Blind boxes originally referred to the sale of boxes of bubble gum, collectible cards, and toys from vending machines, which were especially popular in the 1960s and 1990s (Mvondo et al., 2023). Blind boxes ignite the purchasing enthusiasm of consumers through offering unique uncertainty, novelty, and symbolism (Zhan & Xiong, 2024; Y. Zhang et al., 2022). Between 2015 and 2022, the scale of China’s blind-box market surged from RMB 2.26 billion (USD 0.36 billion) to RMB 14.99 billion (USD 2.23 billion; Z. Zhang, 2021). The maturation of live streaming technology and the ongoing evolution of e-commerce platforms have established blind-box live streaming as a multifaceted consumption phenomenon encompassing entertaining value, purchasing behavior, and social connectivity (Tian, 2025). The distinctive appeal lies in the combination of the mystery of blind boxes with the interactivity of live streaming, thus creating a new shopping experience (Tian, 2025). This sense of mystery and surprise attracts a certain consumer group, sparks their curiosity and buying desire, and correlates with their buying behavior (Gong et al., 2024; Mi, 2022). Studying blind-box live streaming can help researchers understand consumers’ psychological and decision-making processes when facing uncertainty, offering a reference for other industries’ marketing strategies.

 

Impulse buying enables corporations to profit from consumers’ fleeting joy and enthusiasm, and it is especially effective in live stream commerce (Y. Zhang et al., 2024; Zheng et al., 2019). Live stream commerce frequently includes one-time deals or limited-time offers, which can engender a sense of urgency and scarcity that supports impulsive purchasing (Gu et al., 2024; Yuan et al., 2025). Prior research has identified crucial factors related to consumers’ impulsive buying, such as streamers’ characteristics (Bawack et al., 2023; L. Li et al., 2024; X. Li et al., 2024; Ni & Ueichi, 2024), the experience of immersion or flow (Y. Sun et al., 2019), social presence (X. Li et al., 2024; Zheng et al., 2019), and interactivity (Bawack et al., 2023; Liao et al., 2023; Yang et al., 2022). Other researchers have expanded the environmental consideration to include website-quality characteristics, network technology, marketing activities, product types, and e-store atmosphere stimuli (Floh & Madlberger, 2013; Liu et al., 2013). These findings bridge a critical theoretical gap in understanding the psychological mechanisms of online impulsive buying. Nevertheless, few studies have systematically examined how the convergence of blind-box mystery mechanics and live streaming immediacy creates distinct stimulus patterns that alter traditional impulsive buying pathways.

 

Research has demonstrated that viewers’ purchase decisions can be significantly shaped by peer influence through real-time bullet-screen interactions during live streaming (Meng et al., 2021). Emotionally charged comments from other audience members can create a collective affective state that promotes impulsive consumption behaviors (Q. Wang et al., 2025). Studies have employed emotional contagion theory to elucidate the mechanism whereby streamers’ emotional displays evoke corresponding affective responses in viewers, ultimately boosting their purchase intention (L. Li et al., 2024; Y. Zhang et al., 2024). Building on these findings, we conceptualized positive affect as a mediator of the relationship between blind-box live streaming features and consumers’ impulsive buying tendencies.

 

Studies have specifically highlighted the attributes of blind boxes and the psychological motives behind consumer purchases, particularly how they satisfy consumers’ curiosity and desire to collect items (Lee et al., 2025; Mi, 2022; Y. Zhang et al., 2022). The consumption appeal of blind boxes derives from the scarcity effect, uncertainty anticipation, and social engagement (Diao, 2025). Therefore, researchers should consider the attributes of blind boxes to ensure a thorough investigation regarding impulsive buying and emotional contagion.

 

Although scholars have investigated psychological factors and external stimuli in e-commerce live streaming, consumers’ impulsive buying has not been fully studied. Particularly in the context of blind-box live streaming, the synergistic effects of these factors on impulsive consumption remain underexplored, representing a critical gap in the marketing literature. Accordingly, this study adopted the stimulus–organism–response (SOR) framework to systematically examine the antecedent factors and underlying mechanisms driving impulsive purchases in blind-box live streaming contexts. SOR theory proposes that a stimulus influences a person’s internal affective evaluations (organism), which, in turn, leads to an approach or avoidance response (Chen et al., 2025; Floh & Madlberger, 2013). Our proposed model identified live streamers’ characteristics, the live streaming servicescape, and blind-box attributes (stimuli), which act through the mediator of positive affect (organism) to generate impulsive purchasing outcomes (response) in live-commerce settings. This study was designed to extend understanding of the antecedent variables of consumers’ impulsive buying intention and provide actionable insights for blind-box live stream marketing.

Grounding our research in SOR theory, we developed a conceptual model to examine how environmental stimuli and psychological states collectively correlate with impulsive buying decisions in live streaming blind-box scenarios. Live streamers’ characteristics were considered as key stimuli, in that streamers’ active body language, infectious tone, and enthusiastic explanations convey their positive emotions and enhance viewers’ product anticipation and purchasing desires (Hughes et al., 2019; X. Li et al., 2024).

 

A good servicescape can enhance consumers’ emotional experience, making them more willing to immerse themselves in the live streaming atmosphere, which promotes active interaction with both streamers and fellow viewers, ultimately increasing susceptibility to impulsive purchasing (Mari & Poggesi, 2013; Tombs & McColl-Kennedy, 2003; Tong et al., 2023; Wang et al., 2025). Blind-box products offer mystery and randomness, which stimulate consumers’ curiosity and desire for exploration (Gong et al., 2024; Lee et al., 2025).

 

Our theoretical model is shown in Figure 1.

Emotional contagion refers to the process in which a person or group influences the emotions or behavior of another person or group through the conscious or unconscious induction of emotion states and behavioral attitudes (Barsade et al., 2018). Emotional contagion plays a significant role in e-commerce live streaming, influencing consumer behavior through the transmission of emotions from streamers to viewers via both verbal messages and nonverbal cues such as facial expressions, gestures, and vocal tone (Hughes et al., 2019). These methods can deepen the emotional connection between the streamer and the audience, increasing the likelihood of emotional contagion (L. Li et al., 2024). In e-commerce live streaming, positive emotions and excellent interaction skills displayed by the streamer are crucial for enhancing the audience’s emotional experience (Lu et al., 2023). In particular, streamers who are enthusiastic, have a strong sense of humor, and are good at interacting can effectively stimulate the viewers’ excitement, joy, satisfaction, and other positive emotions, which may have a positive relation with transaction data (X. Li et al., 2024).

 

When the streamer reveals the contents of the blind box during the live stream, the viewer’s emotional reactions may be amplified, thereby creating a stronger relationship with the streamer. Thus, we proposed the following hypothesis:

Hypothesis 1: The characteristics of live streamers (e.g., enthusiasm, interactivity, and professionalism) will be positively related to consumers’ positive affect.

 

A servicescape comprises the carefully designed and controlled environmental factors in a broader service setting (Bitner, 1992). It is associated with consumers’ shopping intentions or satisfaction, especially in the context of e-commerce website service settings (Lai et al., 2014; Tankovic & Benazic, 2018; Yadav & Mahara, 2020). The design of a live streaming servicescape, including background music and aesthetic or functional visual stimuli (e.g., lighting, color, or visual complexity) can enhance the audience’s sense of immersion (Tong et al., 2023). Meanwhile, the active interactions and tense atmosphere in live streaming provide a sense of presence to consumers (Ma et al., 2023). The real-time interactivity inherent in live streaming platforms, which encompasses instant commentary, engagement metrics, and virtual gifting, facilitates a dynamic emotional feedback loop between streamers and their audiences (You et al., 2023). Therefore, the better the service setting of blind-box live streaming, the better the sense of presence it creates, allowing consumers to feel immersed, which may enhance their positive emotions.

 

While the atmospherics–affect link of servicescapes has been documented in e-commerce settings (Lai et al., 2014; Tankovic & Benazic, 2018; P. Wang et al., 2025; Yadav & Mahara, 2020), scant research has systematically examined how servicescape cues in blind-box live streaming evoke viewers’ emotions. Given that the core value of blind-box consumption lies not in the physical product per se but in the peak emotional experience of the reveal moment, and that the visual, auditory, and interactive design elements of the live stream constitute key exogenous stimuli that can amplify or attenuate this peak (Gong et al., 2024), we argue that the servicescape effect in blind-box live streaming merits independent investigation. Thus, we proposed the following hypothesis:

Hypothesis 2: The servicescape of blind-box live streaming (e.g., visual design, layout, and interactive features of the live streaming environment) will be positively related to consumers’ positive affect.

 

During live streaming, the emotional reactions of the streamer when revealing the contents of the blind box may be amplified, thereby intensifying the audience’s emotional contagion and enhancing their positive affect. Attributes of blind boxes, such as design, packaging, and intellectual property collaborations, can arouse consumers’ desire to collect (Gong et al., 2024; Hill et al., 2016; Shen et al., 2015). These attributes not only increase the appeal of blind boxes but also elicit positive emotional responses in consumers, such as joy, excitement, and a sense of satisfaction (Zhan & Xiong, 2024). The social attribute of blind boxes allows purchasers to share consumption experiences and unboxing experiences across social networks, which can strengthen emotional bonds among consumers and create a sense of emotional sharing.

 

Offline-setting studies have supported the uncertainty–surprise–positive affect chain (Hill et al., 2016), although whether and how these product cues operate when the surprise is visually amplified in real-time live streaming remains untested. We conceptualized blind-box product appeal as a second-order construct reflected in four core dimensions: mystery, variety, uncertainty, and perceived value. In the live streaming context, these cues simultaneously trigger viewers’ curiosity and collecting motive, which are then converted into positive affect. Thus, we proposed the following hypothesis:

Hypothesis 3: The attributes of blind boxes (mystery, variety, uncertainty, perceived value) will be positively related to consumers’ positive affect.

 

Impulsive buying is a consumer behavior characterized by a sudden, often powerful and persistent, urge to purchase something immediately (Liu et al., 2013), without prior planning or intention (Sharma et al., 2010). Consumers with heightened impulsivity exhibit significantly greater susceptibility to unplanned purchases than those with lower impulsivity, demonstrating attenuated cognitive filtering of extraneous product stimuli during decision-making processes (Park et al., 2012). Viewers’ arousal in live streaming rooms is an important internal mechanism leading to impulsive purchases, and perceived arousal positively affects consumers’ impulsive buying intention (X. Sun et al., 2023). The positive emotions displayed by live streamers when recommending a product can infect the audience by not only stimulating purchasing behavior but also inspiring emotional mimicry (Y. Zhang et al., 2024). Although prior studies have shown that positive affect influences impulse buying across offline and conventional live-commerce settings, whether this relationship remains robust in blind-box live streaming has not been directly tested. Thus, we proposed the following hypothesis:

Hypothesis 4: In blind-box live streaming, consumers’ positive affect will be positively related to their impulsive buying.

 

On the basis of emotional contagion theory, a streamer’s professional explanation and display of products can deepen consumers’ understanding of product information, and high interactivity can close the distance between the streamer and consumers, with both factors increasing perceived trust and thus promoting consumers’ impulsive buying (Guo et al., 2021). Pleasurable emotions have been found to play a mediating role between streamer characteristics and impulsive buying behavior (X. Li et al., 2024).

 

The lively atmosphere of live streaming rooms can bring pleasurable and exciting positive emotions to consumers, which are conducive to enhancing their compliance and triggering impulsive consumption (X. Li et al., 2024). It can be concluded that positive emotions may play a mediating role between live e-commerce promotions and discounts and impulsive buying behavior (Y. Zhang et al., 2023). Therefore, the servicescape in blind-box live streaming may be correlated with consumers’ impulsive buying through the mediator of positive emotions.

 

Researchers have examined the key variables of curiosity (Loewenstein, 1994) and perceived luck (Y. Zhang et al., 2022) in consumption contexts, demonstrating that perceived uncertainty activates consumers’ curiosity, which subsequently influences their purchase decisions (Yu et al., 2022). Positive affect has been found to reduce cognitive deliberation and trigger impulse buying (Beatty & Ferrell, 1998), but it has not been empirically tested as a conduit between blind-box attributes and on-the-spot purchase behavior. Thus, we proposed the following hypotheses:

Hypothesis 5: Positive affect will mediate the relationship between live streamers’ characteristics and consumers’ impulsive buying.

Hypothesis 6: Positive affect will mediate the relationship between the live streaming servicescape and consumers’ impulsive buying.

Hypothesis 7: Positive affect will mediate the relationship between blind-box attributes and consumers’ impulsive buying.

Our target participants were consumers who had watched blind-box live streams within the past 3 months. To ensure representativeness and accuracy, we first distributed the questionnaire in dedicated blind-box communities on Weibo (China’s largest microblogging platform), inside live stream studios on Douyin (the Chinese equivalent of TikTok), and via lifestyle posts and brand-hosted rooms on RedNote (a social-commerce site), all of which are mainstream Chinese social media ecosystems where blind-box content is routinely created and discussed. Then, we asked respondents to forward the link to friends or acquaintances. We clearly stated the purpose of the research and promised strict confidentiality of personal information. We collected data over 12 weeks spanning January to March 2024, distributing 320 questionnaires and received 263 valid responses, for a valid rate of return of 82.19% (see Table 1). Upon completion, all participants were automatically entered into a lottery draw on www.wjx.com featuring 10 blind-box toys (RMB 49/USD 6.89 each) and 20 streaming coupons (RMB 10/USD 1.47 each). The system ensured each participant could win only one prize.

The questionnaire began with seven demographic items. In addition to the item we used to filter out those who were not our target sample (“Have you ever watched blind-box live streaming?”), we asked about the participant’s gender, age, education level, frequency of watching blind-box live streaming, and whether they had purchased blind-box products.

 

The main survey comprised 30 items measured on a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree). To ensure measurement reliability and validity, we operationalized all constructs using established scales from the literature. We took two items from Lu et al. (2023) and one item from Xu et al. (2021) to measure live streamers’ characteristics; four items from Tong et al. (2023) to measure the live streaming servicescape; nine items from Zhan and Xiong (2024) to measure blind-box attributes; three items from X. Sun et al. (2023) and three items from Y. Li and Peng (2021) to measure positive affect; and one item from Liu et al. (2013) and two items from Y. Zhang et al. (2023) to measure impulsive buying

All scales were originally published in Chinese. The versions used in this study were translated from Chinese into English for the purpose of the manuscript. The translators were the first author (fluent bilingual with a master’s degree in consumer behavior) and an independent professional translator (PhD in marketing, 8 years of Chinese market research experience), both of whom followed Brislin’s (1980) back-translation procedure. After the back-translation was complete, three doctoral-level marketing scholars and one blind-box live streaming professional reviewed the item pool. Their comments on wording, cultural fit, and industry jargon were incorporated, producing the final survey instrument shown in Table 2.

We conducted Harman’s single-factor test by loading all measurement items into an exploratory factor analysis. The results showed that before rotation, the first factor explained 43.73% of the variance, which is less than the 50% threshold, indicating that common method bias was within an acceptable range. The maximum correlation coefficient among all variables in the scale was .63, which is less than the .90 threshold, further indicating the absence of substantial common method variance in the dataset.

 

In Table 2, the overall Kaiser–Meyer–Olkin value was .937 (p < .001), indicating that the overall validity of the questionnaire met the minimum standard, and factor analysis could proceed. Cronbach’s alpha coefficient for the overall scale was .95, indicating superior internal consistency. Cronbach’s alpha coefficients also indicated good internal consistency of the individual dimensions’ measurement items (Hair et al., 2013).

 

The measurement model demonstrated an excellent fit across multiple indices (Doll et al., 1994), χ²/df = 1.803, root-mean-square residual = .045 (< .05), root-mean-square error of approximation = .055 (.05–.08), goodness-of-fit index = .866 (> .80), Tucker–Lewis index = .941 (> .90), incremental fit index = .947 (> .90), comparative fit index = .947 (> .90).

According to Table 3 and Figure 2, all factors considered in this study (live streamers’ characteristics, live streaming servicescapes, and blind-box attributes) were significantly and positively correlated with positive affect, thereby supporting Hypotheses 1, 2, and 3. Furthermore, positive affect had a significant positive effect on consumers’ impulsive purchasing behavior, thus supporting Hypothesis 4. These findings underscore the crucial role of positive affect in driving impulsive purchases within the context of blind-box live streaming.

According to Table 4, the results included three specific indirect effects across three paths: (1) live streamers’ characteristics → positive affect → impulsive buying, (2) live stream servicescape → positive affect → impulsive buying, and (3) blind-box attributes → positive affect → impulsive buying. None of the 95% confidence intervals for these indirect effects included zero, indicating significance; thus, Hypotheses 5, 6, and 7 were supported. Through analysis of multiple data in the structural equation model, we concluded that live streamers’ characteristics, live stream servicescape, and blind-box attributes were positively related to consumers’ positive affect. Mediation effect testing showed that positive affect significantly mediated the relationships between live streamers’ characteristics, live stream servicescape, blind-box attributes, and consumers’ impulsive buying.

We found that live streamers’ characteristics, the live streaming servicescape, and blind-box attributes all had a positive relationship with consumers’ impulsive buying. In addition, positive affect was directly correlated with consumers’ impulsive buying and significantly mediated the relationship between the aforementioned independent variables and consumers’ impulsive purchasing behavior. These results are in line with earlier studies (e.g., Hughes et al., 2019; Lu et al., 2023), which reported the impact of live streamers’ characteristics on consumers’ impulsive buying, emphasizing the leading role of live streamers in e-commerce settings. Although existing research has examined aspects such as streamers’ professional level, live streaming atmosphere, and interaction with the viewers, these studies focused on only one or two dimensions. Our study has highlighted that streamers who are well-versed in the information and gameplay of the blind boxes they promote enable consumers to quickly grasp the purchasing process upon entering the live streaming room, thereby stimulating their desire to make impulsive purchases. If the streamer’s live content is unique and entertaining, allowing consumers to feel a strong sense of participation and immersion, then their interest significantly increases, leading to higher viewing frequency and purchase behavior.

 

The existing research is notably sparse when it comes to the specific servicescapes of blind-box live streaming, despite the similarities to other live streaming types (Tankovic & Benazic, 2018; Yadav & Mahara, 2020). Our research suggests that blind-box live streaming can initially attract consumers who are full of curiosity to enter the live streaming room. Due to the innate herd effect, when they see a group of people buying a certain product, it creates a positive purchasing atmosphere, which, in turn, stimulates their desire to buy. If well-planned marketing strategies, such as discounts, promotions, and flash sales, are combined at this time, they may create an urgent purchasing atmosphere among consumers, making them worry that missing the purchase opportunity will cause a perceived loss, thereby prompting them to make purchasing decisions under this sense of urgency. However, a significant difference between blind-box live streaming and other types of live streaming is the potential inclusion of hidden editions in blind boxes. Many live streaming rooms will introduce promotional activities such as additional rewards for drawing hidden editions, which leverages consumers’ gambling mentality. Many people believe they have good luck and are willing to test this supposition (Zhao et al., 2021), which also indirectly increases consumers’ impulsive buying intentions (Y. Zhang et al., 2022).

 

Previous researchers have established that blind boxes bring a sense of excitement, capitalizing on consumers’ curiosity and desire for exploration, and stimulating their desire to make impulsive purchases (Duan et al., 2022; Mvondo et al., 2023). Our study expands on this and combines the attributes of blind boxes with live streaming. Although this experience is not as direct as opening the blind box personally, it can still bring a unique sense of excitement to consumers. Consumers find this novel and interesting, and are willing to stay and watch or even make purchases (Diao, 2025). The identification of positive affect as a mediating construct extends prior work by elucidating the affective transmission processes underlying the relationship between live streamers’ characteristics, servicescapes, blind-box attributes, and impulsive buying (L. Li et al., 2024; Meng et al., 2021). When watching blind-box live streaming, consumers are affected by factors such as live streamers’ characteristics, servicescapes, and blind-box attributes, leading to a series of emotional fluctuations in the live streaming room, such as happiness, satisfaction, excitement, and stimulation, which are positive and proactive emotional experiences. These emotional experiences can change consumers’ feelings and experiences via processes such as emotional contagion, ultimately prompting them to engage in impulsive buying behavior.

Blind-box live streaming is an innovative sales method that not only helps companies expand their sales channels and increase brand exposure but also stimulates consumer purchasing impulses through diversified marketing strategies, thereby improving sales efficiency. Live streamers should improve their professional skills, learn sales techniques, and create an engaging environment to stimulate consumer emotions and drive purchases. The activity level of a live streaming room correlates with consumer emotions. Blind-box live streaming rooms effectively stimulate consumer participation by offering discounts, promotions, and interactive games, leveraging the novelty and curiosity of blind boxes to boost viewer numbers and room activity.

Our research may be overly focused on consumers’ short-term purchasing behavior. In-depth studies are needed of long-term consumption patterns and the impacts and consequences of impulsive buying behavior. Moreover, we recommend that future studies examine the acceptance and preferences of blind-box live streaming across different market segments, along with how to develop effective product positioning and marketing strategies based on these market characteristics.

Barsade, S. G., Coutifaris, C. G. V., & Pillemer, J. (2018). Emotional contagion in organizational life. Research in Organizational Behavior, 38, 137–151. https://doi.org/10.1016/j.riob.2018.11.005

 

Bawack, R. E., Bonhoure, E., Kamdjoug, J.-R. K., & Giannakis, M. (2023). How social media live streams affect online buyers: A uses and gratifications perspective. International Journal of Information Management, 70, Article 102621. https://doi.org/10.1016/j.ijinfomgt.2023.102621

 

Beatty, S. E., & Ferrell, M. E. (1998). Impulse buying: Modeling its precursors. Journal of Retailing, 74(2), 169–191. https://doi.org/10.1016/S0022-4359(99)80092-X

 

Bitner, M. J. (1992). Servicescapes: The impact of physical surroundings on customers and employees. Journal of Marketing, 56(2), 57–71. https://doi.org/10.1177/002224299205600205

 

Chen, Y., Wu, X., Jia, F., Yang, J., Bai, X., & Yu, R. (2025). Exploring the impact of social robot design characteristics on users’ privacy concerns: Evidence from PLS-SEM and FsQCA. International Journal of Human–Computer Interaction, 41(13), 7999–8020. https://doi.org/10.1080/10447318.2024.2402126

 

Diao, Q. (2025). The influence of live streaming on consumer behavior: An analysis of the ‘blind box game of chance’ mechanism. Advances in Economics, Management and Political Sciences, 174, 83–89. https://doi.org/10.54254/2754-1169/2025.21827

 

Doll, W. J., Xia, W., & Torkzadeh, G. (1994). A confirmatory factor analysis of the End-User Computing Satisfaction Instrument. MIS Quarterly, 18(4), 453–461. https://doi.org/10.2307/249524

 

Duan, Y., Xu, S., Wang, Y., Zhang, Y., Wang, Y., & Chen, R. (2022). Blind box over-engagement and suicide risk among adolescents and young adults: Results of a large-scale survey. eClinicalMedicine, 51, Article 101575. https://doi.org/10.1016/j.eclinm.2022.101575

 

Floh, A., & Madlberger, M. (2013). The role of atmospheric cues in online impulse-buying behavior. Electronic Commerce Research and Applications, 12(6), 425–439. https://doi.org/10.1016/j.elerap.2013.06.001

 

Gong, X., Yee, C. L., Lee, S. Y., Saif, A. N. M., Liu, M., & Anonthi, F. (2024). Unveiling the enigma of blind box impulse buying curiosity: The moderating role of price consciousness. Heliyon, 10(24), Article e40564. https://doi.org/10.1016/j.heliyon.2024.e40564

 

Gu, X., Zhang, X., & Kannan, P. K. (2024). Influencer mix strategies in livestream commerce: Impact on product sales. Journal of Marketing, 88(4), 64–83. https://doi.org/10.1177/00222429231213581

 

Guo, L., Hu, X., Lu, J., & Ma, L. (2021). Effects of customer trust on engagement in live streaming commerce: Mediating role of swift guanxi. Internet Research, 31(5), 1718–1744. https://doi.org/10.1108/INTR-02-2020-0078

 

Hair, J., Anderson, R., Babin, B., & Black, W. (2013). Multivariate data analysis (7th ed.). Pearson International. https://elibrary.pearson.de/book/99.150005/9781292035116

 

Hill, K. M., Fombelle, P. W., & Sirianni, N. J. (2016). Shopping under the influence of curiosity: How retailers use mystery to drive purchase motivation. Journal of Business Research, 69(3), 1028–1034. https://doi.org/10.1016/j.jbusres.2015.08.015

 

Hughes, C., Swaminathan, V., & Brooks, G. (2019). Driving brand engagement through online social influencers: An empirical investigation of sponsored blogging campaigns. Journal of Marketing, 83(5), 78–96. https://doi.org/10.1177/0022242919854374

 

Lai, K. P., Chong, S. C., Ismail, H. B., & Tong, D. Y. K. (2014). An explorative study of shopper-based salient e-servicescape attributes: A means-end chain approach. International Journal of Information Management, 34(4), 517–532. https://doi.org/10.1016/j.ijinfomgt.2014.01.010

 

Lee, C., Wyllie, J., & Brennan, S. (2025). Eye-opening! Exploring uncertainty marketing through hedonic blind box collectibles. Journal of Retailing and Consumer Services, 82, Article 104127. https://doi.org/10.1016/j.jretconser.2024.104127

 

Li, L., Chen, X., & Zhu, P. (2024). How do e-commerce anchors’ characteristics influence consumers’ impulse buying? An emotional contagion perspective. Journal of Retailing and Consumer Services, 76, Article 103587. https://doi.org/10.1016/j.jretconser.2023.103587

 

Li, X., Huang, D., Dong, G., & Wang, B. (2024). Why consumers have impulsive purchase behavior in live streaming: The role of the streamer. BMC Psychology, 12(1), Article 129. https://doi.org/10.1186/s40359-024-01632-w

 

Li, Y., & Peng, Y. (2021). What drives gift-giving intention in live streaming? The perspectives of emotional attachment and flow experience. International Journal of Human–Computer Interaction, 37(14), 1317–1329. https://doi.org/10.1080/10447318.2021.1885224

 

Liao, J., Chen, K., Qi, J., Li, J., & Yu, I. Y. (2023). Creating immersive and parasocial live shopping experience for viewers: The role of streamers’ interactional communication style. Journal of Research in Interactive Marketing, 17(1), 140–155. https://doi.org/10.1108/JRIM-04-2021-0114

 

Liu, Y., Li, H., & Hu, F. (2013). Website attributes in urging online impulse purchase: An empirical investigation on consumer perceptions. Decision Support Systems, 55(3), 829–837. https://doi.org/10.1016/j.dss.2013.04.001

 

Loewenstein, G. (1994). The psychology of curiosity: A review and reinterpretation. Psychological Bulletin, 116(1), 75–98. https://psycnet.apa.org/buy/1994-41058-001

 

Lu, B., Li, G., & Ge, J. (2023). Effects of streamer effort and popularity on livestream retailing performance: A mixed-method study. Electronic Commerce Research, 25, 2553–283. https://doi.org/10.1007/s10660-023-09757-7

 

Ma, D., Wang, X., Hu, J., & Li, Z. (2023). The selection of sales channels considering consumer behavior preferences in a co-branding strategy. Electronic Commerce Research and Applications, 62, Article 101309. https://doi.org/10.1016/j.elerap.2023.101309

 

Mari, M., & Poggesi, S. (2013). Servicescape cues and customer behavior: A systematic literature review and research agenda. The Service Industries Journal, 33(2), 171–199. https://doi.org/10.1080/02642069.2011.613934

 

Meng, L. M., Duan, S., Zhao, Y., Lü, K., & Chen, S. (2021). The impact of online celebrity in livestreaming e-commerce on purchase intention from the perspective of emotional contagion. Journal of Retailing and Consumer Services, 63, Article 102733. https://doi.org/10.1016/j.jretconser.2021.102733

 

Mi, L. (2022). Theoretical perspective of behavioral economics: The research on the internal mechanism and consumer behavior of blind-box economy. In F. Balli, A. Y. H. Nee, & S. A. Qalati (Eds.), Proceedings of the 2022 International Conference on Economics, SMART Finance and Contemporary Trade (pp. 1548–1555). Atlantis Press International BV. https://doi.org/10.2991/978-94-6463-052-7_171

 

Mvondo, G. F. N., Jing, F., & Hussain, K. (2023). What’s in the box? Investigating the benefits and risks of the blind box selling strategy. Journal of Retailing and Consumer Services, 71, Article 103189. https://doi.org/10.1016/j.jretconser.2022.103189

 

Ni, S., & Ueichi, H. (2024). Factors influencing behavioral intentions in livestream shopping: A cross-cultural study. Journal of Retailing and Consumer Services, 76, Article 103596. https://doi.org/10.1016/j.jretconser.2023.103596

 

Park, E. J., Kim, E. Y., Funches, V. M., & Foxx, W. (2012). Apparel product attributes, web browsing, and e-impulse buying on shopping websites. Journal of Business Research, 65(11), 1583–1589. https://doi.org/10.1016/j.jbusres.2011.02.043

 

Sharma, P., Sivakumaran, B., & Marshall, R. (2010). Exploring impulse buying and variety seeking by retail shoppers: Towards a common conceptual framework. Journal of Marketing Management, 26(5–6), 473–494. https://doi.org/10.1080/02672570903485097

 

Shen, L., Fishbach, A., & Hsee, C. K. (2015). The motivating–uncertainty effect: Uncertainty increases resource investment in the process of reward pursuit. Journal of Consumer Research, 41(5), 1301–1315. https://doi.org/10.1086/679418

 

Sun, X., Pelet, J.-É., Dai, S., & Ma, Y. (2023). The effects of trust, perceived risk, innovativeness, and deal proneness on consumers’ purchasing behavior in the livestreaming social commerce context. Sustainability, 15(23), Article 16320. https://doi.org/10.3390/su152316320

 

Sun, Y., Shao, X., Li, X., Guo, Y., & Nie, K. (2019). How live streaming influences purchase intentions in social commerce: An IT affordance perspective. Electronic Commerce Research and Applications, 37, Article 100886. https://doi.org/10.1016/j.elerap.2019.100886

 

Tankovic, A. C., & Benazic, D. (2018). The perception of e-servicescape and its influence on perceived e-shopping value and customer loyalty. Online Information Review, 42(7), 1124–1145. https://doi.org/10.1108/OIR-12-2016-0354

 

Tian, Y. (2025). Research on influencing factors of users’ purchase intention of blind box in the model of e-commerce live streaming. In Y. P. Tu & M. Chi (Eds.), E-business: Generative artificial intelligence and management transformation (pp. 153–165). Springer. https://doi.org/10.1007/978-3-031-94187-0_13

 

Tombs, A., & McColl-Kennedy, J. R. (2003). Social-servicescape conceptual model. Marketing Theory, 3(4), 447–475. https://doi.org/10.1177/1470593103040785

 

Tong, X., Chen, Y., Zhou, S., Yang, S., & Jiang, H. (2023). Do atmospheric cues matter in live streaming e-commerce? An eye-tracking investigation. Electronic Commerce Research and Applications, 62, Article 101334. https://doi.org/10.1016/j.elerap.2023.101334

 

Wang, P., Wu, L., Gao, L., & Mattila, A. S. (2025). The servicescape and its impact on consumer satisfaction: A meta‐analysis. Psychology & Marketing, 42(3), 799–816. https://doi.org/10.1002/mar.22152

 

Wang, Q., Liu, L., Turnbull, S. J., & Yoshida, M. (2025). Dynamic analysis of barrage comments on sentimental influence and behavior. Scientific Reports, 15(1), Article 27343. https://doi.org/10.1038/s41598-025-12286-y

 

Yadav, R., & Mahara, T. (2020). Exploring the role of e-servicescape dimensions on customer online shopping: A stimulus-organism-response paradigm. Journal of Electronic Commerce in Organizations, 18(3), 53–73. https://doi.org/10.4018/JECO.2020070104

 

Yang, J., Zeng, Y., Liu, X., & Li, Z. (2022). Nudging interactive cocreation behaviors in live-streaming travel commerce: The visualization of real-time danmaku. Journal of Hospitality and Tourism Management, 52, 184–197. https://doi.org/10.1016/j.jhtm.2022.06.015

 

You, Z., Wang, M., & Shamu, Y. (2023). The impact of network social presence on live streaming viewers’ social support willingness: A moderated mediation model. Humanities & Social Sciences Communications, 10, Article 385. https://doi.org/10.1057/s41599-023-01892-8

 

Yu, C., Cheah, J.-H., & Liu, Y. (2022). To stream or not to stream? Exploring factors influencing impulsive consumption through gastronomy livestreaming. International Journal of Contemporary Hospitality Management, 34(9), 3394–3416. https://doi.org/10.1108/IJCHM-09-2021-1137

 

Yuan, H., Lü, K., & Fang, W. (2025). Machines vs. humans: The evolving role of artificial intelligence in livestreaming e-commerce. Journal of Business Research, 188, Article 115077. https://doi.org/10.1016/j.jbusres.2024.115077

 

Zhan, L., & Xiong, Y. (2024). Research on the influence mechanism of blind box product characteristics on consumers’ purchase intention. In Y. P. Tu & M. Chi (Eds.), E-business. New challenges and opportunities for digital-enabled intelligent future. WHICEB 2024: Lecture Notes in Business Information Processing (Vol. 516, pp. 225–236). Springer. https://doi.org/10.1007/978-3-031-60260-3_19

 

Zhang, Y., Chen, L., & Liu, H. (2025). Dynamic analysis of barrage comments on sentimental influence and behavior. Nature Scientific Reports, 15, Article 12286. https://doi.org/10.1038/s41598-025-12286-y

 

Zhang, Y., Wang, X. Y., & Zhao, X. (2023). Supervising or assisting? The influence of virtual anchor driven by AI–human collaboration on customer engagement in live streaming e-commerce. Electronic Commerce Research, 25, 3047–3070. https://doi.org/10.1007/s10660-023-09783-5

 

Zhang, Y., Zhang, T., & Yan, X. (2024). Understanding impulse buying in short video live e-commerce: The perspective of consumer vulnerability and product type. Journal of Retailing and Consumer Services, 79, Article 103853. https://doi.org/10.1016/j.jretconser.2024.103853

 

Zhang, Y., Zhou, H., & Qin, J. (2022). Research on the effect of uncertain rewards on impulsive purchase intention of blind box products. Frontiers in Behavioral Neuroscience, 16, Article 946337. https://doi.org/10.3389/fnbeh.2022.946337

 

Zhang, Z. (2021). Research on blind box and its marketing strategy. In A. Appolloni, F. Caracciolo, Z. Ding, P. Gogas, G. Huang, G. Nartea, T. Ngo, W. Striełkowski, & S. Joshi (Eds.), Proceedings of the 2021 International Conference on Economic Development and Business Culture (pp. 115–118). Atlantis Press. https://doi.org/10.2991/aebmr.k.210712.020

 

Zhao, J., Li, Z., & Xiong, G. (2021). Effects of luck beliefs on consumers’ variety-seeking behavior. Social Behavior and Personality: An international journal, 49(2), Article 9243. https://doi.org/10.2224/sbp.9243

 

Zheng, X., Men, J., Yang, F., & Gong, X. (2019). Understanding impulse buying in mobile commerce: An investigation into hedonic and utilitarian browsing. International Journal of Information Management, 48, 151–160. https://doi.org/10.1016/j.ijinfomgt.2019.02.010