The Changing Importance of Technology Skills for Accountants

in the Context of Artificial Intelligence

Yangchun Xiong

and Yang Peng

2,*

School of Digital Finance, Guangzhou Huashang Vocational College, Guangzhou, China

School of Business and Trade, Guangzhou Songtian Polytechnic College, Guangzhou, China

Keywords: Artificial Intelligence, Accountant, Technology Skills, Educational Background, Job Requirements.

Abstract: The goal of this study is to demonstrate the impact of the changing importance of technology skill under the

evolution of artificial intelligence on the job requirements for accountants. The analysis is based on data from

the Chinese employment market from 2012 to 2022 under different educational backgrounds. The research

objectives are achieved through multiple regression and relative importance analysis. The analysis indicates

that the changing importance of technology skills have significant effects on the job requirements of

accountants. Trends show that from 2012 to 2020, the relative importance of technology skills decreased.

However, this trend was reversed in 2020. Differences exist in both overall characteristics and trend features

for job seekers with different educational backgrounds. The research findings provide insights for

recommendations on how job seekers and educational institutions should take actions in the context of AI to

promote employment and personal development.

1 INTRODUCTION

In 2012, deep Convolutional Neural Network (CNN)

models achieved significant success in the ImageNet

Large Scale Visual Recognition Challenge. In the

same year Microsoft and Google began employing

deep learning approaches to enhance their speech

recognition systems. In 2020, GPT-3 was launched

for commercial operations, and as a continuation of

GPT-3, OpenAI introduced ChatGPT in 2022. With

the rapid development of Artificial intelligence

(hereinafter referred to as AI), technology skills are

receiving increasing attention. For accountant

positions, technology skills primarily encompass

proficiency in accounting, ERP systems, financial

analysis, taxation, and word processing software

operations. Due to its profound impact, AI has

become a central theme in business education and

practice (Xue et al., 2020). The application of AI can

be found across various business functions

(Bejaković et al., 2020). However, existing research

lacks in-depth insights into the evolution of AI

technology and often overlooks differences in

educational backgrounds during the research process,

making it challenging to thoroughly examine the

https://orcid.org/0000-0003-2145-6376

Corresponding author

impact of AI on job skills requirements. The

accounting industries serve as an example with high

levels of automation in business practices, where

computing technologies have replaced a significant

amount of human simple repetitive labour, giving rise

to demands for technology skills. It is generally

believed that repetitive labour is more prevalent in

accounting roles, making technology skills most

essential. Has the evolution of AI changed this

situation? How does the situation differ for job

seekers in the context of AI? These are intriguing and

worthy questions for in-depth exploration.

Therefore, we use the introduction of CNN in

2012 and GPT-3 in 2020 as demarcation points,

conducts an analysis of the impact of the evolution of

AI on the job skills requirements of accountants by

examining the changes in the relative importance of

technology skills. To showcase and analyse

developments in a specific scientific field, the paper

reviews relevant literature and constructs a research

framework based on critical analysis. The research

focuses on the evolution of concepts related to the

impact of AI on the job requirements of accountants.

This is detailed in the section 2 ('Literature Review').

952

Xiong, Y. and Peng, Y.

The Changing Importance of Technology Skills for Accountants in the Context of Artiﬁcial Intelligence.

DOI: 10.5220/0013447600003929

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 27th International Conference on Enterprise Information Systems (ICEIS 2025) - Volume 1, pages 952-958

ISBN: 978-989-758-749-8; ISSN: 2184-4992

After reviewing and presenting literature relevant

to the research theme, the section 3 ('Data and

Methods') introduces the details for creating task

importance variables (TI) and skill importance

variables (SI) used for the numerical evaluation of

relationships between the studied phenomena. The

basis for the presented measurable indices is online

recruitment data from the Sina Weibo platform

spanning from 2012 to 2022. The analysis of the

impact of SI on TI is achieved through panel

regression with random effects, and the analysis of

changes in the relative importance of job skills is

conducted through Relative Importance Analysis

(RI). The results are presented in the section 4

('Results'). Additionally, the research findings enable

the formulation of recommendations on how

accountant employees and educational institutions

can take action to adapt to the impact of AI, as

discussed in the section 5 ('Conclusion').

2 LITERATURE REVIEW

2.1 AI in Accounting

As a burgeoning force the impact of AI on society has

long garnered widespread attention from researchers

(McAfee et al.,2016). As a crucial component of the

business community, the accounting profession has

also been impacted by AI. Chukwuani and Egiyi

(2020) studied the impact of AI on the accounting

industry, showcasing advancements in automating

accounting processes. Huang (2018) investigated the

application of AI in Chinese tax matters. Chukwudi

et al. (2018) demonstrated the positive impact of AI

on the functions of company accountants in the

southeastern region of Nigeria. Lee & Tajudeen

(2020), through research on the use of various AI-

based accounting software in Malaysian

organizations, found that AI adoption is not limited to

large organizations. They observed organizations

using AI-based accountant software to store invoice

images and fully automate the information capture

process. Luan et al. (2020) discussed the challenges

and directions of AI and big data in education

research, policymaking, and industry, including

accounting and auditing education, policy, and

industry. They argued that effective collaboration

among academia, decision-makers, and professionals

from various disciplines is necessary to fully realize

the potential progress of AI and big data in the face of

the innovations and challenges brought about by the

AI and big data revolution.

Regardless of the future disruptions that AI may

bring to this industry, accountants cannot be replaced

by AI in exercising human creativity and judgment

(Hasan, 2021). AI can help businesses achieve three

key objectives: automate business processes, gain

insights through data analytics, and connect with

consumers and employees. The marginal benefits and

costs of achieving these three goals are crucial

considerations for businesses applying AI (Kokina et

al., 2017).

While the significance of the above studies varies

due to different research perspectives, they provide

not only descriptive but also predictive insights,

offering stakeholders such as employers and schools

valuable insights for decision-making in the AI

revolution (Huang, 2018). Hindered by the

characteristics of early AI technologies, many small

and medium-sized enterprises were unable to afford

or develop customized and dispersed AI systems.

Cost factors and technology barriers limited the full

demonstration of AI potential in the accounting

industry. In June 2020, OpenAI launched the first

commercially available large-scale language model

product, GPT-3. By providing a large language model

interface, developers and users can obtain high-

quality feedback with only basic software skills.

However, researchers after 2020 have not fully

recognized the impact of this breakthrough in AI on

the marginal benefits and costs of the three key

objectives for businesses. Research perspectives

mostly remain confined to the pros and cons, leading

to conclusions that have not kept pace with

technology advancements.

2.2 Research Problem & Hypotheses

The impact of AI on the roles of accountants has been

widely discussed from a business competency

perspective. From a skills standpoint, the relationship

between AI and technology skills has garnered

particular attention. This skill set is built on the

foundation of computers replacing humans in tasks

with clear and repetitive rules, making it more crucial

for the accounts. The influence of AI on human social

skills is deemed to be less significant compared to

technology skills, as studies suggest that due to the

limitations of AI technologies, replacing social skills

is more challenging (Huang, 2018). However, social

skills can enhance communication efficiency across

various roles, allowing employees with higher social

skills to gain a competitive advantage through

information exchange (Deming, 2017). Whether the

evolution of AI technology has altered these

conclusions is a worthy topic for discussion.

The Changing Importance of Technology Skills for Accountants in the Context of Artiﬁcial Intelligence

953

The literature review allowed to determine the

following research hypotheses:

Hypothesis 1. The importance of technology skills

for accountants exhibits a fluctuating pattern.

Specifically, with the rise of deep learning in 2012,

the importance of technology skills increased initially

and then declined, until the emergence of GPT-3 in

2020, marking a renewed ascent in the importance of

technology skills.

Hypothesis 2. The importance of technology skills

fluctuates more among low-educated employees

compared to highly educated ones.

3 DATA AND METHODS

3.1 Data Preparation and Methods of

Calculation

This study utilizes data from recruitment

advertisements on Sina Weibo. The specific steps for

data acquisition and processing are as follows: Firstly,

a Python web crawler is employed to collect textual

content from online job advertisements for

accountants posted between 2012 and 2022. Texts

with lengths less than 50 characters or lacking city

data are filtered out. Subsequently, based on job task

information for accountant and financial advisor

positions from the O*NET database, a keyword

library for job tasks is generated. Additionally, a

keyword library for skill requirements is created

using skill requirement information from the O*NET

database. Next, a blank dataset is constructed with

fields named job task frequency, skill requirement

frequency, educational requirements for applicants,

and city of employment. Using natural language

processing programs, the frequency of job task

keywords is extracted from each job advertisement to

generate the dependent variable

Task

.

Skill

requirement keyword frequencies, educational

requirements, and city data are also obtained to create

the explanatory variables 𝑇𝑆



, 𝑆𝑆



, and the Controls.

The generated data is then inserted into the blank

dataset, resulting in a balanced panel dataset of online

recruitment for 42 cities from 2012 to 2022 to

estimate Equation 1.

3.2 Model and Variables

To test the hypotheses in the second part of the study,

we establish the following Equation 1:

Task



=𝛽



+𝛽



𝑇𝑆



+𝛽



𝑆𝑆



+𝛽



𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠



+𝜀



where the dependent variable

Task



represents the

job tasks of accountants and financial advisors in

different companies across various years. The

explanatory variable gauge the technology skill

requirements of different companies, while 𝑆𝑆



measure the social skill requirements. Controls

include variables that may influence job task content,

and 𝜀



is the disturbance term.

3.3 Methodology

The first goal of the empirical analysis is to ascertain

the impact of changes in the importance of skills on

the job requirements for Chinese accountants from

2012 to 2022. The research process involves two

stages. First, we conduct Ordinary Least Squares

(OLS) tests to empirically examine the relationship

between the changes in technology skills and social

skills importance and job requirements. Second, we

employ Generalized Method of Moments (GMM)

with work experience dummy variables as

instruments to address potential endogeneity issues

(Ye et al., 2015).

The second goal of the empirical analysis is to

identify the relative changes in importance between

technology skills and social skills from 2012 to 2022.

To achieve this objective, we employ the Relative

Importance (RI) analysis method (Krasikova et al.,

2011). The fundamental idea of RI is to compare the

relative importance of different explanatory variables

after the model has been formed.

4 EMPIRICAL RESULTS

4.1 Ols Results

The OLS regression results are presented in Table 1.

It is shown that technology skills are significantly and

positively correlated with the job tasks of accountants,

with a substantial coefficient. Although social skills

also exhibit a significant positive correlation with the

tasks of accountants, the coefficient is smaller. This

suggests that, in the Chinese labour market from 2012

to 2022, relative to social skills, technology skills are

more critical for the tasks of accountants. Technology

skills have previously replaced manual skills, and

now they may face potential substitution by AI.

ICEIS 2025 - 27th International Conference on Enterprise Information Systems

954

Table 1: OLS regression.

Accountan

’s tasks

TS 0.794***

[0.063]

SS 0.078***

[0.019]

Constant 8.134***

[0.696]

Control Yes

City dummies Yes

Year dummies Yes

N 7515

0.298

4.2 Relative Importance Analysis

In contrast to examining the impacts of different skills

on accountant’s tasks, our primary concern is the

relative importance of technology skills. This can be

achieved through the Relative Importance (RI)

analysis method.

RI primarily focuses on ranking predictor

variables according to their relative importance by

comparing the additional contribution of these

variables to the variance across all possible sub-

models. The additional contribution of a predictor

variable refers to the increase in explained variance

when that variable is added to a given sub-model. For

ease of analysis, we adopt the method by Krasikova to

standardize the reported RI values below (Krasikova

et al., 2011). Specifically, the RI for all variables is

consolidated into RI total. Subsequently, the ratio of

RI for each variable to RI total is calculated to obtain

standardized contributions. This standardization

advantageously ensures that the sum of the

standardized contributions for all explanatory

variables equals 1, facilitating a more straightforward

comparison of the relative importance of each variable

with others.

Table 2 presents the results of the Relative

Importance (RI) analysis.

Table 2: Relative importance (RI) analysis.

Year

Overall High educatio

Low education

SS TS SS TS SS TS

2012 33% 67% 5% 95% 36% 64%

2013 15% 85% 1 0% 18% 82%

2014 0% 1 15% 85% 1% 99%

2015 9% 91% 60% 40% 10% 90%

2016 20% 80% 28% 72% 18% 82%

2017 6% 94% 6% 94% 6% 94%

2018 29% 72% 53% 47% 20% 80%

2019 36% 64% 12% 88% 35% 65%

2020 57% 43% 24% 76% 60% 40%

2021 44% 56% 36% 64% 44% 56%

2022 39% 61% 36% 64% 36% 64%

All 46% 54% 35% 65% 45% 55%

4.2.1 The Overall Characteristics of

Accountant Positions

Technology skills are relatively more important than

social skills for accountants, with a relative

importance of 54% for technology skills and 46% for

social skills. This overall characteristic is more

pronounced in high-education samples (65%)

compared to low-education samples (55%).

China's higher education system is divided into

undergraduate education and vocational education

through the National College Entrance Examination

(Gaokao). Those with higher Gaokao scores are

identified as having higher intellectual abilities and

enter the undergraduate education level (High

education), while those with lower scores enter the

vocational education level (Low education). It is

generally believed that undergraduate education

focuses more on theoretical learning, while

vocational education emphasizes skill development,

as skills learning is considered to have lower

difficulty compared to theoretical learning. However,

Table 2 reflects that with the evolution of AI

technology, employers set higher requirements for

technology skills for individuals with higher

educational qualifications.

4.2.2 Trend Characteristics of Accountant

Positions

The relative importance of technology skills

experienced a brief increase from 2012 to 2014, but it

steadily declined from 2014 onwards, while the

importance of social skills increased annually. This

trend reversed in 2020(Figure 1).

Figure 1: Comprehensive Evaluation of Relative

Importance.

One possible explanation is that as the automation

level of accountant tasks deepened, employers began

to place increasing emphasis on the technology skills

of accountants, with the rise of deep learning in 2012

The Changing Importance of Technology Skills for Accountants in the Context of Artiﬁcial Intelligence

955

intensifying this focus. However, as AI gradually

infiltrated software-assisted business processes in

accountant positions, the importance of technology

skills gradually diminished. In the process of

integrating AI tools with accountant tasks, employers

gradually realized that traditional accountant staff,

familiar with the nature and processes of the business,

were sufficient for making judgments, while the rest

could be delegated to AI. In a complex organizational

environment, employees with higher social skills

undoubtedly had a greater advantage in achieving the

goal of obtaining business information from other

departments (Deming, 2017). During this phase, AI

tools exhibited a decentralized characteristic.

The introduction of GPT-3 in commercial use in

2020 changed the decentralized nature of AI tools.

GPT-3, a powerful tool encompassing various

capabilities such as judgment, analysis, and

processing, rendered the importance of employees

being familiar with business gradually less significant.

However, using this powerful tool to meet

personalized demands requires a certain level of

technology skills, leading to an increase in the relative

importance of technology skills over social skills.

Furthermore, like the rise of deep learning in 2012,

the appearance of GPT-3 sparked another wave of

public enthusiasm for AI, inadvertently heightening

the importance that employers placed on technology

skills.

This trend feature is even more pronounced in the

low education sample compared to the high education

sample (Figure 2). If the impact of breakthrough

events in AI technology (the rise of deep learning and

the commercialization of GPT-3) heightened people's

attention to technology skills, the gradual decline in

the emphasis on technology skills reflects the actual

demand for technology skills in accounting. Thus, the

demand for highly educated talent in accountant

positions becomes more stable, while the demand for

lower-educated talent exhibits greater volatility.

Figure 2: Comparison of Relative Importance across varied

educational backgrounds.

4.3 Endogeneity Issues

There may be endogeneity issues between task

requirements and skill demands, such as changes in

job task requirements leading to changes in skill

requirements. Although the work content and task

requirements for accountants have seen minimal

long-term changes. To address this potential

endogeneity issue, we conducted Generalized

Method of Moments (GMM) tests on job

requirements and occupational skills.

We chose work experience dummy variables as

instrumental variables. Work experience dummy

variables are significantly correlated with skill

requirements, while the relationship between work

experience and job task requirements is less clear.

Employers have higher skill requirements for

experienced job seekers, but we cannot conclude that

employers will lower skill requirements due to a lack

of experience in job seekers. So, we consider work

experience dummy variables as fine instrumental

variables for skill requirements. The results of the

GMM regression are shown in Table 3. We find that

the results of the GMM tests are generally consistent

with the benchmark OLS tests.

Table 3: GMM estimation.

Accountin

tasks

TS 0.819***

[0.075]

SS 0.067***

[0.025]

Control Yes

Constant 13.256

[8.326]

N 7515

0.288

This table reports the results of the GMM regressions of the

accountant tasks on technology skills, social skill and some

control variables. The robust t statistics are in parentheses.

***, **, * Significance at 1,5, and 10%, respectively.

4.4 Robustness Test

The focus of this paper is the relationship between

task requirements and skill demands. In practice,

there may be different measurement methods for skill

demands, leading to potential measurement errors.

This paper primarily defines technology skills and

social skills based on O*NET Online requirements,

while DEMING provides different definitions for

information usage skills and social skills. In

robustness tests, we combine DEMING (2017)'s

social skills and O*NET on line's social skills as a

new social skills variable and integrate DEMING

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(2017)'s information usage skills and O*NET on

line's technology skills as new technology skills

variables (Deming, 2017).

Table 4 reports the results of regressions using the

new variables. We employed both OLS and GMM,

and the results of the two tests are generally consistent.

These results are generally consistent with Table 1

and Table 3. The results of the Relative Importance

(RI) analysis using new variables are generally

consistent with the results in Table 2. These results

are not reported here to save space.

Table 4: Robustness Test.

OLS GMM

TS 0.663***

[0.035]

0.848***

[0.040]

SS 0.072***

[0.017]

0.175***

[0.039]

Control Yes Yes

Constant 7.966***

[0.704]

6.435

[8.273]

Cit

dummies Yes Yes

Year dummies Yes Yes

N 7515 7515

0.316 0.287

The robust t statistics are in parentheses. ***, **, *

Significance at 1,5, and 10%, respectively.

5 CONCLUSIONS

The theme of discussing the impact of AI on the

labour market is highly popular in the academic

community, inspiring researchers to explore more

research directions and contribute to the development

of this topic. The analysis of this research field

involves both quantitative and qualitative studies.

However, at both the theoretical and empirical levels,

the evolution of AI itself on the labour market has

been overlooked. Simultaneously, the impact of AI

has been exaggerated due to the neglect of different

educational backgrounds. The empirical evidence

obtained from the East Asian countries’ employment

market from 2012 to 2022 will help address these

shortcomings.

However, like any research, this study also has

certain limitations. One of them is the limited

availability of data. Despite obtaining online

recruitment data from Sina Weibo for the years 2012-

2022 through web scraping, many data points were

not acquired due to flaws in web scraping technology

and time limitations in Sina Weibo’s data storage.

Another limitation is the subjectivity of data cleaning

rules, resulting in the exclusion of valuable data from

the scope of the study.

The research findings indicate that the evolution of

AI has led to changes in the relative importance of

skills, affecting the task requirements for accounts.

Overall, technology skills are crucial for accounting

practitioners, especially for highly educated

employees.

From the trend perspective, the importance of

technology skills exhibits fluctuating characteristics,

rising with breakthroughs in AI and gradually falling

until another technological breakthrough occurs. This

feature is more pronounced among low-educated

employees, indicating that the evolution of AI

technology has a greater impact on them.

The findings of this study provide inspiration for

decision-making among relevant stakeholders.

Maintaining economic growth and promoting

employment are important economic goals for

policymakers. Encouraging the application of AI

across various industries can reduce business costs,

improve work efficiency, and thus contribute to

economic growth. However, for the highly automated

accounting industry, there is a potential conflict

between cost reduction and social responsibility that

policymakers should consider when formulating

policies. The impact of AI on employment, especially

for low-educated populations, poses a challenge for

policymakers. Common policies including skills

training tailored to low-educated populations can

solve short-term labour shortages during peak

industry demand, while it leads to the waste of human

resources during off-peak seasons. Furthermore, the

use of most AI tools does not require complex skills,

further reducing the necessity of skills training. The

policymakers could consider implementing flexible

policies to address the impact of AI on accountants.

For instance, establishing subsidies or incentive

programs tailored to various skill levels could support

the transition of lower-educated accountants amid

technological changes. Additionally, enhancing

technical training for lower-educated accountants and

encouraging collaboration between businesses and

educational institutions to provide practical skills

training can help mitigate employment disparities

caused by technological shifts.

As a highly digitized industry, continuously

updating technological skills often leaves accounting

practitioners, especially those with lower educational

backgrounds, in a state of anxiety. The typical

response is to participate in various technology skills

training programs. Meanwhile, training in skills that

appear unrelated to AI is often overlooked, yet these

may be precisely the skills that cannot be replaced by

AI. This study also found that employers have a more

stable demand for employees with higher academic

background, which may suggest that improving

academic qualifications is more important than skills

training for accounting practitioners. When adopting

The Changing Importance of Technology Skills for Accountants in the Context of Artiﬁcial Intelligence

957

AI, employers should carefully balance cost savings

with employee welfare, considering ways to enhance

efficiency through technology without entirely

relying on technology to replace human labour.

Employees should stay informed about industry

trends and actively pursue new skills and career

adjustments to navigate the challenges and

opportunities presented by AI.

The rise of deep learning and the emergence of

ChatGPT have increased people's attention to AI and

directly expanded society's demand for training in

technological skills. Employers are even willing to

hire low-educated employees who were previously

ignored to meet recruitment needs. However, if the

actual impact of AI on the accounting industry is

lower than expectation, employees with lower

academic background will be affected even more.

Thus, schools should update educational programs in

response to industry demands, emphasizing training

in non-technical skills such as critical thinking and

problem-solving, which are less susceptible to

automation.

ACKNOWLEDGEMENTS

This paper is funded by the project "Potential Impacts

and Mechanisms of Large Language Models on the

Accounting Industry" (Project No. 2023WTSCX330)

from Guangzhou Huashang Vocational College.

The data that support the findings of this study are

available in https://github.com/xiongyc98/data1.

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