HR Analytical Competency in Service Industry: A Case Study
V.V Sateesh Kumar Annepu
Research Scholar,
GITAM School of Business,
GITAM (Deemed to be University),
Visakhapatnam, Andhra Pradesh, India
https://orcid.org/0000-0003-4998-7638
Dr. T. Sowdamini
Assistant Professor,
GITAM School of Business,
GITAM (Deemed to be University),
Visakhapatnam, Andhra Pradesh, India
https://orcid.org/0000-0002-5134-0940
Abstract
In the rapidly evolving landscape of the service industry, the role of Human Resources (HR) professionals has become increasingly complex and demanding. This paper explores the analytical competencies essential for HR professionals in the service sector through a comprehensive case study. The goal of the study is to pinpoint the crucial analytical abilities and subject areas that HR professionals need to be proficient in this fast-paced workplace.
The research employs a mixed-methods approach, combining statistical tools and questionnaire survey with HR professionals from various service industry sectors. The data collected provides valuable insights into the analytical competencies most prized by HR practitioners and their perceived impact on organizational effectiveness.
By giving insight on the changing nature of HR jobs in the service industry and the rising significance of analytical capabilities in HR professionals' skill sets, this study adds to the body of HR literature. In order to keep up with industry trends, it emphasizes the importance of continual training and development for HR professionals. In the end, the study emphasizes how crucial analytical skills are to improving HR's strategic contribution to the achievement of service sector firms.
Keywords: Analytical Competencies, HR Professionals, Service Industry.
Introduction
The service sector has become a crucial sector with a direct impact on the world economy in today's quickly changing economic landscape. Organizations in this sector have particular difficulties as a result of the spread of technology, shifting customer expectations, and a competitive employment market, which call for the presence of HR specialists who are highly competent and flexible. HR professionals in the service sector need a special set of skills to address these issues head-on, with analytical abilities being of utmost importance.
The service sector is distinguished by its intangible products and the crucial role that people play in providing high-quality services. Recruitment, development, and retention of talent are the responsibilities of HR professionals in this industry to guarantee the consistent provision of great customer experiences. Since they allow for data-driven decision-making, strategic planning, and the creation of cutting-edge HR practices, analytical skills have become crucial for HR professionals in the service sector.
This paper explores the multifaceted landscape of analytical competencies within the HR function of the service industry. It delves into the following key areas:
This paper aims to provide a thorough understanding of how HR professionals can adapt to and thrive in an ever-changing business environment, ultimately contributing to the success of their organizations, by shedding light on the crucial role of analytical competencies within the HR function of the service industry.
Review of Literature:
Here's a review of the literature on the analytical competencies of HR professionals in the service industry, along with references:
Ulrich and Brockbank (2005) claim in their key book that HR has changed from being a simply administrative function to a strategic partner in enterprises. This transition is especially important in the service sector, where HR is vital to guaranteeing customer and employee happiness.
In their 2017 article, Marler and Boudreau (2017) emphasized the value of HR analytics and described analytical competencies as including knowledge of data analysis, statistical modeling, and the capacity to convert HR measurements into useful information.
The capacity of HR practitioners to use HR technology and data analytics tools successfully is important, especially in the service business where knowing worker dynamics is crucial, according to a study by Van Den Heuvel and Bondarouk (2017).
Rasmussen et al. (2018) offer insights into the strategic role that analytical HR techniques, like workforce planning and predictive analytics, play in the service sector. They contend that these actions link personnel management plans to corporate goals.
According to empirical research by King, Kylie. (2016), businesses with a higher degree of service quality tend to use HR analytics in decision-making. These insights assure a motivated and well-trained workforce.
Ployhart and Moliterno (2011) point out obstacles HR professionals must overcome to develop analytical skills, such as poor data quality, a reluctance to adopt new technologies, and resistance to change.
Marler and Boudreau (2017) point to cultural opposition to data-driven decision-making as a major obstacle to the adoption of HR analytics.
In order to predict workforce trends and improve employee engagement, Schiemann (2016) predicts that HR analytics in the service sector will substantially use artificial intelligence and machine learning in the future.
Davenport (2019) discusses how incorporating big data analytics and predictive modeling into HR procedures would fundamentally alter how the service industry makes strategic decisions.
According to the literature, analytical skills are crucial for HR professionals in the service sector to adapt to the ever-changing environment, make data-driven decisions, and strategically contribute to organizational success. The future of HR analytics in the service industry is likely to incorporate cutting-edge technology like AI and predictive modeling, but there are still issues like data quality and cultural resistance that need to be addressed.
Objectives
Hypotheses
Research Methodology
Analysis of Data
The first part of the questionnaire collected information about respondents’ demographics and job profile, and the data pertaining to same is presented in table 1
Table 1: Demographic & Professional Profile of Respondents
Gender |
N |
Percentage |
Work Experience |
N |
Percentage |
Male |
54 |
45.76 |
Less than 5 Years |
49 |
41.53 |
Female |
64 |
54.24 |
5 to 10 Years |
40 |
33.90 |
Transgender |
0 |
0.00 |
More than 10 Years |
29 |
24.58 |
Total |
118 |
100 |
Total |
118 |
100 |
Age |
N |
Percentage |
Position in HR Dept |
N |
Percentage |
20-30 Years |
11 |
9.32 |
HR Assistant |
49 |
41.53 |
30-40 Years |
25 |
21.19 |
Asst. Manager |
26 |
22.03 |
40-50 Years |
39 |
33.05 |
Manager |
29 |
24.58 |
50-60 Years |
36 |
30.51 |
Sr. Manager |
11 |
9.32 |
Above 60 Years |
7 |
5.93 |
HR Director |
3 |
2.54 |
Total |
118 |
100 |
Total |
118 |
100 |
Type of Service Industry |
N |
Percentage |
HR Functional Area |
N |
Percentage |
FMCG & Retail |
25 |
21.19 |
Recruitment & Selection |
27 |
22.88 |
Healthcare |
19 |
16.10 |
Training & Development |
35 |
29.66 |
Information Technology |
31 |
26.27 |
Compensation Management |
26 |
22.03 |
Banking & Finance |
28 |
23.73 |
Succession Planning |
18 |
15.25 |
Telecommunication |
15 |
12.71 |
Employee Engagement |
12 |
10.17 |
Total |
118 |
100 |
Total |
118 |
100 |
One of the objectives of this research is to identify HR Analytical Software being used by service industry, so this section presents the data pertaining to this objective in following sub-sections:-
Table 2: Number of HR Analytical Softwares Used
No of Softwares Used |
N |
Percentage |
Only One |
41 |
34.75 |
Two |
32 |
27.12 |
Three |
28 |
23.73 |
Four |
12 |
10.17 |
More than Four |
5 |
4.24 |
Total |
118 |
100 |
Table 3: HR Analytical Softwares used in Service Industry
Softwares Used for Analytics |
N |
Percentage |
MS Excel |
91 |
77.12 |
SPSS |
47 |
39.83 |
SAS |
32 |
27.12 |
R |
39 |
33.05 |
Python |
28 |
23.73 |
Tableau |
31 |
26.27 |
Qlik View |
17 |
14.41 |
By using the analytical software various statistical calculations can be done to analyse and interpret the data. From the extensive review of literature the most used statistical tools were identified and listed. This list was given to HR professionals and they were asked to indicate their proficiency on five point scale ranging from basic to expert. The scale items were described as follows:-
Table 4 is showing the count and percentages of proficiency levels for each statistical tools; further table 5 is presenting the mean, standard deviations and coefficient of variations for each statistical tool. From the mean score it can be inferred that HR professional are having advanced proficiency in performing basic statistical calculations i.e. averages and percentiles etc.
The respondents indicated that they can perform measures of dispersion, correlation, regression, ANOVA, factor analysis, reliability, sampling techniques and multivariate techniques under the supervision of experts. That indicates their intermediate proficiency about these tools. The respondents also said that they can prepare statistical reports to make statistical results understandable under the guidance of expert.
HR professionals said that they have learned about few tools recently by experience or in training programs i.e. causal paths, six sigma analysis and treatment v/s control groups. It was observed that none of the statistical technique was found in basic category that means all the HR professionals were having bear minimum proficiency in statistical analysis.
Table 4: Frequency Distribution of Analytical Competency of HR Professionals
Proficiency Level |
Basic |
Novice |
Intermediate |
Advanced |
Expert |
|||||
Items |
N |
%age |
N |
%age |
N |
%age |
N |
%age |
N |
%age |
Performing basic statistical calculations - Averages (Mean, Median), Percentiles |
16 |
13.56 |
18 |
15.25 |
22 |
18.64 |
32 |
27.12 |
30 |
25.42 |
Calculating statistically significant differences - Range, Variances, Standard deviation |
25 |
21.19 |
17 |
14.41 |
31 |
26.27 |
27 |
22.88 |
18 |
15.25 |
Performing Correlation, Regression |
24 |
20.34 |
27 |
22.88 |
25 |
21.19 |
24 |
20.34 |
18 |
15.25 |
Performing ANOVA, Factor Analysis |
29 |
24.58 |
25 |
21.19 |
27 |
22.88 |
21 |
17.80 |
16 |
13.56 |
Selecting sample, designing survey item, Verifying validity and reliability |
12 |
10.17 |
29 |
24.58 |
39 |
33.05 |
27 |
22.88 |
11 |
9.32 |
Using Advanced multivariate models (Structural equations models, Bivariate / multivariate choice models, Cross-level models) |
20 |
16.95 |
24 |
20.34 |
38 |
32.20 |
22 |
18.64 |
14 |
11.86 |
Identify causal paths |
21 |
17.80 |
42 |
35.59 |
45 |
38.14 |
4 |
3.39 |
6 |
5.08 |
Six Sigma analysis |
31 |
26.27 |
34 |
28.81 |
35 |
29.66 |
9 |
7.63 |
9 |
7.63 |
Formulate treatment vs. control groups |
29 |
24.58 |
40 |
33.90 |
31 |
26.27 |
15 |
12.71 |
3 |
2.54 |
Preparing statistical reports to make statistical results understandable |
25 |
21.19 |
32 |
27.12 |
34 |
28.81 |
10 |
8.47 |
17 |
14.41 |
Table 5: Mean, S.D. and C.V. about Analytical Competency of HR Professionals
Items |
Mean |
S.D. |
C.V. |
Proficiency Level |
Performing basic statistical calculations - Averages (Mean, Median), Percentiles |
3.41 |
1.86 |
0.55 |
Advanced |
Calculating statistically significant differences - Range, Variances, Standard deviation |
2.97 |
1.83 |
0.62 |
Intermediate |
Performing Correlation, Regression |
2.87 |
1.84 |
0.64 |
Intermediate |
Performing ANOVA, Factor Analysis |
2.75 |
1.85 |
0.67 |
Intermediate |
Selecting sample, designing survey item, Verifying validity and reliability |
2.97 |
1.25 |
0.42 |
Intermediate |
Using Advanced multivariate models (Structural equations models, Bivariate / multivariate choice models, Cross-level models) |
2.88 |
1.53 |
0.53 |
Intermediate |
Identify causal paths |
2.42 |
0.97 |
0.40 |
Novice |
Six Sigma analysis |
2.42 |
1.38 |
0.57 |
Novice |
Formulate treatment vs. control groups |
2.35 |
1.13 |
0.48 |
Novice |
Preparing statistical reports to make statistical results understandable |
2.68 |
1.68 |
0.63 |
Intermediate |
Table 6 is depicting the overall analytical competency of HR professional considered under study. It can be seen that 26.27% respondents were having good analytical competency and the analytical competency of 29.66% respondents was average. However analytical competency level of majority of respondents (44.07) was found to be bad.
Table 6: Overall Analytical Competency of HR Professionals
Overall Proficiency Level |
N |
Percentage |
Good |
31 |
26.27 |
Average |
35 |
29.66 |
Bad |
52 |
44.07 |
Total |
118 |
100 |
Further overall analytical competency of HR professionals was ascertained with respect to type of service industry as shown in table 7. It can be observed that analytical competency of HR professionals working in IT industries was highest (2.19) followed by HR employees of FMCG (1.84) and telecommunication (1.73). It was observed that HR professional of healthcare (1.68) and banking & finance (1.54) were having least analytical competency.
Table 7: Service Industry wise Overall Analytical Competency of HR Professionals
Type of Service Industry |
Bad |
Average |
Good |
Total |
Mean |
Rank |
FMCG & Retail |
12 |
5 |
8 |
25 |
1.84 |
2 |
Healthcare |
11 |
3 |
5 |
19 |
1.68 |
4 |
Information Technology |
6 |
13 |
12 |
31 |
2.19 |
1 |
Banking & Finance |
17 |
7 |
4 |
28 |
1.54 |
5 |
Telecommunication |
6 |
7 |
2 |
15 |
1.73 |
3 |
Total |
52 |
35 |
31 |
118 |
Although it has been observed that HR professional working in different service industries are possessing different analytical competency, still to measure significant difference in analytical competencies of HR professional following hypothesis has been taken under study:-
H01:There is a no significant difference in analytical competency level of HR professionals with respect to type of service industry
Ha1: There is a significant difference in analytical competency level of HR professionals with respect to type of service industry
To test this hypothesis ANOVA test was applied and results received are presented in table 8. At 5% level of significance the value of F-statistic is significant which leads to the rejection of null hypothesis so it can be concluded that there is a significant difference in analytical competency level of HR professionals with respect to type of service industry.
Table 8: ANOVA test result to measure difference in analytical competency level of HR professionals with respect to type of service industry
Source of Variation |
Sum of Squares |
Degree of Freedom |
Mean Sum of Squares |
F-Ratio |
p-value |
Result |
Between Samples |
5834.6 |
4 |
1458.650 |
8.129 |
0.000 |
Significant |
Within Samples |
20276.127 |
113 |
179.435 |
|||
Total |
26110.727 |
117 |
Level of Significance=5%
The review of literature highlighted that analytical competency of professionals differ with respect to demographic variables, so in this research this hypothesis was framed:-
H02:There is no significant impact of demographic variables on analytical competencies of HR professionals
Ha2: There is a significant impact of demographic variables on analytical competencies of HR professionals
Firstly the data of HR analytical competency was cross tabulated with the demographic profile of respondents and then chi-square test was applied as presented in table 9. The value of chi-statistic was found to be significant for gender and work experience of respondents whereas it was not significant for the age of respondents. So it can be concluded that gender and work experience of HR professionals have significant impact on their analytical competencies.
Table 10 is showing the mean analytical competency level of HR professionals with respect to their gender and work experience. It was found that male HR professionals (2.00) were more competent in statistical analysis as compared to the female HR professionals. In work experience category the employees having work experience of 5 to 10 years were having the highest competency in statistical analysis.
Table 9: Chi-Square test result to measure impact of demographic variables on analytical competencies of HR professionals
Demographic Variable |
Overall Proficiency Level |
Chi-Square Value |
p-Value |
Significance |
||||
Good |
Average |
Bad |
Total |
|||||
Gender |
Male |
13 |
28 |
13 |
54 |
25.74 |
0.000 |
Significant |
Female |
18 |
7 |
39 |
64 |
||||
Total |
31 |
35 |
52 |
118 |
||||
Age |
20-30 Years |
3 |
5 |
3 |
11 |
11.32 |
0.183 |
Not Significant |
30-40 Years |
8 |
11 |
6 |
25 |
||||
40-50 Years |
9 |
10 |
20 |
39 |
||||
50-60 Years |
9 |
6 |
21 |
36 |
||||
Above 60 Years |
2 |
3 |
2 |
7 |
||||
Total |
31 |
35 |
52 |
118 |
||||
Work Experience |
Less than 5 Years |
8 |
9 |
32 |
49 |
26.27 |
0.000 |
Significant |
5 to 10 Years |
19 |
11 |
10 |
40 |
||||
More than 10 Years |
4 |
15 |
10 |
29 |
||||
Total |
31 |
35 |
52 |
118 |
Level of Significance=5%
Table 10: Analytical competencies of HR professionals with respect to Demographic Variables
Demographic Variable |
Mean |
|
Gender |
Male |
2.00 |
Female |
1.67 |
|
Work Experience |
Less than 5 Years |
1.51 |
5 to 10 Years |
2.22 |
|
More than 10 Years |
1.79 |
Conclusion and Recommendations
Acknowledgements
Funding
This research received no external funding.
Authors' contributions
Both 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.
Consent for publication
All the authors have provided their consent for publication in the PBR journal
Availability of data and materials
Not Applicable
Competing interests
The authors declare no conflict of interest.
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