Wooliscroft, BenPhillips, MeganHoo, Khim Kheong2025-09-032025-09-032025http://hdl.handle.net/10292/19747In today’s competitive retail landscape, consumers are increasingly price-sensitive, with store price image emerging as a central factor influencing both purchase decisions and store choice. To sustain competitive advantage, pricing strategies are central to retail success, with retailers striving to optimise sales revenue, enhance profitability, and communicate a trustworthy commitment to low prices. One prominent approach is the use of price guarantees (PGs), including price-beating guarantees (PBGs) and automatic price-adjustment promises (APPs), which are frequently promoted in advertising campaigns to reinforce retailers’ claims of offering the lowest price and to reassure cost-conscious shoppers. Yet their effectiveness critically depends on how consumers perceive the believability and credibility of such advertising. If consumers trust these promises, PGs can build confidence, satisfaction, and long-term loyalty. Conversely, if they are perceived as marketing gimmicks, they may fuel scepticism, diminish trust, and undermine strategic value. Despite their widespread adoption, empirical evidence remains limited on how the believability of PG advertising translates into consumer trust, satisfaction, and behavioural outcomes in real-world retail environments. This thesis addresses the gap through a two-stage research design. First, a systematic literature review of 82 studies (1991–2022) synthesises developments across six dimensions: research fields, topics, theoretical foundations, methodologies, respondent groups, and product categories. The review reconceptualises PGs through the Mechanism-Based Dual-Function Model of PGs (MB-DFM-PG), framing them as both strategic tools (signalling price competitiveness) and psychological tools (building consumer trust). It also identifies significant gaps across six dimensions concerning the believability of different PG formats in practice. Second, the thesis develops and empirically tests the PG-Believability-Trust-Behavioural (PG-BTB) model, integrating Signalling Theory and Expectation-Confirmation Theory (ECT). A quasi-longitudinal field study with 1,219 Supercheap Auto Loyalty Club-Plus members in New Zealand examined consumer responses to PBGs and APPs. Using covariance-based structural equation modelling (SEM: AMOS-28), the study tested causal pathways linking advertising believability, trust, store price image, service quality, satisfaction, and repurchase intention. The findings provide four significant contributions: (1) Extends Signalling Theory by showing that PGs are interpreted as signals of price competitiveness and also as cues for service reliability and retailer integrity. (2) Advances ECT by applying it in a real-world quasi-longitudinal analysis, demonstrating how the confirmation of expectations with PGs influences satisfaction and repurchase intentions. (3) Offers a dual-theory integration, showing how signals (pre-purchase) and confirmations (post-purchase) interact in shaping consumer trust and loyalty. (4) Presents the first empirical comparison of PBGs and APPs within a single study, showing that APPs' automated process by reducing consumer effort is more effective at fostering trust and satisfaction, while PBGs require clear, transparent communication and simpler procedures to achieve credibility. Overall, this thesis enriches the academic literature by bridging conceptual models and field-based empirical evidence in PG advertising. It provides actionable insights for retailers in New Zealand, Australia, and globally on designing and implementing PG strategies that are credible, trustworthy, and effective in building enduring customer relationships. Beyond retail, the findings hold implications for broader marketing domains where trust, believability, and expectation-confirmation are critical drivers of consumer behaviour.enprice guaranteeprice-beatingautomatic price-adjustmentadvertising believabilityconsumer trustsatisfactionrepurchase intentionThesisOpenAccess