ПІДПРИЄМНИЦЬКІ НАМІРИ СТУДЕНТІВ УНИВЕРСИТЕТІВ. Національний Университет Кораблебудування імені адмирала Макарова

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1 SWorld March MODERN DIRECTIONS OF THEORETICAL AND APPLIED RESEARCHES 2015 Менеджмент и маркетинг - Управление производством и развитие предприятия УДК : Солесвік М.Б. ПІДПРИЄМНИЦЬКІ НАМІРИ СТУДЕНТІВ УНИВЕРСИТЕТІВ Національний Университет Кораблебудування імені адмирала Макарова Пр. Героїв Сталінграда 9, Миколаїв, Україна Solesvik, M. ENTREPRENEURIAL INTENTIONS OF UNIVERSITY STUDENTS National University of Shipbuilding 9 Geroiv Stalingrada Ave., Nikolaev, Ukraine Аннотація: дослідження має на меті визначити вплив основних факторів на вибір підприємницької кар'єри серед українських студентів, які вивчають бізнес та економіку. Стаття заснована на теорії планової поведінки та теорії підприємницької події. Емпіричні дані дослідження отримані на основі опитування, проведеного в трьох університетах в Україні в 2007 році з використанням двохетапного підходу для аналізу вимірювань і моделювання структурними рівняннями. Була протестована інтегрована модель підприємницьких намірів. Отримані результати свідчать про адекватність інтегрованої моделі, що поєднує змінні теорії планової поведінки та моделі підприємницької події, в поясненні дисперсії у намірах студентів стати підприємцями. 1

2 Ключові слова: підприємницькі наміри, Україна, теорія планової поведінки. Abstract: The study seeks to determine the impact of underlying factors on selfemployment career intentions among Ukrainian business students. The paper draws on the theory of planned behavior and the entrepreneurial event theory. The empirical data of the research are derived from a survey conducted at three universities in Ukraine in Utilizing a two-step approach for measurement analysis and structural equation modeling, the integrated model of the entrepreneurial intentions was tested. The results suggest a good predictive power of the integrated model combining variables of the theory of planned behavior and the entrepreneurial event model in explaining variance in the selfemployment intentions of the students. Keywords: entrepreneurial intentions, Ukraine, theory of planned behaviour. Introduction. It is believed that the intent is a good predictor of the human behavior in various areas of the life. One of the most popular theories used to predict behavior through intention is Ajzen s (1991) theory of planned behavior (TPB). This theory together with its predecessor the theory of reasoned actions of Ajzen and Fishbein (1980) was tested in various fields and proved to be a good predictor of individual s activity (for example, the future study plans of undergraduate students (Ingram, Cope, Harju, & Wuensch, 2000), purchasing decisions in virtual stores (Barkhi and Wallace, 2007), expert decision support systems (Workman, 2005), and many other research projects). Entrepreneurship scholars also utilized the theory of planned behavior to explore entrepreneurial intent and behavior based on various samples, including students (Krueger, Reilly, & Carsrud, 2000; Iakovleva & Solesvik, 2014; Liñan & Chen, 2009; Solesvik, 2013; Solesvik et al., 2013; Solesvik et al., 2014). There are, however, other perspectives applied in prediction of the self-employment intentions. Several theories were constructed especially for entrepreneurship: Shapero-Sokol s (1982) model of entrepreneurial event, Bird s (1988) model of implementing 2

3 entrepreneurial ideas. These models have not got significant empirical support compared to the Ajzen s theory of planned behavior (Krueger et al., 2000). Furthermore, Bandura s (1977, 1994) self-efficacy theory is used in entrepreneurial intent research as well. Shook with colleagues (2003) recommend reducing a number of competing models explaining entrepreneurial intentions. They encouraged scholars to concentrate on improving existing models and creating a single integrated model. This work is aimed to fill these research gaps. The goal of the present paper is to test the theory of planned behavior, the entrepreneurial event theory (SEE), and integrated model that combines variables from the theory of planned behavior and SEE theory (Solesvik et al., 2012). The research question of the study: Does the integrated model explain variance in entrepreneurial intentions better than the theory of planned behavior model and SEE model separately. The paper focuses on the investigation of the career choice based on one-country sample of students. The article is organized as follows. The next section explains theoretical background of the models proposed to test. Further, I explain methodology and research design. The results of confirmatory factor analysis and structural equation modeling using AMOS are presented in the subsequent section. In the final part I discuss the results obtained and give implications for further research. Literature review. In the paper, five constructs influencing on self-employment intent are under the scrutiny. They are a part of the integrated model of the entrepreneurial intentions (Iakovleva & Kolvereid, 2007). Attitude to entrepreneurship, subjective norm, and perceived behavior control are factors adapted from the Ajzen s (1991) theory of planned behavior. Perceived desirability and feasibility are variables of the Shapero- Sokol s (1982) entrepreneurial event model. (a) Attitude: It is believed that better attitude to the behavior leads to higher probability to follow such behavior by the individual. Ajzen (2001:188) 3

4 defined attitude as the degree to which a person has a favorable or unfavorable evaluation or appraisal of the behavior in question. (b) Subjective norm: The opinion of important others is an influencing factor for behaviors. Under important others, the theorists of planned behavior understand family members, close friends, and people whose opinion is influential for the individual. Environment s opinion may stimulate the entrepreneurial intentions in this case if important others are supporters of entrepreneurship and if their judgments are influential for the person. Thus, a scale for subjective norm measures both attitude of important others and valence of their opinion for the person. (c) Perceived behavior control (PBC) regarding self-employment career selection reflects individuals believe in their ability to become entrepreneurs and cope with the possible difficulties that might occur compared to the employment. (d) Desirability: Desirability indicates attractiveness of starting an own business (Shapero & Sokol, 1982). (e) Feasibility: Feasibility determines the perceived person s confidence that the behavior will be realized, that is she is able to create a new enterprise and do business. In the proposed model, attitude, subjective norm, and perceived behavior control are not associated with the entrepreneurial intents directly, but mediated by desirability and feasibility constructs. Six hypotheses are derived for the analysis to examine the relationship between the variables. Hypothesis 1: Attitude to self-employment will be positively related to desirability. Hypothesis 2: Subjective norm will be positively related to desirability. Hypothesis 3: Subjective norm will be positively related to feasibility. Hypothesis 4: Perceived behavior control will be positively related to feasibility. Hypothesis 5: Desirability is positively associated with entrepreneurial intentions. 4

5 Hypothesis 6: Feasibility will be positively related to entrepreneurial intentions. Methodology and research design. Data collection and sample The primary data were collected in February 2007 at three Ukrainian universities situated in the city of Nikolaev (south of Ukraine): the National University of Shipbuilding, the Humanitarian University named after Petro Mohyla, and the European University. There are around 450,000 inhabitants in the city. The survey intended to check entrepreneurial intentions among senior students - research population consisted of third, fourth, and fifth-year graduates who study economics and business administration. Totally, 193 questionnaires were distributed and received back. The administration of questionnaires in the classrooms during the regular lessons gave a 100% response rate. One questionnaire was not usable for analysis, thus it was excluded from the further examination. The final sample comprised 192 students. The mean age of respondents is (standard deviation is 1.761); a share of female respondents is 70.8 per cent. Method Structural equation modeling (SEM) is used to analyze data in the present study. This method is widely applied in social sciences research: in experimental and observational studies, cross-sectional and longitudinal designs (MacCallum & Austin, 2000). There are six latent variables in the combined TPB-SEE model: attitude to entrepreneurship, perceived behavior control, subjective norm, desirability and feasibility and entrepreneurial intention. The first three latent variables are exogenous and the last three variables are endogenous. Answers collected were put into the SPSS data file. The records were checked for missing data, minimum, maximum, skewness and kurtosis. I have used AMOS in the subsequent analysis. Measures 5

6 Several scales were applied to measure latent variables of the integrated model of the entrepreneurial intentions: attitudes, subjective norms, perceived behavior control, desirability and feasibility, and entrepreneurial intent. The scale for assessing entrepreneurial intention consisting of six items was taken from the Liñan and Chen (forthcoming). The answers are measured on seven-point Likert like scale (1 = absolutely disagree; 4 = neither agree nor disagree; 7 = absolutely disagree). Attitude characterizing the interest of students in entrepreneurship was assessed by five items with the help of 7-pont Likert scale (1 = absolutely disagree; 4 = neither agree nor disagree; 7 = absolutely disagree). The scale to measure attitude was grounded on Gundry and Welch (2001) and Kolvereid and Isaksen (2006) scales. These items include answering the following questions: (1) Being an entrepreneur implies more advantages than disadvantages to me ; (2) A career as entrepreneur is attractive for me ; (3) If I had the opportunity and resources, I would love to start a business ; (4) Being an entrepreneur would give me great satisfaction ; (5) Among various options, I d rather be an entrepreneur. Subjective norms were measured with the help of scale adapted from Kolvereid (1996). Three believe items estimated judgments of important others using 7-point Likert scale identical to one used to evaluate attitudes, where (=1) implied that person should not, and (=7) should become an entrepreneur in the future. Three additional questions were utilized to determine opinion valence of the person s milieu regarding the further career choice. Respondents were asked to indicate on a 7-point scale (ranging from 1=do not care, to 7=care very much) how they care about (a) closest family members ; (b) closest friends, and (c) people important to you opinions. These are motivations to comply measures. The believe items were recoded to a bipolar scale (1=-3; 2=-2; 3=-1; 4=0; 5=+1; 6=+2; 7=+3) and multiplied with the respective motivation to comply item. The scale to determine perceived behavior control was constructed on the basis of Ajzen s (2002) examples of behavior control. Perceived behavior control was measured by four items: (1) If I wanted to, I could easily become an entrepreneur ; 6

7 (2) As an entrepreneur I would have sufficient control over my business ; (3) There are very few circumstances outside my control that may prevent me from becoming an entrepreneur ; (4) It is entirely up to me whether or not I become an entrepreneur. Responses were given on a 7-point scale ranging from 1=absolutely disagree to 7=absolutely agree. Desirability and feasibility were handled based on responses to the six items: (1) It is desirable for me to become an entrepreneur; (2) It is interesting for me to become an entrepreneur; (3) It is attractive for me to become an entrepreneur; (4) It is feasible for me to become an entrepreneur; (5) Becoming an entrepreneur is not a realistic option for me; (6) Starting my own business would be impossible for me. Desirability and feasibility were measured using 7-point scale: 1=absolutely disagree to 7=absolutely agree. Two last questions (FES5 and FES6) contained negative statements and were reverse coded for further analysis (1=7; 2=6; 3=5; 4=4; 5=3; 6=2; 7=1). The means, standard deviations and range of answers received for the observed variables are reported in the Table Insert Table 1 about here Data analysis. Measurement modeling Two-step procedure (Anderson & Gerbing, 1988) was used to test the research models. On the first stage a measurement model is estimated using confirmatory factor analysis with maximum likelihood estimation. On the second stage, the hypothesized relationships are assessed using structural equation modeling. The measurement model of the integrated entrepreneurial intentions model is shown in Appendix A. Estimation of the integrated measurement model s parameters showed the following results (Table 2) Insert Table 2 about here 7

8 There are 237 degrees of freedom, the relationship of chi-square to degrees of freedom (CMIN/df) should not be grater than 3 (Kline, 1998). Goodness-of-fit index (GFI) and adjusted goodness-of-fit index (AGFI) higher than 0.9 and 0.8 respectively indicate a satisfactory fit of the model (Bentler & Bonett, 1980). In the case of integrated measurement model combining the theory of planned behavior and entrepreneurial event models, goodness-of-fit index I and adjusted goodness-of-fit index are lower than good-fit level (0.83 and 0.78 respectively). Tucker-Lewis index (TLI), comparative fit index (CFI) and normed fit index (NFI) are other goodness-of fit indices. CFI value (0.909) was higher than 0.9 guideline, while TLI and NFI values were under 0.9 guideline (TLI=0.895, NFI=0.841). Badness-of-fit index root mean square error of approximation (RMSEA) is that is within the 0.08 border of acceptable model. At this stage of confirmatory factor analysis, error covariances are fixed at zero. Analysis of the model fit indices showed that the model is suitable for further structural equation modeling. Some modifications will be made in order to improve model fit characteristics. The analyzed integrated model was composed from two models: TPB and SEE. In order to answer the research question and check whether the integrated model is better in prediction of entrepreneurial intents, it is appropriate to carry out a confirmatory factor analysis for each of the component models. Confirmatory factor analysis for the theory of planned behavior model (Table 2) indicated that values of chi-square to degrees of freedom ratio, and root mean square error of approximation, and Tucker-Lewis index indicated an acceptable model. Goodness-of-fit index, adjusted goodness-of-fit index, normed fit index were slightly under the guideline for the acceptable model. In general, goodness-of-fit statistics suggests the measurement model for the TPB alone with the data set from Ukraine is good enough for the structural equation modeling. As for the confirmatory factor analysis of the Shapiro-Sokol entrepreneurial event model alone, the goodness-of-fit statistics parameters do not indicate a well 8

9 fitted model. The ratio of chi-square to degrees of freedom is 4.114; root mean square error of approximation of is above of the suggested level of 0.08 (however, some authors name RMSEA between 0.08 and 0.1 as mediocre fit (Browne & Cudeck, 1993). Goodness-of-fit indices are somewhat below 0.9 threshold (GFI=0.849, NFI=0.868, TLI=0.865, CFI=0.896); adjusted goodness-of-fit index of 0.77 is also lower than 0.8 guideline. Next step is to check validity. In confirmatory factor analysis convergent, discriminant, nomological, and face validities shall be verified. Face validity is the first that the researcher checks. Face validity reflects the consistency of the items with the factor definition. It is grounded on the researcher s opinion. Face validity in the study is acceptable. There are three measures of convergent validity: factor loadings, variance extracted, and construct reliability. First, I examined the strength of the relation standard regression weights in AMOS terms. All regression coefficients are within the lower guideline of 0.5, except item s three weight (0.117) measuring perceived behavior control. Additionally, all item loadings were significant, except this item three of perceived behavior control scale (section Regression weights in AMOS output file). Furthermore, the weights of the majority of indicators were over guideline of 0.7 (except ATT1, PBC4, FES2, FES3, and EI1). The non-significance and low factor loading make this item not suitable for further analysis. Thus, the item three of PBC scale shall be removed from the measurement model. The model fit statistics is improved after the deletion (Table 2). Convergent validity The next step was to calculate variance extracted (VE). The AMOS software does not compute variance extracted. This was done with the help of MS Excel (results are shown in Appendix B). Variance extracted should be higher than 0.5 to advocate satisfactory convergent validity. With the present data, the variances extracted of perceived behavior control and feasibility were low: 31.55% and 39.83% respectively. Low variance extracted of perceived behavior control can be explained 9

10 by not significance and small factor loading of the item three measuring PBC. After the deletion of this item, the variance extracted increased to 40.71% (Table 3). It is almost 10 per cent higher than before deletion, however, variance extracted is still lower the guideline. In the case of feasibility, low variance extracted can be explained by negative questioning of the two initial items in the questionnaire (c5 and c6). They were reversely coded for the present analysis. Data screening showed some controversies in answering these questions compared to answers to other questions. For the future research, if the questionnaire will be administered again, it is recommended to reconsider and reconstruct these problematic items in order to increase validity Insert Table 3 about here The third element responsible for convergent validity is construct reliability. For an acceptable model, it should be on the level of 0.7 and higher. Reliability of is acceptable. Construct reliabilities of attitude, subjective norm, entrepreneurial intention, and desirability are over 0.7. Construct reliability of feasibility and perceived behavior control 1 are equal This is deemed to be low, but acceptable. Discriminant validity In order to assess discriminant validity, squared interconstruct correlations (SIC) are calculated and compared to variance extracted. A covariance matrix is made based on data from AMOS output file Correlations: Group number 1-default model. All variances extracted should be larger than squared interconstruct correlations of the related constructs (Kline, 1998). From the Table 4, all squared interconstruct correlations are lower than variances extracted, except two constructs of attitude and 1 After item 3 measuring PBC deletion. 10

11 desirability. High correlation between attitude and desirability (0.95) imply that these two factors may not be distinctive. Such a high correlation may mean that attitudes construct in the theory of planned behavior and desirability in the entrepreneurial event theory have very close meanings and measure similar characteristics Insert Table 4 about here Nomological validity In order to check nomological validity, the interconstruct correlations should be examined. They need to be all positive and significant. From the Table C1 and Table C2 in Appendix C, we can see that covariances are significant and correlations are positive. Thus, the model is nomologically valid. Analysis of standardized residual indicated that the largest residual is (between item two evaluating PBC and item one of subjective norm scale). No residuals exceed 4. This implies an acceptable degree of error. The results of validity check and model fit estimation suggest that integrated model has somewhat low convergent and discriminant validities, as well as slightly low goodness-of-fit indices while other model fit indices and nomological validity advocate an acceptability. The values allowed me to proceed with the estimation of the structural models. Structural modeling The next step is structural equation analysis. The results of the SEM to test the integrated model are presented in Table 5. First, consider the integrated model of entrepreneurial intents. The model was respecified using correlated measurement errors. This allowed improving fit measures. Chi-square is reduced to The normed chi-square (1.527) falls within the recommended range from one to three. Adjusted goodness-of-fit index (0.840) shows the acceptable fit of the model of

12 and higher (Segars and Grover, 1993). Tucker-Lewis index (0.952) and comparative fit index (0.959) support the model validity. The value of RMSEA of is also under the magnitude of 0.08 demonstrating a good fit Insert Table 5 about here Standardized path loadings are shown in Figure 1. The integrated model explained 60.3 per cent of variance in entrepreneurial intentions. This result is better than the outcome of multiple regression analysis based on the same data that was carried out earlier (Solesvik, 2007). In the previous analysis the variance explained in entrepreneurial intentions equaled 52.1 per cent (adjusted R 2 =0.521). All paths from observed to latent variables in the model are highly significant at p<0.001 level. All other paths are also significant on the level of p<0.05 or less Insert Figure 1 about here All hypotheses have got a strong support. Attitude to entrepreneurship, subjective norm, perceived behavior control, desirability and feasibility have significant influence on self-employment intentions of the students. Standardized total effects of these variables on entrepreneurial intentions are given in the Table Insert Table 6 about here It is worthwhile to check here whether the integrated model has better predictive power than theory of planned behavior model and entrepreneurial event model alone. As for two models composing the integrated model of the entrepreneurial intentions, the path analysis showed the following results. The TPB path model fits well with the data (χ 2 = , p<0.001; normed χ 2 =1.563; CFI=0.968; AGFI=0.867; RMSEA=0.054). However, subjective norm did not relate 12

13 significantly with entrepreneurial intent (Figure 2). Other paths were significant (p<0.01). Path loadings are depicted in Figure 2. The total variance explained in entrepreneurial intent by the variables of the theory of planned behavior model was equal 52.7 per cent (the result of the factor analysis is 51.1 per cent) Insert Figure 2 about here The Shapiro-Sokol s model fits the data marginally (χ 2 = , p<0.001; normed χ 2 =2.654; CFI=0.947; AGFI=0.852; RMSEA=0.093). Feasibility and desirability were related significantly (p<0.01) with the entrepreneurial event. Path loadings are presented in Figure 3. The total variance explained in entrepreneurial intent by desirability and feasibility is 40.3 per cent. This means that the integrated model explains considerably higher share of the variance in the entrepreneurial intent variable than each model separately Insert Figure 3 about here Discussion and conclusions. The goal of the present paper was to test three models that explain self-employment intention. Integrating approaches from two different theoretical perspectives - the theory of planned behavior and the entrepreneurial event model - several hypotheses were developed concerning the relationships between desirability, feasibility, attitude to entrepreneurship, subjective norm perceived behavior control, and entrepreneurial intentions. Students who have positive attitudes toward entrepreneurship, are more likely to intend to start own businesses. The same is true for students whose closest environment has a positive standpoint to entrepreneurship. Eventually, graduates can get support and assistance from family members and friends in a new business creation. The perceived behavior control is also positively related to entrepreneurial 13

14 intent. Confirmatory factor analysis revealed that desirability mediates the relationships between attitude and entrepreneurial intent; the associations between perceived behavior control and subjective norm with entrepreneurial intentions are mediated by feasibility. All five constructs when looked simultaneously were significant and predicted entrepreneurial intent. One of the applications of the confirmatory factor analysis is a scale refinement (MacCallum and Austin, 2000). The results of confirmatory factor analysis showed that scales used to measure two latent variables: subjective norm and feasibility should be reconsidered for the purposes of future research. Variance extracted and construct validity of these indicators are under the suggested guidelines. In general, all scales used are good, only minor changes are necessary. The reasons for discrepancy might occur because of translation the questionnaire from English to Russian, and somewhat exuberate wording of the questions in original English version. That is why some purification of the scales is necessary. In particular, it is appropriate to change negative statements to positive in the questions measuring feasibility, and to reformulate the question three evaluating perceived behavior control in order to make it easier to grasp without changing the meaning. References 1. Anderson, J. C. and D. W. Gerbing Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3): Ajzen, I The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50: Ajzen, I Constructing a TPB Questionnaire: Conceptual and Methodological Considerations. Retrieved from: 4. Ajzen, I., & Fishbein, M Understanding Attitudes and Predicting Social Behavior. Englewood Cliffs, NJ: Prentice Hall. 14

15 5. Bandura, A Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2): Bandura, A Self-efficacy. In V. S. Ramachaudran (Ed.), Encyclopedia of Human Behavior, vol. 4: New York: Academic Press. 7. Bentler, P. M., & Bonett, D. G Significance tests and goodness-of-fit in the analysis of covariance structures. Psychological Bulletin, 88: Bird, B. J Implementing Entrepreneurial Ideas: The Case for Intention. Academy of Management Review, 13(3): Browne, M. W., & R. Cudeck Alternative ways of assessing model fit. In K. A. Bollen and J. S. Long (Eds.), Testing structural equation models: Newbury Park, CA: Sage. 10. Gundry, L. K., & H. P. Welch The ambitious entrepreneur: high growth strategies of women owned enterprises. Journal of Business Venturing, 16(5): Iakovleva, T., & Kolvereid, L Entrepreneurial intentions among Russian students - an integrated model. The 5th Interdisciplinary European Conference on Entrepreneurship Research (IECER) "Entrepreneurship and the Region", Montpellier. 12. Iakovleva, T., & Solesvik, M. Z. (2014). Entrepreneurial intentions in post- Soviet economies. International Journal of Entrepreneurship and Small Business, 21(1), Ingram, K. L., Cope, J. G., Harju, B. L. & Wuensch, K. L Applying to graduate school: A test of the theory of planned behavior. Journal of Social Behavior and Personality, 15(2): Kline, R. B Principles and practice of structural equation modeling. New York: Guilford Press. 15. Kolvereid, L., & Isaksen, E New business start-up and subsequent entry into self-employment. Journal of Business Venturing, 21:

16 16. Krueger, N. F., Reilly, M. D., & Carsrud, A. L Competing Models of Entrepreneurial Intentions. Journal of Business Venturing, 15: Linan, F., & Chen, Y.-W Testing the entrepreneurial intention model on a two-country sample. Working Paper no Universitat Autonoma de Barcelona. 18. Liñan, F. & Chen, Y.-W Development and cross-cultural application of a specific instrument to measure entrepreneurial intentions. Entrepreneurship Theory and Practice. 19. MacCallum, R. C., & Austin, J. T Application of structural equation modeling in psychological research. Annual Review of Psychology, 51: Segars, A. H. & Grover, V Re-examining perceived ease of use and usefulness: A confirmatory factor analysis. MIS Quarterly, 17(4): Shapero, A. & Sokol, L The social dimensions of entrepreneurship. In C. A. Kent, D. L. Sexton & K. H. Vesper. Encyclopedia of Entrepreneurship: Englewood Cliffs, N J: Prentice Hall. 22. Solesvik, M Attitudes towards future career choice: Stavanger Center for Innovation Research. University of Stavanger Norway, Solesvik, M. Z Entrepreneurial motivations and intentions: investigating the role of education major. Education+ Training, 55(3), Solesvik, M. Z., Westhead, P., Kolvereid, L., & Matlay, H Student intentions to become self-employed: the Ukrainian context. Journal of Small Business and Enterprise Development, 19(3), Solesvik, M.Z., Westhead, P., Matlay, H., & N. Parsyak, V Entrepreneurial assets and mindsets: benefit from university entrepreneurship education investment. Education+ Training, 55(8/9), Solesvik, M., Westhead, P., & Matlay, H Cultural factors and entrepreneurial intention: The role of entrepreneurship education. Education+ Training, 56(8/9),

17 Table 1 Descriptive Statistics: Integrated Model of Entrepreneurial Intentions (N=192) a Item Quest. Mean SD Range EI1 A EI2 A EI3 A EI4 A EI5 A EI6 A ATT1 B ATT2 B ATT3 B ATT4 B ATT5 B DES1 C DES2 C DES3 C FES1 C FES2 C5_r FES3 C6_r SN1 D1_ SN2 D1_ SN3 D1_ PBC1 E PBC2 E PBC3 E a EI-entrepreneurial intentions, ATT-attitude; DES-desirability ; FES-feasibility; SN subjective norm ; PBC perceived behavior control. 17

18 Table 2 Measurement Modeling Results: Goodness-of-fit Statistics Indices Integrated TPB & SEE TPB SEE model (TPB & SEE) (deleted PBC3) Chi df CMIN/df GFI AGFI PGFI NFI TLI CFI PNFI RMSEA

19 Table 3 Measurement Model Results: Integrated Model - Reliability, Variance Extracted, and Correlations a Number of items Variance extracted, % Construct reliability ATT PBC DES FES SN EI ATT PBC DES FES SN EI a EI-entrepreneurial intentions, ATT-attitude; DES-desirability ; FES-feasibility; SN subjective norm ; PBC perceived behavior control. 19

20 Table 4 Comparison of Variance Extracted and Squared Interconstruct Correlations for the Measurement Model: Integrated Model a Variance extracted Squared interconstruct correlations ATT Construct PBC Construct DES Construct FES Construct SN Construct EI Construct a The bolded are meanings of SIC that are higher than variance extracted. 20

21 Table 5 Structural Equation Model Results: Fit Measures (after Modifications) Indices Integrated TPB SEE model (TPB & SEE) Chi df CMIN/df GFI AGFI PGFI NFI TLI CFI PNFI RMSEA

22 Table 6 Standardized Total Effects: Integrated Model a SB PBC ATT FES DES EI EI FES DES a EI-entrepreneurial intentions, ATT-attitude; DES-desirability ; FES-feasibility; SN subjective norm ; PBC perceived behavior control. 22

23 Attitude 0.85*** Subjective norm 0.13* Desirability 0.59** Entrepreneurial Intention 0.19* Feasibility 0.33*** PBC 0.66*** a N=192, levels of statistical significance: p<0.1; * p<0.05; ** p<0.01; *** p< Fig. 1. Structural Equation Model: Integrated Model a 23

24 Attitude 0.50*** Subjective norm 0.13 ns Intention PBC 0.25** a N=192, levels of statistical significance: p<0.1; * p<0.05; ** p<0.01; *** p<0.001, ns not significant. Fig. 2. Structural Equation Modeling: TPB Model a 24

25 FIGURE 3 Structural Equation Modeling: SEE Model a Desirability 0.52*** Intention Feasibility 0.25** a N=192, levels of statistical significance: p<0.1; * p<0.05; ** p<0.01; *** p<

26 APPENDIX A An Integrated Model of Entrepreneurial Intentions Attitude Subjective norm Desirability Feasibility Entrepreneurial Intention PBC 26

27 APPENDIX B Calculation of the Variance Extracted and Construct Reliability ATT PBC DES FES SN EI Item Reliabilities delta b b b b b e e e c c c c c5_r c6_r d1_ d1_ d1_ a a a a a a Variance Extracted 57.44% 40.71% 75.13% 39.83% 69.60% 62.36% Construct Reliability

28 APPENDIX C Covariance and Correlations TABLE C1 Covariances Estimate S.E. C.R. P Label DES <--> FES *** ATT <--> DES *** PBK <--> FES *** DES <--> EI *** FES <--> EI *** FES <--> SN *** DES <--> SN *** ATT <--> SN *** PBK <--> SN ATT <--> EI *** EI <--> SN *** PBK <--> EI *** PBK <--> DES ATT <--> FES *** ATT <--> PBK *** 28

29 TABLE C2 Correlations Estimate DES <--> FES.409 ATT <--> DES.935 PBK <--> FES.640 DES <--> EI.709 FES <--> EI.534 FES <--> SN.364 DES <--> SN.664 ATT <--> SN.636 PBK <--> SN.340 ATT <--> EI.688 EI <--> SN.530 PBK <--> EI.491 PBK <--> DES.311 ATT <--> FES.443 ATT <--> PBK.403 Стаття відправлена: р. Солесвик М.Б. 29