Deepening big data sustainable value creation: insights using IPMA, NCA, and cIPMA

Depósito de investigación de la Universidad de Sevilla https://idus.us.es/ “This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1057/s41270-024-00321-2” Deepening Big Data Sustainable Value Creation: Insight using IPMA, NCA, and cIPMA Abstract The impact of big data analytics capabilities (BDACs) on firms’ sustainable performance (SP) is exerted through a set of underlying mechanisms that operate as a “black box.” Previous research, from the perspective of IT-enabled capabilities, demonstrated that a serial mediation of supply chain management capabilities (SCMCs) and circular economy practices (CEPs) is required to improve SP from BDACs. However, further insight regarding the role of BDACs in the processes of SP creation can be provided by deploying complementary analytics techniques, namely importance-performance map analysis (IPMA), necessary condition analysis (NCA), and combined importance-performance map analysis (cIPMA). This paper applies these techniques to a sample of 210 Spanish companies with the potential for circularity and environmental impact. The results show that BDACs exert a positive total effect towards achieving SP. However, companies still have the potential to improve and benefit from these capabilities. Additionally, BDACs are a necessary condition (must-have factor) for all dependent variables in the model, including SP. In this case, high levels of BDACs are required to achieve excellence in SP, justifying organizational initiatives that prioritize the improvement of BDACs to achieve SP goals. Keywords: Big data analytics capabilities; Circular economy practices; Supply chain management capabilities; Sustainable performance; IT-enabled capabilities perspective; Importance-performance map analysis (IPMA); Necessary condition analysis (NCA); Combined importance-performance map analysis (cIPMA). 2 Introduction Achieving sustainable development is now a key priority pursued by governments and international institutions (Andersson et al. 2022). Businesses play an essential role in moving toward a sustainable future. As testimony to this, the United Nations (UN) launched the Global Compact (https://unglobalcompact.org/), the largest international initiative that seeks to engage companies from all over the world, along with governments and civil society, in the commitment to the Sustainable Development Goals (SDGs) as part of its 2030 Agenda (United Nations, 2015). The SDGs challenge businesses to evaluate strategic and operational decisions to leverage economic, social, and environmental sustainability opportunities (Jiménez et al. 2021). Indeed, while the SDGs implicitly focus on global change, individual firms must incorporate sustainability into their strategy to ensure viability (Yadav et al. 2022). Addressing the fundamental uncertainty and complexity of sustainability challenges requires innovative methods that help make decisions based on the diverse dynamics of multiple stakeholders and scenarios (Berrone et al. 2023). Such techniques require leveraging the high volume and velocity of information assets (i.e., big data) through organizing for insight, increasing data storage and processing, and using improved methods to achieve actual impact across disciplines (Abrantes and Ostergaard, 2022). The expanded use of big data in infrastructure, analytics, and services has contributed to sustainable development by helping navigate this environment and measuring progress toward SDGs (Hassani et al. 2021). Digital transformation through emerging technologies can lead to methods and approaches that provide improved solutions to societal issues that drive strategy and create value for both the business and society (Pappas et al. 2023). Studies have consistently demonstrated the impact of big data analytics (BDA), including marketing analytics, on creating business value. However, how it affects the sustainability aspects of this value still needs to be explored (El-Haddadeh et al. 2021). Although research is abundant on the impact of BDA on economic performance, its environmental and social considerations, as well as the holistic view of sustainable performance under a triple bottom line form, are lacking and require further investigation (Behl et al. 2022; Cetindamar et al. 2022; Zhu and Yang, 2021). Additionally, there is a lack of theories to explain the underlying mechanisms through which using BDA to extract, integrate, and optimize large-scale data contributes to firm performance and the conditions under which this occurs (Olabode et al. 2022). Specifically, there is an urgent need for empirical studies that reveal how organizations can effectively capture the value of BDA (Persaud and Zare, 2023). To shed light on these issues, an emerging branch of information systems research, the information technology-enabled capabilities perspective, advocates that information technology (IT) can facilitate other organizational capabilities that improve organizational performance (Liu et al. 2020). In short, this perspective argues that a hierarchy of organizational capabilities exists in which higher-order capabilities require technological capabilities to enable them (Felipe et al. 2020). BDA capabilities (BDACs), which are defined as IT capabilities designed for efficient and effective management and exploitation of big data (Cetindamar et al. 2022), can be conceived as one of these enabling capabilities. Consistent with the postulates defended by the perspective of IT-enabled capabilities, an emerging research line posits that integration and coordination of BDACs within other organizational capabilities in the fields of supply chain management (supply chain management capabilities – SCMCs) and circular economy (circular economy practices – CEPs) leverage them in such a way as to impact economic, environmental, and social performance positively (Riggs et al. 2023; Yu et al. 2022). SCMCs facilitate integrating and coordinating internal and external resources within the supply chain and collaboration 3 among elements beyond the organization's boundaries (Kalubanga and Gudergan, 2022). Meanwhile, CEPs comprise corporate activities that promote the optimal use of resources through reusing, recycling, reducing, and remanufacturing, among others, as opposed to a traditional linear production and consumption model (Le et al. 2023). However, this potential benefit of big data contrasts with research showing that big data investment plans are in decline, with firms still attempting to understand the necessary conditions for big data to improve outcomes (Ghasemaghaei, 2020). It is relevant to note that while previous literature has evaluated the mediating role of SCMCs and CEPs in specific BDA value-creation processes (Piprani et al. 2023; Sahoo et al. 2023), an alternative approach, which has not been sufficiently exploited by previous literature on big data sustainable value creation, is the evaluation of existing underperformance of companies in these areas, meaning there is a potential for improving sustainable results through their influence. This information can be provided by importance-performance map analysis (IPMA) (Ringle and Sarstedt, 2016), which has only recently begun to be used in marketing research (Damberg et al. 2024). Notably, our study seeks to answer the following research question: RQ1: Is there room for improvement in the level of big data analytics capabilities reached by companies to positively impact their sustainable performance? Additionally, all previous literature has focused on studying the impact of different capabilities on performance in terms of additive logic, explaining the outcome through sets of variables that are sufficient but not explicitly necessary for influencing the outcome of interest (Richter and Hauff, 2022). This paper proposes that the application of a necessity logic can offer fresh insights into the phenomena under investigation by examining whether the existence or non-existence of a concept (or a particular level of it) ensures the manifestation or non-manifestation of the outcome (Bokrantz and Dul, 2023). For this purpose, the novel technique of necessary condition analysis (NCA) will be applied (Dul, 2020). This methodology will expand the previous knowledge on the effects of BDACs on the variables considered, which has traditionally been addressed from their positive or negative impacts on them. Specifically, we aim to answer the following research question: RQ2: Are big data analytics capabilities a necessary condition (must-have factor) for sustainable performance of firms and specific antecedents of sustainable performance, such as circular economy practices and supply chain management capabilities? Both questions are pertinent to present BDA research on the IT business value creation field and will offer valuable contributions to the associated state of the art. First, this study will provide significant theoretical and empirical insights into BDA mechanisms to create sustainable value using novel research approaches. Thus, this article examines the perspective of IT-enabled capabilities from a causal approach of necessity rather than sufficiency (Dul, 2024). It provides an in-depth understanding of the must-have organizational factors related to big data or facilitated by them that lead to sustainable outcomes. As a result, this paper is among the first to put forward and contrast necessary condition hypotheses, thereby aiding the development of theoretical support for future cases and expanding the practical application of NCA for prospective users. Additionally, this article combines the use of IPMA and NCA in applying the cIPMA (Hauff et al. 2024) approach, representing a pioneering implementation of this technique. Finally, stressing the call for companies to understand the urge for data-driven decisions to respond to highly dynamic markets (Petrescu and Krishen, 2023), this study 4 provides practical guidance to managers seeking to improve the effectiveness of their big data investments and increase their positive impact on sustainable performance. To this end, the present article proceeds as follows. Following the introduction, the article presents a detailed analysis of the theoretical background that underlies our research, encompassing the foundational theoretical and conceptual frameworks, the conceptual model, and the formulation of necessary condition hypotheses. Then, the methodological approach of the research is outlined, highlighting the three techniques utilized: IPMA, NCA, and cIPMA. The following section presents the results, while their subsequent discussion, theoretical and practical implications, limitations, and future research opportunities are exposed in the final part of our paper. Theoretical background IT-enabled capabilities perspective and big data analytics value creation Information technology plays a significant role in enabling organizational success in today's business environment. However, it is widely acknowledged that mere investment in IT infrastructure or the deployment of IT tools does not directly turn into a competitive advantage (Majhi et al. 2022). Instead, IT is leveraged through its impact on certain value-creating vehicles that lead to outperformance (Mikalef et al. 2021). To be more specific, IT capabilities, defined by Bharadwaj (2000) as the firm’s ability to deploy IT that contributes to the development of corporate strategies and operations, will enable the creation or enhancement of other higher-order capabilities that genuinely make a difference in excellence (Mikalef and Pateli, 2017). This theoretical approach is known as the IT-enabled capabilities perspective, which is the pillar of numerous studies that analyze IT value creation processes through the mediation of other organizational capabilities (Benitez, Llorens, et al. 2018). Concerning this approach, there remains a substantial lack of understanding of the underlying mechanisms through which investments in BDA result in organizational performance, despite the promise of their prospective usefulness in organizations (Elia et al. 2022). According to the triple bottom line conceptualization, this fact is particularly apparent in its potential contribution to sustainable performance. Therefore, an exciting field of research emerges to investigate the interconnections between big data and two relevant contemporary business concepts that can influence firms’ sustainable performance: the circular economy (CE) and supply chain management (Persaud and Zare, 2023). To address this research gap, the IT-enabled capabilities perspective provides a suitable framework for identifying those organizational capabilities BDA can enable to produce desirable sustainable outcomes. In this vein, our recent work (Riggs et al. 2023) develops a research model that focuses on the effects on the sustainable performance of big data analytics capabilities (BDACs), which are IT- based organizational capabilities designed for efficient and effective management and exploitation of big data (Cetindamar et al. 2022) (Figure 1). The results showed that the impact of BDACs on sustainable performance (SP) (under a triple bottom line model – that is, economic, social, and environmental results) is fully and sequentially mediated by two other organizational capabilities, namely supply chain management capabilities (SCMCs) and circular economy practices (CEPs). Insert Figure 1 5 However, that study aligns with most of the previous literature in the field, primarily dominated by additive logic to address causal relationships between variables (Richter and Hauff, 2022). This logical approach posits that one or more variables can be sufficient, though not necessary, to affect an outcome. Moreover, these variables can offset each other, which raises a question that traditional additive logic cannot answer: Are BDACs indeed a necessary condition for achieving SP, or even for the two IT- enabled capabilities that have been proven to facilitate SP achievement (SCMCs and CEPs)? Necessity theory: its application to big data analytics capabilities value creation processes Following Dul (2020, 2024), an alternative approach to understanding these phenomena and providing valuable insights is necessity theory. This novel theoretical development is defined as “a theory that employs causal necessity logic for describing the causal relationships between all or a selection of the concepts of a theory” (Bokrantz and Dul, 2023, p. 54). Therefore, this lens posits that the outcome strictly depends on predetermined conditions, which will not occur in their absence. This concept is conveyed through phrases such as “X is needed for Y (output),” “X is critical for Y,” or “X is a requirement for Y,” among others, denoting the presence of a crucial element for the desired result to occur or, conversely, a barrier to overcome (Dul et al. 2023). On this basis, our work seeks to enhance the perspective of IT-enabled capabilities with novel insights by incorporating the causal necessity approach to this theoretical framework. First, to assess the current state of the art in research that formally addresses big data as a necessary condition from the necessity logic approach, a literature review was performed to identify existing articles that could establish a necessity for big data to obtain sustainable performance. A review of the Scopus database began by searching for all items that included necessity terms and big data, resulting in 296 documents (Table 1). When this list was limited to articles only, the results were reduced to 150. After a preliminary review of these articles, only eight addressed big data as an antecedent in the relationships proposed in the articles. Of these eight, only five sources considered big data an antecedent to performance (Table 2). None of them referred to sustainable performance under a triple-bottom-line conceptualization. Novikov (2020) and Ylijoki and Porras (2019) were descriptive and did not include data analysis. Of the remaining three, only Talib et al. (2023) specifically analyzed big data as a necessary condition independent of the additive effects of any other antecedents. Table 2 summarizes the specific contribution that our paper makes to the previous works cited. Insert Table 1 and Table 2 Therefore, the bibliographic search has confirmed the novelty of applying the necessity theory to the already well-established IT-enabled capabilities perspective. This helps to assess the necessary role that BDACs may play in their relationships with SP, CEPs, and SCMCs for a deeper comprehension of the underlying mechanisms of business value creation. 6 Conceptual framework and hypotheses development Big data analytics capabilities Compared to traditional analytics, big data analytics examines data in greater volume, at higher velocity, and from a greater variety of sources (McAfee and Brynjolfsson, 2012). However, these voluminous data have no value per se for companies; instead, value comes from the quality and sophistication of the processes used to handle and analyze them (Ferraris et al. 2019). BDACs refer to specific information processing capabilities based on organizational culture, data management, and skills to synthesize voluminous raw data into a successful business strategy (Kiron et al. 2014). One of the most comprehensive definitions regarding BDACs that the existing literature on information systems has produced is that of Mikalef et al. (2018), who noted that BDACs are those organizational capabilities that leverage IT infrastructure, massive data management, and the skills of the staff involved, and that support the competitive operation of the company. In line with this definition, Akter et al. (2016) conceptualized the three critical dimensions of BDACs: BDA management, BDA infrastructure, and BDA talent. Supply chain management capabilities The functionality of supply chains hinges on numerous relationships among not only their direct players but also many other agents in their business ecosystem, which involve vulnerability to potential disruptions (Sharma et al. 2022). In current turbulent environments, firms and their supply chain members must adeptly address continuous shifts, employing a combination of knowledge and technological tools (Moryadee and Jitt-Aer, 2020). Therefore, supply chain management implies collaboration, process integration, and information sharing among supply chain partners, thus satisfying customers’ needs by leveraging information and technology (Min et al. 2019). Following Wu et al. (2006), SCMCs are the firm’s ability to exploit and assimilate internal and external resources, such as information that allows its supply chain operations. These authors also proposed four critical dimensions for SCMCs, which were as follows: information exchange between supply chain players, interfirm coordination, integration of activities both within and outside the limits of firms, and supply chain responsiveness to all types of environmental changes (Wu et al. 2006). Circular economy practices According to the Ellen MacArthur Foundation (2013), a circular economy (CE) is “an industrial system that is restorative or regenerative by intention and design” (p. 7). Therefore, a CE model means an economic system that replaces end-of-life concepts with reducing, reusing, recycling, and recovering materials to achieve sustainable development (Kirchherr et al. 2017). CE uses cyclical flows of materials, renewable energy sources, and sustainable development to maximize the service of materials and energy (Korhonen et al. 2018). The shift from linear production systems to circular ones requires the implementation of specific practices in business activities (Schroeder et al. 2019). Govindan and Hasanagic (2018) proposed six groups of internal CE practices (CEPs) for practically implementing circular business in companies, which are as follows: (i) governance initiatives, such as indicators systems for monitoring and measuring CE projects; (ii) economic initiatives, which are those activities seeking to obtain economic advantages from CE opportunities; (iii) cleaner production, or those processes that aim at achieving eco-efficient manufacturing; (iv) product development pursuing durable 7 design or reuse; (v) top management support for CE implementation; and (vi) CE knowledge, which means a proactive CE education, both for companies’ employees and community in general. Sustainable performance Sustainable performance is commonly understood to involve three simultaneous objectives: economic prosperity, environmental quality, and social equity (Kirchherr et al. 2017). Indeed, the literature is dominated by the so-called triple bottom-line conceptualization of sustainability (Purvis et al. 2019), which reflects business effects on profitability, nature, and people (Nursimloo et al. 2020). First, economic performance highlights the impacts of corporate activities on the economic system (Elkington, 1997). Second, environmental performance refers to creating value related to the resilience of natural resources (van Buren et al. 2016). Sustainability minimizes the current environmental impact and takes a restorative and regenerative approach to address past damage (Ellen MacArthur Foundation, 2013). Social performance considers elements related to the welfare of stakeholders, such as the health and safety of employees, the creation of employment opportunities for the surrounding community, education and training, and the reduction of risks to the general public (Das, 2018; Paulraj, 2011). Conceptual model Figure 1 illustrates the conceptual model from Riggs et al. (2023). Our findings showed that SCMCs and CEPs fully and sequentially mediated the relationship between BDACs and SP of the organization (Figure 2). The proposed conceptual model and its results serve as the basis of our study to assess the level of importance and the performance of each variable on sustainable performance. Applying the importanceperformance map analysis (Ringle and Sarstedt, 2016), we examine if companies can enhance BDACs performance to achieve excellence in their SP. Additionally, and according to Dul (2020), we will provide theoretical support in the following sections for proposing the necessary condition hypotheses (in this case, BDACs) as the needed precedent for the other dependent variables of our base research model (SCMCs, CEPs, and SP). Finally, focusing on the final dependent variable, SP, combined importance-performance map analysis (Hauff et al. 2024) will help us prioritize initiatives to achieve a high level of performance in SP. Insert Figure 12 Big data analytics capabilities as a necessary condition for supply chain management capabilities Supply chains operate in increasingly complex and uncertain environments, with frequent and unpredictable changing conditions, sometimes even disruptive (e.g., COVID-19 or recent international conflicts). This fact, coupled with the exponential growth and development of IT, has dramatically affected supply chain management, which has become increasingly challenging and demanding. Supply chains are becoming extensive and complex networks of participants, often located far from each other, that require physical and digital interactions between all chain links, including electronic equipment and devices, until reaching the final customer (Novikov, 2020). Under these circumstances, BDA and 8 its related capabilities become indispensable business tools for supporting all agents who must adapt to these new modes of operation. Moreover, BDACs have improved flexibility, reliability, and resource optimization, which can be applied to supply chain management (Manikas et al. 2023). Mandal’s (2019) study revealed that corporate abilities to plan, coordinate, and control BDA serve as “critical enablers” (p. 297) of certain functions required for successful supply chain management in current turbulent business environments, such as supply chain preparedness, alertness, and agility. Similarly, a recent study by Piprani et al. (2023) argued that the application of BDACs will become indispensable for supply chain management if the company wants to influence its efficiency and competitiveness. In their research effort to identify the skills necessary for supply chain management professionals, Stek and Schiele (2021) remarked that big data analysis was found to be a key and required skill. Finally, resulting from an exhaustive systematic review of the literature on BDA and supply chain management, Lee and Mangalaraj (2022) concluded that “BDA plays a vital role in the functioning of supply chain management” (p. 24), highlighting the relevance of monitoring customers’ preferences, supply chain resilience, and visibility. However, we could ask if there could be other organizational capabilities that exert the same effect or that could substitute for BDACs. To justify the impossibility, we use the arguments provided by Rolf et al. (2023). They proposed that companies incorporate real-time supply chain planning, coordination, execution, and control to prevent costly operational disruptions and ensure the timely delivery of goods and services to customers. This implies that these management principles can only be implemented if accurate real-time data are accessible to all relevant stakeholders, allowing them to identify shortcomings, simplify procedures, and mitigate risk. On top of this, Pratt et al. (2023) emphasized two critical factors for managers to rely on BDACs to support their supply chain decision systems: (1) the urgent need for more sophisticated IT tools to coordinate operations and communication among partners and (2) the significant amount of data generated both internally and externally within a supply chain that cannot be handled with conventional IT tools. We can propose the following hypothesis after all the above exposed theoretical support. H1: Big data analytics capabilities are a necessary condition for the development of supply chain management capabilities. Big data analytics capabilities as a necessary condition for circular economy practices The circular economy system seeks to decouple economic profitability from excessive consumption of resources and degradation of nature. Therefore, it advocates the complete elimination of waste or its recovery as valuable raw materials or by-products and prolonging the product's useful life. The academic study of the relationship between the two proposed variables, BDACs and CEPs, is still developing, resulting in an inherent scarcity of theoretical references. However, early studies have highlighted the need to use BDACs to fully capitalize on the potential of CE models, with the success of circularity dependent on the availability of complex and innovative tools that allow the acquisition, sharing, and analysis of large volumes of data (Chiappetta Jabbour et al. 2019). In this sense, BDACs serve as a critical facilitator for the deployment of CEPs by enabling partners to implement the required communication and collaboration through exchanging and sharing a large amount of data (Kamble et al. 2021). 15 into the organizational capabilities that facilitate CEPs and SPs, as well as the role that specific technologies play in these relationships. As Hypotheses 1 and 2 have confirmed, our article contributes to existing research by revealing that BDACs are a key antecedent and must-have factor for both mediating variables in their relationship with SP. Adopting BDACs to achieve SP is a complex link and requires complementary resources to help organizations achieve their full potential (Benitez, Ray et al. 2018). Firstly, the emergence of BDACs represents a novel technological advancement that empowers supply chains to handle large amounts of data (Chen et al. 2015). BDACs help companies track and analyze voluminous data related to the supply chain, allowing them to identify areas of inefficiency and opportunities to optimize resource use. This implies the ability to monitor and optimize processes, identify areas for improvement, anticipate potential problems, and make decisions based on real-time information. Subsequent studies suggest that BDACs improve supply chain agility and adaptability (Fosso Wamba et al. 2020). The development of robust BDACs is required to improve the effectiveness of supply chain management, as Jha et al. (2020) highlighted. Through monitoring and analysis of realtime data, companies can identify potential issues and implement corrective actions before they disrupt product availability. This proactive approach directly influences marketing strategies by ensuring a reliable and efficient supply chain. These capabilities enable organizations to take advantage of the vast potential of big data, resulting in innovation and a greater competitive advantage within their supply chain operations (Tan et al. 2015). With a deeper understanding of the supply chain and a clearer understanding of demand and purchasing patterns, businesses can improve the customer experience by ensuring product availability, providing more accurate delivery times, and offering more efficient customer service. Secondly, incorporating advanced BDACs is critical in implementing CEPs. In the context of CE, BDACs, through marketing analytics activities, allow the identification of opportunities to anticipate customers' changing needs and preferences that may influence the design of future circular products. This statement is consistent with the recommendation from Dam et al. (2019) to leverage BDA to promote collaboration and co-creation of products and services with suppliers and customers. By identifying historical patterns and correlations in the data, companies can make informed decisions on adapting their marketing strategies to maximize impact. Furthermore, BDACs aid in the intelligent selection of environmentally friendly raw materials for production and reduce defects and wastage (Bag and Pretorius, 2022). In this line, de Souza et al. (2021) explain that certain BDACs enable the development of marketing strategies in the retail sector that optimize pricing, purchasing, sales, and operations management, leading to the implementation of CEPs aimed at reducing food waste. In conclusion, without these BDACs, efficient and sustainable supply chain management and CE would be hindered (Tipu and Fantazy, 2023), as the necessary tools to address challenges proactively and effectively would be lacking. Agreeing with this position, scholars such as Kristoffersen et al. (2021) and Bag et al. (2021) underscore the importance of these capabilities in elevating resource orchestration and fostering a sustainable competitive advantage. NCA analysis shows how BDACs are a necessary condition to achieve satisfactory levels of SCMCs, CEPs, and SP. These results support the idea of the necessity logic approach. In particular, bottleneck analysis suggests that BDACs are required to achieve moderate levels of SCMCs and CEPs, which can only be achieved in the presence of BDACs. From the perspective of necessity logic, BDACs are required to achieve adequate levels due to the strict dependence on predetermined conditions (Dul, Hauff, et al. 2023). Necessity logic posits that certain elements are critical and required for a desired outcome to occur. In the context of sustainability, it is argued that the capabilities of big data are a necessary condition because they are essential for informed and strategic decision-making in supply chain management and CE (Talib et al. 2023). 16 The bottleneck analysis also shows that two necessary conditions are required to achieve even low SP levels: SCMCs and CEPs must be at a primary low degree. However, achieving excellence in SP demands BDACs at a medium-high level. Combining the results generated by PLS-SEM and NCA, it can be observed that BDACs are a significant antecedent and a necessary condition for SCMCs and CEPs (must-have factor). Nevertheless, it should be emphasized that ranges of BDACs above moderate levels are required to achieve exceptional SP results. Finally, our study has facilitated the identification that companies still have room for improvement in utilizing BDACs to achieve optimal SP; thus, not prioritizing the development of these capabilities may affect the positive impact of SCMCs and CEPs on SP. Since BDACs are necessary to achieve excellence in SP, it is emphasized to continue developing and strengthening these capabilities. The IPMA results indicate that BDACs can still influence SP improvement and should be driven by management as they are essential performance enablers (Akter et al. 2016). Thus, it shows that the score of BDACs is close to the mean value in terms of importance in its total effect on SP, even above the role played by the SCMCs variable but below the influence generated by CEPs. Building on this using the cIPMA technique, we observe in our sample that many companies (45.7%) have not yet reached the required level of BDACs to achieve SP performance above 80%. This means that improving BDACs should become a priority for these companies. Unless this bottleneck is overcome, investment in the other variables will not lead to excellence in SP. Moreover, the PLS-SEM results indicate that BDACs do not directly affect SP significantly. However, they have a significant total effect as the influence on SP is indirect. These results are consistent with previous studies that found that the relationship between BDACs and SP was not significant and that the implementation of CEPs and SCMCs mediates this relationship (Cheng et al. 2021; Kristoffersen et al. 2021; Riggs et al. 2023). Furthermore, this study supports the theoretical proposition posited by the perspective of IT-enabled organizational capabilities, suggesting that IT resources and capabilities are necessary to enable other organizational capabilities, ultimately affecting performance (Li et al. 2023). This research contributes to expanding the emerging body of literature that employs this perspective to explain and contrast the potential mediating capabilities that lead to creating value from big data (Li et al. 2023; Sáenz et al. 2022). Practical implications The findings of the present study have practical implications. Managers must be aware that more than implementing BDACs is necessary to achieve positive SP - it must be linked to CEPs and SCMCs. In this context, our results have confirmed Hypothesis 3, which validates that BDACs are an essential element but insufficient to improve SP. Meanwhile, the presence of BDACs is necessary, and their connection and alignment with the principles and goals of CE and supply chain management are considered crucial to achieving optimal levels of SP. As confirmed by Hypotheses 1 and 2, our results ensure that BDACs are also necessary for developing CEPs and SCMCs, enablers of SP. Therefore, our study can serve as a valuable guide to help managers consider investing in BDACs due to their importance in improving adaptability and flexibility in the supply chain in terms of information sharing, coordination, integration, and responsiveness, allowing companies to cope with the complexities and uncertainties of the supply chain. BDACs help identify bottlenecks and areas for improvement in the supply chain. By monitoring and analyzing real-time data, companies can proactively detect potential issues and take corrective actions before they negatively affect product availability. This directly affects marketing strategies by ensuring an efficient and reliable supply chain. 17 Moreover, adopting CEPs must be supported using BDACs due to the importance of acquiring, processing, and using large volumes of information and knowledge; hence, the value of big data analytics to enable the ambitious reengineering processes and complex operational systems necessary to transition to a circular economy model. The ability to transform large datasets into valuable information enhances the effectiveness of CEPs, providing a more precise and sustainable approach to resource management and minimization of environmental impacts. BDACs are based on data availability, enabling companies to integrate processes and share resources, extend the product's shelf life, and promote the reuse and recycling of materials, allowing companies to achieve their economic, environmental, and social objectives. Through marketing analytics based on extensive datasets concerning consumer behavior, preferences, and market trends, companies can obtain valuable information on how to design products that are more sustainable and aligned with the principles of CE. For example, they can identify alternative materials that are easier to recycle or develop modular products that can be easily disassembled and repaired. Finally, our findings indicate that high levels of BDACs performance are required to achieve excellence in SP. BDACs help companies identify areas of inefficiency and waste in their operations. BDACs enable companies to collect and analyze data on their stakeholders' opinions, preferences, and needs, including customers, employees, local communities, and interest groups. This helps them better understand the expectations of these groups and make decisions that promote corporate social responsibility and equity. Focusing on marketing analytics, companies can also identify emerging sustainability trends, such as shifts in consumer preferences toward more eco-friendly products or environmental concerns in certain regions. This enables companies to adapt their marketing strategies to capitalize on these trends and meet market demands. Because there is still room for improvement in the performance that companies can achieve in this variable, and many companies have not yet reached a level in BDACs high enough to guarantee a performance above 80% in SP, this leads us to recommend that companies place priority on their efforts and investments in this variable. Limitations and future research The study was limited in several ways. For one thing, it has relied on participant perceptions of their organization’s situation regarding the survey elements. This introduces the risk that subjective impressions of respondents do not necessarily reflect objective reality. In addition, the study has examined only the variables and constructs in one country and used unique qualifiers. For example, the sample was selected based on inclusion within sectors identified by the European Commission as having the potential to benefit from a circular economy model. Therefore, the study should be replicated using different demographic and geographic criteria. Future studies on the necessity of BDACs for sustainable performance could further investigate variables outside of SCMCs and CEPs as alternative necessities for sustainable performance. Current concerns about environmental uncertainty and existing research could indicate that environmental stability is necessary to justify investments in SCMCs and CEPs implementations. Additionally, analyses could consider the necessity of SCMCs and CEPs for sustainable performance independent of BDACs. Lastly, a longitudinal comparison of the results could indicate whether the necessity of these elements depends on the organization's maturity. 18 Conflict of interest statement On behalf of all authors, the corresponding author states that there is no conflict of interest. References Abrantes, B.F. and Ostergaard, K.G. (2022), “Digital footprint wrangling: are analytics used for better or worse? A concurrent mixed methods research on the commercial (ab)use of dataveillance”, Journal of Marketing Analytics, Palgrave Macmillan, Vol. 10 No. 3, pp. 187–206, doi: 10.1057/s41270-021- 00144-5. Akter, S., Fosso Wamba, S., Gunasekaran, A., Dubey, R. and Childe, S.J. (2016), “How to improve firm performance using big data analytics capability and business strategy alignment?”, International Journal of Production Economics, Elsevier, Vol. 182, pp. 113–131, doi: 10.1016/j.ijpe.2016.08.018. Alshurideh, M.T., Al Kurdi, B., Alzoubi, H.M., Ghazal, T.M., Said, R.A., AlHamad, A.Q., Hamadneh, S., et al. (2022), “Fuzzy assisted human resource management for supply chain management issues”, Annals of Operations Research, Springer, doi: 10.1007/s10479-021-04472-8. Andersson, S., Svensson, G., Molina-Castillo, F.J., Otero-Neira, C., Lindgren, J., Karlsson, N.P.E. and Laurell, H. (2022), “Sustainable development—Direct and indirect effects between economic, social, and environmental dimensions in business practices”, Corporate Social Responsibility and Environmental Management, pp. 1–15, doi: 10.1002/csr.2261. Bag, S. and Pretorius, J.H.C. (2022), “Relationships between industry 4.0, sustainable manufacturing and circular economy: proposal of a research framework”, International Journal of Organizational Analysis, Vol. 30 No. 4, pp. 864–898, doi: 10.1108/IJOA-04-2020-2120. Bag, S., Pretorius, J.H.C., Gupta, S. and Dwivedi, Y.K. (2021), “Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities”, Technological Forecasting and Social Change, Elsevier Inc., Vol. 163 No. November 2020, p. 120420, doi: 10.1016/j.techfore.2020.120420. Baig, M.I., Yadegaridehkordi, E. and Nizam Bin Md Nasir, M.H. (2023), “Influence of big data adoption on sustainable marketing and operation of SMEs: a hybrid approach of SEM-ANN”, Management Decision, Emerald Publishing, Vol. 61 No. 7, pp. 2231–2253, doi: 10.1108/MD-06-2022-0778. Behl, A., Gaur, J., Pereira, V., Yadav, R. and Laker, B. (2022), “Role of big data analytics capabilities to improve sustainable competitive advantage of MSME service firms during COVID-19 – A multitheoretical approach”, Journal of Business Research, Elsevier Inc., Vol. 148, pp. 378–389, doi: 10.1016/j.jbusres.2022.05.009. Benitez, J., Llorens, J. and Braojos, J. (2018), “How information technology influences opportunity exploration and exploitation firm’s capabilities”, Information and Management, Elsevier B.V., Vol. 55 No. 4, pp. 508–523, doi: 10.1016/j.im.2018.03.001. Benitez, J., Ray, G. and Henseler, J. (2018), “Impact of Information Technology Infrastructure Flexibility on Mergers and Acquisitions”, MIS Quarterly, Vol. 42 No. 1, pp. 25–43, doi: 10.25300/MISQ/2018/13245. Berrone, P., Rousseau, H.E., Ricart, J.E., Brito, E. and Giuliodori, A. (2023), “How can research contribute to the implementation of sustainable development goals? An interpretive review of SDG literature in management”, International Journal of Management Reviews, John Wiley and Sons Inc, doi: 10.1111/ijmr.12331. 19 Bharadwaj, A.S. (2000), “A Resource-Based Perspective on Information Technology Capability and Firm Performance: An Empirical Investigation”, MIS Quarterly, Vol. 24 No. 1, pp. 169–196, doi: 10.2307/3250983. Bokrantz, J. and Dul, J. (2023), “Building and testing necessity theories in supply chain management”, Journal of Supply Chain Management, John Wiley and Sons Inc, Vol. 59 No. 1, pp. 48–65, doi: 10.1111/jscm.12287. van Buren, N., Demmers, M., van der Heijden, R. and Witlox, F. (2016), “Towards a circular economy: The role of Dutch logistics industries and governments”, Sustainability (Switzerland), Vol. 8 No. 7, pp. 1– 17, doi: 10.3390/su8070647. Çankaya, S.Y. and Sezen, B. (2019), “Effects of green supply chain management practices on sustainability performance”, Journal of Manufacturing Technology Management, Vol. 30 No. 1, pp. 98–121, doi: 10.1108/JMTM-03-2018-0099. Cetindamar, D., Shdifat, B. and Erfani, E. (2022), “Understanding Big Data Analytics Capability and Sustainable Supply Chains”, Information Systems Management, Taylor and Francis Ltd., Vol. 39 No. 1, pp. 19–33, doi: 10.1080/10580530.2021.1900464. Chen, D.Q., Preston, D.S. and Swink, M. (2015), “How the use of big data analytics affects value creation in supply chain management”, Journal of Management Information Systems, Routledge, Vol. 32 No. 4, pp. 4–39, doi: 10.1080/07421222.2015.1138364. Cheng, T.C.E., Kamble, S.S., Belhadi, A., Ndubisi, N.O., Lai, K. hung and Kharat, M.G. (2021), “Linkages between big data analytics, circular economy, sustainable supply chain flexibility, and sustainable performance in manufacturing firms”, International Journal of Production Research, Taylor & Francis, Vol. 0 No. 0, pp. 1–15, doi: 10.1080/00207543.2021.1906971. Chiappetta Jabbour, C.J., Jabbour Lopes de Sousa, A.B., Sarkis, J. and Filho, M.G. (2019), “Unlocking the circular economy through new business models based on large-scale data: An integrative framework and research agenda”, Technological Forecasting and Social Change, Elsevier, Vol. 144 No. August 2017, pp. 546–552, doi: 10.1016/j.techfore.2017.09.010. Ciavolino, E., Aria, M., Cheah, J.H. and Roldán, J.L. (2022), “A tale of PLS Structural Equation Modelling: Episode I— A Bibliometrix Citation Analysis”, Social Indicators Research, Springer Netherlands, Vol. 164 No. 3, pp. 1323–1348, doi: 10.1007/s11205-022-02994-7. Dam, N.A.K., Le Dinh, T. and Menvielle, W. (2019), “A systematic literature review of big data adoption in internationalization”, Journal of Marketing Analytics, Palgrave Macmillan Ltd., Vol. 7 No. 3, pp. 182–195, doi: 10.1057/s41270-019-00054-7. Damberg, S., Liu, Y. and Ringle, C.M. (2024), “Does culture matter? Corporate reputation and sustainable satisfaction in the Chinese and German banking sector”, Journal of Marketing Analytics, Palgrave Macmillan, Vol. 12 No. 1, pp. 6–24, doi: 10.1057/s41270-023-00259-x. Das, D. (2018), “The impact of Sustainable Supply Chain Management practices on firm performance: Lessons from Indian organizations”, Journal of Cleaner Production, Elsevier Ltd, Vol. 203, pp. 179– 196, doi: 10.1016/j.jclepro.2018.08.250. Dubey, R., Gunasekaran, A., Childe, S.J., Papadopoulos, T., Luo, Z., Fosso Wamba, S. and Roubaud, D. (2019), “Can big data and predictive analytics improve social and environmental sustainability?”, Technological Forecasting and Social Change, North-Holland, Vol. 144, pp. 534–545, doi: 10.1016/J.TECHFORE.2017.06.020. Dul, J. (2016), “Necessary Condition Analysis (NCA)”, Organizational Research Methods, Vol. 19 No. 1, pp. 10–52, doi: 10.1177/1094428115584005. 20 Dul, J. (2020), Conducting Necessary Condition Analysis for Business and Management Students, edited by Stitt, R., SAGE Publications Ltd, London. Dul, J. (2023a), Necessary Condition Analysis (NCA) with R (Version 3.3.3). Dul, J. (2023b), “Advances in Necessary Condition Analysis V1.4”, Bookdown.Org, available at: https://bookdown.org/ncabook/advanced_nca2/ (accessed 15 October 2023). Dul, J. (2024), “A different causal perspective with Necessary Condition Analysis”, Journal of Business Research, Elsevier Inc., Vol. 177 No. March, p. 114618, doi: 10.1016/j.jbusres.2024.114618. Dul, J., Hauff, S. and Bouncken, R.B. (2023), “Necessary condition analysis (NCA): review of research topics and guidelines for good practice”, Review of Managerial Science, Springer Science and Business Media Deutschland GmbH, Vol. 17 No. 2, pp. 683–714, doi: 10.1007/s11846-023-00628-x. El-Haddadeh, R., Osmani, M., Hindi, N. and Fadlalla, A. (2021), “Value creation for realising the sustainable development goals: Fostering organisational adoption of big data analytics”, Journal of Business Research, Elsevier Inc., Vol. 131, pp. 402–410, doi: 10.1016/j.jbusres.2020.10.066. Elia, G., Raguseo, E., Solazzo, G. and Pigni, F. (2022), “Strategic business value from big data analytics: An empirical analysis of the mediating effects of value creation mechanisms”, Information and Management, Elsevier B.V., Vol. 59 No. 8, doi: 10.1016/j.im.2022.103701. Elkington, J. (1997), Cannibals with Forks: The Triple Bottom Line of 21st Century Business, Conscientious Commerce., New Society Publishers. Ellen MacArthur Foundation. (2013), Towards the Circular Economy. European Commission Directorate-General for Communication. (2020), Circular Economy Action Plan: For a Cleaner and More Competitive Europe, European Commission, Publications Office of the European Union, doi: doi/10.2779/05068. Felipe, C.M., Leidner, D.E., Roldán, J.L. and Leal‐Rodríguez, A.L. (2020), “Impact of IS Capabilities on Firm Performance: The Roles of Organizational Agility and Industry Technology Intensity”, Decision Sciences, Vol. 51 No. 3, pp. 575–619, doi: 10.1111/deci.12379. Ferraris, A., Mazzoleni, A., Devalle, A. and Couturier, J. (2019), “Big data analytics capabilities and knowledge management: impact on firm performance”, Management Decision, Emerald Group Holdings Ltd., Vol. 57 No. 8, pp. 1923–1936, doi: 10.1108/MD-07-2018-0825. Fosso Wamba, S., Dubey, R., Gunasekaran, A. and Akter, S. (2020), “The performance effects of big data analytics and supply chain ambidexterity: The moderating effect of environmental dynamism”, International Journal of Production Economics, Elsevier, Vol. 222, p. 107498, doi: 10.1016/J.IJPE.2019.09.019. Ghasemaghaei, M. (2020), “Improving Organizational Performance Through the Use of Big Data”, Journal of Computer Information Systems, Taylor and Francis Inc., Vol. 60 No. 5, pp. 395–408, doi: 10.1080/08874417.2018.1496805. Govindan, K. and Hasanagic, M. (2018), “A systematic review on drivers, barriers, and practices towards circular economy: a supply chain perspective”, International Journal of Production Research, Taylor and Francis Ltd., Vol. 56 No. 1–2, pp. 278–311, doi: 10.1080/00207543.2017.1402141. Gupta, S., Chen, H., Hazen, B.T., Kaur, S. and Santibañez Gonzalez, E.D.R. (2019), “Circular economy and big data analytics: A stakeholder perspective”, Technological Forecasting and Social Change, Elsevier Inc., Vol. 144, pp. 466–474, doi: 10.1016/j.techfore.2018.06.030. 21 Hassani, H., Huang, X., Macfeely, S. and Entezarian, M.R. (2021), “Big data and the United Nations sustainable development goals (UN SDGs) at a glance”, Big Data and Cognitive Computing, MDPI AG, Vol. 5 No. 3, doi: 10.3390/bdcc5030028. Hayajneh, J.A.M., Elayan, M.B.H., Abdellatif, M.A.M. and Abubakar, A.M. (2022), “Impact of business analytics and π-shaped skills on innovative performance: Findings from PLS-SEM and fsQCA”, Technology in Society, Vol. 68, p. 101914, doi: 10.1016/j.techsoc.2022.101914. Henseler, J. (2021), Composite-Based Structural Equation Modeling: Analyzing Latent and Emergent Variables, The Guilford Press, New York, NY. Jha, A.K., Agi, M.A.N. and Ngai, E.W.T. (2020), “A note on big data analytics capability development in supply chain”, Decision Support Systems, Elsevier B.V., Vol. 138, doi: 10.1016/j.dss.2020.113382. Jiménez, E., de la Cuesta-González, M. and Boronat-Navarro, M. (2021), “How small and medium-sized enterprises can uptake the sustainable development goals through a cluster management organization: A case study”, Sustainability (Switzerland), MDPI AG, Vol. 13 No. 11, doi: 10.3390/su13115939. Kalubanga, M. and Gudergan, S. (2022), “The impact of dynamic capabilities in disrupted supply chains— The role of turbulence and dependence”, Industrial Marketing Management, Elsevier Inc., Vol. 103, pp. 154–169, doi: 10.1016/j.indmarman.2022.03.005. Kamble, S.S., Belhadi, A., Gunasekaran, A., Ganapathy, L. and Verma, S. (2021), “A large multi-group decision-making technique for prioritizing the big data-driven circular economy practices in the automobile component manufacturing industry”, Technological Forecasting and Social Change, North-Holland, Vol. 165, p. 120567, doi: 10.1016/J.TECHFORE.2020.120567. Khan, S.A.R., Shah, A.S.A., Yu, Z. and Tanveer, M. (2022), “A systematic literature review on circular economy practices: challenges, opportunities and future trends”, Journal of Entrepreneurship in Emerging Economies, Vol. 15 No. 4, pp. 754–795, doi: 10.1108/JEEE-09-2021-0349. Kirchherr, J., Reike, D. and Hekkert, M. (2017), “Conceptualizing the circular economy: An analysis of 114 definitions”, Resources, Conservation and Recycling, Elsevier BV, Vol. 127 No. September, pp. 221– 232, doi: 10.1016/j.resconrec.2017.09.005. Kiron, D., Prentice, P.K. and Ferguson, R.B. (2014), “The Analytics Mandate”, MIT Sloan Management Review, Vol. 55 No. 4, p. 1. Korhonen, J., Nuur, C., Feldmann, A., Birkie, S.E., Honkasalo, A. and Seppälä, J. (2018), “Circular Economy: The Concept and its Limitations”, Ecological Economics, Elsevier B.V., Vol. 143, pp. 37– 46, doi: 10.1016/j.ecolecon.2017.06.041. Kristoffersen, E., Mikalef, P., Blomsma, F. and Li, J. (2021), “Towards a business analytics capability for the circular economy”, Technological Forecasting and Social Change, Elsevier Inc., Vol. 171, doi: 10.1016/j.techfore.2021.120957. Le, T.T., Ferraris, A. and Dhar, B.K. (2023), “The contribution of circular economy practices on the resilience of production systems: Eco-innovation and cleaner production’s mediation role for sustainable development”, Journal of Cleaner Production, Elsevier BV, p. 138806, doi: 10.1016/j.jclepro.2023.138806. Lee, I. and Mangalaraj, G. (2022), “Big Data Analytics in Supply Chain Management: A Systematic Literature Review and Research Directions”, Big Data and Cognitive Computing, MDPI, 1 March, doi: 10.3390/bdcc6010017. Li, L., Lin, J., Luo, W. and Luo, X. (2023), “Investigating the effect of artificial intelligence on customer relationship management performance in e-commerce enterprises”, Journal of Electronic Commerce Research, Vol. 24 No. 1, pp. 68–83. 22 Liu, Y., Lee, Y. and Chen, A.N.K. (2020), “How IT wisdom affects firm performance: An empirical investigation of 15-year US panel data”, Decision Support Systems, Elsevier B.V., Vol. 133, doi: 10.1016/j.dss.2020.113300. Majhi, S.G., Anand, A., Mukherjee, A. and Rana, N.P. (2022), “The Optimal Configuration of IT-Enabled Dynamic Capabilities in a firm’s Capabilities Portfolio: a Strategic Alignment Perspective”, Information Systems Frontiers, Vol. 24, pp. 1435–1450, doi: 10.1007/s10796-021-10145- 5/Published. Mandal, S. (2019), “The influence of big data analytics management capabilities on supply chain preparedness, alertness and agility: An empirical investigation”, Information Technology and People, Emerald Group Holdings Ltd., Vol. 32 No. 2, pp. 297–318, doi: 10.1108/ITP-11-2017-0386. Manikas, I., Sundarakani, B. and Shehabeldin, M. (2023), “Big data utilisation and its effect on supply chain resilience in Emirati companies”, International Journal of Logistics Research and Applications, Taylor and Francis Ltd., Vol. 26 No. 10, pp. 1334–1358, doi: 10.1080/13675567.2022.2052825. McAfee, A. and Brynjolfsson, E. (2012), “Big data: The management revolution”, Harvard Business Review, Vol. 90 No. 10, pp. 60–68. Mikalef, P., Pappas, I.O., Krogstie, J. and Giannakos, M. (2018), “Big data analytics capabilities: a systematic literature review and research agenda”, Information Systems and E-Business Management, Springer Verlag, Vol. 16 No. 3, pp. 547–578, doi: 10.1007/s10257-017-0362-y. Mikalef, P. and Pateli, A. (2017), “Information technology-enabled dynamic capabilities and their indirect effect on competitive performance: Findings from PLS-SEM and fsQCA”, Journal of Business Research, Elsevier Inc., Vol. 70, pp. 1–16, doi: 10.1016/j.jbusres.2016.09.004. Mikalef, P., Pateli, A. and van de Wetering, R. (2021), “IT architecture flexibility and IT governance decentralisation as drivers of IT-enabled dynamic capabilities and competitive performance: The moderating effect of the external environment”, European Journal of Information Systems, Taylor and Francis Ltd., Vol. 30 No. 5, pp. 512–540, doi: 10.1080/0960085X.2020.1808541. Moryadee, C. and Jitt-Aer, K. (2020), “Exploring the nexus between the absorptive capacity, corporate sustainability, supply chain agility and manufacturing firm performance”, International Journal of Supply Chain Management, Vol. 9 No. 2, pp. 360–367, doi: 10.59160/ijscm.v9i2.4611. Nitzl, C. and Chin, W.W. (2017), “The case of partial least squares (PLS) path modeling in managerial accounting research”, Journal of Management Control, Springer Berlin Heidelberg, Vol. 28 No. 2, pp. 137–156, doi: 10.1007/s00187-017-0249-6. Novikov, S. V. (2020), “Data science and big data technologies role in the digital economy”, TEM Journal, UIKTEN - Association for Information Communication Technology Education and Science, Vol. 9 No. 2, pp. 756–762, doi: 10.18421/TEM92-44. Nursimloo, S., Ramdhony, D. and Mooneeapen, O. (2020), “Influence of board characteristics on TBL reporting”, Corporate Governance, Vol. 20 No. 5, pp. 765–780, doi: 10.1108/CG-06-2019-0187. Olabode, O.E., Boso, N., Hultman, M. and Leonidou, C.N. (2022), “Big data analytics capability and market performance: The roles of disruptive business models and competitive intensity”, Journal of Business Research, Elsevier Inc., Vol. 139, pp. 1218–1230, doi: 10.1016/j.jbusres.2021.10.042. Pappas, I.O., Mikalef, P., Dwivedi, Y.K., Jaccheri, L. and Krogstie, J. (2023), “Responsible Digital Transformation for a Sustainable Society”, Information Systems Frontiers, Springer, 1 June, doi: 10.1007/s10796-023-10406-5. 23 Paulraj, A. (2011), “Understanding the relationships between internal resources and capabilities, sustainable supply management and organizational sustainability”, Journal of Supply Chain Management, Vol. 47 No. 1, pp. 19–37, doi: 10.1111/j.1745-493X.2010.03212.x. Persaud, A. and Zare, J. (2023), “Beyond Technological Capabilities: The Mediating Effects of Analytics Culture and Absorptive Capacity on Big Data Analytics Value Creation in Small- and Medium-Sized Enterprises”, IEEE Transactions on Engineering Management, Institute of Electrical and Electronics Engineers Inc., doi: 10.1109/TEM.2023.3249415. Petrescu, M. and Krishen, A.S. (2023), “A decade of marketing analytics and more to come: JMA insights”, Journal of Marketing Analytics, Palgrave Macmillan, Vol. 11 No. 2, pp. 117–129, doi: 10.1057/s41270-023-00226-6. Phoon, K.K., Ching, J. and Cao, Z. (2022), “Unpacking data-centric geotechnics”, Underground Space (China), Tongji University, Vol. 7 No. 6, pp. 967–989, doi: 10.1016/j.undsp.2022.04.001. Pieroni, M.P.P., McAloone, T.C. and Pigosso, D.C.A. (2019), “Business model innovation for circular economy and sustainability: A review of approaches”, Journal of Cleaner Production, Elsevier Ltd, Vol. 215, pp. 198–216, doi: 10.1016/j.jclepro.2019.01.036. Piprani, A.Z., Khan, S.A.R., Salim, R. and Khalilur Rahman, M. (2023), “Unlocking sustainable supply chain performance through dynamic data analytics: a multiple mediation model of sustainable innovation and supply chain resilience”, Environmental Science and Pollution Research, Springer Science and Business Media Deutschland GmbH, Vol. 30 No. 39, pp. 90615–90638, doi: 10.1007/s11356-023-28507-8. Pratt, J.A., Chen, L., Kishel, H.F. and Nahm, A.Y. (2023), “Information Systems and Operations/supply Chain Management: A Systematic Literature Review”, Journal of Computer Information Systems, Vol. 63 No. 2, pp. 334–350, doi: 10.1080/08874417.2022.2065649. Purvis, B., Mao, Y. and Robinson, D. (2019), “The Concept of Sustainable Economic Development”, Environmental Conservation, Vol. 14 No. 2, pp. 101–110, doi: 10.1017/S0376892900011449. Raut, R.D., Mangla, S.K., Narwane, V.S., Dora, M. and Liu, M. (2021), “Big Data Analytics as a mediator in Lean, Agile, Resilient, and Green (LARG) practices effects on sustainable supply chains”, Transportation Research Part E: Logistics and Transportation Review, Elsevier Ltd, Vol. 145 No. February 2020, p. 102170, doi: 10.1016/j.tre.2020.102170. Richter, N.F. and Hauff, S. (2022), “Necessary conditions in international business research–Advancing the field with a new perspective on causality and data analysis”, Journal of World Business, Elsevier Inc., Vol. 57 No. 5, p. 101310, doi: 10.1016/j.jwb.2022.101310. Richter, N.F., Schubring, S., Hauff, S., Ringle, C.M. and Sarstedt, M. (2020), “When predictors of outcomes are necessary: guidelines for the combined use of PLS-SEM and NCA”, Industrial Management & Data Systems, Emerald Group Holdings Ltd., Vol. 120 No. 12, pp. 2243–2267, doi: 10.1108/IMDS- 11-2019-0638. Riggs, R.L., Roldán, J.L., Real, J.C. and Felipe, C.M. (2023), “Opening the black box of big data sustainable value creation: the mediating role of supply chain management capabilities and circular economy practices”, International Journal of Physical Distribution and Logistics Management, doi: 10.1108/IJPDLM-03-2022-0098. Ringle, Christian M., Wende, S. and Becker, J.-M. (2022), “SmartPLS 4.”, Oststeinbek: SmartPLS GmbH. Ringle, C.M. and Sarstedt, M. (2016), “Gain more insight from your PLS-SEM results the importanceperformance map analysis”, Industrial Management and Data Systems, Emerald Group Publishing Ltd., Vol. 116 No. 9, pp. 1865–1886, doi: 10.1108/IMDS-10-2015-0449. 24 Rolf, B., Jackson, I., Müller, M., Lang, S., Reggelin, T. and Ivanov, D. (2023), “A review on reinforcement learning algorithms and applications in supply chain management”, International Journal of Production Research, Taylor and Francis Ltd., Vol. 61 No. 20, pp. 7151–7179, doi: 10.1080/00207543.2022.2140221. Sáenz, J., Ortiz de Guinea, A. and Peñalba-Aguirrezabalaga, C. (2022), “Value creation through marketing data analytics: The distinct contribution of data analytics assets and capabilities to unit and firm performance”, Information and Management, Elsevier B.V., Vol. 59 No. 8, doi: 10.1016/j.im.2022.103724. Sahoo, S., Upadhyay, A. and Kumar, A. (2023), “Circular economy practices and environmental performance: Analysing the role of big data analytics capability and responsible research and innovation”, Business Strategy and the Environment, John Wiley and Sons Ltd, Vol. 32 No. 8, pp. 6029–6036, doi: 10.1002/bse.3471. Schroeder, P., Anggraeni, K. and Weber, U. (2019), “The Relevance of Circular Economy Practices to the Sustainable Development Goals”, Journal of Industrial Ecology, Blackwell Publishing, Vol. 23 No. 1, pp. 77–95, doi: 10.1111/jiec.12732. Sharma, V., Raut, R.D., Hajiaghaei-Keshteli, M., Narkhede, B.E., Gokhale, R. and Priyadarshinee, P. (2022), “Mediating effect of industry 4.0 technologies on the supply chain management practices and supply chain performance”, Journal of Environmental Management, Academic Press, Vol. 322, p. 115945, doi: 10.1016/j.jenvman.2022.115945. de Souza, M., Pereira, G.M., Lopes de Sousa Jabbour, A.B., Chiappetta Jabbour, C.J., Trento, L.R., Borchardt, M. and Zvirtes, L. (2021), “A digitally enabled circular economy for mitigating food waste: Understanding innovative marketing strategies in the context of an emerging economy”, Technological Forecasting and Social Change, Elsevier Inc., Vol. 173, doi: 10.1016/j.techfore.2021.121062. Stek, K. and Schiele, H. (2021), “How to train supply managers – Necessary and sufficient purchasing skills leading to success”, Journal of Purchasing and Supply Management, Elsevier Ltd, Vol. 27 No. 4, doi: 10.1016/j.pursup.2021.100700. Talib, S., Papastathopoulo, A. and Ahmad, S.Z. (2023a), “Sufficiency and necessity of big data capabilities for decision performance in the public sector”, Digital Policy, Regulation and Governance, Vol. 26 No. 1, pp. 18–37, doi: 10.1108/DPRG-05-2023-0057. Talib, S., Papastathopoulo, A. and Ahmad, S.Z. (2023b), “Sufficiency and necessity of big data capabilities for decision performance in the public sector”, Digital Policy, Regulation and Governance, doi: 10.1108/DPRG-05-2023-0057. Tan, K.H., Zhan, Y.Z., Ji, G., Ye, F. and Chang, C. (2015), “Harvesting big data to enhance supply chain innovation capabilities: An analytic infrastructure based on deduction graph”, International Journal of Production Economics, Elsevier B.V., Vol. 165, pp. 223–233, doi: 10.1016/j.ijpe.2014.12.034. Tipu, S.A.A. and Fantazy, K. (2023), “Examining the relationships between big data analytics capability, entrepreneurial orientation and sustainable supply chain performance: moderating role of trust”, Benchmarking, Emerald Publishing, doi: 10.1108/BIJ-04-2023-0206. United Nations. (2015), Transforming Our World: The 2030 Agenda for Sustainable Development (A/RES/70/1), United Nations. Wu, F., Yeniyurt, S., Kim, D. and Cavusgil, S.T. (2006), “The impact of information technology on supply chain capabilities and firm performance: A resource-based view”, Industrial Marketing Management, Elsevier, Vol. 35 No. 4, pp. 493–504, doi: 10.1016/j.indmarman.2005.05.003. 31 Tables Table 1 - Review of relevant articles Process Number of Results Scopus Search: (TITLE-ABS-KEY (“necessary condition”) OR ( ( TITLE-ABS-KEY ( necessary ) AND ( TITLE-ABS-KEY ( sufficient ) OR TITLE-ABS-KEY ( insufficient ) ) ) ) AND TITLE-ABS-KEY (“big data”) ) 296 Scopus Search: (TITLE-ABS-KEY (“necessary condition”) OR ( ( TITLE-ABS-KEY ( necessary ) AND ( TITLE-ABS-KEY ( sufficient ) OR TITLE-ABS-KEY ( insufficient ) ) ) ) AND TITLE-ABS-KEY (“big data”) ) AND ( LIMIT-TO ( DOCTYPE , “ar”) ) 150 Review Scopus results for target antecedent includes “big data” 8 Review “big data” results for target outcomes related to performance 5 32 Table 2 - Articles linking big data and performance outcomes Article Theoretical Framework Target Antecedent Target Outcome (Ghasemaghaei, 2020) Resource-based view Big data utilization Organizational performance (Hayajneh et al. 2022) Resource orchestration theory Business analytics capabilities Innovative performance (Novikov, 2020) (No specific theory) Big data as a liquid product Profitability (Talib et al. 2023) Dynamics capabilities perspective Big data analytics infrastructure Big data analytics management Big data analytics personnel Decision-making performance (Ylijoki and Porras, 2019) Virtual value chain Data/Information/Knowledge/Wisdom IT value creation theory This Article Necessity theory applied to IT- enabled capabilities perspective Big data analytics capabilities Sustainable performance 33 Table 3 - Demographic data of the respondents Frequency Percentage (%) Current Position Director of Environmental Sustainability/CSR 17 8% Quality Manager 127 60% Operations/Production Manager 28 13% Supply Chain Manager 1 0% General Manager 10 5% Other 27 13% Total 210 Firm Size 50 to 249 179 85% 250 to 1000 28 13% More than 1,000 3 1% Total 210 34 Table 4 - PLS-SEM and NCA for dependent constructs Construct SCMCs PLS-SEM NCA Direct effect Indirect effect Total effect CE-FDH effect sizes (d) Support BDACs 0.582*** 0.582*** 0.247*** H1: Yes Construct CEPs PLS-SEM NCA Direct effect Indirect effect Total effect CE-FDH effect sizes (d) Support BDACs 0.397*** 0.212*** 0.609*** 0.157*** H2: Yes SCMCs 0.364*** 0.364*** 0.243*** Construct SP PLS-SEM NCA Direct effect Indirect effect Total effect CE-FDH effect sizes (d) Support BDACs 0.012ns 0.472*** 0.484*** 0.107* H3: Yes SCMCs 0.144* 0.232*** 0.376*** 0.212*** CEPs 0.636*** 0.636*** 0.206*** Note: * p < 0.05; *** p < 0.001; ns: non-significant 35 Table 5 - Bottleneck CE-FDH table (percentages) Outcome Condition Outcome Conditions Outcome Conditions SCMCs BDACs CEPs BDACs SCMCs SP BDACs SCMCs CEPs 0% NN 0% NN 9.34 0% NN NN NN 10% NN 10% NN 9.34 10% NN NN NN 20% NN 20% NN 11.80 20% NN 9.34 0.11 30% NN 30% NN 11.80 30% NN 9.34 2.20 40% NN 40% NN 11.80 40% NN 11.80 9.24 50% NN 50% 10.51 18.57 50% NN 11.80 9.24 60% 12.01 60% 10.51 18.57 60% NN 11.80 9.24 70% 39.32 70% 10.51 35.78 70% NN 33.86 36.22 80% 52.09 80% 23.68 42.63 80% NN 43.08 36.22 90% 91.89 90% 72.21 46.59 90% 64.60 48.02 61.82 100% 91.89 100% 83.33 72.89 100% 68.09 59.38 61.82 Note: NN = Not necessary. 36 Table 6 - Bottleneck CE-FDH table for sustainable performance (SP) Outcome Conditions Percentage (and number) of cases that do not reach the necessary levels required for SP SP BDACs SCMCs CEPs 0% 0 (0) 0 (0) 0 (0) 10% 0 (0) 0 (0) 0 (0) 20% 0 (0) 0.5 (1) 0.5 (1) 30% 0 (0) 0.5 (1) 1.4 (3) 40% 0 (0) 1.0 (2) 2.9 (6) 50% 0 (0) 1.0 (2) 2.9 (6) 60% 0 (0) 1.0 (2) 2.9 (6) 70% 0 (0) 12.4 (26) 16.2 (34) 80% 0 (0) 22.9 (48) 16.2 (34) 90% 45.7 (96) 32.9 (69) 49.5 (104) 100% 51.0 (107) 61.9 (130) 49.5 (104) 37 Annex Construct / Dimension / Indicator Big data analytics capabilities. Source: Raut et al. (2021) Our organization is capable of parallel computing to address voluminous data. Real-time assess of data and information has helped our organization in better decision making. Our information systems are capable to handle semi-structured and unstructured data. Truthfulness and accuracy of data has helped our organization. Data driven intelligence has made decision making more effective. Our organization has good infrastructure and facilities to support analytics. Interchange ability of services (cloud, mobile, and analytics) plays key role. Analytics personnel are proficient with programming, data management, new tools etc. Supply chain management capabilities (HOC). Source: Wu et al. (2006) Information exchange My firm exchanges more information with our partners than our competitors do with their partners. Information flows more freely between my firm and our partners than between our competitors and their partners. My firm benefits more from information exchange with our partners than do our competitors from their partners. Our information exchange with our partners is superior to the information exchanged by our competitors with their partners. Coordination My firm is more efficient in coordination activities with our partners than are our competitors with theirs. My firm conducts transaction follow-up activities more efficiently with our partners than do our competitors with theirs. My firm spends less time coordinating transactions with our partners than our competitors with theirs. My firm has reduced coordinating costs more than our competitors. My firm can conduct the coordination activities at less cost than our competitors. Integration My firm develops strategic plans in collaboration with our partners. My firm collaborates actively in forecasting and planning with our partners. My firm projects and plans future demand collaboratively with our partners. Collaboration in demand forecasting and planning with our partners is something we always do in my firm. My firm always forecasts and plans activities collaboratively with our partners. 38 Construct / Dimension / Indicator Responsiveness Compared to our competitors, our supply chain responds more quickly and effectively to changing customer and supplier needs. Compared to our competitors, our supply chain responds more quickly and effectively to changing competitor strategies. Compared to our competitors, our supply chain develops and markets new products more quickly and effectively. In most markets, our supply chain is competing effectively. The relationship with our partners has increased our supply chain responsiveness to market changes through collaboration. Circular economy practices (HOC). Source: Riggs et al. (2023). Governance initiatives Our organization has developed a set of performance indicators in order to evaluate our recycling, reuse and remanufacture initiatives in supply chain. Our organization has increased the number of employees in circular economy positions. Economic initiatives Our organization favors economic growth opportunities that have minimal environmental impact. The price of our products includes costs associated with reuse, recycle and remanufacturing. Cleaner production Our organization has experienced increased eco-efficiency in production through the use of reduce, reuse, recycle, recover, redesign, and remanufacture. Our organizational purchasing processes consider sustainability factors in addition to price. Our organization experiments with new strategies in supply chain to constantly improve our circular economy efforts. Our firm collaborates with other organizations in order to make it possible to reuse / recycle / remanufacture. Our organization classifies the materials as either those candidates for reuse / recycle / remanufacture, or those that can be safely returned to the environment. Our organization reviews logistical routes and modes for constant improvement in terms of sustainability. Our organization utilizes equipment specifically designed to produce output that can be remanufactured. Our organization has implemented measurable data gathering systems to measure the environment performance in regards of the initiatives by implementing circular economy in supply chain. Product development Our organization’s products are designed as durable products for multiple cycles of use and for disassembly and reuse. Management support Top managers of our organization actively endorse the circular economy efforts in supply chain. 39 Construct / Dimension / Indicator Knowledge Our organization supports education and awareness programs that support recycling, remanufacturing and reuse among actors in our supply chain. Our organization implements formal training programs that educate our workforce about circular economy concepts and benefits in the supply chain. Our organization demonstrates visionary thinking and technical creativity to implement circular economy in supply chain. Sustainable performance (HOC). Source: Çankaya and Sezen (2019). Economic performance Decrease in cost of materials purchased. Decrease in cost of energy consumption. Decrease in fee for waste discharge. Improvement in earnings per share. Improvement in return on investment. Sales growth. Profits growth. Social performance Improvement in customer satisfaction. Improvement in its image in the eyes of its customers. Improvement in investments on social projects (education, culture, sports). Improvement in relations with community stakeholders, e.g., nongovernmental organizations (NGOs) and community activists. Improved awareness and protection of the claims and rights of people in community served. Improvement in employee training and education. Improvement in occupational health and safety of employees. Improvement in overall stakeholder welfare or betterment. Environmental performance Improvement of an enterprise’s environmental situation. Reduction in waste (water and/or solid). Reduction in air emission. Decrease of consumption for hazardous/harmful/toxic materials. Decrease of frequency for environmental accidents. Note: HOC: Higher-order construct.