Domain Expertise and AI Adoption: Insights into HR Managers’

Unified Perspectives Across Roles and Contexts

Guangming Cao

Digital

Transformation Research Center, Ajman University, P.O.Box: 346 Ajman, U.A.E.

Keywords: HR Manager, Artificial Intelligence, Adoption, Attitude, Intention, Domain-Specific Expertise.

Abstract: The integration of artificial intelligence (AI) offers transformative potential for human resource management

(HRM), yet a significant majority of organizations have yet to adopt AI in HRM practices. While much

research focuses on individual-level factors in technology adoption, limited attention has been given to the

role of domain-specific expertise in shaping HR managers’ perceptions of AI. This study addresses this gap

by exploring HR managers’ attitudes and intentions toward AI adoption and examining whether these

perceptions differ by gender, job role, organizational size, or industry. Survey data from 279 HR managers in

China, analyzed using ANOVA, reveal a largely positive, uniform view of AI adoption, with no significant

differences in demographic or organizational factors. These results suggest that shared expertise within HR

may drive a cohesive understanding of AI’s benefits, challenging conventional models that emphasize

individual or contextual variability in technology adoption. This study contributes to the theoretical

framework of technology adoption by highlighting the role of functional expertise in developing uniformity

and provides practical insights for designing AI training and implementation strategies that resonate across

diverse organizational settings.

1 INTRODUCTION

Artificial Intelligence (AI), defined as the ability of

machines to carry out tasks that traditionally require

human intelligence, is transforming human resource

management (HRM) by streamlining recruitment,

enhancing decision-making, and improving

employee engagement (e.g. Malik et al., 2023;

Prikshat et al., 2023). While advancements in

information technology (IT), big data, and analytics

have broadened AI’s application across industries,

HRM is increasingly recognized as a field for AI-

driven transformation (Brock & von Wangenheim,

2019). However, despite AI’s significant potential, its

adoption within HRM remains limited, with

approximately 75% of U.S. organizations yet to

incorporate AI tools into HRM practices (Maurer,

2024). This low adoption rate underscores the need to

better understand factors influencing HR managers’

perspectives on AI adoption (e.g. Vrontis et al., 2022;

Prikshat et al., 2023).

Research on technology adoption often

emphasizes individual characteristics, such as

hierarchical position, industry, and gender, as

determinants of technology adoption intentions

(Venkatesh et al., 2016). Yet, it is unclear if these

factors significantly influence HR managers’ views

on AI. Domain expertise within HR may foster a

shared understanding and favorable perception of

AI’s utility across roles and industries, creating a

cohesive outlook within the HR field that differs from

established models emphasizing individual

variability. This perspective aligns with research

suggesting that professional expertise can shape

homogenous attitudes toward technology adoption

(e.g. Hoffmann & Soyez, 2010). Therefore, exploring

whether HR managers’ shared expertise leads to

uniform AI adoption attitudes, regardless of

individual characteristics, could enhance existing

technology adoption frameworks.

To address these gaps, this study investigates HR

managers’ perspectives on AI adoption and examines

whether these perceptions vary by demographic and

organizational factors, including gender, job level,

firm size, and industry. This exploration is guided by

two primary questions: (1) What are HR managers’

perspectives on adopting AI in HRM? And (2) Do

these attitudes and behavioral intentions vary

significantly across demographic and organizational

factors?

732

Cao, G.

Domain Expertise and AI Adoption: Insights into HR Managers’ Uniﬁed Perspectives Across Roles and Contexts.

DOI: 10.5220/0013232200003929

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 732-739

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

Drawing on the literature on IT adoption and AI

in HRM, this study challenges traditional adoption

theories that emphasize hierarchical or sectoral

differences. Instead, we examine the potential role of

domain-specific expertise in creating a unified

perspective on AI within HR, where shared functions,

processes, and objectives may foster a common view

of AI’s benefits. While Prior IT adoption research

(e.g. Venkatesh et al., 2003) suggests that adoption

intentions are shaped by individual characteristics

(e.g. Mathieson, 1991; Karahanna et al., 1999),

industry type (Wang et al., 2014), and other

demographic factors (Devolder et al., 2012). This

study explores whether such factors exert a

diminishing influence within a specialized field like

HRM.

The findings reveal that HR managers generally

hold positive and uniform views on AI adoption, with

minimal variation across demographic and

organizational factors. These results suggest that

HR’s domain-specific expertise may create a

cohesive understanding of AI’s potential, challenging

the assumption that individual-level differences

always drive technology adoption patterns. This study

extends the literature on IT adoption by proposing

that functional expertise within specialized domains

can lead to a shared perception of technology’s value,

an insight that warrants further theoretical

exploration.

The following sections outline the literature

review and hypothesis development, research

methods, and empirical results. This study concludes

with a discussion of theoretical and managerial

implications, limitations, and future research

directions.

2 LITERATURE REVIEW AND

RESEARCH HYPOTHESIS

2.1 AI in HRM

AI enables computers to perform tasks traditionally

requiring human intelligence, such as autonomous

learning and adaptive decision-making (Jarrahi,

2018; Samuel et al., 2022). In HRM, AI’s

transformative potential is particularly evident in

recruitment, training, and skill development,

enhancing decision-making in hiring, performance

reviews, mobility, and diversity initiatives(Malik et

al., 2022b; Vrontis et al., 2022). For instance, AI can

analyze job portal data and social media to identify

optimal candidates (Tambe et al., 2019), recommend

skill-gap training, and mitigate biases in evaluations

(Pereira et al., 2023). In some cases, AI outperforms

human capabilities, optimizing recruitment practices

and fostering diversity (Pereira et al., 2023).

Studies have explored factors influencing HR

professionals’ adoption of AI (Suseno et al., 2022)

and its role in enhancing employee experiences

through AI bots (Malik et al., 2022a). However,

effective integration requires a strategic approach that

prioritizes transparency, algorithmic fairness, and

ethical concerns (Chowdhury et al., 2023). Successful

AI adoption in HRM depends on both advanced

technology infrastructure and management’s

capabilities to address ethical and operational

challenges.

Generative AI further contributes by creating

context-sensitive content that evolves through user

interactions (Andrieux et al., 2024; Chowdhury et al.,

2024). Achieving productivity gains from AI in HRM

requires a balanced approach that maximizes AI’s

adaptive benefits while mitigating associated risks.

2.2 Research Hypothesis

Research on HR managers’ perspectives on AI

adoption has gained interest (Chowdhury et al., 2023;

Deepa et al., 2024). Widely used theoretical

approaches to IT adoption, such as the theory of

planned behavior, the theory of reasoned action, the

technology acceptance model, and UTAUT,

emphasize attitudes and intentions as key factors

shaping adoption (Venkatesh et al., 2016). While

intention refers to an individual’s decision to adopt a

technology, it is often influenced by attitude, or “an

individual’s overall affective reaction to using a

system” (Venkatesh et al., 2003, p. 455). Although

UTAUT originally omitted attitude, recent studies

(e.g. Dwivedi et al., 2019) argue that it is central to

shaping behavioral intention, reinforcing its

relevance in exploring HR managers’ adoption of AI

in HRM.

Moreover, HR managers’ views on AI may vary

by job role or decision responsibility. For instance,

research indicates that senior managers often possess

deeper insights into the strategic implications of

emerging technologies (Day, 1994; Savioz & Blum,

2002), while others suggest that AI may be better

suited for operational rather than strategic decisions

(Edwards et al., 2000b). Therefore, senior HR

managers may be more cautious in adopting AI for

strategic tasks. Thus, it is proposed:

H1: Attitudes (H1a) and behavioral intentions

(H1b) toward using AI differ among HR managers

Domain Expertise and AI Adoption: Insights into HR Managers’ Uniﬁed Perspectives Across Roles and Contexts

733

based on their job roles or decision-making

responsibilities.

Additionally, while IT adoption research often

considers industry effects (Venkatesh et al., 2016),

few studies have examined whether AI perceptions

differ by industry. Reports suggest that AI adoption

rates vary significantly across sectors, with industries

like manufacturing and finance leading adoption,

while others, such as construction, lag (Forrester,

2018; Deloitte, 2019). These sectoral differences

could influence HR managers’ attitudes toward AI.

Therefore, it is reasonable to propose that:

H2: The attitudes (H2a) and behavioral intentions

(H2b) towards using AI differ among HR managers

from different industries.

Firm size also plays a role in IT adoption; larger

firms typically have the resources to support AI

initiatives, while smaller firms may face resource

constraints (e.g. Gillon et al., 2014). Therefore, HR

managers from organizations of varying sizes are

likely to differ in their AI adoption perspectives.

Thus, this study proposes that:

H3: The attitudes (H3a) and behavioral intentions

(H3b) towards using AI differ between managers

from different sizes of companies.

Finally, prior research on IT adoption has

identified gender as a moderating factor, citing social

hierarchy and psychological differences (Borghans et

al., 2009; Hovav & D’Arcy, 2012). In HRM, gender

effects on technology adoption are inconclusive, with

some studies noting differences (e.g. Festing et al.,

2015; Guillén et al., 2018) and others finding none

(e.g. Powell, 1990; Sanders & De Cieri, 2021).

Accordingly, this study proposes the following

hypothesis:

H4: The attitudes (H4a) and behavioral intentions

(H4b) towards using AI differ among male and

female HR managers.

3 RESEARCH METHODOLOGY

The two constructs, attitude, and intention to use were

measured using four and three items respectively,

adapted from prior studies (Table 2) (Venkatesh et

al., 2003; Cao et al., 2021).

A questionnaire survey was used to collect 279

responses from HR managers in China. In line with

the research questions, the following data were

collected: first, information about the HR manager’s

profile including the manager’s position and gender

(Table 1a); second, the company’s profile including

industry type and company size (Table 1b); third and

most importantly, the managers’ perspectives on

integrating AI into HRM. As shown in Table 1a, 10%

of the respondents were HR directors; 18% were HR

managers, 52% were HR specialists, and 20% were

HR team leaders. Regarding gender, 51% were male

and 49% were female. As indicated in Table 1b, 20%

of respondents were from the manufacturing sector,

19% from technology, 17% from services, 12% from

the finance and insurance sector, 10% from energy

and public sector, and 22% from other nine industry

sectors. Of all respondents, 19% were from

organizations with 50 to less than 100 employees,

43% having 100 to less than 250 employees, 27%

having 250 to less than 500 employees, and 11%

having 500 or more employees.

Table 1a: Respondent position and gender (n=279).

Respondent Position % Gende

HR Directo

10 Male 51

HR Manage

18 Female 49

HR Specialist 52

HR team leade

Table 1b: Industry type and company size (n=279).

Industry type % Firm size %

Manufacturing 20 50 to less than 100 19

Technology 19 100 to less than 250 43

Services 17 250 to less than 500 27

Finance and insurance 12 500 or more 11

Energy & public secto

Othe

4 ANALYSIS AND RESULTS

The survey results were analyzed using the SPSS®

statistics 29 to examine HR managers’ perspectives

on adopting AI in HRM. Each question was assessed

using a five-point Likert scale, ranging from 1

(strongly disagree) to 5 (strongly agree). Table 2

provides a summary of the responses. Notably, over

80% of the HR managers surveyed expressed a

positive attitude toward using AI in HRM, and more

than 84% indicated an intention to use AI in HRM

practices.

Table 2: % of respndents’ answers (n=279).

Item 1 2 3 4 5

Usin

AI-enabled HRM s

stems is a

ood idea 0 1 12 51 36

Usin

AI-enabled HRM s

stems is a foolish idea

52 29 18 1 0

I like the idea of usin

AI-enabled HRM s

stems 0 1 11 62 25

Usin

AI-enabled HRM s

stems would be

leasant 0 1 14 50 35

I intend to use AI-enabled HRM s

stems in the future 0 2 14 44 40

I would use AI-ena

led HRM s

stems in the work

lace 0 1 13 55 31

lan to use AI-enalbed HRM s

stems for m

work 0 1 12 51 36

-reverse-coded

Interestingly, only up to 18% of HR managers

expressed a “neutral” stance on adopting and

ICEIS 2025 - 27th International Conference on Enterprise Information Systems

734

intending to use AI in HRM, indicating that relatively

few had “no opinion”−a position often associated

with neutrality in prior methodological studies

(Sturgis et al., 2014; Nadler et al., 2015). This limited

neutral response suggests that most HR managers in

this study had a clear perspective and likely a solid

understanding of AI applications in HRM.

Concerning H1a/b, A one-way ANOVA was

performed using latent variables rather than their

associated indicators to understand if HR managers’

attitudes and intentions differ significantly among

managers with different positions. The ANOVA

result indicated that while the assumption of

homogeneity of variance was not violated as the two

significance values for Levene’s test were 0.941 and

0.119 respectively, there were no statistically

significant differences at the p<.05 level in the scores

of attitude and intention to use for the manager with

different positions. As a result, H1a and H1b were

rejected.

To test H2/b that positing HJR managers' attitudes

and behavioral intentions towards using AI differ

among HR managers from different industries, a one-

way ANOVA was also conducted to examine if there

was a statistically significant difference in scores for

the different industry groups. The results indicated all

mean scores were not statistically different. Thus,

H2a/b was rejected.

To test H3a/b H3 assuming that attitudes (H3a)

and behavioral intentions (H3b) towards using AI

differ between HR managers from different sizes of

companies, a one-way ANOVA was performed. The

two variables’ significance Levene’s scores were

0.232 and 0.597, suggesting the assumption of

homogeneity of variance was not violated. There was

a statistically significant difference at the p<0.05

level only in attitude scores for the four groups of

organizations (Group 1: 50 to less than 100

employees; Group 2: 100 to less than 250; Group 3:

250 to less than 500; and Group 4: 500 or more)

[F=2.720, p=0.045]. However, post-hoc comparisons

using the Tukey HSD test indicated that there were no

significant differences among the groups. Thus,

H3a/b was rejected.

Finally, an independent-sample t-test was

performed to compare the intention to use scores for

male and female HR managers (H4a/b). There was no

significant difference in scores for males (M=4.12,

SD=0.62) and females (M=4.21, SD=0.50; t(277)=-

1.278, p=0.202). The attitude scores for male and

female HR managers also showed no significant

difference for males (M=3.29, SD=0.32) and females

(M=3.38, SD=0.36; t(277)=-1.969, p=0.066). As a

result, H4a/b was rejected.

5 DISCUSSION

5.1 Discussion

Research suggests that AI can significantly enhance

HRM processes and functions (e.g. Malik et al.,

2022b; Vrontis et al., 2022), underscoring the need to

understand HR managers’ perspectives on the

adoption of AI within HRM. This study also explores

whether notable differences exist in HR managers’

attitudes and intentions toward adopting AI based on

job role (H1a/b), industry (H2a/b), organizational size

(H3a/b), and gender (H4a/b).

Regarding differences in attitudes and intentions

across HR roles (H1a/b), the findings reveal no

significant variation among HR managers at different

organizational levels. This consistency suggests that

regardless of position, HR managers share a similar

understanding of AI’s potential benefits in HRM.

This result contrasts with previous research, which

suggests that senior management plays a critical role

in adopting IT (Jeyaraj et al., 2006; Brock & von

Wangenheim, 2019; Duan et al., 2019) and often

spearheads responses to major technological trends

(e.g. Day, 1994) to maintain competitiveness (Savioz

& Blum, 2002). Additionally, it challenges the view

that AI is more suited to operational rather than

strategic levels (Edwards et al., 2000a) and the belief

that individual characteristics heavily influence

users’ behavioral intentions (e.g. Wang et al., 2014).

Unlike these studies, this study’s findings focus

specifically on HR managers’ perspectives on AI in

HRM, irrespective of their decision-making

responsibilities.

About attitudes toward and intentions to integrate

AI into HRM across different industries (H2a/b)-

specifically manufacturing, technology, professional

services, finance and insurance, energy and public

sector, and others study finds no statistically

significant differences. This result appears

inconsistent with the notion that AI adoption varies

widely by industry (Forrester, 2018; Deloitte, 2019).

This seeming inconsistency may be due to differences

in study scope; previous research examined AI

adoption across general industry contexts, whereas

this study focuses specifically on AI adoption in

HRM. This discrepancy highlights an intriguing area

for further research.

Concerning perspectives on AI adoption across

organizations of different sizes (H3a/b), this study’s

findings run counter to expectations and diverge from

previous studies (e.g. Gillon et al., 2014). The result

suggests no empirical support for significant

differences in AI perceptions between managers in

Domain Expertise and AI Adoption: Insights into HR Managers’ Uniﬁed Perspectives Across Roles and Contexts

735

small. Medium, and large organizations, indicate

similar outlooks on AI’s role in HRM regardless of

organizational size.

Lastly, concerning gender-based differences in

attitudes toward and intention to use AI in HRM

(H4a/b), the study finds no empirical support for such

differences. This result aligns with research

suggesting no significant gender difference in HRM

roles (e.g. Powell, 1990; Sanders & De Cieri, 2021),

but contrasts with prior studies suggesting a gender-

based difference in technological adoption (Borghans

et al., 2009; Hovav & D’Arcy, 2012) and in other

HRM research (e.g. Festing et al., 2015; Guillén et

al., 2018). This finding contributes new empirical

insights in the context of AI in HRM, reinforcing the

view of gender neutrality in HRM technology

adoption.

The lack of differences in attitudes and behavioral

intentions indicates that HR managers, irrespective of

job level, industry, organizational size, or gender,

generally perceive AI as beneficial for enhancing HR

functions. This uniformity likely stems from the

specialized nature of the HR field, where managers

engage with a consistent set of functions, processes,

and objectives. Such shared professional focus may

cultivate a collective understanding of tools and

technologies that enhance HRM, leading to similar

views on the value and applications of AI. Moreover,

HR managers’ expertise within this specialized

domain likely reduces variability in attitudes and

intentions toward AI use, aligning their perspective

on its practical implications for HR processes. This

alignment offers a promising avenue for further

research into how domain-specific expertise shapes

technology adoption, which is a point reinforced in

other sectors (Hoffmann & Soyez, 2010; Kamal et al.,

2011; Nakandala et al., 2024). For instance, both

Nakandala et al. (2024) and Kamal et al. (2011)

highlight the importance of domain-specific

knowledge and expertise in facilitating technology

adoption across various fields.

5.2 Theoretical Implications

This study reveals that HR managers’ shared

expertise may act as a unifying factor in their

perceptions of AI’s value, regardless of individual

differences such as job level, industry, or gender. This

suggests that technology adoption frameworks could

be refined to account for the effects of domain-

specific expertise within specialized fields like HRM.

Unlike traditional models, which often prioritize

individual differences (Devolder et al., 2012; Wang

et al., 2014), this finding underscores the potential for

specialized knowledge to drive homogeneity in

technology adoption attitudes, opening the door for

frameworks that consider how collective expertise

within functional areas may shape uniform

perspectives on technology.

The study’s finding that HR managers exhibit

consistent attitudes toward AI, regardless of seniority,

challenge established perspectives (Jeyaraj et al.,

2006; Brock & von Wangenheim, 2019; Duan et al.,

2019) that often link seniority to adoption likelihood.

In specialized domains like HR, where collective

expertise appears to guide the perception of AI,

hierarchical roles may have less impact on

technology-related attitudes. This suggests a need for

theoretical frameworks that explore how shared

functional expertise can override hierarchical

distinctions, thereby offering a fresh lens for studying

technology adoption in expertise-driven

environments.

Contrary to the widely held view that industry

context strongly influences technology adoption

levels, this study highlights that in specialized

functions such as HRM, industry boundaries may be

less relevant to shaping technology perceptions. This

points to a theoretical distinction between function-

specific and industry-specific technology adoption

patterns, suggesting that, within certain specialized

domains, functional expertise can lead to

homogeneity in technology attitudes. Future studies

should explore this distinction, assessing whether a

functionally unified view of technology adoption

holds across other areas where professional

objectives and processes are similarly aligned.

The absence of significant gender-based

differences in HR managers' attitudes and intentions

toward AI adoption supports the notion of gender

neutrality in specialized professional roles (e.g.

Powell, 1990; Sanders & De Cieri, 2021). This result

implies that, in the HR domain, shared expertise and

a cohesive professional culture may promote

egalitarian views on technology adoption,

diminishing the relevance of gender as a

differentiating factor. This calls for further theoretical

work on whether similar gender-neutral perceptions

of technology exist in other specialized fields,

particularly those where shared domain expertise

might similarly drive homogeneity in technology-

related attitudes.

5.3 Managerial Implications

Given the consistency of AI perceptions among HR

managers across job levels, industries, and

organizational sizes, organizations can develop

ICEIS 2025 - 27th International Conference on Enterprise Information Systems

736

unified, standardized AI training programs in HRM,

this uniformity suggests that HR managers would

respond positively to a cohesive training framework

that covers AI’s applications in HRM, thereby

reducing the need for highly customized training

based on hierarchical or organizational distinctions.

Since HR managers appear to share similar

attitudes towards AI’s role, regardless of their

position or organizational setting, fostering cross-

level collaboration in AI initiatives may enhance

adoption efforts. Encouraging collaborative efforts

among HR professionals can lead to the faster

integration of AI technologies and smoother

transitions, as professionals are already aligned in

their perceptions of AI’s benefits.

For AI vendors and consultants working with HR

departments, the absence of industry-based

differences implies that implementation strategies for

HRM AI applications can be broadly applicable

across sectors. This allows for more streamlined

marketing and deployment strategies that focus on

HR-specific needs rather than industry-specific

variations, potentially reducing costs and increasing

the scalability of AI solutions.

The lack of gender-based differences in attitudes

and intentions towards AI adoption suggests that HR

departments may be particularly well-suited to

equitable AI adoption practices. Managers can

leverage this finding to promote inclusive technology

policies within HRM, reinforcing gender neutrality in

AI initiatives and fostering a supportive environment

for all employees engaging with new technologies.

Organizations should consider the potential of

functional expertise as a unifying factor when

implementing new technologies like AI. In HRM, this

may mean prioritizing insights from HR professionals

with deep functional knowledge over hierarchical

decision-making structure, as HR professionals

appear to hold a consistent, collective understanding

of AI’s value in the HR process. This approach may

streamline technology adoption and enhance the

effectiveness of technology applications in HR

functions.

5.4 Limitations and Future Research

First, the present study primarily aims to offer a

descriptive and exploratory view of HR managers’

perspectives on AI adoption and potential perception

differences among managerial groups. Future

research could expand this work by examining

various factors, such as technological readiness,

external pressures, and organizational culture (Grover

et al., 2020), that may affect HR managers’ views on

AI in HRM. Additionally, studies could investigate

how domain-specific expertise contributes to

homogeneity in AI perceptions within HRM, building

on the notion that shared expertise may drive uniform

adoption attitudes.

Second, the findings of this study are based on a

sample of HR managers in China, which may limit

the generalizability of the results. Future research

could test this work in diverse geographic and cultural

contexts to explore whether the observed

homogeneity in AI perceptions is consistent across

different countries and cultural environments.

Comparative studies would also help uncover any

cultural nuances that may affect how domain

expertise shapes technology adoption across regions.

Finally, as an exploratory study using descriptive

and traditional statistical analysis methods, this

research provides an initial understanding of AI

adoption perceptions. Future research could employ

structural equation modeling to investigate the

relationships among specific factors, such as

hierarchical roles, gender, and industry, and their

influence on AI attitudes within HR. Additionally,

qualitative approaches could deepen insights into the

motivations, challenges, and strategic implications of

AI adoption in HRM, offering a richer understanding

of how specialized expertise and functional alignment

shape technology perceptions and behaviors in HR.

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