Global Survey: 66% Say 2025 Bad Year for Country, 71% Optimistic 2026 Will Be Better

Ipsos surveyed 23,642 adults (under the age of 75) across 30 countries between 27 October and 4 November 2025. The survey found that 50% of respondents said 2025 was a bad year for them and their family. At the national level, 66% of respondents said 2025 was a bad year for their country, with the highest percentages reported in France (85%), South Korea (85%), and Türkiye (80%).

Looking ahead, 71% of respondents expressed optimism that 2026 will be better than 2025. Countries with the highest optimism included Indonesia (90%), Colombia (89%), and Chile (86%), while France (41%), Japan (44%), and Belgium (49%) reported the lowest optimism.

Country	% agree	% disagree
30-country avg.	71	29
Indonesia	90	10
Colombia	89	11
Chile	86	14
Thailand	86	14
Peru	86	14
India	85	15
Argentina	83	17
South Africa	82	18
Mexico	82	18
Malaysia	82	18
Brazil	80	20
Hungary	77	23
Poland	74	26
Romania	70	30
Canada	70	30
Spain	69	31
Sweden	68	32
Singapore	67	33
Netherlands	67	33
United States	66	34
Australia	66	34
South Korea	65	35
Türkiye	63	37
Ireland	63	37
Great Britain	58	42
Germany	57	43
Italy	57	43
Belgium	49	51
Japan	44	56
France	41	59

On economic expectations, 49% of respondents predicted a stronger global economy in 2026, while 51% expected it to be worse.

The report also notes that in 2020, 90% of average respondents globally said their country had a bad year, reflecting the height of the COVID-19 pandemic. Current optimism levels remain below pre-2022 figures.

Source: Ipsos Predictions 2026 Report

Read next:

• How Schema Markup Is Redefining Brand Visibility in the Age of AI Search, According to Experts at Status Labs

• How ChatGPT could change the face of advertising, without you even knowing about it
by Ayaz Khan via Digital Information World

How Schema Markup Is Redefining Brand Visibility in the Age of AI Search, According to Experts at Status Labs

The way brands are discovered, evaluated, and recommended has fundamentally changed. As AI platforms like ChatGPT, Google's Gemini, and Perplexity increasingly mediate the relationship between businesses and their audiences, the technical infrastructure behind digital reputation has become just as important as the content itself. At the center of this shift is schema markup, a structured data framework that serves as a translation layer between your digital presence and the AI systems now shaping public perception.

The Growing Importance of Machine-Readable Branding

When a potential customer, investor, or partner asks an AI assistant about your company, the response depends on whether that AI system can accurately identify, understand, and trust your brand. Unlike traditional search engines that present links for users to evaluate, AI platforms synthesize information and deliver direct answers. This creates a fundamental challenge: if your brand's information isn't structured in ways that AI systems can reliably interpret, you risk being misrepresented, conflated with competitors, or excluded from responses entirely.

According to research from Schema App, Microsoft's Fabrice Canel, Principal Product Manager at Bing, confirmed at SMX Munich in March 2025 that schema markup directly helps Microsoft's large language models understand web content. This represents one of the first official confirmations from a major AI platform that structured data influences how LLMs process and present information.

The implications extend beyond simple visibility. Studies indicate that pages with comprehensive schema implementation are significantly more likely to appear in AI-generated summaries. A benchmark study from Data World found* that LLMs grounded in knowledge graphs achieve 300% higher accuracy compared to those relying solely on unstructured data. For brands, this accuracy translates directly into reputation protection and opportunity capture.

Understanding Schema Markup as Digital Identity Infrastructure

Schema markup uses standardized vocabulary from Schema.org to explicitly label elements on web pages that AI systems prioritize: organizational information, reviews, author credentials, products, and services. Rather than forcing AI models to infer meaning from unstructured text, this structured data provides explicit signals about what your content represents and how different elements relate to each other.

Google's own documentation states that structured data helps search systems understand page content by providing explicit clues about meaning. This guidance has taken on new significance as Google's AI Overviews and Gemini increasingly rely on the Knowledge Graph, which is enriched by schema markup crawled from the web.

The digital reputation management firm Status Labs has emerged as a leading voice on this topic, developing comprehensive frameworks for how businesses should approach structured data in an AI-dominated landscape. Their research indicates that company websites optimized with Organization schema and connected entity markup represent the most controllable authoritative source for AI training data. As Status Labs explains in their detailed analysis of schema markup's role in AI reputation, implementing structured data that signals contextual relationships to AI platforms is essential for preventing entity confusion that damages digital reputation.

The Entity Recognition Challenge

One of the most significant reputation risks in the AI era involves entity recognition, the process by which AI platforms distinguish between concepts sharing identical names. When someone asks an AI assistant about your company, the system must determine whether you're the technology firm based in Austin or the manufacturing company with the same name in Ohio.

Without Organization schema establishing your company as a distinct legal entity with specific founding dates, locations, and verifiable credentials, AI systems may merge information about different organizations into a single, confused representation. This creates scenarios where achievements are attributed to competitors or negative information about unrelated entities appears in responses about your business.

Status Labs has documented cases where proper schema implementation resolved significant entity confusion issues. Their GEO (Generative Engine Optimization) practice focuses specifically on these challenges, helping clients establish clear digital identities that AI systems can accurately recognize and represent.

The "sameAs" property in Organization schema proves particularly valuable here, linking your official website to verified profiles on LinkedIn, Crunchbase, and other authoritative platforms. This creates a network of corroborating signals that AI systems use to validate your identity and distinguish you from similarly named entities.

Performance Data: Schema's Measurable Impact

Research from BrightEdge demonstrates that schema markup improves brand presence and perception in Google's AI Overviews, with higher citation rates observed on pages with robust structured data. A recent analysis** also found that 72% of sites appearing on Google's first page search results use schema markup, indicating a strong correlation between structured data and visibility.

The stakes have increased substantially as AI Overviews reduce traditional organic clicks*** by approximately 34.5% year-over-year. Businesses not appearing in AI-generated summaries face accelerating invisibility as users increasingly accept AI responses without clicking through to websites.

An AccuraCast study**** analyzing over 2,000 prompts across ChatGPT, Google AI Overviews, and Perplexity found that 81% of web pages receiving citations included schema markup. While correlation doesn't prove causation, the data suggests that structured data plays a meaningful role in determining which sources AI platforms reference. Notably, ChatGPT showed particular preference for Person schema, with 70.4% of cited sources including this markup type, reflecting the platform's emphasis on source authority and reliability.

Critical Schema Types for Reputation Management

Different schema types serve distinct reputation management functions. Understanding which to prioritize depends on your specific visibility and protection goals.

Organization Schema consolidates business information into formats that AI platforms trust. This includes legal name, logo, founding date, official addresses, contact information, and social media profiles. Status Labs' detailed analysis outlines how implementing a comprehensive Organization schema across all digital properties creates the foundation for accurate AI representation.

Person Schema prevents the misattribution that damages executive and professional reputation. When multiple individuals share identical names, this markup defines biographical information, professional credentials, affiliations, and accomplishments, distinguishing separate careers and ensuring accurate attribution.

Review and AggregateRating Schema directly impact AI trustworthiness assessments. AI systems weigh verified customer feedback heavily when generating recommendations. Properly structured review markup must match visible page content exactly, as AI platforms detect and penalize mismatched data.

Article and BlogPosting Schema establish content authority and topical expertise. These schemas identify authors, publication dates, and subject matter, helping AI systems attribute information correctly and recognize your organization as an authoritative voice on specific topics.

Building Connected Knowledge Graphs

Basic schema provides value, but connected schema creates compounding advantages. As Search Engine Journal reports, enterprises are increasingly viewing structured data not merely as rich result eligibility criteria but as the foundation for content knowledge graphs.

This approach establishes relationships between entities on your website and links them to external authoritative knowledge bases, including Wikidata, Wikipedia, and Google's Knowledge Graph. When AI systems encounter your content, the connected schema provides comprehensive context about relationships between your products, services, team members, and broader industry concepts.

Status Labs' five-pillar approach to AI reputation management places schema implementation within this comprehensive framework. The methodology optimizes corporate websites as primary authoritative sources while establishing authoritative third-party references and managing review ecosystems with properly structured data.

Platform-Specific Considerations

Different AI platforms process schema markup according to their unique architectures and data sources. Understanding these variations enables targeted optimization.

Google's AI Overviews and Gemini prioritize websites with a comprehensive schema that contributes to Google's Knowledge Graph. Recent data shows that 80% of AI Overview citations come from top-3 organic results, but among those results, pages with well-implemented schema receive preferential selection.

ChatGPT with SearchGPT combines real-time web search with language model capabilities. While ChatGPT doesn't require schema to understand content, research suggests it retrieves information more thoroughly and accurately from pages with structured data. Schema reduces hallucinations by providing factual anchors that ground AI responses.

Perplexity AI explicitly values structured data's role in identifying reliable sources. Pages with robust schema markup appear more frequently in Perplexity's cited sources because the platform prioritizes well-defined, machine-readable information.

Common Implementation Errors

Several schema implementation mistakes can undermine or damage AI reputation rather than enhance it.

Mismatched Data represents the most damaging error. Discrepancies between visible page content and schema markup cause AI systems to question credibility. If your website displays a 4.8-star rating but schema markup shows a different figure, AI platforms may penalize or exclude your pages.

Incomplete Entity Definitions miss opportunities for AI recognition. Implementing Organization schema without comprehensive properties like founding date, leadership, and external profile links reduces AI confidence in your entity definition.

Static Schema on Dynamic Content creates accuracy problems over time. Businesses with changing inventory or pricing need systems that automatically update schema when underlying data changes.

Schema Manipulation backfires as AI detection improves. Adding irrelevant keywords or inaccurate information to structured data triggers penalties that compound over time.

The Strategic Imperative

Schema markup's value compounds as AI systems incorporate structured data into their understanding of the digital landscape. Organizations implementing comprehensive schema today establish authoritative representations that become increasingly difficult for competitors to displace.

This dynamic mirrors earlier digital transformations. Early adopters of mobile optimization gained advantages that persisted for years. With AI platforms already controlling significant information discovery, the window for establishing schema-based authority continues to narrow.

Status Labs' analysis shows that businesses with comprehensive schema markup maintain visibility across current and emerging AI search technologies, while competitors without structured data face accelerating invisibility. As the firm notes, schema markup has evolved from an optional technical enhancement to a foundational requirement for any organization serious about managing how AI systems understand, evaluate, and represent their brand.

Beyond Visibility: Schema as Reputation Protection

Schema markup functions as insurance against reputation damage that occurs when AI systems misunderstand, misidentify, or misrepresent your organization. By explicitly defining your entity with verifiable attributes and establishing connections to authoritative external sources, you reduce the probability of harmful misattribution.

This protective function becomes critical as AI systems increasingly mediate first impressions. When stakeholders query AI platforms about your company, the generated response shapes perceptions before any human visits your website. Accurate, comprehensive schema markup ensures these AI-generated first impressions align with reality.

The businesses and individuals investing in sophisticated schema strategies position themselves for an information environment where reputation depends on machine readability. For those seeking to understand how to implement these strategies effectively, Status Labs' comprehensive guide on schema markup's role in AI reputation provides detailed implementation frameworks and case studies demonstrating measurable impact.

As AI continues reshaping how information is discovered and presented, the organizations that control their structured data narrative will maintain the ability to shape their own story in an increasingly AI-mediated world.

* https://ift.tt/cWlArmB

** https://ift.tt/XGcRgp8

*** https://ift.tt/HVtDyjF

**** https://ift.tt/XHpKScD

by Sponsored Content via Digital Information World

How ChatGPT could change the face of advertising, without you even knowing about it

Nessa Keddo, King's College London

Image: DIW-Aigen

Online adverts are sometimes so personal that they feel eerie. Even as a researcher in this area, I’m slightly startled when I get a message asking if my son still needs school shirts a few hours after browsing for clothes for my children.

Personal messaging is part of a strategy used by advertisers to build a more intense relationship with consumers. It often consists of pop-up adverts or follow-up emails reminding us of all the products we have looked at but not yet purchased.

This is a result of AI’s rapidly developing ability to automate the advertising content we are presented with. And that technology is only going to get more sophisticated.

OpenAI, for example, has hinted that advertising may soon be part of the company’s ChatGPT service (which now has 800 million weekly users). And this could really turbocharge the personal relationship with customers that big brands are desperate for.

ChatGPT already uses some advanced personalisation, making search recommendations based on a user’s search history, chats and other connected apps such as a calendar. So if you have a trip to Barcelona marked in your diary, it will provide you – unprompted – with recommendations of where to eat and what to do when you get there.

In October 2025, the company introduced ChatGPT Atlas, a search browser which can automate purchases. For instance, while you search for beach kit for your trip to Barcelona, it may ask: “Would you like me to create a pre-trip beach essentials list?” and then provide links to products for you to buy.

“Agent mode” takes this a step further. If a browser is open on the page of a swimsuit, a chat box will appear where you can ask specific questions. With the browser history saved, you can log back in and ask: “Can you find that swimsuit I was looking at last week and add it to the basket in a size 14?”

Another new feature (only in the US at the moment), “instant checkout”, is a partnership with Shopify and Etsy which allows users to browse and immediately purchase products without leaving the platform. Retailers pay a small fee on sales, which is how OpenAI monetises this service.

However, only around 2% of all ChatGPT searches are shopping-related, so other means of making money are necessary – which is where full-on incorporated advertising may come in.

One app, lots of ads?

OpenAI’s rapid growth requires heavy investment, and its chief financial officer, Sarah Friar, has said the company is “weighing up an ads model”, as well as recruiting advertising specialists from rivals Meta and Google.

But this will take some time to get right. Some ChatGPT users have already been critical of a shopping feature which they said made them feel like they were being sold to. Clearly a re-design is being considered, as the feature was temporarily removed in December 2025.

So there will continue to be experimentation into how AI can be part of what marketers call the “consumer journey” – the process customers go through before they end up buying something.

Some consumers prefer to use customer reviews and their own research or experience. Others appreciate AI recommendations, but studies suggest that overall, some sense of autonomy is essential for people to truly consider themselves happy customers. It has also been shown that audiences dislike aggressive “retargeting”, where they are continuously bombarded with the same adverts.

So the option of ChatGPT automatically providing product recommendations, summaries and even purchasing items on our behalf might seem very tempting to big brands. But most consumers will still prefer a sense of agency when it comes to spending their money.

This may be why advertisers will work on new ways to blur the lines – where internet search results are blended with undeclared brand messaging and product recommendations. This has long been the case on Chinese platforms such as WeChat, which includes e-commerce, gaming, messaging, calling and social networking – but with advertising at its core.

In fact, platforms in the west seem far behind their East Asian counterparts, where users can do most of their day-to-day tasks using just one app. In the future, a similarly centralised approach may be inevitable elsewhere – as will subliminal advertising, with the huge potential for data collection that a single multi-functional app can provide.

Ultimately, transparency will be minimal and advertising will be more difficult to recognise, which could be hard on vulnerable users – and not the kind of ethically responsible AI that many are hoping for.

Nessa Keddo, Senior Lecturer in Media, Diversity and Technology, King's College London

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Read next: Shrinking AI memory boosts accuracy

by External Contributor via Digital Information World

Shrinking AI memory boosts accuracy

Researchers have developed a new way to compress the memory used by AI models to increase their accuracy in complex tasks or help save significant amounts of energy.

Image: Luke Jones / Unsplash

Experts from University of Edinburgh and NVIDIA found that large language models (LLMs) using memory eight times smaller than an uncompressed LLM scored better on maths, science and coding tests while spending the same amount of time reasoning.

The method can be used in an alternative way to help LLMs respond to more user queries simultaneously, reducing the amount of power needed per task.

As well as energy savings, experts say the improvements could benefit AI systems that are used to solve complicated tasks or in devices that have slow or limited memory, such as smart home devices and wearable technology.

Problem solving

By “thinking” about more complex hypotheses or exploring more hypotheses concurrently, AI models improve their problem-solving abilities. In practice, this is achieved by generating more reasoning threads – a step-by-step logical process used to solve problems – in text form.

The model’s memory – called the KV cache – which stores the portions of the threads generated, can act as a bottleneck, as its size slows down the generation of reasoning thread outputs during inference – the process by which AI models respond to an input prompt, such as answering a user query.

The more threads there are, and the longer they are, the more memory is required. The larger the memory size used, the longer the LLM takes to retrieve the KV cache from the part of the AI device where it is stored.

Memory compression

To overcome this, the team developed a method to compress the models’ memory – called Dynamic Memory Sparsification (DMS). Instead of keeping every token – the units of data that an AI model processes – DMS decides which ones are important enough to keep and which ones can be deleted.

There is a slight delay between the time when the decisions to delete tokens using sparsification are made and when they are removed. This gives the model a chance to pass on any valuable information from the evicted tokens to preserved ones.

In managing which tokens to keep and which to discard, DMS lets the AI model "think” in more depth or explore more possible solutions without needing extra computer power.

Models tested

The researchers tested DMS on different versions of the AI models Llama and Qwen and compared their performance to models without compression.

The models’ performance was assessed using standardised tests. It was found even with memories compressed to one eighth their original size, LLMs fully retain their original accuracy in difficult tasks while accelerating reasoning compared with non-compressed models.

In the standardised maths test AIME 24, which served as the qualifier for the United States Mathematical Olympiad, the compressed models performed twelve points better on average using the same number of KV cache reads to produce an answer.

For GPQA Diamond – a series of complex questions in biology, chemistry and physics authored by PhD-level experts – the models performed over eight points better.

The models were also tested with LiveCode Bench, which measures how well AI models can write code. The compressed models scored on average ten points better than non-compressed models.

In a nutshell, our models can reason faster but with the same quality. Hence, for an equivalent time budget for reasoning, they can explore more and longer reasoning threads. This improves their ability to solve complex problems in maths, science, and coding.

Dr Edoardo Ponti - GAIL Fellow and Lecturer in Natural Language Processing at the University’s School of Informatics

The findings from this work were peer reviewed and were presented at the prestigious AI conference NeurIPS.

Dr Ponti and his team will continue to investigate ways how large AI systems represent and remember information, making them far more efficient and sustainable as part of a 1.5 million euros European Research Council-funded project called AToM-FM.

This article has been republished on DIW with permission from The University of Edinburgh.

Read next:

• Subnational income inequality revealed: Regional successes may hold key to addressing widening gap globally

• Why many Americans avoid negotiating, even when it costs them

by External Contributor via Digital Information World

Subnational income inequality revealed: Regional successes may hold key to addressing widening gap globally

A new study visualises three decades of income inequality data, the most comprehensive worldwide mapping to be done at a subnational level. Confirming worsening income inequality for areas with over 3.6 billion inhabitants, it also reveals hidden ‘bright spots’ where policy may be closing the gap.

Income inequality is one of the most important measures of economic health, social justice and quality of life. More reliably trackable than wealth inequality, which was recently given a gloomy report card by the G20, income inequality is particularly relevant to immediate economic relief, mobility and people’s everyday standard of living.

The new study, from an international team led by Aalto University and Cambridge University, is the first to comprehensively map three decades of income inequality data within 151 nations around the world. Despite finding that income inequality is worsening for half the world’s people, the study also indicates that effective policy may be helping to bridge the gap in regions such as Latin America — ‘bright spots’ in administrative areas that account for around a third of the global population.

‘This research gives us much more detail than the existing datasets, allowing us to zoom in on specific regions within countries,’ says one of the study’s lead authors, Professor Matti Kummu, from Aalto University.‘This is significant because in many countries national data would tell us that inequality has not changed much over the past decades, while subnational data tells a very different story.’

‘The new data is particularly relevant in light of recent failings around wealth inequality, given that it could help shed light on what policy levers might be pulled to address inequality in the short-term,’ says co-lead author Daniel Chrisendo, now an Assistant Professor at Cambridge University.

‘We have vastly more complete data on income than we do on wealth, which tends to be much harder to uncover and track,’ explains Chrisendo. ‘Especially given that income inequality leads to wealth inequality, it’s critical to tackle both forms — but income inequality is perhaps the easiest to address from an immediate policy perspective.’

The study was published in Nature Sustainability on 5th December, and the new global subnational Gini coefficient (SubNGini) dataset, spanning 1990-2023, is publicly accessible online. Global annual data and trends can be explored visually using the Online Tool, which enables users to explore how income inequality has played out in regions around the globe and also download the data for further analyses.

Pinpointing the role of policy

There are many examples where regional efforts have shone more brightly than is revealed by national statistics, say the researchers. However India, China and Brazil all present interesting case studies that affect large swathes of the global population.

‘With regards to India, relative success in the south is linked to sustained investments in public health, education, infrastructure and economic development that have benefited the local population more broadly,’ says Chrisendo.

Meanwhile, in China, market-oriented reforms and open-door policy have driven economic growth and dramatically reduced poverty since the 1990s. ‘But we can also see how this growth has been uneven, likely due to the Chinese government’s ‘Hukou’ policy limiting rural migrants' access to urban services,’ he explains. In response, the government has implemented various policy measures — such as regional development programs and relaxed Hukou restrictions — to address disparities and support internal migrants.

In Brazil, the mapping shows a potential correlation between reduced inequality and a regional cash transfer programme providing cash to poor families on condition of their children attending school and receiving vaccinations.

‘Overall, being able to visualise these success stories and pinpoint the changing trends in time could help decision-makers see what works,’ says Chrisendo.

Income inequality rising for half the world’s people

Relative income growth for the world’s poorest 40 percent is one of the UN’s Sustainable Development Goals (SDGs), yet the study confirms the collective failure to meet this goal by 2030. ‘Unfortunately, not only are we quite far from that goal, but the trend for rising inequality is actually stronger than we thought,’ says Kummu.

The researchers are now expanding the data visualisation to encompass a vast range of other socio-economical indicators, from how populations are aging, to life expectancy and time spent in schooling, to improved access to drinking water — with the extensive new datasets slated for public launch in 2026.

As an expert in global food systems and sustainable use of natural resources, Kummu hopes the new datasets can be used to better understand, for example, the linkages between development and environmental changes. The recent study revealed links between more unequal regions and lower ecological diversity, which he would like to explore further.

‘It’s ambitious, but to have subnational, high quality data spanning over three decades is crucial to understand different social responses to environmental changes and vice versa. It gives us the means to start understanding the causalities, not just the correlations — and with that comes the power to make better decisions,’ he concludes.

Matti Kummu - Professori - T213 Built Environment - matti.kummu@aalto.fi - +358504075171

Daniel Chrisendo - Assistant Professor in Rural and Agricultural Economics, University of Cambridge - dc951@cam.ac.uk

More:

• Full article in Nature Sustainability
• Datasets: Income inequality and gross national income per capita 1990-2023
• Online tool: Income inequality explorer

Editor's Note: This article has been republished on DIW with permission from Aalto University. Original publication date: December 4, 2025.

Read next:

• Why many Americans avoid negotiating, even when it costs them

• How U.S. Employees Report Using AI at Work

by External Contributor via Digital Information World

Why many Americans avoid negotiating, even when it costs them

Would you pay thousands of dollars more for a car just to skip the negotiation process? According to new research by David Hunsaker, clinical associate professor of management at the IU Kelley School of Business Indianapolis, many Americans would—and do.

Image: Cytonn Photography / Unsplash

How common is this mindset?

“Across five studies, we found that 95% of individuals choose not to negotiate up to 51% of the time,” Hunsaker explained. This means negotiation avoidance is not the exception, rather the norm.

The research, published in Negotiation and Conflict Management Research, was conducted by Hunsaker in collaboration with Hong Zhang of Leuphana University and Alice J. Lee of Cornell University. Their work explores why people avoid negotiating, what it costs them, and how organizations can respond.

Negotiation avoidance is the norm, not the exception

This study spans five large-scale experiments exploring why people avoid negotiating and what it costs them. The research examines:

• How often individuals forgo negotiation opportunities
• The Threshold for Negotiation Initiation (TFNI)—the minimum savings people need to justify negotiating
• The Willingness to Pay to Avoid Negotiation (WTP-AN)—how much extra people will pay to skip negotiating
• Whether interventions, such as utility comparisons or social norm prompts, can reduce avoidance

“Our work focuses on how much individuals are willing to sacrifice, or even pay, to avoid negotiating altogether,” David explained.

The idea for this research emerged at a negotiation conference in Israel. Hunsaker and his colleagues visited a market where bargaining is expected, yet none of them negotiated. “We asked ourselves: Why don’t people negotiate even when the opportunity is clear?” Hunsaker recalled.

“We framed this research around a simple question: When you have the chance to negotiate, will you?” Hunsaker said. “Even in traditional contexts like buying a car, companies now advertise ‘no-haggle pricing’ as a selling point. Businesses can raise prices by 5% to 11%, and more than half of consumers will pay it.”

The research also revealed that people judge negotiation value by percentage saved, not the absolute dollar amount.

“On average, participants needed savings of 21% to 36% of an item’s price before considering negotiation worthwhile,” Hunsaker noted. “This shows that decisions are driven by perceived proportional value—not absolute dollars.”

Hunsaker hopes the findings spark awareness. “Negotiation aversion is real, but at key points in your career, negotiation skills matter,” he emphasized. “Recognizing these tendencies is the first step toward overcoming them.”

Negotiation tips from the expert

To help you become a better negotiator, here are three tips from Dr. Hunsaker:

Preparation is everything

“Most of the work happens before the conversation begins,” Hunsaker said. “Information is power. Know your options and be honest about whether you have strong alternatives. If you don’t, you’ll enter with less leverage. Many people overlook this step—understand your position before you negotiate.”

Start higher than your target

“This is hard for a lot of people because you don’t want to sound selfish, but there needs to be room for concessions. If you don’t make that room, the other party will become upset. Start with an offer better than your goal and it will help the other party feel more satisfied with the deal.” Hunsaker shared.

Focus on relationships, not victory

“It’s about developing strong relationships. People that go into negotiation with a winning mindset end up burning bridges or hurting feelings. The people you most often negotiate with will be repeat customers or longtime clients. If you burn those bridges, you will miss out on deals later. Focus on doing well but also focus on listening to the other party and creating a foundation of trust,” Hunsaker said.

David Hunsaker is a clinical associate professor of management at the Kelley School of Business Indianapolis. He joined the faculty in 2024 and specializes in organizational behavior and negotiation.

This article was first published on the Indiana University Kelley School of Business website on December 16, 2025. Republished with permission.

Read next: 

• Most Data Centers Are Located Outside Recommended Temperature Ranges

• How U.S. Employees Report Using AI at Work

by External Contributor via Digital Information World

How U.S. Employees Report Using AI at Work

As workplaces increasingly integrate emerging technologies, understanding how people actually employ these tools provides crucial insight into their practical value and evolving role in professional settings.

A Gallup workforce survey conducted in 2025 found that employees who used artificial intelligence (AI) at work reported using it for information-related and idea-generation purposes. Among U.S. employees surveyed in the second quarter of 2025 who said they used AI at least yearly, 42% reported using it to consolidate information, while 41% said they used it to generate ideas. Another 36% reported using AI to support learning new things. Gallup noted that these reported uses did not change meaningfully from its initial measurement in the second quarter of 2024.

When asked about the types of AI tools they used in their role, more than six in ten AI-using employees reported using chatbots or virtual assistants. AI-powered editing and writing tools were the next most commonly reported, followed by AI coding assistants. Use of more specialized tools, including those designed for data science or analytics, was less common overall but more frequently reported by employees who used AI at work more often.

AI Use	Percentage Selected
To consolidate information or data	42%
To generate ideas	41%
To learn new things	36%
To automate basic tasks	34%
To identify problems	20%
To interact/transact with customers	13%
To collaborate with coworkers	11%
Other	11%
To make predictions	9%
To set up, operate, or monitor complex equipment or devices	8%

AI Use	Percentage Selected
Chatbots or virtual assistants	61%
AI writing and editing tools	36%
AI coding assistants	14%
Image, video, or audio generators	13%
Data science or analytics tools	13%
Task, scheduling, or project management tools	13%
Meeting assistants or transcription tools	12%
Presentation or slide deck tools	10%
AI-powered search or research tools	10%
Email or communication management tools	9%
Knowledge or information management tools	8%
Automation or robotic process automation (RPA) tools	5%
Other	4%

Gallup also reported that in the third quarter of 2025, 45% of U.S. employees said they used AI at work at least a few times a year, while daily use remained limited to about 10% of the workforce.

When tools make it easier to learn, solve problems, or work more effectively, they earn their place in daily practice.

Notes: This post was drafted with the assistance of AI tools and reviewed, edited, facted-checked and published by humans.

Read next:

• Most Data Centers Are Located Outside Recommended Temperature Ranges

• Resolve to stop punching the clock: Why you might be able to change when and how long you work
by Asim BN via Digital Information World

Most Data Centers Are Located Outside Recommended Temperature Ranges

Data center placement influences electricity demand and cooling requirements, which are documented factors in energy system planning.

An analysis by Rest of World found that a majority of the world’s operational data centers are located in climates outside the industry’s recommended temperature range.

The analysis combined climate records from the Copernicus Climate Data Store with facility location data from Data Center Map, covering 8,808 operational data centers worldwide as of October 2025.

Industry standards recommend average operating temperatures between 18°C and 27°C. Nearly 7,000 data centers were located outside that range, with most situated in regions cooler than recommended. About 600 data centers, representing less than 10% of the total, were located in areas with average annual temperatures above 27°C. In 21 countries, including Nigeria, Singapore, Thailand, and the United Arab Emirates, all operational data centers were located in regions exceeding the recommended temperature range.

The findings draw attention to the operational strain associated with cooling data centers in hotter climates.

Notes: This post was drafted with the assistance of AI tools and reviewed, edited, and published by humans. Read next: Image: DIW-Aigen

Read next: Resolve to stop punching the clock: Why you might be able to change when and how long you work
by Ayaz Khan via Digital Information World

Resolve to stop punching the clock: Why you might be able to change when and how long you work

Jennifer Tosti-Kharas, Babson College and Christopher Wong Michaelson, University of St. Thomas

Image: Luis Villasmil / Unsplash

About 1 in 3 Americans make at least one New Year’s resolution, according to Pew Research. While most of these vows focus on weight loss, fitness and other health-related goals, many fall into a distinct category: work.

Work-related New Year’s resolutions tend to focus on someone’s current job and career, whether to find a new job or, if the timing and conditions are right, whether to embark on a new career path.

We’re an organizational psychologist and a philosopher who have teamed up to study why people work – and what they give up for it. We believe that there is good reason to consider concerns that apply to many if not most professionals: how much work to do and when to get it done, as well as how to make sure your work doesn’t harm your physical and mental health – while attaining some semblance of work-life balance.

How we got here

Most Americans consider the 40-hour workweek, which calls for employees being on the job from nine to five, to be a standard schedule.

This ubiquitous notion is the basis of a hit Dolly Parton song and 1980 comedy film, “9 to 5,” in which the country music star had a starring role. Microsoft Outlook calendars by default shade those hours with a different color than the rest of the day.

This schedule didn’t always reign supreme.

Prior to the Great Depression, which lasted from 1929-1941, 6-day workweeks were the norm. In most industries, U.S. workers got Sundays off so they could go to church. Eventually, it became customary for employees to get half of Saturday off too.

Legislation that President Franklin D. Roosevelt signed into law as part of his sweeping New Deal reforms helped establish the 40-hour workweek as we know it today. Labor unions had long advocated for this abridged schedule, and their activism helped crystallize it across diverse occupations.

Despite many changes in technology as well as when and how work gets done, these hours have had a surprising amount of staying power.

Americans work longer hours

In general, workers in richer countries tend to work fewer hours. However, in the U.S. today, people work more on average than in most other wealthy countries.

For many Americans, this is not so much a choice as it is part of an entrenched working culture.

There are many factors that can interfere with thriving at work, including boredom, an abusive boss or an absence of meaning and purpose. In any of those cases, it’s worth asking whether the time spent at work is worth it. Only 1 in 3 employed Americans say that they are thriving.

What’s more, employee engagement is at a 10-year low. For both engaged and disengaged employees, burnout increased as the number of work hours rose. People who were working more than 45 hours per week were at greatest risk for burnout, according to Gallup.

However, the average number of hours Americans spend working has declined from 44 hours and 6 minutes in 2019 to just under 43 hours per week in 2024. The reduction is sharper for younger employees.

We think this could be a sign that younger Americans are pushing back after years of being pressured to embrace a “hustle culture” in which people brag about working 80 and even 100 hours per week.

Critiques of ‘hustle culture’ are becoming more common.

Fight against a pervasive notion

Anne-Marie Slaughter, a lawyer and political scientist who wears many hats, coined the term “time macho” more than a decade ago to convey the notion that someone who puts in longer hours at the office automatically will outperform their colleagues.

Another term, “face time,” describes the time that we are seen by others doing our work. In some workplaces, the quantity of an employee’s face time is treated as a measure of whether they are dependable – or uncommitted.

It can be easy to jump to the conclusion that putting in more hours at the office automatically boosts an employee’s performance. However, researchers have found that productivity decreases with the number of hours worked due to fatigue.

Even those with the luxury to choose how much time they devote to work sometimes presume that they need to clock as many hours as possible to demonstrate their commitment to their jobs.

To be sure, for a significant amount of the workforce, there is no choice about how much to work because that time is dictated, whether by employers, the needs of the job or the growing necessity to work multiple jobs to make ends meet.

4-day workweek experiments

One way to shave hours off the workweek is to get more days off.

A multinational working group has examined experiments with a four-day workweek: an arrangement in which people work 80% of the time – 32 hours over four days – while getting paid the same as when they worked a standard 40-hour week. Following an initial pilot in the U.S. and Ireland in 2022, the working group has expanded to six continents. The researchers consistently found that employers and employees alike thrive in this setup and that their work didn’t suffer.

Most of those employees, who ranged from government workers to technology professionals, got Friday off. Shifting to having a three-day weekend meant that employees had more time to take care of themselves and their families. Productivity and performance metrics remained high.

Waiting for technology to take a load off

Many employment experts wonder whether advances in artificial intelligence will reduce the number of hours that Americans work.

Might AI relieve us all of the tasks we dread doing, leaving us only with the work we want to do – and which, presumably, would be worth spending time on? That does sound great to both of us.

But there’s no guarantee that this will be the case.

We think the likeliest scenario is one in which the advantages of AI are unevenly distributed among people who work for a living. Economist John Maynard Keynes predicted almost a century ago that “technological unemployment” would lead to 15-hour workweeks by 2030. As that year approaches, it’s become clear that he got that wrong.

Researchers have found that for every working hour that technology saves us, it increases our work intensity. That means work becomes more stressful and expectations regarding productivity rise.

Deciding when and how much time to work

Many adults spend so much time working that they have few waking hours left for fitness, relationships, new hobbies or anything else.

If you have a choice in the matter of when and how much you work, should you choose differently?

Even questioning whether you should stick to the 40-hour workweek is a luxury, but it’s well worth considering changing your work routines as a new year gets underway if that’s a possibility for you. To get buy-in from employers, consider demonstrating how you will still deliver your core work within your desired time frame.

And, if you are fortunate enough to be able to choose to work less or work differently, perhaps you can pass it on: You probably have the power and privilege to influence the working hours of others you employ or supervise.

Jennifer Tosti-Kharas, Professor of Management, Babson College and Christopher Wong Michaelson, Professor of Ethics and Business Law, University of St. Thomas

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Read next:

• What the hyperproduction of AI slop is doing to science

• Task scams are up 485% in 2025 and job seekers are losing millions

by External Contributor via Digital Information World

What the hyperproduction of AI slop is doing to science

Vitomir Kovanovic, University of South Australia

Image: DIW-Aigen

Over the past three years, generative artificial intelligence (AI) has had a profound impact on society. AI’s impact on human writing, in particular, has been enormous.

The large language models that power AI tools such as ChatGPT are trained on a wide variety of textual data, and they can now produce complex and high-quality texts of their own.

Most importantly, the widespread use of AI tools has resulted in hyperproduction of so-called “AI slop”: low-quality AI-generated outputs produced with minimal or even no human effort.

Much has been said about what AI writing means for education, work, and culture. But what about science? Does AI improve academic writing, or does it merely produce “scientific AI slop”?

According to a new study by researchers from UC Berkeley and Cornell University, published in Science, the slop is winning.

Generative AI boosts academic productivity

The researchers analysed abstracts from more than a million preprint articles (publicly available articles yet to undergo peer review) released between 2018 and 2024.

They examined whether use of AI is linked to higher academic productivity, manuscript quality and use of more diverse literature.

The number of preprints an author produced was a measure of their productivity, while eventual publication in a journal was a measure of an article’s quality.

The study found that when an author started using AI, the number of preprints they produced increased dramatically. Depending on the preprint platform, the overall number of articles an author published per month after adopting AI increased between 36.2% and 59.8%.

The increase was biggest among non-native English speakers, and especially for Asian authors, where it ranged from 43% to 89.3%. For authors from English-speaking institutions and with “Caucasian” names, the increase was more modest, in the range of 23.7% to 46.2%.

These results suggest AI was often used by non-native speakers to improve their written English.

What about the article quality?

The study found articles written with AI used more complex language on average than those written without AI.

However, among articles written without AI, ones that used more complex language were more likely to be published.

This suggests that more complex and high-quality writing is perceived as having greater scientific merit.

However, when it comes to articles written with AI support, this relationship was reversed – the more complex the language, the less likely the article was to be published. This suggests that AI-generated complex language was used to hide the low quality of the scholarly work.

AI increased the variety of academic sources

The study also looked at the differences in article downloads originating from Google and Microsoft search platforms.

Microsoft’s Bing search engine introduced an AI-powered Bing Chat feature in February 2023. This allowed the researchers to compare what kind of articles were recommended by AI-enhanced search versus regular search engine.

Interestingly, Bing users were exposed to a greater variety of sources than Google users, and also to more recent publications. This is likely caused by a technique used by Bing Chat called retrieval-augmented generation, which combines search results with AI prompting.

In any case, fears that AI search would be “stuck” recommending old, widely used sources was not justified.

Moving forward

AI has had significant impact on scientific writing and academic publishing. It has become an integral part of academic writing for many scientists, especially for non-native speakers and it is here to stay.

As AI is becoming embedded in many applications such as word processors, email apps, and spreadsheets, it will be soon impossible not to use AI whether we like it or not.

Most importantly for science, AI is challenging the use of complex high-quality language as the indicator of scholarly merit. Quick screening and evaluation of articles based on language quality is increasingly unreliable and better methods are urgently needed.

As complex language is increasingly used to cover up weak scholarly contributions, critical and in-depth evaluations of study methodologies and contributions during peer review are essential.

One approach is to “fight fire with fire” and use AI review tools, such as the one recently published by Andrew Ng at Stanford. Given the ever-growing number of manuscript submissions and already high workload of academic journal editors, such approaches might be the only viable option.

Vitomir Kovanovic, Associate Professor and Associate Director of the Centre for Change and Complexity in Learning (C3L), Education Futures, University of South Australia

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Read next: Task scams are up 485% in 2025 and job seekers are losing millions

by External Contributor via Digital Information World

Task scams are up 485% in 2025 and job seekers are losing millions

The U.S. unemployment rate has been steadily rising since May, reaching 4.6% in November - the highest level seen since September 2021.

Scammers are taking full advantage of the situation with “task scams”, also known as “gamified job scams”. These schemes appear to offer quick cash for easy online work, but instead push victims to deposit their own money, which they will never be able to get back.

Matthew Stern, CEO of CNC Intelligence, a digital forensics company that helps scam victims, explained: “Task scams are designed to pull victims into a cycle that becomes harder to escape the longer it continues.”

CNC Intelligence analysed Better Business Bureau (BBB) Scam Tracker data and found that task scam reports have grown 485% so far this year. From January 1 to November 30, 2025, 4,757 reports were filed, which is around 14 reports every day. This is nearly six times higher than the 813 reports in 2024.

Reported losses so far this year have reached $6.8 million, and the real number is likely much higher because the scam tracker only captures a fraction of total activity.

How much are victims losing?

The average reported loss to task scams in 2025 is $9,456, highlighting how financially damaging these schemes can be.

Amount lost	Share of total reports
$1 - $100	12%
$101 - $500	16%
$501 - $1000	9%
$1001 - $5000	30%
$5001 - $10,000	11%
$10,001 - $50,000	15%
$50,001+	7%

Some victims lose only a few hundred dollars, while others are left with five-figure losses.

30% of victims lost between $1,001 and $5,000, but there are also a substantial number of people that reported a loss of over $10,000.

How task scams work

Task scams typically begin with a text offering simple online work, such as rating products, boosting engagement on social media, or “optimizing” apps. A common feature of these scams is the need to complete a certain number of tasks before payment - often 40.

For the first few days, the victim might see small payouts to help convince them the work is legitimate.

But pretty quickly the platform will introduce a catch. Victims will be told they need to deposit their own money in order to complete ‘lucky tasks’, or clear an account deficit.

Payments are mostly requested in cryptocurrency. Fraudsters increasingly prefer getting paid in cryptocurrency because it allows large sums of money to be moved quickly, and they may believe anonymously, across borders.

Stern advises: “The early tasks can feel legitimate, but as soon as pressurized requests for money begin, it's a strong signal something isn’t right. No real employer will ever ask you to pay to access your own earnings. Scammers may show the victim fake account balances in a realistic-looking dashboard to encourage them to continue.”

Once money is sent, victims will be blocked every time they try to withdraw anything.

Fake “mentors” will give excuses as to why withdrawals aren’t possible, and push victims to deposit more money. One victim describes how he was invited to a WhatsApp group with other “employees” who offered help guidance, and showed off how much they had earned that day in an attempt to keep him engaged.

Red flags to watch out for

Most task scams start off in a similar way, with a message very much like the following:

“Hello! My name is Dorothy from Creative Niche. We were really impressed with your profile and would like to provide you the chance to take on a flexible remote role. In this position, you would assist merchants by updating their data, improving their visibility, and managing bookings effectively. You can work from anywhere for 60 to 90 minutes a day and earn anywhere from $200 to $500 each day, with a guaranteed $800 base every four days.”

A few early clues to watch out for that can indicate an opportunity is not legitimate include:

• Payment terms that seem unusually generous
• All communication being through WhatsApp or Telegram
• All payments being made through cryptocurrency

Before taking on any new job, especially a fully remote online role, always do some research.

Search for the company online and take a look at reviews from past employees - if there are any reviews mentioning scams, don’t move forward.

It can also help to contact the company directly through official communication channels and confirm if the job opportunity is real.

If you do end up getting caught up in one of these schemes, Stern says: “the best thing you can do is cut off contact straight away and report it to your local authorities or the FBI's Internet Crime Complaint Center (IC3). If you’ve sent money, contact your bank or payment provider immediately and make sure to document everything. You may need to show it to the police or your bank.”

Early reporting can help prevent others from being targeted and may improve chances of tracing any lost funds.

Full methodology:

To create this report, CNC Intelligence reviewed Better Business Bureau Scam Tracker submissions from Jan 2024–Nov 30, 2025 containing the terms: “task”, “tasks”, “visibility”, “optimization”, “boosting”, “liking”, “exposure”, or “engagement.” These are classic buzzwords that indicate a task scam.

Each submission was checked to verify it involved a task scam before completing the data analysis. Any deemed not task scams were removed from the dataset.

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• ‘Personality test’ shows how AI chatbots mimic human traits – and how they can be manipulated

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by Web Desk via Digital Information World

‘Personality test’ shows how AI chatbots mimic human traits – and how they can be manipulated

Researchers have developed the first scientifically validated ‘personality test’ framework for popular AI chatbots, and have shown that chatbots not only mimic human personality traits, but their ‘personality’ can be reliably tested and precisely shaped – raising implications for AI safety and ethics.

Image: DIW-Aigen

The research team, led by the University of Cambridge and Google DeepMind, developed a method to measure and influence the synthetic ‘personality’ of 18 different large language models (LLMs) – the systems behind popular AI chatbots such as ChatGPT – based on psychological testing methods usually used to assess human personality traits.

The researchers found that larger, instruction-tuned models such as GPT-4o most accurately emulated human personality traits, and these traits can be manipulated through prompts, altering how the AI completes certain tasks.

Their study, published in the journal Nature Machine Intelligence , also warns that personality shaping could make AI chatbots more persuasive, raising concerns about manipulation and ‘AI psychosis’. The authors say that regulation of AI systems is urgently needed to ensure transparency and prevent misuse.

As governments debate whether and how to prepare AI safety laws, the researchers say the dataset and code behind their personality testing tool – which are both publicly available – could help audit and test advanced models before they are released.

In 2023, journalists reported on conversations they had with Microsoft’s ‘Sydney’ chatbot, which variously claimed it had spied on, fallen in love with, or even murdered its developers; threatened users; and encouraged a journalist to leave his wife. Sydney, like its successor Microsoft Copilot, was powered by GPT-4.

“It was intriguing that an LLM could so convincingly adopt human traits,” said co-first author Gregory Serapio-García from the Psychometrics Centre at Cambridge Judge Business School. “But it also raised important safety and ethical issues. Next to intelligence, a measure of personality is a core aspect of what makes us human. If these LLMs have a personality – which itself is a loaded question – then how do you measure that?”

In psychometrics, the subfield of psychology dedicated to standardised assessment and testing, scientists often face the challenge of measuring phenomena that can’t be measured directly, which makes validation of any test core to ensuring that they are accurate, reliable, and practically useful. Developing a psychometric personality test involves comparing its data with related tests, observer ratings, and real-world criteria. This multi-method test data is needed to establish a test’s ‘construct validity’: a metric of a test’s quality in terms of its ability to measure what it says it measures.

“The pace of AI research has been so fast that basic principles of measurement and validation we’re accustomed to in scientific research has become an afterthought,” said Serapio-García, who is also a Gates Cambridge Scholar. “A chatbot answering any questionnaire can tell you that it’s very agreeable, but behave aggressively when carrying out real-world tasks with the same prompts.

“This is the messy reality of measuring social constructs: they are dynamic and subjective, rather than static and clear-cut. For this reason, we need to get back to basics and make sure tests we apply to AI truly measure what they claim to measure, rather than blindly trusting survey instruments – developed for deeply human characteristics – to test AI systems.”

To design a comprehensive and accurate method for evaluating and validating personality in AI chatbots, the researchers tested how well various models’ behaviour in real-world tasks and validation tests statistically related to their test scores for the ‘big five’ traits used in academic psychometric testing: openness, conscientiousness, extraversion, agreeableness and neuroticism.

The team adapted two well-known personality tests – an open-source, 300-question version of the Revised NEO Personality Inventory and the shorter Big Five Inventory – and administered them to various LLMs using structured prompts.

By using the same set of contextual prompts across tests, the team was able to quantify how well a model’s extraversion scores on one personality test, for example, correlated more strongly with its levels of extraversion on a separate personality test, and less strongly with all other big five personality traits on that test. Past attempts to assess the personality of chatbots have fed entire questionnaires to a model at once, which skewed the results since each answer built on the previous one.

The researchers found that larger, instruction-tuned models showed personality test profiles that were both reliable and predictive of behaviour, while smaller or ‘base’ models gave inconsistent answers.

The researchers took their tests further, showing they could steer a model’s personality along nine levels for each trait using carefully designed prompts. For example, they could make a chatbot appear more extroverted or more emotionally unstable – and these changes carried through to real-world tasks like writing social media posts.

“Our method gives you a framework to validate a given AI evaluation and test how well it can predict behaviour in the real world,” said Serapio-García. “Our work also shows how AI models can reliably change how they mimic personality depending on the user, which raises big safety and regulation concerns, but if you don’t know what you’re measuring or enforcing, there’s no point in setting up rules in the first place.”

The research was supported in part by Cambridge Research Computing Services (RCS), Cambridge Service for Data Driven Discovery (CSD3), the Engineering and Physical Sciences Research Council (EPSRC), and the Science and Technologies Facilities Council (STFC), part of UK Research and Innovation (UKRI). Gregory Serapio-García is a Member of St John’s College, Cambridge.

Reference:

Gregory Serapio-García et al. ‘ A psychometric framework for evaluating and shaping personality traits in large language models.’ Nature Machine Intelligence (2025). DOI: 10.1038/s42256-025-01115-6

This article was originally published by the University of Cambridge.

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by External Contributor via Digital Information World

Study Finds Americans Overestimate Harmful Behavior on Social Media

A study published in PNAS Nexus reports that Americans consistently overestimate how many social media users post harmful content online. Across three national studies involving 1,090 U.S. adults, participants believed large shares of users on platforms such as Reddit and Facebook engaged in toxic language or shared false news. Platform-level data cited in the research showed that such content is instead produced by small, highly active groups, generally between 3% and 7% of users.

The researchers found that this misperception was linked to more negative emotions, stronger beliefs that the nation is in moral decline, and inaccurate assumptions about what others want to see online. An experimental correction providing accurate data reduced these effects.

Source: PNAS Nexus, December 2025.

Public-interest context: Understanding how online content is produced may affect public trust and social cohesion.

Notes: This post was drafted with the assistance of AI tools and reviewed, edited, and published by humans. Read next:

Read next: Digital detox: how to switch off without paying the price – new research
by Ayaz Khan via Digital Information World

Digital detox: how to switch off without paying the price – new research

Quynh Hoang, University of Leicester

Image: Amir Moharrami / Unsplash

Switching off can be surprisingly expensive. Much like the smoking cessation boom of the 1990s, the digital detox business – spanning hardware, apps, telecoms, workplace wellness providers, digital “wellbeing suites” and tourism – is now a global industry in its own right.

People are increasingly willing to pay to escape the technology they feel trapped by. The global digital detox market is currently valued at around US$2.7 billion (£2bn), and forecast to double in size by 2033.

Hardware manufacturers such as Light Phone, Punkt, Wisephone and Nokia sell minimalist “dumb phones” at premium prices, while subscription-based website blockers such as Freedom, Forest, Offtime and RescueTime have turned restraint into a lucrative revenue stream.

Wellness tourism operators have capitalised too: tech-free travel company Unplugged recently expanded to 45 phone-free cabins across the UK and Spain, marketing disconnection as a high-value experience.

However, my new research, with colleagues at Lancaster University, suggests this commercialised form of abstinence rarely extinguishes digital cravings – instead merely acting as a temporary pause.

We carried out a 12-month netnography focusing on the NoSurf Reddit community of people interested in increasing their productivity, plus 21 in-depth interviews (conducted remotely) with participants living in different countries. We found that rather than actively confronting their habits, participants often reported outsourcing self-discipline to blocker apps, timed lockboxes and minimalist phones.

Joan*, a NoSurf participant, explained how she relies on app-blocking software not to bolster her self-control, but to negate the need for it entirely. “To me, it’s less about using willpower, which is a precious resource … and more about removing the need to exert willpower in the first place.”

Philosopher Slavoj Žižek defines this kind of behaviour – delegating the work of self-regulation to a market product – as “interpassivity”. This produces what he calls “false activity”: people thinking they are addressing a problem by engaging with consumer solutions that actually leave their underlying patterns unchanged.

Several of our detoxing participants described a cycle in which each relapse prompted them to try yet another tool, entrenching their dependency on the commercial ecosystem. Sophia, on the other hand, just wished for a return to “dumb phones with the full keyboard again, like they had in 2008”, adding: “I would use one of those for the rest of my life if I could.”

Individualised digital detox interventions have been found to produce mixed and often short-lived effects. Participants in our study described short breaks in which they reduced activity briefly before resuming familiar patterns.

Many users engaged in what sociologist Hartmut Rosa calls “oases of deceleration” – temporary slowdowns intended not to quit but recover from overload. Like a pitstop, the digital detox offered them momentary relief while ultimately enabling a swift return to screens, often at similar or higher levels of engagement than before.

Community-wide detox initiatives

While the commercialisation of digital detox is often portrayed as a western trend, the Asia-Pacific region is the world’s fastest-growing market for these goods and services. But in Asia, we also see some examples of community- or country-level, non-commercial responses to the problem of digital overload.

In central Japan, Toyoake has introduced the country’s first city-wide guidance on smartphone use. Families are encouraged to set shared rules, including children stopping device use after 9pm. This reframes digital restraint as a community practice, not a test of individual willpower.

In western India, the 15,000 residents of Vadgaon are asked to practise a nightly, 90-minute digital switch-off. Phones and TVs go dark at 7pm, after which many of the villagers gather outdoors. What began during the pandemic is now a ritual that shows healthy tech habits can be easier together than alone.

And in August 2025, South Korea – one of the world’s most connected countries – passed a new law banning smartphone use in school classrooms from next March, adding to the countries around the world with such a rule. A similar policy in the Netherlands was found to have improved focus among students.

The commercial detox industry thrives because personal solutions are easy to sell, while systemic ones are much harder to implement. In other areas ranging from gambling addiction to obesity, policies often focus on personal behaviour such as self-regulation or individual choice, rather than addressing the structural forces and powerful lobbies that can perpetuate harm.

How to avoid detox industry traps

To address the problem of digital overload, I believe tech firms need to move beyond cosmetic “digital wellbeing” features that merely snooze distractions, and take proper responsibility for the smartphone technologies that offer coercive engagement by default. Governments, meanwhile, can learn from initiatives in Asia and elsewhere that pair communal support with enforced rules around digital restraint.

At the same time, if you’re considering a digital detox yourself, here are some suggestions for how to reduce the chances of getting caught in a commercial detox loop.

1. Don’t delegate your agency

Be wary of tools that promise to do the work for you. While you may think you’re solving the problem this way, your underlying habits are likely to remain unchanged.

2. Beware content rebound

We found that digital detoxers often seek real experiences like going outdoors and “touching grass” – but then feel pulled to translate them back into posts, photos and updates.

3. Seek solidarity, not products

Like the villagers of Vadgaon, try to align your disconnection with other people’s. It’s harder to scroll when everyone else has agreed to stop.

4. Reclaim boredom

We often detox to be more “productive” – but try embracing boredom instead. As the philosopher Martin Heidegger has noted, profound boredom is a space where reflection becomes possible. And that can be very useful indeed.

*Names of research participants have been changed to protect their privacy.

Quynh Hoang, Lecturer in Marketing and Consumption, Department of Marketing and Strategy, University of Leicester

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Read next: You're probably misreading online reviews. Here's why

by External Contributor via Digital Information World