Factors Affecting Interactive-Storytelling Contents Usage: Exploring the Roles of Perceived Interaction, Innovation, Usage Pattern, and Device Type

Interactive technology has also been applied to traditional video storytelling content, such as TV series, films, and animation. An emerging form of interactive content is Interactive Storytelling Content (ISC). ISC is video content in which users create a narrative while making decisions on behalf of the main characters who appear in the content (Netflix, 2020). Like Netflix, ISC is being offered by major global OTT services. Netflix’s first ISC is Puss in Book: Trapped in an Epic Tale, released in 2017, but two years later, the company raised its ISC reputation by releasing Bandersnatch as a spinoff of its famous original content Black Mirror (Elnahla, 2019). As of September 2020 (Netflix, 2020), Netflix had already released nine ISCs.

The expansion of technological infrastructure, such as the proliferation of User Interfaces (UI)- equipped devices (Abreu et al., 2017), the growth of online streaming video platforms, and the adoption of high-speed networks like 5G, have facilitated the development of ISC. Reflecting this, Bandersnatch won the award for Outstanding Television Movie at the 71st Emmy Awards, demonstrating the potential for commercial success of ISC (Television Academy, 2020). Netflix has made it clear that it will produce even more ISCs in the future, fueled by Bandersnatch’s success (Shieber, 2019).

With the expansion of ISC on the online platform, users’ perceptions and behaviors of ISC are of great interest, both theoretically and practically. These mainly include marketing elements (Elnahla, 2019), aesthetic characteristics (Monteiro, 2020), and studies analyzing the characteristics of Bandersnatch by a game-analysis method (de Vries, 2019). They have largely taken the primary approach of case analysis for ISC, while rarely conducting quantitative analysis. Some studies have explored users’ perceptions of ISC but they only included users in their twenties and did not propose variables that reflect the characteristics of ISC, such as perceived interaction (Roth & Koenitz, 2019). However, considering that ISC is different from traditional content in that users actively interact with the content and complete the narrative, as well as its technological innovation in implementing such interactions, we believe it is necessary to examine the influence of overlooked factors in this context. Given the limitations of existing studies, we take into account several distinctive features of ISC. First, users can perceive multidimensional interaction in ISC. Interaction is conceptualized as the dynamic information exchange between users and media (Bucy & Tao, 2007). However, beyond objective interactivity, users’ subjective experiences of these exchanges—what we term ‘perceived interaction’—play a critical role in understanding engagement. Even if interactive elements are present, users may not always perceive them as highly interactive, making perceived interaction a crucial factor in explaining user engagement. In this study, we define perceived interaction as users’ subjective evaluations of their engagement with interactive elements, irrespective of the actual interactivity embedded in the content. This distinction allows us to examine how users interpret and experience interactive storytelling content in a more nuanced way. Prior studies were applied by subdividing the perceived interaction according to the research questions and fields (Sun & Hsu, 2013). We explore perceived interaction that is highly related to ISC and put them into a research model. Second, we expect that users perceive ISC as an innovation, considering that it is a new technology or content. Therefore, we examine how users perceive the innovative attributes of ISC from the perspective of diffusion of innovations. Third, the characteristics of users’ usage patterns are important. Several media studies have predicted user adoption by considering usage patterns of digital content. Although perceived interaction and innovation are related to user perception, the number of ISCs watched and the duration of Netflix subscriptions reflect the user’s actual behavior. Through this, we consider not only user perception but also actual behavior as factors affecting the adoption of ISC from an integrated perspective. Fourth, users can use various devices to consume ISC. For example, Netflix can use ISCs through a variety of internet-connected display devices, including smart TVs, PCs, smartphones, tablets, and laptops (Netflix, 2020). In this study, we divide users into non-portable devices-centered user and portable devices-centered user groups and compare user perception and behavior between them. Finally, considering that the spread of ISC is at an early stage, we apply various outcome variables such as flow experience and satisfaction as well as the intention to continue using ISC as adoption variables.

LITERATURE REVIEW

RESEARCH METHOD

Data Collection and Sample

We commissioned a survey through Macromill Embrain, a global online survey company, to confirm the number of South Korean users of Netflix's ISC. The first survey was conducted

from June 24 to 26, 2020, and only about 5.0% of respondents were identified as ISC users. We believe that the fact that the ISC is a just-released content influenced the low number of ISC users. The main survey was done from July 8 to 13, 2020. Only people who had experience with ISC as a Netflix subscriber participated in the survey; a total of 329 survey responses were collected and used for quantitative analysis (see Table 1).

Table 1.

Sample Profile Comparison

MEASURES

The formulation of the questionnaire was informed by prior research, encompassing inquiries related to perceived interaction, innovation, usage pattern, intention to continuous use, satisfaction, flow, type of device for ISC, and demographic variables (see Table 2).

Table 2.

Questionnaire Items

Except for demographic factors, usage patterns, and types of devices for ISC, all items were measured on a 5-point Likert scale (1, Strongly disagree, to 5, Strongly agree). First, 14 questions were applied based on previous studies that measured interaction (Baron, 1999; Friedl, 2002; Novak et al., 2000; Um et al., 2005) to understand the detailed dimensions of interaction. Um et al. (2005) proposed P2C, P2G, and P2P variables as the interaction of online games. Considering that ISC is similar in interactions to online games, Um et al. (2005) revised it to fit our study. P2G was renamed P2S (Play to Storytelling) because, unlike games, users interact with narrative elements in ISC. By conducting exploratory factor analysis for 14 items, which is a statistical technique used to identify relationships among variables without predefined factors, we excluded 2 items with a factor loading value of 0.60 or less and used 5 items for P2P, 4 items for P2C, and 3 items for P2S. Attributes of innovations are based on the concept of the Innovation Diffusion Theory (Rogers, 2003) and prior research on digital content (Chan-Olmsted & Chang, 2006). Compatibility, Observability, and Trialability were put into the model; each variable contains 3 items. The number of ISCs watched and the duration of a Netflix subscription were applied to the model as factors of usage patterns, and items were constructed based on research on digital television (Chan-Olmsted & Chang, 2006). For the number of ISCs watched, all 329 respondents were asked to choose what they watched from nine ISC services on Netflix as of July 2020. The duration of a Netflix subscription was measured in open-ended questions (months). The statements were: “How long have you been subscribing to the Netflix service?” (months). For ISC, to subdivide users into portable device-centered users and non-portable device-centered users, we investigated devices mainly used when using ISC. The statements were: “What devices do you mainly use to watch ISC?” Respondents selected only one of the four questions, and the group who selected Television and Computer (Desktop or laptop) was defined as non-portable device-centered users, and the group who selected Tablet and Smartphone as portable device-centered users.

The dependent variables are flow experience, satisfaction, and intention to continue using ISC; each variable contains three items. Items for flow experience were constructed based on prior research on flow experiences in online activities (Chang & Zhu, 2012; Novak et al., 2003; Um et al., 2005), and items for satisfaction were based on Um et al.’s (2005) research examining the relationship between the interaction of online game and satisfaction. Finally, the intention to continue using ISC was constructed based on previous studies related to media and content (Cha, 2013) (See Figure 1).

Figure 1.

Research Question Modeling

RESULTS

We examined how perceived interaction, innovation, and usage patterns of ISC affect the satisfaction, continuous use, and flow experience of ISC through multiple regression. We classified users into non-portable device-centered and portable device-centered groups and did exploratory factor analysis using 12 perceived interaction items (see Table 3). We used varimax factor rotation, which is employed in factor analysis to enhance the interpretability by maximizing the variance of the squared loadings and promoting distinct factor structures, and applied an eigenvalue of 1.00 as a factor recognition criterion. We applied the factor- loading value of 0.60 or higher suggested by Hair et al. (2006) as a criterion to decide whether to include the factor. The exploratory factor analysis showed three factors in both groups, and these were similar to those of previous studies (Um et al., 2005). First, in the group of non-portable device-centered users, the factors had a total explanatory power of 65.5%. P2P showed 26.2% explanatory power of the total variance, followed by P2C 23.8% and P2S 15.4%. Similarly, the portable device-centered users group showed 69.3% of total explanatory power, P2P 29.2% of the total variance, followed by P2C 25.9% and P2S 14.1%.

Table 3.

Exploring Factor Analysis of Perceived Interaction on ISC

Cronbach’s α for all items except the P2C items in the portable device-centered user group was above 0.70. Therefore, the reliability of most perceived interaction variables was verified (see Table 3). However, the P2C of the portable device-centered user group showed a Cronbach α of 0.59. Since this value is close to Cronbach’s α minimum criterion of 0.60, we put the P2C of the portable device-centered user group into the study model.

Next, we did a correlation analysis to examine the association between independent variables (see Table 4). In each model, a correlation between independent variables was partially found, and all variables except the correlation coefficient between relative advantage and compatibility showed less than 0.60. After correlation analysis, we did a multicollinearity test, which assesses the degree of correlation among independent variables in a regression model, to grasp the correlation more clearly between independent variables. It showed all of them had more than 1 VIF (Variance Inflation Factor), so we decided that there was no problem with multicollinearity.

Table 4.

Correlation Matrix

We did multiple regression analyses with the three dependent variables on the non-portable device-centered user group (see Table 5). In all, among the interactions, the influence of P2P and P2S interaction was confirmed. For the innovations, the influence of compatibility, relative advantage, and trialability was confirmed. Usage pattern variables were not significant.

Table 5.

Multiple Regression Analysis for the Non-portable Device-centered User Group

Specifically, they explained 52.7% of the total variance of the model for intention to continue using ISC (R² = 0.52). P2P (β = 0.23, p < 0.01), compatibility (β = 0.31, p < 0.01), and trialability (β = 0.23, p< 0.10) positively affected intention to continue using ISC. For the model in which Satisfaction was set as the dependent variable, the independent variables explained 50.0% of the total variable (R² = 0.50). Among the independent variables, P2P (β = 0.16, p < 0.10), P2S (β = 0.18, p < 0.05), relative advantage (β = 0.24, p < 0.01), and trialability (β = 0.22, p < 0.01) positively affected satisfaction. Finally, the explanatory power of the model applying flow experience as a dependent variable was 42.6% (R² = 0.42). For this model, the influence of interaction was not confirmed, and compatibility (β = 0.38, p < 0.01) and relative advantage (β = 0.20, p < 0.10) positively affected the flow experience.

Next, we analyzed the portable device-centered user group model for ISC in the three regressions and confirmed the influence of P2, P2S, complexity, compatibility, relative advantage, and trialability. In addition, unlike the non-portable device-centered user group, the usage patterns, such as the number of ISC and Length of Watching ISC, influenced the dependent variable. Specifically, In the model for intention to continue using ISC, the independent variables explained 51.2% of the total variance (R² = 0.51). In innovations, we confirmed that complexity (β= -0.10, p< 0.05) negatively affects, and compatibility (β = 0.31, p < 0.01) and relative advantage (β = 0.28, p < 0.001) positively affect intention to continue using ISC. In the usage patterns, only the number of ISCs (β= 0.08, p< 0.10) has a positive influence on the intention to continue using ISC. For the satisfaction model, the independent variables explained 57.8% of the total variance (R² = 0.57). Among the interactions, we confirmed that only P2S (β = 0.12, p < 0.01) positively affects satisfaction, whereas the influence of all innovations was confirmed. Specifically, complexity (β = -0.13, p< 0.01) negatively affects, and compatibility (β= 0.25, p< 0.01), relative advantage (β= 0.28, p < 0.001), and trialability (β = 0.18, p < 0.001) positively affect satisfaction. Finally, the explanatory power of the flow experience model was 51.4% (R² = 0.51). Among the interactions, only P2P (β = 0.17, p < 0.01) had a positive effect on flow experience. In innovations, complexity (β = -0.10, p < 0.05) negatively, compatibility (β = 0.21, p < 0.01), and relative advantage (β = 0.33, p< 0.001) positively affect flow experience. For usage patterns, only the length of watching ISC (β = -0.11, p < 0.05) negatively affects the flow experience (see Table 6).

Next, we confirmed that the influence of independent variables between non-portable device-centered users and portable device-centered users was somewhat different (see Table 6). In the model of Intention to continue using ISC, the model explanatory power was somewhat higher for non-portable device-centered users. In addition, the influence of P2P, compatibility, and relative advantage of non-portable device-centered users was confirmed, whereas, in the portable device-centered user group, the influence of complexity, compatibility, relative advantage, and number of ISCs watched was confirmed.

Table 6.

Multiple Regression Analysis for the Portable Device-centered User Group

For the model of satisfaction, P2P, P2S, relative advantage, and trialability affected the satisfaction of non-portable device-centered users. On the other hand, among perceived interaction, only P2S and all attributes of innovations showed a significant influence on the satisfaction of portable device-centered users. The explanatory power of the model was higher for portable device-centered users than for non-portable device-centered users, which is different from the model of Intention to continue using.

As in the model of flow experience and perceived attributes of innovations, compatibility, and relative advantage influenced the flow experience of non-portable device-centered users, the influence of perceived interaction was not confirmed. On the other hand, in the portable device-centered user group, we confirmed the influence of P2P and that complexity, compatibility, and relative advantage influenced flow experience. The explanatory power was somewhat higher for portable device-centered users than for non-portable device-centered users (see Table 7).

Table 7.

Multiple Regression Analysis Camparison

CONCLUSION AND DISCUSSION

In this study, we examined the factors that influence the flow, satisfaction, and continuation intention of ISC by surveying ISC users in South Korea, one of the countries with high digital content consumption. Considering that this is an early empirical study on ISC, we investigated the various nine independent variables included in the categories of perceived interaction, innovation, and usage pattern. In addition, we refined the research design by analyzing the differences between portable device-centered users and non-portable device-centered users.

RQ1 asked whether interaction dimensions including P2P, P2C, and P2C of ISC would affect the three dependent variables. Exploratory factor analysis largely identified interaction as P2P, P2C, and P2, which reflects the perspective of previous studies on interaction (McMillan, 2002; Um et al., 2005). Our results support the fact that interaction can be distinguished in terms of individuals, subjects, and environments depending on each field. In both device-based user groups, the effects of P2P and P2S were confirmed. The effects of P2P can be interpreted in two ways. First, users are more likely to use ISC as a topic source to communicate with others. Previous studies on content have consistently found the influence of social motivation (Borbora & Srivastava, 2012). The effects of P2P are likely due to users’ social motives. Second, we believe that the influence of P2P stems from the desire to encounter new narratives that users have not experienced. A feature of ISC is that users can experience new narratives each time they watch ISC. Users are likely to actively participate in interactions with others to learn about new narratives that user themselves have not discovered. That is, social motivation and interest in new narratives drive P2P, which leads to adoption. In addition, the effects of P2S were found to have a positive effect on satisfaction in both models. ISC contains a large number of plots, and users can choose from a variety of alternative stories (Stern, 2008). Users are also given the impression that they can influence the outcome of the story through interactions by using controllers or touch (Vosmeer & Schouten, 2014). These attributes of ISC consistently allow users to interact with the story, and as a result, positive attitudes toward ISC may be formed. Interestingly, the effect of P2C was not found in the analyses. This indicates that the interaction with the Netflix system is not a burden or an important factor in the use of ISC.

RQ2 examined the effects of perceived innovation attributes of the ISC on flow, satisfaction, and continuous use of ISC. The effects of innovation factors were generally confirmed in the analyses. In the non-portable device-centered user group, compatibility, relative advantage, and trialability, excluding complexity, were significant. Compatibility showed more explanatory power than other innovations. Users have experience with OTT platforms and RPG games, which take formats similar to ISC. Therefore, users may experience high suitability for ISC based on their experience with existing technologies and how they are used, which may lead to innovation adoption (Rogers, 2003). On the other hand, the insignificant effect of relative advantage may result from differences in properties between ISC and existing storytelling content. If the two types of content have similar properties, users are likely to perceive the relative advantage of an innovation such as ISC as high, but if the opposite is true, it is difficult for users to compare their advantages. This result probably means that the two types of media are substitutes, rather than complements. Relative advantage had significant effects on all outcome variables. These results are consistent with most of the previous studies of the diffusion of innovations that proposed relative advantage as an important predictor (Cha, 2013; Chang et al., 2006). We found that relative advantage is an important variable in explaining the adoption of innovation regardless of the nature of each research field.

In the model of the portable device-centered user group, all innovations were identified as significant variables. Note that, unlike the model of non-portable devices, complexity negatively affects all outcome variables, as is consistent with Rogers’ (2003) assertion that the level of adoption decreases when users find innovations to be difficult to understand or use. This result shows that the user interface of portable devices is still difficult to use from the perspective of general users. Although it has become easy to use portable devices such as smartphones, people who are familiar with traditional media, such as TV and PC, find it inconvenient to operate portable devices to use ISC.

In RQ3, the effects of usage patterns were only confirmed in the model of the portable device-centered user group. Similar to Yu et al. (2018), our study also proved that the two variables need to be considered together in the category of usage patterns. Specifically, users who have viewed multiple ISCs are more likely to continue to use ISCs. Thus, as users increase the number of uses, they have an attachment or loyalty to the ISC, and continuation occurs based on this. ISCs offered by Netflix have different genres. For example, Black Mirror: Bandersnatch is a TV series, and You Vs. Wild is reality. Puss in Book: Trapped in an Epic Tale is based on a fairy tale, Minecraft: Story Mode is a game, and Buddy Thunderstruck: The Maybe Pile is a stop-motion series. In an environment with such a variety of genres, users can easily increase the amount of ISC usage, which leads to continuation by mediating perceived interest. Future research needs to explore the mediating variables between the number of ISCs viewed and continuation. In the meantime, our study showed that the shorter a user’s Netflix subscription period is, the more likely the user is to continue ISC. This finding reflects the characteristics of the initial users. Users who have been using the OTT service for a long time are likely to have their own content repertoire. Users form a consistent usage pattern (repertoire) by considering their tastes and convenience in various content environments (Heeter, 1985). Initial users of the service have not clearly formed their usage patterns, so they are likely to actively explore innovative content, including ISC.

This study identified differences in the effects of perception and usage of ISC on continuation intentions and experiences depending on the type of device. Specifically, complexity and usage patterns were confirmed only in the model of the portable device-centered user group. In addition, the adjusted R²s of the portable device-centered user group were relatively high compared to those of the non-portable device-centered user group. These results differ from the previous study by Furió et al. (2013) which claimed that there were no significant differences in user perceptions across devices, indicating that the functional and user interface differences between non-portable and portable devices need to be considered in content usage studies. Users seem to be more likely to have a more consistent experience and perception when using portable devices, which are mainly a smartphone and a tablet, and the UI for interaction is screen touch, which is the same for almost all devices. In contrast, we counted smart TVs and personal computers as non-portable devices. These devices interact through a variety of controllers, such as remote control, keyboard, and mouse. Unlike portable devices with the same UI, non-portable devices have different UIs. Netflix has a cross-platform strategy that allows different devices to use ISCs with the same properties. This makes it clearer that the difference in impact across devices is due to the UI, not the content properties.

In this study, we have integrated the perspectives of three sub-interaction, innovations, and usage patterns. In this new media environment, the integrated model we propose is expected to adequately explain the adoption of innovative content. This study was also an initial study of ISC that expanded the scope of dependent variables by including intention to continue use, satisfaction, and flow experience. In future research, a detailed model can be developed through theoretical elaboration based on the relationship presented in this study.

Our findings have academic implications. First, this study makes a significant contribution to the understanding of ISC adoption by uniquely integrating perceived attributes of interaction, innovation, and usage patterns into a comprehensive framework. This integration is noteworthy as it brings together the subjective perceptions of users—capturing the complexities of interaction and innovation—and usage behaviors, which were often considered separately in previous research. Furthermore, by conceptualizing interaction as a multidimensional construct, this study enhances explanatory power, offering a deeper understanding of how different forms of interaction, such as Person to Person (P2P), Person to Computer (P2C), and Person to Storytelling (P2S), impact user engagement. This multidimensional approach helps to shed light on the nuanced factors influencing the adoption and engagement with ISC. Additionally, by identifying device-specific differences in user experience and perception, this study underscores the critical role of user interface design in adopting digital content. For example, we found that users who primarily consume ISC on portable devices may exhibit different engagement patterns compared to those using non-portable devices, highlighting the need for tailored design strategies. Future research could build on these contributions by exploring the relationships identified in this study with greater theoretical precision.

Our findings have practical implications for the digital content industry. First of all, to drive the adoption and continuation of ISC, users need to be fully aware of the interaction. If providers offer a community where users can share their experiences and explore the opinions of others after viewing ISC, they will perceive a high level of P2P, which will drive adoption. Similarly, there should be a variety of ways to interact within the story. To provide a rich story through interaction, providers need to build complex story flows, which takes a lot of time and money. Therefore, providers need to realize cost savings by considering the actual interactions that change the composition of the story and the ‘fake’ interactions, which give only interaction opportunities and do not affect the story structure. For example, ‘choose what cereal to have for breakfast’, or ‘choose what music the main character is currently listening to’. A study by Vosmeer and Schouten (2014) confirmed that the fake interaction did not differ significantly from the experience gained from the actual interaction. Therefore, it is necessary to form an efficient ISC so that users can access many opportunities for interaction by appropriately applying all dimensions of interaction. One thing we should keep in mind is that the complexity of interaction affects users differently depending on the device they use. According to our findings, increased complexity can negatively impact portable device users. This can be understood by considering the smaller screen size of portable devices compared to non-portable devices. Given the nature of ISC, where additional interaction buttons must be inserted into the content, this suggests that content providers should focus on keeping interactions intuitive and simple when designing the content. Providing simpler and more intuitive interactions can ensure a smoother and more enjoyable user experience. One possible suggestion is to minimize complexity and avoid obstructing the screen with too many buttons by not displaying the options right away. Instead, inform the user that they are in a section where choices are available, and allow them to view the options only when they want to reflect their choice. This approach gives users the option to engage with the complexity selectively, providing them with the flexibility to navigate without being overwhelmed. We also found that users with shorter Netflix subscription periods are more likely to adopt ISC. This can also be interpreted as suggesting that long-term subscribers, who already have loyalty to the platform, show less influence regardless of the new content type, since their loyalty is already established. However, we cannot overlook the fact that, based on these findings, consistently exposing new platform users to ISC content can be an effective promotional strategy that leads further to platform loyalty.

However, this study has the following limitations. First, this study has limitations in that data were collected exclusively from South Koreans, focusing solely on ISC content on Netflix. While Netflix, as the first OTT platform to introduce ISC and one that has garnered significant attention, was chosen as the research subject, the findings may be influenced by the unique context of South Korean users.

Second, the data used in this research are not up to date. Given the rapid advancements in interactive technologies, significant shifts in user perceptions and behaviors, particularly due to the COVID-19 pandemic, and the rapid development of generative AI, the findings may require additional variables to account for the temporal gap and changes in behavioral and technological factors. Further examination is needed to better understand the current dynamics and user engagement with interactive content. Finally, this study has a limitation in that it divided users' device usage types into two categories (non-portable devices centered user & portable devices centered user). While the focus was on examining the differing relationships based on different user interfaces, an explanation for users who use a combination of devices in a hybrid manner may be lacking.

Disclosure Statement

No potential conflict of interest was reported by the author.

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