Engineering Optimal Network Effects via Social Media Features and Seeding in Markets for Digital Goods and Services

Abstract

Firms nowadays are increasingly proactive in trying to strategically capitalize on consumer networks and social interactions. In this paper, we complement an emerging body of research on the engineering of wordof-mouth effects by exploring a different angle through which firms can strategically exploit the value-generation potential of the user network. Namely, we consider how software firms should optimize the strength of network effects at utility level by adjusting the level of embedded social media features in tandem with the right market seeding and pricing strategies in the presence of seeding disutility. We explore two opposing seeding cost models where seeding-induced disutility can be either positively or negatively correlated with customer type. We consider both complete and incomplete information scenarios for the firm. Under complete information, we uncover a complementarity relationship between seeding and building social media features that holds for both disutility models. When the cost of any of these actions increases, rather than compensating by a stronger action on the other dimension to restore the overall level of network effects, the firm will actually scale back on the other initiative as well. Under incomplete information, this complementarity holds when seeding disutility is negatively correlated with customer type but may not always hold in the other disutility model, potentially leading to fundamentally different optimal strategies. We also discuss how our insights apply to asymmetric networks.

Advances in Social Media Research: Past, Present and Future

“…Aggarwal and Singh (2013) find that blogs help managers with their products in the screening stage, and also offer leverage in negotiating better contract terms. Dou et al (2013) research optimizing the strength of a network by adjusting the embedded social media features with the right market seeding and pricing strategies. reveal that the firms are more viable when they integrate social media in purchase and consumption experience, rather than using it as a substitute for soft online marketing.…”

Social commerce—state-of-the-art and future research directions

“…Research theme: enterprise strategies Enterprise strategies represent a further research theme in social commerce research (14 %) (e.g., Adamopoulos and Todri 2014;Alt and Puschmann 2012;Chen et al 2007b;Danaher and Dagger 2013;Dou et al 2013;Goel and Goldstein 2014;Ng and Wang 2013;Oestreicher-Singer and Sundararajan 2012;Onishi and Manchanda 2012;Stephen and Galak 2012;Xu et al 2014). The majority of these articles investigate social shopping websites (7 articles), followed by social gaming websites and virtual worlds (3 articles), social networking sites (3 articles) and blogs, wikis, and microblogs (3 articles).…”

“…For instance, Onishi and Manchanda (2012) explain how social media can best fit a firm's marketing strategy to lead to commercial success. Further, Dou et al (2013) show how firms can strategically exploit the potential of user networks by regulating the usage of social media features in accordance with suitable market seeding and pricing strategies, while Danaher and Dagger (2013) provide a method that enables advertisers to compare the relative effectiveness of multiple media as part of their marketing strategies. Comparably, Zhang and Duan (2014) examine the economic impact of different advertising channels (search engine, social media, 3rd party websites) on sales metrics of major e-commerce mergants such as Amazon, Sears, Macy's, Target and Walmart.…”

Heads or Tails? Network Effects on Game Purchase Behavior in The Long Tail Market

“…Network effects can operate through social influence of close peers or through network externalities. IS literature has identified certain features, such as compatibility of file formats and user interfaces, as able to alter the effects of network effects [12], [13], [22]. There is paucity of evidence as to how software design influences the effects of network effects.…”

“…Hence, in presence of externalities the utility would be: U = V (γθ) − C, where γ is the intensity of network externalities, and θ is the user base of the product.IS literature has a long history of using certain features -such as file format compatibility, UI similarity, collaborative editing, multiplayer-as proxy to estimate network effects [12], [13]. While these features serve as useful if not perfect proxies for network effects, in reality they enable and enhance network effects through social interactions and utility spillovers of network externalities [22]. The intensity of the network externalities can be engineered through careful design [22].…”