DISPLAY OF COVID 19 INFORMATION ON HOTEL WEBPAGES- A CONTENT ANALYSIS

 

Dr. Moin Uddin ¹, Dr. Mohd Imran Siddiquei ²,  Shashank Kathpal ³

 

¹ Assistant Professor, Saudi Electronic University, Saudi Arabia

² Assistant Professor, University of the People, Pasadena, CA, USA

³ Assistant Professor, International School of Business & Media, Bangalore, India

 

1. INTRODUCTION

The economy around the world has faced an unprecedented challenge due to the novel coronavirus (COVID-19). Most industries have witnessed a sharp decline in demand, and tourism is one of the most affected sectors. COVID-19 has forced the government to impose travel restrictions, which has created havoc in the tourism sector Estrada et al. (2020). According to World Travel and Tourism Council, the current pandemic has put 50 million jobs worldwide at risk and the tourism sector on the verge of collapse Guevara (2020). The decline of tourism activity and economic slowdown has made hotels worldwide vulnerable Hoisington (2020). All major events across the globe are either canceled or got postponed due to COVID-19. This has caused a severe plunge in the occupancy rate of hotels. For instance, in the first quarter, Hyatt hotels witnessed a decline in 28% revenue per available room (RevPAR) across the globe Hotel Business. (2020). The RevPAR will continue to witness a decline in the United States of America and Europe Courtney (2020). The data from the Asia-Pacific region also shows a similar pattern, like in February 2020, the Marriott hotels witnessed a 25% decline in RevPAR Wallis (2020).

Literature has indicated the vulnerability of hotels, especially during an unpredicted catastrophe [Hung et al. (2018), Johnson et al. (2008), Paraskevas (2013), Racherla & Hu (2009)]. Each catastrophe carries a distinct challenge to hoteliers and prompts them to carry out measures to deal with the same. This can be seen in various instances, like after the 9/11 attacks, the security of hotels in Hong Kong was toughened. Similarly, after the outbreak of severe acute respiratory syndrome (SARS), the hotels in Korea set up new equipment to address hygiene and health concerns Kim et al. (2005). Hygiene has acquired considerable attention from hoteliers to ensure customer retention, especially during a pandemic Kim et al. (2005). Few terms such as hygiene and cleanliness have been conferred extensively for the sustenance and recovery of the hotel industry Kim et al. (2005). This could be viewed from the fact that hygiene and cleanliness are believed to be perpetrators of a pandemic Tse & Sin (2006). Thus, understanding the level of preparedness of hotels to COVID-19, understanding the terms like hygiene, sanitization, and cleanliness became fundamental. The current study focuses on the measures taken by hotels in response to COVID-19. To examine these measures, the content of the webpages of different categories of hotels operating in the three most important international destinations in India were analyzed. The keywords related to Covid-19 were extracted from the pages of these hotels as reflected in the most prominent tourist aggregator website of India, 'MakemyTrip.com.' The keywords were shortlisted based on the guidelines given by the Government of India and World Health Organization. To analyze the level of emphasis given to Covid-19 by these hotel websites, content analysis was performed. 

 

2. LITERATURE REVIEW

The COVID-19 crisis is still having a profound effect on the working of the hospitality industry, while the hospitality industry is steadily recovering. To ensure workers' and clients' health and safety and increase customers ' willingness to book hotels, hospitality companies in COVID-19 are required to make significant improvements to their services Gössling et al. (2021). The reopening and relaxation of travel restrictions of restaurants and hotels would not automatically bring consumers back Gursoy et al. (2020). The bulk (more than 50%) of customers is not likely to fly and stay at a hotel in the very near future. Just about twenty-five percent of customers have dined at a restaurant, and just about one-third are willing to visit and stay at a hotel during the coming months Gursoy et al. (2020). The guests usually do not always feel relaxed eating, traveling to a destination, and staying at a hotel during a pandemic Gursoy et al. (2020). Due to the lingering fear associated with this pandemic and related diseases, safety will be a significant driving factor in the recovery of the tourism and hospitality sector since the COVID-19 epidemic, Wen et al. (2021). As of late, the relevance of hotel cleanliness and hygiene has become especially important because COVID-19 can be spread by touching surfaces infected with the virus World Health Organization. (2020). The surfaces of hotels are likely to be contaminated, carry significant bacterial activity, and create possible transmission sources Park et al. (2019). Hygiene and cleanliness are crucial to the success of the hotel sector, and after public health crises such as the 2003 SARS outbreak, the focus is increased Kim et al. (2005). Even before Covid 19, hygiene and cleanliness were widely discussed in the literature on tourism. [Chien & Law (2003), Henderson & Ng (2004), Lo et al. (2006)]. In pandemic outbreaks, sanitation and cleanliness problems have been regarded as a cause of disease. Tse & Sin (2006).

As a result of the outbreak of COVID-19, travelers are expected to prefer hotels providing reassuring facilities in terms of hygiene and cleanliness Wen et al. (2021). The most critical safety precautions consumers expect from a restaurant and a hotel are the apparent attempts to sanitize like hand sanitization at the entrance, usage of masks and gloves by workers, social distancing, restricting the number of customers, more thorough and regular cleaning of common surface areas, and staff training in health and safety procedures Gursoy et al. (2020). Studies show that consumers are affected by hygiene and cleanliness conditions when making buying decisions in a service area [Hecht & Martin (2006), Hoffman et al. (2003), Vilnai-Yavetz & Gilboa (2010), Zemke et al. (2015)]. Although most consumers expect the hotel industry to adopt more stringent safety/cleaning protocols, a proportion is ready to pay for these kinds of additional safety measures Gursoy et al. (2020). Many studies have shown sanitation and cleanliness as a primary determinant of hotel choice for tourists [Gu & Ryan (2008), Lockyer (2005)]. As a consequence of high operational costs, the break-up of the hospitality sector relies heavily on the increased demand for their services and goods.

The rapid advancement in information and communication technologies has significantly changed the hotel and hospitality industries in recent decades. The internet has become a useful tool for marketing in tourism Buhalis & Law (2008). The tourism sector has become one of the most significant sectors globally to use the internet as a means for e-business Chiou et al. (2011). Hotel websites are an essential tool of marketing to communicate with customers and affect their intentions to purchase hospitality services Wang et al. (2015). Searching for relevant information while planning trips, like hotel booking, has become a necessary process in travelers' decision-making [Guillet & Law (2010), Ip et al. (2011), Ye et al. (2011)]. The primary factor behind the behavioural intentions of the customer is the need for information that has to be successfully met in electronic transactions Jeong & Gregoire (2003). The customer will shift from one website to another website if the information is not of sufficient quality Hyde (2007). Information quality is one of the most significant factors in the customer's purchase intention [Ganguly et al. (2009), Hahn & Kim (2009), Hausman & Siekpe (2009), Lu et al. (2010)]. The online travel agent portal has   become an essential tool for information search and booking. Online travel agents or aggregators are expanding quickly by selling a bouquet of services like flights, cruises, holiday packages, hotel rooms, and Visa. Online travel agents are proliferating, and it is expected the market share of OTAs will increase from 564.87 billion dollars (2016) to 755.94 billion dollars in 2019 Statista. (2019). There are many online travel agent portals in India like Makemytrip, Yatra, Musafir, Expedia, and Trivago. However, Makemytrip enjoys a 50 percent market share in all verticals.

Given the increasing consumer demand for hotel hygiene, looking to follow the pandemic of COVID-19, increased cleanliness, and hygiene to eliminate or mitigate the spread of the virus can be promoted as a point of sale during and after this pandemic Wen et al. (2021). Therefore, it is vital to determine what makes a customer return to the hospitality industry, and an intensive analysis needs to be done. To direct the operations of hospitality during the COVID-19 pandemics, the industry and academia urgently need to determine strategies for the survival of this severely struggling sector Gursoy et al. (2020). Post COVID-19 pandemic clients are likely to become more worried about general healthcare access Wen et al. (2021). As a result of the global health crisis triggered by COVID-19, travelers are now expected to pay closer attention to the accessibility and quality of health services while making travel decisions Wen et al. (2021). In this context, the leading areas to be discussed in future studies include how hoteliers can design marketing communications content and use marketing communication strategies to highlight their ability to shield visitors from public health emergencies, ensure health and safety across their stay, and ensure customers feel more secure especially during and aftermath of COVID-19 Wen et al. (2021).

It is quite evident from the literature that information search is a significant factor while booking the hotel. In pandemic times, customers have become very sensitive and looking for specific information related to COVID-19 before making hotel bookings Wen et al. (2021). The industry is also looking for solutions that can influence and increase safety among customers and bring them back to the hotel industry. The research aims at understanding; how hotels through online aggregator websites have changed the marketing communication strategy targeting customers with information related to COVID-19 and convincing them about a safe and hygienic stay.

 

3. RESEARCH METHODOLOGY

Research on different forms of communication has gained considerable importance. Despite the flourishing research, a large area of communication remains unexplored. The research on communication is conducted with content analysis Hsieh & Shannon (2005). Content analysis is a technique to analyze the incorporation of any message Cole (1988). This analysis infers the characteristics and effects of any communicated message Holsti (1969). The research on content analysis covers a variety of areas and time frames. Few of these studies deal with concurrent concerns [Kracker & Wang (2002), White & Iivonen (2001), White & Iivonen (2002)] whereas other studies deal with routinized projects and studies. This routinized study analyzes a variety of communications, which includes articles [Green (1991), Marsh & Domas (2003), Nitecki (1993)] statements on these articles Dewdney (1992) advertisements on recruitment [Croneis & Henderson (2002), Lynch & Smith (2001)] and the websites or webpages [Haas & Grams (1998), Haas & Grams (2000), Wang & Gao (2004)].  One of the communication methods focuses on images or the combination of text and images as input for the data Marsh & Domas White (2003) Another popular method is text, which could be analyzed in multiple forms. Analyzing reference interviews is one of the popular ways of doing content analysis which uses text as input Dewdney (1992). Apart from interviews, metaphors are also considered essential for content analysis Green (1991). In the content analysis, the text could be analyzed in the form of statements White & Iivonen (2001) or the form of words [Green (1991), Nitecki (1993)].

The current study examines the covid-19 related information available on the web pages of hotels. To examine the same, the authors aim to find the answers of following research questions:

1)     Is there a difference in the information on Covid-19 on the hotel web pages of different cities?

2)     Is there a difference in the information on Covid-19 on the hotel web pages of different categories of hotels?

To achieve the desired objectives, the authors classified the hotels on two bases:

1)     Based on the city: Top 3 heritage tourist destinations were chosen, i.e., Delhi, Jaipur, and Agra. The contents of the hotels were segregated based on their city. A total of 50 hotels from each city was taken for the data.

H1: There is no significant variation of Covid-19 information on the hotel webpages of Jaipur and Agra city.

H2: There is no significant variation of Covid-19 information on the hotel webpages of Agra and Delhi city.

H3: There is no significant variation of Covid-19information on the hotel webpages of Delhi and Jaipur city.

H4: There is no significant variation between the three cities and within these cities on COVID-19 information on the hotel webpage

2)     Based on rating: Hotels were segregated based on the star ratings. Five ratings were considered for the same, i.e., 5*, 4*, 3*, 2*, and unrated hotels. A total of 30 hotels from each category was taken for the data.

H5: There is no significant variation of Covid-19 information on the hotel webpages of unrated and 2* Hotels.

H6: There is no significant variation of Covid-19 information on the hotel webpages of 2* and 3* Hotels.

H7: There is no significant variation of Covid-19information on the hotel webpages of 3* and 4* Hotels.

H8: There is no significant variation of Covid-19information on the hotel webpages of 4* and 5* Hotels.

H9: There is no significant variation of Covid-19 information on the hotel webpages of unrated and 3* Hotels.

H10: There is no significant variation of Covid-19information on the hotel webpages of 2* and 4* Hotels.

H11: There is no significant variation of Covid-19 information on the hotel webpages of 2* and 5* Hotels.

H12: There is no significant variation of Covid-19 information on the hotel webpages of unrated and 4* Hotels.

H13: There is no significant variation of Covid-19 information on the hotel webpages of unrated and 5* Hotels.

H14: There is no significant variation of Covid-19 information on the hotel webpages of 3* and 5* Hotels.

H15: There is no significant variation between the different categories of hotels and the hotel category of COVID-19 information on the hotel webpage.

Data for the content analysis was collected with the help of makemytrip.com, the largest tourist website aggregator of India, on September 15, 2020. In the aggregator website, we manually note down the content related to covid-19. We choose 23 criteria for selecting the information available on the website. These 23 criteria were selected based on government guidelines. To convert the shortlisted criteria into meaning, an information coding technique was used. Coding makes the qualitative aspect of information in the quantifiable form Neuendorf (2002). To ensure consistency in coding, the information was analyzed using definitions and statements Weber (1990). To ensure validity, face validity is the most common concept used in content analysis Neuendorf (2002). The authors have ensured objectivity in measuring codes by working backward from measuring codes to their determination Neuendorf (2002). To ensure reliability in coding, clear definitions and statements were considered so that the results produced at different periods would be concurrent with the previous results [Haas & Grams (2000), Kracker & Wang (2002), Marsh & Domas White (2003)].

The statements used for coding are as follows Ministry of Health and Family Welfare, & Government of India. (2020):

S.NO	Variables
1	The hotels are clean and hygienic
2	Wipe the surfaces with disinfectant regularly.
3	Regular and thorough hand-washing by employees, employers, and customers.
4	Sanitizing hand rub dispensers in prominent places
5	Good respiratory hygiene
6	Face masks (surgical mask) and/or paper tissues are available
7	Display moments of hand wash, steps of hand wash, and respiratory hygiene at reception through posters
8	The physical distancing of at least 6 feet
9	Installation and use of AarogyaSetuapp
10	Thermal screening provisions
11	Staff should additionally wear gloves
12	Posters/standees/AV media on preventive measures about COVID-19
13	Hotels must adopt contactless processes like QR codes, online forms, and digital payments like an e-wallet.
14	Luggage should be disinfected
15	Disposable menus
16	disposable paper napkins
17	Contactless mode of ordering and digital mode of payment
18	Air conditioning devices should be in the range of 24-30oC
19	frequent sanitation within the premises
20	Cleaning and regular disinfection (using 1% sodium hypochlorite) of frequently touched surfaces
21	Deep cleaning of all washrooms
22	Rooms and other service areas shall be sanitized
23	Gym, pool & Sauna

 

These statements and specific words used in the web pages of hotels were used to perform content analysis. This data was analyzed for variance using one-way ANOVA, i.e., variance in covid-19 information due to the city or the rating of the hotel. ANOVA can only be performed after the homoscedasticity of each different variables is confirmed. The homoscedasticity of each variable is confirmed using Levene’s Test of Equality and Welch correction.

 

4. HYPOTHESIS RESULTS

Table 1

Table 1 City-Wise Results of the Analysis of Variance Descriptive City-Wise Results of the Analysis of Variance
	Sample Size	Mean of Covid-19 Information Display on Website	Std. Deviation	Std. Error
Agra	50	5.36	4.557	.644
Delhi	50	8.34	5.506	.779
Jaipur	50	9.52	5.545	.784
Total	150	7.74	5.477	.447

 

The numbers of hotels used for content analysis are 50 in each city. Hotels of Agra perform lower than Hotels of Delhi and Jaipur in reflecting COVID-19 information on their aggregator website.

Table 2

Table 2 Levene's Test of Equality
Dependent Variable: Covid-19
Levene Statistic	df1	df2	Sig.
2.492	2	147	.086

 

Tests the null hypothesis that the error variance of the dependent variable is equal across groups.

To measure the amount of information variance of COVID-19 on different hotel webpages of any city, Levene's Test of Equality is used Gastwirth et al. (2009). Levene’s test of equality results shows a significance level of .086, which is greater than .05, which means there is no significant variation of Covid-19 information on the hotel webpages of any city. Non significance of Levene’s Test indicates that ANOVA should be performed. However, Levene’s Test of Equality results does not provide any in-depth information Gastwirth et al. (2009). To explore the variance between hotels in detail, Welch Correction, Scheffe Test, and tests of Between-Subjects Effects tests were conducted.

Table 3

Table 3 Robust Test of Equality of Means
Covid-19
	Statistica	df1	df2	Sig.
Welch	9.329	2	97.109	.000

 

Welch Correction:

The result of Welch Correction reflects the significant value of .000, which means there is a significant level of variance in between cities about COVID-19 related information on the hotel website. To find the specific cities showing a significant variation in COVID-19 information, multiple comparisons are conducted using the Scheffe test Miller (1997).

Table 4

Table 4 Multiple Comparisons
(I) City name	(J) City name	Mean Difference (I-J)	Std. Error	Sig.
Agra	Delhi	-2.980*	1.044	.019
	Jaipur	-4.160*	1.044	.001
Delhi	Agra	2.980*	1.044	.019
	Jaipur	-1.180	1.044	.530
Jaipur	Agra	4.160*	1.044	.001
	Delhi	1.180	1.044	.530

 

The Scheffe test results show that hotels in Agra provide significantly different information on COVID-19 than the hotels of Jaipur and Delhi, as they reflect a high level of significance, i.e., .019 and .001, respectively Miller (1997). No significant difference in COVID-19 information was found between Delhi and Jaipur's hotels, as they reflect an abysmal level of significance, i.e., .530. Therefore, we reject a few null hypothesisH1and H2, i.e., Agra and Jaipur/Delhi did not have any significant difference in COVID-19 related information and accept the null hypothesis H3, which states Jaipur and Delhi do not have any significant difference in COVID-19 related information.

Table 5

Table 5 Tests of Between-Subjects Effects
Source	Sum of Squares	Degree of freedom	Mean Square	F	Sig.
Corrected Model	459.640a	2	229.820	8.426	.000
Intercept	8986.140	1	8986.140	329.481	.000
City name	459.640	2	229.820	8.426	.000
Error	4009.220	147	27.274
Total	13455.000	150
Corrected Total	4468.860	149

 

The population variance between the three cities is 8.426 times higher than the variance within the cities. Since the significance level is high, we reject the null hypothesis H4, which means there is a significant variety of information on COVID-19 between cities and within cities. Approximate 10.3% of the variance occurs in the use of COVID-19 related information in between cities and within cities.

Table 6

Table 6 Results of the Analysis of Variance Based on the Category of Hotels Descriptive
	Sample Size	Mean of Covid-19 Information Display on Website	Std. Deviation	Std. Error
Unrated	30	4.90	3.527	.644
2 star	30	3.40	3.979	.727
3 star	30	7.83	4.602	.840
4 star	30	10.40	5.090	.929
5 star	30	12.17	4.829	.882
Total	150	7.74	5.477	.447

 

The number of hotels used for each category of hotel is 30. Two-star hotels reflect the most impoverished information on COVID-19 on their website, whereas five-star hotels reflect the highest COVID-19 information. The unrated hotels show better information of COVID-19 on their website than two-star hotels. There is an increase in the amount of information presented on the website as the star category improves from two-star hotels.

Levene's Test of Equality

Table 7

Table 7 Test of Homogeneity of Variances
Covid-19
Levene Statistic	df1	df2	Sig.
1.890	4	145	.115

 

To measure the amount of information variance on COVID-19 on different hotel webpages of any particular category of hotel, Levene's Test of Equality is used Gastwirth et al. (2009). Levene’s Test of Equality results shows a significance level of .15, which is greater than .05, which means there is no significant variation of Covid-19 information on the hotel webpages of a particular category of hotel. Non significance of Levene’s Test indicates that ANOVA should be performed. However, Levene’s Test of Equality results does not provide any in-depth information Gastwirth et al. (2009). To explore the variance between a different category of hotels in detail, Welch Correction, Scheffe Test, and Tests of Between-Subjects Effects tests were conducted.

Table 8

Table 8 Robust Tests of Equality of Means
Covid-19
	Statistica	df1	df2	Sig.
Welch	20.411	4	72.139	.000

  

The result of Welch Correction reflects a significance value of .000 Miller (1997), which means there is a significant variance between different categories of hotels about COVID-19 related information on the hotel website. Multiple comparisons are conducted using the Tukey test to find the specific category of those hotels showing a significant variation in COVID-19 information.

Table 9

Table 9 Multiple Comparisons
(I) Type of hotel	(J) Type of hotel	Mean Difference (I-J)	Std. Error	Sig.
Unrated	2 stars	1.500	1.147	.687
	3 stars	-2.933	1.147	.084
	4 stars	-5.500*	1.147	.000
	5 stars	-7.267*	1.147	.000
2 stars	Unrated	-1.500	1.147	.687
	3 stars	-4.433*	1.147	.002
	4 stars	-7.000*	1.147	.000
	5 stars	-8.767*	1.147	.000
3 stars	Unrated	2.933	1.147	.084
	2 stars	4.433*	1.147	.002
	4 stars	-2.567	1.147	.172
	5 stars	-4.333*	1.147	.002
4 stars	Unrated	5.500*	1.147	.000
	2 stars	7.000*	1.147	.000
	3 stars	2.567	1.147	.172
	5 stars	-1.767	1.147	.538
5 stars	Unrated	7.267*	1.147	.000
	2 stars	8.767*	1.147	.000
	3 stars	4.333*	1.147	.002
	4 stars	1.767	1.147	.538

 

The Tukey test results show that unrated hotels provide significantly different information on COVID-19 than four-star and five-star hotels, as they reflect a high level of significance of .000 each Miller (1997). Therefore, we reject the null hypothesis H12and H13. No significant difference about COVID-19 information was found in the unrated hotels than two-and three-star hotels, as they reflect a very poor level of significance, i.e.,687 and .084, respectively. Therefore, we accept the null hypothesis H5and H9.

The results of the Tukey test further reflect that two-star hotels provide significantly different information on COVID-19 when compared with three star, four-star, and five-star hotels, as they reflect a high level of significance of .002, .000, and .000, respectively. Therefore, we reject the null hypothesis H6, H7 and H8.

The results of the Tukey test further reflect that three-star hotels provide significantly different information on COVID-19 when compared with five-star hotels, as it reflects a high level of significance of .002. Therefore, we reject the null hypothesisH14. No significant difference on COVID-19 information was found in the three hotels and four-star hotels, as they reflect a very poor level of significance of .172. Therefore, we accept the null hypothesis H7.

Similarly, the results of the Tukey test also reflect that there is no significant difference in COVID-19 information on the website of four-star and five-star hotels, as it reflects an abysmal level of significance of .538. Therefore, we accept the null hypothesis H8.

Table 10

Table 10 Tests of Between-Subjects Effects
Source	Sum of Squares	Degree of freedom	Mean Square	F	Sig.
Corrected Model	1607.427a	4	401.857	20.364	.000
Intercept	8986.140	1	8986.140	455.363	.000
Type of hotel	1607.427	4	401.857	20.364	.000
Error	2861.433	145	19.734
Total	13455.000	150
Corrected Total	4468.860	149

 

The population variance between different categories of hotels is 20.364 times higher than the variance within each category of hotel. The significance level is high, and hence we reject the null hypothesis H15. The result also indicates that 36% of the variance occurs in the use of COVID-19 related information between different categories of hotels and within each hotel category.

 

5. THEORETICAL IMPLICATIONS

The covid-19 has pushed industries across the globe to prepare them for the new normal. In this new normal, high level of attention is given to hygiene and sanitization. This paper can provide useful insights to academicians to comprehend the preparedness of different hotels to handle this new normal. Furthermore, this study provides knowledge about the emphasis given to covid-19 in the communication strategy of any hotel in India. This study also distinguishes the comparative difference in the information regarding covid-19 in a different category of hotels. Since this study provides information on the emphasis given to covid-19 guidelines by hotels of different cities of India, the findings of the study enable the consumers to analyze the precautionary measures taken by different categories of hotels because of Covid-19. The results further explain that apart from best-in-class hotels, the information presented in other categories of hotels does not vary significantly, which could explain the type of messages they want to convey to their customers. Since the segmentation of different categories of hotels is majorly dependent upon the income level of customers, this study could provide insights on the importance given to covid-19 related information by different categories of hotels. Since the customers' expectations profoundly influence the communication strategy of any firm, the study could provide inputs on how the customer perception of hotel services during a pandemic reflected by the different categories of hotels.

 

6. PRACTICAL IMPLICATIONS

Tourists worldwide are concerned about the risk associated with the epidemic, which is influencing their travel behavior Mao et al. (2010). Consequently, it became essential for hotels to address the concerns of customers that transpired due to COVID-19. The COVID-19 has bought an extraordinary situation for hotels around the world. A comparative study between different hotels reflecting their preparedness became vital to guide them about the industry trends and help them become resilient. This study could also help to restore travelers' confidence in hotels by providing them details about the steps taken by different hotels to ensure their safety. The study could provide insights to travelers to choose the desired hotel based on the hotels' emphasis on Covid-19 related guidelines. This study could also be proven useful for different hoteliers interested in knowing the adaptation level of the hotels of three major heritage tourist destinations of India. The findings also suggest that the emphasis on information on covid-19 is similar across the cities, which reflects the homogeneity of policies of hotels across the major tourist destinations of India.

 

CONFLICT OF INTERESTS

None. 

 

ACKNOWLEDGMENTS

This paper and the research behind it would not have been possible without the exceptional support of all the authors. Their enthusiasm, knowledge and exacting attention to detail have been an inspiration and kept our work on track from our first meeting to the final draft of this paper. We have looked over the transcriptions with hard work and unfailing patience. We are also grateful for the insightful comments offered by academia. The generosity and expertise of one and all have made this research complete for publication.

Finally, we would like to thank our family and friends for their love and support throughout the research process. Without their encouragement and support, we would not have been able to complete this research.

 

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