Knowledge-based capital is increasingly important as a source of economic growth worldwide. As startups and innovation-driven enterprises scale up in the domestic economy, their need for access to capital and markets to monetise their intellectual property (IP) and support business growth and expansion is increasing. Given India's position as the world's third-largest startup hub (Panda & Joy, 2021) and the growing recognition of IP assets as growth engines, it is important for India to harness this potential to accelerate inclusive growth. This paper examines how early-stage technology firms measure and capitalise on their IP assets during Series A funding. Although more than 80% of the value of any early-stage technology firm is tied up in IP assets, traditional financial approaches such as discounted cash flow (DCF) and Cost-to-Duplicate methods may be difficult to apply because of market uncertainty, historical losses, and information asymmetry. To address these issues, the paper employs a secondary dataset comprising 120 Series A startup funding deals from 2021 to 2025. Data were gathered from public venture-capital records and institutional sources, including NASSCOM and Inc42, with most startups based in major Indian technology hubs and selected benchmark deals from global technology hubs. Official patent databases in India (IPO), the United States (USPTO), and globally (WIPO) were also used. The authors develop a hybrid model for IP asset valuation that integrates risk-adjusted DCF, Relief from Royalty (RFR), and dynamic TRL probability weighting. The empirical results indicate that formal ownership of IP assets is associated with valuation premiums: 14.2% per patent family and 18.5% for FTO clearance.
India is moving rapidly towards a digital and cashless or less-cash-dependent economy through the Digital India movement. In this journey, various initiatives have been launched by the Government of India, and the Unified Payment Interface (UPI) is one such initiative. It provides a fast and simplified way of transferring funds instantly from one account to another without visiting a bank. Earlier, the Immediate Payment Service (IMPS) was mainly used to transfer small amounts instantly, but over the last 10 years, the way people transfer money has changed drastically. UPI has become so popular that it has replaced physical cash to a great extent in day-to-day transactions. It has now become so common that almost everyone accepts and makes online payments. UPI has revolutionised online payments in day-to-day life. Both UPI and IMPS are important tools for digital banking and are used to transfer funds (within specified limits) instantly using mobile phones. In this article, a comparative study of UPI and IMPS has been conducted on the basis of their annual transaction volume and transaction value over the last 10 years (from 2016-17 to 2025-26). Various statistical tools have been used to analyse and present the data. The study shows the extent to which UPI has been used and how it has surpassed IMPS in popularity over the last 10 years. The global popularity of UPI is also described. Thus, this study examines the impact of UPI on day-to-day activities and its popularity.
Financial budget preparation remains vulnerable to errors arising from data extraction and mapping, spreadsheet manipulation, forecast assumptions, version control, judgemental adjustment and weak reconciliation. Artificial intelligence (AI) is increasingly proposed as a means of reducing these errors, yet the evidence base is fragmented across management accounting, accounting information systems, forecasting, robotic process automation, machine learning, anomaly detection and human-AI decision research. This critical narrative review evaluates how far current evidence supports AI-enabled error reduction in organisational budgeting and where new sources of error emerge. Literature published principally from 2015 to 16 June 2026 was identified through multidisciplinary and business-focused scholarly indexes, supplemented by citation searching and verification against authoritative bibliographic records. The synthesis distinguishes deterministic automation from predictive machine learning, anomaly detection and generative AI because these technologies address different failure modes and carry different assurance requirements. Evidence is strongest for reducing repetitive transfer and processing errors, improving selected accounting estimates and forecasts, and widening exception screening. Direct causal evidence that AI improves end-to-end corporate budget accuracy remains limited, while field evidence increasingly shows that benefits depend on data quality, process standardisation, confidence-aware human intervention and effective internal control. Important countervailing risks include data leakage, model drift, false precision, brittle automation, automation bias and unreliable numerical reasoning by large language models. The review therefore argues that AI should be treated as a layered control and decision-support architecture rather than an autonomous budget preparer. The most defensible design combines governed source data, deterministic calculations, validated forecasting models, exception detection, logged human overrides and continuous performance monitoring. Future research should test these arrangements in real budgeting cycles using common error taxonomies and outcome measures that capture accuracy, rework, reconciliation failures, uncertainty and control effectiveness.
Aims: This study aims to examine the influence of Suco Head leadership and apparatus competence on public service effectiveness, with apparatus job satisfaction as a mediating variable in Suco Hatuquessi, Município Liquiçá, Timor-Leste. Study Design: This study employed a quantitative explanatory research design. Place and Duration of Study: The study was conducted in Suco Hatuquessi, Município Liquiçá, Timor-Leste, during 2026. Methodology: Data were collected through questionnaires using a Likert scale from respondents involved in the provision and receipt of public services in Suco Hatuquessi. The data were analysed using Partial Least Squares–Structural Equation Modelling (PLS-SEM) with SmartPLS 4. The analysis included the evaluation of the measurement model and structural model, as well as direct and indirect effect testing. Results: The findings indicate that Suco Head leadership does not have a significant direct influence on public service effectiveness (β = 0.016; P = .906). Apparatus competence also does not have a significant direct influence on public service effectiveness (β = 0.208; P = .157). However, Suco Head leadership has a positive and significant influence on apparatus job satisfaction (β = 0.252; P = .032), while apparatus competence has a positive and significant influence on apparatus job satisfaction (β = 0.411; P < .001). Apparatus job satisfaction has a positive and significant influence on public service effectiveness (β = 0.348; P = .006). The mediation analysis shows that apparatus job satisfaction does not significantly mediate the relationship between Suco Head leadership and public service effectiveness (β = 0.088; P = .096), but significantly mediates the relationship between apparatus competence and public service effectiveness (β = 0.143; P = .032). Conclusion: The findings demonstrate that leadership and apparatus competence do not directly determine public service effectiveness in Suco Hatuquessi. However, both contribute to apparatus job satisfaction, which plays an important role in strengthening public service effectiveness. In particular, apparatus job satisfaction serves as a significant mediating mechanism in the relationship between apparatus competence and public service effectiveness.
This study examines the dynamic relationship between Brent crude oil spot and futures prices using a time-series econometric framework. Daily spot and futures price data from 31 March 2022 to 31 March 2026 are analysed to assess long-run equilibrium, short-run adjustment, information transmission, and shock responses. The Augmented Dickey-Fuller test is used to determine the order of integration. Both series are non-stationary in levels and stationary after first differencing, indicating integration of order one. Johansen cointegration analysis identifies one cointegrating relationship, supporting a stable long-run association between Brent spot and futures prices. The Vector Error Correction Model indicates that deviations from long-run equilibrium are corrected through adjustment in the spot market. Short-run causality testing using the VAR Granger causality/block exogeneity Wald procedure shows bidirectional predictive causality between changes in spot and futures prices. Impulse response analysis indicates that shocks originating in either market are transmitted to the other but diminish over subsequent periods. Variance decomposition shows that spot-price variation is explained predominantly by its own innovations, whereas futures-price variation reflects substantial contributions from both spot- and futures-market shocks. Residual diagnostics indicate no serious serial-correlation or ARCH problem, although the residuals are not normally distributed. Overall, the findings demonstrate close short- and long-run interaction between Brent spot and futures markets and indicate that information transmission operates in both directions.
