Research Domain Research Articles

Conceptualizing an integrative typology of sales enablement strategy

Recent conceptualizations of sales enablement (SE) provide a foundation for emerging scholarship yet do not capture the vast complexities of developing an SE strategy. SE is not a singular construct. As a point of departure for future scholarship, the authors conceptualize a framework integrating extant knowledge of SE research and theorizing to develop a typology of SE strategy components. To establish a baseline for the typology, this paper first reviews the theoretical foundations of SE. Next, research domains on the periphery of the phenomenon (go-to-market strategy, organizational learning, change management, technology adoption) are reviewed and organized. Building from extant literature, the emergent typology integrates relevant literature streams with the various perspectives of the organization where SE strategy is devised and utilized (top management, sales management, salesperson). In addition to internal organizational factors, the authors include performance indicators, external contingency factors, and a research agenda associated with each SE typology component area (i.e., SE strategy: people, process, and system components). Ultimately, this work contributes to the sales literature by providing a nuanced view of SE strategy.

Perspectives on non-financial conflicts of interest in health-related journals: A scoping review

ABSTRACT The objective of this scoping review was to systematically review the literature on how non-financial conflicts of interest (nfCOI) are defined and evaluated, and the strategies suggested for their management in health-related and biomedical journals. PubMed, Embase, Scopus and Web of Science were searched for peer reviewed studies published in English between 1970 and December 2023 that addressed at least one of the following: the definition, evaluation, or management of non-financial conflicts of interest. From 658 studies, 190 studies were included in the review. nfCOI were discussed most commonly in empirical (22%; 42/190), theoretical (15%; 29/190) and “other” studies (18%; 34/190) – including commentary, perspective, and opinion articles. nfCOI were addressed frequently in the research domain (36%; 68/190), publication domain (29%; 55/190) and clinical practice domain (17%; 32/190). Attitudes toward nfCOI and their management were divided into two distinct groups. The first larger group claimed that nfCOI were problematic and required some form of management, whereas the second group argued that nfCOI were not problematic, and therefore, did not require management. Despite ongoing debates about the nature, definition, and management of nfCOI, many articles included in this review agreed that serious consideration needs to be given to the prevalence, impact and optimal mitigation of non-financial COI.

Trends, progress and future directions of nanomaterial based sensors: a bibliometric overview

PurposeNanosensors have a wide range of applications because of their high sensitivity, selectivity and specificity. In the past decade, extensive and pervasive research related to nanosensors has led to significant progress in diverse fields, such as biomedicine, environmental monitoring and industrial process control. This led to better and more efficient detection and monitoring of physical and chemical properties at better resolution, opening new horizons in the development of novel technologies and applications for improved human health, environment protection, enhanced industrial processes, etc.Design/methodology/approachIn this paper, the authors discuss the application of citation network analysis in the field of nanosensor research and development. Cluster analysis was carried out using papers published in the field of nanomaterial-based sensor research, and an in-depth analysis was carried out to identify significant clusters. The purpose of this study is to provide researchers to identify a pathway to the emerging areas in the field of nanosensor research. The authors have illustrated the knowledge base, knowledge domain and knowledge progression of nanosensor research using the citation analysis based on 3,636 Science Citation Index papers published during the period 2011 to 2021.FindingsAmong these papers, the bibliographic study identified 809 significant research publications, 11 clusters, 556 research sector keywords, 1,296 main authors, 139 referenced authors, 63 nations, 206 organizations and 42 journals. The authors have identified single quantum dot (QD)-based nanosensor for biological applications, carbon dot-based nanosensors, self-powered triboelectric nanogenerator-based nanosensor and genetically encoded nanosensor as the significant research hotspots that came to the fore in recent years. The future trend in nanosensor research might focus on the development of efficient and cost-effective designs for the detection of numerous environmental pollutants and biological molecules using mesostructured materials and QDs. It is also possible to optimize the detection methods using theoretical models, and generalized gradient approximation has great scope in sensor development.Research limitations/implicationsThe future trend in nanosensor research might focus on the development of efficient and cost-effective designs for the detection of numerous environmental pollutants and biological molecules using mesostructured materials and QDs. It is also possible to optimize the detection methods using theoretical models, and generalized gradient approximation has great scope in sensor development.Originality/valueThis is a novel bibliometric analysis in the area of “nanomaterial based sensor,” which is carried out in CiteSpace software.

Research Landscape of Sustainable Marketing: Thematic Analysis and Future Trends

Sustainability has been recognized as an integral business strategy leading to competitive advantage. The aim of the study is to generate a bibliometric blueprint for the sustainable marketing research domain and further identify emerging trends in this area. The data is collected from one of the most prominent bibliographic databases, Scopus. After narrowing our search keywords, a result of 4,889 articles is produced. Following the use of Boolean operators, 1,004 bibliographic resources were found, which were analysed in this study. The data is then visualized using a comparison visualization programme (VOSviewer) to demonstrate the possible outcomes graphically. The research findings are presented in the form of article categorizations into 11 thematic categories. The thematic categories outlined previous studies’ trends and contribution characteristics under the individual category, illustrating their implications. The article sums up the emerging and future themes in the area of sustainability. From the perspective of sustainability, the study demonstrates the importance of a sustainable business environment, green marketing, ecological marketing, sustainable tourism, and sustainability and development. The study brings out the role of multiple stakeholders to prevent the depletion of natural resources, secure waste management, prevent pollution, and cooperate with the nation’s compliance. This will promote economic development, which can be improved if we consider taking wiser steps to prevent the economy from degrading.

Exploring Localized Provoked Vulvodynia: Insights from Animal Model Research.

Provoked vulvodynia represents a challenging chronic pain condition, characterized by its multifactorial origins. The inherent complexities of human-based studies have necessitated the use of animal models to enrich our understanding of vulvodynia's pathophysiology. This review aims to provide an exhaustive examination of the various animal models employed in this research domain. A comprehensive search was conducted on PubMed, utilizing keywords such as "vulvodynia", "chronic vulvar pain", "vulvodynia induction", and "animal models of vulvodynia" to identify pertinent studies. The search yielded three primary animal models for vulvodynia: inflammation-induced, allergy-induced, and hormone-induced. Additionally, six agents capable of triggering the condition through diverse pathways were identified, including factors contributing to hyperinnervation, mast cell proliferation, involvement of other immune cells, inflammatory cytokines, and neurotransmitters. This review systematically outlines the various animal models developed to study the pathogenesis of provoked vulvodynia. Understanding these models is crucial for the exploration of preventative measures, the development of novel treatments, and the overall advancement of research within the field.

STRIDE-DB: a comprehensive database for exploration of instability and phenotypic relevance of short tandem repeats in the human genome.

Short Tandem Repeats (STRs) are genetic markers made up of repeating DNA sequences. The variations of the STRs are widely studied in forensic analysis, population studies and genetic testing for a variety of neuromuscular disorders. Understanding polymorphic STR variation and its cause is crucial for deciphering genetic information and finding links to various disorders. In this paper, we present STRIDE-DB, a novel and unique platform to explore STR Instability and its Phenotypic Relevance, and a comprehensive database of STRs in the human genome. We utilized RepeatMasker to identify all the STRs in the human genome (hg19) and combined it with frequency data from the 1000 Genomes Project. STRIDE-DB, a user-friendly resource, plays a pivotal role in investigating the relationship between STR variation, instability and phenotype. By harnessing data from genome-wide association studies (GWAS), ClinVar database, Alu loci, Haploblocks in genome and Conservation of the STRs, it serves as an important tool for researchers exploring the variability of STRs in the human genome and its direct impact on phenotypes. STRIDE-DB has its broad applicability and significance in various research domains like forensic sciences and other repeat expansion disorders. Database URL: https://stridedb.igib.res.in.

Local Energy Decomposition Analysis of London Dispersion Effects: From Simple Model Dimers to Complex Biomolecular Assemblies.

ConspectusLondon dispersion (LD) forces are ubiquitous in chemistry, playing a pivotal role in a wide range of chemical processes. For example, they influence the structure of molecular crystals, the selectivity of organocatalytic transformations, and the formation of biomolecular assemblies. Harnessing these forces for chemical applications requires consistent quantification of the LD energy across a broad and diverse spectrum of chemical scenarios. Despite the great progress made in recent years in the development of experimental strategies for LD quantification, quantum chemical methods remain one of the most useful tools in the hand of chemists for the study of these weak interactions. Unfortunately, the accurate quantification of LD effects in complex systems poses many challenges for electronic structure theories. One of the problems stems from the fact that LD forces originate from long-range electronic dynamic correlation, and hence, their rigorous description requires the use of complex, highly correlated wave function-based methods. These methods typically feature a steep scaling with the system size, limiting their applicability to small model systems. Another core challenge lies in disentangling short-range from long-range dynamic correlation, which from a rigorous quantum mechanical perspective is not possible.In this Account, we describe our research endeavors in the development of broadly applicable computational methods for LD quantification in molecular chemistry as well as challenging applications of these schemes in various domains of chemical research. Our strategy lies in the use of local correlation theories to reduce the computational cost associated with complex electronic structure methods while providing at the same time a simple means of decomposition of dynamic correlation into its long-range and short-range components. In particular, the local energy decomposition (LED) scheme at the domain-based local pair natural orbital coupled cluster (DLPNO-CCSD(T)) level has emerged as a powerful tool in our research, offering a clear-cut quantitative definition of the LD energy that remains valid across a plethora of different chemical scenarios. Typical applications of this scheme are examined, encompassing protein-ligand interactions and reactivity studies involving many fragments and complex electronic structures. In addition, our research also involves the development of novel cost-effective methodologies, which exploit the LED definition of the LD energy, for LD energy quantification that are, in principle, applicable to systems with thousands of atoms. The Hartree-Fock plus London Dispersion (HFLD) scheme, correcting the HF interaction energy using an approximate CCSD(T)-based LD energy, is a useful, parameter-free electronic structure method for the study of LD effects in systems with hundreds of molecular fragments. However, the usefulness of the LED scheme reaches beyond providing an interpretation of the calculated DLPNO-CCSD(T) or DLPNO-MP2 interaction energies. For example, the dispersion energies obtained from the LED can be fruitfully used in order to parametrize semiempirical dispersion models. We will demonstrate this in the context of an emerging semiempirical method, namely, the Natural Orbital Tied Constructed Hamiltonian (NOTCH) method. NOTCH incorporates LED-derived LD energies and shows promising accuracy at a minimum amount of empiricism. Thus, it holds substantial promise for large and complex systems.

Harmonizing Compliance: Coordinating Automated Verification Processes within Cloud-based AI/ML Workflows

The significance of ensuring security and upholding data privacy within cloud-based workflows is widely recognized in research domains. This importance is particularly evident in contexts such as safeguarding patients' private data managed within cloud-deployed workflows, where maintaining confidentiality is paramount, alongside ensuring secure communication among involved stakeholders. In response to these imperatives, our paper presents an architecture and formal model designed to enforce security measures within cloud workflow orchestration. Central to our proposed architecture is the emphasis on continuous monitoring of cloud resources, workflow tasks, and data streams to detect and preempt anomalies in workflow orchestration processes. To accomplish this, we advocate for a multi-modal approach that integrates deep learning, one-class classification, and clustering techniques. In essence, our proposed architecture offers a comprehensive solution for enforcing security within cloud workflow orchestration, harnessing advanced methodologies like deep learning for anomaly detection and prediction. This approach is particularly pertinent in critical sectors such as healthcare, especially during unprecedented events like the COVID-19 pandemic.

Harmonizing Compliance: Coordinating Automated Verification Processes within Cloud-based AI/ML Workflows

The significance of ensuring security and upholding data privacy within cloud-based workflows is widely recognized in research domains. This importance is particularly evident in contexts such as safeguarding patients' private data managed within cloud-deployed workflows, where maintaining confidentiality is paramount, alongside ensuring secure communication among involved stakeholders. In response to these imperatives, our paper presents an architecture and formal model designed to enforce security measures within cloud workflow orchestration. Central to our proposed architecture is the emphasis on continuous monitoring of cloud resources, workflow tasks, and data streams to detect and preempt anomalies in workflow orchestration processes. To accomplish this, we advocate for a multi-modal approach that integrates deep learning, one-class classification, and clustering techniques. In essence, our proposed architecture offers a comprehensive solution for enforcing security within cloud workflow orchestration, harnessing advanced methodologies like deep learning for anomaly detection and prediction. This approach is particularly pertinent in critical sectors such as healthcare, especially during unprecedented events like the COVID-19 pandemic.

Coaching adaptive skill and expertise in Premier League football academies-paving a way forward for research and practice.

Within the domain of coach education researchers have long called for a paradigm shift, whereby the quality of coaching practice is no longer measured against a checklist of prescribed competencies. This desire to evolve coach education and development, has been aligned to the need to better identify, understand and utilise what adaptive skill and expertise looks, sounds and feels like across specific sport coaching contexts. This paper outlines a broader research plan for the Premier League to drive the progress of research informed practice, in turn shaping a coach development agenda focused on developing adaptive and skilful coaches within Academies. In turn, this is a core feature of the Premier Leagues institutional aim of developing the most skilful coaches in the world. However, in order to begin the process of initiating such a shift in the way things work, there is a need to seek first to understand, before being understood. Therefore, to demonstrate an evidence informed basis to this shift within coach education and development, we ask three questions; (1) Do we understand what the coaches with the highest level of expertise can do? (2) How should we identify coaches with expertise across different contexts? (3) What does coaching expertise research need to do? In answer to these questions, we present the lack of empirical investigation previously conducted in the sports coaching discipline to explore coaching expertise and draw on wider domains to offer possible capacities of skilful coaches who possess expertise. To identify coaches with expertise, coherent with the broader expertise literature, we suggest that this is best conducted via means of social validation. Finally, we offer a road map of investigation designed to explore expertise, formed of a mix of evidence informed methodologies which have not yet been utilised in sport coaching research.

Towards Harnessing the Most of ChatGPT for Korean Grammatical Error Correction

In this study, we conduct a pioneering and comprehensive examination of ChatGPT’s (GPT-3.5 Turbo) capabilities within the realm of Korean Grammatical Error Correction (K-GEC). Given the Korean language’s agglutinative nature and its rich linguistic intricacies, the task of accurately correcting errors while preserving Korean-specific sentiments is notably challenging. Utilizing a systematic categorization of Korean grammatical errors, we delve into a meticulous, case-specific analysis to identify the strengths and limitations of a ChatGPT-based correction system. We also critically assess influential parameters like temperature and specific error criteria, illuminating potential strategies to enhance ChatGPT’s efficacy in K-GEC tasks. Our findings offer valuable contributions to the expanding domain of NLP research centered on the Korean language.

Nanoframes as a Resilient Candidate for a Broader Spectra of Catalysis Challenges

Metal nanoframes have gained tremendous attention in the domain of modern research and development due to their distinctive 3D spatial structure, efficient physiochemical properties, and comparatively good activity. Different strategies have been implicated by the researchers to design nanoframes of varying chemical natures and shapes. Most of the synthetic protocols being adopted to design nanoframes consist of two main steps: nucleation and the growth of solid particles and, secondly, excavation of the interiors. In this context, many synthetic methods are overviewed. To show their unprecedented performance or activity, a few applications in catalysis, biomedicine, theranostics, SERS, the sensing of different materials, the reduction of CO2, etc., are also discussed.

Progressive feature reduction with varied missing data and feature selection for arthritis disease prediction

In the expansive domain of data-driven research, the curse of dimensionality poses challenges such as increased computational complexity, noise sensitivity, and the risk of overfitting models. Dimensionality reduction is vital to handle high-dimensional datasets effectively. The pilot study disease dataset (PSD) with 53 features contains patients with Rheumatoid Arthritis (RA) and Osteoarthritis (OA). Our work aims to reduce the dimension of the features in the PSD dataset, identify a suitable feature selection technique for the reduced-dimensional dataset, analyze an appropriate Machine Learning (ML) model, select significant features to predict the RA and OA disease and reveal significant features that predict the arthritis disease. The proposed study, Progressive Feature Reduction with Varied Missing Data (PFRVMD), was employed to reduce the dimension of features by using PCA loading scores in the random value imputed PSD dataset. Subsequently, notable feature selection methods, such as backward feature selection, the Boruta algorithm, the extra tree classifier, and forward feature selection, were implemented on the reduced-dimensional feature set. The significant features/biomarkers are obtained from the best feature selection technique. ML models such as the K-Nearest Neighbour Classifier (KNNC), Linear Discriminant Analysis (LDA), Logistic Regression (LR), Naïve Bayes Classifier (NBC), Random Forest Classifier (RFC) and Support Vector Classifier (SVC) are used to determine the best feature selection method. The results indicated that the Extra Tree Classifier (ETC) is the promising feature selection method for the PSD dataset because the significant features obtained from ETC depicted the highest accuracy on SVC.

An analytical approach for unsupervised learning rate estimation using rectified linear units.

Unsupervised learning based on restricted Boltzmann machine or autoencoders has become an important research domain in the area of neural networks. In this paper mathematical expressions to adaptive learning step calculation for RBM with ReLU transfer function are proposed. As a result, we can automatically estimate the step size that minimizes the loss function of the neural network and correspondingly update the learning step in every iteration. We give a theoretical justification for the proposed adaptive learning rate approach, which is based on the steepest descent method. The proposed technique for adaptive learning rate estimation is compared with the existing constant step and Adam methods in terms of generalization ability and loss function. We demonstrate that the proposed approach provides better performance.

Research hotspots and trend of wrist arthroscopy: A bibliometrics analysis from 2013 to 2023.

Wrist arthroscopy technology is a surgical technology invented in recent years and widely used in clinical treatment of various wrist diseases. This study uses the methods of bibliometrics and visual analysis to understand the global research status, research hotspots, and future development trends of wrist arthroscopy. The relevant literature of global publications on wrist arthroscopy from 2013 to 2023 was extracted from the Web of Science Core Collection database, and the annual output, cooperation, hot spots, research status, and development trend of this field were analyzed by using the bibliometric software (VOSviewers, CiteSpace, and the R package "Bibliometrix"). A total of 635 articles were included, from 2013 to 2023, the number of publications related to wrist arthroscopy showed an overall upward trend, the USA, France, and China are the top 3 countries in terms of the number of publications, whereas Mayo Clinic is the institution with the highest number of publications, Ho PC holds a core position in this field, keyword analysis indicates that the research hotspots are the applications of wrist arthroscopy in triangular fibrocartilage complex injuries, scaphoid nonunion, and avascular necrosis of the lunate. Wrist arthroscopy has shown tremendous potential in treating various wrist diseases. However, there are still some challenges in its research domain. With continuous deep research, strengthened international collaboration, and ongoing technological advancements, wrist arthroscopy has the potential to become the standard treatment in hand surgery, offering more efficient and safer treatment options for patients worldwide.

Discharge Capacity Estimation for Li-Ion Batteries: A Comparative Study

Li-ion batteries are integral to various applications, ranging from electric vehicles to mobile devices, because of their high energy density and user friendliness. The assessment of the Li-ion state of heath stands as a crucial research domain, aiming to innovate safer and more effective battery management systems that can predict and promptly report any operational discrepancies. To achieve this, an array of machine learning (ML) and artificial intelligence (AI) methodologies have been employed to analyze data from Li-ion batteries, facilitating the estimation of critical parameters like state of charge (SoC) and state of health (SoH). The continuous enhancement of ML and AI algorithm efficiency remains a pivotal focus of scholarly inquiry. Our study distinguishes itself by separately evaluating traditional machine learning frameworks and advanced deep learning paradigms to determine their respective efficacy in predictive modeling. We dissected the performances of an assortment of models, spanning from conventional ML techniques to sophisticated, hybrid deep learning constructs. Our investigation provides a granular analysis of each model’s utility, promoting an informed and strategic integration of ML and AI in Li-ion battery state of health prognostics. Specifically, a utilization of machine learning algorithms such as Random Forests (RFs) and eXtreme Gradient Boosting (XGBoost), alongside regression models like Elastic Net and foundational neural network approaches including Multilayer Perceptron (MLP) were studied. Furthermore, our research investigated the enhancement of time series analysis using intricate models like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) and their outcomes with those of hybrid models, including a RNN-long short-term memory (LSTM), CNN-LSTM, CNN-Gated Recurrent Unit (GRU) and RNN-GRU. Comparative evaluations reveal that the RNN-LSTM configuration achieved a Mean Squared Error (MSE) of 0.043, R-Squared of 0.758, Root Mean Square Error (RMSE) of 0.208, and Mean Absolute Error (MAE) of 0.124, whereas the CNN-LSTM framework reported an MSE of 0.039, R-Squared of 0.782, RMSE of 0.197, and MAE of 0.122, underscoring the potential of deep learning-based hybrid models in advancing the accuracy of battery state of health assessments.

A Data Assimilation Methodology to Analyze the Unsaturated Seepage of an Earth–Rockfill Dam Using Physics-Informed Neural Networks Based on Hybrid Constraints

Data assimilation for unconfined seepage analysis has faced significant challenges due to hybrid causes, such as sparse measurements, heterogeneity of porous media, and computationally expensive forward models. To address these bottlenecks, this paper introduces a physics-informed neural network (PINN) model to resolve the data assimilation problem for seepage analysis of unsaturated earth–rockfill dams. This strategy offers a solution that decreases the reliance on numerical models and enables an accurate and efficient prediction of seepage parameters for complex models in the case of sparse observational data. For the first attempt in this study, the observed values are obtained by random sampling of numerical solutions, which are then contributed to the synchronous constraints in the loss function by informing both the seepage control equations and boundary conditions. To minimize the effects of sharp gradient shifts in seepage parameters within the research domain, a residual adaptive refinement (RAR) constraint is introduced to strategically allocate training points around positions with significant residuals in partial differential equations (PDEs), which could facilitate enhancing the prediction accuracy. The model’s effectiveness and precision are evaluated by analyzing the proposed strategy against the numerical solutions. The results indicate that even with limited sparse data, the PINN model has great potential to predict seepage data and identify complex structures and anomalies inside the dam. By incorporating coupling constraints, the validity of our PINN model could lead to theoretically viable applications of hydrogeophysical inversion or multi-parameter seepage inversion. The results show that the proposed framework can predict the seepage parameters for the entire research domain with only a small amount of observation data. Furthermore, with a small amount of observation data, PINNs are able to obtain more accurate results than purely data-driven DNNs.

The Influence of Personality Traits on Social Entrepreneurship Intentions in the Moroccan Context

The main aim of this study is to investigate the intention of undergraduate students in Morocco to engage in social entrepreneurship. To achieve this, the research will adopt the theory of planned behaviour as the theoretical framework. The study extends the model by including entrepreneurial and pro-social personality traits as additional constructs. For that purpose, a survey was conducted on a sample of 300 young adults using a questionnaire. We find positive relationships between attitudes, perceived control, subjective norms, moral obligation, and social entrepreneurial intentions. Conversely, empathy did not have any influence on social entrepreneurial intentions, attitudes, or subjective norms. Overall, we contribute to the upcoming domain of social entrepreneurship research by investigating the relationship between entrepreneurial and pro-social personality traits and social entrepreneurial intention.

Post-publication research integrity concerns in randomized clinical trials: A scoping review of the literature.

Post-publication handling of integrity concerns in randomized clinical trials (RCTs) is a contentious matter. We undertook a scoping systematic review to map the literature regarding post-publication integrity issues in RCTs. Following prospective registration (https://osf.io/pgxd8) we initially searched PubMed and Scopus but subsequently extended it to include the Cochrane Library, and Google Scholar databases without language, article type or publication time restriction until November 2022. Reviewers independently selected published articles covering any aspect of post-publication research integrity concerns in RCTs. The study findings grouped within domains relating to issues concerning post-publication integrity were extracted in duplicate, verified by a third reviewer, and then tabulated. The initial search captured 3159 citations, of which 89 studies were included in the review. Cross-sectional studies constituted the majority of included studies (n = 34, 38.2%), followed by systematic reviews (n = 10, 11.2%), methodology reviews/studies (n = 9, 10.1%) and other types of descriptive studies (n = 8, 9.0%). A total of 21 articles (23.6%) covered the domain on general issues, 25 (28.1%) in the journal's instructions and policies domain, eight (9.0%) in the editorial and peer review domain, one (1.1%) in the correspondence and complaints (post-publication peer review) domain, 12 (13.5%) in the investigation for concerns domain, six (6.7%) in the post-investigation decisions and sanctions domain, none in the critical appraisal guidance domain, five (5.6%) in the integrity assessment in systematic reviews domain, and 26 (29.2%) in the recommendations for future research domain. A total of 12 of the selected articles (13.5%) covered two (n = 9) or three (n = 3) different domains. Various research integrity domains and issues covering post-publication aspects of RCT integrity were captured and gaps were identified, mostly related with the necessary implications for all stakeholders to improve research transparency. There is an urgent need for a multistakeholder consensus towards creating specific statements for addressing post-publication integrity concerns in RCTs.

Destination competitiveness research over the past three decades: a computational literature review using topic modelling

ABSTRACT The aim of this study is to comprehensively review the topics and themes studied in destination competitiveness research and to enrich tourism researchers’ literature review toolbox with the computational literature review technique. A novel computational literature review framework that can be applied to review tourism research domains characterised by an abundance of literature is developed. Enabled by the developed computational literature review framework with topic modelling, this study offers a comprehensive review of 1,130 destination competitiveness studies. The results identified several promising research topics, themes and theoretical perspectives that can be perused in future destination competitiveness studies.