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The purposes of this paper are to highlight those topics of forensic accounting that have received little or no attention in the forensic accounting research that has been published in forensic accounting research journals; discover what research methods have been most commonly used; and identify research methods that have been infrequently used.

This is a descriptive research study that explores the topics and methods used in forensic accounting research published in forensic accounting journals.

Fraud and quantitative methods make up the largest percentage of topics and research methods published in forensic accounting journals.

Limited to forensic accounting journals. Results suggest forensic accounting researchers are using mimetic topics and methods of accounting research. The absence of diversity in forensic accounting research topics and methods has the potential to compromise the overall contribution of forensic accounting research.

This paper identifies gaps in topics and research methods in forensic accounting research to encourage research in diverse topics using diverse methods that will be valuable to forensic accountants.

This original research is the first to survey and classify research published in forensic accounting journals according to topic and method.

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.

Recent attention to the developments focusing on the educational services has been noteworthy, with the educational environment specifically the smart campus emerging both as a domain and as an opportunity. As a domain worthy of exploration, a number of research efforts are being conceptualized around smart campus initiatives. The existing bouquet of research publications on smart campus provides a testimony of the enthusiasm and also exposes the heterogeneous attempts the domain has witnessed to date. The available evidence is still inadequate to provide clarity on the thrust areas of research around smart campus.

Given the understanding, this study intends to decode the domain to get an early impression of the focus of the research concentration around smart campus. Thereby the study resorts to an automated text-mining approach using Python on contents shortlisted systematically, and published between the period 2010 and May 2022, from select databases.

Based on the analysis it was possible to identify eight themes (i.e. smart campus characteristics, smart campus stakeholders, smart campus frameworks, smart campus technologies, smart campus infrastructure, smart campus evaluation, smart learning environment and smart campus applications) characterizing research efforts within the smart campus literature.

The themes around the smart campus showcase the thrust areas receiving attention. These characterize extant research endeavours in the smart campus domain and can offer useful pointers to researchers going forward. This awareness can also be beneficial to institutional leadership and technology providers intending to implement smart campus initiatives, contributing to the development of the educational environment.

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.

The paper attempts to address the following research questions (RQs): RQ1: What are the main research topics within PSS research? RQ2: What are future trends for PSS research?

Twenty years of research (1999–2018) on product-service systems (PSS) produced a significant amount of scientific literature on the topic. As the PSS field is relatively new and fragmented across different disciplines, a review of the prior and relevant literature is important in order to provide the necessary framework for understanding current developments and future perspectives. This paper aims to review and organize research contributions regarding PSS. A machine-learning algorithm, namely Latent Dirichlet Allocation, has been applied to the whole literature corpus on PSS in order to understand its structure.

The adopted approach resulted in the definition of eight distinct and representative topics able to deal adequately with the multidisciplinarity of the PSS. Furthermore, a systematic review of the literature is proposed to summarize the state-of-the-art and limitations in the identified PSS research topics. Based on this critical analysis, major gaps and future research challenges are presented and discussed.

On the basis of the results of the topic landscape, the paper presents some potential research opportunities on PSSs. In particular, challenges, transversal to the eight research topics and related to recent technology trends and digital transformation, have been discussed.

Examining research topics in a specific area such as accounting is important to both novice and veteran researchers. The present study aims to identify the research topics in the area of accounting and to investigate the research trends by finding hot and cold topics from all those identified ones in the field.

A new dependency-based method focusing on noun phrases, which efficiently extracts research topics from a large set of library data, was proposed. An AR(1) autoregressive model was used to identify topics that have received significantly more or less attention from the researchers. The data used in the study included a total of 4,182 abstracts published in six leading (or premier) accounting journals from 2000 to May 2019.

The study identified 48 important research topics across the examined period as well as eight hot topics and one cold topic from the 48 topics.

The research topics identified based on the dependency-based method are similar to those found with the technique of latent Dirichlet allocation latent Dirichlet allocation (LDA) topic modelling. In addition, the method seems highly efficient, and the results are easier to interpret. Last, the research topics and trends found in the study provide reference to the researchers in the area of accounting.

The selection of the research topic and design crucially determines the success of conducting a research. Thus, the purpose of this paper is to explore the process by which undergraduate students select their research topic and design, as viewed from the lens of motivation theory proposed by Ryan and Deci (2000), which focuses on motivational propensity and consistency.

A longitudinal narrative inquiry was employed to address the gap. In total, 10 Indonesian graduates from different educational backgrounds were purposively selected. The interview focused on their longitudinal experiences from the freshman period to skripsi (undergraduate thesis) supervision period. Data analysis included inductive thematic analysis, member checking by the researchers, and cross-checking by the participants.

The motivational propensity and consistency in selecting research topic and design are highly dynamic. Specifically, autonomy, high curiosity, self-reflection of past interest and perceived competence, and library research become the major factors toward the manifestation of intrinsic motivation in the selected topics and designs. Meanwhile, friends’ preferences, lecturer’s expertise and suggestion, and unclear input from the relevant previous courses result in the manifestation of various types of external motivation.

Therefore, the implementation of dialogic discussion might be more effective when tensions prevail between the department, academic advisor or skripsi supervisors, and students with respect to the selected topics and designs. Moreover, the provision of constructive tasks like case studies, group discussion, and/or research proposal writing can be applied in research methodology course to enhance their repertoire about research topic and design. Autonomy and self-reflection should become the driving objectives.

The exploration of the issue has to be intensively oriented toward prescriptive handbook development in selecting research topic and design. This study sheds light on the explanation of the actual cyclical process of the undergraduate students’ motivational propensity and consistency generated from internal and external regulations, as viewed from the lens of motivation theory.

The purpose of this study is to discover the distribution and trends of existing Offsite construction (OSC) literature with an intention to highlight research niches and propose the future outline.

The paper adopted literature reviews methodology involving 1,057 relevant documents published in 2008-2017 from 15 journals. The selected documents were empirically analyzed through a topic-modeling technique. A latent Dirichlet allocation model was applied to each document to infer 50 key topics. A machine learning for language toolkit was used to get topic posterior word distribution and word composition.

This is an exploratory study, which identifies the distribution of topics and themes; the trend of topics and themes; journal distribution trends; and comparative topic, themes and journal distribution trend. The distribution and trends show an increase in researcher’s interest and the journal’s priority on OSC research. Nevertheless, OSC existing literature is faced with; under-researched topics such as building information modeling, smart construction and marketing. The under-researched themes include organizational management, supply chain and context. The authors also found an overload of similar information in prefabrication and concrete topics. Furthermore, the innovative methods and constraints themes were found to be overloaded with similar information.

The naming of the themes was based on our own interpretation; hence, the research results may lack generalizability. Therefore, a comparative study using different data processing is proposed. The study also provides future research outline as follows: studying OSC topics from dynamic evolution perspective and identifying the new emerging topics; searching for effective strategies to enhance OSC research; identifying the contribution of countries, affiliation and funding agency; and studying the impact of these themes to the adoption of OSC.

This study is of values to the scholars, as it could stimulate research to under-researched areas.

This paper justifies a need to have a broad understanding of the nature and structure of existing OSC literature.

This paper aims to identify the changes of researched destinations along with the Approved Destination Status scheme to investigate the development of research problems, and to track the changes of research topics in the context of the development of China’s outbound tourism. The research methods and applied theories are also identified and analyzed.

Three large databases, namely, ScienceDirect, EBSCOhost and Google Scholar, were selected to search for published articles (i.e. 107 empirical articles and 15 review/policy articles) related to China’s outbound tourism. Thereafter, the retrieved articles were categorized and analyzed. Finally, a theoretical framework was provided for future studies on China’s outbound tourism.

The major findings indicated that the main topics related to China’s outbound tourism included market segmentation, travel motivation and travel behavior. One or two theories are generally applied to investigate a certain topic. The most common research methods are interviews and a questionnaire survey.

Future studies are suggested to apply more theories to investigate one topic. A single theory can also be applied to investigate different topics. Adopting different and innovative research methods, such as online interaction observation, can also be considered.

This study contributes to the literature through an in-depth qualitative assessment of China’s outbound tourism literature, comparing researched destinations with the Approved Destination Status policies, and reviewing articles based on research.

This bibliometric study provides an overview of research related to digital transformation (DT) in the tourism industry from 2013 to 2022. The goals of the research are as follows: (1) to identify the development of academic papers related to DT in the tourism industry, (2) to analyze dominant research topics and the development of research interest and research impact over time and (3) to analyze the change in research topics during the pandemic.

In this study, the authors processed 3,683 papers retrieved from the Web of Science and Scopus. The authors performed different types of bibliometric analyses to identify the development of papers related to DT in the tourism industry. To reveal latent topics, the authors implemented topic modeling based on latent Dirichlet allocation with Gibbs sampling.

The authors identified eight topics related to DT in the tourism industry: City and urban planning, Social media, Data analytics, Sustainable and economic development, Technology-based experience and interaction, Cultural heritage, Digital destination marketing and Smart tourism management. The authors also identified seven topics related to DT in the tourism industry during the Covid-19 pandemic; the largest ones are smart analytics, marketing strategies and sustainability.

To identify research topics and their development over time, the authors applied a novel methodological approach – a smart literature review. This machine learning approach is able to analyze a huge amount of documents. At the same time, it can also identify topics that would remain unrevealed by a standard bibliometric analysis.