A process model for identifying online customer engagement patterns on Facebook brand pages

Vidyasagar Potdar (School of Information Systems, Curtin University, Perth, Australia)
Sujata Joshi (Symbiosis Institute of Telecom Management, Symbiosis International University, Pune, India)
Rahul Harish (School of Information Systems, Curtin University, Perth, Australia)
Richard Baskerville (Department of Computer Information Systems, Georgia State University, Atlanta, Georgia, USA) (School of Information Systems, Curtin University, Perth, Australia)
Pornpit Wongthongtham (School of Information Systems, Curtin University, Perth, Australia)

Information Technology & People

ISSN: 0959-3845

Publication date: 3 April 2018



The purpose of this paper is to develop and empirically test a process model (comprising of seven dimensions), for identifying online customer engagement patterns leading to recommendation. These seven dimensions are communication, interaction, experience, satisfaction, continued involvement, bonding, and recommendation.


The authors used a non-participant form of netnography for analyzing 849 comments from Australian banks Facebook pages. High levels of inter-coder reliability strengthen the study’s empirical validity and ensure minimum researcher bias and maximum reliability and replicability.


The authors identified 22 unique pattern of customer engagement, out of which nine patterns resulted in recommendation/advocacy. Engagement pattern communication-interaction-recommendation was the fastest route to recommendation, observed in nine instances (or 2 percent). In comparison, C-I-E-S-CI-B-R was the longest route to recommendation observed in ninety-six instances (or 18 percent). Of the eight patterns that resulted in recommendation, five patterns (or 62.5 percent) showed bonding happening before recommendation.

Research limitations/implications

The authors limited the data collection to Facebook pages of major banks in Australia. The authors did not assess customer demography and did not share the findings with the banks.

Practical implications

The findings will guide e-marketers on how to best engage with customers to enhance brand loyalty and continuously be in touch with their clients.


Most models are conceptual and assume that customers typically journey through all the stages in the model. The work is interesting because the empirical study found that customers travel in multiple different ways through this process. It is significant because it changes the way the authors understand patterns of online customer engagement.



Potdar, V., Joshi, S., Harish, R., Baskerville, R. and Wongthongtham, P. (2018), "A process model for identifying online customer engagement patterns on Facebook brand pages", Information Technology & People, Vol. 31 No. 2, pp. 595-614. https://doi.org/10.1108/ITP-02-2017-0035

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Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited

1. Introduction

In the present competitive world, the need for delivering value and providing better services has led the organizations to strive for measures to engage its customers even more than before. Various organizations emphasize customer engagement as one of the critical techniques for better customer relationship management. Hence brands regularly engage with customers on Facebook and other social media platforms to expand their loyal customer base. Marketing literature recognizes customer engagement as a relevant area of study (Brodie et al., 2011; Gambetti and Graffigna, 2010; Sawhney et al., 2005; Van Doorn et al., 2010; Vivek et al., 2012) and practitioners also support it (Sashi, 2012). Several models of online customer engagement are proposed in the literature comprising of different engagement dimensions (Bowden, 2009; Court et al., 2009; Van Doorn et al., 2010). Some of these dimensions include communication, awareness, acquisition, conversion, retention, interaction, satisfaction, bonding, recommendation, etc. Such models aim to transform a customer (through an engagement process) to become a brand advocate, i.e. customers start to recommend products/services they have used. However, customers may not experience all these dimensions when they interact with the brands on a social platform such as Facebook. Hence, understanding and identifying the most common patterns of engagement that lead to recommendation/advocacy becomes a significant research problem. Further, the process, through which the customers go to become a brand advocate is also an under investigated area of research. We address these two fundamental problems in this paper.

For this purpose, we studied different customer engagement models/cycles and various dimensions used in these models to achieve online customer engagement. We identified and categorized seven online customer engagement dimensions in our process model, based on the literature review. These dimensions are communication, interaction, experience, satisfaction, continued involvement, bonding, and recommendation. In this paper, we propose a new independent dimension, experience. Customer experience, although cited in many studies was never included as a dedicated independent dimension to assess customer engagement. We later provide the reasoning and justification for the introduction of this dimension. In the remaining sections that follow, we cover the relevant literature that motivates our study. We then describe the proposed conceptual process model. We continue with our data analysis and then present our findings and conclude our paper with a discussion of the study’s broader implications.

2. Literature review and theoretical background

The literature described in this section covers three important aspects that relate to the proposed research. The first section justifies having “experience” as an independent dimension to explain the process of online customer engagement. The second section provides reasons to investigate customer engagement process from a multidimensional perspective. Finally, the third section outlines the key dimensions covered in the existing customer engagement models proposed in the literature.

2.1 Importance of the experience dimension as a part of online customer engagement process model

Experience, i.e. customer experience, in the context of this paper, is proposed as an important dimension to understand and explain online customer engagement. In this study, we propose experience as a separate dimension in the customer engagement process journey. There are two reasons for this selection. First, we found that experience was not considered as an independent dimension in any of the proposed customer engagement process models although it appeared 59 times in the literature that we studied. Second, the current social media explosion has resulted in viral effect wherein people are readily sharing both good as well as bad experiences freely on social media. Such information sharing has raised the customer’s expectation of services, and hence quality customer service has become imperative. Hence, the next logical step in the customer service evolution is customer engagement, and customer experience is a critical element in this equation. “Experience” is different from “interaction” because “experience” is creating a perception in customer’s mind which involves emotions, feelings, and attitudes of a user concerning a particular product or service. Whereas interaction is a process of mutual reciprocation that may not necessarily involve emotion, feelings, or attitudes. We provide further clarification of this concept in Section 3.3 on experience.

Meyer and Schwager (2007, p. 118) define customer experience as “the internal and subjective response customers have to any direct or indirect contact with a company.” Zomerdijk and Voss (2009) state that customer experience is significantly important in experience-centric service industries, where businesses proactively craft unique customer experiences to distinguish their offerings from their competitors. Carbone and Haeckel (1994) suggest that customers receive an experience whenever they purchase a product or service. This experience could be positive, negative or neutral. Such experiences give an opportunity for companies to start emotional engagement with its customer, no matter how simple the product or service is (Berry and Carbone, 2007; Carbone and Haeckel, 1994; Voss and Zomerdijk, 2007). According to the service dominance logic, customer experience is co-creation using various customer interactions with different service elements (Vargo and Lusch, 2004, 2008). In order to get the desired service experience, the service elements must be assembled coherently along the customer’s service journey (Berry et al., 2002).

Customers perceive experiences differently based on their understanding, their educational, and their cultural background. Blythe (1997) agrees and states that customers rely on their past good experiences to analyze future purchase decisions. Purcărea et al. (2008, p. 203) described customer experience management as “as a coordinated effort to accomplish specific goals by improving the quality and consistency of customer interactions – or touch points.” According to Vivek et al. (2012), the benefits that accrue to a customer by experiencing a product and service encompasses their perception of how they benefitted from using as well as experiencing the service (i.e. how best their needs and requirements were satisfied).

According to Live Person (2013) survey, online shoppers are becoming more and more digital savvy, and along-with speed and simplicity, they expect personalized experiences and help in their online purchase journey. As per Retail Touchpoints (2011) survey on customer experience, a majority of the retailers considered social media as the priority for delivering enhanced shopping experience and gaining engagement insights. This survey further reiterates that customer experience plays a vital role on how customers spend, recommend the brand, and remain loyal. The literature discussed above shows that positive customer experiences will result in enhanced levels of customer engagement and hence a new dimension, “experience”, is introduced in the proposed online customer engagement process model.

2.2 Customer engagement: a multidimensional perspective

Customer engagement is defined as a state of activation of individuals that may be cognitive, behavioral or emotional (Kahn, 1990; Schaufeli et al., 2002). Literature has conceptualized customer engagement from four perspectives. The psychological perspective deals with cognitive and affective activities related to customer engagement (Bowden, 2009; Brodie et al., 2011; Higgins and Scholer, 2009). The behavioral perspective deals with behavioral aspects of customer engagement and customer activities (Ahuja and Medury, 2010; Gummerus et al., 2012; Javornik and Mandelli, 2012; Ojiako et al., 2012; Sashi, 2012; Verhoef et al., 2010). The social perspective deals with social and network component of customer engagement (Fliess et al., 2012; Gambetti et al., 2012; Gummesson, 2002). The multidimensional perspective unifies various dimensions of customer engagement (Brodie et al., 2011; Gambetti and Graffigna, 2010; Mollen and Wilson, 2010; Sashi, 2012; Vivek et al., 2012). There is an increasing trend of using a combination of psychological and behavioral approaches (Brodie et al., 2011; Gambetti et al., 2012; Pagani and Mirabello, 2011). However, the multidimensional approach has rarely been adopted in the studies (Brodie et al., 2011). Our study takes a multidimensional approach to assimilate critical dimensions under one umbrella to propose a comprehensive process model for understanding online customer engagement on social media platforms.

2.3 Customer engagement models/cycles

In this section, we describe the existing research related to customer engagement models/cycles. Table I showcases various studies on customer engagement and the dimensions or antecedents which have been used to conceptualize a customer engagement. Considering the classification of engagement dimensions as proposed by Brodie et al. (2011), we observed that the each study has focused on a different set of dimensions for the conceptualization of customer engagement. Conceptualization of customer engagement with simplified or one sided dimensions are questionable, and there is a stress on viewing the phenomena from a more complex viewpoint (Gambetti et al. 2012). This study attempts to reduce the gap in the literature by consolidating the various dimensions appearing in literature into seven dimensions. In this paper, the process of customer engagement has been conceptualized as an end-to-end process right from the start: from the time when the customer becomes aware of an offering through social media platform until the end when the customer becomes an advocate for the company.

Patterson et al. (2006), have described customer engagement as a construct which has four components namely vigor, dedication, absorption and interaction. Haven et al. (2007), defined customer engagement as 4Is – involvement, interaction, intimacy, and influence. Similarly, Court et al. (2009), in their model introduced the concept of customer decision journey for how customers engage. It consists of activities such as initial consideration, active evaluation, purchase, ongoing exposure, and loyalty.

Bowden (2009) did a comparative study of the customer engagement process and showed its difference from the concepts of loyalty and commitment. According to this model, the new satisfied customers reach a state of calculative commitment, whereas the repeat customers with increasing levels of involvement reach a stage of affective commitment, which eventually leads to loyalty. Van Doorn et al. (2010), proposed five customer engagement dimensions, namely customer resources, outcomes (that a customer gets), impact (positive or negative, depending on the overall experience), purpose (or motive of a customer behind engaging with the brand) and the situation (that can vary e.g. complaints).

Brodie et al. (2011), suggests measuring customer brand relationship using dimensions such as involvement, satisfaction, commitment, and trust. They recommended involvement as a necessary predecessor of customer engagement. On the other hand, customer satisfaction, commitment, and trust were the attitudinal predecessors. The study also elaborated social media platform dimensions namely involvement, participation, ease of use and telepresence. Hollebeek (2011, p. 790) defined customer engagement as “the level of an individual customer’s motivational, brand-related and context-dependent state of mind characterized by specific levels of cognitive, emotional and behavioral activity in direct brand interactions.”

Zailskaite-Jakste and Kuvykaite (2012) associated customer engagement stages on social media platforms and brand equity elements. The stages included watching, sharing, commenting, producing and curating. The watching stage results in brand awareness. At the sharing stage, customers share relevant information. Expressing opinions, evaluating brands and services are part of the commenting stage. At producing stage, companies try to engage customers by providing discussion platforms. In curating stage customers themselves create content within the communities.

Using main parameters of customer engagement, Sashi (2012) proposed a customer engagement cycle. The key stages considered in the cycle were connection, interaction, satisfaction, retention, commitment, advocacy, and engagement. Connection is a way by which a company or a customer gets connected. Interaction facilitates customer’s participation in the value addition process on social media platforms. Satisfaction was defined as a necessary step for the continual interaction with the customers, ultimately leading to customer delight. Retention is an enduring relationship between customers and the company. Advocacy concerning social media platform is spreading positive customer experiences and supporting a brand. Engagement is the result, which is the outcome of customer delight, trust, commitment, loyalty, and bonding.

Malciute and Chrysochou (2013) developed a customer brand engagement model by combining dimensions given by Brodie et al. (2011) and mapping them with engagement parameters, i.e. behavioral, emotional, cognitive components suggested by Cheung et al. (2011). The findings of the model indicated that behavioral, cognitive and emotional engagement direct toward creating brand loyalty and spreading word of mouth (WoM). Vivek et al. (2012) proposed a customer engagement model that included participation and involvement as the antecedents for customer engagement that resulted in the following consequences: value, trust, affective commitment, WoM, loyalty, and brand community involvement.

2.4 Conclusions from the literature review

Based on the literature review on traditional customer engagement and online customer engagement, following observations were made.

First, customer engagement through social media is indeed a novel research area, and there is a need to design, develop and empirically test new models that increase our understanding of online customer engagement behavior.

Second, as stated in the literature review of Brodie et al. (2011), the multidimensional approach has rarely been adopted in customer engagement studies. Hence, this paper attempts to reduce this gap in the literature by studying customer engagement from a multidimensional perspective. We propose a comprehensive process model for customer engagement, which accommodates all four perspectives, i.e. psychological, cognitive, behavioral and social. By doing this, our study adds value to the marketing theory by consolidating various repetitive and similar dimensions discussed in literature into seven dimensions for a thorough and comprehensive representation of online customer engagement process. We propose that these seven dimensions help in understanding the role of online social media for achieving customer engagement.

Third, “experience” as a dimension has been assumed in several studies but not modeled. To reduce this gap in the literature, we propose “experience” as a new dimension in the customer engagement process model. Experience has been cited 59 times but was never included as a dedicated and independent dimension to assess online customer engagement.

Lastly, there are several conceptual studies in the literature without any empirical testing. In our study, we develop and empirically test an online customer engagement process model to understand patterns of customer engagement. Section 3 below explains the proposed conceptual process model for online customer engagement.

3. Conceptual process model of online customer engagement

The proposed conceptual process model (Figure 1) comprises seven dimensions, which are communication, interaction, experience, satisfaction, continued involvement, bonding and recommendation.

The company communicates to the customer through social media, which is followed by interaction between customer and company via comments, likes, and posts. Positive interactions can lead to positive customer experiences. Positive experiences will lead to customer satisfaction, which in turn will result in continued involvement. Continued involvement will lead to emotional bonding, which, finally, will result in the customer not only recommending the company to others but also becoming an advocate for the company. We propose “experience” as a new customer engagement dimension, which has not been included as an independent dimension in any customer engagement process model before.

The section below discusses each of these dimensions and develops propositions that summarize their role in online customer engagement process.

3.1 Communication

Communication on social media platforms is an attempt to connect users with the brand by creating awareness and increasing visibility of the brand. Based on our investigation of the literature we understand that the first touch-point between the customer and the company happens when there is communication on the company’s Facebook page (Court et al., 2009; Sashi, 2012; The Insights Groups Ltd, 2014). This communication can be from the company to the customer (or vice versa), e.g. a bank posts a new mortgage offer for the current month on their Facebook page. With this communication medium, the customer becomes aware of the various offerings of the company, and the cognitive/thinking process starts with the communication received, wherein the customer takes into consideration the various offerings, then evaluates the offerings, gets influenced toward company offerings, etc. Hence, communication connects users with the brand by creating awareness and increasing visibility of the brand. Promotional campaigns, showcasing the brand and promoting offers to the users are the part of communication wherein the company provides knowledge about the company offerings and builds brand image and credibility. It leads to the following proposition:


Ads and posts facilitate communication on social media platforms. Communication creates awareness of the various offerings of the company. It starts the cognitive/thinking process about the communication made via the page, and helps customers consider and evaluate offerings. Communication also provides an opportunity for the company to motivate and influence customer’s to create positive referrals.

3.2 Interaction

The next stage is interaction. Interaction is a process of mutual reciprocation or action between the parties involved. It is different from communication. Communication is an act of transmission of ideas, information, feelings, etc. whereas interaction results into some action; it could be two or more events or objects acting upon each other to produce a new effect. In social media, interaction is a conversation between the users and the company which provides users a platform to share their views and opinions. As a part of the interaction, the company can respond to users’ queries, thus helping to develop the interest of users toward the brand. Here, the customer starts a conversation with the company, through questions, likes, queries, etc. Such conversation helps the customers to participate, consider and evaluate various offerings of the company. At this stage companies can influence, motivate, connect and acquire a customer by stimulating their affective component. The growth of online interaction has increased the expectations of customers to be more involved in the consumption process. Interaction encourages customers to contribute to the value addition activities and jointly work with companies for value creation and value extraction (Mollen and Wilson, 2010; Prahalad and Ramaswamy, 2004; Sashi, 2012). Social media helps companies to interact with their users to understand their needs and problems. In the case of positive feedback, companies can praise their customers and thereby create a connection with them. While approaching negative comments, companies should try to address the problems humbly without being defensive but with an aim to resolve the issue. It leads to the following proposition:


Likes, comments and personal messaging facilitate interaction on social media platforms. It helps customers to participate, evaluate, converse and connect with the company. Companies can use this stage to influence, motivate, connect and acquire a customer and stimulate the affective component at this stage.

3.3 Experience

The next stage is experience, which is the process of creating perception within the minds of the customers after they have interacted with the brand. Customers experience the brand at this stage. Experience is different from interaction. Experience, in this paper’s context, is used from customer experience perspective. It is subjective because it is about individual’s perception and thought. For example, in interaction, a person may have some generic queries and tries to get them resolved. Here, they are not emotionally involved as it may be a very routine or generic query. Experience, on the other hand, can evoke emotions depending upon whether it was positive or negative. The senses stimulated and emotions evoked in customer during their encounter with the brand and evaluating them against the expectations of the customer is experience (Shaw and Ivens, 2002). Gilmore and Pine II (2002) and Smith (2013), mentioned five dimensions to measure experience: sensorial, emotional, cognitive, behavioral, and social. Homburg et al. (2009) and Meyer and Schwager (2007) described experience as cognitive and affective evaluation of experience achieved through interaction of the customer with the brand or its product. Depending on the interaction, experience may be positive or negative. Quick action and timely response to negative comments and empathetic attitude to customer complaints create real influence on the customer’s mind. Thus, this helps the companies to gain favorable perceptions of their brand and enhances customer experience. A positive experience makes the users feel like the page is exclusively for them. At this stage, customer evaluates their experience, and the company can try to further influence customer by way of providing enhanced experience, thus creating customer intimacy. Positive experiences will result in customer loyalty and customers referring the company to others. Thus in this stage, the affective component can be further activated to create or initiate bonding with the customer. It can help to create brand perception and positive influence on their current and potential customers. It leads to the following proposition:


Positive experiences through social media can be achieved through the post, events, polls, and innovation. A customer’s evaluation of experiences will activate the affective component, resulting in positive influence, referrals customer intimacy, and customer loyalty.

3.4 Satisfaction

Satisfaction is an outcome based process of evaluation; that is the resultant of experiences about the offerings. A customer is said to be satisfied if the focal brand’s performance is confirmed to be as per the customer’s expectation (Flint et al., 1997). Sashi (2012) mentioned that if interaction leads customer toward satisfaction, then this interaction would continue and lead to engagement. Companies creating positive experience will make customer highly satisfied and vice-versa. Satisfaction is the evaluation based on the customers’ experience. Social media can increase satisfaction by providing ongoing positive experiences. Overall satisfaction can lead to customer retention over a period, where satisfaction occurs due to repurchases and long-standing buyer-seller relationship, thus enhancing customer loyalty. A satisfied customer will continue to engage with the company through social media platform, which can ultimately lead to emotional bonding or customer loyalty with the company. Thus, satisfaction can be suggested as psychological evaluation of the brand by the customer, resulting in emotional attachment and stickiness of customer with the brand. It leads to the following proposition:


Social media platform facilitate customer satisfaction by offering positive experiences via personalized comments, personalized messages and timely replies. It will activate the affective component, help customer evaluate their experiences and will lead to customer loyalty, intimacy, retention, repurchase and continued involvement with the company.

3.5 Continued involvement

Continued involvement is a process of retaining the interest of the customers so that they remain engaged or involved with the organization. Businesses can use social media to retain existing customers and continue to involve them. Customers having positive experiences with a brand on social media tend to involve with the brand more than those having negative experiences (Smith, 2013). Continued involvement with the brand is a psychological commitment about customer’s thoughts, feelings and subsequent behaviors (Gordon et al., 1998). It increases the stickiness and loyalty of customer toward the brand. Such high involvement of customer with the brand increases the brand trust, leading to increases in customer commitment (Bowden, 2009). Through social media, companies continuously monitor brand conversations, which help them to understand their customers’ needs, problems and clarify brand related queries and please them by frequently communicating appropriate offerings which suit their needs. Frequent positive experiences can help to keep users involved with the brand. Continued involvement can be measured by how long users remain involved with a brand via social media. Retaining users by continually interacting and keeping them occupied is continued involvement. It leads to the following proposition:


Continued involvement through social media can be achieved through quizzes, polls, and events and will help create an emotional bonding and intimacy with the company, resulting in the repurchase, customer commitment, customer loyalty and customer retention. Involved customers will be more likely to influence others and create positive referrals for the company offerings.

3.6 Bonding

Next stage in this model is bonding, which is a process of establishing a relationship through frequent and continuous association. Customer satisfaction, trust, and commitment lead to bonding between customers and brands. Bonding is a connection between committed customers and the brand, which leads to powerful branding. Strong bonding delivers the best chances for brands to create loyal customers. Positive experiences generated by continued involvement with the customers help to gain their trust and commitment, which can turn into loyalty by the passage of time. Delighted and loyal customer leads to customer commitment (i.e. calculative and affective commitment) and develops a strong emotional bond between the customer and the brand (Sashi, 2012). Commitment is a relationship between affective and calculative commitment that develops an inner sense of obligation in a customer toward a certain brand (Gustafsson et al., 2005). Affective commitment results from trust and is an emotional bond created due to delighted experience, while calculative commitment leads to brand loyalty (Sashi, 2012). As a result, customer develops biased behavior and fondness toward a certain brand over others, which is a function of mental and emotional processes over the time (Eskafi et al., 2013). It leads to the following proposition:


Effective bonding through social media can be achieved through personalized comments and personalized messages, which will help the company to remain connected with the customer, leading to enhanced trust, commitment, intimacy, satisfaction, customer loyalty and increased referrals.

3.7 Recommendation

The final stage in this model is recommendation which is an act of suggesting or advising that something is suitable for a particular purpose. For social media platforms, this refers to a customer recommending brand offerings and brand experiences to others. By now, the customer has already evaluated the offerings based on experiences, and if the customer is satisfied, it will lead to positive referrals for the company. The behavioral component is activated at this stage when the customer becomes an advocate for the company and refers others to use the company’s offerings. Brands can leverage social media to increase advocacy by impacting emotionally with the content on social media. Smith (2013) mentions that customers who are emotionally attached to the brand and have positive emotional experience because of cognitive attachment are more likely to recommend that brand. Thus, recommendation is an advocacy by the delighted users who promote the brand and spread a positive word-of-mouth on social media. It leads to the following proposition:


Recommendation with the help of social media can be achieved through comments, shares and likes that may result in activating the behavioral component wherein the customer, after evaluation of experiences, becomes an advocate of the company and refers others to use the company offerings.

4. Research methodology

4.1 Netnography research method

We used a non-participant form of netnography. Netnography is designed to study communities and cultures in a computer or virtual communications environment. In marketing field, it opens the door to analyzing a customer natural behavior patterns by studying information that is available publicly from online communities. Using this information, the researcher can better understand and recognize those decisions and requirements that influence customers in these online communities. Traditional netnography comprises of the following five research steps, namely: making cultural entrée, data collection and analysis, providing trustworthy interpretation of the data, conducting ethical research, and providing feedback to the social community. Non-participant form of netnography skips the first and the last steps. Kozinets (2002, p. 63), states that “because it is both naturalistic and unobtrusive - a unique combination not found in any other marketing research method-netnography allows continuing access to informants in a particular online social situation.” There are some advantages of netnography over traditional ethnography marketing research techniques. First, it requires lesser time in comparison and providing a more in depth analysis while also being very cost effective. Second, it provides or allows the research to be conducted in an entirely unobtrusive manner because the researcher does not define observations of customers for netnography.

In this research, the first step essentially relates to identifying the social community that is best suitable for answering the research questions, which in this case are Facebook pages of Australian banks. The second step is to gather data from Facebook either manually or automatically. Here we manually downloaded Facebook posts and comments. Data screening was done to ensure that only relevant data was downloaded to avoid information overload. Seven dimensions of the online customer engagement process model were used to analyze the data and record the observations. The third step is to provide trustworthy interpretation of data. In this study, the first author coded all the data as per the coding table (Table II), the second author selected a sub-sample of the data and coded it again. Pairwise agreement and inter-coder reliability were computed to ensure trustworthy interpretation of data. The fourth step is to conduct ethical research. We adhered to the ethical research guidelines during this research. The fifth step, however, was not incorporated in this research since formal feedback was not provided to the members as we adopted a non-participant form of Netnography.

4.2 Inter-coder reliability for content analysis

Inter-coder-reliability (or inter-coder-agreement) is the term used to explain how reliable the coding is when a message is coded by two or more independent coders (Lombard et al., 2004). Reliability, which specifies the trustworthiness of data interpretation, is specified by considerable agreement of results (Krippendorff, 2004; Lombard et al., 2004; Mouter and Vonk Noordegraaf, 2012). According to Mouter and Vonk Noordegraaf (2012) inter-coder reliability comprises of the following five main steps, namely: determine the scope of the inter-coder-reliability check, draft the protocol, determine the same that is tested, execute the test, select the reliability coefficient, and calculate the coefficient, and assess the results and draw conclusions. The inter-coder reliability check consists of coding and comparing the findings of the coders. To judge the reliability of the coding, at least two different researchers must code the same body of content. According to De Swert (2015), there does not seem to be a consensus standard on the best way to do and report inter-coder reliability. Mathematical measures that are commonly used to report on inter-coder reliability are as follows: percentage agreement, Scott’s pi, Holsti’s method, Cohen’s kappa (Hughes and Garrett, 1990; Krippendorff, 2004; Lombard et al., 2004; Riff et al., 2014; Tinsley and Weiss, 2000).

4.3 Facebook data reliability and validity using inter-coder reliability check

We collected data from Facebook pages of five Australian banks. These five banks collectively occupy more than 80 percent market share in Australia, which represents a sizeable proportion of customers (“Monthly Banking Statistics”, 2017). Customers are quite engaged on these banks Facebook pages when interacting with the bank.

We gathered data from the Facebook pages of these banks over a period of two months. Banks posted different types of posts (e.g. service alert, interest rate announcement, etc.) in different categories to engage with their customers during this time. We selected 12 posts from each bank’s Facebook page because each of these banks made at least 12 posts over a two month period that had more than ten comments each. In total, 60 posts were downloaded with 849 comments in total. We qualitatively analyzed each comment in Microsoft Excel by coding one or more engagement dimensions to derive patterns of customer engagement. We explain the codes in Table II.

As mentioned in section 4.1, data screening was done to ensure that only relevant data were downloaded, which meant, some posts that generated limited activity (e.g. less than ten comments) were excluded from the study. The main reason behind this decision relates to the main objective of this paper, which is to identify the engagement patterns observed in the data set. If the gathered data did not generate any activity, it would not be helpful in understanding the engagement patterns. Hence, it was a conscious decision to be selective with the number of posts and type of posts, to ensure uniformity in data collection.

The first author, first, coded the downloaded comments and the second author coded a subset. We then calculated reliability based on the proportion of the total pairwise comparisons between the coders. The advantage of this method is that it is easy to understand. The overall inter-coder agreement rate ranged from 92.38 to 100.00 percent, with an average of 96.43 percent. These rates are well above the 0.70 which is the minimum recommendation. The resulting inter-code agreement suggests that the coding demonstrated a very high level of reliability and replicability of our coding.

4.4 Results

The coding of the 849 comments resulted in 22 different patterns of customer engagement. Table II shows the patterns along with percentage wise distribution. Figure 2 shows a visual representation of Table III, along with the, seven dimension of the customer engagement model in green color, and all the 22 branches each representing one pattern of customer engagement. To interpret this figure, one should, follow the arrow between two dimensions and notice the number and the percent value. For example, let us see the first pattern, i.e. communication-interaction (C-I). We observed this pattern 117 times (or 14 percent), as is shown above, the arrow that connects C-I. Similar we observed the pattern (C-I-E) on 31 occasions (or 4 percent). Based on our findings, we categorized these patterns and grouped customers to a particular type. In total, we propose six types of customers. They are the recommending, bonding, excited, satisfied, happy and interactive. We now describe them as follows.

4.4.1 Recommending customer

These groups of customers are highly engaged and recommend the product or services they use. Hence we call this type of customer as a “Recommending Customer.”

Observation 1

In our study, we observed nine patterns (nos 6, 8, 9, 11, 13, 17, 18, 20, 22 from Table II) which represented 265 comments (or 31 percent) that resulted in recommendation. Figure 2 shows the patterns that result in recommendation (i.e. end with R) in a light brown color.

Inference 1

This type of customer is fundamental from the brand’s perspective because brand managers want their customers to recommend their products or services. Our findings align with Bhattacharya et al. (1995) and Woisetschläger et al. (2008) research finding which has shown that brand engagement affects recommendation.

4.4.2 Bonding customer

These groups of customers are relatively less engaged than a recommending customer but show bonding toward the brand. Hence, we call this type of customer as a “Bonding Customer.”

Observation 2

In our study, we observed seven patterns (nos 5, 7, 12, 15, 16, 19, 21) which represented 331 comments (or 39 percent) that resulted in bonding.

Observation 3

In our study, 39 percent patterns showed bonding, and 31 percent showed recommendation. Together they form 70 percent (i.e. 16 patterns), which is an excellent outcome for a brands social media campaign.

Observation 4

In our study, we also observed six patterns (nos 6, 8, 11, 13, 17, 20) in which bonding happened prior to recommendation. This represented 27 percent of the patterns.

Inference 2

This type of customer is also significant from the brand’s perspective because they are just one step away from recommending. Hence brand managers should focus on these customers with an aim to convert them to becoming a recommending customer. Our findings align with the conclusions reported in the literature (Jahn and Kunz, 2012; Kim et al., 2008; Woisetschläger et al., 2008). Jahn and Kunz (2012) reported an increase in bonding due to customer engagement. In other words, engagement positively influences the bonding. Kim et al. (2008) also showed that engagement is a driver for bonding.

4.4.3 Excited customer

This group of customers demonstrates continued involvement with the brands Facebook page. Hence, we call this type of customer as an “Excited Customer” because the customer is excited and willing to be continually involved with the brand.

Observation 4

In our study, we only observed two patterns (nos 4 and 10) that end at the continued involvement stage, and they represent only nine comments (or 1 percent).

Observation 5

We also observed continued involvement as part of seven other patterns, which end either at bonding (nos 5 and 15) or recommendation (nos 6, 9, 11, 17, 18). These seven sub-patterns that show continued involvement are nos 5, 6, 9, 11, 15, 17, and 18. The respective number of comments for each of these patterns is 61, 152, 22, 6, 6, 22, and 11. In total, these comments add up to 280 (or 32.9 percent).

Inference 3

What we can empirically infer from these observations is that on most occasions continued involvement either results in bonding or recommendation or both. Hence brand managers should ensure that they continually involve with their customers to increase the number of customers who then become bonding type or recommending type customers. Our findings align with Algesheimer et al. (2005), and Woisetschläger (2008), results of research, which showed an effect of involvement on continuance intentions leading to bonding intentions.

4.4.4 Satisfied customer

This group of customers exhibits satisfaction with their brand engagement. Hence, we call this type of customer as a “Satisfied Customer.”

Observation 6

In our study, we only observed two patterns (nos 3 and 14) that end at the satisfaction stage, and they represent 96 comments (or 11.3 percent).

Observation 7

We also observed Satisfaction as part of eight other patterns, which end either at continued involvement (no. 4), bonding (nos 5, 7, 15, 16) or recommendation (nos 6, 8, 9). In total the respective comments add up to 372 (or 43.8 percent).

Inference 4

What we can empirically infer from these observations is that on 35 percent occasion’s satisfaction resulted in continued involvement, which then led to bonding or recommendation. Our findings align with results of research from Jahn and Kunz (2012), which states that satisfaction leads to bonding and Woisetschläger (2008), who showed that satisfaction leads to recommendation.

Inference 5

On 12 percent occasions, satisfaction leads to bonding.

Inference 6

On 3 percent occasions, satisfaction leads to recommendation.

Hence brand managers should ensure that their customers should be satisfied as it paves the way for continuous involvement, which then progresses toward bonding and recommendation.

4.4.5 Happy customer

This group of customers exhibits happiness with their brand engagement. Hence, we call this type of customer as a “Happy Customer.”

Observation 8

In our study, we only observed one pattern (no. 2) that end at the experience stage, and they represent 31 comments (or 3.65 percent).

Observation 9

We also observed experience as part of eleven other patterns, which end either at satisfaction (no. 3), continued involvement (nos 4, 10), bonding (nos 5, 7, 12) or recommendation (nos 6, 8, 9, 11, 13). In total, the respective comments add up to 487 (or 57.3 percent).

Inference 7

What we can empirically infer from these observations is that on 51 percent occasions experience resulted in satisfaction which then led to continued involvement (1 percent) and bonding (5 percent). This inference aligns with the disconfirmation theory of customer satisfaction which indicates that customers evaluate the service experiences with some set standards of their own. They compare the pre/post service experience which then results in satisfaction or dissatisfaction. The inference also supports the studies of Liljander and Strandvik (1997) and Otto and Ritchie (1996), which infer that experience influences customer satisfaction. Our inference that customer satisfaction leads to continued involvement also aligns with the studies done by (Bhattacherjee, 2001; Hsu et al., 2015; Zhao and Lu, 2012), which infer that there is a positive link between customer satisfaction and continued usage of information systems/social media. Lastly, our inference that satisfaction then leads to bonding align with the studies by (Abubakar et al., 2014; Claycomb and Martin, 2001; Geddie et al., 2005; Narteh et al., 2013; Sin et al., 2005), which infers that there is a significant relationship between satisfaction and bonding. Hence brand managers should ensure that their customers should have a good experience as it paves the way to satisfaction and then to continuous involvement, bonding, and recommendation.

4.4.6 Interactive customer

This group of customers interacts with their preferred brand. Hence, we call this type of customer as an “Interactive Customer”.

Observation 10

In our study, we only observed one pattern (no. 1) that end at the interaction stage, and they represent 117 comments (or 13.78 percent).

Observation 11

We also observed interaction as part of all other patterns except pattern no. 21, leading to experience, satisfaction, continued involvement, bonding or recommendation.

Inference 8

What we can empirically infer from these observations is that Interaction shows customers intention to engage and tapping into customers’ needs at this stage lays the foundation for ensuring a thoroughly engaged customer moving through all the stages of engagement. Our findings align with Jahn and Kunz (2012), research finding which reported that higher interaction leads to higher engagement. Hence brand managers should ensure that when their customers began interacting with them, they should offer equally interactive response and attempt to provide the customer with an excellent interaction experience. This action paves the way to satisfaction and then to continuous involvement, bonding, and recommendation.

5. Discussion and conclusion

5.1 Research implications

This study contributes to existing research on customer engagement and brand engagement literature in the following ways.

First, the modeling “experience” is an original contribution of this research. Existing literature only referred to “experience” in customer engagement models. Such models did not include “experience” as an independent dimension, and it was not empirically validated. In the proposed online customer engagement process model, “experience” is modeled after “interaction” stage and empirical testing is reported. Figure 2 and Table III show 12 engagement patterns that include “experience,” of which five patterns (nos 6, 8, 9, 11, 13) resulted in “recommendation” and seven patterns (nos 5, 6, 7, 8, 11, 12, 13) resulted in “bonding.” This shows that “experience’ plays a fundamental role in engaging customers leading to bonding and recommendation.

Second, the current study makes a theoretical contribution to improving our understanding of the customer engagement process. It develops and empirically tests a process model to explain the routes that customers take to become an advocate. In the literature, we find several customer engagement models proposed by various researchers, however, many of them have not provided significant empirical validation (Sashi, 2012; Van Doorn et al., 2010; Verhoef et al., 2010). In this research, several online customer engagement dimensions proposed in the literature were thoroughly examined to develop a process model to explain customer’s engagement journey. The evaluation of these dimensions resulted in the identification of seven dimensions as part of the process model. We empirically tested and validated this process model. Specifically, this study went beyond conceptualization and applied social exchange theory and the relationship marketing literature to empirically examine the phenomenon, which enriches the existing literature on online customer engagement.

Third, the study identifies the routes taken by the customers in their online engagement journey, thereby empirically validating the stages proposed in the conceptual process model. It also extends the findings of Brodie et al. (2011) about customer engagement being a multidimensional concept. We represented this in our proposed conceptual model through the introduction of the seven dimensions of customer engagement, which also includes customer experience as the new proposed dimension. The customer experience dimension also offers an elaboration of the “Service Dominant Logic” and “the co-creation of value” as proposed and addressed by (2011) and Vargo and Lusch (2004, 2008).

5.2 Managerial implications

This customer engagement process model provides a thorough framework that will help the management to devise and measure an effective customer engagement strategy using social media platforms. It has implications for the managers from the below-mentioned perspectives.

5.2.1 Understand end to end customer engagement process

The seven dimensions provide a guideline to managers as to how social media can be used to engage the customer, what each dimension will result into and which social media tools can be used to measure the dimension. It can be used by managers to understand the end-to-end process of customer engagement through social media from communication to recommendation. It traces the development and progression of a customer from being unaware of service/brand to the time they become an advocate for the brand.

5.2.2 Multidimensional perspective for managers

It provides a multidimensional perspective to managers to effectively inculcate the cognitive, affective and behavioral components of customer behavior using the social media vehicles while formulating customer engagement strategy. Each dimension showcases how the cognitive/affective/behavioral component can be used by managers to motivate and influence customers to achieve results such as customer retention, loyalty, satisfaction, intimacy, repurchase, emotional connect, bonding, involvement, referrals, and advocacy.

5.2.3 Understanding type of customers

Companies can understand the kind of customers who are interacting on their Facebook pages and take timely action to transform them to become an advocate. For example, in this study, we observed 39 percent bonding customers. Companies can dedicate resources to transform these customers so that they can become brand advocates. Companies normally assign a set budget for social media activities and hence knowing which customers to engage with will provide higher economic returns. Hence selecting engaged customers (wittingly or unwittingly) provides more benefits to the companies.

5.2.4 Coding table

Coding table can assist managers to code the posts from their Facebook page to observe different types of engagement patterns.

In summary, we propose that it is necessary for academicians and practitioners to understand that the customer engagement process should be viewed by managers as not just an attempt to satisfy customers but to move beyond that and convert the satisfied customers into advocates who will themselves become brand ambassadors for the company.

5.3 Conclusion, limitations, and future research directions

This study proposes a conceptual process model for identifying online customer engagement patterns through social media tools. Seven customer engagement dimensions were identified and categorized from the literature into this proposed process model. This study is a first attempt at proposing “customer experience” as a new customer engagement dimension. We found customer experience cited in many studies but never included as a dedicated and independent dimension to assess customer engagement. In this study, we tested the online customer engagement process model by studying Facebook pages of five Australian banks by adopting the netnography research methodology.

One of the limitations of the study was the data collection was done from Facebook pages of five Australian banks hence the sample was restricted to the Australian banking sector. The second limitation was that we did not assess customer demography. Further research could look at a comparative analysis of customer engagement in Australian banks and those of banks in other countries. It would give more insight into customer engagement patterns based on the location, people, and culture of the particular country. Finally, we did not discuss our findings with the five banks.

Hence future research can be carried out for validating the research propositions mentioned in this study with the five banks. Further, we can use the proposed process model for assessing customer engagement patterns of similar service oriented industries such as telecom, hotels, education, etc. Additionally, we could incorporate trust as a new dimension in the process model. Kim and Park (2013) assessed the effects of trust on trust performance, particularly in purchase and WoM intentions for social commerce. Hence, future research will look into the impact of trust on customer engagement to further strengthen the proposed customer engagement process model.


Customer engagement process model

Figure 1

Customer engagement process model

Customer engagement patterns on Facebook brand pages of Australian banks

Figure 2

Customer engagement patterns on Facebook brand pages of Australian banks

Customer engagement dimensions from literature review

Author Antecedents/dimensions
Patterson et al. (2006) Vigor, dedication, absorption, and interaction
Haven et al. (2007) 4Is of involvement, interaction, intimacy and influence
Court et al. (2009) Initial consideration, active evaluation, purchase, ongoing exposure, and loyalty
Bowden (2009) Satisfaction, calculative commitment, affective commitment, involvement and trust
Van Doorn et al. (2010) Customer resources, outcomes, impact, purpose, situation
Brodie et al. (2011) Involvement, satisfaction, commitment, trust
Hollebeek (2011) Immersion (cognitive), passion (emotional), and activation (behavioral)
Zailskaite-Jakste and Kuvykaite (2012) Watching, sharing, commenting, producing and curating
Sashi (2012) Connection, interaction, satisfaction, retention, commitment, advocacy, engagement
Malciute and Chrysochou (2013) Behavioral, emotional, cognitive components
Vivek et al. (2012) Involvement, customer→antecedents
Value, trust, affective commitment, word of mouth, loyalty, and brand community involvement→potential consequences

Coding scheme

Coding family Description of codes
Communication Comments or posts started by the company or the brand are coded as communication. Any post or comment that starts a new discussion is considered as a communication and is coded accordingly
Interaction When a customer or a brand posts a reply to the communication or likes a communication, it is coded as being interaction because this action shows some level of interaction between the two parties
Experience Interactions can result in positive and negative experiences. When this is expressed by the customer in the posts or via like button, it is coded as experience
Satisfaction Positive experience may or may not result in satisfaction. If however the customer expresses satisfaction clearly in the comments or via like button, it is coded as satisfaction
Continued Involvement Experience and satisfaction create an environment where the customer willingly participates in continued conversations, when this is observed; it is coded as continued involvement
Bonding Continued positive experiences generated during continued involvement stage results in bonding. When customer expresses bonding either via comments or like button, it is coded as bonding
Recommendation A comment where the customer advocated a brand or recommends others to try the brand then it is coded as recommendation

Customer engagement patterns

No. Pattern Bank 1 Bank 2 Bank 3 Bank 4 Bank 5 Total %
 1 C-I 18 27 27 24 21 117 14
 2 C-I-E 6 9 3 6 7 31 4
 3 C-I-E-S 18 27 12 19 3 79 9
 4 C-I-E-S-CI 0 0 3 2 0 5 1
 5 C-I-E-S-CI-B 6 18 9 11 17 61 7
 6 C-I-E-S-CI-B-R 33 27 36 32 24 152 18
 7 C-I-E-S-B 0 33 24 19 15 91 11
 8 C-I-E-S-B-R 0 9 6 5 4 24 3
 9 C-I-E-S-CI-R 12 0 0 4 6 22 3
10 C-I-E-CI 0 0 3 1 0 4 0
11 C-I-E-CI-B-R 0 3 0 1 2 6 1
12 C-I-E-B 12 9 0 7 11 39 5
13 C-I-E-B-R 0 0 3 1 0 4 0
14 C-I-S 3 6 0 3 5 17 2
15 C-I-S-CI-B 3 0 0 1 2 6 1
16 C-I-S-B 6 0 0 2 3 11 1
17 C-I-CI-B-R 9 3 0 4 6 22 3
18 C-I-CI-R 3 3 0 2 3 11 1
19 C-I-B 18 33 15 22 26 114 13
20 C-I-B-R 9 0 3 4 5 21 2
21 C-B 9 0 0 0 0 9 1
22 C-I-R 3 0 0 0 0 3 0
Total 849 100


Abubakar, M.M., Mokhtar, S.S.M. and Abdullattef, A.O. (2014), “The role of long-term orientation and service recovery on the relationships between trust, bonding, customer satisfaction and customer loyalty: the case of Nigerian retail banks”, Asian Social Science, Vol. 10 No. 2, pp. 209-220.

Ahuja, V. and Medury, Y. (2010), “Corporate blogs as e-CRM tools – building consumer engagement through content management”, Journal of Database Marketing & Customer Strategy Management, Vol. 17 No. 2, pp. 91-105.

Algesheimer, R., Dholakia, U.M. and Herrmann, A. (2005), “The social influence of brand community: evidence from European car clubs”, Journal of Marketing, Vol. 69 No. 3, pp. 19-34.

Berry, L.L. and Carbone, L.P. (2007), “Build loyalty through experience management”, Quality Progress, Vol. 40 No. 9, pp. 26-32.

Berry, L.L., Carbone, L.P. and Haeckel, S.H. (2002), “Managing the total customer experience”, MIT Sloan Management Review, April 15, Vol. 43 No. 3, pp. 85-89.

Bhattacharya, C.B., Rao, H. and Glynn, M.A. (1995), “Understanding the bond of identification: an investigation of its correlates among art museum members”, Journal of Marketing, Vol. 59 No. 4, pp. 46-57.

Bhattacherjee, A. (2001), “Understanding information systems continuance: an expectation-confirmation model”, MIS Quarterly, Vol. 25 No. 3, pp. 351-370.

Blythe, J. (1997), The Essence of Consumer Behaviour, Prentice Hall, London.

Bowden, J.L.-H. (2009), “The process of customer engagement: a conceptual framework”, Journal of Marketing Theory and Practice, Vol. 17 No. 1, pp. 63-74.

Brodie, R.J., Hollebeek, L.D., Jurić, B. and Ilić, A. (2011), “Customer engagement: conceptual domain, fundamental propositions, and implications for research”, Journal of Service Research, Vol. 14 No. 3, pp. 252-271.

Carbone, L.P. and Haeckel, S.H. (1994), “Engineering customer experiences”, Marketing Management, Vol. 3 No. 3, pp. 8-19.

Cheung, C., Lee, M. and Jin, X. (2011), “Customer engagement in an online social platform: a conceptual model and scale development”, paper presented at the International Conference on Information Systems (ICIS), Shanghai, available at: http://aisel.aisnet.org/icis2011/; https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1225&context=icis2011 (accessed August 28, 2014).

Claycomb, C. and Martin, C.L. (2001), “Building customer relationships: an inventory of service providers’ objectives and practices”, Marketing Intelligence & Planning, Vol. 19 No. 6, pp. 385-399.

Court, D., Elzinga, D., Mulder, S. and Vetvik, O.J. (2009), “The consumer decision journey”, McKinsey Quarterly, January 6, available at: www.mckinsey.com/insights/marketing_sales/the_consumer_decision_journey (accessed August 28, 2014).

De Swert, K. (2015), “Calculating inter-coder reliability in media content analysis using Krippendorff’s Alpha”, available at: www.polcomm.org/wp-content/uploads/ICR01022012.pdf (accessed October 30, 2013).

Eskafi, M., Hossein Hosseini, S. and Mohammadzadeh Yazd, A. (2013), “The value of telecom subscribers and customer relationship management”, Business Process Management Journal, Vol. 19 No. 4, pp. 737-748.

Fliess, S., Nadzeika, A. and Nesper, J. (2012), “Understanding patterns of customer engagement-how companies can gain a surplus from a social phenomenon”, Journal of Marketing Development and Competitiveness, Vol. 6 No. 2, pp. 81-92.

Flint, D.J., Woodruff, R.B. and Gardial, S.F. (1997), “Customer value change in industrial marketing relationships: a call for new strategies and research”, Industrial Marketing Management, Vol. 26 No. 2, pp. 163-175.

Gambetti, R.C. and Graffigna, G. (2010), “The concept of engagement: a systematic analysis of the ongoing marketing debate”, International Journal of Market Research, Vol. 52 No. 6, pp. 801-826.

Gambetti, R.C., Graffigna, G. and Biraghi, S. (2012), “The grounded theory approach to consumer-brand engagement”, International Journal of Market Research, Vol. 54 No. 5, pp. 659-687.

Geddie, M.W., DeFranco, A.L. and Geddie, M.F. (2005), “A comparison of relationship marketing and guanxi: its implications for the hospitality industry”, International Journal of Contemporary Hospitality Management, Vol. 17 No. 7, pp. 614-632.

Gilmore, J.H. and Pine, B.J. II (2002), “Differentiating hospitality operations via experiences: why selling services is not enough”, The Cornell Hotel and Restaurant Administration Quarterly, Vol. 43 No. 3, pp. 87-96.

Gordon, M.E., McKeage, K. and Fox, M.A. (1998), “Relationship marketing effectiveness: the role of involvement”, Psychology and Marketing, Vol. 15 No. 5, pp. 443-459.

Gummerus, J., Liljander, V., Weman, E. and Pihlström, M. (2012), “Customer engagement in a Facebook brand community”, Management Research Review, Vol. 35 No. 9, pp. 857-877.

Gummesson, E. (2002), “Relationship marketing and a new economy: it’s time for de‐programming”, Journal of Services Marketing, Vol. 16 No. 7, pp. 585-589.

Gustafsson, A., Johnson, M.D. and Roos, I. (2005), “The effects of customer satisfaction, relationship commitment dimensions, and triggers on customer retention”, Journal of Marketing, Vol. 69 No. 4, pp. 210-218.

Haven, B., Bernoff, J. and Glass, S. (2007), “Marketing’s new key metric: engagement”, Forrester, August, available at: www.forrester.com/report/Marketings+New+Key+Metric+Engagement/-/E-RES42124 (accessed August 28, 2014).

Higgins, E.T. and Scholer, A.A. (2009), “Engaging the consumer: the science and art of the value creation process”, Journal of Consumer Psychology, Vol. 19 No. 2, pp. 100-114.

Hollebeek, L.D. (2011), “Demystifying customer brand engagement: exploring the loyalty nexus”, Journal of Marketing Management, Vol. 27 Nos 7/8, pp. 785-807.

Homburg, C., Wieseke, J. and Bornemann, T. (2009), “Implementing the marketing concept at the employee-customer interface: the role of customer need knowledge”, Journal of Marketing, Vol. 73 No. 4, pp. 64-81.

Hsu, M.-H., Chang, C.-M., Lin, H.-C. and Lin, Y.-W. (2015), “Determinants of continued use of social media: the perspectives of uses and gratifications theory and perceived interactivity”, Information Research, Vol. 20 No. 2 (accessed December 9, 2016).

Hughes, M.A. and Garrett, D.E. (1990), “Intercoder reliability estimation approaches in marketing: a generalizability theory framework for quantitative data”, Journal of Marketing Research, Vol. 27 No. 2, pp. 185-195.

Jahn, B. and Kunz, W. (2012), “How to transform consumers into fans of your brand”, Journal of Service Management, Vol. 23 No. 3, pp. 344-361.

Javornik, A. and Mandelli, A. (2012), “Behavioral perspectives of customer engagement: an exploratory study of customer engagement with three Swiss FMCG brands”, Journal of Database Marketing & Customer Strategy Management, Vol. 19 No. 4, pp. 300-310.

Kahn, W.A. (1990), “Psychological conditions of personal engagement and disengagement at work”, Academy of Management Journal, Vol. 33 No. 4, pp. 692-724.

Kim, J.W., Choi, J., Qualls, W. and Han, K. (2008), “It takes a marketplace community to raise brand commitment: the role of online communities”, Journal of Marketing Management, Vol. 24 Nos 3/4, pp. 409-431.

Kim, S. and Park, H. (2013), “Effects of various characteristics of social commerce (s-commerce) on consumers’ trust and trust performance”, International Journal of Information Management, Vol. 33 No. 2, pp. 318-332.

Kozinets, R.V. (2002), “The field behind the screen: using netnography for marketing research in online communities”, Journal of Marketing Research, Vol. 39 No. 1, pp. 61-72.

Krippendorff, K. (2004), Content Analysis: An Introduction to Its Methodology, Sage Publications, Beverly Hills, CA, available at: https://us.sagepub.com/en-us/nam/content-analysis/book234903

Liljander, V. and Strandvik, T. (1997), “Emotions in service satisfaction”, International Journal of Service Industry Management, Vol. 8 No. 2, pp. 148-169.

Live Person (2013), “4 tips to personalize digital engagement with actionable”, Data, available at: www.liveperson.com/connected-customer/posts/4-tips-personalize-digital-engagement-actionable-data (accessed July 7, 2017).

Lombard, M., Snyder-Duch, J. and Bracken, C.C. (2004), “Practical resources for assessing and reporting intercoder reliability in content analysis research projects”, available at: http://matthewlombard.com/reliability/index_print.html (accessed July 14, 2017).

Malciute, J. and Chrysochou, P. (2013), “Like it or not? customer brand engagement on online social media platforms”, paper presented at the 18th International Conference on Corporate and Marketing Communications (CMC 2013), Fisciano, April, available at: http://works.bepress.com/chrysochou/42/ (accessed September 4, 2014).

Meyer, C. and Schwager, A. (2007), “Understanding customer experience”, Harvard Business Review, Vol. 85 No. 2, pp. 116-126.

Mollen, A. and Wilson, H. (2010), “Engagement, telepresence and interactivity in online consumer experience: Reconciling scholastic and managerial perspectives”, Journal of Business Research, Vol. 63 No. 9, pp. 919-925.

Monthly Banking Statistics (2017), “Australian Prudential Regulation Authority (APRA)”, Monthly Banking Statistics, June, available at: www.apra.gov.au/adi/publications/pages/monthly-banking-statistics.aspx (accessed August 2, 2017).

Mouter, N. and Vonk Noordegraaf, D.M. (2012), “Intercoder reliability for qualitative research: You win some, but do you lose some as well?”, paper presented at the 12th Transport, Infrastructure and Logistics (TRAIL) Congress, Rotterdam, pp. 1-9.

Narteh, B., Agbemabiese, G.C., Kodua, P. and Braimah, M. (2013), “Relationship marketing and customer loyalty: evidence from the Ghanaian luxury hotel industry”, Journal of Hospitality Marketing & Management, Vol. 22 No. 4, pp. 407-436.

Ojiako, U., Chipulu, M. and Graesser, A. (2012), “Correlating service touch‐point preferences with engagement parameters”, Industrial Management & Data Systems, Vol. 112 No. 5, pp. 766-785.

Otto, J.E. and Ritchie, J.R.B. (1996), “The service experience in tourism”, Tourism Management, Vol. 17 No. 3, pp. 165-174.

Pagani, M. and Mirabello, A. (2011), “The influence of personal and social-interactive engagement in social TV web sites”, International Journal of Electronic Commerce, Vol. 16 No. 2, pp. 41-68.

Patterson, P., Yu, T. and De Ruyter, K. (2006), “Understanding customer engagement in services”, paper presented at the Australia and New Zealand Marketing Academy Conference, Brisbane, December 4-6.

Prahalad, C.K. and Ramaswamy, V. (2004), “Co-creation experiences: the next practice in value creation”, Journal of Interactive Marketing, Vol. 18 No. 3, pp. 5-14.

Purcărea, V.L., Raţíu, M., Purcărea, T. and Davila, C. (2008), “Offering memorable patient experience through creative, dynamic marketing strategy”, Journal of Medicine and Life, Vol. 1 No. 2, pp. 198-205.

Retail Touchpoints (2011), “Improving revenue and customer engagement with social media analytics”, available at: https://docmh.com/improving-revenue-and-customer-engagement-with-social-media-analytics-pdf (accessed July 19, 2017).

Riff, D., Lacy, S. and Fico, F. (2014), Analyzing Media Messages: Using Quantitative Content Analysis in Research, 3rd ed., Routledge, New York, NY.

Sashi, C.M. (2012), “Customer engagement, buyer‐seller relationships, and social media”, Management Decision, Vol. 50 No. 2, pp. 253-272.

Sawhney, M., Verona, G. and Prandelli, E. (2005), “Collaborating to create: The Internet as a platform for customer engagement in product innovation”, Journal of Interactive Marketing, Vol. 19 No. 4, pp. 4-17.

Schaufeli, W.B., Salanova, M., González-romá, V. and Bakker, A.B. (2002), “The measurement of engagement and burnout: a two sample confirmatory factor analytic approach”, Journal of Happiness Studies, Vol. 3 No. 1, pp. 71-92.

Shaw, C. and Ivens, J. (2002), Building Great Customer Experiences, Palgrave Macmillan, New York, NY.

Sin, L.Y.M., Tse, A.C.B., Yau, O.H.M., Chow, R.P.M., Lee, J.S.Y. and Lau, L.B.Y. (2005), “Relationship marketing orientation: scale development and cross-cultural validation”, Business-to-Business Relationship Architecture and Networks among Australia, NZ, and Asian Firms, Vol. 58 No. 2, pp. 185-194.

Smith, S. (2013), “Conceptualizing and evaluating experiences with brands on Facebook”, International Journal of Market Research, Vol. 55 No. 3, pp. 357-374.

The Insights Groups Ltd (2014), “The role of communication in creating engagement”, available at: www.insights.com/resources/the-role-of-communication-in-creating-engagement/ (accessed July 19, 2017).

Tinsley, H.E. and Weiss, D.J. (2000), “Interrater reliability and agreement”, in Tinsley, H. and Brown, S. (Eds), Handbook of Applied Multivariate Statistics and Mathematical Modeling, Academic Press, Cambridge, MA, pp. 95-124.

Van Doorn, J., Lemon, K.N., Mittal, V., Nass, S., Pick, D., Pirner, P. and Verhoef, P.C. (2010), “Customer engagement behavior: theoretical foundations and research directions”, Journal of Service Research, Vol. 13 No. 3, pp. 253-266.

Vargo, S.L. and Lusch, R.F. (2004), “Evolving to a new dominant logic for marketing”, Journal of Marketing, Vol. 68 No. 1, pp. 1-17.

Vargo, S.L. and Lusch, R.F. (2008), “Service-dominant logic: continuing the evolution”, Journal of the Academy of Marketing Science, Vol. 36 No. 1, pp. 1-10.

Verhoef, P.C., Reinartz, W.J. and Krafft, M. (2010), “Customer engagement as a new perspective in customer management”, Journal of Service Research, Vol. 13 No. 3, pp. 247-252.

Vivek, S.D., Beatty, S.E. and Morgan, R.M. (2012), “Customer engagement: exploring customer relationships beyond purchase”, Journal of Marketing Theory and Practice, Vol. 20 No. 2, pp. 122-146.

Voss, C. and Zomerdijk, L. (2007), “Innovation in experiential services – an empirical view”, in DTI (Ed.), Innovation in Services, DTI, London, pp. 97-134.

Woisetschläger, D.M., Hartleb, V. and Blut, M. (2008), “How to make brand communities work: antecedents and consequences of consumer participation”, Journal of Relationship Marketing, Vol. 7 No. 3, pp. 237-256.

Zailskaite-Jakste, L. and Kuvykaite, R. (2012), “Consumer engagement in social media by building the brand”, paper presented at the 1st Electronic International Interdisciplinary Conference, Vol. 1, pp. 194-202.

Zhao, L. and Lu, Y. (2012), “Enhancing perceived interactivity through network externalities: an empirical study on micro-blogging service satisfaction and continuance intention”, Decision Support Systems, Vol. 53 No. 4, pp. 825-834.

Zomerdijk, L.G. and Voss, C.A. (2009), “Service design for experience-centric services”, Journal of Service Research, Vol. 13 No. 1, pp. 67-82.

Further reading

Joshi, S., Majumdar, A. and Malhotra, A. (2014), “Enhancing customer experience using business intelligence tools with specific emphasis on the Indian DTH industry”, Shaping the Future of Business and Society, Vol. 11, pp. 289-305.

Corresponding author

Vidyasagar Potdar can be contacted at: v.potdar@curtin.edu.au