In today’s complex and ever-changing world, concerns about the lack of enough data have been replaced by concerns about too much data for supply chain management (SCM). The volume of data generated from all parts of the supply chain has changed the nature of SCM analysis. By increasing the volume of data, the efficiency and effectiveness of the traditional methods have decreased. Limitations of these methods in analyzing and interpreting a large amount of data have led scholars to generate some methods that have high capability to analyze and interpret big data. Therefore, the main purpose of this paper is to identify the applications of machine learning (ML) in SCM as one of the most well-known artificial intelligence (AI) techniques. By developing a conceptual framework, this paper identifies the contributions of ML techniques in selecting and segmenting suppliers, predicting supply chain risks, and estimating demand and sales, production, inventory management, transportation and distribution, sustainable development (SD), and circular economy (CE). Finally, the implications of the study on the main limitations and challenges are discussed, and then managerial insights and future research directions are given.

Today, by increasing public awareness about environmental issues and pressures from governments and other stakeholders, companies have dealt with environmental challenges more than ever. This paper focuses on environmentally sustainable performance using an integrated methodology based on meta-synthesis, Delphi, and structural equation modeling (SEM) techniques which are utilized in different phases. In the first phase, an in-depth review of green human resources management (GHRM) literature is conducted based on the meta-synthesis method, and as a result, 38 codes are extracted. Next, to adapt and customize the codes with the nature of the construction industry, 2 rounds of Delphi method are implemented to extract the expert judgment from a panel of 15 industry professionals, resulting in 21 codes in 7 categories. To validate the developed methodology, a dataset from 33 Iranian construction companies are collected along with 15 factors in 5 categories determined using SEM. The findings reveal that among 9 main GHRM components extracted from the literature, just 5 components including green recruitment and selection, green performance management, green-reward, green-based employee empowerment, and green training have significant and positive relationships with GHRM. Finally, managerial insights, limitations, and future research directions are discussed.

Today, by increasing public awareness about environmental issues and pressures from governments and other stakeholders companies have dealt with environmental challenges more than ever. This paper focuses on environmentally sustainable performance using an integrated method based on meta-synthesis, Delphi, and structural equation modeling (SEM) methods are utilized in different phases of the methodology. In the first phase, an in-depth review of green human resources management (GHRM) literature is conducted based on the meta-synthesis method and as a result, 38 codes were extracted. Then, to adapt and customize the codes with the nature of the construction industry, 2 rounds of the Delphi method are applied to collect the expert judgment from a panel of 15 industry professionals, resulting in 21 codes in 7 categories. Finally, to verify the proposed model the data from 33 Iranian construction companies were collected and by using SEM, 15 factors in 5 categories are presented. The findings indicated that among 9 main GHRM components extracted from the literature, just 5 components including green recruitment and selection, green performance management, green- reward, green-based employee empowerment, and green training have significant and positive relationships with GHRM. The paper also provides some managerial insights and some future directions.

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Good governance plays a key role in the growth and success of organizations and helps them to achieve their strategic goals. A review of the research literature shows that the term good governance is mostly used in relation to governments, and there is no proper understanding of the role of good governance in non-governmental organizations. Due to the significant growth of project-oriented organizations, there is a gap in the literature to explain the position of good governance in project-based organizations. For this reason, there is no good understanding of the term good governance and its application in project-based organizations. The purpose of this study is to identify and prioritize the criteria of good governance in Iranian project-based organizations. To this end, a hybrid approach based on meta-synthesis, thematic analysis, and multi-criteria decision-making methods was used to identify and prioritize good governance criteria in project-based organizations. In the first phase, using the meta-synthesis method, an extensive review of the good governance literature from 2012 to 2020 was conducted and different criteria of good governance were extracted. In the second phase, the thematic analysis method was used to identify good governance criteria through interviews with 10 experts active in project-based organizations. In the third phase, the best-worst method (BWM) method was used to weigh and prioritize the good governance criteria. The results of data analysis indicated that accountability is the most important criterion, and the planning to respond to risks and uncertainties is the most important sub-criterion of the research. Finally, some managerial implications for managers of project-based organizations and some future research directions were provided.