Stephanie R. Seitz and Kaumudi Misra
The purpose of this paper is to bring a more individual focus to social networks in theorizing the social process of knowledge sharing.
Abstract
Purpose
The purpose of this paper is to bring a more individual focus to social networks in theorizing the social process of knowledge sharing.
Design/methodology/approach
The theoretical model proposes that political skill will shape an individual's social network. Further, political skill within a network will influence the degree of complex knowledge sharing, which likely happens through the mechanism of affective- and cognitive-based trust.
Findings
Theoretical implications and future research directions are discussed.
Originality/value
Knowledge sharing is an inherently social process and as such occurs within the context of social networks in an organization. However, research to date has not fully explored the details of how and why complex knowledge sharing happens within a social network. Generally, theory on social networks has focused on structural qualities of a network, rather than the individual characteristics of the members of that network. This paper brings a more individual focus to social networks in theorizing the social process of knowledge sharing.
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Keywords
John Paul Mynott and Stephanie Elizabeth Margaret O'Reilly
Lesson study (LS) is a collaborative form of action research. Collaboration is central to LS methodology, therefore exploring and expanding the understanding of the collaborative…
Abstract
Purpose
Lesson study (LS) is a collaborative form of action research. Collaboration is central to LS methodology, therefore exploring and expanding the understanding of the collaborative features that occur in LS is a priority. This paper explores the features of collaboration in existing publications on LS to consider if, as Quaresma (2020) notes, collaboration is simplistically referred to within LS research.
Design/methodology/approach
Utilising a qualitative review of LS literature to explore LS collaboration through Mynott's (2019) outcome model and Huxham and Vangen's (2005) theory of collaborative advantage and inertia. 396 publications using “lesson study” and “collaboration” as key words were considered and reviewed, with 26 articles further analysed and coded, generating a collaborative feature matrix.
Findings
While collaboration in LS is referred to generically in the articles analysed, the authors found examples where collaboration is considered at a meta, meso and micro level (Lemon and Salmons, 2021), and a balance between collaborative advantage and inertia. However, only a small proportion of LS publications discuss collaboration in depth and, while the matrix will support future research, more focus needs to be given to how collaboration functions within LS.
Originality/value
Through answering Robutti et al.'s (2016) question about what can be learnt from the existing LS research studies on collaboration, this paper builds on Mynott's (2019) outcome model by providing a detailed matrix of collaborative features that can be found in LS work. This matrix has applications beyond the paper for use by facilitators, leaders of LS, and researchers to explore their LS collaborations through improved understanding of collaboration.
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Marcello Mariani, Rodolfo Baggio, Matthias Fuchs and Wolfram Höepken
This paper aims to examine the extent to which Business Intelligence and Big Data feature within academic research in hospitality and tourism published until 2016, by identifying…
Abstract
Purpose
This paper aims to examine the extent to which Business Intelligence and Big Data feature within academic research in hospitality and tourism published until 2016, by identifying research gaps and future developments and designing an agenda for future research.
Design/methodology/approach
The study consists of a systematic quantitative literature review of academic articles indexed on the Scopus and Web of Science databases. The articles were reviewed based on the following features: research topic; conceptual and theoretical characterization; sources of data; type of data and size; data collection methods; data analysis techniques; and data reporting and visualization.
Findings
Findings indicate an increase in hospitality and tourism management literature applying analytical techniques to large quantities of data. However, this research field is fairly fragmented in scope and limited in methodologies and displays several gaps. A conceptual framework that helps to identify critical business problems and links the domains of business intelligence and big data to tourism and hospitality management and development is missing. Moreover, epistemological dilemmas and consequences for theory development of big data-driven knowledge are still a terra incognita. Last, despite calls for more integration of management and data science, cross-disciplinary collaborations with computer and data scientists are rather episodic and related to specific types of work and research.
Research limitations/implications
This work is based on academic articles published before 2017; hence, scientific outputs published after the moment of writing have not been included. A rich research agenda is designed.
Originality/value
This study contributes to explore in depth and systematically to what extent hospitality and tourism scholars are aware of and working intendedly on business intelligence and big data. To the best of the authors’ knowledge, it is the first systematic literature review within hospitality and tourism research dealing with business intelligence and big data.