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					European Journal of Scientific Research ISSN 1450-216X Vol.37 No.2 (2009), pp.337-350 © EuroJournals Publishing, Inc. 2009 http://www.eurojournals.com/ejsr.htm

Moderating Effects of MNCs’ Country of Origin in the Relationship between Technology Recipient Characteristics and Degree of Inter-Firm Technology Transfer
Sazali, A.W Graduate School of Management, Universiti Putra Malaysia 43400 Selangor, Malaysia E-mail: saw639@gmail.com Haslinda, A Faculty of Economics & Management, Universiti Putra Malaysia 43400 Selangor, Malaysia E-mail: hba@putra.upm.edu.my; drhaslinda@gmail.com Jegak, U Faculty of Educational Studies, Universiti Putra Malaysia 43400 Selangor, Malaysia E-mail: jegak@ace.upm.edu.my Raduan, C.R Faculty of Economics & Management, Universiti Putra Malaysia 43400 Selangor, Malaysia E-mail: rcr@putra.upm.edu.my Abstract While realizing that technologies, knowledge, and competencies are the technology supplier’s main source of competitive advantage, the current inter-firm technology transfer (TT) issue in international joint ventures (IJVs) revolves around the extent of degree of technologies that are transferred by the suppliers to recipient partners in terms of tacit and explicit knowledge. Previous studies on intra-firm knowledge transfer have acknowledged the significant influence of technology actors and facilitators/barriers such as the knowledge transferred, source, recipient and contextual/relational characteristics in the knowledge transfer process. Although previous studies have established the significant relationships between technology transfer determinants and technology transfer, however, these relationships could possibly have been influenced by the MNCs’ country of origin (MNCCOO). Based on the underlying knowledge-based view (KBV) and organizational learning (OL) perspectives, the main objective of this paper is to empirically examine the moderating effects of the MNCs’ country of origin in the relationships between technology recipient characteristics: absorptive capacity (ACAP) and recipient collaborativeness (RCOL) and two distinct dimensions of degrees of technology transfer: degrees of tacit and explicit knowledge within IJVs. Using the moderated multiple regression (MMR), the theoretical models and hypotheses in this study were tested based on empirical data

Moderating Effects of MNCs’ Country of Origin in the Relationship between Technology Recipient Characteristics and Degree of Inter-Firm Technology Transfer gathered from 128 joint venture companies registered with the Registrar of Companies of Malaysia (ROC). The results revealed that the MNCs’ country of origin has significantly affected the relationships between technology recipient characteristics (absorptive capacity and recipient collaborativeness) and degree of explicit knowledge; where the relationship was found stronger for Asian MNCs than Western MNCs. However, the MNCs’ country of origin did not moderate the relationship between technology recipient characteristics and degree of tacit knowledge.

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Keywords: Degree of Inter-Firm Technology Transfer, International Joint Ventures, the MNCs’ Country of Origin, Malaysia.

1.0. Introduction
Many studies from knowledge-based view (KBV) perspective have established that MNCs tend to be more protective of their advance technology, knowledge and competencies which embodied in products, processes and management because these strategic valuable resources and competencies are their main sources of competitive advantage (Porter, 1985; Barney, 1991; Peteraf, 1993; Wernerfelt, 1984; Pralahad and Hamel). On the other hand, organizational learning (OL) perspective studies have suggested that technology and knowledge are more likely to be protected by the supplier when the recipients are opportunistic in the collaborative relationship (Inkpen, 1998a; Inkpen and Dinur, 1998; Child and Faulkner, 1998). Thus, in the context of inter-firm technology transfer (TT) through international joint ventures (IJVs), the remaining question is on the extent of TT by foreign multinational corporations (MNCs); especially when transferring their advance technology to local recipient partner (Narayanan and Lai, 2000). While realizing that technologies, knowledge, and competencies are the supplier’s main source of competitive advantage, the current TT issue in IJVs revolves around the extent of degree of technologies (TTDEG) that are transferred by the suppliers to recipient partners in terms of tacit knowledge (new product/service development, managerial systems and practice, process designs and new marketing expertise), and explicit knowledge (manufacturing/service techniques/skills, promotion techniques/skills, distribution know-how, and purchasing know-how) (Madanmohan et al., 2004). This is because from the recipient’s perspective, TT success is not merely possessing the ability to operate, maintain or repair the machineries at the production level (transmission) but it also includes the ability to learn, acquire, absorb and apply new external technologies and knowledge that are organizationally embedded in product materials, physical assets, processes and production, and management capabilities (absorption) (Davenport and Prusak, 1998, 2000). Technology recipient characteristics (TRCHAR) have increasingly become the dominant factors in determining the success or failure of inter-firm technology transfer within IJVs (Inkpen, 2000; Pak and Park, 2004; Minbaeva, 2007; Yin and Bao, 2006). Among the TRCHAR factors that have been identified by literature to influence TT and knowledge KT are absorptive capacity (Cohen and Levinthal, 1990; Hamel, 1991; Lyles and Salk, 1996; Mowery et al., 1996; Lane and Lubatkin, 1998; Lane et al., 2001; Gupta and Govindarajan, 2000, Minbaeva et al., 2003, Minbaeva, 2007; Pak and Park, 2004), experience (Simonin, 1999a, 1999b), prior knowledge and experience (Inkpen, 1998a, 1998b, 2000; Tsang, 2001), knowledge relatedness (Inkpen, 2000), learning capacity (Makhjia and Ganesh, 1997; Parise and Henderson, 2001), receptivity (Hamel, 1991; Baughn et al., 1997), learning intent or objectives (Beamish and Berdrow, 2003; Hamel, 1991; Simonin, 2004; Inkpen and Beamish, 1997; Baughn et al., 1997; Inkpen, 1998a; Mohr and Sengupta, 2002), managerial belief rigidity (Inkpen and Crossan, 1995), and recipient collaborativeness, readiness and method comprehensiveness (Yin and Bao, 2006).

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Sazali, A.W, Haslinda, A, Jegak, U and Raduan, C.R

Although many studies have empirically confirmed the significant effect of knowledge transfer determinants on knowledge transfer outcomes, nevertheless, the effects of TRCHAR on TTDEG in inter-firm TT could possibly have been influenced by other established moderating factors such as size of MNCs, age of JV, MNCs’ country of origin, and MNCs’ types of industry. In other words the variations in TTDEG could have been significantly moderated by these variables. From a literature review, a bulk of studies on knowledge transfer and acquisition in strategic alliance (Szulanski, 1996; Gupta and Govindarajan, 2000; Minbaeva, 2007; Pak and Park, 2004; Lin, 2005; Wang and Nicholas, 2005; Liao and Hu, 2007; Bresman et al., 1999; Mowery et al., 1996; Lyles and Salk, 1996; Kogut and Zander, 1993; Grosse, 1996: Dhanaraj et al., 2004; Hau and Evangelista, 2007) have not tested the impact (strength) of moderating variables on the linear (direct) relationships between technology recipient characteristics and technology or knowledge transfer. However, a number of studies on interfirm KT and knowledge acquisition in strategic alliance and JVs have acknowledged the important role of moderating variables such as: 1) collaborative know-how, learning capacity and alliance duration (Simonin, 1999a), 2) collaborative experience and firm size (Simonin, 1999b), 3) organizational culture, firm size, alliance form, and competitive regime (Simonin, 2004), 4) age of JV (Mohr and Sengupta, 2002), 5) alliance origin and alliance experience (Yin and Bao, 2006), 6) age of JV (Tsang et al., 2004), and 7) environmental challenge (Hau and Evangelista, 2007). Following the recent approach in the strategic alliance literature (Simonin, 1999a, 1999b, 2004; Yin and Bao, 2006; Tsang et al., 2004) and based on the underlying knowledge-based view (KBV) and organizational learning (OL) perspectives, this study fills in the literature gaps by specifically examining the effect of the MNCs’ country of origin (Western vs. Asian MNCs) as a moderating variable in the relationships between the TRCHAR and two distinct dimensions of degree of technology transfer: degrees of tacit (TCTDEG) and explicit (EXPDEG) knowledge. The primary objective is to provide new insights and information on the boundary conditions for TRCHAR-TTDEG relationship (Aguinis, 2004).

2.0. Theory and Hypotheses
2.1. Technology Recipient Characteristics, Degree of Technology Transfer and Moderating Effect of MNCs’ Country of Origin As TT involves the process of transmission and absorption of knowledge (Davenport and Prusak, 1998, 2000), the recipient firm’s ability to absorb the knowledge transferred largely depends on the degree of their absorptive capacity (ACAP). Absorptive capacity (ACAP) is the firm’s ability to recognize, assimilate, and apply to commercial ends the value of new external information (Cohen and Levinthal, 1990). Prior related knowledge, as the important element of ACAP, is critical for organizations to assimilate and exploit new knowledge. By possessing sufficient prior related knowledge, organizations are able to have an adequate ability to absorb new external knowledge (Cohen and Levinthal, 1990). OL literature suggests that another critical element of ACAP is the recipient’s firm intensity of efforts. Intensity of effort is reflected on the amount of energy expended by organizational members to solve problems through organization members directing their considerable time and effort in learning how to solve problems before attempting to solve complex problems (Kim, 1998). Another important dimension of TRCHAR is recipient collaborative (RCOL). RCOL is closely related to the recipient’s learning intent (competitive vs. collaborative intent). The technology recipient firm’s willingness to establish a mutually beneficial and collaborative relationship requires the recipient firm’s honest intention to create common benefits for both JV partners (Yin and Bao, 2006). Studies on inter-organizational learning have suggested that cooperative/collective learning encourages the alliance partners to work together by sharing their knowledge, benefit each other’s complementarities thus providing mutual opportunities to extract potential synergies between their respective competencies (Doz, 1996; Geringer, 1991). Collaborative learning creates an access to the

Moderating Effects of MNCs’ Country of Origin in the Relationship between Technology Recipient Characteristics and Degree of Inter-Firm Technology Transfer

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partner’s knowledge and skills such as product and process technology, organizational skills, and knowledge about new environments (Inkpen, 1995). In the collaborative learning environment; where the recipient’s learning intent is crucial, the transferring partner tends to be more open or transparent in terms of sharing and transferring knowledge to the acquiring partner as it involves mutual exchange of valuable knowledge (Inkpen, 2000). RCOL, which is reflected on the partner’s learning intent, determines the degree of openness or transparency in knowledge sharing and knowledge transfer (Inkpen, 2000). Many empirical studies have established that MNCCOO (nationality) has a significant impact on 1) the propensities of MNCs’ choice of global strategies, 2) organizational structures and control system (Bartlett and Ghoshal, 1989), 3) internal corporate cultures (Egelhoff, 1984; Franko, 1976; Porter, 1990), 4) expected outcomes (Harrigan, 1988), 5) alliance outcomes and performance (Parkhe, 1993), 6) partners’ learning and protection of proprietary assets in an alliance (Kale et al., 2000), and 7) the way how the MNCs operate (Gupta and Govindarajan, 2000). Problems related to cultural differences, opinions, beliefs, and attitude tend to accelerate due to alliance/JV partners’ nationality (Kale et al., 2000). The differences in culture, language, educational background and distance with cross national partners; which act as barriers to inter-organizational learning, impede the inter-partner learning and knowledge transfer (Mowery et al. 1996). However, Yin and Bao (2006) found nationality of alliance’s partners (the U.S, Japan and Western firms) has no significant effect on the relationships between the supplier and recipient factors and tacit knowledge acquisition. H1: The MNCs’ country of origin moderates the relationship between technology recipient characteristics and degree of tacit knowledge in inter-firm technology transfer. H2: The MNCs’ country of origin moderates the relationship between technology recipient characteristics and degree of explicit knowledge in inter-firm technology transfer.

3.0. Methods
3.1. Sample The sample frame was taken from the IJV companies registered with the Registrar of Companies (ROC). As at 1st January 2008, the number of IJVs operating in Malaysia was 1038. Out of this, 850 IJVs were considered as active IJVs and 103 IJVs were either dormant or had ceased operation. Since the focus of this study is on inter-firm TT from foreign MNCs to local companies, 85 IJVs were further eliminated from the population frame because only IJVs that have operated more than 2 years and have at least twenty percent (20%) of foreign equity are eligible to participate in the survey. Therefore, based on the list provided by ROC, which is considered as the most official and original source of information on foreign investment in Malaysia, it was decided that all IJVs (850) be included in the survey. Data collection was conducted in the period from July 2008 to December 2008 using a selfadministered questionnaire. The questionnaires were mailed to 850 active JV companies as listed with ROC using a cover letter. After one month from the posting date the response was found not encouraging. By mid July 2008 there were only 70 responses received from the respondents. Thus, in order to increase the response rate the researcher followed-up through numerous phone calls, e-mails, reminders via letters and personal visits to seek the respondents’ cooperation in the survey. After intensive efforts were made, by mid November 2008 a total of 145 responses (17.05%) were received. Based on literature review, the response rates for mailed questionnaires are usually not encouraging and low (Newman, 2003; Sekaran, 2003). In the Malaysian context, however, a response rate of 15% to 25% is still being considered appropriate and acceptable (Mohammed, 1998; Rozhan, Rohayu and Rasidah, 2001). From 145 responses only 128 questionnaires were usable and 17 questionnaires were returned blank, returned incomplete, or replied but unable to participate in the study.

341 3.2. Instrument and measurement

Sazali, A.W, Haslinda, A, Jegak, U and Raduan, C.R

The main research instrument in this study is the questionnaire. Building on the previous TT and KT studies, the questionnaire adopts a multi-item scales which have been modified accordingly to suit the context of the study: inter-firm TT. Except for degree of technology transfer (TTDEG), all the variables are measured using ten-point Likert Scale (1 = strongly disagree to 10 = strongly agree). For TTDEG, this variable is measured using ten-point Likert Scale (1 = very low transfer to 10 = substantial transfer). The ten-point Likert Scale was selected because 1) the wider distribution of scores around the mean provides more discriminating power, 2) it is easy to establish covariance between two variables with greater dispersion around their means, 3) it has been well established in academic and industry research, and 4) from a model development perspective, a ten-point scale is more preferred (Allen and Rao, 2000). 3.3. Dependent Variable - Degree of Technology Transfer (TTDEG) Following Lyles and Salk (1996), Lane et al. (2001), Gupta and Govindarajan (2000), Dhanaraj et al. (2004), Pak and Park (2004), Yin and Boa (2006) and Minbaeva (2007), this study adopts “a multidimensional operationalization approach” in measuring this construct. This study operationalizes TTDEG as the transfer of technological knowledge from two dimensions: 1) tacit knowledge (TCTDEG) in terms of new product/service development, managerial systems and practice, process designs and new marketing expertise, and 2) explicit knowledge (EXPDEG) in terms of manufacturing/service techniques/skills, promotion techniques/skills, distribution know-how, and purchasing know-how. The respondents were asked to evaluate TTDEG from MNCs to local firms in terms of tacit and explicit dimensions of technological knowledge. The Cronbach Alphas for TCTDEG and EXPDEG were 0.96 and 0.97 respectively. The results of Cronbach Alpha were quite similar to that of Hau and Evangelista (2007) and Yin and Bao (2006). 3.4. Independent Variable 3.4.1. Technology Recipient Characteristics (TRCHAR) This study concentrates on two distinct elements of TRCHAR: absorptive capacity (ACAP) and recipient collaborativeness (RCOL); which have strong theoretical and empirical supports (Cohen and Levinthal, 1990; Hamel, 1991; Simonin, 1999a). This study captures the technology recipient’s capability, willingness to absorb new knowledge and attitude towards learning. 3.4.2. Absorptive Capacity (ACAP) Building on Lane et al. (2001), this study captures ACAP’s critical elements of ability to understand, assimilate and apply new external knowledge. In capturing these critical elements, this study adopts a multi-item scale previously used by the researchers (Lyles and Salk, 1996; Simonin, 1999a; Pak and Park, 2004) to measure the constructs using seven (7) items with respect to statements on the academic background, technical capacity, educational programs, financial support for new ideas, overseas training opportunities, and commitment in terms of personnel and resources (physical, financial, and logistic) to JV. Following Cohen and Levinthal (1990) and Lane et al. (2001), this study also includes one (1) item to assess the local firm’s ability to understand, assimilate and apply new technology transferred by the foreign parent firm. The Cronbach Alpha for ACAP was higher (0.94) than Simonin’s (2004) Cronbach Alpha (0.81). 3.4.3. Recipient Collaborativeness (RCOL) This study measures RCOL in terms of the local partner firms’ learning intent and their collaborative attitudes by using a five (5) items scale in terms of 1) the local partner’s learning objective, 2) the local partner’s desire, determination and will to learn from foreign partner, 3) the technology-recipient’s

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willingness to allow foreign partner to inspect and monitor the use of knowledge acquired from JV, 4) the local partner’s commitment not to compete directly with the foreign partner in the future, and 5) the local partner’s commitment in sharing with the foreign partner the benefits of the critical knowledge acquired from the JV (Yin and Bao, 2006; Thuc Anh et al., 2006; Hamel, 1991; Simonin, 2004). The Cronbach Alpha for RCOL was higher (0.92) than Yin and Bao’s (2006) Cronbach Alpha (0.71). 3.5. Moderating Variable - Country of Origin (MNCCOO) Following the previous studies (Yin and Bao, 2006; Mowery et al., 1996; Kale et al., 2000), MNCCOO is measured by the nationality of the MNCs foreign JV partners based on item coded: 0 = Western MNCs (Unites States and European countries) and 1 = Asian MNCs (Japan and other Asian countries). 3.6. Model and Analysis The moderated multiple regression (MMR) analysis is described as an inferential procedure which consists of comparing two different least-squares regression equations (Aguinis, 2004; Aiken and West, 1991; Cohen and Cohen, 1983; Jaccard et al., 1990). Using the MMR analysis, the moderating effect of the variable (product term) was analyzed by interpreting 1) the R² change in the models obtained from the model summaries, and 2) the regressions coefficients for the product term obtained from the coefficients tables. Prior to conducting the MMR analysis, preliminary analyses were conducted to ensure that there was no violation of the assumptions of normality, linearity, homoscedasticity, and homogeneity of error variance. The population data was carefully examined to avoid the occurrence of 1) Type 1 error; which is the error of rejecting the true null hypotheses at a specified ,, and 2) Type 2 error (β); which is the error of failing to reject a false null hypotheses at a specified power (Aguinis, 2004). In this study, Equation 1 below was used to represent the variables in the ordinary least-squares (OLS) model: (OLS model): Y = β0 + β1X+ β2Z + e (1) To determine the presence of moderating effect, the OLS model was then compared with the MMR model which was represented by Equation 2 below: (MMR model): Y = β0 + β1X+ β2Z + β3X*Z + e (2) where, Y = degree of technology of transfer (TCTDEG and EXPDEG as the dependent variables), X = technology recipient characteristics (absorptive capacity and recipient collaborativeness), Z = a hypothesized binary grouping moderator (MNCCOO; Western vs. Asian MNCs), X*Z = the product between the predictors (TRCHAR*MNCCOO), β0 = the intercept of the line-of-best-of-fit which represents the value of Y when X = 0, β1 = the least-squares estimate of the population regression coefficient for X, β2 = the least-squares estimate of the population regression coefficient for Z, β3 = the sample-base least-squares estimates of the population regression coefficient for the product term, and e = the error term. The moderating variable (product term) is a binary grouping moderator; where the moderating variable MNCCOO was coded using the dummy coding system; 0 = Western MNCs, and 1 = Asian MNCs. This was done because of its simplicity and ease of interpretation of results when making comparisons between different groups (Aguinis, 2004).

4.0. Results
Table 1 and Table 2 show the model summary for both degrees of tacit (TCTDEG) and explicit (EXPDEG) knowledge. The coefficients for all variables for Model 1 and Model 2 (for both TCTDEG and EXPDEG) are presented in Table 3 and Table 4 below.

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Table 1:

Sazali, A.W, Haslinda, A, Jegak, U and Raduan, C.R
Model Summaryc - Degree of Tacit Knowledge
Model Summary c Change Statistics

Model 1 2

R .464a .477b

R Square .215 .227

Adjusted R Square .203 .209

Std. Error of the Estimate 5.182 5.163

R Square Change .215 .012

F Change 17.158 1.895

df1 2 1

df2 125 124

Sig. F Change .000 .171

a. Predictors: (Constant), MNCCOO, TRCHAR b. Predictors: (Constant), MNCCOO, TRCHAR, TRCHAR*MNCCOO c. Dependent Variable: TCTDEG

Table 1 above shows that for Model 1, R = .464, R² = .215 and [F (2, 125) = 17.158, p = .0001]. This R² means that 21.5% of the variance in the TCTDEG is explained by TRCHAR scores and MNCCOO. Model 2 shows the results after the product term (TRCHAR*MNCCOO) was included in the equation. Table 1 also indicates that the inclusion of the product term resulted in an R² change of .012, [F (1, 124) = 1.895, p > 0.05]. The results show no significant presence of moderating effect. To put it differently, the moderating effect of MNCCOO explains only 1.2% variance in the TCTDEG above and beyond the variance by TRCHAR scores and MNCCOO. Thus, it can safely be concluded that hypothesis H1 is not supported.
Table 2: Model Summary c - Degree of Explicit Knowledge
Model Summary c Change Statistics Model 1 2 R .445a .473b R Square .198 .224 Adjusted R Square .185 .205 Std. Error of the Estimate 4.856 4.797 R Square Change .198 .026 F Change 15.432 4.118 df1 2 1 df2 125 124 Sig. F Change .000 .045

a. Predictors: (Constant), MNCCOO, TRCHAR b. Predictors: (Constant), MNCCOO, TRCHAR, TRCHAR*MNCCOO c. Dependent Variable: EXPDEG

Table 2 above shows that for Model 1, R = .445, R² = .198 and [F (2, 125) = 15.432, p = .0001]. This R² means that 19.8% of the variance in the EXPDEG is explained by TRCHAR scores and MNCCOO. Model 2 also shows the results after the product term (TRCHAR*MNCCOO) was included in the equation. Table 2 above indicates that the inclusion of the product term resulted in an R² change of .026, [F (1, 124) = 4.118, p < 0.05]. The results support the presence of a significant moderating effect. To put it differently, the moderating effect of MNCCOO explains 2.6% variance in the EXPDEG above and beyond the variance by TRCHAR scores and MNCCOO. Thus, it can reasonably be concluded that hypothesis H2 is supported. The coefficients table for TCTDEG as shown in Table 3 below depicts the results of the regressions equation for Model 1 and Model 2.

Moderating Effects of MNCs’ Country of Origin in the Relationship between Technology Recipient Characteristics and Degree of Inter-Firm Technology Transfer
Table 3: Coefficientsa - Degree of Tacit Knowledge
Coefficientsa Unstandardized Coefficients B Std. Error 13.246 1.945 .121 .024 2.133 .944 11.957 2.152 .138 .027 8.830 4.956 -.082 .060 Standardized Coefficients Beta .400 .181 .456 .750 -.590

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Model 1

2

(Constant) TRCHAR MNCCOO (Constant) TRCHAR MNCCOO TRCHAR*MNCCOO

t 6.810 4.983 2.259 5.555 5.082 1.782 -1.376

Sig. .000 .000 .026 .000 .000 .077 .171

95% Confidence Interval for B Lower Bound Upper Bound 9.396 17.095 .073 .169 .264 4.002 7.697 16.217 .084 .191 -.979 18.638 -.201 .036

a. Dependent Variable: TCTDEG

Model 1 indicates that TRCHAR was statistically significant (p < 0.001; Beta value = 0.400) and MNCCOO was also statistically significant (p < 0.05; Beta value = 0.181). Equation 3 below shows that for a 1-point increase in TRCHAR, the TCTDEG is predicted to have a difference by .121, given that the MNCCOO is held constant. The regression coefficient associated with MNCOO means that the difference in TCTDEG between Western and Asian MNCs is 2.133, given that TRCHAR is held constant. TCTDEG = 13.246 + 1.945TRCHAR + 2.133MNCCOO (3) The high-order of interaction effects of the MMR test was conducted to differentiate the extent of TCTDEG that was influenced by Western and Asian MNCs within IJVs. Model 2 shows the results after the product term (TRCHAR*MNCCOO) was included in the equation. As indicated in Table 1 the inclusion of product term resulted in an R² change of .012, [F (1, 124) = 1.895, p > 0.05]. Model 2 shows TRCHAR are highly significant (p < 0.001; Beta value = .685). However, both MNCCOO and TRCHAR*MNCCOO were found to be insignificant (p > 0.05). The results did not support the presence of a significant moderating effect. Table 3 also reveals information on the regression coefficients after the inclusion of product term in the equation. The equation for Model 2 is as follows: TCTDEG = 11.957 + .138TRCHAR + 8.830MNCCOO - .082TRCHAR*MNCCOO (4) As indicated above, the interpretation of the regression coefficients is based on the fact that the binary moderator was coded using the dummy code system. The result for Model 2 indicates that for a 1-point increase in the TRCHAR, the TCTDEG is predicted to have a difference by .138, given that MNCCOO is held constant. The interpretation of the regression coefficients for the product term in Equation 4 is that there is a -.082 difference between the slope of TCTDEG on TRCHAR between Western and Asian MNCs. In other words, the slope regressing TCTDEG on TRCHAR is steeper for Asian MNCs as compared to Western MNCs. The TRCHAR and TCTDEG relationship for Western and Asian MNCs is shown in Figure 1 below by creating a graph displaying the relationships for each of the groups (Aguinis, 2004). From the results of descriptive statistics, the value of the mean score for TRCHAR is 6.57; and for the standard deviation (SD) is 1.60. Following Aguinis (2004), the value 1 SD above the mean is 8.17 and the value 1 SD below the mean is 4.97. Thus, using the value of 1 SD above and 1 SD below mean in Equation 4 yields the graph shown in Figure 1. Results based on Equation 4 led to the conclusion that there was no moderating effect of MNCCOO. Although insignificant, Figure 1 below shows that the TRCHAR-TCTDEG relationship is stronger (i.e. steeper slope) for Asian MNCs as compared to Western MNCs. The coefficients table for EXPDEG as shown in Table 4 below depicts the results of the regressions equation for Model 1 and Model 2.

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Table 4:

Sazali, A.W, Haslinda, A, Jegak, U and Raduan, C.R
Coefficientsª - Degree of Explicit Knowledge
Coefficientsa Unstandardized Coefficients B Std. Error 16.102 1.823 .121 .023 .600 .885 14.336 2.000 .144 .025 9.774 4.604 -.113 .056 Standardized Coefficients Beta .433 .055 .515 .896 -.872 95% Confidence Interval for B Lower Bound Upper Bound 12.494 19.709 .076 .166 -1.151 2.352 10.378 18.294 .095 .194 .661 18.887 -.223 -.003

Model 1

2

(Constant) TRCHAR MNCCOO (Constant) TRCHAR MNCCOO TRCHAR*MNCCOO

t 8.833 5.339 .678 7.169 5.738 2.123 -2.029

Sig. .000 .000 .499 .000 .000 .036 .045

a. Dependent Variable: EXPDEG

Model 1 indicates that TRCHAR was statistically significant (p < 0.001; Beta value = 0.433); however MNCCOO was not statistically significant (p > 0.05). Equation 5 below shows that for a 1point increase in TRCHAR, the EXPDEG is predicted to have a difference by .121, given that the MNCCOO is held constant. The regression coefficient associated with MNCCOO means that the difference in EXPDEG between Western and Asian MNCs is 0.600, given that TRCHAR is held constant. EXPDEG = 16.102 + .121TRCHAR + 0.600MNCCOO (5) Model 2 shows the results after the product term (TRCHAR*MNCCOO) was included in the equation. As indicated in Table 2 the inclusion of product term resulted in an R² change of .026, [F (1, 124) = 4.118, p < 0.05]. TRCHAR was found statistically significant (p < 0.001; Beta value = 0.515); and both MNCCOO and TRCHAR*MNCCOO were also statistically significant (both at p < 0.05). The results show the presence of a significant moderating effect. Table 4 also reveals information on the regression coefficients after the inclusion of product term in the equation. The equation for Model 2 is as follows: EXPDEG = 14.336 + .144TRCHAR + 9.774MNCCOO - .113TRCHAR*MNCCOO (6) The result for Model 2 indicates that for a 1-point increase in the TRCHAR, the EXPDEG is predicted to have a difference by .144, given that MNCCOO is held constant. The interpretation of the regression coefficients for the product term in Equation 6 is that there was a -.113 difference between the slope of EXPDEG on TRCHAR between Western and Asian MNCs. The slope regressing EXPDEG on TRCHAR is steeper for Asian MNCs as compared to Western MNCs. The TRCHAR and EXPDEG relationship for Western and Asian MNCs is also shown in Figure 1 below. The value of the mean score for TRCHAR is 6.57 and for the standard deviation (SD) is 1.60. The value 1 SD above the mean is 8.17, and the value 1 SD below the mean is 4.97. Thus, using the value of 1 SD above and 1 SD below mean in Equation 6 yields the graph shown in Figure 1. Results based on Equation 6 led to the conclusion that there was a significant moderating effect of MNCCOO. Figure 1 below indicates that the TRCHAR-EXPDEG relationship is slightly stronger (i.e. steeper slope) for Asian MNCs as compared to Western MNCs.

Moderating Effects of MNCs’ Country of Origin in the Relationship between Technology Recipient Characteristics and Degree of Inter-Firm Technology Transfer
Figure 1: Slopes for both TCTDEG and EXPDEG on TRCHAR for MNCCOO.

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30 25 20 15 10 5 0 -5
Low TCTDEG / EXPDEG (1 SD below mean) Asian MNCs (TCTDEG) Asian MNCs (EXPDEG) High TCTDEG / EXPDEG (1 SD above mean) Western MNCs (TCTDEG) Western MNCs (EXPDEG)

5.0. Discussion and Conclusion
Building on the underlying KBV and OL perspectives, this study has bridged the literature gaps by providing empirical evidence and new insights on the significant moderating effect of MNCs’ country of origin in the relationships between technology transfer characteristics (absorptive capacity and recipient collaborativeness) and two dimensions of degree of technology transfer: degrees of tacit and explicit knowledge using the Malaysia sample. The results suggest that, in comparison, the inclusion of MNCCOO (Western vs. Asian MNCs) in TRCHAR-EXPDEG relationship has a significant moderating effect in changing the degree (volume) of explicit knowledge only (p < 0.05; R- squared change of 0.026) not degree of tacit knowledge (p > 0.05; R- squared change of 0.012). The moderating effect of MNCCOO is shown to be capable of changing the nature of relationship and explains under what conditions TRCHAR causes EXPDEG. The presence of significant moderating effect of MNCCOO has exceeded the linear relationship between TRCHAR and EXPDEG. This is quite interesting given the fact that the role of MNCCOO has received strong theoretical support by the literature (Bartlett and Ghoshal, 1989; Egelhoff, 1984; Franko, 1976; Porter, 1990; Yip et al., 1994). The results further suggest that MNCCOO; whether Western or Asian MNCs, has been established to provide a significant moderating impact in TRCHAR-EXPDEG relationship. The slopes for EXPDEG on TRCHAR for Western and Asian MNCs (Figure 1) indicated that the relationship appeared to be stronger for Asian MNCs as compared to Western MNCs. The results also provide critical information in such that due to cultural differences (distances) and the fact that knowledge in Oriental cultures is more contextual, Asian MNCs in IJVs are relatively more protective of their technologies and knowledge as compared to Western MNCs; which are quite transparent and more open in facilitating knowledge assimilation and acquisition (Hamel, 1991). Since inter-firm technology transfer in IJVs is an organizational learning process (Daghfous, 2004), the recipient partners’ absorptive capacity, intensity of effort and collaborative learning intent are not the only preconditions for a successful technology acquisition (Hamel, 1991). Because of the cultural distances, the receptivity (absorptive capacity) of the recipient partners would not have significant effect on learning if the transferring partners, especially Asian MNCs, are too protective of their technologies. Consistent with Hamel’s (1991) argument, the degree of transparency/openness in Asian MNCs is closely associated with four inherent determinants such as 1) the penetrability of the social

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context which surrounded the supplier partners, 2) attitudes towards recipient partners who have different cultural background, 3) the extent to which the supplier partners’ distinctive skills were encodable and discrete, and 4) the supplier partners’ relative pace of skill building. Therefore, acquiring explicit knowledge from Asian MNCs is equally challenging simply because although explicit knowledge has been explicitly standardized in blueprints, manuals, procedures and instructions by the supplier partners, quite often, it still consists of a highly tacit, complex and specific knowledge; which is difficult to be articulated and understood without the existence of ‘teacher-student’ relationship (Dhanaraj et al. (2004). In this aspect, when compared to Western MNCs, Asian MNCs are most unlikely to transfer a higher degree of explicit technologies to their JV partners. The results further extend the empirical findings by Yin and Bao (2006) who found MNCCOO was not significant in moderating the relationship between supplier, recipient factors and tacit knowledge acquisition. One of the major limitations encountered by this study was the resource constraints; where this study has mainly relied on responses obtained from the top management level of the IJVs. Thus, the scope of respondents could have been extended to include the response from middle and lower management levels in the JVs. Secondly, consistent with the literature, the subjectivity of nature of relationship is difficult to capture. Thus, the nature of relationship between IJV partners could have tremendously affected the results if the respondents perceived that the IJVs were competitive in nature rather than collaborative. Thirdly, due to lack of awareness on academic research the response rate in terms of the number of usable questionnaires, though sufficient, was not encouraging. This has become a major challenge to many researchers who conduct organization studies in Malaysia. Finally, due to time constraints, the types of technology under investigation in this study were limited to tacit vs. explicit knowledge dimension. This empirical study is a response to the need for statistical evidence that has typically been lacking in inter-firm TT literature. Since this study focuses on degree of inter-firm TT, future studies could be conducted to further examine the moderating effecte of MNCs’ country of origin in the relationships between other technology transfer characteristics such as the supplier, relationship and knowledge characteristics and degree of technology transfer. Secondly, the above relationship could also be extended to cover other formal and externalized inter-firm TT agents such as FDIs and licensing. Thirdly, it is worthwhile to extend the tacit and explicit dimension of technology to cover other dimensions of supply chain activities such as production, marketing, management, and distribution. Fourthly, since the IJV literature has highlighted the high instability rate of IJVs in developing countries, future studies could be directed to empirically examine the moderating effect of MNCs’ country of origin in the relationships between degree of inter-firm TT and conflicts, learning outcomes, asymmetric bargaining power, stability of JV, and equity ownership. Finally, future studies could further investigate the effects of few other established moderating variables such as organizational culture, collaborative know-how, prior JV experience, and learning capacity on the above relationships to provide new insights and information on the boundary conditions for technology recipient characteristics-degree of technology transfer relationship.

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