Abstract
Publication Issue:
Volume 1, Issue 7
February-2026
A B S T R A C T
Abstract
Contents creation is very promising in modern technology, digital communication and marketing strategies. The 21st century and global world needs a fast-paced rapid digital environment of developing and creating contents. Contents Creation is producing information intended to engage and inform audiences. Secondary data consisting of variables such as number of views, likes, shares, good or bad comments, total reactions, and a categorized perception variable of 3 were got from existing dataset of media contents. The quantitative research design of descriptive and inferential approaches was applied. The ex-post facto and causal-comparative design aided the analyzes of existing data without manipulating variables. Correlational analysis, One-Way ANOVA, and Multinomial Logistic Regression were used to assess the differences, relationships, and predictions of the audience’s perceptions. The “R” was 0.851 signifying a positively strong correlational coefficient between the observed and predicted values of the dependent variable (number of likes). R-squared of 0.724 (coefficient of determination) revealed 72.4% of the variation in the number of likes as explained by the number of views of contents. P-value of 0.000 signified that the regression model was highly statistically significant meaning the regression coefficient fits the model. The intercept value of 2196.0458, means that when the number of views is zero, the predicted number of likes were about 2196 likes, while the B=0.4459 means that for each additional view, the model predicted an increase of about 44.59% of likes. The study established a clear statistical relationship between audience perception and engagement metrics.
Volume 1, Issue 7
February-2026
A B S T R A C T
Abstract
Contents creation is very promising in modern technology, digital communication and marketing strategies. The 21st century and global world needs a fast-paced rapid digital environment of developing and creating contents. Contents Creation is producing information intended to engage and inform audiences. Secondary data consisting of variables such as number of views, likes, shares, good or bad comments, total reactions, and a categorized perception variable of 3 were got from existing dataset of media contents. The quantitative research design of descriptive and inferential approaches was applied. The ex-post facto and causal-comparative design aided the analyzes of existing data without manipulating variables. Correlational analysis, One-Way ANOVA, and Multinomial Logistic Regression were used to assess the differences, relationships, and predictions of the audience’s perceptions. The “R” was 0.851 signifying a positively strong correlational coefficient between the observed and predicted values of the dependent variable (number of likes). R-squared of 0.724 (coefficient of determination) revealed 72.4% of the variation in the number of likes as explained by the number of views of contents. P-value of 0.000 signified that the regression model was highly statistically significant meaning the regression coefficient fits the model. The intercept value of 2196.0458, means that when the number of views is zero, the predicted number of likes were about 2196 likes, while the B=0.4459 means that for each additional view, the model predicted an increase of about 44.59% of likes. The study established a clear statistical relationship between audience perception and engagement metrics.