Communication structure of the public sector in India: an empirical analysis
Bibliographic record
Abstract
Purpose The purpose of this paper is to report empirical research about the chronological development of the organizational structure, functions (functional groups) and competencies of the corporate communication(/public relation) – CC(/PR) department of the central public sector enterprises (CPSEs) in India. This paper also attempts to identify the specific organizational goals that influence CC(/PR) departmental structure and its effectiveness. Design/methodology/approach In total, 34 selected CPSEs reflecting most of the salient features of the public sector in India are identified. Key personnel (or designates) in the CC(/PR) departments are contacted to take an online survey that is built after analyzing previously reported instruments appropriate in this context. Analyses are conducted using SPSS 10.0. Findings Data analysis shows that in many PSUs, the development of full‐fledged CC departments is still at a nascent stage; however, in other PSUs development of CC is already streamlined with company vision and is mature as a division. Key acceptable PR roles include communication for the desired perception among target audience and brand sustainability. In established CC departments, CC is a strategic management tool, synchronizing all intentional forms of internal and external communications, thus helping the PSUs to define its corporate image and improve corporate performance. Through the built‐in measurement systems, PSUs are encouraged to become global players. Originality/value The paper empirically measures the efficiency of CC(/PR) departments of 34 operating CPSEs concerned with the development of the engineering industry in India. This paper would be of value to researchers and practitioners seeking to promote, practice or influence the structuring of CC(/PR) departments.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".