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The purpose of this paper is to analyze the influence of the green talent dividend on China’s economic growth and regional differences using a theoretical derivation of the Cobb–Douglas production function.

This study develops a measurement model with human capital based on Chinese inter-provincial panel data for 2001–2017, and analyzes the influences on economic growth of employees’ education level, per capita material capital, green labor participation rate and green jobs. The study explores the impact of the green talent dividend on regional economic growth for different regions.

Employees’ education level, per capita material capital, green labor participation rate and green jobs promote China’s economic growth. The dependency ratio hinders economic growth. The green labor participation rate impacts economic growth more than green jobs do. Furthermore, the scale of green talent in China and its dividend effect are regionally unbalanced. Therefore, to fully release the dividend of green talent, the green labor participation rate should be improved to promote the rational flow of talent among regions.

These findings shed light on the talent dividend, provide a theoretical basis for the formulation of relevant talent policies, and show that the demographic dividend can be transformed into the green talent dividend, which has practical significance for the sustainable development of China’s economy given its aging population.

This study provides a macro perspective on the green talent dividend’s impact on economic growth. The Cobb–Douglas production function in this study differs from the traditional micro perspective on green labor.

The purpose of this paper is to elucidate the emerging landscape of procurement analytics. This paper focuses on the following questions: what are the current and future state of procurement analytics?; what changes in the procurement process will be required to enable integration of analytical solutions?; and what future areas of research arise when considering the future state of procurement analytics?

This paper employs a qualitative approach that relies on three sources of information: executive interviews, a review of current and emerging technology platforms and a small survey of subject matter experts in the field.

The procurement analytics landscape developed in this research suggests that the authors will continue to see major shifts in the sourcing and supply chain technology environment in the next five years. However, there currently exists a low usage of advanced procurement analytics, and data integrity and quality issues are preventing significant advances in analytics. This study identifies the need for organizations to establish a coherent approach to collection and storage of trusted organizational data that build on internal sources of spend analysis and contract databases. In addition, current ad hoc approaches to capturing unstructured data must be replaced by a systematic data governance strategy. An important element for organizations in this evolution is managing change and the need to nourish an analytic culture.

While the majority of forward-looking research and reports merely project broad technological impact of cognitive analytics and big data, much of it does not provide specific insights into functional impacts such as the impact on procurement. The analysis of this study provides us with a clear view of the potential for business analytics and cognitive analytics to be employed in procurement processes, and contributes to development of related research topics for future study. In addition, this study suggests detailed implementation strategies of emerging procurement technologies, contributing to the existing body of the literature and industry reports.

The purpose of this paper is based on China’s economic fundamentals. Factor input, structural optimization and institutional reform, which determine the fundamentals of China's economic development, will actively prop up long-term, sustained and stable growth of the Chinese economy and keep China's potential economic growth rate stabilized within a reasonable growth range in the long term.

The fundamentals of economic development of a country are the basic situation of economic operation determined by the country's main factors and the long-term trend thereof, and they have such characteristics as stability, internality and persistence.

Stability refers to economic operation that remains relatively stable within a reasonable growth range at a certain stage of development, and this does not rule out exceptional economic fluctuations in certain years due to the impact of unexpected short-term factors. For instance, the fundamentals of the Chinese economy during the period after the reform and opening-up are characterized by a sustained high growth rate.

Internality refers to the intrinsic quantity and quality of all factors supporting the economic development of a country, especially the quantity and quality of the factors that play a decisive role in the economic development of a country at a specific stage. For instance, demographic dividend and capital formation have bolstered the high-speed growth of the Chinese economy since the reform and opening-up.

There is a critical need to understand current talent development (TD) practices as well as developing a contemporary paradigm that will support a radically different future for TD. The purpose of this paper is to provide an overview of the existing literature on TD, explore the fundamental role of TD in organizations and identify gaps for future directions.

The paper reviews the literature on TD.

Following themes are identified in selected TD articles: varied TD definitions, theoretical perspective for investigating TD, TD practices and effects and implications on training and development. The literature review identifies two levels of TD practices: organizational level and individual level.

The field of TD is rapidly changing because of the accelerating integration of technology and information into daily work environment. There is a critical need to understand current TD practices as well as to develop a contemporary paradigm that will support a radically different future for TD. Scope and boundaries of TD need to be set, so that practitioners can apply the right practice at the right time.

The paper presents the conceptual boundaries of TD in the current existing literature and identifies the gaps.

This study aims to examine the impact of China's “Manufacturing and Internet Integration Development Pilot Demonstration Project” (MIP) policy on the digital transformation (DT) and labor structure optimization (LSU) of manufacturing enterprises, reveal the relationship between DT and LSU at the micro level and investigate the mechanism between them.

This study employs MIP as a quasi-natural experiment and develops a time-varying difference-in-difference (DID) model based on a sample of 2,445 Chinese A-share listed manufacturing enterprises in the Shanghai and Shenzhen markets from 2013 to 2021.

The implementation of MIP significantly increases DT by 0.4366 and optimizes LSU by 0.0507. By enhancing the two mediated variables of organizational learning inputs (SI) and employees' personal digital cognition (PDC), DT can optimize the LSU of pilot enterprises by 0.035 and 0.034, according to the results of the mechanism analysis. The study also reveals that the impact of MIP on LSU is highly heterogeneous. With effects of 0.0691 and 0.0632, the optimization effect is more pronounced in state-owned firms and firms with low ownership concentration, respectively.

This study demonstrates the dual effects of the MIP pilot on DT and LSU. In addition, this study pioneers research on the significance of optimizing the labor structure through SI and PDC on the basis of DT, which provides an empirical foundation for the Chinese Government to expand the scope of MIP pilots and revise policy content, as well as for manufacturing enterprises to upgrade the labor structure.

The purpose of this paper is to propose talent management, in today's competitive marketplace, is critical in organizational success. Real estate organizations are now recognizing the value in attracting and retaining the best, brightest employees in order to achieve higher than average market share and elevated profiles. In the era of the knowledge worker, “talent” – narrowly defined as a core group of leaders, technical experts and other key contributors – are quickly becoming an organization's most important asset. As real estate companies continue to compete for the top tier talent, their strategies must become more integrated.

Based on review of existing literature and expert panel interviews, a framework for integrating talent management strategies focused on multiple generations.

There is clear evidence that innovative recruiting techniques must be geared to attracting the new generation of workers. Additional attention should be given to increasing employee engagement for Generation Y, while at the same time developing strategies focused on knowledge transfer and reverse mentoring.

In an effort to address the growing talent shortages in real estate, coupled with the exiting of retirement of workers, aggressive strategies must be adopted and the framework provides a holistic approach to achieving competitive market share while addressing critical talent issues.

This paper provides real estate practitioners with strategic systems and processes to attract and retain talent in an increasingly competitive market. Practical strategies are presented with a holistic framework.

Big data analytics (BDA) gets all the attention these days, but as important—and perhaps even more important—is big data analytics quality (BDAQ). Although many companies realize a full return from BDA, others clearly struggle. It appears that quality dynamics and their holistic impact on firm performance are unresolved in data economy. The purpose of this paper is to draw on the resource-based view and information systems quality to develop a BDAQ model and measure its impact on firm performance.

The study uses an online survey to collect data from 150 panel members in France from a leading market research firm. The participants in the study were business analysts and IT managers with analytics experience.

The study confirms that perceived technology, talent and information quality are significant determinants of BDAQ. It also identifies that alignment between analytics quality and firm strategy moderates the relationship between BDAQ and firm performance.

The findings inform practitioners that BDAQ is a hierarchical, multi-dimensional and context-specific model.

The study advances theoretical understanding of the relationship between BDAQ and firm performance under the influence of firm strategy alignment.

The authors have attempted to understand how big data and predictive analytics (BDPA) can help retain employees in the organization.

This study is grounded in the positivism philosophy. The authors have used a resource-based view (RBV) to develop their research hypotheses. The authors tested their research hypotheses using primary data gathered using a single-informant questionnaire. The authors obtained 254 usable responses. The authors performed the assumptions test, performed confirmatory factor analysis (CFA) to test the validity of the proposed theoretical model, and further tested their research hypotheses using hierarchical regression analysis.

The statistical result suggests that the various human resource management strategies play a significant role in improving retention under the mediating effect of the BDPA.

The authors have grounded their study in the positivism philosophy. Moreover, the authors tested their hypotheses using single-informant cross-sectional data. Hence, the authors cannot ignore the effects of the common method bias on their research findings. Moreover, the research findings are based on a particular setting. Thus, the authors caution the readers that their findings must be examined in the light of their study limitations.

The study provided empirical findings based on survey data. Hence, the authors provide numerous guidelines to the practitioners that how the organization can invest in creating BDPA that helps analyze complex data to extract meaningful and relevant information. This information related to employee turnaround may guide top management to reduce the dissatisfaction level among the employees working in high-stress environments resulting from a high degree of uncertainty.

The study helps understand the complex factors that affect the morale of the employee. In the high-paced environment, the employees are often exposed to various negative forces that affect their morale which further affect their productivity. Due to lack of awareness and adequate information, most of the employees and their issues are not dealt with effectively and efficiently by their line managers. Thus, the BDPA can help tackle the most complex problem of society in a significant way.

This study offers some useful contributions to the literature which attempts to unfold the complex nexus between human resource management, information management and strategy. The study contributes to the BDPA literature and how it helps in the retention of employees is one of the areas which still remains elusive to the academic community. Moreover, the managers are still skeptical about the application of BDPA in understanding human-related issues due to a lack of understanding of how and to what extent the employee-related information can be stored and processed. This study’s findings further open the new avenues of research that need to be tackled.

Big data-driven supply chain analytics capability (SCAC) is now emerging as the next frontier of supply chain transformation. Yet, very few studies have been directed to identify its dimensions, subdimensions and model their holistic impact on supply chain agility (SCAG) and firm performance (FPER). Therefore, to fill this gap, the purpose of this paper is to develop and validate a dynamic SCAC model and assess both its direct and indirect impact on FPER using analytics-driven SCAG as a mediator.

The study draws on the emerging literature on big data, the resource-based view and the dynamic capability theory to develop a multi-dimensional, hierarchical SCAC model. Then, the model is tested using data collected from supply chain analytics professionals, managers and mid-level manager in the USA. The study uses the partial least squares-based structural equation modeling to prove the research model.

The findings of the study identify supply chain management (i.e. planning, investment, coordination and control), supply chain technology (i.e. connectivity, compatibility and modularity) and supply chain talent (i.e. technology management knowledge, technical knowledge, relational knowledge and business knowledge) as the significant antecedents of a dynamic SCAC model. The study also identifies analytics-driven SCAG as the significant mediator between overall SCAC and FPER. Based on these key findings, the paper discusses their implications for theory, methods and practice. Finally, limitations and future research directions are presented.

The study fills an important gap in supply chain management research by estimating the significance of various dimensions and subdimensions of a dynamic SCAC model and their overall effects on SCAG and FPER.