Aurelia Butler

Researchers from Boston University, Rutgers University, University of Washington, Cornell University, and University of Pennsylvania published a new paper in the Journal of Marketing that proposes that preserving psychological ownership in the technology-driven evolution of consumption underway should be a priority for marketers and firm strategy.
Why does — and what happens when — nothing feels like it is MINE?
Technological innovations are rapidly changing the consumption of goods and services. Consumption is evolving in modern capitalist societies from a model in which people legally own private material goods to access-based models in which people purchase temporary rights to experiential goods owned by and shared with others. For example, many urban consumers have replaced car ownership with car and ride sharing services. Physical pictures occupying frames, wallets, and albums have been replaced with digital photographs that can be viewed at any time and songs, books, movies, or magazines can be pulled down from the cloud. Half the world population now buys, sells, generates, and consumes goods and information online through connected devices, generating vast quantities of personal data about their consumption patterns and private lives.
The researchers say that technological innovations such as digitization, platform markets, and the exponential expansion of the generation and collection of personal data are driving an evolution in consumption along two major dimensions. The first dimension is from a model of legal ownership, where consumers purchase and consume their own private goods, to a model of legal access, in which consumers purchase temporary access rights to goods and services owned and used by others. The second dimension is from consuming solid material goods to liquid experiential goods. The benefits of these consumption changes, from convenience to lower economic cost to greater sustainability to better preference matching, makes legal ownership of many physical private goods undesirable and unnecessary. But their commensurate reduction in psychological ownership — the feeling that a thing is “MINE” — may have profoundly negative consequences for consumers and firms.
Morewedge explains that “Psychological ownership is not legal ownership, but is, in many ways, a valuable asset for consumers and firms. It satisfies important consumer motives and is value-enhancing. The feeling that a good is MINE enhances how much we like the good, strengthens our attachment to it, and increases how much we think it is worth.” Downstream consequences to firms include increased consumer demand for goods and services offered by the firm, willingness to pay for goods, word of mouth, and loyalty.
The researchers propose that the consumption changes underway can have three effects on psychological ownership — threaten it, cause it to transfer to other targets, and create new opportunities to preserve it. Fractional ownership models and the impermanence and intangibility of access-based experiential goods stunt the development of psychological ownership for streamed, rented, and cloud-based goods. In many cases, this results in a loss of psychological ownership, but sometimes it will transfer to the brands (e.g., Disney, Uber, MyChart) and devices through which goods and services are accessed (e.g., smartphones) or transfer to the community of consumers who use them (e.g., Facebook groups, followers, and forums). The greater choice and new channels for self-expression provided by this evolution of consumption, however, also offer new opportunities for consumers to feel as much psychological ownership for these access-based experiential goods and services they consume as they would for privately owned material goods.
These consumption changes and their effects on psychological ownership appear in a framework that is examined across three macro marketing trends: (1) the growth of the sharing economy; (2) the digitization of goods and service; and (3) the expansion of personal data. Exemplary cases explored include ride sharing, the digitization of music, and the expansion of health and wellness data. Each case illustrates why each of these trends is eroding psychological ownership, how it is being transformed, and new opportunities being created for firms to recapture and preserve it — whether in goods and services, intermediary devices like a phone, or at the brand level.
This psychological ownership framework generates future research opportunities and actionable marketing strategies for firms seeking to preserve the value-enhancing consequences of psychological ownership and navigate cases where it is a liability. It highlights many ways in which psychological ownership will continue to be a valuable lens through which to view, understand, forecast, and manage the consumer experience.

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Materials provided by American Marketing Association. Original written by Matt Weingarden. Note: Content may be edited for style and length. More

Researchers have created fundamental electronic building blocks out of tiny structures known as quantum dots and used them to assemble functional logic circuits. More

Flip a coin. Heads? Take a step to the left. Tails? Take a step to the right. In the quantum world? Go in both directions at once, like a wave spreading out. Called the walker analogy, this random process can be applied in both classical and quantum algorithms used in state-of-the-art technologies such as artificial intelligence and data search processes. However, the randomness also makes the walk difficult to control, making it more difficult to precisely design systems.
A research team based in Japan may be moving toward a more controlled walk by unveiling the mechanism underlying the directional decision of each quantum step and introducing a way to potentially control the direction of movement. They published their results on October 16 in Scientific Reports, a Nature Research journal.
“In our study, we focused on the coin determining the behavior of the quantum walk to explore controllability,” said paper author Haruna Katayama, graduate student in the Graduate School of Integrated Arts and Sciences at Hiroshima University.
In classical systems, the coin directs the walker in space: right or left. In quantum systems, a coin is infinitely less reliable, since the walker operates both as a particle stood in one space and as a wave stretched out in every possibility across time.
“We introduced the time-dependent coin of which the probability of landing on heads or tails varies temporally for unveiling the function of the coin,” Katayama said.
This time-dependent coin can shift the walker’s position, the researchers found, but the wave characteristic of the walker obscured how much physical space the coin controlled.
“We succeeded in clarifying the equivalence of two completely different concepts — the equivalence of the rate of change in coin probability and the velocity of the wave — for the first time,” Katayama said. “This unveiled mechanism enables us to control the quantum walk on demand by manipulating the coin with preserving the random process, providing core fundamental elements of innovative quantum information processing technologies such as quantum computing.”
The researchers determined that how quickly the coin flipped directly affected how quickly the wave moved, resulting in some control of the walker’s movement.
“The walking mechanism enables us to tailor quantum walks as we desire by manipulating the coin flipping rate,” Katayama said. “In addition, we have found that the quantum walk with the desired trajectory can be realized on demand by designing the coin. Our results open the path towards the control of quantum walks.”

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Materials provided by Hiroshima University. Note: Content may be edited for style and length. More

Examining data from the first six weeks of their landmark DETECT study, a team of scientists from the Scripps Research Translational Institute sees encouraging signs that wearable fitness devices can improve public health efforts to control COVID-19.
The DETECT study, launched on March 25, uses a mobile app to collect smartwatch and activity tracker data from consenting participants, and also gathers their self-reported symptoms and diagnostic test results. Any adult living in the United States is eligible to participate in the study by downloading the research app, MyDataHelps.
In a study that appears today in Nature Medicine, the Scripps Research team reports that wearable devices like Fitbit are capable of identifying cases of COVID-19 by evaluating changes in heart rate, sleep and activity levels, along with self-reported symptom data — and can identify cases with greater success than looking at symptoms alone.
“What’s exciting here is that we now have a validated digital signal for COVID-19. The next step is to use this to prevent emerging outbreaks from spreading,” says Eric Topol, MD, director and founder of the Scripps Research Translational Institute and executive vice president of Scripps Research. “Roughly 100 million Americans already have a wearable tracker or smartwatch and can help us; all we need is a tiny fraction of them — just 1 percent or 2 percent — to use the app.”
With data from the app, researchers can see when participants fall out of their normal range for sleep, activity level or resting heart rate; deviations from individual norms are a sign of viral illness or infection.
But how do they know if the illness causing those changes is COVID-19? To answer that question, the team reviewed data from those who reported developing symptoms and were tested for the novel coronavirus. Knowing the test results enabled them to pinpoint specific changes indicative of COVID-19 versus other illnesses.

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“One of the greatest challenges in stopping COVID-19 from spreading is the ability to quickly identify, trace and isolate infected individuals,” says Giorgio Quer, PhD, director of artificial intelligence at Scripps Research Translational Institute and first author of the study. “Early identification of those who are pre-symptomatic or even asymptomatic would be especially valuable, as people may potentially be even more infectious during this period. That’s the ultimate goal.”
For the study, the team used health data from fitness wearables and other devices to identify — with roughly 80% prediction accuracy — whether a person who reported symptoms was likely to have COVID-19. This is a significant improvement from other models that only evaluated self-reported symptoms.
As of June 7, 30,529 individuals had enrolled in the study, with representation from every U.S. state. Of these, 3,811 reported symptoms, 54 tested positive for the coronavirus and 279 tested negative. More sleep and less activity than an individual’s normal levels were significant factors in predicting coronavirus infection.
The predictive model under development in DETECT might someday help public health officials spot coronavirus hotspots early. It also may encourage people who are potentially infected to immediately seek diagnostic testing and, if necessary, quarantine themselves to avoid spreading the virus.
“We know that common screening practices for the coronavirus can easily miss pre-symptomatic or asymptomatic cases,” says Jennifer Radin, PhD, an epidemiologist at the Scripps Research Translational Institute who is leading the study. “And infrequent viral tests, with often-delayed results, don’t offer the real-time insights we need to control the spread of the virus.”
The DETECT team is now actively recruiting more participants for this important research. The goal to enroll more than 100,000 people, which will help the scientists improve their predictions of who will get sick, including those who are asymptomatic. In addition, Radin and her colleagues plan to incorporate data from frontline essential workers who are at an especially high risk of infection.
Learn more about DETECT at detectstudy.org.
The study, “Wearable Sensor Data and Self-reported Symptoms for COVID-19 Detection,” is authored by Giorgio Quer, Jennifer M. Radin, Matteo Gadaleta, Katie Baca-Motes, Lauren Ariniello, Edward Ramos, Vik Kheterpal, Eric J. Topol and Steven R Steinhubl.
Funding for the research was provided by the National Center for Advancing Translational Sciences at the National Institutes of Health [UL1TR00255]. More

It’s still easy to think we’re in control when browsing the internet, but a new study argues much of that is ‘an illusion.’ Corporations are ‘nudging’ us online more than we realize, and often in hidden ways. Researchers analyzed click-stream data on a million people over one month of internet use to find common browsing sequences, then connected that with site and platform ownership and partnerships, as well as site design and other factors. More

A University of Kansas interdisciplinary team led by relationship psychologist Omri Gillath has published a new paper in the journal Computers in Human Behavior showing people’s trust in artificial intelligence (AI) is tied to their relationship or attachment style.
The research indicates for the first time that people who are anxious about their relationships with humans tend to have less trust in AI as well. Importantly, the research also suggests trust in artificial intelligence can be increased by reminding people of their secure relationships with other humans.
Grand View Research estimated the global artificial-intelligence market at $39.9 billion in 2019, projected to expand at a compound annual growth rate of 42.2% from 2020 to 2027. However, lack of trust remains a key obstacle to adopting new artificial intelligence technologies.
The new research by Gillath and colleagues suggests new ways to boost trust in artificial intelligence.
In three studies, attachment style, thought to play a central role in romantic and parent-child relationships, was shown also to affect people’s trust in artificial intelligence. Some of the research’s key findings:
People’s attachment anxiety predicts less trust in artificial intelligence.
Enhancing attachment anxiety reduced trust in artificial intelligence.

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Conversely, enhancing attachment security increases trust in artificial intelligence.

These effects are unique to attachment security and were not found with exposure to positive affect cues.
“Most research on trust in artificial intelligence focuses on cognitive ways to boost trust. Here we took a different approach by focusing on a ‘relational affective’ route to boost trust, seeing AI as a partner or a team member rather than a device,” said Gillath, professor of psychology at KU.
“Finding associations between one’s attachment style — an individual difference representing the way people feel, think and behave in close relationships — and her trust in AI paves the way to new understandings and potentially new interventions to induce trust.”
The research team includes investigators from a wide array of disciplines, including psychology, engineering, business and medicine. This interdisciplinary approach provides a new perspective on artificial intelligence, trust and associations with relational and affective factors.
“The findings show you can predict and increase people’s trust levels in non-humans based on their early relationships with humans,” Gillath said. “This has the potential to improve adoption of new technologies and the integration of AI in the workplace.”

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Materials provided by University of Kansas. Note: Content may be edited for style and length. More

Imagine that you are a job consultant. You are sitting across from your client, an unemployed individual.
After locating them in the system, up pops the following text on the computer screen; ‘increased risk of long-term unemployment’.
Such assessments are made by an algorithm that, via data on the citizen’s gender, age, residence, education, income, ethnicity, history of illness, etc., spits out an estimate of how long the person — compared to other people from similar backgrounds — is expected to remain in the system and receive benefits.
But is it reasonable to characterize individual citizens on the basis of what those with similar backgrounds have managed in their job searches? According to a new study from the University of Copenhagen, no.
“You have to understand that people are human. We get older, become ill and experience tragedies and triumphs. So instead of trying to predict risks for individuals, we ought to look at implementing improved and more transparent courses in the job center arena,” says Naja Holten Møller, an assistant professor at the Department of Computer Science, and one of the researchers behind the study.
Together with two colleagues from the same department, Professor Thomas Hildebrandt and Professor Irina Shklovski, Møller has explored possible alternatives to using algorithms that predict job readiness for unemployed individuals as well as the ethical aspects that may arise.

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“We studied how to develop algorithms in an ethical and responsible manner, where the goals determined for the algorithm make sense to job consultants as well. Here, it is crucial to find a balance, where the unemployed individual’s current situation is assessed by a job consultant, while at the same time, one learns from similar trajectories using an algorithm,” says Naja Holten Møller.
Job consultants need to help create the algorithm
The use of job search algorithms is not a well-thought scenario. Nevertheless, the Danish Agency for Labour Market and Recruitment has already rolled out this type of algorithm to predict the risk of long-term unemployment among the citizenry — despite criticism from several data law experts.
“Algorithms used in the public sphere must not harm citizens, obviously. By challenging the scenario and the very assumption that the goal of an unemployed person at a job centre is always to land a job, we are better equipped to understand ethical challenges. Unemployment can have many causes. Thus, the study shows that a quick clarification of time frames for the most vulnerable citizens may be a better goal. By doing so, we can avoid the deployment of algorithms that do great harm,” explains Naja Holten Møller.
The job consultants surveyed in the study expressed concern about how the algorithm’s assessment would affect their own judgment, specifically in relation to vulnerable citizens.

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“A framework must be established in which job consultants can have a real influence on the underlying targets that guide the algorithm. Accomplishing this is difficult and will take time, but is crucial for the outcome. At the same time, it should be kept in mind that algorithms which help make decisions can greatly alter the work of job consultants. Thus, an ethical approach involves considering their advice,” explains Naja Holten Møller.
We must consider the ethical aspects
While algorithms can be useful for providing an idea of, for example, how long an individual citizen might expect to be unemployed, this does not mean that it is ethically justifiable to use such predictions in job centers, points out Naja Holten Møller.
“There is a dream that the algorithm can identify patterns that others are oblivious to. Perhaps it can seem that, for those who have experienced a personal tragedy, a particular path through the system is best. For example, the algorithm could determine that because you’ve been unemployed due to illness or a divorce, your ability to avoid long-term unemployment depends on such and such,” she says, concluding:
“But what will we do with this information, and can it be deployed in a sensible way to make better decisions? Job consultants are often able to assess for themselves whether a person is likely to be unemployed for an extended period of time. These assessments are shaped by in-person meetings, professionalism and experience — and it is here, within these meetings, that an ethical development of new systems for the public can best be spawned.” More