Consumer responses towards Online Behavioural Advertising (OBA) on Facebook

Consumer Responses Towards Online Behavioural Advertising (OBA) on Facebook
Fazeela J Ahsan & Kashmi D Jayathunga
Faculty of Management and Finance, University of Colombo. Sri Lanka

DOI: https://dx.doi.org/10.47772/IJRISS.2023.7892

Received: 09 August 2023; Accepted: 12 August 2023; Published: 17 September 2023

ABSTRACT

Online Behavioural Advertising (OBA) is the practice of tailoring advertising based on an individual’s online activities such as searching keywords and visiting websites. The purpose of this study is to explore the Sri Lankan consumer’s response towards OBA and to examine privacy concerns of OBA. Facebook has been selected, as it is the most famous social media platform in Sri Lanka. Literature depicts privacy concern had a significant trigger on OBA and personalisation factor has also been an indigenous characteristic of OBA. As per Ducoffe’s model (1996) on web advertising, entertainment, informativeness and irritation were considered as the perceptual dimensions demonstrating a relationship with attitude towards an advertisement and leading to consumer responses. Lee and Rha’s (2013) extended model for OBA depicted privacy and personalisation as two other important dimensions of OBA. Accordingly, the conceptual framework was developed and operationalised using previously used measures. Using data from 390 Sri Lankan respondents who are Facebook users in the age group of 18-34 years, the results show that entertainment, informativeness, and personalisation had a positive relationship between attitudes towards OBA, whereas irritation and privacy concerns had a negative relationship. The results indicate that consumer’s attitude towards OBA in fact has a positive impact on the consumer’s response to click on an advertisement. The findings will be of utmost importance for advertising practitioners to not only on developing information-rich and entertaining advertisements but also personalised content of the advertisements. This research study also contributes to an enhanced understanding of online behavioural advertising on Facebook. The findings of the research will be vital as a stepping stone to research in the area of OBA as it is an upcoming area in digital marketing and is known to be the future of advertising.

Keyword: Online behavioural advertising, Facebook, Sri Lanka, Privacy concerns, Personalisation, Sri Lankan consumer attitudes & responses

INTRODUCTION AND LITERATURE REVIEW

The rapid growth of communication technologies and use of internet have enhanced the spread of social networking in the last decade. Use of internet and social media have become a necessity for most Sri Lanka youth. As per Sri Lanka’s key digital statistics published by HootSuite (2021) indicates that the total population in Sri Lanka is 21.37 Million out of which 10.10 million are internet users, out of which 6.4 million are monthly active Facebook users. 32.35% being female users and 67.75% users being male. Also, Internet users have increased by 8.3% and active Social Media users have increased by 30% since April 2019.As per Seven Media Group (2020) and HootSuite (2020) statistics in Sri Lanka indicate that there are 4.1 Million users who use Facebook within the age range of 18-34 years. According to this, it is evident that Sri Lanka is in a position of substantial growth in Internet connectivity and social media usage. Current trend indicates that, social media marketing is one of the best ways of internet marketing in Sri Lanka.  Facebook is outperforming all other social media and is the most popular Social media platform in Sri Lanka.

One of the most frequently used forms of online advertising is based on the web analytics data, which enable advertisers to know what their current and prospective customers are interested in buying based on their past online search behaviours (Kim & Huh, 2017). Scholars have defined this area and has been identified as a trend in online advertising that deserves careful attention. By tracking consumers’ Internet cookie data, advertisers can show each individual consumer advertisement messages regarding what she or he is actually interested in, instead of guessing what she or he might be interested in. This is called online behavioural advertising as defined by Kim and Huh (2017). Online Behavioural Advertising (OBA), which targets specific individuals for advertising based on their online activity, is spotlighted as a service that can provide benefits not only to businesses but also to the online users themselves (Beales, 2011; Dwyer, 2009; Aiolfi et al, 2021).

An example of how behavioural advertising might work is as follows: a consumer visits a travel website and searches for airline flights to Melbourne. The consumer does not purchase any tickets, but later visits Facebook and navigates the newsfeed. While on Facebook, the consumer sees an advertisement from an airline featuring flights from Colombo to Melbourne. Hence, Online Behavioural Advertising (OBA) is broadly defined as the practice of tracking an individual’s online activities in order to deliver advertising tailored to the consumers’ interests (Federal Trade Commission, 2009).

Although there has been growing number of literatures that has examined online advertising in Sri Lanka (Ajanthan, 2017; Ayoobkhan, 2016; Bulankulama, 2017; Farook & Abeysekara, 2016; Samarasinghe et al, 2016; Jayasuriya et al., 2018) limited research (Wijenayake & Pathirana, 2019; Udadeniya et al., 2019) has been conducted on online behavioural advertising on Facebook in the Sri Lankan context. There have been number of researches conducted in other contexts such as United States of America, South Korea, China, Belgium and Austria which have examined the effects of online behavioural advertising on consumers.  However, scholars (Durvasula et al., 2001; La Ferla et al., 2008) believe that consumer’s attitude and behavioural responses toward social media advertising and advertising in general differs from one country to the other. Privacy concerns and knowledge on OBA among consumers will also differ from one country to another. Smit et al (2014) study on OBA was mainly based on European Union and the findings apply to other western societies as well. He also states that research did not focus on other parts of the world such as Asia and Africa. Smit et al (2014) specifically mentions as privacy concerns and perceptions regarding online (advertising) practices might differ between cultures (Wang & Sun, 2010), and internet is globally used, it is vital to know whether regulations regarding OBA disclosure influence knowledge, privacy concerns and online coping behaviour in a similar vein. Therefore, research findings from other country’s contexts cannot be applied to Sri Lankan context.

Due to the fast-growing volume of OBA and its highly personalised nature raising criticisms, researchers in both industry (eMarketer 2014; Criteo 2015) and academic (Lambrecht & Tucker 2013; Smit et al., 2014; Tucker 2014; Bleier & Eisenbeiss 2015; Kim & Huh, 2017; Boerman et al, 2017; Noor et al, 2022) have examined consumers’ knowledge and understanding of OBA, attitude toward OBA in general, the role of consumer trust in moderating the impact of different OBA features on consumer perception and ad click-through intention, and impact of regulations or privacy control options on consumer responses to OBA and other personalised ads. Wijenayake & Pathirana (2019) mentions that even though the OBA is widely used by the organizations to do better marketing, reaction to OBA by the customers has not been quite positive. Wijenayake & Pathirana (2019) further states that observations show that customers tend to avoid OBA and although there is a global trend of increasing ad block it has been very less in Sri Lanka.

Hence, the current understanding of OBA effects is quite limited, calling for more research. OBA is an interesting advertising practice providing both benefits (relevant messages) and risks (privacy infringements) to consumers; thus, consumers have ambivalent and paradoxical attitudes toward it (Smit et al., 2014). Interestingly, results revealed that perceived risks of privacy infringement were generally seen as outweighing the benefits of having relevant advertisement messages through OBA (Ham, 2017). In contrast, Debatin et al. (2009), however, found that benefits of using Facebook outweigh the potential risk of privacy infringement.

Likewise, OBAs could be perceived as invasive and inappropriate if they trigger consumers’ privacy concern because such adverts would likely make consumers be more aware of advertisers’ tracking and sharing consumer online activity data. With the possibility of having both favorable and unfavorable impacts on consumer responses (Bleier & Eisenbeiss 2015), OBA can be a double-edged sword. Therefore, with the arguments within literature and considering the fact that other contextual findings on the same research area cannot be applied to Sri Lanka, the research problem arises on what is the Consumer’s responses towards Online behavioural advertising on Facebook.

Scholars have mostly mentioned that users are not well aware of how this OBA works and how they are exposed to relevant advertisements based on their search history and online behavior. Scholars such as Smit et al., (2014) and Bennet (2001) explains that for this form of targeted advertising, data must be collected, usually by installing ‘cookies. Cookies are small text files that are put on users’ devices, such as laptops, notebooks or smart phones, to facilitate the functionality of a website (first party, session or functional cookies) or to collect profile information for targeted advertising (third-party or tracking cookies). While advertisers stress the utility of OBA in terms of relevant advertising, the use of cookies is heavily debated by policy makers in the US and Europe because of its potential violation of the privacy of Internet users (Bennett, 2011). Almost every website that an individual visit usually collects cookies in order to analyse the profile of the customers. Third-party HTTP cookies are the main mechanism to enable behavioural targeting. By correlating which websites, the user visits, advertisers can build profiles of likely characteristics and interests, and present advertising most likely to lead to the purchase of a given product or service (McDonald & Cranor, 2009). This is the usual mechanism used by social media sites such as Facebook in order to present users with personalised advertisements based on their online behaviour.

In 1995, Ducoffe introduced the concept of advertisement value and defined as the consumers’ perception of the utility or the relative worth of the advertisement. Hamouda (2018) states that based on the uses and gratifications theory (UGT) and Blumler & Katz (1974), which suggests that users choose the media that best fulfil their cognitive and affective needs involving their personal needs and gratification-seeking motives, Ducoffe (1995, 1996) developed the advertising value model. This model is based on three antecedents of advertising value: informativeness, entertainment and irritation and proposed a positive link between advertising value and attitude toward advertising. This advertising value model is the most widely used theory to explain user perceptions and attitudes toward advertising (Murillo et al. 2016). The main research that has applied this model across several contexts such as traditional advertising (Ducoffe, 1995; Logan et al.,  2012), web advertising (Ducoffe, 1996; Brackett & Carr, 2001; Bennett et al. 2006; Lin & Hung, 2009; Logan, 2013), mobile advertising (Xu et al., 2009; Liu et al., 2012; Kim and Han, 2014) and social media advertising specifically also used in the context of Facebook (Logan et al., 2012; Saxena & Khanna, 2013; Dao et al., 2014; Murillo et al., 2016) and also in the context of OBA (Lee & Rha, 2013). Therefore, the researcher believes that the use of Ducoffe’s advertising value model will add more theoretical value and significance.

However, the predominant characteristics of OBA that were identified previously which are, personalisation and privacy concerns have not been reflected in the Ducoffe’s advertising model. Lee and Rha (2013) also agreed that the model does not reflect the differentiated characteristics of OBA, which is its superior quality of individual personalised level targeting and user concerns on privacy and cannot explain the effect that attitude towards advertisement. Lee and Rha’s (2013) study further extended the Ducoffe’s model by adding privacy concerns and personalisation aspects in order to match the special characteristics of OBA. The integration of web advertising effect model of Ducoffe (1996) and Lee and Rha’s (2013) extended conceptual model will be used for the current research in order to captivate the perceptual dimensions of advertising along with prominent aspects of OBA – perceived personalisation and privacy concerns. Literature has been gathered from recent scholarly articles with regards to relevant dimensions and areas such as entertainment, informativeness and irritation. Predominant characteristics of OBA which were reflected from Lee and Rha’s model were personalisation and privacy concerns. At the instance of gathering literature on OBA, it was mostly demonstrated that level of personalisation and privacy concerns were discussed by many scholars. Literature suggested both positive and negative attitudes and consumer responses towards OBA.

Based on Web advertising effect model of Ducoffe (1996) and Lee and Rha’s (2013) model, this study has developed an integrated framework for Facebook OBA. Ducoffe (1996)’s model is representative of theory that explains the effect of online advertising which includes perceptual dimensions of OBA. Yet, this model does not reflect the special and differentiated characteristics of OBA, which is its superior quality of individual level targeting, and cannot explain the effect that attitude toward advertising can have on responses. Therefore, Lee and Rha (2013) proposed extending the model by adding privacy concerns and personalisation as these are very important dimensions of OBA. This research’s conceptual model (figure 1) proposes possible links between perceptual dimensions of OBA, personalisation, privacy concerns, attitude toward OBA and consumer responses on OBA.

Figure 1:Conceptual framework

Source: Researcher’s construction

Table 1: List of Hypotheses

Source: Researcher’s construction

Section 1.1 of the paper introduces the concept and tried to justify the possible reasons behind the study through a brief review of literature. Section 1.2 clearly mentioned the research objectives of the paper. Section 1.3 clarifies the data and methodology of the paper. Finally, section 1.4 interprets the result and provides the conclusion.

Research Objectives

The main objectives of this research are to examine the relationship between perceptual dimension of OBA and attitude toward OBA and also to assess the relationship between privacy concerns and attitude towards OBA. Further, to find out the Sri Lankan consumer’s response to OBA on Facebook.

RESEARCH METHODOLOGY AND DATA ANALYSIS

Based on the statistics available in relation to this area, the population consists of all Facebook users have been 6.2 Million from which 4.1 Million has been in the range of 18-34 years. Sample size is selected from the Morgan table with the highest level of 384 respondents. Convenience sampling was used in order to distribute the questionnaire. As Saunders et al (2011) states convenience sampling involves selecting haphazardly those cases that are easiest to obtain for the sample. As the population is high and the fact that Facebook users were conveniently available for the researcher, this sampling procedure was chosen.

As this study is a quantitative study, the main data collection tool used was self-administered internet – mediated questionnaire. Other studies (Smit et al 2013; Li and Huang 2016; Ham 2017; Tran 2017; Kim and Huh 2017; Duffet 2017; Lee and Rha 2014, Hamouda, 2018) which have conducted research in the areas of Social Media Marketing, attitude towards Facebook advertising, attitude towards OBA, consumer response towards OBA has also used online surveys.

Once the questionnaire was developed, the researcher conducted a pilot test for 36 respondents. Face validity was also carried out by distributing the pilot test among 10 individuals. Although the questions have been pre-tested in previous literature, as this is a new area for Sri Lankan consumers, clarity and meaningfulness of the questions had to be tested. The collected 36 questionnaires were recorded and analysed using the statistical package SPSS. As analysis of the pilot test, reliability of the measures was checked for each construct through Cronbach’s Alpha. The satisfactory level of reliability can be attained through a Cronbach’s alpha of 0.7 or above (Nunnally, 1978).  Results of the pilot test depicts that Cronbach Alpha values are higher than 0.7 for most, which reflects that there is a high internal consistency among the Likert scale items.

The online questionnaire was administered through Facebook itself for a period of 3 weeks. For this study, the reliability of all the variables under investigation was projected using Cronbach’s Alpha coefficient for the internal consistency of the scale. According to the main study results of reliability analysis given in Table 9. Cronbach alpha for each of the dimensions and variable is higher than 0.7, it is concluded that all the scales and constructs used in the study were reliable, thus they can be used to measure the variables under study.

Table 2: Reliability test results

Source: The researcher, based on data analysis

In order to test the relationships between the dimensions of OBA and attitude towards OBA and the relationship between attitude towards OBA and consumer responses the researcher applied the correlation analysis. Results are provided below in Table 3.

Table 3: Correlations test results

Source: Survey Data

As per the above table, Entertainment, Informativeness and Personalisation are highly significant (< 0.001), which reflects that these constructs have a positive relationship as the coefficient correlation is positive. Irritation has a highly significant negative association with attitude towards OBA as coefficient correlation is -0.228. Privacy concern also has a highly significant negative association with attitude towards OBA with -0.022. Furthermore, attitude towards OBA is significantly correlated with consumer responses positively as the coefficient correlation is 0.877.

In order to test the effect of independent variables on the attitude towards OBA dependent variable, multiple regression model has been conducted from SPSS and is presented below in table 4.

Table 4: Model Summary of multiple regression model

Source: Survey data

The Durbin-Watson statistics is depicted as 1.779 which is between 1.5 and 2.5 which means that residuals are independent and that the model is valid. Overall, the multiple correlation is 0.812 which depicts that the independent variables which are the dimensions of OBA has a strong joint association with attitude towards OBA. Attitude towards OBA can be explained with the independent variables to 65% (R² = .659). As the value is more than 60%, model is depicted to be nicely fitted.

Table 5: ANOVA table

Source: Survey Data

Probability of regression ANOVA presented in table 5 is highly significant (.000) corresponding F test statistic is 148.5. As F statistic is highly significant all the dimensions are jointly influenced on the attitude. As the model is jointly significant multiple regression model is more appropriate. Individual effect has been analysed by the ‘Coefficients’ table below.

Table 6: Multiple regression Coefficients

According to the individual effect as depicted in table 6, probability of ENT is highly significant with a positive beta value. It is having 0.515 individual beta value and it indicates that ENT has a highly significant positive effect on ATT. INFO is also highly significant with a positive beta value corresponding probability and coefficient of 0.000 and 0.240 respectively. PERS also has a highly significant positive effect on ATT. Whereas IRR and PRIV consist of significant negative effect on ATT. The individual beta values are -0.094 and -0.162 respectively.  According to the standardised coeffiecients of beta, most influencing dimension is ENT because ENT has the highest coefficient of beta. Second influencing factor is PERS and third factor is INFO.

All the VIF values are less than 10, therefore regression model does not have multi collinearity problem and it indicates that dimensions are not perfectly correlated. Hence, model is appropriate.

From the multiple regression coefficients table, the following model can be developed;

ATT = .425 + (.515ENT) + (.240 INFO) + (-.094IRR) + (.240PERS) + (-.162PRIV)

In order to test the relationship between Attitude towards OBA (ATT) and the consumer responses (CONR), simple regression model has been conducted and findings are presented in Table 7.

Table 7: Simple regression model

Source: Survey Data

The regression model depicts that Consumer responses can be explained with the attitude towards OBA variable to 75% (R² = .754). As the value is more than 60%, model is depicted to be nicely fitted. Durbin- Watson also depicts to be in between 1.5 and 2.5 which means that residuals are independent and that the model is valid.

Table 8: ANOVA

Source: Survey Data

Probability of this regression ANOVA is highly significant (.000), corresponding F test statistic is 1187.9. As F statistic is highly significant all the dimensions are jointly influenced on the attitude. As the model is jointly significant multiple regression model is more appropriate.

Table 9: Simple regression model coefficients

Source: Survey data

According to the table 9 probability of ATT is highly significant with a positive beta value. It is having 0.689 beta value and it indicates that ATT has a highly significant positive effect on CONR. VIF value is less than 10, therefore regression model does not have multi collinearity problem and it indicates that dimensions are not perfectly correlated. Hence, model is appropriate.

The following table 13 depicts the hypotheses verification based on the findings that were analysed above.

Table 10: Summary of hypotheses

Source: Researcher’s construction based on data analysis

FINDINGS AND INTERPRETATION

A main observation through the study was the respondent’s characteristics which reflected that a large proportion of the respondents (52%) had Facebook experience between 5-10 years. Further, majority of the respondents (89.5%) use Facebook daily as a day to day activity.  Searching of products/services online is also quite popular among the Sri Lankan respondents as 35.9% and 34.1% of respondents search all the time and frequently. The key finding from the respondent’s characteristics was that they responded to be exposed to OBA all the time (47%) and frequently (40%). These findings from the respondents demonstrate that consumers are aware of OBA and are exposed to online behavioral advertisements when using Facebook.

Correlations test results confirmed that entertainment, informativeness and personalisation are highly significant and have a positive relationship with attitude towards OBA. Data analysis findings reflected that irritation and privacy concerns seem to have a negative association with attitude towards OBA. Whereas attitude towards OBA is significantly correlated with consumer responses positively. All the hypothesis proposed were accepted and in reference to the past literature findings it will be further discussed in the following sections.

The first objective of the research is to examine the relationship between perceptual dimensions of OBA and attitude towards OBA. The findings of the data analysis (correlation) depicted interesting results. Entertainment, Informativeness and Personalisation dimensions of OBA proved to have a positive relationship between attitude towards OBA and irritation proved to have a negative relationship between attitude towards OBA, which is in line with the previous findings (Brackett & Carr, 2001, Kim et al., 2010, Taylor et al., 2011; McDonalds & Cranor, 2009 and Lee & Rha, 2013).  This also confirms the applicability of Ducoffe (1995, 1996) model on web advertising value and confirms Lee and Rha’s (2013) extended model on OBA.

As for Entertainment, it was stated in previous chapters that most Sri Lankan Facebook users use the site as a distraction to keep away from stressful situations and to enjoy virtual life and as Muntinga et al. (2011) correctly mentioned that users perceive enjoyment, relaxation by using social media sites such as Facebook. This could be one of the reasons as to why Entertainment aspect of OBA was contributed to have a stronger positive relationship between attitude towards OBA as Sri Lankan users seem to enjoy advertisement which is found to be humorous, has colorful content or uses a specific celebrity. For example, some users may even specifically remember an advertisement if its ‘relatable’ or humorous compared to a generic advertisement. Boerman et al (2017) explained that when an advertisement usually addresses entertainment motives it is automatically believed to increase the attention, memory and attitudes toward the advertisement. As for the Informativeness, it was stated previously that consumers are more willing to read and watch content that provides more information about products and services. Specifically, when it comes to OBA, this information about the products will be mostly related to the products they have searched for, so it will be more useful. Petrovici & Marinov (2007) mentioned that one of the main roles of advertising is to reshape the attitude towards advertising with the use of information delivered, as for the Facebook respondents in this survey reported that they consider informativeness of advertisement is quite important. Logan et al., (2012) in their study on comparison of Facebook advertisement and TV advertisements, found out a contrasting result where respondents agreed that TV advertising is a better source of information. Yet, in other contexts such as social media sites in general (Saxena & Khanna, 2013) and on specific social media sites such as Facebook (Logal et al 2012; Dao et al, 2014) and to OBA context specifically (Lee & Rha, 2013) it was found out that informativeness has been a strong prediction of attitude towards advertising. Thus, in consistency with these prior studies, this research also highlights that particular attention given to information content of an OBA is an important predictor of positive attitude towards OBA.

Personalisation also reflected as a significant positive relationship between attitude towards OBA, which is also in line with Lee and Rha (2013) OBA survey findings. Aguirre et al (2015) also agreed that the level of personalisation of an advertisement can bring about positive attitudes and increase on OBA outcomes as well.  As Campbell and Wright (2008) rightly mentioned, consumers show more favorable attitudes towards advertising and the target product of advertising if the advertising is related to them. It was also found that consumers think the biggest benefit of OBA is ‘better target or right person’ (Ur el al., 2012). In contrast to this finding, Ham (2017) study implied that consumers seem uncomfortable and irritated with highly personalised advertisements as they are targeted based on behavioral tracking. The findings of this research are in contrast to Ham’s (2017) as Sri Lankan consumers have a positive attitude towards OBA as the advertisements are more personalised and customised to them.

As per the findings of this research, it was apparent that there is a component of negative attitude towards OBA as irritation was proved to be negatively related to the attitude of consumers towards OBA. This has been in line with Smit et al (2014) findings which also states that consumers got irritated when there is an excessive repetition of OBAs within a short amount of time. Contrast to the findings of OBA creating positive attitude towards OBA, there are other certain scholars (Sun & Wang, 2010; Lee & Rha, 2013) which have found out that irritation had a negative relationship towards attitude toward OBA. However, the strength of the relationship is quite weak (-.208^**) which implies that even though there is some irritation component involved, it is not solid and strong. Lee and Rha (2013) states that it can be predicted that repeat exposure to OBA or excess technology usage will negatively affect attitudes towards OBA and hence the reason of irritation, which is also in par with the current research. The first objective of the research has been met as the findings prove that entertainment, information and personalisation dimensions of OBA has a positive relationship towards attitude towards OBA. Whereas, irritation has a negative relationship towards attitude towards OBA. Overall, the hypotheses developed were accepted.

The second objective of the research is to find out the relationship between privacy concerns and attitude of OBA. Privacy concern was considered as a separate section as it is a highly important characteristic and dimension which goes hand in hand with OBA. As already known, OBA uses personal search history and online behaviour of individuals to target and provide personalised advertisements to users on Facebook. This triggers the privacy concerns within individuals.

Agreeing to all previous research findings (Phelps et al., 2000; Baek & Morimoto 2012; Tucker 2012; Lee & Rha, 2013; Kim & Huh, 2017) this research is also in line with the finding of privacy concerns being negatively related to the attitude towards OBA. However, there appears to be a privacy paradox as stated by Norberg and Horne (2007) that people say they care about privacy and do not intend to share personal data, but in reality, they provide their data in exchange for small benefits or conveniences. The finding of this research will also contribute to this as there is a weak negative relationship between privacy concerns and attitude towards OBA (-.022) than personalisation and attitude towards OBA (.584^**). This demonstrated the privacy paradox which has been explained by other researchers where people are quite concerned about their private data is being used for advertisements, yet they are also fine with being exposed to personalised advertisements based on their personal data. Compared to findings from developed countries such as U.S, U.K where OBA research is conducted and privacy was flagged to be one of the most controversial concerns, it was noticed that the relationship between privacy concerns an attitude is quite weak. It implies that Sri Lankan consumers may not be significantly concerned towards their privacy being violated.

The third objective of the study was to find out the Sri Lankan consumer responses to OBA on Facebook. As per the estimates (0.877) and mean value of 4.1, it was apparent that there is a very strong relationship between attitudes towards OBA and consumer responses. As previously identified, consumer response outcomes of OBA are either a click intention or purchase intention. In this study, the researcher has considered the intention to click outcome as stated in previous chapters. Therefore, as the findings of the results are that if there is a positive attitude towards OBA there will be favorable consumer response of clicking on the advertisement and if there is a negative attitude towards OBA there will be an unfavorable consumer response and will not consider clicking on the advertisement. Although some of the perceptual dimensions of OBA such as entertainment, informativeness and personalisation proved to have a positive relationship towards attitude, there were 2 other dimensions which were privacy and irritation had a negative relationship towards the attitude towards OBA. Therefore, the attitude towards OBA has been a mixed response, this is also in line with many of the research findings (McDonald & Cranor 2010; Ur et al. 2012; Smit et al., 2014; Lee & Rha, 2013; Aguirre et al., 2015). This can be mainly due to the fact that there is a privacy paradox where consumers are not certain whether they want their private data to be shared or whether they actually prefer personalisation. However, as per the response rate from the questionnaire for the relevant questions on consumer responses indicated a mean value of 4.1 which a good response rate is and depicts that it is a favorable response. Respondents have agreed that they will consider paying attention, willing to click and intend to use OBA on Facebook. It can be determined that although there are various dimensions that will give a mixed attitude towards OBA, the overall consumer response tends to be favorable. It was also discovered that positive attitude towards OBA has led to favorable responses to OBA by Sri Lankan Facebook users.

In a theoretical perspective, research with regards to novelty areas such as targeted advertising or OBA is essential to explore consumer responses based on the type of advertising. From the findings of this survey it was evident that privacy and tracking of data were considered by most users on Facebook. Yet, it has not been a very strong factor that influences attitude towards advertising. Personalisation played a more prominent part in contributing to the attitude towards OBA. As this research area is gaining attention from other developed countries, it is also relatively important for emerging economies such as Sri Lanka to shed light on this research area as well.

From a practical perspective, the results of this study provide implications for marketing and advertising practitioners. The implications for advertising practitioners are that companies should pay great attention to providing entertaining, informative and personalised advertisements to the consumers can increase their attitude towards OBA. In order to lessen the gravity of consumers being irritated and avoid the advertisement, in order to attract more consumers, it is essential for OBA to provide information that is useful for the customers and also to make the advertisement campaigns to be entertaining and worthwhile to remember. Marketers and advertising agencies should attempt to take full advantage of the unique features of OBA (Ur el al., 2012) and provide customised advertising suited to the targeted consumers’ interests, because increased relevancy of information should reduce consumers’ annoyance or irritation (Beales, 2011).

The purpose of this research was to explore the Sri Lankan consumer’s response towards online behavioral advertising on Facebook. The significance of this study was for academic purposes and advertising practitioners.It was apparent that entertainment, informativeness, personalisation has a positive significant relationship towards attitude towards online behavioural advertising. Whereas, irritation and privacy concerns had a negative impact on the attitude towards online behavioural advertising. It was concluded that Sri Lankan consumer’s attitude towards online behavioural advertising has mixed responses as they prefer to be exposed to advertisements targeted them, personalized as long as they are of entertaining and of informational value. However, consumers will also have a negative perception due to irritation and privacy concerns triggered from online behavioural advertising. Nevertheless, the consumer responses seem to be favourable and positive as per the findings of the questionnaire and seemed to have a positive relationship with attitude towards online behavioural advertising.

Implications of online behavioural advertising were provided for advertising practitioners to spread awareness on privacy and to secure consumers privacy in order to avoid negative responses and attitudes. It was also stated that advertising practitioners should also be aware that Sri Lankan consumers who use Facebook are more willing to be exposed to entertaining, informative and personalised advertisements. The results of this study confirm that OBA can be a powerful marketing tool for businesses and a favorable advertising mode for consumers by increasing the level of personalisation and privacy concerns while lowering the level of irritation.

BIBLIOGRAPHY

1. Aiolfi, S., Bellini, S. and Pellegrini, D. (2021), “Data-driven digital advertising: benefits and risks of online behavioral advertising”, International Journal of Retail & Distribution Management, 49 (7), pp. 1089-1110. https://doi.org/10.1108/IJRDM-10-2020-0410
2. Bennett, G., Fereira, M., Siders, R., Tsuji, Y. and Cianfrone, B. (2006), “Analyzing the effects of advertising type and antecedents on attitude towards advertising in sport”, International Journal of Sport Marketing and Sponsorship, 8(1), pp. 62-81.
3. Bennett, S. C. (2011). Regulating online behavioral advertising. John Marshall Law Review, 44(4), 899–961.
4. Bleier, A., and M. Eisenbeiss. (2015), “The Importance of Trust for Personalized Online Advertising.” Journal of Retailing 91 (3): 390–409.
5. Campbell, D. E., & Wright, R. T. (2008). Shut-up I don’t care: Understanding the role of relevance and interactivity on customer attitudes toward repetitive online advertising. Journal of Electronic Commerce Research, 9(1), 62-76.
6. Chang-Dae Ham (2017) Exploring how consumers cope with online behavioral advertising, International Journal of Advertising, 36:4, 632-658, DOI: 10.1080/02650487.2016.1239878
7. Ducoffe, R. H. (1996). Advertising value and advertising on the web. Journal of Advertising Research, 36(5), 21-35
8.  F.S Farook and N. Abeysekara (2016). Influence of Social Media Marketing on Customer Engagement. International Journal of Business and Management Invention, 5 (12).
9. Federal Trade Commission (2009). FTC staff report: Self-regulatory principles for online behavioral advertising. Accessed on December 15, 2019
10. Federal Trade Commission (2012). FTC staff report: Protecting consumer privacy in an era if rapid change. Accessed on December 15, 2019
11. HootSuite (2020) Digital in 2020. Retrieved June 20th, 2020, from https://datareportal.com/reports/digital-2020-sri-lanka
12. Hyejin Kim & Jisu Huh (2017) Perceived Relevance and Privacy Concern Regarding Online Behavioral Advertising (OBA) and Their Role in Consumer Responses, Journal of Current Issues & Research in Advertising, 38:1, 92-105, DOI: 10.1080/10641734.2016.1233157. Issues & Research in Advertising, 17:1, 1-18, DOI: 10.1080/10641734.1995.10505022
13. Jin-Myung Lee & Jong-Youn Rha (2013) The Effect of Privacy Concerns and Consumer Trust on Consumer Response to Online Behavioral Advertising for Travel Products, International Journal of Tourism Sciences, 13:3, 1-29, DOI: 10.1080/15980634.2013.11434678.
14. Lee, S. Y., & Rha, Y. S. (2010). Do web users care about banner ads anymore? The effects of frequency and clutter in web advertising. Journal of Promotion Management, 16(3), 288–302.
15. Lee, Seungsin, Younghee Lee, Joing-In Lee, and Jungkun Park (2015), “Personalized e Services: Consumer Privacy Concern and Information Sharing,” Social Behavior and Personality: An International Journal, 43 (5), 729–40.
16. Logan, K., Bright, L.F. and Gangadharbatla, H. (2012), “Facebook versus television: advertising value perceptions among females”, Journal of Research in Interactive Marketing, 6 (13), pp. 164-179.
17. Manel Hamouda, (2018) “Understanding social media advertising effect on consumers’ responses: An empirical investigation of tourism advertising on Facebook”, Journal of Enterprise Information Management, 31 (3), pp.426-445, https://doi.org/10.1108/JEIM-07-2017-0101
18. McDonald, Aleecia M., and Lorrie F. Cranor (2008), “The Cost of Reading Privacy Policies,” Information System: A Journal of Law and Policy for the Information Society, 4 (3), 543–67.
19. Noor, U., Mansoor, M.and Shamim, A. (2022), “Customers create customers!–Assessing the role of perceived personalization, online advertising engagement and online users’ modes in generating positive e-WOM”, Asia-Pacific Journal of Business Administration, 1(4), https://doi.org/10.1108/APJBA-11-2021-0569
20. S.W.G.K. Bulankulama (2017). Examining the Dimensionality of Social Media Marketing in Sri Lankan Hotel Industry. International Journal of Scientific and Research Publications, 7 (10).
21. Samarasinghe et al., (2016). Impact of Social Media on Business Performance: Empirical Study on Apparel Fashion Brand Retailers in Sri Lanka.
22. Saxena, A. and Khanna, U. (2013), “Advertising on social network sites: a structural equation modelling approach”, Vision, 17(1), pp. 17-25.
23. Seven Media Group (2018) Media Landscape 2018 – Sri Lanka. Retrieved January 15, 2019, from http://sevenmediagroup.com/media-landscape-sri-lanka-2018/
24. Smit, Edith G., Guda Van Noort, and Hilde A. Voorveld (2014), “Understanding Online Behavioural Advertising: User Knowledge, Privacy Concerns, and Online Coping Behaviour in Europe,” Computers in Human Behavior, 32, 15–22.
25. Taylor, David G., Jeffrey E. Lewin, and David Strutton (2011), “Friends, Fans, and Followers: Do Ads Work on Social Networks?,” Journal of Advertising Research, 51 (1), 258–75.
26.  U.P.R.P Udadeniya, M.M.H.H Yalegama, A.K.K.D Wickramasinghe, M.Y.S.S Mannapperuma & K.K.N.A Jayasuriya (2019), Online Behavioral Advertising Avoidance in Online Retailing in Sri Lanka. Global Journal of Management And Business Research. 19 (4).
27. Ur, B., Leon, P. G., Cranor, L. F., Shay, R., & Wang, Y. (2012). Smart, useful, scary, creepy: perceptions of online behavioral advertising. SOUPS ’12 Proceedings of the Eighth Symposium on Usable Privacy and Security
28. Wang, Y., & Sun, S. (2010). Examining the role of beliefs and attitudes in online advertising: A comparison between the USA and Romania. International Marketing Review, 27(1), 87–107.
29. Wijenayake, S and Pathirana I.S., (2019). Study on Online Behavioural Add Avoidance in Sri Lankan Online Advertising. Asia Proceedings of Social Sciences. 2(3). 159-163