1.1 Background

Fast-moving consumer goods (FMCG) retailers are bargaining a rapidly changing consumer environment in the current digital market. Retail brands are under increasing pressure to provide consistent and easy-to-use experiences as consumer demand more seamless integration across online, mobile, and traditional channels. As described as “an integrated sales experience that melds the advantage of actual stores with the information rich experience of online shopping” (Massi et al., 2023).

Before entering the omnichannel era, where channels come together to serve the client as a single system, retail in the past changed from single-channel (brick and mortar) to multichannel (parallel online and offline) (Mounaim et al., 2021). Despite its increasing use since 2016, omnichannel has not received enough consumer-centric research, especially in the FMCG industry (Furquim et al., 2023) (Massi et al., 2023).

According to a recent Peruvian research, consumer behavior research shows that communication through an integrated channel, process consistency, and service transparency have a major impact on consumers’ intentions to make purchases, particularly in developing markets (Astete-Meza et al., 2025). In a similar discovered that improved omnichannel integration in FMCG settings increases customer experience and satisfaction, which are essential indicators of  repurchase intentions and loyalty.

The stakes are high: as global FMCG competition intensifies, firms that do not offer integrated omnichannel service risk losing the trust, loyalty and market share of consumers. The importance of channel synchronization, tailored marketing, and a smooth checkout is highlighted by study on Generation Z in FMCG retail, which shows that omnichannel integration has a immediate impact on their purchase intention (Risk M et al., 2024).

Although, there is still a research gap: despite the fact that several studies examine omnichannel in electronics and fashion, few examine how it influences FMCG purchase intention through factors like perceived convenience (Wang et al., 2024; Omar et al., 2023; Raife et al., 2020; Ririn et al., 2022). By methodically examing the mechanism that connect integrated omnichannel strategies to consumer purchase intentions through perceived convenience, this paper seeks to close that gap.

1.2 Problem Statement

Despite the growing popularity of omnichannel strategies in the Fast-Moving Consumer Goods (FMCG) sector, While earlier research has looked at individual factors like checkout ease and customization, Rarely has it examined their combined impacts, especially when mediated by perceived omnichannel convenience. Furthermore, the results of the literature that is now available are often limited to particular retail stores or geographic areas, this restricts their relevance. Research on mediation effects in developing markets, such as India, where digital transformation varies widely and consumer behavior is highly context-specific is severely inadequate. This indifference makes it more difficult for brands to develop consumer-focused, evidence-based omnichannel strategy. Therefore, this study intends to empirically study how, in the FMCG industry channel synchronization, seamless checkout experience and personalized marketing all perform together to affect consumer purchase intention through a perspective of perceived omnichannel

1.3 Research Objective and Questions

The major objective of this research is to examine, using perceived omnichannel convenience as a mediator, how omnichannel strategy factors affect consumers’ intentions to make purchases in the FMCG industry.

Research Questions

1. How does channel synchronization influence FMCG consumers’ intention to buy through their perception of omnichannel?
2. Which aspect a seamless checkout experience affect consumer purchase intention, mediated by perceived omnichannel convenience?
3. Does improving perceived omnichannel convenience through personalized marketing improve purchase intention in FMCG?

• Contribution of the Study

This study offers in depth of research on omnichannel retailing in both theoretical and practical. Theoretically it improves knowing of how omnichannel strategy factors like channel synchronization, seamless checkout and personalized marketing combine together to influence consumer purchases. The study improve current models of consumer decision making, especially in the context of FMCG, by including perceived omnichannel convenience as a mediating factor. In practice, the study offers data-driven insights to retailers and marketers who want to create omnichannel experiences that are effective, integrated and responsive to customer preferences. The results will make strategic investments in technology directly and advancement to the customer experience through the integration of Structural Equation Modeling.

2.1 Omnichannel Strategies in FMCG

2.1.1 Channel Synchronization

SmartPLS SEM was used to analyze a statistical survey with n=307 Romanian Millennials and Gen-Z. Apart from utilitarian and hedonistic value, channel synchronization the alignment of digital and physical channels was found to be a powerful predictor of positive attitude and willingness to utilize omnichannel retail channels. Uses a framework based on UTAUT2 to provide empirical evidence for the impact of synchronization on consumer attitude. It did not however investigate perceived convenience mediation, which study discuss about the context of FMCG (Lulia Diana Nagy et al., 2024). Through SEM analysis, information visibility and smooth channel integration greatly influence customer satisfaction and the intention to make repeat purchases in emerging markets using an expanded S-O-R model. Smooth transitions were not as important as information integration, which is a part of channel synchronization and suggests that you pay attention to aspects of transparency(Melanie Wiese 2024). Retail shoppers in Italy (grocery/FMCG sector, n=1,031) were polled. This implication is mediated by perceived integration quality. supports the use of mediation analysis and confirms channel synchronization as the key IV. Nevertheless, the study directly isolates perceived omnichannel convenience, which is treated as an integration quality (Giada Salvietti et al., 2024).

2.1.2 Seamless checkout experience

The study uses regression analysis to show that a seamless checkout significantly forecast purchase intention based on a statistical survey (n=400) of Egyptian Generation Z FMCG consumers.  Although the study emphasizes direct effects, it does not directly test mediation processes like perceived convenience. This discrepancy supports the thesis that in FMCG, seamless checkout and purchase intention are mediated by perceived convenience (Risk M et al., 2024).  The fact that convenience way not directly examined highlights the value of the study’s clear modeling of perceived convenience as a mediator (Lulia Diana Nagy et al., 2024).

2.1.3 Perceived Omnichannel convenience

The effect of online-offline integration on satisfaction and repurchase intention is mediated by perceived business convenience or the ease of transacting actions across channels, according to a survey ( n = 600) and a moderated mediation analysis test. Facilitators include easy pickup, flexible channel selection and return policies. Convenience is highlighted in the study as a cognitive mediator, the research expands on this by examining FMCG purchase intention with particular omnichannel variables (Shu-Hsien Lion et al., 2024). Through the use of an expanded S-O-R framework in a developing market multiplexed with SEM, they demonstrate how the effects of channel integration on satisfaction and intention are mediated by sharing convenience, or even though it is not called “perceived convenience.” While confirming the mediating role, this implies (Melanie Wiese 2024).

2.1.4 Personalized Marketing

According to a survey based study (Australia n=500) that uses structural equation modeling, omnichannel shopping habits are developed through personalized marketing that improve value consciousness and cognitive engagement through personalized emails, app messages and loyalty programs. These behaviors subsequently have a positive impact on purchasing decision (Neeru Sharma et al., 2024).

2.1.5 Consumer Purchase Intention

Deep analysis of 130 Scopus-indexed publications that look at omnichannel consumer behavior. Describes how common factors influencing purchase intention include channel consistency, convenience, trust and personalization. Demonstrates the value of testing indirect effects through convenience and validates the applicability of the DV. The gap demands in this SLR are addressed by the study (Lu LUO et al., 2023).

2.2 Research Gap and Theoretical Support

2.2.1 Agreement Across Literature: The components of omnichannel strategy, particularly channel synchronization, a smooth checkout process and personalized marketing, are consistently found to have an important impact on consumer purchase outcomes across recent empirical research. Structure like attitude, perceived value and increasingly perceived convenience act as mediators for these influences (Paula et al., 2020). Channel synchronization, for example has been demonstrated to improve consumer trust and lessen decision-making friction. seamless checkout exhibits increase usability and reduce fatigue with choices, contributing to improved consumer behaviors(B2B Daily, 2025).

2.2.2 Research Gaps and Contribution: Even if these results are constant, there are significant research gaps. Perceived convenience as a mediator has not been thoroughly examined in many research, particularly in the FMCG industry, where channel dynamics and consumer behavior suffer from those forming to high involvement groups. Furthermore, previous research has tended to either aggregate omnichannel variables or narrowly focus on specific markets, it restricts the genralizability of the findings.

2.2.3 Methodological Alignment: Qualitative results or single-variable testing are commonly used in the existing study. More compelling evidence of a casual relationship might be found using a quantitative, SEM based approach. Notably, no prior study has directly verified the intermediary impact of perceived convenience in between seamless checkout and purchase intention with regard to the FMCG context, even though some have focused on specific touch-points or information flows using different theoretical lenses. By addressing that gap, this study provides a significant contribution from an empirical perspective.

2.2.4 Theoretical Support: This study makes use of  SOR framework, TAM and UTAUT2. These theories collectively explain how consumer perception and responses are shaped by factors and causes related to technology adoption. The choice to focus on perceived omnichannel convenience in the present theoretical framework is strongly supported by the result of earlier research. Convenience is frequently cited as a crucial component of consumer behavior and its role as mediator is also becoming more widely acknowledged, despite the fact empirical ramifications of this are still not well understood.

1. Conceptual Framework and Hypothesis
   • Theoretical Foundations: This study relies on three fundamental theories: UTAUT2, the Technology Acceptance Model (TAM) and the Stimulus-Organism-Response (SOR) framework. UTAUT2 (Unified Theory of Acceptance and use of Technology) explains consumer behavior regarding use of technology, demonstrating how adoption is influence by performance and effort requirements (Venkatesh et al., 2012). TAM, which comes from information systems, asserts that perceived usefulness and ease of use are key predictors of the acceptance of digital innovations (Davis, E.D. 1989). The SOR model offers an understanding into psychology, implying that environmental cues (omnichannel features) influence internal states (the organism: perceived convenience), leading to consumer responses (such as purchase intention). These theories collectively support the idea that omnichannel elements act as stimuli that shape internal perceptions and affect buying behavior.

3.2 Justification for Variable Selection:

The independent variables Personalized Marketing, Channel Synchronization and Seamless Checkout Experience, were selected due to their strategic significance in cross-channel retail. By guaranteeing constant product information, pricing and promotion across touch points, channel synchronization lowers  reduce the work of consumer (Paula et al., 2020).  Seamless checkout immediately improves convenience by using effective and user friendly payment methods help to decrease friction and cart abandonment. By using consumer data to create personalized offers and marketing speeds up decision making and decreases decision overload (Issac Asante et al., 2024). In the FMCG industry, these variables have rarely been studies in conjunction with perceived omnichannel convenience as a mediator, despite earlier research that connected them to consumer satisfaction and purchasing behavior (Uzir Hossain et al., 2021). In addition to offering practical insights for retail strategy, this integrated approach improves theoretical rigor.

3.3 Conceptual Model

Figure 1. Conceptual Diagram

Source: Authors’own work

3.4 Hypothesis Development

H1. Channel Synchronization Positively Influences Perceived Omnichannel Convenience.

Channel synchronization, which involves providing consistent product information, pricing and promotion across both digital and physical touchpoints, reduces friction and effort during the shopping journey (Jingwen Li and Yaping Chang 2023; Sundjaja et al., 2024). Research indicates that seamless integration allows consumers to switch channels smoothly, enhancing their sense of convenience and control(Chia-Lin Hsu et al., 2023; Neeru Sharma and Johra Ftima, 2024; Paula et al., 2020; Aisha Muthaffar et al., 2024). This finding suggests that synchronization plays a crucial role in shaping perceptions of convenience by minimizing disruptions and uncertainties, thereby supporting its status as a strong antecedent to perceived omnichannel convenience in the FMCG retail sector.

H2. Seamless Checkout Experience Positively Influences Perceived omnichannel Convenience.

A frictionless checkout process, characterized by simplified payment flows and consistent interfaces, significantly contributes to the perceived ease and fluidity of shopping. Industry analyses show that solutions such as tap-and-go or tokenized payments reduce transaction times, lower abandonment rates and improve overall perceptions of convenience(Business Insider). This empirical evidence underscores that a streamlined checkout process strengthens consumers’ assessment of convenience, validating its inclusion as an essential antecedent in the mediation model.

H3. Personalized Marketing Positively Influences Perceived Omnichannel Convenience.

Personalized marketing customizes offers and messaging based on consumer preferences, which effectively reduce cognitive load and increase relevance. Research on omnichannel personalization demonstrates that relevant communication improves perceived value and ease, enabling consumers to make quicker decsions without extensive searching (Chia-Lin Hsu et al., 2024). This creates a smoother shopping experience that enhances perceived convenience, justifying the significance of personalized marketing in shaping convenience perceptions.

H4. Perceived Omnichannel Convenience Positively Influences Consumer Purchase Intention.

Perceived convenience has consistently been shown to influence purchase intention by lowering psychological barriers and effort-related obstacles. Empirical research indicates that when consumers view their omnichannel journey as convenient, their satisfaction, trust and intention to purchase significantly increase (Neeru Sharma and Johra Ftima, 2024; Jingwen Li and Yaping Chang 2023; Sundjaja et al., 2024). This supports the hypothesis that perceived omnichannel convenience is a key factor in driving purchase intention in FMCG contexts.

H5a. Perceived Omnichannel Convenience Mediates the Relationship Between Channel Synchronization and Consumer Purchase Intention.

Channel synchronization simplifies the transition between channels, enhancing the overall shopping experience. However, its impact on purchase intention isn’t always direct; it primarily occurs through the mediation of perceived convenience (Chia-Lin Hsu et al., 2024). When consumers perceive their journey as smooth and consistent across channels, they are more inclined to make a purchase. Studies suggest that synchronized channels help reduce decision fatigue and the time cost associated with shopping, thereby increasing perceived convenience and boosting purchase behavior.

H5b. Perceived Omnichannel Convenience Mediates the Relationship Between Seamless Checkout Experience and Consumer Purchase Intention.

While a seamless checkout process fosters customer satisfaction, it is the resulting convenience that drives actual purchases. Consumers view faster, secure and familiar checkout experience as convenient. This perceived ease reduces abandonment rates and enhances trust booth of which are essential for promoting purchase intent (Paula et al., 2020) . Research confirms that the checkout experience alone is not always sufficient unless it creates a strong perception of convenience and effortlessness, highlighting the critical mediating role of perceived omnichannel convenience.

H5c. Perceived Omnichannel Convenience Mediates the Relationship Between Personalized Marketing and Consumer Purchase Intention.

Personalized marketing improves message relevance, but its impact on purchase intent is most significant when it translates into convenience. By minimizing irrelevant choices and presenting timely offers, personalization saves consumers time and effort (Lemon, K. N et al., 2016). This perceived convenience positively influences consumer behavior, supported by omnichannel retail research indicating that convenience serves as the mechanism through which personalization facilitates purchase intent

4.1 Research Context (Chennai FMCG Sector)

The Fast-Moving Consumer Goods sector in Chennai, India a metropolis with a diversified consumer base and quick digital adoption was the study’s site. Chennai offers a suitable backdrop because of its population’s digital literacy and integration of offline and online FMCG retail channels.

4.2 Research Design

To evaluate the hypothesis, a cross-sectional quantitative survey method was used to collect standardized responses from a large group of consumers in Chennai. This method facilitates the application of structural Equation Modeling (SEM) and allows for the objective measuring of interaction between variables.

4.3 Sampling Technique and Data Collection

Purposive sampling was used to find consumers who had previously shopped for FMCG products both online and offline. 300 respondents’ information was gathered using offline surveys at retail locations and online questionnaires using Google Forms. To make sure respondents had purchased FMCG products within the previous three months, they were screened. Strict adherence was maintained to ethical principles like data privacy and informed consent.

4.4 Measurement

All constructs were assessed using previous validated 8-item surveys on a 5-point Likert scale (1=Strongly Disagree, 5=Strongly Agree) (Appendix) to ensure validity and reliability. Internal consistency was proven over the suggested threshold of 0.70 using Cronbach’s alpha values (α).

Channel Synchronization: Adapted from (Carla Ferraro et al., 2016).  This concept measures the extent to which customers believe that pricing, inventory and information are integrated and consistent across retail channels. For instance: “Product details are the same on the website, mobile app and store.” (α = 0.902)

Seamless Checkout Experience: Adapted from (Susana Costa et al., 2023). Captures the effectiveness, clarity and simplicity of carrying out cross-channel transactions, including features like flexible payment option and error-free processing. An example item is: “The checkout process is quick and straightforward with minimal steps.” (α =0.898 )

Personalized Marketing: Adapted from (Lemon, K. N et al., 2016). Investigating how marketing offers, communication and content can be customized to meet the preference and behaviors of specific customers. For instance: “I receive offers and promotions from the brand that correspond with my past purchases.” (α = 0.887)

Perceived Omnichannel Convenience (Mediating Variable): Adapted from (Ling Jiang et al., 2013). This scale captures the time, effort and process convenience across integrated retail channels. An example item is: “I can easily find and purchase products regardless of the channel I use.” (α =0.86)

Consumer Purchase Intention (Dependent Variable): Adapted from (Lemon, K. N et al., 2016).Evaluates the probability upcoming purchases via omnichannel platforms. Example item: “I plan to buy something from this brand soon.” (α = 0.861)

4.5 Data Analysis Tools

Descriptive statistics, reliability testing and correlation analysis have been conducted using SPSS. Confirmatory Factor Analysis (CFA), composite reliability, AVE and path analysis with bootstrapping (5,000 resamples) were all included in the PLS-SEM using SmartPLS. To verify that multicollinearity was absent, the variance Inflation Factor (VIF) was examined.

• Descriptive Statistics

Table 1. Respondents’ Profile

Source: Authors’own work

The study examined responses from 300 FMCG consumers in Chennai, all of whom had prior experience with both online and offline retail channels. The demographic variables showed relatively high central tendencies, with mean scores indicating a skew towards female respondents aged 31-50 years (M=4.22, SD=0.61 for gender; M=4.38, SD=0.64 for age).

Educational backgrounds were also notably high, with most participants reporting postgraduate-level qualifications (M=4.34, SD=0.71). In terms of occupational status, a significant portion of respondents were employed in the private or government sectors (M=4.41, SD=0.65).

The income distribution reflected higher purchasing power, with an overall mean of 4.36 (SD=0.72), suggesting that many respondents earned above ₹50,000 per month. The shopping-related constructs confirmed an active omnichannel orientation: shopping frequency was high (M=4.35, SD=0.69), while channel preference showed a strong favor for both online and offline shopping (M=4.29, SD=0.76). Respondents reported frequently using both channels for a single brand (M=4.43, SD=0.77).

Measures of skewness ranged from -1.38 to -2.37 and kurtosis ranged from 5.37 to 8.06, indicating a negatively skewed but sharply peaked distribution across demographic and shopping-related items. This suggests strong clustering of responses at the higher end of the scales. Lastly, confidence in using multiple platforms was consistently high (M=4.29, SD=0.78), further underscoring a digitally literate, educated and affluent consumer base in Chennai. This profile supports the suitability of the sample for investing the mediating role of perceived omnichannel convenience in FMCG purchase intentions.

5.2 Measurements Model Assessment

Table 2. Factor Loading and Multicollinearity

Source: Authors’own work

5.2.1 Factor Loading: Initial Factor Loading was conducted to refine the measurement model. Items with low factor loadings (<0.70) or cross-loadings were removed. After item purification, the retained items demonstrated strong loadings on their respective constructs, confirming to subsequent reliability and validity assessments. An initial exploratory factor analysis was conducted to refine the measurement model. Items with low factor loadings or that exhibited cross-loadings were removed, such as POC2 and POC3. After purifying the items, the remaining ones showed strong loadings on their respective constructs, confirming multidimensional. This process ensured that only valued indicators were included in the subsequent assessment of reliability and validity.

5.2.2 Multicollinearity Assessment: Variance Inflation factor values for all indicators ranged from 1.71 to 2.19, which is below the acceptable threshold of 3.3. This indicates that there is no multicollinearity among the predictors.

Table 3. Reliability and Convergent Validity

Source: Authors’own work

5.2.3 Reliability and Convergent Validity: Cronbach’s Alpha, Composite Reliability and Average Variance Extracted were assessed. All constructs reported Cronbach’s Alpha values above 0.85, CR above 0.90 and AVE above 0.50. For instance, Channel Synchronization (α = 0.902, CR = 0.921, AVE = 0.594) and Consumer Purchase Intention (α = 0.861, CR = 0.9, AVE = 0.643) exceeded the recommended thresholds.

Table 4. Discrminant Validity (Fornell-Larcker Criterion)

Source: Authors’own work

5.3 Discriminant Validity

5.3.1 Fornell-Larcker Criterion: The square roots of AVE (diagonal values) were greater than inter-construct correlations. For example, Consumer Purchase Intention (0.802) was higher than its correlations with CS (0.598), PM (0.583), SC (0.545) AND POC (0.82). Similarly, other constructs followed the same pattern.

5.4 Path Coefficients

To test the hypothesized relationships, we analyzed the path coefficients, t-statistics and p-values.

Table 5. Path Coefficients

Source: Authors’own work

Figure 2. Path Model Results

Source: Authors’own work

5.4.1 Hypothesis Testing Results:

Table 6.  Hypothesis Testing

Source: Authors’own work

The study found that Personalized Marketing (β=0.442) was the most significant predictor of Perceived Omnichannel Convenience, followed by channel synchronization (β=0.423) and seamless checkout experience (β=0.337) . Collectively, the model explained 70% (R² = 0.704) of the variance in Perceived Omnichannel Convenience and 67% (R² = 0.673) of the variance in consumer purchase intention. The coefficient of determination (R²) is a key measure for evaluating the explanatory power of a PLS-SEM path model, as highlighted by (Hair et al., 2021). The Q² values for all dependent constructs (POC=0.694, CPI=0.661) were greater than zero, indicating good predictive relevance.

6.1 Interpretation of Results

The findings indicate that all three factors of an omnichannel strategy - Channel Synchronization(CS), Seamless Checkout(SC) and Personalized Marketing(PM) positively impact Perceived Omnichannel Convenience (POC), confirming hypothesis H1 to H3. Among these factors, Personalized Marketing (β = 0.820, p < 0.001), thereby validating hypothesis H4 and establishing convenience as a key factor in consumer behavior within the fast-moving consumer goods(FMCG) sector. Mediation analysis supported hypothesis H5a to H5c, demonstrating that CS, SC and PM indirectly influence CPI through POC. This emphasizes how POC serves as the main connection between omnichannel strategies and customer intentions.

6.2 Comparison with Existing Literature:

The result align with previous research demonstrating that a smooth checkout reduces cart abandonment, targeted marketing increases satisfaction and loyalty and channel synchronization increases trust and minimizes purchase friction (Junbin Wang et al., 2022). This study unifies these characteristics into a unified framework in contrast to previous research that examined them separately and validated perceived omnichannel convenience (POC) as a mediator in FMCG retail. It builds on previous research on omnichannel integration (Severin Friedrich Bischof et al., 2020)  by providing empirical evidence of the relationship between convenience on business strategy and customer behavior.

6.3 Theoretical Contributions:

By identifying POC as a key mediating construct, this study supports the omnichannel retailing paradigm. It highlights channel synchronization, smooth checkout and personalized marketing as the main factors influencing convenience, drawing on Technology Acceptance Theory and customer experience viewpoints. By emphasizing convenience as a deciding element in FMCG purchase where decisions frequently rely more on ease than on on-depth analysis the validated SEM model advances consumer behavior theory.

6.4 Answers to Research Questions:

1. POC was much enhanced by channel synchronization, seamless checkout and personalized marketing.
2. POC’s crucial significance in FMCG retail was confirmed by the fact that it was the best predictor of consumer purchase intention.
3. Purchase intention is linked to omnichannel tactics by POC, according to mediation analysis (H5a-H5c).

Implications

• Theoretical Implications:

By establishing Perceived Omnichannel Convenience (POC) as a key mediating construct that connects channel synchronization, smooth checkout and personalized marketing to customer buy intents, this study enhances omnichannel research. This study confirms convenience’s mediating role in the FMCG industry, in contrast to previous research that saw it as a result or antecedent (Junbin Wang et al., 2022) . By successfully demonstrating the substantial influence of POC on purchase intentions, this research not only enhances customer experience theory (Lemon, K. N et al., 2016) but also advances existing omnichannel integration frameworks.

7.2 Managerial Implications

Channel Synchronization: Aligning price, promotions and inventory in line fosters trust and lowers conflict.

Seamless Checkout: Secure, expedited transactions increase dependability and reduce cart abandonment.

Personalized Marketing: Relevance and loyalty are increased through data-driven offerings and customized messaging.

Retailers should view omnichannel convenience as a competitive advantage sine FMCG purchases are motivated by convenience. Aside from being a technical advancement, integrating synchronization, checkout and personalization tactics increase customer pleasure, loyalty and buy intention.

2. Conclusion and Future Scope

8.1 Summary of Key Findings:

In the fast-moving consumer goods(FMCG) industry, this study examined the effects of three essential omnichannel strategy components on consumer purchase intention (CPI): channel synchronization, a seamless checkout process and personalized marketing. Perceived omnichannel convenience (POC) was also investigated as a mediating variable. The results from the Structural Equation Modeling analysis confirm that all three independent variables significantly improve POC, with PM having the strongest effect, followed by CS and SC. Additionally, POC was found to have a substantial positive impact on CPI. These results highlight that customers’ perception of convenience are the main conduit through omnichannel tactics convert into purchase intentions in the FMCG setting and they establish POC as a critical mediating mechanism.

8.2 Limitations and Future Research:

This study had drawbacks in spite of its contributions. Initially, the study was carried out in a particular FMCG setting inside a constrained geographic area, which would limit the findings applicability to other marketplaces or industries. Second, the cross-sectional survey design may not accurately reflect changing shopping habits in omnichannel settings since it only records customer impressions at a dingle moment in time. Third, there is a chance of response biases when self-reported measurements are used. Finally, the study only looked at three omnichannel elements: channel synchronization, personalized marketing and seamless checkout; it did not look at other potentially significant elements like mobile commerce features, AI-driven services or after-sales support. In order to enhance the framework, future research could expand the scope by include other components like post-purchase satisfaction, technology integration or trust. Contextual variations in how customers view omnichannel convenience may also be found through cross-sector or cross-country research. Finally, as omnichannel strategies and technologies continue to advance, longitudinal techniques may be better able to track changes in customer purchasing intents

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