MEASUREMENT MODEL

The following sections discuss the measurement model from the pretest and the measurement model from the final data. The pretest was conducted in May 2009 on bank middle managers. Based on the analysis slight changes were made to items for the final survey. After the final surveys were administered, a confirmatory factor analysis was conducted to test the final measurement model.

5.1.1 Measurement Model from Pretest

The pretest survey was administered to 31 middle managers across 5 different banks in March 2009. Of the 31 respondents, approximately 20% were with a large, multi-national bank; 50% were with regional banks; and 30% were with community banks. The pretest distribution of bank demographics did not necessarily mirror the expected distribution of the final sample, but it did allow for adequate representation for each banking group. The respondents were all at the senior vice president level or higher. Surveys were administered on paper.

The researcher reviewed each survey to ensure that responses looked reasonable and respondents were deliberate in their responses. All 31 surveys were retained and used in the analysis. There were a total of 12 missing values in the data set. Maximum likelihood estimation method was utilized to impute missing values. The distributions, means, and standard deviations for each variable were examined for normality and appeared acceptable. The covariance matrix, displayed in Table 4, and maximum likelihood estimation procedure was utilized in LISREL to conduct a confirmatory factor analysis.

The four factor model, forwarded by Zahra and George (2002) and measured by Jansen et al. (2005), with all the indicators had a Chi-square of 409.313 (p<=0.001), RMSEA of 0.0954 (90% CI 0.0591; 0.126), and CFI of 0.591. The Chi-square statistic indicates that the model does

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not fit the data. The CFI is far below acceptable; however, the RMSEA approaches an acceptable level. Given the small sample size and that the objective of the CFA was to identify weak items, these scores were considered acceptable to focus attention on the factor loadings.

Standardized loadings, standardized residuals, and modification indices were analyzed and several items appeared problematic. First, item A2 and A5 had low standardized loadings with their assigned variable “Acquisition”. A2 is a reverse-coded question which states “Our bank prefers to rely on knowledge that originated within our organization rather than seek out knowledge that originated outside of our bank’s boundaries.” This question was drafted by the researcher and one of the questions most altered from the original Jansen et al. (2005) survey. Therefore, it was determined that the indicator would be excluded from the final survey. A5 states “Employees regularly approach third parties such as competitors, consultants, partner organizations, newly acquired organizations, and newly hired bankers to acquire new knowledge.” After discussing the question with participants of the pretest it was determined that the position of the respondents for the pretest may have a different understanding of this question than top executives. Thus, it was determined that the question would remain in the final survey. Additionally, interviews following the pretest revealed that the wording of the question may unintentionally limit the respondent’s view of a third party. Thus, the wording of the question was altered to read “We regularly approach third parties to acquire new knowledge (e.g. clients, partner organizations, newly acquired organizations, competitors, consultants, and newly hired bankers).”

Second, T2 had a low standardized loading on its assigned variable “Transformation.” The item states, “Our employees’ record and store newly acquired knowledge for future reference.” Upon follow-up interviews it was revealed that the position of the respondents may have a different understanding of this question than top executives would possess and so the item was retained.

Third, items E2, E4, E5, E6, E7 and E8 all had relatively high but negative loadings with the exploitation factor and E9 had a particularly low loading with the exploitation factor. The wording and intent of items E2, E4, E5, E6, E7, E8, and E9 were reviewed and despite their negative loadings, it was determined that items E2, E4, E5, E6, E7 and E8 best represented the theoretical underpinnings of the exploitation construct. It also appeared that the items that had the highest loadings for the exploitation factor in the initial CFA (E1 and E3) were actually the

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problematic items and caused the negative loadings for E2, E4, E5, E6, E7 and E8, which best represented the theoretical underpinnings of “exploitations.” Items E1 and E3 were obtained from the Jansen et al. (2005) survey and test the extent to which the organization has clearly defined roles and responsibilities for employees (E1) and has employees who share a common language for products and services (E3). After further consideration of these items, it seemed plausible that these items actually do have a negative impact on exploitation. For example, March (1991) discusses how a common language and clearly defined responsibilities may decrease the likelihood that employees explore other alternatives or deviate from their accepted norms. Thus, it serves to reason that the less likely a group is to explore new alternatives or deviate from the accepted standard, the less likely a group would be to incorporate new knowledge to develop something new. Therefore, it was decided that E1 and E3 would be omitted from the analysis presented here. However, it was also decided that E1 and E3 would be included in the final survey because poor performance on the pretest might have been caused by the level of the respondent. The pretest was completed by middle managers, and it seemed possible that responses by middle managers to these items may differ from those from top management.

After reviewing these model weaknesses, a second 4-factor, CFA model was tested using the same data but omitting items A2, A5, T2, E1 and E3, and E9 discussed above. Using the same data to test a second model inherently weakens the validity of results by taking advantage of specific variations within this data set. Prior to publication it will be necessary to collect additional data to test a second model but due to resource limitations this analysis utilized original pretest data.

Results of the CFA support a 4-factor model with Chi-square of 189.769 (p<=0.001), RMSEA of 0.0899 (90% CI 0.0284; 0.132), and CFI of 0.768. Furthermore, results of the chi- square difference test for the first model tested and second model tested suggested a significant improvement in model fit (Chi2diff=219.544, dfdiff=117, p<=.000). Standardized loadings, attached in Table 5, appeared reasonable. Additionally, the correlations between the 4 factors, attached in Table 6, were analyzed. Higher correlations were noted between factors in the same subgroup (i.e. potential and realized absorptive capacity), consistent with theory. Furthermore, correlations between factors in different subgroups appeared acceptable and similar to those

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reported by Jansen et al. (2005). Lastly, standardized residuals were reviewed for problems and all error variances appeared consistent with theory.

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=============================== 5.1.2 Measurement Model from Final Study Survey

The final survey used to test the hypotheses in this dissertation consisted of 22 items to measure absorptive capacity (see Appendix A). In the final survey, four items assessed knowledge acquisition capabilities; four items assessed knowledge assimilation capabilities; six items assessed knowledge transformation capabilities; and eight items assessed knowledge exploitation capabilities. Items were measured on a seven point scale. For the final data collection, two-hundred sixty surveys were returned with 16 containing missing data values for various absorptive capacity items. Surveys with missing values were excluded from the CFA leaving 244 final surveys. Univariate distributions, descriptive statistics, and the covariance matrix were used to assess normality and appeared to support the assumption of normality. A correlation matrix of the final data with descriptive statistics is presented in Table 7.

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The 4-factor model (i.e. acquisition, assimilation, transformation, and exploitation) forwarded by Zahra and George (2002) and tested by Jansen et al. (2005) had relatively poor fit. The 4-factor model, tested by entering the covariance matrix into LISREL and utilizing the maximum likelihood estimation method, had a Chi-squareof 756.060 (p<=0.001), RMSEA of 0.106 (90% CI 0.0979; 0.1140), and CFI of 0.968. CFA results for a 4-factor/22 item model are illustrated in Figure 4. In addition to the poor fit (Kline, 2005), the high correlation between factors was notable and problematic. The factor correlation matrix, presented in Table 8, reveals a high correlation (r = 0.85) between the factors that measure externally-oriented absorptive capacity (i.e. acquisition and assimilation) and a high correlation (r = 0.92) between the factors

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that measure internally-oriented absorptive capacity (i.e. transformation and exploitation). These results may indicate that while conceptually a 4-factor model might best describe the underlying components of absorptive capacity, in practice managers have difficulty mentally teasing apart the components. A further explanation for the results may be that while conceptually all four aspects of absorptive capacity play a unique role in a firm’s ability to utilize external knowledge, specific knowledge practices may support multiple dimensions of absorptive capacity simultaneously.

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Due to the poor results of the 4-factor analysis, a 2-factor model (i.e. externally- and internally-oriented absorptive capacity) was tested. Results of the CFA for a 2-factor/22 item model, illustrated in Figure 5, show that reducing the model to 2 factors reduced the correlations between factors but also decreased model fit. The correlation between the externally- and internally-oriented absorptive capacity factors was 0.699. While this correlation remains higher than desired, the correlation is consistent with theory because both externally- and internally- oriented absorptive capacity are dimensions of the same underlying construct. However, fit indices for the 2-factor model worsened. The 2-factor model including all 22 items, had a Chi- square of 976.61 (p<=0.001), RMSEA of 0.126 (90% CI 0.119; 0.135), and CFI of 0.953.

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Because of the poor fit indices for the 2-factor model, standardized loadings and modification indices were examined for poorly performing items and it was concluded that several items should be eliminated. First, items A1, B2, B4, T1, T2, E1, and E5 appeared problematic because of the low standardized loadings. After reviewing the items and discussing

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items with bank executives, it was determined that in light of the recent banking crisis, items A1, B2, B4, and T1 no longer had the same meaning as in previous years. These items all discuss looking to changes in the markets (see Table 3 for a detailed list of Jansen et al. (2005) items). At the time executives were completing the survey, they were dealing with the 2009 banking crisis. It is likely the nature, salience, and relatively mandated response to the crisis impacted executives’ perception of their attention to external factors, speed of response, and ability to create new products for customers. Therefore, it was decided that these items could be excluded from the measurement model and that the remaining items for externally-oriented absorptive capacity effectively tapped the core principles underlying the externally-oriented absorptive capacity construct.

Additionally, it was determined that items E1 and E5 could be eliminated. Items E1 and E5 test the extent to which organizations have a clear understanding of how activities should be performed and whether employees have a common language concerning business activities, respectively. In the pretest, items E1 and E5 had negative standardized loadings on the exploitation factor. As mentioned in the pretest section of this dissertation, it seemed plausible that these items actually have a negative impact on exploitation. For example, March (1991) discusses how a common language and clearly defined responsibilities may decrease the likelihood that employees explore other alternatives or deviate from their accepted norms. Thus, it serves to reason that the less likely a group is to explore new alternatives or deviate from the accepted standard, the less likely a group would be to incorporate new knowledge to develop something new. Unlike the negative loadings in the pretest, items E1 and E5 had positive but low standardized loadings using the final data, but due to the overall poor performance of these items and the dubious theoretical relationship, it was decided that E1 and E5 could be omitted from the measurement model and that the remaining items adequately tapped the core principle underlying the internally-oriented absorptive capacity construct.

Lastly, it was determined that item T2 could be omitted from the final measurement model. Item T2 had a low standardized loading in both the pretest data analysis and final data analysis. Item T2 measured the extent to which employees record and store newly acquired knowledge for future reference (see Table 3 for precise question phrase). After talking with bank executives, it was determined that item T2 has a different meaning in the banking industry than in other industries. Item T2 states “employees record and store newly acquired knowledge for

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future reference.” Item T2 is intended to capture a dimension of the transformation of new knowledge that involves recalling important external knowledge to transform that knowledge into something new. However, through discussions with bank executives it was determined regulatory requirements and best practices concerning the collection and documentation involved with collecting client financial data, item T2 was more likely to be interpreted by bank executives as a due diligence, monitoring and control issue. Thus, T2 was eliminated from the final measurement model.

Consequently, the CFA testing the final 2-factor model and using the remaining 15 items resulted in a Chi-square of 325.985 (p<=0.001), RMSEA of 0.102 (90% CI 0.0896; 0.114), and CFI of 0.973. Furthermore, a Chi-square difference test, evaluating the relative fit of the 15 item model to the 22 item model, was significant (p<=0.001) in support of a 15 item model. Additionally, a Chi-squared difference test was used to assess the relative fit of a 2-factor model to a 1-factor model and supported the 2-factor model (Chi2diff=431.92, dfdiff=1, p<=.000). Standardized loadings, factor correlations (r = 0.689), modification indices, and standardized residuals were examined and appeared reasonable and consistent with theory. Figure 6 presents a graphical representation of the measurement model including standardized loadings and factor correlations from the CFA.

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A limitation of the method used to test the measure model is the use of the same data set in testing multiple measurement models. Using the same data to test multiple models capitalizes on specific variations within this particular data set. Therefore, prior to publication it will be necessary to collect additional data to test the measurement model.