Browsing by Author "Apondi, Odula Linda"

Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    Data Analytics and Organizational Performance of Kenya Civil Aviation Authority
    (Research Bridge Publisher, 2023-12) Apondi, Odula Linda; Chege, Perris
    Organizational performance, a pivotal metric determining its sustainability and standing among stakeholders and shareholders, was the focal point of investigation in this study within the Kenya Civil Aviation Authority (KCAA) and its relationship with data analytics. Four specific objectives were established: to evaluate the impact of descriptive analytics on KCAA's organizational performance; to assess the influence of prescriptive analytics on the same; to understand the relationship between predictive analytics and KCAA's organizational performance; and to scrutinize the effect of diagnostic analytics on KCAA's organizational performance. The study drew upon three established theoretical frameworks: the Resource-Based View (RBV), the Technology Acceptance Model (TAM), and the Schumpeterian Innovation Theory. The research encompassed 1400 technical and operational staff across KCAA's headquarters in Nairobi, Moi International Airport in Mombasa, and Jomo Kenyatta International Airport in Nairobi, along with airline operators and pilots. A pilot study, conducted with 30 respondents, ensured the reliability and validity of the research instrument. Reliability tests yielded a Cronbach alpha coefficient averaging 0.79, indicating strong reliability, while validity tests confirmed the instrument's validity, with Average Variance Extracted (AVE) values surpassing the 0.5 threshold. The primary study involved 300 randomly selected participants, utilizing questionnaires for data collection. Both descriptive and inferential statistics were employed for data analysis, revealing a strong positive correlation among variables. Specifically, various types of data analytics displayed positive significance: Descriptive Analytics (β = 0.133, t = 2.046, p < 0.05), Prescriptive Analytics (β = 0.198, t = 3.146, p < 0.05), Diagnostic Analytics (β = 0.190, t = 3.089, p < 0.05), and Predictive Analytics (β = 0.120, t = 1.961, p = 0.05). Diagnostic tests affirmed the absence of multi-collinearity, data normality, and heterogeneous data. Respondents collectively acknowledged the significant impact of data analytics on KCAA's organizational performance, with the study concluding that KCAA had not fully leveraged data analytics, leading to the recommendation of a policy framework prioritizing their ongoing big data ICT initiatives, and advocating for regular implementation of diagnostic analytics to enhance aviation performance, employee engagement, and overall organizational success.