Customer Knowledge Development

Abstract

The Customer Knowledge Development (CKD) scale is an established psychometric instrument designed to evaluate the extent to which a new product development (NPD) project team employs an ongoing, emergent, and experiential trial-and-error approach to decipher evolving customer preferences. Conceptualized and validated by Joshi and Sharma (2004) in the Journal of Marketing, the instrument operationalizes knowledge acquisition during the prelaunch stages of innovation—specifically idea generation, concept refinement, product development, and product testing. Comprising eight manifest items scored along a 7-point Likert-type scale ranging from 1 (“Strongly disagree”) to 7 (“Strongly agree”), the CKD scale captures three core constituent dimensions of organizational learning: (1) an iterative and ongoing process sustained through repeated rounds of customer feedback, (2) the emergent derivation of project novelty arising from direct customer interaction rather than planned specifications, and (3) an experiential, action-oriented mode of trial-and-error experimentation.

Psychometric evaluations demonstrate high internal consistency (Cronbach’s alpha exceeding .85 across cross-industry samples), robust convergent validity supported by substantial factor loadings in confirmatory factor analytic frameworks, and distinct discriminant validity from related organizational constructs such as market orientation, customer co-creation, and design flexibility. Methodologically, the scale is scored by averaging or summing the eight items to create a composite index of customer knowledge development. The instrument has become a foundational empirical measurement tool across product management, marketing strategy, organizational psychology, and engineering design literature, offering actionable diagnostic utility for project managers seeking to balance deliberate strategic planning with emergent market responsiveness.

Keywords

Customer Knowledge Development, New Product Development, Organizational Learning, Trial-and-Error Learning, Emergent Novelty, Market Orientation, Product Innovation, Iterative Feedback, Absorptive Capacity, Psychometrics, Innovation Management, Agile Development

Authors

The Customer Knowledge Development measurement model was formulated and psychometrically validated by:

• Ashwin W. Joshi, Ph.D. — Professor of Marketing at the Schulich School of Business, York University, Toronto, Canada. His research centers on customer relationship management, inter-firm governance, knowledge acquisition in high-technology ventures, and product innovation capabilities.
• Sanjay Sharma, Ph.D. — Professor of Management and former Dean at the Grossman School of Business, University of Vermont, Burlington, VT, USA. His scholarship investigates organizational learning, environmental strategy, corporate sustainability, and competitive capabilities in dynamic business environments.

Purpose

The fundamental purpose of the Customer Knowledge Development (CKD) scale is to assess how organizational project teams uncover, interpret, and integrate nuanced customer requirements within dynamic, ambiguous, and rapidly shifting market environments. In traditional models of new product development, knowledge acquisition was largely conceptualized as an upfront, linear, and formal market research activity—such as static focus groups or broad-scale surveys conducted at the nascent phase of a project. However, contemporary innovation environments are characterized by high technological uncertainty, volatile customer preferences, and rapid obsolescence, rendering upfront market research insufficient and often misleading.

The CKD instrument was developed to measure an alternative, adaptive paradigm: customer knowledge development as an experiential, emergent, and ongoing organizational learning process. Rather than relying on rigid, predefined project trajectories, successful innovation teams leverage continuous customer contact across all prelaunch product milestones (idea generation, concept formulation, engineering prototyping, and commercial testing). The instrument measures the degree to which an NPD project relies on iterative interaction to generate novel insights that could not have been anticipated through classical forecasting techniques.

In research contexts, the CKD scale allows scholars to investigate how antecedents—such as decentralized cross-functional team autonomy, psychological safety, transformational leadership, and relational collaborative norms—foster an environment conducive to customer-driven experimentation. Furthermore, it enables researchers to assess the direct and mediated effects of emergent learning on ultimate project outcomes, including technical performance, market launch speed, product advantage, and commercial success.

In applied management and consulting, the scale serves as a comprehensive diagnostic audit. Cross-functional teams can employ the instrument to determine whether their innovation processes genuinely embody agile, adaptive principles or whether they remain constrained by bureaucratic, rigid planning. By identifying deficits in ongoing interaction, unexpected insight generation, or experimental testing, organizations can adjust their development protocols to mitigate the risk of launching products misaligned with actual customer demand.

Psychological Construct

The Customer Knowledge Development construct is rooted in the intersection of organizational cognition, behavioral learning theory, and market knowledge acquisition. Joshi and Sharma (2004) define CKD as a project-level capability characterized by three interrelated behavioural dimensions:

1. Iterative and Ongoing Process

Traditional product development relies on episodic touchpoints—such as conducting concept tests and subsequently insulating the development engineering team until formal launch. In contrast, the iterative facet of CKD reflects continuous, repetitive engagement with target users throughout the entire prelaunch cycle. This dimension captures the structural rhythm of the project, wherein each phase of development is informed, evaluated, and refined based on feedback loops. Psychologically, this corresponds to recursive cognitive processing and negative feedback loops within cybernetic systems: the team perpetually contrasts its developing prototype against authentic user reactions, adjusting design variables before cognitive entrenchment sets in.

2. Emergent Novelty

The second facet assesses the locus and genesis of innovative solutions. In planned development regimes, novelty is predetermined by specifications drafted by product architects. The emergent novelty dimension of CKD captures how groundbreaking, unanticipated insights arise organically out of unscripted dialogue with prospective users. It conceptualizes customers not as passive evaluators, but as interactive stimuli who help reveal latent, unarticulated needs. This aligns with cognitive psychology theories of serendipity and heuristics: novel schemata are synthesized at the boundary between user experience and engineering capability, yielding solutions that neither party could have derived independently.

3. Trial-and-Error Experimentation

The third dimension characterizes the learning mode deployed by the team. Drawing upon experiential learning theory, this facet reflects an action-oriented, behavioral epistemology wherein the team acquires knowledge through hands-on experimentation, probing, and rapid prototype testing rather than abstract cognitive modeling or deductive market analysis. The team builds interim artifacts, presents them to the customer, observes failures, and extracts critical insights from operational breakdowns. This trial-and-error cycle mirrors the scientific method of hypothesis generation, empirical testing, and falsification, driving deep institutional learning within the development unit.

Theoretical Framework

The theoretical architecture supporting the CKD scale integrates several prominent paradigms within psychology, management science, and innovation theory:

Organizational Learning Theory and Experiential Learning

The foundational bedrock of CKD is organizational learning theory, particularly the work of Argyris and Schön on double-loop learning, alongside David Kolb’s experiential learning theory. Kolb posits that learning is an active, iterative cycle wherein concrete experiences provide the basis for observations and reflections, which are subsequently distilled into abstract concepts and tested through active experimentation. The CKD construct manifests this cycle at the team level: project members do not operate as detached processors of static market data; rather, they engage in action-oriented, physical trials where knowledge emerges from direct interaction with the customer environment.

Absorptive Capacity and Dynamic Capabilities

The construct is closely tied to the concept of absorptive capacity introduced by Cohen and Levinthal, as well as the dynamic capabilities framework articulated by Teece, Pisano, and Shuen. Absorptive capacity emphasizes an organization’s ability to recognize the value of new external information, assimilate it, and apply it to commercial ends. CKD operationalizes this absorptive mechanism at the micro-foundational, project-specific level. By embedding iterative feedback mechanisms directly into the engineering workflow, the development team enhances its sensitivity to subtle shifts in consumer preferences, ensuring dynamic capability deployment in real time.

The Knowledge-Based View (KBV) and Sensemaking

From the perspective of the knowledge-based view of the firm, idiosyncratic customer knowledge is among the most valuable, rare, inimitable, and non-substitutable (VRIN) strategic assets. Tacit customer preferences—unspoken values, emotional frictions, and contextual constraints—cannot be captured through standardized quantitative surveys. Instead, as Karl Weick’s sensemaking theory illustrates, organizations must act in order to understand: team action provokes an environmental reaction, which in turn structures shared cognitive frames. CKD embodies organizational sensemaking through hands-on iterative probes.

Validity

The measurement properties of the CKD scale have been established across multiple empirical studies using rigorous psychometric standards:

Construct and Convergent Validity

In their validation study, Joshi and Sharma (2004) tested the scale on a diverse sample of new product development project leaders across multiple manufacturing and high-technology industries. A confirmatory factor analysis (CFA) demonstrated that all eight manifest items loaded strongly and significantly onto their designated latent construct, with standardized factor loadings exceeding the recommended .70 threshold (loadings ranged from .72 to .86, all significant at p < .001). The average variance extracted (AVE) exceeded .50, establishing robust convergent validity and confirming that the variance captured by the latent construct is substantially greater than the variance attributable to measurement error.

Discriminant Validity

Discriminant validity was established through nested model comparisons and the Fornell-Larcker criterion. The CKD scale demonstrated empirical distinctiveness from related organizational and project constructs, including:

• Broad Market Orientation: While market orientation reflects firm-wide cultural norms and information dissemination routines, CKD focuses exclusively on emergent, action-based customer knowledge generation at the project level.
• Formal Development Planning: The correlation between CKD and formal planning measures is moderate to low, demonstrating that emergent learning is psychometrically distinguishable from deliberate milestone planning.
• Design Flexibility: Discriminant tests confirmed that while design flexibility represents the engineering capacity to modify physical architectures, CKD captures the antecedent epistemic process of discovering what modifications are warranted.

Criterion-Related and Predictive Validity

Extensive structural equation modeling (SEM) demonstrates the predictive validity of the scale. Scores on the CKD scale correlate significantly with key project performance metrics:

• New Product Advantage: Projects exhibiting high CKD scores demonstrate significantly superior competitive product advantage, as the emergent insights allow the firm to solve latent consumer problems ignored by competitors.
• Overall Product Performance: Longitudinal tracking indicates that CKD positively predicts commercial sales performance, return on investment (ROI), and customer adoption rates, particularly when operating in environments characterized by high technological volatility and market turbulence.

Reliability

The CKD scale exhibits excellent reliability metrics across empirical implementations:

• Internal Consistency: In the original validation study by Joshi and Sharma (2004), the composite reliability of the scale was .89, and the Cronbach’s alpha coefficient was .88. Subsequent independent empirical studies in manufacturing, software development, and consumer electronics have reported alpha coefficients consistently ranging between .84 and .91, well above the standard .70 psychometric benchmark.
• Split-Half and Composite Reliability: Raykov’s rho and composite reliability metrics evaluated across various validation studies regularly surpass .85, indicating that the manifest indicators share substantial shared variance without redundancy.
• Measurement Invariance: Cross-validation studies across differing technological intensities (e.g., incremental vs. radical innovation projects) have demonstrated configural and metric invariance, confirming that respondents across diverse operational contexts interpret the eight items in a psychometrically equivalent manner.

Factor Analysis

The dimensionality of the CKD scale was evaluated through both exploratory factor analysis (EFA) and confirmatory factor analysis (CFA):

Exploratory Factor Analysis (EFA)

Initial principal components analysis with varimax and oblimin rotations on the candidate item pool revealed a dominant single-factor solution accounting for over 56% of total variance, with all eight items exhibiting primary factor loadings exceeding .68 and minimal cross-loadings on subsidiary dimensions.

Confirmatory Factor Analysis (CFA)

In structural CFA models, researchers have modeled the eight items as both a direct unidimensional construct and as a second-order factor comprising three closely correlated first-order subscales (Iterative Feedback [Items 1–3], Emergent Novelty [Items 4–6], and Trial-and-Error Experimentation [Items 7–8]). The unidimensional model displays strong fit statistics across empirical studies, supporting the practice of aggregating the items into a single composite score:

• Chi-Square / Degrees of Freedom Ratio ($\chi^2/df$): 1.68 to 2.15 (well below the conservative threshold of 3.0).
• Comparative Fit Index (CFI): .96 to .98 (exceeding the standard .95 cut-off).
• Tucker-Lewis Index (TLI): .95 to .97.
• Root Mean Square Error of Approximation (RMSEA): .045 to .062 (with 90% confidence intervals staying below .08).
• Standardized Root Mean Square Residual (SRMR): .032 to .044.

Instrument / Measurement Tool

The structural characteristics and administration details of the Customer Knowledge Development instrument are outlined below:

• Instrument Name: Customer Knowledge Development (CKD) Scale
• Construct Measured: The degree to which an NPD project utilizes an iterative, emergent, and experimental trial-and-error process to understand customer preferences across prelaunch stages.
• Instrument Type: Retrospective, project-level self-report questionnaire administered to project managers, cross-functional team leaders, or core innovation members.
• Number of Items: 8 items
• Subscale Breakdown:
  • Iterative and Ongoing Feedback: Items 1, 2, and 3
  • Emergent Novelty: Items 4, 5, and 6
  • Trial-and-Error Learning: Items 7 and 8
• Response Scale: 7-point Likert scale (1 = Strongly disagree, 7 = Strongly agree)
• Scoring Rules: There are no reverse-coded items. The overall CKD score is calculated by taking the arithmetic mean of all 8 items (yielding an index from 1.00 to 7.00) or by computing the sum (ranging from 8 to 56). Higher scores indicate a greater reliance on iterative, emergent, and trial-and-error learning with customers during product development.
• Administration Time: Approximately 3 to 5 minutes.

Permissions & Fee and Test Year

The Customer Knowledge Development scale was published in 2004 in the Journal of Marketing, an academic journal published by the American Marketing Association (AMA). In accordance with standard academic practice, the scale items are available for scholarly, non-commercial research and educational purposes directly within the published literature without requiring licensing fees. Researchers employing the scale are expected to properly cite the seminal validation paper by Joshi and Sharma (2004). For commercial applications, executive training, or corporate consulting platforms, interested parties should contact the primary authors or the American Marketing Association to verify copyright permissions and terms of use.

References

• Argyris, C., & Schön, D. A. (1978). Organizational learning: A theory of action perspective. Addison-Wesley.
• Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128–152. https://doi.org/10.2307/2393553
• 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
• Joshi, A. W., & Sharma, S. (2004). Customer knowledge development: Antecedents and impact on new product performance. Journal of Marketing, 68(4), 47–59. https://doi.org/10.1509/jmkg.68.4.47.42728
• Kolb, D. A. (1984). Experiential learning: Experience as the source of learning and development. Prentice-Hall.
• Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533. Below are the authentic scale items in their original language as published in the standard psychometric validation studies, without modification or translation to preserve instrument validity and reliability: