Despite the potential of health information technology (HIT) systems to significantly reduce medical errors, streamline clinical processes, contain healthcare costs, and ultimately improve the quality of healthcare, their adoption by hospitals in the United States has been rather slow. To study this adoption process and get insights into the underlying mechanisms, in this work we synthesize the theories on social networks and knowledge transfer. We propose a research framework in which the absorptive capacity of a potential adopter and the collective disseminative capacity of connected adopters act as two key determinants of knowledge transfer in a socioeconomic network, and these two capacities substitute for each other in affecting HIT adoption. We also propose that, in a network setting, the mechanism of knowledge transfer manifests quite differently from that of social contagion in its impact on the diffusion process at different stages of adoption. Using a large longitudinal data set covering adoption decisions of more than five thousand hospitals across a thirteen-year horizon, we find strong support for our hypotheses. Our analysis shows that knowledge flow in provider networks plays a key role in fostering technology diffusion in initial years, allowing the contagion effect to set in sooner for quicker adoption in later years. Therefore, recent efforts at multiple levels to form integrated healthcare delivery networks should accelerate HIT adoption.
In recent years, we have witnessed an unprecedented growth in the security software market. This market is now fiercely competitive with hundreds of nearly identical products; yet, the price is high and coverage low. Although recent research has examined such idiosyncrasies and found the existence of a negative network effect as a possible explanation, several important questions still remain: (1) What possibly discourages product differentiation in such a competitive market? (2) Why is versioning absent here? (3) How does the presence of free alternatives in this market impact its structure? We develop a comprehensive oligopoly model, with endogenous quality and versioning decisions, to address these issues. Our analyses reveal that, although the presence of numerous competitors leads to a greater need to differentiate, the network effect in this market works as a counterweight, incentivizing vendors to sacrifice differentiation in favor of collocating in the top end of the quality spectrum. We explain the reasons and implications of this important finding. We further show that this result is robust and applicable even when versioning by competing vendors or the presence of free software is taken into consideration. Furthermore, given that the presence of free software actually intensifies competitive pressure and heightens the need to differentiate, the role of the network effect in abating differentiation becomes even more discernible.
As the ability to measure technology resource usage gets easier with increased connectivity, the question whether a technology resource should be priced by the amount of the resource used or by the particular use of the resource has become increasingly important. We examine this issue in the context of pricing of wireless services: should the price be based on the service, e.g., voice, multimedia messages, short messages, or should it be based on the traffic generated? Many consumer advocates oppose discriminatory pricing across services believing that it enriches carriers at the expense of consumers. The opposition to discrimination has grown significantly, and it has even prompted the U.S. Congress to question executives of some of the biggest carriers. With this ongoing debate on discrimination in mind, we compare two pricing regimes here. One regime, namely, service pricing, involves pricing different services differently. The other one, namely, traffic pricing, involves pricing the traffic (i.e., bytes) transmitted. We show why the common wisdom, that discriminatory pricing across services increases profits and harms consumers, may not always hold. We also show that such discrimination can increase social welfare.
The usefulness of a software product becomes obvious to consumers only after they get to experience it and, upon experiencing it, they may reach different conclusions regarding its true value. We examine the problem of designing free software trials under a general learning function. Our analyses lead to several new findings. We find that a time-locked trial is optimal only when the rate of learning is sufficiently large. It is not optimal in other situations, even when it has an overall positive effect on consumers' valuations. We also find that positive network effects have a minimal impact on this optimality. Interestingly, we find that neither the optimal trial period nor the optimal price is monotonically increasing in the rate of learning. At moderate rates, the software manufacturer pursues a dual strategy of offering a longer trial as well as a lower price. At higher rates of learning, the manufacturer does the opposite. Our results are robust, and incorporating possibilities such as a trial providing a signal of quality or learning being correlated with prior valuation has little impact on their applicability.
The market for security software has witnessed an unprecedented growth in recent years. A closer examination of this market reveals certain idiosyncrasies that are not observed in a traditional market. For example, it is a highly competitive market with over 80 vendors. Yet the market coverage is relatively low. Prior research has not attempted to explain what makes this market so different. In this paper, we develop an economic model to find possible answers to this question. Our model uses existing classification of different types of attacks and models their resulting network effects. We find that the negative network effect from indirect attacks, which is further enhanced by value-based targeted attacks, provides a possible explanation for the unique structure of this market. Overall, our results highlight the unique nature of the security software market, furnish rigorous arguments for several counterintuitive observations in the real world, and provide managerial insights for vendors on market competition.
Application-based pricing is common in telecommunications. Wireless carriers charge consumers more per byte of traffic for text messages than they do for wireless surfing or voice calls. Such pricing is possible because carriers and handset manufacturers have the ability to tag and meter each application. While tagging and metering are possible in the case of closed platforms such as iPhone, they are not in the case of open platforms such as Android. Android is open source with open application programming interfaces, and anyone can develop applications for it. Because the carriers have little control over applications, Android is inherently disruptive of differential pricing across applications. Users and neutrality advocates support Android, believing that it can increase consumer surplus by disrupting differential pricing. However, we show that the equilibrium under differential pricing is different from the equilibrium under open platforms, and it is particularly so with regard to the sets of consumers served and the quantities consumed. With open platforms, certain consumers are either not served or they are served a quantity that is less than what they would be served under differential pricing. Consequently, the consumer surplus and the social surplus are often lower with open platforms. Similarly, firms are expected to prefer differential pricing. We show that this expectation is also not true under certain circumstances in which open platforms and neutral pricing work like a quasi-bundle.