Purpose Current knowledge management (KM) systems cannot be used effectively for decision-making because of the lack of real-time data. This study aims to discuss how KM can benefit by embedding Internet of Things (IoT). Design/methodology/approach The paper discusses how IoT can help KM to capture data and convert data into knowledge to improve the parking service in transportation using a case study. Findings This case study related to intelligent parking service supported by IoT devices of vehicles shows that KM can play a role in turning the incoming big data collected from IoT devices into useful knowledge more quickly and effectively. Originality/value The literature review shows that there are few papers discussing how KM can benefit by embedding IoT and processing incoming big data collected from IoT devices. The case study developed in this study provides evidence to explain how IoT can help KM to capture big data and convert big data into knowledge to improve the parking service in transportation.
Purpose This paper aims to define a conceptual framework for transforming Big Data into organizational value by focussing on the perspectives of service science and activity theory. In coherence with the agenda on evolutionary research on intellectual capital (IC), the study also provides momentum for researchers and scholars to explore emerging trends and implications of Big Data for IC management. Design/methodology/approach The paper adopts a qualitative and integrated research method based on a constructive review of existing literature related to IC management, Big Data, service science and activity theory to identify features and processes of a conceptual framework emerging at the intersection of previously identified research topics. Findings The proposed framework harnesses the power of Big Data, collectively created by the engagement of multiple stakeholders based on the concepts of service ecosystems, by using activity theory. The transformation of Big Data for IC management addresses the process of value creation based on a set of critical dimensions useful to identify goals, main actors and stakeholders, processes and motivations. Research limitations/implications The paper indicates how organizational values can be created from Big Data through the co-creation of value in service ecosystems. Activity theory is used as theoretical lens to support IC ecosystem development. This research is exploratory; the framework offers opportunities for refinement and can be used to spearhead directions for future research. Practical implications The paper proposes a framework for transforming Big Data into organizational values for IC management in the context of entrepreneurial universities as pivotal contexts of observation that can be replicated in different fields. The framework provides guidelines that can be used to help organizations intending to embark on the emerging paradigm of Big Data for IC management for their competitive advantages. Originality/value The paper’s originality is in bringing together research from Big Data, value co-creation from service ecosystems and activity theory to address the complex issues involved in IC management. A further element of originality offered involves integrating such multidisciplinary perspectives as a lens for shaping the complex process of value creation from Big Data in relationship to IC management. The concept of how IC ecosystems can be designed is also introduced.
Task knowledge structures can be adapted to determine the cognitive abilities of Net newbies and help create support systems to help shape their search experiences.Because of its inexpensive and ubiquitous nature, many users regard the Internet as the first and only point of access to information to meet their needs. Yet it is known generally that information accessed via the Internet is ill formed, unorganized, and difficult to access. This leads to a variety of problems and frustrations for users, especially novices. The primary, and sometimes only, means to access much of this information are Internet search engines. When novices use search engines without strong mental models for information retrieval-especially in complex environments such as the Internet-they are not likely to achieve success at information gathering.Mental models are cognitive constructs of knowl-specific subject domains. A mental model for searchedge and experiences used to interpret the world [7]. ing on the Internet encompasses several overlapping It has been suggested that a mental model can be domains, including searching in general, IT skills, and developed or strengthened by learning and practicing lmowledge about the subject being searched [1]. skills [4]. Mental models represent a collection of Because mental models are complex and difficult knowledge that builds a foundation of understanding to articulate, we must develop ways to identify and and provides the tools for problem solving in a given characterize them. One approach is to capture eledomain. This is different from a system model, which ments of domain knowledge as they are being applied is a conceptual understanding of how a system works or put to use. Such elements are thought to include (often called a conceptual model). Users often under-understanding the structure and ftinctions of a sysstand their mental models only through conceptual-tem, as well as the relationships between them and ization as systems models. For instance, people might their outcomes [5]. Task knowledge structures (TKS) say, "My mental model for searching for information theory argues tlie knowledge people possess and on the Internet is using Google," when in actuality employ while performing tasl<:s can be represented in they are describing a system model. Systems models a structured way. A TKS is a summary of lcnowledge equate to analogies of specific systems, whereas men-that can be called upon when performing tasks or tal models accumulate broader knowledge in order to solving problems associated with a task [3]. The anticipate, interpret, and solve complex problems in notion of linking TKS to mental models is based on
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