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Critical discourse analysis has become an increasingly popular methodology in organization and management studies. In this article, the authors explore the potential for this methodology to be more widely used in strategic management research. They begin by identifying three research approaches that, to a greater or lesser extent, share a concern with the relationship between language and the formulation and implementation of strategy—strategy as a system of shared meaning, strategy as text and talk, and strategy as truth. They then discuss how critical discourse analysis can be used to extend and develop these approaches by exploiting their underlying complementarities. Finally, using the example of a recently completed case study of strategic change in a large banking and financial services institution, they explore the practical implications of applying critical discourse analysis in strategic management research.

The disastrous effect of flood has shown its influence ifn the past, and as a result, millions of dollars infrastructure have been shattered. Even after so much research, still there is no global ubiquitous system that can collect, store and analyze big data and generate the flood prediction results. In this paper, a social collaborative Internet of Things (IoT) based smart flood monitoring and forecasting architecture is proposed with the convergence between big data and HPC. It classifies geographical areas into a web of hexagonal for effective installation of energy efficient IoT devices. All relevant flood causing and flood preventing attributes are sensed using these IoT devices and computed by big data and HPC processing. Singular Value Decomposition (SVD) is used for attributes reduction. The K-mean clustering algorithm is used to predict the current state of flood and flood rating in any location, whereas Holt-Winter's forecasting method is used to forecast the flood. Experimental evaluation is being done on meteorological data collected by the Indian government and results indicated the effectiveness of the proposed architecture.

Knowledge management (KM) dynamics have caused a lack of traceability and loss of explicit and tacit knowledge during a project's lifecycle. In addition, individuals desire ease of use and accessibility, suggesting that social media (SM) should be integrated. For this purpose, this research analyzed a solution with a technical instrument, through a design science research approach, with the intention of answering the research question: How well does knowledge project management work with the integrated use of project management tools? The Social Media for Project Management (SM4PM), a prescriptive framework for guiding the integrated use of SM in project management (PM), was instantiated to evaluate KM in PM in a public security organization. Data collection was done through interviews, direct observations, document analysis, and focus group. These data were analyzed using MaxQdaPlus. After the implementation, SM4PM was refined and redesigned. Results showed that SM support KM in activities related to PM, giving strong evidence that SM4PM can be generalized to solve a class of problems, such as collecting lessons learned naturally during the project lifecycle, managing the knowledge in PM, and understanding the relationship between processes and their integration. As a contribution, the study empirically applied "theory to practice" by instantiating a technical instrument based on the "theory of doing well" and applied "theory from practice" to refine this technical instrument. This applied research solves a class of problems involving KM in PM during the whole project lifecycle with a unique artifact.

Presentation given by Dr Marta Teperek, Research Data Facility Manager, University of Cambridge at the Research Support Masterclass, 23 June 2016, Rotterdam.

Purpose
The purpose of this paper is to investigate when and how to best use social network analysis (SNA) in the supply chain management (SCM) discipline. In doing so, the study identifies SCM phenomena that have been examined from a social network perspective (SNA approach) in the SCM literature and highlights additional SCM phenomena that would be worth investigating using social network research. Then, the study critically investigates the application of SNA as a methodology (SNA method), with the goal of assessing and mitigating methodological risks in future studies.


Design/methodology/approach
This study carries out a systematic literature review of articles published in 11 top-tier SCM journals over a 20-year period.


Findings
First, while social network research has gained momentum especially since 2010, scholars are not yet entirely aware of the many possibilities the SNA approach offers to the SCM field. Second, expanded possibilities also hold for the development of SNA as a method.


Originality/value
The paper guides future SCM research by investigating when SNA is the right approach to use and how SNA as a method should be performed. Theoretically richer and practically more relevant research should result.

This data management plan was created using the OPIDoR (Optimiser le partage et l'interopérabilié des données de la recherche/ Tools and services to support french research data mangement). It describes all data collected and created as part of the postdoctoral research for the project "Circulation of knowledge: between sciences, policies, and practices in education (CIVOIR)" under the scientific direction of Professor Françoise Lantheaume and with the participation of German Fernandez Vavrik, University Lumiere Lyon 2, France. The data produced in this survey was collected through a mechanism specifically created for this project: the cooperative laboratory - CoopLa.

Behavioural data are important for group research and are widely used to inform and validate relevant theoretical propositions. There is more debate about the role of such data in illuminating the psychological processes that underpin group behaviour itself. This article focuses on this debate and outlines that behavioral data are limited because (a) they have difficulty distinguishing between interactions between individuals-as-individuals and individuals-as-group members, and (b) when the psychological group is properly understood it becomes clear that much of group research concerns individuals-as-group members and the emergence of a shared social identity (along with shifts in self-definition from I to we). The implication is that behavioural data per se are limited in being able to infer the very psychological processes that are central to group research. In conclusion, examples are outlined where in combination with a social identity perspective (and related work) behavioural data have been informative in advancing group research.