The Effectiveness Of Online Advertisement An Empirical Study In Coimbatore District

The Effectiveness Of Online Advertisement An Empirical Study In Coimbatore District Sudha Padmanaban Thavathiru Santhalinga Adigalar Arts Science And Tamil College, Perur, Coimbatore, TamilNadu, India ABSTRACT Today marketing is getting extremely competitive as it is a customer centric and customers are smarter, more demanding and less patient with unresponsive vendors. To tackle with these new business drivers Information is a key. And in this back drop Internet is emerging as the biggest trends in Information Technology since it can be used as one of the good vehicle for advertisement. Advertising is a way to display, to make people aware about a product. Advertising is one of the important tools in market promotion. The present article clearly deals with the effectiveness of online advertisement in an efficient manner. Keywords: Internet, online Advertisement and effectiveness. INTRODUCTION Online advertising is a tool of marketing and advertising which uses the Internet to deliver promotional marketing messages to consumers. It includes email marketing, search engine marketing (SEM), social media marketing, many types of display advertising (including web banner advertising), and mobile advertising. Like other advertising media, online advertising frequently involves both a publisher, who integrates advertisements into its online content, and an advertiser, who provides the advertisements to be displayed on the publisher's content. Other potential participants include advertising agencies who help generate and place the ad copy, an ad server which technologically delivers the ad and tracks statistics and advertising affiliates who do independent promotional work for the advertiser. STATEMENT OF THE PROBLEM Compared with traditional advertising, online advertising is different that consumer behavior affects steps of the online advertising management process. This paper focuses on the influence of advertising management on effectiveness of online advertising, based on the interactivity between consumers and advertisers and hence the researcher has chosen this area to study the effectiveness of online advertisements. OBJECTIVES OF THE STUDY To analyze the effectiveness of online advertisement with reference to Coimbatore district. REVIEW OF LITERATURE Kaur (2008) illustrates why some footwear sales companies in Ireland are resistant to using social networking advertising while some companies in the same industry are using this medium extensively to advertise their products. Explanatory study was conducted for this research to explain the factors and reasons of using or not using social networking sites as an online advertising medium by companies. The data was collected through two types of questionnaires. The first type of questionnaire was distributed personally to footwear sales companies. The second type of questionnaire was prepared to discover the attitudes of people about online advertising and distributed online. This research also found that today most of the people believe in the Internet. They are of the view that online advertising is better than traditional advertising. Papadopoulos (2009) aims at analyzing the impact of the type of online user activity as well as of the user s online social context on the effectiveness of internet advertising. The objectives of the thesis are pursued through the design and implementation of an online experiment that simulates four types of www.theinternationaljournal.org > RJCBS: Volume: 05, Number: 03, January-2016 Page 1

online activities that are popular among today s plethora of Web 2.0 applications. Starting from the study of the most influential research works in the area of internet advertising effectiveness and following the principles of their experimental methodology, an online experiment was designed and implemented that collected input from a set of 87 users. The analysis of the obtained input reveals significant correlations between the type of online activity of users and the effectiveness of internet advertising. Furthermore, there is evidence that the content of a webpage and the degree of its congruency to the advertising content play a significant role on the impact of online advertising. Ma and Liu (2010) focusing on how advertising management influence the effectiveness of online advertising. The theoretical framework of this study mainly contains the advertising management and effectiveness of effects model. These two models are combined together in an analytical model where a connection between the two theories is explained that will be used as a foundation in gathering and analyzing the empirical findings. The quantitative research strategy is applied here. The conclusion which can be drawn from this study is that there is clear evidence that good management will improve cognitive, affective and conative degree of consumer behavior. RESEARCH METHODOLOGY The researcher has adopted a convenient sampling method for this study. The sample size of the study is restricted to 250. The study was mainly based on primary data. The structured questionnaires were used for data collection. LIMITATIONS OF THE STUDY The study has been restricted to 250 respondents only. The coverage of this study is limited to Coimbatore district only and may not apply the findings and suggestions to other areas. DATA ANALYSIS AND INTERPRETATION The study explores the important factors that are determining the effectiveness of online advertisements towards customers purchase intention in Coimbatore city is depicted in the table 1. TABLE 1 VARIABLES SPECIFICATION FOR ANALYSIING THE EFFECTIVENESS OF ONLINE ADVERTISEMENT S.No Variables Statements No. I. Quality of Website 1. VAR1 Information quality is significant factor for considering the quality of website and it make customer to be more effected by advertisement. 2. VAR2 Attractiveness of website design can influence on perceived website quality. 3. VAR3 The web site provides various types of credit cards for payment (e.g. Visa, MasterCard, Diners, and American Express. 4. VAR4 I believe that the sort of design which is used in website can grab the attention of customer and make them satisfy. 5. VAR5 I feel trust and feel secure while I am visiting from that company which have provided secure website. 6. VAR6 Online advertisements offers better products in quality. 7. VAR7 It ensures prompt delivery of the product. II.Effectiveness of Website quality on customer advertisement perception 8. VAR8 I am more curies to those website ads which have high quality design. 9. VAR9 Information quality is significant factor for considering in web base advertisement 10. VAR10 The ads which have returned in website which has applied sophisticated design will definitely influence on customer. 11. VAR11 I am so concern about security of website so I am more interested to go through www.theinternationaljournal.org > RJCBS: Volume: 05, Number: 03, January-2016 Page 2

12. VAR12 13. VAR13 14. VAR14 15. VAR15 that web advertisement which already has assured the security for their online users. III.Social network Social networking sites are a good approach to stimulate customer positive perception toward E-advertisement. I do read product reviews through social networking sites before purchasing the product. There are sufficient advertisement about products and services on social networking sites which can improve the effectiveness of advertisement. Do you agree that social networks would be the best advertisement tool in the future? IV.Brand Recognition 16. VAR16 Good brand image always associate with purchase intention. 17. VAR17 I do prefer to consider that advertisement which is belonging to branded product is more effective to stimulate customer purchase intention. 18. VAR18 Online advertisements boost brand awareness among the people. 19. VAR19 Most often I use branded product and services. 20. VAR20 Online advertisements help to build brand loyalty. V.Effectiveness of E-advertisement & Perception of customer toward E- advertise 21. VAR21 Multimedia features in online advertisements give consumers a positive feeling toward the product or service. 22. VAR22 Repeated online advertisements affect the behavior of the consumer. 23. VAR23 Ads animation makes me to recall the ad very easily. 24. VAR24 Pictures in online advertisements give consumers a positive feeling toward the product or service. 25. VAR25 Contents in online advertisements give consumers a positive feeling toward the product or service. 26. VAR26 Multimedia features in online advertisements will stimulate consumers to learn more about the product or service. 27. VAR27 Pictures help generate favorable consumer response to the brand of the product or service. 28. VAR28 Consumers will consider purchasing the product or service based on the contents in the online advertisement. 29. VAR29 Contents in online advertisements will persuade consumers to click on the advertisement. VI.Demography 30. VAR30 Social network has not influence on purchase intention based on gender. 31. VAR31 I strongly believe that effective advertisement can effect on customer purchase intention. 32. VAR32 I believe age has strong contribution on Effectiveness of advertisement. 33. VAR33 I believe educational qualification has strong contribution on customer perception toward E-advertisement. 34. VAR34 I believe income has strong contribution on Effectiveness of advertisement. 35. VAR35 I believe income has strong contribution on customer perception toward E advertisement. VII.Customer Purchase Intention 36. VAR36 The effective advertisement would result to purchase. 37. VAR37 Most of my online purchase has effect by online advertisement. 38. VAR38 Most of web based advertisement influence on my purchase intention. VIII.Information and Customization www.theinternationaljournal.org > RJCBS: Volume: 05, Number: 03, January-2016 Page 3

39. VAR39 An online ad provides smoother and free flow of information. 40. VAR40 Helps in customizing solutions to fulfill clients requirements. 41. VAR41 It responds to customer needs or queries immediately. 42. VAR42 It provides timely information about the upcoming product. IX.Convenience and Enjoyment 43. VAR43 The user can experience real fun and enjoyment with online ads. 44. VAR44 It enables me to shop around for cheapest items. 45. VAR45 An online ad gives the relaxation to the user. 46. VAR46 A web ad makes the work easier by making comparison shopping easy. 47. VAR47 Online ads are so convincing which leads to purchase. 48 VAR48 Online ads are user-friendly in nature. 49. VAR49 The products offered in online ads are very broad which makes the people to feel convenient in shopping. 50. VAR50 Accessibility of the various services offered by online ads. The result of the fitness test regarding factor analysis based on KMO adequacy has been presented in table 2. TABLE 2 KMO AND BARTLETT'S TEST Kaiser-Meyer-Olkin Measure of Sampling Adequacy..837 Bartlett's Test of Sphericity Approx. Chi-Square 2.753 df 1225 Sig..000 Table 2 reveals that the value is 0.837 which is not less than 0.5 and hence satisfactory. So, the factor analysis for the present study is effective and suitable. In the present study, the data matrix comprising a large number of identified variables which are inter-related have been tested for the amount of variance that each variable shares with all other variables and the same has been presented in table 3. TABLE 3 COMMUNALITIES Initial Extraction VAR00001 1.000.668 VAR00002 1.000.642 VAR00003 1.000.691 VAR00004 1.000.649 VAR00005 1.000.680 VAR00006 1.000.651 VAR00007 1.000.608 VAR00008 1.000.616 VAR00009 1.000.658 VAR00010 1.000.660 VAR00011 1.000.650 VAR00012 1.000.684 VAR00013 1.000.661 VAR00014 1.000.633 www.theinternationaljournal.org > RJCBS: Volume: 05, Number: 03, January-2016 Page 4

VAR00015 1.000.766 VAR00016 1.000.750 VAR00017 1.000.772 VAR00018 1.000.783 VAR00019 1.000.761 VAR00020 1.000.843 VAR00021 1.000.553 VAR00022 1.000.601 VAR00023 1.000.552 VAR00024 1.000.602 VAR00025 1.000.616 VAR00026 1.000.667 VAR00027 1.000.587 VAR00028 1.000.556 VAR00029 1.000.628 VAR00030 1.000.639 VAR00031 1.000.616 VAR00032 1.000.547 VAR00033 1.000.681 VAR00034 1.000.622 VAR00035 1.000.560 VAR00036 1.000.622 VAR00037 1.000.688 VAR00038 1.000.595 VAR00039 1.000.586 VAR00040 1.000.651 VAR00041 1.000.575 VAR00042 1.000.601 VAR00043 1.000.729 VAR00044 1.000.732 VAR00045 1.000.672 VAR00046 1.000.653 VAR00047 1.000.698 VAR00048 1.000.810 VAR00049 1.000.758 VAR00050 1.000.842 Extraction Method: Principal Component Analysis. The communalities were shown in table 3 measures that the amount of variance a variable shares with all other variables. It is a proportion of each variable s variance as explained by the principal component. A large communality means a large amount of the variance a variable has extracted by the factor solution. Thus, the table indicates that the extracted communalities are high and acceptable for all the variables. www.theinternationaljournal.org > RJCBS: Volume: 05, Number: 03, January-2016 Page 5

EXTRACTION METHOD: PRINCIPAL COMPONENT ANALYSIS It is necessary that the scale constructed and the components extracted should be able to explain the variance in the data. To measure the important factors that determine the Effectiveness of Online Advertisements towards customers purchase Intention in Coimbatore city, the initial eigen values, extraction sums of squared loadings and the rotation sums of squared loadings have been presented in table 4. TABLE 4 TOTAL VARIANCE EXPLAINED Initial Eigenvalues Component Total % of Cumulative Total Variance % Extraction Sums of Squared Loadings % of Cumulative Total Variance % Rotation Sums of Squared Loadings % of Cumulative Variance % 1 9.015 18.031 18.031 9.015 18.031 18.031 4.881 9.762 9.762 2 6.029 12.058 30.089 6.029 12.058 30.089 4.010 8.020 17.781 3 2.731 5.462 35.551 2.731 5.462 35.551 3.817 7.633 25.415 4 2.247 4.495 40.046 2.247 4.495 40.046 2.618 5.236 30.651 5 1.942 3.884 43.930 1.942 3.884 43.930 2.389 4.777 35.428 6 1.863 3.727 47.656 1.863 3.727 47.656 2.231 4.462 39.890 7 1.783 3.565 51.222 1.783 3.565 51.222 2.090 4.180 44.070 8 1.551 3.102 54.324 1.551 3.102 54.324 2.054 4.108 48.177 9 1.355 2.710 57.034 1.355 2.710 57.034 2.015 4.030 52.208 10 1.308 2.617 59.651 1.308 2.617 59.651 1.866 3.731 55.939 11 1.183 2.365 62.016 1.183 2.365 62.016 1.853 3.707 59.646 12 1.049 2.098 64.114 1.049 2.098 64.114 1.679 3.358 63.004 13 1.010 2.021 66.134 1.010 2.021 66.134 1.565 3.130 66.134 14.978 1.956 68.090 15.893 1.785 69.876 16.869 1.737 71.613 17.791 1.582 73.195 18.754 1.508 74.704 19.707 1.414 76.117 20.671 1.343 77.460 21.655 1.309 78.770 22.630 1.259 80.029 23.610 1.219 81.248 24.597 1.194 82.442 25.573 1.146 83.588 26.552 1.103 84.691 27.518 1.035 85.726 28.493.986 86.712 29.476.951 87.664 30.461.921 88.585 31.449.899 89.484 www.theinternationaljournal.org > RJCBS: Volume: 05, Number: 03, January-2016 Page 6

32.434.868 90.352 33.399.799 91.151 34.374.749 91.899 35.347.695 92.594 36.343.686 93.280 37.338.677 93.957 38.320.640 94.597 39.312.625 95.222 40.290.580 95.802 41.281.562 96.364 42.269.538 96.902 43.244.488 97.390 44.235.471 97.861 45.232.465 98.326 46.210.419 98.745 47.193.386 99.131 48.181.363 99.493 49.163.326 99.819 50.090.181 100.000 Extraction Method: Principal Component Analysis. Table 4 shows that though there are 50 variables that can be extracted, but only thirteen variables can be extracted among the 50 variables which have eigen value more than one. By retaining only those variables with eigen values greater than one, it can be inferred that 18.031 percent of variance is explained by factor 1 and 12.058 percent of variance is explained by factor 2 and 5.462 percent of variance is explained by factor 3 and 4.495 percent of variance is explained by factor 4 and 3.884 percent of variance is explained by factor 5 and 3.727 percent of variance is explained by factor 6 and 3.565 percent of variance is explained by factor 7 and 3.102 percent of variance is explained by factor 8 and 2.710 percent of variance is explained by factor 9 and 2.617 percent of variance is explained by factor 10 and 2.365 percent of variance is explained by factor 11 and 2.098 percent of variance is explained by factor 12 and 2.021 percent of variance is explained by factor 13. Thus all the thirteen factors put together explain the variance to the extent of 66.134 percent. Table 4 also indicates that the total of 66.134 percent variance is not uniformly distributed across all the variables, since it is evident that only the first component accounts for 18.031 percent variance. As the variables are not uniformly distributed, the rotated sum of squared loadings method is used to distribute the variables uniformly across all the factors whose eigen value is more than one. Hence, to show the components loading which are the correlations between the variables and the components, component matrix has been presented in table 5. TABLE 5 COMPONENT MATRIX Component 1 2 3 4 5 6 7 8 9 10 11 12 13 VAR00025.552 -.248.072.032.090 -.172.026.132 -.059 -.265.310.030 -.130 VAR00034.552 -.225 -.309 -.019.203 -.212.228.031 -.085 -.002 -.063 -.028 -.142 VAR00031.536 -.264.008.157 -.173 -.048.078.093 -.090.307 -.278 -.080.032 VAR00027.535 -.266 -.053.080.009 -.215.187.249 -.121.000 -.140.021 -.207 www.theinternationaljournal.org > RJCBS: Volume: 05, Number: 03, January-2016 Page 7

VAR00017.535.374.232 -.181 -.040.054 -.071 -.129 -.063.008 -.226.078 -.415 VAR00026.532 -.279 -.221.065 -.004 -.225.355 7.803 -.180.094 -.134.130 -.028 VAR00047.531 -.146.295 -.092.214.005 -.142 -.124.259.059 -.118 -.095 -.354 VAR00036.526 -.254.085.143 -.218 -.032.175 -.040.251 -.299.108.087 -.037 VAR00037.523 -.262 -.092 -.051 -.013 -.129.277 -.173.289.101 -.246.217.094 VAR00014.516.319 -.170 -.398 -.006.022.034 -.065 -.010 -.208 -.003 -.138 -.100 VAR00035.514 -.254 -.111.172 -.122.050.016 -.126.089 -.072.049.340.158 VAR00045.507 -.201.151 -.040.255.119 -.018 -.342 -.265.149.178 -.133.110 VAR00028.506 -.268.129.093 -.041 -.207.146.251 -.088.224.039 -.068 -.098 VAR00041.503 -.173.009.015 -.008.247 -.147 -.073.313.256 -.107 -.064 -.159 VAR00044.501 -.195 -.239 -.120.202.010.107 -.379.161 -.199.098 -.309 -.077 VAR00010.499.380 -.123 -.373 -.178.071.023.179.079 -.132 -.098 -.094.026 VAR00011.484.384 -.096 -.295 -.239.136 -.079.187.197.017 -.039 -.121 -.014 VAR00042.484 -.205 -.141 -.129.384.004.132.041.022.161.217 -.116.188 VAR00032.471 -.242.193.078 -.198.040.067 -.137.217 -.178.202.163 -.113 VAR00012.462.379 -.199 -.398 -.005.021.087.228 -.030 -.008.043 -.094.239 VAR00050.461 -.190 -.328.309 -.369.177 -.322.087 -.125.183.228 -.023 -.095 VAR00030.461 -.317 -.188.133.001 -.080.218 -.249 -.114 -.057 -.228.042.296 VAR00020.453 -.177 -.347.259 -.389.204 -.354.116 -.146.133.194.034 -.097 VAR00024.444 -.162.176.246 -.095.100 -.102.001 -.277 -.257 -.124 -.242.202 VAR00009.438.430 -.222 -.364 -.166.094.083.133.044.014.011 -.062.181 VAR00016.433.405.220 -.040 -.134.230 -.134 -.250 -.352 -.104 -.184.165 -.025 VAR00022.424 -.165.313.086 -.084 -.015.048.292 -.145 -.157.281 -.263 -.019 VAR00046.396 -.131.277.089.090.350 -.270 -.357.016 -.067.062 -.093.215 VAR00005.199.696.055.242.013 -.134 -.024 -.123.084.083.111.158 -.097 VAR00006.198.689 -.005.295.019 -.099 -.051.032.124 -.014.061.108.078 VAR00003.197.686 -.005.337.123 -.122.055 -.004 -.035.045.054 -.168.014 VAR00008.259.666 -.055.129 -.012 -.102.042.084.144.024.027.086.191 VAR00007.290.656.072.113 -.059 -.036.020 -.037.112 -.036 -.002.187.143 VAR00004.204.652.097.331.130 -.153 -.016 -.060.080.048.085.052 -.019 VAR00002.217.649.030.238.064 -.137.130 -.076 -.005.069.033 -.208 -.145 VAR00001.245.615 -.039.320.113 -.123.142 -.070 -.064.072.001 -.252.023 VAR00019.442 -.094 -.608.142.150 -.006 -.301.024.061 -.197 -.080.062.023 VAR00049.432 -.164 -.605.198.181 -.042 -.262 -.046.088 -.150 -.059 -.004.000 VAR00023.319 -.136.545.020 -.074.015.065.002.197 -.141 -.136 -.180.119 VAR00029.403 -.157.416.159 -.380.088.021.096.255 -.019 -.046 -.043.105 VAR00033.396 -.163.405 -.026.097 -.187.171.213 -.192 -.228.226.269.040 VAR00013.410.290 -.018 -.545 -.069.077 -.044.069 -.043.124.209.182.004 VAR00048.359 -.122.238.030.542 -.079 -.440.282.096 -.006 -.129.054.082 VAR00040 -.012.063 -.077.189.238.627.321.189.078 -.066 -.051.056.008 VAR00039.026.046 -.013.173.236.615.307.139.025 -.027 -.004.060 -.027 www.theinternationaljournal.org > RJCBS: Volume: 05, Number: 03, January-2016 Page 8

VAR00038.005.099 -.065.223.241.522.300.287 -.024.039 -.014.062 -.150 VAR00018.366.015.205 -.054.419 -.080 -.477.342 -.035 -.076 -.207.152.075 VAR00015.479.359.130 -.140 -.005.166.023 -.204 -.502 -.014 -.143.157 -.071 VAR00043.384 -.146.126 -.230.278.036.015 -.205.067.440.382.158.047 VAR00021.397 -.190.245.062 -.153 -.020 -.019.129.019.439 -.136 -.082.190 Extraction Method: Principal Component Analysis. a. 13 components extracted. COMPONENTS EXTRACTED Table 5 shows that the components loading that are the correlations between the variables and the components. It is a general practice that while interpreting a component, importance is given to the larger size of the component loading for a variable. Also, the first component is generally more highly correlated with the variables than the second and so on. Thus, it can be seen from table 4 that the variance is now evenly distributed in a range of 9.762 66.134 percent, which was earlier 18.031 66.134 percent. Varimax rotation (Rotated Component Matrix) was applied for all the 50 variables. However, the factor loading of all the variables was observed and clubbed into thirteen factors, which has been presented in table 6. TABLE 6 ROTATED COMPONENT MATRIX Component 1 2 3 4 5 6 7 8 9 10 11 12 13 VAR00003.799 -.002.089.008 -.165.061 -.004 -.008.027 -.037.058.089.016 VAR00004.792 -.053.015 -.015.031.011 -.028.087.075.041.005 -.031.034 VAR00005.771 -.087.078.034.083 -.054 -.063 -.005.166.067 -.042 -.145.053 VAR00006.764 -.084.145.059.088 -.052 -.036.099.038 -.055.026 -.055 -.082 VAR00001.750.109.097 -.015 -.194.050.039 -.089.029 -.027.059.165.052 VAR00002.736.024.117 -.047 -.149.079.006 -.124.072 -.030.020.034.181 VAR00008.683 -.006.325 -.010.080 -.077.031.074 -.005 -.016.023 -.055 -.145 VAR00007.653 -.055.283 -.042.206 -.087.018.065.175 -.010.015 -.032 -.106 VAR00026 -.034.753.054.094.096.164.149 -.062.112.092 -.005 -.061 -.029 VAR00034 -.013.705.147.115 -.012.201 -.045.070 -.017.121.013.002.165 VAR00030 -.072.645 -.005.061.214 -.065.121 -.022.096.055 -.028.341 -.143 VAR00037 -.048.605.134 -.072.431 -.163.213.032 -.013.152 -.028 -.012.117 VAR00027 -.044.572.069.121.070.368.197.126.067 -.068.014 -.115.148 VAR00009.223.090.759.065.012 -.034.057 -.047.062.054.029.020 -.068 VAR00012.195.149.756 -.001 -.080.080.032.081.018.112.028.048 -.123 VAR00010.172.093.756.038.078.058.046.047.119 -.103.003.023.100 VAR00011.212 -.031.709.159.105.003.157.038.045 -.046.013 -.014.184 VAR00014.155.187.648.011.026.093 -.189 -.006.187.037 -.056.128.229 VAR00013.049 -.045.644.042.062.072 -.013.052.251.355 -.069 -.186.005 VAR00020 -.036.106.105.871.119.114.153 -.022.091.021.001.028.001 VAR00050.004.123.058.863.106.133.190 -.057.041.066 -.005.057.030 VAR00019.048.444.182.531.071 -.133 -.281.324 -.103 -.068.007.149.040 VAR00049.046.490.092.520.059 -.130 -.262.272 -.163 -.022 -.010.190.099 VAR00036 -.003.293.084.112.626.292.052 -.053 -.043 -.041.008.117.129 www.theinternationaljournal.org > RJCBS: Volume: 05, Number: 03, January-2016 Page 9

VAR00032 -.036.133.024.132.615.257.050 -.050.075.127 -.021.061.189 VAR00035 -.018.370.027.292.523.003.059.080.100.151.027.083 -.114 VAR00029.015 -.044.073.054.518.214.511.010.006 -.095 -.002.150.104 VAR00022 -.002.019.106.100.117.686.215.055 -.017.028.029.203.052 VAR00033 -.012.174.009 -.174.314.608.040.227.201.161 -.015 -.066 -.166 VAR00025 -.016.299.085.155.277.581 -.107.171.004.150 -.060.061.103 VAR00028 -.006.384.027.121.053.424.405.074 -.013.136 -.043 -.084.115 VAR00021 -.015.150.056.094.074.064.676.108.023.178 -.059.066.025 VAR00031 -.033.462.047.222.058.082.557.042.106.009 -.010.076.098 VAR00023 -.011 -.028.040 -.269.360.242.372.126.029 -.062 -.023.307.189 VAR00018.050.043.114.029 -.019.133.075.848.135.025 -.035.027.050 VAR00048.030.070 -.018.002.008.142.105.845 -.038.152.004.107.149 VAR00015.235.150.277.018 -.052.084.017.019.755.115.056.123.004 VAR00016.284 -.033.237.071.117 -.007.051.058.738 -.012.017.202.027 VAR00017.286.075.322 -.020.106.070.084.107.561 -.001 -.032 -.078.472 VAR00043 -.021.105.078.017.116.052.111.081.042.811 -.020 -.041.120 VAR00045 -.028.233 -.004.074.006.182.092.054.262.541 -.003.438.117 VAR00042 -.035.391.216.041 -.030.174.039.172 -.190.499.123.190.029 VAR00040.017 -.006.022 -.021.038 -.061 -.037 -.007 -.027 -.026.798.056 -.028 VAR00039.014 -.017 -.004 -.016.038 -.011 -.018 -.023.041.050.758.062.003 VAR00038.078.014 -.023.041 -.085.058.002.005.018 -.016.751 -.098.026 VAR00046 -.013 -.100 -.021.138.299 -.013.109.177.223.285.061.582.134 VAR00024.005.169.005.175.114.308.213.134.212 -.170.015.543 -.052 VAR00044 -.039.423.210.064.154.061 -.246 -.088 -.110.230 -.031.433.394 VAR00047.005.155.062 -.032.227.128.155.292.119.194 -.037.087.657 VAR00041 -.038.146.142.274.240 -.115.305.138.002.200.123.090.459 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 16 iterations. The above table clearly explains that all the fifty variables had been extracted into thirteen factors. Factor 1 consists of Eight variables such as variable 3,4,5,6,1,2,8 and 7 and factor 2 consists of Five variables such as variable 26,34,30,37 and 27 and factor 3 consists of Six variables such as variable 9,12,10,11,14 and 13 and factor 4 consists of Four variables such as variable 20,50,19 and 49 and factor 5 consists of four variables such as variable 36,32,35 and 29 and factor 6 consists of Four variables such as variable 22,33,25 and 28 and factor 7 consists of Three variables such as variable 21,31 and 23 and factor 8 consists of Two variables such as variable 18 and 48 and factor 9 consists of Three variables such as variable 15,16 and 17 and factor 10 consists of Three variables such as variable 43,45 and 42 and factor 11 consists of Three variables such as variable 40,39 and 38 and factor 12 consists of Three variables such as variable 46,24 and 44 and factor 13 consists of Two variables such as variable 47 and 41. www.theinternationaljournal.org > RJCBS: Volume: 05, Number: 03, January-2016 Page 10

CONCLUSION From the above analysis, it is concluded that the online advertising is a kind of operational advertising released on the Internet with carriers of digital codes. The advertising circle even thinks that Internet advertising will surpass outdoor advertising to become the fifth major media, following the traditional television, radio, newspaper, and magazine. Since the first commercial online advertisement appeared in 1997, online advertising has always been favored by many people. It provides many professional knowledge, skills and case analysis on online advertising. Online advertising can reach people all over the world via the Internet all around the clock, without restrictions to domain and time. REFERENCES: 1. Inderjit Kaur (2008), Online social networking as an advertising medium (Footwear sales industry, Ireland), Dissertation in M.Sc [E-business Management], September 2008. 2. Symeon Papadopoulos (2009), Key success factors in Internet advertising with emphasis on online user activity and the social context, Thesis for the Master s degree in Business Administration, May 2009. 3. Jin Ma and Handan Liu (2010), Advertising management influence effectiveness of online advertising A study of white-collar workers in online advertising context, Master s Dissertation in International Marketing, 2010. 4. Kevin Kozlen (2006), The value of banner advertising on the web, Master s Thesis, December 2006. 5. Baird and Eleanor Coumont (2008), Targeted online advertising: Persuasion in an era of massless communication, MBA Thesis, June 2008. 6. Goldsmith, R.E. and Lafferty, B.A., (2002), Consumer Responses to Web sites and their Influence on Advertising Effectiveness, Vol.12, No.4, pp: 318-328. www.theinternationaljournal.org > RJCBS: Volume: 05, Number: 03, January-2016 Page 11