ENTREPRENEURIAL SUCCESS: INTERPLAY BETWEEN SYSTEMIC AND INDIVIDUAL FACTORS VIA NETWORKING Moraima De Hoyos-Ruperto, University of Puerto Rico- Mayagüez Campus

SUMMARY This study explores how the nature of an entrepreneurship environment may provoke desired entrepreneurial success. To illustrate this, our study investigates Puerto Rico’s (P.R.) unexplained stagnant entrepreneurial environment when compared to other high-income countries. A quantitative study using Partial Least Squares (PLS) was conducted to determine: How and to what extent do systemic and individual factors— mediated by inter-organizational and individual social networking activities—impact the likelihood of entrepreneurial success? Our findings reveal that systemic factors in P.R. as a whole are not working as suitable sources for the complementary relationships needed to create an environment conducive to successful entrepreneurship. Meanwhile, Puerto Rican entrepreneurs are not using networks efficiently to overcome the inadequate institutional structure. Therefore, to nurture a successful entrepreneurial environment, policy makers must design a better interconnected entrepreneurial system that will work in harmony with entrepreneurs; while entrepreneurs in turn must be taught to effectively use their networks. Keywords: entrepreneurial success; entrepreneurial education; opportunities; social competence; networking

Primary Contact: Moraima De Hoyos-Ruperto, University of Puerto Rico- Mayagüez Campus and Case Western Reserve University 828 Hostos Avenue, Suite 102, Mayagüez, PR 00680. E-mail: [email protected] / [email protected]

INTRODUCTION Entrepreneurship scholars hold very different beliefs about the nature of entrepreneurship activities (Gartner, 1990) and explanations of its role in desired economic progress (Acs & Szerb, 2010; Birch, 1979; Gartner, Shaver, Carter, & Reynolds, 2004; Low & MacMillan, 1988). However, since entrepreneurship is a complex and dynamic phenomenon (Gartner et al., 2004; Shapero, 1984), different views exist regarding the factors that really spur it (Acs & Szerb, 2010). Hence, researchers must clearly establish the limitations and arguments upon which they are basing their study (Shane & Venkataraman, 2000). Advanced studies on entrepreneurship need to explore the interaction between external factors, such as entrepreneurial opportunities and education and national mindset toward entrepreneurship; and personal factors, such as entrepreneur’s social competence and efficacy and their influences on entrepreneurial performance (Welter & Smallbone, 2011).

This research focuses on entrepreneurs doing business in P.R. because among high-income countries P.R., at 3.1 percent, has one of the world’s lowest rates of early-stage entrepreneurial activity (Bosma et al., 2008) despite the government’s two-decade effort to spur it. Long reliant on the presence of multinational corporations to sustain the economy and historically lax in encouraging local business development, P.R. was hard hit by the elimination of tax exemptions in 2006 that incentivized U.S. subsidiaries to establish locations on the island. Despite several attempts to jumpstart the economy in the wake of their departure, reports from worldwide organizations such as the Global Entrepreneurship Monitor (GEM) (Bosma et al., 2008), the World Economic Forum (WEF) (Schwab, 2009), and the World Bank (2010) certify the disappointing state of entrepreneurship in P.R. Experts blame structural problems rather than a lack of entrepreneurial spirit for entrepreneurship’s failure to flourish in P.R. (Aponte, 2002b).

Based on our previous qualitative research, this paper theorizes that individual-level factors, including entrepreneur self-efficacy (SE) and social competence (SC), and systemic factors such as entrepreneurial education (EDU), opportunities (OPP), and national mindset (MIND) act as sourcing mechanisms that can predict entrepreneurial success. However, while these factors perform as sourcing mechanisms, they are being moderated by other systemic factors such as entrepreneurial policies (POL) and programs (PROG), firm characteristics (FC), and entrepreneur profiles (EP). This study additionally proposes that inter-organizational networks (ONETW) and individual social network activities (INETW) mediate all of the abovementioned relationships with entrepreneurial success (ES). Considering the actual world economic crisis, environmental hostility (HOST) is used as a control variable to provide a possible alternate impacting factor and explanation for the degree of success. 2

Our data suggest that systemic factors as a whole are not working as suitable sources of the complementary relationships needed to create an environment conducive to successful entrepreneurship. Institutional factors are not well interconnected among them to complement entrepreneurs’ challenges. Meanwhile, entrepreneurs are not efficiently using their networks to overcome the inadequate institutional structure. Therefore, a better interconnected entrepreneurial system and more effective individual social networking may be necessary for both practitioners and policy makers to design a successful entrepreneurial environment. THEORETICAL BACKGROUND AND HYPOTHESES Systemic factors such as entrepreneurial education (Levie & Autio, 2007), opportunities (Shane & Venkataraman, 2000), and national mindset toward entrepreneurship (Casson, 2003) and individual factors such as social competence (Baron & Markman, 2000) and perceived self-efficacy (Bandura, 1997) can positively or negatively influence the overall entrepreneurship success of a nation. However, while these factors perform as sourcing mechanisms, they are being mediated by other factors such as inter-organizational network activities (Butler & Hansen, 1991) and/or the entrepreneur’s social networking activities (Hoang & Antoncic, 2003; Johannisson, 1998) as our conceptual research model in Figure 1 below shows. -----------------------------------Insert Figure 1 about here -----------------------------------To address the abovementioned concepts, an empirical study with entrepreneurs was designed to examine the following question: How and to what extent do systemic and individual factors—mediated by inter-organizational and individual social networking activities—impact the likelihood of entrepreneurial success?

Entrepreneurial Success as the End Product Several authors remarked on the importance of using multiple performance dimensions (Randolph, Sapienza, & Watson, 1991; Venkatraman & Ramanujam, 1986). Therefore, this study uses both growth measurements, such as sales growth rates and increases in the number of employees, and profit measurements, such as net profit margin and financial conditions compared with three years prior, through a primary data source—questionnaire—to assess entrepreneurial success based on firm performance (Questionnaire and Construct Definition Table is available upon request).

Systemic Factors as Sources of Entrepreneurial Success Institutions and governments play key roles in fostering environments that produce a continuous supply of new entrepreneurs and enable them to be successful (Lundström & Stevenson, 2005; Welter & Smallbone, 2011). However, there are several points of view regarding the key factors that lead to a successful entrepreneurial environment (Acs & Szerb, 2010; Gartner et al., 2004; Levie & Autio, 2007; Lundström & Stevenson, 2005; 3

Shapero, 1984). For that reason, GEM intended to enhance understanding of the role of intitutional factors through their concept of Entrepreneurial Framework Conditions (EFCs). The EFCs recognize ten different factors that may affect the creation and development of new firms. Among those factors, our study considers opportunities, national mindset, and education as causal factors and individual and inter-organizational networks as mediators building on our earlier qualitative interview responses (De Hoyos et al., 2011). A broad literature review to support our hypotheses follows.

The Role of Entrepreneurial Opportunities in the Possibility of Entrepreneurial Success The literature underscores the importance of recognizing and exploiting opportunities as well as a willingness to accept the opportunities to achieve entrepreneurial success (De Carolis & Saparito, 2006; Krueger, 2000; Shane, 2003; Shane & Cable, 2003; Shane & Venkataraman, 2000; Singh, 2000; Tat-Keh, Der-Foo, & Chong-Lim, 2002). Opportunities, however, are not always perceived in the same way; therefore, how they are presented, the people they are presented to, and how they take advantage of them are crucial (Shane & Venkataraman, 2000). Thus, entrepreneurial alertness is a necessary condition for the success of opportunity identification (Ardichvili, Cardozo, & Sourav, 2003). Therefore, we propose: Hypothesis 1a. Perceived entrepreneurial opportunities will positively impact entrepreneurial success, when controlling for environmental hostility.

Hypothesis 1b. Perceived entrepreneurial opportunities will positively impact inter-organizational networking, when controlling for environmental hostility.

Hypothesis 1c. Perceived entrepreneurial opportunities will positively impact individual social networking, when controlling for environmental hostility.

The Role of National Mindset in Entrepreneurship and Entrepreneurial Success Managing entrepreneurship in a balanced and safe manner in an era of increasing global competitiveness requires, among other things, a shift in the mindset of country leaders, as well as individual citizens, toward an entrepreneurial understanding (Cohen, 2007). In fact, the 2004 European Commission defined entrepreneurship as the mindset and process needed to create and develop economic activity, blending risk-taking, creativity, and/or innovation within a new or existing organization. A national mindset, which includes cultural stereotyping, norms, and values as well as individual attitudes and aspirations, may determine, among other things, industrial structure, the expertise developed, access to institutions, and the likelihood of a successful venture (Casson, 2003; Guiso, Sapienza, & Zingales, 2006). For that reason, it is expected that a country with an adequate entrepreneurial mindset embrace an individual sense of responsibility about what happens around them and also cultivate a collaborative 4

and cohesive environment as part of its entrepreneurial strategy (Aldrich & Zimmer, 1986). When lacking strong cultural support toward entrepreneurship, the best and the brightest individuals do not want to be entrepreneurs and decide to enter some other profession (Guiso et al., 2006). Consequently, policy makers and entrepreneurial advocates need to be aware of the strategies needed to build a national mindset conducive toward entrepreneurship. Therefore, we propose: Hypothesis 2.

National mindset will directly impact entrepreneurial success, when controlling for

environmental hostility.

The Role of Entrepreneurial Education in the Likelihood of Entrepreneurial Success Entrepreneurial education is a cornerstone of entrepreneurial success (Ronstadt, 1987) as the educational system’s structure may influence national development (Todaro, 1981). Consequently, entrepreneurship education is considered one of the principal influencing factors in the venture creation process and on venture success (Levie & Autio, 2007). The World Economic Forum (2009) highlighted the importance of entrepreneurship education and training on entrepreneurial development in the following way: … while education is one of the most important foundations for economic development, entrepreneurship is a major driver of innovation and economic growth. Entrepreneurship education plays an essential role in shaping attitudes, skills and culture—from the primary level up. We believe entrepreneurial skills, attitudes and behaviors can be learned, and that exposure to entrepreneurship education throughout an individual’s lifelong learning path…is imperative (World Economic Forum, 2009: 7-8).

Unfortunately, the 2010 GEM points out that the content of entrepreneurship education is inadequate in most countries (Corduras-Martinez, Levie, Kelley, Saemundsson, & Schott, 2010). Kirby (2003) affirmed that educational systems need to focus not simply on what is taught but how it is taught. Hence, the educational system needs to make individuals capable of acting independently, innovatively, and with the capacity for achieving goals and taking risks to create new sources of wealth and employment (Varela, 2003). Furthermore, students need to develop the entrepreneurial ability to create business ideas, identify and recognize opportunities, set up businesses, and manage their growth (Gibb & Hannon, 2006; Reynolds, Hay, & Camp, 1999; Wilson, Kickul, & Marlino, 2007). On the other hand, Wilson et al. (2007) contend that entrepreneurial education that leads to entrepreneurial success is one that promotes self-efficacy and self-confidence. Moreover, self-efficacy enhanced by education may impact entrepreneurial intention (Zhao, Seibert, & Hills, 2005), perceived feasibility (Peterman & Kennedy, 2003), and successful venture performance (Bandura, 1997). Consequently, we propose:

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Hypothesis 3a. Appropriate content of entrepreneurial education will positively impact entrepreneurial success, when controlling for environmental hostility.

Hypothesis 3b. Appropriate content in entrepreneurial education will positively impact individual selfefficacy, when controlling for environmental hostility.

Hypothesis 3c. Self-efficacy will partially mediate the relationship between entrepreneurial education and entrepreneurial success, when controlling for environmental hostility.

Individual Factors as Sources of Entrepreneurial Success Our qualitative study interview responses lead us to believe there are two key individual factors that need to be taken into account when considering P.R.’s entrepreneurial environment: social competence and perceived selfefficacy (De Hoyos et al., 2011). For that reason, we are concerned with the positive or negative effect of both on entrepreneurial success. In the following, we present the literature review to support our hypotheses. The Role of Entrepreneurs’ Social Competence in Entrepreneurial Success Entrepreneurs’ social competence refers to their ability to interact effectively with others and adapt to new social situations with the purpose of developing strategic relationships that leverage business opportunities and competitiveness (Baron, 2000). Baron & Markman (2003) claim that the higher an entrepreneur’s social competence, the greater their financial success.

To operationalize the entrepreneur social competence construct, this study adopted the four dimensions used by Baron & Markman (2003): Social Perception, Social Adaptability, Expressiveness, and Self-Promotion. Social perception refers to the ability to accurately perceive the emotions, traits, motives, and intentions of others; while social adaptability indicates the ability to adapt to, or feel comfortable in, a wide range of social situations. Expressiveness denotes the ability to express feelings and reactions clearly and openly, and self-promotion points out the ability to design tactics to induce liking and favorable impressions by others. Based on the abovementioned literature, we propose: Hypothesis 4a. An entrepreneur’s social competence will positively impact entrepreneurial success, when controlling for environmental hostility. Hypothesis 4b. An entrepreneur’s social competence will positively impact individual social networks, when controlling for environmental hostility. 6

The Role of Entrepreneurs’ Self-Efficacy in Enhancing Entrepreneurial Success According to Krueger & Brazeal (1994), individuals’ self-efficacy or self-confidence can affect venture decisions and firm performance; and Boyd & Vozikis (1994) claim that self-efficacy is fundamental to moving from entrepreneurial intention to action. However, perceived self-efficacy could be more relevant to entrepreneurial success. As Markham, Balkin, & Baron (2002) point out, individuals are motivated by their perception rather than by their objective ability. Perceived self-efficacy refers to an individual’s assessment of his/her skills and ability to carry out a task, but it could be different in reality (Bandura, 1997). As Wilson et al. (2007) mention, based on Bandura’s studies, “This concept reflects an individual’s innermost thoughts on whether they have the abilities perceived as important to task performance, as well as the belief that they will be able to effectively convert those skills into a chosen outcome” (p. 389).

However, Simon, Houghton, & Aquino (1999) contend that the positive side or view of the aforementioned researchers state that perceived self-efficacy will negatively affect entrepreneurial outcomes because of individual overconfidence or overestimation of skills. As a result, entrepreneurs may overlook contradictory signs and information and harbor higher expectations of success. Following Simon et al.’s (1999) line of thought, we hypothesize: Hypothesis 5a. Perceived self-efficacy will negatively impact entrepreneurial success, when controlling for environmental hostility.

Hypothesis 5b.

Perceived self-efficacy will impact individual social networks, when controlling for

environmental hostility.

The Mediator Role of Individual Social Networking and Inter-Organizational Networking As Audretsch and Thurik (2004) mention, Thorton & Flynne (2003) and Saxenian (1994) argue that “(successful) entrepreneurial environments are characterized by thriving supportive networks that provide the institutional fabric; linking individual entrepreneurs to organized sources of learning and resources” (p. 5). Hence, individual social networking and inter-organizational strategic network activities are important to a successful startup and to an ongoing competitive advantage as they may constrain or facilitate resource acquisition and the identification of new opportunities (Beckert, 2010; Brüderl & Preisendörfer, 1998; Butler & Hansen, 1991; Minniti, 2005; Klyver & Hindle, 2006; Shane & Cable, 2003). For this study, the individual social networking construct represents entrepreneurs engaging in networking 7

activities to enhance his/her entrepreneurial venture (Aldrich & Zimmer, 1986). These entrepreneurial networking activities may occur with other entrepreneurs; personal contacts like relatives, friends, and acquaintances; and entrepreneurial advocates in public agencies, civic organizations, and/or private business associations (Birley, 1985). The aim of those networking activities is to provide assistance to entrepreneurs in the form of expert opinions and counseling, shared experiences and role models, information and resources, and support and motivation (Manning, Birley, & Norburn, 1989). Consequently, we propose: Hypothesis 6. Individual social network activities will positively impact entrepreneurial success, when controlling for environmental hostility.

Additionally, for this research inter-organizational networking consists of formal and/or informal collaborative networking activities among entrepreneurial advocates at the public, private, and civic levels that may facilitate the entrepreneurial process from an idea generating stage, to a development stage and later to a strategic positioning one (Butler & Hansen, 1991; Dubini & Aldrich, 1991; Uzzi, 1996). Those collaborative network activities may include alliances to improve entrepreneurial mechanisms, such as permits and funding, as well as processes like internationalization, knowledge spillover, innovation, and investment in R&D (Audretsch & Thurik, 2004; Saxenian, 1994; Schwab, 2009). Therefore, we propose: Hypothesis 7. Inter-organizational network activities will positively impact entrepreneurial success, when controlling for environmental hostility.

As entrepreneurship is embedded in networks, opening entrepreneurs to social networks may advance or constrain links to better resources and information, as well as offer faster responses to opportunities and challenges (Aldrich & Zimmer, 1986; Klyver & Hindle, 2006; Saxenian, 1994; Shane & Cable, 2003). Furthermore, interorganizational networks may facilitate or constrain the information and resources that could turn opportunities into successful ventures (Aldrich & Zimmer, 1986; Butler & Hansen, 1991).

Additionally, Brüderl & Preisendörfer (2000) contend that venture success is attained only if entrepreneurs make effective use of their networks. Consequently, entrepreneurs with high social competence (Manning et al, 1989) and self-efficacy (Boyd & Vozikis, 1994) are more likely to establish strategic networks that will help them overcome their limited resources and barriers, particularly of information. This was confirmed by Baron & Markman (2003) who found that entrepreneurs’ social networks assist them in gaining access to strategic business contacts, but through the effective use of their social competence. Therefore, we propose: Hypothesis 8a. Inter-organizational network activities will partially mediate the relationship between opportunities and entrepreneurial success, when controlling for environmental hostility. 8

Hypothesis 8b. Individual social networking activities will partially mediate the relationship between opportunities and entrepreneurial success, when controlling for environmental hostility.

Hypothesis 8c. Individual social networking activities will indirectly mediate the relationship between social competence and entrepreneurial success, when controlling for environmental hostility.

Hypothesis 8d. Individual social networking activities will partially mediate the relationship between selfefficacy and entrepreneurial success, when controlling for environmental hostility.

Environmental Hostility as Controlled Cause In this study, environmental hostility is used as a control variable since this contextual factor may affect successful venture activities (Covin, Slevin, & Covin, 1990). Environmental hostility denotes an unfavorable external force for business as a consequence of radical changes, intensive regulatory burdens, and fierce rivalry among competitors, among others (Covin & Slevin, 1989). As entrepreneurship is a complex task that is extremely sensitive to “habitat” (Miller, 2000), environmental hostility is expected to impact firm performance. Hence, environmental hostility was isolated from the determinants integral to this study. Based on all of the aforementioned literature the model proposed is as follows: -----------------------------------Insert Figure 2 about here ------------------------------------

RESEARCH DESIGN AND DATA COLLECTION This is an empirical study that attempts to model the relations, effects, and interactions among variables in the proposed model (see Figure 2) using PLS. PLS is ideally suited for small sample sizes, formative indicators, and data that do not conform to traditional statistical assumptions of normality, homoscedasticity, linearity, and multicollinearity (Chin, Marcolin, & Newsted, 2003). To obtain t-statistics for the paths in our model, in line with Baron and Kenny’s (1986) test, we conducted a bootstrap test using 200 resamples. Before modeling the proposed relations, data screening was done to ensure the meeting of data analysis requirements.

Once the data were free from outliers and adequate for the multivariate analysis, Exploratory Factor Analysis (EFA) was conducted to define the underlying structure of the variables. Following this step, the Confirmatory Factor Analysis (CFA) took place to assess the degree to which the data met the expected structure. For both analyses—EFA and CFA—the respective reliability and validity tests were applied. During the CFA, the proposed model was modified to obtain the best “goodness of fit” model for the proposed relationships. In addition, we 9

examined variance among the groups under consideration in the proposed model through CFA multigroup analysis. Once all the tests and the recommended modifications from the previously mentioned analyses were complete, we proceeded to test the structural hypotheses with the modified structural model to obtain the final model (shown in Figure 3). Details for each of the aforementioned tests and/or procedures are explained in detail in the following sections.

Construct Operationalization This research—conducted online through a web-based survey administered by Qualtrics around an initial qualitative study conducted in 2009 and 2010 and through other research literature—was developed and used to test the proposed model. The study was specifically designed to test the validity of the theoretical measurement model and hypothesized relationships among the constructs. The survey items were derived from existing measures with some adaptations to fit the uniqueness of this research. We relied on existing measures since our intention in this study was not to develop new measures when available items had been validated in prior research.

Measures of systemic factors (POL, PROG, EDU and OPP, and MIND) were adapted from the National Expert Survey (NES) (Reynolds et al, 2005). Measures of individual factors were adapted from different sources. The construct of SE was adapted from Chen, Gully, and Eden (2001); the constructs of SC were adapted from Baron and Markman (2003); and measures of INETW and ONETW were modified to reflect the entrepreneurial networking process adapted from Chen, Zou, and Wang’s (2009) measurements. Finally, the ES construct was modified to reflect the firm performance based upon Chua (2009). We considered the guidelines of Petter, Straub, & Rai (2007) regarding formative vs. reflective for this study. Variables were operationalized as reflective, formative, and categorical as follows: The variables EDU, OPP, MIND, SC, SE, HOST, INETW and ONETW were operationalized as reflective constructs on a five-point Likert scale (with 1=strongly disagree and 5=strongly agree).

The entrepreneurial success-related constructs of firm performance were operationalized as formative through different scales such as sales growth rate and net profit margin, change in the number of employees, and variances in financial conditions over the last three years (Jarvis, MacKenzie, & Podsakoff, 2003). We chose to measure firm performance through sales and employee growth, net profit margin, and financial condition, as well as through formative indicators guided by the literature of that type of measurement (Chua, 2009; Diamantopoulos & Winklhofer, 2001). Likewise, because we had more than two variables predicting our dependent variable, we conducted a multicollinearity test. This test produces a variable inflation factor (VIF) to indicate whether the independent variables are explaining unique variance in the dependent variable or if they are explaining overlapping variance (Petter et al., 2007). The results of the analysis indicate that the predictor variables are 10

separate and distinct (VIF range from 1.01 to 1.51). The initial survey was pre-tested on a group of known entrepreneurs using Bolton’s technique, operationalizing item response theory (Bolton, 1993). During pilot tests, five questions were flagged due to problems in comprehension. Subsequent changes were approved by the testers. Since entrepreneurs’ time is limited, the questionnaire was calculated to be completed within 15 minutes to improve the response rate. This was determined acceptable during pretesting by several entrepreneurs.

Data Collection Sample This study was conducted with current entrepreneurs doing business in different industries and regions in P.R. Complete demographic information is reported in Appendix C. The sample consisted of 135 participants. The data were collected through a survey that assured participants that the study was purely for research purposes and that participation was voluntary. All surveys had the option of being answered in Spanish or English, the thought being that while Spanish is the primary language in P.R., most Puerto Rican entrepreneurs consider English the language of business. Study participants were identified and selected from the Puerto Rico Trade and Export Office official Register of Business. This list is public, but needs to be requested. A total of 1,500 surveys were emailed; 221 were returned, resulting in a response rate of approximately 15%. However, only 135 were returned completed and usable for data analysis. Lower response rates for entrepreneur surveys seem typical when compared with the general population (Dennis, 2003). We tested for response bias based on the time of response (early vs. late) following Armstrong and Overton’s (1977) test. To do this, we conducted a one way ANOVA using the dependent variables (3 observed variables), and using response date as the distinguishing factor. The results of the ANOVA show that there is no significant difference among the values (5.66 to 6.44) for the dependent variable between early and late responders.

There were 464 missing values in the data set, which accounted for 5% (n=8640) of the total number of values in the data set. Since substitution also is acceptable if the number of missing values is less than 5% (Tabachnick & Fidell, 2007), we input the mean value for each missing value. The minimum, maximum, and mean values of all the indicator variables appear to be reasonable.

Statistical Analysis Based on a bivariate outlier analysis at a confidence interval of 95%, we found close to 115 cases of outliers. However, while we expect some observations to fall outside the ellipse, we only deleted 5 respondents that fell outside more than two times (Hair, Black, Babin, & Anderson, 2010). Descriptive statistics, correlations, and 11

Cronbach’s alphas for all the variables are presented in Table 1.

Measurement Model: Exploratory Factor Analysis and Confirmatory Factor Analysis An EFA was used to reveal the underlying structure of the relationship among a set of observed variables. Principal Axis Factoring with Direct Oblimin rotation was performed with valid, reliable, and adequate results (as shown in Appendix A), which based on the collected data validates that eleven factors exist throughout the survey. We chose oblique rotation because of its assumption of correlated variables consistent with our understanding of the issues in this study (Ferketich & Muller, 1990; Field, 2005). Direct Oblimin, which is a particular type of oblique rotation, was selected because it allows factors to be correlated and diminished interpretably (Costello & Osborne, 2005). -----------------------------------Insert Table 1 about here -----------------------------------The KMO measure of sampling adequacy was .687, and Bartlett’s Test of Sphericity was significant (

2

= 3819.86,

df=703, p< 0.000), indicating sufficient inter-correlations. Moreover, almost all MSA values across the diagonal of the anti-image matrix were above .50 and the reproduced correlations were over .30, suggesting that the data are appropriate for factoring. An additional check for the appropriateness of the respective number of factors that were extracted was confirmed by our finding of only 4% of nonredundant residuals with absolute values greater than 0.05.

The selected EFA structure was the one with the eigenvalues greater than 1, which also fit with the eleven expected factors. The solution was considered good and acceptable through the evaluation of three possible models— including 12 and10 factor solutions—and their respective statistical values. During the evaluation process, twelve items (10.4-10.6; 12.3; 14.5; 15.3; 16.3-16.6; 18.1-18.2) were eliminated for their communality values below .50 (Igbaria, Livari, & Maragahh, 1995). The total variance explained was 68.7%, which exceeds the acceptable guideline of 60% (Hair et al., 2010). To test the reliability of the measures, we used a coefficient alpha (Gerbing & Anderson, 1988). Acceptable values of Cronbach’s alpha greater than .70 indicate good reliability (Nunnally, 1978). As statistics presented in Table 1 show, all factors have acceptable reliability.

Convergent validity deals with the extent to which measures converge on a factor upon initial estimate. Convergent validity can be made based on the EFA loadings. Since all of the variables loaded at levels greater than .50 on expected factors, convergent validity is indicated (Igbaria et al, 1995). Discriminant validity measures the extent to which measures diverge from factors they are not expected to quantify. In EFA, this is aptly demonstrated by the lack of significant cross loadings across the factors (over .20 differences). The items belonging to the same scale had factor loadings exceeding .50 on a common factor and no cross-loadings (see Table B1.1). The eleven 12

extracted factors seem to be reflective constructs as each item asks similar things.

We performed the CFA using PLS and began by reviewing the factors and their items to establish face validity. We specified the measurement model in PLS with the eleven factors derived from the EFA, no modifications are considered to improve the original model. Our EFA modified model shows all the reliability coefficients above .70 and the Average Variance Extracted (AVE) above .50 for each construct. The measurements are thus reliable, and the constructs account for at least 50% of variance. In the Correlations Table, (see Table B4) the square root of each construct’s AVE is greater than the correlation between constructs, thus establishing sufficient discriminant validity (Chin, 1998). Each item loads higher on its respective construct than on any other construct, further establishing convergent and discriminant validity (Gefen, Straub, & Boudreau, 2000).

Because we used a single survey to a single sample, we needed to conduct a common method bias test to ensure that the results of our data collection were not biased by this mono-method. To do this, we examined our latent variable correlation matrix for values exceeding 0.900. According to Bagozzi, Yi, & Phillips (1991) this is a strong indication of common method bias. However, the highest correlation we have is 0.396, with an average correlation of .118, and the lowest positive correlation of .013. These values provide sufficient evidence that our data collection was not biased by a single factor due to mono-method.

Structural Model We tested our structural model using PLS-Graph 3.0. We used a PLS method because we had formative factors, which are more accurately estimated (Chin, 1998). We initially took an exploratory approach by testing a fully specified model and then trimming non-significant paths one by one until only significant paths remained. Significance of paths was estimated using t-statistics produced during bootstrapping, using 2000 resamples (see trimmed model in Figure 3).

Next we performed a mediation analysis using causal and intervening variable methodology (Baron & Kenny, 1986). Mathieu and Taylor (2006) indicate that mediator variables are explanatory mechanisms that shed light on the nature of the relationship that exists between two variables. Mediated paths connecting independent variables (Opp, SComp, and SE) to dependent variable (ES) through a mediating variable (ONetw and INetw) were analyzed to examine the direct, indirect, and total effects. For each of the mediation hypotheses being tested (H3c; H8a to H8d), a model was first run without the mediation paths then with the mediator. 13

FINDINGS The estimate path loading results based on PLS, significance, and R2 are presented in Figure 3. To avoid errors in statistical conclusion a validity appropriate power level was established (power level at 0.80, and significance level of .05) and used to compute the effect size to guarantee statistically significant results and control over the possibility of Type I and Type II errors (Hair et al., 2010). The R2 values show that the number of predictors used in this research for ES (Beta=.133; p<.05) and for INETW (Beta=.116; p<01) are sufficient to explain it. We found an acceptable power over .80 (Hair et al, 2010) at 95% and 99% of confidence, respectively. Hence, the independent factors proposed in the model were sufficient to explain both. However, this was not the case for ONETW (.014) and SE (.012). This may be because in both cases our model considered only one predictive factor for the analysis. Additionally, the f-squared for the effect of SC on INETW indicates a small effect (f2=0.84). HOST, which shows a strong and significant negative effect on ES (λ = -.33; p<.01) at 99% of confidence, was included in our model as a control variable. The mediation roles of networking remain as interesting subject details throughout this section. Figure 3 below details the abovementioned results, and Appendix B summarizes the hypotheses and statistical results that will be discussed below. -----------------------------------Insert Figure 3 about here -----------------------------------Systemic Factors as the Roots to Entrepreneurial Success This research suggests that systemic factors such as OPP, MIND, and EDU will directly impact ES. To assert this claim, this examination begins by proposing that perceived entrepreneurial opportunities will positively and directly impact entrepreneurial success (H1a) and inter-organizational (H1b) and individual networks (H1c). Remarkably, our results show that none of those suggestions (H1a to H1c) were sustained (see Figure 3 and Appendix B).

Secondly, this research postulates that a national mindset toward entrepreneurship directly impacts entrepreneurial success (H2). Unfortunately, we did not find a significant direct relationship between MIND and ES. Finally, we hypothesized that entrepreneurial education has a direct (H3) and indirect effect through self-efficacy (H3c) on entrepreneurial success. However, the EDU received by the entrepreneurs surveyed does not appear to be appropriate to provoke a direct significant effect on ES nor indirectly through the enhancement of entrepreneur SE since the hypotheses H3a, H3b, and H3c were not supported (see Figure 3 and Appendix B).

All of the abovementioned results provide the foundation for our first finding: Systemic factors in P.R.— entrepreneurial opportunities, national mindset toward entrepreneurship, and entrepreneurial education—are not suitable sources for boosting entrepreneurial success. 14

The Role of Individual Factors in the Likelihood of Entrepreneurial Success This paper theorizes that individual factors such as social competence (H4a) and self-efficacy (H5a) may act as direct driving forces for entrepreneurial success. In the case of entrepreneur’s SE, this paper suggests that perceived SE will negatively impact ES. Additionally, we hypothesized that social competence (H4b) and selfefficacy (H5b) may affect individual social networking. However, our results show a positive but insignificant direct effect from entrepreneur’s SE on ES (H5a). Moreover, from all of the abovementioned hypotheses, H4b was the only one to show a significant direct relationship between entrepreneur’s SC with INETW (λ= .265; p < .01). Therefore, our second finding is: Entrepreneur’s social competence enhances their individual social networking activities. (See Figure 3 and Appendix B)

The Mediating Role of Individual Social Networking and Inter-Organizational Networking This research hypothesizes that individual social networks (H6) and inter-organizational networks (H7) have a positive direct effect on ES. Surprisingly, our data reveals a significant inverse relationship between INETW and ES (λ = .214; p <.05) and an insignificant but still negative effect between ONETW and ES (see Figure 3 and Appendix B). Hence, our third finding is: Individual social networks have a negative effect on entrepreneurial success.

This paper also suggests that inter-organizational networks mediate the relationship between entrepreneurial opportunities and entrepreneurial success (H8a). Additionally, we theorize that individual social networks mediate the relationship between entrepreneurial opportunities (H8b), social competence (H8c) and self-efficacy (H8d) with entrepreneurial success. Our data reveals an indirect relationship between SC and ES through INETW (H8c). Yet, as previously discussed, the relationship between INETW with ES is negative. Therefore, our finding number four is: Entrepreneur’s social competence indirectly affects entrepreneurial success through the development of individual social networks. However, even when an individual’s social competence enhances their social network, the individual social network diminishes their entrepreneurial success (see Figure 3 and Appendix B).

In summary, this research hypothesizes that systemic factors such as national mindset and entrepreneurial education and opportunities act as positive drivers for entrepreneurial success. Unfortunately, our findings suggest that institutional factors in P.R., as a whole are not functioning as proper providers of the complementary relationships needed to create an environment conducive to successful entrepreneurship. On one hand, entrepreneurial opportunities and education as well national mindset seem to be too far away to act as catalysts for entrepreneurial success. On the other, networks at the individual and inter-organizational levels show an adverse effect on entrepreneurial success. In regard to the individual factors under consideration, our findings suggest that 15

even when social competence shows significant indirect effect on entrepreneurial success through individual networking, the final outcome is not powerful since individual networks have a negative effect on firm performance. On the other hand, perceived self-efficacy obtained through entrepreneurial education seems to be insignificant for entrepreneurial success. Thus, for both individual factors we found a limited complementary relationship with the institutional structure. DISCUSSION This study was conducted with entrepreneurs doing business in P.R. who have diverse entrepreneur and firm characteristics. This by itself may account for a wide range of differences between surveyed groups and the role of each factor under consideration. However, even among those potential differences, the lack of an adequate institutional structure conducive to entrepreneurship is present among all the relevant factors.

A study recently published by the SBA Office of Advocacy categorized P.R. as a country that should be in the economic development stage known as “innovation-driven.” However, their results showed that P.R.—at number17 out of 40 countries surveyed—had not exploited its full potential. In the innovation-driven stage, entrepreneurship plays a more important role in increasing economic growth. This study further specified that institutions need to be strengthened before entrepreneurial resources can be deployed to drive innovation. Consequently, our examination expands upon the abovementioned study by explaining why P.R. has not yet attained the innovation driven stage. It reveals that Puerto Rican institutions are neither suitable nor structured to lead the local economy from an efficiency-driven to an innovation-driven one.

Stevenson & Jarillo (2007) assert that when an opportunity is detected and individuals are willing, the ability to exploit it is vital. In that line of thought, our investigation reveals that the inability of P.R.’s entrepreneurs to exploit opportunites is because of their individual networking barriers. Acs & Szerb (2010) demonstrate that a lack of adequate networking may prevent countries from reaching the next stage of development. In that sense, our findings expand the views of Aponte (2002a), Aponte & Rodríguez (2005), and the 2007 GEM report (Bosma et al., 2008), all of which state that the general population in P.R. recognizes that opportunities exist and want to follow them, but perceive it is not feasible to do so. Thus, we agree that the problem is not the lack of opportunities but add that networks utilized by entrepreneurs at the individual level may represent a barrier to successfully exploiting those perceived opportunities. Furthermore, the literature states that networking as part of the entrepreneurial attitude may affect the general disposition of a country’s population toward entrepreneurs, entrepreneurship, and business start-ups (Acs & Szerb, 2010); and perceptions about entrepreneurship may affect the supply and demand of national entrepreneurial activities (Bosma et al., 2008). Therefore, our finding that individual social networks have a negative influence on entrepreneurial success may explain the contradiction between the positive perceptions toward entrepreneurship reported by Aponte (2002b) and the lower 16

entrepreneurial activity recounted by the 2007 GEM study. However, the reasons why entrepreneur networks at individual level have a negative impact on their success are beyond the scope of this study.

As previously mentioned, beliefs, values, and preferences will have direct impacts on economic outcomes (Guiso et al., 2006). Nonetheless, our study shows that P.R.’s national mindset toward entrepreneurship is not acting as a source of entrepreneurial success. A positive national mindset toward entrepreneurship is essential to developing adequate collaborations and institutional and industrial structures and to responding to perceived opportunities (Aldrich & Zimmer, 1986); this finding may help to explains why the institutional factors as a whole, are not the most adequate for generating a successful entrepreneurial environment.

Changing the mindset of a nation, as Romaguera (2010) states, is an incredibly challenging task that requires changing individual mindsets through a well-designed master plan. But, “as culture takes time to change” initiatives developed in P.R. since the mid 1990s—which include university level courses on entrepreneurship, recognizing entrepreneurial success via media coverage of entrepreneurial award winners and massive awareness of entrepreneurship as a pursuable societal goal—are the seeds of the art, science, and process of making an entrepreneurial miracle happen (Romaguera, 2010).

However, the entrepreneurial miracle is not a mystical product. As Romaguera (2010) exemplifies, it is part of a well-conceived plan of action that primarily requires knowing where we are as a country and where we want to go. It is in this sense that entrepreneurial education in P.R. and internationally seems to be scarce (Aponte & Rodríguez, 2005; Corduras-Martinez et al., 2010). The problem, as many researchers state, starts with the lack of consensus among educators regarding the adequate content of entrepreneurial education (Corduras-Martinez et al, 2010; Gibb, 2011; Varela, 2011) as well as the lack of understanding that entrepreneurial education should be customized to the specific needs of the group they are supposed to influence (Gibb, 2011). Along this line, our study confirms Varela (2011) and Gibb (2011), who show that the entrepreneurial education that has a successful effect on firm performance is the one that can impact entrepreneurs’ competencies, such as self-efficacy, and specific target-groups like new startups and those in the internationalization process. To achieve the desired impact of education, entrepreneurial advocates must determine which groups they want to impact and how. This also requires a blueprint plan with evaluation, measurement, and corrective action.

In conclusion, those with a stake in entrepreneurial success—government administrators, entrepreneurial organizations, business associations, educators, and entrepreneurs—must be aware of all abovementioned discoveries to design a master plan that may lead P.R. to build a successful entrepreneurial environment. 17

CONTRIBUTION TO THEORY & PRACTICE This study examines the impact of systemic and individual factors on entrepreneurial success using the island of P.R. as a case study. Limited scholarly research has been conducted on the entrepreneurial environment in P.R., and examinations of institutional and individual factors together are even scarcer. Moreover, no scholarly work has been conducted that analyzes the mediating roles of individual and inter-organizational networks on the island, making this a groundbreaking investigation.

This research adds to the body of entrepreneurship theory demonstrating the relationships and factors that may facilitate or hinder entrepreneurial success. Our research suggests that a better interconnected entrepreneurial system and stronger individual competencies may be necessary for both practitioners and policy makers to develop a master plan that may contribute to improving the current and prospective entrepreneurial environment. Hence, the results of our research could be used to assist policy makers, entrepreneurial advocacy organizations, and entrepreneurs themselves with carrying out their entrepreneurial goals. Policy makers should be aware of the necessity of designing an integrative system through their policies and programs that help interlock entrepreneurial opportunities and education and a national mindset favorable to entrepreneurship for both current and future generations. Entrepreneurial advocacy organizations, for their part, should continue strengthening the interorganizational networks that now seem to be very helpful for entrepreneurs, yet at the same time review the overall content of their programs. Entrepreneurs themselves should reevaluate the use and composition of their individual networks as well as their entrepreneurial competencies. In that line, academic institutions and entrepreneurial organizations that have programs geared toward entrepreneurs should be informed about the systemic and individual deficiencies so they may enhance their curriculum design for current and future entrepreneurs.

LIMITATIONS The size and composition of our sample may limit the generalization of our findings as our sample is specific to entrepreneurs doing business in P.R. A wide variety of entrepreneurs were included in our sample to take into account their diverse individual and firm characteristics. However, the purpose of this study was to examine the systemic and individual factors that may facilitate or hinder entrepreneurial success in P.R., and our results may provide a basis for other countries. Yet, they should be examined bearing in mind each country’s individual context. In addition, constructs were measured by respondents who self-reported information about their firms and perceptions and may be inherently biased. That being said, potential bias is considered a minimal risk in this case for the development of practice-relevant theory as respondents were not asked to identify themselves or their organizations (Venkatraman & Ramanujam, 1986).

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Appendix A: Measurement Model Results Table A1 Final Pattern Matrixa

Factor Promo Mind ONetw Edu Host Adapt .891 .889 .803 .626 -.935 -.805 -.803 -.793 .945 .933 .795 .788 .739 .947 .878 .613 -.863 -.791 -.721 -.885 -.765 .211 -.703 -.618

(Q16_5) (Q16_6) (Q16_4) (Q16_3) (Q11_2) (Q11_4) (Q11_1) (Q11_3) (Q17_3) (Q17_4) (Q17_1) (Q17_2) (Q17_5) (Q10_2) (Q10_3) (Q10_1) (Q20_1) (Q20_3) (Q20_2) (Q15_5) (Q15_4) (Q15_2) (Q15_1) (Q13_3) (Q13_2) (Q13_4) (Q13_1) (Q12_1) (Q12_2) (Q14_2) (Q14_3) (Q14_4) (Q14_1) (Q16_1) (Q16_2) (Q18_3) (Q18_5) (Q18_4) a rd 12 items deleted/ 3 version

-.237

SE

Opp Perc

Expr

INetw

-.926 -.768 -.743 -.663 .913 .832 .866 .746 .691 .595 .913 .901 -.767 -.766 -.732

26

Table A2: Confirmatory Factor Analysis: Loading and Measurement Properties of Constructs

Construct/ Items 1 Promotion Q16_3_1 Q16_4_1 Q16_5_1 Q16_6_1 2 National Mindset Q11_1_1 Q11_2_1 Q11_3_1 Q11_4_1 3

4

5

6

7

8

9

10

11

Loadings/ Weights

t-Value

0.7564 0.9087 0.9306 0.9025

20.3088 28.9024 30.0956 28.4506

0.8868 0.9141 0.9162 0.8986

36.7491 44.5225 52.5999 39.6549

0.8559 0.8471 0.9333 0.9331 0.8263

26.9619 25.7777 28.1935 30.7195 27.1805

0.7871 0.9252 0.9169

25.1831 26.6255 23.6663

0.9037 0.8547 0.9073

37.2128 54.852 38.4972

0.799 0.8365 0.8514 0.8633

21.3127 24.7434 19.7499 18.709

0.8217 0.8521 0.9086 0.8394

15.1947 19.295 17.6227 18.6761

0.9399 0.9399

86.787 86.787

0.7704 0.8916 0.8576 0.7812

19.7143 27.3927 26.7221 17.3595

0.9545 0.9545

112.9245 112.9245

0.8623 0.8411 0.8648

28.7039 27.7983 27.0316

0.3318 0.3318 0.3318 0.3318 0.3318

11.8367 11.2715 13.953 21.2274 4.5872

Composite Reliability 0.93

Communalities 0.5722 0.8258 0.866 0.8145

0.817

Inter-Organizational Networks

Q17_1_1 Q17_2_1 Q17_3_1 Q17_4_1 Q17_5_1 Education Q10_1_1 Q10_2_1 Q10_3_1 Hostility Q20_1_1 Q20_2_1 Q20_3_1 Adaptability Q15_1_1 Q15_2_1 Q15_4_1 Q15_5_1 Self-Efficacy Q13_1_1 Q13_2_1 Q13_3_1 Q13_4_1 Opportunities Q12_1_1 Q12_2_1 Perception Q14_1_1 Q14_2_1 Q14_3_1 Q14_4_1 Expressiveness Q16_1_1 Q16_2_1 Individual Networking Q18_3_1 Q18_4_1 Q18_5_1

AVE 0.77

0.947 0.7864 0.8357 0.8395 0.8074

0.775

0.945 0.7326 0.7176 0.8711 0.8707 0.6828

0.772

0.91 0.6196 0.8559 0.8408

0.79

0.919 0.8167 0.7305 0.8231

0.702

0.904 0.6385 0.6998 0.725 0.7453

0.733

0.916 0.6753 0.7262 0.8256 0.7047

0.883

0.938 0.8834 0.8834

0.684

0.896 0.5936 0.795 0.7355 0.6103

0.911

0.953 0.911 0.911

0.733

0.892 0.7435 0.7074 0.7479

DV_Formative Firm Performance Q35_1 Q36_1 NPM_AVG SGR_AVG Empl_AVG

0.381

0.746 0.4143 0.4126 0.4376 0.5257 0.1163

27

APPENDIX B: Summary of Hypotheses Results

H1

1a: Perceived entrepreneurial opportunities will positively impact entrepreneurial success, when controlling for environmental hostility.

Path Loading (λ) .051 ns

H0 Accepted NO

1b: Perceived entrepreneurial opportunities will positively impact interorganizational networking, when controlling for environmental hostility.

-.118 ns

NO

1c: Perceived entrepreneurial opportunities will positively impact individual social networking, when controlling for environmental hostility.

.091 ns

NO

H2

2: National mindset will directly impact entrepreneurial success, when controlling for environmental hostility.

-.091 ns

NO

H3

3a: Appropriate content of entrepreneurial education will positively impact entrepreneurial success, when controlling for environmental hostility. 3b: Appropriate content in entrepreneurial education will positively impact individual self-efficacy, when controlling for environmental hostility. 3c: Self-efficacy will partially mediate the relationship between entrepreneurial education and entrepreneurial success, when controlling for environmental hostility.

-.059 ns

NO

.111 ns

NO

4a: An entrepreneur’s social competence will positively impact entrepreneurial success, when controlling for environmental hostility.

.153 ns

NO

4b: An entrepreneur’s social competence will positively impact individual social networks, when controlling for environmental hostility.

.265***

YES

5a: Perceived self-efficacy will negatively impact entrepreneurial success, when controlling for environmental hostility.

.057 ns

NO

5b: Perceived self-efficacy will impact individual social networks, when controlling for environmental hostility.

.086 ns

NO

H6

6: Individual social network activities will positively impact entrepreneurial success, when controlling for environmental hostility.

-.214**

NO,

H7

7: Inter-organizational network activities will positively impact entrepreneurial success, when controlling for environmental hostility. 8a: Inter-organizational network activities will partially mediate the relationship between opportunities and entrepreneurial success, when controlling for environmental hostility. 8b: Individual social networking activities will partially mediate the relationship between opportunities and entrepreneurial success, when controlling for environmental hostility. 8c: Individual social network activities will indirectly mediate the relationship between social competence and entrepreneurial success, when controlling for environmental hostility. 8d: Individual social networking activities will partially mediate the relationship between self-efficacy and entrepreneurial success, when controlling for environmental hostility.

-.015 ns

NO

H4

H5

H8

NO

NO NO

YES

NO

28

APPENDIX C: Survey Respondent Socio-Demographic Information and Firm Characteristics Socio-Demographic Education

Groups

%

Technical College or Less Bachelor’s degree or Higher

23% 77%

Male Female

72% 28%

First Attempt 2 or More

61% 39%

30 or Less 31 or More 30 or Less 31 or More 30 or Less 31 or More 30 or Less 31 or More

81% 19% 56% 44% 40% 60% 10% 90%

Market opportunity Lost job Don't want to work for anyone Family business Wanted to be an entrepreneur

26% 20% 19% 14% 21%

Self-Employed Employee

43% 57% %

Gender

Business Attempt(s)

Age Think 1st Venture Current Venture Current Age Motivations

Family Background

Firm Characteristics Industry

Scope

Region

Years in Current Business

Groups Retail Manufacturing Wholesale Construction Transportation Communication Financial Professional Service Others

22% 12% 7% 12% 4% 2% 0% 41% 1%

Global International Island-wide Regional Local

5% 8% 42% 24% 22%

North South East West Central

36% 8% 8% 44% 4%

1 year or less 2 to 3 years 4 to 5 years 5 to 10 years More than 10 years

9% 27% 13% 23% 38%

29

LIST OF FIGURES FIGURE 1: CONCEPTUAL QUANTITATIVE RESEARCH MODEL

FIGURE 2: Proposed Quantitative Research Model

30

FIGURE 3: PLS Results of Proposed Structural Model without Moderators

LIST OF TABLES Table 1: DESCRIPTIVE STATISTICS, CORRELATIONS, AND CRONBACH’S ALPHAS

Factor

Promo

Mind

ONetw

Edu

Host

Adapt

SE

Opp

Perce

Expre

Promo

(0.9)

Mind

-.171

(.925)

ONetw

.111

-.200

(.926)

Edu

-.069

-.175

.146

(.849)

Host

-.138

.327

-.080

-.162

(.867)

Adapt

-.138

.110

-.026

.076

.011

(.857)

SE

-.123

.075

-.041

-.058

.065

.281

(.874)

Opp

.026

.042

-.060

.039

.014

-.032

-.175

(.868)

Percep

.169

.073

.014

-.006

.028

-.341

-.323

.166

(.842)

Expres

.207

-.212

-.048

.144

-.020

-.142

-.002

.044

.026

(.90)

INetw

-.139

-.013

-.171

-.017

.214

.273

.117

-.079

-.154

.016

INetw

(.817)

Mean

SD

3.7305

.7794

2.8263

1.042

3.0295

.9172

3.0483

1.033

2.9641

1.064

4.4063

.5960

4.3559

.5793

3.4081

1.116

4.0966

.4912

3.6974

.8410

3.2500

.8535

Note. Figures in parentheses are Cronbach’s Alphas.

31

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