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Title<\/u><\/strong>:-<\/strong> Factor Analysis Of Product (HERO Splendor Plus)<\/p>\n Authors:-<\/u><\/strong><\/p>\n Adil (021330024021)<\/p>\n N Vaishnavi P (021330024521)<\/p>\n Kartik Dodiya (021330024551)<\/p>\n Introduction<\/u><\/strong>:- <\/strong>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.<\/p>\n 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:<\/p>\n Objective<\/u><\/strong>:- <\/strong>To reduce the dimension<\/p>\n Data Collection<\/u><\/strong>:-<\/strong> 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:<\/p>\n A total of 58 responses<\/strong> were obtained. The responses were then analyzed using SPSS<\/strong> to perform a factor analysis. Principal Component Analysis (PCA) with Varimax rotation<\/strong> was used to identify the underlying factors.<\/p>\n Data Analysis<\/u><\/strong>:- <\/strong>The analysis consists of three main components: the Kaiser-Meyer-Olkin (KMO) and Bartlett\u2019s Test<\/strong>, the Total Variance Explained<\/strong>, and the Rotated Component Matrix<\/strong>.<\/p>\n KMO and Bartlett’s Test<\/strong><\/p>\n<\/td>\n<\/tr>\n Kaiser-Meyer-Olkin Measure of Sampling Adequacy.<\/p>\n<\/td>\n .806<\/p>\n<\/td>\n<\/tr>\n Bartlett’s Test of Sphericity<\/p>\n<\/td>\n Approx. Chi-Square<\/p>\n<\/td>\n 257.768<\/p>\n<\/td>\n<\/tr>\n df<\/p>\n<\/td>\n 45<\/p>\n<\/td>\n<\/tr>\n Sig.<\/p>\n<\/td>\n <.001<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n \u00a0<\/p>\n Total Variance Explained<\/strong><\/p>\n<\/td>\n<\/tr>\n Component<\/strong><\/p>\n<\/td>\n Extraction Sums of Squared Loadings<\/strong><\/p>\n<\/td>\n Rotation Sums of Squared Loadings<\/strong><\/p>\n<\/td>\n<\/tr>\n Total<\/strong><\/p>\n<\/td>\n % of Variance<\/strong><\/p>\n<\/td>\n Cumulative %<\/strong><\/p>\n<\/td>\n Total<\/strong><\/p>\n<\/td>\n % of Variance<\/strong><\/p>\n<\/td>\n Cumulative %<\/strong><\/p>\n<\/td>\n<\/tr>\n 1<\/strong><\/p>\n<\/td>\n 4.845<\/strong><\/p>\n<\/td>\n 48.448<\/strong><\/p>\n<\/td>\n 48.448<\/strong><\/p>\n<\/td>\n 3.040<\/strong><\/p>\n<\/td>\n 30.402<\/strong><\/p>\n<\/td>\n 30.402<\/strong><\/p>\n<\/td>\n<\/tr>\n 2<\/strong><\/p>\n<\/td>\n 1.152<\/strong><\/p>\n<\/td>\n 11.519<\/strong><\/p>\n<\/td>\n 59.968<\/strong><\/p>\n<\/td>\n 2.957<\/strong><\/p>\n<\/td>\n 29.566<\/strong><\/p>\n<\/td>\n 59.968<\/strong><\/p>\n<\/td>\n<\/tr>\n Extraction Method: Principal Component Analysis.<\/strong><\/p>\n 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\u2019 ratings.<\/p>\n Together, these two components account for approximately 60% of the total variance, which is acceptable in social sciences research.<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n \u00a0 \u00a0 3.<\/p>\n Rotated Component Matrix<\/strong><\/p>\n<\/td>\n<\/tr>\n \u00a0<\/p>\n<\/td>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Component<\/p>\n<\/td>\n<\/tr>\n 1<\/p>\n<\/td>\n 2<\/p>\n<\/td>\n<\/tr>\n Durable Build<\/p>\n<\/td>\n .818<\/p>\n<\/td>\n \u00a0<\/p>\n<\/td>\n<\/tr>\n Integrated Braking System (IBS)<\/p>\n<\/td>\n .782<\/p>\n<\/td>\n \u00a0<\/p>\n<\/td>\n<\/tr>\n Fuel Efficiency<\/p>\n<\/td>\n .758<\/p>\n<\/td>\n \u00a0<\/p>\n<\/td>\n<\/tr>\n i3S Technology<\/p>\n<\/td>\n .707<\/p>\n<\/td>\n \u00a0<\/p>\n<\/td>\n<\/tr>\n Reliable Engine<\/p>\n<\/td>\n .519<\/p>\n<\/td>\n \u00a0<\/p>\n<\/td>\n<\/tr>\n Bright Lighting<\/p>\n<\/td>\n \u00a0<\/p>\n<\/td>\n .838<\/p>\n<\/td>\n<\/tr>\n Analog Instrument Console<\/p>\n<\/td>\n \u00a0<\/p>\n<\/td>\n .782<\/p>\n<\/td>\n<\/tr>\n Suspension System<\/p>\n<\/td>\n \u00a0<\/p>\n<\/td>\n .773<\/p>\n<\/td>\n<\/tr>\n Comfortable Seating<\/p>\n<\/td>\n \u00a0<\/p>\n<\/td>\n .722<\/p>\n<\/td>\n<\/tr>\n Affordability<\/p>\n<\/td>\n \u00a0<\/p>\n<\/td>\n \u00a0<\/p>\n<\/td>\n<\/tr>\n Extraction Method: Principal Component Analysis.<\/p>\n \u00a0Rotation Method: Varimax with Kaiser Normalization.<\/p>\n<\/td>\n<\/tr>\n a. Rotation converged in 3 iterations.<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n \u00a0<\/strong>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.<\/p>\n Conclusion<\/u><\/strong><\/p>\n The factor analysis of the 10 characteristics of the Hero Splendor Plus bike yielded two components that explain almost 60%<\/strong> of the variance in the data. The two components can be interpreted as:<\/p>\n Affordability<\/strong> 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.<\/p>\n 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.<\/p>\n \u00a0<\/p>\n \u00a0<\/p>\n \u00a0<\/p>\n","protected":false},"excerpt":{"rendered":" This post will helps to better understand about the splendor plus bike <\/p>\n","protected":false},"author":139646,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[39],"tags":[2057],"class_list":["post-22095","post","type-post","status-publish","format-standard","hentry","category-marketing","tag-splendorplus-hero-marketresearch","entry"],"_links":{"self":[{"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=\/wp\/v2\/posts\/22095","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=\/wp\/v2\/users\/139646"}],"replies":[{"embeddable":true,"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=22095"}],"version-history":[{"count":1,"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=\/wp\/v2\/posts\/22095\/revisions"}],"predecessor-version":[{"id":22096,"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=\/wp\/v2\/posts\/22095\/revisions\/22096"}],"wp:attachment":[{"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=22095"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=22095"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=22095"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}\n
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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.<\/li>\n
Bartlett\u2019s 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.<\/li>\n<\/ul>\n\n
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