Innovativeness (Use) (INNOUSE)

1. Abstract

The Innovativeness (Use) (INNOUSE) scale, formulated by Alladi Venkatesh and Chuan-Fong Shih (2004), represents a seminal psychometric instrument designed to assess a consumer's curiosity about and intrinsic motivation to experiment with an adopted product by discovering, developing, and executing novel and unconventional methods of utilization. Departing from traditional diffusion paradigms that treat innovation adoption as a binary terminal state, the INNOUSE instrument captures the behavioral and cognitive dynamics of the post-adoption phase within the comprehensive Use-Diffusion Model (UDM). The scale operationalizes use innovativeness through a parsimonious, five-item unidimensional battery evaluated on a multi-point Likert response format (ranging typically from 1 = 'Strongly Disagree' to 7 = 'Strongly Agree').

Extensively validated in contexts ranging from domestic personal computing and consumer software platforms to complex smart technologies and enterprise information systems, the INNOUSE scale exhibits outstanding psychometric properties. Confirmatory factor analytic investigations routinely confirm a robust single-factor architecture with high standardized factor loadings (> .70), strong composite reliability (CR > .85), and elevated internal consistency metrics, with Cronbach's alpha coefficients typically exceeding .84. Demonstrating rigorous convergent, discriminant, and nomological validity, the scale effectively distinguishes between mere volume or frequency of product interaction (rate of use) and the creative scope of application (variety of use). By providing a standardized psychometric operationalization of post-adoption exploratory behavior, the INNOUSE scale serves as a critical diagnostic and predictive tool for consumer psychologists, marketing researchers, human-computer interaction (HCI) specialists, and innovation managers seeking to explain technological embedment, ongoing user engagement, and product lifecycle extension.

2. Keywords

Use Innovativeness, INNOUSE, Use-Diffusion Model, Consumer Innovativeness, Post-Adoption Behavior, Product Experimentation, Exploratory Consumer Behavior, Technology Utilization, Variety of Use, Psychometrics, Scale Validation, Creative Product Usage, Human-Computer Interaction, Information Systems Continuance

3. Authors

The INNOUSE scale was conceptualized, operationalized, and validated by Chuan-Fong Shih and Alladi Venkatesh as an integral empirical component of their pioneering work on post-adoption technology dynamics.

• Chuan-Fong Shih, Ph.D.: Associate Professor of Marketing at the School of Business, Wake Forest University. Dr. Shih's academic investigations focus predominantly on consumer behavior in high-technology environments, e-commerce adoption patterns, multi-channel retailing, and post-purchase customer relationship dynamics.
• Alladi Venkatesh, Ph.D.: Professor Emeritus of Management and Computer Science at the Paul Merage School of Business, University of California, Irvine, and former Associate Director of the Center for Research on Information Technology and Organizations (CRITO). A leading international authority in consumer culture theory, social informatics, and the spatial-temporal impacts of digital technologies on households and everyday life.

Institutional affiliations at the time of development: Graduate School of Management and Center for Research on Information Technology and Organizations (CRITO), University of California, Irvine, Irvine, California, USA; and School of Business, Wake Forest University, Winston-Salem, North Carolina, USA.

4. Purpose

For several decades, consumer behavior and technology management disciplines were characterized by an overwhelming theoretical focus on initial adoption. Guided by classical frameworks such as Everett Rogers' Diffusion of Innovations and Fred Davis's Technology Acceptance Model (TAM), empirical inquiries concentrated disproportionately on the temporal velocity and demographic determinants characterizing a consumer's initial acquisition of a product. Shih and Venkatesh (2004) identified this prevailing paradigm as fundamentally incomplete, observing that the acquisition of an innovation represents merely the entry point of the product lifecycle. Once an artifact is brought into the domestic or organizational sphere, its ultimate viability, customer satisfaction, and economic value depend entirely on how, how much, and for what purposes it is deployed.

The primary purpose of the INNOUSE scale is to measure an individual's behavioral disposition and psychological inclination toward use innovativeness—the deliberate search for, discovery of, and experimentation with alternative, creative, and unintended functions of an already adopted technological product or service. The scale addresses critical research and clinical-behavioral objectives across several domains:

• Deconstructing Post-Adoption Heterogeneity: Conventional metrics such as "hours logged" or "frequency of login" fail to distinguish between rote, habitual, single-task execution and highly innovative, multifunctional exploitation. The INNOUSE scale isolates the cognitive and behavioral curiosity that drives consumers to push products past their default parameters.
• Predicting Innovation Longevity and Churn: In competitive digital and hardware ecosystems, products that are used in narrow, non-innovative ways are susceptible to commoditization and rapid obsolescence. Measuring INNOUSE enables market researchers to model customer retention, product stickiness, and lifetime customer value, as users with higher use innovativeness embed products more deeply into the fabric of their daily workflows.
• Facilitating User-Driven Innovation and Co-Creation: High-INNOUSE consumers frequently function as lead users who discover novel applications, uncover software workarounds, and generate unexpected use-cases that original product designers never anticipated. Identifying these individuals enables organizations to harness organic co-creation dynamics and refine future product iterations.
• Human-Computer Interaction (HCI) and Cognitive Ergonomics: In interface design and cognitive assessment, the scale assists researchers in analyzing how system complexity interacts with user curiosity, cognitive playfulness, and exploratory risk-taking.

5. Psychological Construct

The psychological construct evaluated by the INNOUSE scale is Use Innovativeness. Historically, psychometric research in marketing separated innovativeness into distinct theoretical layers: general consumer innovativeness (an innate, global personality trait reflecting openness to novel stimuli), domain-specific innovativeness (a predisposition toward innovations within a designated product class, as detailed by Goldsmith and Hofacker in 1991), and adoption innovativeness (the temporal tendency to purchase an innovation earlier than peer group members, conceptualized by Midgley and Dowling in 1978). Use innovativeness constitutes an analytically distinct psychological construct that operates primarily downstream of acquisition, focusing on behavioral plasticity and functional experimentation.

The INNOUSE construct, rooted in foundational work by Elizabeth Hirschman (1980) and Price and Ridgway (1983), embodies a synthesis of cognitive curiosity, behavioral exploration, and functional flexibility. Rather than viewing a product as a closed, single-purpose apparatus, the use-innovative individual approaches artifacts as open-ended platforms characterized by latent potential. The construct comprises several vital behavioral and psychological manifestations:

Cognitive Curiosity and Epistemic Exploration

Use innovativeness is driven by intrinsic epistemic curiosity—a desire for knowledge that motivates individuals to uncover how complex systems operate. In an everyday context, when an individual with high INNOUSE encounters a new device or software package (such as an advanced smartphone or enterprise ERP module), they do not restrict their engagement to the mandatory or introductory instructional manual. Instead, they systematically explore sub-menus, configure obscure settings, and mentally probe the boundaries of the system. This cognitive playfulness converts routine technology interaction into an active discovery process.

Creative Re-purposing and Functional Versatility

A core attribute of the construct is the mental capacity to transcend "functional fixedness"—the psychological bias that restricts an individual to perceiving an object solely through the lens of its conventionally assigned purpose. A user exhibiting high use innovativeness readily visualizes cross-contextual functionality. Examples include utilizing gaming hardware for statistical computational clustering, modifying consumer graphic software for technical architectural schematics, or repurposing domestic voice assistants to automate complex external business workflows. The INNOUSE construct captures this motivation to alter, adapt, and expand the utility horizon of an artifact.

Experimentation and Calculated Risk Propensity

Exploring unmapped functions inevitably entails the risk of operational errors, suboptimal system states, or operational failure. Consequently, use innovativeness embodies an affective tolerance for operational ambiguity and a willingness to troubleshoot unexpected outcomes. High-INNOUSE individuals view technical hitches, software glitches, and trial-and-error sequences not as aversive barriers, but as constructive learning events that enrich their overall mastery over the technological medium.

6. Theoretical Framework

The INNOUSE scale is grounded in the Use-Diffusion Model (UDM) formulated by Shih and Venkatesh (2004), which itself synthesizes conceptual currents from Self-Determination Theory, the sociology of technology, consumer creativity theory, and information systems continuance frameworks.

The Use-Diffusion Model (UDM)

The overarching architecture of the UDM maintains that diffusion is an ongoing, multi-stage trajectory consisting of two fundamental axes: pre-adoption diffusion and post-adoption use-diffusion. Within the use-diffusion phase, user behavior is segmented into two primary behavioral dimensions:

1. Rate of Use: The purely quantitative temporal allocation devoted to the technology (e.g., duration, frequency of interaction).
2. Variety of Use: The qualitative diversity of distinct operational contexts, software programs, or functional capabilities accessed by the user.

Shih and Venkatesh posited that rate of use and variety of use do not inevitably move in lockstep. An individual might utilize an office computing terminal for forty hours a week (intense rate of use) exclusively to perform routine data-entry tasks in a single software application (minimal variety of use). Conversely, another user might access the terminal for only five hours weekly but execute sophisticated coding, audiovisual editing, telecommunications, and automated system maintenance. The UDM conceptualizes Use Innovativeness as a decisive individual-difference driver that directly spurs the expansion of Variety of Use, which in turn leads to superior technology integration, elevated user satisfaction, and lower discontinuance rates.

Hirschman's Theory of Consumer Creativity and Cognitive Structure

The roots of INNOUSE extend to Hirschman's (1980) theoretical treatise on innovativeness, novelty seeking, and consumer creativity. Hirschman conceptualized consumers as active information processors whose intrinsic creative ability directs them to recombine existing resources in unprecedented ways. Price and Ridgway (1983) subsequently expanded this paradigm by developing an extensive 44-item battery measuring use innovativeness across five exploratory dimensions (e.g., creativity/curiosity, voluntary simplicity, risk preference). Shih and Venkatesh streamlined, refined, and modernized these theoretical underpinnings into a focused, highly practical psychometric measure specifically optimized to capture technology-mediated exploratory behavior without the psychometric encumbrance of broad lifestyle indicators.

Intrinsic Motivation and Experiential Computing

Theoretical perspectives from Deci and Ryan's (1985) Cognitive Evaluation Theory suggest that behaviors sustained by intrinsic curiosity yield higher cognitive absorption and competence. Within the INNOUSE framework, the act of using technology transcends utilitarian necessity. The instrument relies on the foundational assumption that exploratory use is intrinsically rewarding: the process of discovering a novel method to execute a task delivers autotelic satisfaction, thereby fostering continuous experimentation that operates independently of extrinsic mandates.

7. Validity

The INNOUSE scale has undergone thorough psychometric scrutiny across multiple empirical investigations, demonstrating exceptional construct, convergent, discriminant, and criterion-related validity.

Construct and Nomological Validity

Nomological validity—the degree to which a measure behaves as predicted within a theoretically established network of constructs—was extensively validated in Shih and Venkatesh's (2004) initial study of 1,172 households evaluating personal computing technologies. As hypothesized within the Use-Diffusion Model:

• INNOUSE demonstrated a statistically significant, powerful positive direct path to Variety of Use (standardized path coefficients routinely exceeding $\beta = .35, p < .001$).
• INNOUSE displayed moderate, theoretically consistent correlations with general external communication and information search behaviors, while remaining clearly distinct from structural household constraints (such as family size or baseline income).
• In subsequent nomological testing, INNOUSE positively predicted advanced system integration, personal technological efficacy, user task performance, and willingness to adopt subsequent architectural generations of technology.

Convergent Validity

Convergent validity represents the extent to which the items operationalizing INNOUSE genuinely share a high proportion of common variance. In structural equation modeling (SEM) and confirmatory factor analysis (CFA) assessments:

• All standardized factor loadings for the five indicator items typically exceed the recommended threshold of .70 (ranging observed: .72 to .88, $p < .001$).
• The Average Variance Extracted (AVE) consistently surpasses the conventional .50 benchmark, routinely reaching values between .58 and .68 across distinct consumer technology samples. This demonstrates that the latent construct accounts for the majority of the variance observed among its manifest items rather than measurement error.

Discriminant Validity

To confirm that INNOUSE is not merely a redundant proxy for related psychometric constructs, researchers utilize the Fornell-Larcker criterion and modern Heterotrait-Monotrait (HTMT) ratios of correlations:

• The square root of the AVE for INNOUSE consistently exceeds its inter-construct correlations with other focal dimensions in diffusion research, such as Rate of Use ($r \approx .22$ to $.35$), Perceived Usefulness ($r \approx .30$ to $.45$), and Perceived Ease of Use ($r \approx .25$ to $.38$).
• Critically, INNOUSE exhibits sharp discriminant validity against general Adoption Innovativeness (the time of initial purchase). While an individual may score low on adoption innovativeness (e.g., waiting years before acquiring a smartphone due to economic considerations), their INNOUSE score can remain exceptionally high once the device is obtained. HTMT ratios between INNOUSE and adoption-timing measures regularly fall well below the conservative .85 threshold, establishing strict conceptual distinctiveness.

Predictive and Criterion Validity

Criterion-related validity is evidenced by the scale's ability to forecast behavioral outcomes across longitudinal and cross-sectional designs. High scorers on the INNOUSE scale demonstrate significantly higher rates of feature exploration, deploy a broader suite of applications, configure higher numbers of personalized automation routines, and report greater resilience when confronted with software updates and interface alterations.

8. Reliability

The INNOUSE scale consistently demonstrates high internal consistency and measurement stability across diverse empirical investigations, international samples, and varied technological domains.

Internal Consistency Reliability

Reliability analyses systematically confirm that the five indicators of the INNOUSE scale function as a coherent, highly unified measurement battery:

• Cronbach's Alpha ($\alpha$): In the seminal calibration study by Shih and Venkatesh (2004), the five-item instrument yielded an internal consistency coefficient of $\alpha = .87$. Subsequent replications across mobile technologies, domestic smart internet-of-things (IoT) ecosystems, and organizational enterprise software have mirrored these results, consistently reporting alpha values between $.83$ and $.91$.
• Composite Reliability (CR): Within structural equation modeling frameworks, composite reliability values for INNOUSE routinely fall between $.86$ and $.92$, vastly exceeding the classic psychometric standard of $.70$ for applied research.
• Item-Total Correlations: Corrected item-to-total correlations for each of the five items routinely exceed $.60$, with no individual item displaying excessive uniqueness or measurement instability that would necessitate deletion or item-trimming.

Temporal and Cross-Sample Stability

Test-retest assessments administered across longitudinal panel cohorts (evaluating user trajectories at 6-month and 12-month post-implementation milestones) indicate solid temporal stability ($r_{tt} > .75$), confirming that while use innovativeness can be moderately nurtured or constrained by system design, it predominantly operates as an enduring cognitive-behavioral disposition. Furthermore, multigroup confirmatory factor analyses (MGCFA) have verified measurement invariance (configural, metric, and scalar invariance) across gender cohorts, age demographics, and diverse product categories, confirming that the scale functions equivalently across distinct sub-populations.

9. Factor Analysis

The dimensional structure of the INNOUSE scale has been repeatedly investigated using both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) techniques, corroborating a crisp, parsimonious unidimensional configuration.

Exploratory Factor Analysis (EFA)

Initial exploratory factor extractions (utilizing principal axis factoring and maximum likelihood estimation with orthogonal and oblique rotations) conducted on broad representative technology-user samples yield an unambiguous single-factor solution:

• A single dominant factor emerges with an eigenvalue substantially exceeding unity (eigenvalues typically ranging from $3.10$ to $3.65$), while secondary eigenvalues fall markedly below $0.60$.
• The primary factor accounts for between 62% and 73% of the total item variance, satisfying standard scree-test criteria for unidimensionality.
• No cross-loading anomalies or complex factorial splits appear, confirming that use innovativeness can be modeled directly as a cohesive first-order latent construct.

Confirmatory Factor Analysis (CFA) and Fit Indices

Subsequent confirmatory factor analytic models specified in structural equation modeling software (such as AMOS, LISREL, or Mplus) confirm outstanding goodness-of-fit indices for the unidimensional five-item structure. When fitted to empirical survey data, the measurement model routinely achieves metrics that surpass the most rigorous psychometric standards established by Hu and Bentler:

• Model Chi-Square ($\chi^2$): Minimal, non-significant, or displaying favorable relative ratios: $\chi^2 / \text{df} le 2.50$.
• Comparative Fit Index (CFI): Routinely observed between $.96$ and $.99$ (exceeding the standard $.95$ cutoff).
• Tucker-Lewis Index (TLI): Typically ranges from $.95$ to $.98$.
• Root Mean Square Error of Approximation (RMSEA): Consistently maintains values between $.032$ and $.058$, well within the acceptable threshold for close model fit ($< .06$).
• Standardized Root Mean Square Residual (SRMR): Consistently reported between $.020$ and $.041$ ($< .08$).

Typical standardized factor loadings derived from structural equation modeling of the 5-item scale are detailed in the structural table below:

10. Instrument / Measurement Tool

The INNOUSE instrument is a brief self-report psychometric battery designed for straightforward integration into broader consumer behavior, information systems, or user-experience surveys.

• Instrument Name: Innovativeness (Use) Scale (INNOUSE)
• Primary Developer / Source: Chuan-Fong Shih and Alladi Venkatesh (2004)
• Target Respondent: Adult consumers, technology end-users, employees utilizing software/hardware systems, or household members engaging with modern digital devices
• Administration Format: Self-administered paper-and-pencil survey, online survey platform (e.g., Qualtrics, SurveyMonkey), or embedded digital interface evaluation module
• Total Item Count: 5 items
• Estimated Completion Time: 2 to 3 minutes
• Response Scale Architecture: Typically formatted as a 7-point Likert-type response format:
  • 1 = Strongly Disagree
  • 2 = Disagree
  • 3 = Somewhat Disagree
  • 4 = Neither Agree nor Disagree (Neutral)
  • 5 = Somewhat Agree
  • 6 = Agree
  • 7 = Strongly Agree
• Alternative Formats: Occasionally adapted to 5-point Likert scales without loss of dimensional integrity.
• Scoring Protocol:
  • All five items are keyed in the positive direction (no reverse-scored items).
  • An overall composite score is generated by computing either the arithmetic mean across the five manifest items (yielding an interpretable continuous score from 1.00 to 7.00) or by calculating the summated raw score (ranging from 5 to 35).
  • In advanced structural equation modeling, latent variable modeling utilizing factor score weights or unit-weighted composites is recommended to account for residual variance.

11. Permissions & Fee and Test Year

The INNOUSE scale was formally published in January 2004 within the academic literature by the American Marketing Association (AMA). The formal publication details and licensing guidelines are as follows:

• Year of Formal Publication: 2004
• Original Copyright Holders: American Marketing Association (AMA) and the contributing authors (Chuan-Fong Shih & Alladi Venkatesh).
• Scholarly & Educational Usage: Consistent with standard academic fair-use conventions, the scale may be utilized without monetary fees by academic researchers, doctoral students, and non-profit educational investigators, provided that appropriate formal bibliographic attribution is cited in all resulting theses, dissertations, and peer-reviewed publications.
• Commercial / Corporate Usage: Commercial entities, market research agencies, user-experience testing firms, and product development consultancies seeking to embed the scale into proprietary commercial diagnostics or billable software suites should seek formal written permission and review licensing stipulations through the American Marketing Association or its rights-clearance partners (e.g., Copyright Clearance Center / SAGE Publications).

12. References

The foundational and secondary academic literature supporting the development, psychometric validation, and conceptual application of the INNOUSE scale includes:

• Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351–370. https://doi.org/10.2307/3250921
• 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
• Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. Plenum Press. https://doi.org/10.1007/978-1-4899-2271-7
• 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
• 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
• Hirschman, E. C. (1980). Innovativeness, novelty seeking, and consumer creativity. Journal of Consumer Research, 7(3), 283–295. https://doi.org/10.1086/208816
• 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
• Price, L. L., & Ridgway, N. M. (1983). Development of a scale to measure use innovativeness. In R. P. Bagozzi & A. M. Tybout (Eds.), Advances in Consumer Research (Vol. 10, pp. 679–684). Association for Consumer Research.
• Rogers, E. M. (1995). Diffusion of innovations (4th ed.). The Free Press.
• Shih, C. F., & Venkatesh, A. (2004). Beyond adoption: Development and application of a use-diffusion model. Journal of Marketing, 68(1), 59–72. https://doi.org/10.1509/jmkg.68.1.59.24029
• Venkatesh, A., & Vitalari, N. P. (1992). An emerging distributed work arrangement: An investigation of computer-based supplemental work at home. Management Science, 38(12), 1687–1706. https://doi.org/10.1287/mnsc.38.12.1687

13. Items of the Scale