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The purpose of this study is to understand the effect of different brand positions (underdog vs top dog) and comparative advertising on consumers’ brand attitudes. Additionally, this study also aims to demonstrate the effects of inspiration, self-relevance and empathy on the relationship between brand positioning and comparative advertising.

A two-by-three factorial design was employed with brand positions (underdog vs top dog) and three types of comparative advertising (noncomparative, indirect comparative and direct comparative) as the independent variables. Inspiration serves as the mediator, while self-relevance and empathy act as moderators and brand attitude is the dependent variable.

The results show that different brand positions significantly affect brand attitudes, with respondents having a better brand attitude toward the underdog brand. Brand attitude is partially mediated by inspiration. Self-relevance moderates the relationship between brand positioning and brand attitude. However, brand positioning, comparative advertising and empathy do not have interaction effects.

This study contributes to a better understanding of the effect of psychological variables on brand positioning and comparative advertising.

The results suggest that the underdog setting requires a real and honest story because consumers will spot a fake underdog story, which will damage consumer trust in the brand and harm the brand image.

There is a lack of research using psychological variables to demonstrate the effect of being the underdog brand. This study contributes to the literature by employing psychological variables to illustrate the effect of underdog positioning. These findings can help brands develop branding positioning strategies.

The purpose of this study is to examine the corruption prevalent in the distribution of COVID-19 palliatives during the lockdown and movement restrictions in the country. This study seeks to analyse the current state of corruption in the distribution of COVID-19 palliatives and public health facilities in Nigeria while also providing a legal insight and strategic blueprint to combat corruption. To this end, this study will address the current legal framework for combating corruption and build upon this to formulate a working strategy for tackling corruption in the future.

Using a doctrinal legal research methodology, this study draws upon existing literature, tertiary data sources and information from the Nigeria Centre for Disease Control. The collected data is analysed and compared with current literature to identify key findings. Rent-seeking and utilitarian theories of the law were examined to guide this study. This study offers useful insights into combating corruption. The use of this method is justified, as it enhances the credibility of the findings on the importance of strategies for future emergencies. This legal research approach is consistent with the law and can be easily verified. The empirical aspect of this study involved a survey of multidimensional health-care and economic data set of 36 states in Nigeria plus the Federal Capital Territory on COVID-19 in Nigeria. A survey linearised regression model was estimated to determine the influence of government revenue and public health-care facilities in the control of the virus spread in Nigeria.

This study reveals the need for emphasis on the imperative of combating corruption in the distribution of COVID-19 palliatives and establishing economic resilience through transparent and accountable practices, supported by legal frameworks.

Rent-seeking and utilitarian theories of law are evaluated because of their impacts on combating corruption. The limitation of this study is the intricacy of gathering data on COVID-19 palliatives corruption in Nigeria because of secrecy and the absence of reliable data on the subject.

Estimating the exact number of stolen palliatives and their fiscal impact on Nigeria's economy proves to be a formidable task because of the covert nature of corruption. This study equips policymakers in Nigeria with a better understanding of the legal challenges posed by corruption in the health care sector and provides an effective strategy to combat it.

The lack of reliable data on the extent of palliative theft hinders the ability of lawmakers to enact effective legislation and strategies for combating corruption in the distribution of COVID-19 palliatives and addressing future emergencies in Nigeria. The policy implications of this study can assist policymakers in Nigeria and other countries in formulating measures to combat corruption in the distribution of COVID-19 palliatives and other future emergencies. Furthermore, it recommends the overhaul of anti-corruption laws and mechanisms in Nigeria to ensure effective measures against corruption.

In conclusion, this study contributes to knowledge by proposing a legal model centred on people's participation to enhance transparency and accountability in future palliative distribution processes. This study recommends legal strategies that can effectively address corruption in future emergencies or shocks. This study proposes a strategic blueprint to tackle corruption in the future. This blueprint includes an analysis of existing laws and regulations, as well as potential policy changes and legislative reform. This study also includes recommendations for improved enforcement and oversight mechanisms and for improved public awareness and education. As part of this, this study considers the potential for public–private partnerships to increase transparency and accountability in public health and health-care services.

While most language departments of the university offer service-learning (SL) subjects based on language teaching, such as “Teaching Chinese as a Second Language in Local Schools” and “Serving the Community through Teaching English,” this paper aims to argue that teaching students to teach language(s) is yet to be the best strategy to serve the service recipients.

SL is widely understood as an experiential learning pedagogy that integrates academic focus, reflection and community service and is shown to be impactful. In Hong Kong, the first university that has made SL a graduation requirement is the Hong Kong Polytechnic University (the University). Considering this, new SL courses have proliferated over the past decade. Adopting a narrative inquiry approach, this paper examines personal narratives from a new SL subject aiming to raise awareness of refugees in Hong Kong. The data includes students’ reflective journals, co-created personal narratives and podcasts and semi-structured interviews.

This paper finds that crafting and recording narratives of shared experiences deepens cultural understanding, cultivates empathy and facilitates language learning in a genuine setting.

Ultimately, this paper advocates a well-designed SL that combines language, content and technology as a powerful, transformational experience for both college students and service recipients.

This paper focuses on a brand new SL course, “Storytelling for Understanding: Refugee Children in Hong Kong,” offered in Semester 1, 2022–2023. The subject was developed by the two authors from a language division affiliated to the University. The deliverables were podcast recordings, co-authored and co-edited by the students and the children.

Higher education plays an essential role in achieving the United Nations sustainable development goals (SDGs). However, there are only scattered studies on monitoring how universities promote SDGs through their curriculum. The purpose of this study is to investigate the connection of existing common core courses in a university to SDG education. In particular, this study wanted to know how common core courses can be classified by machine-learning approach according to SDGs.

In this report, the authors used machine learning techniques to tag the 166 common core courses in a university with SDGs and then analyzed the results based on visualizations. The training data set comes from the OSDG public community data set which the community had verified. Meanwhile, key descriptions of common core courses had been used for the classification. The study used the multinomial logistic regression algorithm for the classification. Descriptive analysis at course-level, theme-level and curriculum-level had been included to illustrate the proposed approach’s functions.

The results indicate that the machine-learning classification approach can significantly accelerate the SDG classification of courses. However, currently, it cannot replace human classification due to the complexity of the problem and the lack of relevant training data.

The study can achieve a more accurate model training through adopting advanced machine learning algorithms (e.g. deep learning, multioutput multiclass machine learning algorithms); developing a more effective test data set by extracting more relevant information from syllabus and learning materials; expanding the training data set of SDGs that currently have insufficient records (e.g. SDG 12); and replacing the existing training data set from OSDG by authentic education-related documents (such as course syllabus) with SDG classifications. The performance of the algorithm should also be compared to other computer-based and human-based SDG classification approaches for cross-checking the results, with a systematic evaluation framework. Furthermore, the study can be analyzed by circulating results to students and understanding how they would interpret and use the results for choosing courses for studying. Furthermore, the study mainly focused on the classification of topics that are taught in courses but cannot measure the effectiveness of adopted pedagogies, assessment strategies and competency development strategies in courses. The study can also conduct analysis based on assessment tasks and rubrics of courses to see whether the assessment tasks can help students understand and take action on SDGs.

The proposed approach explores the possibility of using machine learning for SDG classifications in scale.