📈 economics

Enhancing Accounting Decision Quality through Business Intelligence Capabilities with Accounting Information Quality as a Mediator and Technology Investment Readiness as a Moderator

This study demonstrates that Business Intelligence capabilities (data management, analytics, and reporting) enhance accounting decision quality in Jordanian organizations by improving accounting information quality, a relationship that is further strengthened by technology investment readiness.

Original authors: Khaled Aldarab’ah, Huthaifa Al-Hazaima, Tamara Adel Al-maaitah, Asma’a Al-Amarneh, Atef B. Al-Qur'an, Areej Faeik Hijazin

Published 2026-08-13

📖 6 min read🧠 Deep dive

CC BY 4.0

Original authors: Khaled Aldarab’ah, Huthaifa Al-Hazaima, Tamara Adel Al-maaitah, Asma’a Al-Amarneh, Atef B. Al-Qur'an, Areej Faeik Hijazin

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine you are trying to bake the perfect cake, but instead of a recipe, you are staring at a mountain of raw ingredients scattered all over the kitchen floor. You have flour, sugar, eggs, and chocolate chips, but they are mixed with dirt, some are expired, and half of them are in different languages. No matter how talented your chef is, if the ingredients are a mess, the cake will be a disaster. This is exactly the situation many companies face today. They have tons of data, but it's often messy, outdated, or hard to understand. In the world of business science, this is called "data management."

To fix this, companies use "Business Intelligence" (BI). Think of BI as a super-smart kitchen robot that can sort the ingredients, wash the dirt off, measure the sugar perfectly, and even tell you which flavor will sell best next week. But here's the catch: just having the robot isn't enough. The robot needs to be plugged into a working power source, and the chef needs to know how to use the controls. If the kitchen is too old to support the robot, or if the chef is afraid to touch the buttons, the robot sits idle. This paper explores how these "robots" (BI tools) actually help accountants make better decisions, but only if the ingredients are clean (high-quality information) and the kitchen is ready to run the machine (technology investment readiness).

The Big Question: Can a Smart Robot Fix a Messy Kitchen?

This study, conducted by a team of researchers from universities in Jordan, asks a very practical question: Does having fancy business intelligence tools actually help accountants make better decisions? And if so, how?

The researchers didn't just guess; they surveyed 300 real people working in accounting, finance, and management at various organizations in Jordan. They wanted to see if three specific "superpowers" of Business Intelligence could turn messy data into gold. These superpowers are:

Data Management: The ability to organize, clean, and store data so it doesn't get lost or corrupted.
Data Analytics: The ability to use tools to find hidden patterns, predict the future, and spot errors.
Reporting and Visualization: The ability to turn complex numbers into easy-to-read charts, dashboards, and pictures.

The team found that these tools don't magically fix decisions on their own. Instead, they work like a relay race. First, the tools improve the quality of the accounting information (making it accurate, timely, and clear). Then, because the information is so good, the quality of the decisions made by managers goes up.

The "Secret Sauce": Information Quality

The study discovered a crucial middleman in this process. It's not just about having the tools; it's about what those tools produce. The researchers found that Business Intelligence capabilities significantly boost the quality of accounting information. When data is managed well, analyzed deeply, and presented clearly, the information becomes reliable.

Think of it like a high-definition TV. If you have a 4K screen (the BI tool) but you are watching a fuzzy, static-filled broadcast (bad data), the picture will still look terrible. But if you have a clear, high-quality signal (good accounting information), that 4K screen makes the picture look amazing. The study showed that the quality of the information is the key reason why better tools lead to better decisions. In fact, the data showed that the link between having good information and making good decisions was very strong.

The "Power Switch": Technology Investment Readiness

Here is where the story gets even more interesting. The researchers found that having the tools isn't the whole story. There is a "power switch" that controls how well the tools work. This switch is called Technology Investment Readiness.

This concept refers to how ready an organization is to spend money and effort on technology. It includes having the right computers, the right software, trained employees, and a budget to keep everything updated. The study found that this readiness acts as a booster.

Imagine you have a high-performance race car (the BI capability). If you have a full tank of premium gas and a professional pit crew (high technology investment readiness), that car will fly. But if you put it on a dirt road with no gas and a broken engine (low readiness), it won't go anywhere, no matter how fancy the car is. The study confirmed that when organizations are ready to invest in technology, the connection between their BI tools and the quality of their information becomes much stronger.

What the Numbers Say

The researchers used a statistical method called SmartPLS 4 to crunch the numbers from their 300 surveys. Here is what they found:

Data Management had a positive effect on information quality.
Data Analytics had the biggest impact of all three tools on improving information quality.
Reporting and Visualization also helped make information clearer and more useful.
Accounting Information Quality was proven to be the bridge that connects these tools to better decision-making.
Technology Investment Readiness was confirmed to strengthen the relationship between the tools and the quality of the information.

The study measured the "strength" of these relationships. For example, the link between having high-quality information and making high-quality decisions was very strong, with a statistical value showing it explains over 50% of the decision quality. The link between the tools and the information quality was also significant, explaining about 68% of the changes in information quality.

The Verdict

So, what does this mean for the real world? The paper concludes that simply buying expensive software isn't a magic wand. To get better accounting decisions, companies need to do three things:

Clean up their data: Make sure the raw ingredients are good.
Use the tools: Analyze and visualize that data to make it understandable.
Be ready to invest: Ensure they have the money, the training, and the infrastructure to support these tools.

The study suggests that if an organization invests in technology readiness, they will get much more value out of their Business Intelligence tools. Without that readiness, the tools might just sit there, unused or underused. The researchers emphasize that the quality of the information produced is the most important factor in turning data into smart decisions.

In short, you can have the best robot in the world, but if your kitchen is a mess and you haven't bought the right ingredients or trained your chef, you're still going to end up with a burnt cake. But if you clean up the kitchen, train your team, and let the robot do its job, you'll be baking championship-winning cakes every time.

Technical Summary: Enhancing Accounting Decision Quality through Business Intelligence Capabilities

Problem Statement
In an increasingly data-driven business environment, organizations face volatile markets and complex regulatory demands that require accurate, timely, and data-backed decision-making. While Business Intelligence (BI) capabilities are recognized as strategic assets for converting raw data into insights, there is a gap in understanding how these capabilities specifically translate into improved accounting decision quality. Existing literature often treats BI as a direct driver of decisions or focuses on general managerial decisions, neglecting the specific mediating role of accounting information quality and the contextual influence of an organization's technology investment readiness. Furthermore, many organizations invest in BI tools without realizing the necessary conditions (such as data governance and infrastructure readiness) required to maximize their impact on accounting outcomes. This study addresses these gaps by investigating the mechanism through which BI capabilities influence accounting decisions, mediated by information quality and moderated by technology readiness.

Methodology
The study employs a quantitative research design using a cross-sectional survey approach.

Sample and Population: Data was collected from 300 employees in Jordanian organizations, specifically targeting individuals in accounting, finance, auditing, information systems, and managerial roles who possess direct experience with accounting information systems and decision-making processes.
Instrument: A structured questionnaire was utilized, measuring six key constructs on a five-point Likert scale:
1. Independent Variables: Business Intelligence Capabilities, operationalized through three dimensions: Data Management Capability, Data Analytics Capability, and Reporting and Visualization Capability.
2. Mediator: Accounting Information Quality (accuracy, relevance, timeliness, completeness, reliability, understandability).
3. Dependent Variable: Accounting Decision Quality (rationality, timeliness, usefulness of decisions regarding budgeting, cost control, investment, etc.).
4. Moderator: Technology Investment Readiness (financial, technical, and managerial ability to invest in digital infrastructure, training, and system integration).
Analysis: The data was analyzed using SmartPLS 4 (Partial Least Squares Structural Equation Modeling). The analysis followed a two-step process:
1. Measurement Model Assessment: Evaluated for reliability (Cronbach's Alpha, Composite Reliability) and validity (Convergent Validity via AVE, Discriminant Validity via HTMT and Fornell-Larcker criteria).
2. Structural Model Assessment: Tested direct, mediating, and moderating relationships using path coefficients, t-values, and p-values. The study also assessed the coefficient of determination ( $R^2$ ), predictive relevance ( $Q^2$ ), and effect sizes ( $f^2$ ).

Key Results
The structural model analysis supported all ten proposed hypotheses:

Direct Effects on Information Quality: All three BI capability dimensions significantly and positively impact Accounting Information Quality (AIQ).
- Data Analytics Capability showed the strongest impact ( $\beta = 0.337$ ).
- Data Management Capability followed ( $\beta = 0.284$ ).
- Reporting and Visualization Capability also had a significant positive effect ( $\beta = 0.246$ ).
Impact on Decision Quality: Accounting Information Quality significantly and positively affects Accounting Decision Quality ( $\beta = 0.712$ ), indicating that high-quality information is a primary driver of decision quality.
Mediation: Accounting Information Quality significantly mediates the relationships between BI capabilities and Accounting Decision Quality. The indirect effects were significant for all three BI dimensions, with the path from Data Analytics Capability through AIQ to Decision Quality being the strongest indirect path ( $\beta = 0.240$ ). The study notes that while mediation is significant, the relationship between Reporting and Visualization Capability and Decision Quality is partially mediated, as the capability also influences decision quality through other mechanisms or direct paths not fully isolated as full mediation in the text.
Moderation: Technology Investment Readiness (TIR) positively moderates the relationships between all three BI capabilities and Accounting Information Quality. This means that the positive impact of BI capabilities on information quality is amplified when an organization has a higher readiness to invest in technology (e.g., infrastructure, training, system upgrades).
Explanatory Power: The model explains 68.4% of the variance in Accounting Information Quality and 50.7% of the variance in Accounting Decision Quality.

Key Contributions

Theoretical: The study extends the Resource-Based View (RBV) and the DeLone and McLean Information Systems Success Model within the accounting context. It empirically validates that BI capabilities act as strategic resources that enhance decision quality primarily through the mechanism of improving information quality, rather than through a direct link.
Contextual: It introduces Technology Investment Readiness as a critical boundary condition, demonstrating that the efficacy of BI capabilities is contingent upon an organization's financial and technical preparedness to support these tools.
Practical: The findings provide actionable guidance for managers and accountants, emphasizing that investing in BI tools alone is insufficient. Success requires a holistic approach that includes robust data management, advanced analytics training, clear visualization tools, and a strong commitment to technological infrastructure and employee development.

Significance and Claims
The paper claims to fill a critical research gap by integrating BI capabilities, information quality, and technology readiness into a unified model for accounting decision-making. The authors assert that their study clarifies how BI capabilities lead to better decisions (via information quality) and under what conditions they are most effective (high technology readiness).

The study concludes that for organizations to enhance accounting decision quality, they must:

Prioritize data governance and management to ensure data accuracy and integration.
Shift from traditional reporting to advanced data analytics to uncover trends and risks.
Utilize visualization tools to make complex data understandable.
Ensure that investments in BI are matched by investments in the necessary technological infrastructure and human capital (training).

The authors modestly acknowledge limitations, noting the cross-sectional nature of the data (precluding causal inferences over time), the reliance on self-reported perceptions, and the restriction of the sample to Jordanian organizations. They suggest that future research should explore longitudinal designs, different national contexts, and additional BI dimensions such as AI and predictive analytics.

Technical Summary: Enhancing Accounting Decision Quality through Business Intelligence Capabilities

More like this