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Despite considerable development in our understanding of potential over the past two decades, we argue that the failure to adequately conceptualize and manage “potential” in the context of talent management has significantly limited the ability of organizations to meet their talent needs. In this chapter, we begin by defining the concept of potential, calling attention to the need to separate it from performance. We also address the need to specify the target for judgments of potential (e.g., management level, specific roles), along with the identification of constructs to measure. The chapter highlights two contextual variables – gender and culture, including translations of language that describe relevant constructs – that may impact judgments of potential. This chapter concludes by summarizing what we know and by identifying a variety of future directions for research on the important construct of potential.

This article aims to discuss the binary matrix of spatial association which is suggested by Moran, and proposes a new method of the definition of the w matrix to obtain a new space‐time correlation coefficient considering the correlation of both time and space.

From the perspective of the multi‐dimension of space and time, this article proposes a new computational method of a correlation coefficient considering both temporal and spatial factors, based on the analysis of the characteristics of Moran's Global Index and Moran's Local Index. The number of patents granted in mainland China's provinces and municipalities is taken as an example of multi‐dimensional analysis.

The results of quantitative analysis using this space‐time correlation coefficient show that the outcomes calculated by this new correlation coefficient are not only highly correlated with Moran's Index, but also have advantages in analyzing the trends of both spatial and temporal indicators simultaneously, which is verified by the illustration of the algorithm.

Due to a scarcity of data in China, the algorithm is based on data for the last 20 years, which may not be long enough for this research. Although this does not reduce the value of the conclusions of this article, a closer look should be taken at the effectiveness of the new space‐time correlation coefficient in the future.

The results of space‐time correlation coefficient are highly correlated with Moran's Index. In addition, it can not only analyze the “flow” indicators in a certain period but also analyze the “stock” indicators to reflect both space and time changes. These may reflect superiority of space‐time correlation coefficient to Moran's Index.

This new correlation coefficient that considers both temporal and spatial factors and will provide a more scientific and effective tool for spatial econometric analysis in time and space changes of management on society and the economy.

The contribution of the Indian Automobile industry in the economic growth of the country is significantly high. Besides catering to a large domestic market, the automobile industry in India has also captured market shares in many foreign countries successfully in the last few decades. Not only is it an important export-oriented industry of the nation but also the fourth largest exporter of automobiles in Asia. However, in the recent years (2018–2019), it has faced an unprecedented slump. This chapter captures this fact by calculating the growth of car selling for the four quarters of the period 2018–2019 across the Indian states. It primarily tries to find out whether the variation in income and tax levied on petrol and diesel has an impact on the variation in the car selling across the states for the abovementioned time period. It has been proven from our study that higher income of a state has a positive impact, whereas higher tax on petrol and diesel which varies across the states has a negative impact on car selling. Apart from this, this study then distinctively tries to find out whether there exists any neighborhood impact on growth rate of car selling and different tax rate on petrol and diesel on the basis of Moran's Index. It is witnessed that there exists a high level of spatial autocorrelation among the different states in case of growth of a car selling and tax imposition on diesel as well as on petrol. This fact necessitates some degree of regional orientation in formulating an effective policy to revive the automobile industry on the part of the Government.

The purpose of this paper is to explore the sustainable development strategy of green finance under the background of big data.

From the perspective of big data, this paper uses quantitative and qualitative analysis methods to judge the correlation among green finance, environmental supervision and financial supervision. Green finance gives the entropy method to calculate the score of green finance and environmental regulation, and establishes the spatial lag model under the double fixed effects of time and space.

Spatial autocorrelation test shows that economic spatial weight matrix has obvious spatial effect on green innovation. Through the model selection test, the space lag model with fixed time and space is selected. The regression coefficients of green finance, environmental regulation and their interaction are 0.1598, 0.0541 and 0.1763, respectively, which significantly promote green innovation. The regression coefficients of openness, higher education level and per capita GDP are 0.0361, 0.0819 and 0.0686, respectively, which can significantly promote green innovation.

In view of the current situation of large-scale application of big data technology in green innovation of domestic energy-saving and environmental protection enterprises, this paper establishes a fixed time lag evaluation model of green innovation. M-test statistics show that the original hypothesis with no obvious spatial effect is rejected.

Tannic acid (TA) is one of the green corrosion inhibitors for mild steel; its anti-corrosive performance in alkaline water on mild steel when it is used together with polyaspartic acid (PASA) still has not been investigated. The purpose of this study is to develop an effective, biodegradable and environment-friendly novel corrosion inhibitor based on TA and PASA as an alternative to the conventional inorganic inhibitors for mild steel in decarbonised water, which is common in cooling systems.

Corrosion inhibition mechanism is investigated by electrochemical techniques such as polarisation measurements and electrochemical impedance spectroscopy, and results were evaluated to determine the optimum inhibitor concentration for industrial applications. Additionally, practice-like conditions are carried out in pilot plant studies to simulate the conditions in cooling systems. Thus, the efficiencies of the inhibitors are evaluated through both weight loss and linear polarisation resistance measurements. Moreover, the corrosion product is characterised by scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM-EDX) and Fourier-transform infrared spectroscopy (FTIR) analysis.

TA shows high inhibition efficiency especially towards pitting corrosion for mild steel in decarbonised water. PASA addition in the cooling systems improves the inhibition efficiency of TA, and at lower concentrations of TA + PASA, it is possible to obtained better inhibition efficiency than TA alone at higher inhibitor amounts, which is essential in economic and environmental aspect.

A blended inhibitor program including TA and PASA with suggested concentrations in this work can be used as an environmental friendly treatment concept for the mild steel corrosion inhibition at cooling systems.