Male and Female Users’ Differences in Online Technology Community Based on Text Mining

Sun, Bing; Mao, Hongying; Yin, Chengshun

doi:10.3389/fpsyg.2020.00806

Cited by 60 publications

(48 citation statements)

References 61 publications

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“…The SUEST is the most adaptable analysis to estimate the differences in regression coefficients between the two groups (Sun et al, 2020). Therefore, we divide sample firms according to their ownership status (e.g., private and SOEs), and SUEST is performed to validate the difference between the two groups.…”

Section: Resultsmentioning

confidence: 99%

Corporate innovation and trade credit demand: Evidence from China

et al. 2021

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This study investigates the linkage between trade credit demand and corporate innovation. The estimations are robust to a series of covariates, fixed‐effect, instrumental variable modeling, and the difference‐in‐difference approach. Our findings reveal that the nexus between enterprise innovation and trade credit demand is positive and statistically significant. The said attachment is more visible in private, financially constrained, small, and high‐tech enterprises. The study also found that innovative firms relied heavily on trade‐credit financing after the financial crisis of 2008. Managers should carefully design trade‐credit agreements because operating suppliers have an important role in the enterprises' radical growth activities.

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Section: Resultsmentioning

confidence: 99%

Corporate innovation and trade credit demand: Evidence from China

et al. 2021

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“…Table S2 (see Supplementary Materials) summarizes demographic data of subjects recruited for this study. Two main reasons can explain the fact that the main part of our cohort is represented by females: (1) in Italy, the majority of medical students are women [27], and (2) females are more active in computer-mediated communication than men [28].…”

Section: Discussionmentioning

confidence: 99%

Anxiety and Gastrointestinal Symptoms Related to COVID-19 during Italian Lockdown

Abenavoli

Cinaglia

Lombardo³

et al. 2021

JCM

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The first case of infection by SARS-CoV-2 (i.e., COVID-19) was officially recorded by the Italian National Health Service on 21 February 2020. Respiratory tract manifestations are the most common symptoms, such as gastrointestinal symptoms (GISs) like nausea or sickness, diarrhea, and anorexia, and psychological effects may be reported in affected individuals. However, similar symptoms may be observed in healthy people as a consequence of an anxiety state. Methods: We analyzed GISs and anxiety state during the COVID-19 lockdown period; from 9 March 2020 to 4 May 2020. A web-based survey consisting of 131 items was administered to 354 students affiliated with the School of Medicine of the University “Magna Graecia” of Catanzaro; Italy. A set of statistical analyses was performed to analyze the relationships among the answers to assess a correlation between the topics of interest. Results: The statistical analysis showed that 54.0% of interviewed reported at least one GISs, 36.16% of which reported a positive history for familial GISs (FGISs). The 354 subjects included in our cohort may be stratified as follows: 25.99% GISs and FGISs, 27.97% GISs and no-FGISs, 10.17% no-GISs and FGISs, 35.87% no-GISs and no-FGISs. Results indicated an anxiety state for 48.9% of respondents, of which 64.74% also presented GISs. In addition, considered dietary habits, we detect the increased consumption of hypercaloric food, sweetened drinks, and alcoholic beverages. Conclusions: The increase of GISs during the lockdown period in a population of medical students, may be correlated to both dietary habits and anxiety state due to a concern for one’s health.

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“…In particular, student readiness for live or real-time online learning is not yet well understood. Compared with classroom learning, online learning requires higher fundamental computer skills ( Sun et al, 2020 ), the efficiency of human-human and human-machine interaction ( Cuadrado-García et al, 2010 ), as well as studying motivation ( Hartnett, 2016 ). Technology readiness is adopted as one of the independent variables in the hypothetical model of this study.…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of Student's live online learning readiness during the coronavirus (COVID-19) pandemic in the higher education sector

Tang

Chen

Law

et al. 2021

Computers & Education

395

199

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Amid the coronavirus outbreak, many countries are facing a dramatic situation in terms of the global economy and human social activities, including education. The shutdown of schools is affecting many students around the world, with face-to-face classes suspended. Many countries facing the disastrous situation imposed class suspension at an early stage of the coronavirus outbreak, and Asia was one of the earliest regions to implement live online learning. Despite previous research on online teaching and learning, students' readiness to participate in the real-time online learning implemented during the coronavirus outbreak is not yet well understood. This study explored several key factors in the research framework related to learning motivation, learning readiness and student's self-efficacy in participating in live online learning during the coronavirus outbreak, taking into account gender differences and differences among sub-degree (SD), undergraduate (UG) and postgraduate (PG) students. Technology readiness was used instead of conventional online/internet self-efficacy to determine students' live online learning readiness. The hypothetical model was validated using confirmatory factor analysis (CFA). The results revealed no statistically significant differences between males and females. On the other hand, the mean scores for PG students were higher than for UG and SD students based on the post hoc test. We argue that during the coronavirus outbreak, gender differences were reduced because students are forced to learn more initiatively. We also suggest that students studying at a higher education degree level may have higher expectations of their academic achievement and were significantly different in their online learning readiness. This study has important implications for educators in implementing live online learning, particularly for the design of teaching contexts for students from different educational levels. More virtual activities should be considered to enhance the motivation for students undertaking lower-level degrees, and encouragement of student-to-student interactions can be considered.

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Male and Female Users’ Differences in Online Technology Community Based on Text Mining

Cited by 60 publications

References 61 publications

Corporate innovation and trade credit demand: Evidence from China

Corporate innovation and trade credit demand: Evidence from China

Anxiety and Gastrointestinal Symptoms Related to COVID-19 during Italian Lockdown

Comparative analysis of Student's live online learning readiness during the coronavirus (COVID-19) pandemic in the higher education sector

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