The current study aimed to investigate factors influencing decision making using business intelligence (BI). The study used a self-administered survey sent to participants that used business intelligence to collect data on their perception of these variables on decision-making quality.
Introduction
The manufacturing industry is going through a radical transformation known as industry 4.0 which gives rise to smart manufacturing through which technologies such as IoT (internet of things) and CPS (cyberphysical systems) emerge, this can create large amounts of data that can be difficult to analyze using traditional BI methods. However, there is little research on the use of big data and its impact on decision-making (Janssen et al., 2017).
Motivation for the Study
Focus of the study
Problem Statement
The overall issue of the study is based on the premise that the value of BI is currently vague and widely misunderstood. The study seeks to understand how factors influence the quality of decision-making using business intelligence.
Research Hypothesis
Null hypothesis
It is unclear how and which factors influence the quality of decision-making or how it can be improved to ensure well-informed and timely decisions (Janssen et al., 2017). One of the key challenges facing BI implementations is ensuring that high-quality information is conveyed to the output of BI tools for decision-making. Currently, there is very little literature on the role of information quality and system quality in successful BI implementation. Dooley, Levy, Hackney, & Parrish, 2018).
Objectives of the study
Primary objective
Secondary objectives
Research Methodology
Chapter Outline
Conclusion
Business Intelligence
- Information as an asset
- What is Business Intelligence?
- Components of BI
- Benefits of BI
However, it was later established that in 1958 Luhn in an IBM journal entitled "A business intelligent system" first mentioned the concept. However, the use of the concept at that time was more general and was not implemented for the purposes of decision-making (Chee et al., 2009).
Information Quality
Information quality refers to the quality of the system output as perceived by the decision maker. There are a number of techniques to monitor data quality throughout the lifecycle.
System Quality
However, it was found that the decision maker's understanding of the relationships between entities was paramount and recommended that the decision maker be included in the analysis process. It found that higher quality information reduced decisions if the decision maker did not understand the basic relationships between the variables, while those who understood the relationships lead to better quality decisions.
Service Quality – BI Team
The gap between business analysts and decision makers is widened by the business expert's lack of advanced analytical knowledge and further widened by the analyst's lack of business knowledge. It also found that the more credible the analyst's credibility, the more influence the advice firm has.
BI Competency
Strategic BI capability that provides information about new opportunities or threats and is oriented towards risk taking and discovery, provides new innovative products and services. Operational BI capability provides information used to improve day-to-day operations, resulting in the streamlining of activities.
Decision Making Quality
If the participant selected "I agree" .. the survey questions were presented and they could continue, otherwise if "I do not agree" was selected upon submission, the page would close and there would be no participation. Thus, interpersonal skills of the BI team are extremely important for the quality of decision making. This means that only 65.7% of the decision quality can be explained or accounted for by information quality, system quality and BI team service quality.
It turned out that the three variables explained 65.7% of the variance in the quality of decision making. This is consistent with the findings of the study, where there was a strong positive correlation between information quality and decision quality. This is consistent with the study's findings with a strong positive correlation between BI service quality and decision-making quality.
The findings of the study are consistent with the literature review which found that system quality contributes to better decision making and net benefits to the organization (Delone and McLean, 2003; Wixom and Watson, 2001). This was consistent with the study's findings as there was a positive significant relationship between BI team service quality and BI competency as well as system quality.
BI impact on decision making and firm performance
Chapter Summary
It showed how information can be used as an asset in determining business value, and reviewed literature on information quality, system quality, service quality BI competence and decision quality. It also reviewed literature on BI maturity, BI adoption and critical success factors for BI.
Research Paradigm
Research Design
There is a dedicated business intelligence group that is part of the larger IT department. The results show that 90.3% of respondents agree or strongly agree that they will continue to use BI in the future. The results show that 87.8% of respondents agreed or strongly agreed that BI enables decision-making efficiency, while 7.3% disagreed or strongly disagreed.
H0 – Information quality, system quality and BI service quality have no impact on decision-making quality. To determine if the quality of information has a positive impact on the quality of decision making using business intelligence in Hulamin-KZN;. To determine whether BI service quality has a positive impact on the quality of decision making using business intelligence in Hulamin-KZN.
In order to determine quality, a multiple linear regression revealed that the three variables could explain 65.7% of the variance in the decision-making quality. Furthermore, analysis was done using multiple linear regression to find the strength of the three variables on the influence of decision making.
Research Strategy
Research Setting
Target Population
Therefore, since the entire population is so small, the entire population will be used in the research.
The Research Instrument
Survey Instrument Design
The results of the study are analyzed based on the following research constructs: a) Information Quality, b) System Quality, c) BI Service Quality, d) BI Competence, e) Decision Quality. Investigating the factors that influence the quality of decision making using business intelligence in Hulamin-KZN; Determining whether system quality has a positive impact on the quality of decision making using business intelligence in Hulamin-KZN; And.
This is consistent with the findings of the study with a high correlation between BI service quality and decision-making quality.
Data Collection
Data analysis
Validity
Reliability
Elimination of Bias
Researcher Bias
Selection Sampling Bias
Response Bias
Ethical Considerations
Chapter Summary
Introduction
Descriptive Statistics
- Demographics
- Research Construct 1 – Information Quality
- Research Construct 2 – System Quality
- Research Construct 3 – BI Team Service Quality
- Research Construct 4 – BI Competency
- Research Construct 5 – Decision Quality
The results show that 82.9% agree or strongly agree that the business intelligence team is available for support inquiries, while 9.7% disagree or strongly disagree. The results show that 82.9% of respondents agree or strongly agree that the turnaround time is fast, while 7.3% disagree or strongly disagree. The results show that 80.5% agree or strongly agree that the current sophistication of the tool works well, while 4.8% disagree or strongly disagree.
Results show that 34.3% disagree or strongly disagree that they have adopted BI to its full potential.
Inferential Statistics
- Cronbach Coefficient Alpha
- Correlation
- Multiple Linear Regression
- Hypothesis Testing
While the ANOVA is significant, the R-squared does not explain over 80% of the decision quality, we cannot determine a sufficient cause-effect pattern despite the high correlation. H2 – system quality was found to have a strong positive and significant relationship with decision quality with a correlation value of .733** at the 99% interval. H3 – BI team service quality was found to have a strong positive and significant relationship with decision quality with a correlation value of .529** at the 99% interval.
Therefore, the study rejects the null hypothesis and concludes that information quality, system quality, and service quality of the BI team positively affect decision-making quality.
Chapter Summary
Introduction
This is consistent with literature findings according to which high quality information leads to high quality decision making. A value of 0.733 was also in line with literature findings according to which a good infrastructure and ease of use lead to better decision making. This is also consistent with literature in that analytical skills and domain knowledge together with strong BI management lead to better quality decisions and facilitate organizational learning.
This is consistent with literature, as the firm's resources and cooperation affect the firm's capability.
Demographic Data
The third finding is that BI service quality is positively correlated with decision quality, with a Pearson correlation value of 0.529. This is an important contribution to the company's competitiveness from the resource-based view, as the collaboration between analysts and decision-making and the interaction with the BI resources is unique and difficult to copy. Other findings show that while information quality, system quality, and BI service quality are all significantly positive for BI competency, BI service quality has the strongest influence.
Furthermore, inferential analysis was performed to measure how strong the influence of the three variables under study was.
Constructs
Objective 1 – To determine the influencing factors on decision making quality 83
This showed that a significant Pearson correlation value of 0.374 between information quality and BI service quality meant that high quality information improves the delivery of BI services. The research showed that there was a positive, strong correlation between information quality and system quality. A study by (Kowalczyk and Gerlach, 2015) found that the quality of information had a positive influence on the processing capacity of decision makers.
This was the highest percentage for disagreement among all four questions related to information quality.
Objective 3 – To determine if either system quality positively affected decision-
A Pearson correlation value of 0.653 implied that having high quality information improved the overall system quality perception. Wang and Strong (1996) argued that companies should make an effort to ensure that data quality is equal to the effort to ensure product quality. They introduced a framework based on total quality management principles called total data quality management (TDQM).
It must consider the time, scale and frequency of measurement, large organizations take master data management seriously and have dedicated data management departments and strategies to ensure data quality (Otto, 2015).
Objective 4 – To determine if either BI team service quality positively affected
BI infrastructure was significantly correlated with operational BI capabilities and positively correlated with strategic BI capabilities (Fink et al., 2017). However, the strength is not as strong compared to the other correlations found as for the 95% range, while others were for the 99% interval.
Recommendations
Limitations of the study
Recommendations for further Research
Summary of Chapter
Informed consent to participate in research
Questionnaire
Measuring the effects of business intelligence systems: The relationship between business process and organizational performance. An ambidextrous perspective on business intelligence and analytical support in decision-making processes: Insights from a multiple case study. Business Intelligence & Analytics and Decision Quality - Insights into analytics specialization and information processing modes.
Distinguishing the adoption of business intelligence systems from their implementation: the role of managers' personality profiles.
Gatekeeper Letter
Ethical clearance
Turnitin Report
A first step towards service dominant logic as a new approach to overcome challenges in business intelligence. South African small and medium-sized enterprises' reluctance to adopt and use cloud-based business intelligence systems: a literature review. Transforming decision-making processes: a research agenda for understanding the impact of business analytics on organizations.
