In this study we examine the effect of customers' participation in a firm's social media efforts on the intensity of the relationship between the firm and its customers as captured by customers' visit frequency. We further hypothesize and test for the moderating roles of social media activity and customer characteristics on the link between social media participation and the intensity of customer-firm relationship. Importantly, we also quantify the impact of social media participation on customer profitability. We assemble a novel data set that combines customers' social media participation data with individual customer level transaction data. To account for endogeneity that could arise because of customer self-selection, we utilize the propensity score matching technique in combination with difference in differences analysis. Our results suggest that customer participation in a firm's social media efforts leads to an increase in the frequency of customer visits. We find that this participation effect is greater when there are high levels of activity in the social media site and for customers who exhibit a strong patronage with the firm, buy premium products, and exhibit lower levels of buying focus and deal sensitivity. We find that the above set of results holds for customer profitability as well. We discuss theoretical implications of our results and offer prescriptions for managers on how to engage customers via social media. Our study emphasizes the need for managers to integrate knowledge from customers' transactional relationship with their social media participation to better serve customers and create sustainable business value.
We use panel data from multiple wards from two hospitals spanning a three-year period to investigate the impact of automation of the core error prevention functions in hospitals on medical error rates. Although there are studies based on anecdotal evidence and self-reported data on how automation impacts medical errors, no systematic studies exist that are based on actual error rates from hospitals. Further, there is no systematic evidence on how incremental automation over time and across multiple wards impacts the rate of medical errors. The primary objective of our study is to fill this gap in the literature by empirically examining how the automation of core error prevention functions affects two types of medical errors. We draw on the medical informatics literature and principal-agency theory and use a unique panel data set of actual documented medical errors from two major hospitals to analyze the interplay between automation and medical errors.We hypothesize that the automation of the sensing function (recording and observing agent actions) will have the greatest impact on reducing error rates. We show that there are significant complementarities between quality management training imparted to hospital staff and the automation of control systems in reducing interpretative medical errors. We also offer insights to practitioners and theoreticians alike on how the automation of error prevention functions can be combined with training in quality management to yield better outcomes. Our results suggest an optimal implementation path for the automation of error prevention functions in hospitals.