Driving channel integration perception in omnichannel environments: the role of touchpoints

Abstract

Purpose

This study aims to advance knowledge of channel integration, a key feature of omnichannel retailing, by investigating the role of specific touchpoints in delivering a consistent integration perception.

Design/methodology/approach

Quantitative methods were adopted, by testing a model built on the stimulus-organism-response framework. Data collection used a panel survey across the grocery and fashion sectors (1,031 and 739 consumers, respectively). An ordinary least squares regression with clustered standard errors was conducted, combined with a multiple correspondence analysis, followed by a mediation analysis.

Findings

This study identifies touchpoints relevant for channel integration perception and shows that they differ across product category and customer types (first time vs repeat customers). Furthermore, it pinpoints touchpoints that are directly and indirectly related to patronage intention, thereby exposing the mediating role of channel integration. By drawing on categorization theory, it discusses individual touchpoints’ contribution to channel integration perception, at general level and on different customer targets.

Practical implications

This study offers a new vision of channel integration perception that highlights touchpoints’ role. It contributes to the established channel integration quality framework by showing that integrated information is concerned not only with consistency of information across channels but also with the specific touchpoints through which such information is disseminated.

Originality/value

This study provides directly actionable managerial implications, by through strategic insights for customer journey and customer experience design/redesign and by offering a practical methodology for retailers to identify the touchpoints they can leverage to improve their customers’ channel integration perceptions – with consequences for patronage intention.

Citation

Salvietti, G., Ieva, M. and Ziliani, C. (2025), "Driving channel integration perception in omnichannel environments: the role of touchpoints", Journal of Product & Brand Management, Vol. 34 No. 1, pp. 6-20. https://doi.org/10.1108/JPBM-12-2023-4873

1. Introduction

The new omnichannel scenario in retailing has emphasized the role of channel integration as a core feature that can affect customer loyalty (Gao and Huang, 2021) by driving consumer empowerment (Zhang et al., 2018), customer engagement (Lee et al., 2019), customer retention and increased basket size (Cocco and Demoulin, 2022). To achieve such outcomes, retailers must manage their practices (i.e. channel integration execution) so as to ensure its perception by customers. Channel integration perception is, in fact, defined, as the “extent to which customer perceives all information systems and management operations are unified and integrated well across channels” (Shakir Goraya et al., 2022, p. 2). Nevertheless, there is still limited knowledge regarding how channel integration can be effectively achieved to allow customers to experience integrated, seamless, omnichannel journeys. Studies on customers’ channel integration perception have mostly focused on updating or extending the channel integration quality framework (Sousa and Voss, 2006), which is composed of two core dimensions – channel-service configuration and integrated interaction – later followed by a third, assurance quality (Hossain et al., 2020). Only few studies hint to other aspects of omnichannel environments that can influence channel integration perception, such as channels characteristics (Bèzes, 2021) or the number of touchpoints offered by the retailer (Acquila-Natale and Chaparro-Pelàez, 2020). The aforementioned attempts, however, have not clearly identified touchpoints’ contribution to integration perception. Our study proposes therefore to fill this gap, that we deem as fundamental to achieve a clear understanding of integrated omnichannel customer journeys. Nowadays, customers are presented with a variety of touchpoints at each stage of their journey (Tueanrat et al., 2021), each of whom affects their experience (Lemon and Verhoef, 2016). Hence, the present study aims to unveil not only the role of touchpoints in driving customers’ channel integration perception but also their subsequent contribution in building loyalty intentions towards the retailer. The study addresses the role of a wide range of touchpoints, physical and digital, encountered throughout the customer journey, in contributing to channel integration perception. Specifically, we focus on identifying touchpoints’ contribution as far as information processing is concerned, which is one of the main aspects of channel integration perception (Sousa and Voss, 2006). In particular, the dimension of “integrated interaction” in the channel integration quality framework is based on information and process attributes consistency across channels. Touchpoints are defined as interactions between companies and customers, comprising a wide range of technologies and services (Wind and Hays, 2016); as such, they are able to deliver both information and services to customers. We pose, therefore, that the touchpoints that are disseminating such information plus services can influence the way the latter are processed by customers; hence, integration perception.

Building on categorization theory – which illustrates how customers process new information gathered during new shopping experiences – we expect that only certain touchpoints will be significantly related to channel integration perception. In line with the calls by Bèzes (2021) and Rahman et al. (2022), we aim to provide, through this approach, an explanation of the cognitive processes and stimuli driving customers’ choices in integrated omnichannel retailing environments. We also investigate the role of different types of customers (first-time vs repeat customers) and product categories (convenience vs shopping goods) on the basis that cognitive effort in information processing will differ across segments and contexts. Finally, we maintain that channel integration perception delivered through touchpoints is correlated with long-term customer loyalty intentions. Patronage intention has been chosen as the variable measuring loyalty-derived desirable outcomes, such as repeated future purchases from the retailer and positive word-of-mouth (Kim et al., 2008).

Research questions are formulated as follows:

RQ1.

Which are the touchpoints that actively contribute to generating a positive perception of channel integration?

RQ2.

Do touchpoints relevant for channel integration perception differ between first-time and repeat customers?

RQ3.

Does channel integration have a mediating role between touchpoints and patronage intention?

By addressing the above research questions, the present study contributes to advancing understanding of customers’ channel integration perception by investigating it at touchpoint level, which is consistent with a customer journey perspective (Barwitz and Maas, 2018).

Our empirical results provide evidence that touchpoints do play a role in the perception of channel integration and should therefore considered as its antecedents. Moreover, results show that different touchpoints generate channel integration perception in different sectors and, within the same sector, between different types of customers. The differences in channel integration perception may therefore emerge from exposure to the individual touchpoint offered by the retailer and can be traced back to how the information provided by touchpoints is processed in consumers’ minds. Finally, results identify direct and indirect relations between touchpoint exposure and patronage intention, while revealing the mediating role of channel integration.

2. Literature review and theoretical foundation

2.1 Channel integration in omnichannel retailing

The literature presents channel integration as the “degree to which different channels interact with each other” (Herhausen et al., 2015, p. 310). Integrated channels allow retailers to follow customers across channels, record relevant information about them (Saghiri et al., 2017) and create specific promotions and benefits for them (Cao and Li, 2015; Verhoef et al., 2015). In turn, retailers are called to communicate consistently with customers across all integrated channels and to manage operations as to create and maintain a unified brand experience (Shi et al., 2020). Channel integration is, therefore, a matter of both execution – for managers – and perception – for customers (Salvietti et al., 2022). Traditionally, studies (e.g. Shen et al., 2018; Lee et al., 2019) have focused on channel integration by developing the construct of channel integration quality, which is defined as “the ability to provide customers with a seamless and unified experience across different channels” (Sousa and Voss, 2006). Channel integration quality provides a conceptual model based on two main dimensions – namely, channel-service configuration quality and integrated interaction quality – generating channel integration perception. The two dimensions focus, respectively, on customers’ awareness and access to the integrated services offered, and on the consistency of information and process attributes communicated to customers, across channels. Over the years, the channel integration quality framework has been updated, with multiple sub-dimensions identified, and with the recent introduction of the third dimension of assurance quality, concerned with the security of customers’ private data within the (Omni)channel network (Hossain et al., 2020 – see Appendix Table A1 for further details). Nevertheless, some authors have started to suggest that other aspects of omnichannel environments should be considered for inclusion. For instance, Bèzes (2021) suggests that we should consider the perceived characteristics of each channel, attribute by attribute, to evaluate the channels’ contribution to the overall judgement of congruence. Other attempts have been made by addressing retail types (high-end specialty stores, department stores and hypermarkets – Lim et al., 2022). As far as touchpoints are concerned, research is still scarce about their role in creating a consistent and seamless customer perception of channel integration in omnichannel. Gasparin et al. (2022) point out that the adoption of customer-centric perspectives is “essential to unveil the neglected role of perceived connectivity of touchpoints in Omnichannel Journeys” (p. 2).

2.2 Touchpoints

Touchpoints are interactions of any kind between the company or the brand and customers, including information exchange or purchase transactions, each of which constitutes a variable influencing the customer experience (Herhausen et al., 2019) at different stages of the journey (Lemon and Verhoef, 2016). The concept of touchpoints is closely related to that of channels, which are “mediums through which the firm and the customer interact” (Neslin et al., 2006, p. 96); they are also “carriers of touchpoints, that in turn allow instances of contact with consumers” (Santos and Gonçalves, 2021, p. 313). Touchpoints not only contribute to the customer journey within and across the firm’s channels but also may favour encounters with the brand outside its controlled environments (Baxendale et al., 2015). Touchpoints may be classified either according to their functions (Straker et al., 2015, on digital touchpoints) or to the subject managing them (Lemon and Verhoef, 2016, differentiate between brand-owned, retail-owned or third-party touchpoints). In studying the role of touchpoints, touchpoint exposure has been regarded a key dimension to consider. Exposure can be assessed by simply considering reach – e.g. whether the consumer encountered or did not encounter a given touchpoint – or by focusing on frequency of exposure, namely, to what extent a given touchpoint was encountered by the consumer in a given period of time (Baxendale et al., 2015). Touchpoint exposure has been found to influence consumer attitudes and behaviours (Barann et al., 2022). Romaniuk et al. (2013) identified a significant relationship between touchpoint exposure and purchase behaviour in a specific product category. Baxendale et al. (2015) assessed that frequency of exposure to different types of touchpoints influences brand consideration. Ieva and Ziliani (2018) identified a relationship between different levels of frequency of exposure to touchpoints and loyalty intentions. The above contributions point to the need for measuring touchpoint exposure during the customer journey.

The variety of touchpoints to which consumers are exposed substantially reduces the degree of control that companies are able to exert on the overall customer journey, both in the single channel and across channels. Today, customers expect consistent and integrated journeys, regardless of the channel used (Piotrowicz and Cuthbertson, 2014), and at the same time they show preferences for different touchpoints and/or touchpoint combinations (Herhausen et al., 2019). Consequently, touchpoints are a primary component of integrated shopping journeys.

According to Verhoef et al. (2015), in fact, optimized levels of integration in omnichannel create synergies between all channels and all touchpoints; moreover, Acquila-Natale and Chaparro-Pelàez (2020) consider the number of customer touchpoints adopted as one of the six dimensions that measure the degree of integration in omnichannel systems. Touchpoints may therefore contribute to channel integration perception, by allowing information and services to flow between customers and retailers.

Herhausen et al. (2019) show that information exchange through touchpoints is one of the variables influencing the customer experience. Moreover, they identify a specific customer segment that uses different types of touchpoints – with subsequent costs in terms of time and effort – to gain access to more integrated information about products and brands. In addition, retailers are still struggling to understand which integration services, delivered through human and automated touchpoints, increase channel quality for customers, to achieve the desired behavioural outcomes (Swoboda and Winters, 2021). Therefore, the need emerges to investigate the role of touchpoints in contributing to shape customers’ channel integration perception.

3. Conceptual development

3.1 Categorization theory

According to categorization theory, consumers use a cognitive scheme to memorize basic information, to evaluate new shopping experiences (which may relate to the brand, product, channel, touchpoint, etc.) more effectively (Mervis and Rosch, 1981). Hence, consumers strive to exert less cognitive effort, as processing new information can be quite stressful due to its relative novelty. Categorization therefore influences the processes of storage, management and retrieval of information, especially when dealing with potentially overwhelming stimuli. Customers generate primary categories – so-called “perceptual information” – around exemplar or prototype instances, and they rely on them when organizing the additional information they receive (Cantor et al., 1982). If the existing primary categories are in line with new information, the latter is inserted within a pre-defined scheme through a top-down assimilation process. If primary categories are no longer suitable for interpreting new information, the process is one of accommodation. In this case, customers proceed in a bottom-up fashion, leading to a redefinition of the cognitive scheme (Sujan and Bettman, 1989). Regardless of the circumstances, assimilation and accommodation processes are activated based on concrete and abstract cues evoked by new information. Through this process, the encounter of additional stimuli contributes to reinforce already existing categories, other than allowing the creation of new ones. A relevant variable in this process is the frequency of encounter of a certain stimulus, which can influence the categorization process. Specifically, studies have in fact shown that frequency of exposure to a stimulus affects categorization performance (Florian, 1992): individuals are able to produce new categories on the basis of the frequently encountered stimuli, or to associate new items to it, faster and with increased accuracy (Nosofsky, 1988).

In retailing literature, categorization theory was first applied in studies of multichannel environments (Balasubramanian et al., 2005). When faced with the choice among channels – offline, online, mobile – that offer diverse experiences, consumers may develop a form of habit towards one specific channel. This tendency to remain anchored to that channel is caused by the channel evoking – by association – a unique pattern in the consumer’s mind during the customer journey (Balasubramanian et al., 2005). When visiting the retailer multiple times, new channels may be unconsciously perceived by the customer as new elements of the journey, which are difficult to be traced back to the pre-existing primary categories and are therefore hard to appropriately evaluate. The customer will therefore tend to remain tied to the channel he or she is currently using, to reduce the cognitive load arising from evaluating new channels and their related potential opportunities and risks (Broniarczyk and Griffin, 2014).

As far as omnichannel retailing is concerned, authors stress that it is inherently an extremely complex environment for consumers to process (Cortinas et al., 2019; Cocco and Demoulin, 2022). Omnichannel allows multiple choices at each stage of the pre-, mid-, and post-purchase, which increase the variety of disturbances affecting customers’ evaluation and decision processes (Li et al., 2018). As a consequence, Rahman et al. (2022) suggest that, nowadays, customers are channel-agnostic and are developing new and specific cognitive processes for omnichannel environments, which could allow them to simplify their choices and reduce upcoming stress from information processing. Accordingly, we therefore propose, in the following section, that categorization processes are likely to be based on the exposure to touchpoints rather than channels. In fact, Juaneda-Ayensa et al. (2016) emphasize that consumers “no longer access the channel, but rather are always in it or in several [channels] at once” (p. 3), through a customer journey marked by touchpoints. By looking at the increasing importance of touchpoints in shaping the customer experience, and their nature of information providers, it is reasonable to hypothesize they would be customers’ new anchors enabling categorization processes in omnichannel environments.

3.2 Research framework and hypotheses development

To test the relationship between touchpoints and channel integration, the present study proposes a research framework based on the stimulus-organism-response (S-O-R) model. The S-O-R model describes relationships among a stimulus (S), the organism (O) – which represent consumers’ states elicited by the stimulus –, and the response (R), namely the consequent behaviour activated by consumers (Mehrabian and Russell, 1974; Belk, 1975). This framework has been extensively used in channel integration studies (Ma et al., 2023; Pereira et al., 2023). Since the Stimulus (S) can be any marketing-related factor, it is reasonable to consider touchpoints as such. In the present study, as a situational factor, we recognize that customers can interact with and be exposed to a wide range of touchpoints during their journeys. Hence, the availability of multiple touchpoints – especially when managed directly by the retailer – is considered as part of the environmental context in which customers shop. In light of categorization theory (see Section 3.1), touchpoints can be considered as carriers of various types of information – including those related to channel integration – about the retailer. Touchpoints deliver cues to customers, who later use them to evaluate the experience with the retailer and update their perceptions. Exposure to the different touchpoints (see Section 2.2) of the retailer can then influence how customers perceive its channels to be integrated and to deliver a seamless experience.

Next, the Organism (O) comprises not only affective or cognitive states, such as satisfaction and pleasure, but also subjective perceptions (Bagozzi, 1986). Prior studies on channel integration quality have used channel integration in the role of organism to measure the contribution of several variables to customers’ perceptions (Hsieh et al., 2012). In the present study, in line with categorization theory, we expect that frequency of exposure to touchpoints triggers customers’ cognitive categorization processing of information across online and offline environments. In this process, touchpoints operate as anchors enabling the generation of channel integration perception. Channel integration has therefore been included in the model as the organism.

Finally, the Response (R) is any behaviour displayed by consumers, elicited by perceptions and states, such as purchase intention (Kim and Lennon, 2013) or repurchase intention (Chopdar and Balakrishnan, 2020). Following Cheah et al. (2020), in our model, we use patronage intention as the response. The studies by Emrich et al. (2015) and Zhang et al. (2018) show that channel integration can lead to a higher patronage intention. Shakir Goraya et al. (2022) state that “although the integrated channel-retailing model is becoming a trend among new age retailers, the patronage it creates among consumers is still not fully explicated” (p. 1); this offers scope for further research on the relationship between the two variables.

Since this study is concerned with major situational factors – i.e. the touchpoints encountered by customers during their shopping journeys – we incorporate patronage intention as an outcome of channel integration. We expect, in fact, that customers who interiorize a perception of the retailer as integrated across channels will display stronger loyalty intentions. Moreover, we assume and test the mediating role of channel integration on the relationship between touchpoints and patronage intention. The aim is to identify which touchpoints can drive patronage intention on their own and which have an effect on patronage intention only when channel integration is perceived. The research framework is displayed in Figure 1 and features two main effects: the effect of touchpoint exposure on channel integration perception, and the effect of channel integration perception on patronage intention.

3.2.1 Type of customer: first-time vs repeat customers

The literature identifies extensive differences among customers, especially first-time and repeat customers. The latter appear as more prone to make future purchases from the brand/retailer than first-time customers and will be persuaded more easily by the brand’s marketing actions (Petrick, 2004; Woodside and Walser, 2007). Other differences in first-time and repeat customers’ behaviours have been related to the attractiveness of specific channels’ features, with a major focus on online environments (see Tractinsky and Lowengart, 2007, about web-store aesthetics).

Xu and Jackson (2019) demonstrate that channel integration can reduce customers’ uncertainties. As such, it is reasonable to hypothesize that customers will interact with those touchpoints that could allow them to perceive higher integration in their future visits to the retailer. This would be more efficient for them, since complex environments cause confusion and further limit customers’ ability to process and perceive information (Schick et al., 1990). Consistent with categorization theory, we suggest that first-time customers approaching a retailer’s touchpoints will interact with them in a different manner compared to repeat customers. Due to limited information processing capacity, they may easily focus on less information, thus missing various facets of the retailer’s offer, with consequences for their perception of channel integration. We therefore compare first-time and repeat customers, to understand how touchpoint exposure may have a different impact on channel integration perception over time.

4. Methodology

4.1 Data collection

Data were collected between July and September 2021 from two samples of Italian consumers, representative of the Italian population, through a leading consumer panel market research company. An online cross-sectional survey was considered by researchers to be the best and most cost-effective method to measure customer exposure through a wide variety of offline and online touchpoints, customer preferences towards them, and to account for the heterogeneity in retailers’ touchpoint offering.

Grocery retailing (Sample 1) and fashion retailing (Sample 2) were the sectors investigated. They were chosen as to incorporate the effect of different product categories – convenience and shopping goods, respectively – given that customers’ decision-making, cognitive and emotional processes are different (Kushwaha and Shankar, 2013). Only consumers who had recently shopped in the two product categories (at least one purchase in the last month as far as grocery and in the last 6 months as far as fashion retailing) were involved in the study. In each category, we asked them to refer to the retailer they had bought more frequently from. To facilitate the task, consumers were provided with a list of all major Italian retailers that offer online and offline channels and operating in the grocery and fashion sectors, respectively. A total of 2,071 questionnaires were collected via an online survey. After excluding uncompleted questionnaires, 1,031 valid responses were obtained for the grocery sector (Sample 1), and 759 for the fashion sector (Sample 2). Table 1 summarizes the characteristics of both samples.

4.2 Measurement

Interaction with touchpoints was measured through frequency of exposure – i.e. how many times customers encountered each touchpoint – along the line of Baxendale et al. (2015). Customers were provided with a list of touchpoints and asked to indicate how frequently they encountered each of them (a seven-point self-anchored scale, ranging from 1 equal to “never” to 7 equal to “often”) in the last six months. The touchpoint list was based on Wind and Hays (2016) and Herhausen et al. (2019), as well as integrated with industry practice (Table 2).

The list was randomized per each respondent to control for order bias. The scale to measure channel integration was adapted from Zhang et al. (2018): one item for each dimension of the original scale was selected and used to ensure that each dimension was represented in the adapted scale. This procedure has been adopted in previous works investigating channel integration as a first-order construct (Shakir Goraya et al., 2022). Patronage Intention items were adapted from Kim et al. (2008). In addition, several control variables were measured: consumers’ preference to interact with each specific touchpoint (dummy variable), age, gender and residence. Reliability and validity of the constructs and items are provided in Table 3. The internal consistency and composite reliability values are above the required 0.70 threshold (Nunnally, 1994); at item level, the factor loadings of each item also show values above the recommended 0.70 threshold (Gerbing and Anderson, 1988). The average variance extracted (AVE) is greater than 0.50, thus ensuring the scale’s convergent validity.

4.3 Data analysis

To answer RQ1 and RQ2, we measured the relationship between touchpoint exposure and channel integration and between channel integration and patronage intention by using multiple ordinary least squares (OLS) regressions, consistently with previous studies on touchpoints that used regression models (e.g. Baxendale et al., 2015). Since touchpoint exposure is partially dependent on the retailer making touchpoints available to consumers, we adopted clustered standard errors. On the one hand, customers exposed to touchpoints of the same retailer may not be independent of each other. On the other, users’ perception of channel integration may also depend on how the channels are actually designed by the different retailers. Stata v.14 was used to run this procedure.

Finally, to test the mediating role of channel integration, PROCESS v.3.5 was used on IBM SPSS v.24 through Hayes’s (2017) approach, that includes bootstrapping procedures. Following the test, we identified as significant indirect effects those for which the 95% confidence interval did not include zero at the 0.05 level. Finally, among the control variables, we measured touchpoint preference through a set of dummy variables, to control for individual preference for each touchpoint. To summarize information on touchpoint preference we conducted multiple correspondence analysis (MCA) on the aforementioned dummy variables. MCA is an extension of correspondence analysis to more than two variables at once (Greenacre and Blasius, 2006). This technique, applied to categorical variables, identifies a number of latent orthogonal dimensions that allow the researcher to obtain a lower number of variables, equal to the rank of the data matrix (Kaciak and Louviere, 1990). Dimensions emerging from the MCA were chosen, for both sectors, under a 70% variance explained criterion (Greenacre, 1993); this resulted in two dimensions for the grocery sector and four for the fashion sector, which served as control variables summarizing touchpoint preferences. Demographic variables were also used as controls while testing our model; however, they were not significant and therefore their effects were omitted from results.

5. Results

The results are presented in the following three sections, each addressing the study’s respective RQ. Firstly, we focus on the multiple regression conducted at touchpoint level: Section 5.1 discusses differences emerging from the comparison of the grocery and fashion sectors; Section 5.2 provides differences identified between first-time and repeat customers in each of the aforementioned sectors. In Section 5.3, we finally focus on the mediation analysis. For readability purposes, only significant variables are displayed in Tables 4–7 and are briefly discussed below each table.

5.1 Touchpoints exposure and channel integration across sectors

In response to RQ1, we identify touchpoints that are (positively or negatively) significant for channel integration perception. Notably, these touchpoints are different, and of different numerosity, across industries, as reported in Table 4.

In the grocery sector, we identified three touchpoints that are positively and significantly related to channel integration perception: the retailer’s mobile app, loyalty program and communications via email or newsletter. In the fashion sector, five touchpoints are significant, four of which positively: the retailer’s website, loyalty program, communications via email or newsletter, and in-store staff in charge of picking orders placed online by customers. The digital coupons of the brand/store are negatively related to channel integration perception: this might suggest that the monetary incentive customers receive might steer their attention towards the retailer’s prices rather than to integration of their channels.

5.2 Touchpoints exposure and channel integration for repeat and first-time customers

To answer RQ2, differences in significant touchpoints within sectors were identified when distinguishing between first-time and repeat customers. The following differences, which will be further discussed with reference to each sector, show that first-time and repeat customers develop their channel integration perceptions by being exposed to different touchpoints. Only a few touchpoints contribute to developing such perceptions for both customer targets.

5.2.1 First-time and repeat customers in the grocery sector

Table 5 shows results for first-time and repeat customers in the grocery sector. For readability purposes, only significant variables are displayed.

In the grocery industry, the retailer’s mobile app is the only touchpoint able to create a channel integration perception for both first-time and repeat customers. Repeat customers also gain a higher perception of integration from the loyalty program. The loyalty program is non-significant for first-time customers, who in all probability have not yet grasped all the advantages of membership, nor attained any reward, as this usually requires time and a series of interactions to accrue. Repeat customers have a negative perception of channel integration from the retailer’s social media pages; this could be linked to the fact that these pages tend to be used for very general stand-alone communication to support brand building and corporate image. Concerning first-time customers, the retailer magazine is significant: it is able to deliver additional information about the retailer as a whole, also in terms of integrated services and activities offered. We also found that the digital promotional flyer is positively related to channel integration. This is probably because digital flyers are more information-rich and allow customers to receive consistent information on the in-store assortment and prices through a different, digital touchpoint. Gift cards, on the contrary, are negatively significant in driving first-time customers’ perception of channel integration; this suggests that this segment’s main concern is for the economic value of this touchpoint.

5.2.2 First-time and repeat customers in the fashion sector

Significant touchpoints for first-time and repeat customers in the fashion sector are displayed in Table 6, as follows.

In the fashion sector, the mobile app is non-significant, that is a major difference compared to the grocery sector. The touchpoints that create a channel integration perception for both first-time and repeat customers are the website, the in-store order picking staff and the loyalty program. The presence of the website as an important touchpoint for both customer types suggests that fashion retailers have managed to make their websites attractive and rich in omnichannel services and should encourage their customers to further adopt this channel. As for the loyalty program, results further support its role as a fundamental touchpoint for retailers into stimulate customers to explore and perceive the company’s advantages across channels. For repeat customers, to see clothes or shopping bags from the retailer being worn by other customers is positively related to channel integration. Last but not least, for repeat customers, the encounter with digital billboards during sport events is negatively related to channel integration. While the latter may be beneficial for reinforcing brand values and awareness, it is not directly related to what the retailer is offering in terms of shopping experience. Similar considerations can be made for another negatively related touchpoint impacting on first-time customers’ perception: live streaming on social networks or ad hoc platforms. During such activities, customers’ attention is diverted from the brand in favour of the streamer-seller performing the live stream. Hence, first-time customers may encounter the brand having been drawn to the streamer-seller’s personality, appeal and interactivity (Chandrruangphen et al., 2022); they do not feel a consistent link between the live streaming they are experiencing and the brand itself. The fact that live streaming becomes non-significant for repeat customers may be explained by categorization theory: these customers are now able to perceive the streamer and the brand as two separate identities. The final insight regarding first-time customers is the negative perception of integration related to the brand/store’s digital coupons, a result in line that aligns with results for the grocery sector as far as gift cards. Similarly to gift cards, digital coupons are a monetary incentive that concentrates customers’ attention merely on the value they are receiving.

5.3 The mediating role of channel integration on patronage intention

Consistently with the aim of the study and RQ3, we investigated the overall contribution of touchpoints to Patronage Intention, both directly and through channel integration, thus identifying the circumstances in which channel integration assumes a mediation role. The results of the model estimation, with direct and indirect effects of touchpoints, are reported in Table 7. Before proceeding with the mediation analysis, we ensured that there was a significant relationship between channel integration and patronage intention. We found that there is a positive and significant relationship between channel integration and patronage intention, as far as both grocery (p < 0.001) and fashion are concerned (p < 0.001).

For direct effects, we notice that the physical store and offline word-of-mouth are positively related to patronage intention for both grocery and fashion. For the fashion sector only, interacting with in-store staff and cashiers is positively related to patronage intention, whereas home delivery staff is negatively related. These results suggest that home delivery services in fashion probably fall short of customers’ expectations.

Regarding indirect mediation effects, two touchpoints are fully mediated in both grocery and fashion sectors. Loyalty programs (Grocery: M = 0.061, [0.035;0.092]; Fashion: M = 0.075, [0.032;0.125]) must be highly integrated – for example, by allowing customers to redeem their points through multiple channels and to access both physical and digital rewards. Similarly, the mobile app must allow customers to seamlessly retrieve information from multiple channels (Grocery: M = 0.037, [0.009; 0.067]; Fashion: M = 0.052, [0.012;0.010]).

In the grocery sector, channel integration fully mediates the effect of all significant touchpoints (in addition to the loyalty program and mobile app, the website, emails and newsletters, and the retailer’s magazine).

Conversely, in the fashion sector, channel integration fully mediates six touchpoints:

1. loyalty program;

2. mobile app;

3. clothing or shopping bags from the brand;

4. order picking in-store staff;

5. gift cards; and

6. digital promotional billboards.

Here, the website and the email/newsletter are only partially mediated by channel integration, differently from the grocery sector. This might be due to fashion customers being more accustomed to encounter the brand primarily through the website, compared to grocery customers.

6. Discussion and conclusions

Our study investigates the relationship between touchpoints and channel integration in omnichannel settings, and interprets and contextualizes the effect of each significant touchpoint.

Bèzes stresses the need for future research “to deepen the knowledge of the psychological mechanisms that activate and build Omnichannel Integration” (2021, p. 913). Our study responds to this call and, by relying on the categorization theory, provides support for its use to explain cognitive processes in integrated omnichannel settings, in line with Rahman et al. (2022). Due to their limited cognitive resources, customers are drawn to stimuli that simplify their choices. In our model, touchpoints represent these stimuli, as they are a fundamental component of customer journeys that customers can freely choose and use to move around omnichannel environments. We find that customers develop greater perception of integration from those touchpoints that allow them to gather information about the retailer as a whole. For example, touchpoints such as the mobile app in the grocery sector, the website in the fashion sector, and loyalty programs overall, have in fact proven able to deliver a stronger channel integration perception than other touchpoints offered by retailers. In light of the categorization theory, we pose that new information delivered through the aforementioned touchpoints is more easily processed by customers, even when the information provided concerns other channels or touchpoints (e.g. the mobile app communicates in-store promotions). We also identified certain touchpoints that have a negative effect on channel integration perception (e.g. gift cards, digital coupons, home delivery staff, etc.). These touchpoints appear as extremely focused on other aspects characterizing the retailer – such as its prices, or its offering on a specific channel only. Consistent with categorization theory, this means that customers encounter stimuli that are reinforcing cognitive categories that do not concern integration, and that in turn divert customers’ attention from integration to the other information retrieved.

The differences we identified within sectors for first-time and repeat customers, regarding the touchpoints that generate channel integration perception, further encourage the above interpretation and offer insights on how customers’ perception evolves over time. Repeat customers have already gained a broader understanding of the retailer – for example, they might have joined its loyalty program, one of the few touchpoints that are significant for both types of customers – and are therefore able to derive new information from exposure to other touchpoints. This is in line with the categorization stages as described in Multichannel retailing studies (Broniarczyk and Griffin, 2014), as it is based on pre-existing mental categories and journeys that consumers have already processed.

Finally, our study shows that consumer perceptions of channel integration are positively related to patronage intention, which is consistent with previous studies (e.g. Zhang et al., 2018; Shakir Goraya et al., 2022). The abovementioned studies, however, only considered channel integration as a determinant of loyalty-related outcomes, without introducing its own antecedents to the overall picture. Moreover, they focused on customer emotions and states, such as consumer empowerment, whereas we adopt a perspective that is concerned with the firm’s offering and related consequences in terms of customer perceptions. In addition, in this regard, our results demonstrate the mediating role of channel integration on the relationship between specific touchpoints – some of which differ among the grocery and fashion sectors – and patronage intention. This contributes to further advancing knowledge on the effects of touchpoint exposure on customers’ perceptions, specifically with reference to loyalty-related outcomes (e.g. Ieva and Ziliani, 2018; Herhausen et al., 2019).

A final and major theoretical implication of the study concerns the emerging need to consider touchpoints when measuring channel integration perception. Results reveal the crucial role of not only consistency of information across channels and touchpoints, but also the specific touchpoints through which such information is disseminated. Our study, therefore, supports the addition of contextual elements, i.e. touchpoints, to the traditional channel integration quality framework (e.g. Acquila-Natale and Chaparro-Pelàez, 2020). The addition of touchpoints – with the relevant differences identified between sectors – would help to increase the framework’s adherence to the reality of omnichannel environments.

The present study also entails significant managerial implications. It is to be first stressed how retailers nowadays are increasingly focusing on achieving loyalty-driven outcomes for sustainable growth, such as increased revenues and profitability (Lin and Bowman, 2022). Prior studies in literature have extensively shown how customer loyalty and retention can be achieved through the design of customer journeys and user experiences, which have become even more crucial in the omnichannel retailing domain (Japutra et al., 2021; Neslin, 2022). Results from the present study, concerned with the role of touchpoints in driving channel integration perceptions, provide clear suggestions regarding customer journey and user experiences’ design and/or redesign.

A second managerial implication relates to the practice of attribution modelling, and the advancements that could be brought by research on touchpoints. Attribution modelling in retailing is traditionally concerned with sales, especially attribution of sales to the offline or online channels (Nass et al., 2020). New research has explicitly called for new attribution models specific to Omnichannel retailing (Nass et al., 2020) and has suggested that such models should be used to determine how to manage various touchpoints based on customers’ progress through their journeys (Ratchford et al., 2022). By adopting a loyalty-centred perspective, our study proposes an extension of attribution models beyond the simple attribution of sales. We in fact suggest that touchpoints should receive attribution not only for leading customer journeys towards the single purchase but also for their repeated contribution to driving loyalty intentions such as patronage intention.

A third managerial implication lies in the proposal of a specific methodology that can be immediately applied and tested by retailers on their own touchpoints and customers. In fact, our methodology pinpoints the specific touchpoints that are more impactful on customers’ Integration perception – which, as shown, in turn has a positive effect on patronage intention. Retailers will then decide whether to focus on touchpoints that can drive channel integration perception on all targets – e.g. the mobile app in the grocery sector – or whether to prioritize their resources and investments towards touchpoints that are effective for specific targets only. Moreover, since certain touchpoints have clearly proven non-significant for delivering channel integration perception, retailers are encouraged to differentiate their messages across touchpoints, so as to maximize their individual effectiveness.

The study is not without limitations. Firstly, data were collected by means of a cross-sectional survey. Although surveys have been used before for academic studies involving touchpoint exposure (e.g. Romaniuk et al., 2013; Ieva and Ziliani, 2018; Bolton et al., 2022), it might be difficult for consumers to recall their encounters with touchpoints; moreover, customers might be biased in recalling their past behaviours.

Secondly, the study draws from a single market, by involving Italian customers only; to improve the generalization of our results, the framework might be tested in other countries. Similarly, the framework might be extended to more industries and targets than those we included in our study.

Finally, our method only investigated the relationship of individual touchpoint with channel integration. As pointed out by Tueanrat et al. (2021), “there is no definite procedure to collect touchpoint data and map a journey (p. 342)”; however, it would be interesting to understand whether combinations of touchpoints – as occurs in real customer journeys – have an impact on channel integration. Hence, this limitation also represents an opportunity for developing future studies in this area of key importance to omnichannel, so as to comprise further dimensions of interest concerning touchpoints.

Figures

Figure 1

Research framework

Table 1

Sample demographics

Measure	Category	Sample 1 (Grocery) Frequency	Sample 1 (Grocery) (%)	Sample 2 (Fashion) Frequency	Sample 2 (Fashion) (%)
Gender	Male	400	38.8	265	34.9
	Female	631	61.2	494	65.1
Age	20–29 years	32	3.1	15	2.0
	30–39 years	146	14.2	130	17.1
	40–49 years	229	22.2	182	24.0
	50–59 years	244	23.7	202	26.6
	60–69 years	195	18.9	137	18.1
	70–79 years	148	14.4	70	9.2
	80–89 years	37	3.6	23	3.0
Education	Middle-school degree	37	3.6	31	4.1
	High-school degree	159	15.4	122	16.1
	University degree	835	81.0	606	79.8
Affluence	Low	204	19.8	154	20.3
	Below average	289	28.0	219	28.9
	Above average	307	29.8	241	31.8
	High	231	22.4	145	19.1

Source: Authors’ own work

Table 2

Touchpoint lists for the grocery and fashion sectors

Grocery sector touchpoints	Fashion sector touchpoints
Advertising on TV, radio, newspapers, billboards	Advertising on TV, radio, newspapers, billboards
Physical store	Physical store
Offline word-of-mouth	Offline word-of-mouth
Online word-of-mouth	Online word-of-mouth
Retailer’s Facebook or other social media pages	Retailer’s Facebook or other social media pages
Google searches or online advertising	Google searches or online advertising
Mobile app	Mobile app
Website	Website
Cashier and in-store staff	Cashier and in-store staff
Loyalty program and special promotions	Loyalty program and special promotions
Printed promotional flyer	Printed promotional flyer
Digital promotional flyer	Digital promotional flyer
Email / newsletter	Email / newsletter
Printed communications by post	Printed communications by post
Printed coupons of the brand / store	Printed coupons of the brand / store
Digital coupons of the brand / store	Digital coupons of the brand / store
Customer service	Customer service
Home delivery staff (for orders placed online)	Home delivery staff (for orders placed online)
Order picking in-store staff	Order picking in-store staff
Retailer’s gift cards	Retailer’s gift cards
Online games and sweepstakes	Online games and sweepstakes
Retailer’s magazine	Clothing or shopping bags from this brand/shop worn by friends, relatives, acquaintances or strangers
	Sales of clothing from this brand/store via live streaming on social networks (e.g. Instagram) or ad hoc platform
	Video content published on other social media or websites than the retailer’s
	Digital promotional billboards during sport events
	Bloggers and experts promoting the brand/retailer on social media
	Text messages (SMS)

Source: Authors’ own work

Table 3

Construct reliability and validity measures

	Grocery sector = Sample 1, n = 1.031				Fashion sector = Sample 2, n = 759
Constructs and items	Factor loadings	Cronbach’s alpha	Composite reliability	AVE	Factor loadings	Cronbach’s alpha	Composite reliability	AVE
Channel integration		0.80	0.86	0.55		0.86	0.89	0.62
I can find consistent promotions and advertisements in the retailer’s physical store, website and mobile app	0.72				0.80
I can find consistent assortment and prices in the retailer’s physical store, website and mobile app	0.71				0.74
I can find product descriptions and check the retailer’s inventory status at the physical store through its website or its mobile app	0.80				0.86
I can redeem the retailer’s gift coupons, vouchers or loyalty points in its physical store, its website or its mobile app	0.70				0.78
I can return or exchange products purchased online in the retailer’s physical store	0.78				0.78
Patronage intention		0.89	0.93	0.81		0.90	0.93	0.82
I am likely to purchase the products(s) from this retailer	0.93				0.94
I am likely to recommend this retailer to my friends	0.84				0.86
I am likely to make another purchase from this retailer if I need the products that I buy	0.94				0.94

Source: Authors’ own work

Table 4

Multiple regression results for grocery and fashion sectors: standardized beta coefficients

Touchpoints	Standardized regression beta coefficients	t-values	p > |t|
Grocery sector = Sample 1, n = 1,031
Mobile app	0.086	3.56	0.004
Loyalty program	0.175	4.39	0.001
Email / newsletter	0.087	2.35	0.039
Fashion sector = Sample 2, n = 759
Website	0.237	4.47	0.001
Loyalty program	0.156	4.23	0.001
Email / newsletter	0.114	2.35	0.039
Order picking in-store staff	0.122	3.21	0.008
Digital coupons of the brand/store	−0.075	−2.34	0.039

Notes:

Only significant variables (touchpoints) are displayed

Independent variables tested: touchpoints as of Table 2, plus control variables: age, gender, location

Source: Authors’ own work

Table 5

Multiple regression results for repeat and first-time customers in the grocery sector

Grocery sector = Sample 1	Repeat customers, n = 813			First-time customers, n = 218
Touchpoints	Standardized regression beta coefficients	t-values	p > |t|	Standardized regression beta coefficients	t-values	p > |t|
Mobile app	0.094	2.87	0.015	0.087	1.81	0.046
Loyalty program	0.177	4.22	0.001	–	–	–
Retailer’s Facebook or other social media pages	−0.067	−2.64	0.023	–	–	–
Retailer’s magazine	–	–	–	0.202	2.25	0.043
Digital promotional flyers	–	–	–	0.192	2.60	0.025
Retailer’s gift cards	–	–	–	−0.221	−4.42	0.001

Notes:

Only significant variables (touchpoints) are displayed.

Independent variables tested: touchpoints as of Table 3, plus control variables: age, gender, location

Source: Authors’ own work

Table 6

Multiple regression results for repeat and first-time customers in the fashion sector

Fashion sector = Sample 2	Repeat customers, n = 551			First-time customers, n = 208
Touchpoints	Standardized regression beta coefficients	t-values	p > |t|	Standardized regression beta coefficients	t-values	p > |t|
Website	0.243	6.51	0.000	0.268	2.49	0.030
Order picking in-store staff	0.122	2.19	0.051	0.171	2.65	0.023
Loyalty program	0.138	7.68	0.000	0.274	3.76	0.003
Clothing or shopping bags from this brand/shop worn by friends, relatives, acquaintances or strangers	0.133	2.85	0.016	–	–	–
Digital promotional billboards during sport events	−0.124	−3.00	0.012	–	–	–
Sales of clothing from this brand/store via live streaming on social networks (e.g. Instagram) or ad hoc platform	–	–	–	−0.170	−3.33	0.007
Digital coupons of the brand/store	–	–	–	−0.125	−2.85	0.016

Notes:

Only significant variables (touchpoints) are displayed.

Independent variables tested: touchpoints as of Table 3, plus control variables: age, gender, location

Source: Authors’ own work

Table 7

Direct and mediation path analysis in the grocery and fashion sectors

		Grocery sector = Sample 1 n = 1,031					Fashion sector = Sample 2 n = 759
Touchpoints	Direct effect	t-value	Indirect effect	BootSE	Type of mediation	Direct effect	t-value	Indirect effect	BootSE	Type of mediation
Physical store	0.562	0.000	–	–	–	0.305	0.000	–	–	–
Offline word-of-mouth	0.162	0.012	–	–	–	0.199	0.017	–	–	–
In-store staff and cashiers	n.s.	n.s.	n.s.	n.s.	n.s.	0.183	0.013	–	–	–
Home delivery staff	n.s.	n.s.	n.s.	n.s.	n.s.	−0.291	0.038	–	–	–
Loyalty program	–	–	0.0615	0.014	Full	–	–	0.0752	0.024	Full
Mobile app	–	–	0.0373	0.014	Full	–	–	0.0524	0.022	Full
Website	–	–	0.0452	0.018	Full	0.245	0.005	0.0502	0.029	Partial
Email / newsletter	–	–	0.0398	0.016	Full	0.197	0.029	0.1009	0.023	Partial
Retailer’s magazine	–	–	0.0365	0.016	Full	n.a.	n.a.	n.a.	n.a.	n.a.
Clothing or shopping bags from this brand/shop worn by friends, relatives, acquaintances or strangers	n.a.	n.a.	n.a.	n.a.	n.a.	–	–	0.0471	0.023	Full
Order picking in-store staff	n.s.	n.s.	n.s.	n.s.	n.s.	–	–	0.0735	0.030	Full
Retailer’s gift cards	n.s.	n.s.	n.s.	n.s.	n.s.	–	–	0.0157	0.025	Full
Digital promotional billboards during sport events	n.a.	n.a.	n.a.	n.a.	n.a.	–	–	−0.0697	0.034	Full

Notes:

Only significant variables (touchpoints) are displayed.

Independent variables tested: touchpoints as of Table 3, plus control variables: age, gender, location

Source: Authors’ own work

Table A1

Overview of channel integration quality dimensions

Authors	Dimensions
Sousa and Voss (2006); Shen et al. (2018); Lee et al. (2019)	Channel-service configuration quality: channel-service choice breadth and transparency Integrated interaction quality: process consistency and content consistency
Banerjee (2014)	Channel-service configuration quality: channel-service choice breadth and transparency; appropriateness of channels Integrated interaction quality: process consistency and content consistency
Oh and Teo (2010); Gao et al. (2021b)	Information quality: integrated promotion information; integrated product and pricing; integrated transaction information Service quality: integrated information access; integrated order fulfilment; integrated customer service
Quach et al. (2020)	Service integration: service consistency and service transparency
Hossain et al. (2020); Hossain et al. (2020); Gao and Huang (2021 – adapted)	Channel-service configuration: breadth of channel choice; transparency of channels; appropriateness of channels Content consistency: information consistency; transaction data integration Process consistency: system consistency and image consistency Assurance quality: privacy; security; service recovery accessibility

Source: Authors’ own work