Shu‐Chuan Liao, Kuo‐Fong Kao, I‐En Liao, Hui‐Lin Chen and Shu‐O Huang
As library collections increase rapidly, personalized recommender systems have become a very important service for library patrons. The purpose of this paper is to design and…
Abstract
Purpose
As library collections increase rapidly, personalized recommender systems have become a very important service for library patrons. The purpose of this paper is to design and implement a personal ontology recommender (PORE) system by building personal ontologies based on patrons' borrowing records.
Design/methodology/approach
In the PORE system, the traditional cataloging scheme, classification for Chinese libraries, is used as the reference ontology. This reference ontology is transformed to a unique personal ontology for each user based on the mining results from library borrowing records of that user.
Findings
A personal ontology represents a unique user interest on specific subjects. The personal ontology can be used to filter out unsuitable recommendations based only on a keyword matching method. Besides, the recommended books can be organized into the personal ontology, and provide the patron with a user‐friendly interface to access library collections.
Research limitations/implications
The PORE system is currently implemented for Chinese collections. From this paper builds a new version to support English collections by adopting the Library of Congress Classification as the reference ontology.
Originality/value
This paper represents a practical method of building a user's personal ontology and explains the functional use of ontology knowledge.
Details
Keywords
Yuanzhu Zhan, Kim Hua Tan and Robert K. Perrons
In today’s rapidly changing business environment, the case for accelerated innovation processes has become increasingly compelling at both a theoretical and practical level. Thus…
Abstract
Purpose
In today’s rapidly changing business environment, the case for accelerated innovation processes has become increasingly compelling at both a theoretical and practical level. Thus, the purpose of this paper is to propose a conceptual framework for accelerated innovation in a data-driven market environment.
Design/methodology/approach
This research is based on a two-step approach. First, a set of propositions concerning the best approaches to accelerated innovation are put forward. Then it offers qualitative evidence from five case studies involving world-leading firms, and explains how innovation can be accelerated in different kinds of data-driven environments.
Findings
The key sets of factors for accelerated innovation are: collateral structure; customer involvement; and ecosystem of innovation. The proposed framework enables firms to find ways to innovate – specifically, to make product innovation faster and less costly.
Research limitations/implications
The findings from this research focus on high-tech industries in China. Using several specific innovation projects to represent accelerated innovation could raise the problem of the reliability and validity of the research findings. Additional research will probably be required to adapt the proposed framework to accommodate the cultural nuances of other countries and business environments.
Practical implications
The study is intended as a framework for managers to apply their resources to conduct product innovation in a fast and effective way. It developed six propositions about how, specifically, data analytics and ICTs can contribute to accelerated innovation.
Originality/value
The research shows that firms could harvest external knowledge and import ideas across organisational boundaries. An accelerated innovation framework is characterised by a multidimensional process involving intelligence efforts, relentless data collection and flexible working relationships with team members.