Leveraging social media, big data, and smart technologies for intercultural communication and effective leadership: Empirical study at the Ministry of Digital Economy and Entrepreneurship
Bibliographic record
Abstract
The objective of this study was to evaluate the impact of social media, big data, and smart technology on intercultural communication and effective leadership inside the Ministry of digital & entrepreneurship. The main objective was to investigate the influence of these technical elements on organizational behavior and the efficacy of leadership within the particular setting of a government ministry dedicated to digital economy and entrepreneurship. In order to accomplish this goal, a thorough empirical inquiry was done, which included gathering data from important individuals involved in the Ministry. The study intentionally selected a sample size of 379 individuals, who represented various responsibilities within the Ministry. The process of data gathering entailed the distribution of surveys and the conduction of interviews to acquire valuable insights and viewpoints from the participants. The utilization of this approach yielded a resilient dataset that is well-suited for thorough investigation. The study explored the complex connection between the use of social media platforms, the implementation of big data analytics, and the incorporation of smart technologies in influencing the dynamics of intercultural communication and leadership inside the Ministry. The results emphasized the substantial influence of social media in promoting intercultural communication and cooperation among personnel within the Ministry. Moreover, the implementation of big data analytics has become a crucial element in improving decision-making processes, impacting several facets of leadership efficacy, strategic planning, and employee involvement. Smart technologies were recognized as crucial elements in establishing efficient communication channels and facilitating effective leadership practices. The study's findings emphasized the beneficial impacts of utilizing social media, big data, and smart technology in the Ministry of digital & entrepreneurship. The research highlighted the significance of government organizations incorporating these technologies in a proactive manner to foster a work environment characterized by improved multicultural communication, well-informed decision-making and efficient leadership. This study makes a substantial contribution to the comprehension of how technological improvements might influence organizational behavior and leadership practices in a government setting. It provides essential insights for policymakers, leaders, and researchers. The findings have broader significance beyond the Ministry, serving as a basis for additional investigation into the use of technology in intercultural communication and leadership effectiveness inside government institutions.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".