Supply chain and external conditions under which supply chain resilience pays: An organizational information processing theorization

Journal Pre-proof Supply chain and external conditions under which supply chain resilience pays: An organizational information processing theorization

Christina W.Y. Wong, Taih-Cherng Lirn, Ching-Chiao Yang, Kuo-Chung Shang PII:

S0925-5273(19)30441-4

DOI:

https://doi.org/10.1016/j.ijpe.2019.107610

Reference:

PROECO 107610

To appear in:

International Journal of Production Economics

Received Date:

27 June 2018

Accepted Date:

28 December 2019

Please cite this article as: Christina W.Y. Wong, Taih-Cherng Lirn, Ching-Chiao Yang, Kuo-Chung Shang, Supply chain and external conditions under which supply chain resilience pays: An organizational information processing theorization, International Journal of Production Economics (2019), https://doi.org/10.1016/j.ijpe.2019.107610

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Supply chain and external conditions under which supply chain resilience pays: An organizational information processing theorization

Christina W.Y. Wong Professor, Business Division, Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong. Tel: +852-2766-6415. E-mail: [email protected] Taih-Cherng Lirn Professor, Department of Shipping and Transportation Management, National Taiwan Ocean University, No.2, Beining Rd., Jhongjheng District, Keelung City 202, Taiwan, ROC. Tel: +886-2-24622192 ext. 3433. Fax: +886-2-24631903. E-mail: [email protected] Ching-Chiao Yang＊ Professor, Department of Shipping and Transportation Management, National Kaohsiung University of Science and Technology, No. 142, Haijhuan Road, Kaohsiung City 811, Taiwan, ROC. Tel: +886-7-3617141 ext. 23166. Fax: +886-7-3647046. E-mail: [email protected] Kuo-Chung Shang Professor, Department of Transportation Science, National Taiwan Ocean University, No.2, Beining Rd., Jhongjheng District, Keelung City 202, Taiwan, ROC. Tel: +886-2-24622192 ext. 7022. Fax: +886-2-24633745. E-mail: [email protected]

*Corresponding author

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Supply chain and external conditions under which supply chain resilience pays: An organizational information processing theorization

ABSTRACT While firms are increasingly exposed to catastrophes due to global presence of their supply chains, the development of supply chain resilience becomes crucial to businesses. Thus, it is important to examine business values for supply chain resilience under different types and levels of disruptions. Drawing on the organizational information process theory, a theoretical model was developed to examine the moderating effects of the various supply chain disruptions on performance outcomes. Empirical evidence, collected from primary and secondary data sources, suggests that supply chain resilience is found to be positively associated with risk management, market, and financial performance. In particular, supply chain resilience has shown importance in contributing to the risk management and market performance when firms experience high levels of supply side, infrastructure, and catastrophic disruptions. Keywords: Disruption management; Supply chain resilience; Supply chain management; Organizational information processing theory; Structural equation modelling

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1. INTRODUCTION While managing global supply chains and working with numerous international partners, firms are increasingly exposed to risks of supply chain disruptions caused by such unexpected events as supplier shutdown, natural disaster, manmade catastrophes, and terrorists acts. In view of the significant adverse impact of supply chain disruptions to businesses, Innovate UK, the Department for Environment, Food, and Rural Affair of the UK, and the Biotechnology and Biological Sciences Research Council, offer £11 million funding to support firms in the food and drink industry to develop and improve supply chain resilience. These industry intelligence and government funding programs signify the practical significance and potential values of strengthening supply chain resilience to businesses as well as economic development. Supply chain resilience is concerned with the capacity of firms to recover supply chain operations from unforeseen disruptions (Christopher and Peck, 2004; Sheffi and Rice, 2005). It helps mitigate disruptions by planning, preparing, and taking actions in advance (Tomlin, 2006), such as maintaining buffering capacity in production and inventory (Chopra and Sodhi, 2014). When firms face an internal and external impact, resilience capability can allow them to reduce the impact of a disruption and recover to its original. Thus, supply chain resilience has been treated as a dynamic capability of enabling supply chain to effectively adapt, response, and recover from disruptions and 2

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which in turn increasing firms’ competitive advantage (Golgeci and Ponomarov, 2013; Chowdhury and Quaddus, 2017; Yu et al., 2019). Although anecdotal evidence has shown the importance of supply chain resilience in coping with disruptions, prior studies are confined to investigate approaches to prevent (Blackhurst et al., 2011) and mitigate of supply chain disruptions (Papadakis, 2006). The performance impact of firms in response to disruptions is examined in Hendricks et al. (2009). Yet, the performance impacts were measured in terms of stock market reaction, which is largely related to response of investors but not on the performance impact of organizational operations due to disruptions. It is also important to note that the building of resilience requires resources and costs to investment, and it is hard for a firm to monetize the payback. In fact, the firms may never know what types and levels of disruptions they have prevented and what impacts were reduced or avoided thanks to its supply chain resilience capability. Thus, a firm may wonder is the developing of resilience worth it (Pettit et al., 2019)? Prior studies assumed the positive impact of supply chain resilience to performance outcomes due to its ability to cope with disruptions. However, there is little understanding on the strategic and business values of possessing supply chain resilience as a capacity to maintain and acquire resources, and orchestrate them to mitigate operations disruptions. This omission in the literature is undesirable as 3

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managers have little knowledge on the business values of supply chain resilience when experiencing different levels of supply chain disruptions. Also, the idea of continuous learning and improvement of resilience as a firm’s dynamic capability requires a stabilizing mechanism by possessing resources and capacity to cope with disruptions in a supply chain. The organizational information processing theory asserted supply chain resilience as processing mechanisms thus can provide a theoretical foundation for this study to develop and empirically validate a theoretical model that explains the performance outcomes of supply chain resilience, and under what types and levels disruptions supply chain resilience affects performance. The significance of this research is twofold. First, it is intuitively appealing to develop supply chain resilience to cope with unforeseen disruptions in supply chains that are exposed to threats and hazards. However, the building of supply chain resilience may impose additional costs on firms, which incur inefficiency in supply chain management (Bakshi and Kleindorfer, 2009; Zailani et al., 2015; Yang and Hsu, 2018). The cost-effectiveness issue thus should be considered when developing supply chain resilience (Ambulkar et al., 2015; Tukamuhabwa et al., 2015). In particular, supply chain disruption orientation and resource reconfiguration can help firms develop to supply chain disruptions (Tukamuhabwa et al., 2015; Parker and Ameen, 2018). To advance knowledge of the business values of supply chain resilience, a major 4

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contribution of this study is to distinguish the performance impact of supply chain resilience under different types and levels of disruptions, providing insights into the performance impacts of supply chain resilience when facing different situational conditions. On the other hand, organizational information processing theory suggests the importance of “fit” between organizational processing mechanisms (e.g., supply chain resilience) and organizational context (e.g., different types of disruptions) to attain desirable performance (Galbraith, 1973). In this study, we consider supply chain resilience as processing mechanisms for firms to operate their processes, while the different types of disruptions are the unpredictable organizational context that affect the performance results of the processing mechanisms. Thus, based on the contingency approach of organizational information processing theory, this study contributes to knowledge of supply chain resilience by pinpointing the point of “fit” between the levels of supply chain disruptions and supply chain resilience. 2. THEORETICAL BACKGROUND AND HYPOTHESES 2.1 Organizational information processing theory Given an open system, organizations must respond to several uncertain conditions. One is inherent in supply chain operations such as changing customer demand, competitors’ unpredictability, and the complexity of inter-organizational activities. Another is largely uncontrollable by firms such as natural disasters or catastrophes 5

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(Cegielski et al., 2012). Such uncertainties or disruptions make decision complex; thus, information processing capability is strategic important for firms to recover more quickly from catastrophes events. Rooted in the open systems theory of organizations (Boulding, 1953), the organizational information processing theory (OIPT) suggests that the presence of uncertainties and complexity are inherent in business environment and coordination amongst partner firms. Galbraith (1973) pointed out that increasing their information processing capability would help organizations cope with environmental uncertainty and thereby improve the performance. The theory reasons that firms need a stabilizing mechanism by possessing resources and capacity in operations to cope with uncertainties and manage unforeseen events that threaten the normal operations of business processes. The OIPT theory advocates that while there are uncertainties/disruptions introduced in the environment, firms need to have capacity buffers and enough information process capability to cope with the disruptions. As an approach of contingency theory, the OIPT suggests that the most effective strategy to improve performance is the fit between the excess organizational capacity (information processing capacity) and the shock that requires capacity to process (information processing needs) (Tushman and Nadler, 1978). Thus, supply chain resilience can be positively associated with performance when its capacity fits the scale of disruptions. 6

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The OIPT theory provides a holistic theoretical foundation for building a supply chain resilience research framework to explain the performance impact of supply chain resilience by suggesting performance outcomes are contingent on exogenous factors as well as their extent of impact can affect supply chain operations, such as supply chain disruptions. 2.2 Supply chain resilience The development of supply chain resilience is under the assumption that not all events that have an impact on supply chain operations can be prevented. Prior studies pointed out that in the case of alternative suppliers, design information substitutability and portability were required by firms to improve supply chain resilience capability (Fujimoto, 2011; Haraguchi and Lall, 2015). The cause of supply chain disruptions varies from natural to man-made hazards. These unfortunate events have disrupted supply chains of many firms, leading to significant financial lost at the very least. Yet, in some cases, have led to industry-changing consequences that cost reputation and life of businesses (Mukherjee, 2008). These lessons prompt managers and researchers to be increasingly concerned about the development of supply chain resilience to allow timely recovery from disruptions by having available capacity in a supply chain. Though there is no consensus on the definition of supply chain resilience in the literature, it can be defined as the ability of supply chain to return to its original state of 7

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operations or maintain or move to a new, more desirable state after being disturbed (Christopher and Peck, 2004). From the organizational perspective, supply chain resilience can be defined in terms of adjustment to capacity or ability to face disruptions in advance and contributes to strategic awareness and a linked operational management of internal and external shocks (Ponomarov and Holcomb, 2009; Annarelli and Nonino, 2015). Thus, supply chain resilience refers to the behavioral responses of firms, national economics, and systems in the contexts of social and economic (Ponomarov and Holcomb, 2009). While few studies had examined the supply chain resilience and disruption issues by the quantitative methods (Cardoso et al., 2015; Kamalahmadi and Mellat-Parast, 2016b; Yang and Fan, 2016; Ivanov et al., 2017), a systematic review on the theoretical foundations of supply chain resilience can find that the resilience basically can be static or dynamic (Tukamuhabwa et al., 2015; Kamalahmadi and Mellat-Parast, 2016a; Yang and Hsu, 2018). Notably, most studies defined the supply chain resilience and proposed the measurement scale from the capability view (Christopher and Peck, 2004; Ponomarov and Holcomb, 2009; Ambulkar et al., 2015; Takamuhabwa et al., 2015; Kamalahmadi and Mellat-Parast, 2016a, b; Brusset and Teller, 2017; Adobor and McMullen, 2018; Yu et al., 2019). As a part of business continuity strategy, supply chain resilience prepares firms with capacity to cope with and recover from disruptions to the original state of 8

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operations. It reflects an organization’s ability to survive, adapt, response, recovery, and grow confronted with change and uncertainty (Knemeyer et al., 2009; Chowdhury and Quaddus, 2017; Adobor and McMullen, 2018). Thus, researchers also view supply chain resilience as a dynamic capability when founded on the ability of managing disruptions and events to maximize the speed of recovery to its original (Golgeci and Ponomarov, 2013; Chowdhury and Quaddus, 2017; Parker and Ameen, 2018; Yu et al., 2019). To examine the impact of resilience on performance outcomes under dynamic environmental with different types and levels of disruptions, the measures for supply chain resilience in this study were based on the capability view and mainly adapted from Golgeci and Ponomarov’s (2013) work. 2.3 Hypotheses development 2.3.1 Performance impacts of supply chain resilience Figure 1 shows the framework of this study. A rationale for the proposed linkages is provided below.

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Risk management performance H1a, H2a, H3a

SC resilience

Market performance H1b, H2b, H3b

H1c, H2c, H3c

Financial performance  ROA  ROE  Profit

Moderators  Supply side disruptions  Infrastructure disruptions  Catastrophic disruptions

Figure 1 Research framework A large number of empirical studies had demonstrated the performance impacts of supply chain resilience. Pettit et al. (2010) argued supply chain resilience capability can enhance manufacturing firms’ competitiveness and financial performance. The resilience capability allows a firm to quickly respond to changes in the environment, and actively adjust its response strategies to prevent major disruption to its supply chain. Typically, the supply chain resilience capability has been proven to improve customer service (Wieland and Wallenburg, 2013; Peng et al., 2014; Srinivasan and Swink, 2018). Thus, the firms can input more efforts to develop their supply chain resilience capability and which in turn improve their financial, risk performance and market performance (Hendricks et al., 2009; Lee and Rha, 2016; Yang and Hsu, 2018). 10

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While having a collaborative supply chain relationship with a common goal of business continuity, supply chain resilience provides firms with access to available assets across internal functions and supply chain partners, as well as sharing and processing information collected from various functions to plan and coordinate business activities (Dhanaraj and Parkhe, 2006). Supply chain resilience encourages information exchange, joint decision making, and building trust across functions and supply chain partners (Bakshi and Kleindorfer, 2009). Supply chain resilience provides firms with flexibility to effectively respond to business opportunities and market demands (Nohria and Gulati, 1996). Also, the maintenance of supply chain visibility enables firms to be aware of changes of market demands, and collect latest market intelligence, enabling firms to develop products that are meeting customer needs (Pettit et al., 2010). These views on the performance impact of supply chain resilience suggest the influence of situational conditions that result in the change of performance outcomes of supply chain resilience. Specifically, according to the organizational information processing theory, the performance impact of supply chain resilience is likely to be affected by the types and scales of disruptions, when the excess capacity is utilized to achieve performance. 2.3.2 Impacts of disruptions In the events of supply chain disruptions, increasing the information processing 11

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needs to identify alternatives and solutions to mitigate impacts caused by the disruptions. Ambulkar et al. (2015) noted that in a high impact disruption context, namely, supply disruption, logistics/delivery disruptions, in house/ plant disruption, and natural hazards/ political issues, resource reconfiguration can fully mediate the relationship between supply chain disruption orientation and firm resilience. Supply chain resilience can serve as the stabilizing mechanism to provide firms with excess capacity. Based on the real-life cases and the literature, interruptions to supply chain operations are often caused by three common forms of disruptions, namely supply side disruption (Zsidisin and Wagner, 2010; Ambulkar et al., 2015), catastrophic disruption (Kleindorfer and Saad, 2005; Ambulkar et al., 2015), and infrastructure disruption (Culp, 2013; Chopra and Sodhi, 2014; Ambulkar et al., 2015). Supply side disruption is concerned with the reliability of suppliers in supplying and delivering the amount and quality of products needed to support the operations of a focal firm business (Hendricks and Singhal, 2005; Wagner and Bode, 2006). This disruption is related to such issues as supplier quality, delivery dependability, production capacity, technological capability, financial distress, or even bankruptcy, which cause suspension to supply (Tomlin, 2006). Supply side disruption has been found in many real-life cases. For example, the launch and availability of Apple Watch in 2015 were delayed due to the manufacturers of the display panel were unable to 12

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resolve production issues of the flexible OLED display that had a 30% yield rate, resulting in a shortage of display panel supply for production of Apple Watch. Supply chain resilience helps firms mitigate the problem of supply by enabling firms to identify and obtain new source of supply, which are supported by maintaining flexibility in supply management. Also, the characteristics of supply chain resilience such as supply chain visibility, supply chain collaboration and excess resources can help firms in coping with such disruptions (Wieland and Wallenburg, 2013). However, according to the organizational information processing theory, supply chain resilience might not contribute to performance improvement in the time of low supply disruption. The additional capacity that were set aside to helps supply chain operations bounce back cannot be fully utilized to improve performance in face of low supply side disruption, resulting in a waste in resources utilizing that fails to contribute to performance improvement. Thus, we theorize that: Hypothesis 1: The relationships between (a) risk management performance, (b) market performance, and (c) financial performance (i.e., ROA, ROE, and net profit) and supply chain resilience are moderated by supply side disruptions; the relationships are strengthened when the supply side disruption is high. Infrastructure disruption is concerned with the breakdown or failure of such systems as information network, production line, and transportation infrastructure that 13

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halt communication, information sharing, and movement of goods in a supply chain (Chopra and Sodhi, 2014). For example, the American West Coast Longshoremen strike in 2012 which results in a large lost to the American economics. The Hong Kong protest also causes airport disruption which results in the delay or cancel of transportation service in 2019. Such disruption can be caused by accident, system malfunctions, disaster, human errors, or attack that inhibit normal operations of processes in supply chain, and unable to synthesize internal and external information to support supply chain coordination. It is related to systematic vulnerabilities of firms as it disrupts the chain of actions in a supply chain due to loss of connectivity amongst supply chain partners (Snediker et al., 2008). Firms are unable to perform their processes as planned due to the lack of necessary information to precede, the halt of communication with partners, and the absence of materials or goods for trade. The possession of supply chain resilience equips firms with excess capacity and resources, and adaptability in operations. Such capacity enables quick response of firms to the disruption by having flexibility in making changes to products and/or processes to maintain services and fulfill orders to customers. Yet, the excess capacity being kept in organizational processes is likely to have no impact on performance improvement when infrastructure disruption does not occur. We therefore conjecture that: Hypothesis 2: The relationships between (a) risk management performance, (b) market 14

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performance, and (c) financial performance (i.e., ROA, ROE, and net profit) and supply chain resilience are moderated by infrastructure disruptions; the relationships are strengthened when the infrastructure disruption is high. Catastrophic disruption often arises from natural disasters and political crises that interrupt supply chain operations (Kleindorfer and Saad, 2005). This disruption is largely unpredictable due to very limited warning in advance and the impact of disruption is difficult to foresee until the events unfold. Catastrophic disruption can extend for a period of time and has been found with severe financial damages. For example, the wafer fab fire in the Philips semiconductor manufacturing plant in Albuquerque, New Mexico halted production line for approximately six weeks and resulted in a loss of $400 million to Ericsson one year after the fire (Bradley, 2014). Also, the other real-life case is the Trump administration abruptly imposed 10% and 25% tariffs on a number of Chinese products in 2018 (Yu et al., 2019). Due to this catastrophic disruption, Ericsson’s inability to cope with the disruption resulted in severe loss of share in the mobile phone market due to its failure in satisfying market needs due to the shortage of its key components. Considering the damages and supply chain costs caused by catastrophic disruptions, these rare but catastrophic disruptions are an extreme form of variability in supply chains and so significant buffers are needed to cope with and mitigate the 15

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impacts of catastrophic disruptions (Sheffi and Rice, 2005; Bradley, 2015). Park et al. (2013) suggested that firms can improve critical capabilities of supply chain information design, portability, and dispersion, and which in turn adopt robust and responsive supply chain strategies to respond to disruptions caused by catastrophic natural disasters. The supply chain resilience and robustness were determined by the dependence on suppliers, visibility of supply chains, design information substitutability, and design information portability (Haraguchi and Lall, 2015). In the case of the 2011 Thailand floods, Nissan thus can recover more quickly than it major competitors due to its availability of alternative suppliers and design information substitutability (Fujimoto, 2011; Haraguchi and Lall, 2015). In face of catastrophic disruption, being able to respond quickly by acquiring resources and working collaboratively with supply chain partners is critical to firms in coping with the disruptions. A collaborative recovery capability based on the supply chain coordination mechanism could be developed for catastrophic disruption management (Matsuo, 2015). Adaptability of operations is also critical by providing flexibility in recovery. However, the non-existence or low of catastrophic disruption utilize little of the capacity of supply chain resilience, which is likely to result in insignificant performance improvement. Hypothesis 3: The relationships between (a) risk management performance, (b) market 16

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performance, and (c) financial performance (i.e., ROA, ROE, and net profit) and supply chain resilience are moderated by catastrophic disruptions; the relationships are strengthened when the scale of catastrophic disruptions is high. 3. RESEARCH METHODOLOGY 3.1 Sample frame and data collection Considering the manufacturing industry of Taiwan accounts for approximately 88% of the total output of the industrial sector in Taiwan that contributes to 30% of its GDP and has long served as an important production bases for the global supply source of electronic hardware products, including chip, notebook, and liquid crystal display (LCD) (IDB, 2013), the sample of this study was the manufacturing companies listed on the Taiwan Stock Exchange (TWSE) and Gre Tai Securities Market (GTSM) (i.e., Over-the-Counter (OTC) Market and Emerging Stock Market). The sample included a total of 1,180 listed companies, whereas 649 companies listed in TSE, and 531 in OTC. The initial mailing elicited 142 responses. A follow-up mailing was sent to 1,038 nonrespondents after the initial mailing. An additional 94 responses were received after the second mailing. A total of 236 questionnaires were returned with a response rate of 20.0%, which is similar to prior studies using key informants for data collection in operations management (Malhotra and Grover, 1998). Thirty three returned questionnaires were disqualified due to incomplete responses. Thus, the total number 17

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of usable responses was 203. Table 1 summarizes the demographic characteristics of respondents. Table 1 Demographic data of respondents Demographic characteristics

Number of Percentage of respondents respondents 62 30.5 47 23.2 61 30.0 15 7.4 18 8.9

Job title

Vice President or above General manager Manager Director Other

Number of employees

<100 101-200 201-400 401-600 601-1,000

20 38 41 22

9.9 18.7 20.2 10.8

32

>1,001

50

15.8 24.6

Annual revenue of firm <1.0 (billion NT$a) 1.1-2.0

32 47

15.8 23.1

2.1-3.0

30

3.1-5.0 5.1-10.0 >10.1

27 22 45

14.8 13.3 10.8 22.2

Notes: a One U.S. dollar equals approximately 32.5 New Taiwanese (NT) dollars. 3.2 Bias issues To detect the potential problem of non-response bias in this study, we used the extrapolation method recommended by Armstrong and Overton (1977) to compare the early and late respondents by conducting a t-test analysis. Results showed that there were no statistical differences across the 7 measures at the 5% significance level, suggesting that non-response bias is not an issue in this study. 18

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To address the problem of common method variance, we collected data from multiple sources, namely perceptual inputs and objective data. The independent variable measures and the dependent variables (risk management performance, RMP; market performance, MP) were self-reported perceptual measures, while the financial variables were objective measure that was collected from the annual reports of the sample firms. Following Podsakoff et al.’s (2003) suggestions, several steps were conducted to detect if common method variance is an issue in this study. First, respondents were assured that their identity would be confidential and anonymous in the reporting of the results in order to encourage them to answer as honestly as possible. Second, 61.4% of the respondents held a senior position and 69.5% of the respondents had at least seven-year tenure, which are assumed to be knowledgeable about the operations of their firms. Third, we conducted Harman’s one-factor test to ensure that no single factor accounted for the majority of covariance (Podsakoff and Organ, 1986). Result showed seven factors with eigenvalue greater than 1 were extracted from all the measurement items, and explained 73.32% of the variances, with the first factor accounting for only 26.11% of the variance. Since no one factor accounted for a majority of the variance, the common method variance does not appear to be a problem in this study. 3.3 Measurement development 19

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The measures and questionnaire items adopted from prior studies were summarized in Table 2. To ensure linguistic equivalence of the measurements, we followed prior studies to conducted back-translation of the measurement (Dai et al., 2010; Hutting et al., 2014). We first invited the two bilingual executives to translate the measurement scales into Chinese. We then invited the two bilingual supply chain academics to back-translate the measurement scales into English. The panel agreed that the translation consistent, thus suggesting linguistic equivalent of the measurement scales. We then conducted a pilot test with a group of thirty executives of manufacturers to ensure the questionnaire readability and the surveyees’ comprehension on the questionnaires in the formal round of survey. The pilot test resulted in slight modifications to a few wording of the measurement items. Table 2 Measures and questionnaire items Constructs and reflective indicators Loadings C.R. 2 Supply chain resilience (χ =4.42, df=2; CFI=0.99; RMR=0.01; IFI=0.99; TLI=0.98; Cronbach’s alpha= 0.84; Composite reliability= 0.84; AVE= 0.58) (Scale: 1=not at all, 5=to a great extent) (Source: Golgeci and Ponomarov, 2013) SCR1: Our firm’s supply chain can quickly return to its original state after being disrupted SCR2: Our firm’s supply chain has the ability to maintain a desired level of connectedness among its members at the time of disruption. SCR3: Our firm’s supply chain has the ability to maintain a desired level of control over structure and function at the time of disruption SCR4: Our firm’s supply chain has the knowledge to recover from disruptions and unexpected events

0.57

---a

0.85

8.40

0.70

7.52

0.89

8.49

Risk management performance (χ2=N/A, df=N/A; CFI=N/A; RMR=N/A; IFI=N/A; TLI=N/A; Cronbach’s alpha= 0.87; Composite reliability= 0.86; AVE= 0.67) (Scale: 1=not at all, 5=to a great extent) (Source: Self-developed based on interview) RMP1: Comparing with three years ago, our firm has improved the ability to cope with threats. 20

0.79

---

Journal Pre-proof RMP2: Comparing with three years ago, our firm has improved the risk management capability. RMP3: Comparing with three years ago, our firm has improved the operational flexibility.

0.91

11.77

0.74

10.81

Market performance (χ2=N/A, df=N/A; CFI=N/A; RMR=N/A; IFI=N/A; TLI=N/A; Cronbach’s alpha= 0.88; Composite reliability= 0.88; AVE= 0.71) (Scale: 1=not at all, 5=to a great extent) (Source: Kim, 2009) MP1: Comparing with our major competitor, our firm has higher customer loyalty. MP2: Comparing with our major competitor, our firm has higher customer satisfaction. MP3: Comparing with our major competitor, our firm has corporation image improvement.

0.88

---

0.88

14.20

0.77

12.54

Supply side disruption(χ2=14.37, df=2; CFI=0.98; RMR=0.02; IFI=0.98; TLI=0.93; Cronbach’s alpha= 0.91; Composite reliability= 0.91; AVE= 0.71) (Scale: 1=not at all, 5=to a great extent) (Source: Wagner and Bode, 2006) In the past three years, the scale of the followings is observed in our supply chain: SSD1: Poor logistics performance of suppliers (e.g., delivery dependability, 0.86 order fulfillment capacity) SSD2: Poor or inconsistent supply quality 0.87 SSD3: Sudden demise of suppliers (e.g., due to bankruptcy) 0.84 SSD4: Capacity fluctuations or shortages on the supply markets

0.81

--15.45 14.64 14.01

Infrastructure disruption(χ2=17.68, df=2; CFI=0.98; RMR=0.02; IFI=0.98; TLI=0.94; Cronbach’s alpha= 0.94; Composite reliability= 0.93; AVE= 0.77) (Scale: 1=not at all, 5=to a great extent) (Source: Wagner and Bode, 2006) In the past three years, the scale of the following is observed in our supply chain: ID1: Downtime or loss of own production capacity due to local disruptions (e.g., labor strike, fire, explosion, industrial accidents). 0.80 ID2: Interruption or breakdown of internal IT infrastructure. ID3: Loss of own production capacity due to technical reasons (e.g., machine deterioration). ID4: Interruption or breakdown of external IT infrastructure.

---

0.94

16.50

0.82

13.43

0.95

16.83

Catastrophic disruption(χ2=2.53, df=2; CFI=1.00; RMR=0.01; IFI=1.00; TLI=0.99; Cronbach’s alpha= 0.94; Composite reliability= 0.94; AVE = 0.80) (Scale: 1=not at all, 5=to a great extent) (Source: Wagner and Bode, 2006) In the past three years, the scale of the following is observed in our supply chain: CD1: Political instability, war, civil unrest, or other socio-political crises.

0.88

---

CD2: Diseases or epidemics (e.g., SARS, MERS).

0.89

18.21

CD3: Natural disasters (e.g., earthquake, flooding, tsunami).

0.94

20.30

CD4: Terrorist attacks (e.g., 2005 London and 2004 Madrid terrorist attack).

0.87

17.57

Note: a Indicating a parameter fixed at 1.0 in the original solution. 21

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Flynn et al. (1990) argued that firm size often have an impact on organizational practices and abilities in resilience and experience in handling with disruptions. It means large firms may have more resources to implement supply chain practices in uncertain marketplace and thus may achieve better organizational performance than the small ones (Yu et al., 2019). Accordingly, firm size measured by firm age and number of employee was treated as control variable in the model. Measures for the supply chain resilience were from the capability view and treated it as the ability of recovery from disruptions. Four items derived from Golgeci and Ponomarov’s (2013) study were selected to measure the supply chain resilience. Performance analysis is used to measure and compare the actual levels of achievement of specific objectives. The performance outcomes of supply chain resilience generally could be measured in financial and operational or non-financial performances (Venkatrman and Ramanujam, 1986). The objective financial performance was measured by three common indicators, namely ROA, ROE, and profit and was collected from the annual reports of the sample firms listed in TWSE and GTSM database in 2015. On the other hand, prior studies (Wieland and Wallenvurg, 2013; Lee and Rha, 2016) had proved the impact of resilience on operational performance such as risk, customer value, operating efficiency, and product or service quality. The operational performance is here further divided into risk management 22

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performance and market performance. The risk management is the protection of organizations from adverse events (Colicchia et al., 2010). If the firms develop a resilient supply chain, it can help them reduce the likelihood of risk occurrence and face unforeseen events in advance and further to improve its risk management performance. Three items were self-developed based on interview and Zsidisin and Ellram’s (2003) study for measuring risk management performance. With respect to market performance, customers are the key driver for firms to survive in the market. The better customer service quality they provide, the better their market performance will be. It is thus imperative for a firm to be speed and uninterruptedness in serving customers and creating value for them. Three commonly used items were adapted from prior studies to measure the market performance, namely customer loyalty, customer satisfaction, and corporation image. 3.4 Measurement validation and reliability To assess the internal consistency and reliability of the individual measurement constructs, Cronbach's alpha and composite reliability were examined. As summarized in Table 2, Cronbach's alpha and composite reliability of all constructs were well above the suggested threshold of 0.8, which is considered adequate for confirming a satisfactory level of reliability (Fornell and Larcker, 1981; Nunnally, 1984). A confirmatory factor analysis (CFA) on all of the theoretical constructs was subsequently 23

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conducted to ensure the validity of the measurement model. Results, shown in Table 3, indicated that the GFI, AGFI and relative fit indices (IFI, NFI, RFI, CFI) were well above the recommended cut-off value of 0.90. Moreover, the absolute fit indices, such as RMR and RMSEA were below the recommended threshold of 0.05, and the normed Chi-Square (χ2/df) also had a value of 1.372 and fell well within the recommended range of 3.0, suggesting all constructs exhibit a good fit of the data (Hu and Bentler, 1990; Hair et al., 2010). Table 3 Results of CFA analysis for the whole model Latent Factors Factor Standardized S.D. variables loading factor loading

SCR

RMP

MP

FP

Critical ratio

R2

SCR1 SCR2

0.670 0.984

0.587 0.863

0.076 0.069

8.816 14.261

0.345 0.744

SCR3

1.000

0.876

---a

---

0.768

SCR4

0.799

0.701

0.073

10.945

0.491

RMP1 RMP2 RMP3

0.934 1.000 0.866

0.817 0.874 0.757

1.000

0.880

13.155 --12.028 ---

0.667 0.764 0.573

MP1

0.071 --0.072 ---

MP2 MP3

0.988 0.883

0.870 0.778

0.065 0.067

15.200 13.179

0.757 0.605

ROA

0.904

0.924

0.026

34.769

0.854

ROE Profit

1.000 0.867

1.000 0.886

--0.032

--27.094

1.000 0.786

AVE

0.587

0.668

0.775 0.712

0.880

Note: a: Indicates a parameter fixed at 1.0 in the original solution. SCR: supply chain resilience; RMP: risk management performance; MP: market performance; Fit index: χ2=80.956, df=59, χ2/df=1.372, RMR=0.045, RMSEA=0.043, GFI=0.944; AGFI=0.913; NFI=0.960, RFI=0.947, IFI=0.989, CFI=0.989.

Convergent validity was assessed by the statistics significance of factor loadings and average variance extracted (AVE) (Fornell and Larcker, 1981; Anderson and 24

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Gerbing, 1988; Hair et al., 2010). Tables 3 showed that all indicators are statistically significantly loaded to their respective constructs with loadings from 0.5 to 0.9, suggesting that the indicators measure their respective constructs and providing preliminary evidence of convergent validity. In addition, Table 3 revealed the AVE of each construct exceeds the threshold value of 0.5, providing further evidence of convergent validity of the construct (Fornell and Larcker, 1981). The means, standard deviations, and correlations of the theoretical constructs were summarized in Table 4. The correlations among the supply chain resilience, risk management performance, and market performance ranging from 0.416 to 0.621 and are significant at the p<0.01 level, indicating acceptable criterion validity (Nunnally, 1984). We tested the possibility of multicollinearity by calculating the Variance Inflation Factor values (VIFs), which evaluates the degree to which each variable can be explained by other variables (Hair et al., 2010). The VIF value of the constructs is all below the recommended cut-off value of 10, suggesting multicollinearity is not a concern of this study. Although the coefficient of correlation between the supply side disruption (SSD), infrastructure disruption (ID), and catastrophic disruption (CD) are high, following examples are discussed to support the classification of these disruptions. Supply side disruption often occurs under trade dispute among nations. Recent examples include the Japanese government's decision to restrict several high-tech 25

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exports to South Korea and American government prohibit all U.S. companies to use information and communications technology from any entity that might endanger American national security. Infrastructure side disruption occurs from local dispute is exampled by the American West Coast Longshoremen strike in 2012 which results in a large lost to the American economics. Finally, catastrophic disruption is exampled by the devastating floods in Thailand in 2012 that hit the PC hard drive suppliers in 2015. Table 4 Mean, standard deviations, and correlations of the constructs Variables/

Mean

S.D.

SCR

RMP

MP

SSD

ID

CD

ROA

ROE

SCR

3.815 0.538

0.762

RMP

3.807 0.502 .456＊＊ 0.819

MP

3.770 0.580 .416＊＊ .620＊＊ 0.843

SSD

2.548 1.017 -.173＊＊ -.106

-.116

ID

2.266 1.084

-.109

-.087

-.116 .765＊＊ 0.877

CD

2.163 1.142

-.037

-.098

-.061 .692＊＊ .778＊＊ 0.894

ROA

0.044 0.101

.135＊

.082

.084

-.086

-.018

-.004

ROE

0.066 0.222

.126＊

.057

.094

-.077

-.050

-.012 .945＊＊

Profit

0.055 0.311

.105

.058

.054

-.100

-.093

-.048 .820＊＊ .906＊＊

Profit

Constructs

0.843

-------

Note: Square root of AVE is on the diagonal; SCR: supply chain resilience; RMP: risk management performance; MP: market performance; SSD: supply side disruption; ID: infrastructure disruption; CD: catastrophic distribution; ＊＊p< 0.05 (two-tailed); ＊p<0.1 level (two-tailed)

To assess the discriminant validity, a rigorous method is to compare the average variance extracted (AVE) values for any two constructs with the squared correlation between these two constructs (Hair et al., 2010). Discriminant validity exists if the AVE values were greater than the squared correlation, implying a latent construct explain more of the variance in its item measures that it shares with another construct. Table 4 showed that the square root of the AVE for all the constructs is greater than the 26

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correlation between any pair of them, providing evidence of discriminant validity (Hair et al., 2010). 4. HYPOTHESES TESTING To examine the contingency effect of supply chain disruptions on the relationships between supply chain resilience and the various performance outcomes, namely, risk management performance, market performance, and financial performance in terms of ROA, ROE, and net profit, a structural equation model was established in this study. Since the moderating influence (supply chain disruption) is measured in a continuous manner, a new variable that is the product of the main effect (X) and moderating variable (M) was created. Following the steps suggested by previous researchers (Ping, 1995; Cadogan, 2003; Little et al., 2007), a structural equation modeling analysis with interaction term (XM) and control variables in AMOS 21.0 is thus used to test the research hypotheses. If the effect of interaction term (XM) is significant, then the moderating effect was founded, implying the effect of X on Y is dependent on the levels of moderating variable (Little et al., 2007). To mitigate the potential multicollinearity, both dependent and independent variables were mean-centered and used in this study (Jaccard and Turrisi, 2003). Given the large firms may have more resources to implement supply chain practices in resilience and experience in handling with disruptions in uncertain marketplace, they 27

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may achieve better performance than small ones, implying the size of a firm may affect the outcomes gained from the supply chain resilience (Flynn et al., 1990; Brusset and Teller, 2017; Yu et al., 2019). Thus, firm size measure by firm age and number of employee was treated as a control variable in the model. Tree models were performed to examine the contingencies of the relationships of supply chain resilience with the various performance outcomes under three kinds of supply chain disruptions. As shown in Table 5, results indicated that all structural models provide a reasonable fit of the data with fit indices.

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Table 5 Results of moderating effect with SEM Dependent variables Independent variables

RMP ß

MP

t-values

ß

ROA t-values

ß

ROE

t-values

ß

Profit

t-values

ß

t-values

0.041 -0.028 0.207 -0.863 -0.050

0.593 -0.401

Model 1: Supply side disruption (χ2142.542 (122); p=0.099; GFI=0.937; RMR=0.077; CFI=0.992; IFI=0.992; TLI=0.987) Age of firm Number of employee SCR SSD SCR X SSD

-0.050 0.012 0.869 0.147 0.230

-0.752 0.184 5.458 0.935 3.489

-0.054 0.078 0.720 0.062 0.206

-0.791 1.140 5.808 0.609 3.039

0.014 0.046 0.224 -0.896 0.004

0.206 0.663 1.849 -1.115 0.055

0.027 0.061 0.223 -0.995 -0.024

0.384 0.870 1.741 -1.116 -0.343

1.751 -1.115 -0.725

Model 2: Infrastructure disruption (χ2169.077 (122); p=0.003; GFI=0.926; RMR=0.079; CFI=0.983; IFI=0.983; TLI=0.973) Age of firm

-0.062

-0.953

-0.065

-0.983

0.015

0.210

0.025

0.355

0.037

0.532

Number of employee SCR ID SCR X ID

0.012 0.836 -0.172 0.262

0.181

0.074 0.728 -0.092 0.244

1.111

0.045 0.259 0.887 0.042

0.639

0.058 0.269 0.984 -0.001

0.829

-0.030 0.251 0.853 -0.045

-0.431 2.226 0.360 -0.652

6.169 -0.353 4.049

6.426 -0.341 3.684

2.255 0.360 0.605

2.218 0.360 -0.018

Model 3: Catastrophic disruption (χ2161.733 (122); p=0.009; GFI=0.929; RMR=0.079; CFI=0.985; IFI=0.986; TLI=0.977) Age of firm

-0.052

-0.801

-0.056

-0.838

0.010

0.140

0.021

0.303

0.036

0.520

Number of employee SCR CD SCR X CD

-0.005 0.850 -0.172 0.231

-0.072

-0.059 0.730 -0.087 0.233

0.881

0.030 0.262 0.885 0.023

0.435

0.045 0.268 0.986 -0.009

0.645

-0.039 0.247 0.852 -0.041

-0.556

5.977 -0.494 3.537

6.260 -0.461 3.497

2.243 0.505 0.339

Note: significant at the p=0.05 level;  significant at the p=0.1 level; ß is standardized path coefficient. 29

2.166 0.505 -0.133

2.159 0.505 -0.587

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Contingency determinants: Supply side disruption Model 1 examined the moderating effect of supply side disruption on the relationship between supply chain resilience and various performance outcomes. Results showed that none of the control variables had a significant impact on performance outcomes. Table 5 also indicated that supply chain resilience was positively related to risk management performance (ß=0.869, t=5.458), market performance (ß=0.720, t=5.808), ROA (ß=0.224, t=1.849), ROE (ß=0.223, t=1.741), and net profit (ß=0.207, t=1.751). More specifically, supply chain resilience had the highest coefficient on risk management performance, followed by market performance, ROA, ROE, and net profit. The interaction term (SCR X SSD) was found to have a significant impact on risk management performance (ß=0.230, t=3.489) and market performance (ß=0.206, t=3.039), offering support for the moderating role of supply side disruption on supply chain resilience and risk management performance and market performance. The moderating effect of supply side disruption was shown in Figure 2a and b. Results supported the notion that for firms under a high level of supply side disruption, the relationships between supply chain resilience and risk management performance, and market performance are stronger. The findings imply supply chain resilience has a stronger effect on risk management performance and market performance when firms facing high level of supply side disruptions. In particular, 30

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supply chain resilience is more influential to the risk management performance than market performance under a high level of supply side disruption. However, the moderating effect of supply side disruption on supply chain resilience and financial performance in terms of ROA, ROE, and net profit was not found in this study, suggesting hypothesis H1c was not supported in this study. In summary, when firms facing high level of supply side disruption, enhancing its supply chain resilience capability will have a stronger impact on risk management performance and market performance, lending support for H1a and H1b. (2a) Risk management performance

Risk management performance

5 4.5 4 3.5 Low SSD

3

High SSD

2.5 2 1.5 1 Low SCR

High SCR

31

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4.5 4 3.5 Low SSD

3

High SSD

2.5 2 1.5 1 Low SCR

High SCR

Figure 2 Moderating effect of supply side disruptions

Contingency determinants: Infrastructure disruption Similarly, model 2 tested the influence of infrastructure disruption and show that none of the control variables has a significant impact on performance outcomes. Table 5 indicated that supply chain resilience was positively associated with risk management performance (ß=0.836, t=6.169), market performance (ß=0.728, t=6.426), ROA (ß=0.259, t=2.255), ROE (ß=0.269, t=2.218), and net profit (ß=0.251, t=2.226). Similarly, supply chain resilience was found to have highest coefficient on risk management performance, followed by market performance, ROE, ROA, and net profit. The interaction term (SCR X ID) was also found to have a significant impact on risk management performance (ß=0.262, t=4.049) and market performance (ß=0.244, 32

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t=3.684), implying the relationships between supply chain resilience and risk management performance and market performance are contingent on infrastructure disruption which is consistent with the literature and our theorization. Thus, we found support for the contingency role of infrastructure disruption on the relationships between supply chain resilience, and risk management performance and market performance. From Figure 3a and b, the findings supported the notion that supply chain resilience had a stronger positive impact on risk management performance and market performance for firms operating in a high level of infrastructure disruption. The supply chain resilience was also found to be more influential to the risk management performance than market performance under a high level of infrastructure disruption. However, the moderating role of infrastructure disruption on supply chain resilience and financial performance in terms of ROA, ROE, and net profit was not found in this study, indicating hypothesis H2c was not supported in this study. To sum up, under high level of infrastructure disruption in firms’ operations, improving firms’ supply chain resilience will have a stronger positive impact on its risk management performance and market performance, lending support for hypotheses H2a and H2b. (3a) Risk management performance

33

Risk management performance

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5 4.5 4 3.5 Low ID

3

High ID

2.5 2 1.5 1 Low SCR

High SCR

(3b) Market performance

Market performance

5 4.5 4 3.5 Low ID

3

High ID

2.5 2 1.5 1 Low SCR

High SCR

Figure 3 Moderating effect of infrastructure disruptions Contingency determinants: Catastrophic disruption Finally, model 3 assessed the moderating effect of catastrophic disruption on the relationship between supply chain resilience and various performance outcomes. Results showed that none of the control variables has a significant impact on 34

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performance outcomes, but supply chain resilience was positively associated with risk management performance (ß=0.850, t=5.977), market performance (ß=0.730, t=6.260), ROA (ß=0.262, t=2.243), ROE (ß=0.268, t=2.166), and net profit (ß=0.247, t=2.159). Supply chain resilience was found to have highest coefficient on risk management performance. Again, the interaction term (SCR X CD) was found to have a significant effect on risk management performance (ß=0.231, t=3.537) and market performance (ß=0.233, t=3.497), supporting for the contingency role of catastrophic disruption on the relationships between supply chain resilience, and risk management performance and market performance. Figure 4a and b indicated that supply chain resilience is useful in contributing to the risk management performance and market performance measures when firms undergo a high level of catastrophic disruption. Notably, supply chain resilience is more influential to the market performance than risk management performance under a high level of catastrophic disruption. However, the interaction term was not found to have a significant impact on financial performance in terms of ROA, ROE, and net profit, implying the moderating role of catastrophic disruption on supply chain resilience and financial performance was not exist; that is hypothesis H3c was not supported in this study. To sum up, if firms operated undergo a high level of catastrophic disruption, improving its supply chain resilience can have a stronger positive impact on its risk management performance and market performance, lending 35

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support for H3a and H3b. (4a) Risk management performance

Risk management performance

5 4.5 4 3.5 Low CD

3

High CD

2.5 2 1.5 1 Low SCR

High SCR

(4b)Market performance 5 Market performance

4.5 4 3.5 Low CD

3

High CD

2.5 2 1.5 1 Low SCR

High SCR

Figure 4 Moderating effect of catastrophic disruptions 5. DISCUSSION AND IMPLICATIONS 5.1 Theoretical implications 36

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Supply chain resilience is found to be positively associated with market and risk management performance, and ROA, ROE, and net profit, consisting with previous studies asserting that firm resilience has significant impact on financial and nonfinancial performance (Hendricks et al., 2009; Pettit et al., 2010; Wieland and Wallenburg, 2013; Lee and Rha, 2016; Yang and Hsu, 2018; Yu et al., 2019). This advances the literature that it is worth for a firm to invest in the building of resilience capability in this uncertain and turbulent environment. Notably, the development of supply chain resilience can be positioned as a firm’s dynamic capability to effectively return, maintain, and recover from various disruptions and unexpected events in a supply chain which in turn improve its performance, which is consistent with previous studies (Golgeci and Ponomarov, 2013; Chowdhury and Quaddus, 2017; Yu et al., 2019). The findings also indicate that three kind of supply chain disruptions were only found to serve as a moderator variable between supply chain resilience and risk management and market performance, suggesting the performance impacts of supply chain resilience are contingent to the types and levels of disruptions which consistent with previous works (Golgeci and Ponomarov, 2012; Ambulkar et al., 2015). In particular, supply chain resilience was positively related to risk management and market performance under a high level of supply side disruption. Thus, a firm facing 37

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high possibility of supply chain disruptions should engage in building its resilience capability to effectively and quickly return, maintain and recovery from events. Compared to firms with worse resilience capability, they can achieve superior risk management and market performance in this dynamic supply chain. However, supply side disruption does not have moderating effect on the relationship between supply chain resilience and financial performance in terms of ROA, ROE, and net profit, suggesting that supply side disruption does not moderate the relationship. That is supply chain resilience contributes to financial performance regardless the level of supply side disruption. Similarly, infrastructure disruption moderates the relationships between supply chain resilience and the risk management and market performance. This finding suggests that supply chain resilience is of particular importance when in face of a high level of infrastructure disruption. Thus, a firm was suggested to build it resilience capability for improving risk management and market performance under a high level of infrastructure disruption, which can be caused by breakdown of production or IT infrastructure. However, infrastructure disruption was not found to have a moderating effect on the relationship between supply chain resilience and financial performance in terms of ROA, ROE, and net profit, implying supply chain resilience contributes to financial performance regardless the level of infrastructure disruption. 38

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The study also reveals that catastrophic disruption only moderated the effect of supply chain resilience on risk management and market performance. It is important to note that unlike infrastructure and supply side disruption that can be prepared for by maintaining buffering capacity, catastrophic disruption is highly unpredictable with little prevention can be done in advance and it always causes huge losses. Thus, a frim should engage in improving its resilience capability to react and recovery from the catastrophic disruption which in turn reduce the financial losses and improve risk management and market performance. In particular, a firm facing high possibility of catastrophic disruptions could enhance its resilience capability to achieve superior risk management and market performance. Finally, supply chain resilience is regarded as continuous processing mechanisms requiring resources and costs to serve as dynamic capability for a firm to cope with disruptions in a supply chain. The finding contributes to theory by providing empirical evidence that the performance impact of supply chain resilience as resources and organizational processing capacity is contingent on the types of disruptions. This implies that supply chain disruptions can be coped with by maintaining capacity and firms are able to strengthen their risk management and market performance due to the fit of the excess organizational capacity and the need of additional capacity caused by the disruptions. 39

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5.2 Managerial implications This study has several managerial implications. First, a firm often wonders is the developing of resilience worth it? This study finds that supply chain resilience can be viewed treated as a dynamic capability for firm to effectively return, maintain, and recover from the disruptions and has been demonstrated to not only improve firms’ risk management and market performance, but also increase the financial performance in terms of ROA, ROE, and net profit. Managers are thus suggested to invest resources and excess capacity in supply chain operations and improve its resilience capability to cope with the various supply chain disruptions. Second, the firms may never know what types and levels of disruptions they have prevented and what impacts were reduced or avoided thanks to its supply chain resilience capability. The moderating effect of disruptions in terms of supply side, infrastructure, and catastrophic, on risk management and market performance was found in this study. That is the SCR was found to have positive impact on risk management and market performance under different levels and types of disruptions. In particular, a firm facing high possibility of disruptions such as supply side, infrastructure, and catastrophic was suggested to strength its resilience capability with buffering resources and capacity to cope with these disruptions. This enables firms to achieve risk management and market performance improvement in a high level of 40

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supply side, infrastructure, and catastrophic disruptions. Thus, firms should consider establishing supply chain resilience to cope with disruptions that are conventional like supply side, infrastructure and catastrophic disruptions. Finally, unlike supply side and infrastructure disruptions, catastrophic disruption is highly unpredictable and difficult for prevention actions to be done in advance. Catastrophic events may interrupt supply chains for extended periods and resulted in sever financial lost. In particular, supply chain resilience is found to have more influential to the market performance than risk management performance under a high level of catastrophic disruption. Managers were thus suggested to improving firms’ supply chain resilience which in turn will have a higher impact on market performance than risk management performance under a high level of catastrophic disruption. 5.3 Limitations and future research directions Our work can be extended in a number of directions. First, although the empirical evidence supports our theoretical reasoning, a proportion of the variance remains unexplained like most empirical organizational studies. Moreover, supply chain resilience is conceptualized as multidimensional structure. Future research might incorporate other determinants that are instrumental to develop supply chain resilience. Second, this study examined three major disruptions that are often found in real-life cases. Future research might consider examining how the performance impact of supply 41

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chain resilience can be affected by other situational factors, such as environmental uncertainty and environmental munificence, and relational conditions between supply chain partners. The managers are also suggested to assess the risks when face these disruptions. Third, supply chain resilience is found to have no significant influence on the financial performance of these manufacturers under the three type of disruption environment. The part of the reason might be the objective performance (financial index) is an accumulation of many years of a company’s effort (a sort of panel data), while the subjective performance (risk management performance and market performance) is obtained from a cross-sectional survey. Furthermore, according Phillips et al. (2003) noted earnings management via the employment of deferred tax do have impacts on the actual current taxable income and therefore the financial index is not an actual reflection of the current financial performance of a firm. Johnson and Kaplan (1991) also indicated the financial index used sometimes do not have relevance to the real financial health condition of a firm. Thus, future research requires an in-depth future research to investigate the causes and could examine the issues of inefficiencies and negative impact on financial performance. Finally, this study was limited to examine the performance outcomes of supply chain resilience under different disruptions in manufacturing industry and one country. Future research could generalize our research model and findings to the other industries and countries. The model can also help 42

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managers to make a decision when design their supply chain all over the world.

ACKNOWLEDGMENTS This work was supported by the Ministry of Science and Technology in Taiwan (R.O.C) [grant number NSC 101-2410-H-019-024].

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Journal Pre-proof Author Contribution Statement Ching-Chiao Yang is the corresponding author and is responsible for ensuring that the descriptions are accurate and agreed by all authors.  Christina W.Y. Wong: Writing- Original draft preparation, Conceptualization, Methodology, Software, Revising.  Taih-Cherng Lirn: Writing- Reviewing and Editing, Visualization, Revising.  Ching-Chiao Yang: Writing- Reviewing and Editing, Methodology, Software, Validation, Revising.  Kuo-Chung Shang: Writing- Reviewing and Editing, Questionnaire design, Data curation, Investigation, Revising.

Journal Pre-proof Highlights     

The application of the organizational information process theory in SCR research SCR positively contributes to various performance outcomes The moderating effect of supply chain disruptions SCR pays under high levels of supply, infrastructure, and catastrophic disruptions Testing moderating effect with AMOS