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Master Thesis Marketing
EBM867B20
Drivers of Online Customer
Engagement Behavior
Christoph Globke - S2194929
c.globke@student.rug.nl
24.06.2013
2 Preface
This master´s thesis is concerned with the principal topic of customer engagement behavior. This topic has been proposed by the University of Groningen due to its increasing interest throughout the last years. The specific topic of drivers of online customer engagement behavior emerged from the principal topic due to its lack of attention in the past.
Special thanks goes to Prof. Dr. Peter C. Verhoef who assisted me throughout the creation of this thesis and guided me with his feedback on a regular basis. Also, I am grateful to the 202 people that took a few minutes of their time in order to respond to my survey.
Management Summary
A growing amount of research deals with the processes and outcomes of online customer engagement behavior. An increased presence of CEB platforms and an increase in the usage of CEB has a considerable impact on customer´s decision making. This study investigates drivers and triggers causing customers to initiate online customer engagement behavior, since this specific topic has not found much attention yet.
Throughout this research, seven possible drivers of online customer engagement behavior initiation have been developed and discussed. These seven drivers have been divided into customer-based, firm-based and market-based factors. The effect of these seven possible drivers of OCEB initiation have been tested via a survey. The results of this survey show that Customer Involvement and Customer Satisfaction represent the two major drivers of OCEB Initiation. The remaining five potential drivers have been found to be insignificant.
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Table of Contents
1. Introduction ... 5
2. Literature Review ... 8
2.1. Online Customer Engagement Behavior ... 9
2.2. Conceptual Model ... 11 2.3. Customer-based Factors ... 13 2.3.1. Customer Satisfaction ... 13 2.3.2. Involvement ... 14 2.3.3. Customer Learning ... 14 2.4. Firm-based Factors ... 15
2.4.1. Firm Engagement Initiatives ... 15
2.4.2. Brand Image ... 16 2.5. Market-based Factors ... 17 2.5.1. Media Attention ... 17 2.5.2. Competitor Actions ... 18 3. Methodology ... 19 3.1. Research Design ... 19 3.2. Sample ... 19 3.3. Stimuli ... 19 3.4. Measurement ... 20 3.5. Analysis ... 21 4. Results ... 22 4.1. Sample Descriptives ... 22 4.1.1. Demographics ... 22
4.1.2. Dependent and Independent Variables... 23
4.2. Analysis ... 25
4.2.1. Reliability Analysis... 25
4.2.2. Multi-variate Regression Analysis ... 28
4.2.3. Non-linear Effect of Customer Satisfaction ... 30
4.2.4. Hypothesis Testing ... 31
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5. Discussion and Conclusions ... 34
5.1. Managerial Implications ... 37
5.2. Limitations and Further Research... 38
6. Bibliography ... 40
7. Appendices ... 44
7.1. Questionnaire ... 44
7.2. SPSS Output ... 48
7.2.1. Demographics ... 48
7.2.2. Dependent and Independent Variables... 49
7.2.3. Reliability Analysis... 51
7.2.3.1. Coefficient Alphas ... 51
7.2.3.2. Correlation Table ... 57
7.2.3.3. Exploratory Factor Analysis ... 59
7.2.4. Multi-variate Regression Analysis ... 66
5 1. Introduction
The evolvement of the internet and social media has changed the way customers communicate with each other, offering a whole new set of opportunities. New technologies and the rise of social media enables modern customers to gain any information they want at the time and place of their choice (Verhoef & Lemon, 2013). Online communication platforms have evolved which diminish geographical borders, eliminate distances and create connections between customers all over the world. These new media platforms such as Twitter, YouTube or Facebook affect customer relationships and customer-to-firm interactions (Hennig-Thurau, et al., 2010). Through these developments, customers can no longer be considered to be passive since their voice is more powerful than ever before. The role of customers has completely changed. They are no isolated individuals anymore. Instead they are increasingly connected with each other sharing experiences and opinions about certain brands and products. Thus, customers are becoming more active and involved which increases the likelihood of them giving feedback (Kotler, 2010, p. 11).
Customers are becoming more and more resistant towards advertising (Vivek, Beatty, & Morgan, 2012). Instead they consult friends or other users when making decisions about purchases. With their participation and experience sharing, customers can actively influence each other, shape brand images and change purchase behaviors (Hennig-Thurau, et al., 2010). This can occur in a positive and negative manner. This user-generated content made social media a powerful source of information for customers affecting their buying decisions. Firms are now realizing the importance of this non-transactional behavior (Verhoef, Reinartz, & Krafft, 2010).
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influence on the creation or destruction of brand value (Miller, 2007). One of the most popular definitions of the term has been compiled by van Doorn et al. (2010), who defines customer engagement as “the customers’ behavioral manifestation toward a brand or firm, beyond purchase, resulting from motivational drivers” (van Doorn, et al., 2010).
However, only very few of the previous researches on customer engagement have dealt with the issue of what drives customer engagement. Van Doorn et al. (2010) developed a thorough foundation of possible drivers of CEB but they never tested it. The purpose of this research is to develop a comprehensive framework of drivers and triggers of online customer engagement behavior.
This research investigates the effects of customer-based factors, firm-based factors and market-based factors on the initiation of online customer engagement behavior. These three factors include the following variables which are expected to drive OCEB: Customer Satisfaction, Involvement, Customer Learning (customer-based factors), Firm Engagement Initiatives, Brand Image (firm-based factors), Media Attention and Competitor Actions (market-based factors). The goal of this research is to find out which factors drive customers to initiate online engagement behavior and which of these drivers are more essential than others. Thus, the main research question of this research is:
1. Which factors drive online customer engagement behavior initiation?
Additionally, the following research questions are investigated throughout this research: 2. What is the role of customer-based factors on OCEB initiation?
3. What is the role of firm-based factors on OCEB initiation? 4. What is the role of market-based factors on OCEB initiation?
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In the upcoming section the literature review will be conducted in which the dependent variable “Online CEB Initiation” is discussed first. Subsequently, the conceptual model will be presented. The model is followed by an introduction of all the different independent variables including the corresponding hypotheses linked to them.
8 2. Literature Review
In the last years, new internet platforms have evolved which enable customers to engage into a whole new level of interactions. Modern customers can gain any type of information at any time they want. Social media platforms enable customers to actively share consumption experiences and opinions which causes an influence on each other’s behavior (Hennig-Thurau, et al., 2010). Therefore, customers cannot be considered as isolated individuals anymore. Nowadays, firm’s initiatives to influence and persuade customers have become less effective. Modern customers value opinions and advice of other customers, friends or family more than firm-generated messages (Libai, et al., 2010). This type of peer-to-peer word-of-mouth is said to be 10 times more effective than traditional marketing channels such as TV or print advertising when it comes to influencing customers purchase intentions (Roberts & Alpert, 2010).
In today´s world where trust in traditional marketing channels has dropped and customers are more connected to each other, the issue of customer engagement is more relevant than ever (Mollen & Wilson, 2009). Non-commercial information provided by customers can have very positive effects on a firm´s performance such as sales growth, profitability and a substantial competitive advantage (Kumar, et al., 2010). However, these non-commercial information can also destroy firm value when the valence of these interactions are negative (Kaplan & Haenlein, 2010). This emphasizes the importance for firms to actively engage customers and stimulate positive customer engagement.
The topic of customer engagement has been generating a lot of attention from academic researchers throughout the last years (Brodie, Hollebeek, Juric, & Ilic, 2011). Since customer engagement is a really broad term there are different definitions and interpretations describing it. The term reflects a customers´ individual and context-specific engagement with a certain object such as a product, brand or organization (Hollebeek, 2011). Customer engagement can be considered to be either a state or a dynamic process (Hollebeek, 2013). Its intensity can develop over time.
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multidimensional conceptualization involving all three aspects is commonly used and accepted by the majority of researchers.
Academic literature provides various definitions of the term. Brakus et al. (2009) defines customer engagement as “customer´s direct, physical contact-base interactions with a focal brand” (Brakus, Schmitt, & & Zarantello, 2009). Another conceptualization is proposed by Vivek et al. (2011) who define the term as “The intensity of an individual’s participation & connection with the organization’s offerings & activities initiated by either the customer or the organization” (Brodie, Ilic, Juric, & Hollebeek, 2011). On the contrary, Bowden (2009) describes customer engagement as an end state of customer loyalty (Bowden, 2009). Moreover, Higgins and Scholer (2009) define customer engagement as “a state of being involved, occupied, fully absorbed or engrossed in an object” (Higgins & Scholer, 2009). The conceptualisation which is most consistent with the concerned issues of this research has been proposed by van Doorn et al. (2010) who defines customer engagement as “the customers’ behavioral manifestation toward a brand or firm, beyond purchase, resulting from motivational drivers” (van Doorn, et al., 2010).
CEB is a broad construct which includes non-transactional behaviors such as blogging, word of mouth, writing reviews or co-creation. Due to relevance and a growing importance, this study will focus on online customer engagement behavior (OCEB).
2.1. Online Customer Engagement Behavior
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OCEB comprises an interactive experience which occurs during virtual customer-to-customer interactions about a brand-related object (Brodie, Ilic, Juric, & Hollebeek, 2011). Mollen & Wilson (2011) developed a decent definition of OCEB. They define it as “a cognitive and affective commitment to an active relationship with the brand as personified by the website or other computer-mediated entities designed to communicate brand value” (Mollen & Wilson, 2009). These engagement behaviours can have positive and negative consequences for a firm. These behavioural manifestations towards a product or brand are strongly related to the emergence of new electronic media and enable social presence (Gummerus, Liljander, Weman, & Pihlström, 2012). It includes interactions among customers as well as customer-to-firm interactions.
A perquisite for OCEB is the extent of interactivity of the particular online platform. The term is defined as the degree to which users can participate and alter the content of the particular online platform in real time (Mollen & Wilson, 2009).
Consequences and outcomes of OCEB have been thoroughly investigated in previous literature. It has been conceptualized that customer engagement behaviour affects customer welfare, economic surplus, firm performance and also firm value (Brodie, Hollebeek, Juric, & Ilic, 2011).
Members of virtual brand communities share the same interests and undergo experiences together which creates a bond among them. In such virtual communities, members and visitors co-create value for themselves (Brodie, Ilic, Juric, & Hollebeek, 2011).
An important aspect about the issue of drivers of OCEB is that particular drivers of OCEB can also appear as consequences of OCEB in certain situations. The factor “Involvement” gives such an example. While this factor appears to be a driver of OCEB for existing customers, this factor turns out to be a consequence of OCEB for new customers (Bowden, 2009).
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as offline. This research includes a conceptual framework to deliver clarity towards the issue of what drives OCEB. This OCEB initiation can be either of positive or negative valence. The deployed factors causing OCEB initiation are discussed in the upcoming sections.
2.2. Conceptual Model
The first set of independent variables within the conceptual framework deals with customer-based factors to have an effect on OCEB initiation. These customer-customer-based factors consist not only of customer personalities and character traits but also of the way how customers perceive a product or brand. One of the most common reason why customers initiate OCEB is satisfaction or dissatisfaction about a product or service. If a customer is strongly satisfied or dissatisfied, he is likely tell others about his experience (Luo & Homburg, 2007). But not only satisfaction plays a role in the sources of OCEB initiation, also involvement can be an important factor. If customers are highly involved with a brand, product or firm, these customers are likely to share their experiences or join brand communities (Brodie, Ilic, Juric, & Hollebeek, 2011). Another factor is customer learning at which customers observe the behavior of others resulting in imitation behavior which can cause OCEB (Libai, et al., 2010).
The second set of independent variables used as drivers of OCEB initiation within this research are firm-based factors. These factors are controlled by firms and are mainly related to their performance and image. One of the major firm-based factors are firm engagement initiatives to stimulate the initiation of OCEB. Firms do so by forming brand communities or platforms for their customers to share experiences and solved product related problems. These firm initiatives lead a lot of customers to initiate CEB (Tripathi, 2009). The image of a brand or firm also plays a major role. Brands with a very strong brand image are likely to be the topic of customer conversations. (Libai, et al., 2010).
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of OCEB initiation. In addition, competitive actions can also cause OCEB. When firms for example run comparative advertising campaigns, customers can get the feeling that the product they are using is of either superior or inferior quality (van Doorn, et al., 2010). This cognition stimulates customer´s recommending and complaining behavior which often results in the initiation of OCEB.
13 2.3. Customer-based Factors
The first set of factors which influence the initiation of OCEB are customer-based factors. These factors deal with the way how customer´s cognitive activities can drive engagement behavior. The factors which are dealt with in this section are customer satisfaction, involvement and customer learning. All these factors are expected to trigger the initiation of OCEB.
2.3.1. Customer Satisfaction
Customer Satisfaction is one of the most crucial drivers of OCEB. Customer satisfaction is a post-consumption, cognitive process which occurs as an evaluation of purchase or consumption experience (Bowden, 2009). If this evaluation meets or exceeds the expectations, the customer will be satisfied. However, if the experience is below expectations the customer will be dissatisfied. The more it exceeds or falls short of its expectation the stronger is the degree of satisfaction or dissatisfaction. High degrees of satisfaction or dissatisfaction are emotional states which motivate customers to engage in response behavior (von der Heyde Fernandes & Pizzutti dos Santos, 2008). The higher the degree of satisfaction or dissatisfaction, the more likely is a customer to initiate OCEB.
The relationship between customer satisfaction and OCEB is U-shaped, meaning that both very high and very low degrees of satisfaction are likely to generate OCEB (Yang, Hu, Winer, Assael, & Chen, 2012). A high degree of satisfaction leads customers to recommend the product or brand to others. A low degree of satisfaction on the other hand stimulates customers complaining behavior (Javornik & Mandelli, 2012). Nevertheless, the valence of the OCEB can also be neutral. However, neutral OCEB behavior is not common practice as in the majority of cases the valence is either strongly positive or strongly negative (Mazzarol, Sweeney, & Soutar, 2007).
These findings on customer satisfaction lead to the formulation of the first hypothesis:
14 2.3.2. Involvement
Involvement is another possible driver of OCEB. The term is defined as “perceived relevance of an object based on inherent needs, values and interests” (Vivek, Beatty, & Morgan, 2012). It is conceptualized as the level of personal relevance which is affective, cognitive and motivational in nature. Customers who are more involved tend to have a larger depth of processing, engage more in searching behaviour and are more likely to make use of product trials (Vivek, Beatty, & Morgan, 2012).
Research has shown that involved customers are more willing to participate in a firm´s relational marketing activities. Furthermore, involved customers are more open and absorptive toward updates and information from the firm (Ashley, Noble, Donthu, & Lemon, 2010). Since involved customers have a higher level of interest in the firm they are also likely to engage into C2C interactions with other customers about this particular firm. These highly involved customers perceive larger benefits from such brand-related C2C interactions than less involved customers. Thus, a high level of customer involvement can be considered an antecedent of OCEB (Vivek, Beatty, & Morgan, 2012)
Based on these finding, the second hypothesis has been developed:
H2: A high level of customer involvement increases the likelihood of OCEB initiation.
2.3.3. Customer Learning
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has internalized the norms and modes of behavior within this virtual community, the new member is very likely to initiate this engagement behavior as well (Chen, Wang, & Xie, 2011). Furthermore, simple C2C interactions can lead to customer learning entailing OCEB initiation. A conversation between a customer and a friend or another customer can provide the customer with new information. These new information which the customer has learned from the interaction can either generate a strong feeling of attention or a feeling of furiousness which may motivate the customer to share this information with others. These new information gained through the learning process animate the customer to initiate OCEB (Hung & Li, 2007).
These findings suggest the following hypothesis:
H3: A high degree of customer learning stimulates OCEB initiation.
2.4. Firm-based Factors
Firm-based factors are expected to be the second major driver of OCEB initiation. These factors deal with all the aspects which are controlled by the frim. This involves all firm initiated activities towards the customer and their reputation. The firm-based factors which are discussed below include brand image and firm engagement initiatives. Firm-based factors can motivate customers to initiate OCEB (Tripathi, 2009).
2.4.1. Firm Engagement Initiatives
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studied customer expectations towards the firm in order to develop campaigns to stimulate commitment (Voyles B. , 2007).
Examples of such firm initiatives are firm-controlled virtual brand communities or sponsored events. All these examples attract customer´s attention and stimulate a response. The more these initiatives arouse the receiving customer the higher the likelihood of this particular customer to initiate OCEB (Javornik & Mandelli, 2012). Very creative campaigns can create a buzz and motivate customers to share their opinion about it.
Especially firm-controlled virtual brand communities are an important driver of OCEB since they are continuously encouraging customer involvement. Such communities are an important platform for customers’ engagement behavior (Kane, Fichman, Gallaugher, & Glaser, 2009). Not only do they stimulate engagement behavior but also do they strengthen the brand, build relationships with customers and get feedback (Gummerus, Liljander, Weman, & Pihlström, 2012).
These findings can be concluded by the following hypothesis:
H4: A high degree of firm engagement initiatives increases the likelihood of OCEB initiation.
2.4.2. Brand Image
A brand´s image does not only influence the firm´s overall performance but also customer engagement behaviour (Sonnier & Ainslie, 2011). Brand image can be defined as customer’s perception towards a brand and their confidence in the firm to adhere to the promises they give (Sääksjärvi & Samiee, 2011). A brand image is a mechanism of social evaluation which results in an opinion about a certain object or in this case firm (Park & Lee, 2009). Certain images can motivate participation behaviour (Libai, et al., 2010). Brands with a higher perceived image tend to perform better than their competitors.
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of OCEB initiation becomes high (Javornik & Mandelli, 2012). Very weak brand images on the other hand show no evidence of causing OCEB (Moliner Velázquez, Fuentes Blasco, Gil Saura, & Berenguer Contrí, 2010).
According to these findings the fifth hypothesis has been developed:
H5: A very strong brand image increases the likelihood of OCEB initiation.
2.5. Market-based Factors
Another set of factors which influence the initiation of OCEB are market-based factors. These factors includes various activities which are happening within a certain market environment which can influence the behavior of its customers. These activities are related to competitor actions and the role of the media. The environmental cues created by these two can influence C2C interactions substantially (Libai, et al., 2010).
2.5.1. Media Attention
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These findings lead to the following hypothesis:
H6: A strong degree of media attention around a certain brand or product has a positive impact on OCEB initiation.
2.5.2. Competitive Actions
Actions of competitive firms can also have an influence on OCEB initiation. Competitive Actions can be defined as firm-level competitive moves which aim on gaining a competitive advantage at the expense of other firms (Yeung & Lau, 2005). When a competitive firm launces a new product, improves its product or substantially lowers its prices it is likely to cause a customer reaction. Customers might feel that the product they are using is of inferior quality. This type of perception causes frustration and stimulates customer complaining behaviour (von der Heyde Fernandes & Pizzutti dos Santos, 2008).
This effect also occurs during competitive advertising campaigns. Customers can realize that the product they are using is of inferior quality as the product of the firm which runs the competitive advertising campaign leading to complaining behaviour (Moliner Velázquez, Fuentes Blasco, Gil Saura, & Berenguer Contrí, 2010). Anyhow, customers who are users of the brand which runs this campaign are also induced to initiate OCEB. Users might realize through these campaigns that the product they are using is of superior quality than competitive products which improves customer satisfaction and stimulates recommendation behaviour (van Doorn, et al., 2010).
In conclusion, when firms run competitive marketing actions, users of that brand are likely to engage in positive eWOM and non-users of that brand are likely to engage in negative eWOM. Based on these findings, the following hypothesis has been developed:
19 3. Methodology
The goal of this research project is to develop a thorough framework of drivers of online customer engagement behaviour. Possible triggers of OCEB initiation were discussed and analysed throughout the literature review. In this section, the research design, sample, stimuli and measurement are described.
3.1. Research Design
A quantitative research method in form of a survey has been selected in order to provide evidence to proof the seven hypotheses. It will be determined which of the seven proposed possible drivers of OCEB initiation actually do effect the dependent variable. Besides, asking about the participants’ age and gender the survey also measures the seven independent variables and the dependent variable separately.
3.2. Sample
There are no limitations for the target population for this research. Leaving out respondents due to nationality, age or gender was not a necessary step in this research. Participants were asked to fill out an online-based questionnaire. The participants were selected based on the random sampling method. For this research a sample size of 200 respondents has been chosen as a reasonable amount.
3.3. Stimuli
20 3.4. Measurement
In order to judge the conceptual model of this research, every variable has been assessed through the questionnaire. Every construct includes at least three items in order to measure them precisely. The constructs have been measured via statements which participants had to evaluate on a scale from 1 (strongly disagree) to 7 (strongly agree).
For the constructs “Involvement” (Ashley, Noble, Donthu, & Lemon, 2010), “Brand Image” (Martínez Salinas & Pina Pérez, 2009; Verhoef, Langerak & Donkers, 2007), and “Customer Satisfaction” (Aaker, 1996; Grigoroudis & Siskos, 2004) scales have been used that are available throughout the literature. For the construct “Competitors Actions” an existing literature based scale for the construct “Competitive Intensity” has been used (Jaworski & Kohli, 1993). The remaining variables have been measured by scales which were developed for the purpose of this research. Due to the importance of the measurement of the dependent variable, a seven-item scale has been developed for “OCEB Initiation”. Table 1 displays the items used in order to measure the constructs including its sources.
Table 1
Construct & Source Measured Item
Customer Satisfaction I would recommend my mobile phone provider to others. (Aaker, 1996; Grigoroudis &
Siskos, 2004)
I consider the price-performance ratio of my mobile phone provider as very beneficial to me.
In my opinion, the services and offering provided by my mobile phone provider are of high quality.
Overall, I am very satisfied with the performance of my mobile phone provider.
Involvement I closely keep track of the services provided by my mobile phone provider. (Ashley, Noble, Donthu &
Lemon, 2010)
I feel well informed about the majority of offers and services my mobile phone provider has to offer.
I participate in many of the offers and services offered by my mobile phone provider.
Customer Learning Before I make decisions about a new mobile phone provider, I observe which mobile phone providers are used by people around me.
Before I make decisions about a new mobile phone provider, I ask friends and family members about their mobile phone providers and how satisfied they are with them. The opinion of others around me has a strong impact on the choice of my mobile phone provider.
Firm Engagement Initiatives
My mobile phone provider often encourages me to join its brand community. My mobile phone provider often encourages me to join its sponsored events.
I feel that my mobile phone provider tries to create a long lasting relationship with me.
Brand Image My mobile phone provider is a well-known brand. (Martinez Salinas et al.,
2009; Verhoef et al., 2007)
My mobile phone provider is a unique brand. My mobile phone provider is an attractive brand.
My mobile phone provider does not disappoint its customers.
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Media Attention My mobile phone provider has a strong presence in the media.
I very often see my mobile phone provider´s offers throughout various media channels. My mobile phone provider is publically debated a lot.
Competitors Actions The competition in the mobile phone provider industry is really strong. (Jaworski & Kohli, 1993) There are many “promotion wars” among mobile phone providers.
There is a strong price competition among mobile phone providers.
Anything that my mobile phone provider can offer, other providers can match readily. One hears of a new competitive move of a mobile phone provider almost every week.
OCEB Initiation I have posted what I think or feel about my mobile phone provider on a social media platform or brand community before.
I share information about my mobile phone provider online in order to inform others. I promote my mobile phone provider online or leave positive comments about it online. I frequently post something about my mobile phone provider online.
I recently posted something about my mobile phone provider online.
I sometimes complain about my mobile phone provider online or leave negative comments about it online.
I complain about my mobile phone provider or leave negative comments about it online in order to warn others.
3.5. Analysis
Eventually, a multi-variate regression model will be used to test the effects of the seven independent variables on the dependent variable simultaneously. These results will show which constructs cause OCEB initiation and consequently which hypotheses can be accepted. The following regression equation will be used for this research:
(1) OCEB Initiation= b0+ b1CusSat+ b2CusSat2+ b3Inv+ b4CusLea+ b5FiEnIn+ b6BraIm+ b7MedAt+ b8ComAct
22 4. Results
This section discusses the results of the data collection and its analysis. Overall, 260 people have responded to the online survey. However, the responses of 56 respondents had to be removed since they lacked to fill out the complete survey including the questions measuring several independent variables and the dependent variable. In addition, two respondents have been taken out of the sample because they indicated that they do not own a mobile phone. As a result, 202 complete responses have been used for the analysis of this research.
4.1. Sample Descriptives 4.1.1. Demographics
The respondent’s genders were somewhat equally distributed with 56,4% female respondents and 43,6% male respondents. In addition, most respondents were in the age of 16-25 with a quantity of 69,3%, followed by 26-35 with 27,2% and 36-45 with 2% as shown in table 2. In terms of occupation, the majority of respondents were students with a quantity of 69,8%, followed by full-time employed respondents with a quantity of 24,3%. Table 4 illustrates that the majority of respondents participating in this research were German (43,1%), followed by Dutch (23,3%), American (6,4%) and French (5,4%). Nationalities of respondents which occurred less than five times have been placed in the category “Other”.
Frequency Percent 16-25 140 69,3% 26-35 55 27,2% 36-45 4 2% 46-55 2 1% 55+ 1 0,5% Frequency Percent Student 141 69,8% Full-time 49 24,3% Part-time 6 3% Unemployed 6 3%
23 4.1.2. Dependent and Independent Variables
Each construct was measured via multi-item scales. The constructs were aggregated by taking the mean of all items per construct in order to summarize each construct within one variable. Table 5 illustrates the mean values and standard deviations of all the variables. From the seven independent variables, Customer Satisfaction has been found to have the strongest presence among respondents with a mean value of 5,01. The lowest score among the seven independent variables was obtained by “Firm Engagement Initiatives” with a mean score of 3,19.
After the data collection has been conducted, the mean score of the dependent variable “OCEB Initiation” has been found to be 1,87. This relatively low score points out that the majority of respondents is rather inactive in terms of initiating online customer engagement behaviour. Figure 1 illustrates how OCEB Initiation is distributed among the respondents on a scale from 1 (low degree of OCEB Initiation) to 7 (high degree of OCEB Initiation).
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The histogram illustrates that the dependent variable OCEB Initiation is not normally distributed. A Shapiro-Wilk test has been conducted in order to test the variable´s normality. The result is a p-value of 0,00 which strongly indicates a non-normal distribution of OCEB Initiation. The normality test of the residuals also results in a p-value of 0,00 which emphasizes the non-normal distribution of the dependent variable.
As the histogram indicates, the results are strongly skewed to the left. A skewness test resulted in a skewness score of 1,67 and a standard error of skewness of 0,17. This results in a z-score of 9,78 which indicates that the skewness of OCEB Initiation is statistically significant. Due to the non-normal distribution of the dependent variable and its strong positive skewness a multi-variate regression analysis alone might not be an appropriate procedure in this case. Thus, an additional logistic regression analysis will be conducted which is a more appropriate approach when dealing with a non-normal distribution within the dependent variable.
0 20 40 60 80 100 120 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 F re q u e n cy OCEB Initiation
25 4.2. Analysis
4.2.1. Reliability Analysis
In order to measure the internal consistency of the conceptual model, the coefficient alphas have been computed to assess reliability. As indicated in table 5, the majority of coefficient alphas of the multi-item scales used in this research are higher than 0,70. The construct for the dependent variable of this research has scored a 0,94, which indicates a very high reliability of this construct. Only the construct “Firm Engagement Initiatives” has scored a 0,69, which is still a fairly reliable result. Overall, these results point out a high degree of reliability of the constructs used within this research.
Furthermore, an exploratory factor analysis has been conducted as an additional assessment of the reliability of the scales. The results show sufficiently high loadings per item per construct. First of all, the factor analysis has been conducted on each construct separately. The majority of items of each construct were classified to be belonging to the according construct. Only item 1 of “Brand Image” and items 2 and 3 of “Competitors Actions” were classified as not belonging to the according construct.
Then, the factor analysis has been conducted with all items of all the constructs. The results are presented in table 6. The majority of constructs were all classified into different factors. Two items of Competitors Actions were classified into a separate factor. The remaining three
Mean Standard Deviation Coefficient Alpha
Customer Satisfaction 5,01 1,15 0,76
Involvement 3,31 1,29 0,70
Customer Learning 4,74 1,50 0,81
Firm Engagement Initiatives 3,19 1,34 0,69
Brand Image 4,60 1,03 0,74
Media Attention 4,47 1,40 0,80
Competitors Actions 4,93 0,88 0,75
OCEB Initiation 1,87 1,37 0,94
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items of the construct were classified into another separate factor. All items of Customer Satisfaction and three items of Brand Image were classified into one factor. This shows a correlation between the two constructs which is also visible in the correlation table.
Component 1 2 3 4 5 6 7 8 CusSat1 .002 .532 .238 -.368 .107 .147 .294 .036 CusSat2 -.024 .565 -.104 -.136 .061 .076 .227 -.117 CusSat3 .015 .792 .200 .115 -.098 .080 -.069 .105 CusSat4 -.007 .869 .008 -.027 .066 .118 .093 .029 Involv1 .225 .051 .091 .224 .073 .188 .747 .029 Involv2 .414 .098 .030 .230 -.034 .000 .611 -.050 Involv3 .110 .403 -.047 .223 .084 .117 .553 .133 CusLearn1 .100 .140 .020 .000 .831 .101 .127 .133 CusLearn2 .024 .118 -.078 -.001 .805 .284 -.044 .023 CusLearn3 .070 .042 .068 .111 .839 -.030 .022 -.009 FiEnIni1 .131 .083 .224 .712 .042 -.017 .204 .055 FiEnIni2 .040 -.067 .153 .704 .020 -.023 .159 .112 FiEnIni3 .112 .409 .059 .568 -.006 .048 .188 -.042 BraImage1 -.029 .045 .880 .048 -.050 .059 .021 -.057 BraImage2 .087 .631 .461 .135 .085 .167 -.059 -.093 BraImage3 .020 .772 -.125 .008 .174 -.080 .054 -.024 BraImage4 .129 .443 .134 .392 .058 .104 .051 -.245 BraImage5 .080 .625 .324 .331 .069 .117 -.075 -.037 MedAtt1 .059 .109 .757 .136 -.009 -.002 .186 .231 MedAtt2 .007 .188 .813 .266 .011 .109 -.061 .057 MedAtt3 .040 -.197 .463 .480 .255 .071 -.042 .020 ComAct1 .095 .090 .122 -.156 .043 .785 .159 -.180 ComAct2 -.086 -.223 .212 -.184 .130 -.141 .320 .623 ComAct3 .207 .069 .050 .274 .079 .208 -.105 .695 ComAct4 .064 .081 .067 .237 .086 .757 -.064 .253 ComAct5 -.028 .189 .038 -.021 .209 .796 .143 .038 OCEBInit1 .879 .130 .032 .022 .031 .000 .086 .040 OCEBInit2 .875 .070 .018 .082 .048 .046 .034 .018 OCEBInit3 .822 .214 .041 .081 .038 .044 .139 -.138 OCEBInit4 .796 .029 -.002 .155 .006 -.023 .219 -.081 OCEBInit5 .908 .052 .022 .047 .029 .014 .115 .008 OCEBInit6 .818 -.217 -.030 .026 .076 .057 -.029 .162 OCEBInit7 .902 -.101 .014 -.031 .043 .047 -.002 .124
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As a consequence of the outcomes of the factor analysis item 1 of Brand Image, items 2 and 3 of Competitors Actions and item 3 of Media Attention have been deleted from their belonging constructs and not been taken into consideration for upcoming steps within the data analysis. The withdrawal of these items led to the improvement of the coefficient alpha scores of Brand image from 0,71 to 0,74, of Competitors Actions from 0,55 to 0,75 and of Media Attention from 0,71 to 0,8.
The correlation table illustrates the correlations between the eight constructs. Overall, the correlations are moderate and positive. The highest correlation coefficient was found between Brand Image and Customer Satisfaction with a score of 0,666. Overall, these results imply an absence of multicollinearity.
OCEB CS In CL FEI BI MA CA
OCEB Initiation 1
Customer Satisfaction ,046 1
Involvement ,399 ,299 1
Customer Learning ,132 ,169 ,173 1
Firm Eng. Initiatives ,212 ,159 ,422 ,109 1
Brand Image ,150 ,666 ,348 ,233 ,397 1
Media Attention ,081 ,203 ,220 ,074 ,384 ,383 1
Competitors Actions ,108 ,289 ,249 ,292 ,122 ,268 ,183 1
28 4.2.2. Multi-variate Regression Analysis
As discussed in the methodology the seven hypothesis which have been developed throughout the literature review were first tested via a multi-variate regression model. The results show a R Square score of 0,176, meaning that only 17,6% of the variance of OCEB Initiation can be explained by this model. The adjusted R Square amounts to a score of 0,147. The ANOVA test shows a significant result with a p-value of p=0,00, meaning that the combination of these variables significantly explains OCEB Initiation. Moreover, multicollinearity is not an issue since all the VIF values are below 4.
In order to test this nonlinear effect of Customer Satisfaction, a hierarchical regression analysis has been conducted as well. First of all, a new variable has been computed by taking the square of Customer Satisfaction. The conduction of the hierarchical regression led to an improved R Square value of 0,226. The adjusted R Square value also improved from 0,147 to 0,194. The ANOVA test of model 2 which includes the squared Customer Satisfaction variable shows a significant result with a score of p=0,00.
Table 8 illustrates the differences between the two models with and without the squared Customer Satisfaction variable. The results of the regular multi-variate regression imply that only Involvement has a significant impact on OCEB Initiation with a p-value of p=0,00. Involvement affects OCEB Initiation with a Beta coefficient of B=0,41. All the other independent variables have a p-value of p>0,05 and are therefore insignificant. However, when including the nonlinear effect of Customer Satisfaction to the model, the independent variable Customer Satisfaction and the dependent variable OCEB Initiation become significant as well. In model 2, Customer Satisfaction has a strong negative effect on the dependent variable with a Beta coefficient of -1,68. The effect of the squared Customer Satisfaction variable is positive with a score of B=0,17.
29
By applying the regression equation, which has been specified earlier, a model to predict OCEB Initiation has been developed:
OCEB Initiation=3,94- 1,68*CusSat+ 0,17*CusSat2+ 0,32*Inv+ 0,06*CusLea+ 0,05*FiEnIn+ 0,04*BraIm- 0,01*MedAt+ 0,02*ComAct
The regression equation shows that Customer Satisfaction can have the strongest impact on OCEB Initiation. Besides Customer Satisfaction and Media Attention, all variables contribute positively to the Initiation of OCEB. However, an increase in Media Attention even decreases the likelihood of OCEB Initiation. After all, the quality and predictability of this model is limited due to a R Square-value of 0,23 and the fact that only Customer Satisfaction and Involvement have an statistically significant impact on OCEB Initiation.
Model 1 Model 2
Beta Coefficient p-value Beta Coefficient p-value
Customer Satisfaction -0,17 0,12 -1,68 0,00
Cus. Sat. Squared - - 0,17 0,01
Involvement 0,41 0,00 0,32 0,00
Customer Learning 0,06 0,37 0,06 0,30
Firm Engag. Initiatives 0,04 0,61 0,05 0,51
Brand Image 0,11 0,37 0,04 0,77
Media Attention -0,03 0,67 -0,01 0,94
Competitors Actions 0,01 0,87 0,02 0,82
Constant 0,55 0,34 3,94 0,00
30 4.2.3. Non-linear Effect of Customer Satisfaction
As discussed in the in the literature review, the relationship between the variable Customer Satisfaction and OCEB Initiation is U-shaped. This assumption was confirmed by the results of the survey and illustrated via figure 2 which shows how the effect of Customer Satisfaction on OCEB Initiation is distributed.
The outcomes of the hierarchical regression analysis in model 2 emphasise the U-shaped effect that Customer Satisfaction carries out on OCEB Initiation. The negative effect of the Customer Satisfaction variable indicates that an increase in Customer Satisfaction decreases the likelihood of OCEB Initiation. However, the more Customer Satisfaction increases, the smaller its effect becomes due to the positive effect of the squared Customer Satisfaction variable. Eventually, when Customer Satisfaction is large enough it will lead to an increase in the likelihood of OCEB Initiation. From that point on, every increase in Customer Satisfaction increases the likelihood of OCEB Initiation.
In order to find the exact turning point where the effect of Customer Satisfaction changes from negative to positive, the first derivate of the regression equation will be taken with respect to Customer Satisfaction and set equal to 0 (Lynch, 2003). When solving this, the exact amount of Customer Satisfaction at which the variable will start to increase the likelihood of OCEB Initiation will be determined.
0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 O C E B I n it ia ti o n Customer Satisfaction
31
= b0+ b1CusSat+ b2CusSat2 The derivate is:
=
b1+ 2b2CusSatSolving this to the point where Customer Satisfaction reaches its minimum:
CusSatmin=
After inserting the regression estimates into this equation the exact amount of Customer Satisfaction needed in order to turn its effect from negative to positive turns out to be at 4,9. This result implies that increasing the amount of Customer Satisfaction decreases the likelihood of OCEB Initiation until a Customer Satisfaction score of 4,9 is reached. From this particular point on Customer Satisfaction will lead to an increased likelihood of OCEB Initiation.
4.2.4. Hypothesis Testing
32 4.2.5. Logistic Regression Analysis
As discussed earlier, a logistic regression analysis has been conducted supplementary due to the non-normal nature of the dependent variable. In order to conduct the logistic regression analysis two categories of OCEB Initiation have been created. The first category includes all the scores of OCEB Initiation below 1,99 and the second category includes all the scores above 1,99. With this categorization it will be distinguished between “no OCEB Initiation” and “some OCEB Initiation”.
The outcomes of the logistic regression analysis show a Cox&Snell R² score of 0,124 and a Nagelkerke R² score of 0,174. These two results imply low explanatory power of the model. Furthermore, the Hosmer & Lemeshow test shows an insignificant result with a p-value of 0,41 and a Chi-square score of 8,22. Thus, there is no linear relationship between the predictors and the outcome variable, which means that the model has a good predictive value. This is also supported by the Omnibus test with a significant p-value of 0,001. The classification table indicates that the model has an overall hit rate of 74,8%. Also, 94,2% of not initiating OCEB and 33,8% of initiating OCEB have been correctly classified in the model.
Table 9 shows the results of the logistic regression analysis. The nonlinear effect of Customer Satisfaction has been included in the logistic regression just like in the multi-variate regression before. The results show that only Involvement has a significant effect on OCEB Initiation with a p-value of 0,001. During the multi-variate regression analysis Customer Satisfaction has proven to have a significant effect. This significant effect could not be confirmed by the outcomes of the logistic regression analysis.
33 Beta Coefficient p-value
Customer Satisfaction -1,12 0,16
Customer Satisfaction Squared 0,09 0,30
Involvement 0,53 0,00
Customer Learning 0,15 0,22
Firm Engagement Initiatives 0,03 0,84
Brand Image 0,11 0,62
Media Attention 0,09 0,42
Competitors Actions -0,19 0,24
Constant -0,04 0,99
34 5. Discussion and Conclusions
Online customer engagement behaviour is a topic gaining a growing amount of attention lately. Its implications for firms have been extensively discussed throughout various scientific researches (Brodie, Hollebeek, Juric, & Ilic, 2011). However, its drivers have been disregarded to a large extend. This research investigates which factors drive customers to initiate online customer engagement behaviour. Seven possible triggers have been developed throughout this research divided into customer-based, firm-based and market-based factors. The results of the data collection indicate that drivers of OCEB Initiation are limited to customer-based factors, namely Customer Satisfaction and Involvement. It has been shown that neither firm-based factors nor market-firm-based factors play a significant role in the consumer´s decision to initiate OCEB.
Customer-based factors have been found to be the only tangible triggers of OCEB Initiation. In the literature review Customer Satisfaction has been stated as one of the most crucial drivers of OCEB Initiation. This claim was confirmed by the analysis of the data collection. This indicates that the cognitive processes occurring while the purchase evaluation are a major driver of OCEB Initiation.
The confirmation of the U-shaped effect of Customer Satisfaction shows that people tend to complain about a product online when their expectations of the product are not met. The stronger the dissatisfaction the stronger the tendency to vent one´s anger online. The more satisfaction grows the less becomes the tendency of customers to share their opinion online. However, when satisfaction reaches a certain level and consumer´s expectations of the product are exceeded during the evaluation process, the likelihood of OCEB Initiation increases again. From that point on, the higher the satisfaction the higher the likelihood of OCEB Initiation. Thus, highly satisfied customers tend to recommend the product or simply share their positive experience online. Hence, high degrees of either satisfaction or dissatisfaction motivate customers to engage in responsive behaviour.
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interactions online in order stay up-to-date and to learn new information about the product of interest. This has been supported by the analysis of the data collection.
Besides Customer Satisfaction and Involvement which are two customer-based factors which have been found to be significant drivers of OCEB Initiation, Customer Learning does not appear to be a tangible driver. When customers practise observational learning or when they learn new arousing information via C2C interactions, there is no evidence that this motivates them to initiate OCEB. Thus, when people engage in customer learning their main purpose is information acquisition. However, they do not seems to share these new acquired information with others online on a regular basis.
Another research question dealt with the role of firm-based factors on OCEB initiation. The analysis of the data collection has shown that neither Firm Engagement Initiatives nor Brand Image significantly influence customers decision to initiate OCEB. This shows that firm’s campaigns and approaches that aim on stimulating OCEB are to a large extent ineffective because the majority of customers seem to be unaffected by these initiatives. This indicates that the active behavior of customers which are actually participating in brand communities is caused by either involvement or a strong feeling of satisfaction or dissatisfaction. Thus, brand community members which actively participate are not evoked by the fact that the firm invited them to the brand community but by other factors. If there would be no brand community these customers would practice their online engagement behavior on another platform.
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The last research question was concerned with the effect of market-based factors on OCEB Initiation. The analysis of the data collection shows that Media Attention as well as Competitors Actions have no significant impact on OCEB Initiation. In fact, their really high p-values and very low Beta Coefficients indicate that these two factors have practically no effect on consumer’s online engagement behavior initiation. Thus, when media attention is high and there is a buzz around a certain brand or product, consumers are not likely to interact about that online as assumed beforehand. The same counts for competitive actions. Although respondents claimed that there would be a rather high intensity of competitive actions it does not seem to affect their complaining behavior or their likelihood of OCEB Initiation in general. This implies that market-based factors do not affect OCEB Initiation.
The outcomes of this research can be reflected by the approach of van Doorn et al. (2010) to develop potential antecedents of CEB, which have never been tested (van Doorn, et al., 2010). The antecedents of CEB developed by them are similar to the drivers of OCEB Initiation of this research. Van Doorn et al. (2010) present customer-based factors involving attitudinal antecedents as major driver of CEB. These customer-based factors also include Customer Satisfaction and Brand Commitment which is very akin to the construct of Involvement used throughout this research. The driving effect of these two constructs were supported by this research. Van Doorn et al. (2010) also discussed the U-shaped effect of Customer Satisfaction stating that feelings of anger, regret or disgust provoke the initiation of negative word-of-mouth. This finding was also supported by this research.
Similar to this research, van Doorn et al. (2012) also discussed the driving role of Firm Reputation within firm-based factors as a major antecedent. Firm reputation comes close to the construct of Brand Image used within this research. They also mentioned the driving role of firm´s initiatives to develop and support platforms to support customer’s actions. Both of these developed antecedents could not be supported by the outcomes of this research. Furthermore, van Doorn et al. discussed the role of strong media attention and a high degree of competitive actions as antecedents of CEB. Both of these assumptions could not be supported by this research.
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Ultimately, this research has shown that only customer-based factors sufficiently drive OCEB. Neither firm-based factors nor market-based factors showed any sign of impact on engagement behaviour. It shows that people stay relatively immune to external influences when looking at the sources of their OCEB Initiation. It seems that only a customer´s cognitive activities are tangible drivers of engagement activities. External influences that try to stimulate consumer´s engagement behaviour are faced with resistance.
5.1. Managerial Implications
This research provides some valuable information in the field of OCEB for firms. First of all it has been found that firm´s initiatives and campaigns in order to stimulate customer´s engagement behaviour are rather ineffective. Customers turn out to be resistant towards these type of strategies. Thus, firms are advised to reduce their budget on these type of initiatives and pursue different strategies in order to stimulate OCEB Initiation.
Customer Satisfaction has always been an important goal for firms to pursue for various reasons such as driving customer loyalty and retention (Deng, Lu, Wei, & Zhang, 2010). This research reveals that customer satisfaction is also an important driver of OCEB Initiation. Thus, it is crucially important for firms to keep their customer base satisfied. A strongly satisfied customer is likely to share his positive experiences with the brand online. This includes leaving positive comments and reviews for the product at hand. Hence, keeping a customer constantly satisfied over a longer period of time, the customer might turn into a promoter of the brand. Such customers which constantly promote the brand online can be really beneficial for a firm, because the opinions and recommendations of other users are more valuable to customers than firm-generated messages (Hennig-Thurau, Gwinner, Walsh, & Gremler, 2004).
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can accomplish some serious damage to a brand via negative eWOM (Gruen, Osmonbekov, & Czaplewski, 2006).
Customer satisfaction is one way for firms to stimulate OCEB Initiation. Another factor that has been found to be a driver of OCEB initiation is involvement. When customer are involved with a certain product or category, they tend to be more active in terms of OCEB Initiation. Involvement is a motivational state of mind which determines a certain level of importance or relevance of a product to a customer (Ashley, Noble, Donthu, & Lemon, 2010). Thus, it is not really a possibility for marketers to create involvement in order to stimulate OCEB Initiation. However, as Cheung & To (2011) found out, the effect of Involvement can be strengthened by co-creation. High levels of co-creation provide favourable conditions which strengthen a customer´s level of Involvement (Cheung & To, 2011). As this research suggests, a higher degree of involvement increases the likelihood of OCEB initiation. However, for co-creation to accomplish this effect, at least a low level of involvement of the customer has to be existent, since co-creation does not create Involvement. Nevertheless, marketers are advised to facilitate a co-creation procedure during the purchase process if possible or improve the current co-creation procedure, because strengthening the degree of involvement increases the likelihood of OCEB initiation.
5.2. Limitations and Future Research
39
Considering the large amount of internet users, a larger scale research with a larger sample size should be conducted to provide more reliable outcomes.
Seemingly there are further possible drivers of OCEB Initiation which is particularly emphasized by the low R-Square score of the developed model including the seven possible drivers of OCEB Initiation. Thus, a large amount of the variance in OCEB Initiation, namely 77%, still has to be discovered to have a more precise image of what drives consumers to initiate OCEB. Future research could go more in depth about possible drivers of OCEB Initiation, identifying additional drivers which have not been discovered by this research. Another approach for future research could be to investigate how drivers of customer engagement behavior differ between online and offline settings.
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44 7. Appendices
7.1. Questionnaire
Completing this questionnaire takes approximately 5 minutes. Your data will be treated anonymously and will only be used for the purpose of this study.
Thank you for your time!
1. Do you have a mobile phone? ☐ Yes
☐ No
2. What is your current mobile phone provider?
☐ Vodafone ☐ KPN ☐ T-Mobile ☐ Hi ☐ Lebara ☐ O2 ☐ Telfort ☐ E-Plus ☐ Movistar ☐ AT&T ☐ Orange ☐ Verizon ☐ SFR ☐ Sprint ☐ Other
3. How frequently do you call or text with your mobile phone? ☐ Every day
☐ 4-6 days a week ☐ 2-3 days a week ☐ Once a week ☐ Once a month
4. For how long are you a customer of your mobile phone provider? ☐ Less than 1 year
