Leveraging social capital to build the cumulative triple-A supply chain sand cone model

International Journal of Physical Distribution & Logistics Management Leveraging social capital to build the cumulative triple-A supply chain sand cone model Journal: International Journal of Physical Distribution & Logistics Management Manuscript ID IJPDLM-12-2023-0449.R4 Manuscript Type: Research Paper Keywords: triple-A SC, agility, adaptability, alignment, sand cone model, financial performance, social capital International Journal of Physical Distribution & Logistics Management International Journal of Physical Distribution & Logistics Management 1 Leveraging social capital to build the cumulative triple-A supply chain sand cone model Abstract Purpose — Drawing on the cumulative capability perspective, this study tests the sand cone model of the triple-A supply chain (SC) (i.e., AAA: SC-alignment, SC-adaptability, SC-agility), including its financial performance implications. Besides, this study investigates social capital as AAA’ enabler. Design — Structural equation modeling and bootstrapping analysis are used to examine hypotheses using data from 216 companies in China that capture firms’ supply chain management practices in relation to their major suppliers. Findings — We identified a cumulative sand cone sequence of three As: alignmentadaptability-agility to effectively develop a triple-A SC. Furthermore, based on this sequence, SC adaptability can enhance financial performance indirectly through SC agility, and SC alignment can improve financial performance indirectly through SC adaptability and SC agility, which directly and positively affects financial performance. Furthermore, cognitive, structural, and relational capital play different roles in improving AAA. Originality/value — This study contributes to triple-A SC literature by identifying the cumulative sand cone sequence of alignment-adaptability-agility and thus further extends the cumulative capability perspective in operations and supply chain management. Besides, this study: a) deepens our understanding of performance implications of triple-A SC capabilities based on the sand cone model; b) contributes to revealing social capital as an important enabler of triple-A SC capabilities from the complex adaptive system perspective; (c) specifies difference in the pattern of triple-A SC sand cone model across different levels of market turbulence. Keywords Triple-A SC (SC-agility, SC-adaptability, SC-alignment), Sand cone model, Financial performance, Social capital Paper type Research paper Page 1 of 57 International Journal of Physical Distribution & Logistics Management 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 International Journal of Physical Distribution & Logistics Management 2 1. Introduction The framework of supply chain agility, adaptability, and alignment (triple-A SC or AAA SC hereafter) was first proposed by Lee (2004). It suggests enhancing SC with triple-A capabilities to better address the evolving business environment rather than focusing solely on efficiency enhancement and cost savings. The importance of triple-A SC is highlighted in the era of postCovid-19 and geopolitical tensions (Patrucco and Kähkönen, 2021; Khan et al., 2023). For instance, amidst the China-US trade conflict, Huawei's SC has exhibited remarkable AAA capabilities and achieved financial growth despite navigating high environmental uncertainty. Triple-A SC has been gaining increasing academic interest. Although extant empirical research on triple-A SC considers the effect of 3As acting simultaneously, it mainly focuses on their performance outcomes (e.g., Khan et al., 2023; Machuca et al., 2021; Dubey and Gunasekaran, 2016) and antecedents (Iranmanesh et al., 2023; Garrido-Vega et al., 2023). Compared with these studies, only very few studies on triple-A SC consider the interrelationships among AAA (e.g., Eckstein et al., 2015; Dubey and Gunasekaran, 2016; Iranmanesh et al., 2023) (see supplementary material, Appendix A[1]) despite the interest and implications that this may have. Appendix A shows evidence that the existing literature on triple-A SC mainly focused on relationships between two As along with a lack of integrated theoretical framework. Therefore, this research proposes that the cumulative sand cone model enables us to explain satisfactorily the interrelationships of all triple-A SC dimensions, thus contributing to the literature on this topic. Some existing theoretical perspectives (e.g., trade-off model, complementarity perspective, ambidextrous perspective) provide implications for managing interrelated resources and capabilities. For instance, the trade-off model of Skinner (1969) suggests that multiple capabilities must be traded off as the improvement in one capability must come at the expense of another capability due to resource constraints. However, the sand cone model that was rooted in the cumulative capability theory asserts that firms could obtain improvements in multiple capabilities, with improvements occurring in a particular sequence (Größler and Grübner, 2006) and cumulatively reinforcing each other (Rosenzweig and Roh, Page 2 of 57International Journal of Physical Distribution & Logistics Management 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 International Journal of Physical Distribution & Logistics Management 3 2004; Flynn and James Flynn, 2004). On the one hand, there may be a scaffolding effect where the foundational capability (e.g., one A in our case) helps the ensuing capability (e.g., another A) more easily and effectively develop and work (Chen et al., 2023). On the other hand, firms can gain long-lasting improvements if all triple-A SC dimensions are developed cumulatively (Schoenherr and Narasimhan, 2012; Nand et al., 2024). Therefore, this study proposes the first research question (RQ1): What is the sequence of AAA in developing a cumulative triple-A SC based on the sand cone model to improve firm performance? Investigating the enablers of three As is important for providing a more comprehensive understanding of triple-A SC (Feizabadi et al., 2019). However, compared with prior studies examining the performance implications of triple-A SC, relatively scarce research concurrently explored the antecedents of three As, let alone the effective enablers of developing a cumulative triple-A SC sand cone model (see supplementary material, Appendix A[1]). We thus further propose that social capital could be an important driver of cumulatively developing triple-A SC capabilities. The cumulative development of triple-A SC requires resources that cannot be provided by a single manufacturer (Zhang et al., 2023). Social capital (comprised of structural, cognitive, and relational capital) provides unique access to information, knowledge, and resources that span firm boundaries and are embedded in inter-firm relationships (Tsai and Ghoshal, 1998; Inkpen and Tsang, 2005), which is vital for focal firms to unite external partners to cumulatively develop AAA capabilities (Rodrigo-Alarcón et al., 2018). Moreover, some previous studies have indirectly revealed the potential of social capital in promoting triple-A SC capabilities (Gölgeci and Kuivalainen, 2020; Vachon et al., 2009). However, the potential of social capital (comprised of structural, cognitive, and relational capital) in directly enhancing all triple-A SC dimensions has not been thoroughly investigated and confirmed by empirical research. Therefore, this study proposes the second research question (RQ2): How do different dimensions of social capital promote the development of triple-A SC capabilities? This study substantially contributes to the existing body of literature in several aspects. First, it provides a further understanding of the triple-A SC and sheds light on the sand cone sequence of AAA, extending the cumulative capability perspective in operations and supply Page 3 of 57 International Journal of Physical Distribution & Logistics Management 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 International Journal of Physical Distribution & Logistics Management 4 chain management (OSCM). Second, it provides a deeper understanding of the association between cumulative triple-A SC and financial performance based on the sand cone model. Third, it enriches the extant literature concerning both triple-A SC and social capital by revealing the mechanisms of how social capital dimensions enable triple-A SC. This study also provides important guidelines for managers to effectively establish triple-A SC cumulatively by leveraging different types of social capital, allowing them to achieve long-lasting success in triple-A SC development to address changes and gain superior financial performance despite resource constraints. 2. Theoretical background and hypothesis development 2.1 Cumulative capability theory and sand cone model The relationship between multiple manufacturing capabilities is an important element of operations strategy. The trade-off model, originally articulated by Skinner (1969), posits that multiple capabilities are incompatible and need to be traded off as the attainment of superior performance in one capability must come at the sacrifice of another due to the scarcity of resources (Narasimhan and Schoenherr, 2013). The trade-off model has been questioned as Flynn and James Flynn (2004) noted that the trade-offs are no longer feasible in the competitive global environment since firms are pressured to cumulate along multiple capabilities to handle variabilities and compete effectively. Besides, the complementarity perspective indicates that multiple capabilities could interact and operate in a complementary manner as combined bundles to enhance the effectiveness of each other (Misangyi and Acharya, 2014). The ambidextrous perspective emphasizes that a firm can reach an efficient equilibrium between exploitation and exploration activities/capabilities by resolving tensions between them (Andriopoulos and Lewis, 2009). Unlike the above-mentioned theoretical perspectives that manage interconnected capabilities without probing into the sequence among them, the cumulative capabilities perspective posits that firms are able to achieve improvements on multiple capabilities as these improvements could reinforce each other in a cumulative sequence. The best-known sequence of cumulative capabilities is examined through the “sand cone model” proposed by Ferdows Page 4 of 57International Journal of Physical Distribution & Logistics Management 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 International Journal of Physical Distribution & Logistics Management 5 and De Meyer (1990), which advocates that multiple manufacturing capabilities could be accumulated in a specific sequence, with quality as the foundation followed by delivery, flexibility, and cost. The main hypothesis of the sand cone model is that firms can exploit cumulative effects to achieve maximum improvements in multiple capabilities if they follow a specific sequence to develop these capabilities in a manner that reinforces each other (Flynn and James Flynn, 2004; Nand et al., 2024). According to Ferdows and De Meyer (1990), cumulative sand cone sequencing can be extremely important. On the one hand, there might be a scaffolding effect, where a basic and fundamental capability needs to be in place, and based on this, improvements in subsequent capabilities can be made more easily (Flynn and James Flynn, 2004). As such, firms can overcome the limitation that necessitates trade-offs among multiple capabilities under resource scarcity. On the other hand, firms can observe long-lasting improvements in manufacturing competitiveness if the capabilities are built up cumulatively in a particular sequence (Schoenherr and Narasimhan, 2012) since capabilities developed in a cumulative sequence can avoid trade-offs and enhance each other, thus creating cumulative benefits in capability improvements and further logically translating into higher firm performance. This cumulative sand cone model has been widely examined in the OSCM literature. For example, White et al. (2010) applied the sand cone model to derive an effective sequence of the implementation of JIT management practices for superior operational performance. Gold et al. (2017) integrated sustainability into the traditional sand cone model, which initially encompasses the quality-delivery-flexibility-cost sequence. More recently, Chen et al. (2023) extended the sand cone model to identify the most appropriate implementation sequence for the green SCM, showing that the sand cone sequence allows firms to build long-lasting advantages. Molinaro et al. (2024) shed light on the cumulative effects of the three sustainability pillars, with environmental performance at the base, followed by social and financial performance based on the sand cone model. This study seeks to explore the cumulative sand cone sequence of AAA to effectively develop a triple-A SC in a mutually reinforcing manner. Page 5 of 57 International Journal of Physical Distribution & Logistics Management 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 International Journal of Physical Distribution & Logistics Management 6 2.2 Triple-A SC and its dimensions The triple-A SC has been identified as the driver of sustained competitive advantage. Manufacturers build triple-A SC capabilities with SC partners by integrating and coordinating their business strategies and processes (alignment), adjusting and reconfiguring SC structures to address long-term market shifts (adaptability), and responding quickly to short-term changes in demand and supply (agility) (Lee, 2004; Alfalla-Luque et al., 2018; Sodhi and Tang, 2021). Specifically, SC alignment refers to the capability that manufacturers align and integrate strategies, goals, and processes with their SC partners to achieve better performance for both parties (Lee, 2004; Flynn et al., 2010; Feizabadi et al., 2021; Sodhi and Tang, 2021). SC adaptability is the capability that manufacturers and their SC partners effectively reconfigure their strategies, resources, products, technologies, and routines in response to long-term changes (Alfalla-Luque et al., 2018; Marin-Garcia et al., 2018; Feizabadi et al., 2021). SC agility in this research is the capability of manufacturers to quickly respond to short-term changes in demand and supply with their SC partners (Lee, 2004; Alfalla-Luque et al., 2018; Marin-Garcia et al., 2018; Feizabadi et al., 2021). SC alignment, agility, and adaptability interrelate and co-exist in combination to provide firms with superior performance and competitive advantages (Aslam et al., 2018). Especially in a dynamic and uncertain environment fraught with risks, three dimensions of triple-A SC are of great importance that it is infeasible for firms to develop only one or two dimensions at the expense of the others, which leads to a trade-off in the development of AAA. However, within this turbulent environment, the development of triple-A SC encounters numerous challenges, particularly in the realms of inter-firm coordination, collaboration, and the constraints imposed by resource limitations, among other factors. Consequently, firms are often confronted with the difficulty of how to effectively develop all three As. Based on the sand cone model, as proposed by different authors (Ferdows and De Meyer, 1990; Flynn and James Flynn, 2004; Rosenzweig and Easton, 2010), we defend that firms could cumulatively develop all three As by following a particular sequence, as the improvement in a foundational capability Page 6 of 57International Journal of Physical Distribution & Logistics Management 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 International Journal of Physical Distribution & Logistics Management 7 would further reinforce the ensuing capabilities to create cumulative effects. In contrast, we consider that the trade-off model is rigid and inappropriate in an environment with extreme uncertainties because tradeoffs among dimensions of the triple-A SC may lessen firms’ alternatives and would be risky to disrupt SCs (Schonberger, 2007). Although previous studies also applied the complementarity perspective (Feizabadi et al., 2021) or ambidextrous perspective (Wamba et al., 2020) to investigate three As, their interactions, and performance implications, they demonstrate inadequacy in revealing whether three As could be related cumulatively as the sand cone model implies. This creates a great opportunity for us to analyze a sand cone sequence of the triple-A SC, through which all three As could more effectively be developed, and outperform competitors under the uncertain environment. Furthermore, combined with the premises underlying the sand cone model, the evidence for the applicability of a triple-A SC sand cone sequence has also come from previous findings on the positive relationships between two As. For instance, SC alignment has been validated to enhance SC adaptability by previous studies (e.g., Dubey and Gunasekaran, 2016; Iranmanesh et al., 2023; Tickle et al., 2024), and Feizabadi et al. (2019) summarized alignment as an antecedent to adaptability in their review article. In addition, SC adaptability is found to positively impact SC agility (e.g., Eckstein et al., 2015; Aslam et al., 2018). As such, integrating these partial results provides a strong ground for proposing that the sand cone model can be pertinent to the triple-A SC context; that is, all triple-A SC dimensions can be improved cumulatively to reinforce each other (rather than being traded off to weaken each other) by following a particular sequence, which is commented in the following section. 2.3 The cumulative relationships of triple-A SC dimensions As manufacturers are susceptible to environmental changes, it is imperative for them to develop capabilities that enable them to effectively address both long-term changes (adaptability) and short-term fluctuations (agility) with their SC partners to ensure continuous operations of the entire SC. However, it is not a straightforward task to develop these capabilities, necessitating sustained commitment and continuous effort of both parties-manufacturer and its SC partnerPage 7 of 57 International Journal of Physical Distribution & Logistics Management 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 International Journal of Physical Distribution & Logistics Management 8 to meticulously evaluate customer demands, identify emerging markets, and create flexible processes and routines (Whitten et al., 2012; Patrucco and Kähkönen, 2021). SC alignment could be considered as the fundamental base of the cumulative triple-A SC sand cone model because it will not be easy to maintain responsiveness and adaptability to the changing environment if manufacturers and their SC partners are not strategically aligned in terms of strategies, objectives, and processes (Whitten et al., 2012; Tickle et al., 2024). First, SC alignment could enhance SC adaptability. SC alignment ensures a strategically integrated relationship between the manufacturer and its SC partner, which facilitates joint working and resource bundling for their joint reconfigurations and adjustments of SC structure towards long-term changes. Second, the improvement in SC adaptability promotes SC agility. SC adaptability provides alternative solutions and rich practical experience based on various structural reconfigurations towards long-term changes, which facilitates quick response towards sudden short-term changes with ease and proficiency. SC alignment is the base of the triple-A SC sand cone model, which expands to cumulatively improve SC adaptability. Manufacturers’ ability to strategically align and coordinate with partners regarding strategies, processes, and routines promotes their joint reconfigurations and adjustments of SC structure towards long-term shifts (adaptability) (Dubey and Gunasekaran, 2016). Specifically, such integration and coordination not only enable them to jointly predict or sense changes but also enhance inter-organizational learning, which contributes to addressing long-term changes through the reconfiguration of structures, processes, and resource bases with SC partners (Marin-Garcia et al., 2023). In addition, the alignment ensures joint working and planning, thereby facilitating the manufacturer and its SC partners to collaboratively reconfigure toward long-term changes (Whitten et al., 2012; MarinGarcia et al., 2018). Finally, by gaining a comprehensive understanding of each other's strategies, objectives, and plans, the manufacturer can initiate appropriate and effective reconfigurations with SC partners to address any long-term changes that may arise (MarinGarcia et al., 2023). Previous studies further corroborate that SC alignment serves as a fundamental enabling capability and precedes the improvement of adaptability (e.g., Dubey Page 8 of 57International Journal of Physical Distribution & Logistics Management 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 International Journal of Physical Distribution & Logistics Management 15 late responses by comparing several firm characteristics (e.g., industry type, firm ownership, number of employees, fixed assets) and key constructs used in this study collected at two times (Armstrong and Overton, 1977). The t-test results indicated no significant difference, showing that non-response bias was not a serious concern in our research. Since single source data was collected in this study, common method bias (CMB) can potentially jeopardize findings. We followed previous research to incorporate several approaches to check and minimize such bias (Tang and Wen, 2020). First, we tried to prevent CMB during the research design phase by positioning conceptually related variables far apart in the questionnaire to control the consistency of responses. Moreover, we promised the anonymity of answers to alleviate respondents’ concerns and included reverse-scored items to prevent habitual scoring by the respondents. Second, Harman’s one-factor test employing exploratory factor analysis (EFA) was utilized to assess the presence of common method bias. We found seven distinct factors with eigenvalues exceeding 1.0, and the first factor accounted for 15.15% of the total variance, which did not occupy a majority of the total variance (Podsakoff et al., 2003). Third, we conducted confirmatory factor analysis (CFA) for Harman’s one-factor test (Sanchez and Brock, 1996). The model fit indices (χ2 = 3179.29 with degrees of freedom = 434, which yields χ2/df = 7.32; non-normed fit index (NNFI) = 0.73, comparative fit index (CFI) = 0.75; root mean square error of approximation (RMSEA) = 0.22; and standardized root mean square residual (SRMR) = 0.14) are found to be unacceptable according to Schermelleh et al. (2003), and greatly worse compared to those of the measurement model. These results indicate that common method bias is not an issue in this study. Furthermore, the variance inflation factors (VIFs) are also suggested to be used to assess the collinearity (Wang et al., 2023). VIFs among variables in this study were all below 3.3 (highest VIF-2.347), suggesting that there is no serious pathological collinearity and CMB is less likely to contaminate our model (Kock, 2015; Wang et al., 2023). Finally, according to Lindell and Whitney (2001), we used the tenure of respondents as a marker variable, which is not theoretically related to constructs in our study, to further assess the CMB potential. We used the value of the smallest positive correlation (r = 0.02) between the marker variable and other Page 15 of 57 International Journal of Physical Distribution & Logistics Management 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 International Journal of Physical Distribution & Logistics Management 16 latent variables to adjust the correlations between the variables. All significant correlations remained significant after the partial correlation adjustments. Therefore, CMB is not a serious problem in this study. 4.2 Reliability and validity A two-step method is used to assess the reliability of constructs (Narasimhan and Jayaram, 1998). First, the results of EFA (available upon request) suggest that all items possess greater loadings on the intended constructs they are designed to measure while featuring low crossloadings on other factors, thereby demonstrating unidimensionality. Then, composite reliability and Cronbach’s alpha of each construct are computed to check the internal consistency reliability (Wang et al., 2023), with all values exceeding the threshold of 0.70 suggested by Hair et al. (2010). Appendix B in the supplementary material shows detailed information about reliability results[1]. Convergent validity is assessed using CFA, where each item is linked to its respective construct, and the covariance is estimated without constraints. The model exhibits a favorable level of fit, as values of indices satisfy established threshold values (Schermelleh et al., 2003; Hu and Bentler, 1999): χ2 = 752.80 with degrees of freedom = 413, which yields χ2/df = 1.82, RMSEA = 0.066 (acceptable fit), NNFI = 0.98 (good fit), CFI = 0.98 (good fit), and SRMR = 0.050 (good fit). Furthermore, all factor loadings exceed the threshold of 0.50 (range: 0.74– 0.91) and demonstrate statistical significance at the 0.01 level. The obtained results provide evidence of convergent validity (Fornell and Larcker, 1981). In addition, the average variance extracted (AVE) for each construct surpasses 0.50 (range: 0.644 to 0.788), which serves as additional evidence of convergent validity (see Appendix B) (Flynn et al., 2010; Wang et al., 2023). In assessing discriminant validity, square roots of AVE surpass the correlation coefficients between the focal construct and all other constructs (see supplementary material, Table II[1]). As such, discriminant validity is ensured (Fornell and Larcker, 1981). Besides, we employed the heterotrait-monotrait ratio (HTMT) of correlations approach as an additional approach to evaluate discriminant validity in this study (Henseler et al., 2015), which is also highly recommended by recent research (e.g., Wang et al., 2023). HTMT serves as a method to compare the average values of heterotrait-heteromethod correlations (i.e., correlations between indicators across distinct constructs) and monotrait-heteromethod correlations (i.e., correlations between indicators within the same construct). Findings in Table III reveal that the Page 16 of 57International Journal of Physical Distribution & Logistics Management 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 International Journal of Physical Distribution & Logistics Management 17 HTMT ratio of correlations falls below the predetermined threshold of 0.85, meeting the HTMT0.85 criteria and providing further evidence of discriminant validity (Henseler et al., 2015; Clark and Watson, 2016) (see Table III in the supplementary material[1]). 4.3 Hypothesis testing Following previous studies, we applied structural equation modeling (SEM) with the maximum likelihood estimation method using LISREL 8.80 software to investigate direct and indirect effects and test hypotheses in this study. Specifically, following Schroeder et al. (2011) and Bortolotti et al. (2015), we tested whether the proposed sand cone model of triple-A SC (Model 1 in Table IVa) could be considered superior to another possible model (H1a, H1b, and H1c). Therefore, we tested another alternative SEM model (Model 1a in Table IVa), which encompasses all paths from Model 1 along with adding a direct path from alignment to agility (see Model 1 and Model 1a in Table IVa in supplementary material[1]). To assess the model fit, a variety of fit measures were used, including RMSEA, SRMR, NFI, CFI, Akaike information criterion (AIC), Bayesian information criterion (BIC), and Akaike weights (AW). If the sand cone model is valid, we expect Model 1 to demonstrate a superior model fit since it precisely specifies the cumulative sequence of triple-A SC, as the sand cone model proposed. Fit statistics in Table IVa suggest that indices of Model 1 (i.e., sand cone model) well satisfy the established threshold values (Hu and Bentler, 1999; Schermelleh et al., 2003): χ2 = 823 with df = 469, which yields χ2 /df = 1.75 (good fit); RMSEA = 0.059 (acceptable fit); TLI = 0.93 (good fit), CFI = 0.94 (good fit); and SRMR = 0.049 (good fit). Therefore, the sand cone sequence in Model 1 fits well with the data (see supplementary material, Table IVa[1]). In addition, as both models are nested, it is essential to report the value of the χ2 difference test and AIC values in this kind of model comparison (Schermelleh et al., 2003; Wang et al., 2023). We thus conducted the χ2 difference test to calculate the marginal change achieved by introducing an additional direct path compared to the sand cone model in Model 1. In the present case, the χ2 difference between Model 1 and Model 1a ( χ 2 diff ( df diff ) = 823.07 - 822.59 = 0.48 and df diff = 469 - 468 = 1, p-value = 0.4884) is non-significant, suggesting that Model 1 should be retained (Schermelleh et al., 2003). More importantly, Model 1 also has lower AIC and BIC values than Model 1a, as well as higher AW values, which means higher statistical confidence for Model 1 (the one displaying the lowest AIC value) (Wagenmakers and Farrell, 2004; Wang et al., 2023). These results suggest that the model resulting from adding the direct Page 17 of 57 International Journal of Physical Distribution & Logistics Management 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 International Journal of Physical Distribution & Logistics Management 18 path from alignment to agility (Model 1a) deteriorates the model fit, further confirming that Model 1 better represents the sand cone sequence (see supplementary material, Table IVa[1]). For the sequence to be cumulative, the relationship between any two adjacent As should be significantly positive, and the relationship between non-adjacent As (direct effect) cannot be observed as significantly negative. Figure 2 shows significant paths with standardized coefficients. The results indicate a positive relationship between SC alignment and SC adaptability and a positive association between SC adaptability and SC agility. Control variables (firm size, industry type) exhibit no statistically significant effects on proposed relationships within our model. As such, the findings indicate that all paths linking adjacent As are positive and statistically significant. Thus, the above results provide support for H1a and H1b. Then, the magnitude of direct and indirect paths linking alignment and agility is compared. The logic is that three As are sequentially developed when the indirect effect between two non-adjacent As (alignment and agility) surpasses their direct effect. In Table IVa, the alignment-agility indirect effect (0.13) mediated by adaptability is larger than the corresponding direct effect (0.06), supporting our triple-A SC sand cone model hypothesis H1c. ====== Insert Figure 2 about here ====== Moreover, to examine H2, the bootstrapping analysis was applied to analyze the effects of the triple-A SC sand cone model on financial performance (Preacher and Hayes, 2008). First, a positive relationship can be found between SC agility and financial performance (Figure 2). Furthermore, the indirect effect derived from the bootstrapping analysis is deemed statistically significant when 0 is excluded between the lower and upper limits of the confidence interval. Therefore, based on the results in Table IVb, we can conclude that SC alignment exerts a positive and statistically significant effect on financial performance through the mediation of SC adaptability and agility (see Table IVb in supplementary material[1]). Likewise, SC adaptability generates a positive and statistically significant impact on financial performance through SC agility. Therefore, H2 is supported. Results also indicate the following statistically significant positive relationships (Figure 2): relational capital positively impacts SC adaptability, cognitive capital positively impacts SC alignment and SC agility, and structural capital positively impacts SC alignment while negatively impacting SC agility (contrary to hypothesis). The remaining relationships concerning the effects of social capital on triple-A SC are not statistically significant. Thus, H3a, H4a, H4c, and H5b are supported, while H3b, H3c, H4b, H5a, and H5c are rejected. Furthermore, relational, cognitive, and structural capital demonstrate significant indirect effects on triple-A SC capabilities (see supplementary material, Table IVb[1]). Page 18 of 57International Journal of Physical Distribution & Logistics Management 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 International Journal of Physical Distribution & Logistics Management 19 4.4 Robustness checks We conducted several robustness checks to further support our research results (see Appendix C in the supplementary material[1]). Specifically, we conducted an alternative model analysis to assess whether the proposed triple-A SC sand cone sequence could be considered superior to other possible alternative sequences regarding AAA. The results demonstrated that the proposed sand cone sequence (alignment-adaptability-agility) is statistically validated to be a superior sand cone sequence of triple-A SC than the other five alternative models as it has the lowest model AIC and BIC values and the highest AW values (see details in Appendix C1 in the supplementary material[1]). In addition, we followed recent research to further reduce concerns with endogeneity by applying the Gaussian copula approach implemented by Park and Gupta (2012) and described by Hult et al. (2018). We conducted the Gaussian copula analysis using SmartPLS 4, and the results suggested that endogeneity is not a serious concern in this model (see details in Appendix C2 in the supplementary material[1]). In addition, we also included control variables such as firm size and industry type into our model to further rule out the existence of endogeneity derived from omitted variables (Hult et al., 2018). The SEM results demonstrated that the control variables have no effect on our study's proposed relationships. We can conclude that our model results are not unduly affected by endogeneity issues. We also conducted robustness checks with different subsamples. We found that our prescribed sand cone sequence of triple-A SC in different industries, classified as the metal, mechanical, and engineering industry and the electronics and electricity industry, were both supported (see Appendix C3 in the supplementary material[1]). Finally, we conducted a robustness check using alternative financial performance measures. Specifically, we performed additional analysis of H1 and H2 by splitting the financial performance items into long-term (i.e., fp1-fp3) and short-term (i.e., fp4 and fp5) categories. We found consistent results for H1 and H2, thus enhancing the robustness of our findings in terms of short-term and long-term financial performance (see Appendix C4 in the supplementary material[1]). 4.5 Additional analysis We additionally conducted Necessary condition analysis (NCA) using R 4.3.3 software to better understand the relationships between social capital and triple-A SC by predicting the necessity of social capital dimensions for developing certain levels of triple-A SC capabilities Page 19 of 57 International Journal of Physical Distribution & Logistics Management 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 International Journal of Physical Distribution & Logistics Management 20 (see Appendix D in the supplementary material[1]). On the one hand, our results showed that social capital dimensions are necessary for developing triple-A SC capabilities in general, except that cognitive capital is found to be unnecessary for developing agility (Appendix D1 in the supplementary material). The effect size results further revealed that different social capital dimensions demonstrated different necessity effects for developing triple-A SC (see details in Appendix D2 in the supplementary material). On the other hand, we calculated the bottleneck table to present the ceiling line results in a tabular form. The results clearly outlined the necessity levels of the three conditions - structural, cognitive, and relational capital - required to attain a certain level of AAA (see details in Appendix D3 in the supplementary material). For instance, achieving 60% of adaptability necessitates 2.9% of structural capital and 4.1% of relational capital; however, cognitive capital only becomes necessary when firms aim to attain an 80% or higher level of adaptability. Besides, it is important to note that cognitive capital has always been unnecessary for building agility. Agility often requires flexibility to respond to dynamic market conditions. However, shared cognitive capital fosters consistency in decision-making processes, which may introduce rigidity or resistance to change if SC partners are overly committed to specific mental models or values. Thus, too much emphasis on cognitive capital is not needed to respond quickly to a changing environment. Furthermore, we conducted a heterogeneity test by examining the triple-A SC sand cone model across different market turbulence (MT) levels (Appendix E in the supplementary material[1]). MT indicates changes in customers' composition and preferences (Paladino, 2008). The results showed that the alignment-adaptability-agility sequence was supported under a low level of MT (Table E1 of Appendix E) but no longer holds under a high level of MT (Table E2 of Appendix E). In contrast, alignment-agility-adaptability was statistically satisfied and superior to other possible alternative sequences regarding AAA under high MT (Table E3 of Appendix E). In addition, we also conducted a heterogeneity test by examining the triple-A SC sand cone model across varying firm ages (Appendix F in the supplementary material[1]). The results demonstrated that our proposed alignment-adaptability-agility sequence was supported in younger firms. Conversely, in older firms, alignment-agility-adaptability was investigated to be the only statistically satisfied sequence regarding AAA. 5. Discussion and implications 5.1 Theoretical contributions Our results suggest that the triple-A SC dimensions can be cumulatively developed in a Page 20 of 57International Journal of Physical Distribution & Logistics Management 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 International Journal of Physical Distribution & Logistics Management Page 21 of 57 International Journal of Physical Distribution & Logistics Management particular sand cone sequence by leveraging diverse forms of social capital and thus achieve superior financial performance. The research findings therefore carry substantial theoretical contributions and hold notable managerial implications. 5.1.1 Support for the sand cone model of triple-A SC First, this study contributes to triple-A SC and cumulative capability literature by empirically investigating and identifying an effective sand cone sequence of triple-A SC. This study thus answers calls for more empirical research on the interrelationships between three As to effectively develop a triple-A SC (e.g., Eckstein et al., 2015; Alfalla-Luque et al., 2018). Meanwhile, this study especially contributes to the SC adaptability literature, which is the least explored of the three As in the academic literature as proposed by Phadnis (2024). This study investigated its enablers, as well as its interrelationships with alignment and agility based on the cumulative sand cone model. Specifically, our study applied the cumulative capability theory to reveal that these triple-A SC dimensions can be cumulatively developed in an effective sand cone sequence to start with alignment, then adaptability, and finally agility. This result indicates that SC alignment is the foundational basis of the triple-A SC sand cone model, that is, the capability to be strategically aligned with SC partners provides possibilities and opportunities for firms and their SC partners to collaboratively address long-term changes (Whitten et al., 2012). Subsequently, manufacturers have the potential to cumulatively enhance their SC agility by improving SC adaptability, affording opportunities and approaches to efficiently manage short-term operational fluctuations promptly (Eckstein et al., 2015). Furthermore, this study revealed that the identified sand cone sequence (alignmentadaptability-agility) was consistent in different industries, which is a sign of the robustness and reliability of the proposed model. Furthermore, our results contribute to identifying contingencies in the pattern of the tripleA SC sand cone model. Previous studies called for considering contingent factors (e.g., Flynn and Flynn, 2004; Nand et al., 2024) to provide a more comprehensive understanding of the cumulative sand cone sequence. The incorporation of contingencies seems especially insightful, as evidenced by our heterogeneity test. First, our results revealed a difference in the pattern of the triple-A SC sand cone model by market turbulence levels. The alignment-adaptabilityagility sand cone sequence holds under a low level of market turbulence but no longer holds under a high level of market turbulence. In a highly uncertain market, the alignment-agilityadaptability sequence was considered statistically valid and superior to other alternative sequences. Since in a market environment characterized by substantial uncertainty, 21 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 International Journal of Physical Distribution & Logistics Management 22 manufacturers are first compelled to cultivate agility through the enhancement of alignment to prevent the loss of market opportunities and orders. As such, our results add new knowledge on identifying contextual contingencies in the pattern of the triple-A SC sand cone model as shaped by market turbulence. Second, we found that the alignment-adaptability-agility sand cone sequence was supported in younger firms, while in older firms, the alignment-agilityadaptability sequence was supported. These results are logical as older firms, with more redundant resources and well-established SC partnerships, are better positioned to build agility with suppliers for quick response to changes without conflicts and time penalties. On the other side, younger firms are more flexible in partnership and inter-firm cooperation without too many entrenched routines, making it easier for suppliers to reconfigure and adjust SC structure with the firms to adapt to fundamental changes. Third, our results extend the cumulative capability perspective in OSCM research by demonstrating that triple-A SC capabilities can be cumulatively developed in a particular sand cone sequence. Most prior research employed a cumulative capability perspective to investigate the sand cone sequence of manufacturing capabilities (quality-flexibility-deliverycost) (Flynn and James Flynn, 2004). This study innovatively applied the cumulative capability perspective to explore and identify the existence of a sand cone sequence of AAA. Triple-A SC capabilities can be cumulatively developed by firms adhering to the alignment-adaptabilityagility sequence. The findings indicate that the development of earlier As accumulates resources and competencies, which act as the foundation for improvements to be achieved more easily and effectively in subsequent As. This study thus extends the cumulative capability perspective to reveal that the improvements of triple-A SC capabilities can cumulatively reinforce each other and therefore effectively achieve in sequence to address the changing environment and improve firm performance despite resource constraints (Rosenzweig and Easton, 2010). 5.1.2 Improving financial performance through cumulative triple-A SC capabilities This study contributes to revealing the performance implications of cumulative triple-A SC capabilities based on the sand cone model. Most previous studies explored the cumulative sand cone sequence of multiple manufacturing capabilities and called for incorporating performance outcomes into the sand cone model research (e.g., Narasimhan and Schoenherr, 2013). This study thus investigated the impacts of the cumulative triple-A SC capabilities on financial performance. The results indicate that SC agility promotes financial performance, supported by previous studies (Swafford et al., 2008; Gligor et al., 2015). Furthermore, our results Page 22 of 57International Journal of Physical Distribution & Logistics Management 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 International Journal of Physical Distribution & Logistics Management 23 demonstrate that SC adaptability improves financial performance through SC agility, aligning with the conclusions of Eckstein et al. (2015). Finally, SC alignment also exhibits a positive indirect impact on financial performance through adaptability and agility. The findings suggest that the value of alignment is translated through adaptability and agility when addressing changes to enhance financial outcomes, which is consistent with the arguments of Patrucco and Kähkönen (2021), who proposed that SC alignment should be paired with SC adaptability and agility to provide a competitive advantage in an uncertain world. The findings reveal that the impacts of cumulative triple-A SC capabilities on financial performance also adhere to the sand cone sequence (H2), thus enhancing our understanding of the performance implications of triple-A SC based on the cumulative sand cone model. 5.1.3 Developing triple-A SC capabilities through social capital First, this study contributes to triple-A SC and social capital research by revealing the mechanisms of how social capital dimensions facilitate triple-A SC capabilities. It thus answers the call of previous studies (e.g., Eckstein et al., 2015; Garrido-Vega et al., 2023) to investigate effective enablers of triple-A SC. The results offer a comprehensive understanding of the distinct roles that structural, cognitive, and relational capital play in fostering three As. From the CAS perspective, the social capital embedded in the complex SC network (encompassing mutually shared values and norms, efficient information and resource exchanges, and trustworthy inter-organizational relationships) is critical for firms to regulate their complex relationships and overcome environmental uncertainties/changes. First, the results demonstrate that SC alignment can be improved by cognitive and structural capital. Shared values and ideologies can contribute to forming consistent and integrated strategies and goals. Frequent social interactions and effective information sharing allow for joint planning, working, and decision-making between the manufacturer and its SC partners (Flynn et al., 2010; Skipworth et al., 2015). However, relational capital does not seem to be an effective enabler of SC alignment in our study, which is inconsistent with previous research (e.g., Inkpen and Tsang, 2005; Zhao et al., 2011). One possible reason may be that trust is necessary but insufficient in this study to motivate manufacturers and their SC partners to invest significantly in their relationship, share crucial business strategies, and make joint plans to prepare for unforeseen changes. Specifically, trust alone may fall short in reconciling the disparate objectives among various entities within the SC, especially in the absence of mutually recognized values fostered by cognitive capital and heightened transparency achieved Page 23 of 57 International Journal of Physical Distribution & Logistics Management 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 International Journal of Physical Distribution & Logistics Management 24 through structural capital. Besides, high relational capital is likely to evoke opportunistic risk, particularly in a volatile environment, as Villena et al. (2011) noted, thereby deterring harmonious alignment. Second, the results show that SC adaptability is directly improved by relational capital. An exchange relationship founded on trust and commitment enhances the long-term relationship orientation, enabling joint reconfigurations to tackle long-term changes. In addition, the bootstrapping results demonstrated that structural and cognitive capital can cumulatively promote adaptability through alignment (see supplementary material, Table IVb[1]). The reason could be that manufacturers often encounter obstacles in reconfiguring SCs with partners, which requires substantial investments and continuous efforts from both parties, potentially leading to new fluctuations and risks (Yang et al., 2022). In such scenarios, seeking to become strategically aligned with their SC partners through frequent interactions and shared values can make a broad range of joint reconfigurations feasible as the willingness to share interests/ risks grows. Third, our results demonstrate that SC agility is directly improved only by cognitive capital. Similar philosophies and perceptions allow manufacturers and their SC partners to maintain awareness and respond quickly without dispute. However, the results show that structural capital negatively affects SC agility. This is in line with Maurer and Ebers (2006), which states that densely interconnected cooperative structure may foster inertia and relational lock-in, preventing manufacturers from responding quickly with partners. Besides, frequent and diverse interactions with SC partners can lead to information overload, making it timeconsuming to process information, thereby impeding prompt decision-making in response to changes (Skipper and Hanna, 2009). However, the bootstrapping analysis results showed that structural capital could cumulatively improve agility through alignment and adaptability based on the sand cone sequence, which can help attenuate its harm. Moreover, we also found that cognitive capital could cumulatively enhance agility through alignment and adaptability, and relational capital cumulatively improves agility through adaptability based on the sand cone model (see supplementary material, Table IVb[1]). These results further confirmed the proposed sand cone model and revealed the role of social capital in cumulatively improving triple-A SC capabilities. 5.2 Managerial implications This study also offers practical insights for managerial decision-making. First, we suggest manufacturers develop AAA SC capabilities with their partners across the SC. 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Page 32 of 57International Journal of Physical Distribution & Logistics Management 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 International Journal of Physical Distribution & Logistics Management 33 Figure 1. Conceptual model. Figure 2. Estimated structural equation model (only significant relationships). Source: Authors' own elaboration Notes: *p < 0.05; **p < 0.01; ***p < 0.001. Financial performance 0.46*** Structural capital Cognitive capital Relational capital Agility Adaptability Alignment 0.29** 0.18* 0.45*** 0.43*** 0.25** 0.73*** -0.19* Social capital Sand cone of Triple-A SC Sand cone model of Triple-A SC SC Adaptability SC Alignment SC Agility Financial performance H1a H1b Cognitive capital Relational capital Structural capital H4a-b-c H3a-b-c H5a-b-c H2 Social capital Control variables Firm size Industry type Page 33 of 57 International Journal of Physical Distribution & Logistics Management 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 International Journal of Physical Distribution & Logistics Management Supplementary material for “Leveraging social capital to build the cumulative triple-A supply chain sand cone model” Table 1. Company and respondent profiles. % respondents % respondents Region Number of employees Bohai Bay Economic Rim 31.5 100-199 27.8 Yangzi River Delta 26.4 200-499 28.2 Pearl River Delta 24.1 500-999 19.9 Other areas in China 18.1 1,000-4,999 18.5 5,000 or more 5.6 Industry Metal, mechanical & engineering 41.2 Fixed asset (mRMB) Electronics & electrical 19.0 <5 5.1 Textiles & apparel 13.0 5-10 8.8 Chemicals & petrochemicals 8.8 10-20 7.9 Building materials 4.6 20-50 18.5 Publishing & printing 4.6 50-100 18.1 Rubber & plastics 3.7 100 or more 41.7 Food, beverage, alcohol & cigarettes 3.7 Pharmaceutical & medicals 1.4 Position Tenure of current position (years) Top manager (e.g., presidents, CEO, director, and deputy of these positions) 17.6 ≤1 8.3 Middle manager (e.g., manager of purchasing, marketing, and production) 77.8 2–5 34.3 Others (e.g., purchaser and salesman) 4.6 6–10 36.1 11–15 13.4 ≥16 7.9 Source: Authors' own elaboration Table II. Correlations, means, and standard deviations. Construct Mean S.D. 1 2 3 4 5 6 7 8 1. Structural capital 4.61 1.383 0.88 2. Cognitive capital 4.69 1.301 .67** 0.89 3. Relational capital 5.24 1.127 .57** .61** 0.87 4. Alignment 4.15 1.333 .58** .61** .46** 0.83 5. Adaptability 5.00 1.047 .48** .44** .55** .46** 0.80 6. Agility 4.89 1.112 .37** .42** .50** .44** .73** 0.84 7. Financial performance 4.02 1.214 .19** .22** .29** .19** .39** .42** 0.84 8. Marker variable 7.71 5.042 -0.10 -0.14 -0.08 -0.11 -0.09 -0.05 0.021 - Notes: **p<0.01. The square root of AVE is shown on the diagonal of the matrix in bold. Source: Authors' own elaboration Table III. HTMT results. Construct 1 2 3 4 5 6 7 1. Structural capital 2. Cognitive capital 0.737 3. Relational capital 0.622 0.659 Page 34 of 57International Journal of Physical Distribution & Logistics Management 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 International Journal of Physical Distribution & Logistics Management 4. Alignment 0.659 0.691 0.517 5. Adaptability 0.520 0.474 0.595 0.514 6. Agility 0.402 0.460 0.536 0.487 0.789 7. Financial performance 0.210 0.243 0.309 0.209 0.424 0.460 Table IV. Results for sand cone sequence. (a) SEM results associated with hypothesized triple-A SC sand cone sequence. Paths Model 1 Model 1 (a) Alignment→Adaptability 0.25 0.25 Alignment→Agility — 0.06 (n.s.) Adaptability→Agility 0.73 0.72 Fit indices χ2 (df) 823.07(469) 822.59(468) χ2/df 1.75 1.76 RMSEA 0.059 0.059 SRMR 0.049 0.049 TLI 0.93 0.93 CFI 0.94 0.94 AIC 18718.08 18719.59 BIC 19139.99 19144.88 AW 0.68 0.32 Comparing direct and indirect effects Alignment→Agility direct — 0.06 (n.s.) Alignment→Agility indirect 0.14 0.13 (b) Bootstrapping results for indirect effects. Construct Adaptability Agility Financial performance Indirect effects Relational capital 0.29 (0.183, 0.420) Cognitive capital 0.09 (0.039, 0.189)a 0.07 (0.026, 0.154) Structural capital 0.06 (0.012, 0.146) 0.05 (0.008, 0.124) Alignment 0.14 (0.044, 0.267) 0.06 (0.019, 0.125) Adaptability 0.34 (0.221, 0.496) Notes: a The number in parentheses indicates the 90% confidence interval (LLCI, ULCI) for n=1000 bootstrap; (LLCI, ULCI): Lower and upper levels for the confidence interval of indirect effect coefficient. Ali Ada Agi Ali Ada Agi Page 35 of 57 International Journal of Physical Distribution & Logistics Management 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 International Journal of Physical Distribution & Logistics Management Appendix A. Literature review of empirical triple-A SC research Study Antecedent Consequence Inter-relationships Method Theory Alfalla-Luque et al. (2018) Competitive advantage (cost-CA; quality-CA; delivery-CA; flexibilityCA; financial-CA) Survey Resource-based view (RBV); Dynamic capability theory (DCT) Aslam et al. (2020) SC adaptability; SC alignment SC agility; SC resilience SC adaptability-SC agility SC alignment-SC agility Survey DCT Attia (2015) SC performance Survey Attia (2016) Organizational performance Survey Dubey et al. (2015) SC adaptability SC agility; Human performance; Logistics performance SC adaptability-SC agility Survey Dubey et al. (2018) SC visibility Survey RBV Dubey and Gunasekaran (2016) SC alignment; SC adaptability SC agility; SC adaptability; Humanitarian SC performance SC alignment-SC agility SC alignment-SC adaptability SC adaptability-SC agility Survey Feizabadi et al. (2019) Firm performance (financial performance; market performance) Survey Resource advantage theory (RAT); Resource orchestration theory (ROT) Feizabadi et al. (2021) Market performance; Financial performance; Cycle time performance Survey Complementarity theory Garrido-Vega et al. (2023) Competitive environment; Business strategy Survey Contingency theory Page 36 of 57International Journal of Physical Distribution & Logistics Management 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 International Journal of Physical Distribution & Logistics Management Gligor et al. (2020) Firm performance Survey RAT; ROT Huma and Ahmed (2022) Visibility; Flexibility; Velocity Agility-Adaptability Adaptability-Alignment Survey RBV Iranmanesh et al. (2023) Operational SC transparency Blockchain adoption intention SC alignment-SC adaptability; SC adaptability-SC agility Survey RBV; Contingency theory Jermsittiparsert and Kampoomprasert (2019) Supply chain performance SC alignment-SC agility SC alignment-SC adaptability SC adaptability-SC agility Survey Khan et al. (2023) SC analytics Post pandemic disruption performance Survey DCT Machuca et al. (2021) Competitive advantage Survey Contingency theory Marin-Garcia et al. (2018) Survey Sheel and Nath (2019) Competitive advantage; Firm performance Survey RBV; DCT Whitten et al. (2012) SC performance Survey DCT; Complex adaptive system (CAS) Wilujeng et al. (2022) SC performance Survey Daneshvar Kakhki et al. (2023) Data analytics dynamic capabilities Operational performance; Strategic performance Meta analysis (survey studies) DCT Marin-Garcia et al. (2023) Competitive advantage Alignment-Adaptability; Adaptability-Agility Survey ROT Mohaghegh et al. (2024) Digital transformation Sustainable performance Survey ROT Page 37 of 57 International Journal of Physical Distribution & Logistics Management 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 International Journal of Physical Distribution & Logistics Management Appendix B. Measures, reliability, and validity Please indicate the extent to which you agree or disagree with the presented statements regarding your social capital with major supplier, with “1” indicating “strongly disagree” and “7” indicating “strongly agree”. The major supplier refers to the supplier that provides the highest dollar value in terms of your procurement. Factor loading t-value Relational capital (Villena et al., 2011) Cronbach's alpha=0.928; Composite reliability (CR)=0.928; AVE=0.762 Rel1. The relationship between us and our major supplier is characterized by mutual trust at multiple levels 0.91 56.60 Rel2. The relationship between us and our major supplier is characterized by mutual respect at multiple levels 0.85 39.46 Rel3. The relationship between us and our major supplier is characterized by mutual friendship at multiple levels 0.90 55.02 Rel4. We and our major supplier share reciprocity 0.83 34.50 Cognitive capital (Villena et al., 2011) Cronbach's alpha=0.914; CR =0.918; AVE=0.788 Cog1. We and our major supplier share similar business vision 0.82 32.39 Cog2. We and our major supplier have similar corporate culture/values and management style 0.93 62.70 Cog3. We and our major supplier have similar philosophies/approaches to business dealings 0.91 55.97 Structural capital (Villena et al., 2011) Cronbach's alpha=0.905; CR =0.910; AVE=0.771 Str1. There is frequent and intensive interaction between the personnel of us and our major supplier 0.81 29.27 Str2. There is an interaction between the personnel across different levels (e.g., managers and engineers) of us and our major supplier 0.91 53.29 Str3. There is an interaction between the personnel across different functions (e.g., logistics and marketing) of us and our major supplier 0.91 53.34 Please indicate the extent to which you agree or disagree with your triple-A supply chain statements, with “1” indicating “strongly disagree” and “7” indicating “strongly agree”. Alignment (González-Benito, 2007; Sanders, 2008) Cronbach's alpha=0.859; CR =0.865; AVE=0.681 Ali1. We and our major supplier participate in each other’s business strategy formation 0.86 33.18 Ali2. We and our major supplier have a good knowledge of each other’s business objectives 0.74 20.26 Ali3. We make strategic plans with our major supplier together 0.87 34.78 Adaptability (Swafford et al., 2006) Cronbach's alpha=0.927; CR =0.927; AVE=0.644 Ada1. We and our major supplier can change material volumes (materials provided to us) to adapt to disturbance (changes in the environment) 0.75 22.96 Ada2. We and our major supplier can change the material mix to adapt to disturbance 0.81 31.47 Ada3. We and our major supplier can implement engineering changes to adapt to disturbance 0.80 30.09 Ada4. We and our major supplier can change supplier-related human resources to adapt to disturbance 0.77 25.97 Page 38 of 57International Journal of Physical Distribution & Logistics Management 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 International Journal of Physical Distribution & Logistics Management Ada5. We and our major supplier can change supplier-related plans to adapt to disturbance 0.87 44.73 Ada6. We and our major supplier can reduce throughout times to adapt to disturbance 0.81 30.73 Ada7. We and our major supplier can adjust supplier-related processes to adapt to disturbance 0.80 29.08 Agility (Braunscheidel and Suresh, 2009) Cronbach's alpha=0.932; CR =0.933; AVE=0.700 Agi1. We and our major supplier can quickly respond to changes in our input 0.83 36.16 Agi2. We and our major supplier can quickly forecast changes in our input 0.80 29.88 Agi3. We and our major supplier can quickly respond to changes in supplier-related plans 0.89 52.43 Agi4. We and our major supplier can quickly respond to changes in their customer service to us 0.89 53.90 Agi5. We and our major supplier can quickly respond to changes in supplier-related processes 0.86 41.31 Agi6. We and our major supplier can quickly respond to changes in supplier-related human resources 0.74 22.79 Please evaluate your company financial performance (in recent five years) relative to your primary/major competitors, with “1” meaning “much worse” and “7” meaning “much better”. Financial performance (Frohlich and Westbrook, 2001; Narasimhan and Kim, 2002; Vickery et al., 2003) Cronbach's alpha=0.924; CR =0.923; AVE=0.706 Fperf1. Growth in sales 0.74 21.96 Fperf2. Growth in profit 0.87 44.42 Fperf3. Growth in market share 0.80 29.44 Fperf4. Growth in return on investments 0.90 51.62 Fperf5. Growth in return on sales 0.88 46.99 Market Turbulence (Paladino, 2008) MT1: Customers in our markets are very receptive to new product ideas 0.60 11.10 MT2: In our markets, customers' preferences change relatively fast 0.74 16.27 MT3: New customers tend to have product-related needs that are different from those of existing customers 0.67 13.70 MT4: We address different customer base compared with that we did in the past 0.71 14.92 Page 39 of 57 International Journal of Physical Distribution & Logistics Management 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 International Journal of Physical Distribution & Logistics Management Appendix C. Robustness checks for the main result Purpose Analysis Results Main analysis alignment-adaptability-agility Model 1a in Table IV Tests for alternative models Comparison between 6 alternative sequences regarding AAA Consistent (Model 1 in Table C1) Endogeneity analysis Endogeneity test using Gaussian copula approach Consistent (Gaussian Copula Model 7 in Table C2a and Model 14 in Table C2b) Different subsample analysis Subsample: firms in Metal, mechanical, and engineering industry Consistent (Model 1 in Table C3(1)) Subsample: firms in Electronics and electricity industry Consistent (Model 1 in Table C3(2)) Alternative measures of financial performance Short-term financial performance Consistent (Model1 in Table C4-1, Table C4-2) Long-term financial performance Consistent (Model1 in Table C4-3, Table C4-4) Page 40 of 57International Journal of Physical Distribution & Logistics Management 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 International Journal of Physical Distribution & Logistics Management Appendix D1. NCA scatterplot of structural, cognitive, relational capital for triple-A SC Figures D1-1, D1-2 and D1-3 depict scatterplots of social capital dimensions for triple-A SC capabilities with empty spaces above the ceiling lines of CR. This result suggested that social capital is necessary for developing triple-A SC capabilities in general, except that cognitive capital was not deemed necessary for agility in Figure D1-3. Figure D1-1. NCA scatterplot of structural, cognitive, relational capital for alignment. Figure D1-2. NCA scatterplot of structural, cognitive, relational capital for adaptability. Figure D1-3. NCA scatterplot of structural, cognitive, relational capital for agility. Appendix D2. NCA effect size of social capital for triple-A SC. We calculated the accuracy, ceiling zone, scope, and effect size. As shown in Table D2, a medium effect is observed with regard to the necessity of structural capital (d = 0.24), cognitive capital (d = 0.29), and relational capital (d = 0.27) for achieving alignment. Similarly, there is a medium necessity effect of structural capital (d = 0.10) and relational capital (d = 0.10) for adaptability and structural capital alignment cognitive capital relational capital alignment alignment agility agility agility structural capital cognitive capital relational capital adaptability structural capital cognitive capital relational capital adaptability adaptability Page 47 of 57 International Journal of Physical Distribution & Logistics Management 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 International Journal of Physical Distribution & Logistics Management a small necessity effect of cognitive capital for adaptability (d = 0.05). Finally, structural capital demonstrates a small necessity effect for agility (d = 0.07), and relational capital exerts a medium necessity effect (d = 0.13), while cognitive capital is found to be unnecessary for developing agility (d = 0.00). Table D2. Accuracy, ceiling zone, scope, and effect size results Construct Method Accuracy (%) Scope Ceiling zone Effect size (d) P-value Outcome variable: alignment Structural capital CR-FDH 100% 36 8.778 0.244 0 CE-FDH 96.3 36 9.904 0.275 0 Cognitive capital CR-FDH 100 36 10.333 0.287 0 CE-FDH 97.7 36 8.989 0.25 0 Relational capital CR-FDH 100 31.5 8.583 0.272 0 CE-FDH 94.9 51.5 9.674 0.307 0 Outcome variable: adaptability Structural capital CR-FDH 100% 32.571 3.381 0.104 0.011 CE-FDH 98.1 32.571 3.116 0.096 0.007 Cognitive capital CR-FDH 100 32.571 1.571 0.048 0.481 CE-FDH 100 32.571 0.786 0.024 0.565 Relational capital CR-FDH 100 28.5 2.964 0.104 0.096 CE-FDH 99.1 28.5 2.281 0.08 0.154 Outcome variable: agility Structural capital CR-FDH 100% 36 2.556 0.071 0.132 CE-FDH 99.1 36 1.97 0.055 0.191 Cognitive capital CR-FDH 100 36 0 0 1 CE-FDH 100 36 0 0 1 Relational capital CR-FDH 100 31.5 4.208 0.134 0.006 CE-FDH 97.2 31.5 3.465 0.11 0.018 Appendix D3. NCA bottlenecks table We calculated the bottleneck table to present the ceiling line results in a tabular form, thus clearly outlining the necessity levels of the three conditions - structural, cognitive, and relational capital - required to attain a certain level of AAA. For simplicity, we focused on results using the CR ceiling line. First, Table D3-1 shows that firms will not have to put structural, cognitive, and relational capital in place unless their desired levels of alignment exceed 30%, which requires at least 0.4% of structural capital and 1.3% of relational capital. Furthermore, firms that aim to achieve alignment exceeding 50% require low to high levels of cognitive capital (10.2%-88.3%), structural capital Page 48 of 57International Journal of Physical Distribution & Logistics Management 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 International Journal of Physical Distribution & Logistics Management (22.6%-78.2%), and relational capital (25.6%-86.4%). Second, as Table D3-2 illustrates, achieving 60% of adaptability necessitates 2.9% of structural capital and 4.1% of relational capital; however, cognitive capital only becomes a necessary condition when firms aim to attain an 80% or higher level of adaptability. Third, Table D3-3 suggests that only structural capital demonstrates a bottleneck when pursuing 70% of agility. Then, 23.2% of relational capital and 13.4% of structural capital are required to develop 80% of agility, while cognitive capital has always been unnecessary for building agility. Table D3-1. NCA bottlenecks table of social capital dimensions for alignment using CR (in %) Alignment (%) Structural capital Cognitive capital Relational capital 0 NN NN NN 10 NN NN NN 20 NN NN NN 30 0.4 NN 1.3 40 11.5 NN 13.5 50 22.6 10.2 25.6 60 33.7 25.9 37.8 70 44.9 41.5 49.9 80 56.0 57.1 62.1 90 67.1 72.7 74.2 100 78.2 88.3 86.4 Note(s): NN indicates Not Necessary. Table D3-2. NCA bottlenecks table of social capital dimensions for adaptability using CR (in %) Adaptability (%) Structural capital Cognitive capital Relational capital 0 NN NN NN 10 NN NN NN 20 NN NN NN 30 NN NN NN 40 NN NN NN 50 NN NN NN 60 2.9 NN 4.1 70 11.9 NN 11.9 80 23.8 5.2 19.7 90 34.3 10.9 27.5 100 44.7 16.7 35.4 Table D3-3. NCA bottlenecks table of social capital dimensions for agility using CR (in %) Agility (%) Structural capital Cognitive capital Relational capital 0 NN NN NN Page 49 of 57 International Journal of Physical Distribution & Logistics Management 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 International Journal of Physical Distribution & Logistics Management 10 NN NN NN 20 NN NN NN 30 NN NN NN 40 NN NN NN 50 NN NN NN 60 NN NN NN 70 4.4 NN NN 80 13.4 NN 23.2 90 22.4 NN 49.9 100 31.3 NN 76.6 Appendix E. Heterogeneity tests across different market turbulence (MT) levels. We examined the triple-A SC sand cone model across varying market turbulence (MT) levels. MT indicates changes in the composition of customers and their preferences (Paladino, 2008). We first developed high and low groups based on their MT scale scores (see Appendix B). Firms scoring above the mean (i.e., 4.532) were categorized into the “high” MT group (n = 105), while firms scoring below the mean were categorized into the “low” MT group (n = 116). Subsequently, we conducted path analysis for different MT groups. The results in Tables E1 and E2 demonstrated that our proposed sand cone model of triple-A SC (alignment-adaptability-agility) was still validated to be statistically satisfied under a low level of MT but no longer holds under a high level of MT as the relationship between alignment and adaptability becomes insignificant. As such, we took a step further to investigate the sand cone sequence under a high MT. The results in Table E3 showed that alignment-agility-adaptability was statistically satisfied and superior to other possible alternative sequences regarding AAA under high MT. Table E1. Test of hypothesized triple-A SC sand cone sequence under a low MT. Paths Model 1 Model 1 (a) Alignment→Adaptability 0.22 0.22 Alignment→Agility — 0.02 (n.s.) Adaptability→Agility 0.78 0.78 Fit indices χ2 (df) 8.79 (8) 8.69 (7) χ2/df 1.10 1.24 RMSEA 0.030 0.047 SRMR 0.028 0.028 TLI 0.98 0.97 CFI 0.99 0.99 AIC 1242.41 1244.30 BIC 1312.86 1317.46 AW 0.72 0.28 Comparing direct and indirect effects Alignment→Agility direct — 0.02 (n.s.) Alignment→Agility indirect 0.17 0.17 Ali Ada Agi Ali Ada Agi Page 50 of 57International Journal of Physical Distribution & Logistics Management 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 International Journal of Physical Distribution & Logistics Management Table E2. Test of hypothesized triple-A SC sand cone sequence under a high MT. Paths Model 1 Model 1 (a) Alignment→Adaptability 0.15 (n.s.) 0.15 (n.s.) Alignment→Agility — 0.14 Adaptability→Agility 0.56 0.54 Fit indices χ2 (df) 7.62 (8) 4.84 (7) χ2/df 0.95 0.69 RMSEA 0.000 0.000 SRMR 0.027 0.027 TLI 1.01 1.05 CFI 1.00 1.00 AIC 1097.27 1096.50 BIC 1166.28 1168.15 AW 0.40 0.60 Comparing direct and indirect effects Alignment→Agility direct — 0.14 Alignment→Agility indirect 0.07 (n.s.) 0.07 (n.s.) Table E3. Path analysis results of triple-A SC sand cone sequence under a high MT (alignmentagility-adaptability). Paths Model 1 Model 1 (a) Alignment→Agility 0.18 0.18 Alignment→Adaptability — 0.01 (n.s.) Agility→Adaptability 0.57 0.56 Fit indices χ2 (df) 12.92 (8) 12.88 (7) χ2/df 1.62 1.84 RMSEA 0.077 0.089 SRMR 0.033 0.033 TLI 0.90 0.87 CFI 0.97 0.96 AIC 1102.58 1104.54 BIC 1171.58 1176.20 AW 0.72 0.28 Comparing direct and indirect effects Alignment→Adaptability direct — 0.01 (n.s.) Alignment→Adaptability indirect 0.10 0.10 Appendix F. Heterogeneity tests across different firm ages. We examined the triple-A SC sand cone model across different firm ages. We first developed young and old groups based on the median of firm age within the sample. Firms scoring above the median (i.e., 17) were categorized into the “old firms” group (n = 110), while firms scoring below the median were classified into the “young firms” group (n = 106). The path analysis results in Tables F1 and F2 showed that our proposed triple-A SC sand cone model (alignment-adaptability-agility) was supported in younger firms. However, this sequence no longer holds in older firms since the Ali Ada Agi Ali Ada Agi Ali Agi Ada Ali Agi Ada Page 51 of 57 International Journal of Physical Distribution & Logistics Management 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 International Journal of Physical Distribution & Logistics Management direct effect of alignment on adaptability and the indirect impact of alignment on agility both became insignificant. Subsequently, we took a step further to explore the sand cone sequence in older firms. Results in Table F3 showed that alignment-agility-adaptability was statistically satisfied and superior to other possible alternative sequences regarding AAA in older firms. Table F1. Test of hypothesized triple-A SC sand cone sequence in younger firms. Paths Model 1 Model 1 (a) Alignment→Adaptability 0.32 0.32 Alignment→Agility — 0.09 (n.s.) Adaptability→Agility 0.68 0.66 Fit indices χ2 (df) 8.028 (8) 7.043 (7) χ2/df 1.00 1.24 RMSEA 0.006 0.008 SRMR 0.027 0.026 TLI 1.000 0.999 CFI 1.000 1.000 AIC 1140.405 1141.420 BIC 1209.655 1213.333 AW 0.68 0.32 Comparing direct and indirect effects Alignment→Agility direct — 0.09 (n.s.) Alignment→Agility indirect 0.21 0.20 Table F2. Test of hypothesized triple-A SC sand cone sequence in older firms. Paths Model 1 Model 1 (a) Alignment→Adaptability 0.14(n.s.) 0.14 (n.s.) Alignment→Agility — 0.12 (n.s.) Adaptability→Agility 0.64 0.63 Fit indices χ2 (df) 5.991 (8) 4.145 (7) χ2/df 0.75 0.59 RMSEA 0.000 0.000 SRMR 0.022 0.020 TLI 1.003 1.054 CFI 1.000 1.000 AIC 1215.886 1216.040 BIC 1286.098 1288.952 AW 0.68 0.32 Comparing direct and indirect effects Ali Ada Agi Ali Ada Agi Ali Ada Agi Ali Ada Agi Page 52 of 57International Journal of Physical Distribution & Logistics Management 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 International Journal of Physical Distribution & Logistics Management Alignment→Agility direct — 0.12 (n.s.) Alignment→Agility indirect 0.07 (n.s.) 0.06 (n.s.) Table F3. Path analysis results of triple-A SC sand cone sequence in older firms (alignment-agilityadaptability). Paths Model 1 Model 1 (a) Alignment→Agility 0.15 0.15 Alignment→Adaptability — 0.01 (n.s.) 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(2022), "Triple-A strategy: For supply chain performance of Indonesian SMEs", Uncertain Supply Chain Management, Vol. 10 No. 1, pp. 95-100. Page 54 of 57International Journal of Physical Distribution & Logistics Management 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60