Do resource configurations vary by age of venture? For this analysis we divided the pooled sample into two groups based on age of the business, firms less than 10 years old and firms older than 10 years, recognizing that this division combines businesses at various stages of any life cycle model regardless of calendar age. However, firm age by calendar year does provide an objective and comparable measure of the amount of time which the companies have been able to spend on activities related to the business. Previous researchers have used age as a basis for differentiating between young and old ventures (Fredrickson, et al, 1987). Further, we were interested not so much in distinguishing between phases, but in determining if resource mixes were different depending on age. Means and standard deviations for the sample are given in Table 3. Industry was operationalized using manufacturing and service sectors, and sample descriptives show 60% service and 40% manufacturing. The mean size of the businesses was measured both by number of employees (19.57) and sales ($7M). Business owners were 32.2 % female, averaged 49 years, and 77.6% were married.
Means and Standard Deviations of Sample Measures
|Individual Characteristics||Company Characteristics|
|Age||49.07 10.14s.d.||Age||13.05 11.16s.d.|
|Gender (% Female)||32.20%||Industry (% Service)||59.20%|
|Marital Status (% married)||77.60%||Percent Ownership||70.30%|
|Sales 1994||$7.02 MM 3.35E+07 s.d.|
|Employees 1994||19.57 35.84 s.d.|
The means and standard deviations of resource measures for the full sample and subsamples based upon age of the business are reported in Table 4. The results of t-tests show relatively few differences in perceptions of resource favorability between respondents from younger businesses and older businesses. Expertise in technology and telecommunications and international business education of the owner/founder is greater in younger firms while the perception of access to debt is more favorable in older firms. Owner years in the firm and in the position are both more favorable in older firms. Whereas this can be partially explained as an artifact of the measure, it may also be that considered as an indicator of the perceived positive contribution of experience.
The analysis of the means of resource measures by resource types informs us as to what types of resources are considered more favorable by respondents in younger firms and those in older firms. Table 5 provides a summary of the five resources reported respectively as most favorable and least favorable. Equipment, a measure of physical resource, is regarded most favorably out of all resource measures by firms in both age categories. Firms under ten years reported several organizational resources; unique products or services, customer service capabilities, operating efficiencies, and cost structure as other most favorable resources. The expertise of the respondent in technology and telecommunications was a human resource also highly valued. Firms older than ten years also rated organizational resources among the top five, however, the human capital resource of years of experience in the firm was ranked second most favorably within the older firms.
Financial measures, access to equity and access to debt were viewed more unfavorable by respondents in both firm age groups. Respondents in the older firms rated having employees with international experience and their own international business experience as less favorable. And finally, respondents in younger firms lack the advantage of human resource experience years in both the position and the firm and therefore rated those measures less favorably. Our resource questions, however, go beyond the significance of individual resource measures to the relationship between configurations of resources and stage of the firm as represented by firm age. In order to more carefully evaluate those configurations we test the relationship. between resource measures through confirmatory factor analysis, predicting the data reduction procedure to reflect our theoretical resource categories. We test the subsamples separately to see if the resources do create different configurations based upon age of the firm. We use a varimax rotation and set eigenvalues to a minimum of one. Physical and social resources are not included in the factor analysis since they are single item measures. Results shown in Tables 6A and 6B report findings of different resource configurations.
Means, Standard Deviations, and t-test Results
Full Sample and Firm Age Comparison: Resource Measures
|Full Sample||Firms <10 yrs n=71||Firms>=10 yrs n=74||t-test|
|Intnl work experience||3.59||3.64||3.57||0.27|
|Years in firm||3.09||2.13||3.96||-10.46***|
|Years in position||3.09||2.26||3.83||-7.59***|
|Years of education||3.35||3.41||3.26||0.92|
|Intnl Bus Education||3.12||3.38||2.91||2.04*|
|Employees -intnl exp||3.11||3.25||3.04||0.75|
|Customer svc capabilities||3.82||3.91||3.73||0.83|
|Access to debt||3.08||2.88||3.23||-1.53a|
|Access to equity||3.19||3.11||3.25||-0.57|
a p<.10 * p<.05 **p<.01 ***p<.001
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Last Updated 03/19/98