Fantasy of success, fear of failure and entrepreneurial choice: the moderating role of business vibrancy and failure experience

Abstract

Purpose

This study investigates the impact of internal expectations, such as fantasy of success and fear of failure and external factors, such as social environment and past experiences, on entrepreneurial choice.

Design/methodology/approach

Based on achievement motivation and social cognitive theories, the authors construct hypotheses and use secondary data from the Global Entrepreneurship Monitor (GEM) database and Economic Freedom Index report to empirically test the hypotheses. The authors also use propensity score matching to solve the endogeneity issue and test the robustness.

Findings

Internal expectations (fantasy of success and fear of failure) on business outcomes inversely affect entrepreneurial choices, with a vibrant business environment amplifying and past failure experience mitigating these effects.

Originality/value

Due to the economic recession, governments encourage small businesses. Thus, the complexity of individual entrepreneurial motivations and influencing factors necessitate deeper exploration. This study is one of the first research offering insights into entrepreneurial motivations from combined dimensions and providing theoretical support for strategies promoting public entrepreneurship.

Citation

Cheng, Y., Zheng, Y., Schiavone, F. and Escobar, O.R. (2024), "Fantasy of success, fear of failure and entrepreneurial choice: the moderating role of business vibrancy and failure experience", International Journal of Entrepreneurial Behavior & Research, Vol. 30 No. 11, pp. 331-359. https://doi.org/10.1108/IJEBR-10-2023-1103

1. Introduction

The recent economic recession has significantly impacted employment rates worldwide. According to the World Employment and Social Outlook: Trends 2023 report (International Labour Organization, 2023), the global unemployment rate has been projected to increase by 30 million people so far. The economic downturn has compelled workers into lower-quality jobs and made the pursuit of high-paying, decent jobs increasingly challenging. In response, entrepreneurial activities have been gathered attention as a potential means to boost employment rates. Governments are encouraging innovation and entrepreneurship to stimulate economic recovery, especially for small business. Many governments around the world implemented policies to encourage entrepreneurship and small business development as a way to mitigate the economic impact of the crisis. For instance, United States government launched Small Business Administration (SBA) loans, which provided low-interest loans to small businesses and entrepreneurs to help them stay afloat during the pandemic. The agency also offered debt relief and loan forgiveness programs to eligible borrowers. In 2020, the Chinese government launched a policy to support entrepreneurship and innovation, with a focus on small and medium-sized enterprises (SMEs). The policy aimed to create a more favorable business environment, provide funding and tax incentives, and encourage entrepreneurship in key industries such as technology and healthcare.

Hence, fostering entrepreneurial activities has become a pivotal strategy for enhancing employment rates, increasing citizens' income, mobilizing society, and promoting economic growth. Several governments have implemented policies to foster public entrepreneurship as a counter-measure against recession. For instance, during the 2020 pandemic, the Chinese government proposed the development of the “street vendor economy” to facilitate income generation through street vending, a form of public entrepreneurship.

Some entrepreneurs might be encouraged by the supportive policies and make decisions to start a business. Supportive policies can increase entrepreneurs' confidence in their ability to start and grow a business, especially if they feel that the government is providing a supportive environment. More importantly, when entrepreneurs see others who have successfully started and grown a business, they may be more likely to take the leap and start their own business. Starting a business can be a fulfilling experience for entrepreneurs, especially if they are passionate about their idea and want to make a positive impact on their community.

While governments are promoting public entrepreneurship, individual entrepreneurs confront a lot of uncertainties and high failure rates. According to data from Bureau of Labor Statistics (Mclntyre, 2020), 20% of small businesses fail in their first year and 70% of the business owners fail in their 10th year, which discourages people from starting a business, especially during a recession. Most people swing between two extremes regarding starting a business: impulsivity or fear of failure, especially for those entrepreneurs, who have failure experience of running a business. Balancing these two extremes is a complex challenge that entrepreneurs must navigate (Douglas, 2005).

To foster more rational entrepreneurship, it is crucial to study the factors influencing entrepreneurial motivation. This study aims to explore the factors that encourage individuals to engage in entrepreneurship, the experiences that shape mature and rational entrepreneurs, and the role the social environment plays in these dynamics.

Previous studies have predominantly discussed businesses of relatively large size (Shepherd et al., 2015). In contrast, our focus is on individual businesses, a unique case arising during economic recession. Decisions to start such businesses rely heavily on personal information, contrasting starkly with traditional entrepreneurial decisions made by top management groups based on collective capability.

Existing literature has identified various factors influencing people’s entrepreneurial intention or choice (Liñán et al., 2011; Schwarz, 2009; Quan, 2012), primarily dividing these into internal factors (i.e. risk preferences, cognition, gender, age, education, wealth level, past experiences, etc.) and external factors (i.e. social support, culture, economic policies, etc.). However, most of these studies focus on the influence from a single perspective, and few considers the interaction among the social environment, behavioral experience, and individual psychological factors.

Furthermore, previous studies have either focused on factors promoting or inhibiting entrepreneurship (Reddy, 2023; Cacciotti et al., 2016; Dutta et al., 2021; Chandler et al., 1992; Hmieleski et al., 2009), lacking a comprehensive examination of both. The study delves into the effects of the fantasy of success and the fear of failure on entrepreneurial decisions, considering these as psychological conflicting factors. The authors explore the influence of both on entrepreneurial choice from an internal perspective, incorporating the moderating effect of failure experience and business vibrancy from an external perspective.

This study mainly answers the research questions about (1) how individuals’ internal conflicting factors impact entrepreneurial decision-making and (2) how external factors alter the extent of these effects. The authors aim to expand the current understanding of entrepreneurial decision-making by considering the interplay of personal, behavioral, and environmental factors, moving beyond a singular focus on psychological or macroeconomic factors.

The authors construct hypotheses based on the achievement motivation theory and social cognition theory, exploring the factors influencing entrepreneurial motivation from a multidimensional perspective of individual psychological factors (fantasy of success and fear of failure), social environment (business vibrancy), and behavioral experience (failure experience). The authors use secondary data from the Global Entrepreneurship Monitor (GEM) database and the Economic Freedom Index report from the Heritage Foundation to construct a macro-micro mixed dataset including 92 countries and 874,312 valid samples (country and year coverage details reported in Appendix Table A1), and empirically test and validate the models. The authors also conduct additional tests, such as propensity score matching, changing dependent variables to do robustness checks.

The authors propose to make contributions in the domain of small businesses and public entrepreneurship. Firstly, the authors explore the factors influencing entrepreneurial decisions from the perspective of psychological conflicting factors, aiming to provide a scientific understanding of the motives that drive individuals to become entrepreneurs. This investigation sheds light on the internal factors that influence entrepreneurial decision-making. Secondly, the authors test the impact of external factors on the relationship between internal factors and entrepreneurial decisions. By examining how external factors such as market conditions, individual failure experience interact with internal motivations, the authors enrich the problem setting framework and broaden the understanding of the contextual conditions that shape entrepreneurial decisions. Finally, the authors develop insights from both entrepreneur’s perspective and government’s perspective. By considering the needs and challenges of entrepreneurs and aligning them with policy objectives, the authors formulate strategies and policies that promote public entrepreneurship.

The remainder of the paper is structured as follows: Section 2 provides literature review and concepts definition. Next, Section 3 elaborates the hypotheses construction, followed by the data introduction in Section 4. Next, Section 5 provides the empirical analysis and discussion on findings emerged from the results in Section 6. Finally, limitations and future study are presented in Section 7.

2. Related literature

2.1 Entrepreneurship choice

Entrepreneurial choice is influenced by mixed factors. Previous research has identified various internal and external factors. Internal factors include risk preference, cognition, and demographic elements (Kristiansen and Indarti, 2004; Ferreira et al., 2012). For instance, Cramer et al. (2002) suggest that individuals with low risk aversion are more inclined towards entrepreneurship, highlighting the importance of risk preference. Similarly, the cognitive aspects of entrepreneurial decision-making, including cognition about opportunities, risks, and personal abilities, have been emphasized (Shepherd et al., 2015). Demographic factors play a role as well, with Hurst and Pugsley (2015) finding a greater likelihood of entrepreneurship among wealthier individuals with refined taste. Gender and education also matter, with individuals showing more proactive entrepreneurial intentions (García and Moreno, 2010), and a positive correlation existing between education level and entrepreneurial intention (Liñán et al., 2011). External factors like social norms, culture, and economic policies significantly impact entrepreneurial intentions (Moriano et al., 2012; Castano et al., 2015; Yang, 2019), with Fossen and Steiner (2009) finding that tax reductions increase entrepreneurial willingness.

Despite the wealth of research, most studies have examined these factors in isolation. This study contributes to the literature by considering the effect of internal and external factors on entrepreneurial decisions jointly.

2.2 Influencing mechanism

2.2.1 Achievement motivation theory

Achievement motivation theory investigates the internal factors, such as achievement motivation, depending on the balance between the desire for success and fear of failure (McClelland and Atkinson, 1953). The theory suggests that individuals with a high need for achievement are motivated to succeed because of their desire for personal accomplishment, recognition, and self-fulfillment. Fantasy of success can be seen as a way to fulfill these intrinsic motivations, as individuals imagine themselves achieving success and experiencing the associated feelings of pride and satisfaction. Furthermore, the theory suggests that individuals with a high need for achievement are more likely to be motivated by feedback and recognition. Fantasy of success can provide a sense of recognition and validation, as individuals imagine themselves achieving success and receiving recognition for their accomplishments.

This theory has been instrumental in understanding why some individuals prefer entrepreneurship, which inherently involves risk-taking, over more conventional careers (Obschonka et al., 2018). Fantasy of success can be seen as a key component of the Achievement Motivation Theory, as it reflects the drive to succeed, the desire for personal accomplishment, and the need for recognition and self-fulfillment. By understanding the role of fantasy of success in the Achievement Motivation Theory, researchers and practitioners can better understand what motivates individuals to pursue entrepreneurial ventures and how to foster a culture of achievement and success.

Meanwhile, it also explains why some entrepreneurs may be more hesitant to start a new business, even with supportive policies in place. The fear of failure can be a significant barrier to starting a new business. Entrepreneurs who are afraid of failure may be less likely to take risks and pursue their business ideas, even if they have a high need for achievement.

2.2.2 Social cognitive theory

The social cognitive theory proposes a significant psychological framework that explains the interplay between cognitive, behavioral, and environmental influences (Bandura, 1986). This theory suggests a triadic reciprocal causation where individuals are both influenced by environmental and personal factors (Schunk and DiBenedetto, 2020), see Figure 1.

This theory providing insights into how individuals process information, form attitudes, and make decisions about entrepreneurship. It extends our understanding of motivation by accounting for the impact of past behavior and social environment on individual motivation beyond internal personal factors. For instance, Krueger and Brazeal (1994) employ the social cognitive theory to explain how perceived feasibility and desirability influence entrepreneurial intentions. Baron (2008) further uses this theory to elaborate on how cognitive mechanisms shape the discovery and exploitation of entrepreneurial opportunities. It highlights the importance of cognitive process, such as attention, perception, and memory, in shaping entrepreneurship decisions.

However, most of these studies have focused on the influence of social contexts and past behavior on entrepreneurial intentions and actions, leaving a gap in understanding how these factors interact with individuals' internal motivations, such as the desire for success and fear of failure.

2.3 Summary

While both achievement motivation theory and social cognitive theory have been widely applied in entrepreneurship research, integrating both can provide a more comprehensive understanding of entrepreneurial decision-making (Abbasianchavari and Moritz, 2021).

The authors fill this gap by integrating these theories to explore a key conflict in achievement motivation theory: the needs for achievement versus failure avoidance. The authors also investigate how individual behavior and the external social environment, as suggested by the social cognitive theory, influence the underlining mechanism.

This integrated approach allows us to consider not only the personal qualities and motivations of entrepreneurs but also the social and contextual factors that shape these choices. Thus, this study contributes to the existing literature by providing a more nuanced understanding of the complex interplay between individual motivations and contextual influences in entrepreneurial decision-making.

3. Hypotheses development

In the three-dimension interactive model of social cognitive theory, social models are one of the most important factors in the environmental dimension. The theory explains that social beings have a social comparison tendency, which leads individuals to compare with others. When people see others entrepreneurs more successful than themselves, they regard these successful individuals as role models and fantasize about achieving the same success by learning from their behavior, thus promoting individual entrepreneurship decision. This process also enhances individual self-efficacy (Schunk and DiBenedetto, 2020), that an individual’s behavioral motivation is positively influenced by social models, as the “Fantasy of Success.” According to social cognitive theory, “Fantasy of Success” refers to the individual’s expectation of achieving similar success by imitating successful stories and successful entrepreneurs.

One of the most readily available sources of social modeling in people’s daily lives is mass media. Hang (2020) notes that media, as a social tool, plays a significant role in shaping social decisions, cognition, and attitudes. Bibliometric analysis of entrepreneurship research in the media from 2005 to 2017 reveals a focus on the role of media coverage in fostering entrepreneurial spirit. Social Cognitive Theory and Parasocial Interaction Theory (Horton and Wohl, 1956) imply that media portrayals of successful individuals can shape individuals' beliefs about success by providing role models and creating emotional connections. This can lead to the formation of aspirations and beliefs about what is possible and desirable. For instance, media may portray successful entrepreneurs, such as, Elon Musk, as innovators and visionaries, which can influence individuals’ perception on what is required to achieve success.

Once individuals are exposed to mass media, they might be affected by social comparison suggested by Social Comparison Theory. Festinger (1957) suggests that individuals evaluate themselves by comparing their abilities, opinions, and behaviors to those of others. Media, including traditional and social media, provide individuals with a wide range of comparison targets, such as successful entrepreneurs. Exposure to media stories featuring successful entrepreneurs can lead individuals to engage in upward social comparisons, where they compare themselves to those perceived as more successful. This comparison process can shape individuals' aspirations and beliefs about their own potential for success. Furthermore, Cultivation Theory (Gerbner, 1969) suggests that long-term exposure to media, such as social media influencers, business news, reality TV shows, can shape individuals' perceptions of reality, including their beliefs about success, motivating individuals to strive for success.

A wealth of studies have investigated the promotional impact of successful business models shaped by the media on entrepreneurial intention (Van Trang et al., 2019; Nowiński and Haddoud, 2019; Kong et al., 2020). Abbasianchavari and Moritz (2021) find that social models can influence individuals' entrepreneurial intentions and behaviors in various ways. Regions with a high degree of entrepreneurial activities create more entrepreneur role models (ERMs) and consequently further increase entrepreneurial activity. Observing others increases the feasibility of starting a business and motivates more people to do so. Successful entrepreneurs and celebrities featured in media reports have become the most prevalent types of social models, which generates the fantasy of success to impact others’ decisions to take the plunge. The hypothesis is

H1.

The fantasy of success positively impacts entrepreneurship choices.

Brundin and Gustafsson (2013) posit certain emotions, including fear, impact entrepreneurs’ investment decisions. Fear of failure refers to the negative consequences of failure, including the negative emotions and pressure caused by being judged or perceived as a failure. Early personalism research believes that an individual’s fear of failure and risk aversion are intrinsic personality traits (Fenigstein et al., 1975). In the field of entrepreneurship, it is defined as an individual’s concern and fear about potential failure or adverse outcomes in the entrepreneurial process. Cacciotti et al. (2016) borrows the concept of opportunity cost in economics, defining fear of failure as “the fear of losing the opportunity for potential success.” With the development of social cognitive theory, studies find that an individual’s fear of failure is a dynamic process (Cacciotti et al., 2016). In addition to personal traits and social environment, individual’s experiences may also influence her fear of failure, linking the internal and external factors that affect fear of failure together.

Morgan and Sisak (2016) propose a model suggesting a complex interplay among an individual’s entrepreneurial inclination, fear of failure, personal traits, and environmental factors. Cacciotti et al. (2016) explore the correlation between situational characteristics, individual factors, and fear of failure. According to Ng and Jenkins (2018), a strong fear of failure can lead to delay or abandonment of entrepreneurial plans, although entrepreneurship education and experience can mitigate this effect. If the fear of failure becomes overwhelming in entrepreneurship, it can dissuade individuals from taking the associated risks. Based on social cognitive theory, anticipated outcomes and low self-evaluation due to fear of failure can diminish an individual’s confidence in their success (low self-efficacy), potentially deterring entrepreneurial choices. The hypothesis is

H2.

Fear of failure negatively impacts entrepreneurship choices.

According to social cognitive theory, business vibrancy can shape an entrepreneurial climate. Areas with active business activity often have robust competition and resource flow, influencing social modeling effects and the opportunity cost of starting a business. Thereby, business vibrancy may impact the effect of the “Fantasy of Success” and “Fear of Failure” on entrepreneurship decisions.

In vibrant business environments, individuals are more likely to engage in social comparisons (Wang and Li, 2017) and desire for success. Increased business vibrancy can enhance the effect that “Fantasy of Success” imposed on entrepreneurship decisions. Meanwhile, societal pressure and expectations can also be high, leading to a stronger desire for business success and further amplifying the “Fantasy of Success”. The hypothesis is

H3a.

High business vibrancy enhances the positive impact of the “Fantasy of Success” on entrepreneurship choices.

Research indicates that entrepreneurial opportunity cost influences individual motivation (Castaño et al., 2015; Herdjiono et al., 2017). A dynamic market, characterized by intense competition and diverse, high-quality employment opportunities, can raise the opportunity cost associated with high-risk entrepreneurship. Individuals might view employment with established, market-leading companies as a safer success route, thereby increasing the opportunity cost of starting a business and amplifying the “Fear of Failure” concern. Furthermore, in vibrant business environments, entrepreneurs may experience fierce competition and uncertainty, which can increase the risk of failure, augment entrepreneurial pressure and anxiety, and enhance the “Fear of Failure” concern. Thus, business vibrancy may intensify the negative impact of “Fear of Failure” on entrepreneurial decisions. The hypothesis is

H3b.

High business vibrancy amplifies the negative impact of the “Fear of Failure” on entrepreneurship choices.

Several studies have shown that entrepreneurial failure experience can have a negative impact on an individual’s motivation and confidence to start a new business venture. For example, a study by Baron (2008) found that entrepreneurs who had experienced failure in the past were less likely to start a new business venture, and that this effect was mediated by a decrease in their motivation and confidence. This is because failure experiences facilitate a more rational understanding of entrepreneurial risks and challenges. Through firsthand encounters with failure, individuals develop a deeper comprehension of the difficulties and risks involved in entrepreneurship, realizing that success is not easily attained. This realistic perspective serves to temper the “Fantasy of Success” and encourages more thoughtful consideration in making entrepreneurial decisions. In addition, failure experiences prompt individuals to assess their capabilities more cautiously and realistically. Such experiences foster objective judgment and rational self-assessment, making individuals less susceptible to external public opinion (Ucbasaran et al., 2013). Hayward et al. (2006) found that entrepreneurs who had experienced failure in the past were more likely to have a realistic view of their chances of success, which can weaken the positive impact of the “Fantasy of Success” on their entrepreneurship choices. Consequently, “Fantasy of Success” may have a diminished impact on entrepreneurial decision-making. The hypothesis is

H4a.

Entrepreneurial failure experience weakens the positive impact of the “Fantasy of Success” on entrepreneurship choices.

On the other hand, experiences of entrepreneurial failure have the potential to lessen the negative impact of the “Fear of Failure” on entrepreneurial decisions. Firstly, entrepreneurial failure experiences can enhance individual adaptability and resilience, equipping them to better handle future challenges. Individuals may come to realize that failure is not as catastrophic as initially imagined, leading to improved problem-solving skills and adaptability in uncertain environments. Secondly, failure experiences can foster a positive approach to dealing with failure. Through these experiences, individuals can learn from their mistakes, enhance their skillset, and navigate entrepreneurial risks and opportunities with greater rationality (Mueller and Shepherd, 2016). This increased knowledge can support more effective planning and implementation of future entrepreneurial endeavors, fostering determination and confidence throughout the process. Thirdly, entrepreneurial failure provides valuable feedback, enabling individuals to better understand their capabilities and limitations, and facilitating more rational and objective assessments of their circumstances, as well as the potential risks and opportunities involved in future entrepreneurial decisions. The hypothesis is

H4b.

Experience of entrepreneurial failure attenuates the negative impact of the “Fear of Failure” on entrepreneurship choices.

The theoretical research model is shown in Figure 2.

4. Background and data

To answer the research questions, the authors collect data from the Global Entrepreneurship Monitor (GEM) and the Economic Freedom Database by the Heritage Foundation. Specifically, micro-level data for entrepreneurs is from GEM’s Adult Population Survey (APS) database. This database, a core component of the multinational GEM project, provides rich data on global entrepreneurship at the micro-level. The APS database uses local resident registers and census data to construct sample frames, then employs a random number generator to select a representative sample. National macro-level data is obtained from the Economic Freedom Report, a publication by The Heritage Foundation and The Wall Street Journal, which encompasses most global countries and regions.

The authors first horizontally match and merge the two datasets using “year + country” as a key variable. Then, the authors truncate data by keeping the population samples at the working age between the 18 and 65. The final dataset, featuring a mix of macro and micro-level data, contains 93 countries from year 2005–2018, yielding a total of 874,312 valid samples.

4.1 Dependent variable

The study defines entrepreneurship choice (Bstart) based on the definition used in the study (Zheng et al., 2019), which is a binary variable that indicates whether entrepreneurs are trying to start a business. This indicator is cline to taking actions, which is more than the willingness or intention. In the GEM’s APS survey, the corresponding question for this variable is “Are you, alone or with others, currently trying to start a business, including any self-employment or selling any goods or services to others?” If the respondent’s answer is yes, the Bstart variable is assigned a value of 1; otherwise, it is assigned a value of 0. The indicator captures a more concrete and behavioral aspect of entrepreneurship, rather than just a willingness or intention, which may be more closely tied to the actual effects of fantasy of success and fear of failure. Also, trying to start a business is a more proximal outcome to the effects of fantasy of success and fear of failure, compared to willingness or intention. This proximity may make it easier to detect the effects of these psychological factors. By focusing on actual behavior, this indicator could be less susceptible to self-reporting biases, which can be a concern when measuring willingness or intention.

4.2 Independent variables

• (1)

  Fantasy of success (Fantasy). The “Fantasy of success” refers to the individual’s expectation of achieving similar success by imitating successful stories frequently reported by the media. Through frequent coverage of successful stories, the mass media creates a beautiful fantasy for individuals to achieve great success through entrepreneurship, like the protagonists in successful business stories. As mentioned earlier, the mass media can subtly implant this idealized image into information receivers. In the GEM’s APS survey, if the respondent believes that they frequently receive reports about successful business stories from the local mass media, then the Fantasy variable is assigned a value of 1; otherwise, it is assigned a value of 0.

• (2)

  Fear of Failure (Fearfail). The “Fear of failure” in this study is a binary variable that measures the degree of fear of entrepreneurial failure perceived by the survey respondents. The fear could come from the perception of the risk of entrepreneurial failure and its consequences, which can potentially affect individuals’ actions. The GEM’s APS survey cleverly combines this subjective perception with potential behavior by asking “Would fear of failure prevent you from starting a business?”. If the respondent’s answer is yes, the Fearfail variable is assigned a value of 1; otherwise, it is assigned a value of 0.

4.3 Moderating variables

• (1)

  Business Vibrancy (BusVib). The BusVib in this study reflects the level of freedom and competition in commercial activities in a country or region. In areas with high business vibrancy, it is easier to enter or exit the market. Competition could be more intense, since there exist more large and mature enterprises offering a rich and diverse range of business choices. This implies more career development options and employment opportunities. The authors measure the sub-index of business freedom in the Index of Economic Freedom. This index has taken into account factors such as market access, capital costs, and time costs of business activities. The original data ranges from 0 to 100, and the study standardizes the data, mapping it to the range of 0–1. The closer the value is to 1, the higher the level of business vibrancy, and the more active the business activities are, indicating a more competitive market.

• (2)

  Failure Experience (Failure). The variable “Failure” in this study specifically refers to entrepreneurial failure experience. In the GEM APS survey, the corresponding question for this variable is “Have you closed, discontinued or exited a business or other entrepreneurial activity that you previously owned in the past 12 months?” If the respondent’s answer is yes, the Failure variable takes a value of 1; otherwise, it takes a value of 0. Since the GEM APS survey question asks about entrepreneurial failure experience in the past 12 months, which implies that the respondent has already experienced failure before attempting to start a new business. By definition, the “Experience of Failure” variable takes a value of 1 if the respondent has experienced failure in the past 12 months, which means that the failure experience has already occurred before the respondent attempts to start a new business. Thus the variable can be regarded as a lagged variable, so it is less likely to be influenced by the current business startup attempt, which reduces the risk of temporal inconsistency.

4.4 Control variables

Given that individual characteristics may influence the corresponding entrepreneurship choices, the study has controlled individual level factors such as age, gender, income level, education level, business investment experience, social network resources, entrepreneurial skills, and opportunity recognition ability, together with the company level factors such as property rights protection, government integrity, investment freedom, currency stability, and tax burden in the country or region where the respondents were located. Since entrepreneurs need to obey local property rights systems, government relations, financing opportunities, and tax burdens. These factors are closely related to business activities and may also influence individual entrepreneurship choices in the region. In addition, the study uses dummy variables to control for country level fixed effects, see Table 1.

5. Empirical results

5.1 Empirical models

Since the dependent variable is a binary variable, the authors construct the following logit models to test the hypotheses:

• (1)

  The main effect models to test H1 and H2:

(1)Logit(Bstartij)=β0+∑i(αi×Controlij)+εij

(2)Logit(Bstartij)=β0+β1Fantasyij+∑i(αi×Controlij)+εij

(3)Logit(Bstartij)=β0+β2Fearfailij+∑i(αi×Controlij)+εij

(4)Logit(Bstartij)=β0+β1Fantasyij+β2Fearfailij+∑i(αi×Controlij)+εij

Model (1) is the baseline model, which contains all control variables and regulating variables. Model (2) and Model (3) include the two independent variables of Fantasy and Fearfail respectively. Model (4) incorporates two independent variables simultaneously into the baseline model.

Bstartij indicates whether an individual chooses to start a business or not; Controlij represents all control and moderating variables, both at the individual and national levels. Subscript i represents individual and j represents country.

• (2)

  The moderating effect models of “Business Vibrancy” to test H3a and H3b:

(5)Logit(Bstartij)=β0+β1Fantasyij+β2Fearfailij+β3BusVibij+∑i(αi×Controlij)+εij

(6)Logit(Bstartij)=β0+β1Fantasyij+β2Fearfailij+β3BusVibij+β4Fantasyij×BusVibij+∑i(αi×Controlij)+εij

(7)Logit(Bstartij)=β0+β1Fantasyij+β2Fearfailij+β3BusVibij+β4Fearfailij×BusVibij+∑i(αi×Controlij)+εij

(8)Logit(Bstartij)=β0+β1Fantasyij+β2Fearfailij+β3BusVibij+β4Fantasyij×BusVibij+β5Fearfailij×BusVibij+∑i(αi×Controlij)+εij

Model (5) is the main effects model. Model (6) and Model (7) include the interaction term of Fantasy of Success and Fear of Failure with the moderating variable of Business Vibrancy respectively. Model (8) includes both independent variables and the interaction terms with the moderating variable of “Business Vibrancy”.

• (3)

  The moderating effect models of “Failure Experience” to test H4a and H4b:

(9)Logit(Bstartij)=β0+β1Fantasyij+β2Fearfailij+β3Failureij+∑i(αi×Controlij)+εij

(10)Logit(Bstartij)=β0+β1Fantasyij+β2Fearfailij+β3Failureij+β4Fantasyij×Failureij+∑i(αi×Controlij)+εij

(11)Logit(Bstartij)=β0+β1Fantasyij+β2Fearfailij+β3Failureij+β4Fearfailij×Failureij+∑i(αi×Controlij)+εij

(12)Logit(Bstartij)=β0+β1Fantasylij+β2Fearfailij+β3Failureij+β4Fantasyij×Failureij+β5Fearfailij×Failureij+∑i(αi×Controlij)+εij

Model (9) is the main effects model. Model (10) and Model (11) include the interaction term of Fantasy of Success and Fear of Failure with the moderating variable of Failure Experience respectively. Model (12) includes both independent variables and the interaction terms with the moderating variable of “Failure Experience”.

5.2 Results

Descriptive statistics of the variables and the correlation matrix are presented in Table 2 and Table 3. It can be observed that 17% of the overall sample chose entrepreneurship, while 83% did not, which is roughly in line with the real-life situation and confirms the effectiveness of the GEM survey’s random sampling procedure. The age distribution of the sample ranges from 18 to 65 years old. Additionally, the macro-level variables have been standardized with a mean of 0.

5.2.1 The impact of fantasy of success on entrepreneurship choice

The regression results of the effects of Fantasy of Success and Fear of Failure on Entrepreneurship Choice are shown in Table 4. Column (1) presents the baseline model regression results that include all control variables and moderating variables. The coefficients of each control variable are significant and consistent with previous research conclusions. Columns (2) indicates Fantasy of Success has positive effect on individuals’ entrepreneurial decisions at 1% significance level, while Fear of Failure has negative effect on individuals’ entrepreneurial decisions, shown in column (3). Column (4) includes both variables and the magnitudes of the coefficients of the two independent variables remain same. The results support hypotheses H1 and H2.

5.2.2 The moderating effect of business vibrancy

Table 5 shows the moderating effect of Business Vibrancy on the main effects. The regressions include the interaction terms of Fantasy and the moderating variable Business Vibrancy (BusVib), and of Fearfail and Business Vibrancy (BusVib). Column (1) presents the regression results of the main effects including Business Vibrancy. Columns (2) and (3) show the regression results including the interaction terms, respectively. Column (4) show results including both interaction terms in the main effect full model.

The coefficient of the interaction term in column (2) is positive at 1% significance level, indicating that Business Vibrancy enhances the positive effect of Fantasy of Success on entrepreneurship decisions, supporting hypothesis H3a. The coefficient of the interaction term in column (3) is negative, indicating that Business Vibrancy amplifies the negative effect of Fearfail on entrepreneurship decisions, supporting hypothesis H3b. Results in column (4) show the consistency of the results.

5.2.3 The moderating effect of entrepreneurial failure experience

Table 6 shows the moderating effect of Failure Experience on the main effects. The regressions include the interaction terms of Fantasy and the moderating variable Failure Experience (Failure), and of Fearfail and Failure Experience (Failure). Column (1) presents the regression results of the main effects including Failure Experience. Columns (2) and (3) show the regression results including the interaction terms, respectively. Column (4) show results including both interaction terms in the main effect full model.

The coefficient of the interaction term in column (2) is negative at 1% significance level, indicating that Failure Experience weakens the positive effect of Fantasy of Success on entrepreneurship decisions, supporting hypothesis H4a. The coefficient of the interaction term in column (3) is positive, indicating that Failure Experience weakens the negative effect of Fearfail on entrepreneurship decisions, supporting hypothesis H4b.

5.3 Robustness checks

5.3.1 Changing the dependent variable

The authors replace the dependent variable to examine the robustness. In the previous analysis, the dependent variable “Bstart” from the GEM APS survey emphasizes whether individuals are currently engaged in entrepreneurial activities. Considering that entrepreneurship is a complex task that requires a lot of preparation, The authors use the variable Entrepreneurship Intention (Futsup) from the APS survey as the dependent variable as a substitution. The survey question corresponding to the variable is: “You are, alone or with others, expecting to start a new business, including any type of self-employment, within the next three years?” This question incorporates entrepreneurs' future intension of starting a business. The results are shown in Table 7 and Table 8, which are consistent with the previous analysis, supporting the hypotheses proposed in the previous section.

5.3.2 Sub-sample test of different economic development levels

Countries with different levels of economic development have different economic and industrial structures, and people’s attitudes towards entrepreneurship could be different as well. According to the World Bank’s classification of the economic development levels of countries worldwide, the study divides the sample into four sub-samples: low-income, lower-middle-income, upper-middle-income, and high-income areas. The authors rerun the analysis based on the four groups of datasets. The results are shown in Table 9 and Table 10, and the regression results generally support the conclusions of the previous analysis. The signs and magnitudes of the coefficients are consistent with the previous results. More specifically, as the country’s level of economic development increases, the negative impact of fear of failure on entrepreneurship choice becomes stronger. The possible explanation could be that the opportunity cost of starting a business is smaller in areas with poor economic development, because many people may face the dilemma of “either being unemployment or starting a business”. It just as Karl Marx said, “The authors have nothing to lose but our chains”, or as the Chinese saying goes, “the barefoot are not afraid of those wearing shoes”.

5.3.3 Addressing potential endogeneity issues

The authors of this study also address the potential issue of endogeneity arising from reverse causality. Hypothesis 1 suggests that mass media influence the audience’s perception of success through frequent reports on successful businesses, thereby increasing the likelihood of individual entrepreneurial choices. However, based on the selective attention theory (Broadbent, 1958), individuals who already have a predisposition towards entrepreneurship may pay more attention to media coverage of successful business stories, leading to an amplified perception of media coverage frequency. This theory implies that attention is limited, and individuals selectively filter external environmental information based on their subjective goals. This phenomenon is often referred to as the filter theory. To mitigate the potential problem of reverse causality, this study adopts a method similar to the study conducted by Zhou et al. (2021) and utilizes propensity score matching (PSM) to reduce the impact of endogeneity on the hypothesis.

Firstly, the survey sample is divided into treatment and control groups based on individuals' self-perceived exposure to mass media reports on successful businesses (Fantasy of Success = 1/0). Nearest neighbor matching is employed to calculate the propensity score, matching the treatment group with a control group sample that exhibits no significant differences in other characteristics except for the influence of media shaping the fantasy of success. The matched sample is then utilized for regression analysis. The following steps outline the process: a logistic regression model is constructed with the binary variable “fantasy of success” as the dependent variable and individual-level control variables as independent variables to calculate the PSM score. The second step involves conducting a balance test. The results in Table 11 demonstrate no significant differences between the matched treatment and control groups, indicating successful matching. Finally, a regression analysis is performed on the matched sample to examine the relationship between the fantasy of success and entrepreneurial choice. The results in Table 12 reveal that the coefficient of the fantasy of success remains significant, providing support for the hypothesis put forth in this study.

6. Discussion

This study is motivated by the government’s policy of promoting small businesses, acknowledging the complex nature of the decision-making process for entrepreneurs when starting a business. The authors seek to examine the mechanisms underlying entrepreneurs’ decisions from a comprehensive perspective.

The study incorporates the achievement motivation theory and the social cognitive theory to encompass both positive and negative factors, as well as internal and external factors, which collectively influence entrepreneurs’ decision-making process. Then the authors empirically validate the significance and explanation of the research model.

The proposed model includes two independent variables (fantasy of success and fear of failure), two moderators (failure experience and business vibrancy) and one dependent variable (entrepreneurship choice). The results first show fantasy of success has positive effect on the entrepreneurship choice and fear of failure has negative effect, which are consistent with the existing literature (for example, Gielnik et al., 2014; Morgan and Sisak, 2016). The paper also highlights that high business vibrancy enhances the positive impact of fantasy of success on entrepreneurship choices, meaning that when the business environment is active and thriving, individuals are more likely to be influenced by their fantasies of success when making entrepreneurial choices. On the other hand, high business vibrancy also amplifies the negative impact of fear of failure on entrepreneurship decisions, indicating that in a vibrant business environment, individuals are more strongly discouraged by their fear of failure.

Furthermore, the results show experience with entrepreneurial failure weakens the positive impact of fantasy of success on entrepreneurship choices. This suggests that individuals who have previously experienced entrepreneurial failure may be less influenced by their fantasies of success when making entrepreneurial decisions. Similarly, experience with entrepreneurial failure attenuates the negative impact of fear of failure on entrepreneurship choice, meaning that individuals who have experienced failure in the past may be less deterred by their fear of failure when considering entrepreneurial opportunities.

These findings contribute to our understanding of the psychological factors that influence entrepreneurial decision-making and highlight the importance of considering contextual factors such as business vibrancy and personal experiences with failure. The study goes beyond examining individual psychological factors by investigating the role of business vibrancy as a moderator. This adds a valuable contribution to the literature by considering the contextual influence on entrepreneurial decision-making.

6.1 Theoretical contributions

The study aims to contribute to the existing literature in three significant ways. Firstly, while previous studies have primarily focused on examining either the promoting factors or the suppressing factors of entrepreneurship (Cacciotti et al., 2014; Cramer et al., 2002; Engel et al., 2021; Kong et al., 2020), the authors take a comprehensive approach by considering both positive and negative effects on entrepreneurial decisions. By utilizing the achievement motivation theory, the authors explore the psychological conflicts inherent in these opposing effects and provide a logical justification for their examination. Secondly, prior research on entrepreneurial choice has predominantly focused on a single dimension, such as the psychological dimension (Rauch et al., 2007; Vignoles et al., 2008; Tubadji et al., 2021) or the environmental dimension (Li and Zhang, 2010; Wang and Li, 2017; Secundo et al., 2021). In contrast, this study integrates both psychological and environmental factors by employing the social cognitive theory. This comprehensive approach enhances the research framework of entrepreneurial choice by deepening and expanding the understanding of the various impacts on such decisions. Thirdly, the authors examine the moderating effect of failure experience on entrepreneurial decisions, which provides valuable insights into how past entrepreneurial failures can influence subsequent decision-making processes. The study’s focus on the moderating roles of business vibrancy and failure experience adds a new layer of complexity to the understanding of entrepreneurial decision-making. These contributions expand understanding, enrich the research framework, and shed light on the complexities of entrepreneurial decision-making.

6.2 Practical implications

This study offers several key implications that can be drown from the research findings:

• (1)

  Entrepreneur perspective:

At the individual entrepreneur level, it is crucial to view entrepreneurship as a long and enduring journey rather than a high-stakes battle. This perspective requires important mindset adjustments and continuous accumulation of experience. Entrepreneurs should avoid making impulsive decisions driven solely by over-enthusiasm or paralyzing fear. Striking a balance between the allure of success fantasy and the fear of failure is essential. It is important to base decisions on practical experience and rational thinking.

Embrace Reality: While success fantasies can ignite the drive for achievement and influence entrepreneurial choices, it is vital for entrepreneurs to avoid excessive and unrealistic expectations. Instead of fixating on overnight success, it is advisable to adopt a mindset grounded in rational thinking. Objectively evaluating one’s abilities and potential risks, and taking steps to mitigate those risks, becomes crucial. The focus should be on gaining practical experience, starting small, and prioritizing consistent learning and exploration, even in the face of failure.

Confront Failure and Fear: Many entrepreneurs experience fears stemming from self-doubt. To overcome these fears, it is advisable to begin with familiar industries or businesses, engage in low-cost trials, and actively build social capital. Avoiding excessive reliance on debt-financed entrepreneurship can help prevent the entrepreneurial journey from becoming a high-stakes gamble.

These implications highlight the need for entrepreneurs to adopt a balanced and realistic approach to decision-making. By embracing practical experience, managing expectations, and addressing fears and failures proactively, entrepreneurs can enhance their chances of long-term success and navigate the challenges of entrepreneurship more effectively.

• (2)

  Government perspective

From a governmental perspective, promoting public entrepreneurship goes beyond being a strategic endeavor—it is also a social responsibility. To foster a healthy and sustainable entrepreneurial culture, the focus should be on providing scientific and educational guidance rather than relying solely on promotional campaigns. Excessive promotion of entrepreneurship without proper guidance can amplify irrational factors, leading to blind entrepreneurship and wastage of resources. To address this, the following considerations should be taken into account:

Balanced Entrepreneurship Education: Prioritize the balanced development of entrepreneurship quality education and skills education. Quality education in entrepreneurship cultivates entrepreneurial awareness, mindset, and skills, while skills education emphasizes the ability to handle situations and solve problems in entrepreneurial practice. Establishing entrepreneurial training platforms can popularize entrepreneurial education and enable a more rational approach to starting businesses.

By emphasizing a balanced approach to entrepreneurship education and providing practical platforms for new entrepreneurs to gain hands-on experience, the government can promote a more thoughtful and rational approach to entrepreneurship. This approach ensures that aspiring entrepreneurs are equipped with the necessary skills, knowledge, and mindset to make informed decisions and contribute to the development of a sustainable entrepreneurial ecosystem.

Value of Failure Education: Given the global prevalence of high failure rates in entrepreneurship, it is important for governments to encourage society and the knowledge industry to extract valuable lessons from failed entrepreneurship cases (Ripollés and Blesa, 2023). Promoting in-depth research and analysis of these cases by academic institutions, think tanks, and media can lead to the identification of improvement suggestions and strategies. This approach helps to reduce failure risks and minimize the waste of social resources associated with unsuccessful ventures.

Support for Failed Entrepreneurs: Establishing a talent pool that includes individuals who have experienced failure but still possess an innovative spirit, entrepreneurial passion, and potential for re-entrepreneurship is crucial. The government should provide necessary resources such as funding, information, policies, technologies, and low-cost professional assistance to support their re-entrepreneurial endeavors. This support enables them to start again and contribute their valuable experience to new initiatives. These individuals can join other entrepreneurial teams as key members or become part of entrepreneurial advisory teams, fully leveraging their knowledge and insights. Recognizing the value of failed entrepreneurs' experiences enhances the appreciation for their human capital, guides societal attitudes, reduces the cost of entrepreneurial failure, and encourages ambitious entrepreneurs to persevere in pushing forward innovation and entrepreneurship.

6.3 Limitations and future research

This study offers contributions to both achievement motivation theory and social cognitive theory from both theoretical and practical perspectives. The utilization of a large sample dataset across multiple countries enhances the robustness of the empirical findings in this study. However, there are certain limitations of the dataset that prevent obtaining more detailed information beyond secondary data.

Firstly, the dataset does not distinguish between different types of businesses. There are distinctions between innovative entrepreneurship driven by individuals with an entrepreneurial spirit and survival entrepreneurship pursued by individuals seeking livelihood. Understanding these differences can provide valuable insights as the nature of these businesses may impact the evaluation of dependent variables.

Secondly, the responses in the dataset only indicate whether individuals choose to start a business or not, without capturing the degree of willingness to do so. Introducing a scale variable could provide more nuanced information about the likelihood of individuals taking the entrepreneurial plunge, leading to more sophisticated results.

Lastly, the study primarily focuses on objective conditions such as social resources and employs a quantitative empirical research method based on secondary data. Future research can explore additional dimensions by incorporating qualitative research methods such as interviews and case studies. Combining qualitative and quantitative approaches can enrich the research materials, enhance the completeness of information, and provide a more comprehensive understanding of entrepreneurial choices.

These limitations present opportunities for future research. Identifying different types of entrepreneurship, incorporating scale variables to assess willingness, and integrating qualitative research methods can further advance the understanding of entrepreneurial decision-making processes and provide a more comprehensive analysis of the subject matter.

7. Conclusions

Drawing from achievement motivation theory and social cognitive theory, this study investigates how psychological and socio-environmental factors jointly influence entrepreneurial decisions. The authors argue that internal motivations, both positive and negative, directly impact entrepreneurs' decisions to start a business. Additionally, a stimulating business environment amplifies the effects of these motivations, shaping entrepreneurial choices in both directions. This suggests that in a dynamic business environment, individuals are more susceptible to the allure of success and the fear of failure, influencing their entrepreneurial decisions accordingly. Significantly, the study reveals that experiences of entrepreneurial failure can mitigate the influence of these effects on entrepreneurial choices. This implies that individuals who have encountered failure are less swayed by success fantasies and more resilient to fear of failure, ultimately impacting their inclination towards entrepreneurship. The empirical findings reinforce these tensions.

These findings offer valuable insights for both entrepreneurs and policymakers. Entrepreneurs can gain a better understanding of their internal psychological conflicts and the influence of their social environment on their entrepreneurial decisions, thereby enhancing their chances of success. Moreover, individuals with past entrepreneurial failure experiences should consciously adjust their attitudes and mental states to overcome the fear of failure and embark on the entrepreneurial path once again. For policymakers, fostering a supportive entrepreneurial environment and providing policy support can boost individuals' confidence in entrepreneurship, reduce entrepreneurial risks, and nurture the growth of entrepreneurial activities. Finally, the innovative framework and methods employed in this research present fresh perspectives and serve as a reference for future studies in this domain.

Figures

Figure 1

Theoretical model of social cognitive theory

Figure 2

Conceptual framework

Table 1

Variable definition

Types	Variable names	Variables	Measurement	References
Dependent variable	Entrepreneurship Choice	Bstart	If an individual chooses to start a business, the value is 1; otherwise, it is 0	Zheng et al. (2019)
Independent variable	Fantasy of Success	Fantasy	If an individual frequently receives successful business stories from the media, the value is 1; otherwise, it is 0	Byrne et al. (2019)
	Fear of Failure	Fearfail	If fear prevents individuals from starting businesses, the value is 1; otherwise, it is 0	Cacciotti et al. (2016)
Moderating variable	Failure Experience	Failure	If the individual has entrepreneurial failure experience, the value is 1; otherwise, it is 0	Kong et al. (2020)
	Business Vibrancy	BusVib	Standardize the Heritage Foundation’s business freedom data
Individual level control variables	Age	Age	Age of individuals in the APS	Yang (2019)
	Sex	Sex	Individual sex in the APS. The value is 1 for males and 0 for females	Zheng et al. (2019)
	Income Level	Income	The individual income level in APS is 1 for the bottom 33%, 2 for the middle 33%, and 3 for the upper 33%	Yang (2019)
	Education Level	Educ	The individual level of education in APS ranges from 1 to 5 for never graduated from elementary school, elementary school, middle school, high school, and college, respectively	Hurst and Pugsley (2015)
	Investment Experience	Busang	If the individual in APS have investment experience, the value is 1, otherwise 0	Ferreira et al. (2012)
	Social Networks	Knowent	If the individual in APS knows an entrepreneur, the value is 1; otherwise, it is 0	Zheng et al. (2019)
	Entrepreneurial Skills	Suskill	If the individual in APS has sufficient entrepreneurial skills, the value is 1; otherwise, it is 0	Buchanan and Di Pierro (1980)
	Opportunity Recognition Ability	Opport	If the individual in APS believes that there are suitable entrepreneurial opportunities within 6 months is 1, otherwise it is 0	Shepher et al. (2015)
Country level control variables	Property Right Protection	PropRights	Standardize the Heritage Foundation’s Property Right Protection data	Yang (2019)
	Government Integrity	GovInt	Standardize the Heritage Foundation’s Government Integrity data
	Investment Freedom	InvFree	Standardize the Heritage Foundation’s Investment Freedom data
	Monetary Stability	MonFree	Standardize the Heritage Foundation’s Monetary Stability data
	Tax Burden	TaxBurden	Standardize the Heritage Foundation’s Tax Burden data. A higher index means a lower tax burden	Fossen and Steiner (2009)

Source(s): Table created by authors'

Table 2

Descriptive statistics of the whole sample

Variable names	Variables	N	Mean	SD	MIN	MAX
Entrepreneurship choice	Bstart	874,312	0.170	0.370	0	1
Fantasy of success	Fantasy	874,312	0.600	0.490	0	1
Fear of failure	Fearfail	874,312	0.400	0.490	0	1
Failure experience	Failure	874,312	0.050	0.220	0	1
Business vibrancy	BusVib	874,312	0	1.020	−3.010	2.360
Age	Age	874,312	40.19	13.79	18	65
Sex	Sex	874,312	0.530	0.500	0	1
Income level	Income	874,312	2.060	0.820	1	3
Education level	Educ	874,312	3.040	1.080	1	5
Investment experience	Busang	874,312	0.070	0.250	0	1
Social networks	Knowent	874,312	0.420	0.490	0	1
Entrepreneurial skills	Suskill	874,312	0.550	0.500	0	1
Opportunity recognition ability	Opport	874,312	0.440	0.500	0	1
Property right protection	PropRights	874,312	0	1	−2.480	1.700
Government integrity	GovInt	874,312	0	1	−2.250	2.150
Investment freedom	InvFree	874,312	0	1	−3.070	1.490
Monetary stability	MonFree	874,312	0	1.010	−4.770	2.330
Tax burden	TaxBurden	874,312	0	1	−3.060	2.230

Source(s): Table created by authors'

Table 3

The correlation matrix

	Bstart	Fearfail	Fantasy	Failure	BusFree	Age	Sex
Bstart	1
Fearfail	−0.084***	1
Fantasy	0.080***	−0.005***	1
Failure	0.123***	−0.017***	0.029***	1
BusFree	0.105***	0.027***	−0.043***	−0.054***	1
Age	0.101***	0.009***	−0.009***	−0.011***	0.008**	1
Sex	0.056***	−0.069***	−0.004***	0.017***	0.007*	−0.004	1
Income	0.043***	−0.042***	−0.011***	−0.003***	0.039***	0.033**	0.084***
Educ	−0.001	−0.011***	−0.052***	−0.019***	0.199***	−0.094**	0.029***
Busang	0.113***	−0.040***	0.023***	0.119***	0.023**	−0.007**	0.054***
Knowent	0.180***	−0.049***	0.066***	0.085***	0.065***	−0.110**	0.080***
Suskill	0.236***	−0.154***	0.076***	0.116***	−0.096**	−0.042**	0.114***
Opport	0.187***	−0.099***	0.149***	0.056***	−0.037***	−0.078**	0.047***
PropRights	−0.140***	0.027***	−0.062***	−0.063***	0.641***	0.014	0.011
GovInt	−0.133***	0.024***	−0.051***	−0.062***	0.660***	0.079	0.014
InvFree	−0.109***	0.026***	−0.089***	−0.049***	0.532***	0.009	−0.001
MonFree	−0.106***	0.038***	−0.047***	−0.050***	0.447***	0.007	−0.001
TaxBurden	0.168***	−0.047***	0.108***	0.066***	−0.396***	−0.015	0.001

	Income	Educ	Busang	Knowent	Suskill	Opport	PropRights
Income	1
Educ	0.271***	1
Busang	0.074***	0.049***	1
Knowent	0.118***	0.086***	0.143***	1
Suskill	0.094***	0.061***	0.112***	0.244***	1
Opport	0.066***	0.022***	0.082***	0.212***	0.213***	1
PropRights	0.014***	0.190***	−0.019***	−0.056***	−0.111***	−0.058***	1
GovInt	0.023***	0.206***	−0.015***	−0.043***	−0.106***	−0.058***	0.410***
InvFree	0.002	0.108***	−0.008***	−0.075***	−0.062***	−0.060***	0.327***
MonFree	−0.018***	0.113***	−0.019***	−0.033***	−0.079***	−0.056***	0.663***
TaxBurden	−0.005***	−0.108***	0.056***	0.067***	0.083***	0.102***	−0.532***

	_GovInt	InvFree	MonFree	TaxBurden
GovInt	1
InvFree	0.336***	1
MonFree	0.278***	0.501***	1
TaxBurden	−0.547***	−0.424***	−0.392***	1

Source(s): Table created by authors'

Table 4

Main effects of fantasy of success and fear of failure

	(1)	(2)	(3)	(4)
	Bstart	Bstart	Bstart	Bstart
Independent variables
Fantasy		0.117***		0.112***
		(16.751)		(15.963)
Fearfail			−0.177***	−0.181***
			(−25.823)	(−26.368)
Control variables
Age	0.012***	0.012***	0.012***	0.012***
	(47.294)	(47.629)	(47.337)	(47.522)
Sex	0.178***	0.180***	0.171***	0.173***
	(27.715)	(27.961)	(26.494)	(26.779)
Income	0.019***	0.020***	0.017***	0.019***
	(4.653)	(4.957)	(4.052)	(4.510)
Educ	0.013***	0.014***	0.011***	0.015***
	(3.799)	(4.280)	(3.343)	(4.523)
Busang	0.416***	0.415***	0.413***	0.412***
	(39.730)	(39.680)	(39.466)	(39.383)
Knowent	0.551***	0.549***	0.552***	0.550***
	(82.039)	(81.716)	(82.122)	(81.795)
Suskill	1.122***	1.117***	1.101***	1.093***
	(144.758)	(144.009)	(141.227)	(139.939)
Opport	0.529***	0.517***	0.521***	0.503***
	(78.321)	(76.228)	(76.966)	(73.750)
PropRights	−0.072***	−0.067***	−0.075***	−0.069***
	(−5.960)	(−5.598)	(−6.193)	(−5.730)
GovInt	−0.032*	−0.032*	−0.030*	−0.030*
	(−1.836)	(−1.835)	(−1.731)	(−1.766)
InvFree	−0.210***	−0.212***	−0.214***	−0.219***
	(−18.615)	(−18.770)	(−18.928)	(−19.389)
MonFree	−0.147***	−0.143***	−0.146***	−0.141***
	(−19.012)	(−18.422)	(−18.851)	(−18.135)
TaxBurden	0.107***	0.100***	0.099***	0.090***
	(5.424)	(5.100)	(5.025)	(4.574)
BusVib	0.036***	0.036***	0.038***	0.036***
	(3.724)	(3.683)	(3.956)	(3.674)
Failure	0.547***	0.547***	0.552***	0.552***
	(47.939)	(47.930)	(48.347)	(48.327)
Country fixed effect	YES	YES	YES	YES
Constant	−3.055***	−3.123***	−2.954***	−3.079***
	(−49.912)	(−50.880)	(−48.167)	(−49.849)
N	874,312	874,312	874,312	874,312

Note(s): Z-statistic in parentheses; *p < 0.10, **p < 0.05, ***p < 0.01

Source(s): Table created by authors'

Table 5

Moderating effect of business vibrancy

	(1)	(2)	(3)	(4)
	Bstart	Bstart	Bstart	Bstart
Fantasy	0.112***	0.106***	0.112***	0.105***
	(15.963)	(14.923)	(15.931)	(14.823)
Fearfail	−0.181***	−0.181***	−0.192***	−0.192***
	(−26.368)	(−26.424)	(−27.562)	(−27.567)
BusVib	0.036***	0.064***	0.057***	0.059***
	(3.674)	(6.012)	(5.723)	(4.990)
Fantasy × BusVib		0.043***		0.047***
		(6.331)		(6.839)
Fearfail × BusVib			−0.064***	−0.063***
			(−9.710)	(−9.558)
Control variables	YES	YES	YES	YES
Country fixed effect	YES	YES	YES	YES
Constant	−3.079***	−3.067***	−3.074***	−3.068***
	(−49.849)	(−49.623)	(−49.790)	(−49.659)
N	874,312	874,312	874,312	874,312

Note(s): Z-statistic in parentheses; *p < 0.10, **p < 0.05, ***p < 0.01

Source(s): Table created by authors'

Table 6

Moderating effect of failure experience

	(1)	(2)	(3)	(4)
	Bstart	Bstart	Bstart	Bstart
Fantasy	0.112***	0.119***	0.112***	0.120***
	(15.963)	(16.300)	(15.960)	(16.348)
Fearfail	−0.181***	−0.181***	−0.190***	−0.191***
	(−26.368)	(−26.366)	(−26.585)	(−26.598)
Failure	0.552***	0.609***	0.515***	0.556***
	(48.327)	(30.455)	(36.931)	(20.642)
Fantasy × Failure		−0.084***		−0.090***
		(−3.491)		(−3.691)
Fearfail × Failure			0.112***	0.113***
			(4.712)	(4.774)
Control variables	YES	YES	YES	YES
Country fixed effect	YES	YES	YES	YES
Constant	−3.079***	−3.083***	−3.075***	−3.078***
	(−49.849)	(−49.903)	(−49.794)	(−49.825)
N	874,312	874,312	874,312	874,312

Note(s): Z-statistic in parentheses; *p < 0.10, **p < 0.05, ***p < 0.01

Source(s): Table created by authors'

Table 7

Robustness test of the replacement of dependent variable with entrepreneurship intention (business vibrancy as moderator)

	(1)	(2)	(3)	(4)
	Futsup	Futsup	Futsup	Futsup
Fantasy	0.130***	0.129***	0.130***	0.129***
	(21.744)	(21.371)	(21.720)	(21.363)
Fearfail	−0.140***	−0.140***	−0.146***	−0.146***
	(−24.103)	(−24.120)	(−24.978)	(−24.987)
Fantasy × BusVib		0.013**		0.012**
		(2.267)		(2.048)
Fearfail × BusVib			−0.048***	−0.048***
			(−8.468)	(−8.420)
Control variables	YES	YES	YES	YES
Country fixed effect	YES	YES	YES	YES
Constant	−2.118***	−2.115***	−2.117***	−2.114***
	(−37.979)	(−37.906)	(−37.963)	(−37.892)
N	874,312	874,312	874,312	874,312

Note(s): Z-statistic in parentheses; *p < 0.10, **p < 0.05, ***p < 0.01

Source(s): Table created by authors'

Table 8

Robustness test of the replacement of dependent variable with entrepreneurship intention (failure experience as moderator)

	(1)	(2)	(3)	(4)
	Futsup	Futsup	Futsup	Futsup
Fantasy	0.130***	0.131***	0.130***	0.131***
	(21.744)	(21.146)	(21.744)	(21.162)
Fearfail	−0.140***	−0.140***	−0.142***	−0.142***
	(−24.103)	(−24.103)	(−23.615)	(−23.615)
Fantasy × Failure		−0.013**		−0.012**
		(−2.267)		(−2.048)
Fearfail × Failure			0.048***	0.048***
			(8.468)	(8.420)
Control variables	YES	YES	YES	YES
Country fixed effect	YES	YES	YES	YES
Constant	−2.118***	−2.118***	−2.118***	−2.117***
	(−37.979)	(−37.977)	(−37.968)	(−37.951)
N	874,312	874,312	874,312	874,312

Note(s): Z-statistic in parentheses; *p < 0.10, **p < 0.05, ***p < 0.01

Source(s): Table created by authors'

Table 9

Robustness test of sub-sample (business vibrancy as moderator)

	(1)	(2)	(3)	(4)
	Low-income	Lower-middle	Upper-middle	High-income
	Bstart	Bstart	Bstart	Bstart
Fantasy	0.528***	0.140***	0.115***	0.108***
	(4.335)	(3.640)	(9.761)	(9.237)
Fearfail	−0.005	−0.064*	−0.143***	−0.294***
	(−0.052)	(−1.726)	(−12.667)	(−24.177)
Fantasy × BusVib	0.116*	0.006	0.074***	0.043***
	(1.721)	(0.218)	(6.252)	(3.795)
Fearfail × BusVib	−0.144***	−0.082***	−0.053**	−0.069***
	(−2.595)	(−3.180)	(−2.110)	(−5.128)
Control variables	YES	YES	YES	YES
Country fixed effect	YES	YES	YES	YES
Constant	31.087***	0.156	−1.833***	−3.433***
	(13.664)	(1.093)	(−19.133)	(−64.260)
N	13,684	91,061	293,850	474,921

Note(s): Z-statistic in parentheses; *p < 0.10, **p < 0.05, ***p < 0.01

Source(s): Table created by authors'

Table 10

Robustness test of sub-sample (failure experience as moderator)

	(1)	(2)	(3)	(4)
	Low-income	Lower-middle	Upper-middle	High-income
	Bstart	Bstart	Bstart	Bstart
Fantasy	0.375***	0.164***	0.144***	0.091***
	(7.155)	(8.312)	(12.156)	(8.335)
Fearfail	−0.021***	−0.053***	−0.144***	−0.313***
	(−4.708)	(−2.927)	(−12.661)	(−28.084)
Fantasy × Failure	−0.280**	−0.163***	−0.097**	−0.033
	(−1.990)	(−2.753)	(−2.526)	(−0.821)
Fearfail × Failure	0.090	0.126**	0.175***	0.200***
	(0.699)	(2.332)	(3.372)	(4.981)
Control variables	YES	YES	YES	YES
Country fixed effect	YES	YES	YES	YES
Constant	31.474***	0.279**	−1.823***	−3.416***
	(14.051)	(2.025)	(−19.080)	(−64.251)
N	13,684	91,061	293,850	474,921

Note(s): Z-statistic in parentheses; *p < 0.10, **p < 0.05, ***p < 0.01

Source(s): Table created by authors'

Table 11

PSM effectiveness test results (balance test)

Variable	Mean		p-value
	Treated	Control
Age	40.088	40.179	0.103
Sex	0.5239	0.5228	0.239
Income	2.0503	2.0511	0.352
Educ	2.9951	2.9958	0.751
Busang	0.07339	0.07334	0.248
Knowent	0.45088	0.45261	0.075
Suskill	0.58279	0.58298	0.761
Opport	0.49591	0.49589	0.984

Source(s): Table created by authors'

Table 12

Regression results of samples before and after PSM matching

	(1)	(2)
	Bstart	Bstart
Fantasy before PSM	0.117***
	(16.751)
Fantasy after PSM		0.194***
		(29.121)
Control variables	YES	YES
Country fixed effect	YES	YES
Constant	−3.079***	−2.764***
	(−49.849)	(−161.42)
N	874,312	862,757

Note(s): Z-statistic in parentheses; *p < 0.10, **p < 0.05, ***p < 0.01

Source(s): Table created by authors'

Table A1

The yearly coverage for each country

Country	Year range
RU:RUSSIA	2007–2016, 2018
EG:EGYPT	2008, 2010, 2012, 2016–2018
ZA:SOUTH AFRICA	2006–2006, 2008–2014, 2016–2017
GR:GREECE	2005–2018
NL:NETHERLANDS	2005–2018
BE:BELGIUM	2005–2014
FR:FRANCE	2005–2007, 2010–2018
ES:SPAIN	2005–2014, 2016–2018
HU:HUNGRY	2005–2014, 2016
IT:ITALY	2005–2010, 2012–2014, 2016–2018
RO:ROMANIA	2007, 2009–2014
SW:SWITZERLAND	2018
AT:AUSTRIA	2005, 2007
DK:DENMARK	2008–2009
SE:SWEDEN	2005–2007, 2010–2011, 2013, 2014, 2016–2018
NO:NORWAY	2006–2006, 2008–2013
PL:POLAND	2011–2018
DE:GERMANY	2005–2018
PE:PERU	2006–2014, 2016–2017
MX:MEXICO	2005–2006, 2008, 2011–2014, 2016–2017
AR:ARGENTINA	2005, 2007, 2009–2012, 2014, 2016–2018
BR:BRAZIL	2005–2013
CL:CHILE	2005–2010, 2016–2018
CO:COLUMBIA	2006–2014, 2016–2018
MY:MALAYSIA	2006, 2009–2014, 2016–2017
AU:AUSTRALIA	2005–2006, 2010–2011, 2014–2017
ID:INDONESIA	2006, 2013–2014, 2016–2018
PH:PHILIPPINES	2006, 2013–2014
NZ:NEW ZEALAND	2005
SG:SINGAPORE	2005–2006, 2011–2014
TH:THAILAND	2005–2006, 2011–2014, 2016–2018
JP:JAPAN	2005–2014, 2016–2018
VN:VIETNAM	2013–2014
CH:CHINA	2005–2007, 2009–2014, 2016–2018
TR:TURKEY	2006–2008, 2010, 2012–2013, 2018
IN:INDIA	2006–2008, 2013–2014, 2016–2018
PK:PAKISTAN	2010–2012
IR:IRAN	2008–2014, 2016–2018
CA:CANADA	2005–2006, 2013–2014, 2016–2018
MA:MOROCCO	2009, 2016–2018
DZ:ALGERIA	2009, 2011–2013
TN:TUNISIA	2010, 2012
GH:GAHANA	2010, 2012–2013
NG:NIGERIA	2011–2013, 2016
CM:CAMEROON	2016
AO:ANGOLA	2010, 2013–2014, 2018
BB:BARBADOS	2011, 2013
ET:ETHOPIA	2012
UG:UGANDA	2009–2010, 2013
ZM:ZAMBIA	2010, 2012–2013
MG:MADAGASCAR	2017–2018
NA:NAMBIA	2012–2013
LU:LUXEMBORG	2013–2014, 2016–2018
IE:IRELAND	2007–2008, 2010–2014
IS:ICELAND	2005–2010
CY:CYPRUS	2016–2018
FI:FINLAND	2005–2014, 2016
BG:BULGARIA	2016–2018
LT:LITHUANIA	2012–2014
LV:LATVIA	2006–2010, 2012–2014, 2016–2017
EE:ESTONIA	2012–2014, 2016–2017
HR:CROATIA	2005–2014, 2016–2018
SI:SLOVENIA	2005–2014, 2016–2018
BA:BOSNIA and HERZEGOVINA	2008–2014, 2017
MK:MACEDONIA	2008, 2010, 2012–2013, 2016
CZ:CZECH REPUBLIC	2006
SK:SLOVAKIA	2011–2014, 2016–2018
BZ:BELIZE	20,142,016
GT:GUATEMALA	2005–2011, 2013–2014, 2016–2018
SV:EL SALVADOR	2012, 2014, 2016
CR:COSTA RICA	2010, 2012, 2014
PA:PANAMA	2009, 2013, 2017–2018
VE:VENEZUELA	2011
BO:BOLIVA	2008, 2010, 2014
EC:ECUADOR	2008–2010, 2012–2014, 2016–2017
SR:SURINAME	2013, 2014
UY:URUGUAY	2007–2014, 2016–2018
TO:TONGA	2009
VU:VANUATU	2010
KZ:KAZAKHSTAN	2007, 2014, 2016–2017
HK:HONG KONG(SAR)	2007, 2009, 2016
JM:JAMICA	2005, 2006, 2008–2014, 2016–2017
BD:BANGLADESH	2011
LB:LEBANON	2009
JO:JORDAN	2009, 2016
SY:SYRIA	2009
SA:SAUDI ARABIA	2009–2010, 2016–2018
YE:YEMEN	2009
IL:ISRAEL	2009–2010, 2012–2014, 2016–2018
QA:QATAR	2014, 2014, 2017–2018
GE:GEORGIA	2016
DR:DOMINICAN REPUBLIC	2009

Source(s): Table created by authors'