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The Marketing literature has shown how difficult it is to profile market segments derived with finite mixture models. especially using traditional descriptor variables (e.g., demographics). Such profiling is critical for the proper implementation of segmentation strategy. we propose a new finite mixture modelling approach that provides a variety of model specifications to address this so.-mentation dilemma, Our proposed approach allows for a large number of nested models (special cases) and associated tests of (local) independence to distinguish amongst them. A commercial application to customer satisfaction is provided where a variety of different model specifications are tested and compared.

Dynamic multivariate models ha e become popular in analyzing the behavior of competitive marketing systems because they are capable of incorporating all the relationships in a competitive marketing environment. In this paper we consider VAR models, the most frequently used dynamic multivariate models. The drawback of VAR models is that a large number of parameters have to be estimated. The problem has been addressed in several articles and the usual solution is to treat only the variables of interest as endogenous while the other variables are usually included exogenously without dynamic effects. This treatment imposes restrictions on the marketing system. which requires preliminary analysis. We propose to use canonical correlation for this purpose. Canonical correlation analysis and its associated Wiener-Granger causality testing based on the canonical correlation coefficients are useful tools to test the existence of structural relationships between (lagged) consumer response and (lagged) marketing instruments. The tools are applied on data of market shares and marketing instruments in a market of a frequently purchased consumer goods.

The authors examine the effect of relational constructs (e.g., satisfaction, trust, and affective and calculative commitment) on customer referrals and the number of services purchased, as well as the moderating effect of age of the relationship on these relationships. The research reported, based on data obtained from a large sample of customers of an insurance company, combines archival and survey data. The results provide evidence that supports the moderating effect of relationship age on the relationship between satisfaction, affective and calculative commitment, and the number of services purchased.

This study's prime interest is to assist retail managers in deciding where they are likely to get the greatest response to their expenditures on quality improvement. An empirical test involving 287 consumers reporting on a wide array of stores assesses whether the relationship between three quality dimensions (technical quality, functional quality and relational quality) and store loyalty is moderated by age, gender and store size. The structural equation model indicates that relational quality is the prime driver of store loyalty, seemingly overruling the impact of technical quality and functional quality. Most importantly, the results suggest that older consumers are relatively more strongly affected by technical quality than younger consumers. Another interesting finding is that retailers reap more benefits from their quality investments if they offer relational quality to female as opposed to male consumers. Store size was not found to mitigate the impact of the quality dimensions investigated.

We develop a general approach to factor analysis that involves observed and latent variables that are assumed to be distributed in the exponential family. This gives rise to a number of factor models not considered previously and enables the study of latent variables in an integrated methodological framework, rather than as a collection of seemingly unrelated special cases. The framework accommodates a great variety of different measurement scales and accommodates cases where different latent variables have different distributions. The models are estimated with the method of simulated likelihood, which allows for higher dimensional factor solutions to be estimated than heretofore. The models are illustrated on synthetic data. We investigate their performance when the distribution of the latent variables is mis-specified and when part of the observations are missing. We study the properties of the simulation estimators relative to maximum likelihood estimation with numerical integration. We provide an empirical application to the analysis of attitudes.

Changes in promotional expenditure decisions for a brand, as in other marketing decisions, should be based on the expected impact on purchase and consumption behavior as well as on the likely reactions by competitors. Purchase behavior may be predicted from estimated demand functions. Competitive reactions can be anticipated based on estimated reaction functions, which show how changes in an instrument for one brand depend on changes in one or more instruments for other brands. Economic and other arguments suggest that the parameters of such reaction functions (e.g. reaction elasticities) should exhibit systematic patterns. We consider these parameters in an economic framework that also contains two other useful measures, viz. net- and gross demand elasticities. For a defender, the net demand elasticity is the difference between the (positive) demand effect of a reaction (the competitive reaction elasticity multiplied by the corresponding own-brand demand elasticity) and the (negative) cross-brand effect (cross-brand demand elasticity). The gross demand elasticity captures just the (positive) demand effect of the reaction. Net demand-, gross demand- and reaction elasticities are useful yardsticks for managers. Our empirical results suggest that managers' competitive reactions do take into account the consumer response to cross-brand and own-brand activities. For example, the greater a cross-brand elasticity, the greater the corresponding reaction elasticity. Similarly, the greater the own-brand elasticity, the smaller the corresponding reaction elasticity. These effects are consistent with managerial behavior that aims to preserve market shares. We also find that the frequency of changes in other brands' marketing variables has a negative impact on competitive reactions. Interestingly, we do not find that average within-firm competitive reaction elasticities are smaller than for brands offered by different firms. However, we do find that for one firm, this reaction incidence is far lower for sister brands (presumably due to the use of category management), while for another firm, this incidence is higher relative to non-sister brands, consistent with expectations derived from personal interviews of all brand managers. The latter result suggests that the characteristics of reward or resource allocation systems may influence the competitive intensity between managers in the same firm. (C) 2001 Elsevier Science B.V. All rights reserved.

Conjoint choice designs are frequently applied in practice, and often a base alternative is added to the design. When such a 'no-choice' base alternative is present in conjoint choice experiments a constant term should be added to the design ('X'-) matrix with attribute dummies when effects type and/or linear coding is used for the attribute levels. Not including such a constant may result in a much lower model and predictive fit and even biased estimates for the (linear) attributes.

GLIMMIX is a commercial WINDOWS-based computer program that implements the EM algorithm (Dempster, Laird and Rubin 1977) for the estimation of finite mixtures and mixtures of generalized linear models. The program allows for the specification of a number of distributions in the exponential family, including the normal, gamma, binomial, Poisson, and multinomial distributions. For each of those distributions, a variety of link functions can be specified to relate the expectation of the dependent variable to a linear predictor. Several statistics, including AIC, CAIC and BIC are computed to aid in model selection (cf. Akaike 1974; Bozdogan 1987), missing values are accommodated, and posterior membership probabilities are computed For cases, included or not included in the analysis. Simple discriminant type models dealing with concomitant variables to describe the classes are supported, and a random responder class can be added to the model. Various graphs are provided. A demonstration version of the program can be obtained from http://www/gamma.rug.nl. Before providing some details on the GLIMMIX software, a brief review of a few relevant issues in Mixture modelling are provided.

The authors provide more efficient designs for conjoint choice experiments based on prior information elicited from managers about the parameters and their associated uncertainty. The authors use a Bayesian design procedure that assumes a prior distribution of likely parameter values and optimizes the design over that distribution. The authors propose a way to elicit prior information from managers and show in Monte Carlo studies that the procedure provides more efficient designs than the current procedures. The authors provide an empirical application, in which they elicit prior information on the parameter values and the associated uncertainty from managers. Here, the Bayesian design provides 30%-50% lower standard errors of the estimates and an expected predictive validity that is approximately 20% higher.

In the last decade, marketers have primarily focused on keeping customers. Only recently have they become aware that creating value by cross-selling additional services is also an important aspect of customer relationship management. In this article we investigate how satisfaction and payment equity, defined as the perceived fairness of the price, affect cross-buying at a multiservice provider. We also consider its competitors' performance on these factors. Our results show that the effect of satisfaction differs between customers with lengthy and short relationships. It also shows that payment equity negatively affects cross-buying for customers with long relationships, However, if the prices of the supplier are perceived as fairer than the prices of the competitor, the customers' probability of cross-buying increases. (C) 2001 by New York University. All rights reserved.

Response latencies provide information about consumers' choice behavior in a conjoint choice experiment. The authors use filtered response latencies to scale the covariance matrix of a multinomial probit model and show that this leads to better model fit and holdout predictions, even if the response latencies in the holdout task are not used. The authors provide an empirical application along with a tentative explanation for the findings of the effect of response latencies.

Whereas much previous research, focusing on the comparative fit at the aggregate level, has shown that either tree or spatial representations of dissimilarity judgments may be appropriate, we investigate whether there exist classes of subjects differing in the extent to which dissimilarity judgments are better represented by additive tree or spatial multidimensional scaling models. We develop a mixture model for the analysis of dissimilarity data that entails both representation and measurement model components. The latter involves distributional assumptions on the measurement error and enables estimation by maximum likelihood. The former component allows dissimilarity judgments to be represented either by an additive tree structure or by a spatial configuration, or a mixture of both. To investigate the appropriateness of additive tree versus spatial representations, the model is applied to twenty empirical data sets. We compare the fit of our model with that of aggregate additive tree and spatial models, as well as with mixtures of additive trees and mixtures of spatial configurations, respectively. We formulate some empirical generalizations on the relative importance of tree versus spatial structures in representing dissimilarity judgments.

One of the mysteries of store-level scanner data modeling is the lack of a dip in sales in the weeks following a promotion. Researchers expect to find a postpromotion dip because analyses of household scanner panel data indicate that consumers tend to accelerate their purchases in response to a promotion—that is, they buy earlier and/or purchase larger quantities than they would in the absence of a promotion. Thus, there should also be a pronounced dip in store-level sales in the weeks following a promotion. However, researchers rarely find such dips at either the category or the brand level. Several arguments have been proposed to account for the lack of a postpromotion dip in store-level sales data and to explain why dips may be hidden. Because dips are difficult to detect by traditional models (and by a visual inspection of the data), the authors propose models that can account for a multitude of factors that together cause complex pre- and postpromotion dips. The authors use three alternative distributed lead and lag structures: an Almon model, an unrestricted dynamic effects model, and an exponential decay model. In each model, the authors include four types of price discounts: without any support, with display-only support, with feature-only support, and with feature and display support. The models are calibrated on store-level scanner data for two product categories: tuna and toilet tissue. The authors estimate the dip to be between 4 and 25% of the current sales effect, which is consistent with household-level studies.

The number of brands in the marketplace has vastly in-creased in the 1980s and 1990s, and the amount of moneyspent on advertising has run parallel. Print advertising is amajor communication instrument for advertisers, but printmedia have become cluttered with advertisements forbrands. Therefore, it has become difficult to attract and keepconsumers’ attention. Advertisements that fail to gain andretain consumers’ attention cannot be effective, but attentionis not sufficient: Advertising needs to leave durable traces ofbrands in memory. Eye movements are eminent indicatorsof visual attention. However, what is currently missing ineye movementresearch is a serious accountof the processingthat takes place to store information in long-term memory.We attempt to provide such an account through the devel-opmentof a formal model. We model the process by whicheye fixations on print advertisements lead to memory for theadvertised brands, using a hierarchical Bayesian model, but,rather than postulating such a model as a mere data-analysistool, we derive it from substantive theory on attention andmemory. The model is calibrated to eye-movement data thatare collected during exposure of subjects to ads in magazines,and subsequent recognition of the brand in a perceptualmemory task. During exposure to the ads we record the fre-quencies of fixations on three ad elements; brand, pictorialand text and, during the memory task, the accuracy and la-tency of memory. Thus, the available data for each subjectconsist of the frequency of fixations on the ad elements andthe accuracy and the latency of memory. The model that wedevelop is grounded in attention and memory theory anddescribes information extraction and accumulation duringad exposure and their effect on the accuracy and latency ofbrand memory. In formulating it, we assume that subjectshave different eye-fixation rates for the different ad elements,because of which a negative binomial model of fixation fre-quency arises, and we specify the influence of the size of thead elements. It is assumed that the number of fixations, nottheir duration, is related to the amount of information a con-sumer extracts from an ad. The information chunks extractedat each fixation are assumed to be random, varying acrossads and consumers, and are estimated from the observeddata. The accumulation of information across multiple fixa-tions to the ad elements in long-term memory is assumed tobe additive. The total amount of accumulated informationthat is not directly observed but estimated using our modelinfluences both the accuracy and latency of subsequent brandmemory. Accurate memory is assumed to occur when theaccumulated information exceeds a threshold that varies ran-domly across ads and consumers in a binary probit-type ofmodel component. The effect of two media-planning vari-ables, the ad’s serial position in a magazine and the ad’s lo-cation on the double page, on the brand memory thresholdare specified. We formulate hypotheses on the effects of adelement surface, serial position, and location.The model is applied in a study involving a sample of 88consumers who were exposed to 65 print ads appearing intheir natural context in two magazines. The frequency of eyefixations was recorded for each consumer and advertisementwith infrared eye-tracking methodology. In a subsequent in-direct memory task, consumers identified the brands frompixelated images of the ads. Across the two magazines, fix-ations to the pictorial and the brand systematically promoteaccurate brand memory, but text fixations do not. Brand sur-face has a particularly prominent effect. The more informa-tion is extracted from an ad during fixations, the shorter thelatency of brand memory is. We find a systematic recencyeffect: When subjects are exposed to an ad later, they tend toidentify it better. In addition, there is a small primacy effect.The effect of the ad’s location on the right or left of the pagedepends on the advertising context. We show how the modelsupports advertising planning and testing and offer recom-mendations for further research on the effectiveness of brandcommunication. In future research the model may be ex-tended to accommodate the effects of repeated exposure toads, to further detail the representation of strength and as-sociation of memory, and to include the effects of creativetactics and media planning variables beyond the ones weincluded in the present study.(Brand Advertising; Visual Attention; Brand Memory; Hierarchi-cal Bayes)

The authors examine consumers’ visual attention during repeated exposures to print advertisements using eye-tracking methodology. The authors propose a statistical model comprising submodels for two key measures of visual attention to elements of the advertisement: attention duration and inter- and intraelement saccades. These measures are vital to understanding attention wear-out and the impact of repetition on advertising effectiveness but have not been considered in previous research. The results of two studies show that (1) attention duration decreases significantly across advertising repetitions and (2) attentional scanpaths, as measured through saccades, remain constant across advertising repetitions and across experimentally induced and naturally occurring conditions. Scanpaths obey a stationary, reversible, first-order Markov process. The authors offer implications for attention theory and advertising processing.

Multidimensional scaling has been applied to a wide range of marketing problems, in particular to perceptual mapping based on dissimilarity judgments. The introduction of methods based on the maximum likelihood principle is one of the most important developments. In this article, the authors compare the three available Maximum Likelihood Multidimensional Scaling (MLMDS) methods, namely, MULTISCALE, MAXSCAL, and PROSCAL, and the traditional multidimensional scaling (MDS) method KYST in a Monte Carlo study with 243 synthetic data sets. The MLMDS methods outperform KYST with respect to recovering the perceptual maps. MAXSCAL recovers the true distances between brands somewhat better than MULTISCALE, which is somewhat better than PROSCAL. With regard to distance recovery, the MLMDS methods are quite robust to violations of distributional assumptions. The decision criteria for selecting the number of dimensions are less robust to distributional violations. The results support the use of Consistent Akaike Information Criterion for the selection of the number of dimensions. The authors recommend that dissimilarity judgments be collected on interval scales or on ordinal scales with a substantial number of scale values. The authors discuss implications of the results for the design and analysis of perceptual mapping studies.

We argue that computer simulation can serve as a functional equivalent for the experimental method in sociology, with respect to theory development. To this end Eve present accounts of experimentation and simulation by experimenting/simulating scientists and sociologists of science. From these analyses we conclude desirable features of a simulation method: generality, surprisability and power to separate. That means that it should be widely applicable, capable of surprising the researcher, and capable of separating surprising results that originate from sociological features of the model from those that stem from technical features. We demonstrate three methods that may provide these Features: emergence, fixing points of reference, and comparative response testing. We develop the latter method in greater depth by discussing an exemplary simulation study.

In this study we reinvestigate the effect of time-aggregation for discrete- and continuous-time hazard models. We reanalyze the results of a previous Monte Carlo study by ter Hofstede and Wedel (1998), in which the effects of time-aggregation on the parameter estimates of hazard models were investigated. Whereas the previous study shows that the estimates of the baseline parameters of continuous- and discrete-time models suffer a lot from time-aggregation, we show that the effects on the baseline hazard itself are much smaller, in particular for the continuous-time model. (C) 1999 Elsevier Science S.A. All rights reserved.

This paper is concerned with the spatial representation of market structure calibrated on actual or intended choice data. Previous models developed for that purpose accommodate consumer heterogeneity by estimating parameters for each consumer, typically using the method of maximum likelihood. This approach to heterogeneity avoids assuming any particular distribution of the individual level parameters across the sample, but leads to problems related to the consistency of the estimates, sufficient degrees of freedom, and the validity of asymptotic standard errors and test statistics. Of greater concern is the assumption of independence of the choice observations within the same individual. This assumption is necessary in a maximum likelihood (MLE) framework to make the estimation computationally feasible. However, the marketing and psychology literature (cf. Manrai, 1995; Tversky and Simonson, 1993; Kim et al., forth coming) demonstrates that dependencies among choice alternatives may exist, and negligence to take such covariance into account may lead to inconsistent estimates, reduced predictive validity, and incorrect managerial action. We develop a new multidimensional scaling (MDS) model that estimates spatial market structures from pick-any/J choice data, provides for individual level parameters, and allows for correlations among the choice alternatives across individuals. We provide a Bayesian estimation method that overcomes the traditional problems associated with estimating models with such correlated alternatives. We provide an application to pick-any/J data for various brands of portable telephones. In a comparative analysis, we show that the proposed model outperforms one in which the utilities are assumed to be uncorrelated across choice alternatives. (C) 1998 Elsevier Science B.V. All rights reserved.

Traditional ratings- or rankings-based conjoint analysis has been very popular in commercial practice. Recently, the choice-based conjoint approach has become an attractive alternative for measuring preference structures. However, little is known about the extent to which both approaches produce similar results or about how they compare in terms of predictive accuracy. This paper presents a conceptual and empirical comparison of ratings-based and choice-based conjoint approaches. The authors add to previous empirical investigations by comparing both approaches with respect to differences in relative attribute importances and predictive accuracy while controlling for task order. In particular, the authors compare segment-level (latent class) models. The results show substantial differences in the segment-level relative attribute importances. These results are consistent with previous research and the prominence hypothesis. At the segment and aggregate level the choice-based approach clearly outperforms the ratings-based approach.

We present a model of consumers' price sensitivity that explicitly deals with the existence of so-called psychological price levels or odd prices, i.e. prices ending in an odd number. The model is formulated in a latent class framework, in which splines are used to model utility as a function of prices in consumer segments. The knots in the splines represent psychological prices. Additionally, the model allows for inferences on price expectations and the role of price as an indicator of quality. The model is tailored to the analysis of so-called Gabor-Granger price experiments. We provide an empirical application to the analysis of a Gabor-Granger study, and investigate the performance of our model relative to a competing model. (C) 1998 Elsevier Science B.V. All rights reserved.

Means-end chain theory links products to consumers by postulating hierarchical relations between attributes of the product, consequences of product use and values of consumers. It has served as an important conceptual framework for studies in marketing. The authors investigate the association pattern technique (APT) as a supplement to laddering, the most popular, qualitative measurement methodology in means-end chains research. APT is a structured method for measuring means-end chains, suitable for large-scale surveys. It can be used in personal as well as quantitative mail interviews. APT separately measures the attribute-consequence, and the consequence-value links. The independence of attribute-consequence, and consequence-value links is crucial to the validity of APT. Using loglinear models, we investigate this assumption for empirical data on four different products. Consistent support for independence is found. In addition, we use loglinear models to test the convergent validity of APT and laddering with respect to the content and structure of the means-end chains network that they reveal. The results show that the content of the APT and laddering networks differs. This result is explained from the different task formats. Most importantly, the hypothesis that the structure of APT and laddering networks is the same could not be rejected. (C) 1998 Elsevier Science B.V.

The authors address the situation in which a researcher wants to cross-tabulate two sets of discrete variables collected in independent samples, but a subset of the variables is common to both samples. The authors propose a statistical data-fusion model that allows for statistical tests of association using multiple imputations. The authors illustrate this approach with an application in which they compare the cross-tabulation results from fused data with those obtained from complete data. Their approach is also compared to the traditional hot-deck procedure.

Understanding the competition between products constitutes an important problem area in marketing. The existence of many items in a product category requires that researchers consider placing restrictions on the nature of competition in consumer response models. However, little is known about the relative appropriateness of alternative model specifications that differ in constraints. We propose hierarchical models, and we compare different possible hierarchies against each other and against recently developed nonhierarchical specifications. We discuss how the alternative specifications accommodate asymmetry in competition, We also show analytical linkages between the different models, and we provide a preliminary empirical comparison of these models based on aggregate (market-level) scanner data. (C) 1997 Elsevier Science B.V.

Previous behavioral studies have shown that initial ownership influences individuals' fairness consideration and other-regarding behavior. However, it is not entirely clear whether initial ownership influences the brain activity when a recipient evaluates the fairness of asset distribution. In this study, we randomly assigned the bargaining property (monetary reward) to either the allocator or the recipient in the ultimatum game and let participants of the study, acting as recipients, receive either disadvantageous unequal, equal, or advantageous unequal offers from allocators while the event-related potentials (ERPs) were recorded. Behavioral results showed that participants were more likely to reject disadvantageous unequal and equal offers when they initially owned the property as compared to when they did not. The two types of unequal offers evoked more negative going ERPs (the MFN) than the equal offers in an early time window and the differences were not modulated by the initial ownership. In a late time window, however, the P300 responses to division schemes were affected not only by the type of unequal offers but also by whom the property was initially assigned to. These findings suggest that while the MFN may function as a general mechanism that evaluates whether the offer is consistent or inconsistent with the equity rule, the P300 is sensitive to top-down controlled processes, into which factors related to the allocation of attentional resources, including initial ownership and personal interests, come to play, Previous behavioral studies have shown that initial ownership influences individuals’ fairness consideration and other-regarding behavior. However, it is not entirely clear whether initial ownership influences the brain activity when a recipient evaluates the fairness of asset distribution. In this study, we randomly assigned the bargaining property (monetary reward) to either the allocator or the recipient in the ultimatum game and let participants of the study, acting as recipients, receive either disadvantageous unequal, equal, or advantageous unequal offers from allocators while the event-related potentials (ERPs) were recorded. Behavioral results showed that participants were more likely to reject disadvantageous unequal and equal offers when they initially owned the property as compared to when they did not. The two types of unequal offers evoked more negative going ERPs (the MFN) than the equal offers in an early time window and the differences were not modulated by the initial ownership. In a late time window, however, the P300 responses to division schemes were affected not only by the type of unequal offers but also by whom the property was initially assigned to. These findings suggest that while the MFN may function as a general mechanism that evaluates whether the offer is consistent or inconsistent with the equity rule, the P300 is sensitive to top-down controlled processes, into which factors related to the allocation of attentional resources, including initial ownership and personal interests, come to play.

In this paper, we assess two alternative indicators of opinion leadership, self-reported opinion leadership and degree centrality, on the same dataset. We also investigate the interaction effect of these two indicators and the social network environment on opinion leadership. We use social network and survey data from the mobile telecom industry to analyze opinion leadership in smartphone adoption. We find that degree centrality indicates opinion leadership, but that self-reported opinion leadership indicates opinion leadership only under the right social circumstances. In case of weak to moderate network ties, the effect of self-reported opinion leadership is not significant. However, self-reported opinion leaders more effectively influence their strong ties. This study sheds light on indicators of opinion leadership and provides insights for managers to improve their social marketing campaigns.

This study systematically compares different customer feedback metrics (CFMs) - namely customer satisfaction, the Net Promoter Score, and the Customer Effort Score - to test their ability to predict retention across a wide range of industries. We classify the CFMs according to a time focus (past, present, or future) and whether the full scale of the CFM is used or whether the focus is only on the extremes (e.g., top-2-box customer satisfaction). The data for this study represent customers of 93 firms across 18 industries. Multi-level probit regression models, which control for self-selection bias of respondents, investigate firm-, customer-, and industry-level effects simultaneously. Overall, we find that the top-2-box customer satisfaction performs best for predicting customer retention and that focusing on the extremes is preferable to using the full scale. However the best CFM does differ depending on industry and the unit of analysis (i.e., comparing customers or firms with one another). Furthermore, combining CFMs, along with simultaneously investigating multiple dimensions of the customer relationship, improves predictions even further. (C) 2015 Elsevier B.V. All rights reserved.

In this paper we investigate the degree to which two social influences, namely imitation and coordinated consumption, effectuate inequalities in the motion picture industry. We develop an agent-based model based on micro movie visitors' decision-making that generates the observed macro market outcomes. The simulation model makes use of the findings of an empirical survey amongst 1112 cinema visitors. We find that social influences explain market inequalities and that the impact of coordinated consumption on market inequalities is stronger than the impact of imitation.

De faculteiten Economie en Bedrijfskunde gaan de komende tijd meer samenwerken, en zullen daarbij de maatschappelijke impact centraal stellen. Deze samenwerking is noodzakelijk vanwege de veranderende eisen die de maatschappij en het wetenschapsbeleid stelt aan de discipline.

Recent research has shown that resisting persuasion involves active self-regulation. Resisting an influence attempt consumes self-regulatory resources, and in a state of self-regulatory resource depletion, people become more susceptible to (unwanted) influence attempts. However, the present studies show that a forewarning of an impending influence attempt prompts depleted individuals to conserve what is left of their regulatory resources and thus promotes self-regulatory efficiency. As a result, when these individuals are subsequently confronted with a persuasive request, they comply less (Experiments 1 and 3), and generate more counterarguments (Experiment 2) than their depleted counterparts who were not forewarned and thus did not conserve their resources, and they are as able as non-depleted participants to resist persuasion. (C) 2010 Elsevier Inc. All rights reserved.

Implementation intentions have been shown to effectively change counter-intentional habits. Research has, however, almost solely been concerned with the effectiveness of a single plan. In the present research, we investigated the behavioral and cognitive implications of making multiple implementation intentions targeting unhealthy snacking habits and its underlying processes, linking multiple habitual snacking cues to healthy alternatives. Study 1 revealed that formulating multiple implementation intentions was not effective in decreasing unhealthy snacking, whereas formulating a single plan successfully induced behavior change. By using a lexical decision task in Study 2, it was found that when making a single plan, but not multiple plans, the healthy alternative became cognitively more accessible in response to a critical cue prime than the habitual response. However, when making additional plans in an unrelated domain, the negative effects of making multiple plans were absent. In sum, the current findings suggest that formulating multiple implementation intentions is ineffective when changing unwanted behavior. These reduced effects of multiple implementation intentions do not occur when making the plan but are rather due to interference in the enacting phase of the planning process. Copyright (c) 2013 John Wiley & Sons, Ltd.