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.

Lo 和 Chai 进行的研究评论（[2012]。质量管理文献的定量分析发表于全面质量管理和卓越业务（1996-2010）。全面质量管理和卓越业务，23 (5-6), 629-651 .doi:10.1080/14783363.2012.669553) 和 Dereli 等人。（[2011]。对从 1995 年到 2008 年发表在全面质量管理和卓越业务上的论文进行分析。全面质量管理和卓越业务，22(3), 373–386。doi:10.1080/14783363.2010.532337) 是基于 TQM&BE 的，它们共同涵盖了 1995 年至 2010 年期间。这两项研究的局限性在于，研究期间结束于 2010 年。因此，本研究通过回顾论文扩展了之前的研究发表于 2010 年至 2019 年。TQM&BE 是一个适合分析的对象，因为它是质量管理领域引用的顶级期刊之一，在 TQM 的质量和科学知识方面起着关键作用。

本研究证明了使用（自组织图）SOM 算法构建主题图的可行性。SOM 技术是最流行的人工神经网络 (ANN) 之一。根据这张地图，确定了六个主题领域。此外，2010年后新确定的主题包括BE框架，以及创新、学习、团队和设计。结果还表明，对 TQM&BE 的研究已经从 TQM 的主导地位转向服务质量、（国际标准化组织）ISO 以及创新和学习。最后，结果表明，SOM 可以被视为一种为学术研究评论开发主题图的技术。