Abstract
Saturation is a pivotal concept in qualitative research, playing a fundamental role in enhancing the completeness and robustness of qualitative findings. However, the digital era has presented new challenges and heightened the already existing “saturation controversy” in qualitative research. The plethora of digital data sources, the transformation of data collection methods and the increase in the volume of data in this time of “big data” raises questions about the relevance and applicability of the conventional approaches of assessing saturation. This paper discusses the dynamic landscape of qualitative research, digital transformation of qualitative research and implications for the concept of saturation. Additionally, the emerging challenges encountered by qualitative researchers in determining saturation in the digital age due to high volume of data and unstructured datasets are explored as well as the debates surrounding the continued applicability and relevance of the saturation methodological principle. Furthermore, emerging frameworks and strategies for the conceptualisation of saturation in the digital context are discussed, highlighting the possible incorporation of data visualisation and computational techniques to support judgments about saturation. While ethical considerations and issues of ensuring credibility and trustworthiness have always been critical matters in qualitative research, approaches to ensuring good data governance, data privacy, transparency, and data authenticity in the face of saturation in the digital era are imperative. Lastly, the paper examines the wider implications of these developments, the dynamic role of saturation in qualitative research methodologies as well as the opportunities for methodological advancements and interdisciplinary collaboration. The paper is expected to contribute to a comprehensive analysis of the saturation controversy in the digital era, provide guidance on how the challenges, complexities and controversies concerning this critical methodological principle can be addressed and to theoretically contribute to ongoing debate on the transformation of qualitative research practices in the evolving digital landscape.
Introduction
The rapid digital transformation has not only modified, how people learn (education), how people interact and communicate (society), how businesses conduct their operations (business world), but also how research is conducted (Costa, 2016, 2023; Mhlanga & Moloi, 2020; Mpofu & Mpofu, 2023). The digitalisation of society has also transformed the qualitative research landscape. Digital qualitative research was accelerated by the COVID-19 pandemic as researchers resorted to virtual means of interaction and interviewing (Keen et al., 2022; Rahman et al., 2021; Roberts et al., 2021). While digital platforms provide unparalleled access to data and participants for researchers, they also present new intricacies and challenges raising concerns about central concept of saturation in qualitative research. Digital technologies such as data analytics are associated with an increase in six main data features (Six Vs). These include the volume, velocity, variety, veracity, value and variability (Gandomi & Haider, 2015). Volume focuses on the magnitude, velocity on the speed and flow of data, veracity on the reliability of data sources and collection techniques, and variety describes the structural heterogeneity or diversity. While value explains the informational power of data, variability describes the variations in the velocity of the data flow (Gandomi & Haider, 2015; Vuković et al., 2024). Therefore, the problems linked to the six Vs of digital data can make it challenging to determine when saturation point has been reached. This paper explores the saturation controversy in qualitative research, while discussing the specific challenges and opportunities associated with the digital age.
Hennink et al. (2017, 2019) and Saunders et al. (2018) underscore the importance of saturation in enhancing quality, rigor, validity and credibility in qualitative research. Several researchers have given different definitions to the concept, but while these definitions converge and diverge in some aspects (Chitac, 2022; Guest et al., 2017; Hennink et al., 2017; Saunders et al., 2018; Sim et al., 2018), they all point to importance of the concept in qualitative research. Hennink et al. (2019) describe saturation point as the point where no new codes or themes arise from the data being analysed. Morse (2015) considers it as an important measure to ensure rigor in qualitative data, while Guest et al. (2006) explain it as an important measure to guide the determination of a sample size in qualitative research. It is described as the juncture of “diminishing returns” by Rowlands et al. (2016, p. 40) and that of “information redundancy” by Sandelowski (2008, p. 875). From these definitions, controversies concerning definitions and conceptualizing saturation are evident, while its fundamental role in qualitative is also accentuated. For example the definitional disagreements between theoretical saturation (the adequacy of data to support theoretical claims and meaning saturation (new data not yielding any significant new insights). These definitional variations present subjectivity, potential for inconsistencies and challenges in application in practice.
Concerning saturation Sebele-Mpofu (2020, p. 1) avows “It is viewed as a contemporary measure to alleviate the subjectivity in qualitative research, a yardstick for estimating sample sizes in qualitative research as well as an assurance for rigour and quality. Despite its recognition as a vital tool, it has its own fair share of controversies and contradictions.” The researcher emphasises that the saturation conundrum in qualitative research is driven by the controversy in the definitions of saturation as well as the varying underlying assumptions, hence the importance of explicating the form of saturation adopted and how it was achieved. Accordingly, considering that there are controversies and complexities linked to saturation under traditional qualitative research, it implies that these could even be more compelling in the digital era due to the demands of the digital era as well as the transformative and disruptive nature of technology. How to harness the transformative impact of digital technologies, particularly concerning the saturation principle is a problematic issue in digital qualitative research.
Emphasising the controversy surrounding saturation, Sebele-Mpofu (2020) raised the question whether saturation was a rule, phase, standard or measure. Morse et al. (2014) argue that contention generally surrounds definitional variations of the concept, incompatibility on how to measure it, describe it or explain it. This points to the intricacy in operationalising and conceptualising the concept. Affirming this Saunders et al. (2018, p. 1893) point to the lack of certainty in its conceptualization and use. Citing Fusch and Ness (2015), Sebele-Mpofu (2020, p. 2) posits, “qualitative researchers often find themselves in conundrum on how to address questions such as, what is saturation? How and when does one accomplish it? How does reaching it or not reaching it affect the research? Is the impact the same across qualitative designs, considering they are multiple?”
Revisiting the saturation controversy especially in the context of the digital era is relevant and pertinent, as qualitative researchers struggle with the implications of digital transformation on their methodologies, especially the concept of saturation. The broadening of digital data sources, the evolution in data collection and analysis methods as well as the exponential increase in data due to the emergence and application of digital technologies such as artificial intelligence (AI), machine learning (ML), big data and predictive analytics among others have significantly transformed the qualitative research landscape, strengthening credibility, completeness and rigor of qualitative findings. However, the digital era has brought forward new complexities in determining the saturation point, thus heightening the saturation controversy in qualitative research by increasing the volume of data and introducing unstructured data sets that challenge the application of traditional saturation assessment methods. How to establish when enough is enough has been complicated by the increase in the six Vs (velocity, volume, value, veracity, variability and variety) and the evolving data collection methods as online and social media interactions came into play. Additionally, while traditional qualitative research emphasises on upholding ethical considerations in research including ensuring trustworthiness and credibility, challenges associated with addressing these key issues are amplified in digital qualitative research (Costa, 2023), thus increasing the need and importance of revisiting the concept of saturation. How researchers deal with concerns linked to data governance, quality, transparency, security and privacy while ensuring that rigor and credibility of qualitative research findings is worth exploring. Therefore, this paper addresses the emerging research void in understanding and addressing the saturation controversy in the digital context, hence providing valuable insights for researchers to navigate the complexities and controversy surrounding saturation in the digital landscape, while leveraging the opportunities provided by digital technologies and other emerging technologies in qualitative research.
The relevance and actuality of this research hinges on the rapid technological developments, the evolution of data sources and methodologies as well as the rising importance of adapting qualitative research practices to new opportunities and challenges emerging from the digital age. By exploring the evolving concept of saturation in the digital era, the paper addresses a timely and pivotal issue in qualitative research. Its comprehensive engagement with relevant literature provides a strong scholarly foundation for understanding the concept, while the discussion of methodological and ethical challenges highlights the complexity of applying traditional notions of saturation to contemporary research contexts. The paper’s consideration of practical adaptations, such as computational tools and iterative analysis, adds valuable insights for researchers navigating the digital transformation of qualitative methods, thus meaningful contributing to ongoing debates in qualitative research methodology. Ultimately the paper is envisaged to contribute to the methodological developments and improvement within the qualitative research arena as well as to the ongoing discussions on saturation controversy. This could enhance rigor, robustness and the impact of qualitative research in the digital age.
The research is important because: (1) While qualitative research methods were conventionally dependent on smaller samples and face to face interactions, the widening of data sources raises challenges for defining and achieving saturation in the digital era, thus the need to revisit and redefine saturation. (2) The complexities and challenges linked to the process of attaining saturation in the digital era are intricate. Discussing possible strategies on how researchers can deal with these complexities and methodological challenges such as the difficulties in analysing large digital and unstructured data sets, identifying patterns and the saturation point are heightened by the digital era is critical. Ethical concerns such as consent, privacy and the likelihood of research bias are also widened, yet quality and rigor still remain key, therefore understanding the saturation concept and adapting it to the challenges of research in digital era is critical. Understanding the ways and strategies of adapting qualitative methodologies to the demands of the digital landscape, while addressing saturation is important. (3) The implications for research and reliability are more significant in the face of rising challenges for digital qualitative researchers and if saturation is not adequately addressed, the credibility and generalisability of qualitative findings may be compromised. Through a critical literature review approach this paper discusses the saturation principle, its importance in qualitative research and controversies surrounding the issue concerning traditional qualitative research as well as how qualitative research in the digital era might reduce of magnify the dilemma concerning the principle.
The Evolving Landscape of Qualitative Research in the Digital Era
The digital landscape has resulted in the transformation of nearly all areas of qualitative research from data sources, recruitment of participants, how ethical considerations are addressed, data collection, management and analysis methods; and the dissemination of findings as well as opportunities for collaboration (Bryda & Costa, 2023; Hesse et al., 2019; Palys & Atchison, 2012). The impact on the different forms of qualitative research may vary with those such as online ethnography being more affected than the traditional in-person interviews. Once largely dependent on face-to-face interviews, observations and other interactive ways such as focus groups, qualitative research methodology has broadened to include various digital techniques and technologies. The digital transformation in qualitative research has brought a double-edged sword effect of digital technologies. While opportunities for qualitative researchers have expanded (Bryda & Costa, 2023), risks and challenges have equally been broadened and novel ones have also emerged (Corti & Fielding, 2016; Sackett, 2024). The digital context avails unprecedented possibilities to carry out longitudinal studies, access participants in diverse geographical areas and from diverse backgrounds, as well as to gain deeper insights into actions and views of participants or those relating to the field under study (Subramaniam & Wuest, 2018). The researchers further allude to heightened efficiency, comprehensive interpretation and broader dissemination of research findings. Notwithstanding these advantages, the digital era poses new requirements and challenges linked to the digital divide, the digital skills gap, data management and security as well as ethical considerations (Costa, 2023). Therefore, understanding evolution of qualitative research in the digital era is critical in assessing the saturation controversy in qualitative research in the digital age.
The Saturation Definition Paradox
The saturation principle has its origins in the grounded theory as expostulated by Glaser and Strauss (1967). It emerged as a concept to guide theoretical and interpretive frameworks in qualitative enquiry (Guest et al., 2017; Moura et al., 2021; Sim et al., 2018). Over the years the principle of saturation has been accentuated by researchers as an important tool to address the criticism linked to sampling subjectivity (Buckley, 2022; Hennink & Kaiser, 2022; Sebele-Mpofu, 2021), research credibility and concerns about generalisability in qualitative research (Fusch & Ness, 2015; Saunders et al., 2018; Sebele-Mpofu, 2020). Walker (2012) posits that saturation is a tool for data adequacy and quality in qualitative research. Notwithstanding its increased relevance and recognition, there are still controversies surrounding its nature, purpose, definition and applicability (Constantinou et al., 2017; Lowe et al., 2018; Nelson, 2017). In consonance, Low (2019) argues that in explaining the concept researchers largely focus on explaining their sample sizes, for example how many interviews or focus groups they conducted to reach saturation point, while ignoring clearly defining what it is. Sebele-Mpofu (2020) posits that there is a paucity in methodological research that gives specific guidelines on defining and evaluating saturation, there are no transparent parameters to guide its attainment. Glaser and Strauss (1967, p. 61) defined saturation as an indicator to stop sampling.
Researchers refer to various forms of saturation which include theoretical saturation (Saunders et al., 2018; Starks & Brown Trinidad, 2007), code and meaning saturation (Hennink et al., 2017; Urquhart, 2012), hence the different definitions of the concept. Concerning theoretical saturation, Saunders et al. (2018) explain that it occurs when there is sufficient data that reflects enough concepts to capture a theory. Hennink et al. (2019) describes theoretical saturation from the angle of being able to collect enough and relevant data to philosophically support emerging theories or models. Van Rijnsoever (2015) explains it as the point whereby all codes in the population are observed in the sample. Therefore, theoretical saturation relates to philosophical adequacy but several the question still emerge. When data is considered sufficient to illustrate a theoretical framework? When enough is enough?
Concerning code and meaning saturation, Hennink et al. (2017) defines them as the point where no new or additional codes arise from the data and where no novel insight emerge from the data respectively. In affirmation Urquhart (2012) points to the saturation point as when recurrent codes emerge from the data without any new ones being generated. Walker (2012) explains it as the point where enough information has been collected to replicate the study. Fusch and Ness (2015) explain thematic saturation as the instance where no new themes emerge from further interviewing. These varying definitions make it problematic to fully comprehend the concept and when to strive to attain the point of saturation and from what perspective. Is it be sampling angle, data collection angle, theoretical dimension (Glaser & Strauss, 1967) or at the data analysis stage (Urquhart, 2012) or the data adequacy and information redundancy stage (Fusch & Ness, 2015; Guest et al., 2006). The varying definitions present challenges in conceptualising, operationalising and applying the concept, owing to its various dimensions (Saunders et al., 2018). For example Saturation can be viewed as the point of data sufficiency (stopping data collection criterion) or as conceptual framework for data richness and adequacy. The two perspectives points to possible challenges of subjectivity, context dependence, interpretive challenges and potential bias in applying the concept. Even though the definitions of saturation vary with researchers, the common conceptualisations are the point where new codes, data, themes and information emerge (These are points of sufficiency as well as redundancy of further data collection and analysis). Therefore, saturation can be defined as the point where no new codes, themes or insights emerge.
Saturation is not universal across the different qualitative research designs. It is also a matter of judgement among researchers (Tran et al., 2017). What constitutes the saturation point for one design might not be adequate to justify saturation point for another (Fusch & Ness, 2015). Saturation with respect to phenomenology and meta-analysis may differ. While phenomenology seeks to get an in-depth assessment of a subject matter based on the views and lived experiences of participants, meta-analysis is based on a review of related literature, thus considering saturation based on the explanations, definitions, research process and attainment of saturation based on the previous studies assessed (Sebele-Mpofu, 2020; Wilson, 2015).
The measurement of saturation is another sticking point in the saturation dilemma. What measure do we use to gauge the attainment of saturation? Is it the data sufficiency, codes, meanings or themes? Hennink et al. (2017) lament the narrowness of measuring saturation using codes and suggests that codes saturation can be employed as a foundation towards attaining the meaning saturation, which must be considered the critical form of saturation. This being the point where data accurately and fully captures viewpoints, convergences and divergences of opinions, as well as deep comprehension of data (Hennink et al., 2019; Saunders et al., 2018). Hennink et al. (2019) raise concerns regarding measuring saturation using themes, considering this is as a rather pre-mature assessment. The researchers avow that the emergence of a theme alone without fully understanding it or the information that fully supports it, is a comparative superficial way of measuring the concept of saturation. In compatibility of opinion, Sim et al. (2018) adduces that conceptualising thematic saturation based on the recurrence of themes is a flawed way of assessing saturation as the analytical frame that reflects relationships and meanings is not captured. The recurrence of a theme might not be a reflection of its contribution or impact to the study (Roy et al., 2015). As outlined by Roy et al. (2015) saturation attainment must be based on whether data collected and analysed reflects that all specific dimensions of the study have been captured. Therefore, like code saturation, thematic saturation must be considered in the preliminary assessment of saturation as the research process builds up towards meaning saturation, which recognises the breadth, depth and refinement of the issue being investigated (Hennink et al., 2019).
Tabling another controversy Sebele-Mpofu (2020) avers: “The question is, when will all the necessary information be captured and how can you tell that all critical information is represented within the data? Researchers provide different answers ranging from when new information becomes redundant, nothing coming up, when the topics are well understood and multiple examples can be used to explain phenomenon and where no new codes or themes emerge”
To answer the question of when and how to gauge the point of information sufficiency, Sandelowski (2008, p. 875) avers that saturation is achieved the data and findings of the study are such that the researcher acknowledges “that the properties and dimensions of the concepts and conceptual relationships selected to render the target event are fully described and that they have captured its complexity and variation.” Morse (2015, p. 587) suggests that when the comprehension of the “phenomenon becomes stronger, more evident, more consistent, more comprehensive and more mature,” saturation has been attained. The researcher further explains that in such a case the research report is presented in a comprehensive and clear manner that shows a logical connection of all areas from the objectives, the reviewed literature, methodology and interpretation of findings, thus enhancing the generalisability of findings. The outcome is “findings surprise and delight the reader” (Morse, 2015, p. 588). In consonance, Boddy (2016) submits that reaching the saturation point in one’s research journey enhance the degree to which the findings can be generalised. Offering a contradictory opinion, Saunders et al. (2018, p. 899) posit that the idea of increased generalisability of findings is contrary to propositions by Glaser and Strauss (1967, p. 61) who describe saturation from the perspectives of “theoretical adequacy” and the “explanatory scope of theory.” Saunders et al. (2018) further argue that the variation in the application of the saturation concept points to a misunderstanding of the objectives, measurement and meaning of the principle as conceptualised by various researchers, thus reflecting a saturation dilemma in qualitative research.
From the reviewed literature, it is evident that there are conflicting definitions of the principle (Buckley, 2022; Guest et al., 2020; Low, 2019), thus giving rise to the various meanings and interpretations attached to the principle as well as its attainment. Researchers offer conflicting opinions on its applicability, with O’Reilly and Parker (2013) questioning its multidisciplinary application. Saunders et al. (2018) point to the complexity and opaqueness in conceptualising and operationalising the principle. Adding to the debate, Yang et al. (2022) observes “Due to the diversity of saturation and its judgement standards, the relationship between the different kinds of saturation are complicated and ambiguous.” Oversampling might be an effective way of solving the challenges of sampling subjectivity (Yang et al., 2022).
Therefore, the saturation concept is both a controversial and important concept in qualitative research; hence, the need to explore its applicability in the digital context, considering that the possible opportunities and challenges associated with digital qualitative research may further complicate how the concept is addressed or require further reassessment that recognises the dual implications of digital technologies. The next section explores the role of the saturation principle in qualitative research.
The Role of Saturation in Qualitative Research and Controversy Surrounding the Concept
Saunders et al. (2018) emphasise the need for researchers to be more transparent in how they report on saturation and to thoroughly evaluate how the concept is operationalised and conceptualised, while recognising the likely contradictions and inconsistencies in how the concept is applied. Bowen (2008) raises concerns about the elusive nature of the concept due to lack of systematisation (Bowen, 2008). Hennink et al. (2019) acknowledge the importance of saturation in the determination of sample sizes, describing the principle as a tool that ethical review committees, researchers, supervisors and funders consider in assessing the adequacy of sample sizes. Even though saturation is considered a vital yardstick in assessing sample size adequacy and ensuring rigour in qualitative research, Hennink et al. (2017) raise concerns about the lack of clear delimitation of the concept and how it is applied in sample sizes determination. Questions also relate to when to determine sample sizes to achieve saturation, that is whether apriori (during the data collection or analysis stages or throughout the research process.
The principle of saturation plays a critical role in ensuring robustness, rigor and validity of qualitative research findings (Guest et al., 2020; Yang et al., 2022). The concept reflects how the researcher explored the phenomenon under investigation comprehensively irrespective of which type of saturation is attained, no new codes, insights, themes and meanings emerging points to thoroughness (rigor) (Sebele-Mpofu, 2020). Qualitative researchers have to demonstrate how saturation point was attained in order to give evidence of the depth, extensiveness and completeness of their research process, data gathering methods, analysis and interpretation of findings (Saunders et al., 2018; Sebele-Mpofu, 2020, 2021), yet researchers lament the failure to clear document saturation point achievement by qualitative researchers (Marshall et al., 2013; Rowlands et al., 2016). Additionally, saturation is pivotal in establishing the credibility and trustworthiness of qualitative inquiry findings (Mpofu, 2021). Meaning saturation reflects that the researcher has achieved a comprehensive investigation and understanding of the phenomenon (Sim et al., 2018; Van Rijnsoever, 2015), thus heightening the trustworthiness and credibility of findings and conclusions drawn as well as the recommendations proffered. This points to moving from descriptive meaning to interpretive meaning.
Achieving saturation point ensures that the qualitative findings fully and accurately reflect the lived perspectives and experiences of the study participants, thus strengthening internal validity of the research. Saturation improves the robustness of findings. The non-emergence of novel patterns, themes, codes and insights is considered a reflection of thoroughness in research and representative of the broader population or context under investigation. The robustness of findings is imperative to enhance consistency and transferability of findings or the applicability of findings to similar contexts, thus strengthening the quality of research. Even though saturation contributes significantly to ensuring credibility, rigor, validity and transferability of findings (Hennink et al., 2019; Morse, 2015), the controversy alluded to in the introductory section, there are also significant challenges, complexities and controversies surrounding the underlying assumptions, relevance and applicability of the concept (Morse et al., 2014; Saunders et al., 2018; Sim et al., 2018). As explained by Sebele-Mpofu (2020, p. 4), “The variation in qualitative research designs compound the intricacy of the saturation puzzle (content analysis, ethnographic, phenomenological and meta-analysis) together with the multiple methods and instruments of data collection (literature review, focus groups and interviews among others).”
While acknowledging both the importance and the lack of clarity on the concept of saturation, Naeem et al. (2024) posit that reflexivity is important in qualitative research as “data saturation is a complex phenomenon expanding beyond the theoretical rationale experienced as a before, during and after an iterative and reflective process engaging with, the research participants and data….” More questions arise especially regarding saturation in the digital era. Rahimi & Khatooni (2024) describes saturation as an evolutionary concept that is context–dependent and that manifests in four forms which are code or thematic, theoretical, data and meaning saturation. Therefore, while saturation is pivotal in ensuring rigour and validity (Daher, 2023), the saturation conundrum is likely to widen in the digital era considering the evolution of qualitative research in the digital realm. The next section discusses the evolution of qualitative research in the digital realm, unpacking the emerging opportunities to be harnessed and challenges to be addressed to ensure research effectiveness and credibility.
The Importance of Redefining Saturation Point in Digital Qualitative Research
Saturation point in traditional qualitative researcher is defined as the point at which new information, data, themes and meanings emerge from data collection or analysis (Hennink et al., 2019; Sim et al., 2018). While this concept continues to be applicable in digital contexts, it may need to be adapted to maintain contextual relevance in view of the unique characteristics of the digital qualitative research settings. Taking into consideration the increase in the volume and diversity of data, saturation may need to shift from only considering how many interviewees or FDGs participants are needed to reach saturation (Guest et al., 2020; Hennink et al., 2019) to the volume of data generated by each participant or interviewee. Furthermore, it might be challenging to determine a fixed point of saturation due to the continuous nature of data collection and in some cases the real time collection and analysis of data may further complicate the assessment of saturation. The emergence of new digitally-supported methodologies such as digital ethnography, netnography and social network analysis lead to more complexities (Bryda & Costa, 2023; Fenton & Parry, 2022) and raise questions on whether researchers need to consider methodological saturation as new information can emerge with the incorporation of additional methods. Researchers need to explore factors such methodological saturation, the type saturation targeted, data diversity and volume, ethical considerations and the iterative nature of data collection in digital contexts and how these factors are likely to influence the re-consideration of saturation in digital contexts.
Opportunities and Challenges Associate with Qualitative Research in the Digital Contexts
This section discusses the opportunities and challenges of digital qualitative research in order to contextualise the need for the reconceptualization of the concept.
Opportunities for Qualitative Researchers in the Digital Age
As outlined earlier, qualitative researchers can enjoy several advantages that are associated with the digital environment. Digital technologies such as data analytics are linked to the increase in six main data features and these include volume, velocity, variety, veracity, value and variability (six Vs) (Gandomi & Haider, 2015; Mpofu, 2023). Similarly, digital qualitative research is associated with significant increases in the types, volume, availability, value and speedy access to data and participants (Costa, 2023). Volume focuses on magnitude, velocity on speed and flow of data, veracity on the variability of data sources and collection techniques, and variety refers to the structural heterogeneity or diversity. While value explains the informational power of data, variability describes the variations in the velocity or flow of data (Gandomi & Haider, 2015; Vuković et al., 2024). Figure 1 summarises some the opportunities from literature and it is important to point out these are not exhaustive. Gibson et al. (2005, p. 57) argue that the digital revolution especially through innovation analysing digital audios through Atlas. ti and other Computer–Assisted Qualitative Data Analysis Software (CAQDAS) has increased the possibilities of “using media-rich, naturalistic data in place of transcribed ‘de-naturalised’ forms.” The opportunities are likely to continue to emergence and evolve as the digital evolution is ongoing and new technologies continue to emerge. Opportunities for Qualitative Researchers in the Digital Context.
Widened Data Access Opportunities
The digital environment broadens data access through the diverse sources of data such as online platforms and surveys, social media, websites and blogs (Subramaniam & Wuest, 2018), thus providing thick and rich data. The digital environment enables researchers to have access to a wider pool of participants (Costa, 2023), hence samples can be larger than the traditional small sizes associated qualitative research. Researchers can also have access to different groups of people across different geographical spaces and from diverse backgrounds, thus increasing the likelihood of capturing various opinions and experiences. Furthermore, researchers can use virtual focus groups while exploiting technologies such as video conferencing, Microsoft teams, zoom and google meet as well as WhatsApp (Keen et al., 2022). Online platforms also avail opportunities for researchers to observe and analyse online interactions, conversations and communities. Therefore, geographical limitations associated with costs, travelling and accessibility can be minimised. While access to diverse data sources and research participants have been expanded, question regarding sampling fairness, adequacy and representation emerge.
Faster and Efficient Data Collection
Data collection can be done in real-time through digital technologies, allowing researchers to capture the evolving phenomena as it occurs. Additionally, virtual interviews, focus grounds and online surveys may increase the efficiency of data collection, minimising costs and time linked to traditional methods of collecting data. Affirming the benefits of digital data collection methods, Keen et al. (2022) posit that virtual focus groups and video call interviews enabled access to marginalised populations, ensured the building of notable rapport and allowing synchronous interaction of people across different environments. The researchers further allude to advantages associated with using new interview methods like the Grid elaboration method. Additionally, the study participants’ recruitment process was enhanced as virtual interactions could be scheduled flexible, valuable recordings made allowing for easy transcription (Keen et al., 2022). Therefore, data collection and analysis process can be streamlined through the use of digital technologies and platforms.
Enhancement of Existing and Emergence of New Research Methodologies
In addition to enhancing the existing ways of collecting qualitative research data, digital technologies have led to the emergence of novel research methodologies such social media analysis, digital and mobile ethnography as well as netnography (Fenton & Parry, 2022; Nascimento et al., 2022; Padricelli et al., 2021; Paoli & D’Auria, 2021). These new technologies and methodologies have improved the recruitment and diversity of participants, enable research to be remotely conducted and improved the quality of research (Kaoukaou, 2021), but not without equally pragmatic problems to be addressed by qualitative researchers. Issues of diversity, inclusivity, bias, fair representation, reproducibility and transparency need to be given due consideration otherwise they may compromise research quality. Kaoukaou (2021) also raises five important areas that might cause challenges for netnographic researchers and these include digital transparency, the virtual interviewee paradox, the interpretation used in digital environment, intermittent engagement and the research scope. Therefore, it is evident that the emerge of new research methodologies will have both favourable and unfavourable implication for the concept of saturation and research quality in the digital realm.
Enhanced Data Analysis, Rigour and Quality of Research
Technological advancements have dramatically modified research with the digital realm bringing digital technologies such as big data and ML and other digital platforms that allow for virtual data collection and analysis, thus widening the scope of research possibilities (Costa, 2023). The digital era has resulted in more enhanced and novel ways of analysing data. CAQDAS which include MAXDA, NVivo and ATLAS.ti have made data analysis quicker and easier. The new technologies and software enable researchers to categorise, organise and analyse large volumes of data, visual and audio data. Subramaniam and Wuest (2018, p. 2) state “The improvements in data analysis packages enables qualitative researchers to perform substantive and quality analysis that were not possible in manual methods.” The speed and accuracy associated with these technologies have enhanced the credibility and rigour in qualitative. As an example, ML and AI can sift through large data sets and accurately identify patterns insights that could be challenging to pick through traditional data analysis methods. Additionally, data visualisation tools enhance the understanding, interpretation and reporting of results (Bryda & Costa, 2023). NPL improves data storage, processing speed and automated transcription which supports coding digital recordings directly (Carter et al., 2021).
Opportunities for Collaboration
Digital platforms foster collaboration among researchers, thus facilitating knowledge sharing, the improvement of qualitative research quality and ensuring multidisciplinary perspectives are brought together (Bryda & Costa, 2023; Palys & Atchison, 2012). The multi and interdisciplinary collaboration among qualitative researchers promotes innovation in research methods and projects, through the mix of diverse skills and perspectives.
Quick, Easy and Expanded Ways of Disseminating Findings
The digital environments offer faster, easier and broad ways of reporting and disseminating research findings (Corti & Fielding, 2016; Hays et al., 2015). While acknowledging that the digital ecosystem presents opportunities for disseminating knowledge to wider audiences and through a variety of platforms such as blogs, webinars, seminars, conferences, policy briefs, journal publications and book chapters among other platforms, it also brings challenges such as the potential unethical use of one’s work, lack of control and cyberbullying (Hays et al., 2015). Therefore, the digital era requires researchers to find ethical, effective and sustainable ways of disseminating findings while minimizing the potential challenges.
Challenges for Qualitative Researchers in the Digital Age
While acknowledging several advantages of qualitative research in the digital context such as overcoming geographical boundaries and faster recruitment of participants, Roberts et al. (2021) raise issues concerning the creation of rapport with participants (which is essential for getting rich data and saturation point attainment as outlined by Malterud et al., 2016) and limitations of the digital divide (lack of access to digital tools and internet connectivity). Rahman et al. (2021) advocate for flexibility, adaptation and responsiveness of research methods to meet the demands of the digital context. Figure 2 summarises some of the challenges confronting qualitative researchers in the digital era. Challenges Confronting Qualitative Researchers in the Digital Context.
Authenticity of Participants
Due to the virtual nature of digital interviews, it might be challenging to verify the identity and legitimacy or integrity of virtual research participants. This raises issues of data validity and quality. In addition to other problems of collecting data through interviews and focus groups in the digital realm, Sackett (2024) raise challenges of creating rapport with participants and that of ghosting (where participants just disappear). While the issue of failure to create rapport through digital data collection techniques is a challenge pointed out by several researchers, other researchers argue that it is possible to build the rapport during the planning stages for the interviews, as participants and researchers on the best possible platform to use and the flexible time. Thunberg and Arnell (2022, p. 757) acknowledge that technical difficulties are likely to affect the quality of data collection, but also observe, “concerning sensitive topics (e.g., victimisation, health issues, sexuality, more rich data) can be reached with digital options than in person options, but it can be difficult to capture visual cues.” Therefore, when researching on sensitive topics virtual data collection through interviews and focus groups can be more effective data collection tools. This is because in cases where participants express their views on audios and offer camera that anonymity gives them comfort to freely express themselves, thus giving researchers rich data which might be difficult to collect during face to face interactions. This critical in the attaining the point of saturation.
Digital Skills Gap, the Digital Divide and Technological Limitations
Costa (2023) avows that qualitative research in the digital era requires the development of digital, new analytical and information technology skills. Pope & Costa (2023) emphasise the need for qualitative researchers in the digital context to have soft, technical and ethical consideration related skills. Researchers might not have adequate skills such as data science and mining, digital ethnography, data analytics and ML skills as well as social media analysis competencies to support data collection, management and analysis. The lack of the requisite skills may negatively affect the research process from data collection to the interpretation and reporting of findings. This might compromise research quality contrary to the general expectation that digital technologies enhance the quality of research. Without appropriate skills and knowledge researcher may fail to exploit the possibilities offered by the digital environment.
Concerning the digital divide, communities and individuals with limited access to digital tools (such as computers and smartphones) and poor digital infrastructure such as internet connectivity or have limited digital knowledge may find it difficult to participate in digital qualitative research studies (Pope & Costa, 2023; Sackett, 2024). This might result in the underrepresentation or exclusion of certain demographic groups or marginalised populations, thus skewing the research findings.
Technical issues associated with the use of technology which include software glitches, lack of and or poor internet connectivity may affect data collection and analysis in the digital age. Sackett (2024) calls for technological, ethical and social adaptations to deal with emerging challenges and new protocols of engagement for effective qualitative research in the digital environment.
Increase in Digital Risks, Biases and Ethical Considerations
Hesse et al. (2019) contend that the big data era in qualitative research has not only brought novel data sources, new research methods, emerging researchers and novel ways of data management, but has also driven the rise of ethical and practical challenges for qualitative researchers. The cyberspaces where data is collected for digital qualitative research raise concerns regarding ethical principles that need to be upheld to ensure the validity and reliability of data in the digital contexts (Subramaniam & Wuest, 2018). Emphasising the importance of addressing ethical concerns in the big data qualitative research context, Hesse et al. (2019, p. 560) outline five principles which encompass “(a) valuing methodological diversity; (b) encouraging research that accounts for and retains context, specificity, and marginalized and overlooked populations; (c) pushing beyond legal concerns to address often messy ethical dilemmas; (d) attending to regional and disciplinary differences; and (e) considering the entire lifecycle of research, including the data afterlife in archives or in open-data facilities.”
Excessive Amount of Data and Issues of Data Quality
While it is advantageous to collect large amounts of data that allows researchers to capture diverse perspectives, the huge amounts of data associated with digital data sources and collection methods maybe overwhelming (Costa, 2023; Sebele-Mpofu, 2021). This may make data management and analysis difficult and even the identification of relevant information. The reliability and quality of data can also be a concern as issues of bias, completeness and manipulation might crop up, hence affecting data quality.
The Saturation Dilemma in the Digital Age
Saturation is used is categorised in five ways that include data, theoretical, code, thematic, and meaning saturation. Saturation promotes research quality, credibility and saves time as well as costs in qualitative research (Rahimi & Khatooni, 2024). This section explores the complexities, controversies, debates and the evolving perspectives concerning the saturation controversy, within the context of qualitative research in the digital era.
The literature review covers the evolution of the saturation principle in the changing qualitative research landscape, the saturation controversy, reconceptualisation initiatives, ethical considerations and implications for qualitative researchers, thus examining the diverse perspectives, debates and possible suggestions associated with this pivotal methodological principle. The discussion is guided by the literature reviewed on the evolution of qualitative research in the digital era. The likely possibilities and challenges associated with qualitative research in the digital landscape have positive and negative implications respectively for upholding the saturation principle.
Highlighting the positive impact of digital technologies in qualitative research which can potentially have a favourably influence on saturation point and the quality of findings, Gibson et al. (2005) posit that the use of digital technologies and CAQDAS in data analysis may significantly reduce some of the longstanding methodological problems associated with qualitative research such as the dependence on data transcription (viewed as subjective) as opposed to natural data. The naturalistic nature of digitally transcribed data may increase the transparency of the research process and justifications of saturation attainment. The researchers also raise the lack of consensus on the best recording technology to use for research purposes as a barrier to effective incorporation of audio data in research. Therefore, the saturation controversy in qualitative research is explored guided by the factors presented in Figure 3. Factors Concerning the Saturation Controversy in Qualitative Research in the Digital Context.
Increase in Data Sources
The increase in data sources in the digital age as a result of the emergence and adoption of technologies like big data and AI complicates the saturation attainment dilemma in qualitative research even further. Furthermore, platforms such as online platforms, blogs, social media, archival sources and other sources of large data sets might broaden qualitative research data sources and increase opportunities to access thick and rich data (Sackett, 2024). While this expansion in data sources, access to data, volume of data and possibly the improved quality of data are positive benefits of digitalisation in qualitative research (which is good for saturation attainment), it is important to note that the digital environment brings challenges for the traditional qualitative research (Nascimento et al., 2022) and conceptualisation of saturation. Researchers may face difficulties in determining the point of diminishing returns, code saturation, thematic saturation or even meaning saturation. The large volumes of data may equally aggravate the saturation attainment controversy as it might be challenging to sift through the large amounts of data and ascertain when saturation point has been achieved.
Digital technologies such as big data are associated with the increase in the volume, velocity and variability of data (Mpofu, 2023), hence the increased amount and speed of data collection may create a sense of perpetual incompleteness, thus raising questions about the feasibility of reaching the correct saturation point. In addition, the evolution of almost all sectors of the economy and areas of human interaction due to digital transformation points to the evolution of economic, social governance and cultural phenomena. This points to the dynamism in perspectives, trends, experiences. This ever-evolving characteristic of digital age phenomena can pose challenges for researchers to determine the point when the comprehensive understanding of phenomena has been captured (meaning saturation as outlined by Hennink et al., 2019) or when theoretical saturation has been attained as the subject of investigation may continuously change.
The expansion of data sources, the increase in the six Vs of data in the digital environment and the evolution of phenomena in the digital age signal that researchers need to rethink their methodological approaches to address these specific challenges associated with conducting qualitative research in the digital era (Rahman et al., 2021). To address the possibly increasing saturation controversy, researchers need to explore alternative strategies which include continuous data collection (but until when?), the use of digital technologies in data collection and analysis, iterative analysis and the use of machine learning algorithms for effective data collection, management and analysis as well as the attainment of the true saturation point.
Challenges in the Definition, Conceptualisation, Operationalisation and Measurement of the Saturation Concept
Conceptual debates and discussions on saturation have traditionally centered on scholars questioning the definition, applicability, conceptualisation and operationalisation (Saunders et al., 2018; Sebele-Mpofu, 2020; Sim et al., 2018). Contention has also bordered around the meaning and distinctions between the various forms of saturation, including the implications of these variations and grey areas on how the term is used and understood. The challenges linked to qualitative research in the digital age bring more questions to the applicability and validity of the conventional saturation criteria in the digital context. Researchers should consider discussions around the redefinition, reconceptualisation and re-operationalisation of the concept in the digital era. It is pivotal to explore alternative measures of data sufficiency and possible formulation of new guidelines for determining the adequate sample size and saturation point in the face of the digital era technologies (Sebele-Mpofu, 2021) and the associated challenges for qualitative researchers to adapt their methodologies to the new demands, opportunities and challenges (Rahman et al., 2021) as well as to reconceptualise how saturation continues to enhance the credibility, rigour and relevance of findings in the rapidly changing digital landscape.
There is a paucity in clear and well-established guidelines for assessing saturation in qualitative inquiry in the digital era as traditional approaches may be inapplicable or inadequate. This lack of clear best practices and consensus on the clear methodological roadmap might widen the inconsistencies and controversies which generally exist concerning the methods of measuring saturation.
Complexities Associated with Volume, Diversity, Heterogeneity and Quality of Data
While digital platforms may increase the recruitment of participants resulting in larger samples, thus allowing for diverse and heterogeneous data to be collected, larger samples have both positive and negative implications for reaching saturation point and the quality of research findings. Roy et al. (2015, p. 250) points out that larger samples may pose challenges in the examination and analysis of data thus “limiting the ability to probe data collection, develop emergent questions and contextualise quotes.” Mpofu (2021) portends that verbatim quotes are important evidence for the development, clarification and elaboration of findings to give credibility to the interpretation and conclusions drawn in qualitative research. While Dai et al. (2016) contend that quotations give an audit trail to show the opinions of participants and ensuring that they are not overshadowed by the epistemological stances and views of the researcher, Morse (2015) avers that they give researchers a platform of showing unusual responses. Thus, larger samples may compromise the achievement of saturation and the quality of findings. On a contradictory angle, Vasileiou et al. (2018) and Sebele-Mpofu (2021) argue that larger samples may help discover novel themes and unexpected responses and experiences which are essential for reaching saturation point.
The diversity and heterogeneity of data sources which include online platforms and digital archives increases the complexity of data collection and analysis in qualitative research (Costa, 2023). While the computational methods that employ digital technologies such as NLP and ML can be used for textual analysis and for analysing large amounts of data and unstructured data sets, these techniques may fail to completely capture the insights, meanings and contextual aspects of the data (Bryda & Costa, 2023), which are critical for the attainment of the saturation point and enhancement of qualitative research findings. Therefore, the computational controversies and limitations in understanding the richness and depth of digital data may create problems for qualitative researchers in the determination of the saturation point.
The abundance of data sources and the ease of data collection due to the use of digital and other technologies may potentially drive over-collection and analysis of data (Carter et al., 2021; Roberts et al., 2021), which might not necessarily contribute to gaining deeper understanding and insights into the subject under investigation (achieving meaning saturation) or attaining other forms of saturation. Concerns of possibly unfocused oversaturation of data also emerge. It might also be challenging to ensure the research process, from collection to analysis is strategic and focused to promote research relevance and manageability.
Ethical Consideration and Data Governance
Ethical considerations and data governance concerns that are generally raised by researchers on the application of digital technologies in various areas such as accounting, finance, auditing among other areas (Mpofu, 2023) are also applicable to qualitative research. Researchers allude to ethical concerns such as data ownership, privacy, security, cyberattacks and other risks associated with technology. Research is not immune to the challenges, as concerns of ethical and responsible data collection, management and analysis emerge, especially the protection of personal information. Ethical concerns are likely to heighten in digital contexts due to emerging challenges such as the complexity of ensuring data security in large scale virtual studies, verifying the authenticity of online participants and dealing with potential harm such as cyberbullying during data gathering. Personal information of participants in the digital era is linked to several digital footprints of participants such as financial data, preferences and demographics (Carter et al., 2021). If these are not properly secured, unauthorised access and use of personal information may cause psychological, physical and emotional harm to participants. Traditional ethical safeguards in qualitative research may be inadequate in digital DQR environments. Therefore, addressing these ethical considerations and ensuring robust data governance practices may compound the complexity of assessing saturation as researchers struggle to strike an equilibrium between methodological rigour and fostering ethical integrity in qualitative research.
Lack of Digital Skills, Digital Infrastructure and Other Research Skill Required in the Digital Age
The challenges concerning the lack of appropriate technical, digital and other emotional intelligence skills were extensively discussed under Section Challenges for Qualitative Researchers in the Digital Age. De Villiers et al. (2022) posit that the ability to collect data that is relevant and contextual is dependent on the ontological and epistemological assumptions of the researcher as well as the research choices and skills. For example, to reap maximum benefits of video technology, both the researcher and participant need to be experienced in its use. Testing and practicing on the chosen virtual platform before the actual interview might also create a good relationship between the researcher and participants, thus setting a good environment to explore issues in-depth. On the contrary where there is a mismatch between researcher’s choices and skills, data collection may be compromised. Quality communication is influenced not only by the knowledgeability and information richness of participants but by the skills of the researcher (Malterud et al., 2016; Sebele-Mpofu, 2020). Lack of appropriate skills may hinder the researchers from attaining the saturation point even with the right sample or informational rich participants.
Imposter Participants and the Failure to Create Rapport
Muir (2024) argues that in addition to the challenges faced by digital qualitative researchers, there is an “imposter participant problem.” Imposters were described as people who fake identities or embellish their experiences to suit those required for participation in a study. The researcher observes that while the digital environment allows researchers to recruit participants quickly and from different backgrounds and geographical locations, there are difficulties for researchers to confidently say participants are who they say they are (identity verification is a challenge). Muir (2024) further observes that imposter participants may compromise the quality of qualitative research or pollute the data collected with fake lived experiences and perspectives. As explained by Muir (2024) imposter participants can go to great lengths to research through AI on the subject area under study and appear knowledgeable on the issue, this might lead to reaching a false saturation point based on false ‘information power’. Malterud et al. (2016) emphasise that the information power of participants or a sample is dependent on the objective of the study, sample size specificity, quality of dialogue, knowledgeability of participants and the analysis strategy.
Building rapport with participants is critical in qualitative research because participants are generally free and comfortable to engage in an environment where there is empathy, trust and respect (Sebele-Mpofu, 2020). It might be difficult to create such an engaging environment in a digital environment and this might have negative consequences for reaching the point of saturation.
The continued use of the saturation principle in qualitative inquiry remains critical for promoting completeness, credibility and rigour, yet the challenges concerning its applicability in the digital context need to be addressed. Therefore, researchers need to consider the development of new methodologies or the enhancement of existing ones in line with the digital environment. Therefore, the next section explores possible strategies to reconceptualise saturation in the digital age while maintaining its importance in qualitative research.
Strategies for the Reconceptualization of the Saturation Concept in Qualitative Research in the Digital Context
Carter et al. (2021) adduces that qualitative researchers need to socially, technologically and ethically adapt to the new requirements of researching in digital environments for them to be able to successfully navigate the intricacies associated with the digital contexts. The researchers further argue that while the adaptation can assist in ensuring adequacy in the recruitment of participant, it can also negatively affect the richness of data collected as well as the comfortability of participants to freely participate in the group, thus affecting rapport in both individual and group interactions (for interviews and focus groups respectively) (Carter et al., 2021). This might affect saturation attainment. Therefore, intentional, open-minded and engaging decisions are pivotal to mitigate these possible challenges. Successful research in virtual social environments requires new protocols for engagement before data collection, attention to group numbers and dynamics, altered moderator teams and roles, and new logistical tasks for researchers.
In addressing the saturation dilemma associated with the complexity, diversity, heterogeneity, volume and dynamism of the digital data, qualitative researchers could need to assess saturation from different perspectives (multidimensional) as well as to employ strategies such as data visualisation, iterative assessment computational techniques such as machine learning. These strategies are briefly discussed below.
Re-assessment of the Definition of Saturation Point
Taking into cognisance the idiosyncratic features of digital data and the evolving nature of phenomenon in digital environments , saturation could be defined to take into consideration attributes such as the point of diminishing returns (Rowland et al., 2016) (acknowledging that it might difficult to reach a fixed point where no new, information, themes or insights emerge, hence researchers may consider the point where the likelihood of discovering new insights decreases), robustness and adequacy of findings (sufficiency to support the research’s conclusions) (Saunders et al., 2018), representativeness (representative of the phenomenon being studied or the broader population) and digital context (acknowledging the distinctive attributes of digital data such as diversity, volume and temporal nature). Saunders et al. (2018, p. 1893) argues that the operationalisation of saturation should be guided by “research question(s), theoretical position and analytical framework adopted” and Sebele-Mpofu (2020) calls for contextualisation in conceptualising the scope of saturation. Therefore, saturation in the digital context could be defined as the point where iterative data collection and analysis yields diminishing returns in discovering new themes, insights or meanings, while providing sufficiently robust and representative findings to address the support the research objectives together with the conclusions drawn.
Multidimensional Assessment of Saturation
As explained by Sebele-Mpofu (2020) saturation attainment could be considered through various dimensions, which encompass the data sources, types of data and the analytical viewpoints or even the type of qualitative research design adopted. Researchers could assess saturation of their data analysis and findings not only from a single data source perspective (Mpofu, 2021), but across the broad digital data sources. The multidimensional orientation can contribute to capturing the nuances and complexities ingrained in digital data and its sources. This is vital in ensuring an in-depth understanding of the phenomenon being investigated. Furthermore, triangulation of data sources, theoretical perspectives and analytical methods might help enhance data validation and corroborative digital findings as well as reap other benefits of triangulation (Mpofu, 2021). Researchers might also engage qualitative researchers, digital methodologists, and other digital experts to peer review the research process to boost research rigor credibility.
Repeated and Continuous Assessment of Saturation
In recognition of the complexity and ever-evolving phenomena in the digital era (Bryda & Costa, 2023), researchers could iteratively and incrementally assess the attainment of saturation. They periodically evaluate the emergence of new codes, themes, insights, concepts and meanings as the data collection and analysis process is in progress, as opposed to depending on the one and final assessment of the attainment of saturation. Computational data modelling and textual analysis technologies could be harnessed for the identification of new themes and patterns as they emerge, these could then be validated through further data analysis to support meaning saturation.
Data Visualisation and Interactive Exploration
Through visual representation of data and the emergence of new codes, patterns and themes, data visualisation can assist in enhancing how researchers assess for the attainment of saturation (Troise, 2022), but this is not without uncertainties (Panagiotidou & Vande Moere, 2022). Interactive data visualisation techniques such as network graphs could support the exploration of the digital data landscape could be employed by the researchers, thus improving the speed and cogency in the identification new insights and areas of saturation as well as the assessment of data collection and analysis adequacy.
Multi-Disciplinary Collaboration and Re-Assessment of the Saturation Guidelines
It is important to note that while the above strategies may contribute to resolving the saturation controversy in the digital era, interdisciplinary collaborative efforts by digital technology experts and qualitative researchers may be pivotal in addressing the challenges faced by digital qualitative researchers (Bryda & Costa, 2023). These various stakeholders could come together to establish guidelines and standards to promote ethically, responsible and transparent use of digital data in qualitative research and for assessing digital saturation. The guidelines for digital saturation assessment developed must take into account how ethical considerations and data governance matters will be addressed in view of the implications and complexities of working with large digital data sets and researching in the digital environment. These guidelines could be piloted, tested and empirically validated and refined in line with emerging challenges or changes in the digital the research environment. The interdisciplinary co-operation could help reconcile the gap between traditional qualitative research methods and challenges linked to digital contexts.
Development of Skills and Competencies Relevant for Qualitative Researchers in the Digital Era
The quick-paced technological advancements demand that researchers continue to reskill and upskill as well as to adapt their technological methods to effectively harness digital technologies and to keep up with the dynamic landscape. The development of digital skills such as computer scientists, data scientists and digital technologies specialists such as AI and machine learning experts with a qualitative research orientation could assist in the resolution of the dilemma and also close the digital skills gap. Additionally, to deal with some of the technological challenges, researchers must make informed choices concerning the online platforms and modalities to use, being fully aware of their strengths and weaknesses as well as the possible implications for these choices (Carter et al., 2021). Furthermore, researchers need to fully engage with participants to fully understand the digital infrastructure available to them and plan how to navigate the associated challenges.
Addressing Ethical Considerations and Trustworthiness Issues in the Digital Era
Affirming the critical need to address ethical issues, Carter et al. (2021) posits “To adapt to ethical challenges, researchers should especially consider participant privacy, and ways to build rapport and show appropriate care for participants, including protocols for dealing with distress or disengagement, managing data, and supporting consent.” The digital research era poses an array of ethical challenges and concerns about research trustworthiness for qualitative researchers and these demand careful consideration as failure to adequately address them may have considerable implications for the credibility and integrity of digital qualitative research. If not handled properly, personal data may lead to compromising the principle of beneficence and no maleficence in research (Mpofu, 2021). Therefore, establishing clear protocols for data ownership, informed consent and data sharing is pivotal in the digital landscape where data vulnerability is very high due to the ease in replicability and distribution as well as increased cybersecurity risks. Personal and sensitive data that generally contains personal, sensitive and potentially identifiable information raises fundamental privacy concerns. Data governance and security measures must be robust to ensure confidentiality, integrity of data and to guard against abuse of information, unauthorised access and other potential breaches.
Researcher Reflexivity
Generally qualitative researchers depend on nuanced judgements that demand researcher reflexivity, yet this is often completely ignored during the research journey (Dodgson, 2019; Olmos-Vega et al., 2023). Reflexivity is defined “as a set of continuous, collaborative and multifaceted practices through which researchers self-consciously critique, appraise and evaluate how their subjectivity and context influence the research processes” (Olmos-Vega et al., 2023, p. 241). Ensuring researcher reflexivity and positionality is necessary to enable researchers to critically reflect on their own actions, assumptions, potential biases and the likely impact of their chosen digital technologies and techniques on the research process. When researchers account for the importance of their methodological, interpersonal and contextual actions in the research process, this helps them acknowledge the biases, limitations and contextual factors that might have influenced the research findings (Dodgson, 2019; Olmos-Vega et al., 2023). The accounts might cover issues of negotiating informed consent, ensuring confidentiality and research integrity, power dynamics and dissemination of findings (Reid et al., 2018). This could also facilitate saturation attainment and the reasonability of the findings. Member check might also help researchers assess whether they indeed attained saturation in the digital qualitative research and build transparency and trust of research participants (Morse, 2015; Sebele-Mpofu, 2021). Lastly, researchers must transparently and comprehensively document the research process as well as which saturation form they achieved and how to ensure the reproducibility and credibility of their research (Sebele-Mpofu, 2021; Vasileiou et al., 2018). Highlighting methodological approaches, data collection strategies and the rationale for the decisions concerning saturation enables readers and other researchers to comprehend the rigor and trustworthiness of findings.
Conclusion, Limitations and Directions for Future Research
The review discussed the importance of saturation in qualitative research and outlined the different forms of saturation as well as the controversy surrounding the definition, application and measurement of saturation in qualitative research. This laid ground for unpacking the fundamental areas to consider in the evolution of qualitative research in the digital context. The key areas of considerations in digital qualitative research concerned the expansion of digital data sources, the ephemeral and evolving nature of phenomena, the diversity of participants and widened access to information and participants, ethical implications of digital footprints (personal information of participants) as well as the potential for saturation to crystallise itself differently in digital contexts when compared to traditional qualitative research contexts. The challenges of attaining saturation in digital qualitative research were found to be closely linked to the challenges faced by qualitative researchers in the digital realm. These challenges include the volume and velocity of data, complexities associated with identifying appropriate digital sources of data, ethical considerations and data governance issues. The other challenges include the impermanent and dynamic nature of online contexts, which poses problems for capturing representative and comprehensive data. Ensuring the appropriate richness and depth of qualitative analysis, considering the scale and breadth of digital data was found to be another difficulty. Controversies surrounding saturation in qualitative research in the digital context were found to be associated with the challenges faced by qualitative researchers in the digital landscape. Contention also surrounded the applicability of the traditional saturation measurement criteria in digital environments, the likelihood of digital contexts to introduce new types of biases and skew the representativeness of samples in digital qualitative research, the failure to create rapport, the imposter syndrome and how computational techniques and algorithms from digital technologies such as ML can potential enhance or undermine rigour in qualitative research.
Future researchers could assess the evolving role of saturation in qualitative research methodologies in the digital era. Here researchers could focus on the application of saturation, the dynamic and multidimensional nature in the digital environment and explore alternate saturation measures. Additionally, researchers could also explore ways of integrating digital technologies in qualitative research process, possible new methodologies as well as ways of ensuring digital literacy and the development of digital competencies among qualitative researchers.
Footnotes
Funding
The author received no financial support for the research, authorship, and/or publication of this article.
Declaration of Conflicting Interests
The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Data Availability Statement
Data is available upon request from the author.