Building manufacturing flexibility with strategic suppliers and contingent effect of product dynamism on customer satisfaction

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Building manufacturing flexibility with strategic suppliers and contingent effect of product dynamism on customer satisfaction ⁎

María Jesús Sáenza,b, , Desiree Knoppena,c, Elcio Mondonca Tachizawad a

Zaragoza Logistics Center, MIT Global Scale Network, PLAZA, c/Bari, 55. Edif. Nayade, 50197 Zaragoza, Spain University of Zaragoza, Spain c Operations Management and Information Systems, EADA Business School, c/Aragón 204, 08011 Barcelona, Spain d Department of Business Administration, Universidad Carlos III de Madrid, c/Madrid, 126, 28903 Getafe, Spain b

A R T I C L E I N F O

A B S T R A C T

Keywords: Manufacturing flexibility Inter-organizational learning Product dynamism Buyer-supplier collaboration Strategic suppliers Customer satisfaction

A critical capability sought by an increasing number of firms is manufacturing flexibility, because it allows to effectively respond to dynamic markets. Grounded upon a supply chain perspective, this paper aims to assess antecedents of manufacturing flexibility that stem from the upstream relationships with strategic suppliers. Additionally, it is one of the first to analyze the contingent effect of product dynamism on the impact of manufacturing flexibility on downstream customer satisfaction. We apply structural equation modeling to a sample of 155 companies in order to analyze our hypotheses. Results strongly indicate that buyer-supplier collaboration facilitates inter-organizational learning that in turn allows organizations to develop manufacturing flexibility and increase customer satisfaction. Approaching manufacturing flexibility from a broader supply chain view thus pays off. Moreover, we apply multi-group confirmatory factor analysis to explore the contingent effect of product dynamism on the relationship between manufacturing flexibility and customer satisfaction. Results suggest a stronger impact of manufacturing flexibility on performance in the context of higher product dynamism in companies’ customer markets, confirming the importance of a contingency view to flexibility.

1. Introduction Manufacturing flexibility is seen as a key characteristic of successful firms (Scherrer-Rathje et al., 2014). Several factors help explain the increasing importance of manufacturing flexibility, such as product proliferation, massive customization strategies, or the enormous increase in online retail. Currently, we face a move to an “on demand” economy based on shorter lead times, exemplified in extremis by new initiatives such as Amazon's “one-hour delivery” (Wired, 2015), and shorter development periods, exemplified by Apple´s recent launch of the iPhone 6 (Reuters, 2015). Although the concept of manufacturing flexibility is not new, we have recently seen an increasing number of empirical studies on this issue (Mendes and Machado, 2015; Mishra et al., 2014; Ojha et al., 2015; Pérez Pérez et al., 2016; Tamayo-Torres et al., 2014; UrtasunAlonso et al., 2014). Nevertheless, antecedents that could hinder or leverage manufacturing flexibility remain underdeveloped in the literature, for example antecedents related to upstream relationships with selected suppliers (Mishra et al., 2014; Pérez Pérez et al., 2016). Critical resources may span firm boundaries and be embedded in buyer-supplier relationships (Dyer and Singh, 1998). Inter-organizational learning in ⁎

that regard allows an organization to identify external knowledge and convert it into value for the customer (Lane et al., 2006). In other words, inter-organizational learning allows a buyer to identify relevant suppliers´ knowledge and convert that into an adapted offer to downstream customers (Sáenz et al., 2014). However, previous studies lack empirical evidence measuring the extent to which inter-organization learning contributes to manufacturing flexibility (Mishra et al., 2014). A second research gap is the influence of buyer-supplier collaboration on manufacturing flexibility. Although integration with suppliers has been often mentioned as contributing to manufacturing flexibility, empirical studies on this issue are rare (Mishra et al., 2014; Zhang et al., 2003). For example, the contrast between relational and arm's length approaches to suppliers, although largely discussed in the supply chain management literature (Mahapatra et al., 2012), has received much less attention in flexibility studies. This is relevant, because flexibility strategies do not exist on a vacuum: instead, they interact with supply policies. The firm can work in concert with strategic suppliers to deliver value to the market (Cousins and Spekman, 2003). But, it is not clear how and to what extent buyer-supplier collaboration facilitates the development of manufacturing flexibility. A supply chain perspective on manufacturing flexibility involves not

Correspondence to: Zaragoza Logistics Center, MIT Global Scale Network, PLAZA, c/Bari, 55. Edif. Nayade, Bl.5, 50197 Zaragoza, Spain E-mail addresses: [email protected] (M.J. Sáenz), [email protected] (D. Knoppen), [email protected] (E.M. Tachizawa).

http://dx.doi.org/10.1016/j.pursup.2017.07.002 Received 24 June 2016; Received in revised form 22 June 2017; Accepted 19 July 2017 1478-4092/ © 2017 Elsevier Ltd. All rights reserved.

Please cite this article as: Saenz, M.J., Journal of Purchasing and Supply Management (2017), http://dx.doi.org/10.1016/j.pursup.2017.07.002

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only the appreciation of the impact of upstream relationships with strategic suppliers but also the effect of flexibility on downstream outcomes. A key outcome in that regard is customer satisfaction (Zhang et al., 2003). With rare exceptions (e.g. Camisón and Villar Lopez, 2010), current literature on flexibility has neglected the impact on customer satisfaction and rather focused on broader performance measures (Pérez Pérez et al., 2016). This is a critical issue, since the combination of customers´ and other stakeholders´ actions and decisions ultimately drive a firm's financial performance. A final research gap concerns the relationship between product characteristics and flexibility. Although flexibility is often associated with innovative products (Fisher, 1997) or dynamic environments (Fine, 1998), there is scarce empirical evidence (Gligor et al., 2015; Tamayo-Torres et al., 2014). Actually, recent studies suggest that flexibility may also be important to less dynamic products (Blome et al., 2013). Many sectors traditionally associated with functional products (e.g., the chemical industry) are facing pressures to increase their flexibility (ICIS, 2015). Summarizing, this paper aims to empirically analyze the key antecedents of manufacturing flexibility that stem from upstream relationships with suppliers. In addition, we aim at providing empirical evidence on the effect of flexibility on downstream customer satisfaction, as well as the moderating role of product dynamism in such relationship. The paper is structured as follows. In the next section we review briefly the existing literature on manufacturing flexibility, the expected impact on customer satisfaction, the expected moderating impact of product dynamism, and the nature of the selected antecedents. This analysis allows us to develop a theoretical framework and corresponding hypotheses. The research methodology is subsequently explained, including the sample characteristics, data collection and measurement scales, and the structural equations method used to analyze the data. We then present and discuss the main results derived from the empirical analysis. Finally, we suggest managerial implications as well as future research directions.

Fig. 1. Conceptual framework.

relationship is moderated by product dynamism. Accordingly, we propose a conceptual framework, which is depicted in Fig. 1. In the next sections, we develop each of our research hypotheses. 2.2. Manufacturing flexibility Upton (1994), (1995) considered flexibility as the ability to increase the range of products available, improving a firm's ability to respond quickly and achieving good performance over this wide range of products. This ability is critical in the context of rapidly-changing environments, where customers continuously change habits and preferences. Through the development of flexibility capabilities, firms seek to build enduring sources of competitive advantage. Nevertheless, several strategic factors need to be considered before planning and implementing manufacturing flexibility (Chang et al, 2003; Suarez et al., 1996). For example, firms should not be viewed as a portfolio of assets and isolated businesses, but as a set of mechanisms by which new skills are selected and built (Teece et al., 1997). This process should involve external actors, notably suppliers (Zhang et al., 2003). Saleh et al. (2009), in a literature review on manufacturing flexibility, pointed out the difficulty of measuring this construct, due to its multidimensional nature. In this study, we adopt the manufacturing flexibility items from Suarez et al. (1996), who define four first-order flexibility dimensions, i.e. those that directly affect the competitive position of a firm in the market, and that are readily perceived by the customers: mix, volume, new product and delivery time. Most of the other “lower-order” flexibility types proposed in the literature express their competitive effect through one or more of the first-order flexibility types (Suarez et al., 1996). In particular, we adapt Suarez's flexibility types to a buyer-supplier perspective. This can be justified from the fact that a detailed analysis of more than 100 studies suggests that a firm's manufacturing flexibility is heavily affected by the external environment (Mishra et al., 2014). Indeed, flexibility can be seen as the result of a system of supply chain actors (Seebacher and Winkler, 2013; Vickery et al., 1999). This is in line with recent studies that remark the critical role of suppliers on the development of flexibility (Aissa Fantazy et al., 2009; Arawati, 2011; Blome et al., 2014). For example, Manders et al. (2016), in a recent empirical study in the fast-moving consumer goods industry, found that flexibility predominantly affects the dyadic relationships in a supply chain. Most studies relating flexibility and performance are focused on internal or financial measures of performance (e.g. Arawati, 2011; Martínez Sánchez and Pérez Pérez, 2005; Merschmann and Thonemann, 2011; Nair, 2005). However, other researchers argue that flexibility should be analyzed from a customer-centered perspective (Lummus et al., 2005; Vickery et al., 1999), such as customer satisfaction (Camisón and Villar Lopez, 2010). Thus, we believe the outcome of flexibility should be analyzed with respect to the extent to which they add value to the customer. In the next section, we review the customer satisfaction implications of flexibility.

2. Conceptual framework and hypothesis development 2.1. Overview of the conceptual framework Companies are aware of the importance of aligning their efforts with supply chain partners in order to address market dynamism. Such alignment facilitates the development of capabilities to better meet customer demands (Vickery et al., 1999). More precisely, careful management of supplier relationships allows the development of flexibility capabilities (Jack and Raturi, 2002; Oke, 2005). In this study, we develop and test a conceptual framework that simultaneously addresses antecedents to manufacturing flexibility and a key outcome of manufacturing flexibility. The former relate to upstream relationships with strategic suppliers and the latter to the downstream customer output. Buyer-supplier relationships host interfirm resources and routines such as knowledge-sharing processes (Dyer and Singh, 1998). The interorganizational learning that takes place between buyers and suppliers has been mentioned repeatedly as a vital antecedent to the development of flexibility (Santos-Vijande et al., 2012; Zhang et al., 2003). Buyer-supplier relationships are characterized by different degrees of collaboration. Higher degrees of collaboration are typically associated to higher degrees of inter-organizational learning (Yan and Dooley, 2014). There are also studies that draw a direct impact from collaboration on manufacturing flexibility (Kähkönen and Lintukangas, 2012). At the downstream side (customer outcome) we focus on customer satisfaction to complement the literature that focusses on more general measures. Based on contingency theory arguments, we posit that the effect of manufacturing flexibility on customer satisfaction depends on product characteristics. More specifically, we posit that this

2.3. Customer satisfaction Customer satisfaction has been analyzed extensively in several 2

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framework have produced mixed, inconclusive or negative results (Perez-Franco et al., 2016). From a managerial perspective, the distinction between highly dynamic and less dynamic products is thorny, e.g. there are products in sectors traditionally seen as less dynamic (e.g. food) with high dynamism, as well as products in sectors perceived as highly dynamic (e.g. electronics) with low dynamism. There are several reasons for that: intense competition, fluctuating prices and unstable demand are forcing many companies in less dynamic sectors to specialize in product niches that are very dynamic (e.g., Glanbia, a whey protein producer in the sports and nutrition industry). On the other hand, firms of sectors traditionally considered as “dynamic” are focusing on more stable demand niches to compete on price, such as computer manufacturer Lenovo (who adopts a hybrid supply chain strategy, with more flexibility for innovative products, and a low-cost supply chain for mature products). In fact, Blome et al. (2013) argue that improving flexibility can generate cost savings even in less dynamic sectors. An implicit but relevant implication is that flexibility may be an important managerial dimension even for firms not competing based on flexibility. Recent supply chain management trends seem to support this notion: customers have become more demanding, product life cycles have become shorter, and social media applications have increased the impact of unsatisfied customers (Blome et al., 2013). Thus, we believe that this discussion could benefit from a customer-centered perspective, in particular focusing on the impact on final customer satisfaction. Hence, in this study we argue that highly dynamic products (i.e. products with unstable demand and short life cycles, such as fashion) will correspond to a stronger relationship between flexibility and customer satisfaction. On the other hand, less dynamic products (e.g. products with stable demand and large life cycles, such as commodities) will be associated to a weaker relationship between flexibility and customer satisfaction. Thus, within the particular context of the conceptual framework (see Fig. 1), we aim to examine the following hypothesis in terms of the contingent effect of product dynamism:

areas, particularly in Operations Management studies. Accordingly, Zhang et al. (2003) consider customer satisfaction as the degree to which customers perceive that products and services are worth more than they paid. The dimensions of customer satisfaction relate for example to ratio of price to value, quality, customer loyalty, or delivery performance (Jayaram et al., 2010; Yao et al., 2009; Zhang et al., 2003). Manufacturing flexibility may increase customer satisfaction in several ways. For example, volume flexibility allows firms to increase production in response to unanticipated customer needs and reduce waiting times when demand levels fluctuate. In addition, mix flexibility supports producing items with the features and performance demanded by customers, and a wide variety of products without excessive delays, premium prices or quality decrease, and reduces waiting time for special orders (Zhang et al., 2003). In spite of the great number of studies relating flexibility and performance, results concerning the impact on customer satisfaction are not conclusive (Pérez- Pérez, et al., 2016). Several studies suggest that this relationship is positive (Duclos et al., 2003; Fawcett et al., 1996; Lummus et al., 2005; Vickery et al., 1999), but they are often conceptual or based on anecdotal evidence. Whereas Yang (2014) verified no positive relationship, other studies (Blome et al., 2013; Hartmann and De Grahl, 2011) do verify this relationship. There are some studies that suggest a positive effect on customer satisfaction, but they are focused on other types of flexibility: logistics flexibility (Zhang, Vonderembse and Lim, 2005) or spanning flexibility (Zhang et al., 2006). Thus, more quantitative studies in different empirical contexts are needed to confirm these notions. Therefore, we propose the following hypothesis: H1:. Manufacturing flexibility is positively associated with customer satisfaction. 2.4. Product dynamism According to contingency theory (Lawrence and Lorsch, 1967), structure and process in an organization must fit its context. In a similar vein, the processes within buyer-supplier relationships must fit their context (Saccani and Perona, 2007). Accordingly, we hypothesize that the impact of manufacturing flexibility on customer satisfaction may depend on contextual conditions. Overall, researchers claim that firms should pursue flexible strategies when operating in unstable environments—i.e. volatile demand, complex customer requirements, and high variety (Fisher, 1997; Lee, 2002; Olavson et al., 2010). There is empirical evidence that in uncertain environments more flexible supply chains perform better than less flexible ones (Martínez Sánchez and Pérez Pérez, 2005; Merschmann and Thonemann, 2011). Wagner et al. (2012) verified that firms with a fit between supply/demand uncertainty and supply chain responsiveness levels achieved better financial performance. Likewise, Peng et al. (2013) have analyzed the moderation effect of product dynamism on the relationship between customer integration, plant improvement and innovation capability. However, there is still limited empirical research examining the role of environmental conditions on the effectiveness of flexibility initiatives, especially concerning customer satisfaction. In particular, Liao and Marsillac (2015) claim that more research is needed to understand the tactical elements related to product dynamism and flexibility. In his seminal article, Fisher (1997) define two types of products: functional products, which have high and predictable demand, long product life cycles and low product variety (less dynamic products); and innovative products, which have unpredictable demand, short product life cycles and high product variety (more dynamic products). Innovative products require a more responsive supply chain, whereas functional products are typically associated with more efficient ones. However, empirical evidence of Fisher's categorization is limited and contradictory. Actually, empirical studies attempting to verify this

H2:. Product dynamism moderates the relationship between manufacturing flexibility and customer satisfaction. 2.5. Antecedents stemming from supplier relationships Human and organizational factors play a crucial role in realizing manufacturing flexibility goals (Suarez et al., 1996; Upton, 1994). In particular, the study conducted by Suarez et al. (1996) at plant level in a printed circuit board-industry addressed the empirical relationship between flexibility and non-technological factors. Based on their study, they suggested that non-technological factors, such as close relationships with suppliers appeared to increase mix-based, volume and newproduct flexibility. Achieving manufacturing flexibility requires a wider effort in order to align the different supply chain partners (Zhang et al., 2003). Thus, supplier relationships and the dynamics they host are key to understand how firms build flexibility. In this study, we are particularly interested in inter-organizational learning, because it provides new knowledge that allows firms to continuously adapt to rapidly changing market requirements. Slater and Narver (1995, p. 63) define organizational learning as “the development of new knowledge or insight that has the potential to influence firm behavior”. In particular, organizations learn in order to better adapt to their environment (Dodgson, 1993). Inter-organization learning is the collective learning of organizations in formal organizational collaborations, such as strategic alliances and networks (Larsson et al., 1998). It is a process by which an organization extends its experience to others, with the goal of jointly producing new experiences (Holmqvist, 2004). In the context of buyer-supplier relationships, it refers to the degree to which companies can learn from each other to recognize rapidly major market changes and opportunities. This implies 3

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Based on multi-case evidence, Emberson and Storey (2006) state in that regard: “collaborative practices were at risk when people moved posts or when alternative priorities swept away the arrangement as a sacrifice on the altar of supposedly ‘bigger’ ideas.” (p. 243). In order to test these contradictory statements, we argue that buyer-supplier collaboration is a key antecedent of manufacturing flexibility, but that it has to be translated through a mechanism such as inter-organizational learning in order to impact flexibility. It is through inter-organizational learning that buyers and suppliers actually change their potential repertoire of actions (Cheung et al., 2010; Knoppen et al., 2010) and thus allow development of the flexibility capability. In other words, the direct impact from collaboration on manufacturing flexibility is not significant and the hypothesis to test is:

to commit resources inside the organization as well as to align with key partners (Azadegan et al., 2008). More precisely, inter-organizational learning allows firms to innovate or continuously improve their product/service offerings. It involves exploration, assimilation and exploitation of new ideas (Lane et al., 2006; Sáenz et al., 2014). Blome et al. (2014) argue that inter-organizational learning supports the formation of combinative capabilities, which lead to the realization of market opportunities. For example, knowledge transfer on process innovation may allow the development of more flexible production processes, with direct effects on volume and mix flexibility. Moreover, sharing information about emerging customer needs allows reduction in product development lead times, increasing new product flexibility. However, research that explicitly investigates the link between inter-organizational learning and flexibility is scarce and not conclusive (Yang, 2014). While some studies have detected a positive relationship (Blome et al., 2014), others have not (Braunschneidel and Suresh, 2009). An experiential view of learning would lead us to expect positive outcomes of inter-organizational learning, when learning is grounded in buyer-supplier collaboration. Without such collaboration, learning outcomes may be biased towards existing and proven procedures that may be detrimental for the broader supply chain (Knoppen et al., 2010). Therefore, we propose the following hypothesis: H3:. Inter-organizational flexibility.

learning

positively

affects

H5:. Inter-organizational learning fully mediates the relationship between buyer-supplier collaboration and manufacturing flexibility.

3. Methodology 3.1. Sample characteristics, data collection and measurement scales In order to gather data to test the research hypotheses, we developed a managerial survey. In a preliminary phase, six maximally different dyads were deeply analyzed to better understand the dynamics of the constructs under study. This exploratory procedure, together with an exhaustive literature review, served as the basis for survey-item development. In the second stage, three academics, four supply chain executives and two senior consultants reviewed the questionnaire. In addition, the Survey Quality Prediction (SQP) tool was used to determine the validity and reliability of the questionnaire (Saris and Gallhofer, 2014). Finally, the questionnaire was assessed in a pilot test, and minor adjustments were introduced. The questionnaire was submitted to a sample of 932 Spanish managers in areas related to the study (purchasing, logistics, supply chain management, etc.). To increase the generalizability of the study, we focused on manufacturing companies belonging to multiple industrial sectors when defining the research sample. In particular, the sample encompassed both companies with highly dynamic products and companies with less dynamic products. We obtained a final sample of 155 companies, 79 related to highly dynamic products and 76 related to less dynamic products, which corresponded to a response rate of 17%. This response rate is in line with recent surveys in the supply chain management (SCM) area (e.g., Oke et al., 2013; Narayanan et al., 2015). Respondents had a diversified profile, which corresponded mainly to senior managers of logistics, purchasing, supply chain, or procurement. Table 1 shows the details related to the respondent's profile and the different subsamples regarding company's size. In the survey, respondents were informed that its focus was the

manufacturing

The ability to learn with suppliers is not automatic, since it depends upon contextual variables (e.g., leadership, culture, or strategy) and the organization needs to be familiar with the conditions where learning with its partner takes place (Azadegan et al., 2008). For example, the mechanisms and the extent to which inter-organizational learning occurs are affected by the alignment between parties. For that reason, many firms have initiated actions for developing their supply base in terms of creating inter-organizational teams, incentivizing their qualified personnel and workers to collaborate with those key suppliers, or creating a willingness to implement collaboration goals (Stevenson and Spring, 2009). Repeated buyer-supplier collaborative interactions help develop knowledge bases, capabilities, and resources (Pérez Pérez et al., 2016; Yan and Dooley, 2014) and facilitate mutual learning from experiences (Holmqvist, 2004). Such collaborative interactions should be established with selected strategic suppliers (Saccani and Perona, 2007) and are consequently an expression of the firm´s desire to move from functional thinking to transversal supply chain wide thinking. In general, collaborative relationships deliver results independent of industry or sector (Cousins and Spekman, 2003). Accordingly, we rely on the well-known construct of buyer-supplier collaboration (e.g. Narayanan et al., 2015; Paulraj and Chen, 2007; Yan and Dooley, 2014). This concept is closely connected to the notion of buyer-supplier integration (Braunschneidel and Suresh, 2009; Reichhart and Holweg, 2007), but it is more focused on the relational aspects of such phenomenon. Thus, we propose the following:

Table 1 Profile of the sample.

H4:. Buyer-supplier collaboration positively affects inter-organizational learning. Number of employees

Buyer-supplier collaboration has also been related directly to capability development. Kähkönen and Lintukangas (2012) argue in that regard that collaboration supports the development of “the ability to compete and respond to environmental challenges in the industry, the ability to exploit relational capabilities, and the ability to understand and respond to customer” (p. 69). Narayanan et al. (2015) on the other hand conclude that the relationship between buyer-supplier collaboration and capabilities development is not a straightforward one, but rather a mediated one. In other words, collaboration as such is not a guarantee of success. It is typically developed at more operational levels and therefore remains vulnerable to changes in corporate policy that clash with established collaborative practices (Emberson and Storey, 2006).

Industry

Respondent's experience in the company

4

Category

Percentage

0–49 50–150 151–500 > 500 Food and beverage Electronics Textile Pharmaceutical Others 0–4 years

28.6% 32.5% 24.0% 14.9% 42.8% 27.0% 14.5% 7.9% 7.9% 26,7%

5–10 years 11–20 years > 20 years

29,3% 28,7% 15,3%

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manufacturing firm and its supply relationship with a selected strategic supplier (where “strategic” was defined in terms of degree of collaboration). The buyer-supplier collaboration, inter-organizational learning and flexibility constructs operated at the dyadic level, and in the introduction of the related sections in the survey we emphasized that those questions referred to the selected supplier. We also highlighted that the customer satisfaction and product dynamism constructs operated at the level of the focal firm, since our aim was to assess the customer-related outputs at firm level. The buyer-supplier collaboration construct was built upon the key facets of management commitment, joint action and collaboration achievements. More precisely, to capture management commitment, we asked to what extent the management encouraged collaboration with the selected supplier. To capture joint action, we assessed the existence of a multidisciplinary team, as well as the quality of the personnel composing such team. We also asked about the feedback of collaboration achievements. Answers had to be given on a 5-point Likert scale, ranging from ‘not implemented at all’ to ‘totally implemented’, with labels attached to all answer categories, in order to increase validity and reliability of the responses (Saris and Gallhofer, 2014). The inter-organizational learning construct was based on the previous work from Holmqvist (2004) and Azadegan et al. (2008). It was assessed by asking the respondents whether this particular supplier offered improvement ideas, if these suggestions were put into practice, and if the supplier kept track of progress through communication made in terms of the proposed changes. Moreover, respondents were asked to what extent they shared relevant information with this supplier in an efficient way. The manufacturing flexibility items were based on Suarez et al. (1996). In particular, we asked the respondents to answer the questions with respect to the selected strategic supplier, concerning key dimensions of flexibility, e.g. to what extent they shared a focus on shortening cycle times, adopting productive processes capable of responding quickly to changes in demand volume and variety, and changes in product or component design to better meet the needs of the market (Suarez et al., 1996). The customer satisfaction items encompassed several dimensions of customer-related outputs: price/value ratio, quality, loyalty and delivery time, following Zhang et al. (2003) and Stathopolou and Balabanis (2016). In particular, the inclusion of delivery performance as a customer satisfaction item is justified because customer satisfaction is determined not only by the quality of the product, but also by the quality of its delivery process (Chavez et al., 2014). Similarly, Gligor et al. (2015) consider customer effectiveness as the extent to which customer-related objectives have been met. The measures of customer satisfaction also included customer loyalty, because both concepts are commonly associated in the literature (Stathopoulou and Balabanis, 2016). However, customer satisfaction and reputation have evolved separately in the literature, and are often treated as separate constructs (Selnes, 1993; Bontis et al., 2007). The reason seems to be that reputation is seen as a more long-run and overall measure than customer satisfaction (Selnes, 1993). Finally, product dynamism was measured with a direct question (How would you characterize the products of your firm?) and had to be responded on a dichotomic scale (dynamic versus stable products). Although the use of single-item constructs could raise some concerns, several researchers claim that they can be as valid as multiple-item ones (e.g. Saris and Gallhofer, 2014). In particular, Nair et al. (2016), by a comparison of several sets of survey data, concluded that the decision between multiple-item measures and single-item measures for scale development should depend on the nature of constructs. Accordingly, for concrete constructs, single-item measures are as valid as multi-item measures. Descriptive statistics are provided in Table 2. The Average Variance Extracted (AVE) was calculated in line with Anderson and Gerbing (1988), and Composite Reliability (CR) was calculated in line with

Table 2 Measurement Scales. Factor loadings Buyer-supplier collaboration (Santos-Vijande et al., 2012; Zhang et al., 2003) Please bear in mind the relationship with the selected strategic supplier: R1 There is a multidisciplinary team with staff 0,61 from both companies 0,74 R2 The management encourages collaboration between the employees & their strategic suppliers R3 We assign qualified personnel to collaborate 0,70 with our supplier Inter-organizational learning (Holmqvist, 2004; Azadegan et al., 2008) Please bear in mind the relationship with the selected strategic supplier: 0,62 L1 My supplier offers ideas and suggestions where he sees openings/opportunities for improvement within the process L2 We put supplier's suggestions into practice 0,72 L3 We share relevant information efficiently, 0,44 succinctly and exclusively with our supplier L4 Our supplier evaluates and communicates our 0,84 progress of proposed changes Manufacturing flexibility (Suarez et al., 1996) With the selected strategic supplier, we share a focus on: F1 shorten cycle-times of both ours and our 0,69 supplier´s productive processes 0,82 F2 adopt productive processes capable of responding quickly to changes in demand volume 0,87 F3 adopt productive processes capable of responding quickly to changes in product specifications from our clients F4 fast response to the market with new products 0.67 or components that satisfy new necessities Customer satisfaction (Zhang et al., 2003; Stathopolou and Balabanis, 2016) S1 Our clients are satisfied with the price/value 0,56 ratio of our products S2 Our clients are satisfied with the quality of our 0,77 products S3 Our clients are loyal to our products 0,71 S4 All customer orders are delivered on the 0,56 specified time Product dynamism (Fisher, 1997; Peng et al., 2013) D1 How would you characterize your products? Products that move fast, such as perishables, fashion, and seasonal products. Products that move slowly, such as commodities, and products with stable demand.

AVE

CR

0.47

0.72

0.45

0.76

0.59

0.85

0.43

0.75

Fornell and Larcker (1981). CR is 0.75 or higher for all constructs. AVE is relatively low for customer satisfaction (0.43). Fornell and Larcker (1981) state in that regard that discriminant validity is established if the latent variable accounts for more variance in its associated indicators variables than it shares with other constructs in the same model. In practical terms this means that each construct´s AVE must be compared with its squared correlations with other constructs in the model (Henseler et al., 2015). All constructs of our model met this requirement. Moreover, confirmatory Factor Analysis (CFA) is an important complement to indicate if reliability levels for the constructs are acceptable (Bollen, 1989; Saris et al., 2009). As stated by Lance et al. (2006), rules of thumb for cut-off values are arbitrary and context dependent. Therefore, it is important to gather multiples pieces of information in order to judge the quality of the model. Accordingly, we decided to continue with CFA and the related misspecifications analysis (MI) to further assess the fit of the model and each of the constituting constructs.

5

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3.2. Statistical methods The study builds upon structural equation modeling (SEM) (Bollen, 1989), using maximum likelihood estimation with LISREL (Jöreskog, 1969). We followed a two-step procedure in which we first evaluated the measurement model and subsequently the structural model (Anderson and Gerbing, 1988). Whereas the measurement model quality test builds upon a CFA, the structural model quality test is based on a path analysis. In order to evaluate the moderating impact of product dynamism, we employed a multi-group confirmatory factor analysis (MGCFA). For all of these tests, we complemented the assessment of standard test statistics—e.g., AGFI, GFI, SRMR, NFI, CFI, and RMSEA, all of which are highly dependent upon the power of the test—with an analysis of misspecifications (Saris et al., 2009). The analysis of misspecifications bears in mind the power of the test and is supported by modification indexes (MI) and expected parameter changes (EPC) as provided by LISREL.

Fig. 2. Structural equation modeling results.

= 0.044), showed a misspecification. More precisely, the loading from the F1 item (“shorten cycle-times of both ours and our supplier's productive processes”) on manufacturing flexibility was non-equivalent across both groups. No straightforward guidelines exist regarding the minimum number of equivalent indicators that are required per concept. Steenkamp and Baumgartner (1998) suggest that ideally a majority of factor loadings and intercepts should be equivalent across groups. De Jong et al. (2007, p. 262) argue that formally only one equivalent indicator is required. Based on these guidelines, we decided to set free this one indicator (which is at the low side of the loadings of the four indicators) and test the metric equivalence again. This time test statistics were fine (χ2 = 46.70; df = 43; RMSEA = 0.033) and there were no further misspecifications. Consequently, we proceeded to compare the relationship between flexibility and customer satisfaction among the two sub-samples defined by product dynamism. Subsequently, we tested path model invariance regarding the impact of on customer satisfaction. The test statistics of the base model for the two groups with non-restricted path models were: χ2 = 44.61; DF = 38; RMSEA = 0.047. There were no misspecifications. Subsequently, when imposing an equivalent impact of manufacturing flexibility on customer satisfaction, tests statistics indicated a substantial decrease in fit (χ2 = 54.82; df = 39; RMSEA = 0.072). Thus, the fit in terms of χ2 decreased with 10.21 for just one degree of freedom. A misspecification was reported: the impact from flexibility on customer satisfaction was not equivalent for both groups, characterized by dynamic and stable products. The impact - in terms of common metric completely standardized solution- in the group of companies with more dynamic products was 0.53, whereas the same impact was 0.45 in the group of companies with more stable products. Therefore, Hypothesis H2 is confirmed. Results are summarized in Table 4. In the next section, we discuss the results and managerial implications.

4. Empirical results and analysis In this section, results of the measurement and structural models are analyzed. The test of the measurement model with the CFA of four correlated latent constructs shows good fit indices (χ2 = 108.81; df = 84; χ2/DF = 1.30; RMSEA = 0.045; NFI = 0.93; CFI = 0.98) and more importantly, the complementary analysis of misspecifications (MI) and expected parameter change (EPC) do not suggest any misspecifications such as cross-loadings or correlated errors. Therefore, CFA results suggest that the construct quality is good enough to be used in subsequent path model analyses. Pearson bivariate correlations between all variables are presented in Table 3. The path model analysis, used to test the research hypotheses, shows good test statistics (χ2 = 112.73; df = 87; χ2/DF = 1.30; RMSEA = 0.045; NFI = 0.92; CFI = 0.98) and more importantly, the complementary analysis of MI and EPC does not suggest any misspecifications such as omitted regressors. In order to answer Hypothesis 5, we also tested the alternative model in which buyer-supplier collaboration directly impacts manufacturing flexibility. This direct impact proved to be non-significant (T-value = 1.81). Therefore, we returned to the original path model, in which the parameter estimates are 0.71 (Hypothesis 4), 0.69 (Hypothesis 3), and 0.45 (Hypothesis 1), and all are statistically significant. Results are displayed in Fig. 2. Finally, in order to assess the impact of the product dynamismmoderator variable on the relationship between manufacturing flexibility and customer satisfaction, we performed MGCFA, an extension of SEM, which has become the standard to investigate the degree to which measures and regression parameters are invariant across groups (Chen, 2008). Accordingly, we split the total sample into two sub-samples: companies that are dedicated to dynamic products (n = 76) and companies that are dedicated to stable products (n = 79). Before comparing the path models, we had to determine if the measurement models of both groups were equivalent. First, configural equivalence was confirmed for the two constructs involved in our hypothesis 2, modelled as two correlated first-order factors (χ2 = 44.61; df = 38; RMSEA = 0.047). Subsequently, metric equivalence, despite good test statistics (χ2 = 50.74; df = 44; RMSEA

5. Discussion and implications for practice In this study, we empirically analyzed the antecedents and outcome of a critical capability for modern supply chains—manufacturing flexibility. The results of this study have implications both for theory and practice. From a research standpoint, our study contributes to existing literature in several ways. First, we advance extant literature by analyzing with a quantitative empirical approach the outcome of flexibility in terms of customer satisfaction. The result adds to the current debate on this issue by adopting a customer-centered perspective, i.e. measuring the flexibility outcomes in terms of satisfaction of downstream customers. Second, we confirm empirically the important role of antecedents related to upstream relationships with selected suppliers - i.e.

Table 3 Correlation matrix.

Customer satisfaction Manufacturing flexibility Buyer-supplier collaboration Inter-organizational learning

Customer satisfaction

Manufacturing flexibility

Buyer-supplier collaboration

Inter-organizational learning

1.00 0.45 0.31 0.24

1.00 0.56 0.67

1.00 0.66

1.00

Correlations above 0.10 are significant (Saris et al., 2009).

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constructs that could expand our understanding of antecedents of flexibility related to supplier relationships, such as power or trust. These limitations raise the possibility of further research. First, longitudinal case studies might allow digging deeper into the inter-organizational dynamics behind capability development. This would allow capturing path dependency effects (Teece et al., 1997). In particular, we recommend the use of such case studies to contrast companies that must respond to more or less turbulent environments. The notion of dynamism can then be expanded to include for example technological and competitive changes. Future studies should also replicate the study in different countries and industries. This would increase the generalizability of conclusions and strengthen empirical validity. In particular, it would be interesting to determine if the results concerning contingency effects of product dynamism can be applied in other geographical contexts. Another interesting venue of research would be to perform dyadic studies that analyze the same issues from the perspective of the supplier, e.g. to what extent the inter-organizational learning contributes to supplier performance. Moreover, other potential antecedents to flexibility could also be considered in future research. For example, power and dependence have been traditionally associated with studies on the buyer-supplier relationship (Caniels and Gelderman, 2005) and could potentially affect the development of flexibility across the supply chain. More specifically, the extent to which the power distribution within supply chains potentially hinders or facilitates the development of flexibility deserves further attention. In particular, researchers could analyze whether power exerts a moderating effect over the relationship between interorganizational learning and manufacturing flexibility. Moreover, other theories could be used to broaden the scope of antecedents and implications of flexibility. For example, social capital theory is considered a key perspective for theorizing the connections among organizations (Adler and Kwon, 2002). The central tenet of social capital theory is that networks of relationships constitute a valuable resource and provide members with collectively owned capital (Nahapiet and Ghoshal, 1998). In the last decade, the links between social capital and supply chain management have attracted the attention of several researchers (e.g. Petersen et al., 2008). However, we still do not understand the links between the various dimensions of social capital (i.e., structural, relational, and cognitive) and flexibility. This should be an important research stream in the future. Overall, our study has contributed to advancing knowledge on manufacturing flexibility by providing empirical evidence on its upstream antecedents and customer implications. We hope that further studies draw on these ideas to generate relevant knowledge on this increasingly important issue.

Table 4 Multiple group confirmatory factor analysis. df

χ2

RMSEA

Configural equivalence Metric equivalence

38 44

44.61 50.74

0.047 0.044

Metric equivalence, set free F1-item Path diagram: base model Path model: Equivalent impact from manufacturing flexibility on customer satisfaction

43

46.70

0.033

38 39

44.61 54.82

0.047 0.072

Δ χ2 vs Δ df

6.13 for 6 df 4 for 1 df 10,21 for 1 df

MI/EPC

No misspecs 1 misspec (loading F1item) No misspecs No misspecs 1 misspec (path model estimator)

buyer-supplier collaboration and inter-organizational learning as determinants of flexibility. Third, our study is one of the first to analyze the contingency effects of product dynamism on the effectiveness of flexibility. As mentioned previously, the traditional literature on flexibility as well as the business community have implicitly assumed this notion, but have rarely analyzed it with empirical data. Our research confirms the hypothesis regarding the moderation effect of product dynamism on the relationship between manufacturing flexibility and performance. In other words, the positive and strong impact of flexibility on customer satisfaction varies across companies with dynamic versus stable products. Our first two stated contributions add to the literature that draws a direct impact from inter-organizational learning on performance. In this research, manufacturing flexibility in our model operates as a mediator that takes out the noise of the relationship between learning and performance. For establishing causality, the association “net of other influences” between two constructs is necessary (Bollen, 1989: p. 57). Moreover, and related to the third stated contribution, the increased detail in the research model (i.e., the inclusion of a mediator) and in constructs (i.e., customer satisfaction versus the more general performance constructs) may also have led to an increased possibility to establish the moderating role of product dynamism. Constructs with a broad domain described along multiple dimensions may overall not be affected by certain antecedents, while each of the constituting dimensions are affected in different but opposing and thus neutralizing ways (Saris and Gallhofer, 2014). From a managerial viewpoint, the analysis of flexibility as a key capability can assist managers in identifying underlying practices and processes that are critical to improving the response capacity and increase customer satisfaction.. Results suggest that a supply chain perspective on manufacturing flexibility pays off. In other words, our results confirm the notion that this capability is effectively deployed when developed together with strategic suppliers through inter-organizational learning processes. Furthermore, such learning processes should be preceded by developing buyer-supplier collaboration throughout the functional areas within the company that interact with supply chain partners (e.g., purchasing, quality, engineering, etc.). As such, we confirm the path dependency of inter-organizational learning in contexts of supply chains and their relationships. Finally, results also suggest that the competitiveness of supply chains depend upon the extent to which inter-organizational collaboration and learning are translated into flexibility.

References Adler, P.S., Kwon, S.W., 2002. Social capital: prospects for a new concept. Acad. Manag. Rev. 27 (1), 17–40. Aissa Fantazy, K., Kumar, V., Kumar, U., 2009. An empirical study of the relationships among strategy, flexibility, and performance in the supply chain context. Supply Chain Manag.: Int. J. 14 (3), 177–188. Anderson, J.C., Gerbing, D.W., 1988. Structural equation modeling in practice: a review and recommended two-step approach. Psychol. Bull. 103 (3), 411–431. Arawati, A.G.U.S., 2011. Supply chain management, supply chain flexibility and business performance. J. Glob. Strateg. Manag. 9 (1), 135–145. Azadegan, A., Dooley, K.J., Carter, P.L., Carter, J.R., 2008. Supplier innovativeness and the role of inter-organizational learning in enhancing manufacturer capabilities. J. Supply Chain Manag. 44 (4), 14–35. Blome, C., Schoenherr, T., Eckstein, D., 2014. The impact of knowledge transfer and complexity on supply chain flexibility: a knowledge-based view. Int. J. Prod. Econ. 147 (3), 307–316. Blome, C., Schoenherr, T., Rexhausen, D., 2013. Antecedents and enablers of supply chain agility and its effect on performance: a dynamic capabilities perspective. Int. J. Prod. Res. 51 (4), 1295–1318. Bollen, K.A., 1989. Structural Equations with Latent Variables. John Wiley and Sons, New York. Bontis, Nick, Booker, Lorne, D., Serenko, A., 2007. The mediating effect of organizational reputation on customer loyalty and service recommendation in the banking industry.

6. Directions for future research and study limitations The potential limitations of this study are mostly related to the survey methods employed. Given the cross-sectional nature of the survey, we did not capture the development of capabilities over time. Furthermore, the firm sample is drawn from a single country (Spain). Finally, we did not include in this study important organizational 7

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M.J. Sáenz et al.

management initiatives: effects of competitive intensity and product life cycle. J. Oper. Manag. 30 (5), 406–422. Manders, J.H., Caniëls, M.C., Paul, W.T., 2016. Exploring supply chain flexibility in a FMCG food supply chain. J. Purch. Supply Manag. 22 (3), 181–195. Martínez Sánchez, A., Pérez Pérez, M., 2005. Supply chain flexibility and firm performance: a conceptual model and empirical study in the automotive industry. Int. J. Oper. Prod. Manag. 25 (7), 681–700. Mendes, L., Machado, J., 2015. Employees' skills, manufacturing flexibility and performance: a structural equation modelling applied to the automotive industry. Int. J. Prod. Res. 53 (13), 4087–4098. Merschmann, U., Thonemann, U.W., 2011. Supply chain flexibility, uncertainty and firm performance: an empirical analysis of German manufacturing firms. Int. J. Prod. Econ. 130 (1), 43–53. Mishra, R., K. Pundir, A., Ganapathy, L., 2014. Assessment of manufacturing flexibility: a review of research and conceptual framework. Manag. Res. Rev. 37 (8), 750–776. Nahapiet, J., Ghoshal, S., 1998. Social capital, intellectual capital, and the organizational advantage. Acad. Manag. Rev. 23 (2), 242–266. Nair, A., Ataseven, C., Habermann, M., Dreyfus, D., 2016. Toward a continuum of measurement scales in Just-in-Time (JIT) research - an examination of the predictive validity of single-item and multiple-item measures. Oper. Manag. Res. 9 (1–2), 35–48. Nair, A., 2005. Linking manufacturing postponement, centralized distribution and value chain flexibility with performance”. Int. J. Prod. Res. 43 (3), 447–463. Narayanan, S., Narasimhan, R., Schoenherr, T., 2015. Assessing the contingent effects of collaboration on agility performance in buyer-supplier relationships. J. Oper. Manag. 33–34 (1), 140–154. Ojha, D., White, R.E., Rogers, P.P., Kuo, C., 2015. Information processing-related infrastructural antecedents of manufacturing flexibility - a real options perspective. Int. J. Prod. Res. 53 (17), 5174–5193. Oke, A., 2005. A framework for analysing manufacturing flexibility. Int. J. Oper. Prod. Manag. 25 (10), 973–996. Oke, A., Prajogo, D.I., Jayaram, J., 2013. Strengthening the innovation chain: the role of internal innovation climate and strategic relationships with supply chain partners. J. Supply Chain Manag. 49 (4), 43–58. Olavson, T., Lee, H., DeNyse, G., 2010. A portfolio approach to supply chain design. Supply Chain Manag. Rev. 21. Paulraj, A., Chen, I.J., 2007. Strategic buyer–supplier relationships, information technology and external logistics integration. J. Supply Chain Manag. 43 (2), 2–14. Peng, D.X., Verghese, A., Shah, R., Schroeder, R.G., 2013. The relationships between external integration and plant improvement and innovation capabilities: the moderation effect of product clockspeed. J. Supply Chain Manag. 49 (3), 3–24. Perez-Franco, R., Phadnis, S., Caplice, C., Sheffi, Y., 2016. Rethinking supply chain strategy as a conceptual system. Int. J. Prod. Econ. 182, 384–396. Pérez Pérez, M., Serrano Bedia, A.M., López Fernández, M.C., 2016. A review of manufacturing flexibility: systematising the concept. Int. J. Prod. Res. 54 (10), 3133–3148. Petersen, K.J., Handfield, R.B., Lawson, B., Cousins, P.D., 2008. Buyer dependency and relational capital formation: the mediating effects of socialization processes and supplier integration. J. Supply Chain Manag. 44 (4), 53–65. Reichhart, A., Holweg, M., 2007. Creating the customer-responsive supply chain: a reconciliation of concepts. Int. J. Oper. Prod. Manag. 27 (11), 1144–1172. Reuters, 2015. Apple iPhone 6 screen snag leaves supply chain scrambling, Available at: 〈http://www.reuters.com/article/2014/08/22/us-apple-iphoneidUSKBN0GM0N620140822〉, (accessed 18 March). Saccani, N., Perona, M., 2007. Shaping buyer–supplier relationships in manufacturing contexts: design and test of a contingency mode. J. Purch. Supply Manag. 13 (1), 26–41. Sáenz, M.J., Revilla, E., Knoppen, D., 2014. Absorptive capacity in buyer-supplier relationships: empirical evidence of its mediating role. J. Supply Chain Manag. 50 (2), 18–40. Saleh, J.H., Mark, G., Jordan, N.C., 2009. Flexibility: a multi-disciplinary literature review and a research agenda for designing flexible engineering systems. J. Eng. Des. 20 (3), 307–323. Santos-Vijande, M.L., Lopez-Sanchez, J.L., Trespalacios, J.A., 2012. “How organizational learning affects a firm's flexibility, competitive strategy and performance.”. J. Bus. Res. 65 (8), 1079–1089. Saris, W.E., Gallhofer, I., 2014. Design, Evaluation and Analysis of Questionnaires for Survey Research, 2nd ed. Wiley Interscience, Hoboken, NJ. Saris, W.E., Satorra, A., Van der Veld, W.M., 2009. Testing structural equation models or detection of misspecifications? Struct. Equ. Model. 16 (4), 561–582. Scherrer-Rathje, M., Deflorin, P., Anand, G., 2014. Manufacturing flexibility through outsourcing: effects of contingencies. Int. J. Oper. Prod. Manag. 34 (9), 1210–1242. Seebacher, G., Winkler, H., 2013. A citation analysis of the research on manufacturing and supply chain flexibility. Int. J. Prod. Res. 51 (11), 3415–3427. Selnes, F., 1993. An examination of the effect of product performance on brand reputation, satisfaction and loyalty. Eur. J. Mark 27 (9), 19–35. Slater, S.F., Narver, J.C., 1995. Market orientation and the learning organization. J. Mark. 59 (1), 63–74. Stathopoulou, A., Balabanis, G., 2016. The effects of loyalty programs on customer satisfaction, trust, and loyalty toward high-and low-end fashion retailers. J. Bus. Res. 69 (12), 5801–5808. Steenkamp, J.B.E., Baumgartner, H., 1998. Assessing measurement invariance in crossnational consumer research. J. Consum. Res. 25 (1), 78–107. Stevenson, M., Spring, M., 2009. Supply chain flexibility: and inter-firm empirical study. Int. J. Oper. Prod. Manag. 29 (9), 946–971. Suarez, F.F., Cusumano, M.A., Fine, C.H., 1996. An empirical study of manufacturing flexibility in printed circuit board assembly. Oper. Res. 44 (1), 223–240.

Manage. Decision. 45 (9), 1425–1445. Braunschneidel, M.J., Suresh, N.C., 2009. The organizational antecedents of a firm′s supply chain agility for risk mitigation and response. J. Oper. Manag. 27 (1), 119–140. Camisón, C., Villar López, A., 2010. An examination of the relationship between manufacturing flexibility and firm performance: the mediating role of innovation. Int. J. Oper. Prod. Manag. 30 (8), 853–878. Caniels, M.C.J., Gelderman, C.J., 2005. Purchasing strategies in the Kraljic matrix – A power and dependence perspective. J. Purch. Supply Manag. 11 (1), 141–155. Chang, S.C., Yang, C.L., Cheng, H.C., Sheu, C., 2003. Manufacturing flexibility and business strategy: an empirical study of small and medium sized firms. Int. J. Prod. Econ. 83 (1), 13–26. Chavez, R., Yu, W., Feng, M., Wiengarten, F., 2014. The effect of customer-centric green supply chain management on operational performance and customer satisfaction. Bus. Strategy Environ. 25 (3), 205–220. Chen, F.F., 2008. What happens if we compare chopsticks with forks? The impact of making inappropriate comparisons in cross-cultural research. J. Personal. Social. Psychol. 95 (5), 1005–1027. Cheung, M.S., Myers, M.B., Mentzer, J.T., 2010. Does relationship learning lead to relationship value? A cross-national supply chain investigation. J. Oper. Manag. 28 (6), 472–487. Cousins, P.D., Spekman, R., 2003. Strategic supply and the management of inter- and intra-organisational relationships. J. Purch. Supply Manag. 9 (1), 19–29. De Jong, M.G., Steenkamp, J.B.E., Fox, J.P., 2007. Relaxing measurement invariance in cross‐national consumer research using a hierarchical IRT model. J. Consum. Res. 34 (2), 260–278. Dodgson, M., 1993. Organizational learning: a review of some literatures. Organ. Stud. 14 (3), 375–394. Duclos, K.L., Vokurka, J.R., Lummus, R.R., 2003. A conceptual model of supply chain flexibility. Ind. Manag. Data Syst. 103 (6), 446–456. Dyer, J.H., Singh, H., 1998. The relational view: cooperative strategy and sources of interorganizational competitive advantage. Acad. Manag. Rev. 23 (4), 660–679. Emberson, C., Storey, J., 2006. Buyer-supplier collaborative relationships: beyond the normative accounts. J. Purch. Supply Manag. 12, 236–245. Fawcett, S.E., Calantone, R., Smith, S.R., 1996. An investigation of the impact of flexibility on global reach and firm performance. J. Bus. Logist. 17 (2), 167–196. Fine, C.H., 1998. Clockspeed: Winning Industry Control in the Age of Temporary Advantage. Basic Books, Cambridge, MA. Fisher, M.L., 1997. What is the right supply chain for your product? Harv. Bus. Rev. 75 (2), 105–116. Fornell, C., Larcker, D.F., 1981. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 18 (1), 39–50. Gligor, D.M., Esmark, C.L., Holcomb, M.C., 2015. Performance outcomes of supply chain agility: when should you be agile? J. Oper. Manag. 33 (1), 71–82. Hartmann, E., De Grahl, A., 2011. The flexibility of logistics service providers and its impact on customer loyalty: an empirical study. J. Supply Chain Manag. 47 (3), 63–85. Henseler, J., Ringle, C.M., Sarstedt, M., 2015. A new criterion for assessing discriminant validity in variance-based structural equation modelling. J. Acad. Mark. Sci. 43, 115–135. Holmqvist, M., 2004. Experiential Learning processes of exploitation and exploration within and between organizations: an empirical study of product development. Organ. Sci. 15 (1), 70–81. ICIS, 2015. Market outlook: Anti-fragile' supply chains a new priority in chemicals available at 〈http://www.icis.com/resources/news/2015/01/12/9851645/marketoutlook-anti-fragile-supply-chains-a-new-priority-in-chemicals/〉, downloaded at January 23, 2015. Jack, E.P., Raturi, A., 2002. Sources of volume flexibility and their impact on performance. J. Oper. Manag. 20 (5), 519–548. Jayaram, J., Ahire, S.L., Dreyfus, P., 2010. Contingency relationships of firm size, TQM duration, unionization, and industry context on TQM implementation—A focus on total effects. J. Oper. Manag. 28 (4), 345–356. Jöreskog, K.G., 1969. A general approach to confirmatory maximum likelihood factor analysis. ETS Res. Bull. Ser. 1967 (2), 183–202. Kähkönen, A.K., Lintukangas, K., 2012. The underlying potential of supply management in value creation. J. Purch. Supply Manag. 18 (2), 68–75. Knoppen, D., Christiaanse, E., Huysman, M., 2010. Supply chain relationships: exploring the linkage between Inter-organisational adaptation and learning. J. Purch. Supply Manag. 16, 195–205. Lance, C.E., Butts, M.M., Michels, L.C., 2006. The sources of four commonly reported cutoff criteria: what did they really say? Organ. Res. Methods 9, 202–220. Lane, P.J., Koka, B.R., Pathak, S., 2006. The reification of absorptive capacity: a critical review and rejuvenation of the construct. Acad. Manag. Rev. 31 (4), 833–863. Larsson, R., Bengtsson, L., Henriksson, K., Sparks, J., 1998. The inter-organizational learning dilemma: collective knowledge development in strategic alliances. Organ. Sci. 9 (3), 285–305. Lawrence, P.R., Lorsch, J.W., 1967. Organization and Environment. Harvard University Press, Cambridge, MA. Lee, H.L., 2002. Aligning supply chain strategies with product uncertainties. Calif. Manag. Rev. 44 (3), 105–119. Liao, Y., Marsillac, E., 2017. External knowledge acquisition and innovation: the role of supply chain network-oriented flexibility and organisational awareness. Int. J. Prod. Res. 1–19. Lummus, R.R., Vokurka, R.J., Duclos, L.K., 2005. Delphi study on supply chain flexibility. Int. J. Prod. Res. 43 (13), 2687–2708. Mahapatra, S.K., Das, A., Narasimhan, R., 2012. A contingent theory of supplier

8

Journal of Purchasing and Supply Management xxx (xxxx) xxx–xxx

M.J. Sáenz et al.

Yan, T., Dooley, K., 2014. Buyer–supplier collaboration quality in new product development projects. J. Supply Chain Manag. 50 (2), 59–83. Yang, J., 2014. Supply chain agility: securing performance for Chinese manufacturers. Int. J. Prod. Econ. 150 (1), 104–113. Yao, Y., Dresner, M., Palmer, J.W., 2009. Impact of boundary‐spanning information technology and position in chain on firm performance. J. Supply Chain Manag. 45 (4), 3–16. Zhang, Q., Vonderembse, M.A., Lim, J.S., 2006. Spanning flexibility: supply chain information dissemination drives strategy development and customer satisfaction. Supply Chain Manag.: Int. J. 11 (5), 390–399. Zhang, Q., Vonderembse, M.A., Lim, J.S., 2005. Logistics flexibility and its impact on customer satisfaction. Int. J. Logist. Manag. 16 (1), 71–95. Zhang, Q., Vonderembse, M.A., Lim, J.S., 2003. Manufacturing flexibility: defining and analysis relationships among competence, capability and customer satisfaction. J. Oper. Manag. 21 (1), 173–191.

Tamayo-Torres, J., Barrales-Molina, V., Nieves Perez-Arostegui, M., 2014. The influence of manufacturing flexibility on strategic behaviours. Int. J. Oper. Prod. Manag. 34 (8), 1028–1045. Teece, D.J., Pisano, G., Shuen, A., 1997. Dynamic capabilities and strategic management. Strateg. Manag. J. 18 (7), 509–533. Upton, D.M., 1994. The management of manufacturing flexibility. Calif. Manag. Rev. 36 (2), 72–89. Urtasun-Alonso, A., Larraza-Kintana, M., García-Olaverri, C., Huerta-Arribas, E., 2014. Manufacturing flexibility and advanced human resource management practices. Prod. Plan. Control 25 (4) (303-250). Vickery, S.K., Calantone, R., Dröge, C., 1999. Supply chain flexibility: an empirical study. J. Supply Chain Manag. 14 (3), 16–24. Wagner, S.M., Grosse-Ruyken, P.T., Erhun, F., 2012. The link between supply chain fit and financial performance of the firm. J. Oper. Manag. 30 (4), 340–353. Wired, 2015. Amazon launches one-hour delivery service in NYC. Available at: 〈http:// www.wired.com/2014/12/prime-now/〉, (accessed March18).

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