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The purpose of this paper is to compare the impact of different multi-unit promotions (MUPs) and a single-unit promotion (SUP) on store-level sales and consumer-level purchase probability and quantity decision.

The paper combines two empirical studies. Study 1 applies a hierarchical multiplicative model to store-level sales data for four product categories provided by a large Dutch retail chain. Study 2 presents a laboratory experiment in which the quantity requirements of the two focal MUP frames are manipulated to assess their impact on consumer purchase decisions.

The paper provides empirical evidence for the superiority of the “X for $Y” above “X + N free”, which confirms the hypotheses based on prospect theory, mental accounting and theory about gift-giving. Quantity requirements of four to five units show the largest effects. In addition, the superiority of the “X for $Y” frame holds for functional product categories, but not for the hedonic categories.

The paper provides managerial insights into the relative effectiveness of alternative MUPs and an SUP and how this promotional effectiveness depends on the type of product category and quantity requirements.

This paper combines actual sales data and experimental data. This “mixed approach” extends existing knowledge by comprehensively evaluating two MUP frames, namely, “X + N free” and “X for $Y” promotions, and an SUP.

The purpose of this paper is to describe a method of estimating the likelihood that a person with particular characteristics will imitate a particular new behaviour (i.e. the use of an innovation). This estimation can be used to provide a new form of forecast for the likely market demand for an innovation.

This method, termed imitation analysis, is based on imitation theories from the behavioural sciences and is applied in two recent case studies in The Netherlands: broadcast TV on mobile phones and a mobile friend‐network service.

These cases illustrate how: the market segments with the highest potential can be identified; marketing communication can be focused on specific issues important for each segment (e.g. based on the highest imitation potential); product design can be improved (by highlighting the characteristics with the most room for improving the imitation potential); and market demand can be modelled (the overall chance of imitation occurring).

Management implications for the two services, as well as the usefulness of imitation analysis in forecasting studies, are discussed.

The paper expands on original work published in this journal in 2005, showing the value of the approach in real‐world settings.

The purpose of this paper is to investigate the impact of complaint behaviour and service recovery satisfaction on consumer intentions to repurchase through internet channels.

Using survey data from large consumer samples from 15 European countries, the authors classify consumers according to: whether they had negative experiences with online purchases, whether they complained, and whether they were satisfied with the complaint handling. A logistic regression analysis assesses the effects of these experiences on repurchase intentions.

Remarkable differences arise among the consumers with respect to intentions to repurchase on the internet. Consumers with negative experiences who complained expressed higher repurchase intentions than consumers with no reason to complain and also than consumers who had negative experiences but did not complain. Yet the highest repurchase intentions arose among consumers who complained and expressed satisfaction with the complaint handling, in support of the service recovery paradox in an online setting.

This project is one of the first empirical studies of the consequences of dissatisfaction and complaints related to online purchase behaviour.

A new integrated method based on the means‐end chain (MEC) analysis and factor analysis is proposed for developing intangible product‐mix strategies for convenience store (CVS) chains. With variables obtained from a survey on CVS service items, both analyses were used to derive the hierarchical value factor map (HVFM). The new map can handle and develop strategies for product differentiation, advertising and market segmentation. The purpose of this article is two‐fold: to propose improvements for the traditional MEC method of constructing the service hierarchical value map (HVM) and to formulate effective intangible‐product strategies.

The goal of this paper is to empirically investigate the effects of an loyalty program (LP) introduction and termination, accounting for simultaneous effects of LP designs, cross-customer effects and competition effects. Despite firms across the globe spend billions of dollars on LPs, it is not clear: whether these programs enhance customer loyalty, what happens if a program is terminated and which LP design elements enhance effectiveness of LPs.

The authors empirically investigate to what extent the effects of introducing and terminating a LP depend on: its monetary and non-monetary design elements, customer characteristics and competition. The empirical evidence is based on a bivariate hierarchical linear model, using a large-scale dataset involving 9,783 consumers rating 24 different LPs across eight industries.

While the characteristics of LP are more important in influencing customer behavior when they join the LP, the competitive environment and the duration of membership in an LP are the primary drivers of customer reactions to LP termination. Non-monetary discrimination between members and non-members is a more powerful tool in creating customer loyalty than offering higher discounts or saving points. The effect of discrimination on loyalty sustains when an LP is terminated.

This is the first research to empirically investigate the effect of an LP termination, accounting for simultaneous effects of LP designs and competition effects. The authors measured behavioral intentions in a hypothetical case of LP termination. Future research could assess the effects of LP termination and the moderating role of both monetary and non-monetary design elements on other behavioral loyalty variables based on, e.g. household panel data, when such data on LP terminations across industries becomes available.

When a firm considers the introduction of an LP or changing an existing one, non-monetary discrimination between members and non-members seems to be the most effective tool in building sustainable customer loyalty. Further, offering a relatively low saving rate is a viable way to keep costs down because the savings percentage does not significantly affect loyalty. For the same reason, firms can also consider reducing or eliminating LP-based discounts. The competitive environment and the duration of membership in an LP are the primary drivers of customer reactions to LP termination.

To the best of authors’ knowledge, the potential effects of LP termination have not been addressed in the current literature. The authors empirically assess the effects of LP termination and effects of those programs at the introduction. Understanding the factors that moderate the potential negative impact of terminating an LP is of crucial importance to managers and researchers alike. The paper is of great value for firms that consider introducing, modifying or terminating an LP.

The purpose of this study is to measure the impact of perceived benefits offered by loyalty programmes on store patronage behaviour and repeat purchase behaviour of customers. Another purpose of this study is to assess the impact of mediating and influencing factors on patronage behaviour and customer loyalty.

The convenience sampling method was adopted for the sample selection. Structured questionnaire instruments were distributed to subscribers who are enrolled or subscribed to any loyalty programme and asked them to mark their opinions about subscriptions to a loyalty programme and the benefits or perceived values that customers get from the subscribed loyalty program. To determine respondent responses, closed-end questions and Likert scales were used. A SmartPLS statistical programme was used to analyse the coefficient and p-value of the perceived value of the loyalty programme and its relation with patronage behaviour.

The study finds a significant impact of various loyalty programme determinants on store patronage behaviour, supported by hypothesis testing. Researchers have used variable commitment to the test with satisfaction and trust and found it supportive. Another hypothesis tested for the impact of satisfaction on trust shows a significant relationship. They found a significant relationship between the perceived benefits of a loyalty programme and satisfaction with the store and trust. Another hypothesis tested for the impact of satisfaction on trust shows a similar relationship. The impact of commitment on loyalty behaviour (store patronage) was analysed and found to be significant.

To the best of the authors’ knowledge, to date, this methodology has not been used to measure the influence of loyalty programme values on patronage behaviour in the present context. Based on the understanding that the measurement of the impact of perceived values of loyalty programmes on repeat purchase behaviour could be very useful in providing guidance for decision-making in consumer retention strategy, this paper contributes to the literature on the evaluation of store patronage behaviour with its influencing variables.

While an increasing number of global brands are of emerging country origin, research about emerging global brands remains scare. The purpose of this paper is to provide the first theoretical effort to understand how consumers in the developed regions evaluate global brands from emerging countries. Building on globalization and social identity theory, the paper aims to shed light on the effect of global identity on consumer attitude toward emerging global brands, the process of such effect, and the boundary condition for it as well.

The authors used two non-student surveys in the USA and UK in which respondents’ global identity was measured and two laboratory experiments in which respondents’ global identity was primed. The operationalization of dependent variables is also divergent, either directly measuring attitude toward the global brands from developing countries or measuring consumer relative evaluation. Convergent results were reported from four studies.

The results show that when consumers’ global (vs local) identity is accessible, those from developed regions will show more favorable evaluations of global brands from emerging countries. And this effect is mediated by the positive association between global identity and globalization. Further, this effect emerged when consumers view global and local cultures as compatible with each other but disappeared when consumers view global and local cultures as oppositional to each other.

The findings have practical implications for global brand marketers from emerging economies to enter developed country markets, and to make their brands real global. Specifically, global identity consumers should be targeted and the compatible view of global and local cultures should be pronounced.

Focusing on global brands from emerging countries, this paper examines the global identity effect in developed country markets for the first time. The finding add new knowledge to the literature of globalization, global branding, and assimilation effect of global identity, and help to reconcile the heated debate on whether country of origin is still relevant to the globalized world.

The purpose of this paper is to estimate individual promotional campaign impacts through Bayesian inference. Conventional statistics have worked well for analyzing the impact of direct marketing promotions on purchase behavior. However, many modern marketing programs must drive multiple purchase objectives, requiring more precise arbitration between multiple offers and collection of more data with which to differentiate individuals. This often results in datasets that are highly dimensional, yet also sparse, straining the power of statistical methods to properly estimate the effect of promotional treatments.

Improvements in computing power have enabled new techniques for predicting individual behavior. This work investigates a probabilistic machine-learned Bayesian approach to predict individual impacts driven by promotional campaign offers for a leading global travel and hospitality chain. Comparisons were made to a linear regression, representative of the current state of practice.

The findings of this work focus on comparing a machine-learned Bayesian approach with linear regression (which is representative of the current state of practice among industry practitioners) in the analysis of a promotional campaign across three key areas: highly dimensional data, sparse data and likelihood matching.

Because the findings are based on a single campaign, future work includes generalizing results across multiple promotional campaigns. Also of interest for future work are comparisons of the technique developed here with other techniques from academia.

Because the Bayesian approach allows estimation of the influence of the promotion for each hypothetical customer’s set of promotional attributes, even when no exact look-alikes exist in the control group, a number of possible applications exist. These include optimal campaign design (given the ability to estimate the promotional attributes that are likely to drive the greatest incremental spend in a hypothetical deployment) and operationalizing efficient audience selection given the model’s individualized estimates, reducing the risk of marketing overcommunication, which can prompt costly unsubscriptions.

The original contribution is the application of machine-learning to Bayesian Belief Network construction in the context of analyzing a multi-channel promotional campaign’s impact on individual customers. This is of value to practitioners seeking alternatives for campaign analysis for applications in which more commonly used models are not well-suited, such as the three key areas that this paper highlights: highly dimensional data, sparse data and likelihood matching.