The Impact of Demands and Resources on Engagement, Strain, and Entrepreneurial Success
Bibliographic record
Abstract
Introduction: Entrepreneurs play an essential role in the Australian economy, to drive innovation and create new businesses. However, they face many challenges and are as likely to fail as succeed, which highlights the need to understand what factors may be important for entrepreneurial success to occur and for entrepreneurs to remain in business. Using the Job Demands-Resource (JD-R) framework, it was hypothesised that greater personal and work resources and fewer entrepreneurial demands would increase work engagement and reduce work-related strain, which would consequently increase social and financial success for entrepreneurs. Methods: Entrepreneurs (N=109, 57.8% female) were recruited by snowball methods from Chambers of Commerce and entrepreneurial Facebook groups to complete an online survey. Participants reported demographics, personal (e.g., proactive personality, optimism) and entrepreneurial work (e.g., ‘freedom to carry out work activities’) resources, entrepreneurial demands (e.g., ‘contact with difficult clients or patients in your work’), work engagement, jobrelated strain, and entrepreneurial success (i.e., the business has achieved success in financial (e.g., ‘healthy turnover/sales’, ‘profit growth’) and social (e.g., ‘employee satisfaction’, ‘strong customer relationships’) areas). Hierarchical multiple regressions tested the predictors of work engagement, strain (e.g., ‘I find it difficult to relax at the end of a working day’), and entrepreneurial success as personal resources (Block 1; age, gender, optimism, self-efficacy, proactive personality), entrepreneurial demands (Block 2) and entrepreneurial resources (Block 3). Results: Participants ranged from 17 to 65 years (M=43.6, SD=10.9) and were mostly married or had a partner (79.8%). They worked alone (38.5%), with 1-3 employees (36.7%), or with 4-20 employees (22.0%) and mostly in regional (42.2%) or urban (51.4%) areas. Most had a trade (33%), undergraduate (29%), or postgraduate (19.3%) qualifications and many (70%) had some management experience before starting self-employment. Size of business only affected entrepreneurial success, rather than work engagement or strain, with owners of businesses with 4-20 employees feeling significantly more successful than sole traders or those with 1 to 3 employees. The HMRs explained highly significant variance in work engagement (49.7%), jobrelated strain (39.0%), and entrepreneurial success (23.2%). Greater work engagement was predicted by increased personal resources, specifically as a more proactive personality and more optimism, and greater resources at work, and for women (rather than men). In contrast, entrepreneurial demands alone increased job-related strain (by mediating effect of greater optimism) and reduced feelings that success had been achieved by the business (by mediating effect of greater self-efficacy). Discussion: The JDR was used to frame the work experiences of entrepreneurs, with resources adding to work engagement, whilst demands specific to entrepreneurial businesses strongly predicting increased job-related strain and reduced whether the entrepreneurs felt they achieved success in their business. The findings highlight areas in which entrepreneurs may be assisted to remain feeling engaged, rested, and successful. Providing training to manage demands around workloads, interruptions, and time pressure, as well as to building personal skills and their businesses, which allow creativity, and better business planning, may ensure that entrepreneurs continue in business in the longer term, benefiting themselves, their families and the economy more generally.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".