Impulse control during the online shopping frenzy in China: the role of consumer inertia

The study investigates how positive electronic word-of-mouth (eWOM) during China’s large-scale online shopping festivals (e.g., Double Eleven) influences consumers’ impulse buying and impulse control. It integrates flow theory into the Stimulus-Organism-Response (S-O-R) framework to assess whether and how consumer inertia (affective, cognitive, behavioral) mediates the transition from positive eWOM to impulse buying amid information overload. The research tests the direct effect of positive eWOM on impulse buying (H1), the mediating roles of affective, cognitive, and behavioral inertia (H2a–H2c), and whether behavioral inertia mediates the effects of affective and cognitive inertia on impulse buying (H3–H4). The purpose is to illuminate impulse-control strategies employed by prudent/rational consumers as risk-reduction mechanisms in online shopping frenzies and to enhance understanding of bounded rationality in this context.

Prior work links eWOM to purchase decisions, showing that argument quality (informativeness, persuasiveness, credibility), source credibility, and volume of reviews affect intentions and can trigger impulsive purchases via self-control failure (e.g., Baumeister, Lamberton, Zhang et al.). Although negative reviews often have stronger marginal effects than positive ones, favorable eWOM still drives enjoyment and recommendations that foster impulse buying. Consumer inertia—conceptualized as affective (emotional carryover), cognitive (conservation of mental resources), and behavioral (habitual action)—represents reliance on past responses and can function as a risk-reduction strategy under cognitive load, prompting reflective impulse control and decision postponement. Flow theory suggests that intrinsic engagement involves cognitive and affective states; under continuous positive reinforcement, habitual behaviors (behavioral inertia) may emerge from affective and cognitive states. Accordingly, hypotheses posit: H1, positive eWOM directly increases impulse buying; H2a–H2c, affective, cognitive, and behavioral inertia mediate eWOM’s effect on impulse buying; H3–H4, behavioral inertia mediates the effects of affective and cognitive inertia on impulse buying.

Context: China’s Double Eleven (2020) online shopping festival. Design: Two-stage sampling (pretest and formal survey). Pretest employed exploratory factor analysis (EFA) for item refinement before confirmatory factor analysis (CFA) and structural equation modeling (SEM). Sample: 497 valid responses (convenience sampling). Measures: Five-point Likert scales for positive eWOM (4 items from Chevalier & Mayzlin, α=0.79), affective inertia (4 items from Kuppens et al., α=0.78), cognitive inertia (4 items from Messner & Vosgerau, α=0.76), behavioral inertia (4 items from Anderson & Srinivasan, α=0.78), and impulse buying (4 items from Rook & Fisher, α=0.78). Overall 20-item scale α=0.94. Controls: gender, age, education, occupation, income. Analysis: CFA to assess reliability/validity (SFLs >0.70; CRs ≈0.85–0.86; AVE >0.50). SEM tested direct and mediated paths per the integrated S-O-R and flow-based model. Model fit indices indicated good fit for measurement and structural models (χ²/df≈1.16; GFI=0.964; CFI/IFI=0.992; SRMR≈0.027–0.028; RMSEA=0.018).

- Direct effect: Positive eWOM → Impulse buying (IB): β=0.144, p<0.01 (supports H1). - Mediation by consumer inertia (H2): • eWOM → Affective inertia (AI): β=0.700, p<0.001; AI → IB: β=0.205, p<0.001 (partial mediation; H2a supported). • eWOM → Cognitive inertia (CI): β=0.652, p<0.001; CI → IB: β=0.386, p<0.001 (partial mediation; H2b supported). • eWOM → Behavioral inertia (BI): β=0.215, p<0.001; BI → IB: β=0.170, p<0.001 (partial mediation; H2c supported). - Serial/reciprocal mediation (H3–H4): • AI → BI: β=0.391, p<0.001; BI → IB: β=0.170, p<0.001; AI → IB: β=0.205, p<0.001 (BI partially mediates AI → IB; H3 supported). • CI → BI: β=0.245, p<0.001; BI → IB: β=0.170, p<0.001; CI → IB: β=0.386, p<0.001 (BI partially mediates CI → IB; H4 supported). - Correlations among key constructs ranged r=0.63–0.71 (p<0.01). Reliability strong (CR≈0.85–0.86; SFLs>0.70). Validity supported (AVE>0.50; Fornell-Larcker criterion met). - Model explanatory power: R² AI=0.49; CI=0.42; BI=0.53; IB=0.58, indicating substantial variance explained, with over half of IB variance accounted for.

Findings confirm that positive eWOM directly stimulates impulse buying while also prompting reflective impulse control via consumer inertia. Integrating flow theory with the S-O-R framework reveals that affective and cognitive responses to sustained positive stimuli cultivate behavioral habits that, in turn, contribute to impulse purchases. Conceptually, this positions consumer inertia as a dual-function mechanism: it helps consumers manage cognitive load and secure decision quality, yet under continuous reinforcement it channels affective/cognitive states into behavioral inertia, aligning with brand loyalty and subsequent impulse purchases. Managerially, firms should foster high-quality products and services to encourage positive eWOM and cultivate trust and loyalty, leveraging inertia to support repeat purchases. Socially, because prudent consumers still exhibit partial mediation leading to impulse buying—especially when lacking prior experience—public policy and social marketing interventions are warranted to mitigate problematic impulsive purchases and address issues like fraudulent reviews.

This study advances understanding of impulse control in online shopping by integrating flow theory into an S-O-R model and demonstrating that consumer inertia (affective, cognitive, behavioral) partially mediates the link between positive eWOM and impulse buying. It highlights inertia as a risk-reduction strategy that can both enhance decision confidence and, through sustained reinforcement, contribute to behavioral habits and brand-aligned impulse purchases. Future research should validate the model across platforms and countries, examine additional internal/external triggers of impulse buying, assess consumer confusion as a moderator, and explore post-decision strategies (e.g., service failure/recovery) addressing expectancy violations.

- Context-specific: Conducted within China’s e-commerce environment; generalizability to other countries/platforms requires validation. - Limited stimuli: Focused solely on positive eWOM; other triggers (traits, motives, marketing stimuli) were not modeled. - Potential moderators untested: The moderating role of consumer confusion on inertia–impulse buying and eWOM–impulse buying relationships was not examined. - Post-decision dynamics: Strategies for handling expectancy violations and outcomes at the post-decision stage were outside scope and warrant investigation.