Strategic Insights Research Articles

Application of K-Nearest Neighbor Algorithm for Consumer Behaviour Identification and Product Personalisation Based on Big Data Analysis

The rapid digital transformation has fundamentally altered the retail industry, presenting challenges such as shifting consumer behavior and intensified market competition. This research explores the application of the K-Nearest Neighbor (KNN) algorithm for identifying consumer behavior and developing product personalization systems based on big data insights. Utilizing a dataset comprising 10,000 transaction records from January to December 2023 and 5,302 product types, we implemented the KNN algorithm to predict consumer purchases. The data was processed into 89,908 distinct transaction records. Our evaluation, using 5-fold cross-validation, demonstrated that the optimal performance of the KNN model was achieved with 𝑘=10, yielding a precision of 0.8319, recall of 0.8311, and an F1-score of 0.8312. These findings highlight the effectiveness of KNN in enhancing consumer satisfaction through precise product recommendations. This study provides strategic insights for modern retailers aiming to leverage AI and big data to remain competitive and meet evolving consumer expectations.

Hydrate management strategies for CO2 injection into depleted gas reservoirs

Sequestering CO2 in depleted gas reservoirs using carbon capture, utilization, and storage technologies has emerged as a viable strategy for mitigating global carbon emissions. However, CO2 hydrate formation during injection processes is challenging and poses a risk to the operational integrity and efficiency of sequestration projects. This study investigated CO2 hydrate formation dynamics within silica gel environments that closely mimicked the median pore size of natural sandstone reservoirs, particularly targeting depleted gas fields in the East Sea of Korea. Integrating kinetic insights into experimental hydrate formation assessments and molecular dynamics simulations provided a comprehensive understanding of the CO2 hydrate formation mechanisms, as well as the formation dynamics and stability of the CO2 hydrates. For example, the hydrate formation kinetics were significantly reduced in the small pores, suggesting the feasibility of implementing more economical hydrate inhibitor injection strategies during CO2 injection. Moreover, this study evaluated the inhibitory effects of NaCl and methanol on CO2 hydrate stability by accurately measuring the three-phase (H-Lw-V) equilibria and employing silica gels (6 nm pore size). The presence of methanol and amorphous silica significantly impacted hydrate formation, with methanol potentially influencing the local structure around the hydrate phase and silica hindering hydrate growth through dynamic interactions with CO2 molecules. In addition, we elucidated the complex interplay between CO2, water, and methanol (hydrate inhibitor) within the constrained environments of porous media, offering strategic insights for the development of effective CO2 injection and sequestration strategies. Integrating molecular-level observations with real-world geological modeling presents a novel methodology for enhancing the safety, efficiency, and environmental sustainability of carbon capture and storage operations. The findings of this study provide critical insights into hydrate phase behavior, highlighting the importance of precise equilibrium determination under simulated reservoir conditions to effectively anticipate and mitigate hydrate formation challenges.

Graduate Education in China Meets AI: Key Factors for Adopting AI-Generated Content Tools

Abstract Factors influencing Chinese graduate students’ adoption of AI-generated content (AIGC) tools are examined through partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA). The developed AIGCT-SI model incorporates key elements such as information accuracy, trust, and privacy concerns. PLS-SEM results indicate that performance expectancy, effort expectancy, facilitating conditions, and habit significantly impact students’ intentions, with trust acting as a key mediator, particularly for privacy concerns and social influence. FsQCA reveals seven configurations, demonstrating how combinations of performance expectancy, effort expectancy, and facilitating conditions drive adoption. A bidirectional relationship between privacy concerns and trust is observed, with trust mitigating privacy risks in several configurations. This integrative approach highlights the complex dynamics of AIGC tool adoption and provides strategic insights for their effective use in Chinese graduate education. As the findings are based on the Chinese context, further exploration in other educational settings is encouraged to validate their broader applicability.

A comprehensive review of treasury management practices and their impact on corporate sustainability in emerging economies: Strategic insights from the oil and gas sector

Treasury management practices are critical for ensuring financial stability and fostering corporate sustainability, particularly in the volatile oil and gas sector of emerging economies. This comprehensive review examines the relationship between treasury management practices and corporate sustainability, focusing on strategic insights that can be drawn from the oil and gas industry. The review highlights the importance of effective cash management, risk assessment, and liquidity optimization as key components of successful treasury operations. The study categorizes treasury management practices into several essential areas, including cash flow forecasting, working capital management, investment strategies, and risk management frameworks. Each category is analyzed concerning its contribution to corporate sustainability, emphasizing the need for a holistic approach that aligns financial objectives with environmental and social considerations. The findings indicate that robust treasury management practices can significantly enhance operational resilience, improve resource allocation, and support long-term sustainability initiatives. Furthermore, the review discusses the challenges faced by oil and gas companies in emerging economies, such as economic instability, regulatory uncertainty, and fluctuating commodity prices. These challenges necessitate the adoption of innovative treasury strategies that not only safeguard financial assets but also promote sustainable business practices. By integrating sustainability metrics into treasury decision-making, companies can better manage risks associated with environmental regulations and stakeholder expectations. Additionally, the role of technology in modernizing treasury management is examined, particularly in terms of digital tools and platforms that enhance data analysis and decision-making capabilities. The review underscores the potential for fintech solutions to streamline treasury operations and improve transparency, thereby contributing to enhanced corporate sustainability. In conclusion, this review provides strategic insights into the interplay between treasury management practices and corporate sustainability in the oil and gas sector of emerging economies. The insights presented herein are intended to guide industry practitioners in developing effective treasury strategies that foster sustainable growth while addressing the unique challenges of the emerging market context.

Transform Financial Data into Strategic Insights Using the Sap Business Technology Platform

This article examines the groundbreaking fusion of SAP Business Technology Platform (BTP) and Artificial Intelligence (AI) in the realm of financial reporting, emphasizing their transformative role in translating financial data into strategic insights for executive leadership. By introducing automated commentary generation, this integration enables BTP to convert intricate financial datasets into coherent narratives that furnish decision-makers with actionable intelligence. Positioned at the nexus of AI progress and financial analysis, BTP facilitates the seamless conversion of complex data into narrative or commentary reports, fostering coherence, insightfulness, and accessibility for decision-makers. The discourse delves into the underlying mechanisms of BTP functionality in financial reporting, spanning data interpretation, language processing, commentary generation, and customization, thereby enhancing report efficiency, precision, and accessibility. Through meticulous exploration, this article elucidates BTP's contributions to financial reporting commentary, including enhanced efficiency, accuracy, scalability, and personalized delivery of financial insights. Furthermore, it confronts the challenges inherent in BTP application, encompassing technical intricacies, ethical considerations, and the constraints of current technologies in capturing financial intricacies. Looking ahead, the article envisions a future where ongoing advancements in AI and machine learning refine BTP capabilities, furnishing even more sophisticated and nuanced financial insights to underpin strategic decision-making at the highest echelons of business leadership.

Strategic insights: Unveiling the potential of Pharmaceutical exports from India to Regulated markets

India is a major exporter of medicinal products in the world. India's pharmaceutical sector has grown significantly to contribute largely to the GDP of the nation because of its rapidly expanding economy and abundance of highly skilled labor. However, exporting pharmaceuticals to markets that are subject to regulations can be a difficult procedure that requires a lot of paperwork. This all-inclusive handbook will assist in navigating the documentation and export regulatory requirements for pharmaceutical exports from India to regulated markets. It also provides export projections for the future, enabling exporters to adapt their export strategy. This handbook will give you the knowledge needed to be successful in the pharmaceutical export business, regardless of whether an experienced exporter or a recent entry into the market. A study on Indian Pharmaceutical Exporter together with future forecasts and SWOT Analysis of Indian Pharmaceutical manufacturers.

Analisa Tingkat Kepuasan Pelayanan pada Aplikasi PLN Mobile dengan Metode Service Quality

This study aims to analyze user satisfaction with the PLN Mobile application using the Service Quality (SERVQUAL) method. PLN Mobile is a digital innovation from PLN, offering services such as electricity payment, token purchase, and e-commerce for electrical equipment. The SERVQUAL method assesses the gap between customers' expectations and perceptions across five dimensions: tangibles, reliability, responsiveness, assurance, and empathy. Data collection involved questionnaires focusing on users' perceptions and expectations, complemented by direct observations of user interactions with the application. The findings reveal negative gaps across all Service Quality dimensions, indicating that customer perceptions fell short of their expectations. The largest gap was identified in the assurance dimension (-23.2), followed by responsiveness (-16.88). The tangibles dimension also showed significant shortcomings (-8.48), mainly concerning the application's interface and functionality. While the application performed relatively well in terms of reliability and responsiveness, substantial improvements are needed across all dimensions to enhance user satisfaction. These results offer strategic insights for improving PLN Mobile to become more responsive, reliable, and aligned with user needs in the future.

Spatial Assets Value Extraction and Integrated Utilization of Old Communities: Case of Central Guangzhou, China

Extracting the economic value by the integrated utilization of space in old communities is crucial for encouraging independent participation from enterprises and residents, reducing reliance on government leadership and fiscal investment. This study starts from the active perspective of spatial assets and constructs a value activation framework for old communities by balancing “endogenous demand” and “exogenous opportunities”. By enhancing the “economic value” through the “use value”, five methods for value extraction and overall project utilization paths are proposed, guided by a dynamic “cost-revenue” balance. Using multi-source data, we identify the spatial assets of 1096 old communities in central Guangzhou and apply a market comparison method for an economic value assessment. Additionally, this study offers recommendations on the timing and project portfolios for regeneration efforts, along with strategies for establishing a coordinating implementation entity and fund account. This research provides strategic insights for advancing the regeneration of old communities by tapping into their macro-level economic potential.

The Application of Virtual Reality and Augmented Reality Technology in Digital Marketing

This study investigates the utilization of Virtual Reality (VR) and Augmented Reality (AR) technologies in digital marketing, particularly their impacts on user engagement, brand awareness, conversion rates, and customer experience. With the progression of digital marketing, VR and AR have emerged as significant tools that create immersive and interactive experiences, aiding brands in distinguishing themselves within a competitive market. By analyzing applications such as virtual product demonstrations, interactive advertising campaigns, virtual showrooms, and AR shopping applications, this research underscores the substantial advantages these technologies confer. The findings reveal that VR and AR not only improve user engagement, brand visibility, but also boost customer satisfaction and drive sales. Despite obstacles such as high development costs and technical challenges, the outlook for VR and AR in digital marketing remains promising. This study provides strategic insights for businesses on effectively incorporating these technologies into their marketing frameworks to ensure long-term success and competitive advantage.

Strategic genetic insights and integrated approaches for successful management of blackleg in canola/rapeseed farming

AbstractThis review describes a triumphant narrative in the battle against the devastating plant pathogen complex, Leptosphaeria maculans and L. biglobosa, and the success of the world's second‐largest oilseed crop, canola/oilseed rape. Emphasizing global collaborations, the article explores successfully mitigating this destructive disease in canola/rapeseed production across Australia, Canada and Europe. It highlights how strategies may vary between continents to adapt to specific contexts. While initial resistance (R) genes proved effective, the evolution of the pathogen under crop‐induced disease pressure led to the breakdown of these genes. Now, growers in these regions have been equipped with new tools, allowing them to make informed decisions that help to keep the disease at generally low levels. A pivotal factor in this success has been a deepened understanding of the intricate science underlying the host–pathogen interaction. Concerted efforts of individual laboratories and collaborative initiatives have played an essential role in this success, including novel methods for disease control based on extensive research that has translated into developing highly resistant varieties, enhanced pathogen monitoring, improved cultivar recommendation and integrated management strategies. The review showcases many milestone advancements, including the cloning of numerous avirulence genes within the pathogen, characterization of specific R genes, the development of various molecular tools for monitoring both pathogen and host, the introduction of groundbreaking disease management strategies such as R gene labelling, rotation and stacking, and establishment of a universal pathogen isolate collection that facilitates the exchange of information among multiple laboratories and adds a new dimension to this triumph.

A Multi-Model Output Fusion Strategy Based on Various Machine Learning Techniques for Product Price Prediction

In the digital era, precise product price prediction becomes crucial for enhancing competitiveness in the online marketplace. This paper presents a hybrid model framework that enhances the accuracy of online product price predictions by integrating several machine learning algorithms, including Linear Regression, Decision Trees, and Gradient Boosting. The objective of this approach is to leverage the distinct advantages of each model to address their individual limitations and create a robust unified predictive model. This integration allows for improved handling of complex data relationships and diverse market dynamics that are typical in online sales environments. The results demonstrate that the hybrid model achieves superior prediction accuracy, as reflected in reduced Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) metrics, and an exceptionally high R ² value compared to single-model approaches. These outcomes underscore the efficacy of combining multiple predictive models to enhance the precision of price forecasts in the highly competitive online marketplace. This model fusion strategy not only provides more accurate pricing predictions but also offers strategic insights into the optimal pricing strategies for businesses looking to enhance their market position.

Racial group differences and export market access challenges in agricultural value chains and supply networks of SMEs in South Africa

ABSTRACT Although previous studies provide an insightful and categorised nature of internal and external export barriers experienced by agricultural SMEs in general; there is little, if any, strategic insights on the racial nuances in value chain barriers for most agricultural SMEs that target global export markets. This study posits that export barriers are not uniform and differ based on race and the context in which the agricultural SME is based. Hence, we specifically explore the nature of export barriers per race within the agro-value chains in South Africa and juxtapose these with the dynamic market and historical conditions of the South African market environment. In this multi-case study, we focused mainly on the differences between black and white-owned agricultural SMEs riding on their different supply networks’ capabilities to navigate the export barriers they face. The findings serve to develop seminal knowledge to enhance export capabilities and opportunities for minority groups globally and more importantly to highlight the underlying drivers for racial imbalances in agro-supply chains. Although the findings are geographically skewed to South Africa, the market dynamics are relevant across countries with similar demographic characteristics and provide a platform for future research.

Determinan Kepuasan dan Loyalitas Pelanggan: Harga dan Kualitas Produk Wardah

This study aims to explore the determinants of customer satisfaction and loyalty towards Wardah products, focusing on the role of price and product quality. In the increasingly competitive cosmetics industry, an in-depth understanding of the factors that influence customer satisfaction and loyalty is crucial to the success of the company. The methodology used in this study is qualitative, with a literature study technique involving the analysis of related literature. The results show that product quality has a significant and positive influence on customer satisfaction. Products that meet customer needs and expectations tend to increase levels of satisfaction, which in turn drives loyalty and repurchase intentions. While price also contributes to the perception of value, product quality remains the main factor determining customer loyalty. This study suggests that Wardah needs to prioritize innovation and product quality improvement in their marketing strategy, accompanied by competitive pricing. In addition, positive customer experiences through good service and effective interactions on social media also need to be considered. These findings provide strategic insights for Wardah management to improve customer satisfaction and loyalty, which are expected to strengthen the company's competitive position in the cosmetics market. Thus, improving quality and wisely managing prices are key to achieving long-term success in this industry.

Blockchain adoption factors in agricultural supply chains: A PLS-SEM study

The purpose of this study is to explore factors influencing the blockchain adoption in agricultural supply chains, to make a particular focus on how security and privacy considerations, policy support, and management support impact the blockchain adoption intention. it further investigates perceived usefulness as a mediating variable that potentially amplifies the effects of these factors on blockchain adoption intention, and sets perceived cost as a moderating variable to test its influence on the strength and direction of the relationship between perceived usefulness and adoption intention. through embedding the cost-benefit theory into the integrated tam-toe framework and utilizing the partial least squares structural equation modeling (PLS-SEM) method, this study identifies the pivotal factors that drive or impede blockchain adoption in the agricultural supply chains, which fills the gap of the relatively insufficient research on the blockchain adoption in agriculture field. the results further provide empirical evidence and strategic insights that can guide practical implementations, to equip stakeholders or practitioners with the necessary knowledge to navigate the complexities of integrating cutting-edge technologies into traditional agricultural operations, thereby promoting more efficient, transparent, and resilient agricultural supply chains.

Optimizing the Sweet Potato Supply Chain in Zimbabwe Using Discrete Event Simulation: A Focus on Production, Distribution, and Market Dynamics

This study leverages a Discrete Event Simulation (DES) model to optimize the sweet potato supply chain in Zimbabwe, focusing on production, distribution, and market dynamics under varying climate conditions. The integration of climate data into the simulation model reveals significant insights into the resilience of different sweet potato varieties, particularly highlighting the suitability of yellow-skinned sweet potatoes for harsh climates due to their high resilience and drought resistance. However, market preferences still favor white-skinned varieties despite their vulnerability to climate extremes. The DES model identifies key bottlenecks, particularly in cultivation and transportation, that hinder supply chain efficiency. To address these challenges, the study emphasizes the importance of targeted interventions, such as improving access to irrigation, strengthening pest management, and adopting community-based resource-sharing approaches. These strategies are critical for enhancing both the resilience and efficiency of the supply chain. Additionally, the study highlights the urgent need for adaptive strategies to mitigate the effects of drought on agricultural productivity, especially in regions that heavily rely on crops like sweet potatoes. Overall, this research offers strategic insights for policymakers and stakeholders aiming to improve food security and agricultural productivity in Zimbabwe, as well as in other countries with similar climate challenges.

Big Data Insights into Coastal Tourism: Analyzing Customer Satisfaction at Egyptian Red Sea Dive Resorts

This research aims to explore the relationship between customer satisfaction and various extracted factors at dive resorts in the Red Sea, Egypt, utilizing a hybrid methodology of descriptive and diagnostic analytics applied to online review data. Employing techniques such as KH coder for text analysis, exploratory factor analysis (EFA), and linear regression, this study systematically identifies key elements that influence customer satisfaction. Findings reveal that activities related to diving and marine life markedly enhance guest satisfaction, underscoring the critical role these aspects play in the overall appeal of Egyptian coastal tourism. Conversely, areas such as dining and amenities were identified as needing improvement. The originality of this study lies in its application of big data analytics to dissect and understand customer feedback in a sector-specific context, providing strategic insights for the sustainable advancement of coastal tourism in Egypt. By focusing on dive resorts, this research highlights their integral role in coastal tourism and offers a model for leveraging online customer reviews to enhance service quality and promote sustainable practices within the tourism industry, contributing to the overall growth and sustainability of coastal tourism.

Analyzing the Impact of Vision 2030’s Economic Reforms on Saudi Arabia’s Consumer Price Index

This study examines the relationship between CO2 emissions, labor force participation, foreign direct investment (FDI), and trade openness on the Consumer Price Index (CPI) in Saudi Arabia, within the context of Vision 2030’s economic reforms. Vision 2030 aims to diversify the economy, reduce oil dependency, and promote sustainable growth, making it crucial to understand the factors influencing inflation and economic stability. Using annual data from 2001 to 2022 and the nonlinear Autoregressive Distributed Lag (NARDL) bounds testing approach, the study analyzes both short- and long-term effects. The findings reveal that higher CO2 emissions have a deflationary effect, reducing the CPI in both the short and long term, while FDI shows an inflationary impact with a delayed effect. Labor force expansion contributes to lowering the CPI, reflecting its deflationary pressure, especially over the long term. Trade openness is also examined for its dual effects on CPI, In the short run, both positive and negative trade openness reduce consumer prices, while in the long run, positive trade openness increases inflation, and negative trade openness lowers prices. This shows the differing inflationary impacts of trade openness over time. These findings contribute to the policy discourse on balancing economic growth, environmental sustainability, and inflation management, offering strategic insights for policymakers in alignment with Saudi Arabia’s Vision 2030 objectives.

Opportunities for the use of artificial intelligence in supply chain management

Aim. To evaluate the potential of generative artificial intelligence technology in the context of supply chain management.Objectives. To analyze how artificial intelligence is changing traditional management practices and structures in supply chain management; to identify fundamental shifts brought about by AI, including automation, data-driven decision making, and human empowerment; to examine current statistics on the effectiveness of artificial intelligence in management processes and conclude the potential, promise of this technology in the logistics industry; to provide strategic insight and practicalMethods. The research combines qualitative and quantitative methods to provide a deeper understanding of the problems considered. Based on these methods, an array of relevant statistical data and expert opinions in the field of generative artificial intelligence has been studied.Results. In today’s business environment, supply chains face a number of challenges: globalization, market volatility, changing customer expectations, and force majeure (natural disasters, pandemics, or wars). To meet these challenges and strengthen their market position, companies are increasingly turning to new technologies and innovations. These technologies have the potential to revolutionize the approach to supply chain management, increasing transparency, efficiency and responsiveness. All emerging technologies, from artificial intelligence and blockchain to the Internet of Things (IoT) and robotics, potentially offer opportunities to streamline operations, improve decision-making, and enhance supply chain collaboration.Conclusions. In this article, the authors have shown how artificial intelligence is changing traditional governance structures in supply chain management. Current statistical data on the effectiveness of artificial intelligence in management processes are analyzed, and a conclusion is made about the potential and prospects of this technology in the logistics sphere. Strategic understanding is formed and practical recommendations for organizations seeking to implement new tools of generative artificial intelligence are given.

Financial Projection for Long-Term Corporate Plan of a Holding Insurance Company for The Year 2025-2029

A company's long-term planning is essential for sustainable business growth and success, especially in the dynamic and competitive insurance industry. This study aims to evaluate the company's feasibility in achieving the desired net profit target based on analysis of current and projected financial data, industry analysis, and conformity with the assumptions and strategic initiatives outlined in the company's long-term plan. The research method used was qualitative. The results show strong growth in the company's key financial metrics. Gross written premium (GWP) is projected to increase from IDR 37.6 trillion in 2024 to IDR 54.5 trillion in 2029 at a compound annual growth rate (CAGR) of 8%. This growth is driven by strategic initiatives focused on market expansion, particularly in the property insurance and credit insurance business lines. Net profit is projected to increase from Rp3.3 trillion in 2024 to Rp6.1 trillion in 2029 at a CAGR of 13.2%, due to improved operational efficiency. The company's initiatives in implementing best practices and streamlining operations will lower its expense ratio from 20.8% in 2024 to 20.4% in 2029. The implications of this study provide strategic insights for insurance company management in formulating long-term policies aligned with market growth and operational efficiency. By aligning strategic initiatives and long-term financial planning, companies can be better prepared to face challenges and capitalize on future opportunities, thereby supporting long-term sustainability and profitability.

Enhancing customer satisfaction through IIoT-Enabled coopetition: Strategic insights and impacts

This research investigates the significant role of Industrial Internet of Things (IIoT) to enable Coopetition Network Practices (CNPs) in enhancing the performance of Small and Medium Enterprises (SMEs) within the context of global digital supply chains. Employing a quantitative approach, our study reveals that CNPs contribute to a noTable 51.0 % improvement in factors determining customer satisfaction. This underscores the strategic importance of blending competition with collaboration to refine production processes and align with consumer expectations. Additionally, the research presents a remarkable 69.1 % boost in operational consistency and reports substantial progress in manufacturing flexibility and the value-to-weight ratio, witnessing increases of 125.8 % and 33.2 %, respectively. These improvements are pivotal in optimizing production resources, which in turn, have led to a 29.3 % decrease in customer complaints and a 15.6 % rise in on-time delivery rates. Conversely, a slight decline in the consistency of the value-to-weight ratio was observed, pointing to potential areas for future research. The findings decisively show that CNPs offer concrete advantages by enhancing customer satisfaction determinants and operational efficiency in SMEs. The paper advocates for future studies to directly measure customer satisfaction and to formulate actionable guidelines for the effective implementation of coopetition strategies. This proposed research direction aims to provide solutions to the manufacturing sector's emerging challenges, thereby promoting competitive advantage and growth in the digital era.