Driving customer engagement and citizenship behaviour in omnichannel retailing: evidence from the fashion sector

Abstract

Purpose

This study, grounded in the SOR theory, aims to enrich the understanding of customer citizenship behaviour in omnichannel fashion retail by examining how different customer experiences enhance customer engagement and how that engagement leads to customer citizenship behaviour. The influence of return policies on the relationship between customer engagement and customer citizenship behaviour was also examined.

Design/methodology/approach

Partial least squares structural equation modelling (PLS-SEM) is used to examine the framework of the proposed study with data collected through a survey (n = 251) to examine the opinions of the respondents about the variables mentioned. The authors also assessed the proposed framework using predictive power assessment using PLS predict.

Findings

The study results reveal that customers’ experiences of integration and flexibility in omnichannel retail are positively associated with their engagement. However, customer experiences of connectivity, consistency and personalization do not appear to affect customer engagement significantly in omnichannel retail. The return policy positively moderates the relationship between customer engagement and customer citizenship behaviour in the omnichannel fashion retail context. Predictive power assessment shows that the proposed model has high prediction accuracy.

Originality/value

This study contributes to the marketing literature by investigating different dimensions of consumer experience collectively and its impact on customer engagement and citizenship behaviour. Furthermore, the study contributes to omnichannel retail in fashion industry by testing the return policy as a moderator variable on the relationship between customer engagement and citizenship behaviour.

Objetivo

Este estudio, basado en la teoría SOR, enriquece la comprensión del comportamiento cívico del cliente en el comercio minorista de moda omnicanal examinando cómo las diferentes experiencias del cliente mejoran el compromiso de éste y cómo dicho compromiso conduce al comportamiento cívico del cliente. También se examina la influencia de las políticas de devolución en la relación entre el compromiso del cliente y el comportamiento ciudadano del cliente.

Diseño/metodología/enfoque

Se utilizó la modelización PLS-SEM para examinar el marco del estudio propuesto con datos recogidos mediante una encuesta (n = 251) para examinar las opiniones de los encuestados sobre las variables mencionadas. Los autores también evaluaron el marco propuesto mediante una evaluación del poder predictivo utilizando la predicción PLS.

Conclusiones

Los resultados revelan que las experiencias de integración y flexibilidad de los clientes en el comercio minorista omnicanal se asocian positivamente con su compromiso. Sin embargo, las experiencias de los clientes de conectividad, coherencia y personalización no parecen afectar significativamente al compromiso del cliente en el comercio minorista omnicanal. La política de devoluciones modera positivamente la relación entre el compromiso del cliente y el comportamiento ciudadano en el contexto del comercio minorista de moda omnicanal. La evaluación del poder predictivo mostró que el modelo propuesto tenía una alta precisión de predicción.

Originalidad

El estudio contribuye a la literatura de marketing investigando colectivamente diferentes dimensiones de la experiencia del consumidor y su impacto en el compromiso del cliente y el comportamiento ciudadano. Además, este estudio contribuye a la venta minorista omnicanal en la industria de la moda al probar la política de devoluciones como variable moderadora de la relación entre el compromiso del cliente y el comportamiento ciudadano.

目的

本研究以 SOR 理论为基础, 通过研究不同的顾客体验如何提高顾客参与度, 以及顾客参与度如何推动顾客公民行为, 丰富了对全渠道时尚零售中顾客公民行为的理解。研究还探讨了退货政策对顾客参与和顾客公民行为之间关系的影响。

设计

本文采用 PLS-SEM 模型来检验拟议的研究框架, 并通过调查（n = 251）收集数据, 以检验受访者对上述变量的看法。作者还通过使用 PLS 预测评估预测能力, 对提出的框架进行了评估。

设计

通过调查（n = 251）收集的数据, 使用 PLS-SEM 模型来研究拟议的研究框架, 以考察受访者对上述变量的看法。作者还通过使用 PLS 预测评估预测能力, 对提出的框架进行了评估。

研究结论

研究结果表明, 顾客在全渠道零售中对整合性和灵活性的体验与他们的参与度呈正相关。然而, 顾客在连通性、一致性和个性化方面的体验似乎并未对顾客参与全渠道零售产生显著影响。退货政策对全渠道时尚零售中顾客参与和公民行为之间的关系起到了积极的调节作用。 预测能力评估表明, 所提出的模型具有较高的预测准确性。

独创性

本研究通过对消费者体验的不同维度及其对顾客参与和公民行为的影响进行综合研究, 为营销文献做出了贡献。此外, 本研究通过检验退货政策作为顾客参与和公民行为之间关系的调节变量, 为时尚行业的全渠道零售做出了贡献。

Citation

Salem, S.F. and Alanadoly, A.B. (2024), "Driving customer engagement and citizenship behaviour in omnichannel retailing: evidence from the fashion sector", Spanish Journal of Marketing - ESIC, Vol. 28 No. 1, pp. 98-122. https://doi.org/10.1108/SJME-10-2022-0220

1. Introduction

The emergence of the COVID-19 pandemic triggered significant shifts in global lifestyles and consumer behaviour. This has compelled numerous firms to adapt their business processes to align with the changing consumption habits of their customers (Chatterjee et al., 2021; Naeem, 2020). In Malaysia, online sales of fashion products are projected to reach RM11.4bn (approximately US$2.7bn) by 2025, reflecting a compound annual growth rate (CAGR) of 12.7% from 2020 to 2025. The increasing use of mobile devices, expanding internet penetration and growing popularity of online shopping have been identified as key drivers contributing to this growth. Additionally, the fashion and beauty industry has been recognized as the second-largest contributor to the e-commerce market in Southeast Asia, constituting 20% of the region’s total e-commerce sales. Fashion retailers in Malaysia have embraced digital retailing technologies to provide customers with convenient and personalized shopping experiences, thereby creating an omnichannel retail environment (Rodríguez-Torrico et al., 2023).

The concept of omnichannel retailing encompasses the integration of multiple channels to deliver a seamless customer experience, regardless of the channel used or the stage of the buying process. Customers often engage in purchases across various channels, conduct information searches on one channel and complete purchases on another (Chang et al., 2017; Mosquera et al., 2018). Many retailers have adopted omnichannel strategies to remain competitive in meeting the increasing demand for integrated purchasing experiences (Lee et al., 2019). Customer experience through their online/offline interaction with retailers/brands has gained prominence, with 66% of customers considering it more important than the price of a product or service. Moreover, poor user experience has led 52% of customers to discontinue shopping on a brand’s website (Oi, 2021). However, existing studies on omnichannel shopping primarily provide descriptive insights (Ali et al., 2022; Van Nguyen et al., 2022), indicating the need for more theoretical and empirical research to fully comprehend omnichannel customer experiences.

Previous studies have often focused on specific aspects of the omnichannel journey, such as channel connectivity (Lee et al., 2019) or device quality (Sun et al., 2020). However, this limited perspective fails to completely capture the omnichannel experience. Exploring multiple dimensions of the omnichannel experience is essential for understanding how improving customer engagement can establish a strong connection between brands and their customers (Rahman et al., 2022).

Consumer engagement in omnichannel environments has been linked to various outcomes including word-of-mouth (WOM) (Kang, 2018), trust (Pagani et al., 2019), loyalty (Tyrväinen et al., 2020) and buying behaviour (Rodríguez-Torrico et al., 2017). Conversely, limited research has investigated the impact of customer engagement on customer citizenship behaviour, which refers to the voluntary actions of customers that benefit a brand or an organization (Xie et al., 2017). Such behaviours encompass positive WOM, defending the organization against negative comments, providing recommendations for service improvements and recommending the organization to others. Understanding customer citizenship behaviour and its antecedents has significant implications for enhancing customer satisfaction (Fernandes and Cruzeiro, 2022) and building stronger customer relationships (Mandl and Hogreve, 2020).

Customer citizenship behaviour has been explored in different contexts such as online brand communities in social media marketing (Ibrahim and Aljarah, 2021) and physical retail stores (Gong et al., 2022). However, as indicated by Gerea and Herskovic (2022), there remains a need to investigate the role of WOM within the omnichannel context, which is a crucial component of customer citizenship behaviour.

Understanding customer citizenship behaviour in the fashion industry is crucial for building and nurturing long-term relationships between fashion brands and customers (A. Anaza and Zhao, 2013). Collaborative efforts between fashion brands and consumers are essential for strengthening this relationship (Khoa, 2020). Citizenship behaviour plays a vital role in providing customer support, ensuring the maintenance of this valuable connection.

In an omnichannel environment, fashion consumers take the time to make purchase decisions by relying on reviews and recommendations (Khoa, 2020). They are influenced by opinion leaders and influential figures who guide fashion choices, allowing customers to express themselves in their style (Goldsmith and Clark, 2008). Peers serve as sources of information and cues for what is considered trendy and socially acceptable (Jegham and Bouzaabia, 2022). Aligning with fashion leaders and seeking validation from others strengthens their sense of belonging and enhances their social status (Alanadoly and Salem, 2022). By promoting and enhancing citizenship behaviour, fashion brands can elevate their positions in the market, strengthen customer bonds and maintain relevance in a dynamic and ever-changing industry.

However, product returns pose a challenge for fashion brands. Online stores have experienced higher return rates than their physical store counterparts because of the inability of customers to try on/touch/feel fashion items before purchasing. To address this issue, companies have implemented new return policies to meet customer needs. Although return policies have been examined with customer satisfaction (Pham and Ahammad, 2017) and purchase intention (Jeng, 2017), few studies have explored the moderating effect of return policies on the relationship between customer engagement and citizenship behaviour. Understanding how return policies influence customer behaviour and engagement with a brand provides valuable insights for businesses to optimize their policies and foster positive citizenship behaviour.

To address these gaps, this study develops and empirically tests a conceptual model that examines the connection between customer experience, customer engagement and customer citizenship behaviour based on stimulus–organism–response (SOR) theory (Mehrabian and Russell, 1974). The SOR framework is widely used to explain customer behaviour in various retail contexts, including offline (Kumar and Kim, 2014), online and e-commerce settings (Izogo and Jayawardhena, 2018). By applying the SOR model in the context of omnichannel fashion retailing, this study provides insights into consumer shopping behaviour by investigating the components of the omnichannel experience (connectivity, integration, consistency, flexibility and personalization) as stimuli for customer engagement (conscious attention, enthusiastic participation and social connection), with customer citizenship behaviour serving as the response. The research model incorporates return policy as a potential moderator.

This study makes several contributions to the field of research. Firstly, it comprehensively examines customer citizenship behaviour within the context of omnichannel fashion retailing, shedding light on how customers actively contribute to the success of fashion brands through positive WOM, defending the brand, making recommendations and voluntary engagement. Secondly, the study applies the well-established (SOR) framework to capture the impact of various stimuli, such as connectivity, integration, consistency, flexibility and personalization, on customer engagement. This application enhances our understanding of consumer shopping behaviour in an omnichannel environment. By considering the unique characteristics and dynamics of fashion retailing, industry-specific insights can be applied by fashion brands to enhance customer experiences and build stronger relationships. Thirdly, this study explored the role of return policy as a potential moderator in the relationship between customer engagement and citizenship behaviour. By investigating how return policies influence customer behaviour and their level of engagement with the brand, valuable insights can be provided for fashion retailers to optimize their return policies and foster positive citizenship behaviour. The findings of this study shed light on the drivers of customer citizenship behaviour within omnichannel experiences. Retailers can develop effective strategies to improve customer experience and increase customer engagement. These insights can inform the development of tailored omnichannel strategies, allowing fashion brands to create seamless, engaging and personalized experiences that foster positive customer behaviour and cultivate long-term brand loyalty.

2. Literature review

2.1 “S.O.R.” theoretical background

As a behavioural psychological framework, the SOR theory, was initially proposed by Meharabian and Russel in 1974, and later modified by Jacoby in 2002 (Naqvi et al., 2021). It elucidates the interconnectedness of three fundamental elements of customer behaviour: stimulus (S), organism (O) and response (R). According to this theory, various external “stimuli” exert influence on customers’ internal emotional and cognitive states (referred to as the “organism”), subsequently leading to specific behavioural “responses” within the marketplace (Cham et al., 2021; Lin et al., 2021; Shah et al., 2020; Zafar et al., 2020).

Stimuli encompass external factors beyond customer control and have been observed to induce changes in cognitive and affective states, as well as shape perceptions. This observation pertains to the design features of technologies or technological implications that influence consumer behaviour in the online context (Gu et al., 2023; Kakaria et al., 2023). Previous studies by Islam et al. (2020) empirically demonstrated the impact of website attributes, interactivity, customization, ease of use and customer service on consumer engagement in e-banking. Furthermore, research has found that interactivity, information richness and social presence enhance enjoyment in live streaming (Gu et al., 2023), metaverse telepresence (Jafar et al., 2023) and excitement in online shopping festival environments (Xie et al., 2023). Flavián et al. (2020) highlighted the positive impact of integrated experiences across different channels in omnichannel retailing on consumers’ feelings towards the brand/retailer, influencing long-term consumer–brand relationships. Based on this theoretical standpoint, we hypothesize that interactive, personalized and integrated omnichannel shopping experiences influence consumer engagement with fashion brands as stimulus factors.

On the other hand, the organism comprises internal processes such as cognition, affect and activation, which act as mediators between external stimuli and the resulting customer responses (Islam et al., 2020; Kuo and Chen, 2023; Zafar et al., 2020). Prior investigations have provided empirical evidence of the effectiveness of enjoyment, social support and memorable experiences in shaping behavioural intentions in live-stream shopping (Gu et al., 2023), impulsive buying tendencies in virtual shopping (Kakaria et al., 2023) and engagement and satisfaction in Facebook brand communities (Naqvi et al., 2021). Engagement as consumer behaviour was found to fully mediate the relationship between personalization and value co-creation behaviour in omnichannel environments within branded mobile applications (Tran et al., 2023). Integration in omnichannel retailing led to consumer engagement through brand familiarity in a study conducted by Itani et al. (2023). However, none of these studies focused on consumer experiences as a multi-item construct with enhancing consumer–brand interactions and engagement. In the present study, we conceptualize customer engagement as an organism that mediates the relationship between experiences of different dimensions and citizenship behaviour as a desired outcome.

Lastly, the response component represents the behavioural outcomes, whether positive or negative, triggered by stimuli and influenced by the organism (Liang and Lim, 2020; Mostafa and Kasamani, 2021). For instance, Shiu et al. (2023) examined the impact of immersive experiences and social interactions on online shopping intention, while Gu et al. (2023) provided empirical evidence supporting the positive influence of enjoyment and memorable experiences on continuity intentions for live-streaming shopping.

In this study, we adopted the SOR framework to investigate the antecedents of customer engagement and predict its connection to customer citizenship behaviour. We propose that positive omnichannel experiences elicit affective conscious behaviour, thereby fostering engagement as an organism. We anticipate that affective shifts will occur through the experience of enthusiasm, excitement and pleasure derived from positive omnichannel experiences, ultimately leading to favourable outcomes. Therefore, rigorous statistical testing is applied to examine the constructs’ effect on the response of interest, which pertains to customer citizenship behaviour associated with the firm or brand to which the customer is engaged.

2.2 Customer citizenship behaviour

Customer citizenship refers to voluntary and constructive prosocial behaviour exhibited by consumers towards a firm or brand. This involves engaging in dialogue, collaboration and interactions that promote positive outcomes (Dang et al., 2020; Gong et al., 2022). This behaviour can be understood through the social exchange theory, in which customers tend to engage in supportive behaviours towards a brand as a way of reciprocating high-quality products or experiences they have received (van Tonder and Petzer, 2020). A high level of consumer citizenship behaviour in e-commerce is manifested by sharing positive reviews, making recommendations, spreading positive WOM through social networks, assisting other consumers by sharing instructions, links or information and displaying tolerance when expectations are not met (Abdelaziz and Saad, 2022; Albuquerque and Ferreira, 2021; Izogo et al., 2020; Türkdemir et al., 2023). Saeed et al. (2021) affirmed that customer citizenship supports interaction, leading to long-term profitability and value, as customers contribute to the sustainable development of the organization or brand. In another study by Tonder et al. (2018), interacting with fellow consumers through voluntary helping is an explicit feature of attached consumers towards a brand or a firm in digital retailing environments, and is related to positive retailing experiences. From the consumer’s perspective, engaging in consumer citizenship behaviour fosters a sense of belonging and usefulness and establishes long-term relationships between brands and consumers (Assiouras et al., 2019).

Studies have used different approaches to conceptualize and measure citizenship behaviour. While some studies have treated it as a higher-order construct comprising advocacy, feedback, helping and tolerance (Kim et al., 2020a; Tonder et al., 2018; Woo, 2019), others have regarded it as a one-dimensional measure of consumers’ behavioural outcomes (Anwar et al., 2020; Assiouras et al., 2019; Gong et al., 2022; Izogo et al., 2020). Gong et al. (2022) highlighted the limited research on the influence of retail technologies on consumer citizenship behaviour in the context of e-commerce and digital platforms. This study aims to shed light on the diverse omnichannel experiences that affect consumer engagement through various constructs and investigate how these experiences may positively or negatively shape consumer citizenship behaviours towards omnichannel retailers/brands.

Consumer citizenship behaviour is a critical facet to be examined in this study, considering the dynamic nature of digital retail technologies and the subsequent reduction in direct interaction between consumers and brand representatives. Against this backdrop, emphasis on consumer-to-consumer advocacy and support has become a paramount goal for brands. This elevated achievement holds substantial importance in bolstering brand performance and cultivating favourable consumer relationships.

3. Conceptual model and hypothesis development

The conceptual model for the present study is derived from SOR theory, as illustrated in Figure 1. The model encompasses five external variables, namely, connectivity, integration, consistency, flexibility and personalization, which serve as stimulus constructs. The organism is represented by the direct variable of customer engagement, which comprises three dimensions: enthusiastic participation, conscious attention and social connections. The response is manifested in the outcome variable of customer citizenship behaviour, with potential moderation by the return policy. The proposed model posits a pathway linking the stimulus (omnichannel customer experience), the organism (customer engagement) and the response (customer citizenship behaviour), with the return policy acting as a moderator in the relationship between customer engagement and customer citizenship behaviour. Hypotheses were formulated to examine the proposed model.

3.1 Omnichannel customer experience as a stimulus

The omnichannel concept was developed to describe the expansion of multichannel retailing strategies and refers to the complete integration of a range of retailing channels and touchpoints that shape the overall customer experience within their relationship with the brands (Huré et al., 2017). The integration of multiple channels requires particular strategies to infuse customers into the brand/retailer’s ecosystem, where the ideal for customers is to seamlessly shift between different channels (physical stores, in-store digital tools, online stores accessed on desktops, laptops, mobile devices and social media) at any stage of their purchasing journey for a fully interactive shopping experience (Gök, 2020). From the retailer’s perspective, two main features of an omnichannel need to be fulfilled: a high level of consistency and seamless integration between channels (Huré et al., 2017).

According to Kang (2019), omnichannel consumers who use different channels spend 9% more time in physical stores than those who use a single channel, and approximately 10% more time in online stores. They also engage more interactively with brands and retailers by reviewing and recommending products and services and spreading WOM. Sung et al. (2021) stated that digital retailing is generally positive for the customer experience and enhances intentional and favourable engagement with the retailer and/or brand. Nonetheless, to enhance customers’ experiences in a way that triggers their interaction, studies have shown that it is essential to adopt consistent, seamless, flexible and synchronized services across all customer touchpoints before, during and after purchase. A seamless experience forms the basis for a deeper and mutually beneficial customer–brand relationship (Kang, 2019), leading to satisfaction and enhancing engagement, especially from a digital marketing perspective (Wang and Ramasamy, 2023). Providing such a seamless, integrated experience across different channels while using a technologically personalized shopping journey is essential for the success of an omnichannel strategy (Ameen et al., 2021; Le and Nguyen-Le, 2021). According to Flavián et al. (2019), consumers move between channels to maximize benefits and value (time, cost and convenience) while minimizing potential risk. Based on this argument, we predict the following:

H1.

Customers’ shopping experience in fashion omnichannel retailing stimulates their engagement through (a) connectivity, (b) integration, (c) consistency, (d) flexibility and (e) personalization.

3.2 Customer engagement as an organism

Customer engagement is a multidimensional construct representing the cognitive, affective and behavioural manifestations of customer attachment to, interaction with, participation in, connection with, and sometimes commitment to a product or business identity in a way that creates value for both the customer and the business (Dass et al., 2021). Engagement is normally affected by awareness, motivation and social networks and can be used to predict various types of behavioural outcomes by shaping the decision-making process (Mohammad et al., 2020; Ornelas Sánchez and Vera Martínez, 2021; Rather, 2019). Engaged customers are willing to invest their available resources of time, effort and money in value co-creation behaviour to communicate their loyalty to a brand interactively among their social networks (Loureiro et al., 2017). Naumann et al. (2020) claimed that the WOM of an engaged customer is 20 times more effective than the advertising messages of an organization or brand, and that engagement is also linked to retention, loyalty and purchasing behaviour. Vivek et al. (2014) described engagement in three main dimensions, conscious attention, enthused participation and social connection. In their phenomenological study, they defined conscious attention as the extent of interest a customer has in interacting with an organization or brand through their engagement; enthusiastic participation as the affection and emotions arising from a customer’s interaction with an organization or brand; and social connection as the extent of a customer’s interaction with social networks in the organization, or brand-related activities. The degree of customer engagement affects behavioural intentions (Sung et al., 2021).

By contrast, digital media provides a platform that supports interactivity and affords co-creation behaviour on the part of customers. Studies have shown that consumer engagement leads to co-creation as a higher-order construct and customer citizenship as a co-creation behaviour component (Albuquerque and Ferreira, 2021; Mubushar et al., 2020). Furthermore, Bazi et al. (2020) found that brand co-creation, as an overall construct, was significantly influenced by the degree of customer engagement with social commerce brands. Consumer engagement has been found to significantly impact consumer citizenship behaviour through education as engaged consumers are fully informed (Gong et al., 2022). This argument leads us to the following hypothesis:

H2.

Customer engagement is positively related to customer citizenship behaviour in fashion omnichannel retailing.

3.3 The moderating role of the return policy

One of the main challenges of omnichannel retailing is dealing with product returns and setting return policies. Flexible, personalized, integrated and consistent omnichannel shopping experiences can provide customers with a convenient shopping journey, but they often find product returns and return policies troublesome (Mandal et al., 2021). Integrated omnichannel reduces the number of returns by reducing the uncertainty during the purchasing process (Barta et al., 2023). Studies have found that 51% of omnichannel customers avoided buying from retailers with stringent return policies, 63% did not make a repeat purchase after a troubled return experience and 66% checked the retailer’s return policy in making a purchasing decision (Jin et al., 2020; Mandal et al., 2021). Fashion has one of the highest product return rates, with a reported 35%–38.2% of goods purchased from online stores returned in 2020, compared to 8%–10% from physical stores (Ader et al., 2021; Jin et al., 2020). Because fashion e-customers are unable to touch, try or experience the product before making a purchase, products are often returned as ill-fitting, uncomfortable, defective or not meeting expectations; sometimes, the wrong product is delivered (Tzeng et al., 2020). The integration of a retailer’s omnichannel touch points is essential in these cases to provide customers with easy, hassle-free and efficient experiences and to achieve a competitive advantage (Ahsan and Rahman, 2021; Mandal et al., 2021). Online retailers traditionally applied a return-by-shipping option (with or without shipping fees), but the integrated features of omnichannel retailing have broadened the range of practices, with many cross-channel pickup and return options such as buy-online-return-in-store (BORS) and buy-online-pickup-in-store (BOPS). The adoption of these options has increased among retail channels and has been found to support sales (Jin et al., 2020). In practice, many major fashion brands include BORS in their return policy; for example, Forever 21, Gap, Levi’s, Zara and H&M all offer BORS in combination with other return options (Jin et al., 2020).

The study of Tzeng et al. (2020) on the moderating role of the online/offline retail service relationship in customer satisfaction in China found that hassle-free product returns significantly strengthened the relationship between service quality and customer satisfaction, with return shipping described as “too much hassle” for customers. Another study by Rokonuzzaman et al. (2020) highlighted the relationship between retailers’ return policies and purchasing intentions, with customer reviews or e-WOM tested as moderators. They found that positive customer ratings/reviews and lenient return policies positively influence purchasing intentions. Furthermore, Rintamäki et al. (2021) identified return satisfaction as a mediating construct in the relationship between return experiences and overall satisfaction, loyalty, customer ratings and reviews. Customers and retailers both consider the process of returns as an “unwanted” aspect of the consumption experience, but investing in providing a better return experience can have positive results on both sides through improved customer convenience (Rintamäki et al., 2021). Based on the literature, we predicted the following:

H3.

Return policy in fashion omnichannel retailing moderates the relationship between customer engagement and the degree of performance of customer citizenship behaviour.

4. Method

4.1 Sample and data collection

Data were collected through online questionnaires using purposive sampling and were distributed via Google Forms. Purposive sampling was chosen as an effective method for this study, considering the need for a specific target sample with particular characteristics that align with predefined inclusion criteria (Etikan, 2016).

Only individuals with prior omnichannel fashion shopping experience were eligible for study participation. Participants were presented with three scenarios that reflected different aspects of the omnichannel strategy. These scenarios included options such as “Free home delivery of in-store orders” where participants had visited a physical store and liked a product, but could not find the desired size or colour. They then ordered the product through a mobile app or website and delivered it to their home. Another scenario was “In-store return of online orders” where participants purchased a product online, and had it delivered to their home, but found that the size did not fit or the colour did not meet their expectations, leading them to exchange or return it in a physical store. The final scenario was “Click-and-Collect”, where participants expressed interest in a product in the online/digital store, reserved it online, purchased it at the physical store or simply bought a product online and collected it from the physical store. In addition, there was an option for respondents who did not relate to any of the scenarios.

A pilot study was conducted with 25 omnichannel customers to ensure the validity and reliability of the measurements. The data from the pilot study were used to evaluate the instrument, and feedback from two academics with expertise in the field confirmed that no changes were necessary.

Using the purposive sampling method, we distributed 350 questionnaires to online participants from Malaysia between July and August 2021. After eliminating responses with missing data, unengaged responses, outliers (Hair et al., 2017) and responses from individuals who lacked experience with omnichannel fashion shopping, the final analytical sample comprised 251 completed questionnaires from respondents in Malaysia, resulting in a response rate of 71.7%.

4.2 Measures

All items were rated on a five-point Likert scale, with responses ranging from 1 (strongly disagree) to 5 (strongly agree). The questionnaire items were adapted and validated from existing literature. The omnichannel customer experience was assessed using five constructs: connectivity (five items, Cronbach’s α = 0.87), integration (five items, Cronbach’s α = 0.85), consistency (six items, Cronbach’s α = 0.86), flexibility (four items, Cronbach’s α = 0.84) and personalization (four items, Cronbach’s α = 0.84). We adopted the constructs proposed by Shi et al. (2020).

Customer engagement, as a second-order construct, was measured through enthused participation (six items, Cronbach’s α = 0.89), social connection (three items, Cronbach’s α = 0.88) and conscious attention (four items, Cronbach’s α = 0.92). These constructs were adopted from Lee et al. (2019). The measurement of customer citizenship behaviour involved four items taken from Kim et al. (2020), with a Cronbach’s α of 0.90. The return policy was assessed using five items derived from Cao et al. (2018), with a Cronbach’s α of 0.87. The specific items used to measure each variable are listed in Table 1.

4.3 Preliminary analyses

4.3.1 Mitigation and testing of common method bias.

As all data were collected through self-reported questionnaires, measures were taken to minimize the potential for common method variance (CMV). Firstly, a message introducing the study was sent to all respondents to ensure anonymity and confidentiality of the data. Secondly, the questionnaire items were carefully developed in accordance with the recommendations of Podsakoff et al. (2003) to enhance the respondents’ clarity. Subsequently, a single-factor test for CMV was performed using Maxwell and Harman (1968) approach. Ten factors were extracted, with the first explaining 37.64% of the variance. As this accounted for less than 50% of the variance, it was concluded that serious CMV was not a concern (Podsakoff and Organ, 1986).

4.3.2 Partial least squares structural equation modelling (PLS-SEM).

To comprehensively understand how various aspects of customer experience influence customer engagement and citizenship behaviour, this study used the partial least squares structural equation modelling (PLS-SEM) approach. Due to the complexity and predictive capabilities of the PLS-SEM approach, it was chosen to analyze the data for the proposed research model in this study (Hair et al., 2012; Kautish and Sharma, 2018). SmartPLS 3.0 software package was used to conduct PLS-SEM analysis.

4.3.3 Respondents.

The demographic characteristics of the participants were as follows: 37.1% male and 62.9% female. The age distribution was 17–20 years (12.7%), 21–35 years (82.5%), 36–50 years (4.4%) and > 50 years (0.4%). Regarding educational background, 11.2% of respondents had a diploma/STPM level, 80.5% had a bachelor’s degree and 8.3% had a postgraduate level.

4.3.4 Tests for reliability and validity of the measurement model.

The final model for this study included the reflective first-order constructs of connectivity, integration, consistency, flexibility, personalization, return policy and customer citizenship behaviour, along with the reflective second-order construct of customer engagement (enthusiastic participation, social connection and conscious attention). Customer engagement was, therefore, assessed as a higher-order construct. Ten first-order constructs were collectively evaluated in the initial assessment of the measurement model (Akter et al., 2011). The validity and reliability of the reflective measurement model were assessed (Hair Jr. et al., 2017), and the results are presented in Table 1. The composite reliability values for all constructs were above the threshold of 0.7, indicating the good reliability of the measurement model (Hair et al., 2017). The convergent validity of the data was verified by analysing the average variance extracted (AVE), which exceeded 0.5, indicating satisfactory convergent validity. The Fornell–Larcker criterion matrix was used to assess discriminant validity.

Discriminant validity analysis ensured that the combinations in each row or column did not exceed the value on the diagonal (Byrne, 2013; Hair et al., 2021). As presented in Table 2, the loadings of each construct in every variable (values in italics) were significantly higher than those of other constructs. Hetero-trait/mono-trait (HTMT) correlation ratios were calculated as an additional measure of discriminant validity. The HTMT values, also shown in Table 2, were all below the critical value of one, confirming discriminant validity.

5. Results

5.1 Estimation of the structural model

Following the preliminary analyses, we assessed the structural model. To examine the research hypotheses, we used a PLS algorithm to estimate the path coefficients of the structural model and used the bootstrapping method to test their statistical significance (Ghosh and Bhattacharya, 2022; Hair et al., 2016). The results presented in Table 3 indicate that most of the direct hypotheses are supported, except for the effects of connectivity, consistency and personalization on customer engagement.

Consistent with our hypotheses and expectations, flexibility and integration demonstrated positive relationships with customer engagement, and customer engagement was positively associated with customer citizenship behaviour. These findings confirm the direct hypotheses, except for H1a, H1c and H1e. Additionally, we examined the moderating effects of the return policy using product indicators (Chin et al., 2003). The findings regarding moderating effects were statistically significant (see Table 3). Return policy moderated the relationship between customer engagement and customer citizenship behaviour, supporting H3. The findings provide a clearer illustration of the interaction effect, showing that the relationship between customer engagement and customer citizenship behaviour is stronger in the presence of clear and flexible return policies.

5.2 Predictive power assessment using PLSpredict

Quantitative research without predictive analytics is incomplete. Using predictive analytics allows organizations to forecast customer responses and buying patterns based on historical data. Additionally, it enables the assessment of potential changes in customer citizenship behaviour in response to events such as pandemics. Therefore, in line with the approach advocated by Shmueli et al. (2019), we used the PLS prediction method to evaluate the model’s out-of-sample predictive power and accuracy in forecasting the outcomes of new cases (Shmueli et al., 2019). This assessment focused on the key outcome of the model, customer citizenship behaviour in the present study, rather than discussing prediction errors on all endogenous construct indicators. The results presented in Table 4 show that all indicators of this construct analysed by PLS-SEM yielded smaller prediction errors than linear models, suggesting high prediction accuracy.

6. Discussion

Omnichannel retailing has emerged as the dominant strategy in various product categories, with its continued prevalence projected for the foreseeable future (Adhi et al., 2022). In line with this trend, the present study provides empirical evidence supporting the idea that consumer citizenship behaviour can be enhanced through a comprehensive omnichannel retail experience. By adopting the SOR theory, we investigated the association between the integration and flexibility dimensions of omnichannel shopping experiences and consumer engagement. Our findings are particularly relevant in the context of pandemic and post-pandemic circumstances, where consumers seek cohesive, adaptable experiences that seamlessly integrate online and offline environments. These experiential dimensions effectively evoked consumers’ emotional, cognitive and social engagement with brands and retailers.

Our results align with prior research by Bilro and Loureiro (2020), who underscored the influential role of consumer experiences in shaping engagement with brands and retailers, particularly when supported by retail technologies. Similarly, Samala and Katkam (2020) argue that millennials’ engagement with fashion omnichannel retailers is driven by active participation and involvement through social networks, underscoring the impact of technological elements on consumer engagement.

Contrary to the widespread belief that connectivity, consistency and personalization in an omnichannel environment invariably lead to increased customer engagement, our findings suggest that reality in the fashion retail sector may differ. Connectivity and consistency across channels do not necessarily translate to higher customer engagement in this industry. Factors, such as fashion trends, personal style, pricing and product availability, may exert a more substantial influence on customer engagement. While connectivity and consistency remain important, they do not singularly determine engagement in this context. In addition, emotional connections and brand image often drive engagement in the fashion retail sector. Customers may seek brands that align with their values, aspirations or self-expression. Although connectivity and consistency contribute to a seamless shopping experience, they may not evoke the desired emotional connection or resonate with an intended brand image. Consequently, customers may not perceive sufficient engagement despite the technical aspects of the omnichannel strategies being implemented.

Confirming Lee et al.’s (2019) proposition, our study establishes that customer engagement is a second-order construct comprising three first-order factors: conscious attention, enthusiastic participation and social connection. The cumulative effect of these sub-dimensions is strongly related to customer engagement. Moreover, customer engagement exhibits a positive relationship with customer citizenship behaviour, indicating that strong engagement fostered by attention to all marketing activities across channels leads customers to devote more time, passion and enjoyment to the social aspects of omnichannel shopping. Consequently, they are more inclined to engage in positive citizenship behaviours, including brand promotion, providing feedback and participating in brand-related activities. These findings align with previous research by Bilro and Loureiro (2020) and Gong et al. (2022), which emphasizes the impact of digital technologies on consumer engagement in the retail industry, shedding light on how these technologies facilitate value creation and encourage citizenship behaviours towards businesses.

We also examine the influence of return policies on the relationship between customer engagement and customer citizenship behaviour. The results highlight that consumers are more engaged when offered greater options at each touchpoint, such as BOPS services and the ability to use mobile devices to place orders and select delivery locations. This indicates that a flexible and transparent product return policy encourages engaged customers of omnichannel fashion retailers to recommend the brand and its products, playing a vital role in product marketing. Our findings are consistent with those of Ader et al. (2021), who observed that the COVID-19 pandemic prompted fashion brands to adapt their return policies to align with evolving customer expectations and needs.

7. Implications and limitations

7.1 Theoretical implications

Investigating various dimensions of consumer experience contributes significantly to the marketing literature by shedding light on the factors that drive positive consumer behaviours, such as engagement and citizenship. This study specifically focuses on understanding the drivers of consumer citizenship behaviours within the context of fashion omnichannel retailing in Malaysia by incorporating the SOR theory.

One noteworthy contribution of our study is the examination of the collective effect of overall consumer experiences as stimulators of engagement, whereas previous research has often studied different dimensions of consumer experiences separately (Rodríguez-Torrico et al., 2020). Our findings highlight that, among the five dimensions of experience, integration between different channels and flexibility in channel usage has the highest impact on driving consumer engagement. This aligns with the work of Lee et al. (2019), who investigated the integration of physical and online stores through innovative technologies and observed a positive effect of integration on consumer engagement and willingness to pay for luxury fashion products. Integrating various dimensions of consumer benefits, including store, product, price, services, promotion and order fulfilment, plays a crucial role in stimulating cognitive and affective experiences, as well as driving consumer intention to use (Gao and Fan, 2021) and loyalty (Quach et al., 2022) towards brands implementing omnichannel strategies. Furthermore, a flexible experience significantly influences consumers visiting omnichannel retailers. Similar results were found by Rodríguez-Torrico et al. (2021), who studied the stimulating effect of seamless and flowing experiences at different omnichannel touch points on consumers’ positive WOM. Both these findings emphasize the importance of prioritizing integration and flexibility in creating captivating consumer experiences in fashion omnichannel retailing.

Our findings also support the work of Tonder et al. (2018) regarding the role of retail technologies in enhancing consumers’ citizenship behaviour. We observed that omnichannel retail experiences drive consumer engagement through cognitive, affective and social constructs, ultimately leading to positive citizenship behaviours that favour brands adopting these technologies. These findings underscore the driving force of consumer engagement, which can be cultivated through positive shopping experiences and, in turn, promote favourable citizenship behaviours.

Another significant contribution of our study pertains to the importance of return policies in omnichannel retailing strategies. Given the prevalence of returns in the digital shopping environment, especially in the fashion industry, consumer-friendly return policies are crucial for ensuring positive digital shopping experiences due to factors such as size, quality and personal preferences (De Borba et al., 2021). Considering the influential effect of returns, our study examines how return policies can impact the relationship between consumer engagement and citizenship behaviour. We found that friendly return policies for fashion omnichannel retailers fully moderated the relationship between engagement and citizenship behaviour. This study adds to the marketing literature by highlighting the positive effects of satisfactory return policies on consumers’ shopping experiences. Consequently, fashion omnichannel retailers are encouraged to offer seamless, integrated and flowing shopping experiences across various consumer touchpoints, as these experiences positively impact cognitive, affective and social consumer engagement. Moreover, consumer engagement through different channels and touchpoints ultimately leads to positive citizenship behaviour, with return policies playing a crucial role in this relationship.

In summary, our study enriches the understanding of consumer behaviour in the context of fashion omnichannel retailing by providing insights into the stimulators of engagement and citizenship. The findings emphasize the significance of integration, flexibility, retail technologies and return policies in creating compelling and positive consumer experiences, fostering engagement and encouraging citizenship behaviour.

7.2 Practical implications

The practical implications derived from this study hold significant importance in the development of effective fashion omnichannel strategies. Firstly, retailers must strive for a seamless and fully integrated shopping experience across different touchpoints encompassing both physical and digital channels. The integration and flexibility exhibited throughout the consumer journey emerged as influential factors stimulating favourable citizenship behaviour among fashion consumers. Such positive experiences were found to drive consumers’ cognitive, affective and social engagement with the brand across various touchpoints (Nadeem et al., 2021). Therefore, omnichannel retailers should devise strategies that strategically target consumers’ state of mind, attract their attention through diverse marketing activities on multiple channels, cultivate a social environment akin to a community and foster interactive engagements within this community. Consequently, fashion retailers and brands are encouraged to incorporate supportive services that facilitate seamless experiences and foster engaging relationships with consumers.

Second, the significance of friendly return policies is emphasized as they mediate the relationship between consumer engagement and citizenship behaviours. Omnichannel retailers and brands should establish transparent and easily comprehensible return policies that traverse different retailing channels seamlessly. Adequately training storefronts and employees on diverse return options and regulations shapes consumer citizenship behaviours towards the brand. By offering consumers convenience, hassle-free and efficient return experiences across channels with multiple return options, retailers and brands can attain a competitive advantage and cultivate positive consumer–retailer relationships within a highly competitive market (Abdulla et al., 2022). In summary, the seamless integration of shopping, purchasing and returning experiences through the implementation of innovative omnichannel technologies will positively contribute to creating favourable consumer–brand experiences, supporting brand growth in dynamic and competitive markets.

7.3 Limitations and future research directions

This study has certain limitations that warrant further investigation. Firstly, the research design relied on cross-sectional data, which restricted our ability to establish causal relationships between the components of omnichannel customer experience, customer engagement and customer citizenship behaviour. Therefore, future research endeavours should use longitudinal approaches to unravel the dynamic interplay between these variables over time, providing a more comprehensive understanding of their causal relationships.

Furthermore, it is important to acknowledge the geographic and cultural context of the study as the respondents were exclusively drawn from Malaysia. Future research should replicate the proposed framework in diverse countries and across different cultural settings to enhance the generalizability of our findings and facilitate a more robust analysis of behavioural variations in the omnichannel consumer experience. Such endeavours will offer valuable insights into the nuances of consumer behaviours and shed light on the role of culture in shaping the relationships between omnichannel customer experience, customer engagement and customer citizenship behaviour. Additionally, cross-cultural investigations will provide a more nuanced understanding of how cultural factors shape consumer perceptions and responses to omnichannel retailing, contributing to the advancement of global marketing knowledge.

7.4 Conclusion

In conclusion, this study, grounded in the SOR theory, aimed to enrich the understanding of customer citizenship behaviour in omnichannel fashion retail. The findings highlight the significant role of integration and flexibility in fostering customer engagement, whereas connectivity, consistency and personalization did not demonstrate a significant impact. The study also revealed that return policies positively moderated the relationship between customer engagement and citizenship behaviour. These insights contribute to the knowledge base of fashion retailers by emphasizing the importance of creating seamless experiences, leveraging customer engagement and implementing flexible return policies to cultivate positive customer behaviours and drive brand loyalty. A summary of the main findings and theoretical and managerial implications is provided in Tables 5 and 6.

Figures

Figure 1.

Conceptual model

Table 1.

Results of the assessment of measurement model for first-order constructs

Construct	Item	Scale items	Loading
Connectivity (Cov) (AVE =0.711; CR = 0.925; α = 0.898)	Cov1	I can check inventory status across different channels	0.821
	Cov2	I can query products information across different channels	0.883
	Cov3	I can check products availability and inventory across different channels	0.855
	Cov4	My member accounts across different channels are connected	0.828
	Cov5	My interactions with customer service across different channels are connected	0.828
Integration (Int) (AVE =0.687; CR = 0.916; α = 0.887))	Int1	My interactions across different channels are integrated and considered for each purchase	0.791
	Int2	Product variety (different sizes, colours, etc.) is integrated across different channels	0.827
	Int3	Descriptions of products are integrated across different channels	0.821
	Int4	The launch of new products is synchronous across different channels	0.843
	Int5	Promotion activities are aligned across different channels	0.863
Consistency (Con) (AVE =0.706; CR = 0.935; α = 0.917)	Con 1	Trademarks, brand names and slogans are consistent across different channels	0.804
	Con 2	The purchase expectance feelings are consistent across different channels	0.851
	Con 3	Product images and display are consistent across different channels	0.863
	Con 4	I receive consistent responses through different channels	0.854
	Con5	The quality of products is consistent across different channels	0.86
	Con6	The customer service performance is consistent across different channels	0.809
Flexibility (Flx) (AVE =0.643; CR = 0.878; α = 0.814)	Flx1	I can try on products in physical stores and order them online	0.781
	Flx2	I can choose different channels to get my chosen product	0.834
	Flx3	I can order online and make payment and pick up offline	0.798
	Flx4	The after-sales service is available across brand’s different channels	0.81
Personalization (Per) (AVE =0.742; CR = 0.920; α = 0.884)	Per1	Shopping recommendations are offered according to my purchase’s history and personal information across brand’s different channels	0.875
	Per2	Shopping discounts and privileges are offered based on my purchase history and personal information across different channels	0.889
	Per3	Website browsing pages are customized based on my purchase history and personal information across different channels	0.846
	Per4	Client-specific rewards or member points are offered based on my purchase history across different channels	0.835
Enthused participation (EP) (AVE = 0.759; CR = 0.950; α = 0.936)	EP1	I spend a lot of my free time on my favourite brand applications	0.855
	EP2	I am heavily into my favourite brand	0.882
	EP3	I am passionate about my favourite brand	0.885
	EP4	My days would not be same without my favourite brand applications	0.867
	EP5	I try to fit accessing my favourite brand applications into my schedule	0.880
	EP6	I enjoy spending time on my favourite brand applications	0.858
Social connection (SC) (AVE = 0.840; CR = 0.940; α = 0.905)	SC1	I love accessing my favourite brand applications with my friends	0.896
	SC2	I enjoy using my favourite brand applications more when I am with others	0.932
	SC3	My favourite brand applications are more fun when other people around me also access it	0.921
Conscious attention (CA) (AVE = 0.781; CR = 0.934; α = 0.906)	CA1	I like to know more about my favourite brand applications	0.858
	CA2	I pay a lot of attention to anything about my favourite brand applications	0.901
	CA3	I keep myself updated about things related to my favourite brand	0.910
	CA4	Anything related to my favourite brand grabs my attention	0.865
Return policy (RP) (AVE = 0.767; CR = 0.943; α = 0.924)	RP1	My favourite brands have clear return policy through their different channels	0.841
	RP2	I often do not have trouble getting the returned item through the brand’s different channels	0.901
	RP3	I often do not have to pay a return shipping/restocking fee	0.871
	RP4	It does not take long to return the product	0.877
	RP5	It is easy to return the product that does not meet my expectations through the brand’s different channels	0.887
Customer citizenship behaviour (CCB) (AVE = 0.712; CR = 0.937; α = 0.918)	CCB 1	If I received good product/service from my favourite brand, I commented about it	0.805
	CCB 2	If I experienced a problem, I let my favourite brand know about it	0.829
	CCB 3	After my interaction with my favourite brand, I say positive things about it and its product/service to others	0.877
	CCB 4	After my interaction with my favourite brand, I recommend it to others	0.889
	CCB5	After my interaction with my favourite brand, I encouraged friends and relatives to interact with it	0.85
	CCB6	If I had a useful idea on how to improve product/service, I let my favourite brand know about it	0.808

Table 2.

Fornell–Larcker criterion matrix (discriminant validity for first-order constructs)

Construct	Cov	CA	Con	CCB	EP	Flx	Int	RP	SC	Per
Connectivity	0.843
Conscious attention	0.426	0.884
Consistency	0.614	0.387	0.84
Customer citizenship	0.522	0.686	0.497	0.844
Enthused participation	0.451	0.845	0.411	0.662	0.871
Flexibility	0.469	0.5	0.488	0.531	0.438	0.802
Integration	0.625	0.496	0.622	0.616	0.449	0.543	0.829
Return policy	0.473	0.448	0.4	0.546	0.448	0.384	0.55	0.876
Social connection	0.427	0.652	0.406	0.627	0.676	0.518	0.523	0.549	0.916
Personalization	0.556	0.406	0.492	0.573	0.416	0.57	0.601	0.4	0.485	0.861

Notes:

Cov = connectivity; CA = conscious attention; Con = consistency; CBB = customer citizenship behaviour; EP = enthused participation; Flx = flexibility; Int = integration; RP = return policy; SC = social connection; Per = personalization

Table 3.

Discriminant validity – HTMT ratio

Construct	Cov	CA	Con	CCB	EP	Flx	Int	RP	SC	Per
Connectivity
Conscious attention	0.47
Consistency	0.677	0.421
Customer citizenship	0.574	0.752	0.539
Enthused participation	0.487	0.915	0.439	0.712
Flexibility	0.549	0.583	0.56	0.613	0.5
Integration	0.699	0.548	0.683	0.68	0.48	0.642
Return policy	0.509	0.488	0.429	0.584	0.48	0.435	0.604
Social connection	0.472	0.72	0.442	0.685	0.734	0.604	0.578	0.6
Personalization	0.627	0.453	0.543	0.636	0.452	0.666	0.684	0.437	0.54

Notes:

Cov = connectivity; CA = conscious attention; Con = consistency; CBB = customer citizenship behaviour; EP = enthused participation; Flx = flexibility; Int = integration; RP = return policy; SC = social connection; Per = personalization

Table 4.

Hypothesis testing

Hypothesis	Path	Path coefficient (β)	SE	t-statistics	p-values	Decision
H1a	Cov → Eng	0.132	0.089	1.482	0.146	Not supported
H1b	Int → Eng	0.226	0.085	2.669***	0.009	Supported
H1c	Con → Eng	0.04	0.09	0.443	0.679	Not supported
H1d	Flx → Eng	0.28	0.103	2.712***	0.005	Supported
H1e	Per → Eng	0.094	0.087	1.082	0.298	Not supported
H2	Eng → CBB	0.599	0.049	12.148***	0.000	Supported
H3	Eng*PR → OB	−0.082	0.034	2.421**	0.022	Supported

Notes:

Cov = connectivity; CA = conscious attention; Con = consistency; CBB = customer citizenship behaviour; EP = enthused participation; Flx = flexibility; Int = integration; RP = return policy; SC = social connection; Per = personalization.

*t-values: 1.65 (10 %);

**t-values: 1.96 (5 %);

***t-values: 2.58 (1 %)

Table 5.

PLSpredict

		PLS		LM		PLS-LM
Constructs	Indicators	RMSE	Q2_predict	RMSE	Q2_predict	RMSE	Q2_predict
Customer citizenship behaviour	CCB1	0.796	0.285	0.858	0.169	−0.062	0.116
	CCB2	0.733	0.31	0.779	0.22	−0.046	0.09
	CCB3	0.703	0.362	0.737	0.3	−0.034	0.062
	CCB4	0.697	0.352	0.742	0.264	−0.045	0.088
	CCB5	0.714	0.327	0.772	0.213	−0.058	0.114
	CCB6	0.774	0.275	0.846	0.133	−0.072	0.142

Table 6.

Conclusion, theoretical and managerial implications

Conclusion	Theoretical contributions and practical implications
Integration and flexibility are the most significant drivers to customer engagement in omnichannel fashion retail.	The study brings insights about creating customer experience on fashion omnichannel retail. The results contribute to existing literature by found only integration and flexibility of customer experience are most important drivers to customer engagement. Fashion brands can draw important insights for their strategies. Firstly, prioritizing seamless integration of various channels and touchpoints will enhance the overall customer experience by providing a cohesive and consistent brand journey. Secondly, offering flexibility in terms of delivery options, payment methods and return/exchange policies will contribute to customer satisfaction and loyalty.
Flexible return policies for fashion omnichannel retailers were found to have moderating effect on the relationship between customer engagement and citizenship behaviour.	The result highlights the importance of offering lenient return policies that empower customers to engage actively and exhibit positive behaviours such as advocating for the brand, promoting positive WOM and contributing to the overall success of the fashion retailer. By implementing flexible return policies, fashion retailers can leverage customer engagement to foster customer citizenship behaviour, ultimately driving brand loyalty and growth.