|
Dr. Asha Sharma Assistant Professor Department of Accountancy and Statistics University College of Commerce & Management Studies Mohanlal Sukhadia University, Udaipur |
Intangible assets like Human resources are a very important asset for a firm. Human capital is an important part of intellectual capital. The success of any firm depends on the quality of its human resources, whether it belongs to any sector or any industry. Physical assets are used to increase earning capacity of any business organization, and used to increase productivity, earning capacity, increasing the wealth and profit, market value, economic value added, etc. Nowadays because of the global transition service industry has become one of the leading industries which are mainly based on human resources. This study focuses on the implication in Human Resources Accounting and to measure the impact of HRA on a firm’s performance and Managerial Efficiency. The aim of the study is to measure the contribution of human resources, and their impact on managerial efficiency and valued HRA. Primary data is taken to test the hypothesis. Statistical tools like regression, Structural Equation model and artificial neural network is used to analyze the result.
Keywords: - Human Resources Accounting, Cost and Value, Retention and Productivity, Managerial Efficiency and social responsibility, Firm Performance1.
INTRODUCTION
Success of corporate
undertakings purely depends upon
the
quality
of human resources.
It
is accentuated that; Human element is the most important input in any
corporate enterprise. The investments directed to raise knowledge; skills and aptitudes of the work force of the organization are the investments in human resource.
In this context, it is worthwhile to examine and human resource accounting practices in corporate sector in India and to understand how the HRA is effecting the financial and overall performance of the company. (Sharma, A. 2012).
HRA
comprises the energies, skills, talents and knowledge of people which are, or
which potentially can be applied to the production of goods or rendering useful
services (Syed (2009).
It
is simply an attempt
to identifying
measuring and communicating information about human resources and it ought to be viewed as a metaphor. It’s the way of thinking about management of people. It is
a recording of transactions related to the value of human resources.
It is highly complicated in today's market to
find well knowledge, and highly motivated people. But Human Resource is one of
the most important operations for any organization or business. Without the
human involvement can lose its efficiency in work, and all the areas of
business and levels human efficiency is required with machine efficiency. (Ks,
G. 2018).
2.
LITERATURE
REVIEW
Oko, S (2018) explained in his paper on A Human Resource accounting system
identifies the costs occurrence associated with manpower and separates such
human cost from other costs of business. It is therefore significant in
deciding and affecting corporate investment and employment decisions of
management. It is concluded that capitalizing human
assets would positively impact on performance
and financial position of organizations and recommended its disclosure as
intangible asset in the balance sheet.
Amahalu,
N., Abiahu, M.-F. C., Chinyere, O., & Christian, O. (2016) ascertained the effect of Human Resource Accounting on
Financial Performance of deposit money banks
listed on Nigeria Stock Exchange in their study. The specific objectives are to ascertain the effect or
otherwise of staff cost on return on asset, return on equity and market-to book
value of banks listed on the floor of Nigeria Stock Exchange from 2010-2015.
Sharma, R., & Sharma, A. (2013)
conducted a study with a view to study current practices of human resource
accounting in Infosys limited and to find relationship between human resource
values and rate of return, fixed assets, current assets, total assets and value
added. The researcher found that there is a highly positive correlation between
no. of employees and earning capacity of company. Human assets have equally
contribution like fixed assets in value addition of company. The researcher
also concluded that sound financial health of company is due to its appropriate
human resource value.
Sharma,
A. (2012). Commented on the applicability of HRA system that it can be used to
enhance performance of employees as well as company and to take a variety of
decisions in the area of human resource management. But the number of
organizations that have adopted HRA system in India is low as it is not
compulsory for the Indian organizations to value human resources and mostly
used by public sector but private sectors companies are least interesting.
Still all those companies who are adopting this accounting system are enjoying
their strong financial performance and efficient management.
Sen, D.K., Jain, S.C., Jat, S.L and Saha, R.K. (2008) concluded in their
study whether HR information has any impact on internal decision-making i.e. in the context of personnel management decision-making related to employee recruitment and employee turnover control in banking industry of Bangladesh. It is very important to know the perceptions of the management about the specific uses to which HRA information can be put.
3.
RESEARCH METHODOLOGY
Research methodology comprises the research design, sample design, sources of data, selection of data, various designs and techniques used for analyzing the data. The methodology used for the study at hand is as under:
3.1 Research Design: The research design used for the research problem in hand is causal research as the objective is to determine which variable might be causing certain behavior, i.e. whether there is a cause and effect relationship between variables. In order to determine cause and effect, it is important to hold the variable that is assumed to cause the change in the other variable(s), constant, and then measure the changes in the other variable(s). This type of research is very complex and the researcher can never be completely certain that there are no other factors influencing the causal relationship, especially when dealing with people’s attitudes and motivations.
Total 18 questions were framed, they are segregated
in various dimensions, on the behalf of nature of questions.
Table 1 List of dependent and
independent variables
|
Independent Variable |
|
Dependent Variable |
|
|
Cost and Value (CV) |
14, 15, 16, 18 |
Firm's Performance (FP) |
1, 2, 4, 5, 7, 13 |
|
Retention and Productivity (RP) |
6, 9, 12, 17 |
Management and Society (MS) |
3, 8, 10, 11 |
Independent Variables: Cost and Value (CV), Retention and Productivity
(RP)
Dependent Variables: Firm’s Performance (FP),
Managerial Efficiency and Social Responsibility (MS)
Sample Design: The sample design adopted for the research problem in hand is convenience random sampling.
The following
points are also included in sample design for the purpose of the study:
·
Sample Size: 385 samples are taken but out of them 254 was found suitable.
·
Sampling
Unit: The study includes executives, managers, investors and shareholder.
·
Sample Area: The sample area for the study in hand was metro
cities.
3.2
Objectives
To find out the cost and value of human resource
To know the effect of human resources accounting on
retention and productivity of employees
To understand the impact of the HRA on firm’s performance
To determine the impact of HRA on managerial decision
making capacity
To measure the impact of HRA on social responsibility
3.3 Hypotheses
In order to realize
the above objectives, the following hypothesis has been formulated.
H1: There is no significant
impact of adaptation of HRA on
firm’s financial performance
H2: There is
no significant
impact of HRA on managerial decision making
capacity and social responsibility
3.4 Methods of data collection
For the study in hand, the primary was collected. The primary data for the study was collected directly from target respondents through structured questionnaire. This questionnaire includes the personal information about the respondents. The questions asked to respondents are related to
3.5 Research Technique
Applied
Likert Five-Point scale was applied in order to analyze the results. The percentage response for each category was calculated and the various weights assigned to different opinions as per Likert’s Five Point scale i.e. strongly
agree =5, Agree= 4, Neutral = 3, Disagree = 2, strongly disagree = 1.The mean scores and standard deviation scores were calculated for the same.
4.
RESEARCH ANALYSIS AND IMPLICATIONS OF FINDINGS
Following statistical tests and tools will be used to
meet with above mentioned objectives and for proving the hypothesis:
·
ANOVA
·
Structural Equation model and
·
Artificial Neural Network
For applying this statistical
tools software SPSS 19 and AMOS 26 are used.
4.1
ANOVA
ANOVA is
a collection of statistical models and their associated estimation procedures
(such as the variation among and between groups) used to analyze the
differences among group means in a sample. The ANOVA is based on the law of
total variance, where the observed variance in a particular variable is
partitioned into components attributable to different sources of variation. In
its simplest form, ANOVA provides a statistical test of whether two or more
population means are equal, and therefore generalizes the t-test beyond two
means".
Table 2
|
Model Summary |
||||
|
Model |
R |
R Square |
Adjusted R Square |
Std. Error of the Estimate |
|
1 |
.821a |
.673 |
.663 |
.44634 |
|
a. Predictors: (Constant),
CV2, RP2, RP1, RP4, RP3, CV4, CV1, CV3 |
||||
|
Table 3 ANOVAa |
||||||
|
Model |
Sum of Squares |
df |
Mean Square |
F |
Sig. |
|
|
1 |
Regression |
100.154 |
8 |
12.519 |
62.843 |
.000b |
|
Residual |
48.608 |
244 |
.199 |
|
|
|
|
Total |
148.763 |
252 |
|
|
|
|
|
a. Dependent Variable:
AVfp |
||||||
|
b. Predictors: (Constant),
CV2, RP2, RP1, RP4, RP3, CV4, CV1, CV3 |
||||||
|
Table 4 Coefficientsa |
||||||
|
Model |
Unstandardized Coefficients |
Standardized Coefficients |
t |
Sig. |
||
|
B |
Std. Error |
Beta |
||||
|
1 |
(Constant) |
.952 |
.128 |
|
7.412 |
.000 |
|
RP1 |
.222 |
.032 |
.322 |
6.902 |
.000 |
|
|
RP3 |
.074 |
.035 |
.109 |
2.118 |
.035 |
|
|
RP2 |
.170 |
.033 |
.250 |
5.227 |
.000 |
|
|
RP4 |
.043 |
.035 |
.063 |
1.202 |
.231 |
|
|
|
CV1 |
.062 |
.037 |
.093 |
1.688 |
.093 |
|
CV3 |
.091 |
.039 |
.130 |
2.346 |
.020 |
|
|
CV4 |
.071 |
.037 |
.101 |
1.930 |
.055 |
|
|
CV2 |
.003 |
.039 |
.005 |
.085 |
.933 |
|
|
a. Dependent Variable:
AVfp |
||||||
According
to Table 2, Table 3 and Table 4 it had been found, that there was no
significant difference in the retention & productivity and firm’s
performance. P-Value is found less than .05, the null hypothesis is rejected,
and so alternative hypothesis is accepted. The hypothesis is rejected on the
basis of all the criteria of retention and productivity but it is accepted on
the basis of cost and value. It means there is significant impact of adaptation of HRA on firm’s financial performance.
H2: There is
no significant
impact of HRA on managerial decision making
capacity and social responsibility
|
Table 5 Model Summary |
||||
|
Model |
R |
R Square |
Adjusted R Square |
Std. Error of the Estimate |
|
1 |
.833a |
.693 |
.683 |
.48571 |
|
a. Predictors: (Constant),
CV2, RP2, RP1, RP4, RP3, CV4, CV1, CV3 |
||||
|
Table 6 ANOVAa |
||||||
|
Model |
Sum of Squares |
df |
Mean Square |
F |
Sig. |
|
|
1 |
Regression |
129.988 |
8 |
16.248 |
68.875 |
.000b |
|
Residual |
57.563 |
244 |
.236 |
|
|
|
|
Total |
187.550 |
252 |
|
|
|
|
|
a. Dependent Variable:
AVms |
||||||
|
b. Predictors: (Constant),
CV2, RP2, RP1, RP4, RP3, CV4, CV1, CV3 |
||||||
|
Table 7 Coefficientsa |
||||||
|
Model |
Unstandardized Coefficients |
Standardized Coefficients |
t |
Sig. |
||
|
B |
Std. Error |
Beta |
||||
|
1 |
(Constant) |
.649 |
.140 |
|
4.643 |
.000 |
|
RP1 |
.268 |
.035 |
.347 |
7.660 |
.000 |
|
|
RP3 |
.100 |
.038 |
.130 |
2.624 |
.009 |
|
|
RP2 |
.238 |
.035 |
.312 |
6.730 |
.000 |
|
|
RP4 |
.032 |
.039 |
.043 |
.842 |
.401 |
|
|
CV1 |
.042 |
.040 |
.056 |
1.058 |
.291 |
|
|
CV3 |
.105 |
.042 |
.133 |
2.480 |
.014 |
|
|
CV4 |
.004 |
.040 |
.005 |
.104 |
.917 |
|
|
CV2 |
.033 |
.042 |
.043 |
.789 |
.431 |
|
|
a. Dependent Variable:
AVms |
||||||
According
to Table 5, Table 6 and Table 7, it had been found, that there was no
significant difference in the retention & productivity and firm’s
performance. P-Value is found less than .05, the null hypothesis is rejected, and
so alternative hypothesis is accepted. The hypothesis is rejected on the basis
of all the criteria of retention and productivity but it is accepted on the
basis of cost and value. It means there is significant impact
of HRA on managerial decision making capacity and social responsibility
4.2 STRUCTURAL EQUATION MODEL
To check the reliability and validity of the result,
measurement model is prepared. It is tried to find the fitness of model. Structural Equation Model is a technique or
tool of testing existing theory. It is an application of combination of
exploratory aspect, experimental aspect and descriptive aspect for solving a
problem.Reflective or formative construct is designed and fitness is checked
through measurement and structural model.
Measurement
Model
Figure1 Measurement Model
EMPIRICAL ANALYSIS AND FINDING
Table 8 CMIN (Measurement
Model Evaluation)
|
Model |
NPAR |
CMIN |
DF |
P |
CMIN/DF |
|
Default model |
42 |
259.499 |
129 |
.000 |
2.012 |
|
Saturated model |
171 |
.000 |
0 |
||
|
Independence model |
18 |
386.015 |
153 |
.000 |
2.523 |
|
Zero model |
0 |
2268.000 |
171 |
.000 |
13.263 |
Measurement
Model Evaluation
Assessment
of the convergent and discriminant validity considered item loadings, CMIN (Chi
square min). The model exhibited a good fit with the data, thus; x2=259.44
with 129 df, x2/df = 2.012, p = 0.000; It is considered good if it’s
ranged between 1 to 3 (CMIN/df). It is 2.012 which is suggesting adequate
reliability.
Table 9 RMR, GFI
|
Model |
RMR |
GFI |
AGFI |
PGFI |
|
Default model |
.094 |
.886 |
.848 |
.668 |
|
Saturated model |
.000 |
1.000 |
||
|
Independence model |
.588 |
.830 |
.810 |
.742 |
|
Zero model |
.662 |
.000 |
.000 |
.000 |
Construct reliability
Table 9 presents
the Good Fit Index (GFI) = 0.886; Root Mean Residual (RMR) = 0.094. GFI value
must be > 0.80 and SRMR < 0.08 (Alalwan, et al., 2018) for adequate model
fit.
The
study considers GFI and RMR for construct reliability. In Table 3, value IS
0.886 which is above 0.80, is considered good for fitness of model.
4.3 ARTIFICIAL NEURAL NETWORK
Neural network technique is used to
predict the demand for higher education and to prove the hypothesis. A computational neural network is a set of non-linear data
modeling tools consisting of input and output layers plus one or two hidden
layers.
Multilayer Perceptron
(MLP) Procedure is applied to measure and predict further study. They map
relationships implied by the data. The MLP feed-forward architectures, meaning
that data moves in only one direction, from the input nodes through the hidden
layer of nodes to the output nodes.
|
Table 11 Case Processing Summary |
|||
|
|
N |
Percent |
|
|
Sample |
Training |
176 |
69.6% |
|
Testing |
77 |
30.4% |
|
|
Valid |
253 |
100.0% |
|
|
Excluded |
0 |
|
|
|
Total |
253 |
|
|
The case processing summary in table
11 shows that 176 cases or 69.6 % are assigned to the training sample, 30.4%
are assigned to testing time, which is used to train the model and 77 cases are
assigned to the testing sample which is used to validate the model.
|
Table 12 Model Summary |
|||
|
Training |
Sum of Squares Error |
51.419 |
|
|
Average Overall Relative
Error |
.294 |
||
|
Relative Error for Scale
Dependents |
AVfp |
.316 |
|
|
AVms |
.272 |
||
|
Stopping Rule Used |
1 consecutive step(s) with
no decrease in errora |
||
|
Training Time |
0:00:00.12 |
||
|
Testing |
Sum of Squares Error |
26.233 |
|
|
Average Overall Relative
Error |
.277 |
||
|
Relative Error for Scale
Dependents |
AVfp |
.301 |
|
|
AVms |
.253 |
||
|
a.
Error computations are
based on the testing sample. |
|||
The following model summary table 12
displays information about the results of the neural network training the sum
of square error is equivalent to 51.419 in the training samples and the
relative error is .301 for training. So errors are very minutes.
|
Table 13 Network Information |
|||
|
Input Layer |
Covariates |
1 |
RP1 |
|
2 |
RP2 |
||
|
3 |
RP3 |
||
|
4 |
RP4 |
||
|
5 |
CV1 |
||
|
6 |
CV2 |
||
|
7 |
CV4 |
||
|
8 |
CV3 |
||
|
Number of Unitsa |
8 |
||
|
Rescaling Method for
Covariates |
Standardized |
||
|
Hidden Layer(s) |
Number of Hidden Layers |
1 |
|
|
Number of Units in Hidden
Layer 1a |
7 |
||
|
Activation Function |
Hyperbolic tangent |
||
|
Output Layer |
Dependent Variables |
1 |
AVfp |
|
2 |
AVms |
||
|
Number of Units |
2 |
||
|
Rescaling Method for Scale
Dependents |
Standardized |
||
|
Activation Function |
Identity |
||
|
Error Function |
Sum of Squares |
||
|
a. Excluding the bias unit |
|||
Table 13 gives information about
the network. It describes the process of working. It works into three-layer:
input layer, hidden layer, and output layer. It shows there are 8 units working
under input layer, 7 units are under hidden layer, and 1 unit is working under
the output layer.
|
Table 14 Independent Variable Importance |
||
|
|
Importance |
Normalized Importance |
|
RP1 |
.199 |
93.7% |
|
RP2 |
.212 |
100.0% |
|
RP3 |
.095 |
45.0% |
|
RP4 |
.115 |
54.2% |
|
CV1 |
.082 |
38.8% |
|
CV2 |
.054 |
25.4% |
|
CV4 |
.071 |
33.5% |
|
CV3 |
.172 |
81.4% |
Table 14 shows importance on how
the network classifies the prospective applicants. So, statistical models will
help in this situation. The highest importance is due to RP2 (Does HRA favour and develop to promote Human Resources in continuing
of the concerns?) (100%), RP1 (Being considered as most important assets of the concern help in
increasing productivity) (97.3.3%), and CV3 (Monetary and non- monetary rewards effected Employees for outstanding Performance) (81.4%)
and RP 4 (Training and re-training improve
employee’s skill increase productivity
of employees) (54.2%). Rest of the weighed is due to
remaining factors.
Fg-2
Input, hidden and output layer
Figure 2 gives the network information.
It describes the process of working. It works into three layer: input layer,
hidden layer, and output layer. It is a complete connected graph of input,
hidden layer and output respectively. It
also synaptic weight which is categorized as less than 0 and more than0. The
layers which are grey in colour have impacted more than 0. These layer
describing out of the entire factor which components have more weight or more
important.
CONCLUSION
The usefulness of human
resource accounting is concluded in providing the estimates and measuring the cost occurred on acquiring,
promoting, training, development.
HRA is equally beneficial
for employees and management. The managerial decisions can be taken on behalf of the statement of HRA. By analyzing the statement, productivity if employees can be measured and can motivate to attain their goals and enhancing their work efficiency. On the other hand employees got a psychological satisfaction to be assumed them as important assets and involving them into decision-making process, call for suggestion, participating in voting right and right
in
ownership.
All the approaches i.e.
ANOVA, SEM, ANN used for measuring result says almost same result that HRA
influences firm’s performance, managerial efficiency. Finally we can say that the output of HRA system can be used to enhance performance of employees as well as company and to take a variety of decisions in the area of human resource management. Result of ANOVA presenting the facts
that HRA (factors of retention of employees and productivity) mostly affect
output of the firm. Highly correlation among dependent and independent variable
is found in measurement model by Structural equation model. Artificial Neural
Network explained that the highest impact
of Human Resources Accounting is considered due to retention and productivity.
Trained employees are capable to enhance productivity and it leads to increases
firm’s performance. So, it can be said those companies who are adopting this accounting system are enjoying their strong financial performance and efficient management.
Annexure:
|
Questionnaire |
||
|
Human asset accounting significantly affects organizations’
performance. |
firm's performance |
FP1 |
|
Human Resource Accounting assist to face the acute competition in the
era of globalization? |
firm's performance |
FP2 |
|
The consumer and stakeholders also be affected positively by showing
HRA as an asset by Indian company? |
Management and society |
MS1 |
|
HRA is a measurement of the cost and value of people as organizational
resources |
firm's performance |
FP3 |
|
Is it beneficial for the progress of the companies and to enhance
profits? |
firm's performance |
FP4 |
|
Being considered as most important assets of the concern help in
increasing productivity |
retention and productivity |
RP1 |
|
Has it a great hand in the success of an organization? |
firm's performance |
FP5 |
|
Is it beneficial in communicating information in order to facilitate
effective management? |
Management and society |
MS2 |
|
Does HRA favour and develop to promote Human Resources in continuing
of the concerns? |
retention and productivity |
RP2 |
|
Does HRA inspire towards the social responsibilities? |
Management and society |
MS3 |
|
Does HRA help in taking managerial decision? |
Management and society |
MS4 |
|
Whether HRA is useful for improving to retain good/ skilled
employees? |
retention and productivity |
RP3 |
|
Training and development cost are incurred in order to Improve Firm
performance |
firm's performance |
FP6 |
|
The cost of providing shelter affects employees performance |
cost and value |
CV1 |
|
Cost incurred on Safety and health of the workers promote employee’s
commitment |
cost and value |
CV2 |
|
Monetary and non- monetary rewards effected Employees for outstanding Performance |
cost and value |
CV3 |
|
Training and re-training improve employee’s skill increase productivity of employees |
retention and productivity |
RP4 |
|
Travel cost are incurred in order to improve in turnover rate of
employees |
cost and value |
CV4 |
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