Portfolio Entrepreneurs in China: A Mixed Methods Study
Bibliographic record
Abstract
Entrepreneurs who found and manage at least two businesses simultaneously are portfolio entrepreneurs. Portfolio entrepreneurs have been shown to run fast-growing businesses and contribute significantly to wealth creation. Extant literature has examined how portfolio entrepreneurs differ from entrepreneurs who only run one business at a time and found differences in their demographic backgrounds, human capital, cognition, and other dimensions. However, extant research has treated portfolio entrepreneurs as one homogeneous group, when in reality, there is considerable heterogeneity within this group of entrepreneurs. In this dissertation, I aim to discuss one important distinguishing factor among portfolio entrepreneurs, namely the existence of synergy between businesses established and managed by the same portfolio entrepreneur. Portfolio entrepreneurs are categorized into two groups: synergizers (who run businesses that create greater aggregate value if managed as part of a portfolio than if managed separately) and non-synergizers (who run businesses that are independent of each other).\nThis dissertation investigated the following research questions based on the distinction made between synergizers and non-synergizers. First, I explored factors at the product, firm, and industry level that enable individuals to become synergizers rather than non-synergizers. Next, I examined how these two groups of portfolio entrepreneurs behave differently when making decisions regarding the hiring of top managers in their new ventures. I relate the differences in their decision-making to an outcome measure, namely portfolio entrepreneurs’ job satisfaction.\nI chose to collect data from a major Asian economy, China. I employed a mixed-methods approach to answer these research questions. A qualitative study of 33 Chinese portfolio entrepreneurs was followed by two quantitative studies which used a sample of 105 portfolio entrepreneurs. A survey questionnaire was designed and administered to collect the quantitative sample of portfolio entrepreneurs. Snowball sampling was used in order to reach as many potential participants as possible in the collection of both qualitative and quantitative data.\nFrom analyzing the quantitative sample, my results showed that entrepreneurs who produce more complex products tend to become synergizers rather than non-synergizers.\nCompared to non-synergizers, synergizers tend to hire more experienced top managers, a decision that ultimately leads to higher entrepreneurial job satisfaction. These findings highlight that heterogeneity of portfolio entrepreneurs is an important area of research, and the novel distinction of synergizers and non-synergizers has the potential to expand the theoretical discussion on portfolio entrepreneurship.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".