{"id":23716,"date":"2026-01-07T12:18:52","date_gmt":"2026-01-07T06:48:52","guid":{"rendered":"http:\/\/www.sachdevajk.in\/?p=23716"},"modified":"2026-01-07T12:18:52","modified_gmt":"2026-01-07T06:48:52","slug":"application-of-one-way-anova-in-analyzing-consumer-ratings-of-smartphone-brands","status":"publish","type":"post","link":"http:\/\/www.sachdevajk.in\/?p=23716","title":{"rendered":"Application of One-Way ANOVA in Analyzing Consumer Ratings of Smartphone Brands"},"content":{"rendered":"

Application of One-Way ANOVA in Analyzing Consumer Ratings of Smartphone Brands<\/strong><\/p>\n

Tejas Dashrath Tathe (021331025478)\u00a0<\/strong><\/p>\n

Introduction<\/span><\/b><\/p>\n

In today\u2019s highly competitive smartphone market, consumer perception and brand preference play a crucial role in determining a company\u2019s success. Brands such as Samsung, Lenovo, Apple, XOMI, and OnePlus compete not only on technological innovation but also on customer satisfaction, pricing, design, and brand image. Understanding whether consumers perceive meaningful differences among these brands is important for marketers, manufacturers, and researchers.<\/span><\/p>\n

Statistical tools like Analysis of Variance (ANOVA)<\/b> are widely used to compare the mean responses of more than two groups simultaneously. This study applies a one-way ANOVA<\/b> to analyze consumer ratings of selected smartphone brands and to examine whether there is any statistically significant difference in their mean ratings.<\/span><\/p>\n

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Literature Review<\/span><\/b><\/p>\n

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  1. Kotler and Keller (2016)<\/span><\/b> emphasized that brand perception significantly influences consumer purchasing decisions, especially in technology-driven markets such as smartphones. Their work highlights the importance of measuring customer satisfaction to understand competitive positioning.<\/span><\/li>\n
  2. Malhotra (2019)<\/span><\/b> discussed the application of ANOVA in marketing research, stating that one-way ANOVA is an effective method to compare consumer opinions across multiple brands and identify whether observed differences are statistically meaningful.<\/span><\/li>\n
  3. Aaker (2014)<\/span><\/b> explained that strong brand equity leads to higher customer loyalty and perceived quality. Comparative studies using statistical techniques help organizations evaluate their brand standing relative to competitors.<\/span><\/li>\n
  4. Gupta and Gupta (2019)<\/span><\/b> noted that survey-based rating scales combined with ANOVA provide reliable insights into consumer preferences and are commonly used in business and social science research.<\/span><\/li>\n<\/ol>\n

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    Objectives of the Study<\/span><\/b><\/p>\n

      \n
    1. To analyze consumer ratings of selected smartphone brands.<\/span><\/li>\n
    2. To compare the mean ratings of Samsung, Lenovo, Apple, XOMI, and OnePlus.<\/span><\/li>\n
    3. To determine whether there is a statistically significant difference among brand ratings.<\/span><\/li>\n
    4. To apply one-way ANOVA as a statistical tool for brand comparison.<\/span><\/li>\n
    5. To provide insights that may help marketers understand consumer perception of smartphone brands.<\/span><\/li>\n<\/ol>\n

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      Data Collection<\/span><\/b><\/p>\n

      The study is based on primary data<\/b> collected through a structured questionnaire. Respondents were asked to rate different smartphone brands\u2014Samsung, Lenovo, Apple, XOMI, and OnePlus\u2014on a numerical rating scale. The collected responses were compiled in a spreadsheet and used for statistical analysis.<\/span><\/p>\n

      The data was quantitative in nature and suitable for one-way ANOVA, as it involved comparing the mean ratings of more than two independent groups. The analysis was carried out using statistical software to ensure accuracy and reliability of results.<\/span><\/p>\n

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      ANOVA Table (One-Way)<\/b><\/p>\n\n\n\n\n\n\n\n
      \n

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

      		\n

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

      		\n

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

      		\n

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

      		\n

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

      		\n

      p-value<\/span><\/b><\/p>\n<\/td>\n<\/tr>\n<\/thead>\n

      \n

      Between Groups<\/span><\/p>\n<\/td>\n

      		\n

      25.0221<\/span><\/p>\n<\/td>\n

      		\n

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

      		\n

      6.2555<\/span><\/p>\n<\/td>\n

      		\n

      1.1344<\/span><\/p>\n<\/td>\n

      		\n

      0.3415<\/span><\/p>\n<\/td>\n<\/tr>\n

      \n

      Within Groups<\/span><\/p>\n<\/td>\n

      		\n

      1097.3896<\/span><\/p>\n<\/td>\n

      		\n

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

      		\n

      5.5145<\/span><\/p>\n<\/td>\n

      		\u00a0<\/td>\n\u00a0<\/td>\n<\/tr>\n
      \n

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

      		\n

      1122.4118<\/span><\/p>\n<\/td>\n

      		\n

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

      		\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      \u00a0<\/span><\/b><\/p>\n

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