Target your customers contextually

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[Master thesis]

Target your customers contextually

- Explaining contextual targeting’s effect within

the banking market

Authors: Anton Johnsson & Alex Berlin

Supervisor: Dr. Christine Tidåsen Examiner: Dr. Anders Pehrsson Semester: VT21

Subject: Marketing

Course: Degree Project in Business Administration (master)

Course code: 4FE25E Course credit: 15 hp

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Abstract

Purpose: The purpose of this explanatory study was to explain how contextual targeting affects customer attitudes, privacy concerns, trust, and loyalty.

Literature Review: The reviewed literature included concepts that are important within the banking market. Such as attitude (cognitive, affective, and conative), privacy concerns, trust, and loyalty. Based on the literature review the authors proposed six hypotheses that together formed the research model.

Methodology: A positivistic and deductive research approach was adopted in the form of a quantitative research design. Primary data was collected through an online questionnaire created in Google Forms which generated a total of 132 responses.

Descriptive statistics, Cronbach’s alpha, and Pearson's correlation stood as a basis to analyze and interpret the data. Lastly, regression analysis was conducted in order to address the authors' proposed hypotheses stemming from the literature review.

Findings: The study found that contextual targeting had a significant positive effect on the three components of attitudes, cognitive, affective, and conative. Contextual targeting did not have a significant positive effect on privacy concerns, rather a negative effect in this study. Trust and loyalty were significantly positively affected by contextual targeting. The findings implied that contextual targeting positively affected visibility, awareness, preferences among customers of banks as well as their final purchase decision. Also, that contextual targeting affected trust and loyalty towards banks positively implying the positive aspects of utilizing the marketing strategy.

Keywords - Contextual targeting, Attitudes, Cognitive, Affective, Conative, Privacy concerns, Trust, Loyalty

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Acknowledgement

The authors would like to thank Dr. Christine Tidåsen for her continuous support. Thank you for your guidance and feedback throughout the entire process.

Also, a thank you to Dr. Anders Pehrsson for his feedback and support during seminars throughout the semester.

Lastly, the authors would like to thank all of the respondents who participated in the questionnaire and enabled the authors to finalize this degree project. Thank you!

_______________ _______________

Anton Johnsson Alex Berlin

aj223qb@student.lnu.se ab224gw@student.lnu.se

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List of Figures

FIGURE 1RESEARCH MODEL ...15

FIGURE 2REVISED RESEARCH MODEL ...34

List of Tables

TABLE 1OPERATIONALIZATION TABLE ...19

TABLE 2DESCRIPTIVE STATISTICS ...28

TABLE 3CONCEPT SUMMARY,DESCRIPTIVE STATISTICS ...28

TABLE 4CRONBACH’S ALPHA ...29

TABLE 5PEARSON’S RCORRELATION TABLE ...30

TABLE 6REGRESSION ANALYSIS ...32

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1. Introduction

The first chapter begins with a background on digitalization, digital advertising, and the banking market in Sweden. The background is followed by the problem discussion where the authors review previous research within the field and present the relevance of further research within digital marketing in terms of contextual targeting’s effect on customers' attitudes, privacy concerns, trust, and loyalty. After that, the problem formulation takes place followed by purpose, research questions, and delimitations of the study.

1.1 Digitalization

In the early stages of the 20th century, it was claimed with skepticism that marketers someday in the future would be able to reach the right customer, at the right time with customized advertisements through the collection of data. Since then, digitalization and the use of the internet have dramatically increased and created a digital transformation (Ozcelik & Varnali, 2019). Digitalization can be explained as: “the action or process of digitizing; the conversion of analogue data (esp. in later use images, video, and text) into digital form” (Stolterman & Fors, 2004 cited in Parviainen et al., 2017, pp. 64). Hence, accommodating tangible products or services into digital versions in order to create advantages (Henriette et al., 2015). Digitalization is believed to have contributed to significant economic growth within society (Ozcelik & Varnali, 2019). At the beginning of 2021, there were 4,6 billion active internet users worldwide which translates into almost 60% of the world’s population (Statista (1), 2021). In Sweden, that number is significantly higher with almost 88% of the population using the internet. A total of 9,12 million internet users and the number of users are estimated to reach 9,8 million users in the coming years in Sweden (Statista (2), 2021).

1.2 Digital advertising

As a result of digitalization, businesses are nowadays faced with increased competitiveness due to more simple and effective ways of reaching consumers with information, through the digital world. Nowadays, businesses need to master new technology and it has also been proven that consumers prefer businesses that possess great digital knowledge (Shpak et al., 2020). In regards to digitalization and the increasing use of digital mediums, digital advertising has become a vital component for businesses (Albaum et al., 2016). In 2019, 325 billion US dollars were spent on digital advertising on a global scale. These numbers are expected to reach 389 billion US dollars in 2021 after a small drop in 2020 as a result of the covid-19 pandemic (Statista (3), 2021). In Sweden, it is a similar case where the expenditure on digital advertising has been increasing in the last decades. In 2019, 23 billion SEK were spent on digital advertising which tops the list, by far, of advertising expenditure in Sweden (Statista (4), 2021).

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Performing and utilizing digital advertising also brings challenges for businesses due to the high level of competition in the market (Poveda-Bautista et al., 2013). Consumers are nowadays targeted with numerous advertisements every time they use their phone or computer. It is, therefore, crucial to catch the consumers attention by showing relevant advertisements to them (Arzubov et al., 2017). As a result, advertising has also become an important method to foresee consumers' next move. However, the collection of personal data from consumers has raised privacy concerns (Mutimukwe, Kolkowska &

Grönlund, 2020). The marketing strategy named contextual targeting is an approach that does not collect personal data but is instead based on content analysis. Rather than collecting personal data, contextual targeting collects content data from the users display network (Google, 2021) in order to place relevant advertisements on platforms and web pages (Chen et al., 2019; Wu et al., 2013). Furthermore, contextual targeting is used to create an advantage in a competitive market through targeting consumers with timing and with relevant information (Narang & Shankar, 2019). The reasoning behind contextual targeting is to find ways of reducing consumer advertising that is perceived meaningless, which implies targeting potential customers with preferences based on previous data (Chen et al., 2019), through the display network (Google, 2021). Therefore, contextual targeting is of relevance nowadays since it does not collect personal data which makes it a rather unique marketing strategy compared to other behavioral targeting strategies that do collect personal data (Song et al., 2018).

Due to the newly formed General Data Protection Regulation (GDPR) that replaced the personal data act 2018 it has once again changed marketers' way of reaching their consumers. The regulation was created to support the protection of personal data (Imy, 2021). GDPR has enabled consumers to opt into behavioral targeting rather than opt out of it (Kint, 2017). One area within the GDPR is labelled profiling and automated decisions which are defined as regulations for evaluating a person's traits. Marketers within banks operate with profiling to be able to foresee consumers preferences, interest and behavior (Ikano Bank, 2021). The legal ground for user profiling and automated decisions within banks is to be sure that collected data exists as a legitimate interest, legal obligation, fulfilment of covenant or consent from the customer. In the case of consent, the customer must accept their data being used in marketing strategies like contextual targeting (Ikano Bank, 2021).

1.3 Banking market in Sweden

Sweden ranks as the second country in the EU in terms of readiness for digital banking, stemming from digitalization, according to the EU commission (Svenska Bankföreningen, 2019). The banking market in Sweden consists of three larger banks, Handelsbanken, SEB and Swedbank since Nordea Bank Abp moved their business to Finland back in 2018 (Statista (5), 2021). However, Nordea is still operating within the Swedish market and offers financial services together with the abovementioned. There are also smaller banks operating in the Swedish market such as DanskeBank (News Powered by Cision, 2021), Ikano Bank and Länsförsäkringar Bank (Svenska

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Bankföreningen, 2019). This creates competition on the market and according to the EU commission, 48% of the Swedish bank customers have shifted between their financial service suppliers within the last five years. This translates to Sweden having the second- highest conversion rate of bank customers in the last five years with the average conversion rate in the EU being 29% (Svenska Bankföreningen, 2019). According to Van Esterik-Plasmeijer and van Raaij (2017) , customers' trust towards a bank is vital.

Implementing marketing strategies that are collecting personal data and invading personal privacy is therefore a sensitive subject (Van Esterik-Plasmeijer & van Raaij, (2017). Banks are argued to be focusing on similar integrated marketing communications in order to create a competitive advantage (Hoque et al., 2018). Loyalty, knowledge, technical skills and privacy concerns could all affect customers' feelings towards banks and could also be the reason behind shifting from one bank to another (Carson et al., 2004: Sindwani and Goel, 2016: Sreejesh et al., 2016: Okazaki et al., 2020). As contextual targeting does not collect personal data but instead uses content data the customer’s attitudes towards this marketing strategy is therefore of interest. Uncovering the effects of utilizing contextual targeting as a marketing strategy to obtain and attract new customers within the banking market is therefore of interest. Also, contextual targeting is argued to be a useful marketing technique to increase efficiency in digital advertisements (Vassio et al., 2020).

1.4 Problem discussion

As a result of digitalization, the traditional way of reaching and interacting with customers has changed into a more digitalized setting which implies that businesses nowadays interact with customers differently (Watson et al., 2018). Contextual targeting is one marketing strategy that has not been carefully investigated in terms of the customers' attitude effects of being targeted with content based on the customers' preferences, which implies a gap in the literature. In addition to this, Thanh Khoa (2021) implies that previous research has mainly focused upon utilitarian aspects of customers rather than the mind of customers towards trust and loyalty attitudes. Song et al., (2018) suggested that even though contextual targeting strategies showed signs of increased interaction rate, the actual conversion rate of customers who already had a relationship with a competitor was low. These results claim that contextual targeting is merely reaching customers' cognitive attitude and will not lead to any new enquiries when customers are familiar with another actor on the market. Yeun-Chun et al., (2014) argued that contextual targeting could affect the recognition level in a positive direction but the strategy is not strong enough to affect customers' attitudes to a more favorable one towards a company. Kononova et al., (2020) also suggested that problems could occur for marketers using contextual targeting due to the difficulties of reaching the affective attitude. Furthermore, Kononova et al., (2020) claim that more irrelevant advertising could be seen as being easier to process and perceived as more entertaining than customized contextual targeting being more powerful.

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When implicating contextual targeting within the banking market it is unknown how the strategy impacts the bank's more vital customer aspects like trust level, loyalty and privacy concern. The unknown impact of this marketing strategy that collects user data, instead of personal data, could be causing a low level of trust from customers towards a certain company (Aguirre et al., 2015). Trust is claimed to be the most vital factor for any financial institution to acquire and maintain customers (Knell & Stix, 2015). The authors suggest that low levels of trust could cause crises and breakdowns of banks. The low level of trust can also have negative effects on the customer's willingness to let a company use their collected data for marketing campaigns (Miltgen & Smith, 2019). A low level of trust can also lead to negative attitudes towards a company and also negative perceptions of displayed ads (Wang, Genc & Peng, 2020). It is also claimed that loyalty is highly correlated with what kind of advertising customers are exposed to (Budianto et al., 2019). Therefore, if contextual targeting shows negative impacts on customers attitudes it is proper to assume that this has a possibility to affect customers' loyalty towards their current bank. Another challenge for marketers within banking is the increasing privacy concerns when using marketing strategies that are collecting data which can have a negative impact on the trust towards the bank (Van Esterik-Plasmeijer

& van Raaij, 2017). The invasion of privacy and the collection of data has received more and more attention lately which could have an effect on the trust towards the banks (Mutimukwe, Kolkowska & Grönlund, 2020) and as argued previously, customers trust towards banks is vital (Van Esterik-Plasmeijer & van Raaij, 2017). Privacy concerns can also create negative feelings from customers resulting in them not being willing to share additional information. Also, if customers believe that personal data has been collected and senses that the data could be used in an improper way, then it will affect the company just as much (Sreejesh et al., 2016).

Heavily personalized advertising can create the feeling from customers that their privacy is being violated (Jung, 2017). Usually, it does not even have to be proven that the data has been used in an improper way, it is knowledge and belief from the customers that matter the most (Sreejesh et al., 2016). Berger (2010) discussed the balance between collecting data from customers and privacy concerns before the GDPR was statutory.

Beger (2010) concluded that a marketing strategy collecting personal data is beneficial for companies but at the same time, customers could rise against the violation of privacy.

The violation of privacy could harm the customers and therefore also affect customers' trust towards the company who is behind the ads, in a negative way (Berger, 2010). This study will therefore focus upon contextual targeting which is a rather unique marketing strategy that does not collect personal data (Song et al., 2018) and is used as a marketing technique to increase efficiency in digital advertising (Vassio et al., 2020) through the users display network (Google, 2021). Also, banks spend a considerable amount of their budget on marketing expenses, especially through digital means in order to acquire new customers but also to interact with their existing customers (Acar & Temiz, 2017).

Mogaji and Danbury (2017) investigated advertising strategies and consumers attitudes towards banks in the U.K and argued for future research to explore the attitudes towards banks in other countries since the media differs between countries. Therefore, this paper

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will investigate the attitudes towards a specific marketing strategy, contextual targeting in the setting of Swedish banks. Lastly, as Zephaniah, Ogba and Izogo (2020) explained, the banking market across the globe has undergone substantial digital transformation while research has barely kept pace with the developments, especially digital marketing communication strategies. Which further strengthens the theoretical and practical relevance of this paper.

1.4.1 Problem formulation

Taking the problem discussion, earlier conducted research within contextual targeting and the common factors in the banking market into consideration. There is a theoretical gap identified within how relevant advertisements based on collected content data affect banks customers attitudes. In the way of creating awareness and knowledge, belonging to the cognitive component of attitude. Also, the way of establishing feelings and emotions belongs to the affective component. Thirdly, how bank customers' attitudes are affected in the conative component where preference and purchases take place. Further, it is also suggested that there have been several concerns with using contextual targeting.

The feeling of privacy being violated could be the most challenging one which is also the one that has been appearing within the last years. It is therefore relatively unknown what effects this can have on customer attitude and how it eventually affects the customers' important aspects such as trust towards a bank. Further, the banking market is dependent on trust and trust can lead to loyalty in the longer perspective. The fact that the small amount of research conducted within the area shows that the marketing strategy contextual targeting is not effective enough to affect attitudes in a way that changes the purchase or acquisition decision of a customer (Song et al., 2018: Yeun-Chun et al., 2014: Kononova et al., 2020) which gives this paper theoretical relevance due to the fact that these results can perceive banks to change marketing strategies.

1.5 Purpose and research questions

The purpose of this paper is to explain how contextual targeting affects customers attitudes, privacy concerns, trust and loyalty.

1.5.1 Research questions

1. How does contextual targeting affect customers' cognitive, affective and conative attitude?

2. How does contextual targeting affect customers' perceived privacy concerns, trust and loyalty?

1.6 Research boundaries

The study is delimited to one marketing strategy, in the form of contextual targeting. The strategy is delimited through the lack of personal data collection like behavioral or geographical targeting can be considered to be (Narang & Shankar, 2019). The research

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is also delimited to the sample selection meaning the authors could thereby only generalize based on the specific population.

1.7 Report structure

Following the introduction chapter, this paper will present a literature where six hypotheses will be stated, based on the literature review, forming the research model for this paper. Followed by the method chapter, results and analysis chapter, discussion, conclusion and finally managerial implications, limitations and suggestions for further research.

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2. Literature review, hypotheses and model

The literature review gives account for the theoretical concepts that laid the foundation for the data collection and analysis. This chapter gives a meticulous review of contextual targeting and attitudes. It also reviews banks' more vital customer foundations such as privacy concerns, trust and loyalty to be able to see if there are any correlation between these and how they are affected by contextual targeting.

2.1 Contextual targeting

Contextual targeting is defined as when companies present relevant ads based on customers' content preferences (Hanson, 2016: Zhang and Katona, 2012). Earlier research conducted by Chen et al., (2019) suggested that using suitable terms within the advertising is one way to efficiently find relevant consumers. One reason behind the success of contextual advertising is the relevance of the surrounding content (Broder et al., 2007). Contextual advertising uses certain keywords to match the right advertising content to fit a certain consumer (Chen et al., 2019). Contextual targeting is all about targeting the consumer at the right time (Lu, Xue & Zhao, 2016). Lee, Lee and Yang (2017) argued that contextual advertising together with new technology can maximize the effect of advertisements. As a result of the latest developments within technology, companies can use automated content analysis where companies can figure out customers preferred content and then target them with relevant advertisements (Chen et al., 2019).

That is, without using personal data, which makes contextual targeting work on a larger scale (Song et al., 2018).

Narang and Shankar (2019): Lu, Xue and Zhao (2016) claimed that contextual targeting creates positive attitudes if customers have been targeted at the right moment. Contextual targeting within financial services is an important aspect contributing to advertising effectiveness. Also, placing advertisements relevant to the content on the page will enhance the involvement from consumers as well as the attitude towards the advertisement (Wang, 2011). Narang and Shankar (2019) also suggested contextual targeting to be increasing a company's awareness among customers. Song et al.,(2018) investigated how contextual competitive targeting affected companies, concluding that using contextual advertising has positive effects on awareness due to the increasing clicks it causes. Contextual targeting also affected the final purchase decision which could be afflicted by brand trust and loyalty (Song et al., 2018).

Wu et al., (2013) explained that the positioning of a contextual advertisement is also of importance. Both customers' thoughts and feelings could be reached by advertising through contextual targeting but for customers to actually go through to the final purchase decisions it is also afflicted by trust and loyalty towards a certain company (Song et al., 2018). Wang, Genc and Peng (2020) showed that entertainment was a key factor for creating value and satisfying needs for consumers in advertising. Even though there was a chance of already consisting preferences within contextual targeting, customers doing

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online searches for a product or services usually do not have any preferences and are therefore open for advertising to affect their awareness and preferences (Song et al., 2018). According to Zhang and Katona (2012) contextual targeting consists of two factors that are having a positive impact on profit. First, using this advertising method will let the company target customers that have preferences and needs for products or services that the company offers. Secondly, it could allow your business to purchase content analysis that your competition needs to secure new revenue, which could be helpful in a competitive market. Shaohua et al., (2019) suggested that contextual advertising has an impact on consumer behavior if the ads are considered to be informative and customized (Shaohua et al., 2019). Contextual targeting reduces the irritation level of customers due to more customized marketing as it is contextualized (Lian, Cha & Xu, 2019).

2.2 Attitudes

Attitude is a psychological inclination, and non-visible since it takes place in a person's mind (Eagly & Chaiken, 1993). Attitudes is described as an evaluation of an object of a thought (Arzubov et al., 2017). Can also be described as: “Attitudes are internal feelings and behaviors are their external symbols” (Hamidizadeh et al., 2012 pp. 131). Malhotra (2005) explained attitude as a summarized evaluation of an object or thought. An attitude affects everything a person holds in their mind (Arzubov et al., 2017). Including perceptions of people, groups and ideas (Bohner & Wanke, 2002). According to Patel and Davidsson (2019): Hamidizadeh et al., (2012) attitudes could also be described as a deeper evaluation or feelings within the consumer's mind. Hamidizadeh et al., (2012:

Schiffman and Kanuk (2004): Vishal (2014) claimed that attitudes consist of three components which are cognitive (belief or evaluation), affective (feeling or emotion) and conative (response or action). An attitude towards something can either be positive, negative or neutral (Walley et al., 2009).

One way of leading consumers from unawareness of a product or service into an actual purchase decision could be done through advertising. That is, taking the consumer through the different attitude-components (Lavidge & Steiner, 1961). Firstly, a consumer may not be aware of a product or service, hence, unawareness. Second and third step includes what the consumer thinks about a product or service belonging to the cognitive component. Here, the consumer creates a belief or evaluates the company, product or service. The fourth and fifth step belongs to the affective component. Here, consumers create feelings or emotions towards the company, object, product or service. In the last component, conative response, the consumer responds to the previous steps and possibly takes action and decides to attain a product or service, or the consumer decides not to (Schiffman and Kanuk, 2004: Vishal, 2014: Lavidge and Steiner, 1961).

2.2.1 Cognitive

Hamidizadeh et al., (2012) suggest that the cognitive component can be described as a person's thoughts and beliefs (Schiffman & Kanuk, 2004) about a phenomenon

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enduringness or its velocity. Cognitive or cognition, refers to memory based information (Argyriou & Melewar, 2011). Furthermore, the cognitive component is about the knowledge and beliefs a person has over a product or service (Fill, 2013). It is also claimed that the cognitive responses are affected by external expressions such as advertisements (Petty & Briñol, 2014). Aesthetic factors play an important role in online advertising (Wu et al., 2013). Hamidizadeh et al., (2012) claimed that advertising consisting of informative, credible and relevant content reflects positively on the cognitive response which also results in more efficient advertising. Even though relevant content, the authors claim that consumers' attitudes do not change directly when going through the cognitive component which determines that this component does not directly lead to active purchases (Hamidizadeh et al., 2012). Lavidge and Steiner (1961) claimed that cognitive response is created through awareness and knowledge where knowledge can be related with informative content. Any consumer or person has to go through the cognitive and affective phase to form an attitude towards something (Hamidizadeh et al., 2012). The cognitive component is the place where consumers store and organize information in their minds (Fishbein & Ajzen, 1975) which is an important step to recognize before consumers reach the affective stage. In the affective stage the consumers form opinions based on the cognitive phase (Ahn & Back, 2018).

2.2.2 Affective

The affective component is somebody's feelings about a certain situation or process (Ajzen, 1989). For example, feelings of being good or bad (Hamidizadeh et al., 2012). It is in the affective phase where consumers establish liking and preference towards a specific product, brand or company (Lavidge & Steiner, 1961). Lavidge and Steiner (1961) first work showed that advertising could be used to presvie consumer's feelings towards a positive degree before reaching a level where the company's product or service is the consumer’s preferred choice (Mokhtar, 2016). In the affective phase, changes in attitudes could also occur through advertising, referred to as cognitive-affective inconsistency (Conner et al., 2021). Implying that the cognitive and affective evaluations (attitudes) are not always consistent (Maio, Esses & Bell, 2010). The affective component is a more dominant influencer compared to the cognitive component in how consumers form their attitudes and are therefore believed to significantly influence the way a consumer forms their attitude towards a product, service or company (Chen, Kim

& Lin, 2015). Consumers usually go through the cognitive and affective phase where feelings and emotions emerge (Vishal, 2014). Advertising consisting of entertainment and a low level of irritation are two aspects that affect consumers' affective response.

Also, the cognitive- and affective components are positively affected when the consumers are feeling happy compared to feelings of sadness, which is directly affected by entertainment, credibility, irritation and informativeness (Hamidizadeh et al., 2012).

Marketers should therefore focus on stimulating liking which has a positive effect on preference towards a certain brand or company (Duffett, 2020). Wang and Zhang (2002) argued there to be four major factors influencing a person's attitude towards advertising:

entertainment, irritation, informativeness and credibility (Ducoffe, 1996) which leads to

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the conative component and response which is the last component of an attitude. The place where consumers make decisions.

2.2.3 Conative

Conative response, also referred to as the behavioral component (Vishal, 2014) consists of two levels. Consumers create preferences in the affective stage (Chen, Kim & Lin, 2015), in the form of feelings or emotions. This leads to a desire to attain a certain product or service which is the first step in the conative response. In the second stage the desire leads to an actual purchase, the acquisition of a product or a service (Lavidge & Steiner, 1961). These two parts combined, are the actual purchase level of an attitude where the consumers behavior is being shown (Fill, 2013). Advertising in this specific part of the process plays the role of encouraging consumers to press the action button. In the form of reminders with certain ads at the perfect timing (Mokhtar, 2016). Noela et al., (2018) suggested that the conative response is a result of how the cognitive and affective components are influencing and predicting the actual buying behavior. Hence, the behavioral intention from a consumer based on their attitude (Vishal, 2014). The conative component shows the tendencies towards a product or service which is a reflection of the cognitive and affective responses (Oktavia, Reflinda & Kardena, 2020). Further, the conative component is believed to better predict actual buying behavior of consumers than the two other components of attitude, cognitive and affective (Schim et al., 2001).

On the other hand, the conative component is the outcome of the other two components, cognitive and affective, which results in the conative response being more predictable (Park, Stoel & Lennon, 2008). Hence, combining all of the three components, cognitive, affective and conative leads to the actual creation of an attitude (Noela et al., 2018).

2.3 Attitudes towards advertising

Attitudes towards advertising, also referred to as advertising attitude, is the stimulus from a consumer after being exposed to an advertisement or marketing communication (Lutz, 1985). Boateng and Okoe (2015) suggested that consumers' attitudes towards advertising determine the actual effect of the ad. Also, the most important factor to successfully advertise is to create value for consumers which leads to positive attitudes towards a product or service (Boateng & Okoe, 2015). The overall attitude towards advertising has a positive significant impact on the attitude towards online advertising (Souiden, Chtourou & Korai, 2017) It is shown that consumer value creation is accomplished when the consumer is feeling high levels of entertainment and informativeness but also a low degree of irritation (Hamidizadeh et al., 2012). Ducoffe (1996) also suggested that entertainment creates a positive feeling towards a company. Ducoffe (1996) further revealed that informativeness and entertainment arise from what kind of content is delivered within the advertising. Wang et al., (2002) added to Ducoffe’s (1996) findings that interactivity also affected the attitude towards advertising, later confirmed by Salamzadeh, Ariffin and Aun (2018). The credibility in a company's advertising also affects consumer attitudes where Boateng and Okoe (2015) suggested that trust and credibility were mostly affected by which platform the consumer is targeted by the

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advertisement. Moreover, an advertisements' effect on consumers is correlated with the reputation of the company as well (Boateng & Okoe, 2015). MacKenzie et al., (1986) argued that if a consumer has a positive attitude towards an advertisement they will also feel positively against the company behind the commercial. Consumers who are being targeted with relevant advertisements matching the content of a page are believed to enhance their attitude (Wang, 2011). An environment where the consumers think they are safe affects the cognitive response in a positive manner (Hamidizadeh et al., 2012).

At the same time when consumers are having a feeling of trust towards the company, it creates credibility which makes the advertising more efficient (Boateng & Okoe, 2015).

On the other hand, if consumers feel a level of distrust against a company it can trigger a high level of irritation which would have a negative effect. Hence, having a negative effect on the attitude. But then again, consumers are positively affected when targeted with relevant content. (Hamidizadeh et al., 2012).

As contextual targeting increases consumers awareness (cognitive attitude component) towards companies (Narang and Shankar, 2019: Song et al., 2018: Wang, 2011), reaching consumers feelings and emotions (affective component) (Song et al., 2018: Chen et al., 2019) and has an effect on consumers purchase intention (conative component) (Shaohua et al., 2019: Zhang and Katona, 2012) the authors propose the three following hypothesis.

H1: Contextual targeting positively affects cognitive attitudes of customer of banks H2: Contextual targeting positively affects affective attitudes of customers of banks H3: Contextual targeting positively affects conative attitudes of customers of banks

2.4 Privacy concern

Privacy concern in this context means the concerns of disclosing private individual data online or digital (Wu et al., 2012). The general idea with privacy online is to keep personal data out of reach for others (Ghosh & Singh, 2018). Privacy means the collection of user data in the digital world (Wu et al., 2012). Privacy concerns shift from individual to individual which makes it difficult to actually measure a general effect on consumers' attitudes. There is a clear difference between what individuals think about privacy concerns and how they actually act. Research conducted regarding privacy effects on consumers by Okazaki et al., (2020) suggested that both positive feelings and trust are decreasing if consumers are feeling that their privacy is being invaded. Additionally, Okazaki et al., (2020) found it of significance when the conative component was poorly handled, privacy concerns could affect consumers' purchase decision. It was also claimed that violating consumers' privacy could lead to a dislike of advertising and therefore also a dislike of a company (Martin, 2018: MacKenzie et al., 1986). It is also suggested that it is how consumers perceive advertising that evaluates their privacy concerns (Okazaki et al., 2020). Mutimukwe, Kolkowska and Grönlund (2020) further underline privacy concerns as an important aspect in the digital sphere, where privacy concerns are context- specific implying they depend on types of services. Reducing the individual's privacy

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concerns can be done through enhancing trust towards a service or company which can make the consumer more willing to disclose personal information (Mutimukwe, Kolkowska & Grönlund, 2020). Trust is also believed to be the single most explaining factor in terms of privacy concern, in the digital sphere. The more trust from a consumer the less private information concern they stress, hence privacy concern (Bergström, 2015)

However, Jung (2017) explained that heavily personalized advertising can backfire in the way that consumers feel that their privacy is being challenged or invaded. Because of the fact that contextual targeting does not collect personal data and instead uses content analysis to serve consumers with relevant advertising (Broder et al., 2007: Chen et al., 2019), the authors suggested the following hypothesis based on previous research within privacy concerns online. Implying a positive effect in the sense that it decreases perceived privacy concerns, hence a positive effect.

H4: Contextual targeting positively affects perceived privacy concerns of customers of banks

2.5 Trust

Trust within advertising can be defined as a prospect of consumers ability, reliability, integrity and beliefs that the company delivers what the advertisement promises (Sirdeshmukh et al., 2002). Wang, Genc and Peng (2020) claims that trust factors consumers' perception of a brand and therefore also affects its sales and revenue. Also, distrust will have the opposite effect, instead of increasing sales, it will decrease which is even more essential for advertising online. Trust is shown to be the second most important factor when advertising online and vital for companies to create preferences for consumers (Wang, Genc & Peng, 2020). McAllister (1995) suggested that trust is vital for any company advertising online but can be overlooked if consumers' knowledge is on a high level. Chatterjee and Chaudhuri (2005) investigated if companies with relatively high trust from consumers are more likely to have more efficiency in their digital advertising. Findings were that consumers have a tendency to recognize certain advertisements from companies they have levels of trust for. Meaning that consumers do not have to be exposed severely to advertising but still be affected (Chatterjee &

Chaudhuri, 2005). Pintado et al., (2017) investigated trust’s impact on advertising efficiency. There was not any significance between accepting digital advertising and trust but the findings were that trust can be used as a barrier against irritation in a company's advertising. Implying that consumers with high levels of trust towards a company will not be as irritated when targeted could affect companies advertising (Pintado et al., 2017).

Ke, Chen and Su (2016) investigated brand knowledge as a factor to build trust in an online environment. There was a significant relationship between knowledge and trust which could demonstrate how to build trust online. Brand knowledge was shown to be highly affected by awareness and the image of the brand or company. Suggests that awareness and image are two significant factors to obtain consumer trust Ke, Chen and

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Su (2016). Increased trust from consumers towards a company can lead to improved perception, increased efficiency, more noticeable and more attractive advertisements (Chatterjee & Chaudhuri, 2005). The banking market is highly dependent on trust.

Building trust within the banking industry is an important aspect (Mogaji, Farinloye &

Aririguzoh, 2016) and the financial sector in general (Gillepsie & Hurley, 2013).

Fungáčová, Hasan and Weill (2019) found that countries with a higher degree of income per capita have a lower degree of trust towards banks. Furthermore, women tend to trust banks more than men and that as people get older their trust towards banks tends to decrease (Fungáčová, Hasan & Weill, 2019). Building trust is an important aspect to recognize in order to develop customer loyalty (Akhgari et al., 2018) which led the authors to the following hypothesis.

H5: Contextual targeting positively affects trust of customers of banks

2.6 Loyalty

Loyalty towards a brand is described as: “a deeply held commitment to rebuy or re- patronize a preferred product/service consistently in the future, despite other situational and marketing factors that have the potential to induce switching behaviour” (Oliver, 1999 pp. 34). Further, consumer trust is explained as the reliable antecedent of customer loyalty in terms of transactions online (Nguyen & Khoa, 2019). Brand loyalty can be defined as a customer's attachment towards a certain brand or company (Fang et al., 2012).

Starr and Rubinson (1978) claimed that there is a positive significance between attitudes and loyalty. Therefore, brand loyalty is proven to be relevant towards contextual targeting effects on customers. Neal and Strauss (2008) suggested that brand loyalty consists of attitude and behavioral dimensions, where attitude reflects customers satisfaction towards a purchase. The behavioral (conative) dimension describes the customer's buying behavior (Noela et al., 2018). It shows if a customer is likely to purchase a product repeatedly. Zephaniah, Ogba and Izogo (2020) explained that marketing communication in terms of advertising is a significant predictor of customer loyalty. Keller (1993) suggested that loyalty is inflicted with the customer's knowledge about the brand.

Therefore, it can be said that a higher knowledge of a brand or product could lead to positive effects on a customer's loyalty (Keller, 1993). Kaytaz Yigit and Tigli (2018) explained that customers with a higher level of loyalty have a tendency to pay more for a product or service, in order to attain it, because they want to stay with the company and brand. Krishnamurthi and Raj (1991) claimed that customers with low levels of loyalty are more sensitive to the price level. Aaker (1996) suggested that strong loyalty could make customers choose a certain company or brand even though the quality and price is less beneficial. This is categorized as one of the strongest indicators that the company has created a high level of loyalty (Aaker, 1996). No customer will stay loyal if they are not satisfied, suggesting that loyalty can be connected with the affective and cognitive

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component. This can be compared with a positive attitude towards the company as well as to the conative/behavioral responses, which is reflected in repeated purchases (Fornell, 1992).

In terms of banking and loyalty, customers are believed to have higher loyalty towards banks they trust (Van Esterik-Plasmeijer & van Raaij, 2017). Levy and Hino (2016) explained loyal bank customers as possessing a favorable attitude towards the bank. Also, to buy from it repeatedly as well as recommending it to others (Levy & Hino, 2016).

Having effective advertising contributes to developing loyal customers. Hence, in order to improve consumer loyalty to the bank, marketers should work with effective advertising (Yeneneh, Negash & Adane, 2018). Based on the literature regarding brand loyalty and the positive effect advertising has on customers loyalty the authors propose the following hypothesis.

H6: Contextual targeting positively affects loyalty of customers of banks

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.8 Research model

Combining the reviewed literature above, the authors suggested the research model below in Figure 1 Research model. Based on previous research implying the positive aspects of contextual targeting (Chen et al., 2019; Broder et al., 2007: Lu, Xue & Zhao, 2016: Narang and Shankar, 2019: Wang, 2011) the authors suggested six hypotheses that will be tested further in this thesis. Together, these six hypotheses form the following research model, that contextual targeting has a positive effect on the three attitude components: cognitive, affective and conative. Also, based on previous research contextual targeting is suggested to have a positive effect on privacy concern (Okazaki et al., 2020: Martin, 2018: Mutimukwe, Kolkowska and Grönlund, 2020) since it does not collect private individual data. Instead, it uses consumer’s display networks to serve the consumers with relevant contextualized advertisements (Google, 2021), enabling the consumers to feel a sense of trust towards the company behind the advertisement, as it does not collect private individual data. Hence, decreasing privacy concerns meaning a positive relationship between contextual targeting and privacy concern. Further, the banking industry is dependent on trust (Mogaji, Farinloye & Aririguzoh, 2016), and trust is also an important aspect within advertising (Wang et al., 2020) implying that contextual targeting positively affects the relationship between the two concepts (Pintado et al., (2017; Ke, Chen and Su, 2016). Also, if privacy concerns are positively affected, it could create a sense of trust towards the company, from the consumers. Implying that both privacy concerns and trust are positively affected. Advertising is believed to be a significant predictor of loyalty (Zephaniah, Ogba & Izogo, 2020) and consumers' attitudes towards contextual targeting being argued to be positively affected by contextual targeting, led the authors to propose that contextual targeting has a positive effect on customer's loyalty, in line with Levy and Hino (2016). As trust and loyalty have a positive relationship (Van Esterik-Plasmeijer & van Raaij, 2017). The authors propose that both trust and loyalty also be positively affected by contextual targeting.

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Figure 1 Research model

H1: Contextual targeting positively affects cognitive attitudes of customers of banks H2: Contextual targeting positively affects affective attitudes of customer of banks H3: Contextual targeting positively affects conative attitudes of customers of banks H4: Contextual targeting positively affects perceived privacy concerns of customers of banks

H5: Contextual targeting positively affects trust of customers of banks H6: Contextual targeting positively affects loyalty of customers of banks

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3. Method

The following chapter focuses on the methodological processes that are applied in this study. The chapter begins with the scientific and research approach followed by the research method, research design and data collection method. After the questionnaire has been explained the operationalization table is presented. Followed by the chosen data analysis and quality criteria.

3.1 Scientific approach

This paper was based on a positivistic approach, defined as the paper's outcome aiming to be seen as positive progress for society (Patel & Davidson, 2019). Using positivism meant that the authors did not let their emotional, religious or political orientation reflect the paper's results (Patel and Davidson, 2019: Wallén, 2008). Therefore, the authors of this paper have focused on being as objective as possible. A direct association with positivism is the verification principle which is defined as every piece of theory can be transferred into verifiable observations (Patel and Davidson, 2019: Bryman and Bell, 2017). The definition matches this paper's aim where the theory was conducted first and laid the ground for the research questions and hypotheses. Therefore, after reviewing the gathered literature the authors found a research gap in the form of the attitude towards contextual targeting in the banking market. Where the existing literature has argued trust, loyalty, privacy concerns to be important concepts for banks to consider based on the consumers perspective. The proposed hypotheses were based on previous literature which the research gap then was to address through testing existing research in the identified gap (Bryman & Bell, 2015).

3.2 Research approach

As this research paper found its form based on earlier conducted research and identified a gap that needed to be further developed, the authors undertook a deductive approach.

A deductive approach is defined as when hypothesis and research questions are shaped from existing theory and are formed after relevant theory is in place (Bryman and Bell, 2017: Jacobsen, 2002). Further, a deductive approach focuses on the relationship between the conducted research in this paper and the already existing research. The deductive approach is about testing already existing theories and concepts (Malhotra, 2010). This paper, therefore, took a deductive approach which implies testing existing research, theories and concepts which was conducted through hypothesis testing. According to Bryman and Bell (2015) a deductive approach is normally synonymous with a quantitative research method where numbers and data are gathered to test the proposed hypothesis (Henn, Weinstein & Foard, 2006). The authors set out to test the already existing and established theories within marketing, attitudes, banking, loyalty, trust and privacy concerns to test it in relation to the marketing strategy being contextual targeting.

Further, Geuens and De Pelsmacker (2017) argued the crucial importance of substantial contribution within the research field which the authors of this paper truly believe they

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contributed with. As existing research within the banking industry has been focusing on attitudes, trust, privacy and loyalty, the authors of this paper believed to be contributing to the marketing communication research field in an online context with contextual advertising as the main contribution. As Zephaniah, Ogba and Izogo (2020) deemed a necessity.

3.3 Research method

As a positivistic approach, hence, a deductive approach normally takes a quantitative research method (Henn, Weinstein & Foard, 2006). Further, the fact that the literature review led the authors to propose six hypotheses, a quantitative study came naturally in that sense. A quantitative research method is according to Bryman and Bell (2015) the collection of generalizable numerical data with an objective approach to exhibiting the relationship between theory and deductive research. To qualify as generalizable data the method usually needs to consist of more participants than other methods (Henn, Weinstein & Foard, 2006). Thrane and Torhell (2019) claim that a quantitative approach is based on the objective to understand what people think, decide and act on. The authors also claim that with that knowledge it is possible to discover new facts in a specific area.

Bryman and Bell (2015) therefore suggested that a quantitative approach usually consists of surveys in the collection of data. Based on that, the authors constructed an online questionnaire created in Google Forms because of the positive aspects associated with it.

Such as cost-beneficial, ability to reach out to respondents with minimal effort (Vasantha Raju & Harinarayana, 2016). Through the collection of hard data, the numbers were then analyzed to either reject or accept the proposed hypothesis in this research (Bryman &

Bell, 2015). Further, Goertzen (2017) argues that quantitative research is a sufficient research method when trying to back up claims about impacts and outcomes, which the authors did when addressing the hypothesis. Lastly, though addressing the hypothesis provided evidence whether the proposed hypothesis, based on previous theory, where statistically significant (Goertzen, 2017). Addressing the hypothesis is seen as positive progress to society as it either accepts or rejects previous research and therefore contributes with the knowledge to the society (Patel & Davidson, 2019).

3.4 Research design and Data collection method

This study utilized a cross-sectional research design which according to Rindfleisch et al., (2008) is a less costly research design in comparison with other approaches. In terms of a cross-sectional research design, the descriptive design is the most frequently used by researchers (Malhotra, 2010). A cross-sectional research design revolves around the collection of data on multiple cases but at one specific moment of time, on a given sample of a population. The authors gathered data during a total of 7 days. Furthermore, the generalizability of a cross-sectional research design is limited to the exact population participating in the sample at that given moment of time. Through the collection of quantitative and quantifiable data, the researchers were investigating the relationship

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between the variables in the research to explain the relation and variation between the cases collected (Bryman & Bell, 2015). As this paper used a cross-sectional research design, the data was collected through primary data which according to Malhotra (2010) is beneficial since the data collected concerns addressing the purpose and research question of the study being conducted. The primary data was collected through an online questionnaire argued to be a cost-efficient tool and a suitable instrument to gather data in a time-efficient manner (Bryman and Bell, 2015: Vasantha Raju and Harinarayana, 2016). The questions asked in the questionnaire were open-ended implying that after each statement the respondent had to answer and take a standpoint in the statement which is preferable nowadays (Krosnick & Presser, 2010). The respondents filled in the survey using a five-point Likert scale for each statement. Likert scales can be used to gather data that later on will be statistically treated and analyzed through correlation and regression analysis (Joshi et al., 2015). The respondents were asked to answer each statement where a score of one represented “completely distance me” and a score of five “completely agree” (Bryman & Bell, 2015).

3.4.1 The design of the survey

The survey started with some personal factual questions. These questions consisted of different personal information questions, such as age, gender completed education and income (Bryman & Bell, 2017). The second chapter consisted of a question of familiarity regarding banks in Sweden to get a general idea of the level of familiarity with banks in Sweden today. Throughout the third chapter, the questions regarding contextual targeting were formed after three different attitude components and were based on the literature review. This enabled the authors to categorize what part of an attitude could be affected by contextual targeting. The fourth chapter in the survey was based on earlier research regarding banks impact on privacy concern, trust and loyalty. Where the statements were formed to get a general idea of how contextual targeting affected these vital concepts within the banking industry.

3.4.2 Respondent effort and survey fatigue

Survey fatigue is an important aspect to consider when collecting primary data through an online questionnaire (Bryman & Bell, 2015). There is always a possibility that respondents decrease their involvement in answering as they go through the questions.

Implying respondents losing motivation and giving fewer thoughts to answer the questions (Whelan, 2008). The authors, therefore, kept the statements short but informative to delve with this concern. Also, when the questionnaire was distributed online, mainly through Facebook, the authors underlined the importance of answering with the highest possible attention and effort as the respondents played an important part in this research.

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3.5 Operationalization

The operationalization table allows the researchers to measure existing theoretical concepts and theories (Bryman & Bell, 2015). Also, through operationalizing the concepts enables the researchers to measure the concepts and theories (Jacobsen, 2012).

Table 1 Operationalization table

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3.6 Sampling, Pre-test and ethical considerations

A sample is explained as a subset of a population that is being used in an investigation or research according to Bryman and Bell (2015). The author's purpose was not to investigate all of the individuals found within a population but rather a subset of individuals found within a given population. In this research, the authors targeted consumers who were familiar with banks in Sweden. In line with Geuens and De Pelsmacker (2017) implying that even though a random sample is preferable it does not guarantee the representativeness of a particular population. Since the questionnaire was available online, respondents who did not have access to the internet had not the possibility to answer the questionnaire. This called for a non-probability sampling implying that all individuals within the population did not have the same randomness to be selected in the study. This study was therefore limited to the respondents having access to the questionnaire and through the various forums, it was shared through, such as Facebook and other social media channels (Bryman & Bell, 2015). Non-probability sampling is also referred to as convenience sampling, where the authors utilized the population they had access to. This meant that the sample was not generalizable for the entire population within Sweden. However, the authors set out to investigate the banking market in Sweden and a filter question was utilized to filter between respondents who deemed to be of relevance for this study (Malhotra, 2010). A total of 132 responses was collected which is above the deemed acceptable predictor of a sample size of 104 + m.

Where m stands for the number of independent variables (Green, 1991). The authors strived for gathering as many responses as possible but the total collection of 132 responses was deemed acceptable since this research consisted of one independent variable (104+1=105).

As the authors used a self-completion questionnaire, the one’s responsible for the questionnaire were not physically attending when respondents were answering. To verify and test that the questionnaire was working correctly and no misunderstandings were occurring when the questionnaire was sent out for data collection, a pre-test was conducted which was an important measure to undertake. Because of the importance the questions being clear and explicit to the respondents (Bryman & Bell, 2015). The questionnaire was sent out to eight respondents who matched the criteria of relevance (Hair et al., 2014) and tested the questionnaire. The respondents supplied the authors with relevant feedback to refine the questionnaire accordingly, after that the questionnaire was sent out to gather data. When conducting research especially through an online questionnaire, ethical and societal considerations are two aspects of importance to consider (Bryman & Bell, 2015).

The authors have a responsibility when approaching individuals to answer the questionnaire, both from an ethical standpoint but also from a societal viewpoint. As an example, promoting a product or service that has an underlying reason to promote it or fundraise it falls into unethical practices (Malhotra, 2010). Another ethical consideration the authors undertook was to ensure anonymity during the whole process. In terms of

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societal concerns, moral and legal orders are to be maintained at all times (Social Research Association, 2003) which the authors were well aware of. As this paper did not set out to conduct research on a specific bank, rather the general banking market in Sweden, the authors are convinced they have been objective throughout the entire research process. Also, as the independent variable, contextual targeting does not collect personal data but instead uses content data to target consumers. The authors believe they have contributed with knowledge towards companies, especially banks, but also individuals that using and disclosing personal data is not a necessity to target consumers through a marketing strategy. Lastly, as the carried out research focuses on contextual targeting effects, the findings can be interpreted as to be focusing towards companies that are utilizing this marketing strategy, rather than towards individuals. However, the authors believe they are contributing with a rather general research on the effects of contextual targeting. Benefiting both individuals and companies when interpreting and analyzing the data to enhance the understanding of the concept contextual targeting and its effects.

3.7 Data analysis

The data collected from the questionnaire was analyzed in SPSS (Statistical Package for Social Sciences). To test the proposed hypotheses, significance testing and regression analysis was conducted to find relations and significance between the data (Hair et al., 2014). Further, as the authors undertook a deductive approach and utilized a quantitative study, descriptive statistics was used to measure the relationship between the variables.

Firstly, the mean, median, mode, standard deviation, skewness and kurtosis was analyzed to investigate the data. Standard deviation showed the average amount of variation, skewness told the authors the distribution of the data which the kurtosis also did but vertically (Malhotra, 2010: Hair et al., 2014). Norman (2010) argued that the level of skewness for ordinal data does not always have to be within the -1 to 1 range as non- normally distributed data is not that sensitive when it's ordinal. Secondly, the authors undertook a correlation analysis, R square (R²), between the variables to tell the relationship between them. R² can take a value between -1 and 1, where 0 represents no relationship. If the value is positive, the relationship is positive and likewise, if the value is negative the relationship is negative. The R square tells the goodness of fit since it concerns how the dependent and independent variables influence each other. Also, the adjusted R square (R²) was utilized as it measured the number of independent variables and also considered the size of the sample to find the correlation (Malhotra, 2010).

Regression analysis was then conducted which explored the relationship between the independent and dependent variables together with the utilized control variables discovering the variation between them. Lastly, as the authors set out to address the proposed hypothesis, significance testing was conducted. The chosen confidence interval (CI) was 95% which implied that the coefficient was of significance when a (alpha) was

≤ 0,05. Hence, the CI determined whether the hypothesis were to be accepted or rejected based on the level of significance (Malhotra, 2010: Hair et al., 2014). The regression

Target your customers contextually

[Master thesis]