While the issue of gaining access to survey respondents is often considered in the research methods literature, the focus is usually on gaining cognitive access within a single organisation. In this chapter, based on our own experiences of conducting a UK national survey, we focus on difficulties associated with gaining physical access to respondents holding a particular role in a large number of organisations. Based on the challenges we eventually overcame, we make a number of key recommendations for researchers.
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Mark N.K. Saunders and David E. Gray
Professor Peter Allen
Many doctoral students live a difficult period while defining their research question. The broadness of initial thoughts often leads to an ocean of information. The process of learning how to select proper information and networking within their research field is a key to developing a proper research question.
Professor Pierpaolo Andriani and Professor Giuseppe Carignani
This chapter discusses the complex analogy between biological evolution and technological innovation, focusing in particular on the novel construct modular exaptation. After carefully defining exaptation – a biological concept whose technological analogue is useful in innovation studies – the chapter explores its epistemological bases, arguing that the etiological concept of function – a biological tenet – is valid also in the technological domain. The complex analogy extends to biological and technological functional modules, providing the main building block on which modular exaptation can be founded. Establishing a complex analogy enables the description of the two domains via the same relational structure. In turns this allows the transferability of knowledge from the base domain to the target domain, and vice-versa. The complex analogy can therefore be considered a methodological tool for understanding complex systems in general and technological innovation in particular, as discussed in the final section of the chapter.
Professor Peter Allen
A methodology implies a purpose which here is to reveal and understand what patterns and structures exist in social systems and how, why and when they occur. In the natural sciences, we can perform repeatable experiments that allow us to find robust general laws by induction and make predictions about specific behaviour by deduction. In social systems, however, agents inhabiting a situation are really in co-evolution with each other and their environment, hence constantly changing over time. This makes induction for general laws much harder and predictions on the basis of deduction questionable. Complexity Science provides a ‘scientific’ basis for evolutionary, qualitative changes, revealing the impossibility of guaranteed prediction. We use several examples to show how complexity and evolution involve changing systems of changing elements – both qualitative and quantitative. Our interpretive frameworks (models) do not make predictions about the world but about themselves thus making, through reflexivity, evasive action more likely.
Assistant Professor Sanjay Kumar Palit, Associate Professor Santo Banerjee and Assisant Professor Sayan Mukherjee
The socio-economic scenario of a country reflects its social, economic, political, ideological, ethical, cultural, or communicative habits, making its proper analysis for different countries quite challenging. Complexity science has provided some new methods and tools for dealing with this challenge. Country-level Gross Domestic Product (GDP) and population are the two most important issues in the socio-economic context. In order to show the effectiveness of different nonlinear tools in analysing socio-economic data, the authors implemented three popular nonlinear tools: recurrence rate, mean conditional recurrence (MCR) and complex networks (CN) to analyse country level GDP and population data to validate the derived results with the standard conclusions based on general theories of economics. recurrence rate is used to show how two non-identical systems get synchronized through their phase spaces. MCR detects the driver and response system in synchronized states and CN reflects the overall scenarios of the complex systems by its various statistical measures.
Dr Lesley Kuhn
This chapter describes and demonstrates a complexity informed qualitative social research approach, associated methods and techniques. To theorize human experience and sense making from findings and ideas of complexity studies in nature, translation and interpretation is required. The chapter explains these translations and interpretations based on the assumption that qualitative research, like complexity, has to take a radically relational approach to interpreting interrelationships between sense makers, fragments of knowledge, cultures, histories, futures and aspirations. It sets out how complexity offers a paradigmatic orientation to assist qualitative researchers in discerning nonlinearly-based pattern and order. Vortical postmodern ethnography as an inquiry approach is described along with the narrative generating method of coherent conversations and the techniques of fractal narrative analysis and attractor narrative analysis. Vortical postmodern ethnography effectively re-conceptualizes a number of problematic issues in ethnographic inquiry. The chapter concludes with a demonstration of this inquiry approach and techniques in a research project.
Professor Henrik Jeldtoft Jensen
Dr Babak Pourbohloul, Dr Krista M. English and Dr Nathaniel Hupert
The increase in emerging infectious diseases has led to the allocation of significant time and resources for the development of pandemic preparedness plans worldwide. Nevertheless, real-time management of emerging disease outbreaks is often marked by confusion and uncertainty as decision-makers are challenged to make impactful decisions with little time and incomplete information. Health authorities typically approach such threats by individual level interventions, such as vaccines and antivirals. This does not, however, detail how these targeted interventions and countermeasures should be used to optimally benefit total population health. Mathematical modelling of complex systems represents the bridging science that is needed. This chapter discusses the conceptual design and structure of mathematical models of communicable diseases, using transmission dynamics in the context of respiratory-borne pathogens within human populations. It demonstrates the necessity of assembling appropriate expertise related to mathematical modelling, epidemiology, public health, virology, and clinical management to ensure valuable quantitative decision-support tools to assist policymakers at the time of crisis.
This chapter uses a PhD project to explain how patterns of unexpected findings and an abnormal case were used to develop new ideas that resulted in successful completion of the PhD and a number of publications.