Discrete choice models of firms’ strategic decisions

This paper outlines the methods and applications related to the nascent area of empirical discrete games in marketing. Many key strategic decisions firms make involve discrete choices such as deciding the location of a new store, determining where in product space to position a product, or what options to offer in a service contract. These decisions are fairly complex and typically involve the consideration of a number of demand, cost, and competitive factors. What makes these discrete choices particularly interesting (and challenging to analyze) is that they are interrelated with the choices of other firms because firms take into account the actions of their competitors when making their own decisions. We describe the basic problem of dealing with interrelated discrete choices in a game-theoretic framework and present the various estimation methods available. A discussion of the existing applications and future research opportunities concludes the article.     

Identification and semiparametric estimation of a finite horizon dynamic discrete choice model with a terminating action

We study identification and estimation of finite-horizon dynamic discrete choice models with a terminal action. We first demonstrate a new set of conditions for the identification of agents’ time preferences. Then we prove conditions under which the per-period utilities are identified for all actions in the agent’s choice-set, without having to normalize the utility for one of the actions. Finally, we develop a computationally tractable semiparametric estimator. The estimator uses a two-step approach that does not use either backward induction or forward simulation. Our methodology can be implemented using standard statistical packages without the need to write specialized computational routines, as it involves linear (or nonlinear) projections only. Monte Carlo studies demonstrate the superior performance of our estimator compared with existing two-step estimation methods. Monte Carlo studies further demonstrate that the ability to identify the per-period utilities for all actions is crucial for counterfactual predictions. As an empirical illustration, we apply the estimator to the optimal default behavior of subprime mortgage borrowers, and the results show that the ability to identify the discount factor, rather than assuming an arbitrary number as typically done in the literature, is also crucial for obtaining correct counterfactual predictions. These findings highlight the empirical relevance of key identification results of the paper.     

The use of multinomial choice analysis in international business research

Many strategic decisions made by firms involve a choice among several discrete alternatives. International Business (IB) scholars are often interested in modelling the factors that potentially influence these (multinomial) choices: these factors might include not only characteristics of the firms making the choices but also attributes of the alternative choices. This paper provides a succinct and intuitive introduction to the possible applications of multinomial choice models in IB research. We briefly outline the theory behind discrete choice modelling, and then explain how multinomial choice models may be estimated (including how the datasets need to be formatted) and how the significance of the coefficient estimates as well as the diagnostic statistics may be tested and interpreted.     

Naming strategies as a tool for communication: application to movie titles

Abstract

Product naming is regarded as one of the most important communication decisions for firms to deliver information on their new products, particularly in the case of products with unobservable attributes, such as motion pictures, music, books, and games. Despite its importance, there has been little research on “how to name a product” as a communication decision. Hence, we propose a conceptual framework to describe naming decisions as two-stage strategic decisions. The first-stage decision involves “what kind of information to be communicated via product names,” which we call the “information choice” strategy. The second-stage decision is how to express this information through product names, which we call the “expression” strategy. We applied a two-level hierarchical Bayesian model to a data set consisting of opening weekend box-office sales, names, and release dates for 393 movies released in seven countries. Our empirical study provides useful findings on movie naming. First, the information choice for movie titles significantly impacts movies’ viewership. Second, the effects of “what to choose” depend on “how to express”. Third, significant interaction effects occur between information choice strategies and product characteristics, which implies that naming strategies depend on the product’s characteristics. One particularly noteworthy finding in this study is that although it is common sense to avoid negative wording in product names in most industries, negative content in movie titles positively impacts box-office sales.     

Small business survival and sample selection bias

Analyses of small business and the factors affecting their survival are fairly common in the research literature. The level of research interest may stem from the fact that in the US, only about half of all new small businesses survive after 4 years (Headd 2003). However, research attempting to understand the phenomenon that employs data using only information from and about surviving firms may lead to erroneous conclusions regarding the factors that influence firm survival and failure. In this paper, we provide evidence that omitted information about the firms that disappear from the research data over time leads to biased coefficient estimates. Comparing the Heckman two-step estimation approach of switching regression models to a semi-parametric Cox hazard model, the Accelerated Failure Time (AFT) model, we conclude that the Cox ATF approach is the most appropriate model for firm survival analysis.     

Estimating discrete-choice games of incomplete information: Simple static examples

We investigate the computational aspect of estimating discrete-choice games under incomplete information. In these games, multiple equilibria can exist. Also, different values of structural parameters can result in different numbers of equilibria. Consequently, under maximum-likelihood estimation, the likelihood function is a discontinuous function of the structural parameters. We reformulate the maximum-likelihood estimation problem as a constrained optimization problem in the joint space of structural parameters and economic endogenous variables. Under this formulation, the objective function and structural equations are smooth functions. The constrained optimization approach does not require repeatedly solving the game or finding all the equilibria. We use two static-game models to demonstrate this approach, conducting Monte Carlo experiments to evaluate the finite-sample performance of the maximum-likelihood estimator, two-step estimators, and the nested pseudo-likelihood estimator.     

Strategic reference points in family firms

Family firms are classically seen as risk averse organizations, and this is evident in their generally lower R&D investments compared to non-family firms. Recent research, however, challenges this predominant view and suggests that family firms can embrace higher strategic risk when faced with threats to their family-centered goals. Still, the internal and external conditions that drive variations in the strategic risk taking behaviors of family firms are little known and understood. This article adds to this literature by developing and testing a conceptual model of strategic risk taking that incorporates behavioral theory, family business literature, and the logic of the strategic reference point theory. With recognition that the interplay between family and economic goals determines heterogeneity in strategic actions of family firms, this model suggests that family managers respond differentially to the feedback information regarding internal and external reference points, and consequently identifies key drivers of variation in the R&D investment behavior of family firms. By examining the pattern in R&D investments of 437 Spanish private manufacturing firms from 2000 to 2006, this study shows how strategic inputs, strategic outputs, and external benchmarks produce variations in strategic decisions about R&D investments in family and non-family firms. The findings offer insights into how internal and external reference points are considered in family firms’ decision making, thereby contributing a deeper understanding into the circumstances under which family goals cope or collide with the economic goals of the firm, and how this influences strategic risk decisions in family firms.     

Markov chain Monte Carlo for incomplete information discrete games

This paper outlines a Bayesian approach to estimating discrete games of incomplete information. The MCMC routine proposed features two changes to the traditional Metropolis–Hastings algorithm to facilitate the estimation of games. First, we propose a new approach to sample equilibrium probabilities using a probabilistic equilibrium selection rule that allows for the evaluation of the parameter posterior. Second, we propose a differential evolution based MCMC sampler which is capable of handling the unwieldy posterior that only has support on the equilibrium manifold. We also present two applications to demonstrate the feasibility of our proposed methodology.     

Spatial differentiation in the supermarket industry: The role of common information

In this paper, I investigate the geographic location decisions of supermarkets to infer their tradeoffs between locating close to favorable demand conditions and differentiating themselves geographically from rivals. The model is based on a discrete-choice game between two types of supermarkets, and incorporates firm uncertainty arising from firm- and location-level private information as well as researcher uncertainty arising from location-level common information. Thus the model addresses the concern that firms’ actions may be based on factors that are unobservable to the researcher, thus correlated conditional on observables. The estimates reflect a significant level of common information. Importantly, I find that ignoring unobserved location heterogeneity results in biased estimates of both the competitive effects and the effects of location-specific observables on profits. Counterfactual predictions are therefore misleading if unobserved location heterogeneity is unaccounted for.     

A simple method for estimating preference parameters for individuals

This paper demonstrates a method for estimating logit choice models for small sample data, including single individuals, that is computationally simpler and relies on weaker prior distributional assumptions compared to hierarchical Bayes estimation. Using Monte Carlo simulations and online discrete choice experiments, we show how this method is particularly well suited to estimating values of choice model parameters from small sample choice data, thus opening this area to the application of choice modeling. For larger sample sizes of approximately 100–200 respondents, preference distribution recovery is similar to hierarchical Bayes estimation of mixed logit models for the examples we demonstrate. We discuss three approaches for specifying the conjugate priors required for the method: specifying priors based on existing or projected market shares of products, specifying a flat prior on the choice alternatives in a discrete choice experiment, or adopting an empirical Bayes approach where the prior choice probabilities are taken to be the average choice probabilities observed in a discrete choice experiment. We show that for small sample data, the relative weighting of the prior during estimation is an important consideration, and we present an automated method for selecting the weight based on a predictive scoring rule.     

China's outward foreign direct investment: Location choice and firm ownership

This article evaluates the international location decisions made by public listed Chinese firms during the period 2006-2008, using a Poisson count data regression model. Further, we categorize the firms into state-controlled and privately owned according to majority ownership. We find that the determinants of internationalization differ based on ownership. State-controlled firms are attracted to countries with large sources of natural resources and risky political environments. Private firms are more market seekers. Although all firms have strategic intent, the attraction is commercially viable technology rather than core research content. Our findings also show that existing theories can sufficiently explain the actions of private Chinese firms, but adjustments are needed to understand the behavior of state-controlled multinationals.     

Using stated response choice data to enrich revealed preference discrete choice models

There is growing interest in exploring the view that both revealed preference (RP) and stated preference (SP) data have useful information and that their integration will enrich the overall explanatory power of RP choice models. These two types of data have been independently used in the estimation of a wide variety of discrete choice applications in marketing. In order to combine the two data sources, each with independent choice outcomes, allowance must be made for their different scaling properties. The approach uses a full information maximum likelihood estimation procedure of the hierarchical logit form to obtain suitable scaling parameters to make one or more data sets comparable. We illustrate the advantages of the dual data strategy by comparing the results with those obtained from models estimated independently with RP and SP data. Data collected as part of a study of high speed rail is used to estimate a set of illustrative mode choice models.     

Learning About Computers: An Analysis of Information Search and Technology Choice

We estimate a dynamic model of how consumers learn about and choose between different brands of personal computers (PCs). To estimate the model, we use a panel data set that contains the search and purchase behavior of a set of consumers who were in the market for a PC. The data includes the information sources visited each period, search durations, as well as measures of price expectations and stated attitudes toward the alternatives during the search process. Our model extends recent work on estimation of Bayesian learning models of consumer choice behavior in environments characterized by uncertainty by estimating a model of active learning—i.e., a model in which consumers make optimal sequential decisions about how much information to gather prior to making a purchase. Also, following the suggestion of Manski (2003), we use our data on price expectations to model consumers’ price expectation process, and, following the suggestion of McFadden (1989a), we incorporate the stated brand quality information into our likelihood function, rather than modeling only revealed preference data.Our analysis sheds light on how consumer forward-looking price expectations and the process of learning about quality influence the consumer choice process. A key finding is that estimates of dynamic price elasticities of demand exceed estimates that ignore the expectations effect by roughly 50%. This occurs because our estimated expectations formation process implies that consumers expect mean reversion in price changes. This enhances the impact of a temporary price cut. Finally, while our work focuses specifically on the PC market, the modeling approach we develop here may be useful for studying a wide range of high-tech, high-involvement durable goods markets where active learning is important.JEL Classification: C15, C33, C35, C42, C51, C52, D83, D84     

Strategic intent and performance: The role of resource allocation decisions

The notion that a firm's strategic intent can affect its performance through managerial actions has become prominent in the organization literature. In this research, we propose that strategic aggressive firms will foster decisions that favor holding low levels of slack and low levels of R&D investments, resulting in increased firm ROI, and that a firm's risk preference will moderate the indirect effect of strategic intent on performance. Findings from moderated mediation analyses on data from 130 firms in manufacturing industries support our hypotheses. Specifically, the indirect effect of a firm's strategic intent on a firm's performance is moderated by its risk aversion, such that when risk aversion is high, the indirect effect of strategic intent on performance through slack is strengthened. Similarly, the indirect effect of strategic intent on firm performance through R&D investments is strengthened, when risk aversion is high.     

Configurations of strategic R&D decisions and financial performance in small-sized and medium-sized firms

This paper links the strategic decisions made in R&D during the financially turbulent period of 2009 to the firm's financial health in the period 2010–2013. The focus is on decisions made in R&D-active small and medium-sized enterprises in terms of absorptive capacity, open innovation, type of R&D, and the organizational structuring of R&D. Based on a representative set of R&D-active firms in Belgium, qualitative comparative analysis reveals that the outcomes in terms of financial performance related to optimal configurations of strategic R&D decisions depend on the firm's size and on the time-lag under consideration. Managers in small-sized firms are advised to pay particular attention to a more functionally-structured R&D approach in configurations of strategic R&D decisions. To increase medium-term financial performance, managers in medium-sized firms benefit from more engagement in research-oriented activities, more in-house innovation, and the enhancement of absorptive capacity in sets of strategic R&D decisions.     

The Joint identification of utility and discount functions from stated choice data: An application to durable goods adoption

We present a survey design that generalizes static conjoint experiments to elicit inter-temporal adoption decisions for durable goods. We show that consumers’ utility and discount functions in a dynamic discrete choice model are jointly identified using data generated by this specific design. In contrast, based on revealed preference data, the utility and discount functions are generally not jointly identified even if consumers’ expectations are known. The separation of current-period preferences from discounting is necessary to forecast the diffusion of a durable good under alternative marketing strategies. We illustrate the approach using two surveys eliciting Blu-ray player adoption decisions. Both model-free evidence and the estimates based on a dynamic discrete choice model indicate that consumers make forward-looking adoption decisions. In both surveys the average discount rate is 43 percent, corresponding to a substantially higher degree of impatience than the rate implied by aggregate asset returns. The estimates also reveal a large degree of heterogeneity in the discount rates across consumers, but only little evidence for hyperbolic discounting.     

Sequential choice designs to estimate the heterogeneity distribution of willingness-to-pay

Two prominent approaches exist nowadays for estimating the distribution of willingness-to-pay (WTP) based on choice experiments. One is to work in the usual preference space in which the random utility model is expressed in terms of partworths. These partworths or utility coefficients are estimated together with their distribution. The WTP and the corresponding heterogeneity distribution of WTP is derived from these results. The other approach reformulates the utility in terms of WTP (called WTP-space) and estimates the WTP and the heterogeneity distribution of WTP directly. Though often used, working in preference space has severe drawbacks as it often leads to WTP-distributions with long flat tails, infinite moments and therefore many extreme values. By moving to WTP-space, authors have tried to improve the estimation of WTP and its distribution from a modeling perspective. In this paper we will further improve the estimation of individual level WTP and corresponding heterogeneity distribution by designing the choice sets more efficiently. We will generate individual sequential choice designs in WTP space. The use of this sequential approach is motivated by findings of Yu et al. (2011) who show that this approach allows for superior estimation of the utility coefficients and their distribution. The key feature of this approach is that it uses Bayesian methods to generate individually optimized choice sets sequentially based on prior information of each individual which is further updated after each choice made. Based on a simulation study in which we compare the efficiency of this sequential design procedure with several non-sequential choice designs, we can conclude that the sequential approach improves the estimation results substantially.     

价格规制与企业资本结构的选择——来自我国上市公司的经验证据

受规制企业的资本结构与政府价格规制密切相关。本文建立了一个两阶段动态博弈模型并提出了相关的理论假设,以检验受规制企业资本结构决策与政府价格规制强度之间的关系。理论分析表明,资本结构选择作为企业的战略工具,能提高企业的谈判地位,迫使规制机构提高产品价格。因而企业面临的价格规制越严厉,越有动力提高负债水平。本文利用沪、深两地的受规制上市公司为样本进行的实证研究有效地支持了本文上述的假设。     

Pecking Order Theory versus Trade-Off Theory: are service SMEs�� capital structure decisions different?

This paper seeks to analyse if the capital structure decisions of service small and medium-sized enterprises (SMEs) are different from those of other types of firm. To do so, we consider four research samples: (i) 610 service SMEs; (ii) 126 service large firms; (iii) 679 manufacturing and construction SMEs; and (iv) 132 manufacturing and construction large firms. Using the two-step estimation method, the empirical evidence obtained in this study shows that the capital structure decisions of service SMEs are different from those of other types of firm. Service SMEs’ capital structure decisions are closer to the assumptions of Pecking Order Theory and further removed from those of Trade-Off Theory compared with the case of other types of firm.     

Coevolution of home country support and internationalization of emerging market firms

This study explores the role of diverse home country support for the internationalization of emerging market firms through a coevolutionary approach. A coevolutionary approach can improve our understanding of the complexities of comprehensive institutional transition in emerging market economies which allows firms to adapt and leverage institutional support for internationalization. Based on institutional theory and the strategic choice perspective, we present a coevolutionary model of mutualism and competition among businesses, government officials and industry players through multidirectional interactions. Our research offers an alternative and nuanced explanation of selective and relevant home country support, the role of independence of firms, support criteria and reinforcement of governance for internationalization of emerging market firms. This study contributes to the literature of home country support for internationalization through an enrichment of the institutional framework and strategic choice with a political perspective.