We examined the relationship between trust in the medical system, medication adherence, and hypertension control in Southern African American men. The sample included 235 African American men aged 18 years and older with hypertension. African American men with higher general trust in the medical system were more likely to report better medication adherence (odds ratio [OR] = 1.06), and those with higher self-efficacy were more likely to report better medication adherence and hypertension control (OR = 1.08 and OR = 1.06, respectively).

Purpose -The purpose of this paper is to use a theoretical model to create a scale to predict medical tourism (MT) intentions.Design/methodology/approach -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.Findings -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.Research limitations/implications -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.Originality/value -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 study introduces the theory of planned behavior to health care marketers by extending and replicating a prior study that predicted student's intention to engage in medical tourism. Based on a sample of 164 usable survey responses, our findings suggested that the MEDTOUR scale (developed and introduced a prior study) is robust and works reasonably well with a national sample. Based on these findings, MEDTOUR appears to be worthy of further consideration by health marketing scholars.

PurposeThis 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.Design/methodology/approachSurvey 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.FindingsSupport 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.Research implicationsNetwork 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.Originality/valueThis paper applies network effects theory to help explain the suboptimal adoption rates of an important healthcare technology.

This study examined the effects of public hospitals' privatization on financial performance. We used a sample of nonfederal acute care public hospitals from 1997 to 2013, averaging 434 hospitals per year. Privatization was defined as conversion from public status to either private not-for-profit (NFP) or private for-profit (FP) status. Financial performance was measured by operating margin (OM) and total margin (TM). We used hospital level and year fixed effects linear panel regressions with nonlagged independent and control variables (Model 1), lagged by 1 year (Model 2), and lagged by 2 years (Model 3). Privatization to FP was associated with 17% higher OM (Model 2) and 9% higher OM (Model 3), compared with 3%, 4%, and 6% higher OM for privatization to NFP for all three Models, respectively. Privatization to FP was associated with 7% higher TM (Model 2) and privatization to NFP was associated with 2% higher TM (Model 3).