Knowledge Acquisition in Supply Chains: The Effect of
Buyer-Supplier Relationships
J.J.H. (JAAP) DRIEVER
S2367483
University of Groningen
Supervisor: Chengyong Xiao
Co-Assessor: Prof. Dr. D.P. van Donk
Faculty of Economics and Business
Words: 8047
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Knowledge Acquisition in Supply Chains: The Effect of
Buyer-Supplier Relationships
ABSTRACT
4 INTRODUCTION
Researchers and practitioners have recognized that knowledge acquisition contributes to competitive advantage (Dyer, Nobeoka 2000, Zheng et al. 2014; Ichijo and Nonaka, 2006). To acquire knowledge on a global basis, a company should participate in a supply network. Dyer and Nobeoka (2000: p.364) found that ‘a network can be more effective than a firm at the generation, transfer, and recombination of knowledge’. Therefore, for an organization to achieve competitive advantage, it should participate in a network and establish relationships within the network. Through its relationships, an organization could acquire relevant knowledge. However, buyer-supplier relationships in global supply chains are complex and need further investigation (Alcacer et al., 2014). A better understanding of buyer-supplier relationships and its effect on knowledge acquisition will contribute to effectiveness of knowledge acquisition and competitive advantage of organizations.
The literature so far has treated buyer-supplier relationships as one-dimensional regarding to its effect on knowledge acquisition. Buyer-supplier relationships have been positively associated with knowledge acquisition and considered as a valuable source (Carey et al., 2011; Dyer and Hatch, 2016). Weak ties are associated with acquisition of novel information, whereas strong ties enable specific knowledge sharing and acquisition (Molina-Morales and Martínez-Fernández, 2009). However, to capture the emerging dynamics, Kim and Choi (2015) argue that relationships differ on a continuum of relational intensity and relational posture. That is, buyer-supplier relationships have more than one dimension. This paper intends to contribute to current literature by assessing the effect of buyer-supplier relationships on knowledge acquisition by applying the expanded typology of Kim and Choi. In this way, the paper tries to respond to the call of Alcacer and Oxley (2014) to get a deeper understanding of the drivers within buyer-suppliers relationships on knowledge acquisition. Furthermore, the paper provides a more specific characterization of knowledge. In order to do so, a differentiation between technological versus technological knowledge is made. Finally, exploring the effects of buyer-supplier relationships on acquiring different types of knowledge will not only extend the current literature, it could also have major managerial implications. The practical contributions could provide managers with strategies for effective buyer-supplier relationship for the acquisition of different types of knowledge.
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To explore this question, we will conduct a survey research. A survey research in order to test the applicability of the expanded typology of Kim and Choi and to explore its effect on the acquisition on technological and market knowledge. We gathered the data at SMEs in the Netherlands. Because, not only global players have recognized the importance of knowledge as economic resource, also SMEs have started to implements projects regarding to knowledge management (Bodrow, W., 2006).
6 THEORETICAL BACKGROUND
Knowledge acquisition
The literature have considered inter-organizational knowledge transfer processes in other but related ways. Knowledge sharing is related to the process of knowledge acquisition (Darr et al., 1995; Lyles and Salk, 1996). Van Wijk et al. (2008) provided antecedents, which are important predictors of (inter-) organizational knowledge exchange. Antecedents of knowledge transfer refers to knowledge-, organizational- and network characteristics.
Knowledge ambiguity is a key characteristic of knowledge and will have major impact on the process of knowledge transfer. Ambiguity emerges from effects of tacitness, specificity and complexity (Reed and DeFilippi, 1990). Ambiguity will contribute to an organizations competitive advantage, since it is hard to imitate. However, if ambiguity is high it will hinder transferability (Van Wijk et al., 2008). Recipients have to learn and need explanation how to implement acquired knowledge.
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that very strong ties are detrimental for acquisition of specific, complex product and process knowledge acquisition. Reasons are the increased risk of heavy obligations, collective blindness and supplier opportunism. Therefore, a moderate level of relational ties is optimal. Furthermore, Zheng et al. argue that formal and informal mechanisms will effect such specific knowledge acquisition. Detailed contracts will support acquisition of knowledge in case of weak or moderate ties. However, if ties become stronger, such detailed contracts will signal distrust. Because detailed formal contracts conflict with the self-enforcing nature of strong ties, and eventually will impede inter-organizational knowledge exchange.
The cognitive dimension represent the degree of shared vision and systems and cultural differences between organizations. Shared vision and systems contribute to ‘mutual understanding and provide a crucial bonding mechanism that helps different actors to integrate knowledge’ (Van Wijk et al., 2008: p. 835). Therefore, shared vision and systems are likely to promote to knowledge acquisition. Cultural distance could hinder the capability of an organization to transfer core competencies to foreign markets and increases operational difficulties as well, which in result will impede knowledge transferring (Van Wijk et al., 2008). Battistella et al. (2016) provide also factors of distance which effect knowledge exchange. Organizational distance refers to ‘the mode of organization through which the source and the receiver perform the transfer’, physical distance to ‘the difficulty, the time and the cost of communication. Knowledge base distance denotes ‘the degree source and the receiver are in possession of similar knowledge’ and the normative distance refers to ‘the extent to which the parties of knowledge transfer share social aspects of behavior in their context.
Technological and Market Knowledge
8 Technological knowledge acquisition
Within the technology-driven model (‘technology push’), ‘innovation is primarily linked to the generation and exploitation of technological knowledge developed in research and development (R&D)’ (Sammarra and Biggiero, 2008: p.804). Technological knowledge is defined as ‘knowledge about how to produce goods and services’ (Bohn, 1994). To be more specific, technological knowledge entails scientific and experimental knowledge, which are used in order to improve the know-how and capabilities on the production process (Howell et al., 2003). By acquiring technological knowledge, an organization’s ability to respond to the rapidly changing technological environment improves (Alcacer and Oxley, 2014; Sammarra and Biggiero, 2008). If an organization aims to acquire a certain technology, internal processes have to adapt and knowledge, which is necessary to apply the newly acquired technology, have to be integrated (De Toni et al., 2011; De Toni et al., 2012). The know-how about how to implement, use and practice the new technology also have to be transferred (Howells, 1996; Malik, 2002; Cummings and Teng, 2003). Strong relationships motivate organizations to ensure that the technology and related knowledge is entirely understood by the recipient and could take advantage of the acquired information (Hansen, 1999).
Market knowledge acquisition
9 Expanded buyer-supplier typology
Kim and Choi (2015) noted the recognition in literature that a single-dimensional methodology for categorizing buyer-supplier relationship poorly describes the complex nature of relationships within a supply chain context. The typology revisit the conventional wisdom that close, cooperative buyer-supplier relationships are purely good and arms-length, adversarial relationships entirely bad. Suppliers have to navigate through different and often conflicting needs of their diverse customers and buyers should balance building deep relationships with suppliers while taking the competitive environment among suppliers in mind.
The framework includes two different dimensions of buyer-supplier relationships. These dimensions are relational posture and relational intensity, which capture the affective and operational connection between organizations. ‘The basic constructs of relational posture include commitment, trust, information sharing, relational norms, and conflict resolution, which together represent relational quality or attachment’ (Kim and Choi, 2015: p.63). The relational posture dimension range from adversarial to cooperative. ‘Relational intensity addresses transactional strength and volume, reflecting economic interdependence between a buyer and supplier’ (Kim and Choi, 2015: p.63). The strength of relational intensity is reflected in terms of interaction frequency, asset-specificity, operational interdependence and multiplexity. When the intensity between buyer and supplier is classified as low according to these indicators, the relationship is considered as arms-length and as closely-tied if levels are high.
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and supplier opportunism. A ‘transient buyer-supplier relationship’ is characterized by relational ambiguity and supplier flexibility. Both supplier and buyer have alternative options and therefore are not interested in more extensive knowledge sharing or joint undertakings (Anand and Ward, 2004). A strategic advantage of this type of relationships, due to its arms-length nature, is that supplier is exposed to larger number of relational ties and is highly adaptable to a particular course of action (Stam and Elfring, 2008; Zahra and Filatotchev, 2004). Finally, within a ‘gracious buyer-supplier relationship’ the supplying party may have connections to various other companies due to the arms-length nature of the relationship (even to buyer’s competitors) (Das and Teng, 1998). Therefore, the buying party has a lack of control over its supplier but will remain a in a cooperative relationship hoping for future benefits. Because, a supplier could be a source of innovation in the future (Axelrod, 1984). Due to arms-length intensity, the supplier could possibly aid as a channel for new ideas and market knowledge (Fleming et al., 2007).
The expanded BSR typology related to technological and market knowledge acquisition
Due to the knowledge characteristics of technological knowledge, ambiguity is likely to be high. Technological knowledge refers to ‘how to produce goods and services’ (Bohn, 1997). This know-how is related to tacit knowledge and is revealed by its application (Grant, 1996). Recipients need explanation and need to be trained in order to take advantageous use of the acquired knowledge (Hansen, 1999). This process requires commitment and effort from both partners. Due to the closely tied and cooperative characteristics of a ‘deep’ relationship, reliable flows of information are enabled. Therefore, all ingredients for efficient and effective knowledge acquisition are present and most technological knowledge will be acquired within a ‘deep’ relationship.
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to be shared on a regularly basis between partners. However, the posture of the relationship is adversarial and partners perceive each other as ‘necessary evil’ (Kim and Choi, 2015). This will hinder the ease of knowledge transfer (De Long and Fahey, 2000). Especially due to tacitness of technological knowledge, ambiguity is likely to be high. Therefore, less technological knowledge will be acquired within a ‘sticky’ relationship compared to a ‘deep’ relationship, because buying partner lacks the commitment to pay effort in the understanding of the acquired knowledge by the recipient.
Still, more technological knowledge is acquired within a ‘sticky’ relationship than within a ‘gracious’ relationship due to the high levels of intensity. Within a ‘gracious’ relationship, despite low relational intensity, partners established a cooperative relationship. A cooperative relationship is established in order to benefit from it in the future (Axelrod, 1984). Therefore, there will be knowledge shared on technological basis as well.
Contrary to a ‘deep’ relationship, there are no foundations for knowledge sharing within a ‘transient’ relationship (Kim and Choi, 2015). Therefore, we expect the lowest level of technological acquisition within this relational type. Supplier and buyer have alternative options and do not intend to establish a long lasting relationship. Therefore, both parties do not have the intension to share valuable knowledge with each other. Taking these observations in mind, we formulated the following hypotheses:
H1a: Within a ‘deep’ relationship most technological knowledge will be acquired H1b: Within a ‘sticky’ relationship more technological knowledge will be acquired compared to a ‘gracious’ and ‘transient’ relationship, but less as compared to a ‘deep’ relationship
H1c: Within a ‘gracious’ relationship, less technological knowledge will be acquired compared to a ‘deep’ and ‘sticky’ relationship, but more as compared to a ‘transient’ relationship
H1d: Within a ‘transient’ relationship, no technological knowledge will be acquired
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expect that within a ‘deep’ relationship most market knowledge will be acquired. Because, due to high intensity and a cooperative posture partners are committed to create value together in order to increase performance and innovativeness (Van Wijk et al., 2008). In this way, partners help each other to stay competitive. Also for market knowledge acquisition counts the fact that all ingredients which are necessary for effective and efficient knowledge sharing are present. Therefore, a within a ‘deep’ relationships most market knowledge will be acquired.
However, regarding to market knowledge acquisition, we expect that more market knowledge will be acquired within a ‘gracious’ relationship than within a ‘sticky’ relationship. Within a ‘gracious’ relationship, a supplier may have relationships with the buyers’ competitors as well (Das and Teng, 1998; Kim and Choi, 2015). Therefore, a supplier could better notice signals about the market. Because the nature of the ‘gracious’ relationship (commitment, trust, information sharing, relational norms, and conflict resolution), it is likely that partners are willing to share valuable knowledge about the market. Furthermore, within a ‘sticky’ relationship, there is an asymmetry of information and animosity and partners do not intend to invest in the relationship. So, valuable knowledge about the market is not likely to be acquired within a ‘sticky’ relationship, because partners are not aiming to help each other by providing valuable knowledge about the market. Though, still more market knowledge is acquired within a ‘sticky’ relationship than in a ‘transient’ relationship due to the characteristics of a ‘transient’ relationship. A ‘transient’ relationship is not likely to be a great predictor for market knowledge acquisition, because all foundations for effective and efficient knowledge sharing/acquisition are missing (Kim and Choi, 2015; Van Wijk et al., 2008). The relationship is characterized as being adversarial and arms-length, and therefore missing the predictors of knowledge sharing. We draw the following hypotheses:
H2a: Within a ‘deep’ relationship most market knowledge will be acquired
H2b: Within a ‘gracious’ relationship more market knowledge will be acquired compared to a ‘sticky’ and ‘transient’ relationship, but less as compared to a ‘deep’ relationship
H2c: Within a ‘sticky’ relationship, less market knowledge will be acquired compared to a ‘deep’ and ‘gracious’ relationship, but more as compared to a ‘transient’ relationship
13 METHODS
Sample and procedure
In order to test our hypotheses, we conducted a survey research. Within business and management research, data is frequently gathered through surveys and a questionnaire is an appropriate tool to collect data (Adams et al., 2014). An online questionnaire (via Qualtrics) was designed in collaboration with three other researchers, under supervision of a researcher from the University of Groningen. The questionnaire consisted of four different topics, which all were related to knowledge acquisition and creation between or within small or medium sized enterprises (SMEs). We collected the data at SMEs in the Netherlands, which are operating in several kinds of industries, such as manufacturing, logistics, service providers, energy and agriculture. In total 306 questionnaires were distributed and 155 SMEs responded. However, only 86 of the responses were valid. The response rate was 50.7 percent. Among the SMEs, the average age of the organizations was 34 years and the average number of employees was 90. We received responses from CEOs, supply chain managers and purchasing managers. Respondents were between 18 and 65 years old.
Measures
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The collected data was analyzed through SPSS. We conducted a reliability, unidimensionality and validity test for each construct. Reliability of the constructs refers to consistency of the measurement and validity reflects the adequacy of what we intend to measure (Adams et al., 2014). We performed an exploratory factor analysis (EFA) on the scales of relational posture, relational intensity, technological knowledge acquisition and market knowledge acquisition. To test the adequacy of the approach, we also analyzed the Kaiser-Meyer-Olkin (KMO) measure. This measure relates ‘magnitudes of observed correlation coefficients to partial correlation coefficients’ (Adams et al., 2014: p. 223). The KMO measure showed a ‘middling’ sampling adequacy, KMO = 0.711 and is likely to be caused by the relatively small sample size. Bartlett’s test of sphericity determined that values in the correlation matrix significantly differs from an identity matrix, χ2 (231) = 889.337, p < 0.001 (Adams et al., 2014).
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Component Variable
1 Technological Knowledge Acquisition
2 Market Knowledge Acquisition
3 Relational Intensity
4 Relational Posture 1 – Trust
5 Relational Posture 2 – Commitment + MKA 4 6 Relational Posture 3 – Relational norms and
Information sharing
7 Relational Posture 4 – Conflict Resolution + RI 3 and RI 1
Table 1: summary rotated component matrix
Relational posture
To measure relational posture, we used the relational posture dimension by Kim and Choi (2015). This dimension includes commitment, trust, information sharing, relational norms, and conflict resolution. The following operational definitions are given by Kim and Choi (2015: p.69): commitment reflects ‘each party’s intention to maintain the relation even at the cost of short-term sacrifices’. Trust refers to ‘the level of expectancy held by the parties of the partner’s reliability and faithfulness’. Information sharing is expressed at ‘the extent which parties disclose information that may be beneficial for the other’. Relational norms are defined as ‘a party’s perception of whether its partner shares the understanding regarding mutually accepted behaviors’. Finally, conflict resolution is ‘the extent to which interfirm conflicts are managed amicably and developmentally’.
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within the scale seems logical, due to the different concepts relational posture contains. Therefore, the construct is not consistently measuring what it supposed to. Finally, the corrected item-total correlations showed that all items except item seven (0.203) had a good correlation with the total score of the scale (> 0.3) However, together the items represent the posture of the relationship as proposed by Kim and Choi.
Dimension Constructs Item
1 Trust RP 1 (.915), RP 3 (.827)
2 Commitment RP 2 (.700), RP 4 (.810)
3 Relational Norms RP 5 (.792)
Information Sharing RP 6 (.719)
4 Conflict Resolution RP 7 (.796)
Table 2: dimensions within relational posture with factor loading.
Relational intensity
For measuring the relational intensity between buyer and supplier, we are going to use the relational intensity dimension by Kim and Choi (2015). This dimension reflects the transactional strength and volume, reflecting economic interdependence between a buyer and supplier. Kim and Choi (2015: p.69) also give operational definitions for the constructs of relational intensity: interaction frequency is defined as the ‘frequency in various domains of buyer–supplier interactions’. Asset specificity related to ‘the extent of relation-specific investments made by each party’. Operational interdependency refers to ‘the economic value of the exchange tie (in volume and product) and lack of substitutes’. And finally, multiplexity entails ‘the extent to which two firms engage in joint activities above and beyond their regular exchange’. An example item from the questionnaire is ‘Our company interacts more often with B, as compared with other customer companies’.
17 Technological knowledge
In order to measure to which extent organizations acquire technological knowledge from its buying business partner, we use the definition from Bohn (1997) and Howell et al. (2003). We operationalize it by measuring to which extent a buyer receives knowledge about how to produce certain goods or services. An example of an item used is ‘Our company has gained a lot of knowledge from B about how to produce goods and/or services’. The combined questions related to the acquisition of technological knowledge have a Cronbach’s alpha of 0.89. The corrected item-total correlations revealed that all items have a good correlation with the total score of the scale (> 0.3). In addition, exploratory factor analysis revealed that each item (N=5) belonged to one single dimension and the degree to which items contribute to the dimension is significant. Eigenvalue of the combined items (3.46) explains 69.2% of the variance within the scale. Therefore, consistency and adequacy of the construct technological knowledge acquisition are ensured.
Market knowledge acquisition
Market knowledge is going to be measured by the definition of De Luca and Atuahene-Gima (2007) and (Li and Calantone, 1998). Market knowledge refers to the extent to which an organization acquires knowledge from its supplier about the market. Specifically, the extent to which it acquires knowledge about the behaviors and need of its customers and its competitors as well. ‘B provides us with significant knowledge about customers’ needs and behaviors’ is an example which we used in order to measure the degree of market knowledge acquisition. The combined questions related to market knowledge acquisition have a Cronbach’s alpha of 0.80, which is good. Finally, exploratory factor analysis shows that three items are measuring what they are supposed to measure, except item 4. Item 4 has a higher factor loading on RP 2, which is related to commitment within a buyer-supplier relationship. Furthermore, item 4 has a corrected item – total correlation of 0.4 and if item is deleted Cronbach’s alpha increases to 0.84. Considering these observations, we decided to delete item 4 for the construct of market knowledge acquisition.
18 RESULTS
In this section, we will present the results of our data analysis; first we will present the results of the cluster analysis, and then the results of the hierarchical regressions test. Using single items of relational posture and intensity as the criteria of cluster analysis, we formed four clusters. First by hierarchical cluster analysis, then we forced SMEs into the clusters by non-hierarchical cluster analysis. We compared each cluster to the degree of technological or market knowledge acquisition by a one-way ANOVA test.
Cluster analysis
The purpose of the cluster analysis is to divide the heterogeneous sample into homogeneous groups, based on active variables of relational posture and intensity. The active variables contained all the items used for measuring ‘relational posture’ and ‘relational intensity’. From the agglomeration schedule, we find that four clusters is the optimal solution. We noticed relatively drastic change in coefficients from four to three clusters. Therefore, the number of four clusters seems appropriate. In addition, the dendrogram (see appendix B) gives a visual representation from which we can derive that four clusters are formed.
Hierarchical cluster analysis showed four different clusters and Kim, and Choi (2015) came up with four different types of buyer-supplier relationships. Therefore, we will push the SMEs in four different clusters. By using a non-hierarchical clustering method (K-means), we were able to do so. The figure below shows the results of the cluster analysis. In order to decide which cluster belongs to which relationship type, we use the ‘quadrant classification’ method (Ross et al., 2009). Therefore we conclude that cluster 1 = ‘transient’ (rp. 3.73, ri. 2.16), 2 = ‘sticky’ (rp. 3.59, ri. 3.93), 3 = ‘deep’ (rp. 4.76, ri. 4.57) and 4 = ‘gracious’ (rp. 4.55, ri. 3.19). For
STICKY N = 18 DEEP N = 36 TRANSIENT N = 11 GRACIOUS N = 21
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example, cluster three scores superior on both dimensions and thus belongs to the ‘deep buyer-supplier relationship’.
Comparing means
In order to analyze if the degree of technological knowledge acquisition (TKA) differs between buyer-supplier relationships, we conducted a One-way ANOVA test of buyer-supplier relationships on TKA. The results of this One-way ANOVA test revealed a significant difference (F = 9.929, p = .000) between groups. Therefore, we can conclude that buyer-supplier relationships (as described by Kim and Choi) influence the degree of TKA.
To analyze if the level of market knowledge acquisition (MKA) differs per buyer-supplier relationship, we also conducted a One-way ANOVA test. In this case we exploited a test of buyer-supplier relationship on MKA. This One-way ANOVA was significant (F = 6.732, p = .000), which means that buyer-supplier relationships have influence on the degree of MKA as well. The table in Appendix C shows the output of the ANOVA analysis and the corresponding statistically significant cross-group difference.
Multiple comparisons
After we found significant cross-group differences (revealed by One-way ANOVA), we wanted to know which specific clusters differed. The multiple comparisons table provided us with the answers (Appendix D). First we noticed, for TKA, that there is a statistically significant difference between a ‘transient’ relationship and: (1) a ‘sticky’ relationship (p = 0.016), (2) a ‘deep’ relationship (p = 0.000) and (3) a ‘gracious’ relationship (p = 0.015). While a ‘transient’ relationship is characterized as adversarial and arms-length, the results suggest that improvement/increase in posture and/or intensity would lead to a significant difference in the level of TKA. Therefore, relational posture and intensity are compensatory to each other. For example, a high intensity within a buyer-supplier relationship would compensate an adversarial posture in order to acquire technological knowledge from a buyer (and vice versa).
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technological knowledge (p = 0.070). This would infer that a higher level of intensity between supplier and buyer results in a higher level of TKA, since intensity within a ‘deep’ relationship is higher compared to a ‘gracious’ relationship. Additionally, the mean difference between ‘sticky’ and ‘deep’ (p = 0.116) and ‘sticky’ and ‘gracious’ (p = 1.000) are not statistically significant. So, if we stick to a significance level of 0.05 there is no statistical evidence that the mean level of TKA differs between a ‘deep’, ‘sticky’ and ‘gracious’ relationship.
For MKA we notice the same pattern, however with different significance levels. In this case there is a statistically significant difference between a ‘transient’ relationship and: (1) a ‘sticky’ relationship (p = 0.002), (2) a ‘deep’ relationship (p = 0.000) and (3) a ‘gracious’ relationship (p = 0.002). However, there is no significant mean difference between the ‘deep’ and ‘sticky’ (p = 1.000), ‘deep’ and ‘gracious’ (p = 0.989) and ‘sticky’ and ‘gracious’ (p = 0.995) relationships. These results provide statistical evidence that there is no difference at the degree of MKA between these three types of relationships. Therefore, relational intensity and relational posture are compensating dimensions for MKA as well. For example, a cooperative posture will compensate an arms-length intensity in order to acquire market knowledge (and vice versa). Furthermore, a Tukey post hoc test for MKA showed that a ‘deep’ relationship has the highest mean score (3.29 ± 0.72), then ‘sticky’ (3.28 ± 0.69), followed by ‘gracious’ (3.23 ± 0.56) and ultimately ‘transient’ (2.23 ± 0.96).
21 DISCUSSION
The previous section showed the results of our research. This section will elaborate on our findings, discuss the theoretical and managerial implications. Limitations of the study will be addressed and final conclusions are being made.
Theoretical implications
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blindness and supplier opportunism. Studies have also recognized the risks of high levels of trust between partners, since it could lead to collective blindness (Lane et al., 2001; Yli-Renko, 2001). Kim and Choi, who describe ‘supplier rigidity’ as a possible negative result of a ‘deep’ buyer-supplier relationship, also recognize these dynamics.
We expected that more technological knowledge would be acquired within a ‘sticky’ relationship compared to a ‘gracious’ one, and more market knowledge be acquired within a ‘gracious’ relationship compared to a ‘sticky’ one. In terms of absolute mean scores, the results of technological knowledge acquisition were in line with our expectations. On the other hand, our absolute results regarding market knowledge acquisition were not. However, results did not show significant difference between a ‘deep’, ‘sticky’ and ‘gracious’ buyer-supplier relationship on mean scores of technological and market knowledge acquisition. Therefore, a closely-tied intensity could compensate an adversarial posture in order to acquire technological and/or market knowledge. Contrarily, a cooperative posture could compensate an arms-length intensity as well.
Within a ‘sticky’ relationship, partners consider each other as ‘necessary evil’ (Kim and Choi, 2015). Literature suggests, due to frequent interaction, operational interdependency, asset specificity and multiplexity, knowledge sharing between partners increases as well (Reagans and McEvily, 2003; Rowley et al., 2000). Our results corroborate the importance of relational tie strength in order to acquire knowledge as well. Furthermore, He et al. (2013) argue that high interdependence is a catalyst for knowledge transfer. However, within this kind of buyer-supplier relationship relational posture is more adversarial (or less cooperative) compared to a ‘deep’ and ‘gracious’ relational type. Therefore, our study challenge existing literature which argue the importance of trust within a buyer-supplier relationship in order to acquire knowledge (Battistella et al., 2016; van Wijk et al. 2008; Hansen 1999; Reagans and McEvily 2003; Lane et al., 2001; Inkpen 2000).
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had to evaluate their main business partner. Furthermore, plausibility of our arguments is unsure. For example, He et al. (2013) found that increasing availability of alternatives negatively affects knowledge acquisition. Therefore, in case of a ‘gracious’ buyer-supplier relationship this would lead to less knowledge acquisition. Nevertheless, this could explain the difference in absolute mean scores between a ‘sticky’ and ‘gracious’ relationship.
Since the results did not show significant differences, our research suggests that a cooperative posture could compensate an arms-length intensity in order to acquire knowledge. Due to an amicable relationship and low frequency of interaction, buyers tend to stay in contact with a supplier hoping for future benefits of the relationship (Axelrod, 1984). Our results suggest that only having a cooperative relationship in terms of social aspects would be appropriate to acquire technological or market knowledge. This will corroborate the importance of social elements (trust) within a relationship and on the other hand challenge tie strength in terms of intensity.
Between a ‘transient’ buyer-supplier relationship and ‘deep’, ‘sticky’ and ‘gracious’, differences in technological and market knowledge acquisition were significant and therefore our hypothesis is supported. A relationship between buyer and supplier which is adversarial and arms-length is not a great predictor of knowledge acquisition. Suppliers having a ‘transient’ relationship within our research, indicated that they do not acquire technological or market knowledge from their business partner. This is completely in line with existing theory on inter-organizational knowledge sharing/acquisition. Because foundations as interaction frequency and/or trust between partners are missing, which have been proved to be great antecedents of inter-organizational knowledge acquisition (Battistella et al., 2016; Van Wijk et al., 2008).
However, our findings imply that either increasing intensity or improving posture would allow a supplier to acquire technological and/or market knowledge from their business partner. Nevertheless, the unidimensionality and validity of relational posture and intensity appeared to be poor. We will elaborate on this in our limitations section.
Practical implications
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Suppliers could benefit from not having high relational intensity or posture with its buyer and still acquire knowledge, because each relational type has its advantage and disadvantage (Kim and Choi, 2015). For example, relational stability is a benefit from the ‘deep’ buyer-supplier relationship. However, the relationship could lead to supplier rigidity. The benefit of a ‘sticky’ buyer-supplier relationship is that the supplier could engage in opportunistic activities. Partners consider each other as ‘necessary evil’ and the stronger party is exploiting the adversarial posture in order to acquire larger shares of relational benefits (Kim and Choi, 2015). However, the relationship is also characterized by its lack of synergy. A ‘gracious’ buyer-supplier relationship differs not significantly from a ‘deep’ relationship with respect to technological knowledge acquisition (p = 0.07). However, it does not seem to be most favorable relationship to acquire technological knowledge. Due to the arms-length intensity, it is likely that alternative partners are also available which is detrimental for the acquisition of knowledge (He et al., 2013). Nevertheless, within a ‘gracious’ buyer-supplier relationship, the supplier could benefit from the lack of control of the buyer. While the buyer is hoping to acquire knowledge which stems from supplier innovation (Kim and Choi, 2015). Finally, in case suppliers would like to acquire knowledge from their partners, our results do not recommend a ‘transient’ relationship with their buyer.
However, due to the quality of our measurements (e.g. relational posture and intensity) it is hard to justify practical implications as well. These limitations will be discussed in the following section.
Limitations and future research
Our examination of the effect of buyer-supplier relationships on technological and market knowledge acquisition. The quality of our measurements of relational posture and intensity appeared to be too inadequate. It remained unclear what our construct ‘relational posture’ was actually measuring, since the construct consists four different dimensions. Reliability of relational intensity improved after removing one item and still every aspect of relational intensity was being captured. However, validity of the construct remained poor and affected implications negatively. When items are reflecting different dimensions of a concept, it makes little sense to combine the items into one construct. However, we aggregated them in order to capture the relational posture and intensity dimension completely. This will have major impact on the appropriateness of theoretical and practical implications.
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we conducted a combined survey research with other researchers, we had to take the length of the questionnaire in mind. Therefore, we were not able to measure both dimensions thoroughly. Moreover, the suppliers only assessed buyer-supplier relationships and perspectives of buyers are missing within our research. Whipple et al. (2010) attempted to inspect buyers' perspectives of performance and satisfaction in relational exchanges. The suppliers assessed their main buying business partners, which could have influenced our hypothesis about market knowledge acquisition. Relationships with these partners are likely to be persistent for a long(er) time, and therefore to be intense and cooperative as well. Long-term ties between buyer and supplier are generally equated with exquisite information sharing (Gulati, 1998).
Nonetheless, poor validity of the constructs of posture and intensity means that capturing buyer-supplier relationships in two dimension remains complex. Furthermore, it is troublesome to capture all aspects of buyer-supplier relationship within one framework. However, our exploration of the effect of different relational types on the acquisition technological and market knowledge would offer interesting possibilities for future research. Future research could improve validity of both dimensions of the expanded buyer-supplier relationship typology. Kim and Choi also acknowledge that the expanded typology should be polished both theoretically and empirically. Furthermore, the effect of buyer-supplier relationships could be examined in more depth. By assessing suppliers and their relationships with multiple buyers, a broader view of different relational types will be reflected.
Conclusion
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32 APPENDICES
APPENDIX A Rotated Component Matrixa
Component
1 2 3 4 5 6 7
TKA 2. B provides our company with very useful know-how on process and product development.
,808 ,143 ,099 -,133
,119 ,145 -,077 TKA 5. Our competences on production improves a lot with the
information our company receives from B.
,802 ,124 ,201 ,044 -,046
,215 ,192
TKA 3. With the information of B, our company is getting much better at the execution of product and process development.
,794 ,051 ,137 ,199 ,094 ,125 -,141 TKA 1. Our company has gained a lot of knowledge from B about
how to produce goods and/or services.
,764 ,183 ,065 ,019 ,261 -,024
-,047 TKA 4. Our company has gained a lot of knowledge on product
improvement from this customer company
,730 ,250 ,247 ,087 ,070 ,155 ,050
MKA 2. From B, our company gains a lot of information about competitor activities.
,195 ,830 ,043 ,019 ,280 ,029 ,101
MKA 3. B provides us with significant knowledge about customers’ needs and behaviours.
,294 ,796 ,083 ,130 -,043
,040 -,047 MKA 1. Our company acquired a lot of information from B about
the market.
,166 ,794 ,268 ,017 ,106 ,175 ,063
RI 5. Our company has extensively invested in production equipment to do work for B.
,091 ,106 ,831 ,061 ,076 ,198 ,171
RI 2. Our company interacts more often with the B, as compared with other customer companies.
,215 ,114 ,695 ,157 ,024 -,125
,147
RI 6. Our company has been engaged extensively in joint activities with B.
,368 ,204 ,668 ,146 ,184 -,185
-,155 RI 4. Our firm has committed significant time and resources to
train and develop our personnel for B.
,188 ,094 ,550 ,119 ,363 ,472 -,157 RP 1. Our company trusts B to keep its promises. ,021 ,035 ,037 ,915 ,058 ,191 ,035
RP 3. B is a trustworthy company. ,068 ,072 ,170 ,827 ,197 ,193
-,124 RP 4. Both our company and B often go out of our way to help
each other.
,131 ,009 ,117 ,164 ,810 ,271 -,148 RP 2. Our company on occasion makes sacrifices to help B. ,247
-,030
,102 ,192 ,700 ,042 -,030 MKA 4. The knowledge our company acquires from B is mainly
about competitors’ behaviours.
,062 ,420 ,079 ,260 ,552 -,092
-,282 RP 5. Our firm receives a fair compensation from B for what we
put into our relationship.
33
RP 6. B regularly shares its proprietary information with our company.
,228 ,203 ,102 ,212 ,351 ,719 -,088 RP 7. When there are disagreements, my firm and B tend to blame
each other. ,052 ,008 -,185 ,278 ,115 -,085 ,796
RI 3. It will be easy for B to substitute another supplier for the component(s) our company provides.
,126 -,293
,134 -,082
,021 ,266 ,723
RI 1. Any change in our firm’s production will have a significant impact on B’s day-to-day operations.
,281 ,184 ,310 ,315 ,095 ,088 -,532
34 APPENDIX B
Cluster analysis (hierarchical and non-hierarchical)
Hierarchical
Agglomeration Schedule
Stage
Cluster Combined
Coefficients
Stage Cluster First Appears
Next Stage
Cluster 1 Cluster 2 Cluster 1 Cluster 2
36
Non-hierarchical Final Cluster Centers
Cluster
1 2 3 4
Relational Posture 3,73 3,59 4,76 4,55
Relational Intensity 2,16 3,93 4,57 3,19
Number of Cases in each Cluster
Cluster 1 = Transient N= 11,000
2 = Sticky N= 18,000
3 = Deep N= 36,000
4 = Gracious N= 21,000
37 APPENDIX C
One-way ANOVA Test
ANOVA
Sum of Squares df Mean Square F Sig.
TKA Between Groups 14,985 3 4,995 9,929 ,000
Within Groups 39,744 79 ,503
Total 54,730 82
MKA Between Groups 10,372 3 3,457 6,732 ,000
Within Groups 39,546 77 ,514
38 APPENDIX D Multiple comparisons Tukey HSD Dependent Variable
(I) Cluster Number of Case (J) Cluster Number of Case Mean Difference (I-J) Std. Error Sig. 95% Confidence Interval Lower Bound Upper Bound
TKA Transient Sticky -,83636* ,27446 ,016 -1,5567 -,1160
Deep -1,31065* ,24517 ,000 -1,9541 -,6672 Gracious -,81636* ,26625 ,015 -1,5152 -,1176 Sticky Transient ,83636* ,27446 ,016 ,1160 1,5567 Deep -,47429 ,20968 ,116 -1,0246 ,0760 Gracious ,02000 ,23398 1,000 -,5941 ,6341 Deep Transient 1,31065* ,24517 ,000 ,6672 1,9541 Sticky ,47429 ,20968 ,116 -,0760 1,0246 Gracious ,49429 ,19882 ,070 -,0275 1,0161 Gracious Transient ,81636* ,26625 ,015 ,1176 1,5152 Sticky -,02000 ,23398 1,000 -,6341 ,5941 Deep -,49429 ,19882 ,070 -1,0161 ,0275
MKA Transient Sticky -1,05398* ,28069 ,002 -1,7911 -,3169
Deep -1,06194* ,24859 ,000 -1,7147 -,4091 Gracious -,99773* ,26901 ,002 -1,7042 -,2913 Sticky Transient 1,05398* ,28069 ,002 ,3169 1,7911 Deep -,00797 ,21727 1,000 -,5785 ,5626 Gracious ,05625 ,24037 ,995 -,5750 ,6875 Deep Transient 1,06194* ,24859 ,000 ,4091 1,7147 Sticky ,00797 ,21727 1,000 -,5626 ,5785 Gracious ,06422 ,20195 ,989 -,4661 ,5945 Gracious Transient ,99773* ,26901 ,002 ,2913 1,7042 Sticky -,05625 ,24037 ,995 -,6875 ,5750 Deep -,06422 ,20195 ,989 -,5945 ,4661
39
Descriptives
N Mean Std. Deviation Std. Error
95% Confidence Interval for Mean
Minimum Maximum Lower Bound Upper Bound
TKA Transient 11 2,3636 ,94157 ,28389 1,7311 2,9962 1,00 4,20 Sticky 17 3,2000 ,60828 ,14753 2,8873 3,5127 2,00 4,00 Deep 35 3,6743 ,68098 ,11511 3,4404 3,9082 2,00 5,00 Gracious 20 3,1800 ,69555 ,15553 2,8545 3,5055 1,40 4,20 Total 83 3,2843 ,81697 ,08967 3,1059 3,4627 1,00 5,00 MKA Transient 11 2,2273 ,95822 ,28891 1,5835 2,8710 1,00 4,00 Sticky 16 3,2813 ,69447 ,17362 2,9112 3,6513 2,25 5,00 Deep 34 3,2892 ,72336 ,12405 3,0368 3,5416 1,25 4,50 Gracious 20 3,2250 ,55548 ,12421 2,9650 3,4850 2,00 4,00 Total 81 3,1276 ,78992 ,08777 2,9529 3,3022 1,00 5,00 TKA Tukey HSDa,b
Cluster Number of Case N
Subset for alpha = 0.05
1 2 Transient 11 2,3636 Gracious 20 3,1800 Sticky 17 3,2000 Deep 35 3,6743 Sig. 1,000 ,174 MKA Tukey HSDa,b
Cluster Number of Case N
Subset for alpha = 0.05
1 2 Transient 11 2,2273 Gracious 20 3,2250 Sticky 16 3,2813 Deep 34 3,2892 Sig. 1,000 ,994
a. Uses Harmonic Mean Sample Size = 17,520.
40 Questionnaire
Relational Posture
1. Our company trusts B to keep its promises.
2. Our company on occasion makes sacrifices to help B. 3. B is a trustworthy company.
4. Both our company and B often go out of our way to help each other.
5. Our firm receives a fair compensation from B for what we put into our relationship. 6. B regularly shares its proprietary information with our company.
7. When there are disagreements, my firm and B tend to blame each other. (D)
(D) = reverse-coded.
Relational Intensity
1. Any change in our firm’s production will have a significant impact on B’s day-to-day operations. 2. Our company interacts more often with the B, as compared with other customer companies. 3. It will be easy for B to substitute another supplier for the component(s) our company provides. (D) 4. Our firm has committed significant time and resources to train and develop our
personnel for B.
5. Our company has extensively invested in production equipment to do work for B. 6. Our company has been engaged extensively in joint activities with B.
(D) = reverse-coded.
Technological Knowledge Acquisition
1. Our company has gained a lot of knowledge from B about how to produce goods and/or services. 2. B provides our company with very useful know-how on process and product development.
3. With the information of B, our company is getting much better at the execution of product and process development.
4. Our company has gained a lot of knowledge on product improvement from this customer company 5. Our competences on production improves a lot with the information our company receives from B.
Market Knowledge acquisition
