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Using a survey of 349 respondents, we investigate the triggers of word of mouth (WOM) in four durable categories and compare this evidence with previous findings for services. For these durables, positive word of mouth (PWOM) is mostly triggered by advertising and customer satisfaction with the product, while negative word of mouth (NWOM) is rare and mostly triggered by the content of conversation and the perception that other persons need advice. This contrasts with previously established findings for services where advertising has little effect on PWOM and dissatisfaction has substantial effect on NWOM. These differences have important implications: they suggest that durable ads should be tested to check that they trigger PWOM and that service providers should pay more attention to the satisfaction derived from the service experience.

Are referred customers more valuable than customers recruited through advertising/promotion? This question is answered using data accumulated from surveys covering the satisfaction, recommendation, retention, spending and mode of acquisition of customers. The database comprises 6578 records and covers 15 product/service category groupings. Overall, referral customers are somewhat more satisfied, recommend their brand more, and have superior retention when compared to customers recruited through advertising/promotion. These findings suggest that referral customers are more profitable than advertising customers but, unexpectedly, their superiority over advertising customers rests mainly on their greater retention rather than their higher level of recommendation. On this evidence, referral customers bring somewhat more benefit to a firm than those recruited through advertising/promotion. Projections for a 7-year period illustrate the magnitude of these benefits. Also discussed and illustrated is a possible secondary outcome – social amplification.

The volume and impact of positive and negative word of mouth (PWOM, NWOM) are investigated in relation to the market shares of brands. We find that the volumes of PWOM and NWOM are closely related to market share. By contrast, the average impact of instances of PWOM and NWOM shows no direct relationship with market share. When the direct influence of market share is removed, we find that small brands have somewhat more WOM than is warranted by their size, whereas large brands have slightly less, and this effect is stronger in the case of NWOM. The evidence presented here provides norms for evaluating WOM data and assists in the development of WOM metrics.

This paper furthers the understanding of the decision process used by managers when extending their brands. In particular, we investigate the extent to which the model of extension decision process outlined by Ambler and Styles (1997) for fast moving consumer goods is relevant to the luxury sector. We find that the broad structure and components of the A & S model extend to the luxury sector. However, some modifications are required to reflect the differences between luxury and fast moving consumer goods. In contrast with fast moving consumer goods, growth, rather than defence, is the main driver for extending luxury brands. The marketing function, rather than R&D, has the strongest influence on brand extension decisions in the luxury sector. Tradition and brand heritage are identified by luxury goods experts both as the first driver and an important decision criterion for brand extensions.

This paper is concerned with the way in which positive word of mouth (PWOM) about brands spreads their usage. We find that brand users, who have heard positive comments on their brand, offer nearly twice as much PWOM as users who have not heard such comments. We identify a transmission mechanism that underpins the production of PWOM; specifically, that social amplification underlies this effect. While brands are at the core of our investigation, background theory comes from the literature on diffusion and the adoption of new products. We explain the social basis of new product adoption and argue that social amplification works alongside the classic infectious disease model of diffusion and results in further adoptions when the extra WOM reaches non-users. We support this account with evidence using data from studies on branded mobile phones, movies, vacation destinations, hotels, restaurants and fashion stores. It is proposed that recommendation received from others stimulates more PWOM because it provides a script which the receiver of the recommendation can use in subsequent conversations, and we offer empirical support for this proposal.

Consumer loyalty may be defined as a singular concept, usually as an attitude toward the loyalty object or as repeatpatronage behaviour; alternatively, the definition may combine attitude and behaviour in either an additive or an interactive expression. We argue that definitions of loyalty are useful if they predict phenomena such as recommendation, search and retention (loyalty outcomes). In three consumer fields, we find that combination measures of customer loyalty often perform poorly as predictors of loyalty outcomes compared with singular measures since recommendation is predicted by attitude but not by repeat patronage, whereas retention and search behaviour are predicted better by repeat patronage than by attitude. We also find that the prediction of loyalty outcomes is not improved by the inclusion of an interaction term in the model. On this evidence, we argue that combination concepts of loyalty are of limited value. Further, we find that there is no form of loyalty that consistently predicts all the different loyalty outcomes and, therefore, we should abandon the idea of a general concept of loyalty.

Negative information normally has more impact on attitude and cognition than positive information, but there is evidence that positive word of mouth (PWOM) usually has more effect on purchase intention than negative word of mouth (NWOM). We explain how this apparent inconsistency may come about when measures of attitude and intention are used as indicators of behavior. We argue that on/off behaviors, such as purchase, should be predicted better by a measure of intention, rather than attitude, and that consideration should be given to a measure of intention that is proportional to the maximum change that could occur as this reflects the movement toward or away from purchase. A large database is analyzed to show the differences between proportional and absolute measures. In addition, we examine the proportions of respondents reaching certainty (i.e. 0% or 100% probability of purchase) after NWOM and PWOM. Findings from these new analyses show that PWOM has more impact than NWOM on purchase intention (both proportional and absolute), but a higher proportion of respondents reach certainty after NWOM.

This paper examines the reasons that people give for switching services. It is designed to test previous work and to extend knowledge by showing how the frequencies of different reasons vary by type of service. Unlike previous work using Critical Incident Technique (CIT), surveys are used to gather data on switching reasons. It is argued that CIT tends to select in favour of event-related reasons and against condition-related reasons. The research reveals a much lower frequency of event-related reasons for switching and more condition-related reasons compared with earlier work, particularly when services are delivered at a specific location. The research is extended to different types of switching. When customers have to switch, they often cited conditions; when they switch because of dissatisfaction with a supplier, they give more event-based reasons; when they discover a better supplier without previous dissatisfaction, they most often cite competition reasons. These findings have practical implications. First, previous work has claimed that service switching is preventable because event-related reasons can often be avoided by management; the present work, by showing that avoidable reasons are rarer, shifts the focus from customer retention to customer acquisition. Second, this work informs practitioners about the reasons that are likely to be given by defectors in their service category; this assists strategy decisions on retaining and acquiring customers.