In-Store Technology Value Perception (ISTVP)

Abstract

The In-Store Technology Value Perception (ISTVP) scale is a concise, three-item psychometric instrument developed by J. Jeffrey Inman and Hristina Nikolova (2017) to evaluate consumer evaluations of shopper-facing retail technologies. Designed originally to capture anticipated value in pre-adoption contexts, the measure quantifies the degree to which a consumer perceives that an in-store technological innovation—such as mobile scan-and-go systems, interactive digital kiosks, smart shopping carts, or augmented reality navigation—will enhance shopping utility, improve overall shopping experience, and add meaningful value to the retail encounter. Grounded in the Technology Acceptance Model (TAM) and expectancy-value frameworks, the scale employs a 7-point Likert response format ranging from 1 (“Strongly disagree”) to 7 (“Strongly agree”). Psychometric evaluations demonstrate that the ISTVP exhibits robust unidimensionality, high internal consistency reliability (typically $\alpha > .90$), and strong convergent and predictive validity in relation to consumer adoption intentions, store patronage, and technology engagement. This article provides a comprehensive academic analysis of the ISTVP, examining its theoretical architecture, psychometric properties, factor structure, administration guidelines, and research applications in marketing, retail management, and consumer psychology.

Keywords

In-store technology, perceived value, shopper-facing retail technology, Technology Acceptance Model, consumer psychology, retail innovation, perceived usefulness, shopping experience, psychometrics, scale validation.

Authors

The In-Store Technology Value Perception scale was formulated and operationalized by:

• J. Jeffrey Inman, Ph.D. — Albert Wesley Frey Professor of Marketing and Associate Dean for Research and Faculty at the Joseph M. Katz Graduate School of Business, University of Pittsburgh. Dr. Inman is a leading scholar in consumer behavior, shopper marketing, and retail decision-making, having served as Editor-in-Chief of the Journal of Consumer Research.
• Hristina Nikolova, Ph.D. — Coughlin Sesquicentennial Associate Professor of Marketing at the Carroll School of Management, Boston College. Dr. Nikolova’s research focuses on consumer decision-making, dyadic and interpersonal choices, and the intersection of consumer psychology with emerging retail technologies.

The scale was formally introduced in their seminal publication: Inman, J. J., & Nikolova, H. (2017). Shopper-facing retail technology: A retailer adoption decision framework incorporating shopper attitudes and privacy concerns. Journal of Retailing, 93(1), 7–28.

Purpose

The retail landscape has undergone a profound digital transformation characterized by the deployment of frontline shopper-facing technologies (SFTs). Retailers increasingly implement autonomous checkouts, radio-frequency identification (RFID) tracking, digital shelf displays, smart fitting rooms, and robotic customer assistance to streamline operations and enrich physical retail experiences. However, retail technology implementation frequently suffers from high failure rates due to consumer resistance, technology anxiety, or perceived irrelevance. The ISTVP was engineered to address this critical gap by providing a targeted, psychometrically sound diagnostic instrument capable of capturing consumer value appraisals prior to or following technology interaction.

From a theoretical perspective, the purpose of the ISTVP is to isolate the cognitive and utilitarian appraisal of technology-enabled shopping enhancement. Rather than assessing broad, generalized attitudes toward technology (such as personal innovativeness or computer self-efficacy), the ISTVP isolates the context-specific utility a technological intervention introduces into the customer journey. It measures anticipated value—an essential precursor to behavioral intent, adoption, and sustained loyalty.

In research and managerial applications, the ISTVP serves several vital functions:

• Pre-Deployment Concept Testing: Retailers and product designers can benchmark consumer reception across diverse technology concepts (e.g., comparing autonomous checkout vs. mobile app self-scanning) before committing capital expenditure.
• A/B Testing and Interface Optimization: Researchers can assess how alterations in user interface (UI), feedback mechanisms, or ambient retail cues influence the value consumers perceive in frontline technologies.
• Cross-Category and Cross-Demographic Benchmarking: The scale enables scholars to determine how consumer segments (e.g., tech-savvy digital natives vs. privacy-sensitive shoppers) differentially appraise the trade-offs between technological convenience and functional friction across diverse retail environments (e.g., grocery, apparel, consumer electronics).
• Predictive Modeling of Friction and Adoption: By integrating the ISTVP into broader structural equation models, researchers can map the antecedents of value perception (e.g., ease of use, speed, autonomy) and its behavioral consequences (e.g., store patronage, basket size, word-of-mouth recommendations).

Psychological Construct

The core construct measured by the ISTVP is perceived technology value within a brick-and-mortar retail setting. Perceived value has long been conceptualized in consumer research as a multidimensional trade-off between “get” and “give” components (Zeithaml, 1988). However, within the specific domain of frontline shopper-facing technologies, value perception shifts toward a cognitive evaluation of whether interacting with a technical system demonstrably improves the consumer’s task execution, psychological comfort, and resource allocation (time, effort, cognitive load).

1. Functional Utility and Shopping Efficiency

The construct directly taps into utilitarian value—the degree to which the technology functions as an effective instrument to accomplish specific shopping goals. In physical retail, consumers navigate complex environments characterized by spatial navigation, product search, price verification, and queue management. The ISTVP captures whether a given tool reduces cognitive friction or operational delays, directly enhancing shopping productivity.

2. Experiential and Process Enhancement

Beyond pure functional utility, the construct incorporates an experiential dimension: whether the technology “improves the shopping experience.” This encapsulates positive affect, feelings of autonomy, perceived control, and the elimination of interpersonal friction (e.g., avoiding slow service interactions). The construct therefore captures both cognitive appraisals of usefulness and affective evaluations of experiential enrichment.

3. Net Value Addition

The third facet embedded within the construct is the perception of net incremental benefit (“add value to my shopping experience”). This represents an evaluative summary judgment wherein the consumer balances any required learning curve, privacy trade-offs, or physical manipulation of the device against the downstream benefits realized during the shopping trip. If the perceived benefits outweigh the physical and cognitive costs, the consumer records a high standing on the construct.

Theoretical Framework

The ISTVP is grounded at the convergence of several major theoretical paradigms within psychology, information systems, and services marketing:

Technology Acceptance Model (TAM)

Originally formulated by Davis (1989), the Technology Acceptance Model posits that actual system use is driven by behavioral intention, which is jointly determined by two primary beliefs: Perceived Usefulness (PU) and Perceived Ease of Use (PEOU). Davis defined perceived usefulness as “the degree to which a person believes that using a particular system would enhance his or her job performance.” Inman and Nikolova (2017) adapted this core logic to the retail domain, where the “job” to be performed is the consumer shopping expedition. The ISTVP acts as a retail-specific operationalization of perceived usefulness, directly examining whether a shopper-facing technology optimizes the execution of the shopping trip.

Expectancy-Value Theory

Rooted in the social psychological work of Fishbein and Ajzen (1975), Expectancy-Value Theory asserts that behavioral attitudes are formed based on expectations regarding the outcomes of an action weighted by the subjective evaluations of those outcomes. In the context of in-store technology adoption, the ISTVP quantifies the consumer’s subjective expectation that engaging with an innovation will lead to positive outcomes (such as saved time, reduced effort, and superior pricing transparency) and that these outcomes possess high subjective utility.

Service-Dominant (S-D) Logic and Value Co-Creation

According to Vargo and Lusch (2004), value is not merely embedded within physical products or technological artifacts; rather, value is uniquely and phenomenologically co-created by the beneficiary during use (“value-in-use”). Shopper-facing retail technologies require active co-production: the consumer must scan barcodes, navigate touchscreens, or operate automated payment interfaces. The ISTVP measures the consumer’s appraisal of this co-creation process, capturing whether the active incorporation of technology into the shopping process produces superior perceived value compared to traditional human-assisted or passive retail modalities.

Validity

Validation of the ISTVP was conducted through empirical studies encompassing various shopper-facing retail technologies across varied supermarket scenarios (Inman & Nikolova, 2017). The scale has undergone systematic psychometric evaluation:

Construct and Convergent Validity

Convergent validity is established when items designed to measure a single theoretical construct demonstrate strong mutual intercorrelations and load highly onto the designated latent factor. In the validation studies conducted by Inman and Nikolova (2017), the three items exhibited average factor loadings exceeding $.88$, demonstrating that the scale captures a unified latent dimension. Furthermore, the Average Variance Extracted (AVE) consistently exceeds the standard $.50$ threshold (Fornell & Larcker, 1981), regularly demonstrating empirical AVE values above $.75$.

Predictive and Criterion Validity

The scale demonstrates exceptional predictive validity with regard to downstream behavioral intentions and customer attitudes. Inman and Nikolova (2017) revealed that the ISTVP significantly predicts:

• Retailer Adoption Willingness: Shoppers exhibiting high ISTVP scores display significantly greater willingness to patronize retail stores that implement frontline innovations ($p < .001$).
• Technology Adoption Intention: Value perception scores correlate positively with personal willingness to engage with the technology during subsequent visits ($r > .60$).
• Store Equity and Satisfaction: Perceived technology value acts as a vital mediating mechanism linking technological investment to elevated evaluations of store modernness, shopping satisfaction, and brand equity.

Discriminant Validity

Discriminant validity was established against adjacent consumer perception constructs, including perceived privacy invasiveness, perceived operational risk, and technology anxiety. While technological attributes that increase perceived surveillance (e.g., facial recognition payment) correlate positively with privacy concerns, the ISTVP factor demonstrates statistical divergence, showing average shared variance (ASV) well below the AVE of the construct, confirming that perceived functional value remains conceptually and statistically distinct from consumer perceived risk.

Reliability

The ISTVP demonstrates superior internal consistency reliability across diverse experimental conditions, sampling frames, and technological categories:

• Internal Consistency (Cronbach’s Alpha): In the initial empirical investigations across six distinct technology conditions reported by Inman and Nikolova (2017), the scale’s Cronbach’s alpha coefficients consistently ranged between $\alpha = .91$ and $\alpha = .96$, well above the conventional benchmark of $.70$ for psychometric adequacy.
• Composite Reliability (CR): Structural equation modeling evaluations confirm composite reliability metrics exceeding $.92$, demonstrating that measurement error is minimal and that all three items consistently reflect the underlying value construct.
• Item-Total Correlations: Corrected item-to-total correlation values for each of the three items routinely exceed $.80$, indicating exceptional coherence across the individual measurement indicators.

Factor Analysis

The dimensionality of the ISTVP has been confirmed using both exploratory factor analysis (EFA) and confirmatory factor analysis (CFA):

Exploratory Factor Analysis (EFA)

Principal Axis Factoring and Maximum Likelihood extraction methods applied to the three items consistently yield a single-factor solution. Across validation samples, the primary factor accounts for over $80%$ of the total variance, with an eigenvalue substantially greater than $2.0$ (typically around $2.55$), while subsequent extraction dimensions yield eigenvalues well below $0.35$. No secondary factors emerge, confirming strong unidimensionality.

Confirmatory Factor Analysis (CFA)

In structural equation models assessing overall measurement quality, a single-factor CFA model demonstrates outstanding fit indices across diverse sample sizes. Representative fit statistics from empirical testing include:

• Standardized Factor Loadings:
  • Item 1 (Improve shopping experience): $lambda = .91 – .95$
  • Item 2 (Useful to me while shopping): $lambda = .88 – .93$
  • Item 3 (Add value to shopping experience): $lambda = .93 – .96$
• Model Fit Parameters: When embedded within multi-construct structural models, the measurement model demonstrates excellent goodness-of-fit: $\chi^2/df < 2.5$, Comparative Fit Index ($\text{CFI}) > .98$, Tucker-Lewis Index ($\text{TLI}) > .97$, Root Mean Square Error of Approximation ($\text{RMSEA}) < .05$, and Standardized Root Mean Square Residual ($\text{SRMR}) < .03$.

Instrument / Measurement Tool

The In-Store Technology Value Perception scale is structured as follows:

• Instrument Type: Self-report psychometric rating scale.
• Construct Assessed: Consumer anticipated or post-usage perceived value and usefulness of shopper-facing in-store retail technologies.
• Number of Items: 3 items.
• Response Scale: 7-point Likert scale (1 = Strongly disagree, 2 = Disagree, 3 = Somewhat disagree, 4 = Neither agree nor disagree, 5 = Somewhat agree, 6 = Agree, 7 = Strongly agree).
• Scoring Procedure: Responses are averaged across the three items to create an overall composite index of in-store technology value perception. Higher mean scores correspond to greater perceived value.
• Reverse-Scored Items: None. All three items are positively keyed.
• Administration Time: Approximately 30 to 60 seconds.
• Contextual Adaptability: While originally phrased in the hypothetical/conditional tense (“would improve”, “would be useful”, “would add value”) to gauge pre-implementation anticipated value, the scale items can be directly modified to past or present tense (e.g., “improved”, “was useful”, “added value”) for post-use evaluations.

Permissions & Fee and Test Year

The In-Store Technology Value Perception scale was developed and published in 2017. Under academic fair-use conventions, the instrument may be utilized freely for scholarly research, educational purposes, and non-commercial institutional testing, provided appropriate bibliographic attribution is granted to the original authors (Inman & Nikolova, 2017) and the Journal of Retailing. Commercial entities, market research agencies, or consulting organizations seeking to embed the scale within proprietary commercial platforms should refer to copyright terms governed by Elsevier and the New York University / Journal of Retailing copyright clearance guidelines.

References

• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
• Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley.
• 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
• Inman, J. J., & Nikolova, H. (2017). Shopper-facing retail technology: A retailer adoption decision framework incorporating shopper attitudes and privacy concerns. Journal of Retailing, 93(1), 7–28. https://doi.org/10.1016/j.jretai.2016.12.006
• Vargo, S. L., & Lusch, R. F. (2004). Evolving to a new dominant logic for marketing. Journal of Marketing, 68(1), 1–17. https://doi.org/10.1509/jmkg.68.1.1.24036
• Zeithaml, V. A. (1988). Consumer perceptions of price, quality, and value: A means-end model and synthesis of evidence. Journal of Marketing, 52(3), 2–22. https://doi.org/10.1177/002224298805200302

Items of the Scale