chapter iv profile of entrepreneurs and their...
TRANSCRIPT
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CHAPTER IV
PROFILE OF ENTREPRENEURS AND
THEIR BUSINESS PERFORMANCE
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Profile of Entrepreneurs and their Business Performance
Demographic Profile of the Entrepreneurs
Comprehensive study of the various demographical factors of the
entrepreneurs such as age, gender, place of origin and qualification was made
by finding out cross relationship between the variables. The cross tab procedure
was applied by the application of SPSS software. The results are tabulated and
the corresponding interpretations are presented in the following pages.
TABLE 4.1
GENDER AND AGE OF THE RESPONDENTS
Present age groups of the entrepreneurs Gender
20-30 31-40 41-50 51-60 Total
Male 15 (10.0%) 35 (23.3%) 43 (28.7%) 57 (38.0%) 150 (100.0%)
Female 12 (24.0%) 21 (42.0%) 15 (30.0%) 2 (4.0%) 50 (100.0%)
Total 27 (13.5%) 56 (28.0% 58 (29.0%) 59 (29.5%) 200 (100.0%)
Source: Primary data.
As could be seen in Table 4.1, male and female ratio is 3:1. Male
members dominate in all the age groups. Interestingly two-thirds of the male
respondents are in the age group of 40-50, whereas the same proportions of
women entrepreneurs are found in the younger age, i.e. in 20s or 30s. It may be
inferred from the data that male entrepreneurs entered business at a younger
age and they have been around in business for quite some time. In contrast,
female entrepreneurs were almost negligible in the higher age group with just
4%. It could be due to the fact that female literacy and their entry into
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organized activity, more particularly entering business arena is a recent
phenomenon. The various women empowerment progress and incentives and
specialized schemes would have prompted women to seek an entrepreneurial
career, which is more pronounced in the last few years. Hence a substantial
number of women respondents are found in the age groups of 20–30 and 31-40.
TABLE 4.2
AGE AND EDUCATION OF THE RESPONDENTS
Age Qualification
20-30 31-40 41-50 51-60 Total
Non-School 7
(87.5%) 0
(`.0%) 1
(12.5%) 0
(.0%) 8
(100.0%)
High School Pass/Pre-university
10 (50.0%)
5 (25.0%)
4 (20.0%)
1 (5.0%)
20 (100.0%)
Graduation/Post Graduation/Professional
10 (5.8%)
51 (29.7%)
53 (30.8%)
58 (33.7%)
172 (100.0%)
Total 27
(13.5%) 56
(28.0%) 58
(29.0%) 59
(29.5%) 200
(100.0%)
Source: Primary Data.
An attempt was also made to check the relationship between the
qualification and age of the respondents. Table 4.2 shows, that 172 out of 200
respondents (86%) studied up to graduation and above. Thus, a very high
proportion of educated entrepreneurs are operating in Bangalore. It may be
seen that an overwhelming majority of the respondents had higher education,
thus it is evident that entrepreneurs with good academic credentials and training
chose to enter business. Though entrepreneurial history all over the world
shows that many entrepreneurs have made a mark in business without higher
education, it is found of late, people with good education and training are
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entering business. They possess undergraduate, post graduate and professional
qualifications.
TABLE 4.3
PLACE OF ORIGIN AND AGE GROUPS Age groups of entrepreneurs
Origin 20-30 31-40 41-50 51-60 Total
Local Kanadiga 16
(34.0%) 12
(25.5%) 8
(17.0%) 11
(23.4%) 47
(100.0%)
Local Non-Kanadiga 6
(6.3%) 30
(31.3%) 31
(32.3%) 29
(30.2%) 96
(100.0%)
NRI Kanadiga 0
(.0%) 4
(19.0%) 10
(47.6%) 7
(33.3%) 21
(100.0%)
NRI Non Kanadiga 2
(7.4%) 7
(25.9%) 7
(25.9%) 11
(40.7%) 27
(100.0%)
Foreign Nationals 3
(33.3%) 3
(33.3%) 2
(2.2%) 1
(11.1%) 9
(100.0%)
Total 27
(13.5%) 56
(28.0%) 58
(29.0%) 59
(29.5%) 200
(100.0%)
Source: Primary data.
The relationship between place of origin and age as presented in Table
4.3, shows that bulk of the entrepreneurs are young. About 66.6% of them fall
in the age group of 20-30 and 31-40. The local non Kanadiga people are
maximum (48.0%) in number and are almost double the number than that of
the local Kandigas (23.5%). It was found people from all the states of India
found excellent business opportunities in Bangalore. Added to that, there were
the incentives and subsidies offered by the government of Karnataka and the
accommodative spirit of the local population. The positive attitude and warmth
that the local people are known for, attracted many non Kanadigas to flock to
Bangalore and try their luck in business. Thus, the post liberalization period
since early 90s witnessed the establishment of a large number of business
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ventures. The other interesting fact is the presence of the foreign entrepreneurs
in Bangalore. Though the number of foreign entrepreneurs, (9 members out of
200), is a small number, it proves the point that tourism as a business lures
foreign entrepreneurs also. Personal interviews revealed that these people were
mostly in providing software solutions to tourism units and came under the
purview of ‘vendors to tourism’. Similarly, the presence of the NRI
entrepreneurs (48 out of 200), shows that about one-fourth of the respondents
have some foreign connections. We understand tourism requires a high level of
international networking and these NRI back ground people have a natural
advantage in providing the global touch.
TABLE 4.4
GENDER AND PLACE OF ORIGIN
Entrepreneurs’ place of origin
Gender Local Kanadiga
Local Non-
Kanadiga
NRI Kanadiga
NRI Non Kanadiga
Foreign Nationals
Total
Male 41 (27.3%)
71 (47.3%)
17 (11.3%)
18 (12.0%)
3 (2.0%)
150 (100.0%)
Female
6 (12.0%)
25 (50.0%)
4 (8.0%)
9 (18.0%)
6 (12.0%)
50 (100.0%)
Total 47 (23.5%)
96 (48.0%)
21 (10.5%)
27 (13.5%)
9 (4.5%)
200 (100.0%)
Source: Primary Data.
The study of gender with place of origin as presented in Table 4.4 shows
that among male members 47.3% were local non Kanadiga and 18% NRI non
Kanadiga members, showing 65.3% of the male entrepreneurs were from
outside the state. Among the female respondents, 50.0% were local non-
Kanadiga and another 18.0% non Kanadiga NRI (totaling 68%). The trend
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clearly shows that in both the male and female entrepreneur categories, the
immigrants constitute a substantial portion.
TABLE 4.5
GENDER AND EDUCATION
Qualification
Gender Non-
School High School Pass / Pre-university
Graduation / Post Graduation/
Professional
Total
Male 7 (4.7%) 14 (9.3%) 129 (86.0%) 150 (100.0%)
Female 1 (2.0%) 6 (12.0%) 43 (86.0%) 50 (100.0%)
Total 8 (4.0%) 20 (10.0%) 172 (86.0%) 200 100.0%)
Source: Primary Data.
The relationship test between respondents’ gender and educational
qualification as per Table 4.5 shows a high level of qualification for both male
and female - together constituting 86.0% having graduation/professional/post-
graduation achievements. Other significant finding is that there are 7 men and 1
woman (total 8) entrepreneurs who did not have any formal education even up
to school level, but still made it in tourism business.
Age of Entrepreneurs and Delivery of Quality Service
An attempt was made to find out the association of age of the
entrepreneurs and delivery of quality service through testing the following
hypothesis:
Ho: No significant association is established between the age of the
respondents and entrepreneurial business performances in terms of
delivering quality tourism and allied services.
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This hypothesis has two components as one predictor (independent) and
the other is dependent, as the age would be the independent variable. The
dependent variable consists of different aspects of the quality venture
performance. They relate to size of the business, motivation of the
entrepreneur, growth of the venture, competition, branding, venture break-even
time and venture life-span. Each of these variables is tested with the
independent variable ‘age of the entrepreneur’. Cross tab and Pearson Chi–
Square test have been applied separately for each combination. All the
individual dependent variables’ association results are consolidated to the
combined dependent variable ‘quality venture performance’.
TABLE 4.6
TYPE OF BUSINESS AND AGE
Age groups of the entrepreneurs Pearson
Chi-square Category of
Tourism Business
20-30 31-40 41-50 51-60 Total
Hotels 3
(12.0%) 5
(20.0%) 7
(28.0%) 10
(40.0%) 25
(100.0%)
Restaurants 13
(56.5%) 7
(30.4%) 2
(8.7%) 1
(4.3%) 23
(100.0%)
Travel Agencies/ Tour Operators
2 (3.4%)
16 (27.1%)
25 (42.4%)
16 (27.1%)
59 (100.0%)
Transport Operator
4 (18.2%)
4 (18.2%)
6 (27.3%)
8 (36.4%)
22 (100.0%)
Tourism Business Vendors
1 (2.9%)
12 (34.3%)
6 (17.1%)
16 (45.7%)
35 (100.0%)
Entertainment Service Providers
4 (11.1%)
12 (33.3%)
12 (33.3%)
8 (22.2%)
36 (100.0%)
Total 27
(13.5%) 56
(28.0%) 58
(29.0%) 59
(29.5%) 200
(100.0%)
60.708
.000
Source: Primary Data.
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The study of the relationship between the type of business and their the
respondents age as presented in Table 4.6 reveals that entrepreneurs operating
hotels are mostly older; those operating restaurants are relatively younger;
travel agents / tour operators fall mostly in the middle aged group; transport
operators are uniformly distributed over all the age groups; while entertainment
service providers are middle aged. The study also reveals the age groups of 31-
40, 41-50 and 51-60 are equally distinguished in all the business categories. It
is also evident that restaurant business mostly draw younger people whereas
hotels, travel agencies and entertainment providers tend to be middle-age and
above. Thus, there is a strong association between the various categories of
tourism ventures and the age of the entrepreneurs. Pearson Chi-square test
show a significance value of .000, showing the null hypothesis is rejected. This
confirms the fact there is effective association between categories of business
and the age of the entrepreneur.
TABLE 4.7
RESPONDENTS AGE AND SIZE OF BUSINESS
Age of the Respondents
Pearson Chi-
Square
Size of Business
20-30 31-40 41-50 51-60 Total
Small Scale 26
(26.0%) 32
(32.0%) 24
(24.0%) 18
(18.0%) 100
(100.0%)
Medium Scale
1 (1.3%)
22 (27.8%)
26 (32.9%)
30 (38.0%)
79
(100.0%)
Large Scale 0
(.0%) 2
(9.5%) 8
(38.1%) 11
(52.4%) 21
(100.0%)
Total 27
(13.5%) 56
(28.0%) 58
(29.0%) 59
(29.5%) 200
(100.0%)
38.311 .000
Source: Primary Data.
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The study of business size and age is shown in Table 4.7. It could be
seen that maximum units were small scale (50%) followed by were medium
(39.5%) and large scale 10.5%. Most of the tourism units tended to be small
and medium in nature. The small scale units are run by young and middle aged
people; the medium scale units are owned by middle and elderly people and the
large scale units are operated by the elderly. Classification of the size of the
firm has been done according to Govt. of India grading of industries as: Units
with total assets < 1 crore – small scale; > 1crore < 10crores – medium scale; >
10 crores – large scale. Thus we can see managing assets worth 10 crores and
above require the experienced professionals with good exposure to business.
There were many respondents who worked in reputed tourism organizations,
achieved their expertise and then quit their jobs to start their ventures. Thus a
number of interesting relationships exist between the size of the venture and the
age of the entrepreneurs. The Pearson Chi–Square test produces a value of
significance of .000, proving the null hypothesis is rejected. This further
confirms that size of the venture has association with age of the entrepreneurs.
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TABLE 4.8
MOTIVATION TO START VENTURE AND
THE AGE OF THE RESPONDENTS
Age groups of the entrepreneurs Chi-Square
Motivation to Start
Entrepreneurship 20-30 31-40 41-50 51-60 Total
Urge to Achieve 20
(17.9%) 32
(28.6%) 28
(25.0%) 32
(28.6%)
112
(100.0%)
Excess Funds 1
(5.6%) 7
(38.9%) 6
(33.3%) 4
(22.2%) 18
(100.0%)
Professional Expertise
0 (.0%)
12 (27.9%)
14 (32.6%)
17 (39.5%)
43 (100.0%)
To Capitalize Demand
0 .0%
2 33.3%
2 33.3%
2 33.3%
6 100.0%
Family Business Support
6 (28.6%)
3 (14.3%)
8 (38.1%)
4 (19.0%)
21 (100.0%)
Total 27
(13.5%) 56
(28.0%) 58
(29.0%) 59
(29.5%) 200
(100.0%)
19.003 .008
Source: Primary Data.
Motivation to start venture and the age of the respondents as
demonstrated by Table 4.8 revealed ‘The urge to achieve’ was the single
motivational force found common across all age groups. This proves the
achievement motivation is strong for entrepreneurs of all age groups and it
supersedes all other choices. ‘Professional expertise’ does not have any score in
the 20-30 years age group and in other three age groups the response increases
progressively with the age groups. This is understandable as the younger
entrepreneurs do not have work experience. Thus the eldest group shows
maximum affinity for professional expertise. Thus there is relationship between
motivation to start entrepreneurship and the age of the entrepreneurs. This fact
is further confirmed by the chi–square test significance value of .008 that
suggests the null hypotheses should be rejected. There is considerable amount
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of association between motivation to start business and age of the
entrepreneurs.
TABLE 4.9
VENTURE GROWTH AND AGE OF RESPONDENTS
Age Chi-
Square Progress
in the Business 20-30 31-40 41-50 51-60 Total
Loss Making 4 (36.4%) 5 (45.5%) 0 (.0%) 2 (18.2%) 11 (100.0%)
Break Even 7 (25.0%) 7 (25.0%) 10 (35.7%) 4 (14.3%) 28 (100.0%)
Moderate Profits 11 (10.5%) 33 (31.4%) 27 (25.7%) 34 (32.4%) 105 (100.0%)
High Profits 5 (8.9%) 11 (19.6%) 21 (37.5%) 19 (33.9%) 56 (100.0%)
Total 27 (13.5%) 56 (28.0%) 58 (29.0%) 59 (29.5%) 200 (100.0%)
.048 21.160
Source: Primary Data.
Relationship of business growth with the age of the respondents study
as projected by Table 4.9 reveals out of the 11 failure cases 4 are from 20-30
age group and 5 are from 31-40 age group. These shows the younger
respondents lack the tenacity to face bad times. This is due to the intense
competition in the tourism business and the high level of service delivery skills
required which may not be present in the 20-30 groups. The best profit making
track belong to 31-40 and 41-50 groups and the failure rate is also the least in
these categories. This finding confirms the fact that one needs to possess
considerable tourism service operational skills to run their ventures. The results
thus show there is association between growth of business and age of the
entrepreneurs. The significance value of the chi-square test of .048 suggests the
null hypothesis is rejected.
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TABLE 4.10
COMPETITION AND AGE OF RESPONDENTS
Age
Competition 20-30 31-40 41-50 51-60
Total
Yes 21 (13.0%) 42 (26.1%) 48 (29.8%) 50 (31.1%) 161 (100%)
No 6 (15.4%) 14 (35.9%) 10 (25.6%) 9 (23.1%) 39 (100%)
Total 27 (13.5%) 56 (28.0%) 58 (29.0%) 59 (29.5%) 200 (100%)
Source: Primary Data.
As per Table 4.10, it is seen that respondents across all the age groups
agree that they faced strong competition. There is almost an unanimous view
that strong competition exists. It could also be due to the fact that respondents
who are 40s and 50s in age are more conscious of the existing competition in
their respective businesses. On the other hand, the younger entrepreneurs’ less
acknowledgement of competition shows that they are either less experienced to
assess the impact of competition or are more courageous to take on the
competition.
TABLE 4.11
BRAND BUILDING EFFORTS OF THE RESPONDENTS Age
Brand Building 20-30 31-40 41-50 51-60
Total
Yes 16 (11.7%) 37 (27.0%) 38 (27.7%) 46 (33.6%) 137 (100%)
No 11 (17.5%) 19 (30.2%) 20 (31.7%) 13 (20.6%) 63 (100%)
Total 27 (13.5%) 56 (28.0%) 58 (29.0%) 59 (29.5%) 200 (100%)
Source: Primary Data.
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Study of brand building with age as per Table 4.11, shows an interesting
picture in the ‘yes’ category. The number of responses increasing
proportionately with the individual age groups. This shows, as the age of the
entrepreneurs advances the capacity and urge to build brand also increases.
Brand building requires deep knowledge of the market and years of brand
management experience that can be expected in senior people. The respondents
were found busy in activities as selective advertising, developing of logo and
brand slogan, direct marketing, product positioning and publicity – all aimed to
strengthen their brand image. That is the reason 51-60 years age group has the
maximum number of respondents who built a brand of their own in business.
TABLE 4.12
RESPONDENTS’ AGE AND BUSINESS BREAK-EVEN PERIOD Age Break
Even time 20-30 31-40 41-50 51-60 Total
Chi-Square
Within One Year
17 (26.6%) 25 (39.1%) 16 (25.0%) 6 (9.4%) 64 (100%)
2-5 Years 9 (8.3%) 25 (23.1%) 36 (33.3%) 38 (35.2%) 108 (100%)
6-10 Years 1 (3.6%) 6 (21.4%) 6 (21.4%) 15 (53.6%) 28 (100%)
Total 27 (13.5%) 56 (28.0%) 58 (29.0%) 59 (29.5%) 200 (100%)
33.482 .000
Source: Primary Data.
Break-even of business as presented in table 4.12 shows that in the case
of 108 respondents, their businesses had broke even in the first 2-5 years of
operation. Again, interestingly all these units broke-even in 2-5 years are
owned by respondents who are in their 40s and above. Whereas in the within
one year’, category it can be seen the concentration is more towards the young
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groups of 20-30 and 31-40. This shows that the young entrepreneurs tend to
break even within one year. It could be largely due to the fact that younger
respondents own business in the niche area of tourism and are relatively small
in size. There are certain associations among the age and break-even time of
the ventures. The chi–square significance value also shows .000, indicating the
null hypotheses that the variables are not related, should be rejected.
TABLE 4.13
AGE OF THE RESPONDENTS AND VENTURE LIFE SPAN
Age Venture Life Span
20-30 31-40 41-50 51-60 Total
Pearson Chi-
Square
One Year 20 (50.0%) 14 35.0%) 4 (10.0%) 2 (5.0%) 40 (100%)
2-4 Years 6 (7.1%) 37 (43.5%) 29 (34.1%) 13 (15.3%) 85 (100%)
5-10 years 1 (1.3%) 5 (6.7%) 25 (33.3%) 44 (58.7%) 75 (100%)
Total 27 (13.5%) 56 (28.0%) 58 (29.0%) 59 (29.5%) 200 (100%)
112.336 .000
Source: Primary Data.
Venture life span and age of entrepreneurs’ as shown in Table 4.13,
reveals that 85 ventures aged 2-4 years are owned by respondents in the age
group of 31-40 years, followed by 37 owned by respondents in the age group of
41-50 years. However in the next highest category with response of 75 the
concentration is on 41-50 years group with 25 and 51-60 years group with
score of 44. This show the elderly people are more visible in ventures that
exhibited longer life span. In contrast The chi–square test produces a
significance value of .000 that suggests the null hypothesis is reject, the units
owned by younger respondents are relatively young and mostly start-up units.
There are clear associations between venture life span and age of the
entrepreneurs.
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The above mentioned eight aspects of business as: category of business,
size of the business, venture motivation, venture growth, competition,
branding, venture break-even time and venture life-span all have been analyzed
quantitatively as well as qualitatively to test whether there exists any
association between these variables and the age of the entrepreneur. Result
show there are relationships between each variable and age of the entrepreneur.
Thus all the business aspects put together will constitute ‘Entrepreneurial
business performances in terms of delivering quality tourism and allied
services’; that in turn is also significantly associated with the age of the
entrepreneurs. So age of the entrepreneurs does have significant association
with business performance. The following null hypothesis is therefore rejected.
Ho: No significant association is established between the age of the
respondents and the business performance in terms of delivering
quality tourism and allied services
Gender vis-a-vis Tourism Enterprises
In an attempt to find out the role of the gender of the respondents and its
association with venture opportunities and challenges, it was decided to
examine the following hypothesis.
Ho: The gender of the respondents is in no way associated with the
tourism and allied entrepreneurial business opportunities and
challenges:
The independent variable gender with the group of dependent variables
that sum up the combined dependent variable business opportunities and
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challenges, were analysed using cross tabs and chi-square test. The dependent
variables are categories of business, size of business, motivation, venture
progress, competition, branding, break-even time, and venture life span. Each
one of this dependent variable was tested with the independent variable gender.
Cross tabs and Chi–Square test was applied using SPSS. The results and
interpretations are presented in the following pages.
TABLE 4.14
ENTREPRENEURS GENDER AND TYPES OF BUSINESS
Categories of Tourism Business
Hotels Restaurants Travel Agencies/
Tour Operators
Transport Operator
Tourism Business Vendors
Entertain--ment
Service Providers
Total Chi Square
Male 22
(14.7%) 16
(10.7%) 48
(32.0%) 20
(13.3%) 27
(18.0%) 17
(11.3%) 150
(100.0%)
Female 3
(6.0%) 7
(14.0%) 11
(22.0%) 2
(4.0%) 8
(16.0%) 19
(38.0%) 50
(100.0%)
Total 25
(12.5%) 23
(11.5%) 59
(29.5%) 22
(11.0%) 35
(17.5%) 36
(18.0%) 200
(100.0%)
21.757 .001
Source: Primary data.
In the study to establish relationship between gender and categories of
business, it was found as shown in Table 4.14, the maximum number of the
male and female respondents were in the travel agencies/tour operation
business. It may be noted that hotels and restaurants were dominated by the
male respondents. Vendors of tourism and entertainment business also had
more male members. The women respondents are found in good number in the
entertainment business provider. The total male to female entrepreneurs’ ratio
was 3:1. Thus male members clearly outnumbered the women in all the
categories of business. The Chi-square test produced a result of .001, denoting
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the null hypotheses can be rejected; thereby confirming the associations
between the gender of the entrepreneurs and the type of business.
TABLE 4.15
RESPONDENTS’ GENDER AND SIZE OF BUSINESS
Size of Business Organization Chi Square Gender
Small Scale Medium Scale Large Scale Total
Male 75 (50.0%) 57 (38.0%) 18 (12.0%) 150 (100.0%)
Female 25 (50.0%) 22 (44.0%) 3 (6.0%) 50 (100.0%)
Total 100 (50.0%) 79 (39.5%) 21 (10.5%) 200 (100.0%)
1.627 .443
Source: Primary Data.
The gender and size of business results presented in Table 4.15 shows a
fairly uniform distribution. Male and female respondents constitute the same
proportion between small, medium and large enterprises. It appears that gender
does not influence the size of the units. Both male and female respondents are
found to have set up establishments of different sizes in Bangalore. The Chi-
square test of significance show score of .443 suggesting the null hypotheses is
not rejected. Thus, it is accepted that size of the unit is not gender dependent.
TABLE 4.16
GENDER AND MOTIVATION TO ENTER BUSINESS
Gender of the entrepreneurs Chi-Square Motivation to Start Entrepreneurship Male Female Total
Urge to Achieve 78 (69.6%) 34 (30.4%) 112 (100.0%)
Excess Funds 13 (72.2%) 5 (27.8%) 18 (100.0%)
Professional Expertise 35 (81.4%) 8 (18.6%) 43 (100.0%)
To Capitalize Demand 5 (83.3%) 1 (16.7%) 6 (100.0%)
Family Business Support 19 (90.5%) 2 (9.5%) 21 (100.0%)
Total 150 (75.0%) 50 (25.0%) 200 (100.0%)
5.631 .228
Source: Primary Data.
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As could be seen in Table 4.16, the highest numbers of female
respondents motivated by ‘urge to achieve’ were in the ratio of 2:1 with the
male respondents in this category. Notable factor is that eight members out of
50 women respondents were driven by ‘professional expertise’. It indicates
that many women entrepreneurs had much less work experience or technical
expertise to utilize in their ventures compared to male respondents. The
Chi–square test shows the score .228 suggesting the null hypothesis is not
rejected. Thus, it is concluded that motivation to start a business unit is not
gender dependent.
TABLE 4.17
VENTURE GROWTH AND RESPONDENTS GENDER
Gender of entrepreneurs Venture Growth
Male Female Total
Chi-Square
Loss Making Unit 9 (81.8%) 2 (18.2%) 11 (100.0%)
Break Even 22 (78.6%) 6 (21.4%) 28 (100.0%)
Moderate Profits 79 (75.2%) 26 (24.8%) 105 (100.0%)
High Profits 40 (71.4%) 16 (28.6%) 56 (100.0%)
Total 150 (75.0%) 50 (25.0%) 200 (100.0%)
.847
.838
Venture growth and gender as shown in Table 4.17 reveal the units’
growth as measured by profit making potential of the business. Slightly more
than half (105 out of 200) of the units are making moderate profits while 28%
of the units (56 out of 200) are making high profits. Further, the percentage of
high profit ventures between male and female respondents shows that many
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units owned by female respondents are making profits compared to the ones
owned by male respondents. However, the results show there are proportionate
distribution of male and female respondents in all the three categories as ‘loss
making’, break-even’, ‘moderate profits’ and ‘high profits’ categories.
Difference in gender does not have significant association in business growth.
The chi-square test result shows significance value of .838 indicating the null
hypothesis is not rejected.
TABLE 4.18
PERCEPTION OF COMPETITION AND
GENDER OF RESPONDENTS
Gender Strong Competition
Male Female Total
Yes 125 (77.6%) 36 (22.4%) 161 (100.0%)
No 25 (64.1%) 14 (35.9%) 39 (100.0%)
Total 150 (75.0%) 50 (25.0%) 200 (100.0%)
Source: Primary Data.
As regards the perception of competition between male and female
respondents, on the whole, both the categories are fully aware of the intensity
of competition in their respective business, though the percentage of female
respondents who do not perceive the competition as presented in Table 4.18, is
slightly higher compared to male respondents (28% and 16.7% respectively). It
shows a large number of respondents got into niche business where the
competition is not severe.
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TABLE 4.19
GENDER AND BRAND BUILDING EXERCISE
Gender Brand Building
Male Female Total
Yes 99 (72.3%) 38 (27.7%) 137 (100.0%)
No 51 (81.0%) 12 (19.0%) 63 (100.0%)
Total 150 (75.0%) 50 (25.0%) 200 (100.0%)
Source: Primary data. The relationship between brand building and gender of the respondents
in Table 4.19 reveal out of 200 sample respondents, 137 respondents are keen
in brand building. Among this 99 respondents were male and 38 respondents
were female. The result shows that the male respondents were almost three
times that of female respondents. This shows women respondents were more
actively engaged in brand building. Business details of some of these women
respondents have been mentioned in the subsequent part of this chapter.
TABLE 4.20
VENTURE BREAK-EVEN PERIOD AND GENDER
Gender Break Even Period
Male Female Total Chi-Square
Within One Year 41 (64.1%) 23 (35.9%) 64 (100.0%)
2-5 Years 84 (77.8%) 24 (22.2%) 108 (100.0%)
6-10 Years 25 (89.3%) 3 (10.7%) 28 (100.0%)
Total 150 (75.0%) 50 (25.0%) 200 (100.0%)
7.575 .023
Source: Primary Data.
163
The break-even period and gender matrix as shown in Table 4.20 reveal
that the maximum numbers of 108 respondents were in 2-5 years group. In this
group the male to female ratio were 4:1.Totally 64 respondents broke even
within 1 year and the male is to female ratio was 2:1. This show the majority of
the entrepreneurs (totally 172) could break even within 5 years. Bangalore
tourism entrepreneurs had the fortune of early break-even period. Out of the
total 50 female respondents, 48 of them could break-even in the first 5 years.
This also indicated most of the establishments were small to medium with less
capital requirement and high return on investment. The chi-square test gives a
value of .023, showing the null hypothesis is rejected. It can be concluded that
break-even period has association with the gender.
TABLE 4.21
VENTURE LIFE SPAN AND RESPONDENTS GENDER
Gender Venture Life Span
Male Female Total
One Year 25 (62.5%) 15 (37.5%) 40 (100.0%)
2- 4 Years 59 (69.4%) 26 (30.6%) 85 (100.0%)
5-10 years 66 (88.0%) 9 (12.0%) 75 (100.0%)
Total 150 (75.0%) 50 (25.0%) 200 (100.0%)
Source: Primary Data.
Table 4.21 shows that the maximum number of respondents in the
category of the venture life span and gender study falls in 2-4 years bracket
with 85 as the score. Here male to female ratio works out to 2:1. Next in line
comes 5-10 years category with the total response score of 75; the male to
female ratio stand as 7:1. Clearly the male members are more in number in
164
ventures with longer life span. The one year category shows the male to female
ratio as 3:2. That is the female representation is clearly high in the businesses
that require less life span.
In the case of gender and its association with the different aspects of
business as: categories of business, size of business, motivation, venture
growth, competition, branding, break-even time, and venture life span, it may
seen that there are associations with the gender in deciding entrepreneurial
progress. At the same time, there are few occasions (three cases) where the chi-
square significance value did not reject the null hypothesis of the individual
aspect. Thus we can conclude by saying that gender has less influence on the
course of entrepreneurial business. Members of both sexes tend to have similar
influence on the business and its success and their differences do not bring any
major change. Age comparatively has much more influence in deciding the
destiny of the entrepreneurial business.
Ho: Gender of the entrepreneurs is no way associated with the tourism
business opportunities and challenges
The above mentioned null hypothesis however is rejected.
Respondents place of origin and venture success
In order to assess the relationship between the place of origin and
business success the following null hypotheses was introduced.
HO: There is no significant association between the place of origin of the
entrepreneurs and the factors determining success in the tourism
ventures.
165
The independent variable ‘place of origin of entrepreneur’ was used to
find out cross relationship with a number of dependent variables that add up to
‘entrepreneurial venture success.’ These variables were: type of business,
organizational structure, motivation, innovations, venture growth, competition,
brand building, break-even period and venture life span.
The independent variable place of origin has been tested with each of
the above mentioned variables applying crosstabs and chi-square test using
SPSS. The results and interpretations are presented in the following pages.
TABLE 4.22
RESPONDENTS SIZE OF BUSINESS AND THEIR PLACE OF ORIGIN
Entrepreneurs Place of Origin
Size of Business
Organization
Local Kanadiga
Local Non-
Kanadiga
NRI Kanadiga
NRI Non Kanadiga
Foreign Nationals
Total Chi-Square
Small Scale 30
(30.0%) 41
(41.0%) 10
(10.0%) 13
(13.0%) 6
(6.0%) 100
(100.0%)
Medium Scale 16
(20.3%) 44
(55.7%) 10
(12.7%) 6
(7.6%) 3
(3.8%) 79
(100.0%)
Large Scale 1
(4.8%) 11
(52.4%) 1
(4.8%) 8
(38.1%) 0
(.0%) 21
(100.0%)
Total 47
(23.5%) 96
(48.0%) 21
(10.5%) 27
(13.5%) 9
(4.5%) 200
(100.0%)
21.403 .006
Source: Primary Data.
The study of size of business and origin of domicile as presented in
Table 4.22 reveals that the non Kanadiga (local & NRI) respondents were
found operating large ventures (19 out of 21). The Kanadiga (local & NRI)
respondents were found largely in small ventures. There were 6 foreigners in
the small scale and 3 foreigners in the medium scale units. Further in regard to
domicile status, migrant to local ratio was 2:1. Thus, there is an association
166
between venture size and entrepreneurs place of origin. The chi-square test
shows a significance score of .006; suggesting that the null hypothesis is
rejected. Thus, we may infer that there is an association between venture size
and entrepreneurs place of origin.
TABLE 4.23
TYPE OF BUSINESS AND THEIR PLACE OF ORIGIN
Entrepreneurs’ place of origin Chi –
Square Category of Tourism Business
Local Kanadiga
Local Non-
Kanadiga
NRI Kanadiga
NRI Non Kanadiga
Foreign Nationals
Total
Hotels 9
(36.0%) 9
(36.0%) 0
(.0%) 6
(24.0%) 1
(4.0%) 25
(100.0%)
Restaurants 13
(56.5%) 5
(21.7%) 1
(4.3%) 3
(13.0%) 1
(4.3%) 23
(100.0%)
Travel Agencies/Tour Operators
8 (13.6%)
41 (69.5%)
7 (11.9%)
3 (5.1%)
0 (.0%)
59 (100.0%)
Transport Operator
8 (36.4%)
8 (36.4%)
4 (18.2%)
2 (9.1%)
0 (.0%)
22 (100.0%)
Tourism Business Vendors
5 (14.3%)
14 (40.0%)
7 (20.0%)
9 (25.7%)
0 (.0%)
35 (100.0%)
Entertainment Service Providers
4 (11.1%)
19 (52.8%)
2 (5.6%)
4 (11.1%)
7 (19.4%)
36 (100.0%)
Total 47
(23.5%) 96
(48.0%) 21
(10.5%) 27
(13.5%) 9
(4.5%) 200
(100.0%)
72.191 .000
Source: Primary Data.
Table 4.23 shows, that the relationship between the type of business and
the domicile status of the respondents was significant. It shows among the 25
hotels, 9 were run by Kanadiga and other 9 were run by non Kanadigas.
Similarly, in the transport operator category, out of 22 units 8 were operated by
Kanadigas and 8 were operated by non Kanadigas. In the restaurant category
out of 23 units the Kanadiga dominated with 13 as against 5 by non Kanadiga
167
respondents. In the other categories, the non Kanadiga outnumbered the locals.
The overall trend was that restaurant business needed the local market,
location, manpower knowledge and provisions procurement abilities and thus
the Kanadigas were comfortable in this segment of business. In the business
like travel agencies, tourism vendors and entertainment service providers, the
migrants far outnumber the locals. The later mentioned categories require
professional expertise with global networking that the migrants could bring
with them. The variables, categories of business and entrepreneurs place of
origin are thus associated. The significance value of the chi-square test is .000,
indicating that the null hypothesis is rejected. This confirms the fact the type of
business has association with the entrepreneurs’ place of origin.
TABLE 4.24
ORGANIZATIONAL STRUCTURE AND
RESPONDENTS PLACE OF ORIGIN
Entrepreneurs’ place of origin Chi-
Square Organization
Structure Local
Kanadiga Local Non-
Kanadiga
NRI Kanadiga
NRI Non Kanadiga
Foreign Nationals
Total
Single Unit 28
31.5%) 34
(38.2%) 9
(10.1%) 13
(14.6%) 5
(5.6%) 89
(100.0%)
Branches in Karnataka
16 (40.0%)
12 (30.0%)
6 (15.0%)
6 (15.0%)
0 (.0%)
40 (100.0%)
Branches in Other States
3 (5.2%)
45 (77.6%)
3 (5.2%)
5 (8.6%)
2 (3.4%)
58 (100.0%)
Branches in Overseas
0 (.0%)
5 (38.5%)
3 (23.1%)
3 (23.1%)
2 (15.4%)
13 (1 00.0%)
Total 47
(23.5%) 96
(48.0%) 21
(10.5%) 27
(13.5%) 9
(4.5%) 200
(100.0%)
45.659 .000
Source: Primary Data.
168
With regard to the structure of the unit in terms of its spread of
operations, namely whether the business operates from a single location or
multiple locations, the results as shown in Table 4.24, reveal that close to half
of the sample respondents (89 out of 200) have their business in a single
location, followed by (58 out of 200) respondents who have branches in other
states as well. Interestingly, immigrant respondents were found to have their
business in multiple locations. The NRIs (both local and immigrant) have their
units at both single and multiple locations.
The Chi–square test shows significance value of .000 and thus the null
hypotheses can been rejected. It can be concluded as entrepreneurs’
organization structure and their place of origin are connected in many ways.
TABLE 4.25
MOTIVATION AND RESPONDENTS PLACE OF ORIGIN
Respondents place of origin
Motivation to Start Entrepre-
neurship
Local Kanadiga
Local Non-
Kanadiga
NRI Kanadiga
NRI Non Kanadiga
Foreign Nationals
Total Chi-Square
Urge to Achieve
24 (21.4%)
56 (50.0%)
11 (9.8%)
14 (12.5%)
7 (6.3%)
112 (100.0%)
Excess Funds 4 (22.2%)
9 (50.0%)
1 (5.6%)
4 (22.2%)
0 (.0%)
18 (100.0%)
Professional Expertise
6 (14.0%)
24 (55.8%)
4 (9.3%)
7 (16.3%)
2 (4.7%)
43 (100.0%)
To Capitalize Demand
1 (16.7%)
3 (50.0%)
2 (33.3%)
0 (.0%)
0 (.0%)
6 (100.0%)
Family Business Support
12 (57.1%)
4 (19.0%)
3 (14.3%)
2 (9.5%)
0 (.0%)
21 (100.0%)
Total 47 (23.5%)
96 (48.0%)
21 (10.5%)
27 (13.5%)
9 (4.5%)
200 (100.0%)
25.427.003
Source: Primary Data.
169
Enterprise creation is the result of an individual’s motivation. Table 4.25
reveals that 112 members out of 200 respondents were drawn into business by
the ‘urge to achieve’. Thus, more than half of the respondents were driven by
David McClelland’s Need to achieve motivation. Family financial support also
played an important role in motivating many local entrepreneurs as their roots,
family, friends were all in Bangalore. Professional expertise was the second
highest motivation with 43 out of 200 respondents. In that order was the
avaibility of surplus funds. In this case the respondents were mostly NRIs with
sizable savings. It is clear that motivation to start entrepreneurship is connected
to entrepreneurs’ place of origin in many ways. The chi-square test shows the
significance value is .003, denoting the null hypotheses should be rejected
thereby confirming there is valid association between entrepreneurs’ place of
origin and motivation.
TABLE 4.26
RESPONDENTS DISPOSITION TOWARDS INNOVATION
Respondents place of origin Innovation in
Tourism Industry Local Kanadiga
Local Non-
Kanadiga
NRI Kanadiga
NRI Non Kanadiga
Foreign Nationals
Total Chi-square
No Changes 16
(39.0%) 17
(41.5%) 4
(9.8%) 3
(7.3%) 1
(2.4%) 41
(100.0%)
Introduced New Concept
13 (24.5%)
24 (45.3%)
7 (13.2%)
6 (11.3%)
3 (5.7%)
53 (100.0%)
Experimented with New Concept But Failed
2 (13.3%)
9 (60.0%)
2 (13.3%)
2 (13.3%)
0 (.0%)
15 (100.0%)
Successful in Exploiting New Ideas
10 (20.8%)
23 (47.9%)
7 (14.6%)
8 (16.7%)
0 (.0%)
48 (100.0%)
Revolutionized the Existing System
6 (14.0%)
23 (53.5%)
1 (2.3%)
8 (18.6%)
5 (11.6%)
43 (100.0%)
Total 47
(23.5%) 96
(48.0%) 21
(10.5%) 27
(13.5%) 9
(4.5%) 200
(100.0%)
20.459 .015
Source: Primary Data.
170
With regard to the respondents’ disposition towards their attitude to new
ideas and innovations, Table 4.26 shows that 53 out of 200 respondents
introduced new concepts now and then. This trend has signs of Peter Drucker’s
argument that innovation is the main ingredient for entrepreneurship.1 48 out of
200 respondents claimed they were ‘successful in exploiting untapped
demand’. Whereas 43 respondents claimed they ‘revolutionized the existing
system’. This had signs of Joseph Schumpeter’s thesis ‘creative destruction’.2
There were 14 out of 200 who claimed the ‘experimented with new concept but
failed’ but are still successful about their business. The chi–square test shows
significance value of .015 and therefore the null hypothesis is rejected.
TABLE 4.27
VENTURE GROWTH AND PLACE OF ORIGIN OF RESPONDENTS
Entrepreneurs’ place of origin Chi-
Square Progress in the
Business Local
Kanadiga Local Non-
Kanadiga
NRI Kanadiga
NRI Non Kanadiga
Foreign Nationals
Total
Loss making
4 (36.4%)
2 (18.2%)
1 (9.1%)
3 (27.3%)
1 (9.1%)
11 (100.0%)
Break Even
6 (21.4%)
16 (57.1%)
1 (3.6%)
5 (17.9%)
0 (.0%)
28 (100.0%)
Moderate Profit
29 (27.6%)
48 (45.7%)
13 (12.4%)
7 (6.7%)
8 (7.6%)
105 (100.0%)
High Profit
8 (14.3%)
30 (53.6%)
6 (10.7%)
12 (21.4%)
0 (.0%)
56 (100.0%)
Total 47
(23.5%) 96
(48.0%) 21
(10.5%) 27
(13.5%) 9
(4.5%) 200
(100.0%)
23.010 .028
Source: Primary Data.
171
Table 4.27, shows, growth of business and place of domicile. It may be
seen that immigrant entrepreurs seen to be doing well compared to the native
entrepreneurs. Migrants were double in number than the locals in making
moderate profits. Again they were four times their local counterparts in
making good profits. 11 out of 200 were making loss and were prepared to
close down or change their business. Thus it is evident that the adage survival
of the fittest holds good in Bangalore tourism industry. The chi-square test
produces the significance value of .028, indicating the null hypothesis is
rejected.
TABLE 4.28
COMPETITION AND PLACE OF ORIGIN OF RESPONDENTS
Origin of Domicile Chi-
Square Strong
Competition Local
Kanadiga Local Non-
Kanadiga
NRI Kanadiga
NRI Non Kanadiga
Foreign Nationals
Total
Yes 37
(23.0%) 77
(47.8%) 17
(10.6%) 24
(14.9%) 6
(3.7%) 161
(100.0%)
No
10 (25.6%)
19 (48.7%)
4 (10.3%)
3 (7.7%)
3 (7.7%)
39 (100.0%)
Total 47
(23.5%) 96
(48.0%) 21
(10.5%) 27
(13.5%) 9
(4.5%) 200
(100.0%)
4.389 .495
Source: Primary Data.
Table 4.28 demonstrates, 161 respondents out of 200, claim they faced
strong competition. 161 respondents answered ‘yes’ with regarde to the
competition they face in the business ‘yes’ The overall picture emerging is
immaterial of their place of origin the entrepreneurs were subject to
competition and a few were not subject to competition. The chi–square test of
significance gives a result of .495 showing the null hypotheses is not rejected.
172
TABLE 4.29
BRAND BUILDING AND RESPONDENTS PLACE OF ORIGIN Entrepreneurs’ place of origin
Brand Building
Local Kanadiga
Local Non-
Kanadiga
NRI Kanadiga
NRI Non Kanadiga
Foreign Nationals
Total Chi-
Square
Yes 24
(17.5%) 73
(53.3%) 14
(10.2%) 22
(16.1%) 4
(2.9%) 137
(100.0%)
No 23
(36.5%) 23
(36.5%) 7
(11.1%) 5
(7.9%) 5
(7.9%) 63
(100.0%)
Total 47
(23.5%) 96
(48.0%) 21
(10.5%) 27
(13.5%) 9
(4.5%) 200
(100.0%)
13.708 .008
Source: Primary Data.
The relationship between brand building and place of origin as shown in
Table 4.29, demonstrates an overwhelming 137 members out of 200 agreeing
they are very keen in brand building. The migrants were three times than that of
the locals in the act of building brands. This again shows the migrant
entrepreneurs were more professional and resorted to more aggressive
marketing. An interesting find is equal number of locals saying yes and also no
to brand building. The chi-square test show the significance value of .008,
which indicates the null hypothesis is rejected.
TABLE 4.30
BREAK –EVEN PERIOD AND RESPONDENTS PLACE OF ORIGIN
Respondents place of origin Chi-
Square Break Even
Period Local
Kanadiga Local Non-Kanadiga
NRI Kanadiga
NRI Non Kanadiga
Foreign Nationals Total
Within One Year
14 (21.9%)
32 (50.0%)
6 (9.4%)
5 (7.8%)
7 (10.9%)
64 (100.0%)
2-5 Years
25 (23.1%)
51 (47.2%)
13 (12.0%)
17 (15.7%)
2 (1.9%)
108 (100.0%)
6-10 Years
8 (28.6%)
13 (46.4%)
2 (7.1%)
5 (17.9%)
0 (.0%)
28 (100.0%)
Total 47
(23.5%) 96
(48.0%) 21
(10.5%) 27
(13.5%) 9
(4.5%) 200
(100.0%)
12.252 .140
Source: Primary Data.
173
The study of break-even period and place of domicile was significant to
test the business prudence of the operators. Table 4.30 projects 54% of the
respondents took 2-5 years; and 32% of the respondents took a year for the feat.
This shows that majority (86%) of the tourism entrepreneurs of Bangalore
could break even very early, i.e., within the first 5 years. Within this early
break-even period of less than 5 years there are 39 local players and 83
migrants. Once more the results show that the migrants are more aggressive in
business. Another interesting fact is that 7 out of total 9 foreigners could break
even within a year. This show the foreigners were in less capital outlay and
high return on investment ventures, mostly in IT solutions to the tourism
industry. The chi–square test produces the significance value of .140
suggesting the null hypothesis is not rejected.
TABLE 4.31
VENTURE LIFE SPAN AND RESPONDENTS PLACE OF ORIGIN
Respondents place of origin Chi-
Square Venture Life Span
Local Kanadiga
Local Non-
Kanadiga
NRI Kanadiga
NRI Non Kanadiga
Foreign Nationals
Total
One Year
15 (37.5%)
16 (40.0%)
1 (2.5%)
4 (10.0%)
4 (10.0%)
40 (100.0%)
2- 4 Years
15 (17.6%)
42 (49.4%)
12 (14.1%)
12 (14.1%)
4 (4.7%)
85 (100.0%)
5-10 years
17 (22.7%)
38 (50.7%)
8 (10.7%)
11 (14.7%)
1 (1.3%)
75 (100.0%)
Total 47 (23.5%)
96 (48.0%)
21 (10.5%)
27 (13.5%)
9 (4.5%)
200 (100.0%)
13.607 .093
Source: Primary Data.
Table 4.31, shows that the maximum number of ventures (42.5%) were
operating for 2-4 years; and 37.5% of them were there for 5-10 years; leaving
174
the balance 20% to the less than 1 year group. Likewise the NRIs (Local and
migrants) were also scattered non uniformly in all categories of venture life
span. Thus no logical relationships could be established between the variables.
The chi–square test produced significance value of .093; denoting the null
hypotheses cannot be rejected.
TABLE 4.32
MOTIVATION TO START VENTURE AND RESPONDENTS
PLACE OF ORIGIN
Respondents place of origin
Chi-Square Test
Motivation to Start
Entrepreneurship Local
Kanadiga Local Non-
Kanadiga
NRI Kanadiga
NRI Non Kanadiga
Foreign Nationals
Total
Urge to Achieve 24
(21.4%) 56
(50.0%) 11
(9.8%) 14
(12.5%) 7
(6.3%) 112
(100.0%)
Excess Funds 4
(22.2%) 9
(50.0%) 1
(5.6%) 4
(22.2%) 0
(.0%) 18
(100.0%)
Professional Expertise
6 (14.0%)
24 (55.8%)
4 (9.3%)
7 (16.3%)
2 (4.7%)
43 (100.0%)
To Capitalize Demand
1 (16.7%)
3 (50.0%)
2 (33.3%)
0 (.0%)
0 (.0%)
6 (100.0%)
Family Business Support
12 (57.1%)
4 (19.0%)
3 (14.3%)
2 (9.5%)
0 (.0%)
21 (100.0%)
Total 47
(23.5%) 96
(48.0%) 21
(10.5%) 27
(13.5%) 9
(4.5%) 200
(100.0%)
25.427 .063
Source: Primary Data.
Table 4.32 shows that entrepreneurial motivation spread across the place
of origin revealed the migrants were double the number than the locals in terms
of ‘urge to achieve’ and they were four times than that of their local
counterparts in ‘professional expertise’ Thus, migrants by and large had much
more work experience that channelised their thinking to start the units. Almost
all the NRI (local and from other states) were motivated by ‘the urge to
175
achieve’. The NRI population in Bangalore was sizeable and they brought with
them the knowledge and gained by working overseas. The foreign nationals
also were mostly driven by the achievement factor (77.8%) and professional
expertise (22.2%) in their respective category. The chi-square test show
significance value of .063 that is more than .050 and thus the null hypothesis of
the variables not being related are not rejected.
TABLE 4.33
INNOVATIONS AND ENTREPRENEURS’ PLACE OF ORIGIN
Respondents place of origin
Innovations in Tourism Industry
Local Kanadiga
Local Non-
Kanadiga NRI
Kanadiga NRI Non Kanadiga
Foreign Nationals
Total Chi-
Square
No Changes 16
(39.0%) 17
(41.5%) 4
(9.8%) 3
(7.3%) 1
(2.4%) 41
(100.0%)
Introduced New Concepts
13 (24.5%)
24 (45.3%)
7 (13.2%)
6 (11.3%)
3 (5.7%)
53 (100.0%)
Experimented with New Concept But Failed
2 (13.3%)
9 (60.0%)
2 (13.3%)
2 (13.3%)
0 (.0%)
15 (100.0%)
Successful in Exploiting New Ideas
10 (20.8%)
23 (47.9%)
7 (14.6%)
8 (16.7%)
0 (.0%)
48 (100.0%)
Revolutionized the Exiting System
6 (14.0%)
23 (53.5%)
1 (2.3%)
8 (18.6%)
5 (11.6%)
43 (100.0%)
Total 47
(23.5%) 96
(48.0%) 21
(10.5%) 27
(13.5%) 9
(4.5%) 200
(100.0%)
20.459 .015
Source: Primary Data.
Table 4.33 shows, the degree of changes effected by respondents in their
respecting business. 41 respondents out of 200 did not make any change and
were busy with their routine. On the whole immigrant enterprises were more
adept in the introducting and adapting to changes. The chi–square test produced
the significance value of .015 suggesting the null hypothesis is rejected.
176
TABLE 4.34
PERCEPTION OF COMPETITION AND RESPONDENTS PLACE OF
ORIGIN
Origin of Domicile Strong
Competition Local Kanadiga
Local Non-
Kanadiga
NRI Kanadiga
NRI Non Kanadiga
Foreign Nationals
Total Chi-
Square
Yes 37
(23.0%) 77
(47.8%) 17
(10.6%) 24
(14.9%) 6
(3.7%) 161
(100.0%)
No 10
(25.6%) 19
(48.7%) 4
(10.3%) 3
(7.7%) 3
(7.7%) 39
(100.0%)
Total 47
(23.5%) 96
(48.0%) 21
(10.5%) 27
(13.5%) 9
(4.5%) 200
(100.0%)
4.389 .495
Source: Primary Data.
Enterprenurs’ perception of competition as revealed by Table 4.34
shows uniform distribution of yes and also no responses as. The ratio of the
migrants to locals was 2:1 in both the categories of yes and no responses. The
yes and no scores were in the ratio of 4:1. The chi-square test produced
significance value of .495 suggests that the null hypothesis is not rejected.
In the study of the effect of the entrepreneurs place of origin on the type
of business, organizational structure, motivation, and changes brought, venture
growth, competition, brand building, break-even period and venture life span, it
was found that place of origin had good association with almost all the aspects.
In a few cases (four) the chi-square test did not reject the null hypotheses and
these results were close to 0.05 suggesting marginality. Thus, it can conclude
that there is considerable association between the entrepreneurs’ place of origin
and the factors determining the scope of success in diverse entrepreneurial
ventures in the tourism industry.
177
Comparatively, age has stronger association than gender and place of
origin in influencing the birth, growth progress and success of entrepreneurial
businesses.
HO: There is no significant association between the place of origin of the
entrepreneurs and the factors determining the scope for success in
the diverse entrepreneurial ventures in tourism industry.
The above mentioned null hypothesis is thus rejected.
Business Performance of the Respondents
An attempt was also made to find out several factors that are responsible
for the growth of ventures. In order to survive in a highly competitive
international market a good amount of marketing efforts are required. The
relationship between type of business, brand building and competition they
encountered, have been analyzed. Subsequently, to examine the business
performance, the break-even period and the life span of the business are studied
in relation to the type of business. The results and interpretations are presented
below.
TABLE 4.35
TYPES OF BUSINESS AND BRAND BUILDING
Brand Building Category of Tourism Business Yes No Total
Chi-Square test
Hotels 20 (80.0%) 5 (20.0%) 25 (100.0%)
Restaurants 14 (60.9%) 9 (39.1%) 23 (100.0%)
Travel Agencies/ Tour Operators
44 (74.6%) 15 (25.4%) 59 (100.0%)
Transport Operator 9 (40.9%) 13 (59.1%) 22 (100.0%)
Tourism Business Vendors
27 (77.1%) 8 (22.9%) 35 (100.0%)
Entertainment Service Providers
23 (63.9%) 13 (36.1%) 36 (100.0%)
Total 137 (68.5%) 63 (31.5%) 200 (100.0%)
12.490 .029
Source: Primary Data.
178
There was an overwhelming response to the need for brand building
with 137 units agreeing out of 200 respondents who were aware of the branding
exercise and its usefulness to business. Tourism business vendors however out
number other category of enterprineurs (77%) with regard to the effort put in
branding the products. Bangalore as such and the inhabitants are branded
conscious. Entrepreneurs are aware of this and all the categories of
entrepreneurs have executed their efforts to build and popularize their brands.
Vendors seen to have realized the need for branding quite well as they have to
establish strong relationship with the entrepreneurs based on the credibility of
brand. The chi-square test shows significance value of .029 which explains that
the null hypothesis (of the variables not related) is rejected.
TABLE 4.36
TYPE OF BUSINESS AND COMPETITION
Category of Tourism Business Strong
Competi-tion
Hotels Restau-rants
Travel Agencies/
Tour Operators
Trans-port
Operator
Tourism Business Vendors
Entertainment Service
Providers
Total Chi-Square Tests
Yes 21
(13.0%) 20
(12.4%) 48
(29.8%) 17
(10.6%) 30
(18.6%) 25
(15.5%) 161
(100.0%)
No 4
(10.3%) 3
(7.7%) 11
(28.2%) 5
(12.8%) 5
(12.8%) 11
(28.2%) 39
(100.0%)
Total 25
(12.5%) 23
(11.5%) 59
(29.5%) 22
(11.0%) 35
(17.5%) 36
(18.0%) 200
(100.0%)
4.389 .495
Source: Primary Data.
The results in Table 4.36 clearly show a large majority of the
entrepreneurs, (161 out of 200) encounter strong competition in their business.
179
All the categories of entrepreneurs are facing the competition without
exception.
Similarly, the establishments that claimed no competition, (39 out of
200) are distributed across all the categories. It is clear that the nature of
business does not have any bearing on having the degree of competition. The
chi-square test shows a significance value of .495 which denotes the null
hypothesis of the variables not related, is not rejected.
TABLE 4.37
TYPE OF BUSINESS AND BREAK-EVEN PERIOD
Break even time period of the ventures Chi-
Square Category of
Tourism Business
Within One Year
2-5 Years 6-10 Years Total
Hotels 3 (12.0%) 12 (48.0%) 10 (40.0%) 25 (100.0%)
Restaurants 13 (56.5%) 9 (39.1%) 1 (4.3%) 23 (100.0%)
Travel Agencies/ Tour Operators
23 (39.0%) 28 (47.5%) 8 (13.6%) 59 (100.0%)
Transport Operator
7 (31.8%) 15 (68.2%) 0 (.0%) 22 (100.0%)
Tourism Business Vendors
5 (14.3%) 27 (77.1%) 3 (8.6%) 35 (100.0%)
Entertainment Service Providers
13 (36.1%) 17 (47.2%) 6 (16.7%) 36 (100.0%)
Total 64 (32.0%) 108 (54.0%) 28 (14.0%) 200 100.0%)
35.751 .000
Source: Primary Data.
Tourism business is vast and of diverse nature where all the categories
have complete different style of operations. Table 4.37 clearly shows that an
airline is the most capital intensive industry, next come the hotels, the tourism
vendors, the restaurants, transport services. The travel/tour services are perhaps
180
the least capital intensive. Further, it is interesting to note that 10 out of 25
hotels (40%) needed 6-10 years to break even. Whereas 56.5% of the
restaurants brokeeven within one year. 47.2% of the travel agents took 2-5
years to break even. Similarly 68.2% of the transport operator needed 2-5 years
to break even. Tourism vendors with 77.1% and Entertainment service
providers with 47.2% both needed 2-5 years to break even. Thus we can see the
hotel industry is the most capital intensive and has moderate return on
investment with relatively longer period break-even. Restaurants are less
capital intensive and have higher return on investment with low break even
period. The other categories like transport, vendors and entertainment have
proportionate capital investment and return on investment and break-even
period is 2-5 years. The chi–square score shows a significance value of .000
that suggests the null hypothesis stands rejected.
TABLE 4.38
TYPE OF BUSINESS AND LIFE SPAN
Venture Life Span Chi-
Square Category of Tourism
Business One Year 2- 4 Years 5-10 years Total
Hotels 9 (36.0%) 7 (28.0%) 9 (36.0%) 25 (100%)
Restaurants 11 (47.8%) 10 (43.5%) 2 (8.7%) 23 (100%)
Travel Agencies/ Tour Operators
9 (15.3%) 29 (49.2%) 21 (35.6%) 59 (100%)
Transport Operator 3 (13.6%) 9 (40.9%) 10 (45.5%) 22 (100%)
Tourism Business Vendors
2 (5.7%) 13 (37.1%) 20 (57.1%) 35 (100%)
Entertainment Service Providers
6 (16.7%) 17 (47.2%) 13 (36.1%) 36 (100%)
Total 40 (20.0%) 85 (42.5%) 75 (37.5%) 200 (100%)
28.432 .002
Source: Primary Data.
181
The study on venture life span is necessary to understand the longivity
of the ventures. As could be seen Table 4.38 apart from the restaurants all other
ventures were in business for 5-10 years. Thus, the tourism enterprises had a
fairly long life span. The scores also show there were many new establishments
in all the categories which are just one year old. The overall objective of the
study was to find out the life span of the units for the last 10 years of the
liberalization period. Result show that new units were established almost every
year during the last 10 years. All these units were operating successfully. The
chi–square result shows the significance value at .002 that indicates the null
hypothesis is rejected.
Friedman Mean Rank Chi–Square test application
Some of the major ingredients that influence the creation and success of
business are sources of capital, promotional measures, type of tourism business,
reasons for success, and obstacles faced, if any, need to be examined in detail.
Following hypothesis has been introduced and Friedman mean rank Chi-Square
test is applied.
H5: Items with the mean rank value representing sources of capital,
business promotion, nature of tourism business, reason for success
and obstacles are independent to each other.
An attempt is made to rank the various aspects under each head,
according to its order of importance and investigate the reasons for its
respective position. Further, it was also attempted to find out whether all these
individual factors are related or independent.
182
TABLE 4.39
STATEMENT OF MEAN RANK OF SOURCES OF
CAPITAL AND CHI–SQUARE TEST
Sources of Capital Mean Rank Chi-Square
Own Fund 4.90
Banks 4.00
Financial Institutions 4.00
Friends and Relatives 2.40
Venture Capitals 3.40
Personal Loan 2.30
12.427
Sig..029
Friedman Test. Source: Primary Data.
The study of the sources of capital as shown in Table 4.39 reveals that
the most frequently used source of capital is own funds, followed by funds
supplied by banks and financial institutions. Among these various sources, own
funds stands as the most important and mostly utilized at the mean rank of
4.90. Entrepreneurs by and large use their own funds to maintain control and
the next options are the traditional banks at 4.00 and financial institutions also
at 4.00. The financial environment of Karnataka in general and Bangalore in
particular is quite positive, where funding from financial institutions for the
various business proposals is liberal. The emergence of venture capital is also
evident as the next indicator at the mean rank of 3.40. Truly, venture capitalists
of national and international fame have set their shops successfully in
Bangalore. The funds supplied by friends and relatives (2.40) and personal
loans (2.30) are less sought after options. It shows that entrepreneurs are
professional minded and have self-confidence to mobilize resources through
institutional means. The chi–square test show the significance value of .029
183
which indicates the variables are independent to each other, but they are all
significant interms of sources of business funds.
TABLE 4.40
MEAN RANK OF BUSINESS PROMOTION AND CHI-SQUARE TEST
Business Promotion Mean Rank Chi–square test
Advertising 3.56
Electronic Channel 2.91
Physical Channel 2.72
Sales Promotion 2.69
Personal Selling 3.11
40.268
.000
Friedman Test. Source: Primary Data.
Data related to business promotion as shown in Table 4.40 reveals that
the most popular method adopted is advertising at the mean rank of 3.56.
Tourism business requires extensive use of advertising in all possible Medias.
Posters, banners, visual representations and cinema/video presentations can
truly project the spirit of tourism. Personal selling or direct marketing score the
second highest position of mean rank with 3.11. Today the travel and tourism
trade believe in approaching the customers directly in person, through
telephone or through the internet. The electronic channel is indicated as the
third most popular method at 2.91 where the website reservations, CRS, GDS
and all other online marketing forms come in. International tourism requires
fast and high level of networking. The next mean rank is occupied by physical
channel at 2.72 where the wholesalers and retailers play their role. Travel
agents, hotels, vendors, entertainment providers all use this at different levels.
The lowest of mean rank is held by sales promotion at 2.69. The indicator is
184
close to other methods, signifying all tourism entrepreneurial units use personal
selling in a less frequent manner. The chi–square test result produces a
significance value of .000 that show each of the various promotion methods is
independent and does not affect each other. But collectively they constitute
important promotional measures.
TABLE 4.41
MEAN RANK OF TOURISM TYPES WITH
CHI-SQUARE TEST
Tourism Types Mean Rank Chi-Square
Transit to Mysore 4.15
Urban Tourism 1.86
Business Tourism 4.10
Leisure Tourism 1.83
Medical Tourism 3.07
419.204 Sig.000
Friedman Test. Source: Primary Data.
The mean rank Table 4.41, shows ‘Transit to Mysore’ with highest
position with the mean rank 4.15. Bangalore traditionally is the entry point for
Bangalore–Mysore–Ooty itinerary. This still holds good and many a
businessman in tourism industry commercially exploit the same. Business
tourism has surprisingly come second in rank with 4.10; contrary to the popular
belief that it should be the first. The importance of business in Bangalore has
been discussed in chapter III. Medical tourism scores the third rank with 3.07
showing the emerging new trend in Bangalore. Wellness/health/medical
tourism is fast gaining grounds with a number of health therapy centers, spas,
nursing homes and world class hospitals.3 This is followed by urban tourism at
1.86 and leisure tourism at 1.83 with close scores. The study reveals there are
185
significant urban tourism and leisure tourism happenings and the presence of
several attractions that draw tourists to Bangalore. The chi-square test result at
.000 signifies all these individual aspects are not related to each other; but all
together they form the tourism business scope the entrepreneur can promote
and serve.
TABLE: 4.42
MEAN RANK OF REASONS FOR VENTURE SUCCESS AND
CHI-SQUARE TEST
Reason for success Mean Rank Chi-Square
Strong Unique Selling Proposition 2.91
Aggressive Marketing 2.49
Tenacity and Dedication 4.30
Constant Product Development 2.27
Good Financial Management 3.03
.422 Sig. .000
Friedman Test. Source: Primary Data.
As regards the contributing factors for success (Table 4.42), tenacity and
dedication take the highest rank (4.30). The capacity to meet all odds and still
keep the venture going is clearly indicated by most of the respondents as the
strongest. Good financial management comes second (3.03) as competition is
intensifying by the day. Strong USP (2.91) comes next as market forces are
high and the product has to be unique, given the growing competition.
Aggressive marketing (2.49) and constant product development (2.27) comes
next in the order once again shows the compulsions of market forces. The
significant finding tenacity and dedication is rated by the respondents’ way
above all the other qualities. That shows hard work and perseverance pay in the
long run and contribute immensely for success. The chi–square significance
186
value is .000 showing the factors are individual and not connected to each other
but together they decide the entrepreneurial success.
TABLE 4.43
MEAN RANK OF LIMITATIONS/BARRIERS
TO VENTURE AND CHI-SQUARE TEST
Limitations/Barriers Mean Rank Chi-Square
Bureaucratic hurdles 4.37
Increasing cost of business 4.80
Dominance of MNC 4.06
Attrition 3.88
Civic Problems 2.60
Personal Problems 1.31
485.703 Df
Sig. .000
Friedman Test. Source: Primary Data.
As regards the limitations/barriers faced by the entrepreneurs as could
be seen in Table 4.43, the highest mean rank is occupied by ‘increasing cost of
businesses at 4.80. This reflects the fact that Bangalore is the costliest city in
India. All the respondents opined this as the greatest hurdle they confront with.
Next in close order comes ‘bureaucratic hurdle’ at 4.37 and ‘dominance of
MNCs’ at 4.06. Some of the state government policies are restrictive that
hamper the growth of business. The entry of multinationals has further added to
the woes of the respondents as many find it difficult to face the competition
from the MNCs as they bring in advanced technology and huge resources.
Attrition/staff problems were the next barrier, at 3.88 followed by civic
problems with 2.60 and personal problems as last at 1.31. The tourism staff was
skilled and good in communication skills and thus tended to change jobs
frequently. Frequent power cuts, bad roads, traffic jams, and over population
187
have given enough grounds for civic problems. The chi-square result shows
significance value of .000 suggesting that all these factors ranked are individual
and do not have connection between one and other. But they create obstacles to
the entrepreneurs.
Target Market for Tourism Entrepreneurs
A clear picture emerges regarding the business promotion methods and
the types of tourism business the entrepreneurs were engaged in and also the
reason for their success and the hurdles they faced. Target markets that
produced tourists and kept the entrepreneurs in business had to be investigated.
Secondary data sources produced data on international tourism that can be
exploited by the entrepreneurs.
In 2007 there were over 903 million international tourist arrivals, with a
growth of 6.6% as compared to 2006. International tourist receipts were USD
856 billion in 2007. Despite the uncertainties in the global economy,
international tourist arrivals during the first four months of 2008 followed a
similar growth trend.
However, as a result of economic crisis of 2008, international travel
suffered a strong slowdown beginning in June 2008, with growth in
international tourism arrivals worldwide falling by 2% during the Boreal
summer, while growth from January to April 2008 had reached an average
5.7% compared to its 2007 level. Growth from 2006 to 2007 was 3.7%, as total
international tourism arrivals from January to August were 641 million tourists,
up from 618 million in the same period in 2007.4
188
The country specific and India’s standing in the global tourism scenario
is presented below. The World Tourism Organization reports the following 10
countries as the most visited in 2007 by number of international Tourists:
TABLE 4.44
MOST VISITED COUNTRIES
Rank Country UNWTO Regional Market
International Tourist Arrivals
(2007)
International Tourist Arrivals
(2006)
1 France Europe 81.9 million 79.1 million
2 Spain Europe 59.2 million 58.5 million
3 United States North America 56.0 million 51.1 million
4 China Asia 54.7 million 49.6 million
5 Italy Europe 43.7 million 41.1 million
6 United Kingdom Europe 30.7 million 30.7 million
7 Germany Europe 24.4 million 23.6 million
8 Ukraine Europe 23.1 million 18.9 million
9 Turkey Europe 22.2 million 18.9 million
10 Mexico North America 21.4 million 21.4 million
Source: United Nations World Tourism Statistics, 2007.
In the above list the extract showing only the top ten, as per Table 4.44
India ranks 42nd with 5 million international arrivals, tying up with Brazil
placed at 41st with the same 5 million international tourists arrivals, during
2007.
It was essential to find out the business volume of tourism that the
prominent nations were doing as mere arrivals did not reflect the bulk of
revenue earned from tourism. Thus studies on total earnings from tourism by
leading nations were collected through secondary data sources.
189
TABLE 4.45
INTERNATIONAL TOURISM RECEIPTS
Rank Country UNWTO Regional
Market
International Tourist Receipts
(2007)
International Tourist Receipts
(2006)
1 United States North America $ 96.7 billion $ 85.7 billion
2 Spain Europe $ 57.8 billion $ 51.1 billion
3 France Europe $ 54.2 billion $ 46.3 billion
4 Italy Asia $ 42.7 billion $ 38.1 billion
5 China Europe $ 41.9 billion $ 33.9 billion
6 United Kingdom Europe $ 37.6 billion $ 33.7 billion
7 Germany Europe $ 36.0 billion $ 32.8 billion
8 Australia Oceania $ 22.2 billion $ 17.8 billion
9 Austria Europe $ 18.9 billion $ 16.6 billion
10 Turkey Europe $ 18.5 billion $ 6.9 billion
Source: United Nations World Tourism Organization statistics 2007.5
Statistical records show that the most visited countries are not the same
as highest tourism revenue earnings generating countries. As far as the tourism
receipts are concerned as shown in Table 4.45, United States takes the
undisputed lead by earning 96.7 billion dollars during 2007 as against 85.7
billion during 2006. Thus it was essential to study the highest spending
countries in tourism activities. The high spending countries reflect the potential
for foreign tourists who would actually generate international tourism. The high
spending countries would be of marketing interest to India and to other
countries aspiring to make it big in global tourism.
190
TABLE 4.46
INTERNATIONAL TOURISM TOP SPENDERS
Rank Country UNWTO Regional Market
International Tourist
Expenditure (2007)
International Tourist Expenditure (2006)
1 Germany Europe $ 82.9 billion $ 73.9 billion
2 United States North America $ 76.2 billion $ 72.1 billion
3 United Kingdom Europe $ 72.3 billion $ 63.1 billion
4 France Europe $ 36.7 billion $ 31.2 billion
5 China Asia $ 29.8 billion $ 24.3 billion
6 Italy Europe $ 27.3 billion $ 23.1 billion
7 Japan Asia $ 26.5 billion $ 26.9 billion
8 Canada North America $ 24.8 billion $ 20.5 billion
9 Russia Europe $ 22.3 billion $ 18.2 billion
10 South Korea Asia $ 20.9 billion $ 18.9 billion
Source: United Nations World Tourism Organization Statistics 2007.
The World Tourism Organization sources present that for the 5th year in
a row, German tourists continue as the top spenders as presented in Table 4.46.
A study by Dresdner Bank forecasted that for 2008, Germans and Europeans in
general will continue to be the top spenders, because of the strength of the Euro
against the U.S. dollar–with strong demand for U.S. destinations rather than
others.6
The WTO statistics further show that France and Spain attract tourists
most and get the maximum tourist arrivals. In fact 9 destinations that have huge
volume of business are European. All these European tourist destinations such
as as France, Spain, Italy, United Kingdom, Germany Ukraine, Russia, Austria
and Turkey are rich in their unique cultures and possess multi attractions for
191
tourists. Various interviews with international tourists reveal the attractions in
their mind about the European destinations as follows.
France – Artists paradise – painting, sculpting, folk music, theatre,
street performers. Enchanting Country side; the best wine regions of the world;
Unending valleys of vineyard; Lovers and honeymooner's paradise;
mesmerizing perfumes; the unforgettable French Cuisine that perhaps taught
the world the culinary art and skills; an old and rich history; Mecca for fashion
designing and also the modern vibrating night life of Paris.
Spain – Glorious history, beautiful architecture; beautiful people with
colorful customs as costumes; music; dance forms and lifestyle vibrant
festivals; traditional bull fight and bull taming sports; rich cuisine with full
bodied wine and exciting spirits; modern tourist entertainments and nightlife.
Italy – The Glorious Roman history; the ruins of the Roman Empire; the
warm sunshine and cool breeze of the Mediterranean region; the vineyards of
the Tuscany region; the distinctive Italian cuisine with the wines; the touch of
artistic class with paintings, sculptures that draw its heritage from Michael
Angelo and Leonardo Da Vinci; the romance of the Gondolas in Venice; and
the ultimate religious haven for Christians at the Papal abode of the Vatican
City.
The rest of the 6 destinations are also endowed with multi attractions
that are spread over a wide spectrum and each unique in itself. The other
interesting feature was, all these European destinations had the best modern
transportation, the best hotels and the state of art tourist facilities. This lesson
192
should be learnt by promoters of Indian tourist destinations and the tourism
service providing entrepreneurs. Tourists rush to destinations that are unique
and are capable of offering a package, that can provide existing experience in
culture, architecture, music, dance, nature, weather, food, wine and spirits,
sports, entertainment, people and their lifestyle – it is the total unique
experience that makes a Country a top tourist destination. All these should be
backed up by upgraded modern tourists' facilities to suite the quality conscious
global traveler.
The next interesting analysis made is most of these European
destinations together with United States and three Asian destinations with
Australia feature in all the three tables. This shows there is a strong relationship
between the most popular destinations, the highest tourism revenue earning
nations, and the highest tourism spending nations. The Global tourism
consciousness seems to be polarized on this group of countries where the
highest traffic, the highest spending, and the highest receipts take place. So
when the respondents opined that tourism will grow globally and will be
concentrated in a few countries in years to come, they were correct and aware
of the actual happenings.
A stark reality that automatically emerges is that China ranks 4th in most
visited destinations and 5th in top Tourism Receipts and the Top Tourism
Spenders simultaneously. China proves to be one of the most successful
nations, attracting tourists as well as spending on tourism and earning from
tourism. In Asia, what China possess India does not possess? This question
193
should work on all tourism professional's mind, including the entrepreneurs.
China apparently having a not so open culture, conservative communist
ideology, population not so conversant in English language and not an expert in
international affairs – could achieve such a feat in international tourism. In the
light of all these, it is differenting to note that India is found lagging at an
insignificant 42nd position in Global tourism ranking. Observation and
interviews with tourists and employees of tourism units reveal that China is
endowed with certain unique strengths like very old history and culture,
Mongolian – Buddhism – Communism – Neo Capitalism evolution, Popular
Chinese Cuisine. Chinese art, craft, dances, festivals, Chinese mysticism,
popular martial arts and sports. This is backed up with modern tourism
facilities of Beijing, Hong Kong and Shanghai that sports world class airports,
hotels and transportation. The neighboring popular hub of Singapore has added
to the popularity of Chinese tourism circuit. So the above mentioned analysis
holds good for China. China's silent success of global tourism can be a strong
case study for India. A number of questions should be raised and solutions
achieved, before India can really match China in economic development, in
global tourism or in hosting the Olympics.
As discussed earlier, it is high time that tourism entrepreneurs evolved
strategies focused on specific markets. Travel agents, tour operators, hotels and
airlines can target the lucrative markets of Germany, United States, United
Kingdom, France, China, Italy, Japan, Canada, Russia and South Korea. These
nationals are the top spenders in tourism activities and their demands have to be
194
met to ensure their patronage. The entrepreneurs should study tourism demand
determinants like preference of region, weather, and scenic beauty, preference
of activities as adventure, leisure, historical, eco appreciation, urban life and
entertainments, or merely the joy of traveling, of all these affluent tourists.
Surprisingly three Asian countries are a part of this group. China, Japan and
South Korea nationals are high spenders on tourism activities. China, Japan and
South Korea must have some ethnic attachment with India. Buddhism can be a
strong ethnic link. Even the people, their habits, food and customs may have
common oriental touch. The best advantages being these countries are not far
away and are well connected by air.
Application of Factor Analysis
The researcher applied Factor Analysis based on Principal Component
Analysis on 34 variables of entrepreneurship that were found to be prominent
in the entrepreneurship literature. The objective was to find out if there were
any definite patterns in these variables and also establish the most dominating
factors that facilitated entrepreneurs operation. These variables were as
follows:
Entrepreneurial Abilities
• Business operating ability
• Risk taking ability
• Service recovery ability
• Profit making ability
• Trade knowledge level
• Social responsibility ability
• Quality consciousness
195
• Innovation ability
• Self-motivation level
• Customer relations ability
• Employee relations ability Venture performance
• Venture life span
• Venture success rate
• Product uniqueness
• Customized product
• Customer satisfaction
• Marketing thrust
• Tourism development
• Benefit to society
• Contribution to economic growth
• Professionalism in services
• Use of technology
• Training standards
Environment for entrepreneurship
• Post liberalization policies
• Government incentives
• Financial institutions aid
• Economic recession setback
• Industrial / commercial growth
• Tourism infrastructure
• Income level of tourism
• Tourism friendly culture
• Income level of tourists
• Civic facilities
• Law & order situation
• Availability of educated manpower
Data gathered on these variables were presented in the Likert scale of 1–
7, with 1 denoting least importance and 7 denoting most importance. The
information thus gathered was put to i) Kaiser – Meyer–Olkin Measure of
Sampling Adequacy and Bartlett’s test of Sphericity, ii) Catell’s Scree Plot iii)
196
Principal Component Analysis: Rotation Method: Varimax with Kaiser
Normalization. Statistical Process for Social Sciences (SPSS) software has
been used. The extracted result of the SPSS out put has been analyzed as per
the following report.
TABLE 4.47
KMO AND BARTLETT'S TEST
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
.763
Bartlett's Test of Sphericity Approx. Chi-Square 1231.335
Df 231
Sig. .000
Source: Primary Data.
As presented in the Table 4.47, Kaiser-Meyer-Olkin measure of
sampling adequacy gives the output of .763. This according to Kaiser is
sufficient sampling adequacy for factor analysis. Anything above .500 can be
considered to be adequate. So, the sample size 200 is considered to be adequate
as per the KMO and Bartlett’s test. Bartlett’s test of sphericity to run the factor
analysis as it is significant at .000. Thus, anything less than 0.05 is mostly
considered in social science research as significant to reject the null
hypotheses. The variables are related to each other and it is worth applying
factor analysis to find out a definite pattern among them.
197
TABLE 4.48
COMMUNALITIES EXTRACTED BY PRINCIPAL
COMPONENT ANALYSIS
Initial Extraction
Risk taking ability 1.000 .487
Trade Knowledge level 1.000 .677
Innovation ability 1.000 .598
Venture success rate 1.000 .713
Product uniqueness 1.000 .570
Customer Satisfaction 1.000 .746
Marketing thrust 1.000 .750
Benefit to society 1.000 .513
Use of technology 1.000 .711
Training Standards 1.000 .700
Post Liberalization policies 1.000 .721
Government incentives 1.000 .667
Financial institutions aid 1.000 .655
Economic recession setback 1.000 .563
Industrial/commercial growth 1.000 .664
Tourism infrastructure 1.000 .725
Income level of tourists 1.000 .482
Tourism friendly culture 1.000 .479
Civic facilities 1.000 .610
Law and order situations 1.000 .580
Availability of educated manpower 1.000 .526
Profit making ability 1.000 .673
Extraction Method: Principal Component Analysis.
These 34 variables are put to factor analysis extraction method of
Principal Component Analysis. According to Kaiser Normalization,
communalities more than 1.000 is relevant. Thus, out of 34 variables, total 22
communalities are extracted, as demonstrated by Table 4.48. Thus, the above
mentioned extracted communalities are effective to throw light on
entrepreneurship. Communality shows how much of each variable is accounted
for by the underlying factor taken together. A high value of communality
198
means that not much of the variable is left over after whatever the factors
represent is taken into consideration.7
TABLE 4.49
STATEMENT OF TOTAL VARIANCE EXPLAINED OF
THE FACTORS EXTRACTED
Com- ponent
Total
Initial Eigen values
% of
Variance
Cumu-
lative
%
Total
Extraction Sums of Squared Loadings
% of
Variance
Cumu-
lative
%
Total
Rotation Sums of Squared Loadings
% of
Variance
Cumu-
lative
%
1 4.456 20.256 20.256 4.456 20.256 20.256 2.916 13.255 13.255
2 2.840 12.911 33.167 2.840 12.911 33.167 2.420 10.998 24.253
3 1.705 7.750 40.917 1.705 7.750 40.917 2.263 10.287 34.540
4 1.452 6.599 47.516 1.452 6.599 47.516 1.614 7.338 41.878
5 1.295 5.886 53.402 1.295 5.886 53.402 1.600 7.273 49.151
6 1.057 4.804 58.206 1.057 4.804 58.206 1.520 6.907 56.058
7 1.005 4.567 62.773 1.005 4.567 62.773 1.477 6.714 62.773
Extraction Method: Principal Component Analysis. Source: Primary Data.
Total variance shows (Table 4.49) that the initial Eigen values of 7
components being extracted. Principal Component Analysis extracted
components with Eigen values more than 1.000. It can be clearly seen the first
7 factors are responsible for a cumulative 62.773%. Thus majority of the total
Eigen values are covered by these 7 components. The first component has the
largest variance (20.256) from the mean and thus considered to be most
powerful. The second component has second largest variance from the mean
and thus second in value. And so further variances become less and less and the
value of corresponding components becomes less and less.
199
Diagram 4.1
Scree Plot
Component Number
21191715131197531
Eig
en
va
lue
5
4
3
2
1
0
Source: Primary Data
Cattel’s scree plot shows a reconfirmation of facts whether the
components are 7, or less or even more than that. The graph in Diagram 4.1 is
plotted with Eigen value and corresponding component number. It can be
clearly seen after the 7th component the scree falls freely. This denotes 8th
component onwards, till the last component can be rejected. The other fact to
be noted is components 1 to 7 have Eigen value 1 or more. 8th component
onwards the Eigen value is less than I.000. Thus, according to Kaiser all these
components (8 to 21 of the diagram) are to be rejected.
200
TABLE 4.50
ROTATED COMPONENT MATRIX OF
THE EXTRACTED SEVEN FACTORS
Name of the Factors Indicators of Entrepreneurship Factor
Loadings Cornbac Alpha
Factor-1(Venture
Success factor) Post Liberalization policies
.821
Venture success rate .811
Customer Satisfaction .671
Trade Knowledge level .590
Factor-2 (Business
Environment)
Civic facilities .731
Availability of educated manpower
.678
Tourism friendly culture .593
Government incentives .567
Factor-3 (Product
Development) Product uniqueness
.723
Innovation ability .702
Profit making ability .697
Factor-4 (Growth
opportunities) Marketing thrust
.682
Industrial/commercial growth .599
Law and order situations .555
Factor-5 (Tourism
venture scope) Tourism infrastructure
.805
Risk taking ability .524
Income level of tourists .523
Factor- 6 (Venture
planning) Economic recession setback
.662
Financial institutions aid .653
Benefit to society
Factor-7 (Training
and Development) Training Standads
.777
Use of technology .769
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. Rotation converged in
10 iterations. Source: Primary Data.
Extraction method of Principal Component analysis has been applied to
rotation of varimax with Kaiser Normalization. The rotation was converged in
10 iterations using SPSS. The output as per Table 4.50 shows Rotated
201
Component Matrix where 7 components each with few variables have been
extracted. The factor name selected fairly covers the bunch of variables shown
in the matrix. As the first component decided is Venture success factor as it
covers all the variables in component no. 1: Post liberalization policies,
Venture success rate, Customer Satisfaction, Trade knowledge level. Since
component no.1 has the highest variance and thus considered to be most
powerful, it has been selected as responsible for venture success. The same
procedure is followed for all the seven factors decided for the corresponding
seven components as follows:
• Venture success factor
• Business environment
• Product development
• Growth opportunities
• Tourism venture scope
• Venture planning
• Training and development
According to the factor analysis, these seven factors are most
responsible for the operation of entrepreneurial establishments.
The prominent variables with high loading are also identified following
Kaiser’s principle as:
• .500 to less than .500 – to be ignored
• .600 to .700 – poor
• .700 to .800 – moderate
• .800 to .900 – good
• More than .900 – excellent
202
The post liberalization policies .821, venture success rate .811, tourism
infrastructure .805 – all are considered good. The study can identify these as
the strongest indicators to bring in success. Similarly next in importance are
civic facilities .731, product uniqueness.723, training standards .777, use of
technology .769 – all are considered to be moderate. Factor analysis further
identifies the above mentioned indicators for effective venture management.
The extracted seven factors are subjected to correlation test to find out in what
way the factors were related to each other. The correlation table and the
interpretations are given below.
TABLE 4.51
MATRIX SHOWING CORRELATIONS BETWEEN
THE SELECTED SEVEN FACTORS Name of
the
Factor
F1 F2 F3 F4 F5 F6 F7
F1 1 .449(**) -.194(**) .248(**) .029 .253(**) .036
. .000 .006 .000 .685 .000 .613
200 200 200 200 200 200 200
F2 .449(**) 1 -.077 .371(**) .123 .314(**) .062
.000 . .280 .000 .082 .000 .382
200 200 200 200 200 200 200
F3 -.194(**) -.077 1 .191(**) -.034 .263(**) .203(**)
.006 .280 . .007 .630 .000 .004
200 200 200 200 200 200 200
F4 .248(**) .371(**) .191(**) 1 .113 .379(**) .132
.000 .000 .007 . .110 .000 .063
200 200 200 200 200 200 200
F5 .029 .123 -.034 .113 1 .016 -.037
.685 .082 .630 .110 . .826 .602
200 200 200 200 200 200 200
F6 .253(**) .314(**) .263(**) .379(**) .016 1 .194(**)
.000 .000 .000 .000 .826 . .006
200 200 200 200 200 200 200
F7 .036 .062 .203(**) .132 -.037 .194(**) 1
.613 .382 .004 .063 .602 .006 .
200 200 200 200 200 200 200
**Correlation is significant at the 0.01 level (2-tailed). Source: SPSS output of Primary Data.
203
The correlation Table 4.51 shows a matrix of all the seven factors. A
score of +1.0 denotes perfect positive correlation; score of -1.0 denotes perfect
negative correlation. Negative correlation means when one is more the other is
less and vice versa. A score of 0 denotes there is absolutely no correlation.
The seven factors are as follows:
• Venture success factor – F1
• Business environment – F2
• Product development – F3
• Growth opportunities – F4
• Tourism venture scope – F5
• Venture planning – F6
• Training and development – F7.
An attempt is made to find the relationship between the factors that
significantly constitute the entrepreneurial opportunities in tourism business.
Some of the relations are explained to express the overall reading of the
correlation table. The most notable feature is SPSS suggested significance level
of 0.01 as against the normal accepted value of 0.05; thus making the
correlations more specific. The result of two tailed correlation where two
results for each pair is given, thus both the scores are considered. The table also
shows the sample size as 200 for all the operations.
- Table shows correlation between F1 and F2-average positive (.449) one end and no relation (.000) at other end,
- Between F1and F3 – low negative (-194) one end and no relation (.006) at other end,
- Between F1 and F4 – low positive (.248) one end and no relation (.000) at the other end,
204
- Between F1 and F5 – very low positive (.029) one end and fairly high positive (.685) other end,
- Between F1 and F6 – low positive (.253) one end and no relation (.000) on other end,
- Between F1 and F7–very low positive (.036) one end and fairly high positive (.613) on other end. . Likewise the entire matrix can be examined for the correlation of a pair of factors at a time.
Examination of the table in the above mentioned procedure shows there
is not a single correlation where both the ends show no relation (using the set
significance level at 0.01). Thus the conclusion is all the seven extracted factors
are in some way or other connected to each other. Research result can be
confirmed as the extracted factors are all related to entrepreneurship and are
responsible for its success. Between one and another factor they are also related
as in turn they all relate to the source theme ‘Entrepreneurship’.
Association of the size of the entrepreneurial firm with the selected factors
It was also examined whether the extracted seven factors of
entrepreneurship vary with the size of the establishment. The following
hypothesis was used to achieve the objective.
H5: No significant difference exists between the size of the
entrepreneurial unit and the seven factors (Venture Success,
Business Environment, Product Development, Growth
Opportunities, Tourism venture scope, Venture planning and
Training & Development) influencing the attitude, ability,
performance, strategic thinking, and managerial decisions of
entrepreneurs.
205
It was attempted to find out whether the size of various tourism
establishments was responsible for making any difference in the seven factors
selected by factor analysis. The process adopted was to analyze the mean and
standard deviation of each factor against the three different sizes of the
establishment as small, medium, and large scale.
TABLE 4.52
STANDARD DEVIATION OF SEVEN FACTORS (F1 TO F7) SET
ACROSS THE ESTABLISHMENT SIZE
N Mean Std.
Deviation Maximum
F1 Small Scale 100 18.7700 4.62504 26.00
Medium Scale 79 17.7468 4.87923 25.00
Large Scale 21 18.4286 5.58186 25.00
Total 200 18.3300 4.83092 26.00
F2 Small Scale 100 19.7600 2.59806 24.00
Medium Scale 79 19.3671 3.17513 25.00
Large Scale 21 19.3333 2.74469 25.00
Total 200 19.5600 2.84727 25.00
F3 Small Scale 100 15.2100 3.25761 21.00
Medium Scale 79 15.5949 2.92434 20.00
Large Scale 21 15.6190 2.97449 20.00
Total 200 15.4050 3.09173 21.00
F4 Small Scale 100 14.9500 1.87689 18.00
Medium Scale 79 15.0253 2.34780 18.00
Large Scale 21 14.3810 2.45919 18.00
Total 200 14.9200 2.13457 18.00
F5 Small Scale 100 11.7200 2.04534 17.00
Medium Scale 79 11.8101 1.90193 16.00
Large Scale 21 11.7143 1.76473 15.00
Total 200 11.7550 1.95296 17.00
F6 Small Scale 100 15.2200 1.98774 19.00
Medium Scale 79 15.1646 2.27827 20.00
Large Scale 21 15.8095 1.91361 20.00
Total 200 15.2600 2.09867 20.00
F7 Small Scale 100 9.9200 1.34600 13.00
Medium Scale 79 9.8228 1.38453 13.00
Large Scale 21 10.0476 1.02353 12.00
Total 200 9.8950 1.32770 13.00
Source: SPSS output of Primary data
206
The standard deviation (Table 4.52) gives a clear picture of the fact that
the variance level decreases with every factor. The standard deviation of F1 is
the largest (for all firm sizes); standard deviation of F2 is second largest (for all
firm sizes) and so on to show standard deviation of F7 is the least (for all firm
sizes). So it is confirmed factor one (FI) is the strongest factor for
entrepreneurship as the variance is largest from the mean. The strength of the
factors gets reduced in descending order. This reconfirms that the factor
analysis test is correct where similar result is projected when FI, F2, F3, F4, F5,
F6, & F7 were extracted and the strength were also in descending order.
Apart from establishing the above mentioned fact, there are no other
visible patterns between the different sizes of the firms. That is the variance
from the mean of individual firm sizes are close to being equal. It is clearly
seen from the standard deviation and also from the maximum deviation
columns in every factor category the values of the firm sizes are almost equal.
There are no or insignificant difference of various sizes of entrepreneurial firms
as associated with the selected seven factors of entrepreneurship.
ANOVA test on selected factors and firm size groups
Subsequently, Analysis of Variance (ANOVA) was applied to test the
variance of the seven factors as related to the entrepreneurial firm sizes. Test
was applied to the firm size (small, medium, and large) in association with the
seven factors.
207
TABLE 4.53
ANOVA TABLE OF SEVEN SELECTED FACTORS AS ANALYZED
BETWEEN AND WITHIN FIRM SIZE GROUPS
The factors Sum of
Squares Df Mean
Square F Sig.
F1 Between Groups 46.430 2 23.215 .995 .372
Within Groups 4597.790 197 23.339
Total 4644.220 199
F2 Between Groups 8.019 2 4.009 .492 .612
Within Groups 1605.261 197 8.149
Total 1613.280 199
F3 Between Groups 7.615 2 3.807 .396 .674
Within Groups 1894.580 197 9.617
Total 1902.195 199
F4 Between Groups 7.068 2 3.534 .774 .463
Within Groups 899.652 197 4.567
Total 906.720 199
F5 Between Groups .397 2 .199 .052 .950
Within Groups 758.598 197 3.851
Total 758.995 199
F6 Between Groups 7.221 2 3.611 .818 .443
Within Groups 869.259 197 4.412
Total 876.480 199
F7 Between Groups .964 2 .482 .271 .763
Within Groups 349.831 197 1.776
Total 350.795 199
Source: SPSS output of Primary Data.
The ANOVA table 4.53 shows the sum of square (SS), degree of
freedom (df), Mean square (MS), F – ratio (F) and significance (Sig.). For the
present research the first column and the last column are relevant. It can be
clearly seen FI to F7 – all the seven factors have significance value much
higher than accepted set value of .050. The null hypothesis is not rejected.
Thus, in all the seven cases the null hypothesis of the variables is not related to
each other is not rejected. Test proves that there is no significant difference of
factors. It may be informed from the result of the one way ANOVA test that no
difference of opinions exist across the small, medium, and large entrepreneurs
208
on seven key factors that represent true scope for entrepreneurial opportunities
in tourism in the high tech city Bangalore.
Thus both the tables of standard deviation and the ANOVA conclusively
prove the fact – The size of the entrepreneurial firms is not in any way
responsible for altering the seven selected factors.
Entrepreneurs of allied activities demonstrating the seven factors
The seven factors were also present amongst the ‘allied activities
operators’. Secondary data provided sufficient inputs to identify these ‘allied
activities operators’ who were found operating with the seven factors as:
Venture Success, Business Environment, Product Development, Growth
Opportunities, Tourism venture scope, Venture planning and Training &
Development.
Here are some cases of innovative products of entrepreneurial
establishments of tourism and allied activities in Bangalore city:
� Anju Sudarshan's Theatre Café at Ranga Shankara a premier theatre hall
that attracts lot of tourists.
� Ms. Manjul Gupta's service to tourists through her health centre 'Body
Craft' a spa spread across four floors operating on Western and Oriental
styles of health care.
� Eagleton Golf village, a 500 acre golf village with 170 acres of golf
course, designed by Australia firm Pacific Gold Coast and promoted by
entrepreneur Arun Kumar to promote golf tourism. The complex house
209
resort, private villas, golf academy, clubhouse measuring 3 lakh sq. ft.
with restaurants, golf accessories shop and golf school.
� Levitate a tourists, artifact shop with souvenirs as rings, semi-precious
stones, bangles, beads, boutique shirts, skirts, belts, boxes, carry bags –
promoted by the entrepreneur Meghna Khanna, a MBA graduate who
gave up her corporate dreams to pursue her true calling.
� A number of painters and artists of Bangalore have sold their paintings
to hotels and restaurants. These paintings and murals adorn the lobbies,
banquet halls, dining halls, restaurants, bed rooms, corridors of the best
5 star hotels in Bangalore. The selected art galleries also attract a
number of tourists and city of art lovers. These artists and painters have
undoubtedly contributed to the cause of tourism and tourists are perhaps
their greatest admirer. The list of the art entrepreneurs include reputed
names as Paresh Hazra, Amitab Sengupta, Ravi Mandlik, Peter Hayman,
Henry salt, G. Gangatharan, Karunakaran, William Miller, R. Rowyer,
G. Adirekar, G. Shenoy, Kiran Mallapur, M.S. Murthy, Yusuf Arrakal
and Nambiar.8
� 'Bangalore Habba' the unique cultural festival that is performed by 2000
artists performing in almost 100 locations across the city over one month
promoted by two women entrepreneurs Nandini Alva and Padmini Ravi.
Both international and domestic tourists throng the city during
December to enjoy Bangalore Habba.
210
� Hansgrohe and Jaguar have ventured into innovative toilet and sanitary
fittings for 5 star hotels in Bangalore. The product range includes sensor
taps, Strass Swarovski, Indroform, fiber optic lighting, wall brackets and
crystal chandeliers. The entrepreneurs have also set up a number of
orientation camps to explain the usage of their products to architects and
interior decorators of hotels, motels and resorts, in Bangalore.
� Entrepreneurs venturing into the new concept of 'Tea Bar' in Bangalore
were found to be successful and highly demanded by the tourists.
Detailed report is narrated in the annexure chapter.
o Infinitea – English style tea bar promoted by Gaura Saria.
o My Tea House – A venture by tea-historian Anil Rawat serving
tea from different parts of the world.
o Some of the star hotels also started their own tea bars to
supplement the growing demand of the tourists in Bangalore.9
There were imposing shopping malls that were conceived and
successfully operated by entrepreneurs. Findings based on observation and
secondary data confirmed these malls provided the main attraction to urban
tourists as they housed state of art shops, restaurants, cinema halls, gaming
centers, beauty parlors, entertainment outlets – all that tourists need to spend
their time with, in the atmosphere of glamour, sophistication and excitement.
Findings are documented in details in the annexure section, of the following
malls:
211
1. Forum Mall – 100,000 sq.ft mall at Koramangala promoted by
entrepreneur Neeraj Duggal, the present vice president.
2. Bangalore Central (BC) Mall – 1,20,000 sq. ft. Mall promoted by the
Pantaloon Group, famous for ready-made garments.
3. EvaMall – Country's first mall to be exclusively dedicated to ladies.
4. Garuda Mall – spread over 75,000 sq.m with parking facility for over
1000 cars, the mall is promoted by entrepreneur Uday Garudachar.
5. GopalanMall- has 2,38,000 sq.ft. of retail space, situated on Mysore
Road a popular venture by entrepreneur Dinesh Malpani, CEO of the
jubilant group that launched the Gopalan Mall.
6. Lido Mall – situated on the famous land mark of the familiar Lido
Theatre which actually gave away place for the mall, promoted by the
original owners who thought Mall business was a better option.10
• Entrepreneurs in Bangalore ventured into amusement Parks, as the state
government offered incentive by reducing entertainment tax from 20%
to 10% and were considering a request for further reduction to 5%. The
detailed report on, Wonder La, the best and most popular amusement
Park off Mysore Road at Hejjala, promoted by entrepreneurs
Kochouseph Chittilappily, the present managing director of Wonder La
Holidays Pvt. Ltd. is documented in annexure section.
• Entrepreneurship in Hotel Management software was also quite
prominent in the IT City. Press release extract of one such enterprenuer,
Vishnu Murali Konduru the venturer of Saastra Software as a service for
212
tourism transportation and accommodation, has been documented in the
annexure section.
There were considerable amount of entrepreneurship enterprises on
health therapies and fitness centers, scattered all over the city, catering to
the needs of the health conscious globe-trotting tourists, the domestic
visitors and the mobile ITprofessionals. Extracts of the published report
are presented at annexure.
1) TaiChiAcademy- imparts Chinese martial art for tourists. Tai Chi
teaches exercise movements for physical fitness centers are at
Bangalore Palace Grounds, Lourd Vijay's DanceSchool, Active Canvas
Studio, and at Alliance Francaise
2) Yoga Centers- There are various centers as Sudarshan Kriya at the Art
of Living, Siddha Samadhi Yoga, Power Yoga, B.K.S. Iyengar style of
yoga, artistic yoga, Ashtanga Yoga from the teaching of Pattabhi Jois
and Hatha yoga for the spiritually inclined. Report of Rupa Satyam
who rums one of the most popular yoga center have been documented
in the annexure section.
3) 27 years old therapist entrepreneur Sheela Bajaj practices and teaches
meditation. Besides being a chakra and reiki healer, she is also a gem
and crystal therapist. Sheela Bajaj's interview with the media has being
documented in the annexure section.
4) The premier names practicing Chakra therapy, gemology and reiki in
Bangalore include Indira Bhangar, Arnav Medhi and Pam Mehra.
213
Published interview with Arnav Medhi revealed that the later was a
typical MBA till he decided that he wanted out the rat race and become
an entrepreneur in pranic and spiritual healing.
REFERENCES
1) Drucker Peter F. (1985) ‘Innovation and Entrepreneurship – Practice and Principles’. Harper Row Publishers. New York.
2) J. Schumpter (1934) ‘The Theory of Economic Development’. Cambridge Mass: Harvard University Press.
3) Rao Smitha ‘Tourism plans go full throttle’. The Times of India, Bangalore. Jan 11, 2007.
4) United Nations World Tourism Organization Statistics 2007.
5) Ibid.
6) Ibid.
7) Kothari C.R.(2009) Research Methodology, New Age International Pvt. Ltd. publishers. New Delhi.
8) Mendonca Allen. 'Arty facts." India Today. Simply Bangalore September 2007 Vol. 3 Number 8: New Delhi. Pg. 9.
9) Mehra Neeti. 'Blazing the Coffee trail.' Express Hospitality September 1-15, 2006. Pg. 22.
10) Govindarajan Nirmala. 'City In Al League.' The Times of India, Bangalore. Tuesday, October 2, 2007.