Business Insights Research Articles

Does digital transformation increase corporate violations?

AbstractThis study examines whether digital transformation influences corporate violations in China. Using data from Chinese A‐share listed companies from 2007 to 2020, this paper finds that digital transformation increases the incidence of corporate violations. The results remain robust through endogeneity checks, the use of alternative measurements for digital transformation and corporate violations, and subsample analyses. Cross‐sectional analyses suggest that the impact of digital transformation on corporate violations is more salient in non‐state‐owned enterprises, firms with lower institutional investors shareholding ratios, and firms with higher agency costs. Mechanism analyses indicate that digital transformation leads to corporate violations by increasing operational complexity and affecting information quality. Additional tests reveal that effective corporate governance helps reduce the occurrence of corporate violations resulting from digital transformation. This research deepens our understanding of the economic implications of corporate digital transformation and provides valuable insights for businesses undergoing this process.

Cost Reduction and Efficiency Boost Through E-Commerce Strategies

General Background: E-commerce has revolutionized business practices by offering cost reduction, especially in marketing, through advancements in electronics and communication, expanding reach, and reducing operational costs. Specific Background: This study investigates the potential of e-commerce in reducing marketing costs and improving business efficiency, despite challenges faced by many firms. Knowledge Gap: While extensive research exists on e-commerce, the specific relationship between e-commerce strategies and the reduction of promotional costs remains underexplored, especially in terms of practical implementation by companies. Aims: The study explores the link between e-commerce strategies and reduced promotional expenses, highlighting how digital marketing can boost profitability and reduce costs for businesses. Results: Statistical analysis revealed that companies effectively reduced advertising costs, such as salaries, bonuses, and training expenses, by utilizing e-commerce strategies. However, many firms failed to capitalize on further cost-saving opportunities in inventory management, travel, and research expenses. The t-value (17.062) and p-value (0.000) indicate a significant relationship between e-commerce strategies and reduced promotional costs. Novelty: This research provides empirical evidence on the significant cost-saving potential of e-commerce, highlighting its underutilization in areas beyond marketing, such as operational expenses. Implications: The study emphasizes the importance of integrating e-commerce into operational strategies for cost reduction and competitive pricing, providing practical insights for businesses aiming to improve efficiency and profitability. Highlights: E-commerce reduces marketing costs and boosts business efficiency. Firms often miss broader cost-saving potential of e-commerce. Strong correlation found between e-commerce use and lower promotional expenses. Keywords: E-commerce, digital marketing, cost reduction, promotional expenses, business efficiency

Low Carbon Sustainable Diet Choices-An Analysis of the Driving Factors behind Plant-Based Egg Purchasing Behavior.

In the face of escalating climate change, environmental pollution, and global crises, plant-based eggs are a viable solution for environmental conservation and health promotion. This study uses an integrated framework combining value-belief-norm (VBN) theory and the Theory of Planned Behavior (TPB) to explore the factors influencing Taiwanese consumers' inclination towards plant-based eggs. Through convenience sampling, 417 questionnaires were issued and 387 valid responses were received, indicating a 92.8% valid response rate. The analysis indicates that consumers' biospheric and altruistic values significantly enhance their awareness of outcomes, which, coupled with their sense of responsibility, strengthens personal norms. From the TPB perspective, perceived behavioral control is identified as a critical driver of the intention to purchase plant-based eggs, with attitudes and subjective norms playing significant roles. Subjective norms positively impact personal norms, thereby influencing consumers' behavioral intentions. The findings confirm the integrated model's theoretical and practical validity, and offer valuable insights for businesses. By developing adept marketing strategies that consider market dynamics, this study enhances the effectiveness and value of marketing efforts by boosting consumers' engagement with plant-based eggs. These insights contribute to advancing environmental sustainability goals.

Factors Influencing the Purchase Behaviour of Plant-Based Food Products in Thailand: An Extension of the Theory of Planned Behaviour

This study aims to investigate the factors influencing the adoption intention of plant-based food products and to examine the mediating effects on the relationship between adoption intention and actual behaviour. The research is based on the Theory of Planned Behavior (TPB) as the foundational theoretical framework. Convenience sampling has been applied for data collection through an online questionnaire (G-Form), resulting in 582 usable responses. The measurement model was analyzed using Confirmatory Factor Analysis (CFA), and the structural equation model was assessed using Structural Equation Modeling (SEM). The findings indicate that factors positively influencing the adoption intention include attitudes towards plant-based food products, subjective norms, environmental concerns. Word-of-mouth communication was identified as a mediating variable in the relationship between adoption intention and actual behaviour. This research corroborates the Theory of Planned Behavior. Also, it identifies additional factors relevant to the acceptance of plant-based food products beyond the theoretical framework in Thailand. The findings provide valuable insights for business and marketing strategies about plant-based food products.

Millennial Muslim Tourists’ Motivations and Characteristics at Marawa Beach Club

This research aimed to identify the dominant characteristics and motivations of millennial Muslim tourists visiting Marawa Beach Club Padang West Sumatra. Utilizing a quantitative research method, data was collected through the distribution of questionnaires. A simple random sampling technique was employed to select participants from the tourist population. The data were analyzed using quantitative descriptive analysis, with responses evaluated on a Likert scale of 1-5. Results from the questionnaires, completed by 100 respondents, highlighted distinct characteristics and motivations of millennial Muslim tourists compared to other visitor segments. This study reveals the unique appeal of Marawa Beach Club for this demographic. The findings provide valuable insights for tourism businesses, local authorities, and destination marketers aiming to cater to the specific preferences of millennial Muslim travelers. Additionally, this research lays a foundation for understanding evolving travel trends and informs strategies to create a more inclusive and enriching destination experience.

Predicting Customer Purchase Behavior Using Machine Learning Models

In this study, we aim to predict customer purchase behavior using various machine learning models to better understand customer tendencies and enhance marketing strategies. We use a dataset containing demographic and behavioral data, including age, gender, annual income, number of purchases, product category, time spent on the website, loyalty program membership, and discounts availed. Our analysis involves data preprocessing, exploratory data analysis (EDA), and feature engineering. We then train and evaluate six different machine learning models: Logistic Regression, Random Forest, Gradient Boosting, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and XGBoost. The models are assessed using metrics such as accuracy, precision, recall, F1-score, and ROC AUC. Results indicate that ensemble models, specifically Random Forest and Gradient Boosting, outperform the other models in terms of accuracy and ROC AUC. The study concludes that ensemble models are highly effective for predicting customer purchase behavior, providing valuable insights for businesses to tailor their marketing efforts. Future research could explore additional features, more advanced models, and real-time prediction capabilities.

ANALYSIS OF SEGMENTATION AND TARGETING OF JEANS IN JAKARTA

Jeans are a significant part of the global apparel industry, with a vast market. This research aims to analyze how the jeans segmentation is formed and who exactly is the target market. The population in this study were all groups who wear jeans in Jakarta. The technique used in determining the sample was purposive sampling with a total of 161 people. The type of research used in this research is descriptive research using the hierarchical procedure method to search for segmentation. This analysis aims to see whether there are significant differences between each cluster or group formed. Then, in searching for target markets, this research uses a non-hierarchical procedure method. From the results of the analysis, 2 clusters were formed where the 2nd cluster was selected as the target market. It is hoped that the results of this analysis can provide input to jeans business actors to be able to attract more potential consumers and accurately. This study employs sentiment analysis to delve into the preferences and opinions of jeans users in Jakarta, offering fresh insights for businesses to develop more effective marketing strategies and understand market trends, consumer preferences, and growth potential.

Sales Data Visualization to Determine Business Insight Using Metabase in a Global Retail Company

Sales Data Visualization to Determine Business Insight Using Metabase in a Global Retail Company

The Effects of Individual Competencies on the Qualitative Research Interview Process for Business Insights

The Effects of Individual Competencies on the Qualitative Research Interview Process for Business Insights

Transformative Trends: Exploring the Nexus of Innovation, Technology, Blockchain, and Islamic Social Finance for Global Sustainability

Global sustainability is a growing concern, with the Islamic finance system playing a pivotal role in this process. The integration of blockchain technology, a technology that enhances transparency, efficiency, and security, is being explored in the context of Islamic social finance. The study, which combines literature review, case study, and industry expert interviews, highlights the potential of blockchain to revolutionize zakat management, waqf, and sadaqa processes, contributing to global sustainability by promoting fair transactions, wealth distribution, and socioeconomic empowerment. The case study of iBantu's collaboration with the Hidayatullah organization in Indonesia exemplifies the practical application of blockchain in digital philanthropy. The study emphasizes the importance of user-friendly, scalable, and interoperable blockchain solutions for widespread adoption and sustainability. The findings offer valuable insights for policymakers, businesses, and practitioners, suggesting strategic interventions to foster sustainable innovation. By integrating blockchain technology into regulatory frameworks and operations, Islamic social finance institutions can significantly contribute to achieving the Sustainable Development Goals while promoting environmental responsibility and socioeconomic development.

Investment decision and efficiency: Global insights on manufacturing firms amidst energy uncertainties

This study examines the impacts of energy-related uncertainties on manufacturing listed firms' investment decisions and performance in advanced and emerging markets using panel data during the period 2004–2022. Our empirical investigations show that energy-related uncertainties significantly and positively affect firms' investment scale. This indicates a tendency of manufacturing firms to invest more in innovation and technology that reduces their dependence on traditional energy and meets new energy standards. Further, we reveal that nations' digitalization, institutional quality, business support environment, and R&D incentives relieve firm's pressure in increasing investment under energy uncertainties. Our study offers valuable insights for businesses, policymakers, and researchers, and sheds light on the relationships between energy-related uncertainties and strategic investment decisions in the global corporate landscape.

Efficient Resource Allocation in Kubernetes Using Machine Learning

Kubernetes is a distinguished open-source container orchestration system in cloud computing and containerized applications. Google developed it, and the Cloud Native Computing Foundation now maintains it. Kubernetes offers a robust framework for automating application deployment scaling and management, revolutionizing how organizations use their containerized workloads and providing huge flexibility and feasibility. The current paper explores the application of machine learning algorithms to optimize resource allocation in Kubernetes environments. As the complexity of cloud-native applications increases because of various engagements, it is vital to maintain performance and cost- effectiveness. This study also evaluates various machine learning models and techniques and their relevancy in areas such as anomaly detection and enhancing overall cluster utilization. Our findings include machine learning-driven methodologies that will significantly improve performance utilizing historical data. Kubernetes's decentralized nature requires a scalable structure for task scheduling to accommodate dynamic workloads conveniently. The AIMD algorithm, a celebrated method for congestion avoidance in network management, inspires our approach. Computing clusters can be challenging to deploy and manage due to their complexity. Monitoring systems collect large amounts of data, which is daunting to understand manually. Machine learning provides a viable solution to detect anomalies in a Kubernetes cluster. KAD (Kubernetes et al.) is one such algorithm that can solve the Cluster anomalies problem. Enormous Cloud native applications market is projected to reach 17.0 billion USD by 2028, which was USD 5.9 billion in 2023. On par with those numbers is the global Machine Learning (ML) market size, which was valued at $19.20 billion in 2022 and is expected to grow from $26.03 billion in 2023 to $225.91 billion by 2030 (As per Fortune Business Insights). At the conjecture, both markets will take innovation to a new level, offering more adaptive solutions for contemporary cloud infrastructures.

Navigating uncertainties: impact of supply chain resilience on organizational performance, mediated and moderated model: Pakistan manufacturing sector case

PurposeThis study aimed to investigate the relationship between supply chain resilience and organizational performance with innovation as a mediator and information sharing as a moderator.Design/methodology/approachThe study thoroughly explored how supply chain resilience, organizational performance, innovation and information sharing are connected. It used an exploratory approach and quantitative methods. Data were collected from large manufacturing firms through online questionnaire surveys using Google Forms, emails and WhatsApp.FindingsThe findings demonstrated that supply chain resilience positively impacts sustainability efforts. Furthermore, leveraging innovation and effective information sharing mediated and moderated the relationship, playing pivotal roles in enhancing sustainability within the supply chain.Research limitations/implicationsThe study provided actionable insights for businesses to strengthen their sustainability efforts. Managers could utilize these findings to implement strategies that enhance supply chain resilience, drive innovation and promote effective information sharing, ultimately leading to a more sustainable supply chain.Originality/valueThis study contributed to the existing body of knowledge by examining the complex relationships between supply chain resilience, organizational performance, innovation and information sharing in the context of achieving sustainability. The exploration of these components in a holistic manner added originality to the research and shed light on effective strategies for sustainable supply chain management.

Product Quality Improvement through Effective Operational Management

Purpose: This study explores the impact of advanced technologies and strategies on supply chain optimization within operational management, aiming to understand how AI, ML, IoT, blockchain, effective inventory management, supplier relationship management, risk management strategies, and sustainability practices enhance supply chain performance. Research Design and Methodology: The study uses a qualitative research design, including semi-structured interviews with supply chain experts from various industries and document analysis. The thematic analysis identifies patterns and themes, offering a detailed understanding of factors contributing to supply chain optimization. Findings and Discussion: The study reveals advanced technologies significantly improve demand forecasting accuracy, real-time decision-making, and supply chain transparency. Strategies such as JIT, Lean Inventory, EOQ, and VMI optimize inventory levels and reduce costs. Strong supplier relationships and robust risk management strategies enhance supply chain resilience and agility, while sustainability practices lead to cost savings, improved brand reputation, and regulatory compliance. The findings support the hypothesis that technological integration and strategic management enhance supply chain performance, aligning with the resource-based view and dynamic capabilities theories. Implications: This research provides insights for businesses seeking to optimize their supply chains. It highlighting the need for a holistic approach integrating technological solutions with human and organizational factors. The findings offer practical guidance for implementing advanced technologies and strategies, contributing to scientific understanding and practical applications in supply chain management.

The Influence of Digital Retail Environment on Consumer Purchasing Behavior: A Causal Study

As e-commerce continues to evolve, understanding consumer behavior in the context of online shopping becomes increasingly challenging. This study investigates the impact of purchasing attitudes among Saudi customers within the online clothing environment. By collecting 410 responses from residents of Riyadh, Saudi Arabia, the study emphasizes the convenience aspect of online and virtual markets and employs advanced statistical techniques, specifically SPSS-AMOS software, including confirmatory factor analysis, path analysis, and structural equation modeling. The findings reveal several key insights: perceived entertainment, perceived ease of use, and perceived usefulness significantly and positively influence the user experience on websites. The research highlights the importance of website quality indicators, such as perceived entertainment, ease of use, and usefulness, in shaping the user experience within the online clothing environment. It confirms that website quality, encompassing indicators of the user experience, exhibits a significant positive association with online impulse buying behavior. Additionally, the flow experience, which refers to the immersive and enjoyable state when browsing a website, also demonstrates a statistically significant and positive effect on online impulse buying behavior. These findings offer valuable insights for businesses and marketers seeking to understand and adapt to the evolving landscape of e-commerce, specifically concerning online clothing purchases.

Live streaming product display or social interaction: How do they influence consumer intention and behavior? A heuristic-systematic perspective

The live streaming e-commerce industry is developing rapidly, significantly influencing consumer decision-making processes. Unlike previous studies focusing solely on singular outcomes, this research concurrently examines impulse buying and repurchase intentions. It constructs a theoretical model based on the heuristic-systematic model to validate the effectiveness of two types of marketing signals (product-related signals and interactive signals) in live streaming. Additionally, this study introduces the uniqueness of streamers, products, and performance as moderating variables, aiming to elucidate the mechanisms through which different marketing signals in live streaming affect consumer cognition, impulse buying, and repurchase intentions. Through the collection and analysis of 468 survey responses, this research reveals that viewer-streamer interactive signals and viewer-viewer interactive signals positively impact the narrowing of psychological distance. Simultaneously, product-related signals positively influence the enhancing of consumer perceived value. Furthermore, three interesting moderating effects are identified. By integrating signal theory and the heuristic-systematic model, this study provides a clear depiction of the pathway through which consumers process marketing signals in live streaming, offering theoretical support and practical insights for streamers, platforms, and businesses.

CUSTOMER SEGMENTATION USING RFM ANALYSIS

This research presents a comprehensive approach to customer segmentation using Recency, Frequency, and Monetary (RFM) analysis, combining statistical insights, data visualization, and machine learning techniques. The study utilizes a real-world dataset obtained from a retail environment, aiming to categorize customers based on their recent purchasing behavior, visit frequency, and monetary contributions to the store. The code begins with data preparation and exploration, ensuring data integrity by addressing issues such as negative quantities and missing customer identifiers. Following this, the Recency, Frequency, and Monetary metrics are computed, providing a holistic view of customer engagement and spending patterns. Visualizations, including violin plots, histograms, and box plots, are employed to intuitively convey the distribution of these metrics. The research then delves into the quantile-based segmentation of customers, allowing for a more granular classification. Quantiles are calculated to divide customers into four segments for each RFM metric. The resulting quantile labels are applied to the dataset, enabling the creation of a compound RFM quantile that combines recency, frequency, and monetary information. This combined quantile facilitates the definition of distinct customer segments. To further enhance the interpretability of customer segments, the study introduces a set of rules for labeling customers based on their RFM quantiles. These rules yield segments such as "Best Customer," "Loyal Customer," "Big Spender," "Dead Beats," and "Lost Customer." The resulting customer segmentation is presented visually through histograms and a pie chart, providing a clear and concise representation of the distribution of customers across different segments. Moreover, the research integrates machine learning models, including XGBClassifier, and CatBoostClassifier, to explore the potential of automating the segmentation process and predicting customer segments based on historical data. However, the machine learning aspect is introduced with commented-out sections, leaving room for further exploration and experimentation. In conclusion, this research contributes a comprehensive and detailed code implementation for RFM-based customer segmentation. The integration of visualization techniques aids in the interpretation of customer behavior, while the inclusion of machine learning models opens avenues for predictive analytics in customer segmentation. The presented approach provides valuable insights for businesses seeking to tailor marketing and customer relationship strategies based on individualized customer segments. KEYWORDS— Customer Segmentation, Frequency, Monetary Value, Recency, RFM Analysis.

The Determinants of Factors Affecting Customer Requisition of Electronic Fiscal Device (EFD) Receipts

This study aims to identify and analyse the factors affecting customers' requisition of EFD receipts in the Kariakoo Tax Region. Specifically, it examines the awareness levels among customers regarding the benefits of EFD receipts, assesses the perceived Value of EFD receipts by customers, explores the impact of legal and regulatory factors on customer requisition behaviour, investigates the influence of business reputation and trust on customer EFD receipt requests as well as assess the ease of receipt issuance and its impact on customer behaviour. Descriptive statistics were used to collect and analyse data to describe the characteristics of the determinants of factors affecting customers' requisition of EFD receipts. Also, the quantitative approach was used to collect numerical data through surveys to quantify the extent of the determinants' influence. The sample size was based on statistical considerations and the population under study. The researcher develops a structured questionnaire with a mix of closed-ended and Likert scale questions to gather quantitative data and also conducts in-depth interviews with a subset of participants to gather qualitative insights. The results suggest a moderate level of awareness about the benefits of EFD receipts. The Perceived Value of EFD receipts is generally positive, with customers acknowledging their importance in ensuring transparency and compliance with regulations. Also, customers are more likely to seek EFD receipts from businesses with a positive reputation and high trust. On the other hand, businesses that streamline issuing EFD receipts are likely to experience higher compliance rates. The study provides valuable insights for businesses, policymakers, and regulatory bodies to enhance the effectiveness of EFD systems by addressing the identified areas of improvement in awareness, legal compliance, and ease of issuance; businesses can foster greater customer compliance with EFD receipt requests, ultimately contributing to a more transparent and trustworthy business environment.

Examining the limitations of AI in business and the need for human insights using Interpretive Structural Modelling

The integration of Artificial Intelligence (AI) in business settings is rapidly increasing, yet significant limitations hinder its effective use and adoption. Understanding these limitations and their interrelationships is crucial for enhancing AI implementation. Despite growing research, there is a lack of a comprehensive model that systematically identifies and elucidates the factors influencing AI limitations in business environments. This study employs Interpretive Structural Modeling (ISM), combined with MICMAC analysis and an extensive literature review, to develop such a model. We identified 15 key factors and analyzed their driving and dependence powers to understand their interrelationships. Most factors, such as contextual understanding, transparency, intuition, emotional intelligence, ethics, bias, tacit knowledge, creativity, credibility, and reliability, were found to be autonomous. Accountability and privacy emerged as the strongest driving forces, while trust and adaptability exhibited the highest dependence and lowest driving power. This research offers a comprehensive understanding of AI limitations and their interrelationships, providing valuable insights for managers and businesses. The findings can aid in making more informed decisions about AI implementation and in developing strategies to mitigate these limitations. Furthermore, the study emphasizes the importance of combining AI with human insight to overcome these challenges. However, using the ISM technique could involve subjective judgment from the experts.

Pengaruh Harga Produk, Customer Review Dan Preferensi Pengalaman Pembelian Terhadap Keputusan Pembelian Mahasiswa Manajemen Upgris Pada Shopee Dan TikTok Shop

This research aims to analyze the influence of product prices and customer reviews on purchasing decisions of Management students at PGRI University Semarang (Upgris) on the e-commerce platforms Shopee and TikTok Shop. In the current digital era, e-commerce has become one of the main channels for consumers to make purchases. Two important factors that consumers often consider before making a purchase are product price and customer reviews. The research method used is quantitative with a survey approach. Data was collected through a questionnaire distributed to 36 Upgris Management students who actively use Shopee and TikTok Shop.The research results show that both product price and customer reviews have a significant influence on purchasing decisions. Specifically, product price has a greater influence than customer reviews in determining purchasing decisions on both platforms. Apart from that, there is a difference in the level of trust in customer reviews on Shopee and TikTok Shop, where students tend to trust reviews on Shopee more. It is hoped that these findings will provide insight for e-commerce businesses in designing effective marketing strategies by considering price factors and customer reviews.