The effect of personalized messages and brand humanization on
consumer electronic word-of-mouth
Emilia Oksanen (10228179) University of Amsterdam BSc Economics and Business Academic year: 2013-2014 Semester 2, Block 3
Abstract
This study examines whether the use of personalized messages in a business-to-consumer (B2C) context influences business-to-consumer electronic word-of-mouth (eWOM) behavior. In addition, this study tests whether consumer-perceived brand
humanization mediates this relationship. Past research has found multiple benefits of companies sending personalized messages in order to improve their brand image. Brand humanization also contributes to attracting and retaining customers through a likeable brand personality. As many past studies have reported, consumer eWOM is important for companies to be aware of as it can determine their success or failure. Data was collected for this study through the use of an online questionnaire. Results show that the use of personalized messages does not influence consumer eWOM behavior but does result in brand humanization. The results also indicate that brand humanization does not influence consumer eWOM behavior. This study’s findings conclude that companies should use personalized messages in order to humanize their brand for consumers and to create a likeable brand image and trustworthy personality. However, using personalized messages and brand humanization does not influence a company’s consumers to spread eWOM about the brand. Companies should therefore conduct additional research to find other ways of influencing consumer eWOM.
Table of Contents
Abstract ... 2
1. Introduction ... 5
2. Literature Review ... 8
2.1 Social media and eWOM ... 8
2.2 Personalized messages ... 9
2.3 Branding and brand personality ... 10
2.4 Brand humanization and anthropomorphism ... 10
2.5 Managerial implications ... 11 3. Conceptual Framework ... 12 3.1 Hypotheses ... 12 4. Methodology ... 15 4.1 Research design ... 15 4.2 Sample ... 16 4.3 Data collection ... 17 4.4 Measures ... 17 5. Results ... 20 5.1 Sample characteristics ... 20 5.2 Descriptives ... 21 5.2.1 Control variables ... 21
5.2.2 eWOM behavior of participants ... 22
5.2.3 Data ... 23 5.3 Reliability analysis ... 24 5.4 Correlations ... 24 5.5 Regression analysis ... 25 5.6 T-tests ... 26 6. Discussion ... 28
6.1 Message personalization’s effect on consumer eWOM ... 28
6.2 Message personalization leading to brand humanization ... 29
6.3 Brand humanization’s impact on the likelihood of consumer eWOM behavior ... 30
6.4 Mediating relationships between variables ... 31
6.5 Managerial implications ... 32
6.6 Limitations ... 33
6.7 Suggestions for future research ... 33
7. Conclusion ... 35
Appendix A: Survey ... 39 Appendix B: Personalized message dataset descriptives ... 46 Appendix C: Non-personalized message dataset descriptives ... 47
1. Introduction
Over the recent years, in addition to consumer-to-consumer communication changing drastically, the way in which consumers find and share product information and experiences has also dramatically changed (Hennig-Thurau, Malthouse, Friege, Gensler, Lobschat, Rangaswamy & Skiera, 2010). Thomas (2014) explains that the large amount of online social communities has made it highly important for
organizations to strategically empower their brand through word-of-mouth (WOM). These communities strongly influence the opinions that others develop and sustain about a certain company. It is therefore essential for companies to encourage consumers to engage in positive WOM behavior over negative WOM behavior in order to attract new customers (Schultz, 2014).
The presence of various social media has allowed for consumers to engage in more electronic word-of-mouth (eWOM) behavior. This behavior is defined by Hennig-Thurau, Gwinner, Walsh and Gremler (2004, p. 39) as “any positive or negative statement made by potential, actual, or former customers about a product or company, which is made available to a multitude of people and institutions via the Internet”. Schultz (2014) adds that eWOM is an efficient and more affordable way to promote a brand. eWOM allows consumers to obtain unbiased information on different products and services and to return the favor by sharing their own advice based on experience (Hennig-Thurau et al., 2004). Hennig-Thurau et al. (2004) also state that the reasons why consumers engage in eWOM behavior is due to their desire for social interaction, concern for fellow consumers, and the chance to increase their self-worth and gain economic incentives.
New media such as Facebook, Twitter, Youtube and Google have made it possible for companies to reach and communicate with consumers in different ways as well as to follow their communicating, browsing and purchasing behaviors more carefully (Hennig-Thurau et al., 2010). One way that companies can address consumer eWOM behavior is through personalized messages. The Internet has become one of the most significant forms of media due to its easy access to customization (Ansari & Mela, 2003). This means that companies are able to send customized (i.e. personalized) messages to all of their customers online. Through the use of personalized messages, consumers are able to receive messages online that are modified to meet their specific interests and needs. Ansari and Mela (2003) discuss
the importance of personalized messaging as it catches the consumer’s attention and can result in loyalty to the brand as well as customer lock-in. They add that
personalized messaging reduces the amount of information that a consumer needs to process and therefore helps them make decisions. If the consumer finds the exact product or service that they are looking for, it leads to higher consumer satisfaction. In addition, the customer may feel like they are receiving special attention and this will result in better customer relationship management and an improved brand image (Ansari & Mela, 2003).
Past research has looked into the effect that personalized messages have on consumer perceived brand image as well as on the relationship between the company and its consumer. In addition, past research has studied how brands are humanized through business-to-consumer interactions. The gap identified in the literature is whether brand humanization influences the amount of eWOM the consumer engages in after receiving personalized messages.
An important aspect to consider in this research is whether personalized messages humanize the brand for the consumer. Stinnett, Hardy and Waters (2013, p. 31) define humanization as “attributing human characteristics and traits to non-human entities, such as organizations”. The use of personalized messages attempts to show a company’s compassion and friendliness, which could result in consumer perceived brand humanization. Stinnett et al. (2013) add that through humanization,
personalities can be assigned to the brand. The assigning of personalities to a brand is essential in branding and in creating a brand image.
Keller (2003) discusses the importance of branding and how marketers are in great need of learning more about consumer behavior. Nowadays, managers are finding it challenging to quickly adapt to a changing market environment that is caused by an increase in competition and consumers that know exactly what they want. Knowledge on consumer behavior has never been as important as it is today (Keller, 2003). It is therefore essential for companies to realize how humanizing their brand can influence consumer behavior such as eWOM.
The purpose of this paper is to look into the effect that personalized messages have on eWOM behavior and whether consumer-perceived brand humanization mediates this relationship. Due to the growing popularity of social networking sites (SNSs), this research focuses on eWOM through Facebook, a host of online
is because one can assume that it aids in humanizing a brand due to regular contact between businesses and consumers. Managers need to turn their focus onto the social media phenomenon, which is allowing consumer to use platforms to “create, modify, share, and discuss Internet content” (Kietzmann, Hermkens, McCarthy & Silvestre, 2011, p. 241). It is important for companies to focus on this phenomenon because it can greatly impact their reputation through consumer eWOM behavior. This research will focus on how the use of personalized messages in a business-to-consumer context may alter consumer perceived brand image and the amount that consumers share with their online friends. The research question of this paper is: To what extent does brand humanization mediate the relationship between personalized messages and electronic word-of-mouth (eWOM) behavior? In order to answer this question, an online
experiment in the form of a survey will be conducted amongst different age groups and genders.
Following this introduction is the theoretical framework, which will analyze past research on the topics of eWOM, personalized messages and brand humanization in a more detailed manner. Afterwards, the conceptual framework of this study will be presented and followed by a methodology that will explain the research design. Next, the results section will present the findings of this study that will be explained by the discussion section. Lastly, this paper will end with a conclusion of the overall study conducted.
2. Literature review
The previous section provided a short overview of the topics studied in this research. This section will look into past research in more depth and identify their contributions to the topics. First, social media and its influence on eWOM will be discussed. Next, an overview of personalized messages will be presented along with the importance of branding and brand personality. The concept of brand personality will then be linked to brand humanization and brand anthropomorphism. Lastly, the managerial
implications will be mentioned. 2.1 Social media and eWOM
Social media is a relatively new and important part of integrated marketing
communications (IMC) that differs from traditional marketing (Mangold & Faulds, 2009). Kietzmann et al. (2011, p. 241) go as far as to say that we are “in a midst of an altogether new communication landscape”. Furthermore, social networking sites (SNSs) are nowadays considered an important source of product and brand information in both consumer-to-consumer as well as business-to-consumer marketing (Chu & Kim, 2011). In terms of consumer-to-consumer marketing, consumers are able to communicate with each other on social media without the control of companies over what they say or to what extent they spread it (Mangold & Faulds, 2009). Not only are consumers more willing to post product and brand reviews online, they are more likely to be honest and share their real opinions and thoughts.
SNSs have increased the popularity of consumer eWOM behavior, as
consumers want to create and sustain social relationships. To do so, consumers share their specific product experiences and provide personal opinions of the brand online (Chu & Kim, 2011). Consumer eWOM on SNSs has become extremely important for companies as the consumer’s social media friend list includes people that are
perceived as more reliable than strangers. In other words, people tend to believe that the comments they read on their SNSs are more credible because they are written by individuals whom they know (Chu & Kim, 2011). Mangold and Faulds (2009) discuss how the chance for customers to submit feedback through social media has lead them to feel more engaged with these brands and their products or services.
In addition, social media has improved business-to-consumer marketing as companies have easier access to their consumer segments. Companies have been able
to create strong and stable relationships with their customers through the growing popularity of SNSs (Chu & Kim, 2011). However, the presence of social media has made it critically important for companies to focus on brand transparency (Raghav, Sharma & Khandelwal, 2013). This means that social media allows for consumers to check if a brand is really doing what they claim to be doing. Companies therefore have a harder time earning their consumers’ trust with the presence of social media (Raghav et al., 2013).
This paper will discuss the use of personalized messages in business-to-consumer communication. More specifically, the focus will be on companies sending personalized messages to their consumers through Facebook. Facebook was chosen as the social media platform for this study because it is the most visited one nowadays (Beal, 2014). In addition, Facebook “enables its users to present themselves in an online profile, accumulate “friends” who can post comments on each other’s pages, and view each other’s profiles” (Ellison, Steinfield & Lampe, 2007, p. 1143). The personalized message used in this study’s scenario will be posted on the fictional mobile service provider PhilPhone’s Facebook page. The use of a company’s
Facebook page means that the comments are public for everyone to read. The reason for choosing public messaging in a business-to-consumer context is because the eWOM tested afterwards is also public.
2.2 Personalized messages
The Internet is used by companies for customer interaction in terms of marketing their product, promoting their brand, fulfilling their orders and providing after-sales
support (Tam & Ho, 2006). Personalized messages can be used in all these
interactions between the company and its consumers on social media. Postma and Brokke (2002, p. 137) mention that message personalization can “dramatically increase the effectiveness of communication”. In other words, when customers are receiving the right messages, communication is improved and can lead to an improved brand image.
Past research has also found that the use of personalized messages may not always lead to positive results. White, Zahay, Thorbjernsen and Shavitt (2008) discuss how the use of personalized messages may cause consumers to feel like they are being observed and studied too closely. In other words, personalized messages are likely to be viewed by consumers as being too personal—“extending beyond friendly
recognition to suggest an inappropriate level of familiarity with consumers’ preferences and behaviors” (White et al., 2008, p. 41). However, personalized messages between the company and its customers can benefit both parties by
providing help in making decisions as well as avoiding information overload (Tam & Ho, 2006). Over time, the use of personalized messages can result in the company achieving competitive advantage because of its efficient communication (Tam & Ho, 2006). Competitive advantage in customer communication is important for a
company, especially when building their brand image and personality. 2.3 Branding and brand personality
Personalized messages from a company to its consumers play a big role in forming consumer perceived brand image and brand personality. It is defined as “a vehicle of consumer self-expression” and “instrumental in helping consumers express their actual self, ideal self, or specific aspects of the self” (Belk, 1988; as cited in
Swaminathan, Stilley & Ahluwalia, 2009, p. 986). Swaminathan et al. (2009) add that strong relationships can be developed between the brand and consumer by creating brand personality. Not only is brand personality able to develop consumer
relationships, it can even influence consumer choices and behaviors, therefore influencing the likelihood of consumers making a purchase (Swaminathan et al., 2009). It is important for organizations to keep this in mind when developing their brand and more specifically, their image. Aaker’s (1997, p. 347) research identifies brand personality as a “set of human characteristics associated with a brand”. Therefore, brand personality can be linked to the concept of brand humanization. 2.4 Brand humanization and anthropomorphism
Azoulay (2005) states that when consumers interact with a company, they attach human characteristics to the brand. Through these interactions, brands are
personalized and given an identity (Aaker, 1996). Aggarwal (2004) adds that when consumers associate human characteristics with a brand, they may start to interact with them in similar ways to a human social relationship. Furthermore, brand
humanization increases consumers’ brand liking and familiarity (Puzakova, Kwak & Rocereto, 2013). Brand humanization can also lead to more severe consequences when an organization disappoints its consumers. In other words, when a brand is humanized, consumers will perceive it as being responsible for its actions and most likely develop unfavorable attitudes towards the brand (Puzakova et al., 2013). This
result is stronger when the brand is anthropomorphized than when it is not (Puzakova et al., 2013).
The concept of anthropomorphism has interested academics over many years (Stinnett et al., 2013) and is clearly defined by Puzakova et al. (2013, p. 82) as “attributing mind, intentions, effortful thinking, emotional states, and behavioral features to nonhuman objects”. Brand anthropomorphism has become important in marketing research as it looks at brand personality in more depth (Stinnett et al., 2013). By looking into a consumer’s brand anthropomorphic perspective, marketers are able to gain a better understanding of what a brand’s actual image is and how it is perceived (Stinnett et al, 2013). According to Aggarwal and McGill (2007), people attempt to understand the world around them better by anthropomorphizing. In order to do this, they need to transfer human qualities to the brand such as emotionality, thought and volition (Fournier, 1998).
2.5 Managerial implications
Since eWOM is a cost effective way for companies to market their brand, marketers need to find the right way to promote positive consumer eWOM behavior for their brand and avoid negative eWOM. Companies also need to create their brand image carefully in order to achieve the personality they want their customer segment to identify with. With the use of personalized messages, companies can humanize their brand and create a specific brand image. This will hopefully result in closer relations in a business-to-consumer context and aid in spreading positive eWOM about the company through social media platforms.
3. Conceptual framework
In the previous section, we reviewed past research on the topics of eWOM, brand humanization as well as personalized messages. This section will discuss the conceptual framework used for this specific research.
3.1 Hypotheses
Previous research has shown that the use of personalized messages is an effective way for companies to communicate with their customers (Ansari & Mela, 2003; Tam & Ho, 2006; Postma & Brokke, 2002). With the strong presence of social media
nowadays, it is more important than ever for companies to build a relationship with its customers through personalized messaging. When a company sends the right
messages to its customers, the customers are happier and have a more positive perception of the company and their brand image (Postma & Brokke, 2002). The independent variable of this research is message personalization. The experiences that a customer has with a company impacts their likelihood of engaging in eWOM
behavior. Consumer eWOM behavior is the dependent variable. Therefore, the first hypothesis of this research is:
Hypothesis 1: Message personalization will positively influence consumer eWOM behavior.
Companies are using personalized messages to place a human face to their brand (Ansari & Mela, 2003). The personalization of messages sent from a company to its consumers makes consumers feel like they are getting more attention.
Personalized messages may also make customers feel like the company knows them well and cares about them (Ansari & Mela, 2003). These caring traits of the company may lead to consumer perceived brand humanization. This leads to the second
hypothesis.
Hypothesis 2: Message personalization will positively influence consumer perceived brand humanization.
Next, it is important to test how all this influences consumer eWOM behavior. If consumers perceive a brand to be humanized due to its caring nature, will they be more likely to share their thoughts with their friends on social media? Brand
(2013). Consumers tend to share their positive experiences and thoughts on companies with their friends through WOM. However, do they engage in more eWOM due to perceived brand humanization? This study predicts that a positive brand liking will cause consumers to talk about the brand with friends on Facebook. This results in the third hypothesis.
Hypothesis 3: Brand humanization will positively influence consumer eWOM behavior.
Lastly, this research will look into whether the mediating role of brand humanization will strengthen the predicted relationship between the use of
personalized messages and consumer eWOM behavior. According to Fournier (1998), brand humanization causes consumers to perceive the brand as having human
qualities of emotionality, thought, and volition. This research predicts that if
consumers see these qualities in a company due to a personalized message, they will be more likely to talk about them online to their friends. Since assigning human characteristics to a brand can make the company appear trustworthy, it may lead to consumer e-loyalty. Srinivasan, Anderson and Ponnavolu (2002) found that
consumers engage in positive word-of-mouth as a result of e-loyalty. Therefore, the fourth and last hypothesis is:
Hypothesis 4: Brand humanization positively mediates the relationship between message personalization and eWOM behavior
The conceptual model below shows all three variables tested in this study; message personalization, brand humanization and eWOM. In addition, the model shows the four hypotheses of this research. All relationships are predicted to be positive.
A conceptual limitation of this study is that not all factors influencing the dependent variable eWOM are included. The results and conclusion of this research would be affected if more variables were taken into account. For example, factors such as participant age, gender, occupation and the channel through which the message is sent could influence consumer perceived brand humanization and eWOM behavior in different ways.
4. Methodology
The sections prior to this methodology section discussed relevant past literature as well as the specific conceptual framework for this study. This section will explain the research design and how the research question will be attempted to answer with the use of quantitative data. In addition, the study’s sample and measures will be discussed.
4.1 Research design
In order to test the hypotheses mentioned earlier, quantitative data collection will be performed. This form of data collection gathers numerical data for analysis. More specifically, this study’s quantitative research design is descriptive as the aim is to establish relationships between variables without changing behavior or conditions (Hopkins, 2008). In addition, this research is of the explanatory kind as the aim is to “establish causal relationships between variables” (Saunders, Lewis & Thornhill, 2009, p. 140).
Due to the popularity of questionnaires in explanatory research (Saunders et al., 2009), the hypotheses will be tested through the use of an online questionnaire-based survey. According to Saunders et al. (2009), surveys are appropriate to conduct for this kind of study, as they are useful for collecting and analyzing quantitative data. In addition, surveys make it possible to produce descriptive and inferential statistics. Surveys are a popular means of conducting research as they make it relatively easy to collect large amounts of data in an affordable manner (Saunders et al., 2009). Another benefit of conducting surveys is that their use of standardized questions makes
identifying comparisons between participants much easier. Due to time constraints, this study will be cross-sectional. A cross-sectional study focuses on a particular phenomenon at a specific point in time (Saunders et al, 2009) and subjects participate only once (Hopkins, 2008). Hopkins (2008) defines validity as “how well a variable measures what it is supposed to”. In addition, he mentions that validity is particularly important in descriptive studies. As the amount of people participating in the study increases, the overall validity increases as well (Hopkins, 2008).
The online survey on qualtrics.com involves showing the participants one of two possible scenarios involving them as a customer of the fictional mobile service provider PhilPhone. Both scenarios informed the participant that they had signed up for a mobile contract with PhilPhone and that it was taking longer than promised for
their SIM card to start working. The participant was then asked to imagine that they complained to PhilPhone publicly through their Facebook page. In the first possible scenario, the participant was shown a personalized message from PhilPhone in reply to their Facebook complaint. The second possible scenario was the same as the first, only differing in the reply being unpersonalized. The participant was then asked questions concerning how personalized they found PhilPhone’s reply. Next, perceived brand humanization was measured. The participants were then asked how likely they would be to engage in eWOM behavior after the interaction with PhilPhone on Facebook. They were also asked about their eWOM behavior on a normal day-to-day basis. The survey concluded with simple demographic questions on their gender and age.
4.2 Sample
The use of samples is necessary when it is not possible to access a whole population. Samples are a smaller representation of a particular population. For this specific study, the population is individuals who are active on the social media platform Facebook. This population is reached by distributing the survey on Facebook. The channel through which participants access Facebook (i.e. Internet browsers, mobile phones, iPads, etc) is not studied.
Due to time and budget constraints, it is very challenging to survey this entire population. Therefore, convenience sampling will be used. This form of sampling involves selecting individuals for research that are easiest to get a hold of from the population (Saunders et al., 2009). Convenience sampling saves the researcher a lot of time and other resources however; it means that individuals are chosen with some bias. For this study, help with spreading the online questionnaire-survey will be asked from personal contacts such as family and friends. The survey can be spread to others by email or Facebook.
The use of samples is beneficial as it allows for more organization in data collection as well as more time to check the accuracy of the data collected (Saunders et al., 2009). In order for the data to be statistically tested, it needs to be normally distributed. A normal distribution is achieved when there is a minimum of 30
participants for each category tested in the overall sample (Stutely, 2003, in Saunders et al., 2009). The central limit theorem states that the larger the sample size, the closer the distribution will be to a normal one (Saunders et al, 2009). Therefore, this research
will aim to obtain as many completed questionnaires as possible. The minimum amount of participants needed for this study is 60 as there are two categories: personalized and non-personalized messages. Due to the limited amount of time available for data collection, participants will only participate once in the questionnaire during a specific time period.
4.3 Data collection
Since the population is individuals who are active on Facebook, the questionnaire-based survey will be created online. The site that will be used to do this is
www.qualtrics.com. This site was chosen in particular as it provides various different formats and options for creating customized questionnaires for no fee. The use of an online questionnaire provides multiple benefits such as saving costs by avoiding printing, saving time through quick sharing, and easy access to different geographical locations. As mentioned earlier, the questionnaire will be spread to others through the use of personal contacts such as family and friends.
Before starting the data collection, a pilot study involving 12 participants is conducted. This tests whether the questions are asking what they are supposed to ask and whether the survey is formulated clearly for the participants. Due to the large amount of international participants, both the pilot and final questionnaire are written in English. The final questionnaire includes an introduction page that discusses the overall purpose of the study, reminds participants of full anonymity and lists an email address in case of questions concerning the study. The questionnaire ends with a short note thanking the respondent for their participation in the study as well as a reminder of an email address in case of any questions or concerns regarding the study.
Personal contacts will share the survey link on Facebook in order to spread it to a larger sample. The time frame for participation is approximately two weeks. The data obtained will then be organized and analyzed in order to form the results and discussion sections.
4.4 Measures
Dillman (2007, in Saunders et al., 2009) identifies three different types of data variables that can be gathered through the use of questionnaires: opinion, behavior and attribute. This study will collect all three variables. Attribute variables will be collected in the survey through questions about the participant’s characteristics such as gender and age. Opinion variables will gather information on the participants’
feelings and thoughts towards the personalized messages while behavioral variables look into what individuals have done in the past, will do now or will do in the future in terms of eWOM behavior. For example, opinion variables will involve asking participants to what extent they agree with the statement “PhilPhone could use the same reply for another customer” on a 7-point likert scale from strongly disagree to strongly agree. The participant will reply by choosing the option that best rates their level of agreeing. For an example of a behavioral variable, one question asks the participant to rate how much they agree with the statement “I would tell my Facebook friends about this specific experience with PhilPhone’s customer service” on a 7-point likert scale from strongly disagree to strongly agree.
The independent variable message personalization will be tested in the survey by randomly showing the participant either an example of a personalized or non-personalized Facebook message from the company PhilPhone to the participant (the consumer). The participant will then be asked to rate the personalization of the message by indicating how much they agree with the two statements “PhilPhone could use the same reply for another customer” and “PhilPhone was successful in making their reply original” on a 7-point likert scale from strongly disagree to strongly agree. This will provide the researcher with a clearer understanding of how personalized the participant views PhilPhone’s reply on Facebook.
It is also important to measure the mediating variable brand humanization after finding out how personalized the participant views the social media message example. One of the items measuring perceived brand humanization was adapted from Aggarwal’s (2007) study in which the variable was measured by asking to what extent the object was “like a person”. For example, a question in this study asked the participant to what extent they agreed with the statement “PhilPhone’s brand can be perceived as a person that carries its own personality”. The traits showing humanized characteristics were identified by Fournier (1998) as emotionality, thoughtfulness and volition. These three characteristics were applied to the items in this study measuring brand humanization such as “PhilPhone is compassionate towards its customers” and “PhilPhone is honest with its customers” on a 7-point likert scale from strongly disagree to strongly agree. These items will show to what extent the participants view the brand as being humanized and whether this relates to the amount of
personalization in the message.
Items were adapted from Chu and Kim’s (2011) study, which split up eWOM into opinion seeking, opinion giving and opinion passing. For example, two items adapted and used for this study are “I would tell my Facebook friends about PhilPhone’s company in general” and “I would persuade my friends on Facebook to sign up for a contract with PhilPhone”. These are both from Chu and Kim’s (2011) opinion giving and opinion passing sections testing consumer eWOM that consist of talking to friends about a company as well as persuading them to buy their product or service. All statements testing consumer eWOM behavior will be on a 7-point likert scale from strongly disagree to strongly agree.
Lastly, the control variable of the sample’s regular eWOM behavior will be tested. Three items were included to measure this variable and they were based on items similar to the dependent variable’s, adapted from Chu and Kim’s (2011) research. For example, one of the three items were “My Facebook friends pick their products and services based on what I have told them”. The purpose of this variable is to see to what extent the participants engage in eWOM behavior in their regular life.
After the questions testing the variables of this study, the survey will ask participants questions on their demographics. This is done so that the results can be analyzed using different demographics. Demographic questions in this study will be based on the variables of participant gender and age. Gender questions will provide two possible answers; 1) male or 2) female. Questions concerning age will appear in multiple-choice format so that the participants can be placed into different age groups. These groups are: 1) under 18, 2) 18-24, 3) 25-34, 4) 35-44, 5) 45-64 and 6) 65 and over. Demographic variables are important to include in the survey so that they can be taken into account when analyzing the data (Hopkins, 2008). In addition, these
measures will also act as control variables in further analyses in the results section. The statistical analysis program SPSS Statistics will be used to analyze the data. Descriptive statistics, reliability analyses, bivariate correlations and linear regressions will be performed with the help of the program in order to interpret the results. The following section of results will discuss the various tests in more detail.
5. Results
The previous section discussed the specific research design of this study as well as the sample and measures. This section will provide the results of the study. First,
descriptive statistics and frequencies will be discussed in order to get a general overview of the sample and data collected. Results of the reliability analysis will show the Cronbach’s alphas of the variables measured. Next, the correlations between all three variables will be shown and followed by the regression models to test for the hypotheses and overall mediation.
5.1 Sample characteristics
First, the general characteristics of the sample will be described in order to provide an overview of who participated in this study. 12 participants responded to the pilot study. The use of a pilot study aids in finding out whether questions are appropriate and clear for the participants. There were a total of 79 people who opened the final survey link, however, only 73 responded to the questions. Therefore, 6 participants were removed from the sample. Participants in both the pilot and final study were reached through personal Facebook contacts. Of the total 73 participants, 19 were male (27.1%) and 51 were female (72.9%). 3 participants did not want to reveal their gender. Gender frequencies are shown below in Table 1.1.
Table 1.1: Gender frequencies
Gender Frequencies Percentage
Male 19 27.1%
Female 51 72.9%
Total 70 100%
Of the total sample of 73 people, 5 were under 18 years old (7.4%), 37 were 18-24 year olds (54.4%), 4 were 25-34 year olds (5.9%), 11 were 35-44 year olds (16.2%), 10 were 45-64 years old (14.7%) and 1 was 65 or over (1.5%). 5 participants did not want to reveal their age. Age group frequencies are shown below in Table 1.2.
Table 1.2: Age group frequencies
Age groups Frequencies Percentage
Under 18 5 7.4% 18-24 37 54.4% 25-34 4 5.9% 35-44 11 16.2% 45-64 10 14.7% 65 and over 1 1.5% Total 68 100%
These descriptive statistics and frequencies show that the largest group in the sample was 18-24 year old females. This could be because personal Facebook contacts were used and it is a group that is more likely to want to participate in this research.
5.2 Descriptives 5.2.1 Control variables
All participants were asked the control question of whether or not they had heard of the company PhilPhone prior to the survey. This question tests that there are no biases in recognizing a company before answering questions. It is no surprise that no one had heard of the company prior to the survey because it was fictional. Two other questions were asked to find out whether the participants viewed the situation with PhilPhone presented in the beginning of the survey as reliable. In other words, it is important to test whether the customer service situation is believable in the real world. The items were “I can imagine this kind of contract problem happening with a mobile service provider in real life” and “I can imagine this form of complaining to a
company through Facebook happening in real life” on a 7-point likert scale from strongly disagree to strongly agree. Descriptive statistics were performed on these items, resulting in the means 5.51 and 5.63, respectively. These figures show that overall; the participants found the example scenarios believable. Below are the mean and standard deviation figures of these items shown in table format.
Item Mean Standard deviation “I can imagine this kind of
contract problem happening with a mobile service provider in real life”
5.51 1.36
“I can imagine this form of complaining to a company through Facebook happening in real life”
5.63 1.43
5.2.2 eWOM behavior of participants
Three items were used to test the participants’ views on their personal eWOM
behavior. These items were “I often persuade my Facebook friends to buy products or services that I like”, “My Facebook friends pick their products and services based on what I have told them” and “When I receive product or service related information from a friend, I will pass it along to my other Facebook friends”. The means of these three items are approximately 3.49, 3.68 and 3.04, respectively. They were all measured on a 7-point likert scale from strongly disagree to strongly agree. These means show that the average eWOM behavior of the participants is low. These statistics may affect results if the participants do not normally engage in eWOM behavior on their personal Facebook accounts. These figures, along with the standard deviations of the items, are shown below in table format.
Item Mean Standard deviation “I often persuade my
Facebook friends to buy products or services that I like”
3.49 1.69
“My Facebook friends pick their products and services based on what I have told them”
3.68 1.39
“When I receive product or service related information from a friend, I will pass it along to my other Facebook friends”
3.04 1.43
5.2.3 Data
Appendix B and C show the new variables that were created in order to further analyze the data. Items from the study were combined in order to create the three variables of message personalization, brand humanization and consumer eWOM. The personalized and non-personalized datasets are separated in order to compare the means and standard deviations of the two groups. All items were measured on a 7-point likert scale, meaning that the higher the number, the more strongly the
participant believed in or agreed with the item. For example, scoring a 6 on an item meant that the participant agreed with the statement while a 3 meant they somewhat disagreed with it. The item “PhilPhone could use the same reply for another
customer” in the message personalization variable had to be recoded in order to match the measurement of the personalization scale. In the personalized dataset, the mean of the variable message personalization is 5.38 and shows the participants’ perceived amount of personalization in PhilPhone’s reply. Brand humanization’s mean of 4.41 shows only a small amount of consumer perceived brand humanization. eWOM behavior was not very likely after the personalized message scenario as its mean is only 3.78.
For the non-personalized dataset, message personalization was averaged at 1.85, as shown in Appendix C, indicating a lack of personalization in the message.
Brand humanization’s mean was only 3.34, showing that the participant did not humanize PhilPhone after reading the non-personalized message. Consumer eWOM had a mean of 3.92, also showing that the likelihood of the participant engaging in eWOM behavior is small.
Table 3.1 below shows the descriptives of the variables created by combining both personalized and non-personalized datasets. The mean score for message
personalization is 3.63 and consists of two items. Brand humanization averaged a score of 3.86 and has five items. Consumer eWOM after the interaction with
PhilPhone has a mean of 3.79 and consists of four items. Lastly, the control variable of sample eWOM behavior has a mean of 3.45 and has two items.
5.3 Reliability analysis
In order to find out if the measurement scales can be used for a regression analysis, a reliability analysis needs to be conducted. One important aspect of reliability is a scale’s internal consistency (Pallant, 2005). The Cronbach’s alpha is found by conducting a reliability analysis and is the most common way to find out whether all items in a scale are measuring the same construct (Field, 2009). These values are shown in parentheses in Table 3.1 below. All four alphas are considered good,
according to Field’s (2009) rule that they should be above 0.7. The variables shown in the table were created by using all items from the study (both the personalized and non-personalized dataset). The message personalization variable provided a Cronbach’s alpha of 0.964 for the message personalization items. The Cronbach’s alpha for the mediating variable brand humanization was 0.903. The consumer eWOM variable, measuring the likelihood of the participant engaging in eWOM behavior after their interaction with PhilPhone, had a Cronbach’s alpha of 0.750. No items were deleted for these variables, as this would not improve the alpha values in this case. However, the Cronbach’s alpha for the control variable measuring the sample’s regular eWOM behavior was below 0.7 at 0.695. Therefore, the item “When I receive product or service related information from a friend, I will pass it along to my other FB friends” was deleted resulting in a Cronbach’s alpha of 0.761 for the control variable.
5.4 Correlations
Table 3.1 below also shows the correlations between all four variables. The only significant correlation is between the variables message personalization and brand
humanization, r(73)=0.460, p<0.01. This significant positive correlation supports Hypothesis 2. Contrary to hypothesis 1, there is no correlation between message personalization and consumer eWOM as there is no significance, r(73)=-0.113, ns. Lastly, no correlation was found between brand humanization and consumer eWOM, therefore contradicting hypothesis 3, r(73)=-0.119, ns.
Table 3.1: Descriptives and correlations for all variables (Cronbach’s alphas on diagonal) M SD No. of items 1 2 3 4 1 Message personalization 3.63 1.93 2 (.964) 2 Brand humanization 3.86 1.32 5 .460** (0.903) 3 Consumer eWOM 3.79 1.21 4 -0.113 -.119 (0.750)
4 Sample’s eWOM behavior 3.45 1.55 2 0.241* 0.063 0.282* ( 0.761) Note: N=73. *p<0.05. **p<0.01
Bivariate correlations were also performed on the variables of age and gender. The results show that age is not significantly correlated with brand humanization (r(68)=-0.034, ns) nor with eWOM behavior (r(68)=0.144, ns). In addition, gender is not significantly correlated with brand humanization (r(70)=0.202, ns) nor with eWOM behavior (r(70)=-0.099, ns).
5.5 Regression analysis
To test the hypotheses, a regression analysis was performed. Table 4.1 shows the four regressions performed for the mediation model as well as their coefficients, standard errors (SE), betas (β) and r-squared values (R2). Contradictory to our predictions, Model 1 testing hypothesis 1 with an explained variance of 7.4 percent shows no significant effect of message personalization on consumer eWOM behavior ( β=-0.113, ns, R2=0.074). This shows that hypothesis 1 is not supported. With an explained variance of 21.1%, Model 2 testing hypothesis 2 shows that message personalization has a positive effect with significance on brand humanization (β=0.460, p<0.01, R2=0.211). Hypothesis 2 is therefore supported. Model 3 testing hypothesis 3 performed a regression with 1.4% explained variance and found no significant effect of brand humanization on consumer eWOM behavior (β=-0.119, ns,
R2=0.014). Hypothesis 3 is not supported. With an explained variance of 6.6%, Model 4 testing hypothesis 4 shows that message personalization has no effect on consumer eWOM behavior (β=-0.074, ns, R2=0.066). In addition, the mediating relationship was not significant and therefore, also shows no effect (β=-0.085, ns, R2=0.080). This contradicts the original prediction of a mediating relationship between message personalization, brand humanization and consumer eWOM and therefore does not support hypothesis 4.
Table 4.1: Regressions in mediation model
Model 1: Model 2: Model 3: Model 4:
Depende nt variable
Consumer eWOM Brand humanization Consumer eWOM Consumer eWOM
Coeffi cient SE Beta Coeffi cient SE Beta Coeffi cient SE Beta Coeffi cient SE Beta Constant 4.039 ** 0.304 2.733 ** 0.298 4.204 ** 0.44 1 4.252 ** 0.452 Message personalizat ion -0.071 0.074 -0.11 3 0.316 ** 0.072 0.460 -0.046 0.084 -0.074 Brand humanizati on -0.109 0.10 8 -0.119 -0.078 0.122 -0.085 Sample’s eWOM behavior 0.199 0.093 0.25 5 0.008 * 0.093 0.010 -0.135 0.10 6 -0.147 0.200 0.093 0.256 R2 0.074 0.211 0.066 0.080 Note. N=73. * p<0.05 **p<0.01. 5.6 T-tests
T-tests are performed in order to compare the means of two different groups in a sample (Pallant, 2005). In this study, independent-samples t-tests will be performed because there are two groups (personalized and non-personalized messages) and participants complete the survey only once in a specific time period.
The first independent-samples t-test was conducted to see if there is a significant difference in mean scores for the mediating variable perceived brand
humanization in the two groups. With a Levene’s test significance of 0.217, equal variances are assumed. There was a significant difference in mean scores for brand humanization between the personalized message (M=4.41, SD=1.37) and non-personalized message (M=3.34, SD=1.04; t(71)=3.762, p<0.01) groups. This shows that the amount of personalization in a message does influence different amounts of brand humanization. Therefore, hypothesis 2 is confirmed.
The second independent-samples t-test was performed to see if there is a significant difference in mean scores for eWOM behavior in the two groups. With a Levene’s test significance of 0.509, equal variances are assumed. No significant difference in mean scores for eWOM behavior were found between the personalized message (M=3.65, SD=1.178) and non-personalized message (M=3.92, SD=1.24; t(71)=-0.946, p=0.348) groups. This means that the amount of message
personalization does not influence consumer eWOM behavior. Thus, hypothesis 1 is rejected.
6. Discussion
The previous section presented the sample characteristics and data analysis of this study. This section will discuss the findings of the research and whether or not the hypotheses were supported. In addition, the findings will be compared to previous research as well as used to attempt to answer the research question. Managerial implications will state how the results benefit companies. Lastly, the limitations of this study and suggestions for future research will be discussed.
6.1 Message personalization’s effect on consumer eWOM
As mentioned earlier by Postma and Brokke (2002), customers are happier when they receive the right messages from a company. In other words, the use of personalized messages in business-to-consumer interactions aids in creating a positive customer perceived perception of the company and a desired overall brand image. It was predicted that the personalization of the message from PhilPhone to the customer (the participant) would increase their likelihood of spreading eWOM with their Facebook friends. In other words, it was predicted that personalized messages sent from a company to its consumers is a positive experience for both parties involved. The results did not support this hypothesis. No relationship was found between message personalization and consumer eWOM. Therefore, the results are not in line with past research which claims that personalized messages lead to loyalty (Ansari & Mela, 2003), which then leads to the consumer engaging in positive eWOM behavior (Ranaweera & Prabhu, 2003).
A possible explanation as to why the use of personalized messages did not lead to eWOM behavior could be because some participants may have found the reply too personal or even reaching “an inappropriate level of familiarity” (White et al, 2008, p. 41). White et al.’s (2008) study also found that the use of personalized messages causes customers to feel like they are being observed too closely. This concept was not tested in this study but could be a possible explanation for the lack of eWOM behavior after the interaction with PhilPhone on Facebook and should be looked into in future research.
In addition, Cho, Im, Hiltz and Fjermestad (2002) state that friendly and efficient customer service is the most important factor influencing customer
satisfaction online. This means that consumers expect the best treatment and help with their problems no matter what the scenario is. The use of personalized messages in
business-to-consumer interactions does not necessarily impress a lot of customers because it is becoming more common, especially with the presence and popularity of social media. Kietzmann et al. (2011, p. 249) say that “customer service is the new marketing”, meaning that consumers are in charge of the conversation on social media and that they should be kept happy. Problems should be resolved properly and
consumer input should be enforced. It could be that no matter whether PhilPhone’s reply on Facebook was personalized or non-personalized, the customer (the
participant) was satisfied enough with a reply from the company. Therefore, whether the message was personalized or not did not change the customer’s eWOM behavior. This could be a potential reason as to why the hypothesis was not supported and consumers did not show the likelihood of spreading eWOM after their interaction with PhilPhone.
On the contrary, Ranaweera and Prabhu (2003) state that customers will engage in more positive WOM, the higher their satisfaction levels. Since the scenario in this study consisted of a negative experience (SIM card not working) with the mobile service provider, PhilPhone, this could lead to all participants viewing the interaction as unsatisfying even though the company responded. However,
personalized messages were meant to provide customers with a positive experience, thus influencing eWOM behavior for the personalized message sample group. Further research needs to be conducted in order to better control how satisfying the
company’s Facebook reply is for the customer and whether this affects their eWOM behavior.
6.2 Message personalization leading to brand humanization
It was predicted that message personalization would influence consumer perceived brand humanization because of the findings from Ansari and Mela’s (2003) research. Their research stated that companies are using personalized messages in order to place a human face to their brand. Ansari and Mela (2003) added that when a company uses personalized messages, it makes the customer feel like the company cares about them and knows them well. The results show that message personalization does indeed influence consumer perceived brand humanization. These findings are in line with Ansari and Mela’s (2003) results and conclude that by showing a company’s compassion and friendliness in personalized messages, consumers will perceive the brand as having human-like qualities. The use of a personalized message from
PhilPhone to the customer shows the human qualities of emotionality and thought (Fournier, 1998) as the brand is putting more effort into helping the customer in a caring way. This could be a reason as to why the consumer humanized PhilPhone after receiving a personalized reply to their customer-service problem.
In addition, Aggarwal and McGill (2007, p. 468) stated that humans want to humanize non-human objects in order to “make better sense of the world around them”. Therefore, another reason why the consumer (the participant) humanized PhilPhone in this study could be because they wanted to make more sense of
outcomes that they could not predict. People tend to prefer using things that they are familiar with (Aggarwal & McGill, 2007). If they are not familiar with something, they may attach human-like qualities to it (in this case, PhilPhone’s brand) to better understand certain actions and decision-making. The consumer (the participant) may have humanized PhilPhone because they were uncertain of how quickly their
customer-service problem would be resolved as this was not specified enough in the reply.
Lastly, another reason why the use of personalized messages resulted in consumer-perceived brand humanization could be because close interactions between the consumer and a company cause consumers to assign human characteristics to the brand (Azoulay, 2005). As mentioned earlier, the presence of new media such as Facebook allows companies such as PhilPhone to interact with their consumers by sending personalized messages easily and therefore deepening their B2C connection (Hennig-Thurau et al., 2010). A personalized reply directly to the consumer’s Facebook complaint comment can be perceived by both the company and the consumer as a close interaction, compared to other forms of messaging (i.e. more formal email complaints).
6.3 Brand humanization’s impact on the likelihood of consumer eWOM behavior As Swaminathan et al. (2009) stated, brand personality strongly influences
consumers’ preferences and even consumer choices and behavior. Brand personality is a large part of brand humanization and is even defined by Aaker (1996) as being a set of human characteristics. Therefore, it was predicted that brand humanization would influence the likelihood of consumer eWOM behavior. This hypothesis was not supported. A possible explanation as to why the hypothesis was not supported could be that brand humanization is becoming more common with the presence of
social media. In addition, close business-to-consumer relationships are also becoming more common due to frequent interactions on social media. Humanizing a brand is thus not viewed as something special by consumers and does not provide them with an incentive to spread positive eWOM. This contradicts Mangold and Fauld’s (2009) research, which shows that when consumers know a lot about a company, they are more likely to talk about them. In the case of this study, when the consumer ascribes human-like characteristics to PhilPhone, they should feel a closer connection with them and be more inclined to talk about them online.
Another possible reason as to why the results indicate that brand humanization does not influence consumer eWOM behavior could be that the consumers (the participants) do not want to upset the brand. More specifically, consumers might not want to spread negative eWOM about a company after they perceive it as having human-like qualities such as feelings. According to Waytz, Gray, Epley and Wegner (2010; in Puzakova et al., 2013), humanizing a brand can lead to consumers
perceiving the brand as having emotions. In addition, consumers may view that the brand will form negative impressions and evaluations of them if they spread negative eWOM about the brand to their friends (Epley & Waytz, 2009; in Puzakova et al., 2013). Future research should focus on distinguishing between positive and negative consumer eWOM as this research did not do this.
6.4 Mediating relationship between variables
The last hypothesis predicted that message personalization and brand humanization would predict consumer eWOM behavior better than message personalization alone. In other words, it was predicted that if consumers saw qualities of emotionality, thought and volition (Fournier, 1998) in a company due to their use of personalized messages, they would be more likely to talk about the company with Facebook friends. These traits mentioned by Fournier (1998) show human-like qualities and result in consumer perceived brand humanization. Contradicting the predictions, the results did not show a mediating relationship between the variables of message personalization, brand humanization and consumer eWOM behavior.
Personalized messages and brand humanization were meant to increase customer satisfaction and therefore influence customers to talk about the company to their Facebook friends. Therefore, the findings conflict research done by Ranaweera and Prabhu (2003) whom conclude that the more satisfied a customer is, the more
they will spread positive WOM. As mentioned earlier, the lack of a mediating relationship could be due to the fact that brand humanization is becoming more common and may not surprise customers enough to influence them to engage in eWOM. The first hypothesis predicted that personalized messages would increase the likelihood of the consumer engaging in eWOM behavior. As this was not proven to be the case with the findings of this study, it is not surprising that the likelihood of
eWOM behavior does not change by adding brand humanization as a mediator. As mentioned in White et al.’s (2008) study, companies should maximize the utility that consumers will receive from a personalized message before sending it. If this is not done, the B2C interaction will not result in a closer relationship. In the case of PhilPhone, their reply on Facebook to the consumer (the participant) did not provide information on the reason why the mistake occurred nor any compensation for it. In addition, mentioning an immediate solution for the consumer would have increased the utility of the Facebook reply. Therefore, a possible reason as to why the use of personalized messages and brand humanization did not result in consumer eWOM behavior could be that PhilPhone’s Facebook reply did not show enough utility for the consumer. If the consumer had seen utility in the reply, the B2C
relationship would have deepened and thus lead to positive consumer eWOM (White et al., 2008).
6.5 Managerial implications
The results suggest that companies trying to humanize their brand through the use of personalized messaging will not succeed in influencing customer eWOM behavior. In addition, the use of personalized messages will not result in their customers spreading positive information about them to their friends on Facebook. Companies need to find other ways of influencing consumer eWOM behavior. This is important for
companies as it helps spread positive awareness of their brand in an economically feasible way.
However, as the findings show, personalized messages do lead to brand humanization. Companies should focus on how to better personalize the messages they send to their customers in order to improve customer relationships through brand humanization. As mentioned before in Aaker’s (1997, p. 347) research, brand
personality is a “set of human characteristics associated with a brand” and can
brand’s personality portrayed through the use of personalized messages, they become more loyal to the brand (Ansari & Mela, 2003). It is therefore highly beneficial for companies to use personalized messages as a way to obtain consumer perceived brand humanization. The presence of Facebook enables more close interactions between companies and consumers and therefore makes it easier for companies to use personalized messages.
6.6 Limitations
The more respondents there are for a survey, the more reliable the results are. A significant limitation of this study was that only 73 people participated. Although only 60 participants were required, as mentioned before, more respondents would improve the results. In addition, the sample consisted largely of females (72.9%). This results in the limitation of an unbalanced sample. Age groups were also not balanced as the majority of respondents were in the 18-24 age group (54.4%). In order to improve the dataset and results of this study, more participants are needed.
Another limitation of the sample used was that the respondents were found through personal connections on Facebook. This results in slight bias, as they may not be representative of the overall population. For example, the majority of the
participants could be students and this may affect the results. Furthermore, the variables in this study were created using only 2-5 items each. By adding more questions to the survey, the amount of items measuring a variable would increase and most likely improve results. Additional factors that could complement the
understanding of the eWOM behavior results are age, gender and occupation. In order to keep the research feasible, it was not possible to include these factors in this
research.
6.7 Suggestions for future research
Therefore, a suggestion for future research on this topic is to focus more on the differences between eWOM in different age groups, genders and occupations. Age is an important factor to focus on in the topic of eWOM behavior because younger generations are more active on social media. Younger generations could be more likely to share things on social media than, for example, people in the 35-44 age group. It is highly likely that most of the participants in the research sample are students due to the use of a personal network and the fact that the biggest age group is 18-24 years old.
As the findings show, personalized messages do lead to consumer perceived brand humanization. Brand humanization has been mentioned by past research to be very beneficial for companies but further research should be conducted in order to explore the consequences of this. In addition, it would be beneficial to research what other factors influence brand humanization and to find out whether the use of
personalized messages is the best method to achieve this.
This research did not distinguish between positive and negative consumer eWOM and therefore it would be useful for future studies to distinguish between the two. This is important because companies need to focus on promoting positive
consumer eWOM and finding out what causes negative consumer eWOM. This study would have benefited from finding out whether the negative experience with the mobile service provider resulted in negative eWOM or if the company’s reply helped promote positive eWOM.
Future research should also look into consumer eWOM behavior on other social media platforms besides Facebook. It would be beneficial to see if the behavior differed between these sites. For example, Twitter may be a social media site that has more business-to-consumer (B2C) interaction. In addition, Twitter ‘tweets’ have the ability to reach a larger audience, compared to Facebook messages. Future research could go even more in depth on the topic of eWOM by researching the differences in eWOM behavior between different social media channels. These channels include Internet web sites, tablets and mobile phones. Are people more likely to share
information about a specific brand over mobile phone messages or on a computer? In addition, this leads us to the question; are people more likely to engage in eWOM behavior on public platforms (i.e. status updates and comments on company pages) or on private ones (i.e. private message and chat applications)?
7. Conclusion
The presence of new media such as Facebook has enabled companies to communicate with their consumers in new ways (Hennig-Thurau et al., 2010). Moreover, it has improved consumers’ ability to share and browse information concerning companies with each other. This has made it more important than ever for companies to address consumer eWOM behavior, for example, through the use of personalized messages. The purpose of this study was to find out to what extent brand humanization mediated the relationship between message personalization and consumer eWOM behavior in a B2C context on Facebook. Quantitative research through the use of an online
questionnaire was conducted in order to collect data. The results showed no
significant relationship between message personalization (IV) and consumer eWOM behavior (DV). In addition, no significant relationship was found between brand humanization (MV) and consumer eWOM behavior (DV). The only significant
relationship was found between message personalization (IV) and brand humanization (MV). These findings help to conclude that a company’s use of personalized
messages leads to the consumer perceiving the brand as having human-like characteristics. However, this does not influence their likelihood of engaging in eWOM behavior with their online Facebook friends and therefore, there is no mediation effect.
The main contributions of this research are to help companies identify their need to send personalized messages to their consumers if they want to humanize their brand. As mentioned earlier, there are many benefits to brand humanization and therefore more ways to achieve it should be researched. The findings of this study also show that the use of personalized messages is not the best way to influence consumers to talk about a company to their friends on Facebook. It could be that personalized messages are becoming too common with the presence of new media platforms, that people don’t associate the messages with showing company
compassion and care. Companies no longer control conversations that occur online, therefore they need to find more ways to keep online consumers satisfied as their words could damage the company’s reputation. Future research needs to be conducted on this topic in order to explore more factors influencing consumer electronic word-of-mouth because it is a concept that will remain highly important for all companies trying to build and sustain a positive brand image.
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