Online Business and Marketplaces
Abstract
This book covers some fundamental operations of online business and marketplaces. Topics include introduction to online retailing, online marketplaces, analytics and AI for online retailing, supply chain management for online retailing, logistics equipment and technologies for online retailing, order fulfillment for online retailing, last-mile delivery for online retailing, service-oriented platforms, and omni-channel retailing.
The relationship between perception and the impact of online shopping refers to how people think about and understand online shopping, and how this affects their lives. Perception includes things like whether people trust online stores, find them convenient, or worry about risks. The impact of online shopping covers how it affects people's academic performance, finances, and overall lifestyle. For example, if someone thinks online shopping is easy and safe, they might shop more often, which could affect how much money they spend or how well they do in school. Understanding this relationship helps us figure out how online shopping influences people and how we can help them make better decisions when shopping online. This quantitative study investigates the attitudes of Prayagraj university students on internet shopping and its effects on their lives. The study intends to comprehend the relationship between perception and the consequences of internet buying, with a sample size of 147 people. The study investigates students' attitudes towards internet purchasing and its impact on their academic achievement, money management, and general lifestyle by analysing data collected through questionnaires and statistical methodologies. Important elements including ease, perceived hazards, and confidence in online platforms will be examined to determine how they influence students' attitudes and actions towards online buying. The study's conclusions are important because they shed light on how to help kids behave ethically when navigating the internet marketplace, which is beneficial for educators, businesses, and governments. Through enhancing the usability and reliability of e-commerce platforms and resolving any issues, stakeholders may improve students' online buying experiences and encourage more informed decision-making when making purchases online.
Keywords: Perception, Impact, Online shopping, students
In today's digital age, online shopping has become an integral part of consumer culture, with higher education students representing a significant and dynamic consumer group. This study employs a quantitative research design, surveying a sample size of 200 higher education students, to investigate their perceptions towards online shopping. The research assesses various factors, including attitudes, preferences, trust, convenience, and past shopping experiences, to gain a comprehensive understanding of how this demographic interacts with e-commerce platforms. This research study delves into the perceptions of students pursuing higher education regarding online shopping. In the rapidly evolving landscape of e-commerce, understanding the perspectives of this demographic is of paramount importance. The study employs a quantitative research approach and a sample size of 200 students, aiming to provide insights into the factors that shape their attitudes and behaviors towards online shopping. The findings of this study offer valuable insights for e-commerce businesses and educators alike, shedding light on the nuances of student consumer behaviour in the online shopping realm. By identifying key drivers and barriers, businesses can tailor their strategies to better meet the needs and expectations of this demographic, while educators can adapt their curricula to include relevant digital literacy skills.
Keywords: Online Shopping, Higher Education, Perception, Students, Consumer behaviour
This study investigates the factors impacting online shopping behaviour among students of higher education in Prayagraj District. Through simple random sampling, a sample size of 100 students was selected to participate in the study. Utilizing a structured questionnaire, data was collected to examine various aspects influencing online shopping practices. Five key factors including convenience, price, variety, security, and trust were analyzed to understand their significance in shaping students' online shopping behaviour. Results revealed that convenience emerged as the most influential factor, followed by price and variety. Moreover, security concerns and trustworthiness of online platforms significantly influenced students' decision-making processes. The findings provide valuable insights for online retailers, policymakers, and educators to better understand the dynamics of online shopping behaviour among students in higher education. Understanding these factors can aid in the development of strategies to enhance the online shopping experience and foster consumer trust in e-commerce platforms.
KEYWORDS: Online shopping, Consumer behaviour, Higher education students
The rapid growth of e-commerce has significantly influenced consumer behaviour worldwide. This study investigates the impact of online shopping on students pursuing higher education in Prayagraj, India. With Prayagraj's unique socio-cultural context, understanding the dynamics of online shopping among students becomes crucial for businesses and educational institutions alike. The primary objective is to analyze the factors influencing students' online shopping behaviour, including their motivations, preferences, and challenges. It also explores the role of various online shopping platforms and the impact of peer influence and socio-economic backgrounds on their choices. Employing a mixed-methods approach, this research collects quantitative data through surveys and questionnaires and gathers qualitative insights through in-depth interviews and focus group discussions. The study targets a diverse sample of students from higher education institutions in Prayagraj. The findings are expected to contribute to both academic knowledge and practical insights. They will enhance our understanding of online shopping behavior among higher education students and offer guidance to businesses and e-commerce platforms aiming to cater to this demographic. Ultimately, this study aims to bridge the existing knowledge gap and provide valuable insights into the evolving landscape of online shopping in Prayagraj.
Keywords: Online shopping, students, higher education, Prayagraj, impact, e-commerce, consumer behaviour.
Problem definition: In each period of a planning horizon, an online retailer decides on how much to replenish each product and how to allocate its inventory to fulfillment centers (FCs) before demand is known. After the demand in the period is realized, the retailer decides on which FCs to fulfill it. It is crucial to optimize the replenishment, allocation, and fulfillment decisions jointly such that the expected total operating cost is minimized. The problem is challenging because the replenishment-allocation is done in an anticipative manner under a "push" strategy, but the fulfillment is executed in a reactive way under a "pull" strategy. We propose a multi-period stochastic optimization model to delicately integrate the anticipative replenishment-allocation decisions with the reactive fulfillment decisions such that they are determined seamlessly as the demands are realized over time.
Academic/practical relevance: The aggressive expansion in e-commerce sales significantly escalates online retailers' operating costs. Our methodology helps boost their competency in this cut-throat industry.
Methodology: We develop a two-phase approach based on robust optimization to solve the problem. The first phase decides whether the products should be replenished in each period (binary decisions). We fix these binary decisions in the second phase, where we determine the replenishment, allocation, and fulfillment quantities.
Results: Numerical experiments suggest that our approach outperforms existing methods from the literature in solution quality and computational time, and performs within 7% of a benchmark with perfect information. A study using real data from a major fashion online retailer in Asia suggests that the two-phase approach can potentially reduce the retailer's cumulative cost significantly.
Managerial implications: By decoupling the binary decisions from the continuous decisions, our methodology can solve large problem instances (up to 1,200 products). The integration, robustness, and adaptability of the decisions under our approach create significant values.
We present our work with an online retailer, Rue La La, as an example of how a retailer can use its wealth of data to optimize pricing decisions on a daily basis. Rue La La is in the online fashion sample sales industry, where they offer extremely limited-time discounts on designer apparel and accessories. One of the retailer's main challenges is pricing and predicting demand for products that it has never sold before, which account for the majority of sales and revenue. To tackle this challenge, we use machine learning techniques to estimate historical lost sales and predict future demand of new products. The nonparametric structure of our demand prediction model, along with the dependence of a product's demand on the price of competing products, pose new challenges on translating the demand forecasts into a pricing policy. We develop an algorithm to efficiently solve the subsequent multiproduct price optimization that incorporates reference price effects, and we create and implement this algorithm into a pricing decision support tool for Rue La La's daily use. We conduct a field experiment and find that sales does not decrease because of implementing tool recommended price increases for medium and high price point products. Finally, we estimate an increase in revenue of the test group by approximately 9.7% with an associated 90% confidence interval of [2.3%, 17.8%].
We consider a retailer with limited storage capacity selling n independent products. Each product is produced by a distinct manufacturer, who is offered a consignment contract with revenue sharing by the retailer. The retailer first sets a common revenue share for all products, and each manufacturer then determines the retail price and production quantity for his product. Under certain conditions on price elasticities and cost fractions, we find a
unique optimal revenue share for all products. Surprisingly, it is optimal for the retailer not to charge any storage fee in many situations even if she is allowed to do so. Both the retailer's and manufacturers' profits first increase and then remain constant as the capacity increases, which implies that an optimal capacity exists. We also find that the decentralized system requires no larger storage space than the centralized system at the expense of channel profit. If products are complementary, as the degree of complementarity increases, the retailer will
decrease her revenue share to encourage the manufacturers to lower their prices.
Order picking has long been identified as the most labour-intensive and costly activity for almost every warehouse; the cost of order picking is estimated to be as much as 55% of the total warehouse operating expense. Any underperformance in order picking can lead to unsatisfactory service and high operational cost for the warehouse, and consequently for the whole supply chain. In order to operate efficiently, the order-picking process needs to be robustly designed and optimally controlled. This paper gives a literature overview on typical decision problems in design and control of manual order-picking processes. We focus on optimal (internal) layout design, storage assignment methods, routing methods, order batching and zoning. The research in this area has grown rapidly recently. Still, combinations of the above areas have hardly been explored. Order-picking system developments in practice lead to promising new research directions.
Managing the shipment of goods to consumers is one of the central aspects of retail competition on the internet. In this article, we analyze internet retailers’ shipping strategies using data from the internet book retailing industry. We find that, controlling for a variety of observable firm characteristics, firms with lower product prices offer lower shipping fees and higher quality shipping in terms of average delivery time, compared to firms with higher product prices. These patterns cannot be readily reconciled with a large class of models of competition under perfect consumer information. Theories based on imperfect consumer information can explain the findings better. Copyright Springer Science + Business Media, LLC 2006
Purpose
Caring for older adults is an increasingly complex and multi-dimensional global concern. This article provides a comprehensive definition of the older adult care experience and discusses its key components to help practitioners deliver older adult-centered care to maximize well-being outcomes for older adults.
Design/methodology/approach
Based on prior research on service operations, service experience, person-centered care and the unique, evolving needs of older adults regarding their care, this paper develops a conceptual framework in which the older adult care experience is the central construct, and key dimensions of well-being are the outcomes.
Findings
The older adult care experience is shaped by older adults' perceptions and evaluations of the care that they receive. Older adult-centered care has autonomy, dignity, unique needs and social environment as its core dimensions and results in those older adults feel empowered, respected, engaged and connected as part of their experience. The article also discusses how such experience can be evaluated by using quality dimensions from service operations, hospitality and healthcare contexts, and challenges that service firms may face in creating older adult care experience.
Research limitations/implications
Given the changing demographics and unique needs of older adults, it is an imperative for academics and practitioners to have an understanding of what determines older adult care experience to better serve them. Such understanding is important as by creating and fostering older adult care experience, service organizations can contribute to individual and societal well-being.
Originality/value
To the authors' best knowledge, this is the first paper to provide a comprehensive conceptualization of the older adult care experience.
In urban logistics, the last-mile delivery from the warehouse to the consumer’s home has become more and more challenging with the continuous growth of E-commerce. It requires elaborate planning and scheduling to minimize the global traveling cost, but often results in unattended delivery as most consumers are away from home. In this paper, we propose an effective large-scale mobile crowd-tasking model in which a large pool of citizen workers are used to perform the last-mile delivery. To efficiently solve the model, we formulate it as a network min-cost flow problem and propose various pruning techniques that can dramatically reduce the network size. Comprehensive experiments were conducted with Singapore and Beijing datasets. The results show that our solution can support real-time delivery optimization in the large-scale mobile crowd-sourcing problem.
Order picking, the assembly of a customer's order from items in storage, is an essential link in the supply chain and is the major cost component of warehousing. The critical issue is to simultaneously reduce the cost and increase the speed of the order picking activity. The main objectives are to: evaluate various routeing policies in a random storage environment; evaluate the impact of warehouse shape and pick-up/drop-off location; and examine the interaction of the routeing policies, warehouse shape, and pick-up/drop-off location under different pick list sizes. The experimental results clearly indicate that the optimal routeing procedure generates significantly shorter routes than heuristic methods. The composite and largest gap routeing policies are, however, significantly better than simpler heuristic procedures. Further testing, in addition, indicates that the shape of the warehouse and the location of the pick-up/drop-off point can affect the picking efficiency.
Getting the order is not enough. Companies that choose the right e-fulfillment strategies come out ahead.
In #bucket brigade" manufacturing, such as recently introduced to the apparel industry, a production line has n workers moving among m stations, where eachworker independently follows a simple rule that determines what to do next. Our analysis suggests and experiments con#rm that if the workers are sequenced from slowest to fastest then, independently of the stations at which they begin, a stable partition of work will spontaneously emerge. Furthermore, the production rate will converge to avalue that, for typical production lines, is the maximum possible among all ways of organizing the workers and stations. Key words: production line, line-balancing, self-organizing systems, discrete dynamical systems, #xedpoint # School of Industrial and Systems Engineering, Georgia Institute of Technology,Atlanta, Georgia 30332-0205 USA. E-mail: john.bartholdi@isye.gatech.edu y Graduate School of Business, The University of Chicago, Chicago, Illinois 60637 USA. E-mail: don.eisenstein@gsb.uchicag...
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propose a customer-focused framework that can be expressed as an information-fulfillment matrix in Figure 9.4 to help retailers navigate in an omni-channel 5. e-payment and financing, 6. AI and autonomous technology
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Companies that provide these services and products include Alibaba, Amazon, and
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Hu also serves as Academic Scholar at the Cornell Institute for Healthy Futures, on the board of POMS College of Service Operations and the board of INFORMS Service Science section. Previously, she worked at Lawrence Berkeley National Lab
- Dr
Dr. Hu also serves as Academic Scholar at the Cornell Institute for Healthy Futures,
on the board of POMS College of Service Operations and the board of INFORMS Service Science section. Previously, she worked at Lawrence Berkeley National Lab, Morgan
Stanley, and the Yiwu Government -the world's largest wholesale market.