An empirical analysis of buyer-supplier relations of
production companies:
The impact of Intra ICT and Inter ICT on supply chain performance and the
moderating effect of demand uncertainty, technological uncertainty and
market uncertainty
by
Mark Hofkamp
University of Groningen Faculty of Business Administration
MSc. Operations & Supply Chains
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An empirical analysis of buyer-supplier relations of production
companies:
The impact of Intra ICT and Inter ICT on supply chain performance and the
moderating effect of demand uncertainty, technological uncertainty and
market uncertainty
Abstract
Earlier studies have not always found a relation between ICT and supply chain performance. However, within these studies there was little attention for the concepts of Intra ICT and Inter ICT. Furthermore, previous studies did acknowledge that uncertainty could influence the relation between ICT and supply chain performance. Yet, this has not been fully explored. This research will look at the relation that Intra ICT and Inter ICT have with supply chain performance and if demand uncertainty, technological uncertainty and market uncertainty moderate the relation between ICT and supply chain performance. This research analyses the buyer-supplier relations of production companies. The data that is used was collected from suppliers about their relation with key buyers. The analysis of this research reveals a positive relation between Intra ICT and service performance and a positive relation between Inter ICT and service performance. Yet, there was a negative relation found between Inter ICT and cost performance. Uncertainty was not found as a moderator on the relation between ICT an supply chain performance. It is believed that a higher level of uncertainty requires less Inter and Intra ICT. It might be that suppliers need frequent and informal contact with key buyers in order to cope with high levels of uncertainty.
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1. Introduction
Information and communication technology (ICT) has become increasingly important over the last 20 years. Studies have tried to find the relation between ICT and supply chain performance. This has resulted in a number of studies that have found a positive relation between ICT and supply chain performance (Byrd and Davidson, 2003; Sanders, 2008; Ward and Zhou, 2006) and a number of studies that have not found a positive relation (Brynjolfsson, 1993; Sriram and Stump, 2004; Sircar et al., 2000). Unfortunately these studies are not very specific in their measurement of ICT. This could be a reason for these different findings. Furthermore, studies measure ICT either as Intra ICT, Inter ICT or a combination of these two concepts. Therefore, the results can not be compared easily, nor are they easy to interpret. The aim of this research is to investigate the different influences that Inter ICT and Intra ICT have on supply chain performance. Furthermore, this research will show how Intra and Inter ICT influence supply chain performance when companies face high levels of uncertainty, as in previous studies external influences have been researched insufficiently.
Previous studies measured ICT in different ways. They either measure ICT as Intra ICT (Jayaram et al., 2000; Sanders and Premus, 2002), Inter ICT (Cagliano and Da Silveira, 2006; Saeed et al., 2005), or a combination of both (Deveraj and Kohli, 2000; Li and Ye, 1999; Loekis et al., 2009). Furthermore, studies measure the concepts of Intra ICT and Inter ICT differently. Ward and Zhou (2006) only measure ERP systems as Intra ICT, while Jayaram et al. (2000) only measures CAD/CAM. Of the concept of Inter ICT Cagliano and Da Silveira (2006) measure EDI, internet and extranet, while Sanders (2007) only measures E-business. It seems that all of these studies only measured a part of the concept. This could be a reason why they did not always find a positive relation between Intra ICT and supply chain performance or between Inter ICT and supply chain performance. Therefore, this research will contribute by comparing the influences of Intra ICT and Inter ICT on supply chain performance. This will be done by measuring more elements of Intra ICT and Inter ICT than have been during previous studies.
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the relation between Inter ICT and supply chain performance. Uncertainty is considered to be a key dimension of the buyer-supplier relation and a part of the network (Claycomb and Frankwick, 2008), a critical antecedent to supply chain management (Chen and Paulraj, 2004) and will have an adverse effect on supply chain performance unless appropriate measures are taken (Heide and Stump, 1995). Furthermore, it has already been mentioned that most of the published studies fail to study the effects of uncertainty (Van Donk et al., 2008). A number of studies tried to find this moderating effect of uncertainty, but were not specific in their measurements. Jean et al. (2008) measure the moderating effect of environmental uncertainty on the relation between ICT capabilities and performance, while Mendelson and Pillai (1998) only measure the moderating effect of marketing uncertainty on the relation between EDI and supply chain performance. In order to create a better understanding of the moderating effect of uncertainty, this research will further study the moderating effect of uncertainty. This research contributes, because environmental uncertainty will be defined into different kinds of uncertainty, which have not been studied yet as a moderating factor on the relation between Intra ICT and supply chain performance and the relation between Inter ICT and supply chain performance. This way, it will become clear whether the different kinds of environmental uncertainty have the same or a different effect as a moderator and how companies should cope with high levels of uncertainty.
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2. Theoretical background
2.1 ICT, performance and their relation
ICT can be seen as a family of technologies used to process, store and exchange information, facilitating the performance of information-related human activities, provided by, and serving both the public at-large as well as institutional and business sectors (Salomon and Cohen, 1999). The exchange of information and communication along the supply chain is very important and central to the collaboration (Andersen et al., 2008; Chen and Paulraj, 2008; Mohr and Nevin, 1996; Sanders and Premus, 2002). The information and relationships must be managed and developed in order to link with upstream and downstream partners (Bozarth et al., 2009). This is how ICT can contribute to a higher level of performance (Frohlich and Westbrook, 2001).
In the introduction different results have been mentioned of how ICT has an influence on supply chain performance. A possible explanation of these different results is that previous studies have measured ICT in a different way. Perhaps it is better to make a clear distinction of ICT into Intra ICT and Inter ICT, because not every manufacturer uses them the same way (Bergeron and Raymond, 1992; Ward and Zhou, 2006). According to Ward and Zhou (2006) a manufacturer will first implement their Intra ICT (e.g. MRP). Some manufacturers only develop simple Intra ICT without a supply chain focus, while other manufacturers might expand its scope into the supply chain and thus develop the Inter ICT (Ward and Zhou, 2006), therefore, suggesting that it might be better to divide ICT into Intra ICT and Inter ICT. As already mentioned in the introduction, this distinction was not made clear in previous studies.
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conduct business (Youtie et al., 2007). Several authors use different names for this kind of ICT such as: ICT applications (Byrd and Davidsson, 2003; Sanders and Premus, 2002), Hard ICT (Loekis et al., 2009) and information system infrastructure (Jayaram et al., 2000). They all use some aspects of Intra ICT. In order to prevent confusion, the concept will be called “Intra ICT” for the purpose of this research. Examples of Intra ICT are: MRP, APS, ERP, CIM (Ward and Zhou, 2006) and CAD/CAM (Jayaram et al., 2000).
Inter ICT refers to information systems that connect a firm with its suppliers and customers (Ward and Zhou, 2006). Inter ICT is an automated information system shared by two or more companies that allow the transfer of information across organizational boundaries (Ali et al. 2007; Hughes et al. 2003). The ICT makes it possible to exchange rich information effectively, makes data more available, and data exchange can be performed quicker and more reliable. Furthermore, organizations have an easy access to their partners (Cagliano and Da Silveira, 2006). For this kind of ICT several authors also have used different names such as: inter organizational information systems (Cagliano and Da Silveira, 2006), inter organizational systems (Saeed et al., 2005) and inter organizational collaboration technology (Sanders, 2007). For the purpose of this research it will be addressed as “Inter ICT”. Examples of Inter ICT are: electronic mail, internet connection, extranet connection, EDI (Cagliano and Da Silveira, 2006), and an auto data capture system (Sanders and Premus, 2002).
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Intra ICT might have a relation with supply chain performance. It already has been stated that Intra ICT and Inter ICT should be discussed separately (Ward and Zhou, 2006). However, in previous studies Intra ICT was either very specific or very broad including parts of ICT that are not Intra ICT. Loekis et al. (2009) studied the effect of ICT investments. This is a study that did not make a distinction between Intra ICT and Inter ICT. They did find a positive relation with performance. However, the study of Loekis et al. (2009) was about financial performance and not supply chain performance. Byrd and Davidsson (2003) also found a positive relation with performance. Again, they studied the effect on firm performance and not supply chain performance. Sanders and Premus (2002) only partially found evidence that Intra ICT improves supply chain performance. Sircar et al. (2000) and Sriram and Stump (2004) both found no relation between investments in ICT and supply performance. Investments in ICT are considered both Intra ICT and Inter ICT. On the other hand, when studies measure a specific program of Intra ICT there seems to be a relation with supply chain performance. ICT programs such as CAD and CAM (Jayaram et al., 2000) and MRP (Barua and Lee, 1997) have a positive relation with performance. CAD/CAM makes it possible that production is more efficient, faster and of better quality. Thus, CAD/CAM can lead to a higher supply chain performance. Therefore, MRP is used to manage the production planning. Because of good planning MRP can reduce inventory which leads to lower costs. Thus, MRP can lead to a higher supply chain performance. Both studies consider specific programs of Intra ICT. This research studies the concept of Intra ICT. The studies of Jayaram et al. (2000) and Barua and Lee (1997) could indicate that if several Intra ICT programs are combined into one concept, Intra ICT also positively affect supply chain performance. If CAD/CAM and MRP can lead to a higher supply chain performance it could also indicate that the concept of Intra ICT can lead to higher supply chain performance. Therefore, our first hypothesis is as follows:
H1. Intra ICT has a positive and direct relation with supply chain performance.
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not incorporate many kinds of Inter ICT, and they did not study the effect on supply chain performance. Investments made in Inter ICT software such as EDI, extranet and internet have resulted in a positive relation between Inter ICT and performance (Cagliano, 2006). This study did have a focus on supply chain performance, but it did not use that many kinds of Inter ICT. Saeed et al. (2005) also found that Inter ICT has a direct and positive relation with performance. However, this study did not focus on supply chain performance and used only EDI as an example of Inter ICT. Sanders (2007) also found a positive relation for Inter ICT with performance. Again, this study did not focus on supply chain performance and used only internet and e-business technologies as examples of Inter ICT. It seems that EDI, an extranet connection, an internet connection or e-business technologies improves communication with the buyer. It makes it easier for the supplier to provide the buyer with information about products and deliveries. Therefore, it seems that EDI, extranet, intranet or e-business lead to a higher supply chain performance. These previous studies have measured only one or a small numbers of examples of Inter ICT. However, this could indicate that this research will make it clear that Inter ICT can lead to higher supply chain performance. Therefore, the second hypothesis is as follows:
H2. Inter ICT has a positive and direct relation with supply chain performance.
2.2 Uncertainty and its moderating effect
Uncertainty can refer to the state of an organism, that lacks the information about whether, where, when how or why an event has occurred or will occur (Bar-Anan et al., 2009; Original source Knight, 1921). They say that by gaining information, organisms will learn to predict and control their environment and conferring an adaptive advantage. As a result, Bar-Anan et al. (2009) say that uncertainty is generally viewed as an aversive state that organisms are motivated to reduce.
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uncertainty into internal and external uncertainty. Lee (1998) mentions that there lies uncertainty within decision making. According to him there are three components: adequacy of available information, predictability of outcomes and confidence about outcomes. Most studies measure uncertainty as environmental uncertainty (Chen and Paulraj, 2004; Hawkins et al., 2008; Heide and John, 1990; Lee, 1998; Sako and Helper, 1998; Skarmeas et al., 2002). Milliken (1987) defines uncertainty as the inability to assign probabilities to future events, the lack of information about the cause-effect relationships and the inability to predict accurately what the outcomes of a decision might be. Chen and Paulraj (1994) come up with 3 different kinds of uncertainty: Supply uncertainty, demand uncertainty and technological uncertainty. Davis (1993) has almost the same definition. He divides uncertainty into supply uncertainty, customers’ uncertainty and technological uncertainty. However, supply uncertainty has not been included in this research, because of the downstream focus from supply towards the buyers. According to Lee et al. (2008) the two major dimensions are technological uncertainty and market uncertainty. Markets can be unstable or competitors can be aggressive when new companies enter the market (Lee et al., 2008). This research divides environmental uncertainty into demand uncertainty, technological uncertainty and market uncertainty.
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Technological uncertainty is the inability to forecast accurately the technical requirements in the relation (Heide and John, 1990). Technological uncertainty refers to the rate of technological change in the critical product and manufacturing process (Lee et al., 2008). It is a result of changes in the standards or specifications of the components or end products, or from general technological development (Heide and John, 1990). If a company wants to operate successfully in a technological unpredictable environment, the manufacturer has to be able to rapidly change product technology in downstream markets (Joshi and Stump, 1999). Chen and Paulraj (2004) say that technology uncertainty measures the extent of technological changes which is evident within their industry. It may originate from regulatory, social, economic or technological trends in the market (Joshi and Stump, 1999). Schilling and Steensma (2002) say that technological uncertainty could be a reason for companies to increase the vertical integration. However, it is more likely that it creates a boundary due to high risks. If a company wants to be able to successfully operate in a technologically unpredictable environment, the company must be able to rapidly upgrade its product technology (Joshi and Stump, 1999). This research also focuses on the technological modifications of products and the need to change them.
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actions of companies can create an unstable market. Ramdas and Spekman (2000) characterize market uncertainty with the level of market maturity, changes in the market and the length of the product life cycles. Low market maturity, rapidly changes in the market and a short product life cycle contributes to a high market uncertainty. This corresponds with the explanation of complexity and stability about uncertainty by Duncon (1972). This research focus on the product life cycle, the market maturity (Ramdas and Spekman, 2000) and whether the market is volatile (Duncan, 1972).
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research will now tries to find and compare the moderating effect of demand uncertainty, technological uncertainty and market uncertainty on the Intra ICT-supply chain performance relation and the Inter ICT-supply chain performance relation.
A higher demand uncertainty will make it difficult for companies to deal with the fluctuating demand and forecast sales and market trends (Germain et al., 2008). The study of Li and Ye (1999) found that when there is a higher level of uncertainty there will be a stronger relation between ICT and performance. When there is a high level of demand uncertainty there will be a lack of information. In order to cope with this uncertainty companies will invest and use more ICT so they can gather more information. If a company has more information about demand, the company can produce enough, have sufficient products on supply and supply quickly. Intra ICT like MRP and forecasting software can make it possible to collect more information in uncertain environments (Germain et al., 2008). Thus, Intra ICT can lead to a better supply chain performance. However, Fynes et al. (2005) said that more informal communication is needed. Under conditions of high environmental uncertainty, high frequency and informality of communications is needed. A possible explanation for this outcome is that Fynes et al. (2005) did not measure Intra ICT. They measured the supply chain relation in general, not via ICT. Under conditions of low uncertainty, Intra ICT will not result in higher supply chain performance. There is no need to monitor changes in demand, because demand is known. This is why it is expected that under conditions of high demand uncertainty, Intra ICT makes it possible to monitor changes in demand better and makes it possible to react quickly. As results supply chain performance will be higher. When there are conditions of low demand uncertainty, there is no need to monitor demand because there will be no fluctuations of demand. Therefore the next hypothesis is as follows:
H3. Demand uncertainty will moderate the relation between Intra ICT and supply
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The unpredictability of volume is the reason companies want to communicate more with external relations (Hyde and Stump, 1995). When there is a situation of volatile demand, difficulty of forecasting and timing, there is a greater need of communication with external partners in order get more information (Fynes et al., 2005). Cagliano and Da Silveira (2006) found that communication with Inter ICT such as: EDI, extranet and internet will lead to a better supply chain performance. Therefore, it could be that when there is a high level of demand uncertainty, communication via Inter ICT makes it possible for a company to gather more information about demand from the buyer. This obtained information can lead to better, faster and correctly timed reactions. Under conditions of low demand uncertainty, Inter ICT will not result in higher supply chain performance. Demand is known and therefore there is no need to communicate with the buyer about demand. Summarizing, it is expected that under conditions of high demand uncertainty, Inter ICT will positively relate to supply chain performance. When there are conditions of low demand uncertainty, Inter ICT does not relate to supply chain performance. Therefore the next hypothesis is as follows:
H4. Demand uncertainty will moderate the relation between Inter ICT and supply
chain performance: If demand uncertainty is high, there will be a positive relation between Inter ICT and supply chain performance, whereas there will be no relation when demand uncertainty is low.
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performance because there is no need to modify a product. Therefore the next hypothesis is as follows:
H5. Technological uncertainty will moderate the relation between Intra ICT and
supply chain performance. If technological uncertainty is high, there will be a positive relation between Intra ICT and supply chain performance, whereas there will be no relation when technological uncertainty is low.
Due to rapidly changing technology, companies have to exchange and share information more often than when technology is predictable (Fynes et al., 2005; Song, 2001). As the exchange of information increases, the relation between external relation and performance will be stronger (Fynes et al., 2005). Buyers and suppliers have to communicate with each other about what kind of technological modifications are needed. It has already been found that communication with Inter ICT such as electronic mail and internet connection improve supply chain performance (Cagliano and Da Silveira, 2006). It seems that Inter ICT is an easy and fast way to communicate. So when there is a high level of technological uncertainty, communication via Inter ICT makes it possible to gather information about technological modifications quickly. A company can deal with these changes which should lead to higher supply chain performance. Thus it is expected that under conditions of high technological uncertainty, Inter ICT will positively relate to supply chain performance. When there are conditions of low technological uncertainty, Inter ICT does not relate to supply chain performance because there is no need to communicate about technological modifications of the product. Therefore the next hypothesis is as follows:
H6. Technological uncertainty will moderate the relation between Inter ICT and
supply chain performance: If technological uncertainty is high, there will be a positive relation between Inter ICT and supply chain performance, whereas there will be no relation when technological uncertainty is low.
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of market uncertainty more communication is needed to cope with the uncertain situation. When there is a high level of market uncertainty, the higher level of communication will lead to higher performance (Sicotte and Bourgault, 2008). The environment of businesses with high market uncertainty is rich with information. A higher frequency of changes results in larger volumes of information that need to be communicated (Mendelson and Pillai, 1998). In order to cope with the rapidly changing environment, companies have to adjust their internal operations to match the changes (Mendelson and Pillai, 1998). Increasing numbers of competitors and product life cycles results in a higher level of market uncertainty. With the use of Intra ICT the supplier can achieve a higher supply chain performance. It seems that with the use of Intra ICT (e.g. CAD/CAM) a supplier is able to react quicker to changing trends in the market. This could result in a higher supply chain performance. Summarizing, if there is a high level of market uncertainty companies should use more Intra ICT. This makes it possible for companies to react quicker to shorter product life cycles or changes in the market. Thus, it is expected that under conditions of high market uncertainty, Intra ICT will positively relate to supply chain performance. When there are conditions of low market uncertainty, Intra ICT does not relate to performance. Therefore the next hypothesis is as follows:
H7. Market uncertainty will moderate the relation between Intra ICT and supply
chain performance: If market uncertainty is high, there will be a positive relation between Intra ICT and supply chain performance, whereas there will be no relation when market uncertainty is low.
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suppliers can react faster with the use of electronic links. This results in timely information (Mendelson and Pillai, 1998). It seems that when there is a high level of market uncertainty, Inter ICT results in higher supply chain performance. Inter ICT makes it possible to generate information in a fast and flexible way. The supplier can react quicker to changes in the market. Summarizing, if there is a high level of market uncertainty and companies want to react quickly to market shifts, more communications and use of Inter ICT is needed to get the right information. This will result in higher supply chain performance. When there are conditions of low market uncertainty, Inter ICT does not relate to performance. Therefore the last hypothesis is as follows:
H8. Market uncertainty will moderate the relation between Inter ICT and supply
chain performance: If market uncertainty is high there will be a positive relation between Inter ICT and supply chain performance, whereas there will be no relation when market uncertainty is low.
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3. Methodology
3.1 Procedures
This study is related to a Master Thesis project within the faculty of Business Administration of the University of Groningen. The hypotheses of this study were tested with the use of data gathered using a questionnaire. Starting point of this research was a list generated by the Chamber of Commerce. This list contained manufacturing companies with SIC codes in the range of 33-38. Previous studies at the University of Groningen also gathered information using the same questionnaire: one Master student and 2 groups of Bachelor Thesis students.
The data collection took place in several phases. First, companies were selected from the list of the Chamber of Commerce. The internet was used to check that the companies supplied other production companies. Subsequently, these companies were approached by phone. During this first conversation, the purpose of this research was briefly explained and it was made clear how the companies could contribute to this research. When a company agreed to participate, a suitable person in the company was selected to fill in the questionnaire. For example, this person could be from sales, marketing, ICT or the director. The company selected this person based on the knowledge he or she had in order to fill in the questionnaire. He or she had to answer questions on a key buyer of their company. Depending on the preference of the respondent, the questionnaires were sent by email or postal mail. However, for this research all questionnaires were sent by email. The email message contained the questionnaire and an additional letter explaining the research. This letter also assured that data used from the questionnaire would be kept confidential. When questionnaires were not returned after a month the respondents were contacted again by phone. The companies were contacted until the questionnaires were returned or until they indicated that they would not participate any longer.
3.2 Respondents
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of 21%. A Master student sent 64 questionnaires and 25 were returned. This is a response rate of 39%. In 2009 a second group of Bachelor students sent the questionnaire to 510 companies and 85 were returned. This is a response rate of 16.7%. For this research 50 questionnaires were sent and 26 were returned. This is a response rate of 52%. In total, 899 questionnaires were sent and 194 questionnaires were returned. This is a response rate of 21.6%.
The number of employees that work in a manufacturing company varies. From the 194 questionnaires most people work in a company with 101-1000 employees (45.9%) or in a company with 21 to 100 employees (37.1%). The smallest group of people works in a company with 1001-5000 employees (4.6%). In addition, 5.2% work in a company with over 5000 employees and 7.1% work in a company with less than 20 employees. The length of the relation with a buyer ranges from 0 to 30 years with an average of 7.1 years (SD=5.4).
3.3 Measurements
A questionnaire was used to measure the variables Intra ICT, Inter ICT, demand uncertainty, technological uncertainty and the supply chain performance. The respondent had to answer the questions based on a five-point Likert scale. The measurement of the variables will be discussed here.
Intra ICT was measured using 9 items, based on Ward and Zhou (2006) and Jayaram et al. (2000). The respondent had to answer to what extent technologies are used in his or her company. An example of a technology is: “Forecast-Demand Management Software”. Based on a five-point Likert scale, respondents had to answer ranging from no use to high use.
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Demand uncertainty was measured using 4 items, based on Chen and Paulraj (2004). The respondent had to indicate if he or she would agree with a statement. An example of a statement is: “The total volume of products delivered to the key buyer fluctuates drastically from week to week”. Based on a five-point Likert scale the respondent had to answer ranging from totally disagree to totally agree.
Technological uncertainty was measured using 4 items, based on Chen and Paulraj (2004). The respondent had to indicate if he or she would agree with a statement. An example of a statement is: “Our company frequently needs to carry out technical product modifications, in order to remain competitive”. Based on a five-point Likert scale the respondent had to answer ranging from totally disagree to totally agree.
Market uncertainty was measured using 4 items, based on Ramdas and Spekman (2000) and Mendelson and Pillai (1999). The respondent had to indicate if he or shewould agree with a statement. An example of a statement is: “The maturity of the market that our firm is in is high”. Based on a five-point Likert scale the respondent had to answer ranging from totally disagrees to totally agree.
Supply chain performance was measured using 6 items for cost performance and 6 items for service performance, based on Gimenez and Ventura (2003). The respondent had to indicate if the company’s performance improved compared to three years ago. An example of a performance is: “The production costs related to the key buyer”. Based on a five-point Likert scale the respondent had to answer, ranging from a significant increase to a significant decrease of performance.
3.4 Data analysis
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Based on the outcome of this factor analysis, the Pearson correlation is used to find an indication if there are any direct relations.
21 4. Results 4.1 Results factor analysis
Table 4.1 presents the results of the factor analysis. This was performed with varimax rotation. First, the concepts of cost and market uncertainty had to be recoded, because they were stated in opposite direction. Based on the Kaiser Meyer Olkin measure of sample, items had to be greater than 0.5. The concepts needed a Cronbach Alpha greater than 0.6 (Field 2005). Then the factor analysis was performed.
For the variable Intra ICT, 6 items were deleted. At first, it seemed that Intra ICT loaded on 2 factors: Intra planning ICT and Intra Production ICT. However, there was a high cross loading and Intra production ICT had an eigenvalue below one. Therefore, items had to be deleted. This resulted in one concept of Intra ICT, based on three items with a Cronbach Alpha of .71. For the variable Inter ICT, one item was deleted. Inter ICT has a Cronbach Alpha value of .67.
There are three different kinds of uncertainty. Demand uncertainty has a Cronbach Alpha of .83 and no item was deleted. For technological uncertainty no item was deleted and it has a Cronbach Alpha of .76. Finally, the concept of market uncertainty had to be deleted. Market uncertainty had a very low Cronbach Alpha. After two items were deleted, the Cronbach Alpha was .43. It had to be greater than .6. Therefore, market uncertainty as a concept was removed. This means that hypotheses H7 and H8 can not be tested.
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Table 4.1 - Results of principal component analysis of Intra ICT, Inter ICT, Demand uncertainty and technological uncertainty
1 2 3 4
Intra ICT (α=.71)
Please indicate to what extent these technologies are used in your company.
Forecast-Demand Management Software .01 -.04 .30 .81
Advanced Planning and Scheduling (APS) .03 .21 .12 .74
MRP/ MRP II -.06 .05 .02 .78
Inter ICT (α=.67)
Please indicate to what extent these technologies are used.
Use an auto data capture system with our key buyer (e.g. Bar coding. RFID). -.02 .04 .77 .16
Use EDI with the key buyer. -.06 -.09 .73 .25
Have an internet connection with the key buyer. -.13 .10 .53 .08
Have an extranet connection with the key buyer. -.01 .02 .78 -.04
Demand Uncertainty (α=.83)
Please indicate the degree to which you agree with each statement with regard to your key buyer.
The total volume of products delivered to the key buyer fluctuates drastically from week to week. .84 .09 -.05 .03 The mix of products delivered to the key buyer changes considerably from week to week. .83 .07 .04 -.01 The total buying volume of products delivered to the key buyer is difficult to predict. .74 .27 -.16 -.04
The required mix of products delivered to the key buyer is difficult to predict. .80 .18 -.13 -.04
Technological Uncertainty (α=.76)
Please indicate the degree to which you agree with each statement with regard to your key buyer.
These products are characterizes by a lot of technical modifications. .15 .78 -.03 -.05
Our company frequently needs to carry out technical product modifications. .02 .85 .00 .12
The rate of precoess obsolescence is high in our industry .26 .69 .09 .10
The production technology necessary to produce these products changes frequently. .13 .63 .08 .07
Eigenvalue 3.464 2.871 1.572 1.322
Percentage of variance explained 23.093 19.140 10.477 8.811
Items
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Table 4.2 - Results of principal component analysis of performance factors
Items Factor
1 2
Service (=α.61)
Provide an indication of the improvement of your customer service to the key buyer relative to three years ago. In case the relationship with your key buyer is shorter than three years, please refer to the improvement of your customer service since the start of the relationship.
Responds to the key buyer needs in terms of product mix. .75 -.11
Responds to the special requirements of the key buyer. .71 .09
Provides the quantities ordered by the key buyer .68 -.01
Notifies the key buyer in advance about late deliveries or stock-outs .77 .06
Costs (=α.70)
Provide an indication of the improvement of your organization’s performance relative to three years ago.In case the relationship with your key buyer is shorter than three years, please refer to the improvement of your performance since the start of the relationship
The cost-to-serve the key buyer. -.17 .73
The production costs related to the key buyer. -.07 .75
The transport costs related to the key buyer. .18 .63
The administrative costs related to the key buyer. .06 .58
Eigenvalue 2.19 1.85
Percentage of variance explained 27.40 23.14
4.2 Correlations
Table 4.3 presents the means, standard deviations and correlations between the variables. A positive and significant relation was found between Intra ICT and Service (r = .15, p<.05). There was no relation found between Intra ICT and cost. A negative and significant relation was found between Inter ICT and Cost (r = -.15, p<.05). Finally, a positive and significant relation was found between Inter ICT and Service (r = .15, p<.05).
24 4.3 Multiple hierarchical regression analysis
In order to test the hypotheses a multiple hierarchical regression analysis was performedª. In the first step Cost and Service were added as dependent variables. As independent variables Intra ICT, Inter ICT, demand uncertainty and technological uncertainty were added. The independent variables were standardized to reduce the possibility of multicollinearity (Aiken and West, 1991).
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Step two in table 4.5 shows that our fourth hypothesis H4: “Technological uncertainty will moderate the relation between Intra ICT and performance: If technological uncertainty is high, there will be a positive relation between Intra ICT and performance, whereas there will be no relation when technological uncertainty is low”, has to be rejected, as no significant moderating effect was found on Intra ICT and cost (β = -.02, n.s.) or on Intra ICT and service (β = .01, n.s.).
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relation between Inter ICT and performance: If demand uncertainty is high, there will be a positive relation between Inter ICT and performance, whereas there will be no relation when demand uncertainty is low”, has to be rejected, as no significant moderating effect was found on Inter ICT and cost (β = .06, n.s.) or on Inter ICT and service (β = -.03, n.s.).
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5. Discussion
5.1 Discussion of hypotheses
Intra ICT
This research started with an analysis of the relation between ICT and supply chain performance. The first hypothesis predicted a positive relation between Intra ICT and supply chain performance. This was tested for both cost performance and service performance. A positive relation was found between Intra ICT and service performance. This corresponds to the study of Jeffers et al. (2008), who indicated that ICT firstly improves service. It turns out that when manufacturers use Intra ICT, they can generate information. With this information, manufacturers can be more flexible, can react quicker to changes in demand, and they can deliver faster. It corresponds to the study of Jayaram et al. (2000), that the use of Intra ICT can improve service performance.
This research found no positive relation between Intra ICT and cost performance. Like in previous studies conducted by, for example, Li et al. (2008), there is no significant result that can be found. Reasons for this could be that companies are dealing with the productivity paradox of Brynjolfsson (1993). Although benefits of ICT investments are high, the relation between the input and the output is not found (Bharadwaj et al., 1999; Brynjolfsson, 1993; Stratopoulos and Dehning, 2000). There is also the possibility of lags. Lags due to learning or adjustment might result in the lack of benefits. However, it could be that learning and adjusting of individuals and the organization is needed. This might result in larger benefits in the long run (Brynjolfsson, 1993; Deveraj and Kohli, 2000; Stratopoulos and Dehning, 2000).
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represent Intra ICT there would be a positive relation with cost performance (Theodorou and Florou, 2005).
Inter ICT
The second hypothesis predicted a positive relation between Inter ICT and supply chain. This was tested for both cost performance and service performance. A positive relation was found between Inter ICT and service performance. This corresponds to the studies of Auramo et al. (2005), Cagliano and Da Silveira (2006), Hughes et al. (2003) and Jayaram et al. (2000). Thus, it seems that Inter ICT such as: EDI, internet and extranet makes it possible for suppliers to communicate faster and easier with buyers. They can easily communicate about orders, delivery, delays, etc. This results in a higher supply chain performance.
A negative relation was found between Inter ICT and Cost performance. This implies that investments in Inter ICT lead to higher costs. The development of Inter ICT technologies as EDI and internet helps to improve responsiveness towards buyers. Furthermore, it creates possibilities of faster and easier access to internal and external information. There is also higher quality of information. Therefore, it seems that Inter ICT is designed for service performance. A better, faster and easier way of communication with buyers is made possible. There is no objective of reducing costs, merely a higher quality of communication and sharing information in order to improve service performance (Auramo et al., 2005). Another reason might be that the types of costs do not give a good representation. Costs such as transport costs might not be included in this case and could have attributed that there was an increase of costs. Therefore it seems that Inter ICT resulted in an increase of costs, while actually costs increased because transport cost were included.
Demand uncertainty
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Supply chain performance was tested via service performance and cost performance. In both situations the relation did not turn out to be stronger. The question in this situation might be why demand uncertainty should moderate the relation. Slater and Narver (1994) already questioned whether it is necessary for a company to act differently in an uncertain situation. This statement can be confirmed with the results of a small interview. We happened to have a conversation with an employee of one of the companies that participated in this research. This was not someone from the Sales department, but someone from purchasing. He has contact with other companies that participated in this research. For certain projects it is not exactly known which and how many materials or products are needed. Therefore, the supplier would also not know what the specific demands of the buyer are. In this situation, Intra ICT of the supplier does not improve supply chain performance. In fact, the supplier is not using Intra ICT any differently than when there is a low level of demand uncertainty. The supplier just waits until the buyer comes with its order. The supplier maintains a high stock level of standard products so they can deliver quickly. Suppliers do not keep every product on stock. Products made to order have a waiting time. As this happens at all suppliers the buyers just have to accept this. Thus, it seems that in order to deal with high levels of demand uncertainty, suppliers have high stock levels of standard products, so they can deliver immediately.
Intra ICT might be useful to forecast demand. This could be relevant for smaller buyers. However, the questionnaire was based on the key buyer. Perhaps the key buyer does not have a fluctuating demand and the less important buyers do have a fluctuating demand. In situation of high uncertainty companies might start to cooperate more with their key buyer (Matanda and Freeman, 2009). Furthermore, a close long-term relationship can be an effective means to reduce uncertainty (Su et al., 2008). Therefore, it might be possible that the buyer-supplier relationship is not under influence of demand uncertainty.
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of high uncertainty, frequent information is necessary to deal with the uncertainties. Frequent communication should then have a positive effect on performance (Mohr and Nevin, 1990).
In addition, under conditions of high environmental uncertainty no formal ICT is required. Companies benefit more from frequent and informal communication (Fynes et al., 2005). An explanation could be that ICT requires more standard and formal processes between supplier and buyer. This is more difficult in situations of high demand uncertainty. This result also came forward from the Pearson correlation. Table 4.3 in section 4.2 of this research made it clear that Inter ICT has a significant correlation of -.15 with demand uncertainty. This indicates that more demand uncertainty results in less use of Inter ICT. therefore, it could be that under conditions of high demand uncertainty, Inter ICT is not being used because companies benefit more from informal and frequent communication.
Technological uncertainty
This research has tried to discover the moderating effect of technological uncertainty on the relation between Intra ICT and supply chain performance. However, there was no moderating effect found of technological uncertainty on the relation between Intra ICT and supply chain performance. Again, supply chain performance was split up into service performance and cost performance, but in both situations no moderating effect was found. New occurring problems might be the cause that there is no stronger relation between Intra ICT and supply chain performance. An increase of technological uncertainty results in an increase of complexity. Unanticipated problems can start to increase. Due to new product development there might also be a higher failure rate (Oh and Rhee, 2008). The higher level of technological uncertainty can cause more problems and failures in production. This could result in higher costs and a decrease of flexibility and a decrease in the number deliveries being made on time.
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has the right production software, he can easily introduce technical modifications. However, after the factor analysis the production software was no longer factored in. Intra ICT only included planning and forecasting software. This might explain why no moderating effect of technological uncertainty was found on the relation between Intra ICT and supply chain performance.
This research also tested if technological uncertainty moderates the relation between Inter ICT and supply chain performance. However, proof of a moderating effect of technological uncertainty on the relation between Inter ICT and supply chain performance was not found. This effect was found neither on relation between Inter ICT and service performance nor on relation between Inter ICT and cost performance. This confirms the studies of Jaworski and Kohli (1993) and Slater and Nevin (1994), who also did not find the moderating effect of technological uncertainty. They suggested that a moderating effect of technological uncertainty is not found because it is more difficult to create a competitive advantage. Furthermore, it is likely that the supplier and the key buyer discuss technological modifications, of the product, during face to face contact instead of by Inter ICT. This is confirmed by the study of Fynes et al. (2005), who stated that under conditions of high environmental uncertainty frequent and informal communication is needed, while Inter ICT requires more standardized and formal processes. It was further confirmed by the interview mentioned in the discussion of demand uncertainty. If a buyer wants technical modifications of the product, the buyer and supplier arrange a meeting. Accompanied by someone of the production department, the buyer and supplier will have face to face contact when they discuss the desires of the buyer and the possibilities of modifying the product. Therefore, it seems that under conditions of high technological uncertainty there is no stronger relation between Inter ICT and supply chain performance, because there is more face to face contact.
5.2 Practical implications
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invests in Inter ICT, they can have a better and faster communication with their buyer resulting in a higher service performance. With internet connection or the use of EDI it is possible to provide the buyer quickly with information about delivery times, delays or possible other problems.
Furthermore, it seems that when suppliers face high levels of demand uncertainty or technological uncertainty, there is no stronger relation between Intra ICT and Inter ICT. Suppliers might need to communicate in a different way. Not using formal ICT, but frequent informal contact, such as face to face contact. This research should help suppliers understand that Intra ICT and Inter ICT are useful and can improve service performance. Although, under conditions of high demand uncertainty and high technological uncertainty, Intra ICT and Inter ICT do not have a stronger relation with supply chain performance.
5.3 Limitations of research
The formulation of questions could be the reason that the same answer is given to different questions. All questions are based on the five-point Likert scale. It would be easy for a participant to give the same answer for multiple questions. This would not give a correct representation of reality. Perhaps it would be better to have a smaller number of questions on one subject, ask open questions or use a different scale than the five-point Likert scale. This might prevent participants giving the same answers to different questions. As a result there could be less or more use of Intra ICT and Inter ICT or a higher level of demand and technological uncertainty. Furthermore, there is a number of questionnaires that were returned incompletely. Questions remained unanswered. This might have caused an incomplete representation of reality.
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Another reminder for this research is that there has been is an economic crisis for several years. Companies are dealing with rising costs and fewer turnovers. This could have been the cause companies did not noticing a decrease in costs but an increase. This could be a reason why there was no relation found between Intra ICT and cost performance.
For this research, manufacturing companies with SIC codes in the range of 33-38 were contacted. However, there are different kinds of manufacturing companies. Some companies manufacture simple standardized products. There are also companies who manufacture complex project based products. The use of ICT and investments in ICT would be completely different. It seems to be easier to use Intra ICT and Inter ICT in simple standardized situations, rather than complex turbulent situations. The level of uncertainty would also be different in both situations. This could have influenced results of this research and perhaps it would be better to look at these companies in separate researches.
5.4 Future research
This research shows how different concepts of ICT can contribute to supply chain performance. From the participating manufacturers there are companies that produce products make-to-stock or make-to-order. There are also manufacturers with high complexity and low complexity. Some companies used every type of ICT from the questionnaire and some companies used hardly any type of ICT. The question is to what extend supply chain management plays a role in the use of ICT. Companies do not use every type of ICT and do not need all types of information. Future research could study which type of company, depending on the type of production, needs which ICT.
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performance. This could also be the case for the moderating effect of demand uncertainty on the relation planning ICT and supply chain performance.
It also seemed that the concept of market uncertainty has not been fully explored. Due to possibly incorrect questions, a potential moderating effect has not been found. In a future research it might be interesting to look at other characteristics of market uncertainty. More representatives for market uncertainty could be the number of competitors, how low thresholds are for new competitors to join the market, how high thresholds are for competitors to leave the market and investments that should be made to join the market. In a future research, it would also be interesting to know not only the supplier’s opinion, but also the buyer’s opinion. If both fill in a questionnaire it is possible to create a better representation of the perceived levels of uncertainty and investment in ICT.
Previous studies, as the study of Van Donk et al. (2008), mentioned that most studies fail to address business conditions. This research has tried to study the effects of uncertainty. Another business condition is complexity. The study of Bozarth (2009) already showed that complexity is of influence on manufacturing costs. Future research can study whether internal and external complexity moderates the relation between ICT and supply chain performance.
5.5 Conclusion
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Previous studies acknowledged the importance of business condition. Yet, not many studies put much emphasis on business conditions. As uncertainty is a key dimension in the buyer supplier relation this research tried has tried to discover whether uncertainty moderates the relation between Intra ICT and supply chain performance and the relation between Inter ICT and supply chain performance. Uncertainty has been defined as demand uncertainty, technological uncertainty and market uncertainty. Based on a factor analysis, market uncertainty turned out to be unreliable and was left out from this research.
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