No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Is a picture really worth a thousand words? - On the role of images in e-commerce
Abstract
In online peer-to-peer commerce places where physical examination of the goods is infeasible, textual descriptions, images of the products, reputation of the participants, play key roles. Visual image is a powerful channel to convey crucial information towards e-shoppers and influence their choice. In this paper, we investigate a well-known online marketplace where over millions of products change hands and most are described with the help of one or more images. We present a systematic data mining and knowledge discovery approach that aims to quantitatively dissect the role of images in e-commerce in great detail. Our goal is two-fold. First, we aim to get a thorough understanding of impact of images across various dimensions: product categories, user segments, conversion rate. We present quantitative evaluation of the influence of images and show how to leverage different image aspects, such as quantity and quality, to effectively raise sale. Second, we study interaction of image data with other selling dimensions by jointly modeling them with user behavior data. Results suggest that "watch" behavior encodes complex signals combining both attention and hesitation from buyer, in which image still holds an important role when compared to other selling variables, especially for products for which appearance is important. We conclude on how these findings can benefit sellers in a high competitive online e-commerce market.
... The inclusion of a real picture effectively increases auction success and the value of the final bid. Product images also provide a clear and attractive picture in a trusted way, which could convey information to potential customers about the product and its quality (Di et al., 2014). Francis et al. (2013) emphasize that a product image should be recognizable, accurate, and high quality and users should be provided clickable thumbnail images leading to several views of the product. ...
... Thumbnail images are a popular technique for many retailers to offer a variety of products (Collier & Bienstock, 2006). Di et al. (2014) examine three aspects of images that are the display format, the number of images per product, and image quality. Their results indicate that a large, high-quality, and greater number of images provide more information for buyers, thus increasing the chance of success in online selling, particularly for products relying more on visuals such as Clothing, Shoes & Accessories. ...
... Although the study of Zhang et al. (2021) indicates that the aesthetic and technical scores of the cover image are helpful in the classification of project success and the study of de Larrea et al. (2019) reveals the significant impact of total images on restaurant CF success, this study shows the contrary results indicating that thumbnail image's property in terms of inside-text and popular image (labels) are not significant drivers of CF success. This could be explained by the insignificant effect of total images on funding success in the study of Thapa (2020), the insignificant effect of the number of pictures on CF success in the study of Li et al. (2021), and the mixed importance of image quality due to the product category in the study of Di et al. (2014). The number of pictures is also not listed in the top five visual information items important to the prediction model using Indiegogo data in the study of Blanchard et al. (2022). ...
... This statement resonates with many of us, reflecting the widely recognized power of visual representations in effectively conveying concepts. For example, in the communication of a product for sale, it is highly beneficial to have effective and realistic visualizations of the product and its features [1]. The increasing investments in augmented reality (AR), virtual reality (VR), and mixed reality (MR) [2] further confirm the importance of graphic representations [3]. ...
... In today's market, potential customers have become increasingly accustomed to the visualization of products. Di et al. [1] provided evidence that images play a significant role in increasing buyer attention, trust, and conversion rates. Specifically, their research suggests that increasing the number of product images, which enhances the overall visual representation of the product, effectively improves sell-through. ...
... Specifically, their research suggests that increasing the number of product images, which enhances the overall visual representation of the product, effectively improves sell-through. This result highlights the importance of providing a comprehensive visual experience to potential buyers to drive better sales outcomes [1]. This change in consumer behavior highlights the importance of visualization in helping customers customize products according to their individual needs [8]. ...
The increasing attention and investments in augmented reality (AR), virtual reality (VR), and mixed reality (MR) further highlight the importance of graphic representations as communication tools. However, numerous online configurators lack advanced visualization and very few utilize virtual reality. Considering the expense associated with advanced visualizations, it becomes crucial to understand the incremental utility of such visualizations within the configuration process. This positioning paper aims to call for and pave the way towards a deeper understanding of the role and value of visualization in configurators, not limiting to AR, VR, and MR but considering all forms of visualization.
... Nowadays, a common practice we can see is that customers upload pictures/videos of the purchased products and provide visual feedback [4], [5], besides (/instead of) the language feedback [3]. Such visual reviews can convey crucial implicit information regarding service and product quality, product handling during transit/delivery, user experience, etc., which C. Adak is with the Dept. of CSE, IIT Patna, India-801106, S. Chattopadhyay is with Dept. of CSE, IIIT Guwahati, India-781015, and Muhammad Saqib is with Data61, CSIRO, Australia-2122. ...
... Moreover, people often rely more on visual review than language one. Therefore, analyzing such visual reviews is becoming important [4]. ...
... ; e <t,t > = f A (r <t−1> , a <t > ); (4) where, f A is the alignment model, which is basically a feedforward neural network [14]. The decoder f D has total T y timesteps. ...
With the proliferation of the e-commerce industry, analyzing customer feedback is becoming indispensable to a service provider. In recent days, it can be noticed that customers upload the purchased product images with their review scores. In this paper, we undertake the task of analyzing such visual reviews, which is very new of its kind. In the past, the researchers worked on analyzing language feedback, but here we do not take any assistance from linguistic reviews that may be absent, since a recent trend can be observed where customers prefer to quickly upload the visual feedback instead of typing language feedback. We propose a hierarchical architecture, where the higher-level model engages in product categorization, and the lower-level model pays attention to predicting the review score from a customer-provided product image. We generated a database by procuring real visual product reviews, which was quite challenging. Our architecture obtained some promising results by performing extensive experiments on the employed database. The proposed hierarchical architecture attained a 57.48% performance improvement over the single-level best comparable architecture.
... In addition, the scope of the application is limited due to the requirement for users to disclose personal information. Furthermore, in e-commerce, product images often carry more weight in decision-making than product titles or summaries [10]. We hypothesize that user preferences for furniture recommendations are more strongly influenced by product images-specifically, attributes like style (genre), shape, and texture-than by text or personal information. ...
... As the category-based profile, each item's shape, texture, and color are crucial to capturing favorite furniture on e-commerce platforms [10]. The second is User Profile (Images), created from the images of items users have interacted with. ...
We propose a novel recommendation model for diversifying furniture recommendations and aligning them more closely with user preferences. Our model builds upon the Recommender Variational Autoencoder (RecVAE), known for its effectiveness and ability to overcome overfitting by linking user feedback with user representation. However, since RecVAE relies on implicit feedback data, it tends to exhibit bias towards popular items, potentially creating a recommendation filter bubble. While previous work has proposed user profiles learned from a user’s personal information and the textual data of an item, we propose user profiles generated from the image data on the item given the points of interest when selecting items in e-commerce and the ease of data acquisition. We hypothesize that to capture user preferences and provide tailored furniture recommendations accurately, it is essential to incorporate both reviewed text information and visual data on furniture pieces. To utilize user preferences well, we incorporate the Conditional Variational Autoencoder (CVAE) architecture, where both the encoder and decoder are conditioned on a user profile indicating the user’s preference information. Additionally, the user profile is trained to capture the user’s preference for a specific predefined style. We trained our models using MovieLens-20M and the Amazon Furniture Review Dataset, a new dataset dedicated to furniture recommendations. As a result, on both datasets, our model outperformed previous models, including RecVAE. These findings show the effectiveness of our user profile approach in diversifying and personalizing furniture recommendations.
... In the environment of consumer information decision-making, image and text are undoubtedly two extremely key information carriers that have an impact on the consumer's choice process that cannot be ignored. Images, as an intuitive and vivid visual medium, can help consumers better understand and identify intangible goods on the internet, effectively convey the core information of goods, and then guide consumers' purchase behavior [15,16]. In addition, image plays an important role in the communication of electronic wordof-mouth. ...
... In addition, image plays an important role in the communication of electronic wordof-mouth. High-quality product pictures can significantly enhance consumers' trust in word-of-mouth information, interest in products, and purchase intention [15,16]. This is because product pictures can often shape consumers' initial impressions of products for the first time and provide detailed product displays by simulating real items, helping consumers to have a more comprehensive understanding of all the details of products, thus improving consumers' interaction efficiency and having a profound impact on their purchase decisions [17][18][19][20]. ...
Although the importance of the content of a webpage in retail business performance is widely recognized, there are few empirical studies on the importance of text and image information on the homepage in retailer performance. How will consumers sift through this information? Does text and image information affect consumers’ purchasing behavior? Using a data set of a Chinese convenience chain store, we attempt to clarify the influence of the brand in the title, the emotional atmosphere on the picture, and the product images of the homepage on the picture on retail business performance by employing a panel fixed-effects negative binomial regression model and a panel fixed-effect regression. Our results show that mentioning the product brand in the event title and presenting clear product images significantly enhance retailer performance in online community group buying. It is noteworthy that emotional descriptions have a greater impact on retailer performance compared to rational descriptions. In practice, this study provides a new perspective and reference for online community group buying platforms to better attract consumers and maintain sustainable development.
... Photos were desired to speed up, simplify, and secure the selection process (Pp1-Pp3). DI et al. (2014) confirm that images can increase the attention, trust, and conversion rate of the buyer. HASAN (2016) also stated that the visual design (with images) of a website is crucial for assessing website quality, where good images can increase the quality perception of the product and website (DI et al., 2014). ...
... DI et al. (2014) confirm that images can increase the attention, trust, and conversion rate of the buyer. HASAN (2016) also stated that the visual design (with images) of a website is crucial for assessing website quality, where good images can increase the quality perception of the product and website (DI et al., 2014). The colour selection, font size, and use of photos were also perceived as useful. ...
E-commerce in agriculture is gaining increasing attention, but market penetration is currently low, and companies are barely exploiting its full potential. Identifying and satisfying farmers’ expectations of e-commerce websites for farm inputs is crucial to reduce this opportunity loss. This paper presents a qualitative case study using the “thinking aloud” method, investigating the factors of an agricultural e-commerce site that need to be improved to increase customer satisfaction. The results reveal that farmers’ dissatisfaction with and reluctance to engage in agricultural e-commerce are linked to deficiencies in the store design. These deficiencies are especially apparent in the incongruent design of off- and online stores. Congruity is needed not only in terms of price but, more importantly, in terms of design (e.g., navigation, product order) and services. However, this is often lacking. High channel congruence improves trust in the online provider and keeps perceived transaction costs low. The study emphasizes the importance of customer centricity and a channel integration strategy in agricultural trade and provides indications of which elements lead to higher customer satisfaction.
... The platform might employ artificial intelligence (AI) methods, such as ML models, to derive information from the image of such an item; e.g., for cars, it might compute a price estimate, or assign the car to a specific category. 1 In turn, a human (i.e., a potential customer) might search the online platformthereby looking at the same input image provided to the ML modeland make a purchasing decision that might at least partially be based on the image. 2 A malicious merchant -i.e., an attacker -might aim to simultaneously mislead (a) the ML model so that the item for sale is assigned a more favorable category, and (b) the human to make her buy the item. ...
... Overall, using a classifier loss for AS somewhere in between , , e.g. for = 0.5, yielded no clear gain, but added significant complexity, i.e., hyperparameters , , in addition to . In the course of writing the paper, we also investigated various other hyperparameter settings, e.g., increasing and ∈ [0.05, 0.8] (the weight of the classifier losses), ∈ [0.05, 1] (the maximal loss per sample) and ∈ [1,3] (the weight of the classifier loss for ∈ {0.35, 0.65}). All yielded qualitatively similar effects, i.e., they increase reconstruction loss but yield better classifiable AS-such a trade-off can be unavoidable as shown in our theoretical treatment. ...
Adversarial samples mostly aim at fooling machine learning (ML) models. They often involve minor pixel-based perturbations that are imperceptible to human observers. In this work, adversarial samples should fool both humans and ML models, which is important in two-stage decision processes. We perform changes on a higher abstraction level so that a target sample exhibits properties of a desired sample. Technically, we contribute by deriving a regularization scheme for autoencoders incorporating a classifier loss for smoothly interpolating between wildly different samples. The realism and effectiveness of generated samples are confirmed with a user study and other evaluations. Our experiments consider neural networks of four architectures, assessed on MNIST, FashionMNIST, QuickDraw and CIFAR-10. Results show that our scheme leads to superior performance compared to existing interpolation techniques: on average, other methods have an 11% higher failure rate when producing a sample that is of any of two interpolated classes. Furthermore, our attacks work in both white-and black-box settings.
... Melalui katalog produk sebagai media promosi, sebagaimana penjelasan Kotler dan Amstrong (2012:202) bahwa promosi merupakan penyebar informasi yang dapat memengaruhi konsumen agar membeli produk yang ditawarkan perusahaan. Di et al. (2014) menyatakan bahwa gambar mampu mendorong transaksi dalam belanja online. Xin et al. (2014) juga menyatakan bahwa presentasi produk visual mampu memengaruhi perilaku belanja konsumen saat estetika visual disajikan lewat foto produk yang bisa ditangkap dengan bantuan teknik pemrosesan gambar modern. ...
... Kualitas foto terutama foto katalog produk untuk pebisnis online akan membantu menarik minat beli konsumen (Xin et al., 2016). Sebagaimana dalam penelitian Di et al. (2014) yang meneliti tentang peran gambar (foto) dalam e-commerce menyatakan bahwa gambar (foto) atau informasi multimedia lainnya mambu mendorong peningkatan transaksi belanja online. ...
This study aims to identify the factors considered by online shop owners in choosing photo catalogue services. This research is quantitative type. This study's data sources are primary data conducted by distributing questionnaires to research respondents. This research sample is 54 online shop owners who use product catalogue photo services. This research's variables are product attributes consisting of the brand, quality, features, design, price, and guarantee. The data collection technique uses questionnaires to 54 samples of product catalogue service users. In this case, the weighting questionnaire was filled in, done with Osgood's semantic differential measurement scale. This study's data analysis techniques were factor analysis, factor feasibility test, and correlation analysis. Are six factors to consider online businesspeople using product catalogue photo services. Product quality factors include high photo resolution, proper photo brightness; the resulting photo focuses on the product and colour match. The Feature Factor includes distinctive features, exceptional light-dark levels, and distinctive themes. Design factors include photo clarity, photo editing, and ready-to-upload photos. Factors include price according to quality, affordable prices, discounted prices, and price variations. The warranty factor includes product warranty items, a money-back guarantee, and a photo re-guarantee. Promotional factors that are informative, attractive promotions, promotions that are easy to remember, service readiness, problem-solving, services that are as promised and responsive to complaints
... Visual features play key roles in the purchasing process, which is possibly the most crucial factor in the decision making of a buyer [19]. Typically, a buyer will look at product pictures to obtain a general idea of the overall characteristics of the product before making his/her purchase decision. ...
... To demonstrate the impact of images in the prediction process, Di et al. [19] discussed the role of images in ecommerce by clarifying the impact of images across various dimensions. The authors stated that the "watch" behavior encoded complex signals in which the image played a key role compared to other selling variables. ...
Recent years have witnessed the rapid development of online shopping and ecommerce websites, e.g., eBay and OLX. Online shopping markets offer millions of products for sale each day. These products are categorized into many product categories. It is crucial for sellers to correctly estimate the price of the second-hand item. State-of-the-art methods can predict the price of only one item category. In addition, none of the existing methods utilized the price range of a given second-hand item in the prediction task, as there are several advertisements for the same product at different prices. In this vein, as the first contribution, we propose a deep model architecture for predicting the price of a second-hand item based on the image and textual description of the item for different sets of item types. This proposed method utilizes a deep neural network involving long short-term memory (LSTM) and convolutional neural network architectures for price prediction. The proposed model achieved a better mean absolute error accuracy score in comparison with the support vector machine baseline model. In addition, the second contribution includes twofold. First, we propose forecasting the minimum and maximum prices of the second-hand item. The models used for the forecasting task utilize linear regression, LSTM, and seasonal autoregressive integrated moving average methods. Second, we propose utilizing the model of the first contribution in predicting the item quality score. Then, the item quality score and the forecasted minimum and maximum prices are combined to provide the item’s final predicted price. Using a dataset crawled from a website for second-hand items, the proposed method of combining the predicted second-hand item quality score with the forecasted minimum and maximum price outperforms the other models in all of the used accuracy metrics with a significant performance gap.
... Data on sale ranks, list price, customer rating, number of reviewers, and days released from e-commerce sites could be used to forecast market demand, estimate cost and price elasticity, and even evaluate the optimality of pricing strategies [11]. Nowadays, not only texts but also images can be mined for competitors' products reputations [39]. Properties of images such as display formats, image quality, the number of views can affect buyers' intention, stimulate trust and improve the transaction rate [39], [40]. ...
... Nowadays, not only texts but also images can be mined for competitors' products reputations [39]. Properties of images such as display formats, image quality, the number of views can affect buyers' intention, stimulate trust and improve the transaction rate [39], [40]. ...
The digital transformation enables enterprises to mine big data for marketing intelligence on markets, customers, products, and competitor. However, there is a lack of a comprehensive literature review on this issue. With an aim to support enterprises to accelerate the digital transformation and gain competitive advantages through exploiting marketing intelligence from big data, this paper examines the literature in the period from 2001–2018. Consequently, 76 most relevant articles are analyzed based on four marketing intelligence components (Markets, Customers, Products, and Competitors) and six data mining models (Association, Classification, Clustering, Regression, Prediction, and Sequence Discovery). The findings of this study indicate that the research area of product and customer intelligence receives most research attention. This paper also provides a roadmap to guide future research on bridging marketing and information systems through the application of data mining to exploit marketing intelligence from big data.
... Visual content in e-commerce refers to the visual material such as image, video, audio to represent product and services provided in e-commerce (Chocarro et al., 2022). By giving consumers a tangible feel for the product, it helps bridge the gap between digital and physical shopping experiences (Di et al., 2014). According to eye-tracking research, product images are often the first thing that catches a user's attention on an e-commerce website; however, text information may be more attention-grabbing than product images (Liu et al., 2024). ...
This study aims to examine the influence of Social Media Marketing, Affiliate marketer, and Visual Content on product purchasing decisions at Travel E-Commerce among users in Denpasar. The method used in this study is a quantitative method with a survey approach using an online questionnaire distributed to 96 user respondents in Denpasar. Data collection was conducted by using a Likert scale to measure respondents' perceptions of the influence of the three variables on purchasing decisions. The data analysis technique used was multiple linear regression, with validity, reliability, and classical assumption tests to ensure valid results. The results of the study show that Social Media Marketing, Affiliate marketer, and Visual Content have a significant effect on purchasing decisions.
... Many models of using the Internet to make money and start a business have been developed. There are three main ways to sell goods on the Internet, namely short videos, pictures-and-words, and live streaming [1][2][3]. The model of live streaming is the most profitable at the moment, with the most people engaged in it. ...
With the development of the Internet, online shopping has become a part of people's daily lives, and new marketing strategies appear. This paper first analyzes the current mainstream online marketing strategies, namely short video, pictures-and-words, and live streaming. Then, this paper focuses on the factors affecting consumer decision-making, typically in live streaming marketing. Further, the paper discusses the negative impacts of live streaming on consumers, such as inducing consumption and concealing important information about the goods, which lead to impulsive spending. One of the main concerns is the current marketing tactics in live streaming which negatively affect the health of the youth, especially the sales related to games during live streaming with a gambling nature. The negative impacts are resulted from the failure of platforms in regulating the content of live streaming, as well as a lack of comprehensive cyber laws. It is recommended that platforms should strengthen the regulation of live streaming, increase the protection of teenagers and improve the quality of live streamers.
... Extensive previous research have emphasized the importance of images in e-commerce scenarios (e.g. [6,10,11]). In recent years, there is a growing interest in investigating the visual compatibility between different items. ...
Building a successful recommender system depends on understanding both the dimensions of people's preferences as well as their dynamics. In certain domains, such as fashion, modeling such preferences can be incredibly difficult, due to the need to simultaneously model the visual appearance of products as well as their evolution over time. The subtle semantics and non-linear dynamics of fashion evolution raise unique challenges especially considering the sparsity and large scale of the underlying datasets. In this paper we build novel models for the One-Class Collaborative Filtering setting, where our goal is to estimate users' fashion-aware personalized ranking functions based on their past feedback. To uncover the complex and evolving visual factors that people consider when evaluating products, our method combines high-level visual features extracted from a deep convolutional neural network, users' past feedback, as well as evolving trends within the community. Experimentally we evaluate our method on two large real-world datasets from Amazon.com, where we show it to outperform state-of-the-art personalized ranking measures, and also use it to visualize the high-level fashion trends across the 11-year span of our dataset.
... The conventional reliance on detailed written descriptions is undeniably foundational, yet the transformative power of images in enhancing these descriptions cannot be overstated. Quality imagery has a critical role in capturing attention and bolstering consumer confidence, ultimately driving purchase intentions (Chen and Teng, 2013;Di et al., 2014;Zakrewsky et al., 2016). ...
Introduction
The sorting of sequences of images is crucial for augmenting user engagement in various virtual commercial platforms, particularly within the real estate sector. A coherent sequence of images respecting room type categorization significantly enhances the intuitiveness and seamless navigation of potential customers through listings.
Methods
This study methodically formalizes the challenge of image sequence sorting and expands its applicability by framing it as an ordering problem. The complexity lies in devising a universally applicable solution due to computational demands and impracticality of exhaustive searches for optimal sequencing. To tackle this, our proposed algorithm employs a shortest path methodology grounded in semantic similarity between images. Tailored specifically for the real estate sector, it evaluates diverse similarity metrics to efficiently arrange images. Additionally, we introduce a genetic algorithm to optimize the selection of semantic features considered by the algorithm, further enhancing its effectiveness.
Results
Empirical evidence from our dataset demonstrates the efficacy of the proposed methodology. It successfully organizes images in an optimal sequence across 85% of the listings, showcasing its effectiveness in enhancing user experience in virtual commercial platforms, particularly in real estate.
Conclusion
This study presents a novel approach to sorting sequences of images in virtual commercial platforms, particularly beneficial for the real estate sector. The proposed algorithm effectively enhances user engagement by providing more intuitive and visually coherent image arrangements.
... Selain itu pembeli juga dapat menerima informasi lebih banyak mengenai produk. Dalam sebuah foto produk terdapat rangsangan visual yang memuat informasi, emosi, estetika dan status sosial [6]. Belajar dari paparan Wei dkk tersebut, penulis berusaha memberikan pemahaman kepada bagi pemilik UMKM Jatinegara Kaum agar memasang foto produk yang menarik perhatian, enak dipandang serta merangsang pembelian sehingga pembeli tertarik. ...
Abstrak-Pelaku UMKM banyak mengalami kesulitan dalam mengelola media sosial instagram. Salah satunya dalam memoto foto produk yang akan digunakan sebagai alat promosi di instagram. Untuk itu diperlukan bimbingan teknis dalam menghasilkan foto yang instagramable produk UMKM. Tujuan dari penelitian ini adalah untuk memaparkan pemanfaatan trik fotografi sederhana untuk menghasilkan foto instagramable produk UMKM Jatinegara Kaum. Metode yang digunakan adalah menggunakan metode partisipatori dari peserta UMKM untuk medemonstrasikan foto session pada produk-produk UMKM. Hasil kegiatan ini peserta dari owner UMKM sudah berhasil mepraktikkan cara memfoto produknya menggunakan handphone dan menggunakan lampu meja belajar untuk pencahayaan bila belum memiliki budget untuk membuat mini studio. Foto closeup diperlukan untuk membuat konsumen lebih tertarik pada produk. Selain itu, diperlukan kreativitas dalam menghasilkan foto instagramable produk UMKM. Kata kunci: fotografi, instagram, foto produk, UMKM Abstract-Many UMKMs experience difficulties in managing Instagram social media. One of them is taking product photos that will be used as a promotional tool on Instagram. For this reason, technical guidance is needed in producing instagrammable photos of UMKM products. The aim of this research is to explain the use of simple photography tricks to produce instagrammable photos of Jatinegara Kaum UMKM products. The method used is to use participatory methods from MSME participants to demonstrate photo sessions on UMKM products. As a result of this activity, participants from UMKM owners have succeeded in practicing how to photograph their products using cellphones and using study table lamps for lighting if they don't have the budget to make a mini studio. Closeup photos are needed to make consumers more interested in the product. Apart from that, creativity is needed in producing instagrammable photos of UMKM products. 1. PENDAHULUAN UMKM di Jatinegara Kaum merupakan UMKM binaan yang dibina oleh Kelurahan dan mendapatkan supervisi dari Kecamatan dan Jakpreneur. Saat ini UMKM binaan tersebut baru berjalan pada bidang kuliner. Walaupun merupakan UMKM binaan, pelaku UMKM di Jatinegara Kaum belum sepenuhnya dapat memaksimalkan bisnis yang dijalankannya. Hal tersebut dikarenakan bisnis rumahan yang dijalankan oeh pelaku UMKM yang merupakan Ibu Rumah Tangga tersebut beawal dari sekadar hobi memasak dan kuliner. Oleh karena itu, pentingnya diajarkan kepada pelaku UMKM mengenai keahlian lainnya agar bisnis rumahan yang dijalankan bisa naik kelas. Pelaku UMUM Jatinegara Kaum dalam menjalankan bisnisnya tidak sendirian. Bertumbuhnya minat masyarakat menjalani usaha kuliner skala usaha mikro, kecil dan menengah membutuhkan berbagai inovasi untuk dapat mempromosikan produk jualannya. Tidak terkecuali pada UMKM Jatinegara Kaum. Banyak pelaku UMKM Jatinegara Kaum yang menggunakan Instagram untuk melakukan promo karena keunggulan yang dimiliki instagram. Agar bisa unggul dalam promosi dibutuhkan keahlian pelaksana UMKM agar dapat foto produk yang dapat menumbuhkan minat beli pelanggan [1]. Pelaku UMKM selain berjualan produk juga dituntut untuk inovatif dalam memasarkan produknya [2]. Penelitian sebelumnya dilakukan [3] menemukan bahwa foto produk yang diunggah oleh pelaku UMKKM berpengaruh kepada minat beli pelanggan. Mereka menemukan bahwa semakin menarik foto yang diunggah semakin berminat netizen untuk membeli produk tersebut [4]. Disamping itu kehadiran foto produk juga menjadi
... WhatsApp is the only hope in trying to sell online either through WhatsApp Status or through personal chat, coupled with an unattractive way of posting (for example, blurry photos, an unbalanced ratio between photos and backgrounds, or how to take pictures of products that look unattractive). Visual images are a powerful channel for conveying important information to online shoppers and influencing their choices (Di et al., 2014). Images with good quality always result in more interactions (Li & Xie, 2019). ...
The COVID-19 pandemic transformed the industry into the strongest online industry of the last decade. One of the commodities that have experienced faster sales since the COVID-19 pandemic is healthy food; fruit is one of them. As a response to the crisis, online fruit traders have emerged using social media. Unfortunately, not all community groups can adapt to technology. Fruit traders who stay in the kiosk (selling offline) are included in the new left-behind group due to their inability to technology adaptation. This study aims to differentiate the sales flow of fruit commodities (before and after the appearance of online sales) and describe the advantages and disadvantages of each group (online traders and kiosk/offline traders). The data to answer the two research goals were processed in a qualitative descriptive way. The results show that online traders have a shorter sales flow than kiosk traders. The main advantages of online traders include using social media, which makes it easier to find market and customer information, low prices, and ownership of transportation. Meanwhile, the main disadvantages of offline fruit traders are the limited quantity of human resources, the inability to operate smartphones and social media, and not always having transportationKeywords: New Competition, Fruit Sales Flow, Online Trader, Kiosk Merchant
... There are different types of aspects affecting the laptop price like Random access memory, Read-only memory, CPU, Graphical Processing Unit (GPU), model, display touchscreen etc.[4]. Visual features also play a vital role in affecting the cost and it is the most important factor for the decisionmaking of any buyer[2] [3] [10][18] [22] ...
With the rapid enhancement of modern technology, we are more engaged with online shopping due to its high comfort, ease to use, safety etc. So we find a problem for laptop product evaluation in the online as well as offline market. The demand for laptops were rapidly increased after the lockdown in India. In the June quarter of 2021, 4.1 million units were shipped and which is the highest shipment in five years. In laptops, the price is acquired from its RAM, ROM, CPU, GPU, Touch screen, model, trends etc. Sometimes it is very much difficult for the customer as well as the retailer to fix a price with the certain characteristics of laptops so that both can evaluate the price and be satisfied with it. So we are going to develop a model for predicting the laptop price as per its properties. Because of any casual customer, this model will help in selecting and deciding on a laptop whether to buy or not, and also will reduce the time and effort that anyone will have to spend manually researching the market price. This paper will focus on Human-centric computing applications for laptop price prediction because it can be analyzed by those well- structured data that itself enhanced machine learning techniques, easily representable as a set of qualified parameters etc. So, we will develop an attribute-based prediction model for laptops using Regression machine learning algorithm.
... Dalam social media marketing, penggunaan foto produk yang menarik tidak hanya berfungsi sebagai suguhan visual untuk memberikan informasi penting terkait produk, tetapi juga berperan dalam mempengaruhi konsumen untuk memutuskan dan membeli barang yang dijual secara online (Di et al., 2014). Oleh karena itu, di era yang serba online seperti saat ini, sudah menjadi hal yang imperatif bagi pelaku UMKM yang ingin memasarkan dan mengiklankan produknya secara online untuk memiliki produk yang menarik. ...
The rapid development of social media platforms has encouraged the trend of social media marketing. Nowadays, many business owners use social media to market their products online. However, out of a total of 59.2 million MSMEs in Indonesia, only about 8% have used social media platforms to promote their products. Therefore, this community service aims to provide information on how to use Facebook Ads, Instagram Ads, TikTok Ads, and Youtube Ads platforms as well as the information about the effect of product photos and marketing language to boost sales for MSMEs owners in Solo area, Central Java. Participants were benefited with the information needed to apply AdSense ads in the designated popular social media platforms. For participants categorized as the digital immigrant group, they need more intensive assistance in understanding in depth how to implement the AdSense ads, potentially due to their age and digital literacy constraints.
... Issues such as business intelligence, marketing intelligence, healthcare, security, and customer well-being are also tied to big data characteristics, which is why big data is interesting to researchers, businesspeople, and politicians. On the other hand, a large part of the big data related to click-through and relocation data through mobile devices requires high speed, which can be used for short-term forecasting with high accuracy [1][2][3][4][5][6][7][8][9][10][11][12][13][14] That's why big companies like Google and Facebook, which are skilled at analyzing huge amounts of data, are looking to build new businesses and explore big data. Manyika et. ...
Annotation: Big data has been increasingly considered by experts and company owners due to its direct impact on the development of businesses and companies, so that big data management increases the efficiency of important business decisions. With the increasing use of the Internet and the advancement of information storage technology, there is a vast amount of information that can be used and properly examined. This amount of information is called big data and can be used to make important decisions for the development of a company. All the while, many start-ups and even large corporations are unsure how to use big data. In addition to looking at big data and its role in decision making, this article examines how to properly extract information to support decision making.
... To alleviate this problem, online fashion platforms offer various product information including categorical features such as fabric, occasion, length, etc. and visual imagery or video content in different setups. Studies have shown that product visual information (images/videos) plays a vital role in customer's confidence, click-through rate, and purchase decision [1,2]. However, due to the high costs in creating quality professional content, product images are often produced for only one single product size that flatters or best fits the fashion model who is wearing it. ...
Amidst the rapid growth of fashion e-commerce, remote fitting of fashion articles remains a complex and challenging problem and a main driver of customers' frustration. Despite the recent advances in 3D virtual try-on solutions, such approaches still remain limited to a very narrow - if not only a handful - selection of articles, and often for only one size of those fashion items. Other state-of-the-art approaches that aim to support customers find what fits them online mostly require a high level of customer engagement and privacy-sensitive data (such as height, weight, age, gender, belly shape, etc.), or alternatively need images of customers' bodies in tight clothing. They also often lack the ability to produce fit and shape aware visual guidance at scale, coming up short by simply advising which size to order that would best match a customer's physical body attributes, without providing any information on how the garment may fit and look. Contributing towards taking a leap forward and surpassing the limitations of current approaches, we present FitGAN, a generative adversarial model that explicitly accounts for garments' entangled size and fit characteristics of online fashion at scale. Conditioned on the fit and shape of the articles, our model learns disentangled item representations and generates realistic images reflecting the true fit and shape properties of fashion articles. Through experiments on real world data at scale, we demonstrate how our approach is capable of synthesizing visually realistic and diverse fits of fashion items and explore its ability to control fit and shape of images for thousands of online garments.
... Ürün görsellerindeki herhangi bir bulanıklık veya pikselleşme, ürünün kalitesiz olduğunu düşündürecek ve müşterileri uzaklaştıracaktır. Bunun sonucunda, e-ticaret sitelerinde yüksek kaliteli fotoğraflara sahip olmak, mutlak bir zorunluluktur ve müşterilerin satın almalarındaki karar verme sürecinde yardımcı olabilmektedir (Sean, 2019;Di, Sundaresan, Piramuthu ve Bhardwaj., 2014). Popüler olan "zoom" ve "kaydırma" özellikleri gibi çevrimiçi görselleştirme teknolojilerini benimsenmesi, ürün deneyimini kolaylaştırarak dönüşüm oranlarını artıracaktır (Song ve Kim, 2012). ...
Altyapı sağlayıcılar e-ticaret ekosisteminde köprü görevi görmektedir. E-ticaret ortamında iletişimin direkt olmaması bu köprüye daha
da önem kazandırmaktadır. Bu çalışmada, altyapı/arayüz sağlayıcı ve
yöneticileri perspektifinden e-ticaret alanında müşteri memnuniyetini
etkileyebilecek faktörler araştırılmaktadır. E-ticaret alanında müşteri
memnuniyetini etkileyen faktörlerle ilgili, sektördeki uzmanların görüşlerinin AHS yöntemi ile değerlendirilmesi yapılmıştır. Sonuçlara
göre Tedarik zincirinin hizmet sağlama halkasında yer alan uzmanlar
deneyimleri çerçevesinde öncelikli unsurun ürünün iyi fiyatlanmış ve
talebi karşılamaya yönelik olması gerektiği görüşünü ortaya koymuştur. Arayüz ikincil faktördür. Uzman görüşlerine göre müşteri ilişkilerinin rolü ise ancak bu iki faktörde e-ticaret başarılıysa etkinlik göstermektedir. Uzmanlar lojistik ile ilgili unsurlara sıralamada en sonda yer vermiştir
... For example, Xue and Muralidharan (2015) found that green visuals increase the positive assessments of environmental claims. Di et al. (2014) found that visual images increase buyers' attention, trust, and conversion rate. Therefore, we suggest that making the recycled components within a product visible to the customers, these images can influence reinforce the positive effects of using waste materials of customers directly into the products for that respective customer. ...
Our current take-make-dispose economic model faces a vital challenge as it extracts resources from the natural environment at faster rates than that the natural environment can replenish. A circular economy where businesses lower their negative impact on the natural environment by transitioning towards recycling business models (RBMs), one of the four principles of circularity, is suggested as a promising solution. For a RBM to become viable, collaboration among several stakeholders and across several industries is required. In addition, the RBM should be scalable to make a positive impact. Hence, developing RBMs is complex as organizations need to consider multiple principles imposed by the recycling, collaborative, and scalability dimensions of these business models (BMs). In addition, these principles often remain general and not actionable to the practitioners. Therefore, in this study, we researched the practical guidelines for viable RBMs that are also collaborative and scalable. The empirical setting is the reuse of textile fibers to develop biocomposite products. We studied three cases using a research-through-design approach. We contribute to the literature on RBMs by showing the six minimum practical guidelines for recyclability, collaboration, and scalability. We draw implications for within sector collaborations and advance the thought that lease constructs challenge the scalability of RBM.
... Users often make buying decisions purely by analyzing visual images and textual information available in print/digital advertisements, commercials and e-commerce websites [12][13]. As the popularity of e-commerce grows, a greater number of people every day are increasingly relying on visual representations of actual products for making buying decisions. ...
23 popular woodworking tool handle designs were collected to develop a reference visual catalog. Survey data was collected from 19 male craftsmen (11 carvers, 8 carpenters) regarding their most frequently used tools along with recommendations on handle designs suitable for these tools. Opinions were also collected regarding the most “liked” and “disliked” handle designs on the catalog. Subjective preferences and recommendations of handle designs for the most frequently used tools were also collected from a group of 58 undergraduate students of Design (41 male, 17 female). Participant responses revealed that woodcarvers most "liked” an ivory-colored, Japanese style, circular cross-section, regular-sized handle with a hooped top. Carpenters most "liked" a wenge-colored, rectangular-sectioned bulky handle with a hooped top. Male Design students most "liked" a golden-honey-colored, London pattern handle with an octagonal central section and domed wooden top. Female Design students most "liked" a beech-colored, bulky pear-shaped round handle with a hooped top. Overall, 12 different tools which included different sizes of u-gouges, v-parting tools, fishtail-chisels, firmer-chisels, mortise-chisels, and an in-denting tool were found to be the most frequently used implements by the craftsmen. On an aggregate for these 12 tools, an espresso-colored, bulky pear-shaped round handle with a hooped top was found to be the most recommended handle and a Bubinga-colored elongated pattern maker type handle was the least recommended handle. Results from this paper should help researchers and manufacturers gain qualitative insights into subjective preferences and biases that may exist for and against certain handle design features in the context of woodworking tool research and development
... A product description in a typical e-commerce marketplace is usually accompanied by a related image gallery. Whether those images are uploaded by the sellers themselves, or sourced by the e-commerce platform, they tend to considerably enrich user experience and provide customers with a valuable informational resource at various stages of their decision-making process [5,18]. ...
Image galleries provide a rich source of diverse information about a product which can be leveraged across many recommendation and retrieval applications. We study the problem of building a universal image gallery encoder through multi-task learning (MTL) approach and demonstrate that it is indeed a practical way to achieve generalizability of learned representations to new downstream tasks. Additionally, we analyze the relative predictive performance of MTL-trained solutions against optimal and substantially more expensive solutions, and find signals that MTL can be a useful mechanism to address sparsity in low-resource binary tasks.
Visualization plays a critical role in the purchasing process, particularly for customized products, as it improves customer engagement and decision-making. Despite the importance of product configurators in presenting customized products, there is limited research on the characteristics of visualization modes that configurators employ. This study aims to address this gap by developing an evaluation framework consisting of 11 descriptive variables: embodiment, presence, interactivity, authenticity, realism, media richness, avatar similarity, functional control, visual control, interaction richness, and vividness. Each variable of the framework is defined and exemplified by practical examples. These variables, derived from the literature on e-commerce and customer experience, offer a structured framework to describe and compare product visualization modes in configurators.
Using a large, comprehensive dataset of nearly 31 million reviews in 26 product categories from Amazon.com, this study investigates the influence of trust cues on the helpfulness of online product reviews, which extant literature reached mixed conclusions. In addition, the effect of user-provided images on review helpfulness is examined by different product attributes. Based on extensive analysis of a large dataset, this study found that verified purchase badges and user-provided images significantly and positively influence review helpfulness. Additionally, the interaction of these trust cues, as well as the number of user-provided images positively influence review helpfulness. Further, this study suggests that the positive effect of user-provided images is more substantial for search products.
With the recent shift toward the digital world, online customers are increasing rapidly. To fulfill the market demand, competitive market strategies are introduced in the e-commerce industry. Analyzing customer feedback is becoming one of the integral parts of this market strategy, and indispensable to a service provider. In recent days, it can be noticed that customers upload purchased product images with their review scores. In this article, we undertake the task of analyzing such visual reviews, which is very new of its kind. In the past, the researchers worked on analyzing language feedback. However, here, we do not take any assistance from linguistic reviews that may be absent, since a recent trend can be observed where customers prefer to quickly upload visual feedback through a smartphone instead of typing language feedback. We propose a hierarchical architecture, where the higher-level model engages in product categorization, and the lower-level model pays attention to predicting the review score from a customer-provided product image. We generated a database by procuring real visual product reviews, which was quite challenging. Our architecture obtained some promising results by performing extensive experiments on the employed database. The proposed hierarchical architecture attained a 26.26% more accuracy than the single-level best comparable architecture.
Purpose: The purpose of this study was to determine purchasing decisions on the GoFood application as measured by two aspects, namely product photos and customer ratings. This research can help food and beverage sellers, especially restaurants, as GoFood partners, display attractive product photos to influence consumers' purchasing decisions. Research methodology: This research is quantitative research with a survey method through an online questionnaire. The sampling technique used was purposive sampling, where the respondents were consumers who had bought food and beverages on the GoFood application, as many as 101 respondents. The analysis technique in this study is an analysis of the characteristics of the respondents; validity and reliability test; classical assumption test; correlation coefficient test (r); coefficient of determination test (R2); F test; and t test. Results: The results of the study simultaneously show that the product photo (X1) and customer rating (X2) variables simultaneously affect purchasing decisions through the GoFood application. The product photo (X1) and customer rating (X2) variables on purchasing decisions indicate that the better the product photos displayed, the greater the purchasing decisions made by consumers on the GoFood application. Limitations: Researchers only focus on the GoFood application, while there are still many other food and beverage service provider applications and the number of respondents is still very low to describe the actual situation. Contribution: enriching knowledge in the field of digital marketing, specifically about the effect of product photos and customer ratings on purchasing decisions. Keywords: 1. Product Photos 2. Customer Ratings 3. Purchase Decisions
Product images are essential for providing desirable user experience in an e-commerce platform. For a platform with billions of products, it is extremely time-costly and labor-expensive to manually pick and organize qualified images. Furthermore, there are the numerous and complicated image rules that a product image needs to comply in order to be generated/selected. To address these challenges, in this paper, we present a new learning framework in order to achieve Automatic Generation of Product-Image Sequence (AGPIS) in e-commerce. To this end, we propose a Multi-modality Unified Image-sequence Classifier (MUIsC), which is able to simultaneously detect all categories of rule violations through learning. MUIsC leverages textual review feedback as the additional training target and utilizes product textual description to provide extra semantic information. Based on offline evaluations, we show that the proposed MUIsC significantly outperforms various baselines. Besides MUIsC, we also integrate some other important modules in the proposed framework, such as primary image selection, noncompliant content detection, and image deduplication. With all these modules, our framework works effectively and efficiently in JD.com recommendation platform. By Dec 2021, our AGPIS framework has generated high-standard images for about 1.5 million products and achieves 13.6% in reject rate.
Previous studies have demonstrated that images are of great importance in attracting people’s attention and motivating them to take action. Various attributes (e.g., colors, aesthetics, and embedded objects) related to images are considered driving factors. Among which emotions in images, in particular, play a critical role in stimulating individuals, based on the Stimulus–Organism–Response theory. Consequently, many researchers put great efforts to understand image emotions, ranging from developing theoretical models to a broad spectrum of applications. Due to the complex and unstructured characteristics of images, identifying image emotions is challenging. Although some significant progress in image emotion classification has been achieved, inherent constraints still remain unaddressed. For example, acquiring a sufficiently large amount of labeled data to train a good model is costly and inevitably requires lots of human efforts. Besides, building a generalized model applicable to different datasets still needs a deep exploration. Image emotions are very subjective, which also makes such a classification task difficult. This paper proposes a general meta-learning framework for the few-shot image emotion classification, called Meta-IEC. Meta-IEC provides the capability of: (i) adapting to a similar dataset but new classes that have not been encountered before, and (ii) generalizing to a completely different dataset where emotion classes are unseen in the training dataset and only very few labeled images are available. Meta-IEC is also able to capture the uncertainty and ambiguity during the meta-testing, where we implement a hierarchical Bayesian graphical model to understand latent relationships among various parameters between meta-training and meta-testing. Extensive experiments on three commonly used datasets empirically demonstrate the superiority of our method over several state-of-the-art baselines. For example, our meta-learning based model can achieve performance improvement up to 5+%. We also provide some managerial implications on parameter sensitivity and label selection of meta-training and meta-testing.
The most labour-intensive stage of machine learning (ML) modelling is the appropriate preparation of correct dataset. This paper aims to show transfer dataset approach in image segmentation use case to lower labour intensity. Moreover, we test the effectiveness of this approach by training deep learning models on our prepared dataset. The models achieved high-performance metrics, even on very hard test data.
Over the years, online retailers have increasingly embraced live chat to improve customer service and facilitate transactions. We examine how online sellers’ use of live chat influences their online traffic conversion into sales. Like brick‐and‐mortar stores, a fundamental task for online stores is to convert traffic into purchases, an important problem in retail service operations management. We argue that live chat can increase online sales conversion by performing the functions of informing and persuading. We explore how the two roles of live chat interact with existing information regarding sellers and their products, such as product descriptions, ratings, price, and reviews, as these types of information also influence consumers’ perceived product quality and help mitigate uncertainties in product fit to their needs. We apply a random‐coefficient model in a Bayesian hierarchical framework and test the model using a panel dataset from Taobao, the leading C2C platform in China. Our results suggest that live chat has a positive impact on conversion and that the strength of this positive effect depends on seller and product characteristics. The positive effect is stronger when product information on web pages is less comprehensive (where live chat manifests an informative role), and when the perceived value of the product is higher (where live chat manifests a persuasive role) with either a higher product rating or a lower product price. Our results provide relevant and useful implications for online merchants and platform owners.
Fotografi adalah teknik yang selalu ada dalam kehidupan kita, tidak jarang dalam teknik fotografi selalu menampilkan sisi yang realistik pada setiap visual. Korporat dan UMKM tidak jarang selalu menggunakan teknik fotografi pada medianya, namun selama ini strategi dan model visualnya selalu menggunakan teknik fotografi pada media. Urgensi pada penelitian ini yang menarik untuk dikaji dan di paparkan dalam artikel penelitian ini. Penelitian dengan judul “Strategi Visual Untuk Menyampaikan Citra Korporat Dan UMKM Ke Stakehokder Melalui Teknik Fotografi” mengambil studi kasus pada visual fotografi PT Swabina Gatra (anak perusahaan Semen Indonesia) dan UMKM Batik, CV Bintang Abadi. Penelitian ini bersifat deskriptif, dan mendukung pola merancang dengan teknik fotografi pada media. Pengumpulan data menggunakan metode pengumpulan portofolio dari kedua sampe diatas, selain itu penelitian ini melakukan wawancara, observasi dan literatur ilmiah.
The purpose of this study is to analyze factors affecting on online shopping behavior of consumers that might be one of the most important issues of e-commerce and marketing field. However, there is very limited knowledge about online consumer behavior because it is a complicated socio-technical phenomenon and involves too many factors. One of the objectives of this study is covering the shortcomings of previous studies that didn't examine main factors that influence on online shopping behavior. This goal has been followed by using a model examining the impact of perceived risks, infrastructural variables and return policy on attitude toward online shopping behavior and subjective norms, perceived behavioral control, domain specific innovativeness and attitude on online shopping behavior as the hypotheses of study. To investigate these hypotheses 200 questionnaires dispersed among online stores of Iran. Respondents to the questionnaire were consumers of online stores in Iran which randomly selected. Finally regression analysis was used on data in order to test hypothesizes of study. This study can be considered as an applied research from purpose perspective and descriptive-survey with regard to the nature and method (type of correlation).The study identified that financial risks and non-delivery risk negatively affected attitude toward online shopping. Results also indicated that domain specific innovativeness and subjective norms positively affect online shopping behavior. Furthermore, attitude toward online shopping positively affected online shopping behavior of consumers.
Product search engine faces unique challenges that differ from web page search. The goal of a product search engine is to rank relevant items that the user may be interested in purchasing. Clicks provide a strong signal of a user's interest in an item. Traditional click prediction models include many features such as document text, price, and user information. In this paper, we propose adding information extracted from the thumbnail image of the item as additional features for click prediction. Specifically, we use two types of image features -- photographic features and object features. Our experiments reveal that both types of features can be highly useful in click prediction. We measure our performance in both prediction accuracy and NDCG. Overall, our experiments show that augmenting with image features to a standard model in click prediction provides significant improvement in precision and recall and boosts NDCG.
Older consumers comprise a growing but under-represented segment of Internet users. However, compared to many younger groups, members of this segment often possess more discretionary time and income. This presents a significant opportunity for marketers of Internet related products and services. In order to better understand older individuals’ attitudes and motivations concerning Internet usage, phenomenological interviews were conducted among six Internet users and six non-users. From the emic perspective of the informants, and the etic interpretation of the transcripts, the following six themes characterizing differences between Internet using and Internet non-using older individuals emerged: Reference group affiliation, Technology schema, Resistance to change, Nature of social relations, Perception of reality, and Physical dexterity. The marketing implications of these findings are identified and discussed.
Consumers shop online for both goal-oriented and experiential reasons. However, goal-oriented motives are more common among online shoppers than are experiential motives. This article identifies and discusses attributes that facilitate goal-oriented online shopping, including accessibility/convenience, selection, information availability, and lack of unwanted sociality from retail sales help or shopping partners such as spouses. Importantly, consumers report that shopping online results in a substantially increased sense of freedom and control as compared to offline shopping. While consumers are more likely to describe offline rather than online shopping in experiential terms, evidence of experiential motivations for online shopping is emerging. Also, while closing transactions at web sites is one important e-commerce goal, companies should not lose site of the continuing importance and power of their web site as an information and communications vehicle.
While a large number of consumers in the US and Europe frequently shop on the Internet, research on what drives consumers to shop online has typically been fragmented. This paper therefore proposes a framework to increase researchers' understanding of consumers' attitudes toward online shopping and their intention to shop on the Internet. The framework uses the constructs of the Technology Acceptance Model (TAM) as a basis, extended by exogenous factors and applies it to the online shopping context. The review shows that attitudes toward online shopping and intention to shop online are not only affected by ease of use, usefulness, and enjoyment, but also by exogenous factors like consumer traits, situational factors, product characteristics, previous online shopping experiences, and trust in online shopping.
This research investigates eBay auction features that influence auction outcomes: likelihood of transaction or whether the item was actually sold in the auction (auction success) and value of the last bid (auction effectiveness). Specifically, we study several seller options available to the seller to increase the final price and successful end to the auction. We investigate the effectiveness of the term "New In Box" in the auction's heading, the use of an actual or cut picture, the initial price set by the seller, use of a reserve price and acceptance of a credit card in increasing the likelihood of the auction ending successfully, and at increasing the final price. Along with other independent variables, the impact of factors dealing with the auction pictures is examined. Hundreds of auctions (423) for two financial calculators were examined in this study. Findings show that utilization of certain risk-reducing auction features positively influence outcomes of these eBay auctions. These features include level of the starting bid, mention of "New in Box," inclusion of a real picture of the unit being sold, and the inclusion of a stock picture. In addition, findings indicate that certain risk-enhancing auction features negatively influence eBay auction outcomes. These features include the presence of a reserve price and the mention of "Wear" in the auction.
As use of the Internet has increased, many issues of trust have arisen. Users wonder: will my privacy be protected if I provide information to this Internet vendor? Will my credit card remain secure? Should I trust that this party will deliver the goods? Will the goods be as described? These questions are not merely academic. A recent Boston Consulting Group study revealed that one out of ten consumers have ordered and paid for items online that never were delivered (Williams, 2001). This year consumers filed around 11,000 complaints with the Federal Trade Commission alleging auction fraud, a figure up from the 107 lodged in 1997. It is no wonder that people are increasingly worried about whom to trust in online interactions. This paper explores the conditions under which online trust thrives and looks at examples of best and worst corporate practices. Online trust issues arise in a wide array of forums – chat rooms, news postings, e-catalogues, and retail transactions, to name a few. This paper focuses primarily on the online retail market, but the analysis applies to informational and entertainment sites as well.
Are trust and risk important in consumers' electronic commerce purchasing decisions? What are the antecedents of trust and risk in this context? How do trust and risk affect an Internet consumer's purchasing decision? To answer these questions, we i) develop a theoretical framework describing the trust-based decision-making process a consumer uses when making a purchase from a given site, ii) test the proposed model using a Structural Equation Modeling technique on Internet consumer purchasing behavior data collected via a Web survey, and iii) consider the implications of the model. The results of the study show that Internet consumers' trust and perceived risk have strong impacts on their purchasing decisions. Consumer disposition to trust, reputation, privacy concerns, security concerns, the information quality of the Website, and the company's reputation, have strong effects on Internet consumers' trust in the Website. Interestingly, the presence of a third-party seal did not strongly influence consumers' trust.
Web-based electronic stores have become more and more popular. However, there are not many guidelines, nor theories, showing what features of a store would work and why. This paper develops a set of functional guidelines for designing electronic stores and classifies them into three categories: motivational, hygiene, and media richness factors. An empirical study was conducted to evaluate the relative effect of these factors. The results show that the store design does have an effect on consumer purchase decision. A two-factor theory is plausible: hygiene factors are the major concern when consumers decide whether to shop electronically, while motivational factors play a key role when consumers choose among different electronic stores. Media richness factors are, in general, less important. The implication of the findings is that, for a web store to beat its electronic competitors, providing good transactional support is the key. If they would like to attract customers from traditional stores, special attentions must be paid to the hygiene factors.
Internet auctions have recently gained widespread popularity and are one of the most successful forms of electronic commerce. We examine a unique dataset of eBay coin auctions to explore the determinants of bidder and seller behavior. We first document a number of empirical regularities. We then specify and estimate a structural econometric model of bidding on eBay. Using our parameter estimates from this model, we measure the extent of the winner's curse and simulate seller revenue under different reserve prices. Copyright 2003 by the RAND Corporation.
The study of online consumer behavior is one of the most important research agendas in management information systems and marketing science. However, there is very limited knowledge about online consumer behavior because it is a complicated social-technical phenomenon and involves too many antecedent factors. Most prior studies in this area often offered inconsistent or even conflicted results due to using various simple research models in order to achieve parsimony. This study aims to overcome the drawback of previous studies and examines up to ten antecedent factors in one research model. ^ This study extends theory of planned behavior (TPB) by including ten important antecedents as external beliefs to online consumer behavior. The ten antecedents are identified by prior studies, mostly in the areas of management information systems and marketing science. This study is conducted with a survey of 288 college students who have online shopping experiences. The collected survey data is used to test each hypothesis developed in the research model. The results of data analysis confirm perceived ease of use (PEOU) and trust are essential antecedents in determining online consumer behavior through behavioral attitude and perceived behavioral control. The findings also indicate that cost reduction helps the consumer create positive attitude toward purchase. Further, the findings show the effects of two constructs of flow - concentration and telepresence on consumer’s attitude. Concentration is positively related to attitude toward purchase, but telepresence likely decreases attitude due to the consumer’s possible nervousness or concern about uncertainty in the online environment. ^ One of the main contributions of this study is that it provides valuable empirical evidence to prove that the TPB-based research model can well handle up to ten external beliefs with combining the partial least squares (PLS) statistical analysis. According to the findings, it is reasonable to conclude the TPB-based model has high potential to manipulate more than ten external beliefs without compromising the model’s goodness of fit.
A systems model is used to illustrate the information flow between three subsystems in e-commerce: Store Environment, Customer, and Web Technology. In the process of purchase, a customer makes several decisions: to enter the store, to navigate, to purchase, and to pay. This artificial environment must be designed so that it can support customer decision-making. At the same time it must be pleasing and fun, and create a task with natural flow. The paper summarizes existing research and theories, and provides a few suggestions for future research.
This research investigates eBay auction properties that influence auction outcomes: effectiveness (ability to attract bidders) and success (whether the item was actually sold in the auction). Hundreds of auctions (1,273) for three popular financial calculators—the HP 12c, the HP 10b and BA II plus—were examined to assess the impact of these auction properties. Findings show that utilization of certain auction features influence both outcomes. These features include starting price and reserve price for both calculators. Other features impact only auction effectiveness: wear and the presence of a picture for the HP10 and BA II plus; and wear and ability to pay with credit cards for the HP 12c. Further, others auction factors influence only auction success: abnormally high first bids and whether the seller is a recognized company rather than an individual for the HP 10b and BA II plus; and the length of time the seller has been selling on eBay, the presence of a picture, and whether the seller was a recognized company rather than an individual for the HP12c.
A model of demand for the Internet and other information sources is presented that treats the Internet as a production factor employed in producing benefits of search. Based upon the premise that the Internet is most efficient at providing information about functional attributes and price, several propositions are developed about its use and its impact on the use of other information sources. The model is supported by empirical evidence, using the example of Internet deployment in the search for a new automobile.
Although auctions have been examined extensively in economics, and to some degree in marketing, on-line auctions are only beginning to receive research attention. Further, in both economics and marketing the research on auctions has relied primarily on rational, economic theories. This article investigates how particular on-line auction features impact two important outcomes: auction success and final closing price. Traditional economic theories as well as theories from marketing and psychology are employed to provide a broader picture of on-line auctions. Specifically, several key factors related to auction success and closing price for four types of sterling flatware in an on-line auction site (eBay) are examined. The findings show that, across all four piece types, a reserve auction format, the relative opening price, and the number of bids unexplained by a low or high opening price are associated with both auction success and final closing price. © 2003 Wiley Periodicals, Inc.
Since George A. Akerlof (1970), economists have understood the adverse selection problem that information asymmetries can create in used goods mar-kets. The remarkable growth in online used goods auctions thus poses a puzzle. Part of the solution is that sellers voluntarily disclose their private information on the auction webpage. This defines a precise contract — to deliver the car shown for the closing price — which helps protect the buyer from adverse se-lection. I test this theory using data from eBay Motors, finding that online disclosures are important price determinants; and that disclosure costs impact both the level of disclosure and prices.
The Internet interface poses a difficulty for buyers in evaluating products online, particularly physical experience and durable goods, such as used cars. This increases buyers' product uncertainty, defined as the buyer's perceived estimate of the variance in product quality based on subjective probabilities about the product's characteristics and whether the product will perform as expected. However, the literature has largely ignored product uncertainty and mostly focused on mitigating buyer's seller uncertainty. To address this void, this study aims to conceptualize the construct of product uncertainty and propose its antecedents and consequences in online auction marketplaces. First, drawing upon the theory of markets with asymmetric information, we propose product uncertainty to be distinct from, yet affected by, seller uncertainty. Second, based on auction pricing theory, we propose that product uncertainty and seller uncertainty negatively affect two key success outcomes of online marketplaces: price premium and transaction activity. Third, following information signaling theory, we propose a set of product information signals to mitigate product uncertainty: (1) online product descriptions (textual, visual, multimedia); (2) third-party product certifications (inspection, history report, warranty); (3) auction posted prices (reserve, starting, buy-it-now); and (4) intrinsic product characteristics (book value and usage). Finally, we propose that the effect of online product descriptions and intrinsic product characteristics on product uncertainty is moderated by seller uncertainty. The proposed model is supported by a unique dataset comprised of a combination of primary (survey) data drawn from 331 buyers who bid upon a used car on eBay Motors, matched with secondary transaction data from the corresponding online auctions. The results distinguish between product and seller uncertainty, show the stronger role of product uncertainty on price premiums and transaction activity compared to seller uncertainty, empirically identify the most influential product information signals, and support the mediating role of product uncertainty. This paper contributes to and has implications for better understanding the nature and role of product uncertainty, identifying mechanisms for mitigating product uncertainty, and demonstrating complementarities between product and seller information signals. The model's generalizability and implications are discussed.
In this paper we study the importance of image based features on the click-through rate (CTR) in the context of a large scale product search engine. Typically product search engines use text based features in their ranking function. We present a novel idea of using image based features, common in the photography literature, in addition to text based features. We used a stochastic gradient boosting based regression model to learn relationships between features and CTR. Our results indicate statistically significant correlations between the image features and CTR. We also see improvements to NDCG and mean standard regression.
For buyers and sellers alike, there's no better way to earn one another's trust in online interactions.
yin comparing multiple products on the samescreen all have adverse effects on electronic shopping[2]. Can customers find what they want in the stores?Are customers aware of what products are available?After all, diligence in browsing a store is not a virtueretailers should expect from its online customers.We review online retail store attributes such as thenumber of links into the store, image sizes, numberof products, and store navigation features. By reviewingthe user...
This paper introduces a model for analyzing marketplaces, such as eBay, which rely on binary reputation mechanisms for quality signaling and quality control. In our model sellers keep their actual quality private and choose what quality to advertise. The reputation mechanism is primarily used to determine whether sellers advertise truthfully. Buyers may exercise some leniency when rating sellers, which needs to be compensated by corresponding strictness when judging sellers' feedback profiles. It is shown that, the more lenient buyers are when rating sellers, the more likely it is that sellers will find it optimal to settle down to steady-state quality levels, as opposed to oscillating between good quality and bad quality. Furthermore, the fairness of the market outcome is determined by the relationship between rating leniency and strictness when assessing a seller's feedback profile. If buyers judge sellers too strictly (relative to how leniently they rate) then, at steady state, sellers will be forced to understate their true quality. On the other hand, if buyers judge too leniently then sellers can get away with consistently overstating their true quality. An optimal judgment rule, which results in outcomes where at steady state buyers accurately estimate the true quality of sellers, is analytically derived. However, it is argued that this optimal rule depends on several system parameters, which are difficult to estimate from the information that marketplaces, such as eBay, currently make available to their members. It is therefore questionable to what extent unsophisticated buyers are capable of deriving and applying it correctly in actual settings.
Online auctions have recently gained widespread popularity and are one of the most successful forms of electronic commerce. We examine a dataset of eBay coin auctions to explore features of online bidding and selling behavior. We address three main issues. First, we measure the extent of the winner's curse. We find that for a representative auction in our sample, a bidder's expected profits fall by 3.2 percent when the expected number of bidders increases by one. Second, we document that costly entry is a key component in understanding observed bidding behavior. For a representative auction in our sample, a bidder requires $3.20 of expected profit to enter the auction. Third, we study the seller's choice of reserve prices. We find that items with higher book value tend to be sold using a secret as opposed to posted reserve price with a low minimum bid. We find that this is, to a first approximation, consistent with maximizing behavior. We also develop new techniques for structurally es...
We propose an analytical framework for studying bidding behavior in online auctions. The framework focuses on three key dimensions: the multi-stage process, the types of value-signals employed at each phase, and the dynamics of bidding behavior whereby early choices impact subsequent bidding decisions. We outline a series of propositions relating to the auction entry decision, bidding decisions during the auction, and bidding behavior at the end of an auction. In addition, we present the results of three preliminary field studies that investigate factors that influence consumers' value assessments and bidding decisions. In particular, (a) due to a focus on the narrow auction context, consumers under-search and, consequently, overpay for widely available commodities (CDs, DVDs) and (b) auction starting prices lead to higher winning bids, but only when comparable items are not available in the immediate context. We discuss the implications of this research with respect to our understanding of the key determinants of consumer behavior in this increasingly important arena of purchase decisions. - 2 - Web-based auctions have become one of the greatest successes of the Internet, success that has not diminished even after many other web-based services have lost their initial popularity. The growing importance of online auctions has attracted the attention of consumer researchers, who have studied such issues as herding behavior (Dholakia & Soltysinski, 2001), the impact of reserve prices (Hubl & Popkowski Leszczyc, 2001), the role of expertise (Wilcox, 2000), and the effects of auction formats (Lucking-Reiley, 1999). Still, our understanding of buyer (bidder) behavior in online auctions is rather limited. In particular, acquiring an item through online auctions is different i...
Online auctions: User experience insights from ebay
- A Haywood
Antecedents of desirable consumer behaviors in electronic commerce
- N Sukpanich
- L Chen
An investigation of university students' on-line shopping behavior
- H.-J Han
- R Ocker
- J Fjermestad
User experience insights from ebay
- Haywood A.
Sukpanich and L. Chen. Antecedents of desirable consumer behaviors in electronic commerce
- N Sukpanich
- L Chen
- Sukpanich N.