TourisminIndia_Group No 1

8/10/2019 TourisminIndia_Group No 1

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A study of Indian Tourism Sector and

the impact of other sectors

Group - 1

Members

Name Student ID Email ID Section

Amit Kumar Swain 1301-018 [email protected] A

M V S Srikanth 1301-108 [email protected] A

Sachin Jaiswal 1301-399 [email protected] A

Saswati Sunayana 1301-196 [email protected] A

Sidheshwar Birajdar 1301-218 [email protected] A

Sudipta Goswami 1301-420 [email protected] A

Sulabh Gupta 1301-572 [email protected]


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Abstract

Tourism, is not just a leisure activity anymore. Large amount of revenue is generated from this

sector and that adds to the many other potentials of this industry such as employment

generation, improved lifestyle of residents, cross culture communication etc. Tourism in a way,is related to every other industry, though the connection has not been focussed on in many

research papers. This paper attempts to find a relation between the dependent and independent

variables of tourism industry, handicraft industry, IT industry and hospitality industry. While in

some cases, the hypothesis formed comes out with a clean chit, in some other cases it has

been rejected since the conditions of hypothesis did not come true.


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Acknowledgement

As a part of our curriculum, we completed our research on the topic and submitted an account

of our work. During the research, we have utilized the resources of the institute and taken help

from professors, seniors and colleagues. This is an attempt to express our gratitude for all thoseacts of cooperation.


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Introduction:

Tourism evolved as a sector and created its own space in the market in the last two decades

only. Even though India has always been a place of interest for the visitors, tourism was not one

of the key sectors which were focussed on immediately after independence. In fact, asacknowledged in National Tourism Policy, 2002, the growth of Indian tourism sector, is

dependent on many other sectors. Also the growth of this sector has direct or indirect impact on

these sectors. (http://www.academia.edu/1855785/TOURISM_IN_INDIA). The campaign

Incredible India gave a new direction to the perception towards tourism. This is evident from

the figures that say tourism and hospitality sector alone accounted for $2468.39 billion in the

period April 2000 - April 2011. Considering the increasing tourist arrival driven by business,

leisure and medical reasons at a compound annual growth rate of 8 percent, the future of this

sector seems a safe bet. Government initiatives to facilitate international air travel with better

connectivity, allow investments from private and foreign sources and develop infrastructure

have added value to the lucrative business models of tourism industry.(http://www.asa.in/pdfs/surveys-reports/Tourism-in-India.pdf).

Literature Review:

In this section we went through the work of other researchers in the same area. Themethodology used was studied with much care so that we can jot down our thoughts on similarlines. To start with, Liu and Wall presented a paper on planning tourism employment: adeveloping country perspective. This research paper tells us to take a holistic approach fortourism planning. It says that tourism shouldnt be about planning only for the visitors but

planning for both the visitors and the local residents. So the tourism plans should include

Human Resources development as a major focus area. Also the form of tourism should fit wellwith the human resource capabilities of the area else the local people would find it difficult toparticipate fully.

Tourism in developed countries a taken as an economic endeavour so it is well planned but itdeveloping countries it is not taken as a leading economic sector. Also most of the benefits ofthe economic proceeds are taken up by the government and the tourism companies rather thatthe local residents.

So Liu and Wall proposed a conceptual framework which focuses on using training andeducation as tools to facilitate the entry of local residents and job advancements in tourism

sector. Thus tourism should be considered as a social engineering tool to promoteadvancement of local residents.

T Asli D.A. Tasci gives us an idea about how much influence different factors have on the imageof a destination as perceived in the minds of tourists. This analysis is based on a large datasetof Michigan Regional Travel Market Survey. The factors which are considered are based onboth cognitive and affective components. A regression analysis was conducted on all the factors
http://www.academia.edu/1855785/TOURISM_IN_INDIAhttp://www.academia.edu/1855785/TOURISM_IN_INDIAhttp://www.academia.edu/1855785/TOURISM_IN_INDIAhttp://www.asa.in/pdfs/surveys-reports/Tourism-in-India.pdfhttp://www.asa.in/pdfs/surveys-reports/Tourism-in-India.pdfhttp://www.asa.in/pdfs/surveys-reports/Tourism-in-India.pdfhttp://www.asa.in/pdfs/surveys-reports/Tourism-in-India.pdfhttp://www.academia.edu/1855785/TOURISM_IN_INDIA


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and finally it was concluded what all factors decide the destination image. The destination imageis considered to be a function of the following selected variables

DI = f (R, G, A, I, S, OTE, V, SS)

Where

DI = Respondents image of the study destination

R = Respondents race

G = Respondents gender

A = Respondents age

I = Respondents total annual household income

S = Respondents state of residence

OTE = Respondents overall travel experience

V = Prior visitation to the study destination

SS = Survey season

Onome Daniel Awaritefes study on Evaluating Tourism Resource Areas in Nigeria forDevelopment was conducted to find out the distribution of population in 101 identified touristdestinations in Nigeria. Though there were major tourist attraction places in all the destinations itwas found out the majority of tourism was concentrated in the Lagos and Yankari/Jos/Abuja.This was because of the easy accessibility and the cultural and natural resources present inthose areas. This study thus establishes the conditions required for destination planning andmarketing approaches required for the other regions.

Moller et al. tried to study the changing travel behaviour of Austrias ageing population and its

impact on tourism. As the average age of population is increasing continuously and thisphenomenon is more visible in the western countries, it is important to consider their

preferences in all the industry. This paper researches on the effect of this aging population ontourism industry. It tries to find out whether the tourism interest changes according to age. Thusa research was conducted in Austria with a participant age group of 55+ and their leisure and

travel habits were analysed.

It was found out that they didnt change their travel habits considerably but their preferences

were changed like they now preferred longer stays and travelling off season. Thus it wasconcluded that as the travel and leisure behaviour are build up gradually during the life span of

the people they dont get changed completely with age.

Lindroth et al. put attempt to study why creativity was important in the tourism sector also. Withrising creative influence in every sector the tourism sector also needs organisational creativity

along with cultural competence. This is achieved by researching in a town of Porvoo in Finland.Creative tourism is an extension to normal tourism. It offers visitors with an opportunity todevelop their creative potential by active participation in learning experienceswhich arecharacteristic of the holiday destination where they are undertaken (Richards and Raymond,2000). The needs of the visitors are changing and the completion among destinations areincreasing. It was concluded that the tourism is affected with a wide range of factors like socio-physical environment, logistics, traffic, accessibility marketing, service structure, networking etc.


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According to Zehrer and Siller, although motivation is merely one variable explaining travellers

behaviour, it's considered to be one of the most important. Hence, nature-based commercialenterprise resources as non-market product or destination product have their own economicvalue that is often neglected. This paper seeks to look at this issue. This study attempts to

quantify the value and significance of nature and landscape for tourists travel motivation forvacations. The study was conducted across both the summer and winter seasons. The surveyshows that notably within the summer season Nature/Landscape is that the most significanttheme for summer vacation nowadays and it can be even more in 2020. Therefore, it clearly isone in all the foremost essential travel motives within the eyes of the consultants. Nature-basedvacation encompasses a robust significance and price for the province, with Nature among thehighest 3 strengths within the Delphi survey.

Travel photos are often symbols reflective inner feelings of the photographers. They additionallyfunction as records that store travel expertise of the photographers. By content analysing onehundred forty five travel photos submitted to the New York Times, Pan et al.s paper aims toexplore the relationships among motivations, image dimensions and affective qualities ofplaces. It was established that image dimension of natural resources like flora and fauna,

wealth of countryside and beaches are often associated with pleasant and arousing

feelings toward a destination. On the other hand, image dimension of culture, history and art isfrequently associated with pleasant quality of an area. These three association rules are

successively frequently coupled to intellectual travel motivation. Photos that induce arousingand pleasant feelings are typically taken in long shot, at eye -level angle, with stark densitylevel and with single-person composition.

Sirakaya and Woodside discussed about building-block propositions for making useful theoriesof decision making by travellers by a qualitative review of the tourist decision-making literature.This article describes trends in developing someone destination selection models besides

examining decision-making propositions from the literature, the article covers resolution ofimportant issues which are needed for creating advances in understanding, describing, andpredicting tourist's decision-making. It tests theories like Advancing consumer decision makingin tourism and tests different consumer decision making theories and critically examines each. Italso examines various travel decision models and states their contributions and limitations.

Croes and Vanegas Sr. put forward their study to examine the econometric estimates so as toelucidate tourist arrivals to Aruba from the United States, Netherlands and Venezuela. Thestudy specified a dynamic economics model for modelling short term as well as the long-termresponses. It calculated both linear and log-linear functions, and it applied the BoxCoxstatistical procedure to determine the suitable practical form. The inclusion of Venezuela as adeveloping country allowed the comparison of the behaviour of tourism demand in

comparatively rich and poor countries. The results showed to how much extent the cross-country behaviour of demand differs with regard to changes in effective costs and exchangerates. It was found out that price and exchange rate are considerably more important andcompelling pull conditions for Venezuelan tourists than the American and Dutch tourists. So thisfinding reflects the stage of economic development of Venezuela with reference to the other twocountries. For all countries, the foremost result is the importance of the income variable,followed by the exchange rates and relative prices.This study will assist future formulation of


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macroeconomic policies as well as market and pricing strategies in an exceedingly small ormicro-state economy.

Ashworth and Page performed their research on urban tourism that remained a standardizedtheme within the expansion of tourism research since the 1980s and several other seminalpapers have reviewed the state of research and its progress towards a bigger recognition. This

Progress in tourism Management review article moves our understanding and knowledge of theresearch agendas inside urban tourism by examining the paradoxes associated with suchagendas thereby suggesting to adopt a holistic approach that interconnects with the widerdomain of the social sciences and those of urban studies and the notion of world cities. Thisstudy explains that to study urban tourism you must first embrace its theoretical critiques whichplay a significant role in place imagery.

Sirgy et al. tried to find out and examine a theoretical model that links community residentsfeelings about tourism impact. These impact may be economic, cultural, social andenvironmental. It also tests their satisfaction with particular life domains like community well-being, material well-being, health and safety well-being, emotional well-being and overall lifesatisfaction. The model also suggests that the strength of these relationships is moderated bythe relative success of tourism development in the community. This model was tested using asurvey of 321 respondents from various communities having different level of tourismdevelopment. The results showed that how the factors considered were all supportive of theoverall model.

Hjalager reviews the various research on the topic of innovation of tourism. The differentcategories of innovation are addressed. These include innovation in product, marketing,managerial, process and institutional. Various other innovations like entrepreneurship, existenceof territorial industry clusters and technology push are also acknowledged. Knowledge isidentified as a key factor for both the occurrence and also the nature of innovations. This studyshows that there is still very limited systematic and empirical evidence about the level of

innovative activities and their influence on the destination and national economies. Therefore ithas been concluded that there should be more quantifiable and qualitative study about theimplications of innovation in tourism.

Qu et al conducted a study on the fact that there were many research establishing thesignificance of destination branding in the field of tourism there was no conceptual developmentabout this fact. Thus this study aims to develop a theoretical model for destination branding andtest it. This study shows that a unique image as a relatively new component of destination brandassociations. The brand associations include the affective, cognitive and unique imagecomponents. It was proposed that the image of any destination is a mediator between its brandassociations and the tourists future plans like their intentions to revisit and recommend. The

results showed that image is influenced mainly by three types of brand

Associations. It was also showed that Image is a critical mediator between brand associationsand tourists future behaviours. And it was fund out that unique image had the second highest

influence on the overall image formation followed by the cognitive evaluations.

The last paper by Thomas et al. was about understanding small firms in tourism observed thatthe small firms in tourism have been making impact on the agendas of the policy makers forquite a while now, but still the academic interest over this issue has not been consistent. Thispaper tries to show impact of small firms by reviewing disciplinary studies which have impacted


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the present understanding of small firms. It also shows how this knowledge leads to widertourism understanding. It was established that there were not sufficient evidence to establishthat the small firms impact tourism in a positive or negative way. Thus this area should be moreresearched.

Objective of our study:

The objective of this study remains to study various factors affecting tourist footfall and revenue

generation of Indian tourism sector. We intend to come up with a model function that can

establish relationship between independent and dependent factors of tourism sector and some

other sectors such as IT sector, hospitality sector and handicraft industry. We chose four

different areas that touch the lives of Indians from a diverse range of age group, income range

and job profile.

Scope of study:

Our study focuses on tourism sector and its importance for the overall growth of Indian economy

and how it affects other industries. Previously there have been studies on demand function

models with the help of which we can determine the effect of independent factors such as

income, price, transportation cost, foreign exchange rate etc. As mentioned in the literature

survey we have gone through research papers on the econometric study in other countries.

These models helps optimizing the revenue of tourism industry with the help of the independent

factors. However, according to our best knowledge, this interdependence of sectors has not

been studied properly and we see some scope of research here.

We put forward a hypothesis that there are independent factors in other sectors which havedirect or indirect impact on tourism. In this report we have tried to determine the effect of these

independent factors on the growth of tourism industry in terms of revenue generated and tourist

arrival. The results can be used to justify an attempt to tap the yet unexplored areas of this

sector.

Hypothesis formulation:

The hypothesis of this research makes a claim that change in number of tourist footfall can be

expected with change in percentage penetration of IT industry and number of approved hotels in

India. So if we define the actual number of tourist footfall and the expected number of

footfall, where = a+ b, the form of hypothesis can be given as

:

:

Methodology:


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Starting with handicraft sector, we studied a survey given by ministry of tourism and culture on

the expense of foreign tourists on handicrafts. 58 percent of tourists were male, age of 70

percent of the tourists was in the range of 25 to 44 years, about 36 percent tourists were

business executives, 10 percent were students and 27 percent were from service industry. Also

43 percent of the tourists come in the more than average income category and 32 percent in

average income category thus making a perfect bias free sample space. The expenditurepattern of the tourists was studied. The total and per capita expenditure on handicrafts were

estimated separately. The handicraft items were grouped under 12 major heads. The impact of

various factors such as age group, income range, city of stay and number of days of stay was

studied separately.

Coming to IT sector, we observed that various issues tourists used to face are being addressed

by latest innovations in information technology. With the increase in number of internet users

the number of tourist arrival in India also shows a positive trend. We analysed the data available

and came up with a hypothesis that the percentage penetration of internet can be described as

an independent factor affecting the growth of tourism industry.

Finally, we took hospitality industry into consideration. We studied the trend of foreign tourist

arrival in India, the occupancy rate of hotels by foreign and domestic tourists and tried to find a

connection between the number of approved hotels in India and tourist footfall, number of

approved hotels being the independent variable.


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Data Collection:

Number of Foreign Tourist Visits in India

(1997 to 2012)

(In Million)

Year

No. of Foreign Percentage

Tourist Visits (%) Change

over the

Previous Year

1997 5.5 9.3

1998 5.54 0.7

1999 5.83 5.3

2000 5.89 1.1

2001 5.44 -7.8

2002 5.16 -5.1

2003 6.71 30.1

2004 8.36 24.6

2005 9.95 19

2006 11.75 18.1

2007 13.26 12.8

2008 14.38 8.5

2009 5.17 -2.2

2010 5.78 11.8

2011 63.09# 9.2

2012 66.48# 5.4

Note: #: In Lakh.

Source: Ministry of Tourism, Govt. of India.


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From the above graph, it can be seen that the number of foreign tourist visits to India remained

almost constant during the period of 1997 to 2001. The number of foreign tourist visits started to

grow consistently from 2002 onward till 2008 after which there was a sharp dip in the number of

foreign tourist arrivals to India. This dip could be justified by the onset of recession period in

2009.

Foreign Exchange Earnings from Tourism in India

(1991 to 2012)

Year

Rs. in % In US$ %

Crore Change Million Change

1991 43180 - 1861 -

1992 59510 37.8 2126 14.2

1993 66110 11.1 2124 -0.1

1994 71290 7.8 2272 7

1995 84300 18.2 2583 13.7

1996 10046 19.2 2832 9.6

1997 10511 4.6 2889 2

1998 12150 15.6 2948 2

1999 12951 6.6 3009 2.1

2000 15626 20.7 3460 15

2001 15083 -3.5 3198 -7.6

2002 15064 -0.1 3103 -32003 20729 37.6 4463 43.8

2004 27944 34.8 6170 38.2

2005 33123 18.5 7493 21.4

2006 39025 17.8 8634 15.2

2007 44360 13.7 10729 24.3

2008$ 50730 14.4 11747 9.5

2009$ 54960 8.3 11394 -3

2010 64889 18.1 14193 24.6

0

2

4

6

8

10

12

14

16

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Number of foreign tourist visits


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2011 77591 19.6 16564 16.7

2012 94487 21.8 17.74^ 7.1

Note: $: Advance Estimates.

^: In US$ Billion.

Source: Ministry of Tourism Govt. of India.

From the above graph it can be seen that the foreign exchange earnings from tourism in India

has grown substantially over the years. As we have seen above that over the years the foreign

tourist visits have grown substantially and hence has grown the foreign exchange earnings from

tourism in India.

For handcraft industry, data was collected taking all the diversified factors that can affect

tourism revenue into consideration which included profile of tourists such as gender, age,

profession, income group and country of origin, number of days of stay in India, currency used

and city of departure into consideration. The expenditure pattern of the tourists was studied

taking daily per capita expenditure and total expenditure into account separately.

Percentage penetration of IT industry was studied year wise. Also, we focussed our attention on

online travel agencies. The trend of share of tourism sector B2C e commerce space was

observed. We also tried to find give a picture of the distribution of online travel sales in India

across bus travel, air travel, railways, hotel booking and hotel deals.

Data for hospitality was taken from indiastats.com. We concentrated on number of hotels in

India and the bookings. Information regarding average number of days of stay, share of

domestic tourists and foreign tourists and distribution across 5 star, 4 star, 3 star, 2 star, 1 star

and heritage hotels were also collected.

0

10000

2000030000

40000

50000

60000

70000

80000

90000

100000

Rs. in Crore

Rs. in Crore


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Data analysis:

For the analysis of data related to hospitality sector, we studied two parameters such as the

number of approved hotels in India and the percentage share of foreign tourists in the hotel

bookings. The data was taken for the period 2002-08.

Regression Statistics

Multiple R 0.343570657

R Square 0.118040796

Adjusted R Square -0.058351045

Standard Error 263.2293459

Observations 7

Coefficients StandardError

t Stat P-value

Lower95%

Upper95%

Lower95.0

%

Upper95.0

%

Intercept 1855.03338 328.581479

4

5.6455

81

0.002

42

1010.

388

2699.

679

1010.

388

2699.

679

X Variable -25.77596181 31.5092496

4

-

0.8180

4

0.450

542

-

106.7

73

55.22

114

-

106.7

73

55.22

114

The simple linear regression analysis with number of approved hotels as input parameter (x)

and tourist footfall as the input parameter (y) year wise gave the t stat value 5.64 and R square

value 0.118. As the t stat value exceeds critical t value 2.306 with 8 df, our null hypothesis

doesnt get rejected. Some unknown but non-zero value of b exists and there definitely lies


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some correlation between the independent and dependent parameter. Also, from the R square

value it can be inferred that 11.8 percent of tourist footfall can be explained by number of

approved hotels in India.



R Square 0.244337782

Adjusted R Square 0.093205338


Observations 7

Coefficients Standard

Error

t Stat P-

value

Lower

95%

Upper

95%

Lower

95.0%

Upper

95.0%

Intercept 20.240 3.090882 6.548 0.001244 12.29478 28.18551 12.29478 28.1855107

X Variable 0.376 0.296399 1.27

15

0.259

485

-

0.385

05

1.138

791

-

0.385

05

1.13879

122

Similar results were delivered when percentage share of hotel booking was taken as input

parameter (x) rest all conditions remaining the same. The t stat value and R square value were

found to be 0.244 and 6.548 respectively. Again it can be interpreted that since the t value

exceeds 2.306, we can assume safely that our hypothesis holds true and doesnt get rejected.

Also, it can be said that 24.4 percent of tourist footfall can be explained by the percentage share

of foreign tourists. This can be attributed to the initiatives being taken to improve the experience

of foreign tourists by Government and other institutions in India.




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R Square 0.589399969

Adjusted R Square 0.486749961


Observations 6

Coefficients Standa

rd

Error

t Stat P-

value

Lower

95%

Upper

95%

Lower

95.0%

Upper

95.0%

Intercept 0.64819403 1.1056

45

0.5862

59

0.5891

89

-

2.4215

7

3.7179

56

-

2.4215

7

3.7179

56

X Variable 1 0.275608549 0.1150

18

2.3962

14

0.0746

62

-

0.0437

3

0.5949

51

-

0.0437

3

0.5949

51

However, as we observed the regression analysis of tourism industry taking percentage

penetration of internet in the country as input variable (x) and tourist footfall as input variable (y)

the t stats value observed is 0.586 i.e. less than 2.306. Hence the hypothesis doesnt hold true

here. One of the probable explanation for this could be that the internet penetration in our

country in that period was perhaps had more to do with social networking, online shopping and

other areas instead. (Indolia and Chauhan, 2012)

To dig deeper into this, we also collected information regarding the share of online travel sales

in B2C ecommerce space. The share changed from 78 percent in 2009, 78 percent in 2010, 76

percent in 2011, and 73 percent in 2012 to 71 percent in 2013. However, to study such recent

information required many other recent information that was found to be beyond the scope of

this project.

For handicraft industry, we set out null hypothesis (H0) that the amount spend by differentgroups of people according to Age, Sex , Profession, Income are equal.

And our Alternative hypothesis(H1) is that these are unequal.

From this analysis it was established that the main factors which differentiate the spendingpatterns are Age, Income and Profession. While grouping in terms of sex didnt have anysignificant difference in the amount spent on handicrafts for the foreign tourists.

The data have been analysed using Microsoft excel. The main facts have been attached in thisdocument below.


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Though we have tested our hypothesis there might be some errors as the data was taken in2001-02 but still it was a large scale survey containing 12250 participants and thus we assumethat it will be comprehensible for our analysis.

Analysis of Tourism expenditure variance according to sex.

SUMMARY

Groups Count Sum Average Variance

Male 10 7145 714.5

403856.94444444

4

Female 10 5105 510.5

197824.72222222

2

ANOVA

Source of

Variation SS df MS F P-value F crit

Between

Groups 208080 1 208080

0.69166142672

3434

0.41649687373

2558

4.41387341917

057

Within

Groups 5415135 18

300840.8

33333333

Total 5623215 19

Thus by performing a single factor Anova over the distribution of tourists according their sex andtheir spending on handicrafts we find that the amount spent by different groups of peopleaccording to sex are not significantly different as the calculated F with 95% confidence intervalis lesser that the F critical value. Also the P value is greater that our threshold level of 0.05.


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So the spending patterns of tourists grouped in terms of sex are not different.

Analysis of Tourism expenditure variance according to Profession.

SUMMARY


Service 10 1136 113.6 10946.7111111111

Business 10 4118 411.8 126439.733333333

Industry 10 4720 472 177662.222222222

Agriculture 10 1724 172.4 27821.1555555556

Students 10 432 43.2 2090.17777777778

Others 10 124 12.4 118.488888888889

ANOVA

Source of


Between

Groups

1867424.3333

3333 5

373484.8

66666667

6.49391159

447654

0.0000855361

41734

2.3860698615

7422

Within

Groups 3105706.4 54

57513.08

14814815

Total

4973130.7333

3333 59


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Thus by performing a single factor Anova over the distribution of tourists according theirprofessions and their spending on handicrafts we find that the amount spent by different groups

of people according to professions are different as the calculated F with 95% confidence intervalis much larger and the F critical value. Also the P value is less that our threshold level of 0.05.

So the spending patterns of tourists grouped in terms of their profession are significantlydifferent.

Analysis of Tourism expenditure variance according to Age.

SUMMARY



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ANOVA

Source of


Between

Groups

1867045.533333

33 5

373409.106

666666

6.15374

1191498

51

0.0001396540

45435

2.3860698615

7422

Within

Groups 3276720.8 54

60680.0148

148148

Total

5143766.333333

33 59

Between

Groups

1741277.666666

66 9

193475.296

296296

2.84314

3875712

58

0.0087659804

0622

2.0733511634

7462

Within

Groups

3402488.666666

67 50

68049.7733

333333

Total

5143766.333333

33 59

Thus by performing a single factor Anova over the distribution of tourists according to their ageand their spending on handicrafts we find that the amount spent by different groups of peopleaccording to age are different as the calculated F with 95% confidence interval is much largerand the F critical value. Also the P value is less that our threshold level of 0.05. So the spending

patterns of tourists grouped in terms of their profession are significantly different.


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Analysis of Tourism expenditure variance according to Income.

SUMMARY


Below Average 10 1137 113.7

8792.6777777777

8

Average 10 4113 411.3

169592.23333333

3

More than

Average 10 4720 472

186750.66666666

7

High 10 1721 172.1

21704.766666666

7

Very High 10 431 43.1 1248.1

ANOVA

Source of


Between

Groups

1424696.

32 4

356174

.08

4.5888261438

687

0.00341434431

2874

2.57873918431

156

Within Groups 3492796 45

77617.

688888

8889

Total

4917492.

32 49

Thus by performing a single factor Anova over the distribution of tourists according their incomelevel and their spending on handicrafts we find that the amount spent by different groups ofpeople according to income are different as the calculated F with 95% confidence interval is


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much larger and the F critical value. Also the P value is less that our threshold level of 0.05.Sothe spending patterns of tourists grouped in terms of their profession are significantly different.

We also tried to find out the cumulative effect of three variables such as fraction of foreigntourists in the total number of hotel bookings in India, number of hotel rooms and penetration ofIT in percentage on tourist footfall. The result we obtained in multiple regression analysis is

given below.


Multiple R 0.935184

R Square 0.874569

Adjusted R

Square 0.686423

Standard Error 1712767

Observations 6

Coefficients StandardError t Stat P-value Lower 95% Upper95% Lower95.0% Upper 95.

Intercept 11053274 9594925 1.151992 0.368436

-

30230357.11 52336906 -3E+07 52336905

X Variable

1 112.3738 103.4206 1.08657 0.390742

-

332.6092231 557.3567

-

332.609 557.356

X Variable

2 0.269719 0.082195 3.281436 0.081656

-

0.083939258 0.623377

-

0.08394 0.623376

X Variable

3 -554285 274130.4 -2.02197 0.180546

-

1733772.606 625203.1

-

1733773 625203.0

Compiling the results, the equation obtained from regression analysis is as follows,

Where refers to number of approved hotel rooms in India, refers to percentage of IT

penetration in India and refers to percentage of foreign tourists in hotels.

With an adjusted R square value of 0.686423, it can be safely inferred that there exist some

effects of the above mentioned three factors on the total tourist footfall.

Findings:From the analysis performed, we gathered that hospitality industry has a positive impact on

tourism sector. The total number of hotels and the quality of service do matter to attract more

number of tourist to Indian tourist destinations. The penetration of IT however does not follow

the pattern for which we tried to come up with some probable explanation.

The handicrafts industry in India is one of the major sectors in India which provides direct and

indirect employment to about 76 lakh persons as per data from Export Promotion Council for

Handicrafts (EPCH) in 2001-02 shows. It valued the total export of handicrafts from India in

2001-02 as Rs. 6769.5 crores. Moreover this sector is of keen interest as it requires relatively

low capital investment and draws a steady source of foreign exchange.


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Thus a survey was done by the National Productivity Council (NPC) to find out how much theforeign tourists spend in India and the main products purchased by the tourists. From thissurvey it was found out that the total expenditure on handicrafts by all the foreign tourists hasbeen pegged at Rs. 2985.154 crores in 2001. This roughly calculates to about 44% of the totalestimated export value of handicraft items in India in 2001-02 comparing data of EPCH.

According to Tourist Statistics provided by Department of Tourism in 2001, India had earnedforeign exchange of Rs. 14344 crores from the tourism sector alone. Thus the foreign touristsexpenditures on handicrafts in India formed about 21% of their total spending in India.

Thus it can be understood from the data of Export promotion Council of Handicrafts that theforeign tourism sector is highly influences the handicrafts industry of India.

Managerial Implications:

There are many factors that motivate tourism in India like the nature and landscapes, theadventurous Himalayas, deserts in Rajasthan, beaches of Goa, lush green forests in Odisha

and Kerala. There are also factors like rich cultural heritage also attracts the tourists. The varietyin language, climate, traditions and religion has continually been a matter of curiosity for thetourists. Also Indias handloom and handicraft products have been major attraction. Thereforethe foreign tourists generally purchase generously.

And if special attention is given to improving the condition of the tourist attraction points andaccessibility of these spots, it can work wonders to increase the revenue of tourism industry.

Conclusion:

Hereby we conclude our report stating that hospitality sector and handicraft sector have at least

some impact on the growth of Indian tourism sector. This is a sector that not only puts a brandimage of our country on the world map, but also generates revenue and creates employment. If

well taken care of, this has the potential to give a big push to Indian economy. Though the

impact of IT sector was not that evident in this report, the future of tourism industry sure will

benefit the IT industry and vice versa. Government initiatives, campaigns such as incredible

India bring in a lot of publicity to the tourist places, which in turn works for the maintenance of

the spot as well.


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References

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3. Awaritefe, O. D. (2007). Evaluating Tourism Resource Areas in Nigeria.

4. Moller, K. W. (2007). The Changing Travel Behaviour of Austrias Ageing

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Web References

1. http://www.academia.edu/1855785/TOURISM_IN_INDIA

2. http://www.asa.in/pdfs/surveys-reports/Tourism-in-India.pdf

3. http://tourism.gov.in/writereaddata/Uploaded/Guideline/020920110558156.pdf

4. dkc.engo.in/files/.../ICT-and-Tourism-Challenges-and-Opportunities.pdf

5. http://www.saarj.com/images/download/ACAD,OCT.2011%20COMPLETE%20%

20PDF/ACADEMICIA%20%20MAY%202012,%20PAPERS%20PDF/ACADEMIC

IA%20MAY%202012,%20PAPERS,%20PDF/5.15,%20Umakant%20Indolia.pdf

6. https://eprints.usq.edu.au/245/1/Pease.pdf

7. http://ayanaant.vivavoyages.in/wp-content/uploads/2013/07/online-white-

paper.pdf
http://www.academia.edu/1855785/TOURISM_IN_INDIAhttp://www.asa.in/pdfs/surveys-reports/Tourism-in-India.pdfhttp://tourism.gov.in/writereaddata/Uploaded/Guideline/020920110558156.pdfhttp://dkc.engo.in/files/.../ICT-and-Tourism-Challenges-and-Opportunities.pdfhttp://www.saarj.com/images/download/ACAD,OCT.2011%20COMPLETE%20%20PDF/ACADEMICIA%20%20MAY%202012,%20PAPERS%20PDF/ACADEMICIA%20MAY%202012,%20PAPERS,%20PDF/5.15,%20Umakant%20Indolia.pdfhttp://www.saarj.com/images/download/ACAD,OCT.2011%20COMPLETE%20%20PDF/ACADEMICIA%20%20MAY%202012,%20PAPERS%20PDF/ACADEMICIA%20MAY%202012,%20PAPERS,%20PDF/5.15,%20Umakant%20Indolia.pdfhttp://www.saarj.com/images/download/ACAD,OCT.2011%20COMPLETE%20%20PDF/ACADEMICIA%20%20MAY%202012,%20PAPERS%20PDF/ACADEMICIA%20MAY%202012,%20PAPERS,%20PDF/5.15,%20Umakant%20Indolia.pdfhttps://eprints.usq.edu.au/245/1/Pease.pdfhttps://eprints.usq.edu.au/245/1/Pease.pdfhttp://www.saarj.com/images/download/ACAD,OCT.2011%20COMPLETE%20%20PDF/ACADEMICIA%20%20MAY%202012,%20PAPERS%20PDF/ACADEMICIA%20MAY%202012,%20PAPERS,%20PDF/5.15,%20Umakant%20Indolia.pdfhttp://www.saarj.com/images/download/ACAD,OCT.2011%20COMPLETE%20%20PDF/ACADEMICIA%20%20MAY%202012,%20PAPERS%20PDF/ACADEMICIA%20MAY%202012,%20PAPERS,%20PDF/5.15,%20Umakant%20Indolia.pdfhttp://www.saarj.com/images/download/ACAD,OCT.2011%20COMPLETE%20%20PDF/ACADEMICIA%20%20MAY%202012,%20PAPERS%20PDF/ACADEMICIA%20MAY%202012,%20PAPERS,%20PDF/5.15,%20Umakant%20Indolia.pdfhttp://dkc.engo.in/files/.../ICT-and-Tourism-Challenges-and-Opportunities.pdfhttp://tourism.gov.in/writereaddata/Uploaded/Guideline/020920110558156.pdfhttp://www.asa.in/pdfs/surveys-reports/Tourism-in-India.pdfhttp://www.academia.edu/1855785/TOURISM_IN_INDIA

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