The current quantitative longitudinal study compares the performance of 135 small trade and service businesses that participated in a coaching project run by the Ministry of Industry, Trade and Labor in Israel, in an attempt to assess the effect of strategic planning, entrepreneurship, human and financial resources, and market orientation on business performance in a direct model (i.e., path analysis) as well as in a Structural Equation Model (SEM). Findings generally supported the hypotheses regarding entrepreneurship, financial resources and market orientation, which were found to affect performance in both models. Contrary to expectations, strategic planning and human resources did not affect performance in any of the models. Moreover, findings indicated some differences between the two statistical analysis methods. In the SEM analysis, the effect on performance of both entrepreneurship and market orientation was smaller than their effect in the direct model, while the opposite was true with respect to financial resources.
A. Introduction
Numerous studies have tried to assess the factors that lead to better performance of small businesses. These studies generally reflect the connection between one or two explanatory variables and firm performance (e.g., strategic planning and performance, Yusuf and Saffu, 2005; market orientation and performance, Kara et al., 2005). The construction of a model that includes several explanatory variables in examining the simultaneous relations between all these variables and performance in a longitudinal study would enhance the reliability of the research and our understanding of how these variables interact in improving performance. Moreover, financial performance represents the narrowest conceptualization of firm performance (Venkatraman and Ramanujam, 1986). Consequently, a wider approach that includes other performance indicators is needed.
The current study is quantitative longitudinal research on small trade and service businesses that participated in a coaching project run by the Israeli Ministry of Industry, Trade and Labor. The study included a comprehensive longitudinal assessment of the effect of four variables – strategic planning, entrepreneurship, resource usage (human and financial), and market orientation on performance of small businesses. These variables represent managerial, operational and behavioral activities within the firm, which in previous studies were found to have an affect on performance of small businesses (e.g., entrepreneurship and performance, Wiklund, 1999; market orientation and performance, Pelham, 2000; strategic planning and performance, Wijewardena et al., 2004). This broader approach enables a better assessment of the possible antecedents contributing to performance of small businesses. Furthermore, defining the construct of small business performance by using financial and operational measures as well as subjective and objective operational measures, facilitates a more comprehensive measurement of the effect that includes not only retrospective measures, but also reflects fluctuations in the business operation and its chances of future success (Covin and Slevin, 1989; Reichel and Haber, 2005; Venkatraman and Ramanujam, 1986).
First, theoretical background on the potential effect of each of the four variables – strategic planning, entrepreneurship, resource usage (human and financial), and market orientation – on performance is presented. Then the methods used in the research are explained, followed by the results. The last section provides a discussion of the results and the main conclusions, followed by the limitations of the current study and suggested future research.
B. Literature Review
Strategic Planning and Performance
Beginning in the mid 1970s scholars began to distinguish between small and large businesses in terms of needs, level of sophistication and range of strategic planning. Bracker and Pearson (1986), Rue and Ibrahim (1998), Perry (2001) and Wijewardena et al. (2004) have all formulated definitions of strategic planning, taking into account the uniqueness of small businesses and the fact that small businesses cannot draw on management and material resources in a manner similar to that of large organizations.
The findings of empirical studies indicate a correlation between strategic planning and performance. The findings, however, are mixed. A survey by Miller and Cardinal (1994) of twenty-six experimental studies identified a significant positive correlation between strategic planning and small business performance. Bracker and Pearson (1986) discovered a significant increase in income and remuneration per entrepreneur in businesses that prepared strategic plans (the highest of four designated levels of strategic planning) compared to other businesses. No significant increase was found in salary expenditures divided by the sum total of sales. Rue and Ibrahim (1998) found a significant differentiation in the rate of sales increase in small businesses that incorporated written planning (basic or sophisticated), as opposed to small businesses that did not. Wijewardena et al. (2004) defined three levels of planning: no written planning, basic planning and detailed planning. The findings indicated that the level of planning stands in direct proportion to the level of increase in sales. Yusuf and Saffu (2005) classified three levels of planning: low, moderate and high. A correlation was found only between increase in sales and the low level of planning. No correlation was found between strategic planning and increases in market share or profitability.
Entrepreneurship and Performance
Entrepreneurship as a process within the organization has been identified by scholars as a crucial element to the firm’s success (Davis et al., 1991). In the competitive environment in a world with an ever-more global economy, innovativeness and proactiveness can determine survival and ultimately success (Porter, 1990). Entrepreneurship is crucial since it initiates general economic development as well as improvement in the performance of individual firms. Moreover, entrepreneurship is a key factor in achieving competitive advantage and a larger financial return (Covin and Slevin, 1991). According to Zahra and Covin (1995), entrepreneurially oriented firms are capable of directing themselves to choice market segments, where they can charge premium prices prior to the penetration of competitors. Such firms detect changes in the market and promptly respond, which is why they are the first to commercialize new opportunities.
Many empirical studies have defined entrepreneurship based on the definition of Miller and Friesen (1982), which relies on three dimensions: innovativeness, risk-taking, and proactiveness. The findings of these studies support a significant positive correlation between entrepreneurship and small business performance (Covin and Slevin, 1989, 1990; Smart and Conant, 1994; Wiklund, 1999; Wiklund and Shepherd, 2003, 2005).
Human Resources, Financial Resources and Performance
Westhead et al. (2001) found that human and financial resources need to be incorporated into the research model they constructed for small businesses. The small business entrepreneur/owner is to be emphasized as a human resource of paramount importance. According to Storey (1994), the entrepreneur’s experience, expertise and abilities are generally considered to be primary parameters of influence over the firm’s survival and development. Mullins (1996) claimed that the entrepreneur’s decision-making capacity strongly affects organizational processes that constitute the foundations of competitive advantage as well as of growth. Rangone (1999) has defined the entrepreneur as a “unique” resource that supports all other resources.
Several studies shed light on the relationship between human or financial resources and small business performance. Cooper et al. (1994) found that human resources, and particularly the owner’s education, are correlated with growth. Moreover, knowledge of the industry and financial resources contribute to growth as well as to the firm’s survival. According to Westhead (1995), the founder’s experience affects performance and survival in hi-tech enterprises over a period of six years from the date of their establishment. Brush and Chaganti (1999) examined small trade and service-oriented businesses. Their study designates two dimensions of human resources – owner resources and owner commitment. A significant positive correlation was found between these two dimensions and net cash flow. They found no correlation between them and the log of employment growth. The findings of Westhead et al. (2001) support the hypothesis that if the firm’s founder possesses significant prior knowledge of the industry, it is to be expected that the firm will register profitability beyond the means of its competitors. Premaratne (2001) indicated a correlation between subsidies granted to the firm and increase in sales, but no correlation between subsidies and profitability. Wiklund and Shepherd (2005) found a significant positive correlation between access to capital and performance. Pena (2004) examined the relationship between human resources (e.g., education, management experience, prior entrepreneurial experience, implementation of ideas acquired in previous workplaces) and an increase in profit, sales and the number of employees. A positive correlation was found between education and the implementation of ideas acquired in previous workplaces, and increase in the number of employees and sales. However, Chrisman et al. (2005) that utilized education and prior experience as control variables, indicated on correlation between prior experience and an increase in the number of employees and in sales, but no such correlation with education was found.
Market Orientation and Performance
The concept of market orientation places the customer at the center of firm activity (Dalgic, 1998; Pelham, 2000). Market orientation leads to better satisfaction of customer needs and to an organizational obligation on the part of the employees (Narver and Slater, 1990).
Research conducted by Kohli and Jaworski (1990) enables a better understanding of the concept of market orientation and the types of behavior associated with the marketing concept. Their study lays a theoretical foundation regarding the expectation that market orientation will lead the firm to better performance.
Other studies also support the positive correlation between market orientation and performance in small businesses (e.g., Appiah-Adu and Singh, 1998; Homburg et al. 2002; Kara et al. 2005; Pelham, 2000; Shun-Ching and Cheng-Hsui Chen, 1998).
Based on the literature cited above, five basic hypotheses were defined:
H1: Strategic planning positively affects the level of performance of the small business.
H2: Entrepreneurship positively affects the level of performance of the small business.
H3: Human resources positively affect the level of performance of the small business.
H4: Financial resources positively affect the level of performance of the small business.
H5: Market orientation positively affects the level of performance of the small business.
These hypotheses, which have already been tested in previous studies as "stand-alone" hypotheses, were tested in our study using a model that simultaneously examines the relations between all these variables and performance in a longitudinal study. Such an approach, which has not been used previously in the context of the study of small businesses, can enhance our understanding of how these variables interact to improve performance. The method and the results are described in the following sections.
C. Method
Sample
Data was collected on the firms that participated in a coaching program conducted by the Israeli Ministry of Industry, Trade and Labor through two separate surveys at two designated times. (0) designates the time of contact between the firm and the coaching project; (1) designates a period of one year after the firm entered the project. The sample included only small trade and services firms to control the disparities between the various industries with respect to performance and the firm's profitability (Beard and Dess, 1981; Miller and Tolouse, 1986). The investigated unit in this research was the Small Business, defined by four parameters:
(a) Number of employees – five to fifty
(b) Firm age – over two years
(c) Independent business (subsidiaries or units of large companies were excluded)
(d) Business which is not dominant in the market in which it operates.
Between March 2004 and March 2005, 183 businesses completed the questionnaire at time (0). At time (1), 135 of those businesses completed the questionnaire again. Forty-eight small businesses did not remain with the study through completion. Of these, twenty-seven dropped out of the coaching project. Eleven could not be contacted, or alternately, the business had been shut down. Ten declined to continue their participation in the study.
Three stages preceded the final form of the questionnaire. The initial stage consisted of a brainstorming session, which included scholars with considerable experience in statistical research and construction of questionnaires. The second stage consisted of interviews with the owners of five small businesses. Some of the issues addressed during the interviews were the clarity of the questionnaire, how it corresponded to the specific industry in question, the clarification of ambiguous concepts or wording and the length of the questionnaire. An exploratory study was conducted in the third and last stage; twenty-seven questionnaires were distributed among small businesses participating in the standard coaching project. Preliminary statistical checks were carried out within this framework.
The questionnaires were distributed to small business owners by fax or e-mail, followed by phone contact.
Measures
Independent variable, Strategic planning: The level of strategic planning was examined by one primary criterion – whether the plans had been written, and by two secondary criteria – the detail and scope of strategic planning and the period of time it covered. The evaluation of strategic planning was based on a measurement instrument formulated by Robinson and Pearce (1983). To better grasp the secondary criteria, we defined five layers which indicated, with a rising level of detail, the strategic planning, replacing the two layers of the original questionnaire, as well as three possible time scales: up to one year, up to two years and up to three years, replacing the one-time scale of three years in the original questionnaire (see Appendix). The overall grading of the level of strategic planning was a product of the level of detail and the scope of time.
Independent variable, Entrepreneurship: Based on the measurement instrument developed by Covin and Slevin (1989, 1990), the entrepreneurship level was measured by six items, using a 7-point semantic differential type scale anchored by descriptive phrases (see Appendix). Cronbach alpha coefficients were calculated for both measurements: time (0) α = 0.68; time (1) α = 0.56.
Independent variable, Human resources: The level of human resources was measured using five items, based on the dimension of owner resources (Brush and Chaganti, 1999). Human resources measurement was carried out only at designated time (0). Three items were specified by years of experience: prior managerial experience, previous business ownership, and owner’s industry experience. The remaining two items examined the owner’s education: formal education – a four-point ordinal scale from 1, no formal education, to 4, Ph.D. level education, and business education – a four-point ordinal scale from 1, no business education, to 4, regularly and consistently participates in various business management courses (see Appendix). The overall grading of the business’s level of human resources was the equivalent of the grades' average. Factor analysis detected two dimensions: owner experience (three items, α = 0.77), and owner education (two items, α = 0.36). The last dimension was omitted from the research due to its low internal consistency.
Independent variable, Financial resources: Previous research indicates that financial resources are defined as any source of capital – cash capital, stocks and assets of various levels of liquidity. Furthermore, the capacity of raising funds is itself a financial resource. The measurement instrument applied to financial resources constituted these definitions and included three items. Each of the items was scored by the respondents on a Likert scale ranging from 1 = low extent to 5 = high extent (see Appendix). Cronbach alpha coefficients were calculated for both measurement times: time (0) α = 0.76; time (1) α = 0.81.
Independent variable, Market orientation: Based on the measurement instrument developed by Pelham (2000), market orientation level was measured using six items. For each of these items the respondents were required to provide a score using a 5-point semantic differential type scale anchored by descriptive phrases (see Appendix). Cronbach alpha coefficients were calculated for both measurement times: time (0) α = 0.78; time (1) α = 0.67.
Dependent variable, Small business performance: Firm performance is a multi-dimensional concept, the measurement of which is complex (Brush and Vanderwerf, 1992). According to Venkatraman and Ramanujam (1986), financial performance represents the narrowest conceptualization of firm performance and is measured through an examination of financial indicators. Operational performance consists of those key parameters which may lead to an improvement in financial performance.
Objective performance measures are most often financial ones. Nevertheless, even when they are available, scholars face difficulties in obtaining exact measurements (Dess and Robinson, 1984). Where small private businesses are concerned, gathering objective data is a difficult task, as this data is not available to the general public (Dess and Robinson, 1984; West and Meyer, 1998). Moreover, small businesses are very sensitive to the disclosure of information relating to their performance (Bantel, 1998; Covin and Slevin, 1989, 1990). Reichel and Haber (2005) utilized both objective and subjective measurements in their study. We have therefore defined the structure of small business performance by using both financial and operational measures, and including subjective as well as objective measures.
Subjective Assessment: For the subjective measurement we referred to the second part of a questionnaire modified by Covin and Slevin (1989) from an instrument developed by Gupta and Govindarajan (1984), which measures satisfaction. In both times [(0), (1)] respondents were asked to indicate, on a Likert scale ranging from 1 = highly dissatisfied to 5 = highly satisfied, the extent to which they are currently satisfied with their small business performance on each of the financial and operational performance criteria (see Appendix).
Objective Assessment: Respondents were asked to indicate at time (1) the rate at which the business’ performance changed throughout the previous year, in relation to each of the performance criteria (see Appendix), for example, Cash flow: below -20%, -20%, -15%, -10%, -5%, 0, 5%, 10%, 15%, 20%, above 20%.
The measurement instrument initially included thirteen subjective items and five objective items. A varimax rotated factor analysis carried out on the thirteen subjective performance measures and the five objective performance measures elicited the following factors:
1. Financial performance; subjective measurement – This consists of seven measures. Cronbach alpha coefficients were calculated for both measurements: time (0) α = 0.86; time (1) α = 0.79. The measure increase in number of employees was omitted.
2. Operational performance; subjective measurement – This consists of two dimensions: creating new opportunities and human resources. Creating new opportunities includes two measures, opportunities within the firm’s existing market and opportunities in new markets. Cronbach alpha coefficients were calculated for both measurements: time (0) α = 0.78; time (1) α = 0.80. The measure customer satisfaction was omitted. Human resources include two measures. Cronbach alpha coefficients were calculated for both measurements: time (0) α = 0.77; time (1) α = 0.77.
3. Financial performance; objective measurement – This consists of three measures: cash flow, rate of increase in sales and earnings before interest and tax (EBIT). A Cronbach alpha coefficient calculation was calculated once, for the change from time (0) to time (1); α = 0.81. The measure increase in number of employees was omitted.
4. Operational performance; objective measurement – This consists of one measure, fluctuation in market share, measuring the change from time (0) to time (1).
5. Overall success; subjective measurement – This is an overall subjective performance measure of the small business, taken at time (0) and time (1).
Control, Age, and Size of the Small Business: Age and size are the two control variables most commonly used. In most cases, the two variables are treated as one comprehensive control unit. Firm age and size can affect both its management techniques and the accuracy of the firm’s performance measurement. In this study age was measured by the number of years a business was in existence, and size was measured according to the number of full-time employees.
D. Results
Descriptive statistics and correlations among the research variables are presented in Tables 1 and 2. Research variables were encoded to simplify subsequent presentation of data, as follows:
Independent variables: Entrepreneurship (E); Market Orientation (MO); Strategic Planning (SP); Human Resources - Owner Experience (OE); Financial Resources (FR).
Dependent variables: Performance (P); Financial Performance, Subjective (FP_S); Operational Performance, New Opportunities, Subjective (NO_S); Operational Performance, Human Resources, Subjective (HR_S); Financial Performance, Objective (FP_O); Operational Performance, Objective (OP_O); Overall Success, Subjective (OS_S).
Table 1
Means, Standard Deviations, and Correlations for Mediating Variable E, MO, SP, OE, FR, and Dependent Variable P in Measurement Time (0)
Table 2
Means, Standard Deviations, and Correlations for Mediating Variables
E, MO, SP, OE, FR, and Dependent Variable P in Measurement Time (1)
Descriptive statistics and correlation coefficients (Tables 1 and 2) indicate the following:
- No significant correlation was found between strategic planning and performance, with the exception of a low, but significant, correlation with new opportunities (NO_S) at measurement time (0).
- No significant correlation was found between owner's experience (OE) and performance, with the exception of a significant correlation with human resources (HR_S) at measurement time (1).
- Both measurements indicate a significant correlation between entrepreneurship, financial resources, market orientation and each of the six indicators used to specify performance (OP_O, FP_O, OS_S, HR_S, NO_S, FP_S) with the exception of non-significant correlations between entrepreneurship, financial resources and human resources (HR_S).
- Results concerning the contribution of strategic planning and owner's experience (OE) to performance are inconsistent with previous studies. Possible explanations are:
The small percentage of small businesses which reported any sort of strategic planning
implementation in our sample: only 41 out of 183 (23.5%) businesses at time (0), and only 41 out of 135 (30%) at time (1).
Difficulties in understanding questions were related to owner's experience (OE), despite rephrasing of the questions after the exploratory study.
Two stages were employed in testing Hypotheses 1-5: Stage 1 - Path analysis was performed to assess the contribution of each of the five independent variables in explaining the variation in performance. Stage 2 – Testing the simultaneous relations between all research variables by Structural Equation Modeling (SEM) using AMOS 7 software.
Latent construct of performance was explored. Six parceled indicators (OP_O, FP_O, OS_S, HR_S, NO_S, FP_S) were used to specify the performance’s latent construct at measurement time (1), and four parceled indicators (OS_S, HR_S, NO_S, FP_S) were used to specify the performance’s latent construct at measurement time (0).
In SEM analyses, model fit was estimated using four fit indices: the non-normed fit index (NNFI) – values above .90 represent an acceptable fit (Bentler and Bonett, 1980); the comparable fit index (CFI) – values above .90 represent an acceptable fit (Bentler, 1990); the incremental fit index (IFI) – values above .90 represent an acceptable fit and the root mean square error of approximation (RMSEA) – values below .08 represent an acceptable fit (Steiger, 1980). We did not use the chi-square fit index due to its extreme sensitivity to large sample sizes. Factor loadings and correlations among the latent construct variables of performance are presented in Tables 3 and 4.
Table 3
Factor Loadings of Latent Construct (P), Measurement Time (1)
Table 4
Latent Construct Correlations (P), Measurement Time (1)
Stage 1: Using path analysis, we examined the influence of each of the five independent variables - strategic planning, entrepreneurship, financial resources, owner's experience and market orientation, in explaining the longitudinal effect on performance, while controlling for firm age and size. The research model for path analysis is presented in Figure 1. Results are shown in Table 5.
Figure 1
Path Analysis Model: The Longitudinal Effect of Each Independent Variables SP, E, MO, FR, and OE on P in Measurement Times (0) and (1), Controlled for Firm Age and Size
Table 5
Path Analysis Model: Standardized Parameters of the Longitudinal Effect of Each Independent Variables SP, E, MO, FR and OE on P in Measurement Times (0) and (1), Controlled for Firm Age and Size.
Stage 2: Structural Equation Modeling (SEM) was employed for examining the manifested indicators' – strategic planning, entrepreneurship, financial resources, owner's experience and market orientation – longitudinal effect on the latent construct of performance, controlled for age and size of the firms. The model fit the data well (TLI = .866, CFI = .924, IFI = .932, RMSEA = .051); the results are presented in Figure 2.
Strategic planning (β = -.005, p < .994) and owner's experience (OE) (β = .014, p < .861) were found to have an insignificant effect on performance, while entrepreneurship (β = .211, p < .011), market orientation (β = .357, p < .001), and financial resources (β = .213, p < .019) had a significant positive effect on performance.
Figure 2
Standardized Parameters of the Longitudinal Effect of SP, E, MO, FR, and OE on P in Measurement Times (0) and (1), Controlled for Firm Age and Size
The results, as described in stages 1 and 2, indicate as follows:
- Hypothesis 1 was rejected. Both stages indicate an insignificant positive effect of strategic planning on performance.
- Hypothesis 2 is supported. Both stages indicate a significant positive effect of entrepreneurship on performance. Entrepreneurship’s coefficient decreased from β= .325 in the path analysis to β = .211 in the SEM analysis.
- Hypothesis 3 was rejected. No effect on performance was detected in either stage in relation to owner's experience (OE).
- Hypothesis 4 is supported. Path analysis indicates a significant positive effect of financial resources on performance (β = .191, p < .056), while SEM analysis indicates an even higher coefficient and significance (β = .213, p < .019).
- Hypothesis 5 is supported. Both stages indicate a significant positive effect of market orientation on performance. Market orientation’s coefficient decreased from β= .407 in the path analysis to β = .357 in the SEM analysis.
E. Discussion and Conclusions
Chrisman et al. (2005) found a correlation between prior experience and an increase in the number of employees and sales; contrary to these findings, our findings suggest that owner experience (OE) does not have a significant effect on performance, in either the path analysis or the SEM analysis. These research results are in line with previous inconclusive findings regarding the effect of human resources on performance. This is similar to Chrisman et al. (2005), which did not support the correlation between education and performance measures, and Pena (2004), which did not support the correlation between experience measures and performance.
The lack of evidence for the effect of strategic planning on performance in either path analysis or SEM analysis, contradicts Miller and Cardinal's (1994) findings, which identify a significant positive correlation between strategic planning and small business performance. In contrast, it may be considered as a continuation of previous research that found mixed results regarding the correlation between strategic planning and performance (e.g., Bracker and Pearson, 1986; Yusuf and Saffu, 2005). However, it is important to note that only 23% to 30% of the subjects in our study had implemented any form of strategic planning.
Our findings indicate that financial resources have a significant positive effect on performance, and support previous research findings such as Wiklund and Shepherd's (2005).
In contrast to previous findings regarding the effects of strategic planning and owner's resources, the literature reflects much more conclusive evidence for the correlation between market orientation, entrepreneurship and performance. Our research findings, both in the path analysis and SEM analysis, support the findings of studies suggesting a significant positive correlation between market orientation and performance (Appiah-Adu and Singh, 1998; Homburg et al. 2002; Kara et al. 2005; Pelham, 2000; Shun-Ching and Cheng-Hsui Chen, 1998) and entrepreneurship and performance (Covin and Slevin, 1989, 1990; Smart and Conant, 1994; Wiklund, 1999; Wiklund and Shepherd, 2003, 2005).
Two stages of statistical analysis have been conducted to evaluate the hypotheses. The research model was constructed to highlight existing differences between the unique effect of each independent variable (i.e., path analysis) and the overall simultaneous effect, while taking into account the covariance between independent variables (using SEM analysis). Results indicate some differences between the two statistical analysis methods. When using SEM analysis, both entrepreneurship and market orientation experience are decreased in their level of effect on performance, while the opposite occurs regarding financial resources.
The inclusion of the longitudinal effect and controlling for firm age and size, both in the path analysis and SEM analysis, enhance the credibility of the research findings.
Based on Venkatraman and Ramanujam's (1986) statement that financial performance represents the narrowest conceptualization of firm performance, we tried to establish a more comprehensive performance construct by adding operational performance measures to the financial measures. Adding operational measures enabled a more comprehensive and credible assessment of the effect of the studied variables on performance.
In summary, it is our belief that a longitudinal study employing path analysis and structural equation modeling, which enable simultaneous accounting for the effects of all variables on small business performance, provides more credible results than have been previously obtained. We therefore can conclude from our findings that small businesses performance is mainly affected by market orientation, entrepreneurship and accessibility of the firm to financial resources. The effect of strategic planning is questionable and could not be supported in our study, due to the small number of small businesses that employed even the lowest level of strategic planning.
F. Limitation and Future Research
This study is based on a quantitative longitudinal research of small trade and service businesses that participated in a coaching project initiated by the Israeli Ministry of Industry, Trade and Labor. Its findings must be interpreted with care, as it was conducted only within specific industries. The latent constructs used in the SEM analysis that were computed for performance have not been used in previous studies.
The results extracted from path analysis varied significantly from those of SEM analysis regarding the level of the effect on performance carried by each of the independent variables. The reason for this may be the simultaneous relations between all research variables and the covariance of independent variables taken into account by the SEM analysis. In that regard, the finding that the correlation between market orientation and entrepreneurship was the primary cause for differences between the path analysis results and the SEM results should be further investigated in future research.
Only a small portion of subjects in this study implemented some form of strategic planning. The significant positive correlation between strategic planning and financial performance found in that sub-sample [N = 35, P = 0.037, β = .354 in measurement time (1)] suggests the need to take the effect of strategic planning on performance into consideration, despite mixed results in the literature (e.g., Yusuf and Saffu, 2005).
Previous research findings regarding the effect of human resources on performance are inconclusive (Pena, 2004; Chrisman et al., 2005; Haber and Reichel, 2007). However, we believe that this study’s findings, which did not show a significant correlation with human resources are a result of technical problems of clarity and structure of the questions, as well as the manner of data collection, and therefore further investigation is needed to establish a more adequate measurement instrument that will allow an assessment of the effect of human resources on small business performance.
Finally, the variable increase in number of employees, in both types of measurements – subjective and objective, was omitted from further analysis due to the factor analysis results. It remains to be investigated if an increase in number of employees can in fact serve as a relevant measure of performance.
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