Managerial heuristics of pioneering innovative (PI) entrepreneurs: an exploratory study
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Bibliographic record
Abstract
Managerial heuristics are defined as the decision-rules guiding the less programmed decisions of entrepreneurs/executives. A review of literature on entrepreneurship suggests that the scene is dominated by the search for economic, socio cultural and psychological factors to explain entrepreneurial behavior. The managerial heuristics of entrepreneurs are rarely studied. Moreover, the traditional research has generally viewed entrepreneurship as a uni-dimensional construct and expected the same set of factors to explain all kinds of entrepreneurship. The literature survey showed no large sample studies of innovative entrepreneurs. The present study compares the pioneering innovative (PI) entrepreneurs with low PI entrepreneurs on the managerial heuristics implied in their decisions. \nData for the study were collected from 138 stories of first generation entrepreneurs published in Indian business magazines, books and a Canadian book. In addition, interviews of 26 first generation entrepreneurs were also conducted. A pilot study of the relatively less programmed decisions reported in 40 of the published stories revealed 186 decision - rules (managerial heuristics). Each of the 138 stories was then rated for the presence or absence of these heuristics in it, on a three-point scale. A PI index was developed based on ten different types of innovation, such as introduction of a new product/new method, identification of a new source of supply/new market, use of a new marketing strategy/ a new way of managing finance, development of a new culture/structure., and so forth. Each entrepreneur was assigned an innovativeness score (PI index) based on the presence or absence of these ten types of innovation. Similar scores were obtained from the interview data for the 26 entrepreneurs. Finally demographic data were collected on the nature of the entrepreneur and his enterprise. \nTo test the reliability of the researcher’s ratings, two random samples from the published cases were selected and rated separately for heuristics and PI index items by two independent raters. One trained in management and the other untrained. The inter-rater correlation between the researcher and the untrained rater was 0.91, and between the researcher and the trained rater was 0.94. \nFor the purpose of comparative analysis, a high PI group and a low PI group were identified. Those cases scoring in the upper third of the PI index were assigned to the high PI group entrepreneurs (N=52): those scoring in the lower third were assigned to the low PI group (N=46). \nThe data have yielded the following results: \n1)\tOut of the 186 heuristics, 77 had significantly different (at p<=0.05) group means for the high PI and low PI groups. These were called PI heuristics. The remaining 109 heuristics were referred to as the general entrepreneurial heuristics. \n2)\tA classification of the above two sets of heuristics into various functional areas showed up substantial differences between common entrepreneurs and the PI entrepreneurs in terms of their heuristic orientations. \n3)\tA hierarchical factor analysis of the PI heuristics yielded six PI orientations. \n4)\tA hierarchical factor analysis of the general entrepreneurial heuristics yielded 8 general entrepreneurial orientations. \n5)\tA discriminant analysis showed that the PI orientations could discriminate between the low PI and the high PI groups with a probability of misclassification of 0.12. However, the addition of the general entrepreneurial orientations increased the discriminatory power and brought down the probability of misclassification to 0.80. \n6)\tA stepwise regression analysis of PI index scores on the 14 PI and general orientations showed that eight of the (five PI and three general) explained 50% of the variance in the PI index. \n7)\tCorrelations between the ten types of innovation and the 186 heuristics showed that different sets of heuristics are associated with different types of innovation. For instance, cultural innovation is significantly (at p<=0.01) correlated with the largest number (47) of heuristics, and government relations innovation with the fewest (8). \n8)\tA classification of the high, moderate and low PI cases according to region, time period, and industry and their Chi-square analyses showed that there was no significant difference in the promotions. Thus the hypothesis of environmental determinism was not supported. \n9)\tA cluster analysis of the PI and the low PI groups separately on the basis of the six orientations revealed seven types of high PI cases and six types of low PI cases. \nThe study has its limitations arising mainly out of the use of journalistic data. However, the findings have thrown some light on the policy orientations of pioneering innovative entrepreneurs. The potential impacts on understanding indigenous models of management and on the training and development of entrepreneurs are discussed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it