Search results

Numerous research tools exist but their usage among researchers across the different phases of the research cycle of scholarly communication remains unknown. This research aims to address this knowledge gap by mapping the research tools frequently used by global researchers against the various phases of the research cycle of scholarly communication.

This research adopts a descriptive research design and conducts a cross-tabulation of secondary data consisting of 20,663 useable responses in a global survey of research tools for scholarly communication. This research also administered a survey to academic experts to classify the research tools according to traditional, modern, innovative and experimental categories.

This research reveals the six phases of the research cycle (i.e. discovery, analysis, writing, publication, outreach and assessment) and the research tools of scholarly communication frequently used by researchers worldwide in each phase as a whole and by roles, disciplines, regions and career stages. Notably, this research indicates that most of the research tools used by researchers are classified as “modern” and “innovative”.

The original insights herein should be useful for both established and early career researchers to gain and share research insights, as well as policymakers and existing and aspiring service providers who wish to improve the utility and usage of research tools for scholarly communication. Notably, this research represents a seminal endeavor at enhancing a global survey (secondary research) using a follow-up expert survey (primary research), which enabled the organization of research tools for scholarly communication into four refined categories. In doing so, this research contributes finer-grained insights that showcase the importance of keeping up with the advancement of technology through the use of modern, innovative and experimental research tools, thereby highlighting the need to go beyond traditional research tools for scholarly communication.

This paper aims to investigate the knowledge and experience of Zambian teaching faculties with scholarly communication and dissemination practices.

The researchers used a survey to quickly obtain information about a large sample of individuals of interest. The study population consisted of faculties from two Zambian public universities with research and publication experience. The researchers used random sampling techniques. A total of 125 valid responses were received from the selected population.

The findings show that most respondents agreed that publishing in open-access journals increased visibility and readership, had a more significant impact, facilitated collaboration and interdisciplinary research, was cost-effective and provided accessibility. Google Scholar was the most commonly used platform, followed by ResearchGate and ORCID.

This study’s limitations focus on only two Zambian public universities. This study’s practical implications include improving the universities’ open-access policies and educating faculties on the benefits of open access.

This study’s originality lies in exploring the Zambian teaching faculties’ perceptions of open access and academic social networking sites. The results of this study can help universities and researchers in Zambia to understand the importance of scholarly communication and dissemination practices and help them implement effective policies for promoting open-access publishing, institutional repositories and academic social networking sites.

Pervasive health-care computing applications in medical field provide better diagnosis of various organs such as brain, spinal card, heart, lungs and so on. The purpose of this study is to find brain tumor diagnosis using Machine learning (ML) and Deep Learning(DL) techniques. The brain diagnosis process is an important task to medical research which is the most prominent step for providing the treatment to patient. Therefore, it is important to have high accuracy of diagnosis rate so that patients easily get treatment from medical consult. There are many earlier investigations on this research work to diagnose brain diseases. Moreover, it is necessary to improve the performance measures using deep and ML approaches.

In this paper, various brain disorders diagnosis applications are differentiated through following implemented techniques. These techniques are computed through segment and classify the brain magnetic resonance imaging or computerized tomography images clearly. The adaptive median, convolution neural network, gradient boosting machine learning (GBML) and improved support vector machine health-care applications are the advance methods used to extract the hidden features and providing the medical information for diagnosis. The proposed design is implemented on Python 3.7.8 software for simulation analysis.

This research is getting more help for investigators, diagnosis centers and doctors. In each and every model, performance measures are to be taken for estimating the application performance. The measures such as accuracy, sensitivity, recall, F1 score, peak-to-signal noise ratio and correlation coefficient have been estimated using proposed methodology. moreover these metrics are providing high improvement compared to earlier models.

The implemented deep and ML designs get outperformance the methodologies and proving good application successive score.

Study on knowledge-based systems for scientific publications is growing very broadly. However, most of these studies do not explicitly discuss the knowledge management (KM) component as knowledge management system (KMS) implementation. This background causes academic institutions to face challenges in developing KMS to support scholarly publication cycle (SPC). Therefore, this study aims to develop a new KMS conceptual model, Identify critical components and provide research gap opportunities for future KM studies on SPC.

This study used a systematic literature review (SLR) method with the procedure from Kitchenham et al. Then, the SLR results are compiled into a conceptual model design based on a framework on KM foundations and KM solutions. Finally, the model design was validated through interviews with related field experts.

The KMS for SPC focuses on the discovery, sharing and application of knowledge. The majority of KMS use recommendation systems technology with content-based filtering and collaborative filtering personalization approaches. The characteristics data used in KMS for SPC are structured and unstructured. Metadata and article abstracts are considered sufficiently representative of the entire article content to be used as a search tool and can provide recommendations. The KMS model for SPC has layers of KM infrastructure, processes, systems, strategies, outputs and outcomes.

This study has limitations in discussing tacit knowledge. In contrast, tacit knowledge for SPC is essential for scientific publication performance. The tacit knowledge includes experience in searching, writing, submitting, publishing and disseminating scientific publications. Tacit knowledge plays a vital role in the development of knowledge sharing system (KSS) and KCS. Therefore, KSS and KCS for SPC are still very challenging to be researched in the future. KMS opportunities that might be developed further are lessons learned databases and interactive forums that capture tacit knowledge about SPC. Future work potential could identify other types of KMS in academia and focus more on SPC.

This study proposes a novel comprehensive KMS model to support scientific publication performance. This model has a critical path as a KMS implementation solution for SPC. This model proposes and recommends appropriate components for SPC requirements (KM processes, technology, methods/techniques and data). This study also proposes novel research gaps as KMS research opportunities for SPC in the future.

This study aims to examine the appropriate measures needed toward achieving sustainability of renewable energy production among developing and middle-income countries.

This study uses semi-annual panel data covering the period 2000–2020 among 152 developing and middle-income countries and Cox proportional hazard model for the analysis.

Estimates indicate that effective operations of environmental institutions, investment in research and development, subsidizing the production of renewable energy, government investment in producing renewable energy and investment in renewable energy production made by the private sector will contribute immensely toward achieving sustainability of renewable energy production.

This study recommends that governments should rationalize their expenditures to mobilize enough resources for investment in renewable energy production. Again, operations of environmental institutions should be enhanced through giving their managers’ performance contracts and licensing its employees. Enabling environment should be created for private sector to increase their investment in renewable energy production.

Empirical studies have been carried out exploring measures to deal with climate change. Nonetheless, the appropriate measures needed toward achieving sustainability of renewable energy production among developing and middle-income countries have not been explored in existing empirical studies. Hence, this study fills the gap in existing empirical studies.

This survey-based study aims to explore the research data dissemination and requesting practices of Arab researchers. It investigates the reasons, types, methods, barriers and motivations associated with data sharing and requesting in the Arab research community.

A cross-sectional survey was conducted with 205 Arab researchers representing various disciplines and career stages. Descriptive statistics were used for data analysis.

The study found that 91.2% of Arab researchers share data, while 56.6% access data from others. Reasons for sharing include promoting transparency and collaboration while requesting data is driven by the need to validate findings and explore new research questions. Processed/analysed data and survey/questionnaire data are the most commonly shared and requested types.

This study contributes to the literature by examining data sharing and requesting practices in the Arab research community. It provides original insights into the motivations, barriers and data types shared and requested by Arab researchers. This can inform future research and initiatives to promote regional data sharing.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-06-2023-0283

In the digital era individuals are overwhelmed by huge amount of readily available information. The information provided at the time of COVID-19 crisis is increasingly available. The purpose of this paper was to investigate individuals’ perceived feelings due to the plethora of information during COVID-19 pandemic in Greece in Spring 2020.

This study was conducted through a Web-based questionnaire survey posted on the Google Forms platform. The questionnaire consisted of closed-ended, seven-point Likert-scale questions. The data collected were subjected to a principal component analysis. The retained principal components (PCs) were subjected to statistical analysis between genders and among age groups and professional status with the nonparametric criteria Mann–Whitney U and Kruskal–Wallis.

Responses by 776 individuals were obtained. Seventeen original variables from the questionnaire were summarized into three PCs that explained the 71.7% of total variance: “affective disorders,” “uncertainty issues and inaccurate information worries” and “satisfaction and optimism.” Participants partly agree that the received amount of information on the disease caused them feelings of uncertainty about the future and worries about relatives’ lives, but also satisfaction with developments in the country. Females seem to experience stronger perceived feelings of “affective disorders” (p < 0.001) and reported higher degree of agreement about “uncertainty issues and inaccurate information worries.”

The recorded feelings caused by the volume of available information may have forced people accept the necessary precautionary behavioral changes that had contributed to the Greek success in preventing spread of the disease in Spring 2020.

The challenge of predicting changes in aggregate income and stock prices is one that has occupied the research agendas of economists. This paper aims to use the consumption–income ratio and the dividend–price ratio to predict future income and stock prices.

To examine the stability of the consumption–income ratio and the dividend–price ratio, the authors run a two-variable, two-lag reduced-form VAR in the vein of Cochrane (1994), using a lag of each respective ratio as exogenous to the VAR. Additionally, the authors estimate an AR(4) model for income and prices.

The consumption–income ratio and the dividend–price ratio remain key to understanding future movements in income and stock prices. The consumption–income ratio significantly predicts future income in the USA, and aggregate income is easier to predict than consumption in the VAR model. The dividend–price ratio does not significantly predict future price growth. Consumption and dividend shocks have lasting impacts on income and prices.

The consumption–income ratio and the dividend–price ratio are still key to understanding future movements in income and stock prices. The consumption–income ratio significantly predicts future income in the USA, and aggregate income is easier to predict than consumption in the VAR model. However, the dividend–price ratio does not significantly predict future price growth, a change from previous research from the 1990s, despite the increasing complexity of stock markets. Consumption and dividend shocks have lasting impacts on income and prices and appear to be significant drivers in both the short- and long-run variance in income and prices.

Wikipedia's inclusive editorial policy permits unrestricted participation, enabling individuals to contribute and disseminate their expertise while drawing upon a multitude of external sources. News media outlets constitute nearly one-third of all citations within Wikipedia. However, embracing such a radically open approach also poses the challenge of the potential introduction of biased content or viewpoints into Wikipedia. The authors conduct an investigation into the integrity of knowledge within Wikipedia, focusing on the dimensions of source political polarization and trustworthiness. Specifically, the authors delve into the conceivable presence of political polarization within the news media citations on Wikipedia, identify the factors that may influence such polarization within the Wikipedia ecosystem and scrutinize the correlation between political polarization in news media sources and the factual reliability of Wikipedia's content.

The authors conduct a descriptive and regression analysis, relying on Wikipedia Citations, a large-scale open dataset of nearly 30 million citations from English Wikipedia. Additionally, this dataset has been augmented with information obtained from the Media Bias Monitor (MBM) and the Media Bias Fact Check (MBFC).

The authors find a moderate yet significant liberal bias in the choice of news media sources across Wikipedia. Furthermore, the authors show that this effect persists when accounting for the factual reliability of the news media.

The results contribute to Wikipedia’s knowledge integrity agenda in suggesting that a systematic effort would help to better map potential biases in Wikipedia and find means to strengthen its neutral point of view policy.

This study aims to provide insights into Airbnb’s exit from the China market, specifically from the perspective of platform competition.

Based on the understanding of the evolution and competition of peer-to-peer (P2P) accommodation market in China and the double-edged network effects, this viewpoint article provides an in-depth discussion on Airbnb’s withdrawal from the China market.

This viewpoint paper contends that Airbnb faced increasing challenges in establishing unique competitive advantages against local rivals, who demonstrate a capacity for innovation and maintaining loyal customers in adapting to the market evolution. Consequently, this may inhibit Airbnb from attracting the mainstream market and lead Airbnb into a negative spiral, exacerbated by indirect network effects.

This study offers insights into the failure of an internationalisation process, drawing from an analysis of the competitive environment and the competition strategies of P2P accommodation platforms in China. These findings have practical implications for the implementation of international business strategies in P2P accommodation.

本观点文章旨在基于平台竞争的视角对爱彼迎退出中国的现象进行阐释

基于对中国共享住宿/民宿市场的演变和竞争、双边网络效应的理解, 本文对爱彼迎退出中国进行了深入探讨。

本文提出, 爱彼迎在建立独特竞争优势以应对本地竞争对手方面面临日益严峻的挑战, 因为本地竞争者在适应市场变化方面展现出突出的创新和留客的能力。这可能会阻碍爱彼迎吸引主流市场, 并导致其跌入由间接网络效应加剧的恶性循环中。

基于对中国共享住宿平台的竞争环境和策略的分析, 本文洞悉了一个国际化失败的案例。本文的发现对于共享住宿的国际化策略实施具有实践意义。

Este artículo de opinión pretende aportar ideas sobre la salida de Airbnb del mercado chino, concretamente desde la perspectiva de la competencia entre plataformas.

Basándonos en la comprensión de la evolución y la competencia del mercado de alojamientos P2P en China, así como en los “efectos de red de doble filo”, este artículo de opinión ofrece una discusión en profundidad sobre la retirada de Airbnb del mercado chino.

Este documento sostiene que Airbnb enfrentó desafíos crecientes para establecer ventajas competitivas únicas frente a sus rivales locales, demostrando estos últimos una capacidad de innovación y del mantenimiento de la lealtad de sus clientes al adaptarse a la evolución del mercado. En consecuencia, esto puede inhibir a Airbnb para atraer al mercado principal y llevar a la empresa a una espiral negativa, exacerbada por efectos de red indirectos.

Este estudio ofrece una visión del fracaso de un proceso de internacionalización, a partir de un análisis del entorno competitivo y de las estrategias de competencia de las plataformas de alojamiento P2P en China. Estos resultados tienen implicaciones prácticas para la aplicación de estrategias empresariales internacionales en los alojamientos P2P.