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The purpose of this paper is to provide scientific guidance for the integration of industrialization and information (TIOII). In recent years, TIOII has promoted the development of intelligent manufacturing in China. However, many enterprises blindly invest in TIOII, which affects their normal production and operation.

This study establishes an efficiency evaluation model for TIOII. In this paper, entropy analytic hierarchy process (AHP) constraint cone and cross-efficiency are added based on traditional data envelopment analysis (DEA) model, and entropy AHP–cross-efficiency DEA model is proposed. Then, statistical analysis is carried out on the integration efficiency of enterprises in Guangzhou using cross-sectional data, and the traditional DEA model and entropy AHP–cross-efficiency DEA model are used to analyze the integration efficiency of enterprises.

The data show that the efficiency of enterprise integration is at a medium level in Guangzhou. The efficiency of enterprise integration has no significant relationship with enterprise size and production type but has a low negative correlation with the development level of enterprise integration. In addition, the improved DEA model can better reflect the real integration efficiency of enterprises and obtain complete ranking results.

By adding the entropy AHP constraint cone and cross-efficiency, the traditional DEA model is improved. The improved DEA model can better reflect the real efficiency of TIOII and obtain complete ranking results.

This study is an analysis that aims to understand the rationale behind the concept of value creation contained in the integrated reporting (IR) framework. As such, the authors examined the quality of the disclosures made in integrated reports by measuring the level to which the six capitals (6Cs) have been integrated into disclosures on value creation.

The IR framework’s value creation model focuses on six content elements and three guiding principles. Hence, the present analysis combines content analysis with quantitative measures in the form of a bespoke Integrated Disclosure Index. The index measures the level of integration found in the disclosures instead of the mere presence or absence of mentioned capitals, content elements and guiding principles in isolation. The present sample comprised the 2016 integrated/sustainability reports for 184 listed companies sourced from the Integrated Reporting Examples Database.

The 6Cs are well disclosed in form but only partially disclosed in substance. Further, overall levels of integration between the capitals, the content elements and the guiding principles are higher than average. Disclosures on materiality, business models and stakeholder relationships are somewhat lacking, as are the related medium- and long-term disclosures on outlook.

The paper contributes to the academic debate on IR by building a case for holistically assessing the substance of integrated reports. Considering that the IR value creation model can underpin and align with the 17 UN sustainable development goals, the authors show how the fundamental concept of the 6Cs sustaining value creation is understood and implemented differently across the various elements and principles of the IR framework.

This research also provides guidance for overcoming some of the practical hurdles associated with assessing the quality of reports because the authors provide tools for spotlighting the substance of disclosures over their form.

This paper delves into the substance of integrated reports by assessing how well the 6Cs have been integrated into disclosures on the content elements and guiding principles of the IR framework. In contrast to previous IR research that has mainly analysed capital, elements and principles in isolation, the authors develop an index assessing the integration of these three fundamental concepts of IR.

Financial integration has played an essential role in achieving economic growth in the members of the Association of Southeast Asian Nations (ASEAN). However, its effects on economic growth in the region in the long run have been underexamined. This paper examines these effects for the ASEAN member countries.

A fully modified ordinary least squares (FMOLS) estimation is used to take into account two critical econometric issues in panel data analysis, including (1) cross-sectional dependence and (2) slope heterogeneity. The dynamic ordinary least squares estimation is also used for robustness analysis. The authors use the generalized least squares estimation to examine the effects in the short run.

This study’s empirical results confirm the important role of financial integration to economic growth in the ASEAN countries in the short term. However, the effects appear to disappear in the long term. The authors also find capital, labor, and human development positively contribute to economic growth in the region. International trade plays a significant role in supporting economic growth in the ASEAN in the short run. However, its effect seems to weaken in the long run.

The growth effects of financial integration in the ASEAN region in the long term have largely been neglected. As such, the authors examine these effects using updated data on financial integration. The authors extend this study’s analysis by considering foreign direct investment and financial depth as the alternative proxies for financial integration. Other estimation technique is also used as the robustness check.

The digital economy has become a key force supporting the high-quality development of tourism. This paper discusses the coupling coordination relationship and spatiotemporal evolution path of digital economy and tourism in China's provinces.

This paper uses the entropy method to measure the development level of digital economy and tourism, and establishes coupling coordination model and spatial autocorrelation model to study the interaction between the two industries.

Results show that the development levels of the two industries are rising, which spatially show a progressively decreasing pattern of east-middle-northeast-west. The coupling coordination degrees of the two industries have increased steadily, but the overall level is still near maladjusted. Spatially, the positive correlation is increasing, but the incongruity of spatial agglomeration is still significant. The coupling coordination evolution path in the provinces shows differentiated characteristics. The migration path is mainly concentrated in Zones I and II. The eastern region has an obvious trend of extending to Zone III, where the tourism industry was the most affected by the pandemic.

The study helps clarify the industrial coupling and coordination relationship in various regions and formulate regional tourism digital transformation strategies to promote the high-quality development of China's tourism industry.

This paper enriches the research on the relationship between digital economy and tourism from the perspective of industrial integration. The development commonality of China's tourism digital transformation summarized provides theoretical reference and demonstration for the coordinated development of China's tourism.

The export competitiveness has only calculated on only two aspects either comparatively advantageous or comparatively disadvantageous products for India or China. There is not any thorough study that has been undertaken for Indian manufacturing sector at a segregated level along with that of China. So, in the light of these shortcomings, the purpose of this study is to analyse the dynamics of export competitiveness of indian manufacturing sector vis-à-vis its emerging counterpart, china in the global market.

A modified revealed comparative advantage index has been used in two different phases of 2001–08 and 2010–18 to find the dynamic pattern of manufacturing exports of India and China in the world market.

The study revealed that India has shown a positive response in increasing its competitive positioned products from low-technology to medium-technology products during the study period. There has been a decline in the competitive positioned products of China and simultaneously China’s threatened product lines have shown an immense increment over the years. Moreover, Indian exports are concentrated to few low-technology and resource-intensive products, that share more than 50% of total exported value for its manufacturing in the global market, whereas, China is much diversified and the exported value is more scattered over its manufactured items.

The study does not include the factors that impacted the export competitiveness of the sample economies and thus adds a limitation to this study.

As there is very limited research on dynamics of export competitiveness of Indian manufacturing exports at harmonised system 6-digit level with China, this study fulfils the gap.

The aim of this study is to discern the role of digital finance in driving rural industrial integration and revitalization. Specifically, it intends to shed light on how the deep development of digital finance can contribute to the optimization and transformation of the rural industrial structure. The research further explores the particular effects of this financial transformation in the central and western regions of China.

This research studies the influence of digital finance on rural industrial integration across 30 Chinese provinces from 2011 to 2020. Utilizing the entropy weight method, a comprehensive evaluation index system is established to gauge the level of rural industrial integration. A two-way fixed effects model, intermediary effect model, and threshold effect model are employed to decipher the relationship between digital finance and rural industrial integration.

Findings reveal a positive relationship between digital finance and rural industrial integration. A single threshold feature was identified: beyond a traditional finance development level, the marginal effect of digital finance on rural industrial integration increases. These effects are more noticeable in central and western regions.

Empirical outcomes contribute to policy discourse on rural digital finance, assisting policymakers in crafting effective strategies. Understanding the threshold of traditional finance development provides a new perspective on the potential of digital finance to drive rural industrial integration.

With the rapid development of the financial market, stock index futures have been the one of important financial instruments. Predicting stock index futures accurately can bring considerable benefits for investors. However, traditional models do not perform well in stock index futures forecasting. This study put forward a novel hybrid model to improve the predictive accuracy of stock index futures.

This study put forward a multivariate deep learning framework based on extreme gradient boosting (XGBoost) for stock index futures price forecasting. First, the original sequences were decomposed into several sub-sequences by variational mode decomposition (VMD), and these sub-sequences were reconstructed by sample entropy (SE). Second, the gradient boosting decision tree (GBDT) was used to rank the feature importance of influential factors, and the top influential factors were chosen for further prediction. Next, reconstructed sequence and the multiple factors screened were input into the bidirectional gate recurring unit (BiGRU) for modeling. Finally, XGBoost was used to integrate the modeling results.

For the sake of examining the robustness of the proposed model, CSI 500 stock index futures, NASDAQ 100 index futures, FTSE 100 index futures and CAC 40 index futures are selected as sample data. The empirical consequences demonstrate that the proposed model can serve as an effective tool for stock index futures prediction. In other words, the proposed model can improve the accuracy of stock index futures.

In this paper, an innovative hybrid model is proposed to enhance the predictive accuracy of stock index futures. Meanwhile, this method can be applied in other financial products prediction to achieve better forecasting results.

This study aims to investigate the dependence structure and volatility spillovers among two strategic commodities (crude oil and gold) and a set of Islamic and conventional regional stock market indices, while examining the Ramadan effect

The empirical strategy consists of two complementary measures of dependence and connectedness. This study first uses copulas to examine the dependency between the markets considered, then spillovers compute the magnitude of the connectedness among them.

The copulas analysis shows that Frank’s copula appears to better capture the relationship between most asset returns and highlights the almost absence of extreme dependence and, therefore, the existence of diversification opportunities. Moreover, the connectedness analysis suggests that gold is a net volatility receiver and provides, thereby, greater diversification benefits compared to crude oil. In addition, the high levels of time-varying connectedness support strong integration among the financial markets studied, specifically during the COVID-19 crisis period. Furthermore, the connectedness among the markets studied increases during the Ramdan subperiods, supporting shift contagion among financial markets considered during this religious holiday.

The results provide investors with a better understanding of the nature as well as the magnitude of the interdependences between commodity markets and a set of Islamic and conventional regional stock markets. Indeed, it is of paramount importance for investors to clearly understand how Islamic and conventional markets are segmented or integrated during stress and stress-free periods, as well as the effect of the month of Ramadan on the interdependence among markets, to better assess risks, diversify portfolios and implement more effective hedging strategies.

While a considerable body of literature examines financial contagion and volatility transmission between financial markets, there is still much to be said regarding connectedness among commodity and stock markets, particularly when it comes to studying the effects of religious holidays on the interaction between conventional and Islamic assets. This paper fills in this gap by focusing on the dependence structure as well as the connectedness between Islamic stock indices, conventional stock indices, gold and crude oil for six different regions, while examining the Ramadan effect.

This study aims to establish a reliable index to identify interdisciplinary breakthrough innovation effectively. We constructed a new index, the D_Div index, for this purpose.

The D_Div index incorporates the degree of interdisciplinarity in the breakthrough index. To validate the index, a data set combining the publication records and citations of Nobel Prize laureates was divided into experimental and control groups. The validation methods included sensitivity analysis, correlation analysis and effectiveness analysis.

The sensitivity analysis demonstrated the D_Div index’s ability to differentiate interdisciplinary breakthrough papers from various categories of papers. This index not only retains the strengths of the existing index in identifying breakthrough innovation but also captures interdisciplinary characteristics. The correlation analysis revealed a significant correlation (correlation coefficient = 0.555) between the interdisciplinary attributes of scientific research and the occurrence of breakthrough innovation. The effectiveness analysis showed that the D_Div index reached the highest prediction accuracy of 0.8. Furthermore, the D_Div index outperforms the traditional DI index in terms of accuracy when it comes to identifying interdisciplinary breakthrough innovation.

This study proposed a practical and effective index that combines interdisciplinary and disruptive dimensions for detecting interdisciplinary breakthrough innovation. The identification and measurement of interdisciplinary breakthrough innovation play a crucial role in facilitating the integration of multidisciplinary knowledge, thereby accelerating the scientific breakthrough process.