2.7 Conceptual Framework
Figure 1: Conceptual Framework
(Source: Author)
It is clearly evident from the above figure that a possible relationship exists between the confidence of decision makers and the consistency of their judgment. And Confidence and Consistency are both independently affecting Quality.
2.8 Research Hypothesis
Based on the analysis of the literature that has been conducted above, there is a positive relationship between confidence and consistency. It seems like more confidence leads to more consistency. Therefore, the first hypothesis will be defined as followed.
However, relationship between confidence and consistency has implications for the quality of decision making. Sometimes, a person who is confidence can make a good quality of decision making. But if someone who are arrogant and strongly trust himself, then he may be overconfidence. This may cause the bad quality of decision making. Then I suppose confidence have a maximum value. Before that point, confidence will lead to positive results. However, if the confidence is found to be over the maximum point, it will result in negative outcome. Similarly, it is inferred that consistency may also have a maximum value. Hence, it is posited that
H2a: There is a positive relationship between confidence and quality of decision making.
H2b: There is a negative relationship between confidence and quality of decision making.
H3a: There is a positive relationship between consistency and quality of decision making.
H3b: There is a negative relationship between consistency and quality of decision making.
CHAPTER 4: ANALYSIS OF DATA
4.1 Chapter Introduction
The data analysis chapter of the research is the most crucial component that plays a significant role in the accomplishment of the research. The research aims to analyse the impact of confident decision makers on the consistency of judgement in organizations. The chapter is important because it transforms the accumulated data into appropriate findings. The data is analysed through Statistical Package for Social Sciences (SPSS) software in which reliability, multiple regression and correlation tests have been run by the researcher.
4.2 Survey Response Rate
The response rate is also known as completion rate of the study. The survey response rate is determined by the number of people who answered the survey questions divided by the number of respondents in the sample. The concept is applicable in both the qualitative and quantitative researches (Johnson and Wislar, 2012). It is analyzed that a low survey response rate can result in sampling bias if the number of non-responses becomes equal to the participants who generate the outcome for the research. In this research the sample size was 70 and the researcher approached 70 people to get their responses. The researcher makes sure that he/she gets accurate and complete responses of the people participated in the study to reduce biasness in the research. The response rate is 100% in the research because all of the people in the sample took part in the study and give answers that were asked in the questionnaire.
4.3 Demographic analysis
The demographic analysis presents summary about the demographic factors of the respondents such as age, gender, income, occupation, experience etc. In this particular research, I test about gender and working experience.
Graph 1: Working Experience
Graph no.1 presents the percentages of participants who have high medium and low experience in the firm. It is analyzed that 22.86% participants have less than 2 year experience while 24.29 % respondents have 2- years. The percentage of respondents having 6-10 year experience was 27.14% and who have more than 11 years were 25.71%.
Graph 2: Gender of the respondents
Graph no. 2 represents the percentages of male and female respondents involved in the study. It is analyzed that 54.29% respondents were female while 45.71% were male.
4.4 Data Analysis
The analysis of the data collected by the researcher has been carried out with the SPSS software. Some important tests have been carried out by the researcher in order to obtain meaningful results. The tests applied in the current study are reliability test, correlation test and regression test.
4.41 Reliability Statistics
The reliability test shows the consistency of the responses taken from the respondents. Cronbach alpha is the most common measure used for measuring internal consistency (reliability) of the responses generated by the respondents of a research. The accepted value of Cronbach’s Alpha is 0.7 and higher value is considered to be good. However, the result of this reliability test is enough for the researcher to further precede the study towards its completion. The researcher also tests the reliability of the variables if the current study which are confidence, consistency and quality (Leech, Barrett, and Morgan, 2014).
The first variable of the study is confidence. In the preset study the researcher is finding out the reliability of confidence through the reliability test. The variable consists of statements to check the general perception of respondents regarding the confidence in decision making. The number of items in this variable is seventeen. The researcher applies the reliability test using the SPSS. As shown in the table the value of reliability is .711 which means that the data is valid and the value of the reliability is acceptable.
The second important variable of the study is consistency. The variable contains ten items and the reliability test has been run by the researcher using the SPSS software. The second variable includes statements for evaluating the consistency of decision making. The questions were related to analyze the patterns adopted by respondents of the study in their daily decision making. It is clearly mentioned that the reliability of the consistency variable is .702 which is also higher than 0.7. It means that the value of reliability is acceptable.
The third variable which is under investigation is quality. The Cronbach alpha is also measured for the checking the internal consistency of the items. The variable contains eleven statements in total that were based on the general knowledge. The main purpose is to analyze the quality of decisions made by the participants rather than analyzing their perception. The total number of items in this variable is eleven and the value of Cronbach alpha is .821. The value is acceptable as well as good. The high value of Cronbach alpha indicates that there is a high level of internal consistency for the scale used by the researcher in the current study.
4.42 Correlation Test
The correlation is the most important and useful statistics which is applied on the variables of the study. Correlation defines the relationship that exists between variables of a research. Past studies indicate that it tells the degree of relationship between variables which can be identified with the application of correlation techniques. The summary of the correlation is known as correlation coefficient (r). The acceptable range of the values is -1.0 to +1.0 (Mayers, 2013).
The ranges of the Pearson correlation are also defined. The range of 0.1 to 0.3 indicates a poor relationship, values above than 0.3 to 0.5 indicates a weak relationship, correlation values ranging between 0.5 to 0.7 indicates a moderate relationship and higher values till 1.0 means the relationship is extremely strong.
4.3 Multiple Regression Analysis
Multiple Regression analysis is a statistical tool used for investigating relationships among variables. By applying a regression analysis test on the data a researcher will determine the impact of one variable on the other. For example, determining the effect of price on the demand can be evaluated through regression analysis. Regression analysis allows a researcher to know the impact of one variable on the other and it will help the researcher to generate a regression equation. The regression equation helps to describe the relationship between one or more predictor variables and the response variable.
When P value is lower than 0.05, it means there is a significant association between the independent variables ad dependent one. When P value is higher than 0.05, it indicates no significant relationship between the independent variables ad dependent one. According to the results, the p value between consistency and quality of decision making is 0.00, so it shows the relationship between consistency and quality of decision making is significantly positive. The P value between confidence, gender, working experience and quality is 0.282, 0.479, and 0.978. All of these are higher than 0.05, so it is not significant.
4.5 Hypothesis Assessment Summary
CHAPTER5: DISCUSSION
After learning about the literature evidence in support of consistency and confidence affecting the quality of the decision making in the organizational context, we were interested to perform the quantitative analysis using the multiple regression as the statistical tool. To perform the research work correctly, we relied on SPSS, which happens to be highly credible statistical software. As already explained in one of the paragraphs of this dissertation, validating the impact of variables such as consistency and confidence on the decision making is very important because while the process of decision making is very intimidating and crucial because of the repercussion, which each decision will have on the organization. However, in order to be comprehensive in our research approach, we also added work experience and gender as two another independent variables and hypothesized their impact on the quality of the decision making. Discussed below are the findings of the statistical research work:
5.1 Significance of the regression model
Referring to the outcome of the multiple regression model, we found that the regression model has adjusted r-square of 52.60%. This indicates that the four independent variables have a sustainable explanatory power and more than half of the variation in the quality of the organizational decisions is affected by confidence, consistency, gender and work experience. Consequently, we can accept this regression model and proceed with the discussion of the results.
5.2 Impact of consistency, confidence, gender and work experience on quality of decision making
Based on the outcome of the ANOVA table, it can be seen that out of the four independent variables, only consistency contributes significantly towards the quality of the decision making at a 5% level of significance. Apart from this, no other independent variable, be it confidence, gender or work experience is contributing significantly in explaining the quality of the organizational decisions. The reasons for the different degree of influence of these variables may be as follows:
-Scatter Plot of confidence v/s quality
-Scatter Plot of Consistency and Quality
As we may notice from the above scatter plots, while confidence and consistency are both positively related to confidence, however, regression output confirms the sole significance of consistency in determing the quality of decision making.
Even the academic fraternity proposes its favor for consistency over confidence. For instance, an article authored by Gigerenzer and Gaissmaier in 2011 postulated that since the business environment is turning highly dynamic, business managers and leaders need to access every situation differently and with stability as such decisions have significant importance to all the stakeholders of the company. (Gigerenzer, 2011) Therefore, negative attributes related with other variable such as overconfidence associated with confidence, diversity issues with gender participation and pin-pointed expertise on only functional aspect with the work experience, are likely to avoid the decision maker in adopting a holistic approach towards the decision making process.
Therefore, both from the literature and statistical approach, we conclude that consistency is the single most influential factor that affects the quality of the decision making.
5.3 Correlation between consistency and confidence
Going through multiple literature evidences, we came across researchers with confronting results over the relationship between confidence and consistency. For instance, Dala and Bonaccio, 2010, cited that confidence does help in taking prudent decisions, which helps in achieving the overall goals of the organization. However, if the decision maker turns overconfident, he cannot be consistent in his approach. Similarly, McDevitt and Giapponi define a confident man with an attribute of consistency in his approach. (McDevitt, 2007) The author quotes,’’ overview of confident decision makers' personality and establishes that individuals who are equipped with such trends tend to judge situations from different angles before making a decision. The objective of evaluating situations is that it reduces any making decisions that are incoherent with the objectives of the organization. (McDevitt, 2007)
Henceforth, it was really important for us to learn whether there is any correlation between confidence and consistency. Ironically, our correlation test confirmed that there is negative correlation between confidence and consistency and the two variables are not related to each other. This confirms that while from the literature point of view, confidence and consistency are two important variables affecting the quality of the organizational decisions. However, the statistical results confirm that there is no relation between these two variables and only consistency, as a variable, affects the quality of the decision making.
