Management accounting practices mediate the relationship between enterprise resource planning system and sustainability performance under moderating role of corporate governance

Published Dec 11, 2025 on Dryad. https://doi.org/10.5061/dryad.r2280gbs3

Data files

Dec 11, 2025 version files 47.28 KB

Raw_data_MAP_(11.12.25).xlsx

36.79 KB
README.md

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Abstract

This study examined the effect of Enterprise Resource Planning (ERP) system factors on the performance of listed firms in Vietnam, which is intervened by Management Accounting Practices (MAPs). Besides, the study investigated the moderating role of corporate governance in the relationship between MAPs and firm performance. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), the findings reveal that technical support has the strongest influence. Additionally, organizational strategy and user involvement are key drivers, emphasizing the importance of structured processes and stakeholder engagement in MAPs, which enhancing firm performance. The study also highlights the moderating role of corporate governance in strengthening the effect of MAPs on firm performance. Aligned with the Resource-Based View (RBV) and Dynamic Capabilities Theory (DCT), the results suggest that well-integrated, ERP-driven MAPs contribute to a sustained competitive advantage and improved firm performance.

Access this dataset on Dryad: https://doi.org/10.5061/dryad.r2280gbs3

Overview

This dataset contains firm-level survey responses used to investigate how Enterprise Resource Planning (ERP) systems influence sustainability performance, with Management Accounting Practices (MAPs) acting as a mediator and Corporate Governance (CG) as a moderator. No experimental procedures were conducted.

Description of the Data and File Structure

The dataset is provided as a single Excel file (Raw_data_MAP_(11.12.25).xlsx) containing two sheets.

Sheet 1 — Data

This sheet includes raw, anonymized survey responses.

Each column represents a coded item (e.g., OS1, ET3, MAP2).
Each row represents a respondent.
Empty cells indicate missing responses due to skipped or declined questions.

All items were measured on a 5-point Likert scale:
1 = Strongly disagree
2 = Disagree
3 = Neutral
4 = Agree
5 = Strongly agree

No personally identifiable information is present.

Sheet 2 — Explanations of Constructs and Items

This sheet provides an overview of the measurement constructs and the coding structure used in the dataset.

Clarification of “Items”

In this dataset, “items” refer to individual survey statements/questions used to measure each latent construct.
Each item corresponds to a survey question that respondents answered using a 5-point Likert scale.

The original survey contained full-text statements for each item.
However, before data entry, all survey statements were coded into item identifiers (e.g., OS1, ET2, MAP3).

This means that the Excel data file contains only the coded items, not the raw text of each question.

To ensure transparency, Sheet 2 explains:

the constructs,
the item codes,
and the mapping between constructs and item identifiers.

Because the survey was encoded, the full wording of each item is not embedded in the data sheet.
This is why the data appear as numerical Likert values (1–5) corresponding to the coded items.

A complete reconstruction of the measurement model is provided below.

Factor	Code	Items
Organizational strategy	OS	OS1	OS2	OS3	OS4
Education and Training	ET	ET1	ET2	ET3	ET4
User involvement	UI	UI1	UI2	UI3
Technical support	TS	TS1	TS2	TS3
Management Accounting Practices	MAP	MAP1	MAP2	MAP3
Firm performance	FP	FP1	FP2	FP3
Corporate governance	CG	CG1	CG2	CG3

No	Items	Code/Items
I	Organizational strategy	OS
1	Our firm ensures that strategic objectives are consistently aligned with operational activities and internal accounting controls.	OS1
2	Our business strategy requires the use of relevant and comprehensive management accounting information to support decision-making.	OS2
3	Our firm prioritizes the development of core and distinctive capabilities as part of its strategic orientation to gain competitive advantage.	OS3
4	Our firm places strong emphasis on effectively implementing strategic plans across all departments and functional units.	OS4
II	Education and training	ET
1	Employees receive adequate training to understand ERP concepts, system logic, and the integration of business processes across departments.	ET1
2	The training programs provide detailed guidance on ERP functions that support accounting tasks and management reporting.	ET2
3	Employees are given hands-on ERP practice that enhances their confidence and competence in performing accounting-related activities.	KM3
4	ERP training improves our ability to use the system effectively for financial and managerial decision-making.	KM4
IV	User involvement	UI
1	User involvement across departments enhances the integration of accounting information, thereby improving budgeting, cost management, and performance evaluation.	UI1
2	Users provide continuous feedback that improves ERP-based accounting processes, making management accounting more flexible and responsive to business changes.	UI2
3	Cross-departmental user participation strengthens the integration of accounting information, leading to better budgeting, cost control, and performance assessment.	UI3
V	Technical support	TS
1	The technical support team ensures accurate data processing and stable ERP system operation, thereby enhancing the quality of our management accounting practices.	TS1
2	Technical support assists in customizing and adjusting ERP functions to align with our firm's accounting processes and reporting requirements.	TS2
3	Technical support enables us to fully utilize ERP tools, such as dashboards and analytical features, to improve financial planning, cost management, and decision-making.	TS3
VI	Management accounting practice	MAP
1	Our management accounting system provides timely and relevant information that supports strategic and operational decision-making across departments.	MAP1
2	Our management accounting practices effectively support budgeting, cost management, and performance evaluation through ERP-based reports and analyses.	MAP2
3	Our management accounting practices are flexible and can be quickly adapted to changes in the business environment and organizational strategy.	MAP3
VII	Firm performance	FP
1	The use of management accounting practices, such as budgeting, performance measurement, and cost control, has improved our firm’s operational efficiency.	FP1
2	Management accounting practices have contributed to improved financial performance, including higher profitability and more effective cost management.	FP2
3	Management accounting practices have strengthened our firm’s ability to achieve strategic objectives and maintain long-term sustainability.	FP3
VIII	Corporate governance	CG
1	Our corporate governance system ensures that managers are held accountable for using management accounting information appropriately in decision-making.	CG1
2	Our corporate governance practices enhance transparency and improve the reliability of management accounting information used for planning and performance evaluation.	CG2
3	Strong corporate governance in our firm ensures that insights and analyses from management accounting practices are effectively implemented to improve organizational performance.	CG3

Data Collection and Ethics

Data were collected between November 2024 and January 2025 using a convenience sampling approach.
Participants completed the questionnaire during class sessions based on availability.
Respondents were informed about the academic purpose of the study, confidentiality protections, and their right to withdraw at any time.
Verbal consent was obtained prior to participation and documented in a participation log.
No minors participated; therefore, parental consent was unnecessary.
All data were anonymized and used solely for academic research.
The study complies with ethical standards regarding confidentiality, data protection, and academic integrity.

Code / Software

No analysis scripts are included in this deposit.
The dataset can be analyzed using:

Microsoft Excel
SmartPLS software

This study employs a quantitative research approach utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM) to investigate the research question. Data processing is conducted using SmartPLS software following a structured analytical procedure, including descriptive analysis, factor loading and convergent validity assessment, reliability testing of scales, discriminant validity evaluation, and multicollinearity assessment. Additionally, model fit is assessed using R² or adjusted R² values.

The recruitment of participants for this study took place from November 2024 to January 2025. A convenience sampling technique was used, where participants were invited to complete the questionnaire based on their availability during class hours. All participants were informed about the study’s academic purpose, the confidentiality of their responses, and their right to withdraw at any stage without any repercussions. Verbal consent was obtained from all participants before they took part in the study. This consent process was witnessed by the research team and documented through a record of participation. Since the study did not involve minors, parental or guardian consent was not required. No personally identifiable information was collected, and all responses were anonymized and aggregated for analysis. This study adheres to strict ethical standards to ensure participant confidentiality and privacy. The collected data is solely used for academic purposes, and the findings are presented in a manner that safeguards the anonymity of all participants.

Besides, the f-square (f²) statistic in SmartPLS is a key effect size measure that assesses the impact of an independent variable on a dependent variable in a partial least squares structural equation modeling (PLS-SEM) framework. It helps determine whether an exogenous construct has a substantive impact on the endogenous construct. According to (Cohen, 2013), the effect size values are interpreted as follows: Small effect: if f² ≥ 0.02; Medium effect: if f² ≥ 0.15; Large effect: if f² ≥ 0.35.