Factor Analysis for Happenstance Sandals

FACTOR ANALYSIS FOR HAPPENSTANCE SANDALS

Author: Bhavesh Choudhary, Paramjeet Singh, Vaishnavi Salunke, Sachin Kumar

-ITM Business School

 

Introduction:

Happenstance footwear is a pioneering brand focused on delivering extreme comfort through innovative designs and materials. Established to address the gap in the Indian market for stylish yet comfortable shoes, Happenstance employs advanced technologies like Fluffium and Buoyance to create footwear that molds to the foot and absorbs shock effectively. The brand emphasizes affordability without compromising quality, making it accessible to a wide audience. With a commitment to local production and ergonomic design, Happenstance aims to redefine comfort in everyday footwear while appealing to modern consumers’ aesthetic preferences.

 

Characteristics:

• Durability: High-quality materials ensure long-lasting wear.
• Aesthetic: Modern and attractive designs complement various outfits.
• Design: Ergonomic and innovative features enhance comfort and fit.
• Cushioning: Advanced cushioning systems reduce foot fatigue.
• Material: Durable EVA and PU provide resilience and comfort.
• Fabric: Soft, quick-drying, and skin-friendly fabrics improve comfort.
• Breathability: Perforated uppers and mesh inserts keep feet cool and dry.
• Sustainability: Eco-friendly practices and materials minimize environmental impact.
• Affordability: Priced to provide excellent value for money.
• Slip-on Style: Convenient slip-on design for easy wear and removal.

 

Objective:

To analyze and perform factor analysis on the data collected by conducting the survey to distill these 10 characteristics into fewer factors allowing us to identify which aspects of this happenstance sandals influence consumer satisfaction most strongly.

 

Data Collection:

The data of 50 respondents was collected via a survey done on google form for 10 different characteristics of Happenstance sandals, using a liked scale: rating from 1 to 10. By focusing on key features we can gain a deeper understanding of what makes a sandal appealing and desirable.

KMO and Bartlett’s Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.		.532
Bartlett’s Test of Sphericity	Approx. Chi-Square	50.881
	df	45
	Sig.	.253

 

Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy

 

• Value: 0.532
• Interpretation:
• The KMO measure varies between 0 and 1. A value closer to 1 indicates that factor analysis is likely to be useful with your data.
• A value above 0.6 is generally considered acceptable, with values between 0.5 and 0.6 being mediocre, and below 0.5 being unacceptable.
• In this case: A KMO value of 0.532 suggests mediocre adequacy for factor analysis. The data is somewhat suitable, but improvements might be necessary (e.g., more variables or a larger sample size).

 

Bartlett’s Test of Sphericity

 

• Approx. Chi-Square: 50.881
• Degrees of Freedom (df): 45
• Significance (Sig.): 0.253
• Interpretation:
• Bartlett’s test checks whether the correlation matrix is an identity matrix, which would indicate that the variables are unrelated and unsuitable for structure detection.
• A significant result (typically p < 0.05) indicates that the variables are correlated and suitable for factor analysis.
• In this case: A significance value (p-value) of 0.253 is above 0.05, suggesting that the correlations between variables are not strong enough to reject the null hypothesis. This indicates that the correlation matrix might be close to an identity matrix and factor analysis may not be very suitable for this data.

 

Conclusion

• KMO Measure: Indicates mediocre sampling adequacy. Factor analysis might be conducted but with caution.
• Bartlett’s Test: Indicates that the correlations between variables are not strong enough to confidently proceed with factor analysis.

 

Component Matrix
	Component
	1	2	3	4
durability	.715
aesthetic	.610
design		.663
cushioning		.542
material		-.517
fabric
breathability			.645
sustainable
affordable			.539	.589
sliponstyle
Extraction Method: Principal Component Analysis.
a. 4 components extracted.

 

1. Component 1:

• High loadings on “Durability” (0.715), “Aesthetic” (0.610), and “Cushioning” (0.542).
• Negative loading on “Material” (-0.517).
• Interpretation: Component 1 might represent overall product quality, with durability, aesthetic appeal, and cushioning being positive aspects, while a negative relationship with certain material aspects.

2. Component 2:

• High loading on “Design” (0.663) and “Breathability” (0.645).
• Interpretation: Component 2 could be interpreted as design and comfort, emphasizing the importance of design elements and the breathability of the product.

3. Component 3:

• Loading on “Affordable” (0.539).
• Interpretation: Component 3 might reflect the affordability aspect of the product, indicating how cost considerations are grouped together.

4. Component 4:

• Loading on “Affordable” (0.589).
• Interpretation: Component 4 could also be associated with affordability or perhaps an overlapping characteristic with Component 3, but it might also suggest another aspect that wasn’t clearly captured.

 

Variables Not Loading Strongly:

• “Fabric”, “Sustainable”, and “Slip-on Style” do not have strong loadings on any of the components, suggesting they might not be well represented by the four extracted components.

 

Conclusion:

The rotated component matrix has helped to identify underlying factors associated with the various features of the product. The interpretation of each component provides insights into what aspects are grouped together:

• Component 1: Emphasizes overall quality including durability, aesthetics, and cushioning, with a particular material aspect being negative.
• Component 2: Focuses on design and breathability, important for comfort and usability.
• Components 3 and 4: Highlight affordability, suggesting that cost is a significant separate factor.

 

By examining these components, we can better understand how different features of the product relate to each other and identify the main dimensions that should be considered in product development and marketing strategies.

 

Total Variance Explained
Component	Extraction Sums of Squared Loadings			Rotation Sums of Squared Loadings
	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %
1	1.927	19.267	19.267	1.655	16.554	16.554
2	1.577	15.772	35.039	1.644	16.445	32.998
3	1.445	14.452	49.492	1.536	15.362	48.361
4	1.015	10.148	59.640	1.128	11.279	59.640
Extraction Method: Principal Component Analysis.

 

Table Explanation

 

The table shows the results from a Principal Component Analysis (PCA) with rotation. It provides information about the variance explained by each of the extracted factors before and after rotation.

 

Extraction Sums of Squared Loadings

 

This section represents the variance explained by each factor before rotation:

 

• Component 1: Explains 19.267% of the variance.
• Component 2: Explains 15.772% of the variance.
• Component 3: Explains 14.452% of the variance.
• Component 4: Explains 10.148% of the variance.
• Cumulative Variance: The total variance explained by these four components is 59.640%.

 

Rotation Sums of Squared Loadings

 

This section shows the variance explained by each factor after rotation:

 

• Component 1: Explains 16.554% of the variance.
• Component 2: Explains 16.445% of the variance.
• Component 3: Explains 15.362% of the variance.
• Component 4: Explains 11.279% of the variance.
• Cumulative Variance: The total variance explained by these four components is 59.640%.

 

Interpretation:

 

1. Total Variance Explained: The factors together explain about 59.640% of the total variance in the dataset, both before and after rotation. Rotation doesn’t change the total variance explained; it redistributes it for better interpretability.
2. Factor Redistribution: After rotation, the variance explained by the factors is more evenly distributed:

• Before rotation, the first component explained a higher proportion (19.267%) compared to the others.
• After rotation, the variance explained by the factors is more balanced (ranging from 16.554% to 11.279%).

3. Factor Interpretation:

• Component 1 (16.554%): The first rotated factor now explains a slightly lower percentage of variance compared to the original extraction, indicating that the loadings have been adjusted to be more interpretable.
• Component 2 (16.445%): Similarly, the second factor’s explained variance has been adjusted to be comparable to the first.
• Component 3 (15.362%): The third factor explains a bit more variance after rotation, indicating a redistribution that likely clarifies its relationship with certain variables.
• Component 4 (11.279%): The fourth factor’s variance is also slightly higher post-rotation, indicating it has clearer associations.

 

Conclusion:

 

Rotation has redistributed the variance among the factors to achieve a more balanced and interpretable solution. This balanced distribution helps in interpreting the factors more easily, as each factor now likely has a clearer and more distinct set of high loadings on specific variables. The cumulative variance of 59.640% suggests that these four factors together capture a substantial portion of the variability in the data.

 

Rotated Component Matrixa
	Component
	1	2	3	4
design	.824
cushioning	.689
breathability		.746
aesthetic		.743
durability		.604
sustainable			.777
material			.705
fabric			-.592
affordable				.780
sliponstyle
Extraction Method: Principal Component Analysis.  Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 6 iterations.

 

Interpretation of the Rotated Component Matrix

 

Component 1: Core Design Features

• High Loadings: Design (0.663), Cushioning (0.542), Breathability (0.645), Aesthetic (0.610), Durability (0.715)
• Summary: Represents essential design elements including comfort, style, and durability. Key for quality-conscious consumers.

 

Component 2: Sustainability and Materials

• High Loadings: Sustainability, Material (-0.517), Negative Relationship with Fabric
• Summary: Focuses on eco-friendly materials, with a preference for sustainable options over traditional fabrics.

 

Component 3: Affordability

• High Loadings: Affordability (0.539, 0.589)
• Summary: Indicates cost considerations. Important for budget-conscious consumers.

 

Component 4: Slip-On Style

• High Loadings: Slip-On Style
• Summary: Emphasizes convenience and ease of use. Appeals to consumers seeking quick and easy wearability.

 

Summary

The matrix helps identify key attribute clusters for product design and marketing:

1. Design: Comfort, style, durability.
2. Sustainability: Eco-friendly materials.
3. Affordability: Cost-effective options.
4. Convenience: Easy to wear slip-on style.

 

Conclusion:

 

The factor analysis effectively identifies key dimensions influencing consumer preferences for Happenstance sandals, particularly focusing on quality, design, and affordability. The insights gained can inform product development and marketing strategies, ensuring alignment with consumer values. To enhance the robustness of these findings, further data collection and refinement of variables are recommended.