Factor Analysis OF Splendor Plus

 

Title:- Factor Analysis Of Product (HERO Splendor Plus)

Authors:-

Adil (021330024021)

N Vaishnavi P (021330024521)

Kartik Dodiya (021330024551)

Introduction:- Factor analysis is a statistical method used to identify the underlying relationships between variables and reduce data into smaller, interpretable factors. In this study, a factor analysis was performed on 10 characteristics of the Hero Splendor Plus bike to explore how these characteristics cluster into meaningful groups or components.

The Hero Splendor Plus is a popular motorcycle known for its fuel efficiency, reliability, and affordability. Understanding how these and other features are perceived by users can help in designing better marketing strategies and product improvements. The characteristics analyzed include:

• Reliable Engine
• Fuel Efficiency
• Comfortable Seating
• i3S Technology
• Integrated Braking System (IBS)
• Durable Build
• Suspension System
• Analog Instrument Console
• Bright Lighting
• Affordability

Objective:- To reduce the dimension

Data Collection:- Data was collected using a Google Form, which was circulated among classmates. The respondents were asked to rate each of the 10 characteristics using a 5-point Likert scale:

• 1: Strongly Disagree
• 2: Disagree
• 3: Neutral
• 4: Agree
• 5: Strongly Agree

A total of 58 responses were obtained. The responses were then analyzed using SPSS to perform a factor analysis. Principal Component Analysis (PCA) with Varimax rotation was used to identify the underlying factors.

Data Analysis:- The analysis consists of three main components: the Kaiser-Meyer-Olkin (KMO) and Bartlett’s Test, the Total Variance Explained, and the Rotated Component Matrix.

1.  

KMO and Bartlett’s Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.		.806
Bartlett’s Test of Sphericity	Approx. Chi-Square	257.768
	df	45
	Sig.	<.001

 

• Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy:806
  The KMO measure assesses whether the sample is adequate for factor analysis. A value of 0.806 indicates that the sample is suitable for this type of analysis. Generally, KMO values above 0.7 are considered good.
• Bartlett’s Test of Sphericity: Chi-square = 257.768, df = 45, p < 0.001
  Bartlett’s Test checks if the correlation matrix is significantly different from an identity matrix. The significant result (p < 0.001) suggests that the correlations between the variables are sufficient to proceed with factor analysis.

2.  

Total Variance Explained
Component	Extraction Sums of Squared Loadings			Rotation Sums of Squared Loadings
	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %
1	4.845	48.448	48.448	3.040	30.402	30.402
2	1.152	11.519	59.968	2.957	29.566	59.968
Extraction Method: Principal Component Analysis. Factor analysis extracted two components, which together explain 59.968% of the total variance in the data. This is a satisfactory percentage, indicating that these two components capture a majority of the variance in the respondents’ ratings. Component 1: Before rotation, this component has an eigenvalue of 4.845 and explains 48.448% of the variance. After rotation, it explains 30.402% of the variance. Component 2: This component has an eigenvalue of 1.152 and explains 11.519% of the variance before rotation. After rotation, it explains 29.566% of the variance. Together, these two components account for approximately 60% of the total variance, which is acceptable in social sciences research.

3. Rotated Component Matrix
	Component
	1	2
Durable Build	.818
Integrated Braking System (IBS)	.782
Fuel Efficiency	.758
i3S Technology	.707
Reliable Engine	.519
Bright Lighting		.838
Analog Instrument Console		.782
Suspension System		.773
Comfortable Seating		.722
Affordability
Extraction Method: Principal Component Analysis.  Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 3 iterations.

 The rotated component matrix shows how much each characteristic loads onto the two components. The loading values indicate the correlation between the characteristics and the components.

• Component 1 can be interpreted as representing “Performance and Efficiency”. It includes characteristics such as Durable Build, Integrated Braking System (IBS), Fuel Efficiency, i3S Technology, and Reliable Engine. These features are more related to the bike’s operational performance and efficiency.
• Component 2 can be interpreted as representing “Comfort and Features”. It includes characteristics such as Bright Lighting, Analog Instrument Console, Suspension System, and Comfortable Seating. These features enhance the rider’s experience and comfort.
• Affordability does not load significantly on either component, which may suggest that affordability is perceived independently or could require further exploration.

Conclusion

The factor analysis of the 10 characteristics of the Hero Splendor Plus bike yielded two components that explain almost 60% of the variance in the data. The two components can be interpreted as:

1. Performance and Efficiency: This includes mechanical and operational features like the braking system, fuel efficiency, and engine reliability. These are likely important to users who value the bike’s performance and economic operation.
2. Comfort and Features: This includes features related to rider comfort and ease of use, such as the lighting system, seating comfort, and suspension.

Affordability did not show significant association with either of the two components, indicating that further analysis may be needed to explore its role in customer satisfaction or decision-making.

This analysis provides valuable insights into which characteristics are likely to influence customer satisfaction and can help guide future product development, marketing strategies, and customer service improvements.