Omnichannel usage influence on webrooming-showrooming: the moderating influence of consumer segment and product price

The rise in internet usage, mobile devices and social networks has significantly altered the traditional purchase decision-making process (Verhoef et al., 2015; Aiolfi et al., 2022). Marketers are now strategically blending brick-and-mortar stores with online platforms (Wu et al., 2023) to allow consumers to seamlessly navigate between offline and online touchpoints during their purchasing journey (Aw et al., 2021; Wu et al., 2023). Consumers use digital touchpoints for checking product availability, information gathering, price comparisons and time and cost savings, all while enjoying convenience and accessibility from anywhere (Zhu et al., 2018). Physical stores, in contrast, offer benefits like salesperson assistance, tactile experiences and immediate gratification (Aw et al., 2021). The integration of online and offline touchpoints often involves showrooming (offline search followed by an online purchase) and webrooming (online search followed by an offline purchase) approaches (Flavián et al., 2020; Guo et al., 2022; Wu et al., 2023).

Understanding customer segmentation in this context helps identify factors that drive segment preferences for specific shopping patterns (Neslin, 2022). Previous research has explored customer segments based on psychographic factors like price consciousness, innovativeness, time pressure and demographics (Sands et al., 2016; Schneider and Zielke, 2020). This study argues that value consciousness, perceived personalisation and privacy and security perceptions are pivotal in shaping utility within the omnichannel context, necessitating their inclusion in customer segmentation research. Beyond mere price comparisons, consumers strategically evaluate the balance between product quality, benefits and costs, and anticipate efficient and satisfying shopping experiences (Mishra et al., 2021). It is imperative to recognise that value consciousness drives consumers to seek optimal value propositions across online and offline touchpoints within the omnichannel environment. Personalisation in omnichannel retail tailors the shopping experience based on customer data, including purchase history, browsing behaviour, demographics and interactions (Rahman et al., 2022), enhancing utility by streamlining search efforts and offering exclusive interactions (Zhang et al., 2022a, 2022b). Privacy and security assurances significantly influence utility by addressing concerns and reinforcing positive outcomes with online touchpoints (Hossain et al., 2020; Xuan et al., 2023).

Beyond shopper segmentation, marketers must analyse the impact of pricing on showrooming and webrooming, as highlighted by Hsieh et al. (2024). Existing research by Jain and Shankar (2023) and Shankar and Jain (2023) has primarily focused on specific shopping behaviours related to luxury products in multichannel contexts. Previous omnichannel studies indicate that consumers’ choice of online and offline touchpoints often hinges on price comparison (Sánchez-Torres et al., 2022). However, a significant gap exists in understanding how price points influence specific shopping patterns, such as showrooming and webrooming. Recently, scholars like Sharma et al. (2023) have emphasised the need to explore omnichannel shopping behaviours across high and low price points (Sharma et al., 2023).

The present research addresses the above gaps in the literature by analysing the moderating influence of shopper segments and price points on specific shopping behaviours such as showrooming and webrooming. The research objectives of this study are to investigate the extent to which anticipated utility factors – value consciousness, personalisation and privacy and security shape different segments of shoppers in an omnichannel context; analyse the moderating influence of the identified segments on showrooming and webrooming; examine how pricing, specifically the comparison between cheap versus expensive products, moderates showrooming and webrooming. Leveraging anticipated utility theory (Quiggin, 1982), which suggests that individuals consider future outcomes when making decisions, this study proposes a conceptual framework to examine how consumers’ forward-looking perspectives influence their decision-making processes during the search and purchase stages.

Phase 1 of the study identifies omnichannel shopper segments, and Phase 2 discusses the influence of these segments and product price on showrooming and webrooming.

Omnichannel shopping encompasses a consumer’s seamless and effortless interaction across multiple touchpoints throughout the purchasing process. These touchpoints include physical stores, websites, mobile apps and social media platforms. Two primary aspects characterise omnichannel usage: frequency and range. Frequency refers to the regularity with which customers access the brand’s omnichannel services (Sun et al., 2020). Range, on the other hand, involves the simultaneous utilisation of at least two channels within a single shopping process (Verhoef et al., 2015). The integration of channels allows consumers to move fluidly between online and offline environments, creating a cohesive and comprehensive shopping experience.

Within this omnichannel framework, researchers have identified two specific shopping patterns that exemplify the interplay between physical and digital channels: showrooming and webrooming (Goraya et al., 2022; Guo et al., 2022; Sánchez-Torres et al., 2022). Showrooming describes consumer behaviour where individuals visit physical stores to engage with products firsthand, gaining valuable insights and experiences. Following this engagement, they choose to purchase online, attracted by potential cost savings, accessibility and the convenience offered by online shopping platforms (Schneider and Zielke, 2020). Showrooming not only bridges the tactile experience of in-store visits with the efficiency of online transactions but also underscores the importance of seamless integration between physical and digital touchpoints (Guo et al., 2022).

Conversely, webrooming involves consumers starting their purchasing journey by researching products online, comparing prices, reading reviews and scrutinising features (Guo et al., 2022; Sánchez-Torres et al., 2022). They then transition to making their final purchase in the physical retail store, aiming to capitalise on the immediacy of in-store purchases or take advantage of the in-store promotions (Wu et al., 2023). Webrooming seamlessly blends the advantages of online platforms, such as information accessibility and quick comparisons, with the tangible experience and assurance offered by in-person shopping (Goraya et al., 2022).

To gain a deeper understanding of these omnichannel behaviours, this study uses anticipated utility theory to explore the diverse motivations driving omnichannel consumers’ touchpoint preferences. By examining the factors influencing touchpoint choice at different stages of the purchasing process, the study aims to uncover underlying reasons why consumers engage in showrooming and webrooming behaviours.

The theory of anticipated utility, proposed by Quiggin (1982) elucidates how individuals navigate uncertain situations, by envisioning diverse outcomes and assessing the utility associated with each potential scenario. In an omnichannel context, this theoretical framework provides valuable insights into consumer behaviour across various touchpoints – both online and offline – each offering distinct possibilities. Consumers mentally simulate experiences with each touchpoint, considering the feelings and cognitive perceptions linked to these scenarios that shape their overall anticipated utility.

The anticipated utility theory posits that consumers’ exploration and utilisation of omnichannel services are rooted in their desire to enhance convenience, accessibility and overall satisfactory shopping experience. Retailers’ omnichannel services enhance consumers’ anticipated utility by ensuring seamless transitions between online and offline touchpoints (Goraya et al., 2022; Wu et al., 2023). Consumers envision a seamless integration of online and offline touchpoints (Verhoef et al., 2007) and strategically choose the touchpoints based on the expected utility from each interaction in the omnichannel shopping environment.

The anticipated utility framework also elucidates phenomena such as showrooming, where consumers foresee the benefit of hands-on evaluation in physical stores before making online purchases (Arora et al., 2022). Conversely, in webrooming scenarios, consumers, guided by anticipated utility, envision a pathway where the validation and experience gained through online research complement their choice of offline touchpoints for purchase (Schneider and Zielke, 2020).

Omnichannel literature offers limited insights into consumer segments (Sharma et al., 2023; Neslin, 2022). Past studies have solely focused on factors driving showrooming behaviour (Fiestas and Tuzovic, 2021). Other studies (e.g. Schneider and Zielke, 2020) do not consider variables potentially shaping the segments. While numerous studies have explored consumer segmentation focusing on demographic and psychographic characteristics (Aw et al., 2021; Burns et al., 2019; Flavián et al., 2020; Jain and Shankar, 2023; Maggioni et al., 2020), the studies were conducted in the context of multichannel retailing. This study considers demographics (i.e. age, income and gender) and psychographic factors (i.e. values) (Neslin, 2022) such as customers’ value consciousness, perceived personalisation and privacy and security considerations for identifying omnichannel consumer segments. Then the moderating influence of segments on showrooming and webrooming behaviours is examined.

Value consciousness, a psychological trait, involves meticulous evaluation of a product relative to cost, seeking optimal value (Itani et al., 2019). In omnichannel shopping, value-conscious customers strategically calculate to minimise resource use (time, effort and money) while maximising outcomes. They use webrooming and showrooming mechanisms to optimise their shopping experience (Goraya et al., 2022; Zhu et al., 2018). This process is supported by anticipated utility theory, involving factors such as product availability, tangibility, information access, choice confidence, efficiency, effectiveness, convenience and search-process satisfaction (Santos and Gonçalves, 2019; Zhu et al., 2018). This aligns with the concept of “smart shopping perception”, where consumers aim to maximise outcomes while minimising resource expenditure (Flavián et al., 2020). Value consciousness varies in intensity and holds implications for customer segmentation (Itani et al., 2019). As anticipated utility theory suggests that the intensity of utility perceptions is influenced by individual characteristics, value-conscious customers, with distinct utility considerations, represent specific needs, preferences and decision-making pathways. Therefore, segmenting customers based on value consciousness is crucial for understanding the utility perceptions of different consumer groups within the omnichannel context (Mishra et al., 2021).

Personalisation significantly reduces search efforts, saves time and addresses uncertainties about product quality and fit, boosting consumer confidence in purchase decisions (Holdack et al., 2022). According to anticipated utility theory, the utility of personalisation is rooted in reducing uncertainty and enhancing the overall shopping experience. Thus, aligning with theory, consumers benefit from not having to sift through irrelevant options, making the shopping process more efficient and enjoyable (Gao and Huang, 2023). Customers who find personalisation relevant trust the system’s ability to accurately understand their preferences and needs (Gao and Huang, 2023). Personalisation also fosters a sense of exclusivity and relevance, contributing to more memorable and satisfying interactions and thereby increasing perceived value. By catering to individual preferences, personalisation enhances the emotional and experiential dimensions of the shopping experience, as highlighted by anticipated utility theory. Furthermore, personalisation plays a critical role in customer segmentation, as noted by Schneider and Zielke (2020) and thereby used as a segmentation criterion in this study.

In the omnichannel context, ensuring privacy and security is crucial to enhancing the customer experience. Privacy involves protecting personal information and allowing individuals to control its use (Thaichon et al., 2023), aligning with anticipated utility theory’s emphasis on anticipating positive outcomes. Transparent data handling and obtaining consent, as advocated by Hossain et al. (2020) and Xuan et al. (2023), enhance the perceived utility of online touchpoints by instilling consumer confidence in responsible information management. Security concerns involves consumers’ perception of measures protecting their information and financial transactions (Rahman et al., 2022; Zhang et al., 2019; Petrescu et al., 2020) such as protecting from unauthorised access, addressing the risk of data leakage, etc. According to the anticipated utility theory, these heightened risk perceptions reduce customers’ intention to use online touchpoints, as the potential negative outcomes of privacy and security concerns outweigh the anticipated positive utility (Alrawad et al., 2023). However, privacy and security, previously mostly overlooked in customer segmentation research. Considering consumer concerns about their privacy (Ameen et al., 2021) and transaction security poses greater financial and psychological risks, this study listed privacy and security as segmentation criteria.

Consumer demographic factors, such as age, income and gender, significantly influence preferences for online and offline touchpoints etc. are key for segmentation study. Younger, tech-savvy consumers favour digital touchpoints (Guo et al., 2022), while older demographics prefer traditional offline options. Income levels also shape shopping values and touchpoint choices (Shankar and Jain, 2023). Besides, gender can also affect shopping behaviours and channel preferences (Ameen et al., 2021).

Data was collected from 512 panellists of a market research company based in New South Wales, Australia. An online questionnaire was developed using a five-point Likert scale (strongly disagree = 1 to strongly agree = 5), incorporating existing items (Table 1). In line with the conceptualisation of omnichannel usage, individuals who used both online and offline touchpoints in a single transaction were recruited. Two additional screening criteria were applied: participants had to be at least 18 years old and have made a minimum of three purchases in the past three years (van Dolen et al., 2004).

Data collection was supported by internal faculty funding from one of the co-author’s universities. Respondent anonymity was ensured. The questionnaire included a broad spectrum of consumer goods (e.g. clothing, footwear, accessories, cosmetics, furniture, electronics and groceries). These products include both necessity and luxury items potentially providing insights into the consumer decision-making processes. Respondents were asked to select either a cheap or an expensive product from the given categories and use the chosen product as the reference point for answering.

The gender distribution among participants was nearly equal, with 49.7% male and 49.9% female, while a small percentage (0.40%) chose not to disclose their gender. Approximately 50.8% of participants were between 18 and 45 years old, 29.8% were aged 46–60 and about 19.4% were over 60 years old.

The composite reliability of the constructs surpassed the threshold of 0.70 (Nunnally, 1978) and the average variance extracted was higher than the correlation values with other constructs (Table 2), ensuring construct reliability and discriminant validity.

The study performed a cluster analysis to identify groups of respondents with similar profiles (Fatima and Mascio, 2020). Statistical Package for Social Sciences version 29 was used to run cluster analysis with various variables, including age, gender, length of omnichannel use, income, value consciousness, personalisation and privacy-security. Two distinct clusters appeared. The first cluster (Cluster 1) is dominated by males (95.1%), mostly aged between 31 and 35 years and with annual gross income above $120,000. Most of this cluster members have more than four years of omnichannel shopping experience. On the contrary, second cluster members are predominantly female (99.6%), aged between 26 and 30 years and income below $50,000. Like Cluster 1, they also have more than four years of omnichannel shopping experience.

Value consciousness emerges as the most critical factor in both clusters, with Cluster 2 exhibiting a higher mean value (3.94 mean value out of 5) than Cluster 1 (3.86 mean value out of 5). Privacy and security perception is the second important driver in both clusters, with Cluster 2 showing a slightly higher mean value (3.52 out of 5) than Cluster 1 (3.47 out of 5). This indicates Cluster 1 is more concerned with their data and transactions than Cluster 2. Both clusters value personalisation, but Cluster 2 values it slightly more (3.45 out of 5) than Cluster 1 (3.42 out of 5). The mean comparison tests support that Cluster 2, predominantly female-dominant, has higher values of value consciousness, personalisation and privacy-security than Cluster 1, primarily male-dominant. This finding aligns with existing research indicating that more value-conscious consumers may also appreciate personalised offerings to optimise their spending (Alimamy and Gnoth, 2022) and are likely to seek privacy and security in the shopping environment (Xuan et al., 2023). In addition, the income disparity between the two clusters may contribute to the variations in their value consciousness, with the female-dominant group potentially being more budget-conscious due to their lower income levels.

While previous studies have explored gender differences in online shopping behaviour, a notable gap exists in understanding gender-based shopper segments in omnichannel research. Ameen et al. (2021) highlighted higher privacy risk concerns among female shoppers, whereas Alrawad et al. (2023) found no gender-specific differences in online shopping risk. Sharma et al. (2023) noted a skewed gender ratio favouring women in studies on shopping behaviour. However, this study argues that factors beyond gender are crucial in analysing omnichannel shopping behaviour. Identifying shopper profiles based on socio-economic and psychographic factors offers a more comprehensive approach to understanding preference for online/offline touchpoints.

Considering the existing gap in omnichannel literature concerning the influence of product price and consumer segments (Neslin, 2022; Sharma et al., 2023), Phase 2 of this study will specifically examine how these factors moderate specific omnichannel behaviours, such as webrooming and showrooming. Unlike broader studies that focus on general consumer transitions between channels, this research will investigate how different consumer segments and product pricing distinctly affect these shopping patterns.

Considering the diverse shopping behaviour as identified in Phase 1 consumer segments, Phase 2 considers these consumer segments as moderators. For instance, Cluster 2, a female-dominant group with lower income, is value conscious, values personalised offers, prioritises savings and seeks the best deals and may actively browse digital touchpoints to maximise utility (Verhoef et al., 2015). Furthermore, they are more confident in retailers’ privacy and security measures than Cluster 1. In contrast, Cluster 1, a male-dominant group with higher income, may use digital touchpoints for research but prefer purchasing in physical stores due to lower security perception and privacy concerns, to avoid sharing personal information online (Xuan et al., 2023). As they do not bother with the associated switching costs from online to offline, such as time, travel expenses and effort (Aw et al., 2021; Wu et al., 2023), their lower value consciousness may also lead to a preference for offline purchases. Kleinlercher et al. (2020) mentioned that males often switch to offline touchpoints after online research. With these considerations, the study proposes the following hypothesis:

Cluster 2 shoppers, characterised by their high value consciousness, tend to visit physical stores to assess products but defer purchases to capitalise on online cost savings like free delivery and online discounts, as supported by (Fiestas and Tuzovic, 2021). In contrast, Cluster 1 shoppers may rely on in-store sales staff for assistance, prioritise immediate in-store purchases and may not prioritise personalised online experiences (Aw et al., 2021; Fiestas and Tuzovic, 2021; Kleinlercher et al., 2020). Their moderate privacy and security perception regarding digital transactions lead them to prefer physical stores. Given these distinctions, the study puts forth the following hypothesis:

Previous studies have highlighted consumers’ preferences for luxury product shopping patterns, often starting with online searches for initial exploration due to digital touchpoints’ convenience but finalising purchases offline to mitigate perceived risks (Shankar and Jain, 2023). However, studies conducted in developing countries like India suggest consumers prefer offline searches and in-store visits for luxury products (Jain and Shankar, 2023), possibly due to lower trust in eCommerce platforms (Kaur and Khanam Quareshi, 2015). In Western countries like Australia, where online retailing is established and trusted, consumers may have fewer reservations about purchasing luxury items online due to secure payment systems and consumer protection laws (Australian Competition and Consumer Commission, 2025). Yet, previous omnichannel research indicates that consumers’ choice of online/offline touchpoints depends on price comparison and the need to physically inspect the product (Sánchez-Torres et al., 2022). Thus, there is a gap in omnichannel literature regarding consumer shopping routes for expensive versus cheap products. For expensive products, consumers may favour offline searches and online purchases to benefit from in-store experiences while availing of online discounts (Arora et al., 2022). In contrast, for cheaper products, consumers may lean towards online searches followed by in-store purchases (webrooming) to gather information and compare features before visiting in person (Kleinlercher et al., 2020). Moreover, discounts like free shipping are typically unavailable for cheaper products. The study thus hypothesises:

Figure 1 below shows the conceptual model for this study.

SmartPLS 4 (Hair et al., 2020) was used to perform partial least squares structural equation modelling (PLS-SEM) analysis for analysing the research model. The SmartPLS is a widely accepted tool in consumer behaviour research (Bangun et al., 2023; Jadhav et al., 2023) and does not require normally distributed data (Hair et al., 2020). No latent construct correlated with any other construct more than 0.9 (Bagozzi et al., 1991) (see Table 3). Harman single-factor test (Hair et al., 2020) result is satisfactory with a 40.63% cumulative percentage, confirming common method bias is within the permissible limit. A consistent PLS algorithm was conducted to examine the reliability and validity issues and added in Table 3 to compare to standard PLS algorithm output.

The items met the suggested outer loading criteria of 0.70 or above (Henseler et al., 2016) (Table 1). Item reliability was assessed with a bootstrapping analysis of 5,000 subsamples. The standardised loadings were found to be highly significant (p-value < 0.001) for all items demonstrating item reliability. The composite reliability is from 0.842 to 0.882 (Table 3), satisfactorily above the threshold of 0.70 (Nunnally, 1978). While most correlation values are lower than relevant average variance extracted values (Fornell and Larcker, 1981), the showrooming value (0.690) is lower than its correlation with webrooming (0.747). However, the correlation value is below 0.8, indicating that the construct is sufficiently distinct (Hair et al., 2022). The heterotrait-monotrait ratio of correlations (HTMT) results in Table 4 can be considered to ensure the discriminant validity. In certain cases, Fornell and Larcker criterion is not effective and thereby, HTMT has been widely accepted as a more reliable tool for assessing discriminant validity (for its higher specificity and sensitivity rate (of 97%–99%) (Hair et al., 2022; Henseler et al., 2015; Ab Hamid et al., 2017). f² values (omnichannel usage to showrooming, 0.152 and to webrooming, 0.131) are also accepted as standard (Hair et al., 2019; Cohen, 1988).

To assess the out of sample predictive validity of the model, PLSpredict was conducted with 10 folds and 10 repetitions (Shmueli et al., 2019). All the Q² values are higher than zero (Q² > 0), which established the predictive significance of the model (Falk and Miller, 1992; Wong and Wong, 2025). In most of the cases, the Root Mean Squared Error (RMSE) values (Table 5) are lower than Linear Regression Model (LM) indicating medium predictive power of the model (Awasthi and Kumar, 2022; Zhang et al., 2022a, 2022b; Shmueli et al., 2019).

Structural model as shown in Figure 2 summarises the results.

The results of the PLS-SEM analysis (Table 6) show that omnichannel usage has a significant positive impact on webrooming (t-value = 8.04, p-value < 0.01, path coefficient 0.340) and showrooming (t-value = 7.75, p-value < 0.01, path coefficient 0.363). Again, a consistent PLS bootstrapping algorithm was conducted and comparing the results with the standard PLS algorithm, it showed consistent results for webrooming (t-value = 8.06, p-value < 0.01) and showrooming (t-value = 7.60, p-value < 0.01).

Multigroup analysis (Henseler et al., 2016) with Smart PLS 4 examined the moderation impact of shopper segments as identified in Phase 1 and pricing (expensive versus cheap). Multigroup analysis using PLS is a widely accepted popular tool for moderation among recent scholars (Asif et al., 2022; Sharma, 2023). Bayesian information criterion (BIC) values (Hair et al., 2022) for multiple models (model with both independent variables: showrooming = −60.94 and webrooming = −51.29, model with one independent variable: showrooming = −60.74 and webrooming = −52.79), further justifies using moderators in the theoretical model. Multigroup analysis (Table 7) reveals that the shopper segment has a significant moderation impact on webrooming (t-value difference = 2.271, p-value difference = 0.024) but not on showrooming (t-value difference = 1.433, p-value difference = 0.153). Thus, hypothesis H1a is accepted, and hypothesis H1b is rejected. For usage to webrooming link, Cluster 1 members have a stronger impact (t-value = 8.035) than Cluster 2 members (t-value = 4.266).

Product price (expensive vs cheap products) is a significant moderator on omnichannel usage to showrooming relationship (t-value difference = 2.169, p-value difference = 0.031) (Table 7). Confidence intervals for moderation relationships are also summarised in Table 7. However, moderation impact is insignificant on the usage to webrooming links (t-values difference = 0.830, p-value difference = 0.407). Thus, hypothesis H2b is accepted, and hypothesis H2a is rejected. For the usage to showrooming relationship, the expensive products have a higher moderation effect (t-value = 8.838) than the cheaper products (t-value = 5.066).

The study explores omnichannel consumer segments based on value consciousness, personalisation, privacy and security. The study also investigates whether omnichannel usage influence varies with expensive and cheaper products and across consumer segments, to webrooming and showrooming.

In Phase 1, cluster analysis revealed two distinct shopper segments: one predominantly male, with higher income levels but lower levels of value consciousness, personalisation and perceived privacy-security; and the other one is mostly female, with lower income levels but higher levels of value consciousness, personalisation and perceived privacy-security. This finding advances omnichannel shopper segments beyond gender, emphasising the importance of considering socio-economic and psychological factors (Neslin, 2022). While the male-dominant segment prefers online touchpoints during the search (Kleinlercher et al., 2020), the female-dominant segment consistently favours online channels due to their higher value consciousness and perceived personalisation, albeit lower privacy and security concerns. This contradicts previous findings (Ameen et al., 2021), but may be explained by varying shopping experiences. Notably, no variations were found in showrooming behaviour between clusters (Burns et al., 2019), likely due to inherent product category influences. Items such as clothing, furniture and home appliances are examples where consumers, regardless of gender, may prefer to see, touch, or try out the product in person before buying.

Phase 2 of the study suggests no difference in webrooming between expensive and cheaper products; indicating consumers’ strong desire for informed decisions regardless of price point (Flavián et al., 2020). Cheaper products tend to elicit single-channel usage, whereas expensive products may lead to varied shopping patterns. This aligns with previous research indicating that consumers use a single channel (online or offline) for low-risk informational or low-risk experiential products (Guo et al., 2022). In contrast, for expensive products, consumers find the physical store more appealing due to the availability of services, assistance and product testing. They subsequently use online touchpoints for finalising their purchases (Jain and Shankar, 2023). However, the finding disagrees with the previous research by Guo et al. (2022) which found that high-end clothing and cosmetics may lead to either type of shopping behaviour (showrooming and webrooming). The study’s broader age representation and consideration of diverse product categories contribute to nuanced findings, underscoring the complexity of omnichannel shopper behaviour.

This study makes a significant contribution to omnichannel literature by investigating consumer segments and their influence on specific shopping patterns, such as showrooming and webrooming. Using anticipated utility theory, it highlights the omnichannel shopping utility that encompasses socio-economic and psychographic factors, including value consciousness, personalisation and perceived privacy-security. These factors shape distinct consumer segments and move beyond traditional gender-based categorisations.

Unlike previous research (e.g. Maggioni et al., 2020), this study delves deeper into value consciousness as a comprehensive psychological trait incorporating both monetary and non-monetary aspects. It also expands the concept of personalisation beyond social contact (e.g. Schneider and Zielke, 2020) to include tailored recommendations and communication based on past transactions, relevant for both online and offline touchpoints. In addition, the study introduces privacy and security perceptions as a significant factor for consumer segments, a novel variable not previously explored (Schneider and Zielke, 2020).

While earlier studies (Schneider and Zielke, 2020; Fiestas and Tuzovic, 2021) focused on showrooming behaviour, this study considers both showrooming and webrooming, systematically exploring the factors shaping both types of shopping patterns and bridging a gap in the omnichannel literature. A valuable contribution is the exploration of segments’ moderating effect on webrooming and showrooming. The findings reveal that while both segments are likely to showroom, they differ in their webrooming behaviour, providing a nuanced understanding of consumer patterns.

Furthermore, the study responds to the need highlighted by Sharma et al. (2023) by examining how consumer shopping behaviours vary by high versus low price points, addressing a notable gap in existing omnichannel research (e.g. Sánchez-Torres et al., 2022).

Finally, the study’s inclusion of broader age representation offers insights into how different age groups influence shopping behaviours. While most of the early literature leans towards younger consumer behaviour (Aw et al., 2021; Flavián et al., 2020; Goraya et al., 2022; Shankar and Jain, 2023; Jain and Shankar, 2023; Wu et al., 2023), this study attempts to break the myth by focusing on the broader age range of consumers with webrooming and showrooming behaviour.

Omnichannel marketers should tailor customised strategies to accommodate the distinct demands of identified consumer segments in this study while integrating various touchpoints to ensure a cohesive customer experience. For the male-dominant consumer segment, which prefers online touchpoints during the search phase, companies should prioritise optimising their online platforms. Enhancing the user experience, providing relevant information and streamlining the online research process can effectively engage this segment. Facilitating smooth transitions to offline touchpoints is vital, which can be achieved through features like real-time product availability and sales staff assistance in nearby physical stores (Kim and Han, 2023). Using data analytics to personalise online experiences can ensure a seamless transition to offline stores, where online-found information and deals are honoured, building purchase confidence. Marketers need to implement an omnichannel strategy that allows this consumer segment to initiate their search on a digital channel (e.g. social media), continue it on another digital channel (e.g. website) and finalise the purchase in a physical store (Kim and Han, 2023). They should ensure that websites and mobile apps are user-friendly, easy to navigate and optimised for various devices to provide relevant and engaging content that guides customers to navigate digital touchpoints (Kim and Han, 2023; Lee and Hsieh, 2019).

To address the preferences of the female-dominant group, retailers should invest in advanced personalisation and data analytics to provide customised recommendations, curated collections and personalised discounts based on previous purchases and browsing behaviour. This creates an exclusive and personalised online shopping experience. Retailers should prioritise transparency and security by clearly communicating robust security measures, encryption protocols and secure payment gateways to instil trust (Xuan et al., 2023). Transparency regarding data handling is essential, and showcasing genuine customer reviews and testimonials online offers social proof (Thaichon et al., 2023). Encouraging satisfied customers to share their experiences, mainly focusing on value for money and personalised service, can influence this segment’s buying decisions. Additionally, marketers should create informative content, such as blog posts or videos, to educate the female-dominant group about product features and benefits. Providing comprehensive buying guides and FAQs may also assist them in making well-informed purchase decisions.

For expensive products, marketers should emphasise the advantages of the in-store experience while highlighting the convenience and variety available through online shopping. Crafting marketing messages that align with the preferences of high-end consumers is crucial, emphasising the value of offline and online touchpoints. Cheaper products, on the other hand, should focus on a single channel, either online or offline, based on consumers’ behavioural data for a specific company. This approach allows efficient resource allocation and provides a more straightforward shopping experience for consumers seeking lower-cost products.

While this research focused on dissecting the purchase decision-making process with two unidirectional phases: showrooming and webrooming, real-world consumer behaviour may have a complex interplay between virtual and physical touchpoints. Therefore, future studies would benefit from exploring a myriad of online-offline touchpoint combinations, rather than merely examining unidirectional sequences. For instance, consumers might initiate their journey by seeking product information online, proceeding to physically test the product in a store and ultimately making the purchase online – a sequence encompassing research, testing and buying (Fernández et al., 2018).

Besides, upcoming researchers could delve into the impact of emerging technologies such as augmented reality (AR), virtual reality (VR), artificial intelligence (AI) and anthropomorphism, to get an overall idea of touchpoint preferences across different segments (Álvarez Márquez and Ziegler, 2023). Future studies may examine how touchpoint preferences and usage evolve across different consumer segments as often segments maintain consistent touchpoint preferences or adapt based on evolving shopping behaviours, such as omnichannel habitual behaviour (Sun et al., 2020).

Future researchers could also examine the moderating influence of the psychological factors of value consciousness, personalisation and privacy-security on omnichannel shopping patterns.

One of the methodological limitations is that the current study used survey data, however, future research could leverage longitudinal data to get more in-depth insights. Finally, the study focused exclusively on Australian consumers, which might limit the generalisability of the findings. Thus, further research should investigate different country contexts to provide a more comprehensive perspective.