Shopping motivations, retail attributes, and retail format choice in a transitional market : evidence from Vietnam

The purpose of this two-phase, sequential mixed method study is to examine the impact of shopping motivations (utilitarian motivation and hedonic motivation), retail format attributes and demographics on the retail format choice of either a supermarket or a traditional market in a transitional Vietnamese economy. The first phase was a qualitative study which explored the link between shopping motivations and retail format attributes, using in-depth interviews with sixteenth shoppers in Hochiminh city. The reason for gathering qualitative data is to enable the researcher to develop the scale of shopping motivation constructs that is contextually relevant to Vietnam, taking into consideration its cultural backdrop and pace of economic development. This is because similar scales developed in a Western context might not be suitable in measuring and determining motivation constructs in Vietnam. The qualitative study aims to adjust and modify these scales as well as assist in discovering retail format attributes and determining demographics variables that might impact on Vietnamese shoppers’ choice retail format. Based on the findings from this qualitative study, combined with extensive, extant literature reviews, a second phase study was undertaken to develop two instruments. These instruments were used to survey consumers shopping mainly for two kinds of products for their households: non-food products and processed food products. The sample size for the non-food products study was 276 shoppers, and the sample size of the processed-food products study was 301 shoppers. The surveys were conducted in 24 districts of Hochiminh city. Logistic regression modeling was used to analyze the data. Results from the study indicated that shopping motivations (utilitarian motivation and hedonic motivation) impact significantly on retail format choice in a transitional, Vietnamese economy. In addition, “location” and “time convenience” were traditional market attributes that were strong predictors in shoppers’ choice of traditional markets. “Merchandise selection” was an important supermarket attribute in predicting shoppers’ choice of supermarkets. Finally, age and the average household income monthly of Vietnamese were found to be important predictors of retail format choice for only processed food products but not for non-food products

Bayesian Approach For Early Stage Event Prediction In Survival Data

Predicting event occurrence at an early stage in longitudinal studies is an important and challenging problem which has high practical value. As opposed to the standard classification and regression problems where a domain expert can provide the labels for the data in a reasonably short period of time, training data in such longitudinal studies must be obtained only by waiting for the occurrence of sufficient number of events. On the other hand, survival analysis aims at finding the underlying distribution for data that measure the length of time until the occurrence of an event. However, it cannot give an answer to the open question of how to forecast whether a subject will experience event by end of study having event occurrence information at early stage of survival data?\u27\u27. This problem exhibits two major challenges: 1) absence of complete information about event occurrence (censoring) and 2) availability of only a partial set of events that occurred during the initial phase of the study. Thus, the main objective of this work is to predict for which subject in the study event will occur at future based on few event information at the initial stages of a longitudinal study. In this thesis, we propose a novel approach to address the first challenge by introducing a new method for handling censored data using Kaplan-Meier estimator. The second challenge is tackled by effectively integrating Bayesian methods with an Accelerated Failure Time (AFT) model by adapting the prior probability of the event occurrence for future time points. In another word, we propose a novel Early Stage Prediction (ESP) framework for building event prediction models which are trained at early stages of longitudinal studies. More specifically, we extended the Naive Bayes, Tree-Augmented Naive Bayes (TAN) and Bayesian Network methods based on the proposed framework, and developed three algorithms, namely, ESP-NB, ESP-TAN and ESP-BN, to effectively predict event occurrence using the training data obtained at early stage of the study. The proposed framework is evaluated using a wide range of synthetic and real-world benchmark datasets. Our extensive set of experiments show that the proposed ESP framework is able to more accurately predict future event occurrences using only a limited amount of training data compared to the other alternative prediction methods

A probabilistic paradigm for the parametric insurance of natural hazards.

This is the author accepted manuscript. The final version is available from Wiley via the DOI in this record.There is a pressing need for simple and reliable risk transfer mechanisms that can pay out quickly after natural disasters without delays caused by loss estimation, and the need for long historical claims records. One such approach, known as parametric insurance, pays out when a key hazard variable exceeds a predetermined threshold. However, this approach to catastrophe risk, based on making deterministic binary predictions of loss occurrence, is susceptible to basis risk (mismatch between payouts and realized losses). A more defensible approach is to issue probabilistic predictions of loss occurrence, which then allows uncertainty to be properly quantified, communicated, and evaluated. This study proposes a generic probabilistic framework for parametric trigger modeling based on logistic regression, and idealized modeling of potential damage given knowledge of a hazard variable. We also propose various novel methods for evaluating the quality and utility of such predictions as well as more traditional trigger indices. The methodology is demonstrated by application to flood-related disasters in Jamaica from 1998 to 2016 using gridded precipitation data as the hazard variable. A hydrologically motivated transformation is proposed for calculating potential damage from daily rainfall data. Despite the simplicity of the approach, the model has substantial skill at predicting the probability of occurrence of loss days as demonstrated by traditional goodness-of-fit measures (i.e., pseudo-R2 of 0.55) as well as probabilistic verification diagnostics such as receiver operating characteristics. Using conceptual models of decisionmaker expenses, we also demonstrate that the system can provide considerable utility to involved parties, e.g., insured parties, insurers, and risk managers.Benjamin Youngman’s research was supportedby the Willis Research Network

Information gain that convective-scale models bring to probabilistic weather forecasts

Ensemble prediction systems, run nowadays at convective-scale by several operational and research forecasting centres, undoubtedly provide a large amount of data. Whether and how the utilisation of these data can lead to additional valuable information for the probabilistic prediction of specific weather phenomena is an open challenge. Here, the information gain of the Met Office convective-scale ensemble relative to its lower resolution global model counterpart (33 km) is quantified for the sea breeze phenomenon. It occurs on small spatio-temporal scale but influenced by the large-scale conditions, well represented by the global ensembles. Sea breeze is also an atmospheric counterpart of the so called density currents and could lead to high impact weather, especially when colliding with other sea breezes or mesoscale flows. In the first part of the thesis, a new set of numerical simulations of colliding density currents is presented, with the aim of understanding the dynamics of collision. A novel parametric formula for predicting the collision angle is shown to agree well with numerical data. This can be useful for future parametrizations of these processes. Probabilistic forecasts of the occurrence of sea breezes have been then produced, compared and verified over an extended period. This motivated the development of a novel method for the automatic identification of the sea breeze from convective-scale ensembles. This method can be in principle applied to every coastline. A Bayesian approach is instead used to extract information from the coarser resolution ensemble. It is trained on paired high/low resolution ensemble member, but it can be trained also on observations. This method creates a statistical forecast of the high-resolution ensemble member, given the knowledge of the global ensemble predictors alone. In the last part of the thesis, the same methodology has been applied to the prediction of wind gusts. Comparison of the two forecasting methods, using a variety of well established verification metrics all lead to the same conclusion: although the Bayesian forecasts have potential skill for the prediction of event occurrence, the convective-scale ensemble is shown to be more skillful

A Review of Expertise and Judgment Processes for Risk Estimation

A major challenge of risk and reliability analysis for human errors or hardware failures is the need to enlist expert opinion in areas for which adequate operational data are not available. Experts enlisted in this capacity provide probabilistic estimates of reliability, typically comprised of a measure of central tendency and uncertainty bounds. While formal guidelines for expert elicitation are readily available, they largely fail to provide a theoretical basis for expertise and judgment. This paper reviews expertise and judgment in the context of risk analysis; overviews judgment biases, the role of training, and multivariate judgments; and provides guidance on the appropriate use of atomistic and holistic judgment processes

Design of an event-based early warning system for process operations

This thesis proposes a new methodology to design an event-based warning system as an alternative to the conventional variable-based alarm system. This study initially explores the options for grouping process variables for alarm allocation. Several grouping methods are discussed and an event-based grouping procedure is detailed. Selection of the key variables for a group is performed considering the information that the variables contain to distinguish between an abnormal and a normal condition. The information theory is used to quantify the information content of a variable about an event to select the key variables. The cross-correlation analysis between pairs of key variables is used to identify the redundant variables. Simulation study using the model of a continuous stirred tank reactor (CSTR) is used to demonstrate the methodology. The proposed event-based early warning system utilizing online measurements is detailed in the thesis. In this approach, warnings are assigned to plant abnormal events instead of individual variables. To assess the likelihoods of undesirable events, the Bayesian Network is used; the event likelihoods are estimated in real time utilizing online measurements. Diagnostic analysis is conducted to identify root-causes of events. By assigning warning to events, the methodology results in significantly lower number of warnings compared to traditional variable-based warning (alarms) system. It also enables early warning of a possible event along with an efficient diagnosis of the root-causes of the event. Experimental testing using a level control system is presented to demonstrate the efficacy of the proposed method. Simulation study using the model of a CSTR is also presented to demonstrate the performance of the algorithm. Both, experimental and simulation studies, have shown promising results

Computational Models of Information Processing

The necessity and desire to understand the nature of information permeates virtually all aspects of scientific endeavors in both natural and social systems. This is particularly true in research that seeks to understand how various forms of organizations arise and function. This dissertation is dedicated towards understanding one important concern in the study of information in organizations: that of information integrity. There are two aspects of information integrity that are interesting to examine in management contexts: First, because the definition and quantification of information is often nebulous, the onus to preserve information integrity, from ensuring a successful information transfer to minimizing distortion, to a large extent falls on the information recipient. Second, unlike in physical systems, in which there tends to be a clear demarcation between the information processing system and the environment, the boundary between the two in management contexts is usually more diffuse, which can give rise to complex interactions. The structure of the environment can thus have a significant influence on an information recipient\u27s ability to process information and to preserve its integrity. I present two computational models to develop theories about these aspects: In the first, I look at how an organization\u27s strategic effort to acquire (and therefore receive) information co-evolves with its absorptive capacity in different types of environment. Here, loss of information integrity is defined by acquisition failure. The model suggests that an exploitative information acquisition strategy could better preserve information integrity and eventually generate a more diverse knowledge stock than an explorative strategy could, thereby challenging common assumptions. The model also highlights several environmental and cognitive parameters that modulate the relationship between information acquisition strategy and its outcome. In the second model, I look at information processing in the context of event forecasting. The model is built on the idea that events have structural signatures that are given by the web of causal relationships from which those events arise. As forecasters receive information about an event, their failure to preserve the integrity of the event\u27s structural information hurts forecast performance, but interestingly, some events have structural characteristics that buffer against this effect

Goal characteristics predict the occurrence of goal-related events through belief in future occurrence

peer reviewedWhile previous studies have highlighted the role of episodic future thinking in goal pursuit, the underlying cognitive mechanisms remain unexplored. Episodic future thinking may promote goal pursuit by shaping the feeling that imagined events will (or will not) happen in the future - referred to as belief in future occurrence. We investigated whether goal self-concordance (Experiment 1) and other goal characteristics identified as influential in goal pursuit (Experiment 2) modulate belief in the future occurrence of goal-related events and predict the actual occurrence of these events. Results showed that goal self-concordance, engagement, and expectancy had an indirect effect on the actual occurrence of events, which was (partially) mediated by belief in future occurrence. The mediating role of belief supports the view that belief in future occurrence when imagining events conveys useful information, allowing us to make informed decisions and undertake adaptive actions in the process of goal pursuit