Harmonizing Rigor,Relevance,and Insight: A Mixed Inductive Approach for Qualitative Analysis With the Gioia Methodology and the GABEK® Method

Abstract

Qualitative research is currently engaged in a significant debate regarding the balance between rigor and relevance within the field of organizational studies, emphasizing the need for flexible methodologies to capture the complexity of real-world practices while maintaining structured analysis processes. This paper focuses on the integration of the GABEK® Method and the Gioia Methodology, both employing inductive reasoning, Gestalt Theory, and Grounded Theory while possessing distinct characteristics that can complement each other to overcome shortcomings within one method. The study explores their application in understanding organizational culture change in professional service firms amidst the rising trend towards teleworking. Additionally, the paper highlights the absence of prescriptive rules in the Gioia methodology for conducting gestalt analysis and discusses adaptations made to incorporate GABEK® features. Through systematic data reduction and thematic analysis, the study reveals deeper meanings and interrelationships within the data, contributing to a nuanced comprehension of cultural dynamics in contemporary workplaces. Ultimately, this research underscores the importance of methodological innovation in enhancing the rigor and depth of qualitative studies, advocating for approaches that are adaptable to the evolving flexibility demands of organizational research.

Keywords

GABEK® method gioia methodology qualitative analysis qualitative research qualitative research design

Introduction

Qualitative research in organizational studies faces an ongoing and multifaceted debate regarding rigor and relevance (Brinkmann et al., 2014; Harley & Cornelissen, 2022; Roulston, 2010). Rigor in qualitative research typically refers to the systematic and self-reflective design, data collection, interpretation, and communication processes that ensure the quality and credibility of findings (Mays, 2000). In contrast, relevance pertains to the applicability and utility of qualitative research findings in real-world contexts, which often necessitates a degree of flexibility in research design; overly prescriptive measures may undermine the richness inherent in qualitative methodologies (Barbour, 2001). Establishing rigorous standards remains challenging due to the inherent subjective and contextual nature of qualitative inquiry, which stands in stark contrast to the more standardized approaches commonly found in quantitative research (Tong et al., 2012). While output-oriented approaches can enhance the credibility of qualitative research, they may inadvertently impose rigid structures that fail to accommodate the nuanced nature of qualitative inquiry (Reynolds et al., 2011).

The design of qualitative research is inherently complex, encompassing a variety of approaches and practices (Marshall & Rossman, 2016). Traditional qualitative methodologies often encounter significant challenges in ensuring rigor, validity, and reliability (Morse, 1994). To achieve rigor in qualitative research, it is essential to substantiate and reflect on the methodologies employed in categorization, analysis, and theory generation (Pilnick & Swift, 2011). Conversely, flexibility within the research process allows for the exploration of intricate organizational issues (Fade & Swift, 2011; Symon & Cassell, 2004). In the ongoing debate surrounding qualitative methods, the inherent flexibility of these approaches can be both an asset and a source of contention, with reliance on templates for qualitative analysis sometimes undermining rigor (Harley & Cornelissen, 2022). This perspective aligns with the broader discourse on the rigor-relevance debate, where the challenge lies in balancing methodological rigor with the practical applicability of research findings (Schelp & Winter, 2009).

Current methodologies still struggle to integrate the ongoing debate surrounding rigor versus relevance. For example, Mayring’s (2019) and Krippendorf’s (2018) qualitative content analyses emphasize a systematic approach that seeks to ensure rigor through predefined categories and coding schemes, thus providing a structured framework for data analysis (Nicmanis, 2024; Schultz & Reinhardt, 2022). However, these methods are often critiqued for their potential rigidity, which may limit the flexibility necessary to capture the richness of qualitative data. In contrast, the qualitative content analysis of Kuckartz (2012) advocates for a more flexible approach, allowing themes and categories to emerge during the analysis process, thereby enhancing the relevance of findings in specific contexts. Nonetheless, it has faced criticism for its tendency to fit responses into established frameworks of predefined categories and coding schemes (Nicmanis, 2024). Grounded theory (Glaser & Strauss, 1967) is another prominent methodology that focuses on generating theory from data through iterative coding and constant comparison, allowing for flexibility in the analysis process. This approach aligns with inductive reasoning principles, enabling researchers to adapt their analyses as new insights emerge. However, Grounded Theory also presents challenges, including the potential for methodological confusion and inconsistency in its application (Backman & Kyngäs, 1999), as well as concerns regarding subjective interpretation of data, which raises questions about the validity and reliability of the theories generated (Hall & Callery, 2001).

In conducting qualitative research using an example of organizational research, we encountered the challenges discussed in the literature. Consequently, we had to determine how to design our qualitative research project to address both rigor and relevance effectively. In this section, we propose a novel integration of the Gioia Methodology (Gioia et al., 2013) and the GABEK® Method (Zelger, 2003) that tackle the specific challenges encountered in qualitative analysis. Compared to other qualitative methodologies, such as those proposed by Kuckartz (2012) and Mayring (2019), both the Gioia Methodology and the GABEK® Method prioritize maintaining the informants’ voices throughout the research process. In contrast to Grounded Theory, both methods employ rules for analyzing the data. By using a structured yet flexible coding process, these methodologies enable researchers to capture the nuances of participants’ experiences while simultaneously developing theoretical insights (Gioia et al., 2013; Zelger, 1995, 2003). Both methods are particularly well-suited for exploring nascent concepts that lack adequate theoretical reference in the existing literature and encourage a more iterative and reflexive approach to qualitative research. Still, both do have their shortcomings (Mees-Buss et al., 2022).

The Gioia Methodology is a well-established inductive framework for qualitative research that incorporates elements of abductive reasoning (Magnani & Gioia, 2023). It is designed to facilitate the systematic analysis of qualitative data while preserving the perspectives of informants. This methodology emphasizes the creation of a visual data structure that aids in understanding how data are analyzed and interpreted. The process typically involves three key stages: first-order coding, second-order coding, and the development of aggregate dimensions (Gioia et al., 2013).

The GABEK® Method is distinctive in its foundation on the theory of linguistic gestalt and is complemented by the WinRelan® software, which facilitates its application (Buber & Kraler, 2000). It is a qualitative research strategy designed to analyze complex social phenomena through a structured and systematic approach. Developed by Zelger (2003), GABEK® facilitates the analysis of large, unstructured, and often controversial language data samples, making it particularly useful in contexts with limited research history. After preparing the data by dividing the text into text units, the content analysis consists of three rounds of coding, including keyword coding, coding of evaluations, and coding of causal relationships. The last step of analysis is the building of Gestalts based on a cluster analysis (Buber & Kraler, 2000).

Both methods share commonalities in their reliance on inductive coding, Grounded Theory principles, and Gestalt Theory. Furthermore, they incorporate a structured procedure for data analysis. This overlap suggests a promising potential for their synthesis, allowing researchers to leverage the strengths of both approaches while overcoming their limitations, as well as the limitations associated with other established content analysis methods, such as those of Kuckartz, Mayring, and Grounded Theory. To illustrate the practical application of this combined approach, we present a case study from organizational research.

The paper is structured as follows: First, we provide an overview of the Gabek® Method and the Gioia Methodology to familiarize readers with the foundations and the steps of analysis. Second, we compare both approaches to derive similarities, differences, and complementarities. Third, we introduce our new approach that combines the GABEK® Method and the Gioia Methodology for comprehensive data analysis, applying it to our research project on organizational culture change in professional service firms (PSFs). Finally, we discuss our experiences with the application of this new approach and draw conclusions.

Comparative Analysis of the GABEK® Method and Gioia Methodology: Similarities, Differences, and Complimentaries

The GABEK® Method and the Gioia Methodology show similarities and differences in qualitative data analysis. In this section, the foundational principles of each method are outlined (see Table 1 for a summary), followed by a detailed description of their respective data analysis procedures (see Table 2). Finally, the discussion addresses the potential for integrating both approaches through effective data display techniques.

Table 1.

Comparative Analysis of the Backgrounds of the GABEK® Method and the Gioia Methodology.

	GABEK® method	Gioia methodology
Approach/theory	Perceptive gestalt theory (Stumpf, 1939)	Grounded theory (Glaser & Strauss, 1967)
Approach/theory	Linguistic gestalt theory (Zelger, 1994; 1999b, 2003)	Social construction of reality (Berger & Luckmann, 1967)
Aim	Structured analysis of unorganized verbal data according to rule-based process steps to understand them better and organize knowledge (Zelger, 2019)	Systematic step-by-step analysis for rigorous qualitative data analysis generates theoretical perspectives (Gioia et al., 2022; Magnani & Gioia, 2023)
Key works	Zelger (1991)	Gioia and Chittipeddi (1991)
Key works	Zelger (1999a)	Gioia et al. (2013)
	Zelger (2000)	Gioia (2021)
Data	Texts (open questions, interviews, documents) (Buber & Kraler, 2000)	Texts (Magnani & Gioia, 2023)

Table 2.

Comparative Analysis of the Data Analysis Incorporated in the GABEK® Method and the Gioia Methodology.

	GABEK® method	Gioia methodology
Data preparation	Dividing the text into structured units and applying criteria (Buber & Kraler, 2000)	—
Data reduction	Keyword coding, coding of evaluations, coding of causalities (Zelger, 2008)	First-order concepts, second-order themes, aggregated dimensions (Gioia et al., 2013)
Type of coding	Inductive keywords (Maxwell, 2013; Zelger, 2000)	Inductive and abductive (Magnani & Gioia, 2023)
Data analysis	Understand fundamental values, aims, measures, conditions (Buber & Kraler, 2000)	Categorical analysis (Gioia & Thomas, 1996)
Data analysis		Gestalt analysis (understand what is happening) (Gioia, 2021)
Data display	Network graphics, causal nets, list of evaluations, gestalt tree (Buber & Kraler, 2000)	Data structure (tree-diagram) (Gioia et al., 2013)
Software	WinRelan®	No associated software
Results	Explanations, hypothesis, theories (Buber & Kraler, 2000)	Insights, hypothesis, theories (Magnani & Gioia, 2023)
Fields of application in research	Strategic management, tourism management, service management, human resources management research (Haas, 2019)	Organization and management research (Gehman et al., 2018)

One of the most noticeable distinctions between the GABEK® Method and the Gioia Methodology is their terminology. While Zelger (1991) refers to his approach as a method, Gioia and Chittipeddi (1991) classify theirs as a methodology. However, the Gioia approach is also described as a method (e.g., Yu et al., 2022) or an approach (e.g., Murphy et al., 2017).

The GABEK® Method, which stands for “Ganzheitliche Bewältigung von Komplexität” (translated as holistic management of complexity), is a structured inductive analysis approach for qualitative data. This method focuses on qualitative data, mainly linguistic statements, enabling researchers to capture and present social situations in their full complexity (Zelger, 2000). Building on the concepts of perception gestalt as articulated by Stumpf (1939) and Smith (1988), the GABEK® Method advances these ideas by applying them to linguistic gestalt. A linguistic gestalt refers to a complex unit formed by interconnected nodal terms, where individual statements function as distinct components. Collectively, these components are perceived as a meaningful whole, akin to a cohesive text group. From these gestalts, higher-order structures can be created, including hypergestalt and hyperhypergestalt, which are visually represented in a gestalt tree (Zelger, 1999b; Zelger & Oberprantacher, 2002). Interview data derived from open questions is mainly used. Notably, Grounded Theory is implicitly incorporated into the GABEK® Method, as Zelger emphasizes the use of “natural language statements generated with minimal intervention,” suggesting that interviewers should limit their questions to avoid guiding participants’ responses. For further theoretical insights, readers may refer to works by Zelger (1991, 1995), and Zelger (1999a). For the application of the method in a research setting, refer, for example, to Schultz and Reinhardt (2023), Rhein and Sträter (2021), Haas (2019), Mueller (2015), Raich et al. (2014), and Hielscher and Will (2014).

The Gioia Methodology represents a systematic approach to qualitative research that adheres to the principles of Grounded Theory (Glaser & Strauss, 1967; Murphy et al., 2017), emphasizing inductive coding derived from textual data (Gioia, 2021). Rooted in the social construction of reality (Berger & Luckmann, 1967) within the philosophy of science, the Gioia approach is best understood as a comprehensive methodology rather than merely a method (Corley & Gioia, 2011; Gioia & Chittipeddi, 1991; Gioia et al., 2013; Magnani & Gioia, 2023). This methodology employs both induction and abduction (Magnani & Gioia, 2023), with the primary objective of generating Grounded Theory rather than testing hypotheses (Gehman et al., 2018; Gioia & Chittipeddi, 1991) and employs gestalt analysis as a meta-level analysis to comprehensively capture the phenomena under investigation. It facilitates the development of alternative perspectives and provides a detailed description of results, analyzing data from multiple viewpoints based on any text. For a more comprehensive theoretical overview, readers are encouraged to consult Corley and Gioia (2011). Applications of the Gioia Methodology can be found, for example, in works by Kjaergaard et al. (2011), Nag and Gioia (2012), and Magnani and Gioia (2023).

Both methodologies aim to systematically organize qualitative data to yield new theoretical insights (see Table 2). The GABEK® Method is motivated by the desire to organize knowledge (Zelger, 2019), while the Gioia Methodology seeks to enhance the credibility and comprehensibility of qualitative analysis during the review process (Gioia et al., 2013; Magnani & Gioia, 2023). Both approaches employ an inductive analysis strategy (Maxwell, 2013; Zelger, 2000), with the Gioia Methodology also incorporating abductive elements (Magnani & Gioia, 2023). The GABEK® Method utilizes WinRelan® software for its step-by-step data analysis, whereas the Gioia Methodology does not have an associated software tool. The WinRelan® software provides various data display options, including network graphics, causal nets, and gestalt trees (Buber & Kraler, 2000), while the Gioia Methodology is characterized by a manual representation of the data structure in the form of a tree diagram (Gioia et al., 2013). The integration of the WinRelan® software enhances this analytical process by ensuring adherence to these established rules, thereby supporting researchers in their data analysis activities. In comparison to other Computer-Assisted Qualitative Data Analysis Software (CAQDAS), such as Atlas.ti or MAXQDA, WinRelan® presents distinct advantages, particularly in its methodological integration (Mueller et al., 2011).

According to Zelger (2000), the GABEK® Method provides a systematic, step-by-step framework for analyzing qualitative data. Each step within this process is governed by specific rules derived from the theory of linguistic gestalt:

• Step 1: Preparing the data: Dividing the text into structured units and applying criteria. The method begins by segmenting text, such as transcribed interviews, into coherent “text units.” A text unit is defined as a unit of coherent thought that typically comprises three to nine relevant keywords. In the WinRelan® software, these text units are displayed (manually or automatically) on index cards, allowing researchers to analyze smaller, manageable segments of text (Zelger, 1991; Zelger & Oberprantacher, 2002). Each index card can also be manually associated with criteria, such as the interviewee’s name or personal information (e.g., gender, age, position), facilitating within-project comparisons.

• Step 2a: Keyword coding and network graphics: Each index card is manually coded with three to nine keywords that encapsulate the semantic content of the text unit. This coding process is based on the “philosophical concept of comprehension and explanation” (Zelger & Oberprantacher, 2002), allowing verbal information to be represented in a formal indexing system. To minimize redundancy, researchers replace synonyms in keyword codes. The WinRelan® software can then create network graphics, connecting codes that co-occur within a sense unit. These linguistic networks help organize textual data, providing insights into the implications of the unstructured interview data (Zelger, 2008).

• Step 2b: Evaluation coding and evaluation lists. In this step, if the statements of the interviewees contain evaluative elements regarding a keyword, researchers can code these evaluations. Concepts can be designated as positive, negative, or neutral. The evaluation list reflects the value systems of the interviewees, indicating which topics they wish to support or avoid (Buber & Kraler, 2000; Zelger, 2000).

• Step 2c: Causal coding and causal nets. The GABEK® Method also supports causal coding, capturing the interviewees’ expressed causal assumptions. Researchers code the type of effect and, if applicable, whether the interviewee perceives the mentioned causality positively or negatively (Zelger, 2000). Causal nets visually represent these relationships. (Zelger, 2000).

• Step 3: Gestalt building and Gestalt trees. Building on the previous coding steps, the creation of Gestalts provides a concise overview of the content and structure of the data. A linguistic gestalt is an abstract entity consisting of a meaningful group of text units that adhere to syntactic, semantic, and pragmatic rules. It represents a semantic implication arising from various statements within a text group (Zelger & Oberprantacher, 2002). Coherent text groups are formed through automatic cluster analysis, grouping similar text units while differentiating them from others. This process generates higher levels of gestalts, such as hypergestalts and hyperhypergestalts, culminating in a gestalt tree that summarizes the analysis (Buber & Kraler, 2000).

According to Gioia et al. (2013), the Gioia Methodology facilitates a systematic analysis of qualitative data through the following steps. Through its structured, step-by-step approach, the Gioia Methodology ensures the rigor and traceability of qualitative content analysis (Gioia & Chittipeddi, 1991; Magnani & Gioia, 2023).

• Step 1: Coding of first-order concepts, second-order themes, and aggregate dimensions. The data is subjected to open coding, leading to the identification of summarizing concepts for specific sections. These concepts are derived directly from the informants’ language and represent their perspectives. The goal is to capture the essence of what participants are communicating without imposing preconceived categories. Following the identification of first-order concepts, researchers engage in a process of axial coding to group these concepts into second-order themes. This step involves analyzing patterns and relationships among the first-order concepts, allowing for a more abstract understanding of the data. These categories are synthesized into second-order themes, which are then aggregated into theoretical dimensions that encapsulate the identified phenomena. These dimensions represent higher-order constructs that encapsulate the overarching patterns identified in the data (Gioia, 2021; Gioia et al., 2013).

• Step 2: Displaying the data structure. A data structure resembling a tree diagram is created based on the three levels of first-order concepts, second-order themes, and aggregated dimensions. This structure visually represents the relationships among the individual levels. This hierarchical structure helps to clarify the relationships between different themes and provides a comprehensive view of the phenomena under study (Gioia, 2021; Gioia et al., 2013).

• Step 3: Interpreting the Gestalts. In this phase of result analysis, the objective is to gain a comprehensive understanding of the underlying dynamics present within the data. Through the process of gestalts analysis, researchers interpret the categories, data, and connections that emerge from the analysis. This interpretation involves synthesizing the insights derived from both first-order concepts and second-order themes to construct a coherent narrative that reflects the complexities of the social phenomena under investigation. By engaging in this interpretative process, researchers can uncover deeper meanings and relationships within the data, ultimately contributing to the generation of Grounded Theory and enhancing the overall rigor and relevance of the qualitative research (Gioia, 2021; Gioia et al., 2013).

In summary, both the GABEK® Method and the Gioia Methodology incorporate inductive reasoning, Grounded Theory, and Gestalt Theory. A comparative examination of their processes reveals that the two approaches can complement each other in qualitative analysis (Schultz & Reinhardt, 2022, 2023). Both approaches involve several rounds of coding in the primary data reduction process. The data in both approaches is displayed in Gestalt tree diagrams, which vary in their level of detail.

By comparing the data display, we see that the coding of first-order concepts is comparable to the coding of keywords, as both are inductively generated and reflect participants’ perceptions. In addition to mere keyword coding, the GABEK® Method adds information about causalities and evaluations. At the next level, the Gioia Methodology consolidates first-order concepts into second-order themes, effectively reducing complexity. In contrast, the GABEK® Method achieves this through the formation of gestalts, a process that is supported by cluster analysis and rule-based procedures. At the next level, the Gioia Methodology distills second-order themes into aggregated dimensions, which can be compared to the final summary step of the GABEK® Method. However, the GABEK® Method includes additional rule-based steps for forming hypergestalts and hyperhypergestalts to reduce complexity further. In summary, while individual steps of the GABEK® Method and the Gioia Methodology can be compared, significant differences remain, particularly regarding the rule-based steps facilitated by the WinRelan® software, which creates more levels than the Gioia Methodology.

New Approach: Combining the GABEK® Method and the Gioia Methodology for Comprehensive Data Analysis

The comparison between the GABEK® Method and Gioia Methodology reveals notable similarities in terminology, foundational theoretical concepts, and a structured approach to data analysis. Unlike other qualitative data analysis methods (Hwang, 2008; Kuckartz, 2012; Kuckartz & Rädiker, 2019; Mayring, 2019) that may lack the integrated theoretical framework shared by these methodologies, combining the GABEK® Method and the Gioia Methodology optimizes qualitative research outcomes, as a combination of methods can overcome the limitations of a single method (Kelle, 2006). On the one hand, the GABEK-WinRelan® method may introduce complexity through its detailed inductive coding process, potentially leading to a proliferation of keywords. This observation is supported by the caution expressed by Gioia et al. (2013), who indicate that such coding may result in an excessive number of categories. On the other hand, the Gioia methodology does not provide rules on how to conduct the gestalt analysis as a meta-level analysis to comprehensively capture the phenomena under investigation (Corley & Gioia, 2011). To mitigate this issue, some researchers (e.g., Haas, 2019; Schultz & Reinhardt, 2022, 2023) have adapted GABEK® to incorporate Gioia’s abductive and deductive elements alongside their inductive analysis strategy (Mueller et al., 2011). Our research follows this trajectory, leading to a novel integration of both methodologies designed to enhance the rigor and utility of qualitative inquiry. As shown in Figure 1, our understanding of this integration provides a fresh perspective on the combination of GABEK® with Gioia:

Figure 1.

The Combination of the Gabek® Method and Gioia Methodology (Own Figure Based on Buber & Kraler, 2000; Gioia et al., 2013).

The combined approach begins with preparing the data, where the GABEK® Method facilitates the initial step of segmenting data into sense units. These units are imported into the WinRelan® software, where the keyword coding and synonymization are conducted before transitioning to the Gioia Methodology, where they are summarized abductively into second-order themes and aggregated into dimensions. Furthermore, additional coding evaluations and causalities provide the basis for detailed graphics and lists to illustrate details in the results and for Gestalt building using cluster analysis in WinRelan. The outcomes can be visualized in three gestalt tree versions:

- Version I (based on Gioia), which merges inductive elements with abductive summarization, facilitating a more explicit connection to existing literature and enhancing interpretative analysis

- Version II (based on the cluster analysis in WinRelan®), which remains close to the original text, retaining inductive elements, thus providing new insights for theory from the data but potentially providing lesser clarity for academia

- Version III, conceived as a synthesis of gestalt tree versions I and II, aims to overcome the limitations inherent in both individual approaches while harnessing their collective strengths. This version addresses critiques surrounding the reliability and replicability of qualitative research through a structured step-by-step analysis process facilitated by the WinRelan® software. It also addresses the lack of the construction of higher-order constructs that encapsulate overarching patterns when focusing only on inductive insights.

These contrasting versions of the gestalt trees enhance the understanding of the phenomena under investigation and lead to results that address research queries, propose hypotheses, and formulate theoretical assumptions. Each version serves specific purposes, wherein Version I aligns insights with broader academic discourse, while Version II identifies new findings. Version III serves as a robust methodological framework that not only reinforces the rigor of the research process but also embraces the applicability of the qualitative conclusions, effectively bridging theoretical insights with practical relevance in organizational studies.

The Research Project in Organizational Research

The research project examines organizational culture change in professional service firms (PSFs) and integrates the GABEK® Method and the Gioia Methodology. The study utilizes a qualitative research design characterized by an exploratory approach (Ghauri et al., 2020). It investigates the impact of the increasing trend of teleworking (Smite et al., 2023) on organizational culture, specifically exploring the relationship between the two.

To address the research question, 47 problem-centered interviews were conducted using a semi-structured interview guideline (Witzel, 2000). These interviews, which ranged from 35 to 80 min in duration, took place via video conferencing tools or in-person from May to July 2023. The sample consisted of employees and managers from various PSFs, with 24 employees and 23 managers participating. The demographic breakdown included 36 male and 11 female interviewees (see also Table 3). To ensure a robust retrospective evaluation, only individuals who had been with their organizations for at least six months prior to the implementation of coronavirus protection measures and associated teleworking regulations were included. The average length of organizational affiliation among participants was ten years.

Table 3.

Interviewees.

Type of company	Role	Gender distribution	Number of interviewees
Auditing and/or (tax) consultancy firm	Employee	Female	1
	Employee	Male	6
	Manager	Female	2
	Manager	Male	9
IT consulting company	Employee	Female	1
	Employee	Male	2
	Manager	Male	1
M&A and restructuring company	Manager	Male	2
Management consultancy	Employee	Female	2
	Employee	Male	12
	Manager	Female	5
	Manager	Male	4

The interviews were transcribed, anonymized, and analyzed using the combined methodologies of GABEK® and Gioia, as illustrated in Figure 1.

Two researchers independently coded the data and subsequently refined their findings through ongoing negotiations regarding the application of relevant coding strategies. This iterative process reflects a commitment to maintaining methodological rigor while facilitating a balanced exploration of the data, ensuring that the complexities of qualitative inquiry are adequately addressed. Such collaborative coding efforts enhance the reliability and validity of the results by incorporating diverse perspectives and minimizing individual biases in the interpretation of qualitative data. Ultimately, this approach aligns with the overarching goals of qualitative research, which seeks to generate meaningful insights while remaining adaptable to the evolving nature of the data.

Preparing the Data and Data Reduction

The 47 transcribed interviews from the organizational research project serve as the database for this study. To manage the complexity inherent in the interview data, data reduction techniques are employed to streamline the number of words and sentences, thereby facilitating content analysis. The interview texts are segmented into text units, which are then imported into the WinRelan® software and presented on index cards. This process yielded a total of 538 index cards from the 47 interviews. During the initial coding phase, we applied keyword coding alongside meta-linguistic criteria. The meta-criteria included personal characteristics such as the gender of the interviewees.

When coding the keywords following the changes in organizational culture, we identified 839 keywords. Through synonymization, terms such as “teleworking” and “home office” were combined, resulting in a reduced set of 735 keywords. Since the object language coding in WinRelan® is limited to single-word entries, we revisited the associated sentences to translate these into first-order concepts, adhering to the principles of the Gioia Methodology.

Subsequently, we manually processed the remaining 735 keywords to categorize them into second-order themes and aggregated dimensions outside of the WinRelan® software, following the Gioia framework. This involved employing an abductive coding approach, wherein terms like “office” (keyword) were distilled into “workplace” (second-order theme) and “artifact” (aggregated dimension). This meticulous process culminated in the identification of 106 second-order themes and three aggregated dimensions. Additionally, we coded seven evaluations and 43 causal relationships within the WinRelan® software.

Data Display and Result Analysis

The data reduction steps implemented through the combination of inductive and abductive coding enable us to present the data in various formats. Figure 2 illustrates an excerpt of the data structure derived from the Gioia Methodology. The analysis indicates that organizational culture has transformed across multiple levels, as demonstrated by the interview data. Specifically, the three levels of culture identified by Schein (2010) have undergone different changes in response to the influence of teleworking.

Figure 2.

Section of the Tree Diagram (Version I) of the Research Project (Created Manually).

The impact of teleworking has led to modifications in the artifacts of organizational culture. For instance, there has been a shift in leadership dynamics, which now reflects the new cultural artifacts. Additionally, new communication channels, such as Zoom and Microsoft Teams, have gained prominence. The dress code has also become more relaxed, and new teleworking policies have been implemented.

At the level of values, established shared values are now being practiced more prominently, as the influence of teleworking has heightened awareness of these principles. Additionally, new values, such as diversity, have emerged due to the increased family-friendliness associated with teleworking, resulting in greater trust in individuals’ personal responsibilities stemming from positive experiences with remote work.

At the deepest level of assumptions, attitudes toward teleworking have fundamentally changed. Employees are now trusted more when working remotely, and teleworking has become a standard expectation.

Figure 3, generated using the WinRelan® software, provides a network graphic illustrating the relationships between the keywords “values” and “assumptions.” In this graphic, a line indicates the presence of co-occurring keywords on an index card, while the accompanying numbers reflect the frequency of these connections throughout the database. This presentation effectively visualizes how the keywords for values and assumptions relate to various other terms.

Figure 3.

Network Graphic Values and Assumptions (Created With WinRelan®).

The keywords “values” and “assumptions” are interconnected, revealing associations with diverse topics. For example, the influence of teleworking has impacted issues related to diversity, sustainability, and work-life balance. Furthermore, work-life balance is linked to communication, which also relates to “assumptions.” The term “assumptions” encompasses topics such as openness, transparency, and acceptance. The graphic underscores that many keywords possess multiple connections with each other, highlighting the complex interplay among these concepts.

Table 4 presents a list of evaluations expressed by the interviewees. The first column indicates whether the evaluation is positive, marked with a “+”, or negative, marked with a “–”. The second column displays the frequency of each evaluation. In contrast, the third column identifies the evaluated term, and the fourth column indicates the index card number where the evaluation can be found. For instance, one interviewee stated, “But there has been a clear development, in my opinion, a very positive one” Av7. Based on this statement, we assigned a positive rating to the keyword “development.” This suggests that the interviewee perceives the changes related to teleworking in their company as positive. Conversely, another interviewee remarked, “This could also have negative consequences in the medium term, as people may no longer switch off properly” Ap5. The complete context of this statement indicates that the interviewee anticipates potential adverse outcomes resulting from changes associated with teleworking.

Table 4.

List of Evaluations (Created With WinRelan®).

Type of evaluation (positive + or negative −)	Amount of mention	Object codes	Number index card
+	1	Development	Av7
+	1	Communication	Ap5
−	1	Consequences	Ab8
−	1	Team	Af1
+	1	Change	Aa1
−	1	Time	Af3
+	1	Cooperation	Ap2

Figure 4 displays a causal net for the keyword “telework,” illustrating the causal relationships expressed by the interviewees. The circles represent evaluations of decreasing relationships, indicating that as X increases, Y decreases. Conversely, arrows signify increasing relationships, where an increase in X leads to a rise in Y. A line without an arrow accompanied by a circle indicates that the interviewees provided both increasing and decreasing evaluations, which effectively neutralize each other. The numbers along the lines correspond to the index card numbers that contain the evaluations of these causal relationships. For example, one interviewee remarked, “Well, it happens more distantly, i.e., on-call or not in the presence here in the office, but from somewhere” Ao7. This statement suggests that increased teleworking has led to a greater sense of “distance.” Another interviewee stated, “We have fewer travel activities” Qa1, indicating that the rise in teleworking has resulted in a reduction of business-related travel as customer appointments are increasingly conducted digitally. The keyword “communication” reveals both increases and decreases in communication due to teleworking. One interviewee noted, “Communication has become more, at least from the company management” Ap7. At the same time, another mentioned, “I would just like to emphasize once again that you noticed immediately after Corona and also during Corona that communication was less and also significantly cooler” Ad3. This indicates that while there may be increased communication from management, the overall communication experience has diminished and become less engaging as a result of increased telework.

Figure 4.

Causal Net Telework (Created With WinRelan®).

Depending on the database, the different levels of the design tree can be created using the specified rules and analysis steps. Figure 5 presents a Gestalt tree generated solely in WinRelan®, illustrating its various levels. Utilizing the data from the research project in organizational research and adhering to the rules and cluster analysis of GABEK-WinRelan®, we constructed a Gestalt tree that extends to the hyperhypergestalt level. This Gestalt tree serves as a comprehensive summary of the organizational research project. The tree begins with the hyperhypergestalt labeled “Impacts,” which leads to several detailed hypergestalts. These include the hypergestalts “Establishment,” “Flexibility,” and “New Characteristics.” However, for the hypergestalts “Togetherness,” “Process & Rules,” “Ways of Working,” and “Digitization,” no corresponding hyperhypergestalt is formed. On the right-hand side of the tree, various gestalts are described, such as “Acceptance of Home Office” and “Hybrid as Normal” for the hypergestalts “Establishment” and “Togetherness.” This illustrates that a new sense of togetherness and flexibility defines the altered organizational culture. “Togetherness” is characterized by a redefined normal, exemplified by a hybrid work model that promotes flexibility, enabled by new leadership responsive to employees’ needs.

Figure 5.

Gestalt Tree Version II (Created With WinRelan®).

Figure 6 presents version III of the gestalt tree, which combines elements from both the GABEK® Method and the Gioia Methodology. Compared to the gestalt tree shown in Figure 5, this updated version has yielded new and more fundamental insights from the research project. We utilize abductive elements starting from the level of hypergestalts. The gestalt tree commences with the overarching summary “Organizational Culture,” which then branches out into more detailed levels, culminating in various gestalts on the right. This general summary leads to the subsequent level of hyperhypergestalts. The transformed organizational culture is primarily attributed to the main themes of “New Manifestations of Everyday Life” and “Values.” These two hyperhypergestalts further reveal detailed hypergestalts, resulting in categories such as “Leadership” under “Manifestations of New Everyday Life” and “Flexibility” under the hypergestalt “Values.” The right-hand side of the tree outlines different gestalts, including “New Leadership Communication” and “Flexible and Needs-Oriented Leadership” associated with the hypergestalt “Leadership.” The transformed organizational culture is characterized by manifestations in everyday life and values that arise from new forms of communication and increased flexibility. Communication is redefined as a means of conveying values, while flexibility emerges as a new corporate principle.

Figure 6.

Gestalt Tree Version III (Created With WinRelan® and Manually).

Discussion, Limitations, and Future Research

The findings of this study highlight the complementary interplay between the GABEK® Method and the Gioia Methodology, showcasing their combined potential to enhance qualitative analysis of organizational culture change within professional service firms (PSFs) amidst the rise of teleworking. Both methodologies utilize inductive reasoning (Gioia et al., 2013; Zelger, 2003), linguistic Gestalts (Smith, 1988), and Grounded Theory (Glaser & Strauss, 1967), allowing researchers to uncover first-order concepts and second-order themes that reveal deeper relationships within the data. The Gioia methodology and the GABEK® method already meet the demand of overcoming limitations associated with other established content analysis methods, such as those of Kuckartz (2012), Mayring (2019), and Grounded Theory (Glaser & Strauss, 1967). However, the Gioia Methodology lacks prescriptive guidelines for conducting a comprehensive gestalt analysis (Corley & Gioia, 2011), which is essential for understanding complex social phenomena, whereas the GABEK® Method is sometimes critiqued for following the steps too strictly.

To address these limitations, researchers like Schultz and Reinhardt (2023) have effectively adapted the GABEK® Method to incorporate Gioia’s elements, thus enriching the analytical framework by combining abductive and deductive reasoning alongside inductive strategies. Mees-Buss et al. (2022) have sought to mitigate the shortcomings of the Gioia methodology through the application of hermeneutic principles. Our proposed combination of the GABEK® method and the Gioia methodology aims to build upon these efforts, providing a more comprehensive analytical framework.

Moreover, this study emphasizes the importance of flexibility in qualitative research design to effectively capture the complexities of evolving work environments. Rigid methodologies can compromise the richness of qualitative data, as highlighted by Mays (2000) and Tong et al. (2012), who emphasize the balance between maintaining rigor and ensuring real-world relevance. By merging the strengths of the GABEK® Method and the Gioia Methodology, this research advocates for ongoing methodological innovation in qualitative studies, paving the way for more effective strategies to address contemporary organizational challenges where the findings not only adhere to research standards but also hold practical significance within real-world contexts.

While the integration of the GABEK® Method and the Gioia Methodology provides a promising framework for analyzing organizational culture change, it also presents certain limitations that warrant consideration. The lack of prescriptive guidelines within the Gioia Methodology for conducting gestalt analysis, which can hinder researchers’ ability to comprehensively capture the complexities of the phenomena under investigation, has provided an area for improvement. The adaptation proposed by Schultz and Reinhardt (2023), aim to bridge this gap by incorporating elements of the GABEK® Method: However – unlike our approach – they cluster keywords to form linguistic gestalts, suggesting that gestalts can be viewed as first-order concepts, hypergestalts as second-order themes, and aggregated dimensions as hyperhypergestalts. We, instead, propose to differentiate between results derived from the GABEK® Method and the Gioia Methodology (with gestalt trees Version I to III). Consequently, it becomes imperative to navigate these methodological limitations while ensuring that the combined approach remains robust and effective in analyzing intricate social dynamics.

The practical implications of this study highlight the importance of integrating the GABEK® Method and the Gioia Methodology for researchers and practitioners exploring organizational culture change, particularly in the context of teleworking. By combining these frameworks, organizations can benefit from a comprehensive analytical approach that facilitates deeper insights into the dynamics of remote work environments. This integration not only enhances the rigor of qualitative research but also ensures that findings are relevant and applicable in real-world settings. Ultimately, this research underscores the potential for enhanced qualitative analysis to guide decision-making in professional service firms, promoting a culture that effectively navigates the challenges and opportunities presented by remote work arrangements.

Future research should focus on several key areas to build on the findings of this study regarding the integration of the GABEK® Method and the Gioia Methodology. First, l researchers may explore the application of this integrated methodology across various sectors to determine its generalizability and effectiveness in diverse organizational contexts, which could mitigate the limitations related to the specific environments studied in this research. Additionally, future studies should consider refining the gestalt analysis aspect of the Gioia Methodology. Finally, a comparative analysis of the effectiveness of combining these methodologies with other established qualitative approaches, such as thematic analysis or grounded theory, would provide valuable insights into best practices for rigor and relevance in qualitative research. By pursuing these avenues, future studies can enhance methodological robustness in qualitative analysis and contribute to more comprehensive insights into organizational culture change in the context of evolving work environments.

Footnotes

Acknowledgments

We thank Prof. Josef Zelger for his thoughtful comments.

ORCID iD

Julia Mueller-Seeger

Ethical Statement

Ethical Approval

At the start of the study, the University did not have an ethics or institutional committee for the social sciences.

Consent to Participate

Written and audiotaped verbal permission to conduct the interviews for the purposes of this research was obtained by all respondents, who were fully informed about the purposes of this research and how their responses would be used and stored.

Consent for Publication

The participants agreed to be part of the study if the individual participant cannot be identified by the data presented.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared that there are no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data cannot be openly shared in order to protect the participant’s privacy.*

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