In this article, a content analysis is performed for the papers that appeared in Total Quality Management and Business Excellence. A total of 1132 papers which appeared in the volumes from 6 to 19 are analysed into several different dimensions. Country origins and academic unit of authors (engineering/basic sciences/management sciences, etc.), research methods (library search/conceptual study/case study/simulation/field survey, etc.) and unit of analyses (individual/company/firm/regional/national/international level) are included in this analysis. In addition to those attributes, keywords of the papers and the content of case studies (manufacturing/service/information technology) are computed as well. Association mining has also been performed to determine the consequential relationships among the attributes.

In order to establish an innovation culture, a set of organizational procedures and practices called “Innovation Management”, which may differ among companies, should be followed. At the enterprise level, systematic innovation management becomes more complicated. A number of works covering various aspects of this subject have been published. However, a systematic synthesis of all of these contributions is still lacking in management literature. In this review, we aim to analyze and classify the main contributions published on the topic of innovation management systems/standards in management literature, seeking to discover the gaps which still remain in the literature, and to outline future avenues of research in this domain. More than 70 articles in Innovation Management Systems/Standards (IMS/St) studies published in peer-reviewed journals during 2006–2020 are reviewed and analyzed systematically by searching the science databases ScienceDirect, Scopus and Emerald, etc., and using Google Scholar and Mendeley Elsevier to identify related terms. A complete and accurate view of the latest literature on IMS/St is provided, which identifies the main topics developed in the management literature on IMS/St, as well as significant gaps, and demonstrates the low maturity level of the current state of the field. This paper contributes theoretically to the development of literature on IMS/St and provides a clear understanding of the state of the field during the period 2006–2020, shedding light on the research needed in the future in this field of study. From a managerial perspective, it can help companies to better understand the implications of IMS/St, and to harvest the best benefits from the implementation of IMS/St. Our study also answers these three important questions: 1. What are the main topics developed in the management literature on IMS/St so far? 2. Are innovation management standards mature from a practical point of view? 3. What are the main research gaps in management literature, and how could future avenues of research be shaped?

Fabric quality has an important role in the textile sector. Fabric defect, which is a highly important factor that influences the fabric quality, has become a concept that researchers are trying to minimize. Due to the limited capacity of human resources, human-based defect detection results in low performance and significant loss of time. To overcome human-based limited capacity, computer vision-based methods have emerged. Thanks to new additions to these methods over time, fabric defect detection methods have begun to show almost one hundred percent performance. Convolutional Neural Networks (CNNs) play a leading role in this high-performance success. However, Convolutional Neural Networks cause information loss in the pooling process. Capsule Networks is a useful technique for minimizing information loss. This paper proposes Capsule Networks, a new generation method that represents an alternative to Convolutional Neural Networks for deep learning tasks. TILDA dataset as source data for training and testing phases are employed. The model is trained for 100, 200, and 270 epoch times. Model performance is evaluated based on accuracy, recall, and precision performance metrics. Compared to mainstream deep learning algorithms, this method offers improved performance in terms of accuracy. This method has been performed under different circumstances and has achieved a performance value of 98.7%. The main contributions of this study are to use Capsule Networks in the fabric defect detection domain and to obtain a significant performance result.