This paper aims to provide a comprehensive understanding and guideline for conducting qualitative research with a flexible-pattern-matching design. Moreover, the growing questions on the well-developed grounded theory also call for avenues with more rigorous analysis processes and stronger relationships with the existing literature (Cuervo-Cazurra et al. 2014, 2019), there is a need to gain more insights into its foundation, its relation to established qualitative methods, and how it best informs theory building. In this paper, to ensure the consistent use of terms and avoid confusion, we refer to the approach that involves the flexible pattern matching logic as “flexible pattern matching”.ĭespite or rather because of the growing number of studies applying the principles of this type of flexible pattern matching (Bouncken and Barwinski 2020 Gatignon and Capron 2020 Sinkovics et al. However, before Sinkovics ( 2018) systematically categorized pattern matching into the three overarching categories, scholars did not differentiate between these distinct approaches, referring to all three as pattern matching. Further theorizing and/or theory development is then triggered by mismatches between theoretical patterns and observed patterns, or by the emergence of unexpected observed patterns (Bouncken and Barwinski 2020 Bouncken et al. Recently, there is an emerging body of work in this category that builds on a systematic or semi-systematic review of the literature to define the initial theoretical patterns that are then matched to the data (Bouncken and Barwinski 2020 Sinkovics et al. Eisenhardt’s ( 1989) theory building approach from cases and King’s ( 2004) template analysis are examples of the flexible pattern matching logic. It allows the interaction of deductive and inductive components, thus combining rigor with a high level of flexibility.
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In contrast, flexible pattern matching spans the space between partial and full pattern matching. In the qualitative domain, full pattern matching entails the operationalization of multiple theoretical explanations for a specific phenomenon to determine which explanation is the most accurate (Yin 2009 Yin and Moore 1988). Full pattern matching generally includes all statistical methods (Trochim 1985 Trochim and Donnelly 2008). Top-down partial pattern matching employs visualization techniques to specific bodies of literature to identify innovative research questions (Sinkovics 2016). The purpose is to start with the data and identify patterns that emerge from it. Grounded theory, as put forward by Glaser and Strauss ( 1968), and the Gioia ( 2004) method of structural coding represent examples of bottom-up partial pattern matching. Partial pattern matching aims to engage the investigator’s mental models in the theorizing process.
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This pattern matching framework allows the organization of the diverse qualitative techniques into three overarching categories namely partial pattern matching, full pattern matching, and flexible pattern matching.
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In this way, the approach provides readers with a roadmap to reproduce the researchers’ thought processes and how they arrived at the conclusions they present in their manuscript.
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As a technique, pattern matching applies this logic and provides a set of guidelines with respect to how to make the researchers’ internal mental models as explicit as possible. The reason comes from the observers’ interpretation they do it through a path-dependent lens developed through their past experiences and based on the amount of available information at the time of the observation. However, while some patterns can be described and interpreted with a high level of accuracy, others can only be interpreted with a certain level of probability (Hammond 1966). As a logic, the aim is to identify and describe patterns with as much accuracy as possible. Pattern matching is both a logic and a technique.
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The framework organizes the diverse qualitative techniques into different categories based on the degree of the pattern matching. The pattern matching framework put forward by Sinkovics ( 2018) creates a balance between the extremes of rigid standardization and complete anarchy.