Online shopping experience drives online shopping intention: role of omnichannel shopping trust

Abstract

Purpose  This study proposes a model in which online shopping intention is a consequence of the online shopping experience in an omnichannel environment.

Theoretical framework  The experience is shaped by the interaction between a consumer's cognitive and emotional experiences, with omnichannel shopping trust playing a crucial mediating role in the relationship between online shopping experience and purchase intention. The theoretical model is based on the Stimulus-Organism-Response (S-O-R) framework.

Design/methodology/approach  Data analysis was performed using structural equation model analysis. Systematic random probability sampling was used to recruit 600 consumers, all of whom had experience with omnichannel shopping in Chilean retail settings.

Findings  Online shopping intention results from the interaction between cognitive and emotional experiences. Omnichannel shopping trust partially mediates the effect of the online shopping experience on purchase intention.

Practical & social implications of research  This study has important implications for digital platform managers and retailers who want to deliver an optimal omnichannel experience.

Originality/value  In an omnichannel environment, the retailer needs to establish a strong connection through the cognitive and emotional experience of the shopper.

Keywords:
Experience; omnichannel; cognitive; emotional; shopping; trust

1 Introduction

Advances in information and communication technologies have disrupted traditional retail sales (Chou et al., 2018) and influenced the consumption habits of digital buyers. Retailers must engage customers through multiple channels (Cummins et al., 2016; Verhoef et al., 2015), with 30% of retailers optimizing their omnichannel journeys (Statista, 2020). According to the Technology Acceptance Model (TAM), the online shopping experience has significantly improved (Davis, 1989; Le & Nguyen-Le, 2021) and is a key determinant of online shopping intention (Kazancoglu & Aydin, 2018; Rippé et al., 2017; Lemon & Verhoef, 2016). Evidence suggests that an omnichannel environment increases the likelihood of online purchases (Silva et al., 2019), with omnishoppers preferring showrooming over webrooming (Schneider & Zielke, 2020). Despite the critical role of the online shopping experience, its formation remains unclear.

Previous studies have only considered cognitive factors (Park et al., 2015) or emotions (Kawaf & Tagg, 2017) in the online shopping experience. This point of view represents a gap that this study attempts to address. To improve the digital channel in an omnichannel environment and in line with Damasio (1994), this study suggests that online shopping experiences are configured by an integrated network of both cognitive (Sun et al., 2020) and emotional (Wu & Chang, 2020) experiences. Cognitive experiences include timely delivery and payment security (Izogo & Jayawardhena, 2018), while emotional experiences comprise interactive design (Shen et al., 2018; Constantinides, 2004) and personalized attention (Blasco-Arcas et al., 2016).

Our study investigates the - relationship between the digital environment and the consumer using the Stimulus-Organism-Response (S-O-R) model (Mehrabian & Russell, 1974), adopting a simultaneous approach to analyze the proposed model, as the mind operates non-linearly (Chopdar & Balakrishnan, 2020; Kühn & Petzer, 2018; Druckman & Lacey, 1989) and engages concurrently during online shopping (Jacoby, 2002). While trust has been shown to mediate the impact of service quality on online repurchase intention (Zhe et al., 2023), eWOM on online purchase intention (Ilhamalimy & Ali, 2016), and relative advantage on online purchasing attitudes (Chetioui et al., 2021), there is limited evidence on the mediating role of trust in the effect of omnichannel experience on omnichannel intention.

The virtues of the omnichannel experience have been highlighted in different countries. For example, it has been used in China to increase market share and improve product promotion (Sun et al., 2020). In the United Kingdom, channel transparency and uniformity reduce perceived risk (Xu & Jackson, 2019). In Germany, continuous channel innovation increases customer value (Menrad, 2020). Given the lack of studies on these characteristics, this study was conducted in Chile, which has an annual GDP of US$ 316.77 billion. Chile is considered a developing country (Statista, 2023).

Therefore, this study proposes a model in which online purchase intention results from the online shopping experience in the environment. It examines whether this experience is shaped by the interaction between the consumer's cognitive and emotional experiences, and whether omnichannel shopping trust plays a mediating role in the relationship between online shopping experience and purchase intention.

2 Literature review

Recent developments in omnichannel retail research have highlighted the increasing complexity of consumer behavior in integrated shopping environments. Recent studies show evolving patterns in consumer decision-making processes, particularly regarding the integration of digital and physical channels. Contemporary research highlights the growing importance of seamless channel integration (Solem et al., 2023), the role of artificial intelligence in personalizing shopping experiences (Calvo et al., 2023; Khare et al., 2023), and the impact of post-pandemic behavioral changes on omnichannel preferences (Sharma & Dutta, 2023). These findings provide a context for understanding current omnichannel shopping dynamics and inform our theoretical framework.

2.1 Omnichannel experience

The omnichannel environment stems from the seamless integration of offline and online shopping experiences (Shen et al., 2018). Research shows that customer experiences involve cognitive and emotional components (Gentile et al., 2007; Verhoef et al., 2009). Despite the various offline and online touchpoints in omnichannel systems (Hossain et al., 2019), omnishoppers tend to favor online shopping (Novak et al., 2000) due to the positive shopping experience (Mathwick & Rigdon, 2004). The online shopping experience is a model of experiential value, characterized by an esthetic environment, engaging interactions, excellent service, and return on investment for each purchase (Mathwick et al., 2001).

The literature categorizes the esthetic environment and engaging interactions as emotional experiences, while excellent service and return on customer investment are cognitive experiences (Izogo & Jayawardhena, 2018). Thus, the experiential value associated with emotional (Pentina et al., 2022; Kawaf & Tagg, 2017) and cognitive (Hong et al., 2004) states or both (Gao et al., 2021; Rose et al., 2012) is relevant to consumers' interactions with both physical and virtual environments during shopping. Cognitive involvement in retail increases emotional involvement (Petermans et al., 2013). Understanding consumers' emotional and cognitive perceptions can predict their online purchasing behavior (Pappas et al., 2016).

2.2 Timely delivery experience

Recent studies suggest that the timely delivery of omnichannel systems offers various opportunities for omnishoppers (Wang et al., 2019). The literature on omnichannel logistics asserts that channel integration allows customers to complete their shopping journey promptly and seamlessly (Verhoef et al., 2015). Channels under the same brand assist omnishoppers in resolving issues efficiently, enhancing their search, shopping, and post-shopping experiences (Simangunsong & Subagyo, 2021; Verhoef et al., 2022).

Omnishoppers can search for a product on a retailer's mobile app, buy it on the website, and pick it up in-store (Flavián et al., 2020). Among omnichannel delivery services, shopping online and collecting in-store is a key benefit (Fisher et al., 2019; Kusuda, 2022; Gao & Su, 2017). Omnichannel retailers ensure unified distribution and allow product returns at physical stores regardless of the purchase channel (Hübner et al., 2016).

2.3 Payment security experience

Payment security remains a significant issue in e-commerce. Omnishoppers often avoid online shopping due to the lack of secure payment tools or concerns about their credit card information being accessed (Aziz & Wahid, 2018; Adnan, 2014). Studies indicate that omnishoppers' attempts to shop frequently fail due to incomplete online transactions. To address these concerns, some omnichannel retailers offer the flexibility to choose the payment channel (Nguyen et al., 2022). Omnishoppers can complete their transactions online using traditional methods or pay later in-store (Jin et al., 2020).

To enhance payment security, some retailers allow online orders without immediate payment, with the option to pay later at a physical store using cash or credit card (Oracle, 2023). Additionally, some stores encourage the use of mobile phones to place orders online, pick up in-store, and pay via mobile applications. Furthermore, a cash-on-delivery option has been implemented to improve security, where retailers deliver products to omnishoppers before requesting payment (Banerjee, 2018).

2.4 Interactive shopping experience

Physical stores remain essential in omnichannel (Verhoef et al., 2015). Unlike traditional stores that prioritize sales, omnichannel retailers encourage window shopping in showrooms (Sit et al., 2018; Arora et al., 2017, 2019). Showrooms enable omnishoppers to interact with products prior to purchase (Liao & Cheung, 2001) and order online if items are unavailable (Gao & Su, 2017). Omnichannel retail technologies such as free Wi-Fi, RFID, interactive screens, smart boards, and virtual mirrors with 3D body scanning systems enhance interactive experiences to meet omnishopper demands (Oracle, 2023; Ameen et al., 2021).

2.5 Personalized attention experience

Personalized attention creates the perception of more options for omnishoppers and enables retailers to focus on their preferences (Chang & Li, 2022; Chang & Chen, 2008; Tsai & Huang, 2007). Personalization involves selecting or filtering information for individuals (Schubert & Koch, 2002) and co-creating experiences (Prahalad & Ramaswamy, 2000), with over 80% of omnishoppers expecting consistent personalized attention (Oracle, 2023). To meet customer needs and enhance product uniqueness, customer experience, and enjoyment, retailers gather data on omnishoppers' interaction behavior, including store visits, Facebook activity, website searches, and nearby sign-ups (Brynjolfsson et al., 2013). This information enables retailers to offer personalized suggestions and discounts, thereby increasing purchase intention (Kaczorowska-Spychalska, 2017).

2.6 Online shopping intention

Online shopping intention results from cognitive convergence (Pappas et al., 2016). Omnishoppers who use multiple channels (Juaneda-Ayensa et al., 2016) exhibit smart shopping behavior (Flavián et al., 2020). Their intelligent characteristics (Kang, 2018) drive webrooming and showrooming to determine purchase details (Mukherjee & Chatterjee, 2021). Mobile devices have transformed omnishoppers' decision-making and intentions through their ubiquity, hyperconnectivity, and geolocation, enabling in-store assistance and online purchases (Faulds et al., 2018; Fiestas & Tuzovic, 2021). Recent studies confirm omnishoppers' preference for showrooming (Schneider & Zielke, 2020), citing low webrooming and favorable price perceptions (Dahana et al., 2018) as relevant factors. Showrooming reinforces the positive impact of cross-channel integration on retailer appeal (Li et al., 2018).

2.7 Omnichannel shopping trust

Both cognitive trust and emotional trust play an important role in service relationships (Johnson & Grayson, 2005) and are important elements of commerce (Komiak & Benbasat, 2006). Omnishoppers want to trust their vendors and websites when shopping online. According to the literature, trust can be interpreted as a multidimensional concept. Trust in an e-commerce environment can be defined as an omnishopper's perception of the seller's honesty, benevolence, and competence (Artigas et al., 2017). Trust helps omnishoppers overcome uncertainty and perceptions of risk from online providers (Harrigan et al., 2021). When omnishoppers trust a website and believe the environment is risk-free, their online purchases increase (Mangin et al., 2014). Trust is considered a key factor influencing the attitudes and online shopping intentions of omnishoppers (Jadil et al., 2022). Sel (2015) argued that trust influences the experience of omnishoppers, in addition to their online shopping intentions.

2.8 S-O-R model

Mehrabian and Russell (1974) showed that environmental stimuli (S) elicit emotional reactions (O) that lead to behavioral responses (R). Evidence indicates that the SOR paradigm has been used to study the cognitive and emotional experiences of omnishoppers (Gao et al., 2021). Findings highlight elements such as channel integration (S), trust and satisfaction (O), and patronage intention (R) (Zhang et al., 2018), as well as physical environment, mobile phone, Internet, and social media (S), pleasure-arousal dominance, environmental quality perception (O), and shopping intention (R) (Lazaris et al., 2022). Emotions, cognition, and biochemical responses are interconnected (Damasio, 1994). The SOR model is proposed to represent an interactive, networked process (Jacoby, 2002) in which stimuli, organisms, and responses form a tapestry of the individual's conscious experience (Druckman & Lacey, 1989).

3 Hypotheses

The development of our research hypotheses followed a systematic process grounded in theoretical foundations and empirical evidence. The cognitive-emotional framework established by Gentile et al. (2007) and further developed in recent studies provides the theoretical basis for H1, which suggests the integration of cognitive and emotional experiences in omnichannel environments. H2 emerges from the technology acceptance literature and recent omnichannel behavior studies, while H3 builds upon trust research in digital commerce. Each hypothesis was formulated to address gaps in the current understanding while building upon established theoretical frameworks.

3.1 Online shopping experience configuration

Regardless of whether contact occurs through offline or online channels, various dimensions constitute an omnishopper's experience (Chang & Li, 2022). The online shopping experience includes physical, ideological, pragmatic, and social dimensions (Trevinal & Stenger, 2014). It also includes sensory, emotional, cognitive, pragmatic, lifestyle, and relational dimensions (Gentile et al., 2007). Essential components such as the smoothness of the experience and perceived privacy risk have also been noted (Quach et al., 2022). Some studies highlight emotional and cognitive dimensions as crucial elements (Hao et al., 2015). From this perspective, we propose the following hypothesis:

• H1. In an omnichannel environment, consumers' cognitive and emotional experiences are positively influenced by their online shopping experience.

3.2 Online shopping experience as a key antecedent of online shopping intention

Consumers' cognitive and emotional perceptions are key antecedents of their shopping behavior (Pappas et al., 2016). Similarly, customers' cognitive and emotional implications positively impact online shopping intentions (Ma et al., 2020). Furthermore, marketing studies confirm that customers' cognitive and emotional attitudes positively affect online shopping intentions (Suparno, 2020). Consumers' emotional and cognitive experiences with devices positively impact online shopping intentions (Mishra et al., 2021). From this perspective, we propose the following hypothesis:

• H2. In an omnichannel environment, the online shopping experience directly and positively impacts online shopping intentions.

3.3 Mediating role of omnichannel shopping trust

Omnichannel shopping trust plays an important role in mediating the effect of collaborative marketing on customer loyalty (Chen et al., 2022). It also mediates the impact of previous online shopping experiences on consumers' future online purchase intentions (Weisberg et al., 2011). Similarly, online shopping trust plays a mediating role in the effect of the online shopping experience on online repurchase intention (Dewi & Giantari, 2022); it plays a key role in mediating the effect of a previous online shopping experience (website appearance, online promotions, and security) on online repurchase intention (Zhu et al., 2020). From this perspective, we propose the following hypothesis:

• H3. Omnichannel shopping trust mediates the effect of the online shopping experience on online shopping intention.

Figure 1 shows the proposed empirical model.

Figure 1
Theoretical model

4 Methodology

The theoretical model was verified through structural equation analysis based on the stimulus-organism-response (S-O-R) framework. Systematic random probability sampling was used to recruit 600 consumers with omnichannel shopping experience in Chilean retail stores.

4.1 Description of scales

The dimensions for measuring the constructs in this study were derived from the relevant literature (Supplementary Material). The scale for online shopping experience in an omnichannel environment includes cognitive elements such as timely delivery and payment security, and emotional elements such as interactive design and personalized service. The online shopping intention and omnichannel shopping trust scales mediate the effect of the online shopping experience on purchase intention using similar procedures and were refined based on the analysis of De Wulf and Odekerken-Schröder (2003). A qualitative research technique using focus groups including regular omnichannel and e-commerce shoppers, academics, executives, and experts guided discussion and identified dimensions that best represent the constructs. Zaichkowsky’s (1985) method for measuring product involvement was modified to analyze participants' opinions.

Participant recruitment followed a structured protocol to ensure sample representativeness and data quality. Initial participants were identified through the university's consumer database, with additional recruitment through professional networks and social media platforms. Screening criteria included previous omnichannel shopping experiences within the past six months and regular use of both online and offline shopping channels. The focus groups were conducted in sessions of 6-8 participants, led by experienced moderators using a semi-structured interview guide. Each session lasted approximately 90 minutes and was recorded for subsequent analysis. The focus group discussions explored participants' cognitive and emotional experiences during omnichannel shopping, their perceptions of trust, and the factors that influence their purchase intentions. Sessions were conducted until theoretical saturation was reached, resulting in eight focus groups.

Participants rated each item as representative, somewhat representative, or not representative. Dimensions with high consensus were retained and formed the final scales for the questionnaire (Lichtenstein et al., 1990). A quantitative pretest with 40 participants used exploratory factor analysis and Cronbach's alpha to validate each dimension. Statements were clearly written and experiences were collected using a 7-point Likert scale.

4.2 Data collection

The population size of 50,000 corresponds to consumers who have experience with omnichannel shopping in Chilean retail stores (Supplementary Data 1 – Survey Data). This was confirmed by an open declaration by the participants. This population was obtained from the database of the Center for Experimental Social Sciences of the University of Santiago de Chile. In addition, the ideal minimum sample size to best represent the population was calculated as n = 384, considering N= 50,000, a confidence level of 95%, and a margin of error of 5% (Taherdoost, 2016). Random probability sampling was used to select the participants in the sample (Etikan & Bala, 2017); specifically, a systematic sampling technique was employed. Given that the size of both the population (N = 50,000) and the desired sample (n = 600) were known (Table 1), the sampling interval (i) was determined to be 83. A random number between 1 and 83 was then selected, which was 25. The sample consisted of the 25th, 108th, and 192nd units up to the 49,942nd. This procedure resulted in a final sample that participated in the study.

Table 1
Sample Profile

Gender of Respondents	%	Marital Status	%
Male	37	Married	26
Female	62	Single	60
Other	1	Divorced or Separated	11
Total 100		Other	3
		Total	100
Age Range of Respondents	%	Education	%
> 39	44	Incomplete secondary	1.8
≤ 39	56	Complete secondary	8.7
Total	100	Incomplete technical (high school/vocational training)	7
Current Employment	%	Complete technical (high school/vocational training)	13
Self-employed	12.2	Incomplete university	22.3
Unemployed and not looking for work	1.7	Complete university	30.5
In a situation of disability without the possibility of working	0.3	Incomplete post-graduate studies	13.2
Part-time job	10.5	Complete post-graduate studies	3.5
Full-time job	35.5	Total	100
Looking for a job	13.6
Housewife	3
Retired	4.2
Student	16.8
No experience/does not know	2.2
Total	100

• 1 US$ = 803.84 Chilean pesos, as of April 2023 (BCC, 2023).

5 Results

5.1 Assessment of the measurement model

After data collection, psychometric analysis was conducted to confirm that the scales had a reasonable degree of validity, reliability, and dimensionality (Supplementary Data 2 – Survey Codebook). First, exploratory factor analysis was conducted. Factor analysis of the principal components was performed using varimax rotation to identify items that did not belong to their dimensions (Hair et al., 1998). Based on this procedure, it was not necessary to eliminate indicators from the evaluated scales (Table 2). All indicators yielded optimal degrees of unidimensionality, with factor loadings greater than 0.4 (Larwood et al., 1995).

Table 2
Confirmatory Factor Analysis of Scales

Subscales	Variable	Factor loading	Variance explained (%)	Own value
Online shopping experience
Cognitive experience
Delivery experience	Dee1	0.73	51.19	2.56
	Dee2	0.79
	Dee3	0.79
	Dee4	0.75
	Dee5	0.48
Payment experience	Pae1	0.65	46.76	2.34
	Pae2	0.75
	Pae3	0.75
	Pae4	0.73
	Pae5	0.52
Emotional experience
Interactive experience	Ine1	0.71	51.91	2.60
	Ine2	0.66
	Ine3	0.71
	Ine4	0.82
	Ine5	0.69
Personalized experience	Pee1	0.76	54.18	2.71
	Pee2	0.66
	Pee3	0.86
	Pee4	0.60
	Pee5	0.77
Online purchase intention
	Opi1	0.84	79.46	3.97
	Opi2	0.89
	Opi3	0.92
	Opi4	0.94
	Opi5	0.86
Omnichannel shopping trust
	Ost1	0.84	74.90	3.75
	Ost2	0.83
	Ost3	0.87
	Ost4	0.90
	Ost5	0.88

A reliability analysis using Cronbach's alpha, construct reliability, and average variance extracted (AVE) tests was conducted according to Jöreskog and Sörbom’s (1993) criteria: 1) exclude indicators with weak convergence to the latent variable (Student's t-value > 2.28, p < 0.001); 2) remove variables with standardized coefficients below 0.5; and 3) exclude indicators with a linear R² ratio < 0.3. As a result, items Dee5, Pae1, Pae5, Ine2, Pee1, and Pee4 were excluded. All other items had standardized coefficients > 0.5 and R² ratios > 0.3. The multidimensional nature of the online shopping experience was confirmed using a rival model strategy (Steenkamp & van Trijp, 1991), comparing a unidimensional (first order) model with a multidimensional (second-order) model. The second-order model showed a better fit (Table 3), confirming the multidimensionality of the online shopping experience.

Table 3
Multidimensional Analysis of Online Shopping Experience

Fit measurements	Model fit	Recommended value	Rival models
			Model 1	Model 2
			Removed items
				Dee5; Pae1; Pae5; Ine2; Pee1; Pee4
Absolute	NCP	Minimum	1516.457	394.384
	SNCP	Minimum	2.527	0.657
	RMSR	Minimum	0.950	0.950
	RMSEA	< 0.08	0.079	0.068
Incremental	NNFI	High (close to 1)	0.453	0.915
	IFI	High (close to 1)	0.573	0.929
	CFI	High (close to 1)	0.568	0.929
Parsimony	AIC	Minimum	1759.457	668.384
	χ2 / df	[1; 5]	12.233	3.738

The optimal scales were obtained using this procedure. The reliability of each scale was confirmed (Table 4). According to Henseler et al. (2015), Cronbach's alpha (limit 0.7) and composite reliability (limit 0.7) were used. According to Fornell and Larcker (1981), AVE analysis (limit 0.5) was conducted.

Table 4
Reliability and Validity of Constructs

Scales	Variable	Cronbach's alpha	Composite reliability	Average variance extracted
Online shopping experience
Cognitive experience
Delivery experience	Dee1	0.748	0.860	0.605
	Dee2
	Dee3
	Dee4
Payment experience	Pae2	0.740	0.825	0.541
	Pae3
	Pae4
Emotional experience
Interactive experience	Ine1	0.760	0.843	0.518
	Ine3
	Ine4
	Ine5
Personalized experience	Pee2	0.766	0.853	0.541
	Pee3
	Pee5
Online purchase intention
	Opi1	0.934	0.950	0.793
	Opi2
	Opi3
	Opi4
	Opi5
Omnichannel shopping trust
	Ost1	0.915	0.937	0.747
	Ost2
	Ost3
	Ost4
	Ost5

Theoretical analysis indicates that these scales have strong content validity. An analysis was conducted to determine whether the refined scales met convergent and discriminant validity to verify construct validity (Bagozzi & Yi, 1988). Convergent validity was confirmed as all standardized coefficients from the confirmatory factor analysis (CFA) were significant at 0.001 and greater than 0.5 (Bagozzi, 1981). Discriminant validity was established using a confidence interval test. The intervals of correlations between latent variables in the CFA model did not include 1, indicating discriminant validity (Bagozzi, 1981; Anderson & Gerbing, 1988). Additionally, this was confirmed by comparing the χ2 values of the proposed CFA model with alternative models in which latent variable pairs were restricted to 1. The proposed model showed discriminant validity (Table 5), as evidenced by significantly lower χ2 values than alternative models (Bagozzi & Phillips, 1982).

Table 5
Discriminant Validity

Confidence Interval Test		Difference χ2 (df)
Bi-variate relationship	Confidence intervals
Full model
Online shopping intention - Omnichannel shopping trust	0.481 – 0.493	1574.8 (1)	1479.1(335)
Online shopping intention - Personalized experience	0.338 – 0.342	1561.1 (1)
Online shopping intention - Interactive experience	0.435 – 0.439	1619.9 (1)
Online shopping intention - Payment experience	0.508 – 0.512	1604.9 (1)
Online shopping intention - Delivery experience	0.323 – 0.335	1619.5 (1)
Personalized experience - Omnichannel shopping trust	0.165 – 0.169	1582.2 (1)
Interactive experience - Omnichannel shopping trust	0.286 – 0.290	1631.4 (1)
Payment experience - Omnichannel shopping trust	0.162 – 0.166	1671.5 (1)
Delivery experience - Omnichannel shopping trust	0.143 – 0.155	1644.8 (1)
Interactive experience - Personalized experience	0.478 – 0.490	1534.3 (1)
Payment experience - Personalized experience	0.362 – 0.374	1558.8 (1)
Delivery experience - Personalized experience	0.289 – 0.293	1555.2 (1)
Payment experience - Interactive experience	0.629 – 0.637	1579.5 (1)
Delivery experience - Interactive experience	0.401 – 0.409	1601.9 (1)
Delivery experience - Payment experience	0.006 – 0.568	1566.3 (1)
Online shopping experience
Delivery experience- Payment experience	0.585 – 0.589	617.5 (1)	364.3(71)
Delivery experience - Interactive experience	0.373 – 0.377	572.6 (1)
Personalized experience - Delivery experience	0.387 – 0.395	626.6 (1)
Payment experience - Interactive experience	0.616 – 0.620	463.0 (1)
Personalized experience - Payment experience	0.452 – 0.460	541.5 (1)
Personalized experience - Interactive experience	0.555 – 0.559	442.6 (1)

• Note: All coefficients are significant at the 0.001 level.

5.2 Assessment of the structural model

The structural model constructed with reflective indicators (MacKenzie et al., 2005) was assessed using structural equation modeling. IBM® SPSS® Amos 26 was used for this procedure. In line with Bagozzi (1981), the values obtained from the fitted model were within an acceptable range: IFI = 0.929, CFI = 0.929, RMSEA = 0.068, standardized χ² = 3.738, and p < 0.001. By calculating the standardized λ, it was observed that the online shopping experience is formed by cognitive experience (λ = 0.92; p < 0.001) and emotional experience (λ = 0.87; p < 0.001).

Cognitive experience comprised timely delivery experience (λ = 0.62; p < 0.001) and payment security experience (λ = 0.92; p < 0.001). Emotional experience comprised interactive layout experience (λ = 0.81; p < 0.001) and personalized attention experience (λ = 0.70; p < 0.001). Figure 2 shows that the online shopping experience configured in this way directly and positively affects online shopping intention (β = 0.63; R² = 0.39; p < 0.001).

Figure 2
Structural model

The R² value (0.39) obtained in the model indicates that 39% of the variance in online purchase intention is explained by the online shopping experience, representing moderate explanatory power. This result can be attributed to the complex and multifaceted nature of omnichannel shopping behavior, where other factors not included in the model may also influence purchase intention. The lower λ values observed for the timely delivery (λ = 0.62) and personalized attention (λ = 0.70) constructs compared to other constructs such as payment security (λ = 0.92) and interactive layout (λ = 0.81) suggest that other potentially relevant factors could increase the explanatory power of the model. For instance, factors such as logistical convenience, product variety, returns policy, and previous shopping experiences may also significantly influence purchase intention in the omnichannel context (Li et al., 2018; Verhoef et al., 2022). This observation is consistent with previous studies that have identified multiple determinants of purchase intention in omnichannel environments (Zhang et al., 2018; Lazaris et al., 2022).

The mediating role of omnichannel shopping trust in the effect of online shopping experience on purchase intention was also evaluated. The results revealed that the relationship between online shopping experience and purchase intention was partially mediated by online shopping trust (19.1%) (Table 6).

Table 6
Mediation Analysis

Omnichannel shopping trust mediation analysis
Online shopping experience (Ose) -> Omnichannel shopping trust (Ost)-> Online shopping intention (Osi)
c = Ose on Osi		a = Ose on Ost	b = Ost on Osi		c`= Ose on Osi in the presence of Ost
Total effect: (Ose->Osi) - c		Direct effect: (Ose->Osi) - c`		Indirect effects: (Ose->Osi) (a*b)
Estimated β	p-value	Estimated β	p-value	Estimated β	p-value
0.791	< 0.001	0.640	< 0.001	0.151	< 0.001
95% Confidence interval
Lower	Upper	Lower	Upper	Lower	Upper
0.650	0.916	0.5170	0.767	0.0878	0.226
% Mediation
100		80.9		19.1

The results obtained in this assessment confirmed the hypotheses proposed in this study (Table 7).

Table 7
Status of Hypotheses

Hypothesis	Value	Path			Result
H1	(+)	Cognitive and emotional experiences configure the online shopping experience			Supported
H2	(+)	Online shopping experience		online shopping intention	Supported
H3	(+)	Omnichannel shopping trust mediation
		Online shopping experience		online shopping intention	Supported

Although no experimental techniques were used in this study to test the S-O-R model, sufficient evidence was found in the specialized literature on interactive marketing and consumer behavior that the S-O-R model can be manifested simultaneously in the order considered: online shopping experience → omnichannel shopping trust → online shopping intention (Jai et al., 2021; Jai et al., 2014). The empirical results in Tables 6 and 7 support all three hypotheses proposed in this study.

Hypothesis 1 posits that cognitive and emotional experiences configure the online shopping experience in an omnichannel environment. This hypothesis is strongly supported by the standardized λ coefficients obtained in our structural model. Cognitive experience showed a robust relationship (λ = 0.92, p < 0.001) with the online shopping experience, while emotional experience also showed a strong connection (λ = 0.87, p < 0.001). These high lambda values indicate that both dimensions are fundamental to the online shopping experience. Furthermore, the constituent elements of each dimension also showed significant relationships: within cognitive experience, payment security (λ = 0.92, p < 0.001) and timely delivery (λ = 0.62, p < 0.001); within emotional experience, interactive layout (λ = 0.81, p < 0.001) and personalized attention (λ = 0.70, p < 0.001).

Hypothesis 2 proposes that the online shopping experience directly and positively impacts shopping intentions. This hypothesis is supported by the significant direct effect (β = 0.63, p < 0.001) observed between these constructs, with an R² value of 0.39. This indicates that the online shopping experience explains 39% of the variance in online shopping intentions, which is a substantial explanatory power in behavioral research. The strength of this relationship demonstrates that a positive online shopping experience significantly influences consumers' online shopping intentions in an omnichannel context.

Hypothesis 3, which suggests that omnichannel shopping trust mediates the effect of online shopping experience on purchase intention, is supported by the mediation analysis results presented in Table 6. The analysis reveals a partial mediation effect, where the total effect of the relationship (c) is 0.791 (p < 0.001); the direct effect (c') is 0.640 (p < 0.001); the indirect effect (a*b) is 0.151 (p < 0.001); and the mediation accounts for 19.1% of the total effect.

These results show that while there is a significant direct effect of online shopping experience on purchase intention, trust plays a significant mediating role, explaining approximately one-fifth of the total effect. The confidence intervals (lower bound: 0.0878, upper bound: 0.226) do not include zero, confirming the statistical significance of the mediation effect.

The comprehensive support for all three hypotheses by multiple statistical indicators strengthens our theoretical framework. It provides robust evidence for the proposed relationships between online shopping experience, trust, and shopping intentions in an omnichannel environment. These findings are significant as they demonstrate the direct relationships between constructs and the complex mediating mechanisms in omnichannel consumer behavior.

6 Discussion

In an omnichannel environment, there is consensus on the elements of the online shopping experience. Schneider and Zielke (2020) analyzed the showrooming behavior of omnishoppers and confirmed that the online shopping experience is a multidimensional construct that includes cognitive and emotional experiences. This in is line with Verhoef et al. (2009) and Gentile et al. (2007). In this study, timely delivery and secure payment systems were crucial to the cognitive experience, while interactive shopping and personalized attention were vital to the emotional experience.

This finding contrasts with Gao et al. (2021) and Rose et al. (2012), who considered cognitive and emotional experiences as unidimensional or partial responses to external factors. In an omnichannel environment, this study shows that the online shopping experience, which includes both cognitive and emotional experiences, directly and positively influences online shopping intention, which differs from Ma et al. (2020) and Suparno (2020), who viewed these experiences as individual attitudes that separately influence shopping intention. Within an omnichannel context, this study regards cognitive and emotional experiences as integral, interactive components of the online shopping experience.

It is critical to examine the influence of the multidimensional experience on online purchase intention, rather than focusing solely on cognitive and emotional experiences. This study confirms that omnichannel shopping trust partially mediates the influence of the multidimensional omnichannel experience on online purchase intention, consistent with Hao et al. (2015) and partially consistent with Giantari et al. (2013). However, a comparison of scales reveals differences (Supplementary Data 3 – Appendix A); while Giantari et al. (2013) included timely delivery and product quality in the omnichannel shopping trust scale, this study considers timely delivery as a cognitive stimulus component of the omnichannel experience scale for omnishoppers (Vashishtha & Kumar, 2016) and product quality as part of quality orientation (Ling et al., 2010). The results confirm that the online shopping experience directly and positively influences online purchase intention in an omnichannel environment, suggesting an integrated emotional and cognitive network (Damasio, 1994). The SOR model proposed by Jacoby (2002) in an omnichannel context can be expressed as online shopping experience + omnichannel shopping trust + online shopping intention, co-occurring. Consistent online shopping experiences in an omnichannel environment can enhance retailers' omnichannel management, consistent with research in Germany (Menrad 2020).

Our analysis revealed several noteworthy findings, including unexpected results that merit further discussion. The relatively lower impact of timely delivery (λ = 0.62) compared to payment security (λ = 0.92) contradicts some previous findings in the literature (Khan et a., 2023) and suggests a potential shift in consumer priorities. This unexpected result might be attributed to the increasing sophistication of delivery systems and changing consumer expectations in the post-pandemic environment. Another surprising finding was the moderate strength of the relationship between emotional experience and purchase intention, which suggests that emotional factors might play a more complex role than previously theorized.

The practical implications of these findings are particularly relevant for omnichannel retailers. The strong influence of payment security suggests that retailers should prioritize investments in secure payment systems and communicate their security measures to customers. The moderate impact of emotional factors suggests that retailers should balance their resources between functional improvements and emotional engagement strategies. These findings also highlight the need for integrated approaches that address both cognitive and emotional aspects of the shopping experience while maintaining strong security measures.

7 Conclusion

The findings of this study support the proposed theoretical model. In an omnichannel environment, online shopping involves both cognitive and emotional experiences. The cognitive experience includes timely receipt of purchases and secure payment systems, while the emotional experience includes interactive shopping and personalized service. The study also confirms that in an omnichannel setting, the online shopping experience positively impacts online shopping intentions. Omnichannel shopping trust partially mediates the effect of the online shopping experience on purchase intention. This research suggests that the SOR model in an omnichannel context is dynamic and simultaneous rather than static and linear.

7.1 Managerial implications

Our findings offer insights for managers developing and implementing omnichannel retail strategies. The results show that omnichannel management requires a balanced approach to both the cognitive and emotional aspects of the shopping experience.

The cognitive dimension requires special attention to delivery management and payment security systems. Managers should implement comprehensive delivery solutions, including real-time tracking systems and flexible delivery options such as home delivery, in-store pickup, and locker collection. Given the high impact of payment security (λ = 0.92) on the shopping experience, retailers should prioritize implementing robust security measures, including multi-factor authentication and biometric verification, while maintaining transparent transaction processes. Furthermore, seamless integration between online and offline inventory systems is fundamental to providing accurate stock information and enhancing the overall cognitive experience.

Regarding the emotional dimension, managers should create immersive digital environments that enhance customer engagement. This can be achieved by implementing interactive features such as virtual try-on capabilities, 360-degree product views, and augmented reality applications. The personalization aspect (λ = 0.70) requires special attention. This suggests that retailers should invest in AI-driven recommendation systems and develop targeted communication channels that create customized shopping experiences based on customer behavior and preferences.

Trust-building mechanisms play a critical mediating role and should be integrated throughout the customer journey. This includes maintaining transparency around delivery times and costs, incorporating authentic customer reviews and ratings, and prominently displaying security certifications. Managers should also ensure a consistent brand experience across all touchpoints through unified customer service protocols and integrated loyalty programs that span online and offline channels.

To maintain and improve these initiatives, retailers should implement comprehensive performance monitoring systems that track cognitive and emotional elements of the experience. Regular customer feedback mechanisms and measures of channel integration effectiveness are essential for continuous improvement. The relatively lower impact of timely delivery (λ = 0.62) suggests that managers should investigate and enhance this aspect of their operations, possibly through improved logistics systems and delivery partnerships.

These managerial implications underscore the importance of a holistic approach to omnichannel retailing, where cognitive and emotional elements are carefully balanced to create a seamless and engaging shopping experience that builds trust and encourages continued patronage.

7.2 Limitations

The representativeness of the sample for the entire omnishopper population in Chile remains to be determined, as the bias introduced by drawing from a specific group of 50,000 people limits the generalizability of the findings. To mitigate this bias, stratified random probability sampling should be used to identify omnishoppers with webrooming and showrooming behaviors. This is important for improving retailers' strategic marketing efforts in an omnichannel environment. Furthermore, the need for an experimental study to verify consumers' simultaneous adoption of the S-O-R model complicates its generalization in an omnichannel context, and such a study should be conducted in line with Jai et al. (2021).

While this study provides valuable insights into omnichannel shopping behavior, several limitations should be acknowledged, particularly regarding cultural context and its influence on cognitive and emotional factors. The study was conducted in Chile, a country with distinct cultural characteristics that may influence shopping behavior differently from other regions. Chilean consumers typically exhibit collectivist values and high uncertainty avoidance (Hofstede, 2021), which may affect their trust formation and risk perception in omnichannel environments differently from consumers in more individualistic or risk-tolerant cultures.

The cognitive aspects of the shopping experience, particularly the relatively lower impact of timely delivery (λ = 0.62) and higher importance of payment security (λ = 0.92), may reflect specific cultural preferences and concerns. For instance, Chile's historical economic volatility and resulting consumer caution regarding financial transactions could influence the emphasis on payment security. Similarly, the emotional dimensions of the shopping experience, including personalized attention (λ = 0.70), may be particularly salient in Latin American cultures, where personal relationships and service interactions are highly valued. Furthermore, Chile's unique retail infrastructure and digital maturity level may affect how consumers integrate online and offline shopping channels. While Chile has one of the highest internet penetration rates in Latin America, digital shopping behaviors and channel preferences may differ significantly from more mature e-commerce markets in North America, Europe or Asia. The varying levels of technological infrastructure and digital literacy across different regions of Chile may also influence the generalizability of our findings.

These cultural and contextual limitations suggest several important considerations for future research. Cross-cultural studies comparing omnichannel shopping behaviors across different cultural contexts could provide valuable insights into the universality or cultural specificity of our findings. Research in countries with different levels of digital maturity, different cultural dimensions (such as individualism-collectivism, power distance, and uncertainty avoidance), and different retail infrastructures would be particularly valuable. Additionally, studies that examine how cultural factors moderate the relationship between cognitive and emotional experiences and shopping intentions could enhance our understanding of omnichannel behavior in a global context.

Although the demographic composition of the sample is representative of urban consumers in Chile, it may not fully capture the diversity of omnichannel shoppers in different cultural contexts or socioeconomic groups. Future studies should consider more diverse samples from different cultural backgrounds, age groups, and socioeconomic levels to provide a more comprehensive understanding of omnichannel shopping behavior.

7.3 Future research

Our study opens up several promising avenues for future research that could enhance our understanding of omnichannel shopping behavior. Future research would benefit from employing a variety of methodological approaches, including longitudinal studies to track behavioral changes over time and cross-cultural analyses to examine the validity of the model across different cultural contexts. Mixed-method approaches that combine quantitative data with qualitative insights from in-depth interviews, observational studies, and focus groups could provide a richer understanding of emotional responses and decision-making processes in omnichannel environments.

The cognitive and emotional constructs identified in this study warrant further investigation. Future research could explore additional delivery experience variables and emerging payment security technologies, especially given the relatively small impact of timely delivery (λ = 0.62) found in our study. The role of artificial intelligence in the decision-making process and its impact on cognitive experiences is another promising research direction. Regarding emotional experiences, researchers should investigate the impact of emerging technologies such as virtual and augmented reality on customer engagement, and the influence of social shopping and brand community involvement on emotional connections.

Context-specific applications of our model could yield valuable insights. Industry-specific studies across different retail sectors could reveal differences in the importance of cognitive and emotional factors. Additionally, examining how emerging technologies affect the shopping experience and investigating the model's resilience during market disruptions could provide practical insights for practitioners. The impact of various moderating factors, such as technology readiness, age, digital literacy, and risk perception, could further enhance our understanding of omnichannel shopping behavior.

Future studies should also consider examining additional mediating variables beyond trust, such as customer satisfaction, brand attachment, and channel integration quality. These investigations could provide a more comprehensive understanding of the mechanisms that drive omnichannel shopping behavior. Moreover, research into behavioral outcomes, including post-purchase behavior, customer loyalty development, word-of-mouth effects, and repeat purchase patterns, could offer valuable insights into the long-term effectiveness of omnichannel strategies.

These research directions would advance the theoretical understanding and provide practical insights for practitioners navigating the evolving landscape of omnichannel retailing. The combination of methodological diversity, construct enhancement, contextual applications, and behavioral outcome analysis would provide a more comprehensive framework for understanding and optimizing omnichannel shopping experiences.

SUPPLEMENTARY MATERIAL

Supplementary Data 1 – Survey Data

Supplementary Data 2 – Survey Codebook

Supplementary Data 3 – Appendix A

Supplementary material to this article can be found online at https://doi.org/10.7910/DVN/QSXDFD

• Reviewers:
  Eduarda Remor Bastos; Anderson Rosa
• Evaluation process:
  Double Blind Review
• This article is open data
• How to cite:
  Marinao-Artigas, E., Wei-Shen, S., Limongi, R., & Laroze, D. (2025). Online shopping experience drives online shopping intention: role of omnichannel shopping trust. Revista Brasileira de Gestão de Negócios, 27(1), e20240057. https://doi.org/10.7819/rbgn.v27i01.4292
• Financial support:
  National Council for Scientific and Technological Development - 303041/2023-4.
• Open Science:
  Marinao-Artigas, Enrique; Wei-Chen, Shih; Limongi, Ricardo; Laroze, Denise, 2025, "Online Shopping Experience as Driving of Online Shopping Intention: Role of Omnichannel Shopping Trust", https://doi.org/10.7910/DVN/QSXDFD, Harvard Dataverse, V2, UNF:6:U1WLQSJ1egncM91N/kiSfQ== [fileUNF]
• Plagiarism analysis:
  RBGN performs plagiarism analysis on all its articles at the time of submission and after approval of the manuscript using the iThenticate tool.

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• Responsible editor:
  Prof. Dr. João Maurício Gama Boaventura

Data availability

Marinao-Artigas, Enrique; Wei-Chen, Shih; Limongi, Ricardo; Laroze, Denise, 2025, "Online Shopping Experience as Driving of Online Shopping Intention: Role of Omnichannel Shopping Trust", https://doi.org/10.7910/DVN/QSXDFD, Harvard Dataverse, V2, UNF:6:U1WLQSJ1egncM91N/kiSfQ== [fileUNF]

Publication Dates

• Publication in this collection
  04 Apr 2025
• Date of issue
  2025

History

• Received
  16 Aug 2024
• Accepted
  18 Feb 2025