The Relationship between Personalization and Customer E-loyalty: Customer
Trust and Customer Satisfaction as Mediators
Dicky D. Hidayat
S3156176
University of Groningen
Faculty of Economics and Business
Msc. Marketing Management
Abstract
Nowadays, personalization in advertisements is more common than ever, especially in commerce, in order to gain trust and satisfy their customers and most importantly, to gain their e-loyalty. The purpose of this study is to find out whether moderate personalization in advertisements gives a positive impact to customer e-loyalty directly or whether this relationship is significantly mediated by customer trust and customer satisfaction in order to give a positive impact. The research was done through a survey made in Qualtrics and distributed to respondents from The Netherlands and Indonesia and an amount of 478 respondents were successfully collected. The study shows that moderate personalization in advertisement (i.e., mentioning customers by name) is indirectly gives a positive impact on customer e-loyalty through the mediation of customer trust and customer satisfaction. Without the mediators, moderate personalization is proven not effective in enhancing customer e-loyalty as some customers might perceive moderate personalized advertisement to be less or not personal to them.
Keywords:
CHAPTER 1
INTRODUCTION
There is an increasing number of websites that personalize the search results based on user’s personal preferences which then display the most relevant information (Kim & Gambino, 2016). Personalization itself is automated and does not need the user’s explicit input to create individualized content (Treiblmaier et al., 2004). To offer an improved online search experience for users, many developers set up algorithms in websites that customize their content automatically according to personal information about individual users collected in an open or hidden manner (Sundar and Marathe, 2010). Personalization is assumed to be an effective marketing strategy to promote customer loyalty (Coelho and Vilares, 2006). For example, personalized content related to the customer’s preferences will result in more positive attitudes and more positive behavioral intentions toward the online content (Kim and Gambino, 2010). In particular, the key benefits of personalization include an appearance in customer trust and customer satisfaction (Coelho and Vilares, 2006).
the effect of personalization on customer e-loyalty is not direct but rather through increasing customer satisfaction (Coelho and Vilares, 2006). Thus, it is assumed in the present study that customer satisfaction causes customer e-loyalty, which is based on the reason explained by Bolton and Lemon (1999) that satisfied customers are more likely to have a greater usage level of a service than those who are not satisfied, and they tend to have a stronger repurchase intention and to recommend the product/service to their friends (Zeithaml, Berry, and Parasuraman, 1996). It is also hypothesized in the present study that customer satisfaction mediates the relationship between personalization in advertisements and customer e-loyalty. By investigating the mediating effects of both customer trust and customer satisfaction to the relationship between personalization and customer e-loyalty, this study would contribute to understand the importance of building customer trust and satisfaction through personalization in online advertisements in order to increase customer e-loyalty.
The research question that this study aims to answer is: How can companies increase
CHAPTER 2
LITERATURE REVIEW
2.1 Personalization
The growth of interest in one-to-one marketing over the decades (Peppers and Rogers, 1993) has brought the topic of personalization of products, services, and communications to a great significant position in marketing theory and practice (Ball et al., 2006). The basis of personalization is attached in a detailed comprehension of the customer. The significance of personalization is important even when communicating with the customer (Gavurová et al., 2018). For example, a report shows that a personalized experience leads to an overall better customer experience as well as increasing customer loyalty (Hussain, 2019). In general, the aim of personalization is to bring the right message to the right person at the right time (Tam and Ho, 2006).
Li (2006) explained that there are two types of personalization; actual personalization and perceived personalization. The first refers to the extent where advertisers has used personal information obtained from the consumer while the latter refers to what the consumer feels, whether the ad is personal or not. Consequently, an ad could be perceived as personal or vice versa even when the advertiser has personalized an ad. An example for this, which will be used in this study, would be addressing a consumer with his/her first name which is a form of moderate personalization.
different devices enhances and deepens customer relationships (Aguirre et al., 2015). In their study, Steinhoff et al. (2018) explain that the adoption of mobile shopping channels boosts customers’ order frequency with order sizes and rates rise up after they start to use mobile shopping. The rise of order frequency may happen because mobile channels can aid the establishment of habitual buying and interacting with the online sellers, unconstrained of time and space (Wang et al., 2015). This rise of order frequency can be translated into the intention to continue to patronize and continue the relationship with the company and as mobile channels help to establish habitual buying, consumers are supposed to repurchase or re-patronize from the company and would even give positive word-of-mouth and willing to expand purchasing beyond the initially-purchased line of services or products. All the things mentioned can be referred to as customer loyalty. As this study is focused on online personalization, thus e-commerce, we can refer this loyalty as customer e-loyalty, which will be explained in the following section.
Finally, personalization in online interaction can be achieved through ad personalization in which firm decides the marketing content, products or services that fit with an individual based on a consumer’s previous behaviour (Arora et al., 2008). Research has proven that personalization in ads effectively increase the recipient’s perceived personalization and that personalization creates a match between what is being shown in the ad and the characteristic of the recipient of the ad (Petty Wheeler and Bizer, 2000). With all being said, it is assumed that personalization in an online ad would increase perceived personalization and thus leads to the increase of customer e-loyalty. Moreover, Komiak and Benbasat (2006) explained that personalization is directly increase trust. This theory is also align with the paper of Aguirre et al. (2015) which stated that personalization could have an impact on customer trust through taking advantage of trustworthy website by capitalizing on the potential spillover of trust from the website to an advertisement. This would lead to consumers to believe that advertisements follow the norms of the websites on which they appear.
2.2 Customer E-Loyalty
reasons why customer loyalty is important for modern-day business. The first reason is that customers are a scarce resource in which it is attainable to reach out for old customers rather than a new one. Second, customer loyalty benefits the company in terms of profitability and revenues. Customer loyalty is translated into benefits including gaining new customers from word-of-mouth communication and cost reduction, as well as a rise in profits from cross-selling and up-selling.
As the use of Internet rise, consumers are more focused nowadays on buying products and services online (Kariyawasam and Wigley, 2017) and customer loyalty is one of important drivers of success of e-commerce (Rachjaibun, 2007). Within the e-commerce context, customer loyalty is referred to as e-loyalty (López-Miguens and Vázquez, 2017; Toufaily et al., 2016). Toufaily, Ricard, and Perrien (2013) explain that e-loyalty is customer’s willingness to retain a stable relationship in the future and to engage in a continuous behaviour of visits and/or purchase of online products/services through the company’s website. The significant difference of e-loyalty with the traditional loyalty is that Internet users can access web sites with just a click (Jeon, 2009). In this study, I would like to find out whether the presence of moderate personalization in an advertisement will enhance customer e-loyalty, as studied Ball et al. (2006) in which they found that personalization, besides enhances trust and satisfaction (which will be explained in the following paragraphs), also enhances loyalty.
H1 : There is a positive relationship between moderate personalization in advertisements and
customer e-loyalty
2.3 Customer Trust
of the nature of the situation at hand, in this case, the environment for e-commerce which has various risk (e.g. security risk) involved.
Furthermore, trust in the transacting vendor is significant for the consumer to take risk which is related with a given transaction (McCole et al., 2010). On the other hand, Soh et al. (2009) defined trust in advertising as “confidence that advertising is a reliable source of product/service information and willingness to act on the basis of information conveyed by advertising”. They also argued that consumers should have trust in ad-conveyed information for advertising to function effectively as a source of information an perceived as useful by consumers in their decision makings. Advertisers want their brands to be preferable by the customers to others, having the intention to keep purchasing their brands in the future, and finally transforming that intention into action of purchasing their brands (Mutum et al., 2013). Nguyen et al. (2013) explained in their study that trust can have an immediate impact on the decision the customers will make in the future, including continuing the purchase with the company, which is a form of customer loyalty. Various factors can impact customer trust, but this study emphasizes exclusively in personalization in advertisements. Like most scholars, for instance White et al. (2008) which explain that tailored advertising trigger reactance in which customers would feel threatened, thus reduces their trust to the company, research on whether personalization positively impact customer trust is limited. On the other hand, as Ball et al. (2006) have found that personalization causes trust and loyalty. Thus, in the present study, I would like to investigate whether personalization have a positive impact on customer trust and whether customer trust have a positive impact on customer e-loyalty. Thus, the discussions have led to the following hypotheses:
H2a : Personalization will have a positive impact on customer trust
H2b : Customer trust will have a positive impact on customer e-loyalty
2.4 Customer Satisfaction
making and buying process that can be done easily, and the ability for customers to see the available products with reviews written by other customers (Francisca and Hapsari, 2018).
Knowing what creates customer satisfaction has becoming even more crucial nowadays as more e-retailers promise their customers creating a satisfying online experience. But first, we have to know what is satisfaction. Satisfaction is defined as a result of difference between what is expected and what is experienced (Albaity and Melhem, 2017). Customer satisfaction thus means the customer’s perceived balance between the expected performance of the product before buying it and the actual performance of a product after it has been consumed (Hayati, Suroso, Suliyanto, and Kaukab, 2020). Fornell (1992) explains that satisfaction might be an overall feeling and customers have an idea of how a product or service is compared to the “ideal” norm. Thus, when a product or service performance is below expectation, dissatisfaction tends to take place.
Furthermore, As proposed by Coelho and Vilares (2006), personalization should create a transaction that is more satisfactory and a more satisfactory relationship between the customers and the company throughout the time. In addition, they also explained that personalization shows an effect on customer loyalty, but the effect is not direct but rather through improving service satisfaction. I argue that the mediating role of customer satisfaction in the relationship between moderate personalization in advertisement and customer e-loyalty makes sense because I assume that customers that are satisfied with the company and generally satisfied with the advertisement (e.g., when the ad display the product they are expecting) would enhance their e-loyalty to the company. Thus, from above discussions, the hypotheses could be expressed as follow:
H3a : Personalization will have a positive impact on customer satisfaction
H3b : Customer satisfaction will have a positive impact on customer e-loyalty
H4 : The relationship between personalization and customer e-loyalty will be mediated by
2.5 Conceptual Model and Hypotheses
Figure 1 displays the conceptual model of this study with the hypotheses included.
Figure 1 Conceptual Model
CHAPTER 3
METHODOLOGY
3.1 Research Design
The relationship between personalization, customer e-loyalty, customer satisfaction, and customer trust were tested with one-way between participant experimental design. The independent variable, personalization, included two levels: ‘Moderate personalization’ and ‘no personalization’. Before the manipulation experiment, participants were asked about the frequency of online shopping they did in the past 3 months, their names, age, and gender. After being exposed to the manipulation, participants answered questions in relation to the perceived personalization and the mediator variables “customer satisfaction” and “customer trust”. The dependent variable was “customer e-loyalty”.
3.2 Sampling Strategy and Sample
The research was targeted to everyone in the Netherlands and Indonesia; hence, the study was conducted in both English and Indonesian. ’50 respondents per factor’ was used as the rule of thumb for the purpose of factor analysis (Pedhazur & Scmelkin, 1991), thus, a minimum of 200 respondents were needed as there are four factors within the conceptual model. Tabachnick and Fidell (1996) explained that a total of ~200 respondents can be seen as ‘fair’, ~300 as ‘good’, ~500 as ‘very good’ and ~1000 as an ‘excellent’ size of respondents. In the end, the amount of 478 respondents was obtained.
3.3 Experimental Manipulation: Moderate Personalization
Two scenario-based experiment were used where participants were randomly assigned to one of these scenarios and they were prompted to take a perspective of a specific person in the following way:
buying a unique two-person umbrella that you can use together with your loved one or a friend for some time now, especially since the rain season has started. Then, you stumbled upon a page on your social media called ‘ParaplUS’. ParaplUS sells many types of two-person umbrellas in different colours and shapes. Since you are interested in buying an umbrella fast, you decide to follow the page ParaplUS.
The following day, you frequently receive the following advertisement on your social media timeline from the company Wet-No-More:
Hey guys [no personalization condition] /name of the participant [moderate personalization condition] ! Are you looking for a unique umbrella where you can use it together with your loved
one or a friend? We have just what you are looking for! Introducing ‘UmbrellUS’, a unique innovation for an umbrella that can fit for two people!”
The advertisement from Wet-No-More shows the exact model for the two-person umbrella that you were looking for. Moreover, you were not aware about the existence of the company
Wet-No-More before receiving this advertisement as you never encountered this company previously.
You also noticed that the page ParaplUS and the company Wet-No-More are not connected with each other.”
names and in the second scenario, they were not, but rather they were greeted with “Hey guys!”. The current study targeted to investigate the difference between the extent of personalization instead of the difference between personalized and non-personalized ads. A fictitious brand was used to avoid any bias of previous loyalty to any actual brands.
3.4 Procedure and Measure
At the start, participants were welcomed and asked if they had any social media account/s. Then, participants read the scenario in which they were asked to imagine themselves that they need an umbrella soon and they were randomly assigned to one of the two conditions. They either were exposed to the moderate personalized ad of a fictional brand (i.e., including their first name), or they were exposed to the non-personalized ad (i.e., no name included). Next, they received questions related to the manipulation check, personalization, and the mediators, trust and satisfaction. These items were randomized to establish a cause-and-effect relationship, to yield an accurate analysis, and it was expected that the participants should not differ significantly with respect to the potential confounding variables. This is aligned with Goodhue and Lolacono (2002) who showed grouping questions resulted in a artificially higher Cronbach’s alpha than intermixed questions, and that “intermixing questions results in a small but systematic improvement in actual reliability”. Finally, the dependent variable, e-loyalty towards the brand was measured.
Moreover, confounding variables are used to control the influence of socio-demographics to the results and to see if they would have an impact on the examined relationships. Age and the frequency of using social media were included as the confounding effects and these are adopted from the study by Boerman et al. (2017). Age was used because it was assumed to give affect the hypothesized relationships because according to Boerman et al. (2017), younger people seem to be less likely to oppose to personalization. The perceived relevance of the product (i.e., a uniquely designed umbrella) was added because probably some participants would prefer using a raincoat or drive a car when going out, rather than using an umbrella. The frequency of using social media was added as based on the study by Lee et al. (2015) and Miyazaki (2008), someone will be more tolerant for personalized advertising and would likely to see any personalization in an ad to be personal.
specifically for me” and “The ad targeted me as a unique individual” and they were measured on a five-point Likert scale from 1 “strongly disagree” to 5 “strongly agree” was used instead of seven-point as it would reduce the frustration level of respondents and increase response rate and quality (Sachdev & Verma, 2004).
Customer E-loyalty. The items that were used to measure customer e-loyalty were adapted from the study by Lu, Wu, and Hsiao (2019). Participants were asked to indicate the extent to which they were agree or disagree with the following five items and for each item on a Likert scale ranging from 1 ‘strongly disagree’ to 5 “strongly agree”: “I say positive things about the products to other people after reading this on my social media timeline”, “I would recommend the products to those who seek my advice after reading this mobile ad”, “I would encourage friends and relatives to buy the products after reading this on my social media timeline”, “I would post positive messages about the products on social media after reading this on my social media timeline”, “I intend to continue to buy products from the same company after reading this on my social media timeline”.
Customer Satisfaction. The measurement of customer satisfaction was conducted by asking four items and was adapted from the study Oliver (1980) including “I would be satisfied with my decision to purchase UmbrellUS from Wet-No-More company”, “If I would purchase UmbrellUS, I would feel positive about buying it from Wet-No-More company”, “It would feel ‘right’ to buy UmbrellUS from No-More company”, and “My choice to purchase UmbrellUS from Wet-No-More company would be a wise one”. Regarding these items, participants were asked to indicate the extent in which they agreed or disagreed with the statements on a five-point Likert scale was used.
3.5 Principal component analysis
Factor analysis (FA) was used initially in the analysis of the data in order to assure the validity and the reliability of our constructs. Before that, Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy was used to know if it was appropriate to perform a factor analysis by using multiple methods and to establish correlation and covariance between variables. The threshold is that variables should be larger than 0.50 (Malhotra, 2010). The following test was Bartlett’s test of sphericity to confirm that variables are indeed correlate with one another and are appropriate for factor analysis. For these conditions to be met, null hypothesis of the test should be rejected (Bartlett, 1950). Finally, principal component analysis was used to check any communalities. Malhotra (2010) explained that the amount of variance that is explained by the factors should be more than 0.40. After FA was ensured to be appropriate, the analysis was able to be started.
3.6 Assumption check: linearity, normality, and independence
Assumption checks regarding the assumptions of linearity, normality, and independence were needed to be performed before conducting multiple linear regression analysis (Newbold, 2013). Linearity assumption can be viewed at the scatterplot of the relationship between the dependent variable and the independent variables and the amount to which it deviates from linearity. Moreover, normally distributed data is what was wanted to be established in this study. In order to check the normality assumption, a Normal P-P Plot and a histogram were used. Finally, auto-correlation in the data was needed to be checked as this should not be the case when checking the independence assumption. To do this, Durbin-Watson test were used and the value between 1.50< d <2.50 which tells that residuals are independent and there is no autocorrelation should be met (Newbold, 2013).
3.7 Analysis
CHAPTER 4: RESULTS
4.1 Data cleaning
The number of respondents obtained after I closed the Qualtrics survey was 478 respondents. This data had to be cleaned as there were some respondents who did not completed the survey, thus after the cleaning, I proceeded with 330 useable respondents, which according to Tabachnick and Fidell (1996), this can be considered as a ‘good’ number of respondents.
4.2 Validity and reliability of measures
To test the convergent and discriminant validity and reliability, a factor analysis was used. The Kaiser-Meiyer-Olkin (KMO) measure of sampling adequacy was conducted and revealed that it was appropriate to mix the personalization items (i.e., continuous variable), satisfaction items, trust items, and e-loyalty items into a factor analysis as all values exceeded 0.50 (Field, 2009). Moreover, Bartlett’s test of sphericity for all variables was significant, which means that they were uncorrelated from one another. Results for validity and reliability can be seen in Table 1. Next, there were no multicollinearity issues as the variable inflation factors values are less than the maximum level of 5 (Ringle et al., 2015) or 10 (Hair et al., 1995). The results can be seen in Table 2. Furthermore, communalities were checked using the principal component analysis (PCA) and that the amount of variance that is explained by each of the variables exceeded 0.40. All the tests established that FA is appropriate for the sample in this study. The complete results for FA and reliability analysis can be seen on Table 3. Finally, ANOVA tests were also used in order to determine if the difference in mean values of our variables is significantly different. The test shows a significant result, which means that the variables in this study are all significantly different from each other. The results can be seen in Appendix B section.
Table 1
Validity and Reliability Test
Items KMO Bartlett’s Test (sig.)
Personalization .50 .000
Satisfaction .79 .000
Trust .78 .000
Table 2 Multicollinearity test Constructs Tolerance VIF Personalization 0.91 1.09 Satisfaction 0.46 2.20
Trust 0.48 2.08
Table 3
Factor Analysis and Reliability Analysis
Construct and items Factor
loadings Commu-nalities Eigenvalues/ %Variance explained Cronbach’s alpha (α ) 1. Personalization 1.53/76.52 0.69
The ad seemed to be designed specifically for me 0.88 0.77 The ad targeted me as a unique individual 0.88 0.77
2. Customer Loyalty 2.74/68.37 0.84
I will say positive things about UmbrellUS to other people after reading this ad on my social media timeline
0.86 0.73
I would recommend UmbrellUS to those who seek my advice after reading this ad on my social media timeline
0.87 0.75
I would post positive messages about UmbrellUS on social media after reading this ad on my social media
0.81 0.66
I intend to buy UmbrellUS from Wet-No-More company after reading this ad on my social media timeline
0.77 0.50
3. Customer Satisfaction 2.10/74.95 0.89
I would be satisfied with my decision to purchase
If I would purchase an ImbrellUS, I feel positive about buying it from the company Wet-No-More
0.89 0.79 It would feel “right” to buy UmbrellUS from the
company Wet-No-More
0.91 0.82 My choice to purchase UmbrellUS from the
company Wet-No-More would be a wise one
0.86 0.740
4. Customer Trust 2.58/64.49 0.81
I trust the company Wet-No-More would keep my best interest in mind while purchasing UmbrellUS
0.78 0.611 I do not find it necessary to be cautious with the
company Wet-No-More
0.72 0.520 I think the company Wet-No-More seems to be
trustworthy
0.87 0.740 I trust the performance of the company
Wet-No-More to be good
0.84 0.70
4.3 Assumption check: linearity, normality, and independence
Most the assumptions have been fulfilled which can be seen in Appendix A. The P-P Plots seem to follow a straight line and that the histogram shows an approximately normal distribution. Thus, it can be said that the data in this study is normally distributed. Finally, the result of the Durbin-Watson test, which can be seen in Appendix B (b), indicates that the value of the model in this study lies between 1.50 < d < 2.50, which tells us that there is no autocorrelation.
4.4 Manipulation Check
because not all respondents in the moderate personalized ad condition (i.e., being mentioned by name) would perceive the ad as personal to them and by seeing that the t-test result is insignificant, the manipulation of personalization in this study is not successful.
4.5 The Main and Mediating Role of Both Customer Satisfaction and Customer Trust on the Relationship between Personalization and E-Loyalty
well (B=0.41, p=0.00), which then provides the support for Hypothesis 3b. Finally, in addition to the significant direct effects of personalization on the mediators (Hypothesis 2a and 3a) and the mediators with the dependent variable, customer e-loyalty (Hypothesis 2b and 3b), personalization significantly gave an impact on customer e-loyalty when the mediators were not included (B=0.20 p=0.00). When the mediators were included in the model, personalization did not significantly explain customer e-loyalty anymore (B= 0.03 p= 0.34), suggesting a full mediation. Moreover, the indirect effect of personalization through the mediators was significant, as the 95% confidence intervals did not include zero (BSAT=0.10, SE=.03, 95% CI [.052,.162]), thus providing support for
Hypothesis 4. All of the data can be seen on Table 4 below.
Table 4 Mediation model
Path Estimate T-Statistic P-Value
CHAPTER 5
DISCUSSION, CONCLUSION, LIMITATION, AND FUTURE RESEARCH
reading the ad on their social media timeline (Lu et al., 2019). Next, the results show that personalization has a significant positive impact on customer satisfaction, in a way that the more they perceived an ad as personal, the more they will likely to be satisfied when they purchase something from the company,thus supporting Hypothesis 3a. This satisfaction can be expressed in various ways including the feeling of satisfaction with the decision to purchase the product, the positive feeling that would emerge after purchasing the product, the ‘right’ feeling to buy the product, and the conclusion that it is wise to purchase that product from the chosen company (Oliver, 1980). Moreover, the results of this study show that the customer satisfaction has a positive impact on customer e-loyalty in a way that the more customers feel satisfied by their purchase, their e-loyalty would increase, thus supporting Hypothesis 3b. Most previous studies were on direct relationships among personalization, customer trust, customer satisfaction, and customer loyalty/e-loyalty. The final results of this study extend the indirect effect of customer trust and customer satisfaction in the relationship between personalization in advertisement and customer e-loyalty. The results showed that in order for personalization to have a positive impact on customer e-loyalty, customer trust and customer satisfaction play their roles indirectly in order to give a significant positive impact. This study shows that moderate personalization (i.e., mentioning readers by their names) is not enough to increase customer e-loyalty. It emphasizes the importance of developing customer trust and customer satisfaction together with improving personalization in advertisements to enhance a greater customer e-loyalty to the company. To improve personalization in advertisements, marketers could make their advertisements shareable to a wide audience, understand how their customers use their devices, together with other behavioral data, and segmenting their audience so that they could display the most relevant data to the right customers at the right time.
The main limitation of this study relates to the choice of the personalization level in the advertisement. As this study only uses moderate personalization by mentioning the participants’ names, not all participants would perceive this as personal to them.
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Appendix A: Assumptions check for multiple regression analysis including customer E-loyalty as the dependent variable
Figure 1 Figure 2
Histogram P-P Plot
Appendix B: ANOVA & Durbin-Watson test a)
ANOVA Model Sum of
Squares df Square Mean F Sig. Regression 140.95 3 46.98 150.08 0.000b
Residual 102.06 326 0.31 Total 243.01 329
a. Dependent Variable: Average Loyalty
b. Predictors: (Constant), Average Trust, Average Personalization, Average Satisfaction
ANOVA Average
Personalization Squares Sum of df Square Mean F Sig. Between Groups 1.57 1 1.57 1.851 0.175 Within Groups 279.05 328 0.85 Total 280.62 329 b) Model Summaryb
a. Predictors: (Constant), Average Trust, Average Personalization, Average Satisfaction b. Dependent Variable: Customer E-Loyalty
Model R R2 Adjusted R2 Std Error of
the Estimate
Durbin-Watson
