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        <datestamp>2026-09-22T05:38:28Z</datestamp>
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          <dc:title>Supplementary file 1_The role of social media in shaping purchasing behavior among consumers in the Kingdom of Bahrain.pdf</dc:title>
          <dc:creator>Fatima Abdullah Al-Sahhaf (25087843)</dc:creator>
          <dc:creator>Sherif Badran (24678271)</dc:creator>
          <dc:subject>Communication Studies</dc:subject>
          <dc:subject>Bahraini consumer</dc:subject>
          <dc:subject>consumer purchasing behavior</dc:subject>
          <dc:subject>digital marketing</dc:subject>
          <dc:subject>elaboration likelihood model</dc:subject>
          <dc:subject>eWOM</dc:subject>
          <dc:subject>Instagram</dc:subject>
          <dc:subject>social commerce</dc:subject>
          <dc:subject>social media</dc:subject>
          <dc:description>&lt;p&gt;Grounded in Uses and Gratifications Theory (UGT), the Stimulus–Organism–Response (S-O-R) Model, and the Elaboration Likelihood Model (ELM), this study examines how social media use is associated with purchasing behavior among Bahraini consumers across five decision-journey stages: Need Recognition, Information Search, Evaluation of Alternatives, Purchase Decision, and Post-Purchase Behavior. A cross-sectional survey was administered to 384 active social media users; data were analyzed with Pearson correlations, one-way ANOVA (Tukey HSD), and independent-samples t-tests (IBM SPSS v. 27; Cronbach's α = 0.87). Social media influence was strongest at Evaluation of Alternatives (M = 3.54, SD = 0.98; r = .487, p &lt; .001) and weakest at Post-Purchase Behavior (M = 2.48; r = .241, p &lt; .001), revealing a “silent consumption” asymmetry; all five stages were significant (H1 supported). Instagram dominated platform usage (53.3%; M = 4.21) as the primary cognitive-evaluative tool, while TikTok showed a disproportionate influence-to-usage ratio (M = 3.85, 17.5%) via shoppertainment mechanisms. One-Way ANOVA confirmed significant between-platform differences, F(5, 378) = 12.91, p &lt; .001, η&lt;sup&gt;2&lt;/sup&gt; = .146 (H2 supported). No significant differences were found across gender (p = 0.963), age (p = 0.093), education (p = 0.708), or income (p = 0.328)—all with negligible-to-small effect sizes—consistent with a “demographic convergence” pattern that may plausibly be linked to algorithmic personalization, though the mechanism itself was not directly measured. Findings advance stage-differentiated consumer behavior theory and offer actionable implications for social commerce strategy, influencer policy, and GCC digital advertising regulation.&lt;/p&gt;</dc:description>
          <dc:date>2026-09-22T05:38:28Z</dc:date>
          <dc:type>Dataset</dc:type>
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          <dc:identifier>10.3389/fcomm.2026.1897772.s001</dc:identifier>
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          <dc:rights>CC BY 4.0</dc:rights>
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