Knowledge management practice of medical cloud logistics industry: transportation resource semantic discovery based on ontology modelling
| DOI | https://doi.org/10.1108/JIC-03-2020-0072 |
| Published date | 29 September 2020 |
| Pages | 360-383 |
| Date | 29 September 2020 |
| Subject Matter | Information & knowledge management,Knowledge management,HR & organizational behaviour,Organizational structure/dynamics,Accounting & finance,Accounting/accountancy,Behavioural accounting |
| Author | Fuli Zhou,Yandong He,Panpan Ma,Raj V. Mahto |
Knowledge management practice
of medical cloud logistics industry:
transportation resource semantic
discovery based on
ontology modelling
Fuli Zhou
College of Economics and Management, Zhengzhou University of Light Industry,
Zhengzhou, China
Yandong He
School of Intelligent Systems Engineering, Sun Yat-sen University,
Shenzhen, China and Research Center on Modern Logistics,
Tsinghua Shenzhen International Graduate School, Tsinghua University,
Shenzhen, China
Panpan Ma
College of Computer and Communication Engineering,
Zhengzhou University of Light Industry, Zhengzhou, China, and
Raj V. Mahto
Anderson School of Management, The University of New Mexico, Albuquerque,
New Mexico, USA
Abstract
Purpose –The booming of the Internet of things (IoT)and artificial intelligence (AI) techniques contributes to
knowledge adoption and management innovation for the healthcare industry. It is of great significance to
transport the medical resources to requiredplaces in an efficient way. However, it is difficult to exactly discover
matched transportation resources and deliver to its destination due to the heterogeneity. This paper studies the
medical transportation resource discovery mechanism, leading to efficiency improvement and operational
innovation.
Design/methodology/approach –To solve the transportation resource semantic discovery problem under
the novel cloud environment, the ontology modelling approach is used for both transportation resources and
tasks information modes. Besides, medical transportation resource discovery mechanism is proposed, and
resource matching rules are designed including three stages: filtering reasoning, QoS-based matching and user
preferences-based rank to satisfy personalized demands of users. Furthermore, description logic rules are built
to express the developed matching rules.
JIC
22,2
360
The authors would like to thank anonymous referees and editors for their valuable comments and
advice. This study is supported by the following projects: the Henan Province Philosophy and Social
Science Planning Project (2020CZH012), the Think-tank Programme of Henan Science & Technology
(grant no. HNKJZK-2020- 41C), the Scientific Rese arch Starting Fund from ZZU LI (grant no.
2018BSJJ071), the China Postdoctoral Science Foundation (grant no. 2019M660701), and Major
Application Research Program of Philosophy and Social Science in Henan Higher Education Institutions
[grant number 2019-YYZD-18].
Credit author statement: Fuli Zhou and Yandong He designed and wrote this research. Panpan Ma
performed the model verification, and contributed to a lot at the draft preparation. Raj V. Mahto provided
many constructive comments.
Disclosure statement: No potential conflict of interest was reported by the authors.
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/1469-1930.htm
Received 3 March 2020
Revised 4 June 2020
4 July 2020
Accepted 1 August 2020
Journal of Intellectual Capital
Vol. 22 No. 2, 2021
pp. 360-383
© Emerald Publishing Limited
1469-1930
DOI 10.1108/JIC-03-2020-0072
Findings –An organizationaltransportationcase is taken as an example to describe themedical transportation
logistics resourcesemantic discovery process under cloud medical service scenario. Results derived from the
proposed semanticdiscovery mechanism could assistoperators to find the most suitable resources.
Research limitations/implications –The case study validates the effectiveness of the developed
transportation resource semantic discovery mechanism, contributing to knowledge management innovation
for the medical logistics industry.
Originality/value–To improve task-resource matching accuracy under cloud scenario, this study develops a
transportation resource semantic discovery procedure from the viewpoint of knowledge management. The
novel knowledge management practice contributes to operational management of the cloud medical logistics
service by introducing ontology modelling and creative management.
Keywords Cloud medical logistics, Ontology modelling, Transportation resource semantic discovery,
Rule reasoning, Resource-task matching
Paper type Research paper
1. Introduction
With the development of cloud computing, Internet of Things(IoT) and Web service, human
society, information space and physical things would be closely connected. Advanced
techniquesand management philosophies,regarding as intellectual capitals,have been widely
used in organizational operations (Pirozzi and Ferulano, 2016;Evans et al.,2015;Zhou et al.,
2019a). Innovativemedical modes have been generated recentlyin healthcare industry under
Internet environment, for instance, online consultation, smart diagnosis, family medical
treatment and hometelehealth (Pirozzi and Ferulano, 2016;Peng et al., 2007;Shour,2003).
Knowledge, as a crucial part of intellectual capital, its effective absorption and application
lead to operational efficiency improvement (Petty and Guthrie, 2000). With the adoption of
innovative technologies and the Internet of things (IoT), intellectual capitals show an
increasing influence on performance improvement and brand reputation promotion by
knowledge management practices (Su et al., 2020;Zhou et al., 2018a). The organizational value
can be created by the successful operation of intellectual capitals, and organizational profits
can be achieved by knowledge acquisition, diffusion and management (Bourouni et al., 2015;
Su et al., 2018). The Internet-based technology and platform economy motivate the sharing
and opening of operational management with redundant information and big data.
The profitableknowledge that bringsfinancial benefits was calledthe intellectual capitalof
organizations.Knowledge management (KM) and intellectual capital management (ICM) have
covered multiple dimensions of the organizational operations (Mouritsen and Larsen, 2005).
The first focuses on facilitating and managing knowledge-related activities and the latter
concentrateson building and governing intellectual assets (Wiig,1997;Marchior i and Franco,
2020). These two management blocks should be interwoven with other segments to build a
competitivefirm and an industrial ecosystem.Intellectual capitalsincluded dimensionalassets
created through intellectual activities ranging from absorbing new knowledge (learning and
spreading)to creating valuable relationshipsand business (Marchioriand Franco, 2020). There
existing interrelationships among intellectual capital items and relational capital can affect
human capitalthrough organizational dimension (Liu and Jiang, 2020).The ethical knowledge
is a crucial component of intellectual capitals for poststructuralist (McPhail, 2009). For the
continuous improvement of the organization or the industry, the tacit knowledge must be
transferredto the explicit one, and dimensionalcapitals also must be transferredto intellectual
capitals by the innovative technology andcreative management practice.
The soaring gathered knowledge around organizations, industries and societies makes it
difficult to effectively organize and deploy the necessary industrial resources for a certain
purpose. To improve the operational efficiency of medical resources circulation, the sharing
philosophy and Internet-based tools are employed to innovate operational management by
gathering heterogeneous healthcare resources into a sharing platform Papa et al.,2018). In
addition,innovation managementmodes are also developedunder big data era,where plenty of
Knowledge
adoption and
management
innovation
361
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