Enterprise Information Systems Contribute to our special issue on Social Media Data in Business Decision-Making

Enterprise Information Systems

The social media platforms, such as Twitter, Facebook and YouTube, provide rich data sources for managers to capture and to analyse the potential customer comments in order to support evidence based business decision-making. However, most of the social media platforms often provide high volume data that only contain a low density of useful information. For example, unlike survey data, a tweet only contains very little relevant information for marketing researchers to analyse customer’s preferences. Both academics and practitioners endeavour to find ways in developing a thoughtful and holistic approach to analyse social media data. Such social media data include a variety of structured and unstructured data, including text data (comment), meta-data, and network data that make the analytical process more difficult and challenging. Researchers are further challenged to find new research techniques to cross-validate the reliability of social media data.

Due to the newness of social media data, the value of social media data is still not fully revealed. There are several barriers to overcome for utilising the social media data as a major source of information in decision-making and establishing strategies in companies. The obstacles involve (i) identifying data sources, (ii) capturing relevant datasets, (iii) the integration of various structured and unstructured data, (iv) examining the reliability and validity of social media data. Existing research focuses on the development of information processes, data-mining, and network analysis models to discover information about customer behaviour. However, the utilisation of integrating various social media data, the robustness of analysis and theories behind analytical frameworks are rarely explored and discussed.

This special issue focusses on contemporary research related to social media data and customer interactions on social media platforms. Particularly, those papers that pinpoint new theories, combine different research techniques and contain breakthroughs in utilising social media data are of considerable interest. Specifically, it addresses the following questions:

  • How do various information system theories provide a useful lens for understanding the development of the best data analytical models in social media data and their control mechanisms?
  • What research techniques are used to establish the reliability and validity of social media data?
  • What could new theories in various areas (e.g. sociology, applied psychology, business and management) be related to the development of social media analytics models and offer a strong justification for managers' effective decision-making process and/or better understanding for customer interactions?
  • How do companies integrate new data sources with social media data to establish new business strategies and how do they implement these strategies in their daily business operations in order to enhance their performance?

Topics of interests include, but are not limited to, the following issues:

  • Information diffusion analysis in social media network.
  • Customer behaviour and influence analysis.
  • The relationship between social media platform management practices and their impact on performance.
  • Consumer risk perception and decision-making in social media platform.
  • Company’s strategies for responding to concerns raised by contemporary customers linked with social media.
  • The use of social media for business crisis communication.
  • Social media rumour identification and mitigation in business scandal.
  • Managing fake information and/or comments on social media platform.
  • New research techniques and their combinations to establish the reliability and validity of social media data.
  • Trust and credibility in social media data.
  • Product innovation using social media data.
  • Shaping and establishing marketing strategies by adopting any social media data-driven approaches.
  • Getting industrial insights by using case studies of social media data analytics.
  • Data mining for social networks and influential users’ detection.
  • Differences between the customer-generated content from different platforms (e.g. Twitter, Facebook, YouTube, and so on) and their impacts on evidence based business decision-making.

Submission instructions

Original papers describing completed and unpublished work not currently under review by any other journal/magazine/conference/workshop are solicited. Previously published conference/ symposia/ workshop papers or technical reports MUST be clearly clarified by the authors (at the submission stage) and an explanation should be provided how such papers have been extended to be considered for this special issue.

Prospective authors should submit via the submission site of Enterprise Information Systems. Submitting authors must answer ‘Yes’ to a question of “Is this submission for a special issue?” and then select “Social Media Data in Business Decision-Making” from the drop-down menu.

Editorial information

  • Guest Editor: Ying Kei (Mike) Tse, The York Management School, The University of York, UK (mike.tse@york.ac.uk)
  • Guest Editor: Chun Ho (Jack) Wu, Supply Chain and Information Management, Hang Seng Management College, Hong Kong (jackwu@hsmc.edu.hk)
  • Guest Editor: Pervaiz Akhtar, Hull University Business School, University of Hull, UK (Pervaiz.Akhtar@hull.ac.uk)
  • Guest Editor: Yongyi Shou, School of Management, Zhejiang University, China (yshou@zju.edu.cn)