Research shows geographic proximity influences the impact of electronic word of mouth (eWOM). Recommendations over social media are more effective the closer those making the recommendations and those receiving the recommendations are physically located.
The Internet age, some argue, heralds “the death of distance.” For example, aside from occasional utilitarian factors, such as shipping costs, geography is irrelevant to a purchasing decision. Unlike physical stores, the geographic location of online vendors is irrelevant to the consumer.
Along these same lines, one might assume that geographical proximity is even less relevant for electronic word-of-mouth (eWOM), that is, recommendations and discussions about products and services made over social media. The discussion between two college friends or two cousins sharing opinions about an online fashion store will be the same whether they live in the same town and thousands of miles apart.
While such conclusions might seem logical, research from Emory University’s Goizueta Business School shows that assumptions about the impact of geography on eWOM—or lack thereof—are mistaken.
The data for the research is drawn from a special two-month Twitter purchasing and recommendation program presented in conjunction with American Express. When Twitter users made a purchase through the program, the purchase might, depending on the formatting of the purchasing message, appear automatically in the user’s feed—making the purchase visible to the user’s followers.
Reviewing the purchase activities of 133,000 Twitter users, the researchers were able to identify
The researchers deliberately focused on user-follower dyads: they compared the purchasing behaviours of groups of two individuals—one individual being the original purchaser who disseminated through Twitter information about his or her purchase, the second individual being the purchaser’s Twitter follower who received the message concerning the purchase.
With access to the geographic locations of the Twitter users in their database, the researchers could compare purchasing behaviour to geographic proximity—that is, whether distance between the original purchaser and the follower made a difference in the likelihood that the follower would make the same purchase.
The data revealed that distance did make a difference in the follower’s purchase decision: the closer the original purchaser was located to the follower, the greater the likelihood that the follower was influenced by the purchaser’s eWOM.
For example, a decrease of 10 miles in the distance between user and follower increased the likelihood of a follower making the same purchase by almost 13%. In the other direction, an increase of 100 miles between user and follower reduced the likelihood of a follower purchase by more than 25%, and an increase of 1000 miles by nearly 40%. Thus, a follower living in New York City will be approximately 25% less likely to pay attention to word-of-mouth recommendations from Philadelphia than to word-of-mouth recommendations from New York City—and nearly 40% less likely to listen to word-of-mouth recommendations from Miami.
There might, of course, be a number of other explanations for why a user’s eWOM message influences a follower—notably, whether the user and follower share similar preferences and behaviours. Socio-demographic similarities might also explain the influence of a user on a follower. A follower may be more likely to be influenced by an eWOM message from a user of a similar age or the same ethnicity.
When through exhaustive statistical analysis, the researchers removed these and other factors—such as the advertising budgets of the products in the locations involved, the expertise of the sender (based on how often the sender commented on the product category), and small city effects (the fact that rural recipients would be more likely to purchase online than urban recipients)—from the equation, the result remained the same: the closer a follower viewing an eWOM message was located to the user sending the message, the greater the effectiveness of the message.
Once they had established the correlation between distance and the impact of word of mouth, the researchers extended their study to determine why geography made a difference. Their conclusion: location-based social identity, which relates to the connection between your physical location and who you feel you are as a person.
Two factors that strengthen location-based social identity are political homogeneity (the majority of people in the region have the same political views) and local community hardships (the region suffered significant natural disaster(s) or other calamities in the past five years).
The study revealed that when either of these factors was applicable, the relationship between geographical distance and the impact of eWOM was stronger. This relationship was also strengthened when Twitters users explicitly put their location in their Twitter profiles, further reinforcing location-based social identity as the reason geography plays a role in the influence of electronic word-of-mouth.
The research has implications for marketing efforts focused on electronic word-of-mouth.
When striving to engineer eWOM in social media, companies should incorporate geographic proximity as a characteristic linking disseminators and recipients of electronic word of mouth—rather than focusing solely on difficult-to-measure shared personality strengths and tie strength (the strength of the bond between individuals).
Companies can also incorporate local cues into their advertising content and in the design of their viral marketing campaigns and ad content. And while increasingly sophisticated social media-based marketing communication strategies leverage connections between users, geography is one connection typically overlooked.
Beyond advertising strategies, companies can increase the effectiveness of their content curation by including geographic proximity in deciding which consumer-generated content to disseminate.
Geographic location should also be incorporated into the design of algorithms, such as the algorithms used to generate whom-to-follow recommendations.
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