University of Professional Studies

This study examines the impact of CEO trust and organizational commitment on global partnerships and sustainable industrial development (SDG 9) among small and medium-sized enterprises (SMEs) in emerging economies, specifically Ghana. It explores the mechanisms through which internal organizational dynamics facilitate external collaboration aligned with Sustainable Development Goal 17 (Partnerships for the Goals). A quantitative research approach was adopted, employing stratified random sampling to survey SME CEOs across various industrial sectors in Ghana. Data collection involved a self-administered questionnaire utilizing validated scales. Structural equation modeling (SEM) tested the hypothesized relationships among CEO trust, organizational commitment, global partnerships, and sustainable industrial development. The results confirm that CEO trust significantly predicts global partnerships and organizational commitment. Organizational commitment positively influences global partnerships, driving sustainable industrial development sustainability. Organizational commitment mediates the CEO trust-global partnerships relationship, while global partnerships mediate the CEO trust-sustainable industrial development link. This study enriches social capital theory, highlighting CEO trust and organizational commitment’s role in facilitating effective global partnerships. It advances sustainable development literature, providing empirical evidence on SMEs in emerging economies harnessing internal leadership and collaborative capacity to achieve SDG 9. The findings offer insights for SME leaders, policymakers, and development agencies promoting industrial growth via strategic international partnerships, contributing to sustainable development goals.

In the quest to identify factors stimulating employee morale, this study assesses the relationship between leadership practices and employee morale via job satisfaction. We employ a quantitative approach, surveying 400 Ghanaian hospitality workers. We analyze the cross-sectional data using SPSS via descriptive statistics, exploratory factor analysis (EFA), and mediation modeling (macro-PROCESS). Transformational, transactional, and democratic leadership relate positively to employee morale, whereas autocratic leadership relates negatively. Job satisfaction significantly mediates the relationship between transformational, transactional, and democratic leadership and employee morale, but not in the case of autocratic leadership. Managers in Ghana’s hospitality industry should prioritize democratic and transactional leadership to boost morale via job satisfaction. Having confirmed the efficacy of the four common leadership practices as antecedents of organizational outcomes without violating the principle of multicollinearity, we provide empirical support for the expansion of the full-range leadership theory (FRLT) to include democratic and autocratic styles. Consequently, this study provides empirical evidence for practitioners and contributes to the limited literature on leadership and employee morale in developing countries.

This study sought to ascertain whether Hofstede’s cultural dimensions are good predictors of cyberbullying behaviour among Ghanaian tertiary students, given that cultural values can shape individuals’ perceptions and actions, including what is considered acceptable during conflicts in digital spaces. This study used a quantitative, cross-sectional survey design to collect data from 301 tertiary students in Ghana. The data was analysed using structural equation modelling. The model explained 22.9

Sentiment analysis (also known as opinion mining) is a natural language processing (NLP) technique for determining data’s positive, negative, or neutral nature. The rise of social media platforms such as X (formally Twitter) and Facebook have become great arenas for discourse on racism and mediums of racism ideologies. This study utilized a hybrid sentiment analysis to detect racist tweets using lexicon-based sentiment analysis and a Support Vector Machine. The models’ success in accurately classifying sentiments related to racism highlights its potential for broader applications in the analysis of other social issues. Furthermore, this study contributes to the ongoing discourse on combating racism in the digital age. By shedding light on the sentiments expressed online, it provides valuable insights that can inform policy decisions, advocacy efforts, and public awareness campaigns. The findings underscore the importance of addressing racism not just in the physical world but also in the digital sphere, where harmful ideologies can spread rapidly and widely.

Since the emergence of COVID-19 in December 2019, it has continued to ravage our world up until now. Many lives are at risk because of the menace caused by it. Although manual detection with the reverse transcription polymerase chain reaction (RT-PCR) is still popular and operational, it is costly and time-consuming. In this paper, we propose a novel transfer learning model using pre-trained InceptionV3 to classify infected people from healthy people using chest X-ray images. Even in a seemingly post-COVID era, the study of effective deep models for COVID detection and classification remains imperative, as one, the pandemic is likely to recur, two, monitor a few extant cases, and could be repurposed to other respiratory-based conditions. Methodically, by way of initial preprocessing, images were filtered using the non-local means (NLM) filter and improved in contrast using contrast-limited adaptive histogram equalization (CLAHE). Afterwards, data augmentation techniques with random affine transformation were adopted to augment the training data. Three experiments were undertaken on a publicly available dataset from Kaggle and in the end, our proposed model (modifiedInceptionV3) achieved a remarkable, state-of-the-art-accuracy of 98.6