It’s time to better understand the galaxy of channels we use to shop online and in stores. Photo via Pexels

Back in the Paleolithic Age of online marketing — say, 15 years ago — the idea of online sales as a significant business vehicle for brands such as Target or Walmart was almost unimaginable. Shopping meant going to a store, because stores were where sales happened.

Today, people shop with their computers, via watches and phones, even through their refrigerators. Sellers market on multiple platforms, digital and traditional, including the brick and mortar store. Even the glossy catalogues that arrive in the mail still prompt sales.

Advancing technology has made it possible for consumers to shop not only across a staggering number of channels — but to do so in a constellation of ways. Say a shopper has broken down and decided to buy a wildly popular all-purpose pressure cooker. She might start off using the internet to glean product details and prescreen options. Then she might visit a retail outlet to eyeball the product herself. Finally, after mulling for days, she may impulsively whip out her phone to make the order.

But what determines these particular choices of shopping venues? Rice Business professor Utpal M. Dholakia set out to map this new landscape of consumerism. Joining Dholakia were colleagues Barbara E. Kahn of the Wharton Business School, Randy Reeves of Macy’s Department Stores, Aric Rindfleisch of the University of Wisconsin, David Stewart of the University of California at Riverside and Earl Taylor of the Marketing Science Institute.

Consumer behavior, the researchers knew, is too complex — too all-over-the-map — to develop any sort of quantum marketing theory to explain it. So while interested in answers, the team aimed instead to frame useful questions. Their goal was to bring attention to the multi-channel retailing environment, creating a comprehensive but flexible way to investigate how shoppers navigate the intricate modern marketplace.

More specifically, Dholakia’s team wanted to learn exactly what consumers are finding. What do they do while using various internet and other tools to shop? When do different types of shoppers grab their devices and buy? What obstacles crop up as shoppers wend their ways through this maze of venues? Finally, the researchers wanted to map the vast scope of research issues surrounding this customer behavior.

It was fairly simple to answer the first question: Why do we use such diverse shopping tactics? Usually, it’s about getting the best deal. Some people, however, take their shopping seriously, savoring the idea that they are approaching their task both thoughtfully and thoroughly. Others get a genuine thrill out of the social experience of being part of a community, or from experimenting with different products and ways to buy them. And some shoppers head straight to a certain website or media source because they expect a specific price tag.

Many consumers, Dholakia and his co-researchers found, constantly change the means they use to shop. In one survey of 337 multichannel shoppers, for example, the researchers found that 52 percent reported migrating back and forth from offline to online channels across four product categories including books, airline tickets, stereo systems and wine. This hopscotching from brick and mortar to catalogues, to online and back, could be predicted by certain factors including price, the product they were looking for, how they evaluated the product and even waiting time.

The researchers also found that each type of shopper uses channels differently. Penny-pinchers don’t care where they buy, as long as the price is right. Generalists shop online or in the store because of the overall shopping experience. Traditionalists shun new ways of shopping, and multichannel enthusiasts happily bounce between stores, the internet and catalogues. Finally, the hard-core, store-focused customers will only shop in a place with doors and shelves.

To add a layer of complication, some don’t use channels to shop at all. They just want information. These are the shoppers who pop into to a store to test drive a phone before they buy it online. They study the pressure cooker in a catalogue before they go to the store.

And even within all the online options, there are innumerable detours to explore. Say you want a Nikon camera. You might go to an enthusiasts’ page such as Nikonians.org before you decide which model to buy, whether it’s online or at the local camera shop. Your friendly chat with the guy who owns the local camera store may now turn into a real-time virtual chat with a company representative.

The new marketplace, in other words, has become a dizzying landscape. Shoppers, clearly, have risen to the challenge. Nevertheless, it’s in the interests of sellers and buyers both to understand more deeply not only why we buy what we buy — but where.

------

This article originally ran on Rice Business Wisdom and is based on research from Utpal M. Dholakia, the George R. Brown Professor of Marketing at Jones Graduate School of Business at Rice University.

Ad Placement 300x100
Ad Placement 300x600

CultureMap Emails are Awesome

Houston college joins inaugural workforce accelerator supported by Google

hands-on training

Houston City College (HCC) is one of 15 community colleges from around the country to be selected for the first-ever Workforce Futures Accelerator.

The three-year effort is supported by Google.org, the tech company’s philanthropic arm, and led by the Association of Community College Trustees (ACCT), a non-profit educational organization that represents over 500 community, junior, and technical colleges. The accelerator focuses on helping colleges embed virtual, employer-sponsored training opportunities into short-term workforce training programs, giving participants access to opportunities that they might otherwise receive through internships or other "work-based learning" stints.

"The Workforce Futures Accelerator reflects the Houston City College mission of offering a high-quality, affordable education for workforce training and career development," Pretta VanDible Stallworth, HCC trustee and chair-elect of the ACCT board of directors, said in a news release. "Advancing student success and creating pathways to opportunities ensures that our students are well equipped to succeed and build a secure future in today's economy.”

Through the accelerator, HCC is tasked with fusing online project-based learning opportunities with its workforce education programs. The idea is to give students hands-on experiences working on projects sponsored by employers, allowing them to gain real-world knowledge in the process.

HCC will select two workforce programs that meet the accelerator’s criteria and insert into them into coursework. In the second and third year of the accelerator, the selected colleges are expected to scale the programs by adding instructors and programs to develop a network of to support their continued implementation.

“Participation by HCC will strengthen how we provide students with career-connected learning experiences that complement their classroom education and align with the needs of employers,” HCC Chancellor Margaret Ford Fisher added in the news release. “We are focused on ‘future forward’ strategies to meet the present and future needs of our region’s businesses.”

Two other Texas colleges were chosen to participate in the accelerator: Lamar Institute of Technology in Beaumont and Grayson College in Denison.

The remaining cohort includes:

  • Bergen Community College in Paramus, New Jersey
  • Central Louisiana Community College in Alexandria, Louisiana
  • Clark State College in Springfield, Ohio
  • Great Basin College in Elko, Nevada
  • Heartland Community College in Normal, Illinois
  • Hudson County Community College in Jersey City, New Jersey
  • Manchester Community College in Manchester, New Hampshire
  • Mesa Community College in Mesa, Arizona
  • Mohave College in Kingman, Arizona
  • San Joaquin Delta Community College in Stockton, California
  • San Juan College in Farmington, New Mexico
  • West Virginia University Parkersburg in Parkersburg, West Virginia

New report ranks Texas among top 10 states where AI could disrupt jobs

AI Workforce

A new nationwide report examining where AI could "reshape" the most jobs has ranked Texas No. 9 among the most at-risk states for AI job disruption.

The new SmartAsset report compared all 50 states and the District of Columbia to calculate the estimated percent of the workforce employed in the 26 occupations with the highest AI exposure, as determined by June 2026 research by the Virginia Economic Information and Analytics Division.

The findings revealed that 500,000 Texas workers, or 3.55 percent of the total workforce, are employed in occupations with "high exposure to potential AI disruption."

This also places the Lone Star State as the 9th most at-risk state in the U.S. where AI exposure can lead to "declining hiring demand, wage pressure, task automation, and other forms of disruption."

"States with larger concentrations of highly exposed occupations could experience more pronounced labor-market changes, particularly in roles where core tasks are more vulnerable to AI-driven restructuring," the report's author wrote.

Texas' biggest cities, like Houston and Austin, are known for their thriving tech and business industries, and the study noted that many of the occupations within those sectors are the most at risk. The Virginia Economic Information and Analytics Division said the top five most AI-exposed occupations in the U.S. are: mathematicians, proofreaders, correspondence clerks, court reporters, and media and communication workers. Additionally, computer programmers, database administrators, web developers, telephone operators, and communications equipment operators round out the top 10 most at-risk positions.

These are the 16 remaining occupations most exposed to AI disruption, in order:

  • Data Entry Keyers
  • Statistical Assistants
  • Office Support Workers
  • Interpreters and Translators
  • Database Architects
  • Software Quality Assurance Analysts
  • Medical Transcriptionists
  • Software Developers
  • Writers and Authors
  • Payroll Clerks
  • Web Designers
  • Miscellaneous Computer Occupations
  • Insurance Claims Processors
  • Telemarketers
  • Computer Numerically Controlled Tool Programmers
  • Bookkeeping and Accounting Clerks

A separate SmartAsset report from April 2026 found about 20.5 percent of Texas workers use AI to do their jobs in some capacity. That trend will continue to shift further as employers and employees choose to adopt — or reject — AI implementation.

Across the U.S., Washington topped the list as the state with the highest concentration of AI-exposed jobs, with nearly 5.7 percent of the state's workforce employed in the 26 most at-risk positions. SmartAsset said Washington's high prevalence of technology companies is a significant factor that skyrocketed the state to the top of the list.

"Home to major technology companies including Microsoft, Amazon, T-Mobile and Expedia, the state has large numbers of computer programmers and software developers, two occupations with high exposure," the report said.

Meanwhile, Mississippi ranked No. 51 with the lowest concentration of AI-exposed jobs in the nation. About 22,500 workers in Mississippi, or 1.93 percent of its workforce, are at risk for AI disruption.

The top 10 states where AI could reshape the most jobs are:

  • No. 1 – Washington
  • No. 2 – Virginia
  • No. 3 – District of Columbia
  • No. 4 – California
  • No. 5 – Utah
  • No. 6 – Maryland
  • No. 7 – Colorado
  • No. 8 – New Hampshire
  • No. 9 – Texas
  • No. 10 – North Carolina
---

This article originally appeared on CultureMap.com.

UH lands $1.2M NIH grant to fight superbugs using AI, quantum sensing

drug defense

The fight against antibiotic-resistant bacteria like MRSA is getting science fiction-like upgrades at the University of Houston thanks to a new four-year, $1.26 million grant from the National Institutes of Health.

The university says the recent funding brings total federal support up to $3.5 million for 11 years for the project, which uses AI and quantum-sensing technology to better understand how bacterial proteins develop resistance to drugs.

Any medical professional will tell you that one of the worst things that can happen is almost killing an infection. Bacteria that survive attacks from conventional antibiotic treatments emerge tougher, more resistant and more aggressive than before–making them much harder to treat. A good example is the superbug methicillin-resistant Staphylococcus aureus (MRSA).

UH chemistry professors Yuhong Wang and Shoujun Xu are working on this issue. They know full well that fighting superbugs requires new technology and new approaches, which is what they aim to pioneer with their new grant.

“Drug-resistant bacterial infections such as MRSA are becoming harder to treat, creating an urgent need for faster ways to understand how antibiotics and other small molecules interact with bacterial proteins,” Wang said in a news release.

Wang and Xu’s work centers around GTP, a cellular fuel that can cause tiny changes to a cell's structure when it mutates. Sometimes, those shape changes make it easier for drugs to breach the wall and attack the cells.

The UH scientists are employing AlphaFold, an AI-powered tool that can scan large molecular libraries in seconds. From these models, they can see promising drug combinations for future testing.

Once identified, the team uses their invention, super-resolution force spectroscopy, to monitor the cells. Tiny magnetic beads are attached to genetic material, then magnified to see how strong that material is when pulled. They can measure this incredible microscopic process through an atomic magnetometer, typically used in quantum physics. Combined, all these tools allow a high-definition look at how each molecule might respond to new chemical approaches.

“We're the only chemists in the world that use an atomic magnetometer for biological research,” Xu said. “It's a technique developed by physicists, and there is usually a gap between techniques developed by physicists and biological applications. Yuhong and I have been bridging that gap together for the past 10 years.”

Eventually, Wang and Xu hope to develop powerful software that can be used by drug manufacturers to model cellular responses. With enough predictive data, the software could even get ahead of superbugs’ own mutation, allowing drugs to be developed before new strains arrive.

"We want an algorithm where you input a protein sequence, score the mutation hotspots, and develop new inhibitors before a drug-resistant species even emerges," Wang added.