




































BANGLADESH JOURNAL OF MULTIDISCIPLINARY SCIENTIFIC RESEARCH 10(1) (2025), 11-21 

11 

        MULTIDISCIPLINARY SCIENTIFIC RESEARCH 
          BJMSR VOL 10 NO 1 (2025) P-ISSN 2687-850X E-ISSN 2687-8518 

         Available online at https://www.cribfb.com 

     Journal homepage: https://www.cribfb.com/journal/index.php/BJMSR 

                                                                                                                                                                                                    Published by CRIBFB, USA 
                                                                                                                                     

EVALUATING THE IMPACT OF CHATGPT IMPLEMENTATION 

ON HR PERFORMANCE SKILLS: A CASE STUDY OF ACADEMIC 

INSTITUTIONS IN OMAN          
 

 Boumedyen Shannaq (a)    Ahmed Alabri (b)1    
 

(a) Associate Professor, Management of Information Systems Department, University of Buraimi, Sultanate of Oman; E-mail: 

boumedyen@uob.edu.om 
(b) Assistant Professor, Finance and Administrative Affairs & Supporting Services, University of Buraimi, Sultanate of Oman; E-mail: 

drahmed.s@uob.edu.om 
                     

 
A R T I C L E I N F O 

 
 

Article History: 

 

Received: 25th September 2024 

Reviewed & Revised: 25th September 2024 

to 28th January 2025 
Accepted: 30th January 2025 

Published: 5th February 2025 

 
Keywords: 

 
ChatGPT Implementation, HR  

Performance Skills, User Training,  

ChatGPT Usability, AI for  

Academic Institutions 

 
JEL Classification Codes: 

 

      G32, F65, L66, L25, M41  

       

      Peer-Review Model:  

 

      External peer review was done through  
      double-blind method. 

        

 
A B S T R A C T      

 
Academic institutions increasingly integrate AI-driven tools like ChatGPT to enhance HR performance. 

However, the effectiveness of such adoption depends on various factors. The study investigates the 

impact of ChatGPT implementation on HR performance skills in Omani academic institutions, with user 

training, system usability, and cultural factors as moderating variables. Using a structured 

questionnaire, this study employs survey data collected from 791 HR staff from five academic 

institutions. Structural Equation Modeling (SEM) was used to examine direct and indirect relationships 

within the proposed framework and test the conceptual model. The outcomes show that implementing 

ChatGPT enhances the performance of HR skills predictably (B = 0.092, P = 0.003). User training 

showed a partial mediation effect (B = 0.090, t = 4.615, P = 0.000), which is evident as a key facilitator 

of the relationship between ChatGPT adoption and HR outcomes. In the same way, system usability was 

found to have a lesser but significant partial mediation effect (B = 0.019, t = 1.715, P = 0.043). However, 

organizational culture did not moderate the relationship significantly, B = 0.005, t = 0.922, P = 0.178. 

The findings of this study suggest that integrating AI-enabled tools like ChatGPT in academic HR 

settings requires a user-centered system development approach and targeted training. The results 

contribute to the literature by establishing a theoretical foundation for understanding how technology 

applications influence institutional efficiency and goal attainment, particularly in regions where 

education significantly impacts socio-economic development. 

 
 

© 2025 by the authors. Licensee CRIBFB, USA. This open-access article is distributed under the 
terms and conditions of the Creative Commons Attribution (CC BY) license 
(http://creativecommons.org/licenses/by/4.0).  

            

 

INTRODUCTION 

Businesses use artificial intelligence to transform human resource management systems, including recruitment practices, 

employee decision-making capabilities, and interaction methods (Pandey, Singh, Arora, & Batta, 2024). International 

companies invest in AI recruitment platforms and workforce management tools that workforce management tools that 

enhance process automation and operational efficiency (Mamuli et al., 2025). The assessment of ChatGPT and similar AI 

language models for HR performance advancement has not been extensively researched. Current research explores machine 

learning use in recruitment and HR decision-making (Budhwar et al., 2023; Ajayi & Udeh, 2024; Thakral et al., 2023) yet 

omitted essential insights about the full effects on staff development as well as strategic human capital management 

functions from ChatGPT adoption. Facing a worldwide growth trend of educational institutions adopting ChatGPT, 

professionals now have the opportunity to enhance their HR performance capabilities, particularly in talent recruitment and 

staff evaluation, along with employee retention initiatives. HR departments use artificial intelligence models to analyse 

unstructured employee feedback, enhancing their ability for qualitative workforce evaluation and improved decision-making 

(Chukwuka & Dibie, 2024). The success of ChatGPT in HRM functions depends on three key elements, including system 

usability alongside user training and organizational culture, which shape how the system is adopted and its resulting 

performances. This research addresses the knowledge gap regarding the effect of ChatGPT on human resource performance 

throughout academic institutions in Oman by providing empirical findings. Successful integration of AI technology by HRM 

                                                      
1Corresponding author: ORCID ID: 0009-0009-9743-211X 
© 2025 by the authors. Hosting by CRIBFB. Peer review is the responsibility of CRIBFB, USA.  

https://doi.org/10.46281/bjmsr.v10i1.2281 

 
To cite this article: Shannaq, B., & Alabri, A. (2025). EVALUATING THE IMPACT OF CHATGPT IMPLEMENTATION ON HR PERFORMANCE 

SKILLS: A CASE STUDY OF ACADEMIC INSTITUTIONS IN OMAN. Bangladesh Journal of Multidisciplinary Scientific Research, 10(1), 11-21. 

https://doi.org/10.46281/bjmsr.v10i1.2281 

http://creativecommons.org/licenses/by/4.0/)
http://creativecommons.org/licenses/by/4.0/)
https://www.openaccess.nl/en
https://doi.org/10.46281/bjmsr.v10i1.2281
https://orcid.org/0000-0001-5867-3986
https://orcid.org/0009-0009-9743-211X


Shannaq & Alabri, Bangladesh Journal of Multidisciplinary Scientific Research 10(1) (2025), 11-21

 

12 

departments depends on solving three main challenges between tool usability, technical support, and cultural acceptance. 

HR performance enhancement through AI lacks enough research about how user training measures interact with system 

usability features and organizational cultural backgrounds (Vrontis et al., 2022; Ardichvili, 2022). This research investigates 

how implementing ChatGPT affects HR performance skills within Omani academic institutions by evaluating user training, 

system usability, and organizational culture as essential moderating variables. The analysis of this research focuses on 

ChatGPT's contribution to executive decisions and task solving and its effects on employee productivity. This research 

evaluates the human resource practices at academic institutions in Oman to demonstrate how artificial intelligence can 

enhance HR competency among Gulf-region universities. The paper follows this structure: Section 2 surveys literature about 

AI in human resources management, Section 3 describes the research design, and Section 4 shows analysis results, after 

which Section 5 examines future study directions. 

 

LITERATURE REVIEW 

This paper thoroughly analyses existing research through multiple subsections before establishing connections between 

study questions and hypothesis development with available theoretical knowledge. 

 

AI Applications in HR and Chabot’s 

Research into AI applications for HR, mainly Chabot's, has primarily focused on their role in recruitment and selection   

(Panday et al., 2024; Majumder & Mondal, 2021; Jierasup & Leelasantitham, 2024; Vassilakopoulou et al., 2023). Studies 

indicate that Chabot interviews create favorable outcomes for both companies and candidates, improving efficiency in 

government recruitment. Chabot supports HR functions such as onboarding, staff engagement, training, and development. 

These findings align with H1, which hypothesizes that the effective implementation of ChatGPT systems improves HR 

performance skills. 

AI-driven HR management systems (HRMS) are becoming critical for HR operations, with security experts 

recognizing Chabot’s as essential for secure and efficient human resource management  (Pandey, Singh, Arora, & Batta, 

2023). Given the importance of system usability in HR Chabot applications, these studies support H3, which proposes that 

ChatGPT usability moderates the relationship between system implementation and HR performance skills. 

 

HR Performance Skills in Academic Institutions 

Academic institutions require advanced HR capabilities to maintain competitiveness in a knowledge-based economy (Alam, 

2022; Kutieshat & Farmanesh, 2022; Hossain, 2024). HR processes integrated with technology allow for strategic decision-

making and improved value generation in education and research (Rangriz et al., 2011; Khotimah et al., 2024). This aligns 

with RQ1, which evaluates the impact of ChatGPT systems on HR performance skills in academic institutions in Oman. 

Training and assessment methodologies play a crucial role in HR skill development and reinforce the importance 

of H2, which highlights user training as a moderating factor (Lussier, 2019). Additionally, motivated and well-trained HR 

professionals drive productivity and competitiveness (Singh et al., 2024), which links to RQ2, which examines how user 

training, system design, and cultural factors moderate ChatGPT’s impact. 

 

AI and HR: Opportunities and Challenges 

AI in HR streamlines hiring, training, payroll management, and employee engagement (Savastano et al., 2024). AI-driven 

HR systems enhance performance management and career development (Hajrizi & Shaqiri, 2024; Pande, Moon, & Haque, 

2024). These studies relate to RQ3, which seeks to identify the most effective ChatGPT components for improving HR 

performance. 

Small and mid-sized organizations increasingly use AI-driven bots for HR functions such as employee servicing 

and survey management (Mamuli et al., 2025; Chukwuka & Dibie, 2024). Automating repetitive HR tasks allows 

professionals to focus on strategic initiatives. AI-driven Chabot also collects and analyzes confidential HR data   (Kooli, 

2023), supporting H4, which suggests that organizational culture moderates the ChatGPT-HR performance relationship. 

 

ChatGPT and AI-Driven HR Solutions 

Advancements in Chabot technology, particularly ChatGPT, have enabled AI to handle multiple HR tasks (Luo et al., 2022). 

AI-driven HR platforms improve response accuracy and operational effectiveness (Szandała, 2025). Institutions integrating 

AI solutions into HR processes experience improved workforce management and service delivery. These studies support 

RQ4, which explores how aligning ChatGPT systems with institutional goals impacts HR performance outcomes. 

The optimization of Chabot communications through machine learning depends on selecting appropriate responses 

and maintaining effective interactions (Sharma et al., 2022). AI technology promises to revolutionize HR operations within 

academic institutions, reinforcing H1 regarding the impact of ChatGPT implementation on HR performance skills. 

 

Higher Education and HR Development in Oman 

Oman's higher education institutions focus on quality curricula, research, and community engagement (Raj et al., 2023). 

Institutional success depends on motivated HR professionals with intense training and development programs (Almamari et 

al., 2025). With increasing student enrollment and faculty development initiatives, Oman’s institutions benefit from AI-

driven HR solutions (AlHarthi, 2024; Hajrizi & Shaqiri, 2024; Jahan, 2023). These findings align with RQ1, which examines 

ChatGPT’s impact on HR performance skills. 

 

 



Shannaq & Alabri, Bangladesh Journal of Multidisciplinary Scientific Research 10(1) (2025), 11-21

 

13 

Implementing ChatGPT in Oman’s HR Departments 

Omani academic institutions implementing ChatGPT in HR aim to enhance recruitment, employee engagement, and 

workflow efficiency (Thottoli et al., 2025). ChatGPT’s interactive capabilities support candidates and staff by improving 

communication and operational processes. These findings support RQ4, exploring how ChatGPT’s alignment with 

institutional goals affects HR performance. 

The literature review highlights how AI can enhance HR functions, particularly ChatGPT. By linking past research 

to the research questions, objectives, and hypotheses, this review establishes a strong foundation for investigating 

ChatGPT’s role in HR performance within academic institutions in Oman. 

 

MATERIALS AND METHODS 
The study investigates how ChatGPT systems affect performance skills among HR professionals working in academic 

institutions in Oman through a case study approach. When contemporary phenomena exist within their real-world context, 

it is best to use a case study method that handles unclear boundaries between the subject and its surrounding elements. 

Multiple studies employ it as an effective tool to develop and verify these theories while extracting generalized findings. 

The research incorporated qualitative and quantitative data collection through the case study approach for detailed analysis 

of intricate matters. 

 

Research Design 

This study uses a mixed-methods approach for its research methodology. Both qualitative and quantitative data collection 

methods form part of the research design. 

 A series of focus groups analyzed HR practices, including performance barriers and challenges higher education 

staff members face. 

 A survey confirmed the conceptual framework presented in Figure 1 and all the hypotheses derived from 

published research. HR professionals and academic staff members from Oman were selected as participants. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 1. The proposed conceptual framework 

 

Research Questions 

The following question will be of particular importance regarding this research topic; 

 What is the evaluation of the impact of ChatGPT systems implementation on the performance skills of the 

Academic institutions in Oman HR? 

 What roles do user training, system design, and cultural issues play in moderating the relationship between 

ChatGPT systems and HR performance skills? 

 Which particular components of ChatGPT systems are most effective in improving the ability of HR performance 

skills? 

 How does the alignment of ChatGPT systems to deliver institutional goals impact HR performance results? 

 

Research Objectives 

 This study aims to examine the results of implementing its systems on performance skills in human resources at 

the Academic institutions in Oman HR. 

 To test the mediating effect of user training, system usability, and organizational culture regarding the link 

ChatGPT Systems 

Implementation 

(IV) 

Organizational 

Culture 

ChatGPT 

Usability 

User Training 

HR performance 

skills (DV) 



Shannaq & Alabri, Bangladesh Journal of Multidisciplinary Scientific Research 10(1) (2025), 11-21

 

14 

between ChatGPT systems and HR performance skills. 

 To determine the details of causation, we need to know what ChatGPT systems effectively improve HR 

performance skills. 

 To test the impact of the ChatGPT systems alignment with the institutional goals on HR performance output. 

 

Hypotheses 

 H1: Effective implementation of ChatGPT systems improves performance skills in HR. 

 H2: User training plays a moderating role in the relationship between the implementation of ChatGPT systems 

and the level of performance skills among organizational human resources. 

 H3: ChatGPT usability moderates the link between implementing ChatGPT systems and HR performance skills. 

 H4: Some organizational culture plays a middle role in modulating the correlation between ChatGPT systems 

implementation and HR performance skills. 

 

Mapping of Research Questions, Objectives, Variables, and Hypotheses 

The study establishes connections between its research inquiries and objectives, process variables, and established 

hypotheses. 

Figure 2 shows how research questions produce corresponding objectives and variables while developing hypotheses that 

create a systematic framework for this study. 

 

 
 

Figure 2. Mapping Research Questions, Objectives, Variables, and Hypotheses 

 

Data Collection 

Seven91 academic staff members from five institutions in Oman took part in the study using a survey questionnaire available 

in ChatGPT Implementation and HR Performance (2024). Research data collection used quantitative methods, with surveys 

administered before and after system implementation for participants to report their views on ChatGPT system deployment. 

The questionnaires contained the General Training Scale (GTS), which served as a tool for examining HR 

performance levels. Employee confidence ratings and their understanding and perception of ChatGPT's usefulness went 

under evaluation through the GTS measurement system. The research gathered information about participant demographics 

through data points that included their job position alongside HR experience and gender in addition to age range, educational 

attainment, and specific ChatGPT usage habits. The research also received open-ended feedback from participants. 

Qualitative data came from six HR staff and leadership members who regularly utilized ChatGPT for HR services. The 

individuals contributed important information by taking surveys and examining service logs and call records. 

 

Cleaning Data 

Responses' quality assessments preserved their position within the approved five-point Likert scale values (1–5). The 

analysis of missing data revealed no critical data errors. Standard deviation (SD) examined for data outliers revealed results 

where SD-Min equaled 0.470 and SD-Max reached 1.523. The standard deviation calculations demonstrated that the 

collected data was appropriate for additional analysis. 

The study employed descriptive statistics to check that skewness and kurtosis values met Structural Equation 

Modeling (SEM) data analysis requirements. The data showed appropriate measurement readiness for analysis based on 

acceptable skewness and kurtosis values, according to (Hair et al., 2021). 

 



Shannaq & Alabri, Bangladesh Journal of Multidisciplinary Scientific Research 10(1) (2025), 11-21

 

15 

Strengths and Limitations 

The research benefits from its dual qualitative and quantitative methodology, which yields a complete understanding of 

Human Resources performance and ChatGPT system functions. The dual use of focus groups and survey data allowed 

researchers to combine multiple resources for better results. 

Relying solely on participants' self-reported data could compromise the research results due to potential user bias 

effects. The research focused exclusively on academic institutions across Oman, which created boundaries for its ability to 

present findings applicable to diverse settings. 

 

Data Analysis 

Assessment Model: Validity and Reliability                     
Reliability and validity were used using 'Cronbach's Alpha' and 'Composite Reliability (CR).' On using factor analysis, any 

item that had a factor loading of less than 0.700 was eliminated from the study. The following items have been excluded 

due to factor loadings below the threshold of 0.7: 

(DV) HR-Performance_Skills 0.581, (IV) ChatGPT_Implementation 0.645, (Mv1) User_Training0.457,1 (Mv2) 

ChatGPT_Usability 0.411, (Mv3) Organaizational_Culture0.551,2. (DV)  HR-Performance_Skills 0.688. A similar 

process has also been followed for all opportunities less than 0.700. Figure 3 below shows the data obtained with all the 

other items included. Figure 4 below shows the data after excluding the remaining items. 

This was arrived at after analyzing several tests ranging from AVE to HTMT.  Table 1 shows the reliability and 

validity of the remaining items and the factor loading each item. All alpha values and CRs exceeded the recommended cut-

off of 0.700, making reliability highly desirable. For the convergent validity, tests such as AVE and CR were used, all of 

which had to be more than or equal to 0.500 and 0.700. Convergent and discriminant validity was established through the 

analysis of the cross-loadings of the items, revealing that factor loadings were higher than the cross-loadings, thus suggesting 

discriminant validity. Moreover, 'multicollinearity' was checked separately for each indicator by using VIF values less than 

5, showing no 'multicollinearity' problems. Applying the same tastes about factor loadings more than cross-loadings, as 

indicated in Table 2 and Table 4, provided an affirmation of the discriminant validity of all items. 

 

 
 

Figure 3. Conceptual Model before removing indicators below 0.7 

 



Shannaq & Alabri, Bangladesh Journal of Multidisciplinary Scientific Research 10(1) (2025), 11-21

 

16 

 
Figure 4. Conceptual Model after removing indicators below 0.7 

 

Table 1. Item Loadings, Reliability, and Validity 

 
 Factor 

Loading 
Cronbach's 

alpha 
Composite reliability 

(rho_a) 
Composite reliability 

(rho_c) 
Average variance 

extracted (AVE) 

1.(IV)  

ChatGPT_Implementation 

 0.847 0.851 0.897 0.685 

2. (IV)  0.850     

3. (IV)  0.803     

7. (IV)  0.859     

8. (IV) 0.794     

2. (DV) HR-

erformance_Skills 

 0.794 0.799 0.866 0.618 

2. (DV)  0.809     

3. (DV)  0.803     

4. (DV)  0.781     

5. (DV)  0.750     

3. (Mv1) User_Training  0.708 0.711 0.837 0.631 

2. (Mv1)  0.810     

3. (Mv1)  0.798     

4. (Mv1)  0.774     

4. (Mv2)  

ChatGPT_Usability 

 0.688 0.712 0.824 0.610 

2. (Mv2)  0.724     

3. (Mv2)  0.825     

4. (Mv2)  0.790     

5. (Mv3) 

Organaizational_Culture 

 0.723 0.744 0.842 0.640 

2. (Mv3)  0.767     

3. (Mv3)  0.837     

4. (Mv3)  0.794     



Shannaq & Alabri, Bangladesh Journal of Multidisciplinary Scientific Research 10(1) (2025), 11-21

 

17 

Discriminant Validity 

Further discriminant validity was checked using the Heterotrait-Monotrait method (HTMT) and Fornell & Larcker criteria, 

with detailed output shown in Table 2 and Table 3. 

 

Table 2. Heterotrait-monotrait ratio (HTMT) 

 
 ChatGPT_Implementation ChatGPT_Usability HR-

Performance_Skills 

Organaizational_Culture User_Training 

ChatGPT_Implementation           

ChatGPT_Usability 0.438         

HR-Performance_Skills 0.251 0.189       

Organaizational_Culture 0.222 0.257 0.171     

User_Training 0.235 0.144 0.691 0.224   

 

Table 3.  Fornell-Larcker criterion 

 
 ChatGPT_Implem

entation 
ChatGPT_Us

ability 
HR-

Performance_

Skills 

Organaizational_

Culture 
User_Tra

ining 

ChatGPT_Implementation 0.828         

ChatGPT_Usability 0.346 0.781       

HR-Performance_Skills 0.208 0.143 0.786     

Organaizational_Culture 0.174 0.180 0.138 0.800   

User_Training 0.183 0.098 0.523 0.163 0.794 

 

Structural Model 

The next step in the current sequence of studies was to assess the structural models to test hypotheses postulated in this 

study. As for testing the hypotheses, especially in mediation analysis, the role of the Structural Model is played. This 

configuration of effects in the present study is aligned within a theoretical structure to distinguish between a variable's direct, 

indirect, and total impact to identify its moderating role as an intermediate between an independent and dependent variable. 

This method assesses the degree of such associations and their significance, which expands an understanding of the factors 

that explain the outcomes noted. There were potential issues with mediation probing; thus, mediation analysis was necessary 

to investigate the mechanisms and reasons behind specific effects, which might occur in the model. A bootstrapping process 

was done to test and present all 6 proposed hypotheses. Figure 5 shows the results of the bootstrapping analysis, while the 

results of the hypothesis testing are determined in Table 4 and Table 5. 

 

 
Figure 5. Bootstrapping process 

 

 

 



Shannaq & Alabri, Bangladesh Journal of Multidisciplinary Scientific Research 10(1) (2025), 11-21

 

18 

RESULTS 

Table 4 demonstrates the results of testing the direct hypothesis: "The effective implementation of ChatGPT systems 

improves performance skills in HR." 

 

Table 4. Testing direct hypothesis (H1) 

 
H1 Original sample 

(O) 
Standard deviation 

(STDEV) 
T statistics 

(|O/STDEV|) 
P values 

ChatGPT_Implementation -> HR-Performance_Skills 0.093 0.034 2.742 0.003 

 

As presented in Table 4, H1 confirms a positive correlation between the implementation of ChatGPT and HR 

performance skills. The original sample coefficient is 0.093, indicating a small effect, whereas the T statistic is 2.742. The 

p-value of 0.003 supports the hypothesis (H1) at a 5 percent significance level. 

Table 5 contains the results of testing mediation hypotheses (H2, H3, and H4) by evaluating total effect, direct effect, and 

specific indirect effect analysis. It displays results, including hypothesis coefficients B, their t-values t, upper and lower 

boundaries UL and LL, and P values. 

 

Table 5. Testing Mediation hypothesis (H2, H3 and H4) 

 
Total effect Direct Effect Specific indirect effect 

B P B  Hypothesis B t UL LL P Results  

0.183 0.000 0.092 0.003 H2 

ChatGPT_Implementation -> 

User_Training -> HR-Performance_Skills 

0.090 4.615 0.06 0.125 0.000 Partial 

Mediation 

H2: 

Accepted 

0.345 0.000 0.092 0.003 H3 

ChatGPT_Implementation -> 

ChatGPT_Usability -> HR-

Performance_Skills 

0.019 1.715 0.001 0.039 0.043 Partial 

Mediation 

H3: 

Accepted 

0.173 0.000 0.092 0.003 H4 

ChatGPT_Implementation -> 

Organaizational_Culture -> HR-

Performance_Skills 

0.005 0.922 -

0.004 

0.015 0.178 No 

Mediation 

H4: 

Rejected 

 

DISCUSSIONS  

Table 5 tests the mediation effect of User Training, ChatGPT Usability, and Organizational Culture on the relationship 

between ChatGPT Implementation and HR Performance Skills. The study evaluates the full mediation power and direct and 

indirect effects between variables to understand attribute involvement in hypothesis relationships. 

The research shows that training users effectively leads the path to connecting the new implementation of ChatGPT 

with enhanced human resources performance capabilities. The relationship between ChatGPT Implementation and HR 

Performance Skills produces a significant influence (B = 0.183, P = 0.000). An evaluation confirms that user training 

substantially indirectly impacts the relationship (B = 0.090, t = 4.615, P = 0.000). Previous studies confirm that practical 

user training improves technological system adoption and utilization. The training provided to users results in improved 

performance through building confidence and increased competence within the organization. User Training is substantive 

in the results alongside ChatGPT Implementation, which maintains its impact on HR Performance Skills. 

The mediational power of ChatGPT Usability (H3) stands lower than the overall relationship. Usability partially 

mediates the connection between ChatGPT Implementation and HR Performance Skills by having an indirect impact of 

0.019 (B = 0.019, t = 1.715, P = 0.043). The total effect of this implementation method remains strong (B = 0.345, P = 

0.000). Despite its importance, Usability does not have a similar impact on User Training, according to the findings. 

According to the results, a user-friendly design is essential in boosting HR Performance Skills through its significant 

mediation effect. 

The evaluation revealed that Organizational Culture (H4) failed to demonstrate significance as a mediating factor 

in this research. The sizeable relationship between ChatGPT Implementation and HR Performance Skills (B = 0.173, P = 

0.000) shows no substantial connection with Organizational Culture (B = 0.005, t = 0.922, P = 0.178). Organizational Culture 

does not act as a mediator based on confidence intervals ranging from 0.015 to -0.004 in this study, thus indicating cultural 

factors may have less impact than training and Usability when moderating the relationship between ChatGPT 

Implementation and HR Performance Skills. Contrary to other studies, this research shows cultural alignment did not play 

a significant role in achieving technology adoption success despite its known importance for cultural alignment. 

The analysis results create essential implications for all research questions within the study. The findings show that 

implementing ChatGPT increases HR Performance Skills according to RQ1. The study results demonstrate significance 

regarding both total and direct effects, which indicates ChatGPT has strong potential to boost HR productivity, decision-

making, and coordination talent. According to research findings, User Training is an effective moderator to improve the 

connection between ChatGPT systems and HR performance skills. The impact of Usability on the relationship remains 

weaker than training when it comes to ChatGPT. The relationship between ChatGPT systems and HR performance skills 

remains unaltered when Organizational Culture is evaluated as a mediator in this context. 

The evaluation reveals that User Training and Usability are the most effective elements of ChatGPT systems when 

enhancing HR performance abilities. According to previous research findings, training, and system usability can predict 

successful technology adoption with user satisfaction. The minimal research exposure to organizational culture requires 



Shannaq & Alabri, Bangladesh Journal of Multidisciplinary Scientific Research 10(1) (2025), 11-21

 

19 

additional study since previous investigations show that cultural fit is essential for technology adoption and performance 

effects. 

The results imply that ChatGPT systems must align with institutional goals before fully maximizing their impact 

on HR performance. However, direct testing was not conducted for RQ4. According to the study results, system 

effectiveness increases when organizations align their objectives with the system, where direct and total effects demonstrate 

strong support. 

This study's findings support previous research about the essential nature of training for technology adoption as 

established by literature. Research has established that training enhances user abilities and confidence, improving 

performance outcomes. According to the established literature, system usability acts as a mediator since researchers 

repeatedly highlight system usability as vital for user satisfaction and task performance. In this study, the non-significance 

of Organizational Culture indicates the relative insignificance of cultural factors toward technology adoption. This research 

suggests that successfully integrating ChatGPT might prioritize training along with usability components rather than 

organizational culture at the same level. 

According to research results, organizations should create detailed training programs for their HR staff to use 

ChatGPT systems effectively. Investments in improving user interface designs while ensuring the system supports HR 

functions will boost this system's effectiveness. Even though organizational culture did not show statistically significant 

effects in this study, promoting technology adoption remains beneficial among employees. 

 

CONCLUSIONS  

The research examined ChatGPT deployment's effects on strengthening human resources performance capabilities for 

academic Omani institutions. The research findings demonstrate that ChatGPT implementation positively affects HR 

operational performance, while User Training and System Usability partially explain this impact. User training is an integral 

component in enhancing both the adoption and effectiveness of ChatGPT in HR functions, but system usability provides 

supplementary benefits. 

The present research adds value by analyzing how User Training and Usability function as middle factors 

connecting ChatGPT deployment to HR performance outputs. Organizations must invest in quality training and user-friendly 

system design to reach ChatGPT's maximum potential when implemented in HR operations. 

The research provides theoretical growth to existing studies about technology implementation and adoption by 

showcasing the essential role that training programs and system design play in technology integration success. Managers 

and HR practitioners need to focus their investments on system usability combined with training programs to achieve 

improved HR performance together with effective AI tool implementation like ChatGPT. 

The research includes some restraining factors that need consideration. The research evaluation applies explicitly 

to academic institutions in Oman and does not establish broader applicability across different industries or geographic areas. 

Additional research should explore the restricted use of Organizational Culture as a mediation variable because cultural 

influences may exceed its function in new contexts. 

Further research should investigate organizational culture and consider alternative factors that affect how well 

ChatGPT implementation succeeds in other situations. Research improvements can be achieved by analyzing a broader 

scope of industries across different regions to confirm the worldwide validity of these results. 

 

 
Author Contributions: Conceptualization, B.S. and A.A.; Methodology, B.S.; Software, B.S.; Validation, B.S. and A.A. Formal Analysis, B.S.; 

Investigation, A.A.; Resources, A.A.; Data Curation, B.S.; Writing – Original Draft Preparation, B.S.; Writing – Review & Editing, A.A.; Visualization, 
B.S.; Supervision, A.A.; Project Administration, A.A.; Funding Acquisition, A.A. Authors have read and agreed to the published version of the manuscript. 

Institutional Review Board Statement: Informed consent was obtained from all subjects involved in the study since it is voluntary, and the statement is 
included on the first page of the questionnaire  

Funding: Research was funded by the University of Buraimi, Sultanate of Oman. The authors acknowledge the university’s support in facilitating this 
study. 

Acknowledgments: The authors would like to express their sincere gratitude to the University of Buraimi, Sultanate of Oman, for their invaluable support 

throughout the research process. Their funding and continuous encouragement have been instrumental in completing this study. 

Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. 

Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available 
due to restrictions. 

Conflicts of Interest: The authors declare no conflict of interest.                                                                                                                                                                                                                                   

 

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