Corporate Sustainability: Study of Factors that Affect Corporate Towards Organizational Sustainability in Today Fast-Changing World
Bibliographic record
Abstract
Sustainability has always run through social and economic activities. As significant economies have competed for their interests in the past few years, this situation has caused a global economic depression. Additionally, this situation worsened due to the spread of the COVID-19 pandemic around the world. The international politics, economy, and Culture are undergoing unprecedented destruction and challenges from the COVID-19 pandemic that has caused businesses in many countries and regions to close or are about to face bankruptcy. More and more employees are getting laid off every day since the COVID-19 began. Even employees who are employed are feeling unsecured. So, in the face of uncertainties and difficulties facing corporations worldwide, we need to find a better way to extend the company's life cycle in a more structured and sustainable manner. The main research question for this study is "What are the factors that have significant effects on organizational sustainability?" In this study, twelve independent variables including Leadership (L.S.), Management (M.N.), Culture (C.T.), Structure (S.T.), Workforce diversity (W.D.), Organizational age (O.A.), Staff age (S.A.), Mindset (M.S.), Technology (T.N.), Organizational dimension (O.D.), Group structure (G.S.), Business locations (B.L.) and one dependent variable called Sustainability (SUS) is the studied. This study aims to understand the structural relationships among these potential variables that could influence corporate sustainability. The dataset utilized to test the hypothesis postulated in this study using Structural Equation Models (SEM). This study suggested that the Leadership, Management, and Staffing Age significantly affects organizations towards organizational sustainability. Considering the different politics, economy, and cultural backgrounds in countries and industries, the study also found that some irreconcilable factors affect the performance of leadership, management, and staff ages. Thus, this study identified effective leadership, management, and staff age as strategies to lead organizations further towards organizational sustainability. The results of this study provide some valuable suggestions for all companies facing the COVID-19 threats right now to bring back to life and become more sustainable in the years to come and provide some evidence for future researchers to explore this field further.
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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 | 0.003 | 0.007 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| 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".