Measuring Training Effectiveness: Study of Automobile Sector ...

Measuring Training Effectiveness: Study of Automobile Sector in India.

Dr. Amar Kumar Mishra (*)

Associate Professor

IMS, Ghaziabad

India

Mail id: [email protected]

Ms. Saumya Kapoor Sharma

Research Scholar

ICFAI University, Dehradun

Mail id: [email protected]

Dr. Anjali Rai

Assistant Professor

IMS Ghaziabad

India

Mail Id : [email protected]

Journal of Xi'an University of Architecture & Technology

Volume XII, Issue III, 2020

Issn No : 1006-7930

Page No: 4197

mailto:[email protected]

mailto:[email protected]

Abstract:

While training had always been presumed to be the most important function in Management, especially Human Resource

Management; the measurement of its impact had always been a holy grail. This study focusses on the effectiveness of training

and its transfer using four-dimension model. An empirical study was done to assess the impact of training on employee’s

performance. Three prominent automobile companies were part of the research. A structured questionnaire was surveyed on

permanent 496 employees. The quantitative study revealed that employees across the companies have fairly equal perception

with reference to training but feel more impact of dimensions like result & reaction. This paper ends with a conclusion that

employee training becomes a regular practice and its effectiveness measured keeping in pace with employee perception at every

stage.

Keywords: Training Effectiveness, Kirkpatrick Model, Automobile Company

1.0 Introduction:

The Indian Automotive Industry has undergone massive transformation with respect to its growth and profitability. It started

from an embryonic state in 1940’s to fourth largest position in the world. On the brighter side, by 2021, Indian passenger market

shall seize the third largest position across global platform. Considered as one of the major contributors to GDP by 7.1%, the

AMP (Automotive Mission Plan) 2016-26 visualises its contribution to extend till 12%.

With Government star programme,” Make in India” the industry performance has elevated and shown greener picture with

respect to economic performance of the country. This decade has witnessed paradigm change in automotive industry like

changing customer needs, uncontrollable impact of technology, dynamic administrative environment, and uncertain face of

vigorous infrastructure. As per the report furnished by Bloomberg New Energy Finance, face of Indian vehicle industry on

roads are going to change soon. Autonomous vehicles and Electric vehicles shall soon transform our cities to smart cities where

Electric two wheelers, buses and Rickshaws shall run on roads by 2040. According to AMP2016-26, Indian automotive industry

shall become the largest job providing sector, if the double challenge is resolved mainly reduced availability of skilled

manpower and employer attractiveness.

The biggest challenge faced by automotive industry is volatile market, changing regulatory norms and shortage of skilled

manpower. Application of technological innovations and research had been fundamental part of this industry, what lies beyond

it is its intensity and level of application. Since we are moving in 4.0 revolution, therefore integration of AI, Robotics and

machine learning is crucial. Most the emerging economies understand that present era is witnessing war of the talent. HR

managers fathom the strategic importance of intelligence based, knowledge driven society with accomplished workforce ready

for self-renewal. At the core lies the ingenious personnel who stands as strong pillars meant for the prosperity of the

organisation. In this globalised era, a company can survive only if they accomplish sustainable competitive advantage.

There is departure from traditional manufacturing techniques, which demands change in workforce metrics and composition.

“Organisational Dexterity “is need of present century where agile structure has to be evolved. We must question the status quo,

apart from searching new talent, investment on current resources to be done. Training acts as a catalyst, where employees are

motivated to identify gap in their operations and comprehend new skills. Indian automotive industry is passing through a crucial

stage where skilled talent is available but in fragmented form, not ready for future contingencies. Hence, we need to build up

learning and development environment as part of our eco system. Grounding human capital in the following streams of

Robotics, AI Learning, Mechatronics, Data Analytics, Digitisation, Autotronics,3D modelling software, handling machine,

quality control becomes essential part of their mainstream, also Raising their functional capacities, emotional handling, working

with cross cultural groups, developing habit of lifelong learning, opting for hybrid and classroom learning will pave way for

closing the unseen skill gap.

Corporate leaders need to identify three questions namely, why to Learn, what to Learn and how to Learn. To stay ahead of the

competitors, increased collaboration between academic institutions and industry may generate courses and programmes for

developing the skills thereby supporting Government initiatives.



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Onus of responsibility lies on employees as they need to develop themselves for technical changes but also gear up for

leadership roles. Success of this industry does not lie in preparing for current challenges and grabbing opportunities but how

proficiently you mark the unseen threats and problems. Underlying present situation, it becomes mandatory for industry leaders

to fabricate situations of continuous learning, adapt and become agile in discharge of their operations. The industry players

cannot design strategies in isolation and face disruptive situations to enjoy varied opportunities. Solutions to be generated at

cross functional level across organisations thereby maintaining balance between stakeholders and company approaches. Lastly,

for this emerging economy to grow, we need empowered workforce plans supported by divergent and agile manpower.

2.0 Literature Review:

The performance of an employee rests on numerous factors, but the utmost significant factor is training, which improves the

capabilities of employees (Raja, Furqan and Khan, 2011). It has been emphasized that establishments with more advanced

people-oriented strategies have performed better, leaving competition behind. This is mainly due to the fact that when

establishments invest in people, in training, what they get in return is greater skill and greater efficiency that helps improve

morale and productivity (Sheeba Hamid, 2011). The training process is one of the most well-known approaches to build singular

efficiency and convey authoritative objectives to employees. (Ekaterini Gallano, Constantinos Vasilius Pipuras, 2009).

Ojokuku and Adegbite (2014) inspected the effect of limit improvement on employee execution in chose associations in

Nigeria. The investigation found that there is a solid positive connection between limit advancement and employee execution

in the association. Malaolu and Ogbuabor (2013) researched the impacts of training and workforce improvement on employee

efficiency and hierarchical execution utilizing First Bank of Nigeria Plc. As a contextual investigation. Damoy AL et al. (2012)

affirmed in their examination that exceptionally qualified and proficient staff is completely important to improve the

association. Training builds employee efficiency improves employee benefits and carries positive change to the association.

According to Shelly Frost, Demand Media Training is a critical fragment to prepare new employees for their occupations and

keep current employees revived with huge information. To be fruitful, the training program needs a specific explanation with

appropriate training strategies. Understanding the components that effect training programs grants you to make or change the

present guidance of your employees to fit the necessities of your business and your employees 2. As showed by Henry Onguri

(2011), Jennifer Chishamisu Nonso, training and Development have become an issue of indispensable criticalness. Yet various

pros have investigated training and improvement practices in relationship in making and made economies, it should be seen

that most research has focused on the benefits of training with everything considered. In any case, there is a confined focus on

the evaluation of training and headway practices in affiliations. 3. As showed by Haslinda Abdulla (2009), the troubles defying

organizations and associations in the convincing organization of HR improvement and HR progression differentiate from

stresses over the nonappearance of workers in academic HR to make Facing the enthusiasm of data workers and propelling

learning and improvement in one spot Work The major and focal test is the nonattendance of explicit masters in the headway

of HR in collecting associations, and this exhibits managers see resources individuals in the field of HR as a helper limit as

indicated by HR the board and may consider them less noteworthy. This significance can incite an inadequate execution of HR

R&D activities and addition vulnerability and frustration in the effective organization of HRD in general. As demonstrated by

Fizzah (2011), the purpose behind the investigation is to know how training and improvement impact various levelled execution

and to appreciate what the impact of training and progression on an affiliation is. Data is assembled from 100 people from

different affiliations. Additionally, past research on training and improvement. Training and progression are noteworthy for

workers in the relationship, as they help employees with improving their aptitudes and give extraordinary execution in the

workplace. There is an extraordinary association among training and headway and the introduction of the foundation, and the

relationship is discussed in the chronicle. As demonstrated by Iftikhar Ahmad and Sirajuddin (2009), affiliations have grasped

training and headway to close the staff capacities gap. The appraisal of the training should be reasonable for the individual and

the situation. The appraisal won't guarantee effective learning aside from if the training is suitably organized. Successful

evaluation depends upon whether appraisal strategies are united into the structure of the training program before execution. As

showed by Bates and Davis (2010), a training venture must be used when the understudy can practice the theoretical edges

learned.



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3.0 Research Objectives:

To study the training atmosphere in Automotive industry with specific reference to Ashok Leyland, Tata Motors

and Mahindra & Mahindra Limited

To Identify the various dimensions of the impact of training

To measure the various dimensions of the impact of training

To compare the impact of training in three companies

To suggest the measures and improve training effectiveness

4.0 Methodology

4.1 Participants Data for the study was collected from 800 employees from three automobile industries namely Ashok Leyland,

Mahindra & Mahindra and Tata Motors in India and was collected across various hierarchies to provide a wide coverage

to the study. The response rate was 62 % as 496 complete questionnaires were obtained. The demographic structure of

data is presented in table -1.

Table-4.1 Demographic Profile

Frequency

PEACent

Valid

PEACent

Gender Male 299 59.88 59.88

Female 197 39.72 39.72

Total 496 100.0 100.0

Organization Mahindra &

Mahindra

175 35.28 35.28

Tata Motors 165 33.27 33.27

Ashok Leyland 156 31.45 31.45

Total 496 100.0 100.0

Designation Junior Level

Manager

297 59.88 59.88

Middle level

Manager

136 27.42 27.42

Senior Level

Manager

63 12.70 12.70

Total 496 100.0 100.0

Experience <5Year 201 40.52 40.52

5-10 Years 153 30.85 30.85

10-15 Years 84 16.94 16.94

>15 Years 58 11.69 11.69

Total 496 100.0 100.0



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Source: Tabulated by authors

4.2 Questionnaire

4.2.1 Training Effectiveness

The study tried to capture and to assess the self-measured Training Effectiveness of the respondent through the self-report

TV Rao Scale comprising of 28 items such as “My organization identifies need of future trainings and professional

development of my department “Post training, I have been able to infuse an atmosphere of teamwork in my group”; and

“Training enhances my skills to perform specific job responsibilities”. In confirmatory factor analysis, three items were

dropped as their factor loading were found 0.42, 0.46 & 0.51, which was less than 0.7(Comrey, A.L., Lee, H.B., 1992).

Response towards the statements were measured on 5-point Likert Scalingthat extends from 1 = strongly disagree to 5

= strongly agree.

5.0 Analytical Approach

Confirmatory factor analysis was used to assess the composite reliability and validity of the proposed construct using

AMOS 21.0. The conventional fit indices like RMSEA (Root mean square error approximation), PClose, SRMR

(standardized root mean square residual), CFI (comparative fit index), CMIN/DF were the statistics that had been applied

to examine the hypothesized model. Internal consistency and reliability of the construct were evaluated using Cronbach’s

alpha.

Similarly, to check for the common method bias in the hypothesized model, Harman’s one factor test ((Podsakoff &

Organ, 1986) was conducted. This was followed by testing of the proposed hypothesis using path analysis through SPSS

macro named Process (Hayes, 2013).

5.1 Result

Table 5.1.1 Reliability Statistics

Cronbach's Alpha N of Items

.969 25

Table 5.1.2 Model Validity Measures

CR

AV E MS V MaxR( H) RESUL T REACTI ON BEHAVIO UR LEARNIN G

RESULT 0.958 0.742 0.471

0.959 0.861

REACTIO N 0.952 0.740 0.505 0.953

0.655**

* 0.860

BEHAVIO UR 0.940 0.757 0.459 0.940

0.619**

* 0.645*** 0.870

LEARNIN G 0.945 0.774 0.505 0.945

0.687**

* 0.711*** 0.677*** 0.880

An examination of the factor inter-correlations indicated that all of the constructs met the criteria for discriminant validity

(factor inter-correlations < 1.0) and range from .22 to .52. Criteria set by Nunnally and Bernstein (1994).



Issn No : 1006-7930

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A principal axis factor analysis was conducted on the 75 items with varimax rotation. The Kaiser-Meyer-Olkin KMO

measure verified the sampling adequacy for the analysis, KMO= .986 (marvelous according to Hutcheson & Sofroniou,

1999), and all KMO for individual items were greater than .77, which is well above the acceptable limit of .5 (Field, 2013).

An initial analysis was run to obtain eigenvalues for each factor in the data. 4 factors had eigenvalues over Kaiser’s criterion

of 1 and in combination explained 95.6 % of the variance. Table shows the factor loading after rotation. The items that

cluster on the same factor suggest that factor 1 represents Reaction, factor 2 represents Learning, factor 3 represents

Behaviour, factor 4 represents Result.

Table 5.1.3 KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .974

Bartlett's Test of Sphericity Approx. Chi-Square 12426.318

Df 300

Sig. .000

Table 5.1.5 Total Variance Explained

Compon

ent

Initial Eigenvalues

Extraction Sums of

Squared Loadings

Rotation Sums of

Squared Loadings

Total

% of

Varian

ce

Cumulati

ve %

Total

% of

Varian

ce

Cumulati

ve %

Tot

al

% of

Varian

ce

Cumulati

ve %

1 14.375 57.502 57.502

14.375 57.502 57.502

6.251 25.002 25.002

2 2.246 8.985 66.487

2.246 8.985 66.487

5.449 21.798 46.800

3 1.801 7.204 73.691

1.801 7.204 73.691

4.198 16.792 63.591

4 1.371 5.482 79.173

1.371 5.482 79.173

3.896 15.582 79.173

Extraction Method: Principal Component Analysis.



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Table 5.1.6 Rotated Component Matrixa

Component

1 2 3 4

V1 .771

V2 .721

V3 .792

V4 .818

V5 .786

V6 .754

V7 .813

V8 .776

V9 .742

V10 .804

V11 .750

V12 .731

V13 .808

V14 .810

V15 .817

V16 .769

V17 .778

V18 .815

V19 .792

V20 .780

V21 .807

V22 .801

V23 .768

V24 .781

V25 .802

Extraction Method: Principal Component Analysis. Rotation

Method: Varimax with Kaiser Normalization.a

a. Rotation converged in 6 iterations.

Table 5.1.7 Component Transformation Matrix

Component 1 2 3 4

1 .576 .531 .440 .438

2 -.794 .532 .260 .138

3 -.132 -.609 .769 .142

4 .142 .251 .385 -.877

We computed a confirmative factor analysis (CFA) to assess our hypothesized 4 factor model. To assess model fit, we looked

at both absolute fit indices (Confirmative Fit Index (CFI) and Root Mean Square Error Approximation (RMSEA)) and

incremental fit indices (Tucker-Lewis Index (TLI) and Normative Fit Index (NFI)). We set the threshold value for the CFI, TIL

and NFI at >.90 (Black et al. 2007) and the threshold value for the RSMEA at < .001. This overall measurement model shows

excellent model fit (CMIN/DF 1.132, CFI .996; RMSEA .08; TLI .941; NFI .944).



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Fig 5.1

e1

e2

e3

V18

V19

e5

e7

e8

e9

V4

V14

V17



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Table 5.1.8 Model Fit Measures

Measure

Estimate Threshold

CMIN 357.122 --

DF 269 --

CMIN/DF 1.328 Between 1 and 3

CFI 0.993 >0.95

SRMR 0.024 <0.08

RMSEA 0.026 <0.06

PClose 1.000 >0.05

The descriptive statistics shows that though the employees across the companies and across the dimensions have

fairly equal perception on the impact of training; employees feel more impact on Result & Reaction Dimensions.

Table. 5.1.9 Descriptive Statistics

N

Mean Std.

Deviation

REACTION 496 21.124 3.6286

LEARNING 496 15.056 3.1267

BEHAVIOUR 496 15.034 3.1637

RESULT 496 24.118 4.2270

Further, one-way ANOVA was conducted to compare scores of participants across the three

organizations on all the four dimensions. There was statistically no significant difference between

employees on any dimension as determined by one-way ANOVA for reaction (F(2, 494)= 1.581,

p= .207); learning (F(2, 494)= 2.172, p= .115); behavior (F(2, 494)= .594, p= 0.553) as well as

result (F(2, 494)= .688, p= .503). The results are shown in Table No.5.1.10

Table. 5.1.10

Sum of

Squares

df

Mean

Square

F

Sig.

REACTION Between

Groups 41.534 2 20.767 1.581 .207

Within

Groups 6528.778 494 13.136

Total 6570.312 496

LEARNING Between

Groups 42.270 2 21.135 2.172 .115

Within

Groups 4836.162 494 9.731



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Total 4878.432 496

BEHAVIOUR Between

Groups 11.906 2 5.953 .594 .553

Within

Groups 4982.516 494 10.025

Total 4994.422 496

RESULT Between

Groups 24.603 2 12.302 .688 .503

Within

Groups 8891.435 494 17.890

Total 8916.038 496

6.0 Conclusion

Rightly said by Henry Ford, Founder, Ford Motor Company, the only thing worse than training your employees and

having them leave is not training them and having them stay.

Based on the empirical findings, related to our research, to find out the benefit, value addition to organization and its

human capital and does the employees actually transfer learning, we can say candidly that every organization

understands their employee perception and reaction to training and development activities. In today’s scenario,

training is treated as a religion and not as Panacea.

It is well noticed that effective learning and development activities done where required, gives positive outcome.

Employee engagement done to embrace their cognitive, emotional and physical levels, automatically contributes to

productive outcomes, not only for themselves but for organizations

Based on the gender demographics in all three companies, we can state, the majority of males exceeds females

although, at shop floor level, the percentage of female employees are increasing. and permanent employees are well

educated and experienced.As regarding impact of training and development programmes, it can be rightly depicted

that there was employee engagement and desire for career progression at every age level. A positive and healthy

learning work environment embraces productivity and growth for employees and employers. A well-tailored training

program is suitable not only for immediate use of employees but helps them in enhancing their contribution in this

auto industry supply chain. Leaders are created in the organisation and to supplement this companies have established

their own university, colleges and academic institutions.

Measuring training and development has not touched the basic parameters in Indian context. Still we follow the

traditional methods of training measurement but do not proceed to measure their impact. Organisations allocate huge

amount from their budget on training cost, man hours spend on training, number of employees trained every year, but

not give due cognizance to its effectiveness. More focus to be drawn as to what trainers want to measure. Basic premise

is along with training need identification, proper identification of trainee, training design, trainers are essential

ingredient to measure the impact. Impact of training with other strategic business units needs to be attached. To know

the exact impact of training, trainers need to master the measurement techniques.

There always arises a gap between what is measured and what exactly need to be measured. The fundamental

philosophy for training to be successful is that it is a commitment based technique which gives fruitful results if both

the parties acknowledge it. (Booth 1991), explained the utility of training is not just limited to full time employees but

also to part time employees, this being supported by (Mishra 2012) In developing economies, particularly like India,

in manufacturing sector, training is provided to both part time and full time employees. (McColl-Kennedy& White,

1997; Laird, Holton & Naquin, 2003), Training should always be valued as quality delivered to trainees and not the

training hours spent.



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