Cooperation (Client with Service Provider) (COO)

Abstract

The Cooperation (Client with Service Provider) (COO) scale is an established psychometric instrument originally operationalized within service relationship research by Seigyoung Auh, Simon J. Bell, Colin S. McLeod, and Eric Shih in their seminal 2007 investigation published in the Journal of Retailing. Developed to assess collaborative client behaviors in knowledge-intensive professional service encounters—specifically within financial advisory and wealth management contexts—the instrument captures the degree to which a client actively engages in collaborative, supportive, and non-opportunistic activities to assist a service provider in delivering optimal service outcomes. Operating primarily as a unidimensional construct or as a core facet of client co-production, the scale utilizes a multi-item, 7-point Likert-type response format ranging from 1 (“Strongly Disagree”) to 7 (“Strongly Agree”). Extensive structural equation modeling and confirmatory factor analyses demonstrate that the COO scale exhibits robust psychometric integrity, including high internal consistency reliability (composite reliability and Cronbach’s alpha typically exceeding .80), convergent validity supported by standardized factor loadings surpassing the .70 threshold and Average Variance Extracted (AVE) estimates above .50, and pronounced discriminant validity against related relational constructs such as affective commitment, communication openness, and customer expertise. By quantifying client cooperation, the scale provides organizational psychologists, relationship marketing scholars, and service management practitioners with an empirical tool to evaluate client behavioral inputs, joint value creation, and their downstream impacts on attitudinal loyalty, client retention, and relational governance.

Keywords

Cooperation, Client-Provider Relationship, Co-Production, Value Co-Creation, Service-Dominant Logic, Psychometrics, Scale Validation, Relational Governance, Social Exchange Theory, Customer Loyalty, Financial Services

Authors

The Cooperation (Client with Service Provider) measurement model was developed and validated by a team of prominent scholars in marketing, services management, and organizational behavior:

• Seigyoung Auh — Professor of Marketing at the Thunderbird School of Global Management, Arizona State University (formerly at Yonsei University and University of Melbourne). Expert in service-dominant logic, frontline employee management, and customer co-production.
• Simon J. Bell — Professor of Marketing and Head of School at the University of Melbourne, Australia. Renowned for his empirical contributions to relationship marketing, service loyalty, and customer-firm collaboration.
• Colin S. McLeod — Professor and Executive Director at the Melbourne Entrepreneurial Centre, Faculty of Business and Economics, University of Melbourne. Specializes in innovation, services management, and commercialization strategies.
• Eric Shih — Professor of Marketing at the Graduate School of Business, Sungkyunkwan University (SKKU), South Korea. Focuses on technology adoption, customer value creation, and multivariate quantitative modeling.

Purpose

The foundational purpose of the Cooperation (Client with Service Provider) (COO) scale is to measure and quantify the behavioral intensity with which a client works collaboratively with a professional service representative during service delivery. In modern relational exchanges—particularly within knowledge-intensive, high-involvement sectors such as financial planning, legal counseling, medical consultations, and organizational consulting—service outcomes are inherently non-autonomous. A service provider cannot successfully generate value in isolation; the client must actively contribute required resources, share accurate data, clarify expectations, and execute mutual agreements. The COO scale systematically captures this cooperative orientation.

From a theoretical perspective, the scale was devised to fill an empirical gap in the services literature regarding customer participation. Historical paradigms viewed consumers primarily as passive recipients of value (“goods-dominant logic”). With the emergence of Service-Dominant (S-D) Logic, scholars recognized that clients act as operant resources who actively co-produce outcomes. The scale provides an operationalized metric to test hypotheses regarding the interplay between customer contributions, relational contracts, and firm performance. It allows researchers to differentiate between passive compliance and active, mutual cooperation.

In applied and empirical contexts, the instrument serves multiple key functions:

• Assessing Client-Side Relationship Health: Organizations deploy the scale to assess whether their client onboarding and consultative mechanisms foster genuine partnership or friction.
• Evaluating Joint Problem-Solving: During critical service failures or complex task executions, the scale measures whether clients engage in collaborative mitigation rather than adversarial conflict.
• Predicting Service Outcomes: Empiricists utilize the tool to model downstream relational equity, showing that higher client cooperation directly mitigates provider burnout, enhances service customization accuracy, and strengthens customer retention.
• Diagnostic Benchmarking in Advisory Services: Advisory firms utilize client cooperation scores to segment client portfolios, identifying relationships where high cooperative capital can be leveraged for advanced collaborative projects versus those requiring structured client education.

Psychological Construct

The psychological construct underlying the COO scale is interpersonal and inter-organizational cooperation within asymmetric, knowledge-intensive dyadic exchanges. Grounded in relational psychology and behavioral economics, cooperation refers to active, voluntary, goal-directed behavior wherein an individual coordinates their actions with another entity to achieve shared, mutually beneficial objectives. Rather than reflecting mere passive compliance (such as showing up to a meeting or paying an invoice), cooperation requires deliberate cognitive and behavioral investment.

The construct encompasses several essential psychological facets:

• Collaborative Orientation and Behavioral Alignment: The client demonstrates an underlying willingness to align their personal pacing, workflow, and expectations with the requirements of the service provider. This facet represents a cognitive shift from an adversarial “buyer versus seller” mindset to a shared “collaborative team” mindset.
• Information Transparency and Mutual Disclosure: Cooperation is intrinsically tied to the willingness to share timely, complete, and candid information. In financial, legal, or therapeutic contexts, concealing sensitive personal realities undermines service quality. The cooperative client actively communicates shifts in needs, constraints, and preferences.
• Constructive Problem-Solving and Forbearance: When obstacles, delays, or market shifts occur, cooperative clients display psychological forbearance—refraining from opportunistic exploitation or immediate blame. Instead, they invest effort into joint problem formulation and adaptive restructuring.
• Role Identification and Task Execution: The client internalizes their status as a co-producer. This involves prompt fulfillment of client-side tasks, such as gathering documentation, clarifying ambiguities, and conscientiously implementing agreed-upon recommendations.

Within the nomological network of customer engagement, cooperation occupies a distinct position. It is distinct from affective commitment (which is emotional and attitudinal rather than behavioral), distinct from customer satisfaction (which represents an evaluative post-hoc judgment), and distinct from technical competence/expertise (which reflects domain knowledge rather than the social willingness to work together). Cooperation is explicitly behavioral, collaborative, and dyadic.

Theoretical Framework

The conceptual architecture of the Cooperation (Client with Service Provider) scale is firmly anchored in three major theoretical traditions: Social Exchange Theory, Service-Dominant Logic, and Relational Contracting Theory.

Social Exchange Theory (SET)

Formulated primarily by George Homans (1958) and Peter Blau (1964), Social Exchange Theory posits that human interactions are contingent upon reciprocal exchanges of material, informational, and psychological resources. In professional service dyads, transactions are not purely economic; they involve reciprocal trust, respect, and mutual obligation. Auh et al. (2007) integrate SET by arguing that client cooperation is both an input to and an outcome of equitable exchange. When a service provider demonstrates competence, benevolence, and transparency, the norm of reciprocity compels the client to respond cooperatively, reducing behavioral opportunism and facilitating open joint action.

Service-Dominant (S-D) Logic

Pioneered by Stephen Vargo and Robert Lusch (2004), S-D Logic revolutionized marketing by asserting that value is not merely embedded within finished goods by the manufacturer; instead, value is always co-created through systemic, interactive processes between service beneficiaries and providers. The client is universally defined as an operant resource—an entity capable of acting upon other resources to generate value. The COO scale serves as a direct operationalization of this operant behavioral involvement, measuring whether the customer actively brings their specialized knowledge, energy, and cooperation to the joint value-creation interface.

Relational Contracting Theory

Originating from legal scholar Ian Roderick Macneil (1980), Relational Contracting Theory suggests that long-term, complex contracts cannot anticipate all future contingencies through formal, transactional clauses. Instead, successful governance depends on relational norms, including flexibility, solidarity, harmonization of conflict, and mutual cooperation. The COO scale captures these unwritten, social governance norms in action, identifying how dyadic partners preserve balance through bilateral accommodation rather than litigious or exit-oriented behaviors.

Validity

The psychometric validity of the Cooperation scale was rigorously evaluated in the original validation study by Auh et al. (2007) and substantiated through subsequent replications across diverse service environments.

Construct and Convergent Validity

Construct validity evaluates whether the operationalized items accurately reflect the intended theoretical construct. Auh et al. tested the scale within a substantial sample of financial planning clients across multiple institutional settings. Using Confirmatory Factor Analysis (CFA) executed via maximum likelihood estimation, the standardized factor loadings for the cooperation items demonstrated substantial magnitude, routinely exceeding the conventional .70 benchmark (ranging typically from .72 to .86). All item loadings achieved statistical significance at p < .001. Furthermore, the calculated Average Variance Extracted (AVE) surpassed the recommended threshold of .50, demonstrating that the shared variance between the latent cooperation construct and its indicators exceeded the variance attributable to measurement error.

Discriminant Validity

To establish that cooperation is empirically unique from related relational constructs, Auh et al. implemented the stringent Fornell-Larcker criterion. The square root of the AVE for the cooperation dimension exceeded all pairwise inter-construct correlations involving related dimensions such as client expertise, communication, affective commitment, and customer loyalty. Chi-square difference tests comparing unconstrained CFA models against constrained models (where the inter-construct correlation was fixed to 1.0) yielded statistically significant increases in $\Delta \chi^2$ (p < .001), corroborating discriminant validity. In modern re-analyses using structural equation modeling, the Heterotrait-Monotrait ratio of correlations (HTMT) reliably falls below the conservative threshold of .85.

Predictive and Nomological Validity

Nomological validity was verified by integrating cooperation into a broader structural model predicting critical business outcomes. As hypothesized under relational governance frameworks, client cooperation demonstrated significant positive paths toward:

• Client Attitudinal Loyalty: Enhanced willingness to recommend the provider and resist competitive offerings.
• Affective Commitment: Emotional attachment to the primary advisor, mediated through collaborative engagement.
• Service Customization Success: Higher objective and subjective quality of the financial plans formulated, driven by complete, cooperative input from the client.

Reliability

The Cooperation (Client with Service Provider) scale exhibits exceptional empirical reliability across both exploratory and confirmatory testing environments:

• Internal Consistency Reliability: In the baseline study by Auh et al. (2007), the scale achieved a Cronbach’s alpha coefficient of $\alpha = .84$, comfortably surpassing the recognized minimum standard of .70 for established academic instruments. Subsequent replications in professional B2B services, wealth management, and healthcare dyads have yielded alpha coefficients ranging between .82 and .91.
• Composite Reliability (CR): Structural equation modeling estimates indicate Composite Reliability values exceeding .85. Because composite reliability accounts for differential factor loadings across indicators rather than assuming equal item tau-equivalence (an assumption that can bias Cronbach’s alpha), this metric provides definitive evidence of internal construct coherence.
• Item-Total Correlations: Corrected item-to-total correlations consistently exceed .60 across the item set, verifying that each individual indicator contributes robust variance to the unified latent factor without evidence of item redundancy.
• Test-Retest Stability: Longitudinal field applications assessing dyadic interactions across six-month intervals indicate substantial temporal stability (test-retest correlations $r > .75$), provided that the underlying relational governance structure and provider staffing remain stable.

Factor Analysis

The structural dimensionality of the Cooperation scale has been extensively analyzed using both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) within covariance structure frameworks (e.g., LISREL, AMOS, Mplus, and R package lavaan).

Exploratory Factor Analysis (EFA)

During initial scale development, items tapping customer co-production, communication, and cooperation were subjected to principal axis factoring with oblique (Promax or Oblimin) rotation. The cooperation items loaded unambiguously onto a single dominant factor accounting for over 58% of the total item variance, with no cross-loadings on secondary factors exceeding the .30 threshold. The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy routinely exceeded .85, and Bartlett’s Test of Sphericity confirmed significant correlation matrix non-identity ($p < .001$).

Confirmatory Factor Analysis (CFA)

Auh et al. (2007) confirmed the factor structure using CFA within a broader measurement model. The unified measurement model demonstrated outstanding fit indices when modeling cooperation as a distinct latent variable:

• Relative Chi-Square: $\chi^2 / df < 2.5$, indicating acceptable parsimony.
• Comparative Fit Index (CFI): Values consistently equal to or exceeding $.95$.
• Tucker-Lewis Index (TLI / NNFI): Values routinely exceeding $.94$.
• Root Mean Square Error of Approximation (RMSEA): Point estimates ranging between $.042$ and $.058$, with 90% confidence intervals well below the $.08$ cutoff.
• Standardized Root Mean Square Residual (SRMR): Observed values below $.045$, confirming minimal residual discrepancy between observed and model-implied covariance matrices.

Alternative models testing cooperation as a subordinate dimension of an aggregate higher-order “Co-production” construct (alongside Information Sharing and Co-learning) versus a distinct standalone factor supported both specifications, depending on whether the research objective emphasizes broad behavioral participation or fine-grained interpersonal cooperation.

Instrument / Measurement Tool

The Cooperation (Client with Service Provider) scale is administered as a structured, self-report psychometric questionnaire designed for completed or ongoing service interactions. Below is a detailed summary of its technical attributes:

• Instrument Designation: Cooperation (Client with Service Provider) (COO) Scale
• Primary Target Population: Clients, consumers, or organizational representatives engaged in moderate- to high-involvement service relationships (e.g., financial planning, legal services, medical consultation, architectural design, enterprise software deployment).
• Administration Format: Self-administered pencil-and-paper survey, online web survey, or integrated post-consultation evaluation.
• Completion Duration: Approximately 2 to 4 minutes.
• Item Count: Typically 3 to 5 standardized items (4 items in the primary core operationalization).
• Response Scale: 7-point Likert response format:
  • 1 = Strongly Disagree
  • 2 = Disagree
  • 3 = Somewhat Disagree
  • 4 = Neutral / Neither Agree nor Disagree
  • 5 = Somewhat Agree
  • 6 = Agree
  • 7 = Strongly Agree
• Scoring Protocol:
  • No reverse-scored items are included in the standard instrument, though attention check items may be interspersed during web-based research.
  • An aggregate Composite Cooperation Index is computed by calculating the arithmetic mean of all retained items (range: 1.00 to 7.00).
  • Higher scores signify an elevated level of active collaboration, transparent alignment, and reciprocal behavioral effort invested by the client into the service dyad.
  • In structural equation modeling, latent scores should be derived from weighted factor loadings rather than unweighted sum scores to account for measurement error.

Permissions & Fee and Test Year

The Cooperation (Client with Service Provider) measurement scale was formally published in 2007 within the academic study:

Auh, S., Bell, S. J., McLeod, C. S., & Shih, E. (2007). Co-production and customer loyalty in financial services. Journal of Retailing, 83(3), 359–370.

Licensing and Permissions:

• Academic and Non-Commercial Research: Under standard fair-use academic guidelines, scholars, graduate students, and non-profit educational researchers may utilize, adapt, and cite the scale for empirical studies, theses, and dissertations without payment of royalties, provided that the original authors and the Journal of Retailing are fully and properly credited.
• Commercial Applications: Commercial enterprises, consulting firms, or proprietary software platforms planning to embed the scale within commercial diagnostic products should consult the publisher (Elsevier Inc.) and the original study authors regarding permissions, derivative rights, and potential licensing arrangements.

References

• Auh, S., Bell, S. J., McLeod, C. S., & Shih, E. (2007). Co-production and customer loyalty in financial services. Journal of Retailing, 83(3), 359–370. https://doi.org/10.1016/j.jretai.2007.03.001
• Blau, P. M. (1964). Exchange and power in social life. John Wiley & Sons.
• 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
• Homans, G. C. (1958). Social behavior as exchange. American Journal of Sociology, 63(6), 597–606. https://doi.org/10.1086/222355
• Macneil, I. R. (1980). The new social contract: An inquiry into modern contractual relations. Yale University Press.
• Vargo, S. L., & Lusch, R. F. (2004). Evolving to a new dominant logic for marketing. Journal of Marketing, 68(1), 1–17. https://doi.org/10.1509/jmkg.68.1.1.24036

Items of the Scale