Ms. Sarsi Shah, Post Graduate Research Scholar, Department of Fashion Management Studies, National Institute of Fashion Technology, Ministry of Textile, Govt. of India, Daman Campus
Mr. Abinay Eligeti, Post Graduate Research Scholar, Department of Fashion Management Studies, National Institute of Fashion Technology, Ministry of Textile, Govt. of India, Daman Campus
Dr Vidhu Sekhar P, Asst Professor, Department of Fashion Management Studies, National Institute of Fashion Technology, Ministry of Textile, Govt. of India, Daman Campus
- Introduction
The swift expansion of fashion e-commerce has completely changed the way that contemporary customers purchase apparel, substituting internet storefronts for traditional retail encounters. The loss of tactile and sensory evaluation is a major drawback of this move from physical stores to online browsing. Before making a purchase, consumers are no longer able to weigh an item, touch the cloth, or try it on to determine how well it fits.
When choosing corporate and business-casual clothing, working professionals have an especially significant issue. Selecting workwear necessitates striking a balance between a number of different criteria, including following corporate dress requirements, guaranteeing comfort during the day, optimizing adaptability in social and professional contexts, and expressing personal style. Online buyers almost exclusively rely on visual clues to assess quality and compatibility because they are unable to physically feel the material.
Color and texture are important stand-ins for touch and feel among these visual cues:
Color: Indicates if a garment is suited for professional meetings, casual offices, or day-to-night wear by communicating tone, sophistication, and contextual appropriateness.
Texture: Gives purchasers an idea of how a garment might feel against the skin and endure throughout the workday by hinting at key fabric qualities including softness, breathability, structure, and durability.
High-resolution photos, close-up details, and dynamic video material become crucial tools for bridging the physical-digital divide because online customers are unable to engage directly with clothing. Consumer doubt is decreased, confidence is bolstered during the browsing process, and the decision-making process is expedited by the clear visual depiction of fabric texture and precise color fidelity.
To maximize digital retailing, fashion firms must better grasp the particular hues and textures that working adults love. E-commerce platforms can successfully decrease purchase hesitancy, increase conversion rates, and reduce expensive return rates caused by unfulfilled visual expectations by customizing product presentation to meet these tastes.
Objectives of the Study
- Identify Preferences: Determine the specific colors and fabric textures working professionals prefer for online workwear.
- Analyze Visual Impact: Examine how high-quality images and videos influence online buying confidence and reduce hesitation.
- Reduce Return Rates: Evaluate how accurate visual details lower return rates by setting realistic product expectations.
- Optimize Digital Merchandising: Provide actionable guidelines for e-commerce brands to present fabrics effectively online.
- Literature Review
2.1. Fabric Colour Preference
Fabric colour preference is about how people choose and feel about the colours of clothing materials. It includes the way colours look, how they make someone feel and how strong or soft the colours appear. In fashion, where customers can’t touch clothes, colour becomes the main thing that grabs attention. It helps shoppers decide if an outfit looks right for work or if it matches their style.
Tian et al. (2024) Looked at how clothing colour affects the emotions of consumers and their behaviour when buying online. They found that the way a colour looks can lead to feelings like happiness, excitement and pleasure. This is especially true in shopping because consumers cannot see, touch or try on clothes before buying. So, what they see on the screen becomes a factor in judging the product. A colour that looks good and fits the style can create a positive impression. This makes the consumer feel more involved with the item. When people feel good about what they see they are more likely to want to buy the product. The study shows how important colour is as a tool in fashion. It plays a role in how customers feel when they first see a garment on a website.
Zhao et al. (2023) Studied how people look at clothing colours using eye-tracking technology. They found that how clear and well-matched the colours are affecting how much effort shoppers need to understand a product. When colours are sharp, consistent and go well together people can take in the information faster. This makes it easier to process the details and leads to decisions. In shopping, where people are often faced with many choices at once cutting down on mental effort matters a lot. Good colour combinations help shoppers notice features faster and make the shopping experience feel smoother. The results show that choosing colours isn’t about looking nice. It also affects how attention is drawn, how information is processed and how fast people decide to buy.
Park and Lin (2022) studied what colours working professionals prefer when choosing clothes. Their results show that people tend to pick more relaxed colours rather than bright or bold ones. This preference seems to be shaped by the professional environments where clothes are worn. Subdued colours are often seen as linked to traits like responsibility, sophistication, reliability and professionalism. For people who work, clothes are not about self-expression but also about showing who they are at work. That’s why they might choose muted shades. These colours help create a fitting look in office settings. The taste for colours also suggests that people connect certain colour traits with perceptions of quality and confidence. This is important for fashion brands that want to reach customers. Choosing the colours can affect how well a garment fits both the practical needs and the social message of the wearer. Knowing these preferences helps brands build colour lines that match the lifestyle and expectations of their audience.
Ahmad et al. (2023) Looked at how online colours match the real colours of clothes and how this affects trust and what happens after the purchase. They found that when the colour shown
online is true to the colour of the item people feel more confident in the information from the seller. This is especially true in fashion because customers can’t touch or see the clothes before buying. If the colour online is different from the one they receive it causes disappointment. Lowers trust in the store. Accurate colour display helps reduce uncertainty and gives people confidence in their choices. Their research also links accuracy to fewer people abandoning their shopping carts and fewer returns especially among working consumers. This means that better digital colour presentation can improve customer happiness and also help the business. As a result, managing colour accurately should be a part of how fashion brands display their products online.
Based on the above review the conclusion comes to Visual colour is the entry point for online shoppers. Because buyers cannot feel the fabric colour sets the mood indicates whether an item fits workplace norm and gives buyers confidence to complete a purchase.
2.2. Fabric Texture Preference
Fabric texture preference represents a consumer’s tactile evaluation of a material’s surface qualities, including its weave, softness, weight and flexibility. Because physical contact is absent in shopping, texture preferences are formed through high-resolution visuals, zoom features and video demonstrations that communicate comfort and material durability.
Creusen et al. (2025), brief video clips that show clothing in motion—more especially, fabric stretching, crinkling, or draping—significantly boost consumer trust. Customers can precisely assess physical characteristics that static photos are unable to capture, such as material thickness, elasticity, and structure, thanks to these dynamic representations.
Song & Kim (2023) showed that consumers may determine material quality through interactive high-resolution zoom capabilities and close-up images of fabric weaves. Customers can visually mimic touch and feel more assured about their purchases by looking at the weave density and small surface features.
Liu et al. (2022) Presenting clear surface texture details significantly reduces perceived purchase risk, according to research by Liu et al. (2022). For office workers who value long term durability and structural integrity in their professional clothes, this visual clarity is essential.
Overvliet et al. (2024) found a clear connection between better fabric quality predictability and high-definition material photography. Brands get greater long-term customer trust and higher post-purchase happiness when their online visuals effectively depict the actual qualities of a garment.
Based on the above review the conclusion comes to Fabric texture is hard to evaluate online, yet working professionals rely on it to judge comfort and durability. High-quality close-ups and dynamic videos act as visual replacements for physical touch.
2.3. Online Purchase Intentions
Online purchase intention shows how likely a consumer is, how willing they are and how they plan to buy a clothing item on an online shopping site. Online purchase intention is driven by things like pictures, how good the product seems, trust in the website and the ease of deciding without trying on the clothing in person.
According to Nguyen (2025), incorporating interactive visual features makes shopping more enjoyable and useful. These solutions immediately promote customer engagement and purchase intent by enabling users to actively investigate product features.
According to Kumar and Sharma (2023), checkout decisions are significantly influenced by a mix of visual clarity, social proof, and sensory representation. High-quality product photographs, genuine user evaluations, and accurate sensory descriptions give customers the assurance they need to finish their orders.
According to research by Zhang and Wang (2022), buyer confidence is greatly increased when clear material specifications are paired with simple, easy-to-use website navigation. Customers are better able to comprehend information and make decisions when visual clutter is removed and precise fabric descriptions are emphasized.
Rahi et al. (2024) showed that clear return policies combined with thorough product descriptions reduce purchasing hesitancy. For full-time workers who want a hassle-free purchasing experience supported by the assurance of hassle-free returns, this combination is especially essential.
Based on the above review the conclusion comes to Purchase intention depends on convenience, product clarity and trust. Busy professionals weigh the convenience of ordering against the risk of receiving disappointing items.
2.4. Working Professionals (Target Demographic)
Working professionals are employed people whose clothing choices are guided by workplace rules and the need for time efficiency and functional usefulness. This group usually looks for durable and low‑maintenance clothes that fit both professional dress codes and all‑day comfort.
The Metropolitan Fashion Consumer Survey (2025) found that working adults value exact product listings, time-saving convenience, and functional utility over trend-driven, impulsive purchases. Since their purchasing judgments are based on real-world necessities, truthful portrayal is significantly more persuasive than ostentatious marketing strategies.
According to Patel and Desai (2023), corporate workers have clear expectations about fit and material compatibility when they buy for workwear. In order to ensure that clothing complies with both formal corporate standards and smart-casual workplace settings, they need precise sizing guidelines and clear fabric details.
According to research by Chen and Lee (2022), time-constrained professionals strongly choose platforms with great visual and functional navigation. Granular filtering options greatly lessen cognitive strain and expedite the search procedure by enabling customers to rapidly sort inventory by certain colors, fabric types, and workplace situations.
As Srivastava and Mishra (2024) showed, urban working adults are willing to pay more when vendors offer accurate, comprehensive fabric information. Transparent material specifications directly reduce the inconvenience of possible returns and support a higher price point by giving customers confidence in the quality of the product.
Based on the above review the conclusion comes to Working professionals are shoppers driven by time limits, income and specific workplace attire needs. They prefer organized platforms that make choosing suitable workwear straightforward.
- Research Methodology
3.1 Research Methodology and Study Type
In order to comprehensively examine the association between fabric attributes—more especially, preferences for color and texture—and online purchase intentions among working professionals, this study uses a quantitative research methodology. Because it allows for the empirical evaluation of consumer preferences and the statistical examination of correlations between visual/tactile factors and purchase behaviour, a quantitative methodology was chosen. Additionally, this study is exploratory in nature because it seeks to learn more about how fabric preferences influence online decision-making in an e-commerce setting when physical product inspection is limited.
3.2 Time Horizon and Research Design
For this study, a cross-sectional descriptive and exploratory research design was chosen. Because it enables the collection of data from the target population at a single, specific point in time to capture current consumer views and behaviours, a cross-sectional framework was judged appropriate. To ensure effective data collection throughout the target demographic, working professionals were given a standardized online questionnaire to complete in order to obtain primary data.
3.3 Techniques for Gathering Data
Both primary and secondary data gathering techniques were used to build a grounded theoretical framework and carry out empirical research. A standardized digital survey questionnaire intended to gauge fabric color preferences, texture assessments, and online purchase intentions was used to collect primary data directly from respondents. In order to develop current theoretical models and contextualize the empirical findings, secondary data was gathered through a thorough literature study of scholarly journals, peer-reviewed articles, and industrial publications.
3.4 Sample Size and Sampling Technique
The study’s target audience consists of working professionals who shop for clothing online and it was kept for a confidence level of 95%. A non-probability convenience sampling technique was used because there was no defined sampling frame that included every working
professional in the target market. Although non-probability sampling restricts direct random selection, it made it possible to effectively reach and include accessible working professionals during the research period. The study successfully collected and processed a final sample size of 114 valid responses (�� = 114), using sample calculator.
3.5 Methods of Data Analysis
JASP, an open-source statistical program, was used for the statistical analysis. To test the empirical assumptions and make statistical conclusions, the gathered dataset was subjected to a thorough quantitative analysis. Cronbach’s alpha testing was used to assess scale reliability and internal consistency. To investigate categorical relationships between respondent variables, chi-square tests of independence were used. Additionally, Analysis of Variance (ANOVA) was used to find significant mean differences in purchase intentions across different participant sub
groups, and Pearson correlation analysis was used to evaluate the direction and strength of relationships between fabric attributes and purchase intent.
3.6 Moral Aspects
To safeguard participant welfare and data integrity, ethical standards were rigorously upheld throughout the research procedure. By not gathering any personally identifiable information, like names, phone numbers, or email addresses, complete participant anonymity was guaranteed. In order to protect respondent privacy and reduce response bias, the survey did not include sensitive demographic factors, such as precise income levels. Respondents were made aware of the study’s academic goal before filling out the questionnaire, and participation was completely optional.
3.7 Scope and Limitations
Regarding the scope of this investigation, a number of methodological limitations should be recognized. First, a bigger sample size might improve the findings’ generalizability to the greater workforce, even though the sample size of 114 respondents offers significant exploratory insights. Second, compared to probability-based random sampling techniques, the use of non-probability convenience sampling restricts external validity. Third, the data gathering process relied on self-reported survey responses, which are prone to response bias and subjective perception. Lastly, the evaluation of fabric texture was restricted to visual
proxies and descriptions within the survey instrument rather than direct physical touch because online shopping lacks tangible tactile clues.
- Data Analysis and Interpretation
4.1 Reliability
Frequentist Scale Reliability Statistics
95% CI
Coefficient Estimate Std. Error Lower Upper
Cronbach’s α 0.6691 0.06876 0.5344 0.8039
Based on the criteria outlined by Putri (2017), a questionnaire is considered reliable if the Cronbach’s Alpha value exceeds 0.60. The scale reliability test yielded a Cronbach’s alpha coefficient of �� = .67 (SE = .07, 95% CI [. 53, .80]). Since the obtained value exceeds the threshold of 0.60, the research instrument is declared reliable and demonstrates acceptable internal consistency for measuring the target variable.
4.2 ANOVA
4.2.1 Frequency to Shop and Practical Workwear Priorities
∙ ��0 (Null Hypothesis): There is no significant difference in Practical Workwear Priorities across different groups of Frequency to Shop.
∙ ��1 (Alternative Hypothesis): There is a significant difference in Practical Workwear Priorities across different groups of Frequency to Shop.
- Practical Workwear Priorities
Cases Sum of Squares df Mean Square F p 5. Frequency to Shop 6.707 2 3.353 2.856 .062 Residuals 130.3 111 1.174
Kruskal-Wallis Test
Factor Statistic df p
- Frequency to Shop 6.182 2 .045
While the parametric One-Way ANOVA suggests a marginal non-significant trend (�� = .062), the non-parametric Kruskal-Wallis test confirms a statistically significant difference (�� = .045). This divergence typically occurs when data distribution departs from normality or contains outliers, making the non-parametric test more reliable. Overall, the findings indicate that how frequently a customer shops significantly influences their priorities regarding practical workwear.
4.2.2 Frequency to Shop and Colour Accuracy Confidence
∙ ��0 (Null Hypothesis): There is no significant difference in Colour Accuracy Confidence across different groups of Frequency to Shop.
∙ ��1 (Alternative Hypothesis): There is a significant difference in Colour Accuracy Confidence across different groups of Frequency to Shop.
ANOVA – 13. Color Accuracy Confidence
Cases Sum of Squares df Mean Square F p 5. Frequency to Shop 1.497 2 0.7483 4.030 .020
Residuals 20.61 111 0.1857
Note. Type III Sum of Squares
Kruskal-Wallis Test
Kruskal-Wallis Test
Factor Statistic df p
- Frequency to Shop 7.650 2 .022
Both the parametric One-Way ANOVA (�� = .020) and the non-parametric Kruskal-Wallis test (�� = .022) yield consistent, statistically significant results. This convergence provides strong evidence that shopping frequency significantly impacts a consumer’s confidence in color
accuracy. Consequently, how often individuals shop plays a meaningful role in determining their level of assurance regarding product color evaluation.
4.2.3 Frequency to Shop and Why Shop Online
∙ ��0 (Null Hypothesis): There is no significant difference in Why Shop Online across different groups of Frequency to Shop.
∙ ��1 (Alternative Hypothesis): There is a significant difference in Why Shop Online across different groups of Frequency to Shop.
ANOVA – 15. Why Shop Online
Cases Sum of Squares df Mean Square F p 5. Frequency to Shop 14.80 2 7.398 3.627 .030
Residuals 226.4 111 2.040
Kruskal-Wallis Test
Kruskal-Wallis Test
Factor Statistic df p
- Frequency to Shop 6.943 2 .031
Both the parametric One-Way ANOVA (�� = .030) and the non-parametric Kruskal-Wallis test (�� = .031) yield consistent, statistically significant outcomes. This strong alignment demonstrates that shopping frequency significantly influences consumers’ motivations for shopping online. Consequently, individuals with differing shopping frequencies prioritize different reasons or drivers when choosing to shop online.
4.2.4 Dress Code and Verified Customer Reviews
∙ ��0 (Null Hypothesis): There is no significant difference in Verified Customer Reviews across different groups of Dress Code.
∙ ��1 (Alternative Hypothesis): There is a significant difference in Verified Customer Reviews across different groups of Dress Code.
ANOVA – 11. Verified Customer Reviews
Cases Sum of Squares df Mean Square F p
4.Dress Code 1.031 2 0.5157 3.195 .045
Residuals 17.92 111 0.1614
Kruskal-Wallis Test
Kruskal-Wallis Test
Factor Statistic df p
4.Dress Code 6.151 2 .046
Both the parametric One-Way ANOVA (�� = .045) and the non-parametric Kruskal-Wallis test (�� = .046) demonstrate consistent, statistically significant results. This alignment provides robust evidence that a consumer’s workplace dress code significantly affects their evaluation or reliance on verified customer reviews. Consequently, individuals working under different dress code requirements display distinct behaviors regarding customer reviews when shopping.
4.3 Correlation
4.3.1 Accurate Screen Colour and Clear Product Visual
∙ ��0 (Null Hypothesis): There is no significant relationship between Accurate Screen Colors and Clearer Product Visuals.
∙ ��1 (Alternative Hypothesis): There is a significant relationship between Accurate Screen Colors and Clearer Product Visuals.
Spearman’s Correlations
Spearman’s rho p
- Accurate Screen Colors – 8. Clearer Product Visuals 0.5472 < .001
The correlation analysis revealed a statistically significant positive relationship between the two variables, ���� = .547,�� < .001. Because the ��-value is less than the standard significance threshold of . 05, the null hypothesis (��0) is rejected in favor of the alternative hypothesis (��1).
4.3.2 Clearer Product Visuals And Practical Workwear Priorities
∙ ��0 (Null Hypothesis): There is no significant relationship between Clearer Product Visuals and Practical Workwear Priorities.
∙ ��1 (Alternative Hypothesis): There is a significant relationship between Clearer Product Visuals and Practical Workwear Priorities.
Spearman’s Correlations
Spearman’s rho p
- Clearer Product Visuals – 9.Practical Workwear Priorities 0.5968 < .001
The correlation analysis revealed a statistically significant positive relationship between the two variables, ���� = .597,�� < .001. Because the ��-value is less than the standard significance threshold of . 05, the null hypothesis (��0) is rejected in favor of the alternative hypothesis (��1).
4.3.3 Practical Workwear Priorities and Fabric Zoom Inspection
∙ ��0 (Null Hypothesis): There is no significant relationship between Practical Workwear Priorities and Fabric Zoom Inspection.
∙ ��1 (Alternative Hypothesis): There is a significant relationship between Practical Workwear Priorities and Fabric Zoom Inspection.
Spearman’s Correlations
Spearman’s rho p
9.Practical Workwear Priorities – 10. Fabric Zoom Inspection 0.5695 < .001
The correlation analysis revealed a statistically significant positive relationship between the two variables, ���� = .570,�� < .001. Because the ��-value is less than the standard significance threshold of . 05, the null hypothesis (��0) is rejected in favor of the alternative hypothesis (��1).
4.4 Chi Square
4.4.1 Accurate Screen Colour and Fabric Zoom Inspection
∙ ��0 (Null Hypothesis): There is no significant association between Accurate Screen Colors and Fabric Zoom Inspection.
∙ ��1 (Alternative Hypothesis): There is a significant association between Accurate Screen Colors and Fabric Zoom Inspection.
- Accurate Screen Colors
- Fabric Zoom Inspection 1 2 3 4 5 Total
1 4 0 3 2 3 12
2 2 3 5 5 1 16
3 1 6 13 5 6 31
4 0 1 10 6 4 21
5 2 0 9 6 17 34
Total 9 10 40 24 31 114
Chi-Squared Tests
Value df p
Χ² 37.96 16 .002
N 114
The analysis revealed a statistically significant association between the two variables, ��2(16,�� = 114) = 37.96, �� = .002. Because the ��-value is well below the standard significance threshold of . 05, the null hypothesis (��0) is rejected in favor of the alternative hypothesis (��1).
4.4.2 Accurate Screen Colors and Product Display Impact.
∙ ��0 (Null Hypothesis): There is no significant association between Accurate Screen Colors and Product Display Impact.
∙ ��1 (Alternative Hypothesis): There is a significant association between Accurate Screen Colors and Product Display Impact.
Contingency Tables
- Accurate Screen Colors
- Product Display Impact 1 2 3 4 5 Total
1 1 5 10 13 8 37
2 8 5 30 11 23 77
Total 9 10 40 24 31 114
Chi-Squared Tests
Value df p
Χ² 10.07 4 .039
N 114
The analysis revealed a statistically significant association between the two variables, ��2(4, �� = 114) = 10.07,�� = .039. Because the ��-value is below the standard significance threshold of . 05, the null hypothesis (��0) is rejected in favor of the alternative hypothesis (��1).
4.4.3 Frequency to Shop and Color Accuracy Confidence
∙ ��0 (Null Hypothesis): There is no significant association between Frequency to Shop and Color Accuracy Confidence.
∙ ��1 (Alternative Hypothesis): There is a significant association between Frequency to Shop and Color Accuracy Confidence.
Contingency Tables
- Frequency to Shop
- Color Accuracy Confidence 1 2 3 Total
1 8 14 8 30
2 9 62 13 84
Total 17 76 21 114
Chi-Squared Tests
Value df p
Χ² 7.718 2 .021
N 114
The analysis revealed a statistically significant association between the two variables, ��2(2, �� = 114) = 7.718,�� = .021. Because the ��-value is below the standard significance threshold of . 05, the null hypothesis (��0) is rejected in favor of the alternative hypothesis (��1).
4.5 Findings & Suggestion
The survey instrument’s reliability ($alpha = 0.669$) for assessing working professionals’ online buying behaviour is confirmed by the quantitative analysis. purchasing frequency has a substantial impact on practical workwear priorities, color accuracy confidence, and online purchasing motives, according to statistical testing. Additionally, the degree to which customers depend on verified customer evaluations when making purchases is directly impacted by workplace dress requirements. Accurate screen colors, better product graphics, practical workwear priorities, and the need for fabric zoom examination were shown to be
strongly positively correlated ($p <.001$). A substantial correlation between color accuracy, zoom capabilities, and overall product display effect is further shown by cross-tabulation.
These results suggest that fashion e-commerce sites aimed at working professionals should employ multi-lighting previews and high-definition color calibration to reduce return risks due to visual disparities. To effectively replace tactile touch with visuals, retailers must provide dynamic fabric movement movies and macro-zoom inspection capabilities. Additionally, to assist consumers in determining the appropriateness of clothing, user evaluations must to be filterable by certain workplace dress standards (e.g., corporate formal vs. smart casual). Lastly, platforms could tailor the browsing experience by dividing communications according on the frequency of purchases, giving regular customers quicker checkouts while providing extensive fabric characteristics for infrequent buys.
- Conclusion
This research shows that minimizing the loss of physical sensory assessment is critical to working professionals’ effective transition from physical retail to fashion e-commerce. Visual indications of fabric quality are crucial replacements for direct touch since full-time personnel have to juggle corporate dress rules, everyday comfort, and time restrictions. The empirical results verify that interactive fabric zoom examination, realistic screen color reproduction, and high-resolution images immediately increase consumer trust and meet practical workplace objectives. Additionally, the statistical analysis reveals that important behavioural and demographic factors—like a person’s frequency of shopping and the dress code at work—significantly influence their reliance on verified customer feedback, motivations for online shopping, and confidence in color accuracy.
In the end, digital product display cannot be seen by fashion shops as only aesthetically pleasing. E-commerce platforms must invest in sensory-bridging technology, such as contextual review filtering, multi-angle drape movies, and high-definition macro visual representations, in order to successfully capture the urban corporate market. Fashion brands can lower cart abandonment, lower return rates due to mismatched expectations, and build long-term consumer trust in an increasingly competitive digital marketplace by directly aligning visual merchandising with working professionals’ functional expectations and risk perceptions.
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