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Language’s Impact: The Influence of CEO’s Written Statements on Investor Sentiment

A recent study presents a crucial insight for CEOs and corporate leaders aiming to establish trust with their investors: the language used to outline the company’s future strategies and the medium through which these are communicated can significantly influence investor decisions. Published in the journal Behavioral Research in Accounting, the study was co-authored by Scott C. Jackson, a professor of accounting at UNLV Lee Business School, and colleagues from the University of Massachusetts Amherst.

The research explored how different communication methods affect investor decisions by observing the reactions of 250 past and prospective MBA student investors to the same message from a CEO. The variations included written messages, videos, and audio recordings. The findings revealed that when CEOs and managers employ specific language styles—such as the present tense or active voice—to discuss future events, these events are perceived by investors as more tangible and probable. However, this effect is primarily observed in written communications.

For messages conveyed through video or audio, the impact is lessened as viewers and listeners focus more on the speaker’s vocal and facial expressions rather than the content of the words spoken. According to Jackson, “Written communication carries greater significance in this context. When individuals read about plans described using immediate language, it makes the future seem nearer and more plausible. Conversely, with video or audio, aspects such as tone or body language become more prominent, weakening the message’s effectiveness.”

This study is particularly relevant when regulatory bodies such as the U.S. Securities and Exchange Commission (SEC) and the Federal Trade Commission are intensifying their efforts to enhance transparency in financial and corporate communications. This includes initiatives to eliminate hidden fees and provide more transparent disclosures of risks. The findings also coincide with the SEC’s increasing focus on the language and tone used in public disclosures. As companies progressively use platforms like YouTube, LinkedIn, and earnings calls to share their visions, these messages’ manner and auditory quality are becoming increasingly critical.

Jackson notes, “We tend to believe that video adds a layer of professionalism and credibility. However, in terms of setting expectations for the future, written communication may in fact be more influential.” The implications of this study extend beyond Wall Street and are significant across various sectors and cities.

Take Las Vegas, a city known for its grand announcements regarding new casinos, sports arenas, and tech hubs. Each significant development is backed by a presentation to investors, city planners, and other key stakeholders. This research suggests that the mode and venue of communication could influence perceptions of feasibility or risk associated with these plans. Jackson also draws on historical examples to highlight the broader implications of media on human perception. He references the 1960 Kennedy-Nixon presidential debates, where television viewers preferred the poised, confident appearance of JFK. In contrast, radio listeners, who focused solely on the content of the discourse, felt Nixon was more convincing. This underscores the significant influence of the communication medium, suggesting that written communications hold more sway than previously considered.

More information: Scott C. Jackson et al, Does Temporal Immediacy Impact Investors’ Judgments? It Depends on Communication Mode, Behavioral Research in Accounting. DOI: 10.2308/BRIA-2023-017

Journal information: Behavioral Research in Accounting Provided by University of Nevada, Las Vegas

Energy-Efficient Artificial Intelligence Inspired by Human Brain Functionality

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Artificial Intelligence (AI) is making significant strides in performing complex calculations and analysing data more swiftly than humans. Yet, these capabilities demand substantial energy, contrasting sharply with the human brain—a marvel of efficiency that executes highly complex computational tasks while consuming minimal energy. As technology firms expand, researchers at Texas A&M University, including Dr. Suin Yi from the College of Engineering, are pioneering a transformative approach. Their development of “Super-Turing AI” mimics the human brain’s efficiency, potentially revolutionising the AI sector by integrating processes that conventional systems handle separately, thus reducing the need for extensive data transfers that current AI systems require.

Today’s AI systems, such as those developed by OpenAI, require significant computational resources and operate within vast data centres that consume enormous amounts of electricity. Dr Yi points out the stark energy disparity: these centres may consume up to a billion watts, whereas the human brain uses a mere 20 watts. The environmental impact and the high operational costs of these data centres call for a sustainable approach to AI, making it imperative to address these issues as AI becomes more integrated into our lives.

Dr. Yi and his team believe that mimicking the brain’s neural processes is the key to overcoming these challenges. Learning and memory functions are intertwined in the human brain, facilitated by synapses that enable neurons to transmit signals. These synaptic connections are modified through a process known as synaptic plasticity, which is essential for forming and altering neural circuits to store and retrieve information. This integrated approach contrasts sharply with conventional computing systems, where training and memory storage occur separately, creating inefficiencies.

Super-Turing AI is groundbreaking because it eliminates the need to transfer large amounts of data between different hardware parts. This model avoids the computationally intensive backpropagation method, which is effective but biologically implausible. Instead, it utilises mechanisms such as Hebbian learning and spike-timing-dependent plasticity, which more accurately reflect the brain’s learning processes and potentially reduce the computational power required.

The practical applications of this research are already demonstrating significant benefits. For instance, a drone equipped with a circuit based on these principles adeptly navigated a complex environment autonomously, learning and adapting in real-time. This method proved to be faster, more efficient, and less energy-intensive than traditional AI methods, showcasing the potential of Super-Turing AI to enhance operational efficiency across various fields.

The implications of Dr. Yi’s research are profound for the future of AI. As industries push to develop more capable AI models, the limitations imposed by current hardware and energy constraints become increasingly apparent. Some new AI applications may even require the construction of additional data centres, amplifying environmental and economic concerns. Looking forward, Super-Turing AI represents a critical step towards sustainable AI development. By redesigning AI architectures to emulate the human brain’s efficiency, we can address economic and environmental challenges, paving the way for a new generation of more innovative, efficient, and environmentally responsible AI. This innovative approach promises to reshape the AI landscape, ensuring that as it advances, it does so in a manner that benefits both people and the planet.

More information: Suin Yi et al, HfZrO-based synaptic resistor circuit for a Super-Turing intelligent system, Science Advances. DOI: 10.1126/sciadv.adr2082

Journal information: Science Advances Provided by Texas A&M University

Does Your Work Bring You Joy? Exploring Insights from Job Satisfaction Statistics

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Sometimes, the gloom extends well beyond the typical Monday melancholy at work. Research conducted at the University of Georgia has indicated that job dissatisfaction can persist throughout the week. This observation suggests that employers and policymakers need to consider the significant economic implications of employee happiness. Susana Ferreira, a UGA College of Agricultural and Environmental Sciences professor, employed an empirical model to explore the connections between job satisfaction, wages, and working environments. The conventional expectation is that employees are compensated fairly according to their working conditions, a concept rooted in the hedonic wage model. This optimistic assumption presupposes ideal job and labour market conditions, with workers being rational, well-informed about their work conditions, and free to switch jobs easily.

However, the study led by Ferreira takes a different approach by focusing on overall satisfaction to gain a deeper understanding of the employees and to investigate the compromises between working conditions and pay. This focus is particularly relevant in rigid job markets where workers might feel trapped. Ferreira, the study’s lead author, observed that employees who are inadequately compensated for their job risks might seek other employment opportunities. Conversely, they might tolerate lower wages if their working conditions are excellent. Despite these possibilities, the least desirable jobs are often the least well-paid, especially in inflexible job markets.

The research involved analysing data from nearly 35,000 European workers across various sectors in 30 countries. Ferreira discovered a consistent pattern: workers facing higher risks generally received lower wages than expected. Yet, the job satisfaction indicators remained effective. Workers with lower wages or those confronting higher risks and worse conditions typically reported greater dissatisfaction with their jobs.

The study also quantified the costs associated with enduring such conditions. Adjusted for U.S. dollars at the time of publication, it was found that workers would need to be compensated approximately $29 per hour to offset all perceived health and safety risks to remain satisfied with their positions. Moreover, avoiding days off due to work-related accidents was estimated to cost $362 annually, while improved workplace conditions were valued at over $12,000 annually.

“If you understand job satisfaction, you can estimate how much more you need to pay your workers to accept higher risks,” explained Ferreira. This statement highlights the crucial role of job satisfaction in the economic valuation of work conditions. The study demonstrates that higher wages and safer work environments can significantly improve worker contentment, leading to numerous benefits for the business.

Ferreira emphasised the importance of recognising workers’ emotions in her conclusions. “Paying attention to how people feel is crucial,” she remarked. Economists can access valuable, overlooked economic information by asking workers about their feelings and collecting data on subjective well-being. Acknowledging employee well-being can foster a more productive work environment, enhance individual performance, and provide broad economic benefits. Ferreira believes this study could pave the way for better methods of estimating and measuring environmental benefits and contributions to welfare in a way that informs policy decisions.

More information: Susana Ferreira et al, Measuring job risks when hedonic wage models do not do the job, Journal of Environmental Economics and Management. DOI: 10.1016/j.jeem.2025.103120

Journal information: Journal of Environmental Economics and Management Provided by University of Georgia

Leasing Apparel for Eco-Friendly Style – Targeted Markets Excel

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Renting clothes has emerged as a potential solution to the vast environmental impact of the fashion industry, which is responsible for nearly ten per cent of global greenhouse gas emissions. Despite the promise of sustainability, the transition to rental business models has faced significant challenges. The most successful examples of clothing rental ventures, particularly those focusing on specific niches such as sportswear, illustrate the importance of close collaborations with suppliers and manufacturers. This approach was highlighted in a detailed study by researchers at Chalmers University of Technology in Sweden, which provided key insights into making clothing rental both viable and successful.

In Sweden, the environmental toll of clothing is mainly attributed to the production of new items, with over 90% of the climate impact coming from this stage. Addressing this, researchers from Chalmers, the University of Borås, and the research institute Rise explored alternative business models. They aimed to mitigate the industry’s environmental damage by extending the lifecycle of clothing through rental systems, which promote the reuse of garments and reduce the need for new production.

Frida Lind, a professor at Chalmers and the study’s lead researcher, notes the commonality of rarely used clothes in people’s wardrobes. She advocates for the rental model, which, by extending garment use, encourages more sustainable consumption patterns. The research team analyzed nine Swedish companies engaged in clothing rental, identifying three main business models: the membership model, similar to a library; the subscription model, where customers pay a monthly fee; and the individual rental model, often paired with related equipment like ski gear.

Achieving profitability has proven difficult for these ventures, primarily due to the complex logistics involved in the rental process. Each garment requires thorough inspection and maintenance before it can be rented again, adding to operational costs. Issues such as warehousing, logistics, and maintenance, including laundry expenses, pose significant financial challenges, particularly for subscription-based models that require substantial initial investment and venture capital.

However, companies that targeted specific markets, like outdoor clothing, tended to perform better. These firms not only met a direct consumer need but also capitalized on their proximity to relevant recreational areas, showing that focusing on specific niches where customer demand is apparent can lead to success. Collaborations with stakeholders, primarily manufacturers and suppliers who prioritize sustainability, provided additional advantages, allowing rental companies to quickly adapt to consumer preferences and garment performance feedback.

Although not the primary focus of this study, existing research supports the environmental benefits of such business models. Production dominates the climate impact of clothing, but extended use and reduced production can significantly lessen ecological damage. Lind highlights the importance of every initiative contributing to the sustainability transition, emphasizing that even if some companies do not survive long-term, their efforts are vital in changing consumer attitudes and enhancing industry knowledge.

In conclusion, the researchers recommend focusing on niche markets with precise customer needs and establishing robust partnerships with suppliers to refine products based on rental experiences. They also emphasize the importance of integrating logistics and transport considerations early in the planning stages to ensure the scalability of the business model. This study from Chalmers University sheds light on the challenges and opportunities within the fashion rental industry. It serves as a crucial resource for decision-makers aiming to foster more sustainable practices in fashion.

More information: Frida Lind et al, Exploring renting models for clothing items – resource interaction for value creation, Journal of Business and Industrial Marketing. DOI: 10.1108/JBIM-04-2024-0281

Journal information: Journal of Business and Industrial Marketing Provided by Chalmers University of Technology

Machine Learning Algorithm Enhances Property Valuation, Benefits Market

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Housing often forms a substantial component of household wealth. Despite its critical importance, accurately measuring property value has historically posed significant challenges. Recent research highlights the influence of a well-regarded machine learning algorithm designed for pricing properties within the U.S. housing market. The findings indicate that the algorithm generally provided benefits across the market.

A collaborative team of researchers from Carnegie Mellon University, New York University, and the University of Toronto conducted this investigation. Their findings are documented in an article published in the journal Marketing Science. The research represents a pioneering effort to elucidate the ramifications of algorithm-generated property value predictions on the housing market, including economic and social outcomes. Param Vir Singh, a professor at Carnegie Mellon’s Tepper School of Business and one of the study’s co-authors, elucidated the significance of their findings, highlighting the groundbreaking nature of their work in assessing the impact of machine-generated property valuations.

Many online real estate platforms utilise proprietary algorithms that leverage vast datasets to estimate property values. These calculated values are then displayed on the platforms’ websites, providing potential buyers and sellers with critical information influencing their market decisions. This particular study focused on ‘Zestimate’, a machine learning algorithm devised by Zillow, among the foremost real estate marketplace companies in the U.S. and a pioneer in publishing algorithm-generated property valuations nationwide. The algorithm is noted for its heightened accuracy in affluent neighbourhoods compared to poorer ones, which has sparked concerns about potential increases in socioeconomic disparities.

The study examined over 4,000 property listings across 140 neighbourhoods in Pittsburgh, Pennsylvania, spanning from February to October 2019. The researchers employed a structural model of the housing market to analyse the effects of Zestimate. In this model, buyers and sellers faced uncertainties regarding property values, with Zestimate providing an unbiased indication.

The results revealed that Zestimate positively impacted both buyers and sellers, leading to an average increase in buyers’ surplus by 5.4 per cent and sellers’ profits by 4.2 per cent. This benefit was primarily attributed to the algorithm’s role in diminishing uncertainty, which allowed sellers to adopt a more patient approach, setting higher reservation prices while waiting for buyers who genuinely valued the properties. According to the study authors, this improved the quality of matches between sellers and buyers.

Moreover, the research found that Zestimate played a role in reducing socioeconomic inequality within the housing market. Both affluent and economically disadvantaged neighbourhoods gained from the algorithm, but the benefits were notably more substantial in poorer communities. For instance, the average increase in seller profits in these neighbourhoods was nearly 5 per cent, significantly higher than in mid-range and affluent areas. Similarly, the buyer surplus in poorer neighbourhoods saw an 8.3 per cent rise compared to lower percentages in other socioeconomic segments. This disparity was primarily due to more significant initial uncertainty in poorer areas, which stood to gain more from new, reliable information about property values.

However, the study also acknowledged certain limitations, including the absence of modelling scenarios where multiple buyers might engage in a bidding war, typically driving up the selling price. Furthermore, the model used in the study assumed buyers considered properties individually rather than exploring multiple listings simultaneously, which could otherwise heighten competition among sellers. Yan Huang, another co-author and associate professor at Carnegie Mellon, underscored the potential of property value prediction algorithms to mitigate uncertainty in real estate markets. Meanwhile, Kannan Srinivasan, also a professor at Carnegie Mellon, emphasised the importance of assessing the differential impacts of such algorithms, advocating for a comparative analysis of different demographic groups under scenarios with and without the algorithm’s intervention to appreciate its effects fully.

More information: Runshan Fu et al, Unequal Impact of Zestimate on the Housing Market, Marketing Science. DOI: 10.2139/ssrn.4480469

Journal information: Marketing Science Provided by Carnegie Mellon University

Enhanced Sales Through Automated Lead Nurturing: Effective Only With the Right Conditions

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Businesses globally are investing heavily in marketing automation, with many operating under the belief that Automated Lead Nurturing (ALN) is a guaranteed catalyst for sales growth. However, a compelling study published in the Journal of Marketing challenges this assumption, presenting a nuanced view of ALN’s effectiveness. The research highlights that ALN does not serve as a universal solution, and its impact on sales conversion varies considerably across different industries and customer demographics.

The study, authored by a team of academics including Johannes Habel from the University of Houston, Nathaniel Hartmann from the University of South Florida, Phillip Wiseman from Texas Tech University, Michael Ahearne from the University of Houston, and Shashank Vaid from McMaster University, delves into the conditions under which ALN can effectively guide leads through the sales funnel. It is revealed that ALN proves most advantageous for new leads, shorter sales cycles, and transactions of lower value. Conversely, in scenarios involving high-stake transactions and returning customers, the effectiveness of ALN diminishes notably.

Johannes Habel articulates a critical insight, noting that while many firms anticipate automating lead nurturing processes will directly escalate sales, the actual outcome heavily depends on specific business contexts. In some instances, ALN can markedly enhance conversion rates; in others, it may only elevate engagement levels without significantly affecting sales figures.

The study highlights several key findings. Firstly, although ALN augments interactions such as email openings and website visits, these enhanced interactions do not uniformly increase sales. The efficacy of ALN is contingent upon the business model and the customer’s profile. Automated, educational content delivered before any salesperson interaction can benefit industries characterised by brief sales cycles and low-value deals. However, its impact is limited when dealing with high-value deals or informed, repeat customers who prefer thorough research and personalised consultations over automated messages.

For business leaders utilising ALN, these insights serve as a stark reminder that the effectiveness of marketing automation is not a foregone conclusion. The researchers stress the importance of tracking substantive metrics that reflect true business impact rather than superficial indicators like click and open rates. Phillip Wiseman emphasises the need for businesses to measure the tangible outcomes of ALN, focusing on metrics that track actual revenue generation and closure rates.

The research advocates for calibrated testing of ALN within specific business environments before full-scale implementation. Companies are encouraged to assess the nature of their customer base to determine if ALN truly adds value or if their leads are already well-informed. Additionally, evaluating the complexity of the sales cycle to ascertain whether automated nurturing fulfils an informative role or if direct engagement is necessary remains crucial. Tracking actual outcomes beyond engagement metrics to gauge ALN’s influence on sales meetings and conversions is recommended. The study also cautions against an excessive dependency on ALN, especially in sectors where relationship-based selling is predominant. While automation can bolster lead engagement, it must complement human interactions rather than replace them. Michael Ahearne advises that in high-value sales scenarios, a hybrid approach that merges automation with customised conversations tends to be more effective.

In summary, as the marketing automation sector continues to expand, this study urges businesses to evaluate the deployment of ALN within their operations critically. While automation can enhance engagement, it does not necessarily translate to sales. Companies that tailor their ALN strategies to align with their unique sales processes will likely achieve more favourable outcomes than those that adopt automation tools indiscriminately. Shashank Vaid underscores the importance of the strategic application of ALN, encouraging businesses to rigorously test, refine, and measure its alignment with their customers’ needs instead of presuming its effectiveness across all leads.

More information: Shashank Vaid et al, Sales Pipeline Technology: Automated Lead Nurturing, Journal of Marketing. DOI: 10.1177/00222429251321417

Journal information: Journal of Marketing Provided by American Marketing Association

Postponing the Transition to Net Zero May Incur Considerable Economic Expenses, Recent Studies Indicate

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A new study from the University of Surrey highlights that delayed and disorderly energy transitions threaten economic and financial stability while amplifying the economic risks associated with climate change. In contrast, earlier transitions will likely be more orderly and have positive economic outcomes.

Published in Ecological Economics, the study explores various energy transition scenarios through simulation, demonstrating that initiating earlier transitions can yield more significant economic benefits than delayed, rapid changes. By examining the interplay among industries, investment patterns, and financial markets, the researchers illustrate that disorderly transitions—characterised by overly rapid changes—can precipitate economic instability and heightened financial risks.

Dr Andrew Jackson, a Senior Research Fellow at the Centre for the Understanding of Sustainable Prosperity and the study’s lead author at the University of Surrey, stressed the importance of moving towards a low-carbon economy. He pointed out that delayed and chaotic transitions could lead to adverse outcomes, including increased inflation, higher interest rates, economic stagnation, and financial instability. He underscored the need for governments to act decisively and without delay to mitigate these transition risks and the direct physical risks from climate change.

The study further suggests that strategic investments in green technologies, supported by a robust financial framework and a clear, credible transition pathway, could facilitate a smoother changeover while ensuring economic stability.

Dr Jackson elaborated on the need for governments to prioritise a communicated orderly transition, thus mitigating the risks linked with delayed and disorganised transitions. He called for re-evaluating current transition strategies to account for the economic and financial costs of delaying action and the initial costs of the transition itself. He emphasised that a comprehensive consideration of all these costs is essential to pave the way for a sustainable future without compromising economic and financial stability.

More information: Andrew Jackson et al, Macroeconomic, sectoral and financial dynamics in energy transitions: A stock-flow consistent, input-output approach, Ecological Economics. DOI: 10.1016/j.ecolecon.2024.108507

Journal information: Ecological Economics Provided by University of Surrey

Seeking Donors? Begin by Considering Their Location

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Amidst the expansion of non-profit organisations, their financial support is paradoxically decreasing. The decade from 2013 to 2023 saw a 25% increase in the number of non-profits registered with the Internal Revenue Service. However, the past year has been marked by a decline in fundraising, with a 3% drop in the amount of money raised and the number of donors. This trend highlights a growing concern about the sustainability of funding for these organisations.

Vijay Mahajan, a marketing professor at Texas McCombs, identifies the primary challenge facing non-profits as the low response rates to fundraising solicitations. He attributes this mainly to a lack of quality donor data, which complicates the targeting of fundraising appeals. Non-profits often have detailed records on active donors, such as how much and how frequently they contribute. However, the cost of gathering and maintaining similar data on potential donors, such as those who have yet to contribute, is typically prohibitive.

In his recent research, Mahajan suggests a novel approach to this problem. Instead of relying on historical donation data to forecast who might respond to fundraising appeals, non-profits could use community-clustered profiles. These profiles are built using publicly available data and can identify potential supporters’ locations based on demographic, financial, and social characteristics.

Mahajan explains that these community profiles provide deep insights into donor behaviour. By accessing secondary data sources at the state and ZIP code levels, non-profits can comprehensively understand who is likely to respond to their appeals. This method of targeting potential donors is innovative and potentially transformative for fundraising strategies.

The study categorises potential donors into three groups: current active, lapsed, and new prospects. Mahajan argues that community-clustered data can be instrumental in effectively targeting all three categories. He hypothesises that people with similar backgrounds and lifestyles tend to cluster in specific communities, which can be identified through this data.

Mahajan collaborated with Shameek Sinha from the University of Auckland, Sumit Malik from the University of Liverpool, and the late Frenkel ter Hofstede from Texas McCombs to test this theory. They used a large dataset provided by the Direct Marketing Educational Foundation, which included data on 429,310 donors who had contributed at least once over 15 years. The researchers mapped 44 different characteristics to create detailed profiles for each community, finding that factors such as gender, household size, and financial status were significant predictors of donation potential.

These findings suggest practical applications for non-profits. Fundraising teams can enhance donor data with information from community-clustered profiles to improve response rates. Where information on potential new donors is limited or costly, these profiles can help identify where these individuals are likely to reside, allowing for more targeted and cost-effective fundraising strategies. According to Mahajan, this approach not only maximises the use of available data but also significantly reduces the need for expensive data collection and analysis, making it an invaluable resource for resource-strapped non-profits.

More information: Vijay Mahajan et al, Retain, reactivate or acquire: Can nonprofits reliably use community profiles as an alternative to past donation data?, Journal of Business Research. DOI: 10.1016/j.jbusres.2024.114997

Journal information: Journal of Business Research Provided by University of Texas at Austin

Variations Between National Climate Objectives and the Public’s Readiness to Engage in Climate Initiatives

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In efforts to tackle climate change, nations that are signatories to the Paris Agreement on Climate Change must periodically present voluntary commitments detailing the percentage by which they aim to reduce their greenhouse gas emissions. These commitments are known as Percentage Reduction Pledges (PRPs). Professor Dr Heinz Welsch, an environmental economist from the University of Oldenburg in Germany, has conducted an empirical analysis to examine how these national climate commitments align with the willingness of citizens in various countries to contribute to climate change mitigation, a concept he terms Willingness to Contribute (WTC). His findings were published in the respected journal Ecological Economics.

Professor Welsch’s research highlights that significant country-specific factors influence both the PRPs and the WTC. These factors include per-capita income, levels of emissions, and average temperatures, all of which he found to correlate positively with the national climate pledges. Interestingly, these same factors correlate negatively with the citizens’ willingness to contribute, suggesting a complex relationship between national goals and individual readiness to act. Moreover, his study identified a relationship between these factors and levels of satisfaction with democracy, indicating a broader socio-political context influencing climate action.

The scope of Welsch’s study is extensive. It compares the climate targets of 123 nations, updated last in 2021, against data from the Global Climate Change Survey, which polled nearly 130,000 people across 125 countries in 2021 and 2022. A significant revelation from this survey was the strong support for climate action, with 89% of participants desiring more political intervention from their governments against climate change and 69% expressing readiness to allocate 1% of their income towards climate mitigation measures.

Welsch’s analytical model explores the interplay between cost-benefit calculations, ethical considerations, and personal preferences in shaping national climate policies and individual contributions to climate mitigation. His findings underscore higher per-capita income and emissions correlate with more ambitious governmental climate targets. However, they also reduce citizens’ willingness to contribute to climate action. The influence of average temperatures further complicates this inverse relationship—warmer countries show greater willingness among citizens to support climate action than their colder counterparts, who nonetheless set more ambitious climate targets.

Looking at Germany specifically, the data aligns with Welsch’s broader findings. Despite being one of the leaders in climate ambition, with a pledge to cut emissions by 39.7% from 2019 to 2030, German citizens show a comparatively lower willingness to contribute financially to climate action. This indicates a potential dissonance between governmental climate objectives and public readiness to support these through personal financial sacrifices.

Welsch’s study points to a critical tension inherent in the United Nations’ climate ethics, which advocate for “common but differentiated responsibilities” across nations. This principle emphasises fairness and equity but appears to clash with the realpolitik of cost-benefit analyses prevalent among the public. In colder, wealthier, and more emission-intensive countries, there tends to be a lower public willingness to engage in costly climate measures. This is partly because people in these countries may perceive that the direct impacts of climate change will be less severe, or they fear negative economic repercussions from stringent climate policies.

Furthermore, the study suggests that a discrepancy between ambitious climate targets and public willingness to support these measures correlates with lower satisfaction levels regarding democracy. This indicates a broader political challenge: advancing climate policies that require public sacrifices without fostering disenchantment or resistance that radical political forces could exploit.

Professor Welsch proposes a potential solution to this dilemma—a climate fund. This would involve using revenues from emission taxes to support economically vulnerable groups, thus mitigating the economic and social impacts of climate protection measures. Such a strategy could bridge the gap between national climate ambitions and public willingness, fostering a more harmonious approach to combating climate change while supporting democratic stability. This suggestion offers a pragmatic approach to resolving the tensions between national policies and individual actions in the face of global climate challenges.

More information: Heinz Welsch, Are national climate change mitigation pledges shaped by citizens’ mitigation preferences? Evidence from globally representative data, Ecological Economics. DOI: 10.1016/j.ecolecon.2025.108520

Journal information: Ecological Economics Provided by University of Oldenburg

Annual Economic Impact of Long COVID in the U.S. Estimated Between $2.0 and $6.5 Billion

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According to recent findings published in the Journal of Infectious Diseases, long COVID-19 cases may inflict significant financial damage on the U.S. economy, with annual costs estimated between $2.01 and $6.56 billion. This study, conducted by the Public Health Informatics, Computational, and Operations Research (PHICOR) team at the CUNY Graduate School of Public Health and Health Policy, in collaboration with the CUNY Institute for Implementation Science in Population Health and Baylor College of Medicine, utilises a sophisticated computer simulation model to analyse the economic impact of long COVID. The model estimates that each case of long COVID could result in societal costs ranging from $5,084 to $11,646, predominantly due to productivity losses.

Bruce Y. Lee, MD, MBA, the study’s senior author and a professor at CUNY SPH, emphasised the significant burden COVID imposes on society, highlighting the direct healthcare costs and substantial productivity losses affecting businesses nationwide. According to Lee, the general public could eventually bear these financial burdens through higher insurance premiums and taxes. The model developed for the study simulates scenarios where an individual of a specified age contracts the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), with varying probabilities of developing long-term COVID and experiencing a range of symptoms over time. These symptoms can significantly reduce an individual’s productivity at work or school and lead to a need for various medical interventions.

The simulation results reveal that the overwhelming majority of costs associated with long-term COVID-19—approximately 95%—stem from reduced productivity. This includes absenteeism and presenteeism, where employees are less effective at work. When extrapolated to all COVID-19 cases, the model suggests there are currently between 44.69 and 48.04 million long COVID cases in the U.S., costing the economy billions annually. The study also factors in direct medical costs, which contribute to the overall economic strain while only constituting a small fraction of the total costs (1.04%).

Peter J. Hotez, MD, PhD, professor and dean of the National School of Tropical Medicine at Baylor College of Medicine and co-author of the study, commented on the broader implications of long COVID, noting that the chronic disabilities resulting from the condition could potentially match or exceed the more immediate impacts of COVID-19, such as deaths and hospitalisations. This study not only puts into perspective the direct and indirect costs associated with long COVID but also underscores the ongoing efforts to understand and mitigate its impact on society. As the study indicates, increasing the estimated prevalence of long COVID from 6% to 10% could raise the total annual societal costs to $3.34 billion, highlighting the dynamic nature of this ongoing health and economic crisis.

More information: Peter J. Hotez et al, The Current and Future Burden of Long COVID in the United States, Journal of Infectious Diseases. DOI: 10.1093/infdis/jiaf030

Journal information: Journal of Infectious Diseases Provided by CUNY Graduate School of Public Health and Health Policy

Neural Markers of Price Perception: Unveiling the Brain’s Response to Cost Evaluation

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Russian researchers have unveiled insights into how the brain processes purchasing decisions through a study that combines electroencephalography (EEG) and magnetoencephalography (MEG). Published in the journal Frontiers in Human Neuroscience, the findings suggest that the brain reacts almost instantaneously to deviations from expected product prices. This response involves critical brain areas associated with reward evaluation and learning from past experiences, indicating that value perception is not merely a conscious choice but is significantly influenced by automatic cognitive processes.

The study was motivated by everyday experiences where consumers encounter various prices for products ranging from food to technology and services, which often appear either overpriced or suspiciously cheap. The team, comprising researchers from HSE University and the Russian neuromarketing firm Neurotrend, investigated whether the brain actively evaluates prices or if this process occurs subconsciously. During the experiment, participants were shown images of smartphones from brands like iPhone, Nokia, and Xiaomi, followed by hypothetical prices. Participants then had to decide if the subsequent labels of ‘expensive’ or ‘cheap’ accurately described these prices while their brain activity was monitored using EEG and MEG techniques.

Significant findings emerged when prices substantially differed from the market norm, triggering a strong N400 signal in the brain, an impulse usually elicited by unexpected information. The study noted that exorbitantly high prices caused a more intense N400 signal than unusually low prices, suggesting a scepticism towards unrealistic discounts. Moreover, the brand of the product influenced the brain’s response; for instance, a broader range of prices triggered the N400 signal for Xiaomi, indicating a less definitive public understanding of its market value.

Andrew Kislov, a doctoral student at HSE’s Faculty of Social Sciences and co-author of the study, reflected on his curiosity during his undergraduate days about whether it was possible to deduce acceptable pricing through brain activity. The research confirmed this possibility, highlighting the ethical considerations of such invasive methods, even though they pose no real threat to consumer privacy. Kislov’s insights suggest a cautious approach to how much researchers should intrude into an individual’s subjective perceptions.

Vasily Klucharev, the head of the International Laboratory of Social Neurobiology and chief researcher, emphasized that the brain’s immediate response to non-optimal prices links directly to regions involved in reward assessment and decision-making. This rapid, automatic response precedes conscious evaluation, suggesting that our brains are pre-wired to assess economic value long before we make a deliberate choice. This insight is crucial for understanding the foundational cognitive processes influencing purchasing decisions.

The implications of this study are significant for marketers, as noted by Anna Shestakova, Director of the HSE Institute for Cognitive Neuroscience and another chief researcher. Traditional consumer surveys often fail to capture the complexity of price perception as they can reflect conditioned responses rather than genuine opinions. By collaborating with Neurotrend, the researchers were able to delve deeper into the neurocognitive aspects of price evaluation, offering a more objective and nuanced understanding of consumer behaviour. This approach could transform how companies predict consumer reactions to new products, providing a strategic edge in the highly competitive market landscape.

More information: Aleksei Gorin et al, Neural correlates of the non-optimal price: an MEG/EEG study, Frontiers in Human Neuroscience. DOI: 10.3389/fnhum.2025.1470662

Journal information: Frontiers in Human Neuroscience Provided by National Research University Higher School of Economics

Revising the Influencer-Brand Collaboration Framework – A Novel Approach for Achieving Success

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Influencer marketing has dramatically transformed how brands connect with their audiences, yet it often fails to reach its full potential due to systemic issues. A recent study published in the Journal of Marketing investigates these challenges, specifically the power imbalances that frequently undermine these collaborations—titled “Sponsored Content as an Epistemic Market Object: How Platformization of Brand–Creator Partnerships Disrupts Valuation, Coproduction, and the Relationship Between Market Actors,” the research was conducted by Zeynep Arsel of Concordia University, Maria Carolina Zanette of NEOMA Business School, and Carolina da Rocha Melo of the ALDO Group. Their work provides a deep dive into the dynamics of sponsored content and highlights why traditional influencer marketing strategies often fail to generate long-term value.

The study sheds light on the importance of authenticity and audience trust influencers bring to brand collaborations. Zeynep Arsel notes that when brands impose too much control, it diminishes the value that influencers add. She says, “Our research indicates that these imbalances frequently disadvantage both creators and brands.” This control not only stifles the creative freedom of influencers but also devalues their expertise, ultimately affecting the authenticity that underpins effective influencer marketing.

One significant insight from the research is the negative impact of power imbalances and short-term focus within these partnerships. Brands often script influencer content and focus on immediate metrics like reach or sales, undermining the influencers’ creative contributions. Furthermore, influencers feel compelled to meet brand expectations, sometimes resorting to unethical practices like buying fake followers. This leads to a cycle of mistrust, where brands escalate surveillance and control, further weakening the partnership.

The study also discusses the broader implications of short-term thinking in influencer marketing. Focusing primarily on sales and immediate engagement metrics overlooks the potential for building brand loyalty and deeper audience connections. Maria Carolina Zanette emphasizes, “These collaborations work best when both parties trust each other and align on long-term goals. Short-term thinking can undermine these partnerships, hurting everyone involved.”

Finally, the research offers actionable recommendations for brands and influencers aiming to improve their collaborations and for brands, recognizing the independence of influencers, shifting focus from short-term metrics to long-term outcomes like audience loyalty, and fostering trust by treating influencers as equal partners. For influencers, Carolina da Rocha Melo advises, “Brands need to respect influencers as creators with their own audiences and goals. At the same time, influencers must professionalize their operations to balance creative freedom with business demands.” These strategies can help cultivate more equitable and prosperous partnerships in the influencer marketing industry.

More information: Zeynep Arsel et al, Sponsored Content as an Epistemic Market Object: How Platformization of Brand–Creator Partnerships Disrupts Valuation, Coproduction, and the Relationship Between Market Actors, Journal of Marketing. DOI: 10.1177/00222429241296459

Journal information: Journal of Marketing Provided by American Marketing Association