Netnography, is a specific type of qualitative social media research. It adapts the methods of ethnography, is understanding social interaction in contemporary digital communications contexts. You can think of netnography as a particular set of actions for doing research within and about social media. Netnography is a specific set of research practices related to data collection, analysis, research ethics, and representation, rooted in participant observation. In netnography, a significant amount of the data originates in and manifests through the digital traces of naturally occurring public conversations recorded by contemporary communications networks. Netnography uses these conversations as data. It is an interpretive research method that adapts the traditional, in-person participant observation techniques of anthropology to the study of interactions and experiences manifesting through digital communications (Kozinets 1998).
The term netnography is a portmanteau combining "Internet" or "network" with "ethnography". Netnography was originally developed in 1995 by marketing professor Robert Kozinets as a tool to analyze online fan discussions about the Star Trek franchise. The use of the method spread from marketing research and consumer research to a range of other disciplines, including education, library and information sciences, hospitality, tourism, computer science, psychology, sociology, anthropology, geography, urban studies, leisure and game studies, and human sexuality and addiction research.
Though netnography is developed from ethnography and applied in the online settings, it is more than the application of qualitative research in the form of traditional ethnographic techniques in an online context. There are several characters that differentiate netnography from ethnography.
Netnography is also similar to ethnography in these ways:
Key components of netnography include emotion/story, the researcher, key source person, and cultural fluency.
Netnography combines rich samples of communicative and interactions flowing through the internet: textual, graphic, audio, photographic and & audio-visual. The data then will be analysed using content analysis, semiotic visual analysis, interviews (online and in person), social network analysis and the use of big data analytic tools and techniques. These techniques are employed to find the emotional story behind a subject.
This what differentiate netnography to big-data analysis that often relies on machine (sentiment analysis, word cloud) and also to digital ethnography or digital anthropology. These terms (netnography, digital ethnography, and digital anthropology) are often used interchangeably, but they are very different.
The difference between netnography and digital ethnography could be seen in several ways, but the most obvious one is the research motivation and methodology determined by the purpose. Netnography focuses on internet users forming an online community which is highlighted from the substantial daily life, while digital ethnography only treat the digital world as a place to extend their offline data collection to complement the ethnographic research. The methodological framework between them are not typically different, since netnography mainly use online qualitative techniques and use online quantitative research as a supplement occasionally, while digital ethnography combines both quantitative (e.g., network and co-word analysis) and qualitative (e.g., sentiment and content analysis) techniques.
To find the emotional story, big data analysis is often used as a complementary technique, usually at the beginning of the research. However, instead of scooping a huge amount of data and relying on machine to analyse it, the strength of netnography is contextualized data, human-centered analysis, and resonant representation.
The researcher is not simply a person who knows how to run a specific software but a living, breathing individual whose personality will enrich the research. In netnography, to find the necessary emotion, the story behind the individuals, the researcher has to have a deep understanding of the culture that surrounds the data that he uses. They have to immerse themself in the community where they source their data. A human being is a very complex being, and the language that we use, regardless of the language itself, has depth. It has nuance, symbolism, sarcasm, to name a few. Not to mention context. What is acceptable or positive in one culture might be the total opposite in others. Unearthing the layers is a complicate and delicate process no algorithm can currently perform.
For example, if a researcher wished to understand the sentiment of a brand's customers or potential consumers towards a specific brand, the easiest thing to do is perhaps analyze the comments section of the brand's website. However, should there be a substantial number of comments that are using sarcastic language, solely using a machine-generated algorithm will give the wrong conclusion.
The key to understand the culture is to find rich data from a key source person, the third factor of netnography. Using the same examples, to find the reason behind perception of brand or the reason behind a brand loyalty, a netnographer needs to comb through the comments section to find the gold mine.
One examples of a gold mine is a genuine comment written by a person with a very strong emotions towards the brand either positive or negative. On the other hand, the netnographer may find a person who either loves or hates the brand with every fiber of their being. The netnographer should find this data and analyze it. This small but in-depth data could be the answer to the research question.
The goal of a netnographer is cultural fluency. Cultural fluency means that at the end of the research, the researcher should be fluent in the symbolic language of the site and even so knowledgeable about the users that they have an almost biographical authority regarding them.
Unlike the fetishization of big data and its attempt to portray a generic, characterization of markets in online communities (i.e., frequency of brand engagement), netnography enables researchers "to argue for a central tenet" (Kozinets, 2016, p. 2) that emerges from the collected data that represents a particular market. Netnography has an advantage over ethnography in that it focuses primarily on the context of textual communication and any affiliated multimedia elements, whereas ethnography focuses primarily on physical forms of human communication (e.g., body language) (Bartl et al., p. 168). Since Netnography uses spontaneous data and conducts observation without intruding online users, it is regarded as more naturalistic than other approaches such as interviews, focus groups, surveys and experiments (Kozinets, 2015). While online communication has a relatively shorter duration in efficiency when compared to human communication, the speed in collecting online communication is much faster and far less expensive than traditional in-person ethnography and other qualitative methodologies like focus groups or interviews (Kozinets 2002). It is also a challenging approach involving work to tackle unpredictable and abundant data (Kozinets, 2015).
The need to understand the cultural meaning of online communities (e.g., Reddit; LinkedIn) has grown exponentially since the appraisal of Web 2.0 interfaces (i.e., user-generated content), along with other technological advances. One can no longer assume that people are isolating themselves from the physical world with technology, but rather view technology such as computer-mediated communication and digital information as a gateway that allows them to interact with familiar and, at times, anonymous users on a given occasion. Furthermore, cultural practices within the physical world are extended to, and enhanced by, these online communities, where people can choose a dating partner, learn about a religion and make brand choices, just to name a few examples. With ethnography's influence on netnography, this research method enables the researcher to link the communication patterns in order to understand the tacit and latent practices involved within and between these online communities of interest (Mariampolski, 2005). As Kozinets (Kozinets 1998, p. 366) pointed out, "these social groups have a 'real' existence for their participants, and thus have consequential effects on many aspects of behaviour, including consumer behavior" (see also Muniz and O'Guinn, 2001).
People participating in these online communities often share in-depth insights on themselves, their lifestyles, and the reasons behind the choices they make as consumers (brands, products etc.). Such insights have the potential of becoming something actionable. More specifically, this means that the researcher will be able to present an unknown and unseen truth to his/her client (Cayla & Arnold, 2013) so that they are able to make better decisions in engaging with a target community, whether it be in a form of an advertising or a non-profit campaign. While netnography has been predominantly applied within the field of marketing (Bengry-Howell, 2011), its methods can help researchers and their clients within social sciences to create an empathetic understanding of people's cultural behavior via online, and to allow the researcher and clients to 'immerse themselves' in the consumer domain (Kozinets, 2002; Piller et al., 2011; in Bartl et al., 2016, p. 167). The following information provides a systematic process to search for, collect and analyze data (Bartl et al., 2016, p. 168; see also Kozinets, 2000, 2010)
Netnography offers a range of new insights for front end innovation, providing:
Netnography collects data from Internet data, interviews data and fieldnotes
As with grounded theory, data collection should continue as long as new insights are being generated. For purposes of precision, some netnographers closely track the amount of text collected and read, and the number of distinct participants. CAQDAS software solutions can expedite coding, content analysis, data linking, data display, and theory-building functions. New forms of qualitative data analysis are constantly being developed by a variety of firms (such as MotiveQuest and Neilsen BuzzMetrics), although the results of these firms are more like content analyses of than ethnographic representations (Kozinets 2006). Netnography and content analysis differed in the adoption of computational methods for collecting semi-automated data, analyzing data, recognizing words and visualizing data (Kozinets, 2016). However, some scholars dispute netnography's distance from content analysis, preferring to assert that it is also a content analytic technique (Langer & Beckman 2005).
Distinct from data mining and content analysis, netnography as a method emphasizes the cultural contextualizing of online data. This often proves to be challenging in the social-cues-impoverished online context. Because netnography is based primarily upon the observation of textual discourse, ensuring trustworthy interpretations requires a different approach than the balancing of discourse and observed behavior that occurs during in-person ethnography. Although the online landscape mediates social representation and renders problematic the issue of informant identity, netnography seems perfectly amenable to treating behavior or the social act as the ultimate unit of analysis, rather than the individual person.
Research ethics may be one of the most important differences between traditional ethnography and netnography. Ethical concerns over netnography turn on early concerns about whether online forums are to be considered a private or a public site, and about what constitutes informed consent in cyberspace (see Paccagnella 1997). In a major departure from traditional methods, netnography uses cultural information that is not given specifically, and in confidence, to the researcher. The consumers who originally created the data do not necessarily intend or welcome its use in research representations. Netnography therefore offers specific guidelines regarding when to cite online posters and authors, how to cite them, what to consider in an ethical netnographic representation, when to ask permission, and when permission is not necessary (Kozinets 2002; cf. Langer & Beckman 2005).
Compared to surveys, experiments, focus groups, and personal interviews, netnography can be less obtrusive. It is conducted using observations in a context that is not fabricated by the researcher. Netnography also is less costly and timelier than focus groups and personal interviews.
The limitations of netnography draw from the need for researcher interpretive skill, and the lack of informant identifiers present in the online context that can lead to difficulty generalizing results to groups outside the sample. However, these limitations can be ameliorated somewhat by careful use of convergent data collection methods that bridge offline and online research in a systematic manner, as well as by careful sampling and interpretive approaches (Kozinets 1998, 2002). Researchers wishing to generalize the findings of a netnography of a particular online group to other groups must apply careful evaluations of similarity and consider using multiple methods for research triangulation. Netnography is still a relatively new method, and awaits further development and refinement at the hands of a new generation of Internet-savvy ethnographic researchers. However, several researchers are developing the techniques in social networking sites, virtual worlds, mobile communities, and other novel computer-mediated social domains.
Below are listed five different types of online community from a netnographic analysis by Kozinets (see Kozinets ref. below for more detail). Even though the technologies, and the use of these technologies within culture, is evolving over time, the insights below have been included here in order to show an example of what a market-oriented "netnography" looked like:
As research practice, netnography has 12 roughly temporal, nonexclusive and often interacting process levels (Kozinets, 2015):
According to Kozinets, any netnography will fall into one of four categories: auto, symbolic, digital or humanist. These types of netnography are defined by distinctive axiologies and foci. In order to visualize how a netnography is defined one should Imagine a simple 2X2 figure. Along the figure's x-axis we see that a netnography can be defined by whether or not it supports or challenges the status quo of business and management. In this way we determine a netnography's axiological representation orientation as either "critical", meant to disrupt, or "complementary", meant to assist in decision making. If we turn our focus to the y-axis of our imaginary figure, we see then that a netnography can also be categorized by its analytic field focus, or what it examines based on its orientation. A netnography can be deemed "global" if its focus is on a larger and more general system, or we can think of it as "local" if it narrows its scope to particular iterations of that more general system.
Through the combination these distinct parameters we can end up with the four types of netnography:
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