Reviving of Travel and Tourism Industry: A study focused on consumer preference for Travel post COVID

Reviving of Travel and Tourism Industry: A study focused on consumer preference for Travel post COVID

Prateek Sapra 

© 2020 JETIR October 2020, Volume 7, Issue 10

Abstract

The Novel Corona Virus (COVID-19) has changed the world as it was world as usual. Economies of majority of nations have drop down while situation among poor or developing nations is even worse. Since peoples are avoiding travel in order to maintain social distance hence Travel and tourism industry is the highly affected industry. Majority of worldwide airlines, travel companies, tour operators and other wings associated with Travel and tourism industry are struggling to survive in this pandemic era. Lot of peoples have lost their jobs as organizations are operating with minimal bandwidth and are getting lean. This research paper focuses on potential and future of Travel and tourism industry post COVID era. Tools used to analyze the findings include secondary data using desk research and primary data with the help of survey data to understand the driving factors based on consumer preferences. This research paper brought out several parameters that make an impactful presentation. Scope of the study is to analyze first hand findings which can further help decision makers in industry to take appropriate steps and prepare effective business strategy.

Keywords: Reviving Travel, COVID-19, Consumer preference, Change in demand

 

1.     Introduction

How it started- China reported about COVID-19 to World Health Organization’s country office on 31^st December 2019. By Mid of February more than 80,000 persons already got affected and international flights has already spreaded the virus across the globe. Post gauging the situation World Health Organization announcement global pandemic then countries started imposing travel restrictions and majority of worldwide nations has shuttered down their doors for international, intra state travel. This started impacting the several wings connected with travel and tourism industry. Airplanes were grounded, Travel offices got temporarily closed, Hotels and Restaurants were either temporarily closed or all empty. Starting with “Diamond princess” wherein 700 plus confirmed cases were detected. Seas become the trapped places as 10 odd ships were in sea/ocean and ports denied them to dock.

 

1.1 Travel industry and COVID-19

Worst phase in history of global travel industry- organization “World Travel and Tourism Council” on March 13^th 2020 has warned that COVID-19 pandemic could reduce 50 million travel and tourism jobs globally wherein Asia is expected to be the worst affected while figures released by statista on August 21st 2020 were even worse which indicates that 100.8 million peoples in travel and tourism have already lost their jobs globally and as stated by WTTC in March Asia is worst affected region which comprise of 62.89% of job loss globally.

 

Source: Statista accessed on 20th Sep.’20

 

USA based consultancy McKinsey in one of their report have estimated that 13.4 million jobs from restaurant industry, 3.6 million jobs from food preparation and serving, 2.6 million jobs from restaurant servers followed by 1.3 million jobs from restaurant cooks/chefs are at risk

 

While the biggest aviation body “IATA” estimated that RPK (revenue passenger kilometers) will be -38% as per year on year trend (2019 Vs 2020) which comprise expected damage (revenue loss) of US$252 billion. Multiple global carriers have requested for state aid and few of them have even filled bankruptcy, inhibited refunds. IATA also added that most of airlines have less than three months of liquidity and will not be able to survive for extended period of air travel restrictions.

According to the review of literature and aviation metrics published by IATA it has been observed that demand for domestic travel is expected to recover faster than international demand. Research has further analyzed that domestic air travel in China is growing way faster than any other nation.

Below mentioned statistics clearly demonstrate that Global domestic travel is increasing way quicker than international one, though multiple other parameters are also attached which include restrictions on international air travel i.e. all countries are still not accepting international visitors, strict guidelines for e.g. Thailand tourism board has recently started accepting international tourists though tourists need to undergo 14 days mandatory quarantine followed by 90 days of minimum stay in their country and other nations are also imposing similar conditions for international tourists. 

 

 Study also indicate that during business as usual days USA accommodate highest number of domestic air passengers (587 Million in 2018 which was more than their population count of 331 Million) followed by China at 2^nd rank (515 Million in 2018) while India stood at third rank globally in terms of domestic air travel passengers (116 Million in 2018).

Below mentioned metrics indicate that across globally China has recovered with intense pace in terms of Domestic air travel.

Appeal to survive- Global bodies like “WTTC” is trying to coordinate with different nations to open their borders for international travel followed by “UN aviation task” is also appealing nations to allow tourists to travel without quarantine restrictions post collecting COVID negative report (test conducted in last 48 hours). This appeal is in regards to save drowning jobs and revive economy of the states as multiple direct and indirect jobs are associated with travel and tourism industry.

On parallel stage it was quiet disheartening to see that no aid has been sanctioned by Indian government for travel and tourism industry while wings associated with travel and tourism i.e. Hospitality industry followed by professions like Pilots are among highest tax payer to the government.

Peoples who were under impression that travel is only about leisure and taxi service must have got to know about impact of travel in our daily lives.

 

1.2 The Digital journey

The way travel and tourism industry has dipped. Industry is expected to grow with similar pace too. History is the evidence as peoples went for travel even after 2^nd world war too, though business travel might get impacted due to rapid use of technology as global lockdown has taught the world to work remotely using digital platforms i.e. Zoom, Microsoft Team’s, Skype applications have been used to conduct business meetings, coaching classes, presentations etc.

On parallel track Tourism businesses that do not invest in digitalization will struggle to survive, collapse of Thomas cook U.K. is recent example.

Tech-driven digital native companies are some of the largest and fastest growing in the tourism sector. These include well-known examples such as Skyscanner, Expedia, Booking.com and Airbnb.

Since the world and India is getting digital. Indian government need to assist small medium entrepreneurs associated with Travel and tourism industry as over 85% of Travel setups belong to SMEs. The common problem they face while going digital include Inadequate access to internet, Insufficient resources, skills, financial resources and connectivity as majority of rural areas especially in country like India still doesn’t have good internet connectivity. Policy makers need to develop a forward looking agenda and ensure access to comparable and timely data. Tourism businesses that do not invest in digitalization will struggle to survive in near future.

 

2. RESEARCH METHODOLOGY

2.1 Statement of the research problem

To understand the reviving potential of Travel industry post COVID era and undertake an investigation into its effectiveness in the backdrop of customer centric approach

 

2.2 Research objectives

The objectives of the study are:

1.      To understand consumer preference for travel post COVID

2.      To analyze driving factors to bring tourism and travel industry back on track

 

2.3 Data collection

Data was collected using close ended questions. The study is qualitative in nature

 

2.4 Universe of the study

Considering ongoing pandemic situation online survey with the help of Google form has been distributed across Delhi NCR. (Sample size – 100)

 

2.5 Statistical Tools

Analysis has been achieved with the assistance of SPSS 23.0 and Microsoft Excel 2010.

 

2.6 Profiling Respondents

 

2.7 Problem Analysis

2.7.1 Consumer preference for traveling post COVID

 

 

Table 1.1- One Way ANOVA – Gender wise consumer preference

 

ANOVA
		Sum of Squares	df	Mean Square	F	Sig.
Will you consider the density of the destination you are visiting (Considering social distancing)	Between Groups	7.197	1	7.197	3.393	.069
	Within Groups	203.619	96	2.121
	Total	210.816	97
Will you rely on a travel agency for itinerary planning	Between Groups	.726	1	.726	.309	.580
	Within Groups	227.900	97	2.349
	Total	228.626	98
Will you prefer Brand names to book your trip i.e. Make my trip, Go-Ibibo, Chain hotels like Hyatt, Leela, etc.	Between Groups	11.406	1	11.406	5.600	.020
	Within Groups	197.583	97	2.037
	Total	208.990	98

 

Null hypothesis for question - Will you consider the density of the destination you are visiting (Considering social distancing) has not been rejected which indicates that there is no variance between the Gender wise perception.

Null hypothesis for question - Will you rely on a travel agency for itinerary planning has not been rejected which indicates that there is no variance between the Gender wise perception.

Null hypothesis for question - Will you prefer Brand names to book your trip i.e. Make my trip, Go-Ibibo, Chain hotels like Hyatt, Leela, etc. has been rejected which indicates that there is variance between the Gender wise perception. Raw data also analyze that Female respondents prefer brand names more than males.

 

 

Table 1.2- One Way ANOVA – Age wise consumer preference

 

ANOVA
		Sum of Squares	df	Mean Square	F	Sig.
Will you consider the density of the destination you are visiting (Considering social distancing)	Between Groups	6.333	5	1.267	.570	.723
	Within Groups	204.484	92	2.223
	Total	210.816	97
Will you rely on a travel agency for itinerary planning	Between Groups	10.326	5	2.065	.880	.498
	Within Groups	218.300	93	2.347
	Total	228.626	98
Will you prefer Brand names to book your trip i.e. Make my trip, Go-Ibibo, Chain hotels like Hyatt, Leela, etc.	Between Groups	6.251	5	1.250	.573	.720
	Within Groups	202.739	93	2.180
	Total	208.990	98

 

Null hypothesis for question - Will you consider the density of the destination you are visiting (Considering social distancing) has not been rejected which indicates that there is no variance between the Age wise perception.

Null hypothesis for question - Will you rely on a travel agency for itinerary planning has not been rejected which indicates that there is no variance between the Age wise perception.

Null hypothesis for question - Will you prefer Brand names to book your trip i.e. Make my trip, Go-Ibibo, Chain hotels like Hyatt, Leela, etc. has not been rejected which indicates that there is no variance between the Age wise perception.

  

 

Table 1.3- One Way ANOVA – Occupation wise consumer preference

ANOVA
		Sum of Squares	df	Mean Square	F	Sig.
Will you consider the density of the destination you are visiting (Considering social distancing)	Between Groups	3.932	3	1.311	.596	.619
	Within Groups	206.884	94	2.201
	Total	210.816	97
Will you rely on a travel agency for itinerary planning	Between Groups	3.181	3	1.060	.447	.720
	Within Groups	225.445	95	2.373
	Total	228.626	98
Will you prefer Brand names to book your trip i.e. Make my trip, Go-Ibibo, Chain hotels like Hyatt, Leela, etc.	Between Groups	5.563	3	1.854	.866	.462
	Within Groups	203.427	95	2.141
	Total	208.990	98

 

Null hypothesis for question - Will you consider the density of the destination you are visiting (Considering social distancing) has not been rejected which indicates that there is no variance between the Occupation wise perception.

Null hypothesis for question - Will you rely on a travel agency for itinerary planning has not been rejected which indicates that there is no variance between the Occupation wise perception.

Null hypothesis for question - Will you prefer Brand names to book your trip i.e. Make my trip, Go-Ibibo, Chain hotels like Hyatt, Leela, etc. has not been rejected which indicates that there is no variance between the Occupation wise perception.

 

Table 1.4- One Way ANOVA – Educational qualification wise consumer preference

ANOVA
		Sum of Squares	df	Mean Square	F	Sig.
Will you consider the density of the destination you are visiting (Considering social distancing)	Between Groups	4.751	3	1.584	.722	.541
	Within Groups	206.066	94	2.192
	Total	210.816	97
Will you rely on a travel agency for itinerary planning	Between Groups	8.338	3	2.779	1.199	.315
	Within Groups	220.288	95	2.319
	Total	228.626	98
Will you prefer Brand names to book your trip i.e. Make my trip, Go-Ibibo, Chain hotels like Hyatt, Leela, etc.	Between Groups	6.335	3	2.112	.990	.401
	Within Groups	202.655	95	2.133
	Total	208.990	98

 

Null hypothesis for question - Will you consider the density of the destination you are visiting (Considering social distancing) has not been rejected which indicates that there is no variance between the Educational qualification wise perception.

Null hypothesis for question - Will you rely on a travel agency for itinerary planning has not been rejected which indicates that there is no variance between the Educational qualification wise perception.

Null hypothesis for question - Will you prefer Brand names to book your trip i.e. Make my trip, Go-Ibibo, Chain hotels like Hyatt, Leela, etc. has not been rejected which indicates that there is no variance between the Educational qualification wise perception.

 

 

Table 1.5- One Way ANOVA – Monthly Income wise consumer preference

ANOVA
		Sum of Squares	df	Mean Square	F	Sig.
Will you consider the density of the destination you are visiting (Considering social distancing)	Between Groups	8.422	4	2.106	.990	.417
	Within Groups	185.056	87	2.127
	Total	193.478	91
Will you rely on a travel agency for itinerary planning	Between Groups	14.336	4	3.584	1.552	.194
	Within Groups	203.234	88	2.309
	Total	217.570	92
Will you prefer Brand names to book your trip i.e. Make my trip, Go-Ibibo, Chain hotels like Hyatt, Leela, etc.	Between Groups	1.620	4	.405	.179	.949
	Within Groups	199.111	88	2.263
	Total	200.731	92

 

Null hypothesis for question - Will you consider the density of the destination you are visiting (Considering social distancing) has not been rejected which indicates that there is no variance between the Monthly Income wise perception.

Null hypothesis for question - Will you rely on a travel agency for itinerary planning has not been rejected which indicates that there is no variance between the Monthly Income wise perception.

Null hypothesis for question - Will you prefer Brand names to book your trip i.e. Make my trip, Go-Ibibo, Chain hotels like Hyatt, Leela, etc. has not been rejected which indicates that there is no variance between the Monthly Income wise perception.

 

 

Table 1.6- One Way ANOVA – Marital Status wise consumer preference

ANOVA
		Sum of Squares	df	Mean Square	F	Sig.
Will you consider the density of the destination you are visiting (Considering social distancing)	Between Groups	.847	1	.847	.387	.535
	Within Groups	209.970	96	2.187
	Total	210.816	97
Will you rely on a travel agency for itinerary planning	Between Groups	2.335	1	2.335	1.001	.320
	Within Groups	226.291	97	2.333
	Total	228.626	98
Will you prefer Brand names to book your trip i.e. Make my trip, Go-Ibibo, Chain hotels like Hyatt, Leela, etc.	Between Groups	2.917	1	2.917	1.373	.244
	Within Groups	206.073	97	2.124
	Total	208.990	98

 

Null hypothesis for question - Will you consider the density of the destination you are visiting (Considering social distancing) has not been rejected which indicates that there is no variance between the Marital Status wise perception.

Null hypothesis for question - Will you rely on a travel agency for itinerary planning has not been rejected which indicates that there is no variance between the Marital Status wise perception.

Null hypothesis for question - Will you prefer Brand names to book your trip i.e. Make my trip, Go-Ibibo, Chain hotels like Hyatt, Leela, etc. has not been rejected which indicates that there is no variance between the Marital Status wise perception.

 

  

2.7.2 Transportation mode preferred by consumers post COVID

 

Table 1.7- One Way ANOVA – Gender wise consumer preference for availing transportation mode post COVID

ANOVA
		Sum of Squares	df	Mean Square	F	Sig.
What transportation mode would you like to prefer [Air (Take a flight)]	Between Groups	2.519	1	2.519	1.853	.178
	Within Groups	92.467	68	1.360
	Total	94.986	69
What transportation mode would you like to prefer [Self drive - Own vehicle]	Between Groups	.119	1	.119	.190	.664
	Within Groups	54.376	87	.625
	Total	54.494	88
What transportation mode would you like to prefer [Rent a Car - Chauffeur driven]	Between Groups	.492	1	.492	.462	.499
	Within Groups	67.108	63	1.065
	Total	67.600	64
What transportation mode would you like to prefer [Public transport - Train, Bus etc.]	Between Groups	.766	1	.766	.433	.513
	Within Groups	113.173	64	1.768
	Total	113.939	65

 

Null hypothesis for question - What transportation mode would you like to prefer [Air (Take a flight)] has not been rejected which indicates that there is no variance between the Gender wise perception.

Null hypothesis for question - What transportation mode would you like to prefer [Self drive - Own vehicle] has not been rejected which indicates that there is no variance between the Gender wise perception.

Null hypothesis for question - What transportation mode would you like to prefer [Rent a Car - Chauffeur driven] has not been rejected which indicates that there is no variance between the Gender wise perception.

Null hypothesis for question - What transportation mode would you like to prefer [Public transport - Train, Bus etc.] has not been rejected which indicates that there is no variance between the Gender wise perception.

 

 

Table 1.8- One Way ANOVA – Age wise consumer preference for availing transportation mode post COVID

ANOVA
		Sum of Squares	df	Mean Square	F	Sig.
What transportation mode would you like to prefer [Air (Take a flight)]	Between Groups	16.115	5	3.223	2.615	.033
	Within Groups	78.870	64	1.232
	Total	94.986	69
What transportation mode would you like to prefer [Self drive - Own vehicle]	Between Groups	6.047	5	1.209	2.072	.077
	Within Groups	48.447	83	.584
	Total	54.494	88
What transportation mode would you like to prefer [Rent a Car - Chauffeur driven]	Between Groups	2.633	4	.658	.608	.658
	Within Groups	64.967	60	1.083
	Total	67.600	64
What transportation mode would you like to prefer [Public transport - Train, Bus etc.]	Between Groups	9.341	5	1.868	1.072	.385
	Within Groups	104.598	60	1.743
	Total	113.939	65

 

Null hypothesis for question - What transportation mode would you like to prefer [Air (Take a flight)] has been rejected which indicates that there is variance between the Age wise perception.

Null hypothesis for question - What transportation mode would you like to prefer [Self drive - Own vehicle] has not been rejected which indicates that there is no variance between the Age wise perception.

Null hypothesis for question - What transportation mode would you like to prefer [Rent a Car - Chauffeur driven] has not been rejected which indicates that there is no variance between the Age wise perception.

Null hypothesis for question - What transportation mode would you like to prefer [Public transport - Train, Bus etc.] has not been rejected which indicates that there is no variance between the Age wise perception.

  

 

Table 1.9- One Way ANOVA – Occupation wise consumer preference for availing transportation mode post COVID

ANOVA
		Sum of Squares	df	Mean Square	F	Sig.
What transportation mode would you like to prefer [Air (Take a flight)]	Between Groups	10.881	3	3.627	2.846	.044
	Within Groups	84.104	66	1.274
	Total	94.986	69
What transportation mode would you like to prefer [Self drive - Own vehicle]	Between Groups	2.182	3	.727	1.182	.322
	Within Groups	52.312	85	.615
	Total	54.494	88
What transportation mode would you like to prefer [Rent a Car - Chauffeur driven]	Between Groups	5.850	3	1.950	1.926	.135
	Within Groups	61.750	61	1.012
	Total	67.600	64
What transportation mode would you like to prefer [Public transport - Train, Bus etc.]	Between Groups	3.432	3	1.144	.642	.591
	Within Groups	110.508	62	1.782
	Total	113.939	65

 

Null hypothesis for question - What transportation mode would you like to prefer [Air (Take a flight)] has been rejected which indicates that there is variance between the Occupation wise perception.

Null hypothesis for question - What transportation mode would you like to prefer [Self drive - Own vehicle] has not been rejected which indicates that there is no variance between the Occupation wise perception.

Null hypothesis for question - What transportation mode would you like to prefer [Rent a Car - Chauffeur driven] has not been rejected which indicates that there is no variance between the Occupation wise perception.

Null hypothesis for question - What transportation mode would you like to prefer [Public transport - Train, Bus etc.] has not been rejected which indicates that there is no variance between the Occupation wise perception.

  

 

Table 1.10- One Way ANOVA – Educational Qualification wise consumer preference for availing transportation mode post COVID

ANOVA
		Sum of Squares	df	Mean Square	F	Sig.
What transportation mode would you like to prefer [Air (Take a flight)]	Between Groups	11.856	3	3.952	3.138	.031
	Within Groups	83.130	66	1.260
	Total	94.986	69
What transportation mode would you like to prefer [Self drive - Own vehicle]	Between Groups	4.834	3	1.611	2.758	.047
	Within Groups	49.660	85	.584
	Total	54.494	88
What transportation mode would you like to prefer [Rent a Car - Chauffeur driven]	Between Groups	1.340	2	.670	.627	.538
	Within Groups	66.260	62	1.069
	Total	67.600	64
What transportation mode would you like to prefer [Public transport - Train, Bus etc.]	Between Groups	6.304	3	2.101	1.210	.313
	Within Groups	107.635	62	1.736
	Total	113.939	65

 

Null hypothesis for question - What transportation mode would you like to prefer [Air (Take a flight)] has been rejected which indicates that there is variance between the Educational Qualification wise perception.

Null hypothesis for question - What transportation mode would you like to prefer [Self drive - Own vehicle] has been rejected which indicates that there is variance between the Educational Qualification wise perception.

Null hypothesis for question - What transportation mode would you like to prefer [Rent a Car - Chauffeur driven] has not been rejected which indicates that there is no variance between the Educational Qualification wise perception.

Null hypothesis for question - What transportation mode would you like to prefer [Public transport - Train, Bus etc.] has not been rejected which indicates that there is no variance between the Educational Qualification wise perception.

 

  

Table 1.11- One Way ANOVA – Monthly Income wise consumer preference for availing transportation mode post COVID

ANOVA
		Sum of Squares	df	Mean Square	F	Sig.
What transportation mode would you like to prefer [Air (Take a flight)]	Between Groups	13.300	4	3.325	2.831	.032
	Within Groups	72.819	62	1.175
	Total	86.119	66
What transportation mode would you like to prefer [Self drive - Own vehicle]	Between Groups	1.470	4	.367	.740	.568
	Within Groups	38.747	78	.497
	Total	40.217	82
What transportation mode would you like to prefer [Rent a Car - Chauffeur driven]	Between Groups	7.259	4	1.815	1.779	.145
	Within Groups	60.179	59	1.020
	Total	67.437	63
What transportation mode would you like to prefer [Public transport - Train, Bus etc.]	Between Groups	8.349	4	2.087	1.230	.308
	Within Groups	100.089	59	1.696
	Total	108.438	63

 

Null hypothesis for question - What transportation mode would you like to prefer [Air (Take a flight)] has been rejected which indicates that there is variance between the Monthly Income wise perception.

Null hypothesis for question - What transportation mode would you like to prefer [Self drive - Own vehicle] has not been rejected which indicates that there is no variance between the Monthly Income wise perception.

Null hypothesis for question - What transportation mode would you like to prefer [Rent a Car - Chauffeur driven] has not been rejected which indicates that there is no variance between the Monthly Income wise perception.

Null hypothesis for question - What transportation mode would you like to prefer [Public transport - Train, Bus etc.] has not been rejected which indicates that there is no variance between the Monthly Income wise perception.

 

 

Table 1.12- One Way ANOVA – Marital Status wise consumer preference for availing transportation mode post COVID

ANOVA
		Sum of Squares	df	Mean Square	F	Sig.
What transportation mode would you like to prefer [Air (Take a flight)]	Between Groups	8.953	1	8.953	7.076	.010
	Within Groups	86.033	68	1.265
	Total	94.986	69
What transportation mode would you like to prefer [Self drive - Own vehicle]	Between Groups	1.315	1	1.315	2.152	.146
	Within Groups	53.179	87	.611
	Total	54.494	88
What transportation mode would you like to prefer [Rent a Car - Chauffeur driven]	Between Groups	1.819	1	1.819	1.743	.192
	Within Groups	65.781	63	1.044
	Total	67.600	64
What transportation mode would you like to prefer [Public transport - Train, Bus etc.]	Between Groups	.008	1	.008	.004	.948
	Within Groups	113.932	64	1.780
	Total	113.939	65

 

Null hypothesis for question - What transportation mode would you like to prefer [Air (Take a flight)] has been rejected which indicates that there is variance between the Marital Status wise perception.

Null hypothesis for question - What transportation mode would you like to prefer [Self drive - Own vehicle] has not been rejected which indicates that there is no variance between the Marital Status wise perception.

Null hypothesis for question - What transportation mode would you like to prefer [Rent a Car - Chauffeur driven] has not been rejected which indicates that there is no variance between the Marital Status wise perception.

Null hypothesis for question - What transportation mode would you like to prefer [Public transport - Train, Bus etc.] has not been rejected which indicates that there is no variance between the Marital Status wise perception.

 

  

2.7.3 Factors influencing consumers to purchase travel products post COVID

 

Table 1.13- One Way ANOVA – Gender wise consumer preference for Factors influencing to purchase travel products post COVID

ANOVA
		Sum of Squares	df	Mean Square	F	Sig.
Factors influencing you to purchase travel products post COVID [Hygiene]	Between Groups	.677	1	.677	1.622	.206
	Within Groups	38.429	92	.418
	Total	39.106	93
Factors influencing you to purchase travel products post COVID [Social distancing]	Between Groups	.062	1	.062	.119	.731
	Within Groups	47.164	91	.518
	Total	47.226	92
Factors influencing you to purchase travel products post COVID [Promotional offers]	Between Groups	.008	1	.008	.008	.929
	Within Groups	85.256	85	1.003
	Total	85.264	86
Factors influencing you to purchase travel products post COVID [Travel Experience]	Between Groups	.002	1	.002	.003	.954
	Within Groups	60.320	91	.663
	Total	60.323	92

 

Null hypothesis for question - Factors influencing you to purchase travel products post COVID [Hygiene] has not been rejected which indicates that there is no variance between the Gender wise perception.

Null hypothesis for question - Factors influencing you to purchase travel products post COVID [Social distancing] has not been rejected which indicates that there is no variance between the Gender wise perception.

Null hypothesis for question - Factors influencing you to purchase travel products post COVID [Promotional offers] has not been rejected which indicates that there is no variance between the Gender wise perception.

Null hypothesis for question - Factors influencing you to purchase travel products post COVID [Travel Experience] has not been rejected which indicates that there is no variance between the Gender wise perception.

            

 

Table 1.14- One Way ANOVA – Age wise consumer preference for Factors influencing to purchase travel products post COVID

ANOVA
		Sum of Squares	df	Mean Square	F	Sig.
Factors influencing you to purchase travel products post COVID [Hygiene]	Between Groups	1.026	5	.205	.474	.795
	Within Groups	38.080	88	.433
	Total	39.106	93
Factors influencing you to purchase travel products post COVID [Social distancing]	Between Groups	1.026	4	.256	.488	.744
	Within Groups	46.200	88	.525
	Total	47.226	92
Factors influencing you to purchase travel products post COVID [Promotional offers]	Between Groups	3.316	5	.663	.655	.658
	Within Groups	81.949	81	1.012
	Total	85.264	86
Factors influencing you to purchase travel products post COVID [Travel Experience]	Between Groups	1.170	5	.234	.344	.885
	Within Groups	59.153	87	.680
	Total	60.323	92

 

Null hypothesis for question - Factors influencing you to purchase travel products post COVID [Hygiene] has not been rejected which indicates that there is no variance between the Age wise perception.

Null hypothesis for question - Factors influencing you to purchase travel products post COVID [Social distancing] has not been rejected which indicates that there is no variance between the Age wise perception.

Null hypothesis for question - Factors influencing you to purchase travel products post COVID [Promotional offers] has not been rejected which indicates that there is no variance between the Age wise perception.

Null hypothesis for question - Factors influencing you to purchase travel products post COVID [Travel Experience] has not been rejected which indicates that there is no variance between the Age wise perception.

 

 

Table 1.15- One Way ANOVA – Occupation wise consumer preference for Factors influencing to purchase travel products post COVID

ANOVA
		Sum of Squares	df	Mean Square	F	Sig.
Factors influencing you to purchase travel products post COVID [Hygiene]	Between Groups	.288	3	.096	.223	.880
	Within Groups	38.818	90	.431
	Total	39.106	93
Factors influencing you to purchase travel products post COVID [Social distancing]	Between Groups	.541	3	.180	.344	.794
	Within Groups	46.685	89	.525
	Total	47.226	92
Factors influencing you to purchase travel products post COVID [Promotional offers]	Between Groups	1.234	3	.411	.406	.749
	Within Groups	84.030	83	1.012
	Total	85.264	86
Factors influencing you to purchase travel products post COVID [Travel Experience]	Between Groups	.952	3	.317	.476	.700
	Within Groups	59.371	89	.667
	Total	60.323	92

 

Null hypothesis for question - Factors influencing you to purchase travel products post COVID [Hygiene] has not been rejected which indicates that there is no variance between the Occupation wise perception.

Null hypothesis for question - Factors influencing you to purchase travel products post COVID [Social distancing] has not been rejected which indicates that there is no variance between the Occupation wise perception.

Null hypothesis for question - Factors influencing you to purchase travel products post COVID [Promotional offers] has not been rejected which indicates that there is no variance between the Occupation wise perception.

Null hypothesis for question - Factors influencing you to purchase travel products post COVID [Travel Experience] has not been rejected which indicates that there is no variance between the Occupation wise perception.

 

 

Table 1.16- One Way ANOVA – Educational qualification wise consumer preference for Factors influencing to purchase travel products post COVID

ANOVA
		Sum of Squares	df	Mean Square	F	Sig.
Factors influencing you to purchase travel products post COVID [Hygiene]	Between Groups	.315	3	.105	.244	.866
	Within Groups	38.791	90	.431
	Total	39.106	93
Factors influencing you to purchase travel products post COVID [Social distancing]	Between Groups	.845	3	.282	.541	.656
	Within Groups	46.381	89	.521
	Total	47.226	92
Factors influencing you to purchase travel products post COVID [Promotional offers]	Between Groups	10.285	3	3.428	3.795	.013
	Within Groups	74.980	83	.903
	Total	85.264	86
Factors influencing you to purchase travel products post COVID [Travel Experience]	Between Groups	1.659	3	.553	.839	.476
	Within Groups	58.663	89	.659
	Total	60.323	92

 

Null hypothesis for question - Factors influencing you to purchase travel products post COVID [Hygiene] has not been rejected which indicates that there is no variance between the Educational qualification wise perception.

Null hypothesis for question - Factors influencing you to purchase travel products post COVID [Educational qualification distancing] has not been rejected which indicates that there is no variance between the Occupation wise perception.

Null hypothesis for question - Factors influencing you to purchase travel products post COVID [Promotional offers] has been rejected which indicates that there is variance between the Educational qualification wise perception.

Null hypothesis for question - Factors influencing you to purchase travel products post COVID [Travel Experience] has not been rejected which indicates that there is no variance between the Educational qualification wise perception.

 

 

Table 1.17- One Way ANOVA – Monthly Income wise consumer preference for Factors influencing to purchase travel products post COVID

ANOVA
		Sum of Squares	df	Mean Square	F	Sig.
Factors influencing you to purchase travel products post COVID [Hygiene]	Between Groups	1.035	4	.259	.590	.671
	Within Groups	37.288	85	.439
	Total	38.322	89
Factors influencing you to purchase travel products post COVID [Social distancing]	Between Groups	.252	4	.063	.115	.977
	Within Groups	45.995	84	.548
	Total	46.247	88
Factors influencing you to purchase travel products post COVID [Promotional offers]	Between Groups	1.280	4	.320	.310	.870
	Within Groups	81.529	79	1.032
	Total	82.810	83
Factors influencing you to purchase travel products post COVID [Travel Experience]	Between Groups	1.821	4	.455	.671	.614
	Within Groups	56.988	84	.678
	Total	58.809	88

 

Null hypothesis for question - Factors influencing you to purchase travel products post COVID [Hygiene] has not been rejected which indicates that there is no variance between the Monthly Income wise perception.

Null hypothesis for question - Factors influencing you to purchase travel products post COVID [Social distancing] has not been rejected which indicates that there is no variance between the Monthly Income wise perception.

Null hypothesis for question - Factors influencing you to purchase travel products post COVID [Promotional offers] has not been rejected which indicates that there is no variance between the Monthly Income wise perception.

Null hypothesis for question - Factors influencing you to purchase travel products post COVID [Travel Experience] has not been rejected which indicates that there is no variance between the Monthly Income wise perception.

 

 

Table 1.18- One Way ANOVA – Marital Status wise consumer preference for Factors influencing to purchase travel products post COVID

ANOVA
		Sum of Squares	df	Mean Square	F	Sig.
Factors influencing you to purchase travel products post COVID [Hygiene]	Between Groups	.125	1	.125	.294	.589
	Within Groups	38.982	92	.424
	Total	39.106	93
Factors influencing you to purchase travel products post COVID [Social distancing]	Between Groups	.018	1	.018	.035	.852
	Within Groups	47.208	91	.519
	Total	47.226	92
Factors influencing you to purchase travel products post COVID [Promotional offers]	Between Groups	.104	1	.104	.104	.748
	Within Groups	85.160	85	1.002
	Total	85.264	86
Factors influencing you to purchase travel products post COVID [Travel Experience]	Between Groups	.066	1	.066	.099	.754
	Within Groups	60.257	91	.662
	Total	60.323	92

 

Null hypothesis for question - Factors influencing you to purchase travel products post COVID [Hygiene] has not been rejected which indicates that there is no variance between the Marital Status wise perception.

Null hypothesis for question - Factors influencing you to purchase travel products post COVID [Social distancing] has not been rejected which indicates that there is no variance between the Marital Status wise perception.

Null hypothesis for question - Factors influencing you to purchase travel products post COVID [Promotional offers] has not been rejected which indicates that there is no variance between the Marital Status wise perception.

Null hypothesis for question - Factors influencing you to purchase travel products post COVID [Travel Experience] has not been rejected which indicates that there is no variance between the Marital Status wise perception.

 

 

2.7.4 Chi-Square analysis: Case processing summary- purpose of trip for next trip post COVID

It is a summary of effect of selected demographic variables (gender, age, marital status, income, education and occupation) on the overall consumer with regards to purpose of trip for next trip post COVID.

Table 1.19- Case Processing Summary with regards to purpose of trip for next trip post COVID

Case Processing Summary
	Cases
	Valid		Missing		Total
	N	Percent	N	Percent	N	Percent
Gender * What would be the purpose of your next Trip	100	100.0%	0	0.0%	100	100.0%
Age * What would be the purpose of your next Trip	100	100.0%	0	0.0%	100	100.0%
Occupation * What would be the purpose of your next Trip	100	100.0%	0	0.0%	100	100.0%
Educational Qualification * What would be the purpose of your next Trip	100	100.0%	0	0.0%	100	100.0%
Monthly Income * What would be the purpose of your next Trip	100	100.0%	0	0.0%	100	100.0%
Marital Status * What would be the purpose of your next Trip	100	100.0%	0	0.0%	100	100.0%

 

 

Table 1.20- Chi-square Analysis: Gender and purpose of next Trip

Gender * What would be the purpose of your next Trip Crosstabulation
Count
		What would be the purpose of your next Trip						Total
			Adventure	Business	Leisure	Other	Pilgrimage
Gender	Female	0	9	0	28	6	0	43
	Male	1	15	4	28	5	4	57
Total		1	24	4	56	11	4	100

 

Table 1.20.1

Chi-Square Tests
	Value	Df	Asymptotic Significance (2-sided)
Pearson Chi-Square	8.803a	5	.117
Likelihood Ratio	12.117	5	.033
N of Valid Cases	100
a. 7 cells (58.3%) have expected count less than 5. The minimum expected count is .43.

 

As the significance value in the above selected demographic variables cross-tabulation value is greater than 0.05 so H0 is not rejected. Hence it can be concluded that there is no variance in Gender wise preferences.

 

Table 1.21- Chi-square Analysis: Age and purpose of next Trip

Age * What would be the purpose of your next Trip Crosstabulation
Count
		What would be the purpose of your next Trip						Total
			Adventure	Business	Leisure	Other	Pilgrimage
Age	21-30	0	13	2	34	5	1	55
	31-40	1	8	0	14	6	1	30
	41-50	0	1	0	3	0	1	5
	51-60	0	0	2	5	0	0	7
	61 or Above	0	1	0	0	0	0	1
	Under 20	0	1	0	0	0	1	2
Total		1	24	4	56	11	4	100

 

Table 1.21.1

Chi-Square Tests
	Value	Df	Asymptotic Significance (2-sided)
Pearson Chi-Square	40.833a	25	.024
Likelihood Ratio	31.198	25	.183
N of Valid Cases	100
a. 31 cells (86.1%) have expected count less than 5. The minimum expected count is .01.

 

As the significance value in the above selected demographic variables cross-tabulation value is less than 0.05 so H0 is rejected. Hence it can be concluded that there is variance in Age wise preferences. Below mentioned observation from raw data also indicate majority of respondents prefer to travel for leisure purpose and maximum respondents preferring leisure travel belong to youth age group.

 

 

Table 1.21.2

Purpose of Travel	Age group						Grand Total
	21-30	31-40	41-50	51-60	61 or Above	Under 20
Adventure	13	8	1		1	1	24
Business	2			2			4
Leisure	34	14	3	5			56
Other	5	6					11
Pilgrimage	1	1	1			1	4
(blank)		1					1
Grand Total	55	30	5	7	1	2	100

 

 

Table 1.22- Chi-square Analysis: Occupation and purpose of next Trip

Occupation * What would be the purpose of your next Trip Crosstabulation
Count
		What would be the purpose of your next Trip						Total
			Adventure	Business	Leisure	Other	Pilgrimage
Occupation	Business	0	2	1	5	0	0	8
	Service	0	11	2	32	7	3	55
	Student	1	8	1	17	3	1	31
	Unemployed	0	3	0	2	1	0	6
Total		1	24	4	56	11	4	100

 

Table 1.22.1

Chi-Square Tests
	Value	df	Asymptotic Significance (2-sided)
Pearson Chi-Square	8.894a	15	.883
Likelihood Ratio	9.819	15	.831
N of Valid Cases	100
a. 19 cells (79.2%) have expected count less than 5. The minimum expected count is .06.

 

As the significance value in the above selected demographic variables cross-tabulation value is greater than 0.05 so H0 is not rejected. Hence it can be concluded that there is no variance in Occupation wise preferences.

 

Table 1.23- Chi-square Analysis: Educational Qualification and purpose of next Trip

Educational Qualification * What would be the purpose of your next Trip Crosstabulation
Count
		What would be the purpose of your next Trip						Total
			Adventure	Business	Leisure	Other	Pilgrimage
Educational Qualification	Doctorate	0	2	1	7	0	0	10
	Graduate	0	9	0	18	6	2	35
	Post Graduate	1	11	3	31	5	1	52
	Under Graduate	0	2	0	0	0	1	3
Total		1	24	4	56	11	4	100

 

Table 1.23.1

Chi-Square Tests
	Value	df	Asymptotic Significance (2-sided)
Pearson Chi-Square	18.985a	15	.214
Likelihood Ratio	19.279	15	.201
N of Valid Cases	100
a. 18 cells (75.0%) have expected count less than 5. The minimum expected count is .03.

 

As the significance value in the above selected demographic variables cross-tabulation value is greater than 0.05 so H0 is not rejected. Hence it can be concluded that there is no variance in Educational Qualification wise preferences.

 

 

 Table 1.24- Chi-square Analysis: Educational Qualification and purpose of next Trip

Monthly Income * What would be the purpose of your next Trip Crosstabulation
Count
		What would be the purpose of your next Trip						Total
			Adventure	Business	Leisure	Other	Pilgrimage
Monthly Income		1	2	0	2	1	1	7
	20,001 - 40,000	0	6	0	16	2	1	25
	40,001 - 50,000	0	1	0	9	3	0	13
	50,001 - 75,000	0	3	0	9	1	1	14
	75,001 Above	0	4	2	11	1	1	19
	Below 20,000	0	8	2	9	3	0	22
Total		1	24	4	56	11	4	100

 

Table 1.24.1

Chi-Square Tests
	Value	df	Asymptotic Significance (2-sided)
Pearson Chi-Square	31.382a	25	.177
Likelihood Ratio	25.677	25	.425
N of Valid Cases	100
a. 29 cells (80.6%) have expected count less than 5. The minimum expected count is .07.

 

As the significance value in the above selected demographic variables cross-tabulation value is greater than 0.05 so H0 is not rejected. Hence it can be concluded that there is no variance in Monthly Income wise preferences.

  

 

Table 1.25- Chi-square Analysis: Marital Status and purpose of next Trip

Marital Status * What would be the purpose of your next Trip Crosstabulation
Count
		What would be the purpose of your next Trip						Total
			Adventure	Business	Leisure	Other	Pilgrimage
Marital Status	Married	0	8	4	27	4	1	44
	Unmarried	1	16	0	29	7	3	56
Total		1	24	4	56	11	4	100

 

Table 1.25.1

Chi-Square Tests
	Value	df	Asymptotic Significance (2-sided)
Pearson Chi-Square	8.235a	5	.144
Likelihood Ratio	10.153	5	.071
N of Valid Cases	100
a. 7 cells (58.3%) have expected count less than 5. The minimum expected count is .44.

 

As the significance value in the above selected demographic variables cross-tabulation value is greater than 0.05 so H0 is not rejected. Hence it can be concluded that there is no variance in Marital Status wise preferences.

 

  

2.7.4 Chi-Square analysis: Case processing summary- likely destination for next trip post COVID

It is a summary of effect of selected demographic variables (gender, age, marital status, income, education and occupation) on the overall consumer with regards to likely destination for next trip post COVID.

Table 1.26- Case Processing Summary- likely destination for next trip post COVID

Case Processing Summary
	Cases
	Valid		Missing		Total
	N	Percent	N	Percent	N	Percent
Gender * Likely destination for your Next Trip	100	100.0%	0	0.0%	100	100.0%
Age * Likely destination for your Next Trip	100	100.0%	0	0.0%	100	100.0%
Occupation * Likely destination for your Next Trip	100	100.0%	0	0.0%	100	100.0%
Educational Qualification * Likely destination for your Next Trip	100	100.0%	0	0.0%	100	100.0%
Monthly Income * Likely destination for your Next Trip	100	100.0%	0	0.0%	100	100.0%
Marital Status * Likely destination for your Next Trip	100	100.0%	0	0.0%	100	100.0%

 

 

Table 1.27- Chi-square Analysis: Gender and likely destination for next trip post COVID

Gender * Likely destination for your Next Trip Crosstabulation
Count
		Likely destination for your Next Trip				Total
			Foreign (Beyond country)	I have no travel plans	Within Country
Gender	Female	0	4	6	33	43
	Male	1	3	11	42	57
Total		1	7	17	75	100

 

Table 1.27.1

Chi-Square Tests
	Value	df	Asymptotic Significance (2-sided)
Pearson Chi-Square	1.768a	3	.622
Likelihood Ratio	2.138	3	.544
N of Valid Cases	100
a. 4 cells (50.0%) have expected count less than 5. The minimum expected count is .43.

 

As the significance value in the above selected demographic variables cross-tabulation value is greater than 0.05 so H0 is not rejected. Hence it can be concluded that there is no variance in Gender wise preferences.

 

  

Table 1.28- Chi-square Analysis: Age and likely destination for next trip post COVID

Age * Likely destination for your Next Trip Crosstabulation
Count
		Likely destination for your Next Trip				Total
			Foreign (Beyond country)	I have no travel plans	Within Country
Age	21-30	0	2	12	41	55
	31-40	1	3	3	23	30
	41-50	0	0	1	4	5
	51-60	0	2	1	4	7
	61 or Above	0	0	0	1	1
	Under 20	0	0	0	2	2
Total		1	7	17	75	100

 

Table 1.28.1

Chi-Square Tests
	Value	df	Asymptotic Significance (2-sided)
Pearson Chi-Square	11.581a	15	.710
Likelihood Ratio	10.916	15	.759
N of Valid Cases	100
a. 19 cells (79.2%) have expected count less than 5. The minimum expected count is .01.

 

As the significance value in the above selected demographic variables cross-tabulation value is greater than 0.05 so H0 is not rejected. Hence it can be concluded that there is no variance in Age wise preferences.

  

 

Table 1.29- Chi-square Analysis: Occupation and likely destination for next trip post COVID

Occupation * Likely destination for your Next Trip Crosstabulation
Count
		Likely destination for your Next Trip				Total
			Foreign (Beyond country)	I have no travel plans	Within Country
Occupation	Business	0	2	1	5	8
	Service	0	3	10	42	55
	Student	1	1	5	24	31
	Unemployed	0	1	1	4	6
Total		1	7	17	75	100

 

Table 1.29.1

Chi-Square Tests
	Value	df	Asymptotic Significance (2-sided)
Pearson Chi-Square	7.963a	9	.538
Likelihood Ratio	6.559	9	.683
N of Valid Cases	100
a. 11 cells (68.8%) have expected count less than 5. The minimum expected count is .06.

 

As the significance value in the above selected demographic variables cross-tabulation value is greater than 0.05 so H0 is not rejected. Hence it can be concluded that there is no variance in Occupation wise preferences.

  

 

Table 1.30- Chi-square Analysis: Educational Qualification and likely destination for next trip post COVID

Educational Qualification * Likely destination for your Next Trip Crosstabulation
Count
		Likely destination for your Next Trip				Total
			Foreign (Beyond country)	I have no travel plans	Within Country
Educational Qualification	Doctorate	0	1	0	9	10
	Graduate	0	3	8	24	35
	Post Graduate	1	3	9	39	52
	Under Graduate	0	0	0	3	3
Total		1	7	17	75	100

 

Table 1.30.1

Chi-Square Tests
	Value	df	Asymptotic Significance (2-sided)
Pearson Chi-Square	5.160a	9	.820
Likelihood Ratio	7.842	9	.550
N of Valid Cases	100
a. 11 cells (68.8%) have expected count less than 5. The minimum expected count is .03.

 

As the significance value in the above selected demographic variables cross-tabulation value is greater than 0.05 so H0 is not rejected. Hence it can be concluded that there is no variance in Educational Qualification wise preferences.

  

 

Table 1.31- Chi-square Analysis: Monthly Income and likely destination for next trip post COVID

Monthly Income * Likely destination for your Next Trip Crosstabulation
Count
		Likely destination for your Next Trip				Total
			Foreign (Beyond country)	I have no travel plans	Within Country
Monthly Income		1	1	0	5	7
	20,001 - 40,000	0	2	5	18	25
	40,001 - 50,000	0	1	2	10	13
	50,001 - 75,000	0	0	2	12	14
	75,001 Above	0	1	3	15	19
	Below 20,000	0	2	5	15	22
Total		1	7	17	75	100

 

Table 1.31.1

Chi-Square Tests
	Value	df	Asymptotic Significance (2-sided)
Pearson Chi-Square	17.343a	15	.299
Likelihood Ratio	11.366	15	.726
N of Valid Cases	100
a. 18 cells (75.0%) have expected count less than 5. The minimum expected count is .07.

 

As the significance value in the above selected demographic variables cross-tabulation value is greater than 0.05 so H0 is not rejected. Hence it can be concluded that there is no variance in Monthly Income wise preferences.

 

  

Table 1.32: Chi-square Analysis: Marital Status and likely destination for next trip post COVID

Marital Status * Likely destination for your Next Trip Crosstabulation
Count
		Likely destination for your Next Trip				Total
			Foreign (Beyond country)	I have no travel plans	Within Country
Marital Status	Married	0	2	6	36	44
	Unmarried	1	5	11	39	56
Total		1	7	17	75	100

 

Table 1.32.1

Chi-Square Tests
	Value	df	Asymptotic Significance (2-sided)
Pearson Chi-Square	2.472a	3	.480
Likelihood Ratio	2.884	3	.410
N of Valid Cases	100
a. 4 cells (50.0%) have expected count less than 5. The minimum expected count is .44.

 

As the significance value in the above selected demographic variables cross-tabulation value is greater than 0.05 so H0 is not rejected. Hence it can be concluded that there is no variance in Marital Status wise preferences.

 

 

3. Findings and Conclusions

High internet penetration rate is helping emerging nations like India to revive economy during pandemic. India is second largest online market just after China. Though tourism require physical presence and that’s the reason our industry got worsly impacted due to pandemic though digital transactions followed by digital campaigns, digital marketing are helping Industry to revive. Consumer preference in terms of different demographics- Gender, Age, Occupation, Educational Qualification, Monthly Income and Marital status may vary while choosing travel products post Covid.

 

3.1 Key points observed based on demographics of travellers (n= 100 respondents)

Gender wise preference variance

Gender wise preference variance while preferring Brand

The result of ANOVA analysis revealed that there is variance between Gender wise perceptions about preferring Brand names while choosing travel products it may include hygiene, safety factors due to pandemic. Raw data also analyze that Female respondents prefer brand names more than males.

 

Age Wise preference variance

The result of ANOVA analysis revealed that there is variance between Age wise perceptions about choosing transportation mode as Air travel

The result of Chi square analysis revealed that there is variance between Age wise perceptions while choosing the purpose of next trip. Raw data also indicate majority of respondents prefer to travel for leisure purpose and maximum respondents preferring leisure travel belong to youth age group.

 

Occupation Wise preference variance

The result of ANOVA analysis revealed that there is variance between Occupation wise perceptions about choosing transportation mode as Air travel

  

Educational Qualification Wise preference variance

The result of ANOVA analysis revealed that there is variance between Educational Qualification wise perceptions about choosing transportation mode as Air travel

The result of ANOVA analysis revealed that there is variance between Educational Qualification wise perceptions about choosing transportation mode as Self-drive - Own vehicle

The result of ANOVA analysis revealed that there is variance between Educational Qualification wise perceptions for factors influencing to purchase travel products post COVID [Promotional offers]

 

Monthly Income Wise preference variance

The result of ANOVA analysis revealed that there is variance between Monthly Income Wise perceptions about choosing transportation mode as Air travel

 

Marital Status Wise preference variance

The result of ANOVA analysis revealed that there is variance between Monthly Income Wise perceptions about choosing transportation mode as Air travel

 4. Conclusions

1.     The majority of respondents contributed in this research were young and well educated.

2.     Study interprets that demographic wise preferences of consumers may vary.

3.     Majority of travelers preferred to travel for leisure purpose for their next trip post COVID.

4.     This study observed that there is variance in gender wise preferences while choosing brands while female travellers are keener to choose brands due to hygiene and social distancing related factors. 90.6% female respondents agreed that hygiene is one of factor influencing them to purchase post COVID while 88.3% females respondents agreed that social distancing is one of factor influencing them to purchase post COVID.

5.     There is variance among consumers while preferring Air travel as mode of transport post COVID while majority of 61.6% young travelers prefer to opt for air travel.

6.     On parallel track majority of graduate and post graduate respondents prefer to opt for self-drive as mode of transport post COVID while remaining educational categories has mix responses which indicate that educated persons are keener to prefer self-drive.

7.     Comparatively married travellers prefer to opt for air travel as compared to unmarried travelers.

8.     Majority of 75% travelers responded that they would like to opt for domestic destination due to multiple factors attached.

 

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