A new book by Barbara Plester, an associate professor at the University of Auckland Business School, examines how communication, humour, and happiness are being reshaped in the age of hybrid work. Hybrid Happiness: Fun and Freedom in Flexible Work explores the social and emotional consequences of dividing time between home and office, and how these shifts are altering the fabric of workplace life.

What makes us happy

Drawing on immersive fieldwork in two organisations—a technology firm dubbed “Gecko” and a food manufacturer called “Firefly”—Dr Plester observed employees across all levels, from CEOs to recent hires. Over four weeks embedded in each company, she watched, listened, and interviewed, taking a grounded-theory approach that let themes emerge naturally rather than testing a preconceived hypothesis. “My research on fun and humour evolved into a study of happiness,” she notes, “because participants consistently linked these ideas together.”

The book captures both the freedoms and frictions of flexible work. A chapter titled “The Emotional Landscape of Hybrid Work” examines the strain that digital communication imposes on emotional understanding. Reading nuance through chat messages, emojis and GIFs, Dr Plester argues, has become a new workplace skill—and a potential minefield. “Emotions are increasingly mediated through technology,” she writes. “Pictorial language can enliven conversation and convey playfulness, but it can just as easily be misinterpreted.”

Another chapter, “Tech-Powered Freedom,” delves into questions of trust, surveillance, and the erosion of boundaries between home and office. Trust, Plester argues, is the cornerstone of successful hybrid arrangements. When employees feel trusted, they tend to perform better and with greater engagement. One worker at Firefly put it bluntly: “If you don’t trust your employee, why employ them?”

Encouraging safety

In “Psychological Safety in Hybrid Fun,” Plester considers the paradox of “forced fun”—activities designed to build camaraderie but which can leave employees feeling coerced or infantilised. Her research suggests that contrived cheerfulness may foster cynicism rather than connection. True enjoyment, she writes, depends on the freedom to participate—or not—without fear of judgment.

The book concludes that happiness in hybrid work cannot be manufactured through corporate enthusiasm alone. It flourishes instead in cultures that prize trust, empathy, and flexibility—qualities that, in an era of emojis and blurred boundaries, matter more than ever.

Recruitment has seldom been an enjoyable affair, and while it’s hard to argue that AI is making it better, you sense that it is a factor in the rise of AI in recruitment. This desire to remove ourselves from the stress and tedium of submitting or wading through application after application.

Dealing with the biases

Of course, it’s increasingly evident that the systems we’re using to do all of this also come with their fair share of biases. What’s more, we’re also not very good at spotting biased outputs when they’re presented to us by AI. So, given the important role recruitment plays, the reliance on these tools comes with a large degree of risk.

Research from the University of Washington found that when we use AI to help us select candidates, we’re more likely to make biased choices than if we make decisions on our own. The researchers presented over 500 volunteers with identical resumes, but some were presented as being from white candidates, some from Black candidates, some from Hispanic candidates, and some from Asian candidates (all were from male candidates).

The results showed that the volunteers strongly followed the advice given to them by the AI, regardless of whether it was biased or not. Indeed, even when the AI was extremely biased, people didn’t shift their recommendations a great deal.

“In one survey, 80% of organizations using AI hiring tools said they don’t reject applicants without human review,” the researchers explain. “So this human-AI interaction is the dominant model right now. Our goal was to take a critical look at this model and see how human reviewers’ decisions are being affected. Our findings were stark: Unless bias is obvious, people were perfectly willing to accept the AI’s biases.”

Fair recruitment

An obvious first step to ensure that bias doesn’t pervade AI-driven recruitment is to ensure that the models and systems themselves aren’t biased. Indeed, the study shows that when AI made unbiased recommendations, this typically resulted in humans making unbiased decisions.

There are also things that we can do that don’t rely on the tech companies to change. For instance, implicit bias training can be effective as it reminds us that the technology can make discriminatory decisions. This was underlined by research by the Haas School of Business, which suggested a good approach is for companies to foster a culture of bias awareness in AI, which allows individual employees to report biased outcomes and support them in addressing these issues with their supervisors.

Ultimately, a fairer application of AI in recruitment is likely to require a collective effort from recruiters, developers, and policymakers. We can’t rely on recruiters alone to fix this issue, nor indeed on developers to do so on our behalf. Indeed, research from University College London shows that while some developers take these societal externalities seriously, many others view downstream problems as someone else’s to fix.

Moving forward

We’ve seen in numerous other applications of AI that it can be useful when it helps us to think, and very unhelpful when it does our thinking for us. Recruitment is no different, but these studies remind us that AI can also lead us astray when it serves us biased recommendations.

As such, it’s important that we better understand that AI decisions mirror the data used to train it, with all of the biases that come with that. Awareness is a key part of being able to work not only more effectively with AI but also more fairly with it. We shouldn’t use it to shortcut our own thought processes, but rather as one input among many. Embedding bias awareness training, such as implicit association testing, into hiring processes can help ensure that human judgment remains critical and reflective.

We should also be much more demanding of the companies making these systems to ensure that not only are datasets tested for bias during development, but also that outputs are continuously monitored for biases throughout operations. Just as “bug hunting” is a common part of traditional software development, so too should “bias hunting” be a part of modern AI tools. This would require implementing clear and easy-to-use means for recruiters to report when recommendations are biased.

Meanwhile, the onus also lies with those creating and deploying these systems. Developers must actively test for and mitigate bias during model training, and organizations should insist on transparency around how recommendations are generated. Regulation will likely play a growing role too, helping ensure these systems align with broader societal and ethical expectations.

There’s clearly something a bit broken with recruitment at the moment, with no party particularly happy with the status quo. AI can undoubtedly play a role in making things better. It might even help us to make recruitment fairer. It won’t happen by osmosis, however. It requires us to design, deploy, and critique it at every step so that humans are always in the driving seat.

Synthesising four decades of global studies, the researchers find that the relationship between urban greenness and mental health follows an inverted-U pattern: psychological well-being improves as greenery increases—but only up to a point. Beyond a moderate threshold, benefits plateau and may even reverse. The study, published after analysing 87,000 records across five continents, offers the most comprehensive evidence yet that moderation, not maximisation, is key.

The right dose

The optimal dose, the authors find, is remarkably precise. For greenery seen at eye level, the streets, parks, and plazas people actually move through, mental health peaks when roughly half (around 53%) of the visual field is green. Top-down views, such as canopy coverage from satellite data, show similar results. Below about a quarter or above four-fifths, the psychological pay-off declines.

The logic is intuitive. A city that is too barren is stressful; one that is too lush can feel isolating, poorly lit, or impractical. The findings echo broader theories—from the Yerkes–Dodson law to hormesis—showing that moderate stimuli often elicit optimal human responses.

For policymakers, the message is pragmatic. Cities need not carpet every available surface in foliage. Instead, they should design “just enough” green—prioritising street-level trees, pocket parks and visible vegetation where people actually experience them. Over-greening can divert scarce land from housing, services, and infrastructure—an especially acute concern in densely packed Hong Kong, where verdant hills rise above cramped flats.

The study’s broader contribution is theoretical as well as practical. By defining a generalised “dose–response curve” between greenness and mental health, it may lay the groundwork for an emerging law of environmental psychology: that balance, not abundance, best nurtures well-being.

The analysis, published in the European Sociological Review, draws on the German Family Panel between 2008 and 2021. It finds that women still shoulder around 70% of housework and childcare, despite remote work becoming far more common, rising from one in ten parents before the pandemic to nearly half of fathers and a third of mothers by 2021. The shift, in other words, has expanded the potential for fairness but not guaranteed it.

Uneven share

When mothers work from home, the researchers found, they tend to do even more domestic labour, particularly when they or their partners hold conventional views about men’s household responsibilities. Only in households where fathers regard care as a shared duty does remote work appear to level the load.

The authors argue that encouraging men to work from home could help close the gap, but warn that structural reforms alone will fall short unless social attitudes change too. Policies such as flexible working rights and well-paid, ringfenced paternity leave in a child’s early years can help reset expectations about who cares. Many fathers, they note, are keen to do more; the question is whether governments and employers will enable them.

“Structural policies such as the right to flexible work must be accompanied by efforts to challenge gender stereotypes,” the authors conclude, “beginning with perceptions of men’s roles in family life.” Remote work, it seems, is only as equalising as the beliefs that govern the home.

Research from the Olin Business School at Washington University in St. Louis suggests that entrepreneurial zeal pays off, but only when it delivers results. Analysing data from around 650 employees at Fortune 500 companies, the researchers found that workers who exhibited entrepreneurial behaviour were significantly more likely to launch internal ventures, such as new products or improved processes. A one-point rise in entrepreneurial activity increased the likelihood of doing so by 50%.

Promotion chances

Those who successfully created such ventures were, in turn, far more likely to be promoted and, particularly in large firms, to receive higher pay. Merely having ideas was not enough; transforming them into tangible outcomes was what earned career dividends. As the authors note, “entrepreneurial behaviour alone did not guarantee success—execution did.”

Size also mattered. Smaller firms tended to reward innovators with promotions rather than pay rises, perhaps reflecting tighter budgets. In larger organisations, where managers are more distant from employees, creating an internal venture was a strong signal of leadership potential. In a follow-up experiment, participants reviewing résumés for a promotion were more likely to select candidates who had built internal ventures, viewing them as future leaders.

For ambitious employees, the lesson is clear: innovation must be visible to be valuable. Turning an idea into something concrete, however modest, can boost both credibility and compensation.

For firms, fostering such behaviour requires more than hackathons and suggestion boxes. Training helps, but so does giving employees ownership of their ideas, from conception to implementation. Cross-functional collaboration—where marketing meets engineering or logistics meets design—often sparks the most productive innovation.

Managers, however, must learn to see entrepreneurial employees not as troublemakers but as catalysts. Human-resources departments can help by reframing risk-taking and even failure as part of learning, not grounds for reprimand. Companies that manage this cultural shift will not only keep their innovators but reward them, and themselves, in the process.

Usage follows trust downhill. Between February and July, employees’ use of sanctioned AI tools dropped by 15%. Yet nearly half of frontline workers with access to corporate AI say they use unofficial alternatives instead. The problem, then, is not antipathy to AI per se, but scepticism toward the tools their employers prescribe. Many workers feel these systems are being foisted upon them—imposed, not adopted—and, worse, that they are being asked to help train the very technologies that might replace them.

Building trust

With so much money riding on the successful integration of AI, companies are scrambling to fix the trust deficit. Deloitte highlights five approaches that firms—its clients and others—are using to restore confidence.

Start by measuring mistrust. Deloitte’s own index parses trust into four dimensions—reliability, capability, transparency and “humanity”—allowing managers to isolate what is eroding confidence. When trust rises, so do positive behaviours: high-trust employees are almost twice as likely to recommend their employer and more willing to acquire new skills. If staff balk at AI’s opacity, greater communication or employee involvement can steady nerves.

Invest in skills, not redundancies. Fears of automation replacing humans remain acute, but labour shortages in sectors such as logistics and healthcare mean that few firms can simply “hire their way” into an AI-ready workforce. Companies that use AI to redesign jobs rather than eliminate them reap better results. Training that blends technical fluency with human skills—judgement, empathy, adaptability—boosts performance when workers collaborate with intelligent systems. Employees who receive hands-on training report trust levels 144% higher than those who do not. Sweden’s IKEA offers a tidy case study: after launching an AI chatbot to handle half of customer inquiries, it reskilled 8,500 call-centre staff into design-advice roles rather than shedding them. In warehouses, drones have taken over laborious stock counts, freeing workers for more valuable tasks. The company plans to train 70,000 employees in AI literacy by 2026. Turnover has dropped, and morale is up.

Design AI with workers, not just for them. Many systems fail because they are delivered as fait accompli. Walmart takes a different tack: its internal AI foundry, Element, develops tools through iterative pilots with store associates. A scheduling app, refined with input from staff, now includes shift swapping and availability preferences—small features that matter when juggling childcare or second jobs. Another tool offers real-time translation across 44 languages, incorporating Walmart-specific vocabulary. Workers trust the tools because they helped build them.

Allow experimentation—safely. If AI adoption requires trial and error, firms must stop punishing the latter. Colgate-Palmolive’s “AI Hub” lets employees create no-code assistants tailored to their jobs. A plant manager in Greece built a system that translates dense German machinery manuals into practical troubleshooting steps; an HR employee created a “goals coach” trained on corporate values. Thousands of such assistants now exist, and the most effective are scaled company-wide.

Empower middle managers. Adoption ultimately depends on the people closest to the front line. Workers trust their direct managers far more than the corporate centre—by roughly 20 percentage points—and weekly check-ins boost trust by nearly 60%. At Intuit, efforts to increase AI use stalled until the company gathered 150 frontline “experimenters” for a hands-on training day led by mid-level managers. Participants returned to their teams as evangelists, turning a corporate initiative into a peer-driven movement.

Across these cases, one lesson stands out: trust is not a soft metric but a hard constraint. AI will deliver on its promise only when workers feel that it enhances, rather than threatens, their work. Companies that understand this may find that AI adoption becomes not a battle to be won, but a momentum that builds itself.

“Immigrants support the core democratic principles to a similarly high degree as people without a migratory background,” the researchers report.

High support

Across 30 European countries, the average level of support for democratic values—such as free elections, equal rights, minority protection, and independent courts—scores 8.56 out of 10 among migrants, compared with 8.48 among non-migrants. In Germany, the figures are almost identical: 2.67 versus 2.66 on a three-point scale. “These very high mean values hardly differ between the individual groups,” the study notes.

A small but statistically significant difference emerges between immigrants from highly authoritarian states—such as Eritrea, Saudi Arabia, and Iran—and those from more democratic countries, including India, Turkey, and Romania. Those accustomed to authoritarian rule tend to express slightly weaker democratic attitudes, while those with longer experience of democratic systems show marginally stronger support. The gap, however, is modest. Broadly, the researchers conclude, democratic convictions are widely shared across cultural and national lines and tend to deepen with exposure to democratic life.

A small minority of immigrants reject democracy altogether, amounting to a mid–single-digit share of the population—virtually identical to the proportion among non-migrants. Anti-democratic sentiment, the study stresses, is not a migration-related phenomenon but a marginal undercurrent present in all social groups.

The findings draw on two large-scale datasets. The tenth wave of the ESS includes tens of thousands of respondents across Europe, among them over 2,000 first-generation migrants. The fourth wave of the Integration Barometer, compiled by Germany’s Expert Council on Integration and Migration (SVR), surveys around 15,000 people, including some 7,000 with a migration background and 4,000 first-generation immigrants. Both surveys assess key indicators of liberal-democratic commitment.

Thus far, it’s not proved to be easy to truly understand the environmental impact data centers have, not least because the industry is required to self-report a lot of the time. A recent study from Cornell aims to do a better job.

Environmental impact

The researchers utilize data analytics and a dollop of AI to develop what they believe is a state-by-state analysis of data centers’ environmental impact. The study found that the current rate of growth in data center capacity would pump up to 44 million metric tons of C02 into the atmosphere by 2030. That’s equivalent to adding around 10 million cars to America’s roads.

The situation is no better when it comes to water either, with growth in AI set to drain around 1,125 million cubic meters of water per year. That’s equivalent to the annual household water usage of 10 million Americans. Perhaps unsurprisingly, this makes it practically impossible for the United States to meet its net-zero targets.

Are things irreparable? The researchers don’t think so, and outline a roadmap that could make the AI transformation more sustainable. This includes things like faster grid decarbonization and smart siting. If these steps are taken, the researchers believe that the carbon emissions could drop by 73% and water usage by 86%.

“Artificial intelligence is changing every sector of society, but its rapid growth comes with a real footprint in energy, water and carbon,” the researchers explain.

“Our study is built to answer a simple question: Given the magnitude of the AI computing boom, what environmental trajectory will it take? And more importantly, what choices steer it toward sustainability?”

Testing the footprint

The true environmental footprint of AI was compiled by gathering data across a range of fields, including manufacturing, financial, and even marketing data. The aim was to understand not only the growth in the sector but also the locations of this growth. They then combined this with data on resource consumption and power systems to see how it all tied together with changes in the climate.

“There’s a lot of data, and that’s a huge effort. Sustainability information, like energy, water, and climate, tends to be open and public. But industrial data is hard, because not every company is reporting everything,” the researchers explain. “And of course, eventually, we still need to be looking at multiple scenarios. There’s no way that one size fits all. Every region is different for regulations. We used AI to fill some of the data gaps as well.”

Suffice it to say, it’s not enough simply to project the impact of AI on the environment. The researchers also wanted to provide guidance so that the seemingly inevitable investment in AI infrastructure is made in as sustainable a way as possible.

Location matters

The results show that there generally isn’t one single thing that has an overwhelming impact. Instead, it’s a combination of decarbonizing the electricity grid, making sure data centers are efficiently operated, and the location that collectively can make a difference.

The last point was perhaps the most interesting, though. For instance, at the moment, the hyperscalers locate data centers in areas with water shortages already. Similarly, in areas like northern Virginia, the rapid rise in data centers is placing significant strain on the local infrastructure.

Simply locating data centers in areas with more ample water supplies could reduce water demands by over 50%, especially if coupled with greater cooling efficiency. If this was also combined with improvements to grid efficiency, it could reduce water usage by around 86%.

The researchers argue that states in the Midwest or the so-called “windbelt” would be ideal for this. This would include Nebraska, Texas, and Montana.

Decarbonization is key

While location is crucially important, the researchers don’t ignore the fact that decarbonizing the electricity grid remains hugely important, if also significantly harder to achieve. They say that if the pace of decarbonization doesn’t improve to meet the rising demand placed by data centers, then emissions could rise by around 20%.

“Even if each kilowatt-hour gets cleaner, total emissions can rise if AI demand grows faster than the grid decarbonizes,” the researchers explain. “The solution is to accelerate the clean-energy transition in the same places where AI computing is expanding.”

The authors acknowledge, however, that while decarbonization is important, it’s not a silver bullet. Indeed, even if we achieve much higher levels of decarbonization than we are now, it might not be enough given the rapacious appetite of the industry. It’s estimated that by 2030, even with high levels of renewable energy, we’d need 28 gigawatts of wind or 43 gigawatts of solar capacity to reach net zero.

Ultimately, we need a lot more coordination between the hyperscalers and utilities, and regulators to ensure that AI can develop in a way that doesn’t drain water and energy resources (or contribute massively to emissions).

“This is the build-out moment,” the researchers conclude. “The AI infrastructure choices we make this decade will decide whether AI accelerates climate progress or becomes a new environmental burden.”

Extroverts tend to bask in the glow of workplace camaraderie. When invited to post-office gatherings, they report higher satisfaction with their jobs and themselves. Their more reserved colleagues, however, often experience the opposite: heightened anxiety, stress, and a desire to retreat.

Unwanted pressure

“We assume that socialising with co-workers is inherently positive,” note the researchers. “But those invitations are not necessarily always good.” What is intended as a friendly gesture can quickly turn into a social obligation, weighed down by unspoken expectations to attend.

The study drew on multiple field experiments and surveys of hundreds of full-time employees. It found that after-hours invitations—to dinners, parties, or bowling nights—are warmly received by those already comfortable in social settings. These employees tend to feel appreciated and included.

For the shy or socially anxious, however, the same invitations provoke unease. “When you already struggle with social interactions, the expectation to engage can be stressful,” the researchers observe. Gratitude for inclusion coexists with dread of performance.

Even those who decline such invitations may not escape the fallout. The anticipation alone—deciding whether to attend, worrying about who else will be there, or fearing the consequences of saying no—can cause tension and distract from work. “If I say yes, how long will it take? If I say no, will I be excluded?” one participant mused. The calculus of collegiality, it seems, is mentally taxing.

Showing discretion

The findings suggest that both employees and managers might do well to exercise discretion. Recognising one’s own social preferences and limits can buffer against the strain of obligatory merriment. Equally, those issuing invitations should consider timing and intent.

“When we initiate an invitation, it can have unintended consequences for that day’s performance,” the researchers warn. “You may think you’re being kind, but the recipient might instead be thinking, ‘What on earth am I going to do?’”

A convivial culture, in short, is not built on compulsion. For some workers, the happiest hour may be the one spent quietly heading home.

A consortium of election researchers from across Europe has examined how populist movements frame their messages on Facebook. Their conclusion is unequivocal: populist parties are far more likely than their mainstream counterparts to foreground controversial subjects when courting voters.

Strategic move

This, the researchers suggest, is no accident. By thrusting polarising themes to the forefront, populists seek to shift public debate onto terrain that plays to their strengths.

The 2024 elections to the European Parliament unfolded amid a climate of crisis. Russia’s war in Ukraine raged on, migration was once again high on the political agenda, and climate change continued to preoccupy voters. Against this unsettled backdrop, some parties turned to “wedge issues”—topics designed to divide electorates and stir passions, often to the detriment of their rivals’ credibility and competence.

The researchers analysed 8,748 Facebook posts from political parties in 13 European countries, assessing both the prevalence of contentious subjects and the tone in which they were presented. The results were striking, if not wholly unexpected. Populist parties, they found, not only rely more heavily on controversial issues but also frame them in a distinctly populist idiom—replete with anti-liberal rhetoric and sharp “us versus them” distinctions.

In groups and out groups

Such messaging, the study suggests, is designed to exclude outsiders and reinforce a sense of group identity. It exemplifies the style of digital communication that has come to define populist politics: emotive, simplistic, and adversarial.

Digital platforms, by rewarding clarity and outrage, tend to flatten complex debates into binary choices. When several hot-button issues—migration, climate, war—compete for attention, the result is often heightened polarisation and diminished nuance.

As the researchers note, certain themes can serve as a “Trojan horse” for populist actors. Migration, for instance, is not merely invoked to debate border policy but to cast doubt on liberal democracy itself. The ultimate goal, they warn, is not to solve problems but to redefine the terms of public discourse.