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Big Data Analytics is
a recent and rapidly evolving field in technology driven
business world, and private and public organizations are eagerly
waiting to collect the promised results. Empowered by
advancement of information and communication technology, the
volume and complexity of data are growing exponentially. Big
Data is formed of large, diverse, complex, longitudinal, and
distributed data sets generated from various instruments,
sensors, Internet transactions, email, video, click streams, and
other sources. It is commonly characterized in three or more Vs:
volume, velocity, variety, and additionally value, veracity etc.
Big Data Analytics is characterized by the requirement of
advanced data storage, management, analysis, and visualization
technologies, which traditional business analytics is not able
to offer. These technologies include, among others: interfusion
of various data sources, real-time analysis, online analytical
processing, business performance management, data mining,
machine learning, cloud computing, distributed processing,
parallel algorithms, and parallel DBMS.
Big Data Analytics generates new opportunities for the benefit
of our society, but it also introduces challenges. Applications
of Big Data Analytics are expected to change the world, how
people and organizations are doing things in the future, as it
provides increasing awareness and deeper insight on various real
world and virtual phenomena. It is to change business models,
management and decision making processes in companies and public
organizations, and to affect usage of resources in creating
products and services.
Electronic commerce is one of the most promising and high impact
application areas of Big Data Analytics2. It has already
transformed markets when adopted by many leading electronic
commerce vendors. Wide adoption of social media and
crowdsourcing applications in various forms still offer new
possibilities for collecting and analysing Big Data for
supporting business, as well as unexplored opportunities for
developing Big Data Analytics based information services for
customers. Even if Big Data Analytics research is at its
currents stage significantly technology driven, focusing on such
topics as data mining and cloud computing technologies2, there
is and evident need for also understanding it better from the
electronic commerce point of view.
Subject Coverage
The objective of this Special Issue is to present the current
state of research and practical experiences on Big Data
Analytics from the viewpoint of electronic commerce research.
The disciplines can cover any area of electronic commerce,
including computer science, information technology, information
systems, information management, telecommunications, business
administration, law, social sciences, financial services, and
other related fields. Particularly we like to see
interdisciplinary papers presenting innovative applications and
uses of Big Data Analytics in electronic commerce that are able
to connect theory with practice. We are looking for experiences
of successful Big Data Analytics applications as well as
critical views and challenges. We encourage submitting papers
that present genuine, rigorous research on electronic commerce
in transit, which shows how Big Data Analytics is related with
business practices, social, cultural and legal environments,
personal privacy and security concerns, information systems, and
emerging smart environments and device technologies.
Topics of interest include, but are not limited to, the
following:
1. New opportunities in business environment
- Changes of eCommerce in business and public sector
services attributed to Big Data Analytics
- Big Data Analytics based services innovation
- Business models built on Big Data Analytics
- Big Data Analytics in business ecosystems
- Big Data Analytics with public and open data
- Big Data Analytics and data markets
2. Big Data Analytics and strategy
- Big Data Analytics strategies in eCommerce
- Impact of Big Data Analytics in eCommerce strategies
- Big Data Analytics in strategic decision making
3. Management of electronic commerce
- Combining Big Data Analytics with eCommerce processes
- Embedding Big Data Analytics in eCommerce practices
- Application of Big Data Analytics methods and tools in
eCommerce
- Technological challenges of applying Big Data Analytics in
eCommerce
- Big Data fusion from different sources
4. Legal issues in applying Big Data Analytics
- Privacy issues in Big Data Analytics
- Applying Privacy by Design in eCommerce
- Big Data Analytics and IPR
5. Research on Big Data Analytics in electronic commerce
- Big Data Analytics research challenges
- Research approaches to Big Data Analytics in eCommerce
- Big Data Analytics based methods for eCommerce research
Notes for Intending Authors
We are seeking original, innovative, and scientifically rigorous
papers presenting practical experiences, methodological
challenges, or impacts of Big Data Analytics from the viewpoint
of electronic commerce. Especially empirical research, case
studies or theory based qualitative and quantitative studies,
are welcome.
Submitted papers should not have been previously published nor
be currently under consideration for publication elsewhere.
Author guidelines can be found at
http://www.jtaer.com/author_guidelines.doc. All submissions will
be refereed by at least three reviewers. Submissions should be
directed by email to
jtaer.big.data@utalca.cl
For more information, please visit the following web site:
http://www.jtaer.com
Important dates
- Full paper submission: March 30, 2015
- Notification of acceptance: June 15, 2015
- Revised submission: July 20, 2015
- Final acceptance notification: August 24, 2015
- Camera ready version of paper: September 21, 2015
- Publication: January – May, 2016
Guest Editors
Dr. Jouni Markkula
Senior Research Fellow
Department of Information Processing Science
University of Oulu
Finland
Dr. Marikka Heikkilä
Senior Research Fellow
Centre for Collaborative Research
Turku School of Economics, University of Turku
Finland
Prof. J. Christopher Westland
Department of Information & Decision Sciences
University of Illinois
USA
Prof. Zhangxi Lin
The Rawls College of Business Administration
Texas Tech University
USA
Prof. Jukka Heikkilä
Dept. of Management and Entrepreneurship
Turku School of Economics, University of Turku
Finland