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Student engagement is regarded as a critical educational outcome. However, it has proven to be elusive to educators within technical domains, such as marketing research and analytics, which has inspired the exploration of experiential course design elements. Client-sponsored projects (CSPs) have become a popular tactic to meet this objective in such courses. The authors utilize a mixed-methods design to examine CSPs and their effectiveness in marketing research and analytics courses. In Study 1, qualitative research yields student insights regarding the desired characteristics of a CSP. Study 2 illustrates that CSPs boost student engagement through perceptions of course relevance. However, students' subjective interest in the client represented a boundary condition for this effect. That is, CSPs did not offer value to students who lacked inherent interest in the client. In Study 3, a discrete choice model analysis outlined what attributes make clients interesting for students. Finally, Study 4 utilizes a field experiment to identify project-framing tactics that increase student interest through enhanced client familiarity. A series of recommendations are provided to maximize the efficacy of CSPs to marketing research and analytics courses.

We developed the Leakage Risk Monetization Model (LRiMM) which integrates simulation of CO2 leakage from geologic CO2 storage reservoirs with estimation of monetized leakage risk (MLR). Using geospatial data, LRiMM quantifies financial responsibility if leaked CO2 or brine interferes with subsurface resources, and estimates the MLR reduction achievable by remediating leaks. We demonstrate LRiMM with simulations of 30 years of injection into the Mt. Simon sandstone at two locations that differ primarily in their proximity to existing wells that could be leakage pathways. The peak MLR for the site nearest the leakage pathways ($7.5/tCO2) was 190x larger than for the farther injection site, illustrating how careful siting would minimize MLR in heavily used sedimentary basins. Our MLR projections are at least an order of magnitude below overall CO2 storage costs at well-sited locations, but some stakeholders may incur substantial costs. Reliable methods to detect and remediate leaks could further minimize MLR. For both sites, the risk of CO2 migrating to potable aquifers or reaching the atmosphere was negligible due to secondary trapping, whereby multiple impervious sedimentary layers trap CO2 that has leaked through the primary seal of the storage formation.

College graduates need better preparation for and experience in data analytics for higher-quality problem solving. Using the curriculum innovation framework of Borin, Metcalf, and Tietje (2007) and case study research methods, we offer rich insights about one higher education institution's work to address the marketing analytics skills gap. Specifically, we describe how a focal marketing department developed an innovative curriculum based on foundational pillars of learning styles (Bloom's Taxonomy), experiential learning theory, and marketing analytics. The effectiveness of the curriculum is reviewed in light of criteria such as established learning goals and student satisfaction.

A non-traditional methodology of polling in small areas, called bellwethers, improves the accuracy of forecasts drawn from a large area. Bellwether areas are small government units (such as cities or counties) which exhibit voting patterns close to actual election outcomes in the larger areas of which they are a part (such as states). The bellwether methodology of one polling organization is described in detail using data from the 2012 US Senate election in Massachusetts. An overview of the performance of the bellwether methodology is provided. In use at Suffolk University Political Research Center since 2003, bellwethers combined with state polls correctly forecast election outcomes in 97% of trials where a clear winner could be determined (i.e., no ties). The contribution of this work is to offer an open-source methodology for improving accuracy in election forecasting. [ABSTRACT FROM AUTHOR]