How AI micro-short dramas shape users’ sharing behavior: visual persuasion, emotional empathy, and communication transformation—evidence from China

Abstract

Background:

Under the background of media convergence, micro-short dramas have become a popular form of communication because of their serial narrative structure and lightweight viewing features, and they are increasingly being integrated with digital technologies such as Artificial Intelligence Generated Content.

Methods:

Drawing on survey data from Chinese users with prior exposure to AI micro-short dramas, this study develops an integrated framework to examine users' sharing behavior. Grounded in media convergence, the framework combines visual persuasion theory, the Elaboration Likelihood Model (ELM), and the SIPS model, and incorporates visual persuasion features, immersive experience, perceived information density, emotional empathy, and sharing behavior to construct a sequential explanatory framework of “visual features–cognitive/experiential processing–emotional empathy–sharing behavior”.

Results:

The results show that the visual persuasive features of AI micro-short dramas significantly promote users' sharing behavior by creating immersive experiences, increasing perceived information density, and triggering emotional empathy.

Discussion:

This study enriches the research on the sharing behaviors of users of AI micro-short dramas, and provided useful references for improving content creation and dissemination methods in practical scenarios such as “AI + content dissemination”.

1 Introduction

In recent years, digital platforms, mobile interfaces, algorithmic recommendations, and audio-visual production have become increasingly interconnected, enabling media content to circulate across multiple platforms while reshaping the relationships among content producers, platforms, and audiences (Jenkins, 2004; Sang and Qu, 2023). This process is widely referred to as “media convergence”. This deepening media convergence has fostered numerous new communication tools and platform-based audiovisual forms. Among them, online micro-short dramas have expanded rapidly through compressed storytelling, lightweight viewing, and algorithm-driven circulation, thereby reshaping conventional audiovisual production and distribution and becoming a distinctive form of platform-based digital content (Yang, 2024). Online micro-short dramas usually refer to “online audio-visual programs with each episode ranging from several dozen seconds to about 15 min, featuring relatively clear themes and main plots, and having relatively continuous and complete storylines” (NRTA, 2022). According to Sensor Tower (2025), ReelShort and DramaBox ranked first and second on both the global revenue and revenue-growth charts for overseas short-drama applications in Q1 2025, generating USD 130 million and USD 120 million in in-app revenue, respectively. This market performance not only indicates that micro-short dramas are developing a considerable commercial monetization capability, but also demonstrates their international dissemination potential in global platform markets such as the United States, Southeast Asia, Latin America and India. Further analysis reveals that the rapid expansion of micro-short dramas is not merely the growth of a single content category. It is closely related to the structural changes in global media consumption patterns, including the preference for mobile viewing, the content distribution mechanism driven by algorithm recommendations, and the increasing demand from users for short-duration, intense plot, and continuous narrative content. Meanwhile, driven by technological advancements and content innovation, the formats and dissemination boundaries of micro-short dramas continue to expand. Especially with the integration of artificial intelligence technology, micro-short dramas are showing new development trends in content generation efficiency, visual presentation methods, and narrative expression styles. AI enhances the efficiency of script generation, virtual character development, scene construction, and audiovisual synthesis. At the same time, it reduces content production and iteration costs, making micro-short dramas easier to produce across platforms, distribute quickly, and promote across markets. For example, the AI + micro-short drama “Sanxingdui: Future Revelation” has achieved over 160 million views, and the related topic on Douyin has garnered over 135 million views. “Mohini”, an AI-driven music short drama launched through a collaboration between Equinox Virtual and Google Cloud, also reflects the exploration of AI in overseas short-form content creation, audiovisual expression, and social media dissemination. Although these cases come from different markets and platform contexts, they reflect broader trends in AI-generated content, platform-based dissemination, and high levels of user engagement, echoing the wider expansion of AI beyond technological and industrial applications into digital communication practices. Overall, AI micro-short dramas should not be simply understood as entertainment products, but rather as an emerging audiovisual format in which AI is increasingly integrated into content creation, visual expression, and platform-based distribution (Zhang, 2024). However, alongside the rapid expansion of micro-short dramas, the market has also seen a growing influx of homogeneous, formula-driven, and uneven-quality content. Deficiencies in narrative design, value articulation, and aesthetic quality may gradually erode user patience and trust, thereby creating new challenges for the sustainable development and effective dissemination of AI micro-short dramas.

Similar to short videos, long-form videos, live streaming, and other forms of online video content, AI micro-short dramas are primarily distributed through mobile platforms and algorithmic recommendation systems, with the core objective of capturing user attention and increasing engagement. However, as a media form shaped by the deep integration of artificial intelligence and audiovisual production, AI micro-short dramas differ from conventional online videos in their imagery, markability, and structurality: In terms of visual presentation, conventional online videos often rely on real-location shooting and traditional editing to convey narrative and emotional content. In contrast, AI micro-short dramas employ AIGC, virtual modeling, intelligent synthesis, and audiovisual coordination to rapidly generate visually intensive, imaginative, and conceptually distinctive scenes. Such AI-enabled visual construction can strengthen sensory appeal and facilitate social media engagement (Risqo et al., 2023). In terms of markability, traditional online videos typically shape characters through relatively complex and multidimensional narratives. In contrast, AI micro-short dramas tend to rely on highly stylized virtual characters, distinct character archetypes, and easily recognizable symbolic cues. These features help enhance viewers' memory, recognition, and impression formation (Forster, 1985). In terms of structurality, AI micro-short dramas are embedded within interactive and platform-based dissemination mechanisms, stimulating public emotions and driving continuous spread through likes, bullet comments, live chat interactions, and algorithmic recommendations (Li et al., 2022). Moreover, compared with the immediacy and fragmentation of short videos, AI micro-short dramas combine serialized storytelling with lightweight viewing, forming a distinctive narrative structure suited to mobile and fragmented media consumption (Hu and Li, 2024; Shen and Yin, 2024). Taken together, these features constitute a unique visual persuasion system of AI micro-short dramas. Nevertheless, how these visual persuasion features shape users' online sharing behavior remains insufficiently examined.

The influence of the visual persuasive features of AI micro-short dramas on online sharing is a complex process involving the joint effect of multiple factors. In digital video and social media contexts, prior studies have shown that immersive experience can strengthen users' psychological engagement and intention formation (Fang et al., 2019), information density on short video platforms influence users' interpretations and behavioral responses (Lu et al., 2023), and emotional empathy plays an important role in promoting interpersonal communication and the social sharing of content (Rimé et al., 2020). Furthermore, some studies have incorporated both immersive experiences and social influencing factors into the analysis of the mechanisms that shape users' intentions (Zheng et al., 2022). Some scholars also regard presence (a cognitive factor) and natural empathy (an emotional factor) as sequential mediating variables, and study their progressive influence on consumers' behavioral intentions (Chen et al., 2024). In this research context, although existing literature has revealed the driving factors of online video sharing willingness from different aspects, most of them have focused on a single mechanism or a specific situation. However, compared with general online videos, AI micro-short dramas exhibit more distinct technical generation and content expression characteristics. Currently, there has been no systematic exploration specifically targeting this emerging form of AI micro-short dramas. To systematically uncover the complex pathways between the unique visual persuasiveness of AI-generated micro-short dramas and users' online sharing behaviors, it is urgently necessary to establish a comprehensive theoretical framework.

As a branch of online behavior, user online sharing is a core element for the effective dissemination of emerging media such as AI micro-short dramas (Yang et al., 2022). It has extended to multiple fields including Short video marketing (Shen and Wang, 2024), consumer values (Gustavo et al., 2022), and advertising content characteristics (Lu et al., 2024). Among them, the Elaboration Likelihood Model (ELM) and the SIPS model (Sympathize, Identify, Participate, Share and Spread) have been widely applied in the research of user online sharing behavior. For instance, some research has explored the impact of short video language on users' sharing behavior and their willingness to share through the ELM (Feng et al., 2023; Fu et al., 2024). There are also studies that take the SIPS model as a framework to explore the impact of short videos on sharing and the problems existing in marketing promotion (Lv, 2023; Zhu and Luo, 2022). To further uncover the pathways of sharing behavior among emerging media users, scholars have also attempted to integrate source credibility and source attractiveness into the ELM model, or to combine ELM with the planned behavior theory (Li and Liang, 2025; Shi et al., 2018). Some scholars have also attempted to expand the SIPS model to systematically explain the complex sharing intentions of users on social media platforms (Chang et al., 2019). They have integrated the SIPS model with the Emotional Contagion theory to explore the transmission mechanism of negative emotions among social media users (Gao, 2025). The above exploration provides us with effective ideas for studying the formation mechanism of user sharing behavior in AI micro-short dramas.

In summary, although existing research has made valuable explorations into the drivers of users' online sharing behavior from a multidisciplinary perspective, most studies have focused on static and single-path mechanism testing. Against the backdrop of the rapid global expansion of short-drama applications and the growing integration of AIGC into content production, existing studies remain insufficient in explaining the multi-stage dynamic mechanism of AI micro-short dramas, which progresses from technological generation and cognitive processing to emotional arousal and behavioral response. However, while the ELM helps explain how users process persuasive messages through the central and peripheral routes, it offers limited insight into the subsequent behavioral shift from emotional engagement to active sharing. In contrast, although the SIPS model captures the process from emotional identification to sharing and diffusion, it relatively lacks theoretical support regarding the deeper interplay among information processing mechanisms, technological features, and psychological processes. In summary, from the perspective of media convergence, this study integrates relevant theories from psychology, consumer behavior, and management to construct a theoretical framework that combines the ELM model with the SIPS model. The research explores how the distinctive visual persuasion features of AI micro-short dramas, namely imagery, markability, and structurality, are associated with users' sharing behavior through immersive experience, perceived information density, and emotional empathy. By doing so, it develops a sequential explanatory pathway linking visual stimuli, cognitive and experiential processing, emotional empathy, and behavioral engagement. The study contributes to a deeper theoretical understanding of user behavior mechanisms in AI micro-short dramas, expands the applicability of related theories within intelligent generative media contexts, and also provides insights for content creation, user operations, and cross-market communication practices of AI micro-short dramas in global platform environments.

2 Theoretical background and research hypotheses

2.1 Theoretical background

Persuasion studies originated from classical persuasion theories. Messaris (1998) proposed three characteristics of visual persuasion: imagery (IMG, simulating real people or objects to evoke emotions), markability (MRK, images also have marking functions), and structurality (STRU, non-rigorous logical visual structure). The theory has been extended to various media types such as video and advertising (Simola et al., 2014; Ryan, 2019), effectively explaining how visual content influences user attention, memory, and attitude formation. However, AI micro-short dramas are not merely visual media in the conventional sense. Compared to traditional videos or advertisements, AI micro-short dramas possess stronger technological generativity, visual symbolism, narrative compression, and platform interactivity. Accordingly, this study adopts visual persuasion theory as the fundamental theoretical framework for explaining “media stimuli”. Based on this, imagery, markability, and structurality are identified as the three core visual characteristics of AI audiovisual content. Specifically, imagery captures the AI-enabled richness of content, genre diversity, and the clarity and vividness of visual presentation. Markability refers to the symbolic cues embedded in drama titles and character construction, especially AI-generated virtual characters and stylized character designs that enhance recognition. Structurality reflects platform-based interaction and the narrative organization of AI micro-short dramas, including likes, bullet comments, serialized storytelling, and lightweight viewing. Thus, visual persuasion theory addresses the question of “how AI micro-short dramas exert influence through which visual characteristics”.

Based on this, relying solely on visual persuasion theory is still insufficient to explain how users mentally process these visual features after encountering them. To reveal the underlying mechanisms by which visual stimuli influence users' psychological responses, this paper further introduces the Elaboration Likelihood Model (ELM). The ELM posits that individuals typically engage in two types of processing pathways when receiving persuasive messages: One is the central route, where users deeply process information based on its quality, relevance to content, and rational thinking. The second is the peripheral route, where users primarily rely on external cues and emotional inspiration to form quick judgments (Petty et al., 2009; Teng et al., 2014). In AI micro-short drama scenes, visual features closely related to the plot theme, informational content, and narrative continuity are more likely to trigger users' central route (Huber, 2016). Visual features closely related to the style, character tags, likes and comments, and visual impact, which are more easily and quickly recognized, are more likely to trigger users' peripheral route (Koh and Cui, 2022). Thus, the ELM addresses the question of “through which psychological processing routes users respond to the visual characteristics of AI micro-short dramas”.

However, the ELM primarily explains the information processing and attitude formation process, offering relatively limited insight into how users further engage in social interactions and communication behaviors after attitudes have been formed. As an emerging medium highly dependent on platform dissemination and user engagement, the communication effectiveness of AI micro-short dramas is reflected not only in whether users are persuaded, but also in whether they are willing to recommend, forward, and share. To explain this behavioral transformation process, this paper further introduces the SIPS model. The SIPS model emphasizes that in social communication environments, users undergo a dynamic evolution process of “empathy - validation - participation - sharing - diffusion”, effectively explaining the behavioral chain through which users transition from emotional identification to active dissemination in digital media contexts (Dentsu, 2023). However, if all five links of the SIPS model are incorporated into the analytical framework, it will not only be difficult to highlight the key nodes in the dissemination mechanism of AI micro-short dramas, but also easily lead to a complex model structure and a scattered focus. In fact, with their high-density narrative, strong emotional rhythm and serialized structure (Hu and Li, 2024; Shen and Yin, 2024), AI micro-short dramas often trigger intense emotional empathy among users in a very short time, making “emotional empathy” (EE) the key starting point for subsequent behaviors. The “sharing behavior” (SB) is the terminal outcome and value realization node of the dissemination chain. Therefore, this study distills the SIPS model into the core causal chain of “emotional empathy-sharing behavior”. This approach not only retains the behavioral evolution logic core of the original model, but also makes the theoretical focus more concentrated. Meanwhile, considering the narrative characteristics of AI micro-short dramas, such as their strong immersive experience (IE) and high perceived information density (PID), they play a crucial contextual role in driving EE towards SB (Hu and Li, 2024). This study embeds it as a key situational variable into the SIPS framework to enhance the model's adaptability and explanatory power for the unique dissemination logic of AI micro-short dramas. Therefore, the SIPS model in this paper refers to “how users transform processed psychological responses into sharing behaviors”. In summary, this paper maps the three components to the three stages of “visual stimulus recognition - user psychological processing - communication behavior conversion”, establishing an integrated explanatory framework of “visual features - cognitive/experiential processing - emotional empathy - sharing behavior”.

2.2 Research hypotheses

2.2.1 The influence of visual persuasion in AI micro-short dramas on IE

The visual persuasive features of AI micro-short dramas that can quickly attract a large number of viewers are specifically reflected in three aspects: IMG, MRK and STRU.

2.2.2 The influence of visual persuasion in AI micro-short dramas on PID

2.2.3 The mediating role of IE

2.2.4 The mediating role of PID

2.2.5 The chain mediating effect of IE and EE

2.2.6 The chain mediating effect of PID and EE

Figure 1

3 Variable measurement and research design

3.1 Research context, sample screening, and data quality control

Artificial intelligence is increasingly integrated into the creation, production, and dissemination of micro-short dramas, reshaping key processes such as scriptwriting, virtual character design, visual scene construction, and algorithmic content distribution. In China, these AI-enabled practices have produced several highly visible cases, reflecting strong user engagement and the growing communicative potential of AI-generated audiovisual content. This study does not regard Chinese users as fully representative of global micro-short drama audiences. Instead, it treats the Chinese market as a theoretically meaningful and empirically relevant context. With its early-mover advantage, large-scale user base, active content innovation, and internationally expanding micro-short drama applications, the Chinese market provides an appropriate setting for examining the communication mechanisms of AI micro-short dramas (Wang and Liang, 2025).

3.2 Variable measurement

This study adopted the online questionnaire survey method for data collection. The scale design is based on mature scales and adapted to the research context. The reliability and validity tests and item iteration optimization were carried out through a pre-survey (with 113 valid questionnaires). In the pre-survey stage, statistical analysis and manual screening were comprehensively applied to compare the score differences of each item between the original questionnaire and the revised one, and to identify the items with significant deviations. On this basis, through online interviews, further explore possible cognitive biases of the respondents in semantic understanding, expression habits and situational adaptability, and accordingly make semantic corrections and structural optimizations to the scale. Additionally, the interview data were not used for hypothesis testing but served as supplementary qualitative evidence to enhance the semantic clarity, contextual appropriateness, and content validity of the measurement items.

The final scale composition is detailed in Table 1. Due to space limitations, this study does not elaborate on the above revision process. The measurement of each variable includes: IMG (3 items), MRK (2 items), STRU (6 items), IE, PID, EE (3 items), and SB (3 items), all of which are measured using a five-point Likert scale. The questionnaire also collected demographic information such as gender, age, and educational attainment.

3.3 Sample selection

The research collected 386 valid questionnaires through Credamo, with the sample covering different genders (43.8% male and 56.2% female), ages (90.9% under 40 years old), and educational backgrounds (97.4% with college degrees or above), ensuring the representativeness and comprehensiveness of the study. The sample characteristics are shown in Table 2.

3.4 Data analysis strategy

This study conducts data analysis in the following sequence: variable relationship testing, structural model testing, and mediation effect testing. First, the reliability and convergent validity of the scale were assessed using Cronbach's α, composite reliability (CR), and average variance extracted (AVE). Second, discriminant validity was assessed using the Fornell-Larcker criterion and the HTMT ratio test. Again, the Pearson correlation analysis was conducted to preliminarily examine the relationships among variables. Finally, given that the research model includes multiple latent variables and continuous mediation paths, path analysis was conducted using structural equation modeling (SEM) with maximum likelihood estimation (MLE), and the bootstrap resampling method was employed to test the mediation and chain mediation effects.

4 Empirical testing and analysis of results

4.1 Reliability and validity tests

This study mainly employed Cronbach's α coefficient for reliability analysis. As shown in Table 3, the Cronbach's α coefficients of all variables were greater than 0.8 (Hair et al., 2010), and the CR was above the threshold of 0.7 (Fornell and David, 1981), indicating that the reliability of all variables was good. In terms of validity testing, the AVE of each variable is all above the threshold of 0.5 (Fornell and David, 1981), indicating that there is a high convergent validity among the variables.

According to Table 4, the square root of the AVE for each variable is greater than the correlation coefficient between that variable and other variables, which indicates good discriminant validity (Hair et al., 2014).

To further examine the discriminant validity among the latent variables, this paper employs the HTMT ratio for the test. According to Table 5, the HTMT values among the latent variables range from 0.404 to 0.667. All of these values are below the strict threshold of 0.85 and the more lenient threshold of 0.90. This indicates that the constructs have good discriminant validity (Hair et al., 2021; Henseler et al., 2015).

4.2 Correlation analysis

This study explores the relationships among variables through Pearson correlation analysis. As shown in Table 6, all variables are significantly positively correlated (R > 0) and significant at the 99% confidence level (Cohen, 1988).

4.3 Structural model validation

4.3.1 SEM model fit test

As the model involves latent variables and multiple mediating paths, this study employs SEM and MLE for hypothesis testing. The model is shown in Figure 2, and the goodness-of-fit results are presented in Table 7: CMIN/DF = 2.405 (1–3), RMSEA = 0.060 < 0.08, which is acceptable. IFI, TLI, and CFI are all greater than 0.9 (Hu and Peter, 1999), reaching an excellent level. It can be seen that the model fits well.

Figure 2

4.3.2 Results of hypothesis testing for SEM model path relationships

The results of the hypothesis test are shown in Table 8. To enhance the robustness of the structural model results, this study further employed Hayes's (2017) Bootstrap to test each path. The Bootstrap was set to run 5,000 times, and a 95% bias-corrected confidence interval was calculated. If the confidence interval does not include 0, it indicates that the path is significant. IMG → IE (β = 0.301, p < 0.05), IMG → PID (β = 0.344, p < 0.05), and the 95% Bootstrap bias-corrected confidence intervals are [0.140, 0.452] and [0.206, 0.483] respectively, neither of which includes 0. This indicates that H1 and H4 are supported. Further analysis shows that all the remaining paths are also significant. Therefore, hypotheses H2–H6 are supported as well. The test results of all paths indicate that the theoretical relationships proposed in the model are consistent with the empirical results. Therefore, hypotheses H1–H6 are all supported.

4.3.3 Test results of mediating effect

The data in Table 9 indicates that IE has a significant mediating effect between IMG and EE. The 95% bias-corrected bootstrap confidence interval is [0.066, 0.257], and the percentile bootstrap confidence interval is [0.065, 0.255]. Neither of these intervals includes 0. Therefore, Hypothesis H7 is supported. Similarly, Hypotheses H8–H18 are also supported.

5 Discussion

5.1 Direct effect

5.1.1 The impact of visual persuasion characteristics of AI micro-short dramas on IE

The visual persuasive features of AI micro-short dramas significantly influence IE through central and peripheral routes. IMG: The plot is rich in content, providing users with IE (Jacob et al., 2019). AI micro-short dramas come in many types and can meet users' different needs. Meanwhile, AI continuously optimizes images, scenes and special effects, providing technical support for a more immersive viewing experience. MRK: The drama title is a condensed version of its plot (Ding, 2022), helping users to filter and draw their attention. The flat character design empowered by AI makes the characters vivid and recognizable, easy for users to remember and enhancing the sense of immersion and engagement (Forster, 1985). STRU: TL and BC convey the popularity of short dramas to users. SF makes the plot more coherent and the content more abundant, creating a more immersive and continuous plot environment (Rózsa et al., 2022). And LF meets the current users' characteristic of fragmented memory.

5.1.2 The impact of visual persuasive characteristics of AI micro-short dramas on PID

The visual persuasive features of AI micro-short dramas significantly influence PID through central and peripheral routes. IMG: AI micro-short dramas convey rich information to users through technical means such as algorithm generation, virtual modeling and intelligent synthesis (Li, 2024). The rich variety of types enables users to filter based on their preferences and big data. High-definition picture quality provides users with a good viewing experience. MRK: The drama title can convey information such as the plot theme and values. AI-generated characters often possess highly distinct, labeled and uniformly styled features. Such more recognizable virtual images can enhance the memory points of the characters, making it easier for users to identify and accept the information carried by the characters (Clay, 2000). STRU: TL and BC conveys information about the public's affection. SF enables AI micro-short dramas to offer more frequent and dense content output while maintaining plot coherence. LF makes AI micro-short dramas have the characteristics of convenient viewing and targeted dissemination. Users can watch them at any time with existing devices.

5.2 Mediating effect

5.2.1 The mediating effect of IE and PID

IE plays a mediating role in the relationship between visual persuasion and EE. IE make users willing to participate and persist in the activity (Csikszentmihalyi, 1975). During the viewing process, users are influenced by these AI-driven visual persuasion features, constantly driven by novel plot settings, the fusion of virtual and real audio-visual effects, and intense sensory stimulation, thus generating a strong IE. This sense of immersion further intensifies users' emotional investment in the plot scenarios and the fates of the characters, thereby triggering EE related to the plot.

PID plays a mediating role in the relationship between visual persuasion and EE. The visual persuasive feature of AI micro-short dramas endows them with stronger advantages in information generation and compressed expression. By leveraging features such as intelligent generation, multi-modal fusion, and efficient content arrangement, AI micro-short dramas can more concisely present multi-level information and enhance the compactness and novelty of content expression. Therefore, users can more easily find topics that interest them, capture their attention, and evoke empathy with their own experiences and emotions. As a result, even within a short viewing time, users are more likely to be emotionally touched and cognitively engaged, thereby further stimulating EE (Sedej, 2019).

5.2.2 The chain mediating effect of IE, PID, and EE

IE and EE play a chain mediating role in the relationship between visual persuasion and SB. By leveraging unique features such as intelligent generation, virtual-real fusion, and high-density audio-visual expression, AI micro-short dramas enable users to have an IE (Csikszentmihalyi, 2000), thereby evoking EE. This EE and sense of participation prompt users to engage in social interaction behaviors, such as sharing (Chen, 2018). This further encourages them to share AI micro-short dramas on social media platforms, thereby expanding the reach and influence of AI micro-short dramas.

PID and EE play a chain mediating role in the relationship between the visual persuasion and SB. AI micro-short dramas convey a large amount of content through intelligent image generation, multi-modal fusion expression, and high-frequency information output, thereby enhancing users' understanding of the plot setting, technological novelty, and value connotation, increasing their sense of identification, immersion, and EE, and ultimately promoting users' social interaction (Conover et al., 2011).

6 Implications and suggestions

6.1 Implications

Drawing on insights from visual persuasion theory, the ELM, and the SIPS model, this study develops an interdisciplinary framework for explaining users' SB in the context of AI micro-short dramas. This framework offers a new theoretical perspective for understanding how AI-generated content influences user behavior through the combined effects of visual symbols, IE, information processing, and emotional activation. At the same time, it also provides strong theoretical support and decision-making references for promoting the production, dissemination practice and strategy optimization of AI micro-short drama content.

Based on the distinct characteristics of AI micro-short compared to traditional online videos, such as SF and LF, this study extends and redefines the IMG, MRK, and STRU elements within the ELM framework. Meanwhile, AI micro-short dramas can leverage generative expression to offer users stronger IE and higher PID, and further incorporate EE as a variable, thereby providing a multi-layered explanation for the formation process of user SB. This approach extends the application of the SIPS model to the field of intelligent content generation and dissemination. It also indicates that AI micro-short dramas do not merely influence user behavior through superficial visual appeal. Instead, they can gradually guide users from content exposure to social sharing through the combined effects of immersion, information perception, and EE, thereby refining the theoretical understanding of the formation mechanism of SB.

Under the context of AI micro-short dramas, this study empirically reveals a complete chain of “visual persuasion - IE/PID - EE - SB”, breaking through the existing research that mostly stays at the analysis paradigm of single paths or parallel mediations. This discovery provides empirical support for research on the dissemination effect of AI micro-short dramas. It also deepens the theoretical understanding of the connection between visual persuasion theory, the ELM model, and the SIPS model. Meanwhile, in the context of AI-generated content, users' SB exhibits more distinct multi-stage, hierarchical, and interactive characteristics. This offers new analytical perspectives for future research on the dissemination of AI content and the mechanisms of user responses.

6.2 Suggestions

Research shows that the visual persuasive features of AI micro-short dramas can significantly influence users' IE and further affect their SB. Therefore, when platforms and content creators engage in the production of AI micro-short dramas, they should pay more attention to the collaborative design of IMG, MRK, and STRU. In terms of IMG, the advantages of AI generation technology in scene construction, character creation, special effects presentation and image optimization should be fully exploited. By providing content with greater visual impact and a sense of integration between reality and virtuality, the user's sense of immersion can be enhanced. In terms of MRK, content creators should refine drama titles and strengthen character construction to help users quickly identify the thematic focus of the content and develop viewing interest within a short time. In terms of STRU, it is necessary to enhance the continuous appeal of the content through serialized narratives, bullet comment interactions, like feedback, and lightweight rhythm design. For AI micro-short dramas, creators need to keep improving intelligent generation, spectacular expression, and highly recognizable style. These features can help users feel more immersed. They can also create a better basis for later SB.

PID played an important mediating role in the process where the visual persuasive features of AI micro-short dramas influenced users' EE and SB. This shows that the communication effect of AI micro-short dramas depends not only on whether they are interesting to watch, but also on whether they convey information effectively. Therefore, content producers should focus on enhancing the information-carrying capacity per unit of time during the creative process. They should fully leverage the advantages of AI micro-short dramas in algorithmic generation, multi-modal integration, and condensed content expression, organically integrating plot information, product information, emotional information, and value information. Specifically, creators can be achieved by strengthening the signaling function of titles, refining character labels, incorporating plot-relevant information, and embedding product-related expressions within specific narrative contexts, thereby helping users quickly identify key plot points and value propositions within a limited viewing time. At the same time, creators should avoid information overload and unbalanced expression. Too much information may reduce users' understanding and acceptance. By making information more compact, clear, and relevant, AI micro-short dramas can better improve users' cognitive engagement and increase their willingness to share further.

The results further indicate that EE plays a central role in both the IE → EE → SB and PID → EE → SB pathways, highlighting its importance in explaining users' SB in the context of AI micro-short dramas. These findings suggest that users’ willingness to share depends not only on technological novelty and information richness, but also on the extent to which they develop emotional empathy during viewing. Therefore, the operators of AI micro-short dramas should pay more attention to cultivating the mechanism of EE. The AI's content generation capabilities can be harnessed to create more compelling plot scenarios, character fates, and value conflicts, thereby enhancing users' emotional engagement through narrative expression. In this way, users can better understand the content and gradually form value recognition. Especially in the context of social media dissemination, EE often more readily translates into interactive behaviors such as comments, likes, and shares. Therefore, AI micro-short dramas should consider technical appeal, information expression, and emotional connection in their content design. Only in this way can they move beyond simple viewing and more effectively encourage social sharing.

As AI micro-short dramas are still in a phase of rapid development, their content formats, technical features, and platform mechanisms continue to evolve. Future research could further expand to different countries, platform types, genres, and user groups, enabling cross-cultural and cross-platform comparative analyses. Meanwhile, future research could also focus on differences among various groups in information reception, emotional interaction, and sharing psychology, revealing how group characteristics influence users’ communication behaviors.

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