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Examining absorptive capacity in supply chains: linking responsive strategy and firm performance.


Supply chains have been theorized as networks of firms consisting of three distinct flows: product, information, and finances (Carter, Rogers & Choi, 2015; Ketchen, Crook & Craighead, 2014). The focus of this research is on information. While, information has never been so readily available to aid management in making decisions (Ellram & Cooper, 2014), the ability to make sense of and use information has become a dire bottleneck (Bendoly, 2016; Chen, Chiang & Storey, 2012). Information processing is critical because performance is not driven by obtaining information alone, but rather it results from how effectively information is also dealt with and applied in decision-making (Mason-Jones & Towill, 1997; McAfee, 2003). Moreover, information processing has increased in importance as firms face amplified pressure to respond to customer demands for innovative products (Azadegan, Dooley, Carter & Cater, 2008). Given that much of the information needed to improve performance resides outside the boundaries of the firm with customers and suppliers, absorptive capacity has emerged as an important concept in supply chain management which captures the accumulation of a firm's information processing activities (Azadegan, 2011; Saenz, Revilla & Knoppen, 2013).

Absorptive capacity is defined as the degree to which a firm acquires, assimilates, transforms, and applies information to improve performance (Zahra & George, 2002). For example, as firms increasingly involve their suppliers in their product development efforts (McGinnis & Vallopra, 1999a,b, 2001; Zsidisin & Smith, 2005), absorptive capacity enables organizations to achieve greater responsiveness (Fisher, 1997; Lee, 2002) by acquiring and applying information across firm boundaries (Rebolledo, Halley & Nagati, 2009; Saenz et al., 2013). As a focal construct, absorptive capacity is an important phenomenon which SCM scholars have identified as warranting a better understanding (e.g., Azadegan, 2011; Bellingkrodt & Wallenburg, 2013; Saenz et al., 2013). While absorptive capacity is thought to be a particularly important organizational attribute for responsiveness, there is little empirical evidence regarding appropriate structural linkages involving the construct (Liao, Welsch & Stoica, 2003). Furthermore, little is known about how firms develop absorptive capacity, or why some firms demonstrate this competence while others do not (Azadegan, 2011; Saenz et al., 2013). In this study, we examine absorptive capacity as an organizational design that enables a firm to capture outside information, disseminate it, and use it to create value (Galbraith, 1973, 1974). Our research model explains links among responsive strategy, absorptive capacity, and firm performance, and investigates how firms develop absorptive capacity by examining the curvilinear effect of responsive strategy.

The theorized model is tested using data from two collection periods of the International Manufacturing Strategy Survey (IMSS). The model is tested using data from the IMSS IV, containing 711 firms, and is then replicated on a second sample of 677 firms collected 4 years later during the IMSS V. Our results reveal three primary contributions involving responsiveness, absorptive capacity, and firm performance. First, absorptive capacity is shown to be motivated by a firm's responsive strategy. Second, absorptive capacity fully mediates the relationship between responsive strategy and firm performance, showing that absorptive capacity is a necessary competence for firms that aim to deliver innovative products to customers. Third, we reveal a U-shaped relationship between responsive strategy and absorptive capacity, indicating that when firms attempt to blend efficient and responsive strategies, their ability to develop absorptive capacity is diminished. Lastly, a secondary contribution is made from a methodological perspective: we were able to operationalize absorptive capacity in a manner consistent with Zahra and George (2002), as a second-order construct consisting of four-first-order dimensions.

The remainder of the paper is structured as follows. In the next section we develop the main constructs of the study. We pay specific attention to absorptive capacity, complementing its use in recent extant studies (Azadegan, 2011; Saenz et al., 2013). We next develop and introduce the study's hypotheses. This is followed by a description of our study's methodology and presentation of the results. The paper ends with a discussion of the implications for theory and practice, limitations, and suggestions for future research.


In this section, we will define the three major constructs in our study, responsive strategy (RS), absorptive capacity (AC), and firm performance (FP). The origins of RS have been traced to Fisher (1997), who identified this strategy as the right fit for products that are highly innovative and therefore must operate in an uncertain environment. This concept was further refined by Lee (2002) to include flexibility to the customers' requirements. As market dynamics have increased for most firms and product clockspeed has increased, the reliance on responsiveness for a competitive advantage has grown (Peng, Verghese, Shah & Schroeder, 2013). Responsive strategy is defined in this research as the degree to which a firm's business strategy reflects the importance of (1) new product frequency; (2) more innovative products; and (3) a wide product range in winning orders from customers (Chaffee, 1985; Fisher, 1997; Gunasekaran, Lai & Cheng, 2008; Kim & Lee, 2010; Randall, Morgan & Morton, 2003; Roh, Hong & Park, 2008; Storey, Emberson & Reade, 2005).

Cohen and Levinthal (1990, p. 128) first introduced AC, arguing that "[...] the ability of a firm to recognize the value of new, external information, assimilate it, and apply it to commercial ends is critical to its innovative capabilities." They and others suggested that AC can serve to explain a firm's resource allocation decisions for innovation activity (Cohen & Lenvinthal, 1990; Daft & Lengel, 1986; Lane & Lubatkin, 1998). Zahra and George (2002) extended this conceptualization to include acquisition, assimilation, transformation, and application of information. In a supply chain sense, AC is important because the most relevant information for a firm resides outside its boundaries, which not only makes information acquisition more difficult, but requires effective dissemination and application of that information (Lane & Lubatkin, 1998). Absorptive capacity is particularly useful for firms managing a RS given the uncertainty they face and the consequent value derived from effective information processing (Fisher, 1997; Galbraith, 1973, 1974).

Due to the central notion of AC in the current research, we provide a detailed review of the existing literature, as shown in Table 1. We expand on the literature review framework by Deng, Doll and Cao (2008) and focus on recent AC articles dealing with responsiveness, innovativeness, and/or performance. Our review of the literature indicates that AC plays a significant role in increasing innovative capacity and responsiveness in a variety of research contexts. In terms of definition and measurement of the AC construct, it is apparent that conceptualizations have differed by study. We can conclude that AC has been measured in a variety of ways ranging from proxies such as R&D investment (Cohen & Levinthal, 1990; Griffith, Redding & Van Reenen, 2003; Stock, Greis & Fischer, 2001) to more behavioral measures (Gluch, Gustafsson & Thuvander, 2009; Malhotra, Gosain & El Sawy, 2005). While previous attempts to measure AC provide important insights into the phenomenon, they fail to capture AC in an accurate theoretical way reflecting the accumulation of the four key dimensions (Azadegan, 2011). That said, an exception was identified in the construction management literature, where Gluch et al. (2009) tested the four dimensions described by Zahra and George (2002). However, while Gluch et al. (2009) measured the four AC dimensions, the authors tested each variable independently using regression, and therefore did not confirm that the four dimensions of AC are encompassed within a higher (second)-order construct. Saenz et al. (2013) did conceptualize AC as a second-order construct with three-first-order dimensions consisting of exploration, assimilation, and exploitation using composite measures as opposed to individual measurement items. Given the three dimensions of their conceptualization, the authors were unable to distinguish between a correlated model and second-order model of AC; however the authors chose to model the construct in the second-order. Therefore, the measurement and validation of a comprehensive second-order, four-dimensional conceptualization of AC (Zahra & George, 2002) would make a useful contribution to the literature. While in our research we rely on a previously developed survey, the measures that we use are proxies that are aligned with Zahra and George (2002) and are consistent with the previously described studies on AC, which were more narrowly focused. Next, we describe the four dimensions of AC in the context of supply chain responsiveness.

Supply chain information acquisition is defined as the degree to which a firm uses electronic tools (i.e., EDI) to communicate with customers and suppliers regarding RFx (Request for quotes, information, and proposals) and for content and knowledge management (Handheld & Lawson, 2007; Sanders & Premus, 2002). As mentioned, while some scholars have used proxies such as R&D spending (Cohen & Levinthal, 1990; Griffith et al., 2003; Stock et al., 2001), others have proposed more direct measures to measure AC in terms of IT use for information exchange and processing (Malhotra et al., 2005). The use of electronic tools can facilitate knowledge acquisition by linking suppliers, the focal firm and customers in the supply chain, especially in the context of innovation seeking firms (Sambamurthy, Bharadwaj & Grover, 2003; Wynstra & Ten Pierick, 2000). As a result, infrastructure development within the firm and among the firms in the supply chain has emerged as a primary focus for IT departments (Sanders & Premus, 2002; Kahn, Maltz & Mentzer, 2006). This knowledge acquisition may be most critical during the early stages of innovation development (Handheld & Lawson, 2007; Nellore, 2001) when the foundation of problem solving or knowledge application is created. Therefore, a firm's IT infrastructure is critical for acquiring information from customers and suppliers to ultimately increase firm performance (Byrd, Pitts, Adrian & Davidson, 2008).

New product development (NPD) assimilation is defined as the degree to which a firm has implemented programs to (1) integrate product development and manufacturing through quality function deployment (QFD), design for manufacturing (DFM), etc.; and (2) improve product development/innovation performance and manufacturing through platform design, etc. (Adler, 1995; Doll, Hong & Nahm, 2010; Fosfuri & Tribo, 2008; Hong et al., 2005; Hong & Hartley, 2011; Huang & Rice, 2009). These activities assimilate information acquired from customers and suppliers, allowing for the inclusion of superior technologies and materials in the early stages of product design (Lakemond, Echtelt & Wynstra, 2001; Petersen, Handfield & Ragatz, 2005; Song & di Benedetto, 2008; Yan & Dooley, 2014). Thus, it is insufficient to simply acquire information, but rather benefits emerge through leveraging suppliers' expertise in a cooperative fashion (Morris & Carter, 2005; Nellore, 2001; Salvador & Villena, 2013; Tan & Tracey, 2007; Tracey, 2004). This infers the importance of information assimilation during NPD as a dimension of AC (Parker, Zsidisin & Ragatz, 2008; Rogers, Lambert & Knemeyer, 2004).

We define supply chain transformation as the degree to which a firm engages in a series of actions intended to bring significant changes in the supply chain, including changes in the firm's supplier or customer portfolios, supplier/customer development, and the coordination of the flow of goods (Azadegan, 2011; Liao, Hong & Rao, 2010; Vonderembse & Tracey, 1999; Voordijk, 1999). It is boundary-spanning, involving a series of action plans that intend to implement substantial changes in supply chain infrastructure (Giannakis & Croom, 2004; van Hoek, Vosm & Commandeur, 1999; Roh, Min & Hong, 2011). It is identified from an AC perspective as inter-organizational network restructuring (Carlile, 2004; Zahra & George, 2002). Examples of supply chain restructuring include restructuring supply strategy, implementing supplier development, increasing the level of coordination of planning decision and flow of goods with suppliers, rethinking distribution strategy and increasing the level of coordination of planning decisions and flow of goods with customers (Quesada, Syamil & Doll, 2006; Zsidisin & Smith, 2005). Supply chain transformation enables the firm to move closer to commercializing information acquired through the supply chain and assimilated in NPD (McGinnis & Vallopra, 1999, McGinnis & Vallopra, 2001).

Operational application is defined as the degree to which a firm achieves its operational goals, specifically related to key processes (i.e., procurement), delivery, and manufacturing (Jitpaiboon, Dobrzykowski, Ragu-Nathan & Vonderembse, 2013; Randall et al., 2003; Wu et al., 2010). OA is the manifestation of the previously described IT and boundary-spanning information processing activities aimed at specific operational performance dimensions (Song, van der Bij & Weggeman, 2005; Taps & Steger-Jensen, 2007). While various conceptualizations of operational performance exist, in the context of AC and innovation, time to market (or delivery speed) and flexibility are logical aims for information processing (Jitpaiboon et al., 2013; Leuschner, Charvet, and Rogers, 2013). In a consistent fashion, other scholars have suggested that AC should manifest as NPD performance (Stock et al., 2001), and flexibility and innovation performance (Zahra & George, 2002).

Finally, as shown in Table 1, there are few studies that empirically link strategy to AC and FP. We view this as an evolution since AC was previously often linked with NPD. In addition, the notion of what is necessary to achieve a RS requires the measurement of success at the firm level. Financial measures were chosen to measure FP in this study as "prior research has shown that managerial assessments of company performance are highly correlated with internally obtained objective performance indicators" (Sabherwal & Chan, 2001; pg. 19). In addition, it has been suggested that market share and sales represent financial measures that are more clearly related to innovation activities than measures such as Return on Investment (Jeong & Hong, 2007; Kim, Cavusgil & Calantone, 2006; Terpend, Krause & Dooley, 2011). For these reasons, firm performance is defined as the degree of improvement and the level of the firm's market share and sales.


Information processing theory (IPT) posits that coping with information is an organization's main task and that more information has a positive link with performance (Galbraith, 1973, 1974). An integration of IPT with one of the central tenets of AC suggests that an ample flow of information is necessary, but it should also be acquired, assimilated, transformed, and applied in a useful way. This is especially important when firms follow a RS because uncertainty is higher in such an environment, increasing the need for AC. Galbraith (1974) describes a framework of how organizations can organize themselves, starting with rules and resulting in a hierarchy, which then leads to goals. Those goals are outlined to guide the behaviors of employees and lead to business results that can be measured in financial terms (Shinkle, 2012). In addition, IPT is specifically important when there is increased uncertainty, as firms require more "information that has to be processed between decision-makers" (Galbraith, 1974, p. 28). We also suggest that in today's interconnected supply chains, often those decision-makers reside in different firms. In our model, we specifically measure the existence of a RS as a set of goals for the firm (Christopher, 2000), which is more important in highly-dynamic, high clockspeed, and uncertain external environments (Peng et al., 2013; Trautmann, Turkulainen, Hartmann & Bals, 2009). According to Galbraith (1974), the organization can achieve those goals by four methods: the creation of slack resources, the creation of self-contained tasks, investment in vertical information systems, and/or the creation of lateral relations. The creation of slack resources and self-contained tasks endeavor to reduce the need for information processing, while information technology and boundary-spanning lateral relationships aim to increase the information processing capacity of a firm (Galbraith, 1974; Trautmann et al., 2009). Given that information processing, not reducing the need to process information, is the central tenant of AC, we draw on IPTs emphasis on information technologies and boundary spanning in theorizing our model (see Figure 1).

In competitive, high clockspeed marketplaces that are commonplace today, we expect that firms that follow a RS are more successful (Azadegan et al., 2008; Gunasekaran et al., 2008; Maloni & Benton, 2000; McGinnis & Kohn, 2002; Peng et al., 2013). In order words, a firm that invests in information technology (IT), connecting the firm with its customers and suppliers (Jansen, Frans, Van Den Bosch & Volberda, 2005; Johnson & Vitale, 1988), is increasing its AC (Malhotra et al., 2005), which should result in better firm performance (Ahmed, Hayder & Khan, 2014; Cegielski, Allison Jones-Farmer, Wu & Hazen, 2012). From a supply chain view, AC can be observed in the use of IT for knowledge acquisition in a specific goal-driven context such as with an aim for responsiveness, assimilation of this information during new product development (NPD), supply chain restructuring for transforming information, and ultimately application of this information in an operational fashion (Azadegan, 2011; Saenz, Revilla & Knoppen, 2013). These four AC dimensions are either manifested in or fueled by IT use and/or boundary-spanning relationships (Jansen et al., 2005). For example, supply chain information acquisition and supply chain transformation involve IT use and/or boundary-spanning relationships, while NPD assimilation and operational application are enhanced by IT use and boundary-spanning activities, and increase the firm's information processing capability (Jitpaiboon et al., 2013; Leuschner, Rogers, and Charvet, 2013). In this way, the four AC dimensions collectively translate the firm's goals for responsiveness into improved performance. For example, consider a firm that integrates IT systems with customers and suppliers, assimilates that information, and transforms its supply chain. If this firm fails to realize operational application, it would not have truly increased its information processing capacity, and would not successfully link its responsive strategy to improved overall firm performance (Shinkle, 2012) (Figure 2).


Responsive Strategy and Absorptive Capacity

A responsive strategy (Fisher, 1997; Lee, 2002) heightens uncertainty for the firm and thus increases the need for information processing (Daft & Lengel, 1986; Galbraith, 1974; Lane & Lubatkin, 1998). This is reflected in the literature which suggests that a RS is implemented through innovation, requiring both information acquisition and application (Gunasekaran et al., 2008; Roh et al., 2008; Storey et al., 2005). External linkages formed for knowledge acquisition can deliver on a RS through improvements in resource allocation and speed, among other benefits, thus extending the market base for a product (Jeong & Hong, 2007). The knowledge acquired through these boundary-spanning linkages therefore enables a firm to develop a deep understanding of their customers' needs as well as organize supplier resources to deliver on those needs, which is essential in implementing a RS focused on the customer. Knowledge acquisition through the use of IT alone, however, is insufficient for the successful implementation of an RS (Gunasekaran et al., 2008). Even when IT is available, organizational and behavioral barriers must be overcome in effectively assimilating and applying the knowledge which is acquired (Dobrzykowski & Tarafdar, 2015; Storey et al., 2005). In other words, knowledge must not only be acquired, but processed, assimilated, and integrated into firm practices to realize positive outcomes. Innovation responses hinge upon collaborative cross-functional decision making which is afforded by supply chain information acquisition (Flint, Larsson, Gammelgaard & Mentzer, 2005). Recognizing the potential benefits, many managers are redesigning relationships in hopes of improving innovation responses to produce better customer alignment, more frequent product launches, and shorter time to market (Adler, 1995).

Supply chain transformation occurs when a firm engages in a series of actions intended to bring significant changes in the supply chain, including changes in the supplier and/or customer portfolios, supplier/ customer development, and coordination of the flow of goods. Changes in the supplier portfolio involve careful supplier selection based on their quality performance, lead times, and timely delivery. In the context of complex customer requirements, supply chain transformation can impact speed and lead time (Doll et al., 2010). Responsiveness is possible through shortened product cycles and delivery time of suppliers. On the customer side, a focal firm's key activities are to streamline the distribution network so that finished products are delivered on time to the customers. Coordination with customers allows procurement of component parts from suppliers to be better planned (Hong et al., 2005; Doll et al., 2010). For these reasons, it is reasonable to expect that a firm would engage in supply chain information acquisition, NPD assimilation, supply chain transformation, and operational application of information when implementing a responsive strategy. Thus, we hypothesize the following:

H1: Responsive strategy is positively related to absorptive capacity.


Absorptive Capacity and Firm Performance

The firm's ultimate goal in implementing a responsive strategy is improved financial performance (Kim & Lee, 2010). However, firm performance is not easily achieved given that a RS increases uncertainty, therefore reducing stability in the firm's environment (Daft & Lengel, 1986; Gunasekaran et al., 2008; Lane & Lubatkin, 1998). In the face of increased variation in customer requirements, the firm benefits from increasing its information processing capabilities and often increases its IT linkages with customers (Galbraith, 1974; Sambamurthy et al., 2003). This sensitivity to customer requirements increases variation in the product offering and increases reliance on suppliers (Azadegan et al., 2008). Suppliers then expand their focus to include their customers' (the focal firm's) requirements (Reuter, Foerstl, Hartmann & Blome, 2010). Supply chain transformation is a way to enhance the firm's operational capabilities through supplier performance and therefore is often necessary to improve financial outcomes (Terpend, Tyler, Krause & Handfield, 2008). In other words, executing a RS and realizing the benefits of financial performance hinge on the information processing activities (Daft & Lengel, 1986; Dobrzykowski, Callaway & Vonderembse, 2016; Galbraith, 1974; Lane & Lubatkin, 1998), which we conceptualized as the AC of the firm (Cohen & Levinthal, 1990; Zahra & George, 2002). In fact, we posit that without AC, it is unlikely that a RS will improve financial performance. Thus, we hypothesize the following:

H2: a) Absorptive capacity is positively related to financial performance, and b) absorptive capacity fully mediates the relationship between responsive strategy and firm performance.

The Nature of the Relationship between Responsive Strategy and Absorptive Capacity

As discussed, AC is useful in the context of a responsive strategy and has been shown to drive firm performance in other settings as well. Yet, variation exists in firms' ability to develop AC (see Azadegan, 2011; Saenz et al., 2013; Fosfiiri & Tribo, 2008; Huang & Rice, 2009; Gluch et al., 2009; among others in Table 1). As such, a logical curiosity extends beyond the linear associations established among AC and other variables, toward developing a deeper understanding of how some firms create AC while others do not. Given that the overarching aim of this study is to examine how a RS influences firm performance vis-a-vis AC, we carefully investigated the literature dealing with the influence of supply chain strategy on practices (Aitken, Childerhouse & Towill, 2003; Childerhouse, Aitken & Towill, 2002; Fisher, 1997; Qi, Boyer & Zhao, 2009; Skinner, 1974; Vonderembse, Uppal, Huang & Dismukes, 2006).

Fisher (1997) provides seminal thinking in this area by linking supply chain strategy to product characteristics, resulting in physically efficient (efficient) and market responsive (responsive) supply chain strategies. The efficient strategy aims to serve customers who favor low cost over innovation and is well-suited for functional products characterized by longer product life cycles and lower product variety. Functional products have stable demand, and slow changing design characteristics and production requirements (Vonderembse et al., 2006). "Their stability invites competition, which often leads to low profit margins" (Fisher, 1997, p. 106). Conversely, the RS aims to serve customers who favor innovation and is appropriate for innovative products characterized by shorter product life cycles and higher product variety. Innovative products are new or derivative products aimed at new customers and are designed to be adaptable to fast changing customer requirements in the face of uncertain demand and unstable product designs (Vonderembse et al., 2006). These factors can reduce competition, and given that customers are often willing to pay a premium for innovative products, this strategy yields higher product profit margins (Fisher, 1997). A third hybrid strategy referred to as leagile or lean/agile also exists (Goldsby, Griffis & Roath, 2006; Qi et al., 2009; Vonderembse et al., 2006). This hybrid strategy attempts to incorporate elements from the efficient and responsive strategies through managing multiple products, some of which are innovative and some of which are functional, or by employing a decoupling point whereby the upstream and downstream sections of the supply chain for a single product operate different strategies (Goldsby et al., 2006). We refer to this hybrid strategy as efficient/responsive (Qi et al., 2009).

Effective strategy implementation is predicated on a firm's ability to focus on a narrow set of priorities and develop the appropriate competencies necessary to achieve those priorities. Skinner (1974) introduced the idea of focus in manufacturing as the concept that simplicity, experience, repetition, and task homogeneity develops competence (see also Childerhouse et al., 2002). Fuller, O'Conor and Rawlinson (1993) extended the focus to the supply chain context in explaining the need to "... avoid averaging: A Diseconomy of Scales ... Thus customers who need specialized products quickly but unpredictably tend to be underserved, while customers with more commodity-like products were overcharged" (Aitken et al., 2003, p. 130).

Absorptive capacity can be useful when managing the efficient strategy because the firm benefits from acquiring, assimilating, transforming, and applying information given that inventory levels are low to reduce costs for customers who still expect reliable quality products to be available when needed, making stock outs unacceptable (Aitken et al., 2003). Likewise, AC is useful when managing the RS because it enables the firm to effectively acquire, assimilate, transform, and apply information about changing customer requirements given the short product life cycles and need for innovation (Vonderembse et al., 2006). Focus is problematic when considering the requirements of the efficient/responsive strategy as it attempts to deliver on outcomes desirable for functional products (cost and quality) as well as innovative products (speed and variety) (Fisher, 1997). Indeed, Childerhouse et al. (2002: p. 687) found that "No one demand [supply] chain strategy can best service all these requirements. Hence, focus is required to ensure demand [supply] chains are engineered to match customer requirements. Such focus is enabled via segmentation on the basis of each product's characteristics."

The efficient/responsive strategy is difficult to effectively implement due to the "need to master two different and sometimes conflicting management styles," (Qi et al., 2009, p. 671). When employing an efficient/responsive strategy, simplicity, experience, repetition, and task homogeneity are reduced (Skinner, 1974). While AC has been shown to positively influence both a firm's efficiency and innovation performance (Saenz et al., 2013), AC may be difficult to develop under an efficient/responsive strategy because this approach requires managing the upstream and downstream differently (Qi et al., 2009). Our operational definition of RS consists of survey items measuring (1) new product frequency; (2) more innovative products; and (3) wider product range, thus grounding our strategy variable in Fisher's (1997) description of innovative products. Therefore, higher scores reported for these items indicate that the firm is focused on providing innovative products (e.g., new computer chips and software upgrades) and a RS, while lower scores reflect firms focused on providing functional products (e.g., small appliances or hand tools like toasters or saber saws) and an efficient strategy (Fisher, 1997; Vonderembse et al., 2006). Firms reporting scores near the center of the range indicate that they provide hybrid products (e.g., automobiles or other assembled products) and employ an efficient/responsive strategy (Vonderembse et al., 2006). Thus, we would expect that low scores (more focus on functional products and an efficient strategy) and high scores (more focus on innovative products and a responsive strategy) will be associated with higher levels of AC. Firms with medium scores, indicating hybrid products and an efficient/responsive strategy, ought to display lower levels of AC. Thus, we hypothesize the following:

H3: Responsive strategy has a positive U-shaped, second-order polynomial relationship with absorptive capacity.


The relationships under study are tested using a primary dataset collected from 711 plant managers or manufacturing executives during the International Manufacturing Strategy Survey IV (IMSS). The measures and path relationships are validated in a robustness test using a secondary sample of 677 manufacturers from a subsequent data collection initiative 4 years later (IMSS V). The robustness test is discussed later in the Results section. The primary dataset was collected from key respondents represent firms from 23 countries. Table 2 displays the demographic details of the sample. The survey instrument was developed in English and translated into the local language for distribution in non-English speaking countries. These translators served as research coordinators, who were university professors in the area of supply chain and operations management in most cases. This was done in an effort to ensure reliable translation by a research coordinator familiar with business and operations concepts. Firms were contacted in advance of the initial wave of mailings to assess participation interest. This was done in an attempt to ensure an acceptable response rate, which was realized as the lowest response rate was 25 percent in any individual country. IMSS is a multiphased research project which has produced research published in major supply chain and operations journals (Venkatraman, 1990; Voss & Blackmon, 1998).


Measurement items were theorized from the literature discussed earlier based on their appropriateness for measuring the domain of each variable, and testing of the relationships among the variables under study (Keller, Savitskie, Stank, Lynch & Ellinger, 2002). The independent variable, responsive supply chain strategy, was measured using three items assessing the importance to the firm of offering new products, innovative products & a wide range of products (Gunasekaran et al., 2008; Miller & Roth, 1994; Roh et al., 2011). The mediating variable, absorptive capacity, was measured as a second-order construct comprised of four-first order dimensions--supply chain information acquisition, new product development assimilation, supply chain transformation, and operations application. Supply chain information acquisition was measured using three items assessing the degree of the firm's use of electronic tools for knowledge management and request for proposals with customers and suppliers (Ellinger, Daugherty & Keller, 2000; Handheld & Lawson, 2007; Hong, Dobrzykowski & Vonderembse, 2010). New product development assimilation was measured using three items assessing the degree of the firm's use of action programs to increase organizational and technological integration between product development and manufacturing, and increase product development performance through platform design (Galbraith, 1974; Hong et al., 2005; Doll et al., 2010). Supply chain transformation was measured using four items assessing the degree of the firm's use of increased coordination with customers and suppliers, restructuring the distribution strategy, and supplier development and rating programs (Liao, Hong & Rao, 2010). Operational application was measured using three items assessing the degree of the firm's operational performance with regard to procurement and manufacturing lead time, and delivery speed (Jayaram & Vickery, 1998; Jitpaiboon et al., 2013; Randall et al., 2003; Roh et al., 2011). The dependent or outcome variable in this study, firm performance, was measured using three items assessing the degree that the firm improves its market and financial levels--specifically related to sales and market share given the focus of this study on responsiveness to customers (Jeong & Hong, 2007; Randall et al., 2003). Finally, given that uncertainty is an important aspect of IPT, we developed a control variable to measure the extent to which the respondent characterized the plant's external environment as uncertainty. The item states "How would you describe the external environment?" using a Likert-type scale with anchors consisting of "declining rapidly" (1) and "growing rapidly" (5). We recoded the responses to capture neither declining nor growing as a value of 1 (original responses of 3 were recoded to equal 1), declining or growing moderately as a value of 2 (original responses of 2 or 4 were recoded to equal 2), and declining or growing rapidly as a value of 3 (original responses of 1 or 5 were recoded to equal 3). The items appear in the Appendix along with the mean and standard deviation of each item.

Measurement Model Results

Following Anderson and Gerbing (1988), confirmatory techniques were used to test the measurement and structural models (Garver & Mentzer, 1999) in Analysis of Moment Structures (AMOS) 19.0. The measurement model and fit statistics are [X.sup.2]=287.72, d.f. = 148, [X.sup.2]/d.f. = 1.93, RMSEA = .04, GFI = .96, AGFI = .95, CFI = .97. All model fit statistics meet generally accepted thresholds. These results were obtained after correlating the error terms of two items measuring supply chain transformation which is to be expected given that they both measure the phenomena (Rabinovich, Dresner & Evers, 2003).

Table 3 displays variable means, standard deviations, and ranges of the t-values for the items measuring each substantive construct. All of the t-values for the item loadings are highly significant and above the generally accepted threshold of .60. Two exceptions were retained for an item assessing current market share as a measure of firm performance (loading = .47) and an item assessing the use electronic tools with key/strategic suppliers for content and knowledge management as a measure of information acquisition (loading = .52). Both items were retained given their statistical significance at p < .01 (t = 10.82 and 12.08, respectively) as well as their theoretical significance in measuring each substantive construct (Salvador & Villena, 2013). Cronbach's alpha values are displayed in Table 3 and range from .72 to .94, indicating acceptable reliability. The composite reliability values, which are also displayed in Table 3, range from .74 to .86 and also provide evidence of construct reliability (Nunnally, 1978). Average Variance Extracted (AVE) values for the variables under study all indicate convergent validity by generating values >.5. Table 3 also displays correlations among all latent variables and the AVE for each variable is displayed on the diagonal, followed by the square root of each respective AVE. Each AVE square root is greater than the correlations in its corresponding row and column indicating discriminant validity (Fornell & Larcker, 1981; Koufteros, Vonderembse & Doll, 2001). Finally, ANOVA was used to test for mean differences among the variables by country and no statistical differences were found which increases the global generalizability of our findings.

Common Method Bias

Preventive measures to mitigate common method bias (CMB) were undertaken during the data collection for this study. The questions measuring the predictor and criterion variables were segmented into different sections of the survey. Different scale anchors/formats were employed for items measuring independent and dependent variables. Such procedural remedies reduce the likelihood of CMB by making it difficult for respondents to link the targeted measures together (Podsakoff, MacKenzi, Lee & Podsakoff, 2003). The data were tested for the presence of CMB following data collection. The Harman (1967) single-factor test failed to produce evidence of CMB. Finally, the confirmatory single-method factor test advocated by Podsakoff et al. (2003) examined the effects of a latent method factor in the measurement model. The relationships among all of the hypothesized measurement items and their respective constructs remained statistically significant. Furthermore, the average variance explained for the latent method factor items was calculated, producing a value of .16 which is considerably less than that of the substantive constructs under study (.68). In analyzing these results individually and collectively, CMB was not found to be problematic.


Validation of Absorptive Capacity as a Second-Order Construct

Given our conceptualization of AC as a second-order factor, we begin by testing whether a second-order factor for AC accounts for the relationships among the first-order dimensions (Tanriverdi, 2006). Four tests are available to compare first-order and second-order factor models: (1) the goodness of fit statistics for the two models (Grover, Teng & Fiedler, 2007; Tippins & Sohi, 2003; Venkatraman, 1990); (2) the significance of the second-order factor loading values (Tippins & Sohi, 2003; Venkatraman, 1990); (3) the target coefficient (T) statistics (Marsh & Hocevar, 1985); and (4) the significance of the structural relationships between first-order factors from the measurement model and a criterion variable of interest, in this case Firm Performance (FP) (Venkatraman, 1990).

Model fit statistics for the first-order model linking each first-order factor of AC to FP is [X.sup.2]/d.f. = 2.35, RMSEA = .04, GFI = .97, AGFI = .95, and CFI = .97, while the second-order model linking AC as a second-order construct to FP is [X.sup.2]/d.f. = 2.37, RMSEA = .04, GFI = .96, AGFI = .95, and CFI = .97. Both models provide comparable fit statistics, thus the second-order model ought to be accepted due to parsimony with fewer parameter estimates and more degrees of freedom (Grover et al., 2007; Tippins & Sohi, 2003). All of the second-order factor loadings are significant at p < .01, with SCIA producing a loading = .62, t = 8.81; New Product Development Assimilation (NPDA) producing a loading = .69, t = 8.88; SCT producing a loading = .74, t = 6.88; and OA producing a loading = .42, t = 6.88. These results also support the presence of AC as a second-order factor (Grover et al., 2007; Tippins & Sohi, 2003). The T-coefficient is .96 ([X.sup.2] of 140.98 for the first-order correlated model divided by [X.sup.2] of 146.57 for the second-order model) which is >.80, the generally accepted cutoff, thus providing further evidence of the existence of a second-order construct (Marsh & Hocevar, 1985).

The final test compared two models with the first model linking each first-order factor of AC to FP and the second linking AC to FP as a second-order construct. The first-order model produced one significant relationship at the p < .01 level for the link between OA and FP (coefficient = .16, t = 3.23). The second-order model produced a statistically significant relationship at p < .01 between AC as a second-order construct and FP (coefficient = .19, t = 3.52), indicating that the second-order conceptualization of AC is appropriate (Venkatraman, 1990). These results also illuminate the individual roles of the first-order factors of AC in mediating the link between RS and FP. Responsive strategy is related to all of the first-order factors; however, only operational application is a mediator by virtue of its association with FP. (1)

Path Model Mediation Results

Results were next analyzed for the structural model and are displayed in Table 4. The structural model provides evidence of adequate model fit: [X.sup.2]/ d.f. = 1.96, RMSEA = .04, GFI = .96, AGFI = .95, and CFI = .97. The uncertainty variable was linked to each variable in the model as a control. Hypothesis 1 postulates a positive relationship between RS and AC, and is supported at p < .01 (coefficient = .25, t = 4.69). Hypothesis 2a posits a positive relationship between AC and FP, and H2b postulates that AC fully mediates the relationship between RS and FP. Testing these hypotheses requires an examination of both the direct effect of AC on FP as well as the direct and indirect effects of RS on FP. The results reveal that AC has a strong positive direct effect on FP at p < .01 (coefficient = .19, t = 3.40), providing support for H2a. The direct effect of RS on FP is not supported (coefficient = .02, t = 0.43), while the indirect effect is significant (coefficient = .05, p < .001). These results support H2b, and suggest that the effect of RS is fully mediated by AC. The uncertainty control variable is not statistically significant with any of the substantive variables in the model with the exception of RS, which is marginally significant (coefficient = .07, t = 0.98).

Curvilinear Results

Hierarchical multiple regression was employed to test the posited, positive curvilinear U-shaped effect of RS on AC (H3). Following Aiken and West (1991), RS was mean-centered prior to analysis to reduce the likelihood of multicollinearity and aid in the interpretation of our results. All of the variables produced acceptable variance inflation factors slightly greater than 1, diminishing concerns over multicollinearity (Neter, Kutner, Nachtsheim & Wasserman, 1996). The results are displayed in equations 1, 2, and 3 in Table 5. The control variable capturing uncertainty is entered first in equation 1, followed by a test for the main effect of RS in equation 2, and finally a squared term of RS to form a basic curvilinear model is added in equation 3 (Aiken & West, 1991). The change in [R.sup.2] and significant F-value (8.57, p < .001) comparing equations 2 and 3 reveals a curvilinear relationship between RS and AC. The positive coefficient (.11, p<.01) indicates a U-shaped relationship which is illustrated in Figure 3. Taken together, these results support H3, and suggest that firms with low and high levels of RS (i.e.: focus on efficiency or responsiveness) produce higher levels of AC than firms near the mean. (2)

Robustness Check and Validation

In an effort to validate the robustness of the main path model results from the primary sample, we validated the hypothesized model using a disparate, secondary subsequent IMSS sample (Hair, Black, Babin, Anderson & Tatham, 2006). IMSS is a multiphased, partially longitudinal research project containing five discrete data collection initiatives and subsequent datasets. Herein, we analyzed the IMSS IV data to test the hypothesized research model and validate the results from the first survey. A successive data collection initiative, IMSS V, used an instrument with substantial overlap to that used in IMSS IV, repeating 16 of 19 items and resulting in a sample of n = 677 (see Table 6). Two items from IMSS IV measuring Information Acquisition--(1) Indicate to what extent you/ your key/strategic customers use electronic tools (Internet or EDI based) with you for content and knowledge management; and (2) Indicate to what extent you use electronic tools (Internet or EDI based) with your key/strategic suppliers for content and knowledge management were not included in IMSS V. Instead, these items were replaced with (1) Indicate to what extent you use electronic tools with your key/strategic customers for contract and document management; and (2) Indicate to what extent you use electronic tools with your key/strategic suppliers for contract and document management, in the IMSS V dataset. In addition, one item from IMSS IV measuring firm performance, Market share current (%), was omitted from the IMSS V dataset. With this in mind, we accepted the two new replacement items measuring Information Acquisition in IMSS V along with the 16 repeated items and retested the hypothesized model (with 18 items in total).


The methods used to examine construct validity were repeated for the IMSS V dataset (see Table 7). Again, construct reliability was measured using Cronbach's alpha and composite reliability; these values meet or exceed the commonly held threshold of .70 (Nunnally, 1978). All factor item loadings are statistically significant, and all AVE values are >.50, both of which indicate convergent validity. Evidence of discriminant validity is provided by comparing the square root of each variable's AVE to the corresponding correlations of that variable with others in the model (Fornell & Larcker, 1981).

An invariance test was conducted to examine the measurement properties of the IMSS IV and IMSS V datasets. Results reveal that the two datasets are not invariant when testing the full model (18 items). However, deleting item V5 reveals that the two datasets are measurement invariant (see Table 7). In testing the two datasets, the TF baseline model has a model fit of [X.sup.2] = 795.88, df= 208, [X.sup.2]/df = 3.83, RMSEA = .045, GFI = .94, AGFI = .92, and CFI = .92, which are within acceptable cutoffs (Hair et al., 2006) and are consistent with previous studies using similar methods (Cao & Zhang, 2011; Jean, Daekwan & Sinkovics, 2012). In examining item level measurement invariance, each factor loading was constrained to be equal across the subsamples. The [X.sup.2] difference between the unconstrained and constrained models is 14.74 with 11 degrees of freedom, which is not statistically significant (p = .20). This indicates that the factor loadings are invariant across the two samples (IMSS IV and IMSS V) (Goldsby et al., 2013).

Upon confirming measurement invariance, the structural model (without item V5) was examined using the IMSS V dataset. The structural model provides evidence of adequate model fit, featuring [X.sup.2] = 313.39, df = 112, [X.sup.2]/df = 2.80, RMSEA = .05, GFI = .95, AGFI = .93, and CFI = .95. Hypothesis 1 postulates a direct relationship between RS and AC. Hypothesis 1 is supported at p < .001 (coefficient = .52, t = 8.55). Hypothesis 2a posits a positive relationship between AC and FP and H2b postulates that AC fully mediates the relationship between RS and FP. Testing these hypotheses requires an examination of both the direct effect of AC on FP as well as the direct and indirect effects of RS on FP. The results reveal that AC has a strong positive direct effect on FP at p < .05 (coefficient = .23, t = 2.05), supporting H2a. The direct effect of RS on FP is not supported (coefficient = .01, f = 0.15), while the indirect effect is significant (coefficient = .11, p < .01) indicating that the effect of RS is fully mediated by AC and supporting H2b. The structural path relationships were tested for invariance and the only statistically significant finding (indicating that the path coefficients differ between the samples) appears for the path between RS and AC ([DELTA][X.sup.2] = 6.90, [DELTA]df = 1, p < .01), indicating that while the relationship between RS and AC is statistically significant in both samples, the relationship is stronger in the IMSS V data compared to the IMSS IV data. Thus, the IMSS V dataset reaffirms the findings of the hypothesized model tested using the IMSS IV dataset.


The overarching aim of our study is to develop and explain the role of absorptive capacity (AC) in translating strategy into performance. Given this, the contributions of our study center in three key areas. First, responsive strategy (RS) has a positive impact on AC. Second, AC is positively related to financial performance (FP), and fully mediates the relationship between RS and FP. Third, our findings suggest that the relationship between RS and AC is curvilinear and U-shaped. Lastly, AC was conceptualized as a second-order construct with four-first-order dimensions consisting of supply chain information acquisition, new product development assimilation, supply chain transformation, and operational application of this information. The robustness of the structural model results was validated using two large, discretely collected global datasets (IMSS IV with 23 countries followed by IMSS V with 19 countries). The remainder of this section highlights the theoretical and managerial implications, limitations, and suggestions for future research.

Theoretical Implications

This study contributes to the scholarly understanding of AC within an information processing theory (IPT) framework (Galbraith, 1973, 1974). As supply chains deal with the coordination of products, information, and financial exchanges, this research contributes to a better understanding of information flows (Carter et al., 2015). While, product flows have been studied extensively, there is a need for more studies on information management approaches in the supply chain (Byrd et al., 2008). AC is motivated by strategic drivers which increase uncertainty (Daft & Lengel, 1986; Lane & Lubatkin, 1998). RS drives AC which in turn mediates the relationship between strategy and performance. It is worth noting that these results were obtained after controlling for uncertainty in the firm's external environment which is relevant given the conceptual links drawn in the literature between uncertainty and IPT (Daft & Lengel, 1986), and RS (Fisher, 1997). The only statistically significant control relationship in the model linked uncertainty to RS at p < .10, consistent with the literature.

The path model findings extend Gunasekaran et al. (2008), Storey et al. (2005), and Dobrzykowski et al. (2016), who suggest that knowledge acquisition by itself is insufficient to capitalize on a RS. Instead, acquiring information through boundary-spanning relationships fuels actions which translate information into innovative new products (Galbraith, 1973, 1974). The potential value of information should be realized through supply chain transformation and operational applications (Zahra & George, 2002). Indeed, in testing the first-order factors which comprise AC, we found that RS is positively related to all four dimensions, while only Operational Application leads to firm performance. This finding provides an important augmentation to the implications derived from testing AC as a second-order construct. Here, we provide a more nuanced understanding of AC by revealing how AC might actually influence performance (Gluch et al., 2009).

The results for the main path model were validated using a subsequently collected dataset which supported the model. However, in examining structural level invariance between the two models, it is worth noting that the more recent IMSS V dataset showed that the relationship between RS and AC increased in statistical significance since data collection for IMSS IV. While the relationship between RS and AC is significant in both datasets, this variance indicates that AC may be increasing in importance for firms implementing a RS. As such, AC may be even more important today than previously thought when the ideas originated (Cohen & Levinthal, 1990; Zahra & George, 2002). The rise in information processing needs may also reflect challenges in decision-making in the emergence of big data (Galbraith, 1973, 1974; Waller & Fawcett, 2013).

Next, previous studies have identified but not examined potential causes of the variance that exists in firms' abilities to develop AC (see Azadegan, 2011; Saenz, Revilla & Knoppen, 2013; and Table 1 for detailed list of AC studies). We explore these potential causes by examining the relationship between strategy and AC, building an argument grounded in Skinner's (1974) concept of focus. Our results reveal that when a firm focuses on efficiency or responsiveness, it develops higher levels of AC through simplicity, experience, repetition, and task homogeneity (Childerhouse et al., 2002; Skinner, 1974). Focus enables firms to avoid diseconomy of scales in their supply chains that emerge when attempting to blend the two strategies by pursuing an efficient/responsive approach (Fuller et al., 1993). The hybrid efficient/responsive strategy may cause the firm to struggle to produce AC, which was shown earlier in the path model to be a key mediator in translating strategy into firm performance. This finding reinforces important trade-off decisions that managers need to make among cost and quality in managing functional products, and speed and variety in producing innovative products (Fisher, 1997). We show that these trade-off dilemmas influence information processing activities and practices, and not firm performance, given that RS does not appear to directly drive performance. This complements Qi et al. (2009), who acknowledged the challenges in implementing an efficient/responsive strategy while examining its positive effects on outcomes directly.

Finally, our study provides a modest methodological contribution to the measurement approaches employed in the AC literature (see Table 1). We conceptualized and tested a comprehensive second-order model to measure AC with four dimensions. This extends the work of previous authors who have used a variety of measures ranging from single-item proxies to multi-item psychometric measures (Cohen & Levinthal, 1990; Griffith et al., 2003; Stock et al., 2001; Malhotra, Gosain & El Sawy, 2005). Our results also contribute to the studies that have conceptualized but were unable to measure AC as a second-order construct (Gluch et al., 2009; Saenz et al., 2013). This nuanced measurement of AC provides a more thorough understanding of the relationships with other factors by testing AC at the second-order, aggregated level as well as the first-order, decomposed level.

Managerial Implications

For practitioners, the results of the overall mediation model provide important insights. Responsiveness is shown to require a multidimensional competency in the firm, namely AC. This emphasizes the human-behavioral aspects involved in achieving customer closeness or responsiveness through supply chain information acquisition, new product development assimilation, supply chain transformation, and operational application. Our findings suggest that implementing the dimensions in isolation may mute success. The t-values from the first-order correlated model testing may provide insights into the impact of each AC dimension on firm performance. Specifically, Operational Application is the only statistically significant variable on firm performance. This indicates that firm performance is most closely linked to Operational Application, suggesting that Operational Application ought to be preceded by Supply Chain Transformation, New Product Development Assimilation, and Supply Chain Information Acquisition. As such, our findings stress the importance of all four dimensions of AC collectively comprised as an antecedent of firm performance. For example, while commonly attempted in industry, the implementation of IT tools alone may be insufficient for success.

Thus, executives should take a more holistic perspective, recognizing that investment in people and processes as well as technology is crucial for success. In a more actionable sense, managers would be well-served to better integrate their organizations in NPD including design efforts and technologies. In transforming their supply chains, managers ought to implement supplier development programs, improve supplier and customer coordination and rethink their distribution strategy. The application of information for responsiveness should be manifested in improved delivery speed, and reductions in procurement and manufacturing lead times. Ultimately, through these actions, managers should recognize the AC capabilities of the organization when striving for responsiveness to achieve better financial performance in terms of sales and market share.

Next, leaders are well-served to recognize the criticality of the trade-off decisions they make among priorities for cost, quality, speed, and variety in delivering functional and innovative products to the market. It is tempting to fall into the trap of striving to operate on the supply chain frontier, aiming to deliver on all four attributes of performance using an efficient/responsive strategy (Qi et al., 2009). Our results provide guidance that the implementation challenges of this hybrid approach manifest themselves in lower levels of AC, specifically the operational application of information. Thus, an intense focus on providing either a narrow range of functional products with infrequent new product introductions, or conversely, a wide range of innovative products with frequent new product introductions are both effective approaches that lead to AC and improved firm performance. A straddling of these two strategies is shown to lead to poor information processing in the firm which ultimately reduces sales and market share.

Finally, the results should also be considered in light of the extensive press about Supply Chain Analytics (see for example: Pettey, 2015). While analytics are likely to benefit companies that invest in it, the organizational design features captured in absorptive capacity are critical to maximizing the value of their investment. Such investments should therefore be considered a part of the holistic improvement of the firm's information processing capability rather than isolated tactics to gain specific capabilities.


While our study makes some important contributions, our findings should be considered within the limitations of our research. While we took reasonable procedural steps to mitigate common method bias and used commonly employed statistical tests to test for its presence, the fact that our data were collected using a survey method from single respondents indicates that bias may be possible. As discussed in our literature review, we acknowledge that AC may have antecedents in addition to a RS. Therefore, we anticipate numerous interesting studies exploring the source of AC. Likewise the operationalization of the four-first-order AC constructs, within the IMSS survey, may not be the ideal way to measure the construct, although we believe this is an improvement to previous research (Azadegan, 2011; Saenz et al., 2013).

Suggestions for Future Research

Based on the exploratory nature of this study, we provide the following suggestions for additional studies centered on AC. The concept of AC has been studied from a cross-sectional, firm-level perspective, but we urge authors to consider additional research approaches that take a more behavioral perspective, such as experiments or social network analysis. While we had some initial evidence supporting the notion that responsiveness has increased over time, thus increasing the need for additional increased AC, this potential effect should be tested formally. Longitudinal studies often provide for deeper understanding of a phenomenon and increase the generalizability of the results (Goldsby & Autry, 2011). Due to the limitations of the dataset used in this research, it would be interesting to devise and validate a new construct that is more widely applicable in the supply chain context.

It would be interesting to decompose the second-order factor to examine the relationships among the acquisition, assimilation, transformation, and application constructs. The extent to which firms, even within the same industry, use each dimension of AC (e.g., IT use) may differ (Dobrzykowski, 2012). A deeper analysis may help to understand how these factors and the relationships among these factors differ in various supply chain contexts. Possible examples could be national culture, emerging markets versus highly developed markets, supply chain strategies, and industry sectors (Dobrzykowski, Saboorideilami, Hong & Kim, 2014).

Likewise, the investigation of how different types of information are processed would be worthwhile (Wilson, Dobrzykowski & Cazier, 2008). Lastly, it is important to recognize that we have considered the issue of new product development (NPD) in a general manner. Future studies should investigate specific projects and what contributes to the success of individual product introductions. We expect there to be differences in the type of projects or products that a firm introduces and the level of absorptive capacity required.
Survey Items and Descriptive Statistics

Survey Items                                             Mean     SD

Responsive Strategy (RS)--3 items ([alpha] = .76)

Consider the business unit's competitive strategy. Consider the
current importance of offering:

new products more frequently to win                      3.41    0.92
orders from your major customers.

more innovative products to win orders                   3.57    0.88
from your major customers.

a wider product range to win orders                      3.43    0.81
from your major customers.

(Scale anchors: not important = 1; very important = 5)

Supply Chain Information Acquisition (SCIA)--3 items ([alpha] = .73)

Indicate to what extent do you/your:

key/strategic customers use electronic                   2.65    1.19
tools (Internet or EDI based) with you
for content and knowledge

key/strategic customers use electronic                   2.63    1.21
tools (Internet or EDI based) with you
for RFx (request for quotation,
proposal information).

use electronic tools (Internet or EDI                    2.60    1.11
based) with your key/strategic suppliers
for content and knowledge

(Scale anchors: none = 1; high = 5)

NPD Assimilation (NPDA)--3 items ([alpha] = .74)

Action programs ...

Increasing the organizational                            2.82    1.00
integration between product
development and manufacturing.

Increasing performance of product                        2.92    1.01
development and manufacturing
through platform design,
standardization, and modularization.

Increasing the technological integration                 3.03    1.10
between product development and
manufacturing through e.g..

(Scale anchors: none = 1; high = 5)

Supply Chain Transformation (SCT)--4 items ([alpha] = .86)

Action programs ...

Rethinking and restructuring                             2.57    0.99
distribution strategy in order to change
the level of intermediation.

Increasing the level of coordination of                  3.16    1.10
planning decisions and flows of goods
with customers including dedicated

Increasing the level of coordination of                  3.19    1.04
planning decisions and flow of goods
with suppliers including dedicated

Implementing supplier development                        2.57    0.98
and vendor rating programs.

(Scale anchors: none = 1; high = 5)

Operational Applications (OA)--3 items ([alpha] = .77)

How does your current operational performance compare with your main

Procurement lead time.                                   2.59    0.82

Delivery speed.                                          2.99    0.91

Manufacturing lead time.                                 2.80    0.86
(much worse = 1, much better = 5)

Firm Performance (FP)--3 items ([alpha] = .71)

Sales compare to three years ago.                        3.43    0.77

Market share compare to three years                      3.36    0.78

Market share currently.                                  2.55    0.80

(Scale anchors: deteriorated > 10% = 1; stayed about the same = 2;
improved 10%-30% = 3; improved 30%-50 = 4; improved > 50% = 5)

Uncertainty (control)--1 item

How would you describe the external environment (Scale anchors:
declining rapidly = 1; growing rapidly = 5. Responses were recoded:
neither declining nor growing = 1 (original responses of 3 recoded to
1), declining or growing moderately = 2 (original responses of 2 or 4
recoded to 2), and declining or growing rapidly = 3 (original
responses of 1 or 5 recoded to equal 3).


Rutgers University


University of Toledo


Rowan University


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(1) We tested a partially mediated model linking Responsive Strategy to each first-order factor of AC and Firm Performance. The relationships emanating from Responsive Strategy to each of the first-order dimensions of AC are statistically significant at p < .01. Only Operational Application is statistically significant on Firm Performance at p < .05.

(2) The same hierarchical regression approach was used to examine the relationship between Responsive Strategy and Operational Application given that Operational Application was found to be the only variable linking Responsive Strategy and Firm Performance. The results revealed a similar positive U-shaped curvilinear relationship between Responsive Strategy and Operational Application.

David D. Dobrzykowski (PhD., University of Toledo) is an Assistant Professor in the Department of Supply Chain Management, and Co-Director of the Masters in Healthcare Services Management program at Rutgers Business School, Rutgers University. Dr. Dobrzykowski's research investigates information processing and the coordination of work in supply chains. His publications have appeared (or are forthcoming) in Journal of Supply Chain Management, Journal of Operations Management, Decision Sciences, Service Science, International Journal of Production Research, International Journal of Production Economics, Journal of Service Management among others. Prior to academe, he enjoyed a 13-year industry career, serving in Chief Executive Officer and Vice President roles in the healthcare sector.

Rudolf Leuschner (Ph D., The Ohio State University) is an Assistant Professor in the Department of Supply Chain Management, and Co-Director of the Master of Science in Supply Chain Management program, at Rutgers Business School, Rutgers University. His research interests focus on the end-to-end supply chain and the integration of its three primary flows: product, information and financial. Other interests include logistics customer service, supply chain strategy, and generalizability of research and replication, with a specific focus on meta-analysis. His work has appeared in the Journal of Supply Chain Management and the Journal of Business Logistics. He is also a co-author of Supply Chain Management: Processes, Partnerships, Performance.

Paul C. Hong (Ph.D., University of Toledo) is a professor of Operations Management at the University of Toledo. Dr. Hong is the recipient of numerous research awards including Journal of Operations Management Best Paper Finalist Award (2006), 2011 Emerald Literati Network Awards for Excellence and 2015 University of Toledo Outstanding Research and Scholarship Award. He is international network coordinating chair of the annual Global Supply Chain Management Symposium and Workshop. His recent books are Building Network Capabilities in Turbulent Competitive Environments (co-authored with Youngwon Park), Practices of Global Firms from Korea and Japan (2012) and Business Success Stories from the BRICS (2014). He is currently working on projects related to developing growth engine industries and entrepreneurial innovation for base of pyramid (BOP) customers.

James J. Roh (Ph.D., University of Toledo) holds a Ph.D. in Manufacturing Management, an M.B.A. and an M.A. in Economics from the University of Toledo, and a B.A. in Economics from Dongguk University. The recipient of the New Jersey Bright Idea Award in 2014 and Rohrer College of Business Alumni Advisory Council Award for Scholarly Achievement in 2012, Dr. Roh has actively engaged in research in responsive supply chain management and published papers in journals such as Business Horizons, Information & Management, International Journal of Production Research, International Journal of Production Economics, and Production Planning & Control, among others.
Absorptive Capacity Literature

                                                       Definition and
                                                      Applications of
                Authors and Year                        Absorptive
Analysis        (Sample Context)       Context            Capacity

Inter-          Newey (2010)       Open innovation   Supplier-and
organizational  (anti-influenza                      customer-types of
                drug development)                    absorptive

                Park and Kang      Technology        A process to
                (2009) (62         alliance and      understand,
                technology         knowledge         internalize
                alliance cases     transfer between  external
                between teacher    firms             knowledge, and the
                and student firms                    determinant to
                in Korean IT                         successful
                industry)                            introduction of
                                                     technology and
                                                     Measured as R&D

                Zhen and Anand     Inter-            The aggregation of
                (2009) (161        organizational    the prior relevant
                engineering units  knowledge         knowledge or
                of multinationals  transfer between  experiences of the
                in the Chinese     focal company     individual members
                automotive         and partner       of a recipient
                industry)          company           community

                Azadegan (2011)    Translating       The cumulative
                (136               supplier          ability to
                manufacturers      innovation into   recognize,
                and 272            firm              assimilate and
                suppliers)         manufacturing     replicate
                                   performance.      knowledge

Organizational  Fosfuri and Tribo  Antecedents of    Potential
                (2008) (2,464      potential         absorptive
                Spanish firms)     absorptive        capacity was
                                   capacity--the     measured using
                                   first two         seven survey items
                                   dimensions from   assessing the
                                   Zahra and         importance of
                                   George (2002)     external

                Gluch et al.       Green innovation  Capability that
                (2009) (246                          links knowledge
                companies from                       generated outside
                Swedish                              the company to
                construction                         knowledge
                industry)                            generated within
                                                     the company.
                                                     Measured by four
                                                     and exploitation

                Huang and Rice     Innovation        A processual
                (2009) (292        performance       capability that
                Australian                           builds on a stock
                manufacturing                        of prior knowledge
                SMEs)                                to facilitate the
                                                     absorption of
                                                     externally gained

                Joshi, Chi, Datta  The relationship  A set of
                and Han (2010)     between IT-       organizational
                (110 firms from    enabled           routines and
                Information Week   knowledge         processes by which
                500)               capabilities and  firms acquire.
                                   firm innovation.  assimilate,
                                                     transform. and
                                                     exploit knowledge
                                                     can produce
                                                     Measured by IT-
                                                     enabled potential
                                                     and realized
                                                     capacity, and
                                                     social integration

                Zhou and Wu        Technological     Absorptive
                (2010) (192        capability in     capacity is not
                Chinese firms)     product           specifically
                                   innovation        defined or
                                                     measured; rather a
                                                     firm's learning
                                                     capability is
                                                     represented by

                Liao, Wu, Hu, and  Knowledge         Defined and
                Tsui (2010) (362   acquisition and   measured as
                Taiwan's           innovation        second-order
                knowledge-         capability        construct that
                intensive                            includes
                industries)                          communication with
                                                     the external
                                                     environment, level
                                                     of know-how and
                                                     experience within
                                                     the organization,
                                                     diversity and
                                                     overlaps in the
                                                     structure, and

Group           Nemanich et al.    Individual and    Defined as the
                (2010) (100 US-    project team      capabilities of
                based research     level absorptive  evaluation,
                teams)             capacity          assimilation, and
                                                     Measured as four
                                                     capability, team
                                                     shared cognition
                                                     capability, team
                                                     apply capability.
                                                     Shared cognition
                                                     is a newly added

                                           Findings Related to
                      Dependent                 Innovation
Analysis              Variable                 Performance

Inter-          Inbound and             Inbound open
organizational  outbound                innovation
                open innovation         involved customer
                                        absorptive capacity
                                        and outbound
                                        innovation required
                                        supplier absorptive

                The performance of      Absorptive capacity
                technology alliances.   improves the
                                        performance of
                                        technology alliance

                Individual knowledge    Collective absorptive
                and collective          capacity is more
                knowledge               effective
                                        in transferring
                                        compared to their
                                        individual level of
                                        absorptive capacity.
                                        For innovation,
                                        collective absorptive
                                        capacity needs to be
                                        cultivated more.

                Manufacturer            Absorptive capacity
                performance             moderates the link
                                        between supplier
                                        innovation and firm
                                        performance with
                                        routine and

Organizational  Innovation              Firms involved in R&D
                performance             partnerships and
                                        transactions in R&D
                                        demonstrate higher
                                        ability to understand
                                        and assimilate
                                        from the external

                Innovation and          Knowledge
                performance in          transformation and
                green                   exploitation positively
                activities              affect green innovation
                                        and performance.
                                        Acquisition and
                                        assimilation indirectly
                                        influence them.

                R&D intensity as a      Absorptive capacity
                proxy for innovation    improves innovation
                performance             performance

                Ideated innovation as   Realized absorptive
                patent application      capacity increases
                counts and              ideated and
                commercialized          commercialized
                innovation as new       innovation. Social
                product and service     integration capacity
                introduction counts     moderates between
                                        ideate innovation and

                Exploration and         Technological capability
                exploitation            fosters exploitation at
                                        an accelerating rate
                                        but it has an inverted
                                        U-shaped relationship
                                        with exploration.

                Innovation capability   Absorptive capacity
                                        improves innovation

Group           Team apply              The capability of R&D
                capability              team members to
                                        evaluate and assimilate
                                        external knowledge
                                        improves collective
                                        assimilation capabilities,
                                        enhancing the chances
                                        of innovation.

Sample Demographics

Country                    n        %

Argentina                  44       6.2
Australia                  14       2.0
Belgium                    32       4.5
Brazil                     16       2.3
Canada                     25       3.5
China                      38       5.3
Denmark                    36       5.1
Estonia                    21       3.0
Germany                    18       2.5
Greece                     13       1.8
Hungry                     54       7.6
Ireland                    15       2.1
Israel                     20       2.8
Italy                      45       6.3
New Zealand                30       4.2
Norway                     17       2.4
Portugal                   10       1.4
Sweden                     82      11.5
Netherlands                63       8.9
Turkey                     35       4.9
United Kingdom             17       2.4
United States (USA)        36       5.1
Venezuela                  30       4.2
Missing values              0       0.0

Total                     711      100%

Plant Size
Employees                n       %

<100 emps                 134      18.8
101-200 emps               95      13.4
201-500 emps              154      21.7
501-1,500 emps            121      17.0
>1,501 emps               118      16.6
Missing values             89      12.5

Total                     711      100%

ISIC code                   n         %

28--fabricated            271      38.1
metal products

29--machinery             147      20.7
and equipment

30--office and             16       2.3

31--electrical             92      12.9

32--radio, television,     39       5.5
and communication

33--medical,               29       4.1
and optical

34--motor vehicles,        68       9.6
trailers, and semi-

35--other                  41       5.8

Missing values &            8       1.1

Total                     711    100.1%

Composite Reliability (CR), Average Variance Extracted (AVE), and

                                              CR = .77
               [mu]   [sigma]   t-values    [alpha] = .76

1. Resp        3.47     .72     13.3-14.2      .54/.73

2. SC Info     2.63     .94     12.1-15.3       .206

3. NPD         2.93     .84     13.5-15.0       .237

4. SC          2.87     .82     17.2-18.7       .120

5. Ops.        2.79     .72     14.9-15.5       .085

6. Firm        3.11     .62     10.8-12.1       .065

                     2               3               4
                 CR = .74        CR = .75        CR = .86
               [alpha] = .73   [alpha] = .74   [alpha] = .81

1. Resp

2. SC Info        .50/.71

3. NPD             .392           .50/.71

4. SC              .476            .513           .60/.77

5. Ops.            .275            .339            .270

6. Firm            .093            .144            .110

                     5               6
                 CR = .78        CR = .74
               [alpha] = .77   [alpha] = .71

1. Resp

2. SC Info

3. NPD

4. SC

5. Ops.           .54/.73

6. Firm            .190           .50/.71

The AVE for each variable is shown in bold on the diagonal
immediately followed by the square root of the AVE for discriminant
validity testing.

SEM Path Model Results (n = 711)

Hypotheses                       Direct Coeff.   T-Stat   p-Value

H1: Responsive Strategy [right      .25 ***       4.69     .001
arrow] AC

H2: AC [right arrow] Firm           .19 ***       3.40     .001

H2: Responsive Strategy [right     .02 (n/s)      0.43     .666
arrow] Firm Performance

Hypotheses                       Indirect Coeff.   p-Value

H1: Responsive Strategy [right         --            --
arrow] AC

H2: AC [right arrow] Firm              --            --

H2: Responsive Strategy [right       .05 **         .003
arrow] Firm Performance

Model fit: [X.sup.2]/df = 1.96, RMSEA = .04, GFI = .96, AG FI = .95,
and CFI = .97; ([dagger]) p < .1,  * p < 05, ** p < .01, *** p <
.001; (n/s) Not statistically significant; Direct and indirect
relationships tested; Controls: uncertainty (market dynamics) linked
to all variables.

Hierarchical Regression Results

                         Dependent Variable: Absorptive Capacity (AC)

                         Equation 1                   Equation 2
                        AC = f (UNC)               AC = f (UNC, RS)

Variables                    Beta        t-Value     Beta      t-Value

Uncertainty (UNC)       .06 ([dagger])    1.69       .05        1.40

Responsive Strategy                                  .20 **     5.53


[R.sup.2]               .004                         .045

Adjusted [R.sup.2]      .003                         .043

Change of [R.sup.2]                                  .041

F-value of change of                               30.55 ***

                          Equation 3 AC = f
                        (UNC, RS, [RS.sup.2])

Variables                  Beta       t-Value

Uncertainty (UNC)        .04           1.16

Responsive Strategy      .21 **        5.60

Responsive               .11 **        2.93

[R.sup.2]                .057

Adjusted [R.sup.2]       .053

Change of [R.sup.2]      .011

F-value of change of    8.57 ***

([dagger]) p < .1, * p < .05, ** p < .01, *** p < .001.

Summary of IMSS IV and IMSS V Measurement Items for Robustness Check

Item     IMSS IV (n = 711)               IMSS V (n = 677)

         Responsive Strateqy             Responsive Strateqy

V1       Wider product range             Wider product range

V2       Offer new products              Offer new products
         frequently                      frequently

V3       Offer more innovative           Offer products that are more
         products                        innovative

         Information Acquisition         Information Acquisition

V4       Content & knowledge             Contract & document
         management w/suppliers          management w/suppliers

V5 (a)   RFx w/customers                 RFx w/customers

V6       Content & knowledge             Contract & document
         management w/customers          management w/customer

         NPD Assimilation                NPD Assimilation

V7       Design integration              Design integration

V8       Organizational integration      Organizational integration

V9       Technological integration       Technological integration

         Supply Chain Transformation     Supply Chain Transformation

V10      Supplier development            Supplier development

V11      Coordination w/suppliers        Coordination w/suppliers

V12      Rethink distribution            Rethink distribution
         strategy                        strategy

V13      Coordination w/customers        Coordination w/customers

         Operations Applications         Operations Applications

V14      Delivery speed relative to      Delivery speed relative to
         comp                            comp

V15      Manufacturing lead time         Manufacturing lead time
         relative to comp                relative to comp

V16      Procurement lead time           Procurement lead time
         relative to comp                relative to comp

         Firm Performance                Firm Performance

V17      Sales compared to 3 years       Sales compared to 3 years
         ago                             ago

V18      Market share compared to 3      Market share compared to 3
         years ago                       years ago

V19      Market share current (%)        --No replacement item--

(a) Item omitted from robustness analysis owing to results of
invariance testing.

IMSS V Composite Reliability (CR), Average Variance Extracted (AVE),
and Correlations

                                           CR = .78
               Mean    SD    t-Values    [alpha] = .78

1. Resp        3.27   0.91   10.4-10.5      .56/.75#

2. SC Info     3.31   0.97   9.8-10.3        .159

3. NPD         2.97   0.94   10.9-11.1       .444

4. SC          2.77   0.88   11.3-11.9       .437

5. Ops.        3.31   0.54   10.6-11.0       .089

6. Firm        2.83   0.82    6.0-8.2        .098

                     2               3               4
                 CR = .79        CR = .75        CR = .81
               [alpha] = .78   [alpha] = .77   [alpha] = .79

1. Resp

2. SC Info       .55/.74#

3. NPD             .329          .51/.71#

4. SC              .449            .636          .51/.71#

5. Ops.            .151            .167            .205

6. Firm            .147            .167            .096

                     5               6
                 CR = .78        CR = .78
               [alpha] = .77   [alpha] = .74

1. Resp

2. SC Info

3. NPD

4. SC

5. Ops.          .54/.73#

6. Firm            .181          .63/.79#

The AVE for each variable is shown in bold on the diagonal
immediately followed by the square root of the AVE for discriminant
validity testing; These results were obtained after deleting item V5
during the invariance analysis. See Table 5 for V5.

Note: The AVE for each variable is indicated with #.
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Article Details
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Author:Dobrzykowski, David D.; Leuschner, Rudolf; Hong, Paul C.; Roh, James J.
Publication:Journal of Supply Chain Management
Article Type:Statistical table
Geographic Code:1USA
Date:Oct 1, 2015
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