Virtual inter-organizational communities of practice (IOCoPs) enable professionals belonging to different organizations to exchange and share knowledge via computer-mediated interactions. Since knowledge sharing is socially embedded, contextual factors likely play an important role in encouraging individuals' community participation. Specifically, professionals in IOCoPs are embedded in two different social environments: the virtual community where they interact with online peers and organizations where they utilize their knowledge. Therefore it is important to simultaneously study motivating factors generated from these two different contexts, including peer effects within and organizational influences outside the virtual community. In this research, we apply a novel econometric identification method to analyze a unique dataset collected from a virtual IOCoP in the financial trading sector. We find that, after controlling for individual level characteristics, contextual motivating factors from peers and organizations are influential both quantitatively and qualitatively in determining community participation. Differentiating multiple-level motivating factors across different contexts enables us to shed light on various mechanisms that IOCoPs can apply to engage collective learning and knowledge management across organizations.

With the availability of large-scale network data, peer influence in social networks can be more rigorously examined and understood than before. Peer influence can arise from immediate neighbors in the network (formally defined as cohesion or direct ties with one-hop neighbors) and from indirect peers who share common neighbors (formally defined as structural equivalence or indirect ties with two-hop neighbors).While the literature examined the role of each peer influence (direct or indirect) separately, the study of both peer network effects acting simultaneously was ignored, largely due to methodological constraints. This paper attempts to fill this gap by evaluating the simultaneous effect of both direct and indirect peer influences in technology adoption in the context of Caller Ring Back Tone (CRBT) in a cellular telephone network, using data from 200 million calls by 1.4 million users. Given that such a large-scale network makes traditional social network analysis intractable, we extract many densely-connected and selfcontained subpopulations from the network. We find a regularity in these subpopulations in that they consist either of about 200 nodes or about 500 nodes. Using these sub-populations and panel data, we analyze direct and indirect peer influences using a novel auto-probit model with multiple network terms (direct and indirect peer influence, with homophily as a control variable). Our identification strategy relies on Bramoullé et al.'s (2009) spatial autoregressive model, allowing us to identify the direct and indirect peer influences on each of the extracted subpopulations. We use meta-analysis to summarize the estimated parameters from all subpopulations. The results show CRBT adoption to be simultaneously determined by both direct and indirect peer influence (while controlling for homophily and centrality). Robustness checks show model fit to improve when both peer influences are included. The size and direction of the two peer influences, however, differ by group size. Interestingly, indirect peer influence (structural equivalence) plays a negative role in diffusion when group size is about 200, but a positive role when group size is about 500. The role of direct peer influence (cohesion), on the other hand, is always positive, irrespective of group size. Our findings imply that businesses must design different target strategies for large versus small groups:for large groups, businesses should focus on consumers with both multiple one-hop and two-hop neighbors;for small groups, businesses should only focus on consumers with multiple one-hop neighbors.

Software components such as application programming interfaces (APIs) provided by external developers are vital to online digital platforms. Although APIs generally increase the variety of products according to anecdote, the precise relationship between the categories of APIs and product variety is not yet known. We find that APIs, regarding their use frequency, are categorized into three groups. The core is a group of frequently used APIs, whereas the periphery is a group of sparsely used APIs. In a large and mature platform ecosystem, an additional group of APIs, the regular core, mainly provided by third-party developers, emerges. APIs in the regular core are the main driver of product variety. However, we also find that the strength of this effect diminishes in a newly created product category when most of the new products are built by duplicating the usage of APIs from other products. A platform owner can stimulate developers’ creativity by acting as a bridge between digital product providers and third-party developers. It can collect functional needs from third-party developers and then share them with product providers. Therefore, the latter can build APIs that developers need.

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