PRODUCTIVITY OR UNEXPECTED DEMAND SHOCKS: WHAT DETERMINES FIRMS' INVESTMENT AND EXIT DECISIONS?

We investigate the roles played by unexpected demand shocks, besides productivity, on firms' capital investment and exit decisions. We propose a practical approach to recover unexpected firm‐level demand shocks using inventory data. The recognition of demand shocks and inventory also improves the productivity estimation. The empirical results indicate that although productivity and demand shocks are both significant factors determining firm behavior, the former is more dominant for investment decision and the latter is more salient for firm exit. These findings confirm that unexpected demand shocks, besides persistent productivity, are important factors when analyzing capital investment and firm exit decisions.     

A combined approach using AHP and DEMATEL for evaluating success factors in implementation of green supply chain management in Indian manufacturing industries

The present work aims to evaluate the success factors (SFs) associated with the implementation of green supply chain management (GSCM) from the Indian manufacturing industry perspective. Initially, 24 SFs relevant to GSCM adoption and implementation were identified from the literature and inputs received from relevant experts. Subsequently, a structural model was proposed by means of combining the Analytical Hierarchy Process (AHP) and the Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach to evaluate these SFs at the tactical, operational and strategic levels in GSCM adoption. The AHP method helps in determining the relative importance/priorities of the SFs to GSCM adoption. In addition, the DEMATEL method captures the interactive relationships among the SFs, and analyses them on the basis of causal effect mapping. The model proposed can help industrial managers/practitioners to formulate short-term as well as long-term flexible decision strategies in efficiently managing a green supply chain.     

Assessment of CSR based supply chain performance system using an integrated fuzzy AHP-TOPSIS approach

The objective of this research was to identify Corporate Social Responsibility (CSR) criteria as well as propose and prioritise the alternatives to improve the supply chain performance system of an organisation. To meet the objective, firstly, CSR-based criteria, sub-criteria and alternatives were identified based on the literature review and discussion with the field experts, taken from automobile industries located at the Delhi region of India. Then, kappa statistics has been applied to check the internal consistency between identified criteria and sub-criteria. After finding the evidence of internal consistency, a hierarchical-type model has been developed and analysed using a multi-criteria decision-making as an integrated fuzzy analytical hierarchy process (AHP) and a fuzzy technique for order performance by similarity to the ideal solution (TOPSIS) approach. To make the results more robust and feasible, a sensitivity analysis has also been performed.     

Balancing Profitability and Customer Welfare in a Supermarket Chain

We investigate the impact of price discrimination by a large Chicago supermarket chain. First we measure the impact of the chain's current zone-pricing policy on shelf prices, variable profits and consumer welfare across its stores. Using the chain's database to simulate a finer store-specific micro-pricing policy, we study the implications of this policy on profits and welfare. We show how a store-pricing policy that is constrained to offer consumers at least as much surplus as a uniform chain wide pricing policy still enables the retailer to generate substantial incremental profits.To ensure our pricing problem exhibits a well-defined optimum, we use the parsimonious, mixed-logit demand function that allows for flexible substitution patterns across brands and also retains a link to consumer theory. We discuss the issue of price endogeneity when estimating the demand parameters with weekly store-level data. Standard instrumental variables techniques used to account for such endogeneity also seem to increase the magnitudes of own-price elasticities thereby offsetting the problem encountered by previous researchers of predicted prices from a demand model exceeding those in the actual data.     

Managing Channel Profits: The Role of Managerial Incentives

The existing literature has examined how manufacturers can enhance profits by employing specific channel structures and channel coordination mechanisms. In this paper, we examine the implications of strategically designed managerial incentives for channel performance in a duopoly. We first analyze how equilibrium outcomes (especially manufacturer profits) are altered when the manufacturers provide their channel managers with strategically designed incentives. Following that, we examine how optimal channel structure decisions are altered when manufacturers provide their managers with strategic incentives, i.e., we examine how strategic incentives moderate optimal channel structure decisions. In contrast with the existing literature, we find that an asymmetric channel structure with one manufacturer employing a profit-maximizing retailer and the other integrated manufacturer providing strategic incentives for the channel manager in charge of pricing, is an equilibrium outcome under certain conditions. We then compare how the implications of strategic incentives differ from those of channel structure decisions and channel coordination initiatives, and discuss when and why strategic incentives yield superior outcomes from the manufacturer’s perspective. Our results shed light on the sparsely researched role of managerial incentives in the channel context.     

Modeling and Forecasting the Sales of Technology Products

Managers in technology product markets require sales response models that provide substantive insights into the effects of marketing activities as well as reliable sales forecasts. Such markets are characterized by frequent introductions and withdrawals of multiple models by different companies. Thus, the data available on the performance of any individual model is scarce. A second characteristic is that the effects of product attributes and marketing activities could change over time as different types of consumers participate in the market at different points in time. Given sparse data, it becomes critical to specify a model that allows pooling of information across brand-models while at the same time providing brand-model specific parameters. We accomplish this via a hierarchical Bayesian model specification. Further, to capture the effects of changing consumer preferences over time, we specify a time varying parameter model. Our modeling framework therefore, integrates a hierarchical Bayesian model within a time varying parameter framework to develop a dynamic hierarchical Bayesian model. We employ data on digital cameras in the U.S. market to estimate the parameters of our proposed model. We use thirty-three months of national level data on the digital camera market with the data series beginning very close to the inception of this product category. We find that while there is little variation in reliance of benefits by early adopters, the second wave of adopters focus on Ease of Use followed by later adopters who rely on Storage and Image Quality. Looking at the elasticities of demand with respect to the various benefits, we find that at around the halfway point of our data series, the industry as a whole would have been better off investing in increasing image quality rather than storage if costs associated with the two are equal. However, at the end of the time horizon both benefits appear to have about equal impact. Further, the relative benefits of improving these attributes vary across brands and points in time. We then generate single period and multiple period ahead sales forecasts. We make different assumptions about information availability and find that the average (across brand-models and time) MAPE ranges from 7.5 to 14.5% for the model. We provide extensive comparisons of our model with 4 potential alternatives and find that our model outperforms these alternatives on the nature of substantive insights obtained as well as in forecasting out-of-sample especially when there is a very short time window of data.     

Buy high,sell low: How listed firms price asset transfers in related party transactions

We examine a sample of 254 related party and arms’ length acquisitions and sales of assets in Hong Kong during 1998–2000. Our analysis shows that publicly listed firms enter deals with related parties at unfavourable prices compared to similar arms’ length deals. Firms acquire assets from related parties by paying a higher price compared to similar arms’ length deals. In contrast, when they sell assets to related parties, they receive a lower price than in similar arms’ length deals. With the exception of audit committees, corporate governance characteristics have limited impact on transaction prices. Firms with audit committees on their boards pay lower prices to related parties for acquisitions and receive higher prices from related parties from divestments.     

Investigating the effects of a line extension or new brand introduction on market structure

Market structure has been characterized by the locations of brands in the perceptual space of households (perceptual map) and the importance weights associated with the attributes of this map. It is hypothesized that a line extension or a new brand introduced into this market could potentially affect the brand locations or the importance weights. We show how a simple extension of a recent approach to market structure analysis enables us to empirically identify which, if any, of these two aspects of market structure is affected by the line extension or new brand. An empirical application to the detergent product market is provided.