A STUDY ON THE USE OF PERSONALIZED FEATURES IN ONLINE TRAVEL SHOPPING WEBSITES Varsha Agarwal* 1

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1 ISSN: IJMRR/Nov. 2015/ Volume 5/Issue 11/Article No-2/ Varsha Agarwal / International Journal of Management Research & Review A STUDY ON THE USE OF PERSONALIZED FEATURES IN ONLINE TRAVEL SHOPPING WEBSITES Varsha Agarwal* 1 1 Research Scholar, Christ University, Institute of Management, Bangalore, India. ABSTRACT Personalization in online travel shopping is the process of providing the products and services on websites according to the travelers preferences. Personalization plays an important role in increasing attractiveness of consumers for online shopping of travel related products and services. This study is conducted to examine how consumers perception toward personalization features of online travel shopping websites influence consumer intentions to purchase online tickets by identifying the important factors behind consumer intention to make bookings online. The result of this study shows the effect of personalization factors on consumers perception and purchase intentions for online travel shopping websites and their implications for marketers in the travel industry. The variables tested in the empirical study are gender, age, occupation, income and education and personalization features of online ticket booking websites. In order to find out the perception and purchase intentions of online shoppers a closed ended structured questionnaire was designed for primary data collection. Data was analyzed using the following statistical tools namely Descriptive Statistics, Factor Analysis and Linear Regression Analysis. This study is the first study which examines the effect of personalization features on the perception of consumers and their purchase intention with regard to the online travel shopping. Online marketers need to focus on adoption of attractive personalized features and strategies to attract potential consumers. Keywords: Online travel shopping, Online shopper, Perception, Purchase Intentions INTRODUCTION In e-commerce travel is the leading competitive sector and it is improving with a faster rate. The increased use of internet is encouraging independent travel, and travelers search information and deals online. Social media is playing important role in travel industry and it is focusing not only on short term sales but on increasing customer loyalty also for longer term and development of brand reputation. Smart phones and tablets are huge success and it is making mobile applications an important channel of booking travel services. Travelers geo-localization also allowing real time sales for products that are location based. The revolution in travel industry is giving central role to the consumers. And with this companies are also getting benefits. Because of these developments in the market, travel companies are thinking about their old models of business and focusing on acquiring the knowledge about the expectations of customers. They are starting fruitful communications with consumers. In the advanced economy online transactions are becoming the integral part of sales in travel *Corresponding Author

2 industry. And this is continuing to show stronger growth rates in comparison with traditional channels in the travel sector. According to Sahney et, al. (2014) for many Indians, booking of tickets via internet for travel in train was their first introduction with the online shopping. The government portal irctc.co.in for booking of railway tickets and travel firm Makemytrip.com, which is also listed in NASDAQ, created revolution in the travel industry. It changed the scenario where buying train tickets meant waiting in long queues at railway booking counters. Technology has an important role in online travel industry and providing exponential growth. Earlier this industry was having high opaqueness. Due to the penetration of internet scenario has been changed. At global level lot of advanced information with communication technology has been incorporated in online travel industry. These technologies are used for the development of travel product, marketing, distribution and employee training in the travel sector. They are indispensable for knowing and satisfying the changing demands of the consumers. In the environment of ecommerce, personalization is playing important role in improving the service levels as well as improving customer loyalty according to Shaw (2003). Many online marketers are now offering highly personalized products as well as services in a wider range of categories. This is transforming the practice of retailing into consumer oriented from retailer oriented. This process of retailing involves customization of products and services for individual customer needs. Etailers are allowing the consumers in choosing their own services according to their preferences by adoption of the new technologies in personalization. Online travel industry created revolution in India for planning and buying the travel product and service. Travel websites have introduced the ease and convenience of the operators and expanding the choices for consumers. Now travelers can simply search on internet for destinations of their choice. They can evaluate the available options and can take decisions. These travel portals are emerging as one stop shop for all travel related needs in place of mere ticket agents. The future of the online travel industry has been marked by consolidation and new players are crating ventures into this sector. These collaborations will certainly lead to success of the online travel industry. It has been revealed that when a customer shops online from companies those offer personalized products and services than companies can get the information about consumers very easily and at cheaper cost. It helps companies to gather more information about users. And it helps them in predicting users preferences and online choice pattern. This personalized information can help company to formulate further business strategies and designing of interface and communication with the potential customers. Numes et, al. (2001) described the process of personalization as a way of artificial intelligence use. It helps in the analysis of demographic profile of consumers. Companies can give further recommendations about the preference patterns of consumers. REVIEW OF LITERATURE The review of literature gives a fair idea about the work done in the subject area, the views and observations made by different researchers and the gaps which need to be filled. The literature review covers the basic concepts of personalization features in e-tailing, and perception and intentions starting with a general review of ecommerce and e-tailing. Copyright 2012 Published by IJMRR. All rights reserved 1046

3 Studies regarding personalization in ecommerce Pauline de Pechpeyrou (2009) conducted a study to know the value given by consumers to the online personalization. The objective of the study was comparing the behaviour and attitudes of consumers with respect to personalized selling and random selling. It was found out that personalized items got more clicks than random ones. Additionally, a flavour of personalization was added up to the positive attitude towards the website. This study examined the attributes of service available on the websites of women apparel and difference from attributes available on the websites of men s apparel with regard to the nine dimensions of e-service quality. The websites differ in providing online services in a manner that women s apparel websites offered more services that better the e-service quality than that of men s websites. Studies regarding consumers perception and purchase intentions in an e-commerce context Sorce et, al (2005) conducted a study to investigate the buying behaviour of younger as well as older and effect of their attitude on online shopping. The conclusion of the study made it clear that older online buyers found lesser products in comparison with younger consumers. There existed variance in the behaviour of shoppers on the basis of their attitudes. Thamizhvanan et, al. (2013) conducted a study to determine the purchase intentions of Indian consumers for online shopping.along with previous online buying familiarity and online belief, product positioning, impulse buying alignment and superiority positioning were found to be significant buying orientation aspects for buyer online buying intention, as per the detailed literature analysis. The research concluded that prior online purchase experience, impulse purchase orientation and online trust have noteworthy influence on the customer purchase intention. Studies regarding e-commerce in travel industry Sahney et, al. (2014) conducted a study to know the motivation of buyers towards online shopping in the context of Indian railway. This study was theoretical and it conceptualizes the motivation as an example with respect to the online shopping and tested it empirically. The main aim of the study was to find out the important factors of motivation those are affecting the decisions of people for online shopping and to make an integrated model. Leica et, al. (2012) conducted a study for generalization of user behaviour for online travel shopping websites and developed a model for website acceptance. The study focused on analysis of tourist behaviour for travel websites. It clarified the users intentions for these websites use on the basis of their determinants. As a result of concentration on blog the relationship between reasoning as well as behavioural variables was found out and it was concluded that it can differ according to particular website. A study was done by Law et, al. (2008) to find the difference between perceptions of browsers who had browsed a websites and buyers who had completed online shopping. Empirical results suggested that quality factors were considered significant by website users and they were usually contented with travel websites. Purchase intention in the users of these websites had a positive view. Research conclusions suggested that customer satisfaction was positively related with travel Copyright 2012 Published by IJMRR. All rights reserved 1047

4 website quality, and that it was related to purchase intention. Woodside et, al. (2011) conducted a study to find dominance of the tourism destination and the usefulness of the marketing websites. The dominance of the tourism destination could be defined as number of tourists visiting each year to residential population of the destination. A multi item metric was created for the checking of the usefulness of the website of destination marketing. 40 destination marketing websites were judged based upon the tools, as a part of the study. The conclusions of the study also indicated a noteworthy relationship between marketing website usefulness and tourism destination dominance. PURPOSE OF THE STUDY This study is conducted to examine how consumers perception toward personalization features of online travel shopping websites influence consumer intentions to purchase online tickets by identifying the important factors behind consumer intention to make bookings online. Thus, the focus of the study is measuring consumer perception and matching to intentions to purchase online using personalization features by determining whether consumer perceptions of personalized services in an online travel shopping website is a key determinant predicting consumers intentions to purchase. RESEARCH OBJECTIVES Objectives of the study are as follows: 1) To identify the personalization features of selected online shopping websites; 2) To find the demographic profiles of online shopping consumers; 3) To analyze the perceptions of consumers towards online shopping websites; 4) To analyze the impact of factors of personalization features of the online travel shopping websites on the purchase intentions of consumers; RESEARCH HYPOTHESES This research proposes the following hypothesis to be tested empirically based on the literature review. H 11: There exist significant differences in the perception of consumers of different demographics towards personalized features of selected online travel shopping websites. H 12: Personalization factors have a significant impact on consumers purchase intentions towards online travel shopping websites. RESEARCH METHODOLOGY In order to find out the perception and purchase intentions of online shoppers a closed ended structured questionnaire was designed for primary data collection with sample size of 650. The location chosen for survey was Bangalore. Statistical tools such as descriptive analysis, factor analysis and regression analysis were used to analyze the data and meeting the objectives. This section provides a description of the methods, tools and techniques used to conduct the research. Data Collection: Data has been collected from primary sources for the purpose of this study, from respondents who book tickets online. The data was obtained by using the survey method Copyright 2012 Published by IJMRR. All rights reserved 1048

5 through the administration of structured questionnaires to the respondents. Non probabilistic convenience sampling was used for the primary data collection. Statistical technique: Data was analyzed using the following statistical tools namely Descriptive Statistics, Factor Analysis and Linear Regression Analysis. Descriptive statistics was used to capture the demography of consumers. Cross tabulation was used to compare and analyze the perceptions of consumers towards personalization features of selected online shopping websites. Factor analysis was performed to identify important personalization factor of online travel shopping websites. Regression Analysis was used to analyze the impact of personalization factors of the online shopping websites on the purchase intentions of consumers. ANALYSIS AND INTERPRETATION Demographics of the Respondents This section provides an understanding about the demographic characteristics of the sample. Table 1 gives information about gender, marital status, education, income, occupation and age. Results in table 1 shows that majority 56.3 respondents are male who use online shopping for booking travel related services percent of the respondents are married and 41.7 percent of them are single percent has cleared their HSC, 29.6 percent are undergraduates and rest is 23.6 percent post graduates and 20.1 above post graduates. Majority of respondents 45.7 percent have family income of Rs 30,000 to 50,000 per month followed by 38.2 percent, who earn above 50,000. Majority 32.7 percent of respondents are salaried people followed by 26.6 percent students percent of respondents are housewives who shop online. It can be inferred from the table that the majority of the respondents fall under the age group of with 37.7 percent. After that 26.1 percent of respondents belong to age groups. Table 1: Demographic Characteristics of respondents Gender Frequency Percent Male Female Total Marital Status Frequency Percent Single Married Total Education Frequency Percent HSC UG PG Above PG Total Income Frequency Percent Below 30, ,000-50, Above 50, Total Occupation Frequency Percent Student Salaried People Self Employed House Wife Total Age Frequency Percent Less than Above Total Copyright 2012 Published by IJMRR. All rights reserved 1049

6 Use of internet and online shopping websites usage To get an understanding of perception of consumers towards online shopping websites, the responses from online shoppers were analyzed with the help of descriptive statistics. The analyzed data has been presented in a graphical form followed by an interpretation. The demography of consumers for online shopping websites can be understood by analyzing the comfort level of respondents with internet, no. of times they purchased products online in last one year, no. of shopping websites used in last one year and most frequently used online shopping travel websites. Comfort level with internet use This table 2 represents respondents comfort level with the internet use. Table 2: Comfort level with internet use Frequency Percent Very Uncomfortable Somewhat uncomfortable Neutral Somewhat Comfortable Very Comfortable Total It can be understood from table 2that majority of the respondents 47.6 feel somewhat comfortable with the use of internet, followed by 40.4 percent who feel very comfortable in using internet. No. of times products purchased online in last 1 year Table 3 represents no. of times products purchase online in last one year by respondents. Table 3: No. of times products purchased online in last 1 year Frequency Percent More than Total From Table 3 it can be understood that majority of the respondents 44.9 percent shop online frequently and bought 6 to 10 products online in past one year followed by 43 percent respondents who shop above 10 products over internet in last one year. It shows the consumers perception towards online shopping. No. of online shopping websites used in last 1 year This table 4 represents no. of online shopping websites used in last 1 year by respondents. Table 4: No. of online shopping websites used in last 1 year Frequency Percent Less than More than Total From Table 4 it can be understood that majority of the respondents 46.1 percent has used 3 to 5 online shopping websites for online shopping of products and services, followed but 42 percent who used more than 5 websites in last one year for shopping over internet. It clearly shows the perception of consumers towards online retailers. Copyright 2012 Published by IJMRR. All rights reserved 1050

7 Most frequently used online shopping website for travel bookings This table 5 represents most frequently used travel websites for travel booking by respondents. Table 5: Most frequently used online shopping website for travel bookings Frequency Percent Yatra.com Makemytrip.com Irctc.co.in Total From table 5 it can be understood that majority of the respondents 41.7 percent use Makemytrip.com website for booking their travel related services online. Similarly 41.2 percent of respondents use Irctc.co.in website for their ticket booking needs. Only 17.1 percent respondents use Yatra.com website for their travel related needs. This shows the perception of consumers towards each online travel shopping website. Personalization Factors behind Consumers Purchase Intentions Factor analysis was performed to reduce the 18 independent variables into 7 important personalization factors. To know the important personalization factors that influence purchase intentions of consumers for online travel shopping websites factor analysis was performed. The variables were formed as questions on a five point scale and respondents were asked to answer them. These eighteen statements are the independent variables which then get reduced to seven factors. Factor analysis was used to reduce dimensions of the eighteen independent variables into seven factors using Principal Component Analysis. Table 6: KMO and Barlett s Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy..579 Bartlett's Test of Sphericity Approx. Chi-Square df 153 Sig Significant value indicates that this is not an identity matrix. Hence factor analysis can be performed. Based on the above output, the KMO = This shows that this model is adequate for performing factor analysis. Table 7: Total Variance Explained Component Initial Eigenvalues Extraction Sums of Squared Rotation Sums of Squared Total % of Variance Cumulati ve % Total % of Variance Cumulati ve % Total % of Variance Cumulati ve % Copyright 2012 Published by IJMRR. All rights reserved 1051

8 Table 8: Rotated Component Matrix Component I prefer price filter option availability on websites Online shopping websites helps in comparing products of different 0.82 brands to a large extent. I compare features of services when I shop online This website offers me ability of personalizing a service by my 0.70 preference set. Ratings provided by different consumers help me in choosing a 0.88 service. Online marketers maintain robust content management and update it 0.82 on regular basis. I can check status of my bookings online easily Travel websites is the one stop to shop all travel related services 0.83 like hotels, taxy, flights etc. When I login to the websites, they offer me other supported 0.75 services also like hotels Shopping selection aids such as recommendations, FAQs, or expert's comments plays important role in purchase decisions. Online shopping of travel services provides competitive price deals Some e-tailors offer occasional and seasonal deals for travel related 0.84 services. Online shopping also provides good return policy and guaranteed 0.72 cash back if I cancel my bookings or do not want to use the services. I like options to save my personal information This website offers good customer services such as a phone 0.82 number, , or chatting. Etailers offer good and responsive enquiry services like options to save my financial information such as credit card 0.80 number. Online travel retailers offer more reward programs such as bonus points or miles The Rotated Component Matrix indicates, based on factor loadings that these eighteen components were reduced into seven factors. Details of the factors are given in below table 9. Copyright 2012 Published by IJMRR. All rights reserved 1052

9 Table 9: List of Factors S.N. Factor Name Variable Factor Reliability 1 I prefer price filter option availability on websites Online shopping websites helps in comparing products of 0.82 Convenience different brands to a large extent. I compare features of services when I shop online This website offers me ability of personalizing a service by 0.70 my preference set. 2 Website Ratings provided by different consumers help me in Content choosing a service. Online marketers maintain robust content management and 0.82 update it on regular basis. I can check status of my bookings online easily One stop shop Travel websites is the one stop to shop all travel related services like hotels, taxy, flights etc Competitive Deals 5 Guaranteed Cash Back 6 Responsivenes 7 Loyalty Programmes 1. Convenience When I login to the websites, they offer me other 0.75 supported services also like hotels Shopping selection aids such as recommendations, FAQs, or expert's comments plays important role in purchase decisions. Online shopping of travel services provides competitive 0.90 price deals. 3Some e-tailors offer occasional and seasonal deals for 0.84 travel related services. Online shopping also provides good return policy and 0.72 guaranteed cash back if I cancel my bookings or do not want to use the services. I like options to save my personal information This website offers good customer services such as a 0.82 phone number, , or chatting. Etailers offer good and responsive enquiry services like options to save my financial information such as 0.80 credit card number. Online travel retailers offer more reward programs such as bonus points or miles. This is the most important factor and captures 22.4 percent information in total. Consumers prefer to compare the products always before making purchase. So availability of option of comparing products online causes a great level of convenience for consumers and hence this factor is very important to discuss. When consumers busy and have lesser time for shopping, in that time availability of comparison of products sitting at home is proving very comfortable, useful and helpful to them. It saves both time and energy. These days are large number of websites which offer products from different brands in one online store. Hence consumers are getting attracted towards such features of online shopping websites. Hence marketers should focus in this dimension to attract large number of consumers. 2. Website Content This factor alone contributes to percent in total information. Content of website and its timely management is very important and necessary to stay competitive. These days online Copyright 2012 Published by IJMRR. All rights reserved 1053

10 e-tailers are focusing on regular content management of websites to provide updated and timely information to consumers. It increases the reliability of online seller and buyer gets desired information about the product and service. 3. One Stop Shop This factor captures percent in the total information. These days travel websites are offering travel products along with other supported services like hotel booking, taxi and many more. Hence they are evolving as one stop shop for all consumer needs. Marketers can make their strategies for future enhancement in the websites. 4. Competitive Deals Factor competitive deals captures percent of information in total. Travel websites offer attractive and competitive deals for consumers for special occasions and seasons. Hence consumers go for booking their tickets online in comparison with traditional medium of ticket booking. 5. Guaranteed Cash Back This factor contributes to percent in the total information. Online travel websites provide guaranteed cash back if the booking is cancelled by consumer or by Travel Company. Hence it produces trust among the shoppers and they go for booking their tickets online. It is an easy process of cancellation and getting the cash back. 6. responsiveness factor contributes to percent information in total. Online travel websites provide responsive enquiry services and act to customer feedbacks. Hence it makes easier for customers to solve their queries and gives them satisfaction. 7. Loyalty Programmes Loyalty programmes has captured percent information in total. Online travel shopping websites various kind of loyalty programmes to consumers such as bonus points, extra miles, coupons and many more. Hence they try to retain their customers and offer them attractive deals to increase their loyalty. On the basis of reliability only four factors were taken for further analysis out of 7 factors. One stop shop, guaranteed cash back and loyalty programmes were showing less reliability. Hence these factors were not included in further analysis. Consumers Perception towards personalization factors of online shopping travel websites Consumers perception plays very important role behind their purchase intentions towards online travel shopping. Hence perception of consumers has been measured here with performing ANOVA between demographic variables and personalization factors of online shopping websites. It shows whether there is any significance difference exists or not in the perception of consumers for personalization factors of online travel shopping websites with regard to their demographic profile and most used travel shopping website. Copyright 2012 Published by IJMRR. All rights reserved 1054

11 Analysis of Variance between Gender and Personalization Factors Gender wise association of Personalization factors is tested to find whether there is any significant difference between different levels educated online shoppers in association with Personalization factors. The test is conducted at the 5 percent significance level. Table 10: ANOVA for difference is Personalization Factors across the Gender of the shoppers. Convinience Website Content Competitive Deals Sum of df Mean F Sig. Squares Square Between Groups Within Groups Total Between Groups Within Groups Total Between Groups Within Groups Total Between Groups Within Groups Total The above shown ANOVA table10 is the test result of whether there is any significant difference between the two genders in perceiving personalization factors. Form the ANOVA table it is clear that the P value is greater than 0.05 that there is no significant difference between the perception of male and female shoppers towards the personalization factors. The test is conducted at 5% significance level. Hence male and female shoppers have similar perception towards online travel shopping websites. Analysis of Variance between Marital status and Personalization Factors Marital Status wise association of Personalization factors is tested to find whether there is any significant difference between different levels educated online shoppers in association with Personalization factors. The test is conducted at the 5 percent significance level. Table 11: ANOVA for difference in Personalization Factors across the Marital Status of the shoppers Convinience Website Content Competitive Deals Sum of df Mean F Sig. Squares Square Between Groups Within Groups Total Between Groups Within Groups Total Between Groups Within Groups Total Between Groups Within Groups Total The above shown ANOVA table11 is the test result of whether there is any significant difference (0.05) between the two marital statuses in interpreting personalization factors. Form the ANOVA table11 it is clear that there is no significant difference between the Copyright 2012 Published by IJMRR. All rights reserved 1055

12 perceptions single and married shoppers towards 4 personalization factors. Hence married and single status online shoppers have similar perception towards online travel shopping websites. Analysis of Variance between Education and Personalization Factors Education wise association of Personalization factors is tested to find whether there is any significant difference between different levels educated online shoppers in association with Personalization factors. The test is conducted at the 5 percent significance level. Table 12: ANOVA for difference in Personalization Factors across the Education level of the shoppers Convenience Website Content Competitive Deals Sum of df Mean F Sig. Squares Square Between Groups Within Groups Total Between Groups Within Groups Total Between Groups Within Groups Total Between Groups Within Groups Total ANOVA table 12 shows that there is a significant difference in product comparison convenience factor with the different education level groups. So it can be concluded that there is significant difference in the perception of different education groups with regard to product comparison convenience. Post Hoc Test The post hoc test helps to explore all the possible pair wise comparison of means and show the significant variables in the group wise. It will test the least significant difference of the variables under the factor. In the product comparison convenience factor the first group HSC has significance difference only in group PG. the group UG shows significance in only PG group. The third group shows significance in all the other three groups. Table 13: Multiple comparisons for education groups and comparison convenience Dependent Variable (I) Education (J) Education Mean Difference (I-J) Std. Error Sig. Convenience HSC UG PG Above PG UG PG * Above PG HSC PG * Above PG HSC * UG * Above PG * HSC UG PG * Copyright 2012 Published by IJMRR. All rights reserved 1056

13 Analysis of Variance between Family Income and Personalization Factors Family Income wise association of Personalization factors is tested to find whether there is any significant difference between different income groups of online shoppers in association with Personalization factors. The test is conducted at the 5 percent significance level. Table 14: ANOVA for difference of Personalization Factors across the family income of shoppers Convenience Website Content Competitive Deals Sum of df Mean F Sig. Squares Square Between Groups Within Groups Total Between Groups Within Groups Total Between Groups Within Groups Total Between Groups Within Groups Total ANOVA table14 shows the significant difference in competitive deals factor with the different income groups. So it can be concluded that there is significant difference in the perception of different income groups with regards to competitive deals. Post Hoc Test Table 15: Multiple s for income levels and Competitive deals Dependent Variable (I) Income (J) Income Mean Difference (I-J) Std. Error Sig. Competitive Deals Below 30,000 30,000-50,000 Above 50,000 30,000-50, Above 50, * Below 30, Above 50, Below 30, * ,000-50, In the competitive deals factor the first group (Below 30,000) has significant difference in the group Above 50,000. Analysis of Variance between Occupation and Personalization Factors Occupation wise association of Personalization factors is tested to find whether there is any significant difference between different occupations of online shoppers in association with Personalization factors. The test is conducted at the 5 percent significance level. Copyright 2012 Published by IJMRR. All rights reserved 1057

14 Table 16: ANOVA for difference of personalization factors across the Occupation of shoppers Convenience Website Content Competitive Deals Sum of df Mean F Sig. Squares Square Between Groups Within Groups Total Between Groups Within Groups Total Between Groups Within Groups Total Between Groups Within Groups Total ANOVA table 16 shows the significant difference in product comparison convenience factor with the different occupation groups. So it can be concluded that there is significant difference in the perception of different occupation groups with regards to product comparison convenience. Post Hoc Test Table 17: Multiple comparisons for Occupations and comparison convenience Dependent Variable (I) Occupation (J) Occupation Mean Difference (I-J) Std. Error Sig. Convenience Student Salaried People Self Employed House Wife Salaried People Self Employed House Wife * Student Self Employed House Wife * Student Salaried People House Wife * Student * Salaried People * Self Employed * In the product comparison convenience factor group house wife has significant difference between all three occupation groups student, salaried people and self-employed. Analysis of Variance between age and Personalization Factors Age wise association of Personalization factors is tested to find whether there is any significant difference between different age groups of online shoppers in association with Personalization factors. The test is conducted at the 5 percent of significance level. Copyright 2012 Published by IJMRR. All rights reserved 1058

15 Table 18: ANOVA for difference of personalization factors across the Age of shoppers Convenience Website Content Competitive Deals Sum of Squares df Mean Square F Sig. Between Groups Within Groups Total Between Groups Within Groups Total Between Groups Within Groups Total Between Groups Within Groups Total ANOAV table18 shows the significant differences in product comparison convenience factor with the different age groups. So it can be concluded that there is significant difference in the perception of different age groups with regards to product comparison convenience. Post Hoc Test Table 19: Multiple s for age and product comparison convenience Dependent Variable Convenience (I) Age (J) Age Mean Difference Std. Error Sig. (I-J) Less than * Above Above * Less than * * Above Less than * Above Less than * In the product comparison convenience factor group less than 20 shows significant difference only in the group Analysis of Variance between most used travel websites and personalization factors Website used wise association of Personalization factors is tested to find whether there is any significant difference between particular travel websites online shoppers in association with Personalization factors. The test is conducted at the 5 percent significance level. Copyright 2012 Published by IJMRR. All rights reserved 1059

16 Table 20: ANOVA for difference of Personalization factors across the most used travel websites by shoppers Convenience Website Content Competitive Deals Sum of Squares df Mean Square F Sig. Between Groups Within Groups Total Between Groups Within Groups Total Between Groups Within Groups Total Between Groups Within Groups Total ANOVA table20 shows the significant difference in with the different website groups. So it can be concluded that there is significant difference in the perception of different website groups with regard to. Post Hoc Test Table 21 Multiple s for most used travel websites and customer responsiveness Dependent Variable (I) Most used Travel Websites (J) Most used Travel Websites Mean Difference (I-J) Std. Error Sig. Yatra.com Makemytrip.com Irctc.co.in Makemytrip.com * Irctc.co.in * Yatra.com * Irctc.co.in Yatra.com * Makemytrip.com In the customer responsiveness factor website group Yatra.com has significance difference between the website group Makemytrip.com and itctc.com. Personalization Factors and impact on Consumers purchase intentions Correlation and Regression analysis is performed to analyze the impact of personalization factors of selected online travel shopping websites on consumers purchase intentions. Here relationship between purchase intentions and personalization factors is also found out. Regression analysis performed to know the level of impact on consumers purchase intentions due to personalization factors. Relationship between Consumers purchase intentions and Personalization Factors Correlation analysis is conducted to find out the relationship between the dependent variable purchase intentions and all the four personalization factors. This analysis will interpret the relevance of study by analyzing the relation between variable. Correlation is conducted among all the independent variables (personalization factors) to know the inter correlation. Copyright 2012 Published by IJMRR. All rights reserved 1060

17 Table 22: Correlation Analysis Purchase Intentions Convinience Website Content Competitive Deals Purchase Intentions Convinience Website Content Competitive Deals Pearson 1 Correlation Sig. (2-tailed) N 597 Pearson.571 ** 1 Correlation Sig. (2-tailed).000 N Pearson ** ** 1 Correlation Sig. (2-tailed) N Pearson.333 **.380 ** Correlation Sig. (2-tailed) N Pearson.425 **.115 ** ** Correlation Sig. (2-tailed) N The correlation table22 indicates that correlation analysis is significant at 5 percent significance level. There is a positive correlation between the dependent variable purchase intentions and three independent personalization factors. The only personalization factor website content has negative correlation with the dependent variable. It is found that purchase intentions are positively correlated to product comparison convenience (r=0.571), competitive deals (r=0.333), customer responsiveness (r=0.425) and. comparison convenience shows the high level of correlation with r= The correlation table 22 also shows that there is an inter correlation among the independent variables and most them are positively correlated to each other. It is found that product comparison is positively correlated with competitive deals (r=0.380), customer responsiveness (r=0.115). Website content has negative correlation with three independent variables. Impact of Personalization Factors on Consumers purchase intentions Regression analysis is conducted to find out the impact of personalization factors on consumer purchase intentions. In the regression analysis consumer purchase intention is the dependent variable and independent variables are product comparison convenience, website content, competitive deals, and customer responsiveness. The four personalization factors which are considered as independent variables are used to test if personalization factors significantly influenced consumers purchase intentions. Table 23: Analysis of Variance for Purchase Intentions of Consumers 1 Model Sum of df Mean F Sig. Squares Square Regression b Residual Total Copyright 2012 Published by IJMRR. All rights reserved 1061

18 In the ANOVA table 23 P=.000 indicates that overall the model applied is significantly good enough to predict consumer purchase intention. It indicates that the study is relevant and it has got a significant importance. Table 24: Model Summary for Purchase Intentions of Consumers Model R R Square Adjusted R Square Std. Error of the Estimate a The model summary table 24 of the regression indicates that the 4 predictors of consumer purchase intention (the independent variable product comparison convenience, website content, competitive deals, customer responsiveness) represent 53 percent of the variance. It means that 53 percent of the purchase intention of the consumers is affected by personalization factors, while the rest may be due to other variables. Table 25: Coefficients of Regression for Purchase Intentions of Consumers 1 Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta (Constant) Convenience Website Content Competitive Deals From the coefficient table25 it is clear that there is significant relations (at 5 percent significance level) of consumer purchase intentions with all personalization factors (independent variables). Hence it is concluded that all the four personalization factors product comparison convenience, website content, competitive deals, and customer responsiveness influenced consumers purchase intentions. All the personalization factors significantly impacts consumers purchase intentions. The regression equation: y = a + b 1 x 1 + b 2 x 2 + b 3 x 3 + b n x n + standard error y = dependent variable (consumer purchase intentions) x 1 = Convenience x 2 = Website Content x 3 = Competitive Deals x 4 = a = 0.23 (constant) b 1 =.78 b 2 = -.58 Copyright 2012 Published by IJMRR. All rights reserved 1062

19 b 3 =.19 b 4 =.54 In the above table25 product comparison convenience has got the high coefficient value of.78, so product comparison convenience has got more impact on consumer purchase intentions. The regression equation is given by: IMPLICATIONS OF RESEARCH The finding of this study proves that personalization is a part of online shopping and it cannot be separated. It has more positive effect on consumers in comparison with products and services alone. This study is the first study which examines the effect of personalization features on the perception of consumers and their purchase intention with regard to the online travel shopping. This study also indicates that proper implementation of personalization features can bring more positive and powerful results and can increase the consumers purchase intentions. Web portals have to include the new and preferred personalization features in their websites to become competitive in travel industry. This will be helpful in increasing the competitiveness. The results of this study have some important implications. The findings of this study suggest that use of personalization features in the online travel websites can definitely encourage the positive perception of consumers and can make higher the intentions of consumers. Implementation of personalization features in the online travel shopping websites and personalization factors will influence the purchase intentions of consumers. It can be implicated from this study that online travel companies and their users are focusing on the adoption of the personalization features and their implementation in the online travel shopping websites. Both government and public sector service providers are incorporating the innovative personalization features in online travel industry for attracting potential consumers. SUGGESTIONS AND RECOMMENDATIONS This study is conducted to investigate the perception of consumers and their purchase intentions with respect to personalization features in the travel websites. comparison convenience is the most important personalization factor behind the purchase intentions of consumers. Hence all the three website designers Yatra.com, Makemytrip.com and Irctc.co.in should focus on other personalization factors like competitive deals and customer responsiveness. These website marketers should focus on providing more competitive deals to consumers according to their preferences. For example these online travel shopping websites can offer special discounts for students in their summer holidays. It will encourage consumers to plan their trip in that season and companies can get benefit from it as well as consumers will also feel to get advantages. Also they can offer some special packages for senior citizens to travel to their holy destinations. Yatra.com website should provide more Copyright 2012 Published by IJMRR. All rights reserved 1063

20 customer responsive approach and it can improve on it by giving dedicated customer care no. and online support. Makemytrip.com should focus on website content management and should try to make it more informative for customers. Personalization factor of Irctc.co.in has highest impact on consumers purchase intentions. Hence other online shopping websites can refer to it and can improve on accordingly. In this study it has been revealed that demographic profile of consumers also plays major role on perception of consumers. SCOPE FOR FURTHER RESEARCH There is a scope for conducting further research study for solving the managerial level problems for helping the managers. So that can use the personalization in a better way and can adopt it. It will help them in formulating new marketing strategies. In the same field further studies can be conducted with use of random sampling with scientific selection for compensating the shortcomings of this study. Research in this subject can also include the combination of personalized services in online travel industry and personalized features provided by other industries in the industry of online shopping. LIMITATIONS OF THE STUDY The major limitations of this study are with respect to the number of websites chosen for survey. Only three websites Yatra.com, Makemytrip.com and Irctc.co.in were chosen to conduct the study. Another major limitation is location chosen. Only Bangalore has been chosen for study. Hence this study may not reflect the perception and purchase intentions of all the online travel shoppers. CONCLUSION Analysis shows that online shoppers prefer personalized services in online travel websites. Although, consumers have started using online shopping websites for booking of tickets, but it needs to go long way to find considerable market share for companies in the field of online marketing. To get them out of this traditional way of booking tickets and other travel related services, can be a challenge. Online marketers need to focus on adoption of attractive personalized features and strategies to attract potential consumers. REFERENCES [1] Jr SF, Gelb JW. Consumer Privacy Regulation, Enforcement, and Litigation in the United States. Business Lawyer 2003; 58(3): [2] Kambil A, Nunes PF. Personalization? No thanks. Harvard Business Review 2001; 79(4): [3] Law R, Bai B. How do the preferences of online buyers and browsers differ on the design and content of travel websites? International Journal of Contemporary Hospitality Management 2008; 20(4): [4] Munoz-Leiva F, Hernandez-Mendez J, Sanchez-Fernandez J. Generalising user behaviour in online travel sites through the Travel 2.0 website acceptance model. Online Information Review 2012; 36(6): Copyright 2012 Published by IJMRR. All rights reserved 1064