The Role of Organizational DNA in Improving Organizational Performance: A Study on the Industrial Companies in Egypt
Bibliographic record
Abstract
Purpose: This paper attempts to highlight the significant role of organizational DNA in improving Organizational Performance (OP). Research Design/Methodology: Using Booz Allen Hamilton, 2002; Neilson, et al., 2003; 2004; Booz, 2004; Neilson, et al., 2005; Holoday, 2005; Remecker & Bowdin, 2005; Neilson, 2006; Vanmullem & Hondeghem, 2007; Soroush, et al., 2013 of organizational DNA, the study develops a number of hypotheses and tests them. This research is an applied form in terms of its goals and descriptive in terms of the method of data collection. Three groups of employees at industrial companies were examined. Of the 372 questionnaires that were distributed, 300 usable questionnaires were returned, a response rate of 81%. Findings: This study reveals that the four building blocks of organizational DNA (organizational structure, decision rights, motivators, and information) have a significantly direct effect on OP. Practical implications: The study suggests that the industrial companies can improve OP by influencing its organizational DNA, specifically, by developing the organizational structure, decision rights, motivators, and information. The study provided a set of recommendations including the necessity to pay more attention to the dimensions of organizational DNA as of a key source for organizations to enhance the competitive advantage which is of prime significance for OP. Originality/value: The study observes that there is a critical shortage of studying organizational DNA in Egypt and that a greater understanding of the factors that influence the OP, including organizational structure, decision rights, motivators, and information, is of great importance. Therefore, this study is to examine the relationship between organizational DNA and OP among employees in industrial companies in Egypt.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".