Adoption and diffusion of organizational innovation, the winning mantra for growth: A case of manufacturing SMEs in Pakistan
Bibliographic record
Abstract
This multiple case study qualitative research examined the impact of adoption and diffusion of innovation on Small and Medium Enterprises (SME’s) growth in the hostile business landscape of Khyber-Pakhtunkhwa, Pakistan. This research is intended to investigate research data and consequent findings based on an interview protocol that was purposefully developed from extant literature, complemented by an initial pilot study of two pharmaceutical SMEs. The researcher conducted 20 interviews, guided by the semi-structured interview protocol offered to the respondents beforehand after sorting their informed consent. The 20 participants represented the different hierarchal levels of the 08 case study of pharmaceutical from the two industrial clusters of Khyber Pakhtunkhwa, Pakistan, located at the Hayatabad Industrial Estate, Peshawar, and the Rashkai Industrial Estate, Nowshera. The analysis of the data presented findings and corroborated the research propositions that those SMEs that are structurally entrepreneurial and adopt innovation amenably, are open to mobility and tourism, yield satisfactory results in terms of their growth as compared to those that are inertial and unentrepreneurial. Similarly, the results offer confirmation that the effectiveness of government agencies that are explicitly formed to address the problems of small businesses is insufficient. They rather create hindrances than assistance due to the excessive delays in approving innovative ideas and conceptions by these related organizations and ministries. Moreover, the proposed framework offers pragmatic recommendations to contextualize entrepreneurial culture and innovative structures in SMEs and their essential factors in critical environmental circumstances.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".