Abstract
PurposeThis study aims to analyze four text-mining studies of quality management (QM) to illustrate and problematize how the research on quality has informed the quality paradigm since the 1980s. By understanding history, one can better manage current developments.Design/methodology/approachThe findings are based on a meta-analysis of four text-mining studies that explore and describe 11,579 research entries on quality between 1980 and 2017.FindingsThe findings show that the research on quality during the past 30 years form a research paradigm consisting of three operational paradigms: an operative paradigm of backend quality orbiting around QM, total QM (TQM) and service quality; an operative paradigm of middle-way quality, circling around the International Organization for Standardization (ISO), business excellence frameworks (BEFs) and quality awards; and an operative paradigm of frontend quality, revolving around reliability, costs and processes. The operative paradigms are interconnected and complementary; they also show a divide between a general management view of quality and a hands-on engineering view of quality. The findings indicate that the research on quality is a long-lived standalone paradigm, supporting the notion of quality being a genuine academic entity, not a fashion or fad.Research limitations/implicationsThe empirical basis of the study is four text-mining studies. Consequently, the results and findings are based on a limited number of findings.OriginalityText-mining studies targeting research on quality are scarce, and there seem to be no prior models that depict the quality paradigm based on such studies. The perspectives presented here will advance the existing paradigmatic discourse. The new viewpoints aim to facilitate and deepen the discussion on current and future directions of the paradigm.
Subject
Strategy and Management,General Business, Management and Accounting,Business and International Management,General Decision Sciences
Reference120 articles.
1. Total quality management: a literature review and an agenda for future research;Production and Operations Management,1995
2. AlSumait, L., Barbará, D., Gentle, J. and Domeniconi, C. (2009), “Topic significance ranking of LDA generative models”, in Buntine, W., Grobelnik, M., Mladenić, D. and Shawe-Taylor, J. (Eds), Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Dublin, Ireland, Springer, Berlin, pp. 67-82.
3. A systematic literature review on total quality management critical success factors and the identification of new avenues of research;The TQM Journal,2017
Cited by
6 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献