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1 – 2 of 2Nkeiru 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.
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Keywords
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.
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