Topic analysis of studies on total quality management and business excellence: an update on research from 2010 to 2019

• Published in: Total quality management & business excellence 2022-07, Vol.33 (9-10), p.1039-1055
• Main Authors: Hsu, Sheng-Hsun, Chen, Yu-Fan, Chou, Ying-Chyi
• Format: Article
• Language: English
• Subjects: Quality management; research review; self-organizing map; text mining; topic modeling; Total quality
• ISSN: 1478-3363; 1478-3371
• DOI: 10.1080/14783363.2021.1916392

The research reviews conducted by Lo and Chai ([2012]. Quantitative analysis of quality management literature published in total quality management and business excellence (1996-2010). Total Quality Management & Business Excellence, 23(5-6), 629-651. doi:10.1080/14783363.2012.669553) and Dereli et al. ([2011]. An analysis of the papers published in total quality management & business excellence from 1995 through 2008. Total Quality Management & Business Excellence, 22(3), 373-386. doi:10.1080/14783363.2010.532337) were based on TQM&BE, and together they covered the period between 1995 and 2010. The limitation of these two studies is that the study period ended in 2010. Accordingly, this study extends those previous studies by reviewing papers published between 2010 and 2019. TQM&BE is a suitable object for analysis because it is one of the top journals cited in the quality management field and plays a key role in the quality and scientific knowledge of TQM. This study demonstrated the feasibility of using the (self-organizing map) SOM algorithm to build a topic map. The SOM technique is one of the most popular artificial neural networks (ANNs). Based on this map, six topic areas were identified. In addition, the new identified themes after 2010 include the BE framework, and innovation, learning, team, and design. The results also showed that research on TQM&BE has undergone a shift in focus from a dominance of TQM to service quality, (International Organization for Standardization) ISO, and innovation and learning. Finally, the results showed that the SOM can be considered as a technique for developing topic maps for academic research reviews.