Bibliographic record
Abstract
AbstractBeing an entrepreneur or continuing to work as a regular employee is a difficult decision a number of people are dealing with when choosing their career. The entrepreneurs can contribute for the economics' growth. However, because of the high rate of failure businesses, the failed entrepreneurs have to deal with amorous difficulties. This study has conducted to identify the life of the entrepreneurs who had failure experiences from the time after their failure to the recovery time of them. The quantitative method was conducted to find the relationship between the entrepreneurial human capital and the learning process from failure, the restart intention. The finding of this research indicated positive impact of entrepreneurial human capital and failure learning on restart intention. The research also provided evidences to scholars who are developing literature review of entrepreneurship to help them with a new researching direction about entrepreneurs' intentions and behaviors by using Motivation-Opportunity-Ability perspectives. Based on this study, failed entrepreneurs would be provided with different points of view about their collapse, as well as found some helpful mechanisms to use as resources and drew useful lessons from their failure to build up optimism in their future entrepreneurial career.Keywords: entrepreneurship failure, entrepreneurial learning, entrepreneurship restart intention, entrepreneurial intention, human capital1. Introduction1.1 Research IntroductionBeing an entrepreneur or continuing to work as a regular employee is a difficult decision a number of people are dealing with when choosing their career. Becoming an entrepreneur, who establishes, organizes, controls, and takes responsibility for a new business, can offers a person more chances to solve some difficulties so that many people may desire to be entrepreneur rather than a mere employee (Segal, Borgia, & Schoenfeld, 2005). An individual chooses to be an entrepreneur for a wide range of reason. According to Gilad and Levine (1986), there are two kinds of closely related reasons of entrepreneurial motivation, the push theory and the pull theory. Negative external factors can be seen as the reason behind people who are pushed into becoming entrepreneurs, for example job loss, job issues, discomfort in traditional working environment, inflexible schedules and even insufficient incomes. On the other hand, the pull theory referred to the individual's internal factors including desire for independence, self-fulfillment, power, reputation and more which are the main factors impacting the decide to be an entrepreneur. Moreover, being entrepreneurs, people expect to be rewarded with the wages of employment (Van Praag & Cramer, 2001) and also hope for brighter future in their life. Keeble, Bryson, and Wood (1992), Orhan and Scott (2001) and Segal et al. (2005) argued that the pull factors have more influences in entrepreneurial decision-making than the push factors.However, deciding to become entrepreneurs, people have to deal with countless difficulties, challenges, and confrontations ahead. The threat can be derived from external environment as well as internal one. The critical role of entrepreneurship research which can be seen as the effect of entrepreneurial activities in establishing new business has been a key influence on the economy growth, employee and innovation (Guerrero, Rialp, & Urbano, 2008).However, in the research conducted by Knott and Posen (2005), the author indicated that around 80-90 percent of new firm ultimately failed, a shocking figure. Approximately 10-20 percent of the surviving firms let the entrepreneurs achieve their business establishing goals such as high incomes, elevated reputations, and realistic ambition. Furthermore, the data from the U.S. Census Business Information Tracking Series in the study conducted by Headd (2003) mentioned that the failure was not the same in the new firms. …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".