The relationship between innovation and knowledge strategies: its impact on business performance

International Journal of Knowledge Management Studies

Volumn 1, Issue 3-4, 2007, Pages 388-422

  Donate Manzanares, Mario ^a   Guadamillas Gómez, Fátima ^a  

^a UNIVERSITY OF CASTILLA LA MANCHA   (Spain)

Author keywords

firm performance; innovation strategy; knowledge strategy; moderator effect

Indexed keywords

EID: 76349089861     PISSN: 17438268     EISSN: 17438276     Source Type: Journal    
DOI: 10.1504/ijkms.2007.012532     Document Type: Article

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91. This is not to say that we consider TIS from a functional perspective. Rather, this strategy would be linked to the manufacturing area (owing to the consideration of product and process technologies) but it is not an exclusive strategy for the operations management department.
92. Although some authors emphasise some knowledge creation tools (see Alavi and Leidner, 2001; Alavi and Tiwana, 2003), in this study it has been considered that for this process to be able to develop, organisational conditions that promote it are more important (Nonaka and Takeuchi, 1995). Therefore, they have already been included as ‘implementation support systems’. On the other hand, some of these tools (especially those based on IT) are the same that have been considered as storage, transfer and application tools, so their non-inclusion avoids some item repetition. In any case, the indicated KM methods (or tools) are not separable from the knowledge creation function (Grant, 2002).
93. The Barlett sphericity test explores the null hypothesis that the correlations matrix is an identity matrix.
94. The measurement of survey fitness of Kaiser-Meyer-Olkin is an index, which compares the magnitude of the correlation coefficients observed with the magnitude of the partial correlation coefficients. If the value of the measurement of survey fitness is under a value of 0.6, wich is considered the limit (Hair et al., 1999), it may not be pertinent to use the factor analysis with these data.
95. The function 1/√x was used to normalise the variables.
96. This variable had to be transformed through the natural logarithm Ln(x), in order to normalise it.
97. In this case, four factor analyses were applied, one for each of the dimensions considered. The reason was to confirm the existence of unidimensionality of some constructs (conception, objectives), and in other cases to find out the structure of data and to analyse the discriminating validity (KM tools, implementation support systems).
98. Pearson coeffients show negative values due to the transformation made on the original variables in order to normalise them (1/√x). Correlation between protection methods based on external factors and the number of product innovations obtained over the past 3 years was measured, because it is more probable that products are protected through patents (rather than processes). Also, the correlation between protection methods based on internal factors and the number of process innovations obtained over the past 3 years was measured, because it is more probable that these innovations are protected by methods based on hiding knowledge (Grant, 2002).
99. All of the objective measures were calculated as an average of the data that corresponds to the past 2 years (based on information availability). Therefore, the financial results available for 2001 and 2002 were used. On the other hand, each of the subjective measures of financial performance (ROE, ROS and sales growth) were calculated as the average of three items: average performance for the past 3 years, average performance for the past 3 years in comparison with the main competitor and average performance for the past 3 years compared with the industry average. 10SABI (Sistema de Análisis de Balances Ibéricos): Iberian Scorecard Analysis System. Fomento de la Producción 30.000: Manufacturing Promotion-30.000.
100. SABI (Sistema de Análisis de Balances Ibéricos): Iberian Scorecard Analysis System. Fomento de la Producción 30.000: Manufacturing Promotion-30.000.
101. The control variable age of the firm was eliminated from the analysis, because it damaged the significance of the model. Also, the dummy variables corresponding to sectorial belonging were eliminated, because there was not any significant increase of the R2 coefficient. The only variable included in the model is company size (transformed through the 1/√x function), because previously this has appeared to be significantly correlated with business performance.
102. Moreover, Pardo and Ruiz (2002) prove that if the value showed by the condition index is under 15, there is no collinearity problem between the variables. Problems would begin to arise with values between 15 and 30, and there would be serious problems with values over 30. This value is 4.909 for the second model.
103. As previously indicated, a series of exploratory factor analyses were applied to the different dimensions of KS to confirm the structure of the data (Hair et al., 1999) and the unidimensionality of the different constructs previously specified. In this case, it is important to remark that when the factor analysis is applied to the knowledge management tools dimension, the construct knowledge protection was grouped into two factors. The first one included patents, brands and first-mover advantages. This was called ‘protection based on external factors'. The second one grouped together secrecy, knowledge characteristics and complementary assets. This one was called ‘protection based on internal factors'. It is also remarkable that following the indications of Hair et al. (1999), average values were used as inputs in the cluster analysis and not factor scores, as certain collinearity is advisable.
104. The alternative procedure is called ‘k-means’, which requires the previous specification of the number of groups to have and to only use a method for the agglomeration of the cases (Pardo and Ruiz, 2002).
105. This method begins choosing the more distant k cases (k is the number of clusters specified a priori), and next the cases are grouped by their proximity to the closer centre and the value of the centre is updated as often as new cases are incorporated. When all the cases have been assigned to one of the k conglomerates, an iterative process begins to calculate the final centres of these k clusters (Pardo and Ruiz, 2002).
106. On this point, Hair et al. (1999) state that the selection of the reference category is a researcher’s decision, based on the research objectives. But the selection of one or another reference category does not change the research results, due to the fact that dummy variables are symmetrical.
107. A different model was built for each group of interactions. Only the interactions that corresponded to each of the components (variables) of the TIS were considered, in order to make their interpretation easier (see Zahra and Nielsen, 2002; Flor, Camisón and Oltra, 2004).
108. Here, we use the ‘equifinality’ term to point out that, on their own, knowledge strategies would not significantly impact on business performance. Thus, if equifinality arise, KS would need adjusting with other elements (e.g. technological strategy, business strategy, organisational design) to achieve such an impact.
109. The estimation of the ‘non-standardised’ coefficients of the independent variable in each of the regression model with interactions – considering the reference group (all the strategies would adopt value 0 and only the independent (quantitative) variables remain in the model) – corresponds to the slope of the variable of the regression of the dependent variable over the independent one in this group, that is, to the effect of the interaction of that group.
110. This implies carrying out some additional calculations that are not shown in this study: three new codifications of the dummy variables (one for each calculation of the whole model for each reference variable), calculation of interaction terms for each modification, specification and estimation of the new regression equations.
111. This does not necessarily imply that business performance is better than the rest of the groups, but the interaction has a significant and positive effect for that specific group.