For a marketer who is about to deliver bad news to a customer, an AI representative will improve that customer’s response. This would be the best approach for negative situations such as unexpectedly high price offers, cancellations, delays, negative evaluations, status changes, product defects, rejections, service failures, and stockouts. However, good news is best delivered by a human.
Researchers from University of Kentucky, University of Technology Sydney, and University of Illinois-Chicago published a new paper in the Journal of Marketing that examines the customer response and satisfaction implications of using AI agents versus human agents.
Are we more forgiving of an artificial intelligence (AI) agent than a human when we are let down? Less appreciative of an AI bot than a human when we are helped? New research examines these questions and discovers that consumers respond differently to favorable and unfavorable treatment at the hands of an AI agent versus another human.
Consumers and marketing managers currently are in a period of technological transition where AI agents are increasingly replacing human representatives. AI agents have been adopted across a broad range of consumer domains to handle customer transactions, including traditional retail, travel, ride and residence sharing, and even legal and medical services. Given AI agents’ advanced information processing capabilities and labor cost advantages, the transition away from human representatives for administering product and services is expected to continue. However, what are the implications for customer response and satisfaction?
The researchers find that when a product or service offer is worse than expected, consumers respond better when dealing with an AI agent. In contrast, for an offer that is better than expected, consumers respond more favorably to a human agent. Garvey explains that “This happens because AI agents, compared to human agents, are perceived to have weaker personal intentions when making decisions. That is, since an AI agent is a non-human machine, consumers typically do not believe that an AI agent’s behavior is driven by underlying selfishness or kindness.” As a result, consumers believe that AI agents lack selfish intentions (which would typically be punished) in the case of an unfavorable offer and lack benevolent intentions (which would typically be rewarded) in the case of a favorable offer.
Designing an AI agent to appear more humanlike can change consumer response. For example, a service robot that appears more humanlike (e.g., with human body structure and facial features) elicits more favorable responses to a better-than-expected offer than a more machinelike AI agent without human features. This occurs because AI agents that are more humanlike are perceived to have stronger intentions when making the offer.
What does this mean for marketing managers? Kim says, “For a marketer who is about to deliver bad news to a customer, an AI representative will improve that customer’s response. This would be the best approach for negative situations such as unexpectedly high price offers, cancellations, delays, negative evaluations, status changes, product defects, rejections, service failures, and stockouts. However, good news is best delivered by a human. Unexpectedly positive outcomes could include expedited deliveries, rebates, upgrades, service bundles, exclusive offers, loyalty rewards and customer promotions.”
Managers can apply our findings to prioritize (vs. postpone) human to AI role transitions in situations where negative (vs. positive) interactions are more frequent. Moreover, even when a role transition is not entirely passed to an AI agent, the selective recruitment of an AI agent to disclose certain negative information could still be advantageous. Firms that have already transitioned to consumer-facing AI agents, including the multitude of online and mobile applications that use AI-based algorithms to create and administer offers, also stand to benefit from our findings. Our research reveals that AI agents should be selectively made to appear more or less humanlike depending upon the situation.
For consumers, these findings reveal a “blind spot” when dealing with AI agents, particularly when considering offers that fall short of expectations. Indeed, the research reveals an ethical dilemma around the use of AI agents – is it appropriate to use AI to bypass consumer resistance to poor offers?
“We hope that making consumers aware of this phenomenon will improve their decision quality when dealing with AI agents, while also providing marketing managers techniques, such as making AI more humanlike in certain contexts, for managing this dilemma,” says Duhachek.
Now you see me, now you don’t: How subtle ‘sponsored content’ on social media tricks us into viewing ads
People are not as good at spotting them as they think. If people recognized ads, they usually ignored them – but some, designed to blend in with your friends’ posts, flew under the radar.
How many ads do you see on social media? It might be more than you realize. Scientists studying how ads work on Instagram-style social media have found that people are not as good at spotting them as they think. If people recognized ads, they usually ignored them – but some, designed to blend in with your friends’ posts, flew under the radar.
“We wanted to understand how ads are really experienced in daily scrolling — beyond what people say they notice, to what they actually process,” said Maike Hübner, PhD candidate at the University of Twente, corresponding author of the article in Frontiers in Psychology. “It’s not that people are worse at spotting ads. It’s that platforms have made ads better at blending in. We scroll on autopilot, and that’s when ads slip through. We may even engage with ads on purpose, because they’re designed to reflect the trends or products our friends are talking about and of course we want to keep up. That’s what makes them especially hard to resist.”
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The scientists wanted to test how much time people spent looking at sponsored versus organic posts, how they looked at different areas of these different posts, and how they behaved after realizing they were looking at sponsored content. They randomly assigned 152 participants, all of whom were regular Instagram users, to one of three mocked-up social media feeds, each of which was made up of 29 posts — eight ads and 21 organic posts.
They were asked to imagine that the feed was their own and to scroll through it as they would normally. Using eye-tracking software, the scientists measured fixations — the number of times a participant’s gaze stopped on different features of a post — and dwell time, how long the fixations last. A low dwell time suggests that someone just noticed the feature, while a high dwell time might indicate they were paying attention. After each session, the scientists interviewed the participants about their experience.
Although people did notice disclosures when they were visible, the eye-tracking data suggested that participants paid more attention to calls to action — like a link to sign up for something — which could indicate that this is how they recognize ads. Participants were also quick to recognize an ad by the profile name or verification badge of a brand’s official account, or glossy visuals, which caused participants to express distrust.
“People picked up on design details like logos, polished images, or ‘shop now’ buttons before they noticed an actual disclosure,” said Hübner. “On brand posts, that label is right under the username at the top, while on influencer content or reels, it might be hidden in a hashtag or buried in the ‘read more’ section.”
Although the scientists found that the ads often went unnoticed, if people realized that the content wasn’t organic, many of them stopped engaging with the post. Dwell time dropped immediately.
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This was less likely to happen to ads that blended in better, with less polished visuals and a tone and format more typical of organic content. If ad cues like disclosures or call-to-action buttons weren’t noticed right away, they got similar levels of engagement to organic posts.
“Many participants were shocked to learn how many ads they had missed. Some felt tricked, others didn’t mind — and that last group might be the most worrying,” said Hübner. “When we stop noticing or caring that something is an ad, the boundary between persuasion and information becomes very thin.”
The scientists say these findings show that transparency goes well beyond just labelling ads. Understanding how people really process ads should lead to a rethink of platform design and regulation to make sure that people know when they’re looking at advertising.
However, this was a lab-based study with simulated feeds, and it’s possible that studies on different cultures, age groups, or types of social media might get different results. It’s also possible that ads are even harder to recognize under real-life conditions.
“Even in a neutral, non-personalized feed, participants struggled to tell ads apart from regular content,” Hübner pointed out. “In their own feeds which are shaped around their interests, habits, and social circles it might be even harder to spot ads, because they feel more familiar and trustworthy.”
Personalized pricing can backfire on companies, says study
If part of the product’s value depends on how many people are using it, think a social media network or e-commerce platform, not being able to see what others are being charged means consumers are fuzzier about how many people are likely to buy in and join the network.
Personalized pricing, where merchants adjust prices according to the pile of data about a consumer’s willingness to pay, has been criticized for its potential to unfairly drive-up prices for certain customers.
But new research shows that the practice can also hurt sellers’ profits.
Consumers commonly experience personalized pricing through digital coupons or other discount offers they receive either as potential customers or after making a purchase. Other recent examples include the practice of “Buy Now, Pay Later” plans that bundles the sale of a product with a subsidized loan, which can offer different prices to different customers based on their willingness to pay, and airlines using artificial intelligence to customize prices for individual airfares.
Companies can tweak their prices according to data about a customer’s digital footprint, including their buying preferences, location, lifestyle and even what kind of digital device and operating system they use—all in pursuit of squeezing maximum profit out of the buyer.
The downside though, says Liyan Yang, a professor of finance and the Peter L. Mitchelson/SIT Investment Associates Foundation Chair in Investment Strategy at the University of Toronto’s Rotman School of Management, is that this practice typically obscures the price information available to other consumers, an important factor in their decision to buy.
When prices are transparent to everyone and they’re low, “you know that on average, more people will be buying,” says Prof. Yang.
But if part of the product’s value depends on how many people are using it, think a social media network or e-commerce platform, not being able to see what others are being charged means consumers are fuzzier about how many people are likely to buy in and join the network.
The upshot? “Consumers are going to spend less,” says Prof. Yang.
The researcher put those ideas under a theoretical microscope when he and former Rotman PhD student Yan Xiong, who is now an associate professor at University of Hong Kong Business School, used mathematics and game theory to model what happens when consumers can’t see what other people are being charged for a network-based product. Their models revealed that a company ultimately charged more when prices were concealed compared to when they were transparent, leading to lower profits.
Luckily for companies, there are workarounds. Using similar modelling, the researchers found that the profit pitfall could be avoided through some kind of corporate commitment or backstop related to keeping prices low even as a company also pursued profits.
That could be done by the company committing to keep prices within a certain range or at least to lowering prices through a corporate social responsibility program, by developing a good reputation among consumers, by initially offering low prices that are transparent to attract consumers with a lower price threshold, or through the use of price caps either mandated by government or voluntarily adopted by the company.
Another option is for a government to require companies to charge the same price to all customers, a strategy promoted in China, the European Union and the United States where personalized pricing practices have become an issue.
While companies typically dislike regulation, Prof. Yang points out that theoretically at least, some form of price restriction may lead to better corporate profits in the end.
“There are trade-offs,” he says, adding that regulators would have to “gauge precisely” where the limits should be to hit the pricing sweet spot that optimizes profits to the company.
The study appeared in the Journal of Economic Theory.
Time plays a key role in consumer behavior, especially concerning the purchasing patterns of vulnerable groups in society who have been ridiculed in offensive and discriminatory ads. Ben-Gurion University researcher Dr. Enav Friedmann examined the long-term reactions of consumers from discriminated groups after exposure to offensive advertising. Such advertising often manifests in marketing messages that demean excluded groups, reinforce harmful stereotypes, or cross social norms.
Their findings were published last month in Psychology & Marketing. Dr. Friedmann is a member of the Department of Business Administration at Ben-Gurion University of the Negev. She is the head of the LBM research lab, which focuses on marketing,
“The social and psychological implications of such advertisements are profound,” explains Dr. Friedmann. “Socially, they normalize prejudice, perpetuate stereotypes, and undermine efforts to achieve equality. We decided to examine these conflicts of social identity combined with consumer behavior. This is a topic that hasn’t been researched enough, but it has significant implications for individuals, groups, and businesses in society.”
The Study’s Approach
To this end, three independent experiments were conducted. They examined the impact of exposure to insulting advertisements or those excluding vulnerable groups (women and people of color) at two time points: immediately upon exposure to the ad, and then 10 days or a month later.
The offensive ads were designed to be inspired by authentic advertisements from companies, which contained offensive content toward women and people of color. A total of 640 women and men, both light-skinned and dark-skinned, participated in all the experiments and answered questions related to the brand and their personal feelings.
Key Findings
In the first experiment, a hypothetical ad for a body soap brand called “BubbleSoap” was presented, with a racist implication toward people of color. A dark-skinned family was shown in the ‘before’ image and a light-skinned family in the ‘after’ image. It was found that dark-skinned participants who felt their ethnic group was severely discriminated against, and tended to identify less with their group, showed a higher purchase intention for the BubbleSoap brand ten days later compared to participants who did not feel their ethnic group was discriminated against.
The second experiment involved an offensive advertisement toward women for a real brand. Participants were randomly exposed to either non-offensive sexist ads or offensive sexist ads. The offensive version was identical but included the text: “Women, I’m sick of you! I get tired of all of you so quickly,” with the well-known tagline below: “You’re not you when you’re hungry.” This ad was inspired by real candy bar ads that mock the idea of men respecting women and aggressively disparage women under the guise of sarcastic humor.
After about a month, it was found that women who identified their gender group as significantly discriminated against, and tended to identify less with the female group, were more likely to choose the brand that offended their group. The choice was made at each time point by choosing between three chocolate brands. Of course, the respondents’ initial preference for the offensive brand was considered.
In the third experiment, neurological measurements were taken using an EEG device in a lab experiment for a construction company. Participants were randomly exposed to either offensive or non-offensive sexist ads. The offensive version included the text: “She thinks she understands… In big decisions, don’t let her decide!” Participants were asked to describe their feelings toward the brand at two points in time. The researchers measured the activation of the participants’ right and left frontal brain regions during a brand feeling task. After ten days, among women who identified their group as significantly discriminated against, and tended to identify less with the female group over time, increased activity was found in the left frontal areas (compared to the right) of the brain. These areas are known in the literature to indicate a desire to approach a stimulus.
Photo by Marcus Herzberg from Pexels.com
The Paradoxical Phenomenon
The findings revealed a paradoxical phenomenon: participants who reported high levels of perceived discrimination against their group, and over time tended to identify less with the offended group, actually showed an increasing preference for the brand that insulted their group. This was measured through purchase intention, actual product choice, or brain responses indicating an approach toward the brand.
This phenomenon aligns with theories of disidentification, a process in which individuals from vulnerable groups come to understand the long-term consequences of harm to their group (reduced self-esteem and group-esteem).
Those who feel their group is significantly discriminated against and tend to reduce their identification with the group in order to protect their sense of self-esteem, tend to do so by approaching the object that harmed their group over time.
“The research findings deepen our understanding of how identity threats affect responses in advertising contexts and highlight the ethical considerations brands must address when formulating campaigns,” explains Dr. Friedmann. “This research delves into the psychological complexity of identity regulation as a result of exposure to threatening content for consumers.”
Implications and Recommendations
The study results do not suggest that offensive-discriminatory advertising is an effective marketing strategy. Most participants exposed to this content did not demonstrate more positive attitudes or behaviors than those in the control group; rather, it was a specific limited group of people who reacted positively to it. On the contrary, such advertisements can exact a significant psychological toll on individuals belonging to discriminated groups. These findings reinforce the importance of adopting an ethical approach to identity-based marketing and avoiding tactics that exploit social vulnerability for strategic profit.
In accordance with the study’s findings, the researchers recommend adopting an approach that involves enforcement and clear criteria to prevent harm to various population groups.
“Enforcement against offensive and discriminatory marketing is essential to protect the well-being of individuals and foster a more egalitarian society. As a society, we must develop specific criteria for controlling offensive advertisements, as is customary in the UK, and impose significant financial penalties on those who violate them,” concluded Dr. Friedmann.
The Research Team
The research team included: Eliran Solodoha from the Peres Academic Center, Sandra Maria Correia Loureiro from the University of Lisbon, and Lior Aviali, LBM Lab Manager, from Ben-Gurion University of the Negev.