Consumers often take on debt at different points in time. As a result, debts may be months, if not years, apart in age. In this research, the authors ask whether and how debt age affects installment debt prepayment decisions. While the ideal strategy for prepaying older (vs. newer) debt depends on specific account parameters (e.g., interest rate, monthly payment, etc.), in many circumstances it is financially advantageous to prioritize paying down newer debt. This is because debt amortization (i.e., repayment) schedules often result in reduced interest payments when newer installment debts are paid first. However, across eight studies, including a secondary dataset of consumer loans, the authors find that consumers prefer to prepay older debt first—even when it is financially disadvantageous to do so. The authors term this the “FIFO (first-in-first-out) preference” and demonstrate that consumers prioritize older (vs. newer) debt prepayment because they feel they have invested greater effort (i.e., mental or physical work and energy) to repay it. Accordingly, reducing the effort required to repay an older debt (e.g., through automated payments) or shifting consumers’ focus from invested effort to remaining effort attenuates the FIFO preference. These findings offer implications for theory, managerial practice, and consumer welfare.

Purpose The purpose of this study is to enhance the accuracy and comprehensiveness of sentiment influence analysis in online reviews by addressing three critical limitations: improving sentiment classification accuracy, considering the combined effect of multiple emotions and mitigating biases in traditional usefulness voting methods. Design/methodology/approach This study employs a novel hybrid machine learning framework that integrates information entropy, principal component analysis (PCA) with K-means clustering and a bidirectional encoder representations from transformers-based sentiment classification model. Over 290,000 online reviews across four product categories were analyzed to predict perceived usefulness, calculate sentiment loss rates and systematically quantify sentiment influence. Findings The results demonstrate that different product types are influenced by positive, negative or mixed sentiments to varying extents. Specifically, economical products were predominantly influenced by positive sentiments, while applied products (e.g. medical apps) showed greater sensitivity to negative sentiments. Experiential and enjoyment products were equally influenced by both sentiments, highlighting the need for balanced strategies. Research limitations/implications The study’s data are primarily from the Chinese market; future research could extend the framework to international datasets. Additionally, although focused on e-commerce scenarios, the methodology could be expanded to other domains and integrated with hybrid models combining machine learning with regression testing. Practical implications Businesses can leverage this framework to optimize resource allocation effectively, target marketing strategies more accurately and improve customer satisfaction and loyalty by responding strategically to consumer emotions reflected in reviews. Social implications Accurate sentiment influence analysis empowers consumers with better information for decision-making and encourages businesses to align closely with genuine consumer needs and emotional feedback, thereby enhancing overall consumer welfare and market transparency. Originality/value This research pioneers the integration of qualitative sentiment classification with quantitative emotional influence measurement using machine learning. It introduces a novel metric – sentiment information loss rate – to quantify sentiment impacts precisely and explores the combined effect of multiple emotions, challenging the traditional one-to-one sentiment contagion model.

The increasing penetration of renewable energy resources and the rising volatility of wholesale electricity prices introduce significant uncertainty into tariff design and retailer procurement decisions. Conventional tariff-setting approaches typically rely on deterministic forecasts or limited scenario analyses, which may underestimate tail risks and fail to ensure equitable cost allocation among consumers and retailers. Moreover, existing regulatory frameworks often lack an integrated mechanism for jointly considering consumer welfare, retailer profitability, and system-level financial risk exposure, particularly under distributional uncertainty. To address these tensions, we propose a two-level model in which the upper-level regulator maximizes a risk-adjusted social welfare metric that incorporates consumer surplus, retailer surplus, and penalties for variance and tail risk, while the lower-level retailers optimize their own profit given retail tariffs, wholesale procurement, and imbalance penalties. The framework embeds stochastic demand elasticity across heterogeneous consumer segments and introduces hedging portfolios composed of forwards and call options to capture realistic financial risk management strategies. Distributional robustness is incorporated through a Wasserstein ambiguity set that models uncertainty in wholesale prices, renewable availability, and demand response distributions. Methodologically, the model is reformulated as a tractable single-level mixed-integer program using Karush–Kuhn–Tucker conditions for the lower-level retailer problem, Big-M linearizations for complementarity constraints, and epigraph-based linearizations for variance and conditional value-at-risk terms. This reformulation enables efficient solution by state-of-the-art solvers and provides convergence guarantees. To enhance scalability, the model is equipped with a Benders decomposition procedure that separates scenario-based risk evaluation from tariff and hedging decisions. Computational experiments demonstrate convergence within forty iterations to sub-percent optimality, confirming tractability for realistic day-ahead instances with 24 hourly blocks and 100 stochastic scenarios. A case study based on a stylized 1,100 MW wholesale system, with ten thermal generators and five renewable sites, illustrates the economic and operational implications of alternative tariff designs. Results show that real-time pricing yields the highest expected welfare, exceeding time-of-use by nearly one million dollars per day, but also exposes consumers to bill variability of up to 25–30 percent in high-risk scenarios. Time-of-use tariffs achieve a balanced compromise, improving expected welfare by 15 percent relative to flat pricing while maintaining tail risks within manageable bounds. Flat tariffs, while stable, impose high hidden welfare losses due to inefficient resource allocation. Hedging portfolios shift markedly across regimes: forwards dominate under flat tariffs, mixed portfolios emerge under time-of-use, and option-heavy strategies prevail under real-time pricing. Shadow price analysis further reveals that affordability constraints bind most strongly in evening hours under real-time pricing, underscoring the tension between efficiency and equity. Sensitivity tests on the ambiguity radius show that welfare losses under distributional robustness are modest for flat and time-of-use tariffs but pronounced for real-time pricing, reflecting its direct exposure to tail distributions.

This paper examines the impact of India’s GST 2.0 (Goods and Services Tax) reform on the Fast-Moving Consumer Goods (FMCG) sector, with a particular focus on consumer welfare, consumption patterns, and tax pass-through dynamics. Implemented in September 2025, GST 2.0 rationalised India’s indirect tax structure by consolidating multiple slabs into three primary rates: 5% for essential goods, 18% for standard goods, and 40% for luxury and sin goods. Given the FMCG sector’s high volume, low margins, and central role in household consumption, these changes have had direct implications for prices, affordability, and expenditure distribution across households. Using secondary data, policy documents, and a hypothetical consumption-basket analysis, this study evaluates both the benefits and limitations of GST 2.0. The findings suggest that while the reform improves affordability and equity for necessity-heavy consumption baskets, implementation frictions and incomplete pass-through limit its full consumer-welfare potential.

The rapid evolution of digital markets, data-driven business models, and artificial intelligence has reshaped the global competition landscape, raising complex legal and policy challenges. Contemporary competition law must address market concentration among digital gatekeepers, the competitive implications of data dominance, and the emergence of algorithmic collusion. At the same time, issues surrounding platform neutrality, gig-economy labour relations, and the rise of killer acquisitions demand more sophisticated regulatory frameworks. The intersection of privacy and competition, as well as the competitive effects of vertical integration in digital ecosystems, further complicates antitrust enforcement. Beyond digital markets, new areas of concern include labour-market monopsony, sustainability agreements, and the growing need for cross-border regulatory coordination. In India, proposed reforms such as the Digital Competition Act signal a shift toward ex ante regulation to address systemic risks posed by dominant digital enterprises. This essay analyses these emerging challenges and argues that modern competition law must remain dynamic, globally aligned, and technologically informed to ensure fair markets and long-term consumer welfare.

Objectives The growing dominance of digital platforms has raised significant human rights concerns globally, and Albania is no exception. As Big Tech firms increasingly influence economic, social, and political life, their practices have implications for privacy, freedom of expression, non-discrimination, and democratic participation. Traditional antitrust frameworks, primarily centered on consumer welfare, often fall short in addressing these multifaceted challenges. These developments have necessitated robust competition policies to ensure fair market practices and consumer protection. The digital transformation in Albania has created significant potential for economic growth and innovation, but it has also raised substantial concerns regarding the protection of human rights and the integrity of competitive market practices. Material and methods This paper explores the current state of the digital market in Albania, with a particular focus on its intersection with human rights mainly data protection and freedom of expression and competition rights The research is qualitative, doctrinal, and literature-based, combining legal analysis with policy review and applied examples to critically examine how antitrust and regulatory tools are used to safeguard human rights in the digital age. Results By analyzing the Albanian legal framework, its alignment with European Union regulations, institutional practices, and emerging market trends, the paper highlights key regulatory gaps and proposes policy solutions aligned with European Union standards. Conclusions The study aims to support Albania’s democratic and economic development in the context of its EU integration process.

Food adulteration continues to pose a serious threat to public health and consumer welfare in India despite comprehensive legal frameworks such as the Food Safety and Standards Act, 2006 and the Consumer Protection Act, 2019. This study examines the effectiveness of these laws in safeguarding consumers and highlights the critical role of forensic science—particularly forensic chemistry and toxicology—in strengthening detection, enforcement, and adjudication. While routine food testing identifies common adulterants, advanced forensic techniques enhance the reliability and evidentiary value of analytical findings. The 2025 notification designating the National Forensic Sciences University as a Referral Food Laboratory marks a significant step in integrating forensic science into food safety enforcement. Using a multidisciplinary approach, the research identifies gaps in enforcement and laboratory capacity and emphasizes that meaningful consumer protection requires forensic-driven regulation, institutional coordination, and scientific rigor as part of the right to life under Article 21 of the Constitution.

ABSTRACT This study examines how interest-rate caps affect the demand for payday loans, using aggregate data from British Columbia from 2012 to 2019, during which the province’s maximum fee was reduced from $23 to $17 and then to $15 per $100 borrowed. Estimating a linear demand function via OLS, we find that lowering interest-rate caps significantly increases loan demand. We estimate that the $8 decrease from $23 to $15 raised annual consumer surplus by roughly $28.6 million (2012 CAD). A further reduction to $14, starting in January 2025, is projected to add an additional $3.9 million per year. These results suggest that stricter interest-rate caps within a certain range can yield substantial consumer welfare gains, with no evidence of restricted access to payday loans due to lender exit from the market.

A competition-friendly environment is a must for steady economic growth, which is a driving force for developing countries such as Bangladesh. A cartel is a potential barrier to promoting consumer welfare and overall market development. Hence, competition law is vital in supervising the market with its anti-cartel enforcement policies. Since this is a global phenomenon, countries have experimented and taken different strategies to combat cartels in their jurisdiction accordingly. Being a young competition jurisdiction, Bangladesh has a simple enforcement policy restricted to fines, but the cartel detection rate is quite unsatisfactory. This article aims to find the loopholes for anti-cartel enforcement in Bangladesh. It looks for a sustainable solution, especially introducing dawn raids, leniency mechanisms, and some other structural modifications, taking inspiration from global practices to address cartels in the Bangladeshi market. Dhaka University Law Journal, 2025, 36 (1), 193-207

Against the backdrop of deepening economic globalization, U.S.-China trade relations have emerged as a central issue in international trade. In recent years, research on the U.S.-China trade war has made significant progress, as scholars have analyzed its causes and impacts from multiple dimensions—including trade imbalances, industrial competition, and political factors—providing rich perspectives for understanding this complex economic phenomenon. However, existing research predominantly focuses on descriptive analysis and policy evaluation. A significant gap remains in systematic studies, rooted in David Ricardo’s theory of comparative advantage, that examine the losses suffered by both China and the United States due to tariff measures, as well as the impact of these measures on the global trade system. This study centers on the core theme of “The Economic Impact of Tariff Measures in the U.S.-China Trade War from the Perspective of the David Ricardo Model,” aiming to deeply analyze two critical questions: First, how tariff measures undermine core assumptions of the Ricardo model—such as free trade and fixed factors—thereby distorting the international division of labor based on comparative advantage. Second, what specific losses the escalating tariff barriers impose on China-U.S. trade volumes, industrial development, consumer welfare, and producer surplus, and whether they lead to a significant decline in global resource allocation efficiency. This study holds significant theoretical and practical implications. Theoretically, by revealing how tariff measures undermine classical trade theory assumptions, this study further validates the explanatory power of free trade theory in today’s complex trade environment and offers new perspectives for the development of international trade theory. Practically, the findings provide theoretical grounding for China, the United States, and other countries in addressing trade frictions, warn against the risks of trade protectionism, and advocate for a return to rational cooperation among countries to improve the global trade governance system.

ABSTRACT Understanding the interaction between pricing design and consumer behaviour is essential for both firms and policymakers striving to balance profitability with equity and sustainability in resource use. This study develops a theoretical model of household electricity consumption that links usage decisions to the time consumers devote to conservation under an Increasing Block Pricing scheme. The framework incorporates heterogeneous time sensitivity, whereby consumers experience disutility from the time invested in reducing electricity usage. Using five years of household-level monthly electricity consumption data, we calibrate and estimate the model to identify the optimal tiered price levels and assess how these pricing structures influence firm profitability and consumer welfare.

Purpose: This study investigates how food nationalism, adulteration perception and perceived price shape Turkish consumers’ attitudes and purchase intentions toward imported agricultural foods. Design/methodology/approach: Using an online survey, data from 213 adults were analysed with partial least squares structural equation modelling (PLS-SEM). Findings: The strongest relationship in the model is that perceived price positively affects attitude towards imported products (β = 0.771, p < 0.001). Food nationalism variable significantly but weakly negatively affected attitude (β = –0.103, p = 0.023). No significant relationship was found between perception of adulteration and attitude (β = –0.060, p = 0.221). The effect of attitude on purchase intention is significant and negative (β = –0.377, p < 0.001). The purchase intention scale is reverse scored; therefore, a negative coefficient actually indicates a positive behavioral intention. The explanatory power of the model is high for attitude (R² = 0.65) and weak for purchase intention (R² = 0.142). Research limitations/Implications: Convenience sampling and online collection restrict generalisability; future studies should adopt probability sampling, broader demographics and mixed methods. Adding perceived quality, brand image and health concerns may enhance explanatory power. Social implications: Marketers should highlight quality rather than engage solely in price competition, while policymakers can bolster consumer welfare through price-advantage regulations. Originality/Value: By integrating food nationalism, adulteration perception and price perception into a single structural model, this research provides a holistic, empirically validated framework that advances understanding of imported-food choice drivers in an emerging economy.

The question that this paper aims to address is whether firm incentive is the primary driver of generative AI in FinTech precision marketing, or whether consumer welfare is the primary driver, and how the same algorithms can generate consumer benefit and consumer harm. Theoretical framing employs a double-edged effect approach, and this implies that there are both positives and negative outcomes of the same personalization processes. The research solely employs secondary data, as it combines anonymized FinTech marketing logs, natural-experiment rollout logs, and open-access controlled experiment repositories to mediate by personalization and moderate by transparency. The methods of analysis are descriptive statistics, difference-in-means, linear probability model, difference-in-differences, heterogeneity test and mediation analysis. The initial anticipations are that AI-personalized messages increase aggregate conversion and engagement, however, more significantly they increase subgroup differences and privacy or complaint indicators where there is low transparency which is synonymous with existing findings on usefulness, bias, and cyber risks. To inform policy, the paper proposes governance measures such as transparency labels, fairness audits, and mandatory reporting of parity gaps, practical steps to balance innovation and consumer protection.

ABSTRACT Defaults have emerged as a ubiquitous and powerful nudging tool, attracting considerable research scrutiny in the field of consumer behavior. Default refers to the alternative the consumer receives unless he/she explicitly requests otherwise. This Systematic Literature Review (SLR) focuses on the power of defaults on consumer choice. A domain‐based systematic review is employed to analyze 61 peer‐reviewed articles from Scopus, Web of Science and manual search, published between 2000 and 2025. Drawing on the Theory–Context–Construct–Method (TCCM) framework, this paper presents an up‐to‐date synthesis of research, while setting the future research agenda. The opt‐out engages higher levels of consumer consent but its superiority can be contingent on specific contextual factors. Building on the assertion that “one default does not fit all” we identify conditions that may lessen or reverse default effects. Notably, opt‐in and active choice seem more effective in engaging a more permanent consent. The paper examines the underlying mechanisms, contextual factors, carry‐over effects and ethical considerations. The prospect theory and self‐determination theory serve as the theoretical foundations of default research. Finally, the paper aims at setting the future research agenda by highlighting equivocal findings, areas with limited research attention, and issues that would enhance the effectiveness of defaults in promoting consumer welfare.

Context: the liquefied petroleum gas (LPG) distribution and retail market in Brazil’s Federal District engaged in collusive practices that raised prices and triggered an investigation by the Administrative Council for Economic Defense (Cade), culminating in the cartel’s conviction in 2023. Objective: estimate the overcharge attributable to the LPG cartel and demonstrate the relevance of Cade’s intervention in restoring competition and consumer welfare. Method: combined application of the “Before and After” and Difference-in-Differences (DID) approaches, comparing prices before, during, and after the collusive conduct to isolate the cartel’s causal effect on prices. Conclusions: The estimates obtained from the “Before and After” methodology indicate overcharges ranging from 2.82% to 5.94%, while the Difference-in-Differences model shows values between 3.73% and 4.56%. Considering the most comprehensive specification, which includes time fixed effects and standard errors clustered by neighborhood, the estimated effect loses statistical significance, suggesting that part of the price variation may be explained by common spatial and temporal factors. Overall, the results indicate that, although there is no robust evidence of a price increase directly attributable to the cartel, CADE’s intervention was relevant in dismantling the coordinated conduct and preserving market competition, thereby promoting a more competitive and stable environment for price formation.

Objective: this paper aims to contribute to the discussion about the competitive assessment of sustainability agreements between competitors by identifying and analyzing the position of various competition agencies on the subject. The hypothesis is that competition authorities have rarely addressed the issue, and, when this has been done, they have not yet provided criteria adapted to the economic challenges of the environmental problem, especially when it comes to quantifying benefits. Method: a range of documents from 29 jurisdictions were mapped, with the purpose of determining how many and which authorities have already taken a stand in the debate and what criteria is being applied. Conclusions: the data collected indicate that only 13 of the 29 jurisdictions analyzed have expressed a view on the subject, with different perspectives coexisting among the jurisdictions. We found fragmented guidance and recurring reliance on traditional consumer welfare tools that struggle to account for environmental externalities. Our review identifies five recurring and non-cumulative assessment criteria across jurisdictions: (i) prevention of the exchange of sensitive information; (ii) voluntary participation and possibility of adhesion by third parties; (iii) limitations on combined market share of participants and market coverage; (iv) time limitations on the agreements; and (v) preservation of other competitive aspects. We also document emerging, but sparse, approaches to out-of-market and collective benefits. By mapping these patterns, this paper intends to clarify the current regulatory landscape and to provide a structured foundation to help reduce legal uncertainty and foster sustainability-oriented collaborations.

This study evaluates the potential of an academic livestock laboratory to transition into an academic–agribusiness enterprise by examining ‎market conditions, financial sustainability, and economic value creation. Analysis of household consumption patterns and retailer stocking ‎behavior across Lipa City, Batangas City, Taysan, and Lobo reveals consistent demand for processed pork products and persistent supply ‎gaps. The laboratory’s product lines—Filipino-style sausage, sweet cured pork, cured pork, and sweet pork stew—align well with consumer‎er preferences, indicating that local markets can absorb expanded production and that market demand provides a supportive environment for ‎enterprise development.‎ Financial assessment shows that while the laboratory is capable of generating stable revenues, it faces difficulty achieving full cost recovery ‎using traditional accounting measures. Negative financial net present value, low internal rate of return, and an improving yet inadequate ‎operating self-sufficiency ratio highlight the structural constraints of academic laboratories. These facilities operate with high training costs, ‎intensive supervision, and process limitations that prevent full optimization for profit without compromising instructional and research functions.‎ Economic evaluation offers a contrasting perspective. When non-market benefits—such as human-capital development, extension services, ‎consumer welfare gains from import substitution, local multiplier effects, and environmental offsets from biogas and fertilizer by-‎products—are incorporated, the project produces positive net benefits beginning in 2026. A positive economic net present value, a benefit–cost ratio above one, and a high economic internal rate of return indicate that societal benefits outweigh economic costs.‎ To synthesize these findings, the study presents the Academic–Agribusiness Laboratory Leverage Conversion Model, framing such facilities as hybrid institutions. The model illustrates that financial limitations coexist with substantial economic value, supported by market de-‎mand and circular-resource practices. Overall, the laboratory’s sustainability is strengthened not through full commercialization but through ‎development-oriented operations backed by strategic subsidies and institutional support‎.

The rapid development of the digital economy, marked by the adoption of pricing algorithms, has introduced new dynamics to Indonesia's competition law landscape. Algorithmic systems enable autonomous price setting based on market data learning without direct human intervention. This condition potentially gives rise to algorithmic cartels, a form of market coordination occurring without explicit agreement, yet producing anti-competitive effects similar to conventional cartels. The national legal framework, specifically Law No. 5 of 1999 and KPPU Regulation No. 4 of 2010, remains inadequate to address this phenomenon, as it is still anchored to a traditional paradigm requiring the element of “agreement” as a prerequisite for proving violation. This study aims to analyze the dilemma faced by the regulator (KPPU) in proving the existence of algorithmic cartels against the principle of fair competition in the digital era. Employing a normative juridical approach, this study examines relevant legislation, academic literature, and international policies from the OECD and European Commission. The findings indicate a regulatory gap in Indonesia's competition law regarding proof involving autonomous systems. Furthermore, the KPPU faces conceptual and technical obstacles in determining legal intent (legal intent) and the validity of digital evidence derived from algorithmic systems. The study concludes that proving algorithmic cartels must shift from an intent-based approach to an effects-based approach, which focuses on assessing the economic impact on market structure and consumer welfare. Therefore, strategic recommendations include reinterpreting the element of “agreement” in Article 1, paragraph 7, and Article 11 of Law No. 5 of 1999 to encompass algorithmic coordination that generates anti-competitive effects. Additionally, the KPPU is mandated to develop digital evidence guidelines and strengthen the multidisciplinary institutional capacity to effectively oversee algorithmic behavior. These steps are crucial for Indonesian competition law to adapt to the realities of the digital economy while ensuring justice and legal certainty.

Abstract The digital advertising tax is an increasingly used policy measure, but its consequences must still be better understood. We develop a theoretical model where two countries compete to tax a digital platform that sets its quality and advertising to maximize profits. Although the tax may increase country-level tax revenues, we highlight an unexplored drawback of this policy. The unilateral ability of a country to tax advertising revenues generated in its territories may lead to inefficiently high equilibrium tax rates. A “race to the top”, as opposed to the “race to the bottom” typically observed in tax competition across countries, occurs whenever the quality of the digital platform is sufficiently responsive to an increase in the tax rate and is more decisive than advertising in determining prices and consumer welfare. Our findings emphasize the need for a global initiative on digital platform taxation rather than leaving the decision to individual countries.

Individuals might experience negative utility from not consuming a popular product. With such externalities to nonusers, standard consumer surplus measures, which take aggregate consumption as given, fail to appropriately capture consumer welfare. We propose an approach to account for these externalities and apply it to estimate consumer welfare from two social media platforms: TikTok and Instagram. Incentivized experiments with college students indicate positive welfare based on the standard measure but negative welfare when accounting for these nonuser externalities. Our findings high-light the existence of product market traps, where active users of a platform prefer it not to exist. (JEL D62, D83, D91, L82, Z13)