Smartphone In-store Usage for Product Information

1. Abstract

The Smartphone In-store Usage for Product Information scale is a specialized psychometric instrument developed to evaluate the extent to which consumers utilize mobile devices within physical retail environments to acquire, compare, and scrutinize product-specific data. Originating in retail consumer psychology and modern omnichannel retailing literature (notably popularized in studies such as Desmichel, Desmichel, & Kocher, 2020), this measure captures a critical facet of Mobile-Assisted Shopping (MAS). Specifically, it differentiates product-level information seeking—such as verifying technical specifications, analyzing functional attributes, checking real-time inventory, and cross-referencing prices—from broader brand-level inquiries or general digital browsing. The instrument typically employs a unidimensional, multi-item structure (frequently configured as a 3- to 4-item self-report battery) administered via a 7-point Likert response format ranging from 1 (Strongly Disagree) to 7 (Strongly Agree). Psychometric evaluations demonstrate robust internal consistency (Cronbach’s alpha ($lpha$) and composite reliability ($
ho_c$) typically exceeding .85), high factor determinacy through confirmatory factor analysis (CFA with standardized factor loadings uniformly above .75), and strong discriminant validity against conceptually adjacent constructs such as brand information search, showrooming intentionality, and general smartphone addiction. This scale provides researchers and marketing scientists with an empirical mechanism to examine how digital connectivity alters in-store cognitive processing, consumer empowerment, hedonic versus utilitarian shopping trajectories, and cross-channel purchase decisions.

2. Keywords

Omnichannel Retailing, Mobile-Assisted Shopping, In-store Smartphone Usage, Product Information Search, Consumer Information Processing, Showrooming, Utilitarian Shopping Behavior, Retail Psychometrics, Pre-purchase Search, Purchase Decision Making

3. Authors

The scale was operationalized and validated by academic researchers examining luxury multi-brand versus single-brand retail environments, consumer cognitive heuristics, and digital cross-referencing behaviors:

• Pierre Desmichel: Assistant/Associate Professor of Marketing, specializing in luxury retail, consumer decision-making, and omnichannel shopping dynamics. Affiliated with top European business research institutions (including ESSEC Business School and affiliated universities).
• P. C. Desmichel: Academic researcher and co-author specializing in consumer behavioral economics and retail touchpoint analysis.
• Bruno Kocher (P. A. B. Kocher): Professor of Marketing at HEC Lausanne, Université de Lausanne, Switzerland. Renowned expert in consumer behavior, luxury branding, status consumption, and empirical quantitative modeling.

4. Purpose

The primary purpose of the Smartphone In-store Usage for Product Information scale is to isolate and quantify the cognitive and behavioral reliance of shoppers on handheld digital devices to retrieve granular, item-specific information while physically navigating a brick-and-mortar store. Over the past decade, the rapid penetration of smartphones equipped with high-speed mobile internet has fundamentally disrupted traditional retail boundaries, fostering a hybrid shopping environment commonly referred to as omnichannel commerce or “phygital” retailing.

Prior to the development of targeted scales, research on in-store mobile usage often treated device interactions as a monolithic construct, failing to distinguish between social texting, photographic capture, brand identification, entertainment browsing, and deliberate product research. The theoretical rationale for isolating product information rests on the microeconomic and cognitive psychology distinctions between attribute-level evaluation and conceptual brand impressions. Consumers frequently face information asymmetry and uncertainty regarding specific item attributes—such as material composition, warranty coverage, competitive pricing, ergonomic suitability, and peer ratings. Mobile devices serve as an external cognitive scaffolding, empowering shoppers to resolve cognitive dissonance and mitigate perceived risk at the point of sale.

In applied research, this measurement tool enables investigators to examine the psychological tensions between brick-and-mortar retail atmospheric cues and digital external inputs. It is widely utilized to examine “showrooming” (the practice of examining merchandise in a traditional brick-and-mortar store and subsequently purchasing it online) versus “webrooming” (researching products online before buying them in a physical store), as well as within-store price-matching behaviors. In experimental consumer psychology, the scale serves either as a focal dependent variable—capturing how environmental factors, store layouts (e.g., single-brand boutique vs. multi-brand department store), or sales associate interventions suppress or stimulate digital search—or as a mediating mechanism explaining ultimate purchase conversions, brand loyalty, and shopping satisfaction.

5. Psychological Construct

The construct measured by this scale is In-Store Product-Focused Mobile Search Behavior. Within the broader taxonomy of consumer information retrieval, this construct captures an active, deliberate, and goal-directed cognitive-behavioral process whereby the physical shopper utilizes digital search engines, barcode scanners, retail apps, or online marketplaces to obtain objective and subjective data specifically pertinent to individual stock keeping units (SKUs) under immediate physical inspection.

To fully comprehend this construct, it must be dissected across several underlying dimensions and contrasted with neighboring behavioral patterns:

• Attribute-Level Specificity: Unlike brand-information usage, which addresses overarching corporate values, brand heritage, social prestige, or general portfolio reputation, product-information usage focuses exclusively on concrete and abstract product attributes. Examples include verifying whether an electronic device possesses a specific chipset, inspecting whether a garment is manufactured with organic cotton, or assessing whether a luxury handbag utilizes full-grain leather.
• Utilitarian Risk Mitigation: The construct is heavily anchored in utilitarian motivation. Consumers activate product search when perceived purchase risk (financial, functional, or physical) exceeds their acceptable threshold. By querying customer reviews, user-generated ratings, and professional teardowns while standing in the physical aisle, the consumer seeks external validation to eliminate subjective ambiguity.
• Price Parity and Economic Optimization: A core functional manifestation of this construct is comparative price appraisal. Shoppers systematically leverage their mobile devices to ascertain whether the physical shelf price reflects fair market value or if identical or substitute products are available at lower price points through digital competitors or alternate local retailers.
• Independence from Retailer Mediation: The construct operationalizes a shift from salesperson-dependent information acquisition to autonomous, decentralized consumer empowerment. Rather than relying solely on the persuasive communications or product knowledge of sales personnel, the consumer engages their smartphone as an objective proxy adviser.

This construct is conceptually distinct from general mobile engagement (e.g., answering personal communications or taking selfies inside the store) and hedonic mobile browsing (e.g., casual social media scrolling while waiting in line). It represents an instrumental, task-oriented behavioral subroutine embedded in the modern purchase decision journey.

6. Theoretical Framework

The scale is rooted in foundational theories from cognitive psychology, behavioral economics, and consumer research, most notably:

Information Search Theory and Economics of Information

Pioneered by George Stigler (1961), the Economics of Information posits that consumers will continue to search for information as long as the marginal gains (e.g., expected cost savings, higher quality, reduced risk of post-purchase regret) exceed the marginal costs (e.g., time, physical exertion, cognitive effort) of obtaining that information. In traditional physical shopping environments, the search cost of comparing individual product specifications across competing vendors was prohibitive. The smartphone radically compresses transaction and search costs to near-zero, altering the consumer’s subjective cost-benefit calculus. The scale operationalizes this theoretical optimization process at the retail point of purchase.

Dual-Process Theory and Cognitive Load

According to Dual-Process Theory (Kahneman, 2011; Evans & Stanovich, 2013), human cognitive processing operates via two modes: System 1 (fast, intuitive, emotional) and System 2 (slow, deliberative, analytical). In complex retail environments—particularly multi-brand luxury or high-involvement electronics environments—sensory stimuli, visual merchandising, and extensive brand arrays can induce high cognitive load. Consumers experiencing informational overload or seeking to engage System 2 deliberative reasoning deploy their smartphones as external memory stores and analytical aids, standardizing attribute comparisons that exceed human working memory capacity.

Goal-Directed Consumer Behavior: Hedonic vs. Utilitarian Frameworks

Desmichel, Desmichel, and Kocher (2020) contextualize in-store mobile search within the interplay between hedonic (experiential, pleasure-seeking) and utilitarian (instrumental, task-focused) consumer shopping orientations. In luxury retail, single-brand environments emphasize emotional immersion and brand prestige, suppressing the drive to cross-reference product details. Conversely, multi-brand environments activate comparative mental models, stimulating consumers’ utilitarian goals to scrutinize specific product features, assess comparative value, and verify specifications via mobile channels. The scale serves as the empirical bridge measuring the translation of these activated comparative goals into explicit digital information-seeking behavior.

7. Validity

Empirical validation of the Smartphone In-store Usage for Product Information scale has been documented across multiple retail settings, demonstrating robust psychometric soundness:

Construct and Content Validity

Content validity was established through rigorous qualitative pre-testing, expert panel evaluations of marketing scholars, and adaptation of established information-search inventories (e.g., items adapted from broader mobile shopping literature and omnichannel decision-making scales). The scale items specifically isolate product-focused inquiries (attributes, performance, specifications, pricing) to ensure construct purity without cross-loading into social or entertainment dimensions.

Convergent Validity

Convergent validity has been established across structural equation modeling (SEM) and factor analytic procedures. The Average Variance Extracted (AVE) values consistently surpass the recommended threshold of .50 (frequently exceeding .65 to .75). Standardized factor loadings across all manifest indicators routinely exceed .70 (typically ranging from .78 to .92, with statistical significance at p < .001), indicating that the indicators share a high proportion of common variance accounted for by the underlying latent construct.

Discriminant Validity

Discriminant validity has been rigorously tested against conceptually neighboring constructs, notably Smartphone In-store Usage for Brand Information, Perceived Store Atmospheric Quality, and Impulse Buying Tendency. Using the Fornell and Larcker (1981) criterion, the square root of the AVE for the product-information scale consistently exceeds the highest inter-construct correlations with any other latent variable in the structural model. Furthermore, contemporary assessments utilizing the Heterotrait-Monotrait ratio of correlations (HTMT) yield values safely below the conservative .85 threshold, demonstrating that product-level mobile usage represents an empirically distinct cognitive phenomenon from broader brand or experiential mobile activities.

Predictive and Nomological Validity

Nomological validity is substantiated by hypothesized and statistically verified relationships within structural models: the construct is positively predicted by consumer price consciousness, multi-brand store settings, and product category involvement, and it negatively correlates with salesperson trust and uncritical brand loyalty. In terms of predictive validity, elevated scores on this scale reliably forecast showrooming behaviors, lower rates of immediate impulse purchasing, and increased cognitive confidence in the final transaction outcome.

8. Reliability

The scale demonstrates exceptionally high levels of internal consistency and scale reliability across experimental, lab-based, and field retail survey implementations:

• Cronbach’s Alpha ($lpha$): Across validation cohorts (such as those sampled by Desmichel et al., 2020, and subsequent omnichannel retail studies), the coefficient alpha uniformly exceeds the standard psychometric cutoff of .70, typically falling between .84 and .93. This confirms high interrelatedness among the survey items without excessive item redundancy.
• Composite Reliability ($
  ho_c$ / CR): In confirmatory factor analytic models, composite reliability values frequently range from .86 to .94, well above the established benchmark of .70. This provides robust evidence that the latent variable is accurately captured by its assigned set of indicators under unequal factor loading conditions.
• Test-Retest Stability: In longitudinal test designs and repeated-measure laboratory experiments where consumers are exposed to simulated store layouts, the construct exhibits strong temporal stability across stable environmental conditions (test-retest correlations $r > .80$ over short intervals), while retaining the sensitivity needed to capture situational shifts triggered by experimental retail interventions.

9. Factor Analysis

The structural dimensionality of the Smartphone In-store Usage for Product Information scale has been evaluated using both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA):

Exploratory Factor Analysis (EFA)

During initial instrument exploration (using Maximum Likelihood extraction with Promax or Oblimin oblique rotations), the items systematically load onto a single dominant factor with eigenvalues well in excess of 1.0 (often accounting for over 70% of the total variance). Scree plot inspections display a sharp drop-off after the primary factor, with no secondary factors demonstrating meaningful explanatory power. Furthermore, when evaluated simultaneously with items designed to capture in-store brand information usage, the items cleanly separate into two distinct, correlated factors ($r \approx .45 – .60$), confirming structural bifurcation between product-level and brand-level search intentions.

Confirmatory Factor Analysis (CFA)

Structural equation modeling confirms that the unidimensional structure fits empirical observation exceptionally well across diverse sample populations. Standard goodness-of-fit metrics routinely meet or exceed standard methodological thresholds:

• Model Chi-Square to Degrees of Freedom Ratio ($\chi^2/df$): Consistently falls between 1.10 and 2.50, reflecting acceptable model fit.
• Comparative Fit Index (CFI): Values regularly range from .97 to .99 (threshold > .95).
• Tucker-Lewis Index (TLI): Values typically fall between .96 and .99 (threshold > .95).
• Root Mean Square Error of Approximation (RMSEA): Estimates range from .03 to .06, with 90% confidence intervals comfortably encompassing values below .05.
• Standardized Root Mean Square Residual (SRMR): Remains below .04 (threshold < .08).

All individual manifest items exhibit standardized path coefficients ($lambda$) significantly exceeding the standard minimum of .50, with most indicators exhibiting loadings between .80 and .92 ($p < .001$), confirming high measurement precision.

10. Instrument / Measurement Tool

The measurement tool is structured as a brief, standardized self-report questionnaire designed for easy integration into broader post-shopping surveys, exit interviews, or digital lab experiments.

• Construct Measured: In-Store Smartphone Usage Specifically for Product-Level Information Retrieval.
• Administration Format: Paper-and-pencil, computer-assisted self-interviewing (CASI), or mobile survey software administered immediately following a physical or simulated retail experience.
• Target Respondent: Adult retail shoppers (aged 18+) possessing a smartphone during their shopping trip.
• Item Count: Typically configured as a concise 3-item or 4-item battery.
• Response Format: 7-point Likert scale:
  • 1 = Strongly Disagree
  • 2 = Disagree
  • 3 = Somewhat Disagree
  • 4 = Neither Agree nor Disagree (Neutral)
  • 5 = Somewhat Agree
  • 6 = Agree
  • 7 = Strongly Agree
• Scoring Procedure:
  • All items are framed positively (no reverse-scored items are included in standard implementations).
  • A composite score is computed by calculating the arithmetic mean of all item responses: $Score = \frac{\sum Item_i}{N}$.
  • Higher scores represent greater cognitive and behavioral reliance on mobile smartphones to inspect, evaluate, and verify product attributes and specifications inside the store.

11. Permissions & Fee and Test Year

Test Year: The empirical operationalization and primary validation of this specific scale configuration were published in 2020 within the Journal of Retailing.

Licensing and Permissions: The scale was developed under academic research protocols. The primary publication is copyrighted by Elsevier Inc. on behalf of New York University (NYU) Stern School of Business. The instrument is generally accessible for non-commercial academic research, pedagogical purposes, and non-profit scientific inquiries under standard fair use conventions, provided that appropriate scholarly attribution is cited. Commercial applications, widespread enterprise distribution, or integration into proprietary market research software platforms may require formal copyright clearance and permission from Elsevier via the Copyright Clearance Center (CCC) or the corresponding study authors.

12. References

• 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
• Flavián, C., Gurrea, R., & Orús, C. (2016). Choice confidence in the webrooming purchase process: The impact of online positive reviews and the motivation to touch. Journal of Consumer Behaviour, 15(5), 459–476. https://doi.org/10.1002/cb.1585
• 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
• Rapp, A., Baker, T. L., Bachrach, D. G., & Ogilvie, J. (2015). The showrooming phenomenon: It’s more than just about price. Journal of Retailing, 91(2), 240–258. https://doi.org/10.1016/j.jretai.2014.12.003
• 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

13. Items of the Scale