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.