Supply chain disruption management: Global convergence vs national specificity

Supply chain disruption management: Global convergence vs national specificity

JBR-07827; No of Pages 13 Journal of Business Research xxx (2013) xxx–xxx Contents lists available at SciVerse ScienceDirect Journal of Business Res...

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JBR-07827; No of Pages 13 Journal of Business Research xxx (2013) xxx–xxx

Contents lists available at SciVerse ScienceDirect

Journal of Business Research

Supply chain disruption management: Global convergence vs national specificity Elena Revilla a,1, María Jesús Sáenz b,c,⁎ a b c

Department of Operation and Technology Management, IE Business School, Maria de Molina 12, 5ª Planta, 28006 Madrid, Spain MIT-Zaragoza International Logistics Program, Zaragoza Logistics Center, Ed. Nayade 5. C/Bari 55, Plaza, Zaragoza, Spain University of Zaragoza, Spain

a r t i c l e

i n f o

Article history: Received 22 June 2012 Received in revised form 8 April 2013 Accepted 19 May 2013 Available online xxxx Keywords: Supply chain disruptions Cross-national Universalism Risk management

a b s t r a c t As the supply chain expands overseas, there is a growing need for managing supply chain disruptions from a cross-national perspective. This paper investigates whether or not supply chain disruption management (SCDM) can be universally applied. The universality of the SCDM framework is analyzed through the convergent versus divergent (national specificity) debate. On an empirical level, based on a unique sample of 1403 firms representing 69 countries all over the world and using the GLOBE framework, we compare the level of importance of the eight constructs of our framework and the patterns of relationship between the constructs, across eight country clusters. MANOVA analysis and multiple regression analysis were applied to obtain relevant empirical insights. Surprisingly, our findings suggest that while risk sources are different in the various countries, the implementation of SCDM practices is universal. These results support the existing tension between the convergence theory and the national specificity argument. © 2013 Elsevier Inc. All rights reserved.

1. Introduction Supply chain disruptions and related issues are considered the most pressing concerns facing firms competing in today's global marketplace (Craighead, Blackhurst, Rungtsunatham, & Handfield, 2007). A disruption can be defined as an unplanned and unanticipated situation in comparison with normal supply–demand coordination risks (Hendricks & Singhal, 2003; Kleindorfer & Saad, 2005; Wagner & Bode, 2006). Examples of these include the 9/11 terrorist attacks, the lightning strike at the Philips NV microchip plant in New Mexico, the bankruptcy of Land Rover's exclusive chassis supplier, or the shutdown of all air traffic due to a volcanic eruption in Iceland. Academics and practitioners argue that in the last few years supply chains have become more vulnerable to disruption (Christopher & Lee, 2004; Craighead et al., 2007; Kleindorfer & Saad, 2005; Simangungsong, Hendry, & Stevenson, 2012). This is supported by findings coming from organizational scientists (Perrow, 1984), which indicate that accidents become inevitable or even normal in complex and tightly coupled technological systems. Given this theory, it is not surprising that lengthy and complex supply chains, working with faster speeds, have become more prone to disruptions. Hendricks and Singhal (2003, 2005) analyzed the effects of supply chain disruptions and empirically showed that these events have a significant negative impact on shareholder value and on operating performance (i.e., sales, operating income, return on assets). Their study also indicates that companies that experience a supply ⁎ Corresponding author. Tel.: +34 976 077 606. E-mail addresses: [email protected] (E. Revilla), [email protected] (M.J. Sáenz). 1 Tel.: +34 91 568 97 33.

chain disruption suffer a 33 to 40% decline in stock price compared with industry peers over a three-year period. The implications of supply chain disruptions in the entwined global operations have been evidenced in different kinds of industries over the last decade. Take, for example, the impact of the tsunami catastrophe that struck Japan in March 2011, one of the largest disruptions to global supply chains in modern history, which had important consequences for the electronics industry. Cisco, leader in the communication technology industry, was able to assess the impact of the tsunami for their 300 suppliers in 12 h thanks to their sophisticated supply chain disruption management strategies and therefore. As result, Cisco experienced practically no loss in revenue (Sáenz & Revilla, 2013). Another relevant example is Apple's ipad2, which went on sale just hours after the tsunami hit. Apple had to deal with subsequent shutdowns causing stock shortages and long delays in deliveries, which was frustrating for Apple's customers as well as its shareholders (Neville, 2011). Extant research has not only highlighted the long-term negative effects of supply chain disruptions but has also contributed relevant insights into related issues such as supply chain disruption management (e.g. Kaku and Kamrad, 2011; Manuj & Mentzer, 2008; Sheffi & Rice, 2005; Simangungsong et al., 2012; Speier, Whipple, Closs, & Voss, 2011; Tomlin, 2006; Zsidisin, Melnyk, & Ragatz, 2005). Our research emphasizes the framework developed by Kleindorfer and Saad (2005), one of the few formal theory-building efforts on supply chain disruption management (SCDM), cited in almost all articles written in the field since its publication in 2005. This conceptual framework identifies three main concurrent tasks: specifying sources of risk and vulnerabilities, assessment and mitigation, which, together, are known as SAM (Kleindorfer & Saad, 2005). But this framework

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Please cite this article as: Revilla, E., & Sáenz, M.J., Supply chain disruption management: Global convergence vs national specificity, Journal of Business Research (2013), http://dx.doi.org/10.1016/j.jbusres.2013.05.021

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has not considered the importance of deployment in a continuous loop in order to learn from previous experiences and be even more prepared for the inevitable next crisis (Jüttner and Maklan, 2011; Zsidisin et al., 2005). With an aim to integrate these two relevant contributions in the literature, we reframe the SAM framework developed by Kleindorfer and Saad (2005) and propose a comprehensive SCDM framework as the basis for our theoretical foundations. As supply chains expand overseas (e.g.: supply chain tiers and sub-tiers located in different continents being threatened by different local events combined with global effects), there is a growing need for multi-country and cross-cultural research (Bhattacharyya, Datta, & Offodile, 2010; Naor, Linderman, & Schroeder, 2010). Understanding cultural differences becomes increasingly important since it can provide both the theoretical underpinnings and the practical implications for risk perception and subsequent ways of handling supply chain disruptions. Scholars have debated the effect of national culture on management practices (Naor et al., 2010; Rungtusanatham, Forza, Koka, Salvador, & Nie, 2005). In the management literature, the convergence hypothesis implies the institutionalization of supply chain behavior over regions when there is a risk of disruption. This means that supply chain disruption management involves a universal set of management practices and principles that goes beyond cultural boundaries. Conversely, the divergent or national specificity hypothesis argues that national culture produces values and that these value systems inhibit the cross-cultural transferability and applicability of disruption-management concepts and practices. Thus, even if supply chains face similar disruptions and adopt similar models, deeprooted cultural forces could still enter into play in the way the disruptions are confronted. Consequently, any organizational practice is adapted to the social context, and different practices are found across nations. Coinciding with this surge of interest in supply chain disruptions, there has been a substantial increase in studies applying different research methodologies in the past few years in order to understand the phenomenon and its managerial implications. The majority of the existing publications based their efforts on empirical qualitative studies (e.g., Blackhurst, Craighead, Elkins, & Handfield, 2005; Craighead et al., 2007; Johnson, 2001; Juttner, Peck, & Christopher, 2003; Khan, Christopher, & Burnes, 2008; Norrman & Jansson, 2004; Oke and Gopalakrishan, 2009; Smeltzer and Siferd, 1998; Svensson, 2000; Zsidisin, Ellram, Carter, & Cavinato, 2004; Zsidisin, Panelli, & Upton, 2000). Some other studies take advantage of available secondary data bases and archival data to analyze the impact of disruptions (Altay & Ramirez, 2010; Bhattacharyya et al., 2010; Hendricks & Singhal, 2003, 2005). In the extant literature, there is also research regarding modeling or simulation in this field (e.g., Cachon & Lariviere, 2005; Sodhi, 2005; Tomlin, 2006; Wang & Webster, 2007; Wilson, 2007). However, there is a deficit of academic work in terms of recent survey-based research (for exceptions, see Thun & Hoenig, 2011; Wagner & Bode, 2006), which would allow us to empirically and statistically validate theories in the field. To address these research gaps, this study aims to investigate the universality of the management applicability in the supply chain disruption context. In doing so, we propose a inclusive SCDM framework that let empirically analyze the convergence vs. national specificity debate (Child & Kieser, 1979; Shenkar & Ronen, 1987) on two complementary levels based on a sample of 1403 firms. We compare both the level of importance of the constructs of our SCDM framework and the patterns of relationship between the constructs across eight country clusters, using the Global Leadership and Organizational Behavior Effectiveness (GLOBE) framework. By doing this, we intend to contribute to both the cross-cultural literature and the supply chain management literature. First, although SCDM has gained attention in academia in the past, its management is still in an embryonic phase (World Trade Magazine, 2010). Executives are not adequately prepared to manage supply chain risks.

Through the reframing of the SAM framework (Kleindorfer & Saad, 2005), we provide conceptual clarity for the universality of supply chain disruption practices. Second, we observe that the nature of previous SCDM work is mainly normative, anecdotal, or case study-based (Wagner & Bode, 2006) as opposed to theory-testing. While insightful, the conclusions drawn from these past studies are not as strong as we would expect. Consequently, there is a need for theory-driven empirical research (Oke & Gopalakrishan, 2009; Thun & Hoenig, 2011). In this respect, this paper simultaneously compares and contrasts SCDM practices across countries. Third, despite the growing body of cross-cultural studies, the debate regarding convergence vs. national specificity management practices has not subsided (Rungtusanatham et al., 2005). This debate has in fact become even more important in this era of globalization in which organizations are increasingly expanding across international boundaries (Naor et al., 2010). We base our analysis on a country cluster comparison using the GLOBE study, which provides the most complete data gathered over the last decade on national culture (House, Hanges, Javidan, Dorfman, & Gupta, 2004).

2. Toward a supply chain disruption management framework The existing literature on supply chain risks shows that there are two fairly distinct categories of risks affecting supply chain design and management (Kleindorfer & Saad, 2005; Norrman & Jansson, 2004; Oke & Gopalakrishan, 2009): risks arising from the problems of coordinating supply and demand and risks arising from disruptions to normal activities. This paper focuses on the second category of risks —supply chain disruptions. Supply chain disruptions are defined in the literature as unplanned and unanticipated events that disrupt the normal flow of goods and materials within a supply chain (Craighead et al., 2007; Hendricks & Singhal, 2003; Kleindorfer & Saad, 2005; Svensson, 2000). As a consequence, these disruptions expose firms within the supply chain to operational and financial risks (Stauffer, 2003). A great deal of literature has contributed to the current understanding of how to manage supply chain disruptions. Initially, studies analyzed risk sources and mitigation strategies together, suggesting that sources must be identified or understood first in order to propose adequate mitigation strategies (Oke & Gopalakrishan, 2009). Later on, research proposed integration of the sequencing of complementary tasks in order to efficiently manage a supply chain disruption (Oke & Gopalakrishan, 2009; Rao & Goldsby, 2009). Taking their fundamentals from the theory and practice of industrial risk management (Haimes, 1998), Kleindorfer and Saad (2005) proposed the so-called SAM framework in which three main tasks have to be sequentially completed as the foundation of supply chain disruption management. The three tasks are: specifying risk sources and vulnerabilities, assessment, and mitigation. Although the authors propose a sequence of the three above-mentioned tasks, they stated in their conceptual framework that it reflects the effective integration of the joint activities of assessment and mitigation (Kleindorfer & Saad, 2005, p. 54). Accordingly, we propose the integration of the assessment and mitigation tasks into one joint action, which we call disruption management. Regardless of how extensively it is mentioned in the literature, this framework does not integrate how to close the loop that would (1) enable supply chain partners involved in disruption-management activities to continuously learn from past experiences, and (2) provide feedback on how to handle the sources and impose complementary corrective actions. Previous literature (Jüttner and Maklan, 2011; Zsidisin et al., 2005) has already pointed out that when supply chain disruptions occur, it is important that the firm learns from the experience. Thus, we propose an additional stage for the disruption management that we call “learning feedback”. Next, we justify the elements and their interrelations, which comprise our proposed conceptual framework for SCDM depicted in Fig. 1.

Please cite this article as: Revilla, E., & Sáenz, M.J., Supply chain disruption management: Global convergence vs national specificity, Journal of Business Research (2013), http://dx.doi.org/10.1016/j.jbusres.2013.05.021

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Risk sources: - Market -Supply chain discontinuity -Natural hazards -Socio economic

3. Convergence hypothesis and national specificity arguments

Supply Chain Disruptions

Disruption management: - Internal-operational - Inter-organizational

3

Learning feedback

Fig. 1. Conceptual framework for supply chain disruption management (SCDM).

Following the logic of the figure, SCDM should start considering the different risk sources. While some previous studies investigated the sources, classifying them based on their role in the supply chain as inbound and outbound risk sources (Svensson, 2000, 2002), others proposed a broader perspective for their categorization based on the nature of such a threat, like major natural and man-made disasters, supply risks, forecast, intellectual property, inventory, or capacity (Chopra & Sodhi, 2004). Special attention was made only on risk sources related to supply (Ellis, Shockley, & Henry, 2011; Zsidisin & Wagner, 2010). Recently, Rao and Goldsby (2009) argued that the sources of supply chain risks are derived from industrial (or the market), operational and environmental risks. All of this previous work has led to a common understanding emphasizing the following risk sources as the most relevant ones: the market, supply chain discontinuity, natural hazards, and the socio economic context. According to Rao and Goldsby (2009), market sources include those that may not affect all the sectors of the economy as a whole but rather specific industry or market segments, including price and sale collapse due to competition (Rao & Goldsby, 2009). The risk source known as supply chain discontinuity refers to a delivery or transportation failure on the part of a main supplier (Kleindorfer & Saad, 2005; Rao & Goldsby, 2009). External sources of supply chain disruptions are natural hazards such as hurricanes, floods, or earthquakes (Altay & Ramirez, 2010; Chopra & Sodhi, 2004; Kleindorfer & Saad, 2005; Rao & Goldsby, 2009; Sheffi & Rice, 2005). Socio economic risk sources are those that affect the overall business context across industries—for instance, political and/or economic instability (Ellis et al., 2011; Kleindorfer & Saad, 2005; Rao & Goldsby, 2009; Sheffi & Rice, 2005). Considering, from the SAM conceptual framework (Kleindorfer & Saad, 2005), the nature of the risk assessment and mitigation activities that should be jointly activated for establishing the degree of implementation of management practices, we propose their integration, which we label as disruption-management element in Fig. 1. Traditional organizations have centered their supply chain management efforts in internal practices that have currently been complemented with broader inter-organizational approaches (Lee, 2004). Therefore, two complementary disruption-management practices can be recognized: one emerges from internal actions and operations within companies like business continuity planning or formal security or emergency strategies (Zsidisin et al., 2005), and the other involves actions that should be specifically undertaken along with supply chain partners in order to diminish the effects of supply chain disruptions (Blackhurst et al., 2005; Craighead et al., 2007; Sheffi & Rice, 2005). The final step of our framework entails getting the company and its supply chain partners to identify important lessons learned from how effective the disruption-management practices were (Zsidisin et al., 2005). Jüttner and Maklan (2011) argue that supply chain disruption management is aimed twofold, one at reducing the risk effect and the other at increasing the knowledge about how the company dealt with past disruptions. This post-incident analysis helps to identify relevant lessons learned and to gather feedback from the different implied stakeholders. This is what we term as learning feedback. In summary, all of these interrelated activities form an SCDM framework that integrates how supply chain disruptions are a consequence of the different risk sources, the effects of applying disruption-management practices as well as learning-feedback practices. Consequently, an ongoing SCDM process takes place.

Globalization has extended the geographical reach of supply chains and increased the interdependence among countries, as reflected in the increased intensity of three types of cross-border flows: goods and services, capital, and knowhow (Bhattacharyya et al., 2010; Govindarajan & Gupta, 2001). In this international setting, issues such as organizational members' values and beliefs, and hence national culture can play a significant role (Flynn & Saladin, 2006; Leung, Bhagat, Buchan, Erez, & Gibson, 2005). As a result, it is important to take into account the national culture when organizations deal with supply chain disruption management since it aids in predicting how organizational members will deal with coordination problems with other supply chain partners (Dowty & Wallace, 2010). Culture is the homogeneity of characteristics that separates one human group from another and provides a society's characteristic profile with respect to a set of shared understandings and behavioral patterns (Bhawuk, 2001; Hofstede, 1991a, 1991b; Triandis, 1987). Accordingly, the organizational and managerial phenomena are impacted by how different cultures across different nations perceive the decision-making processes (e.g., Chui, Lloyd, & Kwok, 2002; Gibson, 1999; House, Javidan, Hanges, & Dorfman, 2002; Naor et al., 2010). Over the last years, some scholars have been arguing that the main dimensions of national culture along with countries can be hierarchically ordered (Hofstede, 1980; House et al., 2004; McSweeney, 2002; Schwartz, 1994; Smith, 2006). The GLOBE study provides the most updated and complete data gathered on national culture in 61 different countries (House et al., 2004; Naor et al., 2010). It defines nine different cultural dimensions: uncertainty avoidance, future orientation, power distance, institutional collectivism, human orientation, performance orientation, in-group collectivism, gender egalitarianism and assertiveness (Gupta & Dorfman, 2002; House et al., 2002; Naor et al., 2010). The GLOBE study based its results on empirical validations obtained from the most updated and recent data gathered on national culture. This brought about a very insightful structure for application to further cross-national empirical research classifying ten country clusters based on empirical similarities of their organizational cultures: Eastern Europe, Latin America, Latin Europe, Confucian Asia, Nordic Europe, Anglo, Sub-Sahara Africa, Southern Asia, Germanic Europe, and the Middle East. Scholars have debated the effect of national culture on management practices and examined whether organizational representatives make decisions on the bases of pre-established universal rules or procedures or, on the contrary, they adopt a cultural bias when interacting with another organization (Naor et al., 2010; Rungtusanatham et al., 2005). For example, under universalistic approaches general principles should be strictly enforced without being compromised by cultural bias and, therefore, outweigh particular consideration. However the opposite is true in specific cultures. In that regard, and particularly within the supply chain management literature, a relevant and pertinent discussion should be featured on the dichotomy of whether (1) general theories or so-called etic theories (Harris, 2001; Triandis et al., 1993), which everyone can translate and adopt to their specific conditions, should be developed, or (2) an attempt should be made to develop local, culture-specific theories known as emic theories that are contextual and suitable for a specific situation (Harris, 2001; Triandis et al., 1993). This dichotomy is discussed below. 3.1. The convergent hypothesis argument The original convergence theory (Kerr, Dunlop, Harbison & Myers, 1960) started when the rest of the world seems to have applied western-style practices to finance, organization, production, and marketing (Witt & Redding, 2009). This theory states that as nations become more industrialized they become more structurally alike (Weed, 1979, p. 72). Consequently, as nations develop, organizations embrace work-related behavior common and adopt universal practices about work (Ralston, Holt, Terpstra, & Kai-cheng, 1997). The underlying idea

Please cite this article as: Revilla, E., & Sáenz, M.J., Supply chain disruption management: Global convergence vs national specificity, Journal of Business Research (2013), http://dx.doi.org/10.1016/j.jbusres.2013.05.021

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is that individuals are malleable entities and their behavior is independent of national culture (Adler & Jelinek, 1986). As a result, individuals' behavior is not constrained by national culture (Dastmalchian, Lee, & Ng, 2000). In this respect, the convergence argument specifies convergence across nations and among individuals across nations (Child & Kieser, 1979; Naor et al., 2010; Rungtusanatham et al., 2005). Universal, culture-free business practices would diffuse widely, and inefficiencies and complexities associated with divergent beliefs and practices would disappear (Leung et al., 2005). This would mean less bonding to values, beliefs, and dogmas (Webber, 1969, p. 78) “… placing demands on individuals to alter modes of thinking and behaving…”. This would foster the development of similar human nature and social behavior across nations. Applied to the context of global supply chain disruptions, the convergent hypothesis states that if cultures of the different nations are indeed converging, supply chain managerial practices would become increasingly similar (Griffith & Myers, 2005). Under this hypothesis, the decision makers from different nations within the same supply chain would aggregate the same understanding of the same sources of disruptions and would engage in similar behavior regarding the decisions made in order to impose corrective actions—which would imply similar logic and managerial practices in the decision-making process. Accordingly, when organizations face with decisions related to supply chain disruption and recovery, the decision making process strictly bases on official standards with little consideration to national culture. Sharing best practices in terms of SCDM globally puts objectivity and impersonality as core value of the organizational and ensures that organizations will not make discretionary decisions based on tradition or national cultures. 3.2. The national specificity argument The national specificity argument has its fundamentals in the culture-specific argument posed by Child and Kieser (1979). Contrary to the argument above, it posits that national culture, not industrialized practice, drives individual's personal beliefs, values, and attitudes and that, even if the country becomes developed, the value systems in the organization remain largely unchanged (Ralston et al., 1997, p. 183). Accordingly, managerial practices and assumptions about the nature of people and their behavior may be influenced by national culture, making the potential convergence among cross-country organizations difficult. In a similar vein, Hofstede (1980, 1983) argues that management theory and practice are culturally bound. Culture is a pervasive phenomenon that permeates organizations (Lammers & Hickson, 1979). This argument is often related to specific business practices in many parts of the world, such as guanxi in China (Cai, Jun, & Yang, 2010; Luo, 2000; Park & Luo, 2001), keiretsu in Japan (Dyer & Nobeoka, 2003; Gerlach, 1987) or jeitinho in Brazil (Amado & Brasil, 1991, Duarte, 2006). A study by Ryu, Han, and Frank (2006) on a culture of collectivism in supply chain management in Korea, gives support to this argument. They state that organizations operating under a collectivist culture (such as in Korea) have a better chance of having a long working history with supply chain partners than those that emphasize individualistic attitudes (such as those in western countries). Their findings suggest that managerial practices and their implementation are embedded in the social context of the nation. From the national specificity argument, even when global supply chains are affected by similar disruptions, the perception of the implications of the disruptions and the subsequent disruption-management concepts and practices could differ and be adapted to the specific cultural context and its assumptions (Bhattacharyya et al., 2010; Ellis et al., 2011). Accordingly, national culture shapes the way an organization coordinates decisions related to supply chain disruption management with other supply chain partners. As such, any disruption management practice is not universal but evolves from national culture. It must be

adapted to the social context to maximize its effectiveness (Dabhilkar, Bengtsson, & Bessant, 2007). Therefore, the national specificity argument explains the observed divergence of practices across nations. 3.3. Proposition development These conflicting perspectives (convergence vs. national specificity) represent two well-established intellectual tendencies in the international literature. Although empirical studies on convergence hypothesis arguments have been relatively spare, evidence on the issue seems to conclude that the convergence of various managerial practices is overly optimistic. Kerr (1983) revised the convergence idea and concluded that it seems to have taken place to a degree, but had then the author stopped identifying a general pattern of hybridization, in which managerial practices transferred from one place to another are adapted and reconfigured in the process of fitting them into the new environment. When this debate is applied to the global context of supply chain management, it provides us with the basis for evaluating the cross-national applicability of SCDM. Although the intuition makes us think that the origin of the risk sources are more likely attached to the geography or economic development of the region, it is not clear whether the perception and awareness of the risk can be affected by national culture. Therefore, without being biased toward one of these two opposite perspectives (Naor et al., 2010; Nelson & Gopalan, 2003; Rungtusanatham et al., 2005), we present two alternative sets of propositions to evaluate whether or not our SCDM is universal in its applicability. First, we study how universal the constructs of our SCDM framework are; and second, we study the universality of the pattern of relationships between those constructs. In line with Rungtusanatham et al. (2005), we suggest the following sets of alternative hypotheses regarding the applicability of the four main constructs (and their correspondent sub-constructs) forming the SCDM framework. Accordingly, Propositions 1a and 1b refer to the level of importance of the four constructs of the SCDM framework presented in Fig. 1, and we pose proposition set “a” consistent with the convergence perspective and proposition set “b” consistent with the national specificity perspective: Proposition 1a. The degree of applicability of the constructs of the SCDM framework will be similar across organizations in different countries. Proposition 1b. The degree of applicability of the constructs of the SCDM framework will be different across organizations in different countries. Propositions 2a and 2b contrast the general universality of the proposed SCDM framework under the convergence vs. the national specificity arguments. Consequently, we suggest the following propositions, which refer to the pattern of relationships among these four constructs: Proposition 2a. The pattern of relationships among the constructs of the SCDM framework will be similar across organizations in different countries. Proposition 2b. The pattern of relationships among the constructs of the SCDM framework will be different across organizations in different countries. 4. Methodology 4.1. Data collection This empirical research is based on the application of the survey method (Hair, Black, Babin, Anderson, & Tatham, 2006; Saris & Gallhofer, 2007). A group of academics and researchers led by the Center for Transportation and Logistics (CTL) at MIT, under the MIT Global SCALE Risk Initiative, designed and developed the questionnaire tool based on a thorough literature review. The questionnaire was then validated through a pre-test that allowed us to purify our survey items and rectify any potential deficiencies. Minor adjustments were made on the basis of specific suggestions.

Please cite this article as: Revilla, E., & Sáenz, M.J., Supply chain disruption management: Global convergence vs national specificity, Journal of Business Research (2013), http://dx.doi.org/10.1016/j.jbusres.2013.05.021

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A large-scale, worldwide online survey was used as the base. In each of the different regions, a supply chain management professional association was key in reaching a larger number of supply chain professionals. For example, the Association for Operations Management — American Production and Inventory Control Society (APICS) and the Council of Supply Chain Management Professionals (CSCMP) collaborated in the United States, as did the Spanish logistics association Centro Español de Logistica (CEL) in Spain, among others. They sent out emails to their members asking them to participate in the survey. The data-gathering process took two months (December 2009 through January 2010) with information collected simultaneously in all of the countries. The target respondents of our survey consisted of supply chain managers at decision-making levels and in strategically oriented positions from different cultures, countries, and industries. We measured respondents' backgrounds: age (63.2% were older than 40), gender (82.2% males and 14.4% females), and education (62.1% held a university or master's degree). Respondents averaged 12.9 years of experience in their industry (median = 13 years) and 32.6% of them were senior managers whereas 32% held the position of Vice-President. After screening out spurious and incomplete responses (less than half of the questions answered on the survey) and conducting a missing value analysis (with a result of 1.5% overall), there were 1403 valid complete survey responses for the study, representing 69 countries. Response rate varied among the surveyed countries, but the average response rate was 22%. Given the broad sample for our empirical study, we grouped the different nations according to the country clusters provided by the GLOBE study (House et al., 2004), which has been shown to be the most suitable and recent empirical study for grouping societies based on their organizational culture. Table 1 provides the details related to the different subsamples for each of the country clusters. 4.2. Measures The measurement for the study is based on the multiple-items method, which enhances confidence in the accuracy and consistency of the assessment (Saris & Gallhofer, 2007). We classify risk sources as market, supply chain discontinuity, natural hazards, and socio economic, as explained in the theoretical section of this paper. According to Rao and Goldsby (2009), risk sources derived from the market are due to a price collapse or sales collapse when faced with new competition. Risk sources derived from discontinuity in the supply chain are due to supplier, manufacturing, transportation, or product quality failures (Kleindorfer & Saad, 2005). Risk sources derived from natural hazards are very well identified in the extant literature and are due to hurricanes, tornados, typhoons, earthquakes, tsunamis, floods, or mudslides, etc. (Chopra & Sodhi, 2004; Rao & Goldsby, 2009; Sheffi & Rice, 2005). Risk sources derived from the socio economic are those that affect the overall business context across industries: economic recession, market collapse, protracted labor disputes or sudden currency devaluation (Ellis et al., 2011; Rao & Goldsby, 2009; Sheffi & Rice, 2005).

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Pursuant with the extant literature on disruptions, we assess disruption management through two different managerial dimensions: internal–operational and inter-organizational. Internal–operational practices refer to the management strategies applied within the focal company and include the role of the risk manager, implementing a business continuity plan, following a formal security strategy, or managing emergency operations (Kleindorfer & Saad, 2005; Zsidisin et al., 2005). Interorganizational practices refer to the activities carried out jointly with suppliers or customers in order to reduce the final effect of such a risk (Sheffi & Rice, 2005; Thun & Hoenig, 2011; Zsidisin et al., 2005). According to Zsidisin et al. (2005), we consider two learning feedback practices: simulations of various supply chain risks and disruptions, and analyses of past incidents to identify how processes can be improved. Finally, Supply chain disruptions are identified as events that disrupt the normal flow of goods within a supply chain (Craighead et al., 2007; Hendricks & Singhal, 2003; Kleindorfer & Saad, 2005; Svensson, 2000). According to this literature, supply chain disruptions occur when internal operations are interrupted, the company cannot communicate with supply chain players, the goods supply is lost, or products cannot be delivered or shipped. Details on the measurement items for the eight constructs of the SCDM framework can be found in Appendix A. Since we collected the information on the variables of interest from a single respondent within a single firm as a strategic partner of the same focal buyer, common method bias could present a problem. The potential for common method bias was assessed based on Harman's test as described in Podsakoff, MacKenzie, Lee, and Podsakoff (2003). It consists of loading all of the variables into an exploratory factor analysis and examining the unrotated factor solution. Results revealed seven distinct factors with eigenvalues above 1.0, which together explain more than 63.7% of the variance. The first factor accounted for only 20.9% of the variance. Since a single factor did not emerge and the first factor did not account for most of the variance, common method bias should not be an issue in the data. 4.3. Empirical analysis We followed the Rungtusanatham et al. (2005) approach to examine our propositions at the empirical level. Before testing the empirical propositions, we had to analyze the measurement quality and equivalence. This empirical assessment determines whether or not different groups, when administrated by the same measurement instrument, can yield accurate perceptual responses about some situational issues of interest and whether these transparent differences appear to bias key informant responses about the situational issue (Rungtusanatham, Zhao, & Lee, 2008). 4.3.1. Measurement quality Measurement quality examines construct validity and reliability. We examined construct validity by analyzing the dimensionality of each measurement scale (Hair et al., 2006). In demonstrating that a multiple-item measurement scale is one-dimensional, we use exploratory factor

Table 1 Distribution of the responding firms, depending on the role of the supply chain and size. Cluster

N

Manufacturer

Retailer

Wholesaler

3PL

Other

1–100

101–1000

1001–over 2000

1 Eastern Europe 2 Latin America 3 Latin Europe 4 Confucian Asia 5 Nordic Europe 6 Anglo 7 Sub-Sahara Africa 8 Southern Asia 9 Germanic Europe 10 Middle East Total

26 121 210 67 17 535 171 71 167 18 1403

19 63 127 43 10 380 97 49 124 8 920

2 4 12 8 0 17 11 4 8 2 68

2 9 19 3 3 25 7 0 12 2 82

0 29 29 4 0 28 24 1 4 1 120

3 16 23 9 4 85 32 17 19 5 213

4 34 60 15 3 134 32 19 35 5 341a

13 45 105 27 10 282 92 28 85 3 690a

9 38 35 23 3 104 38 22 42 10 324a

a

The remaining answers didn't provide the data.

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6

E. Revilla, M.J. Sáenz / Journal of Business Research xxx (2013) xxx–xxx

Table 2 Measurement quality results. Measurement scale for

n Cronbach's alpha Cluster 2 Latin America

Cluster 3 Latin Europe

Cluster 4 Confucian Asia

Cluster 5 Nordic Europe

Cluster 6 Anglo

Cluster 7 Sub-Sahara Africa

Cluster 8 Southern Asia

Cluster 9 Germanic Europe

Cluster 10 Middle East

26 2 0.378 4 0.27

121 0.86 0.80

210 0.78 0.78

67 0.83 0.78

17 0.92 0.89

535 0.77 0.75

171 0.74 0.80

71 0.78 0.79

167 0.82 0.70

18 0.95 0.86

3 3 4 3 2 4

0.68 0.74 0.78 0.81 0.50 0.78

0.69 0.66 0.80 0.74 0.56 0.61

0.82 0.64 0.71 0.80 0.69 0.70

0.79 0.58 0.76 0.70 0.63 0.57

0.73 0.63 0.76 0.80 0.61 0.65

0.60 0.65 0.80 0.75 0.63 0.73

0.84 0.72 0.84 0.89 0.74 0.82

0.65 0.64 0.78 0.70 0.51 0.59

0.91 0.70 0.86 0.74 0.58 0.86

1642 (82.09) 2430 (60.74)

1704 (85.21) 1848 (92.42) 1631 (81.53) 1589 (79.45) 2434 (60.84) 3005 (75.13) 2314 (57.85) 2519 (62.99)

1643 (82.15) 1688 (84.38) 1902 (95.10) 2478 (61.95) 2128 (53.19) 2904 (72.60)

1988 (66.27) 1808 (60.25) 2512 (62.79) 1978 (65.94) 1395 (69.75) 1846 (46.15)

2200 (73.33) 1766 (58.88) 2134 (53.35) 2156 (71.86) 1530 (76.52) 2135 (53.38)

2272 (75.74) 1923 (64.09) 2715 (67.86) 2473 (82.44) 1588 (79.41) 2607 (65.18)

Cluster 1 Eastern Europe Num. companies Source-market Source-supply chain discontinuity Source-natural hazards Source-socio economic DM-internal operational DM-interorganizational Learning feedback Supply chain disruption

0.864 0.20 0.57 0.92 0.60 0.77

Factor analytical results: first eigenvalue (% variance) Source-market 2 1.236 (67.78) 1758 (87.91) Source-supply chain 4 a 2504 (62.59) discontinuity Source-natural hazards 3 2429 (80.96) 1939 (64.63) Source-socio economic 3 a 1970 (65.66) DM-internal operational 4 1898 (47.45) 2432 (60.79) DM-inter organizational 3 2594 (86.48) 2165 (72.16) Learning feedback 2 1432 (71.58) 1331 (66.53) Supply chain disruption 4 2403 (60.08) 2413 (60.33) a

2368 (78.93) 1672 (55.73) 2390 (59.76) 1894 (63.14) 1461 (73.03) a

1988 (66.28) 1743 (58.11) 2349 (58.72) 2169 (72.30) 1437 (71.83) 1964 (48.84)

1958 (65.27) 1771 (59.02) 2529 (63.22) 2018 (67.27) 1455 (72.74) 2222 (55.56)

1815 (60.52) 1775 (59.17) 2406 (60.15) 1887 (62.89) 1348 (67.38) 1825 (45.62)

2576 (85.85) 1940 (64.66) 2824 (70.59) 1974 (65.79) 1412 (70.58) 2853 (71.33)

Second eigenvalue > 1.

analysis (Cronbach & Meehl, 1955). The internal consistency reliability was assessed by calculating Cronbach (1951) for each measurement scale by cluster country. Table 2 shows the internal consistency reliability and dimensionality coefficients for each measurement scale by cluster country. Additionally, Appendix A provides the range of factor loadings for each measurement scale by cluster country. For most of the country clusters, the eight constructs appear to show acceptable levels of reliability (Cronbach's alpha > 0.60) and validity (second eigenvalue b 1.00; factor loading > 0.5). The exception was found in Eastern Europe and Nordic Europe. In the first case, four of the nine measurement scales report Cronbach's alpha values below 0.60. Likewise, the second eigenvalue for source–supply chain discontinuity and source-socio economic were greater than 1. For Nordic Europe, the Supply chain disruption and source-socio economic measurement scales show Cronbach's alpha values of 0.57 and 0.58, respectively. Likewise, disruption risk was not found to be onedimensional. One possible explanation for these results may be the relatively small sample size of these two clusters (see Table 2). Therefore, given the reliability and validity construct problems, we decided not to keep working with the supply chains from Eastern and Nordic Europe. Table 2 also shows reliability problems for learning feedback measurement scales in four clusters: Latin America, Latin Europe, Germanic Europe, and the Middle East. A possible explanation of this finding is the small number of items that make up this construct. Although we were aware of this problem, we decided not to omit the clusters for which Cronbach's alpha showed values lower than 0.60 from the analysis, but only worked with this construct for those clusters where it appeared to be reliable. 4.3.2. Measurement equivalence In evaluating measurement equivalence, we use two different instruments: translation equivalence and metric equivalence (Douglas & Craig, 1983). The first one aims to ensure that the same latent construct can be measured by the same set of items in different native languages (Mullen, 1995). Careful and rigorous translating was employed to ensure that the survey content was consistent across the different languages. To maximize translation equivalence (Bensaou, Coyne, & Venkatraman, 1999), we translated the English source questionnaire into the different languages (in our case Portuguese, Brazilian Portuguese, Mexican Spanish,

Spain Spanish, German, Greek, Italian, and Chinese-Mandarin) and followed the Translation, Review, Adjudication, Pre-testing, and Documentation (TRAPD) procedure. This is a team approach to survey translation, which is qualitatively better than the often-used translation–back translation approach (Harkness, van de Vijver, & Mohler, 2007). Metric equivalence is focused on the equivalence in the scoring process—i.e., the way respondents from different countries answer the same question (Mullen, 1995). In order to check this, we compared the magnitudes of the internal consistency reliability for the measurement scales between two country clusters (Rungtusanatham et al., 2005). For most of the measurement scales, the difference in Cronbach's alpha was below 0.2. This difference, which was only higher in five situations, presented the maximum value between Sub-Sahara Africa and the Middle East for source-natural hazards (Cronbach's alpha is 0.31). A minimum difference of 0.01 or 0.02 was found for 27 of these comparisons. Thus, we can conclude that the eight constructs are metric and translation equivalent across the remaining eight country clusters. 4.3.3. Empirical examination of propositions Next, we subject Proposition 1a (and its converse 1b) to empirical evaluation. We use MANOVA analysis to compare the average of the constructs of our framework across multiple country clusters. MANOVA is a generalized form of univariate analysis of variance (ANOVA) which is used in cases where there are two or more dependent variables. Besides identifying whether changes in the independent variable have significant effects on the dependent variables, MANOVA is also used to identify interactions among the dependent variables and among the independent variables (Stevens, 2002). Accepting the convergence hypothesis implies that the average of the construct is equal across clusters, (Proposition 1a). Rejecting the convergence hypothesis means that the average of the construct is not equal across clusters, suggesting that the national specificity argument (Proposition 1b) has merits. For Proposition 2a (and its converse 2b), which evaluates the pattern of relationships among the eight constructs of the SCDM framework, we applied regression analysis by country cluster. In doing so, we examine the bivariate relationships between specific pairs of constructs that are related within the SCDM framework (See Fig. 1). In order to demonstrate support for Proposition 2a for the convergence argument, the statistical significance of the partial regression coefficient for specific pairs of

Please cite this article as: Revilla, E., & Sáenz, M.J., Supply chain disruption management: Global convergence vs national specificity, Journal of Business Research (2013), http://dx.doi.org/10.1016/j.jbusres.2013.05.021

7

relationships across multiple country clusters should lead to the same conclusion. Otherwise, Proposition 2b – related to the national specificity argument – would be favored (Rungtusanatham et al., 2005).

Convergence Convergence Nat. specificity

5. Results

Overall test Wilks' λ = 0.511 (F value = 6092 p b 0.0001). a Learning feedback not reliable in these clusters.

1.63 (0.98) 1.49 (0.97) 1.40 (0.75) a 1.53 (1.00) a 2.47 (1.24) 1.72 (0.92) 2.63 (1.04) Latin Am. and Sub-sahara Africa/Latin Europe and sub-sahara Africa/Anglo and Sub-sahara Africa/Germanic Europe and Sub-Sahara Africa/Southern Asia and Germanic Europe/ (0.98) 1.35 (1.04) (1.06) 1.70 (1.03) (0.93) 2.63 (1.19)

DM-internal– 1.67 (1.00) 1.80 (1.07) 1.34 (0.87) 1.46 operational DM-interorganizational 1.53 (1.10) 1.33 (0.99) 1.45 (0.89) 1.22 a a Learning feedback 1.74 (1.01) 1.61 SC DISRUPTION 2.04 (1.11) 2.00 (0.88) 2.09 (1.08) 2.07

(0.97) 1.41 (1.02)

1.42 (1.05) 1.02 (0.83) 1.53 (0.91) 1.30 (0.92) 1.99 (1.04) Source-socio economic

Nat. specificity Convergence

1.23(1.09) 0.24 (0.50) 0.81 (0.86) 0.45 (0.74) 1.51 (1.32)

Nat. specificity

0.99 (1.06) 0.26 (0.52) 1.14 (1.45) Latin Am. and Confucian Asia/Latin Am. and Subsahara Africa/Latin Am. and Germanic Europe/Latin Europe and Confucian Asia/Latin Europe and Anglo/Confucian Asia and Sub-sahara Africa/Confucian Asia and Germanic Europe/Anglo and Sub-sahara Africa/ Anglo and Germanic Europe/Southern Asia and Sub-sahara Africa/Southern Asia an Germanic Europe 1.44 (1.03) 0.99 (0.90) 1.10 (1.41) Latin Am. and Sub-sahara Africa/Latin Europe and sub-sahara Africa/Anglo and Sub-sahara Africa/Germanic Europe and Sub-Sahara Africa 1.59 (0.97) 1.71 (0.96) 1.47 (1.29)

2.18 (1.10) 1.97 (1.11) 2.07 (1.04) 2.24 (0.99) 2.33 (1.03)

Source-SC discontinuity Source-natural hazards

Nat. specificity Convergence 1.84 (1.01) 1.66 (1.10) 1.26 (1.17) Confucian Asia and Anglo/Confucian Asia and Subsahara Africa/Confucian Asia and Latin Am. 2.02 (0.99) 1.87 (0.95) 2.21 (0.97) 1.39 (1.07) 1.45 (0.96) 2.05 (1.04) 1.39 (0.92) 1.42 (1.03)

Cluster 4 Confucian Asia Cluster 3 Latin Europe Cluster 2 Latin America

Source-market

Measurement scale for

Table 3 MANOVA results.

Means (standard deviation)

Cluster 6 Anglo

Cluster 8 Cluster 7 Sub-Sahara Southern Asia Africa

Cluster 9 Germanic Europe

Cluster 10 Middle East

Bonferroni test differences in mean levels at p b 0.05

Argument accepted

E. Revilla, M.J. Sáenz / Journal of Business Research xxx (2013) xxx–xxx

As stated above, MANOVA analysis was used to compare the average for the eight constructs that make up our conceptual framework across different country clusters (see results in Table 3). The MANOVA results, controlling for the effects of the company role on the supply chain (manufacturer, retailer, wholesaler, 3PL, and others) and size (measured in terms of the number of employees), indicate that differences across country cultures in term of risk sources at p b 0.05 exist. So, the national specificity argument for risk sources underlying Proposition 1b appears to be supported over convergent Proposition 1a. The only exception in terms of risk source is found in the construct regarding supply chain discontinuity. More specifically, Proposition 1a is only supported for the source–supply chain discontinuity dimension, suggesting that its level is similar across countries. Table 3 also shows that the mean adoption levels of disruption management dimensions and learning feedback, at p b 0.05, are equal across countries, suggesting a rejection of Proposition 1b in favor of the alternative Proposition 1a, for the convergent argument. These results, which continue to be valid despite the presence of size and company role effects, suggest that disruption management and learning feedback do not change despite the presence of country differences and that the national specificity argument has no intrinsic value. Finally, our findings do not support the convergent argument for supply chain disruption, suggesting that not all countries face the same level of supply chain disruptions. We then applied regression analysis by country clusters to examine the bivariate relationship between specific pairs of constructs according to our SCDM conceptual framework and as expressed by Propositions 2a and 2b. The partial regression coefficients (and their statistical significance levels) for our SCDM relationships across eight validated country clusters are reported in Table 4. Although we find a mix of results, the national specificity argument seems to prevail. Results also indicate that, for some clusters, previously hypothesized relationships do not appear to be significant. Examining Propositions 2a and 2b at p b 0.05 for the pattern of relationships among risk sources and supply chain disruptions, we found important differences. While Proposition 2a, which is behind the convergent argument, is supported for the risk sources known as supply chain discontinuity and socio economic (except for the Middle East); Proposition 2b, which is behind the national specificity argument, is supported for the risk sources known as market and natural hazards. On a detailed level, Table 4 shows that the effect of source-market on supply chain disruption was not significant in Confucian Asia and the Middle East. The importance of this effect for Germanic Europe (p b 0.1) was lower than in the other clusters. Likewise, the impact of source-natural hazards was not found to be significant in the Middle East and barely significant (p b 0.1) in Southern Asia. Important differences across country clusters were also found when we analyzed the impact of disruption management and learning feedback on supply chain disruption. Thus, when it comes to the pattern of relationships among disruption management and supply chain disruption, the national specificity argument underlying Proposition 2b appears to be supported over the convergence argument underlying Proposition 2a. When examining all of these differences, it is important to note that some of them are due to a lack of significance of the proposed relationship underlying the SCDM framework (Fig. 1). Table 4 also suggests a rejection of Proposition 2a in favor of the national specificity argument of Proposition 2b for the pattern of relationships among risk sources and disruption management. So, important differences appear when we assess the effect of the set of risk sources on disruption management. Finally, we observe that, with the exception of the Middle East, the convergence argument underlying Proposition

Please cite this article as: Revilla, E., & Sáenz, M.J., Supply chain disruption management: Global convergence vs national specificity, Journal of Business Research (2013), http://dx.doi.org/10.1016/j.jbusres.2013.05.021

8

Suggested relationships (XY)

βxy by country

Argument Accepted Cluster 10 Middle East

Cluster 2 Latin America

Cluster 3 Latin Europe

Cluster 4 Cluster 6 Anglo Cluster 7 Sub-sahara Africa Cluster 8 Cluster 9 Confucian Asia Southern Asia Germanic Europe

Source-market→SC disruption

0.28⁎⁎⁎

0.29⁎⁎⁎

0.16 nsa

0.26⁎⁎⁎

0.40⁎⁎⁎

0.48⁎⁎⁎

0.15⁎

Source-SC discontinuity→SC disruption Source-natural hazards→SC disruption

0.63⁎⁎⁎ 0.34⁎⁎⁎

0.54⁎⁎⁎ 0.22⁎⁎⁎

0.59⁎⁎⁎ 0.33⁎⁎

0.51⁎⁎⁎ 0.15⁎⁎⁎

0.52⁎⁎⁎ 0.18⁎⁎

0.64⁎⁎⁎ 0.22⁎

0.54⁎⁎⁎ 0.21⁎⁎

Source-socio economic→SC disruption Source-market→DM-internal operational

0.50⁎⁎⁎ 0.44⁎⁎⁎ (-)0.11 nsa 0.04 nsa

0.42⁎⁎⁎ 0.07 nsa

0.32⁎⁎⁎ 0.16⁎⁎⁎

0.32⁎⁎⁎ 0.16⁎

0.33⁎⁎⁎ 0.31⁎⁎

0.24⁎⁎⁎ (-) 0.11 nsa

Source-SC discontinuity→DM-internal operational Source-natural hazards→DM-internal operational Source-socio economic→DM-internal operational Source-market→DM-interorganizational

0.10 nsa

0.30⁎⁎

0.17⁎⁎⁎

0.08 nsa

0.31⁎⁎

0.20⁎

0.22⁎⁎ a

(-)0.08 ns

a

(-) 0.04 ns

(-)0.09 nsa 0.07 nsa (-) 0.24⁎⁎ a

(-) 0.07 nsa a

0.13 ns

(-)0.12⁎⁎

(-) 0.12 ns

0.30⁎⁎

(-) 0.05 nsa

(-) 0.03 nsa

0.1⁎

0.01 ns

a

0.22 ns

a

a

Source-SC discontinuity →DM-interorganizational Source-natural hazards →DM-interorganizational Source-socio economic →DM-interorganizational DM-internal operational→SC disruption

0.11 ns

DM-interorganizational→SC disruption

0.24⁎⁎

0.04 ns

0.19 ns

b

b

b

b

b

b

0.75⁎⁎⁎ 0.75⁎⁎⁎ 0.24⁎

DM-interorganizational→learning feedback DM-internal operational→learning feedback Learning feedback→SC disruption a

a

0.15 ns

(-) 0.07 ns

0.10 ns

0.07 nsa

(-) 0.03 nsa

0.30⁎⁎

0.18⁎

(-) 0.008 nsa 0.25⁎

Not significant. Learning feedback not reliable in these clusters. c Exception of Middle East ⁎ p b 0.10. ⁎⁎ p b 0.05. ⁎⁎⁎ p b 0.01. b

a

a

a

0.14⁎⁎⁎

a

a

a

(−) 0.02 nsa No; Clusters 4, 9 and 10 — different 0.81⁎⁎⁎ Yes a 0.23 ns No; Clusters 8 and 10 — different Yes Cluster 10 is different 0.15 nsa 0.36 nsa No; Clusters 2, 7, 9 and 10 — different 0.61⁎⁎ No; Clusters 2, 7 and 9 — different a (-) 0.06 ns No

0.13 ns

0.02 ns

(-) 0.1 nsa

0.35⁎⁎

(-) 0.10 nsa (-) 0.25 nsa

0.21⁎⁎

0.16 nsa

(-) 0.03 nsa 0.16 nsa

0.11 ns

a

a

0.11 ns

a

(-) 0.05 ns

Similar across clusters ? p b 0.05

a

0.17 ns

0.16⁎⁎⁎

0.13 nsa

0.44⁎⁎⁎

0.16⁎⁎⁎

0.08 ns

a

0.20 ns

0.57⁎⁎⁎ 0.58⁎⁎⁎ 0.1⁎⁎

0.55⁎⁎⁎ 0.55⁎⁎⁎ 0.05 nsa

0.68⁎⁎⁎ 0.69⁎⁎⁎ 0.33⁎⁎

(-) 0.07 nsa (-) 0.007 nsa No, only Clusters 4 and 8 — similar 0.23⁎⁎ 0.64⁎⁎ No; Clusters 2, 3, 4, and 7 — different a a 0.09 ns 0.36 ns No; Only Clusters 2 and 6 — similar b b Yes; Cluster 10 — different b b Yes; Cluster 10 — different b b No; only Clusters 6 and 8 — similar

a

0.09 ns

Nat. specificity

Nat. specificity

0.30⁎⁎

0.01 nsa

a

Nat. specificity

No

(-) 0.01 nsa

(-) 0.12 ns

a

Convergencec Nat. specificity

Nat. specificity

0.008 ns

(-) 0.05 ns

a

Convergence Nat. specificity

No; only Clusters 4 and 8 — similar No, only Clusters 2 and 7 — similar No

0.23⁎

a

Nat. specificity

Nat. specificity Nat. specificity

Nat. specificity Nat. specificity Nat. specificity Convergence Convergence Nat. specificity

E. Revilla, M.J. Sáenz / Journal of Business Research xxx (2013) xxx–xxx

Please cite this article as: Revilla, E., & Sáenz, M.J., Supply chain disruption management: Global convergence vs national specificity, Journal of Business Research (2013), http://dx.doi.org/10.1016/j.jbusres.2013.05.021

Table 4 Regression results.

E. Revilla, M.J. Sáenz / Journal of Business Research xxx (2013) xxx–xxx

2a prevails when analyzing the relationship between learning feedback and disruption management. 6. Discussion This paper investigates whether or not SCDM is universal in its applicability. The debate regarding the convergent argument vs. the national specificity argument in a supply chain disruption context has been further intensified by the greater complexity and global reach of many supply chains—the hallmarks of economic activity in the 21st century. Recognizing this imperative, risk managers from companies as well as their insurance partners are showing an active interest in a more comprehensive SCDM framework that transcends national boundaries. In this research, we built upon the SAM framework developed by Kleindorfer and Saad (2005) and complement it with an additional stage: learning feedback (Zsidisin et al., 2005). This means our data set offers an ideal setting for comparing both the level of importance of the eight constructs of our SCDM framework and the patterns of relationship between the constructs across eight country clusters (Latin America, Latin Europe, Confucian Asia, Anglo, Sub-Sahara Africa, Southern Asia, Germanic Europe and the Middles East), based on the GLOBE study (House et al., 2002, 2004). The diversity and representativeness of the different sizes, roles, and country cultures of the companies that shape a global supply chain are guaranteed with the complementary subsamples analyzed in this research. 6.1. The cross-cultural effects of each construct Analyzing the comparative results of the applicability of SCDM constructs from a strict perspective, our findings show that the implementation of disruption management at the internal and inter-organizational level as well as learning feedback practices appear to be universal. This provides evidence that national differences have a weak impact on disruption management and learning feedback and that risk managers work to implement best practices. This finding also reinforces prior research suggesting that national culture does not determine the degree to which operational practices can be applied across countries (Brannen, 1995; Naor et al., 2010). According to Birds and Stevens (2003), it is clear that one of the major effects of globalization is the creation of a new class of managers who belong to an emergent global culture. Previous literature has not always been consistent in relation to the national specificity argument vs. the convergent argument when it comes to operational practices. Some studies argue that Japanese practices such as Lean, JIT, and TQM have been successfully adopted by many other countries, which would support the convergent argument (Schroeder & Flynn, 2001). Other studies, however, show that the levels of adoption of TQM practices across multiple countries are different and conclude in favor of the national specificity argument (Rungtusanatham et al., 2005). Likewise, Sheffi and Rice (2005) provide practical examples in the context of the Nokia and Ericsson electronics supply chain of how important it is not to underestimate the contribution of culture to an organization's flexibility and resilience while managing supply chain disruptions with strategic suppliers like Philips (Sheffi, 2005).

9

On the other hand, we conclude against the convergent argument and in favor of the national specificity argument when we compare how often supply chain risks may arise from different sources. The comparative results show that the Sub-Sahara Africa cluster faces the biggest political and economic instability, which is significantly different from Latin America and the three most developed country clusters, Germanic Europe, Latin Europe, and Anglo. In terms of sources from natural hazards, important differences are shown between Germanic Europe, Latin Europe, and Sub-Sahara (which showed the lowest probability of disruption due to this cause) and the rest of the clusters. At this point, our explanation is clear. Natural hazards depend basically on the geographic location of the country, and socio economic sources present evidence of the political and social uncertainty of the country. Market is the risk source dimension that shows fewer differences between countries. It refers to unexpected changes in the demand due to changes in the competitiveness of the industry. This means that under conditions of global competitiveness, the differences associated with market risk tend to disappear. A significant difference in favor of the Anglo cluster is found when compared with Confucian Asia. One possible explanation for this result may be the relatively higher competitive uncertainty that these markets face due to the current rapid evolution of the industry in the regions from the Confucian Asia cluster. Regarding risk sources, the only exception was found in supply chain discontinuity, where the convergence argument is accepted. This exception could possibly be related to the fact that the implementation of disruption management appears to be universal. Supply chain discontinuity is the only risk source dimension that emerges from the supply chain, and consequently it shows a similar behavior across country clusters. Additionally, the comparative results for supply chain disruption in Table 3 provide support for the national specificity argument. The level of disruption experienced by supply chains is different across country clusters. Germanic Europe shows the lowest level of disruption, which is significantly different from Sub-Sahara Africa and Southern Asia. On the contrary, Sub-Sahara Africa, the country cluster that faces the highest level of supply chain disruption, shows significant differences with the three most developed country clusters—Germanic Europe, Latin Europe, and Anglo. Finally, no statistical differences appear to be detected among these three most developed country clusters. Table 5 gives the GLOBE cultural dimensions for the eight country clusters used in our study and, lets us explain our results in terms of the national culture. Cultures with a high degree of uncertainty avoidance, future orientation and performance orientation such as Confucian Asia or Anglo and Germanic Europe show low levels of supply chain disruption occurrence. This is especially relevant for the Germanic European culture, which additionally shows less vulnerability in terms of source-natural hazards and source-socio economic. 6.2. The cross-cultural effects of the patterns of relationships Next, we examine the underlying patterns of relationships in our SCDM framework. At first glance, the national specificity argument seems to dominate. The results in Table 4 indicate that the SCDM patterns

Table 5 Globe culture scales per country cluster. Culture scales

2 3 4 6 7 8 9 10

Latin America Latin Europe Confucian Asia Anglo Sub-Sahara Africa Southern Asia Germanic Europe Middle East

Uncertainty avoidance

Future orientation

Power distance

Institutional collectivism

Human orientation

Performance orientation

In-group collectivism

Gender egalitarianism

Assertiveness

3.62 4.18 4.42 4.42 4.27 4.10 5.12 3.91

3.54 3.68 4.18 4.08 3.92 3.98 4.40 3.58

5.33 5.21 5.15 4.97 5.24 5.39 4.95 5.23

3.86 4.01 4.80 4.46 4.28 4.35 4.03 4.28

4.03 3.71 3.99 4.20 4.42 4.71 3.55 4.36

3.85 3.94 4.58 4.37 4.13 4.33 4.41 3.90

5.52 4.80 5.42 4.30 5.31 5.87 4.21 5.58

3.41 3.36 3.18 3.40 3.29 3.28 3.14 2.95

3.54 3.72 4.54 3.89 3.99 4.65 3.07 3.39

Please cite this article as: Revilla, E., & Sáenz, M.J., Supply chain disruption management: Global convergence vs national specificity, Journal of Business Research (2013), http://dx.doi.org/10.1016/j.jbusres.2013.05.021

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of relationship are mostly not statistically similar. Accordingly, we could conclude that the SCDM framework does not appear to be universally applicable across countries. This finding is consistent with the results of Rungtusanatham et al. (2005), which indicate that the Deming-based TQM relationships are different across countries. Next, we discuss the different sets of relationships depicted in Table 4 presenting first those relationships that have as dependent variables supply chain disruption, then learning feedback, and finally disruption-management practices. 6.2.1. Supply chain disruption The patterns of relationship between risk sources and supply chain disruption were found to be similar at p b 0.05 (Table 4). If the Middle East cluster is not considered, only three exceptions are observed within the pattern of relationships among these two construct sets. No statistical significance was found in the hypothesized association between the market dimension of source risk and supply chain disruption for Confucian Asia. Germanic Europe and Southern Asia present differences due to a lower level of significance (p b 0.1) of the relationship, in the first case between source-market and supply chain disruption and in the second case between source-natural hazards and supply chain disruption. Looking at the Middle East, we observed that this is the cluster with the biggest differences. The only similarity found was for the relationship pattern between the supply chain discontinuity and supply chain disruption. We did not find any more statistical significance in the proposed relationship between the risk sources and supply chain disruption. This result may be associated with the relatively small sample size of this cluster. The managerial implications of this general result is that a supply chain manager not only should correctly identify the risk sources affecting the focal firm but also should be able to identify the consequences of such risk sources among the global supply chain partners in order to reduce the effect of supply chain disruption (Bhattacharyya et al., 2010). In accordance with Zsidisin et al. (2005), the first task in the SCDM process is creating awareness, which is developed when the firm recognizes that it is exposed to the risk sources and realizes the potentially serious consequences of such disruptions. Regarding national culture, the Anglo culture is the one which values the impact of sources of risks on supply chain disruption least. This culture shows the highest degree of uncertainty avoidance and institutional collectivism. Southern Asia, the culture that presents the highest level of human orientation is the one that significantly gives less importance to the impact of source-natural hazards on a potential disruption, even when its risk of natural hazard can be considered relatively high in this region of the world, as demonstrated by recent inclemency. Another interesting finding in the context of our study is that, while the proposed relationship between the internal–operational dimension of disruption management and supply chain disruption shows statistical significance – although at different levels (with the exception of Latin Europe and Sub-Sahara Africa) – the proposed association between the inter-organizational dimension of disruption management and supply chain disruption only appears to be significant for the Latin America and Anglo clusters. Similarly, the relationship between learning feedback and supply chain disruption only seems significant at a p b 0.05 level in the Southern Asia and Anglo clusters, and the magnitudes of these relationships are low. This suggests that most of the time inter-organizational disruption-management practices and learning feedback have no expected impact on supply chain disruption in all of the world's regions. These results are relevant considering the extant literature on supply chain collaboration. They are not consistent with the idea that supply chain collaboration (Christopher & Peck, 2004) and knowledge management (Chapman, Christopher, Juttner, Peck, & Wilding, 2002; Simangungsong et al., 2012; Zsidisin et al., 2005) can significantly help mitigate disruptions. This suggests that future research on supply chains should endeavor to identify differences in terms of supply chain design that influence collaboration and learning feedback. These results indicate that cultures with uncertainty avoidance and performance oriented scales are those in which the implementation of

disruption management and learning feedback practices imply a lower level of supply chain disruption. These are the cases of Anglo y Latin American culture when DM-Interorganizational is evaluated and the cases of Southern Asia and Anglo cultures when DM-internal operational and learning feedback are considered. 6.2.2. Learning feedback To complete the exploration of our SCDM framework, we examine the pattern of the relationship between disruption management and learning feedback, without forgetting the implicit limitations of the learningfeedback construct previously mentioned. We find this positive relationship to be invariant across the countries. This result is consistent with the idea that the SCDM cycle should integrate a practice by which managers could learn from past experiences and take a continuousimprovement view (Chapman et al., 2002; Zsidisin et al., 2005). This relationship pattern seems to be universal (as per Table 4 and excluding Middle East) and can be interpreted as a common understanding in the context of a global supply chain that past experiences regarding the application of disruption management practices must serve as a basis for learning. 6.2.2.1. Disruption management. Finally, we incorporate the comparison of the proposed patterns of relationship between risk sources and disruption management. The level of statistical significance found is rather low, and strictly speaking we have to accept the national specificity argument. The hypothesized relationship between the supply chain discontinuity risk source and the DM-internal operational dimension of disruption management shows the biggest statistical equivalence across countries compared with the other relationship patterns between the dimensions of these two constructs. If we compare the magnitude of the relationships between sources and disruption management, we observe that only two country clusters – Anglo and Southern Asia – show statistical significance for roughly half of the relationship sets. This finding is consistent with our previous result and confirms the tendency to utilize disruption management practices in global supply chains, independently on risk sources as specific to the context or the country. Likewise, it suggests that further empirical research needs to verify the role of the pattern of relationships among these two construct sets. Given that risk sources significantly influence supply chain disruption, we postulate that in order to be effective SCDM should be contingent upon the risk source and should vary across countries. This suggestion is aligned with the arguments posed by other authors, but it is yet to be empirically verified (Craighead et al., 2007). Overall, these results illustrate important differences in institutional collectivism as well as human oriented culture profiles concerned with encouraging and rewarding collective distribution and the well-being of others, respectively. More precisely, country clusters with higher (Confucian Asia, Anglo, Sub-Sahara Africa and Southern Asia) and lower (Latin America, Latin Europe and Germanic Europe) degrees of institutional collectivism and human orientation show that the interorganizational disruption management efforts increase when the risk level grows. Therefore, our findings seem to suggest that these two cultural dimensions make organizations collaborate with supply chain partners in order to diminish the effects of supply chain disruption. 7. Conclusions and limitations This study started by claiming that the vulnerability of a supply chain is a key issue that must be considered if it is to be success. However, this statement can be interpreted as very general and vague if we do not consider the heterogeneity of the different vulnerabilities and how they are perceived, interact and affect the overall supply chain. As verified in this paper, SCDM is a complex paradigm when applied to the real supply chain arena. The paper complements the SCDM literature with a comprehensive and complete framework. Our proposed framework presents the causal relationships in order >to diminish the effects of supply chain

Please cite this article as: Revilla, E., & Sáenz, M.J., Supply chain disruption management: Global convergence vs national specificity, Journal of Business Research (2013), http://dx.doi.org/10.1016/j.jbusres.2013.05.021

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disruptions, integrating the sources from which risks could arise (market, supply chain discontinuity, natural hazards and socio economic), disruption-management practices at the internal–operational and interorganizational levels, as well as learning from experience. Consequently, an ongoing SCDM process takes place. This framework offers enhanced guidance to help firms systematically assess the extent to which supply chain disruptions could potentially affect them and thus arm themselves with the necessary capabilities to overcome them. It also helps reveal where these capabilities should be located depending on the regions of the globe where the threats and their sources could appear. Our statistical results indicate that the level of importance of supply chain risks arising from different sources at the market, natural hazards, and socio economical dimensions are different across countries. However, the level of implementation of SCDM practices appears to be universal all over the world. These two distinctive results suggest the importance of better aligning of the perception and assessment of risk sources with the corresponding implementation of disruption-management practices, not only considering the different nature of threats but also taking into account the different regions where these risks could arise so as to diminish their effects. In doing this, the paper contributes to the literature in several complementary ways. First, it answers scholars' calls to expand cross-national research within the operations-management field to developing countries. The international business literature is dominated by a focus on developed countries in trying to identify operations management best practices. Comparing Eastern and Western developed countries is often done (Li, Mobley, & Kelly, 2011; Naor et al., 2010). However, we address this gap by broadening the debate regarding the convergent argument vs. the national specificity argument in a supply chain disruption context to additional country clusters, which group countries according to common features of organizational culture. The introduction in the study of regions such as South America, Africa, the Middle East, and parts of Asia into a unique, integrated empirical study, which contrasts the different practices observed across the world, enriches the debate found in the existing international management literature. In addition, whereas the crosscultural literature and the supply chain disruption literature tend to be descriptive and qualitative in nature, this work features an empirical, quantitative investigation of the variations across countries. We surveyed 1403 firms operating in 69 different countries, and we grouped them into 10 country clusters. From an empirical point of view, this grouping methodology has been demonstrated to be the most suitable way to group the countries according to national organizational cultures (House et al., 2002, 2004). This research also provides supply chain managers with a framework so that they may deal with supply chain disruptions in a global context, especially when the supply chain is intensive in transactions with countries with different patterns of behavior or when the supply chain is subjected to important potential or actual disruptions that require holistic understanding of the potential effects. In particular, practical management implications are provided according to the interactions among specific countries that are involved in such a global supply chain. These implications are pertinent in today's global economy when multinational corporations invest overseas but establish their supply chain design groups in local headquarters, with limited awareness of the different risk perceptions of their key and strategic supply chain partners (Metters & Verma, 2008). The study must be viewed in the light of some limitations. We did not take into account the existence of various national cultures inside each cluster since our data were adjusted to the GLOBE study. However, studies that rely on the Hofstede or GLOBE data sets normally assume a unified cluster culture (Naor et al., 2010). Furthermore, although the main focus regarding disruption-management is on two strategies – internal operational and inter-organizational – that shed light on their complementary effects on diminishing the disruption, a more detailed level of analysis of the different but associated disruption-management practices is needed.

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Finally, moving forward, two potential research opportunities are noteworthy. First, further research should be conducted in order to verify and gain a deeper understanding of the role of disruption management and learning feedback practices in the relationship between risk sources and supply chain disruptions. Further studies should be conducted in order to verify whether the direct effect of risk sources on supply chain disruptions (already verified in this study) would be moderated by the disruption-management and learning-feedback practices. Likewise, other cultural frameworks could be employed in future research (Detert, Schroeder, & Mauriel, 2000) to enhance our knowledge and understanding of the link between culture and SCDM. Appendix A. Measurement scales for SCDM framework Range of loadings for country clusters Risk sources: market How often has your supply chain been disrupted by these events? Consider only MAJOR disruptions. 1 Price collapse due to a new competitor 0.786–0.961 2 Sales collapse due to a new competing product 0.786–0.961 Risk sources: supply chain discontinuity How often has your supply chain been disrupted by these events? a Consider only MAJOR disruptions 1 Raw material supplier failure 0.627–0.922 2 Finished goods manufacturing failure 0.777–0.888 3 Transportation carrier failure 0.714–0.904 4 Product quality failure 0.667–0.924 Risk sources: natural hazards How often has your supply chain been disrupted by these events? Consider only MAJOR disruptions 1 Hurricanes, tornados, or typhoons 0.708–0.924 2 Earthquakes or tsunamis 0.816–0.966 3 Floods or mudslides 0.503–0.966 Risk sources: socio economic How often has your supply chain been disrupted by these events? a Consider only MAJOR disruptions 1 Economic recession or market collapse 0.727–0.860 2 Protracted labor disputes 0.716–0.880 3 Sudden currency devaluation 0.525–0.869 Disruption management: internal-operational Tell us about supply chain risk management at your company 1 We have a risk manager or group 2 We have a business continuity plan 3 We have a formal security strategy 4 We have an emergency operations center

0.683–0.854 0.582–0.864 0.541–0.895 0.591–0.882

Disruption management: inter-organizational Tell us about supply chain risk management at your company 1 We actively work on supply chain risk management 2 We work with customers on supply chain risk management 3 We work with suppliers on supply chain risk management

0.541–0.894 0.793–0.951 0.792–0.972

Learning feedback Tell us about supply chain risk management at your company 1 We simulate different supply chain risks and disruptions 2 We analyze incidents to identify process improvements

0.816–0.891 0.816–0.891

Supply chain disruption How frequently have you experienced the following types of supply chain disruption? Consider MAJOR disruptions only 1 Your own internal operations are interrupted (e.g. power failure, machine breakdown, fire, etc.) 2 You cannot communicate with vendors, customers or other sites (e.g. systems fail, internet down, etc.) 3 You lose supply of quality materials (e.g. supplier fails or cannot deliver, bad product quality, etc.) 4 You cannot ship or deliver your products (e.g. no transportation, ports closed, roads blocked, etc.)

0.593–0.844 0.671–0.858 0.581–0.884 0.665–0.804

a

Second eigenvalue > 1 for Cluster 1 Eastern Europe.

Please cite this article as: Revilla, E., & Sáenz, M.J., Supply chain disruption management: Global convergence vs national specificity, Journal of Business Research (2013), http://dx.doi.org/10.1016/j.jbusres.2013.05.021

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Please cite this article as: Revilla, E., & Sáenz, M.J., Supply chain disruption management: Global convergence vs national specificity, Journal of Business Research (2013), http://dx.doi.org/10.1016/j.jbusres.2013.05.021