1.1. Background: The Omnichannel Imperative in Fashion Retail

The global fashion industry is undergoing a structural transformation, primarily driven by the exponential growth of e-commerce and escalating digital engagement among consumers. This digital shift, facilitated by increased smartphone penetration and technological advancements, has fundamentally reshaped the retail landscape from a traditional multi-channel model into an emergent omnichannel ecosystem. In this environment, women represent a dominant consumer segment whose purchasing behavior is complex, guided by a sophisticated balance between convenience and assurance.

Traditional brick-and-mortar stores continue to hold vital strategic importance, primarily by offering tangible product experiences, the ability to physically try on garments (trial options), and personalized assistance from sales staff. Conversely, online platforms provide superior benefits in terms of wider product assortment, competitive pricing, and unparalleled ease of purchase. The strategic challenge for contemporary fashion brands lies in integrating these disparate channels—a concept known as Omnichannel Customer Experience (OCE)—to provide a seamless journey where connectivity, consistency, and personalization reduce consumer uncertainty. However, the intrinsic sensory dependence of fashion products (e.g., fit, fabric quality) means that achieving true seamlessness is particularly challenging, often resulting in a persistent customer expectation mismatch between the online and in-store environments.   

1.2. Problem Statement and Research Gap

Despite the maturation of e-commerce, fashion brands frequently encounter difficulties in establishing unified brand perception and confidence across digital and physical platforms. The online channel, while offering convenience, introduces significant perceived risks, particularly concerning product uncertainty, such as size mismatch, delayed delivery, and concerns over authenticity. This environment creates a critical "trust divide," where retailers lack clarity on the specific cognitive and behavioral factors—such as brand trust, authenticity, and loyalty—that lead women to prefer one channel over the other.   

Prior academic research has extensively studied online purchase intention in isolation. However, a systematic, comparative analysis that quantifies and contrasts the impact of Perceived Risk, Brand Authenticity, and Promotional Influence on channel preference within the rapidly evolving Indian fashion retail context remains essential. This study addresses this gap by empirically investigating the foundational mechanisms that build or erode trust, thereby offering actionable insights for developing cohesive omnichannel strategies.

1.3. Objectives and Theoretical Framing

This study utilizes a cross-sectional descriptive design to achieve the following specific objectives :   

1. To compare women’s buying preferences for fashion products based on channel-specific factors such as convenience, product availability, delivery speed, trial options, and satisfaction with return policies.   
2. To analyze the extent to which Perceived Risk (specifically relating to fit, quality, and authenticity concerns) dictates channel preference and compare differences in brand trust and buying behavior between online and offline channels.   
3. To determine the relative influence of key purchase drivers, including brand trust, brand loyalty, brand image, and promotional strategies, on channel preference.   

The analysis is anchored in two established theoretical frameworks: the Theory of Perceived Risk (TPR)  and the Omnichannel Customer Experience (OCE) model. TPR provides the lens through which consumer hesitation regarding e-commerce is understood, focusing on how risk dimensions (financial, performance, psychological) impede adoption. Brand Authenticity is integrated as a critical antecedent to channel trust, acting as a direct countermeasure to product uncertainty.   

THEORETICAL FRAMEWORK AND LITERATURE REVIEW

2.1. Omnichannel Retail and the Concept of Seamlessness

The transition to omnichannel retailing is necessitated by consumer expectation for flexibility and consistency across all touchpoints. The OCE framework emphasizes dimensions such as Connectivity, Integration, Consistency, and Personalization as key elements for a successful customer journey. High Channel Integration Quality (CIQ) is critical for achieving perceived fluency, which subsequently influences customer attitudes and purchase intentions. Poor CIQ—manifesting as inconsistent inventory, disjointed branding, or varying pricing strategies between channels—erodes consumer trust and increases the likelihood of loyalty attrition. By contrast, a unified strategy enhances convenience, minimizes barriers, and builds brand loyalty.   

• Hypothesis H1: High channel integration quality, evidenced by consistency in inventory, service, and policy across channels, is positively related to overall brand loyalty.

2.2. The Determinants of Perceived Brand Authenticity and Image

Brand perception factors, including perceived quality, overall image, and authenticity, are pivotal in how women evaluate fashion products and influence purchase confidence and loyalty across platforms. Consumers today often align themselves with brands that reflect their personal beliefs and values, meaning authenticity—defined by dimensions such as realism and aesthetics—creates a sense of reliability that encourages repeat business. When analyzing primary drivers of purchase decisions, brand image (24.8%) and product quality (21.9%) were cited as the most influential factors by respondents, significantly outweighing price (5.7%). This high weight given to non-monetary, quality-related attributes suggests that the consumer segment studied views fashion purchases as high in symbolic and hedonic value, making them acutely sensitive to failure or disappointment regarding brand promises. Authenticity, therefore, acts as a critical signal of genuine quality and intent.   

• Hypothesis H2: Perceived brand image and product authenticity are foundational antecedents that positively moderate the relationship between channel experience (online/offline) and purchase intention.

2.3. Perceived Risk Theory (TPR) in Fashion Retail

The Theory of Perceived Risk posits that consumer choices are driven by attempts to reduce anticipated unpleasant consequences. In the context of online fashion retail, consumers face multiple risk dimensions, including financial risk, time-loss risk, psychological risk, and product performance risk. For clothing and apparel, product performance risk—specifically issues related to size, fit, material quality, and color variation—is recognized as the most significant barrier to online adoption. The underlying data confirms that challenges faced in online shopping include size mismatch, color variation, and poor fabric quality, which all stem from the inability to physically inspect the product. Brands can mitigate this uncertainty through assurance mechanisms, such as providing customized information, personalized recommendations, and detailed product imagery, which collectively aim to lower performance risk perceptions.   

• Hypothesis H3: High perceived product risk (authenticity and fit concerns) negatively and significantly impacts the preference for the online channel.

2.4. Trust Mechanisms and Risk Reduction Strategies

Procedural trust, particularly confidence in post-purchase policies, is paramount in mitigating perceived risks. A lenient and reliable return policy serves as a risk-decreasing behavior that translates into higher purchase intention. The empirical finding that a substantial segment of consumers (40.4%) believes offline stores offer better and more reliable return/exchange processes underscores that policy confidence remains a major bottleneck for digital platforms. This lack of procedural assurance online, combined with the inherently high product risk of fashion, results in a preference for physical channels where assurance is immediate.   

Retailers are leveraging technology to bridge this trust deficit. Tools such as Virtual Try-On (AR/VR) and high-quality product visualization enhance the user’s mental imagery, thereby reducing perceived fit and performance risk without requiring physical interaction. Furthermore, to combat rising consumer skepticism regarding product authenticity, blockchain technology is emerging as a solution. Blockchain can provide an immutable record of a product's provenance and supply chain journey, directly enhancing transparency and consumer confidence in the product's genuineness.   

• Hypothesis H4: Confidence in return/exchange policies acts as a stronger driver of online purchase intention than deep price discounts, due to its role in mitigating financial and time-loss risks.

2.5. The Influence of Marketing Communication and Social Validation

Brand communication strategies significantly influence buying behavior across both channels. In the digital sphere, fashion influencers serve as key market drivers. They act as expert endorsers, utilizing perceived authenticity and credibility to lower consumer risk perceptions and promote purchase intention. However, this influence can be moderated by consumer skepticism regarding financial sponsorship. In the physical environment, in-store promotions and staff interaction rely on building interpersonal trust, which is often crucial for converting high-involvement purchases. The finding that social media plays a major role in brand discovery and that influencer collaborations are highly persuasive highlights the importance of social validation in the pre-purchase phase.   

• Hypothesis H5: Influencer collaboration credibility and perceived authenticity significantly enhance the attitude towards the brand and reduce perceived risk in online fashion purchases.

RESEARCH METHODOLOGY

3.1. Research Design, Approach, and Context

This study adopted a Descriptive-Cum-Comparative Research Design to systematically examine the relationships between women’s fashion brand perception and their buying preferences across online and offline channels. A quantitative approach, utilizing structured questionnaires, was employed to collect measurable data on variables such as brand trust, satisfaction, buying frequency, and perceived risk. The research was conducted in selected urban and semi-urban areas in India, focusing on women consumers actively engaged in purchasing apparel, footwear, and accessories.   

3.2. Sampling Procedure and Demographics

The target population comprised women aged 18–45 years, including students, working professionals, and homemakers. The study achieved a total sample size of 800 respondents. A combination of purposive and convenience sampling techniques was used to ensure that all respondents possessed prior experience with both online and offline fashion shopping channels, thereby providing a basis for comparative analysis.   

The demographic profile of the sample provides crucial context for interpreting the behavioral findings.

Table Title: Sample Demographics and Consumer Profile (N=800)

The data indicates a notable skew toward older demographics, with women above 45 years forming the largest share (39.6%). Furthermore, Business Owners constituted the largest occupational segment (24.8%), while working professionals represented the smallest group (9.2%). The majority of shoppers fell within the low-to-mid monthly budget bracket of ₹1,000–₹3,000 (27.7%). This profile suggests that the findings are most representative of established, value-conscious shoppers who may prioritize verified, reliable transactions over aggressive digital adoption, which helps contextualize the subsequent findings on channel preference and trust.   

3.3. Data Collection and Instrumentation

Primary data was collected through a structured questionnaire comprising Likert scale, multiple-choice, and open-ended questions. The questionnaire was segmented to capture: demographics, Brand Perception (quality, reputation, reliability), Buying Preferences (frequency, channel choice), Trust and Loyalty, Promotional Influence, and Channel-Specific Challenges. The measurement of Perceived Authenticity was inferred from questions related to product reliability, perceived quality, and consumer confidence in online purchases, aligning with established constructs of realism and control. Secondary data was gathered from academic journals and industry reports to contextualize current trends.   

3.4. Data Analysis Strategy

The analysis employed descriptive statistics, including means, percentages, and frequency distributions, to profile the consumer base and quantify observed behaviors. Comparative analysis was used extensively to contrast preferences and satisfaction levels between the online and offline environments. The findings derived from this robust descriptive analysis serve as the quantitative foundation for future advanced statistical modeling (e.g., Structural Equation Modeling or PLS-SEM) required to formally test the proposed hypotheses (H1-H5) regarding causal relationships and the mediating role of trust. It is noted that, due to the reliance on self-reported data, the responses may include personal biases or social desirability bias, particularly concerning sensitive data points like budget and the prioritization of quality over price.   

RESULTS

4.1. Comparative Brand Performance Across Channel Experiences

The results demonstrate a clear segmentation in which brands excel across different channels, highlighting a fundamental fragmentation in omnichannel execution.

The data shows a diverse distribution of brand preferences, with MANGO emerging as the most frequently purchased brand at 22.1%, indicating its strong brand appeal and perceived quality among respondents. Westside follows with 17.2%, suggesting its affordability and accessibility contribute to its popularity. ONLY also retains a competitive position at 13.9%. In contrast, brands like FabIndia (4.1%) and H&M (6.6%) hold smaller shares, reflecting either niche appeal or less frequent purchases. Notably, the “Others” category at 11.5% signifies that consumers still engage with a wide range of alternative brands beyond the major listed ones, highlighting a fragmented and highly competitive market landscape

Table Title: Brand-Channel Alignment: Perception Across Digital and Physical Experiences

ZARA emerged as the top brand for perceived product authenticity and reliability (29.2%) and also led the perception for the most appealing online shopping experience (27.7%). This suggests ZARA has successfully cultivated a strong, consistent digital brand image and quality assurance. Conversely, MANGO dominated the physical retail environment, being selected by the largest share of respondents (27.6%) as offering the most satisfying in-store shopping experience. This channel-brand mismatch confirms that operational and experiential capabilities are not uniformly integrated across channels, reinforcing the notion that the market may still be operating in a multi-channel rather than a truly seamless omnichannel.   

4.2. Quantification of the Trust Divide and Perceived Risk

The analysis revealed a pronounced preference for physical shopping, confirming the prevalence of risk-averse behavior in this consumer segment. Offline shopping was favored by 52.7% of respondents, compared to only 16.4% who shop exclusively online, with the remainder (30.9%) preferring a mixed approach.   

Trust in Authenticity (H3 Validation)

A substantial majority of consumers expressed concern regarding the genuineness of products bought online. The data shows that 68.4% of consumers either distrust online products outright (35.5%) or trust them only sometimes (32.9%). This split demonstrates a significant credibility gap in e-commerce authenticity, confirming that consumers perceive the risk of receiving a counterfeit or misrepresented product as high, which directly validates H3: high perceived product risk negatively impacts online channel preference.   

Policy Confidence and Procedural Risk (H4 Validation)

Satisfaction with procedural assurances strongly favored physical retail. Over 40% (40.4%) of respondents explicitly stated that offline channels offer better and more reliable return and exchange policies, indicating stronger confidence in in-person interactions. This finding emphasizes that procedural trust acts as a critical risk mitigation factor. The fact that consumers feel more secure with physical return processes validates H4, suggesting that mitigating financial and time-loss risks through robust policies is a stronger determinant of channel trust than price promotions alone.   

4.3. Influence of Drivers and Promotional Effectiveness

The reported influence factors confirm that the consumer segment prioritizes value and reputation over cost reduction. Brand Image (24.8%) and Product Quality (21.9%) were identified as the two most influential factors shaping buying decisions. In stark contrast, Price was the primary influence for only 5.7% of respondents, suggesting that for this market segment, perceived premium attributes and credible branding are more valued than affordability alone.   

Promotional Efficacy (H5 Validation)

In terms of marketing effectiveness, promotional strategies driven by social validation and physical interaction were most persuasive. Influencer Collaborations (37.2%) and In-Store Promotions (34.6%) were the strongest drivers of buying behavior, significantly outweighing traditional digital methods like Instagram Ads (18.1%) or Email/SMS offers (10.1%). This supports H5, indicating that third-party endorsements (influencers) or physical reassurance (in-store staff) are highly effective in building confidence and driving purchase intent.   

The impact of financial incentives was moderate. Discount offers were reported to influence buying decisions “Rarely” (25.5%) or “Sometimes” (22.6%) by the majority of respondents. While online discounts attract 20% of respondents, the overall moderate influence confirms that price serves as an incentive but does not override the fundamental importance of quality and brand value in the purchasing calculus.   

DISCUSSION

5.1. Interpreting the Dominance of Offline Preference in terms of Perceived Risk

The strong consumer preference for the physical retail environment (52.7%) is not solely attributable to a lack of digital literacy but rather represents a rational, risk-averse behavior specific to the fashion category. As confirmed by the theory of perceived risk, the inherent inability to inspect products remotely leads to high perceived product uncertainty. For a product segment carrying high hedonic and symbolic value, the consequence of purchase failure (e.g., poor fit or quality) extends beyond monetary loss, incurring psychological and social risks. Thus, the physical store remains the necessary channel for risk reduction, as it allows for tactile verification, immediate trial, and assurance of product performance, thereby directly supporting the negative relationship posited in H3. The presence of a significant proportion of older, established consumers (39.6% aged 46+) in the sample further reinforces this interpretation, as these consumers often prioritize stability and verified transactions over convenience and aggressive digital adoption.   

5.2. Addressing the Authenticity Trust Deficit and Procedural Assurance

The high level of consumer, with 68.4% of respondents expressing distrust or uncertainty regarding online authenticity , poses the single greatest threat to digital growth for fashion brands. This product genuineness online creates a demand for absolute assurance in the reversal process, forming a critical Risk-Trust-Assurance Loop. Consumers default to the channel that offers the best assurance, which, for 40.4% of respondents, is the offline store due to perceived confidence in physical returns. This observation strongly validates H4, confirming that procedural risk mitigation is a more powerful driver of channel preference than basic discounting.   

5.3. Strategic Integration for Omnichannel Competency

The data highlights a competitive fragmentation where brand strengths are channel-specific (ZARA strong digitally, MANGO strong physically). Optimal Omnichannel Customer Experience (OCE) requires achieving high Channel Integration Quality (CIQ). When brands exhibit such channel-specific dominance, it demonstrates that their operational and experiential capabilities are not consistently unified. This lack of process consistency prevents consumer trust built in one environment from flowing smoothly to the other, consequently sustaining the market’s reliance on risk-averse physical transactions.  To achieve superior OCE, brands must focus on balancing their capabilities: digitally-dominant brands (e.g., ZARA) must enhance the sensory experience and staff engagement in their physical locations, while physically-dominant brands (e.g., MANGO) must aggressively adopt risk-reducing digital visualization tools to make the digital experience as more impactful.   

5.4. Leveraging Social and Experiential Influence (H5 Validation)

The pronounced effectiveness of both Influencer Collaborations (37.2%) and In-Store Promotions (34.6%) underscores that trust-building mechanisms centered around validation are crucial for driving purchasing intent. In the digital sphere, consumers utilize social proof from influencers as a surrogate for physical trial, compensating for the uncertainty inherent in remote purchases. Retailers should strategically partner with influencers who exhibit high perceived authenticity and expertise to effectively reduce the psychological and performance risk associated with online fashion. In the physical realm, the success of in-store promotions emphasizes the enduring value of personalized interaction and the staff’s role in building interpersonal trust, which is highly influential in the decision-making process for fashion.   

6. FINDINGS, CONCLUSION AND FUTURE RESEARCH

6.1. Summary of Theoretical and Empirical Contributions

This comparative analysis provides empirical confirmation that women’s fashion buying behavior in an omnichannel environment is critically defined by the management of perceived risk. While brand loyalty itself is largely brand-driven (with 54.5% showing no difference in channel loyalty), the selection of the purchase channel is decidedly risk-driven. The study successfully quantified the significant consumer concern regarding online authenticity (68.4%) and the high preference for offline procedural assurance (40.4% preferring physical returns), demonstrating the explanatory power of the Theory of Perceived Risk in understanding the offline primacy observed in the data. Furthermore, the findings highlight that communication strategies based on social validation (influencers) or physical assurance (in-store experience) are most effective in countering channel uncertainty.   

6.2. Managerial Implications

1. Prioritize Risk Mitigation over Price: Retailers should strategically focus on reducing Perceived Product Risk (e.g., through advanced technology implementation) and Procedural Risk (e.g., establishing transparent, seamless, and cross-channel return policies) rather than relying solely on deep discounts to drive online sales (H4).
2. Invest in Channel Integration Quality (CIQ): Operational investments must target consistent inventory mapping and service delivery across all touchpoints (H1). Brands must ensure that the experiential quality of the physical store (e.g., MANGO's dominance) is matched by the usability and efficiency of the digital platform (e.g., ZARA’s appeal), reducing channel fragmentation.
3. Harness Authenticity as a Trust Anchor: Given the consumer’s reliance on Brand Image and Quality, brands should aggressively adopt authenticity-enhancing technologies, such as blockchain traceability, to provide verifiable provenance, thereby directly addressing the core 68.4% consumer authenticity deficit (H2).
4. Optimize Social and Interpersonal Touchpoints: Leverage influencer marketing as a core component of the digital trust strategy, selecting partners based on perceived authenticity and expertise to reduce performance risk (H5). Simultaneously, invest in retail staff training to reinforce the physical store as the definitive channel for quality assurance and personalized experience.

6.3. Limitations of the Current Study

The findings of this descriptive and comparative study are subject to several methodological limitations. The use of purposive and convenience sampling, combined with geographical restrictions, limits the generalizability of the results to the entire Indian women consumer segment. The reliance on self-reported data carries the potential for personal bias or social desirability bias in responses, which may skew the reported priorities of quality and brand over price. Furthermore, the cross-sectional nature of the research restricts the ability to perform longitudinal analysis, preventing the assessment of how consumer behavior and loyalty evolve over extended periods or across different fashion seasons. The rapid rate of technological advancement also means the findings regarding online behavior are time-bound.   

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