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The use of structured literature review methods like bibliometric analysis is growing in the management fields, but there is limited knowledge on how they can be facilitated by technology. Hence, we conducted a broad overview of software tools, their roles, and limitations in structured (bibliometric) literature reviewing activities. Subsequently, we show that several software tools are freely available to aid in searching the literature, identifying/ extracting relevant publications, screening/assessing quality of the extracted data, and performing analyses to generate insights from the literature. However, their applications may be confronted with several challenges such as limited analytical and functional capabilities, inadequate technological skills of researchers, and the fact that the researcher's insights are still needed to generate compelling conclusions from the results produced by software tools. Consequently, we contribute toward advancing the methodologies for performing structured reviews by providing a comprehensive and updated overview of the knowledge base of key technological software tools and the conduct of structured or bibliometric literature reviews.

This paper contains a theoretically inspired discussion of recent Norwegian controversies related to the management of public library space as a civil public sphere.

This study engages with theories of civil public spheres and their application within a Nordic context. The theories are applied in discussions of recent controversies related to the management of Norwegian public libraries as civil public spheres, as represented in professional journals and press articles.

Through the discussion, it becomes apparent that the value of neutrality and librarians' inclusive practices on the part of societal minorities might be conflicting when managing public libraries as civil public spheres.

This paper engages with recent library controversies in Norway and discusses them in light of recent scholarly work on library activism in a Nordic context, as well as recent theorizations of civil public spheres in the Nordic countries. It thus connects ongoing discussions among Norwegian librarians with recent library research and ongoing theorization of civil public spheres within the Nordic model.

This paper seeks to revisit the interrelationship among tourists’ perceived value of the destination, tourist satisfaction and destination loyalty in the heritage tourism site of Hoi An. In addition, the moderating role of tourists’ perceived crowding, which has become remarkably common at the site, in such a triangle relationship will also be explored. In other words, this study aims to validate an extended model of perceived value – tourist satisfaction – destination loyalty – perceived crowding.

The study collects data from 403 tourists who visited Hoi An during peak season through an online questionnaire. The data were later analysed using AMOS and Warp partial least squares.

The results validate the significant and positive correlation among perceived value, customer satisfaction and destination loyalty. Also, perceived crowding was confirmed to affect the relationship among these three variables negatively. In terms of academic contributions, this paper empirically proved that low levels of tourist satisfaction and destination loyalty among tourists who highly value their visiting experience at World Heritage Sites (WHS) can be caused by perceived crowding.

So far, current literature has investigated the direct (either positive or negative) relationship between perceived crowding and post-visit behaviours of tourists (Nie et al., 2022; Papadopoulou, Ribeiro, & Prayag, 2023; Stemmer, Gjerald, & Øgaard, 2022). Broadening this area of research, the authors of this paper used the social interference theory and the stimulus-overload theory to explain the low level of tourist satisfaction and destination loyalty among tourists who highly value their visiting experience at WHS.

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.

By building and examining an integral model, the principal objectives of this research are to systematically explore how indirect and direct network externalities lead to user loyalty toward WeChat through the mediating effect of perceived gratifications.

The data were collected through an online survey of 688 young people in Mainland China. To empirically assess the conceptual model, zero-order correlation analyses and structural equation modeling were carried out utilizing web-based data.

Path analysis results demonstrate that indirect network externalities and direct network externalities exert a significant impact on users' hedonic gratifications and utilitarian gratifications. Moreover, the study discovers the significant mediating influences of utilitarian gratifications on the association between indirect network externalities and user loyalty.

Theoretically, this article may extend the scope of diverse studies on the association between network externalities and perceived gratifications and offer fresh insights into how mobile social media could actually improve user loyalty through enhancing perceived values among younger generation. Practically, this research assists mobile social media practitioners in retaining users and gaining competitive advantages over rival applications.

Although the extraordinary growth of WeChat has successfully become the dominant media by which individuals develop interpersonal network and contact with others, the roles of perceived gratifications between network externalities and user loyalty toward WeChat have not yet been investigated in depth. These obtained outcomes not only enrich the existing literature regarding the relationship between network externalities and affective response, but also offer fresh insights to mobile social media designers, marketers and users.

This study aims to explore the good crowding effect among Indian domestic travellers during the COVID-19 pandemic in the context of the city destination. This study uses the framework of social motivation theory to achieve the objective.

This study adopted a qualitative research design by taking the case of Shimla, Himachal Pradesh. Using purposive sampling, semi-structured interviews were conducted with 37 respondents, and themes were drawn manually.

The analysis found four themes that create a good crowding effect among domestic tourists, namely, convenience and price; familiarity and place attachment; social affiliation; and safety. The themes indicated that despite the pandemic, and constant occurrences of new variants, Indian domestic tourists’ on-site attitude towards crowding was favourable.

Firstly, the good crowding effect during the pandemic could have been better understood using empirical data. Secondly, the results cannot be generalized, specifically for developed economies.

This study offers practical implications to destination managers and local administrative bodies for whom achieving sustainability in urban tourism has always been concerning. These include developing infrastructural facilities, encouraging cultural activities in city centres and improving the perception of safety to sustain the good crowding effect.

The affective dimension involved in making a travelling decision played a significant role in the post-pandemic phase. While suppliers needed survival, tourists needed social affiliation and escape from the mandated home isolation due to multiple phases of COVID-19 lockdown in India. This study adds value to society by emphasising that the need for social affiliation among travellers remains intact, and the tourism industry should embrace this transformation.

While most of the pandemic-related studies criticised crowd and tourists’ crowd averting behaviour, this study reported that the good crowding effect could also be an outcome owing to different factors. Therefore, this study offers distinctive nuance of tourists’ behaviour in the post-COVID-19 phase, allowing destination managers and tourism stakeholders to re-think their strategies.

Environmental sustainability is quickly becoming one of the most critical issues in industry development. This study aims to conduct a systematic literature review through which the author can provide various research areas to work on for future researchers and provide insight into Industry 4.0 and environmental sustainability.

This study accomplishes this by performing a backward analysis using text mining on the Scopus database. Latent semantic analysis (LSA) was used to analyze the corpus of 4,364 articles published between 2013 and 2023. The authors generated ten clusters using keywords in the industrial revolution and environmental sustainability domain, highlighting ten research avenues for further exploration.

In this study, three research questions discuss the role of environmental sustainability with Industry 4.0. The author predicted ten clusters treated as recent trends on which more insight is required from future researchers. The authors provided year-wise analysis, top authors, top countries, top sources and network analysis related to the topic. Finally, the study provided industrialization’s effect on environmental sustainability and the future aspect of automation.

The reliability of the current study may be compromised, notwithstanding the size of the sample used. Poor retrieval of the literature corpus can be attributed to the limitations imposed by the search words, synonyms, string construction and variety of search engines used, as well as to the accurate exclusion of results for which the search string is insufficient.

This research is the first-ever study in which a natural language processing technique is implemented to predict future research areas based on the keywords–document relationship.

The purpose of this paper is to compare the effectiveness of the various text mining techniques that can be used to classify maintenance work-order records into their respective failure modes, focussing on the choice of algorithm and preprocessing transforms. Three algorithms are evaluated, namely Bernoulli Naïve Bayes, multinomial Naïve Bayes and support vector machines.

The paper has both a theoretical and experimental component. In the literature review, the various algorithms and preprocessing techniques used in text classification is considered from three perspectives: the domain-specific maintenance literature, the broader short-form literature and the general text classification literature. The experimental component consists of a 5 × 2 nested cross-validation with an inner optimisation loop performed using a randomised search procedure.

From the literature review, the aspects most affected by short document length are identified as the feature representation scheme, higher-order n-grams, document length normalisation, stemming, stop-word removal and algorithm selection. However, from the experimental analysis, the selection of preprocessing transforms seemed more dependent on the particular algorithm than on short document length. Multinomial Naïve Bayes performs marginally better than the other algorithms, but overall, the performances of the optimised models are comparable.

This work highlights the importance of model optimisation, including the selection of preprocessing transforms. Not only did the optimisation improve the performance of all the algorithms substantially, but it also affects model comparisons, with multinomial Naïve Bayes going from the worst to the best performing algorithm.

The purpose of this study is to explore to which extent data mining research would be associated with the library and information science (LIS) discipline. This study aims to identify data mining related subject terms and topics in representative LIS scholarly publications.

A large set of bibliographic records over 38,000 was collected from a scholarly database representing the fields of LIS and the data mining, respectively. A multitude of text mining techniques were applied to investigate prevailing subject terms and research topics, such as influential term analysis and Dirichlet multinomial regression topic modeling.

The findings of this study revealed the relationship between the LIS and data mining research domains. Various data mining method terms were observed in recent LIS publications, such as machine learning, artificial intelligence and neural networks. The topic modeling result identified prevailing data mining related research topics in LIS, such as machine learning, deep learning, big data and among others. In addition, this study investigated the trends of popular topics in LIS over time in the recent decade.

This investigation is one of a few studies that empirically investigated the relationships between the LIS and data mining research domains. Multiple text mining techniques were employed to delineate to which extent the two research domains would be associated with each other based on both at the term-level and topic-level analysis. Methodologically, the study identified influential terms in each domain using multiple feature selection indices. In addition, Dirichlet multinomial regression was applied to explore LIS topics in relation to data mining.