How competencies creates economic value

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The study purpose is to explore the attributes and competencies that business executives (CEOs) possess that enable them to create real economic value for their respective organisations. The study is informed by the observation that even though EVA is extensively utilised to measure executive performance and is also used as a compensation tool for the remuneration of business executives, it seems not to be utilised in the recruitment and selection measures of these executives. In addition, seems not to be utilised during the executives’ skills gap analysis which should be a precursor to executive development. The previous chapter presented the literature of economic value added, competencies (and how competencies can be related to creating economic value in organisations and the results of a research that argues that the appointments of CEOs have an impact (negative or positive) to the share price and volume traded on the stock exchange.
In order to seek answers to the research questions in this study, a mixed method approach was followed, specifically a Qualitative Dominant Sequential Exploratory Design. The first phase of the study was qualitative with the aim of finding out firstly what attributes and competencies do CEOs possess and secondly how do they use these competencies to create economic value. The quantitative phase was focused on developing the index based on the attributes and competencies identified during the qualitative phase.
The population of interest was the top 50 companies listed on the JSE, which have had the same CEO for the period of five consecutive years or more. This was followed by calculating the companies’ EVA over a period not exceeding 10 years, then workout the cumulative EVA (CEVA).
Data were collected by field interviews, observations and analysis of companies’ documents (these included annual reports, internet interviews with business analysts and other relevant related documents). Primary data and secondary data from company reports were managed and organised using a data analysis software called ‘Atlas ti’. Once this phase was concluded, then the fourth step of the approach outlined by Lucia and Lepsinger (1999: 67-112) on how to develop the competency model from scratch was followed. Lucia and Lepsinger (1999) and Spencer (2003) provide steps to follow while developing the competency model from the scratch, namely:
• Step 1: Develop a performance criteria;
• Step 2: Analyse criterion sample;
• Step 3: Determining the data collection methodology;
• Step 4: Collecting data, conducting interviews and focus groups, Edgar and Lockwood (2011) further support this approach, by reporting that to discover competencies analysis of corporate documents and interviews of corporate personnel is necessary;
• Step 5: Performing job observations;
• Step 6: Analyse data;
• Step 7: Develop an interim competency model; and
• Step 8: Validate competency model.
Participants were given an informed consent and were made aware that the participation is voluntary and that their privacy and confidentiality will be ensured at all times (see Appendix A). The study is located in the pragmatic paradigm with the focus mainly on what and how the research could best be done. Subsequent sections detail the rationale for following the stated methodology in conducting the study. Figure 3.1 provides the road map of this chapter.

Research Design

Doyle Brady and Byrne (2009) argue that the first question a researcher asks when deciding on methodology is to ascertain which approach will best suit the research question, and by extension which approach is best suited to achieving the purpose of the study. A mixed methods design was used to conduct this study and obtain data from CEOs who for the purpose of this study shall be referred to as executives. Creswell and Plano Clark (2007:5) define mixed methods as:
“… a research design with philosophical assumptions as well as methods of inquiry. As a methodology, it involves philosophical assumptions that guide the direction of the collection and analysis and the mixture of qualitative and quantitative approaches in many phases of the research process. As a method, it focuses on collecting, analysing, and mixing both quantitative and qualitative data in a single study […]. Its central premise is that the use of quantitative and qualitative approaches, in combination, provides a better understanding of [the] research problem than either approach alone”.

Mixed Methods

Mixed methods approach aims to provide the researcher with an approach that can better explain and /or answer the research question(s). This method allows the researcher to optimise the benefits of both the positivist quantitative method and the post-positivist/constructivist qualitative approach. Bryman (2007) asserts that mixed methods researcher analyse and interpret research in such a way that the two components are mutually illuminating, particularly when these two approaches are integrated. In addition, Jogulu and Pansiri (2011:698) assert that by employing mixed methodology in management and business research is beneficial because of the gained exposure to “multiple data types and variations in data analysis techniques”.
Given that the literature and previous studies fail to provide evidence of variables or competencies responsible for economic value creation, within the context of EVA, then developing an index needs to be preceded by the identification of those variables or competencies. Mixed methods research has become the best and comprehensive approach for this study. The rationale for conducting mixed methods research designs identified by Greene, Caracelli and Graham (1989) and Bryman (2006) further supports this design as a best choice for answering the research question in this study. These include, though not limited to (also acknowledged by Doyle, et al., 2009):
• Triangulation- mixed methods design allows for greater validity in this study by seeking substantiation between qualitative and quantitative data.
• Complementary/completeness – using the combination of research approaches provided a more complete and comprehensive picture of this study phenomenon.
• Offsetting weaknesses and providing stronger inferences – utilising mixed methods allowed for the limitations of each (qualitative and quantitative) to be minimised while leveraging on their strengths thereby providing stronger and more accurate inferences.
• Answering different research questions – this approach helped in answering the questions that could not been possible to answer with either, and provided for “a greater repertoire of tools to meet the aims and objectives” (Doyle, et al., 2009: 178) of this study.
• Explanations of findings – mixed methods approach assisted in having a deeper understanding with regard to the link between value creation and competencies/variables. These identified variables became a necessary framework for the development of an index. Without relying on literature for determining the components of the index, results attained from a qualitative approach provided these answers.
• Hypothesis development and testing – a qualitative phase of the study assist in developing hypotheses to be tested in a follow-up studies.
• Instrument development and testing – a qualitative study assisted in generating items for inclusion in a survey questionnaire used in a quantitative phase to develop an index.
This study followed what Mason (2006) call ‘qualitatively driven’ approach to mixed methods since this approach offers vast potential for generating new ways of understanding the complexities and context and enhancing the capacities for social explanation and generalisation. Mason (2006) further provides reasons why mixed methods are better placed to answer research questions in social science research, thus transcending the complexities of this research. She believes that mixed methods:
• encourages researchers to think out of the box – for exploring new dimensions of experience in social life, and intersection between these.
• enhances our capacity for theorising beyond macro and micro.
• enhances and extends the logic of qualitative explanation – a qualitative logic works by seeking to comprehend the distinctive dynamics; this involves exploring as fully as possible the situational contours and context, while the quantitative logic charts and sometimes aims to predict wide patterns and changes in social phenomenon.
Using mixed methods approach was also found to increase audience (Bryman, 2007) from differing ontological and epistemological inclinations. This helped in building bridges by “marry-ing an objectivist with a constructivist” (Bryman, 2007:16). However, like any other methods, mixed methods design has its strengths and its weaknesses. Johnson and Onwuegbuzie (2004) highlight these in Table 3.1 below.

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Mixed Methods Research – Pragmatic Paradigm

The notion of paradigm refers to a set of ontological (nature of reality), epistemological (the nature of knowledge) and methodological (how do we obtain the knowledge) assumptions. These are assumptions made about the nature of social reality and the manner in which we can move towards this reality. In addition, Harrits (2011:152) highlights that research paradigm also refers to “a common research practice, existing within a research community, and carrying with it shared identity as well as a specific a specific problem or set of problems that are regarded as particularly significant in relation to the advancement of knowledge”.
Scholars like Morgan (2007) and Johnson, Onwuegbuzie and Turner (2007) suggest that the pragmatism presents a suitable research paradigm within which mixed methods research can be founded. Without entering into the paradigm wars debate, and also ensuring that the study views are not misquoted or misrepresented, the researcher acknowledges that there is a great value in the distinctions between deductive and inductive, objectivity and subjectivity, and generalisability and context approaches. However, this study finds comfort in the pragmatic paradigm, by the virtue of what the study aims to achieve. Morgan (2007) argues that pragmatic approach is to rely on abduction, inter-subjectivity and transferability (see Table 3.2).
• Abduction – abductive reasoning move back and forth between induction and deduction (Morgan, 2007), converting qualitative observations into possible propositions or theories, and then assessing this propositions through action. The goal in the pragmatic paradigm is to search for useful connection points between qualitative and quantitative reasoning.
• Inter-subjectivity – this implies that the researcher has to “work back and forth between various frames of reference” (Morgan, 2007:71). This aspect represents the emphasis to be placed on the process of communication and shared meaning. Morgan (2007) further indicates that inter-subjectivity represent the response to issues of incommensurability, in that there is no problem with the assertion that there is a single ‘real world’, and that all individuals have their own interpretation of that world. This approach specifically addresses the concern that studies of similar nature have been conducted before, and failed to establish the relationship between individuals (and their competencies) with the creation of value.
• Transferability – the extent to which we can take things that we learn (including those we do not learn) with one type of method in one particular setting and make the most appropriate use of that knowledge in other circumstances.
In closing the paradigm discussion, Johnson and Onwuegbuzie (2004) present the characteristics of pragmatism, accordingly, the researcher believe that this approach was best suited to better answer the research questions (see Table 3.3).

Typology of mixed methods approach in this study

Bryman (2006) argues that the dimensions out of which the typologies are constructed draw attention to the different aspects of a multi-strategy research. In conducting the typology for this study, the researcher followed the approach preceded by various scholars (Creswell, 2003; Creswell et al., 2003; Morgan, 1998; Morse, 1991 and Tashakkori and Teddlie, 1998) in order to the construct the four pillars that were used to develop a typological framework for this study (see Figure 3.2). These four pillars of typology of mixed methodology research are as follows:
• Prioritising the approach (qualitative or quantitative first);
• Timing of data collection;
• Level of integration – at what point of research do these methods come together; and
• Purpose – what is the purpose of this integration.
In answering these questions in Figure 3.3, the decision tree for mixed methods (Creswell et al., 2003; Creswell and Plano Clark, 2007) and the typology for mixed methods (Creswell and Plano Clark, 2007). The researcher constructed the following typology (this is also represented graphically in Figure 3.3):
• The study was of a nature where qualitative methods were dominating. This approach according to Johnson et al. (2007) is the type of a mixed research in which one relies on a qualitative, constructivist-poststructuralist-critical view of the research process, while recognising the addition of quantitative data. This approach was necessary in allowing the researcher to go deeper in identifying those variables/competencies that executives have in order to be able to create value in organisation, secondly unearthed the complexities related to how these executives actually create value in their respective organisations.
• The second decision had to do with whether data is collected concurrently or sequentially, that is the timing. Given the lack of previous data on finding out those competencies responsible for creating Economic Value in organisation, this dictated that data be collected sequentially.
• The purpose of this study was exploratory in nature and used for developing an instrument, ‘Instrument Development Model’ (Doyle et al., 2009:181).
• With regard to data integration, this occurred at the data interpretation level by “connecting the data” (Doyle et al., 2009:180) linking the qualitative findings to the quantitative approach.
In conclusion, the design that underpinned this study can be referred to as Qualitatively Dominant, Sequential Exploratory Design (QD-SED).



This section presents the process and criteria for selecting the sample, the instrument (s) utilised to collect data and the manner in which data were analysed.

Sampling design

Relevant population – it has been discussed before that the study will be mainly qualitative then followed by a quantitative approach. This study accepts EVA as the superior measure of real economic value, based on this acceptance (also detailed in Chapter 2) attempted to identify companies that are listed on the JSE that are using EVA with no success. Then the alternative approach was to create
a population based on the following criteria (40-5-10):
• The company should be on the top 40 companies listed on the JSE;
• The company should have had the same chief executive officer for a period of five years; and
• Then calculated the company EVA over a period of 10 years, and the company should have created economic value (that is positive EVA), cumulative EVA (CEVA).
Study sample – Teddlie and Yu (2007:88) assert that “there is no widely accepted typology of mixed methods sampling strategies”, and the literature related to the sampling strategies of mixed methods is still in infancy, meaning that this created a challenge in sampling for this study. Based on the pragmatic approach and the study design, the sample needed to be in the following ways:
• Be in line with the study design; and
• Be able to answer the research question.
Sequential mixed methods sampling (SMMS) approach was undertaken in this study. The example of a SMMS procedure followed in the study QUAL-QUAN whereby the methodology and results from the first strand (QUAL) is employed in the second strand (QUAN). The strand approach is discussed in detail in Kemper, Stringfield and Teddlie (2003). Table 3.4 and Figure 3.4 provide the guidelines for mixed methods sampling and the actions taken to satisfy these guidelines in this study sampling process (Teddlie and Yu, 2007).
Unit of Analysis – in this study the unit of analysis was the companies’ CEOs. The focus of this study was to identify the attributes that CEOs have and are critical for creating economic value in their organisations to also find out how they create this value.

List of Figures
List of Tables
List of Equations
1.1. Introduction
1.2. Background
1.3. Research problem and question
1.4. Research aims and objectives
1.5. Relevance and significance of the study
1.6. Scope and limitations
1.7. Methodology
1.8. Chapters Outline
2.1. Introduction
2.2 Measures of value
2.3 Economic Value Added (EVA)
2.4 How competencies creates economic value
2.5 Competency modelling and assessments: assumptions and shortcomings
2.6 Leadership Theory
2.7 The role of CEO in moving financial markets
2.14 Competencies of chief executive officers
2.15 Conclusion
3.2 Introduction
3.3 Research Design
3.4 Mixed Methods
3.5 Mixed Methods Research – Pragmatic Paradigm
3.6 Typology of mixed methods approach in this study
3.7 Methodology
3.8 Limitations
3.9 Ethical considerations
3.10 Conclusion
4.1 Introduction
4.2 Financial Excellence
4.3 Planning and vision creating strategy
4.4 Competitiveness enhancing strategies
4.5 Other value creating supporting strategies
4.6 Co-X attributes and competencies from other executives
4.7 Conclusion
5.1 Development of CEO competency model
5.2 Quality of the CEO competency model
5.3 Statistical analysis for quantitative results
5.4 Sustainable economic value competencies index (SEVCI) construction: more than a competency model
5.5 Conclusion
6.1 Competencies for value creation
6.2 Implications for theory and business
6.3 Limitations and weaknesses
6.4 Future research
6.5 Conclusion

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