An Empirical Exploration of the Success Dynamics of Online Tourism Aggregators in India

This study is the reflection of the various factors affecting the online tourism aggregators in this Techno enthusiastic world. In this study the factors affecting tourism aggregators are identified and analysed for the purpose of measuring their success. Growing in number of web clients and utilization of web, alongside headway in data innovation is putting forth relative information in the virtual space. Indian tourism is having new opportunities as many foreign investments are made on the hotels, which in a way is promoting the India tourism. Besides many tourism applications are also popping up each day. Even in the recent times, the government has come up with investments plans towards the tourism industry as well as the technical base for India. Hence it gives an extensive scope for the industry to grow. The latest promotional efforts and celebrity endorsements in the tourism industry has created revolutionary impact and took Indian Tourism to the next level. Hence, the conglomerate of technology and tourism and their mutual impact is the integral part of the research. This research focuses on assessing impact on the online conversion of travellers. A logistic regression model in SPSS has been designed to find significant relation between the independent variables and dependent variable. It also analyses the factors which are affecting significantly in the success of the aggregators specific to tourism industry. This paper seeks to explore the industry from the perspective of technology and provide a deeper insight of the same.


Introduction
According to Gustave Flaubert (1849), "Travel makes one modest. You see what a tiny place you occupy in the world." Tripping is claimed to be one of the most favourite leisure source or stress buster round the globe. As Indian Prime Minister Narendra Modi rightly stated ""Brand India" is built of 5 T"stalent, tradition, tourism, trade and technology". In this study we tried to stitch a relationship between Tourism and Technology through various aspects of the tourism industry. It starts with the Indian tourism history to present scenario. The paper speaks about how technology stepped inside the industry of Indian tourism and revolutionized it like never before. The country has witnessed a parallel growth of both the Indian tourism industry and technology affecting it. As statistics claims that tourism has a contribution of 9.9% in global GDP and as surveyed that there are more than 2 billion+ internet users. Hence, the consolidation between the industry and internet can be highly beneficial in terms of growth of the industry (Adenwala, 2014). Firstly, this paper identifies the various tourism aggregators and their services. (Akehurst, 2018) has claimed that customers through internet portals not only can gain awareness about tourist destinations but also can get access to the various hotels available there and match them according to their requirement. The increasing number of internet users in the country has forecasted strong impact on the growth of the various industries where tourism and hospitality is one of the major ones (Waghmare., 2012). The study finally analyses the tourism aggregators and what factors affecting them.
As claimed by Keith Bellows, National Geographic Society "There are some parts of the world, once visited, get into your heart and won"t go. For me, India is such a place" India can be considered as one of the most preferred tourist destinations. Besides, post liberalization there has been a huge development in the Service sector which is helping to build technical base for tourism industry as well. According to World Travel and Tourism Council (WTTC) the Indian tourism industry in recent years is also going through a period of accelerating growth in terms of GDP contribution, precisely 9.6% of India"s GDP. Similarly, every day we are witnessing many new tourism aggregators with innovative tourism services are entering the market. Hence, attempt has been made to identify those factors which are contributing to the success of tourism aggregators in India. Therefore, the study provides a deeper insight in the field of tourism as it deals with a wider perspective.

Tourism in India
India has a very diverse market of tourism. No other countries in the world has such geographical and cultural diversity, hence India is always a preferred tourist location round the globe. Besides, India is also a cheaper tourist location hence it is a major attraction for foreign tourists which is contributing to the growth of tourism industry in India. Presently, Indian tourism is having new opportunities as many foreign investments are made on the hotels, which in a way is promoting the India tourism also as claimed by Dev and Kuckreja (1989). Kulkarni (2015) in the article discusses about the MAKE IN INDIA mission on Indian tourism context. The study is done on the platform of Karnataka. One of the major aspects in this study is FTAs in India (travel and transport).The article claims that there has been a remarkable growth and also new opportunities have arrived in the tourism context with the introduction of Make in India mission. As this mission till a great extent brought about technological revolution, hence, a new dimension was added to the industry which was beneficial in nature.

E-Commerce in Tourism
E-Commerce has brought about positive transformation in Tourism industry. Tourism industry can be considered as catalyst for other areas. Tourism helped to bring about vigorous changes which led the other industries to adopt the changes. Hence, tourism didn"t only develop its own self but other areas too or more precisely the economy as a whole. IT development has been beneficial for the consumers and buyers in a number of ways. Mamaghani (2009) explained that, "95% web users have searched the internet to gather travel related information, 93% has visited the destination websites, and lastly one half used e-mails to gather travel related information". Ecommerce in Indian Tourism context is an effective tool in promoting and attracting tourists, accelerating growth in the industry and having positive and negative impact of it on the local service providers. In some cases internet is promoting the local service provides but in some cases as well it is substituting the local service providers (Kant and Sharma, 2016). There has been researches using qualitative data focusing on the various relationships between economic growth and ICT, tourism in ICT, E Commerce in tourism. ICT tools helping in building up knowledge about the tourism industry by bringing various information"s which can be helpful for the growth and development of the industry is the conclusive answer ICT tools hence forth is also contributing in the growth of the industries as people can be more educated about the tourist spots and their accessibility to them (Shanker., 2008).There has been intensive growth of the E-commerce"s in Indian tourism market as per survey on annual exchange rates through online transactions like online travel, ticket booking (pricing). The increasing number of internet users in the country has been very impactful in the growth of the various industries where tourism and hospitality is one of the major ones (Waghmare., 2012). New business opportunities can pave a way through E-Commerce as it also helps in searching and analysing various areas or scopes where new business models can be set up. Hence forth tourism also pitched into E-Commerce. Werthner and Ricci (2004) researched on the change in the structure of the industry with the entry of E commerce in travel and tourism by the analysing the behaviours of active users and active buyer through a survey on the E-commerce portals of travelling tourism industry. The most important conclusion derived, is the creation of new business opportunities to attract customers. E-Commerce also brought about new aspects in the sector like customers can do customization of their travel plans through online portals. The whole study conveys that there have been positive impacts caused due to the entry of IT in this tourism industry. E-Commerce is beneficial for the small scale tourism enterprises as the process is profitable in nature and requires less intermediaries. As seen in the SMTE in the urban areas of Sydney and Melbourne and rural areas of Victoria, Bendigo using secondary data-Trends in internet connection (source: Senses 2004). The SMTEs in these areas benefited due to the enforcement of E-Commerce (Davidson and Burgess, 2006). Internet can be an effective tool in promoting budget tourism packages of SMTEs in the South Pacific Regions and is very effective tool for the tourist to gather information and also booking purposes. Promotion of small scale budget tourism in Fiji through E-Commerce is an effective way of promotion and highly beneficial too. Places like Fiji, Samoa and Tonga has been impacted beneficially by using internet economically to promote and also carry out business practices of tourism industry through E-Commerce (McMaster et al., 2005).

Challenges of E-Commerce and Aggregators specific to Tourism Industry
E-commerce cannot be implemented without the empowerment of stakeholders, to get the benefits of the internet and E commerce. Traditional methods are still prevailing in tourism as E-Commerce in tourism is a very recent concept. The education and benefits of E-Commerce is needed to be conveyed to the stakeholders because they play a very crucial role in any industry or organization as evaluated in the context of SMTEs in Korea (Kim, 2014). As the E-Commerce in Tourism till some extent is based on AR or Augmented Reality, hence it is very important to have a strong technical base. But, there are certain limitations regarding building a strong technical base for AR (Kounavis et al., 2012). Problem even lies in the degree of adoptability of the AR or specifically the E-Commerce in tourism by the consumers. That further calls for the importance of education among the consumers regarding the benefit of these (Kounavis et al., 2012). There lies a big issue among the aggregators of tourism industry regarding their less of concentration on consumer wants and demands rather on cross firing which tends them to lose customers (Carey et al., 2012). Lastly, the aggregators" specific to tourism industry should adopt to change or bring about change according to situations on a faster pace, where these aggregators seem to lack. With every passing day evolutions have been occurring at a fast pace in technology which is contributing in changing the whole business scenario. Therefore, the aggregators should be quick adaptable to any changes and should change themselves accordingly (Carey et al., 2012).

Online Consumer Buying Behaviour in Tourism
Internet helps the enterprises to know about the buying behaviour or the buying trend of the customers. It also helps the enterprises to understand the expectations of the buyers so that the enterprises can work on it to accelerate the sales. And, internet also plays a vital role in travel information, travel booking which also contributes to the growth of this industry. Berbegal-Mirabent et al. (2016) in their article did a sampling survey through online questionnaires on general public in Spain regarding men"s and women"s purchasing behaviour towards tourism .In their study they found that the men and women have some similarities and also some parity in terms of buying behaviour of tourism related services. Even their attitude and loyalty differs. Hence, depending on that the web portals needs to be customized so that it can satisfy the desires of each group as suggested in the study. Lastly, individual Buying Behaviour is highly dependent on online consumer reviews. This particular has some pros and cons in its own way. Even the consumers can be segmented depending on their reflexes towards these reviews and can be handled on their own way (Markus and Law., 2015).

Digital Tourism
Digitalization represents an exciting opportunity for the travel ecosystem as it impacts every element of travel and tourism value chain. As per the paper the themes which are important to the digital transformation over the next decade or so are the traveller"s experience, travel ecosystem being digitally enabled, changes in the operational processes, protecting the privacy of traveller data. Avinash et al. (2016), found through their research that there is a significant impact of digital marketing on the travel and tourism industry in India. The population of age range 26 to 35years who use internet predominantly and have willingness to travel are satisfied with the various services offered by the digital marketing platform. Majority of them prefer e-commerce websites for instant information and booking facilities. The websites of the travel aggregators provide end to end information and customized packages, deals, discounts. Phutela and Dasgupta (2013), in his research found the factors those help building customer trust among in the online environment. They listed the trust factors as dependability, reliability of information, responsibility in using the information, ability, integrity and benevolence. The research paper emphasizes the various dimensions of internet purchase attitudes which include risk reduction, flexibility, convenience, trendiness. The various factors that help in building trust include consumer perceptions regarding the web safety, perceived web vendor reputation, and website quality. Gretzel et al. (2015) defined smart tourism and its ecology and laid tourism"s business and technological foundation. They have highlighted the current trends in smart tourism. Smart tourism they defined as the technological, economic and social developments fuelled by technologies that rely on latest technologies like sensors, big data, open data and new ways of connectivity and exchange of information. Xianga et al. (2015) emphasized that the traditional channels such as online travel continue to dominate the travel planning. They also emphasized the prevalence of social media among the travellers who belong to Generation Y. It was found that a portion of travellers keep using online channels for information and transaction. Pavelka (2016) studied the young travellers from Edinburg and found that there is a strong relationship of the travellers with their phone during travel as a majority of the travellers were found to be comfortable, easy, safe and less risky with their travel when they carry smart phone with them. Benyon et al. (2014) concluded that, to create substantive user experiences (UX) for visitors, digital tourism is an important means. They explained with multiple instances of tourist experiences which creates a blended space provided by a combination digital content and physical location. They suggest that the digital tourism experiences need to focus on the appropriateness of the digital content and the spatial and aesthetic characteristic is the physical location so that it will be helpful to measure the quality of the user experience.

Research Design
The investigation attempts a blend of exploratory research and causal research. Exploratory inquire about plan has been utilized as a part of request to pick up top to bottom experiences into the examination region and perception of different issues related the tourism aggregators. In the meantime, causal research configuration was utilized to inspect the effect of different online builds or factors on the online visitor change.

Data Sources
To lead the exploration, both essential and also auxiliary sources were used to gather information. Essential (direct) information was gathered straightforwardly from the respondents by managing organized survey arranged for information accumulation. As the examination is planned to discover the achievement variables of the online tourism aggregators in India, the essential information was gathered just from the respondents who were private part representatives, government segment workers, independently employed, understudies and homemakers. EBSCO and Emerald and Google Scholar were the significant research databases used by specialist to gather wanted research papers and articles. Ndubisi (2007), Gaurav (2008) and Alrubaiee and Al-Nazer (2010) in their investigation depended upon an organized poll as an estimation instrument. In this investigation the outline of the poll was predominantly in view of different thing estimation scales got from the previous look into contemplates. The different things were distinguished from past research and further adjusted by the prerequisite of this investigation. In the poll, all the chose things identified with the web interfaces and ascribes identified with online travel aggregators were displayed as explanations alongside five-point Likert scale (from 1 = strongly disagree to 5 = strongly agree) in an assertion of the announcement.

Sampling Design
The sample was collected in a due course of three months where people of various age groups, economic and social status were approached and questionnaires were filled. Different platforms of data collection and people from varied social groups enabled to ascertain and design a scattered data set or sample. As Saunders et al. (2009) suggested that in survey research, probability sampling is preferred over non-probability sampling. Simple random sampling was employed for the survey of the present research for authentic pattern of sample and hence dependable results.
In order to develop the sampling frame, the visitors who contact the travel and tourism aggregators both offline and online were obtained from the city Hyderabad.

Sample Size
In behavioural research studies, it is advisable to follow a thumb rule of for selecting the sample size based on the acceptable confidence levels (Roscoe, 1975). For quantitative studies a sample size in the range of 30 to 500 is advised. According to Comrey and Lee (1992), more than 200 sample size is a fair number of sample size. The sample for this study was 291, and the visitors contacting various offline and online travel and tour aggregators in Hyderabad were considered as sampling units in the sample. Due care was taken to avoid the bias while administering the survey questionnaire. After removing the incomplete and invalid responses, the sample size was 232 which was still in the range of fair sample size.

Data Collection
Various tourism forums both offline and online were approached in order to collect the data. Offices of various tourism aggregators were visited for the purpose of collecting data helping to derive a scattered and diverse sample.
After collection of primary data and analysing it, data will be used to do Logistic Regression using IBM SPSS. This study will also investigate the correlation between the independent and dependant variables. We will develop a logistic regression model in SPSS to find significant relation between the independent variables and dependent variable in order to analyse the factors which are affecting significantly in the success of the aggregators specific to tourism industry.

Data Characteristics
The data forecasted few characteristics like the dominant age group was 21-30 years of age (49%), followed by age group below 20 years and between 31 -40 years both holding 22%, then age group above 51 years (5%) and finally 31-40 years (2%). On the basis of gender the sample comprised of 77% female and 33% male. On the basis of income the dominant income group was with monthly income between INR 25,000-50,000 (47%), followed by income group above INR75, 000 (23%), then individuals with monthly income between INR 50,000-75,000 (19%) and lastly of income group below INR 25,000 (11%). The last diversity was on the basis of occupation where private sector employees dominated the sample with 43%, followed by government sector employees (21%), then selfemployed and students both holding 16% and lastly, the homemakers (4%).

Data Analysis
SPSS or Statistical Package for the Social Sciences is the most drifting measurable bundle apparatuses prevalently utilized for controlling and examining exceedingly complex information with simple guidelines. Here SPSS has been utilized to break down the information.

Logistic Regression
Logistic Regression is a statistical method where one or more independent variables are analysed to determine their significance on a particular outcome. The outcome with a dichotomous variable where there is only possibility of two outcomes. It can be binomial or multinomial. Binary logistic regression is the method of choice if your dependent variable is binary (Dichotomous) where we attempt to explore the relative influence of continuous or categorical independent variables over the dependent variable. It also seeks to assess interactional effects between the independent variables. In this case the outcomes can be either Yes or No, when it comes online tourist conversions (Miles and Shevlin, 2001). It is a prediction done with categorical variable.

Purpose of Logistic Regression
Logistic Regression is generally used when there are two categories of dependant variables. Logistic Regression is a convenient analysis in the tool SPSS. Logistic Regression is the best analysing method when the independent variables are categorical or a combination of continuous and categorical.
Logistic regression involves fitting an equation of the form to the data: Logit (p) = a + b1X1 + b2X2 + b3X3 + … Here the" p" denotes the probability that the case is in a particular category here it is the dependant variable of Online Tourist Conversions. In the equation, "a" denotes the constant of the equation and "b" denotes the coefficient of the predictor variables. "X" denotes the independent variables of Smooth Automation, User adaptability, online payment, Review Methodology and attributes of user-interface like Responsive design, Minimalistic Design and Simple Checkout process.

Assumptions of Logistic Regression
Following assumption are typically made while performing the logistic regression during the study. Logistic Regression does not assume linear relationship between dependent and independent variables. The dependent variables should be dichotomous in nature. The groups should be mutually exclusive and exhaustive in nature. A case may only be in a single group and every case should be a part of one of the groups.  Step 1 Step  Binary logistic regression is used when you have a categorical dependent variable with two levels of target group. Typically a zero is used to refer to the base group of the result group and a 1 refers to a target group. After running the model using IBM SPSS 22.0, the Output table 1 shows the null model which shows the prediction accuracy. It means what is the prediction accuracy when there are no predictor variables and the output shows the accuracy is 66.8 percent. Table 3 shows the Chi-square model test value which is significant. This indicates that our predictor model fits better than the model with no predictor variable. Now the model is significantly better than the NULL model as the overall classification rate of the model is now 80.2 percent. As per the Table 4, there are positive coefficient for the predictor variables online payment and Checkout. It shows that as the scores on the predictor variables increase, the probability of falling into the target group increases. having negative coefficients shows that as the scores on these predictor variables increase, the probability of falling into the target group decreases.

Odd Ratio Analysis
The column Exp (B) in the table of significance rate i.e., Table 4 refer to the odds ratio. Odd ratio reflects the change in the odds as a function of change in predictor variables. Odds ratio amount of change in odds in every one unit change in the predictor variable but the change is multiplicative as opposed to additive. For instance, every increase in online payment by one unit, the odds increase by a factor of 1.023. To elaborate, the visitor who prefers online payment is 1.023 times more likely to be converted as customer online than a visitor who does not prefer to make an online payment. If the odd ratio is greater than one then the odds are increasing and if it is less than one we interpret that the odds are decreasing. The variable checkout is also having an odd ratio greater than 1.
The confidence interval values for the corresponding variables shows the upper and lower limit for the odd ratio. If the value of 1 falls between the upper and lower limits of the confidence intervals then you cannot reject the NULL and essentially we can conclude that the predictor is not having significant impact on the target group membership. If 1 falls outside the upper and lower bound of the interval then that the predictor is significant impact on the target group membership. In our test, other than the variables smooth, reviews, responsive the rest of the variables are having significant impact on the target group variables.
The tables show that Adaptability to online tourism aggregators has P-value 0.001 (<0.05) and T-value 11.615 (>1.96). Hence, it can be derived that Customer adaptability is significant at 99% over the success of the online tourism aggregators. Online payment has generated P-value 0.032 (< 0.05) and T-value is 4.585 (> 1.96). Hence it can be derived that Online Payment option is also significant at 99% over the success of the online tourism aggregators. Review Methodology has generated P-value 0.053 which is negligibly greater than 0.05 and the T-value is 3.753 (>1.96). Hence, it can be derived that Review methodology is significant at 90% over the success of the online tourism aggregators. Lastly, the smooth automation of online tourism aggregators has generated P-value of 0.355 (>0.05) and the T-value is 0.855 (< 1.96). Hence, it can be derived that Smooth Automation of the system has no significance over the success of the online tourism aggregators. In case of User Interface Attributes, above analysis shows that for minimalistic design and simple checkout process forecasted P-value 0.008 and 0.000 respectively (<0.05) whereas T-value 6.969 and 13.642 respectively. Hence, it can be derived that Minimalistic Design and Simple Checkout Process is significant at 99% over the success of online tourism aggregators. Whereas the Responsive design having P-value 0.68 (>0.05) will not have significance over the success of the online tourism aggregators.

Discussions
The paper seeks to get the result where it clearly forecasts the factors which are contributing in the success of tourism aggregators, through analysing the independent variables with respect to the dependant variable. The selected dataset was scattered among the various age groups, gender, occupation as well the income groups. The dominant age group and gender was 21-30 years and females respectively. Similarly, the dominant occupation and income level was private sector and INR 25,000-50,000 respectively. Through the analysis the independent factors of online payment system, adaptability towards online tourism aggregators, minimalistic design and simple check out process were the most vibrant factors satisfying the above conditions. This result helped to bridge the gap because it forecasted the in depth knowledge not just about the industry but also brought up a new perspective about the tourism aggregators and success factors. This study helped to scrutinize this area from core and built up a base for one of recent gifts of technology that has consolidated with one of India"s fastest growing, highest revenue earning sector, which is tourism. This study will also further be beneficial for the travel aggregators to gain knowledge about their customer"s preferences. It also initiates a further scope to study the Consumer Buying Behaviour and Consumer Relationship Management in terms of online tourism aggregators for their future growth and success.

Conclusion
Tourism in India is a booming industry and presently technology has fuelled in its accelerating growth. This study focused on the tourism aggregators and intended to analyse their "Success Factors". Through an in depth analysis of a primary data of size 232 which is scattered in characteristics, few significant factors has come under focus. Through the study it has been derived that adaptability towards online tourism aggregators, online payment system and review methodology has played significant role in the success of the Online Tourism Aggregators. Taking into consideration the user interface, Minimalistic design and Simple Checkout process of the online tourism aggregators has played a significant role in the success of the online tourism aggregators. Hence, we can conclude that this study has been successful to reveal the success factors behind the tourism aggregators trending in the market.

Managerial Implications
The result shows that online payment and checkout process is significantly influential in the conversion of tourists through online tourism aggregators. Hence, the aggregators may put more concentration on making them even more convenient to the customers at the same time getting adapted to the changing trends.
The responsive design and the review methodology are also some of the integral parts because today"s customers are impatient and believe in on the go service at fingertips. They also like to choose everything which can be customized and personalized on the basis of their choices.
Minimalistic design does not show much of a significant influence on the results but in due course of time, with increasing clutter in these various online portals, minimalistic design of the portals may pop up to be one of the influential factors.