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This study aims to use a variant from the family of discrete choice models, i.e. the logit model, to analyse the relationship between the Y dependent and X explanatory variables. This model addresses the linear probability model's main drawback by constraining the probabilities of the Y outcome between 0 and 1. The logit model also offers an extra advantage, in that it can provide odd ratio estimations.

This is a compact case written specifically to teach statistics, econometrics and research method. It has an accompanying data set for the case-users to do hands-on statistical analyses. The data set has been collected from a questionnaire survey from the students enrolled in Attitune, i.e. the music school that the case protagonist founded.

The case revolves around a relatively new music school, Attitune Music, established in July 2017 in the heart of the capital city of a northern state in Malaysia. Michael Lee Wei-Pin was the founder of Attitune Music Sdn. Bhd. He was also one of the four music instructors of Attitune Music. His speciality instruments were the guitar and the piano. The case opens with the case protagonist, Michael, pondering over Attitune’s performance in terms of its music students’ enrolment. Attitune faced a major challenge – its student enrolment had remained more or less constant since its establishment. Low and/or constant number of students could ultimately translate into stagnant or even worse, shrinking revenues for Attitune. To attract more students, Michael had been toying with the idea of injecting new elements into Attitune’s music lessons, something different from what other music schools were offering and that could be unique selling points for Attitune. With this in mind, Michael surveyed Attitune’s students to gather information that could help him gauge the potential and feasibility of his idea.

This case is well positioned to be perhaps the pioneer Malaysian teaching case to be written to teach courses in statistics, econometrics and research methods. The case can be easily adapted to teach at either the introductory or at an advanced level.

Agricultural Trade, Farm Management, Economics of Food Safety

Both undergraduate and postgraduate studies in Agribusiness and Agricultural Economics.

The pineapple production sector plays a very significant role in the Ghanaian horticultural industry. Production and export of fresh pineapple has been Ghana’s most developed high-value supply chain. However, the introduction of the GlobalGAP food safety standard in 2007 resulted in a fall in smallholder farmers’ participation in exportable pineapple production and subsequently led to declining trends in pineapple exports. The Ghanaian horticultural industry received quite a number of interventions over the years aimed at revitalizing the horticultural export sector and enhancing international competitiveness. However, the pineapple export sub-sector is still constrained with production and market access challenges meaning the sector struggles to survive.

The GlobalGAP standard compliance case is an appropriate way of explaining how smallholder farmers make informed decisions concerning the adoption of new farm practices. The case presents a careful evaluation of technical, institutional and socio-economic factors influencing a farmer’s decision to comply or not to comply with the GlobalGAP standard. Students should be able to apply farm management decision-making concepts and tools such as profit maximization and binary choice modelling techniques to explain a farmers’ final decisions on GlobalGAP standard compliance. This case should enable students to appreciate key factors constraining agricultural export trade performance in developing countries. The case should also contribute to students’ understanding of smallholder farmers’ decisions on food safety standards compliance, particularly GlobalGAP, and the challenges associated with the entire compliance process. Moreover, this case should provide students with possible policy considerations geared towards making food safety standards compliance easier, effective and sustainable in developing countries so as to enhance market access while ensuring food quality and safety along high-value food supply chains.

Teaching Notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes.

CSS 7 Management Science

This case, along with its B case (UVA-QA-0865), is an effective vehicle for introducing students to the use of machine-learning techniques for classification. The specific context is predicting customer retention based on a wide range of customer attributes/features. The specific techniques could include (but are not limited to): regressions (linear and logistic), variable selection (forward/backward and stepwise), regularizations (e.g., LASSO), classification and regression trees (CART), random forests, graduate boosted trees (xgboost), neural networks, and support vector machines (SVM).

The case is suitable for an advanced data analysis (data science, machine learning, and artificial intelligence) class at all levels: upper-level business undergraduate, MBA, EMBA, as well as specialized graduate or undergraduate programs in analytics (e.g., masters of science in business analytics [MSBA] and masters of management analytics [MMA]) and/or in management (e.g., masters of science in management [MScM] and masters in management [MiM, MM]).

The teaching note for the case contains the pedagogy and the analyses, alongside the detailed explanations of the various techniques and their implementations in R (code provided in Exhibits and supplementary files). Python code, as well as the spreadsheet implementation in XLMiner, are available upon request.