What Is Qualitative Research? An Overview and Guidelines

Qualitative research prioritizes the specific circumstances and settings in which phenomena occur, ensuring findings are grounded in real-world contexts. In studying consumer behavior, this means considering the macro (e.g. PESTEL) and micro (e.g. brands, intermediaries, and retailers) factors influencing choices. The implications are that strategies and solutions can be developed with a clear understanding of the relevant contextual factors.

Qualitative research probes deeply into specific contexts, capturing rich, nuanced insights into complex phenomena. For example, when exploring consumer behavior, qualitative research can unveil the underlying motivations driving purchasing decisions. This depth equips researchers and practitioners with a comprehensive understanding, enabling more informed and tailored interventions.

Qualitative research seeks to understand phenomena in their entirety, considering all relevant aspects and their interrelations. For instance, examining consumer behavior from multiple angles (e.g. behavioral, cultural, demographic, geographic, psychographic, psychological, and social) provides a comprehensive view. This holistic perspective facilitates a more complete understanding critical to theory development, as well as targeted solutions resulting in good return on value.

Qualitative research values and thus actively seeks out the experiences and perspectives of participants. In exploring consumer behavior, this approach ensures that the viewpoints of the actual consumers (e.g. older adults) are integral to the findings. The outcome is research that is more grounded, authentic, and reflective of the participants’ realities.

Qualitative research allows for adjustments in methodology (flexibility) based on emerging data and insights (reflexivity). If initial interviews about consumer preferences reveal unanticipated responses (e.g. social desirability), researchers can adapt their approach accordingly (e.g. individual to projective or third-person techniques). Such reflexivity and flexibility ensure that the research process remains sensitive to bias while staying responsive and relevant.

Qualitative research often involves smaller, specific samples, which may not represent broader populations. Studying consumer behavior in a particular demographic may yield insights that are not applicable elsewhere. The implication is a potential limitation in the applicability of the findings.

To address the limited generalizability of qualitative research, researchers can explicitly state the boundaries and contexts of their study, ensuring that readers understand the limitations. Additionally, enhancing the research design through triangulation (Denzin, 1978), such as using multiple data sources (e.g. participants of varied populations, types of data such as audio and visual recordings, field notes, and vignettes) or analytical perspectives (i.e. methodological, theoretical; e.g. investigators, methods, and lenses), can also contribute to a more robust and potentially generalizable set of findings. While some constructivist and interpretivist researchers may critique triangulation for presupposing an objective reality (Sutrisna, 2009), this perspective overlooks the value of triangulation in enhancing the depth, compellingness, and credibility of subjective interpretations. Employing multiple data sources or analytical perspectives does not negate the subjective nature of social reality; rather, it provides a more comprehensive understanding, akin to gathering diverse perspectives to describe an elephant fully rather than in isolation. This due diligence, encouraged by triangulation, is not about claiming objectivity but about ensuring that our subjective interpretations are as informed and nuanced as possible, thereby reducing the risk of one-dimensional or unnecessarily biased views.

Qualitative research methods, such as in-depth interviews, can be time consuming and require substantial resources. For example, a comprehensive study of consumer behavior might entail lengthy interviews with numerous participants. The implication is a demand for significant investment in terms of effort, time, money, and storage, among many of the resources that may be required.

To mitigate the resource-intensive nature of qualitative research, efficient project management is essential, encompassing careful planning and judicious allocation of resources (e.g. time management). Leveraging software tools specifically designed for qualitative data analysis can streamline processes, reducing time and resource demands. Additionally, investing in training for researchers can enhance efficiency in data collection and analysis, ensuring a more effective utilization of available resources.

Qualitative research data are often rich and voluminous, posing challenges in analysis and interpretation. Analyzing detailed consumer narratives requires meticulous effort to extract meaningful patterns. The implication is a need for expertise and time in handling complex qualitative data.

To navigate the complexity inherent in qualitative research, a systematic and organized approach to data management and analysis is crucial. Implementing clear coding frameworks and engaging in peer debriefing sessions can enhance consistency and reliability in data interpretation. Investing in skill development and training for researchers can also provide them with the necessary tools to effectively analyze and manage complex qualitative data.

Quality of qualitative research hinges on the researcher’s abilities, particularly in data collection and analysis. The subtleties of consumer behavior might be missed or misinterpreted without the requisite disciplinary knowledge or methodological skillset. This underscores the importance of having well-trained and experienced researchers for qualitative research.

To ensure the quality of qualitative research amidst the requisite high level of researcher skill, ongoing training and professional development become paramount. Access to education or training and resources on the latest methods and techniques in qualitative research can enhance researcher capabilities. Additionally, fostering a culture of collaborative learning and mentorship, where less experienced researchers can learn from and be guided by more seasoned colleagues, further ensures the development of requisite expertise and skills.

Qualitative research is interpretive in nature and thus opens up possibilities for subjective influences. A researcher’s own beliefs and values could inadvertently color the interpretation of human behavior. This introduces the need for rigorous reflexivity and checks to maintain alignment to the participant’s interpretation or the research paradigm and theories informing the researcher’s interpretation.

To counteract potential unacknowledged⁴ researcher bias in qualitative research, a commitment to reflexivity is required, where researchers consistently reflect upon and question their own biases and preconceptions throughout the research process. Maintaining a transparent and detailed audit trail that documents all research decisions can further mitigate bias, while seeking external feedback through peer review provides an additional layer of scrutiny.

Grounded theory is a systematic approach to theory development, wherein theory emerges naturally from research data. Initiated by Barney G. Glaser and Anselm L. Strauss (1967), grounded theory was developed as a counterbalance to the dominance of theory-driven research, responding to the belief that qualitative research was merely a precursor to quantitative research (Cullen & Brennan, 2021; Goulding, 2006; Suddaby, 2006).

The essence of grounded theory lies in its inductive nature (Corbin & Strauss, 2015; Glaser & Strauss, 1967). The process, which is systematic and iterative in nature, begins without preconceived theories. Instead, grounded theory fosters an environment where theories organically evolve as an interplay of data collection, preparation, and analysis. Notably, the concurrent activities of collecting, coding, and comparing data (the “3Cs”) enable researchers to make sense of what is happening, leading to the gradual development of theories (e.g. concepts, themes, relationships, and frameworks), which are intimately connected to the data.

Grounded theory, as proposed by its founders Glaser and Strauss (1967), hinges on the notion that the validity of a theory is contingent upon the process of its derivation. This represents a departure from deductive methods that start with theories to form hypotheses or propositions, which are then empirically tested and verified. Instead, grounded theory develops theories directly from the data, refining them through a continuous loop of data collection, coding, and comparison, ensuring the resultant theory is a genuine reflection of the phenomenon under study. In this regard, literature review should be delayed as much as possible in order to remain as neutral as possible (Glaser, 1978).

However, it is important to note that conducting a preliminary review of the literature can still be valuable in grounding a grounded theory study. This initial review is not meant to find theories to guide the research but rather to identify gaps and shortcomings in existing studies. This approach can help in articulating the need for a grounded theory research, highlighting areas where current knowledge is lacking or where existing theories fall short. This early engagement with the literature can provide a context for the research, helping to frame the study without predisposing the researcher to specific theoretical outcomes.

Grounded theory is renowned for its effectiveness in examining human behavior and subjective experiences. This approach shines when existing theories are inadequate, as it pioneers the development of new theories to better explain complex social processes or phenomena. Structured guidelines deliver a rigorous and transparent roadmap for researchers to systematically interpret qualitative data (Cullen & Brennan, 2021). Furthermore, grounded theory’s adaptability has made it a staple across various scholarly fields, solidifying its status as a multidisciplinary approach (Bryant & Charmaz, 2007). In this regard, grounded theory has a distinctive capacity for fostering the development of innovative concepts and eschewing the notion of fixed social structures, thereby acknowledging the dynamic nature of social realities. More than a mere procedural approach, grounded theory serves as a foundational strategy for elucidating processes and contributing to the theoretical lexicon, focusing on theory construction (e.g. hypothesis generation) rather than theory verification (e.g. hypothesis testing).

Despite its status as an influential approach for qualitative research, grounded theory has witnessed divergences in its applications and interpretations. The once unified front presented by its co-creators, Glaser and Strauss (1967), eventually frayed, with marked differences becoming particularly evident after Strauss and Corbin (1990). Glaser (1992) criticized this work, defending what he considered the authentic form of grounded theory—an approach underpinned by inductive reasoning and the emergence of concepts, championing the researcher’s creativity within a defined procedural structure. Conversely, Strauss and Corbin (1990) advocated for stringent validation and a more methodical, systematized approach. Kelle (2007) encapsulates this methodological schism as a debate between the flexibility of Glaser’s emergent coding families and Strauss’s prescriptive coding paradigm focused on systematically uncovering causal conditions and contextual interactions. This academic debate, termed by Glaser (1998) as a “rhetorical wrestle,” continues to stir discussion and evolution within qualitative research circles.

To reconcile the methodological debate between Glaser’s creative, flexible approach and Strauss’s systematic, criterion-focused strategy in grounded theory, a synthesized process is envisioned herein that embraces the cyclical nature of research, iterating through three concurrent activities—that is, collection, coding, and comparison (the “3Cs”)—wherein open, axial, and selective (OAS) coding, theoretical sensing, and theoretical sampling continue until theoretical saturation is achieved so that theoretical synthesis is enabled (the “4 TSs”), signaling the completion of the cycle (Figure 2). This harmonized approach combines Glaser’s advocacy for creative emergence within data and Strauss’s emphasis on methodical scrutiny to yield a robust, grounded theory. This iterative cycle ensures that the theory develops in complexity (breadth and depth), with each stage revisiting the data continuously, refining the analysis, and ensuring that the emerging theory remains anchored in empirical evidence while also benefiting from structured methodological rigor.

Grounded theory’s primary strength lies in its inductive nature, which allows for the emergence of rich, organic theories that can explain complex social phenomena in a manner that is deeply connected to empirical data. This emergent quality encourages the discovery of novel insights, particularly in areas where existing theories are insufficient or silent. Furthermore, its systematic approach ensures rigorous data analysis, which enhances the trustworthiness of the research findings. The versatility of grounded theory allows for its application across a myriad of disciplines (i.e. it is not limited to any one discipline), fostering interdisciplinarity and innovation in qualitative research.

Despite its versatility, grounded theory is not without critique. The approach’s flexibility can sometimes lead to a lack of consistency in application, which can raise questions regarding the reproducibility of studies. The process can also be time consuming and resource intensive due to the iterative nature of data collection and analysis. Moreover, the approach’s emphasis on inductive reasoning can potentially downplay the role of existing literature and theoretical frameworks, which may result in a reinvention of concepts that have already been well-established within certain disciplines.

To counter the above shortcomings, researchers can implement several strategies. Ensuring a clear and transparent articulation of the research process can mitigate issues related to consistency and reproducibility. Leveraging existing theories as sensitizing concepts rather than as definitive frameworks can incorporate a balanced view of the literature without stifling the emergence of new theories. Employing rigorous methodological checks, such as triangulation, member checking, and peer debriefing, can enhance the trustworthiness of the grounded theory research. Moreover, strategies to enhance reflexivity, such as maintaining reflexive journals and engaging in regular discussions with research peers, can help to ensure that the researcher’s biases are acknowledged and accounted for throughout the research process.

Phenomenology is a qualitative approach that focuses on the lived experience of individuals (Figure 3 and Table 5). Popularized by Edmund Husserl (1913) and Martin Heidegger (1927), phenomenology seeks to reveal the essence of phenomena by exploring how they are experienced from the first-person perspective. As the study of phenomena, phenomenology is interested in the appearance of things (or things as they appear in our experience; i.e. the “things”), the ways we experience things (i.e. the “experience”), and the awareness, qualia, intentionality, and meaning of things and experiences (i.e. the “consciousness”; Smith, 2018).

Essentially, things can manifest in many ways, such as bodily awareness (e.g. feeling comfortable or uncomfortable when using a product), embodied action (e.g. ease or ways of using a product), and social activity (e.g. using a product with others), among others (e.g. events, interactions, objects, time, self, and people). Similarly, the experience of things can come in many forms, including emotion (e.g. excitement or disappointment in a product), thought (e.g. considering the benefits and costs of a product), memory (e.g. nostalgia of past experiences with a brand), imagination (e.g. envisioning using a product in daily life), desire (e.g. aspiring to own a luxury product), and volition (e.g. deciding to purchase a particular product).

Understanding of consciousness through analysis or reflection is imperative in phenomenology, which is enriched by various enabling conditions like bodily gestures, cultural or historical contexts, embodiment, language, and social practices. These elements provide a backdrop against which the consciousness is experienced and understood, and within this backdrop, phenomenology explores several key dimensions of human consciousness:

Phenomenology, as a qualitative research tradition, finds its roots in the philosophical work of Husserl (1913), particularly his development of transcendental phenomenology, also referred to as descriptive phenomenology. This approach is characterized by its rigorous effort to provide detailed, depersonalized descriptions of lived experiences. The cornerstone of Husserl’s phenomenology is the concept of epoché, a process of “bracketing” or setting aside preconceived notions and biases. This technique is central to descriptive phenomenology and is employed to access the phenomena of consciousness in their purest form, untainted by external theories or subjective interpretations by the researcher. Here, the focus is on describing the structures of experience as they present themselves to consciousness, without imposing any interpretation or external meaning. This approach seeks to uncover the essential qualities of experiences by examining them from a first-person perspective. Husserl emphasized that through epoché, researchers could isolate the essential features of any given experience, allowing them to describe the fundamental nature of consciousness and phenomena. Indeed, the practice of epoché demands a high level of reflexivity and self-awareness from the researcher; it requires a suspension of judgments and the setting aside of the natural attitude—the everyday assumptions and beliefs about the world. In doing so, the researcher endeavors to reach a level of description that is as free from personal bias and presuppositions as possible, capturing the lived experience in its most authentic and essential form. Thus, transcendental phenomenology, as conceived by Husserl, is not just about cataloging experiences but about understanding and describing the structures of consciousness that enable these experiences.

In contrast to Husserl’s descriptive phenomenology, interpretative phenomenology, as developed by Heidegger (1927), Husserl’s student and assistant (Horrigan-Kelly et al., 2016), weaves together existential and hermeneutic elements to offer a richer, more contextually grounded understanding of human experience. Heidegger’s approach, deeply rooted in existence, emphasizes “being in the world,” a concept that places consciousness within the broader tapestry of an individual’s existence, including their interactions with others and their environment. Beyond this existential lens, Heidegger’s incorporation of hermeneutic principles marks a significant methodological shift. Hermeneutics, the art and science of interpretation, becomes central in understanding how individuals make sense of their experiences. Heidegger posits that our comprehension of experiences is inextricably linked to our historical, cultural, and linguistic backgrounds. This perspective acknowledges that meanings are not inherently present in the world but are actively co-constructed by individuals within their unique life contexts. Interpretative phenomenology, therefore, extends beyond merely describing experiences; instead, it delves into interpreting these experiences, uncovering the layers of meaning embedded within them. This process involves a deep engagement with the participant’s perspective, understanding not just what their experiences are, but how these experiences are imbued with personal and cultural significance. Researchers in this tradition focus on the narrative aspects of experience, exploring how individuals articulate and make sense of their experiences and how these narratives are shaped by broader societal narratives and discourses. In this regard, interpretative phenomenology often involves a reflexive dialogue between the researcher and the phenomena under study. Researchers recognize their own interpretative role in the research process, reflecting on how their perspectives and contexts influence the interpretation of the data. This reflexivity is key to understanding the co-constructed nature of knowledge in interpretative phenomenology.

While both descriptive and interpretative phenomenology aim to elucidate the nature of experiences, they differ in their philosophical orientation and methodological emphasis. In descriptive phenomenology, the researcher aims to bracket out their prior knowledge and biases to capture the essence of experiences in their purest form. Conversely, in interpretive phenomenology, the researcher’s pre-existing knowledge and experience are considered essential to fully comprehend and elucidate the meanings inherent in the lived experiences of individuals. Both methods, however, contribute to a richer understanding of phenomena by offering complementary perspectives on the complexity of human experiences. This entails a detailed exploration of participants’ narratives to reveal the meanings embedded within their experiences. Therefore, phenomenology distinguishes itself by focusing on the richness of subjective experience, emphasizing depth over breadth.

The process of conducting phenomenological research, whether through a descriptive or interpretative lens, involves rigorous and iterative stages, ensuring a deep engagement with the phenomenon of interest. These stages—described herein as reduction, reflection, and refinement (3Rs)—adapt fluidly to the philosophical orientation of the study. These stages might unfold as follows:

Phenomenology’s primary strength lies in its commitment to understanding the nuances of human experience, offering a profound immersion into individual perspectives, thereby enabling researchers to capture the subtleties of subjective experience that other methods, especially quantitative ones, may overlook. This approach is particularly potent in areas where the complexity of human consciousness and richness of personal experience are central to the inquiry, allowing for the emergence of deeper meanings and novel insights that can inform theory and practice in fields as diverse as business, psychology, and sociology, all of which have relevance for marketing.

Despite its strengths, phenomenology is not without its critiques. One of the primary shortcomings is the subjective nature of the approach, which can raise questions about the trustworthiness of the findings. The intensive, interpretative process can also be time consuming and requires a high level of skill and reflexivity on the part of the researcher to ensure the integrity of the analysis. Additionally, the process of bracketing, crucial to reducing bias in descriptive phenomenology, can be challenging to achieve in practice, leading to potential contamination of the findings with the researcher’s preconceptions.

To address these shortcomings, researchers can employ several mitigation strategies. Triangulation, using multiple researchers or methods to confirm findings, can enhance the credibility of the research. Maintaining a reflective journal can help in articulating and managing personal biases throughout the research process. Peer debriefing and member checking, wherein findings are reviewed by peers or participants, can further validate the research outcomes. Presenting rich, thick descriptions of the data can also help in providing context, allowing others to assess the applicability of the findings to different settings.

Ethnography, derived from the Greek words “ethnos” (meaning “people”) and “grapho” (meaning “to write”), has evolved into a principal approach within the field of anthropology (McGranahan, 2018). The theoretical underpinnings of ethnography are rooted in the early anthropological work of Bronislaw Malinowski (1922) and Margaret Mead (1928), who emphasized the importance of cultural immersion and participant observation to understand societal structures and dynamics from the emic, or insider, perspective (White & Cooper, 2022).

Netnography, coined by Robert Kozinets (1998), extends ethnographic practice to the study of interactions in online communities and cultures. This is a form of ethnography adapted for the digital age, reflecting the ubiquity of online social experiences and the richness of data available in these virtual environments. Kozinets’s work not only recasts traditional ethnographic tools for the internet but also introduces new methodological considerations unique to the digital context.

In practice, ethnography is executed through the immersive engagement of researchers within a community to observe and participate in its daily routines. This immersion can span several months or even years, allowing the ethnographer to develop an in-depth understanding of the community’s social dynamics and cultural practices. Active participation, coupled with vigilant observation, provides a rich tapestry of qualitative data. Ethnographers conduct interviews, collect life stories, and engage in daily activities, all while maintaining a reflexive stance to interpret the cultural symbols and meanings within the context they are observed. This methodological commitment enables a profound comprehension of a community’s way of life, beliefs, and interactions, thereby uncovering the underlying patterns that might elude more superficial forms of inquiry.

Nonetheless, the scope of conducting an ethnography extends beyond the confines of a single community (e.g. a village in a single geographical location). For instance, when studying household behaviors like water consumption, ethnographers may choose to live in multiple households across different geographical locations, each for a duration of 1 to 2 weeks. This cumulative approach, potentially spanning several months, enables a comprehensive understanding across various settings. Immersing themselves in different micro-environments, ethnographers can gather rich insights, reflecting the diverse practices and interactions that characterize contemporary social dynamics. The work of Brennan et al. (2015) in social marketing illustrates how ethnography can illuminate the environmental and socio-cultural factors influencing behaviors. Through the lens of ethnography, researchers can transcend superficial observations, delving into the experiences and perspectives of individuals and communities. This deep engagement facilitates the uncovering of underlying motivations, barriers, and facilitators of behavior, enabling the development of more effective, culturally congruent understanding of the social systems in which behaviors are embedded alongside equivalent socially responsible marketing interventions.

In contrast, netnography is applied to online communities and digital interactions, involving the practice of observing and participating in virtual environments where individuals gather to co-create, interact, and share experiences and opinions (Kozinets, 2020; Kozinets & Gambetti, 2020). This methodological approach requires researchers to immerse themselves within digital spaces such as blogs, forums, and social media platforms to analyze the content created by users, including textual conversation, multimedia elements, and the subtleties of digital communication. Through an iterative process of engagement, a netnographer collects and interprets data, allowing for an in-depth understanding of online culture and behavior. The insights gleaned offer valuable implications for understanding consumer preferences, uncovering trends, and informing strategies in the digital domain, making netnography a powerful tool for academics and businesses interested in the intersection of commerce, culture, and technology, all of which have relevance for marketing.

Crucially, netnography extends beyond mere analysis of extensive online posts, a task suited for content analysis. True netnography, as conceived by Kozinets (2020), necessitates deep immersion within online communities, mirroring the experience of virtually residing in these digital spaces over substantial periods. This approach is vividly illustrated in the work of Kozinets and Seraj-Aksit (2024), who dedicated over two and a half years to engage with a Subreddit community focused on financial investing. Their methodical engagement—characterized by regular visits, comprehensive reading, reflective contemplation, and meticulous data preservation—ventures beyond superficial analysis, aiming to unravel the dynamics, prevailing norms, and cultural underpinnings of digital communities, thereby setting netnography apart from more elementary forms of online content analysis. Notably, the recent work of Kozinets at al. (2024) resonates with Brennan et al.’s (2015) advocacy for the impactful potential of ethnography in fostering social change, extending this premise to digital realms. This extension is embodied in the concept of “transformative netnography,” a novel approach that amalgamates netnography with action research. Leveraging digital platforms’ unique affordances, such as social media, and an interventionist stance, transformative netnography catalyzes ecological and societal advancements, thereby enhancing both the scope of application and impact of netnographic research.

Taking a leaf from Taylor and Francis (2013), ethnography begins with the identification of a distinct culture or social group the researcher wishes to study (Step #1). This is followed by determining the critical variables and dynamics within the cultural setting (Step #2). A thorough review of related literature to gain background knowledge and theoretical framing is essential (Step #3). The next step involves gaining access to the community, which may require building trust and establishing consent (Step #4). Once admitted, the researchers immerse themselves into the culture, either as an active participant or as an observer, to gain a deep understanding of the social dynamics and behaviors (Step #5). Finding key informants who can provide inside perspectives is a crucial element of the data collection process (Step #6). The actual data gathering is conducted through various methods, including observations, interviews, and analysis of artefacts and documents (Step #7). Following data collection, the researcher must then describe the cultural beliefs, behaviors, and patterns identified (Step #8), and use these descriptions to develop broader insights and theories about the culture under study (Step #9).

In the same vein, the above steps can be reconfigured for netnography in the digital realm, starting with the selection of an online community or culture for investigation (Step #1). The researcher then identifies significant interaction patterns and themes within the online setting (Step #2). This is coupled with a literature review to understand the current state of knowledge about online behavior or phenomenon (Step #3). Gaining entrance into the online space often involves joining the community, while maintaining ethical standards around transparency and consent (Step #4). The researcher then engages in virtual ethnography by observing and sometimes participating in online interactions to grasp the nuances of the digital culture (Step #5). Identifying informants within the community who can provide depth and context to the observed phenomena is a key step (Step #6). Data is primarily collected through the curation of digital artifacts, such as posts, chat logs, and multimedia content (Step #7). The subsequent description of the online culture is crafted from these digital narratives (Step #8), leading to the development of theories that explain the behaviors and norms of the virtual community (Step #9).

The strengths of ethnography lie in its ability to provide in-depth insights and finer-grained understanding of cultural and social processes. This approach excels in delivering a holistic view of a community’s life, adeptly capturing complex human behaviors in their natural environment. Particularly effective in uncovering tacit knowledge and implicit norms that might elude more structured research methods, ethnography allows researchers to penetrate beneath the surface of observable phenomena. Its power rests in the ethnographer’s emic perspective, which can unveil the contradictions and subtleties within a culture that quantitative data may overlook. This method’s flexibility is also a significant asset, as it adapts to new developments and findings throughout the research process, often leading to discoveries beyond the scope of initial propositions, if any. Moreover, the narrative richness of ethnographic research makes its findings not only more comprehensive but also engaging, offering accessible insights that deeply resonate with a wide audience.

Likewise, netnography shares the above strengths and further leverages the expansive reach of the digital world. This innovative approach grants access to an immense pool of naturally occurring data, spanning diverse geographies and time zones, and it often does so with lower costs and reduced intrusion compared to conventional methods. Particularly adept at monitoring real-time developments, netnography shines in decoding the swiftly evolving dynamics of online communities. This approach leverages the immediacy and interconnectedness of the digital world to offer insights into contemporary cultural and social trends, making it an invaluable tool for understanding the pulse of virtual communities.

Despite their strengths, both approaches face challenges. Ethnography can be particularly time-consuming and resource-intensive, often requiring extended periods of fieldwork to deeply understand the cultural nuances and social dynamics of a community. The prolonged engagement necessary for ethnographic research demands significant investment in both time and resources, which can be a constraint for researchers. Additionally, the subjective nature of data collection and interpretation in ethnography raises concerns about researcher bias. Personal beliefs, values, and experiences of the ethnographer can inadvertently influence the observation and analysis process. To mitigate these challenges, several strategies can be employed. Rigorous training in reflexivity is essential, where ethnographers are encouraged to constantly reflect on their biases and the influence of their background on the research process. This self-awareness helps in minimizing the impact of personal biases on the study’s findings. Implementing triangulation methods can also enhance the validity of the research. Using multiple data sources, methods, theories, or even investigators (Denzin, 1978), researchers can cross-verify the data and interpretations, adding breadth and rigor to the study. Furthermore, clear and well-defined research protocols, along with regular peer reviews and consultations, can provide checks and balances against potential biases and ensure that the research stays on course.

Moreover, ethnographic research navigates complex ethical dilemmas, such as balancing the need for deep immersion with respecting the autonomy and privacy of individuals. More specifically, ethnographers often find themselves in intimate proximity to sensitive aspects of participants’ lives, raising questions about the boundaries or extent of observational research. Maintaining confidentiality becomes particularly challenging in closely-knit communities where individual stories may be easily identifiable. Ensuring informed consent is an ongoing process, requiring constant vigilance to recognize and respect participants’ changing boundaries and comfort levels. Ethnographers must therefore employ ethical strategies that are as dynamic and adaptable as their research environments, continuously engaging with participants to uphold ethical standards while deeply exploring their cultural contexts.

For netnography, the transient nature of online data and issues of authenticity pose significant challenges. The often anonymous nature of online interactions presents a risk of misrepresentation, as individuals may portray themselves differently in digital spaces compared to offline settings. To mitigate these issues, researchers must adopt a careful and ethical approach to data collection. This includes thoroughly understanding the context of online interactions, utilizing methods or tools to verify identities where feasible, and being acutely aware of the digital footprint that their research activities leave behind. Ethical considerations are paramount, especially in respecting the privacy and confidentiality of online participants. Systematically addressing these shortcomings, researchers can uphold the integrity of ethnographic and netnographic studies, ensuring that their findings remain robust and trustworthy.

Action research as an approach has its intellectual genesis in the early 20th century, with roots in the works of John Dewey’s (1903, 1910) pragmatism and Kurt Lewin’s (1939) field theory, wherein Dewey’s advocacy for experiential learning and Lewin’s emphasis on understanding individuals within their social context laid the groundwork for an approach that seeks to blend research and practice in pursuit of knowledge creation and social change.

Fundamentally, Lewin’s (1946) ideas has led to the birth of action research, for which its premise suggests that social problems cannot not be understood without trying to change them, and thus, research should go beyond fact finding and involve an action component to drive positive change (Walter, 2009). In this regard, action research is a democratic and dynamic approach, where research is not conducted on but with communities.

Action research is implemented through the iteration of planning, acting, observing, and reflecting (Lewin, 1946). The cyclical nature of action research creates a dynamic process through which change can be both implemented and studied in real-time. This process is deliberate, reflective, and adaptive, aiming to bring about change that is grounded in evidence and reflective learning.

Action research brings a unique set of strengths to the table, with its iterative, participative, and problem-solving approach offering a dynamic avenue for both inquiry and intervention. Its cyclical nature fosters continuous improvement and adaptation, making it highly effective in environments that require ongoing refinement and responsiveness. The collaborative aspect of action research ensures that the knowledge created is relevant and actionable, as it involves stakeholders who are often the end-users of the research outcomes. Furthermore, by integrating reflection into every cycle, action research perpetuates a learning culture that consistently seeks to understand the consequences of actions, leading to more sustainable and impactful changes. The pragmatic blend of theory and practice allows for immediate application of findings, thereby reducing the gap between research and real-world application.

Despite its practical appeal, action research is not without its challenges. One significant limitation is the potential for subjectivity and bias, given the researcher’s involvement in the action being studied. To mitigate this, action researchers can employ methodological rigor, such as triangulation of data sources, to validate their findings. Another challenge is the potential for conflicts of interest, given the dual role of the researcher as both observer and participant. Establishing clear ethical guidelines and roles can help navigate these challenges. For example, in a community-based project, managing the dual role of a researcher involves a clear strategy that typically comprises maintaining transparent communication with the community about the research purpose, consistently seeking consent, and establishing boundaries between participation and observation. While the researcher might engage in developing strategies with the community, they also need to step back regularly to observe and record outcomes objectively. This can involve scheduling specific periods for active involvement (e.g. helping to organize a local event) and other times for detached observation and data collection. Additionally, keeping a reflective journal where personal reflections are distinctly noted apart from observational data can help in maintaining this balance. This approach ensures that while the researcher contributes to the community’s efforts, their data collection and analysis remain focused on the research objectives while mitigating bias. Furthermore, the change-driven nature of action research can also be disruptive, and if not managed carefully, can lead to resistance within the community or organization involved. This necessitates the development of change management strategies, such as stakeholder engagement and transparent communication throughout the research process. Moreover, the scope of action research can become unwieldy if not properly delimited, which calls for a focused approach with clearly defined boundaries and objectives. Acknowledging these potential shortcomings and proactively addressing them, action researchers can enhance the credibility and effectiveness of their work.

General inquiry serves as a strategic research alternative, filling the methodological space not adequately addressed by grounded theory, as well as other qualitative approaches such as phenomenology, ethnography or netnography, and action research. Unlike grounded theory, which seeks to inductively derive theory from data, general inquiry may not aim for theory development but instead focuses on exploring a broad range of questions through methods like narrative inquiry, historical inquiry, and case studies.

Narrative inquiry is particularly beneficial for capturing the richness of perceptions, making it a compelling choice when the objective is to understand individuals’ perspectives on issues. This approach serves as an alternative to phenomenology, which concentrates more on exploring the essence of lived experiences, often times on a single phenomenon, rather than individual perspectives, which may involve various issues.

Historical inquiry provides a lens to examine past events and contexts, yielding insights into the long-term development of phenomena and offering explanations for trends that might surface, for example, in quantitative research. This approach can be a more fitting alternative to ethnography or netnography in contexts where in-depth community immersion is not feasible or when the research goals extend beyond the scope of current or recent cultural contexts. Furthermore, historical inquiry’s broader longitudinal reach makes it more pragmatic for studies that require an extensive time frame, setting it apart from the more present-focused approaches of ethnography and netnography.

Case study contributes depth to general inquiry through their intensive examination of particular instances (e.g. activity, event, process, time, and community—individual, group, and organization), revealing the dynamics and complexities of a phenomenon within its real-world setting. This approach stands as a suitable alternative to action research, especially in scenarios where co-creation of solutions or direct intervention by the researcher is not the primary focus. Case studies allow for a more observational and descriptive analysis, focusing on documenting and understanding rather than actively participating in change, which is central to action research. This makes case studies ideal for explorations that require a deep, contextual understanding without the imperative of co-created or researcher-led action.

It is essential to recognize the distinction between single and multiple case studies. In a single case study, data is analyzed within the context of that specific case, where themes emerge distinctly within its unique framework. Here, disaggregation serves to dissect the case into analyzable elements, yet these elements remain tethered to their source, preserving the case’s integrity and specificity. Conversely, in multiple case studies, data is initially disaggregated to examine each case on its own terms but is subsequently reaggregated to identify cross-case patterns, themes, or insights. This reaggregation process is not about diluting individual case identities but rather about synthesizing insights to understand broader phenomena or variations across cases. The goal of multiple case studies is thus to compare and contrast, seeking to understand how and why differences or similarities occur across cases, which can enrich theoretical frameworks or contribute to theory development.

When deciding between a single case study and multiple case studies, researchers should consider the scope and objectives of their research. A single case study is often selected for its uniqueness, offering a detailed exploration of a critical (e.g. a brand’s comprehensive recovery strategy following a high-profile failure), exceptional (e.g. a brand’s isolated failure that garnered significant public attention and led to industry-wide policy changes), or revelatory (e.g. a brand’s successful transformation of failures into long-term customer loyalty) case (Yin, 2009). This approach allows for an in-depth understanding of the case, making it invaluable for understanding specific, unique, or extreme instances. However, the findings from a single case study may have limited generalizability and are more vulnerable to criticisms of their representativeness. Whereas, multiple case studies involve examining more than one case, facilitating the investigation of differences between and within cases. This approach enhances the robustness and generalizability of the findings, allowing for broader exploration and comparison. Multiple case studies can provide more compelling evidence for theory development, as similarities and differences can be analyzed across various contexts. However, they require more resources and time and may involve more complex data analysis and synthesis across cases. Ultimately, the choice between a single or multiple case study approach depends on the research question, the depth of contextual understanding required, and the resources available to the researcher. Both approaches offer valuable insights but must be chosen judiciously to align with the research objectives and constraints.

In employing these approaches—that is, narrative inquiry, historical inquiry, and case study—within the domain of general inquiry:

While grounded theory is characterized by its methodological rigor in the systematic development of theory, general inquiry offers flexibility in understanding phenomena through various lenses without the constraints of developing or aligning with existing theoretical frameworks. For instance, historical inquiry allows for an investigation of trends over time, which may offer context to quantitative findings without necessitating the creation of a new theory, as grounded theory would require. Similarly, narrative inquiry can provide rich, detailed insights into individual experiences that stand alone in their significance and do not necessarily serve to ground a broader theory. This flexibility makes general inquiry particularly relevant when the aim diverges from the intrinsic goals of phenomenology, which is to uncover the essence of experience, or ethnography and netnography, which delve deeply into cultural contexts. Action research, with its pragmatic approach to problem-solving, also contrasts with general inquiry’s broader exploratory focus that might not be confined to the iterative cycles of action and reflection. Thus, general inquiry stands as a complement or alternative to these approaches, offering a valuable pathway for research that seeks to elucidate the complexities of human behavior and perspective, inform practice, or provide a comprehensive narrative to quantitative trends, without the singular focus on theory generation that grounds grounded theory.

General inquiry boasts versatility, permitting researchers to navigate beyond the limitations of established qualitative and quantitative approaches. Its strengths lie in its adaptability and capacity to generate contextual, finer-grained insights. With narrative inquiry, historical inquiry, and case study, researchers can illuminate the complexities and richness of perceptions, the evolution of phenomena across time, and the peculiarities of specific instances. This approach is especially adept at answering the “why” behind observations and trends, offering a qualitative complement to quantitative data.

However, general inquiry is not without its critiques. Its open-ended nature can sometimes lead to a lack of clarity in methodological direction and theoretical grounding (if unspecified), which may be perceived as less rigorous compared to more structured qualitative methods. The breadth of focus inherent in general inquiry might also lead to an overreliance on the subjective interpretation of the researcher, which could introduce bias and reduce the replicability of the study. Additionally, the richness of detail that is a hallmark of this approach may result in voluminous data, posing challenges for concise analysis and presentation.

To address the aforementioned shortcomings, clear and rigorous methodological protocols must be established at the onset of research. This involves articulating the research questions with precision, setting out the criteria for data collection and analysis, and maintaining a reflective stance to mitigate researcher bias. Triangulation of data sources and methods can enhance the trustworthiness of the findings. Furthermore, incorporating peer review during the inquiry process can introduce checks and balances, ensuring methodological integrity. Finally, while the depth of data is a strength, it is crucial to synthesize findings effectively, presenting them in a manner that is both accessible and comprehensive, ensuring that the implications for theory and practice are evident and actionable.

Archives encompass a broad range of pre-existing documents and records that are utilized by researchers as a means to glean pertinent information for their studies. Within this domain, there are two primary categories: traditional documents and online materials.

Traditional documents, such as company brochures, journals, magazines, and newspapers, are indispensable for conducting historical and contextual analyses. They offer a retrospective view, allowing researchers to trace the evolution of ideas, opinions, and events over time. This longitudinal perspective can be crucial for understanding the present in light of the past. However, these sources can sometimes lack contemporaneity and may not reflect current realities or the rapid changes in societal attitudes.

Online materials, conversely, provide a rich vein of data that is both current and expansive. Customer reviews, blog posts, online forums, and social media content can yield real-time insights into public opinions and behaviors. The interactive nature of online platforms also offers a glimpse into the discourse and communication patterns that characterize today’s digital era. Nonetheless, the use of online materials as archival data is not without challenges. The transient nature of online content and the potential for a lack of authenticity due to anonymous or pseudonymous contributions can pose validity concerns.

To mitigate these shortcomings, researchers must approach archival data with a critical eye. Cross-verification of facts, consideration of source bias, and corroboration with data from other methods are essential steps to ensure the richness and trustworthiness of insights gleaned from archival sources. Furthermore, when it comes to online materials, employing algorithms and tools to authenticate data and filter out noise can enhance the quality and usability of the information collected.

In-depth interview represents a qualitative data collection method characterized by one-on-one interactions between the researcher and the participant. The researcher engages with individuals to gain a profound understanding of their experiences and perspectives regarding a specific subject matter.

In distinguishing between semi-structured and unstructured in-depth interviews, it is essential to understand the varying degrees of flexibility and guidance they offer. Semi-structured interviews utilize a guide or prompts to explore suggested issues and questions, yet they afford the interviewer the flexibility to probe deeper into responses or follow emerging themes, maintaining a balance between consistency and adaptability. Unstructured interviews require a high level of skill from the interviewer to maintain a prolonged conversation without relying on an interview guide. This approach allows for the exploration of topics in great depth, guided purely by the participant’s responses and the interviewer’s ability to navigate the conversation. Each type serves distinct research purposes: semi-structured interviews offer a mix of depth and comparability, ideal for exploring complex topics with some direction, while unstructured interviews are suited for delving into the nuances of personal experiences, revealing insights that may transcend the initial scope of the study.

However, it is important to note that there is, arguably, no such thing as a “structured interview”—characterized by a set of predetermined, fixed questions that allow for little to no deviation—in the context of qualitative research. If the goal is to gather specific, comparable information across participants, then what is essentially happening is a survey. Surveys may be administered by enumerators or researchers who ask participants questions and fill in responses on their behalf. Therefore, surveys administered orally should not be confused with in-depth interviews, which seek to explore complex issues in greater detail.

The strength of in-depth interviews lies in their inherent flexibility and depth; they are particularly adept at uncovering the complexities and peculiarities of human thought and experience. This technique is highly effective for exploring sensitive topics where in-depth understanding and the context of personal stories are crucial. The intimate nature of in-depth interviews facilitates a detailed exploration of personal narratives, allowing researchers to uncover not just what participants think, but why they think in certain ways. Through this method, new insights can emerge that were not initially anticipated by the researcher, providing a richer, more complex picture of the subject under study. Interviewers can adjust their line of questioning in real-time, responding to the direction of the conversation and pursuing interesting avenues that may arise spontaneously. This adaptive approach can reveal the dimensions and layers of individual experiences, contributing significantly to theory-building and the generation of new hypotheses.

Despite these strengths, in-depth interviews have their shortcomings. They are time-consuming, often requiring substantial investment in recruitment, preparation, conducting the interviews, and analyzing the results. The method’s reliance on the articulateness of participants and their willingness to share can also be limiting. There may be issues of recall bias or social desirability bias, where participants tailor their responses to what they perceive the interviewer wants to hear. Additionally, the findings from in-depth interviews are inherently subjective and cannot be readily generalized to larger populations.

To mitigate these challenges, researchers must employ a range of strategies. They should ensure that interviews are carefully structured to provide direction yet allow for sufficient flexibility. Building rapport with participants is key to encouraging openness and mitigating social desirability bias. Researchers can also use techniques such as member checking, where participants review the accuracy of the interview transcripts, to increase the validity of the data. Triangulation, the use of multiple data sources or methods to confirm the findings, can further bolster the study’s robustness. When analyzing data, a critical and reflexive approach must be adopted, being aware of one’s own biases and the potential influence they could have on the interpretation of the data. Through these measures, the value of in-depth interviews as a method for gaining deep qualitative insights can be fully realized.

Focus group is a qualitative data collection technique that captures the experiences and perspectives of participants through facilitated discussions within a small collective. This method is particularly potent in exploring people’s attitudes, ideas, and reactions to certain subjects or stimuli in a social setting. The interplay of conversation in focus groups can unearth a tapestry of insights, as individuals respond to and build upon the perspectives of others. The group setting can encourage participants to explore and articulate thoughts that might remain underdeveloped in solitary reflection.

The recommended size for a focus group typically ranges from 5 to 10 participants. This size is considered optimal for several reasons:

One of the core strengths of focus group interviews is their capacity to reveal the social processes that shape individual opinions and behaviors. As participants interact, they exchange and modify views, providing a window into the complexities of social dynamics and consensus-building. This interaction can be particularly illuminating when studying cultural norms, shared experiences, or group decision-making.

However, focus group interviews are not without limitations. The group dynamic, while beneficial in many respects, can lead to issues such as groupthink (Whyte, 1952), where a desire for harmony or conformity results in an erroneous or poor decision-making outcome. Dominant participants can skew the discussion, and quieter members may feel intimidated or less inclined to share dissenting views. Additionally, the presence of a researcher or moderator might influence responses, a phenomenon known as the moderator effect (Fern, 1982).

To mitigate these potential drawbacks, skilled moderation is essential. An experienced facilitator can ensure equitable participation, drawing out quieter group members while tempering dominant voices. The selection of participants is also critical; a well-composed group that reflects a diverse range of perspectives can enhance the richness of the discussion. Structuring interviews around a carefully designed guide can keep conversations focused and productive. Triangulation with other data sources can confirm the validity of the findings and provide a more robust understanding of the studied phenomenon.

Observational research stands as a cornerstone of qualitative inquiry, offering a lens through which researchers can witness firsthand the complexity of cultural norms and social interactions and processes. Engaging directly with the environment, observers can gather information that transcends the self-reported data from interviews or surveys, capturing the subtleties of human behavior and interaction in natural settings.

The principal strength of observational research lies in its ability to provide a rich, detailed understanding of a setting. Observation allows researchers to see how people behave in their natural environment, which can be particularly revealing when there is a difference between what people say and what they actually do. Observational data can contextualize and enrich findings from other qualitative methods such as in-depth or focus group interviews by adding layers of meaning and understanding to reported experiences or behaviors.

Despite its strengths, observational research is not without its challenges. One significant limitation is the Hawthorne effect (French, 1953), where individuals alter their behavior because they are aware they are being observed. There is also the issue of subjectivity; observations can be colored by a researcher’s biases or preconceptions. Moreover, there can be ethical and practical limitations to observation, particularly in sensitive settings where privacy is a concern, or where the presence of an observer may be intrusive or alter the phenomenon being studied.

To mitigate these shortcomings, researchers can employ several strategies. Unobtrusive observation techniques can be used to minimize the Hawthorne effect, such as observing from a distance or using recording devices. To address subjectivity, multiple observers can be used to cross-check and corroborate findings. Furthermore, researchers can be trained to recognize and bracket their biases. Regarding ethical concerns, obtaining informed consent, where possible, and ensuring confidentiality are paramount. Finally, blending observational data with other methods (triangulation) can help to validate findings and provide a more comprehensive understanding of the research subject.

In qualitative research, the use of interviews, whether in-depth (individual) or in focus groups, is a fundamental technique for gathering rich data. The management and structuring of these interviews are crucial for ensuring that the data collected is both comprehensive and insightful. Table 7 summarizes key aspects of interview guides and management for in-depth and focus group interviews.

In-depth interviews aim to attain a profound understanding of individual perspectives. Interview guides for this format should be designed to encourage open-ended responses, allowing interviewees the flexibility to share personal stories and experiences. Questions should be sequenced to follow a natural, conversational flow, adapting to the direction of the discussion. Probing plays a crucial role; it should be tailored to dive deeper into the individual’s responses. The interviewer’s role is pivotal in building rapport and trust, as the one-on-one setting demands a focused, empathetic, and patient approach. The timing of these interviews is flexible (e.g. 30–60 minutes per interview), often guided by the depth and richness of the information being shared. Managing the discussion is primarily respondent-led, giving the interviewee space to guide the conversation. Closure of the interview should be reflective, summarizing the individual insights gleaned.

Focus groups aim to explore a range of views and understand group dynamics. The interview guide for focus groups requires questions that stimulate interaction and group discussion. Sequencing of questions should be more structured to efficiently manage group dynamics and maintain focus within the limited time. Probing in focus groups is broader, designed to elicit diverse perspectives. The moderation is facilitative in nature, balancing turn-taking and ensuring equitable participation among all group members. Unlike individual interviews, focus groups have stricter time constraints (e.g. 90 minutes) and require a moderate level of flexibility to keep the discussion on track. The moderator leads the discussion, striving to balance group participation while maintaining focus on the research topics. Closure in focus groups involves summarizing the group’s consensus and highlighting differing viewpoints.

Both in-depth and focus group interviews gain substantial value from the process of pilot testing. This essential phase serves multiple purposes: it tests the efficacy of the questions in drawing out deep, meaningful insights and assesses the functionality of the interview guide in real-world settings. For in-depth interviews, pilot testing helps refine questions for clarity and depth, ensuring they resonate on a personal level and encourage comprehensive storytelling. It also provides an opportunity for researchers (as interviewers) to practice and refine their probing techniques and rapport-building skills, which are critical for eliciting candid and in-depth responses. For focus groups, pilot testing is crucial for evaluating the dynamics of group interaction elicited by the questions. It allows researchers (as moderators) to observe how the questions facilitate discussion, manage group dynamics, and ensure balanced participation from all members. This stage also lets researchers practice their facilitation skills, particularly in managing the flow of conversation, addressing dominant voices, and encouraging quieter participants to share their thoughts. Pilot testing in focus groups can also reveal if the planned timing for each segment is realistic and if the flow of questions maintains the group’s engagement.

Adhering to these guiding principles in interview guide construction and management, researchers can optimize the data collection process in qualitative research, ensuring that the information gathered is both rich in content and relevant to the research objectives.

Probing techniques in qualitative research are critical to dive deeper into the thoughts, feelings, and experiences of participants, going beyond surface-level responses. They are particularly useful in exploring complex issues where participants may not immediately articulate their thoughts or may not be fully aware of their underlying motivations. Three probing techniques avail for qualitative research: personal probing, projection techniques, and vignettes (Table 8).

The coding process in qualitative analysis is a foundational element, systematically unfolding into three interconnected techniques: open coding, axial coding, and selective coding (Strauss, 1987; Strauss & Corbin, 1990). Each stage builds upon the previous, transitioning from identifying and labeling discrete data segments in open coding to organizing these into categories during axial coding, and ultimately integrating these into a coherent theme in selective coding (Figure 2). This structured approach ensures a comprehensive and detailed examination of the data, laying the groundwork for meaningful insights (Figure 4).

Thematic analysis, as outlined by Virginia Braun and Victoria Clarke (2006), is a methodical approach for analyzing qualitative data. This approach involves six stages, ranging from initial familiarization with the data to the generation of codes, searching for overarching themes, reviewing and refining these themes, and finally reporting the narrative. This process emphasizes the transformation of raw data into a coherent story that not only aligns with the research question but also contributes to the existing body of knowledge. Spiggle (1994) offers useful ideas or principles on analysis (i.e. categorization, abstraction, comparison, dimensionalization, integration, iteration, and refutation) and interpretation (i.e. grasping meanings, seeking patterns in meanings, and crystalizing phenomenon) that can be used to strategically strengthen these stages. To the best of the author’s knowledge, synthesizing the analytical and interpretative principles proposed by Spiggle (1994) with the methodical framework of Braun and Clarke (2006) represents a pioneering synthesis of insights from two highly regarded sources in the field of qualitative research.

Credibility in qualitative research is akin to the concept of internal validity in quantitative research, but it is adapted to the context of qualitative methodology (Lincoln & Guba, 1985). Essentially, credibility refers to the degree to which the findings of a study are a true representation of the phenomena being investigated. Credibility hinges on the authenticity of the data and the appropriateness of the researcher’s interpretation, aiming to ensure that the study’s conclusions are genuinely reflective of reality (e.g. participants’ experiences and perspectives). This involves a deep and empathetic understanding of the context and the subjective realities of those being studied. Credibility is about believability and trust in the data and the researcher’s interpretation, essentially answering the question: “Are these findings credible to those who experienced the phenomena?” Establishing credibility in qualitative research involves a multifaceted approach that enhances the authenticity and appropriateness of the study’s findings. This includes strategies such as prolonged engagement, persistent observation, triangulation, member checking, peer debriefing, negative case analysis, and referential adequacy.

Prolonged engagement bolsters credibility by having researchers spend extended time in the field, which is crucial for gaining in-depth insights. Having sufficient time to immerse in the field allows for a thorough understanding of the study context, the establishment of trust with participants, and the observation of phenomena as they naturally unfold over time. This deep immersion helps in identifying and understanding patterns, behaviors, and subtleties that short-term engagement might miss, thus adding richness and depth to the research findings.

Persistent observation adds to credibility when researchers are focused and pay consistent attention to detail within the research setting. This is not just about being the researcher being present in the field; rather, it is about actively noticing, scrutinizing, and documenting ongoing activities, interactions, and changes. Such continuous, attentive observation helps uncover hidden aspects and deeper meanings within the data, providing a more comprehensive and finer-grained understanding of the research phenomenon.

Triangulation enhances credibility by combining multiple data sources, approaches, theories, or investigators. For instance, corroborating interview data with documentary analysis and observations adds layers of evidence that strengthen the study’s conclusions. Therefore, triangulation helps to identify and mitigate potential biases inherent in any single method, person, source, or theory.

Member checking elevates credibility whereby researchers take preliminary findings back to the participants for their review and validation. This ensures the accuracy of the researcher’s interpretations and conclusions. Participants can confirm, refine, or challenge the researcher’s findings, thus enhancing the study’s credibility and ensuring that the results truly reflect their perspectives and experiences.

Peer debriefing strengthens credibility when researchers engage peers or mentors in discussions about the research process and findings, providing an external perspective that can challenge researchers’ assumptions and interpretations. This process helps to maintain a critical and objective stance, ensuring that the study’s findings are well-grounded.

Negative case analysis further strengthens credibility when researchers deliberately look for and analyze cases that contradict the prevailing narratives or themes. This strategy is vital for a balanced and comprehensive analysis as it prevents selective attention to data that only supports preconceived notions. Incorporating and explaining disconfirming evidence, researchers can refine and strengthen their arguments, ensuring a more robust and well-rounded understanding of the research topic.

Referential adequacy solidifies credibility when researchers preserve a portion of the original data (e.g. audio recordings, interview transcripts, and raw field notes) to compare with the final interpretations or conclusions drawn from the analysis. This strategy serves as a means to validate the research findings by enabling the researcher (or an external auditor) to refer back to the original data at any point, verifying that the interpretations and conclusions are firmly anchored in the actual data collected. The practice of referential adequacy ensures that the researcher’s interpretations and thematic analyses are representative and true to the participants’ accounts and experiences. Through a retrospective examination of the data, researchers can demonstrate that their findings and interpretations are credible and substantiated by concrete evidence. This method enhances the trustworthiness of the research by providing a robust way to confirm that the interpretations are indeed an accurate reflection of the data.

Through these strategies, credibility in qualitative research is not just claimed but rigorously established, ensuring that the findings genuinely represent the participants’ views and experiences. This comprehensive approach to establishing credibility is key to producing robust, meaningful, and trustworthy qualitative research.

Dependability in qualitative research refers to the consistency and stability of the research findings over time and under similar conditions, and thus, it parallels the concept of reliability in quantitative research, addressing the need for research findings to be repeatable and consistent if conducted again under similar circumstances (Lincoln & Guba, 1985). Dependability acknowledges that while qualitative research often explores dynamic, evolving phenomena, the methods and processes used should exhibit a level of consistency that allows the findings to be dependable. This aspect of trustworthiness is particularly important as it reassures us that the findings are not arbitrary or capricious, but are the result of a well-thought-out and consistently applied research process. It considers the changing contexts and realities of qualitative research, ensuring that such changes do not unduly influence the research outcomes. To establish dependability in qualitative research, various strategies are employed, most notably inquiry audit and triangulation.

Audit inquiry enhances dependability by having an external evaluator review the research process and decisions. This comprehensive evaluation typically includes examining the research question, design, data collection and analysis methods, and the final conclusions. The evaluator assesses the consistency and logical progression of the research steps, ensuring each decision is well-founded and transparent. For researchers, preparing for an inquiry audit requires detailed documentation of the entire research process, including methodological approach, data collection instruments, data itself, and analysis notes. This documentation should be organized and accessible, allowing the auditor to trace the researcher’s decisions and verify the study’s dependability.

Triangulation is another robust method for enhancing dependability. This involves using multiple approaches to gather and interpret data, which might include combining different data collection methods (e.g. in-depth and focus group interviews), using multiple data sources (e.g. interviews, observations, and reflective journals), or involving several different researchers (e.g. emerging and established, female and male, and scholar and practitioner) in the analysis process. The rationale behind triangulation is that the use of diverse methods or perspectives can corroborate findings, minimizing the biases or subjective interpretations that might arise from a single method or source. When implementing triangulation, researchers should ensure that each method or source is aligned with the research objectives and that the integration of different approaches is coherent and adds depth to the analysis.

Through these strategies, qualitative researchers can significantly bolster the dependability of their findings. An inquiry audit provides a rigorous external examination of the research process, while triangulation brings a multifaceted perspective to the data analysis, both of which collectively contribute to producing dependable qualitative research outcomes.

Confirmability in qualitative research refers to the degree of alignment maintained in the study, and it is akin to the concept of objectivity in quantitative research (Lincoln & Guba, 1985). This criterion assesses whether the findings, interpretations, and conclusions are rooted in the data, paradigm, and/or theory, and not unduly influenced by the researcher’s personal biases, interests, or motivations. Confirmability emphasizes the importance of ensuring that the outcomes of the research are shaped by the respondents and the conditions under which the data were collected and analyzed, rather than by the researcher’s unfounded subjective viewpoints. This requires a demonstration that the data, paradigm, and/or theory, and not the researcher’s personal biases, interests, or motivations, have driven the study’s results. Confirmability is crucial for establishing the trustworthiness of qualitative research, as it assures that the findings are based on the participants’ experiences and perspectives as well as paradigm and/or theory, if any, and thus are consistent and replicable under similar conditions. To establish confirmability, several strategies can be employed.

Audit trail for confirmability encompasses a detailed documentation of the research process that allows an external auditor to review and confirm the findings are based on the data, paradigm, and/or theory, and not the researcher’s personal biases, interests, or motivations. This documentation should include the initial data, analysis notes, decision-making processes, and changes made during the study. The emphasis is on transparency and the ability to trace back every interpretive decision to its source in the data, paradigm, and/or theory, ensuring that the conclusions drawn are firmly rooted in the data collected and guiding principles.

Member checking for confirmability entails presenting the findings or interpretations back to the participants for their validation. This process helps ensure that the researcher’s interpretations align with the participants’ experiences and perceptions and are not solely a product of the researcher’s subjective understanding. This is a crucial step in verifying that the data and subsequent conclusions are authentic and representative of the participants’ perspectives.

Peer debriefing for confirmability involves engaging with impartial colleagues who can review the research and challenge the researcher’s findings and interpretations. This method serves as a check against researcher bias, as these colleagues can help identify blind spots or subjective interpretations that the researcher might have missed. The goal is to ensure that the conclusions are supported by the data and are not merely reflections of the researcher’s preconceptions.

Reflexivity for confirmability pertains to the researcher actively reflecting on and documenting their own biases, perspectives, and methodological decisions throughout the research process. This practice is crucial for identifying and mitigating the researcher’s subjective influence on the study. By being reflexive, researchers maintain a critical awareness of their role in the research and the potential impact they have on both data collection and analysis. This involves questioning how the researcher’s background, assumptions, and interactions with participants may shape the data gathered and the interpretations made. Maintaining a reflexive journal, where these reflections are systematically recorded, is a key method of demonstrating reflexivity. This journal not only provides a space for self-examination but also serves as a transparent record showing how personal biases were acknowledged and managed to safeguard the confirmability of the research findings.

Triangulation for confirmability involves using multiple data sources, methods, theoretical perspectives, and researchers to corroborate the findings. This strategy helps to ensure that the research findings are consistent and can be confirmed across different methods or viewpoints, which, in turn, provides a way to cross-check data and interpretations, minimizing the potential for bias and improving the alignment of the research.

Through these strategies, researchers can solidify the confirmability of their qualitative studies. These strategies collectively ensure that the findings are a true representation of the data, paradigm, and/or theory, and not unduly influenced by the researcher’s personal biases, interests, or motivations.

Transferability in qualitative research refers to the extent to which the findings of a study can be applied to other contexts or settings, and thus, it is akin to the concept of generalizability in quantitative research but acknowledges the unique and contextual nature of qualitative studies (Lincoln & Guba, 1985). Transferability is about providing enough descriptive detail for readers to evaluate how the findings might apply to other situations or groups. This involves a thorough depiction of the research context, the characteristics of the participants, and the interactions that occurred during the study. Unlike quantitative research, where generalizability is often a primary goal, qualitative research focuses on the depth and richness of understanding specific cases or contexts. Therefore, transferability does not imply that findings are universally applicable, but rather that they provide valuable insights or lessons that could be relevant in similar situations.

Thick description is the primary strategy for establishing transferability in qualitative research, which involves providing a detailed account of the findings that includes not only the data itself but also the context and any other pertinent factors (e.g. emotions and interactions) surrounding the phenomena under study.

Through thick description, researchers enable a more comprehensive understanding of their findings, providing enough contextual and interpretive detail for readers and other researchers to determine the potential transferability of the results to other groups or settings. This approach not only strengthens the trustworthiness of qualitative research but also extends its impact by making the findings more relatable to a broader range of contexts (Figure 6).

Advanced digital recording devices and software have simplified the process of capturing in-depth and focus group interviews. High-quality audio and video recorders ensure that every detail of a conversation is captured, which is crucial for accurate transcription and analysis. In online research settings, tools like Microsoft Teams and Zoom offer built-in recording features, enabling researchers to easily record virtual interactions.

Transcription software such as Dragon NaturallySpeaking, MeetGeek, and Otter.ai have revolutionized the transcription process. These tools use sophisticated speech recognition technology to convert spoken words into written text, significantly reducing the time and effort required for manual transcription. They are particularly beneficial for transcribing lengthy in-depth interviews or focus group discussions, allowing researchers to focus more on analysis and less on the labor-intensive process of transcription. Indeed, many transcription tools now offer integration with qualitative data analysis software. For instance, transcriptions generated software and saved in compatible file formats can be directly imported into NVivo for coding and thematic analysis, streamlining the workflow and reducing the potential for data loss or errors that can occur with manual data transfer.

The advancement of technology in qualitative research has been particularly transformative in the domain of data analysis. Software like ATLAS.ti and NVivo represent a major leap forward, providing robust tools specifically designed for the complexities of qualitative data. These software facilitate an efficient and systematic approach to data organization and analysis, allowing researchers to delve deep into their data. From advanced coding capabilities, which enable meticulous categorization and structuring of large data sets, to thematic development tools that assist in identifying and visualizing key patterns and themes, these software solutions are indispensable for modern qualitative analysis. They offer a range of features that cater to various aspects of qualitative research, ensuring that every nuance and subtlety within the data is captured and analyzed.

Moreover, software for qualitative data analysis are becoming more adept at handling a diverse array of data formats, including textual, audio, and visual materials, allowing for a comprehensive and multi-faceted analysis. The integration of different data types is seamlessly facilitated, enhancing the depth and richness of the research findings. Collaboration features in these software tools enable research teams to work together effectively, ensuring consistency and reliability in the analysis. Offering these sophisticated capabilities, qualitative data analysis software has not only streamlined the research process but has also significantly contributed to the efficiency and rigor of qualitative inquiry, making it an essential component in the toolkit of contemporary qualitative researchers.

The integration of artificial intelligence (AI), machine learning, and deep learning into qualitative research marks a groundbreaking shift, offering new dimensions and capabilities in data analysis (Christou, 2023; Longo, 2019). These technological advances are increasingly being harnessed to enhance the depth, efficiency, and scope of qualitative research, opening up innovative avenues for understanding complex social phenomena.

AI and machine learning are particularly effective in handling and analyzing large-scale qualitative data that would be overwhelming for manual analysis. Natural language processing (NLP), a branch of machine learning, is instrumental in text analysis, enabling the processing of vast amounts of textual data from various sources like interviews, social media posts, and online forums. NLP algorithms can identify themes, sentiments, and patterns in the data, offering insights that might elude traditional qualitative analysis. For instance, sentiment analysis, an application of NLP, allows researchers to gauge public opinion, emotions, and attitudes expressed in text data, providing valuable insights into consumer behavior, political trends, and societal issues.

Deep learning, an advanced subset of machine learning, further extends these capabilities. This technique can analyze not only text but also images and videos, making sense of visual data in a way that mimics human cognitive processes. This ability is particularly valuable in fields like consumer science or media studies, where visual content plays a significant role. Deep learning algorithms can recognize and interpret features and patterns in visual data, offering insights into brand perception, cultural trends, and visual communication strategies.

Moreover, AI-driven tools facilitate predictive analysis in qualitative research. Analyzing existing data, these tools can forecast future trends and behaviors, providing an anticipatory perspective that traditional qualitative methods might not offer. This predictive capability is particularly beneficial for market research, policy planning, and strategic decision-making, where understanding future scenarios is as crucial as analyzing the present.

The integration of AI, machine learning, and deep learning into qualitative research is not without challenges. It requires a careful balance between technological capabilities and the intrinsic human-centric approach of qualitative research. Ethical considerations, especially around data privacy and the interpretive integrity of AI algorithms, are paramount. Nevertheless, the potential of these technologies in qualitative research is immense, promising enhanced efficiency, richer insights, and new possibilities in understanding the complexities of human behavior and social structures. As these technologies continue to evolve, their role in qualitative research will likely become even more significant, redefining the boundaries and capabilities of qualitative inquiry.

Ethical recruitment involves transparent communication about the purpose of the research, the nature of participation required, and the choice to opt out without any penalty. Researchers must avoid coercion or undue influence, especially when working with vulnerable populations. To address this, clear guidelines and protocols should be established for recruitment, ensuring that potential participants are approached respectfully and informed fully about the study’s aims and processes.

In both qualitative and quantitative research, obtaining informed consent from participants is a fundamental ethical requirement that respects their autonomy and right to make informed decisions about their involvement. Consent considerations involve ensuring that participants are fully aware of the nature and scope of the research, what their participation entails, and their rights as participants. This process is not just a formal requirement but also a crucial aspect of establishing a trust-based relationship between the researcher and the participant; it involves clearly communicating all pertinent aspects of the research and obtaining explicit or implicit consent depending on the research context. Explicit consent requires participants to sign a consent form after being fully informed about the research. Implicit consent might be assumed in situations like observing public behaviors or participants’ progression in an online survey, but it is important to ensure that this does not infringe on privacy rights. To facilitate this, researchers provide detailed information sheets that serve as a comprehensive guide for potential participants, outlining various crucial aspects of the research.

Ensuring the safety of both participants and researchers, especially in sensitive or potentially hazardous environments, is a key ethical concern. Risk assessments should be conducted before beginning the research, and measures should be put in place to mitigate any identified risks, ensuring a safe environment for all to participate. Implied consent can be argued for observing individuals in physical and virtual public environments, provided that due diligence is exercised (e.g., de-identification and data aggregation) and that the observations are conducted reasonably (e.g., respecting privacy and ethical standards). However, implied consent is unlikely to be accepted by ethics committees for solicited interviews, as these involve direct interaction and explicit consent is typically required to ensure participants are fully informed and agree to the terms of the study.

Offering incentives for participation is common but should be handled carefully to avoid undue influence. Incentives should be reasonable and not so large as to persuade someone to participate against their better judgment. Researchers must evaluate the appropriateness of incentives based on the context and nature of the study, ensuring they are ethical and do not compromise the voluntary nature of participation.

Researchers should carefully weigh the potential benefits of the research against any risks to participants, such as emotional distress or privacy breaches. A thorough ethical review process should be in place to identify potential benefits and risks, ensuring that the research contributes positively without causing harm to participants.

Protecting participants’ anonymity and confidentiality is essential, especially when dealing with sensitive information. Data should be anonymized wherever possible, and strict confidentiality protocols should be established and followed, including secure data storage and controlled access.

Ensuring secure storage and controlled access to data is critical to protect participant information and maintain trust. Researchers should implement robust data security measures and have clear policies on data retention and destruction after the study’s completion.

In summary, ethical considerations in research require ongoing attention and responsiveness as the research progresses. Ethical protocols should be reviewed regularly, and researchers should remain vigilant to emerging ethical issues throughout the study. Adhering to these ethical principles, researchers can ensure that their work upholds the highest standards of research integrity and respect for participant rights and well-being.