qualitative research design about social media

Analyzing social media data: A mixed-methods framework combining computational and qualitative text analysis

• Published: 02 April 2019
• Volume 51 , pages 1766–1781, ( 2019 )

Cite this article

• Matthew Andreotta 1 , 2 ,
• Robertus Nugroho 2 , 3 ,
• Mark J. Hurlstone 1 ,
• Fabio Boschetti 4 ,
• Simon Farrell 1 ,
• Iain Walker 5 &
• Cecile Paris 2  

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To qualitative researchers, social media offers a novel opportunity to harvest a massive and diverse range of content without the need for intrusive or intensive data collection procedures. However, performing a qualitative analysis across a massive social media data set is cumbersome and impractical. Instead, researchers often extract a subset of content to analyze, but a framework to facilitate this process is currently lacking. We present a four-phased framework for improving this extraction process, which blends the capacities of data science techniques to compress large data sets into smaller spaces, with the capabilities of qualitative analysis to address research questions. We demonstrate this framework by investigating the topics of Australian Twitter commentary on climate change, using quantitative (non-negative matrix inter-joint factorization; topic alignment) and qualitative (thematic analysis) techniques. Our approach is useful for researchers seeking to perform qualitative analyses of social media, or researchers wanting to supplement their quantitative work with a qualitative analysis of broader social context and meaning.

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Introduction

Social scientists use qualitative modes of inquiry to explore the detailed descriptions of the world that people see and experience (Pistrang & Barker, 2012 ). To collect the voices of people, researchers can elicit textual descriptions of the world through interview or survey methodologies. However, with the popularity of the Internet and social media technologies, new avenues for data collection are possible. Social media platforms allow users to create content (e.g., Weinberg & Pehlivan, 2011 ), and interact with other users (e.g., Correa, Hinsley, & de Zùñiga, 2011 ; Kietzmann, Hermkens, McCarthy, & Silvestre, 2010 ), in settings where “Anyone can say Anything about Any topic” ( AAA slogan , Allemang & Hendler, 2011 , pg. 6). Combined with the high rate of content production, social media platforms can offer researchers massive and diverse dynamic data sets (Yin & Kaynak, 2015 ; Gudivada et al., 2015 ). With technologies increasingly capable of harvesting, storing, processing, and analyzing this data, researchers can now explore data sets that would be infeasible to collect through more traditional qualitative methods.

Many social media platforms can be considered as textual corpora, willingly and spontaneously authored by millions of users. Researchers can compile a corpus using automated tools and conduct qualitative inquiries of content or focused analyses on specific users (Marwick, 2014 ). In this paper, we outline some of the opportunities and challenges of applying qualitative textual analyses to the big data of social media. Specifically, we present a conceptual and pragmatic justification for combining qualitative textual analyses with data science text-mining tools. This process allows us to both embrace and cope with the volume and diversity of commentary over social media. We then demonstrate this approach in a case study investigating Australian commentary on climate change, using content from the social media platform: Twitter.

Opportunities and challenges for qualitative researchers using social media data

Through social media, qualitative researchers gain access to a massive and diverse range of individuals, and the content they generate. Researchers can identify voices which may not be otherwise heard through more traditional approaches, such as semi-structured interviews and Internet surveys with open-ended questions. This can be done through diagnostic queries to capture the activity of specific peoples, places, events, times, or topics. Diagnostic queries may specify geotagged content, the time of content creation, textual content of user activity, and the online profile of users. For example, Freelon et al., ( 2018 ) identified the Twitter activity of three separate communities (‘Black Twitter’, ‘Asian-American Twitter’, ‘Feminist Twitter’) through the use of hashtags Footnote 1 in tweets from 2015 to 2016. A similar process can be used to capture specific events or moments (Procter et al., 2013 ; Denef et al., 2013 ), places (Lewis et al., 2013 ), and specific topics (Hoppe, 2009 ; Sharma et al., 2017 ).

Collecting social media data may be more scalable than traditional approaches. Once equipped with the resources to access and process data, researchers can potentially scale data harvesting without expending a great deal of resources. This differs from interviews and surveys, where collecting data can require an effortful and time-consuming contribution from participants and researchers.

Social media analyses may also be more ecologically valid than traditional approaches. Unlike approaches where responses from participants are elicited in artificial social contexts (e.g., Internet surveys, laboratory-based interviews), social media data emerges from real-world social environments encompassing a large and diverse range of people, without any prompting from researchers. Thus, in comparison with traditional methodologies (Onwuegbuzie and Leech, 2007 ; Lietz & Zayas, 2010 ; McKechnie, 2008 ), participant behavior is relatively unconstrained if not entirely unconstrained, by the behaviors of researchers.

These opportunities also come up with challenges, because of the following attributes (Parker et al., 2011 ). Firstly, social media can be interactive : its content involves the interactions of users with other users (e.g., conversations), or even external websites (e.g., links to news websites). The ill-defined boundaries of user interaction have implications for determining the units of analysis of qualitative study. For example, conversations can be lengthy, with multiple users, without a clear structure or end-point. Interactivity thus blurs the boundaries between users, their content, and external content (Herring, 2009 ; Parker et al., 2011 ). Secondly, content can be ephemeral and dynamic . The users and content of their postings are transient (Parker et al., 2011 ; Boyd & Crawford, 2012 ; Weinberg & Pehlivan, 2011 ). This feature arises from the diversity of users, the dynamic socio-cultural context surrounding platform use, and the freedom users have to create, distribute, display, and dispose of their content (Marwick & Boyd, 2011 ). Lastly, social media content is massive in volume . The accumulated postings of users can lead to a large amount of data, and due to the diverse and dynamic content, postings may be largely unrelated and accumulate over a short period of time. Researchers hoping to harness the opportunities of social media data sets must therefore develop strategies for coping with these challenges.

A framework integrating computational and qualitative text analyses

Our framework—a mixed-method approach blending the capabilities of data science techniques with the capacities of qualitative analysis—is shown in Fig.  1 . We overcome the challenges of social media data by automating some aspects of the data collection and consolidation, so that the qualitative researcher is left with a manageable volume of data to synthesize and interpret. Broadly, our framework consists of the following four phases: (1) harvest social media data and compile a corpus, (2) use data science techniques to compress the corpus along a dimension of relevance, (3) extract a subset of data from the most relevant spaces of the corpus, and (4) perform a qualitative analysis on this subset of data.

Schematic overview of the four-phased framework

Phase 1: Harvest social media data and compile a corpus

Researchers can use automated tools to query records of social media data, extract this data, and compile it into a corpus. Researchers may query for content posted in a particular time frame (Procter et al., 2013 ), content containing specified terms (Sharma et al., 2017 ), content posted by users meeting particular characteristics (Denef et al., 2013 ; Lewis et al., 2013 ), and content pertaining to a specified location (Hoppe, 2009 ).

Phase 2: Use data science techniques to compress the corpus along a dimension of relevance

Although researchers may be interested in examining the entire data set, it is often more practical to focus on a subsample of data (McKenna et al., 2017 ). Specifically, we advocate dividing the corpus along a dimension of relevance, and sampling from spaces that are more likely to be useful for addressing the research questions under consideration. By relevance, we refer to an attribute of content that is both useful for addressing the research questions and usable for the planned qualitative analysis.

To organize the corpus along a dimension of relevance , researchers can use automated, computational algorithms. This process provides both formal and informal advantages for the subsequent qualitative analysis. Formally, algorithms can assist researchers in privileging an aspect of the corpus most relevant for the current inquiry. For example, topic modeling clusters massive content into semantic topics—a process that would be infeasible using human coders alone. A plethora of techniques exist for separating social media corpora on the basis of useful aspects, such as sentiment (e.g., Agarwal, Xie, Vovsha, Rambow, & Passonneau, 2010 ; Paris, Christensen, Batterham, & O’Dea, 2015 ; Pak & Paroubek, 2011 ) and influence (Weng et al., 2010 ).

Algorithms also produce an informal advantage for qualitative analysis. As mentioned, it is often infeasible for analysts to explore large data sets using qualitative techniques. Computational models of content can allow researchers to consider meaning at a corpus-level when interpreting individual datum or relationships between a subset of data. For example, in an inspection of 2.6 million tweets, Procter et al., ( 2013 ) used the output of an information flow analysis to derive rudimentary codes for inspecting individual tweets. Thus, algorithmic output can form a meaningful scaffold for qualitative analysis by providing analysts with summaries of potentially disjunct and multifaceted data (due to interactive, ephemeral, dynamic attributes of social media).

Phase 3: Extract a subset of data from the most relevant spaces of the corpus

Once the corpus is organized on the basis of relevance, researchers can extract data most relevant for answering their research questions. Researchers can extract a manageable amount of content to qualitatively analyze. For example, if the most relevant space of the corpus is too large for qualitative analysis, the researcher may choose to randomly sample from that space. If the most relevant space is small, the researcher may revisit Phase 2 and adopt a more lenient criteria of relevance.

Phase 4: Perform a qualitative analysis on this subset of data

The final phase involves performing the qualitative analysis to address the research question. As discussed above, researchers may draw on the computational models as a preliminary guide to the data.

Contextualizing the framework within previous qualitative social media studies

The proposed framework generalizes a number of previous approaches (Collins and Nerlich, 2015 ; McKenna et al., 2017 ) and individual studies (e.g., Lewis et al., 2013 ; Newman, 2016 ), in particular that of Marwick ( 2014 ). In Marwick’s general description of qualitative analysis of social media textual corpora, researchers: (1) harvest and compile a corpus, (2) extract a subset of the corpus, and (3) perform a qualitative analysis on the subset. As shown in Fig.  1 , our framework differs in that we introduce formal considerations of relevance, and the use of quantitative techniques to inform the extraction of a subset of data. Although researchers sometimes identify a subset of data most relevant to answering their research question, they seldom deploy data science techniques to identify it. Instead, researchers typically depend on more crude measures to isolate relevant data. For example, researchers have used the number of repostings of user content to quantify influence and recognition (e.g., Newman, 2016 ).

The steps in the framework may not be obvious without a concrete example. Next, we demonstrate our framework by applying it to Australian commentary regarding climate change on Twitter.

Application Example: Australian Commentary regarding Climate Change on Twitter

Social media platform of interest.

We chose to explore user commentary of climate change over Twitter. Twitter activity contains information about: the textual content generated by users (i.e., content of tweets), interactions between users, and the time of content creation (Veltri and Atanasova, 2017 ). This allows us to examine the content of user communication, taking into account the temporal and social contexts of their behavior. Twitter data is relatively easy for researchers to access. Many tweets reside within a public domain, and are accessible through free and accessible APIs.

The characteristics of Twitter’s platform are also favorable for data analysis. An established literature describes computational techniques and considerations for interpreting Twitter data. We used the approaches and findings from other empirical investigations to inform our approach. For example, we drew on past literature to inform the process of identifying which tweets were related to climate change.

Public discussion on climate change

Climate change is one of the greatest challenges facing humanity (Schneider, 2011 ). Steps to prevent and mitigate the damaging consequences of climate change require changes on different political, societal, and individual levels (Lorenzoni & Pidgeon, 2006 ). Insights into public commentary can inform decision making and communication of climate policy and science.

Traditionally, public perceptions are investigated through survey designs and qualitative work (Lorenzoni & Pidgeon, 2006 ). Inquiries into social media allow researchers to explore a large and diverse range of climate change-related dialogue (Auer et al., 2014 ). Yet, existing inquiries of Twitter activity are few in number and typically constrained to specific events related to climate change, such as the release of the Fifth Assessment Report by the Intergovernmental Panel on Climate Change (Newman et al., 2010 ; O’Neill et al., 2015 ; Pearce, 2014 ) and the 2015 United Nations Climate Change Conference, held in Paris (Pathak et al., 2017 ).

When longer time scales are explored, most researchers rely heavily upon computational methods to derive topics of commentary. For example, Kirilenko and Stepchenkova ( 2014 ) examined the topics of climate change tweets posted in 2012, as indicated by the most prevalent hashtags. Although hashtags can mark the topics of tweets, it is a crude measure as tweets with no hashtags are omitted from analysis, and not all topics are indicated via hashtags (e.g., Nugroho, Yang, Zhao, Paris, & Nepal, 2017 ). In a more sophisticated approach, Veltri and Atanasova ( 2017 ) examined the co-occurrence of terms using hierarchical clustering techniques to map the semantic space of climate change tweet content from the year 2013. They identified four themes: (1) “calls for action and increasing awareness”, (2) “discussions about the consequences of climate change”, (3) “policy debate about climate change and energy”, and (4) “local events associated with climate change” (p. 729).

Our research builds on the existing literature in two ways. Firstly, we explore a new data set—Australian tweets over the year 2016. Secondly, in comparison to existing research of Twitter data spanning long time periods, we use qualitative techniques to provide a more nuanced understanding of the topics of climate change. By applying our mixed-methods framework, we address our research question: what are the common topics of Australian’s tweets about climate change?

Outline of approach

We employed our four-phased framework as shown in Fig.  2 . Firstly, we harvested climate change tweets posted in Australia in 2016 and compiled a corpus (phase 1). We then utilized a topic modeling technique (Nugroho et al., 2017 ) to organize the diverse content of the corpus into a number of topics. We were interested in topics which commonly appeared throughout the time period of data collection, and less interested in more transitory topics. To identify enduring topics, we used a topic alignment algorithm (Chuang et al., 2015 ) to group similar topics occurring repeatedly throughout 2016 (phase 2). This process allowed us to identify the topics most relevant to our research question. From each of these, we extracted a manageable subset of data (phase 3). We then performed a qualitative thematic analysis (see Braun & Clarke, 2006 ) on this subset of data to inductively derive themes and answer our research question (phase 4). Footnote 2

Flowchart of application of a four-phased framework for conducting qualitative analyses using data science techniques. We were most interested in topics that frequently occurred throughout the period of data collection. To identify these, we organized the corpus chronologically, and divided the corpus into batches of content. Using computational techniques (shown in blue ), we uncovered topics in each batch and identified similar topics which repeatedly occurred across batches. When identifying topics in each batch, we generated three alternative representations of topics (5, 10, and 20 topics in each batch, shown in yellow ). In stages highlighted in green , we determined the quality of these representations, ultimately selecting the five topics per batch solution

Phase 1: Compiling a corpus

To search Australian’s Twitter data, we used CSIRO’s Emergency Situation Awareness (ESA) platform (CSIRO, 2018 ). The platform was originally built to detect, track, and report on unexpected incidences related to crisis situations (e.g., fires, floods; see Cameron, Power, Robinson, & Yin 2012 ). To do so, the ESA platform harvests tweets based on a location search that covers most of Australia and New Zealand.

The ESA platform archives the harvested tweets, which may be used for other CSIRO research projects. From this archive, we retrieved tweets satisfying three criteria: (1) tweets must be associated with an Australian location, (2) tweets must be harvested from the year 2016, and (3) the content of tweets must be related to climate change. We tested the viability of different markers of climate change tweets used in previous empirical work (Jang & Hart, 2015 ; Newman, 2016 ; Holmberg & Hellsten, 2016 ; O’Neill et al., 2015 ; Pearce et al., 2014 ; Sisco et al., 2017 ; Swain, 2017 ; Williams et al., 2015 ) by informally inspecting the content of tweets matching each criteria. Ultimately, we employed five terms (or combinations of terms) reliably associated with climate change: (1) “climate” AND “change”; (2) “#climatechange”; (3) “#climate”; (4) “global” AND “warming”; and (5) “#globalwarming”. This yielded a corpus of 201,506 tweets.

Phase 2: Using data science techniques to compress the corpus along a dimension of relevance

The next step was to organize the collection of tweets into distinct topics. A topic is an abstract representation of semantically related words and concepts. Each tweet belongs to a topic, and each topic may be represented as a list of keywords (i.e., prominent words of tweets belonging to the topic).

A vast literature surrounds the computational derivation of topics within textual corpora, and specifically within Twitter corpora (Ramage et al., 2010 ; Nugroho et al., 2017 ; Fang et al., 2016a ; Chuang et al., 2014 ). Popular methods for deriving topics include: probabilistic latent semantic analysis (Hofmann, 1999 ), non-negative matrix factorization (Lee & Seung, 2000 ), and latent Dirichlet allocation (Blei et al., 2003 ). These approaches use patterns of co-occurrence of terms within documents to derive topics. They work best on long documents. Tweets, however, are short, and thus only a few unique terms may co-occur between tweets. Consequently, approaches which rely upon patterns of term co-occurrence suffer within the Twitter environment. Moreover, these approaches ignore valuable social and temporal information (Nugroho et al., 2017 ). For example, consider a tweet t 1 and its reply t 2 . The reply feature of Twitter allows users to react to tweets and enter conversations. Therefore, it is likely t 1 and t 2 are related in topic, by virtue of the reply interaction.

To address sparsity concerns, we adopt the non-negative matrix inter-joint factorization (NMijF) of Nugroho et al., ( 2017 ). This process uses both tweet content (i.e., the patterns of co-occurrence of terms amongst tweets) and socio-temporal relationship between tweets (i.e., similarities in the users mentioned in tweets, whether the tweet is a reply to another tweet, whether tweets are posted at a similar time) to derive topics (see Supplementary Material ). The NMijF method has been demonstrated to outperform other topic modeling techniques on Twitter data (Nugroho et al., 2017 ).

Dividing the corpus into batches

Deriving many topics across a data set of thousands of tweets is prohibitively expensive in computational terms. Therefore, we divided the corpus into smaller batches and derived the topics of each batch. To keep the temporal relationships amongst tweets (e.g., timestamps of the tweets) the batches were organized chronologically. The data was partitioned into 41 disjoint batches (40 batches of 5000 tweets; one batch of 1506 tweets).

Generating topical representations for each batch

Following standard topic modeling practice, we removed features from each tweet which may compromise the quality of the topic derivation process. These features include: emoticons, punctuation, terms with fewer than three characters, stop-words (for list of stop-words, see MySQL, 2018 ), and phrases used to harvest the data (e.g., “#climatechange”). Footnote 3 Following this, the terms remaining in tweets were stemmed using the Natural Language Toolkit for Python (Bird et al., 2009 ). All stemmed terms were then tokenized for processing.

The NMijF topic derivation process requires three parameters (see Supplementary Material for more details). We set two of these parameters to the recommendations of Nugroho et al., ( 2017 ), based on empirical analysis. The final parameter—the number of topics derived from each batch—is difficult to estimate a priori , and must be made with some care. If k is too small, keywords and tweets belonging to a topic may be difficult to conceptualize as a singular, coherent, and meaningful topic. If k is too large, keywords and tweets belonging to a topic may be too specific and obscure. To determine a reasonable value of k , we ran the NMijF process on each batch with three different levels of the parameter—5, 10, and 20 topics per batch. This process generated three different representations of the corpus: 205, 410, and 820 topics. For each of these representations, each tweet was classified into one (and only one) topic. We represented each topic as a list of ten keywords most prevalent within the tweets of that topic.

Assessing the quality of topical representations

To select a topical representation for further analysis, we inspected the quality of each. Initially, we considered the use of a completely automatic process to assess or produce high quality topic derivations. However, our attempts to use completely automated techniques on tweets with a known topic structure failed to produce correct or reasonable solutions. Thus, we assessed quality using human assessment (see Table  1 ). The first stage involved inspecting each topical representation of the corpus (205, 410, and 820 topics), and manually flagging any topics that were clearly problematic. Specifically, we examined each topical representation to determine whether topics represented as separate were in fact distinguishable from one another. We discovered that the 820 topic representation (20 topics per batch) contained many closely related topics.

To quantify the distinctiveness between topics, we compared each topic to each other topic in the same batch in an automated process. If two topics shared three or more (of ten) keywords, these topics were deemed similar. We adopted this threshold from existing topic modeling work (Fang et al., 2016a , b ), and verified it through an informal inspection. We found that pairs of topics below this threshold were less similar than those equal to or above it. Using this threshold, the 820 topic representation was identified as less distinctive than other representations. Of the 41 batches, nine contained at least two similar topics for the 820 topic representation (cf., 0 batches for the 205 topic representation, two batches for the 410 topic representation). As a result, we chose to exclude the representation from further analysis.

The second stage of quality assessment involved inspecting the quality of individual topics. To achieve this, we adopted the pairwise topic preference task outlined by Fang et al. ( 2016a , b ). In this task, raters were shown pairs of two similar topics (represented as ten keywords), one from the 205 topic representation and the other from the 410 topic representation. To assist in their interpretation of topics, raters could also view three tweets belonging to each topic. For each pair of topics, raters indicated which topic they believed was superior, on the basis of coherency, meaning, interpretability, and the related tweets (see Table  1 ). Through aggregating responses, a relative measure of quality could be derived.

Initially, members of the research team assessed 24 pairs of topics. Results from the task did not indicate a marked preference for either topical representation. To confirm this impression more objectively, we recruited participants from the Australian community as raters. We used Qualtrics—an online survey platform and recruitment service—to recruit 154 Australian participants, matched with the general Australian population on age and gender. Each participant completed judgments on 12 pairs of similar topics (see Supplementary Material for further information).

Participants generally preferred the 410 topic representation over the 205 topic representation ( M = 6.45 of 12 judgments, S D = 1.87). Of 154 participants, 35 were classified as indifferent (selected both topic representations an equal number of times), 74 preferred the 410 topic representation (i.e., selected the 410 topic representation more often than the 205 topic representation), and 45 preferred the 205 topic representation (i.e., selected the 205 topic representation more often that the 410 topic representation). We conducted binomial tests to determine whether the proportion of participants of the three just described types differed reliably from chance levels (0.33). The proportion of indifferent participants (0.23) was reliably lower than chance ( p = 0.005), whereas the proportion of participants preferring the 205 topic solution (0.29) did not differ reliably from chance levels ( p = 0.305). Critically, the proportion of participants preferring the 410 topic solution (0.48) was reliably higher than expected by chance ( p < 0.001). Overall, this pattern indicates a participant preference for the 410 topic representation over the 205 topic representation.

In summary, no topical representation was unequivocally superior. On a batch level, the 410 topic representation contained more batches of non-distinct topic solutions than the 205 topic representation, indicating that the 205 topic representation contained topics which were more distinct. In contrast, on the level of individual topics, the 410 topic representation was preferred by human raters. We use this information, in conjunction with the utility of corresponding aligned topics (see below), to decide which representation is most suitable for our research purposes.

Grouping similar topics repeated in different batches

We were most interested in topics which occurred throughout the year (i.e., in multiple batches) to identify the most stable components of climate change commentary (phase 3). We grouped similar topics from different batches using a topical alignment algorithm (see Chuang et al. 2015 ). This process requires a similarity metric and a similarity threshold. The similarity metric represents the similarity between two topics, which we specified as the proportion of shared keywords (from 0, no keywords shared, to 1, all ten keywords shared). The similarity threshold is a value below which two topics were deemed dissimilar. As above, we set the threshold to 0.3 (three of ten keywords shared)—if two topics shared two or fewer keywords, the topics could not be justifiably classified as similar. To delineate important topics, groups of topics, and other concepts we have provided a glossary of terms in Table  2 .

The topic alignment algorithm is initialized by assigning each topic to its own group. The alignment algorithm iteratively merges the two most similar groups, where the similarity between groups is the maximum similarity between a topic belonging to one group and another topic belonging to the other. Only topics from different groups (by definition, topics from the same group are already grouped as similar) and different batches (by definition, topics from the same batch cannot be similar) can be grouped. This process continues, merging similar groups until no compatible groups remain. We found our initial implementation generated groups of largely dissimilar topics. To address this, we introduced an additional constraint—groups could only be merged if the mean similarity between pairs of topics (each belonging to the two groups in question) was greater than the similarity threshold. This process produced groups of similar topics. Functionally, this allowed us to detect topics repeated throughout the year.

We ran the topical alignment algorithm across both the 205 and 410 topic representations. For the 205 and 410 topic representation respectively, 22.47 and 31.60% of tweets were not associated with topics that aligned with others. This exemplifies the ephemeral and dynamic attributes of Twitter activity: over time, the content of tweets shifts, with some topics appearing only once throughout the year (i.e., in only one batch). In contrast, we identified 42 groups (69.77% of topics) and 101 groups (62.93% of topics) of related topics for the 205 and 410 topic representations respectively, occurring across different time periods (i.e., in more than one batch). Thus, both representations contained transient topics (isolated to one batch) and recurrent topics (present in more than one batch, belonging to a group of two or more topics).

Identifying topics most relevant for answering our research question

For the subsequent qualitative analyses, we were primarily interested in topics prevalent throughout the corpus. We operationalized prevalent topic groupings as any grouping of topics that spanned three or more batches. On this basis, 22 (57.50% of tweets) and 36 (35.14% of tweets) groupings of topics were identified as prevalent for the 205 and 410 topic representations, respectively (see Table  3 ). As an example, consider the prevalent topic groupings from the 205 topic representation, shown in Table  3 . Ten topics are united by commentary on the Great Barrier Reef (Group 2)—indicating this facet of climate change commentary was prevalent throughout the year. In contrast, some topics rarely occurred, such as a topic concerning a climate change comic (indicated by the keywords “xkcd” and “comic”) occurring once and twice in the 205 and 410 topic representation, respectively. Although such topics are meaningful and interesting, they are transient aspects of climate change commen tary and less relevant to our research question. In sum, topic modeling and grouping algorithms have allowed us to collate massive amounts of information, and identify components of the corpus most relevant to our qualitative inquiry.

Selecting the most favorable topical representation

At this stage, we have two complete and coherent representations of the corpus topics, and indications of which topics are most relevant to our research question. Although some evidence indicated that the 410 topic representation contains topics of higher quality, the 205 topic representation was more parsimonious on both the level of topics and groups of topics. Thus, we selected the 205 topic representation for further analysis.

Phase 3. Extract a subset of data

Extracting a subset of data from the selected topical representation.

Before qualitative analysis, researchers must extract a subset of data manageable in size. For this process, we concerned ourselves with only the content of prevalent topic groupings, seen in Table  3 . From each of the 22 prevalent topic groupings, we randomly sampled ten tweets. We selected ten tweets as a trade-off between comprehensiveness and feasibility. This thus reduced our data space for qualitative analysis from 201,423 tweets to 220.

Phase 4: Perform qualitative analysis

Perform thematic analysis.

In the final phase of our analysis, we performed a qualitative thematic analysis (TA; Braun & Clarke, 2006 ) on the subset of tweets sampled in phase 3. This analysis generated distinct themes, each of which answers our research question: what are the common topics of Australian’s tweets about climate change? As such, the themes generated through TA are topics. However, unlike the topics derived from the preceding computational approaches, these themes are informed by the human coder’s interpretation of content and are oriented towards our specific research question. This allows the incorporation of important diagnostic information, including the broader socio-political context of discussed events or terms, and an understanding (albeit, sometimes ambiguous) of the underlying latent meaning of tweets.

We selected TA as the approach allows for flexibility in assumptions and philosophical approaches to qualitative inquiries. Moreover, the approach is used to emphasize similarities and differences between units of analysis (i.e., between tweets) and is therefore useful for generating topics. However, TA is typically applied to lengthy interview transcripts or responses to open survey questions, rather than small units of analysis produced through Twitter activity. To ease the application of TA to small units of analysis, we modified the typical TA process (shown in Table  4 ) as follows.

Firstly, when performing phases 1 and 2 of TA, we initially read through each prevalent topic grouping’s tweets sequentially. By doing this, we took advantage of the relative homogeneity of content within topics. That is, tweets sharing the same topic will be more similar in content than tweets belonging to separate topics. When reading ambiguous tweets, we could use the tweet’s topic (and other related topics from the same group) to aid comprehension. Through the scaffold of topic representations, we facilitated the process of interpreting the data, generating initial codes, and deriving themes.

Secondly, the prevalent topic groupings were used to create initial codes and search for themes (TA phase 2 and 3). For example, the groups of topics indicate content of climate change action (group 1), the Great Barrier Reef (group 2), climate change deniers (group 3), and extreme weather (group 5). The keywords characterizing these topics were used as initial codes (e.g., “action”, “Great Barrier Reef”, “Paris Agreement”, “denial”). In sum, the algorithmic output provided us with an initial set of codes and an understanding of the topic structure that can indicate important features of the corpus.

A member of the research team performed this augmented TA to generate themes. A second rater outside of the research team applied the generated themes to the data, and inter-rater agreement was assessed. Following this, the two raters reached a consensus on the theme of each tweet.

Through TA, we inductively generated five distinct themes. We assigned each tweet to one (and only one) theme. A degree of ambiguity is involved in designating themes for tweets, and seven tweets were too ambiguous to subsume into our thematic framework. The remaining 213 tweets were assigned to one of five themes shown in Table  5 .

In an initial application of the coding scheme, the two raters agreed upon 161 (73.181%) of 220 tweets. Inter-rater reliability was satisfactory, Cohen’s κ = 0.648, p < 0.05. An assessment of agreement for each theme is presented in Table  5 . The proportion of agreement is the total proportion of observations where the two coders both agreed: (1) a tweet belonged to the theme, or (2) a tweet did not belong to the theme. The proportion of specific agreement is the conditional probability that a randomly selected rater will assign the theme to a tweet, given that the other rater did (see Supplementary Material for more information). Theme 3, theme 5, and the N/A categorization had lower levels of agreement than the remaining themes, possibly as tweets belonging to themes 3 and 5 often make references to content relevant to other themes.

Theme 1. Climate change action

The theme occurring most often was climate change action, whereby tweets were related to coping with, preparing for, or preventing climate change. Tweets comment on the action (and inaction) of politicians, political parties, and international cooperation between government, and to a lesser degree, industry, media, and the public. The theme encapsulated commentary on: prioritizing climate change action (“ Let’s start working together for real solutions on climate change ”); Footnote 4 relevant strategies and policies to provide such action (“ #OurOcean is absorbing the majority of #climatechange heat. We need #marinereserves to help build resilience. ”); and the undertaking (“ Labor will take action on climate change, cut pollution, secure investment & jobs in a growing renewables industry ”) or disregarding (“ act on Paris not just sign ”) of action.

Often, users were critical of current or anticipated action (or inaction) towards climate change, criticizing approaches by politicians and governments as ineffective (“ Malcolm Turnbull will never have a credible climate change policy ”), Footnote 5 and undesirable (“ Govt: how can we solve this vexed problem of climate change? Helpful bystander: u could not allow a gigantic coal mine. Govt: but srsly how? ”). Predominately, users characterized the government as unjustifiably paralyzed (“ If a foreign country did half the damage to our country as #climatechange we would declare war. ”), without a leadership focused on addressing climate change (“ an election that leaves Australia with no leadership on #climatechange - the issue of our time! ”).

Theme 2. Consequences of climate change

Users commented on the consequences and risks attributed to climate change. This theme may be further categorized into commentary of: physical systems, such as changes in climate, weather, sea ice, and ocean currents (“ Australia experiencing more extreme fire weather, hotter days as climate changes ”); biological systems, such as marine life (particularly, the Great Barrier Reef) and biodiversity (“ Reefs of the future could look like this if we continue to ignore #climatechange ”); human systems (“ You and your friends will die of old age & I’m going to die from climate change ”); and other miscellaneous consequences (“ The reality is, no matter who you supported, or who wins, climate change is going to destroy everything you love ”). Users specified a wide range of risks and impacts on human systems, such as health, cultural diversity, and insurance. Generally, the consequences of climate change were perceived as negative.

Theme 3. Conversations on climate change

Some commentary centered around discussions of climate change communication, debates, art, media, and podcasts. Frequently, these pertained to debates between politicians (“ not so gripping from No Principles Malcolm. Not one mention of climate change in his pitch. ”) and television panel discussions (“ Yes let’s all debate whether climate change is happening... #qanda ”). Footnote 6 Users condemned the climate change discussions of federal government (“ Turnbull gov echoes Stalinist Russia? Australia scrubbed from UN climate change report after government intervention ”), those skeptical of climate change (“ Trouble is climate change deniers use weather info to muddy debate. Careful???????????????? ”), and media (“ Will politicians & MSM hacks ever work out that they cannot spin our way out of the #climatechange crisis? ”). The term “climate change” was critiqued, both by users skeptical of the legitimacy of climate change (“ Weren’t we supposed to call it ‘climate change’ now? Are we back to ‘global warming’ again? What happened? Apart from summer? ”) and by users seeking action (“ Maybe governments will actually listen if we stop saying “extreme weather” & “climate change” & just say the atmosphere is being radicalized ”).

Theme 4. Climate change deniers

The fourth theme involved commentary on individuals or groups who were perceived to deny climate change. Generally, these were politicians and associated political parties, such as: Malcolm Roberts (a climate change skeptic, elected as an Australian Senator in 2016), Malcolm Turnbull, and Donald Trump. Commentary focused on the beliefs and legitimacy of those who deny the science of climate change (“ One Nation’s Malcolm Roberts is in denial about the facts of climate change ”) or support the denial of climate change science (“ Meanwhile in Australia... Malcolm Roberts, funded by climate change skeptic global groups loses the plot when nobody believes his findings ”). Some users advocated attempts to change the beliefs of those who deny climate change science (“ We have a president-elect who doesn’t believe in climate change. Millions of people are going to have to say: Mr. Trump, you are dead wrong ”), whereas others advocated disengaging from conversation entirely (“ You know I just don’t see any point engaging with climate change deniers like Roberts. Ignore him ”). In comparison to other themes, commentary revolved around individuals and their beliefs, rather than the phenomenon of climate change itself.

Theme 5. The legitimacy of climate change and climate science

Using our four-phased framework, we aimed to identify and qualitatively inspect the most enduring aspects of climate change commentary from Australian posts on Twitter in 2016. We achieved this by using computational techniques to model 205 topics of the corpus, and identify and group similar topics that repeatedly occurred throughout the year. From the most relevant topic groupings, we extracted a subsample of tweets and identified five themes with a thematic analysis: climate change action, consequences of climate change, conversations on climate change, climate change deniers, and the legitimacy of climate change and climate science. Overall, we demonstrated the process of using a mixed-methodology that blends qualitative analyses with data science methods to explore social media data.

Our workflow draws on the advantages of both quantitative and qualitative techniques. Without quantitative techniques, it would be impossible to derive topics that apply to the entire corpus. The derived topics are a preliminary map for understanding the corpus, serving as a scaffold upon which we could derive meaningful themes contextualized within the wider socio-political context of Australia in 2016. By incorporating quantitatively-derived topics into the qualitative process, we attempted to construct themes that would generalize to a larger, relevant component of the corpus. The robustness of these themes is corroborated by their association with computationally-derived topics, which repeatedly occurred throughout the year (i.e., prevalent topic groupings). Moreover, four of the five themes have been observed in existing data science analyses of Twitter climate change commentary. Within the literature, the themes of climate change action and consequences of climate change are common (Newman, 2016 ; O’Neill et al., 2015 ; Pathak et al., 2017 ; Pearce, 2014 ; Jang and Hart, 2015 ; Veltri & Atanasova, 2017 ). The themes of the legitimacy of climate change and climate science (Jang & Hart, 2015 ; Newman, 2016 ; O’Neill et al., 2015 ; Pearce, 2014 ) and climate change deniers (Pathak et al., 2017 ) have also been observed. The replication of these themes demonstrates the validity of our findings.

One of the five themes—conversations on climate change—has not been explicitly identified in existing data science analyses of tweets on climate change. Although not explicitly identifying the theme, Kirilenko and Stepchenkova ( 2014 ) found hashtags related to public conversations (e.g., “#qanda”, “#Debates”) were used frequently throughout the year 2012. Similar to the literature, few (if any) topics in our 205 topic solution could be construed as solely relating to the theme of “conversation”. However, as we progressed through the different phases of the framework, the theme became increasingly apparent. By the grouping stage, we identified a collection of topics unified by a keyword relating to debate. The subsequent thematic analysis clearly discerned this theme. The derivation of a theme previously undetected by other data science studies lends credence to the conclusions of Guetterman et al., ( 2018 ), who deduced that supplementing a quantitative approach with a qualitative technique can lead to the generation of more themes than a quantitative approach alone.

The uniqueness of a conversational theme can be accounted for by three potentially contributing factors. Firstly, tweets related to conversations on climate change often contained material pertinent to other themes. The overlap between this theme and others may hinder the capabilities of computational techniques to uniquely cluster these tweets, and undermine the ability of humans to reach agreement when coding content for this theme (indicated by the relatively low proportion of specific agreement in our thematic analysis). Secondly, a conversational theme may only be relevant in election years. Unlike other studies spanning long time periods (Jang and Hart, 2015 ; Veltri & Atanasova, 2017 ), Kirilenko and Stepchenkova ( 2014 ) and our study harvested data from US presidential election years (2012 and 2016, respectively). Moreover, an Australian federal election occurred in our year of observation. The occurrence of national elections and associated political debates may generate more discussion and criticisms of conversations on climate change. Alternatively, the emergence of a conversational theme may be attributable to the Australian panel discussion television program Q & A. The program regularly hosts politicians and other public figures to discuss political issues. Viewers are encouraged to participate by publishing tweets using the hashtag “#qanda”, perhaps prompting viewers to generate uniquely tagged content not otherwise observed in other countries. Importantly, in 2016, Q & A featured a debate on climate change between science communicator Professor Brian Cox and Senator Malcolm Roberts, a prominent climate science skeptic.

Although our four-phased framework capitalizes on both quantitative and qualitative techniques, it still has limitations. Namely, the sparse content relationships between data points (in our case, tweets) can jeopardize the quality and reproducibility of algorithmic results (e.g., Chuang et al., 2015 ). Moreover, computational techniques can require large computing resources. To a degree, our application mitigated these limitations. We adopted a topic modeling algorithm which uses additional dimensions of tweets (social and temporal) to address the influence of term-to-term sparsity (Nugroho et al., 2017 ). To circumvent concerns of computing resources, we partitioned the corpus into batches, modeled the topics in each batch, and grouped similar topics together using another computational technique (Chuang et al., 2015 ).

As a demonstration of our four-phased framework, our application is limited to a single example. For data collection, we were able to draw from the procedures of existing studies which had successfully used keywords to identify climate change tweets. Without an existing literature, identifying diagnostic terms can be difficult. Nevertheless, this demonstration of our four-phased framework exemplifies some of the critical decisions analysts must make when utilizing a mixed-method approach to social media data.

Both qualitative and quantitative researchers can benefit from our four-phased framework. For qualitative researchers, we provide a novel vehicle for addressing their research questions. The diversity and volume of content of social media data may be overwhelming for both the researcher and their method. Through computational techniques, the diversity and scale of data can be managed, allowing researchers to obtain a large volume of data and extract from it a relevant sample to conduct qualitative analyses. Additionally, computational techniques can help researchers explore and comprehend the nature of their data. For the quantitative researcher, our four-phased framework provides a strategy for formally documenting the qualitative interpretations. When applying algorithms, analysts must ultimately make qualitative assessments of the quality and meaning of output. In comparison to the mathematical machinery underpinning these techniques, the qualitative interpretations of algorithmic output are not well-documented. As these qualitative judgments are inseparable from data science, researchers should strive to formalize and document their decisions—our framework provides one means of achieving this goal.

Through the application of our four-phased framework, we contribute to an emerging literature on public perceptions of climate change by providing an in-depth examination of the structure of Australian social media discourse. This insight is useful for communicators and policy makers hoping to understand and engage the Australian online public. Our findings indicate that, within Australian commentary on climate change, a wide variety of messages and sentiment are present. A positive aspect of the commentary is that many users want action on climate change. The time is ripe it would seem for communicators to discuss Australia’s policy response to climate change—the public are listening and they want to be involved in the discussion. Consistent with this, we find some users discussing conversations about climate change as a topic. Yet, in some quarters there is still skepticism about the legitimacy of climate change and climate science, and so there remains a pressing need to implement strategies to persuade members of the Australian public of the reality and urgency of the climate change problem. At the same time, our analyses suggest that climate communicators must counter the sometimes held belief, expressed in our second theme on climate change consequences, that it is already too late to solve the climate problem. Members of the public need to be aware of the gravity of the climate change problem, but they also need powerful self efficacy promoting messages that convince them that we still have time to solve the problem, and that their individual actions matter.

On Twitter, users may precede a phrase with a hashtag (#). This allows users to signify and search for tweets related to a specific theme.

The analysis of this study was preregistered on the Open Science Framework: https://osf.io/mb8kh/ . See the Supplementary Material for a discussion of discrepancies. Analysis scripts and interim results from computational techniques can be found at: https://github.com/AndreottaM/TopicAlignment .

83 tweets were rendered empty and discarded from the corpus.

The content of tweet are reported verbatim. Sensitive information is redacted.

Malcolm Turnbull was the Prime Minister of Australia during the year 2016.

“ #qanda ” is a hashtag used to refer to Q & A, an Australian panel discussion television program.

Commonwealth Scientific and Industrial Research Organisation (CSIRO) is the national scientific research agency of Australia.

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This research was supported by an Australian Government Research Training Program (RTP) Scholarship from the University of Western Australia and a scholarship from the CSIRO Research Office awarded to the first author, and a grant from the Climate Adaptation Flagship of the CSIRO awarded to the third and sixth authors. The authors are grateful to Bella Robinson and David Ratcliffe for their assistance with data collection, and Blake Cavve for their assistance in annotating the data.

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Andreotta, M., Nugroho, R., Hurlstone, M.J. et al. Analyzing social media data: A mixed-methods framework combining computational and qualitative text analysis. Behav Res 51 , 1766–1781 (2019). https://doi.org/10.3758/s13428-019-01202-8

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Do you want to study influencers? Opinions and comments on a set of posts? Look at collections of photos or videos on Instagram? Qualitative Research Using Social Media guides the reader in what different kinds of qualitative research can be applied to social media data. It introduces students, as well as those who are new to the field, to developing and carrying out concrete research projects. The book takes the reader through the stages of choosing data, formulating a research question, and choosing and applying method(s).

Written in a clear and accessible manner with current social media examples throughout, the book provides a step-by-step overview of a range of qualitative methods. These are presented in clear ways to show how to analyze many different types of social media content, including language and visual content such as memes, gifs, photographs, and film clips. Methods examined include critical discourse analysis, content analysis, multimodal analysis, ethnography, and focus groups. Most importantly, the chapters and examples show how to ask the kinds of questions that are relevant for us at this present point in our societies, where social media is highly integrated into how we live. Social media is used for political communication, social activism, as well as commercial activities and mundane everyday things, and it can transform how all these are accomplished and even what they mean.

Drawing on examples from Twitter, Instagram, YouTube, TikTok, Facebook, Snapchat, Reddit, Weibo, and others, this book will be suitable for undergraduate students studying social media research courses in media and communications, as well as other humanities such as linguistics and social science-based degrees.

TABLE OF CONTENTS

Chapter chapter 1 | 24  pages, introduction, chapter chapter 2 | 14  pages, qualitative content analysis, chapter chapter 3 | 19  pages, qualitative visual content analysis, chapter chapter 4 | 21  pages, analyzing social media language with critical discourse analysis, chapter chapter 5 | 24  pages, multimodal critical discourse analysis, chapter chapter 6 | 32  pages, multimodal narrative analysis of video clips, chapter chapter 7 | 15  pages, online ethnography, chapter chapter 8 | 22  pages, focus group interviews, chapter chapter 9 | 4  pages.

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• > Cambridge Handbook of Qualitative Digital Research
• > Social Media Qualitative Research Vignettes

Book contents

• Cambridge Handbook of Qualitative Digital Research
• Copyright page
• Contributors
• Part I Philosophical, Epistemological and Theoretical Considerations
• Part II Methodological Considerations
• Chapter 7 Human Values in a Digital-First World: The Implications for Qualitative Research
• Chapter 8 One Picture to Study One Thousand Words
• Chapter 9 Demystifying the Digital
• Chapter 10 Case Study Research Revisited
• Chapter 11 Social Media Qualitative Research Vignettes
• Chapter 12 Co-Inquiring in a Digital Age
• Part III Illustrative Examples and Emergent Issues

Chapter 11 - Social Media Qualitative Research Vignettes

from Part II - Methodological Considerations

Published online by Cambridge University Press:  08 June 2023

The chapter outlines social media and qualitative research. It describes social media for data collection and different qualitative research approaches to data collection. The chapter describes social media as a phenomenon for research and outlines different levels of social media utilization: individual, work-practice and supra-organizational levels. Vignettes for the different levels are provided and the need for qualitative research concluded.

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• Social Media Qualitative Research Vignettes
• By Alex Wilson , Josh Morton , Boyka Simeonova
• Edited by Boyka Simeonova , University of Leicester , Robert D. Galliers , Bentley University, Massachusetts and Warwick Business School
• Book: Cambridge Handbook of Qualitative Digital Research
• Online publication: 08 June 2023
• Chapter DOI: https://doi.org/10.1017/9781009106436.014

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A qualitative study on negative experiences of social media use and harm reduction strategies among youths in a multi-ethnic Asian society

Ellaisha samari.

1 Research Division, Institute of Mental Health, Singapore, Singapore

Sherilyn Chang

Esmond seow, yi chian chua.

2 Department of Psychosis, Institute of Mental Health, Singapore, Singapore

Mythily Subramaniam

3 Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore

Rob M. van Dam

4 Departments of Exercise and Nutrition Sciences and Epidemiology, Milken Institute School of Public Health, The George Washington University, Washington, DC, United States of America

Swapna Verma

Janhavi ajit vaingankar, associated data.

Our data contain potentially sensitive information and restriction to sharing of data has been imposed by our ethics committee. However, data for this study are available upon reasonable request. The data request can be sent to The Institutional Research Review Committee, Institute of Mental Health, Singapore; Email address: gs.moc.hmi@hcraeserhmi .

This study aimed to expand and inform the emerging body of research on the negative experiences of social media use among youths and how youths deal with them, in an Asian setting, using a qualitative approach.

Data were collected using 11 focus group discussions (FGDs) and 25 semi-structured interviews (SIs) among youths aged 15 to 24 years residing in Singapore who were recruited via purposive sampling. Data were analysed using thematic analysis.

The salient negative effects mentioned by participants include the development of negative reactions and feelings from upward comparisons with others (e.g., others’ achievements and lifestyle), receiving hurtful comments, exposure to controversial content (e.g., political events and social movements), as well as the perpetuation of negative feelings, behaviours, and sentiments (e.g., rumination, unhealthy eating behaviour, and self-harm). Participants also described strategies which they have employed or deemed to be useful in mitigating the negative effects of social media use. These include filtering content and users, taking breaks from social media, cognitive reframing, and self-affirmation, where they identify and change stress-inducing patterns of thinking by setting realistic social, physical, and lifestyle expectations for themselves, and focusing on self-development.

The current results highlight that while youths experience negative effects of social media use, they have high media literacy and have employed strategies that appear to mitigate the negative effects of social media use. The findings can inform various stakeholders involved in helping youths navigate the harms of social media use or provide directions for intervention studies aimed at reducing the harms of social media use.

Introduction

The marked rise in social media use today exemplifies the evolution of the digital landscape. Social media platforms such as Facebook and YouTube continue to dominate the online scene with an estimated 2.9 and 2.1 billion monthly active users respectively [ 1 , 2 ]. Other platforms such as Instagram and TikTok, developed later, have since gained traction, especially among younger audiences with 120 and 100 million active users respectively [ 3 – 5 ]. Using social media sites can arguably be a norm of growing up in the digital age, especially among youths [ 6 ]. This is reflected in research conducted in 2019 among the younger population of the US where 85% of teenagers were reported using YouTube, while 72% used Instagram and 69% used Snapchat [ 7 ].

Social media platforms have drastically changed the way people socialize, share information, present themselves, perceive others, and work [ 8 ]. It is an influential and integral element in today’s interaction and communication which is readily available and easily accessible through multiple devices such as smartphones, computers, and tablets. Furthermore, persistent cues through notifications and variable reward mechanism (e.g., social validation for social media post through likes and comments, infinite scroll etc.) encourage greater social media use. Naturally, this phenomenon has sparked interest among researchers to examine the experiences of social media use, particularly among youths given new social dynamics [ 9 ] and the intricate period of transition to adulthood where young people are experiencing significant developmental and psychosocial shifts including identity exploration [ 10 , 11 ]. It is also a period where youths are experiencing intensified peer relationships, seeking romantic relationships, and engaging with potential or current partners [ 10 ] and may be motivated to gain attention from their peers or to observe peers’ self-presentations.

Arguably, the harms or benefits of social media use depends on how and why people use them, and who uses them. Social media sites provide opportunities for youths to develop and maintain social relationships [ 12 ], cultivate a sense of belongingness, present themselves to others [ 13 ], keep up to date [ 14 ] and even learn about sexual health and identity [ 15 ]. Some studies have reported associations between social media use and psychological well-being, which is a state of wellness where an individual is feeling good and functioning well based on having positive relationships with others, environmental mastery, self-acceptance, personal growth, autonomy, sense of purpose in life and experiencing positive emotions such as happiness and contentment [ 16 , 17 ]. Specifically, judicious use of social media has been found to be associated with several positive psychosocial outcomes such as increased quality of friendship [ 16 ] and social support [ 17 ]. On the other hand, social media use has also been associated with negative experiences such as stress, social isolation, cyberbullying, and mental health issues including depression, anxiety, poor body image and disordered eating [ 18 ], or found to have no substantive links to mental health issues [ 19 – 22 ]. For example, a longitudinal study by Heffer et al. [ 20 ] found that social media use did not predict future depressive symptoms and adolescent girls who experience depressive symptoms tend to use more social media across time, and not vice versa. In another longitudinal study by Coyne et al. [ 19 ], increased time spent on social media was not associated with increased depression or anxiety across adolescents’ developmental period at an individual level.

The literature on social media use has been primarily focused on the association between social media use and its impact on users’ well-being, while factors associated with harm reduction or how young people manage or reduce the harms of its use have received less attention. Notwithstanding, spending less time on social media sites or using social media mindfully was perceived by youths to help mitigate the negative experiences from social media use [ 23 , 24 ]. In addition, high levels of confidence, high level of media literacy and appreciation of individual differences appeared to mitigate the potential negative effects of social media exposure on body image among adolescent girls [ 25 ]. Findings from these studies highlight some perceived effective means of mitigating the negative experiences of social media use. Nonetheless, given the dearth of research on harm reduction strategies for negative experiences of social media use, the current study aimed to contribute to the emerging evidence base on negative experiences of social media use among youths and ways youths reduce the harms of social media use in Asia. Identifying harm reduction strategies is critical as it offers insights into how young people mitigate these negative effects which can inform the development and implementation of interventions aimed at improving health outcomes. A qualitative approach will allow a deeper understanding of the psychological mechanisms and processes (i.e., how and under what circumstances) behind youths’ negative experiences and the ways they deal with them. Thus, we used a qualitative approach to explore the broad experiences of young people in Singapore with social media use to answer the following questions– 1) What are the negative experiences of social media use? 2) What are the strategies youths employ to reduce these harms?

A purposive sampling design was used to obtain the study sample of young people aged 15–24 years, with an approximately equivalent proportion of men and women as well as those belonging to age groups 15–19 and 20–24 years, and three main ethnic groups in Singapore (Chinese, Malay and Indian). Initially, referrals for these participants were sought from colleagues and acquaintances who were provided with the study brochures. Subsequently, participants who had participated were also given the study brochures to disseminate to others and initiate snowball recruitment. Efforts were also made to include young people who had experiences of psychological distress, school drop-out or risky behaviours (e.g., substance use, gang involvement, and incarceration) to ensure greater diversity of the study sample. Referrals for these participants were sought from community-based youth welfare organisations which are providing services to clients with these experiences. Written informed consent was obtained from all participants before they participated in the study. For participants aged below 21 years, parental consent was also obtained. Ethical approval was obtained from the National Healthcare Group’s Domain Specific Review Board (DSRB No. 2020/0228).

Data collection

Data were collected through a combination of semi-structured interviews (SIs) and focused group discussions (FGDs) conducted in English and via online videoconferencing using the Zoom platform (11 FGDs, 21 SIs) or in person (4 SIs) between May 2020 and November 2020. Almost all SIs and FGDs were conducted by the lead female researcher [JAV] who has extensive experience in conducting qualitative studies, while some were conducted by the other trained study team members [ES1, SC, ES2, and YCC] who have also received training in qualitative research and had prior experience conducting qualitative interviews. Data from this study originates from a larger study which examined youths’ interpretation of positive mental health and its associated pathways. As part of that study, participants were asked questions relating to social media use (as seen in Table 1 ) and their mental health including their or their peers’ experience of any pleasant or unpleasant experiences/incidents on social media, their feelings arising from those experiences/incidents and how it influenced their or their peers’ mental health. Interviews ceased once data saturation was reached, where no new information was observed and collected. The present study explored data gathered on the harms of social media use and the strategies youths employ or perceive to be useful against these harms.

Data analysis

All FGDs and SIs were audio-recorded and transcribed verbatim. Data management and coding were conducted on NVivo 11 software. Data were analysed using thematic analysis as informed by Braun and Clark [ 26 ]. Following the inductive approach, five study team members [ES1, SC, ES2, YCC, JAV] identified preliminary codes on the harms of social media use and strategies to avoid those harms from the first six transcripts using the open coding method [ 27 ]. Through thorough and iterative discussions within the study team, codes were generated into higher-order concepts (themes and sub-themes) based on their common properties and formed an initial codebook. Regular discussions were carried out to review, refine and build consensus on the final codebook, which served as a framework for coding of remaining transcripts [ 28 ]. A high level of consensus was reached during the framework development process.

Data from the present study comprise 36 data units– 11 FGDs and 25 SIs. A total of 95 young people (51 women and 44 men; mean age = 20.1 years) participated in the study. Participants belonged to Chinese (n = 32), Malay (n = 27), Indian (n = 28) and other ethnicities (n = 8) such as Filipino or Burmese. Among them, eight participants had a history of psychological distress, school drop-out or risky behaviour (e.g., substance use, gang involvement and incarceration). Table 2 displays information on participants’ sociodemographic backgrounds.

1 Institute of Technical Education

Social media use

All participants reported having experience with social media use. The primary purposes of using social media included sharing information, gathering information, connecting with others, maintaining relationships, and entertainment which occurred on various platforms including Instagram, Facebook, TikTok, Reddit, Twitter, and YouTube. The following results are presented as themes encapsulating the various negative experiences and impact of social media use as experienced and perceived by participants or their peers, as well as practised and perceived mitigations against these harms.

Negative experiences of social media use

Theme 1 : Experience of negative emotions and behaviour from upward comparisons with others . A common theme in the interview was the development of negative reactions from upward comparisons with others. Comparisons with others’ achievements (e.g., school-related, work-related), material possessions, and experiences (e.g., travels), were more saliently discussed during the interviews as opposed to physical appearance, which was noticeably mentioned more by female participants than male participants. Participants noted that comparisons were usually made with peers, social media influencers, and celebrities with narratives on comparisons to peers and social media influencers being more pronounced. The resulting negative reactions to these comparisons included the development of negative feelings (e.g., feelings of inferiority, insecurity, self-consciousness, hurt, and loneliness), negative behaviours (e.g., self-loathing, starving, engaging in unhealthy competitions and overly intense workouts), and negative body image (e.g., idealizing skinny body types).

“ Social media is a platform where there are many many different beauty standards , and I can’t help but tend to compare myself with them . I think over the past one year or so , I tried many , many , many ways to lose weight because I felt that I wasn’t good enough to the point that like I lost too much weight which wasn’t good for myself . ”–FGD03 “ When you’re looking at like , maybe the Instastory of like , some influencer or like , and stuff like that , or people who are like , on holidays and whatnot , then kind of make you feel as though like , you are inadequate or you are missing out or you’re not doing something right , that kind of puts on unnecessary trigger or stress on you . ”–SI10

Furthermore, comparisons with others can result in the erosion of individuality when users feel compelled to follow the trend or be like other users just to fit in. This desire to belong can inadvertently fuel feelings of insecurity.

“ … it’s very natural to then feel , "Okay . We must do this as well because that’s what a lot of our generation people are trying to do . " We’re trying to follow other people . We’re trying to follow to be like someone else . And along that process , we either worsen the insecurities we already have or we don’t feel confident about ourselves . We don’t feel good about ourselves . We don’t want to be ourselves”–FGD10

Theme 2 : Experience of negative emotions from receiving hurtful remarks . Some participants recounted being emotionally affected by hurtful remarks directed towards them or having witnessed their friends suffer from such insults. These hurtful remarks stem from varying sources, including disagreement with or disapproval towards the content (e.g., activities, opinions, or comments) they had shared, and come from both known and unknown contacts. Furthermore, the anonymity afforded by fake social media accounts allows unbounded and harsh criticisms without consequences towards the perpetrator. Following that experience, some removed the content shared or deactivated their accounts.

“ One of my friends , she posted her point of view about this situation and other things . Then there’s this another person who just created another fake account and went on to talk shit about her and attack her saying that "Oh , you shouldn’t have this point of view . Why are you acting this way , " all that stuff . Then it sort of affected her mental health… she was really affected by it that she had to deactivate her account for a couple of weeks until she got back online again–SI14 “ I removed them [family] but they somehow managed to stalk me still . They started talking about me–that I’m sharing my personal life in social media and disgracing the family . So , they caught me and I had to remove all of the videos because it was really very stressful for me . That’s one of the bad experiences . ”–SI21

A participant also recounted how easy it was to have been a bully on social media:

“ …talking to people online through the screen is so much easier . So , it also increases cyberbullying . Yeah , it’s so easy to cyberbully someone because it’s just like… and then just send . Yeah , I’m sorry to say this , but I was a bully also . Yeah , it felt very bad , but I’m not that person anymore . ”–SI07

Theme 3 : Experience of negative emotions from exposure to controversial content . Some participants described experiencing or seeing their peers experience negative feelings from exposure to controversial content. While a broad range of content was discussed, the prominent ones leaned towards global political events (e.g., repression of Uyghurs), natural or man-made disasters (e.g., Beirut explosion), social issues (e.g., misogynistic content and animal cruelty), and social movements (e.g., Black Lives Matter). Consumption of this controversial content had left them feeling disturbed, drained, helpless, overwhelmed, and pessimistic.

“ I see a lot of my friends online , they might overdo it and it might like start to drain them (be)cause they tend to watch a lot of such disturbing videos and- so I think last week , I was speaking to someone and they were saying they feel very upset , and I asked them why . They were like they were watching a lot of animal cruelty videos , and it really affect them a lot”–SI08 “ …I feel like certain situations are full of injustice , for example , the Uyghur situation or how people criticize Muslims a lot in this world . And it just frustrates me because a lot of , yeah , misunderstandings are due to them not wanting to educate themselves . Like the comments are so ignorant and it’s just so frustrating . ”–FGD07

Theme 4 : Perpetuation of negative emotions , behaviours , and sentiments . The perpetuation of negativity through social media use was commonly mentioned by participants. First, social media platforms enable rumination through posts or stories. Second, negative sentiments are sometimes echoed by like-minded communities and exacerbated. Third, social media can encourage the perpetuation of negative behaviours such as self-harm, unhealthy eating behaviour and unhealthy coping styles when users follow self-harm content/accounts and emulate them.

“ If they’re feeling down right , instead of looking at something positive , they end up going on Reddit , and they look at—they enter those communities that are all the same people , and it becomes an echo chamber of negative things , and it gets worse because it’s just the same kind of people . ” FGD08 “ It’s very , very sad because you see that their (friends with eating disorders) whole lives are just engrossed and obsessed with numbers , calories , food , exercise , all these things . And it’s really very , very , very sad and disheartening to see that social media has such a huge role in worsening these things . ”–SI16 “ I work mostly with youths at risk , and the kind of content that you follow online is always more towards the emo genre . Sometimes , it does have the elements of suicide or taking a life , or like self-harm , and all those various aspects , and that became their way of dealing with stress , either they learn to take the action from there , or it just comes ingrained , and they do something similar towards the destructive and that’s maladaptive . ”–FGD08

Mitigation of negative effects of social media use

Theme 5 : Filtering content and users . Most participants described curating content or being selective of users they follow as means to reduce any negative effect social media use can have on their mental health. These include choosing only positive content to follow from the start, filtering out accounts followed (e.g., unfollowing accounts which trigger negative feelings), muting selected users’ posts/stories on social media, and scrolling past negative content.

“ I don’t really feel that way [negative feelings] when I use social media because , yeah , why would I want to make myself feel so bad ? Yeah , I tend to go for the more positive kind of posts instead of those that kind of puts you down”–FGD01 “ Let’s say you have a favourite YouTuber who is so pretty . She’s a beauty vlogger . And you love watching her videos . But every time you watch her videos , you feel so self-conscious about your own face . I think it’s important to make that hard sacrifice to cut it out , to stop watching that YouTuber . ”- SI13

Theme 6 : Taking breaks from social media . Participants also highlighted the importance of being aware of the negative impacts of social media consumption on themselves, such as from social comparisons or absorbing overwhelming content and managing time spent on social media to reduce these impacts. They also mentioned different ways and benefits of taking breaks from social media use. For some, breaks can either be temporary or permanent (e.g., deactivating social media accounts for good), and the benefits include being able to focus on other tasks at hand (e.g., studying/schoolwork) and experiencing boosted mental health.

“ …I think what we don’t realize is that these (social media) are outlets that feed on your energy every single day . So , if it gets to a point where you can’t function or perform well because of it , then you need to give yourself a break from it . ”–FGD7 “ So , for one period , I deleted all my social media like kind of a social media cleanse , and I think a lot of youth do that as well , as they are growing a little bit older , because social media is very superficial , in a sense . ”–FGD2

Theme 7 : Cognitive reframing and self-affirmation . This theme explicates the conscious effort by users to identify and change stress-inducing patterns of thinking by setting realistic social, physical, and lifestyle expectations for themselves and not being swayed by unrealistic social media portrayals. Participants also showed awareness of the superficial nature of the content on social media which does not necessarily mirror complete real-life situations or experiences. In addition, focusing on self-development and self-affirmation were other ways participants noted as safeguards against the pitfalls of social comparisons on social media.

“ … we need to have a stronger sense of reality and understand that social media is really not everything . Like what [participant] said , there’s a backstage and there’s a front stage that you want to portray , and no one wants to show their dirty laundry on social media . They want everything to be good . ”–FGD4 “ So , you see these people’s successes… but then I just try to remind myself that , "Okay . I’ll be happy for them . Yours will come in time , " instead of feeling like , "Oh , why isn’t mine here ? ”–SI03 “ I don’t regularly tell positive affirmations to myself , but maybe once in a while , I will just look in the mirror and say positive things about myself , and it actually makes me feel better immediately , and I think that has an impact on my mood ”– FGD08

Findings from this study expand and inform the emerging body of research on the negative experiences of social media use among a sample of youths in Asian society and how they deal with them. While youths reported experiencing or witnessing their peers experience significant negative effects of social media use, it is promising to note youths’ awareness of these negative effects and their attempts to avoid or reduce them.

Youths’ narratives suggest a predisposition towards upward social comparisons, particularly with peers and influencers on social media sites. This tends to result in negative effects; most of which are reflected in prior studies including the development of negative feelings (e.g., feelings of inadequacy, lowered self-esteem [ 29 ]), as well as an unhealthy mindset (e.g., negative body image [ 30 ]) and behaviours (e.g., disordered eating behaviours [ 31 ]). [ 32 , 33 ]. Scholars suggest that the fundamental and universal desire for comparisons with others serves a variety of functions such as evaluating the self [ 34 ], fulfilling affiliation needs [ 35 ], and being inspired [ 36 ]. Given the social functions of social media sites and the detailed information about others, it may be natural for people to engage in social comparisons either consciously or unconsciously [ 37 ]. Furthermore, unlike real-life situations, social media sites allow people to present an optimized or idealized version of themselves and their experiences [ 38 , 39 ]. It is therefore possible that further exposure to ‘enhanced’ profiles can create more discrepancy between their perceived self and others and perhaps amplify feelings of inadequacy. This could be supported by the finding of Chou and Edge [ 40 ] who examined the impact of using Facebook on people’s perception of others’ lives and found that people who have spent more time on Facebook tend to perceive other social media users as having better lives than they do.

Qualitative studies examining comparisons on social media among young people in Western populations have mostly focused on examining the relationship between social media use and physical or bodily appearance [ 25 , 41 , 42 ] or found the appearance-related social comparison to be discussed by participants when examining the role of social media on mental health [ 43 ]. In such studies, negative impacts from such comparisons were often highlighted. For example, in a study based in the UK by Easton et al. [ 42 ], the authors examined young adults’ (aged 18–25) experience with ‘fitspiration’ (blend of “fitness” and “inspiration”) on social media. They found that comparisons with another’s perceived fitness (content on healthy lifestyle habits, relating to exercise and diet), can give rise to negative effects on their psychological health. In particular, minor negative effects include being frustrated about the deceptive nature of posts and jealousy towards unattainable body appearance while more perturbing effects include negative feelings towards their bodies and unhealthy eating habits. These findings are reflected in the narratives of a few participants in this study, particularly female participants, who had mentioned engaging in upward physical appearance comparisons and resonated with such experiences. On the other hand, a unique finding which emerged from this study is the upward comparison with others’ achievements (e.g., academics, employment) or material possessions and lifestyles. Research exploring cultural differences in social comparisons suggests that people living in countries whose cultures tend to be more collectivistic, rather than individualistic, are more likely to engage in social comparisons [ 44 ] and that Eastern cultures are suggested to be more concerned about one’s relative social standing [ 45 ]. Therefore, it seems unsurprising that youths in Asian society are inclined to seek upward social comparisons with peers’ achievements, such as having a good social network, occupation, and education, which are contributors to a person’s social standing.

Participants highlighted cognitive reframing as a strategy to reduce the harms of social comparisons on social media. Cognitive reframing among participants entails identifying negative patterns of self-evaluations and reinterpreting how they view content on social media, such as reminding themselves of the superficial nature of social media portrayals as well as creating more realistic self-expectations. Research indicates that low self-esteem has been linked to unrealistic standards for self-evaluation [ 46 ] and that negative self-evaluations can occur when discrepancy increases between ideal and real self-image [ 47 ]. In the context of social media, for example, exposure to ‘fitspiration’ content, which tends to involve images and messages praising thinness and high fitness levels [ 48 , 49 ], can lead to increased body dissatisfaction if these ideals are internalized and unattained [ 39 ] Cognitive reframing mentioned by youths, such as setting realistic body image or achievement expectations, can therefore act as a buffer against the development of negative self-evaluations when comparing with unrealistic and unattainable body images or others’ achievements on these platforms.

Narratives of negative feelings from exposure to controversial content are worth paying attention to. Specifically, youths mentioned feeling drained and helpless from bearing witness to cruelty or disasters to which they are unable to contribute to improving the situation. Social media has emerged as a source of news content over the years [ 50 ] and provides many opportunities to be exposed to news incidentally or deliberately through content shared by others within their social networks [ 51 ], or when they follow official accounts of news broadcasters. Media writers have argued that news in the digital age has become increasingly visual, with images taken from various sources, and written to convey excitement and danger, and be fear-laden [ 52 ]. Importantly, user-generated images of important world events are frequently captured on smartphones by witnesses in these events, allowing the audience to view such events in real-time [ 53 ]. Such user-generated images allow news broadcasters to display more intense and shocking visuals that may not have been available or approved in earlier times. It is therefore unsurprising that there is an observed negative effect on mental well-being with exposure to such raw and intense visuals and commentaries.

However, it is arguably reassuring to note that participants are aware of the control they have over social media’s influence on their lives and have exercised proactive self-control strategies to regulate their social media use, such as limiting content seen on social media or keeping off social media. Participants recognize the feeling of relief and benefit to their mental well-being when they step away from stress-inducing content or when they disabled their social media accounts, incidentally, highlighting the pervasiveness of social media in their lives. Indeed, some studies have shown that taking a break from social media positively affects subjective well-being [ 54 – 56 ]. It is also worth noting that keeping off social media among participants appears to involve internal negotiation and contention, alluding to resisting compulsions towards using it.

The negative experiences and harm minimisation strategies reported in this study align well with some emerging initiatives to protect young people from the harms of social media use. An example of such an initiative is #Chatsafe. #Chatsafe was developed to guide and educate young people about communicating safely about suicide on social media [ 57 , 58 ]. The social media campaign was found to be effective in improving young people’s capacity to intervene against suicide online, perceived internet self-efficacy and safety when communicating about suicide on social media [ 58 ]. A similar initiative based on the #Chatsafe guideline was also implemented in Singapore [ 59 ]. Specifically, the #Chatsafe guideline was adapted to the local context of Singapore into a #PauseBeforeYouPost campaign which educates young people on how to conduct safe conversations around mental health issues and engage safely with those who are at risk of suicide. In addition, a #Chatsafe training curriculum is also being developed for youths and caregivers to provide them with the relevant skills and knowledge to engage positively with suicide-related online content and support those around them who are in distress. The effectiveness of this campaign can inspire the implementation of future interventions targeting different needs of youths, as exemplified by the narratives among this sample population, to mitigate negative experiences and outcomes of social media use. For example, users could be taught and reminded of proper etiquette when communicating with others on social media to avoid making hurtful remarks. In addition, self-esteem, which can mediate the effects of upward comparisons on well-being, could also be addressed in these campaigns.

Efforts could also be extended beyond the social media realm as programs in schools. Educators could teach youths how to manage content and conversations on social media and encourage the diversification of content by suggesting accounts that nurture intellectual passions or interests, resilience, and increase self-esteem in them. As exemplified by narrations of adolescents from Burnette et al.’s [ 25 ] study, a supportive school environment and its effective communication of social media-related messages and programmes on accepting differences in body image can contribute to the development of high media literacy and confidence among them.

Strengths and limitations

This study has several strengths. Data were gathered from both FGDs and SIs, and deviant samples, allowing for the generation of broad and rich qualitative data. FGDs are generally more dynamic and allow participants to discuss and expand on their pre-existing ideas in light of points mentioned by other participants, which may not have been uncovered in in-depth interviews [ 60 ]. On the other hand, SIs allows for the gathering of greater insight into the individuals through the discussion of topics in detail [ 60 ]. However, study findings must be considered in the context of several limitations. The nature of the topic–the negative impact of social media use–may be sensitive or controversial to some participants; therefore, participants may limit sharing due to social desirability bias [ 61 ]. In addition, we did not examine whether experiences of social media use differ across individuals with different patterns of social media use, or those from different sociodemographic and sociocultural backgrounds, which may account for differences in experiences of negative effects and subsequently adoption and success of different coping mechanisms. It is recommended that future research delve into this topic further and explore the interaction between an individual’s socioecological environment and their experiences with social media use. Future research could also investigate differences in terms of strategies adopted by Asian and Western youth populations.

Results from the present study indicate that social media can influence youths’ lives today. While social media can enhance learning, connection and communication [ 62 ], the salience of its negative effects on users’ mental well-being drives the need to actively monitor these harms and explore effective ways to steer users away from them. The current results offer a preliminary portrait of the salient negative effects of social media use in a multi-ethnic Asian youth population. It also indicates that while youths experience the negative effect of social media use, they have high media literacy and have employed strategies that appear to mitigate the negative effects of social media.

Acknowledgments

The authors are immensely grateful to all the participants and CHAT Hub, Human Hearts, The Green House Community, and Touch Community for kindly circulating information on our study to their clients.

Funding Statement

This study was supported by the Singapore Ministry of Health’s National Medical Research Council under the Centre Grant Programme (NMRC/CG/004/2013) The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Data Availability

Social Media in Qualitative Research: Challenges and Recommendations

Information and Organization, vol. 27, issue 2, pages 87-99, 2017, https://doi.org/10.1016/j.infoandorg.2017.03.001.

The University of Auckland Business School Research Paper Series

Posted: 22 Sep 2021

Brad McKenna

University of East Anglia (UEA)

Michael David Myers

University of Auckland Business School

Mike Newman

The University of Manchester - Manchester Business School

Date Written: 2017

The emergence of social media on the Internet provides an opportunity for information systems researchers to examine new phenomena in new ways. However, for various reasons qualitative researchers in IS have not fully embraced this opportunity. This paper looks at the potential use of social media in qualitative research in information systems. It discusses some of the challenges of using social media and suggests how qualitative IS researchers can design their studies to capitalize on social media data. After discussing an illustrative qualitative study, the paper makes recommendations for the use of social media in qualitative research in IS. Full paper available at https://doi.org/10.1016/j.infoandorg.2017.03.001.

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• DOI: 10.1016/J.INFOANDORG.2017.03.001
• Corpus ID: 205433765

Social media in qualitative research: Challenges and recommendations

• Brad McKenna , M. D. Myers , Michael Newman
• Published in Information and organization 1 June 2017
• Computer Science, Sociology

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Ethical use of social media to facilitate qualitative research

Affiliations.

• 1 Flinders University, Adelaide, South Australia, Australia [email protected].
• 2 University of Melbourne, Melbourne, Victoria, Australia.
• 3 Flinders University, Adelaide, South Australia, Australia.
• PMID: 25212856
• DOI: 10.1177/1049732314549031

Increasingly, qualitative health researchers might consider using social media to facilitate communication with participants. Ambiguity surrounding the potential risks intrinsic to social media could hinder ethical conduct and discourage use of this innovative method. We used some core principles of traditional human research ethics, that is, respect, integrity, and beneficence, to design our photo elicitation research that explored the social influences of drinking alcohol among 34 underage women in metropolitan South Australia. Facebook aided our communication with participants, including correspondence ranging from recruitment to feeding back results and sharing research data. This article outlines the ethical issues we encountered when using Facebook to interact with participants and provides guidance to researchers planning to incorporate social media as a tool in their qualitative studies. In particular, we raise the issues of privacy and confidentiality as contemporary risks associated with research using social media.

Keywords: Internet; alcohol/alcoholism; ethics / moral perspectives; sociology; young adults.

© The Author(s) 2014.

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9 Data Collection Methods in Qualitative Research

Explore top methods for collecting qualitative data, from interviews to social media monitoring, to gain deeper customer insights for your strategy.

In the world of customer insights, having access to the right data is crucial. Numbers and metrics can provide valuable direction, but they often fail to capture the full picture of how your customers truly feel, what they need, or why they behave in certain ways.

That’s where qualitative research shines. Using multiple qualitative data collection methods is like casting a wider net for insights — the more varied your approach, the better your chances of capturing nuanced feedback that standard surveys might miss.

Whether it’s through in-depth interviews or mining customer chat logs, the diversity of data sources can help build a robust understanding of your customers’ experiences.

In this article, we’ll cover the top methods you can use to collect qualitative data to inform your customer experience strategy .

Table of contents

Qualitative vs quantitative methods, 9 essential qualitative data collection methods.

In-depth Interviews

Focus Groups

Observational Research

Case Studies

Surveys with Open-ended Questions

Ethnographic Research

Customer Support Center Chat History

Social Media Conversation Monitoring

Review Sites

Pitfalls to Avoid in Qualitative Data Collection

Analyzing qualitative data.

When it comes to gathering customer insights, there are two main avenues: qualitative and quantitative research. Both are crucial, but they serve different purposes.

Quantitative methods rely on numerical data. Think of it as your go-to for answering “how many?” and “how much?” questions. It’s all about measurable facts, trends, and patterns. For example, you might run a large-scale survey asking customers to rate their satisfaction on a 1-10 scale, and you’ll get hard numbers to analyze. This kind of data is easy to visualize in graphs and charts, which helps you track customer satisfaction metrics like NPS or CSAT scores over time.

But qualitative methods ? This is where you dig deeper. These methods focus on the “why” and “how,” uncovering insights into the emotions, motivations, and thought processes behind customer behaviors. Instead of numerical data, qualitative research gives you rich, detailed feedback in the form of words. The qualitative data collected through these methods provides detailed and nuanced insights into individuals' or groups' experiences, perspectives, and behaviors. It’s an excellent way to get to the heart of customer experiences and understand their pain points on a human level.

Why Qualitative Research Is Critical for Customer Experience Strategy

Quantitative data can tell you what’s happening, but qualitative data tells you why it’s happening. The qualitative data collected through various methods can explain the underlying reasons behind customer satisfaction scores. If your quantitative research shows a drop in customer satisfaction scores, qualitative research can explain why. By diving into customer stories, open-ended survey responses, or even analyzing chat logs, you gain invaluable insights into where things might be going wrong (or right!).

Let’s dive into the most impactful methods you can use to gather valuable customer insights. Each of these methods offers a unique lens into the customer experience, helping you build a comprehensive understanding of your audience. Understanding both qualitative and quantitative data is essential for building a comprehensive understanding of your audience.

1. In-Depth Interviews

In-depth interviews are one-on-one conversations where the researcher asks open-ended questions , allowing the customer to share their thoughts and experiences in detail. These interviews are incredibly useful when you want to understand the “why” behind customer behavior or preferences. The qualitative data collected through in-depth interviews provides rich, detailed insights into customer behavior and preferences.

Maximizing the method: To get the most out of in-depth interviews, focus on creating a comfortable environment where participants feel free to express their honest opinions. Listen actively, ask follow-up questions, and don’t shy away from allowing the conversation to go off-script if it leads to richer insights.

Example: Imagine you’re an insights manager at a retail brand conducting an in-depth interview with a frequent shopper. By asking about their shopping habits, you can uncover that the customer values sustainability and chooses brands with eco-friendly packaging. This insight could inform future product packaging decisions.

2. Focus Groups

A focus group is a facilitated discussion with a small group of customers – usually around 6-10 people. The goal is to encourage interaction between participants, sparking conversations that reveal insights through group dynamics. The collective experience of a focus group can surface opinions that may not emerge in individual interviews. The qualitative data collected through focus groups can reveal collective opinions and insights that may not emerge in individual interviews.

Maximizing the method: Ensure that the focus group facilitator is skilled at guiding discussions without leading them. It’s important to let the conversation flow naturally, but the facilitator should know when to probe deeper or refocus the group when necessary.

Example: Let’s say a tech company runs a focus group with power users of their app. During the session, one participant mentions a feature they find confusing, which prompts others to agree. This shared feedback provides the company with a clear signal to revisit that feature for usability improvements.

3. Observational Research

Observational research (sometimes called field research) involves observing customers in their natural environment, whether it’s a store, website, or another setting. Instead of asking questions, researchers watch how customers interact with products, services, or environments in real-time. The qualitative data collected through observational research provides real-time insights into customer interactions and behaviors.

Maximizing the method: The key to observational research is to remain unobtrusive. Customers should behave naturally without being influenced by the researcher’s presence. It’s also crucial to take detailed notes on both the behaviors you expected, and any surprising actions that arise.

Example: A coffee shop chain might use observational research to see how customers navigate their in-store experience. Do they head straight to the counter or linger at the menu? Are they confused about the ordering process? These observations could highlight ways to improve the store layout or ordering flow.

4. Case Studies

Case studies are in-depth analyses of individual customer experiences, often focusing on how a product or service has solved a specific problem for them. By following a single customer’s journey from problem to solution, case studies offer detailed narratives that can illustrate the broader impact of your offerings. The qualitative data collected through case studies offers detailed narratives that illustrate the broader impact of your offerings.

Maximizing the method: Choose case study subjects that reflect common challenges or experiences within your customer base. The more relatable the story, the more likely other customers will see themselves in the narrative.

Example: A B2B SaaS company could create a case study around a client that successfully used their software to reduce employee churn. By detailing the challenges, implementation, and results, the case study could serve as a powerful testimonial for potential clients.

5. Surveys with Open-Ended Questions

While many surveys are typically quantitative, surveys with open-ended questions provide a qualitative element by allowing customers to write out their responses in their own words. This method bridges the gap between structured data and personal insights, making it easier to spot recurring themes or unique perspectives. The qualitative data collected through open-ended survey questions bridges the gap between structured data and personal insights.

Maximizing the method: Be strategic with the placement of open-ended questions. Too many can overwhelm respondents, but including one or two at key points in your survey allows for deeper insights without causing survey fatigue.

Example: A travel company might send out a post-trip survey asking, “What was the most memorable part of your experience?” The open-ended responses could reveal customer preferences that the company wasn’t previously aware of, informing future offerings or services.

6. Ethnographic Research

Ethnographic research takes immersion to a new level. In this method, researchers embed themselves in the customer’s environment for extended periods to observe and experience their behaviors firsthand. It’s about gaining a deep understanding of customer culture, motivations, and interactions. The qualitative data collected through ethnographic research provides a deep understanding of customer culture and interactions.

Maximizing the method: This method works best when researchers fully integrate into the customer’s world, whether that’s living among a target community or spending time on-site with customers in their daily routines. It’s a time-intensive process, but the insights can be incredibly rich.

Example: A researcher for a clothing brand might spend several weeks with a group of customers, observing how they shop for and wear clothes in their daily lives. This immersive research could uncover nuanced preferences about fabric types, fit, and style that surveys alone wouldn’t reveal.

7. Customer Support Center Chat History

Your customer support center chat history can be a treasure trove of qualitative data. By analyzing conversations between customers and support agents, you can identify recurring issues, concerns, and sentiments that might not surface in formal surveys or interviews. This method provides an authentic view of how customers feel in real-time as they interact with your brand for problem-solving. The qualitative data collected from chat histories provides an authentic view of customer sentiments in real-time.

Maximizing the method: Use text analysis tools to sift through large volumes of chat data, identifying common themes and patterns. Pay special attention to moments of frustration or satisfaction, as these often hold the key to customer experience improvements.

Example: A software company analyzes its chat history and notices that many customers express confusion about a particular feature. This insight leads the product team to create clearer in-app tutorials, ultimately reducing the number of support requests related to that feature.

8. Social Media Conversation Monitoring

Social media platforms are filled with candid, unsolicited customer feedback. Social media conversation monitoring involves tracking brand mentions, hashtags, and keywords to gauge customer sentiment and uncover insights about your audience. This method gives you access to a wide range of voices, including those who may never participate in formal research. The qualitative data collected from social media conversations offers a wide range of customer insights.

Maximizing the method: Leverage social listening tools to automate the process of monitoring and analyzing conversations across platforms like Instagram, Meta, or X. Be sure to track both direct mentions of your brand and broader industry-related conversations that could reveal trends or shifting customer preferences.

Example: A beauty brand might notice that customers are frequently discussing a competitor’s eco-friendly packaging on social media. By monitoring this trend, the brand could introduce more sustainable packaging solutions to align with emerging customer values.

9. Review Sites

Review sites such as Yelp, Google Reviews, and Trustpilot are another goldmine for qualitative data. Customers who leave reviews are often highly motivated to share their experiences, whether positive or negative. By mining these reviews, you can gather insights into customer satisfaction, product issues, and potential areas for improvement. The qualitative data collected from review sites provides insights into customer satisfaction and areas for improvement.

Maximizing the method: Don’t just focus on star ratings—read through the text of each review to extract the underlying emotions and motivations. Look for patterns in the language used and the specific aspects of your product or service that are frequently mentioned.

Example: A restaurant chain may notice through online reviews that customers often comment on the long wait times during dinner hours. This feedback prompts management to reassess staffing levels during peak times, improving both operational efficiency and customer satisfaction.

As with any research process, there are a few key pitfalls to watch out for when collecting qualitative data. Avoiding these three common mistakes will ensure that your insights are both accurate and actionable.

1. Bias in Data Collection

Bias can creep into qualitative research in many forms, from how questions are phrased in interviews or surveys to how data is interpreted. For example, leading questions might push respondents toward a specific answer. Similarly, during observational research or focus groups, the presence or behavior of the researcher could unintentionally influence participants.

How to avoid it: Ensure your research methods are designed to be neutral and that questions are open-ended. It’s also important to train researchers to minimize their influence during interviews or observations. Using standardized protocols can help maintain consistency across different data collection methods.

2. Over-reliance on a Single Method

While one method may seem like the easiest or most convenient to implement, relying solely on one form of data collection can lead to incomplete or skewed insights. For example, in-depth interviews might provide detailed information, but they won’t capture broad patterns across your entire customer base.

How to avoid it: Combine multiple data collection methods, like surveys, focus groups, and social media monitoring, to get a fuller picture. Each method will reveal different aspects of customer experience, and when analyzed together, they provide more comprehensive insights.

3. Failing to Document the Research Process

One of the easiest ways to undermine the quality of your qualitative data is by failing to document the research process adequately. Without a clear record of how data was collected, analyzed, and interpreted, it becomes difficult to validate findings or replicate the study in the future.

How to avoid it: Keep detailed notes, records, and transcriptions of every stage of the research process. Having a clear audit trail ensures that your findings are credible and can be trusted by decision-makers.

With these qualitative data collection methods at your disposal, you’ll find yourself with a wealth of unstructured qualitative data. While an abundance of data is valuable, it also presents a significant challenge: how to make sense of it all efficiently.

This is where advanced tools and technology come into play.

The Challenge of Unstructured Data

Qualitative research methods produce, by their nature, unstructured data. Whether you’re working with transcripts from focus groups, feedback from review sites, or social media conversations, the data doesn’t neatly fit into rows and columns like quantitative data does. Instead, you’re dealing with text—rich, narrative-driven, and full of context. This makes it incredibly insightful but also hard to analyze manually.

Manually categorizing themes, identifying patterns, and summarizing key takeaways from large datasets is time-consuming and prone to human error. It’s easy to miss out on emerging trends or nuances that could offer strategic value, especially if you're dealing with diverse data sources.

How Kapiche’s AI-Powered Auto-Theming Can Help

Kapiche’s automatic theming feature is designed to solve this problem. By leveraging AI-powered technology, Kapiche cleans, categorizes, and analyzes your text data quickly and accurately. The platform automatically identifies themes, clusters related data points, and even provides summaries that help you interpret what your customers are saying.

For example, Kapiche can scan through customer support chat histories or social media mentions and instantly group similar pieces of feedback together—whether customers are talking about product performance, customer service, or price sensitivity. With these insights readily available, you can take faster action to improve your customer experience.

Benefits of Auto-Theming for Insights Managers

Here's how an auto-theming can transform your qualitative data analysis:

Speed and Efficiency: Automating the process saves you countless hours of manual work.

Comprehensive Analysis: By aggregating data from multiple sources, you get a fuller picture of customer sentiment across various touchpoints.

Uncover Hidden Insights: The AI detects patterns that you might not notice through manual analysis, offering deeper insights into customer behavior.

Actionable Summaries: Instead of wading through raw text, Kapiche provides concise summaries of key themes and trends, enabling you to act on insights faster.

With tools like this at your disposal, the overwhelming task of analyzing qualitative data becomes manageable, empowering your insights team to make data-driven decisions more effectively.

Let Us Help You

Navigating the complexities of qualitative data collection and analysis can be challenging, but you don’t have to do it alone. At Kapiche, we’re committed to helping insights teams like yours make the most of your qualitative customer data.

Our AI-powered auto-theming capabilities simplify the process by automatically categorizing, analyzing, and summarizing your data. This means you can quickly uncover key insights and trends without getting bogged down by the sheer volume of unstructured information.

Ready to see how Kapiche can transform your research process? Click the link below to watch an on-demand demo and discover how our platform can enhance your customer insights strategy.

Book a Demo with Kapiche

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• Open access
• Published: 20 September 2024

Translanguaging pedagogy on the digital stage: exploring Chinese undergraduates’ English grammar learning through DingTalk platform

• Lixuan Sun   ORCID: orcid.org/0000-0001-9593-8488 1  

Humanities and Social Sciences Communications volume  11 , Article number:  1245 ( 2024 ) Cite this article

Metrics details

• Language and linguistics

The internet sphere has become a disruptive platform for education, leading to a reworking of the language instruction scene. This research investigates the intersection of Translanguaging Pedagogy with the dynamic digital platform DingTalk, with the objective of enhancing the English grammar learning experience for Chinese undergraduate students. The goal of this study, which is multilingual context-based, is to investigate the as-yet-unexplored field of using students’ linguistic repertoires in virtual environments. Utilising a mixed-methods methodology, this work incorporates a quasi-experimental design and qualitative investigation to elucidate the complex interaction between Translanguaging Pedagogy and the immersive potential of DingTalk. The study involved a population of 58 Chinese undergraduate students, with qualitative insights further enriched through interviews with 4 participants. The results reveal notable enhancements in English grammatical skills among individuals who participated in the Translanguaging method using DingTalk. These findings underscore the effectiveness of incorporating Mandarin as a cognitive bridge. Qualitative observations shed light on the cognitive and emotional aspects of this new method, unveiling its profound influence on the acquisition of language. The present study aims to alter educational paradigms by illustrating the mutually beneficial connection between pedagogy and technology in the context of improving language acquisition. In light of the global shift towards remote education, this research presents a groundbreaking approach that paves the way for a future characterised by comprehensive and skill-oriented language instruction, fostering inclusivity.

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Introduction.

In the wake of the digital stage, the landscape of education transformed radically, propelling educators and students into the realm of virtual learning (Chen and Hsu, 2021 ; Cretu and Ho, 2023 ). As the physical classroom dissolved into digital pixels, language educators faced a unique challenge: how to maintain the effectiveness of language instruction in a remote environment, preserving the interactive and communicative essence that underpins language learning. This study delves into a pioneering endeavour, exploring the potent synergy of translanguaging pedagogy and the dynamic digital platform, DingTalk, to enhance Chinese undergraduates’ English grammar learning. Educational institutions worldwide grappled with the necessity of remote instruction as lockdowns shuttered campuses (Ali, 2020 ; Tilak and Kumar, 2022 ). Language education, traditionally rooted in face-to-face interactions and immersive experiences, stood at a crossroads (Bonk et al., 2021 ). As Chinese undergraduates embarked on their English language learning journey from homes and dorm rooms, educators were tasked with reimagining pedagogical strategies that could effectively bridge the virtual divide.

Amidst the turmoil, the concept of translanguaging emerged as a beacon of pedagogical innovation (Kiraly and Hernández, 2023 ). Translanguaging encourages learners to embrace their entire linguistic repertoire, dissolving the rigid boundaries between languages and harnessing the inherent cognitive benefits of multilingualism (Almahmoud, 2021 ; Tîrnovan, 2023 ). It has the potential to reshape language education paradigms by cultivating deeper comprehension, critical thinking, and cultural sensitivity (Rahmadani, 2023 ). Nestled within this educational transformation was the dynamic platform, DingTalk. Known for its versatility, DingTalk seamlessly transitions between synchronous and asynchronous interactions, facilitating real-time discussions, multimedia integration, and collaborative projects (Huang et al., 2023 ). Its virtual stage bore witness to the unfolding experiment, where Chinese undergraduates navigated English grammar instruction through the lens of translanguaging.

This study embarks on a journey to explore the nexus of these two powerful forces: translanguaging pedagogy and DingTalk’s virtual stage. The objective of this study is to assess the efficacy and student perceptions of the DingTalk based Translanguaging teaching method in enhancing English Grammar performance among Chinese undergraduate students. Furthermore, it seeks to investigate the perspectives and opinions of students on this novel pedagogical method. Beyond the immediate context of digital stage, this research harbours the potential to reshape language pedagogy, offering an innovative model that amalgamates traditional methods with cutting-edge technology.

Literature review

Translanguaging in language education.

Translanguaging, a concept derived from sociolinguistics and bilingualism studies, challenges conventional language limits and promotes the deliberate use of multiple languages to improve learning (Cenoz and Gorter, 2020 ; García and Kleifgen, 2020 ). Translanguaging proponents argue that learners’ native languages should and may have a substantial impact on their learning of new languages in the context of foreign language instruction (Li, 2022 ). Educators are increasingly acknowledging the potential of technology to close the divide between students’ linguistic backgrounds and formal language training (Yuan and Yang, 2023 ). Translanguaging fosters deeper comprehension, cognitive flexibility, and cultural understanding (Azevedo-Gomes et al., 2021 ; Yüzlü and Dikilitaş, 2022 ). Researchers like Charamba ( 2022 ) and Rajendram ( 2022 ) have championed translanguaging as a pedagogical approach that leverages students’ linguistic repertoires to facilitate language learning and promote critical thinking. The study by Liu and Fang ( 2022 ) examines the evolving concept of translanguaging in the context of multilingualism and foreign language education, highlighting positive stakeholder attitudes and suggesting practical implications for redefining language policies and utilising students’ native languages in English language instruction. Sun and Zhang ( 2022 ) employ a mixed-method approach to investigate the effects of translanguaging in online peer feedback on the writing proficiency of Chinese university students. The findings indicate that translanguaging initially exhibits a positive impact on writing performance. Nevertheless, the study highlights the intricate interaction of elements such as motivation, agency, self-efficacy, and translanguaging awareness in promoting successful language practice.

Online language learning platforms and DingTalk

The digital revolution has opened new avenues for language education (Collins and Halverson, 2018 ). DingTalk, a multifunctional platform developed by Alibaba, emerged as a powerful tool in the digital stage (Gao and Zhang, 2020 ; Valentina et al., 2022 ). Offering real-time communication, collaboration features, and multimedia integration, DingTalk has facilitated the transition to virtual classrooms (Adipat et al., 2021 ; Chen et al., 2021 ). It enables educators to engage students asynchronously or synchronously, creating opportunities for interactive language learning beyond traditional classroom confines (Kurni and Srinivasa, 2021 ). Online collaborative prewriting through DingTalk improves writing performance, decreases anxiety, and garners favourable student attitudes, according to Huang et al. ( 2021 ) who study the effects on EFL learners’ writing anxiety and performance. The study conducted by Jin ( 2020 ) investigates the utilisation and efficacy of computer-based online instruction, specifically through the application of DingTalk, within a higher education environment amidst the pandemic. The findings of the study shed light on the positive aspects of course design and teacher characteristics, while also identifying areas that require enhancement in terms of practical teaching and innovation.

Language education in the digital stage: challenges and innovations

The digital stage necessitated a re-evaluation of language education (Al-Bargi, 2021 ). As face-to-face interactions diminished, educators faced challenges in maintaining learner engagement and effective pedagogy (Tamah et al., 2020 ; Neuwirth et al., 2021 ). However, the crisis also spurred innovative strategies. Flipped classrooms, gamification, and multimedia integration emerged as ways to sustain interaction and motivation (Azar and Tan, 2020 ; Haryudin and Imanullah, 2021 ; Salvador, 2021 ; Inayati and Waloyo, 2022 ; Burlacu et al., 2023 ; Menggo et al., 2023 ; Tang et al., 2023 ). These approaches reinvigorated the role of technology in language education and set the stage for exploring platforms like DingTalk for immersive language learning experiences.

Translanguaging in multilingual contexts: chinese undergraduates and english grammar

Research on translanguaging has predominantly focused on bilingual and multilingual contexts. The literature surrounding Chinese undergraduates’ English grammar learning underscores the significance of instructional strategies tailored to their linguistic and cultural context (Bai et al., 2021 ). Traditional approaches emphasising rule-based instruction are juxtaposed with contemporary methods like communicative language teaching, reflecting a dynamic pedagogical landscape (Tyler, 2012 ). Challenges stemming from interference of first language structures and examination-oriented learning methods are recognised, prompting a call for personalised and technology-integrated approaches (Güneyli et al., 2023 ). The interplay between metacognitive strategies, learner motivation, and self-regulation emerges as a critical factor in optimising grammar acquisition (Wongdaeng, 2022 ). In the case of Chinese undergraduates learning English grammar, the potential application of translanguaging strategies remains underexplored (Fang and Liu, 2020 ). Chinese learners often possess rich linguistic repertoires, including Mandarin and regional dialects (Duff et al., 2013 ). Leveraging these resources through translanguaging may yield deeper understanding and engagement, particularly when integrated within digital platforms like DingTalk.

Although there is a vast amount of literature on translanguaging and online language learning, there is a lack of study examining how translanguaging concepts might be applied on DingTalk to enhance Chinese undergraduates’ English grammar learning.

Research methodology

Procedure design.

The objective of this investigation was to evaluate the efficacy of the DingTalk-based Translanguaging method of instruction in improving the English grammar proficiency of Chinese undergraduates during the digital stage. This investigation is divided into two distinct temporal periods. In order to resolve the initial research question, the quantitative portion implemented a quasi-experimental methodology. During the second phase, a qualitative methodology was implemented to study research question 2. The researcher initiated the study by administering pre-tests in the form of an English grammar assessment and subsequently collecting the students’ results. This was done prior to the implementation of the treatment. During the course of the lesson, the control class was instructed in English grammar, while the experimental class was instructed using a Translanguaging approach that was based on DingTalk. This study aims to explore the potential of combining translanguaging with DingTalk, shedding light on its impact on Chinese undergraduates’ English grammar learning and solve the following research questions.

Research Question 1: Does the DingTalk based Translanguaging teaching method have a substantial impact on the English Grammar scores of Chinese students compared to those who do not get this intervention?

Research Question 2: What are the students’ perspectives on the Translanguaging method of instruction that is implemented through DingTalk?

Research place

This research was conducted at the prestigious Faculty of Foreign Languages in Harbin City, renowned for its exceptional coordination of language courses. The faculty was fully committed to offering students exceptional language learning options and ensuring equal access to top-notch language sessions. It is noteworthy that the investigation was conducted during the digital era, necessitating the integration of a state-of-the-art online learning medium into conventional instruction. As the field of language education evolved, the teaching of English grammar was inextricably linked to the utilisation of this advanced online network platform. Therefore, the researcher deliberately chose to perform the study in this place.

Research participants

A total of 58 Chinese sophomores who met the rigorous eligibility criteria were chosen to participate in the investigation. These diligent students, now in their sophomore year of their bachelor’s degree and pursuing English as their major, exemplify individuals committed to attaining excellence in the field of language. Their ages span from 19 to 20. The determination of the sample size was conducted by a rigorous power analysis, following the criteria of Williams ( 2007 ) which recommend a minimum of 15 persons per group for experimental research. Therefore, the sample size of 58 was deemed sufficient to ensure the experiment’s robustness. The principal analytical tool applied in this study was SPSS 27.0, selected for its capacity to guarantee the precision and correctness of the crucial analyses.

The study was conducted by two proficient professors who possess extensive expertise in the domain of English grammar. A sole instructor oversaw the control class and implemented a comprehensive strategy to teaching English. On the other hand, a separate teacher used DingTalk and the translanguaging pedagogical approach to lead the experimental class. In order to guarantee the fairness and trustworthiness of the evaluation process in the study, the English grammar assessments, which included both the pre-test and post-test, were meticulously graded by two seasoned raters. Both raters were given precise instructions to evaluate the samples using explicitly defined rubrics, guaranteeing that their assessments were consistent and unbiased.

Instrumentation

English proficiency test.

It was critical to ensure the experimental and control classes were equivalent before administering any treatments. This would allow the researcher to establish comparisons between the two classes and enhance their comprehension of the data for both classes. All study participants were required to take an English Proficiency Test (EPT) before the trial began to guarantee that their level of English proficiency was comparable to that of the control and experimental classes. By taking this approach, we hoped to ensure that the two classes of Chinese undergraduates would have comparable levels of English ability, strengthen our control over the experimental variables, and lessen the likelihood of interference.

Grammar examination

To conduct a quantitative analysis on the English grammar examination results, the statistical software SPSS 27.0 was employed. Each of the two classes—the experimental and the control—was given an English grammar examination to complete before the actual lesson began. Both cohorts of students are obligated to take an English grammar evaluation, which must be finished within a 30-min timeframe. After 12 weeks of instruction, students in both the experimental class (who were taught English grammar using the DingTalk-based Translanguaging method) and the control class (who were taught English grammar using the normal PPP strategy) took a post-test. The completion of the test was mandatory within a specified time frame of 30 min.

Semi-structured interview

During the qualitative phase, interviews were carried out with the students to have a better grasp of their perspectives and opinions on the utilisation of the DingTalk translanguaging teaching technique. The experimental class was the only one that participated in the interviews; for 11 weeks, they were exposed to the DingTalk-based Translanguaging method of instruction.

Data analysis

The quantitative portion of the study involved comparing pre- and post-test scores on an English grammar component using SPSS 27.0 and relevant statistical procedures. Careful attention to detail was maintained throughout the assessment. The goal of the study was to compare the two classes’ grammar learning results and determine whether the experimental class showed any significant progress. The qualitative phase involved a thorough study of the participant interview data using the method of theme analysis. A comprehensive analysis was conducted on the interview transcripts in order to extract themes that illuminate the participants’ perspectives and experiences on the novel pedagogical method known as DingTalk-based Translanguaging. The purpose of this mixed-methods study is to assess, in depth, how DingTalk-based Translanguaging has affected the grammar proficiency of Chinese undergraduates. Substantial empirical evidence and enhanced study findings were produced by combining quantitative data analysis with qualitative insights gained from interviews.

DingTalk platform and English grammar learning

Platform design features.

DingTalk, as a multifunctional platform, offers several design features that enhance its suitability for educational purposes, particularly in the context of language learning (Sun et al., 2024 ). The following features are particularly relevant to our study:

Multimodal communication

DingTalk supports text, voice, and video communication, which facilitates diverse modes of language practice and interaction among students and teachers (Sun et al., 2023 ). These resources can be leveraged to explain complex grammar rules visually and auditorily, catering to diverse learning preferences.

Real-time feedback mechanisms

The platform enables teachers to provide immediate feedback on student assignments and language use, which is essential for reinforcing correct grammar structures and addressing misconceptions promptly (Valentina et al., 2022 ; Sun et al., 2024 ). DingTalk integrates tools for formative assessment, allowing instructors to create quizzes, assignments, and polls related to English grammar. Immediate feedback provided through the platform helps students identify areas for improvement and consolidate their understanding (TAN and Boriboon, 2023 ).

Virtual classroom environment

Through its virtual classroom features, DingTalk simulates traditional classroom interactions in an online setting (Mu et al., 2022 ). This environment supports synchronous learning activities such as group discussions, collaborative projects, and peer reviews, fostering a dynamic learning community.

Personalised learning pathways and pedagogy

The platform supports personalised learning pathways through the integration of adaptive content delivery systems that respond to individual student progress and performance data. Machine learning algorithms play a crucial role by continuously analysing user interactions—such as time spent on tasks, quiz performance, and engagement with learning materials. These algorithms recommend personalised learning resources and activities that align with each student’s unique learning needs, thereby optimising learning outcomes. In terms of pedagogy, the platform’s design is grounded in constructivist learning theory, which emphasises the importance of active, student-centred learning. By tailoring content delivery, the platform encourages deeper engagement with the material and supports differentiated instruction, allowing educators to meet diverse student needs effectively (Gligorea et al., 2023 ).

Integration of translanguaging practices

By allowing seamless transitions between Chinese and English within instructional contexts, DingTalk supports translanguaging pedagogies, which have been shown to enhance language learning outcomes (Chen, 2023 ).

By leveraging these digital infrastructure components and functionalities, DingTalk not only enhances the accessibility and flexibility of English grammar learning but also promotes interactive and personalised learning experiences tailored to the needs of diverse student populations.

Pedagogical integration

The design of DingTalk aligns with contemporary pedagogical approaches that emphasise learner-centred, interactive, and technology-enhanced learning environments. Key considerations include:

User interface (UI) and user experience (UX)

The intuitive UI of DingTalk contributes to a positive user experience, promoting sustained engagement in language learning activities.

Data analytics and learning analytics

Leveraging data analytics within DingTalk allows educators to track student progress, identify learning patterns, and tailor instructional interventions to individual needs, thereby optimising the learning experience.

Conceptual model: enhancing English grammar learning through DingTalk platform

The conceptual model presented here aims to elucidate the mechanisms through which the design and functionalities of the DingTalk platform can enhance English grammar learning among Chinese undergraduate students. Drawing upon theoretical frameworks from educational technology and language acquisition, this model integrates key components essential for effective digital pedagogy.

Theoretical framework

The conceptual underpinnings of this model are rooted in several theoretical perspectives:

Technology-enhanced language learning

Leveraging digital platforms like DingTalk aligns with the principles of technology-enhanced language learning (TELL) (Barjesteh et al., 2022 ). According to this framework, integrating technology into language education fosters interactive and engaging learning environments (Huh and Lee, 2020 ).

Constructivist learning theory

The constructivist approach posits that learners actively construct knowledge and meaning through interaction with learning materials and peers (Mvududu and Thiel-Burgess, 2012 ). In the context of DingTalk, this theory supports the idea that students can collaboratively engage with English grammar concepts through interactive features such as group discussions and peer feedback.

Translanguaging pedagogy

Translanguaging theory, rooted in sociolinguistics, advocates for the strategic use of students’ native language resources to enhance understanding and learning in a second language (Burton and Rajendram, 2019 ). It posits that bilingual communication is not merely a matter of switching between languages but rather a complex, integrated use of linguistic resources (Otheguy et al., 2019 ). In the context of the DingTalk platform, translanguaging pedagogy is manifested through the facilitation of bilingual interactions and explanations. This approach supports deeper comprehension of English grammar structures by allowing students to draw on their entire linguistic repertoire, thereby promoting meaningful engagement and cognitive flexibility (Yao et al., 2024 ). This theoretical foundation underscores the transformative potential of translanguaging in educational settings, where it enriches language learning experiences by leveraging students’ linguistic diversity. By encouraging fluid movement between languages, DingTalk not only supports language acquisition but also nurtures a deeper understanding of grammar rules and structures through comparative linguistic analysis (Pan et al., 2023 ). Such pedagogical practices are essential for cultivating inclusive learning environments that recognise and value students’ linguistic identities, thus enhancing overall language proficiency and academic achievement.

Components of the model

Interactive learning features.

DingTalk’s design incorporates features such as real-time messaging, multimedia content sharing, and collaborative tools. These functionalities enable students to engage in interactive grammar exercises, receive immediate feedback from instructors, and participate in peer discussions, promoting active learning.

Pedagogical strategies

The model integrates pedagogical strategies tailored to enhance grammar learning. These include structured grammar tasks, differentiated instruction based on learner needs, and the integration of formative assessment practices facilitated by DingTalk’s assessment tools.

Linguistic and phonetic explanation in practice

Effective grammar learning through DingTalk is strengthened by the platform’s ability to directly integrate linguistic insights into morphology, syntax, and phonetics with practical application. For example, DingTalk’s interactive exercises allow students to dissect the morphological structure of English words, linking this analysis to real-time communication scenarios where syntax rules are reinforced through structured sentence formation tasks. Additionally, DingTalk’s voice messaging and pronunciation tools offer a seamless way to incorporate phonetic explanations. These features enable students to practice and receive immediate feedback on their pronunciation, ensuring they can articulate and recognise correct grammar structures audibly. The synchronisation of phonetic practice with grammar exercises within DingTalk creates a robust linguistic learning environment that supports both the theoretical and practical aspects of language acquisition.

Enhanced learning outcomes

By leveraging DingTalk’s immersive potential, students can experience enhanced learning outcomes in English grammar proficiency. The platform’s ability to facilitate continuous interaction and personalised feedback supports students in overcoming language barriers and mastering complex grammar concepts.

The results of this study demonstrate the revolutionary potential of DingTalk-based translanguaging in improving Chinese undergraduates’ proficiency with English grammar in the digital era.

Findings of research question 1

Both the pre-test and post-test evaluated the English grammar of Chinese students by two raters. A comparison was made between the experimental and control classes using the mean scores provided by the two raters. There are 20 points available for the cumulative score.

Comparison of experimental and control classes’ grammar pre-test results

The distribution of scores for students in the experimental class gradually expanded, forming a pattern that aligns with the characteristics of a normal distribution, as illustrated in Fig. 1 . With the exception of the experimental class, the majority of students in the control class scored in the 12–13 range too. Notably, 11 students in the control class outperformed 7 students in the experimental class in terms of scores. For scores below 12 points, the experimental class had 9 students, while the control class had 7 students, showing a comparable number of students. There was a small difference in the numbers for scores between 13 and 15 points; 8 students from the experimental class and 9 from the control class participated. The experimental class had 3 students with scores between 15 and 16 points, while the control group had no such students. 2 students in each group hit the 16-point mark or higher.

Distribution of grammar pre-test scores for the experimental and control classes.

Using SPSS 27.0, an independent sample t-test was conducted to assess and compare the Grammar scores of the experimental and control classes. The objective was to determine if there was a significant difference between the two classes so that the pre-test data could be further analysed.

The descriptive data for the pre-test outcomes of the grammar classes, both experimental and control, are shown in Table 1 . The average Grammar score for the control group was 12.9655 and for the experimental group of 29 students it was 13.1724. A gap of 0.2069 points separated the experimental class from the control class in terms of performance. The results show that both classes had quite comparable Grammar scores. An independent samples t-test, as shown in Table 2 , can be used to statistically evaluate if the experimental and control classes differ significantly in their pre-test Grammar scores.

Presented in Table 2 are the findings of the independent sample testing that compared the Grammar scores of the experimental and control courses. The results of Levene’s test for variance equality and the corresponding statistical significance (Sig.) for both classes are presented in this table. A pre-test variance of 0.160 shows that there is no statistically significant difference between the control and experimental classes. The significance level (Sig 2-tailed) is 0.664, which is greater than the 0.05 threshold, further indicating that there is no statistically significant difference in the Grammar pre-test scores between these classes.

Comparison of experimental and control classes’ grammar post-test results

As seen in Fig. 2 , the post-test results for grammar show a notable distinction between the control and experimental classes. The experimental class routinely beat the control class on grammar, even though both classes got results over ten. In particular, just 10 students in the control group achieved a score of 13 or above, but 22 students in the experimental class did so. Out of the 29 students in the control class, 26 got scores below 14. Two students in each class scored above 16 points, which is noteworthy.

Distribution of grammar post-test scores for the experimental and control classes.

For statistical analysis, the experimental and control classes’ Grammar scores were subjected to an independent sample t-test in SPSS 27.0. This study aimed to determine if there was a statistically significant difference between the two courses so that further investigation of the post-test data could be conducted.

Results from the post-test on grammar for both the experimental and control classes are detailed in Table 3 . The control class averaged 12.9828 points whereas the experimental class managed 14.1207. These results show that compared to the control class, the experimental class achieved an average of 1.1379 points more on the grammar examination. The two classes clearly have different grammar scores. As seen in Table 4 , an independent sample t-test was performed to ascertain the statistical significance of this difference.

Fig. 4 displays the outcomes of the independent sample test conducted on the grammar scores of both the experimental and control classes. Both variables are statistically significant, as indicated by Levene’s test for variance equality (Table 4 ). The two classes’ post-test variances are identical, according to the computed value of 0.490. With a two-tailed significance (Sig 2-tailed), the statistical analysis shows that the experimental and control classes differ significantly on the grammar post-test. Compared to the previously set significance level of 0.05, the post-test probability of significance is lower at 0.009.

Comparison of the experimental class’s grammar pre- and post-test results

Results for the experimental class’s Grammar exam both before and after the intervention follow a normal distribution, as shown in Fig. 3 . In the beginning, most students got 12 or 13 on the pretest. Likewise, the majority of students’ post-test results fell somewhere in this range as well. Students’ general competency in the grammar course was found to have improved, according to the column distribution in Fig. 3 .

Distribution of experimental class grammar pre- and post-test scores.

A paired sample t-test is used to evaluate the experimental class’s pre- and post-test results in order to find out if there has been a substantial improvement. Tables 5 and 6 display the experimental outcomes.

The descriptive statistics for the experimental class’s pre- and post-test scores on the Grammar exam are shown in Table 5 . From a pre-test average of 13.1724 to a post-test average of 14.1207, the experimental class’s average Grammar score increased significantly after the intervention. After the intervention, the mean score improved by 0.9483 points.

Quantitative data from the Grammar examination reveals a significant improvement in performance compared to the pre- and post-test outcomes. Plus, at the 0.05 level of significance, the paired sample t -test on the pre- and post-test results of the experimental class produced a sig value of 0.000, indicating statistical significance. This changes to be obvious when the significance threshold for grammar scores is set to 0.05. The experimental class showed a statistically significant improvement of 0.9483 points between the pre- and post-tests in the grammar area. When compared to the pre-test numbers, the post-test results show a significantly higher level of achievement. The results show that the DingTalk-based Translanguaging approach to teaching English grammar could help students understand and use the language better.

Comparison of the control class’s grammar pre- and post-test results

The control class’s grammar scores did not change significantly between the pre- and post-tests, as shown in Fig. 4 . After the test, the scores seemed to be more evenly distributed. Initially, a considerable number of students achieved pre-test scores ranging from 12 to 13 points. On the other hand, most students averaged 13–14 on the post-test. The grammar performance of the control class did somewhat better on average.

Column distribution of the control class’s grammar pre- and post-test scores.

The study used a paired sample t-test to look at how the control class’s grammar exam results changed between the first and second assessments. The results are shown in Tables 7 and 8 , respectively.

The descriptive statistics for the pre- and post-test results in Grammar for the control class are shown in Table 7 . There was a small improvement of 0.017 points between the pre- and post-tests, with mean scores of 12.9655 and 12.9828, respectively. There does not seem to be a statistically significant numerical difference between these scores, according to the results in Table 8 .

Table 8 displays the outcomes of the control class’s paired sample t-test. Over the generally recognised threshold of 0.05, the calculated significance value was 0.769. At the 0.05 level of significance, there is no statistically significant change in the control class’s grammar scores between the pre- and post-tests.

It can be concluded from the findings of Research Question 1 that using the DingTalk-based Translanguaging teaching technique has the potential to significantly raise students’ scores on grammar tests. The control class of students, on the other hand, showed no discernible gain in applying their grammar knowledge after completing their whole English education. After twelve weeks of training, there was no appreciable improvement in the Grammar scores of the students in the control class. This result implies that there hasn’t been any discernible progress in their ability to use language in this area. This study shows that students’ grammar scores and general language skills significantly improve when the DingTalk-based Translanguaging teaching approach is used.

Findings of research question 2

After the interview, it may be noted that students’ perspectives regarding English grammar study have altered. It elaborates on the implications of the DingTalk-based Translanguaging teaching approach for students. Student C and D stated that the instructor’s explanations of major sentence patterns in class and the DingTalk homework corrections had substantially increased the students’ grammatical skills. The teacher will employ the utilisation of Chinese language for illuminating grammar complications, while English will be applied for supplemental education. Students are capable of not only learning grammar ideas with clarity, but also refining their English language abilities through practical application. The practice of the instructor revising students’ assignments through DingTalk after class, according to students A and B, had the largest impact on the grammar scores. In conclusion, DingTalk-based Translanguaging method of instruction can contribute to students’ grammar skills.

“I think it is very helpful…the teacher timely modified it through the DingTalk platform, so that I can immediately realize my grammatical structure problems, quickly correct them….”

(SA.19.9.2022.L1-L4)

“…The teacher’s real-time modification made me improve a lot.”

(SB.19.9.2022.L2)

“The teacher will give us some standard sentence patterns and expressions in Chinese when helping us sort out problems in each class…I think this method is very efficient….”

(SC.19.9.2022.L4-L5)

“…Through the teacher’s translanguaging demonstration and after class correction, I can more accurately capture how to write correct sentences to complete the writing task.”

(SD.19.9.2022.L3-L5)

Both Student A and Student B held the viewpoint that the utilisation of DingTalk had resulted in enhanced efficiency in terms of professors’ revision and provision of feedback on students’ home assignments. When engaging in classroom discussions with students regarding English language acquisition, it was often convenient to overlook the importance of grammatical accuracy and the appropriate usage of language. As a result, it became imperative to engage in direct contact and make modifications to the statements on DingTalk following the conclusion of the lesson. In addition, Student C highlighted that the use of DingTalk has the potential to enhance the efficacy of teachers in rectifying grammatical faults in students’ post-class exercises. Simultaneously, Student D underscored the efficacy of employing the group translanguaging approach and receiving teacher feedback post-class as means to acquire certain grammatical patterns and sentence structures, leading to substantial advancements.

The Translanguaging teaching method, which use DingTalk as its platform, has demonstrated its potential as a successful tool for supporting students in rectifying grammatical errors and acquiring specialised sentence patterns and grammatical structures necessary for English language acquisition. The utilisation of DingTalk facilitates users in acquiring knowledge while concurrently optimising their time management. For example, the utilisation of DingTalk to facilitate post-class engagement and communication between students and teachers can effectively aid students in promptly comprehending grammatical structure issues. Through DingTalk, teachers can promptly rectify these issues, thereby enabling students to save time on error correction and enhance their learning efficiency. The blended education methodology, facilitated by the DingTalk-based Translanguaging teaching method, enables students to engage in both online and offline interactions and communication. Consequently, the students’ proficiency in acquiring grammatical knowledge is enhanced. The precision of grammatical structure application is a vital metric of English language proficiency, particularly in the expression of content. In order to successfully convey information, it is essential to present content using grammatically sound frameworks. Consequently, the implementation of the DingTalk-based Translanguaging teaching approach has resulted in an enhancement in the precision with which students employ grammatical structures. The integrated teaching strategy is particularly well-suited for the contemporary educational landscape and the learning requirements of students.

This study demonstrates the ability of combining Translanguaging Pedagogy with the DingTalk platform to improve English grammar proficiency in Chinese undergraduate students. This part examines the consequences of these findings, compares them to previous research, and emphasises the practical and theoretical contributions of this study.

Enhancement of English grammar skills

The findings demonstrate a notable enhancement in the English grammar proficiency of students in the experimental class who actively participated in the Translanguaging approach through the utilisation of DingTalk. The rise in post-test results in comparison to pre-test scores emphasises the effectiveness of this method. The experimental class demonstrated significant progress, as seen by their average post-test score of 14.1207, compared to the control class’s score of 12.9828. This suggests that including Mandarin as a cognitive bridge enhanced the comprehension and utilisation of English grammatical structures.

These findings are consistent with other research that has highlighted the advantages of translanguaging in the field of language teaching. For instance, Fan ( 2022 ) found that pedagogical translanguaging significantly improved grammar learning among Chinese primary school students. Similarly, Zhang and Chan ( 2022 ) demonstrated the effectiveness of translanguaging in a trilingual context, showing enhanced comprehension and application of English grammar rules among EFL learners in Xinjiang.

Cognitive and emotional impact

Qualitative data from student interviews further elucidate the cognitive and emotional benefits of the DingTalk-based Translanguaging approach. Students reported that the method helped clarify complex grammatical concepts, with Mandarin explanations aiding their understanding and retention of English grammar. The immediate feedback and corrections provided via DingTalk were particularly appreciated, as they allowed students to promptly address and rectify errors, leading to enhanced learning efficiency.

These qualitative insights are consistent with Vygotsky and Cole ( 1978 ) sociocultural theory, which posits that learning is a socially mediated process. The DingTalk platform enabled a collaborative learning environment where students could receive timely support from their instructor, fostering a supportive and interactive educational experience. This approach also aligns with theories of formative assessment, where continuous feedback plays a crucial role in student learning and motivation (Black and Wiliam, 1998 ).

The role of digital resources and extramural english

The use of digital resources such as DingTalk extends learning beyond the classroom, aligning with the concept of extramural English, where language learning occurs in informal settings. This study highlights the importance of digital tools in facilitating language acquisition outside traditional classroom environments. Digital resources can provide learners with constant access to language practice and feedback, thereby enhancing their proficiency.

Lin et al. ( 2020 ) demonstrated that contextual gaming approaches could significantly improve EFL students’ grammar learning performance, suggesting that engaging and interactive digital tools can motivate learners and enhance their grammatical skills. Similarly, Calafato and Clausen ( 2024 ) found that vocabulary learning strategies in extramural English gaming positively impacted vocabulary knowledge, underscoring the potential of digital gaming as a valuable educational tool. Rød and Calafato ( 2023 ) also highlighted the relationship between extramural English, self-efficacy, and learning outcomes, indicating that informal learning through digital platforms can significantly influence students’ language proficiency and confidence.

Grammar learning and instruction

Grammar teaching in this study refers to the explicit instruction of grammatical rules and structures, coupled with practical application through exercises and immediate feedback. Previous research has shown mixed attitudes towards grammar instruction. Frøisland et al. ( 2023 ) investigated the impact of age and gender on attitudes towards explicit grammar instruction and found varying levels of acceptance and perceived efficacy across different demographic classes.

Additionally, studies like those by Sobkowiak ( 2022 ) and Almuafa ( 2024 ) emphasise the role of translanguaging practices in grammar learning. Sobkowiak ( 2022 ) highlighted the benefits of translanguaging in Polish EFL classrooms, while Almuafa ( 2024 )‘s work demonstrated how Arabic grammar knowledge could be leveraged to facilitate English grammar comprehension.

Furthermore, Hirosh and Degani ( 2018 ) reviewed the direct and indirect effects of multilingualism on novel language learning, suggesting that multilingual individuals may benefit more from explicit grammar instruction due to their enhanced metalinguistic awareness. This aligns with the findings of the current study, where the use of Mandarin to explain English grammar concepts facilitated a better understanding and application of grammar among Chinese students.

Practical implications

The practical implications of this study are significant, particularly in the context of the global shift towards remote education. The findings suggest that integrating digital platforms like DingTalk with translanguaging pedagogy can effectively enhance language learning outcomes. Educators can adopt similar approaches to leverage students’ linguistic resources, providing explanations in their native language to clarify difficult concepts and using digital tools for continuous assessment and feedback.

Furthermore, the positive reception of the DingTalk-based approach by students indicates its potential for wider application. Institutions can consider implementing similar strategies to support language acquisition, especially in multilingual classrooms where students can benefit from using their full linguistic repertoire to learn a new language.

Theoretical contributions

This study contributes to the existing body of literature on translanguaging and digital learning platforms by providing empirical evidence of their combined effectiveness in language education. It bridges the gap between translanguaging theory and practical application, demonstrating how digital tools can facilitate the implementation of translanguaging pedagogy. The findings support the view that translanguaging is not only a theoretical construct but also a practical approach that can significantly enhance language learning outcomes in digital and multilingual contexts.

Additionally, this research highlights the importance of considering students’ linguistic backgrounds in language instruction, reinforcing the idea that leveraging students’ native languages can be a powerful tool in language education. This approach challenges traditional monolingual methods and supports a more inclusive and effective strategy for teaching English as a foreign language.

MacSwan ( 2022 ) and Seals et al. ( 2020 ) further underscore the value of translanguaging practices, with MacSwan ( 2022 ) exploring codeswitching and bilingual grammar, and Seals et al. ( 2020 ) creating translingual teaching resources based on translanguaging grammar rules. These contributions highlight the theoretical foundation for the practical applications observed in this study.

In conclusion, the integration of Translanguaging Pedagogy with the DingTalk platform presents a promising approach for enhancing English grammar skills among Chinese undergraduates. Future research could further explore the long-term impacts of such digital-translanguaging methods and their applicability in diverse educational contexts.

Challenges and future directions

While the integration of Translanguaging Pedagogy with the DingTalk platform has shown promising results in enhancing English grammar skills among Chinese undergraduate students, several challenges and avenues for future research and implementation should be considered.

Technological infrastructure

One significant challenge is the variability in technological infrastructure and access among students, particularly in diverse geographical or socio-economic contexts. Ensuring equitable access to digital platforms like DingTalk for all students remains a priority to avoid exacerbating educational inequalities.

Pedagogical Training

Effective implementation of Translanguaging Pedagogy requires pedagogical training for instructors. Educators need support and professional development opportunities to effectively integrate translanguaging practices into their teaching repertoire and utilise digital tools optimally.

Linguistic diversity

Managing linguistic diversity within the classroom presents another challenge. While translanguaging can leverage students’ linguistic resources, educators must navigate different language backgrounds sensitively to promote inclusive learning environments without marginalising any linguistic group.

Assessment and evaluation

Developing valid and reliable assessments that align with translanguaging practices and digital learning environments poses a challenge. Traditional assessment methods may need adaptation to accurately measure the learning outcomes achieved through translanguaging and digital tools.

Future directions

Longitudinal studies.

Future research should conduct longitudinal studies to explore the sustained impact of Translanguaging Pedagogy integrated with digital platforms on English language proficiency. Understanding the long-term effects can provide insights into the durability of learning outcomes and student retention of language skills.

Comparative studies

Comparative studies across different educational settings and student demographics would enrich our understanding of the generalisability and effectiveness of translanguaging approaches. Investigating variations in linguistic backgrounds, educational contexts, and technological infrastructures can inform tailored instructional strategies.

Innovative technologies

Exploring and integrating emerging technologies beyond current platforms like DingTalk could open new avenues for enhancing language learning. Virtual reality, artificial intelligence, and adaptive learning technologies offer promising tools for personalised and interactive language instruction.

Teacher education programs

Incorporating translanguaging strategies into teacher education programs can better prepare future educators to leverage students’ linguistic repertoires effectively. Providing ongoing professional development opportunities can enhance educators’ confidence and competence in implementing translanguaging practices.

Policy and curriculum development

Advocating for policies that support translanguaging pedagogies and digital literacy in language education can facilitate systemic changes. Curriculum developers should integrate translanguaging principles into language curricula to reflect contemporary pedagogical approaches.

Ethical considerations

Ethical use of technology.

Ensuring the ethical use of digital platforms in education is crucial. Protecting student data privacy, promoting digital citizenship, and mitigating potential risks associated with online learning environments should be prioritised in translanguaging pedagogy implementations.

Cultural sensitivity

Maintaining cultural sensitivity and respect for diverse linguistic backgrounds is essential in translanguaging practices. Educators must foster an inclusive classroom environment where all students feel valued and empowered to engage in language learning.

Addressing these challenges and pursuing these future directions will advance the field of translanguaging pedagogy integrated with digital platforms. By harnessing the potential of students’ linguistic resources and leveraging innovative technologies, educators can foster more effective and inclusive language learning environments. This research contributes to the ongoing dialogue on enhancing language education through thoughtful integration of translanguaging and digital learning strategies.

Limitations

While this study provides valuable insights into the effectiveness of DingTalk-based translanguaging pedagogy in enhancing English grammar learning among Chinese undergraduate students, several limitations and challenges should be acknowledged.

Limitations of the present study

Quasi-experimental design.

The study utilised a quasi-experimental design to compare outcomes between experimental and control classes. While this design is appropriate for educational research, it may not fully account for all variables that could influence the results, such as individual learner differences and external factors beyond the researcher’s control.

Sample size and generalisability

The study’s sample size of 58 participants, though adequate for the scope of this research, may limit the generalisability of the findings to larger populations or different educational contexts. Future studies with larger and more diverse samples could provide broader insights.

Duration of intervention

The 12-week intervention period, while sufficient to observe immediate effects, may not capture long-term impacts on language proficiency. Further longitudinal studies could explore the sustainability of gains achieved through DingTalk-based translanguaging pedagogy.

Measurement of outcomes

The study focused primarily on English grammar skills as assessed by pre-tests and post-tests. While grammar proficiency is crucial, future research could expand to include broader language competencies and communicative abilities influenced by translanguaging practices.

Challenges of translanguaging pedagogy with DingTalk

Technological constraints.

Despite DingTalk’s robust platform, technical issues such as connectivity problems and interface usability could disrupt the learning process. Variations in students’ access to technology and digital literacy skills may also impact engagement and learning outcomes.

Cultural and contextual factors

The effectiveness of translanguaging pedagogy through DingTalk may vary across different cultural and linguistic contexts. Cultural attitudes towards language use in education, as well as variations in linguistic backgrounds among students, could influence the implementation and outcomes of this pedagogical approach.

Dependency on technology

Relying on digital platforms like DingTalk for language learning may introduce dependencies and challenges associated with technological disruptions. Educators and institutions must navigate these challenges to ensure consistent and effective implementation of translanguaging strategies.

Acknowledging these limitations and challenges is essential for refining the implementation of translanguaging pedagogy with the immersive potential of DingTalk. Future research endeavours should address these issues to enhance the applicability, effectiveness, and sustainability of integrating digital tools in language education contexts.

In the digital stage, which has brought about significant changes to society, this study aims to investigate the incorporation of Translanguaging Pedagogy into the digital platform DingTalk, which has emerged as a crucial tool for educational purposes. This study has shed light on the potential for transformation in the English grammar learning of Chinese undergraduate students by utilising their multilingual repertoires within a virtual learning setting. The conclusion of this inquiry provides significant revelations on the convergence of inventive teaching methods, technological advancements, and the process of acquiring language skills. The results clearly demonstrate that the integration of Translanguaging Pedagogy via DingTalk yields concrete advantages. The results of the quantitative portion of the study revealed noteworthy enhancements in English grammar competency. Additionally, the qualitative analysis offered a comprehensive comprehension of the cognitive and affective aspects of the learning process. The incorporation of Mandarin as a cognitive intermediary simplified the process of understanding and exchanging information, hence enhancing the overall acquisition of language skills.

The research holds broader implications that transcend the narrow domain of English grammar acquisition among Chinese undergraduates. The statement highlights the significant impact that Translanguaging Pedagogy can have, surpassing conventional monolingual methods and adjusting to the requirements of the digital era. The dynamic platform provided by DingTalk offers educators a versatile tool to facilitate interactive and captivating learning experiences that align with the socioconstructivist principles of language acquisition. This study serves as a model for instructors who are seeking creative approaches to maintain successful language training, even when faced with the limitations of distant learning. As we go on our journey, this study serves as a catalyst for additional investigation and advancement of Translanguaging Pedagogy in the digital realm. The inherent longitudinality of language acquisition necessitates the continual adoption and ongoing innovation of instructional approaches in order to optimise their lasting effects. This study encourages educators to reconsider language training in a broader sense, by embracing and valuing linguistic diversity and fostering an inclusive atmosphere that acknowledges and appreciates students’ language identities.

The convergence of Translanguaging Pedagogy and DingTalk in a globally interconnected society, characterised by technological advancements and unanticipated obstacles, signifies the emergence of a novel epoch in the field of language instruction. The utilisation of this approach effectively eliminates the obstacles posed by language boundaries, harnesses the linguistic abilities possessed by students, and enables learners to effectively navigate the intricate dynamics of an increasingly interconnected global society. The study highlights a significant connection between pedagogy and technology, revealing a transformative direction that emphasises the importance of cultural diversity, the development of effective communication skills, and the provision of necessary resources for Chinese undergraduates and learners globally to succeed in a multilingual environment. Within the realm of education, this research serves as a dynamic element of novelty, interconnecting theoretical frameworks, technological advancements, and practical applications. As we draw this investigation to a close, we enter a subsequent level of examination, equipped with discernments and motivation to mould a forthcoming era in which Translanguaging Pedagogy in the digital realm emerges as a lasting foundation of language teaching.

Data availability

The data is not publicly available to protect the participants’ privacy and confidentiality. The data of this study will be available upon request to the corresponding author at [email protected].

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Acknowledgements

2023 “World Language and Culture Research” Project (WYZL2023HL0004) “Research on the Impact of Collaborative Online International Learning (COIL) on the Cross-cultural Competence of English Majors”, China Center for Language Planning and Policy Studies.

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Sun, L. Translanguaging pedagogy on the digital stage: exploring Chinese undergraduates’ English grammar learning through DingTalk platform. Humanit Soc Sci Commun 11 , 1245 (2024). https://doi.org/10.1057/s41599-024-03771-2

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Physical activity from the perspective of older adults: a convergent mixed-method study

• Anna Nilstomt   ORCID: orcid.org/0009-0000-9061-7669 1 ,
• Johanna Gustavsson 2 , 3 ,
• Linda Beckman 4 , 5 ,
• Charlotte Bäccman 1 ,
• Finn Nilson 2 , 3 ,
• Stefan Wagnsson 6 &
• Erik Wästlund 1  

BMC Geriatrics volume  24 , Article number:  768 ( 2024 ) Cite this article

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Metrics details

Older adults are insufficiently physically active, despite its importance for healthy aging. To develop appropriate physical activity interventions, it is necessary to understand their physical activity. This study applies a theoretical perspective, the COM-B model, and a mixed-method design to examine what influences older adults’ physical activity levels with three questions: (1) What individual and external factors predict older adults’ physical activity levels? (2) What do older adults perceive as influencing their levels of physical activity? (3) To what extent do the quantitative results on older adults’ physical activity levels agree and disagree with the qualitative findings on older adults’ physical activity levels?

A convergent mixed-method design was used with questionnaire ( n  = 334) and interview ( n  = 14) data from adults 65 years and older. Regression analyses were used for quantitative measurements: physical activity, age, subjective socioeconomic status, health status, capability, opportunity, motivation, and depression. Content analysis was applied to the qualitative data. The two forms of data were then integrated to provide greater insights than would be obtained by either dataset separately.

The regression analyses showed that previous physical activity, current motivation, health status, and age significantly predicted older adults’ physical activity levels. The content analysis revealed that participants addressed all subcomponents of the COM-B model, indicating its pertinence in understanding how older adults discuss their current physical activity levels. The integrated findings showed convergent and divergent results. Overall results indicated that previous physical activity engagement, present motivation, capability, and opportunity influenced older adults’ physical activity levels.

Conclusions

This study is the first to use this mixed-methods design to examine factors influencing physical activity levels among older adults living in rental apartments with community hosts. The integrated result reveals convergence for findings on motivation and physical capability but divergence on psychological capability, opportunity, and previous physical activity. The findings underscore a complex interplay of factors influencing older adults’ physical activity levels and indicate relevance for the COM-B model. The results can guide future research on theoretically informed interventions to promote physical activity and healthy aging. Future research should clarify the role of opportunity for older adults’ physical activity.

Peer Review reports

Physical activity (PA) is vital for healthy aging as it promotes mobility and independence [ 1 ] and reduces declines in health and functioning [ 2 ]. The World Health Organization (3, p. vii) defines PA as “any bodily movement produced by skeletal muscles that requires energy expenditure”, which implies various activities, such as leisure-time activities like walking and gardening, transportation by bike, household chores, occupational tasks if the person still works, or planned exercise.

Adults aged 65 years or over are recommended to engage in at least 150 min of aerobic activities per week, perform muscle-strengthening exercises twice a week, and practice their balance three days a week [ 3 ], or at a minimum be as active as their abilities and conditions allow [ 3 , 4 ]. Previous research has shown that older adults have a more flexible attitude towards PA, ranging from any activity moving the body and mind to strictly planned activities outside the home [ 5 ]. In Sweden, fewer than 60 percent of older adults aged 65–84 meet the aerobic recommendations [ 6 ], meaning many older adults are insufficiently active. Also, the oldest adults are less likely to engage in PA than those of younger ages [ 7 ], which raises the question of what influences older adults’ PA and how it can be promoted in society? The answer to these questions could improve healthy aging and support the Agenda 2030 sustainable development goal of good health and well-being [ 8 ].

Previous research has identified several individual and external factors that influence older adults´ levels of PA, for example, being physically active earlier in life [ 9 ], wanting to continue with PA [ 10 ], and perceiving their health status as good [ 11 , 12 ]. Also, participating in group activities [ 5 ], having a supportive social network [ 13 ], access to facilities, and favorable weather conditions seem to promote older adults’ level of PA [ 13 ]. Factors that seem to decrease the likelihood of PA engagement among older adults are, for example, physical deterioration of the body [ 14 ], pain, fatigue, fear of falling [ 15 ], low self-confidence [ 14 ], low subjective socioeconomic status (SES) [ 16 , 17 ] and depressive symptoms [ 18 ]. However, to gain a deeper understanding of older adults’ PA engagement, applying a theoretical perspective, such as the COM-B model, is valuable [ 19 ] and increases the chances of designing effective interventions [ 20 ]. Thus, the different individual and external factors linked to older adults’ PA levels can be understood through the COM-B model [ 19 ].

The COM-B model refers to three components – capability, opportunity, and motivation – that must be present to generate a specific behavior, such as PA [ 19 , 21 ]. Capability and motivation relate to intrinsic factors, whilst opportunity relates to external factors. More specifically, capability concerns the individual’s physical and psychological capacity to engage in a behavior, for example, skills, knowledge, and thought processes; opportunity can be social or physical possibilities that allow a behavior to occur; and motivation is a mental process that can be either reflective or emotional and that energizes the targeted behavior as well as including goal-setting, decision-making, habits, and emotional responses [ 21 ]. Each component can directly impact behavior, and opportunity and capability can also indirectly impact behavior through motivation. The behavior can also impact capability, opportunity, and motivation [ 21 ].

The COM-B model is commonly related to PA [ 22 , 23 , 24 ] but has only to a limited degree been used for understanding older adults’ levels of PA. A review by Meredith et al. [ 25 ], which included qualitative studies on older adults, mapped the results to the COM-B model to better understand what influences older adults’ PA engagement. This secondary analysis revealed that all the COM-B components interacted and affected older adults’ PA engagement and that opportunity was the most frequently identified component [ 25 ]. Although reviews can be valuable for summarizing research results, they rely solely on secondary data instead of primary data, which may limit understanding nuances and the ability to establish relationships in the data [ 23 ]. Therefore, it is important to conduct more studies on the levels of PA in older adults, using the COM-B model. These studies should preferably use a mixed-methods approach since the convergence of two forms of data brings greater insights than would be obtained by either type of data separately [ 26 ]. Qualitative methods can provide deeper insights and a more nuanced view of a phenomenon, while quantitative methods enable statistical generalizability [ 27 ].

By understanding what influences older adults’ PA in relation to the COM-B model and two datasets, we can better understand the phenomenon of PA in this population. The COM-B model is useful for analyzing behavior as it is part of the theoretical framework for behavior change interventions [ 19 ]. Thus, we can acquire valuable knowledge that can be used to create and improve interventions that support older adults’ PA engagement. Ultimately, this can contribute to healthier aging.

This study aimed to examine what influences older adults’ levels of PA and how it could be understood in terms of the COM-B model and by means of a convergent mixed-method design. It is a design where quantitative and qualitative data are collected approximately simultaneously and analyzed separately before integration [ 26 ]. In this study, the two datasets were equally emphasized [ 26 ]. This study used quantitative observational data from a cross-sectional study to understand older adults’ PA levels through the COM-B model. The qualitative data from interviews explored PA among older adults. The reason for collecting both quantitative and qualitative data was to converge the two forms of data to bring greater insights into the research problem than would be obtained by either type of data separately. Qualitative methods can provide deeper insights and a more nuanced view of a phenomenon, while quantitative methods enable statistical generalizability to a greater degree [ 27 ]. A pragmatic approach was applied, as this allows using both quantitative and qualitative research methods to collect the data needed to address the study’s research questions [ 26 ]. The National Research Ethics Committee approved the study before recruitment.

The aim was operationalized into three research questions:

What individual and external factors predict older adults’ PA levels?

What do older adults perceive as influencing their levels of PA?

To what extent do the quantitative results on older adults’ PA levels agree and disagree with the qualitative findings on older adults’ PA levels?

Study setting

The recruited participants were 65 years or older and lived in rental apartments owned by the municipality. The apartments are specifically designed to provide independent living facilities for older adults and include a so-called ‘Trygghetsvärd’ (community host) who functions as a social support. Aside from arranging social activities like café meetings and walking groups, the community hosts clean the common areas and assist with simple tasks in the residents’ homes, such as changing light bulbs. The fact that the participants lived in rented apartments means that they differ from the Swedish average. In Sweden, owning a house is the most common housing arrangement, followed by renting an apartment and then owning an apartment [ 28 ]. The difference between the study group and the general population should be considered when interpreting the results.

The quantitative method

A cross-sectional study addressed the first research question: What individual and external factors predict older adults’ PA levels?

Participants

The sample consisted of 334 community-dwelling older adults (71.3% women) in a middle-sized town in Sweden. The majority of participants (65.2%) lived alone. Age was measured as a five-year categorical variable ranging from 65–70 years to 95–100 years. The mode age category was 76–80 years. Almost half of all participants (49.4%) could be classified as sufficiently active, 17.4 percent as moderately active, and 33.3 percent as insufficiently active, according to the Godin-Shepard leisure-time exercise questionnaire [ 29 ]. The participants reported a low degree of depressive symptoms ( M  = 3.98, SD  = 3.84, Range = 0–19). However, 24.4 percent of the sample reported six or more symptoms of depression, which, according to the cut-off score of the Geriatric Depression Scale [ 30 ], might indicate depression.

Procedure and response rate

A pilot survey was tested among a sample of self-recruited older adults ( n  = 6) from the local residential areas. This resulted in a shortened survey that was distributed to a total of 700 older adults in February 2022. Participants returned the survey either by pre-paid postage or through a sealed envelope that the community hosts mailed. One reminder was sent to their postbox and notes were posted on one occasion at the apartment complexes’ entrance to boost the response rate. Upon request from the older adults, the community hosts were available to assist with the questionnaires. This approach aimed to minimize dropouts caused, for example, by visual impairments. The community hosts assisted a total of three older adults. In total, 334 older adults provided informed consent to participate, resulting in a response rate of 48 percent.

Instruments

The Godin-Shepard leisure-time exercise questionnaire (GSLTEQ), adjusted by Godin [ 29 ], assessed the individual’s weekly PA. The participants self-reported on three items their frequencies of at least 15 min of mild, moderate, and strenuous PA per week on an eight-point scale ranging from 0 to 7 days . The standard procedure described by Godin [ 29 ] was used to calculate the total PA score and classify individuals as either sufficiently, moderately, or insufficiently active. The total PA scale index ranges from 0–119, where a higher score indicates an increased PA level. The Cronbach’s alpha was 0.65, and the inter-item correlation was 0.39 in this study.

An adjusted version of the COM-B instrument, constructed by Bäccman, Bergkvist, and Wästlund [ 31 ], was used to assess the COM-B model [ 21 ]. It contained 12 items designed to measure capability (four items, such as “I know why it is important to be physically active”), opportunity (four items; for example, “I have access to facilities and equipment required to be physically active”), and motivation (four items, such as “I really want to be physically active”). Each item was answered on a five-point scale ranging from strongly disagree to strongly agree. The mean for each index, capability, opportunity, and motivation was used for the analysis. In this study, Cronbach’s alphas for the capability, opportunity, and motivation index were 0.80, 0.78, and 0.92, respectively.

Health-related quality of life was assessed by EQ-5D-3L [ 32 ]. The participants classified their health on five dimensions (mobility, self-care, usual activities, pain/discomfort, anxiety/depression) and three severity levels ( no, moderate, or severe problems ). The health levels were transformed into an index value by applying the Swedish value set by Burström et al. [ 33 ]. The index values ranged between 0.3402 and 0.9694, where the former represents the lowest self-rated health-related quality of life and the latter the highest. In this study, the Cronbach’s alpha was 0.60, with an inter-item correlation of 0.24.

Symptoms of depression were assessed with the Swedish version of the Geriatric Depression Scale (GDS) developed by Gottfries, Noltorp, and Nørgaard [ 30 ]. The scale is a screening instrument consisting of 20 items to be answered with a yes or no . Five items were reversed before summing a total score that ranged from 0–20, with a higher score indicating more symptoms of depression. A score of six or more indicates that depression might be suspected, whereas a score of five or lower suggests that depression is unlikely [ 30 ]. The Cronbach’s alpha was 0.86 in this study.

The individual’s perception of their subjective socioeconomic status (subjective SES) was assessed with a single item, asking the participants to rank their socioeconomic status compared to other older adults in the society on a 10-point scale ranging from lowest to highest [ 34 ].

Previous PA experiences were assessed with a single item, asking the person to “Rate the average degree to which you have been physically active (exercised/worked out) in your life up until today.” The answers were given on a five-point scale ranging from a very high degree of physical activity to a very low degree of physical activity .

The questionnaire also included demographic information (age, gender, and cohabitation). Age was measured as a categorical variable with an interval of five years (e.g., 65–70). The lowest age category was 65–70 years and the highest was 106 years and older .

Data analysis

The data were analyzed using descriptive statistics and multiple regression. The data did not indicate any outliers. Only two cases had an age of 96 years or above. Therefore, the two age categories 91–95 and 96–100 were merged into an age category of 91–100.

A hierarchical regression analysis was applied using bootstrapping to obviate skewed data. First, a hierarchical regression with three steps was completed, using forced entry within each step. The reason for this procedure was to test the COM-B model initially before adding characteristics. In the first step, opportunity and capability were included, as these variables are theorized to generate behavior and contribute to motivation. In the second step, motivation was added. The additional individual factors (previous PA, EQ-5D-3L, subjective SES, age, and GDS) were included in the third step. After that, a trimmed regression model with forced entry was completed, containing only the significant variables retained from the hierarchical regression. For both regression models, GSLTEQ was the dependent variable. The significance level was kept at an alpha level of 0.05 and cases were excluded listwise. Statistical analysis was completed with SPSS version 28.

The qualitative method

Semi-structured interviews addressed the second research question: What do older adults perceive as influencing their PA levels?

The sample ( N  = 14) is a subset of the quantitative sample. Among the participants in the qualitative sample, seven were women, 12 lived alone, and nine reported age 80 or younger. The mode age category was 86–90 years. Among the participants, four were classified as sufficiently active, four were deemed moderately active, and six were classified as insufficiently active.

Researcher description

The first and second authors conducted the interviews ( n  = 10 and n  = 4, respectively). The initial coding was done by the first, second, and third authors ( n  = 8, n  = 2, n  = 4, respectively). The coding was calibrated through discussions among the coders, and when uncertainty remained, all the authors were consulted. After that, the first author iteratively refined the coding for all 14 interviews. Our preunderstanding was managed by self-reflection through notes. The research team has experience in quantitative and qualitative studies regarding older adults and behavior change, and its members are from psychology, nursing, public health, sports science, and physical therapy.

Procedure and data collection

The older adults who had participated in the quantitative data collection and had agreed to be contacted for future research were approached. The participants were selected based on their gender (male, female), age (65 to 80 years, 80 years and older), and level of PA (insufficient, moderate, sufficient) derived from their survey answers for sample diversity. A four-cycle purposive sequential sampling procedure was used to contact 35 potential participants, 14 of whom consented in writing to be interviewed. Hence, the included sample is based on the maximum number of consented participants. There was no researcher-participant interaction before the data collection.

One-on-one semi-structured interviews were conducted in March and April 2023. The interviews took place either in the participant’s home or in a secluded area at the community hosts’ office, without the presence of non-participants. The participants chose the option that was most comfortable for them. The participants were informed about the purpose of the study, their right to refrain from answering questions, and that their participation was voluntary.

An interview guide with open-ended questions, organized into four themes, was developed, and follow-up questions were tailored during each interview based on the participant’s response. The themes included the participants’ definition of PA, their experience with PA today and previously, and their thoughts on maintaining PA. While the core of the interview guide remained consistent, the questions were nuanced and refined throughout the interview process in response to participants’ answers. The interviews were audio-recorded and transcribed verbatim. The average duration for the interviews was 70 min. The range of interview length was 31–105 min.

To ensure trustworthiness, we spent lengthy time with the specific population, made notes throughout the research process, and applied investigators’ triangulation by being multiple interviewers and coders discussing the results within the research team.

Entire transcripts were analyzed using qualitative content analysis, a method for systematically interpreting text content through coding and identifying patterns [ 35 ]. The method described by Graneheim and Lundman [ 36 ] was applied to the transcripts with the adjustment of not condensing the meaning units. The text was initially read as a whole, then coded and sorted into categories using abductive reasoning. Through this process, the COM-B model was identified as a relevant framework for categorizing the codes based on the entire sample’s quotes. The analysis was completed in NVivo 14.

The mixed-method

The quantitative results were merged with the qualitative findings to address the third research question: To what extent do the quantitative results on older adults’ PA levels agree and disagree with the qualitative findings on older adults’ PA levels?

Before integrating the two datasets through methodological triangulation, each part was independently completed according to its methodological quality standards to ensure trustworthiness [ 26 ]. After identifying quantitative and qualitative findings, the research team compared the results, discussing convergence and divergence in content by reviewing constructs, scale items, and verbal statements. We also reflected upon discrepancies in findings. Those reflections can be located in the discussion section of this paper. A joint table was created to array the results. The table includes only the statistically significant predictors and the main categories identified in each dataset.

The quantitative results

To investigate the first research question – What individual or external factors predict older adults’ PA levels? – a hierarchical regression model was used, with capability, opportunity, motivation, previous PA, EQ-5D-3L, subjective SES, age, and GDS as predictors, and GSLTEQ as the dependent variable. Descriptive statistics (sample size, mean, standard deviation, median, and range) of the variables are shown in Table  1 .

The hierarchical regression model was significant, R 2  = 0.38, F [8, 294] = 24.52, p  < 0.001, although only the variables motivation, EQ-5D-3L, age, and previous PA were significant (see Table 2 ). The participants’ previous PA had a higher semipartial correlation value ( sr  = 0.30, p  < 0.001) than EQ-5D-3L ( sr  = 0.17, p  < 0.001), motivation ( sr  = 0.12, p  = 0.008) and age ( sr  = -0.12, p  = 0.01).

Therefore, a trimmed regression model that only included the significant variables was completed (see Table 3 ). This model remained significant and continued to explain a total variance of 39 percent, F [4, 305] = 49.32, p  < 0.001. The previous PA still had the higher semipartial correlation value ( sr  = 0.29, p  < 0.001) compared to motivation ( sr  = 0.19, p  < 0.001), EQ-5D-3L ( sr  = 0.18, p  < 0.001) and age ( sr  = -0.13, p  = 0.004).

The qualitative results

We used content analysis to explore the second research question: What do older adults perceive as influencing their levels of PA? The analysis confirmed that all the COM-B subcomponents—physical capability, psychological capability, physical opportunity, social opportunity, reflective motivation, and automatic motivation—were relevant to older adults' PA levels based on the entire sample’s quotes (see Table  4 ).

Physical capability

Physical capability concerns how a long life may take a toll on a person’s body and cause mobility issues and focused on strength and stamina . The participants described different illnesses and ailments, restrictions in mobility, and a general decreased fitness as reasons for reduced strength and stamina. Common ailments were stiffness, tiredness, and pain, frequently leading to avoidance of actions like running or even walking. Restrictions in mobility caused a range of issues, from being unable to stand up by oneself to not being able to walk at all or only walking with a walker: “/…/ Right now, I do nothing because my back hurts so bad, it's not possible … you see … I can barely get out of the kitchen " (Participant 6). In a seemingly downward spiral, decreased general fitness was related to reduced cardio-fitness, strength, and balance. A decreased strength and increased stiffness made lifting items difficult, and stiffness combined with poor balance prevented most physical activities such as biking, dancing, or going to the gym.

Psychological capability

Psychological capability describes a person’s mental functioning and understanding; three subcategories were identified: attention , knowledge , and acceptance . The first concerns decreased attention in traffic or not noticing body signals (such as low blood sugar levels). The second, mainly referred to a lack of knowledge, not knowing how to get to an activity (for example, being unfamiliar with the bus system), or not knowing what appropriate or available activities would be for their health conditions.

But I want to do something: move my body. In every newspaper and on TV, they say that you should move, but how and where? Who should I turn to? Because I can’t go to a gym since I can't stand up right there. (Participant 7)

The third subcategory, acceptance, was a permissive approach that some participants adopted when faced with lost abilities. This strategy seemed to boost their well-being.

Physical opportunities

Physical opportunities concern the external living conditions related to time and inanimate aspects of an environmental system. Aspects that benefited PA levels included time, financial means, activities to choose between, and access to music, equipment, nature, and facilities. The COVID-19 pandemic, unpleasant weather conditions, and physical surroundings sometimes prevented mobility. For example, the stairs into busses hindered rides or the pavement levels prevented getting on and off a bus everywhere. Dark and slippery roads, as well as long distances, were additional barriers: “/…/ When I walk, it’s to go and play bingo, but then I have to stop several times, and I only wish there were more benches along the pathway for me to sit down for a little while …” (Participant 5).

Social opportunities

Social opportunities relate to people and cultural elements of an environmental system. These concerned interpersonal influences, belonging to a community, and the presence of others who encouraged, pushed, or guided the participants facilitated engagement in PA: “I have actually done [some physical activity]. And if I had someone close by who … liked the training, we could motivate each other, and then it would surely be even more [activity]” (Participant 4). Others’ opinions and actions directed older adults’ behavior. When one partner did not engage in PA, the other often reduced or stopped their activities too. Reasons were feelings of guilt or lack of time due to an increased need for them to do the household chores.

Automatic motivation

Automatic motivation concerns emotions and impulses that energize behavior. Three subcategories were identified: impulses and inhibitions, emotions , and motives . The first, impulses and inhibition, concerned relatively unreflective internal forces that propel or restrain actions. Habits of regular exercise or walks were automatic behaviors that facilitated PA. At the same time, a struggle to stop exercising before pain or exhaustion prevented future PA. The second subcategory, emotions, concerns positive and negative feelings for PA. Positive feelings like fun, play, and enjoyment facilitated PA, whereas negative emotions like sadness, fear, and guilt limited activity levels. The fear that restricted behavior was mainly about hurting oneself and losing body functions and abilities.

Like I said, I try to be active ... within my limits. I no longer expose myself to anything extreme, although I feel I should. /…/ This stiffness I have, I wish I didn’t, but to a certain degree, you have to accept it as well when you have passed the age of 80, I think. Perhaps not endangering the body too much. (Participant 2). 

The third subcategory, motives, involved wants and needs that elicited PA and facilitated it as far as mobility issues did not pose a hindrance. The desire to do activities other than PA increased older adults’ motives to be physically active.

Everything comes down to one’s will ... if I don’t want something, I don't want it ... you can’t force someone, it’s quite simple when you come to an understanding ... so ... but sometimes I think about the fact that ... I’ll be 72 years old now, I don’t have damn long left on Earth, and the time I have left I actually want to be a little active. I have no desire to use an electric wheelchair or become a vegetable. (Participant 8)

Reflective motivation

Reflective motivation involves conscious thinking that can ignite behavior, and two subcategories were identified: beliefs, and goals and plans . Beliefs concerned ideas about PA, age and age-appropriate manners, and the self. If one believed participating in PA was valuable and worthwhile, it facilitated their engagement. Statements of PA were often accompanied by imperative thoughts, such as ‘must’ or ‘should.’ Regarding beliefs about age, those participants who identified themselves as old tended to perceive PA as too late to engage in, which often restricted their range of activities. Ideas about the self as an active or inactive person influenced the level of PA, where the former facilitated and the latter hindered. “I don't move my body because I have no interest in it” (Participant 5). The subcategory, goals and plans, involved cognitive representations of desired outcomes and intentions for PA. PA helped participants achieve health and independence by reducing illnesses and ailments, lowering restrictions in mobility, improving general fitness, or boosting well-being.

Yes. No, [the goal of these activities] it’s to keep the body going to remain able to cope as I age. I mean, the muscles disappear, the older they get. Of course, you want to try to remain strong and live a long life. I think it’s super-important to exercise because you stay healthier. If you exercise, you have more stamina, and you have fun while exercising. So no, I think it is very important. (Participant 4)

Other goals were to save money and experience nature. A more or less conscious objective with the PA concerned structure and meaning of the days. For example, getting outside the home allowed the participants to explore and be stimulated. Intentions to be active facilitate an active lifestyle and committing to oneself or others further prompted PA. “/… /I take care of dogs and sometimes when I go out, the weather is miserable, but since I’ve promised her, I’ll go out anyway … /…/even if it’s not very tempting to go out when it rains” (Participant 2).

The mixed-method results

The quantitative results were integrated with the qualitative findings to address the third research question: To what extent do the quantitative results on older adults’ PA levels agree and disagree with the qualitative findings on older adults’ PA levels? This revealed both convergence and divergence (see Table  5 ).

This mixed-method study utilized quantitative and qualitative methods to examine the factors influencing older adults’ PA levels. This allowed a greater insight than would be obtained by either dataset separately, as quantitative methods enable statistical generalizability to a greater degree and qualitative methods can provide deeper insights and a more nuanced view of a phenomenon [ 27 ]. The quantitative analysis of standardized questionnaires was used to identify individual and external factors predicting older adults’ PA levels, and the qualitative analysis of semi-structured interviews was used to gain a better understanding of nuances of what older adults perceive as influencing their PA levels. The mixed-method analysis assessed to what extent the quantitative results on older adults’ PA levels agree and disagree with the qualitative findings on older adults’ PA levels. The discussion will follow the two research questions, answered by quantitative and qualitative methods. After that, an integrated discussion will address the third research question.

Predictors of PA levels in older adults

The findings revealed that the best predictor for older adults’ PA levels was their previous PA engagement, followed by their current motivation, health status, and age, which aligns with prior research [ 7 , 9 , 11 , 23 , 24 , 25 , 37 ]. Non-significant variables in the regression analysis were capability, opportunity, subjective SES, and depressive symptoms. Capability changed to a non-significant predictor when health status and age were included, indicating that body-related capacity matters for older adults’ PA levels, which Jancey et al. [ 14 ] also postulated. This indicates that physical- rather than psychological capability matters for older adults’ PA levels. Our results contradict previous research by not identifying opportunity as a significant predictor [ 22 , 23 , 24 , 25 ]. This may be due to multicollinearity and other factors that matter more to older adults’ PA levels. Additionally, the availability of walking groups in their residential areas may have impacted participants’ perceptions of opportunities, influencing the results. Another possibility is range restriction in the participants’ questionnaire answers in this study or that other research studies have used different items to measure the COM-B variables.

Despite previous negative associations between subjective SES with physical inactivity [ 16 ] and mobility issues [ 17 ], subjective SES was not a significant predictor in our study. The reason could be the homogeneity in the sample, with all participants living in rented apartments reporting fairly similar subjective SES.

Many older adults suffer from depression and in Sweden approximately 10 percent of individuals aged 65–74 use antidepressants, increasing to nearly 20 percent for those aged 85–94 [ 38 ]. The prevalence of depression in our sample (potentially 24.4%) is higher than that of Sweden; however, it is lower than the global equivalent (28.4%) [ 39 ]. Depressive symptoms were not a significant predictor of PA levels, although previous research has shown that depressive symptoms may hinder PA engagement [ 18 ]. The discrepancy in findings may be due to different study designs and sample sizes. Our quantitative study included 334 participants in a single measurement, whereas Lindwall et al. [ 18 ] employed a repeated measurement design with a sample of almost 18,000 participants. In summary, early engagement in PA seems to be a precursor for maintaining an active lifestyle later in life. Still, other factors like the current motivation and age-related health status are also important.

What do older adults perceive as influencing their PA levels?

All subcomponents of the COM-B model were identified as relevant to help understand what influences PA levels among older adults, which validates the results from previous reviews [ 22 , 25 ]. As with all models, the COM-B model is a simplified representation of reality, and the subjectivity in data interpretation might account for differences in findings among researchers. Meredith et al. [ 25 ], who reviewed qualitative studies on older adults’ PA participation and mapped their findings to the COM-B model, perceived fear as an individual vulnerability related to capability. However, we interpreted it as an emotion related to automatic motivation. Nonetheless, our findings and Meredith et al. [ 25 ] suggest that fear of falling or losing physical abilities can limit PA.

It is well known that older adults can face challenges in PA due to reduced strength and stamina, especially if the social and physical environments do not support their health condition. Our results show that all the COM-B model’s subcomponents are relevant and have complex interactions. This has also been acknowledged by Meredith et al. [ 25 ], who reported that a greater portion of their findings was related to social and physical opportunities. Other researchers, who did not only include older adults in their samples, have emphasized physical opportunity while downplaying physical capability and social opportunity [ 22 , 24 ]. This suggests that social environment and physical capabilities become more significant later in life and implicates the importance of analyzing age groups separately, as differences can become camouflaged when combined. In summary, the subcomponents of the COM-B model help explain older adults’ engagement or withstanding of PA. The importance of age-disaggregated analysis is also revealed when comparing our results on older adults to those of other researchers that include samples with not only older adults.

Integrative discussion

The quantitative and qualitative results were both convergent and divergent (see Table  5 ). In the quantitative results, opportunity was not a significant predictor, but it was identified as an influential category in the qualitative findings. This discrepancy may be due to how opportunity was measured in the quantitative analysis. The qualitative analysis revealed additional nuances of opportunity that were not assessed in the survey. For example, the qualitative analysis suggested that PA levels are influenced by the existence of PA facilities as well as the physical surroundings that govern how a person can access the facility. Therefore, participants may have responded in the survey that facilities for exercising existed without considering if the physical surroundings allowed them to access them. Similarly, the quantitative survey did not include any item on how the ability of a partner to engage in PA affected the participant's opportunity to be active, which was reported as influential in the qualitative analysis. Nonetheless, this divergence calls for more research regarding the role of opportunity for older adults’ PA levels. Regarding capability, the quantitative analysis only reveals physical capability as core to older adults’ PA levels, while both physical- and psychological capability are identified in the qualitative analysis. Motivation was recognized as central in both analyses. The qualitative findings nuanced the quantitative results by indicating both reflective and automatic motivation as relevant. This life-span perspective concerns a distinction between the two analyses, as previous PA was a statistically significant predictor in the quantitative results but did not surface as a category in the qualitative findings. During the interviews, some participants talked about being active in the past, but they did not associate it with their current PA levels. This divergence between the datasets may be related to the qualitative data collection’s focus on individual experiences, while the quantitative approach emphasizes patterns in large groups.

Comparing the quantitative and qualitative data can improve our understanding of what influences PA in older adults. Our findings show that, as people age, their behavior and cognition change, as does their motivation to engage in PA. Pleasurable, meaningful, and social activities reinforce older adults’ PA positively. However, as shown in this study, aging deteriorates a person’s body and can restrict their current PA levels. In these situations, the surrounding opportunities and the individuals’ knowledge of safely engaging in PA matters. Also, people’s previous experience of PA influences their present behavior. In other words, our study indicates that many factors influence older adults’ PA levels in a complex manner. The COM-B model and its subcomponents seem like a relevant model for understanding older adults’ PA levels. In summary, these findings suggest that applying a life-span perspective and considering the COM-B model's subcomponents can help explain why older adults engage in PA or not.

Implications and practical significance

Stakeholders may promote healthy aging and contribute to the 2030 Agenda’s sustainable developmental goal of health and well-being [ 8 ] by utilizing knowledge of factors influencing PA levels in older adults. It is important to recognize that aging can look very different from one person to another, and that this heterogeneity tends to increase with age, peaking at approximately 70 years for various health characteristics [ 40 ]. Our findings reveal both hetero- and homogeneity among the participants. For example, in the qualitative analysis, they all reported reduced strength and stamina, but the reasons varied from biological to behavioral. The individual differences must be considered, but common features allow for PA promotions for healthy aging.

Firstly, this result implies the importance of prioritizing PA at early life stages, as this positively affects PA levels in older adults. However, this alone will not suffice to increase PA levels in the aging population, as their past cannot be changed. Hence, interventions to target PA in older adults are necessary.

Secondly, interventions to increase PA among older adults should review all subcomponents of the COM-B model. This knowledge is valuable since the COM-B model is the hub of The Behavior Change Wheel (BCW) and can be used to develop and evaluate interventions [ 19 ]. According to the BCW, interventions should be developed systematically, and the first stage is understanding the behavior through the COM-B lens [ 19 ]. This can be considered to have been achieved by the present study and the previous study of Meredith et al. [ 25 ]. In the second stage of intervention development; stakeholders can consult the BCW [ 19 ] while using the results from this study to create interventions that are specific to their settings to promote PA among older adults. For example, our research indicates that in addition to providing PA facilities, successful PA intervention may also require attention to the physical surroundings or the person’s knowledge of how to use the facility.

Limitations, strengths, and future research

The mixed-method design is a strength, as two data sets allow a thorough assessment of what influences older adults’ PA levels. For example, the quantitative analysis identified previous PA as an important influencer to older adults’ PA, which is not emphasized in our qualitative analysis nor by Meredith et al. [ 25 ]. Our results regarding the COM-B model validate Meredith et al. [ 25 ] findings. It can, therefore, be concluded that the COM-B model is useful for understanding older adults’ PA. This is valuable information since the COM-B related results can conveniently be transformed into interventions due to its connection with the framework of (BCW) [ 19 ]. Another strength of this study is that our analysis sheds light on the importance of age-disaggregated research when our findings, related to an older population, are compared to prior studies [ 22 , 24 ] that not only include older adults. Future research should preferably age disaggregate their analysis by the recommended five-year age brackets [ 41 ].

A limitation concerns the findings' generalizability or transferability since the drop-out rate was rather large in the quantitative sample from which the qualitative sample was recruited. Additionally, not everyone invited to the qualitative study consented to participate, meaning that those who chose to participate in this mixed-method study may differ from non-participants. However, the sample size in both datasets was relatively sizable, which boosts the statistical power of the quantitative analysis and nuances in the verbal statements in the qualitative analysis. The living conditions of the older adults, included in our study, may differ slightly from other samples used in prior research, challenging result comparisons. Future studies are recommended to include a more diverse population of older adults. Additionally, our sampling resulted in different mode ages in the two samples, which is a limitation as it may mirror different realities among older adults, thereby potentially influencing the result integration. Another weakness within the quantitative dataset concerns the precision of measurement, multicollinearity, and range restriction for the included variables in the regression analysis. A potential limitation of the qualitative study was the lack of member checks. However, to compensate for this, the participants were invited to contact the researchers with adjustments or additional comments on their interviews. To further ensure credibility and trustworthiness, all interviews were conducted within two months. Additionally, multiple interviewers and coders from different disciplines (i.e., investigator triangulation) helped minimize research bias, enhancing the qualitative analysis. Some talkative participants occasionally strayed off-topic, which may have influenced the data collection and the qualitative findings.

The qualitative analysis indicated the importance of PA as a pleasurable activity and previous research has associated subjective well-being [ 42 ] and morale [ 43 ] with older adults’ PA. However, the quantitative analysis did not include these emotional aspects as variables. We suggest that future studies review and clarify the value of positive and negative emotions for older adults’ PA levels. Additionally, longitudinal experimental study designs are needed to clarify the role of physical- and social opportunities for older adults’ PA.

To our knowledge, this study is the first to use a convergent mixed-method design to examine factors influencing PA levels in older adults aged 65 and above who rent apartments from the municipality with access to community hosts, providing a more comprehensive understanding of the topic. It seems that many factors influence older adults’ PA levels in a complex manner, with the integrated result showing convergence regarding motivation and physical capability but divergence in psychological capability, opportunity, and previous PA engagement. The findings also indicate relevance to the COM-B model as a framework for understanding older adults’ PA levels. Overall, we suggest that it is important to consider all the COM-B model’s subcomponents when designing a PA program for older adults and to apply a life-span perspective, as previous PA engagement seems to influence the current level of PA in older adults. However, it is also central to consider their current motivation, capability, and opportunities to understand what influences their PA levels. More research is needed to clarify the role of emotions and opportunities for older adults’ PA levels since the findings are inconsistent. Furthermore, the value of age-disaggregated data is revealed when our findings from samples of only older adults are compared to previous research that does not only include older people.

From a public health perspective, prioritizing PA early in life appears important, as this can positively impact older adults’ PA engagement. Based on our findings, we would make the following recommendations for promoting PA among older adults. Since the findings can be related to the intervention framework of BCW, stakeholders are encouraged to use these results while also seeking further guidance from the BCW to design interventions to improve PA levels and promote healthier aging among older adults. For instance, our findings suggest that it is important to consider the targeted population's physical abilities and offer appropriate options for their health condition when designing an intervention. Also, to alleviate the fear of injury that can hinder motivation for PA, it is central to address older adults’ concerns and provide them with the necessary knowledge to engage in PA safely. The findings also indicate the importance of PA to be fun, playful, and meaningful. This knowledge can be used to frame and present PA options to participants to motivate PA engagement. Challenging age stereotypes and emphasizing that it is never too late to start exercising appears also important. Additionally, to ensure the success of PA interventions, it is also important to consider the physical surroundings and social settings at macro and meso levels. For example, long walking distances with no resting spots or a partner's physical ability may prevent PA engagement. Addressing such issues can help individuals to partake in activities without limitations.

Availability of data and materials

The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.

Abbreviations

• Physical activity

Subjective socioeconomic status

The Behavior Change Wheel

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Acknowledgements

We thank the Community hosts for their assistance during the quantitative and qualitative data collection.

Open access funding provided by Karlstad University. Our work was supported by Grant No. 20210102 from the Kamprad Family Foundation for Entrepreneurship, Research & Charity.

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Anna Nilstomt, Charlotte Bäccman & Erik Wästlund

Department of Political, Historical, Religious and Cultural Studies, Karlstad University, Karlstad, Sweden

Johanna Gustavsson & Finn Nilson

Center for Societal Risk Research, Karlstad University, Karlstad, Sweden

Department of Public Health, Karlstad University, Karlstad, Sweden

Linda Beckman

Department of Health Services Research, Management & Policy, University of Florida, Gainesville, FL, USA

Department of Educational Studies, Karlstad University, Karlstad, Sweden

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AN made substantial contributions to the design of the work, the acquisition, analysis, and interpretation of data, and has drafted and revised the work. JG made substantial contributions to the design of the work, the acquisition, analysis, and interpretation of data, and has drafted and revised the work. LB made substantial contributions to the design of the work, the acquisition, analysis, and interpretation of data, and has drafted and revised the work. CB made substantial contributions to the design of the work, the analysis and interpretation of data and has drafted and revised the work. FN made substantial contributions to the design of the work and the analysis and interpretation of data and has drafted and revised the work. SW made substantial contributions to the design of the work and the data acquisition and has drafted and revised the work. EW made substantial contributions to the design of the work, the acquisition, analysis, and interpretation of data, and has drafted and revised the work. All authors have read and approved the final manuscript.

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AN is a licensed psychologist and a PhD student researching the maintenance of behavior with a focus on PA among older adults. JG is a senior researcher in public health with expertise in injury prevention for older adults and is also a licensed nurse. LB is a senior researcher in public health with expertise in young and older people’s mental health. CB is a senior researcher in psychology with expertise in health and well-being, behavior change, and digitalization. FN is a professor in risk management and a licensed physical therapist. SW is a senior researcher in sport science with expertise in PA and motivation. EW is a senior researcher in psychology with expertise in decision-making, behavior change, and digitalization.

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Correspondence to Anna Nilstomt .

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The participants gave their informed consent before participating in the study. For the quantitative study, participants provided consent by filling out the questionnaire. For the qualitative study, participants provided written consent. The Swedish Ethical Authority approved this study (No. 2020–00950 and No. 2021–05133).

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Nilstomt, A., Gustavsson, J., Beckman, L. et al. Physical activity from the perspective of older adults: a convergent mixed-method study. BMC Geriatr 24 , 768 (2024). https://doi.org/10.1186/s12877-024-05362-x

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Received : 05 April 2024

Accepted : 06 September 2024

Published : 18 September 2024

DOI : https://doi.org/10.1186/s12877-024-05362-x

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