Limitations of Traditional Approaches
In the social sciences, conventional approaches for analyzing human
behavior have largely been classified as qualitative or quantitative.
Interviews, focus groups, ethnographies, and case studies are examples
of qualitative research approaches used to gain a thorough knowledge of
human behaviour and the factors that influence it. They provide rich,
comprehensive data that allows us to understand people’s ideas,
feelings, and experiences. These methodologies excel at providing
context and capturing the nuances of the human experience. Quantitative
research methods, on the other hand, include conducting systematic
empirical investigations using statistical, mathematical, or
computational tools [19]. Surveys, experiments, and observational
studies belong under this category. These approaches enable researchers
to quantitatively measure and evaluate data, making them effective
instruments for testing hypotheses and reaching generalizable findings.
Despite their many advantages, conventional approaches have several
drawbacks, particularly when it comes to recognizing complicated
patterns in human behaviour. In qualitative approaches, the subjective
aspect of data collection and interpretation can contribute to bias
[20]. While these approaches provide depth, they frequently lack
breadth due to their tiny sample sizes. Identifying patterns in
qualitative data can be challenging due to its complexity and richness,
especially when several factors are involved. Quantitative approaches,
while useful for managing huge datasets and drawing generalizable
findings, sometimes rely on stringent assumptions about the nature of
data and the connections between variables. Real-world data frequently
violates these assumptions, rendering the models less accurate.
Furthermore, traditional statistical approaches may fail to capture
nonlinear connections or interactions among a large number of variables,
which are typical in human behavior data. Another key shortcoming of old
approaches is their inability to efficiently collect and evaluate the
massive volumes of data produced in the digital age. Social media posts,
internet purchases, GPS location data, and health records, among other
things, provide a wealth of information on human behavior. Traditional
approaches are inadequate for dealing with such Big Data, given its
volume, diversity, and pace [21]. Furthermore, traditional
approaches frequently evaluate data in a reductionist manner, distilling
complicated events into manageable chunks. While this technique
simplifies the study, it risks overlooking the complexities of human
behavior, in which numerous factors interact in sophisticated ways at
the same time. In contrast, artificial intelligence, with its capacity
to handle large amounts of data, discover complicated patterns, and
learn from data without explicit programming or inflexible assumptions,
has the potential to overcome these constraints. This makes AI an
invaluable tool for social scientists seeking to comprehend the complex
realm of human behavior. However, this potential should be used with
caution, taking into consideration the ethical and practical problems
that AI presents.