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Journal Article

A Critique of Using Grounded Theory as a Research Method  pp1-10

George Allan

© Jul 2003 Volume 2 Issue 1, Editor: Frank Bannister, pp1 - 77

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Abstract

Grounded Theory is a powerful research method for collecting and analysing research data. It was 'discovered' by Glaser & Strauss (1967) in the 1960s but is still not widely used or understood by researchers in some industries or PhD students in some science disciplines. This paper demonstrates the steps in the method and describes the difficulties encountered in applying Grounded Theory (GT). A fundamental part of the analysis method in GT is the derivation of codes, concepts and categories. Codes and coding are explained and illustrated in Section 3. Merging the codes to discover emerging concepts is a central part of the GT method and is shown in Section 4. Glaser and Strauss's constant comparison step is applied and illustrated so that the emerging categories can be seen coming from the concepts and leading to the emergent theory grounded in the data in Section 5. However, the initial applications of the GT method did have difficulties. Problems encountered when using the method are described to inform the reader of the realities of the approach. The data used in the illustrative analysis comes from recent ISIT Case Study research into configuration management (CM) and the use of commercially available computer products (COTS). Why and how the GT approach was appropriate is explained in Section 6. However, the focus is on reporting GT as a research method rather than the results of the Case Study.

 

Keywords: Grounded Theory, codes, concepts, emerging categories, emergent theory

 

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Journal Article

Sequencing Effects in the Analysis of Complex Experiments in Business Research: Mechanisms, Biases, and Recommendations  pp165-178

Peter Kotzian

© Sep 2019 Volume 17 Issue 3, Editor: Ann Brown, pp102 - 191

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Abstract

Experiments in business research became more complex over time, yielding complex sequences of stimuli and measurements. This raises the issue of sequence effects, where effects are found only in specific sequences of the experiment. One case in point is factorial surveys. Here, presenting the stimulus is followed by asking subjects to evaluate several vignettes presented in a certain sequence. The researcher is interested in the effect of the stimulus on responses to vignettes with certain features. As sequence and stimulus can be made uncorrelated by construction, holding the sequence constant or excluding the sequence from the analysis seems to be justified when researchers are only interested in effects of vignette features or the stimulus. In both cases, even if the sequence is relevant for the dependent variable, correlation between sequence and stimulus, the necessary condition for an omitted variable bias, is absent. The effect estimated for the stimulus should thus be unbiased. We show that even in the case where stimulus and sequence are uncorrelated or the sequence is held constant, an omitted variable bias occurs when the effect of the stimulus in a vignette is in its magnitude dependent on the sequence in which the vignettes were presented. Such an effect would be modeled by including a sequence‑stimulus‑interaction term and the omitted variable is this interaction term, which is, by construction, always correlated with each of the constitutive variables. A simulation is presented to illustrate the problem. Implications for experimental research are discussed.

 

Keywords: Experimental Design; Factorial Surveys; Order-effects; Omitted-Variable Bias JEL Codes: C21; C91

 

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Journal Issue

Volume 17 Issue 3 / Sep 2019  pp102‑191

Editor: Ann Brown

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Keywords: entrepreneurs, entrepreneurial skills, mixed-methods, qualitative, quantitative, Rigour, trustworthiness, auditability, credibility, transferability, methods pedagogy, TACT, Problem-based learning, teaching research methods, first year UG business students, business research process, thematic analysis, pattern matching, case study research, deductive qualitative analysis, leading organisational change, mixed method, social media research, Q factor analysis, Q methodology, Q study, Experimental Design; Factorial Surveys; Order-effects; Omitted-Variable Bias JEL Codes: C21; C91, Research methodology; Innovation; Technology; Technological change; Management; Crowdsourcing

 

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