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CHAPTER 1 INTRODUCTION
1.1 Background and Motivation
Over the past years, as the variety of new media increased, the ways consumers acquaint and make decision on goods have transformed dramatically (Hennig-Thurau, 2010). User-generated content on new media platforms and product search engines has been influencing consumer behavior for goods comprehensively (Ghose 2012). However, with the information explosive growth people face a problem of information overload. To solve this problem, the information filtering, for example user referral or curation information, becomes more persuasive. Because of this trend, recommender systems become more widely used, for example Ipeen, Tripadvisor, etc. (Prasad, 2012).
Recommender systems not only collect user experience information, but also gather the optimal amount of knowledge information obtained from mavens who are the expert in some categories. On the other hand, as the more users using the Internet and the social collaboration increasing, the more this trend of using trust-based social recommender systems is perceived. Trust-based social recommender systems provide two notions of the trust: trust about the other users of the recommender and about to the recommendation site (Prasad, 2012).
Mavens, who have abundant information about products, peripheral products and other aspect of market in the particular fields, are highly trusted and likely diffusing the information to other customers and deeply involve in particular market fields, even in categories of product that are not used by them (J. Boster, 2010); therefore, mavens play an important characteristic in social recommender systems. Including the maven’s knowledge and the curation information that
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recommender systems are argued to influence on consumer’s cognitive and behavior (Thorsten, 2010).
Social Recommender system is one of the technologies having drastically impact on customer searching and decision making on goods (Haubl and Trifts, 2000). With the trust of user knowledge and advice, firms can use such technology for providing highly individualized services and products based on numerous data online, such as searching engine and social media (Hennig-Thurau, 2010). On the other hand, customer engagement concept applied comprehensively in market place. (Van Doorn, 2010) Customer engagement defined as the customers’ behaviors go beyond transactions and customers’ behavioral manifestations have a brand- or firm-focus. Customer engagement behaviors are resulting from motivational drivers (cf. MSI, 2010).
Such motivational drivers include word-of-mouth activity, customer-to-customer (C2C) interactions and blogging activity. Therefore, based on the customer engagement concept, the social recommender systems are supposedly to be an appropriate tool to engage customer behavior to acquire the potential customer for firm and brand.
Nevertheless, it has been a gap exists between social recommender system and customer engagement behavior. Actually, when marketers engage customer on new media; most of them focus on social media interaction, for example Facebook, Twitter. However, seldom of them explore the possibility of using the recommender system to engage customer behavior. Senecal and Nantel (2004) claimed that recommenders have capability of altering and building up the customers evoked set. Integration of consumer preferences, such as the following the maven’s topic or the individual’s history records, in recommendation and consumer acceptance in recommender systems can affect
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consumer decision making. This type of customer engagement should be connected to the social recommendation system by which all the new ways in which customers can interact with firms, including purchase and non-purchase behavior (Libai, 2011).
Further, there are variety characters interacting with each other in the social recommendation system. For instance, firms manifest the brand and product on the social recommender system; customers search for the advice for the goods; social recommender system owners organize the both firm and customer on the site, and also held some events and activities. In order to integrate this complex relationship, we expect to establish a digital business ecosystem to build a mutual co-creation relationship between firm, recommendation platform and the customers/mavens on the platform (Omar A., 2013).
1.2 Research Problem
Since social recommender systems are argued to be suitable for engaging customer behavior but it is rare for firm notice on this new media, this study aims to integrate recommender system with the engagement elements, i.e. the customer involvement, interaction, intimacy, and influence (Forrester, 2008), in order to influence customer attitudes and decisions and affects maven by diffusing the ideas or knowledge about firm.
Review of previous research to recommender systems or platforms nowadays, these new media was often used only for searching or filtering information (Prasad 2012), seldom of them have marketing strategy for acquiring and developing customers. We believe that not only the social media
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has the chance to engage customer behavior to influence other customers by E-WOM, but also the social recommendation system has the opportunity to engage customer behavior to referral customers by the abundant of maven’s knowledge and customer experience.
Besides, Doorn (2010) addressed that firms influence CEBs by developing and providing processes and platforms to support specific customer actions, such as browsing the webpage, sharing the topic and referring the product.
Accordingly, these studies will integrate the searching and sharing process on the recommender site to engage the customer behavior. In the social recommender system, customer making decision on the goods via the scores and users’ advices provided by customers on the page; in other words, the most persuasive comments are the knowledge and experience sharing by the mavens.
In this research, we focus on the question:
1. How firms gain the customer engagement behaviors on the social recommender site by mean of maximizing the likelihood of the designated maven knowledge behavior on recommender site compared with the traditional way.
2. In addition, this research also considers the issue about if the framing context with different content type (Entertainment, Information, Incentive) would impact the maven engagement behavior on social recommender?
With these two the issues, we would like to build an ecosystem, which includes the focal firm, brand alliance and customer on the recommender site, in order to obtain the positive engagement cycle (that integrates customer empowerment, e.g., customer comments, customer sharing, with firm strategy, in order to increase more customer engagement and more customer behavior).
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1.3 Research Method
In our vision, we would like to build a mechanism that can take in account well-design ecosystem to assist firms to engage the customer with the recommender system/site.
In our mechanism, we focus on improving the interactions with customer on recommender. According to previous research (cf. MSI 2010, Thorsten, 2010), the most important factor for increasing the CEBs on social recommender system is maven’s knowledge sharing. Therefore, our service system would gather the information which is highly related to a focal firm from different media, for example social media, news, blog, etc. After that, those information will be sent by our system to the mavens who are potentially interested in to advance the maven’s attitude and acknowledge, in order increasing the likelihood of sharing.
On the other hand, our mechanism adopts the ecosystem perspective and considers both focal firm and stakeholders. The mechanism would utilize the framing theory (Dietram, 1999)to cluster external source events and then connect to the stakeholder relationship, and summarize the relative information. Framing theory is based on the mass communication on the media effect, which causes how to spread the particular information on the mass media or individual with the process of Frame building, Frame Setting and Individual-level effect of framing (Dietram, 1999). With the arrangement of information and specific issue, we believe it is mapping to the specialist sphere directly.
To engage the customer behavior from recommender system to focal firm, our mechanism would build an engagement site to integrate all engagement modules. The engagement site can centrally control the customer behavior
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analysis for the information to make the decision for choosing the product.
Furthermore, the engagement site can include not only the recommendation modules, but also other channel modules, for example email module, social media module, search engines module, so that we pool marketing channel information and both existent and potential stakeholders, to formulate a positive ecosystem.
1.4 Purpose and Contribution
In our research, the purpose is to come up with a mechanism to accomplish the customer engagement through recommender system. In addition, this mechanism enables value co-creation within ecosystems, which include the focal firm, stakeholders, and other media. Our mechanism is semi-automatic to gather information and sensor possible external event on the recommender site to advance the customer engagement behavior.
Customer engagement concept is already mentioned on marketing (Harvey 2005). Since the new media growth significantly, firms are focusing customer engagement with social media, and lots of research (Thorsten,2010, Roderick, 2011) has concentrated on it. We would start to dedicate on recommender system and discover the method and system to support it.
Besides, our mechanism regards the ecosystem perspective that can improve the value co-creation with different actors. We aim to connect concept of customer engagement on the recommender system with ecosystem through the engagement site, and fulfill the co-creation with firm and each stakeholders.
As the result of the firm would considerate customer engagement concept and
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co-creation with ecosystem when design the business model and marketing strategy.
1.5 Content Organization
In Chapter 1 is to introduce of our research background, motivations and define the research problem and purposes.
In Chapter 2, which is the literature review section, we briefly discuss and detect our research proposition with the theoretical which is support us define the to reach the specific objectives that are extended from Chapter 1 and build the foundation of research knowledge.
This research is the partial from iEngagement project. Thus, in Chapter 3, we are going to show the entire project and purpose. Meanwhile, identify the position of this research in the project.
In Chapter 4, we developed a conceptual framework from the previous review and finding related on the Chapter 2 and tried to establish architecture with information technology to develop a possible solution that can be realized.
In Chapter 5, we have shown a conclusion and future work.
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CHAPTER 2 LITERATURE REVIEW
In this section, we figure out how to facilitate customer engagement through recommender, we review some existing knowledge to properly position the research. First, we overview the customer engagement behavior concept, identified the key factor on our research. Second, we review the popular recommendation type used in recommender, and identify the suitable type of recommender and discover the suitable factor or person to influence the customer engagement on recommender of our research. Third, we overview knowledge information spreading formation and define the type of knowledge to diffusion through the used acceptance and satisfaction. Then, we briefly introduce and discuss the popular technology used in summarization today, and defined the suitable technology.
2.1 Customer Engagement Behavior
Customer engagement behavior (CEB) first thinks in marketing to consider of value- creating with customer behavior that generate the corporate performance of firm financial (Brodie, 2011). A customer engagement behavior (CEB) is defined as the customers’ behavioral manifestation with a firm or brand focus, and go beyond the purchase, resulting from motivational drivers (Doorn, 2010). Customer engages to the firm with customer-to-firm relationship experience, which may be impact on motivational drivers both in positive and negative (see the figure 2.1). In firm perspective, firm can co-creation with the customer to co-create their service experience. As the relationship on firm and customer, CEBs can relative with the service-dominant (S-D) logic (Vargo and