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Abstract
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1. Introduction
Information communication technology (ICT) is indispensable for today’s business, as it is widely used in various aspects in our life and business activities. The role of ICT in operation management has been widely discussed. It is uncontested that ICT benefits operational performance either directly or indirectly (e.g., Zhang et al. 2011; Bayraktar et al. 2009 etc.). Some researches indicate that ICT is directly conducive to operational performance (Bayraktar et al. 2009; Sanders 2008; Paulraj et al. 2008; Ye & Wang 2013). Some believe that ICT can enhance operational performance by improving functional performance, customer responsiveness, supply chain agility, supply chain management and operational coordination, etc. (e.g., Jayaram et al. 2000; Ngai et al. 2011; Sanders 2008; Zhang et al. 2011). Furthermore, in today’s business environment, which is pressured by a high level of uncertainty and by the unpredictability of the market, it easily leads to disruptions in the supply chain. This dynamic environment encourages organizations to build resilience into their supply chains. However, the role of ICT in supply chain resilience is rarely mentioned. Mensch et al. (2015) state that implementing ICT in collaboration with certain strategies can produce a resilient supply chain. Yet their work is limited on the result of discrete event simulation. Theoreticians have yet to consider the direct impact of ICT on supply chain resilience.
ICT has normally been analyzed into information technology and communication technology (Hollenstein 2004). Most studies consider only the impact of information technology on supply chain resilience; few investigate the role of communication technology. First, as reviewed by Tukamuhabwa et al. (2015), use of information technology is regarded as both a proactive and a reactive strategy for supply chain resilience. It has been suggested that information technology enhances connectivity and facilitates other resilience strategies, such as collaboration. Second, it is claimed that organizational capital resources and communication networks are positively related to supply resilience (Blackhurst et al. 2011). The fact that communication technology is regarded as an intangible asset that can strengthen an organization’s communication network demonstrates that communication technology influences supply chain resilience. Finally, Mensah et al. (2015) provide a rare result upon describing the relationship between ICT directly to supply chain resilience. They used discrete event simulation to show that ICT can facilitate supply chain resilience through certain strategies (e.g., by implementing six sigma, by implementing lean production and JIT delivery, etc.). To sum up, extant studies touch upon the relationship between ICT and supply chain resilience. On one hand, ICT is the most widely used technology; supply chain resilience, on the other hand, is urgently needed to strengthen supply chain in this turbulent environment (Christopher & Holweg 2011). Given the essential roles of ICT and supply chain resilience in today’s business, it is important to analyze their direct impact.
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consequently hinders an organization’s willingness to utilize information technology as a coordination mechanism (Bryan Jean et al. 2014; Pettit et al. 2013). Ngai et al. (2011) indicate that a comparatively simple supply chain may not need sophisticated ICT to facilitate supply chain competence. A less complex supply chain is a simple supply chain construction, while sophisticated ICT mainly contain intensive application on ICT. Accordingly, environmental uncertainty may also influence the role of ICT on supply chain competence. Moreover, one common attribution of uncertainty is supply chain complexity (de Leeuw et al. 2013). Specifically, complexity upstream leads to more turbulence and severely influences the downstream supply chain (according to bullwhip theory). Therefore, this paper considers upstream complexity as a specific attribution of environmental uncertainty, which might influence the impact ICT has on supply chain resilience.
Therefore, for both academic and practical purposes, it is important to understand the relation between ICT usage and supply chain resilience in different conditions. Building on prior research (Blackhurst et al. 2011; Mensah & Merkuryev 2015), this study investigates ICT’s direct impact on supply chain resilience. Since there is a trend to build resilience into supply chains to prevent and to better react to any disruptions, it is essential to determine whether ICT can directly impact resilience. Consequently, the following research question will be addressed: Does ICT usage directly influence supply chain resilience? Furthermore, based on prior research (Bryan Jean et al. 2014; Ngai et al. 2011; de Leeuw et al. 2013), our second question is this: How does upstream complexity influence the relationship between ICT and supply chain resilience?
The focus of this paper is on determining how ICT directly influences supply chain resilience in different degrees of upstream complexity. The study is conducted via survey-based research using data collected from Chinese manufacturers. The results will provide insight into both theoretical and managerial aspects of the problem. First, it will extend the limited studies that use survey-based research to determine the direct relation between ICT and supply chain resilience. Second, it will analyze the ICT and resilience relationship with respect to various complexity conditions. Third, it should provide a basic understanding of building a resilient supply chain so that managers can adjust ICT usage to the complexity level of their upstream supply chain.
The discussion that follows has the following structure: theoretical background, hypothesis development, methodology, results, discussion and conclusion.
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2. Theoretical background
The relevant literature is reviewed in this section. We first discuss concepts and then ground our theories in the study.
2.1. Information communication technology (ICT)
Information communication technology is normally described as an ability to use information technology in external and/or internal organizational communication. Given cheap storage and processing of data, information grows cheaper and easier to access in today’s business environment (Bloom et al. 2014). In contemporary research, ICT is generally distinguished from inter- and intra-organizational. Explicit technologies are indicated for different types of ICT.
First, inter-organizational ICT is known as an external ICT resource, which is used to connect external organizations such as suppliers, customers, partners, etc. Use of inter-organizational ICT usually relates to information systems for linking, sharing and coordinating with external supply chain partners. All information technology is considered that is used to facilitate better connections with external organizations. Typical technologies include Enterprise Resource Planning (ERP), Radio Frequency Identification (RFID), etc. It is possible to transmit and process deep, rich information in a quick and cost effective manner. In addition, the use of inter-organizational ICT results in a higher level of supply chain integration, which eventually improves performance (Jayaram et al. 2000). Second, intra-organizational ICT is known as an internal ICT resource, which is used to improve information quality and acts as a condition for effective supply chain integration. Use of intra-organizational ICT usually relates to internal material planning, scheduling, controlling and monitoring of internal processes. Typical technologies include Material Resource Planning (MRP/MRP II), Advanced Planning Systems (APS) and Manufacture Execution System (MES) for production management, etc. In addition, intra-organizational ICT is able to synchronize information with external and improves information quality.
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2.2. Supply chain resilience
Because today’s supply chain tends to grow in both length and complexity (Blackhurst et al. 2011), risk of disruption frequently occurs (Bozarth et al. 2009; Vachon & Klassen 2002). Disruptions of the supply chain can be related to any potential turbulence of material, goods and information flow. Organizations therefore engage mostly in risk management by identifying risk sources. However, risk of disruption is inevitable in today’s business, especially given risks from unpredictable environmental disasters. Therefore, organizations can use risk management to mitigate risks, but they cannot prevent risks (Skipper & Hanna 2009). To help organizations react better to supply chain disruptions, it is therefore good strategy to develop a resilient supply chain.
Supply chain resilience has been widely discussed in the literature (e.g., Tukamuhabwa et al. 2015; Jüttner & Maklan 2011). A definition of supply chain resilience has been developed through multidisciplinary study: It is “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 & Holcomb 2009, p.131). In this definition, two stages are mentioned. First, before disruption happens, there is the stage of preparation for unexpected events. At this stage, the supply chain needs to develop some proactive strategies (e.g., increasing visibility, inventory management, supply chain collaboration level, etc.) to predict and prepare for future risks. Second, after disruption happens, there is the stage of responding to and recovering from disruption. At this stage, a more resilient supply chain would achieve this with less time and more efficiency, while the cooperation of some reactive strategies (e.g., increasing flexibility, creating redundancy and increasing velocity, etc.) are needed. Our study focuses on both preparation and response phases to fully adopt the capability of resilience.
Although researchers have reached agreement on the definition of supply chain resilience, the underlying elements of the concept differ among researchers. Generally, elements such as efficiency, redundancy, collaboration, flexibility, velocity and visibility are used to define supply chain resilience (Christopher & Peck 2004; Sheffi & Rice Jr. 2005; Jüttner & Maklan 2011; Wieland & Wallenburg 2012; Pettit et al. 2013). Among those elements, supply chain visibility and flexibility name two key strategies that are frequently used to build supply chain resilience (Tukamuhabwa et al. 2015). These two strategies present two aspects of supply chain resilience capability. First, before disruption happens, a visible supply chain enables an organization to visualize and predict upcoming risks. Second, after disruption happens, a flexible supply chain enables an organization to react to and recover from it. Therefore, in this study, we consider two elements of supply chain resilience: supply chain visibility and supply chain flexibility. 2.2.1. Supply chain visibility
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are important to operations (Jüttner & Maklan 2011). Supply chain visibility facilitates the prediction of risks. Furthermore, supply chain visibility also enables organizations to have smooth information flow (Rai et al. 2006). Because of its unique ability to visualize and smooth, supply chain supply chain visibility is one of the most important routes to supply chain resilience. 2.2.2. Supply chain flexibility
Many researches describe flexibility as a reactive strategy used to cope with supply chain uncertainty (Stevenson & Spring 2007). It is argued that a flexible supply chain aids rapid response to and recovery from disruptions. Supply chain flexibility refers to a firm’s ability to adapt to changing environment with minimum time and effort. Researches have also shown that various flexibility practices (flexibility in supplier management, scheduling, delivery and fulfillment etc.) improve supply chain resilience (Craighead et al. 2007; Pettit et al. 2013)
2.3. Upstream complexity
Contemporary organizations tend to extend their upstream supply chains to focus on core business and to improve competitive advantages. Since a complex upstream is harder to manage, it is essential to understand the complexity of upstream supply chains. Researches have studied various triggers on upstream complexity that can help us to better narrow down the concept. Table 2.1 presents drivers that are commonly used to define upstream complexity in the literature.
We first demonstrate two dimensions that Choi and Krause (2006) studied: (1) the number of suppliers and (2) the degree of differentiation of these suppliers. First number of suppliers refers to the amount of current suppliers who are involved in business relations. With few suppliers, it is easier to manage the upstream. Second, differentiation of suppliers refers to the degree to which suppliers differ. It would be easier for a focal company to coordinate with suppliers if all suppliers shared the same characteristics (e.g., had the same operational practices, technical capabilities, etc.).
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Difference in geographical spread Choi & Krause (2006); Brandon-Jones et al. (2014); Bode & Wagner (2015) Horizontal complexity Bode & Wagner (2015) Vertical complexity Spatial complexity Bode & Wagner (2015) Bode & Wagner (2015)
Many researches have studied the impact of individual drivers on operation/plant performance, as not all drivers are detrimental to supply chain performance. One construct—supply differentiation explicit in delivery differentiation—always adversely impacts operational performance (Bozarth et al. 2009; Brandon-Jones et al. 2014). Bozarth et al. (2009) show that long supplier lead-time and high delivery unreliability are significant in reducing plant-level performance. Brandon-Jones et al. (2014) show that supply base complexity (in terms of delivery) leads to decreased performance. This study grounds upstream complexity in term of delivery differentiation, as these two factors may have difference consequences for supply chain performance. They are key drivers of upstream complexity (Bozarth et al. 2009).
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Because we specifically consider supply chain visibility and flexibility as two elements indicating supply chain resilience, we must further specify our hypothesis about these two elements. To begin with, the following arguments are made concerning building supply chain visibility. First, ICT (such as APS, SRM and RFID) would help to improve operational efficiency and order, part and product traceability (Gunasekaran & Ngai 2004). By improving tangible-products traceability, organizations can see from one end of the supply chain to another. Second, an information system would offer superior analytic abilities, thereby providing decision support via forecasting and planning (Bryan Jean et al. 2014). This argument exhibits the superior scenario-based analytic ability of ICT. This analytic ability helps organizations to forecast better so that they can more easily predict unexpected risks. Third, information system can accelerate and visualize flow of information, products and finance (Ngai et al. 2011). This attribute indicates that ICT would enhance the visibility of a supply chain as it makes it possible to visualize and see through the chain. Therefore, based on its positive impact on traceability, forecasting, and information flow, it is concluded that ICT will positively influence supply chain visibility.
Furthermore, we also need to consider the impact on supply chain flexibility. First, one role of intra-organizational ICT is to coordinate order fulfillment by synchronizing resource availability relative to customer demand (Bayraktar et al. 2009). This means that ICT is able to coordinate operations based on customer demand. So ICT would help an operation to be flexible in response to what is happening downstream. Second, information technology facilitates supply chain agility and customer responsiveness (Ngai et al. 2011; Jayaram et al. 2000). Agility refers to how fast and efficient an organization can respond to supply chain uncertainty, which involves the capacity to be flexible. Therefore, since ICT can help an organization increase agility and react better to uncertainty, we may conclude that ICT will positively impact supply chain flexibility.
Hypothesis 1: There is a positive relationship between use of ICT and supply chain resilience.
3.2. The moderating effect of upstream complexity
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Furthermore, Ngai et al. (2011) finds that the importance of ICT competence to an organization depends on the scale of the organization. For example, a small-scale organization has a comparatively simple supply chain and may therefore not require advanced ICT competence to support supply chain flexibility. In contrast, in a large-scale organization with a sophisticated supply chain, advanced ICT competence is very important to the support of supply chain flexibility. Therefore, the influence of ICT competence changes with the sophistication of the supply chain. A complex upstream may come from a sophisticated upstream such that ICT would support supply chain flexibility. On the other hand, a less complex upstream may not require ICT to support supply chain flexibility.
Hypothesis 2: Upstream complexity positively moderates the relationship between ICT and supply chain resilience such that the relationship becomes stronger when upstream complexity is high rather than low. The below figure 3.1 presents the conceptual model of this study. Figure 3.1. Conceptual model 4. Methodology
To test our hypotheses, this section demonstrates the methodology of our study. The structure of this section as: questionnaire development, sample and data collection, and data reduction and analysis.
4.1. Development of questionnaire
The survey questions are derived from extant research with creditable validity and reliability. Constructs are related to ICT implementation, supply chain resilience and upstream complexity.
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adopted from Petit et al. (2013). Supply chain visibility and supply chain flexibility are two dimensions that contribute to the concept of supply chain resilience. Items of supply chain visibility are related to the ability to trace and track real-time information. Items of supply chain flexibility are related to the ability to induce change by means of order quantity, delivery time and production volume. Finally, the items used to measure ICT were adopted from Ward and Zhou (2006). The survey items are developed as Likert-scale items, with values ranging from 1 (“strongly disagree”) to 5 (“strongly agree”). For consistency of scaling, items used to measure upstream complexity are reversely recoded so that the range is from 1 (to indicate less complexity) to 5 (to indicate more complexity).
The original survey questions were translated from English to Chinese and then back to English to ensure that the contents of the English and Chinese texts are aligned. Furthermore, we invited several academic researchers in the field of Operations Management to edit and improve upon the questions.
Pilot tests were made for the Chinese questionnaire. Informants were randomly selected for pilot tests. Their survey results were not counted for final data analysis. The informants were asked to provide feedback about the readability of the questions, confusion of questions and any mistakes. Meanwhile, their completion times were recorded. After the first pilot test, we adjusted the survey in light of the comments and then launched a second pilot test. The second pilot test went better; it took less time to complete, and we received positive feedbacks from informants.
4.2. Sample and data collection
The data was collected within one month from 20 April 2016, to 15 May 2016. It includes responses all from Chinese manufacturing companies. We collected data in China primarily because of the world position of Chinese manufacturing. The World Trade Organization reports that China shared 12.33% of total world exports in 2014, of which 94% exports are from manufactures (WTO trade profile, 2015). Thus, Chinese manufacturing makes a substantial contribution to the contemporary supply chain field. The data allows us to produce generalizable results. We also collected data in China because it was convenient for us to do so. Three of our project members have good access to Chinese manufacture firms, which ensures reliable data.
The initial target population is based on the data pool of one of largest Chinese survey online platform (www.sojump.com). This online survey platform owns 2.6-million sample in its data pool. Sojump.com is a top professional survey site in China. It achieves very high reliability by controlling the uniformity of IP addresses, accounts and respondents’ detailed information. Sojump.com partners with its respondents, who are dispersed in various industries. To assure sample quality, respondents’ background information is regularly verified and updated. Therefore, the reliability of the sample data that Sojump.com provided is assured.
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From that sample library, we selected respondents based on the following criteria: 1) at least 50 employees in company, 2) respondents are in supply chain-related position, and 3) respondents are from manufacturing firms that have their own production lines. In addition, due to the purpose of this study, the sample industries must meet the following requirements: 1) the completeness of its whole supply chain, 2) a multiplicity of different structures, and 3) possible changes with supply chain disruptions in recent years. The possible changes must align with changes in technology, market fluctuation, consumption patterns, etc. (Zimmerman et al., 2016). The starting population was collected in the following sectors via NACE code:
• Manufacture of food and beverages (C.10-11)
• Manufacture of chemicals and chemical products (C.20) • Manufacture of electrical equipment (C.27)
• Manufacture of motor vehicles, trailers and semi-trailers/ other transport equipment (C.29-30)
• Manufacturer of textiles (C.13)
• Manufacture of other non-metallic mineral products (C.23)
Based on the data pool from sojump.com, we originally distributed 380 surveys. The target respondents were reached through social media and/or email. Within four weeks, 157 questionnaires were returned. 121 of those questionnaires are valid—that is, completed in sufficient time. The respondent rate is therefore 43.2%. A basic profile of respondents is presented in Table 4.1. Furthermore, non-response bias was examined to check for differences between data that was received early and late. Two control variables—annual sales revenue and employee numbers—were used to perform a one-way ANOVA analysis. We chose 30 of the earliest and 30 of the latest responses on which to conduct comparisons. The result obtained from annual-sales-revenue difference is F= .782, P= .547, 2. The result obtained from employee number is F= .232, p= .918. Both p-values are insignificant, which means that there is no difference between early and last responses. Consequently, there is no non-response bias in the data (Karlsoon, 2010).
Table 4.1. Profile of respondents
Profile of respondents Number Percentage
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Electronically equipment 41 0.34 Motor vehicles 15 0.12 Textiles 4 0.03 Other non-metallic mineral products 25 0.21 Not indicated 21 0.17 Total 121 1.00 Table 4.2. Control variable
Control variables Number Percentage
Sales revenue in 2015 (in euro) Under 50 million 7 0.04 50 - 100 million 31 0.26 100 - 250 million 52 0.46 250 - 500 million 16 0.12 Above 500 million 15 0.13 Total 121 1.00 Number of employees Under 50 (including) 11 0.11 50 - 100 (including) 24 0.26 100 - 250 (including) 31 0.32 250 - 500 (including) 28 0.16 Above 500 27 0.16 Total 121 1.00 4.3. Data reduction and analysis To determine the quality—i.e., the validity and reliability—of our multi-item constructs, we conducted an exploratory factor analysis (EFA). Factor analysis is conducted to determine the number of latent constructs that underlay a set of items. To make them align with the same coding sequence, supply-base complexity items were recoded as low complexity (presented by low score) and high complexity (presented by higher score).
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alpha value should exceed .70. Both exploratory factor analyses and hypothesis tests were performed by SPSS.
5. Results
This section presents the results of our survey data. We will first address factor analysis to test validity and variability of used constructs, then descriptive analysis. The regression analyses and hypothesis testing will be presented as follow.
5.1. Factor analysis
The result of the principal-component analysis is presented in Table 5.1 below. 11 items were retained to test the retention of factor. After performing an orthogonal rotation, three factors were retained. The initial eigenvalues of each factor are over 1 (1.14, 1.59 and 4.21, respectively). And the total combination of explained variance of three factors is 61.1%, which is larger than the benchmark of 50%. Each factor contains at least three items, and each item exceeds .40, as convention advocates. The items load specifically to one factor without any cross loadings.
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We have information system that accurately track all operations 0.674 We have real-time data on location and status 0.660 Supply chain flexibility Ability to change quantity of supplier's order 0.857 Ability to change delivery times of supplier's order 0.613 Ability to change production volume capacity 0.613 Eigenvalue 1.14 1.59 4.21 Percentage of variance explained (%) 10.32 14.48 38.30 * Scale: completely disagree - completely agree (1-5) 5.2. Descriptive and correlation testing
To begin with, we computed the mean values of items in supply chain resilience, upstream complexity and ICT usage. These deliver one set of values per factor of analysis. In the following study, ICT usage is treated as the independent variable, supply chain resilience is treated as the dependent variable, and upstream complexity is treated as the moderator.
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resilience. The result confirms hypothesis H1, which suggests that firms that use more ICT are subjected to higher resilience capability. By our study content, higher resilience can be regarded as higher supply chain visibility and flexibility. The adjusted R2 stands for explained variance. With adjusted R2= .265 for second model, ICT usage accounts for 26.5% variation of the variable supply chain resilience. In addition, both F-values are at the p< .001 and p< .05 level, which means that both models are significant. Table 5.3. Regression analysis (Hypothesis 1) Supply chain resilience Step Variables 1 2 1 Control Annual revenue 0.116 0.058 Amount of employees 0.158 0.036 2 Main effects ICT usage 0.496** Adjusted R square 0.050 0.265 Δ R Square 0.066 0.218 F 4.153* 15.413** *p < .05; **p < .001 5.4. Regression analysis: moderation role of upstream complexity Table 5.4 presents a second regression model for the purpose of testing the moderation effect. Step 1 states the regression result of the control variables, neither of which is significantly related to the rest. Step 2 adds the main effect and the moderator to the performance regression analysis. It shows that (independent variable) ICT usage (β= .464, p< .001) and (moderator) upstream complexity (β= -.270, p< .001) are both significantly related to (dependent variable) supply chain resilience. The adjusted R2 of
the second model is .327, while the F-value is at a significant level: p< .001.
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2 Main effects ICT usage 0.464** 0.506** Upstream complexity -0.270** -0.276** 3 Interaction effect ICT x Upstream complexity 0.156* Adjusted R square 0.050 0.327 0.345 Δ R Square 0.066 0.283 0.023 F 4.153* 15.561** 13.622** *p < .05; **p < .001 5.5. Hypothesis testing A visualized moderation effect of ICT is demonstrated in Figure 5.5. In this figure, first, the positive relationship between ICT usage and supply chain resilience appear in both less and more complex upstream conditions. Second, the figure shows that the slop of the more complex upstream condition is greater than the slop of the less complex upstream. This means that the influence of ICT on supply chain resilience is larger and more obvious when the upstream is more complex. Third, a less complex upstream supply chain is always more resilient than a highly complex upstream. This finding is in line with the regression result, according to which upstream complexity and supply chain resilience are negatively associated (β= -.276, p< .001).
Figure 5.5. The moderation effect of ICT usage
The results support both hypotheses. Hypothesis one, which concerns the positive relationship between ICT usage and supply chain resilience, is supported by β= .496, p< .001. Hypothesis two, which is about the positive moderation effect on the relationship of ICT and supply chain resilience, is supported by β= .156, p< 0.05. The results are presented in the structural model in Figure 5.6. 1 1.5 2 2.5 3 3.5 4 4.5 5
Low ICT usage High ICT usage
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Figure 5.6. Structural mode (*p< 0.05; **p< 0.001) 6. Discussion and conclusion
Our study makes several contributions to the extant literature concerning issues relating to ICT usage and supply chain resilience. First, we find that ICT usage has a positive influence on supply chain resilience. This means that the more ICT an organization uses, the more resilient its supply chain. Though prior studies have been valuable to our understanding of the link between ICT usage and supply chain agility (e.g., Swafford et al. 2008) and operational performance (e.g., Sanders 2008), our study extends those empirically to show that ICT usage has a direct and significant effect on supply chain resilience. This result extends the role of ICT usage in the risk-management field. Second, we show that upstream complexity has a positive effect on the relationship between ICT usage and supply chain resilience. This means that the more complex the upstream supply chain, the stronger the effect from ICT on supply chain resilience. This complements previous studies which indicate that environmental uncertainty presents a barrier to ICT implementation (Bryan Jean et al. 2014) and that the degree of ICT influences supply chain performance (Ngai et al. 2011). Our findings are discussed in more detail below.
6.1. ICT usage and supply chain resilience
The data analysis result demonstrates a positive relationship between ICT usage and supply chain resilience (β= .496, p< .001). We suggest that ICT usage might lead to a more resilient supply chain in terms of increased supply chain visibility and increased flexibility. The findings support our first hypothesis.
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etc.). We find that ICT is also able to increase the resilience of a supply chain. When an organization invests more in ICT usage, its supply chain will achieve higher resilience. ICT includes technologies such as APS, CIM and MES. It is important to use these ICT technologies because they contribute both visibility and flexibility to the supply chain. Usage of relevant technologies help organizations traces and visualizes product and information flows. It also helps organizations adjust orders, production, delivery time, etc.
6.2. Moderation role of upstream complexity
The data analysis verifies a positive moderation effect (β= .156, p< .05) on the relationship between ICT and supply chain resilience. We suggest that, in a more complex upstream supply chain, the effect of ICT on supply chain resilience is much stronger while ICT has smaller influence on supply chain resilience in less complex upstream situation. Thus, the finding supports our second hypothesis.
Studies show that the influence on supply chain performance depends on various conditions. For example, the impact of ICT differs with different firm scales. Existing research suggests that, in different conditions, the cooperation and degree of benefit obtained from ICT will vary (Bryan Jean et al. 2014; Gunasekaran & Ngai 2004). Building upon this understanding, our results show that the impact level of ICT varies with upstream complexity. We find that ICT makes it possible for supply chain resilience to have a larger influence on a complex upstream supply chain. This is because the management of a very complex upstream supply chain requires more information technology, while management of a less complex upstream supply chain may not require intensive information technology. In other words, a more complex upstream supply chain needs more ICT to develop resilience (in respect of visibility and flexibility) than a less complex upstream supply chain.
In our results, the upstream complexity varies with long supplier delivery lead-time and unreliable supplier delivery. To this extent, upstream complexity, explicitly attributed from long delivery lead-time and unreliable delivery, influences the relationship of ICT and supply chain resilience. This is elaborated as the longer delivery lead-time and the more unreliable delivery, the more necessaries of using ICT to improve supply chain resilience.
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6.3. Managerial implication
This study has identified a lack-of-direction relation between ICT usage and supply chain resilience. We explored this relationship under different upstream complexity conditions. This study also has managerial implication. First, managers who would implement more ICT for the purpose of improving resilience should first analyze the complexity level of the upstream supply chain because ICT has different results on upstream of different complexity. Second, after the complexity level of the upstream is known, managers should implement the right amount of ICT to avoid unnecessary complications.
6.4. Limitation and further suggestion
A limitation of this study is that the measurement of ICT is restricted to the internal phase. Generally, it is better to consider internal and external ICT together or separately. This method would yield more systematic results with which to develop a strategy. Therefore, it would be interesting to consider both internal and external ICT together or separately. Another limitation is that we focused only on the complexity of the upstream supply chain. This is because we wanted to achieve a precise analysis of complexity. However, a complex supply chain condition also includes manufacturing complexity and downstream supply chain complexity (Bozarth et al. 2009). Finally, regarding measured dimensions of supply chain resilience, we have two main foci: visibility and flexibility. The main foci differ in different firms, and more dimensions can be studied. In sum, further research should consider the above-mentioned limitations.
