Requirements for Personalized m-Commerce: What Drives Consumers’ Use of Social Networks?

Requirements for Personalized m-Commerce: What Drives Consumers’ Use of Social Networks?

Laura Becker (wi-mobile Research Group, University of Augsburg, Augsburg, Germany) and Key Pousttchi (wi-mobile Research Group, University of Augsburg, Augsburg, Germany)
Copyright: © 2013 |Pages: 18
DOI: 10.4018/jeco.2013100102
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Abstract

Mobile devices are used to initiate shopping or enhance traditional shopping experiences. The integration of m-commerce with social networking sites enables marketers to provide really personalized offers to customers. The basis for that is an in-depth understanding of customers’ needs and expectations which can be analyzed on social networking sites due to their explicit and implicit presentation of the customers’ context and needs as well as due to their rising popularity. This study investigates the reasons for customers to use online social networks, using the example of Facebook. For that purpose, a structural equation model with formative constructs is developed and tested against data from young Facebook users. The authors’ results show that subjective norm might show no direct effect but, however, it has a large influence which occurs indirectly via perceived usefulness and perceived enjoyment. Additionally, the authors reveal that drivers for the usage intention of social networks show major differences between men and women.
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Introduction

Many customers use their mobile devices in several ways to initiate shopping or enhance traditional shopping experiences (Kumar & Mukherjee, 2013). The integration of m-commerce with social networking sites shows the importance of social factors for customers’ purchasing intentions (Khansa et al., 2012; Lai et al., 2012). However, the acceptance of m-commerce offers is influenced by the customers’ personal characteristics (Kwon et al., 2007; Zhou & Lu, 2011). Thus, personalized offers must be based on an in-depth understanding of the needs and preferences of the customers (Shaw et al., 2001). Personal needs can be identified by self reported needs (e.g., his/her own profile on social networking sites) or by profiling based on data analysis. The data analysis emphasizes two major kinds of customer data for really personalized offers: information from the customers’ context (e.g., time-based or location-based) and (mobile) payment data (Pousttchi and Hufenbach, 2013). The availability of large volumes of data on customers and data mining tools enable effective marketing strategies (Shaw et al., 2001) and personalized m-commerce offers (Liao et al., 2005).

Yang (2012) shows that Facebook users’ involvement affects their advertising, brand and purchasing attitudes. Social networking sites (SNS) are therefore most important to understand and to reach customers for m-commerce. Social networking sites are eligible for the analysis due to their explicit and implicit presentation of the customers’ context and needs as well as due to their rising popularity. SNS are currently among the most frequently used websites (Google, 2011) and mobile apps in terms of users spending half an hour per day on average (Khalaf, 2012). The prototypical user group, young users with higher education (Derickson, 2012; Pagani, 2004) – the so-called digital natives (Prensky, 2001) – is supposed to exceed this average considerably.

An in-depth understanding of customers’ needs and expectations is the basis of personalized m-commerce offers. SNS are a major data source for this. This research aims at a general understanding of their reasons to participate in SNS, expectations on and usage intention of those is required as a prerequisite to use SNS data for personalized m-commerce offers, using the example of Facebook. We assume that user participation in social networks is affected by the strength of the community due to network effects.

For that purpose, a structural equation model is developed focusing especially on the direct and indirect effects of subjective norm. In order to gain a maximum of insight into the reasons, we operationalize our model with formative instead of reflective constructs wherever reasonable (Jarvis et al., 2003; Diamantopoulos & Winklhofer, 2001). We validate the model with data from 1628 young German Facebook users. The outcome is a validated structural equation model that helps to understand the drivers for the use of social networking sites. This understanding builds the basis for the use of SNS data to identify requirements for personalized m-commerce offers.

This manuscript is organized as follows: The following section reviews the existing literature on online social networks and m-commerce. In the next section the research model is developed and the hypotheses are derived. Then, the research model is tested and the results are discussed. The conclusion is drawn in the last section.

Literature Review

The evaluation of the research question requires a closer look at specific motivations for m-commerce and participation in social networks.

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