The Science of Picky Shoppers

Katie Bohn wrote . . . . . . . . .

There are hard-to-please customers in almost every industry, with certain people being picky about which clothes, houses and even romantic partners they will consider.

A new series of studies has found that shopper pickiness can go beyond shopping for the “best” option. The researchers define what it means to be “picky” and also developed a scale for measuring shopper pickiness.

Margaret Meloy, department chair and professor of marketing at Penn State, said the findings could help companies devise the best strategies for satisfying their pickier customers.

“If a company knows they have a lot of picky customers, they may need to change the way they reward salespeople or dedicate specific salespeople to their pickiest customers, because picky shoppers have very narrow preferences and they see perceived flaws in products others wouldn’t notice,” Meloy said. “Alternatively, a company may allow picky shoppers to customize their products to satisfy their idiosyncratic preferences. It’s not just about offering the best products, but offering the products that are best for the picky customers.”

Meloy added that even the most robust promotional strategies, like offering a free gift with purchase, may fail with picky customers.

Previous research has found that about 40% of people have family or friends they would consider “picky,” suggesting the trait is common. The researchers said it might be helpful for retailers to have a better understanding of what being “picky” means for their customer base, and what those customers may need from a product or shopping experience.

Meloy said that while pickiness affects a customer’s shopping habits and therefore affects a company’s business, there hasn’t been much research done on defining pickiness or investigating how it influences a customer’s behavior.

“In marketing, we call customers who want the absolute best version of a product ‘maximizers,’” Meloy said. “But with picky customers, the best is more idiosyncratic. For them, it might not be about getting the best quality, but getting the precise version of a product they have in their head — a shirt in a very precise shade of black, for example. We wanted to explore this a bit more.”

For the paper — recently published in the Journal of Consumer Psychology — the researchers performed a series of studies to create a scale for measuring shopper pickiness and to identify the consequences of that pickiness on customer behavior.

The first series of studies focused on developing the scale. The researchers said they created a series of questions that would help uncover the psychological dimensions of pickiness while also avoiding using the word “picky,” since the word tends to have negative connotations. Once the researchers were confident the scale accurately measured pickiness, they conducted additional studies to examine the possible consequences of pickiness.

The researchers found that people who scored higher on the picky shopper scale tend to have a small window of what they consider acceptable, which the researchers described as having a small latitude of acceptance and a wide latitude for rejection. These shoppers were more likely to reject a free gift when offered as a thank you for participating in a survey.

“This may seem irrational to some people who may not understand why a person would reject things that come at no cost,” said Andong Cheng, an assistant professor of marketing at the University of Delaware who earned her doctorate at Penn State. “We speculate that it could be psychologically costly for picky shoppers to take free items that they don’t like because possessing these items is a source of irritation for these individuals.”

Additionally, the researchers found that picky people didn’t change their opinions based on a product’s popularity. When they were informed that their top choice of a product was less popular than other options, people who scored high on the picky scale weren’t swayed by that information. They stuck with their original selection.

Meloy said the results support the theory that being picky is a general personality trait that isn’t just present in one situation or area of a person’s life.

“We looked at a range of contexts to see whether being picky in one domain meant you were likely to be picky in others,” Meloy said. “Sure enough, individuals who were picky in one domain were picky in other domains. For example, if you tend to be picky while shopping for groceries, you’ll probably be picky shopping for clothes, as well.”

Meloy said the findings also illustrate the importance of a company understanding and tailoring their business practices to their customer base.

“If you know you have a lot of picky customers, you might not want to bother with offering free products or promoting products by saying how popular they are with other people,” Meloy said. “It’s just not going to work as well with picky customers. These companies will need to come up with strategies that give customers more control to better align their idiosyncratic preferences with the company’s offerings.”

Source: The Pennsylvania State University

Suppression of COVID-19 Waves Reflects Time-Dependent Social Activity, Not Herd Immunity


Scientists modeling the spread of COVID-19 showed that a temporary state of immunity arises due to individual differences in social behaviors. This “transient collective immunity”— referring to when the susceptible or more social groups collectively have been infected—gets destroyed as people modify their social behaviors over time. For example, someone who isolated in the early days of the epidemic may at some point renew their social networks, meeting with small groups or large crowds.


Scientists at the U.S. Department of Energy’s (DOE) Brookhaven National Laboratory and the University of Illinois Urbana-Champaign (UIUC) have developed a new mathematical model for predicting how COVID-19 spreads. This model not only accounts for individuals’ varying biological susceptibility to infection but also their levels of social activity, which naturally change over time. Using their model, the team showed that a temporary state of collective immunity—what they coined “transient collective immunity”—emerged during early, fast-paced stages of the epidemic. However, subsequent “waves,” or surges in the number of cases, continued to appear because of changing social behaviors. Their results are published in the Proceedings of the National Academy of Sciences.

The COVID-19 epidemic reached the United States in early 2020, rapidly spreading across several states by March. To mitigate disease spread, states issued stay-at-home orders, closed schools and businesses, and put in place mask mandates. In major cities like New York City (NYC) and Chicago, the first wave ended in June. In the winter, a second wave broke out in both cities. Understanding why initial waves end and subsequent waves begin is key to being able to predict future epidemic dynamics.

Here’s where modeling can help. But classical epidemiological models were developed almost 100 years ago. While these models are mathematically robust, they don’t perfectly capture reality. One of their flaws is failing to account for the structure of person-to-person contact networks, which serve as channels for the spread of infectious diseases.

“Classical epidemiological models tend to ignore the fact that a population is heterogenous, or different, on multiple levels, including physiologically and socially,” said Alexei Tkachenko, a physicist in the Theory and Computation Group at the Center for Functional Nanomaterials (CFN), a DOE Office of Science User Facility at Brookhaven Lab. “We don’t all have the same susceptibility to infection because of factors such as age, preexisting health conditions, and genetics. Similarly, we don’t have the same level of activity in our social lives. We differ in the number of close contacts we have and in how often we interact with them throughout different seasons. Population heterogeneity—these individual differences in biological and social susceptibility—is particularly important because it lowers the herd immunity threshold.”

Herd immunity is the percentage of the population who must achieve immunity in order for an epidemic to end.

“Herd immunity is a controversial topic,” said Sergei Maslov, a CFN user and professor and Bliss Faculty Scholar at UIUC, with faculty appointments in the Departments of Physics and Bioengineering and at the Carl R. Woese Institute for Genomic Biology. “Since early on in the COVID-19 pandemic, there have been suggestions of reaching herd immunity quickly, thereby ending local transmission of the virus. However, our study shows that apparent collective immunity reached in this way would not last.”

“What was missing prior to this work was that people’s social activity waxes and wanes, especially due to lockdowns or other mitigations,” added Nigel Goldenfeld, Swanlund Professor of Physics and director of the NASA Astrobiology Institute for Universal Biology at UIUC. “So, a wave of the epidemic can seem to die away due to mitigation measures when the susceptible or more social groups collectively have been infected—what we call transient collective immunity. But once these measures are relaxed and people’s social networks are renewed, another wave can start, as we’ve seen with states and countries opening up too soon, thinking the worst was behind them.”

Ahmed Elbanna, a Donald Biggar Willett Faculty Fellow and professor of civil and environmental engineering at UIUC, noted transient collective immunity has profound implications for public policy.

“Mitigation measures, such as mask wearing and avoiding large gatherings, should continue until the true herd immunity threshold is achieved through vaccination,” said Elbanna. “We can’t outsmart this virus by forcing our way to herd immunity through widespread infection because the number of infected people and number hospitalized who may die would be too high.”

The nuts and bolts of predictive modelling

Over the past year, the Brookhaven-UIUC team has been carrying out various projects related to a broader COVID-19 modeling effort. Previously, they modeled how the epidemic would spread through Illinois and the UIUC campus, and how mitigation efforts would impact that spread. Last May, they began this project to calculate the effect of population heterogeneity on the spread of COVID-19.

Several approaches already exist for modeling the effect of heterogeneity on epidemic dynamics, but they typically assume heterogeneity remains constant over time. So, for example, if you’re not socially active today, you won’t be socially active tomorrow or in the weeks and months ahead.

“Basic epidemiological models only have one characteristic time, called the generation interval or incubation period,” said Tkachenko. “It refers to the time when you can infect another person after becoming infected yourself. For COVID-19, it’s roughly five days. But that’s only one timescale. There are other timescales over which people change their social behavior.”

In this work, the team incorporated time variations in individual social activity into existing epidemiological models. While a complicated, multidimensional model is needed to describe each group of people with different susceptibilities to disease, they compressed this model into only three equations, developing a single parameter to capture biological and social sources of heterogeneity.

“We call this parameter the immunity factor, which tells you how much the reproduction number drops as susceptible individuals are removed from the population,” explained Maslov.

The reproduction number indicates how transmissible an infectious disease is. Specifically, the quantity refers to how many people one infected person will in turn infect. To estimate the social contribution to the immunity factor, the team leveraged previous studies in which scientists actively monitored people’s social behavior. They also looked at actual epidemic dynamics, determining the immunity factor most consistent with data on COVID-19-related hospitalizations, intensive care unit admissions, and daily deaths in NYC and Chicago. For example, when the susceptible number dropped by 10 percent during the early, fast-paced epidemic in NYC and Chicago, the reproduction number fell by 40 to 50 percent—corresponding to an estimated immunity factor of four to five.

“That’s a fairly large immunity factor, but it’s not representative of lasting herd immunity,” said Tkachenko. “On a longer timescale, we estimate a much lower immunity factor of about two. The fact that a single wave stops doesn’t mean you’re safe. It can come back.”

This temporary state of immunity arises because population heterogeneity is not permanent; people change their social behavior over time. For instance, individuals who self-isolated during the first wave—staying home, not having visitors over, ordering groceries online—subsequently start relaxing their behaviors. Any increase in social activity means additional exposure risk.

“The epidemic has been with us a year now,” said Maslov. “It’s important to understand why it has been here for such a long time. The gradual change in social behavior among individuals partially explains why plateaus and subsequent waves are occurring. For example, both cities avoided a summer wave but experienced a winter wave. We attribute the winter wave to two factors: the change in season and the waning of transient collective immunity.”

With vaccination becoming more widespread, the team hopes we will be spared from another wave. In their most recent work, they are studying epidemic dynamics in more detail. For example, they are feeding statistics from “superspreader” events—gatherings where a single infected person causes a large outbreak among attendees—into the model. They are also applying their model to different regions across the country to explain overall epidemic dynamics from the end of lockdown to early March 2021.

Source: Brookhaven National Laboratory

New Research Identifies Biological Causes of Muscle Weakness in Later Life

A new largescale genetic analysis has found biological mechanisms that contribute to making people more susceptible to muscle weakness in later life, finding that diseases such as osteoarthritis and diabetes may play a large role in susceptibility.

As we get older we lose muscle strength, and in some people this severe weakness impacts their ability to live everyday lives, a condition called sarcopenia. Around 10 per cent of people over 50 experience sarcopenia. Many causes thought to impact likelihood of developing this weakness, which is linked to higher death rates.

In a genetic analysis of over 250,000 people aged over 60 from UK Biobank and 21 other cohorts, an international team led by researchers at the University of Exeter looked at handgrip strength, using thresholds of loss of muscle function derived from international definitions of sarcopenia.

The team, including collaborators from the USA and the Netherlands, then conducted a genetic analysis, and found specific biological mechanisms push some people towards sarcopenia, whilst protecting others. The study, published in Nature Communications identified 15 areas of the genome, or loci, associated with muscle weakness, including 12 loci not implicated in previous analyses of continuous measures of grip strength.

Biomarkers in the blood including red blood cells and inflammation may also share causal pathways with sarcopenia. Together, these results highlight specific areas for intervention or for identifying those at most risk.

Lead author Garan Jones said: “The strongest associations we found were close to regions of the genome regulating the immune system, and growth and development of the musclo-skeletal system. However we also discovered associations with regions not previously known to be linked to musclo-skeletal traits.

“We found that our analysis of muscle weakness in older people shared common genetic pathways with metabolic diseases such as type-2 diabetes, and auto-immune conditions such as osteoarthritis and rheumatoid arthritis. In subgroups of people with increased risk of these conditions, sarcopenia may be a key outcome to look out for and prevent.

“We hope that by understanding the genetic contributions to muscle weakness with age, we will be able to highlight possible therapeutic interventions earlier in life, which would lead to a happier and healthier old age.”

Source: University of Exeter

Research Establishes a New Method to Predict Individual Risk of Cognitive Decline

Hillary Smith wrote . . . . . . . . .

The early prognosis of high-risk older adults for amnestic mild cognitive impairment (aMCI), using non-invasive and sensitive neuromarkers, is key for early prevention of Alzheimer’s disease.

A recent study, published in the Journal of Alzheimer’s Disease, by researchers at the University of Kentucky establishes what they believe is a new way to predict the risk years before a clinical diagnosis. Their work shows that direct measures of brain signatures during mental activity are more sensitive and accurate predictors of memory decline than current standard behavioral testing.

“Many studies have measured electrophysiological rhythms during resting and sleep to predict Alzheimer’s risk. This study demonstrates that better predictions of a person’s cognitive risk can be made when the brain is challenged with a task. Additionally, we learned that out of thousands of possible brain oscillation measures, left-frontal brainwaves during so-called working memory tasks are good predictors for dementia risk,” said lead investigator Yang Jiang, associate professor in the UK Department of Behavioral Sciences and an affiliated faculty member at the Sanders-Brown Center on Aging (SBCoA).

When looking for a specific car in a large parking lot, older persons increasingly make more mistakes and take more time than young people due to brain and cognitive aging. Jiang says it has already been reported that brain waves associated with that type of daily memory task differ in cognitively normal older people and those of patients with memory loss and dementia. For this new study, researchers followed healthy older adults for 10 years. They reported that a specific pattern of frontal brainwaves during an everyday memory task predicts a person’s risk of cognitive impairment roughly five years before clinical diagnosis. This pattern was not observed in older people who remained cognitively normal over the next 10 years.

Jiang says predicting and preventing cognitive decline is very important to allow preventive measures, such as lifestyle changes, and for researchers to help achieve a greater quality of life for the rapidly growing aging population. “Compared to current methods using neuroimaging as biomarkers, this method of measuring can be easily set up in clinics, is non-invasive, fast, and affordable. Also, reliably predicting the risk of cognitive decline in an individual is new. Our older participants will soon be able to have a better version of brainwave tests here at UK.”

Source: University of Kentucky

Is the Definition of Alzheimer’s Disease Changing?

Jonathan Graff-Radford, M.D. wrote . . . . . . . . .

The National Institute on Aging and the Alzheimer’s Association are suggesting changes to the research definition of Alzheimer’s disease. There are new criteria to define what Alzheimer’s disease is and who has it — but only as it relates to clinical trials and research, and not the diagnosis in your doctor’s office.

Previously, Alzheimer’s disease dementia was characterized by symptoms such as memory loss and changes in thinking and cognition. And that’s still the case when your doctor diagnoses Alzheimer’s disease dementia.

The proposed research definition of Alzheimer’s is defined by the presence of biomarkers — a buildup of plaques and tangles in the brain — which are identified by imaging scans of the brain and samples of cerebrospinal fluid. This change allows researchers to better design clinical trials, include the right participants and learn more about the disease in its earlier stages.

Here’s why it’s important: The classic symptoms of Alzheimer’s disease don’t define or diagnose it. They’re a complication of the changes in the brain that do define the disease — and these brain changes can occur long before the symptoms show up. This change in research may lead to earlier diagnosis of Alzheimer’s disease, which will hopefully lead to delayed progression and better treatments.

Source: Mayo Clinic