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Logistic regression (LoR) is a foundational supervised machine learning algorithm and yet, unlike linear regression, appears rarely taught early on, where analogy and proximity to linear regression would be an advantage. A random sample of 50 syllabi from undergraduate business statistics courses shows only two percent of the courses included LoR. Conceivable reasons for this dearth of LoR content is likely related to topic complexity, time constraints, and varying degrees of tool ease of use and support. We propose that these constraints can be countered by: [1] introducing logistic regression early, [2] informed tool selection prioritizing ease of use with comprehensive output, and [3] using/developing innovative, accessible, and easy to understand concept learning aids. This approach would leverage the proximity to linear regression and probability readily embed distributed practice for student understanding of a foundational technique.
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Faculty teaching data analytics at undergraduate level are often faced with the tension created by student under-preparedness, the demands of the course, and time constraints. How do faculty close this gap? In this paper, we propose the use of flow diagramming as an accessible method for interpreting regression analyses, in ways that are time efficient and not alienating to the student. Our study shows that the use of such flow diagrams has a positive effect on student understanding without additional remedial instruction. Time saved can be directed at core learning objectives of the analytics.
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This study investigates reasons behind high employee turnover of IT professionals through the value placed on intrinsic, extrinsic, leisure, and social rewards. It addresses two questions; what are the most preferred work values by IT professionals, and how do they differ from other professions. Data was collected via a survey using subjects from Amazon Mechanical Turk. A sample of 380 respondents was used. Key findings reveal that intrinsic rewards were the most important work values for IT professionals followed by leisure, extrinsic, and social rewards. Furthermore, when compared to the preferences of non-IT professionals, the levels of preferences for intrinsic rewards were statistically significantly higher for IT professionals than all other professions. This study provides valuable insight regarding key motivating factors of IT professionals and offers practical implications for practitioners relating to job characteristics that are most likely to reduce IT turnover and rethink their rewards systems.
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A great deal of work has been done in recent decades examining the impact of emotional intelligence (EQ) and cultural intelligence (CQ) on extra-role job performance. However, little of this research has used both EQ and CQ constructs together in the same analytical model. In addition, the available literature in is virtually silent with regards to the relationships of these constructs in Russia, a country with a significant multicultural environment. This study examines these relationships, using a sample of 189 supervisory and non-supervisory subjects in Russia. The results suggest that motivational CQ may be seen as an antecedent of CQ and EQ, both of which significantly impact extra-role job performance. EQ and CQ factors were also found to mediate the relationship between motivational CQ and extra-role job performance. In addition, necessary condition analysis was completed adding to model insights. These results extend our knowledge to a Russian sample, and not only support the Thomas et al. (2015) approach to CQ, but also suggest significant implications for current EQ-CQ model theories.
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As companies continue to put data and business analytics as their top priority, universities will need to supply students with the appropriate skill sets that meet this demand and offer future opportunities to their graduates. Although business analytics is a new field, many of the required competencies stem from already established areas such as Information/Computer Technology or Information Systems. Using a sample of 225 randomly selected AACSB accredited business schools this study examined the new developments in Business Analytics undergraduate academic programs, and determined the amount of overlap between the Business Analytics and the Information/Computer Technology degree programs. Our findings reveal that approximately 36 percent of the Business Analytics programs overlap with the Information/Computer Technology programs. In addition, the top three required courses in most Business Analytics programs include a Database course, predictive analytics course, and Introduction to Business Analytics. This research provides valuable insight for schools that haven't adopted a Business Analytics degree yet or are looking to improve their existing curriculum. In addition, colleges and universities can now utilize the appropriate Information Systems courses and include them as important foundation and part of their Business Analytics programs.
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