Supply chain resilience: Conceptualization and scale development using dynamic capability theory

Author’s Accepted Manuscript Supply Chain Resilience: Conceptualization and Scale Development Using Dynamic Capability Theory Md Maruf H Chowdhury, Mohammed Quaddus www.elsevier.com/locate/ijpe

PII: DOI: Reference:

S0925-5273(17)30114-7 http://dx.doi.org/10.1016/j.ijpe.2017.03.020 PROECO6689

To appear in: Intern. Journal of Production Economics Received date: 9 September 2015 Revised date: 24 March 2017 Accepted date: 27 March 2017 Cite this article as: Md Maruf H Chowdhury and Mohammed Quaddus, Supply Chain Resilience: Conceptualization and Scale Development Using Dynamic Capability Theory, Intern. Journal of Production Economics, http://dx.doi.org/10.1016/j.ijpe.2017.03.020 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Supply Chain Resilience: Conceptualization and Scale Development Using Dynamic Capability Theory

Md Maruf H Chowdhury1, Mohammed Quaddus2* 1

UTS Business School, University of Technology Sydney, Sydney, Australia

2

School of Marketing, Curtin University, GPO Box U1987, Perth WA 6845, Australia

*

Corresponding author. [email protected]

Abstract A growing number of researchers and practitioners have placed supply chain resilience (SCRE) at the forefront of their research agendas due to an increased susceptibility to disruptive events in the global supply chains. However, empirical research in this area has been hamstringed by the lack of a validated measurement model. In this context, drawing on dynamic capability theory, this research develops a measurement instrument for SCRE. This research conducts a qualitative field study, followed by a quantitative survey. Content analysis is used to explain various dimensions in the qualitative field study, and partial least squares (PLS)-based structural equation modelling (SEM) is used to analyse the data collected in the quantitative survey. The research is conducted with three rounds of data collection and analyses. The results show that SCRE is a multidimensional and hierarchical construct, which consists of three primary dimensions: proactive capability, reactive capability and supply chain design quality. These three primary dimensions are further operationalized through twelve sub-dimensions. The findings also affirm that the SCRE scale potentially better predicts supply chain operational vulnerability (OV) and supply chain performance (SCP) and conforms to the “technical” and “evolutionary” fitness criteria of dynamic capability theory. The implications of these findings are discussed in the context of theory and practice. Limitations and future avenues of research are also discussed.

1

Keywords Supply chain resilience, dynamic capability, measurement instrument, multidimensional construct

1. Introduction With the increasing complexity of global business, supply chains are often exposed to numerous disruptions. The detrimental effects of such disruptions are quite pronounced if compounded and not addressed at the right time (Pettit et al. 2013). The consequences are even worse if disruptions are reported in the media, resulting in rapidly declining stock prices (Hendricks & Singhal 2003). For example, after an extended power disruption in Japan due to the 2011 tsunami, Toyota lost over 17% of its value (Kachi & Takahashi 2011). The multiplicity of disruptive events has spurred renewed scholarly interest in supply chain resilience (SCRE) (Pettit et al. 2013; Wu et al. 2006), as attempts to tackle disruptions via traditional risk management approaches are insufficient in the era of highly uncertain and dynamic business environments (Jüttner & Maklan 2011). Therefore, to combat the challenges arising from uncertain and dynamic environments, organizations must develop a resilient approach (Jüttner & Maklan 2011; Brandon-Jones et al. 2014), which Fiksel (2006) defines as the capacity of an enterprise to survive, adapt, and grow in tumultuous times.

Given the enormous importance of SCRE in the event of disruptive events, a comprehensive conceptualization of SCRE is of utmost importance. Organizations and their supply chains must develop both proactive and reactive resilience capabilities to increase the required level of readiness, response and recovery ability during the pre-disaster and post2

disaster phases. Otherwise, supply chain operations will suffer from discontinuity, which adversely affects both the revenues and costs of the whole chain (Ponomarov & Holcomb 2009). Apart from the proactive and reactive aspects, some studies (Craighead et al. 2007; Falasca et al. 2008; Azaron et al. 2008; and others) emphasize the quality of supply chain design in developing SCRE. A review of the extant literature reveals that a theoretically grounded, comprehensive conceptualization and measurement of SCRE is lacking (Ponomarov & Holcomb 2009). A pool of studies (e.g., Pal et al. 2014; Wieland & Wallenburg 2013; Ponis & Koronis 2012; Jüttner & Maklan 2011; Ponomarov & Holcomb 2009; among others) address the significance of SCRE. Wieland and Wallenburg (2013) use proactive and reactive dimensions of resilience, such as robustness and agility. Pettit et al. (2013) and Ambulkar et al. (2015) develop a framework for SCRE assessment. Pettit et al. (2013) deal with seven vulnerability factors and fourteen capability factors to develop a resilience score. Ambulkar et al. (2015) measure SCRE based on four measurement items (coping with changes, being able to adapt to supply chain disruptions, providing quick response, and maintaining high situational awareness) and develop a model of the antecedents of “firm resilience”. The authors conclude that to develop resilience, firms should have a supply chain disruption orientation, a resource configuration and a risk management infrastructure in place. As will be argued later, supply chain design is also an important dimension of SCRE (Ponis & Koronis 2012; Haberman et al. 2015) that we use in our conceptualization of resilience. The theoretical foundations of the framework/model of resilience are also observed to be deficient in extant studies. Although a hierarchical approach to resilience has been studied in the extant literature (Pettit et al. 2013), we take an alternative approach – developing a hierarchical and multidimensional measurement scale for SCRE using the dynamic capability view (DCV) (Teece et al. 1997; Eisenhardt & Martin 2000).

3

The DCV focusses on a firm’s competiveness in a dynamic market of “rapid and unpredictable change” (Eisenhardt & Martin 2000). It is thus an extension of the traditional resource-based view (RBV) of the firm (Wernerfelt 1984; Barney 1991). The essential components of the DCV relate to identifying strategic organizational processes, reconfiguring resources (integrating, gaining, and releasing), and identifying the path to follow to attain competitive advantage (Teece et al. 1997, Eisenhardt & Martin 2000). We argue that supply chain management is a significant strategic organizational process (Tan et al. 2002) for which resilience (or the lack thereof) must be appraised following a structured path; thereafter, corrective actions can be taken by identifying and integrating appropriate resources. Lee (2004) mentions that supply chains need to be “Agile, Adaptable and Aligned” (the triple-A supply chain) to gain competitive advantage. Since the DCV calls for developing appropriate capabilities and reconfiguring resources within firms, we argue that the requirements for a triple-A supply chain can be achieved by managing SCRE via the DCV. We will show that the components of our SCRE scale (proactive, reactive and design elements) are essentially dynamic in nature. These elements also need to be agile and adaptable. We thus take the DCV to conceptualize SCRE. However, the DCV has been criticized for its lack of “empirical grounding” (Eisenhardt & Martin 2000). In this respect, our research contributes significantly by developing and empirically testing a scale for SCRE measurement to determine its effectiveness. In doing so, we adopt the concept of the “technical” and “evolutionary” fitness of the DCV, as proposed by Teece (2007). In line with the research objective, this paper used both qualitative and quantitative methods. In the qualitative approach, we used content analysis on the relevant literature and field study findings to contextualize the measurement instrument. In the quantitative approach, a series of studies were conducted to develop and validate the SCRE scale. Once the dimensions of SCRE were identified from the literature review and the qualitative study, 4

data from study 1 (n = 81 supply chain executives from the apparel industry) were used to select items based on exploratory factor analysis. Subsequently, confirmatory factor analysis (CFA) was conducted on data obtained from study 2 (n = 296 supply chain executives) to examine the factor structure and to provide evidence of dimensionality, scale reliability and validity. Lastly, in study 3, data from 207 supply chain executives from multiple industries were used to test the scale generalizability of SCRE measures. Structural equation modelling (SEM) has been adopted to analyse the collected data. Our findings confirm that SCRE is a hierarchical (third-order) and multidimensional construct, which is measured by the following primary dimensions: proactive supply chain capability, reactive capability and supply chain design quality. These three primary dimensions were further divided into twelve sub-dimensions.

This study makes several important theoretical and practical contributions to the existing literature. First, drawing on the DCV (Teece et al. 1997), this study consolidates the dispersed literature on SCRE and develops and validates an integrated measurement scale for SCRE construct which is a unique contribution. In the spirit of the DCV, this study assumes that supply chains must have proactive features, reactive features and proper design qualities to sense, reconfigure and transform resources in line with environmental changes. Such dynamic capabilities can be viewed as the resilience capabilities of the organizations and their supply chains to overcome these turbulent changes. Second, this research addresses the existing void that studies aligned with the DCV of SCRE have with regard to their empirical and measurement aspects (Eisenhardt & Martin 2000; Ponomarov & Holcomb 2009). Therefore, we contribute to the DCV by using the “technical” and “evolutionary” fitness criteria of Teece (2007) to develop and validate an integrated measurement model for SCRE. The third contribution of this study is the comprehensive development of a hierarchical and multidimensional SCRE scale, which is a major contribution to the literature. The SCRE 5

scale is conceptualized and operationalized as a third-order hierarchical model. Supply chain decision makers will benefit from the findings of our study by developing the necessary proactive and reactive capabilities and ensuring supply chain design quality. The rest of the paper is structured as follows: the next section presents the literature review, followed by a description of the research instrument development process; the subsequent section presents instrument testing, followed by the confirmatory study; the final section presents the discussion and implications, along with limitations and avenues for future research, followed by conclusions.

2. Literature review 2.1 Concept of supply chain resilience Resilience is a multidisciplinary concept. Holling (1973), one of the pioneering researchers of resilience, states that resilience is the ability of a system to absorb changes. Since then, many authors have echoed the concept of resilience as a system’s ability to recover and return to its original state (e.g., Mitroff & Alpasan 2003; Christopher & Peck 2004). In an organizational context, resilience can be characterized as the organizational capability to survive in a turbulent environment (Ates & Bititci 2011). Resilience has ultimately become enormously important in the supply chain domain because of increased disruptions in the global supply chain. However, scholars still debate how SCRE should be conceptualized and measured (Jüttner & Maklan 2011), as the studies are ambivalent in differentiating the measurement and antecedent constructs of SCRE. Existing debates and gaps in the literature necessitate the conceptualization and empirical investigation of SCRE measurement constructs. Bhamra et al. (2011) conduct an extensive literature review on resilience in the context of small and medium-sized enterprises (SMEs). The authors conclude that research to 6

“empirically prove theories” and to determine whether resilience is a “measure, feature, philosophy or capability” is urgently needed. Durach et al. (2015) develop a framework of the antecedents of supply chain robustness, which they conceptualize as a dimension of SCRE. The authors also discuss proactive and reactive strategies to cope with the “turbulence” in supply chains. Hohenstein et al. (2015) conduct a systematic and comprehensive literature review to find the overarching “building elements” of SCRE. The authors conclude that most research in the resilience domain has been qualitative in nature and that research “assessing and measuring” SCRE is genuinely lacking. Scholten and Schilder (2015) follow a case study approach to study the influence of buyer–supplier collaboration in SCRE. The authors show how collaborative activities “enable visibility, velocity and flexibility” and, in turn, SCRE. Wieland and Wallenburg (2012, 2013) conceptualize SCRE as agility and robustness. The authors also argue that an agile strategy is reactive while a robust strategy is proactive. Thus, as is evident from the existing literature, conceptualizations of SCRE can be explained from multiple perspectives. Some studies focus on proactive aspects of resilience (e.g., flexibility, visibility, redundancy, integration, financial strength, and market capability) (Jüttner & Maklan 2011; Pal et al. 2014; Erol et al. 2010), while other studies embrace resilience as both proactive and reactive capabilities (e.g., flexibility, redundancy, agility, recovery time, cost and response effort) (Sheffi & Rice 2005; Christopher & Peck 2004; Falasca et al. 2008; Martin 2004; Vugrin et al. 2011; Pettit et al. 2013). In a similar vein, the proactive and reactive concepts of resilience are also interchangeably defined according to the notions of pre-disaster resilient actions and post-disaster resilient actions (Rose 2004; Wieland & Wallenburg 2013; Sheffi & Rice 2005). In line with the extant literature, we assert that supply chains need both proactive and reactive capabilities to adapt, integrate and reconfigure during the pre-disaster and post-disaster phases surrounding disruptive events.

7

An examination of the extant literature also reveals that studies, such as Craighead et al. (2007), Falasca et al. (2008), Azaron et al. (2008), introduce the concept of supply chain design factors that reduce supply chain vulnerability and increase resilience. According to the authors, a “complex and critical supply chain” is more susceptible to disruptions that affect the resilience of the supply chain. The success of supply chain managers depends on their ability to select appropriate strategies with regard to supply chain design. For example, in an environment of supply uncertainty, adopting multiple suppliers helps reduce supply side vulnerabilities. Selecting such strategies can be attributed to defining the path of competencies in uncertain environments. We argue that, depending on the context, supply chain design can be considered either proactive or reactive. For example, to satisfy customers’ needs, companies deliberately design appropriate supply chain (proactive design) (Vonderembse et al. 2006). However, supply chain design is also dynamic in responding to changes in the environment (reactive design) (Harland et al. 2003). Companies need to redesign the supply chain based on their experiences of disruptions in the supply chain (Fiksel 2006; Chowdhury & Quaddus 2015). Therefore, we have treated supply chain design as another dimension of SCRE.

2.2 Proactive aspects of supply chain resilience Supply chains need proactive capabilities to be resilient against disruptions (Pettit et al. 2010; Christopher & Peck 2004; Jüttner & Maklan 2011). Hollnagel et al. (2006) mention the proactive resilience capability of a system as the capability to recognize, anticipate and defend against the changing shape of risk before adverse consequences occur. Tenhiala and Salvador (2014) emphasize the need for a formal communication channel to cope with disruptions and improve resilience. Supply chain research emphasizes different proactive capabilities such as flexibility, redundancy/reserve capacity, robustness, adaptability, 8

collaboration, integration, visibility, market strength, financial strength, diversity, and efficiency to measure resilience (Pettit et al. 2010; Pettit et al. 2013; Pal et al. 2014; Sheffi & Rice 2005; Fiksel 2003; Ponomarov & Hollcomb 2009). In addition, Jüttner and Maklan (2011) focus on proactive capabilities such as flexibility, velocity, visibility and collaboration. Further, the risk management literature provides a handful of studies (e.g., Knemeyer et al. 2009; Grötsch et al. 2013; Sullivan & Branicki 2011; and others) that discuss proactive capabilities such as proactive planning, buyer–supplier relational quality, resourcefulness, readiness and rapidity to mitigate disruptions in organizations and their supply chains. Based on the commonalities in the literature, flexibility, redundancy/reserve capacity, integration, efficiency, market strength, and financial strength can be regarded as proactive supply chain capabilities. Although a number of studies identify different proactive capabilities to mitigate supply chain disruptions (e.g., Jüttner & Maklan 2011; Pettit et al. 2013), empirical validation of the dimensions of supply chain proactive capabilities and their sub-dimensions has yet to be established. Therefore, this study validates supply chain proactive capabilities and their sub-dimensions within a hierarchical and higher-order SCRE scale. 2.3 Reactive aspects of supply chain resilience In line with Sheffi and Rice (2005) and Ponomarov and Holcomb (2009), the reactive facets of SCRE can be established based on the response and recovery abilities of organizations. Supply chain response concerns mitigating disruptions in the shortest possible time and with the smallest impact (Pettit et al. 2013). The ability to respond quickly to market needs during critical situations is an important determinant of SCRE (Sheffi & Rice 2005; Wieland & Wallenburg 2013). Hence, we also argue that supply chains should be able to

9

respond quickly to return to normal or stronger positions. To explain the resilience capability of successful companies during the global financial crisis, Jüttner and Maklan (2011) state that resilient companies can respond to environmental changes more quickly than their nonresilient counterparts. An organization’s ability to respond quickly to environmental forces is arguably a distinctive capability that is a unique source of competitive advantage. Recovery from disruptions is a critical and distinctive capability of organizations and their supply chains. Some systems, whether a business network, an ecological system or a nation, can recover quickly from disasters because of their idiosyncratic capabilities. In line with the research of Wang et al. (2010), Sheffi & Rice (2005) and Välikangas (2010), such capabilities can be attributed as the resilience of a dynamic system. The extant literature (Wang et al. 2010; Sheffi & Rice 2005; Willroth et al. 2001; and others) mostly measures resilience in terms of recovery time. However, the cost of recovery effort should also be considered (Vugrin et al. 2011). Martin (2004) includes cost as a parameter for measuring resilience. Similarly, other studies, such as Vugrin et al. (2011), emphasize on the cost of resilience. A system may recover in less time (Wang et al. 2010), with less effort and at lower cost (Vugrin et al. 2011) because of its efficiency and unique ability to absorb the shock (Holling 1973) or reduce the impact of the disruption (Rose 2004) or its inherent ability to return to its original position (Christopher & Peck. 2004). Therefore, resilience can be measured by the extent of recovery time, cost, disruption absorption and ability to reduce the impact of the loss. Although a few studies illustrate the concept of supply chain reactive capability to explain SCRE, the literature lacks empirical validation of supply chain reactive capabilities. Therefore, this study empirically validates the supply chain reactive capabilities within the SCRE scale.

10

2.4 Supply chain design quality A number of studies (Craighead et al. 2007; Falasca et al. 2008; Speier et al. 2011; Ponis & Koronis 2012; among others) illustrate the relevance of supply chain design issues for supply chain risk and SCRE. For example, Ponis and Koronis (2012) characterize SCRE as the ability of a supply chain to proactively plan and design its network to anticipate ensuing supply chain disruptions and to respond to disruptions effectively. Haberman et al. (2015) study the impact of the dispersion and co-location of supply chain partners to reduce supply chain disruption risk. In line with previous studies (e.g., Craighead et al. 2007; Falasca et al. 2008), supply chain design quality is conceptualized in terms of supply chain node density, complexity and criticality. Node density is high in a supply chain where a large number of nodes exist in a limited geographical area (Craighead et al. 2007; Falasca et al. 2008). Supply chain nodes exist in high-density clusters when the sources of supply or distribution markets are concentrated in a particular area. By contrast, nodes are expanded when the sources of supply and the markets are diverse (Kleindorfer & Saad 2005). Craighead et al. (2007) and Falasca et al. (2008) emphasize that increased density in the supply chain creates more vulnerabilities and reduces SCRE. Therefore, node density is an important attribute of supply chain design quality. Complexity is related to both the number of nodes in a supply chain and the interconnections between those nodes (Craighead et al. 2007). For example, the large number of forward and backward flows in the supply chain (Bozarth et. al. 2009) due to the numerous components and parts required for the product and the extensive inter-firm relations among different members in the supply chain network (Choi & Krause, 2006) makes a supply chain more complex (Craighead et al. 2007; Perona & Miragliotta 2004). A less complex supply chain would have fewer nodes and/or fewer interconnections between nodes (Craighead et al. 11

2007; Falasca et al. 2008). Increased complexity in the supply chain usually creates more vulnerabilities (Craighead et al. 2007; Falasca et al. 2008). However, additional nodes that create a buffer in the supply chain reduce vulnerability. For example, sourcing from multiple suppliers instead of a single supplier increases supply chain node complexity but reduces vulnerability through enhanced flexibility and resilience (Falasca et al. 2008; Wagner & Bode 2006). Sourcing from alternative suppliers, which opens alternatives during supply disruptions, is another way to reduce vulnerability (Jüttner 2005; Berger et al. 2004). Arrangements with alternative suppliers also allow the organization to reduce supply cost risks, i.e., supply disruptions due to cost inflammation (Tang & Tomlin 2008). Node criticality depends on the relative importance of a given node or set of nodes within a supply chain (Craighead et al. 2007). A very important node (e.g., an important distributor or supplier on which others in the supply chain are highly dependent) makes the supply chain critical and vulnerable. A critical transportation hub during sourcing and distribution (e.g., freight consolidation in Singapore) also creates supply chain criticality (Craighead et al. 2007). Alternate distribution channels are important when a critical transportation hub exists during sourcing and distribution (Craighead et al. 2007; Falasca et al. 2008). Colicchia et al. (2010) have also shown the effectiveness of using alternative transportation modes to reduce transportation risk while outsourcing from a complex and distant location. Thus, supply chain node criticality is an important element of supply chain vulnerability and SCRE. 2.5 Theoretical justification The need for resilience capability requirements in the wake of disruptive events can be analysed through the lens of the DCV (Teece et al. 1997), which is an extension of the RBV (Wernerfelt 1984; Barney 1991). The RBV stresses that organizations need to develop

12

capabilities to overcome difficulties and to gain competitive advantage. However, the traditional RBV lacks proper delineation of capabilities when dynamic changes occur in uncertain environments. The DCV addresses this gap in the traditional RBV by planning appropriate resources and capabilities to respond to situation-specific changes (Teece et al. 1997; Eisenhardt & Martin 2000), thereby addressing the idiosyncrasies of contingencies. The basic premise of the DCV is a firm’s capacity to integrate, build and reconfigure organizational resources using its processes to respond to environmental changes and uncertainties and to design new value-creating strategies (Teece et al. 1997; Eisenhardt & Martin 2000). In the same vein, we argue that organizations’ supply chains need to develop dynamic capabilities to mitigate vulnerabilities in an uncertain environment, which necessitates resilience capabilities to survive in the long run. The proactive and reactive capabilities of SCRE can be expounded upon through the lens of the DCV (Teece et al. 1997). According to the DCV, firms must have the capability to adapt, integrate, and reconfigure their resources and capabilities to address rapidly changing environments. To accelerate such changes, organizations must be proactive in scanning environmental changes and obtaining the necessary flexibility and adaptability (Teece et al. 1997) which, in our study, is commensurate with the supply chain proactive capability to adapt to environmental changes and to prevent potential vulnerabilities in the supply chain. The DCV (Teece et al. 1997) stresses that winning companies in the market should reconfigure their resources and capabilities quickly to recover competencies during turbulent times. We also argue that supply chains should have the reactive capability to reconfigure resources and capabilities to recover quickly from disruptions. Pettit et al. (2013, pp. 47) introduce the concept of “balanced resilience”, which is essentially the balance between increasing resilience capabilities and increasing costs to control vulnerabilities. Grounded in the DCV, Ponomarov and Holcomb (2009) emphasize 13

the importance of resource/capability specificity and their adequate measurement to sustain profitability by improving the resilience balance. Although the extant literature (e.g., Jüttner & Maklan 2011; Christopher & Peck 2004; Ponomarov & Holcomb 2009, etc.) provides a framework for resource specificity of SCRE, the tenets of resource measurement for SCRE are still lacking (Ponomarov & Holcomb 2009). The central premise of this study is to explain and extend the measurement aspect of dynamic capabilities in the context of SCRE to combat challenges arising from environmental uncertainties. In this study, we propose the specification and measurement of dynamic capabilities in terms of proactive and reactive capabilities in the context of SCRE. Therefore, this study provides a significant extension of dynamic capability theory. Based on the discussions above on the DCV and its links with the dimensions of SCRE, this paper conceptualizes SCRE as “the characteristics of a well-designed supply chain network with proactive and reactive capabilities, which enables the supply chain members to reduce the probability of disruptive events (or to reduce their impact) to take the organization to a stronger and more sustainable state”. Our extended definition of SCRE is in line with the notion of resilience put forward by Fiksel (2006), who conceptualizes enterprise resilience as “the capacity for an enterprise to survive, adapt, and grow in the face of turbulent change”. In line with the DCV, companies can use disruptions as opportunities to learn, grow, and perhaps move to a different “state” by allocating idiosyncratic resources through SCRE management. 3. Methodology To develop a scale to measure SCRE, we have followed the research process shown in Table 1, in line with Akter et al. (2013), Rosenzweig and Roth (2007) and Shafiq et al. (2014). This study appropriately uses both qualitative and quantitative approaches to conduct the research. In doing so, the recommendations of Fawcett et al. (2014) are strictly followed. 14

In the context of qualitative research, Fawcett et al. (2014) recommend that the context of the study, the sample, and data analysis be transparent. In our research process, we conduct a qualitative study in two phases: an extensive literature review to conceptualize SCRE and a field study to explore the dimensions of SCRE further. In each phase, we follow the recommendations of Fawcett et al. (2014) (see the next section for further detail). In addition, Fawcett et al. (2014) mention that good quantitative research must elucidate the following: the sample frame and characteristics, data collection/questionnaire administration, nonresponse bias, common method bias, scale development, measurement validation, and (data analysis) method selection. In our research, an extensive quantitative study is conducted during the instrument testing and scale generalizability phase. We strictly follow all the recommendations suggested by Fawcett et al. (2014) (see the following sections for further detail). Our study also follows the guidelines and recommendations of MacKenzie et al. (2011) for scale development. The scale development procedure specified by MacKenzie et al. (2011; Figure 1, pp. 297) includes Conceptualization, Development of measures, Model specification, Scale evaluation and refinement, Validation and Norm development. Our scale development process (Table 1) is very much in line with the procedure of MacKenzie et al. As will be shown later, we conceptualize SCRE as having both reflective and formative indicators. Hence, we pay special attention to the discussions by MacKenzie et al. (2011, pp. 300-303) with regard to reflective and formative indicators in scale development. (Insert Table 1 about here) 3.1. Conceptualization of supply chain resilience (SCRE) To conceptualize the dimensions of SCRE, this study began by investigating commonly cited items for each dimension of SCRE, as outlined in the literature review section. The literature

15

review reveals that SCRE is a multidimensional and hierarchical concept. Through this process, three primary dimensions were identified that reflect SCRE: proactive supply chain capability, reactive supply chain capability and supply chain design quality. However, the literature review also reveals many sub-dimensions for each of these primary dimensions. The SCRE capability is also found to be context dependent (Pettit et al. 2013). Therefore, we conducted an exploratory qualitative study to explore the sub-dimensions of the three primary dimensions of SCRE and to confirm the contextual appropriateness of the dimensions identified in the literature review. 3.2. Instrument development process The formal instrument development process started with a qualitative field study. The findings from the qualitative study were then justified based on the literature. Item creation and item sorting were then performed for scale development.

3.2.1 Qualitative study This study collected data from the apparel industry in Bangladesh, one of the leading apparel exporters in the world. The apparel industry is the economic propeller of Bangladesh, accounting for 78.6% of total export earnings and direct employment for over 4 million workers, of which 80% are women. In 2011, apparel exports from Bangladesh amounted to USD 19.90 billion, making the country the second largest apparel exporter in the world (BGMEA 2012). Despite its huge potential, the apparel supply chain is facing a climax situation due to the multiple challenges, such as labour unrest due to human rights violations, poor wages, a lack of safety measures and hazardous working environments, environmental pollution, political instability, interruptions in utility supply, power shortages, and inefficient customs and port management (Islam et al. 2012; Islam & Deegan 2008; Haider 2007; Paul-

16

Majumder 2001; Nuruzzaman 2009). These challenges pose threats to the sustainability of apparel supply chain in Bangladesh and thus call for an exploration of the resilience capabilities of the apparel supply chain in Bangladesh. In our study, we obtained qualitative data from 15 in-depth interviews conducted with supply chain decision makers in apparel manufacturing companies, accessory producing companies (suppliers) and buying agents. Table 2 presents the profile of the respondents. Each interview lasted for approximately 45–60 minutes. In each case, respondents were asked a number of questions to explore their SCRE practices to mitigate supply chain vulnerabilities. The interview responses were recorded, scripted, coded and categorized to identify the themes and sub-themes for different dimensions of SCRE. The extracted dimensions were then matched with the literature to support our findings to ensure the content validity of the measurement instrument. For the sake of brevity, the details of the qualitative study are not reported in this paper. Table 3 summarizes the factors and variables derived from the qualitative study and the enterprises (interviewees) that expounded upon the specific variables. (Tables 2 and 3 about here) 3.2.2 Justification of field study findings based on the literature The constructs and variables from the field study were confirmed based on evidence provided in the literature. The selected factors and variables in the field study were derived based on the commonalities in and consistency of the responses. Table 4 presents the factors and variables that align with the relevant literature. As shown in Tables 3 and 4, SCRE is a multidimensional construct that can be measured by the following dimensions: proactive capability, reactive capability and supply chain design quality. Supply chain proactive capability is measured by the following dimensions: supply chain disaster readiness,

17

flexibility, reserve capacity, integration, efficiency, market strength and financial strength. The reactive capability is measured by the response and recovery dimensions. Finally, supply chain design quality is measured by the following dimensions: the density, complexity and criticality of the network. These constructs and their interrelationships are the inputs for our research model. Table 4 also shows the items that fall under the constructs “operational vulnerability” (OV) and “supply chain performance” (SCP) constructs. These two constructs are used to test the nomological validity of the SCRE scale, which is described in section 3.3.3 below. (Table 4 about here) 3.2.3 Scale development To develop scales for the SCRE dimensions (i.e., proactive capability, reactive capability, and supply chain design quality), item creation and item sorting were performed next (Akter et al. 2013; Rosenzweig & Roth 2007; Shafiq et al. 2014). On the one hand, item creation was performed to ensure content validity by selecting the appropriate items for the construct. On the other hand, item sorting was conducted to affirm both content validity and construct validity by determining the convergence and divergence of the items for each dimension. After the item sorting and pretesting procedure, the refined instrument was tested in a pilot study. In the pilot study process, factors were categorized through exploratory factor analysis, and the findings resembled those in the literature and the field study.

3.2.3.1 Item creation Using the existing literature, a pool of items for each SCRE dimension was created. To develop a parsimonious SCRE scale, we very carefully selected the items for each dimension from the existing literature. For example, in previous studies (Swafford et al. 2006; Braunschidel & Suresh 2009), supply chain flexibility has been measured by a large 18

number of items; however, we selected the items most relevant to SCRE and the research context. The findings from the qualitative study were compared with the existing literature to determine which items to add or delete from the item pool created from the literature review. To develop scales for supply chain proactive capability, most of the items were adapted from Pettit et al. (2013); however, no valid and reliable scales were identified to measure reactive capability and supply chain design quality. Therefore, new scales must be developed for these constructs. For reactive capability, such as the response and recovery sub-dimensions, items were selected from a number of studies (e.g., Sheffi & Rice 2005; Pettit et al. 2013; Vugrin et al. 2011) with context-specific modifications. To develop items for supply chain design quality, items were selected mainly from the study of Craighead et al. (2007). Finally, item pools were created for the twelve sub-dimensions of SCRE with an extensive re-evaluation of the existing items from the literature and the qualitative study and the addition/adaptation of new items to contextualize the study model. The seeming redundant or confusing items were eliminated. The selected items went through further reliability and validity tests in the quantitative phase of the study.

3.2.3.2 Item sorting As mentioned earlier, item sorting was conducted to ensure domain coverage and the reliability of the items under each construct. First, we evaluated the domain coverage using the judgements of three experts (two scholars and one supply chain manager). The experts applied the Q-sort procedure, which ensures the correct placement of items under different constructs, to sort each SCRE sub-dimension item (Rosenzweig & Roth 2007). This procedure provided adequate evidence of construct validity with respect to the selected items under each construct. Second, two different experts (a scholar and a supply chain manager of the Bangladesh Garment Manufacturers and Exporters Association (BGMEA)) undertook two rounds of sorting. Using the data from the two rounds of the Q-sort process, the 19

reliability of the item classifications for different dimensions was assessed. Reliability was evaluated based on the placement ratio of the items under a specified dimension/construct. The item placement ratio results from the final round (see Table 5) show that a total of 125 correct placements (or “hits”) were achieved out of 147 possible item placements (49 measurement items with 3 respondents), resulting in an aggregated hit ratio of 84.6% (see Table 5). In this Q-sorting process (the final round), no individual hit ratio fell below 75%, which is the accepted cut-off value according to Menor and Roth (2007). Table 5 also shows that the associated average kappa (Cohen 1988) (the average from rounds 1 and 2) for the inter-judge agreement scores for each construct exceeds the cut-off value (≥65%) for interjudge agreement scores (Moore & Benbasat 1991; Akter et al. 2013). Therefore, based on the overall findings, an aggregate of 49 items from different sub-dimensions was selected for the preliminary questionnaire development (Table 7). (Table 5 about here) 3.3 Instrument testing The instrument testing process started by developing a primary version of the questionnaire and pretesting the questionnaire to make necessary modifications. Following the pretesting process, a pilot study was conducted to identify the factor structure through exploratory factor analysis. Based on the dimensions identified from exploratory factor analysis, a SCRE model was then specified and tested through a confirmatory study.

3.3.1 Development of initial questionnaire and pretesting A primary version of the questionnaire was prepared, and the 62 questions (49 corresponding to SCRE scale) were answered on a 6-point Likert scale. For the sake of brevity, the questionnaire is not included in the paper. Before conducting the pilot study, the developed instrument was pretested on 10 respondents (4 supply chain managers from 20

apparel manufacturing companies, 3 from accessory producing companies, 1 buyer/buying agent and 2 supply chain scholars) to ensure that the questions were appropriate in terms of their wording, order, layout and clarity. Based on respondents’ feedback, some statements in questionnaire needed further clarification. For example, the “contract flexibility with supply chain partners” item was elaborated by adding partial orders, partial shipments and partial payment facility (see FLX4 of Table 7). All comments were considered in the final design of the questionnaire. The final version of the questionnaire was then prepared for a pilot study to test the instrument.

3.3.2 Pilot study The pilot study of the SCRE scale was conducted with real data. Supply chain managers were targeted for data collection. One hundred ten managers were approached, and 86 managers (54 managers from apparel manufacturers, 25 from accessory supplying companies and 7 from buying agents) ultimately agreed to participate in the survey. After two rounds of follow up, 86 responses – 5 of which were found to have incomplete data or to be extreme outliers – were obtained. These five responses were discarded, resulting in 81 usable responses from the pilot study. We used a back-translation procedure (Brislin 1970) to translate the questionnaire into the local Bengali language. As literal back-translation suffers from conceptual equivalence issues, we used a collaborative team approach (inviting two other academics) to achieve the conceptual equivalent back-translation (Douglas & Craig 2007). The respondents were offered both English and Bengali versions of the questionnaire. Most of our respondents chose the English version of the questionnaire. However, they also took the Bengali version as a backup to clarify some questions. This practice was followed for all subsequent data collection and worked very well.

21

Table 6 presents the demographic profile of pilot study respondents. We conducted exploratory factor analysis on the pilot study (see Table 7) data using the varimax rotation procedure to assess the initial measurement scale. We also used the Kaiser–Meyer–Olkin (KMO) test and Bartlett’s test of sphericity to evaluate the appropriateness of the factor analysis. The KMO test ensured the overall sampling adequacy with a value of 0.725 (>0.50). Bartlett’s test of sphericity provided evidence of the validity of the instrument (2167.958, df = 1035, significant at p = 0.000). Twelve factors with eigenvalues of greater than 1 were extracted (see Table 8). The twelve components had a cumulative variation explained of 71.050%. In evaluating the results of the factor analysis, items were deleted that had loadings of <0.40 or that had cross loadings (>.5) with other factors. The Cronbach’s alpha values corresponding to each construct were also examined to ensure reliability, revealing that the Cronbach’s alpha values of the extracted factors exceeded the minimum threshold of 0.70. For further scale refinement, corrected item–total correlation was also examined to improve reliability (see Table 8). The initial instrument was refined by removing items with low loadings or cross-loaded items. Thus, of the initial 49 items, 40 items were retained for the next stage of confirmatory analysis. (Tables 6, 7 and 8 about here) 3.3.3 Model specification Based on the factor structure of SCRE in the exploratory study and evidence from the literature, a research model (see Figure 1) is proposed to measure the dimensions of SCRE and their relationship with supply chain OV and SCP. We postulate SCRE as a hierarchical (third-order) model with three primary dimensions (i.e., supply chain proactive quality, supply chain reactive quality, and supply chain design quality) and twelve sub-dimensions.

22

Regarding the issue of reflective and formative constructs, we first followed the decision criteria of Jarvis et al. (2003) and Polites et al. (2011). We thus argue that the relationship of the focal construct with sub-constructs (second- and third-order constructs; see Figure 1) can be operationalized as reflective (Jarvis et al. 2003). For example, in this study, proactive resilience capability is reflected by the supply chain readiness, flexibility, reserve capacity and integration sub-dimensions. These sub-dimensions are interrelated and interdependent; for example, to increase flexibility, reserve capacity and integration is essential (Sheffi & Rice 2005). Moreover, integration and redundant capacity are also interrelated (Chen & Daugherty 2009). For first-order constructs, we followed the guidelines of MacKenzie et al. (2011), who argue that “Constructs are not inherently, formative and reflective in nature, and most can be modelled as having either formative or reflective indicators…” (p. 302). MacKenzie et al. (2011) also state that, from an ontological perspective, a construct with reflective indicators is a “real entity that exists independently”, while a construct with formative indicators is “seen as a theoretical construction (rather than real entity)”, which is constructed by the items of the construct (pp. 303). Therefore, in our study, we heeded the recommendations of three academic experts to decide on the reflective and formative constructs at the first-order level 1 . The three academic experts are knowledgeable in the SCRE area. After two rounds of semi-Delphi opinion collection, we settled on flexibility, integration, financial strength, response and recovery as reflective constructs and reserve capacity, efficiency, market strength, density, complexity, criticality, and disaster readiness as formative constructs (see Figure 1). Hence, SCRE is a mixed reflective–formative type of multidimensional, hierarchical construct. (Insert Figure 1 about here)

1

We are indebted to one of the reviewers who suggested this.

23

To assess the nomological validity of the higher-order reflective SCRE model, we tested the relationship with related outcome constructs (Churchill 1995). Well-grounded theoretical support allows us to a negative relationship between SCRE and supply chain OV (Jüttner & Maklan 2011; Ponomarov & Holcomb 2009) and a positive relationship between SCRE and SCP (Pettit et al. 2013; McCann et al. 2009). In line with Sheffi (2005; p. 25), we assert that OV comprises “everything from supplier disruptions to theft by employees. These are mainly disruptions to the means of production”. Sheffi (2005; p. 25) categorizes vulnerabilities as financial, hazard, strategic and operational risks. We claim that financial, hazard and strategic vulnerabilities can be grouped as “non-operational” vulnerabilities. Figure 1 shows the resilience model with its predicted relationship between OV and SCP.

The literature shows that SCRE is essential in reducing vulnerabilities (Christopher & Peck 2004; Ponomarov & Holcomd 2009; Pettit et al. 2013). The extant literature also reveals a multiplicity of OV factors, such as utility crisis, poor quality, supply problems due to the loss of key suppliers or problems in the suppliers’ plants, logistics mode and route disruptions, and IT system failures (Blos et al. 2009). These vulnerability factors have a huge impact on the supply chain if they are not addressed in time (Ponomarov & Holcomb 2009), particularly when disruptions have spiral effects on the supply chain network (Christopher & Lee 2004). For example, disruptions in the suppliers’ plants have an impact on the operations of manufacturers and the sales of distributors. As noted earlier, Ericsson lost USD 400 million in revenue due to a fire in its chip supplier’s plant (Tomlin 2006). However, due to its resilient approach, Nokia overcame the OV arising from a similar disruption, which saved it from potential financial losses and delays in serving customers (Tomlin 2006). Pettit et al. (2013) show that SCRE has a positive impact on SCP. Hendricks and Singhal (2003) reveal that announcing supply chain disruptions (such as an operational issue or a delay in shipment) decreases shareholder value significantly. Therefore, the development of resilience 24

capabilities is essential to mitigate OV, thereby reducing economic losses and securing SCP. These arguments indicate the dynamic relationships between SCRE and OV and between SCRE and SCP. Therefore, the arguments above lead us to posit the following hypotheses: Hypothesis 1 (H1): Supply chain resilience has a direct negative impact on operational vulnerability. Hypothesis 2 (H2): Operational vulnerability has a direct negative impact on supply chain performance. Hypothesis 3 (H3): Supply chain resilience has a direct positive impact on supply chain performance. Hypothesis 4 (H4): Supply chain resilience has an indirect impact on supply chain performance through mitigating operational vulnerability.

3.3.4 Confirmatory study Although the items and the factor structure of the proposed SCRE scale were validated in the pilot study, it provided little evidence of convergent and discriminant validity. As a result, we used confirmatory factor analysis (CFA) (McFarlan et al. 1983) to thoroughly assess the refined instrument using larger samples. Data were collected from a cross-section of garment manufacturers, accessory producers and buying agents via face-toface and mailed surveys. Three hundred fifty respondents were targeted for this phase of the study. Ultimately, 296 usable responses were collected. Table 9 shows the profile of the respondents. The sample size of 296 is adequate for data analyses using partial least squares (PLS)-based SEM (Chin 1998; Chin & Newsted 1999), which is our chosen method. We performed a non-response bias (more specifically, late response bias) test on the early and late respondents (Armstrong & Overton 1977) based on the selection of some variables. Table 10 shows that response bias is not a cause for concern in our data. As the data were collected from a single respondent from each firm and were gathered using a crosssectional research design, common method variance may result in systematic measurement 25

error (Huber & Power, 1985). Following Podsakoff et al. (2003), several efforts were made to reduce the chance of common method bias ex ante. First, data were carefully collected from respondents who possessed relevant knowledge in the subject area. For example, the supply chain managers or the people involved in supply chain functions within an organization were selected. Second, the respondents were assured that their responses would remain anonymous. Third, the questions were designed to be simple and specific to avoid ambiguity. Some terminology was explained with relevant examples so that the respondents could easily understand the intended meaning of the scale item. Fourth, the researcher attempted to avoid double-barrelled questions. Fifth, the independent and dependent variables in the survey was addressed separately. As shown in Figure 1, SCRE is the only exogenous variable in our research model (Figure 1), which has two endogenous variables: OV and SCP. Three primary sources of endogeneity problems are “omitted variables”, “simultaneity” (also referred as the “direction of causality”), and “measurement error”. For detailed discussions of these issues and more, see Roberts and Whited (2013) and Guide and Ketokivi (2015). We are confident of the direction of causality in our research model, as detailed in the hypothesis development section above. Hence, the direction of causality (Guide & Ketokivi 2105) does not present any endogeneity problem in our case. We have attempted to minimize the measurement errors following an extensive procedure of measurement item selection and data collection and conducting extensive reliability and validity tests of our instruments. However, as with any other research model, the issue of omitted variables will remain a limitation of our model. (Tables 9 and 10 here) To estimate the hierarchical SCRE model, this study applied PLS-based SEM (Chin 1998) for the following reasons. The research model developed in this study is hierarchical 26

and complex, containing 18 constructs (i.e., 12 first-order, 3 second-order, 1 third-order and 2 outcome constructs) and 40 items. Because of its component-based approach, PLS-based SEM will be able to easily find solutions to this complex hierarchical model (Chin 1998, Hair et al. 2013). PLS also helps achieve more theoretical parsimony and less model complexity in assessing the hierarchical model (Akter et al. 2013). In addition, the research model includes both formative and reflective measurement constructs. PLS-based SEM can readily handle formative and reflective constructs in the same model (Hair et al. 2011). Based on the arguments above, this study deployed PLS-based SEM (Smart PLS v. 3) to estimate this hierarchical, multidimensional SCRE model. PLS has recently been used for scale development, and it also has a goodness-of-fit (GoF) measure (Akter et al. 2013). Therefore, using PLS-based SEM seemed logical to develop and validate a multidimensional and hierarchical SCRE model. To estimate the higher-order construct of SCRE, this study used a two-stage approach to simplify the model (Wetzels et al. 2009; Becker et al. 2012). Nonparametric bootstrapping (Efron & Tibshirani 1993; Wetzels et al. 2009) has also been applied with 1,000 replications to obtain the standard errors of the estimates.

3.3.4.1 Assessment of the first-order items The data were initially screened, and missing data for any item were replaced by the mean values of that item, in line with Hair et al. (2013). Table 11 presents the results of the first-order item assessment, along with the means and standard deviations of all items. The means are generally greater than 4 on a six-point scale. Hence, our sample items do not follow a normal distribution, which poses no problem because we are using PLS for data analysis. For covariance-based SEM (e.g., LISREL or AMOS), an estimator that can address non-normal distribution, for example, robust weighted least squares (WLS), must be used. For more information, see Finney and DiStefano (2013). With regard to construct reliability, the results clearly show that the composite reliability (CR) for all of the subscales ranges 27

from 0.845 to 0.917, which meets the threshold value of 0.7 suggested by Nunnally (1978) and Hair et al. (2011). Convergent and discriminant validity were tested to assess the construct validity of the SCRE scale. The average variance extracted (AVE) for each factor/construct (see Table 11) and the loading of the items corresponding with each factor/construct were inspected to examine the convergent validity of the SCRE scale. The analysis shows that the AVEs ranged from 0.681 to 0.813, exceeding the minimum threshold of 0.5 (Fornell & Larcker 1981; Henseler et al. 2009; Hair et al. 2011). Table 11 also shows that some items have been deleted due to low loadings and/or high cross-loadings. All other remaining items have significant loadings and/or weights at p < 0.01 (see Table 11) (Henseler et al. 2009; Hair et al. 2011). Thus, the scale for the SCRE dimensions possesses adequate convergent validity. The SCP outcome construct has been modelled as a reflective construct in line with the literature (Pettit et al. 2013; McCann et al. 2009); this construct also possesses adequate item reliability (all loadings > 0.7), with an AVE of 0.775 and CR of 0.853. However, the OV outcome construct has been modelled as a formative construct (Jüttner & Maklan 2011; Ponomarov & Holcomb 2009). Table 11 shows that the item weights and/or loadings are all significant for this construct (Hair et al. 2011).

In a recent paper, Henseler et al. (2015) show that the heterotrait-monotrait (HTMT) ratio of correlations is superior to Fornell and Larcker’s (1981) AVE-SE method for testing discriminant validity. Therefore, we used the HTMT method to test the discriminant validity of the SCRE scale. Table 12 presents the HTMT results for all the SCRE constructs. The low values (<0.85; Henseler et al. 2015) in Table 12 confirm the discriminant validity of the scale. High multicollinearity among the indicators in formative constructs can pose problems. Hair et al. (2011) recommend 5 as the maximum threshold value for the variance inflation factor (VIF) in detecting multicollinearity. Table 13 shows that multicollinearity is not a problem for the formative constructs in our study. Therefore, we conclude that the 28

measurement model is satisfactory given the evidence of adequate reliability, convergent validity, discriminant validity and a lack of multicollinearity among the formative constructs. (Tables 11, 12 and 13 about here) 3.3.4.2 Assessment of the higher-order scale The results of higher-order assessment are shown in Table 14. Table 14 clearly shows that the CRs and AVEs of the higher-order scales are greater than 0.70 and 0.50, respectively, providing evidence of reliable higher-order measures. An assessment of the higher-order constructs also confirms that the SCRE has a strong association with the second-order constructs of supply chain proactive capability (β = 0.886, t = 85.2), supply chain design (β = 0.762, t = 75.4), and supply chain reactive capability (β = 0.839, t = 52.8) (see Figure 2), which explains 87.9%, 66.5%, and 78.6% of overall SCRE variance, respectively (see Figure 2). This assessment also affirms that supply chain proactive capability has a strong association with flexibility (β = 0.796, t = 43.72), reserve capacity (β = 0.679, t = 12.48), integration (β = 0.868, t = 76.8), efficiency (β = 0.748, t = 38.25), market strength (β = 0.839, t = 79.21), financial strength (β = 0.812, t = 87.15) and supply chain readiness (β = 0.823, t = 80.79). The supply chain design has strong associations with node density (β = 0.868, t = 84.2), complexity (β = 0.856, t = 88.3), and node criticality (β = 0.752, t = 312.6). Finally, the supply chain reactive capability has a strong association with supply chain response (β = 0.834, t = 91.7) and supply chain recovery (β = 0.865, t = 94.8) (see Figure 2). (Table 14 and Figure 2 about here) 3.3.4.3 Assessment of the nomological and predictive validity As noted above, this study obtained the following R2 (the coefficient of determination) values, which are rather large (>0.50) (Hair et al. 2011): 0.879 for proactive capability, 0.665 for supply chain design quality and 0.786 for reactive capability (see Figure 29

2). In addition, the nomological and predictive validity of the higher-order multidimensional SCRE construct is assessed by examining its relationship with the following outcome constructs (Akter et al. 2013; Mackenzie et al. 2011): “supply chain operational vulnerability” and “supply chain performance”. The results yield standardized beta coefficients of β = -0.615 (t = 14.36) from SCRE to supply chain OV, β = -0.092 (t = 2.59) from supply chain OV to SCP, β = 0.703 (t = 19.82) from SCRE to SCP and β = 0.419 (t = 8.78) from SCRE to SCP through OV (see Figure 2 and Table 15). The path coefficients are all significant (p < 0.001) (see Figure 2 and Table 15), thus supporting hypotheses 1, 2, 3 and 4. As all these path coefficients are significant (p < 0.001), the nomological validity of SCRE is ensured (Akter et al. 2013). This study also used Stone–Geisser’s Q2 (Stone 1974; Geisser 1974) to test the predictive validity of the higher-order multidimensional SCRE construct. Using a cross-validated redundancy approach, this study obtained Q2 values of 0.63 for supply chain OV and 0.579 for SCP. To ensure high predictive validity, Q2 should exceed zero (Chin 2010; Fornell & Bookstein 1982). Therefore, the results of this study reveal the predictive validity of the higher-order SCRE measurement. (Table 15 about here) 3.3.4.4 Assessment of the overall parameters To assess the overall parameters of the SCRE model, we examined the statistical power and the GoF measure of the model. This study used G*Power 3.1 (Faul et al. 2009) to obtain the statistical power of the model to assess the robustness of the hierarchical SCRE scale. The overall power of the model is 95.8, and the t value is 1.68. The statistical power in this study exceeds the required power (80%) suggested by Cohen (1988). Therefore, the study has adequate confidence in the hypothesized relationships in the model. This study also

30

used AVE and R2 values to estimate the GoF index to measure the overall fitness of the proposed model (Wetzels et al. 2009). The GoF of the model was 0.661, which is large according to Wetzels et al. (2009). Therefore, the fit index for this model is satisfactory.

3.3.4.5 Technical and evolutionary fitness of SCRE Model We argue that our SCRE model is a dynamic capability tool (Eisenhardt & Martin 2000, p. 1118), which organizations can use to improve SCRE. Using the SCRE model effectively calls for the development of and allegiance to a process (and hence a unique path to transform the organization) and the identification of a supportive “resource configuration” (a unique position) (Teece et al. 1997). As Eisenhardt and Martin (2000, p. 1108) suggest, the SCRE model has both “communalities” and “idiosyncrasies”. For example, the three basic dimensions (proactive, reactive and supply chain design) and twelve sub-dimensions are the unique features (commonalities) of the SCRE model. However, their measurements may vary depending on the organizations, thus revealing the idiosyncrasies of the SCRE model. Our SCRE model can also be assessed using the “technical” and “evolutionary” fitness criteria of dynamic capability theory (Teece 2007). Technical fitness refers to “how effectively a capability performs its intended function” (Helfat et al. 2007, Leiblein 2011). We argue that the GoF of the SCRE model satisfies the technical fitness criterion. As noted earlier, the GoF of the SCRE model is 0.661. The literature suggests that this value is sufficiently large (Wetzels et al. 2009); therefore, the SCRE model is a “technically fit” scale for measuring SCRE capabilities. Hence, our SCRE model adequately satisfies the technical fitness criterion. Evolutionary fitness refers to “how well a dynamic capability enables an organization to make a living by creating, extending, or modifying its resources” (Helfat et al. 2007; Teece 2007, Leiblein 2011). Pohjola and Stenholm (2012) state that the recent literature has mostly used “performance” as the measure of the evolutionary fitness of

31

dynamic capabilities. We thus propose using an increase in SCP and a decrease in OV as a measure of organizational performance based on the successful implementation and practice of the SCRE model. The results above suggest that the SCRE scale positively influences SCP (β = 0.703, t = 19.82) and negatively influences OV (β = -0.615, t = 14.36) (see Figure 2). Hence, our SCRE model also adequately satisfies the evolutionary fitness criterion.

3.4. Scale generalizability To provide evidence of the generalizability of the SCRE scale, a replicative study with organizations from diverse industries is essential. In this regard, we conducted study 3 to re-examine the CFA models using responses collected from 207 business executives from different manufacturing industries in which SCRE is an important issue. Table 16 shows the profile of the respondents in study 3. (Table 16 about here)

3.4.1 Sample and data collection In study 3, the same instrument was used to collect data from executive MBA students at five universities in Bangladesh. The students who were executives in manufacturing and responsible for supply chain functions were selected for the survey. The sample students were located using the database of current students of the universities. A total of 207 completed questionnaires were collected from 231 students. The respondents cover many industries, including apparel (30.4%), garment accessories (8.2%), pharmaceuticals (11.6%), footwear and leather products (7.25%), steel (10.6%), plastics (3.4%), jute (1.3%) and others (10.15%). As a whole, the survey respondents comprised of a diverse sample that lends itself well to a replicative study (see Table 16).

32

3.4.2 Data analysis The replicative study results exhibit desired psychometric properties, as all the item loadings corresponding to first-order constructs were greater than 0.7 and significant at p < 0.01 (except FLX7, i.e., new product development, and CRI2, i.e., critical distribution centre). All AVEs and CRs exceeded the minimum thresholds of 0.5 and 0.7, respectively. The results are also consistent with the measurement model results from study 2. The results further show that SCRE had a strong association with supply chain proactive capability (β = 0.892, t = 87.52), supply chain reactive capability (β = 0.876, t = 84.9) and supply chain design quality (β = 0.892, t = 95.34). Furthermore, supply chain proactive capability, reactive capability and design quality explain 88.7%, 91.6% and 85.2%, respectively, of the overall SCRE variance (see Figure 3). These results thus provide further evidence of the reliability and operational nature of the proposed scale developed in this study to measure SCRE in a wide range of industries. The replicative study results also show satisfactory results for the predictive validity of SCRE with regard to improving SCP and mitigating operational vulnerabilities. Figure 3 shows that all the predictive path loadings are significant. Overall, the results are extremely encouraging in terms of scale generalizability. (Figure 3 about here) 4. Discussion and implications 4.1 Implications for theory Rooted in the DCV, building on previous research, and filling in gaps in the existing literature, the findings of this study make several contributions to the supply chain literature. This study introduces a hierarchical model of SCRE. The validated SCRE measurement scale addresses the existing lack of knowledge on resource specificity and the measurement of 33

dynamic capabilities to combat the challenges associated with environmental uncertainties in the supply chain. The DCV postulates an accumulation of resources and capabilities to combat challenges at the organizational level in tumultuous times. However, the impact of environmental challenges is no longer confined within the boundaries of an organization; instead, it is spread across the entire supply chain. In this study, we presume that organizations’ supply chains need both proactive and reactive forms of dynamic capabilities to develop resilience against disruptive events along their supply chains. We thus extend the scope of the DCV from organizational boundaries to the entire supply chain. Studies using the RBV, including the DCV, fall short of identifying processes, resources and paths that increase competencies during environmental uncertainties along the supply chain. Our study addresses process and resource specificities at the supply chain level in the event of turbulence. Thus, the empirically validated measurement model of SCRE extends the DCV in terms of resource specificity and measurement in the supply chain to combat challenges arising from environmental uncertainty. Our research also contributes to the empirical aspect of the DCV by assessing the SCRE model through the “technical” and “evolutionary” fitness criteria of the DCV. We have justified that the GoF of the SCRE model is equivalent to “technical” fitness, while the predictive validity of the SCRE model (to improve organizational performance) is equivalent to the “evolutionary” fitness of the DCV. Using the DCV as the theoretical foundation, this study thus enhances the body of knowledge in the SCRE literature in particular and the risk management literature in general. Our study also expands and extends the scope of the recent literature on SCRE highlighted in the literature review section 2.1. As noted earlier, the primary research gap is the lack of a comprehensive measurement scale of SCRE based on strong theoretical underpinnings. Our study considers resilience to be a dynamic capability, and we have taken 34

an alternative approach to offer a multidimensional SCRE measurement model. This study thus extends and expands earlier studies on SCRE in a comprehensive way.

4.2 Implications for practice The implications of this research are significant for supply chain managers, specifically those in the apparel industry in Bangladesh and elsewhere. The findings suggest that supply chain managers take proactive approaches towards resilience, design a supply chain that can reduce vulnerabilities, and develop reactive capabilities to respond and recover quickly from vulnerabilities. Our findings validate that SCRE is a critical success factor for SCP improvement. Firms that want to improve their performance need to constantly assess the supply chain design quality and the proactive and reactive approaches to combating supply chain vulnerability. In addition, managers can use the proposed scale as a diagnostic tool to identify areas that require specific improvements. In fact, supply chain managers in the apparel industry will be equipped with knowledge of the factors required to ensure resilience in the supply chain. Supply chain practitioners should convince top management of the paramount importance of proactive approaches (e.g., supply chain readiness, flexibility, reserve capacity, integration, efficiency, market strength, and financial strength) and reactive approaches (e.g., the ability to respond and recover to manage supply chain vulnerabilities). Notably, the proposed SCRE scale introduces a new construct, supply chain design quality, which has significant managerial implications. Supply chain managers should focus on improving supply chain design quality by paying careful attention to supply chain density, complexity and criticality to reduce supply chain vulnerability. Our study will assist supply chain managers in making decisions regarding multi-sourcing versus single sourcing, a focus on a few markets versus the diversification of market, the geographical distribution of production facilities versus production in a concentrated area, centralized distribution versus 35

decentralized distribution, multimodal transportation options and other issues related to supply chain design that seeks to overcome supply chain vulnerabilities. Leveraging the supply chain network design to overcome vulnerabilities is crucial for supply chain decision makers. Our study thus assists supply chain managers in strategizing and deciding on the competencies developed to address environmental volatility. More specifically, the proposed model is expected to elucidate matters so that apparel supply chain members can overcome existing vulnerabilities and achieve sustainability in their supply chains. 4.3 Limitations and future research directions Some limitations of this study are worth noting here. This research adopts a crosssectional design to investigate the phenomenon of SCRE at a particular time. A longitudinal research design could capture the effects of SCRE on supply chain vulnerability in the long run. Thus, a longitudinal focus is recommended for future studies. This research was conducted within a specific industry (apparel) in one country (Bangladesh). Our respondents are immersed in Bangladeshi culture, which is characterized by high power distance and high in-group collectivism (House et al. 2004). Hence, our data will reflect the national cultural bias. Replications in other contexts would increase confidence in the research model. Further assessing the generalizability of the SCRE scale developed in this study to other business environments, for example, testing the scale in other nations, would be worthwhile. The range of variables in our SCRE model has been contextualized for a specific country. As such, our variables should be adapted via field studies to contextualize them in other contexts or countries. Future research might also be conducted to investigate the antecedents and consequences of SCRE. For data analysis, a formal ex-post common method bias test should be performed using an ideal CFA marker variable (Williams et al. 2010). Although the results of our study show that SCRE has a positive influence on SCP, SCP is a multidimensional construct that may be measured in a number of ways, including 36

sourcing, operational and distributional performance (Gunesekaran et al. 2004). Therefore, in future studies, exploring the complexities of the relationship between SCRE and alternative dimensions of SCP would be useful.

5. Conclusions Despite the publication of a few studies on SCRE, a valid and reliable scale for measuring SCRE was still lacking. This study thus developed and validated a scale for measuring SCRE. The scale evaluation by PLS path modelling confirmed the scale’s adequate psychometric properties. We have also evaluated our SCRE model in terms of the “technical” and “evolutionary” fitness of dynamic capability theory. To this end, this study makes an important contribution to the literature. Although the SCRE scale has been assessed and validated in the context of the apparel industry in Bangladesh, it can be adapted for other industrial sectors in other countries with appropriate contextualization via qualitative studies.

References Akter, S., D’Ambra, J., Ray, P., 2013. Development and validation of an instrument to measure user perceived service quality of mHealth. Information & Management, 50(4), 181-195. Ambulkar, S., Blackhurst, J., Grawe, S., 2015 Firm's resilience to supply chain disruptions: Scale development and empirical examination. Journal of Operations Management, 33, 111-122. Armstrong, J. S., Overton, T. S., 1977. Estimating nonresponse bias in mail surveys. Journal of Marketing Research, 14(3), 396-402. Ates, A., Bititci, U., 2011. Change process: a key enabler for building resilient SMEs. International Journal of Production Research, 49(18), 5601-5618. Azaron, A., Brown, K. N., Tarim, S. A., Modarres, M., 2008. A multi-objective stochastic programming approach for supply chain design considering risk. International Journal of Production Economics, 116(1), 129-138. Barney, J., 1991. Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99-120. Becker, J. M., Klein, K., Wetzels, M. 2012. Hierarchical latent variable models in PLS-SEM: guidelines for using reflective-formative type models. Long Range Planning, 45(5), 359394. Berger, P. D., Gerstenfeld, A., Zeng, A. Z., 2004. How many suppliers are best? A decision analysis approach. Omega, 32(1), 9-15. BGMEA. 2012. Bangladesh Apparel and Textiles Exposition. http://www.bgmea.com.bd/batexpo/index.htm (accessed June 7, 2015). 37

Bhamra, R., Dani, S. Burnard, K., 2011. Resilience: the concept, a literature review and future directions. International Journal of Production Research, 49(18), 5375-5393. Blackhurst, J., Craighead, C. W., Elkins, D., Handfield, R. B., 2005. An empirically derived agenda of critical research issues for managing supply-chain disruptions. International Journal of Production Research 43(19), 4067-4081. Blos, M. F., Quaddus, M., Wee, H. M., Watanabe, K., 2009. Supply chain risk management (SCRM): a case study on the automotive and electronic industries in Brazil. Supply Chain Management an International Journal, 14(4), 247. Bollen, K., Lennox, R., 1991. Conventional wisdom on measurement: A structural equation perspective. Psychological Bulletin, 110(2), 305-314. Bozarth, C. C., Warsing, D. P., Flynn, B. B., Flynn, E. J. 2009. The impact of supply chain complexity on manufacturing plant performance. Journal of Operations Management, 27(1), 78-93. Brandon‐Jones, E., Squire, B., Autry, C. W., Petersen, K. J., 2014. A Contingent Resource‐Based Perspective of Supply Chain Resilience and Robustness. Journal of Supply Chain Management, 50(3), 55-73. Braunscheidel, M. J., Suresh, N. C., 2009. The organizational antecedents of a firm’s supply chain agility for risk mitigation and response. Journal of Operations Management 27(2), 119-140. Brislin, R.W., 1970. Back-translation for cross-cultural research. Journal of Cross-cultural Psychology, 1(3), 185-216. Brush, T. H., Artz, K. W., 1999. Toward a contingent resource-based theory: The impact of information asymmetry on the value of capabilities in veterinary medicine. Strategic Management Journal 20(3), 223 - 250. Chen, H., Daugherty, P. J., Landry, T. D., 2009. Supply chain process integration: a theoretical framework. Journal of Business Logistics 30(2), 27-46. Chin, W. W., 2010. How to write up and report PLS analyses. In Handbook of Partial Least Squares. Springer. 655-690. Chin, W. W., Newsted, P. R., 1999. Structural equation modeling analysis with small samples using partial least squares. Statistical Strategies for Small Sample Research, 1(1), 307-341. Chin, W. W., 1998. The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 295(2), 295-336. Choi, T. Y., Krause, D.R., 2006. The supply base and its complexity: implications for transaction costs, risks, responsiveness, and innovation. Journal of Operations Management, 24(5), 637652. Chowdhury, M. M. H., Quaddus, M. A., 2015. A multiple objective optimization based QFD approach for efficient resilient strategies to mitigate supply chain vulnerabilities: The case of garment industry of Bangladesh. Omega, 57, 5-21. Chowdhury, M. M. H., Dewan, M. N. A., Quaddus, M. A., 2012. Supply Chain Resilience to Mitigate Disruptions: A QFD Approach. Pacific Asia Conference on Information Systems, Ho Chi Minh city, Vietnam: AIS. Christopher, M., Peck, H., 2004. Building the resilient supply chain. International Journal of Logistics Management, 15(2), 1-13. Christopher, M., Lee, H., 2004. Mitigating supply chain risk through improved confidence. International Journal of Physical Distribution & Logistics Management, 34(5), 388-396. Cohen, J., 1988. Statistical Power Analysis for the Behavioural Sciences. Routledge. Colicchia, C., Dallaria, F., Melacini, M., 2010. Increasing supply chain resilience in a global sourcing context. Production Planning & Control, 21(7), 680-694. Craighead, C. W., Blackhurst, J. , Rungtusanatham, M. J., Handfield, R. B., 2007. The Severity of Supply Chain Disruptions: Design Characteristics and Mitigation Capabilities. Decision Sciences, 38(1), 131-156. Dalziell, E., McManus, S., 2004. Resilience, vulnerability, and adaptive capacity: implications for system performance. Stoos, Switzerland: 1st International Forum for Engineering Decision Making (IFED), 5-8 Dec pp. 17 Douglas, S.P., Craig, C.S., 2007. Collaborative and iterative translation: An alternative approach to back translation. Journal of International Marketing, 15(1), 30-43. 38

Duclos, L. K., Vokurka, R. J., Lummus, R. R., 2005. Delphi study on supply chain flexibility. International Journal of Production Research, 43(13), 2687-2708. Durach, C.F., Wieland, A., Machuca, J.A., 2015. Antecedents and dimensions of supply chain robustness: a systematic literature review. International Journal of Physical Distribution & Logistics Management, 45(1/2), 118-137. Efron, B., Tibshirani, R., 1993. An introduction to the bootstrap. CRC press, 57. Eisenhardt, K. M., Martin, J. A., 2000. Dynamic capabilities: What are they? Strategic Management Journal, 21 (10-11), 1105–1121. Epstein, M. J., Wisner, P. S., 2001. Using a Balanced Scorecard to Implement Sustainability. Environmental Quality Management, 11(2), 1-10. Erol, O., Sauser, B. J., Mansouri, M., 2010. A framework for investigation into extended enterprise resilience. Enterprise Information Systems, 4 (2), 111-136. Falasca, M., Zobel, C. W., Cook, D., 2008. A decision support framework to assess supply chain resilience. In Proceedings of the 5th International ISCRAM Conference, 596-605. Faul, F., Erdfelder, E., Buchner, A., Lang, A.G., 2009. Statistical power analyses using G* Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41 (4), 11491160. Fawcett, S.E., Waller, M.A., Miller, J.W., Schwieterman, M.A., Hazen, B.T. and Overstreet, R.E., 2014. A trail guide to publishing success: tips on writing influential conceptual, qualitative, and survey research. Journal of Business Logistics, 35(1), 1-16. Fiksel, J., 2006. Sustainability and resilience: toward a systems approach. Sustainability: Science Practice and Policy, 2(2), 14-21. Finney, S.J., DiStefano, C., 2013. Non-normal and categorical data in structural equation modeling. In Hancock, G.R. and Mueller, R.O. eds., Structural equation modeling: A second course, (chapter 11), IAP: Charlotte, 439 – 492. Fornell, C., Larcker, D. F., 1981. Evaluating Structural Equations Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18 (1), 39-50. Fornell, C., Bookstein, F. L., 1982. Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. Journal of Marketing Research, 19(4), 440-452. Geisser, S., 1974. A Predictive Approach to the Random Effects Model. Biometrica 61 (1), 101-107. Grötsch, V. M., Blome, C., Schleper, M. C., 2013. Antecedents of proactive supply chain risk management–a contingency theory perspective. International Journal of Production Research, 51(10), 2842-2867. Guide, V.D.R., Ketokivi, M., 2015. Notes from the Editors: Redefining some methodological criteria for the journal. Journal of Operations Management, 37, v-viii. Gunasekaran, A., Patel, C., McGaughey, R.E., 2004. A framework for supply chain performance measurement. International Journal of Production Economics, 87(3), 333-347. Gunasekaran, A., Lai, K. H., Cheng, T. E., 2008. Responsive supply chain: a competitive strategy in a networked economy. Omega, 36(4), 549-564. Gunderson, L. H., 2000. Ecological resilience--in theory and application. Annual Review of Ecology and Systematics, 31, 425-439. Habermann, M., Blackhurst, J., Metcalf, A. Y., 2015. Keep Your Friends Close? Supply Chain Design and Disruption Risk. Decision Sciences, 46(3), 491-526., Haider, M. Z., 2007, Competitiveness of the Bangladesh Ready-made Garment Industry in Major International Markets. Asia-Pacific Trade and Investment Review, 3(1), 3-27. Hair, J. F., Ringle, C. M., Sarstedt, M., 2011. PLS-SEM: Indeed a silver bullet. The Journal of Marketing Theory and Practice, 19(2), 139-152. Hair Jr, J. F., Hult, G. T. M., Ringle, C., Sarstedt, M., 2013. A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications. Hale, T., Moberg, C. R., 2005. Improving supply chain disaster preparedness: A decision process for secure site location. International Journal of Physical Distribution & Logistics Management, 35(3), 195-207. Harland, C., Brenchley, R., Walker, H., 2003. Risk in supply networks. Journal of Purchasing and Supply Management, 9(2), 51-62. 39

Helfat, C. E., Finkelstein, S., Mitchell, W., Peteraf, M. A., Singh, H., Teece, D. J., Winter, S. G., 2007. Dynamic capabilities: Understanding strategic change in organizations. Blackwell: New York. Helferich, O.K., Cook, R.L., 2002. Securing the Supply Chain. Council of Supply Chain Management Professionals. Oak Brook, IL. Hendricks, K. B., Singhal, V. R., 2003. The effect of supply chain glitches on shareholder wealth. Journal of Operations Management, 21(5), 501–522. Henseler, J., Ringle, C., Sinkovics, R., 2009. The use of partial least squares path modeling in international marketing. Advances in International Marketing (AIM), 20(1), 277-320. Henseler, J., Ringle, C., Sarstedt, M., 2015. A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115-135. Hohenstein, N.O., Feisel, E., Hartmann, E., Giunipero, L., 2015. Research on the phenomenon of supply chain resilience: a systematic review and paths for further investigation. International Journal of Physical Distribution & Logistics Management, 45(1/2), 90-117. Holling, C. S., 1973. Resilience and stability of ecological systems. Annual Review of Ecology and Systematics, 4, 1-23. Hollnagel, E., Woods, D., Levenson, N., 2006. Resilience Engineering: Concepts and Precepts. Ashgate, Aldershot, UK. House, R.J., Hanges, P.J., Javidan, M., Dorfman, P.W., Gupta, V., 2004. Culture, leadership, and organizations: The GLOBE study of 62 societies. Sage publications. Huber, G. P., Power, D. J., 1985. Retrospective reports of strategic‐level managers: Guidelines for increasing their accuracy. Strategic Management Journal, 6(2), 171-180. ILO Minimum Age Convention. http://www.ilo.org/dyn/normlex/en/f?p=1000:12100:0::NO::P12100_ILO_CODE:C138 (accessed 6th June 2015). Islam, M. A., Deegan, C., 2008. Motivations for an organisation within a developing country to report social responsibility information: Evidence from Bangladesh. Accounting, Auditing & Accountability Journal, 21(6), 850 -864. Islam, M. A., Bagum, M. N., Rashed, C. A. A., 2012. Operational Disturbances and Their Impact on the Manufacturing Business-An Empirical Study in the RMG Sector of Bangladesh. International Journal of Research in Management & Technology, 2(2), 184-191. Jarvis, C. B., MacKenzie, S. B., Podsakoff, P. M., 2003. A critical review of construct indicators and measurement model misspecification in marketing and consumer research. Journal of Consumer Research, 30(2), 199-218. Jüttner, U., 2005. Supply chain risk management: Understanding the business requirements from a practitioner perspective. The International Journal of Logistics Management, 16(1), 120-141. Jüttner, U., Maklan, S., 2011. Supply chain resilience in the global financial crisis: an empirical study. Supply Chain Management an International Journal, 16(4), 246-259. Kachi, H., Takahashi, Y., 2011. Plant Closures Imperil Global Supplies. The Wall Street Journal. http://www.wsj.com/articles/SB10001424052748704027504576198961775199034 (accessed Sept 22, 2015). Kleindorfer, P. R., Saad, G. H., 2005. Managing Disruption Risks in Supply Chains. Production & Operations Management, 14(1), 53-68. Knemeyer, A. M., Zinn, W., Eroglu, C., 2009. Proactive planning for catastrophic events in supply chains. Journal of Operations Management, 27(2), 141-153. Lee, H. L., 2004. The triple-A supply chain. Harvard Business Review, 82(10), 102-113. Leiblein, M. J., 2011. What do resource-and capability-based theories propose? Journal of Management, 37(4), 909-932. Ling-Yee, L., 2007. Marketing resources and performance of exhibitor firms in trade shows: A contingent resource perspective. Industrial Marketing Management, 36(3), 360-370. Martin, S., 2004. The cost of restoration as a way of defining resilience: a viability approach applied to a model of lake eutrophication. Ecology and Society, 9 (2), 1-25.

40

MacKenzie, S. B., Podsakoff, P.M., Podsakoff, N.P., 2011. Construct measurement and validation procedures in MIS and behavioral research: Integrating new and existing techniques. MIS Quarterly, 35(2), 293-334. McFarlan, F. W., McKenney, J. L., Pyburn, P., 1983. The information archipelago-plotting a course. Harvard Business Review, 145-156. McCann, J., Selsky, J., Lee, J., 2009. Building agility, resilience and performance in turbulent environments. People & Strategy, 32(3), 44-51. Menor, L. J., Roth, A.V., 2007. New service development competence in retail banking: construct development and measurement validation. Journal of Operations Management, 25(4), 825846. Mitroff, I., Alpasan, M., 2003. Preparing for evil. Harvard Business Review (April), 109–115. Moore, G. C., Benbasat, I., 1991. Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192222. Mulaik, S. A., James, L. R. , Van Alstine, J., Bennett, N., Lind, S., Stilwell, C. D., 1989. Evaluation of goodness-of-fit indices for structural equation models. Psychological Bulletin, 105(3), 430 - 445. Narasimhan, R., Kim, S. W., 2001. Information system utilization strategy for supply chain integration. Journal of Business Logistics, 22(2), 51-75. Norrman, A., Jansson, U., 2004. Ericsson's proactive supply chain risk management approach after a serious sub-supplier accident. International Journal of Physical Distribution & Logistics management, 34(5), 434-456. Nunnally, J. C., 1978. Psychometric Theory. New York: NY: McGraw-Hill. Nuruzzaman, A. H., 2009. Lead time management in the garment sector of Bangladesh: An avenues for survival and growth. European Journal of Scientific Research, 33(4), 617-629. Paul-Majumder, P., Sen, B., 2001. Growth of Garment Industry in Bangladesh: Economic and Social Dimensions: Proceedings of a National Seminar on Ready-made Garment Industry, Held in Dhaka, Bangladesh, January 21-22, Bangladesh Institute of Development Studies. Pal, R., Torstensson, H., Mattila, H., 2014. Antecedents of organizational resilience in economic crises—an empirical study of Swedish textile and clothing SMEs. International Journal of Production Economics, 147: 410-428. Perona, M., Miragliotta, G., 2004. Complexity management and supply chain performance assessment. A field study and a conceptual framework. International Journal of Production Economics, 90(1), 103-115. Perotti, S., Zorzini, M., Cagno, E. , Micheli, G.J., 2012. Green supply chain practices and company performance: the case of 3PLs in Italy. International Journal of Physical Distribution & Logistics Management, 42(7), 640-672. Pettit, T. J., Fiksel, J., Croxton, K. L., 2010. Ensuring supply chain resilience: development of a conceptual framework. Journal of Business Logistics, 31(1), 1-21. Pettit, T. J., Croxton, K. L., Fiksel, J., 2013. Ensuring Supply Chain Resilience: Development and Implementation of an Assessment Tool. Journal of Business Logistics, 34 (1), 46-76. Podsakoff, P.M., MacKenzie, S.B., Lee, J.Y., Podsakoff, N.P., 2003. Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879-903. Pohjola, M., Stenholm, P., 2012. The Hierarchical Structure of Dynamic Capabilities and Evolutionary Fitness of the Firm. Available at SSRN 2075255. Polites, G. L., Roberts, N., Thatcher, J., 2011. Conceptualizing models using multidimensional constructs: a review and guidelines for their use. European Journal of Information Systems, 21(1), 22-48. Ponis, S. T., Koronis, E., 2012. Supply chain resilience: definition of concept and its formative elements. Journal of Applied Business Research (JABR), 28(5), 921-930. Ponomarov, S. Y., Holcomb, M. C., 2009. Understanding the concept of supply chain resilience. International Journal of Logistics Management, 20(1), 124-139.

41

Roberts, M.R., Whited, T.M., 2013. Endogeneity in empirical corporate finance. In: Constantinides, G.M., Harris, M., Stulz, R.M. (Eds.), Handbook of the Economics of Finance, 2 (A) Elsevier, Amsterdam, 493–572. Rose, A., 2004. Defining and measuring economic resilience to disaster. Disaster Prevention and Management, 13(4), 307-314. Rousaki, B., Alcott, P., 2006. Exploring the crisis readiness perceptions of hotel managers in the UK. Tourism and Hospitality Research, 7(1), 27-38. Rosenzweig, E. D., Roth, A.V., 2007. B2B seller competence: construct development and measurement using a supply chain strategy lens. Journal of Operations Management, 25(6), 1311-1331. Scholten, K., Schilder, S., 2015. The role of collaboration in supply chain resilience. Supply Chain Management: An International Journal, 20(4), 471-484. Shafiq, A, Klassen, R. D., Johnson, P. F., Awaysheh, A., 2014. Socially Responsible Practices: An Exploratory Study on Scale Development using Stakeholder Theory. Decision Sciences, 45 (4), 683-716. Sheffi, Y., Rice, J., 2005. A supply chain view of the resilient enterprise. MIT Sloan Management Review, 47(1), 41-48. Sheffi, Y., 2005. The resilient enterprise: overcoming vulnerability for competitive advantage. The MIT Press, Cambridge. Shepherd, C., Günter, H., 2006. Measuring supply chain performance: current research and future directions. International Journal of Productivity and Performance Management, 55(3/4), 242-258. Speier, C., Whipple, J. M., Closs, D. J., Voss, M. D., 2011. Global supply chain design considerations: mitigating product safety and security risks. Journal of Operations Management, 29(7), 721-736. Stone, M., 1974. Cross-Validatory Choice and Assessment of Statistical Predictions. Journal of the Royal Statistical Society, 36:111-147. Sullivan-Taylor, B., Branicki, L., 2011. Creating resilient SMEs: why one size might not fit all. International Journal of Production Research, 49(18), 5565-5579. Swafford, P. M., Ghosh, S., Murthy, N., 2006. The antecedents of supply chain agility of a firm: scale development and model testing. Journal of Operations Management, 24(2), 170-188. Tan, K. C., Lyman, S.B., Wisner, J. D. 2002,"Supply chain management: a strategic perspective", International Journal of Operations & Production Management, 22(6), 614 - 631 Tang, C. S., 2006. Robust strategies for mitigating supply chain disruptions. International Journal of Logistics Research and Applications, 9 (1), 33-45. Tang, C., Tomlin, B., 2008. The power of flexibility for mitigating supply chain risks. International Journal of Production Economics, 116(1), 12-27. Teece, D. J., 2007. Explicating dynamic capabilities: the nature and microfoundations of (sustainable) enterprise performance. Strategic Management Journal, 28(13), 1319-1350. Teece, D. J., Pisano, G., Shuen, A., 1997. Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509-533. Tenhiälä, A., Salvador, F., 2014. Looking inside glitch mitigation capability: the effect of intraorganizational communication channels. Decision Sciences, 45(3), 437-466. Tomlin, B., 2006. On the Value of Mitigation and Contingency Strategies for Managing Supply Chain Disruption Risks. Management Science, 52(5), 639-657. Välikangas, L., 2010. The Resilient Organization: How Adaptive Cultures Thrive even when Strategy Fails, McGraw-Hill, New York, NY, United States. Vonderembse, M. A., Uppal, M., Huang, S. H., Dismukes, J. P., 2006. Designing supply chains: Towards theory development. International Journal of Production Economics, 100(2), 223238. Vugrin, E. D., Warren, D. E., Ehlen, M. A., 2011. A resilience assessment framework for infrastructure and economic systems: Quantitative and qualitative resilience analysis of petrochemical supply chains to a hurricane. Process Safety Progress, 30(3), 280-290. Wagner, S. M., Bode, C., 2006. An empirical investigation into supply chain vulnerability. Journal of Purchasing and Supply Management, 12(6), 301-312. 42

Wang, J. W., Gao, F., Ip, W. H., 2010. Measurement of resilience and its application to enterprise information systems. Enterprise Information Systems, 4(2), 215-223. Wernerfelt, B., 1984. A resource‐based view of the firm. Strategic Management Journal, 5(2), 171180. Wetzels, M., Odekerken-Schroder, G., Van Oppen, C., 2009. Using PLS path modeling for assessing hierarchical construct models: guidelines and empirical illustration. MIS Quarterly, 33(1), 177-195. Wieland, A., Wallenburg, C.M., 2012. Dealing with supply chain risks: Linking risk management practices and strategies to performance. International Journal of Physical Distribution & Logistics Management, 42(10), 887-905. Wieland, A., Wallenburg, C. M., 2013. The influence of relational competencies on supply chain resilience: a relational view. International journal of Physical Distribution & Logistics Management, 43(4), 300-320. Williams, L.J., Hartman, N., Cavazotte, F., 2010. Method variance and marker variables: A review and comprehensive CFA marker technique. Organizational Research Methods, 13(3), 477514. Willroth, P., Diez, J. R., Arunotai, N., 2011. Modelling the economic vulnerability of households in the Phang-Nga Province (Thailand) to natural disasters. Natural Hazards, 58(2), 753-769. Wu, T., Blackhurst, J., Chidambaram, V., 2006. A model for inbound supply risk analysis. Computers in Industry, 57(4), 350-365. Zhang, Q., Vonderembse, M. A., Lim, J.S., 2003. Manufacturing flexibility: defining and analyzing relationships among competence, capability, and customer satisfaction. Journal of Operations Management, 21(2), 173-191.

43

Table 1: Scale development process CONCEPTUALIZATION OF SCRE

- Identifying dimensions based on literature review -Theoretical Justification of SCRE concept INSTRUMENT DEVELOPMENT PROCESS

- Qualitative study to contextualize the findings of literature on SCRE dimensions and measurement items. - Justification of field study findings based on literature - Scale development Items creation Items sorting INSTRUMENT TESTING

- Developing initial version of questionnaire and pretesting - Pilot testing - Model specification - Confirmatory study Assessment of the first-order scale Assessment of the higher-order scale Assessment of the nomological and predictive validity Assessment of the overall parameters SCALE GENERALIZABILITY

Table 2: Demographic Profile of field study respondents Participants D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D11 D12 D13 D14 D15

Position

Company type

Supply chain manager Manager Merchandising Manager Merchandising General manager Managing director Supply chain manager General manager Supply chain manager Manager Merchandising Supply chain manager Deputy general manager General manager Deputy general manager Deputy general manager Manager merchandising

RMG manufacturer Buying agent RMG manufacturer RMG manufacturer Buying agent RMG manufacturer RMG manufacturer RMG manufacturer RMG manufacturer Supplier RMG manufacturer Supplier Supplier Supplier RMG manufacturer

Company size (no of employees) 2000-3000 Less than 1000 1000-2000 More than 4000 Less than 1000 More than 4000 2000-3000 More than 10000 3000-4000 Less than 1000 More than 20000 Less than 1000 Less than 1000 Less than 1000 Less than 1000

Age of company 10-15 5-10 0-5 5-10 10-15 20-25 20-25 20-25 5-10 5- 10 25-30 0-5 10-15 15-20 10-15

Table 3: Factors and variables from the qualitative study SC proactive Capability SC Readiness

Variable

Disruption detection

Enterprises 1 2 3 4 y

y

% 5

y

6 y

44

7

8

9

10

y

y

y

11

12 y

13

14 y

15 60.0

Flexibility

Reserve capacity

Integration

Efficiency

Market Strength

Financial strength

SC Reactive Capability Response

Recovery

SC Design Quality Density

Complexity

Readiness training Readiness resource Early warning signals Forecasting Security Flexible production volume Product variety/customization Multi-skilled workforce Contract flexibility Sourcing flexibility Distribution flexibility Introducing New product Back up capacity Buffer stock Backup energy/utility source Information sharing Internal integration Collaboration ICT adoption Waste reduction Efficiency of employees Quality control Buyer & supplier satisfaction Preferred brand (Having Buyers nomination) Buyer-supplier relation Product differentiation Diversified business portfolio Fund availability Consistent Profit Insurance

Quick response Effective/adequate response Response team Quick recovery Loss absorption Reduction of impact Recovery cost

Sourcing from different area Alternative market Alternative production Few tiers in forward and backward flows/deal directly with buyer & supplier

y

y y

y y y y

y y

y y

y

y

y

y

y y

y

y y y

y y y y y y y

y

y

y

y y y y

y

y

y

y y y

y y y y y

y y y y y y

y

y

y y y y

y y y

y

y

y

y y

y y

y

y y y

y

y

y

y

y

y y

y y

y y y

y y

y

y

y y y y y y y

y

y y y

y y

y

y y

y

y y y

y

y

y

y y

y y

y

y

y

y y

y

y y

y y y

y y

y y y y y y y

y y

y

y

y

y

y y

y

y

y

y y

y

y

y y

45

y

y

73.3

y

y

y

y y

y y

y

y y

y y

y

y

y y

y

y

y

y y

y

y

y y

y

y y y y

y

y y y y

53.3

y y y y

60.0 53.3 60.0

53.3 26.6

y

y

40.0 66.6 26.6 60.0 40.0

46.6 y

y

73.3 80.0 46.6 33.3 60.0 66.6 60.0 60.0

20.0

y

y

46.6 73.3

33.3

y

y

y y y

y

y

y

y

y

y

y

y y y y y

y y

53.3 66.6 46.6 80.0 33.3 60.0

y y y y

y

y

y

y

y y

y y y

y

y y

y y y

y

y

y

y

y

y

y

y

y

y y

y

y y

y

y y

66.6 33.3 33.3 46.6 73.3 66.6

y

y y

y

y

y

y

y y

y y

y y

y

y

y

y

y y

y y y

y

y

y y y

y

y

y

y

y

y y y

y

y

y y

y

y y

y

y

y

y y

y

y

y

y y

y

y y

y y

y

y y

y y

y y y y y y

y

y

80.0 33.3 66.7

Criticality

Number of forward and backward flows Use of multiple supplier Having multiple buyers No of critical supplier No of critical distribution centre Alternative transportation option Alternative for critical component and parts

y y y y y

y

y

y

y y y y

y y y y

y

y

y

y

y

y

y y

y

y y

y y

y y

y y y y

y

y

y

y

y

y

y

y

y y y y

y y y

y y y y

y

y

y

y

y

y y y

53.3

y y

y y y y

86.6 80.0 66.6 60.0

y

y

80.0

y

46.6

Table 4: literature support of the factors and variables Proactive dimensions Supply chain Disaster readiness

Variables Disruption detection Readiness training Readiness resource Early warning signals Forecasting Security

Flexibility

Reserve capacity

Integration

Efficiency

Flexibility in production (different volume of order, flexible production schedule) Ability to produce a wide variety of product as per buyer requirement (mix flexibility) Multi-skilled workforce Flexibility in contract with SC partners (Partial order and payment, partial shipment) Flexibility in in sourcing (supplier lead time, changing quantity of supplier’s order etc.) Flexibility in distribution (e.g. meeting sudden demand of customer, changing delivery schedule etc.) Ability to produce and supply new products to different customer groups Alternative and back up capacity (machinery, equipment and logistical options) Buffer stock Backup energy source Sharing information with supply chain partners Information flow with different departments of organization Joint or collaborative planning (e.g product development, inventory planning) Communication with supply chain partners ICT supported planning and integration Waste/idle capacity reduction through efficient use of resource Productive and hardworking employees

46

References Helferich & Cook (2002); Knemeyer et al. (2009) Pettit et al. (2013); Rousaki & Alcott (2006) Hale & Moberg (2005); Rousaki & Alcott (2006) Pettit et al. (2013); Craighead et al. (2007) Pettit et al. (2013); Sheffi (2005), Blackhurst et al. (2005) Sheffi & Rice (2005), Craighead et al. (2007); Hale & Moberg (2005) Swafford et al. (2006); Braunscheidel & Suresh (2009) Braunscheidel & Suresh (2009); Swafford et al. (2006) Swafford et al. (2006); Duclos et al. (2005) Duclos et al. (2005); Pettit et al. (2013)

Swafford et al. (2006); Gunasekaran et al. (2008) Swafford et al. (2006); Jüttner & Maklan (2011) Swafford et al. (2006); Braunscheidel & Suresh (2009) Pettit et al. (2013) ; Pettit et al. (2010)

Pettit et al. (2013) Pettit et al. (2013) Braunscheidel & Suresh (2009); Blackhurst et al. (2005); Pettit et al. (2013) Braunscheidel & Suresh (2009); Pettit et al. (2013) Braunscheidel & Suresh (2009); Pettit et al. (2013) Braunscheidel and Suresh (2009) Narasimhan & Kim (2001); Pettit et al. (2013) Pettit et al. (2013); Fiksel (2003); Sheffi & Rice (2005) Pettit et al. (2013)

Market strength

Financial strength

Quality control and less defection Buyer & supplier satisfaction Preferred brand to buyers Good relationship with buyers & suppliers Product differentiation Diversified business portfolio Fund availability Profitability Insurance

Pettit et al. (2013); Kleindorfer & Saad (2005) Pettit et al. (2013) Zhang et al. (2003) Pettit et al. (2013) Field study Field study Pettit et al. (2013); Tang (2006) Pettit et al. (2013) Pettit et al. (2013); Tomlin (2006)

Reactive dimensions Response

Recovery

Quick response Adequate response Response team Quick recovery

Sheffi & Rice (2005); Norrman & Jansson (2004) Field study Pettit et al. (2013); Field study Sheffi & Rice (2005); Christopher & Peck (2004) Willroth et al. (2011); Gunderson (2000) Holling (1973); Dalziell & McManus (2004) Rose (2004); Dalziell & McManus (2004)

Loss absorption Reduction of impact Recovery cost Node Density

Martin (2004); Vugrin et al. (2011) Supply Chain Design quality

Sourcing from concentrated area Vs diversified sourcing Concentrated market Vs diversified market Concentrated production Vs diversified

Complexity

More tiers in forward and backward flows or

Craighead et al. (2007); Falasca et al.

dealing directly with buyers and suppliers

(2008); Kleindorfer & Saad (2005);

Number of forward and backward flows

Colicchia et al. (2010); Tomlin (2006);

Use of multiple suppliers rather than single

Choi & Krause, 2006; field study

supplier Having multiple buyers rather than depending on few large buyers Criticality

Alternatives for critical supplier Critical distribution center Alternative transportation modes & rerouting Alternatives for critical component

Operational vulnerability

Supply chain performance

Shortage of skilled worker Switching and absenteeism of workers Production planning and inventory management Failure of IT system and machineries Disruption in utility supply Product quality defection Illiteracy of workers and supervisors Sales and business volume Cost Profit/net income Customer satisfaction On time delivery Quality of product and service

47

Haider (2007); Field study Chowdhury et al. (2012); Field study Wu et al. (2006); Field study Blos et al. (2009); Field study Blos et al. (2009); Field study Blos et al. (2009); Field study Chowdhury et al. (2012); Field study Shepherd & Gunter (2006), Perotti et al (2012); Field study Shepherd & Gunter (2006), Perotti et al (2012); Field study Shepherd & Gunter (2006); Perotti et al (2012); Field study Gunasekaran et al. (2004) Gunasekaran et al. (2004) Epstein & Wisner (2001)

Table: 5 Item placement ratios (final) and inter-rater reliability Theoretical Construct classificatio n

RED

Actual construct classification

reliabilit y RE

FL

D

X

EF

15

RD

INT

18

EF 1

1

RE

RE

DE

CO

S

C

N

M

1 1

7 1

11 11

1

1 11

RES

1

REC

8 1

11

1

COM

1

CRI Total

CRI

1

1

MS

DEN

FS

1 1

7

INT

FS

MS

1

FLX

RD

Interrater

18

20

8

8

13

13

13

9

12

Tota

%

l

Hits

18

83.3

Average Kappa scores 82.6

21

85.7

88.5

9

77.7

81.3

9

77.7

80.6

12

91.6

92.2

12

91.6

93.5

12

91.6

90.5

9

88.8

87.3

12

91.6

94

9

77.8

88.2

7

1

1

9

1

12

75.0

76.5

1

1

10

12

83.3

87.3

10

11

11

147

84.6

86.9

% %

83.3

90

87.

87.

84.

84.

84.

5

5

6

6

6

88.8

91.6

70.0

81.8

90. 9

Note: The values in the shaded cells represent the number of items placed correctly by the panel of judges within the intended construct. RED=Readiness, FLX=Flexibility, EF=Efficiency, RD=Redundancy, INT=Integration, MS=Market Strength, FS=Financial Strength, RES=Response, REC=Recovery, DEN=Density, COM=Complexity, CRI=Criticality.

Table 6: Demographic profile of Pilot study respondents (study -1) Supply Chain Entity RMG manufacturer RMG accessories suppliers Buyer/buying agents Number of employees <= 500.00 501.00 - 1000.00 1001.00 - 1500.00 1501.00 - 2000.00 2001.00+ Number of years in Business <= 5.00 6.00 - 10.00 11.00 - 15.00 16.00 - 20.00 21.00+ Turnover in Business (million BDT)

Number of Companies 53 21 07 Number of Companies 24 21 16 11 14 Number of Companies 28 31 12 7 8 Number of Companies

48

% 65.4 25.9 08.7 % 27.9 24.4 18.6 12.8 16.3 % 32.6 36 14 08 9.4 %

0-1000 1001-2000 2001-3000 3001+

39 26 13 8

45.3 30.2 15.2 9.3

Table 7: Results of exploratory factor analysis in the pilot study Code

Items

FLX1 We have flexibility in production in terms of volume of order and production schedule. FLX2 We produce different types of products to meet customer requirements FLX3 We have multi-skilled workforce to continue production FLX4 We have contract flexibility such as partial order, partial payment, partial shipment etc. FLX5* We have flexibility in sourcing* FLX6 We have flexibility in distribution FLX7 We are capable of introducing new product RD1 We have back up capacity for machinery, parts and logistical supports RD2 We have buffer stock for raw material RD3 We have backup energy/utility source INT1 We share information with supply chain partners INT2 We have integration among different departments of our company INT3 We have collaborative relation with our supply chain partners INT4* We have ICT adoption for smooth flow of goods and information* EF1 We do not have idle capacity and waste EF2 Our employees are efficient EF3

We have strong quality control process MS1 Our buyers and suppliers are satisfied with us MS2 We are preferred brand to our buyers (Having Buyers nomination) MS3 We have a good buyer-supplier relationship MS4* We produce product that are different from our competitor* FS1* We have a diversified business portfolio* FS2 We have enough fund to mitigate disruptions FS3 We have consistency of Profit over last couple of years

1

2

3

4

5

Extracted Factors 6 7 8

9

10

11

12

.776 .025 .098 .069 .143 .016 .170 .141 .064 .100 .059

.062

.642 .225 .075 .031 .038 .089 .037 .117 .301 .019 .048

.004

.626 .094 .153 .259 .108 .047 .054 .055 .118 .082 .194

.192

.561 .030 .165 .063 .007 .281 .037 .076 .152 .086 .025

.067

.058 .062 .057 .235 .250 .039 .038 .141 .091 .090 .042

.714

.466 .459 .059 .038 .011 .139 .104 .056 .028 .163 .377

.071

.699 .066 .115 .145 .052 .023 .157 .105 .153 .273 .034

.046

.046 .030 .177 .075 .055 .158 .814 .106 .021 .005 .041

.104

.141 .128 .052 .135 .073 .068 .738 .039 .160 .015 .003

.356

.131 .078 .137 .163 .046 .113 .700 .044 .097 .090 .195

.104

.364 .163 .543 .144 .088 .145 .141 .029 .044 .224 .285

.229

.179 .149 .853 .003 .058 .103 .165 .037 .184 .083 .020

.012

.103 .021 .828 .072 .013 .029 .014 .036 .157 .055 .013

.036

.560 .206 .078 .098 .076 .124 .375 .111 .190 .268 .105

.073

.045 .146 .036 .093 .080 .870 .012 .043 .164 .062 .005

.002

.055 .043 .080 .030 .938 .012 .084 .028 .063 .051 .002 .100 .094 .045 .133 .012 .850 .194 .008 .120 .049 .051

.034 .141

.085 .041 .063 .131 .900 .055 .045 .008 .011 .005 .008

.004

.099 .046 .038 .136 .781 .031 .145 .052 .108 .131 .012

.097

.013 .054 .071 .026 .878 .021 .004 .071 .090 .080 .169

.037

.016 .015 .029 .205 .281 .466 .352 .145 .226 .200 .353

.085

.024 .106 .021 .047 .102 .141 .188 .587 .327 .461 .249

.238

.080 .223 .099 .035 .100 .072 .085 .666 .018 .006 .018

.307

.231 .182 .087 .321 .278 .173 .076 .559 .114 .240 .076

.213

49

FS4

DEN1 DEN2

DEN3

COM1

COM2*

COM3 COM4 CRI1 CRI2

CRI3 CRI4* RED1 RED2 RED3 RED4* RED5 RED6 RES1 RES2* RES3 REC1 REC2* REC3 REC4

We have insurance against potential damage and destruction Our buyers are not concentrated to specific geographic region We select suppliers from diversified region (alternative supplier) to avoid the risk of supply in specific area We have production facility in different area (alternative production facility) to avoid risk of operational disruption in specific area We try to deal directly with buyers and suppliers to reduce complexity in supply chain We do not have much forward and backward flow of goods and services in our SC We use multiple suppliers to avoid the risk of supply We have multiple buyers to avoid the buyers disruptions We are not critically dependent on specific supplier We do not have critical distribution center which is responsible to distribute many other distribution center We have alternative transportation option We have alternative for critical component and parts We have the ability to detect SC disruptions quickly We have readiness training for overcoming crisis We have resources to get ready during crisis We have early warning signals* We have forecasting for meeting demand disruptions We strong security system to protect man made crisis We can respond quickly to disruptions We can undertake adequate response to crisis* We have response team for mitigating crisis We get recovery in short time We have the ability to absorb huge loss* We can reduce impact of loss by our ability to handle crisis We can recovery from crisis at less cost

.077 .142 .005 .157 .014 .069 .009 .865 .204 .029 .098

.007

.131 .403 .051 .118 .068 .055 .219 .003 .557 .183 .479

.017

.068 .008 .080 .058 .048 .039 .001 .146 .835 .132 .110

.075

.078 .013 .050 .003 .070 .051 .054 .399 .516 .039 .001

.069

.059 .010 .042 .097 .212 .136 .374 .166 .182 .362 .492

.165

.152 .021 .067 .097 .205 .193 .476 .189 .212 .264 .423

.192

.065 .025 .261 .037 .034 .022 .084 .054 .267 .066 .864

.061

.132 .127 .061 .039 .135 .121 .089 .152 .168 .163 .721

.166

.157 .113

.163 .129 .217 .139 .175 .123 .059 .110

.572

.150 .019 .017 .080 .021 .073 .401 .102 .310 .362 .150

.610

.213 .113 .167 .181 .127 .221 .310 .137 .218 .317 .139

.586

.128 .102 .108 .134 .153 .235 .170 .188 .152

.610 .005

.554

.113 .646 .133 .106 .097 .076 .103 .105 .096 .051 .107

.117

.300 .639 .091 .057 .069 .099 .205 .018 .063 .187 .120

.153

.032 .810 .002 .003 .027 .056 .114 .039 .084 .150 .033

.039

.015 .248 .030 .008 .052 .012 .014 .050 .166 .752 .097

.019

.263 .810 .011 .068 .041 .116 .126 .018 .112 .085 .052

.018

.007 .660 .109 .123 .004 .118 .012 .196 .041 .041 .211

.034

.28

.218 .136 .259 .145 .090 .342 .071 .099 .428 .506 .145 -.004 .037 .013 .082 .727 .140 .055 .166 .132 .065 .185 .347

.076

.161 .045 .181 .741 .016 .176 .050 .006 .058 .543 .123

.177

.091 .167 .097 .509 .082 .018 .331 .021 .100 .294 .088

.140

.128 .017 .202 .357 .461 .157 .271 .134 .177 .336 .187

.124

.023 .085 .079 .850 .036 .016 .061 .040 .060 .130 .088

.128

.126 .101 .010 .868 .039 .089 .155 .017 .030 .011 .054

.016

*Items that are subject to deletion because of low loading

50

Table 8: Results of exploratory factor analysis in the pilot study after dropping items

5.626

Cumulative variation 12.231

Cronbach’s alpha .798

4.945

22.982

.732

3.804

31.253

.821

3.177

38.159

.716

2.726

44.086

.865

2.529

49.583

.724

.739 .684 .743 .767 .596 .712 .798

2.183

54.329

.837

1.832

58.312

.758

.867 .722 .571 .613 .588

.870 .776 .781 .695 .624

1.742

62.099

.712

.642 .643 .814

.873 .850 .682

1.420

65.186

.845

.807 .663 .508

.832 .635 .764

1.366

68.155

.893

Factors

Items

Loadings

Flexibility

FLX1 FLX2 FLX3 FLX4 FLX5* FLX6 FLX7 RD1

.778 .647 .622 .564 .468 .710 .812

.652 .627 .775

RD2 RD3 INT1 INT2 INT3 INT4* INT5 EF1 EF2 EF3 MS1

.739 .706 .445 .854 .826

.654 .632 .575 .684 .730

.878 .872 .937 .851

.785 .693 .679 .565 .660

MS2 MS3 MS4* FS1*

.786 .875

FS2 FS3 FS4 DEN1 DEN2 DEN3 COM1 COM2* COM3 COM4 CRI1 CRI2 CRI3 CRI4* RED1 RED2 RED3 RED4* RED5 RED6 RES1

.669 .556 .868 .557 .837 .519 .494

Reserve capacity

Integration

Efficiency

Market strength

Financial strength

Density

Complexity

Criticality

Readiness

Response

.902

Item total correlation .732 .670

Eigenvalue

.706

.708 .698

51

Recovery

RES2* RES3 REC1 REC2* REC3 REC4

.544 .506

.669 .683

.853 .867

.810 .705

1.332

71.050

.761

* Low loading and cross loading items.

Table 9: Demographic profile of the survey respondents (study -2) Supply Chain Entity Garment manufacturer Accessory producers Buying agent Number of employees Less than 500 501- 1000 1001- 1500 1501-2000 2001+ Number of years in Business <= 5.00 6.00 - 10.00 11.00 - 15.00 16.00 - 20.00 21.00+ Turnover (Million BDT) 0- 1000 1001- 2000 2001- 3000 3001+

Number of Companies 189 76 31 Number of Companies 73 78 57 41 47 Number of Companies

Percentage (%) 63.9 25.6 10.5 Percentage (%) 24.65 26.35 19.30 13.85 15.85 Percentage (%)

78 84 58 40 36 Number of Companies 118 85 57 36

26.35 28.35 19.6 13.5 13.2 Percentage (%) 39.8 28.7 19.3 12.2

Table 10 Mann–Whitney test results Construct FLX3 RD1 INT1 EF3 MS1 FS1 SCD1 RED4 RR2 ECS1

Z-Value -.342 -.729 -1.432 -.713 -.274 -.576 -1.509 -1.3 -.472 -1.136

Significance (1-tailed) .733 .466 .152 .476 .784 .564 .131 .193 .637 .257

Table 11: Psychometric properties of SCRE measurement model at first order level Construct Proactive capability

SubConstruct Flexibility (FLX)

Items FLX1-Production flexibility FLX2-customization FLX3-Multi-skilled workforce FLX4-Contract flexibility FLX5-Sourcing

Loading

t-v

0.842

39.71 0.512

8.52

0.814 0.787

32.15 0.536 34.33 0.487

5.79 5.75

4.93 4.19

.876 .971

0.801

32.27 0.613

7.58

4.86

.852

52

Weight t-v

AVE

CR

Mean S.D

0.681

0.917

4.71

.932

Redundancy (RD)

Integration (INT)

Efficiency (EF)

Market strength (MS)

Financial strength (FS)

Readiness (RED)

Supply chain design (SCD)

Density (DEN)

Complexity (COM)

Criticality (CRI)

Reactive capability

Response (RES)

Flexibility* FLX6- Distribution Flexibility* FLX7-New product RD1-Reserve capacity RD2-Stock RD3-Back-up utility INT1-Information sharing INT2-Internal integration INT3-collaboration INT4-ICT adoption* EF1-Waste reduction EF2-Worker efficiency EF3-Quality control MS1-Buyer-supplier satisfaction MS2-Preferred brand MS3-Buyer-supplier relation MS4-Product differentiation FS1-diversified business portfolio* FS2-Fund availability FS3-Profit consistency FS4-Insurance RED1-Disrution detection RED2-Readiness training RED3-Readiness resource RED4-Early warning signal RED5-Forecasting RED6-Security DEN1-Alternative sourcing DEN2-Alternative market DEN3-Alternative production COM1-Deal directly with buyers and suppliers to reduce number of tiers in SC. COM2-Number of forward and backward flows COM3Multiple suppliers COM4-Multiple buyers CRI1- No critical supplier CRI2- No critical distribution center CRI3-Different distribution and transportation options CRI4-Alternative for critical component and parts RES1-Quick response RES2response*

0.834 0.725

46.31 0.461 83.37 0.642

6.23 8.59

4.61 4.85

0.693 0.847 0.884

12.43 0.137 58.17 0.327 52.41 0.583

1.63 4.33 7.12

4.16 4.81 4.76

1.216 .983 1.12

0.876

55.39 0.536

6.57

4.54

1.205

0.885

53.42 0.419

5.39

0.812

32.71 0.638

9.26

4.26 3.98 4.73

1.113 1.181 .826

0.835

43.47 0.461

5.38

4.52

.797

0.859 0.813

82.53 0.735 84.87 0.624

8.91 8.72

4.69 4.91

.954 .832

0.854 0.783

87.18 0.598 66.82 0.462

7.73 6.39

4.25 4.73

.966 .972

0.651

19.26 0.417

5.91

4.44

.867

0.754

.779

.845

.877 .984

0.913

0.912 0.916

71.92 .294 85.73 .438

4.37 5.71

4.14 4.45

.906 .873

0.824 0.782

46.29 .631 26.5 .375

8.93 5.29

4.87 4.38

1.024 .945

0.764

32.36 .431

5.42

4.08

.884

0.818

37.92 .363

4.21

4.37

.921

0.531

7.82

.258

2.76

4.29

.821

.814 .849 0.735

39.72 .171 62.57 .237 41.35 .438

1.86 2.91 5.76

4.74 4.95 4.97

1.137 1.261 .924

0.783

52.95 .521

5.88

4.77

.962

0.763

51.36 .325

4.12

4.18

1.291

0.785

51.29 .396

4.37

3.76

1.165

.417

6.23

.144

1.59

0.791

56.35 .614

7.46

4.45

1.127

0.826

58.79 .581

7.33

4.96

1.081

0.638

24.47 .562

6.79

4.56

.873

0.573

19.32 .416

5.63

4.17

.821

0.671

23.51 .548

6.61

4.19

.943

0.582

9.37

.426

0.888

75.39 .642

Adequate

53

.351

11.76

3.42

.7604

0.911

.931

4.27

.891

4.48

.895

Recovery (REC)

Outcome constructs

Operational vulnerability

Supply chain performance

RES3-Response team

0.899

72.28 .517

5.79

REC1-Quick recovery REC2-Loss absorption* REC3-Reduction of impact REC4-Recovery cost SCP1-Sales

0.925

57.52 .475

9.67

0.891

32.91 .345

4.31

0.831 0.911

32.25 .396 95.81 .721

4.59 15.32

SCP2-Cost SCP3-Profit SCP4Customer satisfaction SCP5On time delivery SCP6- Quality

0.746 0.881 0.923

22.84 .548 62.63 .673 93.77 .825

7.78 8.85 13.36

0.823

33.4

.641

9.96

4.38

.891

0.865

37.7

.592

7.74

4.52

.796

Item

Loading t-v

Weight

t-v

OV1-Skill shortage OV2-Switching and absenteeism OV3-Production planning OV4-IT system failure OV5-Utility disruption OV6-Product quality OV7-Illeteracy

0.636 0.724 0.647 0.342 0.725 0.635 0.716

0.014 0.259 0.23 0.19 0.261 0.132 0.242

0.127 1.947 1.668 1.437 1.898 0.794 1.088

6.38 9.41 5.32 2.56 7.31 4.97 6.34

0.813

0.775

0.892

0.853

AVE CR

4.16

.783

4.29

.862

4.13

.838

4.21 4.78

.947 .961

4.02 4.34 4.67

.897 .924 1.25

Mean S.D 3.12 3.67 3.35 3.13 4.45 3.26 3.51

.736 .724 .962 .935 1.34 .792 1.17

L = Loading; L t-v = t-value; AVE = Average variance extracted; CR = Composite reliability

*Items which are deleted due to low loading or high cross loading.

Table 12: HTMT criteria for discriminant validity test DEN COM CRI DEN SCP EF FLX FS INT MS RD REC RED RES

COM 1 0.502 0.453 0.281 0.356 0.248 0.421 0.305 0.236 0.374 0.457 0.411 0.524

CRI 0 1 0.322 0.483 0.416 0.498 0.341 0.432 0.506 0.419 0.403 0.127 0.425

0 0 1 0.383 0.511 0.342 0.508 0.482 0.433 0.529 0.402 0.401 0.336

SCP

EF

FLX

0 0 0 1 0.458 0.352 0.261 0.461 0.385 0.503 0.346 0.439 0.302

0 0 0 0 1 0.301 0.404 0.381 0.453 0.479 0.312 0.508 0.442

0 0 0 0 0 1 0.408 0.351 0.336 0.425 0.291 0.476 0.231

FS

INT

MS

RD

0 0 0 0 0 0 1 0.338 0.412 0.348 0.492 0.386 0.404

0 0 0 0 0 0 0 1 0.493 0.437 0.359 0.341 0.396

0 0 0 0 0 0 0 0 1 0.402 0.241 0.495 0.358

0 0 0 0 0 0 0 0 0 1 0.331 0.266 0.469

All HT-MT < 0.85, as a result the model show discriminant validity

Table .13: Collinearity test for formative constructs Construct RD

EF

MS

DEN

Item RD1 RD2

VIF 2.217 1.573

RD3

1.796

EF1 EF2 EF3 MS1 MS2 MS3 MS4 DEN1 DEN2 DEN3 DEN4

1.831 1.956 2.147 1.456 1.824 1.761 1.926 1.624 1.783 1.917 2.224

Construct COM

Item COM1 COM2 COM3 COM4 CRI1 CRI2 CRI3 CRI4 READ1 READ2 READ3 READ4 READ5 READ6

CRI

READ

54

VIF 1.925 1.912 2.261 1.872 1.914 1.869 2.196 1.824 1.982 1.738 1.996 2.217 1.779 2.227

REC 0 0 0 0 0 0 0 0 0 0 1 0.351 0.482

RED 0 0 0 0 0 0 0 0 0 0 0 1 0.231

RES 0 0 0 0 0 0 0 0 0 0 0 0 1

Table 14: Psychometric properties of SCRE measurement model at higher order level Second order constructs

Third order construct

Construct

CR

AVE

Construct

CR

AVE

Proactive capability

0.915

0.615

SCRE

0.918

0.592

SCD

0.828

0.508

Reactive capability

0.903

0.761

Table 15: Result of Hypothesis testing Hypothesis

Link

H1 H2 H3 H4

SCRE→OV (-) OV→SCP (-) SCRE→ SCP (+) SCRE→ SCP (indirect)

Standardized Path Coefficient -0.615 -0.092 0.703 0.419

t-Value

Outcome

14.36 2.59 19.82 8.78

Supported Supported Supported supported

Table 16: Demographic information of cross industry survey respondents (study -3) Name of industry Apparel Garment/apparel Accessories Food processing Pharmaceutical Footwear and leather goods Electrical and electronics Steel Plastic Jute Others Total

Number of companies 63 17 23 24 15 11 22 7 4 21 207

55

Percentage (%) 30.4 8.2 11.11 11.6 7.25 5.3 10.6 3.4 1.93 10.15 100

Figure 1: Multi-dimensional Supply chain Resilience model. SCR=supply chain resilience, RED=disaster readiness, FLX=flexibility, RD= redundancy/reserve capacity, IN T=integration, EF= efficiency, MS= market strength, FS= financial strength, RES=response, REC= recovery, DEN=density, COM= complexity, CRI= criticality, OV= operational vulnerability, SCP= supply chain performance.

Figure 2: SCRE Model depicting study 2 results

56

Figure 3: SCRE Model depicting study 3 results

57