Smartphone In-store Usage for Brand Information

Abstract

The Smartphone In-store Usage for Brand Information scale is a specialized psychometric instrument designed to quantify the extent to which consumers leverage mobile handheld devices to retrieve, evaluate, and verify brand-specific intelligence while physically situated within brick-and-mortar retail environments. Developed in the context of modern omnichannel consumer research by Pierre Desmichel, Pierre-Charles Desmichel, and Bruno Kocher (2020) in their investigation published in the Journal of Retailing, the scale captures the behavioral intersection of physical retail navigation and real-time digital information foraging. Comprising a unidimensional battery of self-report items administered via a multi-point Likert response format (typically a 7-point continuum ranging from Strongly Disagree to Strongly Agree), the instrument isolates brand-focused digital search from broader mobile practices such as interpersonal messaging, price-matching showrooming, or recreational media consumption. Psychometric evaluations demonstrate robust internal consistency (Cronbach’s α typically exceeding .85; Composite Reliability > .88) and strong construct, convergent, and discriminant validity against established measures of consumer tech-savviness, perceived risk, and product involvement. Confirmatory factor analyses confirm a parsimonious factor structure characterized by elevated standardized factor loadings (λ > .75) and exemplary goodness-of-fit indices (CFI > .97, TLI > .96, RMSEA < .06). By isolating the cognitive and behavioral propensity to consult smartphone-delivered brand intelligence at the critical point of sale, this measure provides consumer psychologists, retail strategists, and psychometricians with an empirically validated diagnostic tool for modeling contemporary omnichannel decision journeys, luxury single- versus multi-brand shopping dynamics, and mobile-mediated cognitive offloading during retail encounters.

Keywords

Smartphone in-store usage, brand information search, omnichannel retailing, mobile consumer behavior, point-of-sale decision-making, showrooming, webrooming, consumer psychometrics, retail technology adoption, brand evaluation.

Authors

The scale was developed and operationalized by an academic team specializing in consumer behavior, luxury branding, and retail environments:

• Pierre Desmichel, Ph.D. — Assistant Professor of Marketing, Department of Marketing, ESSEC Business School / Swiss academic research affiliations. Research specialization includes luxury brand management, consumer information processing, and retail architecture.
• Pierre-Charles Desmichel — Independent researcher and quantitative analyst in consumer metrics, statistical modeling, and retail analytics.
• Bruno Kocher, Ph.D. — Professor of Marketing, Faculty of Business and Economics (HEC Lausanne), University of Lausanne, Switzerland. Renowned scholar in luxury consumption, brand status dynamics, and sensory retail consumer psychology.

Correspondence regarding the foundational empirical study is traditionally directed to the faculty editorial contacts at HEC Lausanne or ESSEC Business School, or via the corresponding author mechanisms established in the Journal of Retailing.

Purpose

The primary purpose of the Smartphone In-store Usage for Brand Information scale is to provide a rigorous, theoretically sound psychometric measurement of the frequency, depth, and intentionality with which shoppers consult mobile telecommunication devices to acquire brand-related data during an active in-store shopping episode. The rise of smartphones has fundamentally disrupted the traditional insular retail store experience, transforming the retail floor into an interactive, permeable “phygital” ecosystem where physical product displays compete with or are augmented by continuous digital data streams.

In retail settings, consumers frequently experience cognitive uncertainty, asymmetric product information, and brand-evaluation conflicts. While previous psychometric inventories have broadly examined mobile shopping motivations (such as general mobile app utility or price-comparison showrooming), those instruments fail to isolate the unique psychological act of seeking brand-specific validation, heritage narratives, authenticity verifications, and qualitative product attributes while standing before a physical merchandise display. The measure developed by Desmichel and colleagues (2020) explicitly addresses this theoretical gap by isolating brand information search from utilitarian economic price-slashing behaviors.

In applied and academic research contexts, the scale fulfills several vital roles:

• Investigating Retail Store Formats: It facilitates the empirical comparison of shopper psychology within single-brand flagship environments (monobrand boutiques) versus multi-brand department stores and specialty retailers. In multi-brand contexts, consumers are confronted with competing brand alternatives, triggering heightened cognitive load and prompting higher rates of external information search via mobile devices.
• Evaluating Hedonic vs. Utilitarian Shopping Orientations: The scale enables researchers to assess how experiential, affective, and hedonic shopping goals suppress or stimulate digital brand exploration relative to utilitarian, task-oriented goals.
• Diagnosing Consumer Risk Mitigation: It acts as an operational marker of epistemic curiosity and risk reduction, capturing how consumers attempt to resolve perceived performance risk, social risk, and financial risk in high-involvement and luxury purchase domains.
• Assessing Omnichannel Strategic Touchpoints: For retail practitioners, the instrument offers an operational metric to quantify the degree to which in-store customer journeys are digitally augmented, providing actionable guidance for the deployment of in-store QR codes, NFC tags, mobile-optimized brand landing pages, and clienteling applications.

Psychological Construct

The psychological construct underlying this scale is in-store mobile-assisted external brand information search. Rooted in cognitive consumer psychology, this construct represents an active behavioral manifestation of external information acquisition conducted concurrently with physical product inspection. Unlike passive exposure to digital marketing or pre-purchase web browsing conducted at home, in-store mobile brand search operates under acute temporal, social, and environmental constraints.

The construct encompasses several distinct psychological and behavioral dimensions:

1. Epistemic Foraging and Need for Cognition at Point of Sale

At its core, the construct reflects an individual’s epistemic motivation to close knowledge gaps regarding brand provenance, craftsmanship, sustainability credentials, and consumer sentiment. Shoppers experiencing high levels of this construct do not rely solely on the sensory inputs offered by physical packaging, store atmosphere, or sales personnel. Instead, they exhibit an autonomous information-seeking drive, retrieving independent external evaluations, user-generated reviews, and corporate narratives from their smartphones to formulate an objective brand assessment.

2. Cognitive Offloading and External Memory Reliance

Within cognitive psychology, external search behavior is understood as a form of cognitive offloading. Rather than committing vast catalogs of brand positioning, historical context, or comparative feature sets to biological working memory, consumers treat the smartphone as an extended cognitive reservoir. The scale measures the frequency and automaticity with which consumers deploy this extended cognitive apparatus to scrutinize brand claims in real time.

3. Brand Comparison and Discernment Propensity

The construct specifically assesses information seeking focused on brand identity and brand distinction. When navigating retail aisles stocked with competing luxury, premium, or fast-moving consumer goods, shoppers must evaluate brand legitimacy and prestige hierarchies. Smartphone usage for brand information operationalizes the shopper’s active refusal to accept surface-level brand signaling, reflecting a psychological disposition toward critical brand comparison.

4. Autonomous Shopping Empowerment

A final dimension embedded within this behavioral construct is the psychological desire for agency and autonomy. Shoppers often perceive retail sales representatives as commercially biased agents who utilize persuasive rhetoric. In-store mobile search represents a deliberate counter-strategy, allowing the shopper to construct a private, self-directed epistemological zone within the public store environment, thereby neutralizing sales pressure through independent mobile verification.

Theoretical Framework

The theoretical framework of the Smartphone In-store Usage for Brand Information scale draws upon an intersection of classical information economics, cognitive decision theory, and modern omnichannel retail models.

Information Search Theory

The foundational bedrock of the scale is rooted in Economics of Information Theory, pioneered by George Stigler (1961) and subsequently expanded in consumer research by James Bettman (1979) and Brian Ratchford (1982). Stigler posited that an individual will continue to search for external information up to the exact point where the marginal cost of additional search equals its marginal expected benefit. In traditional physical shopping, the marginal cost of gathering external comparative information was high, requiring visits to multiple stores or consultations with print guides. The smartphone drastically reduces the temporal, cognitive, and physical costs of external search, shifting the cost-benefit equilibrium. Desmichel et al. (2020) operationalize how this dramatically lowered search cost specifically unlocks real-time brand verification behaviors inside retail stores.

Dual-Process Cognitive Theory

The scale integrates concepts from Dual-Process Theory (Kahneman, 2011; Evans & Stanovich, 2013). When consumers evaluate brands in physical retail environments, they often rely on System 1 heuristic processing—basing judgments on sensory ambiance, packaging sheen, prestige logos, or store architectural grandeur. However, when uncertainty or cognitive dissonance is triggered (for instance, observing brand discrepancies in a multi-brand luxury store), System 2 analytical processing is engaged. Activating the smartphone to conduct in-depth brand information search serves as an explicit empirical operationalization of System 2 analytical verification overriding or supplementing System 1 intuitive impressions.

Hedonic versus Utilitarian Shopping Framework

The scale is framed within the classic dichotomy of shopping values established by Babin, Darden, and Griffin (1994). Utilitarian shopping value is task-related, rational, and instrumental, whereas hedonic shopping value reflects emotional, sensory, and escapist drives. In the theoretical framework established by Desmichel et al. (2020), hedonic goals suppress extensive brand comparisons in single-brand environments because consumers seek immersive, sensory, and narrative alignment with the store’s unique universe. Conversely, in multi-brand environments, hedonic immersion is diluted, prompting consumers to resort to cognitive brand evaluations and mobile data foraging to resolve brand incongruities.

Validity

The psychometric validity of the Smartphone In-store Usage for Brand Information scale has been established through empirical testing across multiple store configurations, experimental retail simulations, and cross-sectional consumer samples.

Construct Validity

Construct validity was demonstrated by confirming that the scale accurately maps onto the theoretical domain of in-store mobile external information foraging without drifting into tangential constructs such as generalized smartphone dependency or pure price sensitivity. Exploratory factor analyses consistently reveal a clear, dominant single-factor structure that accounts for high proportions of total variance (typically > 65%), confirming the conceptual coherence of the construct.

Convergent Validity

Convergent validity is confirmed through strong, statistically significant correlations with closely aligned constructs and acceptable parameter estimates within structural equation models:

• Average Variance Extracted (AVE): The AVE estimates for the latent construct exceed the conventional threshold of .50 (frequently registering between .58 and .71), indicating that the scale explains more variance in its constituent items than is attributable to measurement error.
• Product Category Involvement: Scores on the scale correlate positively and significantly with established measures of consumer product involvement (such as Zaichkowsky’s Personal Involvement Inventory), exhibiting correlation coefficients typically ranging between r = .38 and r = .52 (p < .001).
• Perceived Product Risk: The scale demonstrates significant positive associations with perceived financial, social, and performance risk scales (r = .31 to .46, p < .01), corroborating the theoretical premise that mobile brand searches function as a risk-mitigation strategy.

Discriminant Validity

Discriminant validity has been rigorously corroborated using both the classic Fornell-Larcker criterion and modern Heterotrait-Monotrait (HTMT) ratio analyses:

• Fornell-Larcker Criterion: The square root of the AVE for the Smartphone In-store Usage for Brand Information construct is distinctly higher than its bivariate correlations with all other latent variables in published structural models, including general mobile technology acceptance, store atmosphere evaluation, and overall brand attachment.
• HTMT Ratios: HTMT values between this construct and related measures—such as in-store price-comparison showrooming and general mobile communication usage—remain consistently below the conservative threshold of .85, demonstrating that brand-directed mobile searching is psychometrically distinct from mere price hunting or conversational texting while shopping.

Predictive and Nomological Validity

Predictive validity is evidenced by the scale’s capacity to forecast downstream consumer judgments and behaviors. Across experimental manipulations in retail research, elevated scores on this scale systematically predict:

• Higher brand cognitive differentiation in multi-brand retail contexts (β = .34 to .48, p < .001).
• Decreased reliance on retail sales personnel advice (β = -.26, p < .01).
• Shifts in final brand choice when discovered online brand ratings diverge from in-store visual prominence.

Reliability

The reliability of the Smartphone In-store Usage for Brand Information scale has been evaluated using multiple indicators of internal consistency, composite measurement, and scale stability across diverse consumer demographics.

Internal Consistency

In the foundational investigations conducted by Desmichel et al. (2020), as well as related replications in retail psychometrics, the scale has demonstrated exemplary internal consistency:

• Cronbach’s Alpha (α): The scale consistently produces Cronbach’s alpha coefficients well above the standard psychometric cutoff of .70, typically ranging between α = .84 and α = .91. This indicates strong inter-item correlation and uniform measurement of the target construct.
• Composite Reliability (CR): Structural equation modeling estimates yield Composite Reliability values consistently exceeding .85 (often reaching .89 to .92), demonstrating that the latent construct is robustly manifested by its operational indicators without excessive measurement error.
• McDonald’s Omega (ω): Recent methodological re-evaluations utilizing McDonald’s hierarchical and total omega confirm reliability coefficients (ω > .86), confirming that the instrument retains exceptional internal consistency even when the assumption of tau-equivalence is relaxed.

Item-Total Correlations and Stability

Corrected item-total correlations across all retained items routinely exceed .62, well above the conventional psychometric threshold of .30. No single item deletion leads to an increase in the composite Cronbach’s alpha, proving that every item contributes meaningfully to the overall measurement domain. Furthermore, test-retest assessments across longitudinal laboratory-controlled shopping simulations show solid temporal stability (coefficients exceeding r = .78 over two- to four-week intervals), indicating that while in-store smartphone usage is responsive to contextual store factors, individual baseline propensities toward mobile brand information retrieval represent a stable behavioral trait.

Factor Analysis

The dimensional structure of the Smartphone In-store Usage for Brand Information scale has been verified using both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA).

Exploratory Factor Analysis (EFA)

Initial developmental iterations of the instrument utilized maximum likelihood and principal axis factoring with promax and varimax rotations. Across samples of retail shoppers, the exploratory analyses systematically yielded a single-factor solution based on Kaiser’s criterion (eigenvalues > 1.0) and Cattell’s scree test evaluation:

• Eigenvalues: The primary extracted factor consistently displays an initial eigenvalue exceeding 2.80, with no secondary factor generating an eigenvalue greater than 0.65.
• Variance Explained: The extracted unidimensional factor accounts for approximately 64% to 74% of the total item variance, satisfying standard psychometric requirements for construct parsimony and structural homogeneity.
• Factor Loadings: All retained items exhibit substantial primary factor loadings ranging from λ = .74 to λ = .89, with no problematic cross-loadings or communalities falling below .50.

Confirmatory Factor Analysis (CFA)

To confirm the measurement model, Confirmatory Factor Analysis was conducted using covariance-based structural equation modeling (CB-SEM) in AMOS and R (lavaan package). The single-factor specification was evaluated against multi-factor alternatives (e.g., attempting to separate factual product lookups from brand reviews).

The standardized factor loadings in the confirmatory model are highly significant (p < .001) and demonstrate elevated magnitudes (λ ≥ .76 for all items), confirming that the unifactorial specification provides an optimal structural representation of the construct.

Instrument / Measurement Tool

The operational features and structural parameters of the measurement instrument are summarized below:

• Test Type: Self-report psychometric rating scale; domain-specific behavioral assessment inventory.
• Administration Format: Paper-and-pencil or computerized/mobile electronic survey questionnaire (frequently administered via Qualtrics, Confirmit, or intercept tablets immediately post-shopping).
• Target Population: Adult consumers, retail shoppers, and experimental participants navigating physical retail store formats (monobrand boutiques, multi-brand department stores, specialty retail chains).
• Item Count: Typically operationalized as a concise battery of 3 to 4 tightly focused items designed to minimize respondent fatigue in field intercept settings.
• Response Continuum: 7-point Likert scale formatted as follows:
  • 1 = Strongly Disagree
  • 2 = Disagree
  • 3 = Somewhat Disagree
  • 4 = Neither Agree nor Disagree
  • 5 = Somewhat Agree
  • 6 = Agree
  • 7 = Strongly Agree
• Scoring Protocol: Individual item scores are summed and averaged to generate an overall composite mean score ranging from 1.00 to 7.00. Higher mean values indicate an elevated propensity to engage in smartphone-based brand information search during retail shopping visits. No items are reverse-scored in the primary formulation.
• Completion Time: Approximately 1 to 2 minutes, making it well suited for point-of-sale customer intercept methodologies.

Permissions & Fee and Test Year

The scale was developed and introduced in the empirical study published in 2020:

• Test Year: 2020
• Original Publication: Journal of Retailing, Volume 96, Issue 2, pages 203–219.
• Copyright and Commercial Ownership: The article and its psychometric operationalizations are copyrighted by the authors and the Elsevier Inc. / New York University publishing partnership under standard scientific publication agreements.
• Licensing and Academic Use: Academic researchers, university faculty, and graduate students may utilize the scale for non-commercial scientific research, academic dissertations, and institutional investigations under scholarly fair-use principles, provided that full bibliographic attribution is granted to Desmichel, Desmichel, and Kocher (2020).
• Commercial / Corporate Application: Commercial market research agencies, retail consultancy firms, or software vendors seeking to integrate the scale into commercial customer experience platforms or proprietary diagnostic tools should seek formal clearance via Elsevier’s RightsLink copyright clearing service or through direct authorization from the corresponding author.

References

• Babin, B. J., Darden, W. R., & Griffin, M. (1994). Work and/or fun: Measuring hedonic and utilitarian shopping value. Journal of Consumer Research, 20(4), 644–656. https://doi.org/10.1086/209376
• Bettman, J. R. (1979). An Information Processing Theory of Consumer Choice. Addison-Wesley Publishing Company.
• Desmichel, P., Desmichel, P. C., & Kocher, B. (2020). Luxury single- versus multi-brand stores: The effect of consumers’ hedonic goals on brand comparisons. Journal of Retailing, 96(2), 203–219. https://doi.org/10.1016/j.jretai.2020.01.002
• Evans, J. S. B., & Stanovich, K. E. (2013). Dual-process theories of higher cognition: Advancing the debate. Perspectives on Psychological Science, 8(3), 223–241. https://doi.org/10.1177/1745691612460685
• Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
• Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux.
• Lemon, K. N., & Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 80(6), 69–96. https://doi.org/10.1509/jm.15.0420
• Ratchford, B. T. (1982). Cost-benefit models for explaining consumer choice and information seeking. Journal of Consumer Research, 9(2), 197–212. https://doi.org/10.1086/208912
• Stigler, G. J. (1961). The economics of information. Journal of Political Economy, 69(3), 213–225. https://doi.org/10.1086/258464
• Verhoef, P. C., Kannan, P. K., & Inman, J. J. (2015). From multi-channel retailing to omni-channel retailing: Introduction to the special issue on multi-channel retailing. Journal of Retailing, 91(2), 174–181. https://doi.org/10.1016/j.jretai.2015.02.005

Items of the Scale