Terms in journal articles associating with high quality: Can qualitative research be world-leading?

Purpose: Scholars often aim to conduct high quality research and their success is judged primarily by peer reviewers. Research quality is difficult for either group to identify, however, and misunderstandings can reduce the efficiency of the scientific enterprise. In response, we use a novel term association strategy to seek quantitative evidence of aspects of research that associate with high or low quality. Design/methodology/approach: We extracted the words and 2-5-word phrases most strongly associating with different quality scores in each of 34 Units of Assessment (UoAs) in the Research Excellence Framework (REF) 2021. We extracted the terms from 122,331 journal articles 2014-2020 with individual REF2021 quality scores. Findings: The terms associating with high- or low-quality scores vary between fields but relate to writing styles, methods, and topics. We show that the first-person writing style strongly associates with higher quality research in many areas because it is the norm for a set of large prestigious journals. We found methods and topics that associate with both high- and low-quality scores. Worryingly, terms associating with educational and qualitative research attract lower quality scores in multiple areas. REF experts may rarely give high scores to qualitative or educational research because the authors tend to be less competent, because it is harder to make world leading research with these themes, or because they do not value them. Originality: This is the first investigation of journal article terms associating with research quality

Can the quality of published academic journal articles be assessed with machine learning?

© 2022 The Author. Published by MIT Press. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1162/qss_a_00185Formal assessments of the quality of the research produced by departments and universities are now conducted by many countries to monitor achievements and allocate performancerelated funding. These evaluations are hugely time consuming if conducted by postpublication peer review and are simplistic if based on citations or journal impact factors. This article investigates whether machine learning could help reduce the burden of peer review by using citations and metadata to learn how to score articles from a sample assessed by peer review. An experiment is used to underpin the discussion, attempting to predict journal citation thirds, as a proxy for article quality scores, for all Scopus narrow fields from 2014 to 2020. The results show that these proxy quality thirds can be predicted with above baseline accuracy in all 326 narrow fields, with Gradient Boosting Classifier, Random Forest Classifier, or Multinomial Naïve Bayes being the most accurate in nearly all cases. Nevertheless, the results partly leverage journal writing styles and topics, which are unwanted for some practical applications and cause substantial shifts in average scores between countries and between institutions within a country. There may be scope for predicting articles scores when the predictions have the highest probability

Predicting the impact of academic articles on marketing research: Using machine learning to predict highly cited marketing articles

The citation count of an academic article is of great importance to researchers and readers. Due to the large increase in the publication of academic articles every year, it may be difficult to recognize the articles which are important to the field. This thesis collected data from Scopus with the purpose to analyze how paper, journal, and author related variables performed as drivers of article impact in the marketing field, and how well they could predict highly cited articles five years ahead in time. Social network analysis was used to find centrality metrics, and citation count one year after publication was included as the only time dependent variable. Our results found that citations after one year is a strong driver and predictor for future citations after five years. The analysis of the co-authorship network showed that closeness centrality and betweenness centrality are drivers of future citations in the marketing field, indicating that being close to the core of the network and having brokerage power is important in the field. With the use of machine learning methods, we found that a combination of paper, journal, and author related drivers perform better at predicting highly cited articles after five years, compared to using only one type of driver.nhhma

DATA ANALYTICS FOR CRISIS MANAGEMENT: A CASE STUDY OF SHARING ECONOMY SERVICES IN THE COVID-19 PANDEMIC

This dissertation study aims to analyze the role of data-driven decision-making in sharing economy during the COVID-19 pandemic as a crisis management tool. In the twenty-first century, when applying analytical tools has become an essential component of business decision-making, including operations on crisis management, data analytics is an emerging field. To carry out corporate strategies, data-driven decision-making is seen as a crucial component of business operations. Data analytics can be applied to benefit-cost evaluations, strategy planning, client engagement, and service quality. Data forecasting can also be used to keep an eye on business operations and foresee potential risks. Risk Management and planning are essential for allocating the necessary resources with minimal cost and time and to be ready for a crisis. Hidden market trends and customer preferences can help companies make knowledgeable business decisions during crises and recessions. Each company should manage operations and response during emergencies, a path to recovery, and prepare for future similar events with appropriate data management tools. Sharing economy is part of social commerce, that brings together individuals who have underused assets and who want to rent those assets short-term. COVID-19 has emphasized the need for digital transformation. Since the pandemic began, the sharing economy has been facing challenges, while market demand dropped significantly. Shelter-in-Place and Stay-at-Home orders changed the way of offering such sharing services. Stricter safety procedures and the need for a strong balance sheet are the key take points to surviving during this difficult health crisis. Predictive analytics and peer-reviewed articles are used to assess the pandemic\u27s effects. The approaches chosen to assess the research objectives and the research questions are the predictive financial performance of Uber & Airbnb, bibliographic coupling, and keyword occurrence analyses of peer-reviewed works about the influence of data analytics on the sharing economy. The VOSViewer Bibliometric software program is utilized for computing bibliometric analysis, RapidMiner Predictive Data Analytics for computing data analytics, and LucidChart for visualizing data

How to accomplish a highly cited paper in the tourism, leisure and hospitality field

This paper identifies the main factors that affect the citation rate of an article published in the tourism, leisure and hospitality field. Using several regression techniques, it has been identified that the number of references for an article, the reputation of the main author, and obtaining early citations have a major impact on a document’s citation rate. As well as this, by means of a quantitative–qualitative analysis (fsQCA), the most efficient combinations of factors that influence the number of citations received have also been identified. This paper is useful for researchers, editors and readers interested in improving the impact of their researchS