Monthly Archives: September 2026

The Hidden Career Costs of Workplace Climate Activism

Employees who champion climate action within large professional services firms may see career benefits, but mainly when their efforts align with business priorities, according to research from the University of Bath and Leuphana University of Lüneburg. Published in Human Relations, the study found that organisations are more likely to reward environmental initiatives that attract clients, expand sustainability services or strengthen corporate reputation.

By contrast, employees motivated primarily by environmental or societal goals often receive little recognition. Some participants said they kept their climate activism separate from their regular jobs, carried out activities in their own time or avoided discussing their involvement with managers because they feared it could be viewed as a distraction from client work.

“Many organisations encourage employees to speak up about climate and sustainability issues. But our research shows that not all forms of activism are valued equally,” said Dr Stefanie Gustafsson of the University of Bath’s School of Management. “Employees are most likely to be recognised in career terms when their activism can be linked to business priorities, while more purpose-led activism often receives little reward despite requiring significant personal commitment.”

Based on 58 interviews with professionals involved in internal climate activist groups at a large multinational professional services firm, most of them management consultants, the researchers identified three approaches to workplace climate activism. The first involved employees using climate action to build expertise, increase their visibility and advance their careers by developing sustainability skills that could be applied to client work and business development.

A second group, often comprising more senior professionals, used climate activism to build networks, create formal sustainability roles and influence organisational priorities. These employees were particularly successful when they framed climate initiatives around existing business concerns, including innovation, attracting talent and creating future commercial opportunities.

The third group was motivated primarily by environmental and societal purpose rather than career advancement. These employees were more likely to separate activism from their formal careers, carrying out climate-related activities during evenings and weekends or keeping their involvement from managers. One participant characterised this experience simply as an “underground struggle”.

The findings may help explain why workplace sustainability efforts often produce incremental improvements rather than transformative change. Climate activism connected to revenue, reputation or strategic priorities can be incorporated relatively easily into existing organisational structures. At the same time, initiatives that challenge established business practices may struggle to gain recognition and support.

“Our findings suggest that organisations have created new opportunities for sustainability-focused careers, but the underlying systems that determine what gets rewarded have changed less than many people might assume,” said Dr Katharina Hug of Leuphana University of Lüneburg. The researchers conclude that workplace activism can help employees develop sustainability expertise and build supportive communities. Still, organisations may need to rethink how they assess performance, recognition and career progression if they want employees to pursue meaningful environmental change without facing personal or professional costs.

More information: Katharina Hug et al, A Bourdieusian perspective on internal activism and professional careers, Human Relations. DOI: 10.1177/00187267261477600

Journal information: Human Relations Provided by University of Bath

Roman Coins Reveal How the Empire Forged an Integrated Economy

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The digitisation of archaeological collections is transforming the study of ancient history. By combining millions of records with spatial analysis and quantitative methods, researchers can now uncover economic patterns that would be difficult to detect from individual artefacts. A new study of Roman coins demonstrates how this data-driven approach can reveal the economic forces behind Rome’s expansion.

Researchers Eduardo Amaral Haddad and Inácio Fernandes Araújo of the University of São Paulo analysed approximately four million coins dating from 155 BCE to 2 CE. Published in Humanities and Social Sciences Communications, the study suggests that while military conquest initiated Roman expansion, lasting control depended on the economic integration of conquered territories.

Each archaeological coin provides information about where and when it was minted and where it was eventually discovered. Individually, these details reveal relatively little. Analysed collectively, however, millions of coins can trace monetary movement, economic exchange and connections between regions. The researchers examined about four million coins organised into 24,646 hoards and 5,167 pairs of minting and discovery locations.

The team combined several large archaeological databases with geographic information systems and techniques commonly used in regional economics. These resources provided information about coin hoards as well as Roman cities, roads, ports, rivers and sea routes. The researchers then applied mathematical methods normally used to study modern movements of people, goods and income to reconstruct monetary circulation across the ancient Mediterranean.

Their analysis showed that Roman coins were not distributed randomly. Instead, they formed significant clusters along major trade and transportation routes. Coins originating in Rome spread through networks of roads, ports and urban centres, indicating that infrastructure played a crucial role in connecting newly acquired territories with the Roman economic system.

The researchers also developed a model of the Roman economy incorporating relationships among government, households, landowners, merchants, enslaved people and the army. This helped them examine the gradual monetisation of Roman society, as transactions that had once been conducted partly through goods increasingly shifted towards payments using coins.

The findings suggest that the Roman army was particularly important during the early stages of expansion. Soldiers and military suppliers introduced greater monetary circulation into newly conquered regions. But military activity alone did not sustain it. As territories became permanently incorporated, markets, cities, administrative institutions, religious centres and civic structures generated continuing demand for currency. Military expenditure gradually gave way to civilian, administrative and commercial activity.

The researchers also found that coins travelled increasingly farther from their places of origin as Rome expanded. The influence of geographical distance on monetary circulation progressively weakened, suggesting that previously separated regions were becoming connected through common transportation, markets and institutions. The findings indicate that Rome’s long-term strength rested not simply on conquest, but on its ability to transform conquered territories into parts of an increasingly integrated economy.

More information: Eduardo Amaral Haddad et al, Economic footprints: mapping coin circulation and economic networks in ancient Rome, Humanities and Social Sciences Communications. DOI: 10.1057/s41599-026-07815-7

Journal information: Humanities and Social Sciences Communications Provided by Fundação de Amparo à Pesquisa do Estado de São Paulo

Investors See Growing Value in GenAI for Company Research and Stock Analysis

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Generative artificial intelligence (GenAI) is rapidly becoming an important tool for retail investors researching companies and making investment decisions. Nearly half of surveyed investors said they had used GenAI to process financial information or inform investment decisions, according to new research examining how investors are incorporating the technology into their investment activities.

Joe Croom, assistant professor of accounting at Indiana University’s Kelley School of Business, and his co-authors analysed more than 410,000 investor queries submitted to a major brokerage’s GenAI chatbot and surveyed nearly 2,200 investors. The study provides insight into not only how frequently investors use GenAI, but also the kinds of financial questions they ask and how their use changes over time.

“Investors most often use GenAI to help make sense of complex financial information and better understand what is driving market movements,” Croom said. Investors initially tend to use the technology to screen stocks and conduct high-level company evaluations. As they become more familiar with it, however, they increasingly use GenAI for detailed monitoring and interpretation of company-specific news.

Rather than simply asking GenAI which stocks to buy, investors frequently use it as a research assistant. They may ask questions about the health of a company’s profit margins, its financial performance or factors affecting its stock. In this role, GenAI can help investors navigate large volumes of financial information and make complex material easier and faster to understand.

The findings appear in “Generative AI and Investor Processing of Financial Information,” forthcoming in the Journal of Accounting and Economics. Croom’s co-authors are Elizabeth Blankespoor, professor of accounting at the University of Washington’s Foster School of Business, and Stephanie Grant, associate professor of accountancy at the University of Illinois’ Gies College of Business.

Although many investors experimented with the chatbot only briefly, 17.7% became routine users. “Almost half of investors have used GenAI, and nearly a fifth of those use it routinely,” Croom said, noting that the data were collected in 2024 and adoption has likely increased since then. Among surveyed investors, 65% cited faster information processing as a benefit, while 59% valued GenAI’s ability to simplify complex information.

Among investors who had used GenAI, 74% believed it improved their information processing and 80% planned to continue using it. At the same time, users identified significant concerns, including reliability and accuracy (54%), data privacy (50%) and response quality (46%). Non-users were more sceptical, with 55% uncertain that GenAI would improve information processing, although only 24% said they were unlikely to use it.

The researchers also found that investors with greater financial experience and sophistication were leading GenAI adoption and using its advanced capabilities most effectively, raising questions about whether the technology will narrow or widen knowledge gaps among investors. Croom said the findings have implications for regulators developing investor education and safeguards, as well as companies communicating with GenAI-using investors. The research was based on data provided by Public, an investment brokerage and financial technology company.

More information: Elizabeth Blankespoor et al, Generative AI and Investor Processing of Financial Information, Journal of Accounting and Economics. DOI: 10.2139/ssrn.5053905

Journal information: Journal of Accounting and Economics Provided by Indiana University

Sector Indices May Not Tell the Full Financial Story of S&P 500 Companies

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Investors and analysts often compare companies operating within the same sector, assuming that businesses in similar industries share important financial characteristics. However, a study examining all 500 companies in the S&P 500 suggests that conventional sector classifications capture only part of a company’s financial profile.

Researchers in Spain analysed fiscal year 2022 financial statements to investigate how closely companies’ financial structures corresponded with their assigned sectors. The analysis used accounting ratios covering several important dimensions of corporate performance, including profitability, leverage, liquidity, operational efficiency, and cash generation.

The researchers first examined how strongly these financial ratios differed across sectors. They then used seven machine-learning models to determine whether companies’ sectors could be predicted solely from their accounting information. If sector membership closely reflected financial structure, the models would be expected to classify companies with relatively high accuracy.

The best-performing model, K-nearest neighbours, achieved a validation accuracy of 49.3%. This was substantially higher than the 14.8% majority-class baseline, showing that accounting characteristics do contain meaningful information about sector membership. However, the accuracy remained too low for sector classifications to be considered a complete representation of companies’ underlying financial structures.

The researchers therefore turned to unsupervised machine learning, grouping companies according to similarities in their financial characteristics rather than their existing sector labels. “Hence, we used unsupervised learning to group firms by financial similarity rather than by their existing labels,” said corresponding author Ricardo Reier Forradellas of the Catholic University of Ávila. The approach identified nine economically interpretable groups of companies.

Each of the nine clusters contained companies drawn from more than one conventional sector, highlighting financial similarities that crossed traditional industry boundaries. The clusters also generally displayed less internal variation than standard sectors across most of the accounting ratios examined. Nevertheless, some familiar sector-specific financial patterns remained visible, with utilities, real estate, and financial companies more readily identifiable than firms belonging to several other sectors.

“Our findings do not mean that sector classifications are obsolete,” explained Forradellas. “They show that sectors tell only part of the story. When the aim is to compare companies by financial structure, accounting-based peer groups can provide a useful additional perspective.” Such groupings could therefore help investors and analysts identify financially comparable companies that may otherwise be separated by conventional sector classifications.

The researchers also compared company cluster assignments across subsequent annual reporting periods and found only moderate persistence over time, suggesting that financial peer groups can change as companies’ circumstances evolve. “This indicates that these peer groups should be updated rather than treated as fixed categories,” Forradellas added. The researchers conclude that accounting-based clustering can complement existing sector taxonomies, providing an additional tool for financial benchmarking, company peer comparisons, and broader financial analysis.

More information: Ricardo Reier Forradellas et al, Characterization of S&P 500 companies by sector using artificial intelligence: Statistical evidence and machine learning application, The Journal of Finance and Data Science. DOI: 10.1016/j.jfds.2026.100193

Journal information: The Journal of Finance and Data Science Provided by KeAi Communications Co., Ltd.

Nearly 1 in 5 Medicaid-Eligible Adults May Lose Health Coverage Due to Unstable Work Hours

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A new study suggests that nearly one in five Medicaid-eligible adults in expansion states could be at risk of losing health coverage because they may not consistently meet new federal work requirements. Women, unmarried adults, White individuals, and people with lower levels of education were among those more likely to have insufficient or inconsistent work hours.

The requirements, established under the One Big Beautiful Bill Act (OBBBA), are scheduled to take effect nationwide on January 1, 2027. Adults covered through the Affordable Care Act’s Medicaid expansion will generally need to demonstrate at least 80 hours per month of work, volunteering, education, or job training, although certain groups will be exempt. The broader legislation is expected to reduce federal Medicaid spending over the next decade substantially.

Published in JAMA Health Forum, the study found that 19.8 percent of Medicaid-eligible adults in expansion states were at risk of failing to meet the requirements. About 13.6 percent were close to the minimum work-hour threshold, while 7.6 percent reported highly inconsistent work hours. Researchers warned that these patterns could put millions of people at risk of losing coverage once the requirements are implemented.

The findings also challenge the perception that Medicaid recipients who would be subject to the requirements are largely unemployed. More than 66 percent of applicable recipients were participating in the labour force, and among those workers, more than 85 percent reported working an average of more than 35 hours per week. However, meeting an average weekly threshold does not necessarily mean workers can consistently satisfy the requirement every month.

Many Medicaid recipients work in lower-wage industries such as retail, hospitality, food service, healthcare, and agriculture, where schedules can be unpredictable and hours may fluctuate considerably. Workers may also face difficulties involving transportation, affordable childcare, health problems, or limited control over their schedules. These circumstances can make maintaining and documenting 80 hours of qualifying activity each month difficult even for people who are regularly employed.

Researchers analysed federal social and economic data from 2023 to 2025 covering non-elderly adults who were plausibly eligible for Medicaid across the 40 Medicaid expansion states and Washington, DC. They identified people whose weekly hours were close to the 20-hour threshold or whose reported hours varied substantially from their usual schedules. Risk also differed considerably among states, reflecting variations in employment patterns and working conditions.

Demographic differences were also evident. Women had a 22 percent higher probability of insufficient or inconsistent work hours than men, while married enrollees had an 18 percent lower probability of noncompliance than unmarried adults. People with higher education levels faced lower risks than those without a high school diploma. Eligible Black and Hispanic recipients also showed lower risks of noncompliance than eligible White recipients. Single mothers with children aged 14 or older could be particularly vulnerable because the parental exemption generally applies only to parents of younger children or children with recognised disabilities.

The researchers cautioned that their estimates may understate the number of people affected. States must examine at least the previous month of qualifying activity when determining compliance before a Medicaid application and may review as many as three months. Longer look-back periods could make eligibility particularly difficult for people with fluctuating schedules, gig work, part-time employment, or newly obtained jobs, potentially delaying access to Medicaid and widening disparities among workers with unstable hours.

More information: Paul Shafer et al, Medicaid Work-Reporting Requirements Under HR 1 and Insufficient or Inconsistent Work Hours, JAMA Health Forum. DOI: 10.1001/jamahealthforum.2026.2938

Journal information: JAMA Health Forum Provided by Boston University School of Public Health

Why Bigger Isn’t Always Better for Bank Networks

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Interbank lending can help banks cope with unexpected withdrawals by allowing connected institutions to share liquidity when it is needed. But these connections can also introduce vulnerabilities. If banks reduce their own reserves because they expect to rely on their partners, financial stress at one institution may spread across the wider network.

In a new study published in Risk Sciences, researchers developed a theoretical model to explore why banks form interbank credit networks and how the size of those networks affects market efficiency. Their findings suggest that bigger networks do not necessarily produce better outcomes, because the advantages of sharing risk can eventually be outweighed by strategic behaviour among participating banks.

The model considers two closely related decisions made by banks. First, each bank determines how much money to keep in reserve, balancing the potential profits from lending more money against the need to withstand unexpected liquidity shocks. Second, banks decide whether participating in an interbank network would leave them better off than operating independently.

The researchers found that membership in a network creates both cooperation and competition. Banks benefit from being able to share liquidity risk with their partners, which can provide protection when unexpected withdrawals occur. At the same time, individual banks may have an incentive to reduce their own reserves and depend more heavily on the liquidity held by other institutions in the network.

The researchers describe this strategic behaviour as a “free-riding” effect. Although each bank can benefit individually from holding fewer reserves and putting more funds into potentially profitable lending, widespread free-riding can weaken the network as a whole. Lower reserves can reduce banks’ ability to survive liquidity shocks and may ultimately decrease their expected profits.

Network size therefore plays an important role. In smaller interbank networks, the advantages of sharing liquidity risk tend to outweigh the negative effects of free-riding. However, as additional banks join, free-riding becomes increasingly significant. The researchers found a rise-and-fall relationship between expected profits and network size, suggesting that relatively small networks can sometimes be Pareto optimal—where no participating bank can be made better off without making another worse off.

The study also considers networks containing banks of different sizes. Under certain conditions, smaller and larger institutions may have incentives to establish connections even when their deposit sizes differ considerably. The findings offer a theoretical explanation for core-periphery structures commonly observed in banking systems, where a relatively small number of highly connected institutions interact with a much larger group of smaller banks.

The researchers also considered the implications for financial regulation. Implicit government guarantees may encourage institutions to take greater risks and become excessively interconnected because they expect support during periods of financial distress. The findings suggest that appropriately designed capital requirements could help counteract free-riding, discourage excessive interconnectedness, and improve market efficiency in larger banking networks. Overall, the study highlights an important trade-off: interbank connections can strengthen financial institutions through risk-sharing, but expanding those networks too far may create incentives that undermine the very benefits they are intended to provide.

More information: Tongkui Yu et al, Interbank network and market efficiency, Risk Sciences. DOI: 10.1016/j.risk.2026.100059

Journal information: Risk Sciences Provided by KeAi Communications Co., Ltd.

Customer Loyalty Programs Aren’t One-Size-Fits-All, Research Suggests

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Loyalty programs (LPs) can offer valuable savings to households facing rising cost-of-living pressures, but their benefits are not the same for every shopper. As more consumers seek discounts, coupons, gifts and vouchers to reduce everyday expenses, retailers are increasingly investing in these programs. Globally, the loyalty management market is projected to grow from US$17.38 billion in 2026 to US$51.65 billion by 2034.

New research led by Edith Cowan University (ECU) analysed survey data from more than 800 Australian supermarket customers to understand what drives engagement with loyalty programs and how effectively they encourage loyalty to retailers. The findings suggest that customers’ individual characteristics, circumstances and ability to take advantage of rewards all influence whether a program succeeds.

ECU Professor of Marketing and Service Science Sanjit Roy said retailers often reinforce loyalty program use by reminding customers to scan their rewards cards at checkouts. However, customers may question whether the rewards they receive justify sharing their personal data, particularly when discounts and promotions are not sufficiently personalised to their shopping habits.

Despite these concerns, the researchers found that engagement with loyalty programs can lead to stronger engagement with retailers and greater customer loyalty, both in shoppers’ attitudes and purchasing behaviour. However, customers’ ability to wait for discounts can significantly affect how much they benefit from a program.

Dr Saalem Sadeque, Course Coordinator and Lecturer in Marketing at ECU, said a shopper who expects a frequently purchased product to go on sale may be able to postpone buying it and purchase more when the discount arrives. By contrast, someone who urgently needs an essential product, or cannot afford to buy in bulk, may miss the savings despite being loyal to the retailer.

The research found that successful loyalty program engagement depends on a combination of factors, including trust in the retailer, commitment, perceived benefits, the ability to wait for discounts and the ability to search for better deals across different retailers. “Our findings demonstrate that no one factor leads to engagement but rather a combination of all these factors,” Professor Roy said. Poor engagement can also make it more difficult for retailers to predict customer spending and cash flow.

For retailers, transparency and personalisation may be particularly important. Professor Roy said supermarkets should be transparent about pricing and business practices, provide strong customer service and consistently deliver on their brand promises. Retailers could also make better use of customer data to provide benefits that are genuinely relevant to individual shoppers rather than treating customers simply as data points.

Ultimately, the researchers argue that loyalty programs should provide clear and consistent value while building stronger relationships with customers. “A tailored, integrated strategy that aligns a loyalty program’s value with trust and relationship building is essential for sustaining customer loyalty and long-term retailer relationships,” Dr Sadeque said. Personalised offers that reflect customers’ needs and values could increase perceived benefits and make shoppers more likely to engage with loyalty programs actively.

More information: Stephen Skinner et al, Customers’ disposition towards loyalty program engagement, European Journal of Marketing. DOI: 10.1108/EJM-08-2024-0633

Journal information: European Journal of Marketing Provided by Edith Cowan University

Online Reviews Could Leave Users Vulnerable to Cyberattacks

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Posting online reviews may seem like a win-win activity, helping businesses attract customers while giving other consumers useful information. But new research from the McCombs School of Business at The University of Texas at Austin suggests that seemingly harmless reviews may also reveal information about users’ social connections, potentially leaving them and their online friends more vulnerable to cyberattacks.

The study focuses on spear phishing, a targeted form of phishing in which an attacker impersonates someone the victim trusts to persuade them to send money or disclose sensitive information. Yan Leng, assistant professor of information, risk, and operations management at McCombs, notes that phishing has become increasingly costly. Between 2021 and 2023, the FBI’s Internet Crime Complaint Center received nearly one million complaints involving about $305 million in losses.

Leng and colleagues investigated whether attackers could reconstruct users’ social networks simply by examining their online behaviour. Although many review platforms do not publicly display friendship connections, patterns in reviews and ratings may provide clues about who knows or interacts with whom. The researchers examined Yelp data involving 4,299 reviewers from Louisiana and Pennsylvania in 2020, where both reviews and users’ friend lists were publicly accessible.

The researchers first analysed review behaviour to predict connections between users, much as a cyberattacker might. They then compared those predicted relationships with users’ actual friendship networks. Their analysis found that an attacker could correctly identify 49% of social relationships based solely on online behaviour, while incorrectly identifying 10% of unconnected pairs as connected. With a higher false-alarm rate of 20%, as many as 63% of relationships could be identified.

One particularly revealing behavioural signal was review length. The researchers found observable relationships between the lengths of reviews written by connected users. For example, when one friend wrote longer reviews, another might also begin writing longer reviews. In other cases, one person might write shorter reviews that complemented a friend’s longer contributions. Such patterns can create behavioural fingerprints that reveal relationships even when friendship information itself is hidden.

This information could make spear-phishing campaigns more effective. Once attackers infer who is connected to whom, they can impersonate trusted contacts and send targeted scam messages or emails. The researchers found that identifying larger numbers of relationships could substantially increase attackers’ potential financial returns. In the Pennsylvania data, estimated returns increased from 109% for 500 attack attempts to 1,098% for 10,000 attempts.

Existing privacy protections may not fully address this problem because sensitive information does not necessarily have to be directly disclosed to create risk. Instead, attackers may infer relationships from apparently harmless behavioural data. The researchers say review platforms, e-commerce marketplaces, and media-sharing services should therefore examine whether the information they publish could unintentionally expose users’ social networks.

One possible safeguard is to introduce carefully designed “noise” into publicly available data. For example, platforms could subtly modify review text so that its length varies while its meaning remains unchanged, making behavioural patterns more difficult to detect. Simulations suggested this approach could reduce attackers’ financial incentives and sometimes make attacks unprofitable. Leng argues that platforms should therefore protect not only information users explicitly disclose, but also sensitive information that others may be able to infer from their behaviour.

More information: Yan Leng et al, When Behavioral Data Betray Users: A Diagnostic and Protective Framework Against Social Interaction Leakages, Information Systems Research. DOI: 10.1287/isre.2024.1469

Journal information: Information Systems Research Provided by University of Texas at Austin