Product Packaging Metaphors: Effects of Ambiguity and Explanatory Information on Consumer Appreciation and Brand Perception

Product packaging is an important means for communicating product and brand benefits. Research suggests that visual metaphors may be particularly suited in this context, however, the conditions under which metaphors are effective are not yet well understood. In the research reported herein, effects of metaphor ambiguity and explanatory information on consumer appreciation and brand personality perceptions are tested. Study 1 shows that explicit information explaining the metaphor increases consumer appreciation and positively affects brand perception, but only for ambiguous metaphors. Study 2 shows that drawing consumer attention to packaging design by means of a visual packaging cue may be equally or even more effective for enhancing consumer appreciation and steering brand personality perceptions. In addition, Study 2 shows that effects of explanatory information and information type vary with participants’ readiness to engage in metaphor processing. Together, the results provide greater insight into the effects of metaphors in product design and provide guidelines to packaging designers.     

Stock Return Predictability: A Bayesian Model Selection Perspective

Attempts to characterize stock return predictability have resultedin little consensus on the important conditioning variables,giving rise to model uncertainty and data snooping fears. Weintroduce a new methodology that explicitly incorporates modeluncertainty by comparing all possible models simultaneouslyand in which the priors are calibrated to reflect economicallymeaningful information. Our approach minimizes data snoopinggiven the information set and the priors. We compare the priorviews of a skeptic and a confident investor. The data implyposterior probabilities that are in general more supportiveof stock return predictability than the priors for both typesof investors.     

Hedonic price models and indices based on boosting applied to the Dutch housing market

We create a hedonic price model for house prices for six geographical submarkets in the Netherlands. Our model is based on a recent data-mining technique called boosting. Boosting is an ensemble technique that combines multiple models, in our case decision trees, into a combined prediction. Boosting enables capturing of complex nonlinear relationships and interaction effects between input variables. We report mean relative errors and mean absolute error for all regions and compare our models with a standard linear regression approach. Our model improves prediction performance by up to 39% compared with linear regression and by up to 20% compared with a log-linear regression model. Next, we interpret the boosted models: we determine the most influential characteristics and graphically depict the relationship between the most important input variables and the house price. We find the size of the house to be the most important input for all but one region, and find some interesting nonlinear relationships between inputs and price. Finally, we construct hedonic price indices and compare these with the mean and median index and find that these indices differ notably in the urban regions of Amsterdam and Rotterdam. Copyright © 2008 John Wiley & Sons, Ltd.     

Gaming the future of an urban network

Scenario and gaming techniques have a number of complementary characteristics. In this article, the design and evaluation of a gaming-scenario experiment for the exploration of development planning in an urban network in the Netherlands is presented. Two gaming sessions were held using two long-term scenarios (2030) as varying contexts. The combined gaming-scenario approach made it possible to evaluate the impact of external future developments and trends on the administrative and spatial development. Evaluation results of the gaming experiment indicate that the gaming-scenario approach generated new and critical insights on development planning and the future of the urban network. Through the game, the principal, the game leaders but most of all the stakeholder-participants experienced and realized that the management of development planning in urban networks is a very difficult task and is full of pitfalls.     

Optimal dividend distribution under Markov regime switching

We investigate the problem of optimal dividend distribution for a company in the presence of regime shifts. We consider a company whose cumulative net revenues evolve as a Brownian motion with positive drift that is modulated by a finite state Markov chain, and model the discount rate as a deterministic function of the current state of the chain. In this setting, the objective of the company is to maximize the expected cumulative discounted dividend payments until the moment of bankruptcy, which is taken to be the first time that the cash reserves (the cumulative net revenues minus cumulative dividend payments) are zero. We show that if the drift is positive in each state, it is optimal to adopt a barrier strategy at certain positive regime-dependent levels, and provide an explicit characterization of the value function as the fixed point of a contraction. In the case that the drift is small and negative in one state, the optimal strategy takes a different form, which we explicitly identify if there are two regimes. We also provide a numerical illustration of the sensitivities of the optimal barriers and the influence of regime switching.     

Surges and stops in greenfield and M&A FDI flows to developing countries: analysis by mode of entry

This paper investigates the factors associated with foreign direct investment “surges” and “stops”, defined as sharp increases and decreases, respectively, of foreign direct investment inflows to the developing world and differentiated based on whether these events are led by waves in greenfield investments or mergers and acquisitions. Greenfield-led surges and stops occur more frequently than mergers-and-acquisitions-led ones and different factors are associated with the onset of the two types of events. Global liquidity is the factor significantly and positively associated with a surge, regardless of its kind, while a global economic growth slowdown and a surge in the preceding year are the main factors associated with a stop. Greenfield-led surges and stops are more likely in low-income countries and mergers-and-acquisitions-led surges are less likely in resource-rich countries than elsewhere in the developing world. Global growth accelerations and increases in financial openness, domestic economic and financial instability are associated with mergers-and-acquisitions-led surges but not with greenfield-led ones. These results are particularly relevant for developing countries where FDI flows are the major type of capital flows and suggest that developing countries’ macroeconomic vulnerability increases following periods of increased global liquidity. As countries develop they typically become more exposed to merger-and-acquisition-led surges, which are more likely than greenfield-led surges and stops to be short-lived and associated with domestic macroeconomic policies.     

Small firm internationalization,innovation, and growth

This paper studies the effect of internationalization on innovation and firm performance (employment growth and sales growth) taking the interdependencies among the variables into account. Given the potential endogeneity, this study uses theory-driven instrumental variables and structural equation modelling to estimate the direct and indirect effects of internationalization. Firm-level data is obtained from a survey of 150 Dutch small firms that took part in an export promotion program. The main results show that internationalization has a positive impact on innovation. Internationalization increases firm performance directly and indirectly through innovation, while the direct impact of innovation on firm performance is insignificant. We contribute to the literature of export promotion programs as our results suggest that they not only increase small firm internationalization but also affect innovation and firm growth.     

The relationship between cost system complexity,purposes of use,and cost system effectiveness

This paper uses survey data from 133 Dutch, medium-sized manufacturing firms to examine the associations between cost system complexity (in terms of the applied overhead absorption procedures), purposes of use, and cost system effectiveness. First, factor analysis identifies two underlying dimensions of cost system purposes of use, which refers to the range (scope) of purposes for which the cost system is used, among nine widely used purposes: cost system usage for product planning and cost management purposes. Next, the joint effect of cost system complexity and usage for product planning and cost management purposes on cost system effectiveness, as proxied by the intensity of use of and level of satisfaction with the cost system, is examined. The results robustly indicate that at higher (lower) levels of usage for product planning purposes, cost system complexity negatively (positively) affects cost system intensity of use, while at higher (lower) levels of usage for cost management purposes, cost system complexity positively (negatively) affects cost system intensity of use and satisfaction. This implies that when cost system design (i.e., its level of complexity) and its purposes of use are better aligned, the cost system is more effective.     

Bayesian inference in a sample selection model

This paper develops methods of Bayesian inference in a sample selection model. The main feature of this model is that the outcome variable is only partially observed. We first present a Gibbs sampling algorithm for a model in which the selection and outcome errors are normally distributed. The algorithm is then extended to analyze models that are characterized by nonnormality. Specifically, we use a Dirichlet process prior and model the distribution of the unobservables as a mixture of normal distributions with a random number of components. The posterior distribution in this model can simultaneously detect the presence of selection effects and departures from normality. Our methods are illustrated using some simulated data and an abstract from the RAND health insurance experiment.     

House price risk and the hedging benefits of home ownership

Using a repeat-sales methodology, this paper finds that estimates of house price risk based on aggregate house price indices substantially underestimate the true size of house price risk. This is the result of the fact that aggregate house price indices average away the idiosyncratic volatility in house prices. Additional results show that the idiosyncratic risk exceeds the hedging benefits of home ownership. These results imply that for many home owners, owning a house may well add more price risk than it hedges away. These findings are based on a detailed dataset of individual housing transactions in the Netherlands.