Impact of Demonetization on Consumer's Buying Behaviour Towards Online Shopping

2019 ◽  
Author(s):  
Srishti Agarwal
Keyword(s):  
Online Shopping ◽  
Buying Behaviour

Online shopping environments and consumer’s Need for Touch

2019 ◽  
Vol 16 (5) ◽  
pp. 814-826 ◽  
Author(s):  
Rupa Rathee ◽  
Pallavi Rajain
Keyword(s):  
Online Shopping ◽  
New Technology ◽  
Enabling Technology ◽  
Content Type ◽  
Correlation And Regression Analysis ◽  
The People ◽  
Online Retailers ◽  
Shopping Environments ◽  
Buying Behaviour ◽  
Online Buying

Purpose Online shopping has become a commonplace thing nowadays as people can buy products from the comfort of their home. But such environments do not offer a complete sensory interaction as consumers are unable to touch products which is quite important for certain categories of products such as apparels. Therefore, in order to find whether every individual seeks touch equally, the purpose of this paper is to deal with the differences in an individual’s preferences for touch. The study also evaluates customer responses towards the introduction of touch-enabling technology which can, to some extent, compensate for the lack of touch. Lastly, the study includes customers’ views regarding showrooming and webrooming. Design/methodology/approach A total of 203 responses were received through online and offline questionnaires. The data were analysed using ANOVA, correlation and regression analysis through SPSS version 23. Findings The results revealed that gender influenced the Need for Touch (NFT) with women having higher NFT. The people who were high in NFT preferred to buy in-store, whereas their low NFT counterparts were comfortable with both online and in-store options. Lastly, it was found that there was a significant impact of NFT on online buying behaviour. The new technology when used by online retailers would break the barriers that exist between real touch and virtual touch. Originality/value Although previous authors have given several options like mental representations, verbal details and brand image as alternatives to touch but the use of touch-enabling technology can revolutionise the way online products are perceived. The study adds value by relating NFT with online preferences, showrooming and webrooming.

Comparing Predictive Ability of Classifiers in Forecasting Online Buying Behaviour

2020 ◽  
pp. 1279-1296
Author(s):  
Sanjeev Prashar ◽  
S.K. Mitra
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Online Shopping ◽  
Predictive Ability ◽  
Future Research ◽  
Artificial Neural ◽  
Promotional Strategies ◽  
Artificial Neural Network Ann ◽  
Buying Behaviour ◽  
Online Buying

With Internet invading geographic boundaries and diverse demographic strata, online shopping is growing at exponential rate. Expected to grow by 45 per cent to $7.69 billion by the end of 2015, India's ecommerce market has emerged as one of the most anticipated destinations for both multinational and domestic retailers. Since their success will depend on their ability to attract shoppers to buy online, it becomes relevant for them to decipher Indian consumers' attitude and behaviour towards online shopping and to predict online buying potential in India. The effectiveness of marketing and promotional strategies and action plans also will have to be pivoted around the potential available in the market. This empirical study explores the accuracy, precision and recall of four different classifying techniques used in predicting online buying. The forecasting ability of logistic regression (LR), artificial neural network (ANN), support vector machines (SVM) and random forest (RF) in the context of willingness of shoppers' to buy online has been compared. Analysis of the data supported most of the predictions albeit with varying level of accuracy. The outcome of the study reflects the superiority of artificial neural network over the other three models in terms of the predicting power. This paper adds to the knowledge body for online retailers in reducing their vulnerability with respect to market demand and improves their preparedness to handle the market response. Managerial implications of the findings and scope for future research have been deliberated.

scholarly journals Psychological Factors Influencing the Adoption of Web-Based Shopping Behaviour of Female Consumers

2020 ◽  
Vol 3 (2) ◽  
pp. 169-184
Author(s):  
Komal Mehreen ◽  
Robina Roshan ◽  
Mamoona Gul
Keyword(s):  
Psychological Factors ◽  
Online Shopping ◽  
Research Paper ◽  
Common People ◽  
Web Based ◽  
Factors Affecting ◽  
Security Issues ◽  
Female Consumers ◽  
Buying Behaviour ◽  
Online Buying

Online shopping is one of the latest emerging and revolutionary trends influencing the lives of common people. This research paper examines the relationship between demographic variables and internet exposure which are independent variables with the dependent variables i.e. psychological factors and consumer online buying behaviour. People are now moving from conventional shopping towards web based/online shopping because of which they can buy everything from home. The research paper explains the influence of five psychological variables such as security issues, privacy issues, overcharged, fraud/ hackers and lack of trust over the retailer derived from literature. Data from a sample of 298 female students of public and private sector universities of Dera Ismail Khan were collected through the self-developed and standardized questionnaire. Data were analysed by using descriptive statistics and Pearson’s correlation. The statistical analysis of the data reflects a lack of trust over the retailer and privacy concerns are considered as the most relevant factors affecting female consumers’ online buying behaviour.

scholarly journals Determinants of Online Shopping Behaviour in India

2019 ◽  
Vol 8 (3) ◽  
pp. 3946-3950
Keyword(s):  
Online Shopping ◽  
Low Cost ◽  
Indian Society ◽  
Global Studies ◽  
Freedom Of Choice ◽  
Indian Context ◽  
Shopping Behaviour ◽  
Internet Penetration ◽  
Buying Behaviour ◽  
Post Purchase

With low-cost smartphones and affordable data packages Internet penetration is rapidly growing in India. The research identifies the salient features of online customer behaviour in Indian context. An Exploratory factor analysis was conducted and identified determinants that govern consumer buying behaviour. Six factors emerge which were named utilitarian attributes, post purchase issues, Hedonic motives, freedom, intrusion and convenience. These factors are consistent with the global studies, but freedom emerges as a new factor in Indian context. Given the fabric of Indian society making independent choice & freedom of choice is a significant issue, which online shopping portals can use in their marketing strategy.

scholarly journals Online Purchasing Trend in the Retail Industry in Saudi

2019 ◽  
Vol 8 (3) ◽  
pp. 865-868 ◽  
Keyword(s):  
Saudi Arabia ◽  
Online Shopping ◽  
Current Trend ◽  
Primary Data ◽  
Retail Industry ◽  
Online Retailing ◽  
Theoretical Approaches ◽  
The People ◽  
Growth Opportunity ◽  
Buying Behaviour

The concept of online shopping is very popular in the present market scenario. The current trend is indicating the fact that people prefer to shop online because it is easier ad convenient for them. Organizations can easily understand the buying behaviour of the customers by considering different theoretical approaches. The study has indicated that in Saudi Arabia the current trend of online purchasing is at the higher level. People frequently shop online and it is expected that the rate of online shopping in the retail industry in Saudi Arabia will growth further. This study was carried on by using the primary data, which was collected through survey. Survey was conducted with 100 customers in the retail industry in Malaysia. The study used the positivism philosophy for the investigation. The findings in this study are highly important for understanding the latest scenario in the retail industry in Saudi Arabia. The study showed the fact that the people in Saudi Arabia are fond of online shopping. The people are highly motivated for online shopping because of several reasons or benefits that they can enjoy. The findings will be used in the future researches for understanding how the online retail market in Saudi has been developed. This will help to understand the reasons for which the people in this country prefer to shop online. This will also indicate the growth opportunity in the online retailing in Saudi Arabia. The overall findings in this study are highly useful for getting a clear knowledge about the online retailing in Saudi Arabia

Comparing Predictive Ability of Classifiers in Forecasting Online Buying Behaviour

2015 ◽  
Vol 6 (4) ◽  
pp. 54-71
Author(s):  
Sanjeev Prashar ◽  
S.K. Mitra
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Online Shopping ◽  
Predictive Ability ◽  
Future Research ◽  
Support Vector ◽  
Market Demand ◽  
Artificial Neural ◽  
Buying Behaviour ◽  
Online Buying

With Internet invading geographic boundaries and diverse demographic strata, online shopping is growing at exponential rate. Expected to grow by 45 per cent to $7.69 billion by the end of 2015, India's ecommerce market has emerged as one of the most anticipated destinations for both multinational and domestic retailers. Since their success will depend on their ability to attract shoppers to buy online, it becomes relevant for them to decipher Indian consumers' attitude and behaviour towards online shopping and to predict online buying potential in India. The effectiveness of marketing and promotional strategies and action plans also will have to be pivoted around the potential available in the market. This empirical study explores the accuracy, precision and recall of four different classifying techniques used in predicting online buying. The forecasting ability of logistic regression (LR), artificial neural network (ANN), support vector machines (SVM) and random forest (RF) in the context of willingness of shoppers' to buy online has been compared. Analysis of the data supported most of the predictions albeit with varying level of accuracy. The outcome of the study reflects the superiority of artificial neural network over the other three models in terms of the predicting power. This paper adds to the knowledge body for online retailers in reducing their vulnerability with respect to market demand and improves their preparedness to handle the market response. Managerial implications of the findings and scope for future research have been deliberated.

scholarly journals The cognitive comparison enhanced hierarchical clustering

2021 ◽  
Author(s):  
Chun Guan ◽  
Kevin Kam Fung Yuen
Keyword(s):  
Hierarchical Clustering ◽  
Online Shopping ◽  
Information Overload ◽  
Expert Knowledge ◽  
User Preferences ◽  
Analytic Hierarchy ◽  
Proposed Model ◽  
Shopping Experience ◽  
Rating Method ◽  
Buying Behaviour

AbstractThe growth of online shopping is rapidly changing the buying behaviour of consumers. Today, there are challenges facing buyers in the selection of a preferred item from the numerous choices available in the market. To improve the consumer online shopping experience, recommender systems have been developed to reduce the information overload. In this paper, a cognitive comparison-enhanced hierarchical clustering (CCEHC) system is proposed to provide personalised product recommendations based on user preferences. A novel rating method, cognitive comparison rating (CCR), is applied to weigh the product attributes and measure the categorical scales of attributes according to expert knowledge and user preferences. Hierarchical clustering is used to cluster the products into different preference categories. The CCEHC model can be used to rank and cluster product data with the input of user preferences and produce reliable customised recommendations for the users. To demonstrate the advantages of the proposed model, the CCR method is compared with the rating approach of the analytic hierarchy process. Two recommendation cases are demonstrated in this paper with two datasets, one collected by this research for laptop recommendation and the other an open dataset for workstation recommendation. The simulation results demonstrate that the proposed system is feasible for providing personalised recommendations. The significance of this research is the provision of a recommendation solution that does not depend on historical purchase records; rather, one wherein the users’ rating preferences and expert knowledge, both of which are measured by CCR, is considered. The proposed CCEHC model could be further applied to other types of similar recommendation cases such as music, books, and movies.

The Influence of Individual Internal Factors on Impulse Buying Behaviour through Online Shopping

2013 ◽  
Vol 1 (1) ◽  
pp. 59-69
Author(s):  
Siew Lin Chuah ◽  
Chin Chuan Gan
Keyword(s):  
Factor Analysis ◽  
Online Shopping ◽  
External Factors ◽  
Impulse Buying ◽  
Research Focus ◽  
Internal Factors ◽  
Research Findings ◽  
The Individual ◽  
Buying Behaviour ◽  
Buying In

Objective The aim of this research is to identify whether personality, emotions, and hedonic motivation influence impulse buying behaviour when shopping online. Methodology/Technique A total of 270 samples were collected through online. Factor analysis and multiple linear regression were conducted in this research. Findings The result shows that personality and hedonic motivations are positively related to online impulse buying, whereas emotions are not positively related to online impulse buying. Type of Paper: Empirical paper Novelty : Previous research had focused more on the external factors that influence online impulse buying. There is a lack of research focus on internal factors that influence online impulse buying. In this research, the individual internal factors such as personality, emotions, and hedonic motivations are used to clarify the relationships between online impulse buying and the individual internal factors. Keywords: Emotions, Hedonic Motivation, Impulse Buying Behaviour, Online Shopping, Personality.

Click & Collect Retailing: A Study on Its Influence on the Purchase Intention of Customers

2021 ◽  
Vol 9 (2) ◽  
pp. 239-251
Author(s):  
Karthik S ◽  
Kavitha R. Gowda ◽  
Jayanta Banerjee
Keyword(s):  
Purchase Intention ◽  
Online Shopping ◽  
Secondary Data ◽  
Perceived Usefulness ◽  
Primary Data ◽  
Retail Sector ◽  
Proposed Model ◽  
Shopping Experience ◽  
Product Risk ◽  
Buying Behaviour

The retail sector, over the years, has evolved dramatically to provide better service to its customers. With the superior convenience of online shopping and tangible experience of in-store shopping, retail industries are looking forward to integrating both modes, thus embracing omni channel to provide better service to their customers. The prime objective of the research is to investigate the level of influence that using the Click & Collect online shopping mode can have on customer purchase intention and to ascertain the effects that online and offline shopping attributes have on this intention. The study emphasizes the usefulness of integrating both the shopping modes, thus embracing omni channel in the retail sector to provide a better shopping experience to the customers. The primary data were collected from 356 respondents.  Secondary data were collected by reviewing articles, research papers, extant studies and newspaper articles. In the analysis, the buying behaviour through an e-commerce platform and customers’ purchase intentions are taken as the dependent variable. Product risk, online trust, website quality, offline experience and perceived usefulness are identified as the independent variables. The data thus collected were processed for regression tests using IBM SPSS 25 software to analyse the results. The Stimulus-Organism-Response model was deployed as the proposed model for the research. The results obtained from the research will allow retailers to understand the customer's buying behaviour towards the new Click & Collect system better by identifying the key variables that influence their purchase intention. 

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