Diffusion of mobile telephony in India: An empirical study

Diffusion of new technology is an important driver of economic growth. In this paper, diffusion of mobile telephony in India is studied. There is a vast diffusion potential in this country which needs to be exploited efficiently and in a rational way. This paper investigates the social, technological, economical and political (STEP) factors that have influenced the diffusion process of mobile telephony especially the diffusion speed. The epidemic model, which is widely employed in the diffusion studies of mobile telephony, is used for the study. The data is fitted into logistic, gompertz, and bass models by nonlinear least squares and it is found that gompertz model best describes the diffusion process of mobile telephony in India. The study reveals that competition and government intervention played a significant role in accelerating the diffusion speed of mobile telephony by making the technology affordable. It is found that mobile telephony is a substitute for fixed line telephony in India. The findings will be useful in taking managerial decisions with respect to factors in forecasting and controlling the diffusion process of emerging technologies.     

Self-construal and feature centrality

The current research investigates the interactive influence of self-construal and product feature centrality on product judgment tasks. Feature centrality refers to the extent to which a feature is integral to the product concept and its network of correlated features, and contributes to the coherence of the product’s conceptual representation. While the categorization literature suggests that central features (versus less central features) are weighted more when consumers make judgments about a product, across two studies we find such effect is bounded by individuals’ self-construal. In particular, whereas independent consumers, adopting an analytical thinking style, rely more on central features than less central features in product categorization and evaluations, interdependent consumers, employing a holistic thinking style, use both features to form their judgments.     

A rationale for law of technological substitution

This paper links the popular S-shaped curves often used to represent the path of technological substitution with the evolutionary model of a self-organizing industrial system. In particular, the empirical Fisher-Pry law of technological substitution is given a conceptual rationale by treating technological innovations as structural fluctuations which affect an industry's growth path as defined by a set of non-linear differential equations. In so doing, the paper draws heavily on the articles by Allen and Batten.     

International Evidence on Institutional Trading Behavior and Price Impact

This study characterizes institutional trading in international stocks from 37 countries during 1997 to 1998 and 2001. We find that the underlying market condition is a major determinant of the price impact and, more importantly, of the asymmetry between price impacts of institutional buy and sell orders. In bullish markets, institutional purchases have a bigger price impact than sells; however, in the bearish markets, sells have a higher price impact. This differs from previous findings on price impact asymmetry. Our study further suggests that price impact varies depending on order characteristics, firm‐specific factors, and cross‐country differences.     

Macroeconomic variables and the E/P ratio: Is inflation really positively associated with the E/P ratio?

We examine the economywide E/P ratio both over a long time period (1952–2003) and by dividing the entire period into subperiods. We have two main objectives. First, we reexamine the puzzling result documented by Modigliani and Cohn (1979), who find that, contrary to theory, the economy wide E/P ratio and inflation are positively correlated. Our longer period of analysis allows us to divide the entire period into subperiods with differing macroeconomic environments. We find that the Modigliani and Cohn anomaly is period specific. The association between the E/P ratio and inflation reverses from one period to another. Hence, the relation between inflation and the economywide E/P ratio is not stable over a long time period. Second, we analyze the associations between the economywide E/P ratio and its two main drivers as given by the Gordon (1962) model. We find that the economywide E/P ratio (a) is not associated with the real interest rate and (b) is weakly negatively associated with the expected growth rate. Findings for inflation do not change when we include or exclude other E/P drivers in regression specifications.     

House Buying Behavior: An Empirical Study in Cross-Cultural Buyer Behavior

Some preliminary findings concerning experience, motivation, and search are presented from a cross-cultural study of buyer behavior in housing markets. Data were obtained from probability samples of 652 households in Connecticut and Northwest England. The results include comparative analyses of several dimensions of external search, five aspects of experience, reasons for moving, and husband-wife reasons for buying. Predictive models of search are developed with multivariate regression techniques. The findings indicate that the experience and motivational variables are not very powerful predictors of search. Significant cultural differences do occur in the level of many variables, but there are some interesting similarities in the underlying factor structure.     

Sell on the news: Differences of opinion,short-sales constraints,and returns around earnings announcements

Miller [1977. Risk, uncertainty, and divergence of opinion. Journal of Finance 32, 1151–1168] hypothesizes that prices of stocks subject to high differences of opinion and short-sales constraints are biased upward. We expect earnings announcements to reduce differences of opinion among investors, and consequently, these announcements should reduce overvaluation. Using five distinct proxies for differences of opinion, we find that high differences of opinion stocks earn significantly lower returns around earnings announcements than low differences of opinion stocks. In addition, the returns on high differences of opinion stocks are more negative within the subsample of stocks that are most difficult for investors to sell short. These results are robust when we control for the size effect and the market-to-book effect and when we examine alternative explanations such as financial leverage, earnings announcement premium, post-earnings announcement drift, return momentum, and potential biases in analysts’ forecasts. Also consistent with Miller's theory, we find that stocks subject to high differences of opinion and more binding short-sales constraints have a price run-up just prior to earnings announcements that is followed by an even larger decline after the announcements.     

SOA商务敏捷的捷径

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A generalized framework for estimating customer lifetime value when customer lifetimes are not observed

Measuring customer lifetime value (CLV) in contexts where customer defections are not observed, i.e. noncontractual contexts, has been very challenging for firms. This paper proposes a flexible Markov Chain Monte Carlo (MCMC) based data augmentation framework for forecasting lifetimes and estimating customer lifetime value (CLV) in such contexts. The framework can be used to estimate many different types of CLV models—both existing and new. Models proposed so far for estimating CLV in noncontractual contexts have built-in stringent assumptions with respect to the underlying customer lifetime and purchase behavior. For example, two existing state-of-the-art models for lifetime value estimation in a noncontractual context are the Pareto/NBD and the BG/NBD models. Both of these models are based on fixed underlying assumptions about drivers of CLV that cannot be changed even in situations where the firm believes that these assumptions are violated. The proposed simulation framework—not being a model but an estimation framework—allows the user to use any of the commonly available statistical distributions for the drivers of CLV, and thus the multitude of models that can be estimated using the proposed framework (the Pareto/NBD and the BG/NBD models included) is limited only by the availability of statistical distributions. In addition, the proposed framework allows users to incorporate covariates and correlations across all the drivers of CLV in estimating lifetime values of customers.

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An Approach for Determining Optimal Product Sampling for the Diffusion of a New Product

Free samples are an effective means for introducing and promoting a new product. However, product sampling is also expensive. As a result, careful consideration must be given to the question of how many samples should be distributed. To encourage product adoption in any target market, a company needs to determine the “right” amount of sampling. In other words, a firm needs to determine the optimal number of samples that must be available for trial by the innovators, early adopters, and other key consumers who influence the adoption rate of the new product. With too few samples, the product might not reach enough of these key consumers to generate the word-of-mouth recommendations necessary for market success. On the other hand, offering too many free samples is a waste of a company's resources. Dipak Jain, Vijay Mahajan, and Eitan Muller propose a framework for determining the optimal levels of product sampling. In addition to identifying the upper bounds for the sampling levels of both durable and nondurable products, their model identifies the optimal size of product sampling based on such parameters as the coefficients of innovation and imitation, market potential, discount rate, and gross margin. Several observations are made regarding the relationships between the optimal sampling level and the various parameters used in the model. For example, a high sampling level is not appropriate for a product with a high coefficient of innovation. On the other hand, if a product has a high coefficient of imitation, the sampling level should be high because a significant number of trials are necessary for word of mouth to be effective. High sampling levels are also indicated by a high discount rate or gross margin. For durable goods, the optimal level of neutral sampling (i.e., sampling that does not specifically target innovators and early adopters) rarely exceeds 5%, and the maximum level is 7%. The optimal target sampling level is always higher than the corresponding neutral case, but, in most cases, only marginally so. For the parameter ranges chosen in this article, the maximum level for target sampling is approximately 9%. However, it is important to note that the theoretical upper bounds are no more than benchmarks for the maximum possible level of sampling. In practical situations, the optimal level may be considerably lower than these upper bounds. In such cases, the actual values will depend on the values for the various parameters used in the model.