Why we like Google Scholar: postgraduate students' perceptions of factors influencing their intention to use

Alotaibi, Faiz; Johnson, Frances

doi:10.1108/ajim-10-2019-0304

Cited by 9 publications

(6 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This rather speculative assumption of a current retrieval paradigm may be propped up by the observation that the 'ideal' type of a variety-enhancing tool does not even seem to exist yet: there is not yet the fictitious bot that spits out random scientific publications from a huge corpus from centuries of publications of which each work has an equal probability of being listed, regardless of the publication's age, discipline, authors, citational impact or publication outlet. In addition, one of the most frequently used machines for research discovery, Google Scholar [59], is by default a query-based and, therefore, a redundancy-reproducing one (it is even partly citation-based, as it sorts results based on algorithms that take into account the number of citations by default).…”

Section: Discussionmentioning

confidence: 99%

“…In effect, if researchers only used, say, Google Scholar and Mendeley Suggest to discover relevant publications, they would probably reproduce bibliometric redundancy – as is visible in the standard assumption of a Pareto distribution in bibliometrics, according to which 80% of all citations go to just 20% of all research outputs ([59–61]; but note that there is evidence of a gradual change in this pattern, cf. [62]).…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

A typology of research discovery tools

Nishikawa-Pacher

2021

Journal of Information Science

View full text Add to dashboard Cite

There has been a proliferation of new research discovery tools that aid scientists in finding relevant publications. To obtain a general overview of this development, this article generates a conceptual typology of all possible research discovery tools by drawing from the information-theoretical concepts of redundancy/variety. Bibliometric links between scholarly publications can thus exhibit ‘redundancy’ (i.e. expectable linkages between academic works) or ‘variety’ (i.e. original co-occurrence patterns). On the redundancy-reproducing end of the typology are machines that harness extant co-citations or keyword queries, such as academic search engines and paper recommender systems. The variety end of the spectrum harbours services that enable categorial browsing or that suggest publications randomly, such as journals’ tables of contents or random paper bots. The typology has implications for understanding how the design of research discovery platforms may ultimately shape aggregate citational networks of science.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A typology of research discovery tools

Nishikawa-Pacher

2021

Journal of Information Science

View full text Add to dashboard Cite

show abstract

“…It is proven in the small amount of literature we found, even in Google Scholar, which database is more comprehensive than others. As a competent Big Data device, Google continuously updates its performance by improving the database based on academics' criticisms for better service [13], [14].…”

Section: Methodsmentioning

confidence: 99%

A Review of Business Model Transformation in Transportation Business Post Pandemic

Kaukab

Bachtiar

2022

View full text Add to dashboard Cite

This study reviews post-pandemic business models, as demonstrated in transportation business by employing literature study method. Data obtained includes 56 articles published from 2020 to 2022 during Covid-19 pandemic that transform the business model. The results of the study indicate that there are six streams in the literature, namely (1) the impact of Covid-19 on the transportation sector, (2) the built environment for business model transportation, (3) the need for transformation of business models due to new policies, (4) changes in consumer choices and behavior, (5) supply chain management and transportation transformation, and (6) transportation business model planning. The synthesis resulted in a transportation sustainable business model transformation framework involving threat identification, company character, support, built environment, resources and capabilities, design and development, and delivery and evaluation. The transportation business needs to identify aspects of transportation services that need to be improved, adjust internal resources and capabilities, reconfigure the business, and encourage collective agreements to transform the post-pandemic transportation business model. This research contributes to the literature on transform the regular business model. It emphasizes the importance of resources and capabilities to drive business transformation internally and externally in the context of the transportation business.

show abstract

“…UTAUT has been validated extensively in learning and Kaliisa et al (2019) suggested that UTAUT is effective in investigating mobile learning acceptance. For example, Alotaibi and Johnson (2020) examined international postgraduate students' intention to use Google Scholar based on the four constructs of UTAUT. Moreover, Alowayr (2022) extended the UTAUT to investigate mobile learning acceptance in Saudi Arabia and achieved good results.…”

Section: Open Datamentioning

confidence: 99%

Open data: an innovative learning resource for postgraduates

Dai

Zhou

Zhang

et al. 2023

LHT

View full text Add to dashboard Cite

PurposeWith the continuous development of the global COVID-19 epidemic, mobile learning has become one of the most significant learning approaches. The mobile learning resource is the basis of mobile learning; it may directly affect the effectiveness of mobile learning. However, the current learning resources cannot meet users' needs. This study aims to analyze the influencing factors of accepting open data as learning resources among users.Design/methodology/approachBased on the technology acceptance model (TAM) and the unified theory of acceptance and use of technology (UTAUT), this study proposed a comprehensive theoretical research model. Data were obtained from 398 postgraduates from several universities in central China. Confirmatory factor analysis was used to determine the reliability and validity of the measurement model. Data has been analyzed using SPSS and AMOS software.FindingsThe results suggested that perceived usefulness, performance expectancy, social influence and facilitating conditions have a positive influence on accepting open data as learning resources. Perceived ease of use was not found significant. Moreover, it was further shown in the study that behavioural intention significantly influenced the acceptance of open data as learning resources.Originality/valueThere is a lack of research on open data as learning resources in developing countries, especially in China. This study addresses the gap and helps us understand the acceptance of open data as learning resources in higher education. This study also pays attention to postgraduates' choice of learning resources, which has been little noticed before. Additionally, this study offers opportunities for further studies on the continuous usage of open data in higher education.

show abstract

Why we like Google Scholar: postgraduate students' perceptions of factors influencing their intention to use

Cited by 9 publications

References 49 publications

A typology of research discovery tools

A typology of research discovery tools

A Review of Business Model Transformation in Transportation Business Post Pandemic

Open data: an innovative learning resource for postgraduates

Contact Info

Product

Resources

About