Faculty of Economics and Business
MSc Supply Chain Management
Master’s Thesis
EXPLORING THE “OTHER SIDE”: THE CHALLENGE OF BECOMING A PREFERRED CUSTOMER IN
THE SUPPLY MARKET THROUGH CUSTOMER ATTRACTIVENESS AND SUPPLIER INTELLIGENCE
By Maria Ntompridi Student number: 3200299 Email: m.ntompridi@student.rug.nl Supervisor/ University Dr. Niels Pulles
University of Groningen, Faculty of Economics and Business
Co-assessor/ University Dr. Jasper Veldman
University of Groningen, Faculty of Economics and Business
ABSTRACT
TABLE OF CONTENTS
1. INTRODUCTION ... 4
2. THEORETICAL BACKGROUND ... 6
2.1 Preferred Customer Status ... 6
2.2 Customer Attractiveness ... 8
2.3 Supplier Intelligence ... 9
3. HYPOTHESES DEVELOPMENT ... 11
3.1 The Effect of Customer Attractiveness on Preferred Customer Status ... 11
3.2 The Effect of Intelligence on Customer Attractiveness and Preferred Customer Status ... 12
4. METHODOLOGY ... 13
4.1 Research Design ... 13
4.2 Sample and Data Collection ... 14
4.3 Measures ... 15
4.4 Common Method Variance ... 17
4.5 Data Analysis... 17
4.6 Measurement Model Assessment and Construct Validity ... 18
5. RESULTS ... 19
6. DISCUSSION... 22
7. CONCLUSION ... 24
7.1 Managerial Implications ... 24
7.2 Limitations and Future Research ... 25
REFERENCES ... 26
APPENDIX I: T-test Early and Late Respondents ... 30
1. INTRODUCTION
In buyer-supplier relationships it is a common belief that suppliers struggle to become more attractive to buyers (Schiele, Calvi & Gibbert, 2012). However, current supply markets are characterized by a low supplier to buyer ratio. This implies that many competing buying firms source similar resources from shared suppliers (Huttinger, Schiele & Schroer, 2014; Pulles, Veldman, Schiele & Sierksma, 2014). From a resource-based view perspective, a buying firm can obtain a competitive advantage having access to superior resources compared to its competitors (Koufteros, Vickery & Droge, 2012; Pulles, Schiele, Veldman & Huttinger, 2016), or in other words by becoming a preferred customer in the eyes of suppliers. Consequently, the resource environment generates the great challenge for buyers to distinguish themselves from other customers by becoming as attractive as possible with purpose to gain a preferential resource treatment from suppliers (Ramsay & Wagner, 2009; Schiele et al., 2012).
In the literature, the concept of preferred customer has been described by Pulles et al. (2016) as “the buying firm that is able to attain a preferential resource allocation position from suppliers that are shared with competitors” (2016, p.130). Previous studies (e.g., Huttinger, Schiele & Veldman, 2012; Schiele et al., 2012; Pulles et al., 2016) have shown that one of the main mechanisms that firms use for becoming preferred customers is customer attractiveness. According to Schiele et al., “customer attractiveness is based on the expectations that a supplier has towards the buyer at the moment of initiating or intensifying a business relationship” (2012, p.1180). Hence, customer attractiveness is a prerequisite for the development of a buyer-supplier relationship (Aminoff & Tanskanen, 2013) as well as a priority criterion used by suppliers for the allocation of resources to customers (La Rocca, Caruana & Snehota, 2012). However, what is missing in the literature is how the relationship between customer attractiveness and preferential resource allocation can become more effective and strengthened. It can be argued that the effectiveness of this relationship is of paramount importance for a buying firm in order to prevail over its competitors in the market and thus it is interesting to be examined in-depth.
Banerjee & Shukla, 2012), supplier intelligence is a relatively unexplored type of intelligence, although it is an important concept that could improve the effect of customer attractiveness on preferred customer status. Hence, there is no doubt that supplier intelligence deserves a further investigation.
The purpose of this research is to contribute both to the scientific and managerial world. Firstly, this research aims to extend further the concept of intelligence in literature by determining the degree to which supplier intelligence influences the relationship between customer attractiveness and preferred customer status. From a managerial point of view, this study aims to highlight the importance of intelligence as a supply chain management tactic by which buying firms can improve their attractiveness and strengthen or maintain their preferential resource allocation position against their competitors. In order to clarify exactly how the aforementioned concepts affect each other, the following research question will be answered:
To what extent does supplier intelligence influence the effect of customer attractiveness on preferred customer status?
In order to provide an answer to the research question and achieve the research objective, a survey was conducted using a buying firm’s database of suppliers. In particular, data was collected by means of a web-based survey consisted of two questionnaires, one for the buyer and one for the suppliers, with purpose to capture both perspectives. Thereafter, the data was analysed by the partial least squares (PLS) technique, which provides statistically important outcomes.
2. THEORETICAL BACKGROUND
The aim of this section is to discuss previous studies that have investigated the concepts of preferred customer status, customer attractiveness as well as the role of intelligence.
2.1 Preferred Customer Status
Nollet, Rebolledo and Popel (2012) contended that there are several definitions of the preferred customer and there is also no consensus on the practices on how to become a preferred customer. As mentioned in the introduction section, in this study a preferred customer refers to “the buying firm that is able to attain a preferential resource allocation position from suppliers that are shared with competitors” (Pulles et al., 2016, p.130). According to Hunt and Davis, resources refer to “the tangible (physical) and intangible (innovation) entities available to the organization that enable it to produce efficiently and/or effectively a market offering that has value for some market segments” (2008, p.13). Steinle and Schiele (2008) argued that being a preferred customer, by definition, is very advantageous and also that superior resources contribute to a firm’s competitive advantage. While the traditional resource-based theories claim that this can be achieved through the firm’s internal resources (Pulles, Veldman & Schiele, 2016), other theories assert that external resources are also of paramount importance for obtaining a competitive advantage (Steinle & Schiele, 2008). Therefore, the phenomenon of being a preferred customer has gained recently increasing attention from academics and practitioners.
al., 2012; Pulles et al., 2016). Therefore, it can be concluded that for a buying firm attaining a preferred customer status is a determinant factor of its survival.
Schiele et al. (2011) claimed that being a preferred customer in the eyes of key suppliers implies several benefits for the buying firm. A literature review upon this topic led to the construction of Table 2.1, which depicts some benefits for a buying firm which is awarded with a preferential resource allocation from its suppliers.
Table 2.1: Benefits of Preferred Customer Status
Benefits of Preferred Customer Status Reference
Best personnel of suppliers for NPD Steinle & Schiele (2008) Customized products according to
customer’s preferences Steinle & Schiele (2008) Priority against other customers Steinle & Schiele (2008) Access to newest technologies and
scarce resources Pulles, Veldman & Schiele (2016) High-quality products Nollet, Rebolledo & Popel (2012) Reliable and flexible delivery Nollet, Rebolledo & Popel (2012) Cost savings through price reductions Nollet, Rebolledo & Popel (2012) Access to know-how and supplier’s
innovation capabilities Schiele, Veldman & Huttinger (2011)
2.2 Customer Attractiveness
The resource-based view theory claims that a buying firm can attain a competitive advantage by having access to better resources than its competitors (Hunt & Davis, 2008; Koufteros et al., 2012; Pulles et al., 2016). According to Ellegaard, Johansen and Drejer (2003), if the competitive advantage of a buying firm derives from external resources, then it is important for the firm to be able to influence the behavior of its suppliers. They also claimed that customer attractiveness is a strategic approach that a buying organization can use to influence suppliers’ behaviors and actions according to its wishes. For instance, a highly attractive customer can induce a supplier to proceed to a desired price reduction (Ramsay & Wagner, 2009). Consequently, it can be argued that customer attractiveness is a powerful strategy of a buying firm for influencing in favor of itself the allocation of supplier resources. Indeed, previous studies (e.g., Huttinger et al., 2012; Schiele et al., 2012; Pulles et al., 2016) have shown that the concept of customer attractiveness plays a vital role in obtaining a preferred customer status and thus buying firms attempt to become more and more attractive to (key) suppliers.
As aforesaid in the introduction of this paper, customer attractiveness is defined in line with Schiele et al. (2012, p.1180): “customer attractiveness is based on the expectations that a supplier has towards the buyer at the moment of initiating or intensifying a business relationship”. In particular, these expectations refer to the perceived expected value, which motivates two parties to collaborate and develop a buyer-supplier relationship with purpose to create value and mutual benefits (Hald, Cordon & Vollmann, 2009). Hence, the expected value or the expected reward derived from a relationship is the core component of attraction (Hald et al., 2009; La Rocca et al., 2012). This is confirmed by Aminoff and Tanskanen (2013), who stated that attraction is the driving force that brings a buyer and supplier closer in order to create value.
customer attractiveness is more than a cost/ benefit analysis of a relationship’s value, since it is also a determinant factor of maintaining and/ or strengthening a business relationship.
2.3 Supplier Intelligence
Previous studies (e.g., Aminoff & Tanskanen, 2013; Tόth et al., 2014) have shown that customer attractiveness is not perceived by all suppliers in the same way since they have diverse interests, preferences or evaluation criteria for buyers. This implies that “one-size-fits-all” approach by a buying firm cannot endure in the long-term, and each supplier should be treated differently (Ramsay & Wagner, 2009). In order for a buying firm to be able to do so, it is clear that the possession of knowledge (i.e., intelligence) about all suppliers is necessary for meeting their expectations and increasing its attractiveness as well. Moreover, the current uncertain business environment and the supply base rivalry render their ongoing analysis by a firm an imperative need in order to strengthen and improve its market position. This ongoing process analysis of an organizational environment can be achieved by competitive intelligence. This is confirmed by Ben Sassi et al. (2016), who stated that the huge number of competitors and the fierce competition have led more and more managers to incorporate competitive intelligence processes with their decision-making processes, because competitive intelligence helps managers to make better strategic decisions (Adidam et al., 2012). Nasri (2011) also pointed out the importance for a firm to remain informed of its organizational environment in order to be able to adapt its strategies with the purpose to improve its competitiveness. According to Shujahat et al. (2017), making use of valuable information or knowledge is the most strategic source of a firm for obtaining a competitive advantage (Ahmed et al., 2014). In addition, Adidam et al. (2012) claimed that acquiring intelligence is a prerequisite for a successful strategic planning and a competitive advantage as it leads to the recognition of new opportunities and threats, which can have a significant impact to a company’s competitiveness (Du Toit, 2013). In the current literature, there is no consensus on the definition of competitive intelligence. To describe the concept of competitive intelligence, some definitions are stated below.
Strategic and Competitive Intelligence Professionals define competitive intelligence as “the process of monitoring the competitive environment and analyzing the findings in the context of internal issues, for the purpose of decision support” (Colakoglu, 2011, p.1616).
The concept of competitive intelligence is also known as the “actionable intelligence on the entire competitive environment, which includes an enterprise’s competitors, suppliers and customers, as well as its regulatory and political environment” (McGonagle, 2016, p.55).
It is also worth mentioning that many authors (e.g., Rouach & Santi, 2001; McGonagle, 2016) name different types of competitive intelligence, such as market intelligence, competitors’ intelligence, technological intelligence or strategic and social intelligence. Surprisingly, supplier intelligence is an unexplored type of competitive intelligence and therefore this research aims to examine its dynamic influence as supply chain management practice by defining it as “the knowledge that a buying firm has about its suppliers”. In particular, knowledge is measured in terms of the differences between the buyer’s perception and supplier’s reality when it comes tothe aspects“share in turnover” and “trust”. According to Terpend and Ashenbaum (2012), trust is one of the most important and common strategy used by buying firms to influence the suppliers’ behavior. This is confirmed by Pulles et al. (2014), who stated that when a buyer-supplier relationship is based on trust, then the supplier is more willing to allocate superior resources to this buyer without the fear of opportunistic behavior from the buyer. They also argued that the magnitude (small or large) of a buying firm’s share in the supplier’s turnover determines the strategy that a buying firm needs to follow in order to influence the supplier’s behavior in favor of itself. Consequently, misperceptions about the aforementioned aspects between a buyer and a supplier can affect the relationship between customer attractiveness and preferential resource allocation from a supplier. This effect becomes clearer later by explaining the second hypothesis of this study. At this point, Table 2.2 has been constructed depicting the most common benefits of intelligence.
Table 2.2: Benefits of Intelligence
Benefits of Intelligence Reference Contributing to strategic decision making for
becoming more competitive Nasri (2011) Recognizing business opportunities and problems Guimaraes (2000) Providing a means of continuous improvement Guimaraes (2000) Providing knowledge for obtaining a sustainable
competitive advantage Shujahat et al. (2017) Identifying new technologies, innovations and
3. HYPOTHESES DEVELOPMENT
This section includes the hypotheses that this study will test and the subsequent conceptual model.
3.1 The Effect of Customer Attractiveness on Preferred Customer Status
According to Cropanzano and Mitchell (2005), the social exchange theory (SET) explains the concept of customer attractiveness, because the behavior and actions among partners in business relationships are similar to these among persons in interpersonal relationships. Based on SET, the buyer-supplier interactions are interdependent and hence the partners should abide by reciprocity rules (Cropanzano & Mitchell, 2005). In particular, reciprocity rules are rules that regulate behaviors and actions according to the expectations of giving and receiving relational benefits (Nyaga, Lynch, Marshall & Ambrose, 2013; Pulles et al., 2016). Following this line of reasoning, the buyer-supplier relationship can be described as a valuable trade-off. For example, a buyer’s access to scarce raw materials against a tempting price offer to a supplier is such a trade-off. In this case, the buyer tries to become more attractive by a tempting price offer and the supplier voluntarily reciprocates this offer by a preferential resource treatment. This is confirmed by Aminoff and Tanskanen (2013), who stated that mutual attraction between two parties leads to voluntary efforts from both sides to reciprocate and remain attractive. From the aforementioned example, it becomes clear that the supplier’s behavior is driven by her/his perception that the expected value (in this case the expected profits) derived from this relationship is high and hence this customer is perceived as highly attractive. This is explained by Schiele et al. who argued that “a customer is perceived as attractive by a supplier if the supplier in question has a positive expectation towards the relationship with this customer” (2012, p.1180). Consequently, customer attractiveness can be characterized as a relational mechanism by which a buying firm can influence a supplier’s actions in order to gain a preferential resource allocation (Pulles et al., 2016). In particular, the expectation is that the more attractive the buying firm is perceived by a supplier, the more committed the supplier is to allocate better (physical/ innovation) resources to this firm. Thus, the following hypotheses are formulated:
3.2 The Effect of Intelligence on Customer Attractiveness and Preferred Customer Status As noted earlier, there are plenty of benefits for a firm to incorporate intelligence practices into its business. Maybe the most important benefit regarding the supplier intelligence (SI) is the fact that a buying firm, which follows SI practices, becomes aware of the suppliers’ expectations, preferences and interests. This contributes to the elimination of any misperceptions between the buyer and the suppliers. As a result, the buyer is able to meet a supplier’s expectations, preferences or interests and hence the supplier perceives this buyer as highly attractive. According to Pulles et al. (2016), when the buyer meets the supplier’s expectations, the supplier is satisfied and then more willing to allocate superior resources to this buyer, so (s)he becomes a preferred customer. Moreover, the buyer who has SI implements more effective and suitable strategies in each situation (Adidam et al., 2012), such as power, trust or better communication with the suppliers, since each supplier should be treated differently (Ramsay & Wagner, 2009). In this way the buying firm influences the suppliers’ behaviors in regard with the resource allocation. However, when the buyer has poor SI, (s)he can use a strategy that has no or even a negative effect on the suppliers’ actions. In order to make clear the above described situations, an example related to the intelligence’s measure “share in turnover” is used. In the situation where the buying firm has no SI and expects that possesses a high percent share in the supplier’s turnover (e.g., 80%), it could use as strategy power to influence the supplier’s behavior for a preferential resource treatment. However, if the real percent share in the supplier’s turnover is low (e.g., 20%), then this strategy will have no or even a reversed effect on this supplier. It could also lead the supplier to terminate this relationship which would affect negatively the buyer’s performance in the market. On the other hand, if the buyer has SI and is aware of the real percent share in the supplier’s turnover, then (s)he can adapt the followed strategy (e.g., from power to trust) with purpose to increase the effect of its attractiveness on preferential resource allocation. It can be concluded that the knowledge acquired from SI practices leads to more effective and suitable strategies that eventually influence the supplier’s actions in regard with the resource allocation. Hence it is expected that the more supplier intelligence a buying firm has, the more effective the relationship between customer attractiveness and preferred customer status is. Thus, the following hypotheses are formulated:
H2a: Supplier intelligence has a positive effect on the relationship between customer attractiveness and physical resource allocation.
H2a H2b
H1a H1b
Figure 3.1: Conceptual Model
4. METHODOLOGY
This section describes all actions which were taken by the author in order to receive the necessary data for realizing the research objective.
4.1 Research Design
For the purpose of this research, a survey was a more appropriate method than a case study for three main reasons. First, surveys provide hard numbers, i.e. accurate and verifiable numbers, which are necessary to draw precise statistical outcomes (Sincero, 2012). Good statistical significance is very important for answering a “To what extent” research question, because the goal here is to determine the degree of influence of intelligence. Second, using a survey the proposed hypotheses can be tested to see if they are proved or disproved since all hypotheses in a study should be falsifiable. Third, the limited time available for the conduction of this research made a web-based survey more effective in terms of response rate and non-response bias (Karlsson, 2016). Moreover, using a web-based survey, a large number of suppliers can be approached resulting in access to plenty of valuable data for testing hypotheses and increasing the generalization of the results.
Also, this research examines multi-sided in nature constructs and thus it is important to take into account the perspectives of all stakeholders (Roh, Whipple & Boyer, 2013), because there are often misperceptions between the perspective of a buyer and a supplier. On the one hand, the buyer perspective is needed to measure the level of intelligence that has about the suppliers. On the other hand, the supplier perspective is needed to measure how much attractive perceives the buyer in
research is that takes into account a dyadic perspective and hence dyadic data is needed. In particular, dyadic data were collected from matched pairs of the buyer and suppliers, who replied to different questions on the constructs of interest. Therefore, the unit of analysis in this research is the relationship between a buyer and a supplier. Summing up, this research relies on data gathered through means of a web-based survey consisted of two different questionnaires, one for the buyer and one for the supplier, in order to gain insights in both perspectives of the same relationship.
4.2 Sample and Data Collection
The data were collected in collaboration with a Dutch starch manufacturer with worldwide reputation. In particular, this companyproduces a great range of products based on potato starch and potato protein for use in food, industrial and animal nutrition and feed markets. After approaching many companies in different industries by telephone or emails requesting their participation in the survey, I found more interesting to collaborate with the aforementioned company for two main reasons. First, one of the company’s goals is to retain and excel its production methods, which are characterized as highly innovative and sustainable. Hence, the acquisition of superior physical and innovation resources, such as new technologies or pioneer ideas, from suppliers play a vital role in realizing its goal. Second, this company collaborates with many local and international suppliers and it is interesting to examine how much intelligence acquires about all of them and how the high or poor supplier intelligence affects its preferred customer status.
Although 90 surveys were distributed to suppliers, only 46 out of 90 suppliers filled in the survey, resulting in a supplier response rate of 51.1 percent. For each of these 46 suppliers, the buyer filled in the relevant survey in order to match their responses and examine their relationship. Hence, the suppliers filled in the reality values and the buyer filled in the expected values. The values were compared to each other and the differences between them were used as indicator for the latent variable supplier intelligence. On average, the respondents had approximately 11.5 years of working experience for their firms and were almost 6 years personally involved with the buying firm. Table 4.1 depicts an overview of the sample characteristics.
Table 4.1: Sample Characteristics
No. Respondents Average
Country Nederland 13 Germany 21 United Kingdom 6 Switzerland 2 China 4
Years of supplying the buyer 10.60 Years of working experience 11.52 Years of personal involvement 5.89
with the buyer
The final sample size (46) of this study means that more than half of the population (90) has responded to the survey, which represents a non-response rate of 48.8 percent. A survey method runs always the risk of a high non-response rate and a non-response bias (Lambert & Harrington, 1990). However, Babbie contended that “a response rate of at least 50 percent is considered adequate for analysis and reporting” (2007, p.262) and hence the sample used in this study suffices. Moreover, for eliminating the non-response bias, the researcher conducted a comparative t-test by comparing the first and later waves of respondents in order to test for significant differences in their responses (Lambert & Harrington, 1990). The results of the t-test, which can be found in Appendix I, yielded no statistically significant differences (p > 0.05) between the first and later respondents’ data. Although the non-response bias is not excluded from this study, the results of the test indicated that it might not pose a problem.
4.3 Measures
produce high-quality data when they used in online surveys. Therefore, it was expected to draw insightful conclusions. The construct of customer attractiveness relies on measures adopted from the study of Pulles et al. (2016), while the constructs of preferential physical and innovation resource allocation rely on measures adopted from the study of Pulles et al. (2014). The construct supplier intelligence, in terms of “share in turnover” and “trust”, is measured by the absolute differences between the buyer’s perception and the supplier’s reality. Table 4.2 and Table 4.3 below list the specific instruments used for gathering the data from the buying firm and its suppliers.
Table 4.2: Measurements Items - Supplier Questionnaire
Variable Item Item (Loading)
Customer Attractiveness* Cronbach’s alpha = 0.83 Composite reliability = 0.87 AVE = 0.70
- We consider this customer to be an attractive partner for future collaborations.
- We expect positive outcomes from the relationship with this customer.
-Our firm has positive expectations about the value of the relationship with this customer.
0.95 0.71 0.83 Preferential Resource Allocation (Physical)* Cronbach’s alpha = 0.79 Composite reliability = 0.87 AVE = 0.69
Compared to our other customers:
-We grant this customer better utilization of our production facilities.
- We would choose to give this customer priority in the allocation of our products in the case of extreme events (e.g., natural disasters).
- We allocate our scarce materials to this customer in case of capacity bottlenecks. 0.64** 0.94 0.88 Preferential Resource Allocation (Innovation)* Cronbach’s alpha = 0.84 Composite reliability = 0.90 AVE = 0.76
Compared to our other customers:
- We are more willing to share key technological information with this customer.
- We share our best ideas with this customer first.
- We dedicate more innovation resources to the relationship with this customer.
0.82 0.89
0.89 Share in Turnover Please indicate the share (in %) that this customer has in your
firm’s turnover 0% - 100%.
This customer accounts for ….. % in my firm’s turnover.
-0.35***
Supplier Intelligence (measured by trust)
Cronbach’s alpha = 0.63 Composite reliability = 0.64 AVE = 0.53
Please indicate how much trust in this customer you have 0%
- 100%.
-When this customer makes a promise, we trust that this customer has the managerial and technical capabilities to do what it says it will do.
-We believe that this customer would make sacrifices for us to support our firm.
0.31** 0.98 Length of Relationship Cronbach’s alpha = 1.00 Composite reliability = 1.00 AVE = 1.00
How long has your firm been supplying this customer (in years)?
1.00
Notes: * Measured by a five-point Likert scale ranging from 1 (“no, strongly disagree”) to 5 (“yes, strongly agree”). ** Although the items’ loadings are below the threshold value of 0.7, they remained in the model.
Table 4.3: Measurements Items – Buyer Questionnaire
Variable Item Item (Loading)
Share in Turnover Please indicate your firm’s share (in %) in this supplier’s turnover 0% - 100%.
We account for ….. % in this supplier’s turnover. -0.35*** Supplier Intelligence (measured by
trust)
Cronbach’s alpha = 0.63 Composite reliability = 0.64 AVE = 0.53
Please indicate how much trust this supplier has in your firm
0% - 100%.
-When we make a promise, this supplier trusts that we have the managerial and technical capabilities to do what we say we will do.
-This supplier believes that we would make sacrifices for them to support their firm.
0.31** 0.98 Pricing* Cronbach’s alpha = 1.00 Composite reliability = 1.00 AVE = 1.00
Compared to suppliers of similar product, how would you assess the pricing of this supplier?
1.00
Notes: * Measured by a five-point Likert scale ranging from 1 (“much lower prices”) to 5 (“much higher prices”). ** Although the items’ loadings are below the threshold value of 0.7, they remained in the model.
*** This item was removed, because it is very lower than 0.7 and negative.
4.4 Common Method Variance
The common method variance (CMV) phenomenon is a critical methodological concern in many empirical researches, because there is no strong theoretical basis for addressing the CMV (Siemsen, Roth & Oliveira, 2010). To cope with CMV and prevent misleading conclusions, the researcher took several actions in line with the suggestions of Podsakoff, MacKenzie, Lee and Podsakoff (2003). First, with regard to procedural remedies, the researcher collected data on the variables of interest from different sources (i.e., dyadic data from both the supplier and buyer perspective). Second, in order to eliminate social desirability bias, the introduction page of the questionnaire clearly stated that the individual responses would not be made available to other participants of the survey. The highly confidential nature of the questionnaire protected the respondents’ anonymity in order for them to answer the questions as honestly as possible. In addition, participants were told that there were no right or wrong answers and they should provide the most suited answer for their case.
4.5 Data Analysis
better values than the covariance-based approach to structural equation modeling, when there is a small sample size for the data analysis like in this case (Pulles et al., 2016). Consequently, PLS was the most suitable statistical tool for this data analysis.
4.6 Measurement Model Assessment and Construct Validity
To evaluate and ensure the reliability and validity of the measurement model, several tests were conducted. First, the construct item loadings were examined. The item of the variable ‘share in turnover’ was removed, because its loading was very lower than the acceptable threshold value 0.7 (Fornell & Larcker, 1981) and negative. All other item loadings were above 0.7, except from the item “We grant this customer better utilization of our production facilities” and the item “When this
Table 4.4: Mean, Standard Deviation, Squared AVE and Correlation of Constructs
Mean St.dev. 1 2 3 4 5 6
1. Customer Attractiveness 4.173 0.845 0.838
2. Innovation Resource Allocation 3.340 0.970 0.118 0.875
3. Physical Resource Allocation 3.384 0.983 0.179 0.747 0.835
4. Pricing 3.173 0.676 -0.218 0.084 0.084 1.00 5. Supplier Intelligence 29.804 25.980 0.193 0.261 0.310 -0.038 0.733 6. Length of Relationship 10.608 9.636 0.184 0.133 0.133 -0.006 0.162 1.00
Notes: St.dev. = standard deviation. Mean = sample mean. Numbers on the diagonal in bold show the square roots of the AVE. Numbers below the diagonal that are not bold show the correlations.
5. RESULTS
After conducting all the necessary tests for evaluating the reliability and validity of the outer model, the inner model is analysed in order to obtain the results of this research. Initially, using the SmartPLS 3.0 software, the hypothesized direct effects were tested by a standard path-weighting scheme, which produced the estimated standardized beta coefficients (β) for the hypothesized paths and the R-squared values (R2) for the endogenous constructs. Thereafter, a bootstrapping procedure using 1000 subsamples produced the t-values and p-values needed for the assessment of the statistical significance of the direct effects.
0.209 (0.670) 0.122 (0.829) 0.171 (1.108) 0.137 (0.781) 0.306 (0.784) 0.048 (0.293)
Figure 5.1: Model 1, Results of the Structural Equation Model
In the above figure, it is depicted the inner model which shows the standardized beta coefficients (β) and the t-values in parenthesis. The results indicate that, although, all direct effects are positive, there is no significant effect between the constructs, since all p-values are higher than 0.05. In particular, customer attractiveness is positively related to physical resource allocation (β = 0.209, p = 0.503 significant) and positively related to innovation resource allocation (β = 0.306, p = 0.433 non-significant). This means that the hypotheses H1a and H1b are not supported, because the effects are non-significant. In addition, the R-squared values of the endogenous constructs are 0.073 for the physical resource allocation and 0.106 for the innovation resource allocation. Consequently, customer attractiveness accounted for 7.3 percent of the explained variance of physical resource allocation and 10.6 percent of the explained variance of innovation resource allocation. Next, the model 2 including the moderator supplier intelligence helps to understand how it influences the effect of customer attractiveness on preferential resource allocation.
0.350 (0.806) 0.188 (1.311) 0.138 (0.603) 0.122 (0.866) 0.038 (0.146) 0.129 (0.764) 0.181 (0.723) 0.143 (0.951) 0.193 (0.851) 0.268 (1.041) -0.471 (1.822)
Figure 5.2: Model 2, Results of the Structural Equation Model
In the figure 5.2, it is depicted the inner model including the moderating effect, which also shows the standardized beta coefficients (β) and the t-values in parenthesis. The results indicate that the direct effects of customer attractiveness on physical and innovation resource allocation are remained positive but non-significant; β = 0.138, p = 0.546 and β = 0.038, p = 0.884 respectively. However, it is worth mentioning that after adding the moderator, the effect of customer attractiveness on physical resource allocation and innovation resource allocation decreased. In particular, the beta coefficient of customer attractiveness on physical resource allocation decreased from 0.209 to 0.138, while the beta coefficient of customer attractiveness on innovation resource allocation decreased from 0.306 to 0.038. It is also interesting to mention that the moderating effect 2 (i.e., the effect of supplier intelligence on the relationship between customer attractiveness and innovation resource allocation) has a negative β = -0.471 and a strong t-value = 1.822, but a p-value of 0.069; resulting in a notable but not significant effect. Consequently, the hypotheses H2a and H2b are not supported. Finally, customer attractiveness accounted for 21.3 percent of the variance in the physical resource allocation and for 29.5 percent of the variance in the innovation resource allocation, while supplier intelligence accounted for 3.7 percent of the variance in the customer attractiveness. The complete model with all indicators of the latent variables can be found in appendix II.
6. DISCUSSION
For a buying firm, obtaining better resources than its competitors from shared suppliers is a great challenge (Ellram et al., 2013; Pulles et al., 2016). This study proposes that a supplier, who has a positive expectation from the relationship with a customer and hence perceives this customer as highly attractive, is more willing to allocate better physical and/or innovation resources to this customer. In addition, this study introduces the concept of supplier intelligence, a relatively unexplored practice in supply chain management, and it is hypothesized that the more knowledge (intelligence) a buying firm has about its suppliers, the more effective and strengthened the relationship between customer attractiveness and preferential resource allocation is. The results of this study are very interesting and will be discussed in this section.
First, this study revealed a positive but not significant relationship between customer attractiveness and preferential physical and innovation resource allocation in both models. Although the findings showed a non-significant relationship which contrasts the findings of the research of Pulles et al. (2016), the fact that customer attractiveness is positively related to preferential resource allocation is in line with other studies (e.g., Huttinger et al., 2012; Schiele et al., 2012). Also, the positive relationship between customer attractiveness and preferential resource allocation indicated that indeed customer attractiveness is a priority criterion used by suppliers for the allocation of resources to customers (La Rocca et al., 2012). Consequently, the results confirm the importance of being a highly attractive customer in the eyes of suppliers in order to attain a preferred customer status.
customer attractiveness on preferred customer status depending on the magnitude of the supplier intelligence.
A third more interesting finding of this study is the negative moderating effect of supplier intelligence on the relationship between customer attractiveness and innovation resource allocation. This surprising result implies that the more supplier intelligence a buyer has the less effective the customer attractiveness on innovation resource allocation is. A possible explanation can be that although the buyer is aware of the suppliers’ expectations, (s)he implements a strategy that has a negative effect on the suppliers’ actions in regard with the resource allocation. Maybe the buyer makes use of the same strategy on all buyer-supplier relationships. However, other researchers (e.g., Aminoff & Tanskanen, 2013; Tόth et al., 2014) claim that customer attractiveness is not perceived by all suppliers in the same way and therefore a buying firm should differentiate its strategies with the suppliers (Ramsay & Wagner, 2009). This finding is very important and should capture the caution of the buyer because it could lead to the termination of a buyer-supplier relationship and to a poor performance for the buying firm. On the contrary, the data analysis resulted in a positive but not significant moderating effect of supplier intelligence on the relationship between customer attractiveness and physical resource allocation. This result is in line with the hypothesis H2a, but not significant to support it.
7. CONCLUSION
This empirical study provided insights into the effects of customer attractiveness on preferential (physical and innovation) resource allocation from suppliers and the effect of supplier intelligence on the relationship between customer attractiveness and preferential resource allocation. The novelty of this study is the concept of supplier intelligence, which remains in its infancy with respect to its depth of understanding and its highlight of importance as a modern supply chain management practice. Overall, this study contributed to extend further the concept of intelligence in literature by determining the degree to which supplier intelligence influences the relationship between customer attractiveness and preferred customer status. The aim of adding this unexplored concept was to see if the more knowledge the buyer has about their suppliers, the more strengthened the effect of customer attractiveness on preferred customer status is. It is worth mentioning that supplier intelligence is based on dyadic data, i.e., the buyer’s perception and the supplier’s reality, capturing both perspectives.
First and foremost, this study highlights the importance for a buying firm of being a highly attractive customer in the eyes of the suppliers in order to prevail over its competitors and obtain a preferred customer status. Indeed the results showed that customer attractiveness is positively related to preferential physical and innovation resource allocation, although the hypotheses are not supported because the effects are not significant. Contrary to the expectations, it must be concluded that when supplier intelligence is present, the effect of customer attractiveness on preferential resource allocation is weakened instead of strengthened.
7.1 Managerial Implications
7.2 Limitations and Future Research
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APPENDIX I: T-test Early and Late Respondents
Variable Response N Mean Std. Deviation Std. Error Mean SupAllocPhy1 Early Respondents
Late Respondents 23 23 3,347826 3,304348 0,982052 0,702902 0,204772 0,146565 SupAllocPhy2 Early Respondents
Late Respondents 23 23 3,304348 3,478261 1,32921 0,730477 0,277159 0,152315 SupAllocPhy3 Early Respondents
Late Respondents 23 23 3,347826 3,521739 1,368765 0,593109 0,285407 0,123672 SupAllocInnov1 Early Respondents
Late Respondents 23 23 3,478261 3,565217 1,16266 0,727767 0,242431 0,15175 SupAllocInnov2 Early Respondents
Late Respondents 23 23 3,26087 3,347826 1,214211 0,71406 0,25318 0,148892 SupAllocInnov3 Early Respondents
Late Respondents 23 23 3,173913 3,217391 1,15413 0,735868 0,240653 0,153439 CustAttract1 Early Respondents
Late Respondents 23 23 4,391304 4 0,782718 0,852803 0,163208 0,177822 CustAttract2 Early Respondents
Late Respondents 23 23 4,304348 3,956522 0,87567 0,76742 0,18259 0,160018 CustAttract3 Early Respondents
Late Respondents 23 23 4,347826 4,043478 0,884652 0,877924 0,184463 0,18306 SupShareInTurnover Early Respondents
Late Respondents 23 23 4,826087 2,304348 4,10822 1,063219 0,856623 0,221697 SupTrust1 Early Respondents
Late Respondents 23 23 82,13043 82,43478 16,96334 8,825717 3,537101 1,840289 SupTrust2 Early Respondents
Late Respondents 23 23 62,08696 48,69565 22,73946 19,21986 4,741505 4,007619 BuyShareInTurnover Early Respondents
Late Respondents 23 23 14,52174 3,391304 23,98839 2,51792 5,001924 0,525023 BuyTrust1 Early Respondents
Late Respondents 23 23 54,95652 76,86957 34,97594 13,32225 7,292987 2,77788 BuyTrust2 Early Respondents
APPENDIX II: Results of Structural Equation Model
