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John C. Yi
Department of Decision and System Sciences, Haub School of Business, Saint Joseph’s University, Philadelphia, PA 19131, USA

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Journal article
Published: 02 November 2018 in Sustainability
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The application of predictive analytics in higher education has increasingly gained acceptance and interest over the years. In this study, a predictive model is developed to map students’ non-cognitive skills against their class performance. Our predictive analytics model identified the non-cognitive skills that predicted new students’ class performance based on the dataset collected early in the semester. Based on the predictive analytics results, tailored teaching to improve students’ non-cognitive skills was offered in a required class designed for undergraduate business students. The improvement in the average final semester grade for students in the tailored-taught classes based on our predicted analytics approach was 9%, which was higher than that of the class grade taught without the approach. The study finding also demonstrates a long-term, sustainable positive effect to the students with the predictive analytics approach.

ACS Style

John C. Yi; Christina D. Kang-Yi; Flavia Burton; H. David Chen. Predictive Analytics Approach to Improve and Sustain College Students’ Non-Cognitive Skills and Their Educational Outcome. Sustainability 2018, 10, 4012 .

AMA Style

John C. Yi, Christina D. Kang-Yi, Flavia Burton, H. David Chen. Predictive Analytics Approach to Improve and Sustain College Students’ Non-Cognitive Skills and Their Educational Outcome. Sustainability. 2018; 10 (11):4012.

Chicago/Turabian Style

John C. Yi; Christina D. Kang-Yi; Flavia Burton; H. David Chen. 2018. "Predictive Analytics Approach to Improve and Sustain College Students’ Non-Cognitive Skills and Their Educational Outcome." Sustainability 10, no. 11: 4012.

Journal article
Published: 17 August 2016 in Sustainability
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We investigate the impact of early adoption of an innovative analytics approach on organizational analytics maturity and sustainability. With the sales operation planning involving the accurate determination of physician detailing frequency, multiple product sequencing, nonlinear promotional response functions and achievement of the right level of share of voice (SOV), an analytical approach was developed by integrating domain knowledge, neural network (NN)’s pattern-recognition capability and nonlinear mathematical programming to address these challenges. A pharmaceutical company headquartered in the U.S. championed this initial research in 2005 and became the first major firm to implement the recommendations. The company improved its profitability by 12% when piloted to a sales district with 481 physicians; then it launched this approach nationally. In 2014, the firm again gave us its data, performance of the analytical approach and access to key stakeholders to better understand the changes in the pharmaceutical sales operations landscape, the firm’s analytics maturity and sustainability of analytics. Results suggest that being the early adopter of innovation doubled the firm’s technology utilization from 2005 to 2014, as well as doubling the firm’s ability to continuously improve the sales operations process; it outperformed the standard industry practice by 23%. Moreover, the infusion of analytics from the corporate office to sales, improvement in management commitment to analytics, increased communications for continuous process improvement and the successes from this approach has created the environment for sustainable organizational growth in analytics.

ACS Style

John C. Yi; Sungho Kim. Early Adoption of Innovative Analytical Approach and Its Impact on Organizational Analytics Maturity and Sustainability: A Longitudinal Study from a U.S. Pharmaceutical Company. Sustainability 2016, 8, 808 .

AMA Style

John C. Yi, Sungho Kim. Early Adoption of Innovative Analytical Approach and Its Impact on Organizational Analytics Maturity and Sustainability: A Longitudinal Study from a U.S. Pharmaceutical Company. Sustainability. 2016; 8 (8):808.

Chicago/Turabian Style

John C. Yi; Sungho Kim. 2016. "Early Adoption of Innovative Analytical Approach and Its Impact on Organizational Analytics Maturity and Sustainability: A Longitudinal Study from a U.S. Pharmaceutical Company." Sustainability 8, no. 8: 808.

Journal article
Published: 30 April 2011 in Expert Systems with Applications
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Any pharmaceutical company relying heavily on its sales force to detail multiple products knows the importance of optimizing a short time window to detail its products to physicians effectively, in the right sequence. With the trend toward decreasing detailing time that is now averaging less than a minute, the optimization of this period is critical to success, especially in today’s challenging selling environment. This paper develops a knowledge-based approach that integrates domain experts’ knowledge of the definition of promotional responsiveness with a hybrid model of neural networks and a nonlinear program to accurately determine the physician detail equivalent (PDE) weights that reflect the weighted sequence of detail and portfolio size while identifying the physicians who are responsive to details. The output from this approach drives physician detailing planning, as well as planning for market share of detailing volume, which is known as share of voice (SOV) planning. Results based on six months of implementation indicate that the knowledge-based approach performs significantly better than the traditional approach by more than 12% in profit.

ACS Style

John C. Yi. Knowledge-based approach to improving detailing plan in multiple product situations using PDE weights. Expert Systems with Applications 2011, 38, 3835 -3843.

AMA Style

John C. Yi. Knowledge-based approach to improving detailing plan in multiple product situations using PDE weights. Expert Systems with Applications. 2011; 38 (4):3835-3843.

Chicago/Turabian Style

John C. Yi. 2011. "Knowledge-based approach to improving detailing plan in multiple product situations using PDE weights." Expert Systems with Applications 38, no. 4: 3835-3843.