Research trends in quality management in years 2000-2019. Issue 4 (4th May 2020)

• Record Type: Journal Article
• Authors: Wawak, Sławomir; Rogala, Piotr; Dahlgaard-Park, Su Mi
• Is Part Of: International journal of quality and service sciences. Volume 12:Issue 4(2020)
• Page Start: 417
• Page End: 433
• Publication Date: 2020-05-04
• Subjects: Quality management -- Trends -- Text-mining -- Systematic literature review; Service industries -- Quality control -- Periodicals; Service industries -- Quality control -- Statistics -- Periodicals; Service industries -- Management -- Periodicals; Service industries -- Management -- Statistics -- Periodicals; 338.4
• DOI: 10.1108/IJQSS-12-2019-0133
• Languages: English
• ISSNs: 1756-669X

Abstract

Abstract : Purpose: This study aims to demonstrate the suitability of text-mining toolset for the discovery of trends in quality management (QM) literature in 2000-2019. The hypothesis was formulated that as the field of study is mature, the most important trends are related to deepening and broadening of the knowledge. Design/methodology/approach: A novel approach to trend discovery was proposed. The computer-aided analysis of full-texts of papers led to increased reliability and level of detail of the achieved results and helped significantly reduce researchers' bias. Overall, 4, 833 papers from 8 journal dedicated to QM were analysed. Findings: Trends discovery led to the identification of 45 trends: 17 long-lasting trends, 4 declining trends, 11 emerging trends and 13 ephemeris trends. They were compared to the results of earlier studies. New trends and potential gaps were discussed. Practical implications: The results highlight the trends that gain or lose popularity, thus they can be used to focus studies, as well as find new subjects, which are not so popular yet. The knowledge about emerging trends is also important for those quality managers who strive for improvement of their efficiency. Originality/value: The research was designed to bypass the limitations of previous studies. The use of text mining methods and analysis of full texts of papers delivered more detailed and reliable data. Resignation from predefinition of classification criteria significantly reduced researchers' bias and allowed the discovery of new trends, not identified in previous studies.