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.     

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.     

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.     

On the “Invisible Inventory Conundrum” in RFID‐Equipped Supply Chains: A Data Science Approach to Assessing Tag Performance

Recent trade reports suggest that RFID implementation continues to lag lofty projections. A primary concern is that, despite the high cost of implementing RFID systems, realized read‐rates fall short of expectations. This results in the invisible inventory conundrum whereby tagged merchandise may still not be accurately represented in inventory records. Drawing from data science to address this issue, we ask: How can directed data mining models be used to identify laboratory test performance criteria for RFID tags that operate reliably across the idiosyncratic facilities (i.e., unique DCs, warehouses, and stores) that comprise apparel retailers’ supply chains? We investigate this question by advancing a methodology that integrates laboratory test performance data, field tests of RFID tags fixed to apparel items and scanned under normal operating conditions, and the application of five directed data mining models to the integrated data set of laboratory and field test results. Our analyses of 45,416 observations show that two directed data mining models may identify—with near‐100% accuracy—laboratory test criteria that discriminate tags having 99% or greater read‐rates in the field. Accordingly, our study validates a generalizable methodology for identifying technical performance standards for tags that operate reliably within apparel retailers’ supply chains.     

Product familiarity and information processing strategies: An exploratory investigation

A theoretical and empirical examination of the relationship between product class familiarity and usage of information processing strategies is presented. Four major dimensions of information processing strategies are identified and used to conceptualize the linkage between product class familiarity and usage of particular strategies. Research hypotheses are tested in an exploratory study using a laboratory choice situation. Implications of the study with respect to usage of specific strategies and for future research are discussed.     

Forecasting volatility in the Indian equity market using return and range-based models

In this article, we assess the time-varying volatility of the National Stock Exchange in the Indian equity market using unconditional estimators and asymmetric conditional econometric models. The volatility estimate and forecast is computed from the interday return and intraday range-based data of the exchange’s flagship index, CNX NIFTY, for the time period spanning 1 January 2009 through 31 December 2013. These are our findings: First, we determine that the time-varying volatility of the index is asymmetric with qualities of stationarity and leptokurtic distribution. Second, the one-step-ahead volatility forecast derived from the univariate time series parameters through the GJR-GARCH ?????process indicates that the model evaluation criteria of the autoregressive process tends towards range-based models vis-à-vis a return-based model. The validity of this methodology is further analysed with the superior predictive ability test, the outcome of which supports the use of range-based conditional models. Finally, among the evaluated range-based model variants, the model confidence set procedure favours the Yang–Zhang estimator as being better suited to forecast the exchange’s volatility than the ones by Parkinson, Garman–Klass and Rogers–Satchell.