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The purpose of this paper is to use a theoretical model to create a scale to predict medical tourism (MT) intentions.

The theory of planned behavior (TPB) model was applied to MT by creating a 49‐item questionnaire and collecting data from a convenience sample of 453 undergraduate students enrolled in a university located in the USA. Factor analysis was used to evaluate the results, and yielded a MEDTOUR scale containing 29 items.

A regression of the three variables on an intentions scale of participation in MT had an R‐value of 0.587. The model was able to explain around 35 percent of the variance in intentions. Given the general nature of the model and the first attempt at predicting MT, the results are positive.

This research is limited due to the use of a convenience sample of undergraduate students. Further research utilizing additional samples is needed to verify the MEDTOUR scale. In addition, future research can focus on demographic or other areas of interest in relation to the intention to participate in MT.

The creation of the MEDTOUR scale represents a new application of the TPB to the area of MT. This theory‐based scale is offered as a new tool for future research.

This paper aims to examine the role of network effects (defined as increased utility for users of a technology that occurs when adoption increases among other users) in the adoption of electronic medical records (EMR) systems. EMR systems, which have experienced slow adoption rates, promise to improve the efficiency of the healthcare system by facilitating information exchange among physicians caring for the same patients.

Survey responses from physicians are used to test several hypotheses. The authors are interested in how market level EMR adoption was related to physician adoption intentions. The authors also test the “strong ties” notion of network effects by examining whether EMR adoption among generalists, and specialist physicians, had differing influences on adoption intentions in a given market.

Support for network effects is found; each one unit increase in market‐level EMR adoption is associated with a significant increase in overall physician adoption intention in that market. Secondary analyses suggest adoption of EMRs by specialists is significantly predictive of generalists' adoption intentions in a given market. However, as predicted, EMR among generalists does not influence other generalists' intentions; nor does EMR adoption by a specialists influence other specialists' intentions.

Network effects play a role in the EMR adoption among physicians. Decision‐makers wanting to influence adoption should target defined market segments in an effort to build a critical mass of adoption then move to adjacent segments once network effects take hold.

This paper applies network effects theory to help explain the suboptimal adoption rates of an important healthcare technology.