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Managing the Effectiveness of Supply Chain
Information Integration
MSc Thesis D.D. Blokhuis (s2930137)
University of Groningen January 27, 2020 Supervisor: dr. ir. T. Bortolotti Second supervisor: dr. ir. N.J. Pulles
Acknowledgments
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Abstract
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1. Introduction
Recently Colgate Palmolive, an American multinational, received first place in the annual supply chain ranking of IndustryWeek as Colgate Palmolive was selected as the company that could best manage its global supply chain (Blanchard, 2019). According to IndustryWeek, the American multinational realized nowadays’ importance of real-time information integration with its supply chain partners, i.e. supply chain information integration (SCII). SCII is defined as the degree to which a focal firm and its supply chain partners share information about their supply chain activities with each other (Liu, Wei, Ke, Wei & Hua, 2016). For Colgate Palmolive, this factor was critical to its supply chain performance (Blanchard, 2019). In the current era of globalization and increasing competition it is important to integrate information with supply chain partners in order to survive, stay ahead of the competition and eventually deliver the best value to customers (Simatupang & Sridharan, 2008; Wong, Lai & Bernroider, 2015).
This importance is also seen in multiple studies highlighting the positive effect of SCII on operational performance (Barratt & Oke, 2007; Devaraj, Krajewski & Wei, 2007; He, Sun, Ni & NG, 2017; Liu, Ke, Wei & Hua, 2013; Maiga, Nilsson & Ax, 2015). However, the positive effect of SCII is contingent on several factors (Titah, Shuraida & Rekik, 2016; Wong, Boon-itt & Wong, 2011a). As a result, it is useful to know under which specific conditions, i.e. contingency factors, SCII is most effective in terms of performance. Current studies already specified certain contingencies under which SCII leads to performance enhancements (Titah et al., 2016; Wong et al., 2011a; Wong, Lai & Cheng, 2011b; Wong et al., 2015). Firms can adapt to these conditions, but cannot influence most of them (Sousa & Voss, 2008; Wong et al., 2011b). If contingencies for SCII align, it is important to know how to make SCII most effective. This study recognizes the importance of considering manageable factors influencing the effectiveness of SCII. Knowledge of these factors would allow managerial actions to be taken to further enhance the operational performance resulting from SCII, i.e. increasing the effectiveness of SCII.
5 controlled (Sousa & Voss, 2008). While it is important to know under which external conditions SCII gives advantage, knowledge about internal contingencies can be of high value in further enhancing the performance benefits of SCII. Research of Titah et al. (2016) acknowledged this by showing that SCII leads to higher operational performance when objectives of supply chain partners are aligned. Knowing this, managers having implemented SCII can increase its effectiveness by switching to supply chain partners with aligned objectives. In this sense, alignment of objectives is a manageable factor, even though its manageability depends on factors beyond firm control, like availability of suitable supply chain partners the manufacturing firm can switch to. In addition, switching to alternative supply chain partners might involve negative implications (Wagner & Friedl, 2007). Hence, it is valuable to extend the field of research by investigating manageable factors increasing the effectiveness of SCII. In this research SCII is considered as a capability of manufacturing firms. For value enhancement of capabilities, the resource orchestration theory states that managerial leveraging activities are required (Sirmon, Hitt & Ireland, 2007). Regarding information and knowledge related capabilities, like SCII, mobilization and coordination as leveraging activities are particularly useful (Chirico, Sirmon, Sciascia & Mazzola, 2011). In the context of SCII, IT infrastructure integration provides the required coordination to ensure effective information integration (Huo, Zhang & Zhao, 2015; Wu & Angelis, 2007). In line with this, Van Hoek (2001) states performance benefits will not be optimized when information flows across the supply chain remain fragmented, i.e. not integrated. Hence, IT infrastructure integration seems to be a critical and manageable factor (De Haes & Van Grembergen, 2004) impacting the effectiveness of SCII. In addition to coordination, mobilization is important (Chirico et al., 2011). Mobilization includes identifying valuable capabilities and creating a vision highlighting their importance. Supply chain orientation (SCO) management support provides mobilization towards SCII as it highlights the importance of supply chain partners and integrating information with them. In this way, SCO management support ensures that information from supply chain partners is used to a higher degree (Kim & Lee, 2008; Skipworth, Godsell, Wong, Saghiri & Julien, 2015; Xiao-Rong & Sui-Cheng, 2010), which in turn leads to better performance. Therefore, it appears that both IT infrastructure integration and SCO management support are valuable determinants for the effectiveness of SCII.
The effect of these manageable factors is tested by addressing the following research question: How
do IT infrastructure integration and SCO management support affect the performance outcomes of supply chain information integration? This question is answered by a sequential explanatory design,
6 interviews are conducted for interpretation of the findings and for clarifying the contribution to practice as is done in research of Ross and MacIntyre (2018). In latter-mentioned research, held interviews provided valuable insights in explaining null-correlations. These insights revealed other possibly influencing factors that had previously not been identified by literature and were worth investigating in further research. Where survey data can be used to test whether expected relationships hold true, interviews can provide another dimension to initially unexpected null-correlations and give insights into the more complex dynamics behind the relationships. As some of this research’ findings do not align with findings from literature, interviews are of high value in addition to survey research.
Answering previously formulated research question has several contributions. Regarding theory, this research identifies internal contingency factors influencing the effectiveness of SCII. As a result it adds to existing literature that mainly focused on external contingencies affecting the impact SCII has on performance (Titah et al., 2016; Wong et al., 2011a; 2011b; 2015). Furthermore, present research adds to the resource orchestration theory by empirically testing the applicability of this theory’s assumptions in context of SCII as capability. Regarding practice, this study provides managers with knowledge about the role of SCO management support and IT infrastructure integration as manageable factors influencing SCII’s effectiveness. As these factors can be managed in a value enhancing way, managers have more influence on performance effects resulting from SCII. Knowledge about these factors helps to change or control the conditions in a way that leads to SCII being most effective along with mitigating or enhancing the effects of the external contingency factors already discussed in literature.
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2. Theoretical Background
In this theory section the first part provides an overview of relevant literature in the field of SCII and on the resource orchestration theory, an extension of the resource-based view of Barney (1991). The hypothesis development section follows thereafter discussing the development of hypotheses.
2.1 Literature Review
Since the beginning of this century, people started realizing the value of SCII (Fleisch & Powell, 2001; Weston, 2003; Zeng & Pathak, 2003). From then onwards the number of studies on SCII has significantly increased over the years (Devaraj et al., 2007; He et al., 2017; Huo, Han & Prajogo, 2016; Liu et al., 2013; Maiga et al., 2015; Wei, Wong & Lai, 2012). Most of these studies state that SCII positively influences a firm’s operational performance. However, this positive effect seems to be non-universal, conforming the contingency perspective (Titah et al., 2016; Wong et al., 2011a; 2011b; 2015). This perspective states that the best practices in operations management are not universally applicable to all firms (Sousa & Voss, 2008), but that the effectiveness of these practices is influenced by so-called contingency factors (Flynn, Huo & Zhao, 2010). Applied to SCII, this implies that performance benefits resulting from SCII depend on the alignment between SCII and several factors. It is important to identify such factors that can be managed. When earlier investigated contingencies for applying SCII align, then managerial actions can be taken in order to achieve highest effectiveness of SCII.
8 required to create competitive advantage. It provides a vision on capability configuration by emphasizing the desired direction and valued actions to move into this direction. To create such a mobilizing vision, management support is critical as it builds the firm’s values (Mentzer et al., 2001). This is important to ensure support for the identified capabilities as lack of support might hinder the orchestration of capabilities. Following the resource orchestration theory, coordination and mobilization are necessary leveraging activities for value enhancement of knowledge related capabilities (Chirico et al., 2011), such as SCII. Conforming this, deployment as leveraging activity being the exploitation of capability configurations by using a resource advantage or market opportunity (Sirmon et al., 2011), is not considered here.
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2.2 Hypothesis Development
In this subsection the expected relationships are explained. First, this thesis elaborates upon the effect of SCII on operational performance. Thereafter, expected moderating effects are explained by drawing upon the resource orchestration theory. Last, the developed hypotheses are shown in Figure 2.1.
2.2.1 Supply Chain Information Integration (SCII)
SCII acknowledges the value of building and maintaining tight relationships with both customers and suppliers (Flynn et al., 2010), hence information is integrated. In general, SCII is composed of customer information integration (CII) and supplier information integration (SII) (Flynn et al., 2010; Huo et al., 2016). The decomposition of SCII into CII and SII is illustrated in Figure 3.1. As SCII is the degree to which a manufacturing firm and supply chain partners share information with each other (Liu et al., 2016), CII (SII) consists of the manufacturing firm sharing information with the customer (supplier) and the customer (supplier) sharing information with the manufacturing firm. In this research the customers and suppliers from the manufacturing firm’s point of view are considered, i.e. first tier customers and first tier suppliers.
Information integrated with customers includes i.a. information about demand changes, demand forecasting and customer needs (Kulp, Lee & Ofek, 2004; Maiga et al., 2015). Information shared by customers results i.a. in more clarity about customers’ needs (Flynn et al., 2010). When the information shared is clear and accurate, the manufacturing firm can work on purposefully responding to these needs, resulting in end products being of higher value to the customer. Additionally, changes in demand can be identified faster and more easily (ibid.), increasing the ability of the manufacturing firm to respond to these changes. This enhances the flexibility performance dimension. Furthermore, inventory levels can be decreased as a result of integrating demand forecast information, which in turn, results in lower costs (Wu & Cheng, 2008). So, when customers and the manufacturing firm integrate information, the manufacturing firm has more advanced knowledge regarding customer requirements and demand forecasting. This allows the manufacturing firm to provide higher quality products (Maiga et al., 2015). In line with this reasoning, hypothesis 1a is introduced:
Hypothesis 1a. Customer information integration is positively related to a manufacturing firm’s operational performance.
10 firm’s needs and requirements (Wong et al., 2011a), which enables them to better respond to the manufacturing firm’s requirements (Flynn et al., 2010). When information is shared vice versa from supplier to manufacturing firm, the firm gets a good understanding of its supplier’s capabilities and schedules. This helps the manufacturing firm to be more reliable in delivery (ibid.), have fewer errors and lower inventory holding costs (Wong et al., 2015). These improvements all contribute to a better performance of the manufacturing firm. Therefore, hypothesis 1b is proposed:
Hypothesis 1b. Supplier information integration is positively related to a manufacturing firm’s operational performance.
2.2.2 Coordination: IT Infrastructure Integration
According to the resource orchestration theory, when SCII is in place, management has to leverage it effectively in order to realize value creation and create a competitive advantage (Sirmon et al., 2007; Sirmon et al., 2011). Coordination is important for leveraging information and knowledge related capabilities (Chirico et al., 2011), such as SCII. It serves to maintain effective integration of supply chain information (ibid.). Coordination is achieved by integrating IT infrastructure with supply chain partners (Huo et al., 2015; Wu & Angelis, 2007). This integration is important for realizing competitive advantage, since a firm’s IT infrastructure on its own does not create the highest value. IT can be easily acquired by other firms, so any value resulting from IT decreases quickly (Clemons & Row, 1991). In line with this, Yeh, Lee and Pai (2015) state that merely focusing on own internal IT infrastructure is insufficient to respond immediately to rapidly changing market demands. Hence, IT must be integrated. IT infrastructure integration is defined as “the degree to which a focal firm has established information systems for the consistent and high-velocity transfer of supply chain-related information within and across its boundaries” (Angeles, 2009; Rai, Patnayakuni & Seth, 2006). Important to note is that IT infrastructure integration differs from SCII as conforming the definition of SCII information does not necessarily have to be shared via information systems. Vice versa, the definition of IT infrastructure integration does not express the degree of SCII. Hence, IT infrastructure integration is seen as an independent variable consisting of two components, namely data consistency and application integration (Figure 3.2) (Angeles, 2009; Rai et al., 2006; Tai, Wang & Liu, 2014).
11 consistent (ibid.). This decreases operating cost, and thus improving cost-performance. Regardless of the degree of data consistency, performance benefits are expected in case of SCII. However, when consistent data is shared across supply chain partners, the performance benefits as a result of SCII are expected to be even higher because of more efficient coordination and higher operating efficiency. Thus, data consistency is expected to strengthen the effect of SCII on operational performance. Besides data consistency, IT infrastructure integration includes application integration. Application integration relates to a manufacturing firm’s ability to connect with its supply chain partner’s applications in real-time (Wu & Angelis, 2007; Rai et al., 2006). Firms with high application integration are more connected to each other. Because of these improved connections, dependencies between partners in a supply chain can more easily and better be managed (Rai et al., 2006). When dependencies are well managed, activities of supply chain partners are coordinated with each other, since coordination is the process of managing dependencies (Malone & Crowston, 1994). This coordination leads to higher performance (Ross, 2002), among others in terms of delivery. For example, when a supplier has a component shortage, the manufacturing firm can see this in real-time due to application integration. In turn, the manufacturing firm is better able to communicate the expected delivery time to its customers, eventually resulting in a higher delivery reliability of the manufacturing firm, as the delivery to its customers is more accurate. Based on the real-time characteristic of application integration, the information shared among supply chain partners will always be most recent. When having up-to-date information, coordination is high (Ali & Ramhat, 2009) and better decisions can be made using SCII (Redman, 1998), which is expected to strengthen the effect of SCII on performance. When low SCII is coupled with low IT infrastructure integration, the operational performance is expected to be even worse. Low IT infrastructure integration entails low data consistency and low application integration, i.e. low coordination. Due to low application integration, the limited amount of information shared is not the most recent. As a result, decisions being made are based on outdated, limited information, evoking higher negative performance implications like the bullwhip effect (Bottani, Montanari & Volpi, 2010; Ravichandran, 2006) compared to a situation in which limited but not outdated information is shared, i.e. merely low SCII.
In sum, IT infrastructure integration is likely to strengthen the effect of SCII on operational performance. Conforming the resource orchestration theory, having an integrated IT infrastructure leads to coordination that is needed to leverage SCII and create value from it. In line with this reasoning, the following hypotheses are formulated:
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Hypothesis 2b. IT infrastructure integration has a positive moderating effect on the relationship between SII and operational performance.
2.2.3 Mobilization: Supply Chain Orientation (SCO) Management Support.
For leveraging the value of knowledge or information related capabilities, mobilization is needed in addition to coordination (Chirico et al., 2011). Coordination is irrelevant without mobilization as without mobilization important capabilities are not being identified, making coordinating them impossible. Mobilization concerns identification of capabilities needed to create a competitive advantage. This entails creation of a leveraging strategy, i.e. a vision on capability configuration, to enhance value (Sirmon et al., 2007). Management support is critical for creation of this vision as it shapes the direction and values of the firm (Mentzer et al., 2001). By providing this vision on capability configuration, mobilization leads to a better understanding of the direction and valued actions for this direction. The mobilization subprocess is necessary for ensuring support for the identified capabilities as lack of support might hinder the orchestration of capabilities.
Regarding SCII as capability, it is important that a vision is created to highlight the importance of supply chain partners and SCII. This can be done by SCO management support. SCO management support is defined as the degree to which management emphasizes the value of maintaining good relationships with supply chain partners as well as sharing information, risks and rewards with them (Min et al., 2007). In line with this definition, SCO management support includes highlighting the importance of supply chain partners (ibid.). By highlighting this aspect, the manufacturing firm’s employees know that supply chain partners are valuable for the firm’s success. In addition, SCO management support includes emphasizing the sharing of risks, rewards and valuable information with these supply chain partners. In this way, the valued actions for the desired direction, i.e. firm success, are explicitly stressed. In other words, SCII is identified as an important capability for the firm’s success which is expressed by management to the rest of the firm. Hence, mobilization towards SCII is reached by SCO management support.
13 This is expected to positively impact the extent to which information from supply chain partners is used in the manufacturing firm’s operations. More concrete, SCO management support does not lead to higher SCII, but to more effective use of it. SCII as a capability will be more effectively leveraged due to SCO management support, resulting in higher performance benefits. This strengthening effect also works for the operational performance resulting from low SCII. When low SCII is coupled with low SCO management support, the operational performance is expected to be even lower. Due to low SCO management support, SCII is not identified as valuable capability and a vision regarding the importance of supply chain partners and SCII is lacking. Due to this lacking vision, supply chain partners and their information will not be valued by the manufacturing firm. Therefore, in case of low SCII, the limited amount of information that is shared is expected to lead to an even worse operational performance as the manufacturing firm is not at all mobilized towards using this limited supply chain information to its full extent. Therefore, SCO management support is expected to strengthen the effect of SCII on operational performance. Based on the reasoning mentioned afore, hypothesis 3 is stated as follows:
Hypothesis 3. Supply chain orientation management support has a positive moderating effect on the relationship between SCII and operational performance.
In aforementioned reasoning, a moderating effect of SCO management support is expected. However, based on earlier studies’ findings showing the positive effect of SCO management support on performance (Hult, Ketchen & Slater, 2004; Hult et al., 2008; Min et al., 2007; Patel, Azadegan & Ellram, 2013), an additional direct effect of SCO management support is expected. SCO management support guarantees that the firm and its partners are focused on achieving highest value for end consumers, which enhances operational performance (Patel et al., 2013). This leads to hypothesis 4:
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3. Methodology
3.1 Research Design
The aim of this research is to further increase the effectiveness of SCII by investigating manageable factors. Given the well-developed prior literature regarding the effect of SCII on performance, a deduction approach was followed to answer the RQ that is central in this research (De Groot, 1994). Survey is the appropriate research method for this, since previously formulated hypotheses were tested within the well-known domain of SCII.
Besides, in-depth expert interviews were conducted to validate the survey results and to gather a deeper understanding. In-depth interviews are appropriate as these are useful to get detailed information and to give context to the outcome of data (Boyce & Neale, 2006). Interviews are especially useful in explaining null-correlations where significant correlations were expected (Ross & Maclntyre 2018). For this reason, interviews were valuable as some of this research’ findings did not align with findings in literature. The additional explanation that was given to null-correlations could not be acquired by merely using quantitative data. Therefore, interviews have been held to investigate more complex dynamics behind the relationships and null-correlations.
The method used for this research is classified as sequential explanatory design (Creswell et al., 2003), in which first quantitative approach is used, where after a qualitative approach is applied to get a thorough explanation of the quantitative findings. Following this sequential explanatory design, the interview results have been integrated with the quantitative results in the discussion section resulting in high quality of interference (Ivankova et al., 2006).
3.2 Data Collection and Sample
3.2.1 Survey
16 belong to the machinery, electronics or transportation equipment industry. The sample distribution across country and industry is shown in Table 3.1.
As personal contact increases the response rate (Toepoel & Schonlau, 2017), executives of manufacturing firms were first contacted by phone before the questionnaires were sent. During this phone-contact the research purpose of the HPM project was clarified and there was checked whether firms were willing to participate. If this was the case, the plant coordinator of each firm received questionnaires that in turn had to be distributed to the right respondents within each plant. In total, 23 respondents from 12 different departments within each participating plant filled in the questionnaires. Because the data for variables in the conceptual model are collected from different respondent groups, the chance of common method variance is minimized. The respondents for the data used in this research are active in upstream supply chain management, downstream supply chain management, plant management and information system management.
Table 3.1 Sample distribution across country and industry
Country\Industry Electronics Machinery Transportation equiptment Total
America 5 7 3 15 Brazil 5 7 12 24 China 10 16 4 30 Finland 6 6 5 17 Germany 6 13 9 28 Israël 23 3 0 26 Italy 7 17 5 29 Japan 6 7 9 22 Korea 8 5 13 26 Spain 8 7 10 25 Sweden 4 4 1 9 Switzerland 3 6 2 11 Taiwan 19 10 1 30 UK 4 5 4 13 Vietnam 10 7 8 25 Total 124 120 86 330 3.2.2 Interviews
17 partners of Deloitte Consulting with a description of respondents needed and a request to look in their network for the right respondents. This led to sufficient respondents willing to participate in interviews. Respondents were contacted by phone to schedule a face-to-face interview. At the start of the interview, the aim of the interview was explained again, a time indication was given and confidentiality of the answers was guaranteed (Boyce & Neale, 2006). The interviews were recorded in order to not miss any relevant information.
The first two interviews were one-to-one interviews in which one respondent at the same time was interviewed. These interviews led to valuable insights and alternative explanations for unexpected outcomes. However, the respondents were not able to explain all unexpected relationship. Therefore, the third interview was carried out as a small focus group. Focus groups are seen as a useful way to complement and explain statistical results (Breen, 2006). Furthermore, focus groups in general provide a deeper understanding and new insights as compared to one-to-one interviews (ibid.). The focus group in this research consisted of two experienced supply chain managers from different companies. The managers had worked together in the past and felt comfortable discussing the topics with each other. Feeling comfortable within the focus group is important for respondents to engage in discussions (Rabiee, 2004). This helped in generating rich data for explaining unexpected relationships.
3.3 Measurement
3.3.1 Survey
The independent variable of SCII consists of customer information integration (CII) and supplier
information integration (SII). In total, SCII is made up of four constructs: information sharing by customers (BYCUS), information sharing with customers (WITHCUS), information sharing by suppliers (BYSUP) and information sharing with suppliers (WITHSUP). This is illustrated in Figure 3.1. The items
relating to these constructs are asked on a 5-point Likert scale ranging from 1 (‘strongly disagree’) to 5 (‘strongly agree’). For all four of these constructs items are based on the measures as used by Turkulainen, Kauppi and Nermes (2017). Basing the items on prior work increases the content validity of the items. For this reason, the rest of the items are also based on literature. For the dependent variable of operational performance, measurement items are adapted from Matsui (2002). These performance items are categorized in 4 dimensions together determining a manufacturing firm’s
operational performance: quality, delivery, flexibility and costs. This categorization is also used by
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application integration (APPINT) (Figure 3.2). The items to measure these constructs are cited from
Rai et al. (2006). The items relating to application integration are measured on a 5-point Likert scale, where 1 is for ‘not at all’ and 5 is for ‘extensively’. For items concerning data consistency a value of 1 represents ‘strongly disagree’, whereas a value of 5 represents ‘strongly agree’. The same measurement scale is used for the two constructs representing SCO management support: SCO
management support regarding suppliers (SCOSUPL) and SCO management support regarding customers (SCOCUST) (Figure 3.2). The measurement items for SCO management support are adopted
from Min, Mentzer and Ladd (2007). The specific items used to measure all construct can be found in Appendix A. The Cronbach’s alphas of the multi-item constructs are all above 0.7 (Table 3.2), indicating high internal consistency of the items measuring a specific construct.
As the research framework of this research strongly draws upon the resource orchestration theory, it is crucial that the operationalization of IT infrastructure integration and SCO management support closely correspond to respectively coordination and mobilization as meant by this theory. Only if this is the case explicit theoretical implications relating to the resource orchestration theory can be drawn later on. Following the resource orchestration theory, coordination of supply chain information is necessary to maintain effective integration. This effective integration is ensured by data consistency and application integration (Rai et al., 2006). Therefore, the operationalization of IT infrastructure
integration consists of items relating to data consistency (DCON 1, DCON2) and application integration (APPINT1, APPINT2, APPINT3). Related to SCO management support, SCOCUST1, SCOCUST4, SCOSUPL1 and SCOSUPL4 emphasize the value of supply chain partners, whereas SCOCUST2, SCOCUST3, SCOSUPL2 and SCOSUPL3 highlight the importance of SCII. In this way, SCII is identified as
a valuable capability and by highlighting its importance, support for this capability is created. This conforms to mobilization as stated in the resource orchestration theory.
Figure 3.1 SCII specified
Supply chain information integration (SCII)
Customer information integration (CII)
Information sharing by customer (BYCUST)
Information sharing with customer (WITHCUST)
Supplier information integration (SII)
Information sharing by supplier (BYSUPL)
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Figure 3.2 Moderating variables specified
Table 3.2 Cronbach's alpha values for the multi-item variables
Multi-item variable BYCUST WITHCUST BYSUPL WITHSUPL DCON APPINT SCOCUST SCOSUPL Cronbach’s alpha value 0.860 0.905 0.921 0.883 0.888 0.872 0.728 0.784
Since all constructs consist of multiple items, a Confirmatory Factor Analysis (CFA) with varimax rotation and the eigenvalue-greater-than-one rule has been performed to test convergent and discriminant validity of the items measuring a construct. The Kaiser-Meyer-Olkin measure indicates an adequate sample for doing factor analysis as its value is 0.832. This KMO value is above the minimum threshold of 0.5 (Williams, Onsman & Brown, 2010). Besides, the Barlett’s Test of Sphericity is significant (p=0.000) which indicates equal variance across the sample. The results of the factor analysis indicate that 8 factors arise from 36 items. None of the items load on multiple factors, i.e. the cross-factor loadings are less than 0.4, indicating high discriminant validity. In addition, all constructs have high convergent validity, since the items belonging to a construct load highly onto the same underlying factor, i.e. the factor loadings are all above 0.6 (Kline, 2014). The factor loadings and cross-factor loadings are listed in Table 3.4. The items used to measure operational performance are not included in the factor analysis. Despite, the operational performance items are summed together as the performance dimensions are positively and significantly correlated with each other (Table 3.3). This shows validity of the summed-up performance dimensions (Ward, McCreery, Ritzman & Sharma, 1998).
Table 3.3 Correlation matrix of performance items per dimension
Cost Quality Delivery Flexibility PERF1 PERF2 PERF3 PERF4 PERF5 Cost PERF1 1
PERF2 0.484** 1
Quality PERF3 0.347** 0.320** 1
Delivery PERF4 0.347** 0.299** 0.286** 1
Flexibility PERF5 0.255** 0.249** 0.235** 0.552** 1 ** Correlation is significant at the 0.01 level (2-tailed).
IT Infrastructure Integration
Data Consistency(DCON)
Application Integration (APPINT)
SCO management support
Regarding Customers(SCOCUST)
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Table 3.4 Cross-loading table on measurement items for SCII, IT infrastructure integration and SCO management support
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3.3.3 Interviews
For the interviews, open-ended questions were asked and probes were used where needed in order to get the most accurate and complete information possible from respondents. Appendix B provides a general overview of the interview questions. Some questions depend on the answers of respondents. These follow-up questions are not included in the overview as they were different for each interview. The interviews consisted of two parts together lasting approximately one hour per interview, except for the small focus group interview which lasted for two hours. During the first part, the questions were mainly oriented at getting a better understanding of the reasons behind the sharing of information, like “What kind of information do you receive from your buyers/suppliers?” and “Do you think this influences the performance of your firm?”, “In what way?”. In the second part of the interview, the survey results were presented after which these results were discussed and respondents were asked for their thoughts and explanations.
3.4 Data Analysis and Interpretation
For analyzing the survey data, SPSS software was used with which numerical data can be analyzed and tested. To test the expected linear relationship between SCII and operational performance, a linear regression was conducted. The control variables firm size and industry were included in the analysis since firm size (Majumdar, 1997) and industry (Christmann, Day & Yip, 1999) might influence a firm’s operational performance. Before being able to include these control variables, dummy variables have been created for industry as this variable is nominally scaled. In addition, firm size has been transformed by using logarithm to the base 10 as a response to skewness towards large values (Curran-Everett, 2018). Table 3.5 reports the means, standard deviations and correlations for all variables included in the analysis. For all variables except the dependent one, missing values were replaced by the mean. For the dependent variable, operational performance, missing values were pair-wise deleted. This resulted in an actual sample size used of 288.
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4. Results
4.1 Analysis of Direct Effects
In order to test hypotheses 1a, 1b and 4, linear regression was performed. The results of this analysis are shown in the third column (model 2) of Table 4.2a. For hypothesis 1a and 1b, a linear regression was performed to test the effect of respectively CII and SII on operational performance. The results indicate that CII consisting of information sharing by customer and information sharing with customer, does not significantly influence operational performance (p>0.1). Therefore, hypothesis 1a is not supported. Furthermore, SII consisting of information sharing by supplier and information sharing with supplier, neither has a significant influence on operational performance. Accordingly, hypothesis 1b could not be confirmed.
For hypothesis 4, the same linear regression was performed to test the effect of SCO management support on operational performance. The results in the third column of Table 4.2a show that SCO management support regarding customers positively influences operational performance on a 10% significance level (β=0.117, p=0.068). In addition, on a 5% significance level, SCO management support regarding suppliers has a positive effect on operational performance (β=0.146, p=0.031). In line with these results hypothesis 4 is confirmed, partially on a 10% significance level.
4.2 Analysis of Moderating Effects
24 not significant. This is shown by the moderating factors of BSD, WSD, BSA and WSA not being significant on a 10% significance level. Hence, hypothesis 2b is not confirmed.
The regression results in Table 4.2b indicate that SCO management support regarding customers and SCO management support regarding suppliers do not have a significant moderating effect on the relationship between SCII and operational performance. Merely WSC as a moderating factor is significant on a 10% level. The rest of the moderating factors relating to SCO management support are not statistically significant. Therefore, hypothesis 3 could not be confirmed.
Table 4.1 Moderators tested and their corresponding abbreviations
Moderator Component Moderator tested Abbreviation Hypothesis IT infrastructure integration Data consistency (DCON) BYCUST X DCON BCD
H2a WITHCUST X DCON WCD
BYSUPL X DCON BSD
H2b WITHSUPL X DCON WSD
Application integration (APPINT) BYCUST X APPINT BCA
H2a WITHCUST X APPINT WCA
BYSUPL X APPINT BSA
H2b WITHSUPL X APPINT WSA
SCO management support SCO management support regarding customers (SCOCUST) BYCUST X SCOCUST BCC H3 WITHCUST X SCOCUST WCC BYSUPL X SCOCUST BSC WITHSUPL X SCOCUST WSC SCO management support regarding
suppliers (SCOSUPL)
BYCUST X SCOSUPL BCS WITHCUST X SCOSUPL WCS BYSUPL X SCOSUPL BSS WITHSUPL X SCOSUPL WSS
Table 4.2a Regression analysis results of the direct and moderating effects (H2) with operational performance as dependent variable
Variables Model
1 2 3
25 WITHCUST (H1a) 0.071 0.0758 0.0514 0.0687 0.0679 0.0825 0.0891 0.0731 0.0755 BYSUPL (H1b) 0.019 0.0137 0.0432 0.0196 0.0207 0.0213 0.0208 0.0335 0.0272 WITHSUPL(H1b) -0.041 -0.0358 -0.0589 -0.0396 -0.0483 -0.0325 -0.0346 -0.0397 -0.0468 DCON 0.033 0.0596 0.0385 0.0303 0.0363 0.0401 0.0294 0.0372 0.0380 APPINT -0.070 -0.0596 -0.0721 -0.0716 -0.0651 -0.0519 -0.0552 -0.0579 -0.0628 SCOCUST (H4) 0.117 Ɨ 0.1163 Ɨ 0.1050 0.1173 Ɨ 0.1187Ɨ 0.1159 Ɨ 0.1129 Ɨ 0.1160 Ɨ 0.1123 Ɨ SCOSUPL (H4) 0.146* 0.1509* 0.1585* 0.1464* 0.1460* 0.1373* 0.1400* 0.1493* 0.1457*
Moderating effects (when testing H2)
BCD (H2a) 0.1624** WCD (H2a) 0.1220* BSD (H2b) -0.0170 WSD (H2b) 0.0376 BCA (H2a) 0.1034 Ɨ WCA (H2a) 0.0920 Ɨ BSA (H2b) 0.0768 WSA (H2b) 0.0530 Model summary R2 0.042 0.125 0.1521 0.1383 0.1254 0.1264 0.1366 0.1372 0.1331 0.1278 MSE 0.032 0.090 0.8849 0.8993 0.9128 0.9117 0.9011 0.9005 0.9047 0.9102 F-Value 4.192 3.585 4.1124 3.6775 3.2843 3.3163 3.6258 3.6427 3.5198 3.3586 Model significance 0.006 0.000 0.0000 0.0000 0.0002 0.0002 0.0000 0.0000 0.0001 0.0001
** Coefficient is significant at the 0.01 level (2-tailed).
* Coefficient is significant at the 0.05 level (2-tailed).
Ɨ Coefficient is significant at the 0.10 level (2-tailed).
26 SCOSUPL (H4) 0.146* 0.1454* 0.1419* 0.1522* 0.1465* 0.1683* 0.1711* 0.1537* 0.1594*
Moderating effects (when testing H3)
BCC 0.0235 WCC 0.0445 BSC 0.0698 WSC 0.0262 BCS 0.0768 WCS 0.1006 Ɨ BSS 0.0238 WSS 0.0683 Model summary R2 0.042 0.125 0.1256 0.1270 0.1299 0.1256 0.1301 0.1350 0.1257 0.1301 MSE 0.032 0.090 0.9126 0.9111 0.9081 0.9126 0.9079 0.9027 0.9125 0.9079 F-Value 4.192 3.585 3.2912 3.3340 3.4218 3.2911 3.4261 3.5775 3.2939 3.4261 Model significance 0.006 0.000 0.0002 0.0002 0.0001 0.0002 0.0001 0.0001 0.0002 0.0001
** Coefficient is significant at the 0.01 level (2-tailed).
* Coefficient is significant at the 0.05 level (2-tailed).
Ɨ Coefficient is significant at the 0.10 level (2-tailed).
4.3 Post-Hoc Analysis
To get more insights into the significant moderators and their conditional effects, the Johnson Neyman output is generated. From this additional analysis output, the following insights were derived. For 3.5% of the sample that score lowest on data consistency, information sharing by customer is negatively related to operational performance. Information sharing by customer has a positive effect on operational performance for the 22.9% of the sample that has a moderate to high level of data consistency. About 14% of the sample has a relatively high level of data consistency. For these companies, information sharing with customer has a significant positive effect on operational performance. The same significant positive effect of information sharing with customer and information sharing by customer on operational performance is seen for respectively 14.6% and 28% of the sample that score highest on application integration. The significant effects on a 5% significance level are shown in Figure 4.1 and 4.2. From this additional analysis it became clear that even while hypotheses 1a could not be supported because of the insignificant effect of CII on operational performance, for high levels of IT infrastructure integration, the relationship between CII and operational performance becomes significant, hence a moderation effect of IT infrastructure integration.
27 even while SCO management support appeared to be insignificant as moderator, a limited moderation effect of SCO management support regarding suppliers is seen. For really high values of SCO management support regarding suppliers, information sharing with customer is significantly related to higher operational performance.
Figure 4.1 Effect of information sharing by customer on operational performance at values of IT infrastructure integration
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5. Discussion
Important theoretical as well as practical implications resulted from this research. For the theoretical implications interview findings are used as input in explaining null-correlations. Context is given to the outcome of data in the managerial implications. As with every research, this research includes some limitations which are discussed after the implications. Lastly, suggestions are made for extending the current field of research. One of these suggestions is based on a potential underlying factor that surfaced during the interviews. Overall, interview and survey results are integrated and quotes are provided for clarification.
5.1 Theoretical Implications
29 a possible explanation for the insignificant effect of SCII on the operational performance of manufacturing firms.
The idea that coordination as leveraging activity must be present in order to create value from SCII is partially confirmed by the support of hypothesis 2a. Coordination for SCII is achieved through IT infrastructure integration (Huo et al., 2015; Wu & Angelis, 2007). For high levels of IT infrastructure integration, i.e. high data consistency and/or high application integration, CII was found to significantly increase operational performance. This result strengthens the assumption of the resource orchestration theory as it implies that coordination is an important leveraging activity for value creation and performance enhancement of CII. Interviews with experts provided validation for the moderating effect of IT infrastructure integration since this effect is also perceived by experts in practice. Expert 2: “Information integration can lead to success and performance enhancement, but for this two aspects are required: 1) information has to be reliable; 2) information has to be shared on a regular basis”. Reliable information is ensured by the data consistency element of IT infrastructure integration. Application integration ensures that information is shared in real time, i.e. on a regular basis. Hence, a first step towards validation is provided for the IT infrastructure integration moderation effect.
The finding that for an extremely low level of data consistency, i.e. low coordination, operational performance decreases as a result of information sharing by customer, exceeds the boundaries of the resource orchestration theory. According to Expert 1 it requires effort to make sure you and your customers are talking about the same thing. Based on this one can state that in case of really low data consistency, more effort and higher coordination costs are required hindering the performance of manufacturing companies. It suggests that value deterioration resulting from capabilities can occur in case of extremely low coordination. This offers an interesting opportunity for future research as the resource orchestration theory up to now merely focused on the effect of managerial actions on value creation of capabilities. Meanwhile, the effect in terms of value deterioration is not incorporated in this theory.
30 IT infrastructure integration is a crucial condition for a virtual supply chain and the entailing virtual coordination, this might relate to IT infrastructure integration being a significant moderator merely for the effectiveness of information integration in the downstream supply chain, i.e. CII, and not for the effectiveness of information integration in the upstream part of the chain, i.e. SII.
31 research is valuable to sharpen the boundaries of the resource orchestration theory by making explicit when mobilization is required or not.
5.2 Managerial Implications
The results of this research also have practical implications. Managers of manufacturing firms can act on the findings to improve the operation performance of their firm, crucial in light of globalization and increasing competition (Opara & Eboh, 2017). It is important to concentrate on relevant aspects for operational performance and do not significantly invest in aspects that do not impact performance. SCO management support appeared to be relevant for its direct effect on operational performance. For that reason managers are strongly recommended to value relationships with supply chain partners and to emphasize the importance of sharing information, risks and rewards with them. This helps in achieving highest value for end customers (Patel et al., 2013).
Furthermore, managers are advised to take initiative in establishing CII to increase operational performance. Managers who have already applied CII but do not yet experience performance improvements from it should wonder whether the right conditions are in place to leverage the benefits of CII. More specifically, they should ensure high data consistency and/or high application integration, but preferably both. Managers can best spend their time and resources on integrating the manufacturing firm’s IT infrastructure with that of its customers. This insight can yield significant performance improvements as all interviewed experts indicated that the IT infrastructure integration within their supply chain was rather low. In most cases, information flows within a manufacturing firm were integrated by means of an ERP system. However, this integration often did not include information flows from supply chain partners. “The IT system of our firm is neither linked to that of our customers nor to that of our suppliers”, Expert 1 said. Therefore, establishing IT infrastructure integration with customers can bring substantial benefits. Integrating IT infrastructure is a tangible solution that allows for implementation (De Haes & Van Grembergen, 2004). As with every solution investments in time, financial resources and/or human resources are required. It might be that not all firms immediately have the time or resources available to integrate IT infrastructure. When a manufacturing firm suffering from low data consistency does not have resources to integrate IT infrastructure, the firm should pause CII until they do have resources available to achieve high data consistency. Otherwise, in case of extremely low data consistency, its operational performance will deteriorate due to information the manufacturing firm receives from its customers.
32 establishing SII. Instead, the results suggest that the supplier is the one that should take initiative and invest in CII as from the supplier’s point of view CII enhances the respective supplier’s operational performance. However, this finding should be taken with caution as there are always some exceptions. For instance, Expert 2 indicated that this finding does not hold true for the company he works for. The referred company, a high-tech machinery manufacturing firm, needs quality information from its suppliers to be able to operate due to the complexity of its products. The products undergo a complex assembly process requiring strict technical specifications. Expert 2: “Even if two individual components are within their own quality specifications, it still is possible that they do not function together once they are assembled, for example because both components score at the frontier of their quality specifications”. In such case, it might be wise for a manufacturing firm not to wait for the supplier taking initiative in establishing information integration.
Lastly, a specific managerial contribution dedicated to the SAP service line of Deloitte Consulting, the firm that supported this research, can be formulated. Currently, in case of an SAP implementation project for clients in the manufacturing industry, Deloitte Consulting helps to design and configure SAP product offerings in a way that suits its client’s situation. Generally, the IT infrastructure projects have limited integration as they only relate to IT infrastructure integration within the manufacturing firm. In line with the findings of this research, in case of an SAP implementation project Deloitte Consulting could suggest its manufacturing clients to take a broader perspective across their own boundaries instead of a mainly operational perspective limited to the walls of their company. Deloitte could offer support for IT infrastructure integration between its clients and its clients’ customers. Despite the high costs that are incurred in extension of project scope, higher profit and performance returns are expected for Deloitte’s manufacturing clients once having fully integrated their IT infrastructure with that of its customers. From the interviews it appeared that there is indeed a need for IT infrastructure integration across the supply chain. For example, Expert 2 stated “We strive for having one database and IT system with our partners in the future. This will lead to an increase in consistency and reliability of data, which allows us to perform better”. The SAP service line of Deloitte Consulting could play a major role in fulfilling this need.
5.3 Limitations and Future Research
33 investigate whether IT infrastructure integration is also determining the effectiveness of CII in other industries or manufacturing sectors. Contrary to this research’ findings, it may be that for other industries IT infrastructure integration is actually related to the effectiveness of SII.
Furthermore, for measurement of the dependent variable subjective performance measures were used. For each performance dimension, the performance of a manufacturing firm is subjectively scored relative to that of its competitors in the same industry. As discussed in the method section, the change of common method variance was already minimized by having different respondents for each construct of the research framework. However, to improve validation further research should not only take subjective but also objective performance measures, like ROI, into account (Dawes, 1999). As mentioned earlier, this research’ findings imply that value deterioration from capabilities can occur in case of extremely low coordination. This offers an interesting opportunity for future research as the resource orchestration theory up to now merely focused on the effect of managerial actions on value creation of capabilities. However, the effect in terms of value deterioration is not incorporated in this theory. Future research is needed to see whether the resource orchestration theory can be extended by including the value deterioration effect. Future research is not only useful to extend the theory’s boundaries, but also to refine them. For example, the resource orchestration theory can be sharpened by making explicit under which circumstances mobilization is required for value creation of capabilities.
34
6. Conclusion
This research aimed to investigate how IT infrastructure integration and SCO management support affect the performance outcomes of SCII. Where previous research on SCII mainly have studied external contingencies, this study provided a more complete view by investigating tangible, manageable factors for increasing the effectiveness of SCII. These manageable factors were based on the resource orchestration theory having assumed leveraging activities required for value enhancement of capabilities. To answer the research question, a sequential explanatory research design was conducted, consisting of two consecutive phases in one study: a quantitative and a qualitative phase.
From the quantitative phase it became clear that SCII is not directly impacting performance. IT infrastructure integration is a significant determinant, i.e. a moderator, for the effectiveness of CII. This moderating effect is limited to CII and does not apply to SII. SCO management support did not show a moderating effect. However, the direct effect of SCO management support on operational performance was shown. The qualitative phase was highly valuable in addition to the quantitative part. The interview findings provided a possible explanation conforming to Davis’ (1993) technology acceptance model for the irrelevance of SCO management support as moderator. Related to IT infrastructure integration, the interviews served as a first step towards validation as its moderating effect was acknowledged by experts. At the same time, expert indicated low IT infrastructure integration across the supply chain, which shows highly valuable potential of this research’ findings. However, generalizability of the findings might depend on specific characteristics of manufacturing firms and complexity of products being produced. In addition, from interviews it appeared that the size of customer and/or supplier base may be a possibly underlying factor generating the shown moderating effect in this research.
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Appendices
Appendix A. Measurement Items of the Constructs
Respondents were asked on a 5-point Likert scale to indicate their extent of (dis)agreement with the following statements, whereas 1 stands for strongly disagree and 5 for strongly agree.
BYCUST (information sharing by customers)
BYCUST1: Our plant has access to the inventory information of our key customers
BYCUST2: Our plant has access to the production schedule information of our key customers BYCUST3: Our plant has access to the productivity information of our key customers
BYCUST4: Our plant has access to the sensitive information (for example, financial information,
proprietary process information, etc.) of our key customers
WITHCUST (information sharing with customers)
WITHCUST1: Our key customers have access to the delivery information of our plant
WITHCUST2: Our key customers have access to the demand change information of our plant WITHCUST3: Our key customers have access to the demand forecast information of our plant
WITHCUST4: Our key customers have access to the information about the manufacturing capabilities of our plant
WITHCUST5: Our key customers have access to the inventory information of our plant
WITHCUST6: Our key customers have access to the production capacity information of our plant WITHCUST7: Our key customers have access to the quality information of our plant
WITHCUST8: Our key customers have access to the schedule information of our plant
BYSUPL (information sharing by suppliers)
BYSUPL1: Our plant has access to the delivery information of our key suppliers
BYSUPL2: Our plant has access to the demand change information of our key suppliers BYSUPL3: Our plant has access to the demand forecast information of our key suppliers BYSUPL4: Our plant has access to the inventory information of our key suppliers BYSUPL5: Our plant has access to the quality information of our key suppliers BYSUPL6: Our plant has access to the schedule information of our key suppliers
WITHSUPL (information sharing with suppliers)
WITHSUPL1: Our key suppliers have access to the cost information of our plant
WITHSUPL2: Our key suppliers have access to the information about the manufacturing capabilities of our plant
36 WITHSUPL4: Our key suppliers have access to the productivity information of our plant
WITHSUPL5: Our key suppliers have access to the sensitive information (for example, financial
information, proprietary process information, etc.) of our plant
DCON (data consistency)
DCON1: Definitions of key data elements (e.g., customers, orders, part numbers) are common across the supply chain
DCON2: Data stored in different databases (e.g., order status) is consistent across the supply chain
APPINT (application integration)
APPINT1: Supply chain transaction applications, such as order management, procurement, manufacturing and distribution, communicate in real time
APPINT2: Supply chain planning applications, such as demand planning, transportation planning and manufacturing planning, communicate in real time
APPINT3: Supply chain applications with internal application within our organization, such as enterprise resource planning, communicate in real time
SCOCUST (supply chain orientation management support regarding customers)
SCOCUST1: Relationships with our customers are considered to be of critical importance to our plant’s top managers
SCOCUST2: Sharing valuable information with our customers is considered critical by our top managers
SCOCUST3: Our top managers repeatedly tell us that sharing supply chain risks and rewards with our customers is critical to our plant’s success
SCOCUST4: Our top managers support us in resolving conflicts with our customers, when they occur
SCOSUPL (supply chain orientation management support regarding suppliers)
SCOSUPL1: Relationships with our suppliers are considered to be of critical importance to our plant’s top managers
SCOSUPL2: Sharing valuable information with our suppliers is considered critical by our top managers
SCOSUPL3: Our top managers repeatedly tell us that sharing supply chain risks and rewards with our suppliers is critical to our plant’s success
SCOSUPL4: Our top managers support us in resolving conflicts with our suppliers, when they occur
PERF (operational performance)
37
Cost performance dimension
PERF1: Inventory: raw material, work-in-process and finished goods
PERF2: Operating expense: funds spent to generate turnover, including direct labor, indirect labor,
rent, utility expenses and depreciation
Quality performance dimension
PERF3: Conformance to product specifications
Delivery performance dimension
PERF4: Fast delivery
Flexibility performance dimension
PERF5: Flexibility to change product mix
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Appendix B. Interview Questions
This is an overview of the questions that were asked, however part of the questions depended on the answers that were given during the interview. This is not included as it was different for each interview.
Introduction
Demi Blokhuis, University of Groningen, MSc SCM and MSc TOM, thesis intern at Deloitte. Thesis about supply chain information integration. This interview lasts for approximately 1 hour and consists of two blocks: first 30 minutes is mainly focused on orientation, whereas in the last 30 minutes the survey results are discussed and there will be looked for possible explanations for these results. By survey research, relationships between variables can statistically be tested. However, a broader interpretation or explanation for the underlying constructs regarding this relationship is not possible in case of merely using survey research. That is why I use interviews in addition to survey research. Please interrupt me when something is not clear to you or in case you have any questions. In case you cannot or do not want to answer a question, mention it. Can I record this interview?
First half of the interview – orientation (30 min.)
Information integration
o What kind of information do you receive from (key) customers o What kind of information do you receive from (key) suppliers?
o Do you think this has an impact on the performance of your company? In what way?
o What do you think, related to information integration, are the main drivers for success? Why?
IT infrastructure integration
o Data consistency with supplier and with customer
o To what extent are the definitions of key data elements (e.g. customers, orders, part numbers) common across the supply chain? Example?
o To what extent are the data stored in different databases (e.g. order status) consistent across the supply chain?
o Summarize answers on the two questions above (note to myself). Do you think this has an impact on the performance of your company? In what way?
o Application integration
o To what extent do supply chain transaction applications, such as order management, procurement, manufacturing and distribution, communicate in real time?
o To what extent do supply chain planning applications, such as demand planning, transportation planning and manufacturing planning, communicate in real time? o To what extent do internal application within your organization, such as enterprise
resource planning, communicate in real time?
o Summarize answers on the three questions above (note to myself). Do you think this has an impact on the performance of your company? In what way?
SCO management support
o SCOSUPL (supply chain orientation management support regarding customers)
o How important do you as manager think are the relationships with customers? Is this also shown to the rest of the firm?
39 o To what extent does top management supports you in resolving conflicts with customers when they occur? Or: to what extent do you support the rest of the firm in resolving conflicts with customers when they occur?
o Summarize answers on the three questions above (note to myself). Do you think this has an impact on the performance of your company? In what way?
o SCOSUPL (supply chain orientation management support regarding suppliers)
o How important do you as manager think are the relationships with suppliers? Is this also shown to the rest of the firm?
o To what extent do you as manager think it is important to share valuable information with suppliers? Is this also shown to the rest of the firm?
o To what extent does top management supports you in resolving conflicts with suppliers when they occur? Or: to what extent do you support the rest of the firm in resolving conflicts with suppliers when they occur?
o Summarize answers on the three questions above (note to myself). Do you think this has an impact on the performance of your company? In what way?
Second half of the interview – results related (30 min.)
Show PowerPoint
Slide 6
o Customer information integration is shown to enhance performance. Do you see this in your company as well? In what way?
o Supplier information integration is not related to performance. Do you see this in your company as well?
o If yes, why do you think performance is not enhanced by the information received from suppliers?
o If no, why do you think supplier information integration does lead to better performance of your firm? Why might it be that for most manufacturing companies supplier information integration does not have any effect on performance?
Slide 10
o Why do you think data consistency improves the effectiveness of customer information integration?
o Do you see this in your company as well? (depends on answers given in first part) o In your case, would performance from customer information integration be better
(worse) when (in)consistent data is shared? Why is that?
o Why do you think data consistency does not matter for the effectiveness of supplier information integration?
o Do you see this in your company as well?
o Why do you think application integration improves the effectiveness of supplier information integration?
o Do you see this in your company as well?
This question and its answer became irrelevant later on as short after conducting the interviews I figured out that SCII was not correctly measured. First I measured SCII by only including the information received from customers and from suppliers. However, this is not ‘integration’. Therefore, I added information shared with customers and with suppliers in measurement of SCII. As a result, the findings slightly changed, making this small part irrelevant.
o Application integration on its own does not matter for the effectiveness of customer information integration. Why is that you think?
40
This question and its answer became irrelevant later on as short after conducting the interviews I figured out that SCII was not correctly measured. First I measured SCII by only including the information received from customers and from suppliers. However, this is not ‘integration’. Therefore, I added information shared with customers and with suppliers in measurement of SCII. As a result, the findings slightly changed, making this small part irrelevant.
o In your case, would performance from customer information integration be better (worse) when data is (not) shared in real time? Why is that?
o However, application integration and data consistency together (so, forming IT infrastructure integration) highly determine the effectiveness of customer information integration. Can you give an explanation for this?
o Do you see this in your company as well?
Slide 14
