Managerial Decision Making Regarding the Allocation of Project Manager Resources to Projects: The Case of Botswana

Transcription

1 Managerial Decision Making Regarding the Allocation of Project Manager Resources to Projects: The Case of Botswana Lone Seboni, Apollo Tutesigensi, Denise Bower University of Leeds, Faculty of Engineering, Leeds, England Abstract--Purpose To empirically demonstrate the nature of current project manager assignment practices and their impact on important performance variables, in the context of Botswana s multi-project settings. Approach - the research design is composed of country and company layers. This paper reports on Fieldwork 1 of 3 stages, which involved questionnaires and interviews of 53 project managers and 20 project heads from 12 companies in the public and private sector. Findings the results indicate compelling evidence to conclude that the current practices are informal, not objective, not comprehensive and lack a good match between project managers and projects. Significant correlations were found between the following variables (1) extent of formality and project manager rewards, (2) extent of objectivity and project manager performance, (3) extent of comprehensiveness and project success, (4) Correspondence level between project manager and project and the performance variables - project manager rewards, project manager performance and project success. Contributions - (1) empirical justification for problem existence that gives an indication of the state of practice in Botswana currently unknown, (2) Data from a new country, industries and project types compared to existing empirical studies on this topic, (3) development of a conceptual model that adds to the theory behind matching project managers to projects in a multi-project setting. I. INTRODUCTION Research on project manager assignments, defined in this study as a process of assigning a project manager to a project, has revealed that this process is viewed as a key management challenge[1, 2]but fundamental to project success[3-5]. This process becomes more important in multi-project management settings [6-11], where project managers lead several projects concurrently [10, 12]. The reason is that, unlike single project management settings, there are clear links between projects and business strategy[13], such that a mismatch in the project manager assignment may significantly impact on organizational performance[14-16].the fact that there are often too many projects in the pipeline [17] waiting to be implemented by the project managers in multi-project settings is a clear sign of the need to optimize the project manager assignment process in order to maximize the utilization of limited resources[18]. Empirical research on project manager assignments in multiproject environments, a focus for this study, is currently under-studied [19, 20]. A. Research aim, objectives and questions The aim of this paper is to provide empirical justification for the research problem, in terms of nature of existing project manager assignment practices and their impact on performance, in the context of Botswana s multi-project settings. This should be viewed as an extension of existing empirical studies on project manager assignments [21-24], particularly in multi-project management environments in terms of context. For example, with regard to project manager assignments in multi-project management settings, this study will add to new knowledge in terms of findings from a different country, industries and project types, other than: USA, new product and software development projects, high-technology industry[10, 19, 20, 25-28]. The specific research objectives for this paper are :(1) to examine the nature of existing project manager assignment practices, (2) to assess the impact of existing practices on some important performance variables, and (3) to develop a universal conceptual model that can be used as a window to study the project manager assignment practices in more depth in the next phase of this research. The research questions that are used to address the research objectives for this paper are: What is the nature of project manager assignment practices in Botswana, in terms of the following nature of practice (NP) variables: (1) extent of formality, (2) extent of objectivity, (3) correspondence level between project manager and project and (4) extent of comprehensiveness? What is the impact of the above NP variables on the following performance variables: (1) project manager performance, (2) project manager motivation, (3) project success, (4) project manager rewards? What are the important inputs, processes and outputs (high level) that must be considered in the development of a universal conceptual model (subject to context)? Achievement of research objectives for this paper through fieldwork 1 of 3, will feed into the next 2 phases of the research, following empirical demonstration of the research problem. II. THEORETICAL BACKGROUND AND LITERATURE EVALUATION The research problem (i.e., lack of formal, objective and comprehensive processes for project manager assignments that result in a good match between project manager and project) arose from the researcher s experience as a project manager in one of Botswana s multi-project environments. 487

2 Given the author s anecdotal evidence (considered subjective), the next step was a critical appraisal of the literature on project manager assignment processes and methodologies. In the absence of publications that could have been used as objective evidence for the existence of the problem (Botswana context), fieldwork 1 was designed for this purpose. A. Evaluation of project manager assignment processes and methodologies The literature on project manager assignment processes was divided into four streams as illustrated in Table 1. Reviews of literature streams 3 and 4 are included only because these competencies can be used as criteria for selecting project managers and allocating them to projects. The ongoing professional development of project managers is not the focus of this research. The emphasis is on assessing project manager competencies in relation to assigning them to projects. B. Identified gaps in literature streams 1 and 2 and contribution The most comprehensive methodology about how project managers are assigned is proposed by Patanakul et al. [25-27]. This methodology is based on empirical data, unlike the theoretical model proposed by Adams[29], which may not be applicable to managing multiple simultaneous projects. However, the literature on all identified methodologies (directly proposed and implied) do not explicitly consider organizational dimensions (internal and external) that may influence the decision-maker s assignment of project managers to projects. There is potential to close this gap by incorporating the impact of these organizational dimensions into the model for project manager assignment process, with a view towards building on this existing model. Introducing these organizational dimensions is in line with modifying the model on the basis of a universal framework, to make it more consistent with the broader management literature in relation to resource management. However, the comprehensive model proposed by Patanakul [25-27] does take into account organizational dimensions to some extent, under specific processes such as project prioritization and recognition of limitations. Although the model proposed by Patanakul [25-27] represents a solid foundation for this research in terms of being comprehensive, incorporating the broader resource management theory is beneficial, particularly since a positivist view to philosophical reasoning is adopted in the development of a universal conceptual model to build on this existing model. Using a process-based approach, another area for improvement is to include missing links, feedback loops between the model elements and the appropriate symbols[54]. This will allow performance assessments and continuous improvements to be made during the project manager assignment process and enhance the understanding of the literature. There are limited empirical studies that directly propose methodologies for project manager assignments in multi-project settings [10, 19, 20, 25-28, 30], a focus of this research. These identified key empirical studies are significant in terms of findings, contributions and act as a solid foundation for this research. Notwithstanding, these studies are focused predominantly on one country (USA), industry (High-technology) and project types (new product and software development projects). The present study will borrow the logic of the comprehensive model from these studies, modifying it on the basis of the broader management literature, process-based approach and then apply it to Botswana (a different context). This approach is consistent with the following definition of originality: Trying out something in a country that has previously been done in other countries [55-57]. The conceptual model to be developed for this paper (objective 3) will utilize aspects of existing models TABLE 1: IDENTIFIED LITERATURE STREAMS Literature streams Underpinning references Stream 1: Literature directly proposing methodologies/approaches for [29-32] Project Manager assignments [10, 19, 25-28] [33] Stream 2: Literature on implied methodologies/approaches for project [34] manager assignments (i.e. project manager attributes in relation to selecting [35] them for leading projects) [36] Stream 3: Literature on project manager competencies single project management environments Stream 4: Literature on project manager competencies multi-project management settings and unique competencies for managing multiple simultaneous projects [37] [31, 38],[33, 39, 40], [41], [7, 42, 43] [44, 45] [37, 46, 47] [38, 48-50] [6, 7, 9, 51] [13] [48, 52, 53] 488

3 and decompose them into individual processes, with inputs, outputs and feedback loops. There is therefore, potential to address identified gaps as part of a series of small contributions to the understanding of the project manager assignment literature. Existing literature about project manager assignments uses the terms project assignments and project manager assignments interchangeably. This implies that the task of assigning a project to a project manager is the same as that of assigning a project manager to a project. This study makes a distinction between these two tasks to avoid possible confusion. The two tasks are different because in assigning projects to project managers, the decision-maker looks at the projects to assess which projects can utilize the available project manager competencies, given the limitations of the available project managers in the firm. In other words, the pool of project managers is fixed, and the task is to determine which projects a particular project manager can handle. However, when assigning project managers to projects, the decision-maker looks at the project requirements and attributes to determine which project managers competencies can benefit those project attributes. The decision maker has the projects and wants to look for suitable project managers to lead those projects. This opens up opportunities to search for the required project manager competencies not necessarily within the constraints of the existing pool of project managers in the firm. This process (i.e., assigning project managers to projects) can also be used to address both the status quo as well as planning ahead in terms of a forecast for the required project manager competencies, levels of competencies and number of resources (i.e., project managers) required on the basis of the portfolio of projects to be implemented currently (status quo) and over a specified futuristic timeframe. This line of thinking is consistent with the theory of resource management and will be used in this research to avoid confusion in the use of terminology. C. Identified gaps in literature streams 3 and 4 (for next steps of this research) Extensive literature reviewed on project manager competencies, for both single and multi-project management environments, reveal that different project manager competencies are appropriate for different industry sectors and project types [7, 41, 46, 58]. However, literature on project manager competencies appropriate for managing concurrent mineral exploration projects in a mining industry is missing. Majority of the literature on project manager competencies is mainly focussed on the following project types: (1) Construction, (2) New product development or research and development, (3) Information systems (includes information technology), (4) Organizational and business change. There is mention of engineering projects as an application area, though it is not clear what specific types of engineering projects. There is potential to contribute to knowledge in terms of specific project manager competencies (i.e. job task related competencies) appropriate to managing mineral exploration projects in the mining industry (to be addressed in the next stages of this research). D. Summary of Identified gaps and contribution There is potential to address the identified gaps in existing literature on project manager assignments in multi-project settings. Responding to these gaps, through fieldwork 1 of 3, should be viewed as part of a series of small contributions to knowledge, which when taken together, will become significant. For example, empirical demonstration of the research problem (fieldwork 1) in Botswana, represents one contribution to existing knowledge in terms of: (1) another country other than USA, (2) other industries other than Hightechnology, (3) other project types other than new product and software development. III. RESEARCH PROPOSITIONS AND ASSOCIATED HYPOTHESES The research propositions and hypotheses (specific to fieldwork 1) are outlined below, on the basis of existing literature. Although the literature review has identified numerous studies on project manager assignments [10, 19, 23-29, 31, 32], there are limited empirical studies on project manager assignments in multi-project management settings (the principal focus of this study) published between 2003 to 2013[10, 19, 20, 25-28]. Theoretical evidence from the literature on project manager assignments in multi-project settings that supports the author s anecdotal evidence has been found [25, 28]. However, this theoretical evidence is focussed on one country (USA), industry (high-technology) and project types (new product and software development), but points to the absence of formal project manager assignment procedures. This is despite the established impact of the project manager assignment decision on project and organizational performance [4, 5, 28]. Therefore, the first proposition in relation to the research problem in terms of nature of the practice was constructed as follows: Proposition 1 the project manager assignment processes are treated casually, even though they influence the performance of the project manager, project and the organization [25, 28].Four associated research hypotheses to this proposition are: H1 - the practice of assigning project managers to projects is informal (i.e., null hypothesis, denoted by H 0 ). By definition, the alternative hypothesis (H 1 ) is that the practice is formal. H2 - the practice of assigning project managers to projects is not objective. 489

4 H3 - the practice of assigning project managers to projects is such that there is lack of a good match between the project manager and the project. H4 - the practice of assigning project managers to projects is not comprehensiveness. Several studies support the link between the project manager assignment practice and performance of the project manager, project and organization [1, 2, 5, 8, 14, 25, 27, 59]. Therefore, two proposition and associated hypotheses were constructed as follows: Proposition 2 the correspondence level between project manager and project (i.e. good match between project manager and project) is most likely to be associated with the project managers performance [1, 2, 5, 8, 14, 16, 25, 27, 60]. It follows that research hypotheses 5 (H5) can be stated as: H5 a good match between project manager and project is associated with project manager performance. Proposition 3 the correspondence level between project manager and project is expected to be associated with project success [1, 2, 5, 8, 14, 25, 27]. For the purpose of this study, project success is differentiated from project performance. Research hypothesis 6 (H6) can be stated as: H6 a good match between project manager and project is associated with project success. Empirical studies have established a link between the variables, good match between project manager and project and organizational performance [25-27]. One empirical study revealed that considering a good match between a project and a project manager has a positive impact on both business success and reward for performance [19]. Business success is linked to project performance by other researchers other than Patanakul [8, 31]. Patanakul (2009) found that considering similarities among projects in the project manager assignment process has a positive impact on career advancement (referred to in this study as project manager rewards) and resource productivity. Therefore, proposition 4 (P4) and the associated hypothesis were constructed as follows: Proposition 4 correspondence level between project manager and project is linked to project manager rewards [19]. H7 a good match between project manager and project is associated with project manager rewards. Several studies generally suggest a link between the effectiveness of the project manager assignment decision and the performance of both the project and the organization [5, 8, 14, 26-28]. The effectiveness is viewed in terms of ingredients such:(1) usage of formal guidelines (e.g., documentation, management tools and techniques), (2) repeatability or objectivity, (3) all-inclusive approach in terms of the important factors to consider[25, 30]. Business success is linked to project success [8, 19, 31]. It has been found that considering similarities among projects in the project manager assignment process [19], covered in this study under extent of comprehensiveness, has a positive impact on resource productivity and career advancement. Career advancement is covered in this study under the variable, project manager rewards (i.e., opportunities for promotions, performance bonus and career advancements). Resource productivity is included in this study under the variables project manager motivation and performance. Therefore, three propositions (P5 to P7) and associated hypotheses (H8, H9 and H10) were constructed as follows: Proposition 5 the extent of formality in the project manager assignment practice is linked to an influence on project manager rewards. H8 extent of formality is associated with project manager rewards. Proposition 6 the effectiveness of the project manager assignment practice(e.g., objectivity) is linked to resource productivity [19], performance of projects [20, 26], performance of the project manager and hence the organization [25, 30]. H9 - extent of objectivity is associated with project manager performance. Proposition 7 the extent of comprehensiveness in the project manager assignment process is expected to be linked with project manager rewards [19], which is linked to resource productivity, project performance and hence project success [19, 20]. H10 extent of comprehensiveness is associated with project success. Fig. 1 is a summary of the hypothesized model showing identified key influences of NP variables (independent) on PP variables (dependent), in the context of correlation analysis. This means that the correlation between any two variables does not imply causation [61]. Appropriate inferential statistical procedures were used to test for differences between groups, followed by correlation analysis. 490

5 Fig. 1: Hypothesized model showing key relationships IV. RESEARCH DESIGN Firstly, a schematic representation of the research process for all 3 fieldwork stages is presented to give an appreciation of the entire research (Fig. 2). Secondly, details of the research design are outlined, followed by details of fieldwork 1 data collection and analysis (the main component of this paper). Fig. 2: Schematic representation of the research process 491

6 Fig. 3: Summary of the research design This paper reports on all the work completed in fieldwork 1 of 3 (i.e., activities 1 to 6), including the development of a conceptual model to be used in the next stage of the research (activity 7). Fieldwork stages 2 and 3 have not yet commenced. The research design is discussed in terms of the following: selection of cases (at both Country and company level), research variables and data sources [62]; in relation to a plan for data collection for all 3 fieldwork stages. Fig. 3 is a summary of the research design for the entire study. In Fig. 3, specific informant groups and research method (s) are required for each fieldwork stage. For example, project heads and project managers were required for fieldwork 1; which involved both questionnaires and interviews. Project managers were included in addition to project heads, since they are impacted by the assignment decisions made by project managers, hence the need to obtain their views [10]. Both research methods (i.e. questionnaires and interviews) were used in fieldwork 1, with a view to minimize the limitations of each method while maximizing their strengths. A. Selection of cases The research design is decomposed into two layers: country and company levels. Country level (fieldwork 1) - at country level, enumeration was used to select all eligible companies in Botswana. These eligible companies represent the different cases within the population of private and public companies. Each company is considered as a single case. The population in this case refers to all companies in Botswana that operate in a multi-project management environment. Their number is derived from both public and private sector. Public sector- there are a total of 16 Government Ministries [63],of which only 6 engage in a multi-project management setting. Therefore, there are 6 eligible Government Ministries. All 6 Government Ministries were targeted for participation in Fieldwork 1 (i.e. enumeration), using a mixture of two research methods (i.e., questionnaires and interviews). There was no sampling at Country level since every Government Ministry that met the defined criteria for participation was targeted. Justification for targeting all eligible Government Ministries is that their number (i.e. 6) is relatively small, in comparison to other Government Ministries (i.e. 10) that fall outside the population of eligible companies for this study. Private sector - in the private sector, there are a total of 30 companies listed to be operating in Botswana [64]. Only 9 operate in a multi-project management setting and hence eligible. All 9were targeted for participation, although only 6 responded (i.e., achieved sample). There was no sampling at Country level as far as eligible private companies are concerned, hence no sampling techniques used. Justification 492

7 for targeting all 9 eligible companies is the same as that given for eligible Government Ministries. This means that at country level, the population was 15. The remaining companies did not meet the defined criteria for participation in this study. The target sample was 15, since all companies in the population were targeted for participation. All 15 eligible companies were targeted for participation (i.e. enumeration). Justification for enumeration at country level was due to the small population size in comparison to all companies in Botswana (i.e. only 15 out of 46). Enumeration was used for both questionnaires and interviews during fieldwork 1. Target sample (Country level: fieldwork 1) although the target sample for fieldwork 1 at Country level was 15 companies/cases, this does not mean that the achieved sample was 15, due to data collection challenges. Achieved sample (Country level: fieldwork 1) - at Country level, the achieved sample (i.e. number of eligible companies that responded) was 12, out of the possible 15 eligible companies. This number is made up of 6 Government Ministries and 6 private companies. Company level: fieldwork 1 this represents the second layer in terms of the population, target and achieved sample; within the individual companies/cases, each of which can be studied as an independent system. Each case has its own population, from which decisions were made as regards sampling or enumeration. The population at company level refers to the number of eligible informants/respondents within each case/company, as defined in terms of the following three criteria for selection of informants: (A) Senior level executives - responsible for company strategy, (B)Project heads/functional heads/programme or portfolio managers - responsible for making project manager-to-project allocation decisions, and (C) Project managers - responsible for leading multiple simultaneous projects. The population size for each case was determined using the context presented below: Government Ministries there are 6 Government ministries that are eligible to participate in this study. All 6 Ministries were targeted for participation in fieldwork 1. The achieved sample was also 6 (i.e. 100%). Each Ministry has one department responsible for project implementation. In each of these departments, there is 1 director (i.e. a senior level executive) and a head of projects, leading a team of project managers. As a rough estimate, there are 6 project managers in each department. This makes a total of 1 senior level executive, 1 head of projects and 6 project managers. Private companies - there are a total of 9 companies in the private sector that operate in a multi-project management environment (i.e. eligible private companies). All 9 were targeted, although the achieved sample was 6. The population of informants within each company for the 9 private companies is presented below: Banking industry there are 3 commercial banks (out of a total of 5 Banks) that implement multiple projects. Therefore, there are only 3 eligible banks. In bank 1, there is 1 senior level executive (responsible for company strategy), 1 head of projects leading a team of 10 project managers. This makes a total of 1 senior level executive, 1 head of project and 10 project managers. In bank 2, there is 1 senior level executive (responsible for company strategy), 1 head of projects (Head of Operations), who has 2 project managers reporting to him. This makes a total of 1 senior level executive, 1 head of projects and 2 project managers. In bank 3, there is 1 senior level executive (responsible for company strategy), 1 head of projects, who has 2 project managers reporting to him. This makes a total of 1 senior level executive, 1 head of projects and 2 project managers. The remaining 2 banks do not have a project team with multi-project managers, and hence fall outside the population of eligible companies. Telecommunications industry - there are 2 eligible companies in this industry. Company 1 has 1 senior level executive and 1 head of project who supervises a team of 6 project managers. Company 2 has 1 senior level executive and 1 head of project leading a team of 2 project managers. Manufacturing industry - there are 2 eligible companies in this industry that operate in a multi-project management environment. Company 1 has 1 senior level executive and 1 head of project leading a team of 2 project managers. Company 2 has 1 senior level executive and 1 head of project leading a team of 3 project managers. Energy and power industry there is only 1 eligible company in this industry that operates in a multi-project management environment. In this company, there is 1 senior level executive, 1 project head and 9 project managers. Mining industry there is only 1 eligible company in this industry that operates in a multi-project management environment. In this company, there are 2 senior level executives responsible for strategy (one for short-term planning and the other for long-term planning), 3 heads of projects; each leading a team of 6 project managers. This makes a total of 2 senior level executives, 3 heads of projects and 18 project managers. Enumeration was used for fieldwork 1 questionnaires at company level. Justification for enumeration was based on: (1) the need to reach out to a larger population of potential informants, which constitute a small population of eligible cases at Country level and (2) the ease of administering the online questionnaires (using the Bristol online survey tool), which did not require travelling to the various company sites. However, paper-based questionnaires were printed and distributed only to the companies that asked for this alternative, during fieldwork 1. These paper-based questionnaires were distributed to the research custodians in the respective companies in sealed envelopes and collected from them, again in sealed envelopes. Fieldwork 1 questionnaires (company level) - a summary of the population size, target sample, and achieved sample, within each of the 15 eligible cases, is presented in Fig

8 Fig.4: Population, target and achieved sample at company level for fieldwork 1 questionnaires Fig. 5: Breakdown of informants making up the population size for each case Senior level executives were excluded from Fig 4 above, since they were not part of the eligible informants for fieldwork 1 activities. This group of informants will be included only in fieldwork 2, whose aim is to study the research problem in more depth. From this figure, out of a total population of 107 eligible informants for all 15 cases, 46 responded to the questionnaires; yielding a response rate of 43% for questionnaires alone. A survey response rate of about 35% is considered acceptable [65, 66]. A breakdown of the number of informants from the three identified groups of informants, which make up the population size for each case, is presented in Fig. 5. The qualitative parts from fieldwork 1 interviews are out of scope for this paper. Only the quantitative parts from these interviews, which are similar to the questionnaires in terms of measured variables, were analyzed. B. Research variables and data sources The selection of cases (for both country and company levels) are discussed under four main issues: (1) unit of analysis, (2) basic research design, (3) specific research design, and (4) sample design [67].The discussion is given in light of fieldwork 1 activities. (1)Unit of analysis at country level, the unit of analysis is a group of cases. The group of individuals in the company represents one case. Each of the 15 eligible cases will have a group of individuals within the respective bodies/companies, representing the respective cases. There are 15 eligible cases at Country level. However, at company level, the unit of analysis is the individual informants who work for a particular company/case and falls under one of the three groups of informants identified as criteria for eligibility. (2)Basic research design The basic design of this research can be considered non-experimental (i.e. absence of both manipulated independent variables and random assignment of cases to groups), on the basis of the nature of the groups of cases. The nature of the groups of cases will be addressed under the heading, specific research design. (3)Specific research design the number of groups of cases at country level is 15. Enumeration was used to target all these groups of eligible cases, through both questionnaires and interviews. Justification for enumeration, as well as the use of both research methods has already been provided. Constraints faced by the researcher in terms of data collection were considered. For example, the groups of cases/potential participating companies are co-located in a different continent to where the researcher is based; hence the 494

9 use of online questionnaires that allowed the researcher to target all eligible companies and inside informants at country and company level respectively. Comparisons among the different cases or groups (private and public companies) will be explored, including methodological triangulation to determine the validity of findings by contrasting interview and questionnaire data sets[68]. The individual potential participants were identified on the basis of categorization into 3 different groups namely: senior level executives, project heads and project managers. This is consistent with previous studies on project manager assignments [25-27]. The measurement of research variables, interpretations and conclusions will be made on the basis of data collected from these three identified groups, in the context of each individual case or group, as well as the country context. (4)Sample design there was no sampling at Country level for fieldwork 1 questionnaires and interviews. However, a convenience sample was used for fieldwork 1 interviews at company level. Constraints of time and cost, while ensuring research validity, were considered in sample design. For example: travelling time from Europe to Africa, in-country travel to the eligible company sites, preliminary meetings, interviewing, transcribing and travelling back to Europe. Country level - in the absence of sampling at Country level (as already discussed), it can be argued that each of the individual cases, with different population sizes compared to each other, have an equal chance of appearing in the achieved sample that will be used to represent the population; at both Country and company levels. Company level - however, at company level, the population of eligible informants was enumeration since the authority in each case was asked to forward the questionnaire links to all eligible informants within the case. V. RESEARCH METHOD FOR THIS PAPER In order to respond to the research objectives and questions for this study, data was collected from 53 project managers and 20 project heads from 12 out of 15 eligible companies in the public and private sector, using questionnaires (online and paper based) and semi-structured interviews.16 paper-based questionnaires from the project manager data set were unusable for data analysis purposes, yielding a total of 73 usable responses for data analysis purposes (from both data sets). The 53 project managers comprised 34 questionnaire and 19 interview respondents, while the 20 project heads were made up of 12 questionnaire and 8 interview respondents. 26 respondents were from the public sector and 27 from private sector. The sample demographics were measured using two levels of measurement namely scale variables (5-point Likert scale Never, Seldom, Sometimes, Often, Always) and categorical/nominal variables. The analysis of descriptive statistics for the sample demographics among both data sets were considered separately in terms of continuous and categorical variables [69] as displayed in Table 2 (continuous variables) and Tables 3 and 4 (categorical variables). TABLE 2: DESCRIPTIVE STATISTICS FOR CONTINUOUS VARIABLES Project Heads Project Managers Continuous variables N Mean Std. Dev N Mean Std. Dev Years of proj mgt work exp No. of projects implemented/year Min project budget (BWP) Min project duration (months) Valid N (listwise) Proj mgt work exp = project management work experience, Std. Dev = standard deviation TABLE 3: DESCRIPTIVE STATISTICS FOR CATEGORICAL VARIABLES INDUSTRIES Industries Freq Freq % % Valid % Valid % Cum % Cum PMs PHs PMs PHs PMs PHs PMs % PHs 1 Banking Construction Mining Telecommunication Transport Energy Other Total Freq = frequency, PMs = Project Managers, PHs = Project Heads, Cum = cumulative 495

10 TABLE 4: DESCRIPTIVE STATISTICS FOR CATEGORICAL VARIABLES AGE CATEGORY BY COUNTRY Project Managers: Age category Project Heads: Age category Total Table Total Table Country Count Count Count Count Count N N % Country Count Count Count Count Count N N % 1 Botswana Botswana % 2 UK Kenya % 3 Zimbabwe Myanmar % 4 Ukraine SA % 5 Malawi Sri-Lanka % 6 SA India % 7 Zambia Total % 8 Canada Total The different project types covered were mineral exploration, telecommunications (e.g., ICT, IT), construction (e.g., building and maintenance of roads, dams, bridges and buildings), development (e.g., plant refurbishment or operational optimization) and banking related projects. Data collection was preceded by the following: (1) Development of fieldwork research instruments based on existing literature, (2) Acquisition of Country research permits and necessary approvals from authorities and consent from potential respondents, (3) Research ethics review and approvals, (4) Fieldwork 1 risk assessment approval and (5) Pilot testing of fieldwork 1 research instruments. Statistical data analysis followed, using SPSS version 19. The sequence in the data analysis started with exploring differences between public and private companies, the outcome of which was used as a basis to shape further statistical analysis. This is consistent with a systematic approach to scientific data analyses that seeks to establish new knowledge in this study. The results will be presented in terms of: (1) differences between public and private sector, (2) descriptive statistics (i.e., hypotheses tests for the 4 NP variables), and then (3) inferential statistics (correlations between the NP and PP variables). The initial analysis involved exploring differences in terms of the mean scores (on the 5 point scale used) for all variables in terms of the two data sets (i.e., project managers and project heads). This prior analysis was important to establish whether there are significant differences between public and private sector, an outcome of which was used to inform correlation analysis. A. Measurement of research variables/constructs The actual survey questions, measured specific items on a 5 point Likert scale (1.Never, 2.Seldom, 3.Sometimes, 4.Often, 5.Always). These items formed components of an index for each of the 8 respective research variables (appendix A project manager data set shown for illustration). The 8 research variables (i.e., latent variables) were selected on the basis of existing literature[10, 11, 19-28, 30].Only positively worded questions are included to form components of an index, although negatively worded questions were also used to form a pair of questions designed specifically to measure response bias(appendix B project heads data set shown for illustration). The concept of a scale and reliability (e.g., cronbach s alpha) was rejected in favour of an index due to the following: (1) implementation of a scale during data exploration resulted in dropping a lot of measured variables, which may represent valuable information on collected data, (2) the nature of the project manager assignment practice and its performance are different constructs that cannot be combined into one underlying variable, (3) the use of statistical tests for analysis, which suit the concept of an index[62, 70] and (4) the combination of measurement levels for the measured variables e.g., ordinal, interval or even ratio scale [62, 70].Several concepts of factor analysis (i.e., exploratory, confirmatory and principal component analysis) were explored with a view to identify clusters of variables and reduce them to a small number of underlying variables, while retaining as much of the collected information as possible [71]. Factor analysis concepts were also discarded on the following basis: (1)Kaiser-Meyer-Olkin statistic for measuring sampling adequacy was below the recommended threshold of 0.5[72], (2)Bartlett s Test of Sphericity was nonsignificant, with a significant value greater than 0.05 [71] and (3) the determinant of the correlation matrix was greater than the recommended threshold of [71]. B. Index and computation of constructs being studied Two new variables were created in SPSS in order to implement the idea of an index. The first variable was created by using the Transform Compute procedure in order to compute the average scores for the items that form components of the index for each case/participant. Only positively worded questions measured on the same consistent 5 point scale were included to form components of this index. The second variable was created to compute an index score for each case as shown in appendix A. In the case of NP variables (i.e., RV1, RV2, RV7 and RV8), the higher the index score (based on the 5 point scale used), the better the practice and the lower the index score, the worst the practice. Index scores of 100% represent or indicate an ideal situation in terms of the nature of the practice of assigning project managers to projects. Similarly, in the case of PP variables (i.e., RV3 to RV6), the higher the index score (based on the 5 point scale used), the better the performance of the practice. It follows that the lower the index score, the worst the 496

11 performance of the practice. The variations in the measurement of NP and PP variables will be used for correlation analysis. Consider the index for the variable RV1 (extent of formality in the project manager assignment decision) as an illustration of how it was computed. The index score was computed by summing up the scores for all three measured variables/items that form components of RV1, and then diving by 15; the maximum possible sum of scores for these three positively worded questions on the 5 point scale used (i.e., 5*3 = 15). For the purpose of implementing the index, responses to open questions were omitted on the basis that they were coded on a different scale to the 5 point scale and will form the qualitative analysis (out of scope for this paper). The items that form components of each index (appendix A) are independent of each other and hence consistent with the concept of an index, which avoids dropping some of them, as opposed to a scale that has to be comprised of closely related items. The objective was to measure how well the nature of the practice is in terms of an average score/composite score on a number of independent items that indicate something about the extent of the construct being measured (e.g., extent of formality, objectivity, comprehensiveness and so on) in the project manager assignment process. Higher composite scores or index values are desirable since they indicate the extent to which the practice is formal, objective, comprehensive and so on. The degree of departure from the phenomenon/construct being studied can be expressed in different ways. Expressing it as a percentage was considered useful in that it introduces some generalization in terms of a common measurement unit, irrespective of the variable under consideration and the number of components that form a particular index. The percentage value (i.e., an index score) can be used to indicate the degree of departure between the ideal index score (100%) and the observed index score. Consider the below computation of an index score for the latent variable/construct, extent of formality (RV1) as an illustration: RV1 index score = observed sum of scores for the 3 items/15 * 100, where 15 is the maximum possible sum of scores for the 3 items on the 5 point scale used (i.e., 3*5 =15). The outcome is a percentage score that indicates the extent to which the practice is formal. Given the generalization introduced by multiplying the average scores for each latent variable by 100, it follows that the general formula to compute the index for the 8 latent variables (RV1 to RV8) is given by: 100;.(1) Where x represents the latent variable under consideration, n represents the number of items that form components of that specific latent variable/index and y represents the maximum possible sum of score for the n items measured on the 5 point scale. 1. Determining the threshold for level of presence of NP variables In the absence of a recommended cut-off point for the constructs being studied, using the concept of an index, several scenarios were performed to determining the cut-off point for the proportion of index scores that can be classified as: formal and informal (RV1 index), objective and not objective (RV2 index), match and no match (RV7 index), comprehensive and not comprehensive (RV8 index). Table 5 is a summary showing the sum, average and index scores for the project manager data set. Consider RV1 index for illustration. Four scenarios were performed to determine the cut-off for level of presence of formality in the project manager assignment practice as follows: Scenario 1: If 100% is cut-off (i.e. average score of 5), then proportion of formal & informal = 0(0%) & 53(100%) Scenario 2: If 75% is cut-off (i.e. average score of 4), then proportion of formal & informal = 0(0%) & 53(100%) Scenario 3: If 50% is cut-off (i.e. average score of 3), then proportion of formal & informal = 27(50.9%) & 26(49.1%) Scenario 4: If 25% is cut-off (i.e. average score of 2), then proportion of formal & informal = 53(100%) & 0(0%) Although there is no change in the outcome from scenario 1 to 2, the cut-off can be set at 75%. This is because scenario 3 and 4 are considered too lenient and strict respectively, given the original measurement scale (i.e., 1.Never =0%, 2.Seldom =25%, 3.Sometimes =50%, 4.Often =75%, 5.Always =100%). It follows that index scores of 74% and below (i.e., average scores of 3, 2 and 1 on the 5 point scale used) are considered as informal practice while those from 75% and above (i.e., average scores of 4 and 5 on the 5 point scale used) are considered formal practice. The same procedure for determining the cut-off for RV2, RV7 and RV8 yielded a cut-off of 75% across all 4 NP variables. 2. Determining the presence or absence of NP variables The concept of a Binomial test was applied to test the hypothesis that the proportion of index scores that can be categorized as informal is significant, while the proportion of index scores categorized as formal is small enough to be ignored. The index scores for the NP variables were dichotomized by awarding them two categories (i.e., 1 and 0) based on the pre-determined cut-off point of 75%. For example, in the case of RV1, A new variable was created in SPSS and labelled RV1 Binomial. For this new variable, index scores of 74% and below were awarded a 1, indicating an informal practice (i.e. success). Index scores of 75% and above were awarded a 0, indicating a formal practice (i.e. failure). The K-S tests for normality in relation to the 497

12 TABLE 5: SUM, AVERAGE AND INDEX SCORES FOR NP VARIABLES PROJECT MANAGERS Case RV1 sum RV1 RV1 RV2 sum RV2 RV2 RV7 sum RV7 RV7 RV8 sum RV8 RV8 number of scores average Index of scores average Index of scores average Index of scores average Index dichotomized NP binomial variables (i.e. RV1, RV2, RV7 and RV8) indicate that these one sample distributions are non-normal. The two hypotheses for the binomial test in relation to RV1 binomial can be stated as follows: H 0 : the proportion of the two categories, Informal (i.e. success) and Formal (i.e. failure) occur with some hypothesized probability to be determined from the binomial test trials; H 1 : the proportion of the two categories, Informal (i.e. success) and Formal (i.e. failure) do not occur with the hypothesized probability. In other words, the proportion of responses in the success group (Informal) is less than the hypothesized probability. The objective is to test the null hypothesis in terms of the proportion of responses in the success group (Informal) and 498

13 make a conclusion for or against the null hypothesis, using a 95% confidence interval. If p 0.05, the null hypothesis is rejected (i.e. fail to accept the null hypothesis) which means that there is sufficient evidence against the null hypothesis, such that a conclusion can be made to confirm that the proportion of responses in the success group is less than the hypothesized probability. If p > 0.05, the null hypothesis is accepted (i.e., fail to reject the null hypothesis) which means there is compelling evidence to conclude that the proportion of responses in the success group (Informal) is equal to the hypothesized probability of success. The Binomial test trials for the project manager responses to the non-parametric one sample distribution, RV1 binomial, are presented in Fig. 6. The criteria for probability of success is based on the cutoff point of 75% as determined through scenario analysis in terms of the proportion of index scores in the success and failure categories. The results in Fig. 6 indicate that the hypothesized p-value of (i.e. 90.6%) is the highest p- value for which there is no compelling evidence against the null hypothesis. The significance valuep is 0.051, which is greater than 0.05, at a 95% confidence interval. The inference is to accept the null hypothesis that the proportion of informal (success) and formal (failure) index scores of RV1 occur with probabilities of and respectively, at a 95% confidence interval. This means that there is compelling evidence to conclude that the proportion of informal index scores for the whole sample of project managers (irrespective of company type) is significant. The same procedure regarding the binomial tests for the one sample nonparametric distributions in relation to the remaining NP variables RV2, RV7 and RV8 was used. The binomial test results for all NP variables indicate compelling evidence to conclude that the proportion of index scores for Not objective (RV2), No match (RV7) and Not comprehensive (RV8) is significant, for both data sets. C. Measurement of response bias A new variable (RB - Response Bias) was created in SPSS to compute response bias scores for each case. This new variable becomes one sample on which a significance test can then be performed to test the hypothesis that the difference between the scores for positively and negatively worded questions is zero, after reversing the scale for the negatively worded question (in line with measurement consistency). For missing values, it was considered safe to simply drop the few cases with missing values (e.g. 3 cases out of 53 in the case of questionnaires). There were a total of seven pairs of questions/measured variables that were used to test for response bias in view of the project manager data set. Similarly, there were a total of two pairs of questions/measured variables that were used to test for response bias among the project heads data set (appendix B). These measured variables were included in the questionnaire and interview surveys as part of the design, with the objective of measuring response bias for the two data sets. The one sample distributions for response bias were found to be non-normal, as revealed by the Kolmogorov- Smirnov (K-S) tests for normality in relation to both data sets. The same statistical procedure (i.e., binomial test) discussed under determining the presence or absence of NP variables was used to testing the hypothesis that the proportion of response bias is insignificant. Fig. 6:Binomial test results for project manager responses to RV1 binomial 499

14 Following careful examination of the histograms, skewness and kurtosis statistics, as well as K-S tests for normality, data transformations involving logarithm, square root and inverse [73]were explored and discarded on the basis that the transformations did not help to convert the one sample non-parametric distributions to normal. On this note, the variables RB1 to RB7 were dichotomized[73]. The histogram (Fig. 7) for response bias among project manager questionnaire responses (using the variable RB1) is used for illustration. Fig. 7: Histogram for project manager questionnaire respondents to variable RB1 In the above histogram, response bias scores, for responses to RB1, are computed by taking the difference between the scores on the pair of questions (negatively and positively worded) designed to measure response bias. The formula for response bias score (absolute value) is defined by the equation: Response bias = observed score on positively worded question observed score on negatively worded question (2) In the above equation, the word observed means the score given by a case/respondent on the 5 point scale used. The positively worded question and negatively worded question (across all cases/respondents) form one pair of questions designed to measure response bias. However the scale for the negatively worded question was first reverse coded, in line with measurement consistency, prior to computing the response bias score. An output score of 0 represents no difference between the 2 questions used to measure response bias, which indicates no bias. Scores of 1, 2, and 3 indicate different levels of bias. The maximum level of bias was a score of 3, for both questionnaire and interview data sets. On the 5-point Likert scale used, the worst case scenario regarding response bias is a score of 4, which is the difference between the two extreme values in the scale. A score of 4, representing the highest level of response bias, is absent from Fig. 7. Response bias was computed in SPSS by using the transform compute procedure, in order to give scores for all cases/respondents. There are several outcomes with scores of zero (no bias), 1 (minimum level of bias) to 3 (maximum level of bias in the responses to RB1). These outcomes can be organized into two complementary events, biased or not biased. Although bias has different possibilities in terms of the level of bias, the emphasis is not in the level of bias but rather an establishment of whether a respondent is biased or not biased (from dichotomizing the outcomes) and then determining whether there is enough evidence to make a conclusion regarding the statistical significance of the presence or absence of bias. The basis of the analysis is to test the hypothesis that the proportion of no bias is significant while that of bias is small to be ignored. This situation meets the criteria for using a binomial test[61]. Cohen s effect size index, g, for the binomial test [61, 74, 75] was also calculated and used as an additional statistical inference for the presence or absence of bias. In order to confirm whether the 34 questionnaire respondents and the 19 interview respondents are statistically biased or not biased in their responses to the 7 pairs of questions represented in SPSS by the new variables RB1 to RB7, using a formal statistical test, the outcome of the response bias score (absolute value) was treated into two dichotomous categories that were coded as either 0 (representing no response bias) and 1 (representing bias), based on the following assumptions[61]: There is a fixed number of identical responses or trials (i.e., 34 responses for project manager questionnaires and 19 responses for project manager interviews) The outcome of every response/trial to each of the 7 pair of questions can be dichotomized into two categories namely: 0 (no bias) and 1 (bias). These two categories are treated as complementary (i.e., the only two possible outcomes) and mutually exclusive (cannot occur at the same time) The outcomes of the responses/trials are independent The probability of no bias represents success and can be represented as p (no bias or success) = p. The probability of bias represents failure and can be represented as q (bias or failure), where q = 1 p. On this note, the series is a set of Bernoulli trials and a binomial probability model can be used. The reason is that there is a finite number of responses (n = 34 for questionnaire responses and n=19 for interview responses) and the probability of getting no response bias (coded as 0s) is independent. The probability of getting a response bias (coded as 1s) is also independent. However, the probability of getting a score of 0 (no bias) is not known. Several binomial test trials were performed in order to set the p-value for success (i.e. no bias), in relation to response bias to RB1. The objective was to search for the 500

15 highest p-value for success (i.e. no bias) and check the binomial test result in terms of whether there is compelling evidence for no response bias. An initial p-value of 0.99 for no bias (success) was set, with a defined success cut-off point of 0 (i.e., success is less than or equal to zero) to start the Binomial test trials. The two hypotheses can be stated as: Null hypothesis (H 0 ): the proportion of the two categories, not biased (success) and biased (failure) occur with probabilities of 0.99 and 0.01 respectively. This means that the proportion of responses in the success group (not biased) is equal to the hypothesized probability, in this case 0.99; Alternative hypothesis (H 1 ): the proportion of the two categories, not biased (success) and biased (failure) do not occur with probabilities of 0.99 and 0.01 respectively. In other words, the proportion of responses in the success group (not biased) is less than the hypothesized probability. The aim was to test the null hypothesis in relation to the proportion of responses in the success group (not biased) and make a conclusion for or against the null hypothesis, using a set significance level of 95%. If p 0.05, the null hypothesis is rejected (i.e. fail to accept the null hypothesis) which means that there is sufficient evidence against the null hypothesis such that a conclusion can be made that the proportion of responses in the success group is less than the hypothesized probability. If p > 0.05, the null hypothesis is accepted (i.e. fail to reject the null hypothesis) which means there is compelling evidence to conclude that the proportion of responses in the success group (not biased) is equal to the hypothesized probability of success. The Binomial test results for response bias among both data sets indicate sufficient evidence to conclude that the proportion of observed successes (not biased) for all response bias pair of questions used to test for response bias, are statistically significant. Calculated values of Cohen s effect size index, g, for the binomial test [61, 74]all indicate large effects. This means that the degree to which the phenomenon being examined (i.e. not biased) is established is of a large magnitude. This is consistent with the binomial test results and provides additional statistical inference, over and above the significance tests. These two findings (significance test and Cohen s effect size index) leads to the inference that the information obtained from administering the questionnaire is not biased, which in turn, leads to the conclusion that the questionnaires and interviews responses are reliable. VI. RESEARCH RESULTS The objective of fieldwork 1 (an initial exploratory study for this research) was to demonstrate empirically, the existence of the problem in relation to nature of practices and their impact on certain performance variables. The outcomes for this paper are presented in terms of: A. the developed conceptual model and B. findings from fieldwork 1 data analysis. A. Conceptual model The developed conceptual model is illustrated in Fig. 8. In the general theme of inputs labelled A, B and C (Fig 8), the word others is used to appreciate scope for including additional inputs that have not been covered from literature reviews and fieldwork 1 findings. These additional inputs may come from fieldwork 2, whose aim is to study the research problem in more depth, using the developed conceptual model. Additional inputs may also come from ongoing literature reviews. B. Findings from fieldwork 1 data analysis 1. Differences among the groups (factored by company classification public and private) Differences between public and private companies were explored for all variables (RV1 to RV8) among project manager and project heads data sets (i.e., 8 variables for project manager data set and the same 8 variables for project heads data set, making a total of 16). It is necessary to provide sufficient evidence for testing the significance of the difference between private and public companies. Out of 9 eligible private companies at Country level, 6 was the achieved sample. This represents 66.7 % of the population of eligible private companies. Similarly, out of 16 eligible public companies at country level, the achieved sample was 9. This represents 56.3% of the population of eligible public companies at country level. The disproportionality of the two samples (i.e. 6 private companies vs. 9 public companies) is also not a concern since the ratio of private to public companies is 2 to 3. As regards the individual informants, 26 project managers were from public sector while 27 were from private sector. This represents a ratio of 1:1.04, which is again not a concern in terms of making comparisons. The ratio of project heads from public to private sector was 1:1 (i.e., 10 project heads from public sector and 10 from private sector). The objective was to test the statistical significance of the difference between mean scores of participants (project managers and project heads) from private and public sector, in relation to the research variables RV1 to RV8. Since the data is non-parametric, a Mann-Whitney U test was performed to establish whether the difference between private and public sector is not simply due to random causes[61]. Further justification for Mann-Whitney U test as an appropriate non-parametric method lies in the fact that the sample is small and contains a few outliers and extreme values. The independent samples t-test (an alternative to the Mann-Whitney U test) was rejected since the data does not meet the following parametric assumptions: normality and homogeneity of variance [61, 71]. These comparisons were deemed necessary to explore the differences between the 2 groups, in relation to the mean scores that indicate the perceptions of the project managers and project heads as 501

16 Fig. 8: Developed conceptual model for project manager assignments TABLE 6: SUMMARY OF MANN-WHITNEY U TESTS FOR DIFFERENCES BETWEEN GROUPS 502

17 regards the nature of the project manager assignment practice and its impact on performance variables. A summary of the results of the non-parametric Mann-Whitney U test is presented in Table 6 for each data set, in terms of the groups - public and private sector for all 8 research variables. The results of the non-parametric Mann-Whitney U test (following Kolmogorov-Smirnov tests for normality) revealed no differences between public and private sector in 15 out of a total of 16 pairs. Only 1 significant difference between the 2 groups was found for the variable correspondence level between project manager and project in the case of project heads (Table 6). Further details of the Mann-Whitney U test showing the difference between the 2 groups are presented in Fig. 9 Figure 9: Mann-Whitney U test results for RV7 (project heads) The results indicate a difference between private sector (Mean rank = 13.25) and public sector (Mean rank = 7.75) in relation to the mean scores for RV7 (correspondence level between project manager and project). The non-parametric Mann-Whitney U test shows this difference to be significant beyound the 0.05 (95%) level: U = 77.5; p.035 (two-tailed exact significance value). The inference is that the null hypothesis that there is no significant difference between private and public companies in relation to the correspondence level between project manager and project cannot be accepted. Among several indices proposed for measuring effect size (i.e., the magnitude of the difference) for the Mann-Whitney U test, the Glass rank biserial correlation coefficient is considered appropriate [76]. This index is computed using the equation below [76]: 2 M M n n.....(3) where: M 1 and M 2 are the mean ranks of the scores in the two company groups, n 1 and n 2 are the sample sizes. Therefore,.. = (a large effect according to Cohen s guidelines.this means that the resulting difference in the mean ranks between private and public companies, in relation to RV7, is an effect of large size. The full results of this Mann-Whitney U test are reported as follows: The mean rank for public sector (Mean Rank =7.75) was less than the mean rank for private sector (Mean Rank = 13.25). A Mann-Whitney U test showed this difference to be significant beyound the 0.05 level: U = 77.5; p.035 (twotailed exact significance value). The Glass rank biserial correlation = +0.55, a large effect according to Cohen s (1988) classification of effect size index. The interpretation of this result is that the degree to which there is a difference between public and private companies in relation to RV7 is large. The importance of Cohen s effect size index is to indicate awareness regarding the magnitudes of the effects being studied, over and above significance testing [74]. However, Cohen (1988), acknowledges the low level of awareness among behavioural scientists as regards this additional parameter (Cohen s g) as part of statistical inference. One of the reasons cited is that Cohen s g is not commonly reported in research methods textbooks. The 503