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This research proposes to organise and distil this massive amount of data, making it easier to understand. Using data mining, machine learning techniques and visual approaches, researchers and managers can extract valuable insights (on guests' preferences) and convert them into strategic thinking based on exploration and predictive analysis. Consequently, this research aims to assist hotel managers in making informed decisions, thus improving the overall guest experience and increasing competitiveness.

This research employs natural language processing techniques, data visualisation proposals and machine learning methodologies to analyse unstructured guest service experience content. In particular, this research (1) applies data mining to evaluate the role and significance of critical terms and semantic structures in hotel assessments; (2) identifies salient tokens to depict guests' narratives based on term frequency and the information quantity they convey; and (3) tackles the challenge of managing extensive document repositories through automated identification of latent topics in reviews by using machine learning methods for semantic grouping and pattern visualisation.

This study’s findings (1) aim to identify critical features and topics that guests highlight during their hotel stays, (2) visually explore the relationships between these features and differences among diverse types of travellers through online hotel reviews and (3) determine predictive power. Their implications are crucial for the hospitality domain, as they provide real-time insights into guests' perceptions and business performance and are essential for making informed decisions and staying competitive.

This research seeks to minimise the cognitive processing costs of the enormous amount of content published by the user through a better organisation of hotel service reviews and their visualisation. Likewise, this research aims to propose a methodology and method available to tourism organisations to obtain truly useable knowledge in the design of the hotel offer and its value propositions.

Since the first definition of open innovation (OI), the indivisible relationship between this concept and entrepreneurship was undeniable. However, the exact mechanisms by which an entrepreneurial approach may benefit OI processes and vice versa are not yet fully understood. The study aims to offer an accurate map of the knowledge evolution of the OI–entrepreneurship relationship and interesting gaps to be filled in the future.

The study adopted a bibliometric analysis, coupled with a systematic literature review performed over a data set of 106 peer-reviewed articles published from 2005 to 2020 to identify thematic clusters.

The results show five thematic clusters: entrepreneurial opportunities, organisational opportunities, strategic partnership opportunities, institutional opportunities and digital opportunities for OI. Investigating each of them, the authors created a framework that highlights future avenues for further developing the topic.

This study is the first of its kind to systematise, analyse and critically interpret the literature concerned with the topic of the OI–entrepreneurship.

Management research on serendipity, which is defined as a search that leads to an unintended discovery, has increased considerably over the last three decades. In this article, management research on serendipity (up to the end of 2021) is reviewed and synthesised.

A bibliometric analysis was conducted on 85 peer-reviewed articles extracted from the Scopus database, which was then integrated with a systematic literature review.

The bibliometric analysis revealed that management literature on serendipity is framed around four main thematic areas: conceptual boundaries, conditions favouring serendipity and outcomes, foreign market entry and the relationship between serendipity, networks and assets. A systematic literature review was then conducted on each of the identified clusters.

The present article offers a systematised view of the extant body of research on serendipity in management studies. Based on the findings, the main implications and future research agendas are discussed.

The purpose of this paper is to analyze the role that food and beverage (F&B) sensory stimuli play in building non-food-themed touristic experiences, such as wellness tourism experiences.

This paper adopts an asynchronous netnographic approach supported by software (T-Lab, 2021). The study was conducted on a database consisting of 3,141 reviews in English, left by customers of 38 wellness facilities (Spa Retreats) spread across 5 continents.

The analysis reveals that F&B stimuli contribute significantly to the tourist's perception of the wellness experience in a two-fold manner: on the one hand, they support the wellness experience, and on the other, through specific proposals (e.g. wine, vegan, detox, etc.), they qualify and differentiate the wellness experience in a hedonic rather than eudaimonic way.

The present study contributes to managerial literature on the topic of gastronomic tourism and wellness tourism by providing, on an international scale, empirical evidence of (a) the importance and role of F&B touchpoints in hybrid gastronomic experiences; and (b) the presence of a variety of “wellness experiencescapes”.

This study is the first attempt to measure the role of F&B in tourists' perceptions of non-food-themed experiences. The research not only provides new data on the wellness experience through a cross-continental analysis but also offers useful theoretical and managerial insights for the design of wellness tourism experiences.

This study applies bibliometric analysis to explore the evolution of the research paradigm of agility related to management and organisations.

Authors prepared a quantitative study of the review of selected articles using co-citation analysis and bibliographic coupling. Based on the bibliometric analyses, the evolution of the agility field (past, present, and future of agility research) was prepared.

Emergent themes focus on the importance of agility in interpreting organisational responses in the context of issues as diverse as information systems and business intelligence systems, market orientation, strategic alignment and social computing. Future research needs to focus on digitisation in conjunction with informatisation, an important topic for creating a new organisational culture and knowledge management through increased collaboration between humans and machines.

As the authors are aware, this study is one of the first to choose to show the overall development and importance of agility through quantitative bibliometric methods used to assess the value and contribution of scientific productivity and its impact on development.

The emergence of machine learning has opened a new way for researchers. It allows them to supplement the traditional manual methods for conducting a literature review and turning it into smart literature. This study aims to present a framework for incorporating machine learning into financial statement fraud (FSF) literature analysis. This framework facilitates the analysis of a large amount of literature to show the trend of the field and identify the most productive authors, journals and potential areas for future research.

In this study, a framework was introduced that merges bibliometric analysis techniques such as word frequency, co-word analysis and coauthorship analysis with the Latent Dirichlet Allocation topic modeling approach. This framework was used to uncover subtopics from 20 years of financial fraud research articles. Furthermore, the hierarchical clustering method was used on selected subtopics to demonstrate the primary contexts in the literature on FSF.

This study has contributed to the literature in two ways. First, this study has determined the top journals, articles, countries and keywords based on various bibliometric metrics. Second, using topic modeling and then hierarchy clustering, this study demonstrates the four primary contexts in FSF detection.

In this study, the authors tried to comprehensively view the studies related to financial fraud conducted over two decades. However, this research has limitations that can be an opportunity for future researchers. The first limitation is due to language bias. This study has focused on English language articles, so it is suggested that other researchers consider other languages as well. The second limitation is caused by citation bias. In this study, the authors tried to show the top articles based on the citation criteria. However, judging based on citation alone can be misleading. Therefore, this study suggests that the researchers consider other measures to check the citation quality and assess the studies’ precision by applying meta-analysis.

Despite the popularity of bibliometric analysis and topic modeling, there have been limited efforts to use machine learning for literature review. This novel approach of using hierarchical clustering on topic modeling results enable us to uncover four primary contexts. Furthermore, this method allowed us to show the keywords of each context and highlight significant articles within each context.

This study aims to conduct performance and clustering analyses with the help of Digital Government Reference Library (DGRL) v16.6 database examining the role of emerging technologies (ETs) in public services delivery.

VOSviewer and SciMAT techniques were used for clustering and mapping the use of ETs in the public services delivery. Collecting documents from the DGRL v16.6 database, the paper uses text mining analysis for identifying key terms and trends in e-Government research regarding ETs and public services.

The analysis indicates that all ETs are strongly linked to each other, except for blockchain technologies (due to its disruptive nature), which indicate that ETs can be, therefore, seen as accumulative knowledge. In addition, on the whole, findings identify four stages in the evolution of ETs and their application to public services: the “electronic administration” stage, the “technological baseline” stage, the “managerial” stage and the “disruptive technological” stage.

The output of the present research will help to orient policymakers in the implementation and use of ETs, evaluating the influence of these technologies on public services.

The research helps researchers to track research trends and uncover new paths on ETs and its implementation in public services.

Recent research has focused on the need of implementing ETs for improving public services, which could help cities to improve the citizens’ quality of life in urban areas. This paper contributes to expanding the knowledge about ETs and its implementation in public services, identifying trends and networks in the research about these issues.

This paper aims to examine the extent of price clustering in a selection of Islamic stocks listed in Indonesia, Malaysia and Pakistan and also investigates the determinants of the phenomenon at the firm level.

The author test the uniformity of price distribution in the selected securities. Then, the determinants of price clustering were investigated through multivariate analysis based on a binary logistic regression model. Following the arguments of Narayan et al. (2011), who emphasize the importance of considering firm heterogeneity when studying the phenomenon, the author conducts the empirical study at the firm level.

The evidence indicates that Islamic stocks show a mild level of price clustering. Only half of the stocks under analysis rejected the uniformity test in the distribution of prices. In these cases, investors exhibited a preference for prices ending at zero and five. The evidence does not confirm the cultural clustering theories. Price clustering is found to be positively associated with price level and relative bid-ask spread. Overall, the negotiation hypothesis, which predicts that investors prefer round prices to minimize the costs associated with negotiations, best explains most of our results.

The existence of price clustering is difficult to reconcile with the prediction of the efficient market hypothesis that prices should follow a random walk. Moreover, the evidence indicates that Muslim investors share a preference for round prices in some settings, under the assumption that Islamic stocks are mostly traded by Muslim investors.

To the author’s best knowledge, this is the first study to address the subject of price clustering in Islamic stocks.