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Students’ entrepreneurial behavior: international and gender differences

Students’ entrepreneurial behavior: international and gender differences tugrul.u.daim@pdx.edu Technology Management Doctoral Due to a high level of uncertainty, entrepreneurship is generally considered a risky Program, Portland State University, endeavor. This paper explores the factors impacting entrepreneurial behavior in order to 1900 SW 4th, Portland, OR 97201, USA identify new educational opportunities for its development. The paper explores perceived Full list of author information is feasibility and desirability for students in 10 countries. The entrepreneurship role is gender available at the end of the article tested against desirability and feasibility. The requirements for developing this skill set are also studied. A survey instrument was developed, and data was collected from 4281 students. The results indicate that gender impacts entrepreneurship intention and the way it impacts is influenced by which country the students are from. Keywords: Entrepreneurship, International differences, Gender differences, Behavior, Higher education Background The noteworthy contribution of entrepreneurial activities to economies (Keilbach and Sanders, 2008) in terms of growth, innovation, job creation, and poverty re- duction (Lunati et al., 2010) makes entrepreneurship a popular research topic. The OECD-Eurostat Entrepreneurship Indicators Programme defines entrepre- neurs as “those persons (business owners) who seek to generate value, through the creation or expansion of economic activity, by identifying and exploiting new products, processes or markets” (Lunati et al., 2010). Entrepreneurs differ from the rest of the society ostensibly by their propensity to take risk, tolerance for ambiguity, and motivation for self-employment. Hines (1973) sees entrepreneur- ship as a role model and bases his reasoning on a conclusion that entrepreneurs strive for greater realization and accomplishment in comparison to the role that is fostered by non-entrepreneurial activity. According to Summers (2000), the main aspect of entrepreneurship is “the critical combination of the individual, his or her past experience, background and the decision to start an enterprise.” In- creasing interest in entrepreneurship also raised the curiosity for the drivers such as intentions, traits, behavioral patterns, and external and contextual factors leading individuals to entrepreneurship phenomenon. The study of entrepreneurial motivations has a long history. According to Summers (2000), primal publications were mainly focused on traits, such as self- © 2016 Daim et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Daim et al. Journal of Innovation and Entrepreneurship (2016) 5:19 Page 2 of 22 confidence, risk tolerance, and tolerance for ambiguity. On the way to more re- cent intention-based process models (Shapero, 1982), later studies for entrepre- neurial motivation were based on several other perspectives, such as demographic characteristics (gender, age, education, etc.), social factors (family, community, etc.), and external influences (politics, capital availability, etc.) (Summers, 2000). More recent process models for entrepreneurial motivation are “focusing on atti- tudes and beliefs and how they can predict intentions and behaviors” (Segal et al., 2005). These models are mainly based on human cognitive processes to distinguish possible desirable outcomes and to make decisions on the feasibility of acting to obtain those outcomes (Segal et al., 2005). As mentioned above, country-specific factors were examined in relation with entrepreneurship in the literature. For instance, in their study where they com- pared 15 EU member countries and the USA in terms of latent and actual entre- preneurship, Grilo and Irigoyen (2005) indicate that the level of entrepreneurship shows distinct differences across countries. They pointed out that country- specific effects are indicative for both entrepreneurial motivation and activity levels. According to Freytag and Thurik (2007), country-specific effects are sig- nificant for entrepreneurship preferences but in contrast to that result they do not seem to be able to explain entrepreneurial activity. In their 2006-dated paper, Lee et al. 2006 tried to determine the disparities among the examined countries regarding the aspects essential to improve the entrepreneurship education. Also, Carayannis et al. (2003) indicate that there are differences between American and French entrepreneurship students in terms of attitudes and perceptions towards entrepreneurship. Female and male entrepreneurs usually operate in different sectors and pursue different ways to develop their business. Therefore, increased number of female en- trepreneurs means increased entrepreneurship variety in economy (Verheul et al. 2004). Notwithstanding the importance of their contribution in terms of entrepre- neurship variety, the number of female entrepreneurs is lower than that of male entrepreneurs in almost every country in terms of Total Entrepreneurial Activity, except Ghana, Costa Rica, and Australia (Kelley et al. 2010). This result is also supported with the entrepreneurship literature. For instance, according to Grilo and Irigoyen (2005), for the evaluated 15 EU member countries and the USA, the probability of preference for self-employment is notably higher for men com- pared to women. Menzies and Tatroff ’s (2006) work on gender differences on preferences on entrepreneurship education also states that less women are inter- ested in entrepreneurship education compared to men. Zhang et al. (2009) indi- cate that there is a difference between genders regarding the genetic basis of entrepreneurship. The purpose of this paper is to examine whether gender and country of residence differences have a significant impact on entrepreneurial intentions of university stu- dents as measured by perceived feasibility and perceived desirability. So our research question is the following: What are the gender and country differences’ impacts on entrepreneurial intentions of university students? Daim et al. Journal of Innovation and Entrepreneurship (2016) 5:19 Page 3 of 22 This paper focuses on university-level students as a result of the conviction that younger people are more willing to be self-employed (Blanchflower et al., 2001; Grilo and Irigoyen, 2005). According to GEM’s 2010 global report, in the case of age distribution of entrepreneurs, the 24–35 age group has the highest population for almost every geographic region. Since university students generally fall into the 18–24 age group, examining their entrepreneurial intentions as potential future en- trepreneurs might reveal some implications, because according to Ajzen (1991) intention is anterior to act. The next section examines the entrepreneurial behavior literature with a focus on university students and corresponding national setting and gender differences. Then hypotheses are introduced. This is followed by the description of research design and the methodology conducted. The paper concludes with the discussion of the results and the recommendations for future research. Literature review and hypotheses Entrepreneurial motivations have been frequently examined in the literature. Chell and Allman (2003) explored intentions of more technology-oriented entrepreneurs, while Krueger et al. (2000) analyzed differing entrepreneurial intentions. Grilo and Thurik (2005) explored barriers in 15 European countries and the USA and tried to explain differences in those countries in terms of latent and actual entrepreneur- ship. Studies of entrepreneurship attitudes among students have been viewed as an emerging topic due to an increase in the research performed on that subject by authors such as Luthje and Franke (2003), Wang and Wong (2004), Huffman and Quigley (2002), and Johnson et al. (2006). These studies test entrepreneurial atti- tudes against differing behavioral characteristics to elaborate on a model that would be used as a tool for prediction of future behavior. Among the authors who modeled and examined the behavioral relationship between university students and the corresponding national setting are Turker and Selcuk (2008), Wu and Wu (2008), Wang and Wong (2004), Menzies and Tatroff (2006), Verheul et al. (2004), Kourilsky and Walstad (1998), Zhang et al. (2009), Elenurm et al. (2007), Petridou et al. (2009), Shariff and Saud (2009), Liñán (2008), Carayannis et al (2003), and Veciana et al. (2005). In Turker and Selcuk’s (2008) study, similar to Wu and Wu’s (2008) and Lee and Wong’s (2004), educational setting is seen as a significant factor spurring entrepre- neurship. While Wu and Wu (2008) credit educational significance for assisting in realization of potential behavior, Wang and Wong (2004) see this realization eman- ating from appropriate curriculum structure. Liñán (2008) identified the role of perceived skill as an important factor impacting entrepreneurial intention. Shariff and Saud (2009) explored students’ attitudes towards entrepreneurship in Malaysia and found that self-esteem and personal control differences were influential. Carayannis et al (2003) compared French and US students on their attitudes and perceptions of entrepreneurship and identified regional differences. Barriers against entrepreneurial behavior have long been studied. Menzies and Tatroff (2006) explored attitudes of students in Canada as well, but they also looked at gender differences. They identified no differences in attitudes towards taking risks, but fewer women tended to think that entrepreneurship fit Daim et al. Journal of Innovation and Entrepreneurship (2016) 5:19 Page 4 of 22 their personality. They also reported on studies citing how education helped in- crease the number of female entrepreneurs. Verheul et al. (2004) explored similar factors in a US university and found similar results. Kourilsky and Walstad (1998) also identified similar differences in a US-wide study and proposed entrepreneurship-focused curricula. Zhang et al. (2009) explored genetic differences between genders and their impact on entrepreneurship. Petridou et al. (2009) identified that there were differences in attitudes towards entrepreneurship education and perceptions about required skills between the two genders. Eddleston and Powell (2008) examined how gender identity explains what male and female business owners look for from their careers and found that gender identity, represented by the dimensions of masculinity and femininity, serves as a cognitive mechanism that contributes to dif- ferences in business owners’ career satisfaction preferences. Verheul et al. (2004) explored female entrepreneurship in 29 countries and found that similar factors impacted both genders. Grilo and Thurik (2005) also identified gender differences in a study conducted in the general population. Gerry and Marques (2008) identi- fied similar differences in Portugal. Both were exploring entrepreneurship as a choice. However, Fischer et al. (1993) argued against these differences and found that there was no difference in the success rate at the end. Based on the above discussion, our hypotheses below were developed: H1—Gender in different countries makes a difference in students’ attitudes towards entrepreneurship as measured by desirability and feasibility one country at a time. H2—Country of residence makes a difference in students’ attitudes towards entrepreneurship as measured by desirability and feasibility. Methods Shapero’s model (1982), augmented by Krueger and Brazeal (1994), underlines the basis of our research. We draw our conclusions from the reasoning that intentions are predictions for future behavior. Shapero divided all the characteristics that could initiate intentions into two groups which consist of perceived desirability and perceived feasibility (Summers, 2000). Perceived desirability is defined as a subject- ive norm regarding the perceived social support and personal interest to perform the entrepreneurial behavior. Perceived feasibility examines the perceived ease or difficulty of performing the entrepreneurial behavior and the perceived self- competence regarding to entrepreneurship. Accordingly, we suggest in this paper a model (Fig. 1) that provides insight into the entrepreneurial intensions of students in terms of genders and country of residence differences. A survey instrument was developed, and data was collected from 4281 students from Croatia (1918), Slovenia (306), Austria (541), Poland (332), France (442), Lithuania (415), Israel (295), India (16), and other countries (16) of which 2712 were female and 1563 were male students. This paper is a part of a survey that collected data on the perceived feasibility and desirability of students in more than 10 countries. In this theoretical framework, to examine the concept of perceived desirability, students were asked to measure the extent to which they agreed or disagreed with Daim et al. Journal of Innovation and Entrepreneurship (2016) 5:19 Page 5 of 22 Fig. 1 Research framework (solid lines indicate the part of the study addressed in this paper) the following statements regarding their personal level of desirability for starting their own businesses after the completion of their education: (1) “Iwould love to do it”;(2) “My immediate family members would encourage me to do it”;(3) “I would be tense”;and (4) “I would be enthusiastic.” Students answered by choosing a number on a Likert scale from 1 to 6, with 1 representing “notatall” and 6 “extremely.” In order to investigate students’ perceptions on the feasibility of start- ing a new business, the following questions were included in the survey: (1) “It would be very hard to do”;(2) “I am certain that I would be successful”;(3) “I would be overworked”;(4) “I know enough to start a business”;and (5) “Itrust myself.” In each question, students were able to choose their answers on a Likert scale of 1 to 6, this time with 1 being “very much agree” and 6 being “very much disagree.” As a difference from the previous two studies of Dabić et al. (2012a, 2012b), this study used the same data with the addition of survey results from India and some other countries used to evaluate the impact of gender and country of resi- dence differences on entrepreneurial intentions of university students as mea- sured by perceived feasibility and perceived desirability. In this study, countries were analyzed separately in terms of entrepreneurial intention differences based on country of residence and gender. As a result, significant differences were found among countries and genders in terms of desirability and feasibility to- wards entrepreneurship. Results indicated that Poland, Slovenia, and India seem to have little difference between male and female genders whereas responses from Croatia, Austria, France, and Israel revealed quite strong difference among male and female students. In the other study of Dabić et al. (2012a), perceived desir- ability, perceived feasibility, and educational needs in terms of entrepreneurial programs/activities/projects at an academic institution were analyzed from the gender difference perspective only. Results of the analysis showed that there were significant differences between genders’ perception for educational needs to con- struct academic entrepreneurship education and networking and tutoring chan- nels for students. In Dabić et al. (2012b), countries in the sample were clustered into four groups. The first cluster was created from the questionnaires collected in Israel with the reasoning that Israel is the country with a high entrepreneurial culture, a high level of development, but a low level of political integration. The second cluster consisted of the countries which are in the EU for a longer period of time, namely France and Austria. These two countries have a high level of Daim et al. Journal of Innovation and Entrepreneurship (2016) 5:19 Page 6 of 22 economic and political development and integration. The third cluster was com- prised of Lithuania, Poland, and Slovenia and forms a cluster with characteristics of countries that recently joined the EU and are of a medium level of political and economic development and integration. The fourth cluster was formed by only one country, namely Croatia, as it is a country which is awaiting its acces- sion to the EU and which made a number of political and economic reforms but which has a low level of political integration and a lower level of development than the previous three clusters. Results and discussion Starting a new business: desirability As can be seen in Table 1, the average response to the statement “I would love to do it” (x ¼ 4:12) shows a positive attitude regarding the desirability of entrepreneurial activ- ities for students. The highest score among the statements on desirability is with family support (x ¼ 4:50), meaning that students felt they would generally have the benefit of high family encouragement. Also, it is important to note that the same statements have the highest standard deviation (σ = 1.894), indicating relatively high difference among students. The lowest average score in the group is agreement with the statement of being tense as an entrepreneur (x ¼ 4:04). However, relatively low agreement on this factor can be regarded as a positive indicator towards entrepreneurial attitude, since it suggests students are not highly certain such activities will lead to negative emotions, like tension or stress. Furthermore, enthusiasm scores ( x ¼ 4:33 ) indicate positive mood in connection with starting a new business. This measurement has the second lowest standard deviation in the desirability group (σ = 1.487). Country-specific means show differences and will be analyzed in the next section. Starting a new business: feasibility It is interesting to observe the results of student perceptions on feasibility in con- nection with starting a new business. As can be seen in Table 2, the lowest average Table 1 Perceived desirability—descriptive statistics Country Desirability 1 (D1) Desirability 2 (D2) Desirability 3 (D3) Desirability 4 (D4) Croatia 4.66 5.07 4.21 4.70 Austria 3.69 4.33 4.47 4.36 France 4.29 4.44 4.16 4.62 Israel 4.02 4.49 4.40 4.68 Lithuania 1.83 1.97 2.37 1.78 Poland 4.19 4.12 3.51 4.31 Slovenia 4.23 4.97 4.41 4.49 India 4.44 3.81 3.38 4.94 Rest of the World 4.00 4.69 4.50 4.75 All 4.12 4.50 4.04 4.33 Desirability: (D1) I would love to do it; (D2) My immediate family members would encourage me to do it; (D3) I would be tense; and (D4) I would be enthusiastic. Agreement: (1) not at all; (2) slightly; (3) somewhat; (4) moderately; (5) very much; and (6) extremely Daim et al. Journal of Innovation and Entrepreneurship (2016) 5:19 Page 7 of 22 Table 2 Perceived feasibility—descriptive statistics Country Feasibility 1 (F1) Feasibility 2 (F2) Feasibility 3 (F3) Feasibility 4 (F4) Feasibility 5 (F5) Croatia 2.03 2.82 1.86 3.49 2.42 Austria 2.13 3.43 2.08 3.60 2.65 France 2.03 3.30 2.32 4.35 3.26 Israel 2.14 2.60 2.39 3.59 2.42 Lithuania 3.01 3.19 2.98 3.99 2.93 Poland 2.56 3.35 2.50 3.65 3.01 Slovenia 2.28 2.67 2.50 3.45 2.28 India 1.88 2.38 2.00 3.50 2.00 Rest of the World 2.13 3.50 2.00 3.88 2.56 All 2.20 3.00 2.17 3.66 2.62 Feasibility: (F1) It would be very hard to do; (F2) I am certain that I would be successful; (F3) I would be overworked; (F4) I know enough to start a business; and (F5) I trust myself. Agreement: (1) very much agree; (2) strongly agree; (3) mildly agree; (4) mildly disagree; (5) strongly disagree; and (6) very much disagree score (x ¼ 2:17) and standard deviation (σ = 1.112) occur for the question describ- ing how overworked the entrepreneur expects to be. This could lead to the conclu- sion that students have the perception of being overworked if they start their own businesses. As for the certainty of success, the average score (x ¼ 3:00) indicates that students were right in the middle between most affirmative and most negative, meaning, on average, they were neither certain nor uncertain of success. The aver- age score for the question regarding knowing enough to start a business (x ¼ 3:66) is slightly negative, meaning students are a little unsure whether they know every- thing they need to start a business and thus may benefit from some additional education in this area. For the self-confidence question, results show students have a positive perception (x ¼ 2:62). Nevertheless, they agree with the contention that starting a new business is quite hard (x ¼ 2:20). Country-specific means show dif- ferences and will be analyzed in the next section. Based on Table 3, the average of responses for perceived desirability questions 1 through 4 can be seen gender- and countrywise. According to these results, the average of total responses for desirability question 1 regarding attitudes towards entrepreneurial initiatives and that for desirability question 4 regarding enthusiasm about entrepreneurial initiatives seem higher for male students. Furthermore, the average of total scores for desirability questions 2 and 3 regarding family support and work-related stress, respectively, may imply that although female students feel slightly more supported by their families they are inclined to feel more tense about starting a new business. Another interesting outcome is that female students from Poland and India seem to show equal or greater inclination to entrepreneurial ini- tiatives compared to male students from those countries. Also, country-specific means show differences and will be analyzed in the next section. Based on Table 4, the average of responses for perceived feasibility questions 1 through 5 can be seen gender- and countrywise. According to these descriptive sta- tistics, for feasibility questions 1 and 3, regarding the difficulties associated with entrepreneurial activities and being overworked, respectively, female students’ total average scores are smaller than male students’; for questions 2, 4, and 5, regarding Daim et al. Journal of Innovation and Entrepreneurship (2016) 5:19 Page 8 of 22 Table 3 Perceived desirability means by gender and country—descriptive statistics Country Gender Desirability 1 Desirability 2 Desirability 3 Desirability 4 Croatia Female Mean 4.56 5.14 4.29 4.60 Male Mean 4.82 4.95 4.10 4.84 Austria Female Mean 3.54 4.34 4.46 4.22 Male Mean 4.14 4.32 4.49 4.78 France Female Mean 4.08 4.44 4.29 4.47 Male Mean 4.62 4.44 3.95 4.84 Israel Female Mean 3.60 4.23 4.57 4.55 Male Mean 4.51 4.79 4.21 4.82 Lithuania Female Mean 1.82 1.96 2.37 1.77 Male Mean 1.84 1.99 2.36 1.79 Poland Female Mean 4.19 4.27 3.57 4.31 Male Mean 4.19 3.80 3.37 4.31 Slovenia Female Mean 4.12 5.00 4.41 4.44 Male Mean 4.46 4.91 4.40 4.61 India Female Mean 5.60 4.80 3.00 6.00 Male Mean 3.91 3.36 3.55 4.45 Rest of the world Female Mean 4.25 4.75 4.38 4.88 Male Mean 3.75 4.63 4.63 4.63 Total Female Mean 3.99 4.52 4.10 4.23 Female Std. dev. 1.703 1.703 1.517 1.486 Male Mean 4.36 4.46 3.94 4.50 Male Std. dev. 1.616 2.187 1.384 1.475 certainty of success, certainty of having the required knowledge for entrepreneurial activities, and self-confidence, respectively, male students score smaller. These re- sults may imply that female students are more concerned about the difficulties and workload associated with entrepreneurship and they have lower self-confidence and motivation under the assumption of starting a new business. Also, country-specific means show differences and will be analyzed in the next section. Gender and country differences Differences were analyzed in multiple perspectives as seen in Table 5. ANOVA was used in each case, and the detailed results are provided in the following tables. At the 1 % level of significance, ANOVA indicates relevant differences among differ- ent countries for all the previously mentioned questions regarding perceived feasibility and perceived desirability as can be observed in Table 6. Countrywise differences with respect to desirability Based on the significance values for desirability questions 1 through 4, there is a statistically significant difference between countries for the following: desirability question 1 (D1P = 0.000) regarding attitudes towards entrepreneurial initiatives, desirability question 2 (D2P = 0.000) regarding family support, desirability v Daim et al. Journal of Innovation and Entrepreneurship (2016) 5:19 Page 9 of 22 Table 4 Perceived feasibility means by gender and country—descriptive statistics Country Gender Feasibility 1 Feasibility 2 Feasibility 3 Feasibility 4 Feasibility 5 Croatia Female Mean 1.95 2.89 1.81 3.57 2.53 Male Mean 2.14 2.72 1.95 3.38 2.27 Austria Female Mean 2.10 3.53 2.04 3.76 2.78 Male Mean 2.23 3.15 2.20 3.12 2.26 France Female Mean 1.98 3.35 2.30 4.53 3.50 Male Mean 2.11 3.22 2.35 4.07 2.89 Israel Female Mean 2.17 2.78 2.50 3.69 2.61 Male Mean 2.12 2.39 2.27 3.47 2.20 Lithuania Female Mean 2.98 3.35 2.95 4.13 3.10 Male Mean 3.06 2.89 3.04 3.75 2.62 Poland Female Mean 2.50 3.31 2.45 3.67 3.12 Male Mean 2.69 3.43 2.60 3.62 2.77 Slovenia Female Mean 2.23 2.68 2.45 3.57 2.40 Male Mean 2.39 2.64 2.60 3.20 2.01 India Female Mean 1.80 2.80 2.00 3.80 2.20 Male Mean 1.91 2.18 2.00 3.36 1.91 Rest of the world Female Mean 2.13 3.25 1.88 4.13 3.13 Male Mean 2.13 3.75 2.13 3.63 2.00 Total Female Mean 2.16 3.09 2.14 3.76 2.76 Female Std. dev. 1.102 1.101 1.111 1.291 1.247 Male Mean 2.27 2.84 2.22 3.48 2.37 Male Std. dev. 1.105 1.543 1.113 1.257 1.208 question 3 (D3P = 0.000) regarding work-related stress as an entrepreneur, and desirability question 4 (D4P = 0.000) regarding enthusiasm about entrepreneurial initiatives. Countrywise differences with respect to feasibility Based on the significance values for feasibility questions 1 through 5, there is a statistically significant difference between countries for the following: desirability question 1 (F1P = 0.000) regarding level of difficulties associated with entrepre- neurial activities, desirability question 2 (F2P = 0.000) regarding level of certainty associated with success, desirability question 3 (F3P = 0.000) regarding level of excess work associated with entrepreneurial activities, desirability question 4 Table 5 Summary of ANOVA results Among different countries Among different genders Among different genders in different countries Differences in desirability Significant differences Significant differences were Significant differences were were found in all cases found in all but one case found depending on the country Differences in feasibility Significant differences Significant differences were Significant differences were were found in all cases found in all cases found depending on the country Daim et al. Journal of Innovation and Entrepreneurship (2016) 5:19 Page 10 of 22 Table 6 Desirability and feasibility differences between countries—ANOVA Sum of squares df Mean square F Sig. Desirability 1 Between groups 2775.778 8 346.972 159.710 .000 Within groups 9144.115 4209 2.173 Total 11,919.894 4217 Desirability 2 Between groups 3289.738 8 411.217 146.154 .000 Within groups 11,833.948 4206 2.814 Total 15,123.685 4214 Desirability 3 Between groups 1444.878 8 180.610 98.994 .000 Within groups 7673.663 4206 1.824 Total 9118.541 4214 Desirability 4 Between groups 2922.443 8 365.305 240.145 .000 Within groups 6396.598 4205 1.521 Total 9319.041 4213 Feasibility 1 Between groups 379.611 8 47.451 41.907 .000 Within groups 4776.065 4218 1.132 Total 5155.675 4226 Feasibility 2 Between groups 338.531 8 42.316 26.864 .000 Within groups 6639.460 4215 1.575 Total 6977.991 4223 Feasibility 3 Between groups 538.126 8 67.266 60.560 .000 Within groups 4681.749 4215 1.111 Total 5219.875 4223 Feasibility 4 Between groups 327.085 8 40.886 25.896 .000 Within groups 6654.730 4215 1.579 Total 6981.814 4223 Feasibility 5 Between groups 397.251 8 49.656 33.967 .000 Within groups 6177.962 4226 1.462 Total 6575.213 4234 (F4P = 0.000) regarding level of knowledge required for entrepreneurial activities, and desirability question 5 (F5P = 0.000) regarding level of self-esteem. From Table 7, at the 5 % level of significance, ANOVA results for genderwise differ- ences with respect to perceived desirability- and perceived feasibility-related variables can be seen. Genderwise differences with respect to desirability Based on the significance values for desirability questions 1 through 4 in Table 7, there is a statistically significant difference between genders for the following: de- sirability question 1 (D1P = 0.000) regarding attitudes towards entrepreneurial ini- tiatives, desirability question 3 (D3P = 0.001) regarding work-related stress as an entrepreneur, and desirability question 4 (D4P = 0.000) regarding enthusiasm about entrepreneurial initiatives whereas there is no statistically significant Daim et al. Journal of Innovation and Entrepreneurship (2016) 5:19 Page 11 of 22 Table 7 Desirability and feasibility differences between genders—ANOVA Sum of squares df Mean square F Sig. Feasibility 1 Between groups 13.774 1 13.774 11.314 .001 Within groups 5139.783 4222 1.217 Total 5153.557 4223 Feasibility 2 Between groups 58.238 1 58.238 35.508 .000 Within groups 6919.753 4219 1.640 Total 6977.991 4220 Feasibility 3 Between groups 6.668 1 6.668 5.397 .020 Within groups 5211.806 4219 1.235 Total 5218.474 4220 Feasibility 4 Between groups 80.262 1 80.262 49.082 .000 Within groups 6899.205 4219 1.635 Total 6979.467 4220 Feasibility 5 Between groups 149.169 1 149.169 98.202 .000 Within groups 6425.371 4230 1.519 Total 6574.540 4231 Desirability 1 Between groups 140.515 1 140.515 50.267 .000 Within groups 11,776.839 4213 2.795 Total 11,917.354 4214 Desirability 2 Between groups 2.593 1 2.593 0.722 .396 Within groups 15,118.361 4210 3.591 Total 15,120.953 4211 Desirability 3 Between groups 23.155 1 23.155 10.720 .001 Within groups 9093.382 4210 2.160 Total 9116.536 4211 Desirability 4 Between groups 75.164 1 75.164 34.225 .000 Within groups 9243.556 4209 2.196 Total 9318.720 4210 difference between genders for desirability question 2 (D2P = 0.396) regarding family support. Genderwise differences with respect to feasibility Based on the significance values for feasibility questions 1 through 5, in Table 7, there is a statistically significant difference between genders for the following: desirability question 1 (F1P = 0.001) regarding level of difficulties associated with entrepreneurial activities, desirability question 2 (F2P = 0.000) regarding level of certainty associated with success, desirability question 3 (F3P = 0.000) regarding level of excess work associated with entrepreneurial activities, desirability question 4 (F4P = 0.000) regarding level of knowledge required for entrepre- neurial activities, and desirability question 5 (F5P = 0.000) regarding level of self-esteem. ANOVA results for perceived desirability and perceived feasibility differences among different genders in different countries can be seen in the Appendix as Tables (Tables 9 Daim et al. Journal of Innovation and Entrepreneurship (2016) 5:19 Page 12 of 22 and 10). ANOVA results show that there are further significant differences between male and female students in different countries in terms of their attitude towards entrepreneurship. Perceived desirability differences between genders per country ANOVA results (Table 9) for genderwise differences per country, with respect to desirability questions 1 through 4 for the 5 % level of significance, exhibit that for desirability question 1 regarding attitudes towards entrepreneurial initiatives there is a statistically significant difference between genders in Croatia (D1P = 0.000), Austria (D1P = 0.000), France (D1P = 0.000), Israel (D1P = 0.000), and India v v v (D1P = 0.015) whereas there is statistically no significant difference between gen- ders in Lithuania (D1P = 0.863), Poland (D1P = 0.954), Slovenia (D1P = 0.076), v v v and the rest of the world (D1P = 0.568). Based on the significance values for desirability question 2 regarding family support, there is a statistically significant difference between genders in Croatia (D2P = 0.001) whereas there is statistically no significant difference between genders in Austria (D2P = 0.894), France (D2P = 0.974), Israel (D2P = 0.257), Lithuania (D2P = 0.787), Poland v v v (D2P = 0.064), Slovenia (D2P = 0.579), India (D2P = 0.087), and the rest of the world v v v (D2P = 0.855). Based on the significance values for desirability question 3 regarding work-related stress as an entrepreneur, there is a statistically significant difference between gen- ders in Croatia (D3P = 0.002), France (D3P = 0.015), and Israel (D3P = 0.019) v v v whereas there is statistically no significant difference between genders in Austria (D3P = 0.810), Lithuania (D3P = 0.871), Poland (D3P =0.448), Slovenia (D3P = v v v v 0.957), India (D3P = 0.589), and the rest of the world (D3P = 0.559). v v Based on the significance values for desirability question 4 regarding enthusi- asm about entrepreneurial initiatives, there is a statistically significant differ- ence between genders in Croatia (D4P = 0.000), Austria (D4P = 0.000), France v v (D4P = 0.005), and India (D4P = 0.027) whereas there is statistically no signifi- v v cant difference between genders in Israel (D4P = 0.096), Lithuania (D4P = v v 0.874), Poland (D4P =0.970), Slovenia (D4P = 0.303), and the rest of the world v v (D4P = 0.723). Perceived feasibility differences between genders per country ANOVA results (Table 10) for genderwise differences per country, with respect to feasibility questions 1 through 5 for the 5 % level of significance, exhibit that for feasibility question 1 regarding level of difficulties associated with entrepreneurial activities there is a statistically significant difference between genders in Croatia (F1P = 0.000) whereas there is statistically no significant difference between gen- ders in Austria (F1P = 0.227), France (F1P =0.162), Israel (F1P = 0.698), Lithuania v v v (F1P = 0.639), Poland (F1P = 0.102), Slovenia (F1P = 0.179), India (F1P = 0.812), v v v v and the rest of the world (F1P = 1.000). Based on the significance values for feasibility question 2 regarding the level of certainty associated with success, there is a statistically significant difference between genders in Croatia (F2P = 0.001), Austria (F2P = 0.002), Israel (F2P v v v Daim et al. Journal of Innovation and Entrepreneurship (2016) 5:19 Page 13 of 22 = 0.000), and Lithuania (F2P = 0.001), whereas there is statistically no signifi- cant difference between genders in France (F2P =0.209), Poland (F2P = 0.681), v v Slovenia (F2P = 0.751), India (F2P = 0.244), and the rest of the world (F2P = v v v 0.483). Based on the significance values for feasibility question 3 regarding the level of excess work associated with entrepreneurial activities, there is a statistically signifi- cant difference between genders in Croatia (F3P = 0.003) whereas there is statisti- cally no significant difference between genders in Austria (F3P = 0.136), France (F3P = 0.598), Israel (F3P = 0.053), Lithuania (F3P = 0.562), Poland (F3P = 0.198), v v v v Slovenia (F3P = 0.220), India (F3P = 0.384), and the rest of the world v v (F3P = 0.622). Based on the significance values for feasibility question 4 regarding the level of knowledge required for entrepreneurial activities, there is a statistically significant difference between genders in Croatia (F4P = 0.001), Austria (F4P =0.000), v v France (F4P = 0.000), Lithuania (F4P = 0.008), and Slovenia (F4P = 0.012) v v v whereas there is statistically no significant difference between genders in Israel (F4P = 0.080), Poland (F4P =0.699), India (F4P = 0.384), and the rest of the v v v world (F4P = 0.524). Based on the significance values for feasibility question 5 regarding the level of self-esteem, there is a statistically significant difference between genders in Croatia (F5P = 0.000), Austria (F5P = 0.000), France (F5P = 0.000), Israel (F5P = 0.001), v v v v Lithuania (F5P = 0.001), Poland (F5P = 0.004), and Slovenia (F5P = 0.001) whereas v v v there is statistically no significant difference between genders in India (F5P = 0.639) and the rest of the world (F5P = 0.060). Table 8 is a summary of the gender differences. Those cells with an “X” repre- sent no significant difference between genders. All the other cells indicate signifi- cant difference. Croatia seems to be the only country in which females and males exhibit significantly different attitudes regarding all perceived desirability and feasi- bility aspects. Based on Table 8, it can be observed that responses to questions D2 (My imme- diate family members would encourage me to do it), F1 (It would be very hard to do), and F3 (I would be overworked) indicate nearly no significant difference be- tween genders for all cases except Croatia whereas F4 (I know enough to start a Table 8 Summary of perceived desirability and feasibility differences between genders per country Country D1 D2 D3 D4 F1 F2 F3 F4 F5 Croatia Austria x x x x France x x x x Israel x x x x x Lithuania x x x x x x Poland x x x x x x x x Slovenia x x x x x x x India x x x x x x x Rest of the world x x x x x x x x x Daim et al. Journal of Innovation and Entrepreneurship (2016) 5:19 Page 14 of 22 business), F5 (I trust myself), and D1 (I would love to do it) indicate quite a bit difference between genders for the majority of the countries included in the study. This might mean that although both genders are aware of the required work and dedication for starting a new business, generally male students are more self- confident and keen to do it. If Table 8 is analyzed countrywise, then Poland, Slovenia, and India appear not to have considerable amount of difference between male and female genders whereas responses from male and female students from Croatia, Austria, France, and Israel indicate quite strong difference. In the case of India, D1 and D4 are expected to show significant difference in terms of female students scoring higher than males. This result is consistent with the GEM 2002 report where India and Poland are in the top 6 among 29 countries regarding the female share in total entrepreneurial activity. Interestingly, in the same list, Slovenia occupies the 21st position. Conclusions This paper makes significant contributions to the understanding of entrepreneurial per- ceptions among students. One of the key strengths of this study is that it is based on a wide range of data for students from 10 different countries. Thus, the results are not culturally related but reflect more globally oriented intentions. This paper explores the factors impacting entrepreneurial behavior in order to iden- tify new educational opportunities for its development. Specifically, there are three major findings. Significant differences were found between genders and countries on their perceptions of desirability and feasibility towards entrepreneurial behavior. This adds to the findings of prior research on gender differences in entrepreneurial attitudes. Moreover, there were differences in how genders differ in different countries which would require further research. Insights from this study can help educators plan entrepreneurship-oriented pro- grams or courses in a manner that aims to minimize the gender differences in entrepreneurial motivation. Also, policy makers of countries willing to increase the number of female entrepreneurs would benefit from the results regarding which perceptions females show significant differences from males, so they can shape their entrepreneurship-related policies aiming to reduce these differences or alter the perceptions. There were also differences in how countries differ in terms of perceived feasibility and desirability. These differences can result from social secur- ity policies, economic activity, regulatory issues, or sectoral concentration of recent entrepreneurial activities, etc. specific to each country, which can affect the intention of starting a new business negatively. Further research revealing that dif- ferences’ direction would also help policy makers to understand their countries’ po- tential entrepreneurs’ perceptions about those aspects and to alter them. One shortcoming of this study might be the varying sample sizes from different countries. Sample sizes vary from 1918 to 16, and they are not determined rela- tively to the student population in those countries. More balanced sample size from examined countries would lead to more meaningful results. For further research also, the effect of students’ training areas (engineering, business, social sciences, etc.) on their entrepreneurial perceptions can be examined. Daim et al. Journal of Innovation and Entrepreneurship (2016) 5:19 Page 15 of 22 Appendix Table 9 Desirability differences between genders per country—ANOVA ANOVA Country Sum of squares df Mean square F Sig. Croatia Desirability 1 Between groups 29.840 1 29.840 13.322 .000 Within groups 4291.598 1916 2.240 Total 4321.437 1917 Desirability 2 Between groups 17.688 1 17.688 11.284 .001 Within groups 3003.501 1916 1.568 Total 3021.189 1917 Desirability 3 Between groups 15.785 1 15.785 9.263 .002 Within groups 3265.144 1916 1.704 Total 3280.929 1917 Desirability 4 Between groups 26.133 1 26.133 18.588 .000 Within groups 2693.657 1916 1.406 Total 2719.790 1917 Austria Desirability 1 Between groups 36.911 1 36.911 14.370 .000 Within groups 1384.538 539 2.569 Total 1421.449 540 Desirability 2 Between groups 0.046 1 0.046 0.018 .894 Within groups 1392.398 539 2.583 Total 1392.444 540 Desirability 3 Between groups 0.085 1 0.085 0.058 .810 Within groups 786.721 539 1.460 Total 786.806 540 Desirability 4 Between groups 32.178 1 32.178 16.737 .000 Within groups 1036.255 539 1.923 Total 1068.433 540 France Desirability 1 Between groups 30.707 1 30.707 12.411 .000 Within groups 1088.643 440 2.474 Total 1119.351 441 Desirability 2 Between groups 0.002 1 0.002 0.001 .974 Within groups 946.848 440 2.152 Total 946.851 441 Desirability 3 Between groups 11.560 1 11.560 5.979 .015 Within groups 850.669 440 1.933 Total 862.229 441 Desirability 4 Between groups 14.302 1 14.302 7.905 .005 Within groups 796.080 440 1.809 Total 810.382 441 Israel Desirability 1 Between groups 54.667 1 54.667 22.442 .000 Within groups 643.092 264 2.436 Total 697.759 265 Desirability 2 Between groups 20.394 1 20.394 1.292 .257 Within groups 4151.847 263 15.786 Total 4172.242 264 Daim et al. Journal of Innovation and Entrepreneurship (2016) 5:19 Page 16 of 22 Table 9 Desirability differences between genders per country—ANOVA (Continued) Desirability 3 Between groups 8.673 1 8.673 5.589 .019 Within groups 411.245 265 1.552 Total 419.918 266 Desirability 4 Between groups 4.859 1 4.859 2.795 .096 Within groups 458.979 264 1.739 Total 463.838 265 Lithuania Desirability 1 Between groups 0.027 1 0.027 0.030 .863 Within groups 358.950 394 0.911 Total 358.977 395 Desirability 2 Between groups 0.075 1 0.075 0.073 .787 Within groups 402.497 393 1.024 Total 402.572 394 Desirability 3 Between groups 0.021 1 0.021 0.026 .871 Within groups 321.751 393 0.819 Total 321.772 394 Desirability 4 Between groups 0.020 1 0.020 0.025 .874 Within groups 306.425 394 0.778 Total 306.444 395 Poland Desirability 1 Between groups 0.004 1 0.004 0.003 .954 Within groups 406.531 312 1.303 Total 406.535 313 Desirability 2 Between groups 14.849 1 14.849 3.452 .064 Within groups 1337.777 311 4.302 Total 1352.626 312 Desirability 3 Between groups 2.832 1 2.832 0.576 .448 Within groups 1518.898 309 4.916 Total 1521.730 310 Desirability 4 Between groups 0.002 1 0.002 0.001 .970 Within groups 394.269 308 1.280 Total 394.271 309 Slovenia Desirability 1 Between groups 7.720 1 7.720 3.177 .076 Within groups 738.806 304 2.430 Total 746.526 305 Desirability 2 Between groups 0.488 1 0.488 0.308 .579 Within groups 481.185 304 1.583 Total 481.673 305 Desirability 3 Between groups 0.004 1 0.004 0.003 .957 Within groups 421.748 304 1.387 Total 421.752 305 Desirability 4 Between groups 2.017 1 2.017 1.064 .303 Within groups 576.470 304 1.896 Total 578.487 305 India Desirability 1 Between groups 9.828 1 9.828 7.598 .015 Within groups 18.109 14 1.294 Total 27.937 15 Daim et al. Journal of Innovation and Entrepreneurship (2016) 5:19 Page 17 of 22 Table 9 Desirability differences between genders per country—ANOVA (Continued) Desirability 2 Between groups 7.092 1 7.092 3.383 .087 Within groups 29.345 14 2.096 Total 36.437 15 Desirability 3 Between groups 1.023 1 1.023 0.306 .589 Within groups 46.727 14 3.338 Total 47.750 15 Desirability 4 Between groups 8.210 1 8.210 6.138 .027 Within groups 18.727 14 1.338 Total 26.938 15 Rest of the world Desirability 1 Between groups 1.000 1 1.000 0.341 .568 Within groups 41.000 14 2.929 Total 42.000 15 Desirability 2 Between groups 0.062 1 0.062 0.034 .855 Within groups 25.375 14 1.812 Total 25.438 15 Desirability 3 Between groups 0.250 1 0.250 0.359 .559 Within groups 9.750 14 0.696 Total 10.000 15 Desirability 4 Between groups 0.250 1 0.250 0.131 .723 Within groups 26.750 14 1.911 Total 27.000 15 Daim et al. Journal of Innovation and Entrepreneurship (2016) 5:19 Page 18 of 22 Table 10 Feasibility differences between genders per country—ANOVA ANOVA Country Sum of squares df Mean square F Sig. Croatia Feasibility 1 Between groups 15.230 1 15.230 14.211 .000 Within groups 2053.466 1916 1.072 Total 2068.697 1917 Feasibility 2 Between groups 11.942 1 11.942 10.714 .001 Within groups 2135.786 1916 1.115 Total 2147.729 1917 Feasibility 3 Between groups 8.513 1 8.513 8.708 .003 Within groups 1873.149 1916 0.978 Total 1881.662 1917 Feasibility 4 Between groups 16.340 1 16.340 11.625 .001 Within groups 2693.071 1916 1.406 Total 2709.412 1917 Feasibility 5 Between groups 31.051 1 31.051 22.386 .000 Within groups 2657.637 1916 1.387 Total 2688.689 1917 Austria Feasibility 1 Between groups 1.741 1 1.741 1.463 .227 Within groups 641.408 539 1.190 Total 643.150 540 Feasibility 2 Between groups 14.370 1 14.370 9.240 .002 Within groups 838.281 539 1.555 Total 852.651 540 Feasibility 3 Between groups 2.421 1 2.421 2.235 .136 Within groups 584.000 539 1.083 Total 586.421 540 Feasibility 4 Between groups 42.879 1 42.879 17.309 .000 Within groups 1335.276 539 2.477 Total 1378.155 540 Feasibility 5 Between groups 27.350 1 27.350 18.554 .000 Within groups 794.509 539 1.474 Total 821.860 540 France Feasibility 1 Between groups 1.748 1 1.748 1.963 .162 Within groups 391.809 440 0.890 Total 393.557 441 Feasibility 2 Between groups 1.859 1 1.859 1.585 .209 Within groups 516.315 440 1.173 Total 518.174 441 Feasibility 3 Between groups 0.358 1 0.358 0.278 .598 Within groups 565.663 440 1.286 Total 566.020 441 Feasibility 4 Between groups 22.219 1 22.219 12.823 .000 Within groups 762.426 440 1.733 Total 784.645 441 Daim et al. Journal of Innovation and Entrepreneurship (2016) 5:19 Page 19 of 22 Table 10 Feasibility differences between genders per country—ANOVA (Continued) Feasibility 5 Between groups 39.155 1 39.155 19.977 .000 Within groups 862.401 440 1.960 Total 901.557 441 Israel Feasibility 1 Between groups 0.124 1 0.124 0.151 .698 Within groups 222.016 271 0.819 Total 222.139 272 Feasibility 2 Between groups 10.525 1 10.525 14.152 .000 Within groups 200.795 270 0.744 Total 211.320 271 Feasibility 3 Between groups 3.361 1 3.361 3.780 .053 Within groups 239.178 269 0.889 Total 242.539 270 Feasibility 4 Between groups 3.061 1 3.061 3.095 .080 Within groups 266.994 270 0.989 Total 270.055 271 Feasibility 5 Between groups 11.414 1 11.414 10.930 .001 Within groups 282.982 271 1.044 Total 294.396 272 Lithuania Feasibility 1 Between groups 0.476 1 0.476 0.220 .639 Within groups 857.484 397 2.160 Total 857.960 398 Feasibility 2 Between groups 19.815 1 19.815 10.473 .001 Within groups 751.087 397 1.892 Total 770.902 398 Feasibility 3 Between groups 0.672 1 0.672 0.337 .562 Within groups 791.168 397 1.993 Total 791.840 398 Feasibility 4 Between groups 12.899 1 12.899 7.141 .008 Within groups 717.091 397 1.806 Total 729.990 398 Feasibility 5 Between groups 21.679 1 21.679 10.985 .001 Within groups 783.494 397 1.974 Total 805.173 398 Poland Feasibility 1 Between groups 2.398 1 2.398 2.693 .102 Within groups 276.874 311 0.890 Total 279.272 312 Feasibility 2 Between groups 0.972 1 0.972 0.169 .681 Within groups 1777.523 309 5.753 Total 1778.495 310 Feasibility 3 Between groups 1.547 1 1.547 1.662 .198 Within groups 288.441 310 0.930 Total 289.987 311 Feasibility 4 Between groups 0.137 1 0.137 0.150 .699 Within groups 282.358 309 0.914 Total 282.495 310 Daim et al. Journal of Innovation and Entrepreneurship (2016) 5:19 Page 20 of 22 Table 10 Feasibility differences between genders per country—ANOVA (Continued) Feasibility 5 Between groups 8.307 1 8.307 8.188 .004 Within groups 324.665 320 1.015 Total 332.972 321 Slovenia Feasibility 1 Between groups 1.659 1 1.659 1.810 .179 Within groups 278.606 304 0.916 Total 280.265 305 Feasibility 2 Between groups 0.106 1 0.106 0.101 .751 Within groups 317.894 304 1.046 Total 318.000 305 Feasibility 3 Between groups 1.485 1 1.485 1.510 .220 Within groups 299.015 304 0.984 Total 300.500 305 Feasibility 4 Between groups 9.357 1 9.357 6.430 .012 Within groups 442.408 304 1.455 Total 451.765 305 Feasibility 5 Between groups 10.480 1 10.480 11.246 .001 Within groups 282.385 303 0.932 Total 292.866 304 India Feasibility 1 Between groups 0.041 1 0.041 0.059 .812 Within groups 9.709 14 0.694 Total 9.750 15 Feasibility 2 Between groups 1.314 1 1.314 1.479 .244 Within groups 12.436 14 0.888 Total 13.750 15 Feasibility 3 Between groups 0.000 1 0.000 0.000 1.000 Within groups 8.000 14 0.571 Total 8.000 15 Feasibility 4 Between groups 0.655 1 0.655 0.808 .384 Within groups 11.345 14 0.810 Total 12.000 15 Feasibility 5 Between groups 0.291 1 0.291 0.230 .639 Within groups 17.709 14 1.265 Total 18.000 15 Rest of the world Feasibility 1 Between groups 0.000 1 0.000 0.000 1.000 Within groups 19.750 14 1.411 Total 19.750 15 Feasibility 2 Between groups 1.000 1 1.000 0.519 .483 Within groups 27.000 14 1.929 Total 28.000 15 Feasibility 3 Between groups 0.250 1 0.250 0.255 .622 Within groups 13.750 14 0.982 Total 14.000 15 Feasibility 4 Between groups 1.000 1 1.000 0.427 .524 Within groups 32.750 14 2.339 Total 33.750 15 Daim et al. Journal of Innovation and Entrepreneurship (2016) 5:19 Page 21 of 22 Table 10 Feasibility differences between genders per country—ANOVA (Continued) Feasibility 5 Between groups 5.062 1 5.062 4.200 .060 Within groups 16.875 14 1.205 Total 21.938 15 Competing interests The authors declare that they have no competing interests Authors’ contributions All authors contributed to this project equally from inception to the end. All authors read and approved the final manuscript. Acknowledgements The results of this paper are supported by the EU Commission grant Tempus 144713 Fostering Entrepreneurship in Higher Education, FoSentHE. Author details 1 2 Technology Management Doctoral Program, Portland State University, 1900 SW 4th, Portland, OR 97201, USA. Faculty of Economics and Business, University of Zagreb, Zagreb, Croatia. Nottingham Business School, Nottingham Trent University, Nottingham, UK. Department of Industrial Engineering, Istanbul Technical University, Macka, Istanbul, Turkey. 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J., Narayanan, J., Arvey, R. D., Chaturvedi, S., Avolio, B. J., et al. (2009). The genetic basis of entrepreneurship: effects of gender and personality. Organizational Behavior and Human Decision Processes, 110, 93–107. Submit your manuscript to a journal and benefi t from: 7 Convenient online submission 7 Rigorous peer review 7 Immediate publication on acceptance 7 Open access: articles freely available online 7 High visibility within the fi eld 7 Retaining the copyright to your article Submit your next manuscript at 7 springeropen.com http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Innovation and Entrepreneurship Springer Journals

Students’ entrepreneurial behavior: international and gender differences

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Business and Management; Entrepreneurship; Economic Geography; Economic Policy
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Abstract

tugrul.u.daim@pdx.edu Technology Management Doctoral Due to a high level of uncertainty, entrepreneurship is generally considered a risky Program, Portland State University, endeavor. This paper explores the factors impacting entrepreneurial behavior in order to 1900 SW 4th, Portland, OR 97201, USA identify new educational opportunities for its development. The paper explores perceived Full list of author information is feasibility and desirability for students in 10 countries. The entrepreneurship role is gender available at the end of the article tested against desirability and feasibility. The requirements for developing this skill set are also studied. A survey instrument was developed, and data was collected from 4281 students. The results indicate that gender impacts entrepreneurship intention and the way it impacts is influenced by which country the students are from. Keywords: Entrepreneurship, International differences, Gender differences, Behavior, Higher education Background The noteworthy contribution of entrepreneurial activities to economies (Keilbach and Sanders, 2008) in terms of growth, innovation, job creation, and poverty re- duction (Lunati et al., 2010) makes entrepreneurship a popular research topic. The OECD-Eurostat Entrepreneurship Indicators Programme defines entrepre- neurs as “those persons (business owners) who seek to generate value, through the creation or expansion of economic activity, by identifying and exploiting new products, processes or markets” (Lunati et al., 2010). Entrepreneurs differ from the rest of the society ostensibly by their propensity to take risk, tolerance for ambiguity, and motivation for self-employment. Hines (1973) sees entrepreneur- ship as a role model and bases his reasoning on a conclusion that entrepreneurs strive for greater realization and accomplishment in comparison to the role that is fostered by non-entrepreneurial activity. According to Summers (2000), the main aspect of entrepreneurship is “the critical combination of the individual, his or her past experience, background and the decision to start an enterprise.” In- creasing interest in entrepreneurship also raised the curiosity for the drivers such as intentions, traits, behavioral patterns, and external and contextual factors leading individuals to entrepreneurship phenomenon. The study of entrepreneurial motivations has a long history. According to Summers (2000), primal publications were mainly focused on traits, such as self- © 2016 Daim et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Daim et al. Journal of Innovation and Entrepreneurship (2016) 5:19 Page 2 of 22 confidence, risk tolerance, and tolerance for ambiguity. On the way to more re- cent intention-based process models (Shapero, 1982), later studies for entrepre- neurial motivation were based on several other perspectives, such as demographic characteristics (gender, age, education, etc.), social factors (family, community, etc.), and external influences (politics, capital availability, etc.) (Summers, 2000). More recent process models for entrepreneurial motivation are “focusing on atti- tudes and beliefs and how they can predict intentions and behaviors” (Segal et al., 2005). These models are mainly based on human cognitive processes to distinguish possible desirable outcomes and to make decisions on the feasibility of acting to obtain those outcomes (Segal et al., 2005). As mentioned above, country-specific factors were examined in relation with entrepreneurship in the literature. For instance, in their study where they com- pared 15 EU member countries and the USA in terms of latent and actual entre- preneurship, Grilo and Irigoyen (2005) indicate that the level of entrepreneurship shows distinct differences across countries. They pointed out that country- specific effects are indicative for both entrepreneurial motivation and activity levels. According to Freytag and Thurik (2007), country-specific effects are sig- nificant for entrepreneurship preferences but in contrast to that result they do not seem to be able to explain entrepreneurial activity. In their 2006-dated paper, Lee et al. 2006 tried to determine the disparities among the examined countries regarding the aspects essential to improve the entrepreneurship education. Also, Carayannis et al. (2003) indicate that there are differences between American and French entrepreneurship students in terms of attitudes and perceptions towards entrepreneurship. Female and male entrepreneurs usually operate in different sectors and pursue different ways to develop their business. Therefore, increased number of female en- trepreneurs means increased entrepreneurship variety in economy (Verheul et al. 2004). Notwithstanding the importance of their contribution in terms of entrepre- neurship variety, the number of female entrepreneurs is lower than that of male entrepreneurs in almost every country in terms of Total Entrepreneurial Activity, except Ghana, Costa Rica, and Australia (Kelley et al. 2010). This result is also supported with the entrepreneurship literature. For instance, according to Grilo and Irigoyen (2005), for the evaluated 15 EU member countries and the USA, the probability of preference for self-employment is notably higher for men com- pared to women. Menzies and Tatroff ’s (2006) work on gender differences on preferences on entrepreneurship education also states that less women are inter- ested in entrepreneurship education compared to men. Zhang et al. (2009) indi- cate that there is a difference between genders regarding the genetic basis of entrepreneurship. The purpose of this paper is to examine whether gender and country of residence differences have a significant impact on entrepreneurial intentions of university stu- dents as measured by perceived feasibility and perceived desirability. So our research question is the following: What are the gender and country differences’ impacts on entrepreneurial intentions of university students? Daim et al. Journal of Innovation and Entrepreneurship (2016) 5:19 Page 3 of 22 This paper focuses on university-level students as a result of the conviction that younger people are more willing to be self-employed (Blanchflower et al., 2001; Grilo and Irigoyen, 2005). According to GEM’s 2010 global report, in the case of age distribution of entrepreneurs, the 24–35 age group has the highest population for almost every geographic region. Since university students generally fall into the 18–24 age group, examining their entrepreneurial intentions as potential future en- trepreneurs might reveal some implications, because according to Ajzen (1991) intention is anterior to act. The next section examines the entrepreneurial behavior literature with a focus on university students and corresponding national setting and gender differences. Then hypotheses are introduced. This is followed by the description of research design and the methodology conducted. The paper concludes with the discussion of the results and the recommendations for future research. Literature review and hypotheses Entrepreneurial motivations have been frequently examined in the literature. Chell and Allman (2003) explored intentions of more technology-oriented entrepreneurs, while Krueger et al. (2000) analyzed differing entrepreneurial intentions. Grilo and Thurik (2005) explored barriers in 15 European countries and the USA and tried to explain differences in those countries in terms of latent and actual entrepreneur- ship. Studies of entrepreneurship attitudes among students have been viewed as an emerging topic due to an increase in the research performed on that subject by authors such as Luthje and Franke (2003), Wang and Wong (2004), Huffman and Quigley (2002), and Johnson et al. (2006). These studies test entrepreneurial atti- tudes against differing behavioral characteristics to elaborate on a model that would be used as a tool for prediction of future behavior. Among the authors who modeled and examined the behavioral relationship between university students and the corresponding national setting are Turker and Selcuk (2008), Wu and Wu (2008), Wang and Wong (2004), Menzies and Tatroff (2006), Verheul et al. (2004), Kourilsky and Walstad (1998), Zhang et al. (2009), Elenurm et al. (2007), Petridou et al. (2009), Shariff and Saud (2009), Liñán (2008), Carayannis et al (2003), and Veciana et al. (2005). In Turker and Selcuk’s (2008) study, similar to Wu and Wu’s (2008) and Lee and Wong’s (2004), educational setting is seen as a significant factor spurring entrepre- neurship. While Wu and Wu (2008) credit educational significance for assisting in realization of potential behavior, Wang and Wong (2004) see this realization eman- ating from appropriate curriculum structure. Liñán (2008) identified the role of perceived skill as an important factor impacting entrepreneurial intention. Shariff and Saud (2009) explored students’ attitudes towards entrepreneurship in Malaysia and found that self-esteem and personal control differences were influential. Carayannis et al (2003) compared French and US students on their attitudes and perceptions of entrepreneurship and identified regional differences. Barriers against entrepreneurial behavior have long been studied. Menzies and Tatroff (2006) explored attitudes of students in Canada as well, but they also looked at gender differences. They identified no differences in attitudes towards taking risks, but fewer women tended to think that entrepreneurship fit Daim et al. Journal of Innovation and Entrepreneurship (2016) 5:19 Page 4 of 22 their personality. They also reported on studies citing how education helped in- crease the number of female entrepreneurs. Verheul et al. (2004) explored similar factors in a US university and found similar results. Kourilsky and Walstad (1998) also identified similar differences in a US-wide study and proposed entrepreneurship-focused curricula. Zhang et al. (2009) explored genetic differences between genders and their impact on entrepreneurship. Petridou et al. (2009) identified that there were differences in attitudes towards entrepreneurship education and perceptions about required skills between the two genders. Eddleston and Powell (2008) examined how gender identity explains what male and female business owners look for from their careers and found that gender identity, represented by the dimensions of masculinity and femininity, serves as a cognitive mechanism that contributes to dif- ferences in business owners’ career satisfaction preferences. Verheul et al. (2004) explored female entrepreneurship in 29 countries and found that similar factors impacted both genders. Grilo and Thurik (2005) also identified gender differences in a study conducted in the general population. Gerry and Marques (2008) identi- fied similar differences in Portugal. Both were exploring entrepreneurship as a choice. However, Fischer et al. (1993) argued against these differences and found that there was no difference in the success rate at the end. Based on the above discussion, our hypotheses below were developed: H1—Gender in different countries makes a difference in students’ attitudes towards entrepreneurship as measured by desirability and feasibility one country at a time. H2—Country of residence makes a difference in students’ attitudes towards entrepreneurship as measured by desirability and feasibility. Methods Shapero’s model (1982), augmented by Krueger and Brazeal (1994), underlines the basis of our research. We draw our conclusions from the reasoning that intentions are predictions for future behavior. Shapero divided all the characteristics that could initiate intentions into two groups which consist of perceived desirability and perceived feasibility (Summers, 2000). Perceived desirability is defined as a subject- ive norm regarding the perceived social support and personal interest to perform the entrepreneurial behavior. Perceived feasibility examines the perceived ease or difficulty of performing the entrepreneurial behavior and the perceived self- competence regarding to entrepreneurship. Accordingly, we suggest in this paper a model (Fig. 1) that provides insight into the entrepreneurial intensions of students in terms of genders and country of residence differences. A survey instrument was developed, and data was collected from 4281 students from Croatia (1918), Slovenia (306), Austria (541), Poland (332), France (442), Lithuania (415), Israel (295), India (16), and other countries (16) of which 2712 were female and 1563 were male students. This paper is a part of a survey that collected data on the perceived feasibility and desirability of students in more than 10 countries. In this theoretical framework, to examine the concept of perceived desirability, students were asked to measure the extent to which they agreed or disagreed with Daim et al. Journal of Innovation and Entrepreneurship (2016) 5:19 Page 5 of 22 Fig. 1 Research framework (solid lines indicate the part of the study addressed in this paper) the following statements regarding their personal level of desirability for starting their own businesses after the completion of their education: (1) “Iwould love to do it”;(2) “My immediate family members would encourage me to do it”;(3) “I would be tense”;and (4) “I would be enthusiastic.” Students answered by choosing a number on a Likert scale from 1 to 6, with 1 representing “notatall” and 6 “extremely.” In order to investigate students’ perceptions on the feasibility of start- ing a new business, the following questions were included in the survey: (1) “It would be very hard to do”;(2) “I am certain that I would be successful”;(3) “I would be overworked”;(4) “I know enough to start a business”;and (5) “Itrust myself.” In each question, students were able to choose their answers on a Likert scale of 1 to 6, this time with 1 being “very much agree” and 6 being “very much disagree.” As a difference from the previous two studies of Dabić et al. (2012a, 2012b), this study used the same data with the addition of survey results from India and some other countries used to evaluate the impact of gender and country of resi- dence differences on entrepreneurial intentions of university students as mea- sured by perceived feasibility and perceived desirability. In this study, countries were analyzed separately in terms of entrepreneurial intention differences based on country of residence and gender. As a result, significant differences were found among countries and genders in terms of desirability and feasibility to- wards entrepreneurship. Results indicated that Poland, Slovenia, and India seem to have little difference between male and female genders whereas responses from Croatia, Austria, France, and Israel revealed quite strong difference among male and female students. In the other study of Dabić et al. (2012a), perceived desir- ability, perceived feasibility, and educational needs in terms of entrepreneurial programs/activities/projects at an academic institution were analyzed from the gender difference perspective only. Results of the analysis showed that there were significant differences between genders’ perception for educational needs to con- struct academic entrepreneurship education and networking and tutoring chan- nels for students. In Dabić et al. (2012b), countries in the sample were clustered into four groups. The first cluster was created from the questionnaires collected in Israel with the reasoning that Israel is the country with a high entrepreneurial culture, a high level of development, but a low level of political integration. The second cluster consisted of the countries which are in the EU for a longer period of time, namely France and Austria. These two countries have a high level of Daim et al. Journal of Innovation and Entrepreneurship (2016) 5:19 Page 6 of 22 economic and political development and integration. The third cluster was com- prised of Lithuania, Poland, and Slovenia and forms a cluster with characteristics of countries that recently joined the EU and are of a medium level of political and economic development and integration. The fourth cluster was formed by only one country, namely Croatia, as it is a country which is awaiting its acces- sion to the EU and which made a number of political and economic reforms but which has a low level of political integration and a lower level of development than the previous three clusters. Results and discussion Starting a new business: desirability As can be seen in Table 1, the average response to the statement “I would love to do it” (x ¼ 4:12) shows a positive attitude regarding the desirability of entrepreneurial activ- ities for students. The highest score among the statements on desirability is with family support (x ¼ 4:50), meaning that students felt they would generally have the benefit of high family encouragement. Also, it is important to note that the same statements have the highest standard deviation (σ = 1.894), indicating relatively high difference among students. The lowest average score in the group is agreement with the statement of being tense as an entrepreneur (x ¼ 4:04). However, relatively low agreement on this factor can be regarded as a positive indicator towards entrepreneurial attitude, since it suggests students are not highly certain such activities will lead to negative emotions, like tension or stress. Furthermore, enthusiasm scores ( x ¼ 4:33 ) indicate positive mood in connection with starting a new business. This measurement has the second lowest standard deviation in the desirability group (σ = 1.487). Country-specific means show differences and will be analyzed in the next section. Starting a new business: feasibility It is interesting to observe the results of student perceptions on feasibility in con- nection with starting a new business. As can be seen in Table 2, the lowest average Table 1 Perceived desirability—descriptive statistics Country Desirability 1 (D1) Desirability 2 (D2) Desirability 3 (D3) Desirability 4 (D4) Croatia 4.66 5.07 4.21 4.70 Austria 3.69 4.33 4.47 4.36 France 4.29 4.44 4.16 4.62 Israel 4.02 4.49 4.40 4.68 Lithuania 1.83 1.97 2.37 1.78 Poland 4.19 4.12 3.51 4.31 Slovenia 4.23 4.97 4.41 4.49 India 4.44 3.81 3.38 4.94 Rest of the World 4.00 4.69 4.50 4.75 All 4.12 4.50 4.04 4.33 Desirability: (D1) I would love to do it; (D2) My immediate family members would encourage me to do it; (D3) I would be tense; and (D4) I would be enthusiastic. Agreement: (1) not at all; (2) slightly; (3) somewhat; (4) moderately; (5) very much; and (6) extremely Daim et al. Journal of Innovation and Entrepreneurship (2016) 5:19 Page 7 of 22 Table 2 Perceived feasibility—descriptive statistics Country Feasibility 1 (F1) Feasibility 2 (F2) Feasibility 3 (F3) Feasibility 4 (F4) Feasibility 5 (F5) Croatia 2.03 2.82 1.86 3.49 2.42 Austria 2.13 3.43 2.08 3.60 2.65 France 2.03 3.30 2.32 4.35 3.26 Israel 2.14 2.60 2.39 3.59 2.42 Lithuania 3.01 3.19 2.98 3.99 2.93 Poland 2.56 3.35 2.50 3.65 3.01 Slovenia 2.28 2.67 2.50 3.45 2.28 India 1.88 2.38 2.00 3.50 2.00 Rest of the World 2.13 3.50 2.00 3.88 2.56 All 2.20 3.00 2.17 3.66 2.62 Feasibility: (F1) It would be very hard to do; (F2) I am certain that I would be successful; (F3) I would be overworked; (F4) I know enough to start a business; and (F5) I trust myself. Agreement: (1) very much agree; (2) strongly agree; (3) mildly agree; (4) mildly disagree; (5) strongly disagree; and (6) very much disagree score (x ¼ 2:17) and standard deviation (σ = 1.112) occur for the question describ- ing how overworked the entrepreneur expects to be. This could lead to the conclu- sion that students have the perception of being overworked if they start their own businesses. As for the certainty of success, the average score (x ¼ 3:00) indicates that students were right in the middle between most affirmative and most negative, meaning, on average, they were neither certain nor uncertain of success. The aver- age score for the question regarding knowing enough to start a business (x ¼ 3:66) is slightly negative, meaning students are a little unsure whether they know every- thing they need to start a business and thus may benefit from some additional education in this area. For the self-confidence question, results show students have a positive perception (x ¼ 2:62). Nevertheless, they agree with the contention that starting a new business is quite hard (x ¼ 2:20). Country-specific means show dif- ferences and will be analyzed in the next section. Based on Table 3, the average of responses for perceived desirability questions 1 through 4 can be seen gender- and countrywise. According to these results, the average of total responses for desirability question 1 regarding attitudes towards entrepreneurial initiatives and that for desirability question 4 regarding enthusiasm about entrepreneurial initiatives seem higher for male students. Furthermore, the average of total scores for desirability questions 2 and 3 regarding family support and work-related stress, respectively, may imply that although female students feel slightly more supported by their families they are inclined to feel more tense about starting a new business. Another interesting outcome is that female students from Poland and India seem to show equal or greater inclination to entrepreneurial ini- tiatives compared to male students from those countries. Also, country-specific means show differences and will be analyzed in the next section. Based on Table 4, the average of responses for perceived feasibility questions 1 through 5 can be seen gender- and countrywise. According to these descriptive sta- tistics, for feasibility questions 1 and 3, regarding the difficulties associated with entrepreneurial activities and being overworked, respectively, female students’ total average scores are smaller than male students’; for questions 2, 4, and 5, regarding Daim et al. Journal of Innovation and Entrepreneurship (2016) 5:19 Page 8 of 22 Table 3 Perceived desirability means by gender and country—descriptive statistics Country Gender Desirability 1 Desirability 2 Desirability 3 Desirability 4 Croatia Female Mean 4.56 5.14 4.29 4.60 Male Mean 4.82 4.95 4.10 4.84 Austria Female Mean 3.54 4.34 4.46 4.22 Male Mean 4.14 4.32 4.49 4.78 France Female Mean 4.08 4.44 4.29 4.47 Male Mean 4.62 4.44 3.95 4.84 Israel Female Mean 3.60 4.23 4.57 4.55 Male Mean 4.51 4.79 4.21 4.82 Lithuania Female Mean 1.82 1.96 2.37 1.77 Male Mean 1.84 1.99 2.36 1.79 Poland Female Mean 4.19 4.27 3.57 4.31 Male Mean 4.19 3.80 3.37 4.31 Slovenia Female Mean 4.12 5.00 4.41 4.44 Male Mean 4.46 4.91 4.40 4.61 India Female Mean 5.60 4.80 3.00 6.00 Male Mean 3.91 3.36 3.55 4.45 Rest of the world Female Mean 4.25 4.75 4.38 4.88 Male Mean 3.75 4.63 4.63 4.63 Total Female Mean 3.99 4.52 4.10 4.23 Female Std. dev. 1.703 1.703 1.517 1.486 Male Mean 4.36 4.46 3.94 4.50 Male Std. dev. 1.616 2.187 1.384 1.475 certainty of success, certainty of having the required knowledge for entrepreneurial activities, and self-confidence, respectively, male students score smaller. These re- sults may imply that female students are more concerned about the difficulties and workload associated with entrepreneurship and they have lower self-confidence and motivation under the assumption of starting a new business. Also, country-specific means show differences and will be analyzed in the next section. Gender and country differences Differences were analyzed in multiple perspectives as seen in Table 5. ANOVA was used in each case, and the detailed results are provided in the following tables. At the 1 % level of significance, ANOVA indicates relevant differences among differ- ent countries for all the previously mentioned questions regarding perceived feasibility and perceived desirability as can be observed in Table 6. Countrywise differences with respect to desirability Based on the significance values for desirability questions 1 through 4, there is a statistically significant difference between countries for the following: desirability question 1 (D1P = 0.000) regarding attitudes towards entrepreneurial initiatives, desirability question 2 (D2P = 0.000) regarding family support, desirability v Daim et al. Journal of Innovation and Entrepreneurship (2016) 5:19 Page 9 of 22 Table 4 Perceived feasibility means by gender and country—descriptive statistics Country Gender Feasibility 1 Feasibility 2 Feasibility 3 Feasibility 4 Feasibility 5 Croatia Female Mean 1.95 2.89 1.81 3.57 2.53 Male Mean 2.14 2.72 1.95 3.38 2.27 Austria Female Mean 2.10 3.53 2.04 3.76 2.78 Male Mean 2.23 3.15 2.20 3.12 2.26 France Female Mean 1.98 3.35 2.30 4.53 3.50 Male Mean 2.11 3.22 2.35 4.07 2.89 Israel Female Mean 2.17 2.78 2.50 3.69 2.61 Male Mean 2.12 2.39 2.27 3.47 2.20 Lithuania Female Mean 2.98 3.35 2.95 4.13 3.10 Male Mean 3.06 2.89 3.04 3.75 2.62 Poland Female Mean 2.50 3.31 2.45 3.67 3.12 Male Mean 2.69 3.43 2.60 3.62 2.77 Slovenia Female Mean 2.23 2.68 2.45 3.57 2.40 Male Mean 2.39 2.64 2.60 3.20 2.01 India Female Mean 1.80 2.80 2.00 3.80 2.20 Male Mean 1.91 2.18 2.00 3.36 1.91 Rest of the world Female Mean 2.13 3.25 1.88 4.13 3.13 Male Mean 2.13 3.75 2.13 3.63 2.00 Total Female Mean 2.16 3.09 2.14 3.76 2.76 Female Std. dev. 1.102 1.101 1.111 1.291 1.247 Male Mean 2.27 2.84 2.22 3.48 2.37 Male Std. dev. 1.105 1.543 1.113 1.257 1.208 question 3 (D3P = 0.000) regarding work-related stress as an entrepreneur, and desirability question 4 (D4P = 0.000) regarding enthusiasm about entrepreneurial initiatives. Countrywise differences with respect to feasibility Based on the significance values for feasibility questions 1 through 5, there is a statistically significant difference between countries for the following: desirability question 1 (F1P = 0.000) regarding level of difficulties associated with entrepre- neurial activities, desirability question 2 (F2P = 0.000) regarding level of certainty associated with success, desirability question 3 (F3P = 0.000) regarding level of excess work associated with entrepreneurial activities, desirability question 4 Table 5 Summary of ANOVA results Among different countries Among different genders Among different genders in different countries Differences in desirability Significant differences Significant differences were Significant differences were were found in all cases found in all but one case found depending on the country Differences in feasibility Significant differences Significant differences were Significant differences were were found in all cases found in all cases found depending on the country Daim et al. Journal of Innovation and Entrepreneurship (2016) 5:19 Page 10 of 22 Table 6 Desirability and feasibility differences between countries—ANOVA Sum of squares df Mean square F Sig. Desirability 1 Between groups 2775.778 8 346.972 159.710 .000 Within groups 9144.115 4209 2.173 Total 11,919.894 4217 Desirability 2 Between groups 3289.738 8 411.217 146.154 .000 Within groups 11,833.948 4206 2.814 Total 15,123.685 4214 Desirability 3 Between groups 1444.878 8 180.610 98.994 .000 Within groups 7673.663 4206 1.824 Total 9118.541 4214 Desirability 4 Between groups 2922.443 8 365.305 240.145 .000 Within groups 6396.598 4205 1.521 Total 9319.041 4213 Feasibility 1 Between groups 379.611 8 47.451 41.907 .000 Within groups 4776.065 4218 1.132 Total 5155.675 4226 Feasibility 2 Between groups 338.531 8 42.316 26.864 .000 Within groups 6639.460 4215 1.575 Total 6977.991 4223 Feasibility 3 Between groups 538.126 8 67.266 60.560 .000 Within groups 4681.749 4215 1.111 Total 5219.875 4223 Feasibility 4 Between groups 327.085 8 40.886 25.896 .000 Within groups 6654.730 4215 1.579 Total 6981.814 4223 Feasibility 5 Between groups 397.251 8 49.656 33.967 .000 Within groups 6177.962 4226 1.462 Total 6575.213 4234 (F4P = 0.000) regarding level of knowledge required for entrepreneurial activities, and desirability question 5 (F5P = 0.000) regarding level of self-esteem. From Table 7, at the 5 % level of significance, ANOVA results for genderwise differ- ences with respect to perceived desirability- and perceived feasibility-related variables can be seen. Genderwise differences with respect to desirability Based on the significance values for desirability questions 1 through 4 in Table 7, there is a statistically significant difference between genders for the following: de- sirability question 1 (D1P = 0.000) regarding attitudes towards entrepreneurial ini- tiatives, desirability question 3 (D3P = 0.001) regarding work-related stress as an entrepreneur, and desirability question 4 (D4P = 0.000) regarding enthusiasm about entrepreneurial initiatives whereas there is no statistically significant Daim et al. Journal of Innovation and Entrepreneurship (2016) 5:19 Page 11 of 22 Table 7 Desirability and feasibility differences between genders—ANOVA Sum of squares df Mean square F Sig. Feasibility 1 Between groups 13.774 1 13.774 11.314 .001 Within groups 5139.783 4222 1.217 Total 5153.557 4223 Feasibility 2 Between groups 58.238 1 58.238 35.508 .000 Within groups 6919.753 4219 1.640 Total 6977.991 4220 Feasibility 3 Between groups 6.668 1 6.668 5.397 .020 Within groups 5211.806 4219 1.235 Total 5218.474 4220 Feasibility 4 Between groups 80.262 1 80.262 49.082 .000 Within groups 6899.205 4219 1.635 Total 6979.467 4220 Feasibility 5 Between groups 149.169 1 149.169 98.202 .000 Within groups 6425.371 4230 1.519 Total 6574.540 4231 Desirability 1 Between groups 140.515 1 140.515 50.267 .000 Within groups 11,776.839 4213 2.795 Total 11,917.354 4214 Desirability 2 Between groups 2.593 1 2.593 0.722 .396 Within groups 15,118.361 4210 3.591 Total 15,120.953 4211 Desirability 3 Between groups 23.155 1 23.155 10.720 .001 Within groups 9093.382 4210 2.160 Total 9116.536 4211 Desirability 4 Between groups 75.164 1 75.164 34.225 .000 Within groups 9243.556 4209 2.196 Total 9318.720 4210 difference between genders for desirability question 2 (D2P = 0.396) regarding family support. Genderwise differences with respect to feasibility Based on the significance values for feasibility questions 1 through 5, in Table 7, there is a statistically significant difference between genders for the following: desirability question 1 (F1P = 0.001) regarding level of difficulties associated with entrepreneurial activities, desirability question 2 (F2P = 0.000) regarding level of certainty associated with success, desirability question 3 (F3P = 0.000) regarding level of excess work associated with entrepreneurial activities, desirability question 4 (F4P = 0.000) regarding level of knowledge required for entrepre- neurial activities, and desirability question 5 (F5P = 0.000) regarding level of self-esteem. ANOVA results for perceived desirability and perceived feasibility differences among different genders in different countries can be seen in the Appendix as Tables (Tables 9 Daim et al. Journal of Innovation and Entrepreneurship (2016) 5:19 Page 12 of 22 and 10). ANOVA results show that there are further significant differences between male and female students in different countries in terms of their attitude towards entrepreneurship. Perceived desirability differences between genders per country ANOVA results (Table 9) for genderwise differences per country, with respect to desirability questions 1 through 4 for the 5 % level of significance, exhibit that for desirability question 1 regarding attitudes towards entrepreneurial initiatives there is a statistically significant difference between genders in Croatia (D1P = 0.000), Austria (D1P = 0.000), France (D1P = 0.000), Israel (D1P = 0.000), and India v v v (D1P = 0.015) whereas there is statistically no significant difference between gen- ders in Lithuania (D1P = 0.863), Poland (D1P = 0.954), Slovenia (D1P = 0.076), v v v and the rest of the world (D1P = 0.568). Based on the significance values for desirability question 2 regarding family support, there is a statistically significant difference between genders in Croatia (D2P = 0.001) whereas there is statistically no significant difference between genders in Austria (D2P = 0.894), France (D2P = 0.974), Israel (D2P = 0.257), Lithuania (D2P = 0.787), Poland v v v (D2P = 0.064), Slovenia (D2P = 0.579), India (D2P = 0.087), and the rest of the world v v v (D2P = 0.855). Based on the significance values for desirability question 3 regarding work-related stress as an entrepreneur, there is a statistically significant difference between gen- ders in Croatia (D3P = 0.002), France (D3P = 0.015), and Israel (D3P = 0.019) v v v whereas there is statistically no significant difference between genders in Austria (D3P = 0.810), Lithuania (D3P = 0.871), Poland (D3P =0.448), Slovenia (D3P = v v v v 0.957), India (D3P = 0.589), and the rest of the world (D3P = 0.559). v v Based on the significance values for desirability question 4 regarding enthusi- asm about entrepreneurial initiatives, there is a statistically significant differ- ence between genders in Croatia (D4P = 0.000), Austria (D4P = 0.000), France v v (D4P = 0.005), and India (D4P = 0.027) whereas there is statistically no signifi- v v cant difference between genders in Israel (D4P = 0.096), Lithuania (D4P = v v 0.874), Poland (D4P =0.970), Slovenia (D4P = 0.303), and the rest of the world v v (D4P = 0.723). Perceived feasibility differences between genders per country ANOVA results (Table 10) for genderwise differences per country, with respect to feasibility questions 1 through 5 for the 5 % level of significance, exhibit that for feasibility question 1 regarding level of difficulties associated with entrepreneurial activities there is a statistically significant difference between genders in Croatia (F1P = 0.000) whereas there is statistically no significant difference between gen- ders in Austria (F1P = 0.227), France (F1P =0.162), Israel (F1P = 0.698), Lithuania v v v (F1P = 0.639), Poland (F1P = 0.102), Slovenia (F1P = 0.179), India (F1P = 0.812), v v v v and the rest of the world (F1P = 1.000). Based on the significance values for feasibility question 2 regarding the level of certainty associated with success, there is a statistically significant difference between genders in Croatia (F2P = 0.001), Austria (F2P = 0.002), Israel (F2P v v v Daim et al. Journal of Innovation and Entrepreneurship (2016) 5:19 Page 13 of 22 = 0.000), and Lithuania (F2P = 0.001), whereas there is statistically no signifi- cant difference between genders in France (F2P =0.209), Poland (F2P = 0.681), v v Slovenia (F2P = 0.751), India (F2P = 0.244), and the rest of the world (F2P = v v v 0.483). Based on the significance values for feasibility question 3 regarding the level of excess work associated with entrepreneurial activities, there is a statistically signifi- cant difference between genders in Croatia (F3P = 0.003) whereas there is statisti- cally no significant difference between genders in Austria (F3P = 0.136), France (F3P = 0.598), Israel (F3P = 0.053), Lithuania (F3P = 0.562), Poland (F3P = 0.198), v v v v Slovenia (F3P = 0.220), India (F3P = 0.384), and the rest of the world v v (F3P = 0.622). Based on the significance values for feasibility question 4 regarding the level of knowledge required for entrepreneurial activities, there is a statistically significant difference between genders in Croatia (F4P = 0.001), Austria (F4P =0.000), v v France (F4P = 0.000), Lithuania (F4P = 0.008), and Slovenia (F4P = 0.012) v v v whereas there is statistically no significant difference between genders in Israel (F4P = 0.080), Poland (F4P =0.699), India (F4P = 0.384), and the rest of the v v v world (F4P = 0.524). Based on the significance values for feasibility question 5 regarding the level of self-esteem, there is a statistically significant difference between genders in Croatia (F5P = 0.000), Austria (F5P = 0.000), France (F5P = 0.000), Israel (F5P = 0.001), v v v v Lithuania (F5P = 0.001), Poland (F5P = 0.004), and Slovenia (F5P = 0.001) whereas v v v there is statistically no significant difference between genders in India (F5P = 0.639) and the rest of the world (F5P = 0.060). Table 8 is a summary of the gender differences. Those cells with an “X” repre- sent no significant difference between genders. All the other cells indicate signifi- cant difference. Croatia seems to be the only country in which females and males exhibit significantly different attitudes regarding all perceived desirability and feasi- bility aspects. Based on Table 8, it can be observed that responses to questions D2 (My imme- diate family members would encourage me to do it), F1 (It would be very hard to do), and F3 (I would be overworked) indicate nearly no significant difference be- tween genders for all cases except Croatia whereas F4 (I know enough to start a Table 8 Summary of perceived desirability and feasibility differences between genders per country Country D1 D2 D3 D4 F1 F2 F3 F4 F5 Croatia Austria x x x x France x x x x Israel x x x x x Lithuania x x x x x x Poland x x x x x x x x Slovenia x x x x x x x India x x x x x x x Rest of the world x x x x x x x x x Daim et al. Journal of Innovation and Entrepreneurship (2016) 5:19 Page 14 of 22 business), F5 (I trust myself), and D1 (I would love to do it) indicate quite a bit difference between genders for the majority of the countries included in the study. This might mean that although both genders are aware of the required work and dedication for starting a new business, generally male students are more self- confident and keen to do it. If Table 8 is analyzed countrywise, then Poland, Slovenia, and India appear not to have considerable amount of difference between male and female genders whereas responses from male and female students from Croatia, Austria, France, and Israel indicate quite strong difference. In the case of India, D1 and D4 are expected to show significant difference in terms of female students scoring higher than males. This result is consistent with the GEM 2002 report where India and Poland are in the top 6 among 29 countries regarding the female share in total entrepreneurial activity. Interestingly, in the same list, Slovenia occupies the 21st position. Conclusions This paper makes significant contributions to the understanding of entrepreneurial per- ceptions among students. One of the key strengths of this study is that it is based on a wide range of data for students from 10 different countries. Thus, the results are not culturally related but reflect more globally oriented intentions. This paper explores the factors impacting entrepreneurial behavior in order to iden- tify new educational opportunities for its development. Specifically, there are three major findings. Significant differences were found between genders and countries on their perceptions of desirability and feasibility towards entrepreneurial behavior. This adds to the findings of prior research on gender differences in entrepreneurial attitudes. Moreover, there were differences in how genders differ in different countries which would require further research. Insights from this study can help educators plan entrepreneurship-oriented pro- grams or courses in a manner that aims to minimize the gender differences in entrepreneurial motivation. Also, policy makers of countries willing to increase the number of female entrepreneurs would benefit from the results regarding which perceptions females show significant differences from males, so they can shape their entrepreneurship-related policies aiming to reduce these differences or alter the perceptions. There were also differences in how countries differ in terms of perceived feasibility and desirability. These differences can result from social secur- ity policies, economic activity, regulatory issues, or sectoral concentration of recent entrepreneurial activities, etc. specific to each country, which can affect the intention of starting a new business negatively. Further research revealing that dif- ferences’ direction would also help policy makers to understand their countries’ po- tential entrepreneurs’ perceptions about those aspects and to alter them. One shortcoming of this study might be the varying sample sizes from different countries. Sample sizes vary from 1918 to 16, and they are not determined rela- tively to the student population in those countries. More balanced sample size from examined countries would lead to more meaningful results. For further research also, the effect of students’ training areas (engineering, business, social sciences, etc.) on their entrepreneurial perceptions can be examined. Daim et al. Journal of Innovation and Entrepreneurship (2016) 5:19 Page 15 of 22 Appendix Table 9 Desirability differences between genders per country—ANOVA ANOVA Country Sum of squares df Mean square F Sig. Croatia Desirability 1 Between groups 29.840 1 29.840 13.322 .000 Within groups 4291.598 1916 2.240 Total 4321.437 1917 Desirability 2 Between groups 17.688 1 17.688 11.284 .001 Within groups 3003.501 1916 1.568 Total 3021.189 1917 Desirability 3 Between groups 15.785 1 15.785 9.263 .002 Within groups 3265.144 1916 1.704 Total 3280.929 1917 Desirability 4 Between groups 26.133 1 26.133 18.588 .000 Within groups 2693.657 1916 1.406 Total 2719.790 1917 Austria Desirability 1 Between groups 36.911 1 36.911 14.370 .000 Within groups 1384.538 539 2.569 Total 1421.449 540 Desirability 2 Between groups 0.046 1 0.046 0.018 .894 Within groups 1392.398 539 2.583 Total 1392.444 540 Desirability 3 Between groups 0.085 1 0.085 0.058 .810 Within groups 786.721 539 1.460 Total 786.806 540 Desirability 4 Between groups 32.178 1 32.178 16.737 .000 Within groups 1036.255 539 1.923 Total 1068.433 540 France Desirability 1 Between groups 30.707 1 30.707 12.411 .000 Within groups 1088.643 440 2.474 Total 1119.351 441 Desirability 2 Between groups 0.002 1 0.002 0.001 .974 Within groups 946.848 440 2.152 Total 946.851 441 Desirability 3 Between groups 11.560 1 11.560 5.979 .015 Within groups 850.669 440 1.933 Total 862.229 441 Desirability 4 Between groups 14.302 1 14.302 7.905 .005 Within groups 796.080 440 1.809 Total 810.382 441 Israel Desirability 1 Between groups 54.667 1 54.667 22.442 .000 Within groups 643.092 264 2.436 Total 697.759 265 Desirability 2 Between groups 20.394 1 20.394 1.292 .257 Within groups 4151.847 263 15.786 Total 4172.242 264 Daim et al. Journal of Innovation and Entrepreneurship (2016) 5:19 Page 16 of 22 Table 9 Desirability differences between genders per country—ANOVA (Continued) Desirability 3 Between groups 8.673 1 8.673 5.589 .019 Within groups 411.245 265 1.552 Total 419.918 266 Desirability 4 Between groups 4.859 1 4.859 2.795 .096 Within groups 458.979 264 1.739 Total 463.838 265 Lithuania Desirability 1 Between groups 0.027 1 0.027 0.030 .863 Within groups 358.950 394 0.911 Total 358.977 395 Desirability 2 Between groups 0.075 1 0.075 0.073 .787 Within groups 402.497 393 1.024 Total 402.572 394 Desirability 3 Between groups 0.021 1 0.021 0.026 .871 Within groups 321.751 393 0.819 Total 321.772 394 Desirability 4 Between groups 0.020 1 0.020 0.025 .874 Within groups 306.425 394 0.778 Total 306.444 395 Poland Desirability 1 Between groups 0.004 1 0.004 0.003 .954 Within groups 406.531 312 1.303 Total 406.535 313 Desirability 2 Between groups 14.849 1 14.849 3.452 .064 Within groups 1337.777 311 4.302 Total 1352.626 312 Desirability 3 Between groups 2.832 1 2.832 0.576 .448 Within groups 1518.898 309 4.916 Total 1521.730 310 Desirability 4 Between groups 0.002 1 0.002 0.001 .970 Within groups 394.269 308 1.280 Total 394.271 309 Slovenia Desirability 1 Between groups 7.720 1 7.720 3.177 .076 Within groups 738.806 304 2.430 Total 746.526 305 Desirability 2 Between groups 0.488 1 0.488 0.308 .579 Within groups 481.185 304 1.583 Total 481.673 305 Desirability 3 Between groups 0.004 1 0.004 0.003 .957 Within groups 421.748 304 1.387 Total 421.752 305 Desirability 4 Between groups 2.017 1 2.017 1.064 .303 Within groups 576.470 304 1.896 Total 578.487 305 India Desirability 1 Between groups 9.828 1 9.828 7.598 .015 Within groups 18.109 14 1.294 Total 27.937 15 Daim et al. Journal of Innovation and Entrepreneurship (2016) 5:19 Page 17 of 22 Table 9 Desirability differences between genders per country—ANOVA (Continued) Desirability 2 Between groups 7.092 1 7.092 3.383 .087 Within groups 29.345 14 2.096 Total 36.437 15 Desirability 3 Between groups 1.023 1 1.023 0.306 .589 Within groups 46.727 14 3.338 Total 47.750 15 Desirability 4 Between groups 8.210 1 8.210 6.138 .027 Within groups 18.727 14 1.338 Total 26.938 15 Rest of the world Desirability 1 Between groups 1.000 1 1.000 0.341 .568 Within groups 41.000 14 2.929 Total 42.000 15 Desirability 2 Between groups 0.062 1 0.062 0.034 .855 Within groups 25.375 14 1.812 Total 25.438 15 Desirability 3 Between groups 0.250 1 0.250 0.359 .559 Within groups 9.750 14 0.696 Total 10.000 15 Desirability 4 Between groups 0.250 1 0.250 0.131 .723 Within groups 26.750 14 1.911 Total 27.000 15 Daim et al. Journal of Innovation and Entrepreneurship (2016) 5:19 Page 18 of 22 Table 10 Feasibility differences between genders per country—ANOVA ANOVA Country Sum of squares df Mean square F Sig. Croatia Feasibility 1 Between groups 15.230 1 15.230 14.211 .000 Within groups 2053.466 1916 1.072 Total 2068.697 1917 Feasibility 2 Between groups 11.942 1 11.942 10.714 .001 Within groups 2135.786 1916 1.115 Total 2147.729 1917 Feasibility 3 Between groups 8.513 1 8.513 8.708 .003 Within groups 1873.149 1916 0.978 Total 1881.662 1917 Feasibility 4 Between groups 16.340 1 16.340 11.625 .001 Within groups 2693.071 1916 1.406 Total 2709.412 1917 Feasibility 5 Between groups 31.051 1 31.051 22.386 .000 Within groups 2657.637 1916 1.387 Total 2688.689 1917 Austria Feasibility 1 Between groups 1.741 1 1.741 1.463 .227 Within groups 641.408 539 1.190 Total 643.150 540 Feasibility 2 Between groups 14.370 1 14.370 9.240 .002 Within groups 838.281 539 1.555 Total 852.651 540 Feasibility 3 Between groups 2.421 1 2.421 2.235 .136 Within groups 584.000 539 1.083 Total 586.421 540 Feasibility 4 Between groups 42.879 1 42.879 17.309 .000 Within groups 1335.276 539 2.477 Total 1378.155 540 Feasibility 5 Between groups 27.350 1 27.350 18.554 .000 Within groups 794.509 539 1.474 Total 821.860 540 France Feasibility 1 Between groups 1.748 1 1.748 1.963 .162 Within groups 391.809 440 0.890 Total 393.557 441 Feasibility 2 Between groups 1.859 1 1.859 1.585 .209 Within groups 516.315 440 1.173 Total 518.174 441 Feasibility 3 Between groups 0.358 1 0.358 0.278 .598 Within groups 565.663 440 1.286 Total 566.020 441 Feasibility 4 Between groups 22.219 1 22.219 12.823 .000 Within groups 762.426 440 1.733 Total 784.645 441 Daim et al. Journal of Innovation and Entrepreneurship (2016) 5:19 Page 19 of 22 Table 10 Feasibility differences between genders per country—ANOVA (Continued) Feasibility 5 Between groups 39.155 1 39.155 19.977 .000 Within groups 862.401 440 1.960 Total 901.557 441 Israel Feasibility 1 Between groups 0.124 1 0.124 0.151 .698 Within groups 222.016 271 0.819 Total 222.139 272 Feasibility 2 Between groups 10.525 1 10.525 14.152 .000 Within groups 200.795 270 0.744 Total 211.320 271 Feasibility 3 Between groups 3.361 1 3.361 3.780 .053 Within groups 239.178 269 0.889 Total 242.539 270 Feasibility 4 Between groups 3.061 1 3.061 3.095 .080 Within groups 266.994 270 0.989 Total 270.055 271 Feasibility 5 Between groups 11.414 1 11.414 10.930 .001 Within groups 282.982 271 1.044 Total 294.396 272 Lithuania Feasibility 1 Between groups 0.476 1 0.476 0.220 .639 Within groups 857.484 397 2.160 Total 857.960 398 Feasibility 2 Between groups 19.815 1 19.815 10.473 .001 Within groups 751.087 397 1.892 Total 770.902 398 Feasibility 3 Between groups 0.672 1 0.672 0.337 .562 Within groups 791.168 397 1.993 Total 791.840 398 Feasibility 4 Between groups 12.899 1 12.899 7.141 .008 Within groups 717.091 397 1.806 Total 729.990 398 Feasibility 5 Between groups 21.679 1 21.679 10.985 .001 Within groups 783.494 397 1.974 Total 805.173 398 Poland Feasibility 1 Between groups 2.398 1 2.398 2.693 .102 Within groups 276.874 311 0.890 Total 279.272 312 Feasibility 2 Between groups 0.972 1 0.972 0.169 .681 Within groups 1777.523 309 5.753 Total 1778.495 310 Feasibility 3 Between groups 1.547 1 1.547 1.662 .198 Within groups 288.441 310 0.930 Total 289.987 311 Feasibility 4 Between groups 0.137 1 0.137 0.150 .699 Within groups 282.358 309 0.914 Total 282.495 310 Daim et al. Journal of Innovation and Entrepreneurship (2016) 5:19 Page 20 of 22 Table 10 Feasibility differences between genders per country—ANOVA (Continued) Feasibility 5 Between groups 8.307 1 8.307 8.188 .004 Within groups 324.665 320 1.015 Total 332.972 321 Slovenia Feasibility 1 Between groups 1.659 1 1.659 1.810 .179 Within groups 278.606 304 0.916 Total 280.265 305 Feasibility 2 Between groups 0.106 1 0.106 0.101 .751 Within groups 317.894 304 1.046 Total 318.000 305 Feasibility 3 Between groups 1.485 1 1.485 1.510 .220 Within groups 299.015 304 0.984 Total 300.500 305 Feasibility 4 Between groups 9.357 1 9.357 6.430 .012 Within groups 442.408 304 1.455 Total 451.765 305 Feasibility 5 Between groups 10.480 1 10.480 11.246 .001 Within groups 282.385 303 0.932 Total 292.866 304 India Feasibility 1 Between groups 0.041 1 0.041 0.059 .812 Within groups 9.709 14 0.694 Total 9.750 15 Feasibility 2 Between groups 1.314 1 1.314 1.479 .244 Within groups 12.436 14 0.888 Total 13.750 15 Feasibility 3 Between groups 0.000 1 0.000 0.000 1.000 Within groups 8.000 14 0.571 Total 8.000 15 Feasibility 4 Between groups 0.655 1 0.655 0.808 .384 Within groups 11.345 14 0.810 Total 12.000 15 Feasibility 5 Between groups 0.291 1 0.291 0.230 .639 Within groups 17.709 14 1.265 Total 18.000 15 Rest of the world Feasibility 1 Between groups 0.000 1 0.000 0.000 1.000 Within groups 19.750 14 1.411 Total 19.750 15 Feasibility 2 Between groups 1.000 1 1.000 0.519 .483 Within groups 27.000 14 1.929 Total 28.000 15 Feasibility 3 Between groups 0.250 1 0.250 0.255 .622 Within groups 13.750 14 0.982 Total 14.000 15 Feasibility 4 Between groups 1.000 1 1.000 0.427 .524 Within groups 32.750 14 2.339 Total 33.750 15 Daim et al. Journal of Innovation and Entrepreneurship (2016) 5:19 Page 21 of 22 Table 10 Feasibility differences between genders per country—ANOVA (Continued) Feasibility 5 Between groups 5.062 1 5.062 4.200 .060 Within groups 16.875 14 1.205 Total 21.938 15 Competing interests The authors declare that they have no competing interests Authors’ contributions All authors contributed to this project equally from inception to the end. All authors read and approved the final manuscript. Acknowledgements The results of this paper are supported by the EU Commission grant Tempus 144713 Fostering Entrepreneurship in Higher Education, FoSentHE. Author details 1 2 Technology Management Doctoral Program, Portland State University, 1900 SW 4th, Portland, OR 97201, USA. Faculty of Economics and Business, University of Zagreb, Zagreb, Croatia. Nottingham Business School, Nottingham Trent University, Nottingham, UK. Department of Industrial Engineering, Istanbul Technical University, Macka, Istanbul, Turkey. 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