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Article
Publication date: 28 July 2023

Lindsey M. Harper, Elizabeth D. James, Soohyung Joo and Youngseek Kim

Today’s undergraduate students have spent a significant portion of their lives using YouTube for various reasons, whether for entertainment, personal development or academic…

Abstract

Purpose

Today’s undergraduate students have spent a significant portion of their lives using YouTube for various reasons, whether for entertainment, personal development or academic learning purposes. This study aims to investigate how system factors (i.e. reliability, usability and searchability), interaction factors (i.e. provider and user interactions) and content factors (i.e. format, relevance and coverage) affect undergraduate students’ satisfaction with YouTube and their intentions to adopt YouTube for learning purposes.

Design/methodology/approach

This research uses the information systems success model as its theoretical framework to explore the system, interaction and content factors associated with undergraduate students’ satisfaction with YouTube and their intentions to use YouTube for learning. The proposed hypotheses were examined by the structural equation modelling technique based on a survey with 345 undergraduate students at a Southeastern institution in the USA.

Findings

The results indicate that both system factors (including reliability, usability and searchability) and content factors (including format, relevance and coverage) have a statistically significant effect on students’ satisfaction with YouTube. This study also demonstrates that students’ satisfaction with YouTube significantly influences their intentions to use the platform for learning purposes.

Research limitations/implications

The proposed research model provides a novel perspective in understanding the complex nature of students’ adoption of YouTube for learning purposes, led by both system and content factors mediated by satisfaction with YouTube.

Practical implications

This study suggests that when YouTube is intuitive to use and relevant content is added to the platform regularly, students are more likely to adopt this platform for learning purposes. As a result, it is critical that librarians remain aware of information-seeking practices and platforms used by students to tailor approaches to teaching information literacy to help students understand how to use the platform effectively.

Originality/value

Using the information systems success model, this research sheds light on the roles of system and content factors in undergraduate students’ satisfaction with YouTube and their intentions to use it for learning.

Details

The Electronic Library , vol. 41 no. 5
Type: Research Article
ISSN: 0264-0473

Keywords

Article
Publication date: 19 December 2022

Sukjin You, Soohyung Joo and Marie Katsurai

The purpose of this study is to explore to which extent data mining research would be associated with the library and information science (LIS) discipline. This study aims to…

Abstract

Purpose

The purpose of this study is to explore to which extent data mining research would be associated with the library and information science (LIS) discipline. This study aims to identify data mining related subject terms and topics in representative LIS scholarly publications.

Design/methodology/approach

A large set of bibliographic records over 38,000 was collected from a scholarly database representing the fields of LIS and the data mining, respectively. A multitude of text mining techniques were applied to investigate prevailing subject terms and research topics, such as influential term analysis and Dirichlet multinomial regression topic modeling.

Findings

The findings of this study revealed the relationship between the LIS and data mining research domains. Various data mining method terms were observed in recent LIS publications, such as machine learning, artificial intelligence and neural networks. The topic modeling result identified prevailing data mining related research topics in LIS, such as machine learning, deep learning, big data and among others. In addition, this study investigated the trends of popular topics in LIS over time in the recent decade.

Originality/value

This investigation is one of a few studies that empirically investigated the relationships between the LIS and data mining research domains. Multiple text mining techniques were employed to delineate to which extent the two research domains would be associated with each other based on both at the term-level and topic-level analysis. Methodologically, the study identified influential terms in each domain using multiple feature selection indices. In addition, Dirichlet multinomial regression was applied to explore LIS topics in relation to data mining.

Details

Aslib Journal of Information Management, vol. 76 no. 1
Type: Research Article
ISSN: 2050-3806

Keywords

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