Qubahan EcoLead Journal (QELJ) is a quarterly academic journal published by the Qubahan, QELJ publishes original researches in various fields including (Accounting, Economic, Financial and Banking, Statistics, Management and Business ). QELJ is a Peer-Reviewed Open Access journal. QELJ provides immediate, worldwide, barrier-free access to the full text of research articles without requiring a subscription to the journal.
Explaining Closing Stock Prices Using Daily Market Indicators: Evidence from the Global Restaurant Industry
This study examines the extent to which daily market trading variables explain closing stock prices in a dataset of listed restaurant-related firms. The empirical model uses the closing stock price as the dependent variable and the opening price, high price, low price, and trading volume as explanatory variables, with the adjusted closing price as a comparison variable. Python is employed for data preparation, descriptive statistics, visualization, correlation analysis, and linear regression modeling. The literature review draws on 31 studies and shows that accounting information, financial ratios, and market indicators are commonly associated with share prices. However, their explanatory strength varies across markets, industries, and methods. The empirical results show that same-day price variables are strongly associated with the closing price, whereas trading volume has a weaker direct relationship with price levels. The model therefore provides evidence of same-day price explanation rather than a complete future-forecasting system. The study contributes a structured framework for integrating accounting-performance concepts with Python-based market analysis and identifies the accounting data required for a more comprehensive journal-level model.
Copyright (c) 2026 Qubahan EcoLead Journal https://creativecommons.org/licenses/by-nc-nd/4.0
This study examines the extent to which daily market trading variables explain closing stock prices in a dataset of listed restaurant-related firms. The empirical model uses the closing stock price as the dependent variable and the opening price, high price, low price, and trading volume as explanatory variables, with the adjusted closing price as a comparison variable. Python is employed for data preparation, descriptive statistics, visualization, correlation analysis, and linear regression modeling. The literature review draws on thirty-one studies and shows that accounting information, financial ratios, and market indicators are commonly linked to share prices, although their explanatory strength varies across markets, industries, and methods. The empirical results show that same-day price variables are strongly associated with closing price, whereas trading volume has a weaker direct relationship with price levels. The model therefore provides evidence of same-day price explanation rather than a complete future-forecasting system. The study provides a structured framework for integrating accounting-performance concepts with Python-based market analysis and identifies the accounting data required for a more comprehensive journal-level model.
Market Composition and Data-Quality Constraints in Global Listed-Securities Reference Files: Evidence from a Pre-2020 Cross-Market Dataset
This study examines the empirical usability of pre-2020 global listed securities reference data by assessing market composition, venue concentration, and identifier completeness. Using a source file of 237 raw records and 56 fields, the analysis retains 147 observations created before 2020 and applies descriptive frequency analysis, cross-tabulation, robust summary statistics, and field-completeness checks. The findings show that the retained sample is dominated by exchange-traded funds and common equity, which together account for nearly the entire dataset. Listings are geographically diverse but concentrated in a limited number of countries, particularly GB, AU, HK, US, and NL, while trading activity is similarly clustered across major venues and currencies. Security-level identifiers, including ISIN, SEDOL, issuer name, security description, and trading currency, are complete or near-complete, supporting reproducible descriptive and cross-market analysis. However, materially weaker LEI and GICS coverage limits the dataset’s suitability for issuer-level linkage and sector-based empirical designs without supplementary enrichment. The study contributes by reframing securities reference data as research infrastructure rather than passive administrative metadata and by proposing a Reference-Data Usability Assessment Framework (RDUAF) to guide pre-analysis evaluation of dataset coverage, completeness, and research suitability. Overall, the evidence suggests that the examined file is well-suited to security-level market-structure profiling prior to the COVID-19 period but requires additional data integration to support more advanced firm-level, sectoral, or causal finance research.
Copyright (c) 2025 Qubahan EcoLead Journal https://creativecommons.org/licenses/by-nc-nd/4.0
Assessing Fiscal Vulnerability, Macroeconomic Performance, and Inclusive Growth in Selected African Economies Before the COVID-19 Shock: A Cross-Country Analysis
This study examines fiscal vulnerability, macroeconomic performance, public debt dynamics, and inclusive growth before and during the initial COVID-19 shock, using selected African economies and comparative cross-country indicators as empirical material. The paper draws on Python-based statistical processing of fiscal, macroeconomic, institutional, and inclusive-growth data, with the analytical horizon limited to observations through 2020 whenever year information is available. The study combines descriptive statistics, trend and correlation analyses, model comparisons, hypothesis testing, and visual interpretation to evaluate the relationship between fiscal indicators and macroeconomic outcomes. The results indicate persistent fiscal pressure in the Egyptian fiscal-balance series, strong positive associations between institutional-quality indicators and transformed GDP per capita, improved explanatory power when economic freedom is decomposed into its components, and statistically significant provincial differences in inclusive-growth indicators. The results indicate that fiscal sustainability prior to COVID-19 cannot be assessed solely through public debt levels; it is also contingent upon revenue capacity, expenditure composition, inflation, unemployment, institutional integrity, and regional inclusivity. The paper contributes by organizing diverse empirical evidence into an integrated pre-COVID macro-fiscal framework suitable for later econometric extension.
Copyright (c) 2024 Qubahan EcoLead Journal https://creativecommons.org/licenses/by-nc-nd/4.0
Beyond Tourist Arrivals: Tourism Expenditure, Employment, and GDP Contribution in a Cross-Country Panel
This study examines the relationship between tourism activity and economic performance using a merged country-year panel that includes tourism arrivals, inbound tourism expenditure, tourism employment, accommodation indicators, GDP per capita, and air-passenger activity. The analysis is limited to available observations through 2022, allowing the study to compare long-term tourism patterns before and during the COVID period. The merged dataset shows that reported country-level inbound tourist arrivals increased steadily over time and reached their highest observed level in 2019, at about 1.28 billion arrivals. After that peak, arrivals declined sharply, falling to about 362 million in 2020 and partially recovering in the latest available reporting years. Average tourism GDP share across reporting countries reached its highest observed value in 2019, at 5.22 percent, before decreasing during the crisis period. The descriptive results show that large destination countries are not always the most tourism-dependent economies, because tourism dependence is better reflected by GDP share, employment, and expenditure indicators. Exploratory regression results indicate positive associations between tourism employment and tourism GDP share. In the country-and-year fixed-effects model, inbound tourism expenditure is also positively associated with tourism GDP share, while visitor arrivals are less explanatory. Overall, the findings suggest that tourism spending and employment are more meaningful economic channels than arrivals alone; however, the results are descriptive rather than causal because the panel is unbalanced and reporting coverage varies.
Exploring the Tax Revenue–Economic Development Nexus in Iraq During 2014–2019: Evidence from World Bank and Maddison Project Data
This study examines the relationship between tax revenue and economic development in Iraq during 2014–2019, using annual indicators of tax revenue as a share of GDP, GDP per capita, GDP growth, population, estimated tax revenue per capita, and estimated total tax revenue. The period is restricted to 2014–2019 because complete tax revenue observations for Iraq are available only for these years in the dataset used for the analysis. The study applies descriptive statistics, trend analysis, Pearson correlations, and simple OLS regressions to explore whether changes in taxation were associated with selected development indicators. The findings show that Iraq’s tax revenue remained low and unstable, rising from 0.91% of GDP in 2014 to a peak of 2.69% in 2017 before declining to 1.34% in 2019. GDP per capita, measured in current US dollars, declined during the period, while GDP per capita, measured in purchasing-power-parity terms, increased, indicating that conclusions depend partly on the development measure used. Correlation and regression results suggest weak and measurement-sensitive associations between taxation and economic development indicators. The evidence, therefore, supports a cautious interpretation: Iraq showed limited and unstable tax capacity during 2014–2019, and economic growth did not automatically translate into sustained domestic revenue mobilization. The results highlight the importance of strengthening tax administration, widening the tax base, improving compliance, and reducing dependence on volatile revenue sources.
Copyright (c) 2024 Qubahan Techno Journal https://creativecommons.org/licenses/by-nc-nd/4.0
Portfolio Risk Management in Elite Football Clubs: A Statistical and Managerial Accounting Analysis of Real Madrid Before 2022
This study examines risk management at Real Madrid Club from a combined statistical and managerial accounting perspective prior to 2022. The paper treats the club as a high-value sport organization whose risk exposure is created not only by match outcomes, but also by transfer investment, player asset valuation, market-value concentration, and operational pressure. A season-level dataset covering 16 Real Madrid seasons from 2005/06 to 2020/21 is used to construct sporting-risk indicators, including win rate, loss rate, points per match, average match gap, and short-rest rate. These indicators are merged with managerial accounting measures, including net transfer spending, transfer-fee exposure, transfer turnover, player valuation dispersion, and club market-value concentration. The empirical strategy applies descriptive statistics, correlation analysis, visual diagnostics, and ordinary least squares regression using Python, with SPSS, Stata 17, or EViews suggested for verification. The results show that Real Madrid's average win rate was 0.660, average loss rate was 0.178, and average points per match was 2.143. The correlation results also indicate that transfer-fee exposure is positively associated with the composite risk index (r = 0.675), while net transfer spending is positively associated with win rate (r = 0.446). These findings suggest that elite-club risk should be understood as a portfolio of operational, investment, and asset-value risks rather than as a simple sporting outcome. The study contributes a practical framework for the use of football data in management accounting and risk-control decisions.
Retail Performance and Competitive Positioning of Nike and Adidas in Australia in the Period (2016-2017): A Comparative Statistical and Strategic Analysis
This study examines the comparative retail performance of Nike and Adidas in the Australian market using transaction-level data collected between January 1, 2016, and August 1, 2017. The analysis focuses on pre-2020 observations only and evaluates brand performance from statistical, marketing, and management perspectives. The study uses the Fact Table as the main data source and incorporates Managers and Suburb lookup tables to support regional and administrative analysis. A total of 76,466 observations were analyzed for the two brands. Key indicators were constructed, including revenue, cost, profit, and profit margin, based on total units, sale price, and cost price. Descriptive statistics were used to compare overall brand performance, while Welch t-tests, Mann–Whitney U tests, Cohen’s d, and chi-square tests were applied to examine statistical differences and category associations. The findings show that Nike achieved higher total revenue, total profit, and total units sold, indicating stronger scale-based performance. In contrast, Adidas recorded a higher average sale price and a slightly higher aggregate profit margin, suggesting stronger pricing and relative profitability. Category, monthly, state-level, and manager-level analyses further show that brand performance varied across product groups, time periods, regions, and administrative units. The study contributes to marketing and management analysis by demonstrating how transaction-level data can be used to evaluate brand performance. However, the findings should be interpreted cautiously because the dataset does not include advertising expenditure, customer demographics, store size, or inventory information.
The Relationship between Financial Indicators and Market Returns of (S and P 500) Companies in the Period 2012-2016
This paper examines the relationship between accounting-based financial indicators and annual market returns for S&P 500 companies during 2012–2016. The study draws on company identification data, sector classifications, financial statement variables, and adjusted daily stock prices to construct annual measures of stock return, volatility, profitability, leverage, liquidity, research and development intensity, and capital expenditure intensity. The empirical approach combines descriptive statistics, sector-level comparisons, Pearson correlation analysis, and a linear regression model with sector fixed effects and HC3-robust standard errors. The findings indicate clear differences across sectors in firm size, profitability, return volatility, and annual stock performance, suggesting that sector characteristics are important when interpreting financial and market outcomes. However, the associations between individual accounting indicators and annual market returns are generally weak and inconsistent. The final regression model explains only a limited proportion of the variation in annual returns. Capital expenditure intensity, return on equity, and firm size show statistically significant negative relationships with annual returns, whereas other indicators exhibit little explanatory power. Overall, the results suggest that accounting indicators provide useful background information for assessing corporate performance, but they are not sufficient on their own to explain short-term stock-return behavior in large U.S.-listed companies. The evidence highlights the need to incorporate market expectations and broader macroeconomic factors in future research.
Government Expenditure on Education and Human Development in Developing Countries: Evidence from a Pre-2022 Unbalanced Panel Dataset
This study examines the relationship between government expenditure on education and human development in developing countries using a pre-2022 unbalanced panel dataset covering 115 countries and 459 country-year observations. The study employs a descriptive, panel-based analytical framework to assess whether public education spending is associated with improvements in the Human Development Index (HDI). The descriptive results show that average HDI increased from 0.549 in 2000 to 0.657 in 2021, while average government expenditure on education rose from 3.895% to 4.501% of GDP during the same period. These findings indicate that education finance remains an important policy instrument for strengthening human capital and supporting development outcomes. However, the study also emphasizes that higher spending alone does not automatically produce higher human development. The effectiveness of education expenditure depends on how public resources are allocated, governed, and transformed into improvements in learning, access, equity, and welfare. The paper concludes that developing countries should move beyond expenditure expansion alone and focus on improving the efficiency, quality, and institutional management of education spending. The study contributes to the literature by distinguishing between simple spending levels and the broader policy conditions required to convert education finance into sustainable human development gains.
The Impact of Accounting Conservatism on Profit Stability: An Applied Study of Standard Chartered PLC, 2010-2020
This study investigates the impact of accounting conservatism on profit stability using Standard Chartered PLC as an applied banking case for the period 2010–2020. The study is grounded in the view that conservative accounting improves earnings credibility by recognizing losses in a timely manner and applying greater caution in recognizing gains. Using annual reports and published financial statements, the paper reviews the theoretical foundations of accounting conservatism and develops an applied framework that examines profit stability in relation to conservatism, bank size, leverage, growth, and macroeconomic conditions. The discussion indicates that conservative reporting can support more reliable and transparent profit measurement, particularly in the banking sector, where earnings are strongly affected by credit risk, impairment recognition, regulatory requirements, and economic shocks. The case of Standard Chartered PLC shows that conservatism is especially important during periods of uncertainty, including the COVID-19 shock in 2020, when prudent provisioning and timely recognition of losses are critical to maintaining reporting credibility. The study concludes that accounting conservatism can contribute to profit stability when applied in a balanced manner that improves transparency without weakening the relevance of financial information. The findings provide useful implications for bank managers, investors, regulators, and researchers concerned with earnings quality, financial reporting discipline, and banking-sector stability.
Earnings Quality and Firm Value: Merged Evidence from a Nasdaq Company Metrics Dataset
This paper integrates the uploaded article draft with an empirical dataset on Nasdaq companies to produce a data-backed study of the relationship between earnings quality and firm value. The revised design uses 102 Nasdaq-100 companies and historical firm-level ratios for 2018–2021, yielding 333 usable firm-year observations in the main panel after variable availability filters. Because the dataset does not provide a direct discretionary-accruals series or a consistent size variable, the empirical model operationalizes earnings quality as the inverse of the Beneish M-score, so that higher values indicate lower manipulation risk and therefore higher earnings quality. Firm value is proxied by the natural logarithm of the price-to-book ratio. Leverage, liquidity, profitability, and operating efficiency are included as controls. The results show that earnings quality is positively related to firm value in the firm fixed-effects specification, but the effect is only weakly significant and unstable across specifications. By contrast, leverage and gross profit to assets explain valuation more consistently. The evidence suggests that market valuation in the Nasdaq sample responds to earnings quality, but that the effect is conditional, modest, and intertwined with broader fundamental characteristics.
The Impact of Diversity and Inclusion on Employee Engagement: A Restructured Case Study of Microsoft
This paper examines how diversity and inclusion shape employee engagement at Microsoft and extends the earlier draft by adopting a more publication-style structure. The study argues that diversity contributes to engagement only when it is translated into inclusion through fairness, voice, psychological safety, accessibility, and a sense of belonging. Methodologically, the paper adopts a qualitative case-study design grounded in academic and corporate sources published up to 2020. To enrich the analysis, the paper also integrates an uploaded operational dataset containing 5,718 recorded task steps across 165 recordings and 50 processes. Descriptive analysis of the dataset reveals a multi-application, process-intensive work environment in which coordination, clarity, and inclusive managerial support are critical. The findings indicate that Microsoft is best understood as a systems-based case: inclusive leadership, employee voice, accessibility, fair systems, and supportive work design jointly strengthen the conditions that keep employees engaged. The paper contributes by expanding the previous studies section with 20 additional sources, relocating that synthesis under the literature review, and using operational evidence to show why inclusion matters for execution quality and employee morale.
Statistical Profiling of Banking Data for Anomaly Identification:An Applied Study Using a Synthetic Banking Database
This study examines whether statistical analysis can meaningfully distinguish anomalous from non-anomalous records in an openly available banking database. The dataset contains 5,000 observations and 40 variables covering customer, account, transaction, loan, credit-card, and feedback information. For analysis, the original anomaly label was recoded so that 300 records (6.0%) were treated as anomalous and 4,700 records (94.0%) as normal. The empirical design combines descriptive statistics, Mann-Whitney U tests, chi-square association tests, and a logistic-regression benchmark on a hold-out sample. The results show that major numerical variables do not differ significantly between the two classes, while only account type and resolution status exhibit statistically significant but substantively small associations. The benchmark classifier performs weakly (ROC-AUC = 0.490; average precision = 0.062), indicating that the available variables provide limited discriminatory signal. The study therefore contributes less as an operational fraud-detection model and more as a transparent methodological illustration of how class imbalance, weak labels, and synthetic feature spaces can constrain inference. To strengthen the manuscript for journal submission, the present version expands the literature review with pre-2021 sources, introduces a comparison matrix against prior studies, and adds statistical tables and figures derived from the uploaded dataset.
Market Liquidity and Price Dynamics in Tadawul: A Firm-Day Analysis of Saudi Listed Firms, 2001–2020
This study examines trading activity, liquidity, and price behavior in the Saudi Stock Exchange (Tadawul) using a firm-day panel of 600,571 observations for 200 listed companies in 11 sectors from 31 December 2001 to 23 April 2020. The paper follows a descriptive design built on sample profiles, annual and sector comparisons, correlation analysis, one-way analysis of variance, and an ordinary least squares model for the logarithm of traded value. The results show strong market broadening over time, with listed firms increasing from 49 in 2001 to 200 in the 2020 sample. Trading intensity and average traded value peaked in the mid-2000s, especially in 2006, then moderated. Liquidity remained concentrated in a small group of firms and sectors. Traded volume shows the strongest association with traded value, while sector differences in daily returns are statistically significant but economically small. Overall, Tadawul appears to be a larger and more diverse market than at the start of the sample, but one in which liquidity is still unevenly distributed.
Riding on Perceptions: Decoding Customer Choice Dynamics across the Two-Wheeler Industry Landscape
This research investigates the trends of customer perception in three of the leading two-wheeler brands—Yamaha, Bajaj, and TVS—that compete in the motorcycle market in India, specifically in Durgapur, West Bengal. This study examines customer perception using a two-way ANOVA without replication methodology across four essential purchase dimensions—mileage, price, variant availability, and colour. In the analysis of the customer experiences, the data indicated statistically significant perceptual differences across brands, with mileage demonstrating significant difference at the 0.10 level with respect to perceived fuel efficiency, with Yamaha (50.05 km/l) having significantly more mileage perception compared to Bajaj (43.72 km/l) and TVS (41.5 km/l). The analysis also indicated that there are no statistically significant differences in customer perception across price, variants, or colour; suggesting that price point, variants, and colour function in a market with little perceptual differences. These explore how customer migration will remain driven by mileage and that other traditional purchase dimensions will have significant parity across brands in the competitive two-wheeler market. The study develops a better understanding of how customer perception trends informs brand preference and competitive advantage, while providing manufacturers tangible options to develop stronger marketing strategies and product development in an increasingly competitive marketplace.
A study on the implementation of corporate Social responsibility in Thermal Power Company.
The study examines the Corporate Social Responsibility (CSR) practices of a thermal power company located in the West Bengal region of India. Through use of both primary and secondary data in a mixed study paradigm, a total of 46 employees were surveyed in order to learn about trends within the organization, CSR investments and implementation methods. The study found that the company had taken significant steps in recent years to fulfill its commitment to CSR, with 67.39% of respondents reporting that 1-2% of annual revenues were spent on CSR. The findings identified direct financial support to communities, or public donations, as the most prevalent implementation method (43.47%). While community needs were the leading driver of CSR site selection (43%), current socio-environmental issues influenced selection (33%) as well. The study suggests that CSR practices in the power sector are starting to move away from rigid, compliance-based yet weakly operationalized CSR practices and schemes, to being more community-centered. The study contributes to CSR literature by detailing the sectoral specificity of power companies in India for CSR practices, which may be important to managers, as they make CSR implementation decisions according to societal needs and organizational goals.
Financial Resilience of India’s Private Life Insurance Sector: A CARAMEL-Based Assessment
This research paper presents a comparative study of selected private life insurance companies in India using the CARAMEL model. Tawheed B. (2016) The macro-level analysis of the performance of the Life Insurance Corporation (LIC) in India provides valuable insights into its operational efficiency and societal impact. The study focuses on HDFC Life Insurance, ICICI Prudential Life Insurance, SBI Life Insurance, and Max Life Insurance, representing a significant portion of the private life insurance market. The CARAMEL model, which encompasses Capital Adequacy, Asset Quality, Risk Management, Earnings, Market Perception, and Liquidity, serves as a comprehensive framework for evaluating the financial health and performance of these insurance companies. Trivedi S. (2016) The study on risk management tools and techniques in life insurance in India aims to provide valuable insights into developing effective tools for life insurers to analyze customer risks. This research endeavors to conduct a comparative study of three leading private life insurance companies in India: HDFC Life Insurance, ICICI Prudential Life Insurance, and SBI Life Insurance. The findings indicate that HDFC Life Insurance exhibits superior Capital Adequacy, positioning it favorably in terms of protecting policyholders and promoting financial system stability. Max Life Insurance demonstrates better Asset Quality, reflecting its strong financial health and risk management capabilities. All the selected companies show effective risk management practices, which contribute to their financial stability and resilience. SBI Life Insurance is a top performer in Management Soundness, Earnings and Profitability, and Liquidity ratios, highlighting its operational efficiency, profitability, and financial resilience. Madhuri T. and Rao N. (2020) has evaluated the financial performance of selected Indian life insurance companies using the CARAMEL model. Recommendations of the paper suggested LIC's capital position and underwriting expenses while urging private insurers to enhance liquidity for timely liability fulfillment. The study provides valuable insights for regulators, investors, and other stakeholders, supporting informed decision-making and promoting overall stability and sustainability in the insurance industry.
A Systematic Literature Review on Customer Purchase Decision of Restaurant Foods through Food Delivery Apps
Purpose - This systematic literature review assesses the factors that influence customer purchase decisions when ordering restaurant food via food delivery applications. This review synthesizes the available literature on the topic and provides an overview of the current research to identify important determinants, theoretical models, and future research opportunities. Design/methodology/approach - Following PRISMA guidelines, a thorough search was conducted on academic databases including Web of Science, Scopus, Science Direct and Emerald Insight from 2015-2024. The search terms that were used include, "food delivery apps," "online food ordering" "customer purchase decision" and "restaurant delivery platforms." A total of 87 peer-reviewed articles were deemed appropriate to the inclusion criteria for systematic screening. Findings - The study identifies six major dimensions of factors affecting customers' purchase decisions: (1) Technology related factors (e.g. app usability, interface design, and payment security), (2) Service dimensions (e.g. delivery time, order accuracy, and customer support quality), (3) Food quality perceptions (e.g. expected taste, freshness, and presentation), (4) Economic variables (e.g. price, promotions, and delivery fees), (5) Social variables (e.g. reviews, ratings, and social proof), and (6) Individual factors (e.g. convenience orientation, technology readiness, and previous experiences). Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB) will be the primary theories that guide the assessment. Limitations/implications for research - The review only included articles published in the English language and likely reflects geographical bias towards the West. Future research needs to look at cultural differences in this key area of post-pandemic behavior. Practical implications - The findings provide ways that food delivery platforms and restaurants can develop customer acquisition and retention strategies through improved app development; service delivery or by better targeted marketing campaigns. Social implications - This review and its findings further the understanding of what digital transformation means to food services and its effects on consumer behavior patterns. Originality/value - This is the first systematic and comprehensive review to provide an overall organisation of customer purchase decision factors' specific to food delivery applications that contribute to developing a basis for future research and practice.
Digital Marketing Strategies for Educational Certification Courses: A Conceptual Study
Digital learning platforms are becoming more common, and there is a growing need for professional development. This has changed the way educational certification courses are offered. This conceptual study looks at how to use digital marketing methods strategically to promote educational certification programs in today's digital world. This study creates a theoretical framework for understanding the complicated link between digital marketing strategies and promoting educational services by looking at current market trends, consumer behaviour patterns, and new technologies in great detail. The study looks at the most important digital marketing channels, how well they reach the right people, and suggests a unified approach to get more people to sign up for and participate in certification programs. The results show that marketing educational certifications online successfully requires a variety of methods, including content marketing, social media engagement, search engine optimisation, and personalised communication strategies that are tailored to the needs of adult learners and people looking to improve their skills.