<![CDATA[Repurchase intention is typically regarded as an essential measure of customer loyalty and competitiveness in digital markets. With ever-changing individual behavior and technological advancements, recent literature indicates that researchers lack agreement on what motivates customers to replay. In that light, the paper will conduct a systematic synthesis of the underlying determinants and theories of repurchase intention between 2021 and 2025. The study will adopt the Systematic Literature Review (SLR) research methodology, guided by the PRISMA 2020 guidelines, to ensure transparency and replicability. Identifying the materials involved in the synthesis involved four key phases: identification, screening, eligibility, and inclusion. Peer-reviewed articles within the last five years were retrieved from the Scopus database. Narrative synthesis was then conducted among the eligible papers, combining both quantitative and qualitative results to highlight recurring theories and constructs within the field. The analysis indicates that PLS-SEM is the most widely used technique in the field. From a theoretical standpoint, CAT, TPB, and S-O-R models dominate. Trust, satisfaction, perceived usefulness, and service quality stood out as the main constructs that drive repurchase intention, while anger and sadness inhibited re-purchases. Though the studies consider multiple geographical regions, including the US, China, India and Vietnam, among others, few cross-cultural and demographic studies exist. Repurchase intention is, therefore, a result of the interaction between cognitive, emotional, and relational factors. Further research needs to employ advanced analytics, machine learning, QCA, and biometric sentiment analysis to explore emotion- and situational-moderating factors. It is up to marketers to provide customers with emotionally engaging, trustworthy digital platforms where relationships can be developed and sustained. This review is among the first to synthesize evidence on repurchase intention research from 2021 using the PRISMA 2020 framework.]]>
