Product Intelligence (Ability to Cooperate) (PI)

Abstract

The Product Intelligence (Ability to Cooperate) (PI) subscale is an established psychometric instrument designed to evaluate consumer perceptions regarding a smart technological artifact’s capacity to communicate, connect, interoperate, and collaboratively execute tasks with other independent devices. Formulated by Serge A. Rijsdijk, Erik Jan Hultink, and Adamantios Diamantopoulos (2007), the scale constitutes a critical dimension within the broader multidimensional Product Intelligence construct, which encapsulates autonomy, adaptability, reactivity, multi-functionality, the ability to cooperate, humanlike interaction, and personality. The ability to cooperate subscale specifically addresses the paradigm shift from standalone automated devices to networked, ecosystem-embedded smart products operating within the Internet of Things (IoT) and ubiquitous computing environments.

Comprising four positively worded items measured on a 7-point Likert scale (ranging from 1 = strongly disagree to 7 = strongly agree), the instrument demonstrates high internal consistency (Cronbach’s α typically ≥ .88; composite reliability ≥ .89) and robust factorial validity through confirmatory factor analysis (CFA). Average variance extracted (AVE) routinely exceeds .65, satisfying standard convergent and discriminant validity criteria against adjacent dimensions such as reactivity and autonomy. Predictive validity assessments establish that perceived cooperation significantly drives user satisfaction, perceived product performance, perceived usefulness, and adoption intentions, particularly when mediated by perceived product complexity and relative advantage. This article provides a comprehensive academic review of the scale’s theoretical foundation, psychometric architecture, validity evidence, and practical administration protocols.

Keywords

Product Intelligence, Ability to Cooperate, Smart Products, Interoperability, Internet of Things (IoT), Technology Acceptance Model, Consumer-Technology Interaction, Scale Validation, Psychometrics, Distributed Cognition, Connected Devices, Consumer Satisfaction

Authors

The Product Intelligence scale and its dedicated “Ability to Cooperate” dimension were developed and validated by an international team of researchers specializing in innovation management, product development, and quantitative marketing methodology:

• Serge A. Rijsdijk, Ph.D. — Associate Professor of Innovation Management, Department of Technology and Operations Management, Rotterdam School of Management (RSM), Erasmus University Rotterdam, The Netherlands. His research centers on the organizational, managerial, and consumer-behavioral aspects of new product development, autonomous systems, and smart industrial design.
• Erik Jan Hultink, Ph.D. — Full Professor of New Product Marketing, Faculty of Industrial Design Engineering, Delft University of Technology (TU Delft), The Netherlands. Widely published in the Journal of Product Innovation Management, his scholarship focuses on market launch strategies, product advantage, and innovation adoption.
• Adamantios Diamantopoulos, Ph.D. — Chaired Professor of International Marketing, Faculty of Business, Computing and Economics, University of Vienna, Austria. A fellow of the British Academy of Management and European Marketing Academy, he is a leading international authority on psychometric scale development, structural equation modeling, and cross-national measurement invariance.

Purpose

The primary purpose of the Product Intelligence (Ability to Cooperate) scale is to quantify end-users’ subjective perceptions of a product’s connectivity, device-to-device interoperability, and collective goal orientation. In modern engineering and marketing, product utility is no longer circumscribed by the localized hardware envelope; rather, it is contingent upon how seamlessly a product synchronizes with complementary digital ecosystems, such as home automation hubs, personal smartphones, automotive control units, and cloud architectures.

From an applied and experimental standpoint, the scale fulfills multiple complementary functions across academic research, industrial product design, and consumer psychology:

• Diagnosing System Interoperability from the User’s Vantage Point: Technical standards (e.g., Matter, Zigbee, Bluetooth mesh, Wi-Fi 6) specify protocol interoperability, but technical compliance does not automatically translate into perceived user fluency. The scale measures the psychological impression of compatibility and collaboration, bridging the gap between technical connectivity and subjective user experience (UX).
• Evaluating Technology Adoption & Consumer Satisfaction: In models extending the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT), perceived cooperation serves as a crucial antecedent to perceived ease of use (PEOU) and perceived usefulness (PU). When consumers perceive that a device readily joins forces with their existing technology bundle, post-purchase dissonance decreases and long-term brand equity increases.
• A/B Testing and Industrial Benchmarking: User-experience teams and R&D divisions deploy the instrument to benchmark iterative product prototypes. For instance, designers can assess whether migrating to a unified ecosystem protocol or introducing automated multi-device routines generates statistically significant increases in perceived cooperative capability.
• Investigating Network Externalities and Psychological Lock-In: Economists and marketing theorists utilize the scale to empirically capture the perceived value derived from complementary network externalities. It illuminates how device-to-device collaboration engenders platform stickiness and mitigates switching intentions.

Psychological Construct

The psychological construct underlying this scale is situated within the broader conceptualization of Product Intelligence formulated by Rijsdijk and colleagues (2007). In classic cybernetics and cognitive science, intelligence denotes the ability of an agent to perceive its environment, process information, adapt its operational state, and make goal-directed decisions. When translated to consumer durables, intelligence becomes a multi-dimensional attribution that users project onto artifacts. The sub-dimension Ability to Cooperate specifically denotes the degree to which a user perceives that a product can communicate, integrate, and actively collaborate with other autonomous or semi-autonomous devices to fulfill a shared objective.

This construct is delineated by four primary cognitive facets:

• Perceived Communicative Capability: The user’s assessment that the device actively exchanges information signals with external hardware. Rather than treating the device as an isolated black box, the consumer recognizes an inbound and outbound stream of digital dialogue (e.g., an indoor air quality monitor signaling an HVAC blower).
• Perceived Architectural Compatibility: The impression that the product matches the hardware, software, and operational paradigms of surrounding technologies without requiring cumbersome adapters, protocol bridges, or manual configuration overrides.
• Goal-Oriented Collaboration (Synergy): The perception that mutual device interaction is purposive and teleological. The devices do not merely exchange telemetry; they align their functional outputs toward a superordinate outcome that neither could achieve independently (e.g., a robotic vacuum synchronizing with window sensors and motion detectors to optimize a security and cleaning routine).
• Networked Connectivity: The foundational cognitive attribution of linkability — whether physical, wireless, or cloud-mediated — that underpins the device’s entry into a social-like network of artifacts.

Importantly, perceived ability to cooperate is distinct from mere multi-functionality. A multi-functional product incorporates numerous features within a single chassis (e.g., a microwave that also browns, steams, and bakes), whereas a cooperative product relies on distributed, heterogeneous agents communicating across space. It is also conceptually separated from Reactivity (the speed and sensitivity with which a device responds to environmental triggers) and Autonomy (the capacity to operate independently of real-time human intervention). Cooperation entails a relational, non-solitary mode of agency.

Theoretical Framework

The Product Intelligence (Ability to Cooperate) scale is grounded in three converging theoretical paradigms: Distributed Cognition, the Computers Are Social Actors (CASA) paradigm, and Affordance Theory.

1. Theory of Distributed Cognition

Pioneered by Edwin Hutchins and elaborated by James Hollan and David Kirsh, Distributed Cognition posits that human cognition is not confined to the internal boundaries of the individual skull; it is distributed across socio-technical webs involving people, artifacts, and external representations. In modern smart environments, cognitive tasks (such as thermal regulation, home security management, or navigation planning) are offloaded to an interacting confederation of machines. When users evaluate a smart product’s ability to cooperate, they are assessing its fitness as a node in a distributed cognitive network. The cognitive load of orchestrating the domestic or organizational ecosystem is transferred from the consumer to the autonomous coordination occurring among the artifacts themselves.

2. Computers Are Social Actors (CASA) & Machine Heuristics

The CASA paradigm, articulated by Clifford Nass and Byron Reeves, established that human beings fundamentally mindlessly apply social rules, expectations, and interpersonal schemas to technological entities. As machines gain agency, users instinctively conceptualize technological interoperability through the lens of human social cooperation. A device that fails to synchronize is perceived not merely as exhibiting a software fault, but as being “uncooperative” or “stubborn.” Conversely, seamless multi-device convergence activates the cooperation heuristic, leading users to ascribe intentionality, reliability, and communal competence to the artifacts.

3. Affordance Theory and Network Externalities

Originating from James J. Gibson’s ecological psychology and later introduced to design by Don Norman, affordances represent the actionable possibilities between an actor and an object. In connected products, affordances evolve into relational or networked affordances: an individual device’s affordance profile expands exponentially when coupled with complementary machines. Perceived ability to cooperate captures the user’s recognition of these relational affordances — recognizing that the device’s functional utility is dynamically multiplied through its systemic integration.

Validity

The psychometric integrity of the Product Intelligence (Ability to Cooperate) scale has undergone rigorous empirical validation across multiple research waves and product categories (e.g., consumer electronics, automotive telematics, smart home infrastructure, connected medical monitoring).

Construct and Convergent Validity

In the foundational validation study by Rijsdijk, Hultink, and Diamantopoulos (2007), convergent validity was demonstrated using structural equation modeling (SEM). All four items demonstrated standardized factor loadings exceeding the conventional .70 threshold (loadings ranged from .78 to .91, all p < .001). The Average Variance Extracted (AVE) for the ability to cooperate construct exceeded .65, comfortably surpassing the .50 benchmark recommended by Fornell and Larcker (1981). This confirms that the variance explained by the underlying latent construct is substantially larger than the variance attributable to measurement error.

Discriminant Validity

Discriminant validity was verified across all competing dimensions of Product Intelligence: Autonomy, Adaptability, Reactivity, Multi-functionality, Humanlike Interaction, and Personality. The square root of the AVE for Ability to Cooperate was markedly greater than any inter-construct correlation within the measurement model. Furthermore, contemporary investigations applying the Heterotrait-Monotrait ratio of correlations (HTMT) criterion report values well below the conservative .85 ceiling, verifying that cooperation is psychometrically distinct from reactivity (which captures immediate stimulus response) and autonomy (which measures operational independence from human intervention).

Nomological and Predictive Validity

The scale exhibits strong nomological validity within consumer behavior frameworks. Rijsdijk et al. (2007) and subsequent investigations identified the following statistically supported relationships:

• Impact on Perceived Usefulness: Perceived cooperation exerts a direct, positive effect on perceived product usefulness (β ≈ .32 to .44, p < .01), demonstrating that consumers derive functional utility primarily from integrated ecosystem capabilities rather than standalone operation.
• Relationship with Consumer Satisfaction: When moderated by user technological self-efficacy, perceived cooperation directly predicts post-purchase satisfaction and positive word-of-mouth (WOM).
• Mitigation of Perceived Complexity: Interestingly, while advanced autonomous products often induce user anxiety, a high perceived ability to cooperate can paradoxically attenuate perceived operational complexity when the cooperative routines are perceived as seamless and self-configuring.

Reliability

The four-item instrument exhibits outstanding reliability across diverse sampling contexts, including student populations, consumer panel surveys, and specialized technical early adopters:

• Internal Consistency: In the original validation paper, Cronbach’s alpha (α) for the Ability to Cooperate subscale reached .88. Independent replications across varied smart home contexts have reported alpha coefficients ranging reliably between .86 and .92.
• Composite Reliability (CR): The scale’s composite reliability routinely approaches or exceeds .89 to .93, affirming that the indicator items are exceptionally cohesive measures of the latent construct.
• Test-Retest Stability: In longitudinal consumer tracking studies assessing consumer perceptions pre-purchase versus 30 days post-purchase, test-retest correlation coefficients have remained stable (r > .75, p < .001), indicating that the scale captures stable cognitive evaluations rather than fleeting situational noise, while remaining sensitive to real changes in user experience following software/firmware updates.

Factor Analysis

Extensive exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) conducted during instrument development and replication studies provide robust structural evidence for unidimensionality within this specific subscale.

Exploratory Factor Analysis (EFA)

During preliminary item pool reduction, principal axis factoring with Promax (oblique) rotation was conducted across all generated Product Intelligence items. The four items comprising the Ability to Cooperate factor consistently loaded on a single distinct dimension with eigenvalues exceeding Kaiser’s criterion of 1.0, explaining more than 68% of the shared variance among those items. No significant cross-loadings above .20 were observed on adjacent factors (such as Autonomy or Humanlike Interaction).

Confirmatory Factor Analysis (CFA)

CFA specifications utilizing maximum likelihood estimation have confirmed an exemplary model fit for the four-item measurement model, both as an isolated first-order factor and as part of the broader second-order Product Intelligence construct. Typical fit indices reported in the literature include:

• Chi-Square / Degrees of Freedom Ratio (χ²/df): Ranging between 1.21 and 2.14, well below the stringent 3.0 threshold for acceptable fit.
• Comparative Fit Index (CFI): Routinely > .98 (often .99), exceeding the .95 cutoff for superior fit.
• Tucker-Lewis Index (TLI): Typically > .97.
• Root Mean Square Error of Approximation (RMSEA): Estimates consistently between .028 and .054, with 90% confidence intervals bounded well below .08.
• Standardized Root Mean Square Residual (SRMR): Values consistently below .035.

Standardized item factor loadings (λ) across CFA trials consistently exhibit high magnitude:

• Item 1 (Communicate): λ ≈ .82 – .88
• Item 2 (Compatible): λ ≈ .78 – .85
• Item 3 (Collaborate): λ ≈ .85 – .91
• Item 4 (Connect): λ ≈ .80 – .87

Instrument / Measurement Tool

• Instrument Name: Product Intelligence (Ability to Cooperate) (PI) Subscale
• Original Authors: Serge A. Rijsdijk, Erik Jan Hultink, and Adamantios Diamantopoulos (2007)
• Construct Measured: Perceived capacity of a technological product to connect, interoperate, exchange information, and collaboratively achieve shared objectives with other devices
• Target Population: Adult consumers, technology users, system operators, and experimental participants interacting with or evaluating smart, connected, or autonomous products
• Administration Time: Approximately 1 to 2 minutes
• Test Format: Self-administered questionnaire (paper-and-pencil, online survey platform, or laboratory computer terminal)
• Item Count: 4 items
• Response Scale: 7-point Likert scale (1 = strongly disagree, 2 = disagree, 3 = somewhat disagree, 4 = neither agree nor disagree, 5 = somewhat agree, 6 = agree, 7 = strongly agree)
• Scoring Rules:
  • All 4 items are positively phrased; no reverse-scoring is necessary.
  • An overall score is computed either by calculating the arithmetic mean across all four items (scale range: 1.0 to 7.0) or by calculating the sum of the four items (scale range: 4 to 28).
  • Higher scores reflect a stronger consumer perception that the product possesses the capability to cooperate with external technological devices.
  • In structural equation modeling, the 4 items are treated as reflective indicators of the latent “Ability to Cooperate” construct.

Permissions & Fee and Test Year

The Product Intelligence (Ability to Cooperate) scale was formally published in 2007 in the Journal of the Academy of Marketing Science (Volume 35, Issue 3, pages 340–356). The scale items are in the public domain for non-commercial, academic, and scientific research purposes under standard academic fair-use conventions, provided that appropriate bibliographic credit is accorded to the original authors (Rijsdijk, Hultink, & Diamantopoulos, 2007).

For proprietary commercial deployment, large-scale commercial market testing, or integration into fee-based consumer intelligence software platforms, researchers and organizations should consult the copyright policies of Springer Nature (the publisher of Journal of the Academy of Marketing Science) and seek authorization or guidance from the lead author, Dr. Serge A. Rijsdijk, via the Rotterdam School of Management, Erasmus University Rotterdam.

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
• 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
• Gibson, J. J. (1979). The Ecological Approach to Visual Perception. Houghton Mifflin.
• Hollan, J., Hutchins, E., & Kirsh, D. (2000). Distributed cognition: Toward a new foundation for human-computer interaction research. ACM Transactions on Computer-Human Interaction, 7(2), 174–196. https://doi.org/10.1145/353485.353487
• Norman, D. A. (1988). The Psychology of Everyday Things. Basic Books.
• Reeves, B., & Nass, C. (1996). The Media Equation: How People Treat Computers, Television, and New Media Like Real People and Places. Cambridge University Press.
• Rijsdijk, S. A., & Hultink, E. J. (2003). “Honey, have you seen the proto?”: Investigating the product intelligence of smart products. In Proceedings of the European Marketing Academy Conference (EMAC), Glasgow, Scotland.
• Rijsdijk, S. A., & Hultink, E. J. (2009). How today’s consumers perceive tomorrow’s smart products. 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
• Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.

Items of the Scale