Analyzing antecedents affecting the organizational performance of start-up businesses
Bibliographic record
Abstract
Purpose The purpose of this paper is to depict the effects and relative importance of technological, organizational, environmental and managerial factors on the organizational performance of start-up businesses. Design/methodology/approach This research’s primary data was collected from 389 start-up companies in Malaysia. Principle component analysis and the orthogonal model with Varimax rotation method are used to perform exploratory factor analysis test. Structural equation modelling is also used in confirmatory factor analysis to explore the relationships between independent and dependent variables. Findings The findings suggest positive effects of technological and environmental characteristics on the organizational performance of start-up businesses. The managerial characteristics do not have any positive effect on the organizational performance of start-up businesses. The organizational characteristics split into two parts: the availability of internal financial resources, which positively affects the organizational performance of start-up businesses; and the availability of business incubation, which does not have any important effect. Moreover, start-up companies should choose the one with the highest perceived advantage as it would have the most significant positive effect on their organizational performance. In addition, it was detected that venture capitalists’ (VCs) support has the most positive influence on organizational performance and social customer relationship management adoption even more than governmental supports in the context of Malaysia. Originality/value The proposed framework of this research can be used not only as a research tool for examining determinant factors affecting organizational performance of start-up businesses but also by governments, VCs and other investors to detect best-performing start-up businesses.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.003 | 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".