*Corresponding Author ENTREPRENEURIAL CURIOSITY AMONG GENERATION Z: A MULTI-COUNTRY EMPIRICAL RESEARCH Mitja Jeraj* GEA College, Faculty of Entrepreneurship, Slovenia mitja.jeraj@gea-college.si Gokhan Aydin University of East London, School of Arts and Creative Industries, UK g.aydin@uel.ac.uk Abstract This study focuses on entrepreneurial curiosity construct as a predecessor of entrepreneurial activity and ponders this construct among Generation Z in detail using a student sample (N=1068) from two distinct markets in Europe; Slovenia and Turkey. The study applies exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) to further develop and validate a multifactor entrepreneurial curiosity construct that elaborates entrepreneurial activity and curiosity. Two face to face survey studies using a questionnaire form developed using relevant measures were applied and a total of 1150 surveys were obtained. After screening for low quality and incomplete surveys, data from 1068 forms were analysed on SPSS and AMOS to carry out EFA and CFA respectively. The results indicate a four- factor structure that are named as Entrepreneurial Spirit and Leadership, Learning and Personal Development Orientation, Materialistic Orientation, and Technical Curiosity. Moreover, comparisons between Slovenian and Turkish sample indicate significant differences in the four sub-dimensions of entrepreneurial curiosity. Key Words Entrepreneurship; entrepreneur; entrepreneurial curiosity; Z generation; comparative analysis. Advances in Business-Related Scientific Research Journal, Volume 12, No. 1, 2021 13 INTRODUCTION Researchers are becoming more and more interested in understanding how young people impact entrepreneurship and who individuals that will become successful in the field of entrepreneurship are. This is not surprising since entrepreneurship is one of the most important sectors in the global economy. Entrepreneurship has been pointed out as a key contributor to sustained economic growth and development as it not only creates employment, but increases spending in markets, knowledge transfers, employment and innovation as well (Meyer and de Jongh, 2018). In line with that, there is public and political interest to promote entrepreneurship and to create entrepreneurial platforms to establish the best possible conditions to motivate potential entrepreneurs and to develop entrepreneurship. How can we say who is an entrepreneur, who could become one and who cannot be entrepreneur in entire life? A very basic definition of the entrepreneur could be that he/she is the main actor in entrepreneurship responsible for a new venture creation (Fadzil et al., 2019). There is no well- structured definition of entrepreneurs versus non-entrepreneurs. For example, Brockhaus (1982) stressed there is some consistency in psychological characteristics of entrepreneurs as; - Psychological characteristics where a causal link between high need for achievement and small business ownership is not found; an internal locus-of-control belief does not distinguish entrepreneurs, but may identify successful ones; propensity for risk-taking may not be related to either entrepreneurship decision or success; personal values (need for achievement, independence, and effective leadership) may effectively distinguish successful entrepreneurs from the general population. - Effects of previous experience. Dissatisfaction with previous jobs (except pay) characterizes entrepreneurs; an unemployed person is more likely to start a business; and a large percentage of entrepreneurs had role models who were entrepreneurs. - Personal characteristics associated with entrepreneurs. Further, acting entrepreneurially is something that people choose or plan to do (Shaver and Scott 1991). An emerging stream of research concerns the powerful role effect plays in enhancing the success potential of would-be entrepreneurs (Cardon, 2008). Nevertheless, King and Levine (1993) assume some individuals in society intrinsically possess skills to be potentially capable entrepreneurs and further Frydman, Hessel and Rapaczynski (1998) suggest entrepreneurship is clearly in part a matter of human ability. People all over the world find entrepreneurship not only as an alternative to a conventional job but rather as a first choice of professional life. Beside that many young people in the OECD and the EU [European Union] cannot find employment (Green, 2013). In contrast to that, there is a discrepancy in the market, since studies highlight the fact that entrepreneurial activity is lowest among young people under 25 (Harding and Bosma, 2006). Yet there are geographical and cultural differences among young generation’s Advances in Business-Related Scientific Research Journal, Volume 12, No. 1, 2021 14 perception towards entrepreneurship. Within this context, learning the dispositions of young generation and potential entrepreneurs from two countries is the main research objective in this paper. As Fošner and Jeraj (2018) argue, entrepreneurship and thinking about entrepreneurship among students is a very important and fascinating topic, which has both economic and social impact. In that manner, Seemiller and Grace (2017) argue generational research can provide institutions with valuable information to design effective policies, programs, and practices. In order to comprehend how young students from two different countries perceive entrepreneurially connected concepts such as entrepreneurial curiosity and its sub- dimensions, the empirical study was conducted. From this perspective it is wise to research and to understand how young people are affected by entrepreneurial curiosity which is an important component of entrepreneurial psychology. The generation of young people at university today (as of 2020) is commonly named as Generation Z (Gen Z). Different authors define Generation Z in distinct ways, but the consensus is, Gen Z are young adults who were born between 1995 and 2012 (Fister-Gale, 2015). It is necessary to understand the thinking, the behaviour and needs of Gen Z if the society wants to integrate them successfully in the processes of education, work, entrepreneurship and others. According to Seemiller and Grace (2017) not everyone born in a generational period shares the same values or experiences, they do share a common context that shapes their worldview. This is very crucial since ages are usually not as important for integration to specific groups like other attributes as thinking, culture or values. Gen Z are a digitally savvy generation with heavy use of technology, which they see as an instrument for them (Van den Bergh and Behrer, 2016). The beginning and the end of certain generation’s as depicted by Pew Research Center (2019) is provided on Figure 1. Figure 1: The generations defined Source: Pew Research Centre, 2019. Advances in Business-Related Scientific Research Journal, Volume 12, No. 1, 2021 15 Against this backdrop, this study aims to address the following research questions: - What are the Gen Z’s entrepreneurial characteristics? - Are the entrepreneurial characteristics related to the cultural aspects of the specific country similar among all Gen Z members in two research countries? - Are the entrepreneurial characteristics of Gen Z in two researched countries different and why? - How members of Gen Z perceive entrepreneurship and what do they expect from potential life as an entrepreneur? - Can entrepreneurial curiosity scale for students may be refined and become instrumental in understanding differences in entrepreneurship between cultures. This article is structured as follows: the literature on entrepreneurship and entrepreneurial curiosity is reviewed in the first two Sections. The method is detailed and context is provided along with relevant statistics in Section 3, which is followed by the Results section where the analysis outcomes are displayed. Findings are discussed in detail in Section 5 (i.e. Discussion, Conclusion, and answers to Research Questions) along with relevant theoretical and practical implications. Finally, the main contributions of the study are highlighted in the Conclusions sub-section, which also presents the limitations of the study and offers future research avenues. ENTREPRENEURIAL CURIOSITY Curious people are more likely to gather more information in their lives (e.g. Renner, 2006). Since knowledge is constructed from many data and different information, it is reasonable to predict, that curious people can have knowledge that will empower them to be more successful, also in entrepreneurship (e.g. Acs and Varga, 2005). Curiosity can empower young people with the data and information that can help with right decisions when faced with entrepreneurial stimuluses from the society or the environment. When an individual has a novel business/product concept about a certain issue, he/she can start to create a vision of how this can be realized. In that manner, Kirzner (1982) argued that numerous scholars are undoubtedly correct in linking entrepreneurship with the courage and vision necessary to create the future in an uncertain world. Since through mobile devices such as smartphones, fast broadband Internet access at home, school or even on move, Generation Z students have had access to more information than any other generation at their age (Seemiller and Grace, 2017). Consequently, they had the unique opportunity to establish a curious and open mind-set that would allow them to be successful at many areas including entrepreneurship. In this context, entrepreneurial curiosity is a significant Advances in Business-Related Scientific Research Journal, Volume 12, No. 1, 2021 16 concept and issue for entrepreneurs and the young generation who would like to become successful in different arias in the near future. Entrepreneurial curiosity is a positive emotional/motivational system oriented toward investigation in the entrepreneurial framework to learn tasks related to entrepreneurship and incorporate new experiences in order to improve business (Jeraj, 2012; Jeraj and Antončič, 2013; Jeraj 2014a, Jeraj, 2014b). Literature review revealed that entrepreneurial curiosity was connected in different ways with certain determinants from the field of entrepreneurship as entrepreneurial self-efficacy (Jeraj and Marič, 2013), innovativeness (Peljko et al. 2016), openness and company's growth (Jeraj et al. 2015), and several others. Further Jeraj (2014b) operationalized Pre- entrepreneurial curiosity measure, which is a specific factor in the broader field of entrepreneurial curiosity. Pre-entrepreneurial curiosity factor is composed only of entrepreneurial curiosity items that focus to pre-business activities and are necessary to be done before a company is establish of before an entrepreneur starts with a new project. Entrepreneurial curiosity measure was developed according to recommendations of Churchill (1979), Dawis (1987), DeVellis (2003), Gerbing and Anderson (1988), and Hinkin (1995). With the entrepreneurial curiosity measure, society can test individuals and encourage them to become active in entrepreneurship (Jeraj and Antončič, 2013). Since entrepreneurial curiosity measure was developed for individuals that already have some entrepreneurial experiences, authors of this paper decided to use another instrument for measuring entrepreneurial curiosity that was proposed by Jeraj et al. (2014) specifically for students with no or only few entrepreneurial experiences. The entrepreneurial curiosity measure for students was developed with the same procedure as the measure for entrepreneurial curiosity (Jeraj and Antončič, 2013) and tested on a pilot sample. The items were generated by students and the aim was that items would be as diverse as possible (e.g. DeVellis, 2016). After that the items were revised by 5 experts from the field of entrepreneurship. The ones which did not express directly connection to entrepreneurial curiosity were eliminated. After that similar items were modified and combined to present distinct, yet entrepreneurial meaning. The items were integrated into a questionnaire with 5 point Likert (1975) scale from strongly agree to strongly disagree. In the next step the pilot study was conducted in the sample of 50 students. Further the statistical analysis was conducted using Cronbach Alpha coefficient to choose items appropriate for entrepreneurial curiosity scale for students and to eliminate ones, which had low correlations in the frame of the instrument. Considering that this scale has eighteen items in total and have not been applied in a large scale before it can benefit from further testing and refinement. Moreover, elaborating the sub-dimensions of the entrepreneurial curiosity scale may lead to a scale that can offer deeper insights into this potentially multi-faceted construct. Advances in Business-Related Scientific Research Journal, Volume 12, No. 1, 2021 17 METHOD To attain the research aims, two separate survey studies were conducted in Slovenia and Turkey, the focal points of the study. Following these survey studies to measure the Gen Z’s entrepreneurial dispositions, two factor analysis (an exploratory followed by a confirmatory) were carried out by dividing the sample into two. Detailed information on the two countries is provided in the 3.1 Context sub-section which is followed by a detailed discussion on measures, measurement instrument, sampling and data collection. Context Two countries, Slovenia and Turkey, with unique economies and cultures were selected to carry out the study. Turkey, a predominantly Muslim country where liberal and Westernized ways are integrated into the culture, is situated at the crossroads between Asia and Europe. Turkey is the 19th largest economy in the world. Slovenia situated in the Central Europe on the other hand, is similarly strategically located by is smaller in size mainly attributable to the significantly lower population and ranks 85th largest in the world according to 2019 GDP data (World Bank, 2020). Both countries are present strongly on their domestic market as well as export to European Union, which is the main export target for both countries. Yet Slovenia has a significantly higher exports ratio to its GDP compared to Turkey, who relies more on the domestic market compared to Slovenia (Worldbank, 2020). According to the World Economic Forum Global Competitiveness Index 2015, Turkey and Slovenia rank very similarly at 55th and 56th places respectively. In terms of economic freedom Slovenia’s the 52nd freest in the 2020 Index of Economic Freedom with a score of 67.8 (Heritage, 2020). Slovenia, with a very good infrastructure, well-educated workforce, and strategic location has one of Central Europe’s higher per capita GDPs. Turkey’s economic freedom score on the other hand is slightly lower at 64.4, making its economy the 71st freest in the world. The resilience of Turkey’s economy is partly attributable to a dynamic and diversified private sector (Heritage, 2020). For both countries entrepreneurship is a significant driver of economic growth and is required for improving the competitiveness of each country in the global markets. Yet they are considered to be in different economic development phases; Slovenia innovation driven, Turkey Efficiency-Driven (Martínez-Fierro et al., 2016). Other differences are evident in culture as well as entrepreneurial activities. Further economic and demographic data is presented in Table 1. Advances in Business-Related Scientific Research Journal, Volume 12, No. 1, 2021 18 Table 1: Economic, demographic and entrepreneurial data about Slovenia and Turkey Slovenia Turkey (GDP) per capita figures on this page are derived from PPP WorldBank (2020) 40,344 (2020 estimates) 29,327 (2020 estimates) Unemployment rate OECD (2020a) 4,45 (2019) 13,7 (2019) Education Expenditure (%Bud.) Country Economy (2020) 11.75% (2016) 12.84% (2015) Exports % GDP Country economy (2020) 83.49% (2019) 22.76% (2019) Population Worldometer (2020) 2,078,957 (2020) 84,399,931 (2020) Self-employment rates As a percentage of total employment OECD (2015) 18.6 (2014) 34.0 (2014) Share of young people from 15 to 25 years Youth policies in EACEA (2017) and AA (2020) 11,3% (2017) 15.6% (2019) Total early-stage Entrepreneurial Activity Rates among Adults 2018 GERA (2019) 6.4 (Rank 38/48) 14.2 (Rank 15/48) Total early-stage Entrepreneurial Activity Rates among 18-24 Age Group 2018 (GERA, 2019) 3.1 (Rank 45/48) 14.3 (Rank 14/48) Entrepreneurial Intentions (% of adult non-entrepreneurs) (GERA, 2019) 15.3% (Rank 28/48) 29.7% (Rank 14/48) Source: As seen in table in different sectors. To better understand the culture in each country and to get an overview of the drivers of each culture, the Hofstede’s culture compass (The Hofstede Centre, 2020) developed upon research of Hofstede (2001) was used as seen in Figure 2. Figure 2: Cultural Comparison Source: The Hofstede Centre (2020). 71 27 19 88 49 48 66 37 45 85 46 49 0 10 20 30 40 50 60 70 80 90 100 Slovenia Turkey Advances in Business-Related Scientific Research Journal, Volume 12, No. 1, 2021 19 Several aspects of Turkish and Slovenian culture are provided in Table 2 with regards to Hofstede’s methodology to highlight the cultural aspects of each country (The Hofstede Centre, 2020). The cultural aspects may be instrumental in shaping the entrepreneurship landscape in each country. Table 2: Cultural Dimensions of Slovenia and Turkey Cultural Dimension Slovenia Turkey Power Distance “the extent to which the less powerful members of institutions and organisations within a country expect and accept that power is distributed unequally.” Score:71 Score: 66 Both countries have similar high scores indicating that individuals accept a hierarchical order in which everybody has a place. Structure is dependent, hierarchical and superiors are often inaccessible. Power is centralized, subordinates/employees expect to be told what to do and rely on their bosses and on rules. Individualism “the degree of interdependence a society maintains among its members.” Score: 27 Slovenia is a collectivistic society that manifest in a long-term commitment to the member ‘group’ that individuals belong. This may be an extended family, or extended relationships. Loyalty is paramount, and over-rides most other societal rules and regulations. The society fosters strong relationships where everyone takes responsibility for fellow members of their group. Score: 37 Turkey is a collectivistic society where people belong to in- groups (families, clans or organizations). Relationships has a moral base, which is prioritized over task fulfilment. Compared to Slovenia, Turkey is more individualist, indicating that people tend to think more for themselves and how they uniquely can be individually successful. Masculinity Describes which of the following motivates people more: wanting to be the best (Masculine) or liking what you do (Feminine). Score: 19 Slovenia is considered a fairly Feminine society. The focus is on “working in order to live”. People value equality, solidarity and quality in their working lives. In work life, managers strive for consensus and conflicts are resolved by compromise and negotiation. Incentives such as free time and flexibility are favoured. Score: 45 Turkey has an average score that is slightly on the feminine side. Conflicts are avoided in private and work life and consensus at the end is important. Leisure time, when the whole family and friends come together to enjoy life is important for Turks. Compared to Slovenia a higher score in this dimension indicates that the society in Turkey is more driven by competition, achievement and success. Uncertainty Avoidance “the extent to which the members of a culture feel threatened by ambiguous or unknown situations and have created Score: 88 Score: 85 Slovenia and Turkey both have very high uncertainty avoidance. There is an (emotional) need for rules and in order to minimize anxiety, people make use of a great deal of rituals. Codes of belief and behaviour are strict and intolerant of unorthodox behaviour and ideas. Individuals have an inner urge to work long and hard as time is considered as an important resource that equates with money. Precision and Advances in Business-Related Scientific Research Journal, Volume 12, No. 1, 2021 20 beliefs and institutions that try to avoid these” punctuality are the norm, innovation may be resisted and security is an important element in individual motivation. Long-term Orientation Describes how every society has to maintain some links with its own past while dealing with the challenges of the present and future. Score: 49 Score:46 Both Turkey and Slovenia rank in the middle in terms of maintaining some links with its own past while dealing with the challenges of the present and future. No country wide preference for either maintaining time-honoured traditions/norms or to encourage efforts in modern education as a way to prepare for the future is evident. Indulgence “the extent to which people try to control their desires and impulses, based on the way they were raised” Score: 48 Score:49 Slovenia and Turkey both has intermediate scores of 48 and 49, thus no preference is indicated in the extent to which individuals try to control their desires and impulses. Source: The Hofstede Centre (2020). Measures and Measurement Instrument The measure of entrepreneurial curiosity for students in the study was adapted from existing literature on entrepreneurship by Jeraj and Antončič (2013) and conceptualized according to Jeraj et al. (2014) as already presented in section 2. The measure and the related items are provided in Appendix A. Data Collection and Sampling Two survey studies were carried out in Turkey and Slovenia and a total of 1150 questionnaires were collected in the years 2018 and 2019. Out of this total, 1068 were left after semi-filled and low quality (e.g. all answers coded in the same way) forms were excluded from the study. Demographic attributes of the sample attained have been provided in Table 3. Table 3: Sample Demographics Slovenia Turkey Gender N % N % Total Sample 587 55% 481 45.0 Female 273 46.5% 309 63.6 Male 302 51.4% 172 35.8 Missing 12 - 3 - Age 18-19 73 12.7% 125 26.0 20-21 484 84.1% 226 47.0 22-25 18 3.1% 130 27.0 Advances in Business-Related Scientific Research Journal, Volume 12, No. 1, 2021 21 Missing 12 - 3 - Source: Own survey. In the current research there were 587 (55%) respondents from Slovenia and 481 (45%) from Turkey. In the Slovenian sample there were 273 female students (46.5%) corresponding of the total sample. Female respondents in Turkey on the other hand represented of 63.6% (309) students of the total sample. The highest number of students in the Slovenian sample were 20 to 21 year olds (84.1%). A similar situation was observed in Turkey where almost half of the respondents (47%) were in the same 20-21 age group. RESULTS The results of the data analysis are detailed in this section. The descriptive statistics of the items are provided in Appendix B. Exploratory Factor Analysis As a first step, an exploratory factor analysis (EFA) was carried out. As a rule of thumb in EFA analysis, a sample size 4 times the total number of items is deemed satisfactory whereas 10 times the total number of items is preferred in CFA (Chawla and Sondhi, 2016; Hair et al., 2010). Consequently, the sample size is adequate to carry out the factor analysis and the data obtained from the total sample (N=1068) was divided into two as suggested by Churchill (1979). Following random number generation with uniform distribution in SPSS, Sample 1 (n=533) was used to explore the factor structure of the scale through an exploratory factor analysis (EFA). The remaining data, Sample 2, (n=535) was utilized to confirm the validity and the reliability of the initial factor analysis and for further refinement. The EFA analysis was carried out using SPSS 21.0 while CFA was carried out on AMOS software. There were no missing data in the data-set so no imputation method was applied. KMO Sample Adequacy Test and Bartlett Sphericity tests applied in the beginning of the EFA value (KMO: 0.909) indicates that the sample size is sufficient. The result of the Bartlett test was 2  = 2779.9, df = 153, which is significant (p < .001) indicating that data obtained from the sample was suitable for factor analysis. EFA was carried out using principal components analysis and Varimax rotation. The choice of the number of factors in EFA was done by utilizing eigenvalues and the scree plot. There were four factors with eigenvalues over 1. Scree plot visualizes the eigenvalues of factors and highlights the potential elbow points as seen in Figure 3. Advances in Business-Related Scientific Research Journal, Volume 12, No. 1, 2021 22 Figure 3: EFA Scree Plot Source: Own survey. After a first run of EFA, the results of which are provided in Table 4, the items with factor loadings below 0.4 were removed from the analysis one by one and the analysis was re-run each time an item was removed (Hair et al. 2010; Tabachnick and Fidel, 2007). Moreover, items with loadings over 0.32 to multiple factors were removed with the following conditions: (a) less than 0.10 difference between multiple loadings exist (b) the item in questions do not have high content validity (Hair et al. 2010; Tabachnick and Fidel, 2007). Table 4: EFA Results First Run Component Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings Total % of Variance Cumulative % Total % of Variance Cumulative % Total % of Variance Cumulative % 1 5.794 32.191 32.191 5.794 32.191 32.191 3.558 19.769 19.769 2 1.457 8.097 40.288 1.457 8.097 40.288 2.131 11.841 31.610 3 1.086 6.033 46.321 1.086 6.033 46.321 2.098 11.655 43.265 4 1.024 5.687 52.008 1.024 5.687 52.008 1.574 8.743 52.008 5 .864 4.801 56.808 - - - - - - Source: Own survey. As a final step of the EFA, loadings between 0.4 and 0.5 are considered for removal one by one to refine the scale and improve the cumulative variance extracted figures. As a result, only two items (ENTC13 and ENTC16) are excluded from further analysis. The results of the final run of EFA is provided in Table 5. Advances in Business-Related Scientific Research Journal, Volume 12, No. 1, 2021 23 Table 5: EFA Results Final (Third) Run Component Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings Total % of Variance Cumulative % Total % of Variance Cumulative % Total % of Variance Cumulative % 1 4.997 31.234 31.234 4.997 31.234 31.234 3.431 21.444 21.444 2 1.455 9.093 40.328 1.455 9.093 40.328 1.940 12.125 33.569 3 1.155 7.219 47.547 1.155 7.219 47.547 1.826 11.410 44.979 4 1.046 6.540 54.086 1.046 6.540 54.086 1.457 9.107 54.086 5 .848 5.297 59.383 - - - - - - Source: Own survey. The four factors, which were obtained from the factor analysis, described the measurement model in the best way and explained 54.06 % of the total variance. First factor roughly explains 32%, second factor 9%, third factor 7% and lastly fourth factor 6% of the total variance. The cumulative variance explained value of 54% exceeds the minimum suggested variation of 50% (Hair et al., 2010; Tabachnick and Fidell, 2001). The factor loadings of the rotated solution along with item communalities are provided in Table 6. Table 6. Rotated factor loadings of the final EFA Item Component 1 2 3 4 Communalities EntC1 .692 .562 EntC2 .698 .542 EntC3 .468 .367 .468 EntC4 .565 .380 .459 EntC5 .635 .519 EntC6 .402 .445 .389 EntC7 .526 .363 .475 EntC8 .614 .511 EntC9 .796 .670 EntC10 .729 .592 EntC11 .803 .603 EntC12 .572 .392 .466 EntC14 .707 .615 EntC15 .710 .538 EntC17 .622 .599 EntC18 .661 .645 Source: Own survey. Confirmatory Factor Analysis The next step in the scale refinement process was to verify the four- dimensional structure resulting from the EFA. It is advised to conduct a CFA to confirm the proposed scale structure arrived via EFA (Costello and Osborne, 2005; Worthington and Whittaker, 2006). Consequently, the 16 items factor structure obtained in the EFA was applied to a measurement model in AMOS 21 and a confirmatory factor analysis (CFA) using the remaining sample (n=534) was carried out. Figure 4 shows the measurement model that was assessed and validated in terms of goodness of fit, internal reliability, convergent validity and discriminant validity of the factors using the CFA output. Advances in Business-Related Scientific Research Journal, Volume 12, No. 1, 2021 24 Figure 4: Measurement Model Source: Own survey. After an initial run of CFA, items with loadings below 0.5 are considered for removal from the model one by one. The analysis was re-run after item removing step. The good goodness of fit criteria of the final model with 15 items (ENTC6 removed) provided in Table 7 indicates a good fit of the measurement model and the data. Table 7. CFA Goodness of Fit Results Measures Values Perfect Fit Good fit 2  182,334 df 82 p 0.001 2  /df 2.223 2  /sd ≤ 3.00 3.00 < 2  /sd ≤ 8.00 RMSEA 0.048 0 ≤ RMSEA ≤ .05 .05 < RMSEA ≤ .08 RMSEA (.90) 0.039-0.057 SRMR 0.039 0 ≤ SRMR ≤ .05 .05 < SRMR ≤ .10 GFI 0.954 .95 ≤ GFI ≤ 1.00 .90 ≤ GFI < .95 AGFI 0.933 .90 ≤ AGFI ≤ 1.00 .85 ≤ AGFI < .90 CFI 0.948 .97 ≤ CFI ≤ 1.00 .95 ≤ CFI < .97 Source: Own survey. Advances in Business-Related Scientific Research Journal, Volume 12, No. 1, 2021 25 Chi-square test emerged as significant which normally indicates a poor fit. Yet it is known that this test is sensitive to the sample size. Considering the sample used in the analysis exceeds 500, this is an expected outcome. Further criteria were assessed to provide more accurate insights into the goodness of fit. 2  / df value of 2.223 that is below the 3 threshold indicates a very good fit. Similarly, RMSEA, SRMR, GFI and AGFI indicates very good fit. Considering all the goodness of fit indicators together, it can be concluded that the model data fit is at very good levels (Hooper, Coughlan and Mullen, 2008). The resulting items and the standardized factor loadings are provided in Table 8. Table 8: CFA Factor loadings Question # Item Factor Std. Loading EntC1 I explore new things to sell them. F1 .637 EntC4 I constantly research new things. F1 .620 EntC7 Highly ambitious projects are feasible. F1 .548 EntC8 I find planning of the business aims interesting. F1 .653 EntC14 I would describe myself as someone who actively seeks all possible information about different projects. F1 .702 EntC15 When a group is in search of the leader, I have a feeling, I can be the one. F1 .667 EntC18 I am extremely attracted to different innovative projects. F1 .687 EntC2 If I am unsatisfied with certain things, I think about their improvements. F2 .581 EntC3 I am proud of the results, based mainly on my work. F2 .577 EntC5 I always learn something from my mistakes; therefore they don't present a negative connotation to me. F2 .460 EntC12 Learning is a whole life process. F2 .441 EntC9 I want to be independent in my life. F3 .712 EntC10 I want to know how to earn money. F3 .640 EntC11 I am thinking how to improve an application on a phone I don't like. F4 .470 EntC17 I am interested in how different gadgets I use operate. F4 .688 Source: Own survey. Validity Analysis The validity of the constructs in this study was checked with regards to construct (i.e. convergent and discriminant) validity. Convergent validity was assessed by the magnitude and significance of the factor loadings of each indicator on the relevant latent factor. As illustrated in Table 8, almost all the loadings are greater than 0.5 (Gerbing and Anderson, 1988; Bagozzi and Yi, 1998) indicating good convergent validity (Hair et al., 2010). Discriminant validity was evaluated by comparing the squared root of average variance extracted (AVE) value of each factor to its correlations with other factors (Fornell and Larcker, 1981). As illustrated in Table 9, the square roots of AVEs were greater than the correlations with other constructs, indicating that the discriminant conditions are met for each factor. Advances in Business-Related Scientific Research Journal, Volume 12, No. 1, 2021 26 Table 9: Scale Validity Factor1 Factor2 Factor3 Factor4 Factor1 0.697 Factor2 0.542 0.689 Factor3 0.337 0.431 0.844 Factor4 0.443 0.319 0.187 0.799 Note: Square-roots of AVE are provided on the diagonal; correlations are provided below the diagonal. Source: Own survey. DISCUSSION AND CONCLUSION The four factors are contemplated using their relevant contributing items in this section. Moreover, differences in respondents’ perceptions in the two countries for each factor are also discussed in this section. These discussions provide insights into the differing entrepreneurial nature of Generation Z living in two different cultures in Europe. Table 10: Compare means independent samples test Mean s Turke y Sloveni a Mean Differenc e Sig. (2- tailed ) t Std. Error Differenc e 95% Conf.Interval Lowe r Uppe r F1 0.392 -0.321 0.714 0.000 12.40 5 0.058 0.601 0.826 F2 0.135 -0.111 0.246 0.000 4.030 0.061 0.126 0.366 F3 a -0.118 0.096 -0.215 0.001 -3.468 0.062 - 0.337 - 0.093 F4 -0.065 0.053 -0.117 0.056 -1.912 0.061 - 0.238 0.003 a . Scores for unequal variances are provided as the Levene’s test for equality of variances was significant (15.990, p<0.001). Source: Own survey. Elaboration of Factors Factor 1: Entrepreneurial Spirit and Leadership The first sub-dimension of Entrepreneurial curiosity incorporates the highest number of items and is considered as the main factor with the highest amount of variance explained. More complex in nature that the remaining factors, this factor and related items mainly reflect the respondents’ entrepreneurial spirit and leadership aspirations. Ability to take risk, an inherent characteristic of entrepreneurs (e.g. Xu and Ruef, 2004) is also considered under this factor. Ability to think about the details and carry out plans and assume the leader role are among the main elements that lead to success in business life, thus are among the relevant characteristics of Advances in Business-Related Scientific Research Journal, Volume 12, No. 1, 2021 27 entrepreneurs. Therefore this factor is names as the entrepreneurial spirit and leadership. Factor 2: Learning and Personal Development Orientation Factor 2, the second sub-dimension of entrepreneurial curiosity mainly reflects the respondents’ interest in learning new things and developing themselves. Four items such as `Learning is a lifelong process` contribute to make up this factor. The items also consider an individual’s progress and feelings of accomplishment and success as he/she becomes more knowledgeable and better in solving problems. Curiosity is considered among the elements of success in the entrepreneurship literature (e.g. Jeraj and Antončič, 2013) and is an important motivation that drives people towards personal development (e.g. Markey and Loewenstein, 2014). Factor 3: Materialistic Orientation Factor 3 is named as materialistic orientation as the two items that constitute this factor are related to earning money and live independently (using the money earned). This drive for materialism is among those elements that motivates young entrepreneurs (e.g. Urban, 2009). Materialistic orientation is a significant motive for entrepreneurs considering the changing structure of income distribution in developed and developing economies where the Top 10% is mainly constituted of entrepreneurs and business owners and gets more share of the available wealth (OECD, 2020b). Factor 4: Technical Curiosity Factor 4 consists of two items (11 & 17) that are related to the interest and curiosity of an individual towards new technology products and services. It reflects the way an individual thinks on improving existing technologies and come up with new ones. We name this factor as technical curiosity. This is a significant factor that will most likely increase in importance in the digitally transforming business ecosystem where technology companies lead the entrepreneurial landscape. Country Comparisons Materialistic Orientation and Technical Curiosity were found to be higher in Slovenian sample whereas Entrepreneurial Spirit and Leadership, and Learning and Personal Development Orientation were found to be higher in the Turkish sample. These differences may be attributed to various cultural and economic factors inherent to each country and are discussed in the following paragraphs. The higher entrepreneurial spirit may partly be attributed to the higher individualism of Turkish population compared to the Slovenian population (The Hofstede Centre, 2020). Moreover, the higher self-employment rate and unemployment rate (See Table 1) may have lead more young people towards thinking about launching their own ventures. The early-stage Entrepreneurial Activity (TEA) rate among adults also in 18-24 age group is Advances in Business-Related Scientific Research Journal, Volume 12, No. 1, 2021 28 higher in Turkey compared to Slovenia (rank 15/49 vs. 45/49) as highlighted in the Global Entrepreneurship Monitor 2018-19 report by Global Entrepreneurship Research Association (2019). The same report also highlights that Turkish population consider entrepreneurship in a more positive way as a good career choice (Rank 5/47) compared to Slovenian population (Rank 17/47) and Turks have higher entrepreneurial intentions (29.7% vs. 15.3%). Beside that entrepreneurial culture is much more developed and in the roots of a Turkish nation, since entrepreneurship is legal in Slovenia only from 1990. Before the break-up of socialism there were no significant entrepreneurial activity nor courses on entrepreneurship and/or small business management available at the Slovenian universities (Rebernik and Močnik, 1997). High factor scores in Learning and Personal Development Orientation within the Turkish sample may be related to several factors starting with the high unemployment rates in Turkey that motivate young people towards personal improvement and continuous learning to be able to stay competitive. Inherently, the competitive and growth mind-set is also promoted by higher masculinity (compared to Slovenia) in the Turkish culture (The Hofstede Central, 2020). Turkey is considered to have an ‘Improvement-Driven Opportunity/Necessity’ in entrepreneurial activity (Global Entrepreneurship Research Association, 2019). This motive promotes learning and trying to find ways to improve oneself as well as existing products and businesses. On the other hand, international research about entrepreneurial intentions among students (GUESSS) revealed that young Slovenians would like to become entrepreneurs and successors of family businesses but they are not prepared to work so hard for that aim as colleagues from other countries in the sample (Jeraj and Foštner, 2016). In terms of materialistic orientation, Slovenian sample scored higher compared to the Turkish sample. It is evident that Slovenians aspire to be independent in their life by learning how to earn money. They see entrepreneurship as a means to become less dependent to others. This is an interesting finding that contrasts with the Hofstede Centrals’ view that Turkey is a relatively less collectivist culture. This finding puts the spotlight on the changing values of younger generations such as Gen Z in Central Europe who want to be less collectivist and more autonomous. The higher Materialistic Orientation in Slovenia among Generation Z could be a consequence of political transformation of a regime from socialistic to capitalistic by the time of youth of their parents. Consumers weren’t able to choose among many alternative brands or products in Slovenia before 1990 and people usually could choose only among a couple of alternatives. Since capitalistic economy is mainly grounded on continuous consumption (Ritzer and Jurgenson, 2010) parents of Generation Z members could teach their children that they have much more opportunities compared to what they themselves had when young, especially in the frame of materialistic terms. On the other hand, media and marketing are communicating a vast range of choices what could establish this discrepancy between previous and next generation in relation to materialism in Slovenia. Advances in Business-Related Scientific Research Journal, Volume 12, No. 1, 2021 29 The higher technical curiosity of the Slovenian sample may have been triggered by the Central European location of Slovenia which is a neighbour of Austria and Italy. Another reason could be the fact that in Slovenia information technology access is more commonplace relative to Turkey. For instance, Slovenian population is 29.5% more likely to have internet access than in Turkey since approximately 58.3% of the population has internet access in Turkey whereas the same figure is 75.5% in Slovenia (My Life Elsewhere, 2020). A further reason could be the level of innovativeness which is connected to technological development. According to Country Economy (2020) Slovenia ranks 30th while Turkey ranks 50th in country innovation rankings for the year 2018. Moreover, the existing entrepreneurial landscape of each country differs and may get influenced or influence the technical curiosity of the younger population such as Gen Z. Indeed, in Slovenia a significantly higher share of entrepreneurial activity is in Information/Communications Technologies compared to Turkey (Global Entrepreneurship Research Association, 2019). Conclusion The current study provides insight into the entrepreneurship landscape among Gen Z in Europe by analysing evidence from two distinct markets, Slovenia and Turkey. Entrepreneurial curiosity measure for students that focuses on dispositions of potential entrepreneurs regarding pre-business activities and entrepreneurial mind-set have been used to elaborate entrepreneurship among this young generation. Results of the current empirical study on more than 1,000 respondents display thought-provoking differences between Slovenian and Turkish Generation-Z members’ perceptions of entrepreneurial curiosity and stress further opportunities that the two countries have in order to stimulate young people to be active in entrepreneurship. Differing from the existing literature, this study employs pre-entrepreneurial curiosity construct and collects evidence from two European markets and combines it with credible secondary information. In this manner, this study provides insights into the understanding of how members of Gen Z perceive entrepreneurship and their expectations from a potential life as an entrepreneur. One major implication of the current study is that the motives to become entrepreneurs are different in the two countries. The results can help policy makers (i.e. ministry of economy etc.) and education ecosystem to find those motives and specifically work with certain generation or even individuals. The present study and the conclusions drawn from the analyses are limited in several ways. First of all, the perceptions were measured via a questionnaire, a self-reported instrument. Future research founded upon measurable behaviours and/or experimental methods will be of value to complement the findings of this study. Secondly, the sample was chosen using a non-random sampling method, which is unfortunately the case with most similar studies on large generational cohorts. 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EntC5 I always learn something from my mistakes; therefore they don't present a negative connotation to me. EntC6 I think how can I satisfy the needs of people. EntC7 Highly ambitious projects are feasible. EntC8 I find planning of the business aims interesting. EntC9 I want to be independent in my life. EntC10 I want to know how to earn money. EntC11 I am thinking how to improve an application on a phone I don't like. EntC12 Learning is a whole life process. EntC13 I am ready to take a risk when the result of my work is really important. EntC14 I would describe myself as someone who actively seeks all possible information about different projects. EntC15 When a group is in search of the leader, I have a feeling, I can be the one. EntC16 I am prepared to work on projects that attract me without a break. EntC17 I am interested in how different gadgets I use operate. EntC18 I am extremely attracted to different innovative projects. Appendix B: Descriptive Statistics of Constructs Total N=1068 Turkey N=481 Slovenia N=587 Constructs Mean St. Dev. Mean St. Dev. Mean St. Dev. EntC1 3.61 1.070 4.09 .845 3.22 1.077 EntC2 4.20 .748 4.27 .724 4.15 .764 EntC3 4.35 .838 4.55 .743 4.19 .875 EntC4 3.54 .914 3.71 .884 3.40 .915 EntC5 3.82 .891 3.78 .899 3.84 .885 EntC6 3.63 .966 3.81 .923 3.48 .976 EntC7 3.78 .858 3.83 .893 3.73 .827 EntC8 3.77 .921 4.05 .896 3.55 .880 EntC9 4.49 .752 4.38 .805 4.58 .693 EntC10 4.45 .739 4.39 .751 4.51 .728 EntC11 3.08 1.211 2.96 1.192 3.28 1.218 EntC12 4.53 .793 4.59 .767 4.48 .810 EntC13 3.99 .836 4.10 .883 3.90 .785 EntC14 3.46 .974 3.76 .919 3.22 .950 EntC15 3.42 1.130 3.78 1.006 3.12 1.137 EntC16 3.89 .961 3.93 .942 3.86 .976 EntC17 3.85 1.003 3.79 1.042 3.90 .971 EntC18 3.82 .945 4.07 .901 3.61 .931