Consumers buying behavior towards Online Shopping-A study of Literature Review

Singh, Mridulesh; Katiyar, Ankit

doi:10.5958/2321-5763.2018.00076.8

Cited by 5 publications

(4 citation statements)

References 0 publications

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“…Flipkart.com is offering some of the best prices and completely hassle free shopping experience. The whole concept of online shopping has altered in consumer's purchasing or buying behavior and the success of eretailers in India depends upon its popularity, its branding image and its unique policies [7]. Higher income groups and educated people are purchasing more via e-retailing websites; people have hesitations in doing online shopping due to security concerns.…”

Section: Consumer Behavior Towards Online Shoppingmentioning

confidence: 99%

A Review of Factors Affecting Consumer Behavior towards Online Shopping

Kaur¹,

Kochar²

2018

IJEMR

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Online shopping is a form of electronic commerce which allows consumers to directly buy goods or services from a seller over the Internet using a web browser. Online shopping provides a good example of business revolution. Ecommerce has made life Simple and innovative of individual and groups. Consumer behavior on online shopping is different from physical market where he/she has access to see the products online shopping sites are fast replacing traditional or physical shops. Over, the years the trust of the customers for online shopping sites has increased considerably. The increase in the number of these sites on one hand has led to a fierce competition which means better and cheaper products for customers. However, at the same time customers have their privacy concerns when it comes to shopping online. Internet is changing the way consumers shop and buy goods and services and has rapidly evolved into a global phenomenon. The technology oriented factors like guaranteed quality, cash on delivery discounts and promotions are the major specific factors influencing customer’s attitudes toward online shopping. This research paper attempts to analyze the consumer behavior towards online shopping and the various factors influencing trend of online shopping.

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Section: Consumer Behavior Towards Online Shoppingmentioning

confidence: 99%

A Review of Factors Affecting Consumer Behavior towards Online Shopping

Kaur¹,

Kochar²

2018

IJEMR

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“…For example, the single label classification of a garment has only shirts, which can be summarized in the category of multiclassification. Let L = {λ 1 , λ 2 , ..., λ n } be the limited sample space of classification (data set containing n images), L, where a sample element λ i = {(X (i) , Y (i) )|X X , Y Y }, where X = {x (1) , x (2) , ..., x (N) } denotes the sample space of all images in the data set, and N denotes the category classification of clothing in the data set. Y = {y (n) |y {0, 1}} denotes the sample space of label data in the data set.…”

Section: Multiclass Classification and Predictionmentioning

confidence: 99%

“…With the development of an intelligent information field and the gradual popularization of a high-speed bandwidth network (5G), more and more people have begun to pursue the online virtual shopping [1] experience to replace offline shopping. Clothing shopping has always been a manifestation of the human pursuit of beauty.…”

Section: Introductionmentioning

confidence: 99%

High-Accuracy Clothing and Style Classification via Multi-Feature Fusion

et al. 2022

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In recent years, the online selection of virtual clothing styles has been used to explore and expand diversified personal aesthetics, and it is also an overall reform and challenge to the clothing industry. Under the condition of the existing clothing style categories, this paper puts forward a style classification method combining fine-grained and coarse-grained techniques. Furthermore, a new deep neural network is proposed, which can improve the robustness of recognition and avoid the interference of image background through the pan learning and the background learning of image features. In order to study the relationship between the fine-grained attributes of clothing and the whole style, firstly, the clothing types are learned to realize the pre-training of model parameters. Secondly, through the transfer learning of the first stage of the pre-training model parameters, the model parameters are fine-tuned to make them more suitable for identifying the coarse-grained style types. Finally, a network structure based on the dual attention mechanism is proposed to improve the accuracy of final identification by adding different attention mechanisms at different stages of the network to enhance the performance of network features. In the experiment, we collected 50,000 images of 10 clothing styles to train and evaluate the models. The results show that the proposed classification method can effectively distinguish clothing styles and types.

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“…are not present in online stores thus it can assist its clients with reasonably priced merchandise. The ease and comfort provided by these stores for 24x7 have made very easy shopping for consumers worldwide (Singh, 2014).…”

Section: Introductionmentioning

confidence: 99%