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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.

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.

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.

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.

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.

Information literacy (IL) is clearly important for academic performance, as evidenced by literature. It could be defined as a set of abilities, attitudes and experiences that enable people to recognize when they need information to solve an issue. The importance of investigating students’ IL competencies cannot be overstated. This study therefore aims to look into the IL development and competencies of high school students in Accra, as there appears to be a dearth of systematic study on this in Ghana.

To investigate this phenomenon, the study used a survey research design with a mixed-methods approach and a post-positivist research paradigm. A total of 454 high school students, 3 librarians and 3 heads of ICT departments from three senior high schools in Accra participated in this study. This study used two methods: an audit of the IL programmes and practices available at the selected schools as well as IL literacy assessment through the use of a standardized test instrument. The data collection tools used were a semi-structured interview schedule and a questionnaire.

This study found that high school students in Accra had low IL competencies. Again, the findings of this study revealed that inadequate infrastructure and lack of formalized IL instructions in schools hindered the IL development of students.

The author considers the study original both in conceptualization and design. The main question being interrogated stems from identified gaps in the literature and this study intends to fill these knowledge gaps. This study’s originality also stems from the fact that there is a paucity of information on the subject of study in the context of Ghana. This study recommends the need to integrate IL in the school curriculum to ensure effective and efficient IL instructions in high schools.

The explosion of data due to the sophistication of information and communication technology makes it simple for prospective tourists to learn about previous hotel guests' experiences. They prioritize the rating score when selecting a hotel. However, rating scores are less reliable for suggesting a personalized preference for each aspect, especially when they are in a limited number. This study aims to recommend ratings and personalized preference hotels using cross-domain and aspect-based features.

We propose an aspect-based cross-domain personalized recommendation (AsCDPR), a novel framework for rating prediction and personalized customer preference recommendations. We incorporate a cross-domain personalized approach and aspect-based features of items from the review text. We extracted aspect-based feature vectors from two domains using bidirectional long short-term memory and then mapped them by a multilayer perceptron (MLP). The cross-domain recommendation module trains MLP to analyze sentiment and predict item ratings and the polarities of the aspect based on user preferences.

Expanded by its synonyms, aspect-based features significantly improve the performance of sentiment analysis on accuracy and the F1-score matrix. With relatively low mean absolute error and root mean square error values, AsCDPR outperforms matrix factorization, collaborative matrix factorization, EMCDPR and Personalized transfer of user preferences for cross-domain recommendation. These values are 1.3657 and 1.6682, respectively.

This study assists users in recommending hotels based on their priority preferences. Users do not need to read other people's reviews to capture the key aspects of items. This model could enhance system reliability in the hospitality industry by providing personalized recommendations.

This study introduces a new approach that embeds aspect-based features of items in a cross-domain personalized recommendation. AsCDPR predicts ratings and provides recommendations based on priority aspects of each user's preferences.

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.

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.

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.

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.

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.