Addressing the Efficacy of Quality in Qualitative Research: A Review of the Current Discourse

Quality is one of the most debated topics in the history of Qualitative Research Methods (QRM). It establishes the benchmarks, norms and values that a researcher should follow when involved in research. Quality is a critical tool for promoting value, effectiveness and efficiency in research processes. Qualitative research is popular in several disciplines such as local governance studies, sociology, education, gender studies, public management, media studies, human resource management, political science etc. The proponents of Qualitative Research (QR) believe that it has unique characteristics compared to quantitative and mixed research methods. Qualitative Research (QR) focuses on understanding lived experiences through narrative inquiry, field observations, focus study groups, and the use of digital photos. The literature on quality advocates for methodological rigor in QR. The discourse of quality in qualitative research is evolving with time and the evolving trends in technology and AI. This review explores the key characteristics of QR and the evolving trends. It provides a meta-summary of the quality benchmarks identified across the various domains of qualitative research literature. It evaluates the advantages and limitations of using Artificial Intelligence in QR. Findings from the literature revealed that there is scholarly attention on quality components: credibility, transferability, dependability, confirmability, and authenticity. The characteristics of qualitative research call for different quality standards such as trustworthiness, reflexivity, contextual sensitivity, and rigour. Overall, the findings indicate that embracing Artificial intelligence (AI) in Qualitative Research presents opportunities and threats. AI’s ability to manage large-scale and multimodal data has enhanced the collection of qualitative data. AI can produce brief summaries or spot reoccurring patterns by offering real-time insights. The authors of this paper recommend that future studies should evaluate how quality standards in QR are interpreted and implemented across different academic disciplines, cultures and contexts.