Can Negative Word Of Mouth Go Beyond from Merely Catching Attention
to Generating Actual Purchase?
Jung Ah Han, Ph.D. Student, Department of Marketing
Faculty Advisor: Eleanor McDonnell Feit, Department of Marketing
LeBow College of Business
BACKGROUND
The notion of Word Of Mouth (WOM) has received considerable
attention in both business and academic research. Numerous studies have
been conducted to examine the effectiveness of WOM marketing tools
and valence of the WOM. This study further examines how and when the
Negative Word Of Mouth (NWOM) can be beneficial, and how NWOM
can go beyond from merely catching customer’s attention to actual
increase in sales. This study will investigate the impact of NWOM using
time-series modeling.
MODEL AND METHODS
Negativity Effect
Extant research has found that negative traits are given greater weight in overall evaluations than are
positive traits. This “negativity effect” shows that negative information have more effect on
customer’s judgment on product and brand. Ito et al. (1998) also noted that a negativity bias will
yield greater sensitivity to negative information and brain will weigh more heavily on negative
information.
Berlyne (1954) asserted that rarity of negative information makes it surprising and, thus, draws more
attention. In the same sense, Fiske (1980) noted that negative information is usually scarcer than
positive information and suggested that this made negative information more useful than positive
information.
The other explanation for the larger impact of negative information is the “attribution
effect” (Laczniak et al. 2001). For example, a positive WOM may be discounted because of suspicion
that it is arranged by the party of interest. Therefore customers tend to not believe the positive
information whereas they trust more in negative information.
Positive effects of Negative Word Of Mouth
Berger et al. (2010) have empirically proven that “any publicity is good publicity”. They found that
negative publicity can increase purchase likelihood by increasing product awareness for unfamiliar
products.
The upside of the negative publicity can be explained by the sleeper effect (Hannah and Sternthal
1984). Information tends to become dissociated from the message over time, and people may have a
feeling of awareness, or remember they heard something about the product, but the valence may be
forgotten (Skurnik et al. 2005).
Consumers have finite attention, and it is less likely to affect purchasing behavior when it is not on
top of their mind. Most consumers are not aware of every product or brand that is released.
Consideration appears to explain much of choice (Hauser 1978). Therefore, brand awareness can
stimulate consumers to include that option in their choices.
PURPOSE AND HYPOTHESIS
After the birth of the world wide web and explosion of the internet has
led to the rise of new media channels and offered fruitful ground for
WOM marketing tools. More and more consumers use online tools to
share their opinions about product. It has been studied that when
customers make purchase decisions, they trust online WOM posted by
unknown users more than traditional media. User Generated Content
(UGC) is becoming more impactful because of its instant availability, low
cost, ease of use, wide subscription, wide access, and wide reach. This
new form of marketing tools can contain positive or negative valence
made by actual and former customers. Usually companies try to suppress
NWOM, and extant research has focused on how badly NWOM harm
potential sales and how to cope with such circumstances. When an
established brand is at stake, it would be smart to make an effort to limit
the bad press as much as possible. However, if the brand is new and
unknown to the customers, the NWOM may facilitate brand awareness.
Therefore, the impact of NWOM can differ depending on firm’s maturity
and familiarity of the brand to the customers.
Time Series Analysis in WOM
Using time series analysis is beneficial when examining the impact of WOM because it not only
enables to examine the long-term impact but also it allows to examine the feedback effect of WOM.
The VARX model enables to examine how quickly or slowly NWOM travels, and it is possible to
observe the wear-in and wear-out effects. “Wear-in” effects takes a number of months before the
impact of NWOM reaches the peak point and “wear-out” effects takes several months after the peak
before the impact of NWOM dies out completely and revert back to original status (mean-reverting).
The VARX model also tests the two-way influences with a feedback loop. The framework captures
not merely NWOM’s direct impact on the sales, but also the sales feedback impact on future NWOM
over time. This feedback loop is important because it would suggest a vicious cycle of NWOM.
Another key reason to use time series analysis is to observe WOM phenomenon for long time period
because it takes time for the market to react to NWOM. The long-term results explicitly show that the
market does not instantaneously react to NWOM. Rather, customers’ reactions may grow over time,
as the effects of NWOM unfold. In addition, the harmful effects of NWOM do not vanish overnight.
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