The impact of supply chain complexity on supply chain resilience: the moderating effect of supplier integration

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The impact of supply chain complexity on

supply chain resilience: the moderating effect of

supplier integration

Master thesis, MSc Supply Chain Management

University of Groningen, Faculty of Economics and Business

Yuhan Hu S2655683

Supervisor Prof. D.P. van Donk

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Abstract

Abstract Purpose – The purpose of this research is to explore the direct relationship between

supply chain complexity and supply chain resilience (consisting of robustness and agility), and the possibility of using supplier integration as a capability to absorb the negative effects of supply chain complexity on supply chain resilience.

Design/methodology/approach – Based on the previous literature on the content,

measurement and scope of the concept of three selected variables (supply chain complexity, supplier integration and supply chain resilience), a model is presented and tested by linear regression tests. It utilizes survey data collected from 106 suppliers of two automobile manufacturing firms in China.

Findings – It is found that supply chain complexity has a negative impact on building a

resilient supply chain. In addition, supplier integration as the moderator is also proved to positively influence the relationship between supply chain complexity and supply chain agility in a significant way, while no significant moderating effect has been found on the direct relationship between supply chain complexity and supply chain robustness.

Limitations - The limited sample size causes constraints on estimating and testing of more

comprehensive models of the relationship between supply chain complexity and supply chain resilience and leads to issues on significance in interpreting results of data analysis.

Practical implications – The study offers valuable insights into the management of supply

chain complexity and the development of supply chain resilience. This research provided managers with a specific way, integrating suppliers, to accommodate supply chain complexity and increase supply chain agility.

Originality/value – This paper improves the understanding of supply chain complexity

effects and provides a basis for future research, as well as guidance for companies facing complexity challenges. Specifically, it helps to define the direct relationship between supply chain complexity and supply chain resilience, and the moderating effect of supplier integration on the relationship.

Paper type - Research paper

Keywords: supply chain complexity; supply chain resilience; supplier integration; supply

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Under the current dynamic business environment, firms are easily susceptible to disruption events (Knemeyer et al., 2009). It makes effective supply chain management become a challenging task and calls for building resilience into supply chain. A resilient supply chain, in this sense, allows the supply chain to be prepared for unexpected events, reduce the impact of a disruption and strengthens the ability to recover to its original or an even better state (Jüttner and Maklan, 2011; Ponomarov and Holcomb, 2009). At the same time, the dynamic environment is also reflected in the complexity level of businesses activities, as product variety and customization levels increase, and supply chain partners become more geographically dispersed (Bozarth et al., 2009). The complexity of supply chain, with the difficulties in management and controlling, makes it hard to resist or react to potential disruptions (Datta, Christopher and Allen, 2007), which implies a potential negative influence on supply chain resilience.

As such, there is a common belief among both practitioners and scholars that supply chain complexity is one of the most pressing problems in modern supply chains and a key impediment to performance (Bozarth et al., 2009; Bode and Wagner, 2015; Choi and Krause, 2006; Mariotti, 2008). The negative effect of supply chain complexity on the performance in term of operational performance, financial performance are widely discussed (Aitken et al., 2016; Craighead and Blackhurst, 2007; Brandon-jones et al., 2015; Bozarth et al., 2009), but the impact of supply chain complexity on the supply chain performance in terms of resilience has not been clearly defined. In other words, the direct relationship between supply chain complexity and supply chain resilience remains unexplored. Hence, the impact of supply chain complexity on supply chain resilience is investigated in this research.

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integration by investigating information integration to accommodate supply chain complexity in their research. However, they ignored other practices such as logistics that can also contribute to creating competitive capabilities. In this research, we extend this topic by discussing supplier integration as the firm’s capability that is able to absorb the effects of supply chain complexity and increase supply chain resilience. Overall, this paper investigates the relationship between supply chain complexity and supply chain resilience is investigated, and chooses supplier integration as the moderator. The questions can be generated as:

Q1. What is the relationship between supply chain complexity and supply chain resilience?

Q2. What is the moderating effect of supplier integration on the relationship between supply chain complexity and supply chain resilience?

Data collection was carried out in the automotive industry by means of electronic survey for data analysis and hypothesis testing, in order to investigate the relationships mentioned above in a generalized way. As for the theoretical aspect, this study extends the limited studies to determine the direct relationship between supply chain complexity and supply chain resilience. In addition, the possibility of regarding supplier integration as a capability to absorb the impacts of supply chain complexity on supply chain resilience is discussed, which has not touched upon before. From the practical perspective, these answers could help managers to make better decisions in managing supply chain complexity and achieving supply chain resilience by developing suitable competitive capabilities.

In the subsequent section, the literature is reviewed and a conceptual model is generated. Next, a brief discussion of methodology is presented followed by findings and analysis of the data collected, then the discussion and conclusion.

2. Theoretical background

2.1. Supply chain complexity

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from upstream to downstream. Based on Bozarth et al. (2009, p. 80), supply chain complexity is “the level of detail and dynamic complexity exhibited by the products, processes and relationships that make up a supply chain”.

In order to clearly measure the degree of supply chain complexity, the drivers or sources of supply chain complexity should be defined. Most studies agree that supply chain complexity varies at least with differentiation and variety (de Leeuw et al., 2013), which is also fit with the supply chain complexity drivers mentioned in Bozarth et al (2009).

Bozarth et al. (2009) show that differentiation of supply chain partners refers to the degree to which supply chain partners differ. The different and various characteristics (e.g., operational practices, technical capabilities, etc.) of supply chain partners would lead to difficulties for a focal company in management and control. The geographically dispersed supply chain partners result in long lead-time and high delivery unreliability (Caridi et al., 2010; Choi & Krause, 2006). In such way, this study grounds supply chain complexity in term of delivery differentiation. Meanwhile, supply chain complexity is reflected in the fact that product life cycles and technology life cycles shorten while customer requirements for product variety and the need for customization increase (Aitken, Christopher, and Towill 2002; Swafford, Ghosh, and Murthy 2008). Hence, the variety of products and the fluctuant demand are usually considered to be the drivers for supply chain complexity (Ecksteine et al., 2015; Bozarth et al., 2009).

In summary, the differentiation and variety along supply chain from upstream to downstream indicating the level of supply chain complexity.

2.2. Supply chain resilience

Supply chain in today’s business environment tends to grow in both length and complexity (Blackhurst et al. 2011), resulting in the increasing likelihood of disruptions (Bozarth et al. 2009; Vachon & Klassen 2002). Thus, obtaining the capability of responding to changes and recovering from the turbulence becomes important for supply chains (Hohenstein et al., 2015). Drawing from the foundations of multidisciplinary studies, supply chain resilience is defined as “the adaptive capability of the supply chain to prepare for unexpected events, respond to disruptions, and recover from them by maintaining continuity of operations at the desired level of connectedness and control over structure and function” (Ponomarov and Holcomb, 2009, p. 131). As such, a supply chain is resilient if its original stable state is maintained or if a new stable state is achieved. Base on Brandon-jones et al. (2015), supply chain resilience can be understood as performance outcomes.

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“take action before it is a final necessity” and the reactive capability to “recover after experiencing a crisis” (Valikangas, 2010, p. 19). Hence, we follow Wieland and Wallenburg (2013), who concluded supply chain resilience as the proactive capability “robustness” (Husdal, 2010; Shukla et al., 2011) and the reactive capability “agility” (Braunscheidel and Suresh, 2009). In order to add clarity to the measurement of supply chain resilience, the good starting point is to clearly define supply chain robustness and supply chain agility.

Supply chain robustness can be reflected in the ability of a supply chain to resist

change (Wieland & Wallenburg, 2012, p. 890) and maintain its function despite internal or external disruptions (Brandon-jones et al., 2015). Since a robust supply chain is focusing on maintaining its current stable state while resisting the impact of supply chain disruptions, forecasting and preparing for the occurrence of disruptions in order to sustain its operations and continuously deliver service has been emphasized (Meepetchdee and Shah, 2007; Wieland & Wallenburg, 2012). When compared with agility, a robust supply chain endures rather than responds (Husdal, 2010).

Supply chain agility is defined as “the capability of the firm, both internally and in

conjunction with its key suppliers and customers, to adapt or respond in a speedy manner to marketplace changes as well as to potential and actual disruptions” (Braunscheidel & Suresh, 2009, p. 120). It emphasizes on recovering from the turbulence and achieving a new stable state, thus “rapid system reconfiguration in the face of unforeseeable changes” (Bakshi and Kleindorfer, 2009, p. 585) as an important characteristic of an agile supply chain is defined. Therefore, supply chain agility is corresponding with being fast, being responsive (Christopher and Peck, 2004), and being able to reconfigure the supply chain (Bernardes and Hanna, 2009). 2.3. Supply chain complexity and supply chain resilience

Supply chain complexity enables organizations to reach new markets and offer greater product variety (Isik, 2009), but also increases the differentiation and variety of supply chains, hence decreases the ability of resisting and responding to changes (Fridgen, Stepanek, and Wolf, 2014). To be more specific, when a large-scale organization has a sophisticated supply chain, it is expected that a greater amount of managing activities need to be done throughout the supply chain (de Leeuw et al. 2013), hence the capabilities of acting and responding efficiently to changes of originations are reduced (supply chain agility); due to the high variation in suppliers and customers, difficulties in anticipation and preparation for potential disruptions (supply chain robustness) are increased.

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& Wallenburg, 2013). Thus, the ability of resisting changes is affected.

Also, a robust supply chain aims to maintain its current stable state by getting well-prepared for the unexpected disruptions. In order to keep the robustness of supply chain, buffer stocks or resources are required to against the disruptions and allow operations to continue (Brandon-jones et al., 2015). The variety in customer demand and differentiated requirements for customization, however, make it hard to prepare buffer resources. Thus, the hypothesis is generated as:

H1a: Supply chain complexity is negatively related to supply chain robustness.

Secondly, a high variety of customer demand and customer requirements for products is likely to result in product delivery characterized by numerous options and sizes, making the process of adjusting supply chain in response to changes more complicated (Li et al. 2008). In this case, organizations cannot respond to supply chain disruptions efficiently, which in turn reduces supply chain agility. In addition, supply chain complexity may result in delivery complexity, which refers to long lead-times and unreliability of delivery, leading to the requirement of further data (Frank, Drezner, Ryan & Simchi-Levi, 2000) and extended planning time and response time (Simangunsong, Hendry & Stevenson, 2012). Therefore, supply chain agility would be affected since the less organized and efficient supply chain builds a barrier in rapid response. As such we hypothesize:

H1b: Supply chain complexity is negatively related to supply chain agility.

2.4. Supplier integration

Supplier integration is often defined as “the combination of internal resources of the buying firm with the resources and capabilities of selected key suppliers through the meshing of inter-company business processes to achieve a competitive advantage” (Wagner, 2003, p.6). This definition is also aligned with DCV, which suggests that internalization of specific-assets can be a source of firm’s competitive advantage (Barney, 1991; De Vita et al., 2011; Blome et al., 2014). Following Leuschner et al. (2013), supplier integration can be discussed from three dimensions: information integration, operational integration, and relational integration.

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2004). This cognition facilitates suppliers’ willingness of mutual trust and mutual understanding (Myers and Cheung, 2008), further leads to joint goals and strategies, as well as shared risks between suppliers and focal companies (Mishra and Shah, 2009).

2.5. Moderating effect of supplier integration

As an extension of the resource-based view, DCV explains firms’ competitive advantage in changing environments (Teece, Pisano, and Shuen 1997; Eisenhardt and Martin 2000). Dynamic capabilities are defined as ‘the firm’s ability to integrate, build and reconfigure internal and external competences to address rapidly changing environments’ (Teece, Pisano, and Shuen 1997, 516). In this study, suppliers are considered as the firm’s external resources to maintain competitiveness and address changing environments through integration and reconfiguration.

As hypothesized in section 2.3, when the complexity of supply chain is high, increasing difficulties are formed in managing all the involved actors in the supply chain, less capacity is available to communicate with supply chain partners, and preparing buffer stocks for disruptions becomes harder. A firm, therefore, would have a reduced capability of resisting and responding to changes. In this case, supplier integration as a competitive capability may be able to accommodate supply chain complexity and have a positive influence on resilience. First of all, integrating suppliers facilitates joint planning, efficient collaboration and encourages real-time information exchange (Whipple and Russell, 2007; Wieland & Wallenburg, 2013). When a firm is operating in a complex supply chain, supplier integration ensures a better inventory management, which improves material availability and leads to better preparation for unexpected disruptions (supply chain robustness) (Droge et al., 2004). In addition, a long-term relationship with suppliers deepens and broadens the mutual trust and shared responsibility, which is crucial to reduce delivery complexities (Jacobs and Subramanian, 2012). The shared risks and strategies make suppliers more willing to support flows of products to reduce lead times and errors caused by delivery complexity (Li et al., 2016). It generates a quicker response to the market changes (supply chain agility).

Therefore, it can be expected that if the level of supplier integration becomes higher, the expected negative direct relationship between supply chain complexity and supply chain robustness as well as the direct relationship between supply chain complexity and supply chain agility become weaker.Overall, by building a competitive capability through integrating suppliers, the negative effects of supply chain complexity can be partly absorbed, while both supply chain robustness and supply chain agility are enhanced.

Therefore, the second hypothesis is formulated as:

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the lower the negative effects of supply chain complexity on supply chain robustness. H2b: Supplier integration positively moderates the relationship between supply chain complexity and supply chain agility; the higher the level of supplier integration, the lower the negative effects of supply chain complexity on supply chain agility.

The conceptual model is illustrated in figure 2.1:

Figure 2.1. Conceptual model

3. Methodology

In order to answer the research questions and test hypothesis above, survey as one of quantitative research methods is conducted, since the quantitative method can provide a more objective result and there are large validated scales available in extant literature for performing a survey research. The data collection involves several steps: selecting constructs and developing questionnaires based on the extant literature; conducting the matched survey; and collecting the survey responses.

3.1. Sampling and data collection

Data collection will be carried out in the automotive industry by means of the electronic survey, which targets individuals working at a firm as a purchasing/logistics/supply chain/operations manager in the automobile industry with at least 50 employees. The starting population was collected in the following sectors based on the SIC codes (C27-30):

27. Manufacture of electrical equipment

28. Manufacture of machinery and equipment n.e.c.

29. Manufacture of motor vehicles, trailers and semi-trailers 30. Manufacture of other transport equipment

Due to China’s size and economic diversity (Zhao et al., 2006), data was collected in Chinese automotive companies. Data was from the suppliers of two large companies in automobile industry located in Chongqing and Guangzhou respectively. Companies

Robustness

Agility

supply chain resilience H1a (-)

H2a (+)

Supply chain complexity

Supplier integration

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in these two cities are selected because they represent different stages of economic development. Chongqing is in the early stages of economic reform and market formation, while Guangzhou is in a period of a higher degree of economic reform and marketization (Flynn et al., 2010). As they are local brands and leading vehicle manufacturing companies in China, they have especially complex supply chain and they produce a variety of products including passenger cars, microvans, dirt bike engines, entry-level motorcycles, mini-vehicles, and commercial trucks, etc. In addition, both companies rank top 10 in the automobile industry in china in terms of revenue, firm size and production volume. Overall, they can be regarded as representatives of the vehicle manufacturing companies in China. We consider all the 212 suppliers of company A combined with all 237 suppliers of company B as the database in this research. Thus, the initial sample consists of 449 firms.

The data collection took place from early November to mid-December 2016 in China. Based on the data pool, 449 surveys had been distributed. The target respondents were reached through email. Individuals were approached by means of telephone, e-mail and other electronic communication approaches (e.g. Linkedin), follow-up reminder emails are sent to non-respondents after one week. In the first three weeks, 84 surveys were returned. In order to increase the response rate, another phone call in the fourth week was conducted. In the end, 42 surveys were returned in the last three weeks. Overall, within six weeks, 126 questionnaires were returned. 106 of those questionnaires are valid. The respondent rate is therefore 28.1%. The basic information of respondents is presented in Table 3.1. In addition, non-response bias was examined to check for the differences between data that received early (first round) and late (second round). The number of employees is used to perform a one-way ANOVA analysis. 30 surveys in the first round and 30 surveys in the second round are randomly selected to make the comparison. The result obtained from employee number is F= .412, p= .799>.05, which means there is no significant difference between the responses. Therefore, non-response bias is not considered a problem with the data (Karlsson, 2010).

Profile of respondents Number Percentage

Position

President /director 18 17.0%

Supply chain manager 22 20.1%

Planner/buyer/sales 24 22.6%

Production engineer 11 10.4%

Not indicated 31 29.2%

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Annual revenue (in euro)

Less than 50 million 9 8.4%

50 - 100 million 25 23.6%

100 - 250 million 47 44.3%

250 - 500 million 19 17.9%

Above 500 million 6 5.7%

Total 106 1

Table 3.1. Profile of respondents 3.2. Questionnaire development

The questionnaire is derived from extant research in Supply chain management with creditable validity and reliability, consisting of three constructs discussed in the theoretical background section, namely, supplier integration, supply chain resilience and supply chain complexity. All items were measured on the Likert scale (1-5 scale), where 5 being the positive end and 1 being the negative end.

Firstly, the items of supply chain complexity construct are adopted from Bozarth et al. (2009). Based on the differentiation and variety of supply chain (Caridi et al., 2010; Choi & Krause, 2006), the delivery reliability, the level of customization, and the stability of customer demand are measured to determine the complexity level. By using items across the different supply chain complexity sources, the level of complexity in a supply chain can be accurately measured.

Secondly, supply chain robustness and supply chain agility are two dimensions that contribute to the concept of supply chain resilience (Wieland & Wallenburg, 2013). Thus, items related to the ability of a supply chain to resist disruptions (robustness) as well as recover from turbulence (agility) are measured. When it comes to the robustness of supply chain in this questionnaire, items measuring the ability of maintaining the current state are selected. Furthermore, items measuring the responsiveness and flexibility in terms of supply chain agility are adopted from Braunscheidel & Suresh (2009).

Finally, items related to supplier integration are measured based on the willingness of sharing information, the attitudes of long-term relationship, the level of they integrate their information technology and logistics information sharing, long-term relationship, and joint development (Zhao et al., 2008; Li et al., 2016; Prajogo and Olhager, 2012). These items give clear insights on how supply chain partners link with each other and develop jointly.

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into 5 types: less than 50 employees, 50-100 employees, 100-250 employees, 250-500 employees, and more than 500 employees. And the distribution is presented in the table below.

Number of employees Number Percentage

Less than 50 3 2.8% 50-100 21 19.8% 100-250 36 34.0% 250-500 30 28.3% More than 500 16 15.1% Total 106 1

Table 3.2. Control variables 3.3. Data reduction and analysis

In order to determine the quality of data, validity and reliability of the measurement of multi-item constructs will be analyzed independently through Explorative Factor Analysis (EFA) performed by SPSS. Before the test conducted, supply chain complexity items were recorded to keep aligned with the other items’ sequence. Factor analysis is performed to determine the set of items that belonged to each proposed constructs, meanwhile, the KMO (Kaiser-Meyer-Olkin) test was conducted to measure the sample adequacy. The value of KMO test is .764> .60 and the p-value of Barlette’s test is .000< .001, both indicating a satisfactory adequacy for factor analysis. According to the results showed in table 3.3, the items loaded to specifically one factor without cross loading. In addition, all selected items were in excess of the common accept 0.5 standard, suggesting no need to delete any item to improve the model fit (Anderson and Gerbing 1988). The eigenvalue and the explained variance of the solution for all factors are also presented.

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Table 3.3. Factor analysis

4. Result

In this section, the results of descriptive statistics will be presented firstly to illustrate the validity and reliability of constructs, following by the regression analysis and moderation effect.

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As the conceptual model shows, the supply chain complexity is the independent variable, supply chain resilience including supply chain robustness and supply chain agility is the dependent variable, and supplier integration is the moderator. The results of descriptive statistics are presented in table 4.1, indicating the means, standard deviations and inter-factor correlation among factors. As the table shows, the independent variable (supply chain complexity) is negatively related to dependent variable supply chain robustness (r = -.279) and supply chain agility (r = -.407) in a significant way (p < 0.01). In addition, there is a significant and positive relationship between dependent variables and moderator (supplier integration), with r = .479 and .526 respectively and p < 0.01. On the other hand, the number of employees as the control variable did not relate to supply chain robustness nor supply chain agility in a significant way. Therefore, it can be concluded that the number of employees does not influence the selected variables significantly.

Table 4.1. Descriptive statistics and inter-correlations of constructs 4.2. Regression analysis

Linear regression was used to test the direct relationship between independent variable and dependent variables. In order to test hypothesis 1, the first model was conducted and presented in table 4.2. Step 1 shows that the control variable, the number of employees does not have a significant effect on supply chain robustness (p > 0.2), nor supply chain agility (p > 0.9). In this case, the direct effect cannot be explained by the influence of control variable. Step 2 adds supply chain complexity to the regression model. The effect of the supply chain complexity on supply chain robustness (β = -.278, p < .01) and supply chain agility (β = -.407, p < .01) is negative and significant, with adjusted R2

of .073 and .149 respectively. Therefore, the results confirm hypothesis H1a and H1b, which suggest that firms that with more complex supply chain are subject to lower resilience capability. The adjusted R2

stands for

explained variance. In addition, both F-values are at the p <. 01 levels, which means that both models are significant.

Supply chain resilience

Step Variables Supply chain robustness Supply chain agility

1 Control

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2 Main effects

Supply chain complexity -0.278** -0.407**

Adjusted R2 0.04 0.073 0.010 0.149

Δ R2 0.14 0.091 0.006 0.166

F 1.438 5.142** 4.153* 10.214**

*p < .05; **p < .01

Table 4.2. Linear regression analysis results of direct relationship (H1a and H1b) 4.3. Moderating effect

In order to examine the moderating effect of supplier integration on the relationship between supply chain complexity and supply chain resilience, the second regression model was conducted. Firstly, the values (recorded) of supply chain complexity and supplier integration were transformed into standardized z-values. Secondly, the z-values were multiplied to generate a new variable representing the interaction of independent variable and moderator. Then the regression analysis was performed by SPSS and the results are presented in table 4.3 and table 4.4.

As the two tables present, step 1 states the regression result of the control variables, the number of employees is not significantly related to neither supply chain robustness nor supply chain agility. Step 2 adds the main effect and the moderator to the regression analysis. It shows that (independent variable) supply chain complexity is significantly and negatively related to (dependent variable) both supply chain robustness (β= -.166, p< .05) and supply chain agility (β= -.270, p< .05). Meanwhile, the (moderator supply) supplier integration is also significantly related to (dependent variable) supply chain robustness (β= .434, p< .01) and supply chain agility (β= .451,

p< .01), but in a positive way.

The moderation effect is presented in step 3. We test the relation of interaction effect and supply chain resilience. Regarding the moderating effect of supplier integration on the negatively correlated relationship between supply chain complexity and supply chain robustness, a positive effect (β= .054) is found. However, the threshold significant level is not reached (p = .569 > .05). Therefore, the H2a is not supported. In the meantime, the moderating effect of supplier integration on the negatively correlated relationship between supply chain complexity and supply chain agility is confirmed to be positive and significant (β= .245, p< .01), with R2

= .381 and F-value at p < .01. In this case, H2b is supported.

Supply chain robustness

Step Variables 1 2 3

1 Control

Amount of employees -0.117 -0.102 -0.103

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Supply chain complexity -0.166* -0.145* Supplier integration 0.434** 0.427** 3 Interaction effect SC complexity * supplier integration 0.054 Adjusted R2 0.004 0.245 0.239 Δ R2 0.014 0.266 0.268 F 1.438 8.327** 9.266** *p < .05; **p < .01

Table 4.3. Linear regression analysis results of moderating effect (H2a)

Supply chain agility

Step Variables 1 2 3

1 Control

Amount of employees -0.008 -0.036 -0.004

2 Main effects

Supply chain complexity -0.270** -0.195*

Supplier integration 0.451** 0.422** 3 Interaction effect SC complexity * supplier integration 0.245** Adjusted R2 0.002 0.283 0.381 Δ R2 0.012 0.327 0.404 F 4.153* 15.561** 17.128** *p < .05; **p < .01

Table 4.4. Linear regression analysis results of moderating effect (H2b)

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moderating effect of supplier integration. In addition, supply chain with a higher level of supplier integration has a more agile supply chain than the one with a lower level of supplier integration at the starting point when comparing two lines below. This finding also reflects the direct positive impact of supplier integration on supply chain agility (β= .422, p< .01).

Figure 4.1. The moderation effect of supplier integration on supply chain complexity and supply chain agility

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Figure 4.2. Structural model (*p < .05; **p < .01)

5. Discussion and Conclusion

The current study underestimates the importance of supply chain resilience and ignores the impact of supply chain complexity on supply chain. Our study extends the empirical study to show the direction relationship between supply chain complexity and supply chain resilience from robustness side and agility side, and consider supplier integration as a competitive capability to absorb the impact of supply chain complexity. As the results show above, our research question can be answered as follows: supply chain complexity has a negative impact on supply chain resilience on both robustness and agility sides; supplier integration as a moderator is positively related to the relationship between supply chain complexity and supply chain agility, while non-significant moderating effect is found on the relationship between supply chain complexity and supply chain robustness. The results are discussed in more details below.

5.1 Direct relationship

5.1.1 Supply chain complexity and supply chain resilience

The result of data analysis demonstrates a negative relationship between supply chain complexity and supply chain resilience. When comparing the results of H1a and H1b, the negative impact of supply chain complexity on supply chain agility (β = -.407) is found to be larger than that on supply chain robustness (β = -.278). That means the agile supply chain is more susceptible to complexity than robust supply chain.

Supply chain complexity has a larger impact on agility rather than robustness mainly because supply chain agility is more engaged in speed and flexibility, whereas supply chain robustness relies heavily on the preparation for disruptions. The reasons could be discussed from both dimensions. Firstly, the timely and effective response is the first priority in an agile supply chain. The more complex a supply chain is, the more varieties a supply chain has, thus the longer response time it needs (Simangunsong, Hendry & Stevenson, 2012). It can be concluded that supply chain complexity builds a huge barrier for supply chain to cope with changes in a timely and flexible manner.

Robustness

Agility

supply chain resilience H1a: -.278*

H2a: .054

Supply chain complexity

Supplier integration

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Whereas, although supply chain complexity would negatively influence supply chain robustness by increasing difficulties in anticipation and preparation for potential disruptions due to various actors involved, it also has a positive impact on supply chain robustness in other ways. For example, multiple sources of supply would make a supply chain more robust, because the flow of material from supplier A could be sustained if the flow from supplier B is disrupted (Tang, 2006). As mentioned in section 2.2, robustness is more maintaining rather than responding. In this case, the varieties of supply chains decrease the capability of keeping a stable supply chain due to the difficulties in management, while in the other way also provides supply chain with an opportunity to maintain the current state since the dependency on a sole supplier is reduced. Thus, the impact of supply chain complexity on supply chain robustness is less significant than that on supply chain agility.

Overall, the result highlights the direct negative influence of supply chain complexity on building a robust and agile supply chain. Hence, the need for managing and controlling supply chain complexity is emphasized.

5.1.2 Supplier integration and supply chain resilience

Supplier integration as a moderator is also found a strong direct relationship with supply chain resilience on both agility and robustness sides. This finding is also aligned with prior research. For example, Prajogo and Olhager (2012) stated supplier integration facilitates the integrated inventory flow along the supply chain, allowing the enterprise to maintain stable under the changing environment. In addition, Moon et al. (2012) also indicated supplier integration can result in an increased capability of a company to respond to internal and external changes so as to gain or maintain a competitive advantage. This trend calls for building a resilient supply chain with the help of integration to prepare and recover from unexpected disruptions.

5.2 Moderation role of supplier integration

According to the results of hypothesis 2, it suggests that the negative effect of supply chain complexity can be partly offset by the integrated suppliers, as figure 4.1 shows. This trend calls for building supply chain resilience by using relevant strategies. One of supply chain strategies is to develop supplier integration as a competitive capability to cope with disruptions (Somers, 2009).

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in the fast and efficient responsiveness. In other words, a more complex supply chain needs to take more effort on supplier integration to develop an agile supply chain. Meanwhile, an unexpected result is a non-significant moderating effect of supplier integration on the direct relationship between supply chain complexity and supply chain robustness has been found. The probability value of the negative moderating effect is .569, which indicates that positive moderationoccurs but this moderation is not significant at the 0.1 level. In addition, although supplier integration does have a weak positive effect (β = .054) on the relationship between supply chain complexity and supply chain robustness, the value of β is too small to have an obvious effect on the relationship (when compare the results of step 2&3 in table 4.3). On the one hand, according to the prior research, supplier integration can improve material availability through a better inventory management and leads to better preparation for unexpected disruptions (Droge et al., 2004). On the other hand, Norrman and Jansson (2004) illustrated that supplier integration can lead to an increase in mutual influences, as the more integrated suppliers get, the more likely risks in one affect the other links in the chain, resulting in the failure in maintaining a stable state. These two competing pressures may help to explain our non-significant finding. Overall, hypothesis 2a is not accepted, although the low reliability value (Cronbach’s Alpha value .607 < .70) and limited sample size might cause bias on significance.

5.3 Managerial and theoretical implications

From a theoretical perspective, this study identifies a lack-of-direction relation between supply chain complexity and supply chain resilience from robustness side and agility side. Also, by extending Aitken, Bozarth and Garn (2016), we explore supplier integration as a capability for accommodating supply chain complexity. In this research, based on the DCV, supplier integration as the firm’s capability is proven to be able to absorb the effects of supply chain complexity and increase supply chain agility.

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managers with insights that investing in integrating suppliers for different purposes. To be specific, when a firm is operating in a complex supply chain, it would be a good idea to develop supplier integration when manager aims to improve supply chain resilience in terms of agility; whereas, it would be less suitable to invest in integrating suppliers when manager wants to improve supply chain resilience in terms of robustness.

5.4 Limitations and future research ideas

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Reference:

Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological bulletin, 103(3), 411.

Aitken, J., Aitken, J., Bozarth, C., Bozarth, C., Garn, W., & Garn, W. (2016). To eliminate or absorb supply chain complexity: A conceptual model and case study. Supply Chain

Management: An International Journal, 21(6), 759-774.

Blome, C., Paulraj, A., & Schuetz, K. (2014). Supply chain collaboration and sustainability: a profile deviation analysis. International Journal of Operations & Production Management, 34(5), 639-663.

Bakshi, N., & Kleindorfer, P. (2009). Co-opetition and investment for supply-chain resilience. Production and Operations Management, 18(6), 583-603.

Bernardes, E. S., & Hanna, M. D. (2009). A theoretical review of flexibility, agility and responsiveness in the operations management literature: Toward a conceptual definition of customer responsiveness. International Journal of Operations Production Management, 29(1), 30-53.

Blackhurst, J., Dunn, K.S. and Craighead, C.W. (2011). An empirically derivedframework of global supply resiliency. Journal of Business Logistics, 32(4), 374-391.

Bode, C., Wagner, S. M., Petersen, K. J., & Ellram, L. M. (2011). Understanding responses to supply chain disruptions: Insights from information processing and resource dependence perspectives. Academy of Management Journal, 54(4), 833-856.

Bozarth, C. C., Warsing, D. P., Flynn, B. B., & Flynn, E. J. (2009). The impact of supply chain complexity on manufacturing plant performance. Journal of Operations Management,

27(1), 78-93.

Brandon-Jones, E., Squire, B., & Van Rossenberg, Y. G. (2015). The impact of supply base complexity on disruptions and performance: the moderating effects of slack and visibility.

International Journal of Production Research, 53(22), 6903-6918.

BrandonJones, E., Squire, B., Autry, C. W., & Petersen, K. J. (2014). A Contingent Resource Based Perspective of Supply Chain Resilience and Robustness. Journal of Supply Chain

Management, 50(3), 55-73.

Braunscheidel, M.J. and Suresh, N.C. (2009). The organizational antecedents of a firm’s supply chain agility for risk mitigation and response. Journal of Operations Management, 27(2), 119-140.

Bruce, M., Daly, L. and Towers, N. (2004). Lean or agile: a solution for supply chain management in the textiles and clothing industry? International Journal of Operations &

Production Management, Vol. 24 No. 2, pp. 151-170.

Cao, M., Vonderembse, M., Zhang, Q. and Ragu-Nathan, T.S. (2010). Supply chain collaboration: conceptualisation and instrument development. International Journal of

Production Research, 48(22), 6613-6635.

(24)

semiconductor equipment supply chain. Management Science, 49 (12), 1653-1670.

Caridi, M., Crippa, L., Perego, A., Sianesi, A., & Tumino, A. (2010). Do virtuality and complexity affect supply chain visibility? International Journal of Production Economics,

127(2), 372-383.

Chandler Jr., A. D., & Chandler Jr, A. D. (1962). Strategy and structure: chapters in the

history of the industrial enterprise.

Chen, H., Daugherty, P. and Landry, T. (2009a). Supply chain process integration: A theoretical framework. Journal of Business Logistics, 30(2), pp27-46

Chen, F., Drezner, Z., Ryan, J. K., & Simchi-Levi, D. (2000). Quantifying the bullwhip effect in a simple supply chain: The impact of forecasting, lead times, and information.

Management science, 46(3), 436-443.

Choi, T. Y., and D. R. Krause. (2006). The Supply Base and Its Complexity: Implications for Transaction Costs, Risks, Responsive- ness, and Innovation. Journal of Operations

Management, 24 (5): 637–652.

Christopher, M. and Peck, H. (2004). Building the resilient supply chain. The International

Journal of Logistics Management, 15(2), pp. 1-14.

Craighead, C. W., Blackhurst, J., Rungtusanatham, M. J., & Handfield, R. B. (2007). The severity of supply chain disruptions: design characteristics and mitigation capabilities.

Decision Sciences, 38(1), 131-156.

Christopher, M. & Holweg, M., 2011. “Supply Chain 2.0”: managing supply chains in the era of turbulence. International Journal of Physical Distribution & Logisitics Management, 41(1), pp.63–82.

Cole, R. E., & Scott, W. R. (2000). The quality movement & organization theory. Sage Publications, Inc.

Cousins, P. D., & Menguc, B. (2006). The implications of socialization and integration in supply chain management. Journal of operations management, 24(5), 604-620.

Das, T. K., & Teng, B. S. (2000). A resource-based theory of strategic alliances. Journal of

management, 26(1), 31-61.

Datta, P. P., Christopher, M., & Allen, P. (2007). Agent-based modeling of complex production/distribution systems to improve resilience. International Journal of Logistics Research and Applications, 10(3), 187–203.

Devaraj, S., Krajewski, L., & Wei, J. C. (2007). Impact of eBusiness technologies on operational performance: the role of production information integration in the supply chain. Journal of Operations Management, 25(6), 1199-1216.

Das, A., Narasimhan, R., & Talluri, S. (2006). Supplier integration—finding an optimal configuration. Journal of Operations Management, 24(5), 563-582.

Droge, C., Jayaram, J., & Vickery, S. K. (2004). The effects of internal versus external integration practices on time-based performance and overall firm performance. Journal of

(25)

De Leeuw, S., Grotenhuis, R., & van Goor, A. R. (2013). Assessing complexity of supply chains: Evidence from wholesalers. International Journal of Operations & Production

Management, 33(8), 960-980.

Eisenhardt, K. M., & Martin, J. A. (2000). Dynamic capabilities: what are they?. Strategic

management journal, 1105-1121.

Ergun, O., Heier Stamm, J.L., Keskinocak, P. and Swann, J.L. (2010). Waffle house restaurants hurricane response: a case study. International Journal of Production Economics, 126(1), 111-120.

Frohlich, M. T., & Westbrook, R. (2001). Arcs of integration: an international study of supply chain strategies. Journal of operations management, 19(2), 185-200.

Fridgen, G., C. Stepanek, and T. Wolf. (2014). Investigation of Exogenous Shocks in Complex Supply Networks – A Modular Petri Net Approach. International Journal of

Production Research, 1–22.

Flynn, B.B., Huo, B. and Zhao, X. (2010). The impact of supply chain integration on performance: a contingency and configuration approach. Journal of Operations Management, 28(1), 58-71.

Fynes, B., Voss, C., & de Búrca, S. (2005). The impact of supply chain relationship quality on quality performance. International Journal of Production Economics, 96(3), 339-354.

Galbraith, J.R., (1973). Designing complex organizations. Adddison-Wesley.

Calbraith, J. R. (1974). Organization design: An information processing view. Matrix

Organization & Project Management, 381.

Gimenez, C., van der Vaart, T., & Pieter van Donk, D. (2012). Supply chain integration and performance: the moderating effect of supply complexity. International Journal of

Operations & Production Management, 32(5), 583-610.

Gray, J. V., Roth, A. V., & Leiblein, M. J. (2011). Quality risk in offshore manufacturing: Evidence from the pharmaceutical industry. Journal of Operations Management, 29(7), 737-752.

Hallikas, J., Karvonen, I., Pulkkinen, U., Virolainen, V.M. and Tuominen, M. (2004). Risk management processes in supplier networks. International Journal of Production Economics, 90 (1), 47-58.

Hendricks, K. B., Singhal, V. R., & Zhang, R. (2009). The effect of operational slack, diversification, and vertical relatedness on the stock market reaction to supply chain disruptions. Journal of Operations Management, 27(3), 233-246.

Hohenstein, N. O., Feisel, E., Hartmann, E., & Giunipero, L. (2015). Research on the phenomenon of supply chain resilience: a systematic review and paths for further investigation. International Journal of Physical Distribution & Logistics Management,

45(1/2), 90-117.

(26)

1-27.

Krause, D. R., Scannell, T. V., & Calantone, R. J. (2000). A structural analysis of the effectiveness of buying firms' strategies to improve supplier performance. Decision

Sciences, 31(1), 33-55.

Lawrence, P. R., & Lorsch, J. (1967). Organization and environment, division of research. Graduate School of Business Administration, Harvard University, Boston, MA. Isik, F. (2009). An Entropy-based Approach for Measuring Complexity in Supply Chains.

International Journal of Production Research 48 (12): 3681–3696.

Jacobs, B.W. and Subramanian, R. (2012). Sharing responsibility for product recovery across the supply chain. Production and Operations Management, 24(1), 85-100.

Jacobs, M. A., & Swink, M. (2011). Product portfolio architectural complexity and operational performance: Incorporating the roles of learning and fixed assets. Journal of

Operations Management, 29(7), 677-691.

Jüttner, U. and Maklan, S. (2011). Supply chain resilience in the global financial crisis: an empirical study. Supply Chain Management: An International Journal, 16(4), 246-259. Knemeyer, A.M., Zinn, W. and Eroglu, C. (2009). Proactive planning for catastrophic crisis: an empirical study. Supply Chain Management: An International Journal, 16(4), 246-259. strophic events in supply chains. Journal of Operations Management, 27(2), 141-153.

Leuschner, R., Rogers, D. S., & Charvet, F. F. (2013). A meta-analysis of supply chain integration and firm performance. Journal of Supply Chain Management, 49(2), 34-57. Li, W., Chow, P., Choi, T., & Chan, H. (2016). Supplier integration, green sustainability programs, and financial performance of fashion enterprises under global financial crisis.

Journal of Cleaner Production, 135, 57–70.

Manuj, I., & Sahin, F. (2011). A model of supply chain and supply chain decision making complexity. International Journal of Physical Distribution & Logistics Management, 41(5), 511-549.

Mishra, A.A., Shah, R., 2009. In union lies strength: collaborative competence in new product development and its performance effects. Operations Management. 27(4), 324-338.

Milgrom, P., & Roberts, J. (1995). Complementarities and fit strategy, structure, and organizational change in manufacturing. Journal of accounting and economics, 19(2), 179-208.

Meepetchdee, Y. and Shah, N. (2007). Logistical network design with robustness and complexity considerations. International Journal of Physical Distribution & Logistics

Management, 37(3), 201-222.

Mitchell, R. J. (1993). Path analysis: pollination. Design and analysis of ecological

experiments. Chapman and Hall, New York, New York, USA, 211-231.

Myers, M. B., & Cheung, M. S. (2008). Sharing global supply chain knowledge. MIT Sloan

Management Review, 49(4), 67.

(27)

Journal of Operations Management, 27(2), 114-118.

Norrman, A., & Jansson, U. (2004). Ericsson's proactive supply chain risk management approach after a serious sub-supplier accident. International journal of physical distribution

& logistics management, 34(5), 434-456.

Nunnally, J. (1978). C. (1978). Psychometric theory.

Ponomarov, S.Y. and Holcomb, M.C. (2009). Understanding the concept of supply chain resilience. The International Journal of Logistics Management, 20(1), 124-143.

Prajogo, D., & Olhager, J. (2012). Supply chain integration and performance: The effects of long-term relationships, information technology and sharing, and logistics integration.

International Journal of Production Economics, 135(1), 514-522.

Prajogo, D., Chowdhury, M., Yeung, A. C., & Cheng, T. C. E. (2012). The relationship between supplier management and firm's operational performance: A multi-dimensional perspective. International Journal of Production Economics, 136(1), 123-130.

Rungtusanatham, M., Salvador, F., Forza, C., & Choi, T. Y. (2003). Supply-chain linkages and operational performance: A resource-based-view perspective. International Journal of

Operations & Production Management, 23(9), 1084-1099.

Salvador, F., Forza, C., Rungtusanatham, M., & Choi, T. Y. (2001). Supply chain interactions and time-related performances: an operations management perspective. International Journal

of Operations & Production Management, 21(4), 461-475.

Shah, R., & Ward, P. T. (2003). Lean manufacturing: context, practice bundles, and performance. Journal of operations management, 21(2), 129-149.

Shah, R., & Ward, P. T. (2007). Defining and developing measures of lean production. Journal of operations management, 25(4), 785-805.

Shukla, A., Agarwal Lalit, V., & Venkatasubramanian, V. (2011). Optimizing efficiency-robustness trade-offs in supply chain design under uncertainty due to disruptions.

International Journal of Physical Distribution & Logistics Management, 41(6), 623-647.

Scholten, K., Sharkey Scott, P., & Fynes, B. (2014). Mitigation processes–antecedents for building supply chain resilience. Supply Chain Management: An International Journal, 19(2), 211-228.

Scholten, K., & Schilder, S. (2015). The role of collaboration in supply chain resilience.

Supply Chain Management: An International Journal, 20(4), 471-484.

Sivadasan, S., Efstathiou, J., Calinescu, A., & Huatuco, L. H. (2006). Advances on measuring the operational complexity of supplier–customer systems. European Journal of Operational

Research, 171(1), 208-226.

Simangunsong, E., Hendry, L. C., & Stevenson, M. (2012). Supply-chain uncertainty: a review and theoretical foundation for future research.International Journal of Production

Research, 50(16), 4493-4523.

(28)

international customer–supplier relationships. Information & Management, 51(7), 854-864. Sheffi, Y. & Rice Jr., J.B., 2005. A Supply Chain View of the Resilient Enterprise. MIT Sloan

Management Review, 47(1), pp.41–48.

Somers, S. (2009). Measuring resilience potential: an adaptive strategy for organizational crisis planning. Journal of Contingencies and Crisis Management, 17(1), 12-23.

Stock, J. R., & Lambert, D. M. (2001). Strategic logistics management (Vol. 4). Boston, MA: McGraw-Hill/Irwin.

Stecke, K. E., & Kumar, S. (2009). Sources of supply chain disruptions, factors that breed vulnerability, and mitigating strategies. Journal of Marketing Channels, 16(3), 193-226. Suri, R. (2010). It's About Time. Manufacturing Engineering, 145(4), 120-120.

Van der Vaart, T., & van Donk, D. P. (2008). A critical review of survey-based research in supply chain integration. International journal of production economics, 111(1), 42-55. Swafford, P. M., Ghosh, S., & Murthy, N. (2006). The antecedents of supply chain agility of a firm: scale development and model testing. Journal of Operations Management, 24(2), 170-188.

Tang, C. S. (2006). Robust strategies for mitigating supply chain disruptions. International

Journal of Logistics: Research and Applications, 9(1), 33-45.

Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic management journal, 509-533.

Tukamuhabwa, B. R., Stevenson, M., Busby, J., & Zorzini, M. (2015). Supply chain resilience: definition, review and theoretical foundations for further study. International Journal of Production Research, 53(18), 5592-5623.

Thompson, J. D. (1967). Organizations in action: Social science bases of administrative theory. Transaction publishers.

Venkatraman, N., & Prescott, J. E. (1990). Environment‐strategy coalignment: an empirical test of its performance implications. Strategic Management Journal, 11(1), 1-23.

Välikangas, L. (2010). The resilient organization: How adaptive cultures thrive even when

strategy fails. McGraw Hill Professional.

Wieland, A., & Marcus Wallenburg, C. (2013). The influence of relational competencies on supply chain resilience: a relational view. International Journal of when strategy fails. McGraw Hill Professional.

Wieland, A., & Marcus Wallenburg, C. (2012). Dealing with supply chain risks: Linking risk management practices and strategies to performance. International Journal of Physical

Distribution & Logistics Management, 42(10), 887-905.

Whipple, J. M., & Russell, D. (2007). Building supply chain collaboration: a typology of collaborative approaches. The International Journal of Logistics Management, 18(2), 174-196.

(29)

295-320.

Wilding, R. (1998). The supply chain complexity triangle: uncertainty generation in the supply chain. International Journal of Physical Distribution & Logistics Management, 28(8), 599-616.

Yang, Z., Aydin, G., Babich, V., & Beil, D. R. (2009). Supply disruptions, asymmetric information, and a backup production option. Management science, 55(2), 192-209.

Zhao, X., Flynn, B.B., Roth, A.V., 2006a. Decision sciences research in China: a critical review and research agenda—foundations and overview. Decision Sciences 37 (4), 451–496. Zhao, X., Huo, B., Flynn, B. B., & Yeung, J. H. Y. (2008). The impact of power and relationship commitment on the integration between manufacturers and customers in a supply chain. Journal of Operations Management, 26(3), 368-388.

The impact of supply chain complexity on supply chain resilience: the moderating effect of supplier integration