As the automotive industry moves toward electrification, high-performance vehicle manufacturers face the challenge of adopting electrified powertrains while preserving engine sound, mechanical feedback, and driving engagement valued by consumers. This study examines how objective technical knowledge, emotional attachment to internal-combustion-engine (ICE) characteristics, and household income influence consumers' willingness to pay (WTP) and powertrain preferences for high-performance hybrid cars. A quantitative, cross-sectional design was adopted using an online questionnaire completed by 296 respondents with certain automotive and ownership backgrounds. Descriptive statistics, Spearman correlations, chi-square tests, OLS regression, and binary Logit models were used to analysize the relationships between the independent and dependent variables. The results show that ICE emotional attachment was the clearest predictor of WTP: each one-level increase in attachment was associated with a 2.81-percentage-point increase in the premium respondents were willing to pay. Household income showed a weaker, marginally significant positive relationship with WTP, whereas objective technical knowledge had no significant independent effect. None of the three principal variables consistently predicted hybrid powertrain selection. The study concludes that consumers' financial valuation of high-performance hybrid technology is influenced more strongly by emotional value and financial capacity than by technical knowledge, but these factors do not necessarily determine their final powertrain choice.

This research aims to investigate the income inequality problem in Hong Kong. Apart from practical solutions, basic theories are also included. Simpson's Paradox, the Lorenz Curve, and the Gini Coefficient are the three main tools for investigating the degree of change in income inequality and adjusting for errors caused by insufficient data analysis. These researchers would use a combination of theory and real-life data to conduct an overall investigation and analysis of the income inequality problem in Hong Kong, China. The solutions suggested by the government and the other possible methods and policies could both be stated in the final part of the research. Investigating income inequality is very important since it could help scholars to develop a more mature and well-developed understanding of economic phenomena and the effective use of different economic models and theories. At the end of the research, a conclusion could be reached: Although income inequality and the Gini Coefficient are still fluctuating, noticeable improvement could still be recognised. Governments could impose policies such as progressive taxation or social welfare to improve the situation.

The shift from scheduled, channel-based distribution to on-demand, consumer-centric streaming has forced legacy media conglomerates to reconfigure their content libraries, brand assets, and monetization models simultaneously. This study takes Warner Bros. Discovery (WBD) as a revelatory case of legacy-media transformation. The analysis shows that WBD has unique advantages, namely, a highly diversified intellectual property portfolio, a high-quality brand equity of HBO, cross-platform promotion synergy, and a gradually improved monetization mode of mixed streaming media. However, the company's transformation is severely limited by the following factors: brand dilution caused by chaotic content integration, mismatch of time and cash flow between linear TV revenue decline and large-scale streaming media expenditure, heavy debt burden after merger and acquisition, and highly unstable distribution window strategy. In order to overcome the above systematic problems, this paper gives a set of operable solutions, namely, re-establishing obvious brand differences, implementing cross-cycle capital allocation, giving priority to positive deleveraging, and maintaining the company's windowing rules. The findings suggest that sustainable media leadership rests less on catalog scale alone than on an organization's capacity to coordinate and monetize heterogeneous cross-platform assets under financial discipline. This study gives a very critical strategic framework for understanding the digital transformation that diversified traditional media groups are experiencing.

The rising "goods economy", resulting from the conversion of anime game intellectual property (IP) into wealth, emphasizes the significance of the character products. In this context, the secondary market value of anime game IP products becomes an important topic in the "goods economy". This phenomenon can be illustrated by the prize market of "Ichiban Kuji". This paper uses the Dan Heng Ichiban Kuji plush from Honkai: Star Rail as an example. The formation of these values is not merely determined by the supply and demand, but is mainly affected by psychological factors. From the viewpoint of behavioral economics, this paper studies the joint impact of the endowment effect and speculative demand on the secondary market value. The research shows that the endowment effect, caused by the player's attachment to the character, leads to unwillingness to sell and reduces the market liquidity. This shortage of supply facilitates speculative activities. The speculative demand based on the idea of scarcity results in price bubbles. Ultimately, all these cause damage to the IP brand and decrease the benefits of general consumers, such as financial losses and feelings of injustice. To deal with these complicated issues and restore the market balance, this paper proposes official purchase, price control in the secondary market, and consumer rational education. These proposals provide both theoretical and practical guidance for managing the secondary market of game merchandise.

The rise of micro-short dramas is changing the way audiences access and watch film and television content. This study explores how the development of micro-short dramas has reshaped traditional long dramas and in what aspects this change is mainly reflected. The research adopts a comparative analysis of Chinese TV drama cases and combines relevant studies on the narrative rhythm, platform dissemination, and audience viewing behaviour of micro-short dramas. By comparison, it is found that micro-short dramas mainly influence long dramas in three aspects: narrative structure, narrative content, and audience. In terms of narrative, micro-short dramas usually adopt shorter episode durations, faster plot development, earlier conflict settings, and more direct emotional feedback, which also puts pressure on long dramas to increase their narrative pace. In terms of the audience, fragmented viewing and short-video consumption are becoming increasingly common, but viewers still tend to watch long dramas to experience character development, emotional accumulation, and more complete stories. These findings indicate that micro-short dramas are not simply replacing long dramas, but rather have formed a relationship of competition, influence, and coexistence with them. This research helps to understand how the fragmented media environment changes the narrative and viewing methods of film and television, and can also provide practical references for film and television creators, production companies, and platforms to understand the needs of contemporary audiences.

As the new energy vehicle (NEV) industry is rapidly growing and the global decarbonisation trend has been accelerating, the brand effect has become a critical factor in customers selecting a vehicle. But the underlying processes of how brand effect influences the purchase behaviour of consumers, especially in cross-market and cross-brand comparative situations, are under-theorised and under-empirically tested. This paper compares BYD and Tesla, and analyses the role of brand effect on the consumer purchasing intention. Based on Customer-Based Brand Equity (CBBE) and Social Identity Theory, the study constructs a model which is related to brand exposure, brand association, perceived value and purchase intention. The analysis reveals that brand effect reduces the decision costs, fosters corporate innovation, but creates brand premium bubbles, innovation lock-in and information asymmetry. To this end, the study suggests a multi-stakeholder governance model, which includes value-driven corporate branding, independent third-party evaluation systems and consumer brand literacy education, to counteract these negative impacts. The results expand the research on brand equity to the context of consumption of high technology and provide actionable implications for global NEV markets.

In the digital age, the main power over how information is spread is moving away from professional editors and toward smart algorithms; this shows a big shift, from editors acting as gatekeepers to algorithms acting as gatekeepers. This paper takes that shift, from editors deciding what gets through to algorithms deciding what gets through, as its main topic, and it also uses examples such as Douyin and Jinri Toutiao to talk about how these mechanisms work and what social effects they have. The results show that, when algorithms act as gatekeepers, they match information efficiently and precisely and clearly lower the cost for people to get information, so personalization and user experience are improved; at the same time, however, recommendation methods that rely on user engagement tend to favor content that grabs attention, stirs emotions, or looks very similar, and this may weaken information diversity and public discussion. To deal with these problems, this study puts forward three ways to handle them: a balanced recommendation approach that combines "accuracy and diversity," a responsibility system that can be explained, checked, and audited, and shared supervision by editors and algorithms. The study also points out that algorithms and the public good are not necessarily opposites; the key is to use rule design to keep the desire for efficiency inside a framework, so platforms can balance efficiency and trust, and so the study can offer theoretical and practical references for improving the information environment.

This study examines the limitations of using a simple annual price elasticity of demand calculation to measure consumer response in the U.S. gasoline market. Gasoline demand matters for both business pricing and public policy because consumers rely on gasoline for commuting and daily travel, yet they may not adjust consumption quickly when prices change. Using U.S. Energy Information Administration data from 2022 and 2023, the paper compares annual retail gasoline prices with finished motor gasoline product supplied, applies the midpoint elasticity method, and uses a simple linear demand model to interpret the price and quantity relationship. The annual result shows that the average gasoline price fell from about $4.059 to $3.635 per gallon, while product supplied increased only slightly from about 8,810 to 8,945 thousand barrels per day. The estimated PED is about -0.138, which indicates strongly inelastic demand. Further discussion using monthly comparisons suggests that annual averages may hide short-term consumer responses and mix price effects with seasonality, income, travel habits, and vehicle technology. The study concludes that PED remains useful for describing gasoline demand, but simple two-year estimates should be interpreted carefully when supporting pricing or policy decisions.

Though machine learning has been extensively used in financial forecasting, previous literature mainly focuses on measuring model performance based on statistical measures such as R² and RMSE without paying attention to whether those models create any economic benefit. In order to explore whether the improvement in statistical measures creates any economic benefit, this paper establishes a methodology of converting statistical measures into economic values and then performs a comparative analysis on four typical papers that report both kinds of measures. Three patterns about the relation between statistical measures and economic values are found from the analysis, which are positive correlation, negative correlation, and conditional correlation. Although statistical accuracy is a prerequisite for creating economic value, it is insufficient since the transformation from statistical accuracy to economic value faces several restrictions, including over-fitting, signal degradation, and mismatch of performance measures. The findings suggest that investors should not rely solely on statistical metrics for model selection, but rather incorporate risk-adjusted performance measures such as the Sharpe ratio and maximum drawdown, while also accounting for model complexity and signal persistence when constructing trading strategies.

Consumers rely on cognitive shortcuts to evaluate competing products gradually, but when such shortcuts improve judgment and when they produce bias, it remains unclear. This study examines multimodal review cues (e.g., visuals, ratings, valence) and the architecture of platforms and their roles in consumer decision-making in the context of information overload in digital commerce. The analysis shows that when salient information is relevant and complementary, availability cues lower search expenses and boost diagnosticity. But salience-driven preference distortion, extra attention to negative reviews, peripheral visual salience, and self-salience from review ranking systems can also occur. The paper suggests four remedies to address the problems listed above: integrate visual evidence with representative summary information in the reviews, structure the comparison of negative reviews, prioritize product-relevant visual evidence, and offer more transparency and diversity in the ranking of reviews. These strategies can be used to minimize systematic bias arising from differential information exposure and maintain the efficiency of cognitive shortcuts. The study offers implications for consumers, sellers, and platform designers and provides a better understanding of the interaction between the components of digital choice architecture and consumer heuristics in online purchases.