Design of Activity-Based Cost Accounting System for Software Enterprises Against the Background of Big Data Analysis

Xiaoxia Ke

2022

Abstract

Based on the related theory of activity-based costing, this paper analyzes the cost structure and characteristics of software enterprises through big data technology and further evaluates the application necessity and feasibility of activity-based costing in software enterprises in such an era of big data. The basic principles of activity-based costing are adopted to design a set of activity-based cost accounting systems for software enterprises in terms of confirming activities and activity centers, determining resource costs, selecting both resource and activity drivers, deciding costing objects and costing periods, and setting up account systems, aiming to improve the costing accuracy, upgrade the level of cost management, and more facilitate the development of software enterprises.

Paper Citation

in Harvard Style

Ke X. (2022). Design of Activity-Based Cost Accounting System for Software Enterprises Against the Background of Big Data Analysis. In Proceedings of the 1st International Conference on Public Management, Digital Economy and Internet Technology - Volume 1: ICPDI; ISBN 978-989-758-620-0, SciTePress, pages 565-571. DOI: 10.5220/0011752100003607

in Bibtex Style

@conference{icpdi22,
author={Xiaoxia Ke},
title={Design of Activity-Based Cost Accounting System for Software Enterprises Against the Background of Big Data Analysis},
booktitle={Proceedings of the 1st International Conference on Public Management, Digital Economy and Internet Technology - Volume 1: ICPDI},
year={2022},
pages={565-571},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011752100003607},
isbn={978-989-758-620-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Public Management, Digital Economy and Internet Technology - Volume 1: ICPDI
TI - Design of Activity-Based Cost Accounting System for Software Enterprises Against the Background of Big Data Analysis
SN - 978-989-758-620-0
AU - Ke X.
PY - 2022
SP - 565
EP - 571
DO - 10.5220/0011752100003607
PB - SciTePress