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The purpose of this paper is to present the initial relationship between the Classification Research Group (CRG) and the Center for Documentation and Communication Research (CDCR) and how this relationship changed between 1952 and 1970. The theory of normative behavior and its concepts of worldviews, social norms, social types, and information behavior are used to characterize the relationship between the small worlds of the two groups with the intent of understanding the gap between early classification research and information retrieval (IR) research.

This is a mixed method analysis of two groups as evidenced in published artifacts by and about their work. A thorough review of historical literature about the groups as well as their own published works was employed and an author co-citation analysis was used to characterize the conceptual similarities and differences of the two groups of researchers.

The CRG focused on fundamental principles to aid classification and retrieval of information. The CDCR were more inclined to develop practical methods of retrieval without benefit of good theoretical foundations. The CRG began it work under the contention that the general classification schemes at the time were inadequate for the developing IR mechanisms. The CDCR rejected the classification schemes of the times and focused on developing punch card mechanisms and processes that were generously funded by both government and corporate funding.

This paper provides a unique historical analysis of two groups of influential researchers in the field of library and information science.

Undiscovered public knowledge is a relatively unstudied phenomenon, and the few extended examples that have been published are intradisciplinary. This paper presents the concept of ‘facet’ as an example of interdisciplinary undiscovered public knowledge. ‘Facets’ were central to the bibliographic classification theory of S.R. Ranganathan in India and to the behavioural research of L. Guttman in Israel. The term had the same meaning in both fields, and the concept was developed and exploited at about the same time in both, but two separate, unconnected literatures grew up around the term and its associated concepts. This paper examines the origins and parallel uses of the concept and the term in both fields as a case study of interdisciplinary knowledge that could have been, but was apparently not, discovered any time between the early 1950s and the present using simple, readily available information retrieval techniques.

The purpose is to map and discuss two schools of thought in knowledge organization research. The objective of this mapping is to examine the conceptual views and the derived questions and concerns voiced in these two schools and whether they fit with concerns in contemporary digital culture.

The approach is a comparative analysis and discussion.

The comparative analysis and discussion point out the different sets of questions the two schools are concerned with distinct epistemological and ontological implications.

The originality of this article is the naming, mapping and discussion of two schools of research in knowledge with a view to how they fit with problems of ordering, archiving and searching in digital culture.

This study aims to investigate the current state of research using deep learning methods for text classification in the tourism and hospitality field and to propose specific guidelines for future research.

This study undertakes a qualitative and critical review of studies that use deep learning methods for text classification in research fields of tourism and hospitality and computer science. The data was collected from the Web of Science database and included studies published until February 2022.

Findings show that current research has mainly focused on text feature classification, text rating classification and text sentiment classification. Most of the deep learning methods used are relatively old, proposed in the 20th century, including feed-forward neural networks and artificial neural networks, among others. Deep learning algorithms proposed in recent years in the field of computer science with better classification performance have not been introduced to tourism and hospitality for large-scale dissemination and use. In addition, most of the data the studies used were from publicly available rating data sets; only two studies manually annotated data collected from online tourism websites.

The applications of deep learning algorithms and data in the tourism and hospitality field are discussed, laying the foundation for future text mining research. The findings also hold implications for managers regarding the use of deep learning in tourism and hospitality. Researchers and practitioners can use methodological frameworks and recommendations proposed in this study to perform more effective classifications such as for quality assessment or service feature extraction purposes.

The paper provides an integrative review of research in text classification using deep learning methods in the tourism and hospitality field, points out newer deep learning methods that are suitable for classification and identifies how to develop different annotated data sets applicable to the field. Furthermore, foundations and directions for future text classification research are set.

Patterns have proven to be useful for documenting general reusable solutions to a commonly occurring problem. In recent years, several different business process management (BPM)-related patterns have been published. Despite the large number of publications on this subject, there is no work that provides a comprehensive overview and categorization of the published business process model patterns. The purpose of this paper is to close this gap by providing a taxonomy of patterns as well as a classification of 89 research works.

The authors analyzed 280 research articles following a structured iterative procedure inspired by the method for taxonomy development from Nickerson et al. (2013). Using deductive and inductive reasoning processes embedded in concurrent as well as joint research activities, the authors created a taxonomy of patterns as well as a classification of 89 research works.

In general, the findings extend the current understanding of BPM patterns. The authors identify pattern categories that are highly populated with research works as well as categories that have received far less attention such as risk and security, the ecological perspective and process architecture. Further, the analysis shows that there is not yet an overarching pattern language for business process model patterns. The insights can be used as starting point for developing such a pattern language.

Up to now, no comprehensive pattern taxonomy and research classification exists. The taxonomy and classification are useful for searching pattern works which is also supported by an accompanying website complementing the work. In regard to future research and publications on patterns, the authors derive recommendations regarding the content and structure of pattern publications.

This paper aims to improve the Commission of Higher Education (CHE)'s current university classification and develop the Thai Higher Education Classification Model (THEC‐model). This study supports the CHE's initiative to ensure that the future is more science‐oriented by encouraging universities to become National Research Universities (NRUs).

The research applies empirical data and a statistical approach for the THEC‐model's development. The model's results are then compared with the decisions reached earlier by the CHE in selecting public universities as research‐intensive.

The proposed classification criteria for NRUs consist of: research funding; the variety of instructional programmes; the level of instructional programmes; instructors and research staff body; and student body, which have significantly statistically influenced the differences in Y‐variables: research output, citation, and research awards at alpha 0.05. The initial results show that eight universities are selected. The findings are consistent with the 2008 announcement, except for two universities.

The developed THEC‐model benefits academic researchers, university administrators, and policymakers for many reasons. For example, the THEC‐model provides information for academic researchers to determine the important variables for a research university. The model provides information for policymakers to manage higher education effectively to raise the targets for a university.

The THEC‐model criteria were generated by reviewing the classification system in different locations. Such criteria could be applied extensively at domestic and international level. Moreover, the developed THEC‐model is based on a statistical approach and empirical data improved the reliability and would be beneficial to the CHEs in Thailand for further improvement on research‐focused HEI classification criteria in the future.

When a large number of project proposals are evaluated to allocate available funds, grouping them based on their similarities is beneficial. Current approaches to group proposals are primarily based on manual matching of similar topics, discipline areas and keywords declared by project applicants. When the number of proposals increases, this task becomes complex and requires excessive time. This paper aims to demonstrate how to effectively use the rich information in the titles and abstracts of Turkish project proposals to group them automatically.

This study proposes a model that effectively groups Turkish project proposals by combining word embedding, clustering and classification techniques. The proposed model uses FastText, BERT and term frequency/inverse document frequency (TF/IDF) word-embedding techniques to extract terms from the titles and abstracts of project proposals in Turkish. The extracted terms were grouped using both the clustering and classification techniques. Natural groups contained within the corpus were discovered using k-means, k-means++, k-medoids and agglomerative clustering algorithms. Additionally, this study employs classification approaches to predict the target class for each document in the corpus. To classify project proposals, various classifiers, including k-nearest neighbors (KNN), support vector machines (SVM), artificial neural networks (ANN), classification and regression trees (CART) and random forest (RF), are used. Empirical experiments were conducted to validate the effectiveness of the proposed method by using real data from the Istanbul Development Agency.

The results show that the generated word embeddings can effectively represent proposal texts as vectors, and can be used as inputs for clustering or classification algorithms. Using clustering algorithms, the document corpus is divided into five groups. In addition, the results demonstrate that the proposals can easily be categorized into predefined categories using classification algorithms. SVM-Linear achieved the highest prediction accuracy (89.2%) with the FastText word embedding method. A comparison of manual grouping with automatic classification and clustering results revealed that both classification and clustering techniques have a high success rate.

The proposed model automatically benefits from the rich information in project proposals and significantly reduces numerous time-consuming tasks that managers must perform manually. Thus, it eliminates the drawbacks of the current manual methods and yields significantly more accurate results. In the future, additional experiments should be conducted to validate the proposed method using data from other funding organizations.

This study presents the application of word embedding methods to effectively use the rich information in the titles and abstracts of Turkish project proposals. Existing research studies focus on the automatic grouping of proposals; traditional frequency-based word embedding methods are used for feature extraction methods to represent project proposals. Unlike previous research, this study employs two outperforming neural network-based textual feature extraction techniques to obtain terms representing the proposals: BERT as a contextual word embedding method and FastText as a static word embedding method. Moreover, to the best of our knowledge, there has been no research conducted on the grouping of project proposals in Turkish.

The group has continued to meet regularly since the publication of the last bulletin and has welcomed a number of new members and visitors from both home and overseas. Many members who joined at the beginning or very early on in the Group's history still attend regularly, but several long‐standing members have also left, or ceased active participation, in the period under review. Towards the end of 1972 Mr Wells relinquished the chairmanship of the Group, due to pressure of work, and his place was taken by Mr Mills. Another departure, and one that robbed the Group of one of its most active and forceful members, was that of Jason Farradane. He left the country in 1974, and the Group presented him with a book as a memento of many enjoyable and provocative discussions stimulated by his presence at the meetings which he unfailingly attended. It was with great pleasure that he was welcomed back to a meeting while he was visiting this country in January 1976.

Few marketing studies look at service classifications for self‐service technologies (SSTs) and none directly compare consumer‐based perceptions of traditional services to SSTs. To fill this gap, this study aims to examine how customers perceived traditional services and SSTs on service classifications criteria proposed by Lovelock, Bowen and Bell.

In two separate studies consumer ratings for each classification method on each service were obtained. Using multi‐dimensional scaling (MDS), 13 traditional services and 12 SSTs were separately mapped onto a perceptual space of service classifications.

The comparison of the two perceptual spaces reveals that consumers viewed the classifications of convenience, person/object, and delivery for SSTs differently than that for traditional services. The classifications of traditional services were represented by two dimensions of customization/standardization and person/object. In contrast, the classifications of SSTs were represented by two dimensions of customization/standardization and separability/inseparability. Thus the description of the underlying dimensions of services varied by traditional services or SSTs.

It is possible that the results of the MDS were influenced by the use of preset classifications. Results may also be influenced by the authors' choice of MDS method. Further research is needed regarding the classification of SSTs and the use of these classifications for SST design.

This research extends previous consumer‐based classification research by including SSTs. The findings identified separate typologies for SSTs and traditional services. The typologies should be of interest to both researchers and managers who are interested in how SSTs are perceived by consumers.

Projects have high stakes in how they are categorized. The final place of a project within a classification scheme depends on the inclusion or exclusion of certain classification criteria. So far, many researchers and organizations have used a variety classification criteria to construct different project classification schemes. However, most of these classification criteria have been taken for granted and the process of selecting them to categorize projects still remains a black box. The purpose of this paper is to open the black box of classification process and explain how it is reflected in picking the classification criteria.

Drawing on insights from cognitive psychology’s literature, the authors examine the main views of classification process to provide insight into the unknown or implicit reasons that one might have to pick particular attributes as project classification criteria.

The authors argue that classification occurs in the eye of the beholder; it is not only the project’s features per se but also the classifier’s “goals, ideal and preference” or “knowledge of causal relations” that are reflected in the classification criteria.

By elaborating the classification process, the authors brought the project context into the big picture of classification and provide a more rational, and coherent picture of how project classification works. This contributes to a theoretical blind spot, raised by prior researchers, related to the selection of project classification criteria.

Understanding classification processes will reduce the ambiguities, inconsistencies and multiple interpretations of project categories and help practitioners increase their projects’ visibility and legitimacy within an already established classification scheme. These implications help organizations in addressing some of the main obstacles to using categorization in project management practice.

The review of prior work in the category research literature and the insights from this paper will provide project management scholars with a useful toolbox for future research on project classification, which has long been understudied.