The effects of outsourcing practices conducted by organisations in Nairobi
- Authors: Wachira, Wanjungu
- Date: 2015
- Subjects: Contracting out , Industrial management
- Language: English
- Type: Thesis , Doctoral , DPhil
- Identifier: http://hdl.handle.net/10948/4988 , vital:20775
- Description: The purpose of this study is to investigate the relationship between outsourcing and development in Nairobi. The key research question for this study is what are the impacts of outsourcing practices conducted by organisations in Nairobi? Data were obtained from questionnaires distributed in December 2010. A total of 85 profit-making firms in Nairobi with a sample of 165 management employees were selected for this study. The empirical findings obtained relate to four outsourcing theories. Transaction Cost Analysis (TCA) Theory focuses on the cost savings that result from outsourcing. Agency Outsourcing Theory centres on outsourcing firms hiring agents to achieve productivity. Hiring agents may result in permanent staff being retrenched and additional outsourcing personnel being contracted and job creation and/or job loss results. Expectation Confirmation Theory (ECT) emphasises the importance of an outsourcing provider conforming to quality management principles. Resource Based Theory (RBT) proposes that organisations need a collection of resources and capabilities to execute outsourcing successfully. Findings further suggested that outsourcing can yield positive and/or negative outcomes depending on risks encountered, the business environment, company policies, function/s to be outsourced, and the competence and commitment of an outsourcing vendor. To further enhance the positive impact of outsourcing three improvements need to be executed: formulation of standard policies, price regulations, and commitment of outsourcing firms in adhering to set contract deadlines. It is suggested that the following would allow organisations to gain more from outsourcing in the future: the adoption of international/offshore outsourcing practices, more commitment by outsourcing consultants, the standardisation of charges for outsourcing contracts, and the use of new technology that would improve how outsourcing is conducted. It is concluded that the positive impacts of outsourcing would foster development to some extent while the possible negative impact of outsourcing would impede development.
- Full Text:
- Date Issued: 2015
- Authors: Wachira, Wanjungu
- Date: 2015
- Subjects: Contracting out , Industrial management
- Language: English
- Type: Thesis , Doctoral , DPhil
- Identifier: http://hdl.handle.net/10948/4988 , vital:20775
- Description: The purpose of this study is to investigate the relationship between outsourcing and development in Nairobi. The key research question for this study is what are the impacts of outsourcing practices conducted by organisations in Nairobi? Data were obtained from questionnaires distributed in December 2010. A total of 85 profit-making firms in Nairobi with a sample of 165 management employees were selected for this study. The empirical findings obtained relate to four outsourcing theories. Transaction Cost Analysis (TCA) Theory focuses on the cost savings that result from outsourcing. Agency Outsourcing Theory centres on outsourcing firms hiring agents to achieve productivity. Hiring agents may result in permanent staff being retrenched and additional outsourcing personnel being contracted and job creation and/or job loss results. Expectation Confirmation Theory (ECT) emphasises the importance of an outsourcing provider conforming to quality management principles. Resource Based Theory (RBT) proposes that organisations need a collection of resources and capabilities to execute outsourcing successfully. Findings further suggested that outsourcing can yield positive and/or negative outcomes depending on risks encountered, the business environment, company policies, function/s to be outsourced, and the competence and commitment of an outsourcing vendor. To further enhance the positive impact of outsourcing three improvements need to be executed: formulation of standard policies, price regulations, and commitment of outsourcing firms in adhering to set contract deadlines. It is suggested that the following would allow organisations to gain more from outsourcing in the future: the adoption of international/offshore outsourcing practices, more commitment by outsourcing consultants, the standardisation of charges for outsourcing contracts, and the use of new technology that would improve how outsourcing is conducted. It is concluded that the positive impacts of outsourcing would foster development to some extent while the possible negative impact of outsourcing would impede development.
- Full Text:
- Date Issued: 2015
The impact of domain knowledge-driven variable derivation on classifier performance for corporate data mining
- Authors: Welcker, Laura Joana Maria
- Date: 2015
- Subjects: Data mining , Business -- Data processing , Database management
- Language: English
- Type: Thesis , Doctoral , DPhil
- Identifier: http://hdl.handle.net/10948/5009 , vital:20778
- Description: The technological progress in terms of increasing computational power and growing virtual space to collect data offers great potential for businesses to benefit from data mining applications. Data mining can create a competitive advantage for corporations by discovering business relevant information, such as patterns, relationships, and rules. The role of the human user within the data mining process is crucial, which is why the research area of domain knowledge becomes increasingly important. This thesis investigates the impact of domain knowledge-driven variable derivation on classifier performance for corporate data mining. Domain knowledge is defined as methodological, data and business know-how. The thesis investigates the topic from a new perspective by shifting the focus from a one-sided approach, namely a purely analytic or purely theoretical approach towards a target group-oriented (researcher and practitioner) approach which puts the methodological aspect by means of a scientific guideline in the centre of the research. In order to ensure feasibility and practical relevance of the guideline, it is adapted and applied to the requirements of a practical business case. Thus, the thesis examines the topic from both perspectives, a theoretical and practical perspective. Therewith, it overcomes the limitation of a one-sided approach which mostly lacks practical relevance or generalisability of the results. The primary objective of this thesis is to provide a scientific guideline which should enable both practitioners and researchers to move forward the domain knowledge-driven research for variable derivation on a corporate basis. In the theoretical part, a broad overview of the main aspects which are necessary to undertake the research are given, such as the concept of domain knowledge, the data mining task of classification, variable derivation as a subtask of data preparation, and evaluation techniques. This part of the thesis refers to the methodological aspect of domain knowledge. In the practical part, a research design is developed for testing six hypotheses related to domain knowledge-driven variable derivation. The major contribution of the empirical study is concerned with testing the impact of domain knowledge on a real business data set compared to the impact of a standard and randomly derived data set. The business application of the research is a binary classification problem in the domain of an insurance business, which deals with the prediction of damages in legal expenses insurances. Domain knowledge is expressed through deriving the corporate variables by means of the business and data-driven constructive induction strategy. Six variable derivation steps are investigated: normalisation, instance relation, discretisation, categorical encoding, ratio, and multivariate mathematical function. The impact of the domain knowledge is examined by pairwise (with and without derived variables) performance comparisons for five classification techniques (decision trees, naive Bayes, logistic regression, artificial neural networks, k-nearest neighbours). The impact is measured by two classifier performance criteria: sensitivity and area under the ROC-curve (AUC). The McNemar significance test is used to verify the results. Based on the results, two hypotheses are clearly verified and accepted, three hypotheses are partly verified, and one hypothesis had to be rejected on the basis of the case study results. The thesis reveals a significant positive impact of domain knowledge-driven variable derivation on classifier performance for options of all six tested steps. Furthermore, the findings indicate that the classification technique influences the impact of the variable derivation steps, and the bundling of steps has a significant higher performance impact if the variables are derived by using domain knowledge (compared to a non-knowledge application). Finally, the research turns out that an empirical examination of the domain knowledge impact is very complex due to a high level of interaction between the selected research parameters (variable derivation step, classification technique, and performance criteria).
- Full Text:
- Date Issued: 2015
- Authors: Welcker, Laura Joana Maria
- Date: 2015
- Subjects: Data mining , Business -- Data processing , Database management
- Language: English
- Type: Thesis , Doctoral , DPhil
- Identifier: http://hdl.handle.net/10948/5009 , vital:20778
- Description: The technological progress in terms of increasing computational power and growing virtual space to collect data offers great potential for businesses to benefit from data mining applications. Data mining can create a competitive advantage for corporations by discovering business relevant information, such as patterns, relationships, and rules. The role of the human user within the data mining process is crucial, which is why the research area of domain knowledge becomes increasingly important. This thesis investigates the impact of domain knowledge-driven variable derivation on classifier performance for corporate data mining. Domain knowledge is defined as methodological, data and business know-how. The thesis investigates the topic from a new perspective by shifting the focus from a one-sided approach, namely a purely analytic or purely theoretical approach towards a target group-oriented (researcher and practitioner) approach which puts the methodological aspect by means of a scientific guideline in the centre of the research. In order to ensure feasibility and practical relevance of the guideline, it is adapted and applied to the requirements of a practical business case. Thus, the thesis examines the topic from both perspectives, a theoretical and practical perspective. Therewith, it overcomes the limitation of a one-sided approach which mostly lacks practical relevance or generalisability of the results. The primary objective of this thesis is to provide a scientific guideline which should enable both practitioners and researchers to move forward the domain knowledge-driven research for variable derivation on a corporate basis. In the theoretical part, a broad overview of the main aspects which are necessary to undertake the research are given, such as the concept of domain knowledge, the data mining task of classification, variable derivation as a subtask of data preparation, and evaluation techniques. This part of the thesis refers to the methodological aspect of domain knowledge. In the practical part, a research design is developed for testing six hypotheses related to domain knowledge-driven variable derivation. The major contribution of the empirical study is concerned with testing the impact of domain knowledge on a real business data set compared to the impact of a standard and randomly derived data set. The business application of the research is a binary classification problem in the domain of an insurance business, which deals with the prediction of damages in legal expenses insurances. Domain knowledge is expressed through deriving the corporate variables by means of the business and data-driven constructive induction strategy. Six variable derivation steps are investigated: normalisation, instance relation, discretisation, categorical encoding, ratio, and multivariate mathematical function. The impact of the domain knowledge is examined by pairwise (with and without derived variables) performance comparisons for five classification techniques (decision trees, naive Bayes, logistic regression, artificial neural networks, k-nearest neighbours). The impact is measured by two classifier performance criteria: sensitivity and area under the ROC-curve (AUC). The McNemar significance test is used to verify the results. Based on the results, two hypotheses are clearly verified and accepted, three hypotheses are partly verified, and one hypothesis had to be rejected on the basis of the case study results. The thesis reveals a significant positive impact of domain knowledge-driven variable derivation on classifier performance for options of all six tested steps. Furthermore, the findings indicate that the classification technique influences the impact of the variable derivation steps, and the bundling of steps has a significant higher performance impact if the variables are derived by using domain knowledge (compared to a non-knowledge application). Finally, the research turns out that an empirical examination of the domain knowledge impact is very complex due to a high level of interaction between the selected research parameters (variable derivation step, classification technique, and performance criteria).
- Full Text:
- Date Issued: 2015
The South African mining industry towards 2055: scenarios
- Authors: Du Plessis, Rudolf
- Date: 2015
- Subjects: Mines and mineral resources -- South Africa , Geology, Economic -- South Africa , Forecasting -- Study and teaching
- Language: English
- Type: Thesis , Doctoral , DBA
- Identifier: http://hdl.handle.net/10948/4215 , vital:20568
- Description: The strained commodity price environment has triggered strong measures of cost containment and control by global and South African mining industries with workforce reductions, mine closures and shelved projects. Added to this, the South African mining industry is facing an unparalleled number of challenges, including an uncertain regulatory environment, infrastructure constraints, frequent industrial actions, rising costs and shortages of skills. The dynamism of discontinuous change has increased considerably and the South African mining industry is today facing an uncertain future with a blurred outlook. The results of the detailed analysis of future studies theory and practice in this research study support the argument that there is a strong need to fundamentally change the ways of planning for the future of the South African mining industry. The practice of developing new insight through the application of futures studies is central to this process. Today, collective decisions and strategies are progressively more founded on and informed by futures studies. The research study sought to develop insight regarding the future of the South African mining industry through the construction of four scenarios towards 2055: Divided We Fall, where a confident industry is threatened by social divisions as industry transformation is disregarded; Rock Bottom, where weak global economic conditions coincide with lacklustre industry innovation; Rising from Ashes, with similar economic conditions, but the industry responding positively through accelerated industry innovation; and Renaissance, set against positive global economic conditions with the South African mining industry adopting a collaborative, innovative approach to industry transformation. The research study further strived to uncover the preferred future for the South African mining industry as basis for the South African Mine of the Future Vision towards 2055. Throughout the research study, Inayatullah’s pillars of futures studies were applied as a guideline in mapping the present and future, deepening the future, broadening the future through the development of scenarios, and transforming the future by narrowing it down to the preferred. The study provides valuable insight into the driving forces relevant to the South African mining landscape. In addition, it provides insight on how to anticipate the changes these driving forces may bring about for the industry over the next 40 years from a decision-maker’s point of view. It is up to the mining industry to select the road to follow in terms of progress and sustainable development. Through an innovative approach, the creation of an environment of trust, the sharing of values, purposes and benefits, the South African Mine of the Future Vision is attainable. The South African mining industry must commit itself to working in collaborative partnerships with local communities, government, society and labour; stepping boldly into a world of social, environmental, technological and commercial innovation.
- Full Text:
- Date Issued: 2015
- Authors: Du Plessis, Rudolf
- Date: 2015
- Subjects: Mines and mineral resources -- South Africa , Geology, Economic -- South Africa , Forecasting -- Study and teaching
- Language: English
- Type: Thesis , Doctoral , DBA
- Identifier: http://hdl.handle.net/10948/4215 , vital:20568
- Description: The strained commodity price environment has triggered strong measures of cost containment and control by global and South African mining industries with workforce reductions, mine closures and shelved projects. Added to this, the South African mining industry is facing an unparalleled number of challenges, including an uncertain regulatory environment, infrastructure constraints, frequent industrial actions, rising costs and shortages of skills. The dynamism of discontinuous change has increased considerably and the South African mining industry is today facing an uncertain future with a blurred outlook. The results of the detailed analysis of future studies theory and practice in this research study support the argument that there is a strong need to fundamentally change the ways of planning for the future of the South African mining industry. The practice of developing new insight through the application of futures studies is central to this process. Today, collective decisions and strategies are progressively more founded on and informed by futures studies. The research study sought to develop insight regarding the future of the South African mining industry through the construction of four scenarios towards 2055: Divided We Fall, where a confident industry is threatened by social divisions as industry transformation is disregarded; Rock Bottom, where weak global economic conditions coincide with lacklustre industry innovation; Rising from Ashes, with similar economic conditions, but the industry responding positively through accelerated industry innovation; and Renaissance, set against positive global economic conditions with the South African mining industry adopting a collaborative, innovative approach to industry transformation. The research study further strived to uncover the preferred future for the South African mining industry as basis for the South African Mine of the Future Vision towards 2055. Throughout the research study, Inayatullah’s pillars of futures studies were applied as a guideline in mapping the present and future, deepening the future, broadening the future through the development of scenarios, and transforming the future by narrowing it down to the preferred. The study provides valuable insight into the driving forces relevant to the South African mining landscape. In addition, it provides insight on how to anticipate the changes these driving forces may bring about for the industry over the next 40 years from a decision-maker’s point of view. It is up to the mining industry to select the road to follow in terms of progress and sustainable development. Through an innovative approach, the creation of an environment of trust, the sharing of values, purposes and benefits, the South African Mine of the Future Vision is attainable. The South African mining industry must commit itself to working in collaborative partnerships with local communities, government, society and labour; stepping boldly into a world of social, environmental, technological and commercial innovation.
- Full Text:
- Date Issued: 2015