An investigation of sociological influences on the relationships between psychological traits and entrepreneurial orientation of used car entrepreneurs.

ABSTRACT

Sociological influences are an important factor in the success of an entrepreneurial venture. Sociological factors such as education and a supportive environment may have a moderating impact on the relationship between psychological traits and entrepreneurial orientations. A cross-sectional study was conducted on used-car entrepreneurs in a Southern Metropolitan Statistical Area (SMSA). Results of the study support significant positive relationships between psychological traits and entrepreneurial orientations. Moderated regression results support that levels of education moderate the relationships of three of the four sub-constructs of psychological traits and entrepreneurial orientations. A discussion of the findings is provided as well as directions for future research.

INTRODUCTION

Definitional inconsistency and vagueness in understanding who is an entrepreneur and what features constitute entrepreneurial orientation were succinctly pinpointed by Gartner (1989, p. 47) when he advanced an excerpt from the work of Cole (1969):

   My own personal experience was that for ten years we ran a research
   center in entrepreneurial history, for ten years we tried to define
   the entrepreneur. We never succeeded. Each of us had some notion of
   it--what he thought was, for his purposes, a useful definition. And
   I don't think you're going to get farther than that (Cole, 1969, p.
   17).

Recent reviews of the theoretical and empirical research in the entrepreneurship literature have found changes in the dilemma advanced by Cole some thirty-five years ago (Aldrich & Kenworthy, 1999; Aldrich & Martinez, 2001; Busenitz & Barney, 1997; Carland, Hoy, Boulton & Carland, 1984; Cole, 1969; Gartner, 1988, 2001; Knight, 1921; Lee & Peterson, 2000; Lumpkin & Dess, 1996; Lyon, Lumpkin, & Dess, 2000; Schumpeter, 1934; Shane & Venkataraman, 2000).

Brockhaus and Horowitz (1985), in their review of the psychology of the entrepreneur, cautioned that no generic definition of the entrepreneur exist, or if there is, we do not have the psychological instruments to discover it at this time. Lumpkin and Dess (1996) asserted, "... efforts have served to point out the various dimensions of the entrepreneurial process, they have not led to any widely held consensus regarding how to characterize entrepreneurship. This lack of consensus has impeded progress for researchers toward building and testing a broader theory of entrepreneurship, and has made it especially difficult for them to investigate the relationship of entrepreneurship to performance." Hornaday (1992, p. 12) continued, "there is no accepted definition-working or otherwise- of the terms--'entrepreneur' and 'entrepreneurship' ... the lack of consensus...ensnares nearly every empirical or theoretical research effort."

Many researchers have defined entrepreneurship as the creation of a new venture or creation of a new organization (Gartner, 1988). Lumpkin and Dess (1996) are of the opinion that entrepreneurship encompasses every step taken by an entrepreneur in entry to a new business and its concomitant problems of new start-ups. Entrepreneurial orientation encompasses the processes, methods, practices, and decision-making styles managers use to act entrepreneurially.

The entrepreneurial orientation concept as applied to a firm has its origin in the strategic management literature (Bourgeois, 1980; Lumpkin & Dess, 1996; Miles & Snow, 1978). Previous research provided strong empirical and theoretical support for three dimensions (innovativeness, proactiveness, and risk taking) which comprise entrepreneurial orientation (Covin & Slevin, 1989, 1991; Miller, 1983; Miller & Friesen, 1982). Lumpkin and Dess (1996) expanded the dimensions to five with the inclusion of autonomy and competitive aggressiveness. The five dimensions or some of these may determine the success of new business formation (entrepreneurship) or the successful managing of a new business (entrepreneurial orientation). Lumpkin and Dess' contention is augmented by Gartner's (1985) perspective that a new start-up business venture is a multidimensional phenomenon suggesting that each dimension should be considered collectively, not alone, to determine the success of entrepreneurship and/or entrepreneurial orientation.

The "psychological traits" approach to entrepreneurship has been criticized by a number of researchers as unsatisfactory and questionable (Aldrich & Zimmer, 1986; Gartner, 1988; Low & Macmillan, 1988) in explaining entrepreneurial behavior and performance. "In the trait approach the entrepreneur is assumed to be a particular personality type, a fixed state of existence, a describable species that one might find in a picture in a field guide" (Gartner, 1988, p. 48). Gartner proposed that entrepreneurship should be analyzed from the perspective of what an entrepreneur does and not what he is. This paper will take an integrative stance that personality traits viewed alone as suggested by Gartner (1988) is not adequate to explain the phenomenon of entrepreneurship.

As suggested by Gartner (1988) and Vesper (1980), the creation of an organization is a complex process and a contextual event, the outcome of many influences. Vesper (1980) pointed out that the more education and experience an entrepreneur has had in business (especially small business), the more likely it is that the current venture will be a success. Experience enables the entrepreneur to identify potential problems and deal with them before they destroy the venture. To some extent, managerial ability will be a function of the entrepreneur's education and experience, but it is also an inborn skill, which some entrepreneurs are not able to develop. Vesper (1980) further asserts that management practices affect entrepreneurship. "Thus, although there tends to be a generally positive correlation between good management practices and successfulness, the place where their role is strongest tends to be more downstream of startup than prior to or during startup" (p. 51).

This study fosters the aforementioned assertions that demographics and education, and supportive environmental sociological variables moderate the need for achievement, internal locus of control, propensity for risk taking and tolerance for ambiguity psychological traits to enhance entrepreneurial orientation.

PURPOSE OF STUDY

This study empirically examined how sociological factors moderate the relationships between psychological traits and entrepreneurial orientation among entrepreneurs in the used car industry in a "Deep South" Standard Metropolitan Statistical Area (SMSA).

Study Hypotheses

Hypotheses are formulated to empirically investigate (1) the relationship between psychological traits and entrepreneurial orientation and (2) the moderating influences of sociological influences on the relationships between psychological traits and entrepreneurial orientation:

[H.sub.1a]         Need for achievement is positively related to
                     entrepreneurial orientation.

[H.sub.1b]         Internal locus of control is positively related to
                     entrepreneurial orientation.

[H.sub.1c]         Tolerance for ambiguity is positively related to
                     entrepreneurial orientation.

[H.sub.1d]         Risk taking propensity is positively related to
                     entrepreneurial orientation.

[H.sub.2a]         Levels of education moderate the relationship
                     between need for achievement and entrepreneurial
                     orientation.

[H.sub.2b]         Levels of education moderate the relationship
                     between internal locus of control and
                     entrepreneurial orientation.

[H.sub.2c]         Levels of education moderate the relationship
                     between tolerance for ambiguity and
                     entrepreneurial orientation.

[H.sub.2d]         Levels of education moderate relationship between
                     propensity for risk-taking and entrepreneurial
                     orientation.

[H.sub.3a]         Supportive environment moderates the relationship
                     between need for achievement and entrepreneurial
                     orientation.

[H.sub.3b]         Supportive environment moderates the relationship
                     between internal locus of control and
                     entrepreneurial orientation.

[H.sub.3c]         Supportive environment moderates the relationship
                     between tolerance for ambiguity and
                     entrepreneurial orientation.

[H.sub.3d]         Supportive environment moderates the relationship
                     between risk-taking propensity and entrepreneurial
                     orientation.

The remainder of this paper is divided into five parts. In the next section we briefly review the research related to four personality characteristics--need for achievement, locus of control, tolerance for ambiguity, and risk-taking propensity. Then, we identify the research instruments used to collect data on the personality characteristics and entrepreneurial orientation and supportive environment constructs. The sample selection process will be addressed; followed by the analysis of results employing hierarchical and moderated regression analysis to test study hypotheses. Finally, we discuss the implications of our study and future research needs.

LITERATURE REVIEW

Need for Achievement

In McClelland (1961), The Achieving Society, the need for achievement trait has been empirically linked to entrepreneurial behavior. The need for achievement is defined as a tendency to choose and persist at activities that hold a moderate chance of success or a maximum opportunity of personal achievement satisfaction without the undue risk of failure. The author drew diverse samples from business executives representing various functional specialties, general management, sales and marketing, finance, engineering, and personnel. Senior marketing managers were found to have the highest--need for achievement. He posits that needs are learned and therefore, culturally, not biologically determined. He also further noted that some cultures produced more entrepreneurs because of the socialization process that creates a high need for achievement.

In a longitudinal analysis of the need for achievement scores of college freshmen, McClelland (1965) concluded that a high need for achievement is a predictor of entrepreneurship and is based on influences of childhood and adult training and experiences. McClelland's work was initially influenced by Murray's (1938) studies in the development of his Need for Achievement Theory (Fineman, 1977). McClelland shared with Murray the belief that analysis of fantasy is the best way to assess motives, which are primarily based on unconscious state. Through the usage of the Thematic Apperception Test (TAT), which requires the writing of imaginative stories by subjects in response to a set of pictures, the stories were content analyzed for achievement imagery to obtain an n Ach score by the author. Through the correlation studies in the laboratory, McClelland determined that those high in n Ach, as measured by the TAT, tended to exhibit an original five behavioral traits to a reduced three: (1) Takes personal responsibility for finding solutions to problems; (2) Sets moderate achievement goals and takes calculated risks; and (3) Wants concrete feedback regarding performance. McClelland conducted a number of studies demonstrating that high n Ach and the subsequent manifestation of the above behaviors correlated strongly with entrepreneurial success (McClelland, 1961, 1965a).

Studies have lent support to the aforementioned findings (Begley & Boyd, 1987; Brockhaus, 1982; Johnson, 1990; McClelland, 1965b; Miner, Smith & Bracker, 1989; Shaver & Scott, 1991). After reviewing the entrepreneurship literature, Shaver and Scott (1991) corroborated the findings of Johnson (1990) by stipulating overall, achievement motivation is a valid predictor of entrepreneurial behavior.

However, the weakness of McClelland's study is in the categorization of the sample analyzed (Gartner, 1989). Initially, McClelland tested college students for level of need for achievement during their freshman year. About fourteen years later, the sample was tested again when they settled in their chosen careers. McClelland observed that those students who had high n Ach levels were significantly more likely to become employed as business entrepreneurs. The problem or criticism of the study lies with the sample selection. The sample consisted of salesmen, real estate brokers, management consultants, fundraisers, corporate officers, independent business owners and executive assistants. Since the sample did not represent actual entrepreneurs, which is the object of study, it implies that McClelland's findings predicted, at best, individuals with potential entrepreneurial behaviors and not entrepreneurship per se. Low and Macmillan (1988) is of the opinion that McClelland's conclusions can be applied to many individuals, both entrepreneurs and nonentrepreneurs. Some studies have failed to provide support or are inconclusive on the linkage between high need for achievement and exploiting entrepreneurial opportunities (Sexton & Bowman, 1985).

Locus of Control

Locus of control refers to the degree to which an individual perceives events in his or her life to be under his or her control [internal]; or as unrelated to his or her actions and therefore, beyond his or her control [external] (Sexton & Bowman, 1985). People with internal locus of control believe that they can control what happens in their lives. On the other hand, people with external locus of control tend to believe that most of the events in their lives result from luck, being in the right place at the right time, and the behaviors of powerful people. Research indicates that individuals with internal locus of control often have a more expressed need for achievement (Brockhaus, 1982; Gurin et al., 1969; Lao, 1970).

In an empirical study conducted by Khan and Manopichetwattana (1989), they addressed the proposition whether the characteristics of innovative and non-innovative small firms have significant differences. Their sample was comprised of 50 manufacturing small businesses in the Texas area using cluster and correlational analyses to analyze the data. They found a positive relationship between internal locus of control and innovation. Boone, DeBrabander, and Van Witteloostuijn's (1996) empirical research investigation focused on the furniture industry with a sample comprised of small firms and family-owned small businesses (homogeneous population). They were interested in getting at whether chief executive officers or top management team internality had a positive effect on organizational outcomes. Replicating previously tested hypotheses, they found internal locus of control to be associated with company performance. Their findings corroborated prior study findings of (Begley & Boyd, 1986, 1987; Bonnett & Furnham, 1991, Nwachukwu, 1995) that internal locus of control is an important entrepreneurial personality trait.

A number of studies have failed to support the relationship between internal locus of control and an entrepreneur. Neider (1987) conducted a study in Florida with a sample of fifty-two female entrepreneurs to discern the degree of locus of control and entrepreneurs. Using the Rotter's scale measurement, the researcher found no significant relationship between internal locus of control and entrepreneurs. The fifty-two entrepreneurs scored lower than the female population in general. Somewhat similar study findings were revealed in the work of Begley and Boyd (1987). Comparing the internal locus of control of entrepreneurs and managers, their findings showed no significant differences in both groups though both groups were shown to have more internal locus of control than the general population.

The literature tends to indicate that internal locus of control scores do not correspond consistently with entrepreneurship performance differences (Sexton & Bowman, 1985). The summary conclusion is that while internal locus of control is an important trait of an entrepreneur when compared to the general population, it is not only common in entrepreneurs but also, it is a trait that is found in professional managers.

Tolerance for Ambiguity

Frenkel-Bruswik (1948, p. 226) reported a study comprised of 100 adults and 200 California children from ages 9 to 14 years old in which the researcher looked at their attitudes to ethnic prejudice and argued that tolerance for ambiguity is to be conceived as "a general personality variable relevant to basic social orientation."

Entrepreneurial managers are generally believed to tolerate more ambiguity than conservative managers because entrepreneurial managers confront less-structured, more uncertain set of possibilities (Bearse, 1982), and actually bear the ultimate responsibility for the decision (Gasse, 1982; Kilby, 1971). Budner (1962, p. 29) defined tolerance for ambiguity as the "tendency to perceive ambiguous situations as desirable," whereas intolerance for ambiguity was defined as "the tendency to perceive ... ambiguous situations as sources of threat." An ambiguous situation is one in which the individual is provided with information that is too complex, inadequate, or apparently contradictory (Norton, 1975, p.607). The person with low tolerance of ambiguity experiences stress, reacts prematurely, and avoids ambiguous stimuli. On the other hand, a person with high tolerance of ambiguity perceives ambiguous situations/stimuli as desirable, challenging, and interesting and neither denies nor distorts his or her complexity of incongruity.

Theoretically, people who best tolerate ambiguity are those who obtain superior results if their strategic objective is to pursue growth. Entrepreneurs who seek to increase market shares in their respective industries face more uncertain phenomenon than those who seek to increase profitability. The strategy utilized to implement increase in market share is based on conditions of uncertainty, which requires a greater tolerance of ambiguity. Thompson (1967) stipulated that in a determinist world, the higher the number of external dependencies faced by firms, the greater the degree of uncertainty.

Dollinger (1983), with a sample size of 79 entrepreneurs using Budner's (1962) scale, found that entrepreneurs scored high in the tolerance for ambiguity test. The results showed that tolerance for ambiguity trait is positively related to entrepreneurial activity. Gupta and Govindarajan's (1984) data from 58 strategic business units revealed that greater marketing/sales experience, greater willingness to take risk, and greater tolerance for ambiguity, on the part of strategic business unit general manager, contribute to effectiveness in the case of "build" strategic business units; but hamper it in the case of "harvest" strategic business units. Carland et al.'s (1989) research revealed that people who best tolerate ambiguity are also the most innovative. Tolerance for ambiguity is reported to relate to personal creativity (Tegano, 1990) and the ability to produce more ideas during brainstorming (Comadena, 1984).

The above cited findings tend to indicate that creativity and innovativeness requires a certain degree of tolerance for ambiguity. The ability to tolerate ambiguous situations may also be positively related to the risk-taking behavior of the entrepreneur. Risk-taking requires a certain degree of tolerance for ambiguity. In addition, research indicates that individuals with intolerance for ambiguity tend to perceive higher degrees of risk under the same circumstances (Tsui, 1993).

Risk-Taking Propensity

Brockhaus (1980, p. 12) defines risk-taking propensity as "the perceived probability of receiving rewards associated with the success of a situation that is required by the individual before he or she will subject himself/herself to the consequences associated with failure, the alternative situation providing less reward as well as less severe consequences than the proposed situation." Conducting a study sample of 93 small business owners in St Louis County, Missouri and applying a Kogan-Wallach choice dilemmas questionnaire to compare entrepreneur and small business managers' propensity for risk-taking, the researcher found no significant difference in risk-taking propensity between entrepreneurs and small business managers.

Begley and Boyd (1987) conducted a study in New England with a sample of 239 members of a small business association to determine the difference in psychological characteristics of entrepreneurs and small business managers. Using a survey questionnaire to elicit respondent's perceptions, the researchers found significant differences between entrepreneur's risk-taking propensity as compared to small business managers. Sexton and Bowman (1982) found no significant difference in risk-taking behaviors between entrepreneurs and managers. Sexton and Bowman (1985) concluded that risk-taking propensity may distinguish entrepreneurs and managers.

In a study conducted by MacCrimmon and Wehrung (1990), a sample of five hundred chief executives of businesses was drawn to determine the validity of common stereotypes of risk-taking propensity using factor and linear discriminant analyses. The researchers found that the most successful executives were the biggest risk takers; the most mature executives were the most risk averse. Busenitz (1999) examined bias and heuristics of entrepreneurs and found that entrepreneurs do not see themselves as taking more risky ventures. They do not score significantly better than professional managers in risk-taking propensity.

Study findings on the risk-taking propensity of entrepreneurs have basically been uncorroborative, inconclusive and inconsistent. A likely explanation for the divergence in findings may lie in the methodologies applied in previous studies. Numerous study findings that suggest no risk-taking propensity difference between entrepreneurs and managers or non-entrepreneurs used Kogan and Wallach's (1964) Choice Dilemmas Questionnaire (CDQ), which was designed to measure risk attitudes (Stoner, 1961).

Overall, research findings suggest that, on the aggregate, entrepreneurs are moderate risktakers but significantly differ from managers or the general public (Brockhaus, 1982; Low & Macmillan, 1988).

METHOD

Research Instrument

Need for achievement was measured using a three-item, 7-point Likert type scale that was originally developed by Edwards (1959) to measure achievement motivation. The mean score of achievement motivation among respondents was 5.88, which indicated that, on the aggregate, usedcar entrepreneurs possess a high level of achievement motivations.

Internal locus of control was measured using a four-item, 7-point Likert type scale that was originally developed by Rotter (1966) to measure generalized expectancies. The mean score of internality among respondents was 5.70, which indicated that, on the aggregate, used car entrepreneurs possess a high level of internal locus of control.

Tolerance for ambiguity was measured using a three -item, 7-point Likert type scale that was originally developed by Budner (1962) to measure tolerance for ambiguity. The mean score of tolerance for ambiguity among respondents was 5.24, which indicated that, on the aggregate, used car entrepreneurs possess above average level of tolerance for ambiguity.

Risk-taking propensity was measured by a two-item scale that was developed by Kogan and Wallach (1964). The mean score for risk taking propensity among respondents was 3.l1, which indicated that, on the aggregate, used car entrepreneurs have an average level of risk-taking propensity.

Entrepreneurial orientation dimensions ([H.sub.1a-d], [H.sub.2a-d], [H.sub.3a-d]) were measured using an eleven-item, 7-point Likert-type scale that was designed to measure respondents' entrepreneurial orientations. The mean score value among respondents was 4.15, which indicated that, on the aggregate, used car organizations are entrepreneurially oriented. This result is consistent with previous research studies (Chadwick 1998; Covin & Slevin, 1989; Knight 1997; Naman & Slevin, 1993). Table 1 summarizes the descriptive statistics of the study variables.

Supportive environment factors H2a-d and H3a-d were measured using a three-item, 7-point Likert type scale that was designed to assess the adequacy of institutional and legal frameworks, government policies, availability of universities, training and research services to the used car business community. The mean score of supportive environment among respondents was 5.61 which indicated that, on the aggregate, used car entrepreneurs perceived their business environment as supportive in terms of having adequate legal and institution frameworks, favorable government policies, availability of universities, training, research and counseling services, for efficient functioning of private enterprises.

Sample

The sampling frame for this study was a mailing list of the registered used auto dealers and owners of used car lots situated in a "deep" south Standard Metropolitan Statistical area (SMSA). Three hundred fifteen (315) self-reported questionnaires with a self-addressed, stamped return envelope were mailed to the randomly selected auto dealers from the original four hundred and forty (440) registered population list. A total of ninety five (95) questionnaires were returned, completed and usable, representing a 30.16 percent response rate of the 315 mailed questionnaires.

ANALYSIS

Testing of Hypotheses

Psychological Characteristics and Entrepreneurial Orientation

Hypotheses H1a-d were tested employing hierarchical regression analysis. Hierarchical regression is the statistical technique of choice when a single metric dependent variable is presumed related to one or more metric independent variables (Hair, Anderson, Tatham, & Black, 1995). The objective of this statistical procedure is to explain changes in the dependent variable with respect to changes in the independent variables.

Statistical analyses were performed on the full model (need for achievement, internal locus of control, tolerance for ambiguity and risk- taking propensity) employing the hierarchical procedure of SPSS (Morgan & Griego, 1998). Hypothesis H1a states that need for achievement is positively related to entrepreneurial orientation. The results of the regression analysis are shown in Table 2. The first independent variable entered in the hierarchical regression was need for achievement. A significant relationship was found (b = .369, p<. 001), and it explained 13 percent of the variance in entrepreneurial orientations.

Hypothesis H1b states that internal locus of control is positively related to entrepreneurial orientation. Hypothesis H1c states that tolerance for ambiguity is positively related to entrepreneurial orientation. Hypothesis H1d states that risk-taking propensity is positively related to entrepreneurial orientation. Results showed significant relationships between tolerance for ambiguity and entrepreneurial orientation (b = .305, p < .01) with additional variance change of 11 percent explained in entrepreneurial orientations. The positive relationship between risk-taking propensity and entrepreneurial orientations was not significant (b = .174, p. < .10). The positive relationships between internal locus of control and entrepreneurial orientations were not significant (b = 0.081, p. = .394) [See Table 2].

RESULTS

Need for Achievement and Entrepreneurial Orientations

[H.sub.1a]: The need or achievement is positively related to entrepreneurial orientation.

Hypothesis [H.sub.1a] states that need for achievement is positively related to entrepreneurial orientation. Results of the Pearson's correlations suggest significant orientations (See Table 3). Hierarchical regression results suggest significant positive relationships between need for achievement and entrepreneurial orientations. The results indicated that need for achievement had a standardized coefficient beta of = .36, p. < .001. Thus, hypothesis [H.sub.1a] is supported.

These findings converge with other prior empirical research studies that have linked need for achievement to entrepreneurial process (Begley & Boyd, 1986; Johnson, 1990; McClelland, 1961, 1965a; Shaver & Scott, 1991). For example, Miner et al. (1989) advanced the notion that achievement motivation was positively related to firm growth, and personal innovativeness in a sample made up of actual entrepreneurs.

Internal Locus of Control and Entrepreneurial Orientations

[H.sub.1b]:: Internal Locus of control is positively related to entrepreneurial orientation.

Hypothesis H1b states that internal locus of control is positively related to entrepreneurial orientations. Results of the Pearson's correlations suggest significant weak positive relationships between internal locus of control and entrepreneurial orientations. However, results of the hierarchical regression did not suggest significant positive relationships between internal locus of control and entrepreneurial orientations. Thus, Hypothesis [H.sub.1b] is not supported.

Tolerance for Ambiguity and Entrepreneurial Orientations

[H.sub.1c]: Tolerance for ambiguity is positively related to entrepreneurial orientation.

Hypothesis [H.sub.1c] states that tolerance for ambiguity is positively related to entrepreneurial orientation. Results of the Pearson's correlations suggest significant moderate positive relationships between tolerance for ambiguity and entrepreneurial orientations. Results of the hierarchical regression also suggest significant positive relationships between tolerance for ambiguity and entrepreneurial orientations. Thus, hypothesis [H.sub.1c] is supported. Tolerance for ambiguity with a standardized beta coefficient of .31 at a significance level of p. < .01 suggests a significant explanatory power for the twenty five percent variance ([R.sup.2] adj.=.25) explained by the psychological traits equation of entrepreneurial orientations. These findings lend support to prior research studies that linked tolerance for ambiguity to entrepreneurial behavior (Tegano, 1990; Tsui, 1993).

Risk Taking Propensity and Entrepreneurial Orientations

[H.sub.1d]: Risk-taking propensity is positively related to entrepreneurial orientation.

Hypothesis [H.sub.1d] states that risk-taking propensity is positively related to entrepreneurial orientations. Results of the Pearson's correlations suggest a significant weak positive relationship between risk taking propensity and entrepreneurial orientations. Results of the hierarchical regression also suggest positive relationships between risk taking propensity and entrepreneurial orientations but not significant at a specified level. Thus, hypothesis [H.sub.1d] does not have significant support.

Schoonhoven (1981) and Darrow and Kahl (1982) recommended moderated regression analysis as an appropriate statistical technique for testing hypothesized contingency relationships. Covin and Slevin (1989) confirmed that the technique is appropriate because it allows for interaction terms that are implied in all contingency relationships to be directly examined. Also, Arnold (1982) asserted that moderated regression analysis provides the most straightforward method for testing contingency hypotheses where an interaction is implied. Finally, moderated regression analysis is an appropriate method for identifying interaction effects in a format that the significance of the interaction terms are tested only after other independent variables are entered into the equation. Thus, interaction effects are interpreted as being significant only if they explain a greater portion of the variance in the dependent variable than that which is explained by individual independent variables (Covin & Slevin, 1989).

The statistical significance of interaction effects were tested by first regressing the dependent variable on the independent variables, and the hypothesized moderator variables and then adding the interaction terms that represent the cross product of the independent variable and each of the proposed moderator variables (Sharma, Durand, & Gur-Arie, 1981). The moderated regression equations implemented to test hypotheses 2a-d and 3a-d were replicated from the work of Sharma et al. (1981).

Education and Entrepreneurial Orientation

[H.sub.2a], [H.sub.2b], [H.sub.2c], [H.sub.2d]: The interaction of the levels of education is positively related to the psychological traits of need for achievement, internal locus of control, tolerance for ambiguity and risk taking propensity.

The results of the moderated regression analyses are presented in Table 4. The interactions terms of the levels of education and psychological traits were computed using SPSS by multiplying the levels of education variable and each of the four sub constructs of psychological traits (need for achievement, internal locus of control, tolerance for ambiguity and risk taking propensity) to ascertain whether the variance of the two products provided incremental explanatory power of entrepreneurial orientations. The interactions of need for achievement and levels of education variables provided negative variance change of -0.007 at a significance level of p < 0.01.

The interactions of internal locus of control and levels of education provided incremental [R.sup.2] change of 0.074 at a significance level of p<0.01. The interactions of tolerance for ambiguity and levels of education provided incremental [R.sup.2] change of 0.026 at a significance level of p<0.001. The interactions of risk taking propensity and levels of education provided incremental [R.sup.2] change of 0.043 at a significance level of p<0.01.

Overall, the moderated multiple regression results suggest that, the interactions of levels of education and three of the four sub constructs of psychological traits (internal locus of control, tolerance for ambiguity, risk taking propensity) provided incremental [R.sup.2] change or higher explanatory powers of entrepreneurial orientations as hypothesized in [H.sub.2b], [H.sub.2c], and [H.sub.2d].

Supportive environment and entrepreneurial orientations

[H.sub.3a], [H.sub.3b], [H.sub.3c], [H.sub.3d]: The interaction of the terms of supportive enviromnents will show incremental variance change as related to the psychological traits of need for achievement, internal locus of control, tolerance for ambiguity and risk taking propensity.

The results of the moderated regression analyses are presented in Table 5. The interactions terms of the supportive environments and psychological traits were also computed using SPSS by multiplying the supportive environments variable and each of the four sub constructs of psychological traits (need for achievement, internal locus of control, tolerance for ambiguity and risk taking propensity) to ascertain whether the variance of the two products provided incremental explanatory power of entrepreneurial orientations. The interactions of need for achievement and supportive environments variables provided incremental variance change of 0.027 at a significance level of p < 0.001. The interactions of internal locus of control and supportive environments variables provided incremental variance change of 0.096 at a significance level of p < 0.01. The interactions of tolerance for ambiguity and supportive environments variables provided incremental variance change of 0.044 at a significance level of p < 0.001. The interactions of risk taking propensity and supportive environments variables provided incremental variance change of 0.041 at a significance level of p < 0.05.

Overall, the moderated multiple regression results suggest that, the interactions of supportive environments and the four sub constructs of psychological traits (need for achievement, internal locus of control, tolerance for ambiguity, risk taking propensity) variables provided incremental variance change or higher explanatory powers of entrepreneurial orientations as hypothesized in [H.sub.3a], [H.sub.3b], [H.sub.3c], and [H.sub.3d].

DISCUSSION

The theoretical underpinnings for this research study specified that psychological traits relate positively to entrepreneurial orientations and sociological influences such as, levels of education, and supportive environments, moderate the relationships between psychological traits and entrepreneurial orientations.

Results of the Pearson's correlations largely support significant positive relationships between psychological traits and entrepreneurial orientations. However, the results of the hierarchical regression only provide support for significant relationships for two of the four sub constructs of psychological traits (need for achievement, tolerance for ambiguity) and entrepreneurial orientations. Thus, hypothesis ([H.sub.1a]), which states that need for achievement is positively related to entrepreneurial orientations, and hypothesis ([H.sub.1c]), which states that tolerance for ambiguity is positively related to entrepreneurial orientations are supported. Hypotheses ([H.sub.1b]) and ([H.sub.1d]) are not supported.

Moderated regression results support that, levels of education moderate the relationships of three of the four sub-constructs of psychological traits (internal locus of control, tolerance for ambiguity, and risk taking propensity) and entrepreneurial orientations as hypothesized in [H.sub.2b], [H.sub.2c], and [H.sub.2d].

Moderated regression results also suggest that supportive environments moderate the relationships between psychological traits and entrepreneurial orientations as hypothesized in H3a, [H.sub.3b], [H.sub.3c], and [H.sub.3d].

The study was conducted in the service industry, sampling used-car entrepreneurs and owners of used-car lots where no research efforts have previously taken place. Also, the sample was a one time collection of data in one southern metropolitan statistical area of the continental United States. Also, an inappropriate representation of used car dealers with respect to race was not proportionately distributed to get a fair understanding of psychological traits impacting entrepreneur orientations in metropolitan areas where the dealers may be largely African, Hispanic, Asian-American, White or multiracial. Obviously, the findings are inherently limited for generalization purposes.

CONCLUSION

In the used-car industry where the used-car entrepreneur buys quantities of used cars to resell, and the used-car entrepreneurs have no prior knowledge about the state of the used cars; i.e., whether the used cars are good used cars or bad used cars ("lemons"). Regardless of the state of quality of used cars in his or her stock, the entrepreneur is constrained by law to provide a minimum of 30-day warranty on each car sold to any buyer. According to Akerlof (1970), most cars traded will be lemons, and good cars may not be traded at all. The "bad cars" tend to drive out the good (in much the same way that bad money drives out the good money). These points relate to the findings of Palich and Bagby (1995), which state that entrepreneurs rush in to take advantage of opportunities that others fail to see or act upon. Busenitz (1999) proposed that entrepreneurial risk may be explained by recognizing that entrepreneurs used biases and heuristics more, which is likely to lead them to perceive less risk in a given decision situation.

Future data-based studies addressing the impact of psychological traits on entrepreneurial orientations should employ a more representative sample from multiple industries with provisions for inter-industry variations in life cycles. The sample for this study is perhaps acceptable since this is an exploratory study but is far too limited industry-wise to draw any lasting conclusions. Hence, a more representative sample should be drawn to provide stronger results. Therefore, findings are only inferred and should not be interpreted beyond generating hypotheses and/or formulating research questions for future research efforts.

Another issue of major concern is the length of the questionnaire used in this study. Future researchers addressing psychological characteristics and relationships to entrepreneurial orientations should use a shorter questionnaire to improve the mail questionnaire response rate.

Lastly, the psychological construct scales employed in this study have been used in prior entrepreneurship studies. However, the low reliability values for some of the psychological constructs are source of concern. A multiple-item scale should be adapted to measure the respective psychological constructs instead of the single-item scale employed in this study. A multiple-item scale is appropriate for reliability when primary data are collected (Chandler & Lyon, 2001).

A major gap and void in the entrepreneurship literature continue to revolve around the lack of a comprehensive theoretical framework but grounded with accumulative fragmentalism in definitions. Future research efforts should attempt to bridge this gap in the literature.

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J. R. Smith, Jackson State University 

Donatus A. Okhomina, Sr., Alabama State University

Alisa L. Mosley, Jackson State University

Table 1: Descriptive Statistics of Variables

Statistics     Supportive         Need           Internal
               Environment     Achievement       Locus of
                                                  Control

Mean              5.61            5.88             5.70
Median            5.67            6.00             6.00
Mode              6.30            6.30             6.00
Std. Dev.         1.38            1.27             1.09
Kurtosis          0-.15           5.50             2.95
Skewness         -2.48           -1.05            -1.42
Minimum           2.00            1.33             1.00
Maximum           7.00            7.00             7.00
Range             5.00            5.67             5.50

Statistics    Tolerance for       Risk        Entrepreneurial
                Ambiguity        Taking         Orientation
                               Propensity

Mean              5.24            3.11             4.15
Median            5.33            3.00             4.46
Mode              5.33            3.00             4.46
Std. Dev.         1.18            1.21             1.41
Kurtosis          0.44            1.21            -0.07
Skewness         -0.77           -1.10            -0.017
Minimum           2.00            0.00             1.38
Maximum           7.00            5.00             7.00
Range             5.00            5.00             5.62

Table 2: Regression  Results: Psychological Traits and Entrepreneurial
Orientation

Independent Variables         Beta       SE       F      [R.sup.2]

Need for Achievement          369 ***   .093    13.74       .13
Internal Locus of Control     .081      .106
Tolerance for Ambiguity      .305 **    .091
Risk taking Propensity        .174      .032

[R.sup.2]                               .28

Adjusted [R.sup.2]= .25                       Change in [R.sup.2] .15

Only standardized regression coefficients
are shown

N = 94

*** P < 0.001

** P < 0.01

Table 3: Correlation Coefficients

Orientation       Entrepeneurial     Need for      Internal Locus
                   Orientation      Achievement      of Control

Need for              .36 **
Achievement

Internal Locus        .22 *            29 **
of Control

Tolerance for         32 **            -.05             0.10
Ambiguity

Risk-Taking            19 *            -.09             -.01
Propensity

Levels of             .26 **          .25 **           .27 **
Education

Supportive            .31 **           .18 *           .24 *
Environment

Orientation        Tolerance of     Risk Taking
                    Ambiguity       Propensity

Need for
Achievement

Internal Locus
of Control

Tolerance for
Ambiguity

Risk-Taking
Propensity

Levels of              .05             -.00
Education

Supportive            .28 **            .06
Environment

Orientation         Levels of       Supportive
                    Education       Environment

Need for
Achievement

Internal Locus
of Control

Tolerance for
Ambiguity

Risk-Taking
Propensity

Levels of                             .41 **
Education

Supportive
Environment

** = Significant at 0.01 level
* = Significant at 0.05 level

Table 4: Regression Results: Education Moderating the Relationships
Between Psychological Traits and Entrepreneurial Orientations

Entrepreneurial Orientation
(Dependent Variable)               Beta       [R.sup.2]    Change in
                                                           [R.sup.2]

Independent Variables

  Need for Achievement (NA)        .342 ***   .130
  Internal Locus of Control (C)    .051       .014
  Tolerance for Ambiguity (T)      .300 ***   .108
  Risk-Taking Propensity (R)       0.173      .029
  Levels of Education (E)          .143       .018
  [R.sup.2]                                   0.299

  Need for Achievement X
  Levels of Education              .351 **    .123         -.007
  Internal Locus of Control X
  Levels of Education              .297 **    .088         .074
  Tolerance for Ambiguity X
  Levels of Education              .367 ***   .134         .026
  Risk Taking Propensity X
  Levels of Education              .268 **    .072         .043

                                              0.417        0.136

[R.sup.2] =0.417

Change in [R.sup.2] =0.136

Only standardized coefficients are shown

*** p < 0.001

** p < 0.01

Table 5: Regression Results: Supportive Environments Moderating the
Relationships Between Psychological Traits and Entrepreneurial
Orientations

Entrepreneurial Orientation      Beta        [R.sup.2]    Changes in
(Dependent Variable)                                      [R.sup.2]

Independent Variables
  Need for Achievement           .348 ***    .130
  Internal Locus of Control      .055        .014
  Tolerance for Ambiguity        .265 **     .108
  Risk-Taking Propensity         .169        .029
  Supportive Environment         .151        0.020
  [R.sup.2]                                  0.301

  Need for Achievement X
  Supportive Environment         .396 ***    .157         .027
  Internal Locus of Control X
  Supportive Environment         .332 **     .110         .096
  Tolerance for Ambiguity X
  Supportive Environment         .390 ***    .152         .044
  Risk Taking Propensity X
  Supportive Environment         .264 *      .070         .041

                                             .489         0.208

[R.sup.2] =0.489

Change in [R.sup.2] =0.208

Only standardized coefficients are shown

*** p < 0.001

** p < 0.01

* p < 0.05