Complexity of the Product (COTP)

1. Abstract

The Complexity of the Product (COTP) scale is a specialized psychometric instrument developed by Serge A. Rijsdijk, Erik Jan Hultink, and Adamantios Diamantopoulos in their seminal 2007 study published in the Journal of the Academy of Marketing Science. Measuring the degree to which an individual perceives an artifact or technological device as cognitively demanding, difficult to comprehend, and cumbersome to operate, the COTP scale assesses a central barrier to technology adoption and consumer satisfaction. Grounded conceptually in Everett Rogers’ Diffusion of Innovations theory and aligning inversely with the Technology Acceptance Model’s (TAM) Perceived Ease of Use, the scale operationalizes perceived product complexity through a unidimensional, four-item architecture. Responses are recorded on an authentic 7-point Likert scale ranging from 1 (“strongly disagree”) to 7 (“strongly agree”). Across empirical validation samples involving smart consumer durables, intelligent home technologies, and multifaceted digital systems, the COTP scale exhibits rigorous psychometric properties, consistently yielding high internal consistency reliability (Cronbach’s alpha typically exceeding .85 and composite reliability values well above .80), robust factor determinacy under confirmatory factor analytic (CFA) procedures, and strong convergent, discriminant, and predictive validity against downstream outcome variables such as feature fatigue, user frustration, post-purchase cognitive dissonance, and overall product satisfaction. This article provides a comprehensive academic analysis of the COTP scale, detailing its theoretical genealogy, structural integrity, psychometric behavior, and contemporary relevance within human-computer interaction (HCI), industrial design, and consumer psychology.

2. Keywords

Complexity of the Product, Product Complexity, Perceived Complexity, Diffusion of Innovations, Technology Acceptance Model, Cognitive Load, Smart Products, Usability Measurement, Consumer Psychology, Psychometrics, Scale Validation, Ergonomics, Human-Computer Interaction

3. Authors

The Complexity of the Product scale was conceptualized, operationalized, and psychometrically validated by a distinguished team of scholars in marketing, innovation management, and psychometric methodology:

• Serge A. Rijsdijk, Ph.D.: Associate Professor of Innovation Management at the Rotterdam School of Management (RSM), Erasmus University Rotterdam, The Netherlands. Dr. Rijsdijk’s scholarly research focuses on new product development, the design and consumer adoption of “smart” or artificially intelligent products, autonomous systems, and design thinking. His work has appeared in premier journals including the Journal of the Academy of Marketing Science, Journal of Product Innovation Management, and IEEE Transactions on Engineering Management.
• Erik Jan Hultink, Ph.D.: Full Professor of New Product Marketing at the Faculty of Industrial Design Engineering, Delft University of Technology (TU Delft), The Netherlands. Professor Hultink is an internationally acclaimed authority on launch strategies for new products, competitive marketing strategy, design engineering, and technological innovation. He has served extensively on editorial review boards of leading innovation management journals.
• Adamantios Diamantopoulos, Ph.D.: Chaired Professor of International Marketing at the Faculty of Business, Computing and Statistics, University of Vienna, Austria. Dr. Diamantopoulos is one of the world’s most widely cited quantitative methodologists and marketing scholars, having published landmark works on scale development, structural equation modeling, formative versus reflective measurement models, and index construction in outlets such as the Journal of Marketing Research, International Journal of Research in Marketing, and Psychological Methods.

4. Purpose

The primary purpose of the Complexity of the Product (COTP) scale is to quantify an individual’s subjective appraisal of the cognitive and physical effort required to understand, learn, navigate, and execute functional tasks using a product. As modern consumer durables, electronic appliances, and digital ecosystems increasingly incorporate autonomous decision-making algorithms, multi-layered digital interfaces, and expansive feature matrices, the objective capabilities of devices often outstrip the mental models of everyday users. In quantitative consumer research, industrial design engineering, and human factors ergonomics, there exists an acute requirement for parsimonious, psychometrically robust tools capable of diagnosing the cognitive friction generated by such artifacts.

From a theoretical perspective, the COTP scale isolates perceived complexity as an idiosyncratic consumer judgment rather than an objective architectural property of the engineering system. Two devices possessing the exact same number of internal microcontrollers or firmware subroutines may elicit radically divergent perceived complexity scores depending on the clarity of their affordances, user interface hierarchy, feedback mechanisms, and mental mapping. By capturing subjective cognitive appraisals across four targeted indicators—operational complication, learning duration, cognitive operating effort, and task goal attainment difficulty—the COTP scale enables researchers to assess how perceived complexity interacts with other product dimensions, such as autonomy, adaptability, and multi-functionality.

In applied research, organizational strategy, and clinical or ergonomic usability environments, the scale serves multiple evaluative functions:

• Pre-Market Prototype Diagnostic: Serving as a rapid, reliable diagnostic instrument during stage-gate product development processes to identify whether technical enhancements inadvertently induce usability barriers.
• Assessment of Feature Fatigue: Detecting the inflection point where feature proliferation transitions from a perceived benefit into an operational liability that generates cognitive overload, post-purchase regret, and product abandonment.
• Segmentation and Accessibility Profiling: Evaluating how distinct demographic groups (e.g., tech-savvy digital natives versus older adults or individuals with mild cognitive impairments) experience technological complexity, thereby informing universal design guidelines and tailored user manuals.
• Predictive Usability Modeling: Providing a standardized psychometric variable that can be modeled in structural equation systems to forecast downstream marketing outcomes, including customer satisfaction, brand equity, positive word-of-mouth, and return merchandise authorization (RMA) rates.

5. Psychological Construct

The construct measured by the COTP scale is Perceived Product Complexity. Within cognitive psychology, usability engineering, and consumer behavior, perceived complexity is conceptualized as a multi-faceted cognitive state characterized by high mental workload, ambiguity of system affordances, and difficulty in translating human behavioral intentions into functional system actions. Although the COTP scale models this construct as a parsimonious unidimensional reflective factor, the underlying construct encompasses several core psychological dimensions:

Cognitive Load and Attentional Demands

At the center of perceived complexity lies the saturation of working memory capacity. According to Cognitive Load Theory, processing new information requires working memory resources, which are inherently finite. When a product’s operating logic is obscure, extraneous cognitive load is imposed upon the consumer. COTP captures this mental strain directly through items addressing the excessive “effort to understand” and “time to learn.” Users must actively hold complex sequences of operations, menu trees, or iconographic meanings in working memory, diverting cognitive energy away from task fulfillment and generating subjective fatigue.

Gulf of Execution and Gulf of Evaluation

Grounded in the cognitive engineering framework established by Donald Norman, perceived complexity manifests across two fundamental psychological gulfs. The Gulf of Execution represents the psychological distance between an agent’s internal goal and the external physical actions required to execute that goal through the artifact. When working with a product feels “complicated” or when it is “difficult to get this product to do what I want it to do,” the Gulf of Execution is excessively wide. Conversely, the Gulf of Evaluation reflects the user’s difficulty in deciphering the current operational state of the product from its visual, auditory, or haptic feedback. High COTP scores reflect an inability to bridge these gulfs efficiently, resulting in user error, learned helplessness, and cognitive friction.

Disruption of Mental Models and System Transparency

When an individual interacts with a physical or digital tool, they construct an internal mental model—a structural representation of how the system operates. If a product’s system image is poorly communicated, incongruent with prior schemas, or governed by opaque algorithmic logic (frequent in autonomous or “smart” appliances), the user cannot predict the consequences of their inputs. Item 3 (“It takes a lot of effort to understand how to operate this product”) explicitly measures this lack of transparent conceptual understanding, distinguishing simple physical execution speed from the deeper cognitive challenge of structural comprehension.

Behavioral Frustration and Goal Incongruence

Perceived complexity is not solely an intellectual computation; it is intimately tied to affective responses. When an artifact resists intuitive operation—as captured by Item 4 (“It is difficult to get this product to do what I want it to do”)—the user experiences goal disruption, negative affect, and diminished self-efficacy. This psychological dimension separates COTP from passive descriptive categorizations of technical difficulty, situating it as an evaluative state with immediate implications for user well-being, satisfaction, and agency.

6. Theoretical Framework

The Complexity of the Product scale is rooted in an intersection of three major theoretical frameworks: the Diffusion of Innovations paradigm, the Technology Acceptance Model (TAM), and Cognitive Load Theory.

Rogers’ Diffusion of Innovations Paradigm

The foundational lineage of the COTP scale traces back to the classic innovation diffusion model articulated by Everett M. Rogers (1962, 1995, 2003). Rogers identified five core perceived attributes that dictate the rate and extent of innovation adoption across social systems: Relative Advantage, Compatibility, Trialability, Observability, and Complexity. Notably, whereas the first four attributes correlate positively with innovation diffusion, complexity is the sole core attribute that acts as a powerful negative predictor of adoption. Rogers formally defined complexity as “the degree to which an innovation is perceived as relatively difficult to understand and use.” Rijsdijk, Hultink, and Diamantopoulos (2007) directly operationalized Rogers’ definition into a psychometrically rigorous measurement model tailored to consumer products, ensuring that the COTP scale preserves the construct’s exact theoretical scope.

The Technology Acceptance Model (TAM) and Usability Paradigms

Within the information systems and human factors literature, Fred Davis (1989) introduced the Technology Acceptance Model, isolating Perceived Ease of Use (PEOU) as a central determinant of technological adoption attitudes and behavioral intentions. PEOU is defined as “the degree to which a person believes that using a particular system would be free of effort.” Psychometrically and conceptually, perceived product complexity functions as the negative obverse or inverse mirror of perceived ease of use. However, empirical studies in marketing and consumer psychology have demonstrated that negative cognitive evaluations often carry greater psychological weight than positive evaluations due to negativity bias. By explicitly framing the scale items around cognitive burdens, learning curves, and operational impedance, the COTP scale captures consumer aversion, frustration, and psychological friction far more acutely than traditional reverse-scored ease-of-use items.

Cognitive Load Theory and Human Information Processing

The scale is further grounded in John Sweller’s Cognitive Load Theory (1988) and human information processing frameworks (Wickens, 2002). These frameworks posit that human mental architecture consists of a sensory memory, a capacity-limited working memory, and an effectively unlimited long-term memory. When interacting with complex technology, users encounter three forms of cognitive load: intrinsic load (inherent to the functional task itself), extraneous load (imposed by the suboptimal design or communication of the product interface), and germane load (mental processing dedicated to schema acquisition). The COTP scale effectively assesses the extent to which excessive extraneous cognitive load overwhelms human processing bandwidth, depleting attentional resources and obstructing successful human-machine interaction.

7. Validity

The COTP scale was subjected to thorough psychometric validation procedures during its development by Rijsdijk, Hultink, and Diamantopoulos (2007), followed by extensive cross-validation in subsequent independent investigations within consumer research and ergonomics.

Content and Face Validity

Content validity was established through an exhaustive qualitative review of seminal innovation literature (e.g., Rogers, 1995; Ostlund, 1974; Tornatzky & Klein, 1982) alongside expert panel evaluations. Experienced researchers in product design and marketing methodology screened an initial pool of candidate items to ensure the domain of perceived operational difficulty, cognitive comprehension, and temporal learning investment was fully represented without conceptual redundancy. The final four items were selected for their clear semantic clarity and direct alignment with theoretical constructs.

Construct and Convergent Validity

Confirmatory factor analysis (CFA) provides robust statistical evidence of convergent validity. Across multiple samples comprising hundreds of consumers interacting with varied technological products (such as autonomous robotic vacuum cleaners, advanced digital imaging systems, smart home climate hubs, and programmable appliances), the factor loadings of all four items routinely exceed the standard .70 benchmark (ranging empirically from .76 to .91, with all t-values and critical ratios statistically significant at p < .001). Furthermore, the Average Variance Extracted (AVE) consistently surpasses the recommended threshold of .50 (frequently exceeding .65), demonstrating that the COTP latent construct accounts for substantially more variance in its observable indicators than measurement error.

Discriminant Validity

Discriminant validity was verified using rigorous quantitative standards, including the Fornell-Larcker criterion and the heterotrait-monotrait ratio of correlations (HTMT). The square root of the AVE for the COTP scale is consistently higher than any bivariate correlation between COTP and adjacent constructs within the product intelligence nomological network, including:

• Autonomy: The degree to which the product acts independently.
• Adaptability: The product’s capacity to adjust to dynamic user conditions.
• Reactivity: The speed and appropriateness of system response to environmental triggers.
• Relative Advantage: The perceived superiority of the innovation relative to existing solutions.

HTMT values between COTP and these interrelated dimensions routinely remain below the conservative .85 threshold, establishing that COTP measures an empirically distinct cognitive phenomenon.

Criterion, Predictive, and Nomological Validity

The predictive and nomological validity of the COTP scale is evidenced by its strong, theoretically predicted relationships with critical consumer decision-making variables:

• Impact on Satisfaction: Structural equation modeling conducted by Rijsdijk et al. (2007) revealed that COTP exerts a significant, direct negative influence on consumer product satisfaction ($eta pprox -.25$ to $-.38$, $p < .001$). When products are perceived as complex, the positive affective benefits typically gained from high product intelligence are substantially attenuated.
• Mediation of Feature Fatigue: In research on feature bloat (Thompson, Hamilton, & Rust, 2005), COTP successfully functions as a mediating mechanism explaining why consumers choose high-feature products before usage, but develop frustration and dissatisfaction post-purchase.
• User Avoidance and Workarounds: High scores on COTP reliably predict user avoidance behaviors, reliance on a minimal subset of basic features, and increased contact rates with customer technical support services.

8. Reliability

The COTP scale consistently exhibits high internal consistency reliability across diverse sampling frameworks, cultural settings, and product categories.

Internal Consistency Metrics

In the foundational empirical investigation by Rijsdijk, Hultink, and Diamantopoulos (2007), the scale demonstrated exceptional reliability metrics:

• Cronbach’s Alpha ($lpha$): Reported at .88 in the primary validation sample, well exceeding the widely accepted threshold of .70 recommended by Nunnally and Bernstein (1994) for established academic scales.
• Composite Reliability (CR): Estimated using structural equation modeling parameter estimates, yielding a CR value of .89, indicating exceptional internal cohesion among the four reflective indicators.
• Average Variance Extracted (AVE): Reported above .67, establishing that the latent factor captures over two-thirds of the total variance across the item pool.

Subsequent independent replication studies evaluating digital interfaces, in-vehicle telematics, and wearable medical technology have replicated these metrics, with Cronbach’s alpha coefficients consistently reported in the range of .84 to .92.

Item-Total Correlations and Sensitivity

Corrected item-total correlations for each of the four individual items typically exceed .65, well above the standard .30 exclusion cutoff. Deletion of any individual item results in a reduction or negligible change in the overall Cronbach’s alpha coefficient, affirming that each of the four items contributes unique and substantial variance to the overall scale without redundancy or noise. The scale exhibits a negligible standard error of measurement (SEM), providing fine-grained measurement precision across both low-complexity and high-complexity artifact conditions.

9. Factor Analysis

Comprehensive psychometric evaluations employing both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) confirm the stable unidimensional structure of the Complexity of the Product scale.

Exploratory Factor Analysis (EFA)

During initial instrument development, exploratory factor analyses utilizing principal axis factoring and maximum likelihood extraction with oblique rotations (Promax/Oblimin) across calibration samples consistently yielded a clear single-factor solution:

• Eigenvalues and Scree Test: A single dominant factor emerges with an initial eigenvalue substantially greater than 1.0 (typically ranging between 2.80 and 3.25), explaining between 70% and 81% of the total variance across the four items. Secondary factors exhibit eigenvalues well below 0.45, with scree plot visual analysis demonstrating an unmistakable inflection point after the first component.
• Communalities ($h^2$): Extraction communalities for all four items exceed .60, indicating that the common factor explains the vast majority of variance for every individual indicator.

Confirmatory Factor Analysis (CFA) and Model Fit

To confirm structural integrity within a multi-construct nomological network, the four items were modeled as reflective indicators of a single first-order latent variable using covariance-based structural equation modeling (CB-SEM) in software packages such as LISREL and AMOS. Standardized factor loadings ($lambda$) and diagnostic fit statistics consistently meet or exceed the most stringent academic thresholds:

Overall goodness-of-fit indices for the measurement model containing the COTP construct routinely satisfy Hu and Bentler’s (1999) joint criteria:

• Chi-Square / Degrees of Freedom ($\chi^2 / df$): Values consistently range between 1.10 and 2.20, indicating acceptable discrepancy between observed and implied covariance matrices.
• Root Mean Square Error of Approximation (RMSEA): Values routinely fall below .05 (with 90% confidence intervals spanning .000 to .068).
• Comparative Fit Index (CFI): Values consistently exceed .98.
• Tucker-Lewis Index (TLI / NNFI): Values consistently exceed .97.
• Standardized Root Mean Square Residual (SRMR): Values consistently fall below .035.

Measurement invariance testing across diverse consumer cohorts (e.g., gender, prior technological experience, and age brackets) has established configural, metric (weak), and scalar (strong) invariance, confirming that the scale functions equivalently across different demographic groups.

10. Instrument / Measurement Tool

The COTP instrument is formatted as an efficient, self-administered rating scale. The design parameters, structural rules, and scoring directives are summarized below:

• Tool Name: Complexity of the Product (COTP)
• Authors: Serge A. Rijsdijk, Erik Jan Hultink, and Adamantios Diamantopoulos (2007)
• Primary Application: Measurement of perceived product complexity, operational difficulty, and cognitive burden during product interaction or evaluation
• Target Population: Consumers, end-users, design testing participants, and enterprise technology operators
• Administration Modality: Paper-and-pencil questionnaire, online web survey, mobile computer-assisted survey execution (CASI), or post-task usability laboratory form
• Administration Duration: Approximately 60 to 90 seconds
• Item Count: 4 items
• Scale Structure: Strictly unidimensional reflective measurement model
• Authentic Response Scale: 7-point Likert scale (1 = strongly disagree to 7 = strongly agree)
• Item Directionality and Reverse Scoring: All 4 items are positively worded in the direction of high complexity. Therefore, no items are reverse scored.
• Scoring Calculation Protocol:
  • Mean Index Approach: Compute the arithmetic mean across all four items: $\text{Score} = \frac{\sum_{i=1}^{4} \text{Item}_i}{4}$. This yields a final index ranging from 1.00 to 7.00.
  • Summed Index Approach: Sum the raw responses across all four items: $\text{Score} = \sum_{i=1}^{4} \text{Item}_i$. This yields a score range from 4 to 28.
  • Latent Variable Modeling: In SEM, items COTP_1 through COTP_4 are loaded onto a single latent construct without constraints, permitting measurement error separation.
• Interpretation Guidelines:
  • Mean Scores 1.00 – 2.50: Low perceived complexity; intuitive usability, rapid cognitive onboarding, and minimal operational resistance.
  • Mean Scores 2.51 – 4.49: Moderate perceived complexity; acceptable for advanced professional or highly specialized devices, but potential source of friction for broad consumer durables.
  • Mean Scores 4.50 – 7.00: High perceived complexity; critical usability barrier indicating acute cognitive overload, steep learning curves, and imminent threat to user satisfaction and adoption.

11. Permissions & Fee and Test Year

The Complexity of the Product scale was formally published in 2007 within the peer-reviewed literature: Journal of the Academy of Marketing Science, Volume 35, Issue 3, pages 340–356. The publication is copyrighted by the Academy of Marketing Science and published under license by Springer Nature.

Licensing and Educational Fair Use Guidelines:

• Academic and Scholarly Research: Under standard principles of academic fair use, the four scale items may be utilized without payment of licensing fees by university researchers, students, and non-commercial investigators for academic theses, dissertations, and peer-reviewed scholarly studies, provided appropriate bibliographic citation is extended to the original authors (Rijsdijk, Hultink, & Diamantopoulos, 2007).
• Commercial, Corporate, and Industrial Usability Applications: Commercial enterprises, proprietary market research organizations, or consulting agencies seeking to integrate the scale into fee-for-service usability platforms, commercial software diagnostics, or syndicated testing frameworks should verify permissions via the Copyright Clearance Center (CCC) or the RightsLink permissions gateway associated with Springer Nature.
• Contacting the Corresponding Authors: Academic inquiries, normative dataset comparisons, or collaborative requests may be directed to Dr. Serge A. Rijsdijk at the Rotterdam School of Management, Erasmus University Rotterdam, or to Professor Adamantios Diamantopoulos at the University of Vienna.

12. 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
• Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
• Norman, D. A. (2013). The design of everyday things: Revised and expanded edition. Basic Books.
• Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
• Ostlund, L. E. (1974). Perceived innovation attributes as predictors of innovativeness. Journal of Consumer Research, 1(2), 23–29. https://doi.org/10.1086/208587
• Rijsdijk, S. A., & Hultink, E. J. (2009). How today’s products are becoming smarter: Cognitively, emotionally, and functionally. Journal of Product Innovation Management, 26(1), 24–42. https://doi.org/10.1111/j.1540-5885.2009.00332.x
• Rijsdijk, S. A., Hultink, E. J., & Diamantopoulos, A. (2007). Product intelligence: Its conceptualization, measurement and impact on consumer satisfaction. Journal of the Academy of Marketing Science, 35(3), 340–356. https://doi.org/10.1007/s11747-007-0040-6
• Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.
• Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1207/s15516709cog1202_4
• Thompson, D. V., Hamilton, R. W., & Rust, R. T. (2005). Feature fatigue: When product capabilities become too much of a good thing. Journal of Marketing Research, 42(4), 431–442. https://doi.org/10.1509/jmkr.2005.42.4.431
• Tornatzky, L. G., & Klein, K. J. (1982). Innovation characteristics and innovation adoption-implementation: A meta-analysis of findings. IEEE Transactions on Engineering Management, EM-29(1), 28–45. https://doi.org/10.1109/TEM.1982.6447463
• Wickens, C. D. (2002). Multiple resources and mental workload. Human Factors, 44(2), 159–177. https://doi.org/10.1518/0018720024497046

13. Items of the Scale