Market Strategies Research Articles

Market Preparedness for Policy Interventions towards Domestic Sustainable Markets: A study on Thai Rice Supply Chain

Climate change is a critical global issue affecting environments, economies, and societies. Sustainable development, which meets current needs without compromising future generation emerged in the 1987 Brundtland Report, requiring transformation across sectors, including business, where sustainability must be central. Integrating sustainability into market strategies allows businesses to attract environmentally conscious consumers, differentiate in a competitive market, and contribute to a sustainable future. This paper examines the Thai rice supply chain using Fairtrade as a voluntary sustainability standard, exploring consumer awareness, factors influencing sustainable market practices, and market readiness for sustainable products in the Global South. Based on the research project conducted in Thailand by engaging university students by Fairtrade International, it uses desk and field methods to assess the Thai rice supply chain, Thai rice market, and Thai customers. The study is indicative of the market readiness for sustainable products and recommends that state policies are synonymous with established private sustainable standards to strengthen domestic sustainable market ecosystem.

User behavior and energy-saving potential of electric washing machines

With the intensification of the global energy crisis and the increase in environmental awareness, energy-saving problems related to household appliances have garnered widespread attention. Here, the usage patterns of electric washing machine users and their energy-saving potential was mainly explored, so as to improve the current situation that the influencing factors of existing research behaviors were not deep enough and the energy saving potential was not specific enough. A questionnaire survey was used to gather information on 20,840 users, including individual characteristics, energy-saving awareness, and usage behavior. The study analyzed the differences in users’ energy-saving awareness and behavior through a series of analysis methods, and evaluated the energy-saving and water-saving potential of electric washing machines. The results showed that user behavior such as washing mode, washing temperature, and the volume ratio of clothes significantly affected on the energy and water consumption of electric washing machines. Individual characteristics of users such as gender, age, educational background, and family income were strongly correlated with their awareness of and decisions made regarding energy conservation. Improving the energy efficiency of electric washing machines and optimizing user purchasing behavior could result in 38,787.54 GWh national energy savings potential, and 6.90 million tons of water-saving potential. This study will help manufacturers and government departments better understand consumers’ usage behavior regarding electric washing machines, which could allow them to modify their market strategies and bolster the promotion and education of energy efficiency labels for electric washing machines. This also could support the nation’s objectives for environmental preservation, water and energy conservation, and the sale of products with lesser energy efficiency.

INVESTMENT BEHAVIOR OF B-SCHOOL GEN Z AMIDST GEOPOLITICAL TENSION

This study investigates the investment behavior of Gen Z students from business schools (B-schools) in Bangalore, India, amidst geopolitical tensions, focusing on their financial strategies, risk tolerance, and psychological responses. Utilizing data from 52 respondents collected through a structured questionnaire, the research highlights the cautious yet optimistic approach of these finance-trained individuals during periods of market volatility. Findings reveal that while 78.8% of respondents exhibit high-risk tolerance, their preference for holding investments and reliance on both self-research and financial advisors indicate a conservative, informed strategy emphasizing long-term stability. The majority identified retirement savings as a primary investment goal, showcasing a forward-looking perspective despite limited investment experience. The study also uncovers significant psychological impacts, with 32.7% of respondents expressing anxiety during geopolitical tensions, highlighting the role of external uncertainties in shaping investor sentiment. Moreover, respondents demonstrated a nuanced understanding of market dynamics, with 55.8% acknowledging the long-term impact of geopolitical events on the Indian stock market. Their behavior, including a preference for sectoral diversification and reliance on expert analysis, reflects the application of theoretical financial knowledge to practical investment scenarios. By analyzing the unique responses of B-school Gen Z during geopolitical crises, this research bridges gaps in existing literature, offering insights into how finance-educated individuals navigate uncertainty. These findings hold implications for enhancing financial education to build resilience and confidence in young investors while contributing to broader market strategies and risk management practices during periods of geopolitical instability.

A tri-level model for optimal management of active distribution networks enabling two-layer local markets

The deregulation of distribution systems enable local energy trading under the umbrella of local market strategies, which are launched and cleared by local entities like distribution system operators. The emergence of active players connected to distribution networks such as microgrids path the way to two-layer market structures, within which different local markets must coexist and coordinate. This new paradigm advocates for developing new management and market models suitable for multi-level local markets. This paper focuses on this issue. In particular, a new management model for active distribution networks enabling two-layer local markets is developed, which casts as an original tri-level optimization approach. This paper applies the developed methodology to the case in which different microgrids connected to a distribution market launch local markets for energy trading within the microgrid, but it could be applied to similar frameworks such as energy communities or virtual power plants. The new approach stablishes a game-oriented market coordination which preserves the hierarchical feature of the coordinated market strategy. A case study on a 33-bus radial distribution network serves to validate the model and illustrates how active players such as distributed generators and flexible demands perform under local market rules. A number of scenarios are studied under different distributed generation and microgrid penetration, showing that incrementing the number of both leads to improve the economy of the distribution system operator and microgrids. Finally, a sensitivity analysis regarding the renewable generation potential is studied.

Strategic IPO Timing of Technology Innovation-Driven Enterprises: A Differential Game Analysis of Market Returns, Disclosure Costs, and Industry Dynamics

This article develops a dual-oligopoly differential game model to analyze how technological impacts influence product market competition. This model undertakes a systematic examination of the strategic balancing act enterprises engage in when confronted with technological innovation opportunities, weighing market returns against information disclosure costs in the context of IPO decision-making. The findings reveal that the temporal dimension of technological catch-up and the firms’ intrinsic innovation capabilities are pivotal determinants of IPO timing. Specifically, enterprises that have achieved rapid technological parity and possess robust innovative capacities are more prone to expedite IPOs, seeking to secure funding for further technological advancements and market expansion endeavors. Conversely, elevated information disclosure costs may incentivize companies to postpone IPOs, aiming to shield their technological assets and market strategies from premature competitor scrutiny. Additionally, the study highlights the paradoxical influence of intense market competition: while it incentivizes early IPOs to bolster resource acquisition for competitive strategies, it also exacerbates the detrimental effects of disclosure costs on IPO timing. In conclusion, this investigation not only contributes to the theoretical corpus on IPO decision-making but also offers pragmatic guidance for corporate executives in crafting IPO strategies and serves as a valuable reference for fostering the sustainable development of capital markets.

Volatility-managed portfolios in the Chinese equity market

This paper examines the effectiveness of the volatility-timing strategy in the Chinese equity market. We find that the volatility-managed portfolio (VMP) consistently outperforms its original counterpart. This outperformance is primarily driven by stocks with high arbitrage risks, and is further enhanced when considering the price-limit rule in China. The conditional systematic risks of volatility-managed portfolios are significantly lower during market downturns, serving as a hedge against high volatility. Additionally, the multi-factor portfolio constructed from the individual volatility-managed factors outperforms other multi-factor portfolios, especially during periods of heightened investor sentiment or diminished macroeconomic confidence.

Optimizing Mutual Fund Selection with Fuzzy Logic: A Case Study of Nasdaq Exchange

This study examines mutual fund performance through advanced methodologies, emphasizing fuzzy logic and the Fuzzy Sharpe Ratio. A thorough literature review highlights recent developments, such as the integration of machine learning techniques with fuzzy logic models. The research utilizes data from the Bombay Stock Exchange spanning January 1, 2024, to October 31, 2024, applying the Fuzzy Risk-Adjusted Return method. Moreover, an analysis of major technology companies reveals Alphabet Inc as the top-ranked firm, demonstrating its robust performance metrics and establishing it as a premier investment choice. Conversely, Tesla Inc ranked lowest, reflecting comparatively weaker performance. These results illustrate the competitive dynamics and varied performance levels among leading tech companies, influenced by market strategy, innovation, and financial stability. This analysis underscores the value of fuzzy logic in enhancing mutual fund evaluations and positions Alphabet Inc as a strong candidate for investor consideration.

The Relationship between Hexa Helix and Marketing Performance of Food Product SMEs for Souvenirs: The Mediating Effect of Product Innovation and Integrated Marketing

Purpose: Small and Medium Enterprises (SMEs) play an important role in the Indonesian economy, yet their marketing performance remains low due to various interrelated causal factors. This study aims to explore the Hexa Helix as an independent variable that affects the marketing performance of SMEs producing food products for souvenirs, with product innovation and integrated marketing as mediating factors. Methodology/Approach: Data were collected from 448 respondents, who are owners and managers of SMEs in Malang Regency. Data were analysed using WarpPLS due to its flexibility in analysing mediating variables. Findings: The results showed that the Hexa Helix significantly and positively affects the marketing performance of food SMEs for souvenirs. Research Limitation/Implication: This study did not analyse the interactions between helices but looked at the six components as a whole. Future research can reveal the role of each actor and their interactions that determine the important role of the hexa helix. The implications for integrated marketing strategies for SMEs. Originality/Value of paper: Previous studies placed the hexa helix or other helix as a moderating or mediating variable. This research places the hexa helix as an independent variable and proves that multi-stakeholders affect product innovation and integrated marketing in improving marketing performance.

Analysis of Factors Influencing to Hydroponic Purchasing Decision in Pangkalpinang: A Particial Least Squares Structural Equation Modelling (PLS-SEM) Approach

The rapid growth of urbanization and increased health awareness among consumers in Pangkalpinang City have led to a growing interest in hydroponic vegetables. This study aims to analyze the factors influencing consumer purchasing decisions for hydroponic vegetables in Pangkalpinang. Using a quantitative approach, data were collected from 170 respondents through surveys, focusing on economic social, psychological factors, and marketing stimuli. The research employed Particial Least Squares Structural Equation Modelling analysis to identify significant predictors of purchasing decisions. The direct effects analysis indicates that perception (p-value = 0.004), attitude (p-value = 0.001), product quality (p-value = 0.000), and income (p-value = 0.000) significantly influence purchasing decisions, while motivation (p-value = 0.464) and price (p-value = 0.644) do not have a significant effect. The indirect effects analysis shows that price influences purchasing decisions through perception (p-value = 0.000) but not through motivation (p-value = 0.567). Similarly, product quality impacts purchasing decisions through perception (p-value = 0.006), while its indirect effect through motivation is insignificant (p-value = 0.425). These findings suggest that product quality and consumer perception play crucial roles, while price exerts an indirect influence to purchasing decision. This research provides essential insights for hydroponic entrepreneurs, emphasizing the importance of enhancing product quality and consumer perception to improve market strategies.

CEO trustworthiness and investment efficiency: evidence from China

Purpose This study aims to investigate the influence of chief executive officer (CEO) trustworthiness on firm investment efficiency and explores how this relationship varies in different contexts, including product market competition (PMC), institutional investors, media coverage, analyst monitoring and ownership structure. Design/methodology/approach The authors examined a sample of A-Share non-financial firms listed on the Shanghai and Shenzhen Stock Exchanges from 2005–2018 by using panel date regression techniques. The robustness of the findings is affirmed through alternative measures of investment efficiency and various econometric techniques. Further, various endogeneity tests are conducted to confirm that the findings are not affected by potential bias. Findings The authors find a significant positive effect of CEO trustworthiness on firms’ investment efficiency and exhibit that CEO trustworthiness mitigates the issue of underinvestment rather than overinvestment. Further, PMC strengthens the association between CEO trustworthiness and investment efficiency. The influence is more pronounced when institutional investors, media and analyst monitoring are low and in non state-owned firms. Likewise, financial reporting quality is found to be an underlying mechanism for the positive association between CEO trustworthiness and investment efficiency. Research limitations/implications The reliance on a location-specific index of CEO trustworthiness may obscure its true nature, and caution is warranted when generalizing these results to other regions. Practical implications This study suggests that elevating a trustworthy CEO to the firm upper echelon can improve investment efficiency. Policymakers and investors should recognize and leverage the effect of CEO trustworthiness in firms, especially those with weaker governance structures. Originality/value This study enriches the literature about investment efficiency by introducing a novel determinant, CEO trustworthiness and establishes that it acts as an informal social institution that improves firms’ resource utilization in emerging economies with weak governing structures.

Technological innovations of hidden champions: Evidence from patent data

AbstractThe hidden champion concept has received much interest in practice. As market leaders in niche markets, hidden champions represent the success of the (German) Mittelstand. Innovation is a key element of their strategy and their focus and niche market strategy are associated with specific technological capabilities. However, thus far, little quantitative empirical evidence exists about the innovation output of hidden champions. Drawing on a capability perspective and using patent data, the present study analyzes differences between hidden champions and comparable non‐hidden champion firms in their technological innovation. Our results show that hidden champions have a significantly larger technological innovation output but do not have a higher efficiency in their innovation creation compared to other firms from the same industry, size, and age. Moreover, the innovations produced by hidden champions show higher levels of technological depth and indicate lower levels of technological breadth. The sources of technological knowledge of hidden champions seem to be more inward oriented. Finally, innovations of hidden champions have similar technological impact, novelty and quality compared to those of other firms. Overall, our study supports many of the anecdotal beliefs about the innovation of hidden champions contributing to a better understanding of what makes hidden champions different from other Mittelstand firms. Practical implications for hidden champions and Mittelstand firms are discussed.

DO STOCK INVESTMENT STRATEGY AFFECT STOCK PRICES? A CASE STUDY OF SHARIAH-COMPLIANT COMPANIES IN MALAYSIA

This study aims to analyze how the stock market investment strategy using fundamental analysis affects stock prices. Fundamental analysis is an approach used to assess the intrinsic value of a stock based on the company's fundamental factors, including financial statements. This study utilizes historical stock data from 170 Shariah-compliant publicly listedcompanies on Bursa Malaysia and applies various financial ratios and stock valuation methods, such as net profit margin (NPM), price earnings ratio (PER), current ratio (CR), dividend payout ratio (DPR), earnings per share (EPS) and firm size (FS). The sample consists of 170 Shariah-compliant firms from all sectors except for the financial sector of Bursa Malaysia and 20 years maintained listed as status of Shariah-compliant Firms. This research employs panel data estimation techniques for regression analysis with robust panel data estimation technique for statistical inferences. The results indicate that PER, CR, EPS and FS have a positive effect on the stock price. This result confirms that higher in price earnings ratio, liquidity, earnings per share and firm size can increase stock price. The results also found that that NPM and DPR are negatively significant effect to the stock price of Shariah-compliant companies in Malaysia. This research contributes to the understanding of how fundamental analysis methods can be effectively applied in stock market strategies to influence stock prices. It aims to broaden investors' horizons by helping them identify the best strategies using fundamental analysis to generate profits from stock market investments and contribute to economic growth by strengthening economic conditions. This research is among the few studies examining the impact of stock investment decisions using a fundamental analysis approach on the stock prices of Shariah-compliant listed companies in Malaysia.

A Comprehensive Analysis of Game Success: Synergy Between Quality, Brand, and Market

This study scrutinizes the important factors behind the success of video games,Targeting a comprehensive paradigm which merges game quality, brand influence, and market strategies. By investigating the top 100 best-selling titles on Steam, the research looks into the interconnections between creative gameplay, the depth of storytelling, audiovisual quality, and sales results. Analysis of quantitative regression shows that although game quality is important, it is not the main factor for success. A qualitative assessment of high-priced, low-rated games including Overwatch 2 and NBA 2K25, and high-rated, low-selling games like Stardew Valley and Euro Truck Simulator 2, shows how significant brand loyalty, multiplayer interaction, and market segmentation are. This study demonstrates the value of original gameplay, successful marketing approaches, and technological innovations, like VR and superior graphics, in increasing sales and supporting player retention. The results provide key understanding for game developers and marketers who want to improve game performance in a cutthroat and rapidly evolving market.

Tabular Data Models for Predicting Art Auction Results

Predicting art auction results presents a unique challenge due to the complexity and variability of factors influencing artwork prices. This study explores a range of machine learning architectures designed to forecast auction outcomes using tabular data, including historical auction records, artwork characteristics, artist profiles, and market indicators. We evaluate traditional models such as LinearModel, K-Nearest Neighbors, DecisionTree, RandomForest, XGBoost, CatBoost, LightGBM, MLP, VIME, ModelTree, DeepGBM, DeepFM, and SAINT. By comparing the performance of these models on a dataset comprising extensive auction results, we provide insights into their relative effectiveness across different scenarios. Additionally, we address the interpretability of models, which is crucial for understanding the influence of various features on predictions. The results suggest that while some models perform better than others, no single approach offers consistently high accuracy across all cases. This study provides guidance for auction houses, art investors, and market analysts in refining predictive approaches, identifying key challenges, and understanding where further improvements are needed for more accurate data-driven decisions in the art market.

Evolution and Challenges of Insurance Industry in Hong Kong: From a Backward Investment Channel to a Leading Financial Product

The objective of the study is to explore the growth trajectory of Hong Kong's insurance industry and its changing position in the financial market, this study uses historical literature analysis, market data analysis, and case studies to conduct an in-depth investigation of the development of Hong Kong's insurance industry from the 1980s to the present. The study found that the Hong Kong insurance industry has experienced a transformation from a marginal investment channel to a core financial product, and the product attributes have also undergone significant changes with market demand and technological progress. The analysis shows that the growth of the individual life insurance market is mainly driven by factors such as increased consumer awareness, market policy support, and stable economic environment. However, the study also identified challenges facing the industry, such as market saturation, regulatory changes, and declining consumer trust. Based on these findings, it is recommended to adopt diversified product strategies, strengthen consumer education, and improve service quality to maintain the vitality and sustainability of Hong Kong's insurance industry. The results of this study not only reveal the evolution law of the insurance industry, but also have important theoretical and practical significance for promoting the realization of financial freedom, enhancing industry competitiveness, and formulating future market strategies.

Bitcoin price movement prediction by news sentiment using machine learning approach

This study investigates the potential of news sentiment, derived from Google News, in predicting Bitcoin price movements. It explores the correlation between sentiment in news headlines and Bitcoin's market behavior. Employing a data set of news headlines related to Bitcoin from Google News, this research applies sentiment analysis and various machine learning algorithms, including Decision Tree, Random Forest, Logistic Regression, Support Vector Machine, Naïve Bayes, and KNN. The approach involves extracting sentiment scores and correlating these with historical Bitcoin price data. The analysis revealed that Decision Tree and Random Forest algorithms provide a balanced prediction for Bitcoin price movements. Logistic Regression and Support Vector Machine showed high AUC scores but with unbalanced class predictions. Naïve Bayes and KNN were less effective. Overall, sentiment analysis of news headlines can predict short-term Bitcoin price movements to a reasonable degree. This research offers a novel tool for investors and market analysts, enhancing understanding of the impact of news sentiment on cryptocurrency prices. It provides a predictive model to assist in investment decision-making and market analysis. This study contributes to the literature on financial forecasting by integrating sentiment analysis with machine learning for cryptocurrency price prediction. It's one of the few studies to analyze the impact of news sentiment on Bitcoin price, offering a new perspective in the realm of financial technology and digital currencies.

Liposomal topical drug administration surpasses alternative methods in glaucoma therapeutics: a novel paradigm for enhanced treatment.

Glaucoma is a leading cause of permanent blindness. Despite therapeutic advancements, glaucoma management remains challenging due to limitations of conventional drug delivery, primarily topical eye drops, resulting in suboptimal outcomes and a global surge in cases. To address these issues, liposomal drug delivery has emerged as a promising approach. This review explores the potential of liposomal-based medications, with a particular focus on topical administration as a superior alternative to enhance therapeutic efficacy and improve patient compliance compared to existing treatments. This writing delves into the therapeutic prospects of liposomal formulations across different administration routes, as evidenced by ongoing clinical trials. Additionally, critical aspects of liposomal production and market strategies are discussed herein. By overcoming ocular barriers and optimizing drug delivery, liposomal topical administration holds the key to significantly improving glaucoma treatment outcomes.

Application and Development of Behavioural Economics in Consumer Decision Making

Behavioural economics, as an interdisciplinary field integrating psychology and economics, offers new perspectives for understanding consumer decision-making. This paper provides an overview of the core theories of behavioural economics, its applications in consumer decision making, and its developments and challenges in market practice and policy making. First, behavioural economics challenges traditional economic assumptions about rational decision-making by revealing core concepts such as limited rationality, prospect theory and behavioural biases, providing a deeper understanding of consumer behaviour. Second, the paper explores the effectiveness of behavioural economics in practical applications, such as improving firms' market performance and policy implementation by optimising marketing strategies, utilising personalised recommendations and developing behavioural intervention policies. However, behavioural economics still faces challenges in terms of theoretical universality, cross-cultural applicability and dynamic adaptation. Future research directions include theoretical integration, cross-cultural validation, adaptation to technological developments, optimisation of policy practices and intersection studies with psychology. The review in this paper aims to provide reference for academic research and practical application, to promote the further development of behavioural economics, and to provide theoretical support for policy making and market strategies.

Examining dynamics: Unraveling the impact of oil price fluctuations on forecasting agricultural futures prices

This research aims to explore the complex dynamics governing the correlation between oil futures prices and Chinese agricultural futures prices, with a specific emphasis on unveiling the crucial role played by oil futures prices in predicting the trajectory of agricultural futures prices. The study utilizes the Vector Error Correction-Dynamic Conditional Correlation-Multivariate Generalized Autoregressive Conditional Heteroskedasticity (VEC-DCC-MGARCH) model to dissect the interplay among oil, soybean, and corn price series. Additionally, this study integrates the innovative Spatio-temporal Information Recombination Hypergraph Neural Network (STIR-HGNN) model to analyze how oil futures prices contribute to improving the accuracy of forecasting agricultural product prices. Findings indicate numerous connections between oil prices and agricultural futures prices, highlighting the significant role of oil prices in forecasting agricultural futures price movements. The empirical insights derived from this study serve as a valuable compass for futures market participants, urging them to leverage these findings to refine and optimize their market strategies, enhancing their capacity to navigate and capitalize on the intricate complexities inherent in these interconnected markets.

The Augmented Reality (AR) Based on Artificial Intellegence (AI) in Integrated Marketing Communication (IMC) in Tourism Villages

This study examines the implementation of Augmented Reality (AR) based on Artificial Intelligence (AI) in Integrated Marketing Communication (IMC) in tourism villages, driven by the need for advanced technology to sustain the attractiveness of tourist destinations. The main objectives are to explore the potential of AR and AI technologies in enhancing tourist experiences, identify best practices in IMC, and analyze economic and environmental impacts. A literature review approach was used to gather findings from recent studies, indicating that AR provides deeper and more informative interactive experiences, while AI enables personalized experiences tailored to individual tourist preferences. The research methodology involved critical analysis of relevant literature, including case studies from several tourism villages in Indonesia and abroad that have successfully implemented these technologies. The results indicate that the implementation of AR and AI can significantly enhance destination attractiveness, strengthen branding, and support sustainable tourism development through better environmental management and more active involvement of local communities. However, challenges such as implementation costs, data privacy, and technological dependency need to be considered. This research provides a foundation for developing effective integrated marketing strategies using AR and AI, meeting the needs of modern tourists who increasingly demand informative, interactive, and sustainable experiences.