Journal of Business and Digital Innovation

AI-Driven Personalized Learning: Predicting Academic Performance Through Leadership Personality Traits

Nitsa Herzog — 2026-02-26

The study explores the potential of AI technologies in personalized learning, suggesting the prediction of academic success through leadership personality traits and machine learning modelling. The primary data were obtained from 129 master's students in the Environmental Engineering Department, who underwent five leadership personality tests with 23 characteristics. Students used self-assessment tools that included Personality Insight, Workplace Culture, Motivation at Work, Management Skills, and Emotion Control tests. The test results were combined with the average grade obtained from academic reports. The study employed exploratory data analysis and correlation analysis. Feature selection utilized Pearson correlation coefficients of personality traits. The average grades were separated into three categories: “Fail”, “Pass”, and “Excellent”. The modelling process was performed by tuning seven ML algorithms, such as Support Vector Machine, Logistic Regression, K-Nearest Neighbors, Decision Tree, Gradient Boosting, Random Forest, XGBoost and LightGBM. The highest predictive performance was achieved with the Random Forest classifier, which yielded an accuracy of 87.50% for the model incorporating 17 personality trait features and the leadership mark feature, and an accuracy of 85.71% for the model excluding this feature. In this way, the study offers an additional opportunity to identify students' strengths and weaknesses at an early stage of their education process and select the most suitable strategies for personalized learning.

Relationships between Airline Sustainability and Consumer Behaviour: An assessment of the influence of environmental awareness on the decision-making process of European airline customers

Yves Kremer — 2026-02-26

The aviation industry increasingly contributes to the global share of carbon emissions and therefore also to increasing global average temperatures. Although forecasts for growing emissions and solutions to decrease CO2 by aircraft usage are well described in the extant literature, the debate has failed to address how aviation passengers feel influenced in their choice as consumers. The study investigates possible relationships between civil aviation environmental sustainability, passenger environmental awareness and consumer behaviour. We use the European airline industry as a geographical focus of the study. Five hypotheses are developed related to (i) environmental awareness by airline passengers, (ii) the influence of sustainability on ticket booking behaviour, (iii) the influence of Sustainable Aviation Fuel and carbon offsetting on consumer behaviour and (iv) the influence of environmental awareness on airline image and customer satisfaction. To test the hypotheses, a survey method is used to gather data from airline passengers. The results show that environmental awareness is indeed increasing among airline passengers and as such has an influence on consumer behaviour. Our data indicates that the image of the sector is declining due to a perceived lack of urgency by the airlines. At the same time consumers are willing to pay higher ticket prices is airlines invest into sustainability. However, it is found that, although environmental awareness and concern are growing amongst aviation consumers, price is yet the most important factor that influences ticket booking behaviour and passenger satisfaction.

Preserving Cognitive Ownership in Higher Education: A Sustainable Hybrid Pedagogical Framework for Reasoning-Centred AI Integration

Sunika Naz — 2026-02-26

This study explores how distinctive types of generative artificial intelligence (AI) practice, augmentation, co-construction, and replacement shape students’ reasoning skills and sense of cognitive ownership in higher education (HE) academic writing. This research also responds to growing humanitarian concerns about the erosion of student commitment, the undermining of autonomy, and ethical learning in HE. To address this core gap, an explanatory sequential mixed-methods design was employed. Data were collected from 412 UK HE students, complemented with in-depth interviews from 24 participants. Quantitative modelling showed that augmentation strengthens reasoning through reflective engagement, co-construction yields mixed cognitive outcomes, and replacement significantly weakens ownership and efficacy. Qualitative findings revealed subsistent experiences behind these practices: some students articulated no ethical harm by AI-supported reflection, while others exhibited a quiet disarticulation of their self-learning skills. Incorporating these insights, this study proposed the Hybrid Human–AI Reasoning Integrity Model (HHARIM), a sustainable pedagogical framework in HE that centres human reasoning in ethical AI use. The recommended model also highlights cognitive ownership as an essential element and outlines a robust framework for responsible AI use to safeguard learning, ethics, and autonomy in HE. This study contributes theoretically by offering HHARIM as a framework for effectively embedding AI, thereby upholding ethical, sustainable, and human-centred learning. Ultimately, the implications of this proposed model will influence HE systems to encourage sustainable AI pedagogical practices that reinforce academic writing rather than compromise students’ learning efficacy.

Influence Of Personal Branding on Entrepreneurial Success of Fitness Coaches in the UK

Michelle Pazmino — 2026-02-26

Personal branding allows entrepreneurs to develop strong relationships with their customers and drive emotional affinity, trust, and loyalty. Nonetheless, the specific strategies that lead to entrepreneurial success in the fitness industry remain less clear. The present study evaluates the influence of three personal branding strategies (authenticity, attractiveness, and credibility) on the tribalist entrepreneurial success of fitness coaches in the UK. Drawing on data from 169 surveys, the study reveals that when examining each dimension separately, authenticity has a significant influence (β = 0.612, p < .001) on entrepreneurial success, while credibility has a suggestive influence although below the significance threshold (β = 0.186, p = .068), and attractiveness has no significant influence on entrepreneurial success (β = 0.006, p = .944). Moreover, the composite variable of personal branding strategies indicated a significant influence on entrepreneurial success (β = 0.796, p < .001). These findings allow practical recommendations to be provided to fitness coaches to develop an integrated personal branding strategy that encompasses the three dimensions evaluated in this research to maximise their entrepreneurial outcomes. Additional research utilising qualitative or mixed methodology and focusing on both customers' and fitness coaches' perspectives would be valuable to obtain a comprehensive understanding of the influence of personal branding strategies on the tribalist entrepreneurial success of fitness coaches in the UK.

The Effect of Mobile Payment Methods on Customer Decisions on Jumia's Shopping Platform in Nigeria

Promise Akwaowo — 2026-02-26

This paper analyses how mobile payment technologies affect customer buying behaviour on Jumia, a major e-commerce site in Nigeria. Using a quantitative survey of 150 respondents, the study examines three main areas: (1) whether mobile payment options improve conversion rates, (2) whether they build customer trust, and (3) how infrastructure issues like internet and smartphone access play a role. The results show that customer trust strongly predicts both mobile payment adoption and actual purchases (r = 0.451, p < 0.001), whereas infrastructure challenges have a minimal direct impact (r = 0.018, p = 0.811). Correlation analysis further confirms a moderate, positive link between trust and conversion (r = 0.456, p < 0.01). These findings highlight trust and perceived-security as essential for successful digital transactions. The implication is that, although infrastructure issues remain, improved platform reliability is making them less important. The research offers business management insights for building digital trust and simplifying payment systems as mobile commerce gains ground in Nigeria.

Comparative Analysis of Traditional Machine Learning, Deep Learning, and Hybrid Ensemble Models for Anomaly Detection and Web Application Firewall Optimisation

Shiva Nezamzadeh — 2026-02-26

Anomaly detection is an important component of cybersecurity, particularly in safeguarding web application firewalls (WAFs) from malicious traffic. In this study, we perform a comparative analysis of three Machine Learning (ML) approaches: Random Forest (RF), Convolutional Neural Network (CNN), and a stacking ensemble combining RF and CNN with Logistic Regression (LR) as the meta-learner to explore the most effective approach for anomaly detection. To ensure a fair comparison, we trained all models under consistent preprocessing pipelines, including data class balancing using the SMOTE technique to address the common imbalance in attack data. The results of this study showed that the stacking ensemble outperformed the other models, achieving the highest accuracy (99.97%). The CNN model followed closely with comparable accuracy (99.94%), while also offering significant advantages in terms of computational efficiency and interpretability, particularly when supplemented with SHAP analysis. In contrast, the RF model achieved moderate accuracy (80.41%) but demonstrated strengths in interpretability and efficiency. These findings highlight that, with effective preprocessing, a standalone CNN can provide a practical and resource-efficient alternative to more complex ensemble models. The findings of this study highlight the importance of preprocessing in optimising model performance and propose CNN as a suitable solution for real-time cybersecurity applications. Future research should explore these models across diverse datasets, further investigate hybrid deep learning (DL) frameworks, and integrate advanced interpretability methods to enhance model transparency and trust in ML-based security systems.

Mission Statement Attributes and Employee Engagement in the Nigerian Banking Sector: Evidence from Ogun state, Nigeria

Banjo Hassan — 2026-02-26

Nigerian banks have mission statements like many other corporate firms; they struggle to keep their workforce engaged. One critical yet often overlooked factor is the clarity and impact of the mission statement. Literature has not documented the nexus between attributes of these mission statements and employee engagement. As a guiding principle, a mission statement has the potential to inspire and align employees with organizational goals and values. Against transformational leadership theory, this study examines the impact of mission statement on employee engagement in the banking sector in Ogun State. The study employed the descriptive survey design. A sample of 154 employees was selected from nine deposit money banks in Ijebu-Ode, Ogun state for the study. Primary data was collected using a well-validated instrument. The findings from the regression analysis revealed that clarity and specificity of mission statement has positive significant effect on employee engagement. Also, effective communication of mission statement has positive significant effect on employee engagement. Moreover, employee personal connection to mission statement has positive significant effect on employee engagement. The three independent constructs also collectively have significantly predict employee engagement. The study concluded that mission clarity, its effective communication, and employees’ personal connection to it significantly boosts employee engagement. . Organizations aiming to improve engagement should prioritize not only the content of their mission but also how it is shared and internalized, ensuring it resonates personally with employees and drives a shared sense of purpose and direction.

Sentiment Analysis of Public Perceptions on ChatGPT and Generative Artificial Intelligence (GenAI): Model’s Performance Evaluation and Examining Benefits and Risks in Education and Healthcare

Mifta Uddin Khan — 2026-02-26

As GenAI systems become more integrated into daily activities, understanding how people react to these tools is critical for responsible design and governance. This study provides a large-scale, longitudinal analysis of public sentiment toward ChatGPT and GenAI by integrating transformer-based sentiment classification, temporal trend analysis, and sector-specific topic modeling for education and healthcare. Using over one million English-language posts collected between November 2022 and December 2023, we quantify sentiment patterns over time and identify domain-specific themes of perceived benefits and risks. A comparative evaluation of traditional machine-learning (ML) models (logistic regression, support vector machines, random forest), deep learning (DL) architectures (convolutional neural networks, long short-term memory), and Bidirectional Encoder Representations from Transformers (BERT) was conducted. DL models outperform classical ML, and BERT emerges as the most effective classifier, achieving 98% accuracy with a near-balanced profile across accuracy, precision, recall, and F1-score, outperforming traditional approaches. Using the best-performing model, the findings show that ChatGPT sentiment is predominantly positive, alongside a substantial minority of negative sentiments. Topic modeling reveals domain-specific benefits and risks in education and healthcare discourse. In education, ChatGPT promotes personalized learning, accessibility, and teacher support, but it also raises plagiarism, academic dishonesty, and data privacy concerns. In healthcare, GenAI improves patient information, diagnostics, and administrative efficiency, but it also raises concerns about misinformation, ethics, and empathy. Overall, the research provides evidence-based guidance for technology developers, educators, healthcare professionals, and policymakers taking advantage of GenAI while addressing its associated social and ethical issues.

Risk in Outsourced IT Operations: A Systematic Literature Review of Technological Uncertainty, Knowledge Management and Opportunistic Behaviour

Peter Hewes — 2026-02-26

Technological adoption and digital transformation are increasingly enabling organisations to gain a competitive advantage, underpin business processes and create efficiencies. Utilisation of technologies brings several risks such as the risk of cyber-attacks and infrastructure dependency. To mitigate these, and to reap other benefits, organisations are increasingly turning to third-party IT suppliers to innovate and manage their IT estates. The field of IT operations and supply chain management has gained extensive research over the decades; however, none seem to bring these aspects together with technology risk mitigation in a systematic way. This paper systematically reviews 63 articles to ascertain historical research trends to indicate future research interest and consolidate research themes to discuss the gaps in the extant research. Alongside the fact that academic interest is projected to continue, closely coupled with world events, important findings show that technological uncertainty, information asymmetry and opportunistic behaviour are closely coupled in outsourced IT operations, and that knowledge management acts as a key mitigation mechanism which is illustrated by a new conceptual model. The review also reveals that existing research focuses heavily on ex-ante IT operations outsourcing decisions, with limited attention given to the ex-post operational phase, where most risks in IT operations materialise. Several gaps are identified for the field including how knowledge management can be utilised to mitigate technology risk within the IT operations function linking out to technology adoption. More generally, research into the public sector is found to be underreported giving researchers another lens to investigate current research themes with or adopt those previously listed. Overall, the review provides an integrated understanding of technological uncertainty in outsourced IT operations and highlights key opportunities for further research into ex-post phase, specifically long-term knowledge management in sector-specific outsourcing.