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on Sociology of Economics |
| By: | Patsali, Sofia (Université Côte d'Azur, GREDEG, and Université de Strasbourg, BETA, CNRS France); Pezzoni, Michele (Université Côte d'Azur, GREDEG, CNRS, Observatoire des Sciences et Techniques, HCERES, OFCE, Sciences Po, and ICRIOS, Bocconi University, Italy); Visentin, Fabiana (UNU-MERIT, Maastricht University) |
| Abstract: | This study investigates the effect of research independence during the PhD period on students' career outcomes. We use a unique and detailed dataset on the French population of STEM PhD students who graduated between 1995 and 2013. To measure research independence, we compare the PhD thesis content with the supervisor's research. We employ advanced neural network text analysis techniques evaluating the similarity between the student's thesis abstract and supervisor's publications during the PhD period. After exploring which characteristics of the PhD training experience and supervisor explain the level of research similarity, we estimate how similarity associates with the likelihood of pursuing a research career. We find that the student thesis's similarity with her supervisor's research work is negatively associated with starting a career in academia and patenting probability. Increasing the PhD-supervisor similarity score by one standard deviation is associated with a 2.1 percentage point decrease in the probability of obtaining an academic position and a 0.57 percentage point decrease in the probability of patenting. However, conditional on starting an academic career, PhD-supervisor similarity is associated with a higher student's productivity after graduation as measured by citations received, network size, and probability of moving to a foreign or US-based affiliation. |
| Keywords: | Research independence, Early career researchers, Scientific career outcomes, Neural network text analysis |
| JEL: | D22 O30 O33 O38 |
| Date: | 2021–10–15 |
| URL: | http://d.repec.org/n?u=RePEc:unm:unumer:2021036&r= |
| By: | Laitin, David D; Miguel, Edward; Alrababa'h, Ala'; Bogdanoski, Aleksandar; Grant, Sean; Hoeberling, Katherine; Hyunjung Mo, Cecilia; Moore, Don A; Vazire, Simine; Weinstein, Jeremy; Williamson, Scott |
| Abstract: | While the social sciences have made impressive progress in adopting transparent research practices that facilitate verification, replication, and reuse of materials, the problem of publication bias persists. Bias on the part of peer reviewers and journal editors, as well as the use of outdated research practices by authors, continues to skew literature toward statistically significant effects, many of which may be false positives. To mitigate this bias, we propose a framework to enable authors to report all results efficiently (RARE), with an initial focus on experimental and other prospective empirical social science research that utilizes public study registries. This framework depicts an integrated system that leverages the capacities of existing infrastructure in the form of public registries, institutional review boards, journals, and granting agencies, as well as investigators themselves, to efficiently incentivize full reporting and thereby, improve confidence in social science findings. In addition to increasing access to the results of scientific endeavors, a well-coordinated research ecosystem can prevent scholars from wasting time investigating the same questions in ways that have not worked in the past and reduce wasted funds on the part of granting agencies. |
| Keywords: | file drawer problem, null findings, publication bias, registries, research transparency, Prevention |
| Date: | 2021–12–01 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:econwp:qt9cq8j822&r= |
| By: | David Ardia; Keven Bluteau; Mohammad Abbas Meghani |
| Abstract: | Using the structural topic model, we present a landscape of academic finance. We analyze more than 40,000 titles and abstracts published in 32 finance journals over a period ranging from 1992 to 2020. We identify the research topics and explore their relation and prevalence over time and across journals. Our analyses reveal that most journals have covered more topics over time, thus becoming more generalist. |
| Date: | 2021–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2112.14902&r= |
| By: | F\'elix Lirio-Loli; William Dextre-Mart\'inez |
| Abstract: | Introduction: This study analyzes the scientific production in business administration in scientific articles based on modeling partial least squares structural equations (Partial Least Squares Structural Equation Modeling PLS-SEM) in the 2011-2020 period. Methodology: The study is exploratory - descriptive and has three phases: a) Selection of keywords and search criteria; (b) Search and refinement of information; c) information analysis. A method of bibliometric review of the specific literature has been used based on the analysis of predefined indicators and completed with a qualitative content synthesis. Results: A total of 167 publications were analyzed, making correlations from the year, search criteria, authors, impact factor by quartile, and by citation variables. More outstanding scientific production comes from Scopus under the search criteria ((pls AND sem) OR "partial least squares") AND (business OR management), being the figure of 4,870 scientific articles, while Web of Science accumulates 3,946 articles Conclusion: There has been a progressive growth in scientific articles with the PLS-SEM technique from 2011 to 2020. Scopus, compared to WoS, presents a more significant number of scientific productions with this statistical approach. The authors who register scientific articles demonstrate a high H index; in addition, there is an important number of scientific articles with a PLS-SEM approach in universities in Malaysia that could be related to the expansion of higher education in that country, as well as in Singapore, Taiwan, and Indonesia. Finally, business administration, accounting, and economics are outstanding scientific production. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.02760&r= |