scholarly journals Analyzing the impact of Artificial Intelligence in Online business Intelligence

2021 ◽  
pp. 269-275
Author(s):  
Tanmayee Tushar Parbat ◽  
Rohan Benhal ◽  
Honey Jain ◽  
Dr. Vinayak Musale
Keyword(s):  
Business Intelligence ◽  
Recommendation System ◽  
Online Shopping ◽  
Customer Behavior ◽  
Learning Technology ◽  
General Survey ◽  
Recommendation Algorithms ◽  
Online Business ◽  
Research Gap ◽  
The Impact

The replacement of traditional shopping fashion by the varied modes of online shopping in real-time. Due to traditional shopping, most of them are becoming into real feel about the merchandise whichever they buy. The merchandise features are going to be manually realized by the consumers whereas in online shopping all the consumers believe the descriptive summary of the products and therefore the various factors supported the sold historical data. Now a day’s modern shopping method is moving gradually towards hitting a greater number of consumers. Here recommendation system playing an important role in suggesting the merchandise by considering the sooner records and increasing the demand. Many of the consumers are attracted by factors like deals on an item, rating, review, and price of the merchandise. Through these factors, most of the consumers are interested in taking online shopping rather than traditional shopping methods. For suggesting the products to consumers, many sorts of recommendation algorithms are applied using machine learning and deep learning technology to coach the system automatically by observing the customer behavior patterns. But the believing factors of the merchandise are going to be forged some time; in such cases, consumers aren't satisfied with their expectations. the general survey of this paper will address the research gap and opportunities with the advice system.

scholarly journals How Web Shops Impact Consumer Behavior?

2021 ◽  
Vol 15 (3) ◽  
pp. 350-356
Author(s):  
Helena Štimac ◽  
Ivan Kelić ◽  
Karla Bilandžić
Keyword(s):  
Multivariate Analysis ◽  
Linear Regression ◽  
Correlation Analysis ◽  
Reliability Analysis ◽  
Online Shopping ◽  
Customer Behavior ◽  
Descriptive Statistics ◽  
Firm Reputation ◽  
Marketing Actions ◽  
The Impact

The behavior of e-customers is quite unpredictable, which raises additional questions about this topic. The purpose of the paper is to conduct research on e-customers, understand the impact of marketing actions on e-customer behavior and understand the unpredictability of e-customers. Research was conducted on the Mlinar web shop that sells cakes. 284 respondents/buyers had the opportunity to solve questionnaires about behavior after purchase and consumption of product. Different methods have been used in the analysis - descriptive statistics, multivariate analysis (reliability analysis, correlation analysis and linear regression) and analysis of variance (ANOVA). The results showed that most examinees were satisfied with online shopping on the Mlinar web shop and that they are impulsive when online shopping. Saving time is the main reason to buy on a web shop. Research proved that variables such as firm reputation/perceived value, e-satisfaction and online services positively affect the creation of e-loyalty in their users.

scholarly journals Customers’ behavior intension regarding online shopping of general product and insecticide in Thailand

2020 ◽  
Vol 4 (2) ◽  
pp. 184
Author(s):  
DINESH ELANGO ◽  
KRITCHANUT PRAYOONPONG
Keyword(s):  
Linear Regression ◽  
Customer Service ◽  
Online Shopping ◽  
Technology Development ◽  
Global Market ◽  
Customer Behavior ◽  
Growth Strategy ◽  
Online Application ◽  
The Impact ◽  
General Product

The global market of insecticide products is expected to garner around 16.7 Billion US by 2020, registering a CAGR of 5.0% during the forecast period 2014 to 2020. Companies are operating in this market focus on product launches as part of their growth strategy. As the retail sales of global e-commerce have continuously grown, e-commerce has gained the large share from physical retail over the last decade derived from the impact of greater internet access and technology development that make online shopping easier and more convenience. The research analyzes data of customer behavior intention by parallel compare impact of the data from general product and insecticide product. In analyzing data, single Linear Regression and multiple linear regression were employed to investigate the impact and difference between dependent and independent variable. The 400 respondents who are using online application and have online shopping experience were implied to investigate the factors that have intention to decision on purchase product via online channel in this research. The results have been interpreted that people have more concern on purchase of insecticide online in product attitude, customer service, purchase and delivery, and promotion of insecticide. This study concept and valid the customer behavior toward insecticide product for set strategy of the insecticide in online market.

scholarly journals 3bij3 – Developing a framework for researching recommender systems and their effects

2019 ◽  
Author(s):  
Felicia Loecherbach ◽  
Damian Trilling
Keyword(s):  
Recommender Systems ◽  
Large Scale ◽  
Recommendation System ◽  
Field Experiments ◽  
Online News ◽  
Behavioral Measures ◽  
Time News ◽  
Recommendation Algorithms ◽  
Large Scale Field ◽  
The Impact

Today’s online news environment is increasingly characterized by personalized news selections, relying on algorithmic solutions for extracting relevant articles and composing an individual’s news diet. Yet, the impact of such recommendation algorithms on how we consume and perceive news is still understudied. We therefore developed one of the first software solutions to conduct studies on effects of news recommender systems in a realistic setting. The web app of our framework (called 3bij3) displays real-time news articles selected by different mechanisms. 3bij3 can be used to conduct large-scale field experiments, in which participants’ use of the site can be tracked over extended periods of time. Compared to previous work, 3bij3 gives researchers control over the recommendation system under study and creates a realistic environment for the participants. It integrates web scraping, different methods to compare and classify news articles, different recommender systems, a web interface for participants, gamification elements, and a user survey to enrich the behavioral measures obtained.

scholarly journals Enhanced Recommendation System for E-commerce Applications

2019 ◽  
Vol 8 (9) ◽  
pp. 473-478
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Web Applications ◽  
Online Shopping ◽  
User Behavior ◽  
Filtering Algorithm ◽  
Recommendation Algorithms ◽  
Collaborative Filtering Algorithm ◽  
Special Subset ◽  
And Behavior

Recommendation algorithms play a quintessential role in development of E-commerce recommendation system, Where in Collaborative filtering algorithm is a major contributor for most recommendation systems since they are a flavor of KNN algorithm specifically tailored for E-commerce Web Applications, the main advantages of using CF algorithms are they are efficient in capturing collective experiences and behavior of e-commerce customers in real time, But it is noted that , this results in the phenomenon of Mathew effect, Wherein only popular products are listed into the recommendation list and lesser popular items tend to become even more scarce. Hence this results in products which are already familiar to users being discovered redundantly, thus potential discovery of niche and new items in the e-commerce application is compromised. To address this issue , this paper throws light on user behavior on the online shopping platform , accordingly a novel selectivity based collaborative filtering algorithm is proposed with innovator products that can recommend niche items but less popular products to users by introducing the concept of collaborative filtering with consumer influencing capability. Specifically, innovator products are a special subset of products which are less popular/ have received less traction from users but are genuinely of higher quality, therefore, these aforementioned products can be captured in the recommendation list via innovator-recognition table, achieving the balance between popularity and practicability for the user

scholarly journals 3bij3 ‐ Developing a framework for researching recommender systems and their effects

2020 ◽  
Vol 2 (1) ◽  
pp. 53-79
Author(s):  
Felicia Loecherbach ◽  
Damian Trilling
Keyword(s):  
Recommender Systems ◽  
Large Scale ◽  
Recommendation System ◽  
Field Experiments ◽  
Online News ◽  
Time News ◽  
Recommendation Algorithms ◽  
Large Scale Field ◽  
Web App ◽  
The Impact

Abstract Today’s online news environment is increasingly characterized by personalized news selections, relying on algorithmic solutions for extracting relevant articles and composing an individual’s news diet. Yet, the impact of such recommendation algorithms on how we consume and perceive news is still understudied. We therefore developed one of the first software solutions to conduct studies on effects of news recommender systems in a realistic setting. The web app of our framework (called 3bij3) displays real-time news articles selected by different mechanisms. 3bij3 can be used to conduct large-scale field experiments, in which participants’ use of the site can be tracked over extended periods of time. Compared to previous work, 3bij3 gives researchers control over the recommendation system under study and creates a realistic environment for the participants. It integrates web scraping, different methods to compare and classify news articles, different recommender systems, a web interface for participants, gamification elements, and a user survey to enrich the behavioural measures obtained.

Organizational Justice and Employee Sustainability: The Mediating Role of Organizational Commitment

2020 ◽  
Vol 3 (3) ◽  
pp. 12-22
Author(s):  
Mehreen Fatima ◽  
Zeeshan Izhar ◽  
Zaheer Abbas Kazmi
Keyword(s):  
Organizational Commitment ◽  
Organizational Justice ◽  
Developing Country ◽  
Design Methodology ◽  
Direct Relationship ◽  
Banking Sector ◽  
Mediating Role ◽  
Research Gap ◽  
The Impact

Purpose- The primary purpose of the study is to determine the impact of organizational justice (OJ) on employee sustainability. Along with that, it also describes how organizational commitment mediates this direct relationship. This study includes all dimensions of OJ which are distributive, procedural and interactional (interpersonal & informational) within the context of a developing country (Pakistan). Design/Methodology- This study has considered employees working in the banking sector of Pakistan. Two hundred ten questionnaires were received back from employees. Regression analysis was used to analyze direct relationships between variables, while smart partial least squares (PLS) were used for mediation analysis. Findings- Results demonstrated that all hypothesis were accepted and it was also confirmed that organizational commitment (OC) mediates the direct relationship between OJ and employee sustainability (ES). Originality/value- Multidimensional construct of organizational justice was tested in this study, in the context of a developing country (Pakistan), to address the research gap.

Feature-based multi-criteria recommendation system using a weighted approach with ranking correlation

2021 ◽  
Vol 25 (4) ◽  
pp. 1013-1029
Author(s):  
Zeeshan Zeeshan ◽  
Qurat ul Ain ◽  
Uzair Aslam Bhatti ◽  
Waqar Hussain Memon ◽  
Sajid Ali ◽  
...  
Keyword(s):  
Recommendation System ◽  
Sources Of Information ◽  
Multiple Features ◽  
Recommendation Algorithms ◽  
Label Correlations ◽  
Feature Based ◽  
Multicriteria Model ◽  
Implicit And Explicit ◽  
Hybrid Recommendation ◽  
Different Sources

With the increase of online businesses, recommendation algorithms are being researched a lot to facilitate the process of using the existing information. Such multi-criteria recommendation (MCRS) helps a lot the end-users to attain the required results of interest having different selective criteria – such as combinations of implicit and explicit interest indicators in the form of ranking or rankings on different matched dimensions. Current approaches typically use label correlation, by assuming that the label correlations are shared by all objects. In real-world tasks, however, different sources of information have different features. Recommendation systems are more effective if being used for making a recommendation using multiple criteria of decisions by using the correlation between the features and items content (content-based approach) or finding a similar user rating to get targeted results (Collaborative filtering). To combine these two filterings in the multicriteria model, we proposed a features-based fb-knn multi-criteria hybrid recommendation algorithm approach for getting the recommendation of the items by using multicriteria features of items and integrating those with the correlated items found in similar datasets. Ranks were assigned to each decision and then weights were computed for each decision by using the standard deviation of items to get the nearest result. For evaluation, we tested the proposed algorithm on different datasets having multiple features of information. The results demonstrate that proposed fb-knn is efficient in different types of datasets.

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