INTRODUCTION
Online social networks, smartphones and tablet computers are part of our daily activities and changed people’s lives and behavior (Wei, Lee, Lu, Tzou & Weng, 2015). A mobile application is defined as “application software that allows the user to perform a specific task that can be installed and run on portable devices such as smartphones and tablets” (Liu, Au, Choi, 2014: 327). A mobile application differs from general software, as it is optimized for smartphones, it is designed mostly for personal use, it is offered free of charge or for sale, and it is made available for download directly through an online marketplace (Liu, et al., 2014). The introduction of the smartphone into the mobile market has enabled application developers to increase customer engagement by developing downloadable mobile applications (Chiem et al., 2010). This increased customer engagement comes from the fact that a mobile phone is rarely used by any other person than its owner and this allows for highly personalized marketing and targeting (Pousttchi, 2009). According to Ghose and Han (2014), the global mobile app market is expected to grow to US$150 billion over the course of the next two years. Mobile marketing is growing fast, as for 2017 the number of mobile phone users is forecast to reach 4.77 billion (Statista, 2016). Global importance of mobile phones and associated possibilities is thus continuously increasing over the years as the market growth keeps increasing. This enables for continuous opportunities for mobile application developers. However, according to Gartner, an information technology research and advisory company, in 2018, only 1% of mobile applications will generate a profit. A possible major contributor is the fact that people rely almost exclusively on the recommendations from peers and social networks (Gartner, 2014). It is therefore of major importance for application developers, to acquire knowledge about this influence and find out to what degree this influences the purchase intention of their consumers. Despite the fact that knowing what influences your consumers purchase intentions seems important to understand, not a lot of literature covers this topic. Most research focusses on the model of technology acceptance (TAM). This model consists of a number of constructs in relationship with behavioral intentions of consumers. Examples of such papers are those of Wang, Lin, Luarn (2006); Chong (2012); Wu, Wang (2005). What these papers have in common, is that they find different factors that influence purchase behavior, however they fail to include social influence in their research. While, as mentioned before, social influence is a possible major driver of consumer purchase intention. This paper will therefore cover the topic of social influence in relation to the purchase intention of consumers and will enhance our understanding of what drives consumer purchase intentions. Kang (2015), tries to cover the intention to purchase an application to some degree. His research however, does not make a distinction between in-app and application purchases. This paper will differ from that of Kang, as it will also cover the intention to make in-application (in-app) purchases. The choice for covering both the intention to purchase an application as well as the intention to make in-app purchases, lies in the fact that 98% of the revenues generated by the top 200 mobile applications in the Google Play store was created by in-app sales (Schoger, 2013) and most companies realize their revenues by selling in-app purchase items (Cho, 2015). Thus merely looking at application sales only, does not cover the entire sales aspect of the mobile application market, as in-app sales are of major importance. Lastly, this paper will also look if there are differences among educational levels, in the relationship between social influence and the purchase intention of consumers. As according to Ahkter (2003), the divide that separates people based on how they use the internet, has received inadequate attention. This divide has far reaching consequences as it could aid in answering, why some people are more likely to use the Internet for making a purchase than others (Akhter, 2003). To try and fill up the gaps that exists in the literature, this paper will attempt to find out what the impact of social influence is on the purchase intention of consumers and will look for possible differences among educational levels. Thus, the following research question has been formulated:
What is the effect of social influence on the intention to purchase mobile apps, on the intention to make in-app purchases and what is the moderating effect of educational level on this relationship?
Academic relevance of this paper, lies in the fact that it will attempt to fill in the gap that surrounds a major driver of consumer purchase intention. It serves as a basis for covering the sales aspect of mobile applications, by including both application sales and in-app sales. It helps researchers better understand what drives consumers to purchase, with possible implications for differentiation among educational levels. Managerial relevance of this paper, emerges from the fact that it might contribute, to a better understanding of consumers, and might aid application developers in the process of segmenting and targeting their consumers more effectively.
In the following chapter the factors will be defined, the literature surrounding these factors will be covered and reviewed, hypothesis will be proposed based on literary findings, and a conceptual model will be made. In chapter 3, the methodology will be explained, in chapter 4 the results will be shown, and finally in chapter 5, results will be discussed.
2. LITERATURE REVIEW
Based on the literature a conceptual framework has been made. How and why these concepts are connected to one another, will be elaborated below.
Figure 1 Conceptual framework
2.1 Intention to purchase mobile applications
Intention refers to the user perception of how strongly one intends to perform given behavior in the near future (Ajzen, Fishbein, 1980). Whitlark, Geurts and Swenson (1993: 18), define the intention to purchase a mobile application as “the purchase probability associated with an intention category as a percentage of the individuals who will actually buy the product”. Most paid apps are games, entertainment, or social networking apps and users pay little attention to who the provider is (Xu et al., 2011). Free apps are often listed next to their paid substitutions to provide consumers the opportunity to test run a mobile app before they actually engage in the purchasing process (Liu et al., 2014). Kang (2015) covers the topic of paid applications in relationship to users’ attitude towards an application, and finds that ultimately this attitude leads to an increase in the intention to purchase an application. One of the major factors of this increased positive attitude towards applications, is the effect of peer influence on the consumers (Kang, 2015).
2.2 Intention to make in-app purchases
In-app purchases can be defined as, “to buy additional content or services within the app, or to unlock additional levels of game stages”. (Liu et al., 2014: 328). A commonly used strategy to increase the revenue of mobile application companies is selling more in-app (Cho, 2015). Mobile application developers use a multitude of different strategies in order to maximize their profits and the most popular strategy is the ‘freemium strategy’ (Müller, Kijl and Martens, 2011). Although freemium strategy, is not the same as in-app sales per se, they do have a lot in common. Namely, when using the freemium strategy, the revenues of the application are generated by in-app sales and the only difference with normal applications that have in-app sales, is that with the freemium strategy, the initial purchase price of the application is zero. About 90% of the entire collection of mobile applications is dependent on the freemium strategy (Schoger, 2013). Even though this paper will not concentrate sole on freemium strategy only, its importance is addressed in relationship with in-app sales as a major part of these in-app sales are covered by the freemium strategy. Also, Cho (2015) measured several factors influencing the intention to purchase in-app for gaming applications and found factors such as price value, habit, and more specifically social influence.
2.3 Social Influence
Social Influence was defined by Venkatesh et al. (2012: 159) as the “the extent to which consumers perceive that important others (e.g. family and friends) believe they should use a particular technology”. In the research of Schiffman, Kanuk and Wisenblit (2010), it was stated that the influence of social class, culture and subculture, are important input factors that affect how consumers evaluate and adopt products. In the paper of Jamil and Wong (2010), it was found that the intention to buy a certain brand is influenced by social norms and other people’s expectations. The research of Suki (2013), found that the influence of social influences on the dependency of students on smartphones, was significant, and this influence in turn lead to an increase in purchase behavior. Chong (2012) found that social influence is a significant determinant of behavioral intention in the study of m-commerce and consumers of m-commerce are likely to be influenced by their peers, family, media, and other users of m-commerce in forming their behavioral intention. Theories of conformity in social psychological have suggested that group members tend to comply with the group norm, and moreover that these in turn influence the perceptions and behavior of members (Lascu, Zinkhan, 1999). This behavior can be explained using Reference Group Theory, which emphasizes that a consumer’s behavior (e.g., purchasing decision) can be affected by a group of people whose opinions he/she values (Brown and Reingen, 1987; Kotler, 1999). To reduce risk and uncertainty, consumers usually depend on opinions or suggestions from others to evaluate purchases (Brown and Reingen, 1987; Kotler, 1999). Thus based on equivalent fields of research and results, the expectancy is that social influence has a positive effect on the purchase intention of consumers and therefore, the following hypothesis is made:
H1: Social influence has a positive effect on the intention of consumers to purchase an application.
Among literature such as Jamil and Wong (2010); Suki (2013); Chong (2012), there exists an agreement on results as they indicate a positive effect of social influence on the intention of consumers. According to the research of Lee, Shi, Cheung and Lim (2011) social influence has a positive effect on the intention to purchase online. As the opinions of others influence consumers’ purchasing intent. However, Cho (2015) found that social influence did not have a significant effect on the intention to purchase in-app for mobile games. The fact that social influence did not have a significant effect on in-app purchases within gaming applications does not necessarily mean that the same conclusions can be made for all mobile applications in general. Since, there exists a stronger suggestion for a positive effect, the following hypothesis will be made:
H2: Social influence has a positive effect on the intention of consumers to make in-app purchases.
2.4 Educational level
Akhter (2003) found that education affects information acquisition and product evaluation strategies. Since educated consumers are more likely to engage in extended search and use more product related information, that the higher the educational level, the higher likelihood of purchasing over the internet (Akhter, 2003). Bellman, Lohse and Johnson (1999); Li, Kuo and Rusell (1999), found that the higher a person’s educational level, the more online transactions that person is likely to make. And even more specifically, in the research of Park, Yang and Lehto (2007), where they look at the impact of social influence on the intention to use mobile technology, they found that educational level positively moderates the relationship between social influence and intention to use technology. The high education group members mainly shaped their attitude towards mobile technology based on social influence and this formed attitude lead to an increase in their behavioral intention. Educational level affects the information acquisition and product evaluation strategies of consumers and research shows that higher educated consumers are more likely to engage in extended search (Ahkter, 2003).
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