Exploring loyalty drivers for smartphone and mobile carriers

The rapid proliferation of smartphones since the iPhone’s introduction has transformed consumer behavior and expanded mobile functionality across banking, learning, shopping, and social networking. As smartphones increasingly operate in tandem with telecom services, device-carrier interactivity is critical to understanding user experience and loyalty. In markets like South Korea, the fit between device performance and network quality (e.g., optimized 5G connectivity) affects satisfaction and loyalty. Users typically rely on brand image when selecting devices and on corporate image when choosing mobile carriers. Perceptions of price fairness for devices and service fees for carriers further influence satisfaction. Apps and call quality depend on both device and network performance and may jointly shape satisfaction with both. This study addresses a research gap by integrating device and carrier perspectives into a single framework of loyalty formation, distinguishing symmetric factors (brand image vs. corporate image; price fairness vs. perceived fees) and common factors (apps and call quality). The research aims to identify how these determinants affect device satisfaction and mobile carrier satisfaction, and in turn how both satisfactions drive loyalty. The paper presents the theoretical background, proposes a research model with hypotheses, details the methodology, reports empirical results, and discusses implications.

Smartphone (Device): Prior research on smartphone user behavior leverages frameworks such as the Technology Acceptance Model (TAM) and the Expectation-Confirmation Model (ECM), highlighting perceived usefulness, ease of use, satisfaction, and continuance intentions. Studies emphasize the roles of brand image, product features, price, privacy concerns, and electronic word of mouth on purchase and continued use. While brand image, price, and apps have been widely studied, integrated loyalty that jointly considers device and carrier remains underexplored. Telecom service (Mobile Carrier): Research on mobile carriers focuses on service quality, digital inclusion, recovery strategies, trust, commitment, corporate image, switching costs, and pricing. Big data analytics have been used for segmentation and loyalty prediction. Service quality dimensions, corporate image, and perceived switching barriers influence satisfaction and loyalty. Pricing and technological changes contribute to brand switching. This review underscores the need to integrate device and carrier perspectives to understand loyalty in the telecom ecosystem.

Research design and hypotheses: The model conceptualizes loyalty formation along two dimensions—device satisfaction and mobile carrier satisfaction—driven by symmetric factors (brand image, price fairness for devices; corporate image, perceived fees for carriers) and common factors (applications, call quality) expected to influence both domains. Measurement instrument: Constructs were adapted from validated scales for the smartphone context. The survey was authored in English, translated into Korean, and back-translated to ensure accuracy. Expert review and a pilot test (n=15) refined item clarity and completeness. Except for demographics and frequency items, all constructs used 7-point Likert scales. Data collection and sample: An online survey was distributed via cellular retail outlets in South Korea from February 5 to September 23, 2022, using convenience sampling. Informed consent was obtained, participation was voluntary, and incomplete responses were removed, yielding 357 valid responses. Sample characteristics: 55.5% male; largest age group in 20s (28.0%); device brands: Samsung 71.1%, Apple 25.8%; carriers: SKT 57.1%. Sample size planning used Soper’s a priori SEM calculator, indicating a minimum of 218 for the model (9 latent, 26 observed variables, anticipated effect size 0.1, power 0.8, alpha 0.05), which was exceeded. Analysis: Partial least squares structural equation modeling (PLS-SEM) using SmartPLS 4.0 was employed. Common method bias was assessed via Harman’s single-factor test (largest single-factor variance 42.72%). Measurement model reliability and validity were evaluated with composite reliability (CR), Cronbach’s alpha, AVE, Fornell–Larcker criterion, and HTMT; all thresholds were satisfied. Model fit indices indicated acceptable fit (SRMR=0.047; D_ULS and d_G below 95% bootstrapped quantiles). Structural relationships were tested with bootstrapping (5,000 subsamples).

- Measurement model: All CR values > 0.7; lowest Cronbach’s alpha = 0.738; all loadings > 0.70; all AVE > 0.50. Fornell–Larcker and HTMT criteria supported discriminant validity. CMB not a concern (largest single-factor variance 42.72%). Fit: SRMR=0.047; D_ULS=0.837; d_G=0.556. - Structural paths (coefficients, t-values): • Brand image → Device satisfaction: 0.531, t=10.083 (significant; supported). • Price fairness → Device satisfaction: 0.062, t=1.287 (not significant). • Applications → Device satisfaction: 0.264, t=5.635 (significant; supported). • Applications → Mobile carrier satisfaction: 0.156, t=3.730 (significant). • Call quality → Device satisfaction: 0.009, t=0.215 (not significant). • Corporate image → Mobile carrier satisfaction: 0.680, t=17.452 (significant; supported). • Perceived fee → Mobile carrier satisfaction: 0.090, t=2.501 (significant). • Device satisfaction → Loyalty: 0.681, t=19.998 (significant; supported). • Mobile carrier satisfaction → Loyalty: 0.225, t=5.947 (significant; supported). - Variance explained: The model explained approximately 68.8% of the variation in loyalty.

The integrated analysis shows that device and carrier factors jointly shape loyalty. Strong brand image substantially enhances device satisfaction, reinforcing prior findings on brand perception’s role in user evaluations. Price fairness did not significantly affect device satisfaction, potentially reflecting perceptions that smartphone prices exceed expectations but are offset by utility and entertainment value. Applications significantly improved satisfaction with both devices and carriers, consistent with the interdependence of app performance on device capability and network quality. Call quality did not significantly influence satisfaction, possibly due to uniformly high standards that reduce its salience in evaluations. Corporate image strongly influenced mobile carrier satisfaction, indicating that perceived reputation and quality drive positive assessments. Perceived fees were also significantly associated with carrier satisfaction, aligning with expectations that reasonable fees boost satisfaction. Both device satisfaction and mobile carrier satisfaction significantly increased loyalty, with device satisfaction exerting a stronger effect, suggesting device-centric drivers play a more dominant role in overall loyalty formation within the device–carrier ecosystem.

This study advances theory by jointly modeling device and carrier determinants of loyalty, demonstrating that device satisfaction (driven by brand image and applications) and mobile carrier satisfaction (driven by applications, corporate image, and perceived fees) both contribute to loyalty, with the device side exerting a stronger influence. The model accounts for 68.8% of loyalty variance, offering a robust framework extendable to satisfaction, desire, and repurchase intention. Practically, manufacturers and carriers should coordinate on preferred device–carrier bundles, strengthen device brand positioning, and co-develop/app-optimize to enhance performance. Carriers should emphasize corporate image management and price structures aligned with perceived value while partnering with popular device brands to leverage device-centric loyalty. Future research can explore brand imaging strategies with emerging features and pricing models, examine app–network synergies per carrier, and investigate pricing structures that balance profitability and consumer satisfaction.

The study measured a relatively narrow set of loyalty outcomes centered on satisfaction and referral willingness, not fully capturing broader loyalty constructs such as price insensitivity, resistance to competitive offers, and explicit future behavioral intentions. The research did not distinguish voluntary loyalty from customer lock-in (e.g., switching costs, contractual obligations). Future studies should incorporate a wider array of loyalty behaviors and apply frameworks such as Push–Pull–Mooring to differentiate loyalty from lock-in.