Throughout this dissertation we have illustrated the potential of social media to create business value, and touched upon several interesting further research opportunities building on the presented research, such as the development of a theoretical framework for online sentiment creation, a deeper understanding of the role of MGC across different industries and applications, and more research on the use of social media in B2B-settings, both from a theoretical and marketing analytics point of view.
However, many more interesting questions regarding social media (value) remain unanswered to date. For instance, how consistent are the results over different industry types? How consistent are these results over different firm sizes? Which social media platform is most influential for which type of company? What about relatively newer social media such as Instagram, Pinterest and Snapchat and their influence? Which of the social media engagement actions of customers is most important for companies? Next to social media marketing through the social media pages of a company, other forms of social media marketing research continue to be important. Some of these streams (e.g. viral campaigns, influencer modeling) are already heavily researched (Aral and Walker, 2011; Berger and Milkman, 2012; Hinz et al., 2011; van der Lans et al., 2010), while other streams such as social media advertising received only little academic attention (Naylor et al., 2012; Tucker, 2014) and would benefit from more extensive research in order to understand how social media advertising works, to what extent it can increase meaningful firm outcomes and what may be necessary requirements for it in order to be effective.
All social media efforts can be seen as extra touchpoints with the company. These touchpoints become increasingly more difficult to control by the company, as social media are mainly driven by customers. However, social media also offer the opportunity to collect and measure many of these touchpoints. Combining both offline and online information (social media data, website data, internet-of-things related data) allows marketers to build more comprehensive models, and to better assess the relative value of each of these touchpoints. Synergies, spillover and crossover effects are likely to occur across different media and device types, and probably the type of media used might depend on the communication goal (i.e., convey a message or advertisement to a wide audience vs interaction with some customers). These insights could subsequently be used to get more complete insights in communication- mix elements, taking into account the value of touchpoints of the specific media and their specific roles. Thus, many research questions with high practical relevance are still on the table and provide promising avenues for future research (see for instance Wedel and Kannan (2016) for an overview of different research streams in Marketing Analytics).
However, social media also suffer from several potential pitfalls for future research. First, it becomes more and more difficult for companies to obtain social media data. Facebook, for instance, has already strongly tightened its API download policies. This makes it more difficult for both researchers and companies to obtain relevant social media. For instance, the data for the first study can still be collected, if a useful application is developed that uses the posts. A replication of the data for the second study is only partially feasible, since network data are not available anymore, and names of the comments cannot be retrieved anymore by the API. Chapter three data (fan page data) are still feasible to collect, since these are open data. Also social media data from Instagram, Pinterest and Snapchat are not easy to collect, which means companies have to resort to their own collection and statistics (which are often not very detailed). Second, and related to the first point, privacy issues become more and more prevalent (Baesens et al., 2016). Customers are more cautious to share new information, and at the same time social media tools are more restrictive to share information. Moreover, governments are putting in place strict privacy legislations that prescribe and limit the use of personal and detailed information. In the European Union for example, the right to be forgotten will soon be in practice (Macaulay, 2017), and the recently introduced and much bespoken external regulation in the form of GDPR. As a consequence, future marketing-mix (or other types of) models should be designed to cope with privacy regulations limitations and be able to handle anonymized and minimized data (Wedel and Kannan, 2016). While this may limit the practical
153 implementation of the proposed models, the main insights that come from these studies already offer more in-depth understanding of the working mechanisms and importance of social media which is important given the enormous amount of money spent on social media nowadays.
Social media offer the potential to collect data on individuals, but not every customer is a social media user. Thus, there are limitations to the generalizability of the results found using social media. Put in another way, working with social media often lead to selection effects. This is even more present when using mobile application users, who basically self-selected into a study (e.g., using a Facebook application as in Chapter 3). In this case, we need to accordingly adjust the analysis, for instance with Propensity Score Matching or a Heckman selection model. However, the increasingly complex models cannot easily be adapted to include these corrections (at least the Heckman correction). For instance, a combination of panel data with a binary selection and outcome variable already proves to be a serious challenge that has only just been resolved (Semykina and Wooldridge, 2017). Therefore, it is important that these modeling issues will be further resolved to make full use of the social media data.
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