With the development of the economy, especially since the reform and opening up, the actual conditions that China’s economic development faces have also changed, and the development goals have also been adjusted accordingly. However, since the reform and opening up, China’s economy has still largely relied on the extended reproduction of the extension type. It basically develops along a high-input, low-efficiency path. The effectiveness of regional economic development has always been one of the most important debates in economics, and it has attracted great attention in different countries and regions around the world. So far, people have conducted a lot of theoretical and practical research on the effectiveness of regional development and put forward many practical countermeasures. However, it has achieved little in practice, and the original form of high growth and low efficiency has not been effectively reversed. In this paper, economic development is divided into two parts. The first part consists of the impact of changes in the quantity of factors of production on economic growth and the impact of the efficiency of economic growth on the entire economy. The second part consists of the annual average efficiency of economic growth. The average annual contribution of various production factors to economic growth and the economic growth efficiency of changes in the allocation of capital industries were 6.21%, 5.02%, 4.05%, 3.9%, and 3.3%, respectively in the past five years.
The traditional heavy industry creates not only economic value for societies and countries but also serious ecological and cultural damage. This type of industry is not easy to transform and upgrade because of its large-scale and complex characteristics, and its traditional management mode is being challenged. This study focused on the relation between China’s current ecotechnology and ecological innovation goals. This was investigated to research a new technology and goal management method, which would promote the transformation and upgradation of traditional heavy industry. We investigated 11 shipbuilding companies with strong comprehensive capacity in China’s shipbuilding bases, analyzed the viewpoints of 331 senior managers and designers with more than a decade of shipbuilding experience, and referred to the industry technical standards and literature to define types of shipbuilding ecotechnology and ecological innovation goals. Structural equation modeling was conducted to analyze the relation between them. The simulation results demonstrated that four types of ecotechnology (i.e., energy technology, shipbuilding technology, digital technology, and strategic management) represent the key factors affecting the shipbuilding ecological innovation goals. This study is of theoretical significance for traditional heavy industry, and its outcomes encourage the achievement of ecological innovation goals through the application of ecotechnology.
With the disruption of digital technologies, customers have emerged as co-producers in order to reduce costs and enhance productivity. Customer-driven business logic recognizes customer capabilities and sustainable innovation as key factors in the performance of customized manufacturing enterprises. The potential relationship between customers and producers has become a new area of inquiry. Existing research rarely delves into the impact of customer predictive ability on the process of customized production activities, particularly in relation to sustainable innovation, which remains inadequately characterized. Ships serve as a typical representation of customized enterprises. This study explores the underlying drivers of sustainable innovation through customer demand orientation by examining 20-year historical patterns in the global shipping and shipbuilding markets, considering the environmental dependency and temporal correlation characteristics of the shipbuilding market (producers) and the shipping market (consumers). By employing time series and panel data in a machine learning algorithm, specifically the random forest model, the study reveals a strong and statistically significant correlation between new ship deliveries and the Baltic Dry Index (BDI), with larger value ships having a more pronounced impact on the consumer market. The correlation analysis confirms that these two variables, in combination, can comprehensively reflect customer predictive ability and serve as crucial decision criteria for customer investment in new ship production. Furthermore, based on principal component analysis of customer predictive ability and ship innovation levels, as well as Granger causality tests, the study demonstrates that customer predictive ability is a Granger cause of sustainable innovation in customized production. Customer predictive ability influences sustainable innovation in customized enterprises to varying degrees. This research provides valuable insights for shipbuilding companies in terms of engaging in sustainable innovation in international markets and understanding the value of international market customers.
Customer-centric service innovation performance has become a common businesses goal to pursue, particularly for service-oriented manufacturing companies. However, the continuous focus on the impact of enterprise resources and capabilities in service innovation fails to truly consider market orientation and customer capabilities as core influencing factors of service innovation performance at an individual level. This article explores new service behaviors driven by market orientation and customer predictive abilities, revealing the process of customer-driven value creation for sustainable innovation within enterprises. Ships are typical representatives of customized enterprises. This study examines the role of customer predictive capabilities in the sustainable innovation of shipbuilding companies, starting from a 20-year historical analysis of the global shipping and shipbuilding markets. By exploring the market orientation characteristics of the shipbuilding and shipping markets, this study investigates the behavioral impact of customer predictive abilities on sustainable innovation within shipbuilding enterprises. Employing time series and panel data in machine learning algorithms, specifically the random forest model, reveals a strong and statistically significant correlation between new ship deliveries and the Baltic dry index (BDI), with larger value ships having a more pronounced impact on the consumer market. The correlation analysis confirms that these two variables, in combination, can comprehensively reflect customer predictive ability and serve as crucial decision criteria for customer investment in new ship production. Furthermore, based on the principal component analysis of customer predictive ability and ship innovation levels Granger causality tests, this study demonstrates that customer predictive ability is a Granger cause of sustainable innovation in customized production. Customer predictive ability influences sustainable innovation in customized enterprises to varying degrees. This research provides valuable insights for shipbuilding companies regarding engaging in sustainable innovation in international markets and understanding the value of international market customers.
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