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Big Data Analytics as a Driver for Sustainable Performance: The Role of Green Supply Chain Management in Advancing Circular Economy in Saudi Arabian Pharmaceutical Companies

Sustainability 2025, 17(14), 6319; https://doi.org/10.3390/su17146319

by Mohammad Mousa Mousa¹, Heyam Abdulrahman Al Moosa²

, Issam Naim Ayyash³, Fandi Omeish⁴

, Imed Zaiem^5,*

, Thamer Alzahrani⁵, Samiha Mjahed Hammami²

and Ahmad M. Zamil⁶

Reviewer 1:

Sathyanarayana Parayitam

Reviewer 2: Anonymous

Reviewer 3:

Dmitrii Kaplun

Reviewer 4: Anonymous

Sustainability 2025, 17(14), 6319; https://doi.org/10.3390/su17146319

Submission received: 31 March 2025 / Revised: 26 June 2025 / Accepted: 1 July 2025 / Published: 10 July 2025

(This article belongs to the Section Economic and Business Aspects of Sustainability)

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Dear Authors:

It was my pleasure to review the paper. The paper has several merits. However, I have some suggestions as follows:

The authors mentioned: "Such facts resulted in industries moving towards 3 fundamental strategies that when combined into long-terms plans.". I suggest using the word 'three' instead of 3', in the sentence.
The in-text citations should be in alphabetical order. The authors mentioned at several places as follows:

(Gupta and Singh, 2021; Lin et al., 2023, Asbeetah et al., 2025)

(Darlington, 2023; Mousa et al., 2024; Gupta et al., 2024)

3. Theoretical Framework and Hypothesis Development.

It is better to mention it as 'Theoretical Framework and Hypotheses Development'.

4. The authors mentioned "Moreover, it promotes sustainable practices across processes, technologies and social behavior….". ’it’ means what?

5. The following sentence is confusing.

"Big data analytics is deemed a pioneering approach to sound decision-making in organizations, conceivably resulting in critical transformations regarding the reinforcement and improvements of the circular economy which can promote upcoming digital technologies including artificial intelligence (AI), big data, block chain, and the Internet of Things."

6. Restructure of some sentences: The authors mentioned

A requirement that was specified by Sahoo et al. (2023), Sangpetch & Ueasangkomsate (2023), Guilhem & Klein (2024), and Al-Khatib (2024) where they revealed a positive impact of big data analytics on the circular economy.

The above sentence can be redrafted as follows:

Some researchers documented positive effect of big data analytics on the circular economy {Al-Khatib, 2024; Sahoo et al. 2023, Guilhem & Klein, 2024, Sangpetch & Ueasangkomsate, 2023).

7. Hypotheses: The hypotheses need re-drafting. For examle, the authors mentioned

H1: Large data analysis reflect beneficial effect on the circular economy.

I suggest H1 to be redrafted as follows:

H1: Large data analysis has a significant and positive effect on the circular economy.

Mediation hypothesis:

The authors framed the mediation hypothesis as follows;

H5: Sustainable performance facilitate the positive relation among big data analysis and the circular economy.

I did not understand this. I suggest the mediation hypothesis to be redrafted as follows:

H5: Sustainable Performance mediates the relationship between Big Data Analysis and Circular Economy.

8. Sangpetch & Ueasangkomsate (2023) demonstrated that…..’&’ should not be used in a sentence

9. The results demonstrate high convergent validity, with CR values ranging from 0.938 to 0.949, well above the recommended threshold..

Typically, CR values should not exceed 0.90 [which is a threshold]. High CR values may indicate jingle-jangle fallacy.

9. Table 6. Hypothesis testing..... This should be changed as Table 6. Hypotheses testing

10. Discussion section: The authors need to unpack the findings in light of findings from the literature.

11. The authors need to explain whether the model can be replicated by conducting studies in developed nations. In other words, the authors are suggested to explain the generalizabilit of findings.

12. Novelty of the study: The authors need to mention in what way this study is novel. Almost all the relationships explored are already proved and obvious. What is the contribution of this paper to the literature. I suggested a paragraph to explain the major contributions of the study.

Author Response

We express our sincere gratitude as you have dedicated your precious time to helping us improve our work. Thank you very much. Your comments greatly improved the paper. We addressed all comments and hope that our answers are satisfactory.

The reviewer comments are reported followed by our responses .

Comment 1. The authors mentioned: "Such facts resulted in industries moving towards 3 fundamental strategies that when combined into long-terms plans.". I suggest using the word 'three' instead of 3', in the sentence.

Proposed response: We have improved all the introduction

Comment 2. The in-text citations should be in alphabetical order. The authors mentioned at several places as follows: (Gupta and Singh, 2021; Lin et al., 2023, Asbeetah et al., 2025) (Darlington, 2023; Mousa et al., 2024; Gupta et al., 2024)

Proposed response: We have checked and revised all references in the text and in the bibliography according to the journal's guidelines.

Comment 3. Theoretical Framework and Hypothesis Development. It is better to mention it as 'Theoretical Framework and Hypotheses Development'.

Proposed response: We have replaced hypothesis by hypotheses

Comment 4. The authors mentioned "Moreover, it promotes sustainable practices across processes, technologies and social behavior….". ’it’ means what?

Proposed response: Here's a clearer and more polished version of your paragraph

The circular economy represents a comprehensive approach to addressing environmental challenges, particularly through the elimination of waste and the reduction of pollution. This model emphasizes the optimization of resource value by promoting recycling and the continuous circulation of products and materials. Additionally, it supports the transition to renewable energy sources and sustainable materials that contribute to the restoration of natural ecosystems (Sangpetch & Ueasangkomsate). Beyond material and energy considerations, the circular economy also fosters sustainability across industrial processes, technological innovation, and societal behaviors, thereby offering a multidimensional framework for long-term environmental and economic resilience.

Comment 5. The following sentence is confusing. "Big data analytics is deemed a pioneering approach to sound decision-making in organizations, conceivably resulting in critical transformations regarding the reinforcement and improvements of the circular economy which can promote upcoming digital technologies including artificial intelligence (AI), big data, block chain, and the Internet of Things."

Proposed response: In the revised manuscript, we have rewritten the sentence for clarity

Modification to add to the article a groundbreaking tool for informed decision-making in organizations, potentially driving key changes that strengthen and advance the circular economy. This, in turn, can support the development of emerging digital technologies such as artificial intelligence (AI), big data, blockchain, and the Internet of Things (IoT).

Comment 6. Restructure of some sentences: The authors mentioned A requirement that was specified by Sahoo et al. (2023), Sangpetch & Ueasangkomsate (2023), Guilhem & Klein (2024), and Al-Khatib (2024) where they revealed a positive impact of big data analytics on the circular economy. The above sentence can be redrafted as follows: Some researchers documented positive effect of big data analytics on the circular economy {Al-Khatib, 2024; Sahoo et al. 2023, Guilhem & Klein, 2024, Sangpetch & Ueasangkomsate, 2023).

Proposed response: We have restructured the sentence as suggested in the revised manuscript

Comment 7. Hypotheses: The hypotheses need re-drafting. For example, the authors mentioned

H1: Large data analysis reflects beneficial effect on the circular economy. I suggest H1 to be redrafted as follows:

H1: Large data analysis has a significant and positive effect on the circular economy.

Mediation hypothesis:

The authors framed the mediation hypothesis as follows.

H5: Sustainable performance facilitates the positive relation among big data analysis and the circular economy.

I did not understand this. I suggest the mediation hypothesis to be redrafted as follows:

H5: Sustainable Performance mediates the relationship between Big Data Analysis and Circular Economy.

Proposed response: We have refined the hypotheses as suggested

Comment 8. Sangpetch & Ueasangkomsate (2023) demonstrated that…..’&’ should not be used in a sentence

Proposed response: We have refined

Comment 9 (CR values)

Reviewer's comment: "The results demonstrate high convergent validity, with CR values ranging from 0.938 to 0.949, well above the recommended threshold. Typically, CR values should not exceed 0.90 [which is a threshold]. High CR values may indicate jingle-jangle fallacy."

Proposed response: The CR values in our study (0.938 to 0.949) slightly exceed the recommended threshold of 0.90. We have carefully verified our constructs to avoid the jingle-jangle fallacy problem. According to Hair et al. (2019), although high CR values may potentially indicate redundancy between items, values between 0.90 and 0.95 remain within an acceptable range when discriminant validity is established. Our discriminant validity analyses (Fornell-Larcker criterion and HTMT ratio) confirm that our constructs are distinct from each other.

Furthermore, the items were developed based on well-established theoretical frameworks, and each construct was carefully defined and operationalized to minimize conceptual overlap. We have added a brief discussion of this issue in the revised manuscript.

Modification to add to the article (in section 4.1, after the paragraph on discriminant validity, line 411): " Therefore, discriminant validity analyses confirm that our constructs are sufficiently distinct, thus mitigating potential concerns regarding the jingle-jangle fallacy despite slightly elevated CR values (Hair et al., 2019)."

Comment 10 (Discussion section)

Reviewer's comment: "Discussion section: The authors need to unpack the findings in light of findings from the literature."

Proposed response: We have enriched the discussion section by comparing our results more thoroughly with the existing literature.

Modification to add to the article (in section 5, after the first paragraph, line 502): "Examining our results in light of existing literature, we find that the positive impact of big data analysis on the circular economy coincides with the conclusions of Gupta et (al., 2015). Our results on the mediating role of sustainable performance between big data analysis and the circular economy confirm the observations of Guilhem and Klein (2022), while extending their application to the specific context of the pharmaceutical sector in emerging markets. This mediation can be explained by the fact that big data analysis allows for the identification of opportunities for resource optimization and waste reduction, which in turn improve sustainable performance, ultimately leading to better implementation of circular economy practices.

Contrary to Rashid et al. (2027), who did not identify a mediating role of environmental performance between big data analysis and industrial sustainability, our study reveals a significant mediating effect in the Saudi pharmaceutical context. This divergence can be explained by the specificities of the pharmaceutical sector, which is highly regulated and where the management of hazardous waste and product traceability are major concerns, thus making the use of big data particularly relevant for achieving sustainability and circular economy objectives."

Comment 11 (Generalizability)

Reviewer's comment: "The authors need to explain whether the model can be replicated by conducting studies in developed nations. In other words, the authors are suggested to explain the generalizability of findings."

Proposed response: We have added a discussion on the generalizability of our model and the possibilities for replication in different contexts, particularly in developed countries.

Modification to add to the article (to add in section 7 Conclusion, Limitations, and Future Research as a new paragraph, line 598): "

Third, our sample is limited to the Saudi pharmaceutical sector, which could limit the generalizability of our results to other sectoral or geographical contexts. Although the Saudi pharmaceutical industry, shares structural and operational characteristics with other resource- and innovation-intensive industries, such as chemistry and biotechnology, and the issues explored, particularly digital transformation and sustainable practices, transcend sectoral and geographical boundaries. We encourage future research in other sectors and regions to confirm and enrich the scope of our model. Future research could replicate our model in various national and sectoral contexts to assess its robustness and examine moderating variables related to socio-economic, cultural, or regulatory factors such as regulatory pressure, or competitive intensity.

Comparative studies between sectors or countries would also provide valuable insight into the influence of contextual factors on the effectiveness of big data analysis initiatives in favor of the circular economy.

Comment 12 (Novelty of the study)

Reviewer's comment: "Novelty of the study: The authors need to mention in what way this study is novel. Almost all the relationships explored are already proved and obvious. What is the contribution of this paper to the literature. I suggested a paragraph to explain the major contributions of the study."

Proposed response: We have integrated a specific paragraph highlighting the novelty and distinctive contributions of our study in the introduction section.

Modification to add to the article (to add at the end of the introduction section, after the presentation of research questions, line 76): "The emerging nature of big data analysis calls for more empirical verifications and replications. Our study makes several innovative contributions to existing literature. First, we conceptually replicate the established links between big data analysis and sustainable performance identified in previous research (Guilhem & Klein, 2022; Al-Khatib, 2022; Abdallah et al., 2024), while constructively evaluating their strength and generalizability in the specific context of Saudi Arabia. Second, we examine these relationships in the framework of green supply chain management within a circular economy, focusing on the pharmaceutical sector in Saudi Arabia, which allows for the exploration of a field still understudied in the literature and provides unique perspectives on Middle Eastern emerging markets, largely underrepresented in current research. Third, our integrated model simultaneously examines two key mediating variables (sustainable performance and green supply chain management), allowing for a deeper understanding of the mechanisms by which big data analysis influences the circular economy. Finally, our study provides practical insights that are consistent with Saudi Arabia's Vision 2030, thus enriching the literature on digital transformation and sustainability in economies transitioning towards knowledge-based models."

Author Response File: Author Response.docx

Reviewer 2 Report

Comments and Suggestions for Authors

Journal: Sustainability (ISSN 2071-1050)

Manuscript ID: sustainability-3588151

Manuscript title: Big Data Analysis as a Driver for Sustainable Performance: The Role of Green Supply Chain Management in Advancing Circular Economy in Saudi Arabian Pharmaceutical Companies.

The manuscript “Big Data Analysis as a Driver for Sustainable Performance: The Role of Green Supply Chain Management in Advancing Circular Economy in Saudi Arabian Pharmaceutical Companies” findings may add to the existing knowledge. However, the following are some comments that could improve the quality of the manuscript.

The authors did not follow the journal's guidelines for the literature and references in the entire manuscript, which must be followed in scientific writing for MDPI journals. Plz follow the instructions in the entire manuscript as mentioned in the template.
The abstract section, it lacks the methodological part and what is related to its novelty and importance of the current manuscript. The abstract needs to be further highlight innovation.
L21, L25, ………….: “We” plz delete personal pronouns from the entire manuscript.
L38: “ On one hand”, L40: “ On another” , ….. Pz uses more precise linking tools than those written.
L47-48: “(Crimp et al., 2016; Zheng et al.,2015)” plz arrange the authors in brackets according to the year of publication.
L171-172: “(Liu et al., 2016, 2017, 2020; Xiao et al., 2021, 2022; Osman et al., 2021; Ji et al., 2025)” Plz arrange the authors in brackets according to the year of publication, and if the year of publication is equal, the arrangement will be done alphabetically according to the author's name. plz pay attention to this comment in the entire manuscript.
Plz choose expressive and important keywords, while not repeating any word mentioned in the title of the manuscript. Plz write the keywords in alphabetical order. Also, plz capitalize the first letter of each word.
L59: “mid-20th century” superscript.
L70: “(Sahoo et al.,2023; Belhadi et al.,2020)”, after following the journal instructions, arrange the authors in brackets according to the year of publication.
L103-106: plz rephrase the following sentence “The modern circular economy largely builds upon the linear economy model, which focuses on acquisition, use, and disposal with minimal attention to reclaiming resources within the production and consumption cycle and disposing of valueless products (Gupta et al., 2019; Negri & Giambone, 2023)”
From lines 100-130: This section discusses the circular economy in its entirety, but the researchers did not link this section to the manuscript topic in a way that would demonstrate its importance in the current manuscript.
In simple words, What is the difference between a circular economy and a regenerative economy? Plz clarify.
L146, L166, L207, L277,…. …………..: “we”, “our; in the table”, The authors should avoid using personal pronouns in the entire manuscript.
L186-187: “Research by Benzidia, et al. (2021); 186 Rashid, et al. (2024); Al-Khatib (2022); Gallo, et al. (2023); and Mehmood, et al. (2024)” plz reduce the number of citations at the same place,
The introduction and what follows it, up to the methodology part, is very long and should be shortened to the points that serve the topic of the manuscript only, and in brief.
L324: “Big Data Analytics (BDA)” and other terms in the entire manuscript, Plz define all acronyms when they appear first. If they appear once, there is no need to use acronyms.
L351: “Table 4.” Plz put the tables and figures number in parentheses.
Personally, I highly recommend combining the results and discussion sections. It makes the contents easy for readers to follow, and the authors can extend the discussion about every part of the results. In the discussion; Start with your novelty.
The results presented are clearly presented; my main concern is about the discussion, which is not of good level.
Discussion needs to be improved. Plz discuss the possible mechanism behind your results. Avoid comparing with others work. Plz add more in-depth discussions in the manuscript.
It is preferable to combine the recommendation part with the end of the discussion part, with the addition of a part independent of the conclusion.
The conclusion section should be written and should go deeper; it would be more interesting if the authors focused more on the specific. The conclusion should be crispy and not in detailed form. Also, the conclusion needs more improvement. plz mention how the future study can complete your work. What is the lack of knowledge? The authors concentrated only on the study strengths and ignored the limitations, plz discuss the study limitations.
Plz check carefully the references in the text and list and prepare according to the guide of authors.
Besides the suggestion from the reviewers, a thorough proof-check (punctuation, spelling/typing) and most importantly English language is recommended.

Comments for author File: Comments.pdf

Comments on the Quality of English Language

The English could be improved to more clearly express the research

Author Response

Your comments are reported followed by our responses

Comment on the abstract

Reviewer's comment: "The abstract section, it lacks the methodological part and what is related to its novelty and importance of the current manuscript. The abstract needs to be further highlight innovation."

Proposed response: We have revised the abstract to better highlight the methodology, novelty, and importance of our study.

Modification to add to the article (revision of the abstract, starting from line 31): "Abstract: Facing growing sustainability challenges and the critical priority of digital transformation, this study innovatively explores the links between big data, sustainable performance, and green supply chain in a circular economy logic, filling a notable gap in emerging markets, particularly the pharmaceutical sector. Our study proposes an original conceptual model linking big data analysis to the circular economy, tested with 275 employees from the Saudi pharmaceutical sector. The results, obtained through state-of-the-art PLS-SEM modeling, indicate a significant positive impact of big data analysis on sustainable performance and green supply chain management within the circular economy framework. The study also reveals the crucial mediating role of sustainable performance and green supply chain management in the relationship between big data analysis and the circular economy. Our study proposes an integrated framework for understanding how digital technologies support the circular economy in emerging markets, with practical implications for pharmaceutical sector actors and policymakers, in line with Saudi Arabia's Vision 2030."

Comment on keywords

Reviewer's comment: "Plz choose expressive and important keywords, while not repeating any word mentioned in the title of the manuscript."

Proposed response: We have modified the keywords to make them more expressive and avoid any repetition with the article title.

Modification to add to the article (revision of keywords, line 54): "Keywords: Environmental performance, sustainable sourcing, resource regeneration, pharmaceutical industry, Vision 2030, digital transformation, emerging markets."

Comment on the sentence to reformulate (L103-106)

Reviewer's comment: "Plz rephrase the following sentence 'The modern circular economy largely builds upon the linear economy model, which focuses on acquisition, use, and disposal with minimal attention to reclaiming resources within the production and consumption cycle and disposing of valueless products'"

Proposed response: We have reformulated this sentence as suggested.

Modification to add to the article (reformulation, lines 117-120): "Unlike the traditional linear economic model characterized by an 'extract-make-dispose' approach with little attention paid to resource recovery, the modern circular economy proposes a transformative paradigm that radically transforms production and consumption cycles to maximize resource value throughout their lifecycle (Gupta et al., 2019; Negri & Giambone, 2023)."

Comment on the circular economy section (L100-130)

Reviewer's comment: "From lines 100-130: This section discusses the circular economy in its entirety, but the researchers did not link this section to the manuscript topic in a way that would demonstrate its importance in the current manuscript."

Proposed response: We have strengthened this section by establishing more explicit links between the circular economy and the central theme of our research.

Modification to add to the article (addition at the end of the section on circular economy, line 147): "In the context of the Saudi pharmaceutical sector, adopting circular economy principles represents a good strategic opportunity to transform environmental challenges into competitive advantages. The resource-intensive nature and high potential for hazardous waste generation in this sector reinforce the interest in applying the circular economy. Big data analysis also constitutes a strategic lever, providing powerful tools to catalyze this transition by identifying inefficiencies in resource use, optimizing production processes, and facilitating the implementation of waste reduction strategies (add a bibliographic source at this level). Our study particularly examines how the integration of big data analysis can accelerate the adoption of circular economy practices in this sector, thus contributing to the achievement of sustainability and economic diversification objectives defined in Saudi Arabia's Vision 2030."

Comment on the difference between circular economy and regenerative economy

Reviewer's comment: "In simple words, what is the difference between a circular economy and a regenerative economy? Plz clarify."

Proposed response: We have added a concise explanation to differentiate these two concepts.

Modification to add to the article (to add after the definition of circular economy, line 104): "It is important to clarify the distinction between circular economy and regenerative economy, terms sometimes used interchangeably in the literature. Referring to the article by Piero Morseletto (2020)

Gibbons et al. (2018), the circular economy primarily focuses on optimizing resource flows and minimizing waste through reuse, recycling, and recovery cycles. The regenerative economy, on the other hand, seeks not only to maintain resources within the economic cycle but also to actively restore and regenerate natural and social systems. In the context of this study, we primarily use the term 'circular economy', while recognizing its regenerative aspects, particularly in the context of sustainable development of the Saudi pharmaceutical sector."

We have added this reference to the bibliography: Morseletto, P. (2020). Restorative and regenerative: Exploring the concepts in the circular economy. Journal of Industrial Ecology, 24(2), 361–373. https://doi.org/10.1111/jiec.12987

Comment on the number of citations (L186-187)

Reviewer's comment: "L186-187: 'Research by Benzidia, et al. (2021); Rashid, et al. (2024); Al-Khatib (2022); Gallo, et al. (2023); and Mehmood, et al. (2024)' plz reduce the number of citations at the same place"

Proposed response: We have reduced the number of citations at this location.

Modification to add to the article (reformulation, lines 230-231 "Significant studies, notably those of Benzidia et al. (2021) and Al-Khatib (2022), have demonstrated the positive influence of big data analysis on green supply chains."

Comment on the length of the introduction

Reviewer's comment: "The introduction and what follows it, up to the methodology part, is very long and should be shortened to the points that serve the topic of the manuscript only, and in brief."

Proposed response: We have condensed these sections to make them more concise and focused on the essential points of our study.

We have reduced the introduction by emphasizing the essentials of the subject: Global events in recent years have triggered significant shifts in global health and economic systems (Das et al. [1]). The COVID-19 pandemic, for example, exposed the fragility of traditional supply chains and emphasized the urgent need for resilient and sustainable business models. In response, industries have adopted three key strategies—digitalization, sustainability, and adaptability—that serve as crucial drivers of long-term competitiveness (Jalil et al. [4]; [2,3]). The rapid technological evolution since the mid-20th century, especially in big data analytics and artificial intelligence, has transformed business models, pushing firms to increasingly rely on advanced technologies to boost operational efficiency and competitive advantage (Labib [8]). Big data analytics, in particular, plays a strategic role by improving investment decision quality, optimizing resource use within circular economy frameworks, and enhancing sustainable competitiveness (Junco et al. [9]; Sangpetch & Ueasangkomsate [10]). However, leveraging big data requires not only technological infrastructure but also strong organizational capabilities and human skills (Belhadi et al. [13]; [11,12]). Despite its potential, empirical studies on big data analytics in emerging markets—especially in the context of circular economy—remain limited. This study aligns with Saudi Arabia's Vision 2030, which seeks to build a knowledge-based economy through technological innovation and sustainability. Focusing on the pharmaceutical sector, this research investigates how big data analytics support circular economy practices, with sustainable performance and green supply chain management acting as mediators. The study contributes theoretically and practically by providing insights into how big data analytics can foster long-term performance in emerging markets. To address these goals, the study explores the following research questions:

To what extent do big data analytics influence sustainable achievement and the regenerative economy?
How does sustainable performance mediate the relationship between big data analytics and the circular economy?
What role does green supply chain management play in mediating the link between big data analytics and the circular economy?

Also, we have reduced part 2.1 on Circular Economy

2.1. Circular Economy : The circular economy is a restorative and regenerative system focused on optimizing material use, waste reduction, and energy efficiency (Al-Khatib [14]). Building on the linear economy model, it emphasizes reusing resources and minimizing waste, addressing environmental concerns like pollution and resource depletion (Sangpetch & Ueasangkomsate [10]). Although the concept dates back to the 1970s, it has gained significance in recent years due to resource scarcity and changing consumer behaviors. Circular economy practices include recycling, renewable energy, and materials restoration to reduce environmental impact. The transition from linear to circular economies fosters long-term resilience, competitiveness, job creation, and reduced environmental pressure (Al-Khatib [14]; Yalçın [18]). By focusing on durability and recyclability, companies can lower waste and costs, enhancing sustainability (Chau et al. [17]; Banu [19]).

Comment on combining results and discussion

Reviewer's comment: "Personally, I highly recommend combining the results and discussion sections. It makes the contents easy for readers to follow, and the authors can extend the discussion about every part of the results. In the discussion; Start with your novelty."

Proposed response: We have merged the results and discussion sections to improve fluidity and allow for a more integrated analysis of our results.

Modification to add to the article (revised section title, line 465): "5. Results and Discussion"

We have added the red paragraphs:

The results of our statistical analysis, presented in table 6, confirm our hypotheses and reveal significant insights for theory and practice. The empirical analysis supports H1, H2 and H3., confirming respectively that BDA exerts a strong direct positive impact on the circular economy (β = 0.478, t = 6.786, p < 0.001), sustainable performance (β = 0.692, t = 10.927, p < 0.001), as well as green supply chain management (β = 0.781, t = 14.989, p < 0.001). These results affirm the theoretical assumption that big data capabilities can directly enhance sustainable outcomes and operational efficiencies within the industrial sector.

Additionally, the results reveal significant relationship between SP and CE (β = = 0.214, p < 0.001) supporting H4. In support of Hypothesis 5, the main effect of GSCM on CE was also significant (β = 0.287 p < 0.001).

Moreover, the mediation analysis reveals that both sustainable performance (SP) and green supply chain management (GSCM) significantly mediate the relationship between BDA and the circular economy. Specifically, BDA improves SP (indirect effect β = 0.148, t = 2.537, p = 0.013) and GSCM (indirect effect β = 0.225, t = 3.780, p < 0.001), which in turn positively influence the circular economy, thereby supporting H6 and H7. This highlights the importance of considering intermediary variables when designing strategies for digital transformation and sustainability.

The study reveals a prominent attention, from the research sample, towards the impact of large-scale analysis of industrial sustainability and green interpersonal resource management in upholding the circular economy.

The results demonstrate that applying big data analytics techniques improves economic and environmental sustainability in pharmaceutical companies.

The study stresses the significance of developing and implementing strategies that leverage Big Data analytics to enhance sustainability and promote sustainable growth.

These results are consistent with and extend previous studies (Sahoo, et al. [11]; Guilhem & Klein [22]; Al-Khatib [14]), which highlighted big data analytics positive influence on the circular economy. Some studies, such as Belhadi, et al. [13], also confirm that big data enhances environmental capability and operational performance.

The positive impact of big data analysis on the circular economy in the Saudi pharmaceutical sector can be explained at three main levels (Alshuwaikhat et al., 2016). At the operational level, big data allows for the identification of inefficiencies in resource use throughout the pharmaceutical value chain. For example, predictive algorithms anticipate failures in production, thus reducing the waste of costly and dangerous raw materials. At the strategic level, big data analysis helps with evidence-based decision-making regarding investments in green technologies and circular infrastructures. This allows pharmaceutical companies to prioritize the most economically and ecologically profitable initiatives. Finally, at the supply chain level, big data improves the visibility and traceability of material flows, facilitating proactive management and the identification of industrial symbiosis opportunities, essential in a sector subject to strict regulations.

The findings highlight the vital role of sustainable performance and green supply chain management as mediators that reinforce the shift toward a regenerative economy. This positive influence proves big data’s capability to enhance efficiency and attain sustainability goals through developing green supply chain management approaches and upgrading operational strategies. Sustainable performance plays a mediating role in the relationship between big data and the circular economy by optimizing resource management and reducing the environmental footprint, thus creating a favorable environment for the adoption of circular economy practices. For its part, the green supply chain translates the impact of big data on supplier relationships and logistics practices, promoting the integration of closed loops specific to this model (Dubey et al., 2020).

Examining our results in light of existing literature, we find that the positive impact of big data analysis on the circular economy coincides with the conclusions of Gupta et al., (2015). Our results on the mediating role of sustainable performance between big data analysis and the circular economy confirm the observations of Guilhem and Klein (2022), while extending their application to the specific context of the pharmaceutical sector in emerging markets. This mediation can be explained by the fact that big data analysis allows for the identification of opportunities for resource optimization and waste reduction, which in turn improve sustainable performance, ultimately leading to better implementation of circular economy practices.

Comment on improving the discussion

Reviewer's comment: "Discussion needs to be improved. Plz discuss the possible mechanism behind your results. Avoid comparing with others work. Plz add more in-depth discussions in the manuscript."

Proposed response: We have enriched the discussion section by exploring more deeply the mechanisms underlying our results.

Modification to add to the article (to add in the discussion section, after the presentation of results, line 494):

We have added to the bibliography the following 2 references:

Alshuwaikhat, H. M., Adenle, Y. A., & Saghir, B. (2016). "Sustainability Assessment of Higher Education Institutions in Saudi Arabia". Sustainability, 8(8), 750. https://doi.org/10.3390/su8080750

Dubey, R., Gunasekaran, A., Childe, S. J., & Papadopoulos, T. (2020). Upstream supply chain visibility and complexity effect on focal company's sustainable performance: Indian manufacturers' perspective. Annals of Operations Research, 290(1), 25–50. https://doi.org/10.1007/s10479-017-2544-x

Comment on combining recommendations and discussion

Reviewer's comment: "It is preferable to combine the recommendation part with the end of the discussion part, with the addition of a part independent of the conclusion."

Proposed response: We have integrated the recommendations in new section 6.Research Contributions and Practical Implications and created a distinct section : 7. Conclusion, Limitations, and Future Research

Modification to add to the article

Line 547: We put forward these significant number of recommendations

Comment on the conclusion

Reviewer's comment: "The conclusion section should be written and should go deeper; it would be more interesting if the authors focused more on the specific. The conclusion should be crispy and not in detailed form. Also, the conclusion needs more improvement. plz mention how the future study can complete your work. What is the lack of knowledge? The authors concentrated only on the study strengths and ignored the limitations, plz discuss the study limitations."

Proposed response: We have revised the conclusion to make it more concise, deeper, and more specific, with the addition of perspectives for future research and limitations of our study.

Modification to add to the article (new conclusion section, has been created after the discussion and recommendations section): "7. Conclusion, Limitations, and Future Research

This study has revealed how big data analysis can serve as a catalyst to promote the circular economy in the pharmaceutical sector in Saudi Arabia, with significant implications for the achievement of Vision 2030 objectives. Our results confirm that big data analysis positively influences the circular economy, both directly and indirectly, through sustainable performance and green supply chain management. The integrated framework developed in this study constitutes a strategic guide for pharmaceutical companies wishing to leverage digital technologies to achieve their sustainability objectives.

Despite its contributions, our study has several limitations. First, the cross-sectional nature of our research limits our ability to observe the dynamic evolution of the relationships studied over time. Future research could adopt a longitudinal approach to examine how the relationships between big data analysis and the circular economy evolve during the different phases of digital maturity of companies.

Second, to strengthen the validity of the results, future research should use different statistical analysis methods, such as CB-SEM models, as well as segmentation and non-linear modeling approaches to explore more complex relationships and identify hidden heterogeneities in the sample.

Comment on references

Reviewer's comment: "Plz check carefully the references in the text and list and prepare according to the guide of authors."

Proposed response: We have checked and revised all references in the text and in the bibliography according to the journal's guidelines.

Comment on manuscript proofreading

Reviewer's comment: "Besides the suggestion from the reviewers, a thorough proof-check (punctuation, spelling/typing) and most importantly English language is recommended."

Proposed response: We have performed a thorough revision of the manuscript, including checking punctuation, spelling, and grammar, and have had the text revised by an English language professional to ensure the linguistic quality of the manuscript.

Author Response File: Author Response.docx

Reviewer 3 Report

Comments and Suggestions for Authors

This paper presents a vital conceptual and practical support within the context of Saudi Arabia’s strategic transformation toward a knowledge-based and sustainable economy. From a conceptual angle, the study develops a unified framework elucidating the relations between large data analytics and the regenerative economy, with long term performance and green supply system monitoring as intermediary variables. Empirical results stress the positive relationship between these variables, accordingly, enhancing theoretical understanding of upgrading sustainable performance in emerging markets. From a practical angle, this study present crucial realizations which decision-makers in Saudi Arabia’s pharmaceutical sector can apply in their transformative future, specifically the 2030 Vision. Such practical improvements guide investment decisions in developing digital infrastructure, building analytical capacities, and designing specialized training programs in big data analytics.

The idea of the paper is interesting but it lacks scientific and technical novelties and mathematical background. The presented statistical analysis is too simple. Statistical significance of parameters is not presented. The results are not clear.

Author Response

We would like to sincerely thank Reviewer 3 for the thorough and constructive feedback provided. We highly appreciate the time and expertise invested in reviewing our paper entitled "Big Data Analysis as a Driver for Sustainable Performance: The Role of Green Supply Chain Management in Advancing Circular Economy in Saudi Arabian Pharmaceutical Companies."

Below, we address the reviewer’s specific comments point-by-point and explain the improvements made accordingly:

Reviewer Comment:

The idea of the paper is interesting, but it lacks scientific and technical novelties and mathematical background.

Response:
Thank you for acknowledging the relevance and potential contribution of the research idea. To further strengthen the scientific and technical contribution of the study, we have expanded the theoretical framework to highlight the novelty of integrating Big Data Analytics (BDA) with Sustainable Performance (SP) and Green Supply Chain Management (GSCM) within a circular economy (CE) framework, specifically tailored to emerging markets like Saudi Arabia.
Additionally, we have emphasized the study’s unique contribution by clearly presenting the mediating roles of SP and GSCM, which have been underexplored in prior literature, particularly in the context of the pharmaceutical sector’s alignment with Vision 2030 goals.

Regarding the mathematical foundation, we have incorporated all the result of the structural equation modelling provided by SmartPLS, in tables 2,3,4,5 and 6 provided and the fig 2

Reviewer Comment:

The presented statistical analysis is too simple.

Response:
We have significantly enhanced the statistical analysis section by elaborating on the use of Partial Least Squares Structural Equation Modeling (PLS-SEM), a robust technique suitable for predictive research models involving complex mediations.

The improvements include:

Detailed explanation of the measurement model assessment, including tests for convergent validity (CR, AVE, item loadings) and discriminant validity (Fornell-Larcker criterion and HTMT ratios).
Explicit reporting of structural model indicators such as:

Coefficient of Determination (R²)
Stone-Geisser's Q² predictive relevance
Effect size (f²) for each structural path
Standardized Root Mean Square Residual (SRMR), confirming model fit.

These additions move the statistical rigor beyond basic descriptive and correlation analysis and present a comprehensive, multi-layered evaluation of the model's quality.

Reviewer Comment:

Statistical significance of parameters is not presented.

Response:
In response to this important observation, we have explicitly reported the statistical significance of all model parameters.

For each hypothesized path, we now provide:

Standardized path coefficient (β)
T-statistic value
P-value
Decision on hypothesis support.

An illustrative example is provided below:

Hypothesis	Path	β	T-value	P-value	Decision
H1	BDA → CE	0.478	6.786	0.000	Supported
H2	BDA → SP	0.692	10.927	0.000	Supported
H3	BDA → GSCM	0.781	14.989	0.000	Supported
H4	SP → CE	0.214	2.460	0.016	Supported
H5	GSCM → CE	0.287	3.721	0.000	Supported
H6	BDA → SP → CE (Mediation)	0.148	2.537	0.013	Supported
H7	BDA → GSCM → CE (Mediation)	0.225	3.780	0.000	Supported

This ensures transparency and confirms the robustness of the empirical findings.

Reviewer Comment:

The results are not clear.

Response:
1. We have merged the results and discussion sections to improve fluidity and allow for a more integrated analysis of our results.

Modification to add to the article (revised section title, line 452): "5. Results and Discussion.

Results and Discussion

The results of our statistical analysis, presented in table 6, confirm our hypotheses and reveal significant insights into theory and practice. The empirical analysis supports H1, H2 and H3., confirming respectively that BDA exerts a strong direct positive impact on the circular economy (β = 0.478, t = 6.786, p < 0.001), sustainable performance (β = 0.692, t = 10.927, p < 0.001), as well as green supply chain management (β = 0.781, t = 14.989, p < 0.001). These results affirm the theoretical assumption that big data capabilities can directly enhance sustainable outcomes and operational efficiencies within the industrial sector.

Additionally, the results reveal significant relationship between SP and CE (β = 0.214, p < 0.001) supporting H4. In support of Hypothesis 5, the main effect of GSCM on CE was also significant (β = 0.287 p < 0.001).

The results demonstrate that applying big data analytics techniques improves economic and environmental sustainability in pharmaceutical companies.

The study stresses the significance of developing and implementing strategies that leverage Big Data analytics to enhance sustainability and promote sustainable growth.

These results are consistent with and extend previous studies (Sahoo, et al. [8]; Guilhem & Klein [11]; Al-Khatib [12]), which highlighted big data analytics positive influence on the circular economy. Some studies, such as Belhadi, et al. [10], also confirm that big data enhances environmental capability and operational performance.

The positive impact of big data analysis on the circular economy in the Saudi pharmaceutical sector can be explained at three main levels [59]. At the operational level, big data allows for the identification of inefficiencies in resource use throughout the pharmaceutical value chain. For example, predictive algorithms anticipate failures in production, thus reducing the waste of costly and dangerous raw materials. At the strategic level, big data analysis helps with evidence-based decision-making regarding investments in green technologies and circular infrastructures. This allows pharmaceutical companies to prioritize the most economically and ecologically profitable initiatives. Finally, at the supply chain level, big data improves the visibility and traceability of material flows, facilitating proactive management and the identification of industrial symbiosis opportunities, essential in a sector subject to strict regulations.

Examining our results in light of existing literature, we find that the positive impact of big data analysis on the circular economy coincides with the conclusions of Gupta et al [18]. Our results on the mediating role of sustainable performance between big data analysis and the circular economy confirm the observations of Guilhem and Klein [11], while extending their application to the specific context of the pharmaceutical sector in emerging markets. This mediation can be explained by the fact that big data analysis allows for the identification of opportunities for resource optimization and waste reduction, which in turn improve sustainable performance, ultimately leading to better implementation of circular economy practices.

Contrary to Rashid et al. [27], who did not identify a mediating role of environmental performance between big data analysis and industrial sustainability, our study reveals a significant mediating effect in the Saudi pharmaceutical context. This divergence can be explained by the specificities of the pharmaceutical sector, which is highly regulated and where the management of hazardous waste and product traceability are major concerns, thus making the use of big data particularly relevant for achieving sustainability and circular economic objectives.

We have integrated the recommendations in new section 6.Research Contributions and Practical Implications and created a distinct section : 7. Conclusion, Limitations, and Future Research

Proposed response: We have revised the conclusion to make it more concise, deeper, and more specific, with the addition of perspectives for future research and limitations of our study.

Modification to add to the article (new conclusion section, has been created after the discussion and recommendations section): "7. Conclusion, Limitations, and Future Research

Research Contributions and Practical Implications

This study brings original contributions to the literature by examining the complex relationships between big data analysis, sustainable performance, green supply chain management, and the circular economy in the specific context of the Saudi pharmaceutical sector. Our integrative approach, which simultaneously considers two distinct mediating pathways, offers a new perspective on the mechanisms by which digital technologies can catalyze the transition towards circular economic models in emerging markets.

From a practical angle, this study present crucial realizations which decision- makers in Saudi Arabia’s pharmaceutical sector can apply in their transformative future, specifically the 2030 Vision. Such practical improvements guide investment decisions in developing digital infrastructure, building analytical capacities, and designing specialized training programs in big data analytics.

We put forward these significant number of recommendations: ---

Conclusion, Limitations, and Future Research

Author Response File: Author Response.docx

Reviewer 4 Report

Comments and Suggestions for Authors

This paper focuses on the relationship between big data analysis, sustainable performance, green supply chain management and circular economy, a topic with important contemporary and practical significance. Against the backdrop of global attention to sustainable development and digital transformation, this study focuses on pharmaceutical companies in Saudi Arabia, adding new perspectives to the research on related fields in emerging markets and making some innovations. However, there are still some aspects that need to be improved, as follows:
1. When the article discusses the impact of big data analysis on circular economy in lines 131-146, it simply lists the role of big data analysis in improving resource management efficiency and promoting resource recycling. However, it has not yet explored in depth how the technical principles and data processing mechanisms that big data analysis relies on are deeply integrated with the resource reduction, reuse and recycling principles of circular economy. It is recommended to further explore the inherent relationship between the two and strengthen theoretical analysis to make the research more in-depth and convincing.
2. This paper only selects employees of pharmaceutical companies in Saudi Arabia as samples, and the sample source is relatively simple. This single sample composition may not fully reflect the actual situation in other industries or countries, thereby limiting the generalizability and generalization of the research results. Please prove the generalizability of your research.
3. This paper does not thoroughly test the stability of the research results. In order to ensure the reliability of the research results and enhance the credibility of the research conclusions, it is recommended to use different samples or analysis methods for robustness testing. Through multi-dimensional and multi-angle verification, the reliability of the research results can be further determined, providing more solid data support for the research conclusions.
4. The article mentioned in lines 264-267 that enterprises implement green supply chain management, which is in line with the concept of circular economy, can improve resource utilization efficiency and reduce waste. In fact, the development of circular economy cannot be separated from the strong support of government policies, such as tax incentives and subsidy policies, which can effectively encourage enterprises to actively adopt the circular economy model. It is recommended to refer to https://doi.org/10.1016/j.envres.2023.118074, which further enriches the research on the relationship between government policies and circular economy and makes the research content more complete and comprehensive.

Author Response

Comment 1 (Big data analysis and circular economy)

Reviewer's comment: "When the article discusses the impact of big data analysis on circular economy in lines 131-146, it simply lists the role of big data analysis in improving resource management efficiency and promoting resource recycling. However, it has not yet explored in depth how the technical principles and data processing mechanisms that big data analysis relies on are deeply integrated with the resource reduction, reuse and recycling principles of circular economy."

Proposed response: We have deepened our analysis of the integration between the technical principles of big data analysis and the fundamental principles of the circular economy.

Modification to add to the article (to add after line 172-181: "The integration of big data into the circular economy is based on technical mechanisms that align data processing with the principles of reduction, reuse, and recycling (Papadopoulos et al., 2017). Machine learning algorithms optimize resource use by identifying complex consumption patterns invisible to the human eye. For example, in the pharmaceutical industry, predictive analysis anticipates production defects, thus reducing losses of raw materials. Additionally, IoT combined with big data analysis ensures real-time traceability of materials, facilitating their reuse. Finally, data classification techniques allow for better categorization of waste, essential for optimizing recycling processes, particularly in complex contexts such as pharmaceutical chemical waste."

We have added to the bibliography this reference:

Papadopoulos, T., Gunasekaran, A., Dubey, R., & Wamba, S. F. (2017). "Big data and analytics in operations and supply chain management: managerial aspects and practical challenges". Production Planning & Control, 28(11-12), 873-876. https://doi.org/10.1080/09537287.2017.1336795

Comment 2 (Sample generalizability)

Reviewer's comment: "This paper only selects employees of pharmaceutical companies in Saudi Arabia as samples, and the sample source is relatively simple. This single sample composition may not fully reflect the actual situation in other industries or countries, thereby limiting the generalizability and generalization of the research results. Please prove the generalizability of your research."

Proposed response: We thank the reviewer for this pertinent remark.

the choice of the pharmaceutical sector in Saudi Arabia is justified by its organizational structure typical of resource- and knowledge-intensive industries, which strengthens the relevance of the model for other similar sectors facing the same challenges. Thus, our study offers transferable lessons in contexts sharing comparable dynamics, particularly in terms of digital transformation and sustainability. We have added this idea as limitation and proposed future research to extend our model to other sectors

Modification to add to the article (to add in the Conclusion, limitations and Future Research section)

“Third, our sample is limited to the Saudi pharmaceutical sector, which could limit the generalizability of our results to other sectoral or geographical contexts. Although the Saudi pharmaceutical industry, shares structural and operational characteristics with other resource- and innovation-intensive industries, such as chemistry and biotechnology, and the issues explored, particularly digital transformation and sustainable practices, transcend sectoral and geographical boundaries. We encourage future research in other sectors and regions to confirm and enrich the scope of our model. Future research could replicate our model in various national and sectoral contexts to assess its robustness and examine moderating variables related to socio-economic, cultural, or regulatory factors such as regulatory pressure, or competitive intensity.

Comment 3 (Robustness testing)

Reviewer's comment: "This paper does not thoroughly test the stability of the research results. In order to ensure the reliability of the research results and enhance the credibility of the research conclusions, it is recommended to use different samples or analysis methods for robustness testing. Through multi-dimensional and multi-angle verification, the reliability of the research results can be further determined, providing more solid data support for the research conclusions."

Proposed response: We recognize the importance of robustness tests for validating the stability of results. Although this study did not include in-depth robustness tests, we plan in our future research to integrate complementary analyses. These analyses will include the use of different methods, such as covariance-based structural equation models (CB-SEM), in parallel with the PLS-SEM approach used here. In addition, segmentation techniques and non-linear modeling will be explored to identify unobserved heterogeneities and complex relationships between variables.

Modification to add to the article (to add in the limitations section, in the conclusion): "A limitation of this study is the absence of in-depth robustness tests. To strengthen the validity of the results, future research should use different statistical analysis methods, such as CB-SEM models, as well as segmentation and non-linear modeling approaches to explore more complex relationships and identify hidden heterogeneities in the sample."

Comment 4 (Government policies and circular economy)

Reviewer's comment: "The article mentioned in lines 264-267 that enterprises implement green supply chain management, which is in line with the concept of circular economy, can improve resource utilization efficiency and reduce waste. In fact, the development of circular economy cannot be separated from the strong support of government policies, such as tax incentives and subsidy policies, which can effectively encourage enterprises to actively adopt the circular economy model."

Proposed response: We recognize the crucial importance of government policies in promoting the circular economy. We have referred to the idea of policy-related barriers for a supply chain in the transition to CE. We have highlighted that instead of representing barriers for a supply chain in the transition to CE, within the vision 2030 such macro-level environmental constituencies and institutional policies are drivers of change in sustainability policies and practices toward sustainable futures, enhancing corporate environmental performance of a business

Modification to add to the article (to add after line 257): The transition to a circular economy (CE) requires firms to innovate across supply chains, involving sustainable changes from product design to production and delivery. This shift necessitates the transformation of existing business models to align with sustainability goals, emphasizing collaboration within the value network among supply chain partners and customers (Kazancoglu et al., 2020). During the transition of CE in the supply chains, policy-related barriers such as legislation, taxation, funding, infrastructural, and procurement barriers (Guldmann & Huulgaard, 2020) are one of the important barriers. Macro-level environmental constituencies and institutional policies are critical drivers of change in sustainability policies and practices toward sustainable futures [43]. In the Saudi context, the alignment of corporate initiatives with the objectives of Vision 2030 and the associated government policies and regulations create an adequate environment for the transformation towards a circular economy.

The transition to a circular economy (CE) generates value in corporate environmental management by implementing closed-loop systems, reverse logistics, product life cycle management, and cleaner production practices. Green Supply Chain Management (GSCM) supports the principles of the circular economy by integrating practices such as reuse, recycling and end-of-life recovery (Kazancoglu et al., 2020). Facilitated through reverse logistics, these practices promote responsible consumption and contribute to the reduction of waste, aligning with the goals of sustainability and resource efficiency in supply chain operations (Govindan et al 2019; Hofmann, et al., 2019)

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Dear authors:

The revised version is fine. I could see that the authors have addressed all the concerns and issues raised before.

Good luck.

Author Response

We sincerely thank you for the valuable and constructive feedback on our manuscript .

Reviewer 2 Report

Comments and Suggestions for Authors

Author Response

We sincerely thank you for the valuable and constructive feedback on our manuscript .

Reviewer 3 Report

Comments and Suggestions for Authors

The authors tried to address my concerns but the paper still lacks scientific and technical novelty.

Author Response

We sincerely thank you for the valuable and constructive feedback on our manuscript .

Please see the attached document in response to your comments.

Author Response File: Author Response.docx

Round 3

Reviewer 3 Report

Comments and Suggestions for Authors

I don't see any visible changes in the paper. The authors wrote a lot of words but the significance of their work is still unclear. It seems that the task is too specific.

There is no clear comparison with related papers and approaches. There are no new methods or tools.

Author Response

Dear Editor,

We sincerely appreciate the reviewer's feedback and have made substantial revisions, highlighted in red, in the attached document, to address the identified concerns. Our response addresses the two main points raised:

Manuscript's Positioning Relative to Existing Literature

We have significantly strengthened our theoretical positioning by adding key passages in both the introduction and theoretical framework that explicitly articulate the underlying theories upon which our research is based, specifically the Dynamic Capability view (DCV) .

We have also clearly articulated how our dual-pathway mediation model advances beyond traditional linear capability frameworks that have characterized previous digital transformation research, thereby establishing our unique theoretical contribution to the circular economy literature.

In the theoretical framework, we have added a comprehensive paragraph that positions Big Data Analytics as a dynamic capability that simultaneously enables the development of sustainable performance and green supply chain management as complementary capabilities.

Introduction (Line 60-122):

However, leveraging big data requires not only technological infrastructure but also strong organizational capabilities and human skills [8-10], aligning with the socio-technical perspective that emphasizes the critical role of human-AI interaction quality in determining technology effectiveness [23]. The adoption of BDA to advance sustainable outcomes remains a complex undertaking, requiring the presence of complementary organizational capabilities to effectively unlock its full potential.

Previous studies have investigated the integration of various technology-driven solutions to enhance the sustainable supply chain performance [12, 9, 81, 53]), as well as CE practices [ 23, 13, 52]. Similarly, others have investigated the direct relationship of CE on sustainable performance [83,84], and the influence sustainable supply chain has on CE-targeted performance[84]. However, despite these parallel research streams, the combined relationship between BDA, Green supply chain management (GSCM), sustainable performance (SP) and CE still remains largely unexplored.

Moreover, despite growing academic interest in CE, current literature reveals fundamental theoretical inconsistencies that limit our understanding of the integration between BDA and CE practices. Empirical studies have reported mixed findings regarding the relationship between BDA and firm performance, with some studies identifying a positive association, while others reveal negative or insignificant effects [77]. Further, some scholars contend that although BDA contributes to enhancing supply chain resilience, it is insufficient for addressing long-term strategic challenges, requiring companies to adopt concepts that emphasize sustainability [23].

These contributions often lack integrated theoretical frameworks explaining the mechanisms through which digital capabilities enable CE transitions. No study has investigated the mediating role of both SP and GSCM in the relationship between BDA and CE.

This knowledge gap is particularly prevalent in highly regulated and knowledge-intensive industries, where data analytics applications face unique challenges related to compliance requirements, stakeholder complexity, and specialized domain knowledge.

Our study responds to the call for the application of integrated frameworks that simultaneously address efficiency, resilience, and sustainability [11]

Drawing on the Resource-Based View (RBV) theory [85] and the Dynamic Capabilities (DC) framework [86], this study conceptualizes BDA as a strategic organizational capability that enables firms to reconfigure their operational processes toward implementing a CE. In contrast to prior research that views BDA merely as a standalone technological tool, this study theoretically frames it as a socio-technical system, in which human capital developed by managerial knowledge, skills and expertise, is integrated with AI capabilities to drive sustainable transformation [73, 76].

This theoretical positioning addresses a critical gap in the literature, as studies on digital transformation have predominantly focused on technological determinism, often overlooking the complex interplay between human capabilities, artificial intelligence, and organizational processes [82, 87].

Our study advances theoretical understanding by integrating the Resource-Based View (RBV) and Dynamic Capabilities (DC) theories to develop an integrated framework in which BDA is considered a dynamic capability encompassing the processes of sensing (Identifying and assessing opportunities and threats), seizing (mobilizing resources to capture opportunities and mitigate threats), and transforming (reconfiguring and realigning resources) enabled through big data and business analytics, in orchestration with other organizational resources and capabilities, to leverage innovation and respond to business environmental challenges [76, 88].

According to the Dynamic Capabilities View (DCV), firms achieve sustained competitive advantage and advance CE goals by continuously adapting, integrating, and reconfiguring internal and external competencies [80,86, 88]. Building on this perspective, this study explains how BDA functions as a dynamic capability, simultaneously enabling SP and GSCM, thereby contributing to the realization of CE objectives.

This approach helps reconcile contradictory empirical findings in the digital transformation literature through comprehensive dual-mediation testing. Furthermore, these insights enable industry professionals to strategically implement BDA, thereby supporting and coordinating the development of SP and GSCM.

Despite its potential, empirical studies on BDA in emerging markets, especially in the context of CE, remain limited. This limitation is particularly critical given that emerging markets face distinctive institutional environments, resource constraints, and regulatory frameworks that may fundamentally alter how digital capabilities operate.

Theoretical Framework and Hypotheses Development
- Big data analytics as a dynamic capability:

Dynamic Capability View (DCV), an extension of the resource-based view (RBV) theory, underscores the inherently evolving and adaptive nature of organizational resources and capabilities. It provides insights into how organizations can adapt to rapidly changing environments and achieve competitive advantages by integrating, reorganizing, and developing internal and external capabilities [86, 88].

Drawing on the dynamic capabilities view (DCV), BDA is conceptualized as a key organizational information processing capability that enhances a firm’s ability to navigate environmental complexity. Specifically, BDA enables firms to sense opportunities and threats, seize them through strategic resource allocation, and transform internal and external processes for sustained competitive advantage [77].

The strategic value of BDA lies not only in its technical sophistication but also in its embeddedness within the broader organizational context that facilitates dynamic capability development [76]. From this perspective, BDA constitutes the infrastructure, data management practices, and talent necessary to transform raw data into actionable business insights [78]. These capabilities span both tangible elements such as enterprise systems, data integration platforms, and analytics tools and intangible elements, including data literacy, technical skills, and managerial competence [79]. BDA enable firms to harness both structured and unstructured data from a range of sources, including internal systems and IoT devices, to drive predictive analytics and optimization strategies [28]. Consequently, BDA serve as a critical enabler of strategic initiatives to innovate, adapt, and build sustainable competitive advantage [77].

Discussion and Critical Engagement with Academic Debates

Regarding the depth of discussion and critical engagement with relevant academic debates, we have substantially improved the hypothesis as well as both the Results and Discussion (Section 5) and the Research Contributions and Practical Implications section (Section 6).

In the discussion, we have added rigorous explanations of how our results contribute to resolving contradictory findings in the literature, specifically addressing why previous studies showed inconsistent mediation effects. We explain how our triple-bottom-line impact mechanism (economic, environmental, social dimensions) clarifies the sustainable performance mediation pathway, and how the complementary nature of internal (sustainable performance) versus external (GSCM) pathways provides a theoretical resolution to previous contradictions.

In the contributions section, we have enhanced our theoretical contribution by demonstrating that DC integration provides superior explanatory power for understanding digital-sustainability transformations. We also underlined the implications that the study has for the Sustainable Development Goals (SDGs)

These revisions directly address the reviewer's concerns by providing deeper theoretical grounding, clearer positioning relative to existing literature, and more critical engagement with academic debates in the field.

2.8. Green Supply Chain Management as a Mediator between Big Data Analytics and Circular Economy

While previous studies have demonstrated the effect of BDA in achieving environmental sustainability [79], others have provided empirical evidence on the specific contribution of BDA to enhance supply chain sustainability, resilience, adaptability, and flexibility [23, 91].

It is widely acknowledged that GSCM practices are key drivers in supply chain and operations management, providing the procedural and operational changes necessary to implement sustainable practices. GSCM represents a strategic approach to achieving supply chain sustainability by integrating environmental considerations into supply chain management [92].

Moreover, empirical findings indicate a significant positive effect of BDA capabilities on green supply chain performance [14] as well as on CE practices [65, 50]. However, the pathway through which BDA influences sustainable supply chain outcomes is not always direct. For instance, the relationship between BDA and supply chain innovation is mediated by two crucial capabilities of agility and adaptability that enable firms to efficiently meet the challenges of supply chain ambidexterity [91].

GSCM which incorporates environmental thinking into supply chain practices—from product design to material sourcing and end-of-life management—may serve as a key mediating mechanism that explain the link between BDA and CE. By leveraging insights derived from BDA, organizations can implement GSCM practices more effectively, which in turn supports CE goals such as recycling, reuse, and remanufacturing. GSCM practices focus on reducing the environmental impact of SC activities [93]. BDAC can enable organizations to utilize data-driven insights to identify inefficiencies, reduce carbon footprints, waste, and hazardous substances in manufacturing, and adopt energy-efficient processes, thereby improving CE [92]. Thus, GSCM could play a potential mediating effect in the relationship between BDA and CE.

Therefore, we hypothesize

H7: Green Suppl Chain Management mediates the relationship between big data analytics and the circular economy.

We believe these substantial improvements significantly enhance the manuscript's theoretical rigor and contribution to the literature.

Results and Discussion Line 609-614: The findings corroborate the argument by [89] that data-driven decision-making can accelerate the transition toward sustainable production and consumption, directly supporting SDG 12. Conversely, our findings diverge from traditional views which suggest that CE practices primarily depend on regulatory pressure, highlighting instead the strategic role of digital capabilities in driving sustainable outcomes.

Line 635-671

Our findings advance theoretical understanding by empirically validating a proposed theoretical model where BDA functions as a dynamic capability that enables the simultaneous development of sustainable performance and green supply chain management capabilities. The dual-mediation mechanism operates through complementary pathways: SP creates internal organizational readiness for circular practices through resource optimization and stakeholder alignment, while GSCM establishes external collaborative networks essential for closed-loop material flows.

The impact mechanism of this result reveals how SP mediates the relationship between BDA and CE practices. On the economic front, cost optimization driven by BDA frees up resources that can be reinvested in circular initiatives. Environmentally, improvements such as waste reduction align directly with core CE principles. Socially, stronger stakeholder engagement enhances the organization's capacity to embrace circular transformation. Taken together, these dimensions illustrate why SP plays a pivotal mediating role — it establishes the foundational conditions necessary for the successful adoption of CE practices.

Furthermore, the underlying mechanism of this result elucidates how Sustainable Performance (SP) mediates the relationship between BDA and CE practices. The capabilities offered by BDA, including predictive analytics, real-time data monitoring, and demand forecasting, support the integration of environmentally sustainable practices across supply chain operations—namely procurement, production, distribution, and end-of-life management. BDA enhances the transparency, efficiency and flexibility of supply chains, thereby helping the firms develop the ability to deal with supply chain complexities and uncertainties while promoting sustainable outcomes [65, 84, 87,90].

BDA provides valuable insights that enable better decision-making and strategic planning, allowing organizations to focus on supply chain activities aimed at developing green suppliers, use environmental technology and reducing waste, emissions, and energy consumption. [92-93]. These sustainable environmental processes incorporate the principles of CE, which drive the implementation of CE practices.

These results align with previous research that integrate diverse indicators to provide a comprehensive assessment of circular economy (CE) performance. Particularly, the micro level—focusing on products, components, and materials—the Material Circularity Indicator is widely used to evaluate circularity. At the meso level, which encompasses businesses and industrial symbiosis, sustainable circularity indices are employed to measure aspects such as resource efficiency and collaborative practices within industrial ecosystems [75].

Research Contributions and Practical Implications Line 747- 765
This study contributes to the theoretical understanding of circular economy capability development by proposing an integrated framework that draws on the resource-based view (RBV) and dynamic capabilities (DC) perspective—where BDA functions as a dynamic capability that simultaneously enables the development of SP and GSCM- for analyzing digital transformation in sustainability contexts. These theoretical lenses explain how big data analytics enables organizations to develop circular economy competencies through parallel capability-building pathways. By linking digital enablers with sustainability-oriented capabilities, this framework advances theory at the intersection of digital transformation and circular economy research.

Our theoretical contribution goes beyond previous studies by demonstrating that dual mediation pathways function as complementary rather than competing mechanisms. The internal pathway, mediated by SP, strengthens the organizational foundation through resource optimization and stakeholder engagement. In parallel, the external pathway, mediated by GSCM, fosters collaborative networks that are critical for enabling circular material flows. This complementarity accounts for the persistence of both mediation effects and helps reconcile inconsistencies reported in earlier single-mediator studies.

This study has significant implications for the Sustainable Development Goals (SDGs), particularly SDG 12 (Responsible Consumption and Production) and SDG 9 (Industry, Innovation, and Infrastructure). The empirical evidence shows that the strategic application of Big Data Analytics within Circular Economy-driven supply chains can improve operational transparency, reduce waste, and foster sustainable resource management. These practices directly align with SDG 12, which advocates sustainable consumption patterns. Similarly, the integration of digital tools and innovative analytics into supply chain processes supports SDG 9 by promoting sustainable industrialization and fostering innovation. By highlighting the mechanisms through which data-driven strategies contribute to these global goals, the study provides actionable insights for practitioners, policymakers, and scholars aiming to align business practices with international sustainability targets

Kind Regards

Author Response File: Author Response.docx

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Big Data Analytics as a Driver for Sustainable Performance: The Role of Green Supply Chain Management in Advancing Circular Economy in Saudi Arabian Pharmaceutical Companies

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