Drivers of supply chain adaptability: insights into mobilizing supply chain processes. A multi‑country and multi‑sector empirical research

Vol.:(0123456789) Operations Management Research https://doi.org/10.1007/s12063-024-00474-4 Drivers ofsupply chain adaptability: insights intomobilizing supply chain processes. Amulti‑country andmulti‑sector empirical research MichiyaMorita1 · JoseA.D.Machuca2 · JuanA.Marin‑Garcia3 · RafaelaAlfalla‑Luque2 Received: 9 May 2023 / Revised: 12 February 2024 / Accepted: 19 February 2024 © The Author(s) 2024 Abstract Supply chain (SC) adaptability (SC-Ad) implies that SC processes should change and adapt to anticipated structural and market changes. However, when these changes are related to shifts from exploitative to explorative focuses, companies face an inflexibility problem because of involved uncertainties, creating a barrier to obtaining SC-Ad. This research proposes to overcome this barrier by integrating new combinations of the product/market strategy and SC processes and securing their fit over time. To get it, this study proposes two SC-Ad drivers (related to the SC process (ASCOS) and new product development competences (PDC)), which secure the aforementioned fit by reducing its uncertainties and thus ensuring a SC-Ad that responds to emerging competitive changes. Measurement and structural models were assessed following PLSSEM. ASCOS and PDC’ relative importance was analyzed using the importance/performance/analysis procedure. PLS, PLS-predict, and CVPAT were used to analyze model’s in-sample and out-of-sample predictive capacity. ANOVA was used to compare SC-Ad, ASCOS and PDC in different plant groups. Results suggest that ASCOS and PDC are SC-Ad’s drivers, and that the plants with highest SC-Ad values are those with the higher ASCOS and PDC’ values. This expand knowledge about SC-Ad drivers, which represents an important literature gap. In an indirect way, some new light is also added to the topic of ambidextrous management. The adequate generalizability of these results is supported by a) a wide multi-country, multi-informant, and multi-sector sample of 268 plants, b) a good out-of-sample model predictive capacity c) no heterogeneity issues. Keywords Supply chain adaptability· Supply chain processes’ inflexibility· Product development· Product/market strategy· Ambidextrous management· High performance manufacturing· PLS 1 Introduction Supply chain adaptability (SC-Ad) is defined as the capability of directing and enabling supply chains (SC) to adapt its strategies, products, processes and/or technologies to structural changes in the market (Fisher 1997; Lee 2002, 2004; Ivanov etal. 2010; Arana-Solares etal. 2011; Marin- Garcia etal. 2018). The adaptability capability facilitates companies’ reaction to structural changes, such as those in supply, demand and business environment (Eckstein etal. 2015; Christopher and Holweg 2017; Feizabadi etal. 2019). Accordingly, SC-Ad consists of two competences regarding SC processes: a) identifying the goals and necessary changes in SC processes to respond to structural changes in the markets; and b) implementing these changes (Alfalla- Luque etal. 2018). Consequently, SC-Ad is considered crucial for SC’s performance in a complex and turbulent global environment * Jose A. D. Machuca [email protected] Michiya Morita michiya.mor[email protected] Juan A. Marin-Garcia [email protected].es Rafaela Alfalla-Luque alf[email protected] 1 Gakushuin University, Tokyo, Japan 2 GIDEAO-DEFDO, Universidad de Sevilla, Sevilla, Spain 3 ROGLE-DOE, Universitat Politècnica de València, Valencia, Spain M.Morita et al. (Tuominen etal. 2004; Ivanov etal. 2010; Garrido-Vega etal. 2023), in which SC must continually adapt strategies and structures (Gibbons etal. 2003; Ivanov etal. 2010), while considering technology and market focuses (Tuominen etal. 2004; Ivanov etal. 2010; Arana-Solares etal. 2011). The importance of SC-Ad is increasing in the current context as it facilitates the reconfiguration of companies’ SC design to respond to structural changes in the markets (Yang etal. 2022). Although the importance and positive effects of SC-Ad on company’s performances have been acknowledged by the literature (e.g.: Lee 2004; Ivanov etal. 2010; Eckstein etal. 2015; Rojo etal. 2016; Yang etal. 2022; Iranmanesh etal. 2023; Daneshvar Kakhki etal. 2023; Marin-Garcia etal. 2023), the research about how to acquire SC-Ad is still limited (Whitten etal. 2012; Eckstein etal. 2015; Alfalla- Luque etal. 2018; Yang etal. 2022). Regarding SC processes above mentioned, they consist of physical process network structures (from procurement to final delivery to customers) and administrative processes (for designing and managing operations such as physical processes) (Sterman 2000; Ivanov etal. 2010; Abassi and Varga 2022). One significant managerial issue as to SC processes is that companies must adapt those structures and processes to emerging new threats or opportunities timely and appropriately (Skinner 1969; Fisher 1997; Lee 2002, 2004; Ivanov etal. 2010). Although this adaptation is necessary to meet new competitive requirements such as quality, cost and delivery (Lee 2002), such changes have been sometimes considered as a serious managerial challenge because of the inflexibility of SC processes including manufacturing processes, which comes from the consideration of an enormous sunk cost and an immediate loss of efficiency under situations involving the shift (Skinner 1969; Hayes etal. 1988; De Meyer etal. 1989). This inflexibility of existing SC processes sometimes causes a companies’ bias towards exploitative focuses (for current performance), and it is important to prevent companies from having such short-term focus because it could interfere the explorative activities (explorative focus for future performances) such as those related to the adaptation to the aforementioned structural market and competitive changes (Brenner and Tushman 2003). This is important for companies that wish long time prosperity, but the inflexibility of SC processes (Skinner 1969; Hayes etal. 1988; De Meyer etal. 1989), which gives constraints on timely strategic changes could be a significant barrier for the appropriate balance between these two focuses, which is not yet operationally defined. Those factors such as the cost limitations (Boumgarden etal. 2012; Parida etal. 2016), the tensions between explorative and explorative focuses (He and Wong 2004; Van Looy etal. 2005; Hu and Chen 2016) and the dominant competitive focuses in markets like the topmost importance of innovativeness (Luger etal. 2018; Clauss etal. 2021) make the balancing complex and difficult. Successfully facilitating the steering of switchbacks between the mentioned exploitative and explorative activities (Levinthal 1997), we assume, requires the sufficient competence of mobilizing SC processes in terms of timing and scale with clear sense of future directions. Despite the strategic importance of enhancing SC-Ad by resolving this inflexibility issue of SC processes (Skinner 1969; Hayes etal. 1988; De Meyer etal. 1989; Fisher 1997; Lee 2002, 2004; Ivanov etal. 2010), and the mentioned positive effects on performance measures (Skinner 1969; Fisher 1997; Lee 2002,2004; Ivanov etal. 2010; Martínez Sánchez and Pérez Pérez 2005; Eckstein etal. 2015; Rojo etal. 2016; Wamba etal. 2020; Yang etal. 2022; Iranmanesh etal. 2023; Khan etal. 2023; Daneshvar Kakhki etal. 2023), how to drive SC-Ad has not still been well studied and this study seeks to fill this gap. In this sense, this paper aims to expand knowledge about SC-Ad drivers, because addressing the theoretical foundations of dynamic SC capabilities such as SC-Ad, and especially its antecedents, is still in its infancy. This is an important gap as it indicates that managers do not have guidelines to develop a SC-Ad that would make it easier for them to take advantage of new market opportunities (Aslam etal. 2020). At this point, it is worth remembering that, in practice, leading companies have often shown appropriate examples of mobilization of SC processes by integrating the characteristics of product/market strategy and SC processes to create competitive advantages in specific phases of market competitions, as some cases in the automobile industry indicate. Some examples that can be cited are Ford (T-model and efficient SC process characterized by lot-size economy and assembly lines) (Tedlow 1988; Bednarek and Parkes 2021), General Motors (Class-based variety of models and SC processes under multi-divisional structures) (Chandler Jr. 1962; Sloan 1990; Tedlow 1988), and Toyota (a wide range of small fuel-efficient cars and the lean SC process) (Ohno 1988; Womack etal. 1990), all of which have been acknowledged as “winners” during specific phases of automobile market development when prestigious leaders supported these mobilizations. These cases also suggest that seeking a fit between the product/market strategy aims such as low price, variety and quality and the working properties of SC processes lead companies to successful SC-Ad with competitive advantage. Therefore, it is hoped that investigating how to develop this fit will provide us with important clues to obtain a higher SC-Ad. Based on the above, this study seeks to propose drivers for the mobilization of SC processes to implement an effective SC-Ad through securing the competitive fit between the product/market strategy focus and the Drivers ofsupply chain adaptability: insights intomobilizing supply chain processes. A… working properties of SC processes, which is a contribution to the existing literature. It is also important to consider that a successfully established effective fit may turn out to be not effective as competitive situations change, making it necessary to change to a new fit as the cases in the automobile industry previously mentioned which focused on the shift from the lot-size economy focused to (the Ford case), then to the variety focused (the General Motors case) and to the lean focused (the Toyota case) exemplified. Consequently, the objective of this study is to find and propose appropriate drivers of SC-Ad that secure the fit between the product/market strategy focuses and the SC processes over time (embracing the mobilization of the strategic aims and SC processes). To solve this issue, it is important to recall again that SC processes consist of physical process network structures and administrative processes (Sterman 2000; Ivanov etal. 2010; Abassi and Varga 2022), and that to mobilize SC processes, that is, to design, construct, and operate such a network, it is necessary to have the knowledge required to understand what determines the network’s performance given anticipated or planned demand patterns. Without these knowledge drivers, the barriers that generate the inflexibility of SC processes would remain and prevent companies from getting a higher SC-Ad. Regarding this matter, research by Morita etal. (2015, 2018) provides an interesting reference framework for this knowledge. Said authors introduce the concept of absolute SC orientation strategy (ASCOS) and analyzes it in conjunction with the new product development competence (PDC). PDC is related to the SC-Ad competency of determining the goals and requirements of SC processes, and ASCOS is related to the SC-Ad competency of implementing these changes, that is, of configuring and operating the necessary SC processes. Morita etal.’s (2015, 2018) research results show that high levels of ASCOS and PDC lead to high competencies in both existing and new product strength. Therefore, they can be considered potential drivers of SC-Ad in the abovementioned sense. We assume that this approach is expected to contribute to improving company competitiveness overtime by reducing the uncertainty around the feasibility of steering the company’s explorative and exploitative activities by means of ASCOS and PDC, which secure the fit between the product/market strategy and the SC processes over time, so as to get the necessary SC-Ad to adapt to anticipated changes. The present section is followed by the theoretical background of this research and the hypotheses development. Next, we set out the analytical framework, the measurement of the factors involved, and the analysis of the results. The final section of this study offers the discussion and conclusions. 2 Theoretical background andresearch hypotheses Thus far, we have showed that the inflexibility of SC processes makes it difficult to get SC-Ad and then to implement strategic changes of companies, but that this can be overcome by securing a fit that integrates the competences of product/market strategy (strategic focus) and SC processes (SC competence) over time. It was then proposed that the above issue should be addressed by securing this fit through appropriate drivers of SC-Ad in order to enable the successful response to environmental changes, and that absolute SC orientation strategy (ASCOS) in conjunction with the new product development competence (PDC) (Morita etal.’s (2015, 2018)) can be considered potential drivers of SC-Ad in the abovementioned sense. The present section develops the theoretical framework related to these issues and ends with the proposal of the corresponding hypotheses. The issue of SC process inflexibility, i.e., the issue as to how to improve the flexibility of SC processes including manufacturing is an important problem for many companies seeking satisfactory long-term performance as it impedes timely adaptation to new competitive situations (Skinner 1969; Hayes and Wheelwright 1984; De Meyer etal. 1989; Brenner and Tushman 2003; Winkler 2009). The need for flexible SC processes stems from the view that companies’ strategic focuses need to adapt the firms’ SC processes to new competitive requirements such as quality, on-time delivery, and competitive cost (Fisher 1997; Lee 2002,2004; Defee and Stank 2005; Selldin and Olhager. 2007; Flynn etal. 2010; Wagner etal. 2012; Prajogo etal. 2018; Sabri 2019) depending on their strategic focuses, as the concepts of generic strategies (Porter 1981) and product/market strategies exemplify (Ansoff 1957). This research assumes that due to SC process inflexibility, many companies struggling to develop a high SC-Ad capability find it difficult to change existing SC process configurations to adapt to new competitive situations. On the other hand, the fit between competitive requirements (such as the above mentioned) in companies’ markets and their supply process competences to meet these competitive requirements (even in changing environment) is considered to be one of the essential conditions that must be met to sustain high performance (Fisher 1997; Lee 2002, 2004; Defee and Stank 2005; Selldin and Olhager 2007; Flynn etal. 2010; Wagner etal. 2012; Prajogo etal. 2018; Sabri 2019). In this sense, this study assumes that securing this fit (under any changing competitive circumstances) between designed new values that are desirably innovative (e.g., new business or product introductions or new markets through expanded globalization) and the necessary SC process competences presupposes the M.Morita et al. competence of SC processes to be flexible to adjust to the new requirements, which is necessary for achieving a high SC-Ad. In this line, this paper focuses on enablers which secure the above-mentioned competitive fit over time to ensure a SC-Ad that responds to emerging competitive changes. Regarding the mentioned issues, if the competences of SC- Ad previously defined in this study (i.e.: specifying the new goals and requirements to be met by SC processes to respond to structural changes in the markets and to implement these changes (Alfalla-Luque etal. 2018)) are strong, they will reduce the inflexibility of SC processes and improve SC-Ad by redefining the fit between designed values and SC processes. Therefore, SC-Ad would give directions and would show desirable changes for a new fit between the company’s strategic focuses and its SC processes while focusing on strengthening this fit competency. Therefore, this research focuses on proposing drivers that strengthen the two aforementioned SC-Ad’s competencies and hypothesizes that companies should be competitive in both. Morita etal. (2015, 2018)’ works provide a reference framework for these drivers. On the one hand, the abovedescribed first SC-Ad competence (“specifying the goals and requirements to be met by SC processes to respond to structural changes in the markets”) could be considered to be driven by the product development competence (PDC) proposed by the authors, defined as the integration of wisdom for designing and developing new products or services, which involves suppliers, customers, and functional departments such as manufacturing and marketing (Morita etal. 2018). In this sense, PDC is used in this paper to stand for the most representative explorative competence of new value design and development as well as improvements to existing products, and it is conceptualized as the integration of multifunctional explorative competencies. These include the following 4 dimensions, validated in the different rounds of the survey of the High Performance Manufacturing Project (HPM) (about HPM, see Schroeder and Flynn 2001) and used in (Morita etal. 2018): Customer involvement in new product development (NPD), manufacturing involvement in NPD, supplier involvement in NPD and front-end loading in NPD. On the other hand, Morita etal (2015, 2018)’ proposed the absolute SC orientation strategy (ASCOS), which is related to the second SC-Ad competence (“to implement the required changes”) by the design and operation of SC structures and processes to respond to demand patterns. ASCOS is defined as a measure that contains strengths in four aspects: lead time, just-in-time control, quality conformance, and demand stability, which, together, are all functionally important for any SC process to deliver designed values to markets in terms of efficiency (cost and time) and effectiveness (for customers) (Morita etal. 2015, 2018). Therefore, when a company is ASCOS-oriented, it consistently focuses on these operating process factors and makes constant efforts to improve all four. The four mentioned components of ASCOS are fundamental factors that determine the potential performance of SC processes, which consist of linkages between activities in a network structure whose maneuvering competences are effectively and appropriately exploited in any set of products/markets (Morita etal. 2018). In other words, the capability to maneuver in these ASCOS’s four aspects is expected to be transferable and applied to the design, construction, and operation of SC processes to meet the anticipated competitive requirements of newly targeted products or markets detected thanks to an appropriate PDC competence. In this sense, ASCOS is regarded here as the capability to design and implement any necessary operational activities to meet new competitive requirements. Therefore, if sufficiently high, this capability is expected to reduce the tension associated with the proposed necessary explorative initiatives. To a certain extent, this competence has functional meanings similar to those of the operational absorptive capacity (Patel etal. 2012; Rojo etal. 2018) and organizational nurturing of organizational learning (García- Morales etal. 2008; Rojo etal. 2018) but its construct is more specifically defined as the four mentioned operational focuses that, technically, determine potentially flexible SC process competences. Summarizing, when designing and developing product and service values, PDC’s superiority is expected to correctly evaluate the existing strengths and weaknesses of current products and configure future products with a high probability of success. On the other hand, ASCOS competence diminishes SC processes’ inflexibility because it facilitates to design and operate SC structures and processes to respond to demand patterns. For example, a desired reduction in time delays could be improved by ASCOS’ lead time reduction focus. Securing information availability such as on-hand inventory level and reliable demand estimation is supported by ASCOS’ just-in-time control, quality conformance, and demand stability focuses. Therefore, PDC and ASCOS are expected to underlie the necessary dynamic adaptation process competence. Thus, due to the above, PDC and ASCOS are hypothesized to act as drivers of SC-Ad, and Hypothesis 1 is formulated as: Hypothesis 1: ASCOS and PDC are drivers of the SC-Ad. As noted by Morita etal. (2018), in general, companies tend to be more aggressive towards new product development activities than to SC activities, and this generates a conflict between the long- and short-term visions. An attempt at improvement of the PDC type is explorative focused while the ASCOS type is considered to be exploitative-focused. Regarding this matter, it is important to stress Drivers ofsupply chain adaptability: insights intomobilizing supply chain processes. A… that when a company focuses mainly on exploration it risks building tomorrow’s business at the cost of today’s, while focusing on exploitation it risks losing its long-term vision (Birkinshaw and Gibson 2004). It is, therefore, essential to find an appropriate balance of focuses between the two (Birkinshaw and Gibson 2004; Kristal etal. 2010; Feizabadi and Alibakhshi 2022), and easing the tension between these two focuses is a key strategic challenge (He and Wong 2004; Nieto-Rodriguez 2016). Regarding this matter, it is assumed here that, when ASCOS capability is nurtured in companies, it increases SC processes’ flexibility as it lowers organizational barriers to renewing or rebuilding processes, and also contributes to carrying out an appropriate evaluation of new products or markets in terms of resource requirements and new SC process design. Two focuses, explorative and exploitative, can coexist (Adler etal. 2009; Kortmann etal. 2014; Alcaide-Muñoz and Gutiérrez-Gutiérrez 2017; Gutierrez-Gutierrez and Antony 2020). This can overcome the conflict between exploitative and explorative focuses by a balance in which ASCOS and PDC have both high values, which would lead to SC-Ad’s higher values than other possible combinations of values of these variables (e.g.: high value-low value and low value-high value would be prioritizing one of the focuses, leading to the mentioned conflict between long term and short-term goals. And low value-low value will be against the obtention of an adequate SC-Ad). Therefore, ASCOS and PDC can (and should) work together in the long-term as adaptations generally must be made due to changes in product/market, SC structures, processes, and ways of operating as a set or package (Morita etal. 2018). In this sense, as previously said, to maintain high performance over time, companies have to secure two types of fit: a) the fit of companies’ strategic goals/aims to actual market and competitive situations (through PDC in this paper), and b) the fit of companies’ SC processes’ competencies to actual competitive requirements (through ASCOS in this research) (Venkatraman and Camillus 1984). Moreover, effective fits are required consecutively over time between their strategic goals and SC processes (Fisher 1997; Lee 2002, 2004; Ivanov etal. 2010; Arana-Solares etal. 2011; Marin-Garcia etal. 2018). In line with the above comments, this study hypothesizes that the contribution of ASCOS and PDC to the creation of a more reliable and feasible configuration of SC-Ad is higher when both competences are present and are high in value than would otherwise be the case, and that the tradeoff between these two competencies would harm the obtention of a high SC-Ad. This would avoid the conflict between the exploitative and explorative focuses, making it easier for companies to be competent in both kind of activities and, thus, be able to sustain the mentioned fits and companies’ competitiveness in changing competitive situations. As mentioned before, ASCOS and PDC should work in partnership to determine a better SC-Ad, overcoming the conflict of exploitation vs exploration under certain managerial cultures that are capable of generalizing the effectiveness and logic of practices involved in the process management above mentioned (Adler etal. 2009; Kortmann etal. 2014; Alcaide-Muñoz and Gutiérrez-Gutiérrez 2017; Gutierrez- Gutierrez and Antony 2020). Considering the above comments, we can hypothesize that: Hypothesis 2: Plants with higher SC-Ad values are characterized by higher values of the ASCOS and PDC competences. Higher PDC and ASCOS lead to more reliable insights into the feasibility and prospects of proposed changes. Therefore, a competitive set of high ASCOS and high PDC is expected to drive a high SC-Ad. To perform these practices well, companies should be equipped with the abovedescribed constituent competencies of ASCOS and PDC. Figure1 summarizes the conceptual research framework. New product development competences (PDC) Absolute supply chain orientation strategy (ASCOS) PDC1_ Customer involvement in new product development PDC2_ Manufacturing involvement in new product development PDC3_ Supplier involvement in new product development PDC4_ Front-end loading in new product development ASCOS1_Lead time focus ASCOS2_JIT focus ASCOS3_Quality focus ASCOS4_Demand stability focus Supply chain adaptability (SC-Ad) Fig. 1 Conceptual research framework M.Morita et al. 3 Methods 3.1 Sample A wide multi-country, multi-informant, and multi-sector sample was used to provide highly reliable results. The sample was based on the database of the latest round of the international High-Performance Manufacturing (HPM) Project, which surveyed manufacturing plants (with ≥ 100 employees) in Europe, America, and Asia. Cases with missing values in over 15% of the items included in our study were deleted (Hair etal.2022). The final sample consisted of 268 plants from 16 developed and emerging countries (Austria, Brazil, China Finland, Germany, Italy, Israel, Japan, South Korea, Spain, Sweden, Switzerland, Taiwan, UK, USA, Vietnam) in three sectors (electronics, machinery, and automotive components), chosen as they are in hotly-contested global competition and desirable managerial practice effects are expected to be identified in such industries. The 268 valid cases had missing values completely at random (MCAR) (little test, sig 0.097). At this point, all the variables with under 5% values missing per item (mean replacement option) (Hair etal. 2021, 2019) were selected in Smart PLS 4.0.8.9 (Ringle etal. 2022). To identify possible sources of heterogeneity in the sample, some control variables have been introduced into the model (country context, industry as dummy and log plant size), allowing the SC-Ad results to be adjusted for possible differences due to the sample. Furthermore, unobserved heterogeneity has been analyzed to verify whether the weights of the measurement model or the paths of the structural model have different values in the data subsamples (Becker etal. 2013; Marin-Garcia and Alfalla-Luque 2019). 3.2 Measures Our constructs have been adapted from measures defined and validated in previous research. ASCOS and PDC were validated in Morita etal. (2018) and in Morita etal. (2015). All the lower-order composite constructs for ASCOS and PDC (see Table1) have been measured with reflective indicators. Higher-order composite constructs (ASCOS2_JIT focus, ASCOS3_Quality focus, ASCOS, and PDC, see TableA2 in annex) have been modeled using lower-order construct latent variable scores as formative indicators. SC-Ad was validated in Marin-Garcia etal. (2018) and all the first-order and second-order constructs have been considered formative composite constructs (TableA2 in annex). ASCOS represents the competent culture of SC processes, which is consistently seeking lead time reduction, JIT implementation, demand stability, and quality conformance (Morita etal. 2018). PDC represents the competence of wisdom integration within and beyond organizations for new value development (e.g., product development) (Morita etal. 2018). SC-Ad embraces abilities for changes such as changing SC processes and structures in line with market changes, introducing new technologies (e.g., information technologies), and predicting changes such as those in markets (Marin-Garcia etal. 2018). The control variables used in this research are in line with those used in previous research (e.g., Dubey etal. 2019; Machuca etal. 2021; Alfalla-Luque etal. 2023): a) plant size (log10), b) industry (dummy variable; reference category = electronics), and c) country context. Several steps were taken to reduce the risk of common method bias (CMB) (Podsakoff etal. 2003; Schwarz etal. 2017). Different scale anchors were selected for use in the Table 1 Psychometric properties for reflective scales Cronbach's Alpha Rho_A Composite Reliability (Rho_c) Average Variance Extracted (AVE) ASCOS1_Lead time focus 0.711 0.711 0.839 0.635 ASCOS2_JIT1_JIT delivery by suppliers 0.735 0.743 0.813 0.605 ASCOS2_JIT2_JIT link with customers 0.792 0.821 0.858 0.602 ASCOS2_JIT3_Setup time reduction 0.701 0.723 0.827 0.616 ASCOS3_Q1_Feedback To Employees on Quality 0.829 0.855 0.877 0.589 ASCOS3_Q2_Top Leadership on quality 0.875 0.878 0.907 0.619 ASCOS3_Q3_Process Control 0.805 0.808 0.911 0.836 ASCOS3_Q4_Quality Training 0.852 0.876 0.893 0.627 ASCOS4_Demand stability focus 0.626 0.621 0.801 0.574 PDC1_ Customer involvement in new product development 0.823 0.841 0.882 0.652 PDC2_ Manufacturing involvement in new product development 0.826 0.855 0.876 0.588 PDC3_ Supplier involvement in new product development 0.846 0.851 0.896 0.682 PDC4_ Front-end loading in new product development 0.847 0.854 0.884 0.523 Drivers ofsupply chain adaptability: insights intomobilizing supply chain processes. A… same scale and in different parts of the questionnaire to prevent any priming effects; items were randomly listed in scales to prevent item proximity from generating any response patterns (Marin-Garcia etal. 2018); the questionnaires were responded by two people in each function who had not been informed about what the items were intended to measure (Danese etal. 2019). Informant confidentiality was prioritized during the data collection phase. In addition, Harman’s Single-Factor test (Chin etal. 2013; Schwarz etal. 2017) was applied after collecting the data. The correlation matrix was analyzed using principal component with varimax rotation. The obtained results were robust and valid and indicated that several different factors were present with eigenvalues above 1, which indicated the absence of CMB issues (the presence of a single factor would have indicated CMB). Using G-Power 3.1, a post hoc power check with 268 plants and R2 = 0.184 (the lowest value in our analysis, corresponding to Ad3 in the 1st-order model) gives a result of 0.99 power with Alpha 5% and 17 predictors. This power value is higher than the recommended cutoff value of 0.8 (Marin-Garcia and Alfalla-Luque 2019). 3.3 Analysis procedure Measurement and structural models were assessed following the current guidelines for PLS-SEM (Marin-Garcia and Alfalla-Luque 2019; Becker etal. 2023; Sarstedt etal. 2022; Ringle etal. 2023). Mode A was used to calculate the weights of composites with reflective indicators (ASCOS and PDC low-order constructs (LOC)) and Mode B was used for formative indicators (SC-Ad lower order composites, and all 2nd and 3rd higher-order composites) (Hair etal. 2019; Sarstedt etal. 2019). The relative importance of the antecedents to SC-Ad was analyzed using the importance and performance analysis procedure (IPMA) (Hair etal. 2019). An analysis using PLS and PLS-predict was carried out of model in-sample and out-of-sample predictive capacity to show direct relationships of ASCOS and PDC with SC-Ad, respectively (Shmueli etal. 2016;Sharma etal. 2022). This predictive analysis enhances the retrospective character of classic explanatory models and helps to build theories for both explanation and prediction (Liengaard etal. 2021). ANOVA was used to compare ASCOS and PDC in plant groups with different SC-Ad values. 4 Results The figures that represent 1st LOC (Fig. S1), 2nd HOC (Fig. S2) models and the full research model (Fig. S3) and some tables that complement the analyses can be accessed in the online supplementary material (https:// doi. org/ 10. 5281/ zenodo. 78138 33). The items used in the questionnaire and their descriptive statistics are given in TableA1 in the annex. 4.1 Measurement model The constructs in this research clearly meet the established criteria of internal reliability. Regarding reflective LOCs (see Table1), the established cut-off values for composite reliability (CR > 0.7) and average variance extracted (AVE > 0.5) are met (Marin-Garcia and Alfalla-Luque 2019; Sarstedt etal. 2022). Regarding Cronbach’s alpha, only one construct has a value below the cut-off value (0.626) but it is, nonetheless, very close to 0.7. It also shows high composite reliability (0.801) and AVE (0.574), which are the most relevant parameters. In addition, it belongs to a formative construct (ASCOS) and this dimension (1st-order constructdemand stability) is a basic part of ASCOS’s conceptual composition (as demonstrated in Morita etal. (2015, 2018). Therefore, it should be left in the model (Hair etal. 2019). Fornell-Larcker and Heterotrait-Monotrait Ratio (HTMT) have been used to test the Discriminant Validity of reflective constructs (Marin-Garcia and Alfalla-Luque 2019; Sarstedt etal. 2022). Both criteria show global satisfactory results (see TableS1 in online supplementary material). Although “Feedback to employees on quality” and “Quality training” do not meet discriminant validity individually (0.777, higher than the diagonal value (0.767)), they should be considered sub-dimensions of a formative HOC with a VIF value lower than the established cut-off value of 3 (see TableS2 in online supplementary material). In addition, their significance means that they are an essential part of their construct and, so, should be left in the model (Hair etal. 2019). In addition, when the HOC order-2 constructs are evaluated (TableA2 in annex), the corresponding loadings and weights show that this is not a problem and does not negatively affect the proposed model. Regarding the formative constructs, all outer VIF values (see TableS2 in online supplementary material) are lower than the established cut-off value of 3 (Marin-Garcia and Alfalla-Luque 2019; Sarstedt etal. 2022). Also, all weights are significant except two (see TableA2 in annex). However, both have loadings above 0.5 and represent relevant aspects of their construct, so should be retained in the model (Marin- Garcia and Alfalla-Luque 2019; Sarstedt etal. 2022). 4.2 Structural model The results of the structural model analysis for the thirdorder composites are presented in Table2 and Fig.2. They show that the ASCOS and PDC paths on SC-Ad are both M.Morita et al. significant, with no overlap between the confidence intervals (0.499 to 0.674 for ASCOS; 0.017 to 0.185 for PDC). In addition, the mean value of the coefficient of determination (R2) is 0.406, i.e., between the 0.344 and 0.509 confidence intervals. This indicates that the two variables (ASCOS and PDC) jointly explain a moderately high percentage of the variance, which is a sign of the model’s adequate predictive power (insample prediction). Therefore, Hypothesis 1 is confirmed. Further, as a set, the control variables can be considered not to exert any significant influence. The only significant control variable is the automotive sector in the industry control variable, which has a negative path (indicating that automotive sector firms present less SC-Ad (mean = 3.7) than electronics sector firms (mean = 3.9)). Despite being significant, this difference is almost certainly not relevant given that SC-Ad is in the mid-to-high part of the scale used in both sectors. Furthermore, the existence of a possible unobserved heterogeneity has been analyzed through solutions of 1 to 5 groups with finite mixture partial least squares (FIMIX) (Becker etal. 2013; Marin-Garcia and Alfalla-Luque 2019). The results seem to reinforce that a heterogeneity problem is not a real issue in the sample since they support that the solution of a single group can be considered to represent the results obtained. The results show that ASCOS has a greater influence on SC-Ad than PDC (see Table2 and Fig.2). As Table3 shows, Table 2 3rd-order structural model results (reference categories: developing for country context and electronics for industry) Value Standard Deviation T Statistics P Values 5.0% 95.0% Path ASCOS—> SC-Ad 0.589 0.057 10.363 0.000 0.489 0.678 Path PDC—> SC-Ad 0.085 0.053 1.612 0.054 0.020 0.190 Developed—> SC-Ad 0.006 0.121 0.050 0.480 -0.192 0.203 Automotive—> SC-Ad -0.401 0.117 3.418 0.000 -0.584 -0.201 Machinery—> SC-Ad 0.027 0.122 0.226 0.411 -0.177 0.223 Logplantsize—> SC-Ad 0.011 0.051 0.223 0.412 -0.075 0.093 R2 SC-Ad 0.406 0.050 8.141 0.000 0.350 0.514 Fig. 2 3rd Higher-order composites Drivers ofsupply chain adaptability: insights intomobilizing supply chain processes. A… this is mainly due to the significantly more intense relationship of the ASCOS JIT focus with SC-Ad (0.444). Nonetheless, two of the other ASCOS dimensions (ASCOS3_Quality focus, and ASCOS1_Lead time focus) also contribute to this higher influence (0.224 and 0.166, respectively). The IPMA analysis (Hair etal. 2019) based on Table3 (second and third columns) and presented in Fig.3, also indicates that the influence of ASCOS on SC-Ad is, as already stated, is greater than that of PDC. This is, therefore, the construct that contains the most important levers of SC-Ad. It can also be stated that almost all the ASCOS levers are deployed between 63%-71% of the maximum deployment level, so there is still room for improvement. In this case, ASCOS4_Demand stability focus (the least deployed lever (55% of maximum capacity)) is the lever that least affects the development of SC-Ad. That said, the second least-deployed lever, ASCOS2_JIT focus (63.77%), is the LOC with the highest effect on SC-Ad, which indicates that it is the best candidate to use for leveraging SC-Ad in the plants of our sample. Table 3 Total effects of ASCOS and PDC components on SC-Ad and degree of deployment (performance) Standardized Non-standardized Deployment (performance)% ASCOS 0.589 0.573 68.568 ASCOS1_Lead time focus 0.166 0.138 66.994 ASCOS2_JIT focus 0.444 0.335 63.776 ASCOS3_Quality focus 0.224 0.195 71.412 ASCOS4_Demand stability focus -0.135 -0.095 55.219 PDC 0.085 0.084 69.939 PDC1_CustInv (Customer involvement) 0.010 0.009 73.333 PDC2_MnfInv (Manufacturing involvement) 0.020 0.017 70.015 PDC3_SupInv (Supplier involvement) 0.047 0.036 68.039 PDC4_Front End Loading 0.025 0.023 71.603 SC-Ad – – 69.506 Fig. 3 IPMA analysis M.Morita et al. Id Item N Minimum Maximum Mean Std. Deviation Skewness Kurtosis FRONTN04 Our new product development selection process is designed to evaluate ideas based on market and technology risk, investment 266 1.5 5 4.088 0.8451 -0.823 0.102 FRONTN05 Our new product development process is well supported by highlevel executive leadership 266 1 5 3.976 0.8749 -0.82 0.487 FRONTN06 We have a clear vision of product lines and platforms for specific markets 266 1 5 3.856 0.8318 -0.666 0.118 FRONTN08 In our company we have a map that shows the evolution of critical dimensions in the market, technology and the manufacturing 267 1 5 3.322 1.1663 -0.362 -0.843 JITDELN01 Our suppliers deliver to us on a just-in- time basis 266 1 5 3.513 1.1171 -0.563 -0.422 JITDELN02 We receive daily shipments from most suppliers 266 1 5 3.391 1.2137 -0.432 -0.763 JITDELN03 Our suppliers are linked with us by a pull system 267 1 5 3.354 1.2129 -0.515 -0.613 LINKCN01 Our customers receive just-in-time deliveries from us 261 1 5 3.612 1.0327 -0.669 0.041 LINKCN03 We can adapt our production schedule to sudden production stoppages by our customers 261 1 5 3.811 0.8566 -0.644 0.628 LINKCN04 Our customers have a pull type link with us 261 1 5 3.104 1.2279 -0.242 -0.939 LINKCN05 Our customers are linked with us via JIT systems 261 1 5 2.823 1.2093 -0.011 -0.979 MFDESN02 Manufacturing engineers are involved to a great extent before the introduction of new products 267 1 5 3.836 0.8994 -0.804 0.653 Drivers ofsupply chain adaptability: insights intomobilizing supply chain processes. A… Id Item N Minimum Maximum Mean Std. Deviation Skewness Kurtosis MFDESN04 New product design teams have frequent interaction with the manufacturing function 267 1 5 4.011 0.8157 -0.764 0.474 MFDESN05 Manufacturing is involved at the early stages of new product development 268 1 5 3.908 0.9145 -0.824 0.239 MFDESN06 The manufacturing function is key in improving new product concepts 267 1 5 3.755 0.9393 -0.525 -0.174 MFDESN07 Manufacturing is given challenging tasks in the development of new product concepts 267 1 5 3.439 0.9727 -0.423 -0.121 REPMASN01 Our master schedule repeats the same mix of products, from hour to hour and day to day 261 1 5 2.551 1.267 0.311 -1.098 REPMASN02 The master schedule is level-loaded in our plant, from day to day 261 1 5 3.217 1.1416 -0.33 -0.756 REPMASN03 Within our schedule, the mix of items is designed to be similar to the forecasted demand mix 260 1 5 3.737 0.9856 -0.862 0.507 SETUPN01 We are aggressively working to lower setup times in our plant 267 1 5 3.821 0.9683 -0.663 -0.202 SETUPN02 We have low setup times of equipment in our plant 267 1 5 3.494 0.9323 -0.29 -0.38 SETUPN03 Our workers practice setups, in order to reduce the time required 267 1 5 3.238 1.1812 -0.201 -0.935 SINVLN01 Suppliers are involved early in product design efforts 268 1 5 3.58 0.9382 -0.718 -0.064 SINVLN02 We partner with suppliers for the design of new products 268 1 5 3.75 0.8989 -0.807 0.474 M.Morita et al. Id Item N Minimum Maximum Mean Std. Deviation Skewness Kurtosis SINVLN03 Suppliers are frequently consulted during the design of new products 268 1 5 3.666 0.9527 -0.49 -0.45 SINVLN04 Suppliers are an integral part of new product design efforts 268 1 5 3.854 0.9368 -0.766 0.165 SPEEDN01 Fast delivery is the most important criterion used by our customers in selecting us as a supplier 260 1 5 3.454 0.9534 -0.591 0.161 SPEEDN03 Our customers can rely on us for fast delivery 260 2 5 4.069 0.8342 -0.669 -0.212 SPEEDN04 We are selected by our customers because of our reputation for fast delivery 260 1 5 3.446 0.902 -0.398 0.058 TPLEADN01 All major department heads within the plant accept their responsibility for quality 268 1 5 3.994 0.8926 -0.894 0.409 TPLEADN02 Plant management provides personal leadership for quality products and quality improvement 268 2 5 3.875 0.8796 -0.679 -0.054 TPLEADN03 The top priority in evaluating plant management is quality performance 268 1 5 3.618 1.0196 -0.367 -0.826 TPLEADN04 Our top management strongly encourages employee involvement in the production process 268 1 5 4.062 0.7771 -0.834 1.041 TPLEADN05 Our plant management creates and communicates a vision focused on quality improvement 268 2 5 4.03 0.7844 -0.709 0.428 TPLEADN06 Our plant management is personally involved in quality improvement projects 268 1 5 4.052 0.8352 -0.953 0.917 Drivers ofsupply chain adaptability: insights intomobilizing supply chain processes. A… Id Item N Minimum Maximum Mean Std. Deviation Skewness Kurtosis TRAINN01 Specific work-skills training (technical and vocational) is given to hourly employees throughout the organization 268 1 5 3.838 0.8648 -0.671 0.176 TRAINN02 Quality-related training is given to hourly employees throughout the organization 268 1 5 3.507 1.0679 -0.529 -0.429 TRAINN03 Quality-related training is given to managers and supervisors throughout the organization 268 1 5 3.744 0.9346 -0.695 0.043 TRAINN04 Training in basic statistical techniques is given to employees 268 1 5 3.207 1.2144 -0.163 -1.121 TRAINN05 Training in problemsolving techniques is given to employees 268 1 5 3.61 1.0827 -0.648 -0.365 M.Morita et al. Table A2 Loadings and Weights 3rd order HOC 2nd-order HOC LOC composing 2nd-order HOCs Items of LOC Loading Standard Deviation T Statistics P Value 5.0% 95.0% Weight Standard Deviation T Statistics P Value 5.0% 95.0% – SC-Adaptability SC organizational design: Adapt11—> Ad1 0.897 0.073 12.307 0.000 0.752 0.981 0.748 0.118 6.316 0.000 0.530 0.914 – SC-Adaptability SC organizational design: Adapt12—> Ad1 0.706 0.115 6.126 0.000 0.503 0.869 0.466 0.148 3.158 0.001 0.209 0.695 – SC-Adaptability Use of technology Adapt21—> Ad2 0.717 0.117 6.116 0.000 0.500 0.880 0.343 0.172 1.992 0.023 0.062 0.617 – Adaptability Use of technology Adapt22—> Ad2 0.953 0.062 15.425 0.000 0.839 0.998 0.791 0.137 5.766 0.000 0.543 0.970 – SC-Adaptability Medium- and long-term market knowledge Adapt31- > Ad3 0.999 0.077 12.968 0.000 0.867 1.000 0.973 0.166 5.867 0.000 0.676 1.127 – SC-Adaptability Medium- and long-term market knowledge Adapt32—> Ad3 0.504 0.222 2.273 0.012 0.076 0.800 0.056 0.284 0.198 0.421 -0.421 0.493 – PDC PDC1_Customer involvement in new product development CINVLN01 <—PDC1_ CustInv 0.845 0.043 19.568 0.000 0.775 0.894 0.370 0.077 4.785 0.000 0.260 0.497 – PDC PDC1_Customer involvement in new product development CINVLN02 <—PDC1_ CustInv 0.753 0.065 11.554 0.000 0.644 0.833 0.273 0.095 2.880 0.002 0.132 0.415 – PDC PDC1_Customer involvement in new product development CINVLN03 <—PDC1_ CustInv 0.822 0.051 16.164 0.000 0.728 0.875 0.345 0.090 3.840 0.000 0.200 0.465 – PDC PDC1_Customer involvement in new product development CINVLN05 <—PDC1_ CustInv 0.808 0.060 13.571 0.000 0.706 0.870 0.246 0.088 2.789 0.003 0.104 0.370 – PDC PDC2_ Manufacturing involvement in new product development MFDESN02 <—PDC2_ MnfInv 0.818 0.037 22.203 0.000 0.754 0.863 0.347 0.060 5.742 0.000 0.257 0.447 – PDC PDC2_ Manufacturing involvement in new product development MFDESN04 <—PDC2_ MnfInv 0.790 0.044 17.881 0.000 0.710 0.845 0.223 0.066 3.379 0.000 0.117 0.319 Drivers ofsupply chain adaptability: insights intomobilizing supply chain processes. A… 3rd order HOC 2nd-order HOC LOC composing 2nd-order HOCs Items of LOC Loading Standard Deviation T Statistics P Value 5.0% 95.0% Weight Standard Deviation T Statistics P Value 5.0% 95.0% – PDC PDC2_ Manufacturing involvement in new product development MFDESN05 <—PDC2_ MnfInv 0.825 0.043 19.149 0.000 0.747 0.876 0.303 0.068 4.430 0.000 0.202 0.410 – PDC PDC2_ Manufacturing involvement in new product development MFDESN06 <—PDC2_ MnfInv 0.655 0.078 8.369 0.000 0.515 0.754 0.143 0.080 1.782 0.037 0.007 0.251 – PDC PDC2_ Manufacturing involvement in new product development MFDESN07 <—PDC2_ MnfInv 0.729 0.057 12.734 0.000 0.629 0.799 0.268 0.078 3.463 0.000 0.151 0.391 – PDC PDC3_ Supplier involvement in new product development SINVLN01 <—PDC3_ SupInv 0.848 0.030 27.942 0.000 0.792 0.885 0.234 0.053 4.407 0.000 0.143 0.307 – PDC PDC3_ Supplier involvement in new product development SINVLN02 <—PDC3_ SupInv 0.826 0.035 23.398 0.000 0.763 0.876 0.323 0.058 5.562 0.000 0.238 0.422 – PDC PDC3_ Supplier involvement in new product development SINVLN03 <—PDC3_ SupInv 0.836 0.037 22.678 0.000 0.764 0.882 0.339 0.051 6.628 0.000 0.255 0.417 – PDC PDC3_ Supplier involvement in new product development SINVLN04 <—PDC3_ SupInv 0.793 0.042 19.050 0.000 0.719 0.851 0.316 0.058 5.422 0.000 0.230 0.411 – PDC PDC4_Front- end loading in new product development FRONTN01 <—PDC4_ FrontEnd 0.698 0.053 13.232 0.000 0.605 0.773 0.199 0.048 4.149 0.000 0.122 0.276 – PDC PDC4_Front- end loading in new product development FRONTN02 <—PDC4_ FrontEnd 0.763 0.048 16.034 0.000 0.678 0.822 0.205 0.046 4.461 0.000 0.130 0.275 – PDC PDC4_Front- end loading in new product development FRONTN03 <—PDC4_ FrontEnd 0.728 0.050 14.512 0.000 0.637 0.792 0.178 0.056 3.191 0.001 0.093 0.263 M.Morita et al. 3rd order HOC 2nd-order HOC LOC composing 2nd-order HOCs Items of LOC Loading Standard Deviation T Statistics P Value 5.0% 95.0% Weight Standard Deviation T Statistics P Value 5.0% 95.0% – PDC PDC4_Front- end loading in new product development FRONTN04 <—PDC4_ FrontEnd 0.731 0.048 15.307 0.000 0.645 0.797 0.210 0.055 3.829 0.000 0.127 0.299 – PDC PDC4_Front- end loading in new product development FRONTN05 <—PDC4_ FrontEnd 0.753 0.047 15.853 0.000 0.671 0.811 0.209 0.052 4.037 0.000 0.129 0.284 – PDC PDC4_Front- end loading in new product development FRONTN06 <—PDC4_ FrontEnd 0.760 0.044 17.190 0.000 0.685 0.814 0.221 0.044 5.023 0.000 0.153 0.288 – PDC PDC4_Front- end loading in new product development FRONTN08 <—PDC4_ FrontEnd 0.619 0.073 8.527 0.000 0.487 0.716 0.156 0.065 2.414 0.008 0.045 0.251 ASCOS – ASCOS1_Lead Time Focus SPEEDN01 <—ASCOS1_ leadtime 0.775 0.063 12.268 0.000 0.659 0.859 0.403 0.084 4.823 0.000 0.267 0.536 ASCOS – ASCOS1_Lead Time Focus SPEEDN03 <—ASCOS1_ leadtime 0.755 0.070 10.806 0.000 0.628 0.853 0.436 0.091 4.788 0.000 0.282 0.583 ASCOS – ASCOS1_Lead Time Focus SPEEDN04 <—ASCOS1_ leadtime 0.859 0.046 18.746 0.000 0.763 0.909 0.417 0.073 5.704 0.000 0.288 0.524 ASCOS ASCOS2_JIT FOCUS JIT1_JIT delivery by suppliers JITDELN01 <—JIT1_ suppliers 0.870 0.056 15.561 0.000 0.776 0.916 0.526 0.086 6.097 0.000 0.389 0.659 ASCOS ASCOS2_JIT FOCUS JIT1_JIT delivery by suppliers JITDELN02 <—JIT1_ suppliers 0.504 0.140 3.587 0.000 0.234 0.680 0.014 0.149 0.095 0.462 -0.261 0.220 ASCOS ASCOS2_JIT FOCUS JIT1_JIT delivery by suppliers JITDELN03 <—JIT1_ suppliers 0.899 0.051 17.505 0.000 0.819 0.941 0.595 0.092 6.442 0.000 0.450 0.743 ASCOS ASCOS2_JIT FOCUS JIT2_JIT link with customers LINKCN01 <—JIT2_ Customers 0.746 0.105 7.095 0.000 0.579 0.837 0.333 0.114 2.908 0.002 0.163 0.493 ASCOS ASCOS2_JIT FOCUS JIT2_JIT link with customers LINKCN03 <—JIT2_ Customers 0.787 0.100 7.904 0.000 0.670 0.889 0.465 0.136 3.429 0.000 0.297 0.678 ASCOS ASCOS2_JIT FOCUS JIT2_JIT link with customers LINKCN04 <—JIT2_ Customers 0.773 0.111 6.988 0.000 0.595 0.853 0.276 0.102 2.700 0.003 0.112 0.407 ASCOS ASCOS2_JIT FOCUS JIT2_JIT link with customers LINKCN05 <—JIT2_ Customers 0.797 0.117 6.806 0.000 0.606 0.876 0.216 0.122 1.768 0.039 0.014 0.344 ASCOS ASCOS2_JIT FOCUS JIT3_Setup time reduction SETUPN01 <—JIT3_ SetupTime 0.844 0.022 38.616 0.000 0.804 0.876 0.463 0.038 12.335 0.000 0.403 0.526 ASCOS ASCOS2_JIT FOCUS JIT3_Setup time reduction SETUPN02 <—JIT3_ SetupTime 0.801 0.038 20.840 0.000 0.730 0.854 0.500 0.045 11.078 0.000 0.428 0.575 ASCOS ASCOS2_JIT FOCUS JIT3_Setup time reduction SETUPN03 <—JIT3_ SetupTime 0.703 0.058 12.082 0.000 0.598 0.787 0.296 0.044 6.655 0.000 0.222 0.365 Drivers ofsupply chain adaptability: insights intomobilizing supply chain processes. A… 3rd order HOC 2nd-order HOC LOC composing 2nd-order HOCs Items of LOC Loading Standard Deviation T Statistics P Value 5.0% 95.0% Weight Standard Deviation T Statistics P Value 5.0% 95.0% ASCOS ASCOS3_Quality focus Q1_Feedback to employees on quality FBACKN01 <—Q1_ FeedbackToEmployees 0.766 0.043 17.903 0.000 0.686 0.825 0.223 0.036 6.209 0.000 0.164 0.279 ASCOS ASCOS3_Quality focus Q1_Feedback to employees on quality FBACKN02 <—Q1_ FeedbackToEmployees 0.771 0.041 18.925 0.000 0.695 0.828 0.289 0.044 6.641 0.000 0.220 0.360 ASCOS ASCOS3_Quality focus Q1_Feedback to employees on quality FBACKN03 <—Q1_ FeedbackToEmployees 0.691 0.061 11.322 0.000 0.579 0.773 0.172 0.050 3.421 0.000 0.084 0.246 ASCOS ASCOS3_Quality focus Q1_Feedback to employees on quality FBACKN04 <—Q1_ FeedbackToEmployees 0.828 0.029 28.336 0.000 0.776 0.871 0.347 0.041 8.387 0.000 0.286 0.417 ASCOS ASCOS3_Quality focus Q1_Feedback to employees on quality FBACKN05 <—Q1_ FeedbackToEmployees 0.776 0.036 21.752 0.000 0.712 0.826 0.258 0.040 6.378 0.000 0.192 0.324 ASCOS ASCOS3_Quality focus Q2_Top leadership on quality TPLEADN01 <—Q2_ TopLeadership 0.755 0.043 17.588 0.000 0.676 0.816 0.194 0.034 5.764 0.000 0.140 0.252 ASCOS ASCOS3_Quality focus Q2_Top leadership on quality TPLEADN02 <—Q2_ TopLeadership 0.846 0.027 31.652 0.000 0.795 0.882 0.217 0.022 10.008 0.000 0.179 0.249 ASCOS ASCOS3_Quality focus Q2_Top leadership on quality TPLEADN03 <—Q2_ TopLeadership 0.697 0.041 16.919 0.000 0.623 0.758 0.210 0.031 6.767 0.000 0.161 0.262 ASCOS ASCOS3_Quality focus Q2_Top leadership on quality TPLEADN04 <—Q2_ TopLeadership 0.761 0.031 24.532 0.000 0.706 0.807 0.222 0.029 7.725 0.000 0.178 0.272 ASCOS ASCOS3_Quality focus Q2_Top leadership on quality TPLEADN05 <—Q2_ TopLeadership 0.851 0.023 37.519 0.000 0.811 0.883 0.232 0.024 9.826 0.000 0.194 0.271 ASCOS ASCOS3_Quality focus Q2_Top leadership on quality TPLEADN06 <—Q2_ TopLeadership 0.799 0.028 28.482 0.000 0.749 0.841 0.196 0.026 7.667 0.000 0.155 0.237 ASCOS ASCOS3_Quality focus Q3_Process control CNTRLN02 <—Q3_ ProcessControl 0.904 0.175 5.155 0.000 0.708 0.986 0.515 0.279 1.846 0.032 0.119 0.889 ASCOS ASCOS3_Quality focus Q3_Process control CNTRLN05 <—Q3_ ProcessControl 0.925 0.188 4.924 0.000 0.708 0.988 0.578 0.292 1.979 0.024 0.139 0.904 ASCOS ASCOS3_Quality focus Q4_Quality training TRAINN01 <—Q4_ Training 0.709 0.050 14.139 0.000 0.613 0.778 0.211 0.045 4.711 0.000 0.134 0.279 ASCOS ASCOS3_Quality focus Q4_Quality training TRAINN02 <—Q4_ Training 0.810 0.029 27.514 0.000 0.754 0.849 0.227 0.036 6.336 0.000 0.163 0.279 M.Morita et al. 3rd order HOC 2nd-order HOC LOC composing 2nd-order HOCs Items of LOC Loading Standard Deviation T Statistics P Value 5.0% 95.0% Weight Standard Deviation T Statistics P Value 5.0% 95.0% ASCOS ASCOS3_Quality focus Q4_Quality training TRAINN03 <—Q4_ Training 0.745 0.041 18.067 0.000 0.669 0.803 0.209 0.041 5.118 0.000 0.143 0.276 ASCOS ASCOS3_Quality focus Q4_Quality training TRAINN04 <—Q4_ Training 0.832 0.027 31.060 0.000 0.783 0.871 0.279 0.037 7.614 0.000 0.222 0.342 ASCOS ASCOS3_Quality focus Q4_Quality training TRAINN05 <—Q4_ Training 0.855 0.021 40.268 0.000 0.818 0.887 0.326 0.040 8.164 0.000 0.268 0.397 ASCOS – ASCOS4_ Demand Stability Focus REPMASN01 <— ASCOS4_Demand 0.773 0.099 7.840 0.000 0.613 0.895 0.482 0.120 4.029 0.000 0.302 0.670 ASCOS – ASCOS4_ Demand Stability Focus REPMASN02 <— ASCOS4_Demand 0.815 0.073 11.108 0.000 0.683 0.885 0.385 0.095 4.065 0.000 0.216 0.515 ASCOS – ASCOS4_ Demand Stability Focus REPMASN03 <— ASCOS4_Demand 0.679 0.129 5.282 0.000 0.433 0.831 0.462 0.139 3.318 0.000 0.222 0.657 LOC Low-order construct, HOC High-order construct Funding Funding for open access publishing: Universidad de Sevilla/ CBUA. This study has been conducted within the frameworks of the following funded competitive projects:PID2019-105001GB-I00 by MCIN/AEI/10.13039/501100011033 (Ministerio de Ciencia e Innovación- Spain); PY20_01209 (PAIDI 2020- Consejería de Transformación Económica, Industria, Conocimiento y Universidades -Junta de Andalucía); Japan Society for the Promotion of Science, 17K03952, Michiya Morita. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. 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