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This study consolidates the current state of knowledge in customer experience (CX) research by examining literature published over last 20 years (2003–2022). The purpose is to create a holistic snapshot through synthesis of extant CX research; and thereafter, leverage the snapshot to generate directions for future inquiry.

The study uses systematic literature review (SLR) using SPAR-4-SLR protocol to generate a set of 277 articles. We follow it up with scientometric analysis techniques of bibliographic coupling and betweenness centrality measurement. Finally, to extract topics from the full-text content of sampled articles, we carry out topic modelling using BERTopic.

The study unearths following insights: (1) the predominant underlying topics in extant CX research are: service experience, store brand marketing, mall and online shopping, fun and luxury marketing, brand equity and loyalty artificial intelligence (AI) and machine learning (ML) and augmented reality (AR) and virtual reality (VR); (2) bibliographic coupling suggests existence of six clusters in CX research. The study also showcases the nucleus of CX research, flagship research, major publication outlets and representative studies for each extracted topic.

The paper introduces BERTopic to marketing scholars as a novel method of executing topic modelling and thereby, unearthing latent insights.

The study expands the body of knowledge on CX by applying three complementary analytical approaches: SLR, scientometric analysis and topic modelling using BERTopic.

A gap between research and practice is commonly perceived throughout accounting academia. However, empirical evidence on the magnitude of this detachment remains scarce. The authors provide new evidence to the ongoing debate by introducing a novel topic-based approach to capture the research-practice gap and quantify its extent. They also explore regional differences in the research-practice gap.

The authors apply the unsupervised machine learning approach Latent Dirichlet allocation (LDA) to compare the topical composition of 2,251 articles from six premier research, practice and bridging journals from the USA and Europe between 2009 and 2019. The authors extend the existing methods of summarizing literature and develop metrics that allow researchers to evaluate the research-practice gap. The authors conduct a plethora of additional analyses to corroborate the findings.

The results substantiate a pronounced topic-related research-practice gap in accounting literature and document its statistical significance. Moreover, the authors uncover that this gap is more pronounced in the USA than in Europe, highlighting the importance of institutional differences between academic communities.

The authors objectify the debate about the extent of a research-practice gap and stimulate further discussions about explanations and consequences.

To the best of the authors' knowledge, this is the first paper to deploy a rigorous machine learning approach to measure a topic-based research-practice gap in the accounting literature. Additionally, the authors provide theoretical rationales for the extent and regional differences in the research-practice gap.

This study aims to provide a comprehensive analysis of two-sided matching (TSM) research, an interdisciplinary field that integrates both theoretical and practical perspectives. By examining 756 research articles from the Web of Science database, this paper seeks to identify key trends, collaboration patterns and emerging research topics within the TSM domain.

The research utilizes bibliometric analysis combined with a structural topic model to analyze TSM-related articles published between January 1, 2000, and September 30, 2022. The study identifies leading subfields, journals, countries/regions and institutions based on publication volume, total citations and average citations per article. Interaction and collaboration patterns among these entities are examined through co-occurrence and coupling networks. Additionally, five major research topics are identified and explored using topic modeling and co-word networks. This hybrid knowledge mining approach better reveals the inherent structural changes in topic clusters. Topic distribution and network analysis are beneficial in capturing the attention allocation of different entities to knowledge.

The analysis reveals five prominent research topics in TSM: communication resource allocation, stable matching research, computing task assignment, TSM decision-making and market matching mechanism design. These topics represent the main directions of TSM research. The study also uncovers a shift in research focus from theoretical aspects to practical applications. Furthermore, the distribution of knowledge and interaction patterns among key entities align with the identified research trends.

This study offers a novel and detailed overview of TSM research highlighting significant trends and collaboration patterns within the field. By integrating bibliometric methods with structural topic modeling the study provides unique insights into the evolution of TSM research making it a valuable resource for both academic and professional communities.

This study aims to analyze the distribution of novelty among scholarly papers in the field of library and information science (LIS) in China. Specifically, this study explores the distribution of novelty of papers in various journals, research topics and different periods. It is possible to understand the characteristics of LIS research in China and what factors have influenced it.

This paper collects articles published in Chinese library science journals indexed by the Chinese Social Sciences Citation Index from 2000 to 2022. The BERTopic model is used based on abstracts of the papers and to obtain the topic of each paper. Based on the combination innovation theory of reference pairs cited by focal papers, novelty scores of all papers are calculated. Next, this paper analyzes the novelty of papers under different topics. Finally, this paper analyzes the differences in author collaboration patterns across various topics, aiming to explain how these differences relate to the novelty of papers from a collaborative perspective.

This study shows that archival research topics have lower novelty than papers on journal evaluation and patent technology in Chinese LIS. Research papers in this field are gradually becoming more novel over time. Papers on different topics and with varying degrees of novelty exhibit distinct author collaboration patterns, with low-novelty topics more frequently featuring solo authorship, while high-novelty topics tend to involve a higher percentage of inter-institutional collaboration.

This study investigates the novelty characteristics of research papers on different topics in the field of LIS in China. The authors’ contribution includes visualizing research hotspots and trends in the field and analyzing authors’ collaboration patterns at the level of research topics, thereby providing new perspectives on the factors affecting the novelty of these papers.

Challenges related to sustainability have increasingly become pivotal in the realm of business strategy and innovation. Nevertheless, the incorporation of sustainability principles into business strategies and innovative practices remains a subject of ongoing scholarly debate. This paper aims to undertake a thematic literature review on this theme.

Data were gathered from the Scopus, Web of Science and Science Direct databases. The final sample comprised 85 papers. For analytical purposes, this study adopted topic modeling using Latent Dirichlet Allocation (LDA) methodology.

The authors identified five dominant topics concerning the relationship between sustainability, innovation and business strategy. Through a cross-analysis of these topics, the authors theorize that a sustainable innovation strategy encompasses three complementary and interdependent dimensions: capabilities, management and firm. Building on this analysis, the authors outline a research agenda aimed at further exploration and advancement of this theme.

This review enhances the synthesis of research on the theme, prompting reflections on how companies can initiate innovative sustainable actions that align with their business strategy. Additionally, the authors identify specific elements that require improvement to enhance each of the three dimensions of sustainable innovation strategies, such as eco-efficiency, circular economy and the adoption of innovative business models oriented toward services/servitization.

By interweaving sustainability with innovation and business strategy, this study underscores the critical topics that companies and public policymakers should address to support sustainable development at the national level.

While previous literature reviews have focused on the dyadic relationships between sustainability and strategy, or sustainability and innovation, this study extends the boundaries of knowledge by integrating these three concepts into a hybrid theoretical stream.

The set of 2,509 documents related to the human-centric aspect of manufacturing were retrieved from Scopus database and systmatically analyzed. Using an unsupervised machine learning approach based on Latent Dirichlet Allocation we were able to identify latent topics related to human-centric aspect of Industry 5.0.

This study aims to create a scientific map of the human-centric aspect of manufacturing and thus provide a systematic framework for further research development of Industry 5.0.

In this study a 140 unique research topics were identified, 19 of which had sufficient research impact and research interest so that we could mark them as the most significant. In addition to the most significant topics, this study contains a detailed analysis of their development and points out their connections.

Industry 5.0 has three pillars – human-centric, sustainable, and resilient. The sustainable and resilient aspect of manufacturing has been the subject of many studies in the past. The human-centric aspect of such a systematic description and deep analysis of latent topics is currently just passing through.

This study aims to dissect and understand the latent themes of Islamic work ethic (IWE) and explore the driving factors of IWE research.

Structural topic modeling (STM), a sophisticated machine learning technique, was used to analyze a corpus of 205 articles sourced from the Scopus database. These articles cover the 36 years of research on IWE, from 1988 to 2024. Moreover, negative binomial regression was applied to examine the driving factors of IWE research.

The STM analysis unfolds ten topics in conjunction with IWE including individual success, workplace dynamics, organizational work ethics, knowledge management, employee citizenship behavior, financial ethics, job satisfaction, organizational commitment, performance enhancement and leadership. The further STM outputs included word clouds, prevalence proportions, correlation matrix, heatmap, relationship of topics with metadata, topic prominence in the publishing journals and, finally, illustrating trends and future prospects of research on IWE. The results of negative binomial regression reveal that number of authors, article age, journal indexing, authors from multiple countries and number of references are strong drivers of fostering research in IWE, by having significant positive impacts on total citations.

The insights from this study provide valuable guidance for businesses and organizations looking to integrate IWE principles into their operations. By promoting values such as fairness, hard work and ethical behavior, organizations can foster a more inclusive and morally grounded workplace culture. This, in turn, may lead to enhanced employee satisfaction, greater organizational commitment and improved overall performance. Additionally, the emphasis on ethical practices can contribute to broader societal benefits, such as increased trust in business practices and a stronger alignment with social responsibility initiatives.

This is a unique study that explores the latent themes and characteristics of the IWE literature through STM and provides insights on the future research directions. In addition, this study also examines the driving factors of IWE research.

Since passenger safety is critical, aviation maintenance is essential. Aviation maintenance management is changing due to Industry 4.0 (I4.0). According to earlier research, I4.0 technologies improve aircraft manufacturing efficiency and responsiveness through automation, predictive maintenance and process self-optimization. Thus, this study examines I4.0 research and aircraft maintenance's potential interaction.

Using a text-mining methodology, this paper looks at the state of the art in aviation maintenance research in the I4.0 era. We used the topic modeling approach and Latent Dirichlet Allocation (LDA) technique to analyze the abstracts and indexed keywords of 929 research articles on the intersection of aviation maintenance and I4.0, subsequently clustering them into eight topics.

We have mapped out the emerging research trends at the intersection of “aviation maintenance” and “I4.0 technologies”, and presented suggestions for theoretical frameworks, applied frameworks and future lines of inquiry. This paper makes a theoretical contribution to the systematization of literature on I4.0 technologies in aviation maintenance. It provides valuable insight for managers by exploring the implications and opportunities that arise in light of recent innovations brought by I4.0 in aviation maintenance.

This study focuses on the use of Industry 4.0 technologies in aircraft maintenance processes, contributing to the growing research on digital technology in maintenance and maintenance, repair and overhaul (MRO). Furthermore, the study's analysis of the LDA topic model provides valuable insights for future research on using I4.0 technologies to investigate specific areas of application in the context of digital maintenance.

The purpose of this paper is to find out which research topics and methods in information science (IS) articles are used in other disciplines as indicated by citations.

The study analyzes citations to articles in IS published in 31 scholarly IS journals in 2015. The study employs content analysis of articles published in 2015 receiving citations from publication venues representing IS and other disciplines in the citation window 2015–2021. The unit of analysis is the article-citing discipline pair. The data set consists of 1178 IS articles cited altogether 25 K times through 5 K publication venues. Each citation is seen as a contribution to the citing document’s discipline by the cited article, which represents some IS subareas and methodologies, and the author team's disciplinary composition, which is inferred from the authors’ affiliations.

The results show that the citation profiles of disciplines vary depending on research topics, methods and author disciplines. Disciplines external to IS are typically cited in IS articles authored by scholars with the same background. Thus, the export of ideas from IS to other disciplines is evidently smaller than the earlier findings claim. IS should not be credited for contributions by other disciplines published in IS literature.

This study is the first to analyze which research topics and methods in the articles of IS are of use in other disciplines as indicated by citations.

This paper presents a conceptual framework based on an extensive literature review. The aim of this study is to deepen understanding of the relationship between carbon performance and the financial market by applying qualitative research approaches.

The investigation has identified 372 articles sourced from Scopus databases, subjecting the bibliographic data to a comprehensive qualitative–quantitative analysis. The research uses established protocols for a structured literature review, adhering to PRISMA guidelines, machine learning-based structural topic modelling using Python and bibliometric citation analysis.

The results identified the leading academic authors, institutions and countries concerning carbon performance and financial markets literature. Quantitative studies dominate this research theme. The study has identified six knowledge clusters using topic modelling related to environmental reporting; price drivers of carbon markets; environmental policy and capital markets; financial development and carbon emissions; carbon risk and financial markets; and environmental performance and firm value. The results of the study also present the opportunities associated with carbon performance and the financial market and propose future research agendas on research through theory, characteristics, context and methodology.

The results of the study offer insights to practitioners, researchers and academicians regarding scientific development, intricate relationships and the complexities involved in the intersection of carbon performance and financial markets. For policymakers, a better understanding of carbon performance and financial markets will contribute to designing policies to set up priorities for countering carbon emissions.

The study highlights the critical areas that require attention to limit greenhouse gas emissions and promote decarbonisation effectively. Policymakers can leverage these insights to develop targeted and evidence-based policies that facilitate the transition to a more sustainable and low-carbon economy.

The study initially attempts to discuss the research stream on carbon performance and financial markets literature from a systematic literature review.