Round 1

Reviewer 1 Report

Thank you for submitting your research paper for Sustainability. This is an interesting topic. However, there are many concern that need to be addressed by the authors.

1. Further the introduction is a bit weak. Why is the research needed now? Look at how to create a compelling problematization and a hook for readers to be engaged in your study. Problematization is extremely important to showcase why your findings are going to be useful for explaining the phenomenon in the future. Explicitly introduce research questions / research objectives towards the end of the introduction, as bullet points, before sharing how the remaining article is structured.

2. In the methodology and research design section please define the population of the study and sampling technique adopted. Also please justify why your chosen sampling technique and sample size are appropriate. 

3. Have a stronger discussion on the findings before the conclusion section. Discussion should ideally have 2-3 subsections on Contributions to literature, Implications for practice and Limitations and future research directions.

4. It is required to to include the research limitations and explain their limitations as well as how can be addressed by future research. 

Author Response

Manuscript ID sustainability- 2306427

 

Understanding the Relationship between Big Data Analytics Capabilities and Sustainable performance: The Role of Strategic Agility and Firm Creativity

Reviewer 1

Reviewer 1, Comment 1:

1. Further the introduction is a bit weak. Why is the research needed now? Look at how to create a compelling problematization and a hook for readers to be engaged in your study. Problematization is extremely important to showcase why your findings are going to be useful for explaining the phenomenon in the future. Explicitly introduce research questions / research objectives towards the end of the introduction, as bullet points, before sharing how the remaining article is structured.

Our response:

Many thanks for this constructive comment. We have made careful revisions to our introduction section, with particular attention to making the introduction far more focused. To further consider your comment, we developed three research questions and included them towards the end of the introduction section. These changes are marked in red throughout sections 1. Introduction (pp.2-3), which reads as follows:

   Prior research argues that big data has significant effects on operations management practices. Another study further argues that although big data analytics has been in use to understand customer intentions/ behaviours, the use of analytics for improving sustainable performance is less understood. Previous examination argues that organizations are increasingly investing in IT capabilities. While some researchers have established the linkage between big data analytics capability and competitive advantage [22,29] and agility and competitive advantage [32,25], little empirical testing of big data analytics and strategic agility and sustainable performance exists. Hence, our study seeks to close this research gap by exploring the influence of big data analytics capabilities on sustainable performance through the mediating role of strategic agility.

   Insights derived via big data analysis may give chances for business performance improvements [35]. However, firms must also transform these useful insights into actions. Building on the resource-based and dynamic capabilities approaches, as well as the literature on big data analytics capabilities, this research presents significant theoretical contributions. First, it brings a new mechanism—strategic agility—into the interaction between big data analytics capabilities and sustainable performance. This gives a clearer grasp of how big data analytics skills impact sustainable performance. Second, although prior studies have explored this relationship in the settings of major and established economies, we study the indirect relationship between big data analytics capabilities and sustainable performance, based on the business environment of sectors in emerging nations. Third, we further expand the research by analysing the moderating influence of firm creativity on these relationships. This being the case, the following research issues lie within the scope of this study to investigate:

    RQ1: What is the influence of big data analytics capabilities on sustainable performance?

    RQ2: Does strategic agility mediate the link between big data analytics capabilities and sustainable performance?

    RQ3: Does firm creativity moderate the link between big data analytics capabilities, strategic agility, and sustainable performance?

We hope you will agree that the reworked introduction, along with the addition of more up-to-date references and a strengthened standard of argument throughout the paper, have ensured that the revised manuscript is of a much higher standard. Once again, we are grateful for this constructive comment.

 

Reviewer 1, Comment 2:

2. In the methodology and research design section please define the population of the study and sampling technique adopted. Also please justify why your chosen sampling technique and sample size are appropriate. 

 

Our response:

Many thanks for this constructive comment. In response, we have added an entire new paragraph to our sampling and data collection section, with particular attention to making the sampling and data collection process far more focused. These changes are marked in red throughout sections 3.1. Sampling and data collection (pp. 6-7), which reads as follows:

    A positivist research philosophy was utilized with a quantitative approach to validate the proposed framework, and quantitative data were collected using survey questionnaires to address different levels of the study. The data for our analysis were provided by Saudi Arabian engineering manufacturers. In order to create a representative sample, we made sure that our respondents came from a broad variety of backgrounds. Our sample businesses were selected from a registry compiled by the Engineering Council of Saudi Arabia (PEC) that has a database of 3,610 registered firms.

    The initial e-mails were directed to 600 respondents randomly chosen using probability-sampling methods (the managers’ e-mail addresses were randomly selected by a generated sampling system, like random-digit dialling (RDD). Of the original 600 businesses, 410 completed and submitted the survey for analysis. Our participants were “production and R&D managers, operations and IT directors, presidents and vice presidents of analytics, and executives in charge of activities such as purchasing, production, operations and planning, and warehousing”.

    In total, of the 600 companies took part, 410 submitted forms which were valid for further analysis (response rate 68%). Most companies had > 50 employees (73.2%), with more than five years’ experience in the firm (79%), indicating that these organisations have had concerns over their strategic agility, BDACs, creativity, and sustainable performance. Senior managers represented 56%, while general managers who were knowledgeable about the explored issues represented 16.8% (see Table 1).

    Since 410 cases were collected, the current research sample size is a very good and practically acceptable size for the use of Structural Equation Modelling/ LISREL. Another test has been conducted using the following equation suggested by Westland [81], n ≥ 50r 2 – 450r + 1,100, where n is sample size and r is the ratio of indicators to latent variables. Since 410 cases were collected, the current research sample size satisfies the lower sample size threshold for structural equation modeling [81].

 

Reviewer 1, Comment 3:

3. Have a stronger discussion on the findings before the conclusion section. Discussion should ideally have 2-3 subsections on Contributions to literature, Implications for practice and Limitations and future research directions.

 

Our response:

Many thanks for this point, with which we totally agree. In the revised manuscript, we have substantially revised and edited this section (5. Discussion and conclusion) to discuss the main findings of our study (5.1. Key findings), contributions to literature (5.2. Theoretical implications), implications for practice (5.3. Practical implications), and limitations and future research directions (5.4. Limitations and suggestions for future research). These changes can be found in red in section on pages 10-12, which reads as follows:

Discussion and conclusion

5.1. Key findings

Consistently with other researchers, we concluded that strategic agility strongly mediates the connection between BDACs and sustainable performance [17,23,46,78,90,115]. The Saudi Arabia engineering sector  has a growing interest in BDACs to increase firm agility and boost sustainable performance outcomes in the face of fast change brought on by new technologies and increased levels of digitalization. Our conclusion is  understood within this context.

Our study is the first to offer a rigorous empirical test of the distinct effects of big data analytics capability on strategic agility and sustainable performance, which was called for in previous research [73,81]. Our analysis indicated that strategic agility mediated the relationships between big data analytics capabilities and sustainable performance, which is consistent with prior research in this context [36,51,69].

According to previous examination, innovative businesses get deeper insights via data analytics [39,67,81]. Although BDAC is crucial for agility [17,26,45,65,116], no research has looked at the way in which creativity at the company level moderates the connection between BDAC, strategic agility, and long-term success. Our results add to this body of work since they examine how innovation, flexibility, and performance are affected by a company’s level of creativity. Hence, engineering companies which foster an environment that rewards original thinking and a can-do attitude amongst its staff are better able to take advantage of BDACs and adapt to shifting market demands. Strategic agility (the ability “to foresee market demand efficiently, minimise order to delivery cycle times, and conduct customisation” [46,81,93] may be improved by manufacturers whose designing is highly inventive and by BDACs. Our research on the moderating and mediating impacts supports the idea that managers’ technical competence, especially in the area of goal work, provides a fresh viewpoint on issues and intrinsic motivation, as well as an innovative approach to data analytics.

The study showed that resource-based view and its dynamic capability extension could be successfully used for examining relationships in the development of strategic agility and sustainable performance. The study has further added to the use of dynamic capability theory understanding the evolution of process-oriented capabilities based on big data analytics capabilities. Hence, our study is also a response to the call for exploring the importance of big data analytics capabilities in the development of sustainable performance.

 

In addition, our results lend credence to BDA as one of the most important determinants affecting long-term performance [6,23,46,79,81,117]. Contrary to the commonly held belief that businesses must rely on the development of tangible resources or capabilities in order to improve their performance and gain a competitive edge, we present evidence that a company’s intangible resources help it to develop the capacity to use data analytics, plan better adaptation to changing technological resources, and make quicker decisions [10,26,45,67,83,90,103,118]. Earlier studies have shown that BDACs have a constructive effect on agility [17,26,38,54,119]. In spite of this, there has been a dearth of studies that experimentally and in-depth explore how consumer data analysts and organisational creativity affect manufacturing agility and business success.

5.2. Theoretical implications

This research adds significantly to the existing body of knowledge. To begin with, it incorporates the dynamic capabilities viewpoint in order to research the connection between BDACs and strategic flexibility. Despite the fact that the existing research has shown the significance of dynamic skills for BDACs and agility [63,120,121], the manner in which diverse company resources impact on the development of such capabilities has not so far been comprehensively described. Most studies of BDACs to date have used a resource-based perspective to characterise the impact of BDACs on agility. Our conceptual approach adds fresh understanding to the ways in which intangible assets help businesses acquire the skills necessary to become more strategic in their operations. We add to this body of work by specifying the types and quantities of creative resources required by an organisation in order to cultivate such talents to increase its strategic flexibility. Second, although previous works have emphasised the significance of BDACs for firm performance [106,110,122], the mechanisms by which a company’s different resources influence the growth of its performance have not been elucidated. In light of prior examinations, scholars should now look at the innovativeness of businesses to understand how strategic flexibility impacts long-term success [28,54,102,123].

Our research adds to the existing body of work that seeks to define and categorise creative thinking in the workplace [16,25,38,59,102,124]. Our research, following prior study, contributes to this line of inquiry by illuminating how firm creativity and BDACs contribute to strategic agility [104,110,125]. This is important because the prior literature provides little understanding of the way in which organisations develop strategic agility [126]. To add to the problem, we know almost nothing about ways to enhance this effect. Our research is the first to show how BDACs have a more pronounced impact on strategic agility when there is more organisational creativity present. Amabile suggested CTOC in 1997, and it has been discussed extensively in the literature since then [127,128,129]. Our research on the moderating and mediating roles of creativity in business demonstrates how highly innovative companies are better equipped to reap the benefits of BDACs for long-term performance by adopting more flexible approaches to their operations. Our research shows also that innovative thinking inside businesses helps them adapt to new circumstances and succeed in a highly competitive market. Therefore, strategic agility may aid in the development and capture of fresh ideas for mobilising the resources for seizing corporate value and reconfiguring any current set of resources for value generation.

5.3. Practical implications

The findings of this research have several applications for manufacturing companies and their executives. First, the research suggests that innovation inside businesses is crucial to long-term success. Managers engaging in creative behaviours, which ensure that their company will be able to transform novel ideas into resources suitable for boosting BDACs can improve manufacturing lead times, inventory turnover, and procurement lead times. This being so, we propose that manufacturers not only encourage innovation, but also provide BDAC education to employees so that everyone can help predict market trends, learn about customers’ wants and needs, and quickly develop production strategies with the goal of cutting down on manufacturing lead times.

Second, our research encourages production managers to adopt BDA management approaches and to foster more inventiveness in their businesses as a means of fostering agility. Our research may also help business leaders understand how consumer input is the key to competitive strategic agility. Industrial companies’ decision-making processes may be at risk due to a lack of data visualisation skills. A number of earlier studies have identified the inclusion of information from external players as a problematic but crucial part of digital change transition. Finally, our research demonstrates to business leaders that consumer participation as data analysts may reveal shifts in market circumstances, allowing them to better adapt their digital transformation initiatives to speed up the fulfilment of orders. Strategically agile businesses should, therefore, prioritise consumer participation in order to maximise value creation.

5.4. Limitations and suggestions for the future research 

There are a number of caveats to this study that point to potential future avenues of inquiry. If researchers cannot use cross-sectional data to demonstrate cause and effect links, the study’s reliability suffers. This gap might be filled by a long-term approach. The focus of the present study is only on long-term effectiveness. Other marketing performance measures, such as sales growth, product launches, customer retention, etc., should also be explored. We hypothesise that additional possible mediators, such as market orientation, learning orientation, and entrepreneurial orientation, might expand the scope of advantages from strategic agility. Only one possible moderator is investigated in this research. Others, such as technical uncertainty, firm size, and industry, may be investigated in further research. In this investigation, the context of the manufacturing sector informed the choice of agility assessment. Future studies will need to make a few adjustments to this metric before they can apply it to the service sector. This would considerably improve our ability to construct theories and comprehend the liminal states of the investigated connections.

 

Reviewer 1, Comment 4:

4. It is required to include the research limitations and explain their limitations as well as how can be addressed by future research. 

Our response:

Many thanks for this comment. In response, we have included a section on the research limitations and future research directions, which reads as follows:

5.4. Limitations and suggestions for the future research 

There are a number of caveats to this study that point to potential future avenues of inquiry. If researchers cannot use cross-sectional data to demonstrate cause and effect links, the study’s reliability suffers. This gap might be filled by a long-term approach. The focus of the present study is only on long-term effectiveness. Other marketing performance measures, such as sales growth, product launches, customer retention, etc., should also be explored. We hypothesise that additional possible mediators, such as market orientation, learning orientation, and entrepreneurial orientation, might expand the scope of advantages from strategic agility. Only one possible moderator is investigated in this research. Others, such as technical uncertainty, firm size, and industry, may be investigated in further research. In this investigation, the context of the manufacturing sector informed the choice of agility assessment. Future studies will need to make a few adjustments to this metric before they can apply it to the service sector. This would considerably improve our ability to construct theories and comprehend the liminal states of the investigated connections.

We wish to thank Reviewer 1 for such useful comments. We greatly appreciate the time and effort you devoted to your review. We have done our best to address all your suggestions in our revised version, and we hope you agree that the paper is now much stronger and better argued as a result.

 

Reviewer 2 Report

This paper aims to analyze what is the influence of big data analytics capabilities on sustainable performance, the mediating role of strategic agility between big data analytics capabilities and sustainable performance, and the moderating role between big data analytics capabilities, strategic agility, and sustainable performance. In my opinion, the paper is interesting, has potential value-added, and is in keeping with the objectives of Sustainability. 

 

Author Response

Manuscript ID sustainability- 2306427

 

Understanding the Relationship between Big Data Analytics Capabilities and Sustainable performance: The Role of Strategic Agility and Firm Creativity

 

Reviewer 2

Reviewer 2, Comment 1:

1. This paper aims to analyze what is the influence of big data analytics capabilities on sustainable performance, the mediating role of strategic agility between big data analytics capabilities and sustainable performance, and the moderating role between big data analytics capabilities, strategic agility, and sustainable performance. In my opinion, the paper is interesting, has potential value-added, and is in keeping with the objectives of Sustainability. 

 

Our response:

Thank you for this positive comment and your appreciation of our work. We are genuinely grateful for your encouraging response.

We wish to thank Reviewer 2 for such useful comments. We greatly appreciate the time and effort you devoted to your review. We have done our best to address all your suggestions in our revised version, and we hope you agree that the paper is now much stronger and better argued as a result.

 

 

Reviewer 3 Report

The article is interesting.

The article presents a good model

The article presents a good methodology

The article has a good structure

The article has a large bibliographic reference base.

The article is very good.

I recommend adding a more current reference in the bibliography, the latest ones are from 2019, we are in 2023. I only recommend including an updated bibliographic reference to improve the article

Author Response

Manuscript ID sustainability- 2306427

 

Understanding the Relationship between Big Data Analytics Capabilities and Sustainable performance: The Role of Strategic Agility and Firm Creativity

 

Reviewer 3

Reviewer 3, Comment 1:

1.The article is interesting.

The article presents a good model

The article presents a good methodology

The article has a good structure

The article has a large bibliographic reference base.

The article is very good.

I recommend adding a more current reference in the bibliography, the latest ones are from 2019, we are in 2023. I only recommend including an updated bibliographic reference to improve the article

 

Our response:

Thank you for this positive comment and your appreciation of our work. We are genuinely grateful for your encouraging response. In response, we have added more recent and relevant references, which reads as follows:

Tseng, H.T., Aghaali, N., Hajli, N. Customer agility and big data analytics in new product context. Technological Forecasting and Social Change. 2022, 180(1), p.121690.

Awan, U., Bhatti, S.H., Shamim, S., Khan, Z., Akhtar, P., Balta, M. The role of big data analytics in manufacturing agility and performance: moderation–mediation analysis of organizational creativity and of the involvement of customers as data analysts. British Journal of Management. 2022, 33(3), 1200-1220.

Dubey, R., Bryde, D.J., Dwivedi, Y.K., Graham, G., Foropon, C. Impact of artificial intelligence-driven big data analytics culture on agility and resilience in humanitarian supply chain: A practice-based view. International Journal of Production Economics. 2022, 250(2), p.108618.

Zheng, L.J., Zhang, J.Z., Wang, H., Hong, J.F. Exploring the impact of Big Data Analytics Capabilities on the dual nature of innovative activities in MSMEs: A Data-Agility-Innovation Perspective. Annals of Operations Research. 2022, 23(6), 1-29.

Tarn, D.D., Wang, J. Can data analytics raise marketing agility?-A sense-and-respond perspective. Information & Management. 2023, 60(2), p.103743.

Tseng, H.T. Customer-centered data power: Sensing and responding capability in big data analytics. Journal of Business Research. 2023, 158, p.113689.

Mangalaraj, G., Nerur, S., Dwivedi, R. Digital transformation for agility and resilience: an exploratory study. Journal of Computer Information Systems. 2023, 63(1), 11-23.

Chen, Z., Liang, M. How do external and internal factors drive green innovation practices under the influence of big data analytics capability: Evidence from China. Journal of Cleaner Production. 2023, 34(8), p.136862.

We wish to thank Reviewer 3 for such useful comments. We greatly appreciate the time and effort you devoted to your review. We have done our best to address all your suggestions in our revised version, and we hope you agree that the paper is now much stronger and better argued as a result

 

Reviewer 4 Report

 

The authors aim to investigate how big data analytics capabilities might,  impact on sustainable performance through strategic agility. It is argued that big data analytics capabilities have a significant effect on economic, environmental, and social performance. Strategic agility plays a mediating role. The structure of the article is logical and coherent, supported by some relevant literature.

 

However, I would suggest author revise the paper for the following reasons:

 

1.       Abstract (not executive summary): the abstract needs revisions

The abstract should be a total of about 200 words maximum. The abstract should be a single paragraph and should follow the style of structured abstracts, but without headings: 1) Background: Place the question addressed in a broad context and highlight the purpose of the study; 2) Methods: Describe briefly the main methods or treatments applied. Include any relevant preregistration numbers, and species and strains of any animals used. 3) Results: Summarize the article's main findings; and 4) Conclusion: Indicate the main conclusions or interpretations. The abstract should be an objective representation of the article: it must not contain results which are not presented and substantiated in the main text and should not exaggerate the main conclusions.

2.       Research questions and originality.

Please articulate Research questions and originality further, clarifying how your article adds new knowledge to the body of knowledge that already exists in a research area. The authors need to make the “Important new insights” explicit.

3.       Research questions

The authors need to offer a concrete argument on how the four RQs are interrelated for a coherent project.

 

4.       Conclusion

The authors need to improve the conclusion substantially. The conclusion of the article should be clear and concise and try to address the question and explain how you have met the research objective raised in the introduction.

5.       Line 2, environmental, not  environomental

 

6.       Line 163, explain “big data governance”

Author Response

Manuscript ID sustainability- 2306427

 

Understanding the Relationship between Big Data Analytics Capabilities and Sustainable performance: The Role of Strategic Agility and Firm Creativity

Reviewer 4

Reviewer 4, Comment 1:

1. Abstract (not executive summary): the abstract needs revisions

The abstract should be a total of about 200 words maximum. The abstract should be a single paragraph and should follow the style of structured abstracts, but without headings: 1) Background: Place the question addressed in a broad context and highlight the purpose of the study; 2) Methods: Describe briefly the main methods or treatments applied. Include any relevant preregistration numbers, and species and strains of any animals used. 3) Results: Summarize the article's main findings; and 4) Conclusion: Indicate the main conclusions or interpretations. The abstract should be an objective representation of the article: it must not contain results which are not presented and substantiated in the main text and should not exaggerate the main conclusions.

 

Our response:

 

We are grateful for this constructive comment. In response, we have carefully revised and edited the abstract, which reads as follows:

   Abstract: The most successful organizations create businesses that can respond to sudden and unexpected changes in the market. The purpose of this research is to examine how big data analytics capabilities might, through strategic agility, impact on sustainable performance. We grounded our theoretical framework in two perspectives the resource-based view and the dynamic capabilities view. In order to gather data from Saudi Arabian managers, we used the positivist methodology of a survey. Data were collected from 410 managers. The data were analysed using the SEM method. The findings indicated that big data analytics capabilities have a significant effect on economic, environomental, and social performance. They also revealed that strategic agility partially mediates the relationship between the capabilities of big data analytics and sustainable performance. Furthermore, the impact of big data analytics capabilities on strategic agility is stronger in a creative environment, while the strategic agility-sustainable performance relationship is more pronounced in more creative environments. The findings offer firms an insight into the actual benefits that big data analytics may generate and how firms may align the use of big data analytics with industrial conditions to foster sustainable performance.

Reviewer 4, Comment 2:

2. Research questions and originality.

Please articulate Research questions and originality further, clarifying how your article adds new knowledge to the body of knowledge that already exists in a research area. The authors need to make the “Important new insights” explicit.

Our response:

We are grateful for these very constructive suggestions. In response, we have carefully revised section 1. Introduction (in red, pp.2-3), expressing more clearly our research questions and originality, which reads as follows.

   Prior research argues that big data has significant effects on operations management practices. Another study further argues that although big data analytics has been in use to understand customer intentions/ behaviours, the use of analytics for improving sustainable performance is less understood. Previous examination argues that organizations are increasingly investing in IT capabilities. While some researchers have established the linkage between big data analytics capability and competitive advantage [22,29] and agility and competitive advantage [32,25], little empirical testing of big data analytics and strategic agility and sustainable performance exists. Hence, our study seeks to close this research gap by exploring the influence of big data analytics capabilities on sustainable performance through the mediating role of strategic agility.

    Insights derived via big data analysis may give chances for business performance improvements [35]. However, firms must also transform these useful insights into actions. Building on the resource-based and dynamic capabilities approaches, as well as the literature on big data analytics capabilities, this research presents significant theoretical contributions. First, it brings a new mechanism—strategic agility—into the interaction between big data analytics capabilities and sustainable performance. This gives a clearer grasp of how big data analytics skills impact sustainable performance. Second, although prior studies have explored this relationship in the settings of major and established economies, we study the indirect relationship between big data analytics capabilities and sustainable performance, based on the business environment of sectors in emerging nations. Third, we further expand the research by analysing the moderating influence of firm creativity on these relationships. This being the case, the following research issues lie within the scope of this study to investigate:

    RQ1: What is the influence of big data analytics capabilities on sustainable performance?

    RQ2: Does strategic agility mediate the link between big data analytics capabilities and sustainable performance?

    RQ3: Does firm creativity moderate the link between big data analytics capabilities, strategic agility, and sustainable performance?

 

 

 

 

 

 

 

 

 

 

Reviewer 4, Comment 3:

3. Research questions

The authors need to offer a concrete argument on how the four RQs are interrelated for a coherent project.

 

Our response:

Many thanks for this valuable comment, which has led us to substantially revise and improve section 1. Introduction to offer a concrete argument on how the RQs interrelated for a coherent project, which reads as follow:

   Prior research argues that big data has significant effects on operations management practices. Another study further argues that although big data analytics has been in use to understand customer intentions/ behaviours, the use of analytics for improving sustainable performance is less understood. Previous examination argues that organizations are increasingly investing in IT capabilities. While some researchers have established the linkage between big data analytics capability and competitive advantage [22,29] and agility and competitive advantage [32,25], little empirical testing of big data analytics and strategic agility and sustainable performance exists. Hence, our study seeks to close this research gap by exploring the influence of big data analytics capabilities on sustainable performance through the mediating role of strategic agility.

   Insights derived via big data analysis may give chances for business performance improvements [35]. However, firms must also transform these useful insights into actions. Building on the resource-based and dynamic capabilities approaches, as well as the literature on big data analytics capabilities, this research presents significant theoretical contributions. First, it brings a new mechanism—strategic agility—into the interaction between big data analytics capabilities and sustainable performance. This gives a clearer grasp of how big data analytics skills impact sustainable performance. Second, although prior studies have explored this relationship in the settings of major and established economies, we study the indirect relationship between big data analytics capabilities and sustainable performance, based on the business environment of sectors in emerging nations. Third, we further expand the research by analysing the moderating influence of firm creativity on these relationships. This being the case, the following research issues lie within the scope of this study to investigate:

    RQ1: What is the influence of big data analytics capabilities on sustainable performance?

    RQ2: Does strategic agility mediate the link between big data analytics capabilities and sustainable performance?

    RQ3: Does firm creativity moderate the link between big data analytics capabilities, strategic agility, and sustainable performance?

 

Reviewer 4, Comment 4:

4. Conclusion

The authors need to improve the conclusion substantially. The conclusion of the article should be clear and concise and try to address the question and explain how you have met the research objective raised in the introduction.

Our response:

We are grateful for these very constructive and positive suggestions. In response, we have carefully revised section 5. Discussion and conclusion (in red, pp. 10-12), expressing more clearly our contribution to the literature and including the discussion on how to address the question and explain how you have met the research objective raised in the introduction, which reads as follows:

Discussion and conclusion

5.1. Key findings

Consistently with other researchers, we concluded that strategic agility strongly mediates the connection between BDACs and sustainable performance [17,23,46,78,90,115]. The Saudi Arabia engineering sector  has a growing interest in BDACs to increase firm agility and boost sustainable performance outcomes in the face of fast change brought on by new technologies and increased levels of digitalization. Our conclusion is understood within this context.

Our study is the first to offer a rigorous empirical test of the distinct effects of big data analytics capability on strategic agility and sustainable performance, which was called for in previous research [73,81]. Our analysis indicated that strategic agility mediated the relationships between big data analytics capabilities and sustainable performance, which is consistent with prior research in this context [36,51,69].

According to previous examination, innovative businesses get deeper insights via data analytics [39,67,81]. Although BDAC is crucial for agility [17,26,45,65,116], no research has looked at the way in which creativity at the company level moderates the connection between BDAC, strategic agility, and long-term success. Our results add to this body of work since they examine how innovation, flexibility, and performance are affected by a company’s level of creativity. Hence, engineering companies which foster an environment that rewards original thinking and a can-do attitude amongst its staff are better able to take advantage of BDACs and adapt to shifting market demands. Strategic agility (the ability “to foresee market demand efficiently, minimise order to delivery cycle times, and conduct customisation” [46,81,93] may be improved by manufacturers whose designing is highly inventive and by BDACs. Our research on the moderating and mediating impacts supports the idea that managers’ technical competence, especially in the area of goal work, provides a fresh viewpoint on issues and intrinsic motivation, as well as an innovative approach to data analytics.

The study showed that resource-based view and its dynamic capability extension could be successfully used for examining relationships in the development of strategic agility and sustainable performance. The study has further added to the use of dynamic capability theory understanding the evolution of process-oriented capabilities based on big data analytics capabilities. Hence, our study is also a response to the call for exploring the importance of big data analytics capabilities in the development of sustainable performance.

 

In addition, our results lend credence to BDA as one of the most important determinants affecting long-term performance [6,23,46,79,81,117]. Contrary to the commonly held belief that businesses must rely on the development of tangible resources or capabilities in order to improve their performance and gain a competitive edge, we present evidence that a company’s intangible resources help it to develop the capacity to use data analytics, plan better adaptation to changing technological resources, and make quicker decisions [10,26,45,67,83,90,103,118]. Earlier studies have shown that BDACs have a constructive effect on agility [17,26,38,54,119]. In spite of this, there has been a dearth of studies that experimentally and in-depth explore how consumer data analysts and organisational creativity affect manufacturing agility and business success.

5.2. Theoretical implications

This research adds significantly to the existing body of knowledge. To begin with, it incorporates the dynamic capabilities viewpoint in order to research the connection between BDACs and strategic flexibility. Despite the fact that the existing research has shown the significance of dynamic skills for BDACs and agility [63,120,121], the manner in which diverse company resources impact on the development of such capabilities has not so far been comprehensively described. Most studies of BDACs to date have used a resource-based perspective to characterise the impact of BDACs on agility. Our conceptual approach adds fresh understanding to the ways in which intangible assets help businesses acquire the skills necessary to become more strategic in their operations. We add to this body of work by specifying the types and quantities of creative resources required by an organisation in order to cultivate such talents to increase its strategic flexibility. Second, although previous works have emphasised the significance of BDACs for firm performance [106,110,122], the mechanisms by which a company’s different resources influence the growth of its performance have not been elucidated. In light of prior examinations, scholars should now look at the innovativeness of businesses to understand how strategic flexibility impacts long-term success [28,54,102,123].

Our research adds to the existing body of work that seeks to define and categorise creative thinking in the workplace [16,25,38,59,102,124]. Our research, following prior study, contributes to this line of inquiry by illuminating how firm creativity and BDACs contribute to strategic agility [104,110,125]. This is important because the prior literature provides little understanding of the way in which organisations develop strategic agility [126]. To add to the problem, we know almost nothing about ways to enhance this effect. Our research is the first to show how BDACs have a more pronounced impact on strategic agility when there is more organisational creativity present. Amabile suggested CTOC in 1997, and it has been discussed extensively in the literature since then [127,128,129, 130,131,132]. Our research on the moderating and mediating roles of creativity in business demonstrates how highly innovative companies are better equipped to reap the benefits of BDACs for long-term performance by adopting more flexible approaches to their operations. Our research shows also that innovative thinking inside businesses helps them adapt to new circumstances and succeed in a highly competitive market. Therefore, strategic agility may aid in the development and capture of fresh ideas for mobilising the resources for seizing corporate value and reconfiguring any current set of resources for value generation.

5.3. Practical implications

The findings of this research have several applications for manufacturing companies and their executives. First, the research suggests that innovation inside businesses is crucial to long-term success. Managers engaging in creative behaviours, which ensure that their company will be able to transform novel ideas into resources suitable for boosting BDACs can improve manufacturing lead times, inventory turnover, and procurement lead times. This being so, we propose that manufacturers not only encourage innovation, but also provide BDAC education to employees so that everyone can help predict market trends, learn about customers’ wants and needs, and quickly develop production strategies with the goal of cutting down on manufacturing lead times.

Second, our research encourages production managers to adopt BDA management approaches and to foster more inventiveness in their businesses as a means of fostering agility. Our research may also help business leaders understand how consumer input is the key to competitive strategic agility. Industrial companies’ decision-making processes may be at risk due to a lack of data visualisation skills. A number of earlier studies have identified the inclusion of information from external players as a problematic but crucial part of digital change transition [134,135,136,137]. Finally, our research demonstrates to business leaders that consumer participation as data analysts may reveal shifts in market circumstances, allowing them to better adapt their digital transformation initiatives to speed up the fulfilment of orders. Strategically agile businesses should, therefore, prioritise consumer participation in order to maximise value creation.

5.4. Limitations and suggestions for the future research 

There are a number of caveats to this study that point to potential future avenues of inquiry. If researchers cannot use cross-sectional data to demonstrate cause and effect links, the study’s reliability suffers. This gap might be filled by a long-term approach. The focus of the present study is only on long-term effectiveness. Other marketing performance measures, such as sales growth, product launches, customer retention, etc., should also be explored. We hypothesise that additional possible mediators, such as market orientation, learning orientation, and entrepreneurial orientation, might expand the scope of advantages from strategic agility. Only one possible moderator is investigated in this research. Others, such as technical uncertainty, firm size, and industry, may be investigated in further research. In this investigation, the context of the manufacturing sector informed the choice of agility assessment. Future studies will need to make a few adjustments to this metric before they can apply it to the service sector. This would considerably improve our ability to construct theories and comprehend the liminal states of the investigated connections.

 

 

Reviewer 4, Comment 5:

 

5. Line 2, environmental, not  environomental

Our response:

 

Many thanks for pointing this out. This has been undertaken.

 

Reviewer 4, Comment 6:

 

6. Line 163, explain “big data governance”

Our response:

Many thanks for this comment. We have addressed this point through the addition of an explnition of big data governance (in red, p. 5), which reads as follows:

   Previous examination suggests the following five steps to successfully implement BDA in the healthcare setting: (1) establishing big data governance which refers to the capability of a firm to orchestrate all relevant resources in order to maximize the value of information and insight generation to the organization; (2) fostering a culture of open information sharing; (3) educating and preparing key personnel to use BDA; (4) incorporating cloud computing into the organization’s BDA; and (5) generating new business ideas [12,34,62].

We wish to thank Reviewer 4 for such useful comments. We greatly appreciate the time and effort you devoted to your review. We have done our best to address all your suggestions in our revised version, and we hope you agree that the paper is now much stronger and better argued as a result.

 

 

Reviewer 5 Report

Dear Authors,

You use LISREL software for SEM

1. Please put final output image for the structural model

2. Please send me all measurement models I can not see measurement models evaluation (separately for each variable)

3. Please tell me how you calculate HTMT criteria with LISREL software. As far as I worked that is just possible to do with SmartPLS3. Please send me the original output related to the HTMT table from the software.

4. About moderating variables, please provide more explanation on how you measure the moderating variable? How is it robust? Why did not use MAICON criteria?

Author Response

Manuscript ID sustainability- 2306427

 

Understanding the Relationship between Big Data Analytics Capabilities and Sustainable performance: The Role of Strategic Agility and Firm Creativity

Reviewer 5

Reviewer 5, Comment 1:

1. Please put final output image for the structural model

Our response:

Thank you for this suggestion, which we have acted on. Please see Figure.2. Results of structural equation modeling (P.10).

Reviewer 5, Comment 2:

2. Please send me all measurement models I can not see measurement models evaluation (separately for each variable)

Our response:

Many thanks for this comment. All the measurement statistics that have been used to evaluate the measurement model are included in Table.2. Please kindly see Table.2. (p.9).

 

Reviewer 5, Comment 3:

3. Please tell me how you calculate HTMT criteria with LISREL software. As far as I worked that is just possible to do with SmartPLS3. Please send me the original output related to the HTMT table from the software.

Our response:

Thank you very much for your comment. HTMT was calculated in our study using the SmartPLS 3.0 software, however, to avoid any confusion for the readers that maybe caused due to using more than one software within the data analysis, we focused on using the same software (LISREL) for assessing the discriminant validity as follow: “We examined the discriminant validity using the method suggested by Fornell and Larcker (1981) in order to examine the average variance (AVE) extracted for each construct. The overall values were all well above the 0.5 suggested for each construct (Fornell and Larcker, 1981). Further, the square root of the AVE was larger than the correlation with other constructs (Fornell and Larcker, 1981). Table 3 shows the square root of the average variance extracted for each construct along the diagonals. These suggest that the measurement model had good statistical properties.

Reviewer 5, Comment 4:

4. About moderating variables, please provide more explanation on how you measure the moderating variable? How is it robust? Why did not use MAICON criteria?

 

Our response:

Many thanks for this suggestion. We have added more details on how the moderating test was conducted, which reads as follows.

    To assess the proposed moderation effect in the structural model, we performed a hierarchical moderation regression analysis in the macro process [112], in line with the recommendations provided by MacKinnon et al. [113]. A significant relationship was found to exist between big data analytics capabilities and firm creativity (β = 0.29, P < 0.001), strategic agility and environmental performance (β = 0.31, P < 0.001), strategic agility and economic performance (β = 0.25, P < 0.001), and strategic agility and social performance (β = 0.42, P < 0.001). For H4 & H5, our results revealed that the interaction terms contributed to bringing change in the variance explained (adj-R2 = 0.53; p = 0.001). The interaction term was found to be positive and significant (β = 0.37; p < 0.001). Therefore, H4 & H5 were supported.

 

We wish to thank Reviewer 5 for such useful comments. We greatly appreciate the time and effort you devoted to your review. We have done our best to address all your suggestions in our revised version, and we hope you agree that the paper is now much stronger and better argued as a result.

 

 

Round 2

Reviewer 1 Report

Thank you for addressing the reviewers comments. The paper has improved significantly. I am satisfied with the current version. 

Author Response

Manuscript ID sustainability- 2306427

 

Understanding the Relationship between Big Data Analytics Capabilities and Sustainable performance: The Role of Strategic Agility and Firm Creativity

Reviewer 1

Reviewer 1, Comment 1:

Thank you for addressing the reviewers comments. The paper has improved significantly. I am satisfied with the current version. 

Our response:

Many thanks for your positive comment and accepting our paper for publication.

We wish to thank Reviewer 1 for such useful comments. We greatly appreciate the time and effort you devoted to your review. We have done our best to address all your suggestions in our revised version, and we hope you agree that the paper is now much stronger and better argued as a result.

 

Reviewer 4 Report

The authors have addressed the issues raised in my round 1 review.

Author Response

Manuscript ID sustainability- 2306427

 

Understanding the Relationship between Big Data Analytics Capabilities and Sustainable performance: The Role of Strategic Agility and Firm Creativity

Reviewer 4

Reviewer 4, Comment 1:

 

The authors have addressed the issues raised in my round 1 review.

Our response:

Many thanks for your positive comment and accepting our paper for publication.

We wish to thank Reviewer 1 for such useful comments. We greatly appreciate the time and effort you devoted to your review. We have done our best to address all your suggestions in our revised version, and we hope you agree that the paper is now much stronger and better argued as a result.

 

Reviewer 5 Report

Dear Authors,

Your answer related to HTML is not acceptable and persuasive. SmartPLS3 is based on non-normal data and exploratory researches. On the other hands, LISREL is based on normal data and confirmatory researches. Combination of these software is not acceptable.

Author Response

Manuscript ID sustainability- 2306427

 

Understanding the Relationship between Big Data Analytics Capabilities and Sustainable performance: The Role of Strategic Agility and Firm Creativity

Reviewer 5

Reviewer 5, Comment 1:

Your answer related to HTML is not acceptable and persuasive. SmartPLS3 is based on non-normal data and exploratory research. On the other hands, LISREL is based on normal data and confirmatory research. Combination of these software is not acceptable.

Our response:

Many thanks for this very constructive point, with which we totally agree. Our study utilized LISREL for assessing the discriminant validity, these changes are marked in red throughout sections 4.1. Measurement Model (pp.8-9), which reads as follows:

We examined the discriminant validity using the method suggested by Fornell and Larcker (1981) in order to examine the average variance (AVE) extracted for each construct. The overall values were all well above the 0.5 suggested for each construct (Fornell and Larcker, 1981). Further, the square root of the AVE was larger than the correlation with other constructs (Fornell and Larcker, 1981). Table 3 shows the square root of the average variance extracted for each construct along the diagonals. It is therefore reasonable to assume all of the scales display discriminant validity.

We wish to thank Reviewer 5 for such useful comments. We greatly appreciate the time and effort you devoted to your review. We have done our best to address all your suggestions in our revised version, and we hope you agree that the paper is now much stronger and better argued as a result.

 

 

Author Response File: Author Response.pdf

Round 3

Reviewer 5 Report

Please delete this part and just search about this topic. I suggest you delete discriminant validity with SmartPLS and consider:

`To assess discriminant validity using AMOS, researchers typically perform a confirmatory factor analysis (CFA) by testing a hypothesized model that includes multiple constructs. The CFA can indicate whether the proposed measures, or items, are related to one another in a way that is consistent with the theory that underlies the research questions.`