CANADIAN JOURNAL OF APPLIED SCIENCE AND TECHNOLOGY © 2019 OPEN ARCHIVES INITIATIVE | Volume 7| Issue 3 | ISSN: 2356-6173
Moderating Effect of Absorptive Capacity on the Relationship
between Resource Transfer and Innovation Diffusion for
Product Co-Creation
Basil Kusi
1*,Cai Li
1,Shumaila Naz
1,Jerry Mireku-Afful
1 1School of Management, Jiangsu University, 301 Xuefu Road, Zhenjiang 212013, P. R. China
ABSTRACT
Recognizing the growing role of innovation in developing countries under the new field of research
at the intersection of innovation management and Development studies and has been opened. This
paper explores the moderating effect of absorptive capacity in the relationship between resource
transfer and innovation diffusion for product co-creation. The results show that the stronger the
absorptive capacity the higher the innovative performance and vice versa and this is statistically
significant at 95% confidence interval (p=0.000). This means that, the greater an organization's
absorptive capacity, the more probable it will appreciate the value of the knowledge and resources
acquired from the network ties. This enables firms to change the transfer of information and
resources from just acquisition to a more innovation practice. Having the capacity to do as such
should add to any positive outcome that network-based resources could have on resource acquisition
since all the information and resources acquired would have been sifted to explicitly address the
actual needs of the organization.
Keywords
:
moderating; resource transfer, absorptive capacity, product-co-creation
INTRODUCTION
This paper explores the moderating effect of absorptive capacity in the relationship between resource transfer and innovation diffusion for product co-creation. Firstly the first research hypothesis tested the validity in the claim that the competitive industrial networks are driven by both external (state economic policies, infrastructure, intellectual property rights, public-private partnership, cultural linguistic distances and internal factors (human capital, managerial capital, information capital, financial capital and network capital). It has also been established in the extant studies that the acquisition of competitive advantage to drive innovation such as product co-creation is mediated by resource transfer. This section therefore seeks to explore the moderating effect of absorptive capacity on the relationship between resource transfer and innovation diffusion for product co-creation.
Competitive industrial networks are socio-economic business activities by which business people and entrepreneurs meet to form business relationships and to recognize, create, or act upon business opportunities, share information and seek potential partners for ventures. In the second half of the twentieth century, the concept of networking was promoted to help business people to build their social capital (Robison & Ritchie, 2016). In the US, workplace equity advocates encouraged business networking by members of marginalized groups (e.g., women, African-Americans, etc.) to identify and address the challenges barring them from professional success. Mainstream business literature subsequently adopted the terms and concepts, promoting them as pathways to success for all career climbers (Robison & Ritchie, 2016). Since the closing decades of the twentieth century,
"networking" has become an accepted term and concept in the American and many societies across the globe. Since the 2000s, "networking" has expanded beyond its roots as a business practice to the point that parents meeting to share child-upbringing tips to scientists meeting research colleagues are described as engaging in "networking" (Pozzi, 2013; Reger et al., 2017). Many business people contend that business networking is a more cost-effective method of generating new businesses than advertising or public relations efforts. This is because business networking is a low-cost activity that involves more personal commitment than company money. Country-specific examples of informal networking are guanxi in China, blat in Russia, good old boy network in America, and old boy network in the UK. As observed by Gillard, Foster, and Turner (2016) network members continually improve through unique peer review and network-driven processes. These processes make up a program that provides intensive business training in the form of coaching, consulting, and group workshops (Gillard et al., 2016). Network members observe and evaluate each other's companies in real time, learn from each other's successes, and generate positive solutions to their business opportunities to ensure continuous improvement within a time-proven system. Business clusters have become major sources of network ties with proven records in many fields. In Ghana, automobile and wood processing clusters have existed over time. These are geographical concentration of related automobile and wood processing business firms, organizations, and institutions and have the benefit of like firms, institutions, and infrastructure surrounding it. Despite the age and value of the automobile clusters in Ghana to economic growth, the sector has largely escaped the interest of academics as the front-end of the sector is represented by a collection of presumed illiterates, conmen, tricksters, vagabonds and fraudsters etc. even though in reality they act as middle men for very influential people in society. As such the extent to which business networks support resource transfer in the automobile clusters in Ghana remains a missing link in the extant literature. For this reason, there is very little knowledge about how intra-cluster partnership and relationship with other enterprises outside the automobile clusters such as venture capitalists, universities, research institutions, venture partners and social networks significantly influence resource transfer within the automobile cluster and its implications for sustainable cluster. Moreover, it is suggested that sponsorship-based ties with (e.g. governmental agencies and commercial banks) with automobile clusters in Ghana may have
significant influence on emerging automobile SMEs performance. These issues remain outstanding in the extant literature and provoke in-depth empirical studies such as this. We apply a novel logistic regression on an array of relevant information to establish the extent of relationship between network ties and resource transfer and the moderating effect of a firm’s absorptive capacity in this research. This section seeks to find out whether a firms’ absorptive capacity has the power to slow down or increase the speed of converting resources acquired from competitive industrial networks into innovative products. It is thus postulated that;
H1: A firms’ absorptive capacity moderates the relationship between acquisition of competitive industrial networks and innovation diffusion for product co-creation
Data Source
Data was collected from the owners, managers and supervisors of the Kumasi Wood Cluster and Automobile Engineering Cluster that comprise of the Abbosey Okai in Accra and Suame Magazine in Kumasi. A total of 479 respondents were initially recruited to participate in the research that took place over a three-month period. The author adopted but modified items to establish the construct of network ties and its resulting effect on resource transfer from the extant literature. This was constituted into a closed-ended questionnaire that was robustly tested and fine-tuned before administering to the selected respondents. For example, the face validity of the measurement items was assessed by faculty members and doctoral students of the School of Management who acted as expert judges. After several iterations of item editing and refinement, a final constituted closed-ended questionnaire was administered. Eventually, 367 respondents fully completed the questionnaire and their responses were accepted for analysis in the research. In terms of construct measurement, we identified, measured and recategorized the many different types of linkages and ties examined in the literature (sponsorship ties, partnership based linkages etc. into seven different types of organizational ties and linkages based on the extant literature. These were further decomposed into questions to clearly define the existence of each one of the linkages. For example, five questions were asked to examine the presence of linkage to other enterprises while five additional questions were also asked to determine linkage to venture capital. The questionnaire also involved five questions
each to measure the level or degree of linkage to universities, linkage to venture networks, linkage to financial institutions, linkages to social network and linkage to government. Other demographic variables were added such that the entire questionnaire was made up of 40 questions.
The Analytical Procedures
The analytical procedure included two staged statistical analysis to obtain the results. First, we performed factor analysis to investigate and validate the threshold values of organizational ties. We thoroughly verified the basic assumptions i.e. the constant variance and normality and these did not affect the results. The study was determined for its appropriateness of the data for factor analysis by employing Kaiser–Meyer-Olkin measure of sampling adequacy (KMO-MSA) and Bartlett’s Test of Sphericity. We recorded a KMO value of more than 0.60 and a significant value for the Bartlett’s Test of Sphericity. Varimax rotation and principle components analysis were performed for factor analysis. We eliminated all the factors that had factor loadings lower than 0.50 after which we conducted the Cronbach’s alpha reliability analysis. We ensured that all measure of sampling adequacy exceeded the Cronbach’s alpha reliability value threshold level of 0.60 and large and significant Bartlett’s Test of Sphericity. We eliminated 5 items of the initial 35 on the 7-organizational linkage or ties dimensions since they had a factor loading lower than 0.50. The exploratory factor analysis and reliability statistics measures of the accepted 30 variables were composed into seven composite values of linkage to other enterprises, linkage to venture capital, linkage to universities, linkage to venture networks, linkage to financial institutions, linkage to social networks and linkage to government. In the first model, we designated the seven linkages or ties as independent variables to test their effect on a dichotomous resource transfer value. Second, we disaggregate resource transfer into four variables (technology transfer, infrastructure transfer, knowledge/labour, skill transfer) and establish its individual relationship with linkage to other enterprises, linkage to venture capital, linkage to universities, linkage to venture networks, linkage to financial institutions, linkage to social network and linkage to government. In all, we modeled a fitted logistic regression model by considering the situation where the independent variable is nominal scale and dichotomous (i.e. measured at two levels). This case provides the conceptual foundation for all the other situations. We assume that, the independent variable, x, is coded as
either zero or one. The difference in the logit for a subject with x = 1 and x = 0 is
0 1 0 1
(1)
(0)
g
g
.In order to interpret this result, we need to introduce and discuss measure of association termed the odds ratio. The possible values of the logistic probabilities may be conveniently displayed in a 2 × 2 as shown in Table 1.
Table 1 Values of the Logistic Regression Model When the Independent Variable Is Dichotomous
The odds of the outcome being present among individuals with x= 1 is defined as (1) /[1 (1)]. Similarly, the odds of the outcome being present among individuals with x = 0 is defined as
(0) /[1 (0)]
.
Nevertheless, if the coding scheme is different from the (0,1) then the odds ratio formula needs to be modified, but for the purpose of this study all the dichotomous variables were coded using the (0, 1) coding scheme. The interpretation given for the odds ratio is based on the fact that in many instances it approximates a quantity called the relative risk. This
parameter is equal to the ratio
(1)
(0)
. It follows that the odds ratio approximates the relative risk if[1(0)] /[1(1)] 1 . This holds when
( )x is small for both x=1 and 0. A 100(1)% confidence interval (CI) estimate for the odds ratio is obtained by first calculating the endpoint of a confidence interval for coefficient, 1, and then exponentiating these values.Under the assumption that the logit is linear in the continuous covariate, x, the equation for the logit is
0 1
( )
coefficient,
1 gives the change in the log odds foran increase of “1” unit in x, that is
1
g x
(
1)
g x
( )
for any value of x. Most often the value of “I” is not statistically interesting. Hence to provide a useful interpretation for a continuous scale covariate we need to develop a method for point and interval estimation for an arbitrary change of “c” units in the covariate. The log odds ratio for a change of c units in x is obtained from the logit differenceg x c
(
)
g x
( )
c
1 and the associated odds ratio is obtained by exponentiating this logit difference(C) ( i ) exp( 1)
OR OR xc x c An estimate may
be obtained by replacing
1 with its maximumlikelihood estimate
^ 1
( )
. An estimate may be obtained of the standard error needed for confidence interval estimation is obtained by multiplying theestimated standard error of
^ 1
( )
by c. Hence the endpoints of the 100(1)% confidence interval (CI) estimate of OR( )c are ^ ^ ^ 1 1 1 2exp
c
Z
cSE
(
)
Since both the point estimate and endpoints of the confidence interval depends on the choice of c, the particular value of c should be clearly specified in all tables and calculations. Table 1 presents the computations of the crude odd ratio for effect of network ties or network linkages X on resource transfer Exp (B). As explained by Tennant and Pallant (2006) and cited by Shi et al. (2017) the crude odds ratio of factor explains the degree of influence of each of the variables on the dichotomous value of resource transfer which is the dependent variable of study. Firstly, the Wald’s and log likelihood ratio tests were also performed to ascertain the significance of effect of each of the explanatory variables on resource transfer in this model. According to (T. Brown, 2015), a probability value below or equal to 0.05 is considered to be statistically significant. Hence the inclusion of that explanatory variable as important in determining resource transfer Y= 0 or 1. The parameters of the model were estimated using maximum likelihood approach. The estimates for each explanatory variable are interpreted relative to the referenced category.
Results
In table 1, it is estimated that firms located in the Kumasi (cluster) is 0.748 more likely to are more likely to diffuse innovation for product co-creation with resources acquired from competitive networks than those located in the Accra cluster with 95% confidence interval (p-value=0.000) is statistically significant. With an odds ratio of 1.805 and a confidence interval of 95%, it can be predicted that firms with significant number of years of experience are more likely to diffuse innovation for product co-creation with resources acquired from competitive networks than those with few number of years of experience, giving a similar statistically significant result. The analysis also shows that firms with high linkage to other enterprises are 1.498 more likely to diffuse innovation for product co-creation with resources acquired from competitive networks than those with low linkage to other enterprises at 95% confidence interval (p-value=0.036). Similarly, the results indicate that the odds of diffusing innovation for product co-creation with resources acquired from competitive networks from a cluster increases by a factor of 1.183 with a confidence interval of 95% when the linkage to venture capital is higher than those with low ties (p-value=0.018). It is estimated from an odds ratio of 0.004 that firms with linkage to universities are 1.104 times more likely to contribute to diffusing innovation for product co-creation with resources acquired from competitive networks and this is statistically significant at 95% confidence interval (p-value=0.000). In the same regard, firms’ with linkage to financial institutions are 0.004 more likely to facilitate diffusion of innovation for product co-creation and this is statistically significant at 95% confidence interval (p-value=0.008). The odds ratio of 1.329 and a confidence interval of 95% (p-value=0.000), indicates that firms with linkages to social network are 1.853 more likely to diffuse innovation for product co-creation with resources acquired from competitive networks to support resource transfer while firms with strong linkage to government is also determined to be 2.099 more likely to contribute to transfer resources in a cluster giving similar statistically significant results.
Pearson’s Correlation Analysis of Strong Ties and Type of Innovation
In the extant literature it is asserted that resources acquired from strong network ties are more likely to promote radical innovation than those acquired from weak ties (Partanen et al., 2014). That is, commercializing radical innovation demands the establishment of strong ties with customer networks that contribute to research and development (R&D), distributors who provide customer access and strong ties relationships with research institutions which offer R&D inputs (Kozan & Akdeniz, 2014) and then weak ties relationships with managers who offer market knowledge and also provide access to new customer leads. Thus, table nnnnn above presents the Product Moment Correlation Coefficient results of the relationship between a firm’s network ties and the type of innovation. The analysis shows a positive correlation between the two variables (r= 0.574) indicating that strong network ties lead to a radical innovation type rather than an incremental innovation type and this is statistically significant at 99% confidence interval (p=0.000).
Pearson’s Correlation Analysis of Weak Ties and Type of Innovation
Table 3 also presents the Product Moment Correlation Coefficient results of the relationship between a firm’s network ties and the type of innovation it provides. The results shows a positive
correlation between the two variables (r=0.436) indicating that weak network ties lead to incremental innovation type rather than a radical innovation type and this is statistically significant at 99% confidence interval (p=0.001). The main reason for this could be that incremental innovations are not groundbreaking and that they are quite easy to acclimatize with them as such they do not require many interactions with distributors. However, incremental innovations can be commercialized in conjunction with the distribution partners. Customers are normally reached through these partners who also have the responsibility to manage these customers. This conclusion is quite startling due to the fact that these innovations are plug-and-play units. While firms offer their solutions in a turnkey way, some market players will have to be responsible for these plug and play" activities such as installation, in spite of the ease with which they are carried out. This is the reason why incremental innovations usually have to use distribution partners. Moreover, incremental innovations are usually developed especially through the establishment of strong ties relations with universities who contribute to R&D activities as well as providing credibility to the firm. The basis of this contradictory conclusion could be that although universities should generate radical innovations Powell et al. (1996) as cited by Brandes, Borgatti, and Freeman (2016), there could be further development of incremental innovation into commercial products with the establishment of close cooperation with universities and other academic institutions. These academic partners are far from end users of innovation and their needs because these innovations do not require any major variations in the customers’ infrastructure.
Relationship between a Firm’s Absorptive Capacity and Innovation Diffusion
Table 3 presents the Product Moment Correlation Coefficient results of the relationship between a firm’s absorptive capacity and its diffusion for innovation for product co-creation. The analysis shows a positive correlation between the two variables (r=0.646) indicating that the stronger the absorptive capacity the higher the innovative performance and vice versa and this is statistically significant at 95% confidence interval (p=0.000). This means that, the greater an organization's absorptive capacity, the more probable it will appreciate the value of the knowledge and resources acquired from the network ties. This enables firms to change the transfer of information and resources from just acquisition to a more innovation practice
(Cohen and Levinthal, 1990) as cited by (Ince et al., 2016). Having the capacity to do as such should add to any positive outcome that network-based resources could have on resource acquisition since all the information and resources acquired would have been sifted to explicitly address the actual needs of the organization.
Absorptive Capacity and Firm Innovation Performance
Regarding, the effect of absorptive capacity on innovative performance of firms, the study noted that absorptive capacity has a positive correlation with firm’s innovation. Thus, our analysis shows a positive correlation between the two variables (r=0.646) indicating that the stronger the absorptive capacity the higher the innovative performance and vice versa and this is statistically significant at 95% confidence interval (p=0.000). This finding agrees with several initial findings which observed that, a higher absorptive capacity of a firm ultimately results in higher innovation performance. For instance, this finding agrees with the views of Hughes et al. (2014) who emphasized on the importance of capability in the use of external knowledge and thus, a firm’s innovation performance is influenced by its absorptive capacity. Again, this particular finding supports an earlier research by Ferreras-Méndez, Fernández-Mesa, and Alegre (2016) who concluded that having regular interactions with external new knowledge promotes absorptive capacity of a firm. With the issue of the strength and the contributions of specific network ties in facilitating the commercialization of a particular type of innovation, our findings revealed that, for firms to be able to commercialize their innovations successfully, each type of innovation (radical and incremental) requires a particular type and strength of ties. Our analysis therefore indicates that for firms to successfully commercialize incremental innovation they require the establishment of strong network tie relationships with distributors and retailers whiles they require weak tie relationship with universities and R&D institutions. Also, the analysis revealed that, for firms to be able to successfully commercialize radical innovation, they need to form strong ties relationship with R&D institutions, universities and customers whiles establishing weak ties with other partners such as distributors and agents in the network. This development clearly supports (Karlsson & Warda, 2014; X. Liu, Huang, Dou, & Zhao, 2017) views as equivocal in the extant literature. They explain the ambiguity to be the difficult nature to accurately assess the type of a network tie needed to
commercialize a specific innovation. In the end, our analysis of high-growth and innovation oriented automobile firms revealed that R & D partners, particularly those who possess significant and innovative technologies are important not only in the initial phase of firm’s innovative development but also in the periods of commercialization of innovative products as well as during the period of high growth.
ACKNOWLEDGEMENT
The authors are grateful to the China National Social Science Foundation Grant Numbers 14JD009 and 16BGL028 for their support.
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