Informants in Organizational Marketing Research

Organizational research frequently involves seeking judgmental data from multiple informants within organizations. Researchers are often faced with determining how many informants to survey, who those informants should be and (if more than one) how best to aggregate responses when disagreement exists between those responses. Using both recall and forecasting data from a laboratory study involving the MARKSTRAT simulation, we show that when there are multiple respondents who disagree, responses aggregated using confidence-based or competence-based weights outperform those with data-based weights, which in turn provide significant gains in estimation accuracy over simply averaging respondent reports. We then illustrate how these results can be used to determine the best number of respondents for a market research task as well as to provide an effective screening mechanism when seeking a single, best informant.screening;marketing research;aggregation;organizational research;survey research

Institutional Forecasting: The Performance of Thin Virtual Stock Markets

We study the performance of Virtual Stock Markets (VSMs) in an institutional forecasting environment. We compare VSMs to the Combined Judgmental Forecast (CJF) and the Key Informant (KI) approach. We find that VSMs can be effectively applied in an environment with a small number of knowledgeable informants, i.e., in thin markets. Our results show that none of the three approaches differ in forecasting accuracy in a low knowledge-heterogeneity environment. However, where there is high knowledge-heterogeneity, the VSM approach outperforms the CJF approach, which in turn outperforms the KI approach. Hence, our results provide useful insight into when each of the three approaches might be most effectively applied.Forecasting;Electronic Markets;Information Markets;Virtual Stock Markets

The Active Bundle Scheme for Protecting Electronic Medical Records

Adoption of the electronic medical records (EMRs) or electronic health records (EHRs) by healthcare providers will improve the quality of the American healthcare and reduce the annual bill. However, it will also increase privacy threats due to easier dissemination of EMRs/EHRs than “paper” medical records. Current privacy protection solutions for patient EMRs/EHRs have two main limitations: (1) they require an extensive exchange of messages between computer systems of healthcare providers; and (2) they depend only on data encryption. In this position paper, we propose a solution that provides protection for the patients\u27 EMRs/EHRs disseminated among different authorized healthcare provider systems. This is achieved through the use of the construct named active bundles (ABs). ABs keep EMRs/EHRs as sensitive data, include metadata containing privacy policies, and encompass a virtual machine that enforces privacy policies

Informants in Organizational Marketing Research

Organizational research frequently involves seeking judgmental data from multiple informants within organizations. Researchers are often faced with determining how many informants to survey, who those informants should be and (if more than one) how best to aggregate responses when disagreement exists between those responses. Using both recall and forecasting data from a laboratory study involving the MARKSTRAT simulation, we show that when there are multiple respondents who disagree, responses aggregated using confidence-based or competence-based weights outperform those with data-based weights, which in turn provide significant gains in estimation accuracy over simply averaging respondent reports. We then illustrate how these results can be used to determine the best number of respondents for a market research task as well as to provide an effective screening mechanism when seeking a single, best informant

Institutional Forecasting: The Performance of Thin Virtual Stock Markets

We study the performance of Virtual Stock Markets (VSMs) in an institutional forecasting environment. We compare VSMs to the Combined Judgmental Forecast (CJF) and the Key Informant (KI) approach. We find that VSMs can be effectively applied in an environment with a small number of knowledgeable informants, i.e., in thin markets. Our results show that none of the three approaches differ in forecasting accuracy in a low knowledge-heterogeneity environment. However, where there is high knowledge-heterogeneity, the VSM approach outperforms the CJF approach, which in turn outperforms the KI approach. Hence, our results provide useful insight into when each of the three approaches might be most effectively applied

The Hyponatremic Hypertensive Syndrome in a Preterm Infant: A Case of Severe Hyponatremia with Neurological Sequels

Objective. To report the irreversible severe neurological symptoms following the hyponatremic hypertensive syndrome (HHS) in an infant after umbilical arterial catheterization. Design. Case report with review of the literature. Setting. Neonatal intensive care unit at a tertiary care children's hospital. Patient. A three-week-old preterm infant. Conclusions. In evaluating a neonate with hyponatremia and hypertension, HHS should be considered, especially in case of umbilical arterial catheterization. In case of diagnostic delay, there is a risk of severe irreversible neurological damage