This theory aims to explore and model the relationship between video quality and quantity for a given YouTube channel, and how the optimal equilibrium is based on personal preferences. It is quite concept heavy in the beginning, however this is essential in order to understand the analysis and diagrams in later parts.
To begin, this paper will outline the fundamental principles governing video quality in an idealized scenario
Measuring content quality is inherently subjective, as perceptions of what constitutes "high-quality" content vary among individuals. This subjectivity makes it challenging to establish a universally accepted measure of quality. However, one aspect of quality remains universally quantifiable - Time. This brings us to the first assumption of the QvQ theory:
"All things being equal, as the time spent on making a video increases, the quality of the given video increases"
This assumption holds in an ideal scenario where all individuals possess identical editing skills, equipment, and creative abilities. However, in reality, these factors vary significantly across content creators. Despite this limitation, the assumption serves as a useful framework for analyzing the relationship between time investment and video quality
How it is in reality: Some creators may exhibit a lower quality-to-quantity ratio, where each hour spent on production results in a marginal improvement in quality (e.g., a one-unit increase). Others may have a more pronounced relationship, where an hour of work yields a greater enhancement in quality, such as a two- or even four-unit increase.
Moving on, it is important to recognize the presence of diminishing marginal returns in the relationship between time spent on video production and the resulting quality improvements. This implies that as more hours are dedicated to enhancing a video`'s quality, the incremental gains in quality become progressively smaller. The underlying intuition behind this phenomenon is that each content creator possesses a specific skill set, which imposes a natural ceiling on the quality they can achieve without acquiring new competencies.
"All things being equal, as the time spent on improving the quality increases, the less quality each unit of time will add"
How it is in reality: Some creators may experience a sharper decline in marginal quality gains due to a limited skill set, which restricts them to only a few quality-enhancing techniques before they reach the limits of their expertise.
Finally, the third assumption establishes the relationship between video quality and viewership. Intuitively, one can argue that higher-quality videos tend to attract more views compared to lower-quality ones. This is based on the premise that superior content is generally more engaging, features enhanced visual appeal, incorporates more effective hooks, and is overall produced to a higher standard, making it more likely to capture and retain audience attention.
"All things being equal, a video of higher quality should on average receive more views than a video of lesser quality"
How it is in reality: While this relationship should hold in theory, low-quality content frequently goes viral, demonstrating that a video can be produced in half the time and still achieve the same level of viewership as a higher-quality counterpart. However, the key distinction is that, on average, lower-quality videos do not outperform higher-quality ones in terms of viewership.
With the three assumptions established, we now turn our attention to the relationship between quality and quantity in video production. As illustrated in the diagram below, there exists a trade-off between the quantity of videos produced and their corresponding quality.
Determining the exact quality of a video is difficult, and especially if we were to assign numeric values. Hence, this paper will attempt to create 3 different categories of quality in which most videos fall within.
Further research needed on the impact of increased quantity through AI-generated content while maintaining quality, and its effects on average views within specific industries.
Reposting is common on many platforms, and there is arguably an arbitrage opportunity when it comes to reposting content from one social media platform to another.