Impact of Predictive Analytics on Financial Decision Making in business: An empirical

study of Bangladeshi Ceramics Industry

Submitted To
Prof. Nausheen Rahman
Professor, Department of Finance,
Faculty of Master of Professional Finance
University of Dhaka

Submitted By
Md. Atiqur Sobhan
ID: Fin-03-16-017
Batch: 3rd
Program: MPF
 Introduction
Chaouchi (2018) defines predictive analytics as an advanced branch of analysis used to make predictions about
future unknown ocarinas.
• Ceramics Industry’s financial decision making process
The financial decisions are crucial and important as the financial manager is responsible for the decisions. So, a
certain process is to be followed by the top managers to identify the loop holes and execute task through
making a best possible output by strategic financial decision (Eliot 2016).
• Research Question
Do Predictive Analytics have any impact on the decision making process of Ceramics Industry?
• Research objectives
To find out various aspects related to financial decision making
To find out various aspects related to predictive analytics
To find out if predictive analytics has any impact on financial decision making Ceramics Industry
To provide recommendations so that Ceramics organisations can take financial decisions could be made more
effectively
• Hypothesis
H0 - Predictive analytics have no impact on financial decision making of organizations in Bangladeshi
Ceramics Industry.
H1 - Predictive analytics impact financial decision making of organizations in Bangladeshi Ceramics Industry.
 Literature Review

Predictive analytics

Luo (2010)

Eades, 2017
 Research Methodology

• Data collection method

The sample consists of 50 employees randomly selected from various ceramic
companies. You will receive a questionnaire in which you can answer a series of closed
research questions.
• Justification:
In this case the questionnaire is used. This can also be linked to the quantitative
method, since researchers can collect data from a sample that would represent the
population.
• Data Analysis Techniques
There are several techniques that researchers can use to analyze the data they collect
for research. This study used the quantitative method to collect the necessary data from
the sources. The methods are used to analyze the data.
• Correlation analysis
• ANOVA table
• Regression analysis
 Data analysis & Discussion
Table 4: My organization relies heavily on predictive analysis to make
financial decisions.
Dimensions Frequency Percent Valid Cumulative
Percent Percent
Strongly Disagree 2 4 4 4
Disagree 0 0 0 4
Indifferent 1 2 2 6
Agree 10 20 20 26
Strongly Agree 37 74 74 100
Total 50 100% 100%  

Only 4% strongly disagree, 74% strongly agree and 20% agree that your
organization relies heavily on predictive analytics to make financial decisions.
Data analysis & Discussion

Table 5: My organization spends a lot on data collection and analysis of predictive analysis

Dimensions Frequency Percent Valid Cumulative

Percent Percent
Strongly Disagree 2 4 4 4
Disagree 4 8 8 12
Indifferent 4 8 8 20
Agree 15 30 30 50
Strongly Agree 25 50 50 100
Total 50 100% 100%  
 Data analysis & Discussion

Only 4% totally disagree and 8% completely disagree and 50% strongly agree
and 30% agree, which means that your organization has proactively spent a lot
of money on analysis
 Data analysis & Discussion
Table 6: Predictive analysis increases qualitative and quantitative knowledge of the business environment.

Dimensions Frequency Percent Valid Cumula

Percent tive
Percent
Strongly 3 6 6 6
Disagree
Disagree 2 4 4 10
Indifferent 0 0 0 10
Agree 11 22 22 32
Strongly Agree 34 68 68 100
Total 50 100% 100%  

Only 6% of respondents disagree and 4% disagree, 22% agree and 68% agree
 Data analysis & Discussion
Table 7: I think decisions based on predictive analytics are better

Dimensions Frequency Percent Valid Cumula

Percent tive
Percent

Strongly 9 18 18 18
Disagree

Disagree 6 12 12 30
Indifferent 11 22 22 52
Agree 10 20 20 72
Strongly Agree 14 28 28 100
Total 50 100% 100%  

20% agree and 28% totally agree, which means that decisions based on
predictive analysis are better. 18% disagree and 12% disagree. 22% of
respondents could not decide.
 Data analysis & Discussion
Table 8: Most of the data collected does not remain analyzed

Dimensions Frequency Percent Valid Cumulati

Percent ve
Percent

Strongly Disagree 0 0 0 0

Disagree 3 6 6 6
Indifferent 0 0 0 6
Agree 25 50 50 56
Strongly Agree 22 44 44 100
Total 50 100% 100%  

44% totally agree that most of the data has not been analyzed and 50% agree.
Only 6% disagreed and none of the respondents fully agreed.
 Data analysis & Discussion
Table 9: My organization does not have sufficient resources to use predictive analysis more effectively

Dimensions Frequency Percent Valid Cumula

Percent tive
Percent
Strongly Disagree 2 4 4 4
Disagree 10 20 20 24
Indifferent 0 0 0 24
Agree 18 36 36 60
Strongly Agree 20 40 40 100
Total 50 100% 100%  

. 36% agree and 40% totally agree, while 4%

disagree and 20% disagree..
 Data analysis & Discussion
Table 10: I believe predictive analytics is not necessary to make financial decisions
Percent
Dimensions Frequency Percent Valid Cumula
Percent tive
Percent Strongly Agree 12

Strongly 25 50 50 50
Disagree Agree 4

Disagree 17 34 34 84
Indifferent 0 0 0 84 Indifferent 0

Agree 2 4 4 88
Strongly Agree 6 12 12 100 Disagree 34

Total 50 100% 100%  

Strongly Disagree 50

0 10 20 30 40 50 60

Only 12% totally agree and 4% agree that predictive analysis is mandatory, while
34% disagree and 20% disagree.
 Data analysis & Discussion

• Hypothesis testing
The following assumptions were made in the first chapter of this study. In the next
section, they are tested with several statistical studies. These tests accept one
hypothesis and reject the other, so that the investigation reaches a final decision on the
problem.
• H0: Predictive analysis does not affect the financial decision of ceramic industrial
companies in Bangladesh.
• H1: Predictive analysis influences the economic decision-making of ceramic
industrial companies in Bangladesh.
The hypotheses were formulated with regard to predictive analysis as an independent
variable and financial decision making as an independent variable. The tests determine
whether or not the independent variable affects the dependent variable.
The complete questionnaire was attached at the end of the study.
 Data analysis & Discussion

Correlation Analysis:
The correlation analysis determines whether the independent variable and the dependent variable are linked
and whether one effect affects the other. This analysis creates a table where the P-value can be found in the
column "itself". The value is compared with the alpha value 0.05 to make a decision. If the alpha value is
greater than the P value generated by the correlation analysis, the null hypothesis is rejected and the
alternative hypothesis is accepted. On the other hand, if the P value is greater than the alpha value, the
alternative hypothesis is rejected and the null hypothesis is accepted.
According to the correlation table above, the analysis shows a P value of 0.00 and the value including the
alpha value is less than 0.05.
Therefore, the study rejected the null hypothesis and accepted the alternative hypothesis.
For this reason, predictive analysis has an impact on the financial decision-making of ceramic industrial
companies in Bangladesh.
Coefficients
a

Standardized
Unstandardized Coefficients Coefficients
Model B Std. Error Beta t Sig.
1 (Constant) 3.582 .390   9.185 .000

predictive .216 .087 .334 2.478 .017

analytics
a. Dependent Variable: financial decision making
 Data analysis & Discussion

ANOVA Table:
The ANOVA table determines the strength of the two variables. Determine if an independent
variable has an effect on a dependent variable, whether this influence is significant or not.
The table produces a P value and if the value is less than 0.05, the ratio of the variables is
considered to be significant.
The table above shows that the P value assigned to the F value is 0.17 and is less than 0.05.
Therefore, the relationship between the two variables is significant.
In addition, a positive beta value can be observed, which means that the relationship is positive.
Therefore, better financial decisions are expected with better use of predictive analysis.

ANOVAa
Sum of AKIJ Mean AKIJ
Ceramics Ceramics
Model Ltds df Ltd F Sig.
1 Regression 2.593 1 2.593 6.140 .017b
Residual 20.693 49 .422    
Total 23.286 50      
a. Dependent Variable: financial decision making
b. Predictors: (Constant), predictive analytics
 Data analysis & Discussion

Regression analysis:
The table shows that the table gave an R value for AKIJ Ceramics Ltd of 0.324. This means that
33.4% of the dependent variable is explained by the independent variable. The remainder of 67.6%
is explained by some other variables that have not been taken into account in the current study.
Model Summary

Adjusted R AKIJ Ceramics

Model R R AKIJ Ceramics Ltd Ltd Std. Error of the Estimate
1 .111 a
.324 .343 .64986
a. Predictors: (Constant), predictive analytics
 Conclusion

• This research aims to find out various aspects related to predictive analytics and financial
decision making while determining if financial decision making process of organizations in
Bangladeshi Ceramics Industry is anyway impacted by predictive analytics. The research also
intend to provide further recommendations in order improve the financial decision making
process.
• In order to collect data 50 people have been selected randomly who are the employees of
different organizations in the Bangladeshi Ceramics Industry. They have been given a
questionnaire with close ended questions to express their response. The questionnaire contains
one section with 3 demographic questions and another section with 7 statements. The
respondents in the second segment, point out their level of agreement or disagreement to those
statements against a 5 point likert scale.
• The research has concluded that predictive analytics is not optional in today's highly
competitive business world and it improves decision making process as the decisions that have
been taken using this technique are actually better and more effective. Empirical evidence has
been provided proving that the impact of predictive analytics on financial decision making of
organization in Bangladeshi Ceramics Industry is both positive and significant.
 Research limitations and future research

• With more resources a larger scale research is possible regarding the topic. The
research only considers one independent variable and measures only its impact on
the dependent variable. However, a research with multiple independent variables
with a larger sample size could produce another more informative research.
• The research has undertaken quantitative strategy, but another research using a
qualitative strategy could be also conducted in order to gather more insight
regarding the topic.
 Recommendation.

• Priority can be an important tool when collecting data. For the system to be more
efficient, the most important information must be collected.
• The data source should be considered with the utmost importance.
• They need to design their system to make the fewest mistakes and, by prioritizing
tasks, they can improve the process.