E-commerce Companies Research Articles

APLICABILIDADE DE PROGRAMAS DE ENVIRONMENTAL, SOCIAL AND GOVERNANCE (ESG) NO SETOR DO E-COMMERCE NA INDÚSTRIA 4.0

The rise of Industry 4.0 marks a new phase in the digital transformation of industrial processes, encompassing advanced technologies like the Internet of Things (IoT), big data, artificial intelligence (AI), and intelligent automation. In the e-commerce sector, these innovations have facilitated unprecedented operational efficiency, yet the growth in digital capabilities has also led to heightened expectations for sustainable and ethical business practices, aligned with Environmental, Social, and Governance (ESG) frameworks. This study explores the impact of integrating ESG practices within ecommerce, leveraging Industry 4.0 technologies to enhance operational sustainability and transparency. A systematic review of literature and data was conducted, revealing that robust ESG policies in e-commerce contribute to competitive advantage by attracting investment, fostering consumer loyalty, and meeting regulatory standards. Findings show that Industry 4.0 technologies can aid in environmental management by reducing carbon emissions, waste, and energy consumption through optimized logistics and sustainable packaging. The implementation of circular economy principles further demonstrates how e-commerce companies can reduce their environmental footprint, ensuring alignment with consumer demands for accountability. Moreover, sound governance and digital security play crucial roles in maintaining customer trust and protecting sensitive data, underscoring the broader relevance of ESG practices. This study concludes that the integration of ESG with Industry 4.0 technologies is essential for the long-term success of e-commerce, as it fosters a responsible, transparent, and sustainable business model that balances economic growth with social and environmental stewardship.

Optimizing E-commerce Fulfillment: The Strategic Role of Warehouse Management Systems (WMS)

In this paper, we investigate the role of Warehouse Management Systems (WMS) in improving e-commerce fulfillment by looking at how they optimize operations and provide the operational tools required for modern-day e-commerce businesses to fulfill high customer expectations. E-commerce is taking off, powered by lightning-fast technological innovation and a changing consumer mindset...which means there can be enormous pressure to expedite the process of fulfillment to ensure that it's done quickly, correctly, and inexpensively. Utilizing automation, real-time inventory management, robust data analytics, and the ability to integrate seamlessly with other systems are critical to helping e-commerce companies operate efficiently. This research intends to demonstrate, through a deep dive into WMS features and case studies, how this technology can help combat inventory management challenges along with order processing issues such as demand forecasting for customer satisfaction. The insights gained vis-à-vis these analyses establish the role of WMS as a strategic asset for competitive advantage in e-commerce and provide avenues to progress further by incorporating AI and IoT. Keywords Warehouse Management Systems, WMS, e-commerce, fulfillment, inventory management, order processing, logistics, supply chain management, automation, and demand forecasting.

Tendencias emergentes en el marketing digital para el comercio electrónico en 2024

This article examines emerging trends in digital marketing and their impact on e-commerce by 2024. Through a mixed approach combining quantitative and qualitative analysis, technologies such as augmented reality (AR), virtual reality (VR), blockchain, artificial intelligence (AI), voice search, and advanced personalization were investigated. The quantitative analysis was based on data from 50 e-commerce companies, while the qualitative analysis was conducted through in-depth interviews with marketing and technology directors from 10 selected companies. The results indicate that the implementation of AR and VR significantly improves customer experience and conversion rates, although it faces technical and usability challenges. Blockchain technology increases the transparency and security of transactions, reducing fraud, but presents barriers in terms of technical complexity and costs. AI enables advanced personalization, improving content relevance and sales, although it raises concerns about data privacy. Voice search optimization improves customer service efficiency, facilitating faster and more natural interactions, although integration with existing systems can be complex. Finally, advanced personalization is crucial for customer loyalty, providing personalized and satisfying shopping experiences, although it requires a robust technological infrastructure.

An Algorithm for Consolidating Small Shipments to Reduce Costs in International Logistics for E-commerce Businesses

Purpose: This article presents an algorithm designed to optimize the consolidation of small shipments, aiming to reduce costs and improve efficiency in international logistics for e-commerce businesses. The research focuses on the highly trafficked route between China and the USA, particularly the corridor from Shenzhen to Los Angeles. Methodology: By employing a cluster-based optimization model, the algorithm consolidates goods from multiple e-commerce companies into single containers, maximizing space utilization and minimizing transportation costs. Findings: Experimental data reveals a 40% reduction in costs and a 10-day decrease in delivery times compared to traditional methods. Unique Contribution to Theory, Practice and Policy: The algorithm has the potential as a scalable solution for global supply chain logistics.

Scalable probabilistic forecasting in retail with gradient boosted trees: A practitioner’s approach

The recent M5 competition has advanced the state-of-the-art in retail forecasting. However, there are important differences between the competition challenge and the challenges we face in a large e-commerce company. The datasets in our scenario are larger (hundreds of thousands of time series), and e-commerce can afford to have a larger stock assortment than brick-and-mortar retailers, leading to more intermittent data. To scale to larger dataset sizes with feasible computational effort, we investigate a two-layer hierarchy, namely the decision level with product unit sales and an aggregated level, e.g., through warehouse-product aggregation, reducing the number of series and degree of intermittency. We propose a top-down approach to forecasting at the aggregated level, and then disaggregate to obtain decision-level forecasts. Probabilistic forecasts are generated under distributional assumptions. The proposed scalable method is evaluated on both a large propreitary dataset, as well as the publically available Corporación Favorita and M5 datasets. We are able to show the differences in characteristics of the e-commerce and brick-and-mortar retail datasets. Notably, our top-down forecasting framework enters the top 50 of the original M5 competition, even with models trained at a higher level under a much simpler setting.

E-Business and supply chain integration is the ultimate online shopping

E-business has risen as a key empowering influence to drive supply chain integration. Organizations can use the internet to achieve worldwide visibility on their extended trading network. The influence of e-business on supply chain integration can be defined laterally the dimensions of information integration, synchronized planning, coordinated workflow, and new business models. An Important value can be added to e-business by enabling supply chain integration. In this report, the author has taken a popular rising e-commerce company from Bangladesh. The name of this company is AJKERDEAL. It is a Bangladeshi online retail company, that focuses on a low-cost-oriented supply chain to maintain cost efficiency. The company's success is attributed to its superior supply chain practices over local competitors. However, Western multinational retailers may struggle in Bangladesh due to their differing values, cultures, and beliefs, potentially jeopardizing their key market.

PENGARUH WORK-LIFE BALANCE, PENGEMBANGAN KARIR, DAN KOMPENSASI TERHADAP TURNOVER INTENTION PADA GENERASI Z DI E-COMMERCE

The aims this study is to analyze the effect of work-life balance, career development, and compensation on turnover intention in Generation Z in e-commerce marketplace companies in Jabodetabek. This study uses a quantitative method, where primary data is collected through online questionnaires from 161 generation Z employees in Jabodetabek e-commerce companies. The data were analyzed using smartPLS 4.1.0.2 to test the research hypotheses. Based on the analysis, it was found that work-life balance and career development have a negative and significant effect on turnover intention of Generation Z employees in Jabodetabek e-commerce companies. Meanwhile, compensation has a negative but insignificant effect on the turnover intention of Generation Z employees in the same company. However, this research model does not explain well and has weak predictive ability on turnover intention (R²=0.131, Q²=0.073), so it needs to be tested further with a larger population and additional independent variables. This study can help the management of e-commerce marketplace companies to continue to evaluate compensation, improve career development programs, and offer work flexibility to increase the loyalty of generation Z employees.

Analysis of the Digital Transformation Path of Chinese E-commerce Companies

Digital technology has revolutionized China’s e-commerce sector, changing company operations and client relationships. This article analyzes the impact of digital transformation on online purchasing patterns. This article concentrated on the important of cloud computing, AI, and big data have become essential to online retail tactics. Digitalization is fueling economic development and innovation in China as its digital economy grows and impacts GDP. Digital technologies enable e-commerce and other industrial sectors to interact easily, improving operational efficiency and market reach. Moving from isolated firm models to connected platforms is a sign of a more responsive and integrated economy. This integration fosters a value model of co-creation in which stakeholders (including companies) collaborate to develop product and service offerings, improving customer experience and operational productivity. The route to digital maturity is not without challenges. E-commerce companies struggle with inefficient logistics, costly digital transformation, and a dearth of competent digital workers, as this study shows. Companies must make dramatic organizational and technological adjustments to overcome these constraints and maximize digital technologies. The analysis concludes that China’s e-commerce market will depend on enterprises’ digital adoption. E-commerce enterprises must embrace ongoing learning and development and remove digital integration barriers to compete in today’s dynamic online industry. The study illuminates digital transformation and how it will impact e-commerce.

Video Generative AI in Digital Marketing: A SWOT Analysis on E-Commerce

Artificial Intelligence (AI) is reshaping the way various business processes are performed, most rapidly in digital marketing, due to its advanced computational capabilities. ECommerce companies are in advantageous position to adopt and reap the benefits of AI for many reasons including the readily available computational infrastructure and big data related to their users and processes. Meanwhile, big tech companies and startups are competing to create and improve generative AI models, that create high-definition videos based on inputs in the form of text, image or videos. The current research specifically aims to analyse the video outputs of various video generating AI models available for digital marketing purposes in ecommerce businesses and to rationally analyse how those video generative AI models benefit or affect e-commerce companies. To meet the research objectives, mixed methodologies of experimentation and content analysis were deployed. It was observed that the video outputs from the tested models were not yet suitable for digital marketing and appeared underdeveloped. Additionally, the SWOT analysis revealed advantages like low costs, alongside concerns such as intellectual property issues.

Intelligent E-commerce under the Trend of Integration of Artificial Intelligence and E-commerce: A Case Study of Alibaba International Station

Abstract: China has fully entered the era of digital economy, and undoubtedly, the e-commerce industry needs to keep pace with the times and evolve into a new stage of development: integrating artificial intelligence with e-commerce. Nowadays, an abundance of new productivity tools have unveiled new opportunities for the e-commerce industry. This study focuses on Alibaba International, a premier global digital trading platform, to illustrate the applications of artificial intelligence tools in e-commerce, examine both the positive and negative impacts of these tools on e-commerce, and suggest strategies for enhancing e-commerce Operation Management. The aim is to offer enlightenments for small and medium-sized cross-border e-commerce businesses. Through this case analysis, it can be understood that intelligent e-commerce has provided many conveniences for the e-commerce industry, but also faces a series of challenges, such as AI generated false or infringing content, and increased operational management difficulties. In this situation, e-commerce companies should take strengthening the cultivation of intelligent talents as a guarantee, focusing on customer experience and valuing personalized product needs as advantages, and improving operational management efficiency through the reasonable use of favorable policies to bring profits to the enterprise.

The impact of social media discourse on financial performance of e-commerce companies listed on Borsa Istanbul

This research investigates how changes in public discourse on social media (particularly Twitter, now X) influence financial performance of companies listed on the e-commerce XTCRT index of Borsa Istanbul amid the COVID-19 pandemic. Utilising a unique 4-layer network structure, the study integrates daily closing prices, highest and lowest values, and trading volume data of these companies with Twitter discussions on six categories: culture, economy, health, politics, technology, world affairs. By using advanced computational methods such as multilayer network analysis, long short-term memory architecture, nonlinear autoregressive distributed lag analysis, we find a high responsiveness of the e-commerce index to social media content, particularly in discussions related to health, technology, world affairs. The study underscores that social media discussions significantly shape index composition, resulting in noticeable shifts in market dynamics, including fluctuations in stock prices, trading volumes, and market sentiment. Our study advocates for a more advanced approach to market analysis that incorporates the dynamics of online public discussions.

The examining the relationship of telework intensity and work-family conflict: Work autonomy as moderator

Purpose: While telework offers many advantages during the COVID-19 pandemic, it can also increase pressure and lead to greater work-family conflict for workers. Work autonomy plays a key role in moderating the relationships of telework and work-family conflict. The purpose of this study was to examine the influence of work autonomy on the relationship between telework intensity and work-family conflict. Method: This study involved participants from various e-commerce companies in Indonesia with a total of 86 workers who meet the criteria of working telework, married and having children under 18 years. The data of this study were analysed using hierarchical regression analysis that to assess the moderator role of work autonomy in the relationship between the telework intensity and work-family conflict. Result: The study found telework increases work-family conflict, moderated by work autonomy, with higher autonomy reducing conflict. Gender and period of work also known to influence work-family conflict. Conclusion: Based on the findings of this research, companies can help their workers to manage work-family conflict by giving more job autonomy within their telework systems.

Cross-border supply chain coordination of low-carbon agricultural products under the risk of supply uncertainty.

The purpose of this paper is to discuss how cross-border e-commerce enterprises can promote the sustainable development of the supply chain by optimizing the risk of supply disruption and product quality control mechanism of the cross-border supply chain of low-carbon agricultural products in the face of the problem of uneven quality and inventory shortage that prevails in the supply chain of low-carbon agricultural products under the framework of low-carbon economy. Methods: A two-level supply chain model consisting of a risk-averse cross-border e-commerce enterprise and two risk-neutral overseas suppliers is constructed to compare the optimal strategies and their coordination effects under the centralized and decentralized decision-making modes, and to deeply analyze the supply chain's operation mechanism. Further, the quality cost factor is introduced and an option contract model is designed to quantitatively analyze the impact of different decision-making scenarios and parameter changes on the overall supply chain performance. The results of the study show that under the coordination of option contract, the profit of cross-border e-commerce companies tends to decrease as the risk of supply disruption increases, while the profit of suppliers rises accordingly. Meanwhile, improving the quality of agricultural products can significantly improve the overall profitability of the supply chain. The cost-sharing mechanism is positively related to the profit of cross-border e-commerce companies, but negatively affects the profit of suppliers. In addition, the adjustment of the option price is directly associated with the increase of the specific supplier's profit and the decrease of the cross-border e-commerce enterprise's profit. Conclusion: By constructing and analyzing the option contract model considering the risk of supply disruption, this study effectively reveals the key influencing factors and their interactions in the cross-border supply chain of low-carbon agricultural products, and provides cross-border e-commerce enterprises with specific strategies to achieve coordination of the supply chain contract, improve product quality, and mitigate the risk of supply disruption, and then promote the sustainable development of the supply chain of low-carbon agricultural products.

A Study on the Production-Inventory Problem with Omni-Channel and Advance Sales Based on the Brand Owner’s Perspective

This study explores a supply chain product-inventory problem with advance sales under the omni-channel strategies (physical and online sales channels) based on the brand owner’s business model and develops corresponding models that have not been proposed in previous studies. In addition, because the brand owner is a member of the supply chain, and has different handling methods for defective products or products returned by customers in various retail channels, defective products or returned products are included in the supply chain models to comply with actual operating conditions and fill the research gap in the handling of defective/returned products. Regarding the mathematical model’s development, we first clarify the definition of model parameters and relevant data collection, and then establish the production-inventory models with omni-channel strategies and advance sales. The primary objective is to determine the optimal production, delivery, and replenishment decisions of the manufacturer, physical agent, and online e-commerce company in order to maximize the joint total profits of the entire supply chain system. Further, this study takes the supply chain system of mobile game steering wheel products as an example, uses data consistent with the actual situation to demonstrate the optimal solutions of the models, and conducts sensitivity analysis for the proposed model. The findings reveal that increased demand shortens the replenishment cycle and raises order quantity and shipment frequency in the physical channel, similar to the online channel during normal sales. However, during the online pre-order period, higher demand reduces order quantity and cycle length but still increases shipment frequency. Rising ordering or fixed shipping costs lead to higher order quantity and cycle length in both channels, but variable shipping costs in the online channel reduce them. Market price increases boost order quantity and frequency in the online channel, while customer return rates significantly impact inventory decisions.

Penerapan Algoritma Clustering untuk Segmentasi Pelanggan E-commerce berdasarkan Data Pembelian dan Aktivitas

In the current digital era, e-commerce has become one of the main pillars of global trade. With the ever-increasing amount of transaction and user activity data, e-commerce companies are faced with the challenge of understanding and managing diverse customer segments more effectively. This paper discusses the application of clustering algorithms for e-commerce customer segmentation based on purchasing data and user activity. The aim of this research is to identify homogeneous customer groups to support more targeted marketing strategies and increase customer retention. The problem faced is how to process big data originating from user transactions and activities on e-commerce platforms, as well as how to identify patterns that are useful for customer segmentation. The data used in this research includes purchase history, frequency of visits, length of time spent on the site, and interactions with certain products. The solution method applied in this research is the clustering algorithm, especially K-Means and DBSCAN. K-Means is used to group data into a predetermined number of clusters based on the Euclidean distance between data points. Meanwhile, DBSCAN is used to identify clusters with high density and separate data that is considered noise or outliers. Data preprocessing is carried out to clean and normalize the data before being applied to the clustering algorithm. Validation of clustering results is carried out using metrics such as Silhouette Score and Davies-Bouldin Index. The research results show that by applying the clustering algorithm, customers can be grouped into several segments that have similar characteristics. For example, we found groups of customers with high purchase frequency but low transaction value, as well as other groups with high transaction value but low purchase frequency. This information is very useful for companies to design more effective marketing strategies, such as special offers for customers with high transaction values ​​or loyalty programs for customers with high purchasing frequency. The conclusion of this research is that clustering algorithms can be a very effective tool in e-commerce customer segmentation, allowing companies to understand customer behavior patterns and develop more targeted and effective marketing strategies. Thus, implementing this method is expected to improve business performance and overall customer satisfaction.

Approaches to Modelling Marketing Strategies in E-Commerce

Introduction. Increasing the efficiency of business activities requires developing a marketing strategy using e-commerce to attract a wide range of customers and maximise the company's income. The key marketing metric for most companies is the customer lifetime value indicator, which is why studying approaches to forecasting this indicator in e-commerce requires special attention, considering the changing business operation environment. Aim and tasks. The study aims to develop approaches to modelling the marketing strategy of e-commerce enterprises. The tasks are a comparative characterisation of modern marketing planning tools and an empirical analysis of the possibilities of using the BG/NBD model for marketing planning in e-commerce companies, considering the need to implement scenario marketing planning in unstable conditions. Results. A comparison of the characteristics of the main marketing planning tools was conducted according to features such as content and focus, functional purpose, and planning period, which allows more purposeful use of these tools to solve specific tasks. In order to automatically obtain the required data format for modelling, a Visual Basic program was developed, a forecast of customer behaviour was built, and an indicator of customer transactions was determined for further calculation of customer lifetime value (CLV). During the simulation, four parameters of the BG/NBD model were evaluated, and the maximum value of the logarithmic likelihood function equal to 14460.54 was obtained. The expected number of transactions (forecast time = 26 weeks) was determined for each client from the database. After calculations the information was received, the selected client will make 2.35 transactions during the forecast period of 55-80 weeks. The recommended algorithm for using the BG/NBD model as part of a scenario approach for forecasting the cost of the customer's life cycle allows for more flexible forecasting of digital business revenues and building an optimal marketing strategy. Conclusions. An empirical study of the possibilities of using the BG/NBD model for marketing planning in e-commerce companies showed that, for the simulation of indicators that affect a company's marketing strategy, it is crucial at the initial stage to convert the database of customer purchases into the input format required for modelling. Using a scenario approach allows e-commerce enterprises to carry out variable marketing planning and reduce risks under growing uncertainty. A well-designed marketing strategy that can be adapted to different business development scenarios enables an e-commerce company to increase its sales.

MULTI-ENTITY STOCK DEPENDENT MODEL WITH CAPACITY AND MANUFACTURE COST RESTRAINT’S

Inventory has an impact on the manufacturing process as well as supply chain operations. The fundamental goal of this research paper is to optimize the cost associated with inventories and to provide flow less continuous production process in time. Normally, demand rate of any entity in inventory control model are treated as predictable and at the same time constant too, and that the cost associated to unit inventory must be independent and non-variable in nature. Nevertheless, in practical circumstances, the unit price and demand rate of an entity may be interconnected. When the asking for an article is enormous, an entity is manufactured in huge quantities and the static charges of manufacturing being diffused over a multiple component. Henceforth, per unit article cost decreases significantly. i.e., per unit article cot and the demand of an article are related under inverse variation. So, better to consider the demand rate of an article as a variable constraint than to fixed one. In this research article, a mathematical model for multiple articles through permitted and restricted shortage and per article cost based on demand accompanied by upper and lower limits viz restricted storage space and manufacturing expenses has been constructed. Overall, investigating the simultaneous effect of storage space and manufacturing expenses in an inventory model provides valuable insights that enable cost optimization, resource allocation, capacity planning, and risk mitigation. It helps companies make informed decisions and improve their overall operational efficiency and profitability. The Multi-Entity Stock Dependent Model with Capacity and Manufacture Cost Restraints can be customized and used in a variety of sectors that include managing inventory across numerous entities and complicated supply chain networks. Here are a few examples of industries that can benefit from such a model: manufacturing industry, the retail and distributor sector, e-commerce companies, pharmaceutical and healthcare industry, automotive industry and food and beverage industry. The article cost is explored at this juncture in a fuzzy atmosphere and solutions of the model being obtained through KKT condition. Finally, a conclusion is offered in the final portion.

Easily Determining Post-Study System Usability for Anime Community E-Commerce Analysis

The rapid growth of e-commerce as a form of electronic commerce has transformed the global shopping landscape. To maintain user satisfaction and competitiveness, e-commerce sites must effectively understand and improve the usability of their systems after launch. The aims of the study are to improve e-commerce post-launch usability so that anime fans' enthusiasm can be capitalized upon for financial gain and market expansion. In this study, we adopted a combined approach that included user observations, interviews, surveys, and performance measurement for anime community e-commerce analysis Weeboo web commerce. Through this method, we analyze the behavior and views of users towards e-commerce systems. The results show that most users experience a positive experience in shopping online by appreciating the usability of the layout, search process, and fast checkout process. The results indicated that most users have a positive online shopping experience, appreciating the layout, search process, and fast checkout process. The SUS score of 75.375 (grade B) and the overall PSSUQ satisfaction score of 2.0296 indicate that the system is acceptably well-received. The proposed recommendations can help e-commerce companies quickly identify usability issues and implement relevant fixes.

Ranking and Challenges of Supply Chain Companies Using MCDM Methodology

Background: Supply chain companies have merits and demerits regarding operational and economic transactional policies. The effectiveness of supply chain companies corresponds to a cumulative score on a multi-criteria and perspectives-based evaluation. In this paper, we analyse the performances and challenges of several celebrated e-commerce companies to perceive their overall impression of supply chain management. Method: A mathematical model is framed as a multi-criteria decision-making (MCDM) problem with challenges as criteria and companies as alternatives. The criteria importance through inter-criteria correlation (CRITIC) method is used in this paper to adjust weights representing the available data. The ranking of e-commerce companies is evaluated using multi-objective optimization by ratio analysis plus the full multiplicative form (MULTIMOORA) method. Results: This model investigates the most dependent criteria and sub-criteria for the adaptation challenges of supply chain companies (SCCs). Furthermore, the SCCs are prioritized based on various conflicting criteria. Conclusion: Various challenges of SCCs, like logistics constraints, disruptions in supply chains, issues with technology, ethical sourcing and inconsistency between the products’ availability and the pace of consumption, are considered and analysed. We amassed the difficulties as criteria and sub-criteria in a numerical process using the MCDM approach. Additionally, the sensitivity and comparative of several optimal phenomena are analysed based on distinctive combinations of challenges in the ranking arena.

Pengaruh Content Marketing dan Shopping Lifestyle terhadap Impulse Buying pada E-Commerce Lazada

Indonesia's internet user base is growing as a result of the swift progress of technolog,, which also creates intense competition among e-commerce companies, including Lazada. Based on this phenomenon, the research aims to observe the influence of content marketing and shopping lifestyle on impulse buying behavior in Lazada e-commerce. A non-probability sampling method with purposive sampling is applied. The total sample, according to the predetermined criteria, was 98 respondents. Partial Least Square (PLS) is the data analysis technique used. The research findings show that content marketing does not affect impulsive buying in Lazada's e-commerce. Conversely, impulsive buying in Lazada e-commerce is influenced by consumer shopping lifestyles.