Mount Kenya University

<span lang="EN">The rise of social media platforms over the past two decades has reshaped how citizens engage with public life, political discourse, and democratic processes. This paper examines the theoretical and empirical dimensions of that reshaping through a review of existing scholarship in political communication, media studies, and democratic theory. Drawing on Habermasian public sphere theory, digital democracy frameworks, and political communication research, the paper traces how social media simultaneously opened new channels for civic participation and introduced structural problems that now test the foundations of deliberative democracy. These problems include algorithmic filter bubbles, political polarization, coordinated misinformation campaigns, and the growing concentration of communicative power in a small number of private platforms. The paper also considers how networked social movements have used these platforms for collective action, from the Arab Spring to more recent protest waves, and what those movements reveal about the limits and possibilities of platform-mediated politics. The analysis concludes that social media has not simply disrupted democratic participation but has fundamentally altered its conditions, requiring scholars, citizens, and policymakers to revisit foundational assumptions about deliberation, representation, and the public good.</span>

Currently, the transport and logistics business environment are characterized by increasing uncertainties. Last-mile delivery strategies have emerged as important approaches for enterprises to achieve profit and market share objectives by reducing environmental risk experienced. The primary aim of this study was to determine the influence of last-mile delivery strategies on the performance of logistics firms in Mombasa County, Kenya. The research was based on specific objectives, such as route optimization, flexible delivery, green delivery, and crowd-sourced delivery on the performance of logistics firms in Mombasa County, Kenya. Theories, such as the network optimization theory, dynamic capabilities theory, institutional theory, and agency theory, also guided the study. This study employed a cross-sectional survey research design, mainly concentrating on 710 logistic firms in Mombasa County, Kenya. To meet the research objectives, the departmental heads in the transport and logistics department were used as observation units across the firms. The pilot study targeted 25 respondents, 10% of the final study sample size, of which respondents were randomly chosen in each firm. The primary data was collected through self-administered questionnaires that included open and closed-ended questions designed to obtain definite responses. For this study, the quantitative data collected was analyzed by using SPSS version 28 to calculate the response rate using descriptive statistics, including frequencies, percentages, means, and standard deviations. The study findings showed that last-mile delivery strategies namely route optimization, flexible delivery, green delivery and crowd-sourced delivery significantly influence the performance of logistics firms in Mombasa County, Kenya. The study concluded that performance was predicted to improve for every unit increase in these practices. The study further recommended that logistics firms should prioritize having real-time traffic data integrated into their route mapping process. To achieve this, companies should collect up-to-date traffic information from multiple sources, process it through AI-powered route optimization algorithms, continuously monitor changing conditions, and dynamically adjust routes in real time to minimize delays and improve delivery efficiency. This will ensure improved last-mile delivery strategies and improve performance of logistics firms.

The banking sector is being significantly reshaped by the rapid introduction and application of Artificial Intelligence (AI) in the investment decision-making processes of commercial banks. The purpose of this study, therefore, was to analyze perceptions of AI in investment decision-making by senior management in commercial banks in Kenya. The study was anchored by rational decision-making theory, bounded rationality theory, intuitive decision-making theory, heuristic decision-making theory, and the technology acceptance model. The mixed-methods approach was utilized in the study and survey and in-depth interviews with senior management of commercial banks, with secondary information drawn from industry sources. The target population was the senior managers in the investment department in all the thirty-nine commercial banks in Nairobi City. The study adopted a census approach in all the thirty-nine commercial banks. AI applications allow commercial banks to streamline their workflows and improve the ability to forecast true business health, enabling decision-making based on quality data. AI adoption leads to minimized operational risks and dynamic adaptation of investment strategies due to market changes. The study employed a mixed-methods approach to explore the possibilities of AI, the challenges of AI, and the possibilities of applying AI in the banking sector. The data was analyzed using SPSS version 25 for descriptive and inferential statistics. The results show significant positive correlations: leadership commitment with organizational culture (r=0.65, p<0.01), resource availability with leadership (r=0.58, p<0.01), and organizational flexibility (r=0.62, p<0.01). Employee attitudes are positively correlated with leadership (r=0.52, p<0.01) and culture (r=0.55, p<0.01), while perceptions of AI risks are negatively correlated with leadership (r=-0.45, p<0.01). The regression model explains 61% of perception variance (R²=0.61), with leadership showing the strongest influence (β=0.33, p<0.01), followed by culture (β=0.28, p<0.01), resource availability (β=0.22, p<0.01), and employee attitudes (β=0.17, p=0.018). Perceived risks negatively impact perceptions (β=-0.14, p=0.022). The model’s F-statistic is 18.2 (p<0.001), indicating overall significance and emphasizing the importance of organizational support in AI adoption. The report provides actionable recommendations, including augmented AI training programs, investing in AI with high scalability, and developing transparent frameworks for regulating AI in a way that will promote AI use in Kenya’s banking sector. This will enhance the efficiency, accuracy, and optimization of financial decisions. Findings from this study can be used in the formulation of policy issues on the development of AI-specific regulations and guidelines for the prudent use of AI technologies in the financial services industry in evaluating their present investment decision-making, thus contributing to SDG 9 on industry innovation and infrastructure.

Ukabila unazidi kuongezeka katika jamii na kutishia maisha ya usoni ya mataifa mengi ulimwenguni. Katika utafiti huu, tulishughulikia suala la ukabila na athari zake kwa jamii ya mwanadamu. Riwaya ya Ken Walibora Kufa Kuzikana (2006) pamoja na ile ya Jeff Mandila Narejea Nyumbani (2014) zilirejelewa. Utafiti uliofanywa ulilenga kuafikia malengo matatu mahususi yafuatayo ; kubainisha vichochezi vya ukabila katika jamii namna vinavyojitokeza katika riwaya teule. Pili, kuchanganua athari za ukabila kwa jamii ya mwanadamu na mwisho kutathmini mbinu mwafaka za kukabiliana na ukabila kwa mujibu wa riwaya mbili teule. Utafiti huu ulitumia nadharia mseto ambazo ni ile ya Uyakinifu wa kijamii pamoja na ya Usemezano. Utafiti huu ulikuwa wa maktabani ambapo idadi lengwa ilikuwa matini na makala yaliyo na jumbe kuhusu ukabila. Hata hivyo, kupitia kwa mbinu ya kimaksudi tuliteua riwaya za Kufa Kuzikana na Narejea Nyumbani ili kuchunguza namna watunzi wake walivyoangazia suala la ukabila katika jamii. Vile vile, kule maktabani tulisoma magazeti, majarida, tasnifu za wasomi mbalimbali, vitabu vya ziada pamoja na kupekuapekua mtandao ili kurutubisha data inayohusiana na suala la ukabila katika jamii ya mwanadamu. Uchanganuzi wa data ulifanyika kwa njia ya uchambuzi wa kimaelezo na ufafanuzi. Vichochezi vya ukabila, athari zake katika jamii pamoja na mbinu za kukabiliana na uovu huu vilichambuliwa na kuwasilishwa kuambatana na malengo na mihimili ya nadharia zilizodokezwa. Data iliyozalishwa na utafiti huu itakuwa ya manufaa makubwa kwa jamii inayorudishwa nyuma na suala lililopitwa na wakati la ukabila. Mapendekezo yake yatasaidia jamii kukabiliana na ukabila unaozidi kudidimiza maendeleo ya kiuchumi siku baada ya siku

It is essential to choose the correct guests and specialists for political broadcast shows to make sure they are fair, neutral, and ideologically balanced. Even while the media in Kenya is getting better, there are still concerns about partisan representation and a lack of different points of view, especially in devolved areas like Nakuru County. This study examined the influence of guest and expert panel selection on political bias in television shows in Nakuru County. Agenda-setting theory and the political economy of media theory informed the analysis of how guest selection practices contribute to political bias in broadcast programming. The study employed a descriptive research design, concentrating on broadcast journalists, editors, producers, and consumers of political information. The study employed purposive sampling to generate a group of 74 media professionals as the participants. Data was collected by structured questionnaires and in-depth interviews, later analysed using SPSS (Version 30) for descriptive statistics, while thematic analysis was utilised for qualitative data. The findings showed that using the same political pundits over and over, favouring guests with similar political views, and not letting dissenting views be heard all strongly encourage political prejudice. Qualitative findings also emphasised the influence of ownership, political lobbying, and the lack of unbiased specialists as essential structural factors affecting biased panel membership. On the other side, choosing people based on their competence, which is based on their academic credentials, professional experience, and lack of political bias, made conversations more believable and fairer. The study concluded that the selection of individuals for political talk programs significantly influenced the framing of conversations and the ideological direction of televised debates. When shows repeatedly use the same commentators, especially those with known political views, the conversations tend to be slanted in a predictable way. The study recommends that to reduce political bias in broadcast programming, visitors need to be chosen in a clear, diversified, and merit-based fashion. The study also recommended using defined standards for appointing panellists, rotating panellists, and putting up independent expert registers to make political media material fairer and more trustworthy.

The research was conducted to establish the effectiveness of silver nanoparticles embedded in indium tin oxide (ITO) thin film conducting material in causing the textile azo dye effluent degradation. The percentages of azo dye degradation were as follows: 68.4%, 72.5% and 93.1% using electrocoagulation, ITO and ITO-AgNPs coupled electrodes, respectively. The results obtained from the two-factor ANOVA with replication indicated a significant difference (p = 0.0024) when the silver nanoparticles embedded on indium tin oxide electrodes were utilised in the degradation of these azo dyes in the effluent samples. The colour removal was 99.97% showing that the optimised process was effective and efficient in the degradation of these azo dyes in the textile wastewaters.