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This paper is an extension of the current literature on the impact of climate induced natural disasters on the agricultural sector and the first empirical study that investigates the impact of natural disasters on technical efficiency (TE) of rice (also known as paddy) production in Sri Lanka and elsewhere. Severity and intensity of disasters were measured using data on a number of different natural disasters. Data for 22 districts in Sri Lanka representing two rice growing seasons and three agro-ecological regions for the 18 year period from 2000 were collected. The study employed a stochastic production function approach to examine technical efficiencies of different districts and different agro-ecological regions. The results reveal that drought and flood have a significant negative impact on the TE of rice production and that droughts have had the most severe impact. The disasters have decreased the TE of rice production by 2–5% on average. This study highlights the need to change the policies and resource allocation currently based on the traditional knowledge and beliefs about production efficiency during different seasons and in different agro-ecological regions. Such changes need to reflect the global trend of greater climatic variations resulting from extreme weather conditions.

Improving the productive efficiency (also called technical efficiency) of real estate and construction firms (RECFs) is essential to tackle the sustained shortage of housing supply and the increasing unaffordability of housing in developing countries. However, there are few studies that focus on this issue in the context of developing countries; and available literature focuses mainly on firms listed on stock markets. Our study employs the meta-frontier framework to measure the efficiency of 832 small, medium, and large RECFs across Indonesia from 2012 to 2016, using a data envelopment analysis technique. The meta-frontier framework allows for different production technologies and different business environments operated by firms of different sizes. Under the specification of group-specific production frontiers, large firms obtain the highest average efficiency scores (0.694), followed by medium firms (0.529), and small firms (0.479). The technology gaps between the meta-frontier and the group-frontiers for small and medium firms are relatively large, suggesting that the overall industry would be able to achieve remarkable efficiency improvement if firms could access technologies used by more efficient firms. Our results also show that determinants of the efficiency vary across firm groups, suggesting that policy and managerial interventions tailored to each group would have more impact on the overall productive efficiency of the entire industry.

There is an increasing demand to introduce Introductory Business Analytics (IBA) courses into undergraduate business education. Many real-world business contexts require predictive analytics to understand the determinants of a dichotomous outcome; hence, IBA courses should include binary logistic regression analysis. This article provides our reflective discussions on the design of learning activities and assessments to assist business students in learning binary logistic regression in an IBA course. Data on student engagement and learning outcomes are used to shed light on the impacts of teaching logistic regression on student learning and experience. Notably, students opt to focus their assessment work more on logistic regression than on multiple regression analysis, showing the potential attraction of students toward binary logistic regression analysis. We also observed several challenges, mainly related to the use of Excel, that require special attention from instructors.