The evidence base for the study is the Transition to Clean Energy Enterprise Survey, a large-scale data collection effort covering the 2023 to 2024 period and produced by the Economic Research Forum. Unlike most studies of green technology adoption, which rely on small convenience samples of firms already interested in sustainability, this survey is nationally representative in each of the three countries. That matters because MSMEs dominate the MENA private sector in sheer numbers, and their collective energy choices could determine whether the region’s decarbonization targets are achievable. The authors modeled renewable energy technology engagement as a two-stage process: first, a firm considers adopting the technology, and second, it actually adopts it. Splitting the decision this way allowed the researchers to detect where firms drop out of the pipeline.

To explain the first stage, the study builds on the Technology Acceptance Model, a framework from information systems research that explains technology uptake through perceived usefulness and behavioral intention. The authors extended this classic model with a construct called facilitating conditions, which captures whether a firm has access to the resources, infrastructure, and institutional support needed to make a technology work in practice. They estimated country-specific structural equation models, a statistical technique that simultaneously tests how well measured survey items represent underlying constructs and how those constructs relate to one another. The results were consistent across all three countries: facilitating conditions were more strongly associated with behavioral intention than perceived usefulness was. In other words, MENA entrepreneurs form their intentions to adopt renewable energy less on the basis of expected cost savings or environmental benefits and more on the basis of whether they believe the surrounding ecosystem can support the technology.

The second stage of the analysis is where the study delivers its most consequential finding. The researchers estimated sequential logistic regression models, which predict the probability of each stage conditional on the previous one. Intention and facilitating conditions both predicted whether a firm considered renewable energy technology in the first place. But when the outcome was effective adoption, the picture changed dramatically. Effective adoption was associated with facilitating conditions, firm size, ownership of business premises, and access to targeted subsidies. Behavioral intention, the psychological variable that most adoption models treat as the decisive driver, was not associated with effective adoption at all.

This dissociation between intention and action is what economists and psychologists call the intention–behavior gap, and the study documents it with unusual clarity in a developing-region context. The implication is that implementation depends on feasibility rather than motivation. A small manufacturer in Casablanca may fully intend to install rooftop solar panels, but if it rents its premises, lacks collateral for a loan, cannot find qualified installers, and faces no subsidy targeted at firms of its size, the intention never converts into an investment. The structural equation results reinforce this reading: because facilitating conditions shape intention more than perceived usefulness does, entrepreneurs appear to internalize feasibility constraints even at the intention stage, sensing that a technology the ecosystem cannot support is not worth pursuing.

The firm-level variables associated with effective adoption deserve close attention because they point directly to policy levers. Firm size matters, suggesting that larger small enterprises have the balance sheets, cash flow stability, and negotiating power to absorb the upfront capital costs of renewable installations. Premises ownership matters, which is intuitive once the economics of rooftop solar are considered: a business that owns its building captures the full stream of energy savings over the life of the system, whereas a tenant faces the classic split-incentive problem, investing in an asset whose returns accrue to the landlord. Targeted subsidies matter, indicating that public financial support designed for specific firm categories can tip marginal investment decisions, while generic awareness campaigns apparently cannot.

The methodological rigor of the study strengthens confidence in these conclusions. The authors ran a battery of diagnostics that are often skipped in survey-based adoption research. Harman’s single-factor test was used to check for common method variance, the artifact that arises when all constructs are measured from the same respondents in a single instrument. Sensitivity analyses tested whether the adoption results were robust to alternative model specifications and threshold choices. Measurement invariance tests verified that the survey constructs carried the same meaning in Egypt, Jordan, and Morocco, a prerequisite for meaningful cross-country comparison, and cross-country equality tests examined whether the structural paths differed systematically between the three economies. The adoption models were also estimated conditional on consideration, with a formal diagnostic guiding the selection of thresholds, ensuring that the sequential structure was statistically coherent rather than assumed.

For the MENA region, where governments have invested heavily in utility-scale solar and wind projects while small businesses have largely watched from the sidelines, the study reframes the policy problem. Awareness campaigns and information provision, the default instruments of many clean energy programs, address intention, and the evidence suggests intention is not the binding constraint. Programs that expand facilitating conditions, such as concessional financing tailored to MSMEs, technical assistance for system design and installation, and support for landlord–tenant arrangements that resolve the split-incentive problem, address the stage where firms actually drop out. The finding that targeted subsidies outperform generic support suggests that precision matters: a subsidy designed for a ten-person workshop in Amman is more effective than a broad national incentive that the workshop cannot navigate.

The research also carries a broader lesson for technology adoption studies worldwide. Models inherited from consumer software research, where the cost of trying a new application is negligible, translate poorly to capital-intensive energy investments in resource-constrained environments. When adoption requires physical infrastructure, credit access, and long payback periods, the psychological pathway from usefulness to intention to behavior breaks down, and structural variables take over. The MENA findings suggest that researchers and policymakers should treat intention as a necessary but far from sufficient condition, and should measure the feasibility ecosystem with the same seriousness they devote to attitudes. As the region’s MSMEs represent an enormous untapped reservoir of distributed clean energy capacity, converting their abundant goodwill into installed panels and turbines may be one of the highest-leverage climate opportunities of the coming decade.

Subject of Research: Renewable energy adoption by MSMEs in the MENA region

Article Title: Behavioral and economic drivers of renewable energy adoption by MSMEs in the MENA region

Article References: Mourchid, M., & Ezzahid, E. (2026). Behavioral and economic drivers of renewable energy adoption by MSMEs in the MENA region. Discover Sustainability. https://doi.org/10.1007/s43621-026-04793-3

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04793-3

Keywords: renewable energy, MSMEs, MENA region, technology acceptance model, clean energy transition, energy policy, Egypt, Jordan, Morocco, intention-behavior gap, Behavioral, economic

The researchers drew on the National Health and Nutrition Examination Survey, the long-running CDC program that combines structured home interviews, physical examinations at mobile examination centers, and laboratory testing in a multistage probability sample of the U.S. household population. From survey cycles spanning 2007 through 2020, they identified 1,101 children with obesity between the ages of 8 and 15. Because NHANES collects measured height and weight alongside self-reported perceptions, it offers a rare opportunity to compare what children believe about their bodies against what the calipers and stadiometers record, and to trace how those beliefs ripple outward into behavior.

The analytical strategy was layered. Logistic regression models identified the factors associated with two outcomes: whether a child with obesity accurately perceived their own body weight, and whether that child expressed an intention to lose weight. To characterize diet, the team used principal component analysis, a dimensionality-reduction technique that condenses dozens of dietary variables into a small number of underlying dietary patterns, in this case capturing tendencies toward high-sugar and high-energy food profiles. Multivariate analyses then tested how self-perception, weight loss intention, parents’ perception of the child’s weight, and physician-diagnosed overweight shaped both those dietary patterns and the frequency with which children reported actually trying to lose weight.

The first finding concerns who sees themselves accurately. Older children, girls, children whose overweight had been diagnosed by a physician, children whose parents accurately perceived their weight, and children who already intended to lose weight were all independently more likely to perceive their own body weight correctly. Each of these factors reached statistical significance at the conventional threshold. The pattern fits a social ecological reading of the problem: a child’s self-image is not formed in isolation but is calibrated by signals arriving from family, from the clinic, and from the child’s own developmental stage and gendered social environment.

The second finding concerns motivation. Physician-diagnosed overweight and accurate self-perception emerged as the key factors driving weight loss intention. In other words, a clinical conversation in which a doctor names the condition appears to do work that no amount of ambient health messaging accomplishes on its own. A formal diagnosis seems to anchor the child’s perception, and once perception is anchored, intention follows. Parents’ accurate perception also fed into this chain, promoting both the child’s accurate self-assessment and the motivation that flows from it. The authors frame this as a perception-driven cascade: diagnosis sharpens perception, perception generates intention, and intention opens the door to behavioral change.

But the cascade has a bottleneck, and this is where the study becomes most consequential. Accurate self-perception alone did not translate into healthier eating. The translation only occurred when self-perception was paired with weight loss intention, which the researchers describe as a necessary condition for converting accurate perception into dietary patterns with a lower inclination toward high-sugar and high-energy foods. Perception, in this framework, is a prerequisite rather than a trigger. A child who knows they carry excess weight but has no intention to act shows no measurable dietary advantage over a child who misperceives. Intention is the switch that routes the cognitive signal into the behavioral channel.

Parents occupy a pivotal position in that switching mechanism. The study found that parents’ perception of their child’s weight influenced the translation process by which accurate self-perception and intention became healthier dietary choices. This aligns with a substantial body of research showing that many parents of children with obesity underestimate their child’s weight status, a perceptual blind spot that can quietly neutralize the motivational chain before it reaches the child’s plate. When parents see clearly, the child’s own clarity becomes actionable; when parents do not, the signal attenuates. The social ecological model, which treats behavior as the product of nested individual, interpersonal, and institutional influences, predicts exactly this kind of interaction, and the data bear it out.

Then comes the sobering twist. Neither accurate self-perception, nor weight loss intention, nor the healthier dietary patterns themselves were associated with the frequency of self-reported weight loss attempts. The statistical associations simply dissolved at this final stage. The authors interpret this as evidence of a dissociation between intention and behavior, a well-documented phenomenon in behavioral science sometimes called the intention-behavior gap. Children may know where they stand, may genuinely want to change, and may even shift their everyday food choices toward less sugary and less energy-dense options, yet none of that guarantees they will engage in deliberate, repeated weight loss efforts. The motivational machinery runs fine up to the point of daily choices and then stalls when it comes to sustained, effortful action.

The implications of that stall are significant for anyone designing childhood obesity interventions. If accurate perception and strong intention were sufficient, the policy prescription would be straightforward: diagnose earlier, counsel parents better, and watch behavior follow. The data suggest something more demanding. Interventions that stop at raising awareness or stoking motivation may produce children who see themselves accurately and want to change but lack the skills, routines, and environmental scaffolding to sustain change over time. The authors argue that future strategies should build on intention rather than ending with it, adding systematic support for the practical capabilities and surroundings that long-term behavioral maintenance requires. That could mean structured family-based programs, food environment modifications, and follow-up care that treats dietary change as a skill to be trained rather than a decision to be made.

There are limits worth keeping in view. The study is cross-sectional, capturing a snapshot rather than a trajectory, so the direction of the relationships, while theoretically coherent, cannot be established with certainty; a child who intends to lose weight may perceive more accurately, or accurate perception may breed intention, or both may reflect a third influence. The weight loss frequency measure is self-reported and therefore vulnerable to recall and social desirability biases, particularly in a population of children sensitive about their bodies. And the sample, drawn from U.S. national surveys, may not generalize cleanly to other contexts. Still, the core message travels well beyond the dataset: in childhood obesity, seeing the problem is necessary but nowhere near sufficient, the people around the child shape whether the child sees it at all, and the hardest step, turning a clear-eyed intention into a durable habit, is the one that current interventions most often leave unsupported.

Subject of Research: Self-perceived body weight and dietary behavior among children with obesity

Article Title: Children’s self-body weight perception and dietary behavior under multiple influences: a social ecological perspective

Article References: Li, L., Zhang, Y., Zhang, L., & Lin, S. (2026). Children’s self-body weight perception and dietary behavior under multiple influences: a social ecological perspective. BMC Pediatrics. https://doi.org/10.1186/s12887-026-07840-0

Image Credits: AI Generated

DOI: 10.1186/s12887-026-07840-0

Keywords: childhood obesity, weight perception, weight loss intention, intention-behavior gap, socio-ecological model, dietary patterns, NHANES, pediatrics, parental influence, physician diagnosis, cross-sectional study, weight management

The new instrument, called the PEP-Goal, is the first implicit, reaction-time based measure of health goal conflict and facilitation. It is built on the Propositional Evaluation Paradigm, or PEP, a technique developed to assess personal beliefs and attitudes indirectly. In the PEP, participants are presented with statements and must judge whether each proposition is true or false, and the speed of those judgments carries the diagnostic weight. The logic is straightforward but powerful: if a proposition is easy for a person to endorse or reject because it fits their existing mental representation of the world, responses will be fast. If the proposition clashes with that representation, responses slow down. Unlike classical implicit measures such as the Implicit Association Test, which rely on associative strength between concepts, the PEP targets propositional beliefs, the structured, truth-evaluable contents of thought. This matters for goals, because whether someone believes that their career ambitions undermine their exercise routine is a proposition, not a mere association.

The theoretical backdrop for the study is goal systems theory, which treats goals not as isolated desires but as a network of interconnected pursuits. Within this framework, researchers distinguish between horizontal relations, the links between a health goal and other life goals such as work, relationships, or leisure, and vertical relations, the links between a health goal and a person’s deeper personal values, such as security, achievement, or hedonism. Both kinds of relations can facilitate or impede health behavior. A person whose value system emphasizes self-discipline may find healthy eating naturally supported from above, while someone whose leisure goals revolve around social drinking may find the same goal undermined from the side. Previous work, including studies using accelerometer-assessed physical activity, has shown that these intergoal dynamics predict real-world behavior, but the evidence has rested almost entirely on explicit questionnaires, most notably Personal Project Analysis, a method pioneered by Brian Little in which participants list and rate their ongoing personal projects.

Culot and colleagues set out to validate the PEP-Goal against this established self-report standard and, crucially, against objective behavior. Participants completed the implicit paradigm, which presented them with propositions concerning the relations between their health goals and other goals or values, and their response times were converted into implicit conflict and facilitation scores. They also completed an adapted version of Personal Project Analysis designed to capture the same horizontal and vertical relations explicitly. The researchers then tracked participants’ actual physical activity over one week using wearable activity trackers, providing an objective behavioral outcome that does not depend on the honesty or accuracy of self-description. The study was funded by the Fonds de la Recherche Scientifique (FNRS) under grant number 40003190, approved by the ethics committee of the Faculty of Psychology at Université Libre de Bruxelles, and conducted in line with the Declaration of Helsinki. Data and code were made openly available through the Open Science Framework.

The results delivered a striking dissociation. The implicit PEP-Goal scores did not simply mirror what participants reported on the explicit questionnaire; the two measures diverged in notable ways, suggesting that they tap partly different psychological realities. This kind of divergence is exactly what one would expect if explicit reports are filtered through introspection and self-presentation while reaction-time measures capture more automatic evaluative processes. It also echoes a broader and sometimes sobering literature on implicit measures, which has shown that their ability to predict behavior varies widely depending on how well the measure matches the behavior in question. The Belgian team’s design directly addressed one of the most common criticisms of implicit measures by aligning the content of the paradigm precisely with the target: goal relations, rather than generic attitudes toward health.

The most consequential finding concerned prediction. Implicit horizontal PEP-Goal scores, those capturing the relations between health goals and other life goals, uniquely predicted objectively tracked physical activity over the following week, above and beyond what the explicit self-report measures could explain. In other words, the reaction-time paradigm detected something about the structure of participants’ goal networks that questionnaires missed, and that something translated into measurable movement. For a field in which self-reported physical activity is notoriously inflated and often weakly correlated with actual behavior, the demonstration that an implicit measure adds incremental predictive validity over an explicit one is a meaningful methodological advance. It suggests that some of the conflicts and synergies shaping health behavior operate below the level of ready verbal report.

The vertical relations told a different story. Implicit scores capturing the links between health goals and personal values showed no significant predictive value for physical activity. The authors are careful about this null result, and it fits with a nuanced view of how values operate. Personal values, as formalized in Schwartz’s theory of basic values, are relatively stable, abstract motivational commitments. Their influence on concrete daily behavior may be diffuse and mediated by many intermediate steps, making them harder to capture with a quick truth-evaluation task. Alternatively, the vertical propositions used in the paradigm may simply require further refinement before they can register these deeper alignments. Either way, the asymmetry between horizontal and vertical results is itself informative: it implies that the everyday friction between competing goals, rather than abstract value conflict, is where implicit measurement currently pays off.

The practical implications reach into the design of health interventions. Personalized health-behavior programs increasingly try to tailor advice to an individual’s life circumstances, and goal conflict is a natural target: if a program can identify that a patient’s exercise goal is being quietly sabotaged by a competing work goal, it can help restructure schedules, reframe priorities, or build implementation plans that protect the health behavior. A brief, computerized reaction-time task could, in principle, screen for these conflicts faster and more sensitively than lengthy project interviews, and could detect facilitative relations worth exploiting as well as conflicts worth defusing. The authors caution that the tool still needs refinement before it is ready for applied use, but they argue that the current findings offer promising initial evidence for both the predictive validity and the practical utility of the PEP-Goal in health-behavior research and intervention planning.

The study also contributes to a wider methodological conversation in psychology about what implicit measures are and what they can honestly claim to do. Following the normative analysis offered by Jan De Houwer and colleagues, implicit measures are best understood as indirect measures, inferring mental content from performance on a task, rather than as windows onto a separate unconscious mind. On this reading, the PEP-Goal’s success is not evidence of a hidden unconscious, but evidence that the propositional structure of people’s beliefs about their own goals leaves a measurable trace in the speed of their judgments. That trace, the study shows, carries information about future behavior that introspection does not fully supply. As wearable sensors make objective health behavior ever easier to track, pairing them with validated implicit measures of goal dynamics could close the loop between what people believe about their motivations and what their bodies actually do. The PEP-Goal is an early step in that direction, and its horizontal findings in particular suggest that the most important obstacles to healthy living may be the ones people cannot easily put into words.

Subject of Research: Validation of an implicit reaction-time measure of health goal conflict and facilitation for predicting physical activity

Article Title: The PEP-GOAL: validation of a propositional evaluation paradigm as an implicit measure of health goal conflict and facilitation

Article References: Culot, C., Crombez, G., Kassas, S., Leys, C., Smeding, A., & DeSmet, A. (2026). The PEP-GOAL: validation of a propositional evaluation paradigm as an implicit measure of health goal conflict and facilitation. Current Psychology, 45(18), Article 1504. https://doi.org/10.1007/s12144-026-10035-6

Image Credits: AI Generated

DOI: 10.1007/s12144-026-10035-6

Keywords: implicit measure, goal conflict, goal facilitation, health psychology, physical activity, propositional evaluation paradigm, psychometrics, personal values, intention-behavior gap, wearable activity trackers, goal systems theory, behavior change

Professor Youngho Kim and researcher Dojin An at Seoul National University of Science and Technology, known as SEOULTECH, set out to answer a deceptively simple question: does watching sports content on YouTube actually translate into playing sports in real life? Their investigation, published in Volume 14, Issue 4 of the journal Performance Enhancement & Health and made available online on August 12, 2026, examined the motivational drivers behind college students’ use of YouTube sports content and traced whether those drivers led anywhere beyond the screen. The answer, in short, was largely no. Motivation and positive attitudes toward the platform increased the intention to keep watching, but neither factor significantly increased actual physical activity.

The theoretical backbone of the study was the Technology Acceptance Model, or TAM, a widely used framework in information systems research that explains how people come to adopt and use new technologies. TAM proposes that two perceptions are central: perceived usefulness, meaning the belief that a technology helps achieve desired outcomes, and perceived ease of use, meaning the belief that operating it requires little effort. These perceptions shape attitudes toward the technology, which in turn shape behavioral intention, the stated willingness to keep using it. In the classic model, intention is treated as the most immediate psychological antecedent of actual behavior. The Korean researchers wanted to know whether this chain, so often validated in contexts like e-commerce and mobile banking, would hold when the ultimate outcome was not continued viewing but physical movement.

To test this, the team surveyed university students in their twenties who had prior experience watching sports content on YouTube. Participants rated their motivation to use the content and their intention to continue using it on a five-point Likert scale, anchored at one end with “not at all” and at the other with “very much.” The questionnaire also captured the TAM constructs, asking students how useful and how easy to use they found the platform for sports content, and their attitudes toward engaging with it. Crucially, the study did not stop at intention. Sports participation was measured behaviorally, by asking students how frequently they engaged in vigorous, moderate, and light physical activities for at least fifteen minutes per session during a typical week, a structure consistent with established physical activity recall instruments.

The statistical picture that emerged was strikingly asymmetric. Students who were more motivated to use YouTube sports content were indeed more likely to intend to keep using it, and they viewed the platform more favorably through the lens of the Technology Acceptance Model. Perceived usefulness and ease of use contributed significantly to the intention to engage with the content, and TAM partially mediated the relationship between motivation and that intention. In other words, the psychological machinery linking motivation to continued viewing worked exactly as the model predicts. But when the researchers traced the same motivational and intentional factors forward to actual sports participation, the chain broke. Neither motivation nor intention to use YouTube sports content showed a significant relationship with how often students exercised, trained, or played in the real world.

“In our analysis, we found that motivation to use YouTube sports content and technology acceptance increase users’ intention to engage with the content,” Professor Kim explained. “However, these factors do not directly translate into actual sports participation.” This gap between digital engagement and embodied behavior is the study’s central finding, and it carries implications that extend well beyond YouTube. Media campaigns that assume exposure to sports content will produce active audiences may be relying on a link that simply does not exist in the data. The finding also aligns with a broader pattern in behavioral science, where intention is often a weak predictor of behavior, a phenomenon sometimes called the intention-behavior gap. Watching a fitness influencer demonstrate a perfect squat, it turns out, is psychologically closer to entertainment than to exercise.

The reasons for this disconnect are not hard to imagine, even though the study itself measured the relationships rather than the underlying mechanisms. Consuming sports content provides many of the rewards of sports fandom, including excitement, social talking points, and a sense of identity, without any of the costs of physical exertion. The platform’s recommendation algorithms are optimized to maximize watch time, not steps taken or calories burned. A viewer can spend an entire evening immersed in training montages and nutrition advice while remaining entirely sedentary, and the satisfaction derived from that immersion may even reduce the felt need to be active. The TAM framework, designed to explain technology adoption, captures the psychology of continued use but offers no built-in pathway from screen to playing field.

What makes the study valuable is that it does not simply deliver a negative result. The researchers argue that YouTube sports content could be redesigned to close the gap between viewing and doing. “Integrating the following features into digital sports content could help users move beyond passive viewing and engage in sustained physical activity: exercise action plans, links to local sports facilities, social support, and strategies to enhance self-efficacy,” said Professor Kim. Self-efficacy, the belief in one’s own capability to execute a behavior, is a well-established predictor of exercise adherence in health psychology, and it is rarely addressed by highlight reels or aspirational athlete content. Concrete action plans, low-threshold entry points such as follow-along routines, and community features that connect viewers with local opportunities could convert passive consumption into structured activity.

For content creators, platform designers, and public health communicators, the message is that inspiration alone is not intervention. The study suggests that the enormous reach of sports media among young adults represents an underused resource, one that will not deliver physical activity benefits automatically but might do so if content is deliberately engineered to prompt action. Embedding links to nearby facilities, offering graduated exercise guides matched to viewer ability, and building social accountability into the viewing experience are all plausible design responses supported by the study’s recommendations. As digital media continues to absorb the attention that previous generations devoted to playing sports after school, understanding how to turn that attention into movement becomes an increasingly urgent research agenda.

The work, titled “Can YouTube promote sports participation? A technology acceptance model approach to college students’ motivations for using sports content,” was conducted as a survey-based investigation and published in Performance Enhancement & Health. Its authors report no known competing financial interests that could have influenced the findings. For now, the takeaway for the millions of students who fill their evenings with sports videos is a sobering one: the algorithm may know exactly what you want to watch, but watching, by itself, will not get you off the couch. Turning inspiration into perspiration still requires deliberate design, both in the content itself and in the habits of the viewer.

Subject of Research: The relationship between college students' YouTube sports content consumption and their real-world sports participation

Article Title: Watching sports on YouTube Doesn’t mean you’ll get off the couch, new SEOULTECH research shows

Article References: Watching sports on YouTube Doesn’t mean you’ll get off the couch, new SEOULTECH research shows. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: YouTube, sports participation, physical activity, Technology Acceptance Model, college students, digital media, exercise behavior, sports content, health psychology, intention-behavior gap, SEOULTECH, survey research