Introduction
This chapter focuses on the description of methods utilised to answer the research questions and test the hypotheses. The primary purpose of the chapter was to provide detailed information concerning the data collection and analysis to allow replication of the study in the future. First, the chapter focuses in the research design and justification of the choice. Second, the research philosophy or research paradigm is introduced. Third, the population and sampling procedure is described. Fourth, the data collection procedure along with the instrument used for gathering data are described. Finally, the chapter introduces the statistical model and the data analysis procedure used to achieve the results. The chapter is concluded with a brief summary.
Research Design and Justification
There are three types of research designs utilised by business researchers, including qualitative, quantitative, and mixed-method research (Basias and Pollalis, 2018). Quantitative research design requires clearly formulated hypotheses tested using statistical analysis (Saunders, Lewis, and Thornhill, 2019). The design allows the researchers to achieve increased depth in comparison with qualitative research, as large number of participants can be recruited to collect data systematically (Hair, 2015). The central purpose of quantitative research is usually to test very narrow supposition to add very specific knowledge to the current body of knowledge (Creswell, 1994). In simple words, quantitative research deals with numbers, while qualitative research deals with words and concepts (Saunders, Lewis, and Thornhill, 2019).
Qualitative research focuses on concepts, thoughts, and experiences of participants to acquire in-depth knowledge (Creswell, 2007). Qualitative inquiry is usually associated with acquiring data from focus groups, interviews, and observations, while quantitative research uses surveys or experiments to collect primary data (Creswell, 2012). Qualitative studies use narrative analysis, discourse analysis, thematic analysis; and grounded theory, which are associated with less credibility than rigorous quantitative methods (Saunders, Lewis, and Thornhill, 2019).
Bothe qualitative and quantitative methods have their strengths and weaknesses. In particular, quantitative data is associated with high reliability and validity, quantitative analysis is easier to conduct, and findings can be generalised to large populations (Hair, 2015). However, quantitative methods do not allow in-depth understanding of the relationships between concepts and some concepts may be difficult to measure (Hair, 2015). At the same time, qualitative research can provide more in-depth knowledge to explain complex concepts and relationships between them (Hair, 2015). However, qualitative analysis is more difficult to conduct due to increased possibility of bias (Hair, 2015). Moreover, the findings may be impossible to generalise to larger populations due to a limited sample size (Creswell, 2012).
Mixed-method design can be used to compliment for the limitations of each of the two approaches with the benefits. For instance, mixed method research can use quantitative methods to test the relationships between several variables and then qualitative data analysis to explain how and why these variables are correlated (Hair, 2015). However, mixed-method studies require increased time and resources to execute (Saunders, Lewis, and Thornhill, 2019). Moreover, mixed method approach requires experience and skill of the researchers, as the method is associated with increased difficulty and danger of bias (Saunders, Lewis, and Thornhill, 2019). Thus, when making a decision to conduct a mixed-method study, it is crucial to ensure that the research team has sufficient skills and experience to avoid problems.
The purpose of the research was to determine factors that affect the decision-making process in the telecom sector in Qatar. In particular, this research aimed at studying the effect of workplace cultural diversity, employee engagement, vision-based leadership, and risk management on the effectiveness of the decision-making process in the telecom industry in Qatar. Hence, the most appropriate for the study is quantitative, as it can help to study the relationships between two or more variables. While the mixed-method approach is also appropriate to achieve the purpose of the study. However, since the researcher has limited experience, it was decided in favour of the quantitative design.
Research Philosophy
There are three central research philosophies or paradigms, including positivism, constructivism, and pragmatism. Kuhn (1970) defines a research paradigm as “set of common beliefs and agreements shared between scientist about how problems should be understood and addressed” (p. 47). Research philosophies answer three basic questions associated with the research, including ontological, epistemological, and methodological questions. Positivisms supposes that there is only one reality that can be known (Uyangoda, 2015). Constructivism assumes that there are multiple realities and the understanding of the world differs depending on the observer (Kuhn, 1970). Constructivists suppose that the reality should be interpreted, based on the subjective perspectives of people (Kuhn, 1970). In other words, while positivism claims that there is one objective reality that can be measured, constructivism states that measuring the reality is impossible, as it is subjective.
Pragmatism presupposes that reality may be renegotiated and interpreted, which implies that the methods that should be used depend on the purpose of the study. According to Babbie (1998), pragmatists believe that both qualitative and quantitative methods can be used if they solve the problem. While constructivism acknowledges only qualitative methods and positivism presupposes the use of only qualitative methods, pragmatism allows the use of either of both methods depending on the purpose of the study.
This paper uses positivism as the central philosophy, as the researcher believes that there is only one reality that can be measured using rigorous quantitative methods. While pragmatism was also suitable for the purpose of the study and allows the use of quantitative methods, it was decided in favour of adopting positivists’ approach due to the personal preference and ideology of the researcher.
Population and Sampling
The population under analysis are all the employees from the telecom sector in Qatar. As it was measured in Chapter 1 of this thesis, the telecom industry in Qatar is a duopoly with two major competitors, including Vodafone Qatar and Ooredoo. There are approximately 17,000 employees working in offices of the company in different countries (Ooredoo, 2022). Vodafone reported to have 1,200 employees in all its offices in Qatar (Vodafone Qatar, 2022). Therefore, the population size was approximately 18,200 potential participants. The inclusion criteria were working for the company of at least 6 months and experience of participating in the telecom projects in the company they are currently working at.
Simple random sampling was used as the central sampling method for the study. There are probability and non-probability sampling methods that can be used depending on the purpose of the study and the danger of selection bias (Etikan and Bala, 2017). Probability sampling methods are based on the idea of allowing all the population members have a similar chance to participate in the study (Etikan and Bala, 2017). Probability sampling methods are subdivided into simple random sampling, systematic random sampling, stratified sampling, and cluster sampling (Saunders, Lewis, and Thornhill, 2019). Even though the selection bias was not identified as a significant concern of the study, it was decided to favour simple random sampling, as it is associated with increased reliability in comparison with non-probability and other probability sampling methods for this research (Saunders, Lewis, and Thornhill, 2019).
We began the collection process by identifying the groups I want to reach, so I looked for people involved in project decisions at both companies, Ooredoo and Vodafone. Some of the employees were contacted personally through email, while others were contacted through their departments to participate in the survey. The survey was distributed via email after the confirmation was received from all the recruited participants.
It was decided to recruit a sample of 100 participants after the expert’s opinion of the supervisor of this research. A total of 114 surveys were distributed, and 107 surveys were returned fully completed. Thus, the response rate was 93.9%, which is associated with high reliability. The survey was conducted using Survey Monkey, which was identified as a reliable medium for acquiring primary data due to its convenience and availability.
Data Collection
Variables and Instrument
This research focused on assessment of correlations between five variables. The literature review identified four factors that may affect the effectiveness of the decision-making process, including decision-making engagement, cultural diversity, vision-based leadership, and risk assessment. These four variables were used as the independent variables, while the effectiveness of the decision-making process was used as the dependent variable. Along with the dependent and independent variables, the study collected information on demographic variables, including gender, age, education level, employer, position in the company, work experience (technical or managerial), job experience in years, occupation in the telecommunication field, and the fact of taking managerial courses. A self-created instrument was designed to measure the variables. The survey included a total of 35 questions. The questionnaire was divided into six sections, according to the variables that were measured.
The first section included a total of nine demographic questions and one question that confirmed that the participants read and understood the informed consent form. The second question asked the participants to state their biological gender. The third question asked the participants to select their age group out of five categories. The fourth question asked the participants to select one of the six options that best described their level of education. The fifth question asked the participants to select the organisation they worked for out of two options, including Ooredoo and Vodafone Qatar. The sixth question asked about the position of the participant out of five options, including supervisor, head of section, manager, director, and chief of the unit. The seventh question asked if the participant’s experience was in managerial or technical speciality. The eighth question asked about the current occupation of the participants, while the ninth question asked to select one of the categories concerning the number of years of experience in the telecom industry. The final question of the first section asked if the participants passed any advanced courses in management. In summary, the first section provided holistic information about the demographic characteristics of the sample.
The second section of the questionnaire focused on measuring the independent variable, which was the effectiveness of the decision-making process in the telecom projects. The section consisted of five questions based on a seven-point Likert scale. The questions were created based on the literature review, which revealed several aspects of effective decision making (Frankl, 2019; Julmi, 2019; Peterson, 2017; Simon, 1979). These aspects included using factual information during the assessment of situations, making timely decisions, ability of the managers, and clarity of the process (Frankl, 2019; Julmi, 2019; Peterson, 2017; Simon, 1979). The dependent variable was measured as the sum scores for questions from the second section (questions 11-15).
The third section focused on measuring cultural diversity, which was the first independent variable. Cultural diversity was measured using five questions, which explored five dimensions of cultural diversity revealed during the literature review. These dimensions included number of cultures, presence of a dominant culture, and differences between the cultures represented (Ranaivoson, 2007). Cultural diversity was measured as a sum of the scores for the five questions in the third section (questions 16-20) of the questionnaire.
The fourth section of the survey aimed at measuring engagement in the decision-making process. The questions focused on several aspects of engagement in the decision-making process, including the number of stakeholders involved in decision-making, participation of non-managerial staff in the decision-making, managers’ encouragement of different stakeholders to participate in the decision-making process, and how much managers value participation of different stakeholder in decision-making (Khalid and Nawab, 2018; Shaed, Azazi and Samsurijan, 2021; Zhi, Abba and Hamid, 2021). Employees’ engagement in the decision-making process was measured as a sum of scores for the question from the fourth section (question 21-25) of the survey.
The fifth section of the self-created instrument measured the prevalence of vision-based leadership. The section focused asked question about different aspects of vision-based leadership, including the presence of a clear vision statement, the fact that the managers are guided by the vision in the decision-making process, and managers being inspired by the vision, which were discovered during the literature review (David, 2020; Kreutzer, 2019; Rogoz, 2018). The prevalence of vision-based leadership was measured a sum of scores for the fifth section of the questionnaire (questions 26-30).
The last section of the survey focused on determining the adequacy of risk assessment practices in the decision-making process. The section asked if the managers assessed financial, environmental, and IT risks before making the final decision (Samimi, 2020; Settembre-Blundo et al., 2021; Shibani et al., 2022). The risk assessment quality was measured as the sum of score for the five questions from the sixth section of the survey (questions 31-35).
Data Collection Process
This study relied on primary data rather than using secondary data. The use of secondary data is becoming increasingly popular in medical and sociological research, as it is associated with significant benefits (Cole and Trinh, 2017). In particular, secondary data analysis is associated with increased reliability and validity of measurements, as the data s usually collected by highly qualified professionals (Cuttler et al., 2019). Moreover, the use of secondary data is associated with increased cost and time efficiency, which allows to produce evidence at a higher speed (Saunders, Lewis, and Thornhill, 2019). However, despite the evident benefits of secondary data utilization, it was decided against using the secondary data for this research. The primary reason for avoiding secondary data was the inability to find relevant datasets to answer the research questions.
The data was collected automatically using Survey Monkey as the primary means. While Google forms was also considered for data collection as a tool, Survey Monkey was favoured as it provided basic analysis of variables. Additionally, Survey Monkey was found more convenient for the researcher.
A total of two emails were sent to each participant. The first letter was included information on the purpose of the study and the manipulations the participants were expected to do. The invitation letter also stated that there would be no repercussions associated with the rejecting the offer to participate in the study. Additionally, potential participants were informed that they could withdraw from the study anytime before completing the questionnaires. However, excluding the data acquired from the participants after the participant submitted the results would be impossible due to the inability to identify the data by participant. The recruitment letter also included the informed consent form, and the participants were asked to read carefully and confirm their consent with terms of the study. If the participants replied with agreement to participate in the study and confirmed their consent, they were sent the link to the online questionnaires along with the instructions for the questionnaires. After the data was collected, it was automatically stored in the Survey Monkey cloud. The final questionnaire was downloaded for further analysis when the data collection process was over.
Data Analysis
Data Analysis Method Selection
There are two central methods utilized in correlational studies, including Pearson’s correlation coefficient and regression analysis (Creswell, 1994). Pearson’ correlation analysis is the basic method utilized to determine the magnitude of correlation between two variables. The coefficient ranges between -1 and 1, with 1 standing for perfect positive correlation, -1 standing for the perfect negative correlation, and 0 standing for no correlation (Cuttler et al., 2019). While this method is appropriate for determining the level of correlation between two variables, it is inappropriate for determining a combined effect of several independent variables on one dependent variable (Saunders, Lewis, and Thornhill, 2019). Thus, it was decided to use multiple regression analysis to determine the effect of decision-making engagement, cultural diversity, vision-based leadership, and risk assessment on decision-making process effectiveness.
According to Creswell (1994), multiple linear regression is the most common method for quantifying the effect of several independent variables on one dependent variable, which suits the purpose of this study. There are other methods that may be appropriate for quantifying the relationships between several variables, including quadratic regression, multilevel analysis, logistic regression, and ridge regression. However, due to the limited experience of the researcher, it was decided to use multiple linear regression to create a basic quantitative model for quantifying the relationships between the variables mentioned above.
Model Creation and Data Analysis Procedure
In order to measure the effect of the decision-making engagement, cultural diversity, vision-based leadership, and risk assessment on decision-making process effectiveness, a multiple linear regression model was created. This model is provided below
Where:
- DME = decision-making effectiveness;
- CD = cultural diversity;
- VBL = vision-based leadership;
- RA = risk assessment.
The data was analysed using Microsoft Excel 2019 and Statistical Package for Social Sciences (SPSS) Version 26. First, the data was downloaded from Survey Monkey and cleaned using Microsoft Excel. In particular, all the incomplete responses were excluded from the dataset and the blank rows and columns were deleted. Second, the dataset was imported to SPSS, and the variables were computed by adding the scores of the relevant items. Third, descriptive statistics were calculated for the continuous variables and frequency tables were created for the categorical variables. Descriptive statistics were used to test the variables for normality. Fourth, Cronbach’s alpha and Pearson’s correlation analysis were conducted to assess reliability and validity of the instrument. Finally, the regression model was estimated, and coefficients were analysed.
Chapter Summary
This chapter described the methods used to achieve the purpose of the study, which was to determine factors that affect the decision-making process in the telecom sector in Qatar. The analysis demonstrated that the quantitative research design was most appropriate to answer the research questions. The methods of the study were based on the ideas of positivism as the primary research paradigm. A total of 107 participants were recruited from Ooredoo and Vodafone Qatar, which was considered sufficient by the supervisors and the researcher. A self-created instrument with 35 items was used to measure a dependent variable (decision-making effectiveness), four independent variables (decision-making engagement, cultural diversity, vision-based leadership, and risk assessment), and nine demographic variables. The data was collected using Survey Monkey and analysed using SPSS. One multiple regression model was assessed to determine the magnitude of the effect of independent variables on the dependent variable. The results of the analysis are provided in Chapter 4 of this thesis.
Results and Discussion
This chapter introduces the results of quantitative analysis and discussion of these results. On the one hand, this chapter focused on describe the results of quantitative analysis in the most unbiased matter to allow the reader make the conclusions. On the other hand, the results were discussed against the current body of literature and Simon’s decision-making theory to attempt to explain the results of quantitate analysis. First, the chapter describes the sample in terms of demographic variables and provides descriptive statistics for the dependent and independent variables. Second, the results of the regression analysis are introduced to test the four hypotheses. Third, the chapter tests the regression model assumptions, including linearity, normality, absence of multicollinearity, and homoscedasticity. Fourth, the reliability and validity scores are discussed. Finally, the chapter provides discussion of the research results by variable. The chapter is concluded with a brief summary of the chapter.
Results
This section of the chapter focuses on the description of the results of data analysis. The purpose of this section was to provide the reader with a set of tables and figures to visualise the results with minimal bias. Every figure and table is supplemented by a brief discussion. The tables and figures allow the reader to make the decisions about the results of the research.
Description of the Sample
Before discussing the results of the analysis, it is crucial to describe the sample to understand if the sample was appropriate to answer the research question. The sample was described using nine demographic questions, which were described in the Chapter 3 of this thesis. We discuss these nine aspects below as applied to the final sample of 107 participants.
Gender. In the sample, 33 participants identified themselves as females, while 74 participants identified themselves as males. This implies that 30.8% of the participants were males and 69.8% of participants were males. The gender distribution of the samples is provided in Figure 1 below.
Age. Almost 70% of the sample were aged between 25 and 44 years old, regardless of the fact that the sample consisted of decision-makers, including supervisors, heads of sections, managers, directors, and chiefs of units. The distribution of the sample by age is described in Table 1 below.
Table 1. Age distribution
The bar chart provided in Figure 2 below demonstrates that the age distribution of the participants was close to the normal distribution curve.
Education. The education distribution is described in the frequency table provided in Table 2 below. The analysis demonstrates that more than 75% of the sample had a Bachelor’s degree or above.
Table 2. Education distribution
Employer. The sample included 63 participants working for Ooredoo Qatar and 44 participants working for Vodafone Qatar. This implies that 58.9% of the participants worked for Ooredoo, while 41.1% worked for Vodafone Qatar. The distribution of the participants by employer is visualized in Figure 3 below.
Position. The sample was also distributed among five positions. The positions included supervisors, heads of sections, managers, directors, and chiefs of units. The majority of participants (71) were managers and supervisors, which accounted for 66.3%. The distribution of participants’ positions is provided in Table 3 below.
Table 3. Distribution of participants’ positions
Experience Area. This variable determined if the participants had technical or managerial area of experience. The frequency table provided in Table 4 below demonstrated that the there were 35 participants with only technical experience, 33 participants with only managerial experience, 28 participants with both technical and managerial experience, and 11 participants with no technical or managerial experience.
Table 4. Area of experience distribution
Occupation description. This variable focused on the description of the participants’ duties. The most participants (34.6%) were responsible for design, engineering, and construction. The distribution of the sample by occupation description is provided in Table 5 below.
Table 5. Occupation description distribution.
Years of experience. The majority of the participants had significant experience in the area of the telecom projects, which contributes to the reliability and validity of data. In particular, 85% of the participants had more than 5 years of experience working in the sector with 18.7% of the participants with more than 20 years of experience. The distribution of the sample by years of experience is provided in Table 6 below.
Table 6. Years of experience distribution
Additional managerial courses. The majority of participants (86%) did not take any additional managerial courses. However, 15 participants reported to have taken additional managerial courses.
Descriptive Statistics of Variables
This section focuses on the discussion of the descriptive statistics of the variables. According to McClaive, Benson, and Sincich (2018), descriptive statistics serve two crucial roles in analysis of data. First, descriptive provide a bird’s eye view of the data using the measures of central tendency and dispersion, which allows the reader to understand how the data was distributed (McClaive, Benson, and Sincich, 2018). Second, descriptive statistics may be helpful for determining correlation between variables (McClaive, Benson, and Sincich, 2018). Apart from these two benefits of descriptive statistics, such analysis is often used to determine the distribution pattern of variables to determine if it is in accord with the assumption of normality. According to McClaive, Benson, and Sincich (2018), normality of distribution is a crucial assumption for multiple regression analysis. This research analyzed the variables in terms of mean, median, mode, standard deviation, maximum, minimum, kurtosis, and skewness. The summary of the descriptive statistics is provided in Table 7 below.
Table 7. Descriptive Statistics
Decision-making effectiveness. The scores for the decision-making effectiveness varied between 9 and 35, while the possible minimum was 5 and the possible maximum was 35. The mean score was 23.46 with a standard deviation (SD) of 6.43. The distribution was slightly negatively skewed (Skewness = -0.413) and slightly platykurtic (Kurtosis = -0.567). However, the absolute values of both kurtosis and skewness were below ‘1’, which demonstrated almost a perfect fit with the normal distribution curve. Thus, the assumption of normality for this variable was not violated.
Cultural diversity. The scores for the cultural diversity variable fluctuated between 11 and 30, while the possible minimum was 5 and the possible maximum was 35. The mean score was 21.85 with an SD of 3.76. The distribution was slightly negatively skewed (Skewness = -0.369) and very slightly leptokurtic (Kurtosis = 0.086). However, the absolute values of both kurtosis and skewness were below ‘1’, which demonstrated almost a perfect fit with the normal distribution curve. Thus, the assumption of normality for this variable was not violated.
Engagement in decision-making. The scores for the engagement in the decision-making varied between 5 and 35, while the possible minimum was 5 and the possible maximum was 35. The mean score was 22.8 with an SD of 6.5. The distribution was slightly negatively skewed (Skewness = -0.393) and slightly platykurtic (Kurtosis = -0.574). However, the absolute values of both kurtosis and skewness were below ‘1’, which demonstrated almost a perfect fit with the normal distribution curve. Thus, the assumption of normality for this variable was not violated.
Vision-Based Leadership. The scores for the vision-based leadership varied between 11 and 35, while the possible minimum was 5 and the possible maximum was 35. The mean score was 25.1 with an SD of 6.26. The distribution was slightly negatively skewed (Skewness = -0.432) and slightly platykurtic (Kurtosis = -0.821). However, the absolute values of both kurtosis and skewness were below ‘1’, which demonstrated almost a perfect fit with the normal distribution curve. Thus, the assumption of normality for this variable was not violated.
Risk Assessment. The scores for the engagement in decision-making between 6 and 33, while the possible minimum was 5 and the possible maximum was 35. The mean score was 23.34 with an SD of 5.84. The distribution was slightly negatively skewed (Skewness = -0.552) and slightly platykurtic (Kurtosis = -0.341). However, the absolute values of both kurtosis and skewness were below ‘1’, which demonstrated almost a perfect fit with the normal distribution curve. Thus, the assumption of normality for this variable was not violated.
Hypothesis Testing
One multiple regression model was assessed to test three hypotheses. A hypothesis was accepted if the coefficient for the corresponding variable was statistically significant. The identified significance level for the paper was 0.05. The summary of the model and coefficient are provided in Tables 8 and 9 below.
Table 8. Model summary
Table 9. Coefficients
The results demonstrated that overall model was statistically significant with adjusted R2 = 0.696 (F = 61.62, p < 0.001). In other words, the model had high predictive ability, as changes in the independent variables could explain 69.6% of the fluctuations in independent variables. The analysis of coefficients demonstrated that three out four hypotheses should be accepted, as engagement in the decision-making process, vision-based leadership, and risk assessment were statistically significant. The explanations of the results by hypotheses are provided below.
Hypothesis 1. Hypothesis 1 stated that there is a positive correlation between cultural diversity and decision-making effectiveness in the telecom projects in Qatar. The results demonstrated that the coefficient was statistically insignificant (t = 0.862, p = 0.391). Since the p-value was above the threshold of 0.05, the hypothesis was rejected. This demonstrates that cultural diversity had no significant effect on the effectiveness of the decision-making process in the telecom projects in Qatar.
Hypothesis 2. Hypothesis 2 stated that employees’ engagement in the decision-making process had a positive effect on the effectiveness of the decision-making process in telecom project in Qatar. The regression model demonstrated that the coefficient for the engagement in decision-making process was positive and statistically significant (t = 2.231, p = 0.028). Since the p-value was below the threshold of 0.05, the hypothesis was accepted. This confirmed that employees’ engagement in the decision-making process had a positive impact on the effectiveness of decision-making in telecom projects in Qatar.
Hypothesis 3. Hypothesis 3 stated that there was a positive relationship between effectiveness of the decision-making process and vision-based leadership in the Ooredoo and Vodafone Qatar’s projects. The created regression model revealed that the coefficient for vision-based leadership was positive and statistically significant (t = 3.081, p = 0.003). Thus, the hypothesis was accepted, demonstrating that vision-based leadership had a positive effect on the decision-making process in telecom projects in Qatar.
Hypothesis 4. Hypothesis 4 supposed that there was a positive correlation between risk assessment and decision-making effectiveness in the projects of the two companies under analysis. The results demonstrated that the coefficient for risk assessment was positive and statistically significant (t = 3.384, p = 0.001). Since the p-value was below the margin of 0.05, the hypothesis was accepted, which implies that risk assessment practices had a positive effect on the effectiveness of the decision-making process.
Assumptions
There are several assumptions of the regression model that should be tested before accepting the results. The first assumption of regression analysis is normality of the variables, which means that all variables should be normally distributed (McClaive, Benson, and Sincich, 2018). The discussion of this assumption was included in the Section 4.2 of this paper in the analysis of the descriptive statistics. The analysis of descriptive statics revealed that the variables were normally distributed, which implies that the assumption of normality was not violated.
The second assumption of regression is linearity, which assumes that the relationship between the independent variable and the dependent variables is linear. In order to test the hypothesis, a matrix of scatterplots was created to assess the pattern of the relationship between the variables. This matrix is provided in Figure 5 below. The analysis demonstrated that the relationship between all the variables was linear, as the scatterplots looked like ascending lines for all pairs of variables. Therefore, it may be stated that assumption of linearity was not violated.
The third assumption of multiple regression is absence of multi-collinearity. This assumption means that there should be no strong correlations between the predictor variables to affect reliability of findings (McClaive, Benson, and Sincich, 2018). In particular, if there are strong correlations between two or more predictor variables, they do not provide unique insight about the dependent variable, which implies that one or several of the predictor variables should be excluded from the analysis (McClaive, Benson, and Sincich, 2018).
There are two methods that can be used to assess the multilinearity, including variance inflation factor (VIF) and Pearson’s correlation analysis (Denis, 2018). We used VIF to test for the assumption, as we found it more convenient. The VIF scores were provided in Table 9 previously in the chapter. According to Denis (2018), if the VIF score is below 5, the problem of multicollinearity is not a concern for the model the analysis demonstrated that the VIF scores for the independent variables varied between 2.097 and 3.409. Therefore, the assumption of the albescence of multicollinearity was not violated.
The final assumption of multiple linear regression is homoscedasticity, which means that the residuals should be equally distributed. The assumption of homoscedasticity was tested using a standardised residual plot with a fitted line generated by SPSS. This residual plot is provided in Figure 6 below. The results demonstrate that there is no evidence of heteroscedasticity in the plot, which implies that the assumption of homoscedasticity was not violated.
Reliability and Validity
Before moving on to the discussion of the results it is crucial to determine the reliability and validity of the created instrument to assess any biases associated with the assessment of correlations between the variables. First, reliability of the measurements of the variables was tested using Cronbach’s alpha in SPSS. The results of the analysis are provided in Table 10 below.
Table 10. Reliability analysis summary
The results of the reliability analysis demonstrate that Cronbach’s alpha for all the variables was high to extremely high. This indicates that the study produced highly reliable results.
Validity check was conducted by correlating each variable to the sum of all scores using Pearson’s correlation analysis. All the correlations were statistically significant, which stands for high validity of the dataset. The results of the correlation analyses are provided int Tables 11-15 below.
Table 11. Validity of decision-making effectiveness
Table 12. Validity of cultural diversity
Table 13. Validity of engagement in decision-making
Table 14. Validity of vision-based leadership
Table 15. Validity of risk assessment
Discussion
This section attempted to explain the results of qualitative analysis using the information from the literature review and decision-making theory. The section described each hypothesis separately in terms of contribution to the current body of knowledge. Moreover, each section discussed how the results of this study is aligned with the previous body of knowledge and Simon’s decision-making theory.
Hypothesis 1
Hypothesis 1 stated that there was a significant correlation between workplace cultural diversity and the effectiveness of the decision-making process in the telecom projects in Qatar. The results of the analysis revealed that the hypothesis should be rejected, meaning that there is no significant correlation between workplace cultural diversity and the effectiveness of the decision-making process in Qatar. The results of this research are unique, since no previous research touched upon the effect of cultural diversity on the effectiveness of decision-making in project of the particular industry. Thus, this research makes a significant contribution to the current body of knowledge concerning decision-making.
While this study is unique in some sense, it may be stated that the results of this research contradict previous findings. Gomez and Bernet (2019) stated that cultural diversity allows viewing situations from different viewpoints, which improve the outcomes of the decision-making process. Similarly, diversity in the upper management was found to have a positive effect on the effectiveness of the decision-making process in various industries (Hunt et al., 2018; Osahon and Hassan, 2021). This study demonstrated that when talking about decision effectiveness in telecom projects in Qatar, cultural diversity had little impact on the matter.
Such a discrepancy in the results can be explained based on different approaches. First, it should be stated that the result may be associated with low sensitivity of the test. Descriptive statistics of the variable demonstrates that its standard deviation was almost twice as low in comparison with other variables (SD = 3.759). As seen in Figure 7 below, cultural diversity in the telecom sector does not deviate much from the average, which makes it difficult detect its effect.
Another explanation of the phenomenon may be increased effect of the national culture on the decision-making process. According to Hofstede’s theory of cultural dimensions, Qatar has a very high uncertainty avoidance index (80/100), which states that the representatives of the culture are less likely to make innovative decisions (Hofstede’s Insights, 2022). The central benefit of cultural diversity for business is that people can evaluate the situation from various perspectives, which can generate more innovative decisions for a problem (Elia, Petruzzelli and Piscitello, 2019; Hu et al., 2022). However, even though innovative decisions are formulated, they are unlikely to be used due to the influence of the national cultural. Such explanation is in accord with Simon’s decision-making theory, as the theory states that psychological and cultural factors have high influence on managers’ decision-making process (Simon, 1979).
Previous research concerning cultural diversity demonstrated that there were both positive and negative effects of cultural diversity. On the one hand, cultural diversity can increase innovation and allow the companies to cater for more diverse populations (Elia, Petruzzelli and Piscitello, 2019; Hu et al., 2022; Rink, 2016). However, it is possible only if cultural diversity is well-managed (Lambert, 2016). The effect of cultural diversity may also be negative, as it can create barriers for communication, increase tension, and decreased workplace satisfaction (Robertson, 2019). Therefore, in the case with telecom projects in Qatar, the positive sides of cultural diversity may not outweigh the negative sides of cultural diversity. This may be the third possible explanation of the inconsistency of results. In summary, even though the results of this study contradict previous empirical studies, they can be explained by modern social and decision-making theories.
Hypothesis 2
Hypothesis 2 stated that there was a significant correlation between employee engagement in decision-making and the effectiveness of the decision-making process in the telecom projects in Qatar. The results of regression analysis demonstrated that the hypothesis should be accepted demonstrating a positive relationship between the variables. This demonstrates that the higher the employee engagement the higher the efficiency of the decision-making process in the telecom projects in Qatar was. This result was a unique contribution to the current body of knowledge as no previous research focused on studying the relationship between the effectiveness of the decision-making process and employee engagement in decision making in telecom project in Qatar.
The results of this study are consistent with previous studies conducted in other contexts. Ejere and Jarbandhan (2019) demonstrated that employee participation in the decision-making process positively affects the performance of businesses. Shaed, Azazi and Samsurijan (2021) mentioned that participation in the decision-making process positively affects employees’ job satisfaction, job commitment, and sense of belonging, which may improve the decision-making process in the companies.
Simon’s decision-making theory also support the idea that there is a positive correlation between employee engagement and decision-making effectiveness. Simon (1979) states that managers should select the alternative that involves minimal risk and uncertainty as opposed to the decision that maximises output from a limitless number of alternatives. The more people are engaged in the decision-making process the more likely the managers are to identify new possible courses of actions and address risks that were not considered before. However, only relevant stakeholders should be a part of the decision-making process, as only their opinions are of value.
Hypothesis 3
Hypothesis 3 assumed that there was a significant correlation between vision-based leadership and the effectiveness of the decision-making process in the telecom projects in Qatar. The results of hypothesis testing demonstrated that there was significant evidence that the hypothesis should be accepted. In other words, the more dedicated are the decision-makers to vision-based leadership, the more effective is the decision-making process. The results of this study are unique, as no other research focused on the relationship between vision-based leadership and the effectiveness of the decision-making process in telecom projects in Qatar.
The results of this study are in accord with the conclusions of previous research. Gomez and Basco (2022) stated that vision-based leadership allows effective management of stakeholders. Raad, Shirazi and Ghodsypour (2020) stated that leadership based on a strong vision help to make decisions concerning project portfolio to reach the desired goal. Moreover, previous research demonstrated that vision-based leadership is crucial for effective communication of goals and objectives to the employees and stakeholders (Faris, Gaterell and Hutchinson, 2022). Even though no previous research focused on quantifying the relationships between vision-based leadership and effectiveness of decision-making, there is a significant body of qualitative knowledge demonstrating a positive correlation between the two concepts. This study added quantitative evidence to the current body of knowledge concerning the relationship between the effectiveness of decision-making process and vision-based leadership.
The results of the study are also in accord with Simon’s decision-making theory. Simon (1979) emphasised the difference between programmed and non-programmed decision stating that managers should avoid making non-programmed decisions. Non-programmed decisions are often destructive, as they are based not on rules and pre-determined criteria but rather on impulse and ill-structured thought (Simon, 1979). Vision-based leadership helps to format one’s thought and elaborate clear patterns for decision-making, which, in turn, can reduce the probability of taking non-programmed decisions (Simon, 1979). Therefore, the results of this research can be explained by Simon’s decision-making theory.
Hypothesis 4
Hypothesis 4 assumed that there was a significant correlation between risk management and the effectiveness of the decision-making process in the telecom projects in Qatar. The conducted multiple linear regression analysis demonstrated that there was a significant positive correlation between the effectiveness of the decision-making process and risk management. In particular, the higher was the risk management score the higher was the effectiveness of the decision-making process. The results of this research were unique in a sense that there was no previous research that focused on the discussion of the relationship between these variables in the defined context.
The results of this study are coherent with the results of recent research. A study by Settembre-Blundo et al. (2021) demonstrated that multi-dimensional risk management is the key to decision-making success. These dimensions included financial risks, IT risks, sustainability risks, and supply chain risks (Crovini, Santoro and Ossola, 2021; Settembre-Blundo et al., 2021). Moreover, decision-making is closely correlated with risk assessment on the conceptual level (Julmi, 20190. Therefore, it is crucial for the managers to assess all types of risks thoroughly before making the final decision in project. This study contributes to the current understanding of correlations between risk assessment and the effectiveness of the decision-making process by exploring the inter-relationship in the context of telecom projects.
The results of this study are in accord with Simon’s decision-making theory. According to the theory, managers need to assess a wide variety of possible outcomes and determine the most adequate choice (Simon, 1979). The central assumption of the theory is that the decision-maker should select the alternative that involves minimal risk and uncertainty as opposed to the decision that maximises output (Simon, 1979). This implies that managers need to conduct careful risk assessment of all spheres in projects to ensure the best outcomes. Thus, Simon’s decision-making theory provides explanation of the correlation between the effectiveness of the decision-making process and risk assessment.
Chapter Summary
This chapter focused on the description and discussion of research results. First, the chapter focused on the description of the final sample in terms of demographic characteristics and provided descriptive statistics to test for normality. After that a multiple regression model was assessed to test the hypotheses. The analysis demonstrated that three out of four hypotheses should be accepted. Employees’ engagement in decision-making, risk assessment, and vision-based leadership were found to have a positive effect on decision-making effectiveness in telecom projects in Qatar, while cultural diversity had no effect on the dependent variable. The majority of the results were consistent with the current body of knowledge and Simon’s decision-making theory. However, the fact that cultural diversity had no effect on decision-making effectiveness was found to be contradictive to the findings of recent research. Three possible explanations were given to the inconsistency using Simon’s decision-making theory, Hofstede’s cultural dimensions, and conceptual analysis.
Conclusions and Recommendations
Introduction
This final chapter focuses on the discussion of conclusions and recommendations of the research. The primary purpose of the chapter was to summarise the essence of the study and answer the research questions in a clear and distinct matter. First, the chapter analyses limitations of the study based on the characteristics of the sample and research methods. Second, the chapter discusses the answer to RQ1, which focused on determining factors that affected decision-making effectiveness in telecom projects in Qatar. Third, the chapter provides recommendations for concerning how decision-making effectiveness can be improved, which answers RQ2. Fourth, we provide recommendations for future research based on limitations of the study. The chapter is concluded with a brief summary of the implications of the study.
Limitations
Even though the study was conducted using rigorous research methods and provided reliable results, there are several limitations that should be acknowledged before discussing the conclusions of the study. First, the results of the analysis are limited by the scope of the research questions. This research has a narrow focus on decision-making effectiveness in telecom projects in Qatar. Therefore, the results of the study apply only to the context under analysis and cannot be reliably generalised to other contexts. Therefore, managers should apply the results of this study to other industries with caution. The central reason for the is unique characteristics of Qatar’s national culture. According to Hofstede’s cultural dimensions model, Qatari people can be characterised by extremely high power distance, low individualism, and very high power distance (Hofstede’s Insights, 2022). Qatar is also characterised by multinationalism due to an increased number of immigrants from different countries. Moreover, telecom industry may be associated with distinct workplace culture that can make the results of this study inapplicable to other contexts.
Second, the study is limited by the characteristics of the sample. This study used a sample of 107 respondents, which limits the reliability of findings. According to Online Calculator (no date), such sample with an estimated population of 18,200 is associated with a margin of error of 9%. In order decrease the margin of error to 5%, the sample size should be at least 318 participants (Online Calculator, no date). Moreover, there is guarantee that the sample was truly random. Even though researcher selected the participants at random, the questionnaires were given out only to those who could be reached. Thus, the sampling strategy may impose limitations on the research findings.
Third, the results are limited by time and resources of the researcher. The data collection process took around a week, starting from August 9, 2022 until August 18, 2022. The time of the research was regulated by the University’s policies and regulations, which may have affected the research process.
Fourth, the qualitative approach limits the information that knowledge that can be acquired from analysis. This research established significance of influence of three factors on decision-making effectiveness in telecom projects in Qatar. However, the results of this study do not explain the reasons for existence of such of correlations. Even though previous research and Simon’s theory of decision-making provide an explanation to the results, the reliability of the explanation is challenging to measure.
Finally, the results are limited by the abilities and qualifications of the researcher. The research has significant experience in working in telecom projects in Qatar, which helped the researcher understand the peculiarities associated with working in the context of the industry. However, we had limited experience in conducting full-scale research, which may have affected the results of the study. It should be noted that this limitation was addressed by receiving help from supervisors in cases of any difficulties.
RQ1: Factors Affecting Decision-Making Effectiveness
The first research question focused on determining the factors that affected the decision-making process in telecom projects in Qatar. First, a thorough literature review was conducted to identify potential factors that affected decision-making effectiveness. The literature review identified four possible factors that could affected the decision-making process in the context, including cultural diversity, employees’ engagement in the decision-making process, vision-based leadership, and risk assessment. A quantitative model was created that was used to test four hypotheses associated with the factors. The results of the analysis demonstrated that three of the four potential factors had a significant impact on the decision-making effectiveness in the context of interest.
Thus, the answer to RQ1 is that employees’ engagement in the decision-making process, vision-based leadership, and risk assessment had a significant impact on the effectiveness of the decision-making process in telecom project in Qatar. It was explained that employees’ engagement in the decision-making process allowed the managers to assess numerous viewpoints from various stakeholders, which helped the decision-makers to explore more possible outcomes. Vision-based leadership was supposed to help to the decision-makers select the most adequate solutions to problems based on the company’s overall business discourse. Moreover, such an approach to leadership allowed the decision-makers align all the projects and all the processes within these projects to achieve a common goal. Risk assessment was also found to be crucial as it can help to minimise possible losses and dangers in different spheres, such as IT, financials, and sustainability. The results of the study align with the previous body of knowledge and Simon’s decision-making theory, which was selected as the central theoretical framework for the study.
The only finding that did not align with previous research is the fact that cultural diversity was found to have to statistically significant effect on the effectiveness of the decision-making process. Previous research stated that cultural diversity can help to acquire innovative views on issues and tasks due to the assessment from viewpoints of various cultures (Ranaivoson, 2007). Making innovative decisions is associated with improved ability of managers to make decisions that can bring positive changes. However, there are also negative effects of cultural diversity, including increased barriers for communication, growing inter-cultural tension, and decreased workplace satisfaction (Robertson, 2019). Therefore, one of the possible explanations to the inconsistency of this research’s findings with the current body of knowledge may be the fact that the effect of positive sides of cultural diversity did not overcome the negative effects of cultural diversity. However, there may be other explanations to the fact. In particular, the unique characteristics of Qatar as a nation and Qatari national culture may have had an influence on the matter.
RQ2: Recommendations for Improving Decision-Making Effectiveness
This section focused on answering RQ2 which asked how decision-making effectiveness in telecom projects in Qatar can be improved. This question was answered by drawing insights from the results of this study and previous research. The list of recommendations is provided below.
- Increase employees’ engagement in the decision-making process. This study demonstrated that that decreased employees’ engagement in the decision-making process is associated with lower decision-making effectiveness. According to Hofstede’s model of cultural dimensions, representatives of Qatari culture can be characterized by extremely high power distance score of 93 out of 100 (Hofstede Insights, 2022). This implies that Qatari people are prone to strictly hierarchical organisation where everyone has a place (Hofstede Insights, 2022). Thus, engaging the employees in the decision-making process may be difficult achieve. Dom and Ahmad (2019) recommend to conduct a planned culture change that would include employees’ participation as one of the central values of the organisations. This value should be formally included in all the relevant company’s documentation (Dom and Ahmad, 2019). Additionally, employee training should be conducted to encourage them to take an active part in the decision-making process in telecom projects in Qatar.
- Guide the decision-making process with a clearly formulated vision. This study demonstrated that increased reliance on vision-based leadership improves decision-making effectiveness in telecom projects in Qatar. Project managers always align their decisions with the aims and objectives of the project, which ensures the integrity of all the decisions. However, stakeholders may find it difficult to understand how the aims and objectives of the project contribute to the overall company’s success and improve people’s lives (Christenson and Walker, 2008). Therefore, Christenson and Walker (2008) recommend elaborating a clear project vision following a strict protocol and use this vision to guide the decision-making process.
The protocol demonstrates that each vision statement should following criteria: (a) it should be summarised in a powerful phrase; (b) it should describe people, processes, and technology; (c) it should be aligned with organisational goals, objectives, and values; (d) it should provide a sense of importance; (e) vision statement should not be regulated by length, as it can take up to several sentences (Christenson and Walker, 2008). The project vision should be closely connected to the organisational vision, as it can connect several projects together and make provide a sense of unity between projects (Christenson and Walker, 2008).
- Conduct in-depth risk assessment before making decisions. This study demonstrated that there was a significant correlation between risk assessment and decision-making effectiveness in telecom projects in Qatar. Therefore, it is recommended to conduct formal in-depth assessment of all the possible risks before making major decisions (Crovini, Santoro and Ossola, 2021; Settembre-Blundo et al., 2021). In particular, risk assessment should focus on discussion of IT risks, sustainability risks, and financial risks (Crovini, Santoro and Ossola, 2021; Settembre-Blundo et al., 2021). Apart from that, Simon (1979) stated that effective decision-making is associated with assessment of schedule risks, market risks, governance risks, strategic risks, and legal risks. Simon’s decision-making theory recommends creating a formal decision-making protocol that describes a checklist of actions that should be taken before making a major decision (Simon, 1979). Such an endeavour will help to decreased the number of unprogrammed decisions, which will improve the effectiveness of the decision-making process. Therefore, this study recommends creating a formal risk assessment policy to improve decision-making effectiveness in telecom projects in Qatar.
This research does not make any recommendations to managers concerning promotion of cultural diversity to improve decision-making effectiveness in telecom projects in Qatar, as this study found no significant correlations between these two variables. However, the analysis of literature demonstrated that cultural diversity can have both positive and negative impacts (Ranaivoson, 2007; Robertson, 2019). Since Qatari project managers often need to word with diverse project teams, they need to ensure to employ the best diversity management practices to mitigate the negative effects of workplace cultural diversity (PSA, 2017). Lambert (2016) suggests that cultural diversity should be managed based on the concepts of concept of fairness, constant learning, and legitimacy. Such an approach to diversity management is expected to reduce its possible negative effects on decision-making effectiveness.
Recommendations for Future Research
Future research should focus on addressing the limitations of this study. The list of recommendations for future research is provided below.
- Conduct similar research in other contexts to improve generalisability. Even though this research had important implications for project managers, the applicability of its results is limiter due to a very specific target population. Project managers are not recommended to use the recommendations of this research outside Qatar, as Qatari national culture may have a significant impact on managerial practices. Additionally, it may be inappropriate to use the recommendation outside the telecom industry in Qatar. Therefore, future research is required to assess the effect of cultural diversity, employees’ engagement in the decision-making process, vision-based leadership, and risk assessment on the effectiveness of the decision-making process in project management. The instrument and other methods of this study can be replicated to minimise the required effort.
- Increase the sample size. One of the central weaknesses of this research was a very high margin of error of 9.5% due to low sample size. Such a high margin of error may be associated with biases in the research results. Therefore, it is recommended to a acquire data from a sample of at least 318 participants to ensure a margin of error of no more than 5%. Such an effort will improve the reliability of findings by decreasing the probability of errors in statistical analysis. This can be achieved if the data collection period is increased.
- Improve sampling practices. It is crucial to ensure that the sampling strategy for data collection was truly random to maximise reliability of findings. Therefore, future research should aim at improving the sampling practices utilised in this study.
- Conduct a qualitative study to explain the results of this research. The quantitative method of this study limits its ability to explain the results. Therefore, future research should focus on collecting qualitative data from a relevant sample to explain why employees’ engagement in the decision-making process, vision-based leadership, and risk assessment influence the effectiveness of the decision-making process in telecom projects in Qatar. Moreover, future research should attempt to explain why cultural diversity had no effect on effectiveness of the decision-making process in telecom projects in Qatar.
Chapter Summary
This chapter focused on summarising all the conclusions and providing recommendations for two types of stakeholders. First, the chapter established five central limitations of the study, including applicability limitation, limitations due to the characteristics of the sample, time and resource constraint, lack of explanation due to utilising quantitative methods, and limitations associated with the abilities and experience of the researcher. After that, RQ1 was answered by stating that employees’ engagement in the decision-making process, vision-based leadership, and risk assessment had a significant influence on the effectiveness of the decision-making process in telecom projects in Qatar. After that, as set of three recommendations to managers concerning how decision-making effectiveness in telecom projects in Qatar can be improved were given and justified. Finally, the chapter provided four recommendations for future research that were guided by acknowledge limitations of this study.
Abstract
Rationale. In the nearest future, the effect of certain external factors may decrease their influence, which will affect the industry and the economy in general. As a result, the effectiveness of the decision-making process is expected to have an increased influence on the financial performance of the telecom companies in Qatar.
Purpose. This research aims at determining the factors that affect the decision-making process in the telecom sector in Qatar. The primary purpose of the study is to create a list of recommendations for managers in the telecom sector in Qatar to improve the decision-making process on all levels of telecom projects. Literature review revealed that four factors that can influence decision-making effectiveness in telecom projects in Qatar, including cultural diversity, employees’ engagement in the decision-making process, vision-based leadership, and risk assessment.
Method. This research utilised quantitative research methods to test four hypotheses. A sample of 107 employees working in Ooredoo and Vodafone Qatar were recruited and a self-created questionnaire was distributed among them to measure demographic variables, a dependent variable, and four independent variables. A multiple linear regression model was created to test the hypotheses.
Results. The results demonstrated that employees’ engagement in the decision-making process, vision-based leadership, and risk assessment were positively associated with decision-making effectiveness in telecom projects in Qatar, while cultural diversity had no effect on the dependent variable. The results were discussed against the current body of knowledge and Simon’s decision-making theory.
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