A New Approach to Focus Groups
Standard Focus Groups
If you are a user researcher or market researcher, then you have probably run a focus group as a means to solicit user feedback. Focus groups allow researchers to rapidly gather qualitative data on a larger scale. They will commonly be used in tandem with other research methods, including 1:1 in-depth user interviews, usability testing, or survey.
Focus groups involve a careful process of participant recruitment, choosing to bring together groups with similar interests, opinions, or characteristics. Moderators will then compare and contrast focus group sessions to identify divering responses based on the groupings of similar participants. A well orchestrated focus group should be carried predominantly by the group, with the moderator serving only to “focus” the discussion when and where needed.
A New Appraoch
In the new approach being introduced here, I propose focus groups as an appropriate venue for assessing social influence, power dynamics, hive mentality, and in-group/out-group formation. I have personally used focus groups to understand how different social dynamics play out in relation to particularly polarizing intersectionalities between technology, beliefs, experiences, (mis)understandings, and more.
When recruiting for a social-dynamics focus group, you will want to bring together multiple points of view in different combinations across several focus groups. For example, in a hypothetical situation where you have three groups - “People who believe X”, “People who believe Y”, and “People who are unsure of X or Y” -, you would construct fivedifferent focus groups with the following admixture of participant-types:
Focus Group 1: [People who believe X] + [People who believe Y]
Focus Group 2: [People who believe X] + [People who are unsure]
Focus Group 3: [People who believe Y] + [People who are unsure]
Focus Group 4: [People who believe X] + [People who believe Y] + [People who are unsure]
Focus Group 5: Randomized
What one is looking for in regards to social-dynamics is the logic and explanations provided by different factions when communicating their point of view or when aiming to persuade the other of the error of their ways. It is especially potent in the dynamic of Focus Group 4 when “People who believe X” and “People who believe Y” take care to try and sway “People who are unsure” towards X or Y. The conversation may get spirited, but it has, in my experience, always remained civil and considerate of other’s point of view and experience, however much they might disagree.
Again, the goal in these sessions is to observe what evidence X or Y bring to the table to lend evidence to the validity, facticity, or veracity of their point of view. This can manifest as personal stories, heresay, news articles, past experiences, insider testimonials, hyperbole, or proverbs. What one is looking for is any shifts in the in-group and out-group dynamics. Do X and Y start to establish a middle ground of things they agree upon? Do those who believe in Y start to agree to aspects of X, with some even moving entirely to X? What about those participants who identify as neutral at the onset? What happens to their opinions or beliefs? What, if anything, persuaded them in either direction and why? These nuances of social or peer influence can be observed and probed in a social-dymanics focus group.
A fictional example
Let’s say you are running a study for a new wearable virtual assistant, but you worry consumers will regard this as “creepy,” thinking it might be spying on them throughout their day. While past research has shed light on this concern as largely stemming from a general lack of understanding about how this technology works and an endemic distrust in large tech corporations, you want to identify when and how misinformation takes hold so as to intercept with credible information and direction.
You recruit a number of participants that believe digital assistants “spy” on you and distrusts or actively avoid the use of many smart technologies that incorporate virtual assistants. A second set of participants are users of virtual assistants and smart technology, many might be considered “power users” in that they have integrated such technologies across much of their everyday life and routines. And lastly, your third group are those who are unsure of whether or not smart technologies and virtual assistants “spy”. Some may have one or more smart technologies in their home, some may be daily users of virtual assistants. But, their opinions on virtual assistants “spying” are in the middle.
Imagine we have a focus group with half of our participants being people who believe digital assistants “spy” and the other half are our on-the-fence participants. In the focus group discussion that ensues, a participant who believes digital assistants spy on us speaks of their own experiences — “it just randomly starts talking without me asking it anything” — as evidence for why they know the digital assistant is spying. You might also hear such things as — “how else do these companies make money if not by using what we say and do as data to sell?” — presented as plausible business motivation to encourage consumers to incorporate such technologies into their private lives. The logic presented to the neutral party as reason to distrust digital assistants is akin to the core argumentation in much of the misinformation spread around smart technology and digital assistans in social media, news, and hearsay.
As a researcher overseeing the development of a wearable virtual assistant, you would be especially sensitive to those concerns that emerge around the assistant always listening, spying, or collecting data and user information. How to meaingfully combat this misunderstanding before it skews too far towards an “invasion of privacy”? Enter your other participants, those who do not believe virtual assistants are spying! Put these two in a room together and watch and learn. How does each side take-in, digest, and respond to the other? What logic on either side has cache or currency with the other?
Let’s again imagine that those who believe virtual assistants spy make the above reference statement — “it will just randomly start talking without me asking something” — , as evidence for why they know the digital assistant is always listening. The other-side, nonbelievers, might respond in kind to say — “yes that happens to me too, but it isn’t because it is spying on everything I say or do. Its just waiting for its ‘wake-up’ command to start listening. Sometimes something I say maybe sounds close enough.” Perhaps this brings a moment of pause for the participant that views virtual assistants as creepy and they meditate on the parallel between a computer in sleep mode until a button is hit and their virtual assistant awaiting a wake-up command. Imagine the participant that believes virtual assistants are spies responds, “But, how does it hear its wakeup command if it isn’t always listening?”. Regardless of whether or not the skeptics are won over, there is something curious in this social dynamic, a set of logic running deeper and underpinning the diverging beliefs that might prove frutiful for dissemination information around what your wearable virtual assistant can and cannot do.
We have on the one side a belief that the digital assistant is always listening because there is an associated belief that said technology is much more advanced than it actually is. Whereas the group that disagrees with this view, our power users, have come to regard virtual assistants as more rudimentary technology, not disimilar to computer commands of certain inputs resulting in certain discrete outputs. “Is it possible,” you the researcher thinks, “that marketing our technology as ‘cutting edge,’ is actually putting us at a disadvantage?” Perhaps meangifully intervening in misinformation regarding the power of our tech is simply a matter of not positioning it as “technology of the future,” but rather “computing at your convenience with voice command,” drawing closer parallels between existing and famililar technologies as “nearest neighbors” than a far-removed portrayal of a virtual assistant as a quantum leap in AI capabilities.
In Summary
In the closing of this article on a new approach to focus groups, I hope it is apparent that this method can be used to *safely* elicit for observation a microcosm of peer pressure, social influence, persuasion, and dissemination of (mis)information. Like standard focus groups, time and attention should be spent on recruiting the right participants to bring out the social dynamics of interest for your particular research question or product needs. Unlike standard focus groups, however, insights cut across multiple sessions of differently constituted groups, observing shifts in beliefs. To iterate again, what one is looking for in regards to social-dynamics is the logic and explanations provided by different factions when communicating their point of view or when aiming to persuade the other of the error of their ways.
