The Archive
All Papers
Every paper published in the Student Journal of Business and Economics. Each has its own permanent, original SJBE DOI and is freely citable, anywhere.
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This study investigates how artificial intelligence and digital transformation have shaped economic growth in Azerbaijan, drawing on time series data spanning 1996 to 2025. Using correlation analysis, Augmented Dickey-Fuller unit root testing, Granger causality testing, and ordinary least squares regression, the analysis traces the relationship between a composite Digital Economy Index and both real and non-oil GDP growth. The Digital Economy Index is constructed as a weighted composite measure incorporating five standardized indicators: internet penetration (sourced from the International Telecommunication Union), mobile subscriptions per 100 inhabitants (ITU and State Statistical Committee of Azerbaijan), ICT sector value added as a percentage of GDP (State Statistical Committee of Azerbaijan and Ibadoghlu, 2025), the United Nations E-Government Development Index (UN DESA, 2024), and the ratio of cashless payments to total transactions (Central Bank of Azerbaijan, 2024). Each component was min-max normalized to a 0–100 scale and aggregated using equal weights to ensure transparency and replicability. The results reveal a marked structural shift after 2016: while overall GDP growth slowed as oil production declined, the Digital Economy Index rose sharply and became closely linked to non-oil growth. In the post-oil period (2016–2025), this relationship is strong and statistically robust (R2 = 0.823, p < 0.001), with a one-unit rise in the index associated with a 0.208 percentage-point increase in non-oil GDP growth. These findings are interpreted alongside the objectives of the Azerbaijan Artificial Intelligence Strategy (2025–2028), and the study closes with policy recommendations aimed at translating digital gains into lasting, broad-based economic diversification beyond the hydrocarbon sector.
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25 Sep 2026
This independent research paper examines the contemporary economic and operational dynamics of the Middle Corridor (Trans-Caspian International Transport Route) connecting China and European markets via Kazakhstan, the Caspian Sea, Azerbaijan, and Georgia (Middle Corridor 2026). The central research problem addresses hidden logistical friction, demonstrating that total supply chain costs depend heavily on terminal storage and delays rather than basic transport tariffs. Utilizing empirical commercial tariff sheets from DB Cargo Eurasia GmbH (2023), this study models the exact financial impact of border idling and progressive demurrage rates at critical European cargo hubs. Furthermore, it evaluates the deployment of blockchain-backed “Smart Customs” solutions managed by the State Revenue Committee of Kazakhstan, specifically focusing on how decentralized ledger architectures secure electronic consignment notes (e-CMR) to establish multi-national data trust. The empirical findings show that automated transit workflows compress block train declaration intervals from 3 hours to just 30 minutes, yielding a distinct 83.3% time optimization (State Revenue Committee 2026). The study concludes that digital ledger trust and physical infrastructures must develop in tandem, creating a major potential for systemic cost reduction along global trade routes.
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This study examines whether uncertainty in the wording of annual reports (10-K) affects Environmental, Social, and Governance (ESG) performance for 72 US public firms. Findings show a negative relation between uncertain words and ESG performance, indicating that greater use of ambiguous words in firms' financial statements negatively affects firms' ESG scores. This finding shows that market participants may notice that firms may deploy vagueness or ambiguity to disguise their poor performance or negative impact on the environment (i.e., Greenwashing). Findings from this study could provide some insights to investors, managers, and regulators in the capital market by recommending that public firms use more transparent and clear language in their 10-K reports.
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13 Sep 2026
This paper analyses the differing labour market effects created by industrial robotics and generative Artificial Intelligence (AI) within the Australian economy. While industrial robotics and generative AI are often aggregated under the singular banner term of “automation”, treating them as a homogenous phenomenon downplays their radically divergent transmission mechanisms. By utilising a task-based framework, this paper argues that these two technologies propagate via entirely distinct tracks within the Australian economy; automation is not one overall phenomenon, but rather has a multifaceted dual-track impact, stemming from the automation of individual tasks as opposed to whole jobs. Track 1 investigates the substitution forces of robotics within sectors like mining and manufacturing. The capital intensive and highly structured nature of robotics implementation creates concentrated labour market displacement shocks that are felt within local communities. Track 2 examines the geographically diffused augmentation effects of generative AI across the services sector, specifically finance and retail. Driven by low-friction, decentralised implementation, generative AI has introduced an uneven wave of task transformation and restructuring as opposed to systemic job elimination. Via a qualitative review of domestic sectoral data, this study demonstrates that productivity gains realised at a national macro-level fail to capture the localised socioeconomic disruptions and adjustments observed at the community and firm level. By disaggregating these technological mechanisms, this paper leverages Australia’s distinct sectoral landscape to clarify the globally evolving friction between technological replacement and human reinstatement. In relation to controlling externalities, this paper uses the same disaggregation to conclude that because automation is not a singular process, labour market adaptation cannot be a singular response either.
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13 Sep 2026
Cryptocurrency is often described as a decentralized and accessible financial system, but the concentration of wealth among large holders raises questions about how decentralized cryptocurrency markets actually are. This study examines how high-net-worth individuals (HNWIs), particularly cryptocurrency “whales,” influence cryptocurrency markets and how their influence extends beyond their direct transactions. Using a literature review and secondary data analysis, this research examines ownership concentration, market spillover effects, and behavioral responses to whale activity using academic research, blockchain analytics, institutional reports, and case-based evidence. The findings show that a small number of large holders control a disproportionate share of cryptocurrency assets, allowing their transactions to affect market volatility and generate spillover effects across other assets. However, the findings also suggest that whale influence is amplified by the behavior of smaller investors. Investors may interpret whale transactions and announcements as signals of future market movements, leading them to imitate these actions and further amplify price changes. The Tesla Bitcoin case provides an example of how a major institutional announcement can influence market behavior beyond the direct financial transaction. Overall, the findings support the hypothesis that whale influence comes not only from concentrated ownership and trading power, but also from the behavioral responses of other market participants. This suggests that cryptocurrency may be decentralized technologically while remaining concentrated in terms of wealth, influence, and market power.
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10 Sep 2026
Profit-driven behavior has caused long-lasting debates. Does it serve as an impetus to unlock human potential and foster achievement, or does it treat financial gain as the sole objective, thus leading to unethical harm to society? Gaining profit is vital for enterprises to seek survival and expansion, it is undoubtedly an incentive for businesses to find ways to improve. Generating creativity, evaluating multiple alternatives, optimizing process flow, and meeting diverse customer needs, are all driven by this incentive. As a result, innovations and even revolutions occur, which raise the overall economic and technological level, enhancing human capabilities. On the other hand, the temptation for money gain compels some people to lose sight of ethical business practices. In this case, regulations are ignored, public health is put aside, and environmental sustainability is sacrificed in exchange for short-term financial rewards. However, the intention for pursuing profit is not wrong, what needs to be rectified is the path some people take to reach the goal without integrity. Cultivating ethical values and civic responsibility, selecting ethical business leaders, enforcing strong regulations, and establishing a healthy competitive environment can help shape responsible business conduct and ensure the journey of profit acquirement promotes human progress rather than causing trouble for humanity.
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01 Sep 2026
The rapid proliferation of artificial intelligence has dramatically increased global demand for computing power, reshaping industries and accelerating the need for large-scale digital infrastructure. To meet this demand, technology companies have rapidly expanded data center development, often siting facilities in rural and suburban regions of the United States where land is cheaper, and zoning regulations are more permissive. Expansion supports innovation and economic growth; however, it also introduces significant issues for host communities. AI-driven data centers require enormous amounts of electricity and water, largely due to their reliance on graphic processing units (GPUs), which generate substantial heat. Cooling these systems requires significant water use, straining municipal water supplies and, in some cases, threatening the sustainability of local aquifers. These environmental pressures degrade local ecosystems and expose nearby populations to increased health risks. Data center expansion also carries important economic consequences for local communities, especially seen in Morrow County. The need to upgrade electrical grids, water systems, and other infrastructure often shifts costs onto taxpayers and ratepayers, while pollution-related health effects can create additional financial burdens through increased healthcare costs. This paper argues that while AI data centers enable technological progress and regional economic activity, they impose substantial environmental and economic trade-offs through infrastructure strain, resource overconsumption, and public health impacts. However, these outcomes are not inevitable; with stronger policy frameworks, sustainable technologies, and more equitable community agreements, it is possible to better balance the benefits of AI expansion with the well-being of the communities.
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This paper examines the conditional effects of narrow artificial intelligence on the economic performance of small and medium sized enterprises (SMEs) in the United States using secondary research and policy evidence. It argues that narrow AI tends to improve productivity, reduce operating costs, and support profitability by automating routine tasks, improving customer communication, and strengthening decision making. However, these benefits are not automatic. They depend on a firm’s financial capacity, workforce skills, digital readiness, and ability to manage privacy and cybersecurity risks. For many SMEs, adoption remains limited by implementation costs, knowledge, and concerns about data protection and regulational uncertainty. This paper concludes that narrow AI is most beneficial when it is carefully integrated into business operations with adequate training and security support, but without these conditions, effects are often uneven.
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This paper examines country-level associations between secondary school financial literacy, curriculum design, and macroeconomic outcomes in an unbalanced panel of ten selected OECD nations comprising 24 country–wave observations from the 2012, 2015, 2018, and 2022 PISA cycles. It distinguishes integrated curricula, which include investment mechanics, from thrift-based curricula, which emphasize saving and debt avoidance. Ordinary least squares models with wave-year fixed effects and HC3 heteroskedasticity-robust standard errors assess associations with domestic private credit as a percentage of GDP, income inequality measured by the Gini index, and annual real GDP growth. Financial literacy scores and curriculum type are not statistically significant for any outcome under the Bonferroni-adjusted confirmatory threshold (α* = 0.0083 for six tests). Gross secondary school enrollment, a control variable evaluated at α = 0.05, is positively associated with private credit under HC3 inference (β̂ = 1.953, p = 0.004), but its country-level bootstrap 95% confidence interval includes zero. The signal is fragile. It does not constitute robust evidence. Because curriculum assignment is not exogenous and the sample is small, all findings are associational and should not be interpreted causally.
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Beauty supplements have become a popular part of the health and wellness industry, with many people using them in hopes of improving their hair, skin, and nails. Products containing ingredients such as collagen, biotin, and hyaluronic acid are often advertised as being scientifically supported, but available research does not always confirm these claims. This study examined whether the scientific evidence supports the marketing claims used to promote these supplements. Information was gathered from peer-reviewed journal articles, systematic reviews, government publications, and studies on beauty supplement advertising. Each claim was grouped by ingredient and advertised benefit, then classified as having strong, limited, or insufficient scientific support. Descriptive statistics and a graph theory model were used to organize, visualize, and compare the relationships between marketing claims and scientific evidence. Of the eight beauty claims examined, none had strong scientific support. Four were classified as having limited evidence, while the other four had insufficient evidence. Collagen showed limited support for certain skin-related benefits, but biotin had much less evidence despite being heavily promoted. Overall, the findings suggest that many beauty supplement advertisements make broader claims than the available research supports. These findings highlight the gap between marketing messages and the scientific evidence supporting beauty supplement claims.
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Destination Marketing Organizations (DMOs) serve as the primary public-facing information source for U.S. travelers researching where to stay in a destination. This study examines what official DMO websites in the United States choose to include when presenting lodging options for prospective visitors. The paper analyzes 97 official DMO websites selected for being the top ranked counties in terms of individuals hired by the hospitality and tourism industry. The analysis recorded the presence of six distinct accommodation categories: hotels, resorts, vacation rentals, campgrounds and RV parks, bed and breakfasts, and short-term rentals across all 97 destinations. The findings revealed a consistent and non-random pattern of accommodation representation. Hotels appeared on 96.9% of websites with an average of 105 listings per website. Short-term rentals in comparison, appear on fewer than 5% of sites. The rank order of category exclusion aligns closely with the rank order of competitive proximity to the hotel model. In addition, larger markets, despite their increased access to organizational resources, list fewer accommodation types than smaller leisure destinations. These patterns hold regardless of geographic location, market size, and destination type. These findings suggest that official tourism board websites present an incomplete picture of the lodging landscape that favors hotels over all competing accommodation categories. The findings contribute to current conversations about the role of tourism marketing organizations as intermediaries and raise questions about the accuracy of the accommodation landscape these organizations present to the traveling public.
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22 Aug 2026
This paper asks whether stronger observable business signals in a beat listing are associated with higher listed basic lease prices on independent online beat marketplaces. Using a manually coded convenience sample of 100 listings collected from BeatStars and Traktrain, the study constructs a Business Signal Index from three buyer-visible listing attributes: metadata completeness (BPM and musical key), licensing clarity, and website presence. Each attribute is scored on a partial scale, and listings are assigned to Low (0 to 0.5), Medium (1 to 1.5), or High (2 to 3) signal groups. Of the 100 listings, 75 displayed a visible basic lease price and form the basis of the main analysis; the remaining 25 either omitted a price or listed it as negotiable. Within this sample, mean listed basic lease prices were $21.69 for the Low group (n = 20), $24.64 for the Medium group (n = 29), and $28.19 for the High group (n = 26). The difference between the High and Low group means, based on the displayed values, is $6.50, or approximately 30%. This descriptive pattern is consistent with signaling theory, which predicts that observable seller attributes may reduce buyer uncertainty in information-asymmetric markets, but the study cannot establish whether listing signals drive prices, reflect producer experience, or co-vary with unmeasured factors. The analysis is cross-sectional, observational, and exploratory; no causal claims are made. Key limitations include a small convenience sample, no audio-quality measure, no producer-level controls, and a single coder. The study provides a preliminary descriptive baseline and a framework for more rigorous follow-up research.
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Traditional financial literacy frameworks mainly focus on mathematical or institutional knowledge while neglecting cognitive biases and socioeconomic impulses which dictate youth investment behavior on digital platforms. The study’s purpose is to evaluate the extent to which targeted financial literacy interventions with psychological regulation and technical education, addressing loss aversion and Fear of Missing Out (FOMO), can predict disciplined investment intentions and influence risk tolerance amongst high school students. Using a mixed-methods quasi-experimental approach, a sample including high school students (n = 60, grades 10-12), was split into two groups. A treatment group (n = 30) that received a four-week behavioral finance curriculum and a control group (n = 30). Quantitative data was collected through pre- and post-test literacy assessments and self-reported Likert scales regarding investment intentions. Qualitative understanding was gathered via semi-structured interviews analyzed through thematic coding. Quantitative analysis revealed an increase in the treatment group’s mean financial literacy score from 42% to 78%, while the control group showed no significant change. A strong positive correlation (Pearson’s r = 0.65, p < 0.01) was established between post-intervention literacy and in disciplined long-term investment intentions over speculative vehicles associated with high-risk. Qualitative themes validate these findings by demonstrating a clear behavioral shift from speculation driven by social media to strict emotional regulation and diversification. These findings demonstrate that targeted financial education can predict disciplined allocation of assets successfully if cognitive knowledge and behavioral intelligence are utilized. This creates a compelling, empirical argument for including behavioral economics in mandatory high school curricula.
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This paper examines how financial markets and participants price research and development (R&D) expenses in the short term, with a focus on U.S. technology and industrial firms. Though historically R&D has been known to drive long-term firm performance, in the short-term it is not clear to investors how R&D should impact valuation, as it can create uncertainty and potentially delayed returns. Using quarterly financials and stock price data, this study analyzes how changes in R&D intensity can impact stock price performance over intraday, interday, and quarterly time periods. Regressions were performed on the firm, sector, and pooled levels, with the S&P 500 used as a market control to isolate the causal effect at hand. Findings suggest that there is a negative relationship between R&D intensity and short-term share price movement. This effect was found to be the strongest and most significant in technology firms, with the intensity of the effect growing across longer time horizons. These results indicate that investors interpret R&D as a source of uncertainty in the short-term for technology firms.
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This study specifically examines how reliance on mobile app-based investment influences investor behavior compared to human or brokerage/firm-based advice. This study focuses solely on investor behavior among U.S. investors and uses data from the 2024 National Financial Capability Study Investor Survey. This study conducted a quantitative secondary data analysis of 1,297 respondents categorized into non-overlapping advisory source groups. Respondents were seen as mobile app-based users if they relied on popular investments displayed on a mobile trading app and as human or brokerage/firm-based advice users if they relied on financial professionals or any other investment research and tools provided by a brokerage or advisory firm. This study was conducted in RStudio, using survey weights to allow better population-level generalizability. The results showed statistically significant differences across all three outcomes. Human or brokerage/firm-based advice users reported higher short-term investing motivation and higher stock ownership rates, while mobile app-based users reported higher trading activity. The findings show that advisory sources are related to distinct patterns of investor behavior, with mobile app-based influence linked to more frequent trading and human or brokerage/firm-based advice linked to more consistent market participation. The study contributes to behavioral finance research by comparing advisory source groups within a nationally representative investor dataset.
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09 Jun 2026
As environmental concerns become increasingly urgent, investment firms play a growing role in shaping environmental and social outcomes. One prominent way this occurs is through green investing, a type of investing whose reliability relies on companies’ self reported environmental, social and governance (ESG) factors. The concept of green investing has been around for over twenty years, yet its ability to accurately measure an investment firm’s commitment to the environment has been questioned. This paper examines how large investment firms, such as Morgan Stanley and Blackstone, incorporate ESG factors into investment decisions in comparison to smaller impact-focused firms, such as Sonen Capital and Veris Wealth Partners. Through a comparative analysis of the impact of their respective investments, self-reported environmental consciousness and financial scope, this paper finds that ESG factors often serve as risk management tools rather than accurate measures of environmental commitment. These findings suggest that current ESG measures inadequately capture true impact, highlighting the need for a new system to accurately measure these elements in order to guide business and clients to make more informed decisions about who they trust with their investments.
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Three concurrent disruptions have altered the rent-vs.-buy calculus for New Jersey households: median home values reached $569,314 in 2026, mortgage rates doubled from 3% to over 7%, and annual property-tax bills routinely exceed $10,000. This study tests under which conditions purchasing a home in NJ generates greater long-term wealth than renting and reinvesting the difference, and whether existing policy instruments target the variables that most strongly govern that outcome. A 14-variable financial model was calibrated to current NJ data from Zillow, ATTOM, and FRED. Sensitivity analysis ranked all variables by impact on the buyer-minus-renter wealth difference, and Monte Carlo simulation with 10,000 trials modeled outcome uncertainty under correlated distributions across six counties and two return assumptions. We found that holding period, property price, and monthly rent are the dominant outcome drivers regardless of return assumption: under a conservative 8% annual investment return, buying outperforms renting in all six counties within 3–10 years, while under a historical 13.5% return, renting dominates in five of six counties. The SALT deduction cap ranked last among all ten variables tested, with an impact range of approximately $98,000—one-thirty-fifth that of holding period. These results suggest that current NJ homeownership tax policy is misaligned with the variables that most strongly determine wealth outcomes, and that policy instruments targeting mortgage rate access, transaction cost reduction, and housing supply are better positioned to influence the rent-vs.-buy decision than existing tax deductions.
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This paper examines how diversity, equity, and inclusion (DEI), consumer psychology, and neuroscience intersect to shape audience responses to digital advertising. Drawing on research in third-space theory, social neuroscience, and parasocial interaction, it develops a framework for evaluating how advertising communicates belonging and what happens when it fails to do so. This framework is then applied to American Eagle Outfitters’ 2025 campaign featuring Sydney Sweeney, titled “Sydney Sweeney Has Great Jeans.” The campaign’s visual and linguistic emphasis on a singular identity and inherited physical traits, centered on a “jeans/genes” wordplay, is analyzed through the lens of DEI representation, neural reward and threat systems, and third-space community dynamics. Findings suggest that while the campaign achieved significant reach, its structure limited inclusive identification, created interpretive ambiguity, and reduced the likelihood of broad emotional engagement. The paper concludes by proposing evidence-based advertising strategies that integrate authentic representation, community-oriented design, and participatory digital practices to foster stronger, more inclusive consumer connections.
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16 May 2026
The present study utilizes a quantitative cross-sectional survey of 154 valid subjects (respondents) after removing invalid or ineligible subjects, to determine if two motivational processes, discount responsiveness and brand perception, as well as impulse buying tendencies, can predict how often young adults (ages 18–25) make unplanned purchases, and how much money they spend on unplanned purchases. Impulse buying has been studied in relation to promotional cues, brand-related factors, and susceptibility to impulse buying, but prior studies have measured it more as an overall tendency or likelihood of purchase than as a recent behavior with measurable financial consequences. Using descriptive statistics, reliability analysis, Spearman correlations, and multiple regression models, the data were analyzed to examine the relationships of discount responsiveness, brand perception, and impulse-buying tendencies with the frequency and monetary value of unplanned purchases. Approximately 90% of the young adults sampled made at least one unplanned purchase in the previous 30 days, and over one-third of respondents reported spending at least 5,000 rubles on such purchases. Impulse-buying tendencies were consistently the strongest predictors of both the frequency of unplanned purchases and the amount spent on them, while discount responsiveness and brand perception were only descriptively relevant and did not independently predict either outcome. The results of this study indicate that impulse buying should be viewed not only as a psychological tendency but also as a behavior with measurable financial consequences.
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09 May 2026
Rhode Beauty has disrupted the beauty industry through innovative, cost-efficient marketing strategies centered around a large social media presence and direct-to-consumer distribution. The brand leverages founder Hailey Bieber’s strong personal influence and global visibility (amplified by her connection to Justin Bieber) to rapidly build consumer trust and engagement. By prioritizing advertisements on platforms like Instagram and TikTok, Rhode creates a cohesive aesthetic and fosters community-driven participation, allowing consumers to feel directly involved in product development and brand identity. This paper also highlights Rhode’s strong appeal to Gen Z audiences through its minimalist branding, accessible pricing, and alignment with digital culture. Additionally, it analyzes the brand’s strategic use of scarcity, influencer marketing, and how its consistent visual identity contributed to rapid growth and early acquisition success. Overall, Rhode exemplifies a new model of celebrity entrepreneurship that blends authenticity, digital strategy, and cultural relevance.
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This study presents a comparative evaluation using risk-adjusted metrics of performances among various reinforcement learning algorithms: Q-learning, Deep Q-Networks (DQN), and Proximal Policy Optimization (PPO) (Schulman et al.) models. Stock price data for market structures including AAPL, MSFT, and SPY covering the time period from January 1, 2012 until June 1, 2014 were used in the study, while each algorithm had been comprehensively trained, taking into account of various evaluation metrics including Sharpe and Sortino ratios, maximum drawdown, total return, and number of trades. The results had indicated that the Proximal Policy Optimization (PPO) agent had outperformed the two supplemental algorithms in terms of overall profitability. Across a majority of the evaluated metrics and stocks, the Proximal Policy Optimization (PPO) agent consistently outperformed both the DQN and Q-learning regarding general profitability. However, there is a narrow range of market structures alongside stocks within the program, possibly inhibiting more concrete results.
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30 Mar 2026
Recent changes to NCAA rules now allow college athletes to earn money from their name, image, and likeness (NIL), giving them new opportunities but also new financial responsibilities (NCAA, 2021). Many athletes are excited about these chances, but studies show that most financial education programs are short, optional, and designed for general students rather than athletes with irregular or large incomes (Edwards, 2024; Meares et al., 2024). As a result, many athletes have trouble with budgeting, paying taxes, saving, and investing, which can lead to costly mistakes (Soto et al., 2017). This paper looks at the weaknesses of current financial programs and examines targeted programs like “Money 101,” which show that athlete-focused instruction can improve money skills and decision-making (Edwards, 2024). Based on this research, the paper proposes a financial education program designed specifically for college athletes, teaching practical skills for managing NIL income, including taxes, budgeting, and long-term planning. By combining required financial education with NIL opportunities, colleges can help athletes make smarter choices, avoid financial problems, and build long-term financial security (Anderson et al., 2019).
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Pickleball has become one of the most rapidly expanding sports in the U.S., impacting more than just individuals who have developed a liking to the game. In this paper, the increase in players is examined to reveal how it alters such aspects as local communities, amount spent, and speed with courts and equipment being constructed. It deconstructs how the sport is inexpensive and adaptable, making it easy to jump into for children and adults, and then how that leads to the desires of cities to increase the number of courts, classes, and other businesses. The study also discusses how selling equipment, hosting tournaments, sponsorships, and special facilities are bringing in new money and provoking fights about who can play, space, and neighborhoods. The paper connects what players can do to the market response, demonstrates why pickleball is so huge to those interested in the business, why it interests city leaders, and what could slow down growth, accelerate it, or push its boundaries.
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Social isolation and loneliness among aging populations represent a growing public health concern with significant emotional, cognitive, and physical consequences (Kotwal and Cudjoe, 1). This literature review examines the structural, social, and cultural factors contributing to elder loneliness, including family dynamics, technological barriers, and reduced opportunities for meaningful interactions. Drawing on existing research and community-based interventions, the paper evaluates the effectiveness and limitations of traditional programs, youth-oriented nonprofits, and institutional models. Specifically, youth-led programs and microbusiness models are viewed as innovative, sustainable approaches to reducing isolation. These models emphasize consistency, intergenerational connection, and relationship-based support while simultaneously fostering empathy, leadership, and responsibility among younger people. By combining findings from multiple interventions, this paper highlights the potential of youth-driven initiatives to address gaps in current elder care solutions. Ultimately, the study argues that community-based, intergenerational approaches offer a promising pathway for reducing loneliness and strengthening social cohesion across generations.
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07 Mar 2026
Artificial intelligence (AI) agents are rapidly transforming knowledge-intensive work across industries. Unlike traditional automation systems that execute predefined rule-based instructions, modern AI agents autonomously plan, reason, retrieve information, execute workflows, and iteratively refine outputs across domains such as finance, research, operations, and digital commerce. Recent empirical studies demonstrate that generative AI systems significantly increase productivity, particularly in writing, analysis, and structured decision-making environments (Noy and Zhang; Brynjolfsson et al.). This paper expands that literature by examining applied experimentation with Alfred AI, an autonomous agent deployed in small-scale e-commerce environments. Observational evidence suggests that AI agents can replace or augment hundreds of hours of repetitive cognitive labor annually by automating pricing, inventory optimization, monitoring, and data-driven decision support. However, these gains remain constrained by governance complexity, model reliability limitations, orchestration challenges, and the ongoing necessity of human oversight. The findings suggest that AI agents represent scalable cognitive infrastructure, but their long-term effectiveness depends on structured guardrails, human-in-the-loop design, and ethical governance.
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28 Feb 2026
High schoolers and pre-adults have a lot of financial decisions to make throughout their life, but most of them do not really know the effect of money or how their decisions could affect or change their and others’ lives. Based on this, I created the Teen Financial Education Framework (TFEF) to show how social pressure and lack of financial awareness lead to spending choices. I surveyed 50 high school students and split them into two groups: one group received financial knowledge on personal finance and stories about families who needed the money, while the other group did not. The results: the awareness group put 70% of their $1,000 into savings while the control group put 20% of their $1,000 into savings, showing clear difference and growth. This proves that even a little awareness can change teen minds significantly and how financial awareness can influence one’s life.
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Rhode Island ranks among the least affordable housing markets in the United States, with slow growth in new units, an aging housing stock, and rising short-term rental activity contributing to persistent supply constraints. This paper examines how permitting delays affect housing development in Providence, Rhode Island. Using an original dataset scraped from the city’s permitting portal (2015 to 2025), the study constructs project-level measures of permitting and construction timelines and estimates an ordinary least squares regression to identify correlates of delay. The analysis finds that new structures, larger projects, and higher estimated costs are associated with longer permitting times, while year effects suggest modest improvement over time. Although the model explains a limited share of variance (R² ≈ 0.15), the results underscore that unpredictability in permitting is itself a barrier to investment. A brief discussion of policy implications highlights expedited review and digital process improvements as potential avenues to reduce uncertainty and promote supply growth. A limitations section outlines directions for future research, including multi-city comparisons, governance variables, and greater reproducibility.