<p> This praxis develops a comprehensive performance measuring model to help government policy makers in Sub-Saharan African (SSA) countries identify and evaluate their performance in online outsourcing (OO). After assessing different efficiency measurement methods, data envelopment analysis (DEA) was selected for this study. </p><p> Metrics from the World Bank’s proposed framework for assessing countries’ competitiveness in OO are used to develop the DEA model in this research. Due to the presence of missing values in some of the variables in the dataset, a technique called multiple imputation by chained equations (MICE) is used to estimate these missing values. The DEA model is applied to 23 OO input variables and a single output variable called Information and Communication Technology (ICT) service exports. ICT service exports revenues are used by the World Bank to measure a country’s performance in OO. </p><p> Empirical results from the eight SSA countries studied validate that there is a meaningful relationship between ICT service exports revenue and DEA technical efficiency scores. Further analysis indicates that six out of the eight SSA countries are efficient in OO, while two are inefficient in OO. In addition to the efficiency scores, the DEA model produces benchmark information in the form of an efficiency reference set (ERS). The ERS for an inefficient country consists of an efficient country with which it shares similar levels of input and output factors. Thus, through peer comparison, policy makers in inefficient countries will be able to identify factors that may contribute to improving their performance. </p><p> The results from the proposed DEA model demonstrate the actual possibilities of determining the technical efficiencies of countries participating in OO; the use of this model is therefore not limited to SSA countries but can be applied to various world regions identified by the World Bank.</p><p>
| Identifer | oai:union.ndltd.org:PROQUEST/oai:pqdtoai.proquest.com:10981847 |
| Date | 11 December 2018 |
| Creators | Darko-Mensah, Kwadwo |
| Publisher | The George Washington University |
| Source Sets | ProQuest.com |
| Language | English |
| Detected Language | English |
| Type | thesis |
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