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    Answers to Comments on An integrated methodology for green human resource management in construction industry by Masoud Alimardi (https://doi.org/10.1007/s11356-023-26217-9)
    (Springer Heidelberg, 2023) Darvazeh, Saeid Sadeghi; Mooseloo, Farzaneh Mansoori; Aeini, Samira; Vandchali, Hadi Rezaei; Tirkolaee, Erfan Babaee
    [Abstract Not Available]
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    Application of machine learning in supply chain management: a comprehensive overview of the main areas
    (HINDAWI LTD, 2021) Tirkolaee, Erfan Babaee; Sadeghi, Saeid; Mooseloo, Farzaneh Mansoori; Vandchali, Hadi Rezaei; Aeini, Samira
    In today's complex and ever-changing world, concerns about the lack of enough data have been replaced by concerns about too much data for supply chain management (SCM).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.
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    An integrated methodology for green human resource management in construction industry
    (Springer, 2022) Darvazeh, Saeid Sadeghi; Mooseloo, Farzaneh Mansoori; Aeini, Samira; Vandchali, Hadi Rezaei; Tirkolaee, Erfan Babaee
    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 diferent phases. In the frst 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 fndings 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 signifcant and positive relationships with GHRM. Finally, managerial insights, limitations, and future research directions are discussed.