Search results

Can regional economic complexity improve the employment effect, and is the employment structure also affected by heterogeneity? In order to solve these doubts, this paper plans to carry out systematic theoretical analysis and quantitative tests on these problems.

Based on the macroeconomic data of 129 countries from 1995 to 2022, this paper empirically studies the impact of economic complexity on the employment effect and its mechanism channel by building relevant models.

The results show that regional economic complexity can significantly improve the employment effect of society. At the same time, regional economic complexity has an indirect positive role in promoting employment from two aspects: accumulating fiscal surplus and increasing the working-age population. The former can stimulate employment policies by increasing the government’s tax sources, while the latter can increase production specialization and complexity by stimulating demand and increasing employment. In addition, regional economic complexity will lead to job market differentiation, making the boundary between the service sector and the labor market between industry and agriculture and between the informal employment market and the formal employment more obvious, thus triggering the substitution effect of employment.

The possible innovations and marginal contributions of this paper are as follows: First, there are many studies on the influencing factors of employment or unemployment, and this paper provides a new research direction for the literature on the influencing factors of employment effect by using the concept of regional economic complexity for the first time. Second, this paper broadens the research perspective of the employment effect of regional economic complexity, divides the employment substitution effect of various sectors from the perspective of industrial structure and further analyzes the employment promotion effect of regional economic complexity.

Natural calamities like earthquakes, floods and epidemics/pandemics like COVID-19 significantly disrupt almost all the supply networks, ranging from medicines to numerous daily/emergency use items. Supply Chain Resilience is one such option to overcome the impact of the disruption, which is achieved by developing supply chain factors with Artificial Intelligence (AI) and Big Data Analytics (BDA).

This research examines how organizations using AI and BDA can bring resilience to supply chains. To achieve the objective, the authors developed the methodology to gather useful information from the literature studied and developed the Total Interpretive Structural Modeling (TISM) by consulting 44 supply chain professionals. The authors developed a quantitative questionnaire to collect 229 responses and further test the model. With the analysis, a conceptual and comprehensive framework is developed.

A major finding, this research advocates that supply chain resilience is contingent upon utilizing supply chain analytics. An empirical study provides further evidence that the utilization of supply chain analytics has a positive and favorable effect on the flexibility of demand forecasting to inventory management, resulting in increased efficiency.

Few studies demonstrate the impact of advanced technology in building resilient supply chains by enhancing their factors. To the best of the authors' knowledge, no earlier researcher has attempted to infuse AI and BDA into supply chain factors to make them resilient.