Search results

This study proposes the use of cumulative prospect theory (CPT) to predict over- and under-estimation of risks and the counteractive adjustment in a cold chain context. In particular, the purpose of this paper is to address the importance of the socio-demographic characteristics of an individual in influencing risk attitude and the analysis of measurable risk probability.

This study uses CPT as the basis to develop a decision analysis model in which the two functions of value editing and probability weighting are nonlinear to adequately determine the flexible risk attitudes of individuals, as well as their prospects with numerous outcomes and different probabilities. An experiment was conducted to obtain empirical predictions, and an efficient Markov Chain Monte Carlo algorithm was applied to overcome the nonlinearity and dimensionality in the process of parameter estimation.

The respondents overweigh the minor cold chain risks with small probabilities and behave in a risk-averse manner, while underweighting major events with larger ones, thereby leading to risk-seeking behavior. Judgment distortion regarding probability was observed under risk decision with a low probability and a high impact. Moreover, the findings indicate that factors, such as gender, job familiarity and confidentiality significantly influence the risk attitudes and subjective probability weighting of the respondents.

The findings fit the framework of CPT and extend this theory to deal with human risk attitudes and subjective bias in cold chains. In particular, this study enhances the literature by providing an analysis of cold chain risk from both the human decision-making and managerial perspectives. Moreover, this research determined the importance of the socio-demographic characteristics of an individual to explain the variability in risk attitudes and responses.

Managers must consider the issues of flexible risk attitude and subjective judgment when making choices for risk mitigation strategies. Given the focus on counteractive adjustment for over- and under-estimated risk, firms could evaluate cold chain risk more accurately, and thereby enhance their resilience to risky events while reducing the variability of their performance.

The current study is the first to materialize the phenomena of over- and under-estimation of cold chain risks, as well as to emphasize the different characteristics for loss aversion and judgment distortion at the individual level.