1
MASTER THESIS
Inter-Organizational IT Integration and Supply Chain Agility:
the Moderating Effect of Supply Chain Complexity
Author: T.J.A. Kuijper Student number: 2992493
Email: t.j.a.kuijper@student.rug.nl First supervisor: Prof. Dr. D.P. van Donk Second supervisor: Dr. X. Zhang
MSc Supply Chain Management Faculty of Economics & Business
2 Abstract
In previous research, it has been reported that supply chain IT integration is positively related to supply chain agility. This relationship is expected to be influenced by environmental dimensions. This study aims to show how one of these environmental dimensions, supply chain complexity, moderates the relationship between supply chain IT integration and supply chain agility, where the latter consists of supply chain flexibility and supply chain visibility. By using survey data collected from 45 supply chain managers, operational managers and plant managers working at a firms in the food and beverage industry in the Netherlands, we found that inter-organizational IT integration holds a positive relationship with both supply chain flexibility and supply chain visibility. Furthermore, a significant negative moderating influence of supply chain complexity was found on the direct relationship including supply chain flexibility but not for supply chain visibility. The study findings aim to provide insights to managers which should analyze the complexity in their supply chain before investing in (chain-wide) IT integration in order to improve supply chain flexibility and supply chain visibility capabilities. Because, if a firm integrates their inter-organizational IT, a ceiling effect has been found for supply chain flexibility capabilities if a firm faces high supply chain complexity. By combining supply chain complexity sources from Bozarth et al. (2009), which were missing in previous research, this research aims to provide additional insights on the moderating effect of supply chain complexity. Furthermore, avenues for future research are depicted.
Keywords: Supply chain IT integration, Supply chain flexibility, Supply chain visibility, Supply chain agility, Supply chain complexity
Introduction
Firms have been integrating the flow of information to improve supply chain agility (Prajogo & Olhager, 2012). Information flows can be integrated by applying IT integration, which creates opportunities for increased supply chain agility (Mondragon, Lyons & Kehoe, 2004). Furthermore, a positive relationship between supply chain IT integration and supply chain agility has been found by Swafford, Ghosh and Murthy (2008), although it is indicated in their study that the influence of environmental dimensions is still unclear, because the study solely used an internal perspective of the firm. Therefore, this research focuses on the influence of one of these environmental dimensions; supply chain complexity. Supply chain complexity has been chosen as a dimension because the ability of firms to be able to adjust to changing environments is crucial, especially since current reality is being characterized by increasing complexity (Blome, Schoenherr, Rexhausen, 2013).
3 Furthermore, Swafford et al. (2008) only took supply chain flexibility into account to research supply agility, while supply chain agility is considered to be a broader concept than supply chain flexibility alone, but also includes supply chain visibility (among others, Braunscheidel & Suresh, 2009; Ngai, Chau & Chan, 2011). Hence, it is not clear whether supply chain IT integration is positively related to both supply chain flexibility and supply chain visibility, and therefore also supply chain agility. By combining the observed ‘missing’ elements of supply chain complexity, supply chain agility, and the unknown influence of supply chain complexity on the relationship between supply chain IT integration and supply chain agility, a knowledge gap has been found. This master thesis aims to fill this gap, the yet unclear influence of supply chain complexity as a moderator on the relationship between supply chain IT integration and supply chain agility, as also indicated by Blome et al. (2014, p. 315); “Further dimensions such as customer complexity and process complexity should be included in future studies”. To fill this gap, the following research question will be answered in this thesis: What influence does supply chain complexity have on the relationship between supply chain IT integration and supply chain agility?
This thesis aims to provide IT-, supply chain- and risk managers a proper understanding of the influence of supply chain complexity on the relationship between supply chain IT integration and supply chain agility. With these insights, managers can make a better judgment whether to integrate IT with their supply chain partner(s). Furthermore, this research aims to complete the concepts of both supply chain agility by incorporating supply chain flexibility and supply chain visibility and supply chain complexity by incorporating downstream, upstream and internal manufacturing complexity sources. Thereby the knowledge gap mentioned above is reduced and current knowledge on the moderating effect of supply chain complexity is enlarged. If the research question above is answered, not only the influence of supply chain complexity is known, but it is also known whether integrating IT in supply chains enhances supply chain agility capabilities under both low and high supply chain complexity. This will be done by hypotheses testing, for which a survey will be carried out in the food and beverage industry in the Netherlands to collect data for data analysis.
This thesis will be structured as follows: The second chapter will outline the theoretical background, hypotheses and a conceptual model will be constructed. Afterwards, in the third chapter, the methodology will be described. In the fourth chapter, results will be reported. The fifth chapter discusses these results. Finally, in the sixth chapter, conclusions, limitations and directions for future research will be depicted.
2. Theoretical Background 2.1 Supply chain IT integration
4 Because IT usage occurs within a firm’s functions (intra-organizational) and between firms within the supply chain of the firm (inter-organizational) (Swafford et al., 2008; Zhang, Van Donk & Van der Vaart, 2011), IT integration can be achieved both intra-organizational as well as inter-organizational. Because intra-organizational IT integration solely focuses on linking internal systems and aid internal purposes, i.e. finance and accounting (Savitsie, 2007), it gives the firm only a very limited view of the entire supply chain (see also section 2.2 on supply chain visibility). Inter-organizational IT integration on the other hand, can be reached by developing and linking systems which enable the firm to gain information from both customers (downstream) and suppliers (upstream) (Frohlich & Westbrook, 2001). By connecting databases, like POS (Point of Sale) data, forecasting data and inventory levels with supply chain partners, more (accurate) data can be generated and disseminated with customers and suppliers, which contributes to acquiring a more holistic view of the supply chain of the firm (Barratt & Oke, 2007). Because of this better and larger overview of the supply chain, solely inter-organizational IT integration will be taken into account in this thesis. Therefore, instead of studying both direct relationships between intra-organizational IT integration and supply chain agility and inter-organizational IT integration and supply chain agility, solely the latter direct relationship and the influence of supply chain complexity on this relationship will be taken into account in the hypotheses building sections 2.3 and 2.5, as well as in the remainder of this thesis.
2.2 Supply chain agility
Supply chain agility has gained increasing attention in academic research and organizations have acknowledged that agility is essential for their endurance and competitiveness (Gligor & Holcomb, 2012). The definition of Braunscheidel and Suresh (2009, p. 120) fits with the aim of this thesis because also the inter-organizational element of supply chain agility is mentioned: ‘supply chain agility is 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’. Furthermore, this definition clearly explains the reactive characteristic of supply chain agility, which is built on capabilities of a firm in order to being fast and ‘being able to reconfigure’ (Lin, Chiu & Chu, 2006; Wieland & Wallenburg, 2012, p. 890).
Supply chain agility can result in adapting more efficiently to changes in the marketplace and enable the firm to respond more quickly to changes in customer demand, but also to integrate more effectively with suppliers (Mason, Cole, Ulrey & Yan, 2002). Therefore, if firms possess higher agility capabilities, they can respond faster and/or better towards (potential) supply chain disruptions and supply chain risks can be mitigated (Tse, Zhang, Akhtar & MacBryde, 2016). Because supply chain agility enhances risk mitigation, supply chain agility is often viewed as a reactive supply chain capability (Wieland & Wallenburg, 2013), which is based on two key capabilities: supply chain flexibility and supply chain visibility (Braunscheidel & Suresh, 2009; Swafford, Ghosh & Murthy, 2006; Liu, Ke, Wei & Hua, 2013; Narasimhan, Swink & Kim, 2006).
5 dynamic environment and is able to act faster towards both potential and actual disruptions. Therefore, supply chain flexibility can be considered as an important element of supply chain agility. Supply chain visibility implies “the ability to see from one end of the pipeline to the other” (Christopher & Peck, 2004, p. 9). By having systems or business intelligence programs in place, firms can acquire the capability to have a clear (holistic) view of inventories or production schedules up- and downstream in their supply chain. Supply chain visibility can be reached by close collaboration of suppliers and buyers as well as being able to alert supply chain partners about potential disruptions (Christopher & Peck, 2004). Furthermore, visibility capabilities of processing information “can advance know-how and intellectual capital, which enables firms to make informed decisions and take effective actions” (Yang, 2014, as mentioned in Brusset, 2016, p. 49). If a firm has the capabilities in place to acquire a clear view of its supply chain, the firm knows when and where goods are needed and it can adapt or respond faster upon its changing environment. Therefore, supply chain visibility is an important element of supply chain agility as well.
In this thesis, supply chain agility will be addressed by both supply chain flexibility and supply chain visibility. Supply chain flexibility and supply chain visibility will be treated separately in the hypothesis building in section 2.3 below to indicate possible different outcomes in data analysis.
2.3 Supply chain IT integration and supply chain agility
Because supply chain agility is a reactive capability, the focus of this supply chain capability lies on being capable to adapt and respond quick on changes in requirements, demand or (potential) disruptions (Swafford et al., 2008; Fernie, Sparks & McKinnon, 2010; Wong, Lai & Bernroider, 2015b). Inter-organizational IT integration, on the other hand, supports this supply chain practice by enabling firms to exchange information to show these changes in demand or (potential) disruptions, but also digitally interact with supply chain partners, e.g. by altering orders or inventory policies (Wong et al., 2015b). Therefore, by integrating Inter-organizational IT, higher supply chain agility capabilities can be reached when IT is integrated throughout the supply chain (Swafford et al., 2008). Also when the two elements of supply chain agility, supply chain flexibility and supply chain visibility, are observed separately, a positive relationship is expected to hold with inter-organizational IT integration.
As mentioned in section 2.1 above, inter-organizational IT integration can provide better insights into a changing environment. By being capable of observing these changes in the environment or (potential) disruptions of the supply chain, a firm can alter their strategy or processes like changing their product mix to act upon this changes (Swafford et al., 2008). If inter-organizational IT is properly integrated, it is expected that a firm can interact with its supply chain partners by using these IT linkages, e.g. by modifying orders at its supplier. Therefore, if a firm has integrated their IT inter-organizationally, the capabilities of altering its product flows will be enhanced, which leads to a better capability to adapt or respond towards (potential) disruptions and thereby enhancing supply chain flexibility capabilities. The following hypothesis concerning inter-organizational IT integration and supply chain flexibility will be tested:
Hypothesis 1a. Inter-organizational IT integration is positively related to supply chain flexibility.
6 information exchange and therefore can be considered as value-added networks to link the focal firm with both suppliers and customers (Brusset, 2016). Such value-added networks and platforms are able to enlarge the visibility capabilities of a firm by providing routines and processes which enable supply chain partners to work towards a common goal (Christopher, 2000, as in Brusset, 2016). Hence, we expect that inter-organizational IT integration enhances supply chain visibility capabilities. Therefore, the following hypothesis concerning inter-organizational IT integration and supply chain visibility will be tested:
Hypothesis 1b. Inter-organizational IT integration is positively related to supply chain visibility.
2.4 Supply chain complexity
The topic of supply chain complexity received increased attention in academic literature and practice (Bozarth et al., 2009) and makes managing supply chains more challenging (Choi, Dooley & Rungtusanatham, 2001). Socio-technical systems, like supply chains, are considered to be complex when they are “made up of a large number of parts that interact in a non-simple way” (Simon, 1962, p. 468). Although this definition may sound vague, it fits perfectly with complexities which are frequently encountered in supply chains; supply base complexity, for example, can be explained by a large number of parts (suppliers and the focal firm), which interact in a non-simple way (e.g. competition among suppliers) (Choi & Krause, 2006). Therefore, in this thesis, the definition of supply chain complexity by Bozarth et al. (2009, p. 80) will be used, which states that supply chain complexity is “complexity exhibited by the products, processes and relationships that make up a supply chain.” Complexity is not a desirable feature of a supply chain, because it can lead to a lower operational performance, more complicated decision making or (precipitate) disruptions (Craighead, Blackhurst, Rungtusanatham & Handfield, 2007; Narasimhan & Talluri, 2009; Manuj & Sahin, 2011).
Although many researchers developed lists of supply chain complexity sources, many seem incomplete or too focused on a single component of the supply chain; i.e. solely supply base complexity (Choi & Krause, 2006), the manufacturing plant (Martinez-Olvera, 2008) or a two stage supply chain (Sivadasan, Smart, Huatuco & Calinescu, 2010). Henceforth, this thesis will use the supply chain complexity sources mentioned by Bozarth et al. (2009), because the supply chain complexities in their research comprised upstream complexity, as well as downstream and internal manufacturing complexity and therefore better insight is given into the construct of supply chain complexity. These supply chain complexity sources have been chosen because they are aimed to provide insights into supply chain complexity in different parts of the supply chain, have proved to have statistically significant impact on performance (Bozarth et al., 2009) and are being viewed from the manufacturing plant level, which is the focus of this thesis. Three supply chain complexity sources (Bozarth et al., 2009) on the manufacturing plant level are shortly discussed below: Internal manufacturing complexity, downstream complexity and upstream complexity.
7 of a supplier. Internal manufacturing complexity, downstream complexity and upstream complexity together form the supply chain complexity construct and will be treated accordingly in hypothesis building in section 2.5.
2.5 The moderating role of supply chain complexity
This thesis focuses on the influence of supply chain complexity on the direct relationships between inter-organizational IT integration and supply chain flexibility and inter-organizational IT integration and supply chain visibility. When higher levels of supply chain complexity sources are present, they contribute to a higher level of the supply chain complexity construct. Thereby, the difficulty of managing complexity rises, which results in increased information processing and analyzing needs throughout the supply chain (Wong et al., 2015b).
To manage supply chain complexity, more ‘information generation and dissemination’ (Wong et al., 2015b, p. 4) is needed. When less capacity is available to generate and disseminate information with supply chain partners, it is expected that the firm has a reduced capability of acting and responding fast to changes in the environment. Even if inter-organizational IT is integrated to a large extent, employees working at firms which are facing high complex environments might find it difficult to act on this information (Blome et al., 2014). A possible explanation for this is that employees are overwhelmed by the amount of data being shared to such extent that sense-making of this data is hindered, therefore weakening supply chain flexibility capabilities. Therefore, the following hypothesis concerning the moderating effect of supply chain complexity on the direct relationship between inter-organizational IT integration and supply chain flexibility will be tested:
Hypothesis 2a. Supply chain complexity moderates the positive relationship between
inter-organizational IT integration and supply chain flexibility, such that the relationship becomes weaker when supply chain complexity is high rather than low.
Although a firm might integrate inter-organizational IT integration very well, a reduced (over)view of the supply chain is expected to occur if the firm faces a high complexity environment. This might be due to increasing errors in the data, but mostly because the demanded amounts of information to overcome such complexity cannot be generated, disseminated, analyzed or interpreted. This is less likely to happen in low complexity environments for both low and high inter-organizational IT integration (among others, Wong et al, 2015b). Therefore, the ability to look through the supply chain, supply chain visibility, becomes weaker when supply chain complexity levels rise. The following hypothesis concerning the moderating effect of supply chain complexity on the direct relationship between inter-organizational IT integration and supply chain visibility will be tested:
Hypothesis 2b. Supply chain complexity moderates the positive relationship between
inter-organizational IT integration and supply chain visibility, such that the relationship becomes weaker when supply chain complexity is high rather than low.
8 The relationships with their belonging hypotheses are shown in figure 1 below.
Figure 1. Conceptual model 3. Methodology 3.1 Data Collection
Data collection was accomplished by means of a survey, which targeted firms involved in in the food- and beverage industry in the Netherlands which includes more than 50 employees. The data collection took place from mid-April to early May 2016. The initial population consisted of 283 firms, which were subtracted from the databases from the Dutch Chamber of Commerce with industry codes 10 and 11. 167 Individuals, working at a firm as a plant/supply chain/operations manager in a firm within at least one of these industry codes and with at least 50 employees were targeted to be a key informant. To approach firms within our population, employees of companies in our subsample were contacted on LinkedIn. If an employee approved to participate in this research, a link of the electronic survey was send to the employee by email. In order to increase the response rate, follow-up emails were sent to non-respondents after 7-10 days (Karlsson, 2009). Participating individuals were offered to receive a report including descriptive data when the data collection was finished. In the data collection period, we received 71 responses, which results in a response rate of 42,5%. However, 26 responses were disqualified; 25 of them were entered blank or significantly incomplete and one response did not meet our threshold of 50 employees. As a result, 45 entries could be used in data analysis (26,9%).
9 Netherlands, 45 firms (15,9%) participated in our research. However, because the amount of responses is relatively low, a higher confidence interval has to be accounted for.
Table 1. Sample’s participants position in their firm/plant
Position Count Percent
Supply Chain Planner 7 15.5
Supply Chain Manager 8 17.8
Operations Manager 3 6.7
Plant Manager 9 20.0
Not indicated 18 40.0
Total 45 100.0
3.2 Measurement development
The constructs being used in this thesis were part of a larger questionnaire on supply chain integration, complexity and resilience. This thesis uses the following constructs from the questionnaire (as discussed in the theoretical background above); inter-organizational IT integration, supply chain flexibility, supply chain visibility and supply chain complexity. To measure these constructs, items have been derived from questionnaires in extant SCM and ICT research.
The first construct, inter-organizational IT integration was measured by 11 items of Prajogo and Olhager (2012). These items measure for both the supplier and buyer side to which extent IT is integrated with the focal firm, e.g. whether advanced information systems to track and/or expedite shipments are being used with buyers and suppliers, and whether direct computer-to-computer linkages are available. These items give clear insights into IT integration between supply chain partners and the usage of IT in their supply chain(s).
The second construct, supply chain flexibility was measured by four items of Swafford et al. (2008). These items give insights into the capability of a firm to alter their supply, internal processes and deliveries in order to adapt or respond to the environment and (potential) disruptions, e.g. whether the firm is able to change supplier’s orders and whether the firm is able to change production volume capacity.
10 A control variable was used to account for external effects: firm size, which is measured by the amount of employees working at a plant/firm. This variable was measured using a 5 point scale (less than 50 employees, 50-100 employees, 100-250 employees, 250-500 employees, more than 500 employees).
Table 2. Results of EFA
Factor
Items 1 2 3 4
F1: Inter-organizational IT integration Cronbach’s α = .951
Supplier side
There are direct computer-to-computer links with suppliers. .793 Inter-organizational coordination is achieved using electronic links. .858 We use information technology enabled transaction processing. .844 We have electronic mailing capabilities with our suppliers. .861 We use electronic transfer of purchase orders, invoices, and/or funds. .821 We use advanced information systems to track and/or expedite
shipments.
.844
Buyer side
There are direct computer-to-computer links with buyers. .770
Inter-organizational coordination is achieved using electronic links. .818 We use information technology enabled transaction processing. .723 We use electronic transfer of purchase orders, invoices, and/or funds. .793 We use advanced information systems to track and/or expedite
shipments.
.888
F2: Supply chain flexibility Cronbach’s α = .819
Ability to change quantity of supplier’s order. .841
Ability to change delivery times of supplier’s order. .891
Ability to change production volume capacity. .718
Ability to accommodate changes its product mix. .769
F3: Supply chain visibility Cronbach’s α = .860
We have information systems that accurately track all operations. .934
We have real-time data on location and status of supplies, finished goods, equipment, and employees.
.851
We have effective Business Intelligence gathering programs. .871
F4: Supply chain complexity Cronbach’s α = .666 (All items recoded)
All of our customers desire essentially the same products. .654
Our total demand, across all products is relatively stable. .605
Manufacturing demands are stable in our firm. .791
The master schedule is level-loaded in our plant, from day to day. .722
We seek short lead times in the design of our supply chain. .489
We can depend upon on-time delivery from our suppliers. .386
Eigenvalue 12.27 2.44 2.30 1.33
11 3.3 Data analysis and reduction
Validity and reliability of the measurement of each construct will be analyzed by using Explorative Factor Analysis (EFA), this is allowed because both the Kaiser-Meyer-Olkin (KMO) (.696) and Bartlett’s test of sphericity (χ²/df = 2.35 (p < .001)) indicate appropriate adequacy to use factor analysis. By using EFA, it can be seen which proposed items are allowed to load into its proposed constructs. Values of supply chain complexity were recoded such that low complexity comprises a low score, whereas high complexity comprises a high score. A confirmative factor analysis (CFA) with a Varimax rotation is conducted on the items mentioned in section 3.2 to cover unidimensionality of the factors. To test our hypotheses, regression analysis has been performed in SPSS. To avoid multicollinearity problems in data analysis, the main effects have been centered preceding the analysis (Cohen, Cohen, West & Aiken, 2003). To assess the reliability and stability of our constructs, Cronbach’s alphas (α) were calculated per construct (O'Leary-Kelly & Vokurka, 1998). A threshold of 0.7 (acceptable) has been taken into account to assure a good reliability (Nunnally, 1978, as in Karlsson, 2009). The moderating effect of supply chain complexity on both direct relationships was tested using regression in SPSS. To perform this regression analysis, standardized z-values of inter-organizational IT integration and supply chain complexity were used to calculate an interaction effect in SPSS. If this interaction effect is significant for a direct relationship, supply chain complexity significantly moderates this direct relationship (Baron & Kenny, 1986; Wu & Zumbo, 2008).
3.4 Factor Analysis
The results of EFA can be found in table 2 above. As can be observed in this table, for the sake of discriminant and convergent validity, all items loaded higher than 0.30 and did not have significant cross loadings. Therefore, the model can be considered unidimensional. All items loaded significantly into their latent construct on a p < .001 level, therefore the constructs are appropriately reflected by their indicators. The eigenvalues and explained variance of the solution for all factors can also be found in table 2 above. For the Kaiser-Meyer-Olkin measure, a score of .60 has been used as a threshold to reliably use factor analysis and interpret outcomes from regression analysis, which has been met (see section 3.3 above). Furthermore, all constructs have Cronbach’s alphas close to or greater than our threshold of .70, therefore the internal consistency of items loaded into a factor are solid enough to cover a proper reliability.
4. Results
In this section, results from regression analysis on both direct relationships, regression analysis on the moderating influence of supply chain complexity on both direct relationships as well as results from hypothesis testing will be presented.
12 variables as a result of correlation analysis, whereas table 4 reports on regression analysis of the direct relationships.
Table 3. Descriptive statistics and Pearson Correlations
Variable 1 2 3 4 5 1. Firm size 1 2. Inter-Org. IT Integration .083 1 3. SC Flexibility .177 .701** 1 4. SC Visibility .107 .734** .429** 1 5. SC Complexity -.190 -.606 -.562 -.625 1 Mean 3.87 3.42 3.35 3.48 2.76 Standard Deviation 1.180 .940 .826 .998 .673
Notes: ** Correlation is significant at the 0.01 level (two-tailed)
Table 4. Results of regression analysis among control, independent and dependent variables
Variables SC Flexibility SC Visibility
Firm Size .118 .044 Inter-Organizational IT Integration .717** .751** R2 .541 .571 Adjusted R2 .496 .529 F 11.808** 13.331** Change in R2 .541 .571 Change in F 11.808** 13.331**
Notes: ** Correlation is significant at the 0.01 level (two-tailed), Standard regression coefficients reported
Hypotheses 1a and 1b refer to the direct effect of inter-organizational IT integration on supply chain flexibility and supply chain visibility. As can be seen in table 4 above, inter-organizational IT integration has a significant impact on both supply chain flexibility (β =.717, p < .01) and supply chain visibility (β =.751, p < .01). Therefore, hypotheses 1a and 1b can be accepted. These results suggest that a firm gains supply chain flexibility and supply chain visibility capabilities if the extent of inter-organizational IT integration is enlarged.
Table 5. Results of regression analysis among moderating, independent and dependent variables
Variables SC Flexibility SC Visibility
Firm size .043 -.023
Inter-Organizational IT Integration .441** .495**
SC Complexity -.479 -.430
Inter-Org. IT Integration x SC Complexity -.290* -.211
R2 .663 .660
Adjusted R2 .617 .612
F 14.428** 14.430**
Change in R2 .663 .660
Change in F 14.428** 14.430**
Notes: ** Correlation is significant at the 0.01 level (two-tailed), * Correlation is significant at the 0.05 level (two-tailed) Standard regression coefficients reported
13 chain complexity and inter-organizational IT integration and supply chain visibility is not significant (β =-.211, n.s.), hence hypothesis 2b is not supported. To better understand the moderating effect of supply chain complexity, regression equations were translated into simple regression of inter-organizational IT integration and supply chain flexibility (figure 2), with given conditional values of supply chain complexity. In this figure, it can be seen that if a firm faces low supply chain complexity, higher supply chain flexibility capabilities can be reached than in high supply chain complexity environments. The Chi-Square goodness of fit test, χ²/df = 2.35 (p < .001) (appropriate), lies in the range as recommended by Brown and Cudeck (1993) and Muijs (2010). Therefore, sufficient support can be considered for our conceptual model (see figure 1) and can serve as the basis for evaluating our hypotheses. Finally, figure 3 summarizes the results of hypotheses testing in a structural model.
Figure 2. Visualization of the moderating effect of supply chain complexity on the direct relationship between inter-organizational IT integration and supply chain flexibility.
Figure 3: Structural model
14 5. Discussion
The findings concerning the positive direct relationships are in line with both our expectations and the findings of Swafford et al. (2008) and Blome et al. (2014). However, two findings stand out for discussion as they vary from our expectations. First, our findings on the moderating influence of supply chain complexity on the direct relationship between inter-organizational IT integration and supply chain visibility differs from our expectations, because this moderation appeared non-significant in regression analysis. Secondly, a strong ceiling effect of supply chain flexibility capabilities has been found when inter-organizational IT is integrated to a large extent and the firm/plant faces high supply chain complexity.
5.1. Non-significant moderation
In our data analysis, a non-significant negative moderation influence of supply chain complexity on the direct relationship between inter-organizational IT integration and supply chain visibility has been found. The probability value of the negative moderating effect (β = -.211) has a probability value of .116, which indicates that negative moderation occurs, but this moderation is not significant at a probability level of 0.1. Therefore the hypothesis (2b) concerning this influence has not been accepted. Although the difference in the steepness of the slopes presented in figure 4 below seems high, the moderating effect might be non-significant due to the relatively small sample size, because other academia found significant negative moderation for supply chain complexity sources by using larger samples.
Figure 4. Visualization of the moderating effect of supply chain complexity on the direct relationship between inter-organizational IT integration and supply chain visibility.
15 moderator on direct relationships in which supply chain integration is included. Therefore, the direct relationship between inter-organizational IT integration and supply chain flexibility can be significant whereas it is not for supply chain visibility. Although the construct of supply chain complexity as a whole might not have a significant negative moderation influence on the direct relationship between inter-organizational IT integration and supply chain visibility, individual supply chain complexity sources might act as a significant negative moderator, thereby weakening this direct relationship. To test whether the individual supply chain complexity sources would result in different moderating influences on the direct relationship between inter-organizational IT integration and supply chain visibility, a small analysis has been conducted for the downstream and upstream supply chain complexity items to see whether they moderate the direct relationship. This has been done by creating two new interaction variables and performing an additional regression analysis. For the downstream complexity source (β = -.150, p > .1), no significant moderation effect has been found for the interaction variable. However, for the upstream complexity source (β= -.304, p < .1), a significant negative moderation effect has been found. Therefore, upstream complexity significantly weakens the direct relationship between inter-organizational IT integration and supply chain visibility, although more research is needed on this topic. This can be done for example, by enlarging the sample size or by taking into account the supply chain complexity drivers of de Leeuw, Grotenhuis and van Goor (2013) to research the underlying mechanisms of supply chain complexity sources. Furthermore, it is worth noting that both in this additional analysis and in the research of Wong et al. (2015b), no significant moderating of the downstream complexity source has been found. Therefore, downstream complexity might not (significantly) weaken the direct relationship, although additional research on this topic is needed to provide a decisive answer.
5.2. Ceiling effect of supply chain flexibility
16 6. Conclusions
This thesis provides additional insights into inter-organizational IT integration, supply chain agility and supply chain complexity. Our main findings are that inter-organizational IT integration holds positive relationships with both supply chain flexibility and supply chain visibility, as well as the significant negative moderation of supply chain complexity on the direct relationship including flexibility and non-significant negative moderation on the direct relationship including visibility. Therefore, our research question on the influence of supply chain complexity on this direct relationships can be answered as follows: Supply chain complexity has a negative moderating influence on the relationship between supply chain IT integration and supply chain agility, although this negative moderating influence has not been proven statistically significant for supply chain visibility.
6.1 Managerial & Theoretical Implications
Investing in inter-organizational IT integration has been shown to result in higher supply chain agility capabilities (flexibility and visibility), but solely if the firm faces low supply chain complexity. Therefore, managers should take into account to which extent IT will be integrated among supply chain partners by observing the level of supply chain complexity it faces. In this thesis, we reported upon a negative moderating effect of supply chain complexity, which causes a ceiling effect of supply chain flexibility capabilities if high supply chain complexity levels are observed. As can be seen in figure 2 and section 5.2, only an incremental increase in supply chain flexibility capabilities can be gained if a firm integrates their inter-organizational IT to a larger extent if it faces high supply chain complexity. Therefore, managers must take into account the intended goal of integrating inter-organizational IT; if the goal is to gain supply chain flexibility capabilities, the return on investment might be insufficient, while if the goal is to reduce administration costs, IT investments might be justified.
From a theoretical point of view, this thesis contributed in conceptually and statistically relating inter-organizational IT integration with supply chain agility, where the latter is composed of supply chain flexibility and supply chain visibility. Also, supply chain complexity has been extended by taking into account multiple different supply chain complexity sources. However, the results also have implications for future research on this topic; insufficient research has been conducted on the moderating role of individual supply chain complexity sources and their underlying mechanisms to understand how these supply chain complexity sources influence the direct relationships between inter-organizational IT integration and supply chain flexibility or visibility. Also, additional research is needed on how higher supply chain flexibility (and visibility) capabilities can be reached while facing high supply chain complexity.
6.2 Limitations
The small sample size caused significance issues in interpreting results of data analysis, as well as in proper factor loading of the internal manufacturing supply chain complexity source items. This small sample size is the result of time constraints in data collection for this master thesis. Also the length of the questionnaire, which is being used for multiple theses and included more than 150 items, resulted in a lower response rate and relatively many partly filled out responses. Furthermore, the depicted results solely hold for the food and beverage industry in the Netherlands. Due to industry, cultural or country specific characteristics, results might differ for other industries or countries.
6.3 Directions for future research
17 can be done by researching the underlying mechanism of supply chain complexity sources; supply chain complexity drivers (see de Leeuw et al., 2013). Furthermore, the moderation hypotheses used in this thesis should be tested by using a larger sample, thereby confirming or rejecting the results of the hypotheses in this thesis. Also other external influences like market dynamics might have a (moderating) effect on the direct relationship between inter-organizational IT integration and supply chain agility and should be researched further to identify the force field in which the positive direct relationship takes place. Finally, although we did not assume a strong positive relationship between intra-organizational IT integration and supply chain agility, additional research is needed to see what type of relationship holds for these variables.
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