scholarly journals The Impact of Electronic Commerce Environment on User Behavior: The Case of a Complex Product

2001 ◽  
Vol 1 (1) ◽  
pp. 41-53
Author(s):  
Jungjoo Jahng ◽  
Hemant Jain ◽  
Ramamurthy K
Keyword(s):  
Electronic Commerce ◽  
User Behavior ◽  
Complex Product ◽  
The Impact

An experimental study of Web switching behavior

2003 ◽  
Vol 22 (2) ◽  
pp. 87-93
Author(s):  
James Otto ◽  
Mohammad Najdawi ◽  
William Wagner
Keyword(s):  
Experimental Study ◽  
Electronic Commerce ◽  
Web Site ◽  
Waiting Times ◽  
Switching Behavior ◽  
The Internet ◽  
Wait Times ◽  
Web Page ◽  
Extensive Growth ◽  
The Impact

With the extensive growth of the Internet and electronic commerce, the issue of how users behave when confronted with long download times is important. This paper investigates Web switching behavior. The paper describes experiments where users were subjected to artificially delayed Web page download times to study the impact of Web site wait times on switching behavior. Two hypotheses were tested. First, that longer wait times will result in increased switching behavior. The implication being that users become frustrated with long waiting times and choose to go elsewhere. Second, that users who switch will benefit, in terms of decreased download times, from their decision to switch.

scholarly journals Big Data Analytics for Search Engine Optimization

2020 ◽  
Vol 4 (2) ◽  
pp. 5 ◽  
Author(s):  
Ioannis C. Drivas ◽  
Damianos P. Sakas ◽  
Georgios A. Giannakopoulos ◽  
Daphne Kyriaki-Manessi
Keyword(s):  
Big Data ◽  
Cultural Heritage ◽  
Search Engine ◽  
Data Analytics ◽  
User Behavior ◽  
Big Data Analytics ◽  
Data Types ◽  
Search Engine Optimization ◽  
The Impact ◽  
The Web

In the Big Data era, search engine optimization deals with the encapsulation of datasets that are related to website performance in terms of architecture, content curation, and user behavior, with the purpose to convert them into actionable insights and improve visibility and findability on the Web. In this respect, big data analytics expands the opportunities for developing new methodological frameworks that are composed of valid, reliable, and consistent analytics that are practically useful to develop well-informed strategies for organic traffic optimization. In this paper, a novel methodology is implemented in order to increase organic search engine visits based on the impact of multiple SEO factors. In order to achieve this purpose, the authors examined 171 cultural heritage websites and their retrieved data analytics about their performance and user experience inside them. Massive amounts of Web-based collections are included and presented by cultural heritage organizations through their websites. Subsequently, users interact with these collections, producing behavioral analytics in a variety of different data types that come from multiple devices, with high velocity, in large volumes. Nevertheless, prior research efforts indicate that these massive cultural collections are difficult to browse while expressing low visibility and findability in the semantic Web era. Against this backdrop, this paper proposes the computational development of a search engine optimization (SEO) strategy that utilizes the generated big cultural data analytics and improves the visibility of cultural heritage websites. One step further, the statistical results of the study are integrated into a predictive model that is composed of two stages. First, a fuzzy cognitive mapping process is generated as an aggregated macro-level descriptive model. Secondly, a micro-level data-driven agent-based model follows up. The purpose of the model is to predict the most effective combinations of factors that achieve enhanced visibility and organic traffic on cultural heritage organizations’ websites. To this end, the study contributes to the knowledge expansion of researchers and practitioners in the big cultural analytics sector with the purpose to implement potential strategies for greater visibility and findability of cultural collections on the Web.

scholarly journals Ethics of Retailers and Consumer Behavior in E-Commerce

2020 ◽  
Vol 11 (1) ◽  
pp. 107-126
Author(s):  
Zhi Yang ◽  
Quang Van Ngo ◽  
Chung Xuan Thi Nguyen
Keyword(s):  
Electronic Commerce ◽  
Communication Technology ◽  
Purchase Intention ◽  
Purchase Decision ◽  
Information Communication ◽  
Significant Relationships ◽  
Online Retailers ◽  
Relationship Of ◽  
The Impact ◽  
The Relationship

In the era of the information-communication technology with the development of electronic commerce, consumers can buy almost everything anywhere and at any time. One of the greatest benefits of e-commerce has been the convenience and the vast choices which consumers get online. However, some serious issues exist that impede consumers from transaction online. These issues have attracted the attention of many marketing researchers in recent years. In this research, the authors focus on the impact of consumers' ethics perception of e-retailer on their purchase intention and satisfaction. The authors also try to clarify the roles of trust and commitment in the relationship of ethics on consumers purchase decision and satisfaction because of their importance in e-commerce but also the privation in the research of previous studies. The data collected from a survey of 390 online consumers in Vietnam. Results reveal that there are significant relationships between online retailers' ethics and consumers' purchase intention and satisfaction via the mediation of trust and commitment.

scholarly journals Influence Model of User Behavior Characteristics on Information Dissemination

2016 ◽  
Vol 11 (2) ◽  
pp. 209 ◽  
Author(s):  
Shao Chun Han ◽  
Yun Liu ◽  
Hui Ling Chen ◽  
Zhen Jiang Zhang
Keyword(s):  
Human Behavior ◽  
Information Diffusion ◽  
Statistical Physics ◽  
Information Dissemination ◽  
User Behavior ◽  
Activity Patterns ◽  
Propagation Model ◽  
Propagation Time ◽  
Behavior Characteristics ◽  
The Impact

Quantitative analysis on human behavior, especially mining and modeling temporal and spatial regularities, is a common focus of statistical physics and complexity sciences. The in-depth understanding of human behavior helps in explaining many complex socioeconomic phenomena, and in finding applications in public opinion monitoring, disease control, transportation system design, calling center services, information recommendation. In this paper,we study the impact of human activity patterns on information diffusion. Using SIR propagation model and empirical data, conduct quantitative research on the impact of user behavior on information dissemination. It is found that when the exponent is small, user behavioral characteristics have features of many new dissemination nodes, fast information dissemination, but information continued propagation time is short, with limited influence; when the exponent is big, there are fewer new dissemination nodes, but will expand the scope of information dissemination and extend information dissemination duration; it is also found that for group behaviors, the power-law characteristic a greater impact on the speed of information dissemination than individual behaviors. This study provides a reference to better understand influence of social networking user behavior characteristics on information dissemination and kinetic effect.

scholarly journals INTERNET MARKETING SOLUTIONS TRANSFORMATION DUE TO CHANGES IN CONSUMER'S PERCEPTION OF INFORMATION

2021 ◽  
Author(s):  
Igors Babics
Keyword(s):  
User Behavior ◽  
Online Communication ◽  
Marketing Research ◽  
Modern Society ◽  
Internet Marketing ◽  
Online Marketing ◽  
Scientific Article ◽  
Human Thinking ◽  
Internet User Behavior ◽  
The Impact

We have outlined the main aspects of the modern socio-economic space that have led to transformation not only in the business sector but also in human thinking. We have examined the aspects of the studied problem expressed in modern scientific works and explained the need for further study of changes in the thinking processes of consumers in the field of transformation of applied Internet marketing solutions for the requests of Internet users. We have analyzed the dynamics and trends of changes in Internet user behavior, thereby identifying the key aspects that should be taken into account when companies create online marketing strategies. We have proposed a list of steps to optimize the marketing strategy of the business in line with new realities.The relevance of the study is due to the social processes of modern society resulting in the tendency to transform consumers' thinking. COVID-19 and self-isolation have had an impact on this phenomenon, accelerating the massive changeover to online communication and online shopping.The goal of this article is to describe the results of a study of changes in consumer thinking in connection with the transformation of realities caused by the global pandemic.The scientific novelty of the study lies in highlighting the peculiarities of information perception by modern consumers associated with the global pandemic, and in substantiating the ways of transforming Internet marketing solutions for companies in an altered reality.The theoretical importance of the research lies in a better understanding of the reasons and features of the transformation of information perception by consumers in modern realities, as well as in the analysis of scientific works to study the impact of informatization and computerization on society thinking, which can be used to study this component in marketing research, including in online marketing. This is the practical value of this work.The practical value of the study lies in identifying the features of the transformation of the thinking of modern consumers through visitors to the website of Cita Lieta ltd. at ceanocosmetics.com.Like in any scientific article, this one has its research limitations. The author explores the transformation of consumer thinking change using the data from the website analytics of one company in a particular niche.

scholarly journals Retweet Prediction Based on User Behavior

2021 ◽  
Author(s):  
Syeda Nadia Firdaus
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Prediction Model ◽  
Matrix Factorization ◽  
Information Diffusion ◽  
User Behavior ◽  
Past Research ◽  
The Difference ◽  
The Impact

Social network is a hot topic of interest for researchers in the field of computer science in recent years. These social networks such as Facebook, Twitter, Instagram play an important role in information diffusion. Social network data are created by its users. Users’ online activities and behavior have been studied in various past research efforts in order to get a better understanding on how information is diffused on social networks. In this study, we focus on Twitter and we explore the impact of user behavior on their retweet activity. To represent a user’s behavior for predicting their retweet decision, we introduce 10-dimentional emotion and 35-dimensional personality related features. We consider the difference of a user being an author and a retweeter in terms of their behaviors, and propose a machine learning based retweet prediction model considering this difference. We also propose two approaches for matrix factorization retweet prediction model which learns the latent relation between users and tweets to predict the user’s retweet decision. In the experiment, we have tested our proposed models. We find that models based on user behavior related features provide good improvement (3% - 6% in terms of F1- score) over baseline models. By only considering user’s behavior as a retweeter, the data processing time is reduced while the prediction accuracy is comparable to the case when both retweeting and posting behaviors are considered. In the proposed matrix factorization models, we include tweet features into the basic factorization model through newly defined regularization terms and improve the performance by 3% - 4% in terms of F1-score. Finally, we compare the performance of machine learning and matrix factorization models for retweet prediction and find that none of the models is superior to the other in all occasions. Therefore, different models should be used depending on how prediction results will be used. Machine learning model is preferable when a model’s performance quality is important such as for tweet re-ranking and tweet recommendation. Matrix factorization is a preferred option when model’s positive retweet prediction capability is more important such as for marketing campaign and finding potential retweeters.

scholarly journals Retweet Prediction Based on User Behavior

2021 ◽  
Author(s):  
Syeda Nadia Firdaus
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Prediction Model ◽  
Matrix Factorization ◽  
Information Diffusion ◽  
User Behavior ◽  
Past Research ◽  
The Difference ◽  
The Impact

Social network is a hot topic of interest for researchers in the field of computer science in recent years. These social networks such as Facebook, Twitter, Instagram play an important role in information diffusion. Social network data are created by its users. Users’ online activities and behavior have been studied in various past research efforts in order to get a better understanding on how information is diffused on social networks. In this study, we focus on Twitter and we explore the impact of user behavior on their retweet activity. To represent a user’s behavior for predicting their retweet decision, we introduce 10-dimentional emotion and 35-dimensional personality related features. We consider the difference of a user being an author and a retweeter in terms of their behaviors, and propose a machine learning based retweet prediction model considering this difference. We also propose two approaches for matrix factorization retweet prediction model which learns the latent relation between users and tweets to predict the user’s retweet decision. In the experiment, we have tested our proposed models. We find that models based on user behavior related features provide good improvement (3% - 6% in terms of F1- score) over baseline models. By only considering user’s behavior as a retweeter, the data processing time is reduced while the prediction accuracy is comparable to the case when both retweeting and posting behaviors are considered. In the proposed matrix factorization models, we include tweet features into the basic factorization model through newly defined regularization terms and improve the performance by 3% - 4% in terms of F1-score. Finally, we compare the performance of machine learning and matrix factorization models for retweet prediction and find that none of the models is superior to the other in all occasions. Therefore, different models should be used depending on how prediction results will be used. Machine learning model is preferable when a model’s performance quality is important such as for tweet re-ranking and tweet recommendation. Matrix factorization is a preferred option when model’s positive retweet prediction capability is more important such as for marketing campaign and finding potential retweeters.

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