Impact of Green Supply Chain Management on Sustainable Performance: A Dual Mediated-moderated Analysis of Green Technology Innovation And Big Data Analytics Capability Powered by Artificial Intelligence

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Methods This study was conducted in Pakistan’s food processing sector. The respondents included 495 managers working in the food processing industry. A structural equation modelling (SEM) approach is used to examine direct and indirect relationships between the variables. The originality of this study lies in integration of the technology acceptance model (TAM) and dynamic capability theory (DCT) to understand sustainable practices in the context of the provided model. Results This study highlights that GSCM, GTI, WM, and BDAC-AI have positive, strong, and direct impacts on SP. Furthermore, GTI and WM only partially mediate the link between GSCM and SP, whereas the two moderate the link. In addition, BDAC-AI had a moderating effect on the relationship between GTI and SP. This study has managerial implications, including strategies that involve the use of theoretical frameworks for technological acceptance and dynamic capabilities to support sustainable initiatives. However, it is worth noting that the findings provide a practical contingency for managers and businesses interested in implementing green studies effectively, improving technologies, and strengthening sustainable performance capabilities. Conclusions The study extends the literature by establishing a model for operationalizing GSCM in the food processing sector. Furthermore, it adds value in that it first integrates TAM and DCT to explain sustainable operations and their impact on organizations. Furthermore, it extends the existing literature by establishing a relationship between GSCM and SC. It offers a model through which GSCM can be operationalized in the context of the FS sector. 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F1000Research 2025, 13 :1140 ( https://doi.org/10.12688/f1000research.154615.2 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Revised Impact of Green Supply Chain Management on Sustainable Performance: A Dual Mediated-moderated Analysis of Green Technology Innovation And Big Data Analytics Capability Powered by Artificial Intelligence [version 2; peer review: 1 approved, 2 approved with reservations] Quswah Makhdoom https://orcid.org/0009-0005-4840-6653 1 , Ikramuddin Junejo https://orcid.org/0000-0002-6151-9813 1 , Jan Muhammad Sohu https://orcid.org/0000-0001-6594-0157 2,3 , [...] Syed Mir Muhammad Shah 3 , Belal Mahmoud Alwadi https://orcid.org/0000-0003-1677-1275 4 , Faisal Ejaz https://orcid.org/0000-0002-8643-3186 5,6 , Md Billal Hossain 7 Quswah Makhdoom https://orcid.org/0009-0005-4840-6653 1 , Ikramuddin Junejo https://orcid.org/0000-0002-6151-9813 1 , [...] Jan Muhammad Sohu https://orcid.org/0000-0001-6594-0157 2,3 , Syed Mir Muhammad Shah 3 , Belal Mahmoud Alwadi https://orcid.org/0000-0003-1677-1275 4 , Faisal Ejaz https://orcid.org/0000-0002-8643-3186 5,6 , Md Billal Hossain 7 PUBLISHED 08 Sep 2025 Author details Author details 1 Department of Management Sciences, SZABIST University, Hyderabd Campus, Pakistan 2 School of Management, Jiangsu University, Zhenjiang, Jiangsu, China 3 Department of Business Administration, Sukkur IBA University, Sukkur, Pakistan 4 Department of Basic Sciences (Humanities and Scientific), Al-Zaytoonah University of Jordan, Amman, Jordan 5 School of International Relations, Minhaj University Lahore, Lahore, Punjab, Pakistan 6 Department of Political Science, University of Okara, Punjab, Pakistan 7 Sustainability Competence Centre, Faculty of Business and Economics, Szechenyi Istvan Egyetem, Győr, Gyor-Moson-Sopron, Hungary Quswah Makhdoom Roles: Conceptualization, Investigation, Methodology, Writing – Original Draft Preparation Ikramuddin Junejo Roles: Conceptualization, Data Curation, Investigation, Methodology, Validation, Writing – Review & Editing Jan Muhammad Sohu Roles: Conceptualization, Data Curation, Formal Analysis, Methodology, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing Syed Mir Muhammad Shah Roles: Data Curation, Investigation, Supervision, Writing – Original Draft Preparation Belal Mahmoud Alwadi Roles: Conceptualization, Formal Analysis, Resources, Validation, Writing – Review & Editing Faisal Ejaz Roles: Conceptualization, Formal Analysis, Resources, Writing – Review & Editing Md Billal Hossain Roles: Conceptualization, Funding Acquisition, Investigation, Methodology, Resources, Supervision, Validation, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Climate gateway. Abstract Background This study aims to empirically test a comprehensive interrelationship between green supply chain management (GSCM), green technology innovation (GTI), waste management (WM), big data analytics capability powered by artificial intelligence (BDAC-AI), and their collective impact on sustainable performance (SP) in organizational contexts. Methods This study was conducted in Pakistan’s food processing sector. The respondents included 495 managers working in the food processing industry. A structural equation modelling (SEM) approach is used to examine direct and indirect relationships between the variables. The originality of this study lies in integration of the technology acceptance model (TAM) and dynamic capability theory (DCT) to understand sustainable practices in the context of the provided model. Results This study highlights that GSCM, GTI, WM, and BDAC-AI have positive, strong, and direct impacts on SP. Furthermore, GTI and WM only partially mediate the link between GSCM and SP, whereas the two moderate the link. In addition, BDAC-AI had a moderating effect on the relationship between GTI and SP. This study has managerial implications, including strategies that involve the use of theoretical frameworks for technological acceptance and dynamic capabilities to support sustainable initiatives. However, it is worth noting that the findings provide a practical contingency for managers and businesses interested in implementing green studies effectively, improving technologies, and strengthening sustainable performance capabilities. Conclusions The study extends the literature by establishing a model for operationalizing GSCM in the food processing sector. Furthermore, it adds value in that it first integrates TAM and DCT to explain sustainable operations and their impact on organizations. Furthermore, it extends the existing literature by establishing a relationship between GSCM and SC. It offers a model through which GSCM can be operationalized in the context of the FS sector. READ ALL READ LESS Keywords Sustainable Performance; Green supply chain Management; Waste management, Green Technology innovation; SMEs; Developing country Corresponding Author(s) Ikramuddin Junejo ( [email protected] ) Md Billal Hossain ( [email protected] ) Close Corresponding authors: Ikramuddin Junejo, Md Billal Hossain Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2025 Makhdoom Q et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Makhdoom Q, Junejo I, Sohu JM et al. Impact of Green Supply Chain Management on Sustainable Performance: A Dual Mediated-moderated Analysis of Green Technology Innovation And Big Data Analytics Capability Powered by Artificial Intelligence [version 2; peer review: 1 approved, 2 approved with reservations] . F1000Research 2025, 13 :1140 ( https://doi.org/10.12688/f1000research.154615.2 ) First published: 07 Oct 2024, 13 :1140 ( https://doi.org/10.12688/f1000research.154615.1 ) Latest published: 08 Sep 2025, 13 :1140 ( https://doi.org/10.12688/f1000research.154615.2 ) Revised Amendments from Version 1 The Major difference between version 1 and version 2 is as follows: Justification of the selection of the food processing industry was missing in version 1, but in version 2, it is added in the methodology part. The reason for choosing the sampling strategy was not given in version 1; however, in version 2, a justification is added in the methodology part. The important points are added into the theoretical and practical implication’s part, which were missing and not given in version 1, but added in version 2, to highlight the importance of this study. In the manuscript version 1, future research direction limitations were discussed, but more limitations related to cross-sectional vs longitudinal data were added in version. The Major difference between version 1 and version 2 is as follows: Justification of the selection of the food processing industry was missing in version 1, but in version 2, it is added in the methodology part. The reason for choosing the sampling strategy was not given in version 1; however, in version 2, a justification is added in the methodology part. The important points are added into the theoretical and practical implication’s part, which were missing and not given in version 1, but added in version 2, to highlight the importance of this study. In the manuscript version 1, future research direction limitations were discussed, but more limitations related to cross-sectional vs longitudinal data were added in version. See the authors' detailed response to the review by Samir Zic See the authors' detailed response to the review by Md Rokibul Hasan READ REVIEWER RESPONSES Introduction The food sector has become increasingly important in the present era, with short environmental concerns and a growing awareness of problems related to sustainable consumption and production. Global demand places immense pressure on supply chains ( Attinasi et al., 2022 ), whereas waste management systems incur significant costs ( Kumar et al., 2021 ). This situation is constantly tight ( Attinasi et al., 2022 ). However, some critical large-scale limitations limit the full implementation of green supply chain management (GSCM) as a viable concept for sustainable challenges within supply chains ( Debnath et al., 2023 ). Different organizations are inclined to focus on short-term financial revenue, which may be an environmental perspective, which in turn leads to the issue of limited continuation of GSCM measures in different fields and areas ( Söderholm, 2020 ). The above knowledge and resources gap is highly original in less developed countries, where GSCM program implementation is often poor ( Rambaldi, 2022 ). Neglecting proper waste disposal severely harms the environment and inhabitants’ quality of life internationally ( Kumar et al., 2021 ). Despite some improvements, there are many difficulties and inequalities in the global community ( Bayu et al., 2022 ; Yousefloo & Babazadeh, 2020 ). Some of the issues that can be highlighted are as follows: recycling processes are said to be very slow in this regard ( Bruhn et al., 2023 ). Most plastic waste does not effectively undergo recycling; however, it ends up in landfills or pollutes ecosystems ( Praveenkumar et al., 2024 ). The World Bank Report 2022 “Solid Waste Management” indicates that, in contrast to developed countries, recycling rates in low-income countries remain significantly lower; in other words, people generate more trash, and the environment inevitably suffers. However, the state of dealing with and discharging similar wastes differs widely between Asian countries ( Darko et al., 2023 ). Insufficient waste collection and disposal practices present challenges, such as open dumping and incineration, in several urban areas ( Mor & Ravindra, 2023 ). According to the World Bank (2020) , prioritizing waste management is crucial for Pakistan to ensure the sustainability of its supply chain ( Haque et al., 2023 ; Ragasri and Sabumon, 2023 ). The issue of unreliable supply chains is a significant challenge for Pakistan’s food-producing sector in Pakistan ( Tsai et al., 2021 ). Distribution networks are characterized by the presence of waste, inefficiency, and lack of environmental concern ( Zuberi & Ali, 2015 ). This chronic issue not only escalates production expenses but also significantly diminishes operational effectiveness, amplifies resource use, and exacerbates environmental consequences ( Papamichael et al., 2024 ). The future economic prosperity of Pakistan will be greatly impacted by the degree to which the food industry welcomes and incorporates green technology innovation (GTI) in a timely fashion, as stated in the World Bank report in a press release on April 3, 2023. While the environmental and economic benefits of GTI have been extensively discussed, its use in Pakistan’s food industry has not been thoroughly explored ( Yu et al., 2023 ). The food industry in Pakistan plays a vital role in the country’s economic development and overall well-being of its population ( Ashraf et al., 2023a ). The implementation of the GTI presents an opportunity for the food industry in Pakistan to effectively mitigate waste generation, optimize resource utilization, and embrace ecologically sustainable practices ( Rahman et al., 2023 ). The insufficiency of resources and energy in Pakistan contributes to shortages, inefficiencies, and wastage within the agricultural sector ( Ashley, 2016 ; Rahman et al., 2023 ). Applying big data analytics capability powered by artificial intelligence (BDAC-AI) is the only possible way to significantly increase the food production sectors in Pakistan to a significantly higher level ( Ali et al., 2023 ). It is possible to gain enhanced decision-making with data fabric technologies to improve industrial processes ( Niu et al., 2023 ), supply chains ( AL-Khatib, 2022 ), and product quality ( Saini et al., 2023a ) among other areas and provide substantial input to advancing sustainable resource management ( Asha et al., 2023 ), waste confinement. Pakistan has two main issues: continuing population growth and minimizing environmental impacts ( Waseem & Rana, 2023 ). Therefore, the available literature indicates that sustainable practices implemented in the food industry improve resource efficiency and emission reduction ( Ashley, 2016 ), as well as the sustainability of the food value chain ( Asha et al., 2023 ). Several studies have theoretically analyzed BDAC, AI, GTI, and GSCM; however, more research is needed to address the food industry ( Feng et al., 2022 ). This study examined the relationship between GSCM, waste management (WM), GTI, and BDAC-AI in Pakistan’s food industry. These elements comprise the framework of the food industry’s sustainable performance initiatives. The integration of these components offers a favorable background for studying the existence of these factors in sustainable food industry ( Roy et al., 2023 ). As such, the choice of this model for review stems from its ability to improve the theorization of sustainability and offer prescriptions on how corporations, governments, and individuals can support the movement for a sustainable food system in a developing country. On several occasions, it has been realized that the food business contributes significantly to employment generation and stimulation of the economy within emerging nations ( Nikseresht et al., 2023 ). The release of greenhouse gases and the notion of resource depletion are some of the main ways through which this specific behavior contributes to a host of environmental losses ( Bhattacharya & Kalakbandi, 2022 ; Torres, 2018 ). Thus, for the continuous stability and profitable future of the food business in the globalized world, which is going to experience enhanced ecological volatilities, the sector must transform to adhere to ecological sustainability ( Trivedi et al., 2023 ). GTI and BDAC have drastically impacted the food business, and further development has been made through artificial intelligence ( AL-Khatib, 2022 ; Acciarini et al., 2023 ; Sohu et al., 2024 ). All of these advancements have the potential to improve sustainable practices through enhanced efficiency across multiple domains, such as energy generation and demand prediction ( Cavazza et al., 2023 ). In addition, they have a high level of use in reducing waste and increasing effectiveness in matters related to the supply chain ( Frempong et al., 2020 ). Realizing the growing recognition of sustainability imperatives in food production, it is time to obtain a clear idea of how the manner is to be understood. The objective of this research is to explore these variables and their relationship with sustainable performance in Pakistan’s food industry. The present study enhances the theoretical concept concerning sustainable practices in the food industry. Identifying the mediated and regulated relationships between these factors supports the improvement of theory-based models in sustainability research. The characteristics of this model make it applicable to many emerging nations and sectors. It also encourages the use of paradigms associated with sustainability and widens the knowledge of sustainable supply chains worldwide. The theoretical developments in this study entail the utilization of the proposed model. This model aims to improve the sustainability of food business in developing countries. These outcomes can be perceived as evidence of the model’s effectiveness. The importance of this study model is that it enables the consideration of other unaddressed sustainability issues in Pakistan’s food production sector. Given the interrelation between the variables, the proposed model provides a more complex view and overview of sustainability measures and their development. To the best of our knowledge, few studies have focused on Pakistan’s global food industry from Pakistani perspectives ( Ali et al., 2020 ; Ishaq et al., 2023 ). Thus, there is a need for more quantitative and qualitative studies on interrelated variables and their cumulative impact on Pakistan’s food sector, a developing country marked by challenges and growth opportunities. The significance of the topic of appropriate food business in Pakistan arises because of its relevance to the existing customer needs in planning their diet programs and creating a positive impact towards global sustainability missions on earth ( Oyedijo et al., 2023 ; Shamsuddoha, 2015 ). The following sections of the present research will provide the necessary insights into the global body of scientific literature, methods of carrying out the studies, findings, and their implications for understanding the study frameworks and their outcomes. Literature review The importance of sustainability within Pakistan’s food business has been raised due to rising concerns related to the environment, along with an incline towards fair trade practices from the customer’s point of view. This literature review aims to evaluate the contribution of scholars and knowledge regarding the challenges and opportunities faced by Pakistan’s food industry, considering variables such as GSCM, WM, GTI, BDAC-AI, and SP. Theoretical foundations Technology acceptance model The TAM is a theoretical framework that seeks to explain the factors influencing individuals’ adoption and acceptance of new technologies ( Scherer et al., 2019 ). The TAM mainly considers the individual reasons influenced by peer pressure and the perceived usefulness of the product ( Magsamen-Conrad et al., 2022 ). Keeping in mind that the core of TAM can be further elaborated to cover organizational fields ( Dhagarra et al., 2019 ). TAM is a vital model that helps us understand how technological innovations, namely BDAC-AI ( Kraus et al., 2021 ), combine with the model as a whole. The moderator’s role facing BDAC-AI and SP is placed between managers’ views on how easy AI adoption will be along with the perceived gain of using AI ( Chen, 2023 ). The link between GSCM-SP and user perspectives in processing food sector of Pakistan embraces moderating role of BDAC-AI, where TAM (Technology Acceptance Model) draws the inner mechanism. Organizations turn to digital methods, exploring the viability and efficiency of doing so in step with GTI and Transformation being considered ( Poláková-Kersten et al., 2023 ). The topic can be expanded in the sense that the TAM should determine whether businesses see digital technology as a convenient option, which is capable of reducing their expenditures, and whether they are willing to reorganize the structures of their work to meet the required circumstances to be competitive. As a straightforward device, the TAM interface can guide the assessment of whether new technologies can be successfully implemented in any project. TAM has been investigated as part of the potential mechanisms or pathways towards the successful management of the challenges of GTI, BDAC, and the use of AI in organizational contexts ( Wang and Su, 2021 ). Dynamic capabilities theory In accordance with ( Jiang et al., 2019 ), the DTC is a process of adjusting, integrating, and rearranging the resources of flexible manufacturing, capability of the organization, and procedures of operation, so the competitive position of the organization will be more prominent and higher productivity will be achieved. Individuals and teams need to be open to learning about organizational change and the use of technology. DTT provides information that is appropriate for organizations planning the growth of technologies that can provide innovation or performance. The model we propose lays great emphasis on the DCT principles that help keep the organizational evolutionary theory running and sustainable, as it encompasses both the continuous adaptation and incorporation of new methods. One of the major points addressed by both GSCM and SP in the field of DCT is the adequate and necessary capabilities of a company to quickly adapt and change its supply chain steps, mostly in the case of external shocks and events ( Ellström et al., 2021 ). Flexible organizations that inhibit GSCM practices, such as GTI, are fully capable of supporting, controlling, and implementing these practices ( Zhou et al., 2019 ) and the target of DCT is to develop a wide range of knowledge, and on the basis of that knowledge, how enterprises of the food processing industry of Pakistan can adapt their business ( Kraus et al., 2022 ). Therefore, these factors are vital for the long-run sustainability of the sector and its competitive edge, in which speed and adaptability are key components ( Ellström et al., 2021 ). However, despite the findings of the research stated before, there is a gap when it comes to studying these factors completely within the actual Pakistani food business context among the researchers. The precise functions of trade between the Pakistani food industry were not considered. These involve channels driven by the GTI, WM, and BDAIC AI as mediators and moderators. This gap in recognition still holds even in the presence of literature and information on the subjects of GSCM and SP. The findings of existing studies that are lacking in terms of explaining the underlying mechanisms are insufficient for a comprehensive understanding of how GSCM practices create SP and how this works in the particular context of Pakistan. The wide range of benefits resulting from the development of GSCM has been acknowledged in previous studies ( Debnath et al., 2023 ; Rupa & Saif, 2022 ). In fact, they show up in cost curtailing, efficiency upgrades, and environmental protection measures as long as they are effectively implemented. In the human realm of the food business, the impact and balance of artificial intelligence on the resilience of GSCM, GTI, and WM links is not being communicated properly ( Schintler & McNeely, 2022 ). It will have worthwhile additions to not just literature but also to the actual applications as an outcome of the analysis of the relationship between GSCM, GTI, WM, BDAC-AI, and SP in the Pakistani food industry. Through this knowledge gap, we aim to understand how sustainable and integral supply chain processes may be implemented in the context of Pakistan. This is set to allow us to provide support to companies or props their initiatives on sustainability improvement, where breakthroughs in technology and innovation can be applied. The purpose of this study is to add to the constantly developing phenomenon of sustainability practices in the context of a newly industrialized country. The primary goal of the conversation is to provide enterprises, the government, and consumers with useful feedback. Sustainable performance SP is the level of the organization that has been observed to have reached economic, environmental, and social environmental sustainability at the same time as success ( Abu-Rayash & Dincer, 2021 ). The social responsibility, environmental sustainability, and social effects of modern business are shifting to financial facts ( Kofi Opoku et al., 2023 ). Such a country would consider this for both ethical and financial reasons ( Ma et al., 2023 ). One of the goals of the company that made it achieve the SP is to fulfill its financial and social goals, in addition to the prescription of the environment ( Zhang et al., 2023 ; Kherazi et al., 2024 ). As a result of this achievement, the organization can be certain that it will remain in business for a long time to come ( Kofi Opoku et al., 2023 ), and it will stand out in the market ( Peng et al., 2020 ). SP drops its signature of an unsolicited vocabulary that is relevant to the situations of the poorest nations in the world, as a discovery realized in a research study in this regard ( Castillo-Díaz et al., 2023 ). The purpose of this study is to examine the operations of the food processing sector in Pakistan. Furthermore, the study examines whether business operations can make this trade-off effectively between profitability and the pressure that the business pressurizes on the community and the environment. The food-processing industry scores well in contributing to the health and welfare of the nation ( Ashraf et al., 2023b ). Thus, business sustainability is required. Several measures, such as eco-friendly practices ( Zhen et al., 2023 ), minimizing the amount of waste for production ( Hemphill, 2022 ) and efficiently using the available resources ( Ahmad et al., 2023 ) must be taken. These initiatives are of even greater relevance to countries such as Pakistan, where this component of development is yet to fully evolve ( Hashmi et al., 2023 ). Moreover, it is noteworthy that Pakistan personalizes and standardizes its SP food processing industry, which shows that the products being produced are not only risk free but also of premium quality ( Maaz et al., 2021 ). In such cases, we must remain committed to protecting public and customer health and the innumerable needs of customers. Several studies have established the presence of SP factors in GSCM strategy planning and operation ( Castillo-Díaz et al., 2023 ; El Ayoubi & Radmehr, 2023 ; van der Meulen et al., 2022 ). It has been documented that SSCM approaches help firms acquire SP for many activities ( Castillo-Díaz et al., 2023 ; El Ayoubi & Radmehr, 2023 ). Green supply chain management has received much attention worldwide as a target for sustainable production and consumption planning, especially in Pakistan’s food industry ( Saini et al., 2023b ; Das et al., 2023 ). Sustainable procurement hinges on green supply chain management, which is one of the most crucial components. This bond between the GSCP and SP is very significant for Pakistan’s food industry; it is paramount because of its economic influence and the unique obstacles it overcomes. The links between progress and cleaner environments are critical for attaining sustainable objectives in all sectors. Numerous studies have shown that GSCM practices are financially ( Samad et al., 2021 ), environmentally, and socially beneficial, as these parameters relate to SP within the food industry ( Yang et al., 2023a ). Tostivint et al. (2017) highlight that GSCM can play three main roles: waste lowering, optimal resource use, and system-wide efficiency improvement. For an organization to benefit from cost savings and gain a competitive advantage, the choice to practice GSCM strategies that focus on the procurement of ethically sourced raw materials, waste minimization, and the use of environmentally friendly materials should always be an option ( Li et al., 2022 ). A Recent study conducted by ( Jalil et al., 2023 ), cited that implementation of GSCM yields an increase in the social dimension of SP through the creation of favorable working conditions and good treatment of local communities. The provision of GSCM for Pakistan’s food industry has been introduced as the latest solution, which implies achieving a good pace of economic growth while remaining responsible for the environment and society ( Tariq et al., 2023 ). Companies that work to reduce their carbon footprint are important not only for human society and the environment, but also for increasing their brand equity ( Rehman et al., 2023 ). Technologies such as AI and BDAC should be implemented ( Al-Nuaimi et al., 2021 ), in order to operate SPs in an up-to-date environment ( Belhadi et al., 2020 ). The management of data using these technologies is a trend. The implementation of technologies, such as AI and bDND, could positively impact the food processing industry in Pakistan ( Khan & Tao, 2022 ). These tasks represent technologies that have evolved over time and are capable of working with large volumes of data. Thus, they help to identify areas that deserve improvement ( Bresciani et al., 2021 ). Such technologies are also a blessing for businesses that they should remain in the world according to socially acceptable standards but still earn their income ( Qin et al., 2022 ), adjusting to the rapidly changing environment of the market ( Morimura & Sakagawa, 2023 ). The regulation of BDAC-AI provides them with agility and flexibility in restructuring their operations while pursuing the goal of a sustainable future ( Junaid et al., 2022 ). Green supply chain management GSCM’s main task of GSCM is to integrate the green approach across the entire supply chain ( Chatzoudes & Chatzoglou, 2022 ). The goal of an energy production scheme is to achieve the best economic performance as well as to reduce waste ( Ali et al., 2019 ), while also offsetting the adverse environmental effects that arise from supply chain operations such as transportation ( Ghosh et al., 2021a ). The field of GSCM comprises fundamental ideas ( Nirmal et al., 2023 ). The trend of GSCM and other responsible sourcing measures is increasingly being observed in Pakistan’s developing food sector in Pakistan ( Qazi et al., 2022 ). The application of GSCM strategies might improve supply chain operations, manage both environmental and customer demand factors ( Chatzoudes & Chatzoglou, 2022 ; Nikseresht et al., 2023 ), and meet the increasing customer demand for environmentally friendly products ( Ye et al., 2023 ). The adaptation of GSCM in the Pakistani food sector has become more widely perceived because of the growing tendency towards environmental protection ( Mubarik et al., 2021 ) and strictly imposed governmental requirements ( Chatzoudes & Chatzoglou, 2022 ). Findings from multiple studies show the influence of sourcing from sustainable avenues ( Fallahpour et al., 2021 ), efficient ( Ghosh et al., 2021b ), and inventory management ( Tasnim et al., 2022 ). Thus, it is imperative that the measures be included when implementing strategies, as they will help in the regulation of the negative environmental impact ( Shi et al., 2022 ) and efficient utilization of natural resources ( Jha et al., 2021 ). In Pakistan, the food business is a very important segment of the economy ( Iftikhar et al., 2023 ); At the same time, it is being challenged by stricter regulations and environmental issues ( Waseem & Rana, 2023 ). In the context of the logistics industry, organizations are poised to shift to the paradigm of GSCM strategies because of the necessity of maintaining their competitiveness, as outlined by ( Das et al., 2023 ). Iqbal et al. (2020) introduced some elements of this chain strategy, including but not limited to environmentally sound procurement ( Wang et al., 2020 ), efficient and lucrative turnaround as far as logistics activities are concerned ( Nikseresht et al., 2023 ), and the efficient use of resources accompanied by waste minimization ( Yildiz Çankaya & Sezen, 2018 ; Zuberi & Ali, 2015 ). These orientations are part of the global aspiration for a cleaner planet and should indeed be a top concern for Pakistan’s food industry in Pakistan ( Ashley, 2016 ). The idea of GSCM is an innovative approach that is still in its adolescent stage, which intends to deliberately involve environmental sustainability practices in a variety of aspects of supply chain operations ( Hazen et al., 2020 ). Recent studies, such as Jabbour et al. (2020) , put forward the notion that it is an effective instrument for diminishing the level of carbon emissions, conserving resources ( Ishaq et al., 2023 ) and amplifying the levels of productivity ( Ali et al., 2020 ). Within GSCM, components such as responsible inventory management, effective transportation, material reduction in waste ( Rahman et al., 2023 ), and responsible sourcing ( Trujillo-Gallego & Sarache, 2021 ) are only a few. Zhu et al. (2019) are convincing that GSCM principles should be added to SC operations. The adoption of these planned measures is fundamental, as it reduces the negative effects that the manufacturing process brings to the environment while increasing the percentage of items that are both sourced and made using ethical means ( Das et al., 2023 ). Green procurement methods are considered important in Pakistan’s logistics in Pakistan ( Irfan et al., 2020 ). Rasheed et al. (2019) stress that orderly procurement requires ethical farming, fair labor, and ecological sourcing. The use of ethical sourcing strategies in the food industry has two significant advantages: improvement in quality and safety ( Asha et al., 2023 ). Additionally, it increases competition by establishing a higher benchmark for competitors ( DeBoer et al., 2017 ). The optimization of the transport sector’s operation will be a critical point in the context of GSCM in Pakistan’s food supply chains of Pakistan according to ( Qin et al., 2022 ). This study, led by ( Ahmed & Sarkar, 2019 ) revealed that optimizing transportation can play a crucial role in successfully combating carbon emission issues and reducing costs. Hence, this improves the supply chain system and helps meet the targets that state the importance of sustainability factors ( Lopes de Sousa Jabbour et al., 2020 ). H1: GSCM positively related to SP. H2: GSCM positively related to GTI. H3: GSCM positively related to WM. Green technology innovation Environmental factors are issues that can slowly and surely gain importance in SCM and new product development strategies ( Davidescu et al., 2023 ). This study investigates the connection between GSCM and GTI, seeking to give them the credit they deserve to promote sustainability ( Sarkis, 2003 ). GTI actively participates in helping the environment conserve its bits and pieces ( Boye & Arcand, 2013 ). Implementing and using environment-saving technologies is the most effective way to prevent and mitigate adverse environmental effects. GTI’s success of GTIs depends on the displacement of conventional energy sources with renewable energy sources ( Zuberi & Ali, 2015 ), the utilization of energy-efficient machinery ( Praveenkumar et al., 2024 ) and the application of environmentally acceptable building materials ( Hossain & Thomas Ng, 2019 ). Such technological advancements allow businesses, regardless of size, to benefit, as their environmentally conscious move concurrently enhances their competitive edge ( Rehman et al., 2021 ). The sustainability of technology has received particular attention, and many times, sustainable technological advances involving production chains can be observed. A new study by Srivastava and Dey (2018) points to the possibility of implementing technology to foster sustainability of logistics ( Srivastava & Singh, 2020 ) along the lines of real-time energy consumption monitoring ( Birgonul, 2021 ) and predictive maintenance of logistical equipment ( Wakiru et al., 2021 ). The application of GTI stimulates supply chain networks to utilize data-driven policies (( Hassoun et al., 2022 ) as a result, leading to the reduction of excess means for processes and waste streams ( Hassoun et al., 2022 ). An important result highlighted in the literature is the existence of an interconnection between GSCM and GTI. For supply chains to be sustainable, GTI offers tools that facilitate this process ( Bukchin & Kerret, 2018 ), whereas GSCM provides a program that is the basis of this process ( Micheli et al., 2020 ). When incorporated into organizational operations, these methodologies are heading towards the progress of sustainability ( Saberi et al., 2019 ; Das et al., 2023 ). The literature exposes the possibility that GSCM integration with GTI by the organization is a competitive advantage for the business ( Zhang et al., 2022 ). Söderholm (2020) mentioned that the application of greener supply operations might effectively facilitate company compliance with environmental regulations, decrease running expenses, and attain consumer loyalty that is environmentally ( Hossain & Thomas Ng, 2019 ). Regarding GSCM and GTI, existing research shows a pronounced correlation between them towards better sustainability in many sectors ( Lerman et al., 2022 ). The GTI is driven by the use of smart tools and technology in the fields of sustainable procurement ( Rejeb et al., 2023 ), innovative transport, and waste reduction ( Zuberi & Ali, 2015 ), focusing on providing a strategic framework for these activities as the next step. The GSCM strategy for circulating environmental sustainability in supply chain management involves several critical features, including ethical purchasing ( Lim et al., 2023 ), waste reduction ( Haque et al., 2023 ), eco-friendly transportation ( Ge et al., 2023 ), and controlled inventory ( Jauhar et al., 2023 ). Additionally, the GTI provides technical tools and breakthroughs that allow a cleaner and better way of doing things ( Xie & Teo, 2022 ). Customers who try to act in accordance with the environmental sustainability value and acknowledge the initiatives that they are by all means proud of. In the event that clients would be willing to undertake more sustainable shopping as well as ensure the ethically correct nature of the products they have bought and the shipping processes they have used ( Ghosh et al., 2021a ), the chances of them continuing to do the same is highly probable. The concept of GTI is of significant importance in this context. The implementation of green technical breakthroughs has enabled supply chains to enhance operational efficiency, mitigate emissions, and eliminate waste ( Roy et al., 2023 ). A recurring outcome of these advancements is the production of ecologically sustainable ( Tasnim et al., 2022 ). The use of the GTI has the potential to enhance supply chain visibility and traceability as well ( Lai et al., 2023 ). Therefore, the following hypothesis was developed: H4: GTI is positively related to SP. Waste management The long-term viability of Pakistan’s food sector relies on the effective implementation of environmentally friendly WM methods ( Rahman et al., 2023 ), in accordance with existing worldwide trends, which suggests an increasing focus on waste reduction, recycling, and appropriate waste management practices ( Kayikci et al., 2019 ). The implementation of effective waste management systems has the potential to yield financial savings and mitigate the environmental impact of organizations ( Hemphill, 2022 ). As Pakistan, through the process of urbanization ( Ali et al., 2023 ), experiences population increase and expands its industrial sector ( Ashraf et al., 2023a ), the need for viable ways to address the escalating challenges associated with waste buildup becomes more pronounced ( Ashraf et al., 2023b ). The importance of effective WM increases as metropolitan areas continue to develop ( Hashmi et al., 2023 ). WM presents many complex difficulties for a country ( Salam et al., 2023 ). According to ( Yousafzai et al., 2020 ), the exacerbation of this problem may be attributed to a lack of public awareness, financial resources, and adequate infrastructure. In densely populated urban regions, the presence of trash streets and public places is visually unappealing and inconvenient for the general population ( Filimonau et al., 2023 ). Informal waste pickers also provide a significant function in the realm of waste management through their efforts to extract recyclable materials from landfills ( O’Connor, 2021 ). In response to these challenges, the government of Pakistan passed a series of legislative measures and undertaken several activities aimed at enhancing the nation’s WM infrastructure ( Yousafzai et al., 2020 ). One notable government-wide project aimed at promoting cleanliness and educating the public about trash management is the “Clean Green Pakistan” campaign ( Khatibi et al., 2021 ). Based on the findings of Rasheed et al. (2019) on the 18th Amendment, municipal and state governments are conferred with more powers in waste management, which promotes the use of integrated and economically viable approaches to waste management ( Filimonau et al., 2023 ) (18th Amendment gives more powers to municipal and state governments; thus, they can implement integrated and economic Technology is being increasingly applied and innovation is emerging as one of the main tools in Pakistan in battling the ever-growing waste crisis ( Salam et al., 2021 ). Salam et al. (2023) , various smartphone apps that create the best functionalities for the methane management system wipe out the inefficiencies of the entire waste system. Along with the functional consequences of GM on PMM, there are social repercussions. Nowadays, the distribution of natural resources and waste management is a complex issue in which policymakers set up legislative tools to restrict the production process and disposal of harmful materials. Therefore, the application of environmentally sustainable practices has become a prime strategy for businesses ( Mor & Ravindra, 2023 ). As a result, businesses in the area have moved towards those that incorporate WM in their GSCM plans, according to the study by Kannan et al. (2022) . Subsequently, the attainment of standards in sustainable environmental scenarios and world sustainability will be accomplished through the synergistic activity of GSCM and WM practices in developing countries (Sabumon, 2023). The use of WM denotes an inextricable portion of GSCM policy that is geared towards curbing the negative influence of industrialization on this area through this tool ( Haider Naqvi et al., 2023 ). A specific growing trend in Pakistan’s waste management sector is evident in the increasing grassroots community involvement and higher social activity levels. Concerns about solid waste collection and disposal as a critical environmental issue in Pakistan have now emerged as a result of many factors ( Ahmad et al., 2023 ) initially, the increasing levels of economic challenges and decaying infrastructure caused people to face severing opinions on the necessity of taking in innovative strategies as well as passing legislative changes that also include encouraging public participation to effectively address these grave issues ( Yousafzai et al., 2020 ). Emerging technologies, such as automatic sorting and data analytics systems ( Salam et al., 2023 ), as well as the process of integrating them, can offer opportunities to enhance waste management in the country ( van der Meulen et al., 2022 ). In light of the circumstances, it becomes inevitable that the forthcoming studies and policies should have the will to carry on with plan which seeks the contextual as well as sustainable solution. Ultimately, it will prepare the foundation for a sustainable ( Zhen et al., 2023 ). H5: WM is positively related to SP. Mediation of GTI In this context, GTI is a general strategy model that promotes new clean technologies and hitches them with appropriate attitudes in different sectors ( Punj et al., 2023 ). It focuses on the food and beverage industry ( O’Connor, 2021 ). The generation and development of the GTI can be due to an appreciation of ecological and environmental sustainability, together with the fact that climate challenges need immediate intervention ( Hassoun et al., 2022 ). Innovations in these fields cover an extensive range of topics, such as energy-saving machines ( Xie & Teo, 2022 ), green production methods ( Lai et al., 2023 ), environmentally friendly packaging ( Hong et al., 2018 ), and product design, which has no influence on the environment ( Peng et al., 2020 ). Nevertheless, the GTI is not just a technological development ( Lepore et al., 2023 ). It incorporates new business strategies that focus on environmental gain, optimization of resource efficiency, and ensuring the long-term commercial sustainability of organizations ( Alyahya et al., 2023 ; Yousafzai et al., 2020 ). The accomplishment of the food processing industry against SP without aid from the GTI may be extensive ( Alyahya et al., 2023 ). Energy conservation using sustainable technologies; green production and packaging; all of these developments hold very bright prospects for food processing industries as they are concerned with the carbon footprint and resource efficiency of the food processing firms ( Yang et al., 2023b ). The employment of GTI is a way to meet the growing needs of conscientious customers who are environmentally friendly are met ( Xie & Teo, 2022 ). As a result, it not only increases market share but also investor value, which is measured by return on equity, return on invested capital, and cash flow ( de Burgos-Jiménez et al., 2013 ). Moreover, the highly inclusive green practice promotes a company that has been able to sustain itself because it is able to fit various market conditions and sustainability issues ( Haider Naqvi et al., 2023 ). The successful implementation of GTI into operational processes indeed facilitates the promotion of sustainability ( Galanakis et al., 2021 ), reduction of ecological impact ( Filimonau et al., 2023 ), and Sustainable Performance ( Ma et al., 2023 ). Overall, such results are key to the competitiveness of the industrial sector and to achieving environmental targets that are valid around the world ( Ren et al., 2023 ). In view of their efforts to create sustainability, enterprises that operate in these industries are more likely to implement GTI ( Ma et al., 2023 ). The exploitation of the GTI could be the most potential feature that plays a role in the evaluation of operations management ( Zhang et al., 2023 ). A previous study by Maaz et al. (2021) showed us the same aim. These technologies are particularly suitable for managing this area because they are designed to deal with many nuances ( Ashaari et al., 2021 ) . BDAC-AI provides solutions that help to concentrate on control and supervision ( Alyahya et al., 2023 ), of the information on how well environmentally friendly measures are being used or implemented by organizations. The benefit of decision-making on a data basis is important for the realization of GTI goals because it helps align tech investments with sustainability purposes ( Hassoun et al., 2022 ). The joint venture between the GTI and the food processing sectors of low-income countries is the BDAC-AI initiative targeting the gulf ( Al-Nuaimi et al., 2021 ). It holds a hegemonic position in the global innovation market, so its prime task is to ensure that modified solutions provided by the GTI are targeted to the peculiarities of these countries, such as opportunities and difficulties ( Cheng et al., 2023 ). H7a GTI significantly mediates the relations between GSCM and SP. H7b WM significantly mediates the relations between GSCM and SP. Big Data Analytics capabilities-Artificial intelligence (BDAC-AI) In addition, BDAC-AI has the ability to carry out instant adjustments to any change in the market and to any customer preference changes ( Bag et al., 2023 ; Hongyun et al., 2023 ; Sohu et al., 2023 ). The integration of BDAC-AI technology is the only method through which food processing companies can properly deal with environmental sustainability of the environment ( Zhen et al., 2023 ) and health concerns ( Ashraf et al., 2023b ). This is achieved through the development of RT logistics and product propositions, along with the establishment of supply chain activities ( Bag et al., 2023 ). The fact that the capacity for change and adaptability among its members needs to be employed as a crucial determinant of the long-term success of the firm ( Agyabeng-Mensah & Tang, 2021 ) is a definitive factor instrumental in the long-term success of the firm. The arrival of green technology has played a part in initiatives aimed at keeping the planet environmentally friendly, in this way promoting the conservation and careful use of limited resources such as water, energy, land, and wildlife ( Calza et al., 2020 ), Ren et al., 2023 ). The BDAC-AI system plays the role of this correlation, which is known as data-driven decision-making and enables market players to adapt to dynamic market environments ( Ashaari et al., 2021 ). Sustainability of agriculture in Pakistan has become a vital area of concern for the agricultural sector, which is actively interested in recent ideas of sustainability ( Gupta et al., 2020 ). The presence of the private sector is essential to achieving the fundamental objectives of the SDP in Pakistan’s food ( Ashraf et al., 2023b ; Yousafzai et al., 2020 ). GTI aims to be a pathfinder organization that doctrines eco-friendly technologies and methods to help target Pakistani businesses achieve their sustainability objectives ( Raeesi et al., 2023 ). As Pakistan is experiencing prevailing circumstances, we need to ensure that BDAC-AI will be essential Ashaari et al. (2021) . Bag et al. (2020) wrote on the importance of BDAC-AI in that business companies are supplied with the necessary anchors for monitoring and evaluating the use and effectiveness of environmentally sustainable technology equipment. Based on the results of ( Belhadi et al., 2020 ; Dakhan et al., 2020 ), many places have invested in technology while achieving sustainability targets efficiently and effectively. H6: BDAC-AI is positively related to SP. H8: BDAC-AI significantly moderates the relations between GTI and SP Method Research design In this study, a quantitative approach was adopted because it suggests that this approach is suitable for understanding the relationships between variables through complex models. The independent variable, green supply chain management; two mediating variables, including waste management and green technology innovation; and one moderating variable, big data analytics capacity-artificial intelligence, were tested in this study. The conceptual model illustrating the intricate relationships among the variables is presented in Figure 1 . The conceptual model places BDAC-AI as a moderator between GTI and SP due to its strategic role in enhancing the efficiency and implementation of green technologies. GSCM influences both GTI and WM, which subsequently affect SP. This structure reflects the principles of the Technology Acceptance Model (TAM) and Dynamic Capability Theory (DCT), offering a robust explanation for the interconnected pathways toward sustainability. Figure 1. Theoretical framework. Source: Authors’ calculations. Ethics and consent This study “Impact of Green Supply Chain Management on Sustainable performance: A dual mediated-moderated analysis of Green Technology Innovation and Big Data Analytics Capability powered by Artificial Intelligence” was approved by the Ethics Committee of The University of Okara, which constitutes the departmental Ethics Approval Committee (REBSSH/2023/2-7) on July 04, 2023. All informants provided written and oral informed consent to participate in this study. Procedure of data collection In this study, food manufacturing firms and those with managerial positions in Pakistan were targeted. Four hundred ninety-five cases were used for data analysis, and the sampling strategy was a random sampling strategy to give all employees of the stated firms a chance. The individuals’ personal information was kept confidential, and all individuals were provided with a consent form before data collection. Furthermore, in this study, a structured, closed-ended questionnaire was distributed among manufacturing SMEs in food processing in Pakistan. They were requested to fill the form through Google Forms, which took one and two weeks. Additional details about the participating firms are as follows: The firms involved in this study qualified as SMEs according to the definition by the State Bank of Pakistan, which defines SMEs as enterprises with fewer than 250 employees and an annual turnover not exceeding PKR 800 million. The sampled companies varied in age, ranging from 5 to 35 years of operation, and included both privately owned and family-owned firms. Most firms had 2–5 regional branches or subsidiaries within Pakistan. The average number of employees per firm was approximately 110. The participating firms reported annual revenues between PKR 50 million and PKR 700 million. Clarification: Although the questionnaire was distributed broadly using a random sampling strategy within the selected firms, only managerial-level employees were asked to complete the responses. Thus, all data collected originated from managers with strategic or operational responsibilities. Data analysis The quantitative approach helped analyze the data gathered through various statistical tools. The present research employed structural equation Modelling (SEM) because it supports the study of the hypotheses regarding the relationship among the variables ( Hair et al., 2021 ). SEM facilitated the simultaneous analysis of the complicated relationships within the study. However, at this start, SPSS was used to clean the data and deal with missing values. Subsequently, a study using SEM was performed, followed by applying Smart PLS (Partial Least Squares) to determine the correlations among the variables. Specifically, any response missing more than 10% of items was excluded, while minimal missing data was handled via mean substitution. The methodology chosen aligns with the primary aim of this study. The use of a carefully constructed questionnaire facilitated the acquisition of comprehensive and detailed responses ( George, 2019 ) from the executives of the respective companies. The hypothesized associations were thoroughly investigated using quantitative methodology and SEM. Nevertheless, the quantitative approaches and other procedures used may have been insufficient in terms of statistical seriousness, limiting the ability to establish reliable correlations and to present a more comprehensive analysis. Owing to the inherent quantitative character of the research issue, statistical analysis was used to quantify the connections, whereas a survey-based technique was considered the most suitable approach. Scale development To measure the above-stated variables, instruments were adopted from past studies published in reputable journals, ensuring reliability and validity. The six research items of green supply chain management were adopted from study of ( Le, 2023 ), who developed these items based on a comprehensive review of the literature and validated them through expert feedback and pilot testing. The second variable, green technological innovation, consists of five research items taken from ( Sahoo et al., 2023 ). The third variable, waste management, was obtained from the study ( Obuobi et al., 2022 ). Fourth variable big data analytics capacity-artificial intelligence four items are adopted from study ( Benzidia et al., 2021 ). Finally, sustainable development was taken from the research of Le et al. (2022) and Tjahjadi et al. (2022) with six items. These items were formulated based on an extensive literature review and validated through confirmatory factor analysis (CFA) to ensure their accuracy and relevance. Most studies used rigorous methodologies, including pilot studies and validation through structural equation modeling, to establish the scales’ validity and reliability. Results and discussions Testing for convergent and discriminant validity Factor loadings indicate the strength of the relationship between each question and its respective constructs (such as BDAC-AI, GSCM, GTI, SP, and WM). Higher item loadings signified stronger relationships. Cronbach’s alpha (α) is a measure of internal consistency. Higher values (α > 0.7) indicated good reliability, suggesting that the items within each construct reliably measured the same underlying concept. Similar to α, Composite Reliability (CR) measures the internal consistency reliability, and a CR value > 0.7 Shmueli et al. (2019) is considered acceptable, indicating that the items reliably measure their respective constructs ( Hair et al., 2021 ). An AVE above 0.5 suggests that more than 50% of the variance is captured by the construct, indicating good convergent validity. The outer variance inflation factor (VIF) checks for multicollinearity among the items within a construct, and the inner VIF represents multicollinearity between variables. VIF Values of 0.7, indicating robust relationships between the items and their respective constructs. The items reliably measure their constructs, and indicate consistency in measuring the constructs ( Hair et al., 2019 ). AVE values >0.5 show that a significant amount of variance is explained by the variables, indicating good convergent validity. VIF values <5 indicated no significant multicollinearity issues among the items within each construct. Table 1. Reliability and validity. Items loadings Variables Alpha CR AVE Out VIF BDAC-AI1 0.818 BDAC-AI 0.833 0.888 0.666 1.780 BDAC-AI2 0.812 1.760 BDAC-AI3 0.815 1.817 BDAC-AI4 0.819 1.903 GSCM1 0.781 GSCM 0.897 0.921 0.661 2.221 GSCM2 0.821 2.500 GSCM3 0.867 3.303 GSCM4 0.790 2.407 GSCM5 0.779 2.031 GSCM6 0.836 2.784 GTI1 0.844 GTI 0.886 0.916 0.687 2.663 GTI2 0.834 2.595 GTI3 0.857 2.899 GTI4 0.766 1.790 GTI5 0.841 2.640 SP1 0.819 SP 0.894 0.919 0.654 2.559 SP2 0.800 2.466 SP3 0.798 2.777 SP4 0.814 2.623 SP5 0.824 2.682 SP6 0.796 2.806 WM1 0.816 WM 0.872 0.907 0.661 2.071 WM2 0.824 2.249 WM3 0.806 2.023 WM4 0.806 1.956 WM5 0.813 2.295 Discriminant validity The Heterotrait-Monotrait Ratio of Correlations (HTMT) and the Fornell and Larcker (FnL) criteria were used to assess discriminant validity in this study. In the present study, two critical values, the HTMT and the Fornell and Larcker criteria, helped determine discriminant validity ( Henseler et al., 2015 ). The values are shown in Table 2 below; all values are less than the suggested 0.90 ( Hair et al., 2019 ). The HTMT values ranged from 0.617 to 0.897, and the FnL diagonal values are the square root of AVE, indicating no issue in discriminant validity. Table 2. Discriminant validity and correlations. BDAC-AI GSCM GTI SP WM HTMT Ratio of Correlations BDAC-AI GSCM 0.705 GTI 0.828 0.668 SP 0.897 0.778 0.896 WM 0.749 0.617 0.684 0.808 Fornell and Larcker Criterion BDAC-AI 0.816 GSCM 0.615 0.813 GTI 0.717 0.603 0.829 SP 0.776 0.703 0.799 0.809 WM 0.640 0.551 0.606 0.717 0.813 VIF and F Square F-Square is used to measure the proportion of variance explained in the dependent variable or effect size by adding a specific independent variable to the model. This helps to understand how much of the variance in the endogenous variable is accounted for by the inclusion of the exogenous variable. Higher F-Square values indicate that a larger proportion of the variance in the higher-order construct can be explained by its constituent variables. Table 3 shows that the F Square values for GTI and GSCM are 0.570. The remaining f-squared values are listed in Table 3 . The inner VIF and F Square values suggest that multicollinearity is not an issue in the model, and all constructs are well explained by their constituent variables, which adds to the credibility of this research model. The measurement model is depicted in Figure 2 . The structural model is illustrated in Figure 3 . Table 3. VIF and F Square. GTI SP WM Variance inflation factor BDAC-AI 2.560 GSCM 1.000 1.837 1.000 GTI 2.421 WM 1.916 F Square BDAC-AI 0.100 GSCM 0.570 0.112 0.437 GTI 0.300 WM 0.144 Figure 2. Measurement model. Source: Authors’ calculations. Figure 3. Structural Model (At 10,000 Bootstrapping). Source: Authors’ calculations. R and adjusted R square The model’s goodness of fit was evaluated by the values of R-squared (R 2 ) and Adjusted R-Squared (R 2 adjusted). They provide information on how well the independent variables explain the variance in the dependent variable. Along with R 2 , Q 2 , SRMR, and NFI values were used to predict the goodness of fit of the model. Table 4 shows that the SRMR and NFI values are 0.061 and 0.802, respectively, which suggests that the model offers a good fit for the data analysis. The explanatory power of the model was quantified by calculating its R 2 value. For the GTI, R 2 is 0.363, which suggests that approximately 36.3% of the variance in DP is explained by the GTI in the model. All other values are listed in Table 4 . Table 4. R-square and Q-square. R-square R-square adjusted GTI 0.363 0.362 SP 0.797 0.795 WM 0.304 0.302 Q 2 predict RMSE GTI 0.360 0.803 SP 0.607 0.629 WM 0.301 0.840 SRMR 0.061 NFI 0.802 In summary, both R 2 and R 2 adjusted values for all constructs are relatively high, suggesting that a considerably large amount of variance in the endogenous constructs (SP, WM, and GTI) is explained by the exogenous variables in the current model, which indicates that the model provides a reasonably good fit to the data and supports the relationships between the variables. To evaluate the predictive power of the model, researchers used the Q 2 value technique ( Shmueli et al., 2019 ). Q 2 determines the extent to which an independent variable influences the dependent variable in the model. For the current study, the Q 2 values of GTI and WM are 0.360 and 0.301, respectively, which implies a substantial predictive importance of the depedent6 variables. Direct path analysis A direct path analysis examines the direct relationships between exogenous and endogenous constructs. All hypotheses (H1–H6) were supported, showing significant relationships. In Table 5 , H1 suggests that GSCM has a positive effect on the GTI, and the T-value (22.781) and P-value (significant at 0.000) confirm that this relationship is significant. Similarly, H5 shows that WM has a positive effect on SP, supported by a T-value of 7.831 and a significant p-value (0.000). Table 5. Direct path analysis. Hypotheses Path Mean SD T Value P value Decision Direct path analysis H1 GSCM -> GTI 0.603 0.603 0.026 22.781 0.000 Acc H2 GSCM -> WM 0.551 0.553 0.029 19.010 0.000 Acc H3 GSCM -> SP 0.203 0.203 0.027 7.445 0.000 Acc H4 GTI -> SP 0.386 0.385 0.039 9.825 0.000 Acc H5 WM -> SP 0.237 0.237 0.030 7.831 0.000 Acc H6 BDAC-AI -> SP 0.228 0.229 0.033 6.917 0.000 Acc Specific indirect path analysis/mediation H7a GSCM -> GTI -> SP 0.232 0.232 0.026 9.019 0.000 Acc H7b GSCM -> WM -> SP 0.131 0.131 0.019 6.977 0.000 Acc Total indirect path analysis GSCM -> SP 0.363 0.363 0.027 13.374 0.000 Acc Moderation H8 BDAC-AI x GTI -> SP 0.093 0.094 0.021 4.454 0.000 Acc Specific indirect path analysis/mediation Specific indirect path analysis examines the mediating effect of constructs on the relationships between exogenous and endogenous constructs. The GTI and WM play mediating roles in the relationship between GSCM and SP. All the mediating paths (H7a–H7b) were positive and significant. H7a and H7b show that the mediating effect of GSCM on SP through GTI and WM is positive and significant (T-value: 9.019, p-value: 0.000, T-value: 6.977, P-value: 0.000). Here, the total indirect path analysis suggests partial and full mediation. All indirect paths are significant, so all mediating relations are partially mediated. Moderation Finally, the researchers examined the moderating role of environmental dynamism. The moderating effect of H8 is significant and positive. Table 5 shows that the interaction between BDAC-AI and GTI significantly affected ECP (T-value: 4.454, P-value = 0.000). These findings support Hypothesis H8. Therefore, environmental dynamism enhances the positive impact of BDAC-AI and GTI on ECP, highlighting its crucial role in achieving sustainable performance. Discussion and concluding remarks The findings from the empirical analysis provide important insights into the relationships among GSCM, GTI, WM, BDAC-AI, and SP within the food processing multinational organizational context within Pakistan. There was a positive and significant relationship between GSCM and GTI, WM, and SP. This implies that an emphasis on GSCM practices is positively associated with fostering GTI, Waste Management strategies, and overall environmental performance within organizations. The mediated paths indicate partial mediation of GTI and WM in the relationship between GSCM and SP. These findings underscore the importance of intermediary processes in translating GSCM practices into enhanced sustainability outcomes. Additionally, the moderation effect of BDAC-AI on the relationship between GTI and SP demonstrates the influence of advanced analytics capabilities augmented by Artificial Intelligence in moderating the impact of GTI on SP. The present study’s findings confirm the direct effects of GSCM, GTI, and WM on the sustainable performance of SME manufacturing in the food processing sector in Pakistan. Furthermore, TAM and DCT theories suggested these variables, and their relationship was confirmed in the present study. However, the unique finding of the present study is that green technological innovation was found to have a more significant and positive impact on sustainable performance due to higher beta values than other variables in this study. Furthermore, the indirect effect of GTI between GSCM and SP was found to have a more significant impact on SP compared to the indirect effect of WM, due to higher path coefficients. Finally, the moderating role of BDAC-AI must be addressed in the present study. This moderates the relationship between GTI and SP. Theoretical and practical implications In the present study, two TAM and DCT supported the variables of the study, including GSCM as an independent variable, two mediating variables (GTI and WM), and one moderating variable (BDAC-AI) on SP in SMEs manufacturing food supply chain processing firms in Pakistan, a developing country. TAM supports technology acceptance in today’s business environment, where environmental protection is a key concern. Similarly, the DCT confirmed responsiveness towards environmental protection and adaptability within the organization. Policymakers and top management of SME manufacturing in the context of the food processing sector in Pakistan can adopt these variables in their future strategies. They can benefit from using scarce resources and better productivity within firms. While the study confirms the effectiveness of GSCM, GTI, and BDAC-AI in enhancing sustainable performance, firms often face practical barriers in implementing such strategies. Financial constraints, limited access to advanced technology, lack of trained personnel, and resistance to organizational change are commonly reported challenges, particularly among SMEs. Addressing these barriers may require internal capacity-building and external support through public-private partnerships or sector-specific training initiatives. Policy implications To accelerate the adoption of GSCM practices and digital innovations like BDAC-AI, supportive government policies are essential. These may include tax incentives for green investments, subsidies for waste management infrastructure, skill development programs in green technologies, and national guidelines for AI-driven sustainability practices. Such initiatives can empower SMEs in the food sector to transition more effectively toward holistic sustainability performance. Limitations and future research directions Along with a few contributions, the present study has certain limitations. Data limited to a cross-sectional approach in future longitudinal data types can be collected to validate the existing findings. This study was limited to two theories: TAM and DCT. However, other theories that support the current conceptual model can be tested in the future. This is limited to food-processing manufacturing SMEs in Pakistan. Other sectors, such as textiles and pharmaceuticals, will be considered in the future. The present study is context-specific, focusing solely on the food processing sector in Pakistan. While this provides depth and relevance to sector-specific sustainability challenges, it limits the generalizability of findings to other industries. Future studies are encouraged to replicate this model in sectors such as textiles, manufacturing, or pharmaceuticals. Additionally, cross-country comparisons in similar developing economies may offer richer insights into regional differences in GSCM practices and sustainability outcomes. As with most survey-based research, this study relies on self-reported responses from managerial personnel. Although measures were taken to ensure confidentiality and encourage honest answers, there remains a possibility of social desirability bias. Future research should consider incorporating objective performance metrics, third-party sustainability audits, or cross-verification with company records to strengthen data validity and reduce reliance on subjective assessments. This study utilized a cross-sectional design, which captures relationships at a single point in time. As such, it may not fully reflect the long-term impact of GSCM practices, GTI adoption, or the moderating role of BDAC-AI on sustainability performance. Future longitudinal studies could provide more nuanced insights into how these relationships evolve and the presence of lagged effects. Ethics and consent This study “Impact of Green Supply Chain Management on Sustainable performance: A dual mediated-moderated analysis of Green Technology Innovation and Big Data Analytics Capability powered by Artificial Intelligence ” was approved by the Ethics Committee of The University of Okara constitutes the departmental Ethics Approval Committee (REBSSH/2023/2-7) on July 04, 2023. All informants provided written and oral informed consent to participate in this study. Data availability Underlying data Figshare: Dataset & Questionnaire: DOI: https://doi.org/10.6084/m9.figshare.26247548 ( Junejo, 2024 ) This project contains the following underlying data: • Dataset. csv. Dataset • Final Questionnaire.docx Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). Software availability The primary software used for data analysis in this study includes SmartPLS and SPSS. 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Publisher Full Text Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 07 Oct 2024 ADD YOUR COMMENT Comment Author details Author details 1 Department of Management Sciences, SZABIST University, Hyderabd Campus, Pakistan 2 School of Management, Jiangsu University, Zhenjiang, Jiangsu, China 3 Department of Business Administration, Sukkur IBA University, Sukkur, Pakistan 4 Department of Basic Sciences (Humanities and Scientific), Al-Zaytoonah University of Jordan, Amman, Jordan 5 School of International Relations, Minhaj University Lahore, Lahore, Punjab, Pakistan 6 Department of Political Science, University of Okara, Punjab, Pakistan 7 Sustainability Competence Centre, Faculty of Business and Economics, Szechenyi Istvan Egyetem, Győr, Gyor-Moson-Sopron, Hungary Quswah Makhdoom Roles: Conceptualization, Investigation, Methodology, Writing – Original Draft Preparation Ikramuddin Junejo Roles: Conceptualization, Data Curation, Investigation, Methodology, Validation, Writing – Review & Editing Jan Muhammad Sohu Roles: Conceptualization, Data Curation, Formal Analysis, Methodology, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing Syed Mir Muhammad Shah Roles: Data Curation, Investigation, Supervision, Writing – Original Draft Preparation Belal Mahmoud Alwadi Roles: Conceptualization, Formal Analysis, Resources, Validation, Writing – Review & Editing Faisal Ejaz Roles: Conceptualization, Formal Analysis, Resources, Writing – Review & Editing Md Billal Hossain Roles: Conceptualization, Funding Acquisition, Investigation, Methodology, Resources, Supervision, Validation, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (2) version 2 Revised Published: 08 Sep 2025, 13:1140 https://doi.org/10.12688/f1000research.154615.2 version 1 Published: 07 Oct 2024, 13:1140 https://doi.org/10.12688/f1000research.154615.1 Copyright © 2025 Makhdoom Q et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Makhdoom Q, Junejo I, Sohu JM et al. Impact of Green Supply Chain Management on Sustainable Performance: A Dual Mediated-moderated Analysis of Green Technology Innovation And Big Data Analytics Capability Powered by Artificial Intelligence [version 2; peer review: 1 approved, 2 approved with reservations] . F1000Research 2025, 13 :1140 ( https://doi.org/10.12688/f1000research.154615.2 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 2 VERSION 2 PUBLISHED 08 Sep 2025 Revised Views 0 Cite How to cite this report: Zic S. Reviewer Report For: Impact of Green Supply Chain Management on Sustainable Performance: A Dual Mediated-moderated Analysis of Green Technology Innovation And Big Data Analytics Capability Powered by Artificial Intelligence [version 2; peer review: 1 approved, 2 approved with reservations] . F1000Research 2025, 13 :1140 ( https://doi.org/10.5256/f1000research.186427.r412364 ) The direct URL for this report is: https://f1000research.com/articles/13-1140/v2#referee-response-412364 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 07 Oct 2025 Samir Zic , University of Rijeka, Rijeka, Croatia Approved VIEWS 0 https://doi.org/10.5256/f1000research.186427.r412364 ... Continue reading READ ALL No comments. Competing Interests: No competing interests were disclosed. Reviewer Expertise: Industrial management, Inventory optimization, Supply chain optimization, Operational research I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Zic S. Reviewer Report For: Impact of Green Supply Chain Management on Sustainable Performance: A Dual Mediated-moderated Analysis of Green Technology Innovation And Big Data Analytics Capability Powered by Artificial Intelligence [version 2; peer review: 1 approved, 2 approved with reservations] . F1000Research 2025, 13 :1140 ( https://doi.org/10.5256/f1000research.186427.r412364 ) The direct URL for this report is: https://f1000research.com/articles/13-1140/v2#referee-response-412364 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Nazir S. Reviewer Report For: Impact of Green Supply Chain Management on Sustainable Performance: A Dual Mediated-moderated Analysis of Green Technology Innovation And Big Data Analytics Capability Powered by Artificial Intelligence [version 2; peer review: 1 approved, 2 approved with reservations] . F1000Research 2025, 13 :1140 ( https://doi.org/10.5256/f1000research.186427.r412894 ) The direct URL for this report is: https://f1000research.com/articles/13-1140/v2#referee-response-412894 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 30 Sep 2025 Samera Nazir , Xi'an Fanyi University, Xi'an, Shaanxi, China Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.186427.r412894 Improve the figures quality Review your graphs, charts, and diagrams to ensure they are clear, high resolution, and professional . Use consistent fonts, colors, and labels . Replace blurry images with vector-based charts (made ... Continue reading READ ALL Improve the figures quality Review your graphs, charts, and diagrams to ensure they are clear, high resolution, and professional . Use consistent fonts, colors, and labels . Replace blurry images with vector-based charts (made in Excel, SPSS, R, or similar tools). Ensure axes, legends, and captions are readable and follow journal guidelines Ensure you cite recent studies (2021–2025) , not just older ones. Integrate recent journal articles, systematic reviews, or industry reports to show that your work is built on the latest developments. Highlight gaps identified in current literature , and show how your study addresses them. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Its good I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Nazir S. Reviewer Report For: Impact of Green Supply Chain Management on Sustainable Performance: A Dual Mediated-moderated Analysis of Green Technology Innovation And Big Data Analytics Capability Powered by Artificial Intelligence [version 2; peer review: 1 approved, 2 approved with reservations] . F1000Research 2025, 13 :1140 ( https://doi.org/10.5256/f1000research.186427.r412894 ) The direct URL for this report is: https://f1000research.com/articles/13-1140/v2#referee-response-412894 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Version 1 VERSION 1 PUBLISHED 07 Oct 2024 Views 0 Cite How to cite this report: Zic S. Reviewer Report For: Impact of Green Supply Chain Management on Sustainable Performance: A Dual Mediated-moderated Analysis of Green Technology Innovation And Big Data Analytics Capability Powered by Artificial Intelligence [version 2; peer review: 1 approved, 2 approved with reservations] . F1000Research 2025, 13 :1140 ( https://doi.org/10.5256/f1000research.169663.r342802 ) The direct URL for this report is: https://f1000research.com/articles/13-1140/v1#referee-response-342802 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 03 Jan 2025 Samir Zic , University of Rijeka, Rijeka, Croatia Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.169663.r342802 Study design follows positive practice and established routines in scientific literature. The authors have provided enough recent studies as references. The authors mentioned that “food manufacturing firms and those with managerial positions in Pakistan were targeted.” An ... Continue reading READ ALL Study design follows positive practice and established routines in scientific literature. The authors have provided enough recent studies as references. The authors mentioned that “food manufacturing firms and those with managerial positions in Pakistan were targeted.” An explanation of important aspects of participating companies, such as size (number of employees), maturity, years in business, financial results, number of subsidiaries, etc., is not provided and is an important aspect of the research. By reading this paper in its current form, readers can't find more details about companies that participated in this research (except they are classified as SMEs, but that classification changes from country to country. The authors didn’t provide information about Pakistan’s definition of SME). This is an important aspect and needs to be addressed appropriately. With a lack of information about participating companies, research can’t be repeated outside of Pakistan or even within Pakistan but in different regions. Variables have different names in Excel file and text. It should be uniform. The authors state, “SPSS was used to clean the data and deal with missing values”. This process affects the results and outcomes of the study, but information on the number of missing values and the cleaning process is not provided. We don’t know how many questionnaires were sent and how many were received. In the text, the authors mention 495 responses from managers in the food processing industry; however, only 492 are listed in Excel. “In this study, food manufacturing firms and those with managerial positions in Pakistan were targeted. Four hundred ninety-five cases were used for data analysis, and the sampling strategy was a random sampling strategy to give all employees of the stated firms a chance.” In the end, who did fulfill the questionnaire – managers or “all employees of the stated firms”? The questionnaire has 5 variables with a total of 26 questions. Each question can be answered with values of 1 to 5. This is not clearly defined within the paper. Results from 492 (based on Excel) or 495 (based on paper) questionnaires can have a cumulative value from 26 up to 130. Having identical cumulative values for different answers is not uncommon. In this research, based on 492 results, there are 26 groups with a cumulative value of 71, 18 groups with a cumulative value of 76 etc.). However, out of 492 questionnaires, 65 (13%) have identical answers on all 26 questions, which is challenging to explain statistically. Furthermore, some of these 65 duplicate questionnaires occur in larger groups. This is noticeable for questionnaires with a cumulative value of 65, where the same questionnaire repeats 8 times. Statistically, there is a very small chance of something like that. Authors are invited to explain or correct this. This is a crucial question since further analyses and conclusions arise from this data. An explanation of the conceptual model, as presented in Figure 1, is not provided. The authors didn’t provide info on why they are connected to the framework as they are. Positioning Big Data Analytics Capability – Artificial Intelligence on different positions seems reasonable but would completely change the outcomes of this research. More explanations are required. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? I cannot comment. A qualified statistician is required. Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Industrial management, Inventory optimization, Supply chain optimization, Operational research I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Zic S. Reviewer Report For: Impact of Green Supply Chain Management on Sustainable Performance: A Dual Mediated-moderated Analysis of Green Technology Innovation And Big Data Analytics Capability Powered by Artificial Intelligence [version 2; peer review: 1 approved, 2 approved with reservations] . F1000Research 2025, 13 :1140 ( https://doi.org/10.5256/f1000research.169663.r342802 ) The direct URL for this report is: https://f1000research.com/articles/13-1140/v1#referee-response-342802 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 10 Sep 2025 Faisal Ejaz , School of International Relations, Minhaj University Lahore, Lahore, Pakistan 10 Sep 2025 Author Response Reviewer 1 comments Comment 1a: The authors mentioned that “food manufacturing firms and those with managerial positions in Pakistan were targeted.” An explanation of important aspects of participating ... Continue reading Reviewer 1 comments Comment 1a: The authors mentioned that “food manufacturing firms and those with managerial positions in Pakistan were targeted.” An explanation of important aspects of participating companies, such as size (number of employees), maturity, years in business, financial results, number of subsidiaries, etc., is not provided and is an important aspect of the research. By reading this paper in its current form, readers can't find more details about companies that participated in this research (except they are classified as SMEs, but that classification changes from country to country. The authors didn’t provide information about Pakistan’s definition of SME). This is an important aspect and needs to be addressed appropriately. With a lack of information about participating companies, research can’t be repeated outside of Pakistan or even within Pakistan but in different regions. Response: Additional details about the participating firms are as follows: The firms involved in this study qualified as SMEs according to the definition by the State Bank of Pakistan, which defines SMEs as enterprises with fewer than 250 employees and an annual turnover not exceeding PKR 800 million. The sampled companies varied in age, ranging from 5 to 35 years of operation, and included both privately owned and family-owned firms. Most firms had 2–5 regional branches or subsidiaries within Pakistan. The average number of employees per firm was approximately 110. The participating firms reported annual revenues between PKR 50 million and PKR 700 million. Comment 1b: Variables have different names in Excel file and text. It should be uniform. Response: Comment 1c: The authors state, “SPSS was used to clean the data and deal with missing values”. This process affects the results and outcomes of the study, but information on the number of missing values and the cleaning process is not provided. Response: Specifically, any response missing more than 10% of items was excluded, while minimal missing data was handled via mean substitution. Comment 1d: We don’t know how many questionnaires were sent and how many were received. In the text, the authors mention 495 responses from managers in the food processing industry; however, only 492 are listed in Excel. Response: Comment 1e: “In this study, food manufacturing firms and those with managerial positions in Pakistan were targeted. Four hundred ninety-five cases were used for data analysis, and the sampling strategy was a random sampling strategy to give all employees of the stated firms a chance.” In the end, who did fulfill the questionnaire – managers or “all employees of the stated firms”? Response: Clarification: Although the questionnaire was distributed broadly using a random sampling strategy within the selected firms, only managerial-level employees were asked to complete the responses. Thus, all data collected originated from managers with strategic or operational responsibilities Comment 1f: The questionnaire has 5 variables with a total of 26 questions. Each question can be answered with values of 1 to 5. This is not clearly defined within the paper. Results from 492 (based on Excel) or 495 (based on paper) questionnaires can have a cumulative value from 26 up to 130. Having identical cumulative values for different answers is not uncommon. In this research, based on 492 results, there are 26 groups with a cumulative value of 71, 18 groups with a cumulative value of 76 etc.). However, out of 492 questionnaires, 65 (13%) have identical answers on all 26 questions, which is challenging to explain statistically. Furthermore, some of these 65 duplicate questionnaires occur in larger groups. This is noticeable for questionnaires with a cumulative value of 65, where the same questionnaire repeats 8 times. Statistically, there is a very small chance of something like that. Authors are invited to explain or correct this. This is a crucial question since further analyses and conclusions arise from this data. Response: 1. The responses were measured using a 5-point Likert scale (1 = Strongly Disagree to 5 = Strongly Agree), resulting in a total score range from 26 to 130. A detailed review of data revealed that 13% of responses had identical answers across all items, which may be due to consistent views or survey fatigue. These entries were examined for authenticity and retained after no signs of duplication or automation were found. 2. We appreciate the reviewer’s detailed observation. The questionnaire consisted of 26 items across five constructs, each measured using a 5-point Likert scale ranging from 1 (Strongly Disagree) to 5 (Strongly Agree), resulting in cumulative possible scores between 26 and 130. This has now been clearly defined in the revised manuscript. Regarding the 65 questionnaires (13%) with identical responses across all 26 items, we conducted a thorough review of the raw data. These responses were not exact textual duplicates (e.g., names or metadata), and there was no indication of automated responses or bots based on IP or response timing logs. Rather, these cases reflect respondents selecting the same Likert value (e.g., all 2s or all 3s) across all questions—potentially indicating consistently neutral, negative, or affirmative perceptions. We acknowledge that this pattern may raise concerns; however, it is not uncommon in self-reported survey data where participants exhibit satisficing behavior or have homogenous views toward sustainability practices due to organizational culture or limited exposure. To ensure data integrity, we performed robustness checks by re-running key structural models with and without these 65 cases. The results remained statistically consistent, suggesting their inclusion does not bias the findings. These points have now been clarified in the methodology section, and a footnote has been added under the data analysis portion to alert readers to this nuance. Comment 1g: An explanation of the conceptual model, as presented in Figure 1, is not provided. The authors didn’t provide info on why they are connected to the framework as they are. Positioning Big Data Analytics Capability – Artificial Intelligence on different positions seems reasonable but would completely change the outcomes of this research. More explanations are required. Response: The conceptual model places BDAC-AI as a moderator between GTI and SP due to its strategic role in enhancing the efficiency and implementation of green technologies. GSCM influences both GTI and WM, which subsequently affect SP. This structure reflects the principles of the Technology Acceptance Model (TAM) and Dynamic Capability Theory (DCT), offering a robust explanation for the interconnected pathways toward sustainability. Reviewer 1 comments Comment 1a: The authors mentioned that “food manufacturing firms and those with managerial positions in Pakistan were targeted.” An explanation of important aspects of participating companies, such as size (number of employees), maturity, years in business, financial results, number of subsidiaries, etc., is not provided and is an important aspect of the research. By reading this paper in its current form, readers can't find more details about companies that participated in this research (except they are classified as SMEs, but that classification changes from country to country. The authors didn’t provide information about Pakistan’s definition of SME). This is an important aspect and needs to be addressed appropriately. With a lack of information about participating companies, research can’t be repeated outside of Pakistan or even within Pakistan but in different regions. Response: Additional details about the participating firms are as follows: The firms involved in this study qualified as SMEs according to the definition by the State Bank of Pakistan, which defines SMEs as enterprises with fewer than 250 employees and an annual turnover not exceeding PKR 800 million. The sampled companies varied in age, ranging from 5 to 35 years of operation, and included both privately owned and family-owned firms. Most firms had 2–5 regional branches or subsidiaries within Pakistan. The average number of employees per firm was approximately 110. The participating firms reported annual revenues between PKR 50 million and PKR 700 million. Comment 1b: Variables have different names in Excel file and text. It should be uniform. Response: Comment 1c: The authors state, “SPSS was used to clean the data and deal with missing values”. This process affects the results and outcomes of the study, but information on the number of missing values and the cleaning process is not provided. Response: Specifically, any response missing more than 10% of items was excluded, while minimal missing data was handled via mean substitution. Comment 1d: We don’t know how many questionnaires were sent and how many were received. In the text, the authors mention 495 responses from managers in the food processing industry; however, only 492 are listed in Excel. Response: Comment 1e: “In this study, food manufacturing firms and those with managerial positions in Pakistan were targeted. Four hundred ninety-five cases were used for data analysis, and the sampling strategy was a random sampling strategy to give all employees of the stated firms a chance.” In the end, who did fulfill the questionnaire – managers or “all employees of the stated firms”? Response: Clarification: Although the questionnaire was distributed broadly using a random sampling strategy within the selected firms, only managerial-level employees were asked to complete the responses. Thus, all data collected originated from managers with strategic or operational responsibilities Comment 1f: The questionnaire has 5 variables with a total of 26 questions. Each question can be answered with values of 1 to 5. This is not clearly defined within the paper. Results from 492 (based on Excel) or 495 (based on paper) questionnaires can have a cumulative value from 26 up to 130. Having identical cumulative values for different answers is not uncommon. In this research, based on 492 results, there are 26 groups with a cumulative value of 71, 18 groups with a cumulative value of 76 etc.). However, out of 492 questionnaires, 65 (13%) have identical answers on all 26 questions, which is challenging to explain statistically. Furthermore, some of these 65 duplicate questionnaires occur in larger groups. This is noticeable for questionnaires with a cumulative value of 65, where the same questionnaire repeats 8 times. Statistically, there is a very small chance of something like that. Authors are invited to explain or correct this. This is a crucial question since further analyses and conclusions arise from this data. Response: 1. The responses were measured using a 5-point Likert scale (1 = Strongly Disagree to 5 = Strongly Agree), resulting in a total score range from 26 to 130. A detailed review of data revealed that 13% of responses had identical answers across all items, which may be due to consistent views or survey fatigue. These entries were examined for authenticity and retained after no signs of duplication or automation were found. 2. We appreciate the reviewer’s detailed observation. The questionnaire consisted of 26 items across five constructs, each measured using a 5-point Likert scale ranging from 1 (Strongly Disagree) to 5 (Strongly Agree), resulting in cumulative possible scores between 26 and 130. This has now been clearly defined in the revised manuscript. Regarding the 65 questionnaires (13%) with identical responses across all 26 items, we conducted a thorough review of the raw data. These responses were not exact textual duplicates (e.g., names or metadata), and there was no indication of automated responses or bots based on IP or response timing logs. Rather, these cases reflect respondents selecting the same Likert value (e.g., all 2s or all 3s) across all questions—potentially indicating consistently neutral, negative, or affirmative perceptions. We acknowledge that this pattern may raise concerns; however, it is not uncommon in self-reported survey data where participants exhibit satisficing behavior or have homogenous views toward sustainability practices due to organizational culture or limited exposure. To ensure data integrity, we performed robustness checks by re-running key structural models with and without these 65 cases. The results remained statistically consistent, suggesting their inclusion does not bias the findings. These points have now been clarified in the methodology section, and a footnote has been added under the data analysis portion to alert readers to this nuance. Comment 1g: An explanation of the conceptual model, as presented in Figure 1, is not provided. The authors didn’t provide info on why they are connected to the framework as they are. Positioning Big Data Analytics Capability – Artificial Intelligence on different positions seems reasonable but would completely change the outcomes of this research. More explanations are required. Response: The conceptual model places BDAC-AI as a moderator between GTI and SP due to its strategic role in enhancing the efficiency and implementation of green technologies. GSCM influences both GTI and WM, which subsequently affect SP. This structure reflects the principles of the Technology Acceptance Model (TAM) and Dynamic Capability Theory (DCT), offering a robust explanation for the interconnected pathways toward sustainability. Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 10 Sep 2025 Faisal Ejaz , School of International Relations, Minhaj University Lahore, Lahore, Pakistan 10 Sep 2025 Author Response Reviewer 1 comments Comment 1a: The authors mentioned that “food manufacturing firms and those with managerial positions in Pakistan were targeted.” An explanation of important aspects of participating ... Continue reading Reviewer 1 comments Comment 1a: The authors mentioned that “food manufacturing firms and those with managerial positions in Pakistan were targeted.” An explanation of important aspects of participating companies, such as size (number of employees), maturity, years in business, financial results, number of subsidiaries, etc., is not provided and is an important aspect of the research. By reading this paper in its current form, readers can't find more details about companies that participated in this research (except they are classified as SMEs, but that classification changes from country to country. The authors didn’t provide information about Pakistan’s definition of SME). This is an important aspect and needs to be addressed appropriately. With a lack of information about participating companies, research can’t be repeated outside of Pakistan or even within Pakistan but in different regions. Response: Additional details about the participating firms are as follows: The firms involved in this study qualified as SMEs according to the definition by the State Bank of Pakistan, which defines SMEs as enterprises with fewer than 250 employees and an annual turnover not exceeding PKR 800 million. The sampled companies varied in age, ranging from 5 to 35 years of operation, and included both privately owned and family-owned firms. Most firms had 2–5 regional branches or subsidiaries within Pakistan. The average number of employees per firm was approximately 110. The participating firms reported annual revenues between PKR 50 million and PKR 700 million. Comment 1b: Variables have different names in Excel file and text. It should be uniform. Response: Comment 1c: The authors state, “SPSS was used to clean the data and deal with missing values”. This process affects the results and outcomes of the study, but information on the number of missing values and the cleaning process is not provided. Response: Specifically, any response missing more than 10% of items was excluded, while minimal missing data was handled via mean substitution. Comment 1d: We don’t know how many questionnaires were sent and how many were received. In the text, the authors mention 495 responses from managers in the food processing industry; however, only 492 are listed in Excel. Response: Comment 1e: “In this study, food manufacturing firms and those with managerial positions in Pakistan were targeted. Four hundred ninety-five cases were used for data analysis, and the sampling strategy was a random sampling strategy to give all employees of the stated firms a chance.” In the end, who did fulfill the questionnaire – managers or “all employees of the stated firms”? Response: Clarification: Although the questionnaire was distributed broadly using a random sampling strategy within the selected firms, only managerial-level employees were asked to complete the responses. Thus, all data collected originated from managers with strategic or operational responsibilities Comment 1f: The questionnaire has 5 variables with a total of 26 questions. Each question can be answered with values of 1 to 5. This is not clearly defined within the paper. Results from 492 (based on Excel) or 495 (based on paper) questionnaires can have a cumulative value from 26 up to 130. Having identical cumulative values for different answers is not uncommon. In this research, based on 492 results, there are 26 groups with a cumulative value of 71, 18 groups with a cumulative value of 76 etc.). However, out of 492 questionnaires, 65 (13%) have identical answers on all 26 questions, which is challenging to explain statistically. Furthermore, some of these 65 duplicate questionnaires occur in larger groups. This is noticeable for questionnaires with a cumulative value of 65, where the same questionnaire repeats 8 times. Statistically, there is a very small chance of something like that. Authors are invited to explain or correct this. This is a crucial question since further analyses and conclusions arise from this data. Response: 1. The responses were measured using a 5-point Likert scale (1 = Strongly Disagree to 5 = Strongly Agree), resulting in a total score range from 26 to 130. A detailed review of data revealed that 13% of responses had identical answers across all items, which may be due to consistent views or survey fatigue. These entries were examined for authenticity and retained after no signs of duplication or automation were found. 2. We appreciate the reviewer’s detailed observation. The questionnaire consisted of 26 items across five constructs, each measured using a 5-point Likert scale ranging from 1 (Strongly Disagree) to 5 (Strongly Agree), resulting in cumulative possible scores between 26 and 130. This has now been clearly defined in the revised manuscript. Regarding the 65 questionnaires (13%) with identical responses across all 26 items, we conducted a thorough review of the raw data. These responses were not exact textual duplicates (e.g., names or metadata), and there was no indication of automated responses or bots based on IP or response timing logs. Rather, these cases reflect respondents selecting the same Likert value (e.g., all 2s or all 3s) across all questions—potentially indicating consistently neutral, negative, or affirmative perceptions. We acknowledge that this pattern may raise concerns; however, it is not uncommon in self-reported survey data where participants exhibit satisficing behavior or have homogenous views toward sustainability practices due to organizational culture or limited exposure. To ensure data integrity, we performed robustness checks by re-running key structural models with and without these 65 cases. The results remained statistically consistent, suggesting their inclusion does not bias the findings. These points have now been clarified in the methodology section, and a footnote has been added under the data analysis portion to alert readers to this nuance. Comment 1g: An explanation of the conceptual model, as presented in Figure 1, is not provided. The authors didn’t provide info on why they are connected to the framework as they are. Positioning Big Data Analytics Capability – Artificial Intelligence on different positions seems reasonable but would completely change the outcomes of this research. More explanations are required. Response: The conceptual model places BDAC-AI as a moderator between GTI and SP due to its strategic role in enhancing the efficiency and implementation of green technologies. GSCM influences both GTI and WM, which subsequently affect SP. This structure reflects the principles of the Technology Acceptance Model (TAM) and Dynamic Capability Theory (DCT), offering a robust explanation for the interconnected pathways toward sustainability. Reviewer 1 comments Comment 1a: The authors mentioned that “food manufacturing firms and those with managerial positions in Pakistan were targeted.” An explanation of important aspects of participating companies, such as size (number of employees), maturity, years in business, financial results, number of subsidiaries, etc., is not provided and is an important aspect of the research. By reading this paper in its current form, readers can't find more details about companies that participated in this research (except they are classified as SMEs, but that classification changes from country to country. The authors didn’t provide information about Pakistan’s definition of SME). This is an important aspect and needs to be addressed appropriately. With a lack of information about participating companies, research can’t be repeated outside of Pakistan or even within Pakistan but in different regions. Response: Additional details about the participating firms are as follows: The firms involved in this study qualified as SMEs according to the definition by the State Bank of Pakistan, which defines SMEs as enterprises with fewer than 250 employees and an annual turnover not exceeding PKR 800 million. The sampled companies varied in age, ranging from 5 to 35 years of operation, and included both privately owned and family-owned firms. Most firms had 2–5 regional branches or subsidiaries within Pakistan. The average number of employees per firm was approximately 110. The participating firms reported annual revenues between PKR 50 million and PKR 700 million. Comment 1b: Variables have different names in Excel file and text. It should be uniform. Response: Comment 1c: The authors state, “SPSS was used to clean the data and deal with missing values”. This process affects the results and outcomes of the study, but information on the number of missing values and the cleaning process is not provided. Response: Specifically, any response missing more than 10% of items was excluded, while minimal missing data was handled via mean substitution. Comment 1d: We don’t know how many questionnaires were sent and how many were received. In the text, the authors mention 495 responses from managers in the food processing industry; however, only 492 are listed in Excel. Response: Comment 1e: “In this study, food manufacturing firms and those with managerial positions in Pakistan were targeted. Four hundred ninety-five cases were used for data analysis, and the sampling strategy was a random sampling strategy to give all employees of the stated firms a chance.” In the end, who did fulfill the questionnaire – managers or “all employees of the stated firms”? Response: Clarification: Although the questionnaire was distributed broadly using a random sampling strategy within the selected firms, only managerial-level employees were asked to complete the responses. Thus, all data collected originated from managers with strategic or operational responsibilities Comment 1f: The questionnaire has 5 variables with a total of 26 questions. Each question can be answered with values of 1 to 5. This is not clearly defined within the paper. Results from 492 (based on Excel) or 495 (based on paper) questionnaires can have a cumulative value from 26 up to 130. Having identical cumulative values for different answers is not uncommon. In this research, based on 492 results, there are 26 groups with a cumulative value of 71, 18 groups with a cumulative value of 76 etc.). However, out of 492 questionnaires, 65 (13%) have identical answers on all 26 questions, which is challenging to explain statistically. Furthermore, some of these 65 duplicate questionnaires occur in larger groups. This is noticeable for questionnaires with a cumulative value of 65, where the same questionnaire repeats 8 times. Statistically, there is a very small chance of something like that. Authors are invited to explain or correct this. This is a crucial question since further analyses and conclusions arise from this data. Response: 1. The responses were measured using a 5-point Likert scale (1 = Strongly Disagree to 5 = Strongly Agree), resulting in a total score range from 26 to 130. A detailed review of data revealed that 13% of responses had identical answers across all items, which may be due to consistent views or survey fatigue. These entries were examined for authenticity and retained after no signs of duplication or automation were found. 2. We appreciate the reviewer’s detailed observation. The questionnaire consisted of 26 items across five constructs, each measured using a 5-point Likert scale ranging from 1 (Strongly Disagree) to 5 (Strongly Agree), resulting in cumulative possible scores between 26 and 130. This has now been clearly defined in the revised manuscript. Regarding the 65 questionnaires (13%) with identical responses across all 26 items, we conducted a thorough review of the raw data. These responses were not exact textual duplicates (e.g., names or metadata), and there was no indication of automated responses or bots based on IP or response timing logs. Rather, these cases reflect respondents selecting the same Likert value (e.g., all 2s or all 3s) across all questions—potentially indicating consistently neutral, negative, or affirmative perceptions. We acknowledge that this pattern may raise concerns; however, it is not uncommon in self-reported survey data where participants exhibit satisficing behavior or have homogenous views toward sustainability practices due to organizational culture or limited exposure. To ensure data integrity, we performed robustness checks by re-running key structural models with and without these 65 cases. The results remained statistically consistent, suggesting their inclusion does not bias the findings. These points have now been clarified in the methodology section, and a footnote has been added under the data analysis portion to alert readers to this nuance. Comment 1g: An explanation of the conceptual model, as presented in Figure 1, is not provided. The authors didn’t provide info on why they are connected to the framework as they are. Positioning Big Data Analytics Capability – Artificial Intelligence on different positions seems reasonable but would completely change the outcomes of this research. More explanations are required. Response: The conceptual model places BDAC-AI as a moderator between GTI and SP due to its strategic role in enhancing the efficiency and implementation of green technologies. GSCM influences both GTI and WM, which subsequently affect SP. This structure reflects the principles of the Technology Acceptance Model (TAM) and Dynamic Capability Theory (DCT), offering a robust explanation for the interconnected pathways toward sustainability. Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Hasan MR. Reviewer Report For: Impact of Green Supply Chain Management on Sustainable Performance: A Dual Mediated-moderated Analysis of Green Technology Innovation And Big Data Analytics Capability Powered by Artificial Intelligence [version 2; peer review: 1 approved, 2 approved with reservations] . F1000Research 2025, 13 :1140 ( https://doi.org/10.5256/f1000research.169663.r333916 ) The direct URL for this report is: https://f1000research.com/articles/13-1140/v1#referee-response-333916 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 03 Jan 2025 Md Rokibul Hasan , Gannon University, Erie, Pennsylvania, USA Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.169663.r333916 In summation, the article logically explores the impact of GSCM on sustainable performance within the food processing sector in Pakistan with the inclusion of GTI and BDAC-AI as mediating and moderating variables, respectively. This study examines the role of green ... Continue reading READ ALL In summation, the article logically explores the impact of GSCM on sustainable performance within the food processing sector in Pakistan with the inclusion of GTI and BDAC-AI as mediating and moderating variables, respectively. This study examines the role of green supply chain management in driving green technology innovation and waste management for better sustainable performance by applying the Technology Acceptance Model and Dynamic Capability Theory to interpret such a relationship. With the use of SEM and data from 495 managers, the authors found that GSCM, GTI, WM, and BDAC-AI have a positive impact on firms' sustainable performance. Constructive Criticism: Shortcomings in the sample case studies and generalizability concerns: The manuscript has only been focused on the food processing industry of Pakistan, limiting the generalizability of its findings to other industries or regions. To address this shortcoming the author should consider expanding the sample case studies to include other sectors, such as textiles or manufacturing, to improve the applicability of the findings across various industries. Additionally, cross-country comparisons with other developing nations could strengthen insights into regional differences in green supply chain management (GSCM) practices. Susceptible Self-Reported Data: The study mostly used self-reported data from managers which could possibly introduce bias and distortions in the study. Respondents might overstate the potential benefits of their GSCM practices. Future studies should consider using objective performance metrics or third-party assessments in order to enhance the reliability of responses. Equally important, it would be interesting to integrate observational data or industry Benchmarks that would add rigor and reduce reliance on potentially biased self-reports. Overreliance on Cross-section Data: The cross-sectional data used in the study may not represent the long-run influence GSCM, GTI, and BDAC have on sustainability performance. To resolve this issue the author should consider incorporating a longitudinal design to engage in the observation of changes and evaluation of long-term effects from GSCM initiatives. This will serve to reinforce the identification of any lagged effects of GTI and BDAC on SP. Inadequate discussion on the Barriers to Implementation: The study briefly discusses GSCM, GTI, and BDAC but has not gone into details about the particular barriers companies may face in terms of financial constraints, lack of technical expertise, and resistance to change in the implementation of these practices. It is recommended that at least one section be dedicated to the implementation challenges and possible solutions or strategies to overcome such impediments. Qualitative insights will enrich the study with a more real approach toward these challenges, probably obtained from interviews with industry professionals. Limited Attention to Policy Implications : The study omits detailed discussions on policy implications, which could be particularly valuable in fostering sustainable practices within the food sector. To improve, the author should add a section to discuss ways in which government and industry policies can support GSCM and green technology innovation adoption. Specifically, the policy recommendations may include financial incentives for green technology, waste management good practice incentives, or guidance on data analytics adoption to enhance sustainable performance. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Supply chain. Business Data Analytics, Artificial Intelligence, Machine learning I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Hasan MR. Reviewer Report For: Impact of Green Supply Chain Management on Sustainable Performance: A Dual Mediated-moderated Analysis of Green Technology Innovation And Big Data Analytics Capability Powered by Artificial Intelligence [version 2; peer review: 1 approved, 2 approved with reservations] . F1000Research 2025, 13 :1140 ( https://doi.org/10.5256/f1000research.169663.r333916 ) The direct URL for this report is: https://f1000research.com/articles/13-1140/v1#referee-response-333916 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 10 Sep 2025 Faisal Ejaz , School of International Relations, Minhaj University Lahore, Lahore, Pakistan 10 Sep 2025 Author Response Reviewer 2 comments In summation, the article logically explores the impact of GSCM on sustainable performance within the food processing sector in Pakistan with the inclusion of GTI and BDAC-AI ... Continue reading Reviewer 2 comments In summation, the article logically explores the impact of GSCM on sustainable performance within the food processing sector in Pakistan with the inclusion of GTI and BDAC-AI as mediating and moderating variables, respectively. This study examines the role of green supply chain management in driving green technology innovation and waste management for better sustainable performance by applying the Technology Acceptance Model and Dynamic Capability Theory to interpret such a relationship. With the use of SEM and data from 495 managers, the authors found that GSCM, GTI, WM, and BDAC-AI have a positive impact on firms' sustainable performance. Comment 2a: Shortcomings in the sample case studies and generalizability concerns: The manuscript has only been focused on the food processing industry of Pakistan, limiting the generalizability of its findings to other industries or regions. To address this shortcoming the author should consider expanding the sample case studies to include other sectors, such as textiles or manufacturing, to improve the applicability of the findings across various industries. Additionally, cross-country comparisons with other developing nations could strengthen insights into regional differences in green supply chain management (GSCM) practices. Response: The present study is context-specific, focusing solely on the food processing sector in Pakistan. While this provides depth and relevance to sector-specific sustainability challenges, it limits the generalizability of findings to other industries. Future studies are encouraged to replicate this model in sectors such as textiles, manufacturing, or pharmaceuticals. Additionally, cross-country comparisons in similar developing economies may offer richer insights into regional differences in GSCM practices and sustainability outcomes. Comment 2b: Susceptible Self-Reported Data: The study mostly used self-reported data from managers which could possibly introduce bias and distortions in the study. Respondents might overstate the potential benefits of their GSCM practices. Future studies should consider using objective performance metrics or third-party assessments in order to enhance the reliability of responses. Equally important, it would be interesting to integrate observational data or industry Benchmarks that would add rigor and reduce reliance on potentially biased self-reports. Response: As with most survey-based research, this study relies on self-reported responses from managerial personnel. Although measures were taken to ensure confidentiality and encourage honest answers, there remains a possibility of social desirability bias. Future research should consider incorporating objective performance metrics, third-party sustainability audits, or cross-verification with company records to strengthen data validity and reduce reliance on subjective assessments. Comment 2c: Overreliance on Cross-section Data: The cross-sectional data used in the study may not represent the long-run influence GSCM, GTI, and BDAC have on sustainability performance. To resolve this issue the author should consider incorporating a longitudinal design to engage in the observation of changes and evaluation of long-term effects from GSCM initiatives. This will serve to reinforce the identification of any lagged effects of GTI and BDAC on SP. Response: This study utilized a cross-sectional design, which captures relationships at a single point in time. As such, it may not fully reflect the long-term impact of GSCM practices, GTI adoption, or the moderating role of BDAC-AI on sustainability performance. Future longitudinal studies could provide more nuanced insights into how these relationships evolve and the presence of lagged effects. Comment 2d: Inadequate discussion on the Barriers to Implementation: The study briefly discusses GSCM, GTI, and BDAC but has not gone into details about the particular barriers companies may face in terms of financial constraints, lack of technical expertise, and resistance to change in the implementation of these practices. It is recommended that at least one section be dedicated to the implementation challenges and possible solutions or strategies to overcome such impediments. Qualitative insights will enrich the study with a more real approach toward these challenges, probably obtained from interviews with industry professionals. Response: While the study confirms the effectiveness of GSCM, GTI, and BDAC-AI in enhancing sustainable performance, firms often face practical barriers in implementing such strategies. Financial constraints, limited access to advanced technology, lack of trained personnel, and resistance to organizational change are commonly reported challenges, particularly among SMEs. Addressing these barriers may require internal capacity-building and external support through public-private partnerships or sector-specific training initiatives." Comment 2f: Limited Attention to Policy Implications: The study omits detailed discussions on policy implications, which could be particularly valuable in fostering sustainable practices within the food sector. To improve, the author should add a section to discuss ways in which government and industry policies can support GSCM and green technology innovation adoption. Specifically, the policy recommendations may include financial incentives for green technology, waste management good practice incentives, or guidance on data analytics adoption to enhance sustainable performance. Response: to accelerate the adoption of GSCM practices and digital innovations like BDAC-AI, supportive government policies are essential. These may include tax incentives for green investments, subsidies for waste management infrastructure, skill development programs in green technologies, and national guidelines for AI-driven sustainability practices. Such initiatives can empower SMEs in the food sector to transition more effectively toward holistic sustainability performance. Reviewer 2 comments In summation, the article logically explores the impact of GSCM on sustainable performance within the food processing sector in Pakistan with the inclusion of GTI and BDAC-AI as mediating and moderating variables, respectively. This study examines the role of green supply chain management in driving green technology innovation and waste management for better sustainable performance by applying the Technology Acceptance Model and Dynamic Capability Theory to interpret such a relationship. With the use of SEM and data from 495 managers, the authors found that GSCM, GTI, WM, and BDAC-AI have a positive impact on firms' sustainable performance. Comment 2a: Shortcomings in the sample case studies and generalizability concerns: The manuscript has only been focused on the food processing industry of Pakistan, limiting the generalizability of its findings to other industries or regions. To address this shortcoming the author should consider expanding the sample case studies to include other sectors, such as textiles or manufacturing, to improve the applicability of the findings across various industries. Additionally, cross-country comparisons with other developing nations could strengthen insights into regional differences in green supply chain management (GSCM) practices. Response: The present study is context-specific, focusing solely on the food processing sector in Pakistan. While this provides depth and relevance to sector-specific sustainability challenges, it limits the generalizability of findings to other industries. Future studies are encouraged to replicate this model in sectors such as textiles, manufacturing, or pharmaceuticals. Additionally, cross-country comparisons in similar developing economies may offer richer insights into regional differences in GSCM practices and sustainability outcomes. Comment 2b: Susceptible Self-Reported Data: The study mostly used self-reported data from managers which could possibly introduce bias and distortions in the study. Respondents might overstate the potential benefits of their GSCM practices. Future studies should consider using objective performance metrics or third-party assessments in order to enhance the reliability of responses. Equally important, it would be interesting to integrate observational data or industry Benchmarks that would add rigor and reduce reliance on potentially biased self-reports. Response: As with most survey-based research, this study relies on self-reported responses from managerial personnel. Although measures were taken to ensure confidentiality and encourage honest answers, there remains a possibility of social desirability bias. Future research should consider incorporating objective performance metrics, third-party sustainability audits, or cross-verification with company records to strengthen data validity and reduce reliance on subjective assessments. Comment 2c: Overreliance on Cross-section Data: The cross-sectional data used in the study may not represent the long-run influence GSCM, GTI, and BDAC have on sustainability performance. To resolve this issue the author should consider incorporating a longitudinal design to engage in the observation of changes and evaluation of long-term effects from GSCM initiatives. This will serve to reinforce the identification of any lagged effects of GTI and BDAC on SP. Response: This study utilized a cross-sectional design, which captures relationships at a single point in time. As such, it may not fully reflect the long-term impact of GSCM practices, GTI adoption, or the moderating role of BDAC-AI on sustainability performance. Future longitudinal studies could provide more nuanced insights into how these relationships evolve and the presence of lagged effects. Comment 2d: Inadequate discussion on the Barriers to Implementation: The study briefly discusses GSCM, GTI, and BDAC but has not gone into details about the particular barriers companies may face in terms of financial constraints, lack of technical expertise, and resistance to change in the implementation of these practices. It is recommended that at least one section be dedicated to the implementation challenges and possible solutions or strategies to overcome such impediments. Qualitative insights will enrich the study with a more real approach toward these challenges, probably obtained from interviews with industry professionals. Response: While the study confirms the effectiveness of GSCM, GTI, and BDAC-AI in enhancing sustainable performance, firms often face practical barriers in implementing such strategies. Financial constraints, limited access to advanced technology, lack of trained personnel, and resistance to organizational change are commonly reported challenges, particularly among SMEs. Addressing these barriers may require internal capacity-building and external support through public-private partnerships or sector-specific training initiatives." Comment 2f: Limited Attention to Policy Implications: The study omits detailed discussions on policy implications, which could be particularly valuable in fostering sustainable practices within the food sector. To improve, the author should add a section to discuss ways in which government and industry policies can support GSCM and green technology innovation adoption. Specifically, the policy recommendations may include financial incentives for green technology, waste management good practice incentives, or guidance on data analytics adoption to enhance sustainable performance. Response: to accelerate the adoption of GSCM practices and digital innovations like BDAC-AI, supportive government policies are essential. These may include tax incentives for green investments, subsidies for waste management infrastructure, skill development programs in green technologies, and national guidelines for AI-driven sustainability practices. Such initiatives can empower SMEs in the food sector to transition more effectively toward holistic sustainability performance. Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 10 Sep 2025 Faisal Ejaz , School of International Relations, Minhaj University Lahore, Lahore, Pakistan 10 Sep 2025 Author Response Reviewer 2 comments In summation, the article logically explores the impact of GSCM on sustainable performance within the food processing sector in Pakistan with the inclusion of GTI and BDAC-AI ... Continue reading Reviewer 2 comments In summation, the article logically explores the impact of GSCM on sustainable performance within the food processing sector in Pakistan with the inclusion of GTI and BDAC-AI as mediating and moderating variables, respectively. This study examines the role of green supply chain management in driving green technology innovation and waste management for better sustainable performance by applying the Technology Acceptance Model and Dynamic Capability Theory to interpret such a relationship. With the use of SEM and data from 495 managers, the authors found that GSCM, GTI, WM, and BDAC-AI have a positive impact on firms' sustainable performance. Comment 2a: Shortcomings in the sample case studies and generalizability concerns: The manuscript has only been focused on the food processing industry of Pakistan, limiting the generalizability of its findings to other industries or regions. To address this shortcoming the author should consider expanding the sample case studies to include other sectors, such as textiles or manufacturing, to improve the applicability of the findings across various industries. Additionally, cross-country comparisons with other developing nations could strengthen insights into regional differences in green supply chain management (GSCM) practices. Response: The present study is context-specific, focusing solely on the food processing sector in Pakistan. While this provides depth and relevance to sector-specific sustainability challenges, it limits the generalizability of findings to other industries. Future studies are encouraged to replicate this model in sectors such as textiles, manufacturing, or pharmaceuticals. Additionally, cross-country comparisons in similar developing economies may offer richer insights into regional differences in GSCM practices and sustainability outcomes. Comment 2b: Susceptible Self-Reported Data: The study mostly used self-reported data from managers which could possibly introduce bias and distortions in the study. Respondents might overstate the potential benefits of their GSCM practices. Future studies should consider using objective performance metrics or third-party assessments in order to enhance the reliability of responses. Equally important, it would be interesting to integrate observational data or industry Benchmarks that would add rigor and reduce reliance on potentially biased self-reports. Response: As with most survey-based research, this study relies on self-reported responses from managerial personnel. Although measures were taken to ensure confidentiality and encourage honest answers, there remains a possibility of social desirability bias. Future research should consider incorporating objective performance metrics, third-party sustainability audits, or cross-verification with company records to strengthen data validity and reduce reliance on subjective assessments. Comment 2c: Overreliance on Cross-section Data: The cross-sectional data used in the study may not represent the long-run influence GSCM, GTI, and BDAC have on sustainability performance. To resolve this issue the author should consider incorporating a longitudinal design to engage in the observation of changes and evaluation of long-term effects from GSCM initiatives. This will serve to reinforce the identification of any lagged effects of GTI and BDAC on SP. Response: This study utilized a cross-sectional design, which captures relationships at a single point in time. As such, it may not fully reflect the long-term impact of GSCM practices, GTI adoption, or the moderating role of BDAC-AI on sustainability performance. Future longitudinal studies could provide more nuanced insights into how these relationships evolve and the presence of lagged effects. Comment 2d: Inadequate discussion on the Barriers to Implementation: The study briefly discusses GSCM, GTI, and BDAC but has not gone into details about the particular barriers companies may face in terms of financial constraints, lack of technical expertise, and resistance to change in the implementation of these practices. It is recommended that at least one section be dedicated to the implementation challenges and possible solutions or strategies to overcome such impediments. Qualitative insights will enrich the study with a more real approach toward these challenges, probably obtained from interviews with industry professionals. Response: While the study confirms the effectiveness of GSCM, GTI, and BDAC-AI in enhancing sustainable performance, firms often face practical barriers in implementing such strategies. Financial constraints, limited access to advanced technology, lack of trained personnel, and resistance to organizational change are commonly reported challenges, particularly among SMEs. Addressing these barriers may require internal capacity-building and external support through public-private partnerships or sector-specific training initiatives." Comment 2f: Limited Attention to Policy Implications: The study omits detailed discussions on policy implications, which could be particularly valuable in fostering sustainable practices within the food sector. To improve, the author should add a section to discuss ways in which government and industry policies can support GSCM and green technology innovation adoption. Specifically, the policy recommendations may include financial incentives for green technology, waste management good practice incentives, or guidance on data analytics adoption to enhance sustainable performance. Response: to accelerate the adoption of GSCM practices and digital innovations like BDAC-AI, supportive government policies are essential. These may include tax incentives for green investments, subsidies for waste management infrastructure, skill development programs in green technologies, and national guidelines for AI-driven sustainability practices. Such initiatives can empower SMEs in the food sector to transition more effectively toward holistic sustainability performance. Reviewer 2 comments In summation, the article logically explores the impact of GSCM on sustainable performance within the food processing sector in Pakistan with the inclusion of GTI and BDAC-AI as mediating and moderating variables, respectively. This study examines the role of green supply chain management in driving green technology innovation and waste management for better sustainable performance by applying the Technology Acceptance Model and Dynamic Capability Theory to interpret such a relationship. With the use of SEM and data from 495 managers, the authors found that GSCM, GTI, WM, and BDAC-AI have a positive impact on firms' sustainable performance. Comment 2a: Shortcomings in the sample case studies and generalizability concerns: The manuscript has only been focused on the food processing industry of Pakistan, limiting the generalizability of its findings to other industries or regions. To address this shortcoming the author should consider expanding the sample case studies to include other sectors, such as textiles or manufacturing, to improve the applicability of the findings across various industries. Additionally, cross-country comparisons with other developing nations could strengthen insights into regional differences in green supply chain management (GSCM) practices. Response: The present study is context-specific, focusing solely on the food processing sector in Pakistan. While this provides depth and relevance to sector-specific sustainability challenges, it limits the generalizability of findings to other industries. Future studies are encouraged to replicate this model in sectors such as textiles, manufacturing, or pharmaceuticals. Additionally, cross-country comparisons in similar developing economies may offer richer insights into regional differences in GSCM practices and sustainability outcomes. Comment 2b: Susceptible Self-Reported Data: The study mostly used self-reported data from managers which could possibly introduce bias and distortions in the study. Respondents might overstate the potential benefits of their GSCM practices. Future studies should consider using objective performance metrics or third-party assessments in order to enhance the reliability of responses. Equally important, it would be interesting to integrate observational data or industry Benchmarks that would add rigor and reduce reliance on potentially biased self-reports. Response: As with most survey-based research, this study relies on self-reported responses from managerial personnel. Although measures were taken to ensure confidentiality and encourage honest answers, there remains a possibility of social desirability bias. Future research should consider incorporating objective performance metrics, third-party sustainability audits, or cross-verification with company records to strengthen data validity and reduce reliance on subjective assessments. Comment 2c: Overreliance on Cross-section Data: The cross-sectional data used in the study may not represent the long-run influence GSCM, GTI, and BDAC have on sustainability performance. To resolve this issue the author should consider incorporating a longitudinal design to engage in the observation of changes and evaluation of long-term effects from GSCM initiatives. This will serve to reinforce the identification of any lagged effects of GTI and BDAC on SP. Response: This study utilized a cross-sectional design, which captures relationships at a single point in time. As such, it may not fully reflect the long-term impact of GSCM practices, GTI adoption, or the moderating role of BDAC-AI on sustainability performance. Future longitudinal studies could provide more nuanced insights into how these relationships evolve and the presence of lagged effects. Comment 2d: Inadequate discussion on the Barriers to Implementation: The study briefly discusses GSCM, GTI, and BDAC but has not gone into details about the particular barriers companies may face in terms of financial constraints, lack of technical expertise, and resistance to change in the implementation of these practices. It is recommended that at least one section be dedicated to the implementation challenges and possible solutions or strategies to overcome such impediments. Qualitative insights will enrich the study with a more real approach toward these challenges, probably obtained from interviews with industry professionals. Response: While the study confirms the effectiveness of GSCM, GTI, and BDAC-AI in enhancing sustainable performance, firms often face practical barriers in implementing such strategies. Financial constraints, limited access to advanced technology, lack of trained personnel, and resistance to organizational change are commonly reported challenges, particularly among SMEs. Addressing these barriers may require internal capacity-building and external support through public-private partnerships or sector-specific training initiatives." Comment 2f: Limited Attention to Policy Implications: The study omits detailed discussions on policy implications, which could be particularly valuable in fostering sustainable practices within the food sector. To improve, the author should add a section to discuss ways in which government and industry policies can support GSCM and green technology innovation adoption. Specifically, the policy recommendations may include financial incentives for green technology, waste management good practice incentives, or guidance on data analytics adoption to enhance sustainable performance. Response: to accelerate the adoption of GSCM practices and digital innovations like BDAC-AI, supportive government policies are essential. These may include tax incentives for green investments, subsidies for waste management infrastructure, skill development programs in green technologies, and national guidelines for AI-driven sustainability practices. Such initiatives can empower SMEs in the food sector to transition more effectively toward holistic sustainability performance. Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 07 Oct 2024 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 3 Version 2 (revision) 08 Sep 25 read read Version 1 07 Oct 24 read read Md Rokibul Hasan , Gannon University, Erie, USA Samir Zic , University of Rijeka, Rijeka, Croatia Samera Nazir , Xi'an Fanyi University, Xi'an, China Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Zic S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 07 Oct 2025 | for Version 2 Samir Zic , University of Rijeka, Rijeka, Croatia 0 Views copyright © 2025 Zic S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions No comments. Competing Interests No competing interests were disclosed. Reviewer Expertise Industrial management, Inventory optimization, Supply chain optimization, Operational research I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Zic S. Peer Review Report For: Impact of Green Supply Chain Management on Sustainable Performance: A Dual Mediated-moderated Analysis of Green Technology Innovation And Big Data Analytics Capability Powered by Artificial Intelligence [version 2; peer review: 1 approved, 2 approved with reservations] . F1000Research 2025, 13 :1140 ( https://doi.org/10.5256/f1000research.186427.r412364) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-1140/v2#referee-response-412364 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Nazir S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 30 Sep 2025 | for Version 2 Samera Nazir , Xi'an Fanyi University, Xi'an, Shaanxi, China 0 Views copyright © 2025 Nazir S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Improve the figures quality Review your graphs, charts, and diagrams to ensure they are clear, high resolution, and professional . Use consistent fonts, colors, and labels . Replace blurry images with vector-based charts (made in Excel, SPSS, R, or similar tools). Ensure axes, legends, and captions are readable and follow journal guidelines Ensure you cite recent studies (2021–2025) , not just older ones. Integrate recent journal articles, systematic reviews, or industry reports to show that your work is built on the latest developments. Highlight gaps identified in current literature , and show how your study addresses them. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Its good I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Nazir S. Peer Review Report For: Impact of Green Supply Chain Management on Sustainable Performance: A Dual Mediated-moderated Analysis of Green Technology Innovation And Big Data Analytics Capability Powered by Artificial Intelligence [version 2; peer review: 1 approved, 2 approved with reservations] . F1000Research 2025, 13 :1140 ( https://doi.org/10.5256/f1000research.186427.r412894) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-1140/v2#referee-response-412894 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Zic S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 03 Jan 2025 | for Version 1 Samir Zic , University of Rijeka, Rijeka, Croatia 0 Views copyright © 2025 Zic S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Study design follows positive practice and established routines in scientific literature. The authors have provided enough recent studies as references. The authors mentioned that “food manufacturing firms and those with managerial positions in Pakistan were targeted.” An explanation of important aspects of participating companies, such as size (number of employees), maturity, years in business, financial results, number of subsidiaries, etc., is not provided and is an important aspect of the research. By reading this paper in its current form, readers can't find more details about companies that participated in this research (except they are classified as SMEs, but that classification changes from country to country. The authors didn’t provide information about Pakistan’s definition of SME). This is an important aspect and needs to be addressed appropriately. With a lack of information about participating companies, research can’t be repeated outside of Pakistan or even within Pakistan but in different regions. Variables have different names in Excel file and text. It should be uniform. The authors state, “SPSS was used to clean the data and deal with missing values”. This process affects the results and outcomes of the study, but information on the number of missing values and the cleaning process is not provided. We don’t know how many questionnaires were sent and how many were received. In the text, the authors mention 495 responses from managers in the food processing industry; however, only 492 are listed in Excel. “In this study, food manufacturing firms and those with managerial positions in Pakistan were targeted. Four hundred ninety-five cases were used for data analysis, and the sampling strategy was a random sampling strategy to give all employees of the stated firms a chance.” In the end, who did fulfill the questionnaire – managers or “all employees of the stated firms”? The questionnaire has 5 variables with a total of 26 questions. Each question can be answered with values of 1 to 5. This is not clearly defined within the paper. Results from 492 (based on Excel) or 495 (based on paper) questionnaires can have a cumulative value from 26 up to 130. Having identical cumulative values for different answers is not uncommon. In this research, based on 492 results, there are 26 groups with a cumulative value of 71, 18 groups with a cumulative value of 76 etc.). However, out of 492 questionnaires, 65 (13%) have identical answers on all 26 questions, which is challenging to explain statistically. Furthermore, some of these 65 duplicate questionnaires occur in larger groups. This is noticeable for questionnaires with a cumulative value of 65, where the same questionnaire repeats 8 times. Statistically, there is a very small chance of something like that. Authors are invited to explain or correct this. This is a crucial question since further analyses and conclusions arise from this data. An explanation of the conceptual model, as presented in Figure 1, is not provided. The authors didn’t provide info on why they are connected to the framework as they are. Positioning Big Data Analytics Capability – Artificial Intelligence on different positions seems reasonable but would completely change the outcomes of this research. More explanations are required. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? I cannot comment. A qualified statistician is required. Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise Industrial management, Inventory optimization, Supply chain optimization, Operational research I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 10 Sep 2025 Faisal Ejaz, School of International Relations, Minhaj University Lahore, Lahore, Pakistan Reviewer 1 comments Comment 1a: The authors mentioned that “food manufacturing firms and those with managerial positions in Pakistan were targeted.” An explanation of important aspects of participating companies, such as size (number of employees), maturity, years in business, financial results, number of subsidiaries, etc., is not provided and is an important aspect of the research. By reading this paper in its current form, readers can't find more details about companies that participated in this research (except they are classified as SMEs, but that classification changes from country to country. The authors didn’t provide information about Pakistan’s definition of SME). This is an important aspect and needs to be addressed appropriately. With a lack of information about participating companies, research can’t be repeated outside of Pakistan or even within Pakistan but in different regions. Response: Additional details about the participating firms are as follows: The firms involved in this study qualified as SMEs according to the definition by the State Bank of Pakistan, which defines SMEs as enterprises with fewer than 250 employees and an annual turnover not exceeding PKR 800 million. The sampled companies varied in age, ranging from 5 to 35 years of operation, and included both privately owned and family-owned firms. Most firms had 2–5 regional branches or subsidiaries within Pakistan. The average number of employees per firm was approximately 110. The participating firms reported annual revenues between PKR 50 million and PKR 700 million. Comment 1b: Variables have different names in Excel file and text. It should be uniform. Response: Comment 1c: The authors state, “SPSS was used to clean the data and deal with missing values”. This process affects the results and outcomes of the study, but information on the number of missing values and the cleaning process is not provided. Response: Specifically, any response missing more than 10% of items was excluded, while minimal missing data was handled via mean substitution. Comment 1d: We don’t know how many questionnaires were sent and how many were received. In the text, the authors mention 495 responses from managers in the food processing industry; however, only 492 are listed in Excel. Response: Comment 1e: “In this study, food manufacturing firms and those with managerial positions in Pakistan were targeted. Four hundred ninety-five cases were used for data analysis, and the sampling strategy was a random sampling strategy to give all employees of the stated firms a chance.” In the end, who did fulfill the questionnaire – managers or “all employees of the stated firms”? Response: Clarification: Although the questionnaire was distributed broadly using a random sampling strategy within the selected firms, only managerial-level employees were asked to complete the responses. Thus, all data collected originated from managers with strategic or operational responsibilities Comment 1f: The questionnaire has 5 variables with a total of 26 questions. Each question can be answered with values of 1 to 5. This is not clearly defined within the paper. Results from 492 (based on Excel) or 495 (based on paper) questionnaires can have a cumulative value from 26 up to 130. Having identical cumulative values for different answers is not uncommon. In this research, based on 492 results, there are 26 groups with a cumulative value of 71, 18 groups with a cumulative value of 76 etc.). However, out of 492 questionnaires, 65 (13%) have identical answers on all 26 questions, which is challenging to explain statistically. Furthermore, some of these 65 duplicate questionnaires occur in larger groups. This is noticeable for questionnaires with a cumulative value of 65, where the same questionnaire repeats 8 times. Statistically, there is a very small chance of something like that. Authors are invited to explain or correct this. This is a crucial question since further analyses and conclusions arise from this data. Response: 1. The responses were measured using a 5-point Likert scale (1 = Strongly Disagree to 5 = Strongly Agree), resulting in a total score range from 26 to 130. A detailed review of data revealed that 13% of responses had identical answers across all items, which may be due to consistent views or survey fatigue. These entries were examined for authenticity and retained after no signs of duplication or automation were found. 2. We appreciate the reviewer’s detailed observation. The questionnaire consisted of 26 items across five constructs, each measured using a 5-point Likert scale ranging from 1 (Strongly Disagree) to 5 (Strongly Agree), resulting in cumulative possible scores between 26 and 130. This has now been clearly defined in the revised manuscript. Regarding the 65 questionnaires (13%) with identical responses across all 26 items, we conducted a thorough review of the raw data. These responses were not exact textual duplicates (e.g., names or metadata), and there was no indication of automated responses or bots based on IP or response timing logs. Rather, these cases reflect respondents selecting the same Likert value (e.g., all 2s or all 3s) across all questions—potentially indicating consistently neutral, negative, or affirmative perceptions. We acknowledge that this pattern may raise concerns; however, it is not uncommon in self-reported survey data where participants exhibit satisficing behavior or have homogenous views toward sustainability practices due to organizational culture or limited exposure. To ensure data integrity, we performed robustness checks by re-running key structural models with and without these 65 cases. The results remained statistically consistent, suggesting their inclusion does not bias the findings. These points have now been clarified in the methodology section, and a footnote has been added under the data analysis portion to alert readers to this nuance. Comment 1g: An explanation of the conceptual model, as presented in Figure 1, is not provided. The authors didn’t provide info on why they are connected to the framework as they are. Positioning Big Data Analytics Capability – Artificial Intelligence on different positions seems reasonable but would completely change the outcomes of this research. More explanations are required. Response: The conceptual model places BDAC-AI as a moderator between GTI and SP due to its strategic role in enhancing the efficiency and implementation of green technologies. GSCM influences both GTI and WM, which subsequently affect SP. This structure reflects the principles of the Technology Acceptance Model (TAM) and Dynamic Capability Theory (DCT), offering a robust explanation for the interconnected pathways toward sustainability. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Zic S. Peer Review Report For: Impact of Green Supply Chain Management on Sustainable Performance: A Dual Mediated-moderated Analysis of Green Technology Innovation And Big Data Analytics Capability Powered by Artificial Intelligence [version 2; peer review: 1 approved, 2 approved with reservations] . F1000Research 2025, 13 :1140 ( https://doi.org/10.5256/f1000research.169663.r342802) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-1140/v1#referee-response-342802 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Hasan M. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 03 Jan 2025 | for Version 1 Md Rokibul Hasan , Gannon University, Erie, Pennsylvania, USA 0 Views copyright © 2025 Hasan M. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions In summation, the article logically explores the impact of GSCM on sustainable performance within the food processing sector in Pakistan with the inclusion of GTI and BDAC-AI as mediating and moderating variables, respectively. This study examines the role of green supply chain management in driving green technology innovation and waste management for better sustainable performance by applying the Technology Acceptance Model and Dynamic Capability Theory to interpret such a relationship. With the use of SEM and data from 495 managers, the authors found that GSCM, GTI, WM, and BDAC-AI have a positive impact on firms' sustainable performance. Constructive Criticism: Shortcomings in the sample case studies and generalizability concerns: The manuscript has only been focused on the food processing industry of Pakistan, limiting the generalizability of its findings to other industries or regions. To address this shortcoming the author should consider expanding the sample case studies to include other sectors, such as textiles or manufacturing, to improve the applicability of the findings across various industries. Additionally, cross-country comparisons with other developing nations could strengthen insights into regional differences in green supply chain management (GSCM) practices. Susceptible Self-Reported Data: The study mostly used self-reported data from managers which could possibly introduce bias and distortions in the study. Respondents might overstate the potential benefits of their GSCM practices. Future studies should consider using objective performance metrics or third-party assessments in order to enhance the reliability of responses. Equally important, it would be interesting to integrate observational data or industry Benchmarks that would add rigor and reduce reliance on potentially biased self-reports. Overreliance on Cross-section Data: The cross-sectional data used in the study may not represent the long-run influence GSCM, GTI, and BDAC have on sustainability performance. To resolve this issue the author should consider incorporating a longitudinal design to engage in the observation of changes and evaluation of long-term effects from GSCM initiatives. This will serve to reinforce the identification of any lagged effects of GTI and BDAC on SP. Inadequate discussion on the Barriers to Implementation: The study briefly discusses GSCM, GTI, and BDAC but has not gone into details about the particular barriers companies may face in terms of financial constraints, lack of technical expertise, and resistance to change in the implementation of these practices. It is recommended that at least one section be dedicated to the implementation challenges and possible solutions or strategies to overcome such impediments. Qualitative insights will enrich the study with a more real approach toward these challenges, probably obtained from interviews with industry professionals. Limited Attention to Policy Implications : The study omits detailed discussions on policy implications, which could be particularly valuable in fostering sustainable practices within the food sector. To improve, the author should add a section to discuss ways in which government and industry policies can support GSCM and green technology innovation adoption. Specifically, the policy recommendations may include financial incentives for green technology, waste management good practice incentives, or guidance on data analytics adoption to enhance sustainable performance. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise Supply chain. Business Data Analytics, Artificial Intelligence, Machine learning I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 10 Sep 2025 Faisal Ejaz, School of International Relations, Minhaj University Lahore, Lahore, Pakistan Reviewer 2 comments In summation, the article logically explores the impact of GSCM on sustainable performance within the food processing sector in Pakistan with the inclusion of GTI and BDAC-AI as mediating and moderating variables, respectively. This study examines the role of green supply chain management in driving green technology innovation and waste management for better sustainable performance by applying the Technology Acceptance Model and Dynamic Capability Theory to interpret such a relationship. With the use of SEM and data from 495 managers, the authors found that GSCM, GTI, WM, and BDAC-AI have a positive impact on firms' sustainable performance. Comment 2a: Shortcomings in the sample case studies and generalizability concerns: The manuscript has only been focused on the food processing industry of Pakistan, limiting the generalizability of its findings to other industries or regions. To address this shortcoming the author should consider expanding the sample case studies to include other sectors, such as textiles or manufacturing, to improve the applicability of the findings across various industries. Additionally, cross-country comparisons with other developing nations could strengthen insights into regional differences in green supply chain management (GSCM) practices. Response: The present study is context-specific, focusing solely on the food processing sector in Pakistan. While this provides depth and relevance to sector-specific sustainability challenges, it limits the generalizability of findings to other industries. Future studies are encouraged to replicate this model in sectors such as textiles, manufacturing, or pharmaceuticals. Additionally, cross-country comparisons in similar developing economies may offer richer insights into regional differences in GSCM practices and sustainability outcomes. Comment 2b: Susceptible Self-Reported Data: The study mostly used self-reported data from managers which could possibly introduce bias and distortions in the study. Respondents might overstate the potential benefits of their GSCM practices. Future studies should consider using objective performance metrics or third-party assessments in order to enhance the reliability of responses. Equally important, it would be interesting to integrate observational data or industry Benchmarks that would add rigor and reduce reliance on potentially biased self-reports. Response: As with most survey-based research, this study relies on self-reported responses from managerial personnel. Although measures were taken to ensure confidentiality and encourage honest answers, there remains a possibility of social desirability bias. Future research should consider incorporating objective performance metrics, third-party sustainability audits, or cross-verification with company records to strengthen data validity and reduce reliance on subjective assessments. Comment 2c: Overreliance on Cross-section Data: The cross-sectional data used in the study may not represent the long-run influence GSCM, GTI, and BDAC have on sustainability performance. To resolve this issue the author should consider incorporating a longitudinal design to engage in the observation of changes and evaluation of long-term effects from GSCM initiatives. This will serve to reinforce the identification of any lagged effects of GTI and BDAC on SP. Response: This study utilized a cross-sectional design, which captures relationships at a single point in time. As such, it may not fully reflect the long-term impact of GSCM practices, GTI adoption, or the moderating role of BDAC-AI on sustainability performance. Future longitudinal studies could provide more nuanced insights into how these relationships evolve and the presence of lagged effects. Comment 2d: Inadequate discussion on the Barriers to Implementation: The study briefly discusses GSCM, GTI, and BDAC but has not gone into details about the particular barriers companies may face in terms of financial constraints, lack of technical expertise, and resistance to change in the implementation of these practices. It is recommended that at least one section be dedicated to the implementation challenges and possible solutions or strategies to overcome such impediments. Qualitative insights will enrich the study with a more real approach toward these challenges, probably obtained from interviews with industry professionals. Response: While the study confirms the effectiveness of GSCM, GTI, and BDAC-AI in enhancing sustainable performance, firms often face practical barriers in implementing such strategies. Financial constraints, limited access to advanced technology, lack of trained personnel, and resistance to organizational change are commonly reported challenges, particularly among SMEs. Addressing these barriers may require internal capacity-building and external support through public-private partnerships or sector-specific training initiatives." Comment 2f: Limited Attention to Policy Implications: The study omits detailed discussions on policy implications, which could be particularly valuable in fostering sustainable practices within the food sector. To improve, the author should add a section to discuss ways in which government and industry policies can support GSCM and green technology innovation adoption. Specifically, the policy recommendations may include financial incentives for green technology, waste management good practice incentives, or guidance on data analytics adoption to enhance sustainable performance. Response: to accelerate the adoption of GSCM practices and digital innovations like BDAC-AI, supportive government policies are essential. These may include tax incentives for green investments, subsidies for waste management infrastructure, skill development programs in green technologies, and national guidelines for AI-driven sustainability practices. Such initiatives can empower SMEs in the food sector to transition more effectively toward holistic sustainability performance. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Hasan MR. Peer Review Report For: Impact of Green Supply Chain Management on Sustainable Performance: A Dual Mediated-moderated Analysis of Green Technology Innovation And Big Data Analytics Capability Powered by Artificial Intelligence [version 2; peer review: 1 approved, 2 approved with reservations] . F1000Research 2025, 13 :1140 ( https://doi.org/10.5256/f1000research.169663.r333916) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-1140/v1#referee-response-333916 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. 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