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Influencing Consumer Behavior Using Online Social Cues

By Koh Tat Koon, Assistant Professor, Department of Information Systems, Business Statistics & Operations Management, HKUST Business School

PUBLISHED : Monday, 08 January, 2018, 4:12pm
UPDATED : Monday, 08 January, 2018, 4:12pm

[Sponsored Article]

Recent evolutions in technology and consumer behavior offer opportunities and motivation for businesses to use social information in their marketing activities and strategies. The high penetration of social media means many websites now integrate plug-ins to encourage consumers to “like, share, and follow”. As consumers frequently refer to reviews when making their own purchase decisions, businesses are also providing channels for customers to share their experience with others by posting online reviews.

However, social information is about more than “likes, shares, and follows” or user-generated reviews. It also includes online cues about the actions or behavior of other consumers. For example, on certain hotel booking platforms, consumers can see how many other people are currently viewing, or have recently made a reservation, at a particular hotel they are considering (Figure 1). How are consumers affected by the online social cues of other consumers’ actions? Knowing this can help many businesses operating in a highly competitive environment to tilt decisions and behavior in their favor.

Shaping Behaviors with Behaviors

With the proliferation of tech-based platforms, consumers can and do “multi-home”. In other words, they concurrently consider and use several competing platforms for a particular task. For example, many users have accounts with multiple hotel booking platforms or more than one ride-sharing app in their smartphones. Even though these consumers adopt this approach, they generally use the respective platforms to a different extent and prefer one of them.

To achieve better ranking positions in terms of consumer preference, these platforms can influence users by providing information about the behavior of other similar consumers. This is because individuals tend to consider social cues from other people’s actions when making decisions. Such an inclination is part of observational learning that helps individuals choose the appropriate or best response in a given situation. To illustrate, think of a recent vacation. When looking for places to dine during the trip, you probably chose restaurants with at least some patrons, where you thought the likelihood of getting overcharged or having a bad meal would be lower. That was observational learning at work. Your decisions about which restaurants to try were influenced by cues from other diners’ behavior. In the online world, the viewing or booking of hotels by other consumers is also an example of social cues. Another is the product listings and purchase requests posted by sellers and buyers on exchanges such as eBay or Alibaba.

In a study, Professor Mark Fichman and I examined a group of buyers who were multi-homing on two global online exchanges.1 We monitored their posting of purchase requests over a few months to see how their decisions about which exchanges to buy from were affected by the activities of sellers and other buyers. After accounting for other features of the exchanges, we found that the buyer preferences for using an exchange for their purchases were affected by the levels of buying activities going on (as indicated by the number of purchase requests posted by other buyers).

Since the two exchanges in our study were long established and had large user bases, one might have expected buyers to prefer the one with relatively lower levels of activity, where there was less competition and their bargaining power was stronger. However, this was not always the case. Instead, buyers posted comparatively fewer purchase requests on the exchange with a lower level of buying activity. As buying activities on that exchange increased, so did use of it relative to the competing exchange.

Such behavior is consistent with observational learning and indicates that multi-homing buyers referred to the actions of others when deciding which exchange to use. By considering where others made their purchases, the multi-homing buyers could infer whether posting purchase requests on a particular exchange was a correct move in terms of getting better deals and/or minimizing the risk of dealing with low-quality sellers.

We did find, though, that increases in buying activity beyond certain levels on an exchange deterred buyers from posting their purchase requests there. This could happen because more intense competition might lead to weaker bargaining power and less favorable prices. As a result, buyers might be driven to post relatively more of their purchase requests on the competing exchange.

Implications

Increasingly, businesses are harnessing the power of social information in interactions with their target market. Marketers see the importance of getting consumers to “like, share, and follow” on social media in order to increase the reach and engagement of their campaigns. Consumers are also being encouraged to post reviews of their purchases and experiences to complement the product information businesses provide.

However, because today’s technology makes it easy to track and show information about what consumers have done or are doing, businesses should also look into ways to use this information to shape consumer behavior. Unlike asking consumers to like or share on social media or write positive product reviews, using social cues is not as costly and challenging. Information on how many people are viewing or have recently purchased a particular product on a website can be easily retrieved from the system and shown in real time. More importantly, under certain conditions, online social cues can effectively nudge consumers in certain directions. Therefore, businesses should look into using such social information to shape consumer behavior strategically and gain an edge over their competitors.

 

[1] Koh, T. K. and Fichman, M. “Multi-Homing Users’ Preferences for Two-Sided Exchange Networks,” MIS Quarterly, 38:4, 2014, 977-996.