What Signals Are You Sending? How Signal Consistency Influences Consumer Purchase Behavior in Live Streaming Commerce

Abstract

Driven by digital technology, live streaming business is becoming increasingly common worldwide. Unlike traditional online shopping, live streaming commerce integrates real-time interaction, social communication, and e-commerce. It also eliminates the limitations of one-way information transmission and promotes purchasing behavior by conveying signals such as products, brands, and the personal charm of live streamers to consumers through real-time communication. The core issues explored in this study are whether the cues between the signal transmitters and receivers are consistent and how they affect consumers’ purchasing behavior. In this study, the consistency of the signal is measured by five dimensions, namely self–product fit, live streamer–product fit, live content–product fit, danmaku content–product fit, and self–live streamer fit. In order to study this problem, we constructed a structural equation modeling (SEM) model. In the causal relationship between signal consistency and purchase intention, performance responses have also been discussed as mediating variables. Accordingly, signal consistency enables consumers to perceive performance expectancy, whereby consumers believe that products that perform well positively influence satisfaction. To verify these hypotheses, 443 randomly collected valid questionnaires were used in empirical analyses. The results showed that most of our hypotheses were validated, aside from the relationship between self–live streamer fit and perceived performance expectancy. The findings suggest that signal consistency cues such as live streamer–product fit, live content–product fit, danmaku content–product fit, and self–product fit positively influence consumers’ perceived performance expectancy and satisfaction, which in turn promote purchase intention. These findings not only enrich the application of signaling theory in the field of live streaming commerce but also expand consumer behavior theory. Moreover, they also inspire practitioners in live streaming commerce by helping them to improve live streaming sales and bolster their market competitiveness through signal display optimization, content planning, and precision marketing.

1. Introduction

In the contemporary era, the proliferation of digital technology has ushered in a new era of e-commerce, with live streaming commerce at the forefront of this revolution [1,2]. Unlike traditional e-commerce models that rely on static product displays and limited interaction, live streaming commerce has reshaped consumers’ shopping behaviors and experiences by offering real-time, interactive and immersive shopping experiences [3,4]. For example, imagine that you want to buy a lipstick: through traditional online shopping channels, you can only rely on product photos and written descriptions to make your selection. However, in the live streaming commerce environment, it is like entering a cosmetics store where salespeople show the details of the products and tell you the differences between each color by trying different shades on their mouths, and answer questions in real time about colors, textures, and makeup effects for different skin types. Moreover, you can also interact with other viewers by sending messages on the screen. These messages can include positive reviews, negative comments, or questions, and you can also answer others’ questions. Hence, the entire shopping process through live streaming commerce can create a sense of authenticity [5] and trust [6] for consumers. For enterprises, live streaming commerce presents a unique opportunity to connect with consumers directly [7,8], build brand loyalty [9], and drive sales [10]. By leveraging the power of live streaming, companies can showcase their products in a more engaging and persuasive manner, provide personalized recommendations, and address consumers’ concerns in real time [11]. This direct interaction with consumers allows businesses to gain valuable insights into their preferences and behaviors, and to tailor their marketing strategies and product offerings accordingly [12].

Numerous scholars have conducted research on the factors that influence consumer purchase intention in live streaming commerce, exploring various aspects such as live streamers [13], product information disclosure [14], virtual images [8], and live streaming background settings [15,16]. Studies have shown that influencers’ impression management strategies affect purchase intention, with different effects observed in trust transfer and gender differences [17]. Additionally, the way sellers disclose product information, including the consistency between products and how they are promoted, has a significant impact on consumer purchase decisions [18]. Research has also focused on the role of virtual images in live streaming commerce, with studies indicating that more realistic virtual images can enhance consumer purchase intention [19,20,21]. Furthermore, the visual complexity of live streaming backgrounds, the visualization of the production process, and the features of mobile applications have been found to influence consumer purchase intention based on different aspects of live streaming scenarios [22,23]. Recently, scholars have been especially interested in applying signal consistency theory to live streaming e-commerce [24,25,26]. In this realm, a thorough understanding of the intrinsic relationship between signal consistency and consumer purchase intention is highly practical and helps companies accurately formulate marketing strategies. These can include product-related signals [24], streamer-related signals [27], and product feedback signals [28], all of which play a key role in purchase behavior. By deeply analyzing the factors that influence signal consistency, businesses can promote efficient communication between live sales practitioners and consumers, thereby optimizing the shopping experience.

There are still many gaps in the research on the relationship between signal consistency and purchase intention. Merchants need to accurately predict and influence consumers’ performance expectations in the highly competitive live streaming business market. If they misjudge the role of signal consistency in shaping these expectations, they might invest resources in ineffective marketing strategies. For instance, a brand might focus on flashy live streaming content that has low relevance to the product or is inconsistent with the actual functions, thereby leading to consumer disappointment and the loss of sales opportunities. Therefore, understanding the performance response of signal consistency cues for consumers can help enterprises to create more targeted and effective marketing activities and ultimately promote the industry’s development.

In addition, this research aims to contribute to addressing the gaps in previous studies regarding the research content and methods. Unlike existing studies that have usually focused on isolated aspects of signal cues—such as only paying attention to product descriptions or user reviews—our research integrates five different dimensions of signal consistency: live streamer–product fit, live content–product fit, danmaku content–product fit, self–live streamer fit, and self–product fit. In this respect, we can capture the complexity and dynamics of commercial streaming signals through a holistic perspective to provide a more accurate understanding of how signal consistency affects consumer behavior, which is a significant advancement in this field. Moreover, we use a rigorous two-stage SEM-bootstrapping method to analyze the data, which not only verifies the causal relationship but also precisely quantifies the mediating role of perceived performance expectations and perceived satisfaction. It addresses the gaps in previous studies that have relied on linear regression or single-stage SEM, which often overlook complex, non-linear interactions. The results of this study can not only enrich the relevant research of consumer behavior and consumer psychology but can also provide a valuable reference for further developing live streaming commerce.

2. Theoretical Background

2.1. Live Streaming Commerce

Live streaming commerce is a business model that utilizes advanced streaming technology to showcase products or services to the audience in real time and in a comprehensive way, with the aim of attaining efficient sales conversion during the live streaming process [27,29,30,31]. In this model, the live streamer plays a key role [32]. The live streamer conveys the characteristics, functions, advantages, and other information about the product to the audience through vivid, real-time demonstrations and detailed, professional explanations [33]. Simultaneously, there is real-time interaction between the live streamer and the audience, which greatly enhances consumers’ sense of participation and stimulates their desire to purchase [33,34]. Live streaming commerce has many characteristics that differ from traditional e-commerce, such as real-time sales [30], interactivity [31,34], and intuitiveness [27].

The real-time nature of live streaming commerce confers unique advantages, as it enables consumers to obtain the latest information and see dynamic demonstrations of products at the first time [28]. Moreover, consumers do not need to wait for updates on product pages like in traditional e-commerce models to stay up to date with the latest developments. This real-time capability satisfies consumers’ curiosity and their need for timeliness while also keeping them apprised of product trends and helping them make timely purchasing decisions [35].

Interactivity is also a pivotal characteristic that sets live streaming commerce apart from traditional e-commerce [31]. It serves as a dynamic signal transmission mechanism within the live streaming context. During the live streaming process, consumers can promptly ask questions about specific product features, usage methodologies, and after-sales service provisions through “Danmaku” (a real-time, online commentary scrolling across the screen) [19]. The live streamer then addresses consumers’ questions in real time, furnishing comprehensive and precise information. From the signaling theory perspective, consumers in this real-time interaction send out signals regarding their interests, concerns, and preferences. Simultaneously, the live streamer’s timely and detailed responses act as signals of product expertise, reliability, and service quality [34,36]. This reciprocal exchange not only fosters a deeper understanding of the product but also builds trust between the consumers and the live streamer as the transparency and responsiveness demonstrated are strong signals of credibility [37]. Moreover, this interactive engagement also cultivates a vibrant social ambiance, reducing the sense of isolation that consumers might otherwise experience during the shopping journey and substantially increasing enjoyment and active participation in the process [38].

Intuitiveness represents another crucial signal within live streaming commerce that effectively conveys the product’s value proposition [39], quality [40], and usability [41]. It empowers consumers to attain a clearer, more tangible, and more realistic understanding of the product’s appearance, functionality, and other salient features through the live streamer’s demonstrations and displays [24]. From a signaling standpoint, the live streamer’s hands-on demonstration is a robust signal that instills confidence in consumers regarding the product’s authenticity, performance, and suitability for their needs. This immersive demonstration significantly enhances consumers’ comprehension of the product and heightens their inclination to make a purchase [42].

In conclusion, the real-time interactivity and intuitiveness of live streaming commerce create an engaging shopping experience and directly influence consumers’ behavior. When consumers can obtain immediate information, interact with live streamers and other consumers in real time, and clearly understand the products, they are more likely to make purchase decisions, which will ultimately drive sales in the live streaming business industry.

2.2. Signaling Theory

Signaling theory suggests that in a market environment of information asymmetry, the party with an information advantage (such as a company or seller) tends to send a signal to the party with an information disadvantage (such as a consumer). This signal aims to convey information about the quality of the product or service in order to reduce the impact of information asymmetry [43]. For instance, if a live streamer wants to prove that a food product is healthy, they can show customers information such as the ingredient list and calories. In the context of a live streaming business, factors such as live products, live content, danmaku content, live streamers, and other potential buyers can all be regarded as signal sources, with each conveying various information to consumers [44]. When these signal cues are consistent with each other, consumers are more likely to form positive perceptions and evaluations of the product or service [45].

Signal consistency cues refer to various signals in live streaming commerce that can convey information to consumers and coordinate and match with each other from aspects such as live streaming products and content, bullet comment content, hosts, and consumers themselves [23]. In this study, signal consistency cues include live streamer–product fit (LPF), which reflects the degree of fit between the product image and the streamer image; live content–product fit (LCF), reflecting the correlation and adaptability between the product and the live streaming content; Danmaku content–product fit (DCPF), indicating the relevant compatibility between bullet comment content and the product; self–live streamer fit (SLF), which is related to the consistency between consumers’ self-image and the image of the streamer; and self–product fit (SPF), which refers to the fit between consumers’ self-perception and the product image. For instance, when live streaming products are displayed in a way that is consistent with the live streamer’s description, consumers can build initial trust in the products based on intuitive visual information and professional explanations [46]. Furthermore, when the real-time danmaku indicate positive attitudes from other consumers, the barrage serves as a feedback signal from actual users, further strengthening this trust [47]. From the signal theory perspective, when the signals sent by various signal sources are consistent, consumers can build trust in the product based on intuitive visual information, professional explanations, and others’ evaluations, believing that the actual product use can achieve the expected effect, thereby enhancing their satisfaction with the product and purchase intention [23,25,35].

In contrast, inconsistent signals may lead to confusion and distrust among consumers, which can also strongly affect their purchasing decisions [24]. When consumers receive conflicting signals, they may become confused about the true situation of the product and find it difficult to determine whether it can meet their needs and expectations [24,48]. This uncertainty can trigger a sense of distrust among consumers, making them more cautious in their purchasing decisions and even deterring them from purchasing [49].

To sum up, signaling theory has a crucial impact on consumers’ behavior in live streaming commerce. Consistent signals build consumers’ trust, which in turn increases the possibility of them making purchasing decisions. Sellers can apply this theory to optimize their live streaming content, ensuring the consistency of product-related signals, live streamer-related signals, and danmaku content, thereby enhancing consumer trust and promoting sales.

2.3. Performance Response

Performance expectancy and satisfaction are regarded as performance responses in sociology research [50,51]. In the context of live streaming commerce, performance response is defined as the reaction generated in response to the signal cues received [51]. These reactions are used to measure consumers’ subjective feelings about the live streaming e-commerce shopping experience and the impact on future purchase intentions. The performance response mainly comprises perceived performance expectancy and perceived satisfaction.

Perceived performance expectancy is an individual’s subjective judgment and expectation of the performance results of their own behavior [52]. In the context of live streaming commerce, perceived performance expectancy refers to the psychological expectation that consumers can obtain useful cues and a positive transaction experience during the shopping process, thereby generating positive results for their purchasing behavior [50]. Its theoretical foundation is closely related to expectancy theory [53]. Victor Vroom proposed expectancy theory in 1964, suggesting that an individual’s level of motivation depends on their expected value of behavioral outcomes and their perceived likelihood of achieving those outcomes [54]. In the field of organizational behavior and management, perceived performance expectations affect employees’ work motivation and behavioral choices [55,56,57]. For example, when employees believe that hard work can bring good performance evaluations and rewards, they are more likely to contribute effort. Recent research has also addressed perceived performance expectancy as a component of consumer behavior [58,59]. Zhang et al. [60] assessed how consumers’ performance expectations of AI service robots affected their willingness to use them and to share their experiences on social media. Another study explored how consumers’ satisfaction and loyalty can be improved by managing their expectations [61]. As market competition becomes more intense and consumer demand continues to diversify, enterprises must meet consumers’ expectations by improving the perceived performance of products and services and thereby obtain competitive advantages [62].

Perceived satisfaction is regarded as another performance response for signal cues in live streaming commerce. Satisfaction emphasizes the psychological pleasure that is generated after the customer’s demand is met [63]. Wantara and Irawati [64] define satisfaction as the state experienced after contact with a service or event. This state is influenced not only by the controllable service quality of the provider but also by the demand status of the customer. Some scholars assert that satisfaction is composed of multiple factors; therefore, businesses must design the whole process, from the generation of purchase motivation to the end of purchase, while acknowledging that customer satisfaction and feelings may be different at each stage [65,66].

In the context of live streaming commerce, we propose that performance responses have a direct impact on consumer behavior, whereby higher levels of perceived performance expectancy and perceived satisfaction lead to a greater possibility of purchase intention. If consumers expect the product to perform well based on the signals they receive during the live streaming commerce and are satisfied with their purchasing experience, they are more likely to make a purchase and might even become repeat customers. This also indicates that understanding and managing performance responses can help enterprises to increase sales and build customer loyalty in live streaming commerce.

3. Hypotheses Development

In recent years, with the rapid development of e-commerce, many scholars have conducted research on the impact of different fit factors on consumers’ performance reactions in their e-commerce shopping behavior [67,68].

3.1. Hypotheses on Signal Consistency and Perceived Performance Expectancy

Some scholars have explored the relationship between signal consistency and perceived performance expectancy in shopping behavior. Chen [69] suggested that consumers form expectations for product performance based on the information conveyed by product displays during the purchasing decision process. When the product display matches consumers’ inner expectations, they gain confidence in the product [70]. Wang et al. [71] found that consumers refer to evaluation information from other consumers when making purchasing decisions. When this evaluation information matches the actual performance, it enhances consumers’ trust in the product. Franke et al. [72] suggested that consumers will screen and judge product information from various sources based on their own needs and preferences. When product information matches their own needs, they tend to like the product more and have higher expectations regarding it. Liu and Zhao [73] also suggested that consumers’ personal values can influence their perception and evaluation of products. When product features align with consumers’ personal values, consumers have higher expectations for the product.

With the rapid rise of live streaming e-commerce, the academic community has become increasingly interested in how consumers perceive live streamers, products, shopping environments, and other signals that influence purchase decisions, and studies have been carried out from various perspectives. Luo et al. [74] suggested that when the presentation style of the live streamer matches the product features, consumers have higher expectations for product performance. Chen et al. [75] focused on the fit between live streaming content and the product. The study found that if the live streaming content matches the product and is attractive and relevant, it can significantly enhance consumers’ perceived performance expectations. Chen et al. [25] also studied the impact of danmaku content on consumers’ decision making from the perspective of fit. They found that positive and appropriate danmaku content related to the product can enhance consumers’ perceived performance expectations. Chen and Yang [76] also explored the fit between the self and the live streamer. The results indicate that when consumers establish a strong sense of connection with the live streamers, they are more likely to have high perceived performance expectations for the product. Zhang and Zhang [77] studied the role of self–product fit in live streaming consumption and showed that customers’ perceived performance expectations tend to increase when they perceive a good fit between themselves and the product in a live streaming context.

Based on the existing research, we integrated five dimensions into our hypotheses: live streamer–product fit, live content–product fit, danmaku content–product fit, self–live streamer fit, and self–product fit. Moreover, we propose that when the product’s image matches well with that of the live streamer, when the live content matches well with the product, when others’ real-time comments match with the product, and when the image of the live streamer and the product match with consumers themselves, they are more likely to believe that the product can fulfill their performance requirements. Hence, hypotheses H1a–H1e are proposed as below:

H1a–H1e. 

Live streamer–product fit, live content–product fit, danmaku content–product fit, self–live streamer fit, and self–product fit will positively influence the consumers’ perceived performance expectancy.

Although we have established the relationship between signal consistency and perceived performance expectancy, it is worth noting that perceived satisfaction is another key aspect of consumers’ performance responses in live streaming commerce. Given that understanding how signal consistency affects perceived satisfaction can further clarify the complex relationship between these factors and consumers’ purchasing decisions, we now turn to exploring this connection.

3.2. Hypotheses on Signal Consistency and Perceived Satisfaction

Satisfaction is another dimension of performance reaction that has received widespread attention from scholars in the context of consumer behavior [78]. Liu et al. [79] suggested that accurate and useful product information can help consumers better use products, thereby increasing consumer satisfaction. Antonides and Hovestadt [80] indicated that consumers refer to others’ evaluations in their purchasing decisions, and their satisfaction increases when their actual experience matches others’ evaluations. Rijsdijk et al. [81] showed that if a product and related information are highly matched with a consumer’s needs, their satisfaction will increase during the purchase and use process. Liu et al. [82] also suggested that consumers’ subjective perception of the degree to which a product meets their own needs directly affects their satisfaction.

In the context of live streaming commerce shopping behavior, Shang et al. [41] explored how the fit between live streamers and products relates to consumer satisfaction. The results indicate that when there is a high degree of fit between the anchor and the product, consumer satisfaction will significantly increase. Chen et al. [39] found that carefully designed and product-matched live content can significantly improve consumer satisfaction, thereby emphasizing the importance of content fit in enhancing consumer experience. Li et al. [83] stated that relevant and positive danmaku content can enhance consumer satisfaction during live streaming. Their study indicates that barrage, as an interactive element in live streaming, has a measurable impact on consumer satisfaction. Qin et al. [84] observed that when consumers perceive a strong fit between themselves and the live streamer, they are more engaged and connected, thereby exhibiting higher satisfaction. Wang et al. [71] also proposed that when consumers perceive a good fit between themselves and the product, they are likely to feel satisfied in their live shopping experience. Hence, we propose that when the product’s image matches well with that of the live streamer, when the live content matches well with the product, when others’ real-time comments match with the product, and when the image of the live streamer and the product match with consumers themselves, they are more likely to believe that the product can satisfy them. Thus, we propose the hypotheses below:

H2a–H2e. 

Live streamer–product fit, live content–product fit, danmaku content–product fit, self–live streamer fit, and self–product fit will positively influence consumers’ perceived satisfaction.

H1a–H1e and H2a–H2e explored the relationship between signal consistency cues and perceived performance expectations as well as perceived satisfaction, respectively. It is essential to understand how these two performance responses interact with each other given that their relationship can provide more insights into the potential mechanisms of the psychological processes related to consumers’ purchase decisions.

3.3. Hypotheses on Perceived Performance Expectancy and Perceived Satisfaction

Many researchers have explored the relationship between perceived performance expectancy and perceived satisfaction in different fields. Yüksel and Yüksel [85] examined the relationship between consumer expectations and actual experiences and observed that the degree of consistency between the two has a direct and critical impact on consumer satisfaction. Additional studies have verified the relationship between perceived performance expectancy and perceived satisfaction in contexts such as e-learning [86], social commerce [87], mobile payment systems [88], and AI-powered retail [89]. Hult et al. [90] demonstrated that consumer satisfaction after purchasing a product largely depends on the consistency between the actual product experience and the expectations before purchase. Based on these studies, we infer that in the context of e-commerce, consumers are more satisfied when their expectations before purchasing a product or service are highly consistent with their experience during actual use. Hence, we propose the hypothesis below:

H3. 

Perceived performance expectancy positively influences consumers’ perceived satisfaction.

After clearly understanding the relationships among signal consistency, perceived performance expectancy and perceived satisfaction, we now focus on how these performance responses ultimately affect consumers’ purchasing decisions. Given that purchase intention is a key outcome in the study of consumer behavior in live streaming commerce, studying this relationship can bridge the gap between theoretical performance responses and actual purchasing behaviors.

3.4. Hypotheses on Performance Response and Purchase Intention

As the consumer market continues to develop and evolve, consumer performance reactions and purchasing behavior have become important research topics. In addition to studying the psychological reactions, scholars in many fields have examined the relationship between purchase intention and customers’ perceived performance expectancy and perceived satisfaction [58,91,92]. Some studies have emphasized the important role of performance expectations and satisfaction in purchasing decisions [59,93]. Sun et al. [94] examined that when consumers have positive performance expectations for a green product, they are more willing to purchase the product. Feng et al. [95] studied the relationship between performance expectations and purchase intention in the context of electronic product consumption. They noted that consumers’ perceived performance expectations play an important role in shaping their willingness to purchase electronic products and services, indicating that this relationship is equally significant in the digital field. Choi et al. [96] explored the impact of satisfaction on consumers’ willingness to repeat purchases, demonstrating that satisfied consumers are more likely to express willingness to make repeat purchases. Kuo et al. [97] focused on the correlation between consumer satisfaction, repeat purchase intention, and word-of-mouth communication intention. They also found a positive correlation between consumer satisfaction and these two types of willingness. Zhu et al. [98] revealed the role of online reviews in the relationship between satisfaction and purchase intention. Their study indicates that consumer satisfaction that is influenced by online reviews has a positive impact on purchase intention.

In alignment with these researchers’ work, we propose that regarding purchasing behavior in live streaming commerce, the higher the level of consumer perceived performance expectancy and satisfaction, the stronger their willingness to purchase. Hence, we propose the following hypotheses:

H4. 

Perceived performance expectancy positively influences consumers’ purchase intention.

H5. 

Perceived satisfaction positively influences consumers’ purchase intention.

The conceptual framework is shown as Figure 1.

4. Research Method

4.1. Research Design and Data Collection

To test the proposed model and hypotheses, we conducted a random questionnaire survey targeting Chinese consumers with live streaming e-commerce shopping experience. Data collection was facilitated by Credamo, a professional online survey firm in China, which has a large sample database of over 2 million. The questionnaire was published on the platform and randomly assigned to the respondents in the sample database until it attained the required number of responses. Participants received monetary compensation based on their eligibility. Respondents without live streaming e-commerce shopping experience exited after the first screening question and received CNY 1, while qualified participants who completed the full questionnaire earned CNY 5. The survey instrument was developed through rigorous translation protocols. Scale items, originally in English, were translated into Chinese, followed by back-translation to ensure conceptual equivalence with the source material. This dual-language verification process preserved measurement validity while accommodating the target population’s linguistic needs.

A total of 456 questionnaires were collected over two weeks in December 2024. After data validation, 443 responses qualified for analysis.

Table 1. Demographic profile of respondents.

Demographics	Categories	Number	Percentage
Gender	Male	208	46.95%
	Female	235	53.05%
Age	Less than 20	71	16.03%
	21–30	248	55.98%
	31–40	89	20.09%
	41–50	25	5.64%
	More than 50	10	2.56%
Education background	Secondary school or below	13	2.93%
	Junior college	48	10.84%
	Bachelor	310	69.98%
	Master’s	53	11.96%
	PhD	19	4.29%
Income (CNY/month)	Less than 1500	49	11.06%
	1500–2999	125	28.22%
	3000–4999	70	15.8%
	5000–6999	95	21.44%
	7000–8999	83	18.74%
	Above 9000	21	4.74%
Frequently used live streaming commerce platform (choose two platforms)	TikTok	279	62.98%
	Kwai	105	23.7%
	Taobao	302	68.17%
	Red Book	120	27.09%
	WeChat Channels	80	18.06%
Frequency of shopping through live streaming commerce (times per month)	1–3	90	20.32%
	4–6	175	39.5%
	7–9	55	12.42%
	More than 9	123	27.77%

summarizes the sample demographics: 46.95% male and 53.05% female, with 72% of participants aged under 30. Most respondents (86.23%) held at least a bachelor’s degree. Platform usage patterns revealed that 68.17% engaged with Taobao Live (e-commerce-integrated live streaming), and 62.98% utilized TikTok (live streaming-based e-commerce). Notably, 79.69% reported purchasing through live stream platforms at least four times monthly, indicating that this shopping modality is deeply integrated into daily consumer behavior.

4.2. Measurements

All measurement items were adapted from existing literature and adjusted to the research context. We measured live streamer–product fit and live content–product fit using items from Park and Lin [99]. The self–live streamer fit and self–product fit was measured using items from Chen et al. [13], and the danmaku content–product fit was measured based on Yang et al. [100]. We drew measures of perceived performance expectancy and perceived satisfaction from Loureiro et al. [101] and Lee et al. [102], respectively. This study measured purchase intention using items from Levi et al. [103] and Lu and Chen [104].

Table 2. Measurement items.

Construct	Measurement Items	Reference
Live streamer-product Fit (LPF)	1. The product’s image matches well with the image of the live streamer. 2. The pairing of the live streamer with the product is natural. 3. The product is highly appropriate for the live streamer.	[99]
Live Content-product Fit (LCF)	1. The product image matches well with the live content. 2. The product adds rich context to the live content. 3. The product is highly appropriate for the live content.	[99]
Danmaku Content-product Fit (DCPF)	1. The danmaku content and the product are compatible. 2. The danmaku content and the product have a good fit. 3. The danmaku content and the product are relevant. 4. The danmaku content and the product have a good match.	[100]
Self-live streamer Fit (SLF)	1. The image of the live streamer matches well with the image of myself. 2. The image of the live streamer is highly consistent with how I see myself. 3. The live streamer is highly appropriate for me.	[13]
Self-product Fit (SPF)	1. The image of the product matches well with the image of myself. 2. The image of the product is highly consistent with how I see myself. 3. The product is highly appropriate for me.	[13]
Perceived Performance Expectancy (PPE)	1. I easily find products on the live streaming e-commerce platform that are very useful. 2. Using live streaming e-commerce allows me to get useful information faster. 3. Using live streaming e-commerce increases my effectiveness in finding information when buying products.	[101]
Perceived Satisfaction (PS)	1. I feel happy to be able to buy products through live streaming e-commerce. 2. I feel comfortable with the features available when using live streaming e-commerce. 3. I am satisfied with the experience of buying products via live streaming e-commerce.	[102]
Purchase Intention (PI)	1. I am very likely to buy the products from e-commerce live streaming. 2. I would consider buying products from e-commerce live streaming in the future. 3. I intend to buy products from e-commerce live streaming. 4. I will recommend this product to my family and friends.	[103,104]

shows all the measurement items and source of constructs. All constructs were measured using a seven-point Likert scale, ranging from 1 (strongly disagree) to 7 (strongly agree). We also included control variables such as gender, age, educational background, income, frequently used live streaming commerce platforms, and frequency of shopping through live streaming commerce.

4.3. Data Analysis

The study employed a structural equation modeling (SEM) methodology, which is usually used to analyze the complex relationships among variables. We assessed convergent and discriminant validity through confirmatory factor analysis using AMOS 28.0. The evaluation followed three key criteria for psychometric properties: (1) the reliability assessment required composite reliability (CR, indicators for measuring the internal consistency of a set of items in a measurement model) with Cronbach’s α coefficients exceeding 0.7; (2) the convergent validity verification demanded factor loadings above 0.4 and average variance extracted (AVE, used to evaluate the convergent validity of variables in the measurement model, that is, the degree to which multiple items of the same concept can jointly explain the variance of that variable) values surpassing 0.5; (3) the discriminant validity confirmation necessitated that AVE square roots exceed inter-construct correlations [105]. As presented in

Table 3. Measurement model.

Construct	Items	Mean	Factor Loading	CR	AVE	Cronbach’s Alpha
LPT	LPT1	3.9	0.777	0.818	0.6	0.818
	LPT2	3.88	0.81
	LPT3	4.2	0.734
LCPT	LCPT1	4.65	0.769	0.822	0.606	0.82
	LCPT2	4.51	0.785
	LCPT3	4.23	0.781
DCPT	DCPT1	3.73	0.798	0.886	0.661	0.886
	DCPT2	3.62	0.795
	DCPT3	3.6	0.851
	DCPT4	3.81	0.807
SLF	SLF1	4.43	0.726	0.793	0.561	0.792
	SLF2	4.58	0.765
	SLF3	4.52	0.756
SPF	SPF1	5.08	0.723	0.795	0.565	0.793
	SPF2	4.63	0.727
	SPF3	4.91	0.801
PPE	PPE1	5.08	0.726	0.844	0.645	0.842
	PPE2	3.89	0.831
	PPE3	3.87	0.847
PS	PS1	3.85	0.828	0.833	0.625	0.832
	PS2	3.77	0.814
	PS3	4.01	0.726
PI	PI1	3.92	0.836	0.872	0.63	0.87
	PI2	4.08	0.723
	PI3	4.17	0.767
	PI4	4.09	0.843

, the measurement model demonstrated satisfactory psychometric properties through factor loadings, α coefficients, CR values, and AVE metrics. These results aligned with the established thresholds recommended in the methodological literature, confirming the adequacy of the measurement instruments.

Assessing the discriminant validity involved contrasting inter-construct correlations with square roots of AVE metrics. The analysis outcomes presented in

Table 4. Discriminant analysis.

	LPT	LCPT	DCPT	SLF	SPF	PPE	PS	PI
LPT	0.775
LCPT	0.245 **	0.778
DCPT	0.372 **	0.285 **	0.813
SLF	0.319 **	0.275 **	0.317 **	0.749
SPF	0.212 **	0.286 **	0.278 **	0.257 **	0.752
PPE	0.345 **	0.333 **	0.352 **	0.275 **	0.378 **	0.803
PS	0.398 **	0.388 **	0.407 **	0.390 **	0.429 **	0.505 **	0.791
PI	0.420 **	0.265 **	0.379 **	0.376 **	0.269 **	0.454 **	0.444 **	0.794

Note: ** p < 0.01.

confirm that all constructs’ AVE values surpass their corresponding correlation coefficients with other variables. This empirical evidence indicates sufficient differentiation between measurement constructs, satisfying discriminant validity requirements.

The cross-loading analysis presented in

Table 5. Results of cross-loadings.

Construct	LPT	LCPT	DCPT	SLF	SPF	PPE	PS	PI
LPT1	0.82	0.087	0.134	0.111	0.006	0.095	0.122	0.139
LPT2	0.743	0.113	0.285	0.117	0.081	0.072	0.166	0.17
LPT3	0.836	0.024	0.034	0.084	0.076	0.129	0.06	0.178
LCPT1	0.07	0.834	0.046	0.069	0.065	0.11	0.123	0.08
LCPT2	0.017	0.84	0.121	0.086	0.113	0.103	0.051	0.09
LCPT3	0.115	0.788	0.139	0.112	0.099	0.071	0.178	0.072
DCPT1	0.164	0.098	0.812	0.073	0.069	0.124	0.076	0.086
DCPT2	0.102	0.074	0.812	0.103	0.074	0.097	0.105	0.119
DCPT3	0.153	0.129	0.809	0.135	0.099	0.113	0.09	0.171
DCPT4	0.016	0.054	0.84	0.074	0.086	0.052	0.161	0.145
SLF1	0.101	0.096	0.165	0.783	0.055	0.055	0.07	0.058
SLF2	0.104	0.098	0.046	0.81	0.145	0.145	0.113	0.102
SLF3	0.085	0.073	0.126	0.796	0.222	0.222	0.024	0.076
SPF1	0.043	0.022	0.047	0.068	0.821	0.13	0.129	0.056
SPF2	0.047	0.176	0.152	0.039	0.78	0.062	0.109	0.137
SPF3	0.057	0.089	0.087	0.125	0.805	0.151	0.157	0.049
PPE1	0.137	0.039	0.094	0.01	0.075	0.826	0.117	0.185
PPE2	0.069	0.134	0.136	0.118	0.162	0.795	0.178	0.189
PPE3	0.114	0.184	0.159	0.095	0.185	0.762	0.204	0.176
PS1	0.152	0.163	0.18	0.142	0.181	0.178	0.743	0.196
PS2	0.143	0.094	0.158	0.14	0.156	0.192	0.78	0.158
PS3	0.092	0.172	0.13	0.126	0.16	0.151	0.76	0.13
PI1	0.165	0.113	0.123	0.091	0.114	0.106	0.116	0.818
PI2	0.082	0.081	0.146	0.156	0.015	0.2	0.108	0.741
PI3	0.186	0.023	0.109	0.111	0.042	0.13	0.126	0.783
PI4	0.1	0.082	0.157	0.126	0.123	0.128	0.13	0.814

reveals the loading of all constructs attributes on the studying capacity ranged from 0.741 to 0.836 (the bold values in Table 5), which is viewed as an important variable when it is over 0.5 [105]. Furthermore, it also indicates that measurement items exhibited stronger associations with their theoretically assigned constructs than with other factors. This pattern, in which primary loadings exceeded cross-construct correlations, further substantiates the measurement model’s discriminant validity [106], confirming construct distinctiveness as documented in methodological research.

Moreover, the measurement model exhibited satisfactory psychometric properties with acceptable goodness-of-fit indices (X² = 308.564 with df = 271, p < 0.00, X²/df = 1.139, GFI = 0.949, AGFI = 0.934, CFI = 0.993, RMSEA = 0.018). Goodness-of-fit indices of model were adopted from Haier et al. [105]: X²/df ≤ 3; GFI > 0.9; AGFI > 0.9; CFI > 0.9 and RMSEA < 0.08. These results collectively indicate sufficient measurement validity and reliability for subsequent structural analysis.

5. Results

5.1. Results of Hypothesis Testing

We used the maximum likelihood method for estimation by AMOS28.0. The SEM path coefficients results are shown in Figure 2 below. The path coefficient is a key parameter in the SEM for measuring the direct relationship between variables [105]. Its value usually ranges from −1 to +1, with larger absolute values reflecting a stronger relationship between the variables. A positive value (0 < coefficient ≤ 1) indicates a positive effect between variables, a negative value (−1 ≤ coefficient < 0) indicates a negative effect, and a value near 0 indicates that there is almost no direct linear relationship between the variables. Figure 2 shows that the relationships between each variable pair exert a positive influence, with the relationship between perceived satisfaction and purchase intention being the strongest. The weakest relationship strength was the influence of self–live streamer fit on perceived performance expectancy (coefficients = 0.06).

We examined the hypotheses and conceptual framework through structural equation modeling using AMOS 28.0. All fit measures in the structural model had a marginal fit to the data (X² = 347.19 with df = 276, p < 0.000, X²/df = 1.258, GFI = 0.944, AGFI = 0.928, CFI = 0.987, RMSEA = 0.024). The testing results of the hypothesis are comprehensively presented in

Table 6. Summary of results.

Hypothesis	Path	Coefficients	t-Value	p	Supported
H1a	LPF→PPE	0.161	3.333	***	Yes
H1b	LCFP→PPE	0.124	3.073	0.002	Yes
H1c	DCPF→ PPE	0.122	2.625	0.009	Yes
H1d	SLF→PPE	0.059	1.069	0.285	No
H1e	SPF→PPE	0.284	4.746	***	Yes
H2a	LPF→PS	0.187	3.253	0.001	Yes
H2b	LCPF→PS	0.12	2.182	0.029	Yes
H2c	DCPF→PS	0.121	2.529	0.011	Yes
H2d	SLF→PS	0.203	3.116	0.002	Yes
H2e	SPFF→PS	0.283	3.896	***	Yes
H3	PPE→PS	0.346	4.509	***	Yes
H4	LPF→PI	0.416	4.678	***	Yes
H5	PS→PI	0.378	5.551	***	Yes

Note: *** p < 0.001.

.

Based on the results seen in Table 6, all hypotheses are supported except the self–live streamer fit with the perceived performance path. In terms of the relationship between signal consistency cues and perceived performance expectancy (H1a–H1e), live streaming product matching, live content–product fit, danmaku content–product fit, and self–product fit all have a positive impact on consumers’ perceived performance expectancy. The only exception was the unsupported relationship between self–live streamer fit and perceived performance expectancy. These findings indicate that in live streaming commerce, customers’ expectations of product performance can increase when they perceive a fit with the following factors: how products are displayed live, how products are introduced through live content, the feedback of barrage content on products, and how well their cognition matches with products.

However, the lack of support for a relationship between self–live streamer fit and perceived performance expectancy might indicate that the match between the live streamer and the consumer is not a key factor that directly affects consumers’ expectations of product performance. The reason for this result might be related to the characteristics of live streaming commerce in China, where live streamers frequently play the role of “salespersons”, emphasizing utilitarian benefits (such as price discounts or technical specifications) rather than cultivating social connections. Furthermore, the lack of significant influence might also reflect the measurement limitations. For instance, self–live streamer fit was measured through image consistency (for example, “the live streamer’s image matches my own”), which might not capture dynamic aspects of professionalism such as responsiveness to questions. Future research can re-examine this structure by integrating professional behavioral indicators such as real-time problem-solving or technical fluency, providing a new direction for future research.

In terms of the relationship between signal consistency cues and perceived satisfaction, all hypotheses (H2a–H2e) are supported, indicating that live streamer–product fit, live content–product fit, danmaku content–product fit, self–live streamer fit, and self–product fit all have a positive impact on consumer perceived satisfaction. This finding demonstrates the important role of signal consistency cues in improving consumer satisfaction. When various signal cues during the live streaming process match the actual situation of the product and the needs of consumers, consumers are more likely to be satisfied with the purchasing experience.

Additionally, our study validated the positive relationship between perceived performance expectations and perceived satisfaction (H3). This relationship indicates that the higher consumers’ expectations of product performance, the more likely they are to feel satisfied with their actual experience. This finding further emphasizes the importance of guiding consumers’ expectations of product performance during live streaming to ensure they are reasonable and thereby improve consumer satisfaction.

This study also confirms the positive impact of perceived performance expectations (H4) and perceived satisfaction (H5) on consumer purchase intention, which is consistent with previous research on consumer behavior. That is, consumers’ expectations of product performance and satisfaction with the purchase experience are important factors affecting their purchase intention.

Additionally, analyses of control variables (gender, age, education, income, platform usage, and shopping frequency) revealed no statistically significant effects on the structural relationships in the model, which suggests that the hypothesized paths remain robust across demographic and behavioral contexts.

5.2. Additional Analysis for Mediating Effects

The mediating effect refers to the phenomenon through which the independent variable indirectly affects the dependent variable by influencing one or more mediating variables [103]. In this paper, this is the process through which signal consistency cues indirectly affect consumers’ purchase intention by influencing perceived performance expectancy and perceived satisfaction. Accordingly, we conducted an additional analysis of the mediating effects. Traditionally, methods such as the Sobel test are used to test the significance of the product of path coefficients before and after the intermediary variables. However, the Sobel test assumes that the tested variables conform to the normal distribution. VanderWeele [107] noted that since the product of path coefficients usually does not conform to the positive distribution, the results of the Sobel test are not sufficiently accurate. However, the bootstrap self-sampling method can be used to estimate the intermediate effect value and its confidence interval more accurately. The core of the bootstrap self-sampling method lies in bypassing the traditional assumption of a normal distribution and directly constructing the sampling distribution of the product term ab of the mediating effect. Within this process, a large number of bootstrap samples (such as 5000 times) are drawn with replacement from the original sample. Each time, the path coefficients a (independent variable→mediating variable) and b (mediating variable→dependent variable) are calculated, and the value of ab is obtained. After arranging these ab values in ascending order, the 2.5th and 97.5th percentiles are taken to form a 95% confidence interval. If this interval does not contain 0, the null hypothesis ab = 0 is rejected and the mediating effect exists; otherwise, it is not significant. According to this, bootstrapping was used to verify the mediation effect in this study. Specifically, the technique was used to sample perceived performance expectancy and perception for 5000 times in AMOS28.0. The intermediary effect value (non-standardized) and confidence interval of satisfaction and the chain mediation effect formed by the two are estimated. This process yielded the level values of bias-corrected and percentile functions with 95% confidence, as shown in

Table 7. Summary of mediating effects.

Path	Estimate	se	Bias-Corrected			Percentile			Hypotheses
			95%CI			95%CI
			Lower	Upper	p	Lower	Upper	p
LPF-PPE-PI	0.062	0.029	0.016	0.133	0.007	0.012	0.125	0.014	Yes
LCPF-PPE-PI	0.054	0.026	0.012	0.113	0.01	0.01	0.11	0.013	Yes
DCPF-PPE-PI	0.047	0.028	0.007	0.117	0.016	0.004	0.111	0.025	Yes
SLF-PPE-PI	0.02	0.028	−0.025	0.091	0.351	−0.029	0.084	0.443	No
SPF-PPE-PI	0.086	0.034	0.028	0.165	0.006	0.021	0.155	0.012	Yes
LPF-PS-PI	0.066	0.03	0.021	0.14	0.001	0.019	0.134	0.002	Yes
LCPF-PS-PI	0.048	0.022	0.014	0.1	0.004	0.012	0.098	0.006	Yes
DCPF-PS-PI	0.042	0.024	0.005	0.1	0.024	0.003	0.096	0.033	Yes
SLF-PS-PI	0.062	0.03	0.016	0.137	0.003	0.014	0.132	0.005	Yes
SPF-PS-PI	0.078	0.031	0.028	0.152	0.001	0.026	0.147	0.001	Yes
LPF-PPE-PS-PI	0.019	0.012	0.006	0.057	0.001	0.004	0.049	0.005	Yes
LCPF-PPE-PS-PI	0.017	0.01	0.005	0.047	0.001	0.004	0.042	0.004	Yes
DCPF-PPE-PS-PI	0.015	0.008	0.004	0.041	0.005	0.002	0.034	0.016	Yes
SLF-PPE-PS-PI	0.006	0.009	−0.008	0.031	0.27	−0.012	0.024	0.442	No
SPF-PPE-PS-PI	0.027	0.015	0.01	0.079	0.001	0.007	0.066	0.003	Yes

.

According to Preacher and Hayes [108], when the p-value is less than 0.05 and 0 is not included in the lower and upper ranges of bias-corrected and percentile functions with a 95% confidence interval, the indirect effect is present. Based on the above results, it is evident that the mediating effect of perceived performance and satisfaction exists in the relationship between signal consistency and purchase intention. However, it is worth noting that in the paths SLF-PPE-PI and SLF-PPE-PS-PI, the p-value exceeds 0.05, and 0 is contained in the lower and upper value ranges of the bias-corrected and percentile functions with a 95% confidence interval. This shows that indirect effects do not exist in both routes. This means that self–live streamer fit does not directly affect consumers’ perceived performance expectancy, unlike other signal consistency cues. This finding is very important for live streamers because they do not need to overly focus on establishing a strong self-fit with each consumer to enhance performance expectations. On the contrary, they can focus on other aspects, such as product knowledge and live streaming skills. On the other hand, indirect effects exist in the SLF-PS-PI path, indicating that although self–live streamer fit might not directly affect perceived performance expectancy, it can still influence consumers’ satisfaction and purchasing decisions. Therefore, live streamers should still be committed to establishing positive connections with consumers to enhance satisfaction and drive sales. In conclusion, understanding these mediating effects provides actionable insights for live streaming business practitioners, who can utilize this knowledge to optimize their live streaming strategies, enhance consumers’ shopping experience, and ultimately increase sales and market competitiveness.

6. Discussion

6.1. Theoretical Contributions

This research has made substantial progress by systematically integrating signal theory and consumer behavior framework in the context of live streaming commerce. First, this study extends the signal theory beyond traditional e-commerce and identifies and validates five new dimensions of signal consistency: live streamer-product fit, live content-product fit, danmaku content-product fit, self-live streamer fit, and self-product fit. Previous studies have focused on isolated aspects of signal cues, such as product descriptions [24] or user reviews [47]. This work builds a holistic model that captures both real-time and interactive signals in a live streaming environment, which not only bridges the gap between fragmented signal research but also provides a holistic model for dynamic e-commerce environments. By showing how these dimensions work together to shape consumers’ perceived performance expectations and satisfaction, this study bridges the gap between signal consistency cues and performance responses, further elucidating how signal consistency cues drive purchase intent.

Second, the study improves the methodological rigor in the research of live streaming commerce. Prior research on live streaming commerce predominantly relied on linear regression or single-stage SEM [102], which overlooks complex mediation pathways. By using a two-stage SEM-bootstrapping method, this study not only verified the causal relationship between constructions, but also quantified the mediating role of perceived performance expectation and perceived satisfaction. This dual-method framework addresses the limitations of previous studies that relied on linear regression or single-stage SEM, which often overlook complex, non-linear interactions. For instance, while Chen and Yang [13] emphasized direct effects of live streamer characteristics on purchase intention, our analysis reveals that self–live streamer fit indirectly influences intention through satisfaction—a hidden pathway undetectable via traditional methods. This finding indicates that mediation analysis is necessary for uncovering hidden pathways. This methodological innovation provides a replicable template for future studies that aim to disentangle multi-layered consumer decision-making processes in dynamic, real-time settings.

Third, this study enriches consumer behavior theory by contextualizing performance reactions within live streaming commerce. It establishes that perceived performance expectancy and satisfaction are not merely outcomes of product quality but can be dynamically shaped by several signal cues in live streaming shopping behavior. For example, the positive effect of danmaku content–product fit on satisfaction highlights the critical role of real-time communication with consumers in the same space, reflecting a dimension that has not been fully explored in static e-commerce models [30]. In addition, by demonstrating that self–live streamer fit does not directly affect perceived performance expectancy—contrary to the view of self-fit theory [13]—we emphasize that the functional expertise of live streamers (such as professionalism) is more important than identity fit in the context of live-steaming commerce. These insights redefine the boundaries of consumer behavior theory in digital environments.

6.2. Practical Insights

For practitioners in live streaming commerce, the findings provide actionable insights. These recommendations apply to sellers, live streamers, and live streaming commerce platform operators and are aimed at optimizing consumer engagement and drive sales in live streaming commerce. By leveraging the mechanisms of signal consistency theory, practitioners can enhance perceived performance expectations, satisfaction, and purchase intention through the following strategies.

First of all, as the core link connecting brands and consumers, the personal traits and content presentation methods of live streamers directly affect consumers’ cognition and behavioral decisions. Our research confirms that live streamer–product fit and live content–product fit can enhance consumers’ perceived performance expectancy, whereby perceived satisfaction has a positive effect. This reminds brand owners that live streamers and the live streaming content that they select should be consistent with their product and brand characteristics. For instance, diaper brands prefer to choose live streamers who usually share their parenting experiences on social media for sales instead of travel influencers or dance influencers. Moreover, during the live process, live streamers should not only provide high-quality visual effects and detailed descriptions around the features of the diapers but also professionally answer consumers’ real-time questions and create a positive atmosphere for live shopping to increase consumers’ perceived performance expectancy and perceived satisfaction. This, in turn, can lead to increased purchase intention and long-term brand loyalty.

Second, danmaku management is critical, whereby platforms should encourage authentic, product-related feedback while filtering irrelevant or conflicting comments to maintain signal consistency. We suggest that live streaming platforms should optimize the danmaku management system. During the live streaming process, some negative comments can be automatically filtered out, such as those that are not related to the product or are personal attacks. At the same time, it is also possible to highlight the positive and product-related danmaku. For instance, when an audience member comments that the product they purchased from this live stream has a positive performance and is worth recommending, the comment can be highlighted through the danmaku management system. This encourages consumers to give more genuine feedback and maintains a positive signaling environment, thereby enhancing consumer satisfaction.

Third, live streaming commerce platforms can provide personalized recommendations based on consumer self–product fit to heighten satisfaction. Live commerce platforms such as TikTok can use social platform data to analyze segment users. For example, when users frequently watch short videos related to fitness, the platform can recommend relevant live streaming rooms that recommend sports clothing, fitness equipment and courses, etc. Meanwhile, brand live streaming rooms can also conduct data analysis among their fans. For instance, when they find that a large proportion of their fans are young people who are interested in business, casual, and fashion, the live streamers can focus future broadcasts on showcasing suitable outfits for the workplace, offering styling tips, and even sharing stories related to their careers. This kind of customized content based on audience segmentation enhances the consumers’ perception of the product fit, thereby improving satisfaction and purchase intention. Although self–live streamer fit does not directly affect performance expectations, cultivating the professionalism and credibility of live streaming is crucial. For example, a live streamer of technology products should always conduct in-depth research on the products that they promote to accurately answer complex technical questions and provide reliable product comparisons. This professionalism ensures the audience’s satisfaction with their recommendations, even though the host’s personal image might not have a direct impact on the expected product performance.

Through these practical insights, we aim to help sellers transform live streams into immersive, trust-driven experiences that align with consumer expectations, foster loyalty, and drive sustainable growth in competitive markets. These approaches not only help to enhance immediate sales but also build long-term brand equity through consistent, authentic signal transmission. Meanwhile, this research also provides practical help for improving the efficiency and quality of communication between the live streamer and consumers.

6.3. Limitations and Future Directions

Although this study provides valuable insights, there are still some limitations. Firstly, the data were collected exclusively from Chinese consumers, which may limit the generalizability of the findings to other cultural or economic contexts. Culturally, different regions have distinct consumption values, communication styles, and attitudes towards online shopping. For instance, in Western culture, consumers place more emphasis on individualism and the uniqueness of products when shopping, while Chinese consumers are more influenced by group opinions and brand awareness. Therefore, in the context of live streaming business, these cultural differences might lead to different views on signal consistency. For example, the influence of danmaku content–product fit and self–live streamer fit on performance response might differ from the results of this study. If future studies make cross-cultural comparisons, it may be possible to test whether there are differences in the mechanism of exploring signal consistency across different regions. However, China is the main region of live streaming consumers, and the results of this study still have practical value.

Secondly, our results relied on self-reported survey data from a single source, which might have led to some potential biases. For instance, due to recall bias (such as the inaccuracy of retrospection over live streaming experience) or expectation bias (such as reactions being influenced by perceived social norms), participants might exaggerate their purchase intention and satisfaction in the survey. These biases could potentially exaggerate the relationship between signal consistency and performance response, and the results might overestimate the interpretive ability of the model. Future research can mitigate these limitations by integrating self-reports with behavioral metrics (such as actual purchase records from live streaming platforms) or experimental designs that manipulate signal consistency in a controlled environment, as well as through longitudinal surveys before and after making purchases.

Thirdly, other influential factors, such as live streaming platform interface design or promotional activities, might extend the framework of the paper. Since these variables are based on the spontaneous behavior of brands, the present study addressed the five signals that live streaming commerce platforms send to consumers in the process of live streaming commerce, with the aim of obtaining the most practical results. In our future study of live streaming commerce, we plan to consider the influence of additional factors involving brand building or brand trust.

Finally, the study did not differentiate between product categories (such as luxury products or fast-moving goods), which may moderate signal effects. Hence, future research should account for the fact that certain live streaming marketing strategies may be more relevant for specific business categories.