Understanding the Impact of Social, Hedonic, and Promotional Cues on Purchase Intention in Short Video Platforms: A Dual-Path Model for Digital Sustainability

Abstract

In the context of eco-friendly e-commerce, understanding the psychological and experiential mechanisms that drive consumers’ online purchasing behavior is essential for promoting sustainable platform development. This study aims to fill a critical gap in the literature by examining how social interaction, entertainment, and sales promotion influence consumers’ purchase intentions through the mediating roles of perceived value and immersive flow experience. Grounded in the Stimulus–Organism–Response (S-O-R) theoretical framework, we developed a structural model and conducted an empirical analysis using survey data collected from 438 online shoppers. Data analysis was conducted using SPSS and AMOS through SEM. The results show that social interaction and sales promotion significantly enhance both perceived value and flow experience, which in turn positively influence consumers’ purchase intentions. However, entertainment exhibits a negative and significant effect on perceived value and does not significantly affect flow experience, indicating that hedonic content may not always translate into perceived usefulness or deep engagement. Moreover, the influence of social interaction on flow experience was also found to be negative and significant, suggesting that not all forms of interaction necessarily lead to immersive experiences. These findings highlight the complex psychological dynamics in digital consumption. This study contributes original insights by integrating psychological engagement mechanisms with the goal of digital sustainability, offering practical implications for online retailers aiming to enhance user engagement and platform longevity through experience-driven strategies.

1. Introduction

As of December 2024, the number of Internet users in China has reached 1.1 billion, and the Internet penetration rate has reached 78.6%, which highlights its position as the digital economy with the largest scale and most advanced infrastructure in the world [1]. The increase in Internet users has promoted the deep integration of “live streaming + e-commerce”. Live streamers utilize e-commerce platforms and short video platforms through interactive live streaming and community management, changing users’ purchasing habits and driving the rapid development of the e-commerce live streaming marketing industry. With continuous technological upgrades, the e-commerce live streaming industry is expanding and diversifying, becoming more intelligent and personalized to better meet user needs.

Short video platforms represented by Douyin (TikTok in China) and Kuaishou have become important carriers of information dissemination. Data shows that in 2023, the market size of short video advertising reached CNY 450 billion, accounting for 35% of the overall digital advertising market share [2]. Short video advertising has constructed a more consumer-friendly communication path through strong interactivity, high immediacy, and rich content forms. Research indicates that perceived interactivity plays a crucial role in consumers’ sense of participation and emotional connection [3]. For example, users’ interactive behaviors (such as liking, commenting, sharing) can effectively shorten the emotional distance between consumers and brands and enhance trust in brand content [4].

Promotional strategies have also shown a rich variety of forms in short video advertising, including limited-time discounts, personalized recommendations, and interactive incentives [5]. These promotional means can stimulate purchasing behaviors through low-threshold mechanisms and enhance consumers’ perceived value of products or services.

This study is grounded in three main theories. The Stimulus–Organism–Response (S-O-R) theory, originally developed by Mehrabian and Russell (1974) [6], suggests that environmental stimuli influence internal cognitive and emotional states, which in turn affect behavioral responses. Perceived value theory (Zeithaml, 1988) posits that consumers evaluate products based on the balance between costs and benefits [7]. Flow experience theory (Csikszentmihalyi, 1975) describes a state of complete immersion and optimal experience during activities [8]. To fill this gap, this study draws on three complementary theoretical foundations. First, the Stimulus–Organism–Response (S-O-R) theory provides a general framework for studying how external stimuli (e.g., social interaction, entertainment, and promotion) affect internal states and behavioral responses. Second, the perceived value theory explains how consumers weigh benefits and costs when forming purchase intentions. Third, the flow experience theory describes how immersive engagement affects satisfaction and behavioral commitment in digital environments.

Despite the growing popularity of short-video ads on e-commerce platforms, the psychological and experiential mechanisms behind consumers’ responses to these digital stimuli remain underexplored. Although previous studies have highlighted the importance of user engagement and content interactivity in shaping online purchasing behavior, existing research has mainly focused on utilitarian motivations or platform-specific features, while ignoring how social, hedonic, and promotional cues jointly influence consumer psychology in the context of sustainable digital commerce.

In this context, this study aims to achieve three objectives:

(1)

To explore how perceived social interaction, entertainment, and promotion affect consumers’ perceived value and immersive experience in short video ads;

(2)

To analyze how perceived value and flow experience affect consumers’ purchase intention in an environmentally friendly e-commerce environment;

(3)

To identify which types of stimuli have the most significant impact on consumer responses, thereby providing a reference for platform design and content strategy.

The expected contributions of this study are twofold:

First, it enriches the literature on consumer behavior in the field of sustainable e-commerce by integrating the emotional, social, and promotional dimensions into a unified model. Second, it provides practical suggestions for platform managers and digital marketers who seek to cultivate long-term consumer engagement and behavioral loyalty by improving the user experience.

2. Literature Review

2.1. Theoretical Background

Short-form video platforms have rapidly transformed the online content consumption landscape. Platforms such as TikTok, YouTube Shorts, Instagram Reels, and others allow users to create, share, and watch short video clips—typically ranging from a few seconds to several minutes in length—that are highly engaging and interactive [9]. Rapid advances in artificial intelligence have enabled these platforms to use advanced algorithms to curate personalized content feeds based on user preferences and behaviors, thereby optimizing user retention and attention [10]. Digital sustainability refers to the ability of platforms to balance rapid technological development and user engagement with environmental, social, and economic responsibility [11]. This is increasingly important as digital platforms expand globally and their positive and negative impacts become more prominent [12]. Short video platforms have a huge impact on the environment due to their high data and high energy consumption. Recent frameworks emphasize the interaction of society–eco-technology, proposing that the technological system (platform), social system (users and culture), and ecosystem must develop together to achieve overall sustainable development outcomes [13].

In the field of new media, especially on short video platforms represented by Douyin, the concept of PSI has gradually gained prominence. This concept mainly focuses on the interactivity users perceive between themselves and content creators or brands when watching short videos and how this interactivity affects their behaviors and attitudes [14]. On the Douyin short video platform, interactivity is manifested as users being able to immediately comment on, like, share, and even imitate and create related videos. This real-time interactivity greatly enhances users’ sense of participation and belonging. Wu Min points out that for social media advertising to stand out in the highly competitive new media environment, it must possess social attributes, which means that advertisements should encourage user participation, thereby forming an interactive community [15]. Jiang et al.’s research reveals the impact of information exchange during the interactive process on participants’ psychological perceptions [16].

In the new media environment, users are not only information receivers but also information creators and disseminators. This role transformation enables users to gain a higher degree of perceived control during the interactive process, thereby deepening their brand cognition and emotional investment. Liu Shasha emphasizes the importance of fan interaction for the dissemination effect of Douyin short video advertisements [17].

Sales promotion (SP) has a long history in commercial practice, with its core being the use of short-term incentives to stimulate consumer demand and accelerate the sales process. In the new media era, especially in short video advertising, SP has been given new meaning and broader influence. Currently, SP has expanded across multiple levels including personalized sales, product displays, and price discounts [18]. Personalized services, meticulous product displays, and price discounts together constitute effective means of attracting customers and stimulating purchase desires.

Entertainment represents a state or activity that can satisfy individuals’ needs for fun, esthetic experiences, and emotional expression [19]. In marketing, entertainment is regarded as a key factor that can stimulate consumers’ positive emotions and influence their perceptions and attitudes towards brands. Entertaining content can effectively attract audiences’ attention and evoke strong emotional reactions, providing unique dissemination advantages for advertisements [20]. When advertisement content is entertaining, consumers often show higher engagement and longer viewing times, directly increasing the advertisement’s exposure rate and influence [21]. Perceived value (PV), first proposed by Zeithaml, refers to consumers’ subjective evaluation of the balance between costs incurred and benefits received when assessing a particular product or service [6]. Woodruff further enriched this concept, defining it as consumers’ overall assessment of a product’s attributes, performance, and usage experience [22]. Chen and Dubinsky introduced perceived value into the Internet environment, particularly on e-commerce platforms, highlighting components such as platform risk perception, product quality judgments, price reasonableness, and usage convenience [23]. The concept of “Flow Experience” was first proposed by psychologist Mihaly Csikszentmihalyi in 1975, describing a state of self-forgetfulness achieved through high concentration and immersion, accompanied by profound satisfaction and pleasure [24]. Hoffman and Novak introduced flow experience into online shopping contexts, revealing that consumers may experience high concentration, pleasure, and self-control while browsing and shopping online [25]. Recent research on short video platforms shows that when users are interested in content, they become fully engaged and immersed, ignoring external distractions and experiencing pleasure that transcends daily life [26]. Purchase intention (PI) occupies a central position in behavioral economics and marketing, reflecting consumers’ predisposed attitudes towards specific products or services after acquiring information [27]. Ajzen’s theoretical framework emphasizes intention as a predictor of behavior, pointing out that purchase intention is not only an indication of action but also a direct precursor to actual purchase behavior [28].

2.2. Perceived Social Interaction (PSI), Entertainment (E), Sales Promotion (SP), and Perceived Value (PV)

The relationship between perceived social interaction (PSI) and perceived value (PV) has attracted widespread academic attention. In the short video advertising environment, high-quality interactions can increase users’ sense of participation, enabling consumers to perceive greater emotional connections and feelings of brand care [29], which enhances their overall value evaluation. Perceived social interaction (PSI) has a significant positive impact on user perceived value, enhancing their sense of belonging and satisfaction [30]. The dynamic role of perceived social interaction in information transmission significantly enhances users’ value perception [31].

Entertainment factors (E) significantly influence consumers’ perceived value (PV) [32]. Entertainment experiences provide emotional satisfaction and pleasure, and this positive emotional state directly affects consumers’ overall evaluation of products or services. When consumers feel entertainment while watching short video advertisements, they are more likely to transfer this positive emotion to their brand attitude, enhancing perceived brand value.

Sales promotion (SP) can directly enhance consumers’ perceived value by reducing purchase costs. Monetary promotions (such as price cuts and discounts) allow consumers to save real money and thus perceive a higher transaction value [33]. In short video advertisements, sales promotions provide consumers with immediate benefit perception, strengthening recognition of the brand’s price advantage and attractiveness [34]. Sales promotions influence purchase decisions through economic incentives and enhance overall value perception by creating scarcity and urgency [35].

Based on the above analysis, the following hypotheses are proposed:

H1. 

Perceived social interaction positively influences perceived value.

H2. 

Entertainment positively influences perceived value.

H3. 

Sales promotion positively influences perceived value.

2.3. Perceived Social Interaction (PSI), Entertainment (E), Sales Promotion (SP), and Flow Experience (FE)

Perceived social interaction significantly impacts flow experience (FE). Compared to independent situations, scenarios with rich social interactions (such as online live broadcasts, collaborative games, etc.) can more effectively induce flow experience, making the experiencers feel more immersed and enjoyable [36]. Research indicates that highly interactive short video content is more likely to immerse users, leading them into self-forgetful states [36]. Users’ active participation in interactive behaviors further enhances flow experience formation.

Entertainment (E) has a direct promoting effect on flow experience (FE). Entertaining content stimulates users’ interest and curiosity, guiding deeper exploration and experience, which is an important prerequisite for forming flow states [37]. Entertainment content can significantly enhance users’ sense of immersion and enjoyment, and promote the occurrence of flow experience. Especially in live broadcasts, virtual interactions, and other fields, if the content design is interesting and challenging enough, it can quickly stimulate the target users to “get into the state” [38].

Sales promotion (SP) may influence flow experience by stimulating consumers’ interest, increasing emotional investment, and enhancing satisfaction [39]. Huo et al.’s study demonstrated the impact of promotional stimuli on consumers’ impulse buying intention during live shopping, with particular emphasis on the mediating role of flow experience [40]. In scenarios such as live streaming and e-commerce, short-term, highly stimulating promotional activities (such as limited-time discounts and gifts) can enhance user participation and attention, making it easier for users to enter a flow experience state [40]. Based on the above analysis, the following hypotheses are proposed:

H4. 

Perceived social interaction positively influences flow experience.

H5. 

Entertainment positively influences flow experience.

H6. 

Sales promotion positively influences flow experience.

2.4. Perceived Value (PV), Flow Experience (FE), and Purchase Intention (PI)

Perceived value (PV) is crucial in marketing, reflecting consumers’ overall evaluation of products or services. Research consistently demonstrates that perceived value significantly influences purchase intention [41]. Perceived value (PV) is the core antecedent variable that drives consumer purchase intention (PI). Whether in e-commerce, mobile applications, social media, or entertainment and experiential consumption scenarios, PV has a significant positive impact on PI [42].

Flow experience (FE) is regarded as an important means of enhancing user satisfaction and loyalty, particularly in digital media industries [43]. When consumers can enter flow states while using products or services, their positive evaluations and purchase intentions significantly increase [44]. Flow experience (FE) can enhance users’ positive emotions, concentration, and pleasure, and is a key psychological variable to improve users’ purchase intention in scenarios such as digital applications, games, and live broadcasts. During a highly immersive flow experience, users’ purchase conversion rate and consumption intention increase significantly [45]. In short video advertising contexts, the influence of perceived value and flow experience is particularly significant. When users perceive high value and enter flow states while watching advertisements, they are more inclined to react positively, strengthening purchase intention [26]. These factors work together to significantly enhance consumers’ purchase desire.

Based on the above analysis, the following hypotheses are proposed:

H7. 

Perceived value positively influences purchase intention.

H8. 

Flow experience positively influences purchase intention.

Based on the above research background and assumptions, we propose the research model shown in Figure 1.

3. Methodology

3.1. Sampling Method and Participant Recruitment

This study employs a quantitative cross-sectional research design to investigate the underlying mechanisms of consumer behavior within the context of sustainable e-commerce, with a particular focus on users of short video advertising platforms. Given the exploratory nature of this study and the challenges associated with implementing probabilistic sampling in online environments, a non-probability convenience sampling strategy was employed [46]. To ensure the relevance of participants to the research context, two screening criteria were applied: (1) respondents must be active users of short video platforms such as Douyin, Kuaishou, or Xiaohongshu; and (2) they must have previously purchased products as a result of exposure to short video advertisements. These criteria were intended to ensure that participants had direct experience with both the medium and behavior under investigation. The main data collection was conducted through Wenjuanxing, a leading Chinese online survey platform. The survey was distributed via social media communities, including e-commerce interest groups on WeChat and Weibo, and relevant discussion forums. Both private invitations and public group announcements were used to recruit participants. The survey was introduced as an anonymous academic study, and participants were informed that their responses would be kept confidential and used solely for research purposes. To encourage participation, non-monetary incentives such as entry into a gift certificate draw were offered. To ensure relevance to the research topic, a filtering question was included at the beginning of the survey to verify whether respondents had experience purchasing via short video ads. This helped to differentiate active consumers of sustainable e-commerce content from casual or general digital users. Data were collected from 15 December 2024 to 20 January 2025, covering a peak online shopping period, thus enhancing the generalizability of the findings within the e-commerce context. A total of 438 valid responses were retained for analysis. As shown in

Table 1. Demographic characteristics.

	Categories	N	%
Gender	Male	218	49.8
	Female	220	50.2
Age	~18	16	3.7
	18~28	182	41.6
	26~30	82	18.7
	31~40	63	14.4
	41~50	38	8.7
	50~	57	13.0
Highest Level of Education	~High school	153	34.9
	Junior college	75	17.1
	Bachelor’s degree	144	32.9
	Postgraduate~	66	15.1
Occupation	Current student	102	23.3
	Employee of SOE/state-owned institution	34	7.8
	Employee of private enterprise	175	40.0
	Freelancer	57	13.0
	Other	70	16.0
Average Daily Time Spent Browsing Short Videos	~30 min	76	17.4
	30 min~1 h	146	33.3
	1~2 h	120	27.4
	2 h~	96	21.9
App	Douyin (TikTok)	216	49.3
	Kuaishou	73	16.7
	Xiaohongshu	74	16.9
	Bilibili	57	13.0
	Other	18	4.1
Short Video Viewing Duration	~1 year	113	25.8
	1~2 year	93	21.2
	2~3 year	86	19.6
	3 years~	146	33.3

, the sample was balanced by gender (49.8% male, 50.2% female) and comprised a predominantly young demographic, with 41.6% of participants aged between 18 and 28 years. Educational levels were generally high, with 65.1% holding a bachelor’s degree or above. In terms of occupational background, participants included students (23.3%), employees of private enterprises (40.0%), freelancers (13.0%), and public-sector workers (7.8%). This digitally fluent and highly educated cohort aligns well with the population of interest—active users of technology-driven e-commerce platforms—and thus offers an appropriate base for investigating sustainable digital consumer behavior. While the survey did not require participants to identify as “sustainability-oriented consumers,” the inclusion criteria ensured that all respondents were experienced users of e-commerce and short video shopping features, forming a valid proxy for sustainable digital commerce engagement.

3.2. Ethical Considerations and Data Protection

All procedures complied with ethical standards for academic research. Participation was strictly voluntary, and informed consent was obtained prior to initiating the questionnaire. Respondents were informed that the study was anonymous, no personally identifiable information would be collected, and the data would be used solely for non-profit academic purposes. The survey was self-administered online, and all data were stored securely in password-protected files accessible only to the researchers.

3.3. Data Analysis Techniques

The empirical analysis was performed using SPSS 24.0 and AMOS 26.0. Descriptive statistics were used to analyze demographic characteristics and explore initial patterns among the key constructs. Exploratory Factor Analysis (EFA) was conducted to examine construct dimensionality, followed by Confirmatory Factor Analysis (CFA) to assess measurement model fit and establish reliability and validity. Finally, Structural Equation Modeling (SEM) was used to test the hypothesized relationships among constructs and examine both the direct and indirect effects in the conceptual model.

3.4. Measurement Items

The measures of perceived social interaction (PSI), entertainment (E), promotion (SP), perceived value (PV), flow experience (FE), and purchase intention (PI) are shown in

Table 2. Measurement items.

Variable	Sample Items
Perceived Social Interaction Jiang et al. (2015) [47]; Huang, et al (2021) [48]; Ridings, et al (2002) [49]	“I feel that the short video enables effective communication with the brand,” “I feel involved in the short video,” “I feel the short video responds to my needs.”
Entertainment Sweeney & Soutar (2001) [50]	“I find the short video content enjoyable” “Watching this short video makes me feel happy” “This short video is highly engaging” “This short video helps me temporarily escape from everyday worries.”
Sales Promotion Chandon et al. (2000) [51] and Zhao, et al (2015) [52]	“The promotional activities in this short video offer consider-able discounts” “The promotional offers in the short video are time-limited” “The promotions are highly relevant to my needs” “The promotions make me feel I’m getting good value for money” “The promotions create a strong desire to purchase.”
Perceived Value Chen & Dubinsky (2003) [23] and Woodruff (1997) [22]	“I think the products recommended in this short video are worth the price” “This short video provides valuable product information” “The recommended products meet my needs” “Compared to others, the products in this short video offer greater value.”
Flow Experience Hoffman & Novak (1996) [25] and Chen (2014) [53]	“I am completely immersed when watching the short video” “I lose track of time while watching” “I concentrate deeply when watching” “I feel highly engaged while watching” “I forget the environment around me while watching the short video.”
Purchase Intention Gong et al. (2020) [54] and Song (2020) [55]	“I am willing to purchase the product recommended in the short video” “I would like to try the recommended product” “I am likely to purchase the product” “I would consider buying the product featured in the short video.”

. All constructs were measured using a 5-point Likert scale ranging from 5 (strongly disagree) to 1 (strongly agree).

4. Results and Discussion

Confirmatory Factor Analysis (CFA)

The structural model exhibited an overall satisfactory fit to the data, as shown in

Table 3. Results of Confirmatory Factor Analysis.

	Estimate		S.E.	C.R.	AVE	CR
	β	B
PSI6	0.631	1.000			0.395	0.797
PSI5	0.654	1.062	0.092	11.577
PSI4	0.631	0.977	0.087	11.249
PSI3	0.613	1.012	0.092	10.981
PSI2	0.642	1.021	0.090	11.410
PSI1	0.599	0.959	0.089	10.772
E4	0.617	1.000			0.390	0.719
E3	0.598	0.959	0.096	10.034
E2	0.654	1.005	0.094	10.725
E1	0.628	0.991	0.095	10.412
SP6	0.592	1.000			0.338	0.753
SP5	0.571	1.002	0.101	9.922
SP4	0.594	1.094	0.107	10.227
SP3	0.568	1.058	0.107	9.875
SP2	0.561	1.033	0.106	9.784
SP1	0.599	1.073	0.104	10.289
PV1	0.672	1.000			0.436	0.822
PV2	0.666	1.007	0.080	12.569
PV3	0.644	0.931	0.076	12.189
PV4	0.693	1.013	0.078	13.013
PV5	0.651	1.021	0.083	12.308
PV6	0.632	0.953	0.080	11.980
FE7	0.597	1.000			0.401	0.823
FE6	0.653	1.160	0.105	11.054
FE5	0.656	1.106	0.100	11.089
FE4	0.649	1.071	0.097	10.999
FE3	0.680	1.216	0.107	11.376
FE2	0.592	1.015	0.099	10.265
FE1	0.597	1.005	0.097	10.339
PI1	0.687	1.000			0.411	0.736
PI2	0.627	0.921	0.077	11.926
PI3	0.640	0.978	0.080	12.157
PI4	0.609	0.864	0.074	11.603

Model fit: χ²/df = 1.490, p < 0.001; RMR = 0.039; GFI = 0.911; AGFI = 0.896; CFI = 0.956; TLI = 0.951; IFI = 0.956; NFI = 0.878; RMSEA = 0.033. Notes. N = 438; Perceived Social Interaction (PSI); Entertainment (E); Sales Promotion (SP); Perceived Value (PV); Flow Experience (FE); Purchase Intention (PI).

. In this study, several commonly used fit indices were adopted to assess the measurement model’s adequacy. The threshold values were based on established recommendations in the literature: CFI ≥ 0.90 (acceptable), ≥0.95 (good fit); TLI ≥ 0.90; RMSEA ≤ 0.08 (acceptable), ≤0.05 (good fit); and (SRMR) ≤ 0.08. These criteria were adopted by Hu and Bentler [56]. The Chi-square-to-degrees-of-freedom ratio (χ²/df = 1.490) was well below the recommended cutoff of 3.0, while other key indices also supported model adequacy, including RMR (0.039), RMSEA (0.033), CFI (0.956), TLI (0.951), and IFI (0.956). GFI (0.911) and AGFI (0.896) were within acceptable ranges, and although NFI (0.878) was slightly below the conventional threshold, the model fit was deemed sufficient. While the average variance extracted (AVE) values for several constructs fell short of the 0.50 benchmark, all composite reliability (CR) values exceeded 0.70, indicating satisfactory internal consistency. In line with Fornell and Larcker’s (1981) [57] recommendation, high CR can compensate for marginally low AVE in establishing convergent validity. Therefore, the measurement model was considered adequate for hypothesis testing, though the relatively low AVE values suggest that further refinement of certain measurement items could be beneficial in future studies.

Table 4. Means, standard deviations, and correlations.

	Mean	S.D.	PSI	E	SP	PV	FE	PI
PSI	3.7557	0.73258	1
E	3.8316	0.76597	0.665 **	1
SP	3.7443	0.70679	0.701 **	0.579 **	1
PV	3.7352	0.75502	0.726 **	0.624 **	0.703 **	1
FE	3.7303	0.72810	0.652 **	0.598 **	0.705 **	0.720 **	1
PI	3.7003	0.79415	0.619 **	0.518 **	0.673 **	0.712 **	0.718 **	1

Notes. N = 438, ** p < 0.05; Perceived Social Interaction (PSI); Entertainment (E); Sales Promotion (SP); Perceived Value (PV); Flow Experience (FE); Purchase Intention (PI).

presents the key study variables’ means, standard deviations, and Pearson correlation coefficients. All variables were significantly and positively correlated at the 0.01 level, indicating meaningful relationships among the constructs. Perceived social interaction (PSI) was positively associated with entertainment (E) (r = 0.665, p < 0.01), sales promotion (SP) (r = 0.701, p < 0.01), perceived value (PV) (r = 0.726, p < 0.01), flow experience (FE) (r = 0.652, p < 0.01), and purchase intention (PI) (r = 0.619, p < 0.01). Entertainment was also significantly correlated with all other variables, including SP (r = 0.579, p < 0.01), PV (r = 0.624, p < 0.01), FE (r = 0.598, p < 0.01), and PI (r = 0.518, p < 0.01). The strongest correlation was observed between flow experience and purchase intention (r = 0.718, p < 0.01), followed closely by PV and FE (r = 0.720, p < 0.01). None of the correlations exceeded 0.80, indicating no multicollinearity concerns. These results support the hypothesized relationships and justify further testing through structural modeling.

Table 5. Hypothesis analysis results.

Hypothesis	Estimate	S.E.	C.R.	p	Result
H1 PSI → PV	0.760	0.310	2.449	**	Supported
H2 E → PV	−0.590	0.225	−2.623	**	Rejected
H3 SP → PV	0.956	0.223	4.278	***	Supported
H4 PSI → FE	−1.288	0.467	−2.761	**	Rejected
H5 E → FE	0.433	0.323	1.340	0.180	Rejected
H6 SP → FE	1.975	0.362	5.454	***	Supported
H7 PV → PI	0.388	0.120	3.250	**	Supported
H8 FE → PI	0.731	0.146	5.013	***	Supported

Model fit: χ²/df = 1.566, p < 0.001; RMR = 0.040; GFI = 0.948; AGFI = 0.890; CFI = 0.905; TLI = 0.944; IFI = 0.949; NFI = 0.870; RMSEA = 0.036. N = 438 **, p < 0.05, *** p < 0.01. Perceived Social Interaction (PSI); Entertainment (E); Sales Promotion (SP); Perceived Value (PV); Flow Experience (FE); Purchase Intention (PI).

presents the results of hypothesis testing. Specifically, H1 was supported, indicating that perceived social interaction (PSI) significantly enhances perceived value (PV) (β = 0.760, p = 0.014). In contrast, H2 was rejected, as entertainment (E) showed a significant negative effect on PV (β = −0.590, p = 0.009), which contradicted the expected positive association. Similarly, H3 was supported, with sales promotion (SP) exhibiting a strong positive influence on PV (β = 0.956, p < 0.001). Regarding the predictors of flow experience (FE), H4 was not supported; PSI exerted a significant negative effect on FE (β = −1.288, p = 0.006), which again opposed the hypothesized direction. Likewise, H5 was rejected due to the non-significant effect of entertainment on FE (β = 0.433, p = 0.180). In contrast, H6 received support, showing that SP positively influences FE (β = 1.975, p < 0.001). Finally, both H7 and H8 were supported. Perceived value positively affected purchase intention (PI) (β = 0.388, p = 0.001), and flow experience also significantly predicted PI (β = 0.731, p < 0.001). The overall structural model demonstrated acceptable goodness-of-fit indices (χ²/df = 1.566, RMSEA = 0.036, CFI = 0.905, GFI = 0.948), supporting the robustness of the proposed model. To facilitate a clearer understanding of the proposed hypothesized paths and results, they are shown in Figure 2.

5. Conclusions

5.1. Theoretical Contributions

Based on the Stimulus–Organism–Response (S-O-R) theoretical framework, this study constructs a multivariate model of consumer behavior in the context of short videos. By integrating antecedent variables such as perceived interactivity, entertainment, and promotional strategies with path analysis, this study has made important contributions to the sustainable development of the digital marketing field. The theoretical innovation of this study is mainly reflected in the following aspects:

First, it expands the applicable boundaries and influence mechanisms of the perceived value theory. This study verifies the significant positive impact of perceived social interaction on perceived value (PV) (H1), providing a new theoretical perspective for the application of Zeithaml’s perceived value theory in digital contexts. By integrating Wang’s [29] research on brand image in social media advertising and Zhao’s [31] et al.’s research on e-commerce live streaming based on the SOR theory, this study reveals the internal mechanism by which perceived interactivity enhances users’ psychological ownership and emotional involvement, thereby strengthening their cognitive evaluation of brand value. This finding not only enriches the constituent dimensions of the perceived value theory but also provides a theoretical basis for understanding the dynamic process of value co-creation in the digital native environment.

At the same time, the positive impact of entertainment (E) on perceived value (H2) was not confirmed in this study, which contradicts the results of Wang et al.’s brand community theory [29] “grass planting” marketing research. This result also shows that although users pursue pleasure and sensory satisfaction from platform content, because the platform prioritizes emotional appeal and lacks sufficient information disclosure and product transparency, the perceived value brought by their entertainment is easily transformed into a “satisfaction–boredom” cycle, which in turn forms an obstacle to brand trust and purchase intention—the negative impact of the patternization of short video content. A large amount of patterned and homogenized entertainment content can quickly cause users to lose their sense of freshness, and even numb them to the platform’s content [58]. This phenomenon of “modern boredom” is one of the mechanisms by which entertainment fails to improve perceived value and even lowers user evaluation.

In addition, this study verifies the promoting effect of promotional strategies (SP) on perceived value (H3), providing empirical support for the application of loss aversion theory and mental accounting theory in behavioral economics within the digital marketing environment. By integrating Hong’s [34] research on marketing strategies and Linghu et al.’s [35] research on decision-making influence mechanisms, this study reveals the dual mechanisms by which promotional strategies affect consumers’ value evaluation process through reducing perceived costs and enhancing perceived benefits.

Second, it deepens the understanding of the role mechanism of flow experience theory in digital consumption contexts. This study systematically verifies the positive impacts of perceived social interaction (H4) and entertainment (H5). Secondly, it deepened the understanding of the flow experience theory mechanism in digital consumption contexts. This study systematically verified that the positive effects of perceived social interaction (H4) and entertainment (H5) on psychological experience could not be verified. In some digital platforms (such as short videos and social media), this relationship is not always established, and even some hypotheses have not been verified. The study found that when social interaction becomes a formality or is superficial, it can lead to false interaction and social pressure, making it difficult for users to achieve a deep, immersive experience. The homogeneity of entertainment content or stimulation overload will cause the novelty to quickly fade and esthetic fatigue to accumulate, thereby weakening the psychological experience [59].

However, the effect of promotional strategies (H6) on flow experience was verified, which provides an important theoretical extension for the study of Csikszentmihalyi’s [8] flow theory in the field of digital media consumption. It is particularly worth noting that this study is the first to verify the role of promotional strategies in enhancing the flow experience in the context of short video advertising, providing a theoretical basis for understanding how commercial content can achieve marketing goals while maintaining user immersion. Based on Wei et al.’s [39] research on brand rituals and Huo’s [40] research on chain mediation effects, this study reveals that promotional activities promote the formation of a flow state through dual pathways: stimulating exploration motivation and reducing cognitive impedance.

Third, it constructs a dual-pathway-influence model of consumers’ purchase intention in the digital marketing environment. This study verifies the significant positive impacts of perceived value (H7) and flow experience (H8) on purchase intention, providing an important theoretical framework for understanding the decision-making mechanisms of digital native consumers. Based on Bai’s [41] customer value theory, and Wang’s [43] research on consumer behavior, this study demonstrates the synergistic mechanism of the cognitive evaluation pathway (perceived value → purchase intention) and the emotional experience pathway (flow experience → purchase intention) in the consumer decision-making process. At the same time, by integrating Wang’s [43] research on consumer participation, Ouyang’s [44] research on the influence of purchase intention, and Zhang’s [26] research on user behavioral intentions, this study provides a theoretical explanation framework for understanding how emotional immersion translates into behavioral intentions, enriching the application of emotional decision-making theory in the field of digital marketing.

5.2. Practical Contributions

The theoretical model constructed based on this study can provide some management implications for enterprises operating in a sustainable e-commerce ecosystem, especially those that use short video advertising as a core marketing strategy.

First, the results indicate that perceived social interaction, entertainment, and promotional appeal are crucial in shaping user-perceived value and immersive experiences. Therefore, digital marketing merchants or individuals in the context of the digital economy can establish a multidimensional content evaluation system to quantify these key psychological drivers. By tracking and analyzing indicators such as perceived interactivity, hedonic appeal, and promotional value, they can improve their content marketing strategies and increase precision and relevance.

Second, this study provides a systematic framework for optimizing the content ecosystem of short video platforms. Platform managers can utilize user behavior data, emotional response patterns, and value preferences to develop intelligent content recommendation algorithms that align more closely with user expectations. Additionally, the model can guide creators in developing content development toolkits to help them combine content features with user engagement triggers, thereby enhancing marketing efficiency and user satisfaction.

Third, this study provides theoretical support for enterprises that aim to build brand assets through emotional connections. In addition to traditional functional information delivery, enterprises should also establish long-term cognitive and emotional connections with consumers by providing consistent value and immersive brand experiences. Brand strategies based on flow experience can improve short-term purchase conversion rates and long-term brand loyalty, thereby creating sustainable competitive advantages in the digital market.

Fourth, the model lays the foundation for building a scientific marketing performance evaluation system. Companies can develop a more comprehensive evaluation framework that no longer relies solely on traditional indicators, such as click-through rate or conversion rate, but also incorporates indicators like perceived value enhancement, flow experience intensity, and emotional resonance. This multidimensional perspective can provide a more accurate evaluation of marketing ROI and serve as a reference for more effective resource allocation and strategic optimization.

In summary, this study not only contributes to the theoretical understanding of digital consumer behavior and sustainable e-commerce but also provides practical insights for companies seeking to meet the challenges of digital transformation. By incorporating social, emotional, and promotional factors into marketing design and evaluation, companies can better engage consumers, enhance platform experience, and achieve long-term value creation in an increasingly competitive digital environment.

5.3. Limitations and Future Research Lines

This study has some methodological and research context limitations. First, data collection was conducted using an anonymous self-administered online questionnaire, which protected the privacy of participants but may have introduced subjectivity and standard method bias. The lack of behavioral validation also limits the generalizability of the findings. Second, among the proposed hypotheses, H2 (entertainment → perceived value), H4 (social interaction → flow experience), and H5 (entertainment → flow experience) were not supported, suggesting that individual or platform-specific factors may moderate these relationships. Third, this study did not incorporate platform-generated behavioral data, such as user dwell time, click-through rate, or post-ad purchase behavior, which are essential for validating psychological concepts in real-world scenarios. Future studies should expand the sampling scope to encompass diverse user groups and platforms, employ stratified sampling methods, and integrate back-end behavioral data to capture consumer engagement more objectively. In addition, longitudinal or experimental designs can help to clarify causal relationships and reveal the long-term impact of immersive digital marketing strategies in the context of sustainable e-commerce.