The Effects of Online Shopping Context Cues on Consumers’ Purchase Intention for Cross-Border E-Commerce Sustainability

Abstract

China is currently the world’s largest cross-border e-commerce purchaser and destination country. Therefore, how to promote consumer online shopping is the most important goal for cross-border e-commerce sustainability. Meanwhile, the previous research has not empirically verified the precise effect of online shopping context and perceived value on consumers’ cross-border online purchase intention. To address this gap, this study analyzes the online shopping context that determines consumers’ purchase intention and innovatively identifies four cues that promote this consumption behavior in cross-border e-commerce, such as online promotion cues, content marketing cues, personalized recommendation cues, and social review cues. It proposes a theoretical model based on cue utilization theory and stimulus-organism-response model, which introduces this four cues and brand familiarity in analyzing the effects on consumers’ purchase intention in cross-border online shopping (CBOS). In addition, the paper examines the mediating role of perceived functional value and perceived emotional value. Survey data collected 372 cross-border online consumers from China and the PLS-SEM method was used to empirically test the proposed model. The results show that these four cross-border online shopping context cues have a significantly positive impact on consumers’ purchase intention. Brand familiarity has significantly negative moderating effects between the four cues and the perceived functional value, while brand familiarity also negatively moderates the relationship between online promotion cues, social review cues, and perceived emotional value, respectively.

1. Introduction

With the continuous growth of global trade and the rapid advances of the digital society, consumers are increasingly shopping abroad. Cross-border e-commerce booms all around the world [1]. What is more, these trends have stimulated the upgrading of traditional foreign trade modes in multiple dimensions of sustainability. It can offer attractive products to consumers with competitive prices and wide product assortments [2], and greatly shorten the time and space distance between consumers and suppliers [3,4,5]. According to a report published jointly by Accenture and Ali Research [6], the global Business to Customer (B2C) cross-border e-commerce market will balloon in size to $1 trillion in 2020, and more than 943 million people around the world will shop online internationally. China, with more than 200 million B2C cross-border e-commerce consumers, will become the world’s largest cross-border B2C e-commerce consumer market [6]. In view of the importance of the cross-border e-commerce, countries around the world have taken steps to promote sustainable development of cross-border e-commerce. Given the importance of e-commerce in the Digital Single Market (DSM) strategy, the EU is committed to opening up digital opportunities for people and businesses, especially through a wide range of initiatives to promote cross-border e-commerce [5]. The rapid development of cross-border e-commerce in China is famous for its popular “one belt and one road” initiative, which may reshape our way of conceptualizing and managing manufacturing, logistics, and consumption to achieve sustainable growth and development of cross-border e-commerce, especially in emerging cross-border markets [7].

Previous studies on cross-border e-commerce [8,9,10] pay more attention to the positive impact of cross-border e-commerce on economy and its potential growth and future development. There is not much empirical research on the challenges and opportunities for both cross-border e-commerce suppliers and consumers, and less is conducted to address the increasingly intense price competitions, the efficiencies of the retail sector, positive impacts on other production sectors, the interests of individuals and household consumers, labor productivity, domestic production and total value growth, etc. [5]. While cross-border e-commerce is growing rapidly, especially in emerging market countries (such as China, India), there are still many factors that restrict consumers from purchase. Therefore, attracting more online consumers and recognizing factors that influence consumers’ purchase intention, have become an important goal for cross-border e-commerce sustainability [11]. In addition to factors such as Internet penetration and information technology facilities, Internet consumer experience, and perception are also important constraint factors [5]. The main constraints mentioned in the literature are crossing language barriers, little familiarity and trust in the vendors, need of a secure way to pay, cost-efficiency of parcel delivery, etc. [11,12,13]. Many studies [5,14] have noted that demographic attributes, such as gender, age, income, and education, can make differences in the adoption of cross-border e-commerce. However, the impact of these factors is often context-dependent. According PayPal [2], global consumers’ attitude towards cross-border purchase varies from region to region, and North Americans are most likely to be loyal to global or home websites and more sceptical of those from other countries, consumers in the Middle East are most likely to shop cross-border. Von Helversen et al. [14] found strong age differences in the influence of consumer reviews on purchasing decisions. Cross-border e-commerce platform can take a variety of measures to improve online consumer satisfaction, such as providing personalized products [15,16] according to customers’ purchase history and personal information, providing the evaluation of products and their suppliers [17,18,19].

Cross-border online consumption is vastly expanding around the globe, but nowhere can one observe cross-border e-commerce developments like in China [20]. According to PayPal’s survey, the proportion of cross-border online consumers in China to the total number of online consumers has increased from 25% in 2014 to 43% in 2017 [2]. A number of relatively successful B2C cross-border e-commerce platform companies have emerged in China, such as Yangmatou, Xiaohongshu (also known as RED), Netease’s Koala, Tmall Global, AliExpress, etc. [2]. However, comparing to the domestic e-commerce which has formed a relatively stable market competition pattern and enjoyed a leading position in the world, China’s cross-border e-commerce enterprises are still in the growth stage, and the marketing strategies of cross-border e-commerce retail enterprises is also mainly derived from traditional import trade or domestic e-commerce model [7,13,20]. For consumers, cross-border online purchase and domestic online purchase are two significantly different online shopping contexts. In the traditional domestic online shopping context, even if consumers are not familiar with the properties of online shopping products, they can avoid or fundamentally reduce potential losses through seven days return/exchange policy, freight insurance, and other safeguard mechanisms [21]. Under the cross-border online shopping context, it is often difficult for the seller to effectively provide such a guarantee mechanism due to barriers such as international logistics costs, procedures, and tariffs. Therefore, consumers will be more cautious about the cross-border online purchase decision, and the influencing factors of cross-border online purchase intention may be quite different from domestic e-commerce.

The previous research has not empirically verified the precise effect of online shopping context and perceived value on consumers’ cross-border online purchase intention. To address this gap, the aim of this paper is to identify the main categories of external cues that affect consumers’ cross-border shopping, and discover the influence mechanism of the external cues affecting consumers’ willingness to conduct cross-border shopping. The main research questions are: Are online promotion cues, content marketing cues, personalized recommendation cues and social review cues positively affecting consumers’ purchase intention for cross-border commerce? Does consumer perception has a mediate effect between external cues and cross-border purchase intention? Whether consumers’ familiarity with online brand has a moderating effect on the influence of external cues? The innovative conclusions to these questions provide insight in the behavior of online consumers, which can provide empirical research support for B2C cross-border e-commerce enterprises to improve marketing strategies, enhance cross-border online purchase confidence of Chinese consumers, and promote the sustainable development of cross-border e-commerce.

The Section 2 of this paper reviews Cue Utilization theory and Stimulus-Organism-Response theory (SOR). The proposed research model and hypotheses are presented in Section 3. Section 4 presents the methodology, including data description, measurement of variables and the econometric model. Then, Section 5 analyzes the results, and finally the paper is concluded with discussions on the findings and their theoretical and practical implications.

2. Literature Review and Theoretical Background

2.1. Cue Utilization Theory

Cue utilization theory believes that products convey a series of cues that consumers can use to judge their qualities [22,23]. Product cues categorize them as intrinsic cues and extrinsic cues [23]. Intrinsic cues are related to the direct physical properties of the product, including product size, shape, taste, etc. If the physical characteristics of the product itself is not changed, intrinsic cues cannot be changed or controlled by the test [24]. External cues are usually associated with indirect signals and are “product-related attributes, but not part of physical attributes” [24,25]. The value of clues to consumers can be divided into two categories: predictive values and confidence values. The former is the degree to which consumers associate a clue with product quality. The latter is the degree to which consumers have confidence in their ability to correctly judge and use clues [26]. The importance of cues in consumer perceived quality judgment is determined by the predictive value and confidence value of the cue. The cues of high predictive value and high confidence value will play an important role in value evaluation.

In most cases, consumers are more familiar with external cues than with intrinsic cues, so they rely more on external cues when evaluating products. For example, Wheatley et al. [27] found that changes in the physical quality of a product are less likely to be perceived by consumers than price changes. While physical quality cues have a great impact on perceived quality, when the level of physical quality cues increases, its impact becomes smaller. Therefore, consumers are more inclined to use the external cues of products to judge product quality. Zeithaml [28] argued that consumers are susceptible to adjustments in product factors when using internal leads, i.e., the same consumers may use different internal cues for different products. When using external cues, it is not affected by the adjustment of product factors. For example, many products will use prices, brand names, advertisements etc. Because consumers cannot directly observe the intrinsic cues of products, they rely more on external cues to assess product quality [29,30]. Therefore, under the cross-border online shopping context, external cues play a very important role in consumer decision-making.

2.2. Stimulus-Organism-Response Theory

The SOR model [22] based on environmental psychology theory has been widely used to measure the impact of product feature perception on consumer response [31]. According to SOR, environmental stimuli (S) lead to emotional reactions (O), which in turn drive consumer behavioral responses (R). SOR theory indicates that environmental stimuli (S) lead to emotional responses (O), which then drive consumers’ behavioral responses (R). In the study of marketing, emotion is considered to be the body’s response to external environmental stimuli and ends with some kind of action [32]. Similarly, Emotion plays a key role in consumers’ decision-making and buying behavior [33,34]. According to Wilkie [35], consumers deal with emotions when search for information, comparing alternatives, making purchase decisions, and dealing with products that have already been purchased. Therefore, it can be seen that consumers are immersed in certain emotions throughout the purchase process.

Using the S-O-R model, many studies in the retail and service sectors have successfully used environmental stimuli as predictors of emotional responses and consumer behaviors [36,37]. Bagozzi et al. [38] validated the S-O relationship of the S-O-R model and found that consumption-related emotions were formed based on specific assessments made by consumers. A study by Baker et al. [39] found a link between the store environment and the emotional state of pleasure and awakening. Wakefield and Baker [40] argued that the overall architectural design and decoration of the mall is a key environmental factor that excites customers. Donovan and Rossiter [36] first applied the S-O-R paradigm in a retail environment, and they argued that Mehrabian and Russell [22] did not explicitly address the issue of stimulus classification. Meanwhile, in the marketing literature, there are many different ways that people can use the S-O-R paradigm. Some scholars use consumer assessment of external stimuli as part of the S-O-R model [41,42], while others use actual stimuli [43,44].

3. Conceptual Framework and Hypotheses

3.1. Conceptual Framework

According to the S-O-R model of Mehrabian and Russell [22], stimulus refers to attributes that are located in the environment and affect the emotional and cognitive state of the individual, such as product characteristics, promotion, brand reputation, music, price, layout, service, and so on. In the online shopping environment, consumers often face a large number of external leads. Previous studies have suggested that the main cues to consumer purchasing decisions are visual appeal of the website [45], product price [46], third-party guarantee [47], online store reviews [48] and store reputation [49]. However, these previous studies have also found that when consumers face a large number of external cues, these cues play different roles. The information provided by different cues cannot be simply added up. When one consumer receives the comment information of other consumers, the cues such as seals and store reputation are no longer important to the consumer. In the cross-border online shopping context, this study selects four cross-border online shopping context cues as external stimuli to stimulate consumer emotional responses and cognitive processes, which are online promotion cues, content marketing cues, personalized recommendation cues, and social review cues.

In the S-O-R model, the organism is a mediator of the relationship between environmental stimulus and response. It usually refers to an individual’s emotion or cognitive state. Emotion is a general term for an individual’s series of subjective cognitive experiences. It is a psychological and physiological state in which multiple feelings, thoughts, and behaviors are combined. Emotion is often intertwined with mood, temperament, personality, disposition, and motivation. Cognition is an important aspect of emotion. Cognition is a psychological behavior or process that acquires knowledge and understanding through thoughts, experiences, and senses. Cognition includes attention, knowledge formation, memory and working memory, judgment and evaluation, reasoning and “calculation”, problem solving and decision making, language understanding and production. Different analyzing methods are used to analyze cognitive processes in different contexts and disciplines [50]. Hartman [51] first proposed a value model consisting of both emotion and cognition. Due to the constant changing of consumer’s needs, the dimensions of perceived value are gradually changing and advancing with the times. Sweeny & Soutar [52] use functional value as the basis of cognition and emotional or social value as the basis of emotion. Among them, under the cross-border online shopping context, the perceived function value mainly refers to the consumer’s usefulness perception of the various recommended cues received, that is, customers’ completion of the cross-border shopping targets. Functional value and emotional value have been widely concerned in recent years’ research on consumers’ online purchase intention, but few scholars have tested their relationship with other factors affecting e-commerce success [53,54]. In this study, the perceived value on cross-border online shopping context is used to describe the organism variable, including the perceived functional value and perceived emotional value.

According to the S-O-R theory, consumer response refers to the approach or avoidance behavior after the emotional and cognitive processes. Approaching behavior includes positive responses such as purchase intentions, patronage intentions, etc. Behavioral intentions are personal beliefs or feelings on which people base their actions. The consumer’s behavioral intention is a function of product cues such as store image and price. It is a positive assessment of the store’s image, product price and packaging, and other cues that will enhance consumers’ purchase intention, patronage, or loyalty [55,56]. Based on the theory of clue utilization and the SOR model, this study uses cross-border consumer’s purchase intention as the final response of consumers to cross-border online shopping context cues.

In the context of cross-border online shopping, there are fewer internal cues but more external cues consumers can directly have. Therefore, consumers have to rely on external cues to make purchase decisions. When the impact of external cues on consumer perception and purchase intention is discussed, the impact of other factors should be controlled or excluded. In the literature on international marketing, consumers’ previous shopping experience, education level, and foreign product knowledge are often considered to be important factors influencing consumers’ purchase intention [57]. This study assumes that in the context of cross-border online shopping, Chinese consumers’ brand familiarity (BF) may have an impact on the functional or emotional value of external cues. Therefore, in addition to controlling the demographic attributes of Internet shoppers, this study uses brand familiarity as a moderator between cues and perceived value.

The theoretical framework of this study is shown in Figure 1.

3.2. Research Hypothesis

3.2.1. Cross-Border Online Shopping Cues and Perceived Value

In the cross-border online shopping environment, Chinese consumers are faced with a large number of product recommendation information. According to the push body, push content, the paper summarizes the main online shopping contexts that Chinese consumers face into four categories, namely online promotion cues, content marketing cues, personalized recommendation cues, and social review cues.

Online Promotion Cues and Consumer-Perceived Value

Unlike American consumers [58], most Chinese consumers are more sensitive to price concessions, which can be verified by the Double Eleven phenomenon. Initially 11.11 or Double Eleven was known as ‘Singleton’s Day’ playing on the four ‘1′s that form the date and it aimed only at college students in China. Starting in 2009, several major online retailers decided to offer significant discounts on 11.11. At the same time, they attempt to increase sales during National Day of China to Christmas Day. At present, Double Eleven has changed from self-mocking jokes of young people to national shopping festivals [21]. On 11 November 2018, the online shopping turnover reached 213.5 billion yuan [21]. The main promotion of the Double Eleven is the various price-cutting activities.

Some studies suggest that price promotions have an important impact on consumer purchase decisions [59,60]. Previous researches suggest that price promotions can create an economic incentive that attracts consumers to purchase products [61]. Nagadeepa [62] pointed out that various promotions are just like pleasant surprises for consumers, satisfying their desires for good but cheap products, thus increasing their purchase intention. In the online shopping context, if the products offered by the two retailers are identical except for the price, then the consumers tend to think that the retailer offering lower price has stronger corporate strength or enjoy a higher industry status. Therefore, price promotion is also conducive to the improvement of consumers’ perception of emotional value. Based on the above mentioned, the hypothesis is proposed:

Hypothesis 1a (H1a).

Online promotion cues have a positive impact on consumer-perceived functional value in the cross-border online shopping context.

Hypothesis 1b (H1b).

Online promotion cues have a positive impact on consumer-perceived emotional value in the cross-border online shopping context.

Content Marketing Cues and Consumer-Perceived Value

Content marketing (also known as Advertorials in China [63]), as an advertisement, are articles in a newspaper, magazine, or a website which involves giving information about the product. Usually, brands pay publishers for such articles. Marketers use them to educate potential consumers about the characteristics of content marketing of product. By choosing the right media, content marketing can be used for a specific group of people. For example, content marketing in business newspapers would involve educating a group of people who are more interested in economics, markets or financial products. For companies, storytelling is an effective medium to connect with consumers, which is unlike traditional print advertising in magazines, newspapers or on websites as a banner advertising. Content marketing is more detailed than advertising, which helps consumers to know more about products and is usually written by advertising agents or companies themselves.

In the Internet age, the forms and media of content marketing are more diversified. TikTok (also known as Douyin in China, a media app for creating and sharing short videos), and WeChat (a Chinese multi-purpose messaging, social media and mobile payment app) among Chinese netizens are all content marketing methods that online retail enterprises can choose. Online content marketing are always educational and they are constantly presented on the Internet in an interesting way, thus greatly lowering the requirement for the cognitive ability of audience. Therefore, online content marketing are often more effective than traditional advertisements [64,65].

The concealment of content marketing (which is often criticized) and the independence of the news media have made people unwittingly pleasing and thus won over their trust. Some of the content marketing, such as those created by prominent bloggers and Internet celebrities, have even enhanced the perceived value of their fans to the products. Wang and Yang [63] believe that customers can obtain useful information about the goods from the content marketing posted on the e-commerce platform in a short span of time, so that they do not have to make great efforts for shopping. Sharifi Fard [66] pointed out that interesting, novel and unique content marketing enable consumers to focus on the online shopping activities, to have a sense of pleasure and to meet their emotional needs. Based on the above mentioned studies, the hypothesis is proposed:

Hypothesis 2a (H2a).

Content marketing cues have a positive effect on consumer-perceived functional value under the cross-border online shopping context.

Hypothesis 2b (H2b).

Content marketing have a positive effect on consumer-perceived emotional value under the cross-border online shopping context.

Personalized Recommendation Cues and Consumer-Perceived Value

The personalized recommendation cues refer to the user preference data mined and processed by the cross-border e-commerce platform. Hence the cross-border e-commerce platform can recommend products to target consumers. Using consumer demographics, shopping history, and preference information, the e-commerce recommendation platform can generate recommendations that meet users’ needs [67]. Previous studies have shown that personalized recommendations have a significant role in influencing consumer decision-making [68,69]. Personalized recommendations are more easily accepted by consumers than non-personalized re-suggestions (i.e., recommendations generated without regard to consumer preferences) [70], resulting in more clicks than random recommendations [71], and having higher user ratings than random recommendations [72]. In mobile commerce, they also bring a significant increase in pageviews/sales and total sales [73]. The personalization degree of user perception can be used to explain the important role of personalized recommendation in influencing consumer decision-making. The personalization degree of user perception refers to the extent to which consumers believe that the personalized system understands and represents their personal preferences [74]. Komiak and Benbasat [74] found that consumers perceive that the platform’s personalized recommendations can better understand their preferences for products and more personalized needs. The perceived consensus between consumers and recommendation systems is enhanced. Customers are encouraged to accept recommendations. Xiao and Benbasat [75] believe that personalized recommendation is an e-commerce feature that consumers value highly and helps online companies build brand loyalty. Aljukhadar and Senecal [76] pointed out that personalized recommendations based on consumer needs can reduce consumers’ shopping time and make consumers’ shopping process convenient and interesting. Based on the above mentioned, the hypothesis is proposed:

Hypothesis 3a (H3a).

Personalized recommendation cues have a positive impact on consumer-perceived functional value under the cross-border online shopping context.

Hypothesis 3b (H3b).

Personalized recommendation cues have a positive impact on consumer-perceived emotional value under the cross-border online shopping context.

Social Review Cues and Consumer-Perceived Value

Social review, a blend of user-generated contents and social networks, is a virtual community or online platform for people to share ideas, insights, experiences and perspectives with each other. As online media continues to evolve, users actively communicate and share ideas about products and services through blogs, product reviews, wikis, and Twitter on a more regular basis [77]. With the increasing popularity of social review as an effective tool for socialization and information sharing, social e-commerce has come into being [78]. In social e-commerce, consumers are exposed to various technologies or functions, such as user-provided shopping experience and comments, social recommendation and user preferences, etc., which arouse consumers’ enthusiasm to participate in social e-commerce [79].

The technical characteristics of the social commerce platform reflect not only the objective attributes unrelated to the customer, but also the subjective attributes perceived by the customer. According to existing research literature, online user reviews, product reviews, and other information in social review cues have important implications for consumer value perception and purchase decisions. For example, product ratings (or reviews) have a significant impact on perceived trust and can reflect whether a supplier is reliable [80]. Positive reviews often reflect the positive attributes of the product, such as good quality and brand image, while negative reviews often reflect a lack of consumer confidence in the attributes of the product, such as quality and brand image. Cui et al. [81] found that negative reviews exerted a greater impact than the positive reviews did, since they confirmed negative biases. Consumers are not only inclined to read review ratings, but also tend to read the review text before making a purchase decision [82]. The textual sentiment can be viewed as a unique type of cognitive assessment from previous customers, and the assessment provides a useful set of information for potential consumers’ cognitive processing [83,84]. Emotions inferred from the text of the seller’s comments can predict the consumer’s perceived usefulness. Therefore, social review cues such as product ratings and textual comments on online reviews are important factors influencing consumer perceived value and purchasing decisions. Based on the above mentioned, the hypothesis is proposed:

Hypothesis 4a (H4a).

Social review cues have a positive impact on consumer-perceived functional value under the cross-border online shopping context.

Hypothesis 4b (H4b).

Social review cues have a positive impact on consumer-perceived emotional value under the cross-border online shopping context.

3.2.2. Perceived Value and Cross-Border Online Purchase Intention

Consumers’ purchase intention depends on the extent to which the perceived value of the product reaches their expectations [85]. The direct impact of perceived value on consumer purchase intentions has been widely supported by theoretical and empirical researches [86,87]. Perceived value is a multidimensional concept. Many scholars discuss the influence of consumer perceived functional value and emotional value on purchase intention. In a recent study, Ghanbari et al. [88] found that functional and emotional values have a significant impact on consumer’s purchase intention. It is consistent with the views of Hines and Bruce [89], Rubera et al. [90], Asshidin et al. [91], Lim et al. [92], and Koo et al. [42]. This study proposes that the perceived value of consumers plays a mediating role between external cues and cross-border online purchase intention under the cross-border online shopping context. The assumptions is based on the SOR model and clue utilization theory:

Hypothesis 5a (H5a).

The consumer-perceived functional value has a positive impact on their purchase intention in cross-border online shopping.

Hypothesis 5b (H5b).

The consumer-perceived emotional value has a positive impact on their purchase intention in cross-border online shopping.

3.2.3. Adjustment Effect of Brand Familiarity

The effects of brand-related factors on trust and consumer judgment are well known in the marketing literature. Brand familiarity is a continuous variable that reflects the extent of a consumer’s direct and indirect experiences with a brand or product [93]. Since consumers have a different understanding of different products, numerous studies have shown that familiar brands have major advantages over unfamiliar brands in terms of processing and attitudes. Information about familiar brands requires less efforts made by consumers to process, the information is more easily retrieved and stored, and consumers usually prefer these brands [94,95,96]. In particular, research suggests that well-established brands act as a powerful heuristic cue that influences purchase decisions [97]. Familiar brands include many positive associations that guide consumers to judge whether a product or company is trustworthy [98], thus serving as a quality signal when it is impossible to directly test the product [99,100]. The empirical results can be seen from the structural equation model that the degree of familiarity with the brand will affect consumers’ confidence in the brand, thus affecting their intention to purchase the same brand. Türkel et al. [101] revealed that familiar and unfamiliar brands had no difference in consumers’ attitudes toward news, but have different responses to brand and purchase intention.

Previous literature has shown that familiar brands enjoy more cognitive and affective advantages, because they can be recognized and identified more easily. In addition, a consumer’s attitude toward a specific brand is affected by his/her familiarity with the brand. Therefore, the impact of brand familiarity should be controlled when studying the impact of external cues on consumer value perception. This study believes that under the cross-border online shopping context, the higher the Chinese consumers’ familiarity with the brand of imported goods is, the less stimulating and influential the external leads such as online promotion, content marketing, personalized recommendation, and social review are. Based on this, the hypothesis is proposed:

Hypothesis 6a (H6a).

Under the cross-border online shopping context, the higher the consumer’s brand familiarity, the smaller the influence of the online promotion cue on the consumer-perceived functional value/ emotional value.

Hypothesis 6b (H6b).

Under the cross-border online shopping context, the higher the consumer’s brand familiarity, the smaller the impact of content marketing on consumer-perceived functional value/ emotional value.

Hypothesis 6c (H6c).

Under the cross-border online shopping context, the higher the consumer’s brand familiarity, the smaller the impact of personalized recommendation cues on consumer-perceived functional value/ emotional value.

Hypothesis 6d (H6d).

Under the cross-border online shopping context, the higher the consumer’s brand familiarity, the less the influence of social review cues on the consumer-perceived functional value/ emotional value.

3.2.4. Control of Demographic Variables

Online shopping (or purchasing) behaviors tend to differ demographically among individuals [102]. Consumer demographic attributes are typically represented by differences in age, gender, education, income, and nationality [103]. The importance of this demographic data has led previous studies to investigate the manner in which consumer demographic traits (e.g., age and gender) influence online shopping and cross-border online shopping [5,14,104,105,106].

At present, cross-border online shopping by Chinese consumers has become a very common phenomenon. In view of the differences between cross-border online shopping and domestic e-commerce in terms of tariffs, international logistics, and product uncertainty, the impact of demographic variables on consumer’s cross-border online purchase intension will be significantly different from that of domestic e-commerce. In order to control the interference of demographic variables on relevant research hypotheses, this study used demographic variables such as gender, age, education, income, and occupation as control variables.

4. Methodology

4.1. Measurement Development

Based on previous research into online purchase intention, Perceived Functional Value, Perceived Emotional Value, Brand Familiarity, Online Promotion Cues, Content Marketing Cues, Personalized Recommendation Cues and Social Review Cues, survey items for the measurement of each construct were selected and a questionnaire including those items was developed. All items were measured on a five-point Likert scale, from strongly disagree to strongly agree.

The items in the questionnaire were developed by adjusting measures validated by other researchers or by converting the definition of the constructs into a questionnaire format. The constructs in this model were adapted from previous studies and multi-item scales were used for these constructs. Measurements of each construct were modified to fit the research background, and the Chinese version of origin measurements were translated from the original, back-translated and adjusted for cultural adaptation. Measures of Online Promotion Cues with four items were adapted from Nagadeepa et al. [62]. Personalized Recommendation Cues with five items were modified from Lee and Johnson [107] and Zhang et al. [79]. Content Marketing Cues with five items were adapted from [108], Reid et al. [109], and Wang and Yang [63]. Social Review Cues with five items were adapted from Animesh et al. [110] and Zhang et al. [79]. Perceived Functional Value and Perceived Emotional Value with four items respectively, were adapted from Sweeny and Soutar [52], and Wu et al. [111]. Cross-border Online Purchase Intention with four items were adapted from Animesh et al. [110] and Wu et al. [111]. Brand Familiarity with four items were adapted from Kent and Allen [94], Laroche et al. [112], and Wang and Yang [63]. Backward translation (with the material translated from English into Chinese, and back into English; versions compared; discrepancies resolved) was used to ensure consistency between the Chinese and the English version of the instrument.

Questionnaire development contains multiple stages, in order to improve and restructure survey instrument. Initial version of the survey instrument was finished by four professors at the University for testing in advance, each professor has significant professional knowledge of cross-border e-commerce. After obtaining the specialists’ feedback, we modified the wording and structure of measuring items. Then, the revised version was further pilot-tested on 21 participants who had extensive experience in cross-border online shopping. The pilot group represented various workforce demographics, including scholars, professionals, public servants, self-employed, and undergraduate. Finally, the revised version was further tested through an extensive pre-test with 86 responses who had experience in cross-border online shopping, the internal consistency and discriminant validity of the instrument were assessed. Due to low item-to-total correlation (less than 0.50), one item from Personalized Recommendation Cues and one item from Social review Cues were dropped. The refined instrument, in the form of a self-administered questionnaire, was then used to collect the sample data.

4.2. Data Collection

The survey of this study is for Chinese consumers who have experience in cross-border online shopping. In order to maximize a response rate, both online and offline surveys were conducted to collect data. To maintain external validity, data is sampled from various groups such as schools, companies, railway stations, and supermarkets. Data were collected at different times of the day (morning and afternoon) and during different days of the week (Monday through Saturday) to ensure that the samples were representative and unbiased. The data collection process lasted for 8 weeks.

A total of 984 surveys were distributed, of which a total of 661 were returned (a response rate of 67.2%). After eliminating 289 responses due to insincerity or incompleteness through data filtering, a sample of 372 usable responses was ultimately employed in our empirical analysis. The descriptive statistics of the sample are listed in

Table 1. Descriptive statistics of respondent characteristics (N = 372, 1 ¥ is approximately equal to 0.1326€/0.1485 $, the minimum average monthly income in China is approximately equal to 1753 ¥ (231 €/259 $)).

Demographics	Category	Count	Offline Sample	Online Sample	Difference Test
Gender	Female	215	82	133	z = 0.576
	Male	157	66	91	p value = 0.448
Age	18–25	86	28	58	z = −0.448
	26–35	195	87	108	p value = 0.654
	36–45	69	25	44
	46 or older	22	8	14
Education	High school or below	48	19	29	z = −0.727
	Bachelor’s degree	265	102	163	p value = 0.468
	Master’s degree or above	59	27	32
Occupation	Government/institution	84	26	58	χ2(5) = 7.344
	Listed company employees	93	35	58	p value = 0.197
	Unlisted company employees	111	51	60
	self-employed	19	11	8
	Student	55	21	34
	Others	10	4	6
Monthly income	≤5000 ¥	101	45	56	z = −1.359
	5001–10,000 ¥	158	63	95	p value = 0.174
	10,001–20,000 ¥	93	33	60
	≥20,001 ¥	20	7	13
Monthly expenditure on online shopping	≤200 ¥	16	9	7	z = −0.460
	201–500 ¥	88	35	53	p value = 0.645
	501–1500 ¥	164	62	102
	1501–2500 ¥	79	33	46
	≥2501 ¥	25	9	16

. Among 372 participants, 42.2% are males, 57.8% females, and 75.5% are below 35 years old. Most of the respondents have a bachelor’s degree or higher education level. Furthermore, nearly 70% of respondents’ households spend between 200–1500 ¥(26–198 €/29–222 $) on online purchase, and only 4.3% of households spend less than 200 ¥(26 €/29 $) a month on online purchase. We compared the significant differences in the proportion of different attributes of demographic variables, such as gender, age, and education between the two samples. The results showed that differences are not significant.

4.3. Data Analysis Technique

To test the proposed research model, the partial least squares structural equation modeling (PLS-SEM) was used as it is more flexible in terms of data requirements, model complexity and relationship specification to validate a model compared to covariance-based structural equation modeling (CB-SEM) techniques. PLS-SEM is also considered to be an appropriate statistical tool for theoretical exploration, and our research uses it. Path significance was assessed using bootstrap statistics with a total of 5000 resamples and 372 samples. The moderating relationship was evaluated using the product indicator method first developed by Kenny and Judd (1984), and later implemented in PLS by Chin et al. [113]; that is, interaction terms were created by multiplying the indicators of the predictor and moderator constructs. Each moderator and predictor indicator was centered before performing multiplication.

Smart PLS Version 3.26 is used in this analysis.

5. Analysis and Results

5.1. Measurement Model

Following the proposed two-stage analytical procedures, the confirmatory factor analysis was first performed to evaluate the measurement model; then the structural relationships were examined. To validate our measurement model, we evaluated three types of validity, namely content validity, convergence validity, and discriminant validity. Content validity was determined by ensuring consistency between the measurement items and existing literature. This was done by interviewing experienced practitioners and pilot-testing the instrument.

Convergent validity was assessed by examining the internal consistency, composite reliability and average variance extracted from the measures. Cronbach’s alpha and factor loading were used to calculate the internal consistency (reliability) of items used to measure each of the constructs featured in the study. According to MacKenzie et al. [114], the Cronbach’s alpha value should be higher than 0.7 and factor loadings should be greater than 0.7 for the scale to be considered reliable. As shown in

Table 2. Construct reliability and validity.

Constructs	Measure Items	Factor Loading
Online Promotion Cues	There are many forms of promotion on cross-border e-commerce platforms	0.772
	The promotion of merchandise on cross-border e-commerce platforms is very strong	0.701
	Promotion of imported goods on cross-border e-commerce platforms is frequent	0.736
	Cross-border e-commerce platform provides enough merchandising information	0.809
Content Marketing Cues	content marketing on cross-border e-commerce platforms make me feel more familiar with imported goods	0.749
	content marketing on cross-border e-commerce platforms are useful to me	0.709
	I will pay special attention to content marketing on cross-border e-commerce platforms	0.730
	content marketing on cross-border e-commerce platforms can impress me	0.780
	I will trust content marketing on the cross-border e-commerce platform	0.765
Personalized Recommendation Cues	CBOS platform knows what I want	0.798
	CBOS platform understands my needs	0.718
	I like the products recommended by CBOS platforms	0.746
	does a pretty good job guessing what I want and making suggestions	0.731
Social Review Cues	get a good impression of other residents/avatars in the virtual world	0.742
	develop good social relationships with other community members	0.768
	feel like part of the virtual world community	0.775
	willing to share my own shopping experiences with my friends	0.736
Perceived Functional Value	CBOS has good functions	0.748
	CBOS is reliable	0.756
	CBOS fulfills my needs well	0.702
	CBOS is well provided	0.784
Perceived Emotional Value	CBOS would help me to feel acceptable	0.784
	CBOS would make me want to use it	0.757
	CBOS is the one that I would enjoy	0.837
	CBOS is the one that I would feel relaxed about using	0.848
Cross-border Online Purchase Intention	I will seriously consider purchasing imported goods from cross-border e-commerce platforms	0.752
	I am willing to buy imported goods on the cross-border e-commerce platform	0.766
	I will now buy imported goods on the cross-border e-commerce platform.	0.796
	I will buy imported goods on the cross-border e-commerce platform within six months	0.775
Brand Familiarity	I often see ads for product brands recommended by cross-border e-commerce platforms	0.736
	I often see the display and recommendation of commodity brands recommended by cross-border e-commerce platforms	0.764
	I often hear other people discuss the brand of goods recommended by cross-border e-commerce platforms	0.722
	I often buy product brands recommended by cross-border e-commerce platforms	0.730

, Cronbach’s alpha values of the constructs ranged from 0.722 to 0.821, all the loadings of the measures in our research model are above 0.7, which indicates satisfactory internal consistency for confirmation purposes. Although many studies employing PLS-SEM have used 0.5 as the threshold reliability of the measures, 0.7 is a recommended value for a reliable construct. As shown in Table 2, the composite reliability values range from 0.827 to 0.882. For the average variance extracted (AVE) by a measure, a score of 0.5 indicates acceptability. Table 2 shows that the average variances extracted by measures range from 0.545 to 0.652, which are above the acceptability value. These test results demonstrate good convergent validity.

The discriminant validity of the instrument was verified by observing the square root of the average variance. The results in

Table 3. Correlation between Constructs.

	Cronbach’s Alpha	Composite Reliability	OP	CM	PR	SR	PFV	PEV	CBOPI	BF
Online Promotion (OP Cues)	0.750	0.841	0.756 a
Content Marketing (CM Cues)	0.802	0.863	0.545	0.747
Personalized Recommendation (PR Cues)	0.739	0.836	0.558	0.632	0.749
Social Review (SR Cues)	0.751	0.842	0.541	0.571	0.545	0.755
Perceived Functional Value (PFV)	0.737	0.836	0.589	0.644	0.687	0.620	0.748
Perceived Emotional Value (PEV)	0.821	0.882	0.495	0.528	0.614	0.557	0.630	0.807
Cross-border Online Purchase Intention (CBOPI)	0.775	0.855	0.474	0.632	0.670	0.532	0.733	0.646	0.772
Brand Familiarity (BF)	0.722	0.827	0.580	0.671	0.634	0.660	0.705	0.588	0.656	0.738

Note: ^a Diagonal elements represents square-root of AVE (average variance extracted).

confirm the discriminant validity: The square root of the average variance extracted for each construct is greater than the levels of correlations involving the construct. Finally, all items are well loaded onto their own construct, but poorly on other constructs. All of these test results suggest good discriminant validity.

In addition to validity assessment, multicollinearity was also checked due to the relatively high correlations among some variables (e.g., a correlation of 0.687 between PR and PFV). The resultant variance inflation factor (VIF) values for all of the constructs are acceptable (i.e., between 1.609 and 1.982).

5.2. Structural Model

With an adequate measurement model and an acceptable level of multicollinearity, the proposed hypotheses are tested with PLS. The results of the analysis are depicted in Figure 2. The model explains 59.4 percent of the variance in cross-border online purchase intention, 60.5 percent of the variance in perceived functional value, and 46.3 percent of the variance in perceived emotional value. Henseler et al. [115] introduced the SRMR (standardized root mean square residual) as a goodness of fit measure for PLS-SEM that can be used to avoid model misspecification. A value less than 0.10 or of 0.08 (in a more conservative version) are considered a good fit. The SRMR of final estimated model are equal to 0.070, which indicates that the model has a “good fit”.

As shown in Figure 2, H1a, H2a, H3a and H4a which state that external cues such as online promotion, content marketing, personalized recommendation, and social review positively affect the perceived value of cross-border online consumers, are all supported (β = 0.160, 0.216, 0.337, and 0.226, t = 3.578, 3.987, 6.363, and 4.293).

H1b, H3b and H4b which state that external cues such as online promotion, personalized recommendation and social review positively affect the perceived emotional value of cross-border online consumers, are all supported (β = 0.127, 0.394 and 0.274, t = 2.484, 6.728 and 4.749). While, H2b which states that content marketing cues affect the perceived emotional value of cross-border online consumers is not supported.

H5a and H5b which state that perceived functional value and perceived emotional value positively affect consumers’ cross-border online purchase intention, are also supported (β = 0.541 and 0.306, t = 10.907 and 5.987).

H6a is supported. The results show that Brand Familiarity negatively moderate the relationship between Online Promotion Cues/Content Marketing Cues/Personalized Recommendation Cues/Social Review Cues and Perceived Functional Value. H6b was also supported. The results show that Brand Familiarity negatively moderate the relationship between Online Promotion Cues/Social Review Cues and Perceived Emotional Value.

To determine whether the significant moderating effects are substantive, R-square changes resulting from the interaction effects should be examined [116], specifically through the F-test.

Table 4. Hierarchical regression results.

	Dependent Variable: Perceived Functional Value
	Model 1	Model 2	Model 1	Model 2	Model 1	Model 2	Model 1	Model 2
OP Cues	0.271 ***	0.245 ***
CM Cues			0.312 ***	0.279 ***
PR Cues					0.401 ***	0.371 ***
SV Cues							0.274 ***	0.265 ***
BF	0.548 ***	0.509 ***	0.497 ***	0.475 ***	0.451 ***	0.434 ***	0.525 ***	0.491 ***
OP Cues*BF		−0.143 ***
CM Cues*BF				−0.121 **
PR Cues*BF						−0.099 **
SV Cues*BF								−0.108 **
R2	0.546	0.563	0.551	0.563	0.594	0.602	0.540	0.550
ΔR2		0.017 ***		0.012 **		0.008 **		0.010 **

Note: * p < 0.05, ** p < 0.01, *** p < 0.001.

and

Table 5. Hierarchical regression results.

	Dependent Variable: Perceived Emotional Value
	Model 1	Model 2	Model 1	Model 2	Model 1	Model 2	Model 1	Model 2
OP Cues	0.234 ***	0.216 ***
CM Cues			0.247 ***	0.230 ***
PR Cues					0.327 ***	0.321 ***
SV Cues							0.384 ***	0.296 ***
BF	0.448 ***	0.421 ***	0.419 ***	0.408 ***	0.407 ***	0.397 ***	0.304 ***	0.352 ***
OP Cues*BF		−0.102 *
CM Cues*BF				−0.063
PR Cues*BF						−0.033
SV Cues*BF								−0.100 *
R2	0.382	0.387	0.376	0.379	0.441	0.442	0.394	0.403
ΔR2		0.009 *		0.003		0.001		0.009 *

Note: * p < 0.05, ** p < 0.01, *** p < 0.001.

provide the F-test and the effect size results by running nested (hierarchical) models. The results show that the interaction effects increased R² significantly, confirming the significance of the moderating effects hypothesized by H6a and H6b.

In addition, as shown in Figure 2, among the demographic variables, only age, education, and income have a significant impact on Chinese consumers’ cross-border online purchase intention. Gender, occupation, and monthly online shopping spending have no significant impact on cross-border online purchase intention. For the elderly, the education level is graduate and above, and the monthly income exceeds 20,000. The intention of cross-border online shopping is not strong. Perhaps these consumers lack cross-border online shopping experience or skills, or have a better purchasing channel for imported goods than online shopping.

The moderating effects of gender and income (the comparison of Under 10,000 ¥ (1317 €/1476 $) and Over 25,000 ¥ (3294 €/3690 $)) are conducted by comparing the path loadings across two groups. The multi-group analysis approach is used to assess the moderating effects. As shown in

Table 6. Results of multi-group analysis.

	Gender: Group(M)—Group(F)			Income: Group(high)—Group(low)
	Difference	t	p	Difference	t	p
PFV<—OP	0.239	2.525	0.012	0.068	0.629	0.530
PEV<—OP	0.023	0.231	0.818	0.019	0.163	0.871
PFV<—CM	0.043	0.416	0.678	0.111	0.925	0.356
PFV<—PR	0.113	1.123	0.262	0.030	0.255	0.799
PEV<—PR	0.033	0.295	0.768	0.004	0.027	0.979
PFV<—SV	0.065	0.654	0.513	0.168	1.470	0.142
PEV<—SV	0.033	0.295	0.768	0.004	0.027	0.979
CBOPI<—PFV	0.062	0.623	0.534	0.130	1.284	0.200
CBOPI<—PEV	0.014	0.136	0.892	0.124	1.157	0.248

, gender and income have no significant effect on the model overall as a moderating variable. The results also indicate robustness of the proposed model. Further, the cross-border online purchase intention does not increase consistently with age, education, or income.

In order to compare and analyze the influence of four cross-border online shopping context cues on consumers’ intention to purchase, the Bootstrap method (n = 5000) is used to estimate the impact of four online shopping context cues on consumers’ purchase intention and conduct significant test. It can be found from

Table 7. Mediation effect analysis.

	Total Effects	Direct Effects	Indirect Effects	t	p Value
OP Cues—>CBOPI	0.120	-	0.120	3.526	0.000
CM Cues—>CBOPI	0.147	-	0.147	3.682	0.000
PR Cues—>CBOPI	0.291	-	0.291	6.879	0.000
SV Cues—>CBOPI	0.198	-	0.198	5.106	0.000

that online promotion cues, content marketing cues, personalized recommendation cues, and social review cues have significant positive effects on consumers’ cross-border online purchase intentions, but the degree of influence of different factors is significantly different. The influence of personalized recommendation cues and social review cues on consumers’ cross-border online purchase intension is significantly higher than that of online promotion cues and content marketing cues to consumers’ cross-border online purchase intension. In addition, the impact of four online shopping context cues on consumers’ cross-border online purchase intension needs to be transmitted through mediation variables. Further comparing the mediating effect of the two mediator variables on the path of online shopping context cues to consumers’ purchase intension. It can be seen from Figure 2 that the mediating effects of perceived functional value and perceived emotional value are significantly different. The mediating effect of perceived functional value (normalized estimate of 0.510) is significantly higher than the mediating effect of perceived emotional value (normalized estimate is 0.211).

6. Discussion, Implications, and Limitations

6.1. Conclusion and Enlightenment

In order to analyze the influence of external cues on consumers’ cross-border online purchase intention, this paper constructs a conceptual model of extrinsic cues —> perceived value —> cross-border online purchase intention based on clue utilization theory and SOR model. Based on the effective questionnaire collected from 372 Chinese cross-border online shopping consumers, the model proposed by this research is verified, and the following conclusions are further obtained:

(1) Through the intermediary transmission of perceived value, online shopping context cues have a significant impact on consumers’ cross-border online purchase intension. In terms of the impact of external cues on cross-border online purchase intension, the impact exerted by personalized recommendation cues ranks first, that by social review cues second, and that by content marketing cues and online promotion cues rank last. From the numerical estimation of the impact effect, the effect of personalized recommendation seems to be significantly higher than content marketing and online promotion, and the difference effect between the latter two is not obvious.

(2) In terms of the mediating effect of perceived function value and perceived emotional value, the mediating effect of perceived functional value and perceived emotional value is significantly different in numerical value. The mediating effect of perceived functional value far exceeds the intermediary of perceived emotional value effect. It shows that for online consumers, first, they hope the process of purchasing goods online is simple, and the second is to enjoy the pleasure of online shopping. Consumers who buy imported goods on cross-border e-commerce platforms not only obtain excellent quality, but also seek the satisfaction of emotional and social needs. The online shopping brings them pleasure and enjoyable emotional experience, to which some extent will also stimulate their purchase intention. Similarly, because of the younger Internet consumers, they tend to pursue new things, curiosity but lack of patience. It is difficult to spend a long time to browse a website carefully, so the simple and efficient shopping process will arouse their intention to purchase.

(3) Brand familiarity has a certain negative adjustment effect between external cues and cross-border online purchase intention. The higher the consumer’s familiarity with the brand, the less influential the external cues such as online promotion cues, content marketing cues, personalized recommendation cues, and social review cues on the perceived function value, and the influence of social review and online promotion cues on perceived emotional value are.

The four external cues discussed in this paper that influence consumer cross-border online purchase intention further complement Gefen [13], Gomez-Herrera et al. [11], Cardona et al. [9], Giuffrida et al. [15], and Valarezo et al. [5] and other consumers who study the constraints of cross-border e-commerce. The conclusions about Chinese consumers’ cross-border online purchase intention are consistent with the results of PayPal (2018) [2] and AliResearch (2015) [6] on cross-border online shopping consumers in China.

This study verifies the extrinsic cues —> perceived value —> cross-border online purchase intention model based on perceived functional value and perceived emotional value. It has a positive reference value in helping cross-border online shopping providers improve marketing services and reduce psychological distance from customers [15,16]. As cross-border online shopping has a greater uncertainty than domestic online shopping, the primary purpose of Chinese consumers to cross-border online shopping is to buy high-quality overseas goods that meet their expectations, and secondly to enjoy the fun of cross-border online shopping. In order to improve consumers’ perception of the value of various product recommendation information and further enhance consumers’ intention to cross-border online shopping, cross-border e-commerce platforms and sales companies need to design marketing plans that match the cross-border online shopping contexts. In other words, for the cross-border online shopping characteristics and purchase intention of Chinese consumers, more emphasis should be placed on the marketing method of personalized product recommendation, and the content display of personalized recommendation information needs to be targeted according to cross-border online shopping contexts. Social e-commerce is gaining momentum, and Chinese consumers are more likely to believe in other users’ shopping experiences and online reviews when they are engaged in cross-border online shopping. Therefore, cross-border online shopping platforms and sales companies should shape and maintain their good reputation among consumers. Consumers reading content marketing on cross-border e-commerce platforms are not just spending time for fun, but getting useful information about goods and make faster shopping decisions. Therefore, content marketing for cross-border online shopping should highlight the scientific popularity and knowledge of content, rather than fun and entertainment. Cross-border online shopping consumers generally have relatively higher incomes, richer online shopping experience and skills, and most of them are not price-sensitive consumers. Therefore, the traditional retail environment and the price promotion methods commonly used in the domestic e-commerce environment are significantly less effective in cross-border online shopping contexts. Many Chinese consumers do not have rich cross-border online shopping experience, and their skills in international logistics, cargo clearance, customs withholding, foreign language exchange are relatively weak. They are foreign products that are not familiar to Chinese consumers. Promotions on cross-border e-commerce platforms, content marketing and recommendations from friends will allow consumers to experience the value and fun of cross-border online shopping. Therefore, understanding and predicting consumer’s purchase intention has become an important goal for cross-border e-commerce sustainability. This study has great enlightenment for the sustainable development of cross-border e-commerce:

(1) Promotion is indispensable. The most important difference between online shopping and traditional offline shopping is the price advantage, which needs to be firmly grasped by cross-border e-commerce platforms. In order to enhance consumers’ intention to purchase, we can use some promotional means, such as discounts, group-building, and so on. Continuous use of a variety of innovative promotional means to immerse consumers, satisfy personalized preferences, and then enhance the intention to purchase.

(2) Scientific application of advertising strategies to enhance the effectiveness of content marketing. Content marketing is the most widely used way of communication in the world. Appropriate content marketing can enhance the brand image of enterprises and products. Content marketing, as the most common advertisement on cross-border e-commerce platforms, should strictly control the quality of its content. Content marketing should combine the content of the article with the advertisement perfectly, and output more objective, comprehensive, and valuable commodity information as far as possible.

(3) Customized shopping experience. In the era of information explosion, personalized service has penetrated into all aspects of our lives. This is especially true for cross-border e-commerce platforms, which provide consumers with tailor-made commodity information and services. Big data analysis technology is a powerful weapon to provide personalized services for consumers. Cross-border e-commerce platform grasps consumers’ personal information and behavior preference data. Cross-border e-commerce platform can use these data to build a proprietary model for each consumer’s shopping behavior, and provide recommendation services to meet their shopping needs and even potential shopping needs.

(4) The integration of social e-commerce. The process of communicating among consumers can relax their mind and body and make shopping more pleasant. At the same time, younger online consumers are willing to keep up with the trend of fashion. When they see many consumers recommending a product to themselves on cross-border e-commerce platform, “conformity psychology” and “social identity” will stimulate purchase intention, thus speeding up shopping decision-making time. Therefore, cross-border e-commerce platform needs to design such a function of social interactive among consumers.

(5) Consumer-perceived functional value has the greatest impact on their purchase intention, followed by consumer-perceived emotional value. Therefore, cross-border e-commerce platform should firstly try to find effective online shopping context clues to improve consumer-perceived functional value for stimulating the consumers’ purchase intention. This requires cross-border e-commerce platforms to focus on improving the consumers’ purchasing efficiency when creating online shopping context clues, so that they can obtain as much useful commodity information as possible in a short time.

6.2. Research Limitations

There are still some limitations and shortcomings in this paper that need to be improved in the future. First, as an exploratory study of structural equation modeling based on principal component method, the advantage of PLS-SEM lies in its causality prediction. But as for the discussion of the fitting effect of structural model, PLS-SEM is not as good as the traditional structural equation model which is based on covariance. Therefore, more theoretical research is needed on the theoretical universality of the consumer purchase intention research framework for cross-border e-commerce platforms. Secondly, the interpretation effect of the “Online Context Cues —> Perceived Value —> Consumer Purchase Intention” model established by this research, although reaching the proposed criteria, requires further explorations for other explanations from the perspective of causality prediction. Third, the study validated the reliability and validity of measurement tools such as online promotions, content marketing, perceived functional value, and perceived emotional value based on sample data from 86 pre-tested investigators and 372 formal investigators. In view of the large scale, wide distribution and many characteristics of individual social attributes in China, especially in the cross-border online shopping context, there are few tools presented to Chinese consumers for reference. Therefore, several important measurement questionnaires proposed in this study need to be further verified by a larger-scale, range-based random sample survey.