1. Agnes Jumah – MA Strategic Marketing Management – Buyer Behaviour Assignment
“Word-of-mouth communication is the most powerful force for change but the least accessible”.
Discuss this. What do we know about the patterns of word-of-mouth that helps us use it? What
do we need to know still?
INTRODUCTION
Word of mouth (WOM) communication is unique, complex and multifaceted. WOM according to
Wikipedia is defined as a reference to the passing of information from person to person. Originally
the term referred specifically to oral communication (literally words from the mouth), but now
includes any type of human communication, such as face to face, telephone, email, and text
messaging.
This paper will discuss and examine different theories in relation to key actors within networks:
opinion leaders, influentials and hubs and how they influence change. In addition, some thoughts
on how WOM communications can be inaccessible to marketers will be put forward.
WORD OF MOUTH COMMUNICATIONS AND THE KEY PLAYERS
Much research has been carried out on what drives change within WOM communications. To
understand the drives, it is worth mentioning the Multi-step Flow of Communications – see figure
1.
Figure 1 – Multi-Step Flow of Communications
The Multi-Step Flow model builds on both the One-Step model and the Two-Step model by Katz
and Lazarsfeld (1955) and is considered the way in which communications flow takes place where
there are many intermediaries.
So within communications, what is the importance of opinion leaders and influentials within a
social network and in driving change? In reading some of the current literature, it was found that
many theories existed. The three theories examined in this paper are:
1. The Influentials Hypothesis
2. The Everyone Hypothesis*
3. The Hub Hypothesis*
The Influentials Hypothesis – Focus on the Individual. This theory suggests that a few key
individuals (opinion leaders or influentials) influence the decisions of less active others. It has been
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2. Agnes Jumah – MA Strategic Marketing Management – Buyer Behaviour Assignment
theorised that, Influentials act as intermediaries between the media and others in their social
network determining the rate and direction of change. Lazarsfeld et al (1944) cited by Rogers
*Title created for categorisation purposes of this paper only.
(1995), in their seminal work, analysed a presidential election and were able to highlight the role
that opinion leaders played in influencing the voting decisions of others. Rogers (1995, p300) puts
forward the argument that opinion leaders are “individuals who lead in influencing others’
opinions”. Rogers goes further to state that opinion leaders are key to the rate at which innovation
adoption occurs and that once opinion leaders have adopted an innovation and discuss this with
others in their network, the rate at which adoption take place grows exponentially. This
“influentials hypothesis” was also supported by Katz and Lazarsfeld (1955). This thinking has been
developed. It is hard to conceive that WOM communications is as rigid as the Influentials
Hypothesis followers may have us believe. The Influentials Hypothesis suggests that only a select
number of actors assume a very distinct opinion leader role.
The Everyone Hypothesis – Focus on Everyone. Influence can be considered broader than the
Influentials Hypothesis suggests, with different participants taking on different roles depending on
the situation. More recent studies seem to advocate this. Balter and Butman (2005) cited in Word
of Mouth Research: Principles and Applications follow this argument. They stated that:
“WOM is not about identifying a small subgroup of highly influential or well-connected
people to talk about a product of service. It’s not about mavens or bees or celebrities or
people with specialist knowledge. It’s about everybody”.
Allsop, Bassett and Hoskins (2007) also support this thinking: that everyone can adopt different
roles at different times. They have suggested that at different times and in different environments,
an individual consumer can assume any role: opinion leader, former or follower. Their paper was a
useful summation of WOM principles; unfortunately much of it is theoretical, with general
commentary and a large focus on their own branded WOM simulation system. Despite this some
useful points can be drawn. Fill (1995) also argues that opinion leaders and members of the target
audience all have an effect on each other. Keller and Fay (2006) pioneers in the field of WOM
measurement, state that “everyone plays the role of the “sender” and “receiver” in conversations
about brands.” Watts and Dodds (2007) propose that opinion leaders are not the drivers of
innovation and additionally, state “most social change is driven not by influentials, but by easily
influenced individuals influencing other easily influenced individuals”.
The Hub Hypothesis – Focus on Connections. There is another school of thought on what drives
change within network that focuses on hubs or those that are “well connected” i.e. with several
social ties or relationships, within a social network. In his empirical study on married, female
students living in the same apartment block, Arndt (1967, p293) found that initially, wives that
were “well integrated into the social structures were more likely to adopt a new product than
were the isolated ones”. The gap between these two audiences did however narrow over time.
Though responses were low (only 332 comments were received) this study yielded insightful
findings that support the Hub Hypothesis. Arndt’s findings are to some degree generalisable but
some caution must be exercised as it has been suggested that genders perform differently in
WOM communications depending on the product category (Allsop, Bassett and Hoskins, 2007, p
401 and Keller Fay Group Whitepaper, 2006, p4). Field experiments such as Arndt’s are probably
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3. Agnes Jumah – MA Strategic Marketing Management – Buyer Behaviour Assignment
the best way to measure hub activity accurately but as this was a small sample it would be
beneficially to have these results extrapolated or repeated on a larger scale for better accuracy of
offline hub activity. One of the latest pieces of research on hubs has been put forward by
Goldenberg, Lehmann and Hong (2009). In their study that examined the adoption of new
applications on a social networking website, Goldenberg et al researched the difference well-
connected individuals make on the overall course of diffusion. They go further and categorise hubs
into two types: 1) Innovator Hub and 2) Follower Hub. This is interesting concept as it highlights
further implications for the marketer: not only is there a need to locate the hub within a network;
according to Goldenberg et al, it then needs to be determined whether or not the hub is the type
that adopts innovations faster than others (Innovator Hub) or the type that adopts early because
they are in a position that exposes them to other adopters (Follower Hub).
In addition to the above finding, this broad study also suggests that generally hubs:
 adopt earlier as a results of degree (number of connections) and not their own
innovativeness
 control innovation adoption speeds and therefore market growth rates (specifically
Innovator Hubs) because without them and their subsequent connections, many within the
network simply would not be aware of the innovation.
 when analysed can to, some degree, indicate whether a product has any chance in the
market.
The Goldenberg et al research provides some interesting thinking into how hubs control
innovation diffusion; however it would be beneficial to repeat the experiment taking into
consideration the following thinking. Are their findings applicable to all markets – it can be argued
that it is not. Indeed, the authors of the research themselves highlight that it may be unrealistic to
assume that all organisations would have “access to such (complete) data” as they did.
Additionally, the context of the research raises some questions. The research examines online
activity. This allows for accurate measurement however according to Keller and Fay (2009), 90% of
WOM communications is offline so how does this research relate to offline WOM communications
and how can the learning be applied to face-to-face WOM? The sample of the research must also
be taken into consideration. In an online, social networking environment, hubs could be well
connected not as a result of their nature but as a result of the length of time that they have been
members of the site – the authors allude to this by indicating hub status is positively correlated
with the membership term. Finally, the research also states that there is a higher probability of the
hubs being male. Some caution as already stated, is required as genders can act differently in
WOM communications.
From looking at these different theories, reality is likely to resemble a combination of all three
hypotheses. Research has shown hubs exist. Opinions leaders have been supported by leading
thinkers. It has also been shown that everyone at different times both gives and receives WOM
within their network. The key for marketers is to know exactly how these actors influence change
within their own relevant networks, whether it worth investing in these types of influentials and
how best to implement WOM plans. There is still no universal formula. WOM can be sent and
received in numerous ways – even within the same network – which makes it hard to track and
therefore calculate the impact of these actors. More empirical research is required to determine
this.
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4. Agnes Jumah – MA Strategic Marketing Management – Buyer Behaviour Assignment
Having examined some of the hypotheses surrounding the different types of influentials, their role
in networks and how they drive change, the next section will briefly discuss why WOM can be
considered the least accessible form of communication.
HOW WOM CAN BE CONSIDERED INACCESIBLE.
WOM communications or marketing can be inaccessible for a number of reasons, a few of which
are listed below. For clarity, “inaccessible” in this context is defined as: WOM being difficult to
recall, difficult to mimic for experimental reasons and therefore difficult to measure accurately.
Offline WOM and the effects that it exacts on the recipient are hard to measure. In A Holistic
Examination of Net Promoter (2007), the authors cite the work of Rust et al (2000) that notes: “the
effect of [word of mouth] is notoriously hard to measure but it is frequently significantly large”.
WOM communications take place everywhere and anywhere; can be positive, negative or neutral;
a referral or recommendation. Keller and Fay (2006) have suggested other reasons unique to
WOM that make it difficult to measure:
1. Conversations and their subject matter are consumer-driven and diverse
2. WOM can be generated as a result of other actions including marketing (e.g. advertising,
websites, sales promotions) and normal business activity (e.g. customer service)
3. WOM can be “highly ephemeral” with exchanges between consumers being random and
hard to predict
How can accurate measurement and useful results be made accessible to marketers? Godes and
Mayzlin (2004) state that surveys are the “most popular” way of measuring WOM conversations.
Another method is consumer diaries. Both methods are acceptable however the issue with each is
that they rely on consumer recall. East et al (2008, p246) suggests reports gathered in this way
“may be systematically distorted by recall bias”. Being dependent on the memory of consumers
through surveys and diaries will not ensure 100% accurate recordings but appears to be the most
realistic way of collecting information. Perhaps the key is asking specific questions to ensure the
data collected is as accurate is it can be.
Field experiments can be carried out – one of the most cited being Arndt’s (1967) work with
student housewives - these however are often focused on small samples.
WOM role play has also been used in the past. This method is not a realistic enactment of WOM as
it asks the sample what they would do in a particular circumstance. Asking a consumer, what they
would do in theory, can be very different to what they would actually do in reality. As East et al,
(2008, p245) state: “...there is no guarantee that they would do as they claim”. In examining the
results from this sort of experiment, a marketer would be reliant on what a consumer says they
may do. This may provide a good indication of what may happen, but compared to spends on
other media that are stringently measured, it is difficult to justify WOM within the marketing
budget when its research may be based on theoretically scenarios. With most forms of marketing
communications media and vehicles, marketers are aware of what they will receive in return for
their investment: £X advertising rate, in X space, for X time period equals X number of enquiries or
X unit sales, for example. This does not exist for traditional forms of WOM marketing. It is
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5. Agnes Jumah – MA Strategic Marketing Management – Buyer Behaviour Assignment
therefore hard to guarantee what the return on marketing investment will be with a WOM
campaign.
Some online WOM communications are easier to track as they are automatically recorded, i.e. are
not reliant on the consumers’ recollection. Despite this, the measurement of online WOM is not
without its own distinct challenges. Some online conversations can be followed through consumer
generated content such as chatrooms or blog threads however these then need to be sorted. Was
the post positive, negative or neutral? How many people read the post? Was it passed on and if
so, how many read it? And what of emails and texts – how can marketers know the content of
these conversations? Many questions as to the best way of tracking WOM still exist; some theories
and suggestions on improving methods and areas of further research are outlined in the final
section.
AREAS FOR FURTHER RESEARCH OR DISCUSSION
Many questions have been raised in this paper with regards to influentials and the inaccessibility
of WOM. There are still several gaps in our knowledge. Before any further research is carried out,
it would be useful to have clarity on some of the titles relating to influentials. In examining
research papers, it was often found that the terms Influencer, Hub and Opinion Leader were
substituted for each other or interchanged. Keller and Fay (2009) have alone documented five
types of influential:
Formal Position of Authority; Institutional Experts; Media Elites; Cultural Elite and The Socially
Connected. See Appendix 1. With so many categories and definitions being used and interchanged,
and without agreed sector-wide definitions, it will be difficult to conduct research that benefits all
players within WOM marketing.
Regardless of their label, there are always going to be some that talk more than others and with
more connections than others. In an email exchange (November 2009), with Emanuel Rosen,
author of The Anatomy of Buzz Revisited (see Appendix 2), he stated:
“We still have many questions about hubs: Who exactly are those people who talk more
than others and how can they help a marketer spread the word? Are they less important
today as some argue, or are they more important in a connected world?”
Rosen is correct. There are still many questions surrounding hubs and how marketers can identify
them. However, Rosen omits a key point in his email: the hubs that need to be identified, engaged
with and utilised are those that don’t just help a marketer spread the word but help spread the
word profitably. Some work has been done in identifying how WOM can predict or influence
profitability e.g. Incorporating Word-of-mouth Effects in Estimating Customer Lifetime Value –
again more research is needed.
A comparative review of the existing research on online and offline hubs would also be useful.
There may be some learning from both sides that furthers current thinking.
In addition to hubs, more research is needed on the networks in which they operate and how
social ties affect communications. Homophilic networks have been shown to inhibit the overall
diffusion of innovation and information. Goldenberg et al (2009) have suggested that although
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6. Agnes Jumah – MA Strategic Marketing Management – Buyer Behaviour Assignment
network homophily can be a “barrier to innovation when different groups are involved”, where
groups are the same, homophily can increase diffusion. This is supported by Rosen (2009, p112-
113) and Rogers (2003, p305). This makes ties between actors in a network very important. There
is limited work in this area.
The panacea for marketers would be a measurement system for WOM that grades each influence
by importance depending on the market and the number of consumers that are sent an initial
message, email, text or promotion: For each person, you send your message to, they will
communicate with approximately X number of people about your message, for example. This
WOM “message calculator” is a long way off and would require a unified effort from all those
operating in WOM marketing. Keller and Fay (2006) state the “industry has lacked information on
the totality of word of mouth” – this is often the case for media that are either new or hard to
measure. An excellent example of how a new collection of media were classified for the benefit of
marketers and media planners/buyers can be seen in Ambient media: advertising's new media
opportunity? See Appendix 3. When WOM is classified in a suitable system, measurement tools
that work for marketers can be created and used effectively within the marketing and media plan.
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7. Agnes Jumah – MA Strategic Marketing Management – Buyer Behaviour Assignment
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Appendices
1 Keller and Fay Table
2 Emanuel Rosen Email
3 Ambient media: advertising's new media opportunity? WARC Paper
Appendix 1 - Keller and Fay Table
_______________________________________________________________________________
Appendix 2 - Emanuel Rosen Email
_______________________________________________________________________________________
Appendix 3 - Ambient media: advertising's new media opportunity? WARC Paper
Separate document.
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