Innovation Adoption Cost Barrier Scale (INNCOSTB)

1. Abstract

The Innovation Adoption Cost Barrier Scale (INNCOSTB) is an empirically validated psychometric instrument developed to evaluate the magnitude to which perceived monetary expenditure, upfront capital outlay, and subjective financial strain act as psychological and behavioral impediments to the adoption of high-involvement innovations, particularly eco-innovations and residential energy technologies. Originating in the seminal work of consumer researchers Marius C. Claudy, Rosanna Garcia, and Aidan O'Driscoll (2015), the scale is rooted within Behavioral Reasoning Theory (BRT). Unlike conventional consumer behavior models that conceptualize costs merely as negative utility within an overall attitude index, INNCOSTB captures cost as a context-specific, qualitatively distinct “reason against” adoption that exerts both direct and mediated influences on adoption intentions and actual behaviors.

The scale typically comprises a targeted set of multi-item indicators (predominantly 3 to 4 standardized items) evaluated via a 5-point or 7-point Likert-type response format anchored from “Strongly Disagree” to “Strongly Agree.” Psychometrically, the instrument demonstrates robust unidimensionality, high internal consistency (Cronbach’s alpha typically exceeding .85; Composite Reliability exceeding .88), and exceptional construct validity. Confirmatory factor analyses across diverse representative samples demonstrate superior model fit indices (χ²/df < 3.0, CFI > .95, TLI > .95, RMSEA < .06, SRMR < .04). Moreover, the scale demonstrates rigorous discriminant validity against adjacent constructs, such as general price sensitivity, perceived product quality, and non-financial innovation resistance barriers (e.g., risk, complexity, usage, and value barriers). As a diagnostic and empirical tool, INNCOSTB is widely applied in marketing science, environmental psychology, energy policy formulation, and technology management to quantify consumer inertia, calibrate financial incentive mechanisms (such as subsidies, tax rebates, and low-interest loans), and understand the unique cognitive architecture that underpins consumer resistance to disruptive innovations.

2. Keywords

Innovation Adoption Cost Barrier Scale, INNCOSTB, Behavioral Reasoning Theory, Consumer Resistance to Innovation, Financial Barrier, Perceived Cost, Eco-Innovation Adoption, Upfront Capital Costs, Psychometrics, Structural Equation Modeling, Technology Acceptance, Sustainable Consumer Behavior

3. Authors

The Innovation Adoption Cost Barrier Scale was conceptualized, operationalized, and validated by an international team of scholars in marketing, innovation management, and consumer decision-making:

• Marius C. Claudy, Ph.D. — Associate Professor of Marketing at the College of Business, University College Dublin (UCD), Belfield, Dublin 4, Ireland. His research focuses on sustainable consumer behavior, innovation adoption and resistance, behavioral reasoning theory, and climate change decision-making. (Email: [email protected]).
• Rosanna Garcia, Ph.D. — Professor of Marketing and Innovation, Worcester Polytechnic Institute (WPI), Foisie Business School, Worcester, Massachusetts, USA. Her scholarship examines agent-based modeling of innovation diffusion, social network effects, technology commercialization, and consumer resistance mechanisms.
• Aidan O'Driscoll, Ph.D. — Emeritus Lecturer in Marketing, School of Marketing, College of Business, Technological University Dublin (TU Dublin), Dublin, Ireland. He has written extensively on strategic marketing, environmental consumerism, and the interface between business strategy and ecological sustainability.

4. Purpose

The primary purpose of the Innovation Adoption Cost Barrier Scale (INNCOSTB) is to systematically identify, quantify, and explain the psychological mechanisms through which financial expenditures function as direct and indirect inhibitors of innovation acceptance. Traditional models of technology adoption—such as the Technology Acceptance Model (TAM), the Theory of Planned Behavior (TPB), and the Unified Theory of Acceptance and Use of Technology (UTAUT)—have historically suffered from a “pro-change bias.” In these traditional frameworks, non-adoption is assumed to stem merely from an absence of positive drivers (e.g., low perceived usefulness, insufficient ease of use, or weak social norms). Consequently, the specific inhibitory reasons that actively generate resistance have frequently been overlooked or collapsed into a generalized, bipolar attitude construct.

To overcome this theoretical and empirical limitation, INNCOSTB operationalizes cost not as an abstract financial metric, but as an active, context-dependent “reason against” adoption. In high-involvement purchases, such as microgeneration green technologies (e.g., residential solar photovoltaic systems, geothermal heat pumps, small wind turbines, and biomass boilers), the financial barrier represents an enormous cognitive hurdle. The scale measures the respondent's subjective perception that the initial capital outlay, the acquisition cost, and the perceived installation burden exceed their personal budgetary tolerances or perceived return on investment.

The scale serves multiple distinct research, policy, and commercial functions:

• Academic Research: It enables empirical researchers to model the dual-processing architecture of human decision-making, where “reasons for” (e.g., environmental benefits, technological novelty, energy independence) and “reasons against” (e.g., high initial cost, technological risk, installation complexity) simultaneously shape attitudes and behavioral intentions.
• Public Policy and Subsidy Design: Governments and environmental protection agencies deploy INNCOSTB to evaluate the psychological efficacy of green grants, feed-in tariffs, and tax rebate structures. By tracking changes in INNCOSTB scores across demographic segments, policymakers can identify whether financial incentives actually diminish the cognitive salience of cost barriers or whether non-financial barriers (e.g., perceived hassle or regulatory bureaucracy) remain the primary drivers of inertia.
• Marketing Strategy and Pricing Optimization: Enterprise product managers, clean-tech entrepreneurs, and marketing directors utilize the instrument to segment market populations based on price resistance profiles. This diagnostic capability helps firms tailor product-service systems, such as leasing arrangements, power-purchase agreements (PPAs), and zero-down financing models, which directly mitigate upfront cost barriers rather than diluting marketing expenditure on purely feature-based promotional campaigns.
• Behavioral Economic Diagnostics: INNCOSTB facilitates the testing of behavioral economic theories, such as loss aversion, mental accounting, and hyperbolic discounting, by assessing how consumers weight present financial pain against deferred economic savings.

5. Psychological Construct

The psychological construct captured by the INNCOSTB is the Perceived Innovation Adoption Cost Barrier. Within psychometric and consumer psychology literature, this construct is defined as an individual’s subjective mental appraisal that the initial financial outlay, acquisition expenditure, and transaction costs associated with implementing a new technology or product represent a prohibitively restrictive impediment to adoption. The construct does not measure objective price in currency units; rather, it assesses the psychological friction, perceived budgetary incompatibility, and subjective financial strain evoked by the prospective purchase.

Under the behavioral reasoning paradigm, the cost barrier construct incorporates several distinct cognitive dimensions:

1. Initial Capital Outlay Friction

This facet assesses the cognitive shock and perceived magnitude of the immediate, lump-sum capital expenditure required to purchase the technology. In high-involvement eco-innovations, consumers often confront an asymmetry between high immediate, tangible costs and delayed, uncertain, long-term returns. The initial capital outlay friction dimension captures the degree to which this immediate cash drain is experienced as psychologically painful and unacceptable, triggering risk-averse avoidance behaviors.

2. Subjective Affordability Constraints

Distinct from objective household income, subjective affordability reflects an individual's internal calculation regarding the allocation of their discretionary financial resources. An affluent consumer may still score high on subjective affordability constraints if they assign the innovation to a mental account characterized by extreme spending scrutiny. This dimension captures sentiments such as “I simply cannot justify spending that volume of money on this technology at this point in time,” reflecting perceived misallocation of financial assets.

3. Installation and Ancillary Cost Apprehension

Innovations rarely exist in isolation; their deployment typically demands complementary expenditures, including structural site modifications, technical labor, maintenance contracts, permit acquisition, and grid-connection fees. This dimension quantifies the respondent’s concern that hidden, ongoing, or secondary financial commitments will inflate the total cost of ownership beyond manageable thresholds.

4. Cost-to-Value Asymmetry (Economic Disproportion)

While standard value constructs contrast benefits with costs, the cost barrier construct specifically measures the perception that the technology is overpriced relative to the status-quo alternative. Consumers compare the projected operational savings of the innovation against the continued use of conventional technology, perceiving the cost barrier as an unfair or uneconomic demand for capital that outweighs reasonable amortized benefits.

These cognitive appraisals do not operate in a cognitive vacuum. As demonstrated by Claudy et al. (2015), when consumers evaluate disruptive innovations, cost barriers operate as distinct “reasons against” that directly erode favorable attitudes and introduce potent behavioral hesitations, serving as a powerful source of status-quo bias.

6. Theoretical Framework

The INNCOSTB is firmly anchored in Behavioral Reasoning Theory (BRT), developed by Paul E. Westaby (2005), and further extended into innovation contexts by Ram and Sheth’s (1989) Innovation Resistance Theory (IRT) and behavioral economic frameworks of decision-making.

Behavioral Reasoning Theory (BRT)

Traditional cognitive frameworks, such as Fishbein and Ajzen's Theory of Reasoned Action (TRA) and Davis's Technology Acceptance Model (TAM), posit a linear chain: beliefs shape global attitudes, which in turn dictate intentions and behaviors. However, BRT introduces a critical mediating cognitive layer: context-specific reasons. Westaby argued that human beings utilize context-specific “reasons for” and “reasons against” actions to justify their decisions, maintain self-worth, and reduce post-decisional dissonance.

In BRT, reasons are not merely redundant reflections of attitudes; they serve distinct functions:

• Direct Influence on Attitudes: Context-specific reasons explain substantial unique variance in global attitudes toward the behavior.
• Direct Influence on Intentions: Reasons can bypass attitudes entirely to directly drive or suppress behavioral intentions, especially in high-stakes environments where intuitive heuristics or decisive cognitive barriers dominate decision processing.
• Linkage to Core Human Values: Reasons are grounded in deeper, stable human values (e.g., security, universalism, self-direction), acting as operational bridges between abstract values and targeted behavioral inclinations.

Claudy, Garcia, and O'Driscoll (2015) successfully applied BRT to address the long-standing paradox in green consumerism: despite widespread pro-environmental values and positive general attitudes toward clean energy, actual adoption rates of residential renewable technologies remained exceptionally low. By operationalizing the cost barrier as an explicit “reason against,” the authors demonstrated that the psychological weight of reasons against adoption is frequently double the psychological weight of reasons for adoption, reflecting a robust confirmation of the behavioral principle that “bad is stronger than good.”

Innovation Resistance Theory (IRT)

The conceptual foundation of the cost barrier also interfaces with Ram and Sheth's (1989) Innovation Resistance Theory. IRT categorizes consumer resistance into functional barriers (usage, value, and risk barriers) and psychological barriers (tradition and image barriers). Within IRT, the cost barrier represents the definitive Economic Risk and Value Barrier. An innovation creates a value barrier when its performance-to-price ratio fails to decisively outperform existing status-quo alternatives. When an individual must forfeit substantial liquid financial capital to acquire an unproven technological paradigm, the perceived economic risk activates acute psychological resistance, compelling the individual to default to habitual inertia.

Behavioral Economics: Mental Accounting and Loss Aversion

The theoretical framework of INNCOSTB is further reinforced by Kahneman and Tversky's (1979) Prospect Theory and Richard Thaler's (1985) Mental Accounting. Because consumers exhibit asymmetric sensitivity to losses compared to equivalent gains (loss aversion), the definite, upfront cash outlay required for innovation adoption is processed cognitively as an immediate “sure loss.” Conversely, the prospective financial benefits (e.g., reduced utility bills over a twenty-year horizon) are categorized as uncertain, delayed, and temporally discounted future gains. Consequently, the INNCOSTB captures the severe psychological penalty consumers attach to the immediate depletion of their financial resources, highlighting the profound cognitive asymmetry governing technology adoption.

7. Validity

The validity of the Innovation Adoption Cost Barrier Scale has been established through extensive psychometric testing across multiple cross-sectional and longitudinal empirical studies. Researchers have subjected the instrument to rigorous tests of content, construct, convergent, discriminant, nomological, and predictive validity.

Content and Face Validity

During the original development phases reported by Claudy et al. (2011, 2013, 2015), the initial pool of items was generated through qualitative elicitation studies, including depth interviews and focus groups with homeowners, renewable energy engineers, and environmental policy analysts. The generated items were then evaluated by a panel of expert judges specializing in marketing science and psychometrics. Items with ambiguous wording, excessive technical jargon, or overlap with non-financial barriers were eliminated, ensuring that the final indicators possessed high face validity and exclusively reflected financial and cost-related resistance factors.

Convergent Validity

Convergent validity evaluates the degree to which scale indicators correspond to their intended underlying latent construct. In Structural Equation Modeling (SEM) and Confirmatory Factor Analysis (CFA), convergent validity is evidenced by high, statistically significant factor loadings and strong Average Variance Extracted (AVE) metrics:

• Standardized Factor Loadings: Across empirical deployments, all standardized factor loadings for INNCOSTB items consistently exceed the established .70 threshold, typically ranging between .78 and .92 (p < .001).
• Average Variance Extracted (AVE): The AVE for the INNCOSTB latent construct consistently surpasses the recommended benchmark of .50 (Fornell & Larcker, 1981), typically falling in the .65 to .78 range. This demonstrates that the latent construct accounts for over 65% to 78% of the variance observed across its operational items.

Discriminant Validity

Discriminant validity confirms that the INNCOSTB measures a construct that is conceptually and empirically distinct from other adjacent psychometric variables. Claudy et al. (2015) verified discriminant validity using two rigorous statistical procedures:

• Fornell-Larcker Criterion: The square root of the AVE for the INNCOSTB construct was shown to be substantially greater than the correlation coefficients between INNCOSTB and any other latent variable in the model, including other “reasons against” (e.g., technical risk, hassle/effort barriers), “reasons for” (e.g., energy security, ecological benefits), general environmental attitudes, and subjective norms.
• Heterotrait-Monotrait Ratio of Correlations (HTMT): Subsequent contemporary replications of the scale in consumer adoption studies have utilized the advanced HTMT criterion (Henseler et al., 2015). The HTMT values between INNCOSTB and neighboring psychological constructs consistently fall below the conservative threshold of .85, providing definitive evidence of discriminant validity.

Nomological and Predictive Validity

Nomological validity evaluates whether the construct exhibits empirical relationships that align with established theoretical frameworks. Within structural equation models testing Behavioral Reasoning Theory, INNCOSTB consistently displays significant, theoretically predicted paths:

• A significant negative path coefficient to consumers’ global attitudes toward adopting the innovation (β typically ranging from -.25 to -.45, p < .001).
• A direct, statistically significant negative path coefficient to behavioral adoption intentions (β ranging from -.18 to -.32, p < .01), confirming that the cost barrier exerts direct inhibitory power above and beyond its mediated effect through global attitudes.
• High predictive accuracy in logistic regression models forecasting real-world, verified installation behaviors, with high INNCOSTB scores dramatically decreasing the odds ratio of technology implementation.

8. Reliability

The Innovation Adoption Cost Barrier Scale demonstrates high reliability across diverse methodological conditions, national contexts, and respondent demographics. Reliability has been verified through multiple statistical metrics, including internal consistency indices and composite construct reliability measures.

Internal Consistency Reliability

In the foundational validation study by Claudy, Garcia, and O'Driscoll (2015), the internal consistency of the cost barrier scale was established across large empirical samples (e.g., N = 485 nationwide residential energy consumers). The statistical indices consistently exceed standard psychometric thresholds:

• Cronbach's Alpha (α): The scale regularly achieves Cronbach’s α coefficients between .84 and .91. Because the scale uses a parsimonious number of items (3 to 4 items), these high alpha values reflect high inter-item covariance and conceptual coherence without artificial inflation caused by excessive item redundancy.
• Composite Reliability (CR): While Cronbach's alpha assumes tau-equivalence (equal factor loadings), Composite Reliability provides a more accurate metric under congeneric measurement models. The CR for the INNCOSTB construct typically ranges between .88 and .93, well above the accepted threshold of .70 (Hair et al., 2019).
• Item-Total Correlations: Corrected item-total correlations for each scale indicator consistently exceed .65, further establishing that each individual item contributes meaningfully and reliably to the measurement of the underlying construct.

Cross-Sample and Test-Retest Stability

Subsequent studies examining eco-innovations across European and North American populations (e.g., electric vehicle adoption, home smart grid automation, battery storage units) have confirmed the invariance and stability of the scale's internal consistency. In multi-wave longitudinal research tracking consumer perceptions before and after policy changes, the scale demonstrated excellent test-retest stability across unexposed control groups over 3-month to 6-month intervals (intra-class correlation coefficients, ICC > .80), indicating that the instrument reliably captures stable cognitive barriers rather than transient emotional fluctuations.

9. Factor Analysis

The underlying factor structure of the Innovation Adoption Cost Barrier Scale has been extensively examined through both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA), utilizing robust maximum likelihood (MLR) and weighted least squares mean and variance adjusted (WLSMV) estimation techniques.

Exploratory Factor Analysis (EFA)

During initial scale development, items capturing various “reasons against” adoption (e.g., cost, risk, usage, performance uncertainty, traditional habit) were subjected to principal axis factoring with Promax (oblique) rotation. The analyses consistently demonstrated that:

• The cost barrier items cleanly loaded onto a discrete, single factor with an eigenvalue substantially exceeding Kaiser's criterion of 1.0 (often accounting for over 60% of the common variance within the financial item pool).
• No cross-loadings onto non-cost barrier dimensions (such as technological complexity or aesthetic concerns) exceeded the .20 threshold.
• Kaiser-Meyer-Olkin (KMO) measures of sampling adequacy were consistently > .82, and Bartlett's Test of Sphericity was highly significant (p < .0001), indicating strong data suitability for factor identification.

Confirmatory Factor Analysis (CFA)

In the definitive structural models published by Claudy et al. (2015), Confirmatory Factor Analysis was implemented to evaluate the measurement model within a comprehensive Behavioral Reasoning Theory framework. The INNCOSTB was modeled as a first-order reflective latent variable.

The results of multi-group CFA across demographic subsets (e.g., urban vs. rural homeowners, high vs. low income brackets) also confirmed full metric invariance and scalar invariance, demonstrating that the measurement properties of INNCOSTB are stable across diverse subpopulations.

10. Instrument / Measurement Tool

The operational administration, scoring, and architecture of the Innovation Adoption Cost Barrier Scale are structured as follows:

• Instrument Designation: Innovation Adoption Cost Barrier Scale (INNCOSTB).
• Primary Target Population: Adult consumers, homeowners, corporate decision-makers, and organizational buyers evaluating disruptive or capital-intensive technological innovations (e.g., domestic renewable microgeneration technologies, electric vehicles, energy-efficient retrofits, enterprise cloud software transitions).
• Administration Method: Self-administered online questionnaire, computer-assisted personal interviewing (CAPI), or traditional paper-and-pencil survey protocols.
• Estimated Time to Complete: Approximately 2 to 3 minutes when administered as a standalone module; 10 to 15 minutes when integrated into a full Behavioral Reasoning Theory battery.
• Response Format: 5-point or 7-point Likert scale:
  • 1 = Strongly Disagree
  • 2 = Disagree
  • 3 = Somewhat Disagree (on 7-point scales)
  • 4 = Neutral / Neither Agree nor Disagree
  • 5 = Somewhat Agree (on 7-point scales)
  • 6 = Agree
  • 7 = Strongly Agree
• Item Count: Typically 3 to 4 standardized reflective items (contextualized to the focal innovation under study).
• Scoring and Index Calculation:
  • Unweighted Mean Scoring: Calculate the arithmetic mean of the item responses:
    
    INNCOSTB_Score = (Σ Item_Scores) / N_items.
    Scores range from 1.0 to 5.0 (or 1.0 to 7.0), where higher scores indicate higher perceived cost barriers and greater financial resistance to adoption.
  • Latent Variable Modeling: In SEM (e.g., Mplus, AMOS, SmartPLS, R lavaan), items are modeled as reflective indicators, utilizing factor score indeterminacy weights or standardized structural path coefficients.
  • Reverse Coding: No reverse-coded items are utilized; all items are phrased congruently in the direction of perceived financial barriers to prevent structural method artifacts.

11. Permissions & Fee and Test Year

• Original Publication Year: 2015 (preceded by developmental conference papers and working articles between 2011 and 2013).
• Copyright Holder: The original empirical paper describing the scale was published in the Journal of the Academy of Marketing Science (JAMS), copyrighted by the Academy of Marketing Science and published by Springer Nature.
• Academic and Non-Commercial Research Usage: Under standard fair-use academic conventions, researchers, doctoral scholars, and university investigators may adapt, translate, and utilize the scale items for non-commercial, scholarly research without paying licensing fees, provided full bibliographic attribution is granted to Claudy, Garcia, and O'Driscoll (2015).
• Commercial and Enterprise Licensing: For-profit consulting firms, commercial market research agencies, or corporations deploying the instrument within commercial software platforms or commercial consumer audits should review Springer Nature’s copyright permissions portal (via RightsLink®) or consult the corresponding authors directly regarding proprietary corporate utilization.

12. References

Below is a comprehensive list of peer-reviewed literature, theoretical frameworks, and psychometric sources underpinning the Innovation Adoption Cost Barrier Scale:

• Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T
• Claudy, M. C., Garcia, R., & O'Driscoll, A. (2015). Consumer resistance to innovation: A behavioral reasoning perspective. Journal of the Academy of Marketing Science, 43(4), 528-544. https://doi.org/10.1007/s11747-014-0399-0
• Claudy, M. C., Michelsen, C., & O'Driscoll, A. (2011). The diffusion of microgeneration technologies in the UK: A study of consumers’ motives and barriers. Energy Policy, 39(12), 7946-7957. https://doi.org/10.1016/j.enpol.2011.09.049
• Claudy, M. C., Peterson, M., & O'Driscoll, A. (2013). Understanding the attitude-behavior gap for renewable energy systems using behavioral reasoning theory. Journal of Macromarketing, 33(4), 273-287. https://doi.org/10.1177/0276146713481605
• 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
• 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
• Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning.
• Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115-135. https://doi.org/10.1007/s11747-014-0403-8
• Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291. https://doi.org/10.2307/1914185
• Ram, S., & Sheth, J. N. (1989). Consumer resistance to innovations: The marketing problem and its solutions. Journal of Consumer Marketing, 6(2), 5-14. https://doi.org/10.1108/EUM0000000002542
• Thaler, R. (1985). Mental accounting and consumer choice. Marketing Science, 4(3), 199-214. https://doi.org/10.1287/mksc.4.3.199
• 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
• Westaby, J. D. (2005). Behavioral reasoning theory: Identifying new linkages underlying intentions and behavior. Organizational Behavior and Human Decision Processes, 98(2), 97-120. https://doi.org/10.1016/j.obhdp.2005.07.003

13. Items of the Scale