Ming-Shu Yuan
Associate Professor, Graduate Program and Department of Information and Communications, Shih Hsin University, Taiwan (R.O.C.)
E-mail: juanems@mail.shu.edu.tw
Keywords: Conceptual Framework; Data Narrative Analysis; Digital Narrative;
Narrative Elements
【Abstract】
The open data environment brought by big data has led to the turning of digital narrative research, highlighting the importance of “Telling stories with data.” Data narratives is emerging topic, and its concepts, methods, and applications are under developing. This study reviews literature of data narratives, complements relevant content, establishes a conceptual framework of data narrative analysis, and then take a product brand story analysis as an example for the stage of constructing stories in conceptual framework and revealed some narrative elements. Key results of this study are as follows: (1) Data narrative explores the story content or insight in different materials is the primary task. Using information graphics or related visual technology to interpret the story content is an auxiliary work; (2) The data narrative analysis framework can be divided into three stages: exploring data, constructing stories, and telling stories. Each stage involves different steps; (3) Inducting twenty‐four narrative elements of product brand narrative, three of which are classified as common elements and eleven as secondary elements. This research contributes to clarifying the important concepts of data narrative and its research content, as well as constructing a narrative analysis conceptual framework for data. The results can be applied as an analytical reference for further research.
【Long Abstract】
Introduction
Telling stories with data is key to promoting interdisciplinary cooperation using digital narrative methods. According to literature review, telling stories with data, which means data narrative, does not change the nature of the narrative structure when integrating narratives into each type of data. This study proposes that the details and “stories” within data should be considered in addition to its quantitative
DOI: 10.6245/JLIS.201904_45(1).0004
performance. Take for example, “thick data,” which emphasizes that rather than focusing on the data visualization; researchers should analyze and interpret the underlying meaning, context, and story of data to reveal its inherent “emotion” (Madsbjerg & Rasmussen, 2014; Song, 2016). Additionally, different audiences may differ in their understanding and interpretation of the story content featured in infographics. This study infers that this is a result of insufficient systematic organization of the story content elements, which is used to map the relationship between “data” and “stories.” Therefore, this study includes discussion of this topic.
Method
This study conducted a literature review on the aforementioned study background and defined the following three research purposes:
(1) Based on literature review, categorize concepts and research content related to data narrative.
(2) Construct a conceptual framework of data narrative analysis to serve as reference for future relevant empirical research.
(3) Categorize and analyze stories with common story content elements using the example of storytelling in product branding, the results of which will serve as reference for constructing the conceptual framework in the second stage.
Results The significant findings are as follows:
(1) Concepts and Research Content Related to Data Narrative
Numerous studies have indicated that a definition for the concept of “data narrative” has yet to be agreed upon. Some studies have emphasized defining narrative concepts based on the perspective of storytelling (Sadik, 2008; Lai, 2017), whereas others have focused on providing a definition based on data visualization (Few & Edge, 2007; Lee, 2017a). In summary, data narrative emphasizes the necessity of understanding the communication target audience. User perspective should be adopted to identify the optimal method for conveying information and understand the influence of information on the audience. To achieve the goal of effective communication and information communication, this study defines the core concepts of data narrative as follows: (1) focus primarily on the role of narrative, supplemented with relevant data and visual technology; (2) re-construct and re-visualize the narrative subject; and (3) employ language and information processing strategies with which the audience is familiar.
(2) Constructing a Conceptual Framework for Data Narrative Analysis
According to the aforementioned definition of core concepts, related infographic and visual technologies are used to interpret story content in data narratives is a supplemented task.
Therefore, the core task of data narratives is to create a story that reflects the connection between data and story content elements. Because this study aims to serve as a reference for future empirical research on data narrative, establishing a conceptual framework for data narrative analysis is crucial. Relevant studies have been limited to the three stages of data narrative (Lee et al., 2015; Wolff, 2016); this study, however, expands and converts data narrative into an analysis framework (see Figure 1).
Figure 1 Conceptual framework of data narrative analysis
(3) Categorizing and Analyzing Story Content Elements: Using Product Branding Stories as Examples
Control over story content elements is crucial in the story construction stage of data narrative analysis. Because stories can be in the form of brand stories (Herskovitz & Crystal, 2010), personal anecdote or organizational stories (Yuan, 2007), or news stories (Lin, 2015) and so on, their narrative structure and content may differ, resulting in difficulty when establishing a uniform narrative elements. Therefore, this study conducted a literature review based on two
aspects: (1) narrative structure and elements of a story, including discussion of story format and structure (also referred to as “storytelling structure”) and (2) story content elements, which transmits story content and meaning (also referred to as “storytelling content”). In the management of product branding, “storytelling content” is often prioritized over “storytelling structure” to increase commercial profit. Therefore, this study mainly discusses the story that the product brand is telling rather than how the product brand story is told.
This study referenced Kamp (2015) and Kreuter et al. (2007) in the categorization of the story content elements of product branding. Using “characters” and “events” (including company branding and product-related events) as the primary aspects for categorizing story content elements, this study examined the story content of product branding in Taiwanese and foreign literature. The results indicated that “stories regarding company founder and management,” “origin of company brands,” and
“the reasons for beliefs in company brands” were the most commonly mentioned themes of story content elements. In addition to explaining “who am I?” and “why am I here?” of a brand to consumers, brand beliefs also strengthens the emotional connection between consumer and brand. Therefore, the aforementioned three elements are preliminary considered to be the common story content elements.
This study then identified 11 secondary story content elements (e.g., company organizations, consumers, company success stories, and so on) to explain information concerning a brand and strengthen the abstract mental imagery of consumers that connects them to the brand.
Conclusion and Future Work The conclusions of this study are as follows:
(1) Data narrative includes the three critical factors of data, narrative, and visualization. In the data narrative process, the narrative serves as the foundation and data and visualized technology are employed as support for reconstructing and visualizing narrative subject data. Defining data narrative more clearly can facilitate the analysis and visualization of acquired research data with respect to big data and expand in the conduction of data narrative analysis with respect to thick data.
(2) Regarding construction of the conceptual framework of data narrative analysis, the process of data narrative begins with data exploration, undergoes story construction, and ends with storytelling. The conceptual framework constructed in this study should be evaluated in future empirical studies to further adjust and expand the data narrative analysis pattern.
(3) For categorization of the content elements of product branding stories, this study identified three story categories and 24 subcategories. The results can serve as a fundamental reference for the
story construction stage in the conceptual framework. In the future, we will expand on the story content elements of other story types to facilitate the application of these findings in different research contexts.
In terms of academic contributions, this study clarified the development process of data narrative studies, thereby enriching research in the domains related to digital narrative, information and communications. Regarding practical contributions, the conceptual framework of data narrative analysis can assist data analysts in understanding the overall concept from data exploration to data storytelling.
Additionally, the conceptual framework can help product-branding sales professionals to understand the value of data exploration in the story.
【Romanization of references is offered in the paper.】