scholarly journals USING CORRESPONDENCE ANALYSIS TO MAP ONLINE PURCHASE CRITERIA OF DIFFERENT PRODUCT CATEGORIES

2020 ◽  
Vol 11 (1) ◽  
pp. 45-74
Author(s):  
Preeti Thakur ◽  
Anupriya Kaur
Keyword(s):  
Correspondence Analysis ◽  
Product Categories ◽  
Online Purchase ◽  
Purchase Criteria

scholarly journals E-COMMERCE NATIONAL MARKET - STUDY FROM THE PERSPECTIVE OF MANIFESTED DEMAND

2015 ◽  
Author(s):  
Laura Catalina Timiras
Keyword(s):  
Formal Education ◽  
Total Population ◽  
Official Statistics ◽  
Eu Countries ◽  
Product Categories ◽  
Online Purchases ◽  
Online Purchase ◽  
National Market ◽  
Sports Goods

E-commerce market in Romania recorded the lowest level of development compared to other EU countries, in terms of the intensity of online purchase habit of the population of Romania. Thus, in 2014, only 16.7% of those who used the computer in the range of 16-74 years and 10% of the total population of the same age had made online purchases, compared with 63.3% and 50% respectively - the same indicators at EU level. Still, in Romania there was a rapid increase in the share of those who make online purchases, so in 8 years (2007-2014) this indicator increased by 3 times. Regarding the categories of the population by various characteristics, greater orientation towards online purchases is recorded among individuals with high formal education, youth (under 35), with above-average incomes. In terms of product categories purchased online, the most favorite categories are: clothes, sports goods. This analysis is based mostly on official statistics provided by Eurostat.

Triggers of positive eWOM: exploration with web analytics

2019 ◽  
Vol 37 (4) ◽  
pp. 433-450 ◽  
Author(s):  
Sultan Amed ◽  
Srabanti Mukherjee ◽  
Prasun Das ◽  
Biplab Datta
Keyword(s):  
Big Data ◽  
Real Time ◽  
Language Processing ◽  
Product Type ◽  
Support Vector ◽  
Product Packaging ◽  
Online Retail ◽  
Content Type ◽  
Product Categories ◽  
Online Purchase

Purpose The purpose of this paper is to determine the triggers of positive electronic word of mouth (eWOM) using real-time Big Data obtained from online retail sites/dedicated review sites. Design/methodology/approach In this study, real-time Big Data has been used and analysed through support vector machine, to segregate positive and negative eWOM. Thereafter, using natural language processing algorithms, this study has classified the triggers of positive eWOM based on their relative importance across six product categories. Findings The most important triggers of positive eWOM (like product experience, product type, product characteristics) were similar across different product categories. The second-level antecedents of positive eWOM included the person(s) for whom the product is purchased, the price and the source of the product, packaging and eagerness in patronising a brand. Practical implications The findings of this study indicate that the marketers who are active in the digital forum should encourage and incentivise their satisfied consumers to disseminate positive eWOM. Consumers with special interest for any product type (mothers or doctors for baby food) may be incentivised to write positive eWOM about the product’s ingredients/characteristics. Companies can launch the sequels of existing television or online advertisements addressing “for whom the product is purchased”. Originality/value This study identified the triggers of the positive eWOM using real-time Big Data extracted from online purchase platforms. This study also contributes to the literature by identifying the levels of triggers that are most, more and moderately important to the customers for writing positive reviews online.

Risk Factors Discriminating Online Metropolitan Women Shoppers

2019 ◽  
pp. 1043-1055
Author(s):  
G. Rajini ◽  
M. Krithika
Keyword(s):  
Financial Risk ◽  
Online Shopping ◽  
Product Category ◽  
Social Risk ◽  
Security Risk ◽  
Multiple Discriminant Analysis ◽  
Product Categories ◽  
Online Purchase ◽  
Physical Risk ◽  
Product Risk

Online shopping has been really male - oriented as the product categories were limited to software, computers, music and computer accessories. As the product category expanded to clothing, food, home care and toys, women started to adopt online shopping. Nowadays there is a massive surge in online shopping particularly among metropolitan women, as they tend to purchase both necessary and discretionary products. According to the report by Associate Chamber of Trade and Industry of India (ASSOCHAM), over 80% of the online shopping is done by metropolitan women shoppers.The present study investigates the prominent perceived risks of employed women in online shopping through a consumer survey and by applying multiple discriminant analysis .The results of the empirical analysis demonstrated that perceived physical risk and product risk were found vital in discriminating the respondents as intermediate or experts in online shopping .Whereas perceived psychological risk, quality risk, Information Security Risk, Time Risk, Delivery Risk, Social Risk, Source Risk and financial risk perception during online purchase adoption contributes comparatively lesser for discrimination. The findings elucidate how marketers can formulate and implement risk-reducing strategies during online purchasing.

Analysis on Research Paper Publication Recommendation System with Composition of Papers and Conferences Matrices

2020 ◽  
pp. 120-127
Author(s):  
Htay Htay Win ◽  
Aye Thida Myint ◽  
Mi Cho Cho
Keyword(s):  
Social Network ◽  
Dimensionality Reduction ◽  
Correspondence Analysis ◽  
Recommendation System ◽  
Research Paper ◽  
Topic Modelling ◽  
Research Papers ◽  
The Right ◽  
Publication Recommendation

For years, achievements and discoveries made by researcher are made aware through research papers published in appropriate journals or conferences. Many a time, established s researcher and mainly new user are caught up in the predicament of choosing an appropriate conference to get their work all the time. Every scienti?c conference and journal is inclined towards a particular ?eld of research and there is a extensive group of them for any particular ?eld. Choosing an appropriate venue is needed as it helps in reaching out to the right listener and also to further one’s chance of getting their paper published. In this work, we address the problem of recommending appropriate conferences to the authors to increase their chances of receipt. We present three di?erent approaches for the same involving the use of social network of the authors and the content of the paper in the settings of dimensionality reduction and topic modelling. In all these approaches, we apply Correspondence Analysis (CA) to obtain appropriate relationships between the entities in question, such as conferences and papers. Our models show hopeful results when compared with existing methods such as content-based ?ltering, collaborative ?ltering and hybrid ?ltering.

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