E-commerce Demands Research Articles

AIGraphy: Image to Advetisement

Since e-commerce is growing so quickly, excellent product photography is now crucial for High-quality product photography has become crucial for influencing consumer purchasing decisions as e-commerce has grown rapidly. Traditional photography techniques, however, are frequently expensive and time-consuming, which prevents small firms from using them and makes them difficult for larger corporations with dynamic product catalogues. This study investigates the use of generative artificial intelligence (AI) models to improve and automate product photography, providing a scalable and reasonably priced substitute. Using three essential models: ControlNet for regulated image alteration, Stable Diffusion with inpainting capabilities for backdrop generation, and Segment Anything Model (SAM) for accurate segmentation. According to our research, AI-driven product photography is a feasible answer to the demands of contemporary e-commerce since it can generate excellent images on par with conventional techniques. Our research shows that AI-driven product photography can create images of the same caliber as conventional techniques, which makes it a good option for today's e-commerce requirements. Establishing the groundwork for future research into automated, AI-based product photography and its potential to transform the production of visual content in online retail is the goal of this project.

Using 3rd -Party Logistics Services OR Developing Own Capability to Cater to ECommerce Demands - A comparative Analysis

Ecommerce is an inseparable part of modern global lifestyle, connecting people across international borders and enabling sellers to access global. Despite growing massively over the last couple of decades, its growth is far from over. In fact, the pandemic has acted as an accelerator and the growth momentum is continuing despite people returning to the conventional brick and mortar shops. Ecommerce innovations are constantly being rehashed and improved – especially after the global pandemic this transition is now innovating at a much faster pace. For responding to the ever-increasing demand for swifter yet efficient fulfillment of customer order along with timely shipment, a larger number of e- commerce businesses are turning towards third-party logistics or 3PL providers. While it offers them the freedom from the hassles of storing and delivering finished products to the end-users, the human resource that is therefore freed, can then focus on core competencies of the business thus allowing them to give attention to product development, innovation along with marketing and sales. There is always the risk of poor fulfillment and delayed shipments or goods getting damaged in transit which deteriorates customer satisfaction and company profitability. This article compares the use of third-party logistics (3PL) against building in-house capabilities and highlights the virtue of careful selection and employment of experienced 3PL in helping an enterprise ride the anticipated growth wave by offering cost advantages and flexibility.

Performance assessment of a multi-level shuttle system: an optimisation-embedded simulation study on time and energy consumption

ABSTRACT In response to the dynamic demands of modern e-commerce and supply chains, this paper addresses the critical need for adaptability and efficiency through the implementation of automated storage and retrieval systems. By using an optimisation-embedded simulation methodology, the proposed study focuses on a multi-level shuttle system designed to overcome the inherent limitations of traditional storage systems. A comprehensive discrete event simulation model is developed to evaluate the performance of the multi-level shuttle system. This model includes calculations of travel time and energy consumption, taking also into account stochastic handling times associated with repetitive tasks. In addition, a dynamic programming algorithm is integrated in the simulation model to optimise the sequencing of storage and retrieval missions performed by the handling machine. The study systematically investigates various design and operational parameters, including warehouse configurations and capacity, simultaneous handling of multiple unit loads, and the dimensions of the optimisation sessions. For all configurations tested, the research demonstrates operational advantages in the simultaneous handling of two unit loads. Theoretical contributions extend the current state of simulation models for automated warehousing systems. For practitioners, the study provides empirical insights to support decision making in the configuration of automated warehouses.

Analytical model and swapping policy assessment of a vertical lift module – buffer integrated storage system

To meet the rising demands of global trade and e-commerce, efficient warehousing relies on integrated and cooperative material handling systems. This paper investigates the extension of Vertical Lift Module (VLM) storage capability with a Buffer System and assesses the impact of this integration on performance. We developed an analytical model to calculate the expected dual command cycles, forming the basis for evaluating the VLM – Buffer integrated storage system’s performance. Our research emphasises minimising unnecessary swaps between the VLM and the Buffer System to enhance throughput performance. We introduce the Look Ahead Strategy (LAS) to minimise inter-system swaps and develop a Binary Integer Program (BIP) to benchmark its performance. The results indicate that LAS performs on par with BIP, due to its ability to consider product popularity during the final selection of the outbound swapping tote. Through a comprehensive analysis of the analytical model with an empirical correction, utilising Pareto-based order sequences, the results show deviations of less than 1% on average, affirming the analytical model’s accuracy. Our research provides insights on using the VLM-Buffer integrated storage system, emphasising efficient tote swapping policies like LAS for enhanced warehouse operations, and allows managers to assess system performance through scenario-based analyses.

Breaking Barriers: Investigating Technology Adoption in Micro, Small, and Medium Enterprises (MSMEs) Among Low-Income Women Entrepreneurs in Malaysia

In Malaysia, the B40 segment, particularly low-income women entrepreneurs, faces distinct challenges in adopting technology within their businesses. This qualitative study explores the low uptake of technology adoption among these entrepreneurs to understand the underlying dynamics. By addressing the "what" and "why" of low technology adoption, the research aims to provide insights for fostering inclusivity and empowerment within Malaysia's entrepreneurial landscape. The study involved an in-depth focus group discussion with 25 women entrepreneurs from four states across Malaysia, utilizing thematic analysis with Atlas TI. Key findings reveal issues related to technology adoption based on TAM theory. The findings indicate that most entrepreneurs are integrating technology into their business operations, either through social media marketing, e-commerce platforms, cashless payment methods, online delivery services, and machines, regardless of whether they are located in rural or metropolitan areas. The majority of the respondent are utilizing technology through their social media marketing. These situations have arisen as a result of the impact of COVID-19, which has compelled all entrepreneurs to adopt technology, even at a minimal level. The majority use social media platforms like Facebook and WhatsApp for marketing and adopt online banking and cashless payment methods such as DuitNow QR Code, Touch 'n Go, Boost, Shopee Pay, and Sarawak Pay. Despite using online food delivery services like Food Panda and Grab Food, trust issues such as payment delays, non-payment by drivers, and high commission fees make these platforms less appealing. Additionally, entrepreneurs prefer traditional methods over machinery to maintain quality and struggle to meet e-commerce demand due to their small-scale production, limiting their ability to fully use technology for business growth and sustainability. The findings support the theoretical insights of the TAM model and suggest actionable strategies for fostering technology adoption and empowerment among low-income women entrepreneurs in Malaysia.

Development of an Online Daily Goods Shopping Demand Model Using Internet-Based Consumption Behavior Survey Data

With the rapid growth of the e-commerce market, how to cater to the demand for last-mile deliveries to households is an important challenge for both policymakers and practitioners. Therefore, the incorporation of e-commerce-driven delivery demand into a freight transportation analysis is required. In this research, we conduct an internet-based survey in Japan to collect data on past in-person and online shopping behaviors and develop a household-based e-commerce demand model that simultaneously estimates e-commerce delivery demand in regard to the total value and order size (or order frequency) using a framework proposed by Sakai et al. Using the estimated model parameters, we conduct a sensitivity analysis using the model to understand the impact of changes in delivery service on e-commerce delivery demand. Focusing on groceries and household goods, we successfully demonstrate the ability of the model framework and obtain insights into the impact of delivery service characteristics on households’ reliance on e-commerce using the model developed based on real-world data.

Post-Covid challenges and opportunities: rethinking ESG performance in the logistics sector

In the post-COVID era, the logistics sector faces significant challenges and opportunities. These are mostly due to disruptions in global supply chains and a rise in e-commerce demand. To overcome these issues, companies need to adopt digital technologies, reshape consumer demand, and implement flexible work arrangements, with a focus on sustainability. This transition bolstered attention on Environmental, Social, and Governance (ESG) considerations. In this study, we introduce new metrics to evaluate ESG performance in the logistics sector. To resolve inconsistencies in existing approaches, we propose the Standardised Unitless Rating (SUR) methodology, which combines satisfaction scores and criterion relevance. We employed the Interval Type-2 Fuzzy Analytic Hierarchy Process (IT2FAHP) to calculate the relative weights of ESG criteria. The results show that newly established companies with innovative technology have performed better than traditional companies. This proposed method has been validated by comparing it with existing methods. Additionally, we observed the importance of relative weight as compared to criteria with equal weight. The findings suggest that incorporating process metrics could help companies enhance their ESG performance.

The impact of live-streaming interactivity on live-streaming sales mode based on game-theoretic analysis

Many manufacturers who sell products through e-tailers in e-commerce channels launch a real-time interactivity live-streaming channel. The live-streaming interactivity includes consumer-streamer interaction (CSI) and consumer-consumer interaction (CCI). Thus, manufacturers generally hire influential streamers to expand the market size (i.e., Mode L) to improve live-streaming interactivity. However, some manufacturers act as streamers, i.e., merchant live-streaming mode (Mode M), to save the commission costs of hiring influential streamers. Therefore, we explore the manufacturer's live-streaming sales mode choice from a live-streaming interaction perspective through a game-theoretic method. We find when CCI is more positive (negative), Mode L is more helpful (unhelpful) in alleviating the double marginal effect of e-commerce channels than Mode M, increasing (reducing) e-commerce demand, and ultimately increasing (damaging) the e-tailer's profit. However, the size relationship between live-streaming prices in the two sales modes also relies on the streamer's commission rate, in addition to CCI. Finally, the conditions for the manufacturer to profit from either mode rest on the streamer's commission rate and CCI.

E-Commerce Demand Forecasting Using SARIMA Model and K-means Clustering Analysis

E-commerce has become a significant driver in the retail industry within the digital era, with escalating online transaction volumes. Accurate demand forecasting and effective inventory management are crucial for retailers to minimize costs related to overstock and stockouts while maximizing customer satisfaction and revenue. This study explores the application of advanced time series models for demand forecasting by e-commerce retailers, with a particular emphasis on the Seasonal Autoregressive Integrated Moving Average (SARIMA) model. The SARIMA model, an extension of the ARIMA model, incorporates autoregression, moving averages, and accounts for seasonal variations, making it suitable for analyzing data with regular periodic patterns, such as seasonal fluctuations in consumer purchasing behavior. The study methodology includes data preprocessing, establishment of the ARIMA model, construction of a Linear Regression (LR) model, development of the SARIMA model, demand forecasting, and clustering analysis. The SARIMA model demonstrated higher fit and predictive accuracy compared to traditional LR and ARIMA models, as substantiated by the 1-mWAPE and RMSE metrics. The application of the K-means clustering algorithm enhanced the homogeneity of demand forecasting and improved the model's adaptability to new sequence data. Cluster analysis categorized products into four classes, with more similar demand characteristics within each class, aiding the model in more accurately capturing potential demand changes. In conclusion, this research confirms the effectiveness and practicality of combining the SARIMA model with the K-means clustering algorithm for e-commerce demand forecasting.

Research on E-Commerce Retail Demand Forecasting Based on SARIMA Model and K-means Clustering Algorithm

With the rapid development of e-commerce, precise demand forecasting and efficient inventory management have become essential for the success and profitability of retail businesses. This study focuses on demand forecasting for e-commerce retailers using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model and the K-means clustering algorithm. The research utilizes a dataset containing 1996 time series of sales data from various products, merchants, and warehouses, aiming to predict demand changes for the next 15 days. The study initially evaluates three models—Linear Regression (LR), Autoregressive Integrated Moving Average (ARIMA), and SARIMA—by fitting them to historical sales data to forecast future demand. The SARIMA model is identified as the most effective through rigorous evaluation using 1-mWAPE (mean weighted absolute percentage error) and RMSE (root mean square error) metrics. To enhance homogeneity within demand categories, the K-means clustering method is applied to divide products into four distinct groups, further refining the forecasting process.The paper also addresses the challenge of integrating new sequences into the dataset by leveraging clustering results to classify sequences and using cosine similarity to identify analogous historical time series. These matched sequences serve as the basis for demand prediction using the established SARIMA model. The findings highlight the robustness of the SARIMA model in capturing trends and seasonality, providing a reliable framework for e-commerce demand forecasting that can significantly impact inventory strategies and operational efficiency.

How do strategic attributes of electronic commerce impel the perceived value and electronic loyalty of online food delivery applications (OFDAs)

Purpose This study aims to investigate the different attributes of electronic commerce (e-commerce) that determine perceived value and electronic loyalty (e-loyalty) among consumers of online food delivery applications (OFDAs). In this globalized world and competitive environment, e-commerce demands have increased and organizations are giving special attention to web development, website design and functions to hold the current consumers with sustainable performance in the globalized and competitive environment. Almost every industry has been affected by the coronavirus disease 2019 (COVID-19) and changed the way of operational work in many industries. Similarly, the food industry is facing serious challenges and now restaurants started heavily depending on OFDAs.Design/methodology/approach The study was quantitative and data were collected from 509 consumers of the district of Rawalpindi, Punjab Pakistan by using a convenience sampling technique who was the users of OFDAs to evaluate the proposed research model. Confirmatory factor analysis was applied to evaluate the validity of the constructs, and structural equation modeling was employed to test the model through Smart-PLS.Findings Our findings revealed that perceived value has a substantial positive impact on electronic loyalty (e-loyalty). Moreover, results confirmed that perceived value mediates the relationship of electronic privacy (e-privacy), electronic security (e-security), electronic payment (e-payment), usability and electronic innovativeness (e-innovativeness) with e-loyalty.Research limitations/implications This study added to the unified theory of acceptance and use of technology (UTAUT) and technology acceptance model (TAM) by exploring consumers’ intentions for using OFDAs in the framework of e-commerce attributes, perceived value and e-loyalty. Similarly, the study enabled the author to learn more about how people would use the information system after successfully applying the UTAUT.Practical implications This study has significant implications for web developers, application designers, food delivery companies, restaurants and other businesses. Subsequently, it indicates the importance of the incredible attractiveness of OFDAs in boosting users’ intentions to keep using the application.Originality/value This research contributes substantially to OFDAs efforts to continuously increase its meal service platform and improve client satisfaction which resulted in repurchase intent. In addition, the research facilitates OFDA firms to enhance the features of their applications according to clients.

The impact of ICT and e-commerce on employment in Colombia

This study presents new empirical evidence regarding the impact of e-commerce adoption on employment in Colombia, a developing economy. We use firm-level data from the Annual Manufacturing Survey between 2013 and 2018 and differentiate the workforce by skill level and type of contract. We also use multiple correspondence analysis (MCA) to construct a parsimonious set of instrumental variables to account for the endogeneity of e-commerce. Our main finding is that an increase of 1% in the degree of e-commerce penetration increases demand for skilled workers by 0.2% and does not have a negative impact in the demand for unskilled workers. We then delve deep into the evidence and test for complementarity or substitution between skilled labor and ICT capital related to e-commerce implementation by employing a new and specific theoretical framework, which is empirically assessed. Our results confirm that highly skilled workers benefit the most from the introduction of e-commerce and its associated ICTs into a company, exhibiting a high degree of complementarity compared to unskilled workers. Thus, our findings support the hypothesis that ICT utilization and e-commerce adoption lead to labor substitution, particularly for low skill workers. This study recommends that policymakers invest in skill development to meet e-commerce demands, focusing on both upskilling workers to enhance digital literacy and implementing targeted support programs for a smooth transition to the digital economy across all skill levels.

Cross-border dual-channel supply chain decision-making under random demand and tariff conditions.

Given the cross-border e-commerce import tariff and random demands, this study establishes a pricing decision model for cross-border e-commerce dual-channel supply chain, which is composed of domestic manufacturers and overseas retailers, so as to analyze the effects of import tariff and random demand on the pricing, demand and profit of cross-border e-commerce. According to the research, import tariffs have a positive correlation with retailers' retail prices and a negative correlation with manufacturers' direct prices, wholesale prices, demand and profit from direct channels, and profit from retail channels. The export tax rebate policy will lessen the negative effects of import tariffs and maximize the best choices made by manufacturers and retailers.

Agent-based model of last-mile parcel deliveries and travel demand incorporating online shopping behavior

In this paper, we present an extension of the agent-based travel demand model mobiTopp with a last-mile parcel delivery module called logiTopp, in which online shopping choice is modeled explicitly. Online shopping behavior is modeled using logistic and Poisson regression models, which consider both the socio-demographic characteristics of the customer and aspects of their travel behavior. As mobiTopp is a framework that simulates travel demand over one week, we are able to capture interactions between travel behavior and online shopping that do not become apparent in single-day simulations.The results show that the integrated choice model reflects the findings presented in the literature in that male, affluent, young professionals are most likely to (frequently) order parcels online compared to other groups of the population. Application of the agent-based model to a city in Germany shows that socio-demographic and behavioral characteristics are considered realistically within the simulation.The model presented here is a suitable simulation tool for alternative urban last-mile delivery solutions, and the open-source and modular framework allows for transfer to other regions as the underlying choice models are consistent with literature from other spatial contexts.The findings are of interest to transportation planners and policymakers as they contribute to the understanding of how increased e-commerce demand influences the transportation system and solutions to mitigate adverse effects.

Research on E-commerce Demand Forecast Based on Big data Algorithm and AI

Research on E-commerce Demand Forecast Based on Big data Algorithm and AI

Influence of neighborhood characteristics on e-commerce deliveries: The case of Belo Horizonte, Brazil

Business-to-consumer shoppers in e-commerce increased significantly in recent years, along with the number of urban freight trips derived from deliveries. Therefore, understanding the factors that influence e-commerce demand is essential to providing practical solutions to the problems that arise from deliveries. This study assesses the relationship between neighborhood characteristics and the number of urban e-commerce deliveries using negative binomial regression models applied to Belo Horizonte, Brazil data. The number of e-commerce deliveries between 2019 and 2021 was modeled considering the number of retail shops, sociodemographic data, and neighborhood area. The results show that income, number of retail shops, and neighborhood land area positively influence e-commerce deliveries, while household size negatively impacts the number of deliveries. The elasticity values and t-test indicate that the impact of different characteristics changed in 2020, remaining similar between 2019 and 2021. Income was the most impactful variable in the number of deliveries in 2020. The neighborhood characteristics bring knowledge for developing more effective business models for e-commerce delivery services.

Urban warehouses as good neighbors: Findings from a New York City case study

The surge in online shopping has contributed in no small part to the return of warehouses in cities. Urban warehouses allow retail and delivery companies to serve consumer populations faster. They are also a necessary condition for the transition to an urban logistics system that is efficient and sustainable. However, integrating urban warehouses into dense, mixed-use urban areas presents considerable challenges, particularly in coexistence with surrounding communities. This study explores how urban warehouses can become good neighbors, through a case study of New York City. The city has a high e-commerce demand and high population density. In addition, zoning regulation and administration in particular have enabled the development of several warehouses in recent years. The study introduces seven types of practices that can promote the proper integration of warehouses in cities. Scaled from the system (i.e., the city) to the site (i.e., the warehouse), best practices include the following: zoning for innovation as well as nuisance monitoring and mitigation, environmental policy for movement and place, supply chain awareness for citizens/consumers, blocks and buffers for neighborhood design, community conversation, sustainable transportation transition for goods as well as people, and context-sensitive building design.

Congestion-Aware Bi-Modal Delivery Systems Utilizing Drones

With e-commerce demand rising, logistic operators are investing in alternative delivery methods such as drones. Because of their aerial reach, drones can provide much needed utility in the last mile by taking the load off of vehicles delivering parcels to customers on the road. Our goal is to assess the potential drones have in mitigating traffic congestion. To do so, we develop a mathematical model for a bi-modal delivery system composed of parcels carrying trucks and drones, combining it with an optimization problem that can be solved for the socially optimal routing and allocation policy efficiently. Within this formulation, we include multiple stakeholder perspectives by modeling the objective function in terms of both traffic congestion and parcel latency. This allows our model to quantify the impact of drones on reducing traffic congestion, and simultaneously finds the path routing that minimizes the given objective. To account for the effects of stopping trucks on road latency, we simulate roads shared between trucks and cars by utilizing SUMO. We then use quadratic optimization techniques to test our proposed framework on a variety of real-world transportation networks. Our findings highlight the trade-off between reducing traffic congestion and increasing parcel latency—while routing trucks along less time-efficient paths may alleviate traffic congestion, this disproportionately increases the parcel latency. This suggests the need for a balanced approach that considers both factors when solving for the routing policy.

Assessing last-mile distribution resilience under demand disruptions

The COVID-19 pandemic led to a significant breakdown of the traditional retail sector resulting in an unprecedented surge in e-commerce demand for the delivery of essential goods. Consequently, the pandemic raised concerns pertaining to e-retailers’ ability to maintain and efficiently restore level of service in the event of such low-probability high-severity market disruptions. Thus, considering e-retailers’ role in the supply of essential goods, this study assesses the resilience of last-mile distribution operations under disruptions by integrating a Continuous Approximation (CA) based last-mile distribution model, the resilience triangle concept, and the Robustness, Redundancy, Resourcefulness, and Rapidity (R4) resilience framework. The proposed R4 Last Mile Distribution Resilience Triangle Framework is a novel performance-based qualitative-cum-quantitative domain-agnostic framework. Through a set of empirical analyses, this study highlights the opportunities and challenges of different distribution/outsourcing strategies to cope with disruption. In particular, the authors analyzed the use of an independent crowdsourced fleet (flexible service contingent on driver availability); the use of collection-point pickup (unconstrained downstream capacity contingent on customer willingness to self-collect); and integration with a logistics service provider (reliable service with high distribution costs). Overall, this work recommends the e-retailers to create a suitable platform to ensure reliable crowdsourced deliveries, position sufficient collection-points to ensure customer willingness to self-collect, and negotiate contracts with several logistics service providers to ensure adequate backup distribution.

Activity characteristics and perspective development directions of electronic trade platforms in Azerbaijan

Improving e-commerce platforms and determining their prospective development trends is a global problem. This article indicates the relevance of expanding online transactions with the application of industry 4.0 platform technologies in business activities. The sharp increase in demand for e-commerce due to the pandemic has made it one of the key trends of the modern era. Modern electronic commercial systems and technologies are required to be applied in the organization of business activities. The work shows the significance of forming the necessary infrastructure for e-commerce in the country and solving the problem of integration into the global digital ecosystems. The characteristics of electronic commerce technologies in ensuring economic development are determined. The stages of its life cycle are displayed based on the results of the analysis of many research works related to electronic commerce. Aspects of the impact of the application of modern electronic commerce technologies and tools on economic development in commercial activities are explored. The article also highlights the main procedures of online purchase process in the main electronic trading systems on the Internet. It classifies the electronic commerce system according to various characteristics, and studies the aspects of its implementation in Azerbaijan. It proposes the methodology of comparative evaluation of the development rate of electronic commerce technologies and develops systems and relevant indices. In the end, the results of the final scores on the weighting coefficients of the indicators characterizing B2B electronic commerce are introduced. With the application of the latest information and communication technologies, recommendations are proposed on the operational characteristics and prospective development trends of e-commerce platforms in the Azerbaijani segment of the Internet.