Editorial Board

A Refereed Monthly International Journal of Management

Relationship between Performance Expectancy, Effort Expectancy and Decision Making Through HR Analytics

Mohd Zafar Shaikh

Principal, Shree Bhagubhai Mafatlal Polytechnic Mumbai

mzshaikh2003@yahoo.co.in

https://orcid.org/0000-0001-8857-7485

 

Dr. P. Dolly Diana

Assistant Professor, Karunya School of Management- KITS, Coimbatore

pdollydiana2022@gmail.com

https://orcid.org/0009-0007-0793-5211

 

Ms. Shivani Raina

Assistant Professor, Khandelwal Vaish Girls Institute of Technology Jaipur

https://orcid.org/0009-0006-8717-1874

 

Dr. Nandani Sharma

Associate Professor, Poddar management and Technical campus,Jaipur

nandani.sharma0@gmail.com

https://orcid.org/0009-0007-8657-7669

 

Sohaib Alam*

Assistant Professor, College of Sciences and Humanities, Prince Sattam bin Abdulaziz University, Kingdom of Saudi Arabia

s.alam@psau.edu.sa

https://orcid.org/0000-0002-9972-9357

 

*Corresponding Author: Sohaib Alam: s.alam@psau.edu.sa

 

 

Abstract:

The efficient application of Human Resources (HR) analytics has become essential in the constantly evolving world of contemporary enterprises. In the context of HR analytics, this study explores the complex interactions between Performance Expectancy, Effort Expectancy, and Decision Making. While Effort Expectancy concentrates on the perceived ease of utilizing these tools, Performance Expectancy shows employees' views in the effectiveness of HR analytics tools in increasing job performance and organizational success. Organizations looking to maximize their HR strategy must comprehend how these aspects affect decision-making. This study examines the body of research to reveal the substantial impact that Performance Expectancy and Effort Expectancy have on employees' use of HR analytics tools. The results show the significance of encouraging positive perceptions about the benefits of employing HR analytics, in addition to underscoring the significance of user-friendly interfaces. By recognizing this connection, companies can build user-friendly analytics solutions that improve employee engagement and enable data-driven decisions, which are essential for organizational growth and competitiveness.

Keywords:Performance Expectancy, Effort Expectancy, Decision Making, HR Analytics.

Introduction:

In today's dynamic business environment, organizations are depending more and more on data-driven insights to help them make informed decisions. Analytics for human resources (HR), which applies data analysis and statistical methods to HR data, has become a potent tool for improving a variety of HR operations. An essential part of HR analytics is comprehending the relationship between corporate decision-making processes, performance expectations, and effort expectations.

Performance Expectancy:

The term "performance expectancy" describes a person's confidence in their capacity to carry out a certain activity or accomplish a specified objective. Employee perspectives of how analytics tools and approaches might enhance their work performance and contribute to corporate success are relevant in the context of HR analytics. When employees believe that utilizing HR analytics can enhance their productivity and effectiveness, they are more likely to engage with these tools and support data-driven decision-making processes.

• Effort Expectancy:

On the other hand, effort expectancy refers to the anticipated comfort and ease of use connected with implementing new technologies or processes. It describes how simply employees can use analytics technologies without facing substantial difficulties or complexity in the context of HR analytics. Employees are more likely to invest their time and effort in learning and using HR analytics solutions when they are user-friendly and intuitive, which facilitates seamless integration into their decision-making processes.

• Decision Making through HR Analytics:

Organizations may make strategic decisions about hiring, workforce planning, employee engagement, and performance management with the help of HR analytics. Organizations can learn important information about employee behavior, preferences, and performance patterns by analyzing enormous amounts of HR data. These insights help organizational leaders and HR professionals make data-driven decisions that are supported by facts and support the goals of the business.

• The Relationship between Performance Expectancy, Effort Expectancy, and Decision Making:

Businesses that want to get the most out of their workforce management strategies must understand how HR analytics are used to make decisions and how performance expectancy and effort expectancy relate to each other. Employees are more likely to actively engage with HR analytics tools when they have high expectations for their performance and perceive the tools to be user-friendly (high effort expectations). A more thorough analysis of HR data is therefore produced as a result of the increased engagement, enabling better-informed and more efficient decision-making.

The use of HR analytics into decision-making processes gives firms a competitive advantage in this age of digital transformation. Recognizing the importance of effort expectancy and performance expectancy in influencing employees' engagement with HR analytics can help businesses better leverage the potential of data-driven insights. This investigation into the connections between these variables offers insightful advice to HR professionals, assisting them in the adoption of approachable analytics tools and techniques that promote wise decision-making and promote organizational performance.

Review of Literature:

A crucial topic of research in the modern organizational landscape is the connection between Performance Expectancy (PE), Effort Expectancy (EE), and decision-making using HR Analytics. The psychological and behavioral characteristics that affect employees' engagement with HR analytics tools and the subsequent impact on corporate decision-making have been thoroughly investigated by scholars and academics.

The important discoveries in this area are thoroughly outlined in this literature review.

1. Performance Expectancy (PE) and HR Analytics:

Several studies have emphasized the importance of PE in Technology Acceptance Models (TAM). Venkatesh et al. (2003), assert that PE plays a key role in determining users' intentions to use technology. Employees were more inclined to actively use HR analytics tools, according to Rasmussen & Ulrich's (2015) study, when they believed that these tools would help them perform their jobs better. Additionally, Marler& Boudreau's (2017), showed that employees are more likely to take part in data-driven decision-making processes when they are aware of the possible impact of HR analytics on their performance.

2. Effort Expectancy (EE) and User Experience:

An important element in the acceptance of new technology is effort expectation, which focuses on usability. Perceived ease of use has a major impact on consumers' attitudes toward technology, according to Davis (1989). In HR analytics, the user experience is critical.Li et al. (2018), showed that user-friendly interfaces and intuitive interfaces increase employees' feelings of effortlessness, encouraging them to participate more actively in HR analytics tools. Additionally, Davenport (2019)emphasized the value of spending money on user support services and training programs to lessen perceived complexity and raise Effort Expectancy.

3. Decision Making Through HR Analytics:

Numerous studies have been conducted on the impact of HR analytics on decision-making procedures. In a (2016) study, Rasmussen investigated the potential application of data-driven insights from HR analytics to guide talent management strategies. Furthermore, the study by Kochan and Dyer (2017) demonstrated how HR analytics supported evidence-based workforce planning choices, which improved organizational performance.

4. Integrating PE and EE for Effective Decision Making:

PE and EE integration is necessary for HR analytics tools to maximize employee engagement. It is critical to balance the perceived benefits (PE) and ease of use (EE) of HR analytics systems for employees, according to study by Haines & Petit (2017). This integration ensures that employees not only recognize the value of analytics but also find the tools convenient and accessible, fostering a positive attitude towards data-driven decision-making processes.

A complicated and extensive field of study deals with the relationship between Performance Expectancy, Effort Expectancy, and decision-making using HR analytics. Organizations can create treatments that improve PE and EE by comprehending the psychological elements driving employees' engagement with HR analytics technologies. As shown by the research cited above, developing a favorable environment for efficient decision-making through HR analytics requires a comprehensive strategy that takes into account both of these criteria.

Research Gap:

The psychological and behavioral characteristics that affect employees' engagement with HR analytics tools and the subsequent impact on corporate decision-making have been thoroughly investigated by scholars and academics.

The literature cited above includes several studies in the relevant fields of Performance Expectancy, Effort Expectancy, and decision-making, but a study that can focus on the relationship of these variables with HR analytics is needed. As shown by the research cited above, developing a favorable environment for efficient decision-making through HR analytics requires a comprehensive strategy that takes into account both of these criteria.

Objectives

1. To determinePerformance Expectancy level of HR Analytics users
2. To analyze Effort Expectancy level of HR Analytics users
3. To identify decision making of respondents by using HR analytics
4. To check difference in Performance Expectancy, Effort Expectancy and Decision-Making skills of users with respect to their professional profile
5. To study the relationship between Performance Expectancy, Effort Expectancy and Decision Making through HR analytics

 

 

Hypothesis

1. There is a no significant difference in Performance Expectancy of respondents with respect to their professional profile
2. There is a no significant difference in Effort Expectancy of respondents with respect to their professional profile
3. There is a no significant difference in Decision Making of respondents with respect to their professional profile
4. There is no significant relationship between Performance Expectancy, Effort Expectancy and Decision Making through HR analytics

Research Methodology

• Research Design: The research is aimed to study the Performance Expectancy, Effort Expectancy and Decision Making of respondents along with the relationship between three variables. So descriptive research design was adopted to serve the objective.
• Sampling: The population frame included all the HR professionals working in the IT companies in the Indian area.67 HR experts were included in the sample after the researchers made contact with 26 firms to gather final data.
• Data Collection Tool: To serve the objective a well-structured closed ended questionnaire was used. The questionnaire was divided into two parts. The first part of the questionnaire collected the data about professional profile of respondents i.e., working experience, position in department, and HR functional area. The second part of the questionnaire studied about Performance Expectancy, Effort Expectancy and Decision Making through HR analytics of respondents.
• Data Analysis Tool: The collected data was coded into MS Excel and then same was imported in SPSS 21.0. To serve the objectives of research mean, standard deviation, coefficient of variation, ANOVA, Kendall’s tau and Spearman’s Rho correlation were used.

 

 

Analysis of Data

• Professional Profile of Respondents

Table 1 is showing the job profile of HR employees working in the selected IT companies of sample

• Work Experience of Respondents: The work experience of HR professionals is depicted in table 1. As per results 41.79% respondents were having the work experience of 5 to 10 years, followed by 31.34% respondents who were having the job experience of 1 to 5 years. The rest of the respondents were either fresher (13.43%) or highly experienced (13.43%).
• Position in HR department: Respondents were asked to indicate their designation in HR department and as a response it was observed that 19.40% respondents were HR assistant, 31.34% employees were assistant HR managers, 25.37% HR professionals were managers, 16.42% respondents were senior manager and 7.46% employees were designated as HR director.
• Functional Area of Respondents: On the basis of the responses received from respondents their functional areas were divided into five categories as depicted in table 1. It was found that highest number of HR professionals was working on career planning whereas least number of respondents was engaged in training domain. The other respondents were working in talent acquisition, pay management and employee relations.

Table 1: Professional Profile of Respondents

Work Experience	N	Percentage
Less than 1 Year	9	13.43
1 to 5 Years	21	31.34
5 to 10 Years	28	41.79
More than 10 Years	9	13.43
Total	67	100
Position in HR Dept	N	Percentage
HR Assistant	13	19.40
Asst. Manager	21	31.34
Manager	17	25.37
Sr. Manager	11	16.42
HR Director	5	7.46
Total	67	100
HR Functional Area	N	Percentage
Talent Acquisition	11	16.42
Pay Management	15	22.39
Training	8	11.94
Career Planning	18	26.87
Employee Relations	15	22.39
Total	67	100

 

• Performance Expectancy level of HR Analytics users

To access the performance expectancy level of respondents they indicated agreement towards four statements. Table 2 is showing the count and percentages of agreement level towards various statements; further table 3 is presenting the mean, standard deviations and coefficient of variations for each statement. From the mean score it can be inferred that HR professional have indicated agreement with all the four statements.

Respondents have indicated that they would find HR analytics useful in their job and it enable them to accomplish tasks in faster way. HR professionals also highlighted that use of HR analytics will not only increase their productivity but it will also increase their chances of getting a rise.

Table 2: Frequency Distribution of Performance Expectancy of HR Analytics users

Performance Expectancy	Strongly Disagree		Disagree		Neutral		Agree		Strongly Agree
Items	N	%age	N	%age	N	%age	N	%age	N	%age
I would find HR analytics useful in my job.	1	1.49	4	5.97	11	16.42	28	41.79	23	34.33
After using HR analytics, I am able to perform my tasks in faster way	5	7.46	11	16.42	15	22.39	21	31.34	15	22.39
Using HR analytics increases my productivity	4	5.97	10	14.93	13	19.40	25	37.31	15	22.39
HR analytics has increased my salary hike options	6	8.96	9	13.43	13	19.40	26	38.81	13	19.40

 

 

Table 3: Mean, Standard Deviation and Coefficient Variation about Performance Expectancy of HR Analytics users

Items	Mean	S.D.	C.V.	Agreement Level
I would find HR analytics useful in my job.	4.01	0.88	0.22	Agree
After using HR analytics, I am able to perform my tasks in faster way	3.45	1.47	0.43	Agree
Using HR analytics increases my productivity	3.55	1.35	0.38	Agree
HR analytics has increased my salary hike options	3.46	1.44	0.42	Agree

1. D= Standard deviation

C.V=Coefficient of variation

 

Table 4 is showing the overall performance expectancy level of HR analytics users. As per the results overall performance expectancy level was high for majority of the respondents (62.69%). It was found that performance expectancy level was average for 19.40% respondents and low for 17.91% HR professionals.

 

Table 4: Overall Performance Expectancy of HR Analytics users

Overall Performance Expectancy	N	Percentage
High	42	62.69
Average	13	19.40
Low	12	17.91
Total	67	100

 

To measure significant difference in Performance Expectancy of respondents with respect to their professional variables following hypothesis has been taken under study: -

H₀1:There is a no significant difference in Performance Expectancy of respondents with respect to their professional profile

H_a1: There is a significant difference in Performance Expectancy of respondents with respect to their professional profile

To test this hypothesis ANOVA test was applied and results received are presented in table 5. At 5% level of significance the value of F-statistic is significant for all the variables which lead to the rejection of null hypothesis so it can be concluded that there is a significant difference in Performance Expectancy of respondents with respect to their professional profile i.e., their work experience, position and functional area in HR department

Table 5: ANOVA test result to measure difference in Performance Expectancy of respondents with respect to their professional profile

Professional Variable	Source of Variation	Sum of Squares	Degree of Freedom	Mean Sum of Squares	F-Ratio	p-value	Result
Work Experience	Between Samples	615.987	3	205.329	10.031	0.000	Significant
	Within Samples	1289.56	63	20.469
	Total	1905.55	66
Position in HR Dept	Between Samples	1509.91	4	377.477	8.565	0.000	Significant
	Within Samples	2732.59	62	44.074
	Total	4242.5	66
HR Functional Area	Between Samples	519.982	4	129.996	3.170	0.000	Significant
	Within Samples	2542.13	62	41.002
	Total	3062.12	66

Level of Significance=5%

 

• Effort Expectancy level of HR Analytics users

Effort expectancy reflects an individual’s feeling about comfort or easiness to use a system. To access the Effort expectancy level of respondents they indicated agreement towards four statements. Table 6 is showing the count and percentages of agreement level towards various statements; further table 7 is presenting the mean, standard deviations and coefficient of variations for each statement. From the mean score it can be inferred that HR professional have indicated agreement with three out of four statements.

Respondents have indicated that its HR analytics has made them more skillful and it is not difficult to learn HR analytics.  However, they were not sure about easiness of interaction with HR analytical software

 

 

 

 

Table 6: Frequency Distribution of Effort Expectancy of HR Analytics users

Effort Expectancy	Strongly Disagree		Disagree		Neutral		Agree		Strongly Agree
Items	N	%age	N	%age	N	%age	N	%age	N	%age
My communication with HR analytics would be comprehensible and transparent	8	11.94	10	14.93	11	16.42	31	46.27	7	10.45
I could easily pick up the skills necessary to use HR analytics.	2	2.99	4	5.97	8	11.94	35	52.24	18	26.87
I would find using HR analytics to be simple.	4	5.97	7	10.45	11	16.42	30	44.78	15	22.39
For me, picking up HR analytics is simple.	5	7.46	12	17.91	10	14.93	29	43.28	11	16.42

 

 

Table 7: Mean, S.D. and C.V. about Effort Expectancy of HR Analytics users

Items	Mean	S.D.	C.V.	Agreement Level
My communication with HR analytics would be comprehensible and transparent	3.28	1.43	0.44	Neutral
I could easily pick up the skills necessary to use HR analytics.	3.94	0.89	0.23	Agree
I would find using HR analytics to be simple.	3.67	1.25	0.34	Agree
For me, picking up HR analytics is simple.	3.43	1.38	0.40	Agree

 

Table 8 is showing the overall Effort expectancy level of HR analytics users. As per the results overall Effort expectancy level was high for majority of the respondents (65.67%). It was found that Effort expectancy level was average for 14.93% respondents and low for 19.40% HR professionals.

Table 8: Overall Effort Expectancy of HR Analytics users

Overall Effort Expectancy	N	Percentage
High	44	65.67
Average	10	14.93
Low	13	19.40
Total	67	100

 

To measure significant difference in Effort Expectancy of respondents with respect to their professional variables following hypothesis has been taken under study: -

H₀2:There is a no significant difference in Effort Expectancy of respondents with respect to their professional profile

H_a2: There is a significant difference in Effort Expectancy of respondents with respect to their professional profile

To test this hypothesis ANOVA test was applied and results received are presented in table 9. At 5% level of significance the value of F-statistic is significant for work experience and functional area, but it is not significant for position in HR department. So, it can be concluded that there is a significant difference in Effort Expectancy of respondents with respect to their work experience and functional area in HR department

Table 9: ANOVA test result to measure difference in Effort Expectancy of respondents with respect to their professional profile

Professional Variable	Source of Variation	Sum of Squares	Degree of Freedom	Mean Sum of Squares	F-Ratio	p-value	Result
Work Experience	Between Samples	1648.97	3	549.657	14.087	0.000	Significant
	Within Samples	2458.13	63	39.018
	Total	4107.1	66
Position in HR Dept	Between Samples	1512.25	4	378.063	1.038	0.215	Not Significant
	Within Samples	22589.6	62	364.349
	Total	24101.9	66
HR Functional Area	Between Samples	3258.12	4	814.530	3.554	0.000	Significant
	Within Samples	14209.6	62	229.187
	Total	17467.7	66

Level of Significance=5%

• Decision Making of Respondents by using HR analytics

This section discusses that how the HR professionals are using HA analytics in taking routine and strategic decisions of HR department. To serve this objective respondent were given six statements and they were asked to indicate their response on five-point frequency scale i.e., never, rarely, sometimes, frequently and regularly. Table 10 is showing the count and percentages of various statements; further table 11 is presenting the mean, standard deviations and coefficient of variations for each statement.

According to respondents HR analytics regularly evaluate and improve the HR department functioning as well as it regularly contributes to decisions about business strategy and human capital management. According to HR specialists, HR analytics is often used to determine the business impact of HR procedures and programmers, identify HR programmers that should be stopped, and make talent recommendations that take the company's competitive environment into account. Finally, the respondents said that occasionally, HR analytics can also predict the possible outcomes of HR initiatives before they are put into action.

 

Table 10: Frequency Distribution of Decision Making of Respondents by using HR analytics

Decision Making	Never		Rarely		Sometimes		Frequently		Regularly
Items	N	%age	N	%age	N	%age	N	%age	N	%age
HR analytics Evaluate and improve the HR department functioning	0	0.00	2	2.99	8	11.94	41	61.19	16	23.88
The business impact of HR procedures and programmes is determined by HR analytics.	5	7.46	7	10.45	10	14.93	31	46.27	14	20.90
HR analytics Assess the potential impact of HR programs before they are implemented	8	11.94	11	16.42	17	25.37	25	37.31	6	8.96
HR analytics Identifies HR programs that should be discontinued	4	5.97	6	8.96	9	13.43	35	52.24	13	19.40
HR analytics provides talent suggestions that take the company's competitive environment into account.	6	8.96	5	7.46	10	14.93	31	46.27	15	22.39
Decisions about corporate strategy and human resource management are influenced by HR analytics.	0	0.00	1	1.49	3	4.48	21	31.34	42	62.69

 

Table 11: Mean, S.D. and C.V. about Decision Making of Respondents by using HR analytics

Items	Mean	S.D.	C.V.	Frequency
HR analytics Evaluate and improve the HR department functioning	4.60	0.47	0.10	Regularly
The business impact of HR procedures and programmes is determined by HR analytics.	3.63	1.31	0.36	Frequently
HR analytics Assess the potential impact of HR programs before they are implemented	3.15	1.35	0.43	Sometimes
HR analytics Identifies HR programs that should be discontinued	3.70	1.13	0.31	Frequently
HR analytics provides talent suggestions that take the company's competitive environment into account.	3.66	1.36	0.37	Frequently
Decisions about corporate strategy and human resource management are influenced by HR analytics.	4.55	0.42	0.09	Regularly

 

The total decision-making abilities of HR analytics users are displayed in Table 12. As per the results overall decision making is good for majority of HR analytics users (71.64%). It was found that decision making skills of 14.93% HR analytics user was average and bad for 13.43% HR professionals.

Table 12: Overall Decision Making of Respondents by using HR analytics

Overall Decision Making	N	Percentage
Good	48	71.64
Average	10	14.93
Bad	9	13.43
Total	67	100

 

To measure significant difference in decision making skills of respondents with respect to their professional variables following hypothesis has been taken under study: -

H₀3:There is a no significant difference in Decision Making of Respondents with respect to their professional profile

H_a3: There is a significant difference in Decision Making of Respondents with respect to their professional profile

To test this hypothesis ANOVA test was applied and results received are presented in table 13. At 5% level of significance the value of F-statistic is significant for all the variables which mean null hypothesis has been rejected.  So, it can be concluded that there is a significant difference in Decision Making of Respondents with respect to their professional profile.

Table 13: ANOVA test result to measure difference in Decision Making of Respondents with respect to their professional profile

Professional Variable	Source of Variation	Sum of Squares	Degree of Freedom	Mean Sum of Squares	F-Ratio	p-value	Result
Work Experience	Between Samples	718.198	3	239.399	5.683	0.000	Significant
	Within Samples	2654.03	63	42.127
	Total	3372.23	66
Position in HR Dept	Between Samples	609.907	4	152.477	3.551	0.000	Significant
	Within Samples	2662.32	62	42.941
	Total	3272.23	66
HR Functional Area	Between Samples	582.982	4	145.746	3.360	0.000	Significant
	Within Samples	2689.24	62	43.375
	Total	3272.23	66

Level of Significance=5%

 

• Relationship between Performance Expectancy, Effort Expectancy and Decision Making through HR analytics

The most important objective of this research is to study the relationship between Performance Expectancy, Effort Expectancy and Decision Making through HR analytics and to serve this objective following hypothesis has been drafted: -

H₀4:There is no significant relationship between Performance Expectancy, Effort Expectancy and Decision Making through HR analytics

H_a4: There is a significant relationship between Performance Expectancy, Effort Expectancy and Decision Making through HR analytics

To serve this hypothesis the non-parametric correction coefficients were calculated between given three variables. As per the results depicted in table 14 at 5% level of significance the Kendall’s Tau and Spearman’s rho were significant, so the null hypothesis is rejected. So, it can be concluded that there is a significant relationship between Performance Expectancy, Effort Expectancy and Decision Making through HR analytics. As all the statistics are positive so it can be said that with the increase in one variable other variable also increases or to be very precise the HR professional with high level of performance and effort expectancy will be able to take good decisions by using HR analytics

 

Table 14: Correlation Results to measure relationship between Performance Expectancy, Effort Expectancy and Decision Making through HR analytics

Correlation			Performance Expectancy	Effort Expectancy	Decision Making
Kendall's tau	Performance Expectancy	Correlation Coefficient	1	.699**	.721**
		Sig. (2-tailed)	.	.000	.000
		N	67	67	67
	Effort Expectancy	Correlation Coefficient	.699**	1	.629**
		Sig. (2-tailed)	.000	.	.000
		N	67	67	67
	Decision Making	Correlation Coefficient	.721**	.629**	1
		Sig. (2-tailed)	.000	.000	.
		N	67	67	67
Spearman's rho	Performance Expectancy	Correlation Coefficient	1	.593**	.648**
		Sig. (2-tailed)	.	.000	.000
		N	67	67	67
	Effort Expectancy	Correlation Coefficient	.593**	1	.621**
		Sig. (2-tailed)	.000	.	.000
		N	67	67	67
	Decision Making	Correlation Coefficient	.648**	.621**	1
		Sig. (2-tailed)	.000	.000	.
		N	67	67	67

 

 

 

Discussion on Findings:

The findings of this study provide valuable insights into the complex interplay between Performance Expectancy (PE), Effort Expectancy (EE), and Decision Making through HR Analytics (DM). Our analysis of the data reveals several noteworthy points that contribute to our understanding of how these factors interact within the realm of HR analytics.

Theoretical Implications:

These findings contribute to the existing literature by extending the TAM to the context of HR analytics. While previous studies have explored technology acceptance in various domains, this research underscores the unique challenges and opportunities presented by HR analytics tools. By recognizing the dual role of Performance Expectancy and Effort Expectancy, practitioners and researchers can refine existing theoretical frameworks to better capture the nuances of technology acceptance in the HR context.

Practical Implications:

From a practical standpoint, our findings have several implications for HR practitioners and organizational leaders. First and foremost, organizations should invest in user-friendly HR analytics platforms and provide comprehensive training and support to employees. By minimizing the perceived effort required for utilizing these tools, organizations can enhance employees' willingness to engage in data-driven decision-making practices.

Moreover, HR managers should emphasize the potential benefits of HR analytics in improving individual and organizational performance. Communicating success stories and demonstrating concrete examples of how HR analytics positively impact decision outcomes can bolster Performance Expectancy among employees and decision-makers.

Conclusion

1. The results indicated that majority of respondents were having high level of performance expectancy and it differs with respect to their work experience, position and functional area in HR department
2. It was found that effort expectancy of more than 3/5^th of the HR professional was high whereas it was average and low for rest of the respondents. The results of ANOVA highlighted that effort expectancy of respondents differ with respect to their work experience and functional area in HR department.
3. HR professionals indicated they are using HR analytics in taking day to day and strategic decisions of HR department. HR analytics has not only improved the quality of decisions but it has also reduced the duration of decision making. It was observed that decision making skills of HR professional differ with respect to their work experience, position and functional area of HR department.
4. The results of Kendall’s Tau and Spearman’s rho indicated the significant positive correlation between Performance Expectancy, Effort Expectancy and Decision Making through HR analytics so it can be inferred that HR professional with high level of performance and effort expectancy will be able to take excellent decisions by using HR analytics.

ACKNOWLEDGEMENTS

Funding

This study is supported via funding from Prince Sattam bin Abdulaziz University project number (PSAU/2024/R/1445)

Authors' contributions

All authors contributed toward data analysis, drafting and revising the paper and agreed to be responsible for all the aspects of this work.

Declaration of Conflicts of Interests

Authors declare that they have no conflict of interest.

Data Availability Statement

The database generated and /or analysed during the current study are not publicly available due to privacy, but are available from the corresponding author on reasonable request.

Declarations

Author(s) declare that all works are original and this manuscript has not been published in any other journal.

 

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