n this highly competitive environment, business firms have to do miracles to get the attention of customers. As a result, marketers are vigilant about different types of new technologies that can be incorporated into their marketing messages. Online behavioral advertising (OBA) is such new technology that popular in the business world.

Online behavioral advertising (OBA) is targeted advertising which monitors user's personal information such as purchasing history, browsing history and their preferences by the online retailers to create and deliver personalized or customized ads. There's a small file called cookies which store users online information to make customized ads for each uses preferences (Tene and Polonetsky, 2011). These personalized ads are very effective in the global context, most the online retailers and other businesses such as Amazon, Stitch Fix, eBay, Henrys, Etc. Use those behavioral ads to attract customers (Akinwumi, 2018;Cambra, 2018). As online retailers are doing their online transaction in this way, OBA become a more convenient tool in retailing business (Sanje and Senol, 2012). Pauzer (2016) pointed out that personalized ads have a higher click-through rate than the other methods and also 71%of respondents are more attractive towards the tailored shopping habits. In addition to that, the information of the users' behavior is spread more accurately with more relatedness to the customers. Further, Sanje and Senol (2012)said that out of all the advertisements there're only 8% are behavioral advertisement which posted on the internet by the retailers and those have high ability to reach the target audience and more cost-effective than other advertising methods.

Despite the popularity of OBA, the emerging concern on privacy among customers work as a barrier for the growth of online retailing. (Scholz et al., 2019).In addition to that, customer perception of online purchasing work as another insecure in online retailing (Scholz et al., 2019). As a consequence, the privacy tools are being enhanced, and customers increasingly use the ad blocking facility in their online browsing. As a result, created an inability to reach the target customers and loss of online revenue as well (Zhao et al., 2018).

However, what motivates customers to reject these advertisements are still not clear (Li and Huang, 2016). Further, a limited study was done on this topic in the Sri Lankan context. With the purpose to fill that knowledge gap, this study focuses on exploring the factors of online behavioral advertising avoidance in online retailing in within the frame of the western province Sri Lanka.

Online Behavioral Advertising is known as the collection of user data from their computers or devices with regards to their internet activities and behaviors over the periods of time covering multiple web domains under the common objective of using such data to forecast customer interest and preferences in order to present and deliver Online Behavioral Advertisement to those users (Sanje and Senol, 2012). In addition to that, Legge (2015) pointed out OBA is a process which creates an advertisement to the suite with users' needs by understanding their searching behaviors, searching history, and interest. The term called "Cookies" is the specific technology that they use to identify users searching history and action(Kusumawati and Management, 2017). However, in online retailing, these cookie profiles with concerned information about the interest of users to generate personalized/ behavioral advertisements or suggestions of a purchasing (Legge, 2015). Results of that OBA became a very convenient tool in online retailing (Sanje and Senol, 2012). Because it gives many benefits to the advertisers and the online retailers (Bang and Wojdynski, 2016). However, based on some serious issues like privacy concern, users reject those behavioral ads in first sight (Bang and Wojdynski, 2016)

Advertising Avoidance defined as "all actions by media users that differentially reduce their exposure to ad content"(Van den Broeck et al., 2018). According to Munir et al. (2017), that study pointed out three dimensions of advertising avoidance such as cognitive(didn't want to think about the advertisement), affective(viewers hate), and behavioral(viewers reject). Advertising avoidance was happen on traditional media based in demographic characteristic such as age, gender, income and as well as media-related variables and narrow on communication problems in advertising (Speck and Elliott, 1997). Zhao et al. (2018) pointed out advertising avoidance in online platforms are highly different from traditional methods, as reasons they showed internet users are highly goal oriented and also, they concern speed and accessibility. Finally, they focus on two-way interaction with the internet. This study mainly targets on online behavioral advertising avoidance in the online retailing industry. By referring to past studies, there are 66% of users avoid OBA in China (Cho and John Cheon, 2004). 57% of users uncomfortable with customized ads in the USA and 35% avoid that kind of ads (McDonald and Cranor, 2010). In the Australian context, Mathews-Hunt (2016)points out 77% of Australian community do not take any risk of storing their information to generate behavioral advertising.58% of users were not happy to see those behavioral ads, and 83% were ignoring those ads.

Hossain (2018) pointed out that privacy concern defined as "the degree to which a consumer is worried about the potential invasion of the right to prevent the disclosure of personal information to others."According to Tene and Polonetsky (2011)online behavioral advertising tracking users data without permission, and they suggested that tradeoff between privacy & economic efficiency should achieve by a discussion among privacy advocate and advertising experts. In OBA, to create customized ads its trach users behavior, that tracking negatively effects to the users based on privacy concern (Gironda and Korgaonkar, 2018). Munir et al. (2017) mentioned that most of the users highly concern about their privacy in online platforms; therefore, they neglect to share any information which asked from advertisers. concerning privacy in online platforms, most of the users installing adblocker onto their web browser to avoid the behavioral advertisement (Oger et al., 2015). Some of past studies found that privacy had create a considerable impact on the online advertising avoidance (Li andHuang, 2016, Munir et al., 2017).

Perceived personalization is been described as the users getting to know that a particular thing is personalized towards the user and is relatable to them hence personalization is the most driver towards the favorable effects of personalization (Li, 2016). Through that personalization, the specific ad carried out the right message to the right person at a right time in online platforms, and it's recognized as web-based personalization (Li, 2016) .

In addition to that, Personalized advertising is the new trend in the modern digital market were mostly operated in social media platforms (Gironda and Korgaonkar, 2018). As well as Gironda and Korgaonkar (2018) mentioned that there is a restriction on personalized ads when considering some reasons based on privacy. However, in online platforms that customized advertisements are more likable, understandable, memorable, attractive, and most importantly effective (Munir et al., 2017). Rendering to Li (2016), if those online advertisements are not personalized as they should, the users tend to avoid those kinds of ads.

According to Hossain (2018) goal impediment defined as "the perception that one's goal while online (e.g., web browsing, searching for content) cannot be met as a result of online ads, hence leading to ad avoidance." There are two types users that found in online platforms named experienced-oriented and goaloriented (San martín et al., 2009). Internet is the goaloriented platform where users are connecting with a specific work, that can be interrupted by appearing some un useful personalization ads, and it made consumers avoid that kind of personalized ads (Li and Huang, 2016). Also, those distractions build discomforts and displeasure toward the online behavioral ads in online retailing (Hsin Chang and Wang, 2011). Furthermore, Hossain (2018) pointed out, people become frustrated when their specific task interrupt by some unusual online video ads, results of that 56% skipped online video ads exclude without enumerating. Hence, there can be a high probability that the users neglect online ads to achieve their planned goal (Morimoto and Chang, 2009). Speck and Elliott (1997) they found goal impediment was a one of factor effect to the consumers advertising avoidance behavior.

Ad Skepticism is a one of factor to create ad avoidance that defined as "the general tendency to disbelieve the informational claims of advertising" (Bae, 2018).

Baek and Morimoto (2012) mentioned all advertising trends illustrate some disbelief towards ads because customers believe that advertisement has specific actions to attract customers and make a deal with the product. As well the customer who have high skepticism, they try to neglect those ads as possible (Baek and Morimoto, 2012). Ad skepticism was happen any platforms such as TV, Radio, News, and Magazines and also in the online (Khuhro et al., 2017). Furthermore when examine the ad skepticism in online platforms, Ilmudeen (2018) pointed out, customers trust based on what claims in the ads in online, that claim always depend on some factors such as, appeal of the web site, product or service that they offer, quality of service and trust seal, otherwise its turn to skepticism.

According to the past research studies, one of the most influential dependent variables is the Negative experience of the customers, and the information processing gets affected through the consumers' previous experiences and the consumers' learning perspective (Li and Huang, 2016). Many of the users have had much negative practice in relating to OBA and hence feeling uncomfortable based on the warnings and guiding provided by their parents as well as the teachers and others about possible virus and malware attacks by clicking those OBA advertisements (Kelly et al., 2010). Also Kelly et al. (2010), further stated that "advertising avoidance is a prior negative experience, which includes instances in which internet advertising is deceptive, exaggerated, incorrectly targeted or leads users to inappropriate sites."

Li and Huang (2016) discovered that the lack of usefulness and the incentives or benefits are the major causes of the Negative Experiences related to OBA. Boerman et al. (2017) Negative feeling towards OBA caused by higher personalization of the ads and it equals with the theories such as ownership theory, choice theory, and psychological reactance and therefore personalized ads most likely will lead to a perceived loss of choice and control and being a reason for the negative experiences of the OBA. A recent by De Gregorio et al. (2017)study discovered that excessiveness, waste of time towards ads and annoyance to the users are the stronger negative beliefs which will enhance the OBA avoidance.

Baek and Morimoto (2012) mentioned that there is a positive relationship between privacy concern and advertising avoidance. Not only that, another few studies found there is a positive relationship between privacy and advertising avoidance (Hossain, 2018, Li andHuang, 2016). However, Zhang et al. (2015) found, privacy concern was not related to advertising avoidance. Therefore, this study will be focusing on privacy concern as a variable in the context of OBA avoidance in online retailing.

After referring past studies, Baek and Morimoto (2012), pointed out there is a positive relationship between perceived personalization and advertising avoidance. But Li and Huang (2016) and Munir et al. (2017) mentioned there is a negative relationship between perceived personalization and advertising avoidance. Therefore, this study will be focusing on perceived personalization as a factor in the context of OBA avoidance.

Cho and John Cheon (2004), showed there is a significant valid impact between goal impediment and advertising avoidance. Also, Hossain (2018) and Li and Huang (2016) mention that relationship goes with positive impact. Therefore, this study is focusing on goal impediment to take as a factor effective OBA in online retailing.

When exploring the relationship between ad skepticism and advertising avoidance that some of the previous studies mentioned there is a positive relation between ad skepticism and advertising avoidance (Khuhro et al., 2017, Munir et al., 2017, Raziq et al., 2007), Baek and Morimoto (2012) take this ad skepticism as a intervening variable in their study, and also found there is a positive relationship between ad skepticism and ad avoidance. In this study, will looking forward, how the Ad Skepticism impact on the advertising in online retailing.

By referring to past studies, Cho and John Cheon (2004) pointed there is a significant impact between negative experience and advertising avoidance. As well as there is a positive relationship between those two variables (Hossain, 2018, Li andHuang, 2016).Therefore, this study is focusing on negative experience to take as a factor effective OBA in online retailing.

The research conclusions are most significant as they can be used as inputs when processing ideas which are useful in creating strategically essential ways and means to reduce OBA avoidance (Li and Huang, 2016). To build more consumer favorable Behavioral advertising framework, identification of the factors which affects OBA avoidance is a must, and it also allows the marketers and advertisers to redesign their advertising strategies in a way that decreases the avoidance towards OBA (Li and Huang, 2016).

This article will cover a summarized introduction of the topic and the knowledge gap addressed in this research. And also, research objectives and questions that this study is going to discover, as well the benefits that society gains following by this study. From the literature, it explains what are the academic evidence are used to develop the conceptual model which shows the relationship between OBA avoidance and the factors that effect the evasion.