Call for Papers on Predictive Analytics in e-Commerce for Global Economy

Information Technology and Management is seeking submissions to a forthcoming Special Issue on Predictive Analytics in e-Commerce for Global Economy.

Predictive analytics evolution in e-commerce supports the development of the global economy since e-commerce elaborated globally. Predictive analytics techniques are widely used in business intelligence since predictive analytics describes the utilization of statistics and modeling techniques in anticipating future results and performance by scrutinizing the present and classical data. For instance, risk analysis, churn prevention, analysis of the market, forecasting the demands, etc., are all determined with this technology. Huge organizations are presently focusing on the advantages of predictive analytics to make the business grow and develop operational efficiencies.

At the outburst of the pandemic, customers turned to online retail for security and privacy causes. Predictive analytics mainly scrutinizes the browsing methods, payment techniques, and customer buying methods to reduce and eliminate risks. The collaboration of predictive analytics with machine learning abilities will authorize e-commerce businesses to specify protocols and automate risk detection and prevention. Predictive analytics will permit continuous evaluation of user data, while machine learning abilities will generate the most related results and recommendations to users. In every stage of electronic commerce accumulates the information from client actions to supply chain proficiency to social media trends. Data-driven decision-making advances entrepreneurs to focus on powerful predictive analytic tools. E-Commerce assured massive possibilities for business organizations but still, it has to gain a driving position in the global economy.
 
The goal of this special issue is to study the impact of predictive analytics and how it has transformed the shape of the development of the e-commerce industry with time for the global economy. The objective is to seriously evaluate the aspect of predictive analytics in modern-day e-commerce and identify the strengths and weaknesses in the field. List of topics for the special issue include but not limited to the following:

  • Application of predictive analytics for business internationalization
  • Predictive analytics for implementation in marketing by multiple media
  • Real-time demand forecasting algorithms and techniques
  • Predictive analytics for efficient decision making in e-commerce
  • Predictive analytics for cost-effectiveness management
  • Digital transformation and e-commerce in the industry based on predictive analytics
  • Predictive analytics for modern commerce-based evolution in the global economy
  • Predictive analytics of e-commerce on economic growth, market structure, exports, capital market, etc.
  • Emerging e-business models with the aid of predictive analytics
  • Predictive analytics for secured e-commerce
  • Future organization based on predictive analytics with innovative technology

Manuscript Preparation

We expect full-length submissions with a sufficient level of rigor consistent with the high standard of the journal. The submission can use any appropriate method to study the problems related to the theme of this special issue: analysis of data, mathematical analysis, game theories, etc. The authors should try to keep the papers to be no longer than 38 pages double-spaced in a font size of 11 and in Word or PDF format. When submitting, please follow the detailed submission guidelines provided at the journal website’s URL https://www.springer.com/journal/10799/submission-guidelines. When answering submission questions, you will specify that your submission is for this special issue.

Important Dates

First round review decision: July 30, 2022 
Deadline for Revised Papers: September 30, 2022 
Final Acceptance: November 15, 2022

Guest Editors

Subramaniam Ganesan
Electrical and Computer Engineering Department
Oakland University, USA
E-mail: ganesan@oakland.edu

Mustafa Ally
School of Management & Enterprise
University of Southern Queensland, Australia
E-mail: mustafa.ally@usq.edu.au

Liwen Hou 
Department of Management Information Systems
Antai College of Economics and Management
Shanghai Jiao Tong University, China
E-mail: lwhou@sjtu.edu.cn

Nuray Terzi
Department of Economics
Marmara University, Turkey
E-mail: nurayterzi@marmara.edu.tr

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