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ISSN: 2249-7196
IJMRR/July 2017/ Volume 7/Issue 7/Article No-3/759-780
Syamala Devi Bhoganadam et. al., / International Journal of Management Research & Review
*Corresponding Author www.ijmrr.com 759
ISSUES AND CHALLENGES FACED BY SMALL AND MEDIUM
ENTREPRENEURS IN AUTOMOBILE SECTOR- A STUDY AT VIJAYAWADA
REGION
Syamala Devi Bhoganadam*1, Dr. Nune Srinivasa Rao
2
1Research Scholar, Department of Management, K L University, Vaddeswaram, Guntur
(AP), India.
2Faculty Member, National Institute of Micro Small and Medium Enterprises, Yousufguda,
Hyderabad, India.
ABSTRACT
Entrepreneurship is considered as growth strategy for developing a nation in terms of
employment generation, GDP growth, economic development, wealth creation etc.
Entrepreneurship spirit takes places within a framework of both internal and external issues
and challenges. In developing countries like India with the changing economies issues and
challenges faced by SME’s are given much more role. Vijayawada region autonagar is facing
many issues and challenges with changing political changes and laws. The purpose of this
paper is to analyze issues and challenges faced by SME’s at Vijayawada region with financial
performance using theoretical base and supporting articles. The study data was collected from
entrepreneurs at Vijayawada region sample size as 300 entrepreneurs taken from stratified
simple random sampling using descriptive type of research method. The empirical research
adopts multiple linear regression analysis for hypothesis testing. The study reveals that 86%
Cronbach's Alpha value hence shows reliability on data. Hypothesis testing reveals that there
is significant relationship between IEIC, SEIC, SRIC, FIC and HIC with financial
performance. There is no significant relationship between GEIC, PIC, MIC and IIC with
financial performance.
Keywords: SME’s, challenges, issues, financial, performance, finance, infrastructure,
production, marketing.
Abbreviations:
GEIC = General environmental issues and challenges
IEIC = Industry environmental issues and challenges
SCEIC = Socio cultural environmental issues and challenges
PIC = Production issues and challenges
MIC = Marketing issues and challenges
FIC = Financial issues and challenges
HRIC = Human resources issues and challenges
Syamala Devi Bhoganadam et. al., / International Journal of Management Research & Review
Copyright © 2017 Published by IJMRR. All rights reserved 760
IIC = Infrastructure issues and challenges
SME =Small and medium enterprises
MSME =Micro small and medium enterprises
1. INTRODUCTION
Indian economy is considered as upcoming scope of market for world markets. But this thing
is linked with loads of challenges and issues. Indian economy is majorly on agriculture and
SME’s. SME’s in each and every sector are grooming like and then compared to last ten
years. Indian government had separate division for SME’s as MSME (Micro small and
medium enterprises). MSME’s in India are real contributor for Indian economy in terms of
generating drivers for economy and growth for economy. MSME sector has significant
contributors for employment generation, manufacturing sector, industrialization outputs,
economic development etc. There are around more than 6000 products were manufactures
under MSME sector ranging from traditional products to high tech items. From Singh et al
(2010b) it is estimated that MSME’s has 95% industrial units are in small sector unite 40%
are in manufacturing both finished and semi finished products. MSME sector play an
important role in nation’s exports markets. According to the census of MSME (Ministry of
MSME (2011a) the distribution of business results as 67% in registered manufacturing
MSME’s sector, 17% in services sector and remaining 16% were engaged in maintenance
and repairing sectors of MSME’s as shown in Fig 1.
Fig. 1: Classification of SME’s by sector
Source: Final report of 4th all India census of MSME, 2006-2007-Registered sector
As SME’s are growth day by day with full of opportunities and sources to invest etc as being
as backbone for Indian economy still many issues and challenges both internally and
externally. Hence this is an attempt to fill what issues and challenges faced by SME’s along
with them giving suggestions to both government and to MSME sector for further improving
growth in SME’s by reducing these issues and challenges. In present scenario, SME;s are
facing issues relating to internal environment and with external environment where external
environmental issues and challenges is not control with SME entrepreneurs, internal
environmental issues and challenges are controllable with SME entrepreneurs.
2. OBJECTIVES OF THE STUDY
To study brief about SME’s role in India
To study brief about Jawahar Autonagar Vijayawada
Syamala Devi Bhoganadam et. al., / International Journal of Management Research & Review
Copyright © 2017 Published by IJMRR. All rights reserved 761
To study issues and challenges faced by SME’s at Jawahar autonagar in Automobile
Technician’s Association (ATA)
To examine the relationship between issues and challenges faced by SME’s with financial
performance.
To provide suggestions to MSME sector
3. RESEARCH PROBLEM
It is known that for developing economies like India issues and challenges were quite normal.
With changing economies automobile industry is had grown rapidly. Political changes had
lead Vijayawada automobile industry into a dilemma. Many authors highlighted that there are
financial, HR, production and industry related issues faced by automobile sectors. Hence the
present aims to study Vijayawada Jawahar autonagar automobile sector SME’s issues and
challenges
4. REVIEW OF LITERATURE
Small Medium Enterprises (SMEs) play a significant role in promoting economic growth of
countries Mahmood and Norshahafizah (2013). SME’s also contribute for employment and
economic growth of a country Turner and Ledwith (2009), European Commission (2008).
The external environment is the actions performed outside the company that has the possible
potential to affect the company Chuck Williams (2001). Hoskisson and Hitt (2011: 33), the
external environment divides into three main components, namely, the general environment,
industrial environment, and the competitive environment. Naira project hypothesized to find
relationship between external environment with performance and increased productivity with
socio cultural factors. Measurement of company performance is grouped into two, namely
the non-financial performance measurement and financial performance measurement (Morse
and Davis, 1996 in Hiro Tugiman, 2000: 96; Hirsch 1994: 594-607). Overall literature issues
and challenges were included in below table 4.1.
Table 4.1: Review of literature matrix
Authors Variables Identified Description
Musran munizu
(2010)
Internal and external
environmental factors
Internal factors include aspects of HR (owners, managers,
and employees), financial aspects, technical aspects of
production, and marketing aspects.
external factors consist of government policy, socio-cultural
and economic aspects
Haris Maupa,
(2004)
External
environmental factors
External factors like the role of government institutions,
universities, private and NGO
Hoskisson and
Hitt (2011)
External
environmental factors
External environment factors divided into three main
components, namely, the general environment, industrial
environment, and the competitive environment.
Pearce and
Robinson
(2013)
External
environmental factors
External environment consists of a remote environment,
industrial environment, and the operating environment.
Dess,
Lumpkin, and
Taylor (2012)
External
environmental factors
Company's external environment is classified into two,
namely the general environment, which consists of
population demographics, socio-cultural, political and legal,
technological, economic; and competitive environment,
which consists of the power purchaser, provider (supplier),
the threat of new entrants, threat of substitute products, and
intensity of competition in the same industry.
Syamala Devi Bhoganadam et. al., / International Journal of Management Research & Review
Copyright © 2017 Published by IJMRR. All rights reserved 762
Popy Rufaidah
(2012)
External
environmental factors
External environment consists of macro and micro
environment. Macro environment is often referred to as a
remote environment or remote environments, while the
microenvironment called environmental task.
Musram
Munizu (2010)
Internal
environmental factors
Internal factors according which aspects of HR (managers
and employees); financial aspects; technical aspects of
production; and marketing aspects.
Sofyan Indris
& Ina Primiana
(2015)
Internal
environmental factors
Internal factors including marketing, finance, operations,
human resources, and information systems.
Fred R David
(2011)
Marketing factors Fred explained that there are seven basic functions of
marketing: (1) analysis of the customer, (2) the sale of
products / services, (3) planning products and services, (4)
pricing, (5) distribution, (6) marketing research, and (7)
analysis of opportunities.
Fred R David
(2011)
Production factors The use of machines, packing, combine, and maintenance of
equipment are examples of operating activities.
Supratikno
(2004)
Performance
measurement variables
Measuring the performance of the company with two
approaches. The first approach stated that the superior
performance of the company called if it has performed above
average (above average performance) is viewed from a
variety of dimensions, such as market share, financial kineja,
etc. The second approach assesses corporate excellence
implied from the age of the company (corporate longevity).
Gurhan
Gunday et.al,
(2009)
Performance
measurement
Financial performance of a firm measured using Return on
assets (profit/total assets), General profitability of the firm
and Return on sales (profit/total sales); production
performance measured using Production quantity,
conformance quality and production cost; marketing
performance measured using total sales, market share and
Customer satisfaction.
Musram
Munizu (2010)
Performance with
internal and external
environment
The performance of the micro and small enterprise sector is
affected by two main factors namely the external
environment and internal environment.
Ooghi (2000) Company’s growth
with internal and
external factors
The company's growth is the result of two environments in
which the company conducts its business, the internal
environment and the external environment.
Sofyan Indris
& Ina Primiana
(2015)
Internal and external
analysis on the
performance SMEs
There is a significant relationship between internal and
external analysis on the performance of small and medium
industries (SMEs).
Syamala Devi
Bhoganadam
et.al (2017)
Internal and external
issues challenges
From her study most of study inputs were enclosed. Identified
internal issues and external issues as general issues, socio
cultural, industrial as external issues and challenges.
Production, marketing, financial, HRD and infrastructure as
internal issues and challenges.
Jagriti Jaiswal
(2014)
Environmental issues
in SME’s
Lack of transportation, lack of innovative ideas, enhancing
the technology, management of resources, marketing, poor
price competition, connectivity problems and networking etc.
From literature review it is identified that organization performance variable measured using
Gurhan Gunday et.al, (2009) metrics as financial, production and marketing. Supratikno
(2004) market share as performance metrics Hoskisson and Hitt (2011) external
environmental factors as general and industry environment Musran munizu (2010) external
factors include socio cultural aspects. Sofyan Indris & Ina Primiana (2015), Fred R David
(2011), Musram Munizu (2010) and many authors say that internal factors include
production, marketing, financial, HR and infrastructure aspects.
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Copyright © 2017 Published by IJMRR. All rights reserved 763
5. OVERVIEW OF MSMES AND AUTOMOBILE SECTOR IN INDIA AND
VIJAYAWADA
According to district census the overall growth of SME’s from 1995-2012 is 3.6% increase
which indicates that growth of enterprises. Looking ahead of SME”s growth cluster
development, export potential and promoting SME’s complementary services. From the
figures of commissionerate of industries (Hyderabad) it is clear that year by year numbers of
registered enterprises are increased this seems that there is a growth in SME’s sector. After
forming a new state of Andhra Pradesh honourable chief minister Nara Chandra Babu formed
a new automobile policy for upcoming 2020 to achieve growth in Andhra Pradesh. The
program of industrial estates formation was implemented by Andhra Pradesh Industrial
Infrastructure Corporation (APIIC) which initiates formation of cluster development centres
and industrial parks. APIIC also a hub for special economic zones (SEZ) Software
Technology Parks (STP) etc. Under the cluster development program government has
launched Micro Small and Medium Enterprises (MSME) The program of industrial estates
formation was implemented by Andhra Pradesh Industrial Infrastructure Corporation (APIIC)
which initiates formation of cluster development centres and industrial parks. Under the
cluster development program government has launched Micro Small and Medium Enterprises
(MSME). After forming a new state of Andhra Pradesh honourable chief minister Nara
Chandra Babu formed a new automobile policy for upcoming 2020 to achieve growth in
Andhra Pradesh. Association of Lady Entrepreneurs of Andhra Pradesh (ALEAP) is a lady
entrepreneur’s hub for promoting women entrepreneurs with an area of 30 acres.
5.1 Overview of Vijayawada
Vijayawada is a commercial located in the banks of river Krishna in the state of Andhra
Pradesh. Historically the city is known as Bezawada and also known as ‘The place of victory’
from sathavahana’s. After Visakhapatnam, Vijayawada is known as second largest city in
Andhra Pradesh in terms of area 61.88 sq kms. Population of Vijayawada city for 2011
census is about 3% of Krishna district population. The city’s major development is not only
agriculture but also the industrial hubs. The city is having largest industrial transportation
hub as Vijayawada’s railway junction in India. The city is surrounded bay of Bengal in
eastern coastline, south with Guntur district west by nalgonda district and north by khammam
district. City is central hub for industrial transportation and business. Krishna district is
having 18 taluka’s and 50 mandal’s among them 4 industrial hubs.
5.2 Jawahar Autonagar Vijayawada
Jawahar autonagar was formed by taking an initial step of Andhra Pradesh Industrial
Infrastructure Corporation (APIIC) functioning form 1973, which is the major hub for
developing industrial estates, industrial areas, industrial hubs, autonagars etc. Jawahar
autonagar special features on Indian auto components industry which highlights marketing of
automobiles components and the growth of auto mobile servicing sectors. At jawahar
autonagar there are many associations as Industrial Area Local Authority (IALA) which was
formed for locking over the aspects of facilities which is also known as Industrial Area
Service Society (IASS).
Syamala Devi Bhoganadam et. al., / International Journal of Management Research & Review
Copyright © 2017 Published by IJMRR. All rights reserved 764
Among all the autonagars formed by Andhra Pradesh industrial infrastructure corporation
(APIIC) Vijayawada autonagar is the oldest and first autonagar developed for enhancing the
development of SME’s. Vijayawada’s autonagar occupies an area of 276 acres another
autonagar was developed after jawahar autonagar at kannuru with an area of 150 acres. In
Krishna district there are four autonagars namely jawahar autonagar, kannuru autonagar,
machilipatnam (42 acres) and jaggayapet (45 acres) autonagar among all jawahar autonagar
and kannuru autonagar are major for industrial development compared to machilipatnam and
jaggayapet. Both jawahar autonagar and kannuru autonagar around constitute 400 acres.
The basic purposes of autonagar are serving heavy vehicles like Lorries, buses, vans, tractors
etc. Depending on the type of demand nearby area people each autonagar was developed by
serving those needs. Some units manufacture auto components which are in form finished
goods, supplementary goods and unfinished goods as per their requirements and needs.
Autonagars are formed to provide services to automobile industry with their products.
Autonagar was considered as a project to self help auto technicians, in terms of serving needs
of heavy vehicles and provide work space for entrepreneurs in automobile servicing sector.
5.3 Automobile Technicians Association (ATA)
There are many associations pertaining to automobile sectors in Vijayawada. The automobile
technicians association (ATA) formed under section 3 of companies act 1956 is the lead
promoter for special purpose vehicle (SPV), The Vijayawada Auto Cluster Development
Company Ltd (VACDCL) is the promoter for the development of industrial clusters. ATA is
a federation formed with 24 affiliated associations for serving needs of automobile industry
along with SME’s in Vijayawada. VACDCL is a federation formed with 16 affiliated
associations which are directly connected with automobile trade. These associations are
formed in the year 1966. The value additions provided by ATA are training facilities,
infrastructure, technical assistance and certification details etc. Among all the associations
formed with ATA 13 associations were actively associated with automobile sector in jawahar
autonagar Vijayawada. Hence the present considers registered SME entrepreneurs at ATA
who are included in these 13 associations. Following table 5.3 presents 13 major associations
under automobile technicians association (ATA).
Table 5.3: 13 major ATA associations
S. No Name of association
1 The Automobile Technicians Association
2 The Automobile Mechanics Association
3 The Automobile Engineering Workshops Association (for body building of heavy vehicles)
4 The Autonagar Blacksmiths Association
5 The Motor Tinker Gas Welders and Workers Association
6 The Vijayawada Motor Painters Association
7 The Motor Carpenters Association
8 The Autonagar Electrical Association
9 The Autonagar Painters Association
10 The Vijayawada Tyre Re-traders and Vulcanisers welfare Association
11 The Autonagar Clutch / Brake Servicing Technical Association
12 The Autonagar Radiator Works Association
13 The Autonagar Electrical and Battery technicians Welfare Association
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Copyright © 2017 Published by IJMRR. All rights reserved 765
6. HYPOTHESIS FORMULATION
H1: To examine relationship between General environmental issues and challenges with
SME’s financial performance
H2: To examine relationship between Self related issues and challenges with SME’s financial
performance
H3: To examine relationship between socio cultural issues and challenges with SME’s
financial performance
H4: To examine relationship between production issues and challenges with SME’s financial
performance
H5: To examine relationship between marketing issues and challenges with SME’s financial
performance
H6: To examine relationship between financial issues and challenges with SME’s financial
performance
H7: To examine relationship between HRD issues and challenges with SME’s financial
performance
H8: To examine relationship between infrastructure issues and challenges with SME’s
financial performance
6.1 Model for the study
Issues and challenges relating to automobile sector from review of literature matrix are seen
at table 4.1. It is identified that organization performance variable measured using Gurhan
Gunday et.al, (2009) metrics as financial, production and marketing. Supratikno (2004)
market share as performance metrics. Hoskisson and Hitt (2011) external environmental
factors as general and industry environment. Musran munizu (2010) external factors include
socio cultural aspects. Sofyan Indris & Ina Primiana (2015), Fred R David (2011), Musram
Munizu (2010) and many authors say that internal factors include production, marketing,
financial, HR and infrastructure aspects.
Most of the automobile sector companies in Indian economy are facing many issues and
challenges. Major of authors, researchers has done on either on internal or on external issues
and challenges. But in this present study we are going to study both internal and external
environmental issues and challenges faced by automobile sector small and medium
enterprises entrepreneurs.
From overall literature it is divided issues and challenges as external and internal. External
environmental issues and challenges were considered as general environmental, industrial,
socio-cultural and self related. Internal issues and challenges were considered as production,
marketing, financial, HRD and infrastructure. Hence these nine variables are treated as
independent variable for the study. Financial performance is the dependent variable. Finally
the proposed model for study is as shown in below.
Financial performance = f (GEIC, IEIC, SEIC, SRIC, PIC, MIC, FIC, HRIC, IIC)
Syamala Devi Bhoganadam et. al., / International Journal of Management Research & Review
Copyright © 2017 Published by IJMRR. All rights reserved 766
Independent variables are calculated taking support of literature matrix from table 4.1. GEIC
was measured using statements like political, law related, sustainability, poor access to justice
etc. IEIC was measured using statements like threat from buyers, threat from suppliers,
substitute products etc. SEIC was measured using statements like caste, religion, family
background, social networks, education etc. SRIC was measured using statements like legal
status, average income, age of company, category of company etc.
H1
H2
H3
H4
H5
H6
H7
H8
H9
Fig. 6.1: Conceptual model for issues and challenges faced by SME’s
PIC was measured using statements like technical training, lack of power, raw materials,
machinery and equipment etc. MIC was measured using statements like poor promotional
skills, lack of networking, lack of distribution channels, low returns etc. FIC was measured
using statements like lack of credit from bank, non availability of capital, lack of working
capital etc. HIC was measured using statements like lack of skilled employees, lack of job
description, insufficient training etc. IIC was measured using statements like lack of
transportation facilities, lack of industrial estate facilities tec.
Financial performance reflects organization performance which is measured using statements
like return on sales, return on assets and general profitability of firm etc. Return on assets,
return on sales and profitability were calculated using direct satisfactory level of small and
External issues
and challenges
Internal issues
and challenges
GEIC
F
I
N
A
N
C
I
A
L
P
E
R
F
O
R
M
A
N
C
E
SRIC
PIC
SCIC
MIC
HIC
FIC
IIC
IEIC
Syamala Devi Bhoganadam et. al., / International Journal of Management Research & Review
Copyright © 2017 Published by IJMRR. All rights reserved 767
medium enterprise entrepreneurs. From figure 6.1 H1, H2, H3, H4, H5, H6, H7, H8 and H9
are hypothesis build on conceptual theory from review of literature matrix. These hypotheses
were needed to be tested for empirical validation of study.
7. RESEARCH METHODOLOGY
The study adopts finding out relationship between issues and challenges faced by automobile
SME’s with financial performance. Descriptive research design is adopted in the study. The
present study is based on both primary and secondary data. Primary data was collected using
a structured questionnaire given to small and medium enterprise entrepreneurs of automobile
industry from Vijayawada region at jawahar autonagar. Secondary was collected using
journals, reports, magazines also collected form District industrial centres (DIC’s) and
Automobile Technician’s Association (ATA). Questionnaire was developed using a
conceptual model developed for the study.
Questionnaire was distributed among registered entrepreneurs at ATA. To achieve objectives
and for testing hypothesis stratified simple random sampling technique is used, 300 samples
were collected at Jawahar autonagar in Vijayawada using structured questionnaire. Sample
respondents are automobile SME’s at Jawahar autonagar. Structured questionnaires were
given to respondents to obtain their opinion on identified issues and challenges along with
organization performance.
Questionnaire used opinion scales like 5 point Likert scale for defining both independent and
dependent variables. Data was collected from small and medium enterprise (SME)
entrepreneurs using interview and schedule techniques. Collected samples were entered into
SPSS software for testing hypothesis and for data analysis. Statistical test like alpha test for
checking reliability of data was done using SPSS. Testing of hypothesis was done using
multiple linear regression analysis.
7.1 Multiple linear regression model
These eight hypothesis were tested using multiple linear regression (MLR), is a statistical
method for estimating the relationship between a dependent variable and two or more
independent (or predictor) variables.
Dependent variable / outcome variable = Financial performance
Independent variable/predictor variables = issues and challenges faced by automobile
SME’s (GEIC, IEIC, SCEIC, PIC, MIC, FIC, HRIC, IIC variables)
Hence proposed model as follows
Financial performance = f (GEIC, IEIC, SEIC, SRIC, PIC, MIC, FIC, HRIC, IIC)
Assumptions for multiple linear regression model were as multi collinearity, normality and
heteroscedasticity as follows
1. Multi co-linearity problem is checked by Variance Inflation Factor (VIF) value and
constant index value at co-linearity statistics
If VIF should be < 10 then no multi co linearity
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2. Multi co linearity problem is checked by tolerance value at co-linearity statistics
If tolerance is > 0.2 then no multi co linearity
3. Multi co linearity problem is checked by constant index value at co-linearity
diagnostics
If Constant index should be < 30 then no multi co linearity
7.2 Sample profile
A sample of 300 entrepreneurs was collected from SME’s at autonagar region Vijayawada.
Table 7.1 describes sample profile of 300 entrepreneurs along with their mean and standard
deviation values. It is observed that most of the SME’s at autonagar are started before 5 years
and an average they had 10 years of experience. Educational background of entrepreneurs is
bit low hence seeks technical guidance. Major of the SME’s come under micro and small
enterprises. Autonagar’s automobile sector is having maintenance and repairing enterprises a
very few are from manufacturing enterprises.
Table 7.1 Descriptive Statistics
N Minimum Maximum Mean Std. Deviation
Statistic Statistic Statistic Statistic Std. Error Statistic
Educational qualification 300 1 5 2.29 .073 1.259
Religion of entrepreneur 300 1 6 1.33 .034 .595
Caste of entrepreneur 300 1 4 1.44 .034 .596
Gender of entrepreneur 300 1 1 1.00 .000 .000
Marital status of
entrepreneur
300 1 5 2.00 .012 .208
Age of company 300 1 5 4.89 .033 .576
Average monthly income of
family in rupees
300 1 4 1.44 .036 .622
Category of company 300 1 3 1.15 .022 .382
Type of manufacturing
sector
300 4 4 4.00 .000 .000
Valid N (listwise) 300
8. DATA ANALYSIS AND INTERPRETATION
8.1 Lack of technical training
Table 8.1: Lack of technical training
Frequency Percent Valid Percent Cumulative
Percent
Valid Strongly disagree 24 8.0 8.0 8.0
Disagree 21 7.0 7.0 15.0
Neither agree nor disagree 17 5.7 5.7 20.7
Agree 66 22.0 22.0 42.7
Strongly agree 172 57.3 57.3 100.0
Total 300 100.0 100.0
Syamala Devi Bhoganadam et. al., / International Journal of Management Research & Review
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Fig. 8.1: Lack of technical training
Interpretation:
From the analysis it is clear that almost the entrepreneurs are agreed with the statement as
shown in table 8.1. Among 300 entrepreneurs 172 entrepreneurs are strongly agreed and are
facing lack of technical training challenge. 24 entrepreneurs are having strongly disagreed
with the statement, 21 entrepreneurs disagree with the statement, 17 entrepreneurs are in
dilemma state and 66 entrepreneurs are agreed with the statement as shown in figure 8.1.
8.2 Procurement of raw materials
Table 8.2: Procurement of raw materials
Frequency Percent Valid Percent Cumulative Percent
Valid Strongly disagree 83 27.7 27.7 27.7
Disagree 40 13.3 13.3 41.0
Neither agree nor disagree 26 8.7 8.7 49.7
Agree 65 21.7 21.7 71.3
Strongly agree 86 28.7 28.7 100.0
Total 300 100.0 100.0
Fig. 8.2: Procurement of raw materials
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Interpretation:
From the analysis it is clear that almost the entrepreneurs are agreed with the statement as
shown in table 8.2. Among 300 entrepreneurs 86 entrepreneurs are strongly agreed and are
facing challenge regarding procurement of raw materials. 83 entrepreneurs are having
strongly disagreed with the statement, 40 entrepreneurs disagree with the statement, 26
entrepreneurs are in dilemma state and 65 entrepreneurs are agreed with the statement as
shown in figure 8.2
8.3 Lack of availability of capital
Table 8.3: Lack of availability of capital
Frequency Percent Valid
Percent
Cumulative
Percent
Valid Strongly disagree 60 20.0 20.0 20.0
Disagree 30 10.0 10.0 30.0
Neither agree nor disagree 33 11.0 11.0 41.0
Agree 84 28.0 28.0 69.0
Strongly agree 93 31.0 31.0 100.0
Total 300 100.0 100.0
Fig. 8.3: Lack of avialability of capital
Interpretation:
From the analysis it is clear that almost the entrepreneurs are agreed with the statement as
shown in table 8.3. Among 300 entrepreneurs 93 entrepreneurs are strongly agreed and are
facing challenge regarding lack of credit from bank. 60 entrepreneurs are having strongly
disagreed with the statement, 30 entrepreneurs disagree with the statement, 33 entrepreneurs
are in dilemma state and 84 entrepreneurs are agreed with the statement, it is clear that most
of entrepreneurs are facing lack of availability of capital as a challenge as shown in figure
8.3.
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8.4 Non availability of skilled labour
Table 8.4: Non availability of skilled labour
Frequency Percent Valid
Percent
Cumulative
Percent
Valid Strongly disagree 30 10.0 10.0 10.0
Disagree 15 5.0 5.0 15.0
Neither agree nor disagree 28 9.3 9.3 24.3
Agree 51 17.0 17.0 41.3
Strongly agree 176 58.7 58.7 100.0
Total 300 100.0 100.0
Fig. 8.4: Non avialability of skilled labour
Interpretation:
From the analysis it is clear that almost the entrepreneurs are agreed with the statement as
shown in table 8.4. Among 300 entrepreneurs 176 entrepreneurs are strongly agreed and are
facing non availability of labour as a challenge. 30 entrepreneurs are having strongly
disagreed with the statement, 15 entrepreneurs disagree with the statement, 28 entrepreneurs
are in dilemma state and 51 entrepreneurs are agreed with the statement it is clear that
entrepreneurs are unable to find employees as shown in figure 8.4
9. RELIABILITY ANALYSIS
From table 9.1 reliability analysis was done using Cronbach's Alpha test in SPSS. As per
thumb rule if Cronbach's Alpha tests value is more than 70 is acceptable. Hence from the
results it is clear that 86% of data was reliable which is fully acceptable.
Table 9.1: Reliability Statistics
Cronbach's Alpha Cronbach's Alpha Based on
Standardized Items
N of Items
.862 .874 10
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10. ESTIMATED REGRESSION COEFFICIENTS
The study data was analysed using multi linear regression analysis with dependent variable as
financial performance measurement (FPM) and independent variables as general
environmental issues and challenges (GEIC), industrial environmental issues and challenges
(IEIC), socio-cultural environmental issues and challenges (SEIC), self related environmental
issues and challenges (SRIC) production issues and challenges (PIC), marketing issues and
challenges (MIC), financial issues and challenges (FIC), HRD issues and challenges (HIC)
and infrastructure issues and challenges (IIC). Data was collected from 300 small and
medium enterprises entrepreneurs’ selected using random sampling technique. Assumptions
for multiple linear regression are normality, auto correlations, heteroscedasticity and multi co
linearity. All these assumptions were full filled with the data. Hence multi linear regression
analysis was done with the data of 300 samples. From table 10.1 descriptive statistics were
drawn with standard deviation values along with mean values of each variable. Standard
deviation for PIC is high and also with mean score which indicates that points were clustered
around the mean values.
Table10.1: Descriptive Statistics
Mean Std. Deviation N
FPM 7.2633 3.37123 300
GEIC 35.5000 9.86311 300
IEIC 17.3500 6.23086 300
SEIC 18.5733 7.72162 300
SRIC 24.4033 9.38586 300
PIC 53.5600 14.24815 300
MIC 39.5900 9.55788 300
FIC 23.2667 8.01267 300
HIC 33.1700 8.58215 300
IIC 20.6900 5.95416 300
From table 10.2 correlation matrix were drawn between dependent and independent
variables. High correlations between the variables were required for doing multiple linear
regression analysis. Here most of the variables are having satisfactory degree of correlations
among them. For cross sectional data correlations above 30% are acceptable. There appear
significant values between the variables. Hence moving on to multiple linear regression
analysis.
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Table 10.2: Correlations
FPM GEIC IEIC SEIC SRIC PIC MIC FIC HIC IIC
Pearson Correlation FPM 1.000 .185 .164 .335 .870 .361 .321 .356 .306 .043
GEIC .185 1.000 .619 .527 .103 .618 .596 .442 .599 .581
IEIC .164 .619 1.000 .431 .144 .558 .558 .408 .484 .495
SEIC .335 .527 .431 1.000 .256 .514 .428 .330 .347 .260
SRIC .870 .103 .144 .256 1.000 .288 .230 .263 .183 -.029
PIC .361 .618 .558 .514 .288 1.000 .644 .446 .546 .429
MIC .321 .596 .558 .428 .230 .644 1.000 .554 .588 .518
FIC .356 .442 .408 .330 .263 .446 .554 1.000 .485 .521
HIC .306 .599 .484 .347 .183 .546 .588 .485 1.000 .525
IIC .043 .581 .495 .260 -.029 .429 .518 .521 .525 1.000
Sig. (1-tailed) FPM . .001 .002 .000 .000 .000 .000 .000 .000 .227
GEIC .001 . .000 .000 .038 .000 .000 .000 .000 .000
IEIC .002 .000 . .000 .006 .000 .000 .000 .000 .000
SEIC .000 .000 .000 . .000 .000 .000 .000 .000 .000
SRIC .000 .038 .006 .000 . .000 .000 .000 .001 .306
PIC .000 .000 .000 .000 .000 . .000 .000 .000 .000
MIC .000 .000 .000 .000 .000 .000 . .000 .000 .000
FIC .000 .000 .000 .000 .000 .000 .000 . .000 .000
HIC .000 .000 .000 .000 .001 .000 .000 .000 . .000
IIC .227 .000 .000 .000 .306 .000 .000 .000 .000 .
N FPM 300 300 300 300 300 300 300 300 300 300
GEIC 300 300 300 300 300 300 300 300 300 300
IEIC 300 300 300 300 300 300 300 300 300 300
SEIC 300 300 300 300 300 300 300 300 300 300
SRIC 300 300 300 300 300 300 300 300 300 300
PIC 300 300 300 300 300 300 300 300 300 300
MIC 300 300 300 300 300 300 300 300 300 300
FIC 300 300 300 300 300 300 300 300 300 300
HIC 300 300 300 300 300 300 300 300 300 300
IIC 300 300 300 300 300 300 300 300 300 300
From table 10.3 linear multiple regression model was done using enter model with
independent and dependent variables. Table indicates 9 dependent and one dependent
variable were tested for multiple linear regression analysis.
Table 10.3: Variables Entered/Removed
Model Variables Entered Variables Removed Method
1 IIC, SRIC, SEIC, HIC,
IEIC, FIC, PIC, MIC, GEICa
. Enter
a. All requested variables entered.
From table 10.4 model summary of variables were shown. From this we have to see two
values as R square and significance values. We can explain that 79% of total variability of
organization performance is explained by IDV’s of nine issues and challenges as seen from
adjusted R square value. We can explain that 0.000 of significance value which indicates
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Copyright © 2017 Published by IJMRR. All rights reserved 774
there is a significant relationship between issues and challenges with financial performance of
SME’s.
Table 10.4: Model Summaryb
Model R R Square Adjusted
R Square
Std. Error of
the Estimate
Change Statistics
R Square
Change
F Change df1 df2 Sig. F Change
1 .893a .797 .790 1.54381 .797 126.201 9 290 .000
a. Predictors: (Constant), IIC, SRIC, SEIC, HIC, IEIC, FIC, PIC, MIC, GEIC; b. Dependent Variable: FPM
From table 10.5 it is seen that there is a significant relationship between variables. From
above table we can explain that there is a no explanatory power for null hypothesis hence we
reject null hypothesis as sig. value is 0.000 with eight IDV’s.
Table 10.5: ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression 2707.025 9 300.781 126.201 .000a
Residual 691.172 290 2.383
Total 3398.197 299
a. Predictors: (Constant), IIC, SRIC, SEIC, HIC, IEIC, FIC, PIC, MIC, GEIC
b. Dependent Variable: FPM
Coefficients matrix tells about relationship between financial performance and issues and
challenges faced by automobile SME’s as shown in table 10.6. We have to see four values as
tolerance, VIF, significance and unstandardized coefficients. Tolerance value should be
greater than 0.2 indicates there is no multi collinearity among variables. VIF value should be
less than 10 indicate there is no multi collinearity among variables. Significance value
indicates that there is a significance relationship between financial performance with IEIC,
SEIC, SRIC, FIC and HIC. From table 10.6 seeing sig. Value we test hypothesis as Sig. value
is less than 0.13 then we reject null hypothesis and accept alternative hypothesis at 13%
confidence interval. Unstandardized coefficients B value explains the relationship between
DV’s and IDV’s as one unit change will effect B value change in DV’s, sign denotes positive
increase in DV’s or negative decrease in DV’s.
Table 10.6: Coefficientsa
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig. Collinearity Statistics
B Std. Error Beta Tolerance VIF
1 (Constant) -2.299 .460 -4.993 .000
GEIC -.006 .015 -.017 -.402 .688 .380 2.632
IEIC -.050 .020 -.093 -2.554 .011 .526 1.901
SEIC .037 .015 .084 2.507 .013 .630 1.586
SRIC .286 .011 .795 26.955 .000 .806 1.241
PIC .009 .009 .037 .935 .350 .438 2.282
MIC .016 .014 .044 1.085 .279 .426 2.349
FIC .038 .015 .090 2.555 .011 .570 1.753
HIC .048 .015 .123 3.334 .001 .512 1.955
IIC -.028 .021 -.049 -1.317 .189 .503 1.988
a. Dependent Variable: FPM
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From table 10.7 it is seen that co-linearity diagnostics constant value is <30 hence there is no
multi co-linearity problem in the data. Eigen values should range in between 0 to 1. Thereby
variance proportion of respective independent variables. From the above all analysis we can
say that there is no multi collinearity among variables.
Table 10.7: Collinearity Diagnosticsa
Model Dimension
Eigenv
alue
Condition
Index
Variance Proportions
(Constant) GEIC IEIC SEIC SRIC PIC MIC FIC HIC IIC
1 1 9.516 1.000 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00
2 .133 8.472 .00 .01 .03 .00 .59 .00 .00 .00 .00 .03
3 .104 9.553 .01 .00 .01 .66 .01 .00 .00 .04 .01 .02
4 .063 12.337 .02 .01 .23 .13 .03 .01 .00 .56 .00 .01
5 .055 13.151 .15 .01 .52 .03 .06 .00 .00 .17 .03 .01
6 .034 16.792 .06 .00 .03 .06 .08 .24 .06 .02 .10 .44
7 .029 18.069 .48 .12 .05 .00 .18 .02 .05 .05 .29 .08
8 .026 19.089 .14 .02 .09 .04 .04 .36 .01 .02 .41 .21
9 .022 20.852 .07 .79 .01 .08 .00 .02 .07 .09 .11 .20
10 .019 22.391 .07 .05 .02 .00 .02 .35 .81 .05 .04 .00
a. Dependent Variable: FPM
From table 10.8 show residuals statistics which explains highest standard deviation value as
3.008 mean values as 7.263 with other value intersecting 0.000 significance level.
Table 10.8: Residuals Statisticsa
Minimum Maximum Mean Std. Deviation N
Predicted Value 2.1310 14.4974 7.2633 3.00892 300
Residual -4.51307 3.84610 .00000 1.52040 300
Std. Predicted Value -1.706 2.404 .000 1.000 300
Std. Residual -2.923 2.491 .000 .985 300
a. Dependent Variable: FPM
From figure 10.1 it is clear histogram of variables were showing linear curve with standard
deviation value as 0.985. Hence we can move to testing for hypothesis analysis.
Fig. 10.1: Histogram
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11. TESTING HYPOTHESIS
The eight hypothesis were tested using multiple linear regression (MLR), is a statistical
method for estimating the relationship between a dependent variable and two or more
independent (or predictor) variables for the study. From linear multiple regression analysis
the hypothesis were tested and results were noted as follows.
H1: To examine relationship between GEIC with SME’s financial performance
To test this hypothesis we used multiple linear regression model with dependent variable as
financial performance and independent variable as GEIC. From the above table 10.6 it is
clear that GEIC has no relationship (0.688) with financial performance but Unstandardized
Coefficients (B) shows negative value (-0.006). Hence we reject hypothesis.
H2: To examine relationship between Industry environmental issues and challenges
with SME’s financial performance
To test this hypothesis we used multiple linear regression model with dependent variable as
financial performance and independent variable as IEIC. From the above table 10.6 it is clear
that IEIC has significant relationship (0.011) with financial performance with Unstandardized
Coefficients (B) shows negative value (-0.050). It is clear that one value of IEIC will reduce
0.050 value of financial performance. Hence we accept hypothesis.
H3: To examine relationship between socio cultural issues and challenges with SME’s
financial performance
To test this hypothesis we used multiple linear regression model with dependent variable as
financial performance and independent variable as SEIC. From the above table 10.6 it is clear
that SEIC has significant relationship (0.013) with financial performance with
Unstandardized Coefficients (B) shows positive value (0.037). It is clear that one value of
IEIC will increase 0.037 value of financial performance. Hence we accept hypothesis.
H4: To examine relationship between Self related issues and challenges with SME’s
financial performance
To test this hypothesis we used multiple linear regression model with dependent variable as
financial performance and independent variable as SRIC. From the above table 10.6 it is clear
that SRIC has significant relationship (0.000) with financial performance with
Unstandardized Coefficients (B) shows positive value (0.286). It is clear that one value of
IEIC will increase 0.286 value of financial performance. Hence we accept hypothesis.
H5: To examine relationship between production issues and challenges with SME’s
financial performance
To test this hypothesis we used multiple linear regression model with dependent variable as
financial performance and independent variable as PIC. From the above table 10.6 it is clear
that PIC has no relationship (0.350) with financial performance but Unstandardized
Coefficients (B) shows +0.009 value. Hence we reject hypothesis.
H6: To examine relationship between marketing issues and challenges with SME’s
financial performance
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To test this hypothesis we used multiple linear regression model with dependent variable as
financial performance and independent variable as MIC. From the above table 10.6 it is clear
that MIC has no relationship (0.279) with financial performance but Unstandardized
Coefficients (B) shows +0.016 value. Hence we reject hypothesis.
H7: To examine relationship between financial issues and challenges with SME’s
financial performance
To test this hypothesis we used multiple linear regression model with dependent variable as
financial performance and independent variable as FIC. From the above table 10.6 it is clear
that FIC has significant relationship (0.011) with financial performance with Unstandardized
Coefficients (B) shows negative value (+0.038). It is clear that one value of IEIC will
increase 0.038 value of financial performance. Hence we accept hypothesis.
H8: To examine relationship between HRD issues and challenges with SME’s financial
performance
To test this hypothesis we used multiple linear regression model with dependent variable as
financial performance and independent variable as HIC. From the above table 10.6 it is clear
that HIC has significant relationship (0.001) with financial performance with Unstandardized
Coefficients (B) shows negative value (+0.048). It is clear that one value of IEIC will
increase 0.048 value of financial performance. Hence we accept hypothesis.
H9: To examine relationship between infrastructure issues and challenges with SME’s
financial performance
To test this hypothesis we used multiple linear regression model with dependent variable as
financial performance and independent variable as IIC. From the above table 10.6 it is clear
that IIC has no relationship (0.189) with financial performance but Unstandardized
Coefficients (B) shows -0.028 value. Hence we reject hypothesis.
Table 11.1: Summary of testing hypothesis
S. No. Hypothesis Decision
1 H1 Reject
2 H2 Accept
3 H3 Accept
4 H4 Accept
5 H5 Reject
6 H6 Reject
7 H7 Accept
8 H8 Accept
9 H9 Reject
12. FINDINGS FROM THE STUDY
It is found that there is healthy industry environment in jawahar autonagar automobile
SME’s
It is found that 51% of entrepreneurs are not having educational background because
most of SME’s are started long ago where they learnt work by joining as labor
It is found that 45% of entrepreneurs are facing machinery and equipment related issues
and challenges
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It is found that 45% of entrepreneurs are facing demand issues and challenges
It is found that 45% of entrepreneurs are facing lack of promotional skills issues and
challenges in terms of product, business, contests etc.
It is found that 41% of SME’s agree that they are having imperfect knowledge of market
conditions
It is found that 47% of SME’s strongly agree that they are having low returns in business
It is found that 43% of SME’s strongly agree that they are having working capital related
issues and challenges in business
It is found that 57% of SME’s strongly agree that they are having lack of awareness on
financial and government schemes. Hence, it is clear that no awareness is provided to SME’s
regarding financial and government schemes
It is found that 51% of SME’s strongly agree that they are having insufficient training in
terms of production, marketing and financial aspects.
It is found that 58% of SME’s strongly agree that they are having lack of skilled labour.
13. OBSERVATIONS MADE DURING DATA COLLECTION
It is observed that ATA has 13 major associations where there are different issues and
challenges faced by them based their nature of operations. As body building SME’s are
facing HR issues and challenges majorly which is not found a significant relation in the data.
As micro enterprises like tyre repairs, painters, clutch and break are facing an issue with Lack
of Awareness on financial and government schemes. As engineering works, motor repairs
and body building SME’s are facing technical and training related issues and challenges like
problems detecting by using laptops, advanced motors used etc. It is found that all SME’s are
facing infrastructure related issues like drinking water facilities, industrial estate facilities etc.
It is observed that religion and caste are affecting organization performance in terms of
maintaining networking relations, enlarging buyers and suppliers relationships, obtaining raw
materials etc. It is observed that tinkering and motor work enterprises are facing issues
related to lack of skilled employees, lack technical training, sophisticated machinery etc.
14. SUGGESTIONS
IALA (Industrial Area Local Authority) has to take care of infrastructural issues and
challenges along with Andhra Pradesh Industrial Infrastructural Corporation APIIC.
NGO’s, government organizations should take care of provide technical training with
advanced technology used, awareness on available schemes for SME’s, should techno fares
etc.
Most of SME’s are looking for young people in hard core maintenance and repairing of
automobiles.
From data it is suggested that market share of most of SME’s are going down where
necessary actions like building networks, enlargement of business can be taken.
From data it is clear that SME’s are facing socio cultural issues where can be reduced by
imparting religion free and caste free environment.
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It is suggested from data results that political changes made few SME’s sustainability
challenge because of heavy vehicles not entering into city and are moving to Guntur
autonagar.
It is suggested that APSSDC (Andhra Pradesh State Skill Development Corporation) to
encourage unemployed youth at autonagar to reduce lack of unskilled labor challenge.
It is suggested that to motivate not registered SME’s to register Udyog Aadhar SME’s so
that they can avail many MSME schemes, loans etc.
AP MSME policy says that state has large pool of skilled man power where necessary
steps to be taken to encourage them to involve in SME activities.
It is suggested that VMC (Vijayawada Municipal Corporation) should take necessary
actions regarding traffic issues because due to traffic changes in the city autonagar
automobile SME’s are facing sustainability challenge.
It is suggested to state government and Andhra Pradesh MSME policy for implementing
techno up graduation schemes to reach SME’s at autonagar.
It is suggested that young people should be encouraged to take business in automobile so
that advanced and sophisticated technology can be learned easily.
15. CONCLUSION
The study concludes that SME’s at jawahar autonagar vijayawada were facing issues and
challenges. Results show that there is a significant relationship between industrial
environmental, socio-cultural, self related, financial, HRD related and infrastructure issues
and challenges with financial performance. During data collection most of SME’s felt with
production issues and challenges as major but there is no significant relationship still
production issues and challenges are to be considered. Hence this it is concluded that both
internal and external issues and challenges are to be controlled for better financial
performance which reflects organization performance.
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