Formation of customer value through channel integration: Modelling the mediating role of cognitive and affective customer experience in the omni channel retail context

Abstract

Technology and the internet have changed the Indian retail industry. To better serve customers, retailers are combining the benefits of physical locations with the vast amount of online information. Omnichannel commerce is a unique sales experience that combines the benefits of real venues with the information-rich experience of internet shopping. Thus, the main goal of this study is to understand how customers use omni-channel, identify the most important factors affecting their customer experiences, and examine how customer experience affects customer value. With the help of 37 item-based questionnaires a quantitative survey was conducted on consumers who had purchased from multichannel retailers at least one time in the past year. Demographics included age, gender, education, and family income. Further structural equation modeling technique was implied on data to analyze how integrated channels affect customers’ value from interactions. The findings indicate that both cognitive and affective customer experience have significant impact on value creation process of customers. However, the customer drew greater value from integrated promotions, product, and price in the presence of cognitive customer experience, whereas information access was more valuable in the presence of affective customer experience. The findings of this study demonstrate the relevance of comparing the effectiveness of various channel integration strategies.

Keywords:

• Omnichannel
• online retailing
• customer experience
• channel integration
• customer value

JEL Code:

• M31
• L81
• M30
• O30

Reviewing Editor:

• Len Tiu Wright, De Montfort University Faculty of Business and Law, United Kingdom of Great Britain and Northern Ireland

1. Introduction

The retail sector in India has experienced a significant transformation due to the rapid advancements in technology and the widespread use of the Internet (Shankar et al., 2021; Kumar et al., 2022). This evolution has been particularly prominent in the Indian retail industry, as consumer behavior and expectations continue to change. As a result, the integration of technology has become crucial in reshaping the foundations of retail businesses.

In today’s interconnected society, consumers in India are navigating a landscape characterized by increased connectivity (Basu et al., 2023). They now expect brands to provide consistent and excellent service across various touchpoints, such as physical stores, smartphones, and online platforms (Massi et al., 2023; Swaminathan, 2021; Yin et al., 2022). This shift in societal expectations has created a demand for a shopping channel that seamlessly bridges the gap between different retail platforms. Experts and scholars in the retail industry agree that omnichannel retailing is not just a passing trend but the inevitable future of retailing (Sopadjieva et al., 2017; Zhao et al., 2023).

At the core of this transformative retail landscape is the concept of omnichannel retailing, which aims to combine the best aspects of traditional brick-and-mortar stores with the convenience of online platforms (Kondo, 2018; Rigby, 2011; Thaichon et al., 2023). It goes beyond simply integrating technology; it is a strategic business approach that strives to create a seamless and cohesive experience for consumers across all available channels. By embracing omnichannel retailing, businesses can adapt to the changing retail landscape and meet the evolving needs of their customers.

Extensive research has emphasized the crucial role of customer experience in ensuring the long-term viability and profitability of a company (Fang et al., 2021; Gallino & Moreno, 2014; Oh & Teo, 2010; Piotrowicz & Cuthbertson, 2014; Swaminathan, 2021; Ren et al., 2023; Gao et al., 2023). In today’s digitalized retail landscape, where integrated channel environments are prevalent, understanding and improving customer value and experience have become essential not only for survival but also for sustained profitability.

Against this complex backdrop, our comprehensive study aims to delve into the complex dynamics of omnichannel retail environments, with a specific focus on customer experience and value creation. As we navigate through uncharted territory in the evolution of retail, our research seeks to address fundamental questions that go beyond mere academic curiosity. How does the nuanced emphasis on customer experience in omnichannel retail environments contribute to the creation of value for customers? What aspects of channel integration influence the development of customer value, and what areas remain unexplored in this evolving field?

Beyond academia, our study also aims to provide valuable insights for practitioners who are grappling with the challenges and opportunities presented by the complex nature of omnichannel retailing. As we contribute to the academic discourse, we envision our findings serving as a guiding light for retailers striving to optimize their operations and enhance the overall customer experience within the dynamic context of omnichannel retailing.

2. Theoretical background of the study

The omnichannel retailing literature offers a diverse range of perspectives on the changing dynamics between technology, consumer behavior, and retail strategy. In this section, we provide a comprehensive analysis of important literature sources, which provide a detailed understanding of the theoretical foundations that shape our study.

2.1. Omnichannel retailing

Omnichannel retailing, a transformative concept introduced by Cummins et al. (2016), signifies a crucial intersection where technological advancements intersect with significant shifts in consumer expectations. The essence of omnichannel goes beyond mere technological integration; it signifies a substantial transformation in the way consumers interact with retail ecosystems (Alexander & Kent, 2022). A detailed analysis of recent scholarly literature reveals a noticeable trend in consumer preferences, highlighting an increasing inclination towards seamless transitions between online and offline channels.

Contemporary discussions emphasize the necessity for retailers to create a unified brand experience across various touchpoints, aligning seamlessly with the evolving demands of consumers (Gasparin et al., 2022; Yin et al., 2022). This necessity is reinforced by the empirical observation that consumers, in their pursuit of a cohesive brand encounter, seek a convergence of their online and offline interactions (Massi et al., 2023). This strategic imperative not only resonates with the current retail landscape but also anticipates the trajectory of consumer expectations, emphasizing the need for retailers to transcend traditional boundaries.

A notable aspect of this scholarly discourse is the strategic incorporation of cutting-edge technologies into omnichannel frameworks. Augmented reality and artificial intelligence, as prime examples of such technologies, have emerged as crucial considerations in contemporary conversations. Their integration into omnichannel strategies goes beyond mere tactical adoption; it serves as a testament to the industry’s proactive approach in anticipating and surpassing consumer expectations. Augmented reality, with its ability to overlay digital information onto the physical world, and artificial intelligence, with its capacity to enhance personalization and predictive analytics, exemplify the industry’s commitment to fostering a technologically enriched, consumer-centric retail environment.

2.2. Customer value

The concept of customer value is a response to the complex nature of evolving consumer behaviors and preferences (Abid et al., 2022). Recent studies focus on personalized customer experiences and the impact of tailored offerings on perceived value (Alimamy & Gnoth, 2022; Kaabachi et al., 2022; Menidjel & Bilgihan, 2023). This analysis goes beyond surface-level understanding and explores individualized interactions and their influence on consumer perceptions.

One important aspect of this exploration is the rise of social commerce, where consumer interactions are intertwined with social media platforms. Recent research shows how social commerce shapes consumer perceptions of value (Dhaigude & Mohan, 2023; Siregar et al., 2023). User-generated content plays a significant role in amplifying customer experiences and contributing to overall perceived value (Siregar et al., 2023). This relationship between user-generated content and customer value highlights the need for scholarly examination of social commerce.

2.3. Customer experience

In the ever-evolving landscape of customer experience, recent literature highlights the role of omnichannel analytics in understanding and predicting consumer behavior (Sun et al., 2022; Mahadevan & Joshi, 2022). Advanced analytics, including predictive modeling and machine learning, have empowered retailers to customize interactions across channels, elevating the overall customer experience (Rahman et al., 2022; Sun et al., 2022; Rusthollkarhu et al., 2022). Moreover, discussions around ethical considerations in data usage and the implications of privacy concerns on the customer experience have emerged as critical components of the scholarly discourse.

2.4. Channel integration

Channel integration has become synonymous with providing a seamless customer journey, but recent challenges have prompted a revaluation of strategies. Scholars are now delving into the intricacies of managing data silos, ensuring data privacy, and maintaining consistency across diverse channels (Silva et al., 2023). The rise of voice commerce, social commerce integrations, and the blurring lines between online and offline channels have sparked discussions on the future trajectory of channel integration (Silva et al., 2023; Hajdas et al., 2022; Lawry, 2023). The role of emerging technologies, such as blockchain, in securing and streamlining cross-channel transactions is becoming a focal point of research within this domain.

In weaving these additional threads into the fabric of the literature review, we aim to offer a more comprehensive and forward-looking perspective on omnichannel retailing. By addressing recent trends, challenges, and emerging perspectives, this study seeks to contribute not only to the existing body of knowledge but also to the ongoing conversations shaping the future of omnichannel retail environments.

3. Hypothesis and model development

3.1. Integrated promotion (IP)

Integrated Promotion (IP) refers to a marketing strategy that utilizes advertisements to facilitate consumer awareness across multiple channels. This approach is grounded in research findings that highlight the role of advertisements in aiding buyers to comprehend various channels (Gao et al., 2021; Oh & Teo, 2010). Specifically, for consumers characterized by a heightened desire for knowledge, IP emerges as a more impactful strategy (Oh & Teo, 2010). This is particularly relevant in the context of functional information, which plays a crucial role in assisting clients in making well-informed purchasing decisions throughout their shopping journey (Dennis et al., 2014a).

The cognitive consumer experience encompasses the mental processes and perceptions involved in the consumer’s interaction with promotional content. The positive influence of IP on the Cognitive Consumer Experience (CCE) can be attributed to its role in enhancing consumer awareness and understanding across various channels. Gao et al. (2021), Oh and Teo (2010) and Ratchford et al. (2022) suggest that advertisements serve as effective tools for educating buyers about the diverse channels available to them. By providing comprehensive information through integrated promotional efforts, consumers are likely to experience a more enriched and cognitively engaging interaction with the marketing content.

The Affective Consumer Experience (ACE) represents the emotional and attitudinal responses evoked in consumers as a result of their interactions with promotional materials. Building on the findings of Oh and Teo (2010), the impact of IP on the ACE is particularly pronounced for consumers with a high desire for knowledge. This can be explained by the comprehensive nature of IP, which not only caters to informational needs but also creates a more engaging and emotionally resonant experience (Lim et al., 2022). By leveraging integrated promotion, marketers have the potential to evoke positive emotional responses in consumers, leading to a more favourable affective consumer experience.

H1a: The cognitive consumer experience is positively influenced by IP.

H1b: The ACE is positively influenced by IP.

3.2. Integrated price & product (IPP)

Integrated Price & Product (IPP) emerges as a strategic imperative with the overarching goal of imbuing all of a company’s sales channels with accurate and dependable information concerning its merchandise and pricing structure (Zhang et al., 2018; Gao et al., 2021). This operational philosophy stems from the recognition that the dispersion of inconsistent or unreliable data across channels can undermine customer trust and satisfaction. Notably, cognitive customers, characterized by a penchant for detailed information processing, stand to benefit significantly from a unified pricing and product information strategy (Zhang et al., 2018).

Cognitive customers, who exhibit a proclivity for information processing, derive enhanced value from consistent and accurate data across various sales channels (Zhang et al., 2018; Yin et al., 2022; Cocco & Demoulin, 2022). In essence, the assurance of correct and reliable information fosters a more positive cognitive engagement. When consumers encounter uniform and dependable pricing and product details, it cultivates a sense of trust and reliability, enriching their understanding of the company’s offerings. The hypothesis reflects the anticipation that a cohesive and accurate presentation of pricing and product information across channels contributes to a more satisfying and comprehensible cognitive experience for consumers.

As emotional and attitudinal responses are intricately linked to the perceived reliability and consistency of pricing and product information (Zhang et al., 2018; Gao et al., 2021; Abbasi et al., 2022). By ensuring that consumers are presented with accurate and trustworthy details across various channels, IPP seeks to instill a sense of confidence and convenience. This consistency not only builds trust but also simplifies the consumer’s decision-making process, contributing to positive emotional responses. The hypothesis reflects the expectation that the convenience and reliability associated with IPP foster a favorable affective response among consumers, enhancing their overall emotional engagement with the brand.

H2a: The CCE is positively influenced by IPP.

H2b: The affective consumer experience is positively influenced by IPP.

3.3. Integrated transaction information (ITI)

Integrated Transaction Information (ITI) represents a strategic approach that amalgamates data derived from both online and offline transactions, empowering stores to furnish customers with more personalized information and services (Oh & Teo, 2010; Rose et al., 2011). This sophisticated integration of transactional data is anticipated to enhance customer experiences by providing them access to their purchase history, enabling convenient reordering of commonly purchased items, and offering personalized recommendations for future purchases.

The access to comprehensive transaction data contributes to a more informed and engaging cognitive interaction. As highlighted by Oh and Teo (2010) and Rose et al. (2011), the integration of online and offline transaction information allows stores to offer customers personalized insights into their purchasing patterns and history. This personalized approach is expected to enhance the cognitive experience by providing customers with a more accurate understanding of their preferences, facilitating efficient reordering of commonly purchased items, and empowering them to make well-informed decisions (Alyahya et al., 2023). The hypothesis reflects the anticipation that ITI contributes to a more enriched and cognitively engaging experience for customers.

Furthermore, personalized transactional data can evoke positive emotional responses from customers. By leveraging ITI, stores can tailor their interactions with customers, offering personalized recommendations based on their purchase history (Oh & Teo, 2010; Rose et al., 2011). This personalized approach is expected to enhance the overall emotional engagement of customers, making their interactions with the brand more meaningful and satisfying. The hypothesis reflects the expectation that ITI contributes to a more favourable affective response by providing customers with personalized and relevant information, fostering a deeper emotional connection.

H3a: The CCE is positively influenced by ITI.

H3b: The ACE is positively influenced by ITI.

3.4. Integrated information access (IIA)

Integrated Information Access (IIA) encapsulates customers’ capability to access information through various channels, allowing for seamless transitions between different sources (Gao et al., 2021; Oh & Teo, 2010). This strategy is rooted in the understanding that providing integrated access to information enhances consumers’ ability to find relevant data across multiple channels, contributing to a more comprehensive and convenient information-seeking process.

The hypothesis proposing a positive influence of Integrated Information Access (IIA) on the Cognitive Consumer Experience (CCE) is grounded in the recognition that the ability to access information seamlessly through multiple channels enhances consumers’ cognitive engagement. As emphasized by Gao et al. (2021) and Oh and Teo (2010), IIA enables customers to find information efficiently and transition effortlessly between various channels. This fluidity in information access is anticipated to contribute to a more enriched cognitive experience by facilitating a smoother and more comprehensive acquisition of information (Cocco & Demoulin, 2022; Wang et al., 2023). The hypothesis reflects the expectation that IIA positively influences the cognitive dimension of consumer experiences by fostering a more efficient and seamless information-seeking process.

The hypothesis suggesting a positive influence of Integrated Information Access (IIA) on the Affective Consumer Experience (ACE) is based on the understanding that a seamless and integrated access to information contributes to positive emotional responses from consumers. By allowing customers to access information effortlessly across various channels, IIA aims to enhance the overall convenience and satisfaction associated with the information-seeking process (Zhang et al., 2018). This enhanced convenience and accessibility are expected to positively impact the emotional engagement of consumers, making their interactions with the brand more pleasant and satisfying. The hypothesis reflects the anticipation that IIA positively influences the affective dimension of consumer experiences by creating a more user-friendly and emotionally satisfying information access process.

H4a: The cognitive consumer experience is positively influenced by IIA

H4b: The ACE is positively influenced by IIA

3.5. Integrated order fulfillment (IOF)

Integrated Order Fulfillment (IOF) encapsulates the capability of customers to seamlessly execute their entire transaction, encompassing purchase, payment, return, and delivery, across one or more diverse channels simultaneously (Gao et al., 2021; Rose et al., 2011; Zhang et al., 2018). The impact of IOF on consumer experiences is multifaceted, with an emphasis on enhancing the Affective Consumer Experience (ACE) while potentially affecting the Cognitive Consumer Experience (CCE) (Oh & Teo, 2010).

The hypothesis suggesting a positive influence of Integrated Order Fulfillment (IOF) on the Cognitive Consumer Experience (CCE) reflects the understanding that seamless order fulfillment processes contribute to a more positive cognitive engagement (Cocco & Demoulin, 2022). IOF, by allowing customers to complete transactions across different channels efficiently, is anticipated to enhance the overall cognitive experience by streamlining the purchasing process. The convenience and efficiency associated with IOF are expected to positively impact the CCE by reducing friction and complexity in the transactional journey. While there may be challenges associated with integration, the hypothesis contends that the overall cognitive experience is positively influenced by the integrated order fulfillment process.

The hypothesis positing a positive influence of Integrated Order Fulfillment (IOF) on the Affective Consumer Experience (ACE) aligns with the idea that streamlined order fulfillment processes contribute to enhanced emotional responses from consumers. IOF is expected to positively impact the ACE by providing a seamless and integrated transactional experience, reducing potential stress or frustration associated with fragmented processes (Oh & Teo, 2010). The hypothesis reflects the expectation that the convenience and efficiency associated with IOF positively influence the emotional dimension of consumer experiences, fostering a more positive and satisfying overall interaction.

H5a: The CCE is positively influenced by IOF.

H5b: The ACE is positively influenced by IOF.

3.6. Integrated customer service (ICS)

Integrated Customer Service (ICS) is centered around providing uniform and consistent service across all channels, encompassing after-sales support as a crucial component (Oh & Teo, 2010; Zhang et al., 2018). The focus of ICS is not solely on the transactional aspects but extends to creating an enjoyable and pleasurable shopping experience for customers (Barari et al., 2020).

The consistent and uniform service contributes to a more positive cognitive engagement. ICS, by providing reliable and seamless service across channels, is expected to enhance the CCE by reducing uncertainties and challenges associated with customer service interactions (Gasparin et al., 2022). The hypothesis acknowledges that a positive and uniform customer service experience contributes to a more enjoyable and comprehensible cognitive experience for customers.

Also, creating feelings of pleasure and enjoyment during the shopping experience (Barari et al., 2020). ICS is expected to positively impact the ACE by ensuring that customers not only receive effective after-sales support but also experience a seamless and enjoyable service throughout their interaction with the brand. The hypothesis reflects the expectation that the uniform and consistent customer service provided by ICS contributes to positive emotional responses from customers, making their overall shopping experience more pleasurable and satisfying.

H6a: The CCE is positively influenced by ICS.

H6b: The ACE is positively influenced by ICS.

3.7. Customer experience (CE)

Providing an exceptional customer experience has become a paramount objective for contemporary businesses, and it is anticipated to be a key battleground in future business competition (Harris et al., 2003). The culmination of a customer’s perceptions and sentiments about an organization after utilizing its services is conceptualized as omnichannel customer value. The likelihood that customers will continue to engage with a company’s omnichannel services in the future is contingent upon meeting their expectations for both cognitive and affective experiences during the purchasing process. A positive omnichannel experience is posited to foster customer loyalty and increased utilization of omnichannel services.

A positive omnichannel experience contributes to a more enriched cognitive engagement (Lazaris et al., 2022; Tran Xuan et al., 2023). When customers perceive value in the omnichannel services provided by a company, including meeting their cognitive expectations during the purchasing process, it is expected to positively influence their overall cognitive experience. The positive omnichannel customer value contributes to a more satisfying and comprehensible cognitive experience for customers.

A positive overall perception of the organization, as shaped by the omnichannel experience, contributes to positive emotional responses from customers (Mishra et al., 2023; Xuan et al., 2023). If customers perceive value in the affective aspects of the omnichannel services, it is expected to positively influence their emotional engagement. The positive omnichannel customer value contributes to a more pleasurable and emotionally satisfying experience for customers, fostering increased loyalty and likelihood of future engagement.

H7a: The CCE is positively influenced by omnichannel cognitive value.

H7b: The ACE is positively influenced by omnichannel customer value.

Based on all the above-mentioned hypotheses, following model is proposed which is studied in this paper (Figure 1).

Figure 1. Conceptual model.

4. Research methodology

4.1. Design and collection of data

This study acquired data from India, primarily focusing on the Delhi-NCR region, to evaluate our hypotheses. A questionnaire survey was employed as the data collection method. Most Indian retailers are actively striving to provide a hassle-free shopping experience across various channels. The data collection process spanned approximately 6 months, commencing on February 22. The survey was disseminated through various channels, including WhatsApp groups, email, and distribution to relevant society groups.

At the initiation of the study, respondents were provided with a definition and illustration of omnichannel retailing. To ensure data quality, 409 responses were received, but due to respondents not meeting the minimal completion criteria, including responding to all essential questions, failing to answer essential questions, providing inconsistent responses, and incomplete surveys, 100 surveys were excluded. Therefore, the final dataset for analysis comprised 309 responses.

The “10-times rule” was applied to determine the minimum sample size for the Partial Least Squares Structural Equation Modeling (PLS-SEM) analysis. This rule posits that the sample size should exceed 10 times the maximum number of inner or outer model linkages pointing at any latent variable in the model (Hair et al., 2010; Nunnally, 1967). In our case, with 9 latent variables and 14 inner paths, the maximum number of linkages pointed at any latent variable was determined to be 14. Therefore, the minimum sample size required for our study was 10 × 14 = 140.

To shed light on the specifics of the sample chosen, respondents were selected based on (add information about the criteria for respondent selection, e.g., demographic factors, shopping behavior, etc.). This ensures transparency in the sampling process and strengthens the generalizability of the findings.

4.2. Validation of items and measurements

In formulating the basis for our study, we took great care in selecting and validating key dimensions from reputable sources. The dimensions proposed by Oh and Teo (2010) played a crucial role in addressing the complex aspects of channel integration. Additionally, we incorporated the comprehensive framework presented by Rose et al. (2011), which included two fundamental dimensions of customer experience, serving as a strong foundation for our research.

All the constructs utilized in our investigation were meticulously chosen from previously validated measuring scales, ensuring their reliability and relevance to the context of our study. To capture the subtleties of respondent perspectives, our survey instrument was designed using a seven-point Likert scale. This scale ranged from strongly disagree (=1) to strongly agree (=7) for each of the seven components under examination. This deliberate choice facilitated a detailed exploration of participants’ sentiments, enabling a more precise analysis of their responses.

It is important to note that our study maintained consistent and intentional terminology choices. The inclusion of "emotional experiences" in our investigation was not arbitrary; rather, it was a conscious decision aligned with the nuanced language used in the seminal works of Dennis et al. (2014a) and Rose et al. (2011). This deliberate alignment ensured coherence with existing literature and enhanced the accuracy of our analysis by utilizing terms that resonate with the foundational concepts in the field.

Moving beyond theoretical dimensions, our study drew upon empirical insights from previous research. The six elements characterizing channel integration (i.e., Integrated Promotion (IP), Integrated Price & Product (IPP), Integrated Transaction Information (ITI), Integrated Information Access (IIA), Integrated Order Fulfillment (IOF), Integrated Customer Service (ICS)) were adapted from distinguished contributions by Zhang et al. (2018), Oh and Teo (2010), and Gao et al. (2021). These elements were carefully selected to encompass the multifaceted nature of channel integration and ensure the strength of our model.

Furthermore, we combined the notion of omnichannel customer value, which serves as a fundamental aspect of this research, based on the findings of Nasution and Mavondo (2008). This deliberate integration enabled us to capitalize on a reputable and universally acknowledged conceptual framework, thereby augmenting the credibility and dependability of our study.

4.3. Structural Equation Modeling (SEM)

SEM, specifically Partial Least Squares SEM (PLS-SEM), was chosen as the analytical approach due to its suitability for examining mediation effects and handling complex models (Coelho et al., 2017). Smart PLS-4 software was employed for testing the hypotheses and conceptual model. The analysis also considered the impact of independent variables on dependent variables in the presence of mediators and moderators.

To test the mediation effect of customer experience, a bootstrapping study with 10,000 repetitions was conducted to investigate the impact of channel integration on omnichannel customer value, within 97.5 percent confidence intervals (Saari et al., 2021).

5. Data analysis & interpretation

5.1. Demographic information

The demographic makeup of the survey respondents offers valuable context for comprehending the characteristics of the study sample. Among the participants, there was a noteworthy gender distribution, with the majority being female (n = 171, 55.34%), while male respondents made up a slightly smaller yet substantial portion (n = 138, 44.66%) of the overall sample.

In terms of age distribution, the study observed a significant representation from the 31-40 age group, with 106 respondents (34.3%) falling into this category. To evaluate their educational background, the number of years of education was taken into account. It was observed that a significant proportion of respondents had obtained a master’s or doctorate degree before completing their bachelor’s degree.

Regarding household income, the majority of respondents (117, 37.86%) fell within the income level of 25000. Furthermore, the geographical distribution indicated that a predominant proportion of respondents came from metro regions (n = 158, 51.13%), while non-metro participants constituted a noteworthy 48.47% of the total sample.

Occupational diversity was also a distinguishing factor, with a notable number of respondents (n = 88) belonging to occupations other than government, private, student, or business. Following closely, private job holders accounted for 28.16% of the respondents.

The survey also explored participants’ shopping preferences, revealing that a substantial majority (n = 231) favored both online and offline shopping channels. When assessing shopping frequency, a significant percentage of respondents indicated a frequency of 3 to 5 times, making up 41.42% of the total respondents. These nuanced insights into the demographic and behavioral attributes of the study participants contribute to a comprehensive understanding of the sample composition and enhance the interpretative framework for the study.

5.2. Reliability and validity assessment

Fornell et al. (1981) proposed a method to determine discriminant validity, which states that this validity is established when the square root of the Average Variance Extracted (AVE) between two constructs is greater than their inter-construct correlations. The correlation matrix in Table 1 supports this notion, as the inter-construct correlations are lower than the square root of AVE, providing strong evidence of discriminant validity (Hair et al., 2010). It is important to note that, except for IP which has a reliability value of 0.67 indicating weaker reliability, all Cronbach’s alpha and composite reliability (CR) scores exceed the threshold of 0.7, indicating the constructs’ high trustworthiness (Fornell et al., 1981).

Table 1. Correlation matrix for discriminant validity.

	ACE	CCE	CV	ICS	IIA	IOF	IP	IPP	ITI
ACE	0.793
CCE	0.737	0.788
CV	0.729	0.690	0.715
ICS	0.546	0.502	0.553	0.815
IIA	0.602	0.524	0.618	0.555	0.837
IOF	0.577	0.643	0.655	0.600	0.664	0.806
IP	0.624	0.570	0.581	0.596	0.590	0.569	0.710
IPP	0.606	0.616	0.645	0.553	0.574	0.628	0.664	0.792
ITI	0.592	0.531	0.620	0.568	0.713	0.618	0.631	0.665	0.825

Note: Diagonal values refer to square root of AVE.

Table 2 further demonstrates the constructs’ strong convergent validity, with AVE values exceeding 0.5 (Fornell et al., 1981). According to Fornell et al. (1981) criterion, the square root of the average variance extracted (Sqrt AVE) is higher than the correlations between variables, confirming the validity of the discriminant relationships.

Table 2. Results of confirmatory factor analysis and convergent validity.

Constructs/ Items	Loading	Composite reliability	Average variance extracted	Cronbach’s alpha
Integrated promotion (IP)		0.80	0.50	0.67
Both the retailer’s offline and online storefronts have the same brand name, motto, and emblem. (IP1)	0.65
It is possible to find out more about the current in-store incentives by visiting the seller’s website. (IP2)	0.80
I was able to find the offline location’s address and phone number from the seller’s website. (IP3)	0.73
I found commercials for the seller’s online store on brochures, receipts, and shopping bags, as well as on the seller’s website. (IP4)	0.66
Integrated product and price (IPP)		0.83	0.63	0.70
My online and offline transactions are combined in the retailer’s purchase history. (IPP1)	0.81
The merchant recommends future purchases based on recent online and in - person purchases. (IPP2)	0.73
When I shop online, the retailer’s website adapts the pages to my previous purchases, both online and in person. (IPP3)	0.84
Integrated transaction information (ITI)		0.86	0.68	0.77
My online and in-store purchases are recorded together in the retailer’s database for future reference. (ITI1)	0.80
Online and offline, the retailer uses my previous purchases to make recommendations for future purchases. (ITI2)	0.86
The online store of the retailer customizes pages based on my previous online and in - person transactions. (ITI3)	0.81
Integrated information access (IIA)		0.88	0.70	0.79
Using the seller’s website, I can search for items in their retail outlet. (IIA1)	0.84
On its online store, the seller’s offline inventory status can be viewed. (IIA2)	0.83
I can use the offline store’s Internet kiosks to access the online retailer’s information and capabilities. (IIA3)	0.85
Integrated order fulfillment (IOF)		0.85	0.65	0.73
The shop’s coupons and vouchers can be used in-store or online. (IOF1)	0.81
I can pick up my online purchase from the retailer’s physical location and avoid having to deal with shipping costs. (IOF2)	0.77
When picking up an online order, the shop allows me to select a pickup location that is most convenient for me. (IOF3)	0.83
Integrated customer service (ICS)		0.86	0.66	0.75
The retailer’s offline customer service center accepts returns, repairs, and exchanges for items purchased from the seller’s online store. (ICS1)	0.81
After-sale services like email assistance for in-store purchases are available through the retailer’s internet site. (ICS2)	0.83
Interaction with the retailer’s offline shop customer service representative is possible via real-time chat in the online store. (ICS3)	0.81
Cognitive customer experience (CCE)		0.87	0.62	0.80
If I were in the market for a product, the omnichannel service offered by this retailer would assist me in making a more informed choice. (CCE1)	0.78
The omnichannel service of this retailer may enable you to learn more about a product before making a purchase. (CCE2)	0.85
If I wanted to buy something, I could use this retailer’s omnichannel service. (CCE3)	0.73
When I use the omnichannel service of this business, I can learn more about the products, pricing, and special offers. (CCE4)	0.79
Affinitive customer experience (ACE)		0.84	0.63	0.71
It’s a lot of fun to use the omnichannel solution provided by this retailer. (ACE1)	0.76
It’s a delight to make use of the omnichannel service provided by this store. (ACE2)	0.80
The omnichannel service provided by this retailer elicits emotions and sentiments. (ACE3)	0.82
Customer value (CV)		0.92	0.51	0.90
The omnichannel provides the highest-quality services. (CV1)	0.76
The quality of omnichannel services is consistently high. (CV2)	0.71
The omnichannel services are considered very reliable. (CV3)	0.75
I genuinely enjoyed using the services offered by omnichannel. (CV4)	0.73
The retailer’s omnichannel staff treats us with great respect. (CV5)	0.70
I consider the product rates offered are reasonable. (CV6)	0.71
The retailer’s omnichannel offerings are cost effective. (CV7)	0.71
The payment system is considered convenient. (CV8)	0.76
Using the retailer’s omnichannel services is considered prestigious. (CV9)	0.71
I consider the omnichannel services as a status symbol. (CV10)	0.68
I consider using this omnichannel services fits my social status. (CV11)	0.64

To assess the suitability of the nine variables as criteria, Confirmatory Factor Analysis was employed in this study. Table 2 presents factor loadings ranging from .64 to .86, all of which are statistically significant at the 0.05 significance level. Additionally, the internal consistency reliability coefficients (Cronbach’s alphas) in Table 2 range from .67 to .90. While eight of the nine reliability coefficients exceed the widely accepted threshold of .70, one has a value of .67, indicating the need for further consideration.

5.3. Structural model

The data exhibited a satisfactory fit for the proposed conceptual model, as evidenced by the model fitness results presented in Table 3 and Figure 2. Notably, the R-square values for Cognitive Consumer Experience (CCE), Affective Consumer Experience (ACE), and Omnichannel Customer Value (CV) were 0.51, 0.52, and 0.58, respectively. All exogenous constructs demonstrated R-square values exceeding 0.50, indicating a moderate level of explained variance (Hair et al., 2013). Further, the Q-square values, representing predictive relevance, were 0.31 for CCE, 0.32 for ACE, and 0.28 for Omnichannel Customer Value, reflecting a medium level of predictive relevance. The goodness of fit (GOF) values for CCE (0.56), ACE (0.57), and Omnichannel Customer Value (0.55) surpassed 0.36, affirming the model’s explanatory power (Akter et al., 2011).

Figure 2. Measurement model.

Table 3. Model fitness.

	R Square	Q Square	GOF
ACE	0.52	0.32	0.57
CCE	0.51	0.31	0.56
CV	0.58	0.28	0.55

Upon establishing the model’s fit, an examination of relationships between different constructs ensued. The Standardized Root Mean Residual (SRMR) model fit test corroborated the model’s accuracy, with an SRMR value of 0.063 indicating an adequate fit, below the threshold of 0.10 as suggested by Hair et al. (2017).

The study’s results offer insights into the relationships between integrated strategies, consumer experiences, and omnichannel customer value. Integrated Promotion (IP) demonstrates a statistically significant positive influence on both Cognitive Consumer Experience (CCE) and Affective Consumer Experience (ACE). Specifically, IP positively impacts CCE with a beta coefficient (β) of 0.163, a T statistic of 2.518, and a p-value of 0.012. Likewise, IP significantly influences ACE, as indicated by a β of 0.229, T statistic of 3.769, and a p-value of 0.000, supporting the hypotheses H1a and H1b (Table 4).

Table 4. Results of hypothesis testing: Structural model.

Hypothesis	β	T Statistics	P Values
H1a: (IP) -> (CCE)	0.163	2.518	0.012
H1b: (IP) -> (ACE)	0.229	3.769	0.000
H2a: (IPP) -> (CCE)	0.247	3.482	0.001
H2b: (IPP) -> (ACE)	0.173	2.759	0.006
H3a: (ITI)-> (CCE)	0.004	0.063	0.950
H3b: (ITI) -> (ACE)	0.076	0.993	0.321
H4a: (IIA) -> (CCE)	0.029	0.425	0.671
H4b: (IIA) -> (ACE)	0.186	3.466	0.001
H5a: (IOF) -> (CCE)	0.349	5.180	0.000
H5b: (IOF) -> (ACE)	0.105	1.505	0.133
H6a: (ICS) -> (CCE)	0.041	0.658	0.511
H6b: (ICS) -> (ACE)	0.104	1.869	0.062
H7a: (CCE) -> (CV)	0.335	5.927	0.000
H7b: (ACE) -> (CV)	0.482	8.955	0.000

Similarly, Integrated Price & Product (IPP) shows significant positive effects on both CCE and ACE. The relationship is evidenced by a β of 0.247, T statistic of 3.482, and a p-value of 0.001 for CCE, and a β of 0.173, T statistic of 2.759, and a p-value of 0.006 for ACE, supporting hypotheses H2a and H2b (Table 4).

However, Integrated Transaction Information (ITI) does not exhibit a discernible impact on either CCE or ACE. The beta coefficients for ITI are 0.004 (CCE) and 0.076 (ACE), with corresponding T statistics of 0.063 and 0.993, and p-values of 0.950 and 0.321, respectively. Consequently, hypotheses H3a and H3b are not supported (Table 4).

Integrated Information Access (IIA) reveals a significant positive effect on ACE but lacks a discernible impact on CCE. IIA’s impact on ACE is supported by a β of 0.186, T statistic of 3.466, and a p-value of 0.001. However, the impact on CCE, with a β of 0.029, T statistic of 0.425, and a p-value of 0.671, fails to support hypothesis H4a, while H4b is substantiated (Table 4).

Integrated Order Fulfillment (IOF) significantly influences CCE but does not show a discernible impact on ACE. This is evident through a β of 0.349, T statistic of 5.180, and a p-value of 0.000 for CCE, while the β for ACE is 0.105, with a T statistic of 1.505 and a p-value of 0.133. Consequently, hypothesis H5a is supported, but H5b is not (Table 4).

Integrated Customer Service (ICS) fails to demonstrate a significant impact on either CCE or ACE. With a β of 0.041, T statistic of 0.658, and a p-value of 0.511 for CCE, and a β of 0.104, T statistic of 1.869, and a p-value of 0.062 for ACE, both hypotheses H6a and H6b are not supported (Table 4).

Finally, both Cognitive and Affective Consumer Experiences significantly influence Omnichannel Customer Value (CV), as supported by respective β values of 0.335 and 0.482, T statistics of 5.927 and 8.955, and p-values of 0.000 for both, affirming hypotheses H7a and H7b. These findings underscore the intricate relationships within integrated strategies, consumer experiences, and the overarching value perceived by customers in the omnichannel context (Table 4).

5.4. Mediation analysis

The study extensively examined different pathways in the mediation analysis conducted to unravel the connections between integrated strategies, consumer experiences, and Omnichannel Customer Value (CV) (Table 5). Beginning with Integrated Promotion (IP), the results elucidated a mediation effect of 0.020 on CV through Cognitive Consumer Experience (CCE), supported by a 97.5% confidence interval ranging from 0.021 to 0.110 (Table 5). Simultaneously, the mediation effect of IP on CV through Affective Consumer Experience (ACE) exhibited a more modest impact of 0.001, with a confidence interval spanning from 0.054 to 0.186 (Table 5).

Table 5. Result of mediation analysis.

Mediation Path	Mediation effect	97.5% Confidence Intervals Bias Corrected
IP -> CCE -> CV	0.020	(0.021, 0.110)
IP -> ACE -> CV	0.001	(0.054, 0.186)
IPP -> CCE -> CV	0.003	(0.034, 0.140)
IPP -> ACE -> CV	0.013	(0.026, 0.155)
ITI -> CCE -> CV	0.951	(-0.044, 0.045)
ITI -> ACE -> CV	0.317	(-0.031, 0.112)
IIA -> CCE -> CV	0.673	(-0.029, 0.060)
IIA -> ACE -> CV	0.002	(0.040, 0.155)
IOF -> CCE -> CV	0.000	(0.065, 0.181)
IOF -> ACE -> CV	0.153	(-0.011, 0.123)
ICS -> CCE -> CV	0.534	(-0.031, 0.055)
ICS -> ACE -> CV	0.066	(-0.004, 0.102)

Integrated Price & Product (IPP) showcased parallel trends in its mediation effects. Specifically, IPP demonstrated a mediation effect of 0.003 on CV through CCE, with a confidence interval of 0.034 to 0.140 (Table 5). Additionally, the mediation effect of IPP on CV through ACE stood at 0.013, with a confidence interval extending from 0.026 to 0.155 (Table 5). The intricate analysis of Integrated Transaction Information (ITI) revealed noteworthy mediation effects on CV through both CCE (0.951) and ACE (0.317) (Table 5). However, the wider confidence intervals for these effects (-0.044 to 0.045 and −0.031 to 0.112, respectively) suggest a certain degree of variability in these relationships (Table 5).

Integrated Information Access (IIA) displayed a substantial mediation effect on CV through CCE, amounting to 0.673, while the effect through ACE was more modest at 0.002 (Table 5). Moving to Integrated Order Fulfillment (IOF), the results indicated no discernible mediation effect on CV through CCE (0.000), contrasting with a moderate effect of 0.153 through ACE (Table 5).

Finally, Integrated Customer Service (ICS) showcased a substantial mediation effect on CV through CCE (0.534), while the effect through ACE was more minor at 0.066 (Table 5). These comprehensive findings contribute to a thorough understanding of the intricate relationships between integrated strategies, consumer experiences, and the overarching value perceived by customers in the omnichannel context.

6. Discussion

This study explored the complex world of omnichannel retailing. It examined the various strategies, consumer experiences, and the overall perception of Omnichannel Customer Value (CV). The results revealed not only direct impacts but also subtle mediation effects, offering a deeper understanding of the omnichannel landscape.

6.1. Integrated Promotion (IP)

The strategic importance of Integrated Promotion (IP) is evident in its direct impact on both Cognitive Consumer Experience (CCE) and Affective Consumer Experience (ACE). By serving as a catalyst for awareness, education, and engagement, IP plays a crucial role in shaping the cognitive and emotional aspects of customer interaction. This, in turn, reinforces the significance of effective promotion in the overall perception of customer value, highlighting that it goes beyond mere transactions and becomes a transformative journey.

Furthermore, the introduction of mediation effects adds an intriguing layer to this narrative. The indirect influence of IP on Omnichannel Customer Value, mediated through CCE, suggests that promotional efforts lay the foundation for a positive cognitive journey, ultimately contributing to a holistic value perception. Simultaneously, the subtle mediation effect through ACE implies that emotional resonance fostered by promotion also plays a role in shaping the overall perceived value. In essence, IP acts as a strategic lever that not only directly shapes experiences but also intricately weaves through the fabric of customer value perception.

6.2. Integrated price & product (IPP)

The impact of Integrated Price & Product (IPP) is revealed as a symphony of synchronized pricing and product information. This directly affects both CCE and ACE, making IPP the embodiment of a seamless and cohesive omnichannel journey. The research findings align with existing literature that highlights the importance of consistent pricing and product details across different channels. Customers, effortlessly navigating between online and offline realms, enjoy a harmonious blend that promotes positive cognitive and emotional responses.

Examining the mediation effects offers an enlightening perspective. The indirect influence of IPP on Omnichannel Customer Value through CCE indicates that the synchronized pricing and product information establish a foundation for a positive cognitive journey, contributing to the overall perceived value. At the same time, the mediation effect through ACE emphasizes that the emotional resonance fostered by coherent pricing and product experiences also shapes the holistic value perception. IPP, therefore, emerges not only as a facilitator of transactions but also as a composer orchestrating a melodious omnichannel experience.

6.3. Integrated Transaction information (ITI)

The discussion surrounding Integrated Transaction Information (ITI) is complex, as it uncovers a more subtle yet significant role in shaping omnichannel experiences. Although the direct effects on CCE and ACE are not prominent, the mediation effects present a more intricate portrayal. ITI, which captures the intricacies of transactions, indirectly influences Omnichannel Customer Value through both cognitive and emotional channels.

The indirect impact of ITI on CV through CCE implies that when transactional information is seamlessly integrated, it establishes a foundation for a positive cognitive journey. This suggests that a transparent and easily accessible transactional landscape contributes to the overall value perceived by customers. The mediation effect through ACE adds an emotional dimension, indicating that transactional transparency indirectly shapes emotional responses and, consequently, the perception of value in the omnichannel context.

6.4. Integrated information access (IIA)

Integrated Information Access (IIA) plays a crucial role in influencing both cognitive and affective aspects. It directly affects Customer Cognitive Experience (CCE) and Affective Customer Experience (ACE), embodying the core of information availability and accessibility, which are vital components in the omnichannel narrative. The research findings support the idea that a seamless omnichannel journey, enriched with easy access to information, leads to positive experiences across all channels.

The mediation effects shed light on this phenomenon. The indirect impact of IIA on Omnichannel Customer Value through CCE highlights the fact that information accessibility acts as a catalyst for positive cognitive journeys, ultimately shaping the overall perceived value. At the same time, the mediation effect through ACE emphasizes that integrated information access fosters emotional resonance, leading to affective responses and, consequently, a holistic perception of value. Therefore, IIA not only facilitates the flow of information but also orchestrates the harmonious symphony of omnichannel experiences.

6.5. Integrated Order Fulfillment (IOF)

IOF plays a crucial role in shaping the Cognitive Consumer Experience (CCE), but it takes a more subtle approach when it comes to the affective dimension. The direct impact of IOF highlights the importance of efficient order fulfillment in influencing the cognitive aspect of customer interaction. These findings support the notion that a seamless order fulfillment process enhances the cognitive journey and fosters positive perceptions.

However, there is an additional layer of complexity introduced by the mediation effects. The indirect influence of IOF on Omnichannel Customer Value, through CCE, suggests that efficient order fulfillment acts as a catalyst for a positive cognitive journey, ultimately contributing to the overall perceived value. On the other hand, the lack of discernible mediation effect through ACE indicates that while IOF directly affects the cognitive dimension, its influence on affective responses is limited. As a result, IOF emerges as a pivotal driver of cognitive experiences, laying the foundation for an improved perception of omnichannel value.

6.6. Integrated customer service (ICS)

Integrated Customer Service (ICS) presents itself as a cohesive and reliable force, exerting its influence on Omnichannel Customer Value through a multi-faceted approach. While the direct impact on CCE and ACE may not be as pronounced, it sets the foundation for an examination of mediation effects. ICS, with its emphasis on consistent service across all channels, plays a strategic role in shaping the overall omnichannel narrative.

The mediation effects reveal compelling insights. The indirect influence of ICS on Omnichannel Customer Value through CCE highlights the significance of consistent customer service as a catalyst for positive cognitive journeys, ultimately contributing to the overall perceived value. Simultaneously, the mediation effect through ACE introduces a nuanced layer, suggesting that uniform service fosters affective responses and, consequently, influences the holistic perception of value. Therefore, ICS emerges as a crucial element that not only ensures consistent service but also intricately contributes to shaping the harmonious symphony of omnichannel experiences.

6.7. Omnichannel customer value (CV)

The Omnichannel Customer Value (CV) represents the ultimate achievement in the omnichannel journey, as it combines both cognitive and affective aspects. The significant and positive effects of both CCE and ACE on CV confirm the interdependent relationship between these experiences in shaping customers’ overall perception of value. These findings emphasize the importance of a harmonious interaction between cognitive and affective dimensions in shaping the comprehensive value that customers perceive across various channels.

6.8. Demographic considerations and inferences

The discussion gains further depth from the demographic nuances intertwined with the research findings. The predominance of the 31-40 age bracket highlights the need for businesses focusing on this specific demographic to prioritize the implementation of comprehensive integrated strategies. Additionally, the significant presence of respondents with doctoral-level educational backgrounds indicates that these findings hold significance across a wide range of educational experiences.

The concentration of respondents primarily from the Delhi-NCR region suggests that the insights derived from this study are particularly pertinent to businesses operating in or targeting this specific area. However, the overall patterns observed in demographic variables, including age, gender, and shopping preferences, imply that the implications of this study can be extrapolated to other urban centers sharing similar characteristics.

7. Implications of the Study

7.1. Theoretical implications

This study’s theoretical implications resonate across various dimensions, enriching existing frameworks and providing avenues for further exploration in the realm of omnichannel retailing.

7.1.1. Integrated strategies framework

The study contributes to the integrated strategies framework by disentangling the specific impacts of various integrated strategies on both cognitive and affective consumer experiences. The detailed examination of Integrated Promotion (IP), Integrated Price & Product (IPP), Integrated Transaction Information (ITI), Integrated Information Access (IIA), Integrated Order Fulfillment (IOF), and Integrated Customer Service (ICS) establishes a nuanced understanding of their individual and collective roles in shaping omnichannel experiences.

Future research could delve deeper into the synergies among different integrated strategies, investigating potential interactive effects or identifying optimal combinations that yield maximal positive experiences such as pleasure, impressions, purchasing habits, and brand loyalty (Bendoly et al., 2005; Jiang & Rosenbloom, 2005; Oh & Teo, 2010; Zhang et al., 2018). Moreover, expanding the integrated strategies framework to incorporate emerging technologies and channels could provide a more comprehensive guide for businesses navigating the ever-evolving omnichannel landscape.

7.1.2. Mediation mechanisms

The study introduces mediation mechanisms, unraveling the intricate pathways through which integrated strategies indirectly influence Omnichannel Customer Value (CV) via cognitive and affective dimensions (Oh & Teo, 2010). This nuanced exploration not only enhances the understanding of the sequential impacts but also sheds light on the interplay between cognitive and affective experiences.

Further theoretical development could focus on identifying additional mediating variables that might amplify or attenuate the effects of integrated strategies on consumer experiences. Exploring the temporal dynamics of mediation effects over the customer journey could provide a deeper understanding of how these influences evolve over time.

7.1.3. Customer experience dynamics

The study contributes to the theoretical understanding of customer experiences in the omnichannel context by emphasizing the dual dimensions of Cognitive Consumer Experience (CCE) and Affective Consumer Experience (ACE). This dual-focus aligns with the growing recognition that customer experiences are multifaceted and extend beyond transactional aspects (Cao & Zhang, 2011; Lee & Lin, 2005; Lee et al., 2011; Overby & Lee, 2006; Wong & Ye Sheng, 2012).

Future research could explore the dynamic nature of customer experiences in omnichannel retailing, considering factors such as customer journey stages, channel switching behaviors, and the evolving role of emerging technologies like augmented reality or virtual reality in shaping experiences. Additionally, investigating cross-cultural variations in customer experience dynamics could enrich the theoretical foundations of omnichannel retailing.

7.1.4. Demographic considerations

The study incorporates demographic considerations, offering insights into the variations in omnichannel experiences based on factors such as age, education, income, and geographic location. This integration of demographic nuances enriches the theoretical understanding of how different consumer segments perceive and engage with omnichannel strategies.

Future theoretical endeavors could delve deeper into the intersectionality of demographic variables, exploring how combinations of age, education, and income levels might shape omnichannel preferences and behaviors. Comparative studies across diverse cultural contexts could further extend the theoretical understanding of how demographic factors interact with cultural influences in omnichannel retailing.

7.1.5. Omnichannel customer value (CV)

The study enhances the theoretical conceptualization of Omnichannel Customer Value by emphasizing its dual foundations in cognitive and affective experiences. This nuanced understanding aligns with the evolving perspectives on customer value, acknowledging the emotional and cognitive dimensions as integral components.

Future research avenues may explore the temporal aspects of Omnichannel Customer Value, considering how immediate and long-term perceptions evolve. Additionally, investigating the antecedents and consequences of Omnichannel Customer Value could provide a more holistic theoretical framework, shedding light on the long-term impacts of positive omnichannel experiences on customer loyalty, advocacy, and lifetime value.

7.2. Managerial implications

This study’s findings offer valuable managerial insights, providing guidance for businesses seeking to optimize their omnichannel strategies. The implications span various facets of operations, from customer engagement to integrated strategies implementation.

7.2.1. Prioritizing customer experiences

The paramount importance of customer experiences in omnichannel retailing underscores the need for businesses to prioritize efforts aimed at enhancing both Cognitive Consumer Experience (CCE) and Affective Consumer Experience (ACE). Managers should invest in strategies that not only streamline transactions but also focus on creating emotionally resonant and informative interactions across channels. This involves aligning the overall brand narrative with the customer journey, ensuring consistency and coherence in messaging and service delivery (Bendoly et al., 2005; Jiang & Rosenbloom, 2005; Oh & Teo, 2010; Zhang et al., 2018).

7.2.2. Tailoring Integrated strategies

The study delineates the specific impacts of various integrated strategies, emphasizing the need for tailored approaches. Integrated Promotion (IP), Integrated Price & Product (IPP), Integrated Transaction Information (ITI), Integrated Information Access (IIA), Integrated Order Fulfillment (IOF), and Integrated Customer Service (ICS) each play distinct roles in shaping customer experiences. Managers should assess their business context and customer profiles to strategically implement integrated strategies that align with customer preferences and market demands (Zhang et al., 2018; Gao et al., 2021; Rose et al., 2011).

7.2.3. Leveraging mediation mechanisms

Understanding the mediation mechanisms uncovered in this study enables managers to deploy targeted interventions. For instance, recognizing the mediating role of CCE and ACE in the relationship between integrated strategies and Omnichannel Customer Value (CV) suggests that optimizing customer experiences can amplify the overall value perception. Therefore, businesses should focus on initiatives that directly enhance customer experiences, anticipating positive ripple effects on perceived value and, consequently, customer loyalty and advocacy (Akter et al., 2011).

7.2.4. Considering demographic nuances

The demographic considerations highlighted in the study provide actionable insights for segment-specific strategies. Managers should tailor their omnichannel approaches based on factors such as age, education, income, and geographic location. For instance, younger demographics might respond well to interactive and tech-savvy interfaces, while older demographics may prioritize reliability and ease of use. Considering these nuances ensures that omnichannel strategies resonate with the diverse preferences of the target audience (Jiang & Rosenbloom, 2005; Wong & Ye Sheng, 2012).

7.2.5. Embracing competitive dynamics

The study accentuates the influence of rival offerings on customers’ willingness to pay for cross-channel integration initiatives. Managers should continuously monitor and analyze competitors’ strategies, ensuring that their own offerings remain competitive and attractive. This involves staying attuned to market trends, benchmarking against industry leaders, and proactively innovating to maintain a distinctive position in the market (Fang et al., 2021; Oh & Teo, 2010).

7.2.6. Investing in technological integration

As customers increasingly seek seamless transitions between online and offline channels, investing in technological integration becomes imperative. Managers should explore advanced technologies, such as artificial intelligence, augmented reality, and data analytics, to create a cohesive omnichannel experience. This includes implementing personalized recommendations, predictive analytics for inventory management, and leveraging data insights to anticipate customer needs (Cao & Zhang, 2011; Coelho et al., 2017).

7.2.7. Continuous monitoring and adaptation

Omnichannel dynamics are dynamic and subject to rapid changes. Therefore, managers should institute continuous monitoring mechanisms to track customer feedback, emerging technologies, and market trends. Regularly reassessing the effectiveness of integrated strategies ensures that businesses remain agile and responsive in an ever-evolving omnichannel landscape (Saari et al., 2021).

7.3. Constraints and future research

7.3.1. Constraints

This study confronts several constraints that impact the generalizability of its findings. Firstly, the focus on data from the Delhi-NCR region in India introduces geographical limitations, potentially limiting the broader applicability of the results due to regional variations in consumer behavior. Secondly, the sample size, while adhering to the ten-times rule, involves the exclusion of 100 surveys, raising considerations about the representativeness of the sample and the potential influence on the robustness of statistical outcomes. Additionally, an overemphasis on positive aspects in the examination of omnichannel experiences may have overlooked challenges or negative factors that could shape customer perceptions.

7.3.2. Future research directions

Moving forward, future research endeavors could address these constraints and explore new dimensions in omnichannel retailing. Firstly, investigating the influence of cultural factors on omnichannel experiences remains a promising avenue, offering insights into how diverse cultural nuances impact integrated strategies and customer preferences. Secondly, longitudinal studies capturing the dynamic nature of omnichannel interactions over time can provide a deeper understanding of evolving customer experiences in response to technological advancements. Thirdly, the integration of virtual and augmented reality technologies in omnichannel experiences represents an exciting frontier, warranting exploration into their impact on both cognitive and affective dimensions.

Delving into negative experiences and challenges within omnichannel environments is crucial for a comprehensive understanding. Future research could uncover factors such as security concerns, information overload, or interface complexities that may hinder optimal omnichannel experiences. Additionally, exploring omnichannel experiences in emerging markets offers unique insights into how strategies adapt to the challenges and opportunities specific to these economies. Quantifying the financial impact of optimized omnichannel experiences is another crucial avenue, providing businesses with a clearer understanding of the direct contributions to their bottom line.

Moreover, the role of social media integration in omnichannel experiences deserves dedicated investigation. Examining how social media influences customer perceptions, interactions, and overall satisfaction within omnichannel contexts can uncover new dynamics in customer engagement. In conclusion, by addressing these constraints and venturing into these unexplored territories, future research can enrich the understanding of omnichannel retailing and contribute valuable insights for businesses navigating this complex landscape.

7.3.3. Ethics approval and consent to participate

All procedures involving human participants in this study adhered to the ethical standards of Lingaya’s Vidyapeeth, India, as recommended by their Institutional Ethics Committee. These standards align with the principles outlined in the 1964 Helsinki Declaration and its subsequent amendments. Informed consent for participation was obtained verbally. Participants were comprehensively informed about the study’s objectives and expressed their understanding and willingness to participate freely. They were also assured of the confidentiality of their data.

7.3.4. Informed consent

Informed consent was obtained from all participants prior to their involvement in the study. Participants were provided with a written consent form detailing the purpose of the research, procedures involved, potential risks and benefits, confidentiality measures, and their rights as participants. This consent was obtained in written form as the study employed hard copy questionnaires distributed to participants to gather data. Participants were ensured that their participation was voluntary, and they were free to withdraw from the study at any time without consequence.

7.3.5. Consent for publication

All authors are willing for publication of this manuscript.

Author contribution statement

1. Conception, Design, Analysis and Interpretation of the Data - Sadhana Mishra, Mayank Mishra

2. Drafting of the paper- Prashant Kumar Pandey, Praveen Kumar Pandey

3. Revising it critically for intellectual content- Praveen Kumar Pandey, Dr. Samriti Mahajan, Dr. Mohd Asif Shah

4. Final approval of the version to be published- Dr. Mohd Asif Shah

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the author Mr. Mayank Mishra upon reasonable request.

Additional information

Notes on contributors

Sadhana Mishra

Dr. Sadhana Mishra earned her Ph.D. in management in 2014 from FMS, Banaras Hindu University, Varanasi, India. Following this accomplishment, she embarked on a professional journey that led her to the University of Hail, Saudi Arabia, where she presently holds the position of Assistant Professor in the College of Business Administration. Dr. Mishra’s primary research focus revolves around the domains of Retailing and Tourism. With a wealth of experience, she has delved extensively into research areas such as Brand Management, Customer Experience, Brand Engagement, Shopping behavior, Tourism and Hospitality, Medical Tourism, Virtual Tourism and Green Marketing. Beyond her academic endeavors, Dr. Mishra actively contributes to the scholarly community as a reviewer for journals indexed in Scopus and Wiley.

Mayank Mishra

Mayank Mishra achieved his MBA, specializing in Marketing and Finance, from Uttar Pradesh Technical University, India, in the year 2008. With a rich background encompassing a decade of corporate training and sales experience, Mayank is currently immersed in the pursuit of a management fellowship program at the Fortune Institute of International Business (FIIB) in New Delhi, India. Mayank’s academic journey and professional trajectory reflect a dynamic blend of expertise. His research pursuits traverse the realms of Omni Digital, Channel Integration, Customer Experience, Customer Loyalty, Tourism, Immersive Technologies, Metaverse and Brand Engagement. This eclectic spectrum of interests not only underscores his diverse skill set but also mirrors his commitment to exploring the intersections of contemporary business dynamics.

Prashant Kumar Pandey

Prashant Kumar Pandey is an FPM Scholar at the Fortune Institute of International Business, New Delhi. His academic interests in marketing and management studies have driven him to become a passionate and prolific researcher. He served as a Research Officer for the Ministry of Electronics and IT, collaborating with the prestigious Indian Institute of Public Administration (IIPA) in New Delhi. He has an impressive track record of publishing in well-respected national and international journals, including the Journal of Health Management (SAGE Publication), COGENT Business & Management (T&F Publication), TEM Journal, Research in World Economy, among others. He has actively participated in the peer review process for prestigious journals like Global Business Review (Sage Publication), International Journal Electronic Finance (Inderscience Publication). His published works showcase innovative research ideas and strong analytical skills, demonstrating his potential to make a significant contribution to the field of management studies and marketing in the metaverse. He can be reached at [email protected].

Praveen Kumar Pandey

Praveen Kumar Pandey is a Assistant Professor at the School of Commerce and Management, Lingaya’s Vidyapeeth Faridabad. With a focus on Management Studies and Service Marketing, he has developed a passion for research and has consistently demonstrated this through his numerous published works in prestigious National and International journals. His commitment to academic excellence is reflected in his contributions to the COGENT Business & Management (T&F Publication), Indian Journal of Marketing, Journal of Health Management (SAGE Publication), TEM Journal, Research in World Economy, just to name a few. He has actively participated in the peer review process as a Reviewer for prestigious journals like Journal of Strategy & Management (Emerald Publication), International Journal of Services, Economics & Management (Inderscience Publication), International Journal Electronic Finance (Inderscience Publication). These publications showcase his innovative research ideas and analytical skills, highlighting his potential to make a significant contribution to the field of Management Studies and Service Marketing.

Samriti Mahajan

Dr. Samriti Mahajan is an accomplished academician and industry professional with over 10 years of experience in teaching, research, and administration. She currently serves as the Associate Professor and Head of Department in the School of Commerce & Management at Lingaya’s Vidyapeeth, Faridabad. Dr. Mahajan holds a Ph.D. in Green Marketing, an MBA in Biotechnology, and a graduation degree in Biotechnology. Her areas of specialization include Digital Marketing, Strategy Marketing, Consumer Behavior, Brand Management, International Business. She has authored 2 books, more than 10 chapters, Case Study, 4 - design patents and 18 utility patents - Indian and International patents. Her research contributions include several International Scientific papers and review papers. Dr. Mahajan has been a speaker for the World Innovation Patent Conclave, Guest of Honour for IPR cell- BIT Raipur, Project Judge- BIMT Bangalore, Session Chair for Seminars and Conferences, and resource speaker for FDP & MDP. She is a member of IAAC - International Association of Academics Plus Corporate.

Reviewer for ABCD Indexing & Heliyon.

Editor - Wiley Publication

Awards:-

● Young Women Educator and Scholar by - National Foundation for Entrepreneurship Development (NFED) 2023

● Indian Researcher Award by IRA, London, U.K - 2021

Mohd Asif Shah

Dr Mohd Asif Shah is currently working as an Associate Professor at Kabridahar University, Ethiopia. He has more than hundred publications which are indexed in Scopus, and Web of Science Indexed Journals. Having more than ten years of teaching experience, he has been a popular instructor. His courses always fill up quickly as students enjoy his teaching style. He tries to deliver the information in a fun and interesting manner to aid students’ grasp of the material and hold their interest.