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45 results for “E-commerce”
analisis bibliometrik E-commerce AND Tourism Services
<p>mengumpulkan Ujian Akhir Semester Hukum Bisnis Online</p> <p> </p>
THE GROWTH IMPACT ON COMPANY VALUE: EVIDENCE FROM THE E-COMMERCE SECTOR
<p>Thid dataset contains the information about e-commerce companies globally.</p> <p>The indicators include: region of operations (developed / developing), business-model (1st party trade / 3d party trade), key financial indicators</p> <p> </p> <p>Detailed description of variables:</p> <p>1. Year: the period covers 2017-2021, annual data</p> <p>2. Index: number of the company</p> <p>3. EV/GMV_t: trailing mulriple EV/GMV</p> <p>4. EV/GMV_f: forward multiple EV/GMV</p> <p>5. EV/Revenue: trailing multiple EV/Revenue</p> <p>6. EV/Gross Profit: trailing multiple EV/Gross Proift</p> <p>7. EV/EBITDA: trailing multiple EV/EBITDA</p> <p>8. EV/EBIT: trailing multiple EV/EBIT</p> <p>9. Dummy_Dev: dummy variable, which equals 1 for companies, operating on developed markets, 0 - for developing</p> <p>10. Dummy_Ind: dummy variable, which equals 1 for companies which operating model combines a marketplace and retailing, 0 - for retailers</p> <p>11. Dummy_GP: dummy variable, which equals 1 for companies with positive Gross Profit at a given year</p> <p>11. Dummy_EBITDA: dummy variable, which equals 1 for companies with positive EBITDA at a given year</p> <p>12. GMV_g_1yH: GMV growth rate for 1 historical year</p> <p>13. GMV_g_2yH: GMV CAGR rate for 2 historical years</p> <p>14. GMV_g_1yF: GMV growth for 1 forecasted year</p> <p>15. Rev_g_1yH, Rev_g_2yH, Rev_g_1yF, GP_g_1yH, GP_g_2yH, GP_g_1yF, EBITDA_g_1yH, EBITDA_g_2yH, EBITDA_m_1yF: the same as p.12-14 for Revenue, Gross Profit, and EBITDA</p> <p>16. GP_m_1yH: Gross Margin for 1 historical year</p>
THE GROWTH IMPACT ON COMPANY VALUE: EVIDENCE FROM THE E-COMMERCE SECTOR
<p>The data contains financial information on the companies from the e-commerce industry worldwide over the period from 2017 to 2021 (panel data for 173 companies).<br> The list of variables:<br> 1. Current trading multiples (EV/GMV, EV/Revenue, EV/Gross Profit, EV/EBITDA, EV/EBIT)<br> 2. Control variables: Dummy_Dev (1 - developed country, 0 - developing country), Dummy_Ind (1 - primarily marketplace, 0 - primarily retail)<br> 3. Breakeven point (Dummy_GP, Dummy_EBITDA)<br> 4. Growth of financial indicators (GMV, Revenue, Gross Profit, EBITDA, EBIT)<br> 5. Gross Profit margin, EBITDA margin, EBIT margin<br> 6. Standard deviation of forecasted revenues (G_stdev)</p>
Recommender Systems and AI Techniques in E-commerce: An Analysis of Trends and the Research Agenda
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The Impact of Live-Streaming E-Commerce on the Purchase Intention of Immersive Consumers
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Personalize E-Commerce Product Recommendations Based on User Behavior Using Reinforced Learning Algorithms
<p><span>The development of a personalized and adaptive e-commerce product recommendation system will be developed using the Reinforcement Learning algorithm in this study. Initial data is extremely promising in its ability to raise sales conversion: 30% of the products added to the cart are never purchased. Additionally, there is a strong correlation of 0.8 between viewed versus purchased products. Data collection was from 447 Indonesian respondents over a period of June to July 2024. It was collected using an online questionnaire that measures recommendation quality, satisfaction, and ease of use with purposive sampling. Partial Least Squares Structural Equation Modeling was done on the data analysis. From that, it has been found that system quality is positively related to the accuracy, novelty, and diversity of the recommendation. The results further show how this would lead to an improved user experience, satisfaction, and sales conversion with the reinforcement learning-based system. These findings give insight into developing efficient adaptive recommendation systems on e-commerce platforms and open opportunities for further research. </span></p>
E-commerce data set
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E-Commerce Price Prediction Challenge
<h2>Problem Statement</h2> <p>Akshat is new to the market and is unaware of the prices of multiple products. he wants to be assured of the price before making any purchase! Help Akshat out in predicting the prices for Products and derive some conclusive evidences!</p> <h2>What's in there!?</h2> <p>In the dataset, there are products/images/dimensions/prices/ratings and much more for Feature Engineering.</p> <h2>What is the Inspiration from this!?</h2> <p>This type of business problem is typical for any new products being launched into the market and sites like Trivago/Policy Bazaar compare same product over multiple sites to reach a conclusive rate. Can you achieve the same!?</p>
Dataset Questionnaire E-Commerce Uncovered Exploring Key Drivers of Consumer Impulse Buying Behavior
<p>The following dataset is a dataset from a study that investigated Perceived Ease of Use and Perceived Usefulness on Impulsive buying throught Attitude Towards E-commerce.</p>
Data Set Analisis Pengaruh COVID 19 Terhadap Perilaku Konsumtif Masyarakat dalam Penggunaan E-commerce
<p>Data set jawaban kuesioner Analisis Pengaruh COVID 19 Terhadap Perilaku Konsumtif Masyarakat dalam Penggunaan E-commerce</p>
Analisis Pengaruh COVID 19 Terhadap Perilaku Konsumtif Masyarakat dalam Penggunaan E-commerce
<p>Respon kuesioner Analisis Pengaruh COVID 19 Terhadap Perilaku Konsumtif Masyarakat dalam Penggunaan E<em>-commerce</em>.</p>
DylanKaisar/etikaprofesi: Form Survei Penipuan dalam Transaksi e-Commerce
<p>Form Survei Penipuan dalam Transaksi e-Commerce datasets</p>
Background material for report "The Impact of Trust Building Mechanisms on Trust Relationships in C2C E-commerce: A Systematic Review"
<p>Includes all intermediary material related to the conduct of the systematic review.</p>
What drives residents' intentions to adopt e-commerce in rural regions of western China? A case study in Chongqing municipality
<p>Research data</p>
Pengaruh pandemi covid-19 di indonesia terhadap perubahan perilaku pembelian barang kebutuhan di e-commerce
<p>Saat ini, setiap indvidu harus bersiap menghadapi pergeseran kebudayaan dimana setiap pemenuhan kebutuhan harus beriringan dengan perkembangan teknologi. Hal ini menjadi peluang bagi sebagian besar perusahaan <em>E-commerce</em> untuk menyajikan pasar dalam bentuk membeli dan menjual produk secara <em>online</em>. <em>E-commerce </em>mencakupi segala proses pengembangan, pemasaran, penjualan, pengiriman, pelayanan, dan pembayaran para pelanggan, dengan dukungan dari jaringan mitra bisnis yang lebih luas. Saat ini dunia sedang dilanda pandemi Covid-19, pemerintah melakukan upaya pencegahan penyebaran virus ini dengan malakukan pembatasan sosial untuk mengurangi interaksi masyarakat secara langsung, karena itu dalam penelitian yang bersifat kuantitatif ini dilakukan analisis mengenai perubahan pola perilaku oleh masyarakat Indonesia dalam transaksi jual beli di e-commerce sebagai cara pemenuhan kebutuhan.</p>
The Effect of Social Commerce on Purchasing Decisions on E-Commerce Platforms
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Data e-commerce AND intellectual property
<p>Data e-commerce AND intellectual property</p>
Consumer Planned Behaviour toward Domestic and Foreign Products in E-commerce: Post-Discrimination Policy on Product Categories
<p>theory of planned behavior constructs of purchasing domestic and foreign products, product category, and consumer ethnocentrism</p>
Digital Sovereignty: Requirements of Digital Identity Wallets in E-Commerce
<p><strong>Detail artifacts of the research paper "Digital Sovereignty: Requirements of Digital Identity Wallets in E-Commerce" by Max Sauer, Jonas Thessen and Simone Braun. <span><a title="https://doi.org/10.1109/informatics62280.2024.10900753" href="https://doi.org/10.1109/informatics62280.2024.10900753" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/informatics62280.2024.10900753</a> </span></strong></p>
The Impact of Live-Streaming E-Commerce on the Purchase Intention of Immersive Consumers
<p>The Impact of Live-Streaming E-Commerce on the Purchase Intention of Immersive Consumers</p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.