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The role of interfirm ties on the relationship between
component ambiguity and knowledge acquisition: The
supplier’s perspective
Master Thesis
MSc Supply Chain Management University of Groningen
Faculty of Economics and Business 26-06-2017
Laurens Rijnja S3068552
l.rijnja@student.rug.nl
Supervisor: C. Xiao
Co-assessors: Prof. Dr. D.P. van Donk Prof. Dr. J.T. van der Vaart
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Abstract
Purpose – This paper aims to test the conceptualization of component ambiguity as proposed
by Law (2014). Using this conceptualization, the effect of component ambiguity on the acquisition of knowledge will be determined. Furthermore, the moderating role of relational ties on this relationship will be investigated. The main research question of this paper is: What is the effect of relational ties on the relationship between component ambiguity and knowledge acquisition?
Design/methodology/approach – Using a survey, 126 responses from firms in knowledge
intensive industries have been collected. The firms are located in the Netherlands, China or Greece.
Findings – It is shown, contrary to the hypothesis made, that component ambiguity is positively
and significantly related to knowledge acquisition. Furthermore, insufficient evidence exists to suggest a moderating role of relational ties. However, relational ties are shown to be a full mediator in the relationship between component ambiguity and the acquisition of knowledge.
Research limitations/implications – Future research may elaborate on the hypothesis made by
Law (2014) with respect to the effect of component ambiguity on the assimilation of knowledge in addition to its acquisition. Additional research with respect to the antecedents of relational ties is desirable to determine whether relational ties may be present as a consequence of component ambiguity of knowledge held by the buyer. Further research may also determine which factors account for the different effect of organization location on knowledge acquisition for China as compared to the Netherlands.
Practical implications – This paper shows how relational ties may be used as a mechanism in
the acquisition of knowledge characterized by component ambiguity. The ways in which an increased interaction frequency, openness, and other relational characteristics improve the transferability of knowledge that is characterized by component ambiguity is shown, so that firms can acquire this knowledge in order to obtain or maintain their competitive advantage.
Originality/value – This paper expands the literature with respect to knowledge acquisition,
knowledge ambiguity, and component ambiguity in particular. Furthermore it is shown how relational ties interact with the relationship between component ambiguity and knowledge acquisition.
Keywords – Knowledge acquisition; Relational ties; Knowledge ambiguity; Component
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Table of contents
Abstract ... 1
Table of contents ... 3
Introduction ... 3
Theoretical framework & Hypotheses development ... 5
Component ambiguity & Knowledge acquisition ... 5
Relational ties ... 7
Methodology ... 9
Questionnaire development ... 9
Sample & Data gathering ... 10
Reliability & Validity ... 12
Results ... 15
Conclusion ... 17
Discussion ... 18
Limitations & Directions for Future Research ... 20
Appendices ... 26
Appendix A: Survey Questions ... 26
Appendix B: Validity & Reliability ... 27
Appendix C: Hypothesis Testing... 31
Introduction
The sharing, acquisition and assimilation of knowledge is positively related to organizational performance and effectiveness (Cheung, Myers, & Mentzer, 2011; Mesquita, Anand, & Brush, 2008; Quigley, Tesluk, Locke, Bartol, & Smith, 2007; Zhou, Zhang, Sheng, Xie, & Bao, 2014). However, Simonin (1999) noted that knowledge is more immobile than believed. This immobility is argued to be indicative of the underlying construct “knowledge ambiguity”, which refers to “… the inherent and irreducible uncertainty as to what the underlying
knowledge components are and how they interact” (Van Wijk, Jansen, & Lyles, 2008, p. 833).
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required to obtain knowledge that is by definition characterized by high component ambiguity, it fails to recognize the problems which tacitness and complexity of knowledge pose on the acquisition of this knowledge. Furthermore, the way in which relational ties mitigate knowledge acquisition problems arising from component ambiguity of this knowledge, is unknown. As global supply chains become more demand oriented and uncertainty increases as we move away from the final point of sale (Cheung et al., 2011), acquiring knowledge with respect to demand fluctuations and life cycle compression becomes of increased importance for the supplier. In addition, Squire, Cousins, & Brown (2009) note that knowledge transfer in vertical modes of governance deserves a more detailed consideration. Since the buyer’s perspective has been investigated in further depth in more recent literature (Revilla & Knoppen, 2015; Zhou et al., 2014), this papers aims to investigate the knowledge acquisition by the supplier form its major customer (its largest buyer).
(Simonin, 1999) hypothesized three knowledge-specific variables to be determinants of knowledge ambiguity. Both tacitness and complexity were found to be a significant predictor, whereas no significant relationship was observed for the specificity of knowledge. By differentiating between causal- and component ambiguity, (Law, 2014) hypothesized that complexity and tacitness are predictors of component ambiguity. Specificity on the other hand, is argued to result in causal ambiguity. This differentiation may explain the previously found insignificant effect of specificity on the ambiguity of knowledge, but primarily highlights the identification of types of difficulties which arise from the process of knowledge acquisition versus its application. This paper aims to identify the factors influencing the acquisition of knowledge, which means the component ambiguity of knowledge will be taken into account.
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knowledge. Second the moderating role of relational ties on the relationship between component ambiguity and knowledge acquisition will be investigated. The direct effect investigated shows how component ambiguous knowledge affects its acquisition. Furthermore, it will become apparent if and how relational ties may mitigate knowledge acquisition problems caused by component ambiguity of knowledge.
In order to answer the question “What is the role of relational ties on the relationship between component ambiguity and knowledge acquisition?”, a survey has been constructed. Using a survey enables the acquisition of large amounts of data and reduces geographical dependence. After the survey has been reviewed by professionals, it is distributed to suppliers within knowledge-intensive industries (automotive industry, semi-conductors, healthcare, etc.) in the Netherlands, Greece and China. The theoretical background will provide more insight in concepts used, as well as the development of hypotheses. The operationalization of the concepts used, as well as the development of the questionnaire will be discussed in the methodology section. After the results have been analyzed, conclusions can be drawn, based on which limitations and directions for further research will be discussed.
Theoretical framework & Hypotheses development
Component ambiguity & Knowledge acquisition
Knowledge can be either tacit or explicit. The former refers to the abilities, developed skills, experience, undocumented processes, and ‘‘gut-feelings’’ that are highly personal and difficult to reduce to writing (Holste & Fields, 2010). The latter is easily articulated or reduced to writing, is often impersonal and formal in nature (Holste & Fields, 2010). Knowledge that is highly tacit in nature is more context dependent, which makes it more difficult to understand (Lee, Chang, Liu, & Yang, 2007). Furthermore, knowledge that is highly tacit appears vague to knowledge seekers and reduces the articulation of knowledge components, as well as the absorption of this knowledge by non-experts (Law, 2014). This argument is in line with the findings of Zander & Kogut (1995), who found that knowledge that is more explicit in nature is related to an increased speed of transfer. Although tacit knowledge can be argued to be more valuable than explicit knowledge (Dhanaraj, Lyles, & Steensma, 2014), it is apparent that its context dependency, as well as its limitations of articulation and assimilation may reduce the mobility of knowledge.
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complex. Simonin (1999, p. 600) define complexity as “the number of interdependent
technologies, routines, individuals and resources linked to a particular asset of knowledge”.
As complexity increases, it becomes more difficult to illustrate or explicate knowledge (Law, 2014). Furthermore, it is complexity that reduces the understanding of the totality of an asset, which reduces its transferability (Simonin, 1999). Thus, component ambiguity, characterized by varying levels of tacitness as well as complexity, are argued to obscure the content as well as the structure of knowledge. Furthermore, it reduces the transferability of knowledge as the articulation of this knowledge become more difficult (Law, 2014). The conceptualization of component ambiguity and its antecedents, as well as its hypothesized consequences, is presented in Figure 1 below.
Figure 1: Conceptualization component ambiguity (Adapted from Law, 2014)
The traditional resource-based view (RBV) poses that a firm's’ competitive advantage can be attributed to the possession of valuable, rare, inimitable and non-substitutable resources (Barney, 1991; Mesquita et al., 2008). These resources are further defined by Barney (1991) to include all types of assets, organizational processes, knowledge, capabilities, and other sources of competitive advantage which a firm possesses (Lavie, 2006). From this view, the need for acquiring resources that are by definition more ambiguous in order to increase the firm’s competitive advantage, becomes apparent.
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Knowledge transfer has been used to describe “The process through which organizational
actors – teams, units, or organizations – exchange, receive and are influenced by the experience and knowledge of others” (Van Wijk et al., 2008, p. 832). Although this definition would suit
a dyadic investigation of knowledge movement, a different term needs to be used when investigating the flow of knowledge towards one actor. For the purpose of this research,
knowledge acquisition will be used to describe the extent to which an organization acquires
information resources from its exchange partner (Tsang, 2002; Zhou et al., 2014).
As the component ambiguity of knowledge held by a firm’s supply chain partner increases, the focal firm may choose to look to different sources of knowledge in order to establish or maintain a competitive advantage. A firm’s own industrial R&D, its customers , university research, and research institutions are some of the sources widely used as a source of knowledge (Leiponen & Helfat, 2010). Since the component ambiguity of knowledge makes it more difficult to articulate and obscures the content and structure of knowledge, and a firm can turn to other sources of knowledge, the following hypothesis can be constructed:
H1: Component ambiguity is negatively related to the acquisition of knowledge by the supplier. Relational ties
The relational view considers not the firm, but rather interfirm sources in order to explain competitive advantage (Dyer & Singh, 1998; Lavie, 2006; Mesquita et al., 2008). Relational ties, reflecting the strength of a buyer-supplier relationship with a variation in the degree of interaction, trust, mutual commitment and reciprocity (Poppo & Zenger, 2002) have shown to be positively related to knowledge transfer (Reagans & McEvily, 2003; Van Wijk et al., 2008). However, high levels of tie strength are argued to impede the efficiency of knowledge sharing (Lechner, Frankenberger, & Floyd, 2010). Notice that in the relationships above, the terms knowledge sharing and knowledge transfer are used to describe the movement of knowledge. However, the logic used to describe both the negative and positive effects of relational ties on the movement of knowledge can arguably be generalized for the acquisition of knowledge by a supplier.
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since there is no need for shared contexts. However, as the component ambiguity of knowledge held increases, so does the difficulty of transferring this knowledge (Law, 2014). The context in which knowledge with high component ambiguity is embedded may not be shared by both parties of the knowledge transaction in case the level of relational ties is low. Furthermore, low levels of relational ties have shown to be less suited in order to transfer knowledge of a more tacit nature (Zhou et al., 2014).
Alternatively, if the buyer-supplier relationship is characterized by strong relational ties, shared goals foster mutual understanding, as well as the exchange of knowledge by creating higher levels of interaction (Li, Poppo, & Zhou, 2010; Tsai & Ghoshal, 1998). Furthermore, trust between exchange partners causes openness and receptiveness to the acquisition of knowledge (Dyer & Hatch, 2006; Li et al., 2010). It is more likely that firms that have close relational ties share complementarities with its partner’s external resources, or create information sharing routines which facilitate knowledge transfer (Dyer & Singh, 1998; Lavie, 2006; Zhou et al., 2014). This increased interaction, trust and complementarities may resolve the articulation problems which component ambiguity causes. Furthermore, the explication problems resulting from component ambiguity may be overcome by mutual commitment present in strong buyer-supplier relationships. The collaborative know-how resulting from past collaborative experience, is argued to affect the ability of firms to understand and adopt the right procedure for the gathering, interpretation, and diffusion of knowledge (Simonin, 1999). If the knowledge held by the supply chain partner is increasingly tacit and complex, the difficulties of acquiring this knowledge will be reduced in case routines, increased interaction, and collaborative know-how are present to facilitate this transfer. Then, the following hypothesis can be developed:
H2: Relational ties negatively moderate the relationship between component ambiguity and knowledge acquisition.
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Methodology
Questionnaire development
A seven-point Likert-scale is used for all items used, except for continuous nature of the included control variables. The scale used to measure the strength of relational ties is the one by Zhou et al. (2014). The definition of relational ties given in the Introduction is from Poppo & Zenger (2002). The items created by Zhou et al. (2014) are also based on this definition, which indicates they have been created taking the different levels of interaction, closeness, and reciprocity into account, whereas Hansen (1999) used only distance and frequency of interaction to measure the strength of relational ties. Questions with respect to interaction frequency on both social and professional level indicate that multiplexity, as defined by Kim & Choi (2015) is taken into consideration. Furthermore, reciprocity present in these relationships is measured by indicating a degree to which favors are offered between both supply chain partners. Lastly, respondents were asked to indicate the overall shape of the relationship with their largest customer.
Although the acquisition of knowledge has been investigated widely in previous literature, a difference is often made between the acquiring explicit vs. tacit knowledge (Li et al., 2010; Zhou et al., 2014). This distinction is less apparent in the analysis made by Yli-Renko et al. (2001). Therefore, the same items will be used, as they are considered to represent the acquisition of the totality of knowledge. Respondents were asked to indicate a degree to which market knowledge, as well as customer needs and trends were obtained from their largest customer. To measure tacit knowledge acquisition, the technical know-how acquired from their customer is identified. Furthermore, the respondents were asked to indicate the extent to which
most of the technical know-how related to supplying their product/service is acquired from this
customer.
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the product of individuals, but also capabilities.
Existing literature indicates firm age and firm size to be related to knowledge acquisition (Li et al., 2010; Zhou et al., 2008). They have been measured by an indication of the firm’s age in years, and he number of employees respectively. Although the length of the relationship has been argued to affect the knowledge acquired in the buyer-supplier relationship, it is not included in the regression analysis, since the strength of the relational ties present can be argued to encapsulate this duration. The location of the organization has also been added as a control variable, as the degree of knowledge shared in general may be different across the countries selected. China for example, has a long history of using guanxi ties, which are argued to increase a firm’s competitive advantage, and are positively related to knowledge sharing (Chen, Chen, & Huang, 2013)
Sample & Data gathering
The data needed in order to perform a statistical analysis was collected by means of an online questionnaire. Using an online survey enables access to populations that are difficult to reach through other channels (Wright, 2006). Furthermore, large amounts of data can be acquired quickly, and geographical dependence is reduced. A joint-survey created by eight researchers has first been evaluated by professionals and finally distributed among 461 suppliers in China, Greece and the Netherlands. By collecting data from three different countries, different political- as well as social- and environmental contexts are investigated.. The researchers involved in the creation and distribution of the survey are from the selected countries, which will improve the ease of translation, as well as the distribution of said survey. To reduce the possibility of common method bias, respondents have been asked to fill out the survey with one of their colleagues. The respondents were asked to identify one of their five top buyers, about which questions with respect to the relational ties, knowledge acquisition and component ambiguity were asked. The complete transcript of the survey questions used can be found in the appendix.
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originated from private domestic organizations. The frequency table with respect to organization type is shown in Table 2.
Table 1: Frequencies organization location
Table 2: Frequencies organization type
The descriptive statistics of organization age, relationship length and organization size as presented in Table 3, show that the respondents work at firms that employ on average 4442 people, and that these firms are on average 34 years in existence, out of which the relationship with their mean buyer lasts 16 years. However, since the variance of these variables is very high, it is likely that these means can be attributed to outliers, which is corrected for using a logarithmic transformation (see Reliability & Validity).
Table 3: Descriptive statistics control variables
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can be seen from Table 4, which shows the two-tailed Pearson correlations, all created variables correlate significantly. The logarithmic transformation of the control variables show to be significantly correlated to either knowledge acquisition, or at least one of the other variables used in the analysis. Unlike organization type, the location of the organization showed to be significantly correlated to the constructed variables. Therefore, dummy variables were made in order to include it in the regression model. The constructed variables, as well as the logarithms of the control variables that are shown in Table 5 are in the regression analysis.
Table 4: Correlations
Variable 1 2 3 4 5 6
Knowledge acquisition -
Relational ties 0.52** -
Component ambiguity 0.38** 0.37** -
(Log) Organization size 0.13 0.31** 0.06 -
(Log) Organization age -0.23* 0.05 -0.23** 0.44** -
Organization Location 0.34** 0.37** 0.37** 0.52** -0.10 -
** Correlation is significant at the 0.01 level (2-tailed) * Correlation is significant at the 0.05 level (2-tailed)
Reliability & Validity
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Since most researchers recognize the problems that common method bias poses on the accuracy of the measurements made (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003), different steps have been taken to prevent and test for potential common method bias. First, respondents were instructed to complete the survey with one of their colleagues. In case one of the respondents had insufficient information to answer the question accurately, the colleague can answer the question, instead of the respondent answering through common method. Second, a common latent factor has been used to capture the common variance among the variables used. The chi-square difference test for both the constrained- and the unconstrained model shows no significant difference, which means common method bias is likely not to be a serious issue (Chi-square difference = 4.8, df difference = 8, p = 0.779).
Reliability analysis showed that dropping one of the items belonging to knowledge acquisition would increase the Cronbach’s alpha. After dropping this item, removing one of the three remaining items would again lead to a higher Cronbach’s alpha. However, since the Cronbach’s alpha is already above the widely accepted value (0.777 > 0.7), and removing one more item would lead to a two-item scale, which is recognized to be problematic (Eisinga, Grotenhuis, & Pelzer, 2013), the items shown in Table 5 are retained. The deletion of the item belonging to knowledge acquisition does not lead to a misrepresentation of the construct, since it is an extension of the question regarding the market knowledge acquired from the buyer. The only difference is that the extent to which the majority of explicit knowledge is acquired from this buyer is not taken into account. Similarly, the item dropped for the component ambiguity of the knowledge held by the buyer serves as an extension of one of the retained items, it can therefore be argued that the remaining factor still reflects the original construct.
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Table 5: Constructs, items & validity
Construct, source & validity Description of items (retained) Factor loadings
1 2 3
Relational ties (Zhou et al., 2014) α = 0.775
CR = 0.776 AVE = 0.467
Our company has a close social relationship with this buyer There is a sense of “being in the same boat” between our company and this buyer
Our company and this buyer often visit each other
Our company and this buyer often have activities that are purely social, such as after-work get-togethers
0.63
0.64
0.66
0.79
Knowledge acquisition (Yli-Renko et al., 2001)
α = 0.777 CR = 0.792 AVE = 0.570
Our company obtains a substantial amount of market knowledge from this buyer
Our company obtains substantial amounts of technical know-how from this buyer
Our company learns specific skills and competencies from this buyer
0.55
0.76
0.91
Component ambiguity (Lee et al., 2007) α = 0.716
CR = 0.742 AVE = 0.503
Our technology and process know-how is difficult to codify (in blueprints, instructions, formulas, etc.)
Our technology and process know-how is more tacit than explicit.
Our technology and process know-how is the product of many interdependent techniques, routines, individuals, and resources.
0.71
0.88
0.48
Eigenvalues 3.425 1.645 1.453
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Results
To test what the effect of component ambiguity is on the acquisition of knowledge by the supplier, regression analysis can be performed. The variables were checked for normality, using the normal P-P plot. Since there appear to be no significant deviations from the line of best fit, normality of the variables is indicated. In addition, the scatter plot presenting the standardized residual against the standardized predicted value suggests linearity among variables, since none of the included variables show standardized residual larger than 3.3 or smaller than -3.3. Using Mahalanobis distances, potential outliers can be identified. Although some cases show distances greater than the cutoff-value resulting from the chi-squared probability levels, given degrees of freedom equal to the number of independent variables included in the model, none of these cases show Cook’s distance values larger than 1, indicating that these outliers do not significantly influence the ability to predict the outcome of the regression. From the linear regression results, it can be concluded that no multicollinearity was observed, since the variance inflation factors (VIF’s) range from 1.223 to 2.939 (1 < VIF < 3).
Table 6: Regression direct effect
To test the direct effect, a regression analysis was performed with knowledge acquisition as the dependent variable, component ambiguity as independent variable, and several control variables. Table 6 shows the results of the regression, from which we can
Knowledge acquisition Steps Variables 1 2 3 Direct model 1 (OL) China .41* .36* .20 (OL) Greece .05 .12 .06 (Log) OS .022 .04 .00 (Log) OA -.16 -.11 -.18 2 Component Ambiguity .29* .16 3 Relational Ties .39* R2 .20 .27 .38 Adj R2 .17 .24 .35 F 7.47* 8.69* 11.90* ∆R2 .20 .07 .11 ∆F 7.47* 11.09* 20.73*
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conclude that component ambiguity has a significant positive effect on knowledge acquisition (β = 0.29, p < 0.01). In addition, organizational location has a significant effect for China as compared to the Netherlands on the acquisition of knowledge (β = 0.36, p < 0.01). Given the results from the regression, H1 is not accepted.
The investigation of the moderating effect of relational ties on the relationship examined above, requires the addition of the interaction effect, which is a multiplication of the standardized values of both relational ties and component ambiguity. As shown in Table 7, there is insufficient evidence to suggest a moderating effect (p > 0.05). Therefore, we cannot accept
H2. However, as Table 6 shows, the relationship between component ambiguity and knowledge
acquisition becomes insignificant after controlling for relational ties. Therefore, to see whether there is a different mechanism through which relational ties affect this relationship, a mediating effect has been investigated.
Table 7: Regression moderating effect
Knowledge acquisition
Steps Variables 1 2 3 4
Relational ties as moderator on the relationship between component ambiguity and knowledge acquisition 1 (OL) China .41* .36* .20 .16 (OL) Greece .05 .12 .06 .04 (Log) OS .02 .04 .00 .03 (Log) OA -.16 -.11 -.18 -.19 2 Z-Component Ambiguity .29* .16 .15 3 Z-Relational Ties .39* .42* 4 Relational Ties x Component Ambiguity .13 R2 .20 .27 .38 .40 Adj R2 .17 .24 .35 .36 F 7.47* 8.69* 11.90* 10.77* ∆R2 .20 .07 .11 .02 ∆F 7.47* 11.09* 20.73* 2.86
Standardized regression coefficients are reported. *p < 0.05
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to determine whether relational ties may mediate the relationship between component ambiguity and knowledge acquisition, the effect between component ambiguity and relational ties, as well as the relationship between relational ties and knowledge acquisition have to be determined. The standardized coefficients of this analysis, as well as the direct effect controlling for relational ties are shown in Figure 3. The results suggest full mediation (Sobel test statistic = 3.14, p < 0.01).
Figure 3: Mediation coefficients, direct effect controlling for relational ties is between brackets. *p < 0.05
Conclusion
The aim of this paper is to test the relationship between component ambiguity and knowledge acquisition, and to examine the moderating role of relational ties on this relationship. Law (2014) hypothesized a negative relationship between component ambiguity and the acquisition of knowledge. However, the results indicate that there is no direct negative effect of component ambiguity on knowledge acquisition. More specifically, a positive relationship is indicated. Although these findings are in line with the RBV, which highlights the motivation for the acquisition of knowledge characterized by component ambiguity, the problems arising from an increase in knowledge tacitness and complexity are not reflected by the obtained results. Furthermore, although a negative moderating effect of relational ties on the relationship between component ambiguity and knowledge acquisition is hypothesized, insufficient evidence exists to confirm such an effect. However, relational ties show to be a full mediator in this relationship. It can be concluded that relational ties may be used as a mechanism of coping with component ambiguity in order to acquire knowledge characterized by component ambiguity. The positive relationship found under H1 becomes insignificant after the addition of relational ties as a mediator, which means that the positive relationship found can be explained by the mediation of relational ties. Finally, the following conclusion can be drawn: component ambiguity has a positive effect on knowledge acquisition, via relational ties.
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between component ambiguity and knowledge acquisition as proposed by Law, (2014) has been investigated. The positive effect found implies that as the buyer’s knowledge is characterized by high component ambiguity, suppliers do not necessarily turn to other sources in order to acquire this knowledge. This means that a different mechanism exists through which this knowledge is acquired. Furthermore, the role of relational ties as a mediator in this relationship explains the positive relationship found, thereby adding to relational view literature. From a managerial perspective, this paper shows how relational ties may be used as an instrument in the acquisition of complex and tacit knowledge. The ways in which an increased interaction frequency, openness, and other relational characteristics may improve the transferability of knowledge that is characterized by component ambiguity is shown, so that firms can acquire this knowledge in order to obtain or maintain their competitive advantage.
Discussion
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trust present in the buyer-supplier relationship has shown to be especially important for tacit knowledge exchange (Levin & Cross, 2004). This means that problems arising from knowledge tacitness and may be overcome through different mechanisms than the moderation by relational ties.
Since no moderating role has been observed, but instead a mediating effect is indicated, the role of relational ties can be further defined. Instead of changing the strength of the relationship between component ambiguity and knowledge acquisition, evidence suggests relational ties may be in place as a mechanism to acquire knowledge characterized by component ambiguity. To acquire knowledge characterized by component ambiguity as a means of increasing its competitive advantage, firms may deploy relational ties in order to reduce the articulation and transparency problems this knowledge brings forth. By interacting more frequently, the totality of an asset may be better understood, thus increasing the transferability of that asset. Furthermore, as the trust present in relationships with strong relational ties causes openness and receptiveness (Dyer & Hatch, 2006; Li et al., 2010), knowledge that is characterized by component ambiguity is more likely to be acquired. To what degree these ties are a direct consequence of component ambiguity cannot be determined from the data available.
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Limitations & Directions for Future Research
Although data gathering in China is argued not to provide limitations for generalizability (Zhang, van Donk, & van der Vaart, 2016), the majority of the sample obtained originated in China, which may indicate on overrepresentation of Chinese firms. In addition, the industries in which the survey have been distributed were selected based on the intensity of knowledge in that industry. Since mostly knowledge intensive industries have been examined, the generalizability of the findings across industries may be problematic. Although the acquisition of knowledge by the supplier has been investigated, the assimilation of this knowledge is not taken in to account. Future research may elaborate on the hypothesis made by Law (2014) with respect to the effect of component ambiguity on this assimilation. As mentioned, the degree to which relational ties are present as a consequence of component ambiguous knowledge held by the buyer cannot be determined from the results. This indicates that additional research with respect to the antecedents of relational ties is desirable. In addition, the causal relationships between component ambiguity and relational ties may be further investigated. A larger sample size than the one used in this research may be useful in the investigation of these causal relations.
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Appendices
Appendix A: Survey Questions
Source Construct Items (1-7: strongly disagree – strongly agree)
‘customer refers to your firm’s largest customer. (Zhou et al.,
2014)
Relational Ties • Our company has a close social relationship with this customer.
• There is a sense of “being in the
same boat” between our company and customer. • Our company and this customer often visit each other. • Our company and this customer often have activities that are purely social, such as after-work get-togethers.
• Our company and this customer often offer favors to each other.
• The relationship between our company and the customer is in good shape.
(Yli-Renko et al., 2001)
Knowledge acquisition •Our company obtains a substantial amount of market knowledge from this customer.
•Our company obtains valuable information with respect to customer needs and trends from this customer. •Our company obtains substantial amounts of technical know-how from this customer.
•Our company obtains most of its valuable technical knowledge related to supplying our product/service from this customer.
Adapted from (Lee et al., 2007)
Component Ambiguity •Our customer’s technology and process know-how is difficult to codify (in blueprints, instructions, formulas, etc.).
•Our customer’s technology and process know-how is more tacit than explicit.
•Our customer’s technology and process know-how is the product of many interdependent techniques, routines, individuals, and resources.
27 Appendix B: Validity & Reliability
Rotated Component Matrixa
Component 1 2 3 RT_1 ,761 RT_2 ,724 RT_3 ,757 RT_4 ,752 CA_1 ,726 CA_2 ,792 CA_3 ,696 CA_4 ,656 KA_1 ,815 KA_2 ,618 KA_3 ,749 KA_4 ,722
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
