Factors Influencing Social Media in Managing Corporate Reputation for a Christian Organisation in Developing Countries Based on the VT4 Model
Bibliographic record
Abstract
Social media has changed the dynamics of communication and it has become the present truth mantra for information exchange. Social media has in quick succession grown exponentially to reach out to millions of masses than traditional communication media. The question no longer borders on the need for the organization to use social media but on the dynamics of how to establish an online reputation. Management of corporate reputation for organizations on social media cannot fully be effective without establishing influencing factors. Hence, the two objectives of this study were to establish factors influencing social media in managing corporate reputation for Christian organizations and at the same time to establish whether there is any relationship among these factors. A descriptive and survey design was adopted for the study. The population for this study consisted of church members and leaders of the Seventh-day Adventist Church from the three provinces namely, Lusaka, Central ad Copperbelt with a total membership of 608,357. A questionnaire was used in drawing information from respondents, which contained five sections. Two research questions and nine hypotheses were formulated and tested. The data collected was analyzed using Statistical Package for Social Sciences (SPSS version 24.0), for frequency distribution. Further analysis was carried out using linear regression and correlation analysis. From the findings made in the study, there is a significant role played by social media in managing corporate reputation for Christian organizations. It was recommended that management should be more conscious of the factors that influence social media in reputation management, as well as to improve on their inadequacies and address its organizational threats promptly. Further, managers should strive to focus on having a peculiar online presence in line with their given organizational reputation.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".