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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

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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



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


External factors like the role of government institutions,

universities, private and NGO

Hoskisson and

Hitt (2011)

External


External environment factors divided into three main

components, namely, the general environment, industrial

environment, and the competitive environment.

Pearce and

Robinson

(2013)

External


External environment consists of a remote environment,

industrial environment, and the operating environment.

Dess,

Lumpkin, and

Taylor (2012)

External


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.



Popy Rufaidah

(2012)

External


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


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


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)


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)


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.



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).



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



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


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)



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



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



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



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


Disagree 40 13.3 13.3 41.0


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



Interpretation:



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


Disagree 30 10.0 10.0 30.0


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:



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.



8.4 Non availability of skilled labour

Table 8.4: Non availability of skilled labour

Frequency Percent Valid

Percent

Cumulative

Percent


Disagree 15 5.0 5.0 15.0


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:



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



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.



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



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



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


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


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



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


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 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 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 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 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 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


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 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



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.



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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