Achieving plant responsiveness from reconfigurable technology: Intervening role of SCM

Depósito de investigación de la Universidad de Sevilla https://idus.us.es/ Esta es la versión enviada del artículo publicado en: This is an submitted manuscript of a paper published in: International Journal of Production Economics (2019): 15 junio DOI: https://doi.org/10.1016/j.ijpe.2019.06.001 Copyright: Elsevier El acceso a la versión publicada del artículo puede requerir la suscripción de la revista. Access to the published version may require subscription. “This is a Submitted Manuscript of an article published by Elsevier in International Journal of Production Economics on 15/06/2019, available at: https://doi.org/10.1016/j.ijpe.2019.06.001” Achieving plant responsiveness from reconfigurable technology: intervening role of SCM Cesar H. Ortega Jimenez Facultad de Ingeniería-Universidad Nacional Autonoma de Honduras (UNAH); Facultad de Posgrado-Universidad Tecnológica Centroamericana (UNITEC); and Universidad de Sevilla Email: [email protected].hn; [email protected]u Pedro Garrido-Vega Universidad de Sevilla pgarr[email protected] Cristian Andrés Cruz Torres Escuela de Matemática, Universidad Nacional Autónoma de Honduras (UNAH), and Facultad de Ingeniería y Arquitectura - Universidad Tecnológica Centroamericana (UNITEC), Email: [email protected] Abstract The aim of this paper is to examine relationships between the following production programs that lead to the greater plant responsiveness-PR necessary for market needs: strategic reconfigurable system-SRS (reconfigurable technology-RT supported by manufacturing strategy-MS and technology management-TM), with the emphasis on SCM’s intervening role. The research model relating the programs to support and enhance PR is tested using the High-Performance Manufacturing survey database of 330 plants (3 sectors, 16 countries) and CB-SEM with latent variables. First, RT, MS and TM together form SRS, with a significant impact on PR. Second, when theorizing for mediation processes, SCM intervenes the relationship between the SRS program and PR. Third, the intervening role of SCM is also confirmed by testing for mediation processes. Fourth, environmental complexity interacts with the SRS program in its relationship with SCM. Research implications are twofold: (1) more responsive plants need a more holistic view in which SCM must be deployed in conjunction with SRS; and (2) SCM supports the execution of SRS by playing a key role in improving PR, even when contextual factors are present. Applied implications are that plants can achieve greater responsiveness if they match their production resources around RT through SRS programs. Further, due to SCM’s intervening role, SC practitioners should link SCM dimensions to SRS to improve PR. The originality of this paper is that a fuller SRS is defined and its impact on PR is empirically tested. In addition, SCM fit logic is developed theoretically with empirical evidence of its intervention in the relationship between SRS and PR. Keywords: Plant responsiveness, Supply chain management, Reconfigurable technology, Manufacturing Strategy, Technology Management Article Classification: Research paper. Achieving plant responsiveness from reconfigurable technology: intervening role of SCM Introduction Responsiveness is currently considered a critical performance metric of competitive capabilities (Moradlou et al., 2017) and customer responsiveness is one of the end-goals of an end-to-end Supply Chain (SC). So, as important parts of the SC, plants must respond to product and market changes. Plant Responsiveness (PR) therefore plays a crucial role in achieving SC customer responsiveness (Santos Bernardes and Hanna, 2009) as the ability to quickly adjust to any such changes in production plants (Um et al., 2017). PR can be measured as the combinations of time, dependability and flexibility with which plants respond to customer demands (Ortega-Jimenez et al., 2015). In this context, the newly-developed reconfigurable production technologies (RT 1 ) (a set of computer numerical control (CNC) machines that can be rearranged to execute different machining functions required in dynamic manufacturing) could be instrumental in achieving PR. Therefore organizations implement RT to improve responsiveness in their plants (Abdi and Labib, 2017). Indeed, extant research states that RT implementation is essential for gaining a competitive advantage, as it not only enhances PR (Koren et al., 2017), but also Supply Chain Responsiveness (Chandra and Grabis, 2016). However, empirical study of RT as a source of plant responsiveness has been limited, presenting a first gap. Effective RT implementation needs to be supported with adequate in-plant technology management (TM) and an adequate Manufacturing Strategy (MS) (Ortega et al., 2015). This RTTM-MS fit/integration is referred to here as a strategic reconfigurable system (SRS), since it is reconfigurability-based and yields strategy-focused PR as a mechanism to guide plants’ RT management, a distinctive competency that generates SC competitive advantage. In this context, plant SCs can be considered the vehicle that extends PR to customers; so adequate SCM should favor this process. The study of SCM dimensions in plants is therefore considered important as it equips plants with the cutting-edge strategies that they require to build broad responsiveness (Stevens and Johnson, 2016). The strategic view of SCM dimensions in plants could be crucial for enhancing PR, extending it along the SC, and so gaining a competitive advantage (e.g., Barney, 2012). However, few studies exist on this topic, leaving a second literature gap: i.e., whether SCM dimensions implemented in reconfigurable plants have a positive impact on PR. This paper therefore analyzes whether specific SCM dimensions intervene/mediate the SRS→PR relationship. As a third gap, there are mixed results regarding the impact of the SCM and strategy program on customer responsiveness, and therefore on performance and PR (Roh et al., 2014). As this paper is focusing on PR and not on responsive SC, one sub-gap would be the lack of empirical literature discussing the question of the production programs and practices that are important for both SCM and PR, and how they give support. It is also important to identify which PR-enhancing production programs are likely to be mediated by SCM. Likewise, no prior empirical study exists of these relationships. In a research effort to respond to the above questions and address the mentioned gaps, this paper 1 See Appendix (Table A1) for acronym glossary. will analyze SCM’s intervening role in the SRS-PR relationship. PR may be a key that can be measured as an outcome of these relationships due to two main reasons: (1) Plant Responsiveness has become essential for industry competitiveness (Uskonen and Tenhiälä, 2012); and (2) the functionality and production capacity that plants require for these relationships may make them responsive to increasingly interconnected markets (Koren, 2013, 2010; Koren et al., 2000); i.e., the main purpose of this study is to investigate the interrelationships among three variables: SRS, SCM and PR. Based on the above, the main research objectives are: 1) To analyze whether Reconfigurable Technologies contribute to achieving Plant Responsiveness (PR) and whether this benefits from the fit with Technology Management and Manufacturing Strategy, thus forming a Strategic Reconfigurable System (SRS). 2) To analyze the possible effect of SCM mediation in the SRS ➔ PR relationship. The following section introduces the background to the study and the research model. The methodology is then presented, followed by the results and a discussion of the study’s implications. The paper ends with the conclusions and directions for further research. Background and research model In line with the research objectives, this section starts by discussing the impact on PR of RT and SRS (RT combined with MT and TM). The mediating role of SCM on this relationship is subsequently introduced. Next, the role of environmental complexity (EC) as a moderator variable that affects these relationships with SCM is explained. This paper seeks to contribute to research that has set out to discover both of these, as well as to reject or confirm prior knowledge [Pagell in (Boer et al., 2015)]. All the concepts used in this study will be clarified in the corresponding sections. Finally, the research model based around the relationships formed by the previous concepts will be presented, and testable hypotheses formulated. 1) Reconfigurable Technology (RT), Strategic Reconfigurable Systems (SRS) and Plant Responsiveness (PR) The performance measures of any manufacturing program with strategic implications for the plant must be identified. Plant Responsiveness (PR) can be defined as the “propensity for purposeful and timely behavior change in the presence of modulating stimuli” (Santos Bernardes and Hanna, 2009). So, this paper focuses on the aspects of reconfigurable technology emphasized in Koren (2006): its ability to enable existing systems to launch new products rapidly, and to react quickly, efficiently and effectively to changes in e.g., product markets, where there may be changes in specifications, mix, volume and delivery. Other changes can come from environmental regulations, or from machine failures and needs to keep production running in any circumstances. Following Koren (2010, 2013), the present research takes Reconfigurable Technology (RT) as the main platform for the PR-related attributes: reconfigurability, i.e., the plant’s capability, which may consist of groups of machine tools that can be rapidly rearranged and integrated in an openarchitecture environment to respond to the challenges of globalization by boosting productivity and enhancing plant performance (Koren and Kota, 1999). Reconfigurability seeks quicker responsiveness to both markets and customers by allowing for variable production volumes, a high degree of customization, product variety, and short product life (Koren et al., 2018). So, RT enables a cost-effective, rapid response to changing demands by virtue of capabilities and processes that provide modular, agile and reconfigurable tooling and fixturing systems for short lead-time and low-volume, geometrically-complicated parts. It has been argued that Technology Management (TM) is also necessary for plants to achieve reconfigurability (Ortega et al., 2015). Finally, a plant needs a roadmap for its RT and technology practices to achieve PR, i.e., a coherent Manufacturing Strategy (MS) (Hayes and Wheelwright, 1984). Thus, this paper posits that RT, TM, and MS bundles are part of a plant’s Strategic Reconfigurable System (SRS) and support the most specific capabilities and practices (Ortega-Jimenez et al., 2015). Despite the widely- and long-proclaimed benefits of RT (Koren et al., 2017, 1999), a search of empirical literature on RT shows a limited number of papers investigate whether faster PR returns are obtained and results are inconclusive; i.e., due to a variety of challenges, significant results are found in some cases but not in others (Rösiö and Säfsten, 2013). Furthermore, the decision to implement RT without creating strategic links to practices in other technological and organizational production programs (which plants may already be using as a starting-point for integration) would not be able to provide the promised results (Ortega-Jimenez et al., 2012; Schroeder and Flynn, 2001). This may raise concerns among managers, who often criticize production programs for not delivering the results that they expect (Garrido-Vega et al., 2015). In addition, since current technological implementation is relatively new, very few authors, if any at all, have empirically tested for an output such as PR with an input such as RT (as the leading set of capabilities for responsiveness) and TM practices as a further input (e.g., Brunoe et al., 2017). The question is whether the conclusion will be the same if MS strategic practices are also included to create an integrated SRS framework. Consequently, the first hypothesis to be tested is: H1: SRS is positively related to PR 2) SCM’s mediating role in SRS-PR The prior literature shows that links between SCM and other manufacturing programs increase their impact on PR. Much has been written about their benefits for improving performance in general (Nakano and Akikawa, 2014) and customer responsiveness in particular (Luu, 2017; Roh et al., 2014). A great deal of literature also exists on the importance of upstream and downstream SCM and their interaction for customer responsiveness (Tsinopoulos and Mena, 2015). In other respects, from the earliest research into the Strategic reconfigurable system, its main component, reconfigurable technology (RT), has focused on achieving ever faster operational responsiveness, and so is a key factor in the operational performance dimension (Abdi, 2009; Bruccoleri et al., 2006; Weston, 1999; Zhang et al., 1999). However, there are other outstanding important theoretical and empirical questions, such as whether an amalgamated strategic reconfigurable system (SRS) can enhance responsiveness more if core SCM practices are present. Indeed, although a number of authors have found significant relationships between SRS programs taken singly, or any combinations of all three, and responsiveness (Rogers and Daugherty (2013), Sandhu et al. (2014), Thambusamy and Salam (2010), and Williams et al. (2013)), they do not analyze the extent to which the presence of SCM practices affects their findings. On the other hand, the specialized literature shows some evidence of links between SCM and at least one of the programs in isolation or in combinations of any two, but with no references to responsiveness (e.g., Ageron et al., 2012; Chatha and Butt, 2015b; Clegg and Prasanta, 2013; Lai et al., 2015; Mejías et al., 2016; Swafford et al., 2008; Wang et al., 2012). Thus, extant research does not include holistic links of responsiveness and/or practices related to key supplier-customer relationships. It should be noted that SCM plays a key role as a mediator, as PR is transferred along the SC (Luu, 2017). Mediation is a criterion-specific fit perspective conceptualized as intervention. In other words, there is an intervening variable (SCM) between an antecedent variable (SRS) and the consequent variable (PR); i.e., SCM is an intervening variable between SRS and PR. SRS will continue to have a direct effect on PR, although SCM’s intervening effects are expected to enhance the relationship. Some recent literature (Roh et al., 2014) has found relationships between SRS components (RT especially, but also MS and technology), SCM and general responsiveness, enabling us to hypothesize on SRS’ indirect effect via SCM. Therefore, SCM’s intervening role in the SRS-PR relationship is hypothesized as follows: H2m. SCM mediates the SRS–PR relationship. Two direct effects are also hypothesized: H2: SRS is positively related to SCM H3: SCM is positively related to PR 3) Environmental complexity (EC): contextual variable Many other factors influence a plant’s SCM practices, but they cannot be fully identified and eliminated due to data limitations. Nonetheless, according to the literature (Kotha and Orne, 1989), contextual factors such as the plant environmental complexity (EC) might have an impact on effectiveness (McKone and Schroeder, 2002). The HPM literature on contextual theory has addressed contextual variables that impact effectiveness (Garrido-Vega et al., 2015; Machuca et al., 2011). The only contextual variable included in this paper is EC and no comprehensive study will be conducted as it is outside the research scope. The SRS program made up of three bundles of programs is expected to interact with EC and so make the results more robust, i.e., EC inclusion will not affect the earlier relationship with SCM. Considering the independent variable interaction model, how much SRS affects SCM is conditional on the value of EC and how much EC affects SCM is conditional on the value of SRS, as well as both SRS and EC having causal influence on SCM (Luft and Shields, 2006). Therefore, it is hypothesized that: H4. EC–SRS interaction is related to SCM. 4) Research model The theoretical foundation for this study is based on integrating practices, contingency, and continuous improvement (Demeter, 2017). We propose that an infrastructure for customer responsiveness exists and consists of programs such as SRS and IT. This paper therefore proposes that these programs can create an environment that is conducive to plants achieving PR faster. Following the above, the relationships research model (Figure 1) sets out the hypotheses and summarizes the above-mentioned relationships. Our structural relations model is the part inside the rectangle, while the SRS and SCM measurement model is outside. This model is a holistic vision not only of how a system of production programs can be well-integrated in an SRS, but also of how the latter can achieve PR via SCM. Figure 1. Relationships research model: hypotheses Methodology Data collection and sample This paper uses the fourth-round dataset of the international High-Performance Manufacturing survey (data collection completed in 2017). Twelve questionnaires were targeted at twelve specific management positions to minimize the risk of common method bias-CMB. Besides, the common latent factor test found no indications for CMB since delta differences were <0.2 when estimations were compared (Podsakoff et al., 2003). As major differences exist between practices, performance and contextual factors in plants owned by the same company, the unit of analysis is the plant. Also, as some of the analyzed practices may not apply to small plants, it was determined that sample plants should have a minimum of 100 workers (Machuca et al., 2011). In each country plants were stratified into three industries (auto suppliers, electronics, and machinery) that face global but different market competition (Garrido- Vega et al., 2015). Countries were selected for their mix of HP and standard plants, manufacturing strength, and diverse national cultural and economic characteristics to ensure variability in the database. All 330 plants taking part in the survey’s fourth round from 16 countries (Austria /Switzerland, Brazil, China, Finland, Germany, Israel, Italy, Japan, Korea, Spain, Sweden, Taiwan, United Kingdom, United States, and Vietnam) were included in the present research. An online calculator (Soper, 2017) was used to determine a priori whether the sample would be large enough for this research. Based on the Westland (2010) method, the calculator considers the structural equation model’s complexity (measured by number of indicators and latent variables) and the minimum expected effect size. The sample of 158 plants required for this model’s degree of complexity (108 indicators and 19 latent variables) was amply exceeded by our sample of 330. Our sample size also enabled a 0.3 effect size (Cohen, 1988), a 0.05 significance level and a power of 0.8 to be obtained, since it surpassed the 224-minimum required for this effect size. Lastly, some subsamples were taken based on different criteria (e.g., early and late respondents), and no significantly different results were observed. Therefore neither non-response bias nor late-response bias were found (Halbesleben and Whitman, 2013; Johnson and Wislar, 2012). The Hausman test (Hausman, 1978), which satisfies the requirements to be trustworthy (Ketokivi and McIntosh, 2017), indicated that almost 85 % of the variables showed a p value >.01, showing then a small degree of endogeneity. Relationships between observable variables were also tested in the exploratory analysis (Haans et al., 2016) and no quadratic relationship was found. Therefore, they can be assumed to be linear, which justifies adopting CB-SEM structural equation modeling. Finally, measurement equivalence between sectors and between countries was assessed by measurement invariance test (Cheung and Rensvold, 2002). The values in the fit.means model for the sectors are cfi.delta=0.000 and rmsea.delta=0.001 [lower than 0.01 (maximum admissible value)], showing invariance between the three sectors. For the country sample size to be adequate, measurement equivalence was assessed between developed (Finland, Germany, Italy, Japan, Spain, Sweden, Switzerland, UK, USA) and emerging countries (Brazil, China Israel, South Korea, Taiwan, Vietnam). The results show invariance between the two groups (cfi.delta=0.009 and rmsea.delta=0.003 in the fit.means model). Research variables All the first-order scales used in this paper are common factor (reflective) and taken from previous research (see Appendix, Table A2 2 ). Most of the constructs in the structural model are second-order (see Appendix, Table A3). The two third-order constructs are SRS and SCM. All second-order and third-order constructs are type 2 (Jarvis et al., 2003), while the first-order constructs are common factor and the higher-order constructs are composites. All the constructs are justified, and the validation process detailed in the methodology section. Regarding content validity, all the scales in this research were adopted or adapted from HPM project scales (developed from the literature, expert panels, managers, and country pilot tests). All except for RT were validated measures taken from the three previous data collection rounds. As part of the HPM project the items had been confirmed to be consistent with the constructs’ conceptual definitions and the sources of the scales (not only content, but also convergent and criterion validity). The new measure’s (RT (see below)) composition adopted by the HPM project was defined by Reconfigurable Manufacturing experts (Koren, 2010, 2013), and so was consistent with the construct’s conceptual definition. The scales were measured on a 5-point Likert scale (from 1 “strongly disagree” to 5 “strongly agree”) to determine the extent to which respondents at each of the plants agreed with multi-item questions. Following Koren (2010, 2013), this paper takes the SRS sub-scale, RT, as first order and measures it with six capabilities as its dimensions: customization, diagnosability, convertibility, integrability, scalability, and modularity. The TM sub-scale was initially proposed as a secondorder latent variable with five dimensions: Modularization of Products; Manufacturing Involvement in Product Design; Proprietary Equipment; Anticipation of New Technologies; and Effective Process Implementation (Arana-Solares et al., 2019; Ortega-Jimenez et al., 2011a) Machuca et al., 2011). However, only the first two dimensions were valid for the TM second-order 2 Direct links to Appendices have been provided throughout the paper due to space restrictions and to enable smooth and easy reading (see Appendices in https://doi.org/10.6084/m9.figshare.5782152) construct. Finally, Manufacturing-Business Strategy linkage and MS Formulation were used as first-order constructs for the MS second-order concept (Ortega et al., 2012). Hence, this paper considers SRS as a multidimensional scale (third-order composite construct) with RT, TM, and MS as its three sub-scales (Ortega-Jimenez et al., 2015; Garrido-Vega et al., 2015). Several authors propose that SCM is formed of a number of activities, including informationsharing, interorganizational coordination, and quality (Dhaigude and Kapoor, 2017; Min et al., 2007). This study combines these into three groups of dimensions as SCM program components: SC human support, SC quality, and SC integration. Their choice is justified below. Hohenstein and Feisel, and Saenz classify SC human support as an important aspect of SCM, since it is crucial for ensuring a well-integrated SC (Hohenstein et al., 2014; Wong et al., 2015). According to Hohenstein and Feisel "SCM is all about the people who manage SCs" (Hohenstein et al., 2014), while Sweeney and Sweeney add that SCM can be regarded as a human chain (Sweeney and Sweeney, 2013). Finally, the human factor in SCM contributes to the theory of behavioral SCM (Schorsch et al., 2017). The second SCM dimension, SC quality, is a critical issue, since the SC is the backbone of successful operations (Min et al., 2007). The literature also provides clear evidence of the importance of the connection between technology and SC quality (Morgan et al., 2016). SC integration is what links together all the relationships, activities, functions, processes and locations within plants, as well as those of suppliers, customers and others (Huo et al., 2015). It includes the internal integration of functions and external integration with trading partners. Several authors state that SCM integration is critical for reconfigurability in manufacturing (Chandra and Grabis, 2016). Considering the three preceding paragraphs, three SCM multidimensional scale/program (thirdorder composite construct) components were measured as sub-scales/dimensions: SCM human support (SCM-H); SCM quality (SCM-Q); and SCM integration (SCM-I). Following Min et al. (2007), the proposed SCM-H sub-scale comprised five dimensions: Top Management Support- Suppliers; Top Management Support-Customers; SCM Strategic Implications; Interorganizational Coordination and SC Leadership. However, only Interorganizational Coordination was valid, making this sub-scale a first-order construct. SC Quality Focus, Supplier Involvement in Quality Improvement and Ability to Meet Customers’ Quality Needs were proposed as dimensions for a SCM-Q SCM multidimensional scale (Min et al., 2007). Again, only one practice was significant -SC quality focusmaking this sub-scale a first-order construct. SCM-I is theorized as a sub-scale, with SCO Alignment (Customers), SCO Alignment (Suppliers), SC Information Sharing with Suppliers, SC Information Sharing by Suppliers, SC Information Sharing with Customers, and SC Information Sharing by Customers as its dimensions (Min et al., 2007). The last four (all firstorder constructs) were the only valid dimensions, making this sub-scale a second-order construct. PR entails items of speed/time, flexibility and reliability (Garrido-Vega et al., 2015; Ortega- Jiménez et al., 2014; Ortega-Jimenez et al., 2015). A 5-point Likert-scale (1, “strongly disagree” to 5, “strongly agree”) was used to compare plant performance with the competition. The performance measures were obtained from plant managers, who are the people with most knowledge about the plant’s multidimensional performance. Finally, despite the existence of different manufacturing structure perspectives for plant environment (EC), it was proposed that the variable should be constructed from process structure, product line, and organizational scope (Kotha and Orne, 1989). However, when constructing the three scales only “product line” was internally consistent, as was the only scale used for EC. Psychol. 51, 1173–1182. Belsley, D.A., 1991. Conditioning diagnostics : collinearity and weak data in regression, Published in 1991in New York (N.Y.) by Wiley. Wiley. Bergeron, F., Raymond, L., Rivard, S., 2001. Fit in strategic information technology management research: an empirical comparison of perspectives. Omega 29, 125–142. Boer, H., Holweg, M., Kilduff, M., Pagell, M., Schmenner, R., Voss, C., 2015. Making a meaningful contribution to theory. Int. J. Oper. Prod. Manag. 35, 1231–1252. Booth, G.D., Niccolucci, M.J., Schuster, E.G., 1994. Identifying proxy sets in multiple linear regression: an aid to better coefficient interpretation. Dept Agric. For. Serv. Bruccoleri, M., Pasek, Z.J., Koren, Y., 2006. Operation management in reconfigurable manufacturing systems: Reconfiguration for error handling. Int. J. Prod. Econ. 100, 87–100. Brunoe, T.D.T.D., Andersen, A.L.A.-L., Nielsen, K., 2017. Reconfigurable Manufacturing Systems in Small and Medium Enterprises, in: Managing Complexity. pp. 205–213. Carrión, G.C., Nitzl, C., Roldán, J.L., 2017. Mediation Analyses in Partial Least Squares Structural Equation Modeling: Guidelines and Empirical Examples, in: Partial Least Squares Path Modeling. Springer International Publishing, Cham, pp. 173–195. Chandra, C., Grabis, J., 2016. Reconfigurable supply chains: an integrated framework, in: Supply Chain Configuration. New York, NY, pp. 69–86. Chatha, K.A., Butt, I., 2015. Themes of study in manufacturing strategy literature. Oper. Prod. Manag. Cheung, G.W., Rensvold, R.B., 2002. Evaluating Goodness-of-Fit Indexes for Testing Measurement Invariance. Struct. Equ. Model. A Multidiscip. J. 9, 233–255. Cho, E., 2016. Making Reliability Reliable. Organ. Res. Methods 19, 651–682. Clegg, B., and Prasanta, J.M., 2013. Supply chain professionals: A study of competencies, use of technologies, and future challenges. Int. J. Cohen, J., 1988. Statistical power analysis for the behavioral sciences. L. Erlbaum Associates. Demeter, K., 2017. Research in global operations management: some highlights and potential future trends. J. Manuf. Technol. Manag. 28, 324–333. Dhaigude, A., Kapoor, R., 2017. The mediation role of supply chain agility on supply chain orientation-supply chain performance link. J. Decis. Syst. 26, 275–293. Dormann, C.F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carré, G., Marquéz, J.R.G., Gruber, B., Lafourcade, B., Leitão, P.J., Münkemüller, T., McClean, C., Osborne, P.E., Reineking, B., Schröder, B., Skidmore, A.K., Zurell, D., Lautenbach, S., 2013. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography (Cop.). 36, 27–46. Elith, J., H. Graham, C., P. Anderson, R., Dudík, M., Ferrier, S., Guisan, A., J. Hijmans, R., Huettmann, F., R. Leathwick, J., Lehmann, A., Li, J., G. Lohmann, L., A. Loiselle, B., Manion, G., Moritz, C., Nakamura, M., Nakazawa, Y., McC. M. Overton, J., Townsend Peterson, A., J. Phillips, S., Richardson, K., Scachetti-Pereira, R., E. Schapire, R., Soberón, J., Williams, S., S. Wisz, M., E. Zimmermann, N., 2006. Novel methods improve prediction of species’ distributions from occurrence data. Ecography (Cop.). 29, 129–151. Ellram, L.M., Tate, W.L., 2016. The use of secondary data in purchasing and supply management (P/SM) research. J. Purch. Supply Manag. 22, 250–254. Fornell, C., Larcker, D.F., 1981. Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. J. Mark. Res. 18, 39. Garrido-Vega, P., Ortega Jimenez, C.H., de los Ríos, J.L.D.P., Morita, M., 2015. Implementation of technology and production strategy practices: Relationship levels in different industries. Int. J. Prod. Econ. 161, 201–216. Gold, A.H., Malhotra, A., Segars, A.H., 2001. Knowledge Management: An Organizational Capabilities Perspective. J. Manag. Inf. Syst. 18, 185–214. Graybill, F.A., 1961. An introduction to linear statistical models. McGraw-Hill, New York. Haans, R.F.J., Pieters, C., He, Z.-L., 2016. Thinking about U: Theorizing and testing U- and inverted U-shaped relationships in strategy research. Strateg. Manag. J. 37, 1177–1195. Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E., 2010. Multivariate Data Analysis, 7th Edition, 7th ed. Prentice Hall, Upper Saddle River, N.J. Halbesleben, J.R.B., Whitman, M. V, 2013. Evaluating Survey Quality in Health Services Research: A Decision Framework for Assessing Nonresponse Bias. Health Serv. Res. 48, 913–930. Hausman, J.A., 1978. Specification Tests in Econometrics. Econometrica 46, 1251. Hayes, R.H., Wheelwright, S.C., 1984. Restoring our competitive edge : competing through manufacturing. Wiley. Henseler, J., Ringle, C.M., Sarstedt, M., 2015. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J. Acad. Mark. Sci. 43, 115–135. Hohenstein, N.-O.N.O., Feisel, E., Hartmann, E., 2014. Human resource management issues in supply chain management research: a systematic literature review from 1998 to 2014. Logist. Manag. 44, 434–463. Huo, B., Han, Z., Chen, H., Zhao, X., 2015. The effect of high-involvement human resource management practices on supply chain integration. Int. J. Phys. Distrib. Logist. Manag. 45, 716–746. J. C. Westland, 2010. Lower bounds on sample size in structural equation modeling. Electron. Commer. Res. Appl. 9, 476–487. James, G., Witten, D., Hastie, T., Tibshirani, R., 2013. An Introduction to Statistical Learning, Springer Texts in Statistics. Springer New York, New York, NY. Jarvis, C.B., MacKenzie, S.B., Podsakoff, P.M., 2003. A Critical Review of Construct Indicators and Measurement Model Misspecification in Marketing and Consumer Research. J. Consum. Res. 30, 199–218. Johnson, T.P., Wislar, J.S., 2012. Response Rates and Nonresponse Errors in Surveys. JAMA 307, 1805. Ketokivi, M., McIntosh, C.N., 2017. Addressing the endogeneity dilemma in operations management research: Theoretical, empirical, and pragmatic considerations. J. Oper. Manag. 52, 1–14. Kleiber, C., Zeileis, A., 2008. Basics, in: Applied Econometrics with R. Springer New York, New York, NY, pp. 1–38. Kline, R.B., 2016. Principles and practice of structural equation modeling, 4th ed. Guilford Press, New York, NY, US. Koren, Y., 2013. The rapid responsiveness of RMS. Int. J. Prod. Res. 51, 6817–6827. Koren, Y., 2010. The Global Manufacturing Revolution. John Wiley & Sons, Ltd, Hoboken, NJ, USA. Koren, Y., 2006. General RMS characteristics. Comparison with dedicated and flexible systems, in: Dashchenko, A.I. (Ed.), Reconfigurable Manufacturing Systems and Transformable Factories. pp. 27–45. Koren, Y., Gu, X., Guo, W., 2018. Reconfigurable manufacturing systems: Principles, design, and future trends. Front. Mech. Eng. 13, 121–136. Koren, Y., Heisel, U., Jovane, F., Moriwaki, T., Pritschow, G., Ulsoy, G., Van Brussel, H., 1999. Reconfigurable manufacturing systems. CIRP Ann. - Manuf. Technol. 48, 527–540. Koren, Y., Kota, S., 1999. Reconfigurable machine tool. Koren, Y., Mehrabi, M.G., Ulsoy, A.G., Koren, Y., 2000. Reconfigurable manufacturing systems: key to future manufacturing. J. Intell. Manuf. 11, 403–419. Koren, Y., Wang, W., Gu, X., 2017. Value creation through design for scalability of reconfigurable manufacturing systems. Int. J. Prod. Res. 55, 1227–1242. Kotha, S., Orne, D., 1989. Generic manufacturing strategies: A conceptual synthesis. Strateg. Manag. J. 10, 211–231. Lai, K., Wong, C.W.Y., Lam, J.S.L., 2015. Sharing environmental management information with supply chain partners and the performance contingencies on environmental munificence. Int. J. Prod. Econ. Luft, J., Shields, M.D., 2006. Mapping Management Accounting: Graphics and Guidelines for Theory-Consistent Empirical Research, in: Handbooks of Management Accounting Research. pp. 27–95. Luu, T., 2017. Market responsiveness: antecedents and the moderating role of external supply chain integration. J. Bus. Ind. Mark. 32, 30–45. MacHuca, J.A.D., Ortega Jiménez, C.H., Garrido-Vega, P., De Los Ríos, J.L.P.D., 2011. Do technology and manufacturing strategy links enhance operational performance? Empirical research in the auto supplier sector. Int. J. Prod. Econ. 133. McKone, K.E., Schroeder, R.G., 2002. A plant’s technology emphasis and approach. Int. J. Oper. Prod. Manag. 22, 772–792. Mejías, A.M., Paz, E., Pardo, J.E., 2016. Efficiency and sustainability through the best practices in the Logistics Social Responsibility framework. Int. J. Oper. Prod. Manag. 36, 164–199. Min, S., Mentzer, J.T.J.T., Ladd, R.T.R.T., 2007. A market orientation in supply chain management. J. Acad. Mark. Sci. 35, 507–522. Moradlou, H., Backhouse, C., Ranganathan, R., 2017. Responsiveness, the primary reason behind re-shoring manufacturing activities to the UK. Int. J. Phys. Distrib. Logist. Manag. 47, 222–236. Morgan, T.R., Richey Jr, R.G., Autry, C.W., 2016. Developing a reverse logistics competency: The influence of collaboration and information technology. Int. J. Phys. Distrib. Logist. Manag. 46, 293–315. Nakano, M., Akikawa, T., 2014. Literature review of empirical studies on SCM using the SSPP paradigm. Int. J. Prod. Econ. 153, 35–45. Nitzl, C., Roldan, J.L., Cepeda, G., 2016. Mediation analysis in partial least squares path modeling. Ind. Manag. Data Syst. 116, 1849–1864. Ortega-Jiménez, C.H., Domínguez Machuca, J.A., Garrido‐Vega, P., 2014. From lean to reconfigurability: systematic review of high performance manufacturing. Int. J. Manag. Sci. Technol. Inf. 99–131. Ortega-Jimenez, C.H., Garrido-Vega, P., Machuca, J.A.D., 2012. Analysis of interaction fit between manufacturing strategy and technology management and its impact on performance. Int. J. Oper. Prod. Manag. 32, 958–981. Ortega-Jimenez, C.H., Garrido-Vega, P., Pérez Díez De Los Ríos, J.L., García González, S., 2011a. Manufacturing strategy-technology relationship among auto suppliers. Int. J. Prod. Econ. 133, 508–517. Ortega-Jimenez, C.H., Garrido-Vega, P., Pérez Díez De Los Ríos, J.L., García González, S., 2011b. Manufacturing strategy–technology relationship among auto suppliers. Int. J. Prod. Econ. 133, 508–517. Ortega-Jimenez, C.H.O., Machuca, J.A.D., Garrido-Vega, P., Filippini, R., 2015. The pursuit of responsiveness in production environments: From flexibility to reconfigurability. Int. J. Prod. Econ. 163, 157–172. Podsakoff, P.M., MacKenzie, S.B., Lee, J.-Y., Podsakoff, N.P., 2003. Common method biases in behavioral research: A critical review of the literature and recommended remedies. J. Appl. Psychol. 88, 879–903. Polites, G.L., Roberts, N., Thatcher, J., 2012. Conceptualizing models using multidimensional constructs: a review and guidelines for their use. Eur. J. Inf. Syst. 21, 22–48. Rogers, D.S., Daugherty, P.J., 2013. Enhancing service responsiveness: the strategic potential of EDI. Logist. Inf. Roh, J., Min, H., Hong, P., Min, H., 2014. Implementation of a responsive supply chain strategy in global complexity: The case of manufacturing firms. Int. J. Prod. Econ. 147, 198–210. Rösiö, C., Säfsten, K., 2013. Reconfigurable production system design–theoretical and practical challenges. J. Manuf. Technol. Manag. 24, 998–1018. Rosseel, Y., 2012. Lavaan: An R Package for Structural Equation Modeling. J. Stat. Softw. 48, 1–36. Rungtusanatham, M., Miller, J.W., Boyer, K.K., 2014. Theorizing, testing, and concluding for mediation in SCM research: Tutorial and procedural recommendations. J. Oper. Manag. 32, 99–113. Sandhu, S., Smallman, C., Ozanne, L.K., 2014. Environmental Responsiveness and Cost Savings: Effect or Driver?, in: The International Society of Business, Economics, and Ethics Book Series. pp. 55–70. Santos Bernardes, E., Hanna, M.D., 2009. A theoretical review of flexibility, agility and responsiveness in the operations management literature. Int. J. Oper. Prod. Manag. 29, 30– 53. Schorsch, T., Wallenburg, C.M., Wieland, A., 2017. The human factor in SCM. Int. J. Phys. Distrib. Logist. Manag. 47, 238–262. Schroeder, R.G., Flynn, B.B., 2001. High performance manufacturing : global perspectives. John Wiley. Soper, D.S., 2017. Free A-priori Sample Size Calculator for Structural Equation Models - Free Statistics Calculators. Stevens, G.C., Johnson, M., 2016. Integrating the Supply Chain 25 years on. Distrib. Logist. Manag. 46, 19–42. Suzuki, N., Olson, D.H., Reilly, E.C., 2008. Developing landscape habitat models for rare amphibians with small geographic ranges: a case study of Siskiyou Mountains salamanders in the western USA. Biodivers. Conserv. 17, 2197–2218. Swafford, P.M., Ghosh, S., Murthy, N., 2008. Achieving supply chain agility through IT integration and flexibility. Int. J. Prod. Sweeney, E., Sweeney, E., 2013. The People Dimension in Logistics and Supply Chain Management – its Role and Importance, in: Passaro, R., Thomas, A. (Eds.), Supply Chain Management: Perspectives, Issues and Cases. McGraw-Hill, pp. 73–82. Thambusamy, R., Salam, A.F., 2010. Corporate ecological Responsiveness, Environmental Ambidexterity and IT-Enabled Environmental sustainability Strategy. ICIS. Thode, H.C., 2002. Testing for normality. Marcel Dekker. Tsinopoulos, C., Mena, C., 2015. Supply chain integration configurations: process structure and product newness. Int. J. Oper. Prod. Manag. 35, 1437–1459. Um, J., Lyons, A., Lam, H.K.S., Cheng, T.C.E., Dominguez-Pery, C., 2017. Product variety management and supply chain performance: A capability perspective on their relationships and competitiveness implications. Int. J. Prod. Econ. 187, 15–26. Uskonen, J., Tenhiälä, A., 2012. The price of responsiveness: Cost analysis of change orders in make-to-order manufacturing. Int. J. Prod. Econ. Venkatraman, N., 1989. The Concept of Fit in Strategy Research: Toward Verbal and Statistical Correspondence. Acad. Manag. Rev. 14, 423–444. Wang, X., Chan, H.K., Yee, R.W.Y., Diaz-Rainey, I., 2012. A two-stage fuzzy-AHP model for risk assessment of implementing green initiatives in the fashion supply chain. Prod. Econ. Weston, R., 1999. Model‐driven, component‐based approach to reconfiguring manufacturing software systems. Int. J. Oper. Prod. Manag. 19, 834–855. Williams, B.D.B.D., Roh, J., Tokar, T., Swink, M., 2013. Leveraging supply chain visibility for responsiveness: The moderating role of internal integration. J. Oper. Manag. 31, 543–554. Wong, C.W.Y.C.Y., Wong, C.W.Y.C.Y., Boon-itt, S., 2015. Integrating environmental management into supply chains: a systematic literature review and theoretical framework. Int. J. Phys. Distrib. Logist. Manag. 45, 43–68. Zhang, J., Gu, J., Li, P., Duan, Z., 1999. Object-oriented modeling of control system for agile manufacturing cells. Int. J. Prod. Econ.