Information systems capabilities value creation through circular economy practices in uncertain environments: A conditional mediation model

Journal of Business Research 175 (2024) 114526 Available online 1 February 2024 0148-2963/© 2024 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/). Information systems capabilities value creation through circular economy practices in uncertain environments: A conditional mediation model Randy Riggs a , Carmen M. Felipe a , Jos´ e L. Rold´ an a , * , Juan C. Real b a Department of Business Administration and Marketing, Universidad de Sevilla, Seville, Spain b Department of Business Management and Marketing. Universidad Pablo de Olavide, Seville, Spain ARTICLE INFO Keywords: Information systems capabilities Circular economy practices Business performance Environmental uncertainty Partial least squares structural equation modeling Conditional mediation ABSTRACT The link between the field of information technology business value and sustainable activities is a widely discussed topic in academia and management. However, the mechanisms that lead to the impact on business performance are still elusive. The circular economy is one of the most recent approaches to address sustainability challenges. Drawing on the perspective of information technology-enabled organizational capabilities and contingency theory, this study is one of the first to analyze how information systems capabilities enable circular economy practices to become higher-order capabilities that enhance business performance, as well as the moderation that environmental uncertainty exerts on this relationship. Using partial least squares path modeling, empirical evidence demonstrates that circular economy practices fully mediate the relationship between information systems capabilities and business performance. Meanwhile, environmental uncertainty negatively moderates both the impact of circular economy practices on business performance and the mediation of circular economy practices in the information systems capabilities-business performance relationship. For managers, this research demonstrates how embedding information systems capabilities into organizational processes for transitioning to a circular economy model can present economic benefits depending upon the uncertainty within their specific operating environment. Finally, our mediation analysis method combines predictive and causal techniques with partial least squares structural equation modeling and applies a compelling approach to testing conditional mediation effects. 1. Introduction In the field of information systems (IS) research, previous literature has intensely debated the so-called information technology (IT) business value, which is defined as the impact of IT on organizational performance (Cao et al., 2011). Despite decades of study, there is no common position explaining how IT investments contribute to business performance (BP) (Gellweiler & Krishnamurthi, 2021), leaving the question of how to make IT investments profitable for managers unsolved. Furthermore, a recent IBM survey (Morning Consult, 2022), with a sample of 4001 IT or Corporate Strategy managers, found that environmental pressures rank second (30 % of respondents) on the list of factors that drive IT investment in companies, only behind cyber risks (33 %). However, according to the above survey, these pressures also represent the second most relevant barrier hindering IT investment. These managerial concerns reveal the professional need for deeper insights into IT business value regarding its links to sustainable practices. In this vein, one of the most recent reviews of the literature on IT business value research urges us precisely to “explore the business value creation for aligning IT with green initiatives” (Schweikl & Obermaier, 2023, p. 1740). Regarding sustainability, the circular economy (CE) plays a leading role in addressing private companies’ challenges encouraging the adoption of more sustainable manufacturing and consumption patterns (Kristoffersen et al., 2020). CE practices (CEP) emphasize the preservation of the value of products and materials in the operations of firms (Calzolari et al., 2021) through activities such as reusing, repairing, and recycling (Khan & Haleem, 2021). Estimates show that by 2030, in the European Union, the implementation of CEP will positively impact the gross domestic product (GDP) by up to 11 % (Ellen MacArthur Foundation, 2021) and generate 700,000 new jobs (European Commission, 2018). Despite the benefits of CEP, the industry is still slow to abandon traditional linear systems (Guldmann & Huulgaard, 2020; Kristoffersen et al., 2021; Saccani et al., 2023; Yin et al., 2023). Recent studies reveal * Corresponding author. E-mail addresses: [email protected] (R. Riggs), [email protected] (C.M. Felipe), [email protected] (J.L. Rold´ an), [email protected] (J.C. Real). Contents lists available at ScienceDirect Journal of Business Research journal homepage: www.elsevier.com/locate/jbusres https://doi.org/10.1016/j.jbusres.2024.114526 Received 22 December 2022; Received in revised form 20 December 2023; Accepted 15 January 2024 Journal of Business Research 175 (2024) 114526 2 that the most significant drivers for managers to adopt CEP are economic as opposed to environmental (Mishra et al., 2022). This fact aligns with the call expressed by researchers to unveil how CE activities carried out by companies contribute to their performance (Antonioli et al., 2022; Darmandieu et al., 2022; Horbach & Rammer, 2020). Furthermore, the scholarly literature advocates for more in-depth research on CEP to achieve a better understanding of their successful corporate deployment (Sehnem et al., 2022), this demand being in line with the existing appeal for an extended investigation into novel sustainability-related organizational capabilities and processes that can bolster economic performance (Annunziata et al., 2018; de Bezerra & Morioka, 2020). CE has emerged in an eminently multidisciplinary field of knowledge (Bag et al., 2022), and its implementation involves complex organizational transformations (Castro-Lopez et al., 2023). Therefore, insights into their value-creation mechanisms should be addressed by approaching existing relationships between CEP and other organizational capabilities. Previous literature has underlined the relevance of information technologies (IT) advances as critical enablers in implementing CEP in organizations (Yu, Yazan, et al., 2022). In particular, the two prominent areas of research we have presented so far (IT business value and CE) converge at this point: the need to explore the complex underlying mechanisms that link IT and CEP for value creation in companies. Meanwhile, reviews of existing literature on the relationship between CEP and organizational performance agree on the opportunity for future research related to the search for contextual factors that can affect it (Lahti et al., 2018; Mora-Contreras et al., 2023; Yin et al., 2023). Moreover, previous works emphasize the scarcity of empirical studies that examine the role of contingent factors in the relationship between sustainable practices and organizational performance (Maletiˇ c et al., 2018) and, more specifically, they lack empirical evidence to identify specific contingent factors that influence the degree of economic impact of CEP (Darmandieu et al., 2021). For example, the increasing turbulence and complexity surrounding companies, represented as environmental uncertainty (EU), is a critical variable affecting all functions and operations (Gordon et al., 2009). This cruciality of external influence is intensified in studying the proposed variables, which are seriously affected by regulatory and technological changes (Chan et al., 2016). In times of high uncertainty, business priorities change, and commitments to sustainability may lose out to other strategies that aim to strengthen core business activities. Therefore, addressing the influence of EU on the transition from linear to circular business models becomes critical to its achievement (Lahti et al., 2018). Furthermore, examining the potential effects of EU facilitates a seamless shift to CE models. It reveals associated opportunities while offering valuable insights into the strategic management of EU by managerial teams (de Lima et al., 2022). To clarify these topics, this article presents the following research questions. RQ (1): What underlying mechanisms can link IT and CEP to create economic value in companies? RQ (2): Do CEP exert a context-dependent effect on BP, specifically on the uncertainty of the business environment? A recent review of the literature on the effects of CEP on sustainability performance, including BP, reveals that studies that use a theoretical lens to approach this issue are quite scarce (Mora-Contreras et al., 2023). This article aims to address this gap in academic knowledge by drawing on two well-established theoretical frameworks. First, as an extension of the resource-based view (RBV) and theory of organizational capabilities, the IT-enabled organizational capabilities perspective is a prolific line of research advocating that IT resources and capabilities do not directly generate competitive advantage by themselves; rather, their influence on other intermediate processes and organizational capabilities, which are referred to as higher-order capabilities following Grant’s (1996) capabilities hierarchy, impact BP (Benitez et al., 2023). Our work proposes that information systems capabilities (ISC) exert this enabling role for CEP deployment in firms. ISC are defined as the set of abilities to mobilize and deploy IT-based resources in combination with other resources and capabilities (Bharadwaj, 2000). Therefore, CEP enabled by ISC become a higher-order organizational capability that impacts BP, thus playing a mediating function. Based on our review of the literature, only a few studies have examined the use of sustainability-related organizational capabilities as mediating variables enabled by IT in the creation of business value (Ajamieh et al., 2016; Benitez-Amado & Walczuch, 2012; Li & Lin, 2023). Second, contingency theory suggests that various contextual factors influence the form in which different organizational structures and processes lead to improved outcomes (ZareRavasan & Krˇ c´ al, 2021). This theoretical framework provides a solid foundation to investigate whether firms should implement various sustainable practices, such as CEP, depending on different contextual factors that could impact achieving superior performance (Maletiˇ c et al., 2018). To build these relationships, we propose a research model tested in a sample of 210 Spanish companies belonging to sectors considered critical for implementing CEP. The empirical study proposed in this article provides added value by applying advanced PLS-SEM techniques to address complex research objectives, such as those detailed above. This paper provides several academic contributions. It offers new insights into the complex value-creation mechanisms of both IT and CE, finding an exciting intersection between both lines of research. In addition, it extends the perspective of IT-enabled organizational capabilities by expanding the few sustainability-related capabilities previously pointed out by this theory as mediators while providing a higherorder capability related to CE for the first time. As well, it delivers a contingent approach strongly demanded by the CE literature, using EU for this purpose. From the managerial point of view, we offer managers practical guidelines for successfully investing and implementing ISC that facilitate the adoption of CEP, thus improving their impact on BP. Finally, this study provides practical methodological contributions to PLS-SEM. We utilize various techniques to tackle mediation analysis with PLS-SEM through a dual causal and predictive approach. In addition, we apply a compelling method to estimate and test conditional mediation effects. This paper proceeds as follows. First, a background section comprises the theoretical framework, which encompasses the definitions of the variables and the hypotheses that define our theoretical model. The second section explains the methodological approach followed in this study, while the third section describes the main results. Finally, the conclusions and implications are discussed from academic and managerial perspectives. 2. Theoretical framework and research hypotheses 2.1. IT business value research: The IT-enabled organizational capabilities perspective Having vastly overcome the “IT productivity paradox” of Carr (2003), researchers in the field of information systems (IS) have dedicated decades to a thorough examination of the mechanisms by which IT contributes to business value creation (Castillo et al., 2023). The term IT/IS-business value is defined as the effect that IT has on a company’s organizational performance, regardless of whether it is measured by productivity, competitive advantage, or financial results (Melville et al., 2004). Despite the abundance of work, the dissent is still notable for describing how the positive impact of IT on organizational outcomes occurs (Felipe et al., 2020). Existing research reviews on the IT business value issue agree on the fact that IT indeed creates value, not in isolation but as part of synergistic processes with other organizational elements, whose causality remains elusive (Jacks et al., 2011; Kohli & Devaraj, 2003; Kohli & Grover, 2008; Melville et al., 2004; Sabherwal & Jeyaraj, 2015; Schryen, 2013; ZareRavasan & Jeyaraj, 2023; ZareRavasan & Krˇ c´ al, 2021). R. Riggs et al. Journal of Business Research 175 (2024) 114526 3 In alignment with this perspective, one widely supported research stream that seeks to unveil the underlying mechanisms for IT business value creation is the IT-enabled organizational capabilities perspective (Castillo et al., 2023), which is an extension of the resource-based view (RBV) and the theory of organizational capabilities (Barney, 1991). This theoretical approach builds on previous scholars who propose that the organizational effects of IT resources and capabilities materialize in those processes and routines in which they are embedded, thus facilitating other capabilities or business activities that will eventually lead the firm to superior performance (Mithas et al., 2011; Rai et al., 2006; Sambamurthy et al., 2003). This perspective leverages Grant’s (1996) proposal of an ascendant hierarchy of capabilities based on knowledge integration. At the base of the hierarchy, resources and individual knowledge can be combined and integrated to generate lower-order capabilities. These lower-order capabilities are combined to generate higher-order capabilities that require cross-functional integration and provide performance enhancement or competitive advantage for firms. In this vein, IT resources and capabilities (mainly related to IT infrastructure, skills, and management) are conceived as lower-order capabilities that enable higher-order ones, constituting unique competencies that provide firms with sustainable competitive advantages (Ravichandran, 2018). Therefore, and according to this perspective, the actual impact of IT on organizational performance is not directly exerted but occurs through the mediation of other higher-order capabilities (Benitez-Amado & Walczuch, 2012). A range of organizational capabilities has been proposed under the lens of this perspective to influence company results. From our review of the literature, the following are the most supported IT-enabled organizational capabilities: organizational agility or flexibility ( Benitez, Llorens et al., 2018; Benitez, Ray et al., 2018; Chen et al., 2017; Felipe et al., 2020; Lin et al., 2020); absorptive capability (Braojos et al., 2020; Castillo et al., 2021; Cui et al., 2018; Dong & Yang, 2015; Liu et al., 2013); customer management related capabilities (Braojos et al., 2019; Li et al., 2023; Tang et al., 2023; Mithas et al., 2011); innovation-related capabilities (Castillo et al., 2021; Castillo et al., 2023; Pavlou & El Sawy, 2006); supply chain related capabilities (Ajamieh et al., 2016; Gonz´ alez- Benito, 2007; Liu et al., 2013; Rai et al., 2006) and dynamic capabilities (Ilmudeen et al., 2020; Mikalef et al., 2020; Pavlou & El Sawy, 2006). Few studies that advocate for this theoretical perspective identify IT- enabled organizational capabilities related to the sustainability field in companies, and none refers specifically to CE. That is, the only articles found are that of Li and Lin (2023), who test that digital transformation of firms enables a so-called ’green capability’ to positively impact environmental performance; that of Ajamieh et al. (2016), who conclude that green supply chain management capability fully mediates the positive effect that IT infrastructure capability has on firm performance, and that of Benitez-Amado and Walczuch (2012), who empirically demonstrate that the proactive environmental strategy enabled by IT capability fully mediates the relationship between previously mentioned IT capability and firm performance. To the authors’ knowledge, this article is the first to recognize CEP as these higher-order capabilities enabled by ISC that influence BP through the proposed network of relationships that form our nomological model. 2.2. The mediating role of circular economy practices in the relationship between information systems capabilities and business performance In the proposed research model, information systems capabilities (ISC) are the lower-order IT capabilities that will facilitate the deployment of other higher-order competencies to influence superior performance. The concept of ISC arises from applying the resource and capabilities-based view (RCBV), as the ability of an organization to mobilize and deploy IS resources and capabilities, in conjunction with other organizational ones, to achieve its objectives (Bharadwaj, 2000). Previous literature has shaped ISC by integrating IT infrastructure, IS personnel skills (including management capabilities), and business applications to support business strategies and processes jointly (Bendoly et al., 2012). Therefore, ISC are conceptualized in a complex and multidimensional form, generating different theoretical approaches, leading to a lack of convergence (Aydiner et al., 2019; Santhanam & Hartono, 2003). This paper follows the ISC taxonomy produced by Wade & Hulland, 2004, which has been recognized as one of the most consistent and comprehensive (Sore et al., 2023). They conceive ISC as the set of available assets and capabilities based on IS and used by firms to detect and respond to business opportunities and threats, classifying them into three dimensions: (i) outside-in (externally oriented) capabilities: those that anticipate market changes, customer needs, and competitor movements; (ii) inside-out (internally oriented) capabilities: those assets and capabilities deployed from within to respond to market requirements and opportunities; and (iii) spanning capabilities: with the essential role of integrating and establishing links between the other two dimensions mentioned above. The CE paradigm aims to optimize resource use and reduce environmental damages by building a production and consumption system based on a two-stage closed cycle: the early environmental-friendly design of products and processes and the recovery of subsequent investment (Yu, Xu, et al., 2022). CEP can be defined as the specific organizational practices that perform the closed loop mentioned above concerning processes that reduce the negative impact of manufacturing through methods such as reuse, reduction, and recycling (Khan et al., 2021). This conceptualization conforms to the approach taken by Govindan and Hasanagic (2018) in their investigation of the practical adoption of a CE model in a supply chain. They propose the following six groups of internal CEP that we adopt in this paper: (i) governance initiatives (CE management and control practices); (ii) economic initiatives (practices that focus on making CE activities profitable); (iii) cleaner production (practices aimed at enhancing eco-efficiency in manufacturing); (iv) product development (products’ design to extend its lifecycle); (v) management support (top management alignment with CE strategy); and (vi) knowledge (practices aimed at staff and stakeholders training and education about CE, together with enhancing innovation in the field). According to the current literature on CE, the set of CEP to achieve a common goal in the company represents an organizational CE capability (Bag et al., 2021; Centobelli et al., 2020; Ting et al., 2023; Zeng et al., 2017). Under the lens of IT-enabled capabilities and Grant’s (1996) hierarchy, CEP can be considered a higher-order capability that ISC may enable, as they require a wide-ranging crosssectional knowledge integration for their deployment in firms. Moving from a linear to a circular perspective requires radical organizational changes (Bag & Pretorius, 2022). In response to the need for a deeper understanding of the capabilities that help companies transition to CE, researchers have identified digital technologies as one of the critical enablers of such changes (Chowdhury et al., 2022; Kristoffersen et al., 2021). More precisely, deploying technological resources and capabilities provides the firm with the required flexibility, self-organization capabilities, real-time monitoring, and sustainable resource management to address the challenge of CE in business (Sahu et al., 2022). Following Wade & Hulland, 2004 taxonomy for a better understanding, the influence of ISC on CEP can be outlined as follows. First, outside-in capabilities facilitate collaboration and networking with suppliers, outsourcers, customers, and other business partners. These relationships become indispensable for designing and developing CE products or services (de Sousa et al., 2019). Connecting all external parties involved in CEP requires the application of technologies such as networking platforms, cloud computing, and the Internet of Things (IoT) so that the necessary flows of information and resources can remain fluid (Kristoffersen et al., 2021). Moving on to inside-out capabilities, four are significant contributing factors to CEP. The first is to provide the required IT infrastructure. The optimum performance of the closed loop requires an adequate technology infrastructure and database system (Shojaei et al., 2021). Technologies that facilitate a better connection between machines and R. Riggs et al. Journal of Business Research 175 (2024) 114526 4 process automation, such as those derived from Industry 4.0, contribute to optimizing resource flows and energy efficiency advocated by the CE (de Sousa et al., 2019). Furthermore, digital platforms that enable information storage and knowledge sharing among workers will contribute to the required integration between organizational areas that facilitates the management of circular production processes (Chowdhury et al., 2022). The second capability is the IS skills of personnel. Recent studies show that the successful implementation of CEP requires IS-skilled employees. In this vein, Dongfang et al. (2022) conclude that complementing IS skills with Industry 4.0 technologies is essential for deploying circular production processes. According to Borms et al., 2023, the development of servitization business models, which are common in CE, requires personnel trained in technologies to maintain responsive relationships with customers and monitor the status of products. Finally, Chaudhuri et al. (2022) confirm that skills and competencies derived from a digital orientation in SMEs ease the development of CE-related products and processes. The third capability is decision support enabled by real-time data and analytics. Data analytics is an indispensable tool for supporting economic decisions related to CEP efficiency (Riggs et al., 2023). Currently, the information analysis required to predict product life, maintenance of production equipment, estimate material flows, and energy efficiency are performed on a data analytics basis (Kristoffersen et al., 2020). Simulation models based on advanced computational calculations have widely demonstrated their contribution to improving decision support for CE activities in industries such as construction (Yu, Yazan, et al., 2022). Furthermore, data analytics have proven helpful in circular product design, facilitating continuous improvement of the life cycle of products and materials (de Sousa et al., 2019). Governance is the fourth inside-out capability. During the implementation of a circular model, continuous monitoring of the CEP performance in all its dimensions (economic, operational, environmental, etc.) is essential. Enterprise systems supported by advanced IT, such as blockchain or IoT, allow real-time monitoring of key performance indicators (Shojaei et al., 2021). The last of the ISC dimensions, the spanning capabilities, should not be considered in isolation since they enable the interconnection of the other two dimensions. These capabilities facilitate CEP by providing traceability of operations. With the use of technologies such as blockchain, each player in the circular business model, including the more complex inverse supply chain, can be perfectly tracked, allowing the collection of data needed to optimize the use of any resources involved, as well as their potential reuse or recycling (Shojaei et al., 2021). Similarly, the IoT has empirically demonstrated its usefulness for such purposes, allowing automatic location, traceability, and monitoring of the different elements (Kristoffersen et al., 2020). Spanning capabilities also provide integrated management of the entire life cycle. Circular models require strong integration between the different actors, both internal (organizational areas involved in the operations) and external (stakeholders), for their proper functioning (de Sousa et al., 2019). Information systems such as BIM (Building Information Modeling) have proven useful for these purposes. The digital representation of assets facilitates product life cycle management, becoming a collaborative knowledge resource for all parties involved (Yu, Yazan, et al., 2022). Considering all the above theoretical support, which recognizes that ISC facilitate CEP, an analysis shall be performed regarding the impact of CEP on the organization’s business performance (BP), thus confirming their role as higher-order organizational capabilities. BP considers the firm’s value maximization, as reflected in its financial results and economic contribution (Andersson et al., 2022). BP also includes economic benefits through improved market performance and a positive return on investment (Peteraf, 1993). Specifically, and for our theoretical model, this paper considers within BP both the potential cost reduction effects that CEP may introduce in companies’ operations (economic dimension) and the increase in sales and profitability potentially derived from them (financial dimension) (Çankaya & Sezen, 2019). The question of the impact of CEP on BP is still actively being discussed. Some empirical studies show that CEP alone are insufficient to improve economic performance directly (Susanty et al., 2020). In other cases, the position is tempered by the fact that, in the short term, companies that implement CEP tend to benefit in terms of revenue. However, an initial cost increase will only be compensated later (Antonioli et al., 2022). Nonetheless, the most settled trend in the CEP literature is that these practices contribute to improving BP by affecting both its economic and financial dimensions. First, cost reductions are realized through a better overall use of resources that leads to an increase in operational efficiency (Chen & Dagestani, 2023; Khan et al., 2022; Rosa et al., 2019; Yu, Khan, et al., 2022 ), waste reduction (Yu, Khan et al., 2022), savings in unnecessary expenses and minimization of energy consumption (Khan et al., 2022). Second, increased sales and profitability are derived from the emergence of new revenue streams and new sales channels (Khan et al., 2022; Rosa et al., 2019), improved firm reputation and social image (Chen & Dagestani, 2023; Chowdhury et al., 2022; Mazzucchelli et al., 2022; Yu, Khan, et al., 2022 ), improved customer loyalty, and their willingness to pay more for products (Khan et al., 2022; Yu, Khan, et al., 2022; ) and improved financial sustainability (Chowdhury et al., 2022). From all this theoretical support, it can be inferred that these organizations have better costs and overall financial performance (Farooque et al., 2022). Therefore, and from the findings mentioned above, we propose the hypothesis that ISC positively influence CEP, which in turn directly relate to BP (Fig. 1). H1(+): Circular economy practices positively mediate the relationship between information systems capabilities and business performance. 2.3. A contingent approach: The negative moderating effect of environmental uncertainty on the relationship between circular economy practices and business performance Contingency theory conceptualizes organizations as open systems influenced by environmental factors (contingency or moderator variables), strengthening or weakening the relationship between organizational capabilities and performance (Chavez et al., 2023). This theory is significant because it emphasizes that there is no optimum solution for every business. Instead, we must learn how to enhance organizational performance based on the firm’s environmental conditions (Piyathanavong et al., 2022). However, research on CEP from a contingency approach is still underexplored, and evaluations of CEP impacts on firm performance are typically conducted without considering contextual factors (Prosman & Cagliano, 2022). In this line, recent reviews and meta-analyses dealing specifically with the relationship between CEP and different performance measures have claimed to address a contingent approach in research, incorporating potential moderating factors of this link (Lahti et al., 2018; Mora-Contreras et al., 2023; Yin et al., 2023). Although studies on the relationship between CEP and BP are novel, previous literature has focused on analogous relationships over the past decades regarding various sustainable organizational practices, such as the impact on firm performance of corporate social responsibility (Husted & Allen, 2007), green supply chain (Green et al., 2012) or, in general, environmental management practices (Armas-Cruz, 2011). The results in these diverse fields confirm the complex relationship between sustainable practices and BP, with many studies remaining on the contextual elements that exert their influence (Hull & Rothenberg, 2008; Ramanathan, 2018), such as the characteristics of the environment in which the firms operate. Thus, the old question of whether being green pays off should be replaced by asking under which conditions this payout occurs (Ben Lahouel et al., 2022). The existing general literature regarding the contingency approach frequently points to the uncertainty of the organization’s external environment (Wong et al., 2011), constituting a topical research issue in R. Riggs et al. Journal of Business Research 175 (2024) 114526 5 recent years as markets become increasingly competitive (Jia & Li, 2020). We define environmental uncertainty (EU) through two dimensions: complexity (derived from the heterogeneity of the actors as well as the relationships and mutual influences between them) and dynamism (derived from the frequency and rapidity of changes) (Vecchiato, 2012). The moderating effect of EU has been explored in other relationships, resulting in a positive moderation of the impact of eco-innovation on competitive advantage (Nuryakin & Ardyan, 2022), while it had no significant effect on the relationship between green supply chain management practices and the triple bottom line (environmental, social and economic performance) (Tantan & Akda˘ g, 2023), or even leading to a significant adverse effect on the impact of environmental, social and governance (ESG) practices on corporate performance (Gao et al., 2023). These results are disparate and inconclusive. Consequently, this fact invites us to analyze how the influence of EU can be exerted on the proposed positive impact of CEP on BP. For instance, highly uncertain environments demand increased use of critical resources (e.g., monetary funds, specialized equipment, and human resources) in core business activities and innovation. In such a situation, CEP could divert such resources and lead to lower profits (Hull & Rothenberg, 2008). Managers subject to high EU tend to adopt conservative strategies to ensure the smooth functioning of core activities. Therefore, integrating CE into strategic decisions will be more difficult (Lo & Shiah, 2016). Additionally, continuous change and extreme competition lead to the accelerated obsolescence of capabilities in companies, introducing increased costs from continuous upgrades of circular capabilities (Zhang et al., 2020). Moreover, everchanging environmental legislation can affect firms’ profits, as the firm incurs additional costs to adapt to these erratic changes (Aguilera-Caracuel & Ortiz-de-Mandojana, 2013). Lastly, as CEP involve coordinating and integrating different stakeholders, their implementation requires radical organizational and technical changes that generate additional complexity to the already uncertain environments, hindering success and increasing costs (Arag´ on-Correa & Sharma, 2003). In contrast, when environments are predictable, they favor a planned and more straightforward approach to CEP. In response, firms can more easily institute routine practices and benefit economically (Jia & Li, 2020). Similarly, differentiation of firms through innovation becomes less necessary in stable environments. This implies that CEP can provide a competitive differentiation boost that translates into better BP for the firm (Hull & Rothenberg, 2008). Finally, we can concentrate on the two main arguments that theoretically supported the impact of CEP on BP in the previous section. First, we note that CEP are advantageous for cost savings; however, a high level of EU could negatively impact this. In this manner, EU has the potential to cause supply chain disruptions and resource availability issues (de Lima et al., 2022), creating difficulties that even the most well-managed entities, such as the automotive industry, struggle to address, as evidenced by the recent microchip shortage crisis. This effect is particularly pronounced in highly complex organizational systems, such as those required by CE business models. Furthermore, a high EU leads to high inventory costs due to the inherent difficulties associated with planning and forecasting activities (Prosman & Cagliano, 2022). Second, we have provided evidence that CEP promote higher sales and profits. Nevertheless, unexpected fluctuations in demand caused by EU may harm the economic feasibility of CE models’ products and services (de Lima et al., 2022). Considering the above theoretical supports, we can posit the following hypothesis (Fig. 1). H2(−): Environmental uncertainty negatively moderates the impact of circular economy practices on business performance. 2.4. The moderating role of environmental uncertainty in the indirect effect between information systems capabilities and business performance We expect a negative moderated mediation effect of information system capabilities on business performance through circular economy practices. ISC are expected to stimulate firms to engage in CEP. Deploying technological capabilities provides firms with the flexibility, monitoring, and resource management required to address the challenge of implementing the circular economy in business (Sahu et al., 2022). However, the effect of CEP on BP is less favorable in more uncertain environments. According to the argument presented in H2, it is posited that in highly uncertain environments, there is an increased demand for critical resources in core business activities and innovation (Hull & Rothenberg, 2008). Subsequently, adopting CEP can draw away such resources, ultimately decreasing profits. In line with the above exposed, de Lima et al. (2022) argue that while IT can enable CEP, this fact makes these practices more vulnerable to technological and information changes. Therefore, managing those undesirable effects in organizations can require more extraordinary efforts and result in higher costs that would negatively affect the influence of CEP on BP. Hence, we expect a negative moderated mediation result from the multiplication of a positive effect of ISC on CEP and a negative interaction effect between CEP and EU. Consequently, it leads us to posit a conditional mediation hypothesis that the indirect impact of ISC on BP would be contingent on the EU variable (Fig. 1). Fig. 1. Research model. R. Riggs et al. Journal of Business Research 175 (2024) 114526 6 H3(−): Environmental uncertainty weakens the positive indirect relationship between information system capabilities and business performance through circular economy practices. 3. Method 3.1. Sample and data collection In developing a policy framework centered on the transition to a circular economy model, the European Commission identified key sectors that required immediate, all-encompassing, and coordinated actions to address sustainability challenges (European Commission Directorate- General for Communication, 2020). Based on the above selection of industries, this study built its population around seven sectors with a significant presence in Spain, as listed in Table 1. As such, a population of 3572 companies with at least 50 employees was identified. An initial subset of 439 companies was then generated using a random stratified sampling procedure. Since the unit of analysis is the firm, each company is represented by responses from a single individual within that organization. Respondents tended to be top managers and typically worked within organizational functions that involved sustainability, quality, supply chain, operations, or general management. With the assumption that these positions allow for a comprehensive view of the relevant business processes, these managers can make valid assessments of the impact of CEP on their respective firms. Following a preliminary telephone interview, a market research company issued questionnaires in September and October 2021. Of the 439 companies initially sampled, 210 surveys were completed and collected for analysis, resulting in a response rate of 47.8 %. Table 1 lists the results of the demographic data of the respondents. 3.2. Measures Except for the controls, all indicators used a seven-point Likert scale, with 1 representing “strongly disagree” and 7 representing “strongly agree.” Eight capabilities were used to measure the skills, competencies, and abilities that can leverage physical IT resources that compose ISC (Doherty & Terry, 2009). These reside within a second-order structure along the three dimensions of outside-in, spanning, and inside-out capabilities. The work of Riggs et al. (2023), based on the systematic review of Govindan and Hasanagic (2018), provides the measures used for CEP, which form a higher-order construct of six dimensions: governance initiatives, economic initiatives, cleaner production, product development, management support, and knowledge. The survey utilized seven elements based on Çankaya and Sezen (2019) to measure BP. We have considered this measure as a higher-order construct with two dimensions: (i) the economic dimension employed by authors such as Zhu et al. (2007) and Zhu & Sarkis (2004); (ii) the financial dimension arising from the work of Paulraj (2011) and Chan, He et al. (2012). Eleven indicators were used for EU (Lee & Grover, 1999), structured in the two dimensions of dynamism and complexity. Study controls were added in the form of a categorical variable (i.e., industry sector), the number of years since the firm was founded (expressed as age), and the number of employees (expressed as firm size). The primary variables used in our model are design conceptual variables or artifacts. These variables can be viewed as the result of theoretical thought and as mixtures of elements (Henseler, 2017). Consequently, we model our constructs as composites of simpler elements, such as dimensions or facts (Henseler, 2021). 3.3. Data analysis Several factors were considered in the selection of the analysis approach. First, the core constructs constitute a composite measurement model. Second, this study aims to explain the observed phenomena and predict outcomes in similar circumstances. Further, the mix of direct, mediation, and moderation relationships qualifies the research model as complex. Finally, this study uses component scores to implement the disjoint two-stage approach to estimate higher-order constructs. Based on these four characteristics, partial least squares path modeling (PLSPM) (Ciavolino et al., 2022) is well-suited for evaluating the model (Sabol et al., 2023). This study uses SmartPLS 4 software (Ringle et al., 2022) and the SEMinR package in R (Ray et al., 2022) to estimate the predictive contribution of the mediator. 3.4. Common method bias Following the indications of Schwarz et al. (2017), in this study, we tried to use an a priori approach during the research design phase to reduce the adverse effects of common method bias (CMB). For this reason, we followed the main procedural remedies outlined by MacK- enzie and Podsakoff (2012) to try to counteract these potential side effects. However, we have also applied an a posteriori approach. Therefore, we have used the full lateral collinearity test proposed by Kock and Lynn (2012) to detect potential sources of CMB. The result, shown in Table 2, indicates that our model appears unaffected by the CMB problem, as the highest variance inflation factor (VIF) is 2.227, which falls well below the 3.3 threshold. 4. Results 4.1. Measurement model Because our constructs are modeled as composites, Mode A (correlation weights) was chosen as the estimation method for the main variables for the following reasons (Becker et al., 2013; Rigdon, 2016): (1) Mode A is more advisable than Mode B (regression weights) to estimate standardized regression coefficients in small to medium samples and when R 2 Table 1 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 1,000 28 13 % More than 1,000 3 1 % Total 210 Sector Batteries and vehicles 20 10 % Food, water, and nutrients 62 30 % Electronics and ICT 6 3 % Construction and buildings 85 40 % Packaging 15 7 % Plastics 13 6 % Textiles 9 4 % Total 210 Table 2 Common method bias test. Full collinearity VIFs. ISC CEP BP EU Sector Age Size 1.74 2.227 1.319 1.558 1.019 1.069 1.035 Notes: VIF: Variance inflation factor. ISC: Information systems capabilities. CEP: Circular economy practices. BP: Business performance. EU: Environmental uncertainty. R. Riggs et al. Journal of Business Research 175 (2024) 114526 7 is low to medium. Mode A produces a higher out-of-sample R 2 when the sample size and true predictability are moderate. (2) When correlated indicators exist, Mode A is also better for weight estimation. Both conditions are present in our study. ISC, CEP, and BP mainly presented high levels of correlation (and some collinearity issues) in their measures. For that reason, we applied Mode A to those variables. However, that was not the case for EU, so we decided to use Mode B in this case. We found evidence that our variables estimated in Mode A met the usual requirements for reflective measurement models. First, the loadings for the indicators and dimensions (lower-order components) were greater than 0.70 for most of the measures (Table 3). Second, internal consistency reliability was achieved, as both lower- and higher-order constructs had composite reliability values greater than 0.7 (Table 3). Third, the constructs met the convergent validity requirement since the average variance extracted (AVE) values exceeded 0.5 (Table 3). Add to this that all constructs achieved discriminant validity, applying both the Fornell and Larcker criterion and the heterotrait-monotrait ratio (HTMT) of 0.85 (Table 4). The EU variable was modeled in Mode B. Following the Cheah et al. (2023) guidelines for formative constructs, we found that the EU Table 3 Measurement model results. Construct/Dimension/Indicator Loadings Weights CR AVE Information systems capabilities (HOC Mode A) 0.953 0.872 Outside-in capabilities (composite Mode A) 0.909 0.343 0.925 0.86 is_01 Manage linkages to stakeholders 0.923 0.524 is_02 Collect and disseminate external information 0.931 0.554 Spanning capabilities (composite Mode A) 0.958 0.37 0.926 0.862 is_03 Integrate with organizational functions 0.924 0.521 is_04 Develop capabilities responsively 0.933 0.556 Inside-out capabilities (composite Mode A) 0.934 0.357 0.965 0.874 is_05 Establish a portfolio of technologies 0.917 0.277 is_06 Acquire. use and manage IS knowledge 0.934 0.257 is_07 Deliver of applications rapidly 0.945 0.256 is_08 Support and manage IS operations 0.944 0.28 Circular economy practices (HOC Mode A) 0.937 0.713 Governance initiative (composite Mode A) 0.847 0.217 0.873 0.775 ce_gv_01 Develop CE indicators 0.876 0.559 ce_gv_02 Increase CE staff 0.885 0.577 Economic initiatives (composite Mode A) 0.849 0.201 0.833 0.715 ce_ec_01 Favor growth with minimum impact 0.926 0.733 ce_ec_02 Include CE costs in product price 0.757 0.425 Cleaner production (composite Mode A) 0.922 0.22 0.929 0.62 ce_cp_01 Increase efficiency with CE 0.794 0.174 ce_cp_02 Purchase considering sustainability 0.826 0.162 ce_cp_03 New SC strategies for improved CE 0.872 0.192 ce_cp_04 Collaborate for CE 0.695 0.14 ce_cp_05 Classify materials in terms of CE 0.744 0.136 ce_cp_06 Review logistics for sustainability 0.81 0.159 ce_cp_07 Produce for re-manufacturability 0.773 0.149 ce_cp_08 Gather data for SC CE 0.773 0.154 Product development (composite Mode A) 0.667 0.131 1 1 ce_pd_01 Design for disassembly and reuse 1 1 Management support (composite Mode A) 0.859 0.193 1 1 ce_mg_01 Endorse SC CE by management 1 1 Knowledge (composite Mode A) 0.9 0.212 0.934 0.824 ce_km_01 Support education regarding SC CE 0.893 0.363 ce_km_02 Implement SC CE training 0.908 0.342 ce_km_03 Demonstrate creativity in SC CE 0.922 0.396 Business performance (HOC Mode A) 0.934 0.74 Economic performance (composite Mode A) 0.837 0.457 0.86 0.672 pf_ec_01 Decrease material costs 0.744 0.333 pf_ec_02 Decrease energy consumption 0.851 0.41 pf_ec_03 Decrease waste fees 0.859 0.47 Financial performance (composite Mode A) 0.927 0.666 0.909 0.715 pf_ec_04 Improve earnings per share 0.86 0.317 pf_ec_05 Improve return on investment 0.852 0.297 pf_ec_06 Grow sales 0.818 0.295 pf_ec_07 Grow profit 0.851 0.274 Environmental uncertainty (HOC Mode B) n.a. n.a. Dynamism (composite Mode B) 0.778 0.528 n.a. n.a en_un_01 Stable marketing practices 0.829 0.653 en_un_02 High product obsolescence 0.347 −0.051 en_un_03 Easily predictable competitors 0.216 −0.156 en_un_04 Consumer demand easily forecast 0.274 0.128 en_un_05 Few changes in production technology 0.771 0.616 Complexity (composite Mode B) 0.871 0.676 n.a. n.a en_un_06 Large number of competitors 0.157 −0.234 en_un_07 Variety of substitute products 0.383 0.124 en_un_08 Large number of customers 0.555 0.208 en_un_09 Diverse customer requirements 0.889 0.669 en_un_10 Large number of suppliers 0.453 0.045 en_un_11 Diverse supplier products and services 0.638 0.406 Notes: CR: Composite reliability. AVE: Average variance extracted. HOC: Higher-order construct. n.a.: non-applicable. R. Riggs et al. Journal of Business Research 175 (2024) 114526 8 variable does not exhibit any issue with discriminant validity compared to other variables. This is because its highest HTMT ratio is 0.554 (Table 4). Besides, no issues with multicollinearity were observed between the indicators of the lower-order constructs and the two dimensions related to the higher-order construct. Their respective variance inflation factors (VIF) were below 3 (Sarstedt et al., 2020). From the weight analysis, the dimension of complexity represented the most critical lower-order component (LOC) in forming the environmental uncertainty as HOC (Table 3). Both weights were significant. Lastly, we evaluated the external validity of the main composites of our model (e.g., ISC, CEP, BP, and EU) using bootstrap-based saturated model fit tests (Henseler, 2021). SRMR, d ULS, and d G were below their saturated model’s corresponding values of HI95 or HI99 (Table 5). Consequently, we found additional support for the proposed measurement model. 4.2. Structural model Fig. 2 and Table 6 contain the primary structural parameters of Models 1 and 2, which include the relationships posited in our research model (Fig. 1) and a non-hypothesized relationship (ISC → BP), which is necessary to estimate the mediation effect (Nitzl et al., 2016; Rasoolimanesh et al., 2021). In the following sections, we perform the relevant analyses that allow us to test each of the three central structural relationships depicted in Models 1 and 2. 4.2.1. Mediation analysis In this section, we analyze the mediation effect contained in Model 1 (Fig. 2), assuming a double perspective, causal (Nitzl et al., 2016), and predictive (Shmueli et al., 2016) (Table 7). From an explanatory point of view, we will review and apply different complementary and basic techniques to test the mediation effect. Then, from a predictive point of view, we will analyze the predictive contribution of the mediator (CEP) and assess the predictive power of the model containing the mediation effect (Model 1) compared to a model without mediation. Nitzl et al. (2016) indicate that the researcher’s primary objective in mediation analysis is the causal explanation. From this perspective, our mediation analysis first seeks to understand how and why ISC affects BP through an intervening variable, CEP. In light of this, we began by approaching mediation analysis as a model selection case, comparing a base model (without mediation) with an alternative model (with mediation) (Model 1). Considering this objective, we relied on information-theoretic model selection criteria, focusing on the Bayesian information criterion (BIC), which is superior in terms of fit (Sharma et al., 2019). As shown in Table 8, the mediation model was superior to the base model (without mediation) as it had the lowest BIC. Additionally, we computed Akaike weights (Danks et al., 2020) for both models. The results supported the mediation model (Model 1) over the base model. Our next step was to conduct a robustness check to assess the potential presence of nonlinear effects in Model 1. This is a complementary method that will serve to reinforce the results. With this objective in mind, we used interaction terms to represent the quadratic effects of each exogenous construct on each endogenous variable. After performing a bootstrapping routine, the results showed that none of the quadratic terms was significant (Table 9). Therefore, we assume the robustness of the linear effects of Model 1 (Sarstedt et al., 2020). Lachowicz et al. (2018) suggest that for an indirect effect to be considered a causal effect, the assumption of sequential ignorability must be met. This is important from a causal perspective (Hair et al., 2019), particularly in mediation analysis (Rasoolimanesh et al., 2021). In our case, it means that the effects of ISC on CEP and ISC on BP and the effect of CEP on BP are not confounded by omitted variables. Therefore, we performed an endogeneity analysis of the model to identify omitted variables by applying the Gaussian copula approach (Hult et al., 2018). Thus, we tested that the antecedent variables of the model, which may demonstrate endogeneity, had a nonnormal distribution. In this sense, the Cram´ er-von Mises test indicated that ISC and CEP were not normally distributed (Table 10). Next, we performed a Gaussian copula analysis using SmartPLS. The results in Table 10 indicated that none of the Gaussian copulas had a significant p-value (>0.05). We conclude that endogeneity was not present in Model 1, providing additional support for the robustness of the results of the structural model. Considering the causal purpose of the mediation analysis of Model 1, we examined the overall fit of the hypothesized model using multiple indexes of the overall goodness of fit available for PLS-SEM (Henseler, 2021) (Table 5). Thus, the standardized root mean square residual (SRMR) yielded a reasonable value of 0.033, below the typical cut-off of 0.08. Next, we performed several bootstrap-based tests of the overall model fit (SRMR, d ULS , and d G ). According to our findings, all discrepancy values were below the 95th percentile. Therefore, the hypothesized mediating model cannot be discarded, as it is likely true. Finally, we focused on testing the mediation in Model 1. We first examined the possibility of multicollinearity between ISC and CEP variables and found no problems, as the highest VIF value was 1.74. Model 1 (Fig. 2A) included the effects (a and b 1 ) from which the hypothesized mediation relationship (H1) is estimated. To assess the mediating effect (Nitzl et al., 2016), we included the direct effect (c’) Table 4 Discriminant validity. ISC CEP BP EU Sector Age Size ISC 0.934 0.689 0.425 0.418 0.14 0.097 CEP 0.64 0.845 0.53 0.554 0.105 0.132 BP 0.35 0.452 0.883 0.403 0.248 0.103 EU 0.401 0.533 0.354 n.a. 0.048 0.053 Sector 0.123 0.03 −0.076 −0.056 n.a. Age −0.135 −0.102 −0.222 −0.048 −0.04 n.a. 0.084 Size 0.094 0.125 0.086 0.053 −0.012 0.084 n.a. Notes: ISC: Information systems capabilities. CEP: Circular economy practices. BP: Business performance. EU: Environmental uncertainty. The diagonal elements (bold) are the square roots of the AVEs. The Fornell-Larcker criterion is in the lower-left corner; the heterotrait-monotrait ratio (HTMT; italics) is in the upper-right corner; offdiagonal lower elements are the correlations between constructs. n.a.: non-applicable. Table 5 Tests of model fit. Value HI95 HI99 Saturated model SRMR 0.060 0.054 0.067 d ULS 0.323 0.263 0.413 d G 0.081 0.085 0.100 Estimated model SRMR 0.033 0.037 0.042 d ULS 0.073 0.092 0.117 d G 0.039 0.057 0.068 Notes: SRMR: Standardized root mean squared residual. d ULS : Unweighted least squares discrepancy. d G : geodesic discrepancy. HI95: 95 % bootstrap-based percentile. HI99: bootstrap-based 99 % percentile. R. Riggs et al. Journal of Business Research 175 (2024) 114526 9 between ISC and BP as a non-hypothesized relationship. Model 1 showed that while paths a and b 1 were statistically significant using a bootstrap process (10,000 samples), the direct effect (c’) was not. Then, we tested the mediation hypothesis (H1), specifying the indirect impact of ISC on BP through CEP (ab 1 ) (Table 11). We also investigated the total (c) and direct (c’) effects of ISC on BP, controlling for the three control variables. Table 11 shows that ISC did not significantly impact BP (c’) when the CEP variable was included in Model 1. However, the indirect impact of ISC on BP through CP was significant. This indicates that H1 (ab 1 ) is supported, and there was only indirect mediation, implying that CEP fully mediated the relationship between ISC and BP. We estimated the effect size measures for our mediation analysis to complement the significance assessment and better assess the magnitude of the effects, as some researchers recommended (Lachowicz et al., 2018; Rucker & Preacher, 2019). In this way, incorporating effect sizes can be a supplementary aspect in conducting mediation hypothesis testing (Ogbeibu & Gaskin, 2023). On the one hand, we computed the variance accounted for (VAF) index, which compares the indirect (or direct) effect to the total impact (Alwin & Hauser, 1975). We found that 70.1 % of the influence of ISC on BP was due to the indirect effect of CEP, while 29.9 % was direct. This reinforces the role of the CEP variable as a mediator. On the other hand, we estimated the effect size proposed by Lachowicz et al. (2018). In our Model 1, ν ( ν =a 2 ×b 1 2 ) would be interpreted as the variance in BP accounted for jointly by CEP and ISC that corrects for the spurious correlation induced by the order of the variables. Gaskin et al. (2023) proposed the following threshold for understanding effect sizes: 0.01 (small), 0.04 (medium), and 0.09 (large). Table 11 shows that the mediation effect achieves a ν value of Fig. 2. Structural model results. R. Riggs et al. Journal of Business Research 175 (2024) 114526 16 Kumar, A., Mangla, S. K., Luthra, S., & Ishizaka, A. (2019). Evaluating the human resource related soft dimensions in green supply chain management implementation. Production Planning and Control, 30(9), 699–715. https://doi.org/10.1080/ 09537287.2018.1555342 Lachowicz, M. J., Preacher, K. J., & Kelley, K. (2018). A novel measure of effect size for mediation analysis. Psychological Methods, 23(2), 244–261. https://doi.org/10.1037/ met0000165 Lahti, T., Wincent, J., & Parida, V. (2018). A definition and theoretical review of the circular economy, value creation, and sustainable business models: Where are we now and where should research move in the future? Sustainability, 10(8), 2799. https://doi.org/10.3390/su10082799 Lee, C. C., & Grover, V. (1999). Exploring mediation between environmental and structural attributes: The penetration of communication technologies in manufacturing organizations. Journal of Management Information Systems, 16(3), 187–217. https://doi.org/10.1080/07421222.1999.11518261 Li, L., & Lin, J. (2023). Digital transformation for the sustainable development of firms: The role of green capability and green culture. Sustainable Development. https://doi. org/10.1002/sd.2756 Lin, J., Li, L., Luo, X. (Robert), & Benitez, J. (2020). How do agribusinesses thrive through complexity? The pivotal role of e-commerce capability and business agility. Decision Support Systems, 135. https://doi.org/10.1016/j.dss.2020.113342. Li, L., Lin, J., Luo, W., & Luo, X. (2023). Investigating the effect of artificial intelligence on customer relationship management performance in e-commerce enterprises. Journal of Electronic Commerce Research, 24(1), 68–83. http://www.jecr.org/sites/de fault/files/2023vol24no1_Paper5.pdf. Liu, H., Ke, W., Wei, K. K., & Hua, Z. (2013). The impact of IT capabilities on firm performance: The mediating roles of absorptive capacity and supply chain agility. Decision Support Systems, 54(3), 1452–1462. https://doi.org/10.1016/j. dss.2012.12.016 Lo, S. M., & Shiah, Y. A. (2016). Associating the motivation with the practices of firms going green: The moderator role of environmental uncertainty. Supply Chain Management, 21(4), 485–498. https://doi.org/10.1108/SCM-05-2015-0184 MacKenzie, S. B., & Podsakoff, P. M. (2012). Common method bias in marketing: Causes, mechanisms, and procedural remedies. Journal of Retailing, 88(4), 542–555. https:// doi.org/10.1016/j.jretai.2012.08.001 Maletiˇ c, M., Maletiˇ c, D., & Gomiˇ sˇ cek, B. (2018). The role of contingency factors on the relationship between sustainability practices and organizational performance. Journal of Cleaner Production, 171, 423–433. https://doi.org/10.1016/j. jclepro.2017.09.172 Marrucci, L., Daddi, T., & Iraldo, F. (2021). The circular economy, environmental performance and environmental management systems: The role of absorptive capacity. Journal of Knowledge Management, 26(October), 2107–2132. https://doi. org/10.1108/JKM-06-2021-0437 Mazzucchelli, A., Chierici, R., Del Giudice, M., & Bua, I. (2022). Do circular economy practices affect corporate performance? Evidence from Italian large-sized manufacturing firms. Corporate Social Responsibility and Environmental Management, 29(6), 2016–2029. https://doi.org/10.1002/csr.2298 Melville, N., Kraemer, K., & Gurbaxani, V. (2004). Review: Information technology and organizational performance: An integrative model of IT business value. MIS Quaterly, 28(2), 283–322. https://doi.org/10.2307/25148636 Mikalef, P., Krogstie, J., Pappas, I. O., & Pavlou, P. (2020). Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities. Information & Management, 57(2), 103169. https://doi.org/10.1016/j.im.2019.05.004 Mithas, Ramasubbu, & Sambamurthy (2011). How Information Management Capability Influences Firm Performance. MIS Quarterly, 35(1), 237–256. https://doi. org/10.2307/23043496. Mora-Contreras, R., Torres-Guevara, L. E., Mejia-Villa, A., Ormazabal, M., & Prieto- Sandoval, V. (2023). Unraveling the effect of circular economy practices on companies’ sustainability performance: Evidence from a literature review. Sustainable Production and Consumption, 35, 95–115. https://doi.org/10.1016/j. spc.2022.10.022 Morning Consult. (2022). Global Technology Investment Predictions. https://filecache.me diaroom.com/mr5mr_ibmnewsroom/194659/IBM%20-%20Global%20Tech%20In vestment%20Predictions%20Report.pdf. Mishra, R., Singh, R. K., & Govindan, K. (2022). Barriers to the adoption of circular economy practices in Micro, Small and Medium Enterprises: Instrument development, measurement and validation. Journal of Cleaner Production, 351, 131389. https://doi.org/10.1016/J.JCLEPRO.2022.131389 Nitzl, C., Roldan, J. L., & Cepeda, G. (2016). Mediation analysis in partial least squares path modeling. Industrial Management & Data Systems, 116(9), 1849–1864. https:// doi.org/10.1108/IMDS-07-2015-0302 Nuryakin, A. N., & Ardyan, E. (2022). Open innovation strategies and SMES’ performance: The mediating role of eco-innovation in environmental uncertainty. Management Systems in Production Engineering, 30(3), 214–222. https://doi.org/ 10.2478/mspe-2022-0027 Ogbeibu, S., & Gaskin, J. (2023). Back from the future: Mediation and prediction of events uncertainty through event-driven models (EDMs). FIIB Business Review, 12(1), 10–19. https://doi.org/10.1177/23197145221121084 Paulraj, A. (2011). Understanding the relationships between internal resources and capabilities, sustainable supply management and organizational sustainability. Journal of Supply Chain Management, 47(1), 19–37. https://doi.org/10.1111/j.1745- 493X.2010.03212.x Pavlou, P. A., & El Sawy, O. A. (2006). From IT leveraging competence to competitive advantage in turbulent environments: The case of new product development. Information Systems Research, 17(3), 198–227. https://doi.org/10.1287/ isre.1060.0094 Peteraf, M. A. (1993). The cornerstones of competitive advantage: A resource-based view. Strategic Management Journal, 14(April 1992), 179–191. https://doi.org/10. 1002/smj.4250140303. Piyathanavong, V., Huynh, V. N., Karnjana, J., & Olapiriyakul, S. (2022). Role of project management on Sustainable Supply Chain development through Industry 4.0 technologies and Circular Economy during the COVID-19 pandemic: A multiple case study of Thai metals industry. Operations Management Research. https://doi.org/ 10.1007/s12063-022-00283-7 Preacher, K. J., Rucker, D. D., & Hayes, A. F. (2007). Addressing moderated mediation hypotheses: Theory, methods, and prescriptions. Multivariate Behavioral Research, 42 (1), 185–227. https://doi.org/10.1080/00273170701341316 Prosman, E. J., & Cagliano, R. (2022). A contingency perspective on manufacturing configurations for the circular economy: Insights from successful start-ups. International Journal of Production Economics, 249, 108519. https://doi.org/10.1016/ j.ijpe.2022.108519 Rai, A., Patnayakuni, R., & Seth, N. (2006). Firm performance impacts of digitally enabled supply chain integration capabilities. MIS Quarterly, 30(2), 225. https://doi. org/10.2307/25148729 Ramanathan, R. (2018). Understanding complexity: The curvilinear relationship between environmental performance and firm performance. Journal of Business Ethics, 149(2), 383–393. https://doi.org/10.1007/s10551-016-3088-8 Rasoolimanesh, S. M., Wang, M., Rold´ an, J. L., & Kunasekaran, P. (2021). Are we in right path for mediation analysis? Reviewing the literature and proposing robust guidelines. Journal of Hospitality and Tourism Management, 48(July), 395–405. https://doi.org/10.1016/j.jhtm.2021.07.013 Ravichandran, T. (2018). Exploring the relationships between IT competence, innovation capacity and organizational agility. Journal of Strategic Information Systems, 27(1), 22–42. https://doi.org/10.1016/j.jsis.2017.07.002 Ray, S., Danks, N. P., & Calero Valdez, A. (2022). SEMinR: Building and estimating structural equation models. (R package version 2.3.1). https://cran.r-project.org/pac kage=seminr. Rigdon, E. E. (2016). Choosing PLS path modeling as analytical method in European management research: A realist perspective. European Management Journal, 34(6), 598–605. https://doi.org/10.1016/j.emj.2016.05.006 Riggs, R., Rold´ an, J. L., Real, J. C., & 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 & Logistics Management, 53(7/8), 762–788. https://doi.org/10.1108/ IJPDLM-03-2022-0098 Ringle, C. M., Wende, S., & Becker, J. (2022). SmartPLS 4. SmartPLS. https://www.sma rtpls.com. Rosa, P., Sassanelli, C., & Terzi, S. (2019). Circular Business Models versus circular benefits: An assessment in the waste from electrical and electronic equipments sector. Journal of Cleaner Production, 231, 940–952. https://doi.org/10.1016/j. jclepro.2019.05.310 Rucker, D. D., & Preacher, K. J. (2019). Mediation analysis in consumer psychology: Models, methods, and considerations. In F. R. Kardes, P. M. Herr, & N. Schwarz (Eds.), Handbook of research methods in consumer psychology (pp. 373–384). Routledge. https://doi.org/10.4324/9781351137713. Sabherwal, R., & Jeyaraj, A. (2015). Information technology impacts on firm performance: An extension of Kohli and Devaraj (2003). MIS Quarterly, 39(4), 809–836. https://www.jstor.org/stable/26628653. Sabol, M., Hair, J., Cepeda, G., Rold´ an, J. L., & Chong, A. Y. L. (2023). PLS-SEM in information systems: Seizing the opportunity and marching ahead full speed to adopt methodological updates. Industrial Management & Data Systems, 123(12), 2997–3017. https://doi.org/10.1108/IMDS-07-2023-0429 Saccani, N., Bressanelli, G., & Visintin, F. (2023). Circular supply chain orchestration to overcome Circular Economy challenges: An empirical investigation in the textile and fashion industries. Sustainable Production and Consumption, 35, 469–482. https://doi. org/10.1016/j.spc.2022.11.020 Sahu, A., Agrawal, S., & Kumar, G. (2022). Integrating Industry 4.0 and circular economy: A review. Journal of Enterprise Information Management, 35, 885–917. https://doi.org/10.1108/JEIM-11-2020-0465 Sambamurthy, V., Bharadwaj, A., & Grover, V. (2003). Shaping agility through digital options: Reconceptualizing the role of information technology in contemporary firms. MIS Quarterly, 27(2), 237–263. https://doi.org/10.2307/30036530 Santhanam, R., & Hartono, E. (2003). Issues in linking information technology capability to firm performance. MIS Quarterly, 27(1), 125–153. https://doi.org/10.2307/ 30036521 Sarstedt, M., & Danks, N. P. (2022). Prediction in HRM research–A gap between rhetoric and reality. Human Resource Management Journal, 32(2), 485–513. https://doi.org/ 10.1111/1748-8583.12400 Sarstedt, M., Ringle, C. M., Cheah, J.-H., Ting, H., Moisescu, O. I., & Radomir, L. (2020). Structural model robustness checks in PLS-SEM. Tourism Economics, 26(4), 531–554. https://doi.org/10.1177/1354816618823921 Schryen, G. (2013). Revisiting IS business value research: What we already know, what we still need to know, and how we can get there. European Journal of Information Systems, 22(2), 139–169. https://doi.org/10.1057/ejis.2012.45 Schwarz, A., Rizzuto, T., Carraher-Wolverton, C., Rold´ an, J. L., & Barrera-Barrera, R. (2017). Examining the impact and detection of the “Urban Legend” of common method bias. ACM SIGMIS Database: The DATABASE for Advances in Information Systems, 48(1), 93–119. https://doi.org/10.1145/3051473.3051479 Schweikl, S., & Obermaier, R. (2023). Lost in translation: IT business value research and resource complementarity—An integrative framework, shortcomings and future R. Riggs et al. Journal of Business Research 175 (2024) 114526 17 research directions. Management Review Quarterly, 73, 1713–1749. https://doi.org/ 10.1007/s11301-022-00284-7 Sehnem, S., de Queiroz, A. A. F. S. L., Pereira, S. C. F., dos Santos Correia, G., & Kuzma, E. (2022). Circular economy and innovation: A look from the perspective of organizational capabilities. Business Strategy and the Environment, 31(1), 236–250. https://doi.org/10.1002/bse.2884 Sharma, P. N., Liengaard, B. D., Hair, J. F., Sarstedt, M., & Ringle, C. M. (2023). Predictive model assessment and selection in composite-based modeling using PLSSEM: Extensions and guidelines for using CVPAT. European Journal of Marketing, 57 (6), 1662–1677. https://doi.org/10.1108/EJM-08-2020-0636 Sharma, P. N., Sarstedt, M., Shmueli, G., Kim, K. H., & Thiele, K. O. (2019). PLS-based model selection: The role of alternative explanations in information systems research. Journal of the Association of Information Systems, 20(4), 346–397. https:// doi.org/10.17705/1jais.00538 Shmueli, G., Ray, S., Velasquez Estrada, J. M., & Chatla, S. B. (2016). The elephant in the room: Predictive performance of PLS models. Journal of Business Research, 69(10), 4552–4564. https://doi.org/10.1016/j.jbusres.2016.03.049 Shmueli, G., Sarstedt, M., Hair, J. F., Jr., Cheah, J., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict. European Journal of Marketing, 53(11), 2322–2347. https://doi.org/ 10.1108/EJM-02-2019-0189 Shojaei, A., Ketabi, R., Razkenari, M., Hakim, H., & Wang, J. (2021). Enabling a circular economy in the built environment sector through blockchain technology. Journal of Cleaner Production, 294, 126352. https://doi.org/10.1016/j.jclepro.2021.126352 Sore, S., Saunila, M., Ukko, J., & Helkkula, A. (2023). Business-to-business value cocreation: Suppliers’ perspective of essential information systems capabilities. Journal of Creating Value, 9(1), 81–106. https://doi.org/10.1177/23949643221121857 Susanty, A., Tjahjono, B., & Sulistyani, R. E. (2020). An investigation into circular economy practices in the traditional wooden furniture industry. Production Planning and Control, 31(16), 1336–1348. https://doi.org/10.1080/09537287.2019.1707322 Tang, H., Yao, Q., Boadu, F., & Xie, Y. (2023). Distributed innovation, digital entrepreneurial opportunity, IT-enabled capabilities, and enterprises’ digital innovation performance: A moderated mediating model. European Journal of Innovation Management, 26(4), 1106–1128. https://doi.org/10.1108/EJIM-08-2021- 0431 Tantan, M., & Akda˘ g, H. C. (2023). The effect of green supply chain management practices on the sustainability performance of Turkish shipyards. Sustainability, 15 (8). https://doi.org/10.3390/su15086677 Ting, L. S., Zailani, S., Sidek, N. Z. M., & Shaharudin, M. R. (2023). Motivators and barriers of circular economy business model adoption and its impact on sustainable production in Malaysia. Environment, Development and Sustainability.. https://doi. org/10.1007/s10668-023-03350-6 Trujillo-Gallego, M., Sarache, W., & de Sousa Jabbour, A. B. L. (2022). Digital technologies and green human resource management: Capabilities for GSCM adoption and enhanced performance. International Journal of Production Economics, 249, 108531. https://doi.org/10.1016/j.ijpe.2022.108531 Vecchiato, R. (2012). Environmental uncertainty, foresight and strategic decision making: An integrated study. Technological Forecasting and Social Change, 79(3), 436–447. https://doi.org/10.1016/j.techfore.2011.07.010 Vogel, D. J. (2005). Is there a market for virtue?: The business case for corporate social responsibility. California Management Review, 47(4), 19–45. https://doi.org/ 10.1177/000812560504700401 Wade, M., & Hulland, J. (2004). Review: The Resource-Based View and Information Systems Research: Review, Extension, and Suggestions for Future Research. MIS Quarterly, 28(1), 107–142. https://doi.org/10.2307/25148626 Wong, C. Y., Boon-Itt, S., & Wong, C. W. Y. (2011). The contingency effects of environmental uncertainty on the relationship between supply chain integration and operational performance. Journal of Operations Management, 29(6), 604–615. https://doi.org/10.1016/j.jom.2011.01.003 Yin, S., Jia, F., Chen, L., & Wang, Q. (2023a). Circular economy practices and sustainable performance: A meta-analysis. In Resources, conservation and recycling (Vol. 190). Elsevier B.V. https://doi.org/10.1016/j.resconrec.2022.106838. Yu, Y., Xu, J., Zhang, J. Z., Wu, Y., & Liao, Z. (2022). Do circular economy practices matter for financial growth? An empirical study in China. Journal of Cleaner Production, 370, 133255. https://doi.org/10.1016/j.jclepro.2022.133255 Yu, Y., Yazan, D. M., Junjan, V., & Iacob, M. E. (2022). Circular economy in the construction industry: A review of decision support tools based on information & communication technologies. Journal of Cleaner Production, 349, 131335. https:// doi.org/10.1016/j.jclepro.2022.131335 Yu, Z., Khan, S. A. R., & Umar, M. (2022). Circular economy practices and industry 4.0 technologies: A strategic move of automobile industry. Business Strategy and the Environment, 31(3), 796–809. https://doi.org/10.1002/bse.2918 ZareRavasan, A., & Jeyaraj, A. (2023). Evolution of information systems business value research: Topic modeling analysis. Journal of Computer Information Systems, 63(3), 555–573. https://doi.org/10.1080/08874417.2022.2085212 Zeng, H., Chen, X., Xiao, X., & Zhou, Z. (2017). Institutional pressures, sustainable supply chain management, and circular economy capability: Empirical evidence from Chinese eco-industrial park firms. Journal of Cleaner Production, 155(May 2014), 54–65. https://doi.org/10.1016/j.jclepro.2016.10.093 ZareRavasan, A., & Krˇ c´ al, M. (2021). A systematic literature review on 30 years of empirical research on information systems business value. Journal of Global Information Management, 29(6), 1–37. https://doi.org/10.4018/JGIM.288894 Zhang, J., & Li, H. (2022). The impact of big data management capabilities on the performance of manufacturing firms in Asian economy during COVID-19: The mediating role of organizational agility and moderating role of information technology capability. Frontiers in Psychology, 13. https://doi.org/10.3389/ fpsyg.2022.833026 Zhang, Y., Wei, J., Zhu, Y., & George-Ufot, G. (2020). Untangling the relationship between corporate environmental performance and corporate financial performance: The double-edged moderating effects of environmental uncertainty. Journal of Cleaner Production, 263, 121584. https://doi.org/10.1016/j. jclepro.2020.121584 Zhu, Q., & Sarkis, J. (2004). Relationships between operational practices and performance among early adopters of green supply chain management practices in Chinese manufacturing enterprises. Journal of Operations Management, 22(3), 265–289. https://doi.org/10.1016/j.jom.2004.01.005 Zhu, Q., Sarkis, J., & Lai, K. (2007). Initiatives and outcomes of green supply chain management implementation by Chinese manufacturers. Journal of Environmental Management, 85(1), 179–189. https://doi.org/10.1016/j.jenvman.2006.09.003 Randy Riggs earned his doctoral degree in Strategic Management and International Business from the Universidad de Sevilla (Spain), and received his Master’s degree in Business Administration from the University of Texas at Dallas (United States). Randy has been an adjunct professor at Dallas College and has conducted classes for businesses for over twenty years, in subjects as diverse as nuclear power, instrumentation, enterprise resource planning, project management, and business intelligence applications. He is a certified project management professional and performance technologist. His team was awarded recognition as the Best Application Paper at the PLS Conference in September 2022. Email: [email protected]. ORCID: 0000-0002-9419-5069. Carmen M. Felipe holds a Ph.D. in Strategic Management and International Business from the Universidad de Sevilla. She has developed her professional career for more than 20 years in public and private entities related to the promotion and management of R&D, entrepreneurship, and innovation. Since 2020, she joined Universidad de Sevilla as a substitute teaching tutor in the Business Management and Marketing Department. She conducts her research within the group “Capabilities, knowledge, and learning for value creation”, which is integrated in the Andalusian Government Knowledge System. Her research is focused in the area of business organization, more specifically in the study of the value creation mechanisms of information systems and in the dynamic capabilities view. She has also published her work in Decision Sciences, Journal of Business Research, and Sustainability. Email: [email protected]. ORCID: 0000-0002-2964-5713. Jos´ e L. Rold´ an is a Professor of Management in the Department of Business Administration and Marketing, Universidad de Sevilla, Spain. He holds a Ph.D. in Management. His research interests include technology acceptance models, big data analytics capabilities, knowledge management, organizational culture, organizational agility, and partial least squares structural equation modeling (PLS-SEM). His recent contributions have been published in Journal of Business Research, Decision Sciences, Internet Research, Industrial Management & Data Systems, The Data Base for Advances in Information Systems, European Journal of Operational Research, British Journal of Management, International Business Review, European Journal of Information Systems, Computers in Human Behavior, and Industrial Marketing Management, among others. He is on the Data Base for Advances in Information Systems editorial board. He has also served as Guest Editor of the European Journal of Information Systems, Journal of Business Research, European Management Journal, and International Journal of Sports Marketing and Sponsorship. Email: [email protected]. ORCID: 0000-0003-4053-7526. Juan C. Real, Ph.D., is an Associate Professor of Operations Management in the Department of Business Management and Marketing, Pablo de Olavide University (Seville). His current research interests are in the areas of circular economy, supply chain management, lean management and sustainability. He has published papers in International Journal of Physical Distribution & Logistics Management, International Journal of Production Economics, Journal of Cleaner Production, British Journal of Management, International Journal of Technology Management, Industrial Marketing Management, and Personnel Review. Email: [email protected]. ORCID: 0000-0003-4835-0870. R. Riggs et al.