Emerging technologies, Enterprise Systems and Knowledge Management
Enterprises systems are systems that integrate all data and information systems and services as a type of Enterprise Knowledge Directory (EKD: Galup, Dattero, & Hicks, 2003). They are internal to the enterprise and can gather and interpret data and information from outside the enterprise’s boundary (e.g. internet, supply chains, etc.) to serve multi-organizations. Enterprise systems can integrate ICT -related communications; for example, record video conferences for storage, retrieval, analysis and use in decision-making. As an all embracing and integrated organizational-based data, information and intelligence- based system, Enterprise Systems should logically be related to organizational KM and certainly integral to organizational KMS. Surprisingly, however, the literature has barely made this connection.
Knowledge management in the organisation relies in great part on the information systems in which the data are entered, processed, stored
and extracted as the output and the basis for analysis. This type of information system in the context of an enterprise are often called enterprise systems (Przemyslaw, 2014) or Enterprise Resource Planning (ERP) Systems. They require high implementation costs and are normally customized to fit individual organizational requirements.
As indicated by the literature, organizational knowledge sharing is greatly affected by the service quality, system quality and technology in the enterprise systems (Somayyeh and Ali, 2018). For example, at the operational level, business processes are the foundation for designing, implementing, and staff training in the adoption or upgrading of enterprise systems that influence the service quality and system quality. At the same time, managing knowledge life cycles and aligning the analysis to process management is essential to create business value (Christian, 2014).
At the strategic level, Business Intelligence and data analytics are often built-into the enterprise systems to serve executive decision making. These systems can gather key information by sorting and extracting data from the distributed databases in large organisations including multinational and global enterprises. The process is actually a way of articulation as described by Richard, Herschel, and Nory (2005, p50), converting tacit knowledge into explicit knowledge by specifying the purpose of the decision, articulating parameters, objective functions, and relationships, providing what-if analysis, and evaluating the alternatives. They further suggest that there exists a reciprocal interaction effect between KM and BI as the need to correct and validate the computational logic of data requires the sense-making capabilities of knowledgeable people. As such there are implications that to most effectively manage knowledge in organisation requires coordination with the implementing and running of enterprise systems in such areas as design, training, user environment, culture, and top management involvement and IT support.
Despite the critical role of enterprise systems in organizations, there has been little literature studying it in relationship to the management of organizational knowledge and the context of knowledge management systems. For example, it is difficult to locate research investigating how KM is organized and maintained within enterprise system environments, and what the influences of the contextual factors such as the industry type, firm sizes, and top management are. Enterprise systems are designed into many different forms in the markets. Using SAP as an example; it has been adopted by city councils, the army, food industry, airlines, and recently insurance corporations (SAP, 2014). There are also enterprise systems for not-for-profit organizations such as hospital resource planning (HRP) in hospitals and healthcare organizations, computer reservation system (CRS) in the tourism industry, and manufacturing execution system (MES) in production industries.
In addition, the dynamic nature of business and technology can also impact the evolution of the enterprise systems (Wang, Pauleen and Chan, 2013). To reflect rapid changes in recent years, technologies need to be studied with ES and KM in order to understand how KMS may be impacted due to changes wrought by emerging technologies (e.g., IOT, Big Data, social media tools, and artificial intelligent) and ES.
Industry practitioners and academic researchers have focused on the importance of investment and adoption of emerging technologies for quality and productivity improvements. There is a need to see how these factors with the ongoing development of knowledge management literature and the increasing functionality and scales of enterprise systems in the changing business world.
Knowledge Management Research & Practice
Knowledge management is a term that has worked its way into the mainstream of both academic and business arenas since it was first coined in the 1980s. Interest has increased rapidly during the last decade and shows no signs of abating.
What can I contribute?
Submissions are invited to investigate these phenomena. Areas of particular interest include: how new technologies enhance knowledge management in enterprise systems settings; how changes in business affect the evolution of KMS. Prospective topics include, but are not limited to the list below and all social science research methods will be considered.
- Knowledge Management Systems that exist in the various forms of Enterprise Systems, such as Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), Supply Chain Management(SCM), Manufacturing Execution Systems (MES) , Customer Reservation Systems (as used in airline, hotel, and etc.) and Hospital Resource Planning (HRP).
- Relationships between KM and Enterprise Systems and business models, strategies, and business processes
- The level (and kinds) of knowledge stored in enterprise systems, including subordinate, company, enterprise, and multi-national levels
- Emergent technologies and KM in Enterprise Systems, such as no-SQL database, Big Data, cloud computing, IOT, AI, and Industrial 4.0.
- KM via Data Warehouse, Business Intelligence, and Data Analytics in Enterprise Systems
- Change management for KM in the Enterprise Systems: e.g., merger and acquisitions, amalgamation of subordinates, new business development, or other reasons for major systems upgrades and integration
- With respect to emerging technologies:
- IT Organisation for KM in Enterprise Systems
- IT Architecture for KM in Enterprise Systems
- Vendor and customer aspects and perspectives
- Role of Executives
- KM in enterprise systems used in the sharing economy, healthcare industry, not-for-profit organization and government sectors
- Case studies specific to ES and KM on SMEs, large organisations and global enterprises related to the topic of the special issue
- Legal issues, risk management and information security, governance
- Christian Stary, (2014) "Non-disruptive knowledge and business processing in knowledge life cycles – aligning value network analysis to process management", Journal of Knowledge Management, Vol. 18 Issue: 4, pp.651-686.
- David C. Chou, Hima Bindu Tripuramallu, Amy Y. Chou, (2005) "BI and ERP integration", Information Management & Computer Security, Vol. 13 Issue: 5, pp.340-349.
- Evans, N. & Price, J. (2016) . Enterprise information asset management: the roles and responsibilities of executive boards. Knowledge Management and Research Practice, Vol 14: 353 - 361.
- Galup, S., Dattero, R. & Hicks, R. (2003). The enterprise knowledge dictionary. Knowledge Management and Research Practice, Vol 1, p95 -101.
- Przemyslaw Lech, (2014) "Managing knowledge in IT projects: a framework for enterprise system implementation", Journal of Knowledge Management, Vol. 18 Issue: 3, pp.551-573.
- Richard T. Herschel, Nory E. Jones, (2005) "Knowledge management and business intelligence: the importance of integration", Journal of Knowledge Management, Vol. 9 Issue: 4, pp.45-55
- SAP, 2014, extracted on the 3rd of September,2018: from https://news.sap.com/2014/06/sap-nan-shan-life-build-best-practice-solutions-insurance-industry-taiwan/
- Somayyeh Mirzaee, Ali Ghaffari, (2018) "Investigating the impact of information systems on knowledge sharing", Journal of Knowledge Management, Vol. 22 Issue: 3, pp.501-520.
- Wang, William YC*, Pauleen D, and Chan HK, 2013, “Facilitating the Merger of Multinational Companies: A Case Study of the Global Virtual Enterprise”, Journal of Global Information Management, 21(1), pp.42 -58. (SSCI, A)
- Deadline to submit papers for the SI: 30th Nov 2019
- First review round by 28 Feb 2020
- Second review round by 30 April 2020
- Planned Publication: Summer 2020
Manuscripts should be original, unpublished, and not currently under consideration for publication elsewhere. All submission must follow the instructions to authors that can be found on the journal homepage: www.tandfonline.com/kmrp
Other inquiries should be sent clearly indicating in the subject “Special issue in Knowledge Management Research & Practice” to the Guest Editors:
William Yu Chung Wang firstname.lastname@example.org
David Pauleen email@example.com