Can Future Managers and Business Executives be Influenced to Behave more Ethically in the Workplace? The Impact of Approaches to Learning on Business Students’ Cheating Behavior

This study considers the potential for influencing business students to become ethical managers by directing their undergraduate learning environment. In particular, the relationship between business students’ academic cheating, as a predictor of workplace ethical behavior, and their approaches to learning is explored. The three approaches to learning identified from the students’ approaches to learning literature are deep approach, represented by an intrinsic interest in and a desire to understand the subject, surface approach, characterized by rote learning and memorization without understanding, and strategic approach, associated with competitive students whose motivation is the achievement of good grades by adopting either a surface or deep approach. Consistent with the hypothesized theoretical model, structural equation modeling revealed that the surface approach is associated with higher levels of cheating, while the deep approach is related to lower levels. The strategic approach was also associated with less cheating and had a statistically stronger influence than the deep approach. Further, a significantly positive relationship reported between deep and strategic approaches suggests that cheating is reduced when deep and strategic approaches are paired. These findings suggest that future managers and business executives can be influenced to behave more ethically in the workplace by directing their learning approaches. It is hoped that the evidence presented may encourage those involved in the design of business programs to implement educational strategies which optimize students’ approaches to learning towards deep and strategic characteristics, thereby equipping tomorrow’s managers and business executives with skills to recognize and respond appropriately to workplace ethical dilemmas.

Is it time for standards and a common data exchange format for digital image analysis?

Digital image analysis is at a crossroads. While the technology has made great strides over the past few decades, there is an urgent need for image analysis to inform the next wave of large scale tissue biomarker discovery studies in cancer. Drawing parallels from the growth of next generation sequencing, this presentation will consider the case for a common language or standard format for storing and communicating digital image analysis data. In this context, image analysis data comprises more than simply an image with markups and attached key-value pair metrics. The desire to objectively benchmark competing platforms or a push for data to be deposited to public repositories much like genomics data may drive the need for a standard that also encompasses granular, cell-by-cell data.

Modelling mortality: Are we heading in the right direction?

Predicting life expectancy has become of upmost importance in society. Pension providers, insurance companies, government bodies and individuals in the developed world have a vested interest in understanding how long people will live for. This desire to better understand life expectancy has resulted in an explosion of stochastic mortality models many of which identify linear trends in mortality rates by time. In making use of such models for forecasting purposes we rely on the assumption that the direction of the linear trend (determined from the data used for fitting purposes) will not change in the future, recent literature has started to question this assumption. In this paper we carry out a comprehensive investigation of these types of models using male and female data from 30 countries and using the theory of structural breaks to identify changes in the extracted trends by time. We find that structural breaks are present in a substantial number of cases, that they are more prevalent in male data than in female data, that the introduction of additional period factors into the model reduces their presence, and that allowing for changes in the trend improves the fit and forecast substantially.