ICT, supply chain performance and
the effects of demand uncertainty
A resource based view
Author:
L. Nauta
University of Groningen
Faculty of economics and business
MSc BA Operations & Supply Chains
Production and Distribution
ABSTRACT
The effect of Information and Communication Technology (ICT) on performance has been studied extensively over the past ten years. However, results are rather divergent. This research aims at explaining these incongruities in findings by adopting a resource based view (RBV) on the relation between ICT and supply chain performance (cost and service), with varying degrees of demand uncertainty. Based on the RBV, ICT employment is investigated in three stages: ICT investment, ICT usage and ICT capability. Data is gathered using a questionnaire that is conducted in the Netherlands among supplying companies (n = 71). Our findings are consistent with the basic insight of the resource based view; ICT investment and ICT usage are both necessary conditions, but a significant and positive relation was found between ICT capabilities and supply chain service performance. This is an addition to the RBV that assumes that capabilities have a relation with competitive advantage. In contrast to our expectations, there is a significant and positive relation observed between ICT usage and supply chain cost performance in a certain demand environment. High demand uncertainty had a weak negative effect on the relation between ICT usage and cost. Managers should review their ICT, in order to make ICT a contributor to improved supply chain performance, especially if operating in an uncertain demand environment.
Keywords: information and communication technology (ICT), supply chain performance,
1. INTRODUCTION
Nowadays, companies operate in highly competitive environments that put firms under the pressure to improve quality, delivery performance and responsiveness while simultaneously reducing costs (Kannan and Tan, 2006). Most companies use Information and Communication Technology (ICT) in their internal supply chain, but also in the external supply chain with suppliers or customers. Information Technology (IT) provides links that support communication and collaboration along the supply chain and has become an integral component in supply chain management (Byrd and Davidson, 2003). Firms and their supply chain partners must posses appropriate, competitive inter-organizational systems if they want to respond quickly and effectively to changing customers needs and expectations (Hsu et al.; 2008). ICT is necessary in order to reduce uncertainties (Dewett and Jones, 2001) and is the key for competitive advantage (Gupta and Capen, 1996).
ICTs like Electronic Data Interchange (EDI), Material Requirement Planning (MRP), and Radio Frequency Identification (RFID) are considered important for creating competitive advantage (Papastathopoulou et al., 2007). For example, an organization can use EDI in order to be responsive to its customers (Chen and Williams, 1998). However, the debate on the IT productivity paradox suggest that the relationship between ICT and performance remains unclear (Wu et al., 2006; Jean et al. 2008; Brynjolfsson, 1993). Researchers are still struggling to specify the underlying mechanisms linking ICT to performance, there is a need for better theoretical models that trace the path from IT investments to supply chain performance (Bharadwaj, 2000). Although the relation between ICT and performance has been studied extensively, results are different. The objective of this study is to improve the understanding of the contribution of ICT to supply chain performance (cost and service) by building upon the resource based view (RBV), with varying levels of demand uncertainty. The focus will be on supplier-buyer relationships (business to business) of production companies in the Netherlands. ICT, supply chain performance and demand uncertainty are measured from the perspective of the supplier.
confirmed the relationship (Sanders and Premus; 2002) or did not find a direct relationship (e.g. Ward and Zhou, 2006; Devaraj et al., 2007). Other researchers confirmed an indirect relationship (Subramani, 2004; Dehning et al., 2007). This means that there are four different types of results, regarding one relationship. Each result is supported by a number of studies, most researchers confirmed a direct or indirect relationship between ICT and performance (see Zhang et al., 2008). However, findings which may seem similar, actually deal with different aspects of the relationship between ICT and performance (Zhang et al., 2008). The poor quality of data sets may also contribute to the inconsistent results (Li and Ye, 1999). Furthermore, most research focused only on one aspect of performance (Jin, 2006).
Existing literature and a critical review of Zhang et al. (2008) inspired to start with a more comprehensive research. This research adapts a resource based view (RBV) and distinguishes different stages of ICT employment. There are several reasons to adopt a resource based view. “Information System (IS) literature has demonstrated the RBV’s potential in the conceptual analysis of the effects of IT on performance. The RBV is also an appropriate theory for supply chain research” (Lai et al., 2008, p.25). Examining the impact of ICT in a specific setting, can help us to better understand the relationship between ICT and performance (Barney et al., 2001). Other researchers also focused on ICT and performance, but related solely one stage of ICT employment (for an overview see Zhang et al., 2008). This research will fill this gap by distinguishing three stages of ICT and its relation with supply chain performance. For ICT employment three stages are distinguished: ICT investment, ICT usage and ICT capabilities. Based on previous research, each stage is linked to the RBV in order to determine whether each stage has a relation with supply chain performance.
The next paragraphs describe the concepts used for this research. Hypotheses are formulated regarding the direct relationship of ICT and supply chain performance. Hereafter, the concept of demand uncertainty and hypotheses that describe the moderating effects of demand uncertainty are handled. At the end of the first part of this research, the research model is displayed. The second part of this study focuses on the methodology; data collection and procedure, used measures and data analysis. The results of this analysis are treated in the third section. Section four starts with a discussion upon the results. After this discussion, a conclusive is given. Section four ends with managerial implications and directions for future research.
1.1 ICT and its relation with supply chain performance
This paragraph starts with describing the relationship between the RBV and supply chain performance in general. Next, three stages of ICT employment are distinguished, which will be explained in more detail. For each stage a direct hypothesis regarding the relation of ICT and supply chain performance is formulated.
RBV and supply chain performance. The resource based view originates from Penrose (1959). According to the RBV, resources and capabilities drive the performance of a firm (Ravichandran and Lertwongsatien, 2005; Barney, 1991). Resources refer to the available factors that are owned or controlled by the firm, capabilities refer to a firm’s capacity to deploy resources (Amit and Schoemaker, 1993). Firms consist of a bundle of resources and capabilities (Peteraf, 1993), only firms with distinctive or superior resources and capabilities (compared to their competitors) are able to achieve competitive advantage (Parida, 2008). This means that a firm needs capabilities in order to manage and organize the full potential of its resources (Parida, 2008; Barney, 1991). Fundamentals of the resource based view are that resources and capabilities a firm controls should be valuable (have an effect on performance), heterogeneously distributed across companies (not the same across companies) and imperfectly mobile (are difficult to acquire in resource markets or to develop internally), in order to deliver sustained competitive advantage (Barney, 1991; Bhatt and Grover, 2005).
on business performance (internal) (Rivard et al., 2006) such as efficiency, ROI, profitability, etc. (e.g. Wu et al., 2006; Wieder et al., 2006; Byrd and Davidson, 2003). In this research supply chain performance (cost performance and customer service performance) will be included.
RBV and ICT employment. Based on the RBV and other articles (Cui et al., 2006; Bharadwaj, 2000; Zhang et al., 2008), three stages of ICT employment are distinguished in this research: ICT investment, ICT usage and ICT capabilities. A supplier that wants to use ICT with its buyer, has to invest in hardware and software, by investing in ICT the supplier will develop resources. Investment in IT is needed in order to form an IT infrastructure (Cui et al., 2006). Technology and hardware form the core of a firm’s IT infrastructure (Duncan, 1995). IT investment does not guarantee better supply chain performance (Wu et al., 2006). According to Papastathopoulou et al. (2007, p.327) “IT usage within an organization is a necessary condition for ensuring productivity payoffs from IT investments and must be an integral part of models exploring IT – performance links”. This is in line with a statement of Devaraj and Kohli (2003, p.273) that “the driver of IT impact is not the investment in the technology, but the actual usage of the technology”. However, if a supplier uses a particular technology with it’s buyer, it is easily duplicated by other firms, and it often does not provide improved supply chain performance (Wu et al., 2006). ICT investment and ICT usage are both necessary conditions (Mata et al., 1995; Papastathopoulou et al., 2007), but do these stages contribute to supply chain performance? According to Parida (2008) capabilities are needed in order to use the full potential of the resources. Without the capabilities to manage and make better use of these resources, a firm cannot achieve a competitive advantage in the short term or a sustained competitive advantage in the long run (Lai et al., 2008). In the following section it will be discussed whether ICT investment, ICT usage and ICT capabilities, have a relationship with supply chain performance.
ICT investment, ICT usage and ICT capabilities
According to Strassman (1997) there is no relationship between ICT investments and any measure of firm profitability. Although companies highly invested in IT, positive results could not be observed in the US economy (Roach, 1988). There are many companies that invest in ICT, but derive no benefits from it at all (Nolan, 1994). However, Barua and Lee (1997) found significant productivity gains from IT investments. Mata et al. (1995) state that investment in ICT can add value to a firm and adding value is necessary, but is insufficient for sustained competitive advantage. ICT can be duplicated by competitors, which means that it can give competitive advantage on short term but not on long term. For example: hardware or software bought by a firm can be bought by a competitor too. Since investments in IT are easily duplicated by competitors, investments per se do not provide any sustained competitive advantage (Powell and Dent-Micallef, 1997). High investment in information and communication technology does not necessarily result in much benefits for a firm (e.g.: Barney et al., 2001; Brynjolfsson, 1993; Taylor, 2003).
Some articles discuss ICT investments but use variables that focus on ICT usage or ICT capabilities. Only one article of Sircar et al. (2000) describes ‘pure’ IT investments and firm performance. Sircar et al. (2000) used multiple variables for measuring investments and performance. Their measures of performance included sales, market share, income, etc. but did not take supply chain performance aspects into account. This is a pity, because the variables they used for IT investments are covering many aspects. Sircar et al. (2000) did found that IT investment is a very important contributor to firm performance.
Overall, there is confusion about the relationship between ICT investments and performance. Some researchers state that there is a relationship, while other researchers reject these statements/findings. In order to overcome the confusion, this relation will be tested again in our study but with other variables. Investing in ICT might not be a sustainable competitive advantage, however it is necessary and could provide a competitive advantage.
This leads to the following hypothesis:
H1a: ICT investment has a positive relation with supply chain performance
concentrated on the performance effects of IT based Supply Chain Management (SCM) systems. SCM systems coordinate and integrate the flow of material, information, and finances from supplier to manufacturer to wholesaler to retailer to the end consumer (Dehning et al., 2007). They found weak support for the relationship between usage of an IT based SCM system and overall performance, such as increased total inventory turnover. Dehning et al. (2007) did found support for the relationship between IT-based SCM systems and inbound processes (increased raw materials inventory turnover) / outbound processes (increased finished goods inventory turnover). However, they did not find a relation between usage of an IT based SCM system and operations performance (work in process inventory turnover). Research of Sanders and Premus (2002) discovered that firms with high IT usage had significantly higher performance compared to firms with low or medium IT usage. Sanders and Premus (2002) also found that firms with high IT usage had a disproportionate success rate in cost reduction, cycle time reduction, quality and new product development.
Although Dehning et al. (2007) and Sanders and Premus (2002), found a relation between IT usage and performance, it is questionable what can be concluded from the results. Dehning et al. (2007) focused on IT based SCM systems, Sanders and Premus (2002) focused on different IT applications. The used variables for performance are also different; Dehning et al. (2007) included performance measures such as gross margins, return on assets, return on sales, etc. Sanders and Premus (2002) focused on organizational performance measures such as cost reductions, access to new technologies, new innovations, etc. It is difficult to compare the variables used by these researchers.
This results in the following hypothesis:
H1b: ICT usage has a positive relation with supply chain performance
ICT capabilities and supply chain performance. Capabilities refer to a firm’s capacity to deploy resources (Amit and Schoemaker, 1993). Carr (2003) states that the affordability of IT has destroyed one of the most important potential barriers to competitors. According to Carr (2003), even the best IT capabilities quickly become available to all. It can be questioned if ICT capabilities can be a source of sustained competitive advantage. Dewett and Jones (2001) conclude that it is difficult to imitate IT capabilities as they are presented in organization specific information technologies, developed inside the organization over time. Research of Bharadwaj (2000) indicated that firms with high IT capability tend to outperform other firms on a variety of profit and cost-based performance measures. As the opinions about ICT capabilities differ, there is need for further research.
Bharadwaj (2000), classified three types of IT-capabilities: IT infrastructure (physical IT), human IT resources (technical and managerial IT skills) and IT-enabled intangible resources (product quality, customer service). Following Bharadwaj (2000), human IT resources can be further defined in technical IT skills and managerial IT skills (Mata et al., 1995). Mata et al. (1995) define technical IT skills as the know-how needed to build IT applications using the available technology and to operate them to make products or provide services (original source: Capon and Glazer, 1987). Mata et al. (1995) and Copeland and McKenney (1988) found that skills of the IT department are a source of sustained competitive advantage. Dehning and Stratopoulos (2003) also found support for managerial IT skills as a source of competitive advantage.
notify the key buyer in advance about late deliveries or stock-outs. If the operating system of a supplier is performing better than the average level in their industry, administrative costs related to the key buyer might be lower. These capabilities cannot be easily imitated by competitors.
This leads to the next hypothesis:
H1c: ICT capabilities have a positive relation with supply chain performance
1.3 Demand uncertainty and its moderating effect
A lot of firms operate in uncertain and dynamic environments (Johnston et al., 2008). Whatever occurs in the environment is likely to affect the degree of uncertainty experienced by its members (Achrol and Stern, 1988). Uncertainty is a key element for analyzing environments of a supply chain (Miller, 1987; Achrol and Stern, 1988). Environmental uncertainty, refers to the difficulty firms have in predicting the future because of incomplete information or changing conditions. (Germain et al., 2008). The role of information is particularly crucial in the case of industrial markets (Leonidou et al., 2006). Environmental uncertainty has been identified as an important industry contextual factor, that impacts the role of IT in creating a competitive advantage (Kearns and Lederer, 2004; original source: Johnston and Carrico, 1988) and is therefore taken into consideration for this research.
Demand uncertainty and supply chain performance. In research that focuses on the contribution of IT to performance, the impact of uncertainty has not been fully investigated (Jean et al., 2008). Firms that deal with uncertainty in demand, have a greater need for close supply chain relationships than firms operating in a stable demand environment (adapted from Fynes et al., 2004; Chen and Paulraj, 2007). Close supply chain relationships are more likely to be strongly related to supply chain performance under conditions of uncertain demand than with stable demand (adapted from Fynes et al., 2004). Integrated information systems enable firms to obtain better, more timely information and maintain close relationships (Hsu et al., 2008). It is expected that uncertainty in demand can moderate the relation between ICT (investments, usage, capabilities) and supply chain performance. The next section describes the moderating effect of demand uncertainty on the different stages of ICT and its relation with supply chain performance.
ICT investment, supply chain performance and demand uncertainty. The impact of IS investment on performance depends on (uncontrollable) contextual variables, such as the external environment (Choe, 2003). Firms can deal with uncertainty by creating inter-organizational links between customers and suppliers (Choe, 2003). For close supply chain relationships, integrated information systems are needed (Hsu et al., 2008). For integrated information systems, investment in ICT is necessary. For example, a supplier that operates in an uncertain environment need to invest in (buyer specific) ICT in order to have a closer relation with its buyer. The supplier might invest in EDI or wants to get online access to the planning system of their key buyer. Under demand uncertain conditions, investing in these integrated information systems is more likely to be strongly related to supply chain performance.
to be strongly and positively related to supply chain performance under conditions of uncertain demand than under conditions of stable demand.
This leads to the following hypothesis:
H2a: The positive relation between ICT investment and supply chain performance will be stronger in an uncertain demand environment
ICT usage, performance and demand uncertainty. In an uncertain environment, ICT usage is needed in order to cope with changing demands, modifications, etc. The ability to follow business trends and to quickly respond to changing market needs is critical for superior firm performance in such environments (Wade and Hulland, 2004). To cope with demand uncertainty, information sharing (by for exampling using EDI) should be increased to greatly improve performance of the supply chain. Knowing upcoming orders reduces demand uncertainty and lowers system costs (Sahin and Robinson, 2005). Without the usage of ICT in an uncertain environment, the supplier might be to slow to respond to changes in customer demand. Sharing for example point of sale data (POS) between a buyer and its supplier, allows the supplier to reduce its inventory levels (Hsu et al., 2008). This results in improved supply chain performance (for example lowered inventory costs), which can positively influence the supplier-buyer relationship (Hsu et al., 2008).
Usage of specific ICT enables a supplier to obtain better, more timely information and maintain close relationships (Hsu et al., 2008). This close relationship is strongly related to supply chain performance under conditions of uncertainty in demand (Fynes et al., 2004). As such, firms operating in uncertain demand environments have a greater need for more ICT usage than firms operating under conditions of stable demand. In other words, ICT usage is stronger related to supply chain performance under uncertain demand conditions, than under stable demand conditions. Under conditions of stable demand, usage of ICT is less necessary and is not strongly related to supply chain performance. It is thus expected that the positive relation between ICT usage and supply chain performance is weaker under conditions of low demand uncertainty, than under conditions of high demand uncertainty. The hypothesis concerning this relation is:
ICT capabilities, performance and demand uncertainty. Jean et al. (2008) suggest that IT capabilities must fit the external environment, in order to obtain the desired benefits. In an uncertain environment, different capabilities will be needed than in a stable environment in order to achieve superior performance (Wade and Hulland, 2004). Sharing POS data between a buyer and its supplier in order to reduce inventory levels requires a high level of hardware / operating systems performance (capabilities). In an uncertain demand environment it will be very important to react on time to changing demands. If a firm has not developed capabilities in an uncertain demand environment, it will be unable to respond to changes in demand quickly, causing decreased performance. According to Wade and Hulland (2004, p.126) “the ability to quickly respond to changing needs is critical for superior firm performance in turbulent environments”. When demand is changing rapidly, the firm must be able to share information more quickly than when demand is stable (adapted from Fynes et al., 2004). Sharing information could be facilitated by ICT usage, but capabilities are also needed. Having more capabilities under uncertain demand conditions, will also contribute to improved supply chain performance. For improved supply chain performance, capabilities will also be needed under stable demand conditions. However, it is expected that ICT capabilities play a more important role in its relation with supply chain performance under conditions of high demand uncertainty. This results in the following hypothesis:
H2c: The positive relation between ICT capabilities and supply chain performance will be stronger in an uncertain demand environment.
2. METHODOLOGY
2.1 Data collection and procedure
This study is related to a project of the Faculty of Economics and Business of the University of Groningen. Data is gathered by use of an existing questionnaire, composed by a PhD student. The data of the returned questionnaires is merged with a pre-existing database, that consisted of data gathered by Bachelor students. The questionnaire focused on the relationship between a supplier and its key buyer, business to business. The complete questionnaire can be found in Appendix A.
Data collection consisted of three phases. First, a list of the Netherlands Chamber of Commerce (that manages the trade register) is used in order to identify production companies. This initial list consisted of Dutch manufacturing firms with SIC codes in the range of 33-38. However, this list also included production companies that deliver to customers (Business to Consumer). In addition, internet and company websites were used in order to select production companies that deliver to other companies. Next, most companies were contacted by phone because it was needed to identify which person had sufficient knowledge and understanding of the topics of the survey (Dillman, 2007). The correct person to contact was dependent upon the company, at some firms the ICT manager was contacted, while at other firms the supply chain manager or sales manager had sufficient knowledge and understanding. Persons that could not be contacted, have been called various times (van der Velde et al., 2004). If the correct person could not be contacted by phone, information was sent by email.
still no response was received on the first information email, companies were excluded from the phone/email list and registered as non-response.
If the contact person was willing to participate, the questionnaire was sent by email. In this email, companies were reminded that data would be kept confidential. Only for administration purposes, the name of the organization was asked in the questionnaire. In the database, names of the organization and respondents were not used, codes were used instead. Companies were asked if they wanted to receive a benchmark of their scores and if they would like to receive a copy of this research. Questionnaires could be returned by email or by post. When questionnaires were not returned after two weeks, the respondents were contacted by phone or email. Please note that this were companies that stated that they wanted to participate in this research. If the contact person had emailed that he/she wanted to participate, this email was forwarded, including a new questionnaire as attachment. This email was forwarded because we wanted to remind that initially, the contact person did want to participate. The organizations were contacted until they finally sent back the questionnaire or answered that they did not want to participate anymore. Although respondents of our questionnaires were Dutch, the questionnaire was in English.
For this survey, different types of production companies were approached. Some production companies were make to order orientated, other companies produced more standardized products. The participating companies operated in the industrial sector, examples are manufacturers of pumps, bearings, machinery, etc. In total, 343 companies are approached, 71 companies returned a completed questionnaire. This results in a response rate of 20.7%. The initial response rate from the pre-existing database was 16.73%, the response rate of the additional survey was 41.18%. Table 2.1 displays an overview of the number of employees within the firms that participated.
TABLE 2.1 Number of employees
Employees Frequency Percentage (%)
The length of cooperation years between supplier (respondent personally) and key buyer ranged between 0 and 20 years, with an average of 6.83 years (std. deviation 5.22 years). The primary product price ranged between €0,16 and €1.000.000, with an average price of €47.535,74 (std. deviation €189.994,25). Table 2.2 presents the job titles of the respondents.
TABLE 2.2 Job titles of respondents
Title Frequency Percentage (%)
Analyst 2 2.8
Controller 2 2.8
Director 6 8.5
ICT project leader / manager 4 5.6 (Key) Account manager 7 9.8 Customer service / manager 4 5.6
Manager operations 2 2.8
Marketing manager 2 2.8
Production manager 1 1.4
Project leader / manager 2 2.8
Sales / manager 19 26.7
Supply Chain Manager 2 2.8
Manager (other) 7 9.9
Other function 11 15.5
Not specified 1 1.4
Total 71 100
2.2 Measures
The measures of the independent variables (ICT investment, ICT usage, ICT capabilities, demand uncertainty) and the dependent variable (performance) were based on previous literature of other authors. For all questions, five point Likert scales were used. Table 3.1 (results section) displays the scales and items used. The next section describes the measurement scales of this study in more detail.
need minimal internal technologies, therefore the item “Excel or simple database” was added. Although the term “usage” was included and not “investment” specifically, all items focussed on internal ICT investments of the company (not used in the supply chain). This stems with the resource based view that involves resources internal to the organization (Lynch et al., 2000). Investment in these internal resources or infrastructure forms the basis for further usage in the supply chain. For confirmation, one control variable was included: “The IT investments and expenditures made by a company” (Byrd and Davidson, 2003).
Usage of ICT was determined by eight items, four items of Chen and Paulraj (2004) were included. An example of the included items is: “Use electronic transfer of purchase orders, invoices and/or funds with our key buyer”. Based on former related research, one item of van Donk et al. (2007) is included: “Have online access to the planning system of our key buyer”. Three items of Devaraj et al. (2007) were also adapted, an example of these items is: “The key buyer configures or customizes our products online”.
ICT capabilities were measured by eight items. Respondents were asked to indicate the opinion about how the items listed performed compared to the average level in companies’ industry. Seven items of Byrd and Davidson (2003) were included in the questionnaire. An example of an item is: “Business application software performance of our company”. The item “Hardware and operating system performance” (Byrd and Davidson, 2003) was divided into two items: “Hardware performance of our company” and “Operating systems performance of our company”, as it measures two different objects (Zhang, 2008).
Demand uncertainty was assessed by four items of Chen and Paulraj (2004). The respondent was asked to indicate the degree to which he/she agreed with each statement with regard to the key buyer. An example of an item is: “The total volume of products delivered to the key buyer fluctuates drastically from week to week”.
Relative supply chain performance (logistical service performance) was measured by six items of Gimenez and Ventura (2003). The respondent was asked to provide and indication of the improvement of the firm’s customer service relative to three years ago. If the relationship was shorter than three years, it was asked to refer to the customer service improvements since the start of the relationship. One of the items used is “Our organization responds to the key buyer needs in terms of product mix”.
2.3 Data analysis
3. RESULTS 3.1 Factor analysis
Several steps have been taken to secure the reliability and validity of this research. For the whole data set, a factor analysis (Principal Component Analysis; PCA) is performed. Three statistical measures that determine if PCA is applicable are Bartlett’s test of sphericity (p-value <.001), the anti-image correlation matrix and Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy (KMO >.50) (Field, 2005; Wijnen et al., 2002). A reliability analysis is performed in order to develop a reliable scale (Field, 2005). An indicator used for the reliability analysis is Chronbach’s Alpha coefficient, which should be in general >.60 (depends on the data).
Table 3.1 displays all remaining items after factor analysis (varimax rotation) and loadings on the components of the independent variables ICT investment, ICT usage, ICT capabilities and demand uncertainty. Based on previous mentioned statistical measures, several items had to be excluded for some constructs. For ICT investment, six items were deleted, the remaining three items loaded on one factor. The items measuring ICT investment had a Cronbach Alpha of .77. For ICT usage three items were excluded, all remaining items loaded on one factor. Cronbach’s Alpha for ICT usage was .78. Factor loading of ICT capabilities resulted in three items, with a Cronbach Alpha of .72. Five items were excluded due to insufficient or incorrect loadings. All items of demand uncertainty loaded on one factor, with a Cronbach Alpha of .83.
TABLE 3.1
Results of PCA of ICT investment, ICT usage, ICT capabilities and demand uncertainty
Factors
Items 1 2 3 4
ICT investments (α = .77)
Please indicate to what extent these technologies are used in your company…
MRP/ MRP II .07 -.06 .67 .30
Manufacture Execution System for Production Management .14 .05 .87 .03
Product Data Management software .08 .06 .81 .04
ICT usage in the supply chain (α = .78)
Please indicate the degree to which you agree with each statement regarding the usage of ICT…
Interorganizational coordination between our key buyer and our firm is achieved using electronic links. .73 -.07 .30 -.09
Use advanced information systems to track and/or expedite shipments to our key buyer. .73 .01
Have online access to the planning system of our key buyer. .66 -.17 .09 -.01
The key buyer orders our products online. .79 .14 -.09 .21
The key buyer configures or customizes our products online. .76 .14 -.10 .17
ICT capabilities (α = .72)
Please indicate your opinion about how the items listed perform compared to the average level in your industry…
Hardware performance of our company -.01 -.23 .07 .77
Operating systems performance of our company .08 -.01 .22 .81
Business application software performance of our company .13 .18 .01 .74
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. -.08 .72 .15 -.13
The mix of products delivered to the key buyer changes considerably from week to week. .15 .81 .02 -.02
The total buying volume of products delivered to the key buyer is difficult to predict. -.05 .86 -.09 .01
TABLE 3.2
Results of PCA of supply chain performance
Factors
Items 1 2
Absolute performance (α = .75)
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 .79 .12
The production costs related to the key buyer .71 .05
The transport costs related to the key buyer .67 .04
The administrative costs related to the key buyer .84 .06
Logistical service performance (α = .65)
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 .05 .87
Responds to the special requirements of the key buyer .10 .86
Eigenvalue 2.42 1.39
Percentage of variance explained 40.27 23.14
3.2 Correlations and descriptive statistics
The control variable ‘Information technology investments and expenditures of our company’ correlated significantly (r = .220, p = 0.05, one tailed) with the variable ICT internal investment (see p.18). Table 3.3 displays the means, standard deviations and correlations of the other variables. There is no correlation generated between ICT investment and performance (cost and service). No correlation is found between ICT usage and supply chain performance. Positive and significant correlations were determined between ICT capabilities and logistical service performance (r = .40, p = .01).
TABLE 3.3
Means, standard deviations and Pearson correlations
Variable Mean SD 1 2 3 4 5 1. ICT investments 2.88 1.30 2. ICT usage SCM 2.02 .98 .20* 3. ICT capabilities 3.37 .64 .25* .20 4. Demand uncertainty 3.07 .98 .09 .03 .04 5. Service 3.07 .61 .17 .16 .40** .15 6. Cost 3.73 .59 .08 .02 -.00 -.33** -.18
*. Correlation is significant at the 0.05 level (1-tailed). **. Correlation is significant at the 0.01 level (1-tailed). N = 69
3.3 Testing the hypotheses
p.646). Wijnen et al. (2002) also recommend this option. Massive loss of data was not a problem as six cases had to be excluded as maximum.
Table 3.4 displays the results for the dependent variable supply chain performance: cost and service. In all models, the first part of the analysis focused on the relation between ICT and performance.
Hypothesis 1a: ‘ICT investment has a positive relation with supply chain
performance’, is tested in model one and two. In the first model, no significant results were found for the relation between ICT investment and costs (β = .02, n.s.). Model two displays ICT investments and service, no relation is found (β = .22, n.s.). Hypothesis 1a does not hold.
H1b: ICT usage has a positive relation with supply chain performance
In model three, no significant relation is found between ICT usage and cost (β = .10, n.s.). Model four shows that the relation between ICT usage and service is also not significant (β = .15, n.s), this means that hypotheses 1b is not confirmed.
H1c: ICT capabilities have a positive relation with supply chain performance
As shown in model five, the relation between ICT capabilities and cost is not significant (β = .-12, n.s).Significant result were found for the relation between ICT capabilities and service in the sixth model (first part: β = .53, p <.001, second part β = .53, p <.001). This means this hypothesis is partly confirmed; higher ICT capabilities are related to higher logistical service performance.
The second step of the analysis in table 3.4 focused on the moderating effect of demand uncertainty on the relation between ICT and performance.
Hypotheses 2a: The positive relation between ICT investment and supply chain performance will be stronger in a uncertain demand environment, was tested in the second part of the first model (table 3.4) and in the second part of model two. No significant effect is found (model one: β = .12, n.s.; model two: β = -.18, n.s.)
.-29, p <.05). High demand uncertainty has a weak effect on the relation between ICT usage and cost. However, the relation between ICT usage and cost is stronger when demand uncertainty is low (figure 3.1). This means that the positive relation between ICT
usage and supply chain performance (cost) is stronger in a certain (stable) demand environment. Please note the difference with hypotheses 2b, as our results determined that the opposite is true. When demand uncertainty is high, more usage of ICT shows a small decrease in cost performance. No significant interaction effect was found when the moderating effect of demand uncertainty was tested on the relation between ICT usage and service (β = .03, n.s.).
H2c: The positive relation between ICT capabilities and supply chain performance will be stronger in an uncertain demand environment, is tested in the fifth and sixth model. In both models, no significant effect is found (model one: β = -.14, n.s.; model four: β = -.18, n.s.)
FIGURE 3.1
Relation ICT usage and absolute cost performance with demand uncertainty
-0,4 0,0 0,4 0,8 1,2 1,6 2,0 Low High
ICT usage SCM
A
b
s.
c
o
st
p
er
fo
rm
a
n
ce
Dunc high Dunc low Increase in cost performanceFigure 3.2 displays an overview of the final results after testing the hypotheses. A bold line indicates that the hypothesis is supported. It can be concluded that:
- H1c: ICT capabilities have a positive relation with supply chain performance. Higher ICT capabilities are related to higher logistical service performance. - H2b is partly confirmed: Significant interaction was found when demand
uncertainty was tested upon the relation between ICT usage and cost. It was not expected that the positive relation between ICT usage and supply chain performance (cost) is stronger in a certain demand environment. High demand uncertainty has a weak effect on the relation between ICT usage and cost.
FIGURE 3.2
Final results after testing the hypotheses
3.4 Additional results
In table 3.3 significant correlation is found between ICT investment and ICT usage (r = .20, p = .05) and ICT investment and ICT capabilities (r = .25, p = 0.05). Demand uncertainty and cost performance are also significantly correlated (r = .-33, p = .01), higher demand uncertainty leads to decreased cost performance (table 3.3 & table 3.4. In the next section the research results and additional results are discussed.
TABLE 3.4
Results of regression analysisa
* p < .05 ** p < .01, *** p < .001, a Standardized regression coefficients are reported
Model 1: ICT investment, demand uncertainty and cost
Step Variables 1 2
1 Main effects
ICT investment .02 .01
Demand uncertainty -.41** -.42**
2 Two-way interaction
ICT investment x Demand uncertainty .12
∆R² .17** .01
Adjusted R² .14** .14*
N=59
Excluded cases: 71,68,47,36,15,3
Model 3: ICT usage, demand uncertainty and cost
Step Variables 1 2
1 Main effects
ICT usage .10 .14
Demand uncertainty -.45*** -.49***
2 Two-way interaction
ICT usage x Demand uncertainty -.29*
∆R² .21** .08*
Adjusted R² .18** .25***
N=63
Excluded cases: 71,68,62,47,3
Model 4: ICT usage, demand uncertainty and service
Step Variables 1 2
1 Main effects
ICT usage .15 .14
Demand uncertainty .04 .04
2 Two-way interaction
ICT usage x Demand uncertainty .03
∆R² .02 .00
Adjusted R² -.01 -.02
N=63
Excluded cases: 57,28
Model 2: ICT investment, demand uncertainty and service
Step Variables 1 2
1 Main effects
ICT investment .22 .24
Demand uncertainty .17 .18
2 Two-way interaction
ICT investment x Demand uncertainty -.18
∆R² .09 .12
Adjusted R² .05 .07
N=59
TABLE 3.4 CONTINUED Results of regression analysisa
* p < .05 ** p < .01, *** p < .001, a Standardized regression coefficients are reported
Model 6: ICT capabilities, demand uncertainty and service
Step Variables 1 2
1 Main effects
ICT capabilities .53*** .50***
Demand uncertainty .20 .16
2 Two-way interaction
ICT capabilities x Demand uncertainty -.18
∆R² .33*** .03
Adjusted R² .31*** .33***
N=59
Excluded cases: 58,57,39,34,29
Model 5: ICT capabilities, demand uncertainty and cost
Step Variables 1 2
1 Main effects
ICT capabilities -.12 -.14
Demand uncertainty -.39** -.42***
2 Two-way interaction
ICT capabilities x Demand uncertainty -.14
∆R² .17** .02
Adjusted R² .14** .14**
N=58
4. DISCUSSION
Based on existing literature it was expected that ICT investment, usage and capabilities would have a significant correlation with supply chain performance and that demand uncertainty would strengthen this relation. First, key findings and additional findings are discussed. Next, a conclusive is given. After the conclusive, research limitations are described. Hereafter, the theoretical implications and practical implications are addressed. This chapter ends with directions for future research.
4.1 Findings
The objective of this study was to improve the understanding of the contribution of ICT to supply chain performance (cost and service) by building upon the resource based view (RBV), with varying levels of demand uncertainty. Three hypotheses were formulated to analyze the direct relationship between the stage of ICT employment and supply chain performance. Three other hypotheses were defined in order to test the moderating effect of demand uncertainty. This section starts with a discussion upon the findings between ICT and supply chain performance. Hereafter, findings regarding the moderating effect of demand uncertainty will be discussed.
ICT and its relation with supply chain performance.
In this research the average score of ICT investment was 2.88 (SD = 1.3, see table 3.3, p.23), which means that on average firms have invested in ICT, but the standard deviation is high. From all 71 firms, 40 (56%) have not invested in one or more technologies, 28 of these firms (65%) have not invested because the ICT is not needed, for 7 firms (17.5%) the investment is to high. The other 7 firms (17.5%) have other reasons. Thus, from all firms, there is a high number that did not invested in ICT because the investment is not necessary. It seems that firms make specific choices to invest in ICT. Firms will not invest in ICT if it is unnecessary (not value adding), which can also be a reason for not finding a relationship.
Another reason could be the concepts used for measuring IT investment and supply chain performance. Sircar et al. (2000) focused on ‘pure’ IT investments and firm performance. These are other measures than used in this research, which could cause different results. Most previous research that focused on investments, focused on usage actually. It is thus questionable what can be concluded from previous research. In addition, most previous research focused on benefits of IT investments for the organization itself (e.g.: firm performance) and not on a supply chain perspective. More research on ICT investments specifically is needed to confirm our findings.
Overall, it should not be concluded that investment in ICT is totally unnecessary. The additional results in section 3.4 (p.26) show significant correlation between ICT investment and ICT usage and ICT investment and ICT capabilities. In line with Huang and Lui (2005) it can be concluded that IT expenditures do not create a competitive advantage, only by coordinating with other components (such as capabilities) a company can create superior performance.
reason for not finding a relationship is because of other factors such as firm size, industry type, etc. that can also have an effect upon the ICT usage – supply chain performance relationship. Swamidass and Kotha (1998) find no direct relation between Advanced Manufacturing Technology (AMT) usage and performance, but do find that firm size weakly moderates this relationship. Next, in our research the average score of ICT usage was 2.02 (SD = 0.98, see table 3.3, p.23), which means on average low usage. If this research would be performed in other types of industries (with a higher level of IT usage), results might be different.
In this research, it is not confirmed that higher ICT capabilities are related to higher absolute performance (cost). Bharadwaj (2000), Copeland and McKenny (1988), Dehning and Stratopoulos (2003) and Mata et al. (1995), found that ICT capabilities are related to financial performance. However, in most previous research the focus has been on ‘hard’ IT investment numbers and not qualitative judgments (Chan, 2000). Overall, there is not any direct relation found between ICT and absolute supply chain performance (cost). Not finding a relation between ICT and absolute supply chain performance could be caused by several reasons. First, a decrease in for example production cost is not solely related to hardware performance or operating systems performance. There are many other things that can account for a decrease/increase in performance such as organization size, organizational culture (Carmeli and Tishler, 2004) and firm size (Swamidass and Kotha, 1998). Another reason for not finding a relationship between ICT and supply chain performance might be the concept used. According to Gimenez and Ventura (2003) “gaining a competitive advantage is embedded in the concept of relative performance”. This means that absolute cost performance can not lead to competitive advantage. In addition, in this research it is found that ICT investment, usage and capabilities will also not directly lead to improved absolute supply chain cost performance.
low hardware and/or software performance a company will not be able to provide the correct level of logistical service performance to its customers. This ICT capability can not be easily mimicked by competitors. This finding is also in line with the previous citation of Gimenez and Ventura (2003) that “gaining a competitive advantage is embedded in the concept of relative performance”.
Demand uncertainty and its moderating effect. In this research, three
hypotheses focused on the moderating effect of demand uncertainty upon the relation between ICT and performance.
The moderating effect of demand uncertainty on the relation between ICT investment and supply chain performance is not found in this dataset. This is in contrast with previous research of Li and Ye (1999) that found that for firms in a more dynamic environments, greater investment in IT will lead to better performance. Choe (2003) also concluded that the impact of IS investment on performance depends on the external environment. Li and Ye (1999) operationalized environmental dynamism as the standardized variation in industry-level sales revenue, which is different than our variable demand uncertainty. It is thus questionable if demand uncertainty captures the correct dynamics in the environment. Including other sources of environmental uncertainty such as technological uncertainty and supply uncertainty (Chen and Paulraj, 2004) might result in other findings.
Investment in ICT do not seem to be of influence in coping with demand uncertainty. According to Wu et al. (2006, p.497) “Information sharing in the supply chain may reduce demand uncertainty, and the cost of inventories”. To share information in the supply chain and reduce uncertainties, investments in ICT are not necessary per se. For example, firms might not invest in ICT, but communicate by telephone. This could also be an important reasons for not finding an effect upon the ICT investment – supply chain performance relation with certain/stable demand. If demand is certain, a supplier might not invest highly in ICT because it is not necessary to have specific ICT systems.
delivery schedules have to be changed more frequently and at irregular intervals (Germain et al., 2008, p.560). All these changes requires ICT that fits the uncertain
demand environment. It seems that some organization use ICT, but that there is a misfit
with the environment. In other words, the ICT might not suitable for coping with high levels of demand uncertainty, which causes a decrease in cost performance (see Choe, 2003). “Unpredictable demand implies less streamlined and highly variable operational processes” (Germain et al., 2008, p.560). ICT usage has to support this highly variable processes, which could be difficult for firms.
It is found that the positive relation between ICT usage and supply chain performance (cost) is not stronger in an uncertain environment, but in an certain environment (see figure 3.1). Probably, using ICT in the supply chain can be better applied in a certain demand environment than within an uncertain demand environment as less flexibility and responsiveness of information systems is needed in a certain demand environment. Within a stable demand environment, the goal is to design a system for maximum efficiency (Germain et al., 2008). In a stable demand environment more efficiency (decreased costs) can be obtained by usage of ICT. In a stable environment it will be easier to have a correct fit between ICT usage and demand uncertainty. With for example stable delivery schedules and low process variability, ICT can be better adapted and used for increased performance. This is much more difficult for firms operating in an uncertain environment.
demand uncertainty as used in this research. Including other sources of uncertainty might result in other outcomes.
Besides the moderating effect of demand uncertainty upon ICT usage and supply chain cost performance, no other moderating effects are determined. In this research, ICT investment and ICT capabilities do not seem to be of influence for dealing with demand uncertainty. Demand uncertainty has however a significant direct negative relationship with supply chain cost performance (see table 3.3). This finding is in line with research of Carmeli and Tishler (2004) that discovered that firms that operate in a more certain environment perform better than those acting in an uncertain environment. Firms that want to decrease their uncertainties in demand, in order to obtain increased supply chain performance might chose for ICT as the key to do this. As findings in this research show, this is not that straightforward and might even lead to decreased supply chain performance. Demand uncertainty affects supply chain cost performance, however ICT capabilities affect supply chain cost performance stronger.
4.2 Conclusive
This research provided evidence that there is a positive relation between ICT capabilities and supply chain performance (service). ICT capabilities do matter! It is
found that only ICT capabilities are contributing to increased supply chain performance
and not ICT investments or ICT usage. This is a new finding that is not treated in detail in the existing RBV theory. In addition, for measuring supply chain performance, cost and service were included and not on firm performance (‘hard’ expenditures, return on investment, etc.) as in many other studies.
Not finding a relation between ICT investment and supply chain performance and ICT usage and supply chain performance does not mean that these stages are unnecessary. ICT investment is needed for ICT usage and ICT capabilities, development of ICT capabilities without using ICT will make no sense. ICT capabilities are related to supply chain performance, but therefore the other two preceding stages are also important and necessary.
Demand uncertainty had solely significant interaction with ICT usage and cost performance. It is found that the positive relation between ICT usage and supply chain performance (cost) is stronger in a certain environment. High demand uncertainty had a weak negative effect on the relation between ICT usage and cost performance. It seems that some organization use ICT, but that there is a misfit with the demand environment.
4.3 Limitations of this research
on the questionnaire were “to long”, “serious answering the questions takes more than 30 minutes”. Questionnaires were formulated in English, which could cause difficulties in understanding the questions and non response.
From each organization a single respondent completed the questionnaire. In our questionnaire the respondent was asked to self-asses scores about the organization. Risk of this self-assessment is that respondents consistently answer positive or negative on questions, which could cause response bias.
Next, our study is time dependent. The data used in this study is gathered through a one time survey, however, scores changes over time and therefore a longitudinal study or additional case study might give new insights. In order to measure a ‘sustainable competitive advantage’ longitudinal study is necessary. In the questionnaire, performance referred to improvements relative to three years ago. For ICT usage, ICT investment, ICT capabilities and demand uncertainty, this was not asked specifically. For example, a firm that started with EDI in the past week, could answer this question with “high usage”. Some questions were thus more time specific than other questions, which is not completely consistent.
As noticed in our discussion, there could be several other variables that also influence the ICT – supply chain performance relation. For uncertainty, demand uncertainty was included in this research. Supply uncertainty and technological uncertainty might also be of influence on the ICT – supply chain performance relationship. Environmental dynamism (turbulence), munificent (industry maturity) and environmental complexity could also be important factors (see Wade and Hulland, 2004). Contextual factors were limited in this study, as only demand uncertainty was included. However due to this limitation, we could focus on the RBV, ICT, supply chain performance and demand uncertainty in detail.
4.4 Managerial implications
ICT and its relation to supply chain performance is an interesting research topic, with several important managerial implications. Our study demonstrates that investment in ICT and usage of ICT does not lead directly to increased supply chain performance. Managers should invest in ICT, but (even more important) also have an IT department with sufficient technical qualities, in order to turn the investment into increased supply chain performance (see Dibrell et al., 2008). It seems that some organizations invest in ICT and use ICT within their supply chain, but take ICT capabilities less in consideration. The conclusion that ICT does not add value is than imaginable; investing and using ICT is not enough for an increase in supply chain performance.
If high demand uncertainty is perceived, managers have to make sure that their ICT usage fits the demand uncertain environment. Managers that give insufficient attention to ICT usage in an uncertain demand environment, may observe decreased supply chain performance. Internal and external processes need correct ICT support. Overall, managers should consider reviewing their ICT, in order to make ICT a contributor to improved supply chain performance, especially if operating in an uncertain demand environment.
4.5 Future research
used and not only one item is included for measuring a variable. In previous performed research, this is often unclear. What can be concluded from research if the used variables are unclear? Researchers should be open on the used variables, questionnaires, etc. Future research can profit from this. Fourth, longitudinal research could provide insights in whether the stages of ICT are a competitive advantage and the short and long term effects of it. Fifth, besides including suppliers it would be valuable to collect also data from the buying firms (a dyadic perspective; Anderson and Narus, 1990) to obtain a more accurate and balanced view. The findings can be tested in all types of industries.
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