Howard Hao-Chun Chuang

The impact of e-retail characteristics on initiating mobile retail services

The rise and challenges of m-commerce have led to an urgent need to examine how firms adopt the emerging sales channel. Early studies mainly discussed the differences between e-commerce and m-commerce. Our study shows the modular innovation from e-retailing to m-retailing, which changes the core component of service delivery but keeps the operations intact, provides more opportunities for well-entrenched firms. Using a dataset of e-retailers, we find e-retail characteristics have an impact on firms migration to the mobile domain. Firms with online service competencies, economies of scale, and physical outlets are more inclined to exploit opportunities provided by mobile technologies.

Estimating Retail Demand with Poisson Mixtures and Out-of-Sample Likelihood

Estimation of retail demand is critical to decisions about procuring, shipping, and shelving. The idea of Poisson demand process is central to retail inventory management and numerous studies suggest that negative binomial (NB) distribution characterize retail demand well. In this study we reassess the adequacy of estimating retail demand with the NB distribution. We propose two Poisson mixtures – the Poisson-Tweedie (PT) family and the Conway-Maxwell Poisson (CMP) distribution – as generic alternatives to the NB distribution. Based on the principle of likelihood and information theory, we adopt out-of-sample likelihood (OSL) as a metric for model selection. We test the procedure on consumer demand for 580 SKU-store sales datasets. Overall the PT family and the CMP distribution outperform the NB distribution for 70% of the tested samples. As a general case of the NB model, the PTF family has particularly strong performance for datasets with relatively small means and high dispersion. Our finding carries useful implications for researchers and practitioners who seek for flexible alternatives to the oft-used NB distribution in characterizing retail demand.

Impact of value-added service features in e-retailing processes: : An econometric analysis of web site functions

We examine the impact of three classes of website functions (foundational, customer-centered, and value-added) upon e-retailer performance. Using secondary panel data for 2007-2009 on operating characteristics of over 600 e-retailers, our econometric analysis finds that only the value-added service functions are positively associated with changes in e-retail sales revenues across time. We also observe a decreasing marginal impact of deploying additional value-added service features. To account for possible alternate explanations, we control for firm- and time-specific fixed effects, merchant types, merchandise categories, and order fulfillment strategies. By further decomposing e-retail sales revenues into website traffic, conversion rate, and average order value, we find that website functions affect e-retail sales revenues mainly through their impact on website traffic. Our investigation demonstrates the empirical research usefulness of the Voss (2003) conceptual e-service sand cone model. Our results identify for managers where to focus ongoing e-retailing system development efforts, yet suggest that focusing too many retailing capabilities on exploratory and experimental value-added service features may backfire, potentially leading to worsening e-retailer performance.

Fixing shelf out-of-stock with signals in point-of-sale data

Abstract Shelf out-of-stock (OOS) is a salient problem that causes non-trivial profit loss in retailing. To tackle shelf-OOS that plagues customers, retailers, and suppliers, we develop a decision support model for managers who aim to fix the recurring issue of shelf-OOS through data-driven audits. Specifically, we propose a point-of-sale (POS) data analytics approach and use consecutive zero sales observations in POS data as signals to develop an optimal audit policy. The proposed model considers relevant cost factors, conditional probability of shelf-OOS, and conditional expectation of shelf-OOS duration. We then analyze the impact of relevant cost factors, stochastic transition from non-OOS to OOS, zero sale probability of the underlying demand, managers’ perceived OOS likelihood, and even random fixes of shelf-OOS on optimal decisions. We also uncover interesting dynamics between decisions, costs, and probability estimates. After analyzing model behaviors, we perform extensive simulations to validate the economic utility of the proposed data-driven audits, which can be a cost-efficient complement to existing shelf inventory control. We further outline implementation details for the sake of model validation. Particularly, we use Bayesian inference and Markov chain Monte Carlo to develop an estimation framework that ensures all model parameters are empirically grounded. We conclude by articulating practical and theoretical implications of our data-driven audit policy design for retail managers.

Impact of Value-Added Service Features in e-Retailing Processes: An Econometric Analysis of Web Site Functions: Impact of Value-Added Service Features in e-Retailing Processes

We examine the impact of three classes of website functions (foundational, customer-centered, and value-added) upon e-retailer performance. Using secondary panel data for 2007-2009 on operating characteristics of over 600 e-retailers, our econometric analysis finds that only the value-added service functions are positively associated with changes in e-retail sales revenues across time. We also observe a decreasing marginal impact of deploying additional value-added service features. To account for possible alternate explanations, we control for firm- and time-specific fixed effects, merchant types, merchandise categories, and order fulfillment strategies. By further decomposing e-retail sales revenues into website traffic, conversion rate, and average order value, we find that website functions affect e-retail sales revenues mainly through their impact on website traffic. Our investigation demonstrates the empirical research usefulness of the Voss (2003) conceptual e-service sand cone model. Our results identify for managers where to focus ongoing e-retailing system development efforts, yet suggest that focusing too many retailing capabilities on exploratory and experimental value-added service features may backfire, potentially leading to worsening e-retailer performance.

Impact of value-added features in e-retailing processes: An econometric analysis of website functionality

We examine the impact of three classes of website functions (foundational, customer-centered, and value-added) upon e-retailer performance. Using secondary panel data for 2007-2009 on operating characteristics of over 600 e-retailers, our econometric analysis finds that only the value-added service functions are positively associated with changes in e-retail sales revenues across time. We also observe a decreasing marginal impact of deploying additional value-added service features. To account for possible alternate explanations, we control for firm- and time-specific fixed effects, merchant types, merchandise categories, and order fulfillment strategies. By further decomposing e-retail sales revenues into website traffic, conversion rate, and average order value, we find that website functions affect e-retail sales revenues mainly through their impact on website traffic. Our investigation demonstrates the empirical research usefulness of the Voss (2003) conceptual e-service sand cone model. Our results identify for managers where to focus ongoing e-retailing system development efforts, yet suggest that focusing too many retailing capabilities on exploratory and experimental value-added service features may backfire, potentially leading to worsening e-retailer performance.