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Examining the Impact of Culture on Project Performance

Chapter Methodology

Introduction

The methodology chapter provides an in-depth discussion of the research methods that are employed in the current study. The purpose of the current chapter is to detail the significant steps taken in developing the current study’s research method, which then can be replicated by other researchers.

The chapter discusses aspects of the methods employed, from philosophical assumptions to data collection and analysis techniques. The chapter also reviews research methods design appropriateness, a discussion of the population being studied, and its sample included in the current study. Scandura and Williams (2000) have argued that developing a research methodology appropriate for its aims and objectives is essential.

They further assert that project management research can only progress if researchers examine their methods to conduct the study. For project management studies to be impactful, appropriate and meticulous research methods must be chosen for implementation (Scandura & Williams, 2000).

The research methodology is often described as how the discovery of new information can be or existing information/knowledge can be confirmed; further, methods that can field of study are developed (Marais and Marais, 2015).

The methodology chapter’s primary concern is providing a logical explanation using rigorous arguments that infers that a reasonable and coherent method for research has been implemented.

According to Haswell and DaSilva (2015), the research methodology is primarily concerned with the logic of justification”. Hammersley (2010) further adds that the choice of methods implemented in research should be based on the goals and circumstances of the research being pursued. Research methods are also derived from philosophical and methodological commitments discussed in detail in the current.

Benson and Filippaios (2016) have found that more massive fields of study tend to agree on what is considered a standard of research methods that have to be applied. Consequently, academics such as Aw (1997), Creswell (1999), Transfield et al. (2003), Johnson and Onwuegbuzie (2004), Denzin and Lincoln (2011), Molina-Azorin and Fetters (2016) have argued that fields like business, management, and project management which are characterized as trans-disciplinary require a broader spectrum of research methods.

Through the literature review analysis, themes were extracted to aid in the current study’s achievement of its objectives. Therefore, the study must use a methodology that can encompass all the requirements of the objectives. The following sections describe the various elements that were put together to develop the research methodology.

Research Philosophy

Hudson and Ozanne (1988) described the nature of reality as ontology, while epistemology is defined as “the relationship between the research and reliability or how this reality is captured. Carson et al. (2001) noted two very dominant ontological and epistemological traditions often used in research – positivism and interpretivism. Carlson et al. (1988) and Hudson and Ozanne (1988) argued that researchers using the positivist ontology believe that the work is external and that there is only one objective reality to research situations regardless of their perspectives.

Based on this premise, researchers then take a controlled structural approach by identifying a research topic from which appropriate hypotheses are developed. An apt research methodology is then adopted (Carson et al., 1988; Churchill, 1996; Carson et al., 2001). On the other hand, interpretivism ontology and epistemology hold that reality can be multiple and relative (Lincoln and Guba, 1985; Hudson and Ozanne, 1988).

According to Lincoln and Guba (1985), the multiple realities described depending on other systems to hold meanings, as Neuman (2000) described, making it even more difficult to interpret the terms of fixed facts. Carson et al. (2005) argue that any knowledge acquired through this method is socially constructed instead of objectively determined and perceived.

Positivist researchers are known to remain detached from their research participants, which is considered essential to maintaining clear “distinctions between reason and feeling” (Carson et al., 2011). During their research, Carson et al. (2001) explain that positivists maintain a clear difference between science and personal experience to ensure objectivity through the use of consistently rational and logical approaches to research.

Hudson and Ozanne (1988) have argued that positivists’ goal is to produce time and context-free generalizations. This goal is believed to be reached because the positivist school maintains that human actions can be explained due to real causes that precede their behaviour. The research subjects and the researcher are independent of each other and not influencing one another (Hudson and Ozanee, 1988).

Saunders (2003) has found that positivism reflects the acceptance of adopting a philosophical stance of natural sciences. Remenyi et al. (2010) add that there is a greater preference to work in an “observable social reality,” which allows the results of such researches to become generalized to the broader population.

Others like Gill and Johnson (2017) using positivism and its subsequent approaches will allow for a greater focus on developing a highly structured methodology that will allow for replication in other studies. Dumke (2002) agrees with this stance and further explains that positivist philosophical premises produce highly structured methods and allow for generalizations and quantifications of research objectives. These can be evaluated using statistical tools, techniques, and methods.

An in-depth analysis of various research philosophies has concluded that the positivist approach is the current study’s philosophical foundation. Galliers (1991) and Levin (1988) have argued that positivist philosophy believes that reality is stable can be observed and described using an objective perspective.

It is concluded that the positivist philosophy is a scientific viewpoint that allows researchers to evaluate theory using considerable evidence that can be obtained using a rigid research method with the ability to be replicated.

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Research Strategy

There are many methodologies identified in the literature and extensively discussed by Galliers (1991). Galliers (1991) developed a taxonomy of research methodologies reporting that positivist research includes one or a combination of laboratory experiments, field experiments, surveys, case studies, theorem proofs, simulation, and forecasting.

Because of the current study’s requirements, surveys will be the most suitable strategy in conducting research. Surveys allow researchers to obtain data about viewpoints, situations, and practices at a certain point using questionnaires and even interviews, implementing quantitative analytical techniques that enable researchers to make inferences from the data based on existing relationships.

Lam et al. (2001) point out that the use of surveys allows a researcher to study a plethora of variables at one time which is often not possible in laboratory or field experiments. On the other hand, Jaselskis and Recarte Suazo (1994) argue that surveys have a crucial weakness – it becomes difficult to recognize insights related to the causes or processes involved in the measured phenomena.

There is also several source bias like the innate nature of respondents to be prone to self-reflection. The point causes this when a survey is conducted and the researcher through their specific choice of designing the survey.

Research Design

Research is generally conducted by developing terms of the research philosophy and strategy implemented, leading to a set of instruments used in the study to achieve the set objectives. The elements used in developing the current methodology were derived from Creswell (2014), which considers philosophical views, research designs, and methods to build a grounded approach (see fig. 3.1).

Research-Approach-Development-Source-Creswell

Creswell (2014) notes that there are several ways for research approaches to be customized to approach the research that is best suited for the study. Like Creswell (2014), Academics have stated that three main categories form how research can be organized – qualitative, quantitative, and method methods.

However, many academics have argued that each of these research methods is not distinct as overlapping occurs (Lewis, 2015). Creswell (2014) asserts that “qualitative and quantitative approaches show not be seen as distinct categories, that are rigid, dichotomies, or opposites of each other” (p.32).

It is stated by Lewis (2015) that the most generally cited set of characteristics that are used to distinguish between quantitative and qualitative approaches is their nature and methods of gathering data; in that quantitative research is framed using numerical instead of words, or through the implementation of close-ended questions for quantitative hypothesis developed over open-ended interview questions often used in qualitative research.

The current study is based entirely on quantitative research design, derived from its philosophical foundation and research requirements. Kuhn (2012) argued that the quantitative research approach allows a researcher to interpret observations for the sole purpose of uncovering meanings and patterns of a relationship, which further provides for the classification of the phenomena.

The nature of the design of the current study is best described as a descriptive design. The descriptive design describes a variable phenomenon within quantitative research, with data collection being observational. The current study embodies a cross-sectional design which stems from its use of a survey questionnaire for data collection.

Data Collection

Sampling Method

The sample of the study was chosen based on random sampling. No specific requirements were placed on obtaining a sample, and the population chosen was project managers serving in the United Kingdom’s IT industry. A total of 100 respondents is intended to be collected to ensure a wide range of perspectives. The sample was obtained by distributing the survey to select IT companies in London. Visits were made to IT companies for the distribution of the questionnaire. This was done to ensure that many people responded to reach the desired sample size value of 100.

Research Instrument

Based on its philosophical foundations that justify an empirical research strategy, the current study uses a survey as a data collection tool for carrying out the study’s research. A survey is defined as a “means to gather information about the characteristics, actions, or opinions of a large group of people” (Pinsonneault and Kraemer, 1993, p. 77).

Salant & Dillman (1994) have argued that surveys can assess needs, evaluate demands, and examine impacts. The term itself is used in various ways. Still, most generally refer to selecting a relatively large sample of people from a pre-defined population of interest and collecting small amounts of data from these individuals.

The information obtained from a sample is then used to make inferences about the wider population (Kelley, Clark, Brown, and Sitzia, 2003). Denscombe (1998) argues surveys are designed to provide a ‘snapshot of how things are at a specific time.’ Kelley et al. (2003) note that no attempt is made to control conditions or manipulate variables, with surveys not assigning participants into groups.

Surveys are best suitable within descriptive studies but can also be incorporated into exploring facets of conditions or looking for explanations and providing data for testing a set of hypotheses.

Denscombe (1998) asserts that it is essential for researchers to recognize that the survey approach is a research strategy and not a research method. Kelley et al. (2003) highlight the various advantages and disadvantages of using surveys in research illustrated in the table below.

Table 1.1- Advantages and Disadvantages of Survey Research Approach (Source; Kelley et al., 2003)

Evaluation of Survey Research
Advantages Disadvantages
1. The data is empirical in that it produces data from real-world observations. 1. Possibility of neglecting data’s significance if the researcher focuses a great deal on the coverage range. It excludes an adequate account of the implications of specific data sets relevant to the problem or theories.
2. The depth of coverage of numerous individuals or circumstances means that it is more likely than other approaches to obtain data of a representative sample and, as a result, can be generalized to the wider population. 2. Produced data lacks specific details or breadth of the topic that is being investigated.
3. The strategy produces a large amount of data in a short amount of time and is relatively low in cost. Hence, researchers with a short amount of time can use the strategy which assists in planning and delivering strong
results.
3. It isn’t easy to control acquiring a high response rate to a survey, especially when conducted through the post, face-to-face, and over the telephone.

Data Analysis

Reliability Analysis

The data analysis procedures implemented on the obtained data first begin with a reliability and normality test. Reliability of items is necessary for evaluating assessments and questionnaires (Tavakol and Dennick 2011). It is often thought of as mandatory in research to estimate the quantity of alpha to increase the validity and accuracy of interpretations of results for a given data set.

Researchers such as Gliem and Gliem (2003) have asserted the importance of measuring Cronbach’s alpha, especially when using Likert scales, implemented in the current study. DeVeillis (2012), Georgy and Mallery (2003), and Kline (2000) have provided what are known as ‘rules of thumb’ when interpreting alpha’ and checking for internal consistency; α ≥ 0.9 – excellent; 0.9 ≥ α ≥ 0.8- good; 0.8 ≥ α ≥ 0.7- acceptable; 0.7 ≥ α ≥ 0.6 questionable; 0.6 ≥ α ≥ 0.5- poor; and 0.5 ≥ α- unacceptable. Normality testing is conducted by analyzing skewness and kurtosis.

Normality Analysis

Like the reliability test of Cronbach’s alpha, normality testing is also conducted using IBM’s SPSS statistical package tool. Among most normality tests, skewness and kurtosis (K-S) testing are the most commonly used. Elliott (2007) describes the procedure for conducting K-S through SPSS “explore” procedure: Analyze → Descriptive Statistics → Explore → Plots → Normality plots with tests.

The purpose of the K-S analysis is to compare scores in a sample to a normally distributed set of scores that have the same mean and standard deviation (Ghasemi and Zahediasl, 2012). The null hypothesis often quoted in these tests is that the “sample distribution is normal” (Oztuna, Elhan, and Tuccar, 2006). Under the circumstances that the test is significant, the distribution is non-normal (Ghasemi and Zahediasl, 2012; Oztuna et al., 2006).

PLS-SEM

PLS path models consist of three components: the structural model, the measurement model, and the weighting scheme. Whereas structural and measurement models are components in all kinds of SEMs with latent constructs, the weighting scheme is specific to the PLS approach. Tenenhaus et al. (2005) introduce the theory using the European customer satisfaction index (ECSI) and the mobile phone industry’s measurement instrument.

The measurement instrument’s description is available from the help page help(“ECSImobi”) in the samples package. Figure 1 shows all relations between latent variables (LVs) and manifest variables (MVs), the nomological network. Nodes representing LVs are coded as ellipses and those representing MVs as boxes. Contrary to the CBSEM approach, each MV is only allowed to be connected to one LV in the PLS context.

Furthermore, all arrows connecting an LV with its block of MVs must point in the same direction. The connections between LVs and MVs are referred to as measurement or outer models. A model with all arrows pointing outwards is called a Mode A model – all LVs have reflective measures.

A model with all arrows pointing inwards is called a Mode B model – all LVs have formative measurements. A model containing formative and reflective LVs is called MIMIC or a mode C model. PLS path models only permit recursive relationships. They can be expressed as simply connected digraphs.

Rigdon (1998) remarks that structural equation modelling (SEM) has taken up a prominent role in academic literature in many fields. SEM is likely to be the methodology of choice when researchers deal with relations between constructs such as satisfaction, role ambiguity, or attitude. Since SEM is designed for working with multiple related equations simultaneously, it offers several advantages over some more standard methods. It, therefore, provides a general framework for linear modelling. SEM allows excellent flexibility on how the equations are specified.

The development of an evocative graphical language (McArdle 1980; McArdle and McDonald 1984) has accompanied SEM development as a statistical method. Due to this language, complex relationships can be presented conveniently and powerfully to others unfamiliar with SEM.

The partial least squares approach to SEM (or PLS path modelling), initially developed by Wold (1966, 1982, 1985) and Lohm¨oller (1989), offers an alternative to the more prominent covariance-based (CBSEM, J¨oreskog 1978).

Whereas CBSEM estimates model parameters so that the discrepancy between the estimated and sample covariance matrices is minimized, in PLS path models, the explained variance of the endogenous latent variables is maximized by assessing partial model relationships in an iterative sequence of ordinary least squares (OLS) regressions (e.g., Hair, Ringle, and Sarstedt 2011b).

It is worth mentioning that in PLS path modelling, latent variable (LV) scores are estimated as exact linear combinations of their associated manifest variables (MVs) and treats them as error-free substitutes for the manifest variables. Whereas CBSEM requires challenging distributional assumptions, PLS path modelling is a soft-modelling technique with less rigid distributional assumptions on the data. It should be mentioned that PLS path modelling is not to be confused with PLS regression.

According to Chin (1998), it can be argued that depending on the researcher’s objectives and optimistic view of data to theory, properties of the data at hand, or level of theoretical knowledge and measurement development, PLS path modelling is more suitable.

Additionally, the increasing need to model so-called formative constructs has stimulated great interest in applying PLS path models, especially in marketing and management/organizational research (e.g., Diamantopoulos and Winklhofer 2001; Jarvis, MacKenzie, and Podsakoff 2003; MacKenzie, Podsakoff, and Jarvis 2005). The application of PLS path models in marketing is discussed in depth by Henseler, Ringle, and Sinkovics (2009) and Hair, Sarstedt, Ringle, and Mena (2011a).

Research-Approach-Development-Source-Creswell

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