Browsing by Author "Kandiri, John"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Determinants of technology innovation implementation effectiveness in higher education institutions
    (IEEE, 2013) Kandiri, John; Muganda, N.
    Higher education institutions have continued to acquire technologies with alacrity. However, the transition from adoption to application in teaching and learning has been below expectations. This exploratory study investigated the lack of cadence between adoption and effective implementation of educational technology initiatives. The study was based on PHEA-ETI projects that ran between June 2008 and June 2012. The projects entailed implementation of technology initiatives for example animating science content among others. A questionnaire was sent to all persons involved in the implementation of the projects. Out of the 163 targeted respondents, 105 usable responses were received. Team leaders were interviewed with focus groups held with implementation teams. The study adopted: top management, financial motivation, organizational culture. The new model added the variables: team leadership, monitoring and evaluation and innovation efficacy. When the data was analysed using SPSS version 17, the results confirmed determinants from earlier studies while also showing that team leadership and project efficacy were significant factors to consider in technology innovation implementation.
  • Thumbnail Image
    Item
    Hybrid Machine Learning Techniques for Comparative Opinion Mining
    (IJAIDM, 2023-08) Ondara, Bernard; Waithaka, Stephen; Kandiri, John; Muchemi,Lawrence
    Comparative opinion mining has lately gained traction among individuals and businesses due to its growing range of applications in brand reputation monitoring and consumer decision making among others. Past research in sub-field of opinion mining have mostly explored single-entity opinion mining models and the mining of comparative sentences suing single classifiers. Most of these studies relied on a limited number of comparative opinion labels and datasets while applying the techniques in limited domains. Consequently, the reported performances of the techniques might not be optimal in some cases like working with big data. In this study, however, we developed four hybrid machine learning techniques, with which we performed multi-class based comparative opinion mining using three datasets from different domains. From our results, the best-performing hybrid machine learning technique for comparative opinion mining using a multi-layer perceptron as the base estimator was the Multilayer Perceptron + Random Forest (MLP + RF). This technique had an average accuracy of 93.0% and an F1-score of 93.0%. These results show that our hybrid machine learning techniques could reliably be used for comparative opinion mining to support business needs like brand reputation monitoring.