Market Strategies Research Articles

Forecast of Sugarcane Yield in Chongzuo, Guangxi—LSTM Model Based on Fusion of Trend Yield and Meteorological Yield

Purpose: This paper develops a high-precision yield fusion prediction model for the sugarcane industry in Chongzuo, Guangxi, based on the trend yield and meteorological yield using the long short-term memory (LSTM) model to cope with the multiple factors affecting sugarcane production. Decision support is provided to agricultural producers, policymakers, and supply chain managers so that they can plan resource allocation, market strategies, and policy directions more effectively. Methods: The paper modeled trend yield and weather yield separately to explore the complex relationship between the two in influencing sugarcane production. Trend yields were predicted using the exponential smoothing and multilayer perceptron (MLP) models, while meteorological yields were modeled using stepwise regression. The predicted yields were used again as input variables into the LSTM deep learning network to fit the nonlinear relationship between the two yields. Results: The results showed that (1) the fusion strategy of meteorological yield and MLP trend yield adopted by the model was superior to the fusion strategy of meteorological yield and exponentially smoothed trend yield, achieving a very low mean square error (MSE) of 0.011 and a goodness of fit as high as 0.979, which indicated that the model prediction was highly in agreement with the actual yield, confirming the validity of the method. (2) The prediction curve is basically consistent with the trend of actual sugarcane yield, which predicts that the sugarcane yield in Chongzuo, Guangxi, is expected to maintain a stable and small growth trend in the next eight years. (3) The fusion prediction model proposed in this study provides an accurate and practical solution for sugarcane yield prediction in Chongzuo, Guangxi, with the unique advantage of effectively analyzing and integrating the natural and socio-economic factors affecting the yield, which is of significant reference value for the prediction of sugarcane yield in the local area and even in similar ecoregions.

An Analysis of MUJIs Successful Strategy in the Chinese Market

Abstract: The Chinese consumer market has changed significantly in the last few years due to a tendency toward simplicity and a change in customer sentiments. MUJI has effectively entered the Chinese market by using its sustainable development idea and minimalist style, allowing it to adapt to this shift. This paper aims to examine the innovative marketing strategy employed by MUJI in the Chinese market, its capacity to achieve long-term growth, and its role as a symbol of the minimalist lifestyle, while demonstrating an ability to adapt to the changing needs of its customers. In this paper, both literature review and data analysis are employed to provide an in-depth study of MUJIs strategy to enter the Chinese market. The results show that Chinese consumers preference for MUJI products not only stems from their simple and practical design, but also coincides with the concept of minimalist aesthetics in traditional Chinese culture. In addition, MUJI's unique advantages in product quality, brand image and marketing strategy further enhance its competitiveness in the Chinese market. Through specific analysis, it comprehensively discusses the success factors of this Japanese brands minimalist design style in the Chinese market, which provides a reference for the design and marketing of other international brands in the Chinese market.

Research on the Correlation Between Carbon Emission Rights and Energy Market: A Case Study of EUA and Oil Market

This study focuses on the importance of the relationship between carbon and oil in the context of global climate change and energy transition. In particular, this paper explores the link between fluctuations in the price of Brent crude oil in the United Kingdom and changes in the European Union Emission Allowances (EUA), which has significant implications for insight into energy market dynamics and policy making. This study uses an autoregressive (VAR) model to analyze the dynamic relationship between the European Union Emission allowances (EUA) market and the oil market from 2018 to 2024. Through Augmented Dickey-Fuller (ADF) stability test, the VAR model is established, the optimal lag order is determined, and the impulse response and Variance decomposition are analyzed. The results show that the EUA market has a short-term positive impact on the response of the oil market. But this effect has diminished over time, indicating the market's ability to self-regulate and stabilize. These findings provide new perspectives for understanding the interaction between global energy markets and environmental policy tools. The findings highlight the importance of considering these market dynamics when developing market strategies and environmental policies.

Xiaomi: An Analysis of Market Strategy under the High Cost-Performance Ratio

Abstract: Xiaomi Corporation, which was established in 2010 in Beijing, is well-known for its smartphones along with other smart hardware as well as IoT devices. It is the second-largest manufacturer of smartphones in the world, behind Samsung, with a market share of about 12%, most of which run on the MIUI (now HyperOS) operating system. It mainly focuses on providing products that are of excellent quality and performance but still maintain affordability which attracts customers from around the world. This research delves into Xiaomi's adaptive marketing tactics as a reaction to evolving market dynamics and changing consumer demands, and examines Xiaomis market plan by looking at three clear instances. The first is how Xiaomi keeps its strong position in the smartphone market by bringing out improved models every year with a steady price tag. This helps to maintain their image of being very good value for money. Second, the choice of the company to put $300,000 for three years into remaking its logo with Japanese designer Kenya Hara. This highlights how redesigning has a strategic effect on brand image and worldwide identification. Thirdly, this study talks about Xiaomis start in the car industry when it introduced the first car model called Xiaomi SU7 in month April. It shows a major shift from making only smartphones to also producing electric cars.

Predicting Movie Box Office Based on Machine Learning, Deep Learning, and Statistical Methods

Abstract. The global film industry has been proved to impose a significant impact on both culture and the economy, with box office revenue serving as a crucial indicator of a film's commercial success. This study utilizes data from the Kaggle "TMDB Box Office Prediction" competition, encompassing 3,000 films released between 1990 and 2018, to predict movie box office revenue using Random Forest, XGBoost algorithms, and deep learning models such as Bidirectional Long Short-Term Memory (Bidirectional LSTM) and Simple Recurrent Neural Network (SimpleRNN). The goal is to develop a model that accurately predicts movie box office. By comprehensively considering multiple factors such as budget, popularity and film characteristics, this study not only significantly improves the accuracy of box office prediction, but also provides a scientific basis for the formulation of film market strategies. The results demonstrate that the Bidirectional LSTM excels in handling complex time-series data, showing strong trend-capturing capabilities, while XGBoost exhibits greater robustness in dealing with complex data and outliers. These findings can not only provide guidance for making more effective strategies on film production and distribution, but also provide new directions for future research, such as delving into the impact of social media on box office and developing more sophisticated predictive models to adapt to changing market dynamics.

Predicting financial performance with intellectual capital using machine learning

Purpose This study aims to apply machine learning techniques to efficiently predict leisure firms’ financial performance. Accurate financial forecasting is crucial in leisure and tourism, greatly affecting firms’ strategic decisions and competitive positioning. This study emphasizes the roles of intellectual capital to offer a nuanced understanding of how these types of capital influence firm success. Design/methodology/approach Using comprehensive firm-level data, this study examines several machine learning algorithms’ predictive capacity across a spectrum of industry sectors (general, manufacturing, service) to identify the most effective model and training dataset. These tools are used to evaluate financial metrics such as return on sales, return on assets and sales growth. A range of variables are incorporated into this process to enhance model accuracy and relevance. Findings Results demonstrate the support vector machine algorithm’s exceptional performance based on a training data set from the service sector in predicting leisure firms’ return on sales and sales growth. This algorithm is thus an efficacious strategic forecasting instrument. The variables significantly affecting firm performance include demand variation; organizational, product and technological innovation; synergistic innovation between multiple domains; salary levels; market strategy; and the number of employees. Originality/value By integrating advanced machine learning techniques with the strategic management of intellectual capital, this study presents a sophisticated approach to predicting leisure firms’ financial performance. Findings enrich the discourse on firm performance forecasting and offer actionable insights into strategic planning and resource allocation for practitioners in the leisure and tourism sectors.

Integrated Systematic Framework for Forecasting China’s Consumer Confidence: A Machine Learning Approach

This study aims to introduce a novel approach for predicting China’s consumer confidence index (CCI), a key economic indicator that reflects consumers’ confidence in current and future economic conditions. While traditional statistical models and economic indicators are the primary tools for forecasting CCI, their reliance on linear assumptions limits their ability to capture the complex, dynamic relationships inherent in economic systems. In response, this study proposes a two-step method that integrates social network analysis (SNA) and machine learning (ML) to enhance prediction accuracy by accounting for the nonlinear interactions and systemic interdependencies that drive consumer confidence. The use of SNA enables the identification of critical variables and their interconnected roles in shaping consumer sentiment, while ML models, specifically the gradient boosting decision tree (GBDT), leverage these relationships to provide more precise predictions. Utilizing monthly data from 1999 to 2023, the combined SNA and GBDT approach significantly improves the accuracy of CCI forecasts, particularly during periods of high volatility. The results of this study hold substantial value for policymakers, market analysts, and economists, as they offer a systems-oriented framework for economic forecasting. By demonstrating the effectiveness of combining SNA with ML technologies, this research not only advances the methodological toolkit for economic forecasting, but also provides a new lens through which the complex, adaptive nature of economic systems can be better understood and managed. This integrated approach paves the way for future developments in forecasting models that more accurately reflect the evolving dynamics of consumer confidence in a rapidly changing economic environment.

Management of social responsibility strategies of multinational corporations in russia during the war against Ukraine

This study examined how multinational corporations adapted their corporate social responsibility strategies while operating in russia during the ongoing war against Ukraine. Specifically, the analysis investigated the impact of different corporate social responsibility approaches on financial performance, stakeholder trust, and corporate reputation for multinational corporations operating in russia during the war. A game-theoretic model evaluated three distinct strategies: minimal corporate social responsibility engagement (Strategy 1), increased corporate social responsibility involvement (Strategy 2), and a complete exit from the russian market (Strategy 3). The quantitative analysis showed that companies choosing the exit strategy (Strategy 3) gained the highest payoffs for financial performance, stakeholder trust, and reputation. In contrast, minimal corporate social responsibility engagement (Strategy 1) resulted in negative outcomes, including reputational damage and potential exposure to sanctions. On the other hand, increased corporate social responsibility involvement (Strategy 2) produced neutral outcomes, offering short-term benefits but still leaving companies vulnerable to ongoing risks. The sensitivity analysis confirmed the stability of these outcomes. The study concludes that exiting the russian market not only aligns with ethical standards but also ensures long-term sustainability, offering critical insights for corporations navigating corporate social responsibility challenges in war zones. AcknowledgmentsWe sincerely express our gratitude to Prof. Dr. Dr. h.c. Marko Sarstedt, the LMU Fellowship Grant (LMU Ukraine support-fund) for the support of Ukrainian scientists.

Relationship Between Organisational Innovation and Organisation Performance: A Study of Nigeria Bottling Company

In an increasingly competitive business environment, the role of organisational innovation as a driver of performance is more crucial than ever. This study investigates the relationship between organisational innovation and organisational performance with a specific focus on Nigeria Bottling Company. Using a mixed-methods approach, the study explores how innovations in product development, processes, and management systems contribute to enhanced operational efficiency, market share, and financial outcomes. The study adopts a descriptive cross-sectional survey. The target population for this study consisted of two hundred and forty-five (245) staff members from Nigeria Bottling Company. Data was collected through structured questionnaires with key personnel across various departments within the organisation and analysed using descriptive statistics and regression analysis for the hypotheses. The study revealed that film size innovation is vital for a company's success in today's highly competitive business environment and leads to higher productivity, meaning that the same input generates a greater output. Innovatiocontributeste to firms’ success in terms of better performance in the form of higher profits, lower production costs, more efficient working procedures, and better relationships with customers. Furthermore, finding shows that creativity and innovation helps and improves the work of employees together with improving organisational performance. The study concludes that fostering a culture of continuous innovation is essential for sustaining competitive advantage in the beverage industry. It is therefore recommended that technological innovation be incorporated at all levels. The management of the organisation has to design an effective and timely market innovation strategy and communicate it to all staff members to enhance organisational performance and productivity. Organisations should consider innovation that is either market, product or organisation-based to improve and increase the organisational performance as innovation is significant to new product development, product modification and creation of a new market as experienced in new product lines like increase in online marketing, online purchase of different products and global connection of markets into one.

Forecasting Wholesale Electricity Market Prices Considering Bidding Conditions Using Price Sensitivity

This article proposes a sophisticated forecasting model to predict significant price volatility on the Japan Electric Power Exchange (JEPX) due to the growing influence of solar photovoltaics (PV) in the energy mix. As solar power generation continues to grow, it significantly impacts the daily and seasonal price trends in the market. This method employs a neural network that integrates daily prices, net electricity demand (total demand minus PV output), and underlying time‐series data to determine the objective variable, defined as the deviation from the average price over the past week. Incorporating price sensitivity data from JEPX increases the accuracy of the model. These data reflect how prices respond to market‐specified bid adjustments, including a variety of bidding, providing a nuanced understanding of market responses. The results demonstrate that adding multiple price sensitivities significantly improves the model's ability to detect price spikes and provides a robust tool for transmission and distribution system operators to manage risk and optimize their market strategies in an increasingly renewable energy‐dominated landscape. This approach not only addresses daily and seasonal price fluctuations but also aligns with broader sustainability goals as the share of solar PV generation continues to grow.

Research on the Digital Transformation of the Enterprise Marketing Ecosystem Based on Genetic Algorithms

This paper examines the current state of the Enterprise Marketing Ecosystem (EME), especially in the context of Digital Transformation (DT), highlighting both the challenges and opportunities it presents. A significant focus is placed on the potential of genetic algorithms to address the complex optimization problems inherent in this transformation. The core issues faced by enterprises in their digital marketing evolution are elaborated, setting the stage for the exploration of genetic algorithms in this domain. The research delves into the specific steps of the proposed method, utilizing the multi-objective genetic algorithm NSGA-II to optimize various aspects of market strategies. The methodology includes the generation of an initial population, evaluation of fitness according to the tailored needs of the marketing ecosystem, selection, crossover, and mutation operations, followed by the formation of a new generation of solutions. The termination criteria of the algorithm are also discussed. Experimental results are presented, demonstrating the effectiveness of the genetic algorithm in enhancing the DT of the EME. Comparisons with traditional approaches reveal significant improvements in various key performance metrics, underscoring the algorithm’s capability in navigating the complexities of digital marketing optimization.

Insect Production: A Circular Economy Strategy in Iceland

In this review, the multifaceted issue of food security is addressed, emphasizing the need for innovative and culturally appropriate solutions. Exploring insect livestock farming emerges as a potential remedy, offering a pathway to alleviate food insecurity and promote food sovereignty, particularly when integrated with social acceptability. Stakeholder engagement on both production and consumption fronts, coupled with sustained support, is vital for successful implementation. The expanding landscape of commercial insect farming in the West prompts questions about its broader scalability and equitable deployment, especially for vulnerable populations. Existing research gaps underscore the need for a coordinated effort across international, national, and legal frameworks to effectively integrate insect farming into existing agricultural systems. In this review, we have delved into the industrial-scale production processes of mealworms and black soldier flies (BSFs), known for their high protein content and organic waste conversion, covering small and industrial cultivation methods, offering insights into mealworm production life cycles, innovative rearing systems, and harvesting techniques. This review concludes with climate-specific recommendations for insect facilities, stressing the importance of sustainable practices, continuous research and development, effective market strategies and economic feasibilities in Iceland. In the context of escalating demand for sustainable protein sources, industrial-scale insect production emerges as a pivotal player in addressing global food security challenges.

Artificial intelligence in healthcare forecasting: Enhancing market strategy with predictive analytics

This research examines the transformative role of artificial intelligence (AI) and predictive analytics in healthcare market forecasting, with a specific focus on their application at Eli Lilly. AI-driven insights are becoming critical tools in anticipating market trends, assessing the impact of regulatory changes, and optimizing product positioning in the competitive global healthcare landscape. By leveraging machine learning algorithms and vast datasets, AI can identify patterns and correlations that traditional forecasting methods may overlook, offering companies like Eli Lilly a more comprehensive understanding of future market dynamics. The study explores how AI enhances decision-making by predicting healthcare market trends, particularly in response to regulatory shifts such as the U.S. Inflation Reduction Act. This legislative change, aimed at reducing drug prices and increasing market access, presents significant challenges and opportunities for pharmaceutical companies. AI allows firms to model the potential outcomes of such regulatory changes and adjust their market strategies accordingly. For example, predictive analytics can forecast the pricing pressures on specific drugs, helping companies develop competitive pricing models while maintaining profitability. Moreover, the research highlights the use of AI in mitigating risks associated with market volatility. By continuously analyzing real-time data, AI can detect early signals of potential disruptions—whether from economic downturns, competitor actions, or public health crises—allowing companies to take proactive measures. Additionally, AI-driven forecasting helps identify growth opportunities in emerging healthcare markets by analyzing demographic trends, disease prevalence, and healthcare infrastructure development. In conclusion, AI and predictive analytics are revolutionizing how healthcare companies like Eli Lilly approach market forecasting and strategy development. By offering deeper insights into future market conditions, these technologies not only reduce risks but also optimize product positioning and unlock growth opportunities in the global healthcare market. This research underscores the critical importance of AI in shaping the future of healthcare market forecasting. Keywords: Artificial Intelligence, Predictive Analytics, Market Forecasting, Regulatory Impact, Healthcare Strategy

Research on Netflix’s Expansion and Competitive Strategies in the Global Markets

This study examines Netflix’s global expansion and competitive strategies, emphasizing their importance in the rapidly evolving entertainment industry. Netflix’s transformation from a DVD rental service to a leading global streaming platform highlights the significance of localized content, technological innovation, and original content production. The analysis identifies three major challenges for Netflix: navigating diverse regulatory environments, sustaining long-term subscriber growth, and managing content saturation and quality. Proposed solutions include partnering with local streaming services, enhancing recommendation algorithms, and focusing on producing high-quality content. The research offers valuable insights into effective global market entry strategies, underscoring Netflix’s role in fostering cross-cultural exchange and contributing to a more interconnected global community. Although the study relies on secondary data, it underscores the need for future research incorporating primary data to deepen understanding of Netflix’s strategies and their long-term sustainability. By addressing these challenges, Netflix can continue to thrive in the competitive global market and maintain its position as a pioneer in the entertainment industry.

Research on the Positioning and Development Strategy of Archaeopteryx Brand in Chinese Market

As people focus more and more on health, outdoor activities are becoming more and more popular, and Arcteryx, as a leading brand in outdoor activities, has received a lot of attention recently, this paper adopts the literature review and case law method around Arcteryxs itself, and researches on Arcteryxs market strategy, technological innovation, market adaption, market competitiveness, etc., one by one, and this thesis has certain reference value for the positioning and strategy of Arcteryx as well as other outdoor brands in the Chinese market, as well as in brand marketing, product innovation, consumer relationship management, and digital application. This thesis has certain reference value for Arcteryx and other outdoor brands positioning and strategy in the Chinese market, as well as in brand marketing, product innovation, consumer relationship management and digital application. It also points out that the brand recognition and product line concentration in Arcteryxs high-end market are relatively low, and the future should further explore the regional market performance, changes in consumer behavior and the application of virtual technology.

Determinants of the Relationship between Wholesale and Retail Energy Prices in the Czech Republic

Rising energy prices have become a focus of interest for politicians at the international, European, and national levels. Energy prices are the main determinant of profitability or even the existence of the industrial sector. So, it is accepted that energy policy aims to ensure a secure, sustainable, competitive, and affordable energy supply for households and businesses. However, the recent geopolitical situation has significantly affected the energy policy of the European Union, raising concerns about energy security and an energy crisis. This crisis has had social, economic, and political consequences, including energy poverty. In this context, it is essential to understand the determinants of the relationship between wholesale and retail energy prices. This paper presents a model proposal to be used by the Czech Energy Regulatory Authority. The model of average retail prices depends on the type of contract - spot or forward, with the three-year forward contract having the largest weight on the retail price. Therefore, our study shows that long-term contracts significantly impact retail energy prices, highlighting the importance of regulatory monitoring of market strategies. Methodologically, the model is based on a multivariate regression analysis solved with the least absolute deviations and data for the period 2012 to 2023. The findings of the study contribute to a better understanding of the complexity of the dynamics of energy prices and support policymakers while attempting to ensure energy affordability and sustainability.

Brexit and the London housing market: An empirical contribution

This study aims to analyse the impact of Brexit on London’s housing market, exploring socio-economic and regional disparities. By examining property transaction data from 2012 to 2022, the research seeks to understand how Brexit has influenced real housing prices across different boroughs of London. The methodology involves aggregating transaction data from the Her Majesty (HM) Price Paid database and normalizing prices using the Consumer Price Index (CPI) to obtain real price variations. These data were segmented into three distinct periods: pre-Brexit (2012–2016), post-plebiscite Brexit (2016–2019), and post-implementation Brexit (2020–2022). Spatial analysis was conducted using the software Quantum Geographic Information System (QGIS), transforming point data (postcodes) into polygonal data (wards) for better visualization and comparison. The findings reveal significant socio-economic impacts, with traditionally affluent areas such as Westminster, Kensington, and Chelsea experiencing notable declines in real housing prices. Conversely, certain outer boroughs like Newham and Barnet showed resilience, with positive real price variations despite decreased sales. This geographical disparity underscores the uneven distribution of Brexit’s economic consequences, highlighting the critical role of localized economic policies and development projects in mitigating adverse effects. The results confirm existing literature on the polarization and regional inequalities exacerbated by Brexit while providing new insights into the complex interplay of local and global factors affecting housing markets. The findings emphasize the need for targeted policy interventions to address the diverse challenges posed by Brexit, ensuring both affluent and disadvantaged areas receive adequate support. This research is crucial for informing public policy, urban planning, and housing market strategies in a post-Brexit context, promoting equitable and sustainable development across London.

MediGate: a MedTech product innovation development process from university research to successful commercialization within emerging markets

This study proposes a comprehensive MedTech product innovation development framework tailored for university research commercialization within emerging markets. The MediGate framework, built on the Augmented Stage-Gate model, addresses the unique challenges of MedTech innovation, including regulatory compliance, stakeholder engagement, and market dynamics. The framework integrates critical decision-making criteria for different types of inventions to drive academic research toward commercialization in clinical settings. Through detailed case studies, including innovations like albumin strip test, 3D-printed patient-specific implant, COVID-19 nasal spray, and AI platform for depression detection, and iterative refinement, the framework provides actionable guidelines for navigating the complexities of product development. These guidelines ensure alignment with clinical needs, regulatory requirements, and market strategies. The research highlights the importance of early-stage valuation, reimbursement strategies, legal and IP considerations, and manufacturing and quality management. By offering a structured pathway, this research contributes to the theoretical and practical understanding of MedTech commercialization, aiming to enhance innovation success and healthcare impact in emerging markets.

Dinâmicas de imperialismo e colonialismo na era digital

The investigation presents a reflection on data market strategies anchored in imperialism and colonialism in the digital era. It discusses the commodification of internet user data on different digital platforms, as well as strategies to mitigate these practices and studies on the topic. The research is descriptive, bibliographic and qualitative, reflecting on data, information, power and manipulation of society through digital platforms. The bibliographic survey was carried out on the Capes Periodicals Portal considering only articles published in open access and peer-reviewed, covering the entire period available until December 2023. The data market carried out by large technology corporations exerts dominance over the economy and culture, through the control and exploitation of user information. This phenomenon raises concerns related to privacy, concentration of power and digital exclusion. The data market presents significant challenges in the digital era, demanding effective responses from civil society, governments and international organizations. To address this problem, it is essential that Brazil strengthens its technology industry and implements data protection policies.

Innovative financial and operational models for affordable housing: A review of emerging market strategies

Innovative financial and operational models for affordable housing: A review of emerging market strategies