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Article
Publication date: 20 November 2023

Nkeiru A. Emezie, Scholastica A.J. Chukwu, Ngozi M. Nwaohiri, Nancy Emerole and Ijeoma I. Bernard

University intellectual output such as theses and dissertations are valuable resources containing rigorous research results. Library staff who are key players in promoting…

Abstract

Purpose

University intellectual output such as theses and dissertations are valuable resources containing rigorous research results. Library staff who are key players in promoting intellectual output through institutional repositories require skills to promote content visibility, create wider outreach and facilitate easy access and use of these resources. This study aims to determine the skills of library staff to enhance the visibility of intellectual output in federal university libraries in southeast Nigeria.

Design/methodology/approach

A survey research design was adopted for the study. The questionnaire was used to obtain responses from library staff on the extent of computer skills and their abilities for digital conversion, metadata creation and preservation of digital content.

Findings

Library staff at the university libraries had high skills in basic computer operations. They had moderate skills in digital conversion, preservation and storage. However, they had low skills in metadata creation.

Practical implications

The study has implications for addressing the digital skills and professional expertise of library staff, especially as it concerns metadata creation, digital conversion, preservation and storage. It also has implications for the university management to prioritize the training of their library staff in other to increase the visibility of indigenous resources and university Web ranking.

Originality/value

This study serves as a lens to identify library staff skill gaps in many critical areas that require expertise and stimulate conscious effort toward developing adequate skills for effective digital information provision. It sheds light on the challenges that many Nigerian university libraries face in their pursuit of global visibility and university Web ranking.

Details

Digital Library Perspectives, vol. 40 no. 1
Type: Research Article
ISSN: 2059-5816

Keywords

Article
Publication date: 17 May 2023

Tong Yang, Jie Wu and Junming Zhang

This study aims to establish a comprehensive satisfaction analysis framework by mining online restaurant reviews, which can not only accurately reveal consumer satisfaction but…

Abstract

Purpose

This study aims to establish a comprehensive satisfaction analysis framework by mining online restaurant reviews, which can not only accurately reveal consumer satisfaction but also identify factors leading to dissatisfaction and further quantify improvement opportunity levels.

Design/methodology/approach

Adopting deep learning, Cross-Bidirectional Encoder Representations Transformers (BERT) model is developed to measure customer satisfaction. Furthermore, opinion mining technique is used to extract consumers’ opinions and obtain dissatisfaction factors. Furthermore, the opportunity algorithm is introduced to quantify attributes’ improvement opportunity levels. A total of 19,133 online reviews of 31 restaurants in Universal Beijing Resort are crawled to validate the framework.

Findings

Results demonstrate the superiority of Cross-BERT model compared to existing models such as sentiment lexicon-based model and Naïve Bayes. More importantly, after effectively unveiling customer dissatisfaction factors (e.g. long queuing time and taste salty), “Dish taste,” “Waiters’ attitude” and “Decoration” are identified as the three secondary attributes with the greatest improvement opportunities.

Practical implications

The proposed framework helps managers, especially in the restaurant industry, accurately understand customer satisfaction and reasons behind dissatisfaction, thereby generating efficient countermeasures. Especially, the improvement opportunity levels also benefit practitioners in efficiently allocating limited business resources.

Originality/value

This work contributes to hospitality and tourism literature by developing a comprehensive customer satisfaction analysis framework in the big data era. Moreover, to the best of the authors’ knowledge, this work is among the first to introduce opportunity algorithm to quantify service improvement benefits. The proposed Cross-BERT model also advances the methodological literature on measuring customer satisfaction.

Details

International Journal of Contemporary Hospitality Management, vol. 36 no. 3
Type: Research Article
ISSN: 0959-6119

Keywords

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