Using agent-based simulation experiments, we investigate the outcome of SAs between two smaller online search engine companies in competition with a dominant market leader in settings where an advertiser's decision making is the consequence of a combination of NI (e.g., an individual's willingness to follow others' decisions) and IP. In particular, we focus on a context in which the combined search engine company competes with a market leader holding a larger share of the market than the two runner-up "underdogs" combined. Our results indicate that, with the presence of NI and cascading effects, an alliance with "only" 35%-40% combined market share could compete with a leader whose market share, at the time of an alliance, is 60%-65%. Although important, size alone might be insufficient to build the market as suggested by the "vanilla" network effect theory. Another noteworthy finding is that a nonlinear association exists between NI and an alliance outcome; the combined runner-up companies have the best chance of success when the extent of NI is midrange, rather than on the high or low end of continuum. Contrary to the conventional view, this finding might also stimulate discussions among network science researchers. Furthermore, our results suggest that NI substantially moderates the relationship between the combined market share at the time of an alliance and the likelihood of resulting alliance success.
Information-processing networks (IPNs) denote dynamic network-based information-processing structures that operate as coordination mechanisms that transcend formal hierarchies. Despite growing interest in information technology-enabled IPNs, the literature has been silent in exploring the various ontological structures of IPNs and the structural efficiency embedded in each IPN, especially in the event of radical organizational changes. To fill this gap, this study identifies, from the perspective of graph theory, four ontological IPN archetypes that can serve as blueprints for information processing within and across organizations--random, small world, moderate scale free (MSF), and Barabasi. We then assess how each structure reacts to corporate restructuring (e.g., downsizing) and investigate, based on computer simulation, the extent to which each structure preserves a worker's efficiency and the stability of the network structure in the event of downsizing. Two moderating variables are included in the model--that is, scale of downsizing and the reconnection strategy in the presence of downsizing. In this study, downsizing is viewed not only as the simple elimination of individual workers but also as the elimination of the communication and information- processing conduits necessary for effective communication and coordination. We find that when firms implement a relatively small-scale workforce reduction, centralized coordination structures such as MSF and Barabasi are generally more resilient and facilitate better coordination. However, when the downsizing strategy involves massive and severe layoffs, decentralized coordination structures such as random and small world are more durable, and tend to provide a stronger safety net, irrespective of the strategies employed to create new ties. Although this study focused exclusively on the context of downsizing, the results of the study have important implications for other types of organizational restructuring (e.g.,