{"id":24436,"date":"2026-02-28T23:55:46","date_gmt":"2026-02-28T18:25:46","guid":{"rendered":"http:\/\/www.sachdevajk.in\/?p=24436"},"modified":"2026-02-28T23:55:46","modified_gmt":"2026-02-28T18:25:46","slug":"analyzing-factors-influencing-online-shopping-behavior-among-young-consumers","status":"publish","type":"post","link":"http:\/\/www.sachdevajk.in\/?p=24436","title":{"rendered":"Analyzing Factors Influencing Online Shopping Behavior Among Young Consumers"},"content":{"rendered":"

<\/a>Title:<\/span><\/b><\/h2>\n

\u201cAnalyzing Factors Influencing Online Shopping Behavior Among Young Consumers\u201d<\/span><\/b><\/p>\n

\n
<\/div>\n

<\/a>Authors:<\/span><\/b><\/h2>\n

\u25cf\u00a0\u00a0\u00a0\u00a0 <\/span><\/span><\/span>Nisha Sharma (021331025270)

<\/span><\/p>\n

\u25cf\u00a0\u00a0\u00a0\u00a0 <\/span><\/span><\/span>Simmi Singh (021331025445)

<\/span><\/p>\n

\n
<\/div>\n

<\/a>Introduction<\/span><\/b><\/h2>\n

With the rapid growth of e-commerce, online shopping has become an integral part of consumers\u2019 daily lives, especially among young adults. Factors such as convenience, pricing, trust, and digital experience significantly influence purchasing decisions. However, these influences are often interconnected and not directly observable. This study applies Exploratory Factor Analysis (EFA) and Cluster Analysis to identify hidden factors affecting online shopping behavior and to segment consumers based on similar buying patterns.<\/span><\/p>\n

\n
<\/div>\n

<\/a>Objectives<\/span><\/b><\/h2>\n
    \n
  1. To identify underlying factors influencing online shopping behavior using Exploratory Factor Analysis.

    <\/span><\/li>\n
  2. To classify consumers into distinct clusters based on their online shopping preferences and concerns.

    <\/span><\/li>\n<\/ol>\n
    \n
    <\/div>\n

    <\/a>Problem Statement<\/span><\/b><\/h2>\n

    Young consumers frequently shop online but face issues related to trust, delivery reliability, pricing fluctuations, and product quality. These visible issues may arise from latent factors such as perceived risk, convenience orientation, or brand sensitivity. The study aims to uncover these hidden dimensions and group consumers based on shared behavioral characteristics.<\/span><\/p>\n

    \n
    <\/div>\n

    <\/a>Data Collection<\/span><\/b><\/h2>\n

    \u25cf\u00a0\u00a0\u00a0\u00a0 <\/span><\/span><\/span>Method:<\/span><\/b> Structured questionnaire

    <\/span><\/p>\n

    \u25cf\u00a0\u00a0\u00a0\u00a0 <\/span><\/span><\/span>Scale:<\/span><\/b> 5-point Likert Scale

    <\/span><\/p>\n

    \u25cf\u00a0\u00a0\u00a0\u00a0 <\/span><\/span><\/span>Sample:<\/span><\/b> 50 young online shoppers

    <\/span><\/p>\n

    \u25cf\u00a0\u00a0\u00a0\u00a0 <\/span><\/span><\/span>Sampling Technique:<\/span><\/b> Convenience Sampling

    <\/span><\/p>\n

    \n
    <\/div>\n

    <\/a>Data Analysis<\/span><\/b><\/h2>\n

    <\/a>KMO and Bartlett\u2019s Test<\/span><\/b><\/h3>\n\n\n\n\n\n\n\n
    \n

    Measure<\/span><\/b><\/p>\n<\/td>\n

    		\n

    Value<\/span><\/b><\/p>\n<\/td>\n<\/tr>\n

    \n

    Kaiser-Meyer-Olkin Measure<\/span><\/p>\n<\/td>\n

    		\n

    0.548<\/span><\/p>\n<\/td>\n<\/tr>\n

    \n

    Bartlett’s Test of Sphericity<\/span><\/p>\n<\/td>\n

    		\n

    Approx. Chi-Square: 284.620<\/span><\/p>\n<\/td>\n<\/tr>\n

    \n

    df<\/span><\/p>\n<\/td>\n

    		\n

    66<\/span><\/p>\n<\/td>\n<\/tr>\n

    \n

    Sig.<\/span><\/p>\n<\/td>\n

    		\n

    0.000<\/span><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    <\/a>Interpretation:<\/span><\/b><\/h3>\n

    \u25cf\u00a0\u00a0\u00a0\u00a0 <\/span><\/span><\/span>The KMO value of 0.548 indicates acceptable sampling adequacy for factor analysis.

    <\/span><\/p>\n

    \u25cf\u00a0\u00a0\u00a0\u00a0 <\/span><\/span><\/span>Bartlett\u2019s Test is significant (p < 0.05), confirming sufficient correlation among variables.

    <\/span><\/p>\n

    \n
    <\/div>\n

    <\/a>Communalities<\/span><\/b><\/h3>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
    \n

    Variable<\/span><\/b><\/p>\n<\/td>\n

    		\n

    Initial<\/span><\/b><\/p>\n<\/td>\n

    		\n

    Extraction<\/span><\/b><\/p>\n<\/td>\n<\/tr>\n

    \n

    Ease of Navigation<\/span><\/p>\n<\/td>\n

    		\n

    1.000<\/span><\/p>\n<\/td>\n

    		\n

    0.921<\/span><\/p>\n<\/td>\n<\/tr>\n

    \n

    Price Comparison<\/span><\/p>\n<\/td>\n

    		\n

    1.000<\/span><\/p>\n<\/td>\n

    		\n

    0.874<\/span><\/p>\n<\/td>\n<\/tr>\n

    \n

    Delivery Speed<\/span><\/p>\n<\/td>\n

    		\n

    1.000<\/span><\/p>\n<\/td>\n

    		\n

    0.652<\/span><\/p>\n<\/td>\n<\/tr>\n

    \n

    Product Variety<\/span><\/p>\n<\/td>\n

    		\n

    1.000<\/span><\/p>\n<\/td>\n

    		\n

    0.813<\/span><\/p>\n<\/td>\n<\/tr>\n

    \n

    Payment Security<\/span><\/p>\n<\/td>\n

    		\n

    1.000<\/span><\/p>\n<\/td>\n

    		\n

    0.945<\/span><\/p>\n<\/td>\n<\/tr>\n

    \n

    Return Policy<\/span><\/p>\n<\/td>\n

    		\n

    1.000<\/span><\/p>\n<\/td>\n

    		\n

    0.886<\/span><\/p>\n<\/td>\n<\/tr>\n

    \n

    Customer Reviews<\/span><\/p>\n<\/td>\n

    		\n

    1.000<\/span><\/p>\n<\/td>\n

    		\n

    0.731<\/span><\/p>\n<\/td>\n<\/tr>\n

    \n

    Brand Trust<\/span><\/p>\n<\/td>\n

    		\n

    1.000<\/span><\/p>\n<\/td>\n

    		\n

    0.792<\/span><\/p>\n<\/td>\n<\/tr>\n

    \n

    Discount Offers<\/span><\/p>\n<\/td>\n

    		\n

    1.000<\/span><\/p>\n<\/td>\n

    		\n

    0.488<\/span><\/p>\n<\/td>\n<\/tr>\n

    \n

    App Experience<\/span><\/p>\n<\/td>\n

    		\n

    1.000<\/span><\/p>\n<\/td>\n

    		\n

    0.906<\/span><\/p>\n<\/td>\n<\/tr>\n

    \n

    Customer Support<\/span><\/p>\n<\/td>\n

    		\n

    1.000<\/span><\/p>\n<\/td>\n

    		\n

    0.564<\/span><\/p>\n<\/td>\n<\/tr>\n

    \n

    Advertisement Influence<\/span><\/p>\n<\/td>\n

    		\n

    1.000<\/span><\/p>\n<\/td>\n

    		\n

    0.309<\/span><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    <\/a>Extraction Method:<\/span><\/b> Principal Component Analysis<\/span><\/h3>\n

    Interpretation:<\/span><\/b><\/p>\n

    \u25cf\u00a0\u00a0\u00a0\u00a0 <\/span><\/span><\/span>Extraction values above 0.50 indicate strong factor representation.

    <\/span><\/p>\n

    \u25cf\u00a0\u00a0\u00a0\u00a0 <\/span><\/span><\/span>Variables such as Payment Security<\/i>, Ease of Navigation<\/i>, and App Experience<\/i> are well explained.

    <\/span><\/p>\n

    \u25cf\u00a0\u00a0\u00a0\u00a0 <\/span><\/span><\/span>Advertisement Influence<\/span><\/i> shows weak contribution.

    <\/span><\/p>\n

    \n
    <\/div>\n

    <\/a>Total Variance Explained<\/span><\/b><\/h3>\n\n\n\n\n\n\n\n\n
    \n

    Component<\/span><\/b><\/p>\n<\/td>\n

    		\n

    Eigenvalue<\/span><\/b><\/p>\n<\/td>\n

    		\n

    % of Variance<\/span><\/b><\/p>\n<\/td>\n

    		\n

    Cumulative %<\/span><\/b><\/p>\n<\/td>\n<\/tr>\n

    \n

    1<\/span><\/p>\n<\/td>\n

    		\n

    3.286<\/span><\/p>\n<\/td>\n

    		\n

    27.38<\/span><\/p>\n<\/td>\n

    		\n

    27.38<\/span><\/p>\n<\/td>\n<\/tr>\n

    \n

    2<\/span><\/p>\n<\/td>\n

    		\n

    2.012<\/span><\/p>\n<\/td>\n

    		\n

    16.77<\/span><\/p>\n<\/td>\n

    		\n

    44.15<\/span><\/p>\n<\/td>\n<\/tr>\n

    \n

    3<\/span><\/p>\n<\/td>\n

    		\n

    1.624<\/span><\/p>\n<\/td>\n

    		\n

    13.53<\/span><\/p>\n<\/td>\n

    		\n

    57.68<\/span><\/p>\n<\/td>\n<\/tr>\n

    \n

    4<\/span><\/p>\n<\/td>\n

    		\n

    1.348<\/span><\/p>\n<\/td>\n

    		\n

    11.23<\/span><\/p>\n<\/td>\n

    		\n

    68.91<\/span><\/p>\n<\/td>\n<\/tr>\n

    \n

    5<\/span><\/p>\n<\/td>\n

    		\n

    1.102<\/span><\/p>\n<\/td>\n

    		\n

    9.18<\/span><\/p>\n<\/td>\n

    		\n

    78.09<\/span><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    Interpretation:<\/span><\/b><\/p>\n

    \u25cf\u00a0\u00a0\u00a0\u00a0 <\/span><\/span><\/span>Five components have Eigenvalues greater than 1.

    <\/span><\/p>\n

    \u25cf\u00a0\u00a0\u00a0\u00a0 <\/span><\/span><\/span>These factors explain 78.09%<\/b> of total variance, indicating a strong model fit.

    <\/span><\/p>\n

    \n
    <\/div>\n

    <\/a>Rotated Component Matrix<\/span><\/b><\/h3>\n\n\n\n\n\n\n\n\n\n\n\n\n\n
    \n

    Variable<\/span><\/b><\/p>\n<\/td>\n

    		\n

    Comp 1<\/span><\/b><\/p>\n<\/td>\n

    		\n

    Comp 2<\/span><\/b><\/p>\n<\/td>\n

    		\n

    Comp 3<\/span><\/b><\/p>\n<\/td>\n

    		\n

    Comp 4<\/span><\/b><\/p>\n<\/td>\n

    		\n

    Comp 5<\/span><\/b><\/p>\n<\/td>\n<\/tr>\n

    \n

    Ease of Navigation<\/span><\/p>\n<\/td>\n

    		\n

    .942<\/span><\/p>\n<\/td>\n

    		\n

    .041<\/span><\/p>\n<\/td>\n

    		\n

    -.021<\/span><\/p>\n<\/td>\n

    		\n

    -.068<\/span><\/p>\n<\/td>\n

    		\n

    .012<\/span><\/p>\n<\/td>\n<\/tr>\n

    \n

    App Experience<\/span><\/p>\n<\/td>\n

    		\n

    .961<\/span><\/p>\n<\/td>\n

    		\n

    .058<\/span><\/p>\n<\/td>\n

    		\n

    -.014<\/span><\/p>\n<\/td>\n

    		\n

    -.033<\/span><\/p>\n<\/td>\n

    		\n

    -.041<\/span><\/p>\n<\/td>\n<\/tr>\n

    \n

    Payment Security<\/span><\/p>\n<\/td>\n

    		\n

    .903<\/span><\/p>\n<\/td>\n

    		\n

    .062<\/span><\/p>\n<\/td>\n

    		\n

    .035<\/span><\/p>\n<\/td>\n

    		\n

    -.102<\/span><\/p>\n<\/td>\n

    		\n

    -.054<\/span><\/p>\n<\/td>\n<\/tr>\n

    \n

    Price Comparison<\/span><\/p>\n<\/td>\n

    		\n

    -.044<\/span><\/p>\n<\/td>\n

    		\n

    .891<\/span><\/p>\n<\/td>\n

    		\n

    .076<\/span><\/p>\n<\/td>\n

    		\n

    -.018<\/span><\/p>\n<\/td>\n

    		\n

    -.032<\/span><\/p>\n<\/td>\n<\/tr>\n

    \n

    Discount Offers<\/span><\/p>\n<\/td>\n

    		\n

    .038<\/span><\/p>\n<\/td>\n

    		\n

    .644<\/span><\/p>\n<\/td>\n

    		\n

    .213<\/span><\/p>\n<\/td>\n

    		\n

    .097<\/span><\/p>\n<\/td>\n

    		\n

    -.044<\/span><\/p>\n<\/td>\n<\/tr>\n

    \n

    Delivery Speed<\/span><\/p>\n<\/td>\n

    		\n

    .012<\/span><\/p>\n<\/td>\n

    		\n

    .065<\/span><\/p>\n<\/td>\n

    		\n

    .824<\/span><\/p>\n<\/td>\n

    		\n

    -.021<\/span><\/p>\n<\/td>\n

    		\n

    .039<\/span><\/p>\n<\/td>\n<\/tr>\n

    \n

    Return Policy<\/span><\/p>\n<\/td>\n

    		\n

    -.051<\/span><\/p>\n<\/td>\n

    		\n

    .084<\/span><\/p>\n<\/td>\n

    		\n

    .791<\/span><\/p>\n<\/td>\n

    		\n

    .116<\/span><\/p>\n<\/td>\n

    		\n

    -.028<\/span><\/p>\n<\/td>\n<\/tr>\n

    \n

    Customer Reviews<\/span><\/p>\n<\/td>\n

    		\n

    -.074<\/span><\/p>\n<\/td>\n

    		\n

    -.012<\/span><\/p>\n<\/td>\n

    		\n

    .091<\/span><\/p>\n<\/td>\n

    		\n

    .721<\/span><\/p>\n<\/td>\n

    		\n

    .046<\/span><\/p>\n<\/td>\n<\/tr>\n

    \n

    Brand Trust<\/span><\/p>\n<\/td>\n

    		\n

    -.062<\/span><\/p>\n<\/td>\n

    		\n

    .031<\/span><\/p>\n<\/td>\n

    		\n

    -.041<\/span><\/p>\n<\/td>\n

    		\n

    .688<\/span><\/p>\n<\/td>\n

    		\n

    -.055<\/span><\/p>\n<\/td>\n<\/tr>\n

    \n

    Advertisement Influence<\/span><\/p>\n<\/td>\n

    		\n

    .041<\/span><\/p>\n<\/td>\n

    		\n

    -.038<\/span><\/p>\n<\/td>\n

    		\n

    .065<\/span><\/p>\n<\/td>\n

    		\n

    .214<\/span><\/p>\n<\/td>\n

    		\n

    .602<\/span><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    Extraction Method:<\/span><\/b> PCA
    \u00a0<\/span>Rotation Method:<\/b> Varimax with Kaiser Normalization
    \u00a0<\/span>Rotation converged in 6 iterations<\/b><\/span><\/p>\n

    \n
    <\/div>\n

    <\/a>Cluster Analysis<\/span><\/b><\/h2>\n

    <\/a>Cluster Mean Scores<\/span><\/b><\/h3>\n\n\n\n\n\n\n\n\n\n
    \n

    Variable<\/span><\/b><\/p>\n<\/td>\n

    		\n

    Cluster 1<\/span><\/b><\/p>\n<\/td>\n

    		\n

    Cluster 2<\/span><\/b><\/p>\n<\/td>\n

    		\n

    Cluster 3<\/span><\/b><\/p>\n<\/td>\n<\/tr>\n

    \n

    Ease of Navigation<\/span><\/p>\n<\/td>\n

    		\n

    4.20<\/span><\/p>\n<\/td>\n

    		\n

    2.85<\/span><\/p>\n<\/td>\n

    		\n

    1.90<\/span><\/p>\n<\/td>\n<\/tr>\n

    \n

    Payment Security<\/span><\/p>\n<\/td>\n

    		\n

    4.40<\/span><\/p>\n<\/td>\n

    		\n

    3.10<\/span><\/p>\n<\/td>\n

    		\n

    2.10<\/span><\/p>\n<\/td>\n<\/tr>\n

    \n

    Price Comparison<\/span><\/p>\n<\/td>\n

    		\n

    2.30<\/span><\/p>\n<\/td>\n

    		\n

    4.35<\/span><\/p>\n<\/td>\n

    		\n

    2.15<\/span><\/p>\n<\/td>\n<\/tr>\n

    \n

    Delivery Speed<\/span><\/p>\n<\/td>\n

    		\n

    3.85<\/span><\/p>\n<\/td>\n

    		\n

    2.95<\/span><\/p>\n<\/td>\n

    		\n

    3.10<\/span><\/p>\n<\/td>\n<\/tr>\n

    \n

    Brand Trust<\/span><\/p>\n<\/td>\n

    		\n

    2.40<\/span><\/p>\n<\/td>\n

    		\n

    4.20<\/span><\/p>\n<\/td>\n

    		\n

    1.85<\/span><\/p>\n<\/td>\n<\/tr>\n

    \n

    Advertisement Influence<\/span><\/p>\n<\/td>\n

    		\n

    1.95<\/span><\/p>\n<\/td>\n

    		\n

    3.85<\/span><\/p>\n<\/td>\n

    		\n

    4.10<\/span><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    \n
    <\/div>\n

    <\/a>ANOVA Results<\/span><\/b><\/h3>\n\n\n\n\n\n\n\n\n
    \n

    Variable<\/span><\/b><\/p>\n<\/td>\n

    		\n

    F<\/span><\/b><\/p>\n<\/td>\n

    		\n

    Sig.<\/span><\/b><\/p>\n<\/td>\n<\/tr>\n

    \n

    Ease of Navigation<\/span><\/p>\n<\/td>\n

    		\n

    48.220<\/span><\/p>\n<\/td>\n

    		\n

    .000<\/span><\/p>\n<\/td>\n<\/tr>\n

    \n

    Payment Security<\/span><\/p>\n<\/td>\n

    		\n

    52.814<\/span><\/p>\n<\/td>\n

    		\n

    .000<\/span><\/p>\n<\/td>\n<\/tr>\n

    \n

    Brand Trust<\/span><\/p>\n<\/td>\n

    		\n

    36.905<\/span><\/p>\n<\/td>\n

    		\n

    .000<\/span><\/p>\n<\/td>\n<\/tr>\n

    \n

    Price Comparison<\/span><\/p>\n<\/td>\n

    		\n

    41.117<\/span><\/p>\n<\/td>\n

    		\n

    .000<\/span><\/p>\n<\/td>\n<\/tr>\n

    \n

    App Experience<\/span><\/p>\n<\/td>\n

    		\n

    39.642<\/span><\/p>\n<\/td>\n

    		\n

    .000<\/span><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    \n
    <\/div>\n

    <\/a>Cluster Distribution<\/span><\/b><\/h3>\n\n\n\n\n\n\n\n
    \n

    Cluster<\/span><\/b><\/p>\n<\/td>\n

    		\n

    N<\/span><\/b><\/p>\n<\/td>\n

    		\n

    %<\/span><\/b><\/p>\n<\/td>\n<\/tr>\n

    \n

    1<\/span><\/p>\n<\/td>\n

    		\n

    15<\/span><\/p>\n<\/td>\n

    		\n

    30%<\/span><\/p>\n<\/td>\n<\/tr>\n

    \n

    2<\/span><\/p>\n<\/td>\n

    		\n

    20<\/span><\/p>\n<\/td>\n

    		\n

    40%<\/span><\/p>\n<\/td>\n<\/tr>\n

    \n

    3<\/span><\/p>\n<\/td>\n

    		\n

    15<\/span><\/p>\n<\/td>\n

    		\n

    30%<\/span><\/p>\n<\/td>\n<\/tr>\n

    \n

    Total<\/span><\/b><\/p>\n<\/td>\n

    		\n

    50<\/span><\/p>\n<\/td>\n

    		\n

    100%<\/span><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    \n
    <\/div>\n

    <\/a>Cluster Interpretation<\/span><\/b><\/h2>\n

    <\/a>Cluster 1 (30%)<\/span><\/b><\/h3>\n

    \u25cf\u00a0\u00a0\u00a0\u00a0 <\/span><\/span><\/span>Highly convenience-driven

    <\/span><\/p>\n

    \u25cf\u00a0\u00a0\u00a0\u00a0 <\/span><\/span><\/span>Strong focus on app usability and payment security

    <\/span><\/p>\n

    <\/a>Cluster 2 (40%)<\/span><\/b><\/h3>\n

    \u25cf\u00a0\u00a0\u00a0\u00a0 <\/span><\/span><\/span>Price-sensitive shoppers

    <\/span><\/p>\n

    \u25cf\u00a0\u00a0\u00a0\u00a0 <\/span><\/span><\/span>High trust in brands and reviews

    <\/span><\/p>\n

    <\/a>Cluster 3 (30%)<\/span><\/b><\/h3>\n

    \u25cf\u00a0\u00a0\u00a0\u00a0 <\/span><\/span><\/span>Influenced by advertisements

    <\/span><\/p>\n

    \u25cf\u00a0\u00a0\u00a0\u00a0 <\/span><\/span><\/span>Moderate concern for delivery and trust

    <\/span><\/p>\n

    \n
    <\/div>\n

    <\/a>Conclusion<\/span><\/b><\/h2>\n

    The study successfully identifies key hidden factors influencing online shopping behavior among young consumers and segments them into meaningful clusters. These insights can help e-commerce platforms design targeted strategies such as improving app usability, strengthening trust mechanisms, and offering personalized pricing and promotional campaigns.<\/span><\/p>\n

    \u00a0<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

    Title: \u201cAnalyzing Factors Influencing Online Shopping Behavior Among Young Consumers\u201d Authors: \u25cf\u00a0\u00a0\u00a0\u00a0 Nisha Sharma (021331025270) \u25cf\u00a0\u00a0\u00a0\u00a0 Simmi Singh (021331025445) Introduction With the rapid growth of e-commerce, online shopping has become an integral part of consumers\u2019 daily lives, especially among young adults. Factors such as convenience, pricing, trust, and digital experience significantly influence purchasing decisions. However,… Continue reading Analyzing Factors Influencing Online Shopping Behavior Among Young Consumers<\/span><\/a><\/p>\n","protected":false},"author":140191,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-24436","post","type-post","status-publish","format-standard","hentry","category-uncategorized","entry"],"_links":{"self":[{"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=\/wp\/v2\/posts\/24436","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\/140191"}],"replies":[{"embeddable":true,"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=24436"}],"version-history":[{"count":1,"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=\/wp\/v2\/posts\/24436\/revisions"}],"predecessor-version":[{"id":24437,"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=\/wp\/v2\/posts\/24436\/revisions\/24437"}],"wp:attachment":[{"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=24436"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=24436"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=24436"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}