Abstract
Purpose
Drawing on resource orchestration theory, this paper aims to empirically investigate the relationships between digital transformation (DT), triple-A supply chain capabilities (i.e. agility, adaptability and alignment) and sustainable performance. The research focuses on the pharmaceutical industry, which best represents a business environment characterized by volatility, uncertainty, complexity and ambiguity.
Design/methodology/approach
Data were collected at different echelons of a globally oriented pharmaceutical supply chain, with the focal company located in the Netherlands. Empirical data were analyzed with partial least squares – structural equation modelling.
Findings
The findings reveal that DT enhances the triple-A supply chain capabilities. Nevertheless, not all three capabilities are necessary to improve overall sustainable performance. The results highlight that, among the three, only supply chain agility and adaptability significantly mediate the relationship between DT and sustainable performance.
Originality/value
This research supports the literature affirming that not all the triple-A supply chain capabilities equally affect sustainable performance. Moreover, it deepens the understanding of how orchestrating the triple-A capabilities at a firm level fosters overall sustainable performance, facing resource scarcity and investments in DT.
Keywords
Citation
Mohaghegh, M., Blasi, S., Russo, I. and Baldi, B. (2024), "Digital transformation and sustainable performance: the mediating role of triple-A supply chain capabilities", Journal of Business & Industrial Marketing, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JBIM-02-2023-0098
Publisher
:Emerald Publishing Limited
Copyright © 2024, Matin Mohaghegh, Silvia Blasi, Ivan Russo and Benedetta Baldi.
License
Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
Our contemporary business environment is marked by ongoing uncertainties, stemming from ever-changing customer requirements, intense market competition, rapid technological advancements and the pervasive influence of digitalization. Recently, the COVID-19 pandemic and international geopolitical tensions have injected even more uncertainty into the business environments, imposing unprecedented pressure on various industries within the international context (Khan et al., 2022; Troise et al., 2022; Zhang-Zhang et al., 2022) and leading to the collapse of certain supply chains (Craighead et al., 2020; Tunisini et al., 2023). This challenging situation best represents the acronym “VUCA” for increasing volatility, uncertainty, complexity and ambiguity (VUCA) (Bennett and Lemoine, 2014).
Within the VUCA context, the pharmaceutical supply chain (PSC) is a prime example, as it also faces additional challenges (Viegas et al., 2019). First, pharmaceutical products need to be tracked throughout the entire supply chain (Jaberidoost et al., 2013). Second, the temperature-controlled drug storage (i.e. the cold chain for vaccines transportation) has to meet strict environmental and temperature standards (Lin et al., 2020). Third, PSCs are facing the new challenge of balancing sustainability and efficiency in delivery, e.g. drugs are usually transported by air, which ensures service effectiveness but is highly pollutant (Li et al., 2022).
Given these complexities, it becomes imperative to understand how firms can effectively orchestrate their resources and investments in supply chain capabilities. A review of the literature suggests that, in the VUCA era, firms striving to achieve competitive advantage must rapidly and effectively change their modus operandi, becoming more agile, flexible and responsive (Erhun et al., 2021; Mohaghegh et al., 2023; Patrucco and Kähkönen, 2021; Richey et al., 2023). Following Lee’s (2004) proposal of triple-A supply chain capabilities, agility, adaptability and alignment are three key capabilities to be developed for business success. Agility refers to the ability to respond quickly and effectively to changes in the environment or customer demands (Gligor et al., 2022b; Patel and Sambasivan, 2022). Adaptability entails the flexibility to adjust and successfully reconfigure processes in response to changing market needs (Gligor et al., 2022b; Morgan et al., 2023). Alignment involves the ability to fully reap the benefits of collaborations among supply chain actors to achieve maximum economic performance within the supply network (Narayanan and Ishfaq, 2022). Recent disruptions in global supply chains show that companies with well-orchestrated triple-A capabilities are more capable of adjusting their operations, gaining competitive advantage and achieving business success (Skipworth et al., 2023).
However, due to the sustainability pressure imposed by stakeholders involved in supply chains (e.g. customers), business success is no longer solely determined by profitability and economic factors. Rather, it is also dependent on a company's commitment to environmental and social responsibilities (Mohaghegh et al., 2023; Geyi et al., 2020), referred to as sustainable performance across the triple bottom line or triple bottom line (TBL) (Elkington, 1997).
Existing literature proposes the digitalization of supply chain processes as a strategic investment to improve sustainable performance (Wang et al., 2016). However, it is still underexplored how firms, considering the resource scarcity and their substantial investments in digital transformation (DT), can strategically orchestrate triple-A supply chain capabilities for sustainability purposes. Without critically discussing the role of DT, extant studies also suffer from too much emphasis on the aggregate (higher-order) triple-A supply chain capability (Li et al., 2015; Whitten et al., 2012), while neglecting to investigate the individual impacts of these capabilities on sustainable performance (Mohaghegh et al., 2023; Geyi et al., 2020; Eckstein et al., 2015).
The lack of investigation in the literature on identifying the most optimal match between DT and triple-A supply chain capabilities for competitive advantage leads us to explore the mediating effects of (individual) triple-A supply chain capabilities for the DT – sustainable performance relationship. Building upon the notion that DT alone does not necessarily guarantee improved performance (Bai et al., 2022), we propose that firms need to orchestrate the triple-A capabilities to fully realize the potential of DT and translate their investments in digital solutions into sustainable performance. While DT introduces new tools and processes, firms without the ability to agilely respond to changes, adapt to new conditions and align these changes with strategic objectives cannot fully capitalize on the potential benefits of DT for sustainability purposes. The strategic resource/capability orchestration, therefore, is required to ensure that digital tools are not merely implemented but are integrated in a way that supports the firm’s goals and enhances overall performance.
Theoretically, the resource orchestration theory (ROT) can enhance our understanding by explaining why resource/capability orchestration matters and why mediation exists. This theory emphasizes the importance of effectively structuring, bundling and leveraging a firm’s resources/capabilities to achieve competitive advantage (Sirmon et al., 2011; Skipworth et al., 2023). Therefore, it can shed light on which supply chain capabilities should be prioritized to maximize the effects of DT on sustainable performance, especially within the context of resource scarcity.
Using the ROT and in response to the identified gaps in the literature, our research question is as follows:
How does the orchestration of triple-A supply chain capabilities mediate the relationship between digital transformation and sustainable performance across the TBL?
The rest of the paper is organized as follows. Section 2 presents the theoretical framework. Section 3 develops the research hypotheses. Section 4 lays out the research methodology, and Section 5 describes the analysis and results. Section 6 concludes the paper with a summary of the main findings, managerial implications, limitations and directions for future research.
2. Theoretical contextualization of the triple-A supply chain capabilities in a VUCA environment
The recent global disruptions highlight the necessity of enhancing supply chain capabilities to thrive in the VUCA context (Bals et al., 2023; Ramos et al., 2023; Tunisini et al., 2023). Traditional strategies centered on delivering customer value at reduced costs no longer suffice to outperform competitors and sustain competitive advantage in rapidly changing and unpredictable environments (Christopher and Holweg, 2017). Furthermore, research suggests that to reap the benefits associated with the management of supply chains, firms must develop certain capabilities (Eckstein et al., 2015; Erhun et al., 2021). The strategic amalgamation of various resources and capabilities at the firm level holds the potential to significantly enhance overall business performance (Gligor et al., 2020). Accordingly, this study builds on the ROT as a theoretical lens. ROT emphasizes the strategic deployment and rearrangement of a firm's scarce resources to effectively respond to evolving conditions and challenges (Chirico et al., 2011). In detail, ROT posits that creating value for the firm is primarily influenced by how resources are cultivated and used by managers, rather than solely by their ownership (Sirmon et al., 2011; Skipworth et al., 2023). Moreover, the industrial marketing and purchasing (IMP) perspective, which highlights the significance of relationships, interactions and networks within industrial and market contexts (Håkansson et al., 2009), offers an insightful complement to the ROT. While existing studies on resource interaction within IMP provide valuable insights (Baraldi et al., 2012; Gadde and Lind, 2016), we share the viewpoint of Bocconcelli et al. (2020) that the IMP approach could benefit from further development in today's dynamic business environment. This development avenue might involve gaining a nuanced understanding of how firms collaborate and interact with various stakeholders, including suppliers, customers and partners, to optimize resource utilization in a VUCA environment. Furthermore, it underscores that addressing these challenges extends beyond individual firms and necessitates a holistic view at the supply chain level, emphasizing the interdependence and interactions among various actors within the supply network (Jarzabkowski et al., 2021; Tunisini et al., 2023).
Building upon these perspectives, we delve into the interplay of triple-A supply chain capabilities (i.e. agility, adaptability and alignment), sustainability and digitalization. Following Lee’s (2004) triple-A supply chain capabilities, supply chain agility refers to the ability of a supply chain network to quickly and effectively respond to rapid changes in market conditions, customer demands or external disruptions (Gligor et al., 2022b; Patel and Sambasivan, 2022). Supply chain adaptability represents a firm’s capability to modify or reconfigure its supply chain to both short-term changes and long-term market shifts (Gligor et al., 2022b; Morgan et al., 2023). Supply chain alignment refers to the extent to which various components, partners and functions within a supply chain are synchronized and integrated to pursue shared goals and objectives (Narayanan and Ishfaq, 2022).
Together, these capabilities, optimize operations, enhance responsiveness and improve overall supply chain performance (Mak and Max Shen, 2021; Marin-Garcia et al., 2023). However, a study conducted by Gligor et al. (2020) highlights that achieving high levels of agility, adaptability and alignment simultaneously is not mandatory for optimal performance. This prompts a crucial call for further research, particularly facing resource scarcity, where managers are required to establish which investments to prioritize. In this context, our empirical research endeavors to perform empirical analysis with the goal of obtaining a comprehensive understanding of how firms orchestrate triple-A capabilities in the face of resource deficiencies. In particular, this research aims to shed light on the activation of particular supply chain capabilities and investments, such as digitalization, in response to VUCA challenges, as advocated by Tunisini et al. (2023).
3. Hypotheses development
3.1 Digital transformation and triple-A supply chain capabilities
DT refers to the process by which firms reshape their business models and ecosystems through the integration of high-impact digital technologies and capabilities (Nayal et al., 2022). This encompasses various technologies such as Big Data (analytics), Internet of Things (IoT), robotics, machine learning and business-to-business networks (Dean et al., 2017), to name a few. Given that firms, to achieve competitive advantage, are required to integrate internal and external resources, striking a balance between technology and business practices, we conceptualize DT as a comprehensive process involving the integration of advanced digital technologies and capabilities into operations, with a focus on enhancing efficiency, data utilization and connectivity.
Despite being challenging due to the implementation cost of digital technologies, DT is capable of impacting business resources/capabilities, including the triple-A supply chain capabilities (Bai et al., 2022; Mak and Max Shen, 2021). Ding (2018), for example, noted that the adoption of digital technologies helps firms establish more robust and agile processes, characterized by fewer interruptions and defects. Zhou et al. (2023), surveying 223 Chinese companies, empirically report a positive relationship between supply chain digitalization and supply chain agility. The authors also show that supply chain agility partially mediates the relationship between supply chain digitalization and supply chain performance. Al Humdan et al. (2020), in a systematic literature review, highlighted the importance of supportive information technology (IT) and promoted the use of IT tools as the enabler of supply chain agility. Ciampi et al. (2022) also emphasize that, in turbulent environments, firms need to process a vast amount of data to maintain agility. IT-based technologies play a crucial role in this process, enabling firms to sense and respond effectively to environmental changes.
In addition to agility, a synthesis of the literature reveals that in highly turbulent environments, firms must be flexible to adjust to environmental turbulence and to respond to rapid changes through reconfiguring their supply chain processes and transforming their resources/capabilities, i.e. supply chain adaptability (Aslam et al., 2018; Mohaghegh and Größler, 2024). Prior research has emphasized the importance of DT and high-impact digital technologies in facilitating supply chain adaptability. For instance, Zhao et al. (2023) claim that digitalization empowers enterprises to readily reconfigure their internal and external resources/capabilities to adapt and recover more effectively in a dynamic environment. Pettit et al. (2019) argue that blockchain and cloud technology can improve supply chain adaptation capabilities. Also, Zouari et al. (2021) referred to adaptability as an important feature (or component) of supply chain resiliency and empirically indicated that digital tools adoption and digital maturity positively affect supply chain resiliency.
Furthermore, the adoption of digital technologies arguably improves supply chain transparency, efficiency and flexibility, which in turn further stimulates collaboration and communication among supply chain actors, i.e. supply chain alignment (Kamble et al., 2020; Khan et al., 2022; Nayal et al., 2022). As an example, Kache and Seuring (2017) determine supply chain integration as the opportunity offered by Big Data (Analytics). The authors argue that integrated data exchange platforms are necessary for supply chain collaboration and information sharing. Also, Haddud et al. (2017) identified several advantages of IoT implementation in supply chains and highlight improved integration of inter-organizational business processes as a key benefit.
The significance of DT in enhancing supply chain capabilities can be viewed through the lens of ROT as well. While ROT’s application in the realm of technology and digital adoption remains an uncharted territory, it presents a promising avenue for deepening our understanding of how firms strategically manage their resources/capabilities within the DT process (Xu and Pero, 2023). Consequently, we claim that the synergy of digitization and ROT is crucial for analyzing strategic changes and orchestrating resources effectively, thereby enabling distinctive supply chain capabilities that potentially lead to competitive advantage. Amid these research insights, however, it remains uncertain which triple-A supply chain capabilities can be strengthened by DT, particularly within resource-constrained environments. Adopting the ROT lens and acknowledging the resource-scare setting, we propose to examine the individual impact of DT on each supply chain capability separately. We therefore formulate the following hypotheses:
There is a positive relationship between DT and supply chain agility.
There is a positive relationship between DT and supply chain adaptability.
There is a positive relationship between DT and supply chain alignment.
3.2 Triple-A supply chain capabilities and sustainable performance
Several studies state that agility, adaptability and alignment are critical capabilities that allow supply chains to sustain competitive advantage and achieve higher performance (e.g. Feizabadi et al., 2019; Whitten et al., 2012). So far, existing literature in supply chain management, to the best of the authors’ knowledge, mainly investigates the effect of the triple-A supply chain capability (as an integrated construct) on firm performance or supply chain performance (Feizabadi et al., 2021; Li et al., 2015; Whitten et al., 2012) and sometimes focus only on one or two of the triple-A capabilities (e.g. Al Humdan et al., 2020; Aslam et al., 2018). What is less explored, in fact, in the literature is the direct relationship between individual triple-As at the firm level and overall sustainable business performance. Given that the focus on firm performance has evolved from a single economic perspective to a simultaneous and increasing emphasis on environmental and social sustainability dimensions (Zhang et al., 2022), the present study aims to investigate the direct impact of the triple-As in isolation on sustainable performance, giving equal importance to economic, environmental and social dimensions.
In the context of relationships between individual triple-A capabilities and sustainable performance, for example, Gligor and Holcomb (2014) empirically demonstrate that agility positively impacts both operational and relational performance. Agility is not only directly linked to economic performance, as demonstrated by Gligor et al. (2015) and Inman et al. (2011), but appears to be influential for environmental performance. An agile supply chain can minimize inventory requirements, reducing waste and resource consumption (Geyi et al., 2020). Furthermore, agility optimizes transportation and distribution, leading to lower emissions and energy usage, contributing to environmental sustainability (Geyi et al., 2020; Raut et al., 2021). From the TBL perspective, Mohaghegh et al. (2023), based on a sample of Italian manufacturing firms, discover empirical support for the positive relationships between agility and all triple dimensions of sustainable performance, i.e. economic, environmental and social performance.
With a focus on the relationship between supply chain adaptability and sustainable performance, Christopher and Holweg (2011) found profitability to be a direct outcome of supply chain adaptability. Adaptive supply chains are more resilient in the face of potential disruptions, reducing financial losses and environmental damage (Adobor, 2020; Zhao et al., 2019). Furthermore, adaptability enables companies to move toward sustainable practices, which may involve sourcing eco-friendly materials, enhancing energy efficiency or creating more environmentally friendly packaging solutions (Negri et al., 2021). Considering the social sustainability dimension, Mohaghegh and Größler (2024) claimed that easily reconfigured supply chains offer a flexible environment to implement social sustainability practices throughout the supply chain, including supplier evaluation measures aimed at ensuring compliance with social sustainability standards.
Similar to supply chain agility and adaptability, supply chain alignment appears also to be associated with financial performance (Liu et al., 2016) and operational performance (Morgan et al., 2016), both of which are critical to improving the economic dimension of sustainable performance. As discussed by Gligor et al. (2022a), alignment encourages ethical practices throughout the supply chain, such as fair labor practices, which can eventually improve the social dimension of sustainable performance. Using interpretive structural modeling, Mohaghegh and Größler (2024) also demonstrate that supply chain integration (i.e. to collaboratively removing all boundaries to ease the flow of material, resources and information between different actors involved in the supply chain) is a necessary step to enhance all three dimensions of sustainable performance.
Considering the expected benefits of supply chain agility, adaptability and alignment for equally important dimensions of sustainable performance (i.e. economic, environmental and social performance), the resulting hypotheses are as follows:
There is a positive relationship between supply chain agility and overall sustainable performance.
There is a positive relationship between supply chain adaptability and overall sustainable performance.
There is a positive relationship between supply chain alignment and overall sustainable performance.
3.3 The mediating role of triple-A supply chain capabilities
As described earlier, DT has become a strategic requirement for supply chains to gain a competitive advantage in the marketplace (Frederico et al., 2021). The reason can relate to the potential of DT to enhance certain supply chain capabilities such as supply chain visibility, transparency and flexibility (Nayal et al., 2022), supply chain resilience (Ivanov and Dolgui, 2021) and supply chain collaboration (Stank et al., 2019), all of which are necessary to achieve competitive advantage and improve overall business performance (Wang et al., 2016). Therefore, it is plausible to claim that supply chain capabilities can be seen as mechanisms through which DT contributes to firm-level performance (e.g. Nayal et al., 2022; Troise et al., 2022). This claim aligns with the perspectives shared by several studies such as Akhtar et al. (2018) and Bai et al. (2022) that DT affects performance only in association with certain capabilities. As an example, the results of a study conducted by Zhou et al. (2023) revealed that supply chain agility acts as the mediator for the relationship between supply chain digitalization and supply chain performance. The authors argue that technology alone may not ensure business success (Zhou et al., 2023). Instead, the effects of technology on performance can be maximized when agile capabilities are present to partially transfer the effects of technology on performance. Also, with the focus on the role of alignment for the DT-sustainable performance relationship, Zhou et al. (2023) discovered empirical support for the mediating role of supply chain integration (or alignment), specifically in terms of customer collaboration, on the association between digitalization and financial performance.
Although, in this section and in line with previous studies, we hypothesize that DT in association with triple-A supply chain capabilities (i.e. agility, adaptability and alignment) can yield competitive advantage and enhance sustainable performance, it is of particular interest to investigate which triple-A supply chain capabilities can effectively transfer the effects of DT on sustainable performance. Marin-Garcia et al. (2023) emphasized the necessity of investigating triple-A capabilities individually. The authors argue focusing on individual capabilities, rather than addressing them as an aggregate (high order) capability, allows researchers to better understand their isolated effects on performance, thereby finding the most optimal capability match for competitive advancement and performance. This becomes even more critical within the context of resource scarcity and substantial investments of firms in DT, where managers need to strategically prioritize and orchestrate their resources to achieve competitive advantage. Our perspective resonates with Gligor et al. (2020) conclusion that achieving optimal performance does not necessarily demand high levels of agility, adaptability and alignment simultaneously.
According to the hypotheses developed earlier, where agility, adaptability and alignment are assumed to positively influence sustainable performance and being positively impacted by the DT, we suggest that the triple-A supply chain capabilities (separately) at the firm-level mediate the relationship between DT and overall sustainable performance. This leads to finding the most optimal capabilities match to mediate the relationship between DT and sustainable performance. Accordingly, the formulation of the next hypothesis is as follows:
Supply chain agility, adaptability and alignment can serve as mediators for the relationship between DT and overall sustainable performance.
The conceptual model with expected relationships is depicted in Figure 1.
4. Methodology
4.1 Data collection and sample characteristics
This study follows a quantitative cross-sectional research design. To collect data, we developed a questionnaire-based survey using the online tool Qualtrics. The empirical context of this research is a globally oriented PSC, with the focal company located in the Netherlands. We targeted executives at various echelons within the PSC, with a specific focus on those working in supplier firms, pre-wholesale firms and wholesale firms involved in the international distribution of healthcare products. This is because the executives in such positions are required to deal with the strategic decisions related to DT and sustainability issues. The main language used in the questionnaire was English, due to the global orientation of the PSC. The survey was pre-tested and validated by three experts who were involved daily in the PSC process and by research group members. The questionnaire link was distributed to the executives by email. The respondent's names and emails were identified from companies’ websites. Each mailing contained a cover letter explaining the purpose and intention of the survey. Initially, 360 executives were contacted to participate in the study. We received 84 valid and usable responses, representing a response rate of 24%. This seems to be an acceptable response rate as compared with similar research with a response rate of 19% (Ali and Khalid, 2017) or lower (Aljafari and Brown, 2020; Gligor et al., 2015; Mohaghegh et al., 2021).
Data collection from one single respondent through a self-reported questionnaire can be a source of concern regarding common method bias (CMB). We controlled for CMB through both procedural and statistical controls, as suggested by Podsakoff et al. (2003). For the procedural remedies, we used two strategies. First, we assured all respondents the anonymity and confidentiality of their answers, and we also assured them that there were no right or wrong answers. This procedure allowed us to also control for the social desirability bias. Second, we provided descriptions for potentially unfamiliar terms, i.e. the constructs of our study, and avoided complicated syntax to improve clarity and scale items (Podsakoff et al., 2003). Regarding the statistical strategies, we conducted Harman's single-factor test to detect the presence of CMB in our data (Harman, 1967). A CMB is present if a single factor emerges from the factor analysis or one general factor accounts for most of the variance among the variables (Podsakoff et al., 2012). We conducted a confirmatory factor analysis (CFA), which did not highlight the existence of a single factor accounting for most of the variability of the data. The test revealed the existence of eight distinctive factors with eigenvalues > 1.0. The first and largest factor accounted for only 26.5% of the total variance. Therefore, the CMB is not an issue in our analysis.
The sample characteristics are summarized in Table 1, which presents the respondents' profiles. The participants are mainly senior managers such as supply chain managers (22.6%) and members of the top management team (50.0%); they have a clear and in-depth understanding of the constructs studied in the current research. Work experience of more than 15 years for over half of the sample is further proof of respondents’ detailed knowledge. Following a few questions about their positions within the PSC, respondents provided information about their respective firms. Table 2 summarizes the firm profiles. The sample demonstrates a good balance in terms of firm size, encompassing a diverse range of companies: 12 micro (14.3%), 30 small (35.7%), 15 medium (17.9%) and 27 large firms (32.1%).
4.2 Measures
Our items used to operationalize the constructs were based on validated scales in the extant literature (see Online supplementary material). Each construct was measured through multi-item scales. The survey questionnaire was divided into three main sections:
DT;
sustainable performance (i.e. economic, environmental and social sustainability dimensions); and
triple-A supply chain capabilities (i.e. agility, adaptability and alignment).
DT was assessed based on seven items proposed by Nayal et al. (2022). Supply chain capabilities were measured with the help of six items each, as suggested by Gligor et al. (2020). Sustainable performance was considered as a second-order construct consisting of economic, environmental and social sustainability performance, thus following the TBL perspective. The three sustainable performance measures were measured by five items each, as proposed by Mohaghegh et al. (2021). The economic dimension measured “the use of raw materials”, “profitability” and “return on investment”, for example. The environmental dimension focused on “emissions” and “waste”. The social dimension gauged social considerations such as “health and safety of community and employees”. Most survey items used a seven-point Likert-type scale to assess respondents' level of agreement, ranging from 1 (“strongly disagree”) to 7 (“strongly agree”). However, for the social and environmental sustainability dimensions, the scale focused on perceived performance over the past two years. Here, the scale ranged from 1 (“much worse”) to 7 (“much better”).
5. Analysis and results
To evaluate the research model, partial least squares structural equation modeling (PLS-SEM) was used with SmartPLS 4. This method is appropriate when dealing with small sample sizes (Hair et al., 2011). The minimum sample size acceptable for PLS-SEM is 10 times the largest number of structural paths directed at a particular construct in the structural model (Hair et al., 2017). However, relying solely on this rule of thumb is not adequate (Wang et al., 2023). As proposed by Hair et al. (2011), while determining the sample size, the characteristics of the reference population should be taken into consideration. We assert that our sample size can be satisfactory in a business-to-business context within a specific supply chain such as PSC. In addition, our sample size can be compared with similar studies using PLS-SEM. For example, Ali and Khalid (2017) use PLS-SEM to investigate predictive research models in the early stage of theory development, with a sample of 89. Likewise, Aljafari and Brown (2020) used PLS-SEM with a sample of 77 top managers to test their proposed hypotheses.
5.1 Measurement model assessment
As summarized in Table 3, we assessed the validity of the measurement model using established criteria for internal reliability, convergent and discriminant validity (Hair et al., 2011; Wang et al., 2023). Internal reliability of the constructs was measured using Cronbach’s alpha (CA) and composite reliability (CR), being commonly reported values of scale reliability. CA and CR values exceeded 0.70 and hence considered satisfactory as recommended by Fornell and Larcker (1981). Convergent validity was assessed using both individual-item reliability and average variance extracted (AVE). Individual-item reliability was assessed through the standardized factor loading values between each item and its latent construct. Items with standardized loadings below the recommended 0.7 threshold were removed from the model, as recommended in the literature (Hair et al., 2011). Also, all AVE scores were above the threshold of 0.5 suggested by the literature (Fornell and Larcker, 1981). These findings support the convergent validity of the model. Discriminant validity was examined using both the Fornell–Larcker criterion (see Table 4) and the Heterotrait-Monotrait ratio (see Table 5). Considering the former, the square roots of the AVEs of the latent variables were higher than the correlations among the latent variables (Fornell and Larcker, 1981). Based on the latter, the Heterotrait-Monotrait values were within the acceptable range, below the maximum threshold of 0.85 for nonrelated constructs and 0.90 for related constructs (Hair et al., 2011). Therefore, the convergent validity of the model was assured. The results of the measurement model are depicted in Figure 2.
5.2 Structural model assessment
The structural model was checked using the evaluation criteria such as multi-collinearity assessment, significance of the path coefficients, R2 measures, effect size (f2) and predictive relevance (Q2) (Hair et al., 2011; Wang et al., 2023). To check for multi-collinearity, variance inflation factor (VIF) was assessed for each predicting constructs. The VIF values were found to be below the threshold level (i.e. 5), indicating that there were no concerning effects of multicollinearity. Bootstrapping was used to assess the significance of path coefficients. Bootstrapping was performed using a 10,000 subsample following the updated guidelines by Sarstedt et al. (2023). Table 6 displays the results of the bootstrapping procedure for the structural model. Positive and significant relationships existed between DT and the triple-A supply chain capabilities. DT was positively associated with supply chain agility (β = 0.32; p < 0.001), thus supporting H1a. DT positively and significantly influenced supply chain adaptability (β = 0.34; p < 0.1), confirming H1b. H1c is also supported as DT showed a positive and significant effect on supply chain alignment (β = 0.21; p < 0.05).
Empirical evidence was found for H2a as there was a positive relationship between supply chain agility and sustainable performance (β = 0.26; p < 0.1). However, no empirical support was found for H2b, as the correlation coefficient between supply chain adaptability and sustainable performance was not statistically significant. Finally, the positive and statistically significant relationship between supply chain alignment and sustainable performance (β = 0.27; p < 0.001) supported H2c.
To assess the explanatory power of the proposed model, R2 values were evaluated for the endogenous constructs. The values considered satisfactory vary with the discipline (Hair et al., 2017). The R2 value for sustainable performance was 0.16, while supply chain agility, alignment and adaptability had values of 0.10, 0.04 and 0.11, respectively. Furthermore, we assessed the effect size (f2) to measure the predictive power of the structural model. This measure evaluates whether the omission of an exogenous construct from the model substantively impacts an endogenous construct (Hair et al., 2017). The highest f2 for sustainable performance was supply chain alignment with a value of 0.077. Following the guidelines by Hair et al. (2017) values greater than 0.02 indicate a small effect of the exogenous construct on the endogenous one. Finally, we measured the Q2 to assess the predictive relevance of the model. The recommended default of 10 folds and 10 repetitions was used. The Q2 was 0.082, which is greater than 0, indicating that the exogenous constructs have a predictive relevance for sustainable performance as the endogenous construct.
To test the mediating role of the triple-A supply chain capabilities in the relationship between DT and sustainable performance, we began by examining the direct relationships. As Baron and Kenny (1986) emphasize, it is unnecessary to examine the mediation relationship if there is no direct link between the independent variable (IV) and the dependent variable (DV). Therefore, we first checked the direct relationship between our DV (sustainable performance) and IV (DT). Based on the positive and statistically significant relationship between these two variables (β = 0.41; p < 0.001), we then proceeded to investigate the type of mediation (partial or full), following the approach suggested by Nitzl et al. (2016). The mediation analysis results, summarized in Table 7, showed that supply chain agility and supply chain alignment partially mediated the relationships between DT and sustainable performance, thus supporting H3a and H3c. However, H2b was not supported, as no empirical evidence was found for the mediating role of supply chain adaptability.
6. Discussion and implications
This study aims to investigate whether orchestrating triple-A supply chain capabilities, namely, agility, adaptability and alignment, mediates the relationship between DT and sustainable performance. The results highlight that supply chain agility and supply chain alignment act as mediators for the DT-sustainable performance relationship. However, no empirical evidence is found for the mediating role of supply chain adaptability. Considering these findings, firms can allocate their resources more efficiently, investing in supply chain agility and supply chain alignment, where they are more likely to yield tangible benefits in terms of sustainable performance. Prioritizing these two capabilities fosters a long-term perspective on sustainability and equips firms to successfully cope with the VUCA nature of changing market conditions.
6.1 Theoretical implications
The results of this research lead to several theoretical implications. First, we contribute to the evolving body of literature on the triple-A supply chain capabilities. This research emphasizes that these capabilities are essential in navigating the complex and dynamic environment of VUCA within the PSC. The paper affirms a positive relationship between DT and each of the triple-A supply chain capabilities. This highlights how investments in DT enable adjustments to long-term structural shifts, facilitate agile responses to short-term fluctuations and enhance alignment among supply chain actors.
Second, our findings reveal that while supply chain agility and supply chain alignment significantly influence sustainable performance, supply chain adaptability does not have a significant impact. This result is coherent with the study conducted by Gligor et al. (2020), which states that it is not necessary to develop at the same time high levels of agility, adaptability and alignment to achieve better performance. Our paper provides further evidence and corroborates the idea that not all the triple-A supply chain capabilities equally affect sustainable performance. In detail, adaptability refers to the ability to gradually reconfigure supply chain resources to respond to long-term, structural changes. In contrast, DT often involves rapid technological innovations and quick implementation of digital tools and processes. The mismatch in the time horizons might explain why adaptability does not mediate the relationship between DT and sustainable performance (Gligor et al., 2020). Hence, with the current research, we go beyond the interaction approach to triple-A supply chain capabilities, by moving a step forward in the orchestration of the triple-A supply chain capabilities considered in isolation. By understanding the hierarchical view of those capabilities, organizations can navigate modern business complexities and maintain a sustainable competitive advantage. This perspective allows for more nuanced strategies that align with the varying impacts of capabilities (Marin-Garcia et al., 2023). Furthermore, our research also explores underdeveloped or inconclusive areas such as blockchain and information sharing for supply chain adaptability (Phadnis, 2024). Re-examining these relationships can enhance the marketing and Supply Chain Management (SCM) scholars’ understanding of these concepts.
Third, the research expands the conventional focus on economic performance by considering the broader TBL perspective. Traditionally, research in this domain primarily emphasized economic factors (Feizabadi et al., 2019). However, this study broadens the horizon by incorporating the social and environmental dimensions, aligning with the contemporary understanding that sustainable performance should be evaluated holistically. By doing so, it enriches the body of knowledge related to sustainable performance which is a growing but still developing field. By studying supply chain agility and supply chain alignment as mediators in the relationship between DT and sustainable performance in the PSC, characterized by a complex and global nature, this paper adds valuable insights that can be applied to similarly complex supply chains.
Fourth, the paper leverages the ROT to deepen the theoretical underpinnings of the study. The ROT sheds light on the orchestration and integration of key resources/capabilities for gaining competitive advantage and achieving superior performance. Our study extends ROT by exploring the mechanisms behind isolated and distinctive capabilities, specifically identifying agility and alignment as crucial supply chain capabilities. When adeptly managed and orchestrated, these capabilities empower firms to achieve sustainable performance. Our empirical findings highlight DT as a significant driver that enables supply chain agility and alignment, both critical for sustainable performance. This investigation adds a layer of theoretical depth to the ROT, highlighting the intricate relationship between DT and sustainable performance with the mediating roles of agility and alignment, thereby accentuating their strategic significance in enhancing overall sustainability. In this sense, our research aims to address the underdeveloped aspects of capabilities orchestration, advancing the understanding of how managers’ influence can impact the development of distinct capabilities (Fawcett et al., 2022). However, it should be noted that our study does not extensively cover long-term capabilities such as supply chain adaptability in the context of sustainable performance. This absence is attributed to the immediate focus of ROT on short-term, dynamic capabilities, rather than on the long-range, adaptive strategies necessary for sustaining performance over time. Nevertheless, an intriguing contribution of this study is the inherent tension between short-term agility and long-term adaptation, which needs further research according to other studies (Feizabadi et al., 2021; Phadnis, 2024).
6.2 Managerial implications
From the managerial perspective, this study offers valuable guidance to managers who grapple with the challenge of limited supply chain resources. Given the resource constraints, the recommendation is to strategically prioritize the development and implementation of agility and alignment. These two are identified as considerable capabilities in facilitating process reconfiguration, particularly driven by DT. This strategic prioritization ensures optimal utilization of scarce resources for the maximum impact. Furthermore, the findings of the current research provide actionable insights for managers operating in VUCA environments. By focusing on agility and alignment, supply chain managers can effectively navigate and respond to the dynamics of VUCA. Agility enables them to react swiftly and adjust to short-term changes, crucial in turbulent environments. Alignment, on the other hand, ensures that interests and responsibilities are shared effectively, fostering a collaborative and responsive approach. Therefore, our results emphasize a strategic differentiation between adaptability and agility/alignment. It highlights that in response to dynamic changes, agility and alignment are better suited for swift adjustments and short-term changes. Conversely, adaptability involves a gradual reconfiguration of resources to align with long-term structural changes. This understanding equips managers with a nuanced approach, enabling them to tailor their strategies according to the specific nature and time horizon of changes in their operational landscape. In VUCA environments, the amalgamation of these resources and capabilities could potentially serve as the linchpin for the survival and prosperity of companies.
Following ROT, our results also suggest that managers need to treat DT not as a one-off activity, but they should combine it with a implementation of supply chain capabilities for sustainable outcomes. More in detail, the integration of DT with supply chain capabilities is identified as a pathway to attain competitive advantage. This advantage emanates from the digitalization and automatization of operational activities and processes within the supply chain. By leveraging digital technologies and automating processes, operational efficiency and effectiveness are enhanced, contributing to a competitive edge in the market. Furthermore, DT, when combined with supply chain capabilities, enables firms to effectively manage and exploit big data. This efficient use of data can lead to data-driven insights and decision-making, further contributing to competitive advantage.
Finally, the literature has extensively highlighted sustainability issues concerning PSC. Various studies (Guercini et al., 2020; Milanesi et al., 2020) have shed light on the sustainability concerns, emphasizing their multifaceted implications for companies operating in this sector. Notably, the environmental impact of the pharmaceutical sector extends beyond production and encompasses transportation, distribution, usage (Nayal et al., 2022) and the dispersion of drug residues into the environment (Milanesi et al., 2020). The findings strongly advocate that DT represents a highly effective investment for enhancing sustainable performance within PSC. Digitalization can optimize processes, reduce waste and enhance energy efficiency, all of which contribute to a more sustainable supply chain. Allocating resources and effort into effectively integrating digital technologies and strategies is not only beneficial for competitiveness but also aligns with environmental goals and mandates. This insight serves as a clear message to managers, providing a viable response to the escalating governmental pressures and requirements, such as those outlined in the European Green Deal. The emphasis is on orchestrating DT and supply chain capabilities, positioning this integration as a crucial initial step toward the much-needed transition to decarbonization.
6.3 Limitation and future research
Several limitations can be identified, which can provide indications for future research. First, the concepts of DT and sustainability encompass a wide range of aspects, and hence, the constructs used in the current study cannot comprehensively address all the dimensions. Second, the geographical boundaries represent a limitation to our study; our findings could be generalized in future studies by considering geographical diversification, such as the PSC in the USA. Third, to investigate whether the observed impacts of triple-A supply chain capabilities, as mediators in the relationship between DT and sustainable performance, is industry-specific, future studies could conduct interviews with executives and employees at different supply chain echelons in sectors beyond pharmaceuticals. Moreover, as a prospective avenue for future research, it could be interesting to delve into the intricate ways in which specific managerial skills and organizational routines impact resource structuring, bundling and exploitation to steer supply chains toward success in the ever-evolving marketplace of tomorrow. Fourth, due to the nature of our data (cross-sectional), we could not address the problem of endogeneity (i.e. reverse causality between studied constructs). Therefore, we suggest that future studies address this problem using longitudinal data. Moreover, action research can uncover the nuanced interactions between DT initiatives and sustainable performance outcomes, providing actionable recommendations for both scholars and practitioners. Fifth, our study does not conclusively determine the role of supply chain adaptability. More research is needed to understand how short-term changes may influence long-term responses, where the need for supply chain adaptation is more critical (Phadnis, 2024). In this context, future research could use complexity adaptive system (CAS) theory as a valuable lens. CAS views supply chains as evolving systems driven by interactions among components and their environments. This perspective can help scholars explore how supply chains might self-organize, adapt to uncertainties and achieve long-term sustainability. Finally, exploring the role of artificial intelligence (AI) for DT and sustainable performance in VUCA environments presents a promising area for future research. Investigating AI's potential to optimize efficiency, foster sustainability in supply chain operations, and enhance supply chain capabilities will yield valuable insights (Richey et al., 2023). We encourage researchers to use interdisciplinary approaches, drawing from fields such as computer science, business management, sustainability and ethics, to comprehensively explore the multifaceted integration between AI and DT in the new business contest. Exploring these areas can advance our understanding of the development of business strategies that harness the full potential of AI while promoting sustainability, efficiency and triple-A supply chain capabilities.
Figures
Figure 1
Proposed conceptual model
Figure 2
Measurement model
Respondents profile
| Total responses (84) | ||
|---|---|---|
| Category | Frequency | % Distribution |
| Description of function | ||
| Upstream supply chain management | 7 | 8.3 |
| Downstream supply chain management | 16 | 19.0 |
| Supply chain management | 19 | 22.6 |
| Top management | 42 | 50.0 |
| Gender | ||
| Female | 15 | 17.9 |
| Male | 69 | 82.1 |
| Job experience | ||
| Less than 10 years | 7 | 8.3 |
| Between 10 and 15 years | 3 | 3.5 |
| Between 15 and 20 years | 15 | 17.9 |
| Between 20 and 25 years | 35 | 41.7 |
| > 30 years | 24 | 28.6 |
Author’s own work
Firm profile
| Total responses (84) | ||
|---|---|---|
| Category | Frequency | % Distribution |
| Firm size | ||
| Micro < 10 employees or < €2m turnover |
12 | 14.3 |
| Small < 50 employees or < €10m turnover |
30 | 35.7 |
| Medium < 250 employees or < €50m turnover |
15 | 17.9 |
| Large > 250 employees or > €50m turnover |
27 | 32.1 |
| Age of firm | ||
| <1 year | 1 | 1.2 |
| 1–3 years old | 1 | 1.2 |
| 3–5 years old | 6 | 7.1 |
| 5–10 years old | 7 | 8.3 |
| 10–15 yearls old | 9 | 10.7 |
| 15–20 years old | 9 | 10.7 |
| > 20 years old | 51 | 60.7 |
Author’s own work
Construct measure assessment: reliability and convergent validity
| Construct | Items | Standardized loading value | Cronbach’s alpha (CA) | Composite reliability (CR) | Average variance extracted (AVE) |
|---|---|---|---|---|---|
| SC agility | SCAG1 | 0.85 | 0.91 | 0.92 | 0.67 |
| SCAG2 | 0.85 | ||||
| SCAG3 | 0.85 | ||||
| SCAG4 | 0.77 | ||||
| SCAG5 | 0.79 | ||||
| SCAG6 | 0.79 | ||||
| SC adaptability | SCAD1 | 0.80 | 0.90 | 0.93 | 0.67 |
| SCAD2 | 0.85 | ||||
| SCAD3 | 0.79 | ||||
| SCAD4 | 0.82 | ||||
| SCAD5 | 0.85 | ||||
| SCAD6 | 0.82 | ||||
| SC alignment | SCAL4 | 0.66 | 0.65 | 0.80 | 0.58 |
| SCAL5 | 0.87 | ||||
| SCAL6 | 0.73 | ||||
| Digital transformation | DT1 | 0.83 | 0.80 | 0.86 | 0.61 |
| DT2 | 0.71 | ||||
| DT3 | 0.80 | ||||
| DT7 | 0.78 | ||||
| Econ. perf. | Econ3 | 0.94 | 0.90 | 0.94 | 0.83 |
| Econ4 | 0.95 | ||||
| Econ5 | 0.85 | ||||
| Env. perf. | Env1 | 0.89 | 0.92 | 0.94 | 0.77 |
| Env2 | 0.95 | ||||
| Env3 | 0.91 | ||||
| Env4 | 0.86 | ||||
| Env5 | 0.76 | ||||
| Social perf. | Social1 | 0.78 | 0.91 | 0.94 | 0.76 |
| Social2 | 0.83 | ||||
| Social3 | 0.91 | ||||
| Social4 | 0.90 | ||||
| Social5 | 0.92 |
Author’s own work
Construct correlations and discriminant validity: Fornell-Larcker criterion
| Digital transformation | Econ. perf. | Env. perf. | Social perf. | SCAG | SCAD | SCAL | |
|---|---|---|---|---|---|---|---|
| Digital transformation | 0.78 | – | – | – | – | – | – |
| Econ. perf. | 0.16 | 0.91 | – | – | – | – | – |
| Env. perf. | 0.34 | 0.32 | 0.88 | – | – | – | – |
| Social perf. | 0.31 | 0.16 | 0.30 | 0.87 | – | – | – |
| SCAG | 0.29 | 0.16 | 0.22 | 0.27 | 0.82 | – | – |
| SCAD | 0.31 | −0.10 | 0.17 | 0.24 | 0.57 | 0.82 | – |
| SCAL | 0.18 | −0.02 | 0.37 | 0.22 | 0.26 | 0.29 | 0.76 |
Author’s own work
Discriminant validity: Heterotrait–Monotrait ratio
| Digital transformation | Econ. perf. | Env. Perf. | Social perf. | SCAG | SCAD | SCAL | |
|---|---|---|---|---|---|---|---|
| Digital transformation | – | ||||||
| Econ. perf. | 0.24 | – | |||||
| Env. Perf. | 0.40 | 0.57 | – | ||||
| Social perf. | 0.36 | 0.13 | 0.32 | – | |||
| SCAG | 0.28 | 0.19 | 0.20 | 0.27 | – | ||
| SCAD | 0.34 | 0.25 | 0.19 | 0.26 | 0.65 | – | |
| SCAL | 0.25 | 0.43 | 0.45 | 0.27 | 0.34 | 0.35 | – |
Author’s own work
PLS-SEM analysis results
| Hypothesized relationship | HPs | Standardized coefficient | T-statistics | p-value | HP supported or not? |
|---|---|---|---|---|---|
| DT → SC agility | H1a | 0.32*** | 2.62 | 0.009 | Supported |
| DT → SC adaptability | H1b | 0.34** | 2.46 | 0.01 | Supported |
| DT → SC alignment | H1c | 0.21* | 1.76 | 0.07 | Supported |
| SC agility → Sus. perf. | H2a | 0.26** | 1.99 | 0.04 | Supported |
| SC adaptability → Sus. Perf. | H2b | −0.03 (n.s) | 0.24 | 0.81 | Not Supported |
| SC alignment → Sus. perf. | H2c | 0.27*** | 2.97 | 0.003 | Supported |
***p <0.01; **p <0.05; *p <0.1; and not significant (ns)
Source: Author’s own work
Mediation analysis
| Hypothesized relationship | Direct beta without mediation (p-value) |
Direct beta with mediation or c’ (p-value) |
Indirect beta or a *b (p-value) |
Mediation type | HP supported or not |
|---|---|---|---|---|---|
| DT → SC agility → Sus. perf. DT → Sus. perf DT → SC agility SC agility → Sus. perf. |
0.41*** (0.000) | 0.34*** (0.001) | 0.06* (0.1) | Partial mediation | H3a: Supported |
| DT → SC adaptability → Sus. perf. DT → Sus. perf DT → SC adaptability SC adaptability → Sus. Perf. |
0.41*** (0.000) | 0.38*** (0.001) | 0.02 (ns) (0.5) |
No mediation | H3b: Not supported |
| DT → SC alignment → Sus. perf. DT → Sus. perf DT → SC alignment SC alignment → Sus. perf. |
0.41*** (0.000) | 0.36*** (0.000) | 0.05* (0.1) | Partial mediation | H3c: Supported |
***p <0.01; **p <0.05; *p <0.1; and not significant (ns)
Source: Author’s own work
Supplementary material
The supplementary material for this article can be found online.
References
Adobor, H. (2020), “Supply chain resilience: an adaptive cycle approach”, The International Journal of Logistics Management, Vol. 31 No. 3, pp. 443-463, doi: 10.1108/IJLM-01-2020-0019.
Akhtar, P., Khan, Z., Tarba, S. and Jayawickrama, U. (2018), “The Internet of Things, dynamic data and information processing capabilities, and operational agility”, Technological Forecasting and Social Change, Vol. 136, pp. 307-316, doi: 10.1016/j.techfore.2017.04.023.
Al Humdan, E., Shi, Y., Behnia, M. and Najmaei, A. (2020), “Supply chain agility: a systematic review of definitions, enablers and performance implications”, International Journal of Physical Distribution & Logistics Management, Vol. 50 No. 2, pp. 287-312, doi: 10.1108/IJPDLM-06-2019-0192.
Ali, T. and Khalid, S. (2017), “Trust-performance relationship in international joint ventures: the moderating roles of structural mechanisms”, Journal of Business & Industrial Marketing, Vol. 32 No. 7, pp. 962-973, doi: 10.1108/JBIM-02-2017-0025.
Aljafari, A.M. and Brown, T.J. (2020), “Supplier-initiated ingredient/component branding”, Journal of Business & Industrial Marketing, Vol. 35 No. 6, pp. 1023-1035, doi: 10.1108/JBIM-10-2018-0317.
Aslam, H., Blome, C., Roscoe, S. and Azhar, T.M. (2018), “Dynamic supply chain capabilities”, International Journal of Operations & Production Management, Vol. 38 No. 12, pp. 2266-2285, doi: 10.1108/IJOPM-09-2017-0555.
Bai, B., Um, K.-H. and Lee, H. (2022), “The strategic role of firm agility in the relationship between IT capability and firm performance under the COVID-19 outbreak”, Journal of Business & Industrial Marketing, Vol. 38 No. 5, pp. 1041-1054, doi: 10.1108/JBIM-08-2021-0406.
Bals, L., Huang, F., Tate, W.L. and Rosca, E. (2023), “Creating social value at the bottom of the pyramid: elaborating resource orchestration via social intermediaries”, Journal of Business Research, Vol. 168, p. 114209, doi: 10.1016/j.jbusres.2023.114209.
Baraldi, E., Gressetvold, E. and Harrison, D. (2012), “Resource interaction in inter-organizational networks: foundations, comparison, and a research agenda”, Journal of Business Research, Vol. 65 No. 2, pp. 266-276, doi: 10.1016/j.jbusres.2011.05.030.
Baron, R.M. and Kenny, D.A. (1986), “The moderator–mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations”, Journal of Personality and Social Psychology, Vol. 51 No. 6, p. 1173.
Bennett, N. and Lemoine, J. (2014), “What VUCA really means for you (Jan/Feb 2014)”, Harvard Business Review, Vol. 92 Nos 1/2, Available at SSRN: https://ssrn.com/abstract=2389563 .
Bocconcelli, R., Carlborg, P., Harrison, D., Hasche, N., Hedvall, K. and Huang, L. (2020), “Resource interaction and resource integration: similarities, differences, reflections”, Industrial Marketing Management, Vol. 91, pp. 385-396, doi: 10.1016/j.indmarman.2020.09.016.
Chirico, F., Sirmon, D.G., Sciascia, S. and Mazzola, P. (2011), “Resource orchestration in family firms: investigating how entrepreneurial orientation, generational involvement, and participative strategy affect performance”, Strategic Entrepreneurship Journal, Vol. 5 No. 4, pp. 307-326, doi: 10.1002/sej.121.
Christopher, M. and Holweg, M. (2011), “Supply chain 2.0’: managing supply chains in the era of turbulence”, International Journal of Physical Distribution & Logistics Management, Vol. 41 No. 1, pp. 63-82, doi: 10.1108/09600031111101439.
Christopher, M. and Holweg, M. (2017), “Supply chain 2.0 revisited: a framework for managing volatility-induced risk in the supply chain”, International Journal of Physical Distribution & Logistics Management, Vol. 47 No. 1, pp. 2-17, doi: 10.1108/IJPDLM-09-2016-0245.
Ciampi, F., Faraoni, M., Ballerini, J. and Meli, F. (2022), “The co-evolutionary relationship between digitalization and organizational agility: ongoing debates, theoretical developments and future research perspectives”, Technological Forecasting and Social Change, Vol. 176, p. 121383, doi: 10.1016/j.techfore.2021.121383.
Craighead, C.W., Ketchen, D.J. and Darby, J.L. (2020), “Pandemics and supply chain management research: toward a theoretical toolbox”, Decision Sciences, Vol. 51 No. 4, pp. 838-866, doi: 10.1111/deci.12468.
Dean, A.K., Ellis, N. and Wells, V.K. (2017), “Science ‘fact’ and science ‘fiction’? Homophilous communication in high-technology B2B selling”, Journal of Marketing Management, Vol. 33 Nos 9/10, pp. 764-788, doi: 10.1080/0267257X.2017.1324895.
Ding, B. (2018), “Pharma Industry 4.0: literature review and research opportunities in sustainable pharmaceutical supply chains”, Process Safety and Environmental Protection, Vol. 119, pp. 115-130, doi: 10.1016/j.psep.2018.06.031.
Eckstein, D., Goellner, M., Blome, C. and Henke, M. (2015), “The performance impact of supply chain agility and supply chain adaptability: the moderating effect of product complexity”, International Journal of Production Research, Vol. 53 No. 10, pp. 3028-3046, doi: 10.1080/00207543.2014.970707.
Elkington, J. (1997), Cannibals with Forks: The Triple Bottom Line of 21st Century Business, Alternatives Journal, Capstone, Oxford.
Erhun, F., Kraft, T. and Wijnsma, S. (2021), “Sustainable triple‐A supply chains”, Production and Operations Management, Vol. 30 No. 3, pp. 644-655, doi: 10.1111/poms.13306.
Fawcett, S.E., Jin, Y., Brockhaus, S., Vega, D. and Fawcett, A.M. (2022), “Resource orchestration: managers’ role in developing and deploying resources to create distinctive advantage”, Handbook of Theories for Purchasing, Supply Chain and Management Research, Edward Elgar Publishing, Northampton, MA, pp, pp. 168-185.
Feizabadi, J., Gligor, D.M. and Alibakhshi, S. (2021), “Examining the synergistic effect of supply chain agility, adaptability and alignment: a complementarity perspective”, Supply Chain Management: An International Journal, Vol. 26 No. 4, pp. 514-531, doi: 10.1108/SCM-08-2020-0424.
Feizabadi, J., Maloni, M. and Gligor, D. (2019), “Benchmarking the triple-A supply chain: orchestrating agility, adaptability, and alignment”, Benchmarking: An International Journal, Vol. 26 No. 1, pp. 271-295, doi: 10.1108/BIJ-03-2018-0059.
Fornell, C. and Larcker, D.F. (1981), “Structural equation models with unobservable variables and measurement error: algebra and statistics”, Journal of Marketing Research, Vol. 18 No. 3, pp. 382-388, doi: 10.1177/002224378101800313.
Frederico, G.F., Kumar, V., Garza-Reyes, J.A., Kumar, A. and Agrawal, R. (2021), “Impact of I4.0 technologies and their interoperability on performance: future pathways for supply chain resilience post-COVID-19”, The International Journal of Logistics Management, Vol. 34 No. 4, pp. 1020-1049, doi: 10.1108/IJLM-03-2021-0181.
Gadde, L.-E. and Lind, F. (2016), “Interactive resource development: implications for innovation policy”, IMP Journal, Vol. 10 No. 2, pp. 317-338, doi: 10.1108/IMP-08-2015-0043.
Geyi, D.G., Yusuf, Y., Menhat, M.S., Abubakar, T. and Ogbuke, N.J. (2020), “Agile capabilities as necessary conditions for maximising sustainable supply chain performance: an empirical investigation”, International Journal of Production Economics, Vol. 222, p. 107501, doi: 10.1016/j.ijpe.2019.09.022.
Gligor, D.M. and Holcomb, M.C. (2014), “Antecedents and consequences of integrating logistics capabilities across the supply chain”, Transportation Journal, Vol. 53 No. 2, pp. 211-234, doi: 10.5325/transportationj.53.2.0211.
Gligor, D.M., Esmark, C.L. and Holcomb, M.C. (2015), “Performance outcomes of supply chain agility: when should you be agile?”, Journal of Operations Management, Vols 33/34 No. 1, pp. 71-82, doi: 10.1016/j.jom.2014.10.008.
Gligor, D., Feizabadi, J., Russo, I., Maloni, M.J. and Goldsby, T.J. (2020), “The triple-A supply chain and strategic resources: developing competitive advantage”, International Journal of Physical Distribution & Logistics Management, Vol. 50 No. 2, pp. 159-190, doi: 10.1108/IJPDLM-08-2019-0258.
Gligor, D.M., Davis‐Sramek, B., Tan, A., Vitale, A., Russo, I., Golgeci, I. and Wan, X. (2022a), “Utilizing blockchain technology for supply chain transparency: a resource orchestration perspective”, Journal of Business Logistics, Vol. 43 No. 1, pp. 140-159, doi: 10.1111/jbl.12287.
Gligor, D.M., Stank, T.P., Gligor, N., Ogden, J.A., Nowicki, D.R., Farris, T., Idug, Y., Rana, R., Porchia, J. and Kiran, P. (2022b), “Examining the rigor of SCM research: the case of supply chain agility”, Supply Chain Management: An International Journal, Vol. 28 No. 3, pp. 522-543, doi: 10.1108/SCM-12-2021-0575.
Guercini, S., Milanesi, M. and Runfola, A. (2020), “Bridges to sustainable health systems: public-private interaction for market access”, Journal of Business & Industrial Marketing, Vol. 35 No. 12, pp. 1929-1939, doi: 10.1108/JBIM-11-2019-0475.
Haddud, A., DeSouza, A., Khare, A. and Lee, H. (2017), “Examining potential benefits and challenges associated with the Internet of Things integration in supply chains”, Journal of Manufacturing Technology Management, Vol. 28 No. 8, pp. 1055-1085, doi: 10.1108/JMTM-05-2017-0094.
Hair, J.F., Ringle, C.M. and Sarstedt, M. (2011), “PLS-SEM: indeed a silver bullet”, Journal of Marketing Theory and Practice, Vol. 19 No. 2, pp. 139-152, doi: 10.2753/MTP1069-6679190202.
Hair, J.J.F., Hult, G.T.M., Ringle, C.M. and Sarstedt, M. (2017), A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), Sage, Thousand Oaks, CA.
Håkansson, H., Ford, D., Gadde, L.-E., Snehota, I. and Waluszewski, A. (2009), Business in Networks, John Wiley & Sons, Chichester.
Harman, H.H. (1967), Modern Factor Analysis, University of Chicago Press, Chicago, IL.
Inman, R.A., Sale, R.S., Green, K.W. and Whitten, D. (2011), “Agile manufacturing: relation to JIT, operational performance and firm performance”, Journal of Operations Management, Vol. 29 No. 4, pp. 343-355, doi: 10.1016/j.jom.2010.06.001.
Ivanov, D. and Dolgui, A. (2021), “A digital supply chain twin for managing the disruption risks and resilience in the era of industry 4.0”, Production Planning & Control, Vol. 32 No. 9, pp. 775-788, doi: 10.1080/09537287.2020.1768450.
Jaberidoost, M., Nikfar, S., Abdollahiasl, A. and Dinarvand, R. (2013), “Pharmaceutical supply chain risks: a systematic review”, DARU Journal of Pharmaceutical Sciences, Vol. 21 No. 1, p. 69, doi: 10.1186/2008-2231-21-69.
Jarzabkowski, P., Dowell, G.W. and Berchicci, L. (2021), “Strategy and organization scholarship through a radical sustainability lens: a call for 5.0”, Strategic Organization, Vol. 19 No. 3, pp. 449-455, doi: 10.1177/14761270211033093.
Kache, F. and Seuring, S. (2017), “Challenges and opportunities of digital information at the intersection of big data analytics and supply chain management”, International Journal of Operations & Production Management, Vol. 37 No. 1, pp. 10-36, doi: 10.1108/IJOPM-02-2015-0078.
Kamble, S.S., Gunasekaran, A. and Gawankar, S.A. (2020), “Achieving sustainable performance in a data-driven agriculture supply chain: a review for research and applications”, International Journal of Production Economics, Vol. 219, pp. 179-194, doi: 10.1016/j.ijpe.2019.05.022.
Khan, S.A.R., Piprani, A.Z. and Yu, Z. (2022), “Supply chain analytics and post-pandemic performance: mediating role of triple-A supply chain strategies”, International Journal of Emerging Markets, Vol. 18 No. 6, pp. 1330-1354, doi: 10.1108/IJOEM-11-2021-1744.
Lee, H.L. (2004), “The triple-A supply chain”, Harvard Business Review, Vol. 82 No. 10, pp. 102-113.
Li, B., Guo, H. and Peng, S. (2022), “Impacts of production, transportation and demand uncertainties in the vaccine supply chain considering different government subsidies”, Computers & Industrial Engineering, Vol. 169, p. 108169, doi: 10.1016/j.cie.2022.108169.
Li, X., Wu, Q. and Holsapple, C.W. (2015), “Best-value supply chains and firms’ competitive performance: empirical studies of their linkage”, International Journal of Operations & Production Management, Vol. 35 No. 12, pp. 1688-1709, doi: 10.1108/IJOPM-01-2014-0014.
Lin, Q., Zhao, Q. and Lev, B. (2020), “Cold chain transportation decision in the vaccine supply chain”, European Journal of Operational Research, Vol. 283 No. 1, pp. 182-195, doi: 10.1016/j.ejor.2019.11.005.
Liu, H., Wei, S., Ke, W., Wei, K.K. and Hua, Z. (2016), “The configuration between supply chain integration and information technology competency: a resource orchestration perspective”, Journal of Operations Management, Vol. 44 No. 1, pp. 13-29, doi: 10.1016/j.jom.2016.03.009.
Mak, H. and Max Shen, Z. (2021), “When triple‐A supply chains meet digitalization: the case of JD.com’s C2M model”, Production and Operations Management, Vol. 30 No. 3, pp. 656-665, doi: 10.1111/poms.13307.
Marin-Garcia, J.A., Machuca, J.A.D. and Alfalla-Luque, R. (2023), “In search of a suitable way to deploy Triple-A capabilities through assessment of AAA models’ competitive advantage predictive capacity”, International Journal of Physical Distribution & Logistics Management, Vol. 53 Nos 7/8, pp. 860-885, doi: 10.1108/IJPDLM-03-2022-0091.
Milanesi, M., Runfola, A. and Guercini, S. (2020), “Pharmaceutical industry riding the wave of sustainability: review and opportunities for future research”, Journal of Cleaner Production, Vol. 261, p. 121204, doi: 10.1016/j.jclepro.2020.121204.
Mohaghegh, M. and Größler, A. (2024), “Leagile supply chains and sustainable business performance: application of total interpretive structural modelling”, Production Planning & Control, pp. 1-23, doi: 10.1080/09537287.2024.2344063.
Mohaghegh, M., Åhlström, P. and Blasi, S. (2023), “Agile manufacturing and transformational capabilities for sustainable business performance: a dynamic capabilities perspective”, Production Planning & Control, pp. 1-13, doi: 10.1080/09537287.2023.2229264.
Mohaghegh, M., Blasi, S. and Größler, A. (2021), “Dynamic capabilities linking lean practices and sustainable business performance”, Journal of Cleaner Production, Vol. 322, p. 129073, doi: 10.1016/j.jclepro.2021.129073.
Morgan, T.R., Richey, R.G. and Autry, C.W. (2016), “Developing a reverse logistics competency”, International Journal of Physical Distribution & Logistics Management, Vol. 46 No. 3, pp. 293-315, doi: 10.1108/IJPDLM-05-2014-0124.
Morgan, T.R., Roath, A.S. and Glenn Richey, R. (2023), “How risk, transparency, and knowledge influence the adaptability and flexibility dimensions of the responsiveness view”, Journal of Business Research, Vol. 158, p. 113641, doi: 10.1016/j.jbusres.2022.113641.
Narayanan, A. and Ishfaq, R. (2022), “Impact of metric-alignment on supply chain performance: a behavioral study”, The International Journal of Logistics Management, Vol. 33 No. 1, pp. 365-384, doi: 10.1108/IJLM-01-2021-0061.
Nayal, K., Raut, R.D., Yadav, V.S., Priyadarshinee, P. and Narkhede, B.E. (2022), “The impact of sustainable development strategy on sustainable supply chain firm performance in the digital transformation era”, Business Strategy and the Environment, Vol. 31 No. 3, pp. 845-859, doi: 10.1002/bse.2921.
Negri, M., Cagno, E., Colicchia, C. and Sarkis, J. (2021), “Integrating sustainability and resilience in the supply chain: a systematic literature review and a research agenda”, Business Strategy and the Environment, Vol. 30 No. 7, pp. 2858-2886, doi: 10.1002/bse.2776.
Nitzl, C., Roldan, J.L. and Cepeda, G. (2016), “Mediation analysis in partial least squares path modeling”, Industrial Management & Data Systems, Vol. 116 No. 9, pp. 1849-1864, doi: 10.1108/IMDS-07-2015-0302.
Patel, B.S. and Sambasivan, M. (2022), “A systematic review of the literature on supply chain agility”, Management Research Review, Vol. 45 No. 2, pp. 236-260, doi: 10.1108/MRR-09-2020-0574.
Patrucco, A.S. and Kähkönen, A.-K. (2021), “Agility, adaptability, and alignment: new capabilities for PSM in a post-pandemic world”, Journal of Purchasing and Supply Management, Vol. 27 No. 4, p. 100719, doi: 10.1016/j.pursup.2021.100719.
Pettit, T.J., Croxton, K.L. and Fiksel, J. (2019), “The evolution of resilience in supply chain management: a retrospective on ensuring supply chain resilience”, Journal of Business Logistics, Vol. 40 No. 1, pp. 56-65, doi: 10.1111/jbl.12202.
Phadnis, S. (2024), “A review of research on supply chain adaptability: opening the black box”, Journal of Business Logistics, Vol. 45 No. 1, p. e12370, doi: 10.1111/jbl.12370.
Podsakoff, P.M., MacKenzie, S.B. and Podsakoff, N.P. (2012), “Sources of method bias in social science research and recommendations on how to control it”, Annual Review of Psychology, Vol. 63 No. 1, pp. 539-569, doi: 10.1146/annurev-psych-120710-100452.
Podsakoff, P.M., MacKenzie, S.B., Lee, J.-Y. and Podsakoff, N.P. (2003), “Common method biases in behavioral research: a critical review of the literature and recommended remedies”, Journal of Applied Psychology, Vol. 88 No. 5, pp. 879-903, doi: 10.1037/0021-9010.88.5.879.
Ramos, E., Patrucco, A.S. and Chavez, M. (2023), “Dynamic capabilities in the ‘new normal’: a study of organizational flexibility, integration and agility in the Peruvian coffee supply chain”, Supply Chain Management: An International Journal, Vol. 28 No. 1, pp. 55-73, doi: 10.1108/SCM-12-2020-0620.
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, Vol. 145, p. 102170, doi: 10.1016/j.tre.2020.102170.
Richey, R.G., Chowdhury, S., Davis‐Sramek, B., Giannakis, M. and Dwivedi, Y.K. (2023), “Artificial intelligence in logistics and supply chain management: a primer and roadmap for research”, Journal of Business Logistics, Vol. 44 No. 4, pp. 532-549, doi: 10.1111/jbl.12364.
Sarstedt, M., Hair, J.F. and Ringle, C.M. (2023), “PLS-SEM: indeed a silver bullet’ – retrospective observations and recent advances”, Journal of Marketing Theory and Practice, Vol. 31 No. 3, pp. 261-275, doi: 10.1080/10696679.2022.2056488.
Sirmon, D.G., Hitt, M.A., Ireland, R.D. and Gilbert, B.A. (2011), “Resource orchestration to create competitive advantage”, Journal of Management, Vol. 37 No. 5, pp. 1390-1412, doi: 10.1177/0149206310385695.
Skipworth, H.D., Bastl, M., Cerruti, C. and Mena, C. (2023), “Supply networks for extreme uncertainty: a resource orchestration perspective”, International Journal of Operations & Production Management, Vol. 43 No. 5, pp. 677-711, doi: 10.1108/IJOPM-05-2022-0314.
Stank, T., Esper, T., Goldsby, T.J., Zinn, W. and Autry, C. (2019), “Toward a digitally dominant paradigm for twenty-first century supply chain scholarship”, International Journal of Physical Distribution & Logistics Management, Vol. 49 No. 10, pp. 956-971, doi: 10.1108/IJPDLM-03-2019-0076.
Troise, C., Corvello, V., Ghobadian, A. and O’Regan, N. (2022), “How can SMEs successfully navigate VUCA environment: the role of agility in the digital transformation era”, Technological Forecasting and Social Change, Vol. 174, p. 121227, doi: 10.1016/j.techfore.2021.121227.
Tunisini, A., Harrison, D. and Bocconcelli, R. (2023), “Handling resource deficiencies through resource interaction in business networks”, Industrial Marketing Management, Vol. 109, pp. 154-163, doi: 10.1016/j.indmarman.2022.12.016.
Viegas, C.V., Bond, A., Vaz, C.R. and Bertolo, R.J. (2019), “Reverse flows within the pharmaceutical supply chain: a classificatory review from the perspective of end-of-use and end-of-life medicines”, Journal of Cleaner Production, Vol. 238, p. 117719, doi: 10.1016/j.jclepro.2019.117719.
Wang, S., Cheah, J.-H., Wong, C.Y. and Ramayah, T. (2023), “Progress in partial least squares structural equation modeling use in logistics and supply chain management in the last decade: a structured literature review”, International Journal of Physical Distribution & Logistics Management, doi: 10.1108/IJPDLM-06-2023-0200.
Wang, G., Gunasekaran, A., Ngai, E.W.T. and Papadopoulos, T. (2016), “Big data analytics in logistics and supply chain management: certain investigations for research and applications”, International Journal of Production Economics, Vol. 176 No. 2, pp. 98-110, doi: 10.1016/j.ijpe.2016.03.014.
Whitten, G.D., Kenneth, W.G. and Zelbst, P.J. (2012), “Triple-A supply chain performance”, International Journal of Operations & Production Management, Vol. 32 No. 1, pp. 28-48, doi: 10.1108/01443571211195727.
Xu, J. and Pero, M.E.P. (2023), “A resource orchestration perspective of organizational big data analytics adoption: evidence from supply chain planning”, International Journal of Physical Distribution & Logistics Management, Vol. 53 No. 11, pp. 71-97, doi: 10.1108/IJPDLM-04-2022-0118.
Zhang, S., Sun, L., Sun, Q. and Dong, H. (2022), “Impact of novel information technology on IT alignment and sustainable supply chain performance: evidence from Chinese manufacturing industry”, Journal of Business & Industrial Marketing, Vol. 37 No. 2, pp. 461-473, doi: 10.1108/JBIM-08-2020-0407.
Zhang-Zhang, Y., Rohlfer, S. and Varma, A. (2022), “Strategic people management in contemporary highly dynamic VUCA contexts: a knowledge worker perspective”, Journal of Business Research, Vol. 144, pp. 587-598, doi: 10.1016/j.jbusres.2021.12.069.
Zhao, N., Hong, J. and Lau, K.H. (2023), “Impact of supply chain digitalization on supply chain resilience and performance: a multi-mediation model”, International Journal of Production Economics, Vol. 259, p. 108817, doi: 10.1016/j.ijpe.2023.108817.
Zhao, K., Zuo, Z. and Blackhurst, J.V. (2019), “Modelling supply chain adaptation for disruptions: an empirically grounded complex adaptive systems approach”, Journal of Operations Management, Vol. 65 No. 2, pp. 190-212, doi: 10.1002/joom.1009.
Zhou, H., Wang, Q., Li, L., Teo, T.S.H. and Yang, S. (2023), “Supply chain digitalization and performance improvement: a moderated mediation model”, Supply Chain Management: An International Journal, Vol. 28 No. 6, pp. 993-1008, doi: 10.1108/SCM-11-2022-0434.
Zouari, D., Ruel, S. and Viale, L. (2021), “Does digitalising the supply chain contribute to its resilience?”, International Journal of Physical Distribution & Logistics Management, Vol. 51 No. 2, pp. 149-180, doi: 10.1108/IJPDLM-01-2020-0038.
Acknowledgements
We acknowledge the financial support from the Verona Chamber of Commerce (Italy), with special recognition to the late Riccardo Borghero, former Secretary General, for his interest in digital transformations and business logistics. We also thank Suzanne Hoogendoorn for her help with data collection. Finally, we express our gratitude to the editor and the three anonymous reviewers for their insightful comments and suggestions.