Daystar University

Supermarket supply chains face numerous challenges, including ineffective inventory management (resulting in stockouts or overstocking), unpredictable demand, shifting consumer expectations, disruptions from external factors such as labor issues, rising operational costs, and the increasing need for technology integration. This study sought to examine the effect of Artificial Intelligence (AI)-based inventory management on supply chain performance among large supermarkets in Nairobi City County, Kenya. The study was anchored on the Hybrid Intelligence Model as the guiding framework for artificial intelligence applications. The Triple Triangle Constraint Theory was used to explain supply chain performance, while the Technology Acceptance Model provided a framework for linking artificial intelligence adoption and supply chain performance. The study adopted a descriptive research design. The population comprised employees working in the supply chain departments of ten large supermarkets in Nairobi City County, Kenya. The target population included all employees within these departments. A sample size of 70 employees was selected to participate in the study. A pretest was conducted using seven respondents drawn from two Naivas supermarkets in Kiambu County, Kenya. Primary data were collected through structured questionnaires. The collected data were summarized using percentages and means. Inferential statistics, including correlation and regression analysis, were employed to determine the relationships between variables. Data were analyzed using SPSS version 30. The findings revealed that AI-based inventory management had a statistically significant effect on supply chain performance (M = 3.76, SD = 0.46), (R² = 0.913), (F = 618.938, p < 0.01). The study concluded that AI-based inventory management positively influences supply chain performance. The study recommends that, to fully realize the benefits of AI applications, supply chain managers should integrate diverse data sources, invest in robust data infrastructure and skilled personnel, align AI initiatives with strategic business goals, and foster cross-departmental collaboration. The study further recommends that similar research be conducted in other countries, particularly developed economies, to examine how artificial intelligence applications are transforming supply chain performance across different industries.

Mergers and Acquisitions (M&A) are widely pursued as strategies for organizational expansion and competitiveness, yet evidence consistently shows that many fail to achieve their intended outcomes. In Kenya’s financial sector, where mergers have become increasingly common, these failures are often linked to ineffective internal communication and leadership misalignment. Such shortcomings undermine organizational integration and threaten the achievement of Sustainable Development Goal 8 (Decent Work and Economic Growth), which emphasizes inclusive and sustainable economic progress. This study examined how the synergy between internal communication and innovative leadership strategies can enhance merger success and sustainable growth in the financial sector. The research examined two significant cases: KCB Group’s 2019 acquisition of the National Bank of Kenya and Equity Group’s 2020 merger with Banque Commerciale du Congo (BCDC). A qualitative descriptive research design was employed, drawing on 15 semi-structured interviews with senior leaders and communication managers, complemented by analysis of internal documents covering the period 2019–2025. Thematic analysis provided insights into how communication reinforced leadership intent and how leadership lent strategic authority to communication practices. Findings revealed that synergy between leadership and communication produced four key outcomes: reduced uncertainty through transparent messaging, stronger trust through alignment of words and actions, enhanced employee engagement through participatory dialogue, and sustained culture through communication-led sensemaking. When communication occurred without strong leadership support, it lacked authority, and when leadership acted without communicative depth, it created disconnect. Effective mergers required the two dimensions working in tandem to maintain continuity and foster confidence. The study advances communication theory by highlighting synergy as a determinant of leadership effectiveness in change contexts. Practically, it recommends embedding communication-leadership taskforces in M&A processes. For policy, it calls for integrating communicative competence into leadership training within financial institutions.

Supermarkets increasingly face logistical challenges such as fluctuating demand patterns, rising distribution costs, and the need for timely and efficient product delivery. Artificial Intelligence (AI) has emerged as a critical tool for improving logistics decision-making, particularly through route optimization systems that enhance delivery speed, reduce transportation costs, and improve overall supply chain responsiveness. This study examined the effect of AI-Route Optimization on supply chain performance among large supermarkets in Nairobi City County, Kenya. The study was grounded in the Hybrid Intelligence Model, Triple Triangle Constraint Theory, and Technology Acceptance Theory to explain the integration and influence of AI technologies on supply chain outcomes. A descriptive research design was adopted, targeting employees working in the supply chain departments of 10 large supermarkets within Nairobi City County. A sample of 70 respondents was drawn from this population, and data were collected using structured questionnaires. Reliability was tested through a pretest involving seven respondents from selected Naivas branches in Kiambu County. Descriptive statistics, including means and standard deviations, were used to summarize the data, while inferential statistics such as correlation and regression analysis were applied to determine the relationship between AI-route optimization and supply chain performance using SPSS version 30. The findings indicated that AI-Route Optimization had a statistically significant effect on supply chain performance (M = 3.57, SD = 0.43), with the regression model showing strong explanatory power (R² = 0.79) and statistical significance (F = 222.015, ρ < 0.01). The study concluded that AI-driven route optimization positively enhances supply chain performance by improving delivery efficiency, minimizing transport costs, and strengthening service reliability. The study recommends that supermarket supply chain managers invest in advanced AI-based routing tools, integrate real-time data sources such as GPS and traffic feeds, and enhance staff capacity to effectively utilize these systems. Further research is recommended in other geographical contexts to compare the role of AI-route optimization across different retail and logistics environments.

As the field of cultural evolution marks its fiftieth anniversary, it has an opportunity not only to evolve in theory and scope, but also in ethics of practice. This theme issue contributes to that shift by showcasing projects supported by the Cultural Evolution Society's Transformation Fund (CES-TF), which centred equity, diversity and inclusion (EDI). This opinion piece draws on our CES-TF projects, conducted across diverse cultural and geographical settings, to reflect on the challenges and possibilities of making cultural evolution-and human research more broadly-more just. We focus on three relational dimensions in which cultural evolution research can become more inclusive and equitable: fostering equitable cross-cultural research collaborations; building mutually beneficial researcher community collaborations; and cultivating supportive funding and institutional relationships. Our collective reflections, written by grant recipients and the CES-TF lead, emphasize that, while there is no universal model for ethical research, attending to positionality, power dynamics and diverse knowledge systems can foster responsible and impactful research. Striving to make the field more globally representative will enhance our capacity to critically reflect and improve, but real progress requires time, resources and structural reform, supported by institutions, funders and journals committed to embedding EDI at the core of research. This article is part of the theme issue 'Transforming cultural evolution research and its application to global futures'.

The performance of housing projects in Kenya continues to attract scrutiny due to recurring challenges such as cost overruns, project delays, and quality shortfalls that hinder the achievement of affordable housing goals. This study examined the influence of Monitoring and Evaluation (M&E) tools on the performance of housing projects, focusing on the Two Rivers Development Project by Centum Real Estate as a case study. The objectives of the study were to: (1) establish the effect of quantitative M&E tools on housing project performance, (2) determine the effect of qualitative M&E tools on performance, (3) assess the effect of planning and analysis tools on performance, (4) evaluate the effect of technological tools on performance, and (5) examine the moderating role of organizational capacity in the relationship between M&E tools and project performance. The study adopted a descriptive research design grounded in Results-Based Management (RBM) and Systems Theory, emphasizing accountability, feedback, and outcome-based decision-making. The target population comprised 163 project staff involved in planning, implementation, and supervision at the Two Rivers Project. A census approach was used, and data were collected using a structured questionnaire. A pilot test was conducted on 10% of respondents to assess reliability, yielding a Cronbach’s alpha of 0.812, confirming internal consistency. Data were analyzed using SPSS version 28, employing descriptive statistics, correlation, and multiple regression analyses. Diagnostic tests for normality, multicollinearity, and homoscedasticity confirmed model validity. Results showed a strong positive correlation between the application of M&E tools and housing project performance (r = 0.734, p < 0.05). Regression analysis revealed that quantitative tools (β = 0.286, p < 0.05), qualitative tools (β = 0.249, p < 0.05), planning and analysis tools (β = 0.213, p < 0.05), and technological tools (β = 0.198, p < 0.05) significantly influenced project performance. Moreover, organizational capacity exhibited a significant moderating effect (β = 0.154, p < 0.05) on the relationship between M&E tools and performance, suggesting that strong institutional structures amplify the positive impact of M&E systems. The study concludes that integrating diverse M&E tools enhances cost efficiency, timeliness, quality, and stakeholder satisfaction in large-scale housing projects. It recommends that Centum Real Estate and similar developers institutionalize comprehensive digital M&E frameworks, strengthen staff capacity-building programs, and adopt participatory monitoring systems to ensure accountability and sustainability. The study contributes to empirical and theoretical discourse on project management by demonstrating that structured and technology-enabled M&E systems are critical drivers of performance within Kenya’s private housing sector.

The micro, small, and medium enterprises play a significant role in contributing towards the economic development of many countries of the world. It is the leading sector in the creation of job opportunities for millions of young people in the developing countries across the globe, and in Kenya in particular. Despite this significant contribution of this sector towards economic development, the sector faces a myriad of challenges when it comes to access to credit, financial transactions, and financial literacy influencing their performance (World Bank, 2025). The study's purpose was to assess the influence of access to credit on the financial performance of MSMEs in Dagoretti North Sub-County. The research was anchored on the Theory of Financial Inclusion, advanced by Morduch, Quantitative research methods were employed, while correlational research design was used. The target population was 5,112 licensed and registered MSMEs in Dagoretti North Sub-County, Nairobi City County. Based on Yamane’s formula, the sample size of 371 MSME owners/managers was selected. Stratified random sampling and purposive techniques were used to select the sample size. The data was collected using questionnaires with close-ended questions. A pilot study was conducted to establish the validity and reliability of the questionnaire. The quantitative data was analyzed using SPSS. The Pearson correlation test was used to determine the existence of relationships, while regression analysis was used to determine the extent to which the study variables relate with each other. Results revealed a positive and significant relationship between financial performance and credit access (r = .35, p < .01). Overall, the study showed that MSMEs in Dagoretti North benefit a lot from financial inclusion. The study underscored the importance of strengthening initiatives that support financial literacy, access to credit, and transaction convenience, as this has been shown to make MSME businesses thrive.

This study examines the potential of artificial intelligence (AI) to improve doctor-patient communication in resource-constrained public oncology settings in Kenya, addressing critical communication barriers that impact treatment outcomes and patient satisfaction. A qualitative exploratory study was conducted in three public oncology facilities in Nairobi County, employing semi-structured in-depth interviews with 10 clinical officers and physicians. Data were analysed using thematic analysis with NVivo 12 software to identify patterns and themes related to communication challenges and the potential for AI integration. Three major themes emerged from the analysis: significant communication barriers, including the complexity of technical language, time constraints, and cultural mismatches; high clinician interest in AI communication tools despite limited current awareness; and critical requirements for the ethical implementation of AI systems, including the need for explainable AI, cultural adaptation, and preservation of human empathy. A majority of participants (8 out of 10) identified technical language barriers as the most significant challenge, while all participants (10 out of 10) expressed high interest in AI applications such as medical jargon translation, patient distress detection, and real-time conversational support. This study provides the first empirical evidence for a clinician-endorsed, empathy-centric framework for AI integration in resource-constrained settings, positioning AI not as a replacement but as a tool for augmenting human connection. The core conceptual contribution demonstrates AI as an "empathy enhancer" that addresses systemic communication barriers while preserving the relational aspects of healthcare. The findings provide a culturally appropriate, phased implementation framework for AI communication tools in African healthcare contexts, emphasising comprehensive training and ethical oversight. AI-augmented communication has the potential to improve health outcomes, reduce healthcare disparities, and enhance patient satisfaction in resource-constrained settings, directly supporting Kenya's UHC goals by addressing a key social determinant of health: communication. This study offers crucial insights for the development of healthcare technology in similar resource-constrained environments.

The COVID-19 pandemic exposed critical vulnerabilities in both global and national health systems. It underscored the intricate relationship between public health and national security, emphasising the need for robust preparedness frameworks. This study examined how inefficiencies in the medical supply chain could impact Kenya's national security and public health preparedness, identified structural flaws that emerged during the crisis, and provided recommendations to enhance Kenya's preparedness. The study explains the existing structural gaps in the medical supply chain regime in the context of pandemic preparedness. It also examines the threats to Kenya’s health and national security arising from inefficiencies in the medical supply chain. Grounded in resilience theory, this study emphasises collaboration and building strong institutions for better service delivery. To provide well-informed recommendations, the study uses a qualitative approach. Using a case study design, the study draws on Kenya’s specific experiences during the pandemic to provide insights into strengthening national and regional strategies. The study utilised content analysis of documents, publications, and reports to obtain the relevant data. The study established that, whereas the country had a robust health supply chain capability before and during the COVID-19 pandemic, its reliance on global health supply chains during the pandemic exposed the country to health insecurity. It was also evident that for Kenya to be resilient, it must overcome local inefficiencies resulting in financial mismanagement and a lack of anticipatory resilience. It is also imperative for the country to leverage the potential of the East African Community (EAC) to build regional sufficiency. The findings aim to inform key stakeholders, including the Kenyan government’s public health organs.

The purpose of this study was to investigate the perception of teachers on the use of hidden curriculum in classroom management to meet the psychosocial needs of learners in middle primary school in Migwani, Kitui county, Kenya. Formal curriculum broadly aligns with organized, institutionalized learning models such as learning seen in schools, whilst informal learning describes the everyday learning that people experience throughout their lives, and which can go easily unrecognized. The hidden curriculum is widely acknowledged as a blend of both formal and informal educational elements, deriving its unique characteristics from their interaction. A non-formal curriculum can work alongside the formal curriculum and can possess some characteristics of formal systems, such as following institutionalized frameworks, policies and outlined rules which help to teach ethics and morals to the students. This study used Stratified and purposive sampling to select the sample for the study. The sample comprised of 116 class teachers of class 4 and 5. The target population was 473 teachers in 63 public primary schools in Migwani sub-county. Data was collected by use of class teacher perception questionnaire. The reliability coefficient of the instruments was accepted at 0.89 using Cronbach alpha coefficient. The objective of the study was to assess class teacher management skills in the classroom context to meet psychosocial needs of learners. Factors such as: Teachers positive attitude, teachers’ perception of learner’s psychological needs, and teacher’s professional ethics, were found out to influence ways in which psychological needs of learners were met in a class. The study recommended that there is need for class teachers to Clearly articulate classroom norms, behaviors, and expectations to ensure all students understand the implicit aspects of the learning environment. Finally, the study saw it fitting for teachers to implement structured class meetings which can provide a platform for students to express their thoughts and concerns, fostering a sense of community and belonging. This approach will not only address immediate psychosocial needs but also equips students with essential life skills.

Purpose: Critical care nurses (CCNs) should have a supportive work environment to prevent burnout syndrome (BOS) and compassion fatigue (CF). However, Kenyatta National Hospital (KNH) nurses face high levels of BOS and CF due to overwhelming workloads, patient suffering, and inadequate institutional support. This study aims to examine the prevalence of burnout, compassion fatigue, and CS among KNH CCNs, providing insights to inform policy interventions and institutional strategies to support nurse well-being. Methods: This randomized controlled trial at KNH's Critical Care Unit explored burnout, compassion fatigue, and satisfaction among 156 randomly selected critical care nurses. Data collection utilized the Professional Quality of Life questionnaire. Statistical analysis included descriptive statistics, independent t-tests, and Pearson's correlation analysis, using a p-value of <0.05 for significance. Ethical approval was granted by the KNH-UoN Ethics and Research Committee, Daystar University Ethics Board, and informed consent was obtained from participants. Results: Findings revealed that 33% of CCNs experienced moderate to high levels of burnout, with the highest prevalence among nurses with fewer than two years of experience (M = 28.5, SD = 9.3). Compassion fatigue was prevalent, with 76.2% reporting moderate levels. In contrast, compassion satisfaction was relatively high (M = 39.8, SD = 4.9), particularly among nurses with more than five years in their current position (M = 40.9, SD = 4.3, p = .015). Employment status significantly influenced both burnout and CS levels, with temporary nurses experiencing higher burnout and lower CS. Implications: Burnout and compassion fatigue are common among critical care nurses, especially early-career and temporary staff, while experienced nurses benefit from compassion satisfaction. Institutional measures, such as mental health programs, improved nurse-to-patient ratios, permanent employment, and resilience training in nursing curricula, are essential. The Ministry of Health should create policies to reduce workloads, and further research is needed to evaluate workplace interventions for better nurse well-being and patient care.