Impact of family control on information technology investment and information technology adoption in India
Bibliographic record
Abstract
Purpose This study tests the impact of family control on information technology (IT) investment and IT adoption in MSMEs in India. Design/methodology/approach This study employs a survey research design. Micro, small, and medium enterprise (MSME) owners in India were surveyed to test the impact of family control on IT investment and IT adoption. Findings Our empirical results show that family control — measured by family ownership, family member firm management, and/or family CEO duality — increases IT investment and IT adoption in India. Family ownership increases the chances of IT investment and IT adoption by 19.24% and 38.40%, respectively. Firm management by family members increases the chances of IT investment and IT adoption by 11.29% and 18.29%, respectively. CEO duality increases the chances of IT investment and IT adoption by 51.13% and 258%, respectively. Thus, CEO duality has a higher impact on IT investment and IT adoption than family ownership and firm management by family members. Research limitations/implications The empirical results may be generalized only to MSMEs similar to those surveyed in this study. Additionally, this study relied on the perceptions and judgments of MSME owners. Originality/value This study contributes to the literature on the impact of family control on IT investment and IT adoption in the developing economics. This study can help scholars to develop further studies in the family control area. Our findings may help MSME owners to increase family control to survive and prosper into the future. Additionally, MSME management consultants may find the empirical results useful to provide consulting services.
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".