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In order to solve the decision-making problem that the attributive weight and attributive value are both interval grey numbers, this paper tries to construct a multi-attribute grey decision-making model based on generalized greyness of interval grey number.

Firstly, according to the nature of the generalized gresness of interval grey number, the generalized weighted greyness distance between interval grey numbers is given, and the transformation relationship between greyness distance and real number distance is analyzed. Then according to the objective function that the square sum of generalized weighted greyness distances from the decision scheme to the best scheme and the worst scheme is the minimum, a multi-attribute grey decision-making model is constructed, and the simplified form of the model is given. Finally, the grey decision-making model proposed in this paper is applied to the evaluation of technological innovation capability of 6 provinces in China to verify the effectiveness of the model.

The results show that the grey decision-making model proposed in this paper has a strict mathematical foundation, clear physical meaning, simple calculation and easy programming. The application example shows that the grey decision model in this paper is feasible and effective. The research results not only enrich the grey system theory, but also provide a new way for the decision-making problem that the attributive weights and attributive values are interval grey numbers.

The decision-making model proposed in this paper does not need to seek the optimal solution of the attributive weight and the attributive value, and can save the decision-making labor and capital investment. The model in this paper is also suitable for the decision-making problem that deals with the coexistence of interval grey numbers and real numbers.

The paper succeeds in realizing the multi-attribute grey decision-making model based on generalized gresness and its simplified forms, which provide a new method for grey decision analysis.

International experience (IE) has been acknowledged to be the most useful method for developing global leaders. However, not everyone benefits equally from IE. During the last two decades, our understanding of why this is the case and how global leaders learn from IE has rapidly increased. Several individual and organizational enablers facilitating global leader learning from IE have been identified in the literature, as have learning mechanisms that make such learning possible. However, the literature remains fragmented, and there is a great need to integrate the findings in the field. Therefore, the present paper systematically examines peer-reviewed studies on global leaders' learning from IE published between 1998 and 2019. The study contributes to the extant literature by identifying and integrating individual enablers, organizational enablers, and key learning mechanisms from global leaders' IE and by suggesting topics for future research.

We review academic journal articles, chapters in scholarly books, and dissertations that were published in the global leadership domain in 2018 and compare our findings with those of Mendenhall, Li, and Osland’s (2016) earlier review of the 2010–2014 period. Specifically, we trace and discuss relevant changes over time in the number of authors, nature of employed methodologies, linkages of global leadership to related phenomena, and the prevalence of drawing on other conceptual approaches compared to those previously used to study global leadership. We conclude by discussing implications for advancing (1) an integrated body of global leadership theory, (2) novel forms of empirical research, and (3) collaborative global leadership research.

This paper provides a quantitative review of the literature on the repercussions of idiosyncratic information on firms’ cost of equity (CoE) capital. In total, I review the results of 113 unique studies examining the CoE effects of information Quantity, Precision and Asymmetry. My results suggest that the association between firm-specific information and CoE is subject to moderate effects. First, the link between Quantity and CoE is moderated by disclosure types and country-level factors in that firms in comparatively weakly regulated countries tend to enjoy up to four times greater CoE benefits from more expansive disclosure—depending on the type of disclosure—than firms in strongly regulated markets. Second, a negative relationship between Precision and CoE is only significant in studies using non-accrual quality proxies for Precision and risk factor-based (RFB)/valuation model-based (VMB) proxies for CoE. Third, almost all VMB studies confirm the positive association between Asymmetry and CoE, but there is notable variation in the conclusions reached when ex post CoE measurers are used.

Due to the increasing size and complexity of construction projects, construction engineering and management involves the coordination of many complex and dynamic processes and relies on the analysis of uncertain, imprecise and incomplete information, including subjective and linguistically expressed information. Various modelling and computing techniques have been used by construction researchers and applied to practical construction problems in order to overcome these challenges, including fuzzy hybrid techniques. Fuzzy hybrid techniques combine the human-like reasoning capabilities of fuzzy logic with the capabilities of other techniques, such as optimization, machine learning, multi-criteria decision-making (MCDM) and simulation, to capitalise on their strengths and overcome their limitations. Based on a review of construction literature, this chapter identifies the most common types of fuzzy hybrid techniques applied to construction problems and reviews selected papers in each category of fuzzy hybrid technique to illustrate their capabilities for addressing construction challenges. Finally, this chapter discusses areas for future development of fuzzy hybrid techniques that will increase their capabilities for solving construction-related problems. The contributions of this chapter are threefold: (1) the limitations of some standard techniques for solving construction problems are discussed, as are the ways that fuzzy methods have been hybridized with these techniques in order to address their limitations; (2) a review of existing applications of fuzzy hybrid techniques in construction is provided in order to illustrate the capabilities of these techniques for solving a variety of construction problems and (3) potential improvements in each category of fuzzy hybrid technique in construction are provided, as areas for future research.

Accurate prediction of port cargo throughput within Free Trade Zones (FTZs) can optimize resource allocation, reduce environmental pollution, enhance economic benefits and promote sustainable transportation development.

This paper introduces a novel self-adaptive grey multivariate prediction modeling framework (FARDCGM(1,N)) to forecast port cargo throughput in China, addressing the challenges posed by mutations and time lag characteristics of time series data. The model explores policy-driven mechanisms and autoregressive time lag terms, incorporating policy dummy variables to capture deviations in system development trends. The inclusion of autoregressive time lag terms enhances the model’s ability to describe the evolving system complexity. Additionally, the fractional-order accumulative generation operation effectively captures data features, while the Grey Wolf Optimization algorithm determines optimal nonlinear parameters, enhancing the model’s robustness.

Verification using port cargo throughput forecasts for FTZs in Shanghai, Guangdong and Zhejiang provinces demonstrates the FARDCGM(1,N) model’s remarkable accuracy and stability. This innovative model proves to be an excellent forecasting tool for systematically analyzing port cargo throughput under external interventions and time lag effects.

A novel self-adaptive grey multivariate modeling framework, FARDCGM(1,N), is introduced for accurately predicting port cargo throughput, considering policy-driven impacts and autoregressive time-lag effects. The model incorporates the GWO algorithm for optimal parameter selection, enhancing adaptability to sudden changes. It explores the dual role of policy variables in influencing system trends and the impact of time lag on dynamic response rates, improving the model’s complexity handling.

Three-parameter interval grey numbers (TPIGNs) have been extensively studied as an extended form of interval numbers. However, most existing TPIGN multi-attribute decision-making methods only consider the similarity of positions, ignore the similarity of developmental directions and focus primarily on static evaluation. To address these limitations, in this study, the authors propose a dynamic technique for order preference by similarity to an ideal solution (TOPSIS) based on modified Jaccard similarity and angle similarity for TPIGNs.

First, the authors extend Jaccard similarity to a TPIGN environment to represent positional similarity. A simple example is provided to illustrate the limitations of the traditional Jaccard similarity. Then, the authors introduce an angle similarity measure to represent developmental directional similarity. Finally, a dynamic TOPSIS model is constructed by incorporating time-series data into conventional two-dimensional static data. Stage weights are obtained by an objective function designed to maximize the amount and minimize the fluctuation of decision information. A quadratic weighting method is adopted to derive the overall evaluation value of alternatives.

To evaluate the effectiveness of the proposed method, this study takes the pre-assessment of ice disaster and the selection of cooperative enterprises as examples. The authors then provide the results of comparative and sensitivity analyses, which demonstrate the effectiveness and flexibility of the proposed method.

The proposed TOPSIS method in a TPIGN environment can take a more holistic and dynamic perspective for decision-making, which helps mitigate the limitations of single-perspective or static evaluations.

Based on our review of the global leadership literature over the past 5 years, we delineate patterns and themes in the field’s scholarship and then discuss how the papers published in this volume of Advances in Global Leadership relate to those patterns and themes. We then integrate all of the recommendations for future research that appeared in the papers published in this volume and then offer our recommendations for future research in the field as well. We hope our efforts will aid scholars – especially doctoral students – to isolate the gaps that exist in the field and to design and conduct research that will have a greater potential to be published in top-tier journals and to impact the field.

Due to the cross-network effect, two-sided users communicate with each other, producing a coupling network. To study the spread of platform self-operation in two-sided users' marketing and purchasing tactics, this paper considers the differences in reputation acquired by platform-owned and third-party operating channels.

This study proposes a two-layer network with cross-network links: one layer represents the social network of consumers, while the other layer represents the competitive network of buyers. A closed system of differential equations, based on the binary dynamics of the stochastic network, is developed to study the trend and stability points of the platform self-operation dissemination. Then the overall benefits of platform are analyzed to unify the platform diffusion and pricing strategies.

The degree of difference in social influence and cross-network effects affect diffusion synergistically. Cross-network effects hinder diffusion when there is a significant difference of social influence between consumers and sellers but promote diffusion when there is little difference of social influence between consumers and sellers. Additionally, the network weights and reputation gap exhibit a nonlinear correlation with diffusion. For pricing strategy of the platform, it can achieve maximum profit when the pricing of self-operated goods and third-party-operated goods is equal.

This study considers the complex network architecture created by bilateral markets and the dynamic influence of group interactions on product. Additionally, this study takes reputation into account when considering the price and dissemination tactics of various operating channels, offering guidelines for platforms to control merchants and mediate disputes between various operating channels.

Whereas most societal commentators continue to review the historical patterns of men’s leadership in search of models for 21st-century success, few have begun to recognize, let alone appreciate, the equivalent patterns of women’s leadership and the future contributions that women could potentially make as leaders. What could and are women bringing to society as global leaders? Why at this moment in history is there such a marked increase in the number of women leaders? Are we entering an era in which both male and female leaders will shape history, both symbolically and in reality? And if so, will we discover that women, on average, lead in different ways than men, or will we learn that role (global leader) explains more than gender? This chapter reveals the accelerating trends of women joining men in senior leadership positions, establishes the relationship of women leaders to our overall understanding of global leadership, and sets forth an agenda to accomplish much needed research and understanding.