Market Analysis Research Articles (Page 1)

In the modern era, electric vehicles are developing at a rapid speed, and technology and sales have made breakthroughs. Among them, in recent years, many mature brands have been established in the electric vehicle markets of China and the United States, such as Tesla in the United States and BYD in China, and many new brands have emerged. This article mainly studies the political, economic, social, technological, environmental and legal differences between the China and the United States in the development of electric vehicles, and conducts a detailed market analysis of a single brand to better understand the development of electric vehicles.

The threat posed by invasive alien turtle species to aquatic ecosystems in Poland increases annually. The spectrum of species recorded in the wild evolves and follows trends observed in the pet market. After the ban on the trade of slider turtles (Trachemys scripta), Pseudemys spp. dominated the market of pet turtles in Poland. Currently, released specimens are more and more frequently recorded in the wild, and the scale of their invasion may be similar or even higher than what is currently recorded for T. scripta. In 2025, the first report of a released Pseudemys floridana was recorded in Poznań, which is probably the first record of this species in Poland. The analysis of online offers of freshwater turtles present on the market shows that Pseudemys spp. represents almost 50% of all offers. Thus, a risk assessment for potential invasion should be evaluated, and probably the list of invasive species of concern for Poland should be reviewed, including Pseudemys spp.. However, no action against keeping it in private hands should be taken. The main preventive action should be a ban on the import of Pseudemys spp. to Poland.

This research proposes a bioeconomy model that integrates the principles of Marketing 3.0 with the concept of a biorefinery to valorize the Creole-Antillean avocado cultivated in the Montes de María region, Colombia. The study emerges from the absence of commercial strategies that articulate social responsibility and economic viability in the use of agricultural by-products, considering that the current industry is almost exclusively focused on the Hass variety. The methodology employed a mixed-methods approach, combining quantitative and qualitative analyses. Market studies, consumer segmentation, competition and trend analyses were conducted, along with the use of the TradeMap platform to identify export opportunities and the international positioning of avocado-derived products. In the experimental phase, the production processes were validated: bio-oil was extracted through the Soxhlet method using solvents, while chlorophyll and biocontrol agents were isolated with ethanol, ensuring efficiency and scalability. The results obtained through Aspen Plus simulation were validated by comparing software outputs with data reported in the literature. The model includes a corporate social responsibility section that evaluates the regional impact, highlighting job creation, community inclusion, and the strengthening of the social fabric. Results show that in 2023, Mexico led exports with 1,220,919 tons, followed by the Netherlands and Peru, while Colombia reached 114,741 tons, consolidating itself as a country with high growth potential. The findings suggest that the valorization of the Creole-Antillean variety represents a strategic opportunity to diversify the agroindustry, strengthen competitiveness, create employment, reduce waste, and guide investment decisions in bioeconomy, sustainability, promoting rural development and green innovation in Colombia.

IntroductionThere have been increasing concerns about the overproduction of the health workforce in low- and middle-income countries, leading to unemployment, even as recent literature and country reports point to concurrent shortages based on population health needs. This paradox underscores the need for analyses that synthesise and reconcile these contrasting phenomena.MethodsThis is a rapid review of evidence of unemployment for countries that are listed on the 2023 WHO list of safeguard countries, which are countries designated as having critical health workforce shortages. Searches were done in PubMed, Embase and Google for health labour market analyses, peer-reviewed literature and grey literature detailing health workforce physician, nurse or midwife, unemployment. We combined unemployment rates into a weighted estimate (except for graduates) and as trends. We also provided a narrative summary from the literature providing unemployment rate estimates.ResultsSearches yielded 99 estimates from 72 sources, representing 33 of the 55 (60%) safeguard countries. 65 estimates were used for quantitative analysis. The weighted average of unemployment was 26.8% for physicians (across 13 countries), 39.7% for nurses/midwives (across 18 countries) and 26.1% in health workforce-wide estimates (across 8 countries). The unweighted average unemployment rate for new graduates is 54.2% across cadres: 58.4% for physicians across eight countries, 68.8% for nurses among six countries and 46.2% for midwives in two countries reported. Trends indicated declines in some countries but general increases in unemployment since 2020. Key findings included the contribution of education-market discoordination to unemployment, the realities of informal health care labor and shortage-unemployment paradoxes and migration and protests as a response to unemployment.ConclusionThe disconnect between health workforce unemployment and critical shortages in many countries underscores deep systemic inefficiencies and missed opportunities to strengthen health systems. Addressing this misalignment is not only urgent, but also essential for achieving universal health coverage and improving population health outcomes.

Purpose This study explores the market dynamics of reselling platforms within the broader context of online retailing, focusing on how buyers and sellers interact in a two-sided market environment. The goal is to examine how buyers and sellers perceive and engage with reselling platforms differently, focusing on their distinct behavioural patterns and satisfaction drivers within a two-sided market context. Design/methodology/approach Structural equation modelling was used to analyse survey data from 682 active users who have experience with reselling platforms. This approach enabled a detailed examination of the factors influencing user perceptions, trust and satisfaction in the digital retail environment. Findings The results reveal meaningful differences between buyers and sellers in terms of trust, perceived value and platform engagement, underscoring the need for differentiated strategies that accommodate both sides of the market. Buyer–seller interactions showed distinct patterns, underscoring the need for differentiated platform strategies in the retail sector. The findings highlight how platform-specific retail features shape user experience and engagement in resale commerce. Originality/value This study contributes to the retailing literature by extending two-sided market theory to the under-researched domain of reselling platforms. It offers both theoretical and practical insights into consumer behaviour in the resale market and provides strategic guidance for retail platform managers seeking to enhance competitiveness and user engagement.

Amidst the exponential growth of competition on short-form video platforms like Instagram Reels, traditional approaches to creating viral content, which are based on intuition and reactive trend-following, have proven inadequate for achieving stable commercial results. This methodology addresses a key problem: the gap between high reach metrics and low conversion into tangible business outcomes. The purpose of this work is to present a systematic, replicable approach to video content production that not only achieves viral distribution but also effectively monetizes audience attention. The scientific novelty lies in the development and formalization of the author’s proprietary “Viral Conveyor” operational model. This model integrates theoretical foundations from media virology, cognitive psychology, and marketing analysis into a unified production cycle. The methodology systematically deconstructs the phenomenon of virality, offering tools for analyzing trend lifecycles, applying audiovisual triggers for attention retention, using standardized script templates, and evaluating commercial effectiveness through a system of specialized Key Performance Indicators (KPIs). As a result, the creation of viral content is transformed from an unpredictable creative act into a manageable technological process, enabling brands and video production studios to systematically convert views into commercial orders.

The insurance industry is undergoing unprecedented digital transformation, driven by evolving customer expectations, regulatory pressures, and competitive dynamics. This research examines the strategic integration of product development methodologies, core suite platforms, and artificial intelligence solutions to construct a comprehensive digital infrastructure for modern insurance operations. Through analysis of market data, vendor performance metrics, and implementation case studies from 2024-2025, this study reveals that successful digital transformation initiatives can achieve operational cost reductions of up to 48%, processing time improvements exceeding 80%, and revenue enhancements of 25% within five years of implementation. The convergence of cloud-native core platforms, AI-driven automation, and customer-centric product strategies represents a fundamental shift from legacy operational models toward agile, data-driven insurance ecosystems. Current market analysis indicates that while 87% of insurers plan AI adoption by 2024, only 7% have successfully implemented these technologies at scale, highlighting significant opportunities for competitive differentiation through comprehensive digital core architectures.

Purpose – This study provides a comprehensive analysis of the global uranium market and assesses the ability of future supplies to meet growing demand through 2050. The study aims to identify potential imbalances between projected uranium supply and demand, taking into account market and geopolitical factors. Methodology – The study is based on a comprehensive approach that combines econometric analysis and uranium supply curve modeling. Econometric analysis uses a variety of methods to examine the correlation between prices and production volumes, ensuring the reliability of the results. Supply curve modeling shows how uranium availability will change with price changes, allowing for an assessment of market stability under different scenarios. Originality / value – The study analyzes the International Atomic Energy Agency’s forecasts and shows that primary uranium production will remain the main resource, despite the contribution from secondary sources. The study also highlights the importance of reviewing production and export policies in uranium-rich countries and ensuring security of supply. Findings – The main conclusion is that a significant imbalance between the projected uranium demand and supply, especially under medium and high demand scenarios, indicates that a potential shortage may arise by 2035. Kazakhstan is expected to take a leading position in the uranium export market by 2050. In addition, political and energy security issues may contribute to the growth of demand for nuclear energy, leading to the formation of new global partnerships and trade routes. The conclusions provide a deep understanding of market dynamics and contribute to the sustainable development of nuclear energy.

The exponential growth of user-generated content on review platforms like Yelp presents unprecedented opportunities for understanding consumer behaviour and market dynamics through advanced natural language processing. However, existing approaches face critical limitations: traditional topic models fail to capture fine-grained aspect-specific insights, neural methods lack integrated information extraction capabilities, and temporal dynamics modelling remains underdeveloped. Extracting actionable intelligence from unstructured review text is computationally challenging due to inherent linguistic complexity, temporal variability, multi-dimensional sentiment patterns, and the need to understand geographic market variations. These challenges necessitate a comprehensive framework that simultaneously addresses aspect extraction, topic discovery, temporal evolution, and market analysis. We propose the MultiAspect Temporal Topic Evolution with NeuralSymbolic Fusion and Information Extraction (MATTE-NSF-IE) framework, a novel end-to-end system for analysing restaurant reviews. The framework integrates four synergistic components: (1) a transformer-based information extraction module leveraging RoBERTa, VADER, and BERT for aspect ex- traction, sentiment classification, and named entity recognition; (2) a neural-symbolic topic modeling architecture combining Latent Dirichlet Allocation with TF-IDF weight- ing for aspect-aware topic discovery; (3) a temporal forecasting system using ensemble moving average prediction for sentiment trend analysis; and (4) a geographic market analysis module with statistical validation through Mann-Whitney U tests. We evaluated MATTE-NSFIE on the Yelp Open Dataset, analyzing 3,000 highquality restaurant reviews spanning 2005-2018 from 1,467 businesses across 248 metropolitan areas. The information extraction module achieved 70.0% F1- score for aspect extraction, 70.8% for sentiment classification, and 97.2% for named entity recognition. Topic modelling generated eight coherent aspect-specific topics with an 87.5% diversity score and 0.208 NPMI coherence. Temporal analysis achieved a mean absolute error of 17.9% in sentiment forecasting. Market analysis revealed statistically significant geographic patterns (p < 0.05) across 10 major cities, identifying variations in health trends (3.57-4.38), service priorities (0.72-0.78), and price sensitivity differences (0.44-0.57). The framework enables realtime business: intelligence applications, personalised recommendation systems, and comprehensive market analysis. Our approach provides actionable insights for restaurant management, investment decisions, understanding consumer behaviour, and locationbased market intelligence, positioning it for highimpact deployment in both academic research and industry applications.

Recent literature has established that the infusion of sustainability into the curriculum of higher education embodies an important pathway that allows future marketing leaders to rethink the application of sustainability to business and marketing practices. However, to do so, students must develop the appropriate habits of mind that give them confidence to apply theoretical frameworks covered in class to future real-life scenarios. How can instructors best foster this skillset related to sustainability? Drawing on a systematic analysis of sustainability marketing–related syllabi in the United States, this paper finds that application and ability to critically think and discuss are important objectives of sustainability-related classes. We further show that experiential learning with the use of case studies and in-class discussions ought to be seamlessly integrated into the curriculum. A comprehensive list of 104 case studies and discussion of the topics covered in sustainability marketing–related case studies in the United States is provided which serves as a baseline and inspiration for current and future instructors.

Environmental pollution due to used cooking oil waste and the use of synthetic chemical detergents are serious problems that require innovative solutions. This research aims to develop an environmentally friendly detergent based on used cooking oil as an alternative to conventional detergents. The research method uses a qualitative-descriptive approach with stages of pretreatment of used cooking oil, esterification, transesterification, sulfonation to produce Methyl Ester Sulfonate surfactant, and blending with additives. The results of the study show that JelaWash Detergent products have advantages in removing stains, are biodegradable, produce a long-lasting, gentle scent on clothes and skin. Market analysis of 32 respondents indicated positive acceptance with a preference for liquid detergents of 78 percent. The production capacity is designed to be 50,000 tons per year to meet the gap in Indonesia's detergent needs. The conclusion of the study shows that the use of used cooking oil as a detergent raw material can reduce environmental waste while producing high-quality cleaning products that are economical and sustainable.

The relationship between climate risks and commodity markets remains insufficiently explored, especially when analyzed through the lens of high-frequency data. This study seeks to address this gap by investigating the spillover effects of global climate risks, both physical and transitional, on key commodity markets and employs a novel analytical framework. By utilizing newly developed climate risk indices alongside the innovative mixed-frequency spillover measure, this research combines high-frequency climate risk data with the responses of low-frequency commodity prices. Our results highlight notable spillover effects, demonstrating that climate risks serve as the primary drivers of spillovers to commodity markets in a mixed-frequency data context, whereas such effects are not observed within a common-frequency data environment. These findings have important implications for policy-makers and investors, indicating that current market analyses may not capture the influence of climate risk adequately.

This paper explores the contributions and limitations of Generative AI (GenAI), including Chat GPT, in automated content analysis (ACA) in marketing. It compares the performance of several Chat GPT-driven ACA protocols with manual and automated ad hoc dictionary protocols, highlighting methodological and practical implications. The experiment uses a corpus of 992 responses from a consumer survey. It examines the quality of the different protocols for coding verbatim, but also for the design of thematic grids and ad hoc dictionaries . Performance is evaluated on validity and efficiency criteria. On the various key tasks of ACA, Chat GPT performs significantly worse than classical methods based on corpus analysis and researcher participation. The results show that GenAI has reduced validity, with frequent omissions and errors in the coding of specific data. GenAI can intervene in certain stages of ACA and offer time savings, but it requires rigorous and informed human supervision to ensure the validity of the results, highlighting the importance of a hybrid human-machine approach. The article provides a first systematic evaluation of GenAI applied to ACA in marketing, and proposes methodological recommendations, highlighting the fundamental differences between the algorithmic approach and the researchers’ approach based on real data.

There is a global need to reduce greenhouse gases, and industrial applications are one of the hardest-to-abate sectors. These energy-intensive industries require high-temperature heat which predominantly comes from fossil fuels. The United Kingdom National Nuclear Laboratory has developed a model to forecast the demand of both electricity and heat up to the year 2050, therefore providing an estimated demand that nuclear energy could help fulfil. This article uses the model to investigate the market potential for light water reactors and high-temperature gas-cooled reactors to determine the applicable heat markets globally. The analysis shows that the demand will be up to 1257 TWh for light water reactors and up to 2123 TWh for high-temperature gas-cooled reactors by 2050.

Abstract In the DACH (i.e., Germany, Austria, Switzerland) communication associations, women comprise half of the membership and can consequently express their interests in academic research, jobs, and publishing. But how does gender diversity in the DACH countries unfold across the job market, publications, citations, and grants? We present three studies to address this question. In Study 1, we observed the websites of communication departments in Germany, Austria, and Switzerland and content-analyzed the job titles of men and women working at N = 164 institutions. The data revealed that 43% of DACH professors were female. In Study 2, we ran an automatic content analysis to analyze how many publications in the DACH countries versus other countries were authored by men and women between 2000 and 2022 in 90 communication journals and whether authors from the two genders were cited proportionally. We found that women authored 53% of the articles in the 90 communication journals, whereas only 37% of the articles they authored were cited. In Study 3, we applied desktop research to analyze the diversity of grants that were awarded to DACH communication scholars. Women made up between 35% (German Research Foundation) and 43% (Swiss National Science Foundation) of principal investigators that were awarded funds by national funding organizations. We discuss the impact of current practices and suggest some new measures for increasing gender diversity by addressing issues of transparency, recognition, and inclusivity in communication.

This study presents a scenario analysis of Kazakhstan’s electricity market using the PyPSA-KZ model, with a focus on the integration of renewable energy sources (RES). As Kazakhstan transitions towards a low-carbon economy, this study evaluates the technical and economic implications of increasing RES penetration under various scenarios, ranging from 10% to 60% RES shares, with projections targeted for the year 2030. The study simulates system behavior across scenarios and analyzes key indicators, including total system cost, electricity tariff, generation mix, thermal ramping, and CO2 emissions. Results indicate that up to 30% RES integration is feasible without significant structural changes, delivering reduced system costs and emissions. However, scenarios beyond 30% reveal growing flexibility challenges, necessitating investment in grid modernization, energy storage, and flexible backup capacity. The model outcomes are benchmarked against the International Energy Agency’s 2030 carbon neutrality scenarios and show strong alignment, particularly at 45% RES share. Comparative insights are also drawn from international experiences in Denmark and China. This research demonstrates that the PyPSA-KZ model is a powerful tool for planning Kazakhstan’s energy transition and offers data-driven recommendations to support national energy security and climate goals.

The rapid evolution of India's food service industry has been fueled by technological innovation and shifting consumer lifestyles. A significant development is the rise of cloud kitchens or virtual restaurants that operate without traditional dine-in facilities. This study investigates the symbiotic relationship between cloud kitchens and Online Food Delivery (OFD) platforms, analyzing how their mutual dependency, consumer preferences, and operational efficiency are reshaping the food service ecosystem. Using a descriptive research design with primary survey data (N = 60) and secondary market analysis, this study confirms that while this ecosystem is expanding due to consumer demand for convenience and affordability, it faces significant challenges related to brand visibility, platform dependency, and quality perception. Ultimately, the findings suggest that for this ecosystem to achieve long-term sustainability and tap into expansion opportunities in semi-urban markets, operators must focus on brand-building, transparency, and strategic operational integrity

This study analyses the impact of gender-based subordination on women’s labour market outcomes in Mexico. Challenging the assumption of free choice in employment, we reveal how patriarchal constraints limit autonomy. Using Heckman and Heckprobit models, we examine the impact of subordination on wages, job benefits, job change and job satisfaction. Our results show that subordinated women participate less, earn lower wages, receive fewer benefits and report lower satisfaction and higher job-change intentions than independent women. Influenced women also, those whose autonomy has not been quashed but who are not independent, face significant barriers. Our findings highlight the need for policies promoting women’s autonomy to enhance economic inclusion and job quality, urging a re-evaluation of strategies for equitable global labour participation.

Cloud computing has become essential for businesses, providing scalable, flexible, and cost-efficient solutions. As organizations increasingly rely on cloud-based infrastructure, selecting the most suitable provider for specific needs is crucial. This research evaluates four leading cloud platforms—SiteGround, Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP)—to determine the optimal platform for hosting the TAKDA WebCPaSMS proto-model. Using a comparative case study design and content analysis, the study examines provider reliability, security, scalability, cost-effectiveness, and technical support. Findings indicate that SiteGround is ideal for managed cloud hosting and small-scale applications due to its ease of use but lacks traditional compute, storage, and networking services. AWS provides the most comprehensive cloud solutions, Azure integrates best with Microsoft enterprise ecosystems, and GCP excels in AI, data analytics, and cloud-native applications. These insights help organizations select the best cloud platform for their needs.

In 2021-2023, the volume of investments in the crowd-lending market showed more than threefold growth, but the corresponding volume of unfulfilled obligations doubled. Global and Russian experience confirm that the crowd-lending market has great potential, which depends on effective risk management. The purpose of the study is to propose measures to regulate the risks of the Russian crowd-funding market. Research hypothesis: the mechanism of operation of the crowd-lending market is similar to the exchange-traded corporate bond market, which suggests the possibility of adapting individual regulatory mechanisms of the corporate bond market to the crowd-lending market. The scientific novelty of this research lies in the fact that it is the first time an analysis of the Russian crowd-lending market by level of credit risk has been conducted. The comparison of the regulatory mechanisms of the exchange-traded corporate bond market and the crowd-lending market is also original. Furthermore, new measures for managing the risks associated with the Russian crowd-lending market have been proposed. Research methods: grouping, FOREL clustering, comparative analysis. Main results: 1) in the Russian crowd-lending market, 2 groups of participants were observed annually: with zero and moderate risk, groups with high credit risk were present sporadically. When using FOREL clustering, it was revealed that the group of investment platform operators with zero and moderate credit risk is heterogeneous; 2) the common features and differences between the regulation of the exchange-traded corporate bond market and the crowd-lending market are discussed; 3) risk management measures in the crowd-lending market are proposed (quarterly reporting by the platform operator on the share of outstanding obligations in the total volume attracted investments with the establishment of a recommended threshold value for such an indicator; the introduction of a representative of borrowers among the participants of the investment platform to protect their rights and interests; the inclusion of the procedure for dealing with overdue debts in the rules of the investment platform).