empirical research so far. There is no denying that the relationship between the two is important though, exploring the factors affecting the relationship can bring much more reflection and inspiration.

After using stochastic frontier analysis which includes stochastic factors to calculate total factor productivity, this paper makes regressions on the panel data of Chinese manufacturing industries from 2004 to 2013. In addition, it also tests the roles of industry heterogeneity plays in analyzing the relationships between exports and productivity.

It turns out that in China, exports inhibit the growth of productivity overall, and scale effect is not reflected in the export sector. From the perspective of sub-sectors, exports dampen the productivity significantly in labor-intensive industries, which may be because of low learning ability. In industries with low R&D investment, exports also have significant and negative effects on industry productivity, which is because the R&D capital is not efficient and sufficient to be converted to productivity. In industries with high foreign capital rate, exports prohibit the growth of productivity because of the existence of much processing trade. In industries with low competition pressure, exports hinder the productivity because firms in such industries are possibly not competitive enough to survive in the fierce international market.

To the best of the authors’ knowledge, previous research studies did not take the industry heterogeneity into consideration when discussing the effects of exports on productivity. In this paper, cost types, R&D investment, energy consumption efficiency, export destination, foreign investment rate, state-owned ratio, competition pressure and international competitiveness are all discussed in the analysis of export and productivity. This study provides new insights to help understand the mechanism of export and productivity and the conclusions are of rich policy implications.

The purpose of this paper is to explore the dynamic influence of WTI crude oil returns on the stock returns of China’s traditional energy sectors, including oil and gas exploitation, coal mining and processing, petroleum processing and coking, electricity, heat production and supply and mining services.

Hong’s information spill-over test and the DP Granger causality test are applied to investigate the relationship between the two markets. Moreover, a rolling window is introduced into the above two tests to capture time-varying characteristics of the influence of WTI crude oil returns.

The empirical results indicate that, first, there exists significant bidirectional linear causality between WTI crude oil returns and China’s traditional energy sectoral stock returns, but the nonlinear causality appears weaker. Second, the influence of WTI crude oil returns on traditional energy sectoral stock returns has time-varying characteristics and industry heterogeneity both in the linear and nonlinear cases. Finally, the decline of WTI crude oil prices may strengthen its linear influence on the stock returns of traditional energy sectors, while the excessive rise of market values in traditional energy sectors may weaken the linear and nonlinear influence of WTI on them.

The general nexus between international crude oil market and China’s traditional energy stock market is explored both in the linear and nonlinear perspectives. In particular, the dynamic linear and nonlinear influence of WTI crude oil returns on China’s traditional energy sectoral stock returns and its industry heterogeneity are analysed in detail.

The link between openness and innovative performance has been established as an inverted U-shape relationship, namely, the openness-performance connection is not always positive. The purpose of this paper is to introduce the concept of partner heterogeneity to characterize the influence of “quality” changes in partners on innovative performance, that is, the focus of this paper. Given that partner heterogeneity is crucial in explaining open innovative performance, it is also worth placing the examination of this key construct in emerging regions such as China.

The sample selection of this study covers a wide range of industries, but requires that the sample firms be manufacturing enterprises with an open innovation strategy. With opportunities and challenges associated with partner collaboration toward open innovation, the Chinese province of Zhejiang has established its reputation. Thus, empirical data were collected randomly from data pool of Zhejiang Province Economic and Information Commission, as well as a survey questionnaire. Data were using a cross-sectional survey methodology encompassing diverse organizations, industries, and nations.

Empirical testing of this assumption in a sample of 217 manufacturing firms indicates that partner heterogeneities, which are classified as organizational heterogeneity, industry heterogeneity, and national heterogeneity are all positively associated with innovative performance, but the strength of this association is influenced by environmental turbulence. Technological turbulence significantly and positively modulates the relationships of organizational and national heterogeneities with innovative performance. Market turbulence also plays a significant positive role on the relationship between national heterogeneity and innovative performance, while technological and market turbulence roles on the relationship between industry heterogeneity and innovative performance are not confirmed.

This paper refines the connotative dimensions of partner heterogeneity around the core concept of partner heterogeneity in open innovation in the context of emerging region, China. The study presents a systematic, in-depth analysis, and verifies the impact mechanisms of partner heterogeneity in open innovation on innovative performance by integrating the resource-based view, organizational learning theory, and transaction cost theory.

This thesis deeply studies the impact mechanism of digital service trade on the high-quality development of the manufacturing industry from the aspects of technological innovation and industrial structure.

In this thesis, 40 countries from 2010 to 2020 were selected as samples, and the panel fixed-effect model and intermediary effect model were used to empirically analyze the impact path of digital service trade on the high-quality development of global manufacturing.

Overall, digital service trade has a positive impact on the high-quality development of the global manufacturing industry. Through the analysis of the intermediary effect mechanism, it is found that digital service trade can further positively affect the high-quality development of the global manufacturing industry by promoting technological innovation and industrial structure upgrading.

Based on the empirical results, targeted countermeasures and suggestions are given in this paper.

Through the test of national heterogeneity, it is found that in developing countries, digital service trade mainly acts on the high-quality development of the manufacturing industry by promoting industrial structure upgrading.

In developed countries, digital service trade mainly promotes the high-quality development of manufacturing through technological innovation; from the perspective of industry heterogeneity, the three service industries of information and communication technology (ICT), other business services and property have the intermediary effect of technological innovation and industrial structure.

This manuscript suggests that trade in digital services should be promoted as a national trade priority.

This paper aims to use the Covid-19 pandemic situation to conduct an experiment-like study that focuses on industry reactions under stress. Particularly, this study analyzes stock response to eight pandemic related news in 2020 across different industries. This study also investigates the role that the market risk, beta, plays in such stock reactions.

This study computes the cumulative abnormal returns (CAR) around COVID-19 events using adjusted daily stock returns of all stocks in the S&P 500 index between January 2, 2020 and December 31, 2020. This study also sorts all stocks by beta into quintiles and measures the CAR [0, +3] for each quintile around each event date.

This study finds that low beta portfolios exhibit greater abnormal returns (in absolute value) than high beta portfolios during down markets while high beta portfolios exhibit greater abnormal returns (in absolute values) when the market starts to recover. However, this study finds that beta does not seem to explain the abnormal returns reported in various industries during times of negative sentiment. During times of positive sentiment, both the beta effect and industry effect are present.

Extant literature almost unanimously concurs that the COVID-19 pandemic has brought about negative stock reactions to financial markets across the globe. Nevertheless, three interrelated issues have not been explored: market reactions during the subsequent recovery, industry heterogeneity and individual stocks’ risk profile. The study addresses these matters.

The purpose of this paper is to examine the effects of outward foreign direct investment (O‐FDI) on the competitiveness of home‐country export.

This paper employs a six‐year data set from Taiwanese manufacturing data for 15 industries over the period between 1991 and 2007.

The authors find that exports in Taiwan are positively associated with O‐FDI by Taiwanese firms. This finding supports the view that outward FDI complements home country exports and concurs with the majority of earlier empirical findings which focus on developed home countries. The authors also find that such effect is stronger for Taiwanese FDI in China than in other countries and in traditional sectors than in modern sectors.

These findings suggest that location‐and industry‐specific characteristics moderate the strength of the relationship between O‐FDI and home country exports.

The authors examine the industrywise investment preferences of foreign portfolio investors (FPIs), domestic institutional investors (DIIs) and retail investors in the Indian context. They also investigate the factors influencing their preferences.

Using the quarterly shareholdings and returns data of the Indian market from March 31, 2009 to March 31, 2018, the authors employ analysis of variance to study investors' preferences and a random effect panel data model to examine the factors that influence these preferences.

FPIs hold proportionally more stocks in service-oriented industries and large-cap firms, DIIs hold proportionally large numbers of shares in paper industries and retail investors hold proportionally more shares in chemicals and textiles. FPIs prefer stocks with a high export-to-sales ratio and firms registered on a foreign stock market. Domestic investors, especially retail investors, prefer small-cap stocks and firms whose operations require local knowledge. In addition, industry heterogeneity determines investment decisions. Firm-specific and macroeconomic factors that influence investment decisions differ across industries. Finally, government policies and reforms also play a key role in attracting investors.

Policymakers can identify the key variables that influence investment, which can help direct and regulate investment in India and similar emerging markets.

This study fills a research gap by addressing how industry-level heterogeneity affects investors' preferences in terms of the industrywise preferences of different types of investors and the factors that influence their preferences.

In the context of global economic downturn and intense competition, firms are increasingly resorting to supply chains to acquire capital support and achieve sustainability. This study aims to investigate the effect of supply chain finance (SCF) on corporate sustainability performance (CSP) and identifies SCF-related recipes for CSP.

Based on a sample of 1,038 firms that disclose CSP – namely, corporate financial performance (CFP) and environmental, social and governance performance (ESGP) – the authors use a quasi-replication method consisting of empirical analysis with fuzzy-set qualitative comparative analysis (fsQCA) to investigate SCF’s effects on CSP.

The authors find that SCF has a “doing well by doing good” effect on CSP. CFP can promote the positive effect of SCF and ESGP while ESGP’s positive effect on SCF and CFP is nonsignificant. In addition, heterogeneity tests show that SCF’s promoting effect on CSP is affected by high-low CFP and ESGP. The fsQCA results verify the empirical findings and reveal five SCF-related recipes for achieving high CSP.

This study has the following two limitations. First, we do not consider how SCF affects CSP in different industries. There is a need to investigate whether industry heterogeneity changes SCF’s effects on CSP, especially in prominent industries, such as the energy industry, with its high susceptibility to ESGP, and the manufacturing industry, with its extensive application of SCF. It will be important to investigate these industries to better understand SCF’s role in sustainability. Second, we study the secondary supply chain – namely, core firm–suppliers and core firm–customers. The authors do not consider financial institutions (e.g. banks and guarantee institutions). SCF modes that include the participation of financial institutions, such as factoring financing and reverse factoring financing, cater more to the capital needs of diversified firms. In the future, studying specific industries that have made significant contributions to the application of SCF along with others that are more sensitive to environmental governance could better highlight the effect of SCF on sustainability and help supply chain managers understand the application value of SCF. Future research could also extend SCF participants into multiple roles to explore separate effects. Tracking financing demanders, fund providers and credit guarantors could capture SCF characteristics more comprehensively. Methodologically, it will be challenging to accurately measure SCF networks in terms of quantification. In future work, this could be performed with the help of artificial intelligence.

First, our findings indicate that SCF has a “doing well by doing good” effect on core firms. SCF can not only overcome the capital shortage of SMEs but also provide significant benefits to core firms. Second, our findings provide SCF-related recipes to help firms fulfil ESGP obligations without sacrificing CFP under the pressure to “do good.” The authors provide valuable insights and diverse recommendations to help supply chain managers, marketing executives and researchers adjust supply chain management strategies. Third, this work can guide executives in various fields to adopt SCF to achieve sustainability as a risk-mitigation strategy by means of marketing.

This study identifies better, more straightforward SCF-related recipes for CSP (consisting of CFP and ESGP) using a quasi-replication analysis that improves upon conventional methods such as regression analysis, which have limited power. The authors provide valuable insights and diverse recommendations to help managers pursue sustainable development. The findings point to practical guidelines and feasible solutions that can support well-founded operational strategic and management decision-making, which can enhance a firm’s competitiveness under uncertainty and a sluggish economy.

Prior research highlights the vital role of Information and Communication Technologies (ICT) for innovation in response to environmental conditions. However, there is a lack of studies that analyse the determinants of ICT investments on the innovation activities of firms in relation with their impacts on the industrial and competitive dynamics using large data sets. The paper aims to discuss these issues.

In this paper, the authors investigate the effects of ICT investments on the industrial and competitive dynamics for a large and representative panel data set. All the industries are included, and lagged effects of ICT investments are studied. The model is tested on a seven-year panel (2008–2014) of 231 Italian industries using two-stage least squares instrumental-variables estimators with industry time and fixed effects.

The results indicate that munificent industries and higher ICT spending are interrelated facts, showing that in sectors with more growth opportunities firms invest more in ICT and this leads to higher industry concentration, greater profit dispersion and higher competitive turbulence in the sector. Also, the paper shows that SMEs can rarely take advantage of their ICT-based innovation to start high-growth phenomena.

The results suggest that ICT-based innovation may create competitive advantages that are hard to sustain over the long-term raising important implications for managers involved in ICT-enabled innovations and policy-makers involved in building programs to foster innovation.

Against the backdrop of today’s digital transformation, the paper enriches our understanding on the disruptive effects exerted by the digitalization of the innovation process and provides a base to continue the investigation of industrial changes and competitive dynamics.