Buyer-supplier collaboration and supply chain resilience: the
moderating effect of supply chain complexity
Master Thesis
University of Groningen Faculty of Economics and Business
M. Sc. Supply Chain Management
Theme: Complexity, Resilience and Integration in Supply Chains
Supervisor: Prof. Dr. Dirk Pieter van Donk Dr. Xuan Zhang
Name: Mo Guo Student number: S2993651 Email: m.guo.3@student.rug.nl
Abstract
Purpose – This research aims at testing the direct effect of buyer-supplier collaboration on supply chain resilience as well as the moderating effect of supply chain complexity on the relationship between buyer-supplier collaboration and supply chain resilience. In terms of supply chain resilience, it is represented by the combination of supply chain flexibility and supply chain visibility.
Design/methodology/approach – This research used the methodology of survey and collect data from 115 company in the manufacturing industry in China.
Findings – The results show that buyer-supplier collaboration could positively influence supply chain resilience, regarding to both supply chain flexibility and visibility. Supply chain complexity has a negative impact on the relationship between buyer-supplier collaboration and supply chain flexibility.
Research implications – This research provides an evidence that supply chain complexity should be considered in the research of collaboration and supply chain resilience.
Practical implications – This research implies that for managers in a buyer company, they could choose the strategy to collaborate with buyers to construct resilient supply chain. Furthermore, considering the influence of supply chain complexity, managers need to pay attention to supply chain flexibility which will be less strong under the high level of complexity.
Originality/value – This research contributes to validate and generalize the positive influence of buyer-supplier collaboration on supply chain resilience.
Keywords Buyer-supplier collaboration, Supply chain resilience, Supply chain
Table of Contents
1. Introduction ... 3
2. Theoretical Background ... 5
2.1.
Buyer-supplier collaboration ... 6
2.2.
Supply chain resilience ... 7
2.3.
Supply chain complexity ... 8
2.4.
Development of hypotheses ... 10
2.4.1.
The direct effect of buyer-supplier collaboration on supply
chain flexibility and visibility ... 10
2.4.2.
The moderating effect of supply chain complexity ... 11
3. Methodology ... 13
3.1.
Development of questionnaire ... 13
3.2.
Sample and data gathering ... 14
3.3.
Data reduction and analysis ... 17
4. Results ... 18
4.1. Factor analysis ... 18
4.2. Testing hypotheses ... 21
5. Conclusion and discussion ... 24
6. Limitation and further research ... 26
1. Introduction
In this global and vulnerable business environment, supply chain resilience becomes more crucial for an organization to adopt and survive in the current complex business environment, in terms of dealing with supply chain risks and disruptions (Chopra & Sodhi, 2014; Gunasekaran et al., 2015). The positive effect of supply chain collaboration on supply chain resilience has been broadly examined and proved (Hohenstein, et al., 2015; Tukamuhabwa et al., 2015; Kamalahmadi & Parast 2016). Soosay and Hyland (2015) recently provided a comprehensive review of literatures focusing on supply chain collaboration. According to their research, supply chain collaboration consists of multiple dimensions including vertical collaboration, horizontal collaboration and lateral collaboration. In which, the field of “buyer-supplier collaboration” (Soosay & Hyland, 2015, p. 620), as mainly focusing on the upstream (i.e. supply side) of the supply chain, is the most attractive research area in supply chain collaboration (Barratt, 2004; Flynn et al., 2010; Blome et al., 2014; Soosay & Hyland, 2015). However, only few researchers focused on the relationship between buyer-supplier collaboration and supply chain resilience (Scholten & Schilder, 2015), but regarded buyer-supplier collaboration as a strategy to decrease cost and gain competitive advantages (Soosay & Hyland, 2015). It is of upmost importance to investigate on buyer-supplier collaboration. Because in dealing with supply chain risks and disruptions, supplier should be regarded as the first consideration (Blackhurst et al., 2011) and many buyers are facing the challenge of effectively collaborating with suppliers (Li et al., 2010). Therefore, the gap in the study on the relationship between buy-supplier collaboration and supply chain resilience is existing and needed to investigate on. To be specific, as one dimension of supply chain collaboration which could enhance supply chain resilience, buyer-supplier collaboration also has a positive influence on supply chain resilience. The first research question could be proposed as,
Typically, supply chain resilience is a broad concept including multiple perspectives. From the general perspective, it encompasses two counter dimensions, which could be identified as the reactive strategy and proactive strategy (Tukamuhabwa et al., 2015; Kamalahmadi & Parast, 2016). Supply chain flexibility, which measures the ability to deal with uncertainties and changes, is a representative of the reactive perspective (Hohenstein et al., 2015). Supply chain visibility, however, has been regarded as a proactive strategy to enhance the transparency of the supply chain (Brandon-Jones et al., 2014; Gunasekaran et al., 2015). These two dimensions are investigated by most researchers in terms of supply chain resilience (Scholten & Schilder, 2015), hence the combination of them could represent supply chain resilience.
buyer-supplier collaboration (Sivadasan et al., 2010) and supply chain resilience (Sáenz & Revilla, 2014). The second research question could be proposed as,
What is the influence of supply chain complexity on the relationship between buyer-supplier collaboration and supply chain resilience?
By answering these two research questions, at first, this research could contribute to extensively examine the influence of buyer-supplier collaboration on supply chain resilience and increase the generalizability and validity by conducting the survey methodology; secondly, this research involves supply chain complexity as the moderator which could explain the contradiction of whether buyer-supplier collaboration could enhance supply chain resilience thus fills the gap. In addition, this study aims to confirm and validate the managerial strategy that a buyer should collaborate with its suppliers (Skjøtt-Larsen et al., 2007) so that provides incentives to the manager in a buyer company to adopt to collaborate with suppliers.
The structure of this paper is as follow. Section 2 will review relative literatures about buyer-supplier collaboration, supply chain resilience and supply chain complexity as well as construct theoretical background. Section 3 will explain how to implement survey in this research. Section 4 will present main results and findings. Section 5 will provide conclusions and discussions on main findings as well as theoretical and managerial implication. Section 6 will discuss limitation and give suggestions for future research.
2. Theoretical Background
construct the conceptual model for this research.
2.1. Buyer-supplier collaboration
Buyer-supplier collaboration is oriented from Soosay & Hyland (2015) and represents one dimension of supply chain collaboration (Mason et al., 2007). It initially refers to that an organization collaborates with its suppliers and buyers (Barratt, 2004). Even though many former researches focused on the topic of buyer-supplier collaboration, a unique and clear definition of which is still missing. For example, Sepkman et al. (1998) used “buyer-seller relationship” to demonstrate the close relation between buyer and supplier. Squire et al. (2009) included buyer-supplier collaboration into their study but only regarded it as cooperation. Thus the significant importance and the core in constructing buyer-supplier collaboration were ignored. It should be noted that it is difficult for an organization to effectively achieve collaborative activities. The first reason is the requirement of collaboration is relatively higher than pure strategic cooperation (Soosay & Hyland, 2015). It needs the buyer and supplier to work together closely in terms of plans making and operations executing while attempting to achieve the same goal and to gain bilateral benefits (Cao & Zhang, 2011). Besides, collaborative activities between buyers and suppliers aim at keeping long-term relationships by building up dyadic trust, communication and sharing critical information (Yang, 2013) rather than exchanging basic information (Singh & Power, 2009). Moreover, integrated processes, such as logistic activities and raw material transpiration, are important in buyer-supplier collaboration as well (Prajogo & Olhager 2012). For example, Rönnberg-Sjödin (2013) suggested that buyers are able to gain comprehensive knowledge on operating equipment if buyers and suppliers both engage in the process of equipment assembly and installation.
2016). Remaining long-term relationship emphasizes on forming strategic alliance between buyers and suppliers (Soosay et al., 2008). In contrast to short-term relationship, long-term relationship allows both parties to avoid opportunistic behaviors (Li et al., 2010; Nyaga et al., 2013) so that it could result in long-term mutual development (Lambert & Schwieterman, 2012). Furthermore, buyer-supplier collaboration is driven by information sharing (Kamalahmadi & Parast, 2016). Sharing critical and essential information includes sharing goals and sharing plans (Adams et al., 2014; Ho & Lu, 2015). It gives buyers and suppliers the probability to acquire new knowledge (Soosay et al., 2008) to understand the current status of the supply chain and to realize potential risks caused by uncertainty (Ambrose et al., 2010). Therefore, by encompassing both characteristics, in this research,
buyer-supplier collaboration is defined as, in order to achieve the common goal and
gain reciprocal advantages, the buyer collaborate with supplier through building up a long-term relationship and sharing information.
2.2. Supply chain resilience
many existing studies on supply chain resilience, Tukamuhabwa et al. (2015) proposed a feasible, appropriate and comprehensive definition of supply chain resilience that is “the adaptive capability of a supply chain to prepare for and/or respond to disruptions, to make a timely and cost effective recovery, and therefore progress to a post-disruption state of operations – ideally, a better state than prior to the disruption” (p. 5599). This research adopted and adapted this definition to define
supply chain resilience as the ability of an organization to prepare for, react to and
repair from disruptions by building up a flexible and visible supply chain. In this definition, 2 major characteristics (i.e. flexibility and visibility) are involved to describe and characterize supply chain resilience. As aforementioned, these two characteristics represent the two different types of supply chain resilience that are either proactive or reactive (Brandon-Jones et al., 2014; Gunasekaran et al., 2015; Hohenstein et al., 2015).
Table 1 provides a further definition in detail with regard to the aforementioned characteristics.
Table 1: Definition of Flexibility and Visibility
Characteristic Definition
Flexibility The ability for an organization to response to unforeseen events happened in the supply chain.
(Jüttner & Maklan, 2011)
Visibility The capability for a company to detect the status of the supply chain.
(Jüttner & Maklan, 2011)
2.3. Supply chain complexity
2.4. Development of hypotheses
As a conclusion of the literature review, there are potentially either direct effects or moderating effects among the four abovementioned factors. Specifically, on the one hand, buyer-supplier collaboration might positively direct influence supply chain flexibility and visibility; on the other hand, supply chain complexity might negatively moderate those positive effects. The following parts aim at developing hypotheses for the direct and moderating effects.
2.4.1. The direct effect of buyer-supplier collaboration on supply chain flexibility and visibility
In this research, buyer-supplier collaboration consists of two perspectives, which are long-term relationship and information sharing. Through collaboration, buyer and supplier could align their goal towards the same orientation (Vachon & Klassen, 2008). In such a close relationship, suppliers have a stronger responsiveness to assist buyer by providing the flexibility in sourcing (Mendonça Tachizawa & Giménez Thomsen, 2007). In addition, due to the bidirectional relationship between buyers and suppliers, collaborative activities such as sharing information smoothly and efficiently could shorten the recovery time of both parties (Pettit et al., 2010). Because complementary work contributed bilaterally would enhance the competence on both sides (Møller et al., 2003). This extra capability could enhance supply chain flexibility, which allows an organization to keep extra capacities in dealing with fluctuation from downstream or shared capacities in coping with shortages of upstream (Chopra & Sodhi 2004). Thus, the first hypothesis could be proposed as,
H1a: Buyer-supplier collaboration has a positive influence on supply chain flexibility.
through the pipeline from one end to another and outguessing the condition of each node by increasing visibility (Christopher & Peck, 2004). To gain excellent visibility, an organization could put efforts on collecting information and improving knowledge (Brandon-Jones et al., 2014). Developing buyer-supplier collaboration is significantly and essentially helpful to progress these strategies because close relationship could guarantee the safety of data, information and knowledge (Prajogo & Olhager, 2012). Therefore, the second hypothesis could be raised as,
H1b: Buyer-supplier collaboration has a positive influence on supply chain visibility.
2.4.2. The moderating effect of supply chain complexity
As noted before, even though buyer-supplier collaboration is assumed to positively influence supply chain resilience, there might be other influence factors outside the relationship due to change of context and play the role of moderator. Supply chain complexity, which is seen as a context variable in the supply chain must be considered and dealt with (Soosay & Hyland, 2015). In this research, supply chain complexity is taken into consideration because along with the variation of supply chain activities, decisions or strategies on collaboration from an organization might be changed in order to respond to the changes. The logic is, under such a circumstance, the buyer could still maintain the collaborative relationship with its suppliers but the influence of which on the relationship between collaboration supply chain resilience might be moderated (Bode & Wagner, 2015).
in the situation. In other words, the buyer has to sacrifice supply chain flexibility for dealing with supply chain complexity. For example, the demand variability could increase the level of safety stock of suppliers in order to fulfill requirements of buyers (Sivadasan et al., 2010). Safety stock strategy is widely used in the current business environment but an organization incurs lavish costs by maintaining high level of safety stock (Azevedo et al., 2013). This is the situation that due to the limitation of capacity, a buyer needs to make choice. In this circmstance, the impact of collaboration on flexibility will be less significance. Therefore, the third hypothesis could be described as,
H2a: Supply chain complexity has a negative influence on the relationship between buyer-supplier collaboration and supply chain flexibility.
Supply chain visibility requires an organization could see through the supply chain (Jüttner & Maklan, 2011). Buyer-supplier collaboration plays an important role here because it gives the stakeholders the ability to detect not only the intra-organizational status but the inter-organizational status (Soosay & Hyland, 2015). But the manufacturing instability will threaten this relationship by limiting the accuracy of the shared information (Bozarth et al., 2009). Supply chain complexity introduces more uncertainties and dynamics into the supply chain (Bozarth et al., 2009). In other words, under the high level of supply chain complexity, the fluency of the activities is broken. Therefore, it gives more pressure to buyers and forces them to overly focus on competition by limiting the ability to acquire useful information from its suppliers (Haberland et al., 2015). In this situation, the relationship between buyer and supplier is still collaborative, however, the focus of the buyers is shifted from increasing supply chain visibility to other place due to the pressure from supply chain complexity. Collaboration, under such a circumstance, could be less helpful in increasing visibility. Therefore, the fourth hypothesis could be illustrated as,
buyer-supplier collaboration and supply chain visibility.
In conclusion, by summarizing the abovementioned 4 hypotheses, a conceptual model could be drawn as,
Figure 1: Conceptual Model
3. Methodology
The aim of this research is to investigate on the influence of supply chain complexity on the relationship between buyer-supplier collaboration and supply chain resilience, therefore the method of survey is feasible and appropriate in this condition for the purpose of theory testing (Karlsson, 2009). Additionally, by conducting this survey, it is also response to the suggestion of Scholten and Schilder (2015) to test the validity and generalizability of their research findings about the influence of collaboration on supply chain resilience.
3.1. Development of questionnaire
of two academics, in order to guarantee in the translation process questions and items were not changed.
Even though all data collected were share among the group, only questions relevant to this research were selected. All items are measured on a five-point Likert scale (1 = strongly disagree, 5 = strongly agree). According to the conceptual model (see Figure 1), this research consists of 4 dimensions. Firstly, buyer-supplier collaboration was operationalized by using 5 predetermined items adopted from Prajogo and Olhager (2012). These items aim at capturing the level of buyer-supplier collaboration in terms of long term relationship as well as information sharing which were defined as the two perspectives constructing this dimension. Secondly, supply chain flexibility was measured by using 3 predetermined items adopted from Pettit et al. (2013). These items aim at capturing the ability of a firm to response to potential changes that might happen and impact the operation of the supply chain. Thirdly, 3 predetermined items adopted from Pettit et al. (2013) were chosen to measure supply chain visibility. These items aim at capturing the ability of a company to realize the status of activities in the supply chain. Fourthly, supply chain complexity was operationalized by using 5 predetermined items adopted from Bozarth et al. (2009). These items are in a reversed scale thus the results needed to be modified before analysis (i.e. reverse score = 6 – original score). These items focus on three perspectives of supply chain complexity (i.e. upstream, internal and downstream).
3.2. Sample and data gathering
professional survey service provider in China who has extensive access to respondents (over 2.6 millions). It provides sample service including survey distributing to and collecting from the target sample (i.e. industry of the company and position of the respondents). More than 90% of universities, research institutions and organizations in multiple industries in China, therefore the validity and reliability of the data could be guaranteed. The target industry is the manufacturing industry because it is highly relevant to the complexity, resilience and integration in supply chains. On the one hand, a manufacturer is influenced by upstream, internal and downstream complexity and it has incentives to integrate with its stakeholders and build up resilience; on the other hand, a manufacturer plays a role of a buyer which makes it suitable for doing this research. The target respondent was confined to middle or above level managers who have a broader and comprehensive vision on the supply chain operation of their company.
In this 142 collected questionnaires, 20 respondents spent less than 600 seconds to complete the survey which leads to the results from them were not accepted. In addition, for measuring the buyer-supplier collaboration, all items focus on perspectives of both buyers and suppliers. However, as reflected in the results, there are 7 respondents merely focusing on their relationship and activities with buyers, which were seen as inaccurate responses. Therefore, after data elimination, there are 115 valid results retained for the further analysis. The demographic characteristics of these 115 respondents are presented in Table 4 below.
Table 4: Demographic characteristics of respondents
Job title
Manager 39
Director 14
Officer 5
Others or Not specified 47 Industry classification
Electronic or electrical equipment and components 36
Constructional materials 17
Industrial and commercial machinery 10
Food and beverage 3
Transport equipment 11
Pharmaceutical industry 4
Chemical industry 1
Miscellaneous manufacturing industries 9
Not specified 24
Firm size (In 2015, approximately how many employees are in your company?)
Number of respondents
Less than 20 (including) 10
Between 20 and 100 (including) 23
Between 100 and 300 (including) 30
Between 300 and 1000 (including) 25
More than 1000 23
Not specified 4
Work experience (How long have you been working in supply chain management (or relevant position) in this company?)
Less than 1 year (including) 14
Between 1 year and 3 years (including) 30
Between 3 years and 5 years (including) 29
More than 5 years 37
Not specified 5
that respondents are most in the high ranked position and have a clear vision of their organization. The firm size was measured by the number of employees in a company. The sample covers small (29%), medium (48%) and large (20%) size company so that the generalizability is guaranteed. Moreover, the work experience of respondents was measured specifically focusing on the supply chain management position. Over 57% of them have been working in supply chain management in their company more than 3 years so that it further confirms the validity and reliability of the data they provided.
3.3. Data reduction and analysis
Following the guidance of Hair et al. (2009), the data reduction was conducted with all the 27 items in SPSS (version 23) by using the principal component analysis (PCA) with the varimax rotation. The results of communalities indicate that all items share more than 50% of their variance with the other items, which further imply that all items are appropriate to be processed with the rotation (Russel, 2002). Moreover, the results of Kaiser-Meyer-Olkin (KMO) Measure and Bartlett's Test are presented in Table 2 below, in which, the KMO value (0.78, greater than the recommended value of 0.60) indicates that the sample size is adequate for a factor analysis (Hair et al., 2009). Besides, the χ2 is 583.74 (df = 120, p = 0.000), which illustrates that there is
Table 2: KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. 0.78 Bartlett's Test of Sphericity Approx. Chi-Square 583.74
df 120
Sig. 0.000
4. Results
In this section, the main results of the factor analysis and the tests of the four hypotheses are presented.
4.1. Factor analysis
The objectives of a factor analysis are to create clusters of large correlation coefficients between subsets of items as well as to eliminate items which have small coefficient of factor loadings (Hair et al., 2009). Meanwhile, a sequences of tests were implemented by using exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) in order to measure the unidimensionality, construct validity and scale reliability. The consequences of the rotation (i.e. retained items) with the results of Cronbach’s α value, composite reliability (CR) and average variance extracted (AVE) of each factor are presented in Table 3 below.
Table 3: Results of varimax rotation and Cronbach’s α
Component
1 2 3 4
Buyer-supplier collaboration (Cronbach’s α = 0.77, CR = 0.83, AVE = 0.50)
We expect our relationship with key suppliers to last a long time. (BSC1)
0.74
The key suppliers see our relationship as a long-term alliance. (BSC2)
0.72
company. (BSC3)
Key suppliers are provided with any information that might help them. (BSC4)
0.60
We keep each other informed about events or changes that may affect the other party. (BSC5)
0.72
Supply Chain Flexibility (Cronbach’s α = 0.71, CR = 0.74, AVE = 0.50)
Ability to change quantity of supplier’s order. (SCF1) 0.55 Ability to change delivery times of supplier’s order.
(SCF2)
0.79
Ability to reduce development cycle times. (SCF3) 0.75
Supply Chain Visibility (Cronbach’s α = 0.65, CR = 0.73, AVE = 0.47)
We have information systems that accurately track all operations. (SCV1)
0.75
We have real-time data on location and status of supplies, finished goods, equipment, and employees. (SCV2)
0.70
We have regular interchange of information among suppliers, customers, and other external sources. (SCV3)
0.61
Supply Chain Complexity (Cronbach’s α = 0.73, CR = 0.78, AVE = 0.42)
All of our customer desire essentially the same products. (SCC1)
0.61
Our total demand, across all products is relatively stable. (SCC2)
0.74
Manufacturing demands are stable in our firm. (SCC3) 0.73 The master schedule is level-loaded in our plant, from
day to day. (SCC4)
0.53
We can depend upon on-time delivery from our suppliers. (SCC5)
Eigenvalue 4.73 2.11 1.37 1.12 Percentage of variance explained (%) 29.59 42.74 51.29 58.26 In terms of the unidimensionality, which refers to that a group of items measures just one common dimension (Hattie, 1985). As illustrated in Table 3, each item under the same dimension only loads on a single factor (component) without cross-loading. This result confirms the unidimensionality. Besides, each factor is measuring by at least three retained items so that the minimum requirement is met. In addition, the four factors with eigenvalue greater than 1 explain 58.26% of the variance and there is no factor accounts for more than 30% of the total variance.
Furthermore, in order to establish the construct validity, the CFA measurement model was further conducted by using the R project (version 3.3.0) with the lavaan package (version 0.5-20). As shown in the results, the model fit indices (χ2/df = 1.65, p < 0.001,
CFI = 0.87, IFI = 0.88, RMSEA = 0.08, GFI = 0.86) indicates that the new model after the data reduction fits the data well (Jöreskog, 1969). Moreover, comparing to the original model (χ2/df = 1.81, p < 0.001, CFI = 0.75, IFI = 0.76, RMSEA = 0.08,
Table 4: Descriptive statistics and Pearson correlations Mean Standard Deviation 1 2 3 4 5 1. Market experience 4.19 1.17 1 2. Buyer-supplier collaboration 4.15 0.59 0.06 1 3. Supply chain flexibility 3.92 0.63 0.18 0.32** 1 4. Supply chain visibility 3.96 0.59 0.09 0.22* 0.46** 1 5. Supply chain complexity 2.21 0.59 -0.21* -0.28** -0.45** -0.47** 1
*. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed).
The test of scale reliability was conducted by examining the Cronbach’s α and CR. In general, the scores of Cronbach’s α for each factor lie in the acceptable level (greater than or close to 0.70), which means that those items are reliable and consistent (Hair et al., 2009). Moreover, the values of CR for each factor are greater than 0.70 implying that the factors capture more variance than the error components (Zhang et al., 2016). In summary, the abovementioned analysis about collected data confirms the unidimensionality, construct validity and scale reliability of the models.
4.2. Testing hypotheses
and 1.114. Therefore, it confirms that magnitude of multicollinearity within the set of independent variables is negligible (Hair et al., 2009).
Both the direct effect analyses and the moderating effect analyses were completed by using the linear regression in SPSS. In the analysis of the direct effect, the market experience of a company, which was measured by the number of years has the company been in business, was selected as the control variable. Because for the company who has more experience in business, on the one hand, it is more likely to construct a collaborative relationship with it suppliers; on the other hand, it has a greater chance to meet more disruption so that to have a more resilient supply chain. (Ambulkar et al., 2015). The results of which are presented in Table 5 and Table 6 separately.
Table 5: Results of Regression Analyses (direct model)
Supply Chain Flexibility Supply chain Visibility
Market experience 0.09 0.04 Buyer-supplier collaboration 0.32** 0.219* R2 0.13 0.06 Adjusted R2 0.11 0.04 F 8.10** 3.31* ΔR2 0.09 0.05 ΔF 12.04** 5.72* Notes: *p < 0.05; **p < 0.001
collaboration has a positive and significant influence on supply chain flexibility (β = 0.32, p < 0.01) as well as supply chain visibility (β = 0.219, p < 0.05). Thus, both hypotheses cannot be rejected at significance level of 5%.
The moderating effect analysis was conducted by introducing an interaction variable, which is calculated by independent variable multiplied with moderator variable (Fairchild & MacKinnon, 2009). The results of which are presented in Table 6 below.
Table 6: Results of Regression Analyses (moderation)
Supply Chain Flexibility Supply chain Visibility Buyer-supplier collaboration 0.15** 0.07
Supply chain complexity -0.25*** -0.27***
Interaction -0.08* -0.04 R2 0.26 0.24 Adjusted R2 0.24 0.22 F 13.30*** 11.62*** ΔR2 0.26 0.24 ΔF 13.30*** 11.26*** Notes: *p < 0.05; **p < 0.01; ***p < 0.001
Hypotheses 2a and 2b aim at revealing the moderating effects of supply chain complexity. As shown in Table 6, the result indicates that supply chain complexity has a negative and significant moderating effect (β = -0.08, p < 0.05) on the relationship between buyer-supplier collaboration and supply chain flexibility whereas regarding to supply chain visibility, the moderating effect is not significant (β = -0.04, p > 0.05). Therefore, H2a is supported while H2b is not at significance level of 5%.
condition of supply chain flexibility was defined into the low level and the high level. Comparing to the situation of low level of supply chain complexity, buyer-supplier collaboration has a stronger positive influence on supply chain flexibility (refer to the two slopes).
Figure 2: Plots of moderating effect with respect to supply chain flexibility
5. Conclusion and discussion
hand, it reveals that supply chain complexity has a negative influence on creating a flexible supply chain while using the strategy of buyer-supplier collaboration.
The former results conform to expectations and confirm the usefulness of the proposed strategy on developing a resilient supply chain, as suggested in Scholten and Schilder (2015). It indicates that while a supplier attempting to build a closer relationship with its stakeholders, the supply chain could also be more flexible. It means that under the new network among stakeholders in the supply chain, each partner could have a stronger ability to response to changes that might be incurred from either upstream or downstream as well as occurred internally in the company.
The latter results, however, are partially in line with expectations. Specifically, only the moderating effect of supply chain complexity on constructing a flexible supply chain through buyer-supplier collaboration is substantiated. Therefore, the non-significant moderating effect of supply chain complexity on the positive relationship between buyer-supplier collaboration and supply chain visibility deserves specific attention. These research focus on the manufacturing industry, and all items used for measuring supply chain complexity are relevant to production no matter which type of complexity it is (i.e. upstream, internal and downstream complexity). These items reflected the fluctuation of the demand, order and production process. In this situation, if the buyer and supplier has close relationship and share key information as usual, they will still have the clear vision of the status of their supply chain. Comparing to acquiring supply chain flexibility, a supplier could gain supply chain visibility in stable by collaborating with its suppliers.
moderating effect of supply chain complexity has not been elaborately investigated in this specific research area. This research focuses on both dimensions and uses quantitative method to test the relationship therefore fills the gap and increases the validity of the conclusion from previous studies (e.g. Scholten & Schilder, 2015). In addition, this research further confirms that supply chain complexity plays the role of context variable and has the moderating effect. Therefore, researches under the topic of collaboration and resilience should not ignore it, which is in line with the suggestion of Soosay and Hyland (2015).
This research also provides implications for managers. For a buyer, to collaborate with supplier is a good way to build up supply chain resilience, which are crucial for company to survive and recover from disruption. Buyer-supplier collaboration consists of multiple dimensions but at first the manager should focus on two key perspectives which are long-term relationship and information sharing. To build up long-term relationship, managers from both parties need to align the goals of their companies towards a common orientation. They need to collaborate to gain reciprocity which is seen as the foundation of buyer-supplier collaboration. In this process, the information sharing or communication plays a vital role. Information exchange should be more frequently and transparently so that each party could gain effective message efficiently. In conclusion, managers should choose to collaborate with buyer and/or supplier because collaboration could resist the negative influence of supply chain complexity.
6. Limitation and further research
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