https://hdl.handle.net/10646/4362 Thu, 16 Apr 2026 00:08:19 GMT 2026-04-16T00:08:19Z https://hdl.handle.net/10646/4417 The role of knowledge management systems on the export performance of manufacturing firms: Evidence from Zimbabwe Tarambiwa, Edmore; Mafini, Chengedzai There is a general acceptance that Knowledge Management Systems (KMS) are a primary source of value and have taken a center stage in the definition, operation and performance of most business organisations. However, their use within the manufacturing sector in developing countries remains inconsistent. This article investigated the role of KMS in enhancing the export performance of firms operating within the manufacturing sector in Zimbabwe. The study used a quantitative approach in which a survey questionnaire was distributed to 555 managers drawn from 185 manufacturing firms based in Harare. Data analyses involved the use of descriptive statistics, Spearman correlations and regression analysis. The results of the study showed that combined IT/social driven KMS exerted the greatest impact on export performance. The availability of both information technology centered and social centered KMS influences export performance by improving the firm’s export strategy, export commitment, export orientation, export growth, export sales, export profits and export market share. Sun, 01 Jan 2017 00:00:00 GMT https://hdl.handle.net/10646/4417 2017-01-01T00:00:00Z https://hdl.handle.net/10646/4367 Recognition of cross-language acoustic emotional valence using stacked ensemble learning Zvarevashe, Kudakwashe; Olugbara, Oludayo O Most of the studies on speech emotion recognition have used single-language corpora, but little research has been done in cross-language valence speech emotion recognition. Research has shown that the models developed for single-language speech recognition systems perform poorly when used in different environments. Cross-language speech recognition is a craving alternative, but it is highly challenging because the corpora used will have been recorded in different environments and under varying conditions. The differences in the quality of recording devices, elicitation techniques, languages, and accents of speakers make the recognition task even more arduous. In this paper, we propose a stacked ensemble learning algorithm to recognize valence emotion in a cross-language speech environment. The proposed ensemble algorithm was developed from random decision forest, AdaBoost, logistic regression, and gradient boosting machine and is therefore called RALOG. In addition, we propose feature scaling using random forest recursive feature elimination and a feature selection algorithm to boost the performance of RALOG. The algorithm has been evaluated against four widely used ensemble algorithms to appraise its performance. The amalgam of five benchmarked corpora has resulted in a cross-language corpus to validate the performance of RALOG trained with the selected acoustic features. The comparative analysis results have shown that RALOG gave better performance than the other ensemble learning algorithms investigated in this study. Tue, 01 Sep 2020 00:00:00 GMT https://hdl.handle.net/10646/4367 2020-09-01T00:00:00Z https://hdl.handle.net/10646/4366 Ensemble learning of hybrid acoustic features for speech emotion recognition Zvarevashe, Kudakwashe; Olugbara, Oludayo Automatic recognition of emotion is important for facilitating seamless interactivity between a human being and intelligent robot towards the full realization of a smart society. The methods of signal processing and machine learning are widely applied to recognize human emotions based on features extracted from facial images, video files or speech signals. However, these features were not able to recognize the fear emotion with the same level of precision as other emotions. The authors propose the agglutination of prosodic and spectral features from a group of carefully selected features to realize hybrid acoustic features for improving the task of emotion recognition. Experiments were performed to test the effectiveness of the proposed features extracted from speech files of two public databases and used to train five popular ensemble learning algorithms. Results show that random decision forest ensemble learning of the proposed hybrid acoustic features is highly effective for speech emotion recognition. Sun, 01 Mar 2020 00:00:00 GMT https://hdl.handle.net/10646/4366 2020-03-01T00:00:00Z