Injury count model for quantification of risk of occupational injury

Reduction of risk of occupational injuries is one of the most challenging problems faced by industry. Assessing and comparing risks involved in different jobs is one of the important steps towards reducing injury risk. In this study, a comprehensive scheme is given for assessing and comparing injury risks with the development of injury count model, injury risk model and derived statistics. The hazards present in a work system and the nature of the job carried out by workers are perceived as important drivers of injury potential of a work system. A loglinear model is used to quantify injury counts and the event-tree approach with joint, marginal and conditional probabilities is used to quantify injury risk. A case study was carried out in an underground coal mine. Finally a number of indices are proposed for the case study mine to capture risk of injury in different jobs. The findings of this study will help in designing injury intervention strategies for the mine studied. The job-wise risk profiles will be used to prioritise the jobs for redesign. The absolute indices can be applied for benchmarking job-wise risks and the relative indices can be used for comparing job-wise risks across work systems.     

How category characteristics affect the number of store brands offered by the retailer: a model and empirical analysis

Our research examines why retailers offer, not one, but multiple store brands in some product categories. More specifically, we are interested in how certain product category characteristics affect the number of store brands. We model a product category consisting of two incumbent national brands that may differ in strength. The retailer may introduce one or two store brands depending on which maximizes category profits. Our analysis suggests that the retailer is likely to carry two store brands in categories where (i) the national brands are similar in strength; and (ii) the price sensitivity between the national brands is low. Interestingly, the conditions that support the introduction of more than one store brand are quite different than the conditions that would facilitate the introduction of additional national brands. We provide empirical evidence that support our model-based predictions.     

The mobile shopping revolution: Redefining the consumer decision process

The use of mobile devices by consumers and the accompanying response by retailers is rapidly revolutionizing the retail environment. In the past, retailers have focused primarily on the outcome (to purchase or not to purchase) of the consumer decision process, but now mobile technologies give retailers the opportunity to more actively influence the entire consumer decision-making processes. The increasing use of mobile devices by consumers makes shopping a continuous rather than discrete activity that requires retailers to engage with their customers at critical touch points of the decision process in order to provide a more customer-centric experience. This change in focus from the decision outcome to the decision process signifies an important paradigm shift for the retailing industry. After an extensive review of the literature, we identify four pillars that form the foundation for the mobile shopping revolution and represent the essential ways and means through which retailers can engage with consumers during the decision process. We also discuss the different areas in which the pillars can enable retailers to achieve a sustainable competitive advantage in the mobile shopping era.     

Loglinear model for analysis of cross-tabulated coal mine injury data

Mine accidents/injuries can be cross-classified against the variables of interest in a contingency table and their associations can be assessed through aggregate statistics. However, there is a need to develop a coherent and structured procedure for analysis of accident data, which will allow one to identify associations between two or more variables multivariately. In this study, the loglinear model, which has been proposed as a mathematical representation of the contingency table, was applied to accident data from a group of coal mines to assess the associations/interactions between two or more variables multivariately through their main and interaction effects. The case study results revealed that the variables ‘occupation’ and ‘workplace location’ were highly associated with degree of injury. It was also clearly indicated that the workers with more than 20 years of experience exhibited high injury rate patterns. While designing training programs for miners, focused attention should be given to specific categories of workers to reduce accident/injuries at the case study mines.     

A CRITICAL REVIEW ON EVOLUTION OF RISK FACTORS AND FACTOR MODELS

The purpose of this work is to critically evaluate the evolution of risk factors and factor models. A systematic and structured literature review is carried out to observe and understand the past trends and extant patterns/themes in the present research area, evaluate contributions and summarize knowledge, thereby identifying limitations, implications and potential directions of further research. The main message from the study is that evolution of risk factors and factor models are continuous and endless development. Still today over 300 risk factors are identified by the researchers and many other yet to be discovered but out of them all only few are significantly responsible in explaining the stock markets risk return relationship. Study classifies risk factors into two groups: global and specific risk factors. Study answer the question ‘whether evolution of risk factors and factor models are endless development’. Finally, the present study gives an appropriate direction to the future studies to be taken in terms of risk factors and factor models. Due to continuous evolution and changing of nature of the risk factor it seems quite impossible to have a stable efficient factor models that can explain stock market risk return relationship globally in long run.     

TSFDC: A trading strategy based on forecasting directional change

Directional Change (DC) is a technique to summarize price movements in a financial market. According to the DC concept, data is sampled only when the magnitude of price change is significant according to the investor. In this paper, we develop a contrarian trading strategy named TSFDC. TSFDC is based on a forecasting model which aims to predict the change of the direction of market's trend under the DC context. We examine the profitability, risk and risk‐adjusted return of TSFDC in the FX market using eight currency pairs. The results suggest that TSFDC outperforms the buy and hold approach and another DC‐based trading strategy.     

Coherent forecasting for count time series using Box–Jenkins's AR(p) model

During the last three decades, integer‐valued autoregressive process of order p [or INAR(p)] based on different operators have been proposed as a natural, intuitive and maybe efficient model for integer‐valued time‐series data. However, this literature is surprisingly mute on the usefulness of the standard AR(p) process, which is otherwise meant for continuous‐valued time‐series data. In this paper, we attempt to explore the usefulness of the standard AR(p) model for obtaining coherent forecasting from integer‐valued time series. First, some advantages of this standard Box–Jenkins's type AR(p) process are discussed. We then carry out our some simulation experiments, which show the adequacy of the proposed method over the available alternatives. Our simulation results indicate that even when samples are generated from INAR(p) process, Box–Jenkins's model performs as good as the INAR(p) processes especially with respect to mean forecast. Two real data sets have been employed to study the expediency of the standard AR(p) model for integer‐valued time‐series data.     

Trade liberalization,production organization and informal sector of the developing countries

The paper provides an explanation of recent empirical evidence on fragmentation and the expansion of the informal sector in India. We argue that as the prospect of getting a better price in the international market increases, the producers in the formal sector act more like merchants and subcontract production activities to the producers in the informal sectors. Expanding production in the informal sector allows the firm to take advantage of a growing export market. Our theoretical model explains such organizational change in terms of allocation of monitoring effort between marketing and production. The existence of a low-wage informal sector facilitates division of labor and specialization in the formal segment.     

Adverse influence of brand commitment on consideration of and preference for competing brands

This research investigates if commitment towards a target brand limits set size and preference towards competing brands in memory‐based consideration sets across three situations: (1) situations in which the situational goal matches the benefit offered by the target brand; (2) situations in which the situational goal matches the benefit offered by a competitor brand; and (3) situations in which a new benefit is introduced in the category for the first time. Findings confirm the set size and preference biases of committed consumers against a competitor brand in the first two situations. However, in the third situation, committed consumers favorably or unfavorably view the competitor brand depending on whether it is the first brand to introduce the new benefit in the category. © 2007 Wiley Periodicals, Inc.