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Although the importance of green supplier integration (GSI) has been recognized, the knowledge of how it can be enhanced is still limited. Using insights from transaction cost and resource dependence theories, this paper aims to explore how to balance coercive and non-coercive powers to enhance GSI and the mediating role of relationship commitment and the moderating role of relationship closeness.

To validate the hypotheses, this study conducted hierarchical regression analysis and bootstrapping using the survey data collected from 206 Chinese manufacturers.

The results indicate that coercive power undermines normative commitment, while non-coercive power promotes normative and instrumental commitments. Both normative and instrumental commitments enhance GSI. Normative commitment mediates the impacts of coercive and non-coercive powers on GSI, while instrumental commitment only mediates the impact of non-coercive power on GSI. Moreover, supplier trust and dependence negatively moderate the positive link between instrumental commitment and GSI.

Executives should carefully balance coercive and non-coercive powers to encourage firms to maintain good relationships with suppliers and develop common environmental values under different mediating effects of normative and instrumental commitments. However, they should also be aware that high level of trust and dependence can affect the impacts of powers.

This study contributes to GSI literature by opening the “black box” between power and GSI and verifying its boundary conditions.

Based on resource dependence theory and transaction cost economics this study explores how green customer integration (GCI) affects financial performance via information sharing and opportunistic behavior, and the moderating effects of dependence and trust.

This study develops a theoretical model and tests it using data from two-waved survey data of 206 Chinese manufacturers. The hypotheses were tested using hierarchical linear regression analysis.

The results show that GCI has a significant and positive impact on information sharing, but its impact on opportunistic behavior is insignificant. Notably, information sharing has a significant and positive impact on financial performance, while opportunistic behavior has an insignificant impact on financial performance. In addition, dependence negatively moderates the impact of GCI on information sharing and positively moderates the impact of GCI on opportunistic behavior. Trust negatively moderates the impact of GCI on opportunistic behavior.

Although GCI has received widespread attention, how it affects a firm's performance remains unclear. Most previous studies have focused only on its bright side and ignored its dark side. This study highlights how GCI affects financial performance through information sharing and opportunistic behavior, and the moderating effects of dependence and trust. This enriches the understanding of how and under what conditions GCI affects a firm's performance.

The purpose of this study is to explore the inverted U-shaped relationship between green customer integration (GCI) and opportunistic behavior, as well as the moderating effects of contractual control and relational norms.

The authors conducted hierarchical regression analysis using two-waved data from 206 Chinese manufacturing firms to test hypotheses.

The authors found that GCI has an inverted U-shaped effect on opportunistic behavior. Furthermore, both contractual control and relational norms negatively moderate the inverted U-shaped relationship between GCI and opportunistic behavior.

This study uncovers an inverted U-shaped link between GCI and opportunistic behavior by combining transaction cost economics and social exchange theory. Furthermore, this study reveals contractual control and relational norms can be deemed as two boundary conditions affecting the inverted U-shaped GCI–opportunistic behavior relationship. This study also offers managerial implications for firms curbing opportunistic behavior that may result from GCI.

The neo-Kaleckian model follows the ideas of Marx, Keynes and Kalecki, that investment is a key influencing factor in the dynamics of the capitalist mode of production. Through the discussion of different forms of investment decision function, this paper constructs the analysis framework of wage-led and profit-led economic growth regimes.

The model has become an important theoretical paradigm for current Western heterodox economists regarding the research on the impact of functional income distribution on economic growth, and it has a very large impact on both theoretical and empirical research. Starting from Marx's reproduction theory, this article discusses the theoretical shortcomings of the neo-Kaleckian growth regime model.

This paper mainly focuses on three aspects: (1) the ideological legacy of “Smith's Dogma”; (2) neglecting the restrictions on income distribution from the organic composition of capital and the surplus value rate; (3) technological progress and the formation of a new long economic wave.

The authors believe that the neo-Kaleckian model unilaterally emphasizes the demand-side factors in the economy and, unconsciously or not, ignores the role of the supply-side, which makes it encounter certain limitations in explaining long-term growth. Even if some empirical conclusions are employed to bridge functional income distribution and technological progress, there is still a lack of a theoretical basis for accurately describing long-term economic changes using this model. In order to better promote high-quality economic development and accelerate the formation of a new pattern of economic development in which the domestic large-scale cycle is the mainstay and the domestic and international double cycles promote each other, the authors need to adopt a policy combination with the supply-side as the main and the demand-side as the supplement, and to work from both sides.

In the current literature, there is little systematic research on the relationship among adjustment of the income distribution, change in economic structure and improvement of macroeconomic efficiency.

This paper expands Marx's reproduction schema into the “Marx–Sraffa” three-department structure table comprising fixed capital, general means of production and means of consumption and employs China's input–output table from 1987 to 2015 to portray the relationship between income distribution and macroeconomic efficiency under investment-driven growth.

This paper calculates the wage–profit curve of China's economy and evaluates the space of macroeconomic efficiency improvement in China based on the deviation between actual and potential income distribution structure.

The results show that there is a downward trend of the profit rate, which meets Marx's theoretical prediction, and the decline in the profit rate is mainly attributed to an increase in the organic composition of capital arising from the rapid growth of fixed capital investment under extended growth. The analysis of macroeconomic efficiency shows that the space for improving macroeconomic efficiency is extremely limited under traditional growth pattern and that China must transform its economic development pattern and foster new economic growth drivers.

This paper aims to propose two parallel two-step finite element algorithms based on fully overlapping domain decomposition for solving the 2D/3D time-dependent natural convection problem.

The first-order implicit Euler formula and second-order Crank–Nicolson formula are used to time discretization respectively. Each processor of the algorithms computes a stabilized solution in its own global composite mesh in parallel. These algorithms compute a nonlinear system for the velocity, pressure and temperature based on a lower-order element pair (P₁b-P₁-P₁) and solve a linear approximation based on a higher-order element pair (P₂-P₁-P₂) on the same mesh, which shows that the new algorithms have the same convergence rate as the two-step finite element methods. What is more, the stability analysis of the proposed algorithms is derived. Finally, numerical experiments are presented to demonstrate the efficacy and accuracy of the proposed algorithms.

Finally, numerical experiments are presented to demonstrate the efficacy and accuracy of the proposed algorithms.

The novel parallel two-step algorithms for incompressible natural convection problem are proposed. The rigorous analysis of the stability is given for the proposed parallel two-step algorithms. Extensive 2D/3D numerical tests demonstrate that the parallel two-step algorithms can deal with the incompressible natural convection problem for high Rayleigh number well.

Based on organizational information processing theory, this research explores how big data analytics capability (BDAC) contributes to green supply chain integration (GSCI) and the contingency role that data-driven decision culture plays.

Using the two-wave survey data collected from 317 Chinese manufacturing firms, the authors validate the hypotheses.

The results show that big data managerial capability has positive impacts on three dimensions of GSCI, while big data technical capability has positive impacts on green internal and customer integration. Moreover, green internal integration mediates the impacts of big data technical capability and managerial capability on green supplier and customer integration. Finally, data-driven decision culture alleviates the positive impacts of big data technical and managerial capability on green internal integration.

The findings suggest that firms can leverage big data technical and managerial capability to enhance information processing capability for achieving a higher degree of GSCI. Further, the critical role of data-driven decision culture in affecting the link between BDAC and GSCI should not be overlooked.

This research contributes to literature on green supply chain management by revealing the role of BDAC in improving GSCI.

Based on upper echelons theory and social contagion theory, this study aims to explore how to translate leader sustainability orientation (LSO) into green supply chain integration (GSCI) by green entrepreneurial orientation (GEO), as well as the moderating effects of four dimensions of organizational learning capability (OLC).

This study conducts hierarchical regression analysis using multi-sourced survey data collected in 264 Chinese companies to examine hypotheses.

The results indicate that LSO has positive influences on green supplier and customer integration. Furthermore, GEO partially mediates the impacts of LSO on green supplier and customer integration. Managerial commitment positively moderates the impact of LSO on GEO, while other dimensions such as systems perspective, openness and experimentation and knowledge transfer and integration have non-significant moderating effects.

This study enriches the existing research on internal drivers of GSCI and contributes to the understanding of the direct impact of LSO and mediating effects of GEO. This study extends social contagion theory boundaries by investigating the moderating effects of OLC on the relationship between LSO and GEO.

The purpose of this paper is to describe a novel design method to construct lattice structure computational models composed of a set of unit cells including simple cubic, body-centered cubic, face-centered cubic, diamond cubic and octet cubic unit cell.

In this paper, the authors introduce a new implicit design algorithm based on the computation of volumetric distance field (VDF). All the geometric components including lattice core structure and outer skin are represented with VDFs in a given design domain. This enables computationally efficient design of a computational model for an arbitrarily complex lattice structure. In addition, the authors propose a hybrid method based on the VDF and parametric solid models to construct a conformal lattice structure, which is oriented in accordance with the geometric form of the exterior surface. This method enables the authors to design highly complex lattice structure, computational models, in a consistent design framework irrespective of the complexity in geometric representations without sacrificing accuracy and efficiency.

Experimental results are shown for a variety of geometries to validate the proposed design method along with illustrative several lattice structure prototypes built by additive manufacturing techniques.

This method enables the authors to design highly complex lattice structure, computational models, in a consistent design framework irrespective of the complexity in geometric representations without sacrificing accuracy and efficiency.

Background: Artificial intelligence (AI) is a booming sector that has profoundly influenced every walk of life, and the education sector is no exception. In education, AI has helped to develop novel teaching and learning solutions that are currently being tested in various contexts. Businesses and governments across the globe have been pouring money into a wide array of implementations, and dozens of EdTech start-ups are being funded to capitalise on this technological force. The penetration of AI in classroom teaching is also a profound matter of discussion. These have garnered massive amounts of student big data and have a significant impact on the life of both students and educators alike.

Purpose: The prime focus of this chapter is to extensively review and analyse the vast literature available on the utilities of AI in health care, learning, and development. The specific objective of thematic exploration of the literature is to explicate the principal facets and recent advances in the development and employment of AI in the latter. This chapter also aims to explore how the EdTech and healthcare–education sectors would witness a paradigm shift with the advent and incorporation of AI.

Design/Methodology/Approach: To provide context and evidence, relevant publications were identified on ScienceDirect, PubMed, and Google Scholar using keywords like AI, education, learning, health care, and development. In addition, the latest articles were also thoroughly reviewed to underscore recent advances in the same field.

Results: The implementation of AI in the learning, development, and healthcare sector is rising steeply, with a projected expansion of about 50% by 2022. These algorithms and user interfaces economically facilitate efficient delivery of the latter.

Conclusions: The EdTech and healthcare sector has great potential for a spectrum of AI-based interventions, providing access to learning opportunities and personalised experiences. These interventions are often economic in the long run compared to conventional modalities. However, several ethical and regulatory concerns should be addressed before the complete adoption of AI in these sectors.

Originality/Value: The value in exploring this topic is to present a view on the potential of employing AI in health care, medical education, and learning and development. It also intends to open a discussion of its potential benefits and a remedy to its shortcomings.