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AI Video Translation Shows Real Potential, but People Still Do It Better

AI video translation is not yet a complete replacement for human translators, according to new research from the University of East Anglia. The study suggests that while artificial intelligence can be highly effective when speed and basic clarity are priorities, human translators remain essential for conveying tone, cultural nuance and natural-sounding speech.

The research was led by Jiseon Han, a lecturer in digital marketing at UEA’s Norwich Business School, who noted that brands expanding rapidly across borders are increasingly experimenting with generative AI. As companies seek to engage global audiences on platforms such as TikTok, Instagram and YouTube, many are asking whether AI can realistically take over video translation tasks traditionally handled by people. “As brands race to reach global consumers, a new question has emerged,” Han said. “Can generative AI truly replace humans in video translation? We decided to put it to the test.”

To explore this, researchers examined how consumers in different countries responded to marketing videos translated by a generative AI tool compared with those translated and delivered by human speakers. The AI system used was HeyGen, which automatically translates language while also modifying voice and lip movements to match the target language. This mirrors the technology many marketers are already trialling in real campaigns.

The study involved two experiments. One focused on Indonesian consumers, while the other involved audiences in the United States and the United Kingdom. Participants watched marketing videos presented by either native human speakers or AI-generated versions. This allowed the researchers to directly compare perceptions of naturalness, comprehension and engagement across different cultural settings.

The results revealed a mixed picture. Viewers consistently rated AI-translated videos as less natural and less convincingly native-sounding than those performed by humans. However, the AI performed better on language comprehension when translating into English, likely because English dominates the data used to train many AI models. Notably, these differences did not affect engagement: participants were just as likely to like, share or comment on AI-translated videos as on human-translated ones.

Overall, the findings suggest that AI video translation already offers clear practical value but has significant limitations. For marketers, AI can be an efficient solution when speed and straightforward messaging matter most. Yet when communication depends on tone, personality and cultural context, human translators remain difficult to replace. As co-author Risqo Wahid from the University of Jyväskylä observed, consumers still notice when something feels slightly off. The study provides a timely snapshot of where AI video translation stands today, highlighting both its growing potential and the enduring importance of the human touch.

More information: Risqo Wahid et al, Generative AI for Video Translation: Consumer Evaluation in International Markets, Journal of International Marketing. DOI: 10.1177/1069031X251404843

Journal information: Journal of International Marketing Provided by University of East Anglia

An ESMT Berlin study on why initial offers matter and how they succeed in negotiations

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For many years, researchers and practitioners alike have debated a central question in negotiation strategy: is it better to make the first offer, or to wait and respond to the other side? While opinions have long differed, a recent large-scale meta-study offers a clear and evidence-based answer. Drawing on a comprehensive body of prior research, the study shows that negotiators who make the first offer, provided they do so with careful preparation, generally achieve better outcomes than those who hold back.

The researchers analysed 90 existing studies comprising 374 experiments and involving more than 16,000 participants. This breadth allows for firm conclusions, as the results are not dependent on any single context or experimental design. Across this extensive dataset, a consistent pattern emerged: those who initiated negotiations with a first offer tended to secure more favourable final agreements. The findings suggest that the advantage of going first is not a matter of confidence or personality alone, but a systematic effect that appears across many types of bargaining situations.

The study, titled “The Power and Peril of First Offers in Negotiations, was conducted by Martin Schweinsberg, associate professor of organisational behaviour at ESMT Berlin, as part of an international research team led by Hannes M. Petrowsky of Leuphana University. It was published in Organizational Behavior and Human Decision Processes, a leading peer-reviewed journal in management and decision-making research. The rigorous review process required for publication underscores the reliability of the findings and their relevance for both academic and practical audiences.

One reason first offers are so influential lies in their framing effect. The data spans a wide range of negotiations, including salary discussions, property transactions, procurement contracts, and private sales. Across these settings, the first number mentioned often serves as a psychological anchor, subtly shaping how all subsequent offers are perceived. In 81 per cent of the negotiations examined, higher first offers were associated with better outcomes for the party that made them. This anchoring effect helps explain why the opening move can exert such a strong pull on the entire negotiation process.

At the same time, the study emphasises that the power of first offers has limits. Excessively high or unrealistic opening demands increase the risk of negotiations breaking down or leaving the counterpart feeling unfairly treated. This is especially problematic when the relationship continues after the agreement, such as in employment or long-term service arrangements. A perceived loss can later manifest in reduced effort, lower cooperation, or declining performance, turning an apparent short-term gain into a long-term cost. The research also shows that as negotiations become more complex, involving multiple issues rather than a single price, the influence of one initial number weakens, and trust and relationship quality become more important.

Overall, the findings provide clear but nuanced guidance. Making the first offer is often advantageous, but only when it is grounded in solid preparation, realistic expectations, and an awareness of the future relationship with the other party. As Schweinsberg notes, much of negotiation success is determined before the discussion even begins. Those who set clear goals, define sensible ranges, and plan their opening move deliberately are far more likely to achieve outcomes that prove beneficial over the long term.

More information: Hannes M. Petrowsky et al, The power and peril of first offers in negotiations: a conceptual, meta-analytic, and experimental synthesis, Organizational Behavior and Human Decision Processes. DOI: 10.1016/j.obhdp.2025.104448

Journal information: Organizational Behavior and Human Decision Processes Provided by ESMT Berlin

How Neurodivergent Perspectives Shape Stronger, More Adaptive Ventures

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Businesses and policymakers may be overlooking a substantial source of innovation and economic potential by misunderstanding neurodiverse conditions and the biological differences that shape them, according to new research led by the University of Surrey. Too often, conditions such as ADHD, dyslexia, and bipolar disorder are approached solely as clinical challenges or limitations. The study argues that this narrow framing risks excluding individuals whose cognitive differences can actively support entrepreneurial behaviour, innovation, and business growth.

Rather than treating neurodiversity as a fixed deficit, the researchers build on existing entrepreneurship literature to propose a more dynamic view. They suggest that neurodivergent conditions can interact with entrepreneurial environments in ways that unlock distinctive strengths. Traits that may be difficult to accommodate in conventional employment settings, such as heightened impulsivity, unconventional thinking, or intense focus on specific interests, can become valuable assets in contexts that reward creativity, opportunity recognition, and adaptability.

Published in Neurodiversity in Entrepreneurship, the study draws on a systematic review of scientific evidence published between 2011 and 2023. The researchers examined 139 academic papers and identified 28 core studies across business and management. A key contribution of the research lies in its use of organisational neuroscience, integrating biological evidence ranging from brain activation patterns to genetic mechanisms associated with ADHD, dyslexia, and bipolar conditions. This approach allows the authors to link neurological processes more directly to entrepreneurial behaviour.

The findings reveal consistent patterns across different forms of neurodivergence. Entrepreneurs with ADHD often demonstrate high levels of entrepreneurial alertness, alongside strong performance in innovation and risk-taking. Dyslexic entrepreneurs may compensate for challenges in reading and writing by developing advanced delegation skills and a firm strategic overview, enabling faster decision-making and business growth. Meanwhile, traits associated with bipolar conditions are linked to creativity, idea generation, and a willingness to pursue bold and unconventional ventures. Notably, the study does not deny the challenges associated with these conditions but shows that, in the right contexts, they can also underpin meaningful strengths.

Dr Sebastiano Massaro, co-author of the study and Associate Professor (Reader) of Organisational Neuroscience at the University of Surrey, emphasises that neurodiversity is still widely viewed through a deficit-based lens. From a biological perspective, he notes, these conditions exist along a continuum rather than as clear-cut abnormalities. In entrepreneurial settings, there is strong evidence that they can bring valuable capabilities, calling into question the assumption that they are problems to be fixed.

The research calls for a shift in how organisations and policymakers approach neurodiversity. Instead of aiming to normalise cognitive differences, the authors argue for business environments that value and actively harness them. The study also highlights broader policy implications, suggesting that entrepreneurship can provide viable pathways to work and equality for people who are frequently miscategorised as unemployable. By overlooking the biological foundations of neurodiversity in entrepreneurship, universities, businesses, and governments risk missing valuable capability hidden in plain sight.

More information: Giuseppe Bongiorno et al, The Neuroscience of Neurodiversity in Entrepreneurship, Neurodiversity in Entrepreneurship. DOI: 10.1108/S1074-754020250000024005

Journal information: Neurodiversity in Entrepreneurship Provided by University of Surrey

From Union Jobs to Going It Alone: How Blue-Collar Workers Adapt

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In U.S. states with openly hostile labour policies, workers are far more likely to turn to self-employment. Research shows that employees in these environments are up to 53 per cent more likely to start their own businesses, with blue-collar workers particularly prone to doing so out of necessity rather than opportunity. Rather than reflecting a boom in entrepreneurial ambition, this trend stems from declining job security and the erosion of collective protections that once made wage employment more stable.

These findings come from a study published in the Strategic Entrepreneurship Journal, which examines how state labour environments shape workers’ employment decisions. The researchers compared states that have enacted right-to-work laws with neighbouring states where unions retain stronger bargaining power. By focusing on these contrasts, the study isolates the role of institutional labour conditions rather than attributing the rise in self-employment to individual preference or broader economic cycles.

According to coauthor Namil Kim of the Graduate School of Information at Yonsei University, stringent anti-union laws reduce employees’ incentives to remain in traditional jobs while making self-employment relatively more attractive. As union protections weaken, the balance shifts: paid work offers less security, fewer benefits, and diminished bargaining power, prompting some workers to view going it alone as the least risky option available.

Right-to-work laws are central to this shift. These laws prohibit union security agreements, allowing employees to opt out of union membership or dues even when a union represents their workplace. Historically, such legislation has reduced union membership and weakened unions’ ability to negotiate wages, benefits, and job security. While supporters argue that right-to-work laws promote flexibility and a business-friendly climate, the study highlights their less visible effect on workers’ sense of stability.

Empirically, the researchers compared labour outcomes in Michigan and Indiana, both of which adopted right-to-work laws, with neighbouring Ohio and Kentucky, which did not. They tracked workers aged 20 to 34 who moved into self-employment, defined as dedicating at least 15 hours per week to a new business, while controlling for demographics, job tenure, occupation, industry, and state-level economic factors.

The results indicate that anti-union environments do more than alter pay and benefits. After right-to-work laws were enacted, workers in affected states were about 50 per cent more likely to become self-employed, typically by opening small, unincorporated businesses. This pattern was powerful among blue-collar and low-wage workers, many of whom reported feeling pushed into self-employment because their regular jobs had become less secure, not because they had identified a promising opportunity.

Additional evidence supports this interpretation. Right-to-work adoption was associated with a roughly two percentage-point decline in union membership, a modest increase in weekly working hours, and no significant rise in wages. For many blue-collar workers, weakened unions translated into more extended hours, fewer benefits, and greater insecurity, making necessity-driven self-employment a means of protecting or replacing income rather than a path to upward mobility.

More information: Daehyun Kim et al, Anti-labor environments and employee entrepreneurship: Evidence from right-to-work laws, Strategic Entrepreneurship Journal. DOI: 10.1002/sej.70006

Journal information: Strategic Entrepreneurship Journal Provided by Strategic Management Society

Research from ECU shows AI advancing agility and engagement across organisations

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As organisations navigate markets that are increasingly dynamic and unpredictable, artificial intelligence is evolving from a supportive technology into a core strategic driver of marketing agility and firm performance. No longer confined to automating discrete tasks, AI is reshaping how businesses sense market changes, respond to shifting stakeholder expectations, and remain competitive in environments marked by rapid technological disruption.

New research from Edith Cowan University shows that firms which effectively leverage AI technologies are better positioned to become genuinely AI-ready. These organisations can respond more quickly to changing stakeholder preferences, optimise marketing campaigns in real time, and deliver highly personalised experiences at scale. Such capabilities not only improve efficiency but also strengthen stakeholder engagement by ensuring timely, relevant interactions aligned with individual needs.

According to Sanjit Roy, Professor of Marketing and Service Science, the benefits of AI are most significant when it is integrated into a marketing strategy rather than used in isolation. Firms that embed AI across decision-making and engagement processes tend to achieve stronger overall performance and are better positioned for sustained competitive advantage. As Professor Roy notes, the question for businesses is no longer whether to adopt AI, but how effectively they adapt to its use in an increasingly complex business landscape.

The research highlights that AI offers extensive opportunities to deepen stakeholder engagement through personalised interactions, enhanced communication channels, real-time feedback analysis, and improved decision-making. These capabilities enable firms to move beyond transactional relationships and build stronger, more enduring connections with stakeholders, supporting long-term loyalty and trust.

Rather than simply replacing outdated systems, AI serves as a strategic enabler, helping organisations overcome inefficiencies and reconfigure their business models. It enhances agility by enabling firms to respond more rapidly to disruptions and changing market conditions. Importantly, AI plays a dual role: it functions as a distinct organisational capability while also enabling broader dynamic capabilities, including sensing emerging opportunities, seizing them effectively, and transforming organisational resources to meet evolving demands.

The findings further show that AI-driven stakeholder engagement significantly enhances marketing agility, thereby improving overall firm performance. While early results suggested that technological turbulence might weaken these relationships, the evidence ultimately points in the opposite direction. In highly disruptive and fast-changing technological environments, the positive impact of AI-enabled engagement on agility becomes even stronger. In other words, the more turbulent the market, the more critical AI becomes in helping firms remain agile, competitive, and resilient.

More information: Ali N. Tehrani et al, Navigating technological disruptions: the interplay of AI-based stakeholder engagement, marketing agility, and firm performance, European Journal of Marketing. DOI: 10.1108/EJM-06-2024-0535

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

The impact of extreme weather on agricultural trade across US states

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The United States is largely self-sufficient in agricultural food production, supported by extensive storage capacity and a well-developed system of interstate trade. However, the growing frequency and intensity of extreme weather events are placing increasing strain on agriculture, raising concerns about the country’s ability to sustain food production for an increasing population. These pressures highlight the importance of a resilient food supply chain that can withstand climate-related shocks.

A new study from the University of Illinois Urbana-Champaign examines how extreme weather in one part of the country can affect agricultural trade and food manufacturing far beyond the areas directly impacted. Published in the Proceedings of the National Academy of Sciences, the research analyses state-to-state trade flows for agricultural products to show how local climate shocks propagate through national supply chains.

“With climate change, we’re going to experience more intense and more frequent extreme weather events such as drought and flooding, which impact agricultural output,” said lead author Hyungsun Yim, a doctoral student in agricultural and consumer economics. “It’s important to prepare for ways to mitigate climate shocks to food manufacturing.” Sandy Dall’erba, a professor in the same department and founding director of the Center for Climate, Regional, Environmental and Trade Economics, co-authored the study.

According to Dall’erba, the research is the first to systematically map how extreme weather in individual states reduces local agricultural yields and then affects food manufacturing nationwide through interstate trade. For example, a severe drought in Midwestern grain-producing states can disrupt supply chains and influence production in central food manufacturing states such as California, Texas, Illinois, and New York.

Although the US produces roughly 80 to 85 per cent of the food it consumes domestically, this self-sufficiency depends heavily on internal trade and transportation networks. Around 57 per cent of grain production and 77 per cent of livestock output are used as inputs for domestic food manufacturing rather than being sold directly to households. This interdependence makes the system vulnerable to regional climate shocks, even when national production remains relatively strong.

To analyse these dynamics, the researchers combined two decades of data on interstate trade flows with detailed weather information on temperature, precipitation, drought, and excess wetness. One key example was the 2012 Midwestern drought, which sharply reduced grain output in Iowa, Illinois, and Nebraska. These states typically account for about 34 per cent of the US grain trade, but their share fell significantly that year. As a result, Nebraska increased imports to support its livestock sector, while Texas shifted grain sourcing to states such as Kansas, Oklahoma, and Louisiana. Prices for wheat, maize, and soybeans rose by up to 20 per cent, affecting food manufacturers nationwide.

Overall, the study found that a one per cent increase in drought intensity in producing states reduces domestic agricultural exports by 0.5 to 0.7 per cent, leading to an average decline of 0.04 per cent in food manufacturing output. While this downstream effect is relatively small, it reflects both the resilience of the US agrifood system and the need for planning. The authors argue that their findings can help guide investments in infrastructure, storage, transportation, and multi-state coordination to better prepare for future climate shocks.

More information: Hyungsun Yim et al, Impact of extreme weather events on the US domestic supply chain of food manufacturing, Proceedings of the National Academy of Sciences. DOI: 10.1073/pnas.2424715122

Journal information: Proceedings of the National Academy of Sciences Provided by University of Illinois College of Agricultural, Consumer and Environmental Sciences

A novel approach enhances the reliability of statistical estimates

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An environmental scientist might, for example, examine whether exposure to air pollution is associated with lower birth weights in a specific county. Given the complexity of environmental and health data, a researcher could reasonably turn to machine learning to estimate the strength of this relationship. These methods are desirable for their ability to capture complex, nonlinear patterns that are difficult to represent with traditional statistical models. In practice, a model could be trained on existing pollution and health records to estimate how variations in air quality correspond to changes in birth weight outcomes.

While machine-learning techniques are highly effective for prediction, they are far less reliable when the goal is to determine whether two variables are truly associated, and how certain one can be about that conclusion. Some methods address this by providing confidence intervals, which quantify uncertainty. However, researchers at Massachusetts Institute of Technology found that, in spatial settings, these confidence intervals can be deeply misleading. When data vary across geographic locations, standard approaches may report high confidence even when the estimated association is seriously inaccurate.

This problem arises because many variables of interest, such as air pollution, rainfall, or temperature, are spatially structured rather than randomly distributed. Common confidence-interval methods rely on assumptions that break down in such contexts. They often presume that observations are independent and identically distributed, that the statistical model is perfectly specified, and that the data used to train the model closely resemble the data at the location where the association is being estimated. In spatial analyses, none of these assumptions holds reliably, leading to intervals that appear precise but are fundamentally wrong.

The consequences of this failure are significant. A model might claim to be 95 per cent confident that it has captured the true relationship between two variables, such as pollution and health outcomes, when in fact it has missed that relationship entirely. This false sense of certainty can mislead scientists, policymakers, and practitioners into trusting results that should instead be treated with caution. Recognising this risk, the MIT researchers set out to develop a method that could produce confidence intervals that remain valid when data vary across space.

Their solution replaces unrealistic assumptions with one that better reflects how many real-world variables behave: spatial smoothness. Instead of assuming that data from different locations are effectively interchangeable, the new method assumes that values change gradually over space. For example, air pollution levels are unlikely to shift dramatically from one city block to the next, instead tapering off as distance from pollution sources increases. According to Tamara Broderick, this assumption aligns far more closely with the structure of environmental and spatial data.

When tested in simulations and on real datasets, the new approach consistently produced accurate confidence intervals, even in the presence of noise or measurement error. Other commonly used techniques failed to do so. The findings, presented at the Conference on Neural Information Processing Systems, suggest that researchers in fields such as environmental science, economics, and epidemiology could benefit substantially from adopting methods tailored to spatial data. By providing more trustworthy measures of uncertainty, this work clarifies when results can be genuinely relied upon, thereby improving both scientific understanding and decision-making.

More information: David R. Burt et al, Smooth Sailing: Lipschitz-Driven Uncertainty Quantification for Spatial Association, arXiv. DOI: 10.48550/arXiv.2502.06067

Journal information: arXiv Provided by Massachusetts Institute of Technology

Bot armies for sale: global pricing revealed across hundreds of platforms including TikTok and Amazon

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A new website launched today by the University of Cambridge reveals the fluctuating costs involved in building a bot army across more than 500 social media and commercial platforms worldwide. Covering services from TikTok and Amazon to Spotify, and spanning every country, the site tracks daily price changes in the market for fake online accounts, exposing the economic infrastructure behind large-scale online manipulation.

The platform, known as the Cambridge Online Trust and Safety Index (COTSI), allows the public for the first time to monitor real-time market data from what researchers describe as the “online manipulation economy. Developed by the Cambridge Social Decision-Making Lab, COTSI focuses on SIM farms and related services that mass-produce phone numbers and SMS verifications used to create fake accounts. These services openly sell access to hundreds of platforms, enabling activities ranging from artificial popularity boosts and rage-bait content to coordinated influence operations.

A new study, published in the journal Science and based on 12 months of COTSI data, shows how national regulations shape the prices of fake accounts. Verifying accounts for use in the United States and the United Kingdom is almost as cheap as in Russia, despite their very different regulatory environments. By contrast, Japan and Australia have far higher prices, mainly due to stricter SIM card rules, higher costs and photo identification requirements. During the study period running to July 2025, the average price of SMS verification was $4.93 in Japan and $3.24 in Australia, compared with just $0.26 in the US, $0.10 in the UK and $0.08 in Russia.

The analysis also suggests that political events can be detected in these markets. Prices for fake accounts on messaging apps such as Telegram and WhatsApp tend to rise sharply in countries approaching national elections, indicating increased demand linked to influence campaigns. Examining 61 elections held worldwide between mid-2024 and mid-2025, the researchers found that Telegram account prices rose by an average of 12 per cent and WhatsApp by 15 per cent in the month before polling day. Because these platforms display phone numbers and show the country of origin, influence operations often require locally registered accounts, driving up demand for SMS verification.

To build the index, researchers collected open-source data from major providers of fake accounts. Seventeen vendors were identified and ranked by traffic, with prices from the largest used to construct a global index that tracks both cost and availability. COTSI monitors the stockpiles of fake accounts across social media, messaging services, dating and gaming apps, cryptocurrency exchanges, ride-hailing platforms, streaming services, and major brands. The data show particularly high availability for platforms such as X, Uber, Discord, Amazon, Tinder and Steam, with millions of verifications listed for countries including the US, the UK, Brazil and Canada.

The researchers argue that the fake account economy depends on a critical vulnerability: every account requires a phone number and a SIM card. This creates a potential policy choke point. By making SIM cards more complex or more expensive to obtain, governments could raise the cost of online manipulation and suppress malicious activity. By turning a hidden global market into measurable data, the COTSI index aims to help policymakers and platforms better understand, and ultimately disrupt, the business models that sustain bot armies and online manipulation.

More information: Anton Dek et al, Mapping the online manipulation economy, Science. DOI: 10.1126/science.adw8154

Journal information: Science Provided by University of Cambridge

When Doing Good in Finance Turns Counterproductive

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Socially responsible investors often imagine themselves as drivers of environmental or social progress, convinced that by directing capital toward polluting firms, they can help steer them onto a cleaner path. The logic appears intuitive: invest in what is “bad so that it can become “good.” Yet a recent study by finance scholars from the University of Rochester, Johns Hopkins University, and the Stockholm School of Economics reveals a troubling paradox. Instead of hastening reforms, these well-intentioned investors may inadvertently motivate companies to delay environmental improvements, waiting for the moment when an impact-minded buyer arrives.

The researchers illustrate this counterintuitive dynamic through a scenario that reflects real corporate dilemmas. Imagine owning a profitable but polluting factory. You could invest now to make it greener, or you could postpone action, anticipating that a socially responsible investor might later pay a premium precisely because the firm has not yet begun to change. As study coauthor Alexandr Kopytov notes, although the idea may seem surprising at first, it becomes logical once one recognises that many SRIs prefer firms with unrealised potential to those that have already completed their reforms. By delaying improvements, companies keep themselves attractive to investors seeking to make a visible impact.

Compounding this problem is the presence of traditional investors who care only about financial returns. These investors can act as intermediaries, buying polluting firms at lower prices and later reselling them to SRIs at a profit. Because socially responsible investors pay more for firms that still have room to improve, financially motivated owners become tougher negotiators, inadvertently reinforcing incentives for pollution-heavy firms to wait. In this environment, environmental reform becomes something to postpone rather than accelerate.

Seeking solutions, the researchers examine standard investment policies such as excluding polluters from portfolios, but find these approaches insufficient to counteract the underlying incentive structure. A more impactful strategy involves SRIs publicly committing to pay a premium for companies that have already undertaken environmental reforms before any investment is made. This shift in emphasis would encourage managers to reform early to capture the promised benefit.

However, such commitments must be credible, or firms will not trust that the premium will truly materialise. To strengthen credibility, impact investors may need binding rules or public mandates that impose reputational or contractual costs if they fail to honour their commitments. Principles of responsible investing, consistently and transparently applied, help ensure that firms feel secure enough to undertake reforms without waiting for a future buyer.

Ultimately, the study argues for a rethinking of impact investing. Genuine influence often occurs not after investors take ownership, but before, as firms adjust their behaviour to attract the right kind of capital. Good intentions alone are insufficient. Real progress depends on designing investment incentives that reward action now rather than later, ensuring that attempts to promote sustainability do not inadvertently slow it.

More information: Alexandr Kopytov et al, The Pace of Change: Socially Responsible Investing in Private Markets, The Review of Financial Studies. DOI: 10.1093/rfs/hhaf083

Journal information: The Review of Financial Studies Provided by University of Rochester

Leveraging Combined Credit–Debit Data to Reveal Borrowing Patterns and Improve Delinquency Prediction Models

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A recent article in The Journal of Finance and Data Science reports that combining customers’ credit card information with their debit account transactions markedly improves the ability to forecast credit card delinquency. The study, carried out by Håvard Huse of BI Norwegian Business School, Sven A. Haugland of NHH, and Auke Hunneman of BI, introduces a hierarchical Bayesian behavioural model that consistently surpasses prominent machine-learning systems, including XGBoost, GBM, neural networks, and stacked ensemble methods.

Huse notes that relying solely on credit data offers only a limited view of an individual’s financial circumstances. By incorporating debit-side activity, the researchers gain visibility into payday-driven spending, repayment routines, and patterns in income flows—elements that play a decisive role in whether a customer may struggle to meet payment obligations.

Their analysis draws on granular transaction-level data from a central Norwegian bank. Where standard credit-risk models depend predominantly on monthly summary indicators such as balances or credit limits, these traditional markers reveal little about the day-to-day financial habits that underpin repayment outcomes. By modelling behavioural trajectories—how repayment behaviour shifts over time, or how expenditure rises immediately after payday—the new framework provides a richer explanation of both the mechanisms behind delinquency and the individuals most likely to default.

The model’s advantages extend to its ability to generate more precise predictions for individual customers. It also uncovers distinct behavioural groups characterised by differing “memory lengths”, referring to how strongly past financial states influence present repayment patterns. According to Hunneman, customers under financial strain tend to be more affected by their earlier behaviour, and this dynamic is captured far more effectively by the Bayesian specification than by conventional machine-learning tools.

A further strength of the approach lies in its interpretability. While outperforming cutting-edge algorithms, the model remains transparent enough for practitioners to understand the behavioural drivers of risk. As Hunneman observes, accuracy alone is insufficient for financial institutions; they must also be able to trace the patterns that shape customer vulnerability.

The authors illustrate the model’s practical significance by showing that, over a three-month prediction window, financial institutions could realise considerable savings by identifying at-risk cardholders earlier and taking timely action. Haugland emphasises that this improvement is not just a technical gain in predictive power but a means of offering more proactive support to customers who might otherwise slide into serious financial difficulty.

Together, these findings signal an essential evolution in credit-scoring practice: a movement away from static, aggregate measures toward deeper behavioural analytics grounded in the full spectrum of customer transactions.

More information: Håvard Huse et al, Integrating credit and debit data for enhanced insights into borrowing behavior and predictive modeling of credit card delinquency, The Journal of Finance and Data Science. DOI: 10.1016/j.jfds.2025.100166

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

Strategic Slowing of Acquisitions Can Strengthen Corporate Worth

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In the fiercely competitive realm of corporate acquisitions, where firms often pride themselves on rapid expansion, new research suggests that a slower, more deliberate pace between deals can produce more favourable financial outcomes. Rather than signalling hesitation or weakness, taking extra time between transactions may actually enhance a company’s ability to extract long-term value from each acquisition. This finding challenges the long-standing belief that steady, evenly spaced deal-making represents the most effective route to performance gains.

A recent study co-authored by Jerayr “John” Haleblian, a professor of management at UC Riverside’s School of Business, provides robust evidence for this more reflective approach. Published in the Journal of Business Research, the study focuses on what researchers call “experience schedules”, the intervals between successive acquisitions. Company performance was assessed through changes in stock value following each deal, offering a tangible measure of how well firms were perceived to handle their acquisition activities.

The results were striking. Contrary to earlier research that favoured regular, evenly paced acquisition cycles, Haleblian and his co-authors found that investors rewarded companies extending the time between deals with higher stock valuations. This suggests that investors recognise the strategic benefit of allowing sufficient time for organisational learning and integration. According to Haleblian, gradually lengthening the interval between acquisitions gives firms a better chance to learn from prior deals and ultimately maximise the value created by each transaction.

Integration lies at the heart of these findings. Acquisitions promise advantages such as increased market share, added talent, new technologies, or expanded operational assets. However, integrating these elements into an existing corporate framework is complex and time-consuming. When companies rush into successive acquisitions, they risk what Haleblian describes as “acquisition indigestion”, a state in which leadership and staff become overwhelmed by the demands of merging operations, aligning cultures, and establishing new routines. Slower pacing reduces this strain, allowing executives to refine internal processes, integrate employees more effectively, and stabilise the organisational environment.

The study’s conclusions are supported by a large dataset of more than 5,100 acquisitions undertaken by S&P 1500 companies between 1992 and 2012, providing a long-term view of performance patterns. To deepen their understanding, the research team also interviewed seventeen senior executives involved in acquisitions across the chemical, energy, and technology sectors. One executive noted that fewer deals, spaced further apart, allow firms to “focus on extracting the value” of each transaction while placing less pressure on the broader organisation.

Overall, the message for acquisition managers is clear: slowing the pace between deals can foster stronger performance and support long-term value creation. Rather than pursuing growth through relentless momentum, companies may benefit from adopting a more considered rhythm—one that prioritises integration, organisational stability, and strategic learning.

More information: Christopher B. Bingham et al, Experience schedules: unpacking experience accumulation and its consequences, Journal of Business Research. DOI: 10.1016/j.jbusres.2025.115749

Journal information: Journal of Business Research Provided by University of California – Riverside

From abstract threat to urgent reality: What changes when climate risk becomes personal

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A subtle shift in how climate risk is communicated—specifically, referring to a person’s own local area—can significantly boost engagement with disaster preparedness messages, according to new research from the Stockholm School of Economics and Harvard University published in Nature Human Behaviour. The findings suggest that simply making climate threats feel closer to home can prompt far greater public attention to protective guidance. For governments, insurers, and local authorities working to strengthen climate resilience, the study offers a practical and low-cost communication strategy with clear real-world potential.

The evidence comes from a large field experiment involving nearly 13,000 homeowners in bushfire-prone regions of Australia. Researchers tested whether personalised language influenced people’s willingness to seek information about wildfire safety. Participants were sent emails advising them on steps to reduce fire risk, but only some messages referred directly to the recipient’s suburb. By comparing responses, the researchers isolated the effect of localisation on behaviour.

The difference in engagement was striking. Homeowners who received emails mentioning their own suburb were twice as likely to click through for additional information on protective measures as those who received generic messages. A slight change in wording was enough to turn a distant, abstract danger into a personally relevant risk—one that felt tied to the reader’s own home and family.

“Climate risks often feel vague and far away,” explains lead author Nurit Nobel of the Stockholm School of Economics’ Centre for Sustainability Research. “By naming a person’s suburb, the message transformed an uncertain threat into something concrete and understandable. It helped people connect the risk to their own lives, which nudged them towards action.” Her comments highlight a key psychological mechanism: people are far more likely to engage when a hazard is framed as personally relevant rather than general or theoretical.

Although many climate disasters cannot be prevented, homeowners can take simple steps to reduce potential damage. These include clearing gutters, maintaining defensible space around properties, trimming vegetation, and removing flammable materials ahead of fire season. The study’s emails focused on practical, evidence-based measures, showing that actionable advice combined with personalised framing can significantly improve public engagement.

While much previous research has focused on encouraging people to reduce emissions, far fewer studies have examined how to motivate protective behaviour that helps communities adapt to climate risks already unfolding. Conducted in partnership with a central Australian bank, this study addresses that gap by testing behavioural interventions explicitly designed to support climate adaptation rather than mitigation.

The findings arrive at a critical moment. Climate-related disasters such as wildfires and floods are increasing in both frequency and cost. In the United States, the number of billion-dollar climate disasters has tripled since 1980, while parts of Europe have recorded some of the most significant wildfire impacts on record in recent years. Despite these growing threats, persuading individuals to take preventative action remains a persistent challenge—especially when risks feel distant or uncertain.

Although the absolute behavioural changes observed in the study were modest, the researchers stress that even small increases in engagement become highly meaningful when applied at scale. “In real-world settings, modest behavioural shifts can have a substantial cumulative impact,” says co-author Michael Hiscox of Harvard University. “This type of low-cost, scalable intervention can greatly extend the reach of preparedness campaigns and help people act before disaster strikes.”

The researchers now call for further testing of localised messaging across different hazards and cultural settings. They also emphasise the importance of continued collaboration between academics, industry, and public authorities. As climate risks intensify, improving how preparedness information is communicated may prove just as crucial as the information itself.

More information: Nurit Nobel et al, Enhancing climate resilience with proximal cues in personalized climate disaster preparedness messaging, Nature Human Behaviour. DOI: 10.1038/s41562-025-02352-w

Journal information: Nature Human Behaviour Provided by Stockholm School of Economics