Predicting 4G Adoption with Apache Spark: A Field Experiment

Predicting 4G Adoption with Apache Spark: A Field Experiment

Mantian (Mandy) Hu (The Chinese University of Hong Kong, China)
Copyright: © 2017 |Pages: 14
DOI: 10.4018/978-1-5225-1750-4.ch005


Companies have long realized the value of targeting the right customer with the right product. However, this request has never been so inevitable as in the era of big data. Thanks to the tractability of the customers' behavior, the preference information for each individual is collected and updated by the firm in a timely fashion. In this study, we developed a targeting strategy for telecommunication companies to facilitate the adoption of 4G technology. Utilizing the most up to date machine learning technique and the information about individual's local network, we set up a prediction model of consumer adoption behavior. We then applied the model to the real world and conduct field experiment. We worked with the largest telecommunication company in China and used Apache Spark to analyze the data from the complete customer based of a 2nd tie city in eastern China. In the experiment group, we asked the company to use the list we generated as the targets and in the control group, the company used the existing targeting strategy. The results demonstrated the effectiveness of the proposed approach comparing to existing models.
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Research Background

The existence of 4G networks in today’s technology-driven society is an important label of advancement and change. 4G networks are designed to improve wireless capabilities, network speeds and so on. New technology always brings opportunities and challenges (Gobjuka, 2009). One of the challenge is to attract more users to use 4G network. More 4G users means more important status in telecommunication market for telecom operators. As the competitions among operators are increasing, a creative and efficient market strategy is needed to attract more 4G users.

There are already some researches to learn behaviors of 4G users. A structural equation modeling (SEM) is conducted (Subramanian, 1994) and the results show that attitude towards the use of 4G network and the perceived usefulness of it are significant facilitators of users intention to adopt this technology. A study (Rawashdeh, 2015) aimed to examine the relationship between the perceived usefulness, perceived ease of use, perceived entertainment, attitude and the users’ intention toward using the 4G wireless mobile services. The findings reveal that the combination of them are together responsible for determining the users’ intention to use 4G network, factor analysis, correlation and regression analysis are used in the paper.

User choice is influenced by actions taken by others (Leenders, 2002). In particular by actions of close friends (Lee, Hosanagar, & Tan, 2015). There are a lot of underlying research explaining why this is the case, such as information transmission (Katz & Lazarsfeld, 1955), competition (Burt, 1987) and conformity (Menzel, 1960). These are usually called peer influence in the literature [5]. Researchers have completed that peer influence plays a important role in many different applied fields such as in the diffusion of new drugs (Coleman, Katz, & Menzel, 1966), messaging services (Aral, Muchnik, & Sundararajan, 2009), and in the sales of online books (Chevalier & Mayzlin, 2006). Peer influence has also been shown to shape knowledge diffusion and academic performance (Sacerdote, 2001), opinions and product reviews (Muchnik, Aral, & Taylor, 2013).

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