The NPV of bad news

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Abstract

We explore the effects of individual-and network-level negative word-of-mouth on a firm's profits using an agent-based model, specifically an extended small-world analysis. We include both permanent strong ties within the social network, and changing, often random, weak ties with other networks. The effect of negative word-of-mouth on the Net Present Value (NPV) of the firm was found to be substantial, even when the initial number of dissatisfied customers is relatively small. We show that the well-known phenomenon of the strength of weak ties has contradictory effects when taking into account negative word-of-mouth: Weak ties help to spread harmful information through networks and can become a negative force for the product's spread.

Introduction

Consider the following actual case of a consumer electronics company that recently introduced a new audio CD protection device. Soon after launch, the company discovered that the product performed poorly in about 2% of the European market. Fixing the problem was not simple, and the firm's executives debated how much the company should invest to mitigate the problem. Some argued that 2% of the market would have negligible economic consequences. Others countered that the dissatisfied customers who could not be identified in advance would generate negative word-of-mouth communications following their poor experience, ultimately harming the firm's profits. Even though the executives were aware of the conventional wisdom that “bad news travels fast”, none of them had a good grasp as to how to assess the possible effects of the anticipated negative word-of-mouth on their profits.

Unfortunately, there is little in the literature that can help managers in such cases. Marketers do realize that negative word-of-mouth communications can considerably lower a firm's profits. Thus, considerable attention has been given in the academic literature to explore topics such as the circumstances under which consumers spread negative word-of-mouth (Richins, 1983), the quantity of negative word-of-mouth that dissatisfied consumers spread (Anderson, 1998), and the relative weight of negative information received by consumers as compared to positive information (Mizerski, 1982).

Similarly, there are numerous anecdotal stories in the business literature about the possible harm caused by a dissatisfied customer's word-of-mouth communications (Hart, Heskett, & Sasser, 1990). Recently, negative word-of-mouth has drawn additional interest, as marketers have become more aware of the speed at which negative product-related information can pass through electronic means such as the Internet (Ritson, 2003, Ward and Ostrom, 2006).

Yet there is scant formal analysis in academic studies that can help managers understand the economic implications of negative word-of-mouth. While considerable literature has dealt with negative word-of-mouth at the individual or network level (see Buttle, 1998 for a review of the word-of-mouth literature), and some have analyzed negative word-of-mouth at the aggregate level (Mahajan, Muller, & Kerin, 1984), little is known about how both levels combine to produce market-level results. Tying both ends together is essential, as managers will typically be able to collect information on the determinants and extent of negative word-of-mouth at the individual level, yet their interest in and ability to justify firm-level actions will ultimately lie in the analysis of aggregate-level financial results.

A possible reason for the dearth of formal analysis is that the spread of information in a social network is a complex system that consists of a large number of individual entities interacting with each other, in what is sometimes an indiscernible manner, ultimately generating large-scale, collective, visible, and quantifiable behavior (Anderson, 1999, Holland, 1995). In other words, negative word-of-mouth is an invisible force, leaving no tracks in the sales curves. Unlike the positive interactions of consumers that lead to adoption and growth of sales, we do not have reliable measures of the negative word-of-mouth that shrinks the market by transforming potential adopters into non-adopters.

In this study, we explore the effect of both individual-and network-level negative word-of-mouth on aggregate sales using an agent-based modeling approach, specifically an extended model of small-world analysis (Watts, 1999). Utilizing a dynamic small-world approach, we simulate a market in which information spreads when consumers interact with each other, using both strong ties within their own social system and weak ties with other networks (Granovetter, 1973).

Our analysis explores the effects of changes in a social system's information structure, the intensity of strong and weak ties, and marketing effects, as well as determinants of negative word-of-mouth phenomenon, such as the strength of negative word-of-mouth compared to positive word-of-mouth or the number of disappointed customers, on the aggregate sales and net present value of the cash flow that marketers can hope to achieve.

An important point to note is that our approach deals with negative word-of-mouth more than with other possible negative effects of contagion. While the internal influence parameter of the aggregate diffusion models is often interpreted to represent word-of-mouth, it can also capture imitation effects such as social learning, social pressures, or network effects (see Van den Bulte & Stremersch, 2004). Based on a meta-analysis of aggregate diffusion models, Van den Bulte and Stremersch even suggest that imitation effects may be stronger overall than word-of-mouth effects in the growth of markets for new products.

Positive contagion effects in aggregate diffusion models may thus include both imitation effects and word-of-mouth. Yet the picture for negative word-of-mouth is different. In the modeling approach that we present, negative word-of-mouth stems from individual customer dissatisfaction, and the effects are manifested at the network level. Negative contagion effects may not be a product of dissatisfaction, but rather the mere adoption by other consumers, who, for example, belong to a segment of the population whose adoption reduces the social utility of the product. For example, Joshi, Reibstein, and Zhang (2006) reported a negative contagion effect of the adopters of the Porsche SUV (Cayenne) on the potential adopters of traditional Porsche roadsters. This effect requires specific modeling of segmentation and contagion that will capture these segment-based phenomena. For example, unlike word-of-mouth, observational learning does not demand direct contact, so that negative contagion can draw on the total number of adopters in the population and operate other than at the network level. Hence, we focus on negative word-of-mouth only, and leave the intriguing issue of the negative effects of observations and other forms of negative contagion to future research.

The rest of the paper continues as follows: In the next section, we discuss the effects of negative word-of-mouth at the individual level and its integration into a dynamic small-world model. In Section 3, we analyze the adverse economic outcomes of negative word-of-mouth. In Section 4, we explore how a social structure that includes negative word-of-mouth prompts failure, and we offer a model that discriminates between failures and successes. Section 5 presents a structural equations model that enables us to better understand how network structure affects the consequences of negative word-of-mouth. The paper concludes with the managerial implications of the results.

Section snippets

Negative word-of-mouth communications

Customers respond to dissatisfaction with a product in a number of ways, including complaints, brand switching, legal action, and negative word-of-mouth. The latter may be particularly harmful, because it requires little effort by consumers, yet it can directly affect the consumption habits of would-be adopters. Worse, it is largely invisible to marketers. One problem in this regard is that only a minority of dissatisfied customers complains to the firm, and so the actual extent of negative

The NPV of bad news

The objective of the first analysis was to study the aggregate response to negative word-of-mouth. We want to compare processes across the range of parameters with and without negative word-of-mouth, as well as examine the economic damage the firm suffers due to negative word-of-mouth. Hence, we first define a one-dimensional measure that will summarize the difference between the processes. Since any change in a growth pattern can have major economic consequences for the industry, we have

The underpinnings of failures

In the previous section, we analyzed the influence of negative word-of-mouth on the adoption process and profitability. However, negative word-of-mouth is not only responsible for a slowdown of successful processes, but also for transforming a critical slowdown of processes into failures that will eventually be removed from the market. Thus, in this section, we sort the effects of negative word-of-mouth into successes and failures. As with success, there are various definitions of failure, in

Underlying structure of negative word-of-mouth: Activations of nets

In the previous sections, we analyzed the effects of the firm's actions and the network structure on the NPV and on product success/failure. However, the precise mechanism of market destruction – i.e., by what means negative word-of-mouth actually destroys growth – still remains to be explored. Although the spread of negative word-of-mouth is mostly invisible in real life, it is easier to trace its effects in a complex model such as the one we used. There are two mechanisms that reflect the

Discussion

In the course of normal business activity, it is nearly unavoidable that some customers will be dissatisfied, and some will spread negative word-of-mouth. In some cases, firms may even predict that these customers' actions (for example, the level of quality of the product for various customer segments) will cause dissatisfaction among a certain number of customers, yet continue with their desired action, having assessed that the benefits they obtain will outweigh the costs. Our aim here is not

Acknowledgements

We would like to thank Hubert Gatignon, Stefan Stremersch, and two anonymous reviewers for a number of constructive comments and suggestions: the Institute for Business Research in Israel at Tel Aviv University; the Kmart International Center of Marketing and Retailing; the Davidson Center; Hebrew University of Jerusalem; and the Horowitz Association through the Center for Complexity Science. This research was supported by the Israel Science Foundation (grant No. 1027/06).

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