Innovativeness (Technological) (INN)

1. Abstract

The Innovativeness (Technological) (INN) scale is a specialized psychometric instrument developed by Gordon C. Bruner II and Anand Kumar in 2007 to assess a consumer’s specific propensity and psychological motivation to be among the earliest adopters of emerging technological products, consumer electronics, and technology-mediated services. Originating from empirical investigations into consumer behavior and consumer gadget adoption profiles (frequently termed “gadget lovers”), the INN scale operationalizes domain-specific innovativeness within the rapidly evolving technological landscape. Comprising five carefully formulated self-report items administered on a standard multi-point Likert response format (typically a 7-point continuum ranging from Strongly Disagree to Strongly Agree), the instrument captures an individual’s proactive search for, interest in, and readiness to purchase cutting-edge technical innovations prior to widespread market diffusion.

Extensive psychometric evaluations across multiple empirical investigations confirm that the INN scale possesses robust measurement properties. Confirmatory factor analyses consistently demonstrate a unidimensional latent structure characterized by strong, uniform factor loadings (predominantly exceeding 0.80) and exceptional model fit statistics (CFI > 0.98, RMSEA < 0.05). Reliability assessments show high internal consistency, with Cronbach’s alpha and composite reliability coefficients routinely ranging between 0.88 and 0.93. Convergent validity is evidenced by substantial positive correlations with technological product involvement, objective and subjective tech knowledge, lead-user characteristics, and actual adoption velocity. Discriminant validity tests confirm that technological innovativeness is distinct from general personality traits (such as global sensation seeking or novelty seeking), price sensitivity, and non-technological consumer innovativeness. Today, the INN scale is widely applied in marketing research, consumer psychology, human-computer interaction, and high-tech product management to segment consumer populations, test technology acceptance models, and forecast early market traction for disruptive technical breakthroughs.

2. Keywords

technological innovativeness, gadget lovers, consumer innovativeness, domain-specific innovativeness, technology adoption, diffusion of innovations, psychometrics, scale validation, consumer electronics, product involvement

3. Authors

The Innovativeness (Technological) (INN) scale was formulated and validated by scholars specializing in consumer behavior, psychometric measurement, and marketing strategy:

• Gordon C. Bruner II, Ph.D. — Professor Emeritus of Marketing at Southern Illinois University Carbondale (SIUC). Dr. Bruner is an internationally recognized authority on psychometric scales in business research, best known as the creator and lead author of the multi-volume Marketing Scales Handbook series, which catalogs and psychometrically evaluates thousands of measurement instruments utilized in behavioral literature. His research centers on consumer adoption of high-tech innovations, technology acceptance, commercial internet usage, and psychometric scale development.
• Anand Kumar, Ph.D. — Professor of Marketing and former Department Chair at the Muma College of Business, University of South Florida. Dr. Kumar’s scholarly expertise spans consumer psychology, information processing, emotional and cognitive responses to marketing stimuli, and the behavioral drivers governing adoption patterns among specialized consumer segments, including gadget enthusiasts and early technology adopters.

4. Purpose

The primary purpose of the Innovativeness (Technological) (INN) scale is to provide researchers and practitioners with an empirically sound, parsimonious, and reliable psychometric instrument to measure the extent to which an individual exhibits a generalized drive to acquire and use newly introduced technological devices, hardware, and digital services ahead of peers. As consumer markets became inundated with rapid technological advancements—such as personal digital assistants, smartphones, smart home automation, wearable devices, and sophisticated consumer electronics—traditional global measures of innovativeness proved insufficient for capturing behavioral variance specific to high-tech domains. The INN scale addresses this gap by zeroing in on the unique psychological nexus where technological curiosity, early-market engagement, and consumer risk tolerance intersect.

From an academic and theoretical perspective, the instrument facilitates rigorous empirical testing of how individual-level differences influence the initial stages of innovation diffusion. Rather than conceptualizing innovativeness merely as an ex-post behavioral tally (i.e., counting how many gadgets an individual already owns), the INN scale operationalizes the construct as an ex-ante psychological orientation or disposition. This enables scholars to examine causal pathways linking innate psychological traits, domain-specific technology orientations, cognitive evaluation of technology attributes (such as perceived usefulness and perceived ease of use), and actual adoption behavior. It bridges high-level personality research with granular consumer decision-making frameworks.

In applied market research and new product development, the INN scale serves several critical functions:

• Consumer Segmentation: Identifying and isolating the influential “gadget lover” or “tech innovator” demographic, which often acts as market mavens, opinion leaders, and informal product evangelists during new product launches.
• Predictive Analytics and Demand Forecasting: Assessing whether early consumer interest among survey respondents reflects genuine market readiness or idiosyncratic fascination, helping firms minimize the substantial financial risks associated with releasing high-tech hardware and digital platforms.
• User Experience and Product Positioning: Enabling high-tech enterprises to align marketing messages, feature complexity, and onboarding experiences with the distinct cognitive and psychological preferences of technological pioneers versus later, more risk-averse mainstream consumer segments.
• Technology Readiness Profiling: Supporting cross-sectional organizational assessments to evaluate how receptive end-users, employees, or consumers will be toward compulsory or voluntary technological platform transitions.

5. Psychological Construct

The psychological construct underlying the INN scale is technological domain-specific innovativeness. In psychometric theory, innovativeness has historically been conceptualized at two distinct levels of abstraction: global (or innate) innovativeness and domain-specific innovativeness. Global innovativeness represents a generalized, highly abstract personality trait reflecting an individual’s openness to new experiences, cognitive flexibility, and overall tolerance for ambiguity. However, empirical research has repeatedly demonstrated that global innovativeness exhibits weak-to-moderate predictive validity when applied to concrete purchasing decisions within distinct product categories. An individual might display an intense passion for purchasing the latest avant-garde culinary tools while remaining completely indifferent to or intimidated by novel digital electronics.

Consequently, Bruner and Kumar (2007a) anchored the INN scale at the domain-specific level, focusing explicitly on technology-based goods and services. Technological innovativeness reflects a relatively enduring, domain-bound psychological tendency that drives an individual to be intrinsically motivated by technological novelties. This construct incorporates several interconnected cognitive, affective, and behavioral facets:

• Proactive Information Acquisition: High-INN individuals actively scan their environment, subscribe to technology blogs, participate in developer forums, and monitor product launch announcements. Their search behavior is not merely functional or driven by immediate need; it is intrinsically rewarding and exploratory.
• Temporal Precedence Drive (First-to-Adopt Orientation): A defining psychological hallmark of technological innovativeness is the explicit desire to possess cutting-edge technology before others. This dimension involves a combination of intrinsic curiosity (wanting to experience the frontier of capability) and symbolic, social utility (gaining status, self-efficacy, and identity validation from being perceived as a technological pioneer or opinion leader).
• Calculated Technological Risk Tolerance: Early adoption inherently entails navigating unfinished firmware, sub-optimal user interfaces, hardware bugs, high initial price premiums, and rapid obsolescence. Individuals with high technological innovativeness possess high tolerance for these specific technological ambiguities and product failures, perceiving these obstacles as engaging challenges rather than insurmountable barriers.
• Hedonic and Functional Value of Tech: For high-INN consumers, a technological device is rarely a purely utilitarian tool. Instead, the exploration of device architectures, experimental settings, novel interaction modalities, and technical benchmarks provides deep hedonic pleasure and cognitive stimulation.

Unlike transient product involvement, which may peak sharply around a specific hyped product release and vanish shortly thereafter, technological innovativeness is a stable consumer orientation that persists across successive generations of technical hardware, software ecosystems, and digital consumer paradigms.

6. Theoretical Framework

The INN scale is conceptually anchored in several foundational theories from consumer behavior, communication science, and social psychology:

Diffusion of Innovations Theory

The overarching conceptual bedrock of the INN scale is Everett Rogers’ Diffusion of Innovations Theory. Rogers categorized adopters into five distinct segments along an idealized bell-shaped adoption curve: Innovators (2.5%), Early Adopters (13.5%), Early Majority (34%), Late Majority (34%), and Laggards (16%). Rogers posited that “Innovators” are characterized by venturesomeness, high socioeconomic liquidity, the ability to comprehend complex technical knowledge, and a high tolerance for uncertainty. The INN scale operationalizes the psychological profile of Rogers’ “Innovator” category within the technological realm, providing a standardized, continuous psychometric index to distinguish innovators and early adopters from the late majority and laggards.

Domain-Specific Innovativeness (DSI) Paradigm

Methodologically, the INN construct builds directly on the foundational work of Goldsmith and Hofacker (1991), who developed the Domain-Specific Innovativeness (DSI) scale. Goldsmith and Hofacker demonstrated that measuring innovativeness within a circumscribed behavioral domain markedly improves predictive validity over broad personality batteries. While Goldsmith and Hofacker designed their scale to be adaptable to any self-selected category (e.g., fashion, rock music, wine), Bruner and Kumar tailored and refined the measurement properties specifically for technology-based goods and services, eliminating ambiguities and addressing the unique interaction modalities inherent to advanced consumer electronics.

Technology Acceptance and Readiness Frameworks

The INN scale also interfaces directly with Fred Davis’s Technology Acceptance Model (TAM) and Parasuraman’s Technology Readiness Index (TRI). TAM posits that behavioral intentions to use a system are governed primarily by Perceived Usefulness (PU) and Perceived Ease of Use (PEOU). Technological innovativeness acts as a key individual-difference antecedent within TAM: individuals possessing high INN scores assign lower cognitive weight to PEOU hurdles, demonstrating an innate confidence and willingness to master steep learning curves in exchange for early functional or hedonic utility.

7. Validity

The INN scale has undergone extensive empirical validation across multiple consumer samples, demonstrating superior psychometric performance across all primary forms of construct validity.

Construct and Convergent Validity

Convergent validity is established when the scale strongly correlates with theoretical constructs to which it should conceptually relate. In empirical testing conducted by Bruner and Kumar (2007a) and subsequent replications, the INN scale exhibited statistically significant, robust positive correlations with:

• Technological Product Class Involvement: Demonstrating correlations typically exceeding r = 0.65 (p < 0.001), indicating that individuals with high INN scores consider technology deeply personally relevant and central to their lifestyle.
• Subjective and Objective Tech Knowledge: High-INN respondents consistently score significantly higher on factual knowledge batteries assessing technical specifications, microprocessor architectures, wireless protocols, and emerging product roadmaps.
• Opinion Leadership and Market Mavenism: Substantial positive correlations (r ranging from 0.50 to 0.70) confirm that technological innovators frequently act as trusted technical advisors, reviewers, and word-of-mouth amplifiers within their social networks.

Discriminant Validity

Discriminant validity confirms that the INN scale measures a unique construct rather than repackaging existing personality or consumer variables. Utilizing the Fornell-Larcker criterion, the Average Variance Extracted (AVE) for the INN scale consistently exceeds the squared correlations between INN and other measured constructs. Specific discriminant evidence shows:

• The INN scale shares minimal variance with general consumer price sensitivity (r < -0.15), demonstrating that early tech adoption motives operate independently of traditional bargain-hunting or coupon-proneness traits.
• When analyzed alongside measures of domain-specific innovativeness in non-technical categories (such as apparel, food, or home décor), INN exhibits weak or non-significant correlations, proving its precise domain specificity.
• The instrument cleanly differentiates itself from generalized compulsive buying behavior, showing that the drive to purchase gadgets is motivated by technological novelty and functional exploration rather than an uncontrollable, generalized impulse-control pathology.

Predictive and Criterion-Related Validity

The practical utility of the INN scale is underscored by its ability to predict real-world behaviors. Logistic regressions and structural equation models reveal that INN scores significantly predict:

• The actual ownership count of recently introduced consumer electronic devices.
• The elapsed time between a product’s market launch date and the consumer’s date of acquisition (adoption velocity).
• Participation in commercial pre-orders, crowdfunding hardware campaigns (e.g., Kickstarter), and public beta testing programs.

8. Reliability

The reliability of the 5-item INN scale has been confirmed across diverse consumer cohorts, undergraduate student panels, and adult consumer samples. Across published empirical studies, the scale demonstrates exceptional internal consistency that well exceeds the standard academic threshold of 0.70 proposed by Nunnally (1978):

• Cronbach’s Alpha (α): In the initial validation studies by Bruner and Kumar (2007a), the internal consistency coefficient reached α = 0.89. Subsequent independent investigations utilizing the scale in varying product contexts (e.g., mobile commerce, wearable biometric health monitors, virtual reality headsets) have reported Cronbach’s alpha values consistently situated between 0.88 and 0.93.
• Composite Reliability (CR): Structural equation modeling assessments reveal composite reliability metrics routinely surpassing 0.90, demonstrating that the five indicators reliably measure the single underlying latent construct without relying on redundant or weakly contributing items.
• Average Variance Extracted (AVE): AVE values for the instrument reliably range between 0.65 and 0.75, vastly exceeding the recommended 0.50 threshold. This indicates that the latent construct accounts for upwards of two-thirds of the total variance observed in the individual measurement items, with measurement error remaining minimal.
• Test-Retest Reliability: Longitudinal and repeated-measure assessments over multi-week intervals demonstrate robust temporal stability (test-retest correlations r > 0.82), confirming that the scale captures an enduring psychological disposition rather than a fleeting mood state or short-lived response to promotional marketing.

9. Factor Analysis

The dimensional structure of the INN scale has been subjected to rigorous exploratory and confirmatory factor analyses, consistently substantiating a single-factor, unidimensional model.

Exploratory Factor Analysis (EFA)

During initial scale development, exploratory factor analyses (utilizing Principal Axis Factoring and Maximum Likelihood extraction with both orthogonal and oblique rotations) yielded an unambiguous single-factor extraction. Key EFA findings include:

• A single dominant eigenvalue substantially greater than 1.0 (typically accounting for 65% to 75% of the total explained variance).
• A steep scree plot drop-off, with the second eigenvalue consistently falling well below 0.60, confirming the absence of secondary or cross-loading dimensions.
• Uniformly high item factor loadings, with all five items loading onto the primary factor at values ranging from 0.78 to 0.89, with negligible residual variance.

Confirmatory Factor Analysis (CFA)

When specified in structural equation modeling programs (such as AMOS, LISREL, or lavaan in R), the 1-factor CFA model exhibits near-optimal goodness-of-fit indices across diverse population samples. Typical fit parameters reported across literature include:

• Comparative Fit Index (CFI): 0.985 to 0.998 (exceeding the standard 0.95 benchmark).
• Tucker-Lewis Index (TLI): 0.975 to 0.995.
• Root Mean Square Error of Approximation (RMSEA): 0.035 to 0.055, with 90% confidence intervals tightly bounding values below the stringent 0.06 cutoff.
• Standardized Root Mean Square Residual (SRMR): 0.015 to 0.030, indicating minimal discrepancy between the observed sample covariance matrix and the model-implied covariance matrix.
• Normed Chi-Square (χ²/df): Typically yielding ratios between 1.10 and 2.20, within the acceptable 1.0–3.0 range for robust models.

Measurement invariance testing further demonstrates full metric and scalar invariance across demographic groups, confirming that the scale functions identically across male and female respondents as well as across differing age cohorts.

10. Instrument / Measurement Tool

The INN instrument is structured as a concise, self-administered questionnaire optimized for rapid completion and low respondent burden while maintaining strong psychometric precision.

• Instrument Type: Self-report psychometric scale.
• Number of Items: 5 items.
• Administration Format: Paper-and-pencil, online survey engines (e.g., Qualtrics, REDCap, SurveyMonkey), or mobile-responsive digital interfaces.
• Estimated Completion Time: Approximately 1 to 2 minutes.
• Response Format: Multi-point Likert scale, classically formatted as a 7-point continuum:
• 1 = Strongly Disagree
• 2 = Disagree
• 3 = Somewhat Disagree
• 4 = Neither Agree nor Disagree (Neutral)
• 5 = Somewhat Agree
• 6 = Agree
• 7 = Strongly Agree
• Scoring and Index Calculation:
• All 5 items are directly formulated (positively worded), meaning there are no reverse-coded items required in the standard 5-item specification.
• Composite Summed Score: Scores are calculated by summing the numerical ratings across all five items, yielding a possible score range between 5 and 35.
• Mean Index Score: Alternatively, researchers frequently compute an unweighted arithmetic mean across the five items, yielding an index ranging from 1.00 to 7.00.
• Score Interpretation Guidelines:
• Low Technological Innovativeness (Mean: 1.00 – 2.99; Sum: 5 – 14): Represents conservative, risk-averse consumers or technological laggards who delay adoption until existing products are obsolete or unsupported.
• Moderate Technological Innovativeness (Mean: 3.00 – 4.99; Sum: 15 – 24): Reflects mainstream adopters (early and late majority) who adopt technology once reliability is proven, prices drop, and widespread utility is established.
• High Technological Innovativeness (Mean: 5.00 – 7.00; Sum: 25 – 35): Represents authentic “gadget lovers,” lead users, and technology innovators characterized by an active desire to be first to market, high tolerance for emerging bugs, and significant word-of-mouth influence.

11. Permissions & Fee and Test Year

The Innovativeness (Technological) (INN) scale was formally introduced to the academic literature in 2007 through the following seminal publication:

• Bruner II, G. C., & Kumar, A. (2007). Gadget lovers. Journal of the Academy of Marketing Science, 35(3), 329–339. https://doi.org/10.1007/s11747-007-0051-3

Licensing and Academic Usage Permissions:

• Non-Commercial Academic Research: The scale is widely cataloged in psychometric compilations, notably Dr. Gordon C. Bruner’s Marketing Scales Handbook. Under prevailing academic fair-use conventions, the instrument may be utilized free of royalty fees by academic researchers, doctoral candidates, and university faculties conducting non-commercial scholarly inquiries, provided full and proper bibliographic attribution is accorded to Bruner and Kumar (2007) and the Journal of the Academy of Marketing Science.
• Commercial Applications and Proprietary Consulting: Commercial organizations, proprietary market research firms, and enterprise consulting agencies intending to embed the scale into fee-generating software platforms, commercial consumer profiling engines, or for-profit market segmentation studies must ensure compliance with intellectual property guidelines. Researchers should review publisher rights through Springer Nature (the copyright holder of the journal publication) or consult the authors directly regarding commercial permissions.

12. References

• Bruner II, G. C., & Kumar, A. (2005). Explaining consumer acceptance of handheld Internet devices. Journal of Business Research, 58(5), 553–558. https://doi.org/10.1016/j.jbusres.2003.08.002
• Bruner II, G. C., & Kumar, A. (2007). Gadget lovers. Journal of the Academy of Marketing Science, 35(3), 329–339. https://doi.org/10.1007/s11747-007-0051-3
• Bruner II, G. C. (2017). Marketing Scales Handbook: Multi-Item Measures for Consumer Insight Research (Vol. 9). GCBII Productions LLC.
• 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
• Goldsmith, R. E., & Hofacker, C. F. (1991). Measuring consumer innovativeness. Journal of the Academy of Marketing Science, 19(3), 209–221. https://doi.org/10.1007/BF02726497
• Midgley, D. F., & Dowling, G. R. (1978). Innovativeness: The concept and its measurement. Journal of Consumer Research, 4(4), 229–242. https://doi.org/10.1086/208701
• Nunnally, J. C. (1978). Psychometric Theory (2nd ed.). McGraw-Hill.
• Parasuraman, A. (2000). Technology Readiness Index (TRI): A multiple-item scale to measure readiness to embrace new technologies. Journal of Service Research, 2(4), 307–320. https://doi.org/10.1177/109467050024001
• Rogers, E. M. (2003). Diffusion of Innovations (5th ed.). Free Press.
• Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540

13. Items of the Scale