The impact of online and offline experiences on the repurchase intention and word of mouth of women’s fashion products with the intermediate trust factor

Abstract

Customers with a good experience with the product and point of sale will have more repurchase intention and spread the positive information to other customers. The trend of online and offline experience before deciding to buy is increasingly popular, especially in the period of Covid 19. This study builds a model and tests the impact of online and offline experience factors on customer repurchase intention and word of mouth with trust intermediary factor. The research object is Vietnamese women aged 15 to 60 years old, with online and offline women’s fashion experience. The analytical methods used are EFA, CFA, and SEM with SPSS and AMOS software. Survey results on 629 women show that online and offline experiences have a direct impact on trust, promoting repurchase intention and word of mouth; Besides the direct impact, these two experiential factors also indirectly affect through the intermediary trust factor. Research results suggest to marketers that it is necessary to simultaneously build online and offline women’s fashion experiences for customers to build trust, and stimulate repurchase intention and word of mouth.

Keywords:

• Online experience
• offline experience
• repurchase intention
• word of mouth (WOM)
• women’s fashion

Reviewing Editor:

• Len Tiu Wright, De Montfort University Faculty of Business and Law, United Kingdom

SUBJECTS:

• cognitive psychology
• consumer psychology
• business, management and accounting

1. Introduction

Clients need phygital experiences that combine digital and physical best practices. They order items on their phones but pick them up in shop, use augmented reality shopping to test an item virtually before buying it, and connect with a contact center before talking to a human. Phygital integrates physical and digital channels to create a omni-channel experience. During the Covid 19 period, life moved online, and retailers had to axle to digital channels. As we emerge from the pandemic, clients are beginning to get back to typical life and traditional shopping. Despite the fact that they aren’t returning to stores as quickly as before, there is still a significant demand for offline experiences (Blake, 2022).

In the fashion industry, shopping is a social experience. It’s beyond purchasing goods, it’s about human interaction and tangible sensory experience, which is hard to replace by only online experiences. So, a hybrid shopping experience is the future of fashion retail and marketer to link the gap between the offline and the online experience. The fashion business is developing with innovation, and it’s as of now not just about beautiful garments. About making a digital ecosystem that boosts the buyer experience and offers convenience and customization (Ambavle, 2023).

In the academic field, retailers are confronted with the task of effectively managing the customer experience, as it represents a variable that falls within their jurisdiction (Zaid & Patwayati, 2021). The significance of the customer experience in determining a company’s success has been emphasized (Riaz et al., 2021). Furthermore, it has been established that the customer experience plays a crucial role in influencing the sustainability of a company’s competitive advantage (Pei et al., 2020). The online channel currently possesses a greater number of competitive advantages in fulfilling customer demands when compared to the offline channel. According to Mofokeng, (2021), the utilization of online channels provides several advantages, including enhanced access to information, increased convenience, reduced costs, and time expenditures, and the ability to monitor customer purchases based on their identity. Furthermore, the utilization of online channels enables the acquisition of customer insights and facilitates the implementation of tailored promotional strategies, thereby augmenting the overall customer satisfaction consequently leading to an increased likelihood of customers intending to make repeat purchases (Hult et al., 2019).

Customer satisfaction and experience are commonly linked to physical stores in the offline channel or traditional shopping. The offline channel, commonly referred to as a traditional store, offers customers the opportunity to physically interact with products, seek guidance from knowledgeable staff, and benefit from efficient delivery services (Schröder & Zaharia, 2008). These factors collectively enhance the overall customer experience. There is a growing preoccupation among marketers regarding the repurchase intention, which is subject to the influence of multiple factors including brand, price, satisfaction, and experience. It is crucial for these factors to be in congruence with customer expectations (Yasri et al., 2020).

With the ongoing Vietnam fashion retail market answered to be worth US $358 million in 2017, Vietnam is drawing in worldwide industry players such as Zara, H&M, Uniqlo, and many Vietnamese brands to open distribution channels. Regardless of the great development of the online retail portion put at 61% a year. Online retail sales make up just 2.8% of the domestic retail market. For Vietnamese fashion brands, physical stores are still the most common form of retail (Ng, 2018). In addition, Vietnam’s young, tech-savvy population is thirsty for new experiences, which may aid in the expansion of concept stores. Thus, to streamline transformation rates, it’s the ideal opportunity for fashion retailers to consider the omni-channel model—a methodology that gives a coordinated shopping experience to the client—to broaden their online and offline transactions (Ng, 2018).

If a business evaluates the impact of customer experience (including offline and online experience) on changing customers’ future behavioral intentions (repurchase intention and word of mouth), it will be more easier for business to find solutions to influence customer behavior changes in a direction that is beneficial to the business. The objective of this study was to investigate the quantitative cause-and-effect relationship between online, and offline experience and repurchase intention, word of mouth, and trust acts as a mediating dimension. Research results will help to evaluate the importance of online, offline experience in promoting repurchase intention and word of mouth, as well as the role of trust in this relationship. These are topics that are missing from recently published research. This result brings theoretical contributions through building and testing the impact model among the 5 dimensions mentioned above; and suggests management guidelines for fashion retailers to set their distribution channel policies. This research model and research results will help scientists as well as practitioners to generally research the impact of customer experience on customers’ behavioral intentions under the influence of trust.

2. Literature review

2.1 Research overview

2.1.1. Offline customer experience

Offline customer experience is understood as an integrated series of interactions between customers and retail objects, processes, and environments (Verhoef et al., 2009). The concept of customer experience refers to the enduring impact that customers retain following their engagement with products or services. It encompasses the amalgamation of sensory stimuli that customers encounter during these interactions (Shah & Agarwal, 2020). The provision of customer satisfaction, establishment of expectations, cultivation of trust, promotion of loyalty, and development of emotional connections with customers are all significant aspects in which it is involved (Slack et al., 2020). Defining the customer experience dimension can be challenging due to the diverse perspectives in research). Furthermore, the customer experience assumes a pivotal position in the shopping process, incorporating emotional and cognitive aspects in both physical and virtual environments (Demangeot & Broderick, 2006). The affective and cognitive dimensions are intricately linked to the customer experience, going beyond mere transactional worth (Rose et al., 2012).

In the context of a traditional store, customer experience encompasses various aspects such as affordability, open and comfortable spaces, accessibility, retail mix recreational activities, entertainment, promotions, communication and the overall environment (Verhoef et al., 2017). Furthermore, various elements such as the range of products available, the perceived level of quality, the visual appeal and architectural design, the sense of immersion and discovery, the ease of navigation, the opportunities for social interaction, the convenience of the shopping experience, the promotional incentives offered, the diversity of stores within the mall, the ability to compare prices, the potential for role-playing, and the overall attitude of the customer all play a significant role in shaping the customer experience within a conventional retail environment (Sohail, 2015). In addition, various factors including store selection, store service quality, store prices, hedonic value, and store patronage, store design, self-congruity, store atmosphere, store employees, are influential in shaping the customer experience (Ameen et al., 2021).

2.1.2. Online customer experience

A fundamental aspect of online customer experience is usability, referring to the ease of navigation, website responsiveness, and efficient transaction processes (Rose et al., 2012) emphasizing the importance of user-friendly interfaces in enhancing customer satisfaction and reducing cart abandonment rates. The online experience is commonly understood as the overall encounter individuals have while engaging with websites, applications, or virtual platforms with the significance of usability, interactivity, and personalization in shaping positive online experiences (Pentina et al., 2011). Additionally, the role of user experience design and user interface elements in enhancing satisfaction and engagement has been extensively studied (Bilgihan et al., 2016).

Personalization has also emerged as a significant determinant of online customer experience. Tailoring content and product recommendations based on customer preferences and behavior can lead to higher engagement and conversion rates. Moreover, customer service and support play a pivotal role in shaping the online customer experience. Prompt and effective resolution of customer queries and complaints significantly impacts customer perceptions and influences their propensity to return (Jaiswal & Singh, 2020).

The rise of social media has further complicated the dynamics of online customer experience. Consumers now have the power to amplify their experiences through reviews and ratings, influencing the purchasing decisions of others. Companies must actively manage their online reputation to mitigate negative effects (Micu et al., 2019).

Customers are more likely to engage with brands they trust with their personal information. Implementing robust security measures can foster trust and enhance the overall online shopping experience. Customers’ trust in the seller can be strengthened through positive experiences with online shopping, leading to a perception of the seller as reliable (Suhaily & Soelasih, 2017). The study conducted by Miao et al. (2022) provided evidence supporting the notion that trust exerts a favorable influence on customer satisfaction.

The empirical state is a term used by experts to refer to the cognitive aspect of the online customer experience, which includes knowledge and conscious mental processes. The role of an individual’s emotional system in generating the online customer experience is referred to as the emotional state (Suhaily & Soelasih, 2017). The identification of psychological functions and factors has been recognized as a crucial dimension in understanding the online customer experience. This perception subsequently impacts various outcomes, such as benefits received, emotional responses, evaluative judgments, and behavioral intentions. Prior studies have endeavored to investigate the potential ramifications of the online customer experience, and have theoretically posited that the intention to engage in repeat purchases is a noteworthy outcome of improving the online customer experience (Fared et al., 2021).

2.1.3. Repurchase intention

The concept of repurchase intention pertains to the behavior of customers who choose to make subsequent purchases of goods or services that they have previously encountered and are determined to possess favorable attributes and advantages (Liao et al., 2017). In order to foster customer loyalty and encourage repeat purchases, it is imperative for a firm to effectively meet and exceed customer expectations. Customer satisfaction is a crucial determinant that significantly impacts repurchase intention. Customers who are satisfied are more inclined to engage in repeat purchases as compared to customers who express dissatisfaction (Tata et al., 2020). According to Sullivan & Kim (2018), prior studies have demonstrated a positive relationship between repurchase intention and both cost reduction and market share growth. Furthermore, previous research has emphasized the substantial influence of customer satisfaction on the intention to repurchase (Tata et al., 2020).

2.1.4. Word-of-mouth

The concept of WOM credibility refers to the perceived reliability and trustworthiness of reviews or comments (Evgeniy et al., 2019; Mensah, 2020). These reviews and comments have the potential to significantly impact consumers’ purchase decisions (Bhat N.Y., 2020). Furthermore, it has been observed that consumers exhibit an increased level of confidence and a decreased perception of risk when they are exposed to a greater volume of WOM communication. In their study, Berger & Schwartz (2011) show what’s a driver of immediate word of mouth. The identification of influential nodes within WOM marketing networks is a critical area of scholarly investigation. One prevalent approach in the identification of influential online reviewers involves the comparison of accumulated ratings of reviews or authors, as proposed by Al-Dmour et al. (2021).

2.2. Hypothesis development

2.2.1. The influence of customer experience

Customer experience refers to the cumulative impact of various touchpoints and interactions a customer has with a company or brand throughout their journey. Positive customer experiences have been found to have a significant impact on increasing the likelihood of repurchase intention (Homburg et al., 2017). Moreover, customer experiences that exceed expectations can create a sense of delight, which further enhances the likelihood of repurchase intentions and positive WOM (Hsia et al., 2020). However, it is crucial to note that negative customer experiences can have a strong detrimental impact on repurchase intentions. Dissatisfied customers are more likely to switch to competitors, negatively affect brand reputation, and spread negative WOM (Cam Thuy & Ngoc Quang, 2022).

In the digital age, the customer’s experience comes from offline experience and online experience. Offline customer experience encompasses various interactions and touchpoints that occur in physical settings, such as retail stores, service centers, or face-to-face interactions with sales representatives. Positive customer experiences, characterized by high satisfaction levels, emotional connections, and trust, have been found to significantly impact customers’ likelihood to repurchase from the same establishment (Homburg et al., 2017).

Online customer experience encompasses the various interactions and touchpoints that occur in the virtual realm, such as website usability, ease of navigation, personalized recommendations, and responsive customer service. Positive online customer experiences, characterized by high levels of satisfaction, convenience, and personalization, have been found to significantly impact customers’ intention to repurchase from the same website or online platform (Verhoef et al., 2017). Thus, we develop the hypothesis:

1. Offline experience has a positive impact on customer trust

2. Offline experience has a positive impact and drives repurchase intention

3. Online experience has a positive impact on customer trust

4. Online experiences have a positive impact and drive repurchase intention

Positive online experiences can positively influence offline experiences by creating positive expectations and perceptions about the brand or company (Tzyh et al., 2020). A seamless and satisfying online experience can lead to increased trust and credibility in the company, which can carry over to offline interactions and lead to higher levels of customer satisfaction (Pei et al., 2020). On the other hand, negative online experiences can also have adverse effects on offline experiences. Dissatisfaction with a company’s website or online service can create negative perceptions about the brand, leading to lower satisfaction with subsequent offline interactions (Castañeda García et al., 2018). Thus, we develop the hypothesis:

• 5. Online experiences have a positive impact and drive offline experiences

The influence of online and offline customer experience on WOM has been a subject of interest in the marketing and consumer behavior literature. Positive offline customer experiences have been found to significantly impact customers’ likelihood to engage in positive WOM communication (Homburg et al., 2017). Satisfied customers, who have had pleasant interactions and memorable experiences in physical settings such as retail stores or service centers, are more likely to share their positive experiences with others through WOM (Lemon & Verhoef, 2016). Additionally, online and offline experiences that create emotional connections and foster trust between customers and companies can amplify the likelihood of customers recommending the brand to others (Cam Thuy & Ngoc Quang, 2022). Thus, we develop the hypothesis:

• 6. Online experiences have a positive impact and drive word of mouth

• 7. Offline experience has a positive impact and drives word of mouth

2.2.2. Repurchase intention and word of mouth

Existing research suggests that a positive repurchase intention has a significant positive impact on customers’ WOM (Ginting et al., 2023). Satisfied and loyal customers who intend to repurchase from a company are more likely to engage in positive WOM communication, recommending the brand to their friends, family, and social networks (Ngoc Quang & Thuy, 2023). A strong intention to repurchase indicates a high level of satisfaction and trust in the brand, which in turn, motivates customers to advocate for the company (Castañeda García et al., 2018). On the other hand, a negative repurchase intention can also influence WOM, but in an adverse manner. Customers with low repurchase intention due to dissatisfaction or negative experiences may engage in negative WOM, sharing their grievances and discouraging others from patronizing the brand (Ginting et al., 2023). Negative WOM can significantly harm a brand’s reputation and lead to a loss of potential customers (Ngoc Quang & Thuy, 2023). Thus, we develop the hypothesis:

• 8. Repurchase intention has a positive impact and drives word of mouth

2.2.3. Trust and its mediating role in the influence of customer experience on customer repurchase intention and word of mouth

Trust is the customer’s expectation that a company will act in its best interest, even in situations with limited monitoring or control. Establishing trust through perceived competence, transparency, and positive interpersonal interactions is crucial for building strong and long-lasting customer relationships (Ngoc Quang & Thuy, 2023). Trust has a significant impact on customer behavior, influencing various aspects of the customer-business relationship (Nguyen Thi Tuyet et al., 2017). When customers trust a company or brand, they are more likely to make repeat purchases and exhibit loyalty. Satisfied and trusting customers are also more likely to engage in positive WOM, becoming brand advocates and contributing to new customer acquisition (Ngoc Quang & Thuy, 2023). Trust remains a key driver of customer loyalty, repeat purchases, and positive word-of-mouth in the context of customer-business relationships (Han & Ryu, 2012; Samed Al-Adwan, 2019). Trust continues to be recognized as a vital factor in reducing perceived risk, encouraging purchase intentions, and enhancing overall customer satisfaction (Purwanto et al., 2020; Samed Al-Adwan et al., 2022). Thus, we develop the hypothesis:

• 9. Customer trust has a positive effect and drives repurchase intention

• 10. Customer trust has a positive effect and drives word of mouth

• 11. Offline experience mediates the relationship between online experience and customer trust

• 12. Offline experience and customer trust mediate the relationship between online experience and repurchase intention

• 13. Offline experience, customer trust and repurchase intention mediate the relationship between online experience and word of mouth

• 14. Customer trust mediates the relationship between offline experience and repurchase intention

• 15. Customer trust and repurchase intention mediate the relationship between offline experience and word of mouth

• 16. Repurchase intention mediates the relationship between customer trust and word of mouth

With the above hypotheses, the proposed research model is as follows (Figure 1).

Figure 1. The proposed research model.

3. Methodology

3.1. Measurement scale

The scale is designed to inherit previous studies with groups of factors built on the research proposal model including online experience, offline experience, customer trust, repurchase intention, and WOM. The online experience is composed of four sub-factors: Online Product experience (OPEX), Online community experience (Virtual community sense - OCEX), Online aesthetic experience (OAEX), and Online staff service experience (OSEX). Offline experience is composed of four sub-factors: Product experience (PEX), Sale point experience (SPEX), Community experience (COEX), and Staff service experience (SSEX). The observed variables edited by women’s fashion products are presented in Table 1 below.

Table 1. Measurement scale.

Variables	Source
Online experience - Online Product experience (OPEX) Variety of categories and colors (women’s fashion) Guarantee of women’s fashion product quality Fast delivery of women’s fashion product Green and environmentally friendly of women’s fashion product Brand popularity	Pei et al., (2020)
Online experience - Online community experience (Virtual community sense - OCEX) Receive information support in the community (get help when there is a problem, difficulty; get suggestions and find the causes when there is a problem) Get emotional support in the community (get advice, share, listen and care) Interaction with members of the community (comfortable, agreeable, close) Exchange and develop relationships with the community (Connected to the community as a member)	Zhang et al., (2017)
Online experience - Online aesthetic experience (OAEX) Website information (rich information about features, quality, of women’s fashion product characteristics, etc.) Easy site navigation and search (informative content, attractive menu layout, easy-to-use site sequence, etc.) Interaction on the website (meeting customer needs well, making reasonable purchase suggestions, advertising, promotions as required …) Calculate website response that is relevant and relevant to product requests Easy access to other users’ opinions	Pandey & Chawla, (2018)
Online experience - Online staff service experience (OSEX) Good attitude Business proficiency Frequent communication with the customer good service attitude	Pei et al., (2020)
Offline experience - Product experience (PEX) Variety of categories and colors (women’s fashion product) Guarantee of women’s fashion product quality Fast delivery of women’s fashion product Green and environmentally friendly women’s fashion product Brand popularity	Pei et al., (2020)
Offline experience – Sale point experience (SPEX) Proper and attractive arrangement of the women’s fashion product shop Complete service facilities Guidance of service staff throughout the shopping of women’s fashion product Private space to try women’s fashion products *	Pei et al., (2020) *New scale
Offline experience – Community experience (COEX) Advise customers to ask about this store’s women’s fashion product Ask the opinions of customers who shop at this women’s fashion store Share your opinion with the customers of this women’s fashion store Interact with this women’s fashion store’s customers Consider yourself a member of this community of customers who shop at this women’s fashion store	Happ et al., (2021)
Offline experience – Staff service experience (SSEX) Salespeople were friendly and smiling Sale people were knowledgeable Salespeople were truly interacting and conversing Salespeople were helpful and gave advice	Verhoef et al., (2009)
Customer Trust Trust in the capacity, honesty of the website, store, brand (women’s fashion) Believe in promises to customers (money back guarantee and other sales policies) Trust in the women’s fashion products and services provided Trust honest advice in handling support purchases	Modified from Ngoc Quang & Thuy, (2023)
Repurchase intention Likely to buy this women’s fashion product again in the future Anticipate a future purchase of this women’s fashion product Will buy this women’s fashion product again in the future Expected to continue to buy the women’s fashion product again in the future if there is a fluctuation in price	Modified from Cam Thuy & Ngoc Quang, (2022)
Word of mouth (WOM) I will tell my family, friends, acquaintances about the brand’s women’s fashion products, the store I will speak positively about the store’s staff and women’s fashion products, the brand I would recommend to family members, friends, acquaintances about this women’s fashion brand and product I will definitely speak positively about the women’s fashion store, brand, website on social networking sites	Modified from Ngoc Quang & Thuy, (2023)

3.2. Measurement and methodology

Research subjects were identified as Vietnamese women aged 15 to 60 years old, who have had online and offline women’s fashion shopping experiences. Due to the population of this study it is not possible to accurately determine. The authors chose the sampling frame based on the social network Zalo with the leading online shopping application in Vietnam with more than 100 million users. The sampling frame is defined as the list of women (research subjects) who participate in buying women’s fashion online on the zalo shop application from January to April 2023; moreover, they also have the experience of buying women’s fashion at retail locations. During this survey period, there were about 14,700 women’s fashion purchases on online applications. The authors define this as the sampling frame. To correctly identify the research subjects, the questionnaire was designed to add a filter question about offline shopping experience. The draft was designed in bilingual (English, Vietnamese) and checked by a language expert, and then tested by the customer. After editing the questionnaire was sent to the selected research subjects. Its content consists of two parts; in which the first part is personal information, and the second part is their experiences and behaviors in the process of buying women’s fashion. The variables of experience and behavior are designed on a 5-point Likert scale: 1 is strongly disagree and 5 is strongly agree; These scale is arguably the most popular in social research (Robinson, 2014).

Due to the large size of the sampling frame, the simple random sampling method was used with the sample size determined according to Hair et al., (2006); whereby the sample size must be at least five times larger than the observed variable. This study is designed with 48 observed variables so the sample size must be at least 48x5 = 240 elements. To improve the quality of the analysis, we sent 1200 online questionnaires designed through the Zalo application and obtained 629 questionnaires with valid information. This sample size also meets Yamane’s Formula (5% error and population greater than 5000, the sample size is greater than 385). The survey period took place in April and May 2023.

The main analytical techniques used are the reliability and validity test, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modeling (SEM). In the EFA each observed variable is possibly a measure of every factor, and the goal is to determine relationships between observed variables and factors are strongest (The reason for its use is because the variables are used from many different authors or are modified). In the CFA, a simple dimension structure is suggested, each variable can be a measure of only one dimension, and the correlation structure of the data is tested against the hypothesized structure via goodness of fit tests. Finally, the SEM is used to analyze linear structural equations modeling to test the research hypotheses. The obtained data were analyzed on SPSS 22 and AMOS 22 software to verify the quality of the scale; examine the relationships between variables, sub-factors, and factors; testing structural equations modeling. These are the most popular software today to perform these analysis techniques.

4. Research findings

4.1. Measuring the validity of scales and reliability

The large number of variables in the model (48 variables) with many layers, makes it difficult to represent variables and factors on one figure. Therefore, two groups of online and offline customer experience variables are analyzed by exploratory factor analysis (EFA) to reduce dimension and to evaluate the quality of collected data. This EFA method allows to generate representative factors (Factor Scores) for groups of variables by regression technique. The results of EFA show that the extraction value of 36 online and offline customer experience variables is greater than 0.5, so no variable is excluded from the model. Table 2 Total Variance Explained shows the results of the EFA analysis of 36 variables that created 8 factors with Initial Eigenvalues > 1 (minimum value is 1.344). These 8 factors represent 73.099% of 36 online and offline customer experience variables (Initial Eigenvalues, Extraction Sums of Squared Loadings, Rotation Sums of Squared Loadings). This result is much larger than the 50% minimum requirement.

Table 2. Total variance explained.

Component	Initial Eigenvalues			Extraction Sums of Squared Loadings			Rotation Sums of Squared Loadings
	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %
1	8.408	23.355	23.355	8.408	23.355	23.355	4.239	11.776	11.776
2	3.934	10.927	34.282	3.934	10.927	34.282	3.686	10.239	22.015
3	3.572	9.921	44.203	3.572	9.921	44.203	3.482	9.672	31.687
4	3.041	8.446	52.649	3.041	8.446	52.649	3.218	8.938	40.625
5	2.439	6.776	59.425	2.439	6.776	59.425	3.159	8.776	49.401
6	2.151	5.976	65.401	2.151	5.976	65.401	3.139	8.720	58.121
7	1.427	3.965	69.367	1.427	3.965	69.367	2.838	7.884	66.005
8	1.344	3.733	73.099	1.344	3.733	73.099	2.554	7.094	73.099

Extraction Method: Principal Component Analysis.

Source: Author’s primary research.

Table 3 shows: Component 1 (named COEX) represents 5 variables COEX1-COEX5, Component 2 (named OAEX) represents 5 variables OAEX1-OAEX5, Component 3 (named PEX) represents 5 variables PEX1-PEX5, Component 4 (named OPEX) represents 5 variables OPEX1-OPEX5, Component 5 (named OCEX) represents 4 variables OCEX1-OCEX4, Component 6 (named SSEX) represents 4 variables SSEX1-SSEX4, Component 7 (named SPEX) represents 4 variables SPEX1-SPEX4, Component 8 (named OSEX) represents 4 variables OSEX1- OSEX4. The extraction value of 36 variables on components is the lowest at 0.654 of the SPEX1 variable on component 7 and the highest at 0.891 of the OCEX1 variable on component 5; no variable has an extraction value > 0.5 on many components. Thus, it can be concluded that these components represent 36 analytic variables well and none of them are excluded. These components will be used as observed variables in the analysis of structure equation modeling in the following section.

Table 3. Rotated component matrix.

	Component
	1	2	3	4	5	6	7	8
PEX1			.823
PEX2			.833
PEX3			.837
PEX4			.841
PEX5			.823
SPEX1							.654
SPEX2							.863
SPEX3							.790
SPEX4							.885
SSEX1						.825
SSEX2						.868
SSEX3						.884
SSEX4						.824
COEX1	.882
COEX2	.875
COEX3	.864
COEX4	.862
COEX5	.660
OPEX1				.693
OPEX2				.709
OPEX3				.697
OPEX4				.767
OPEX5				.770
OAEX1		.763
OAEX2		.791
OAEX3		.856
OAEX4		.801
OAEX5		.858
OSEX1								.736
OSEX2								.751
OSEX3								.860
OSEX4								.728
OCEX1					.891
OCEX2					.888
OCEX3					.775
OCEX4					.841

Extraction Method: Principal Component Analysis.

Source: Author’s primary research.

Rotation Method: Varimax with Kaiser Normalization.

Dimension reduction technique created 4 online experience variables and 4 offline experience variables, the study obtained an analytical database of 20 variables (4 online experience variables and 4 offline experience variables, 4 trust variables, 4 repurchase intention variables, and 4 WOM variables). These variables were analyzed for exploratory factor analysis (EFA) and subsequent confirmatory factor analysis (CFA) to test uni-dimensionality, the convergent and discriminant variable, and factor according to the recommendations of Anderson & Gerbing (1988). The results of the analysis are presented in Table 4 below.

Table 4. Exploratory factor analysis (EFA) and Confirmatory factor analysis (CFA).

	Online experience OPEX, OCEX, OAEX, OSEX	Offline experience PEX, SPEX, COEX, SSEX	Trust TRUS1-TRUS4	Repurchase intention RPIN1-RPIN4	WOM WOM1-WOM4
Cronbach’s α	0.835	0.806	0.848	0.797	0.818
KMO	0.834	0.851	0.856	0.844	0.838
Bartlett test	p < 0.05	p < 0.05	p < 0.05	p < 0.05	p < 0.05
EFA	66.18%	64.52%	65.38%	68.63%	65.83%
CFA Chi-square/Df	4.914	4.698	4.717	4.879	4.672
GFI	0.937	0.938	0.956	0.934	0.968
CFI	0.949	0.937	0.968	0.958	0.946
RMSEA	0.055	0.059	0.048	0.053	0.061

Source: Author’s primary research.

The measurement model was tested by average variance extracted (AVE) and composite reliability for reliability and validity. To assess the measurement model fit the data, according to Anderson & Gerbing, (1988), the measurement model was confirmed first by confirmatory factor analysis (CFA), followed by structural equation modeling (SEM). According to Arbuckle, (2011), the maximum likelihood method was utilized for approval with the following criteria: Goodness of Fit Index (GFI); TLI (Tucker & Lewis Index); Comparative Fit Index (CFI); Chi squared (CMIN); Chi square adjusted to degrees of freedom (CMIN/df); Root Mean Square Error Approximation (RMSEA) index. The model is appropriate with data when GFI, TLI, CFI > 0.9; CMIN/df < 5; RMSEA < 0.08, if RMSEA < 0.5, the structure model is very good (Byrne, 1994; Hu & Bentler, 1999; Tucker & Lewis, 1973).

The outcomes in Table 4 show that the construction suits the examination information. As a result, we can say that: convergent validity is achieved for the offline experience, online experience, trust, repurchase intention, and WOM factors. In conclusion, measurement models perform well both theoretically and empirically. In the accompanying part, we analyzed the full model fit with Structural Equations Modeling.

We also looked at the Composite Reliability (CR) and the Average Variance Extracted (AVE) to get a broad picture of the scale’s reliability. According to Hair et al. (2006): When CR > 0.7 and AVE > 0.5, the scale is regarded as a convergent value, and the observed variable is not correlated with other observed variables in the same factor. The results of CR and AVE are shown in Table 5.

Table 5. Results of CR and AVE of scales.

No	Scale	Number of observed variables	CR	AVE
1	Online experience	4	0.763	0.567
2	Offline experience	4	0.718	0.641
3	Trust	4	0.805	0.673
4	Repurchase intention	4	0.812	0.536
5	Word of Mouth	4	0.782	0.654

Source: Author’s primary research.

Thusly, with the introduction of the CR and AVE result, it very well may be resolved that the scale acquired from the formal quantitative examination is qualified for testing the research model and the research hypotheses that have been established.

4.2. The structural model by CFA and structural equations modeling (SEM)

The structural model is designed with a single independent factor, online experience, and a single dependent factor, WOM; the remaining three factors are offline experience, trust, and repurchase intention which can act as independent, mediate, and dependent factors, that depend on the model structure hypotheses.

According to Hair et al. (2006), no known absolute value for any of the fit indices suggests a suitable fit. Depending on the sample size, number of measured variables, and communalities of the factors, the results of suitable models vary from situation to situation. Nearly all attempts at fit indices in this structural equation model yield positive results. The fit of the models was assessed using chi-square statistics and various fit indices such as RMSEA, CMIN/DF (CFA), CFI, the goodness of fit index (GFI), Tucker–Lewis Index (TLI), non-normed fit index (NFI). Figure 2 depicts the structural equation model’s predictable calculations as follows: CMIN/DF (CFA) = 5.24; GFI = 0.913; CFI = 0.896; TLI = 0.908, RMSEA = 0.078; so we can conclude that the structural equation model is acceptable. The Table 6 and 7 displays the hypothesis results.

Figure 2. Structural equation model.

Table 6. Direct hypothesis results.

Hypotheses	Linkage	Estimate	P-value	Result
H1	OFEX→TRUS	.229	***	Supported
H2	OFEX→RPIN	.305	***	Supported
H3	OEX→TRUS	.298	***	Supported
H4	OEX→RPIN	.164	.005	Supported
H5	OEX→OFEX	.288	***	Supported
H6	OEX→WOM	.145	.008	Supported
H7	OFEX→WOM	.166	.005	Supported
H8	RPIN→WOM	.472	***	Supported
H9	TRUS→RPIN	.308	***	Supported
H10	TRUS→WOM	.316	***	Supported

Source: Author’s primary research.

Table 7. Direct – indirect – total effect hypothesis results.

Hypotheses	Linkage	Direct Effect	Indirect Effect	Total Effect
H11	OEX→OFEX→TRUS	.298	.066	.363
H12	OEX→ OFEX, TRUS→RPIN	.164	.200	.364
H13	OEX→ OFEX, TRUS, RPIN→WOM	.145	.334	.479
H14	OFEX→TRUS→RPIN	.305	.070	.376
H15	OFEX→TRUS, RPIN→WOM	.166	.249	.415
H16	TRUS→ RPIN→WOM	.316	.145	.461

Source: primary research.

The research results presented in Figure 2 and Table 6 show that the online experience of women’s fashion has a direct impact on the offline experience, customer trust, repurchase intention, and WOM. The regression weights affecting offline experience and customer trust are quite large, respectively 0.288 and 0.298; the regression weights on repurchase intention and WOM are quite small at 0.164 and 0.145, respectively. This result proves that the online experience has motivated customers to check the experience at the point of sale (offline) in the traditional way and initially created a trust for customers; promoting repeat buying behavior and WOM is less effective. This result is consistent with the study of Pandey & Chawla, (2018).

Offline experience has an impact on customer trust, repeat purchase intention, and WOM; with the corresponding regression weight is 0.229; 0.305; 0.166. This result shows that the traditional point-of-sale experience has a stronger impact on customer trust and repeat purchase intention, and a weaker impact on WOM.

Customer trust has a strong impact on repurchase intention and WOM with a corresponding regression weight of 0.308; 0.316. This result proves that this trust factor has the most important role in affecting repurchase intention, second most important to WOM. Repurchase intention, as the independent factor has the strongest impact on WOM, the corresponding regression weight is 0.472. Thus, WOM’s motivation is shaped by customers’ trust and repurchase intentions for women’s fashion.

Calculation results of direct, indirect, and total effect in Table 7 show that online experience has a direct effect on customer trust with a regression weight of 0.298; combined with indirect effect through offline experience (H11), so the total effect is 0.363 (this is the lowest level compared to other hypotheses). The online experience has a direct effect on repurchase intention with a relatively low regression weight of 0.164; however, combined with the effect of two intermediate factors, offline experience, and trust, the total effect reached 0.364 (H12). Online experience has a direct effect on WOM with a low regression weight of 0.145; however, combined with the effect of three intermediate factors, namely offline experience, trust and repeat purchase intention, the total effect reached the highest level of 0.479 (H13). Offline experience has a direct effect on repurchase intention, with a fairly high regression weight of 0.305; combined with the effect of the intermediary factor trust, the total effect reached 0.376 (H14). Offline experience has a direct effect on WOM, with a regression weight of 0.166; however, combined with the effect of two intermediary factors, trust and repurchase intention, the total effect reached a high level of 0.415 (H15). Trust has a direct effect on WOM, with the highest regression weight at 0.316; combined with the influence of the intermediary factor repurchase intention, the total effect reached a high level of 0.461 (H16).

5. Conclusion and implications for managers, limitations, and future research

5.1. Conclusion and implications for managers

5.1.1. Conclusion

In the context of the strong development of e-commerce and the global spread of the covid-19 pandemic, consumer behavior has changed toward online experiences (Sudaryanto et al., 2021). The research results indicate that the online customer experience in the women’s fashion sector drives the traditional offline experience behavior, builds customer trust, and drives repurchase behavior and customer WOM (The same idea of Blake, 2022). Moreover, the offline experience in this fashion industry still plays an important role in the customer’s buying process by creating trust, driving repurchase intention and WOM (The same idea as Ambavle, 2023).

On a theoretical aspect, this research has recognized the costumer experience structure as multidimensional and second-order dimensions. It backs up the idea that the costumer experience dimensionality is dependent on the specific situation under study (Happ et al., 2021; Pandey & Chawla, 2018; Pei et al., 2020). More specifically, the theoretical contributions of this study are: complete and validate the costumer experience structure with 4 sub-factors in both online and offline environments (community experience, product experience, staff service experience, and aesthetic experience); customer trust, repurchase intentions and word of mouth that applied in the field of women’s fashion in Vietnam. The proposed model and its testing results have confirmed the position of these factors in the causal relationship or its mediating role.

The new and different point of this study is to examine the quantitative cause-and-effect relationship between online, and offline experience and repurchase intention, word of mouth; and offline experience, trust, repurchase intention act as a mediating factor. The quantitative results help to assess the importance of online, offline experience in promoting repurchase intention and word of mouth, as well as the role of offline experience, trust and repurchase intention in this relationship as a mediating factor. This result shows theoretical contributions through creating and testing the impact model among the 5 factors cited above; and recommends fashion retailers to set their distribution channel strategies and policies. This model is the main theoretical contribution of this study.

5.1.2. Implications for managers

When comparing the quantitative results of online and offline experiences, it is found that the impact of online experience on trust is higher than that of offline experience (0.298 and 0.229 respectively). This suggests to managers that customers are used to the online experience and in many cases, this form has brought greater convenience and trust; thus, in the field of women’s fashion as well as fashion in general, the inevitable trend is the need to enhance the online experience for customers, this is in line with the technology context, behavioral trends. This result is consistent with the studies of Mofokeng (2021).

If comparing the impact of online and offline experiences on repurchase intention and WOM, the regression weight of offline experience is significantly higher than online (0.305 and 0.164 for repurchase intention, 0.166 and 0.145 for WOM, respectively). Thus, it can be seen that the offline experience is more effective in promoting repurchase intention and WOM. So marketers cannot ignore the offline experience in driving the buying process and its consequences; This traditional channel of experience is still highly effective in this situation. It should be noted that, in addition to the direct effects of online and offline experiences, there are also indirect effects, which makes the results of the total effect much higher. These effects are pervasive driving new behavior and positive customer responses.

The customer’s trust factor in this study plays an independent role that has a strong impact on repurchase intention and WOM. Furthermore, it is the central mediator of the indirect effects on repurchase intention and WOM. So, marketers need to focus a lot of their resources to increase customer trust. This result is consistent with the study of Tarhini & Hayek (2021), Ngoc Quang & Thuy, (2023). The repurchase intention factor has the strongest direct impact and at the same time acts as a mediator to create indirect effects on WOM. This result is consistent with the classical buying behavior models of which have been recognized by marketing researchers.

Looking in detail at the 4 sub-factors that make up the online experience dimension, the importance in descending order are OCEX, OPEX, OSEX, OAEX. This result suggests that marketers need to focus resources on online experience in the above order: Online community experience, Online Product experience, Online staff service experience, and Online aesthetic experience. Similar to the offline experience, the importance in descending order is PEX, COEX, SPEX, SSEX. This result suggests that marketers need to focus resources in this order: Product experience, Community experience, Sale point experience, and Staff service experience.

5.2. Limitations and future research

5.2.1. Limitations

The factors of experience online, offline, trust, repurchase intention and WOM are mentioned by many researchers, especially in the field of fashion. However, previous studies often only selected one of the two experiences as the independent variable. This study analyzed both online and offline experiences at the same time, so the number of variables is high, the data analysis process is separated into two stages, so the error will be higher. Trust, repurchase intention, and WOM factors can be influenced by many other variables such as psychological, personal, social, reference group, and individual value variables; These have not been mentioned in the proposed model as well as the research results. This limitation also opens up further research directions with the addition of the above factors and variables in the research model as independent variables, intermediate variables, control variables, or moderator variables. The women’s fashion market in Vietnam is quite diverse, with many famous brands, and their customer experience options are also diverse. The survey was conducted on customers who have both online and offline experiences, so most of the survey respondents belong to large brands that have both e-commerce and retail shop channels, and small brands with only one type of experience were not covered. The field of women’s fashion is a rather narrow fashion field only for women, so the scope of application of this study should also be limited to this field, using the recommendations of this study for fashion in general needs to be cautious.

5.2.2. Future research

Future studies may develop in the direction of studying the impact of the psychological, personal, social, reference group, and individual value factors with the role of independent, intermediate, control, or moderator factors on the online and offline experience process of customers. These factors can also be analyzed in the interaction with trust, repurchase intention, and WOM. Moreover, with the trend of changing buying behavior and the combined online and offline experience of customers, there are many areas that need to be researched like this; such as the fashion sector in general, home furniture, consumer electronics, and home appliances.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Ngoc Quang Nguyen

Ngoc Quang Nguyen is a lecturer of Marketing at Marketing Faculty – National Economics University (Vietnam). His research interests focus on consumer behavior and marketing management; recent studies related to customer behavioral psychology in retail banking, telecommunications services and mindfulness in hotel services. He is co-author of several books on marketing management and papers in Journal of Engineering and Technology Management, Cogent Business & Management, International Journal of Psychosocial Rehabilitation and International Journal of Advanced Science and Technology, Cogent Social Sciences.

Hoai Long Nguyen

Hoai Long Nguyen is a lecturer of Marketing at Marketing Faculty – National Economics University (Vietnam). He is interested in marketing principle, service marketing, customer relationship management, place marketing and have publish book about that subject. He have some paper publish on Journal: Heliyon, Economics & Sociology, Economics and Business Quarterly Reviews, Journal of Mekong Societies and some Journals of Viet Nam.

Thuy Giang Trinh

Thuy Giang Trinh is a PhD student of National Economics University (Vietnam). Her research focuses on consumer behavior, startups and corporate culture. She has some paper published on Journal of Economics and Development, Journal of Trade Science, Asia - Pacific Economic Review.