Effects of Relational Knowledge Emphasis on New Product Development Strategy–Model

1. Abstract

The Effects of Relational Knowledge Emphasis on New Product Development Strategy Model is an advanced organizational psychometric and managerial assessment instrument formulated by Wang et al. (2023). Developed to evaluate how business-to-business (B2B) enterprises systematically leverage internal and inter-firm cognitive resources, the model operationalizes the core theoretical construct of relational knowledge emphasis and explicates its downstream strategic impacts on new product development (NPD) trajectories. Grounded in the synthesis of relational exchange theory, the knowledge-based view of the firm, and organizational ambidexterity literature, the instrument captures the multi-faceted nature of organizational knowledge repositories and behavioral innovation strategies. The measurement battery contains 24 operational items distributed across validated multi-dimensional subscales: relational knowledge stores (subdivided into interactional knowledge, functional knowledge, and market knowledge), new product development strategies (specifically differentiating technological exploration and technological exploitation), and boundary conditions comprising contextual market dynamics (technology turbulence) and organizational governance (firm state ownership). Empirical items within the model were methodologically synthesized and adapted from foundational psychometric frameworks established by Johnson et al. (2004), Zhou and Wu (2010), and Jaworski and Kohli (1993). Responses across cognitive and behavioral operational items are recorded primarily via 7-point Likert-type scales, complemented by categorical ordinal indices for enterprise governance structures. Psychometric evaluation of the instrument in enterprise-level organizational surveys demonstrates robust construct validity, high composite reliability ranging from 0.73 to 0.87, and satisfactory structural model fit through confirmatory factor analysis (CFA: χ² = 397.49, df = 215, CFI = 0.91, TLI = 0.90, RMSEA = 0.07). The instrument delivers an empirical apparatus for organizational scientists, industrial psychologists, and enterprise strategists seeking to model how collective interpersonal, cross-functional, and competitive inter-organizational knowledge structures dynamically govern ambidextrous innovation capabilities in emerging and turbulent commercial environments.

2. Keywords

Relational Knowledge Emphasis, New Product Development Strategy, Knowledge-Based View, Organizational Ambidexterity, Technological Exploration, Technological Exploitation, Interactional Knowledge, Functional Knowledge, Market Knowledge, Technology Turbulence, State Ownership, Business-to-Business Marketing, Psychometrics, Structural Equation Modeling, Industrial Psychology

3. Authors

The scale and structural model were originated and validated by an international research team of organizational behavior, marketing science, and strategic management scholars:

• Xinchun Wang — John Chambers College of Business and Economics, West Virginia University, Morgantown, WV, United States. Email: [email protected]
• Ruiqi Wei — Emlyon Business School, Écully, France. Email: [email protected]
• Yuerong Liu — Manning School of Business, University of Massachusetts Lowell, Lowell, MA, United States. Email: [email protected]
• Hui Xia (Corresponding Author) — School of Business, Shandong University, Weihai, Shandong, China. Email: [email protected]
• Yanhui Zhao — College of Business Administration, University of Nebraska at Omaha, Omaha, NE, United States. Email: [email protected]

4. Purpose

The fundamental purpose of the Effects of Relational Knowledge Emphasis on New Product Development Strategy Model is to diagnose, quantify, and explain how enterprise orientation toward relational knowledge stores influences strategic postures in technological innovation. In modern industrial ecosystems, manufacturing and technical business-to-business organizations rarely innovate in cognitive isolation; instead, their capability to invent new products or refine existing product architectures depends upon the nature, breadth, and depth of the knowledge accumulated through sustained relationships with primary external stakeholders, including industrial buyers, supply chain partners, and competitive market actors.

From a theoretical standpoint, this measurement system resolves a persistent tension in the strategic management and organizational psychology literature regarding how specialized cognitive repositories drive strategic orientation. While conventional models often aggregate all organizational knowledge into generic measures of absorptive capacity or technological assets, this instrument purposefully differentiates between three distinct categories of relational knowledge stores: interactional knowledge (the cognitive scripts, protocols, and relational competencies required to coordinate, negotiate, and align with external entities), functional knowledge (the operational, operationalized, and process-oriented expertise co-developed with inter-firm partners), and market knowledge (the analytical and regulatory comprehension of buyer ecosystems, institutional constraints, and competitor behaviors). By measuring these dimensions discretely, the model allows researchers to examine how emphasizing one knowledge store over another shifts an enterprise’s strategic allocation toward either exploitative innovation (refining existing technologies) or exploratory innovation (discovering breakthrough technical paradigms).

From an applied organizational and psychometric perspective, the instrument serves multiple vital functions:

• Empirical Research and Hypotheses Testing: Provides scholars with a standardized, psychometrically validated tool to evaluate structural models connecting inter-firm learning to strategic outputs, organizational agility, and competitive performance across emerging and established industrial contexts.
• Diagnostic Benchmarking for Enterprise Leadership: Enables chief technology officers, R&D directors, and strategic planning executives to evaluate organizational competence profiles. By calculating relative scores across interactional, functional, and market knowledge, executive teams can identify asymmetric dependencies, cognitive blind spots, and misalignments between current relational assets and desired innovation postures.
• Moderated Contingency Analysis: Facilitates rigorous contextual modeling by embedding boundary measures such as technology turbulence (the rate of technological obsolescence and disruption in the operational sector) and state ownership (institutional governance configurations). This allows researchers and enterprise consultants to discern how institutional pressure and environmental volatility amplify or suppress the strategic utility of relational knowledge repositories.

Ultimately, the instrument operationalizes the psychological and behavioral transition from static information gathering to active strategic alignment, capturing how the subjective value that an organizational culture assigns to relational processes dictates its exploratory versus exploitative trajectory in product development.

5. Psychological Construct

The scale operationalizes a multidimensional system composed of distinct cognitive, behavioral, and environmental constructs. At the structural epicenter lies the concept of Relational Knowledge Emphasis, conceptualized as an enterprise’s strategic focus, valuation, and systematic prioritization of knowledge embedded within inter-organizational dyads and supplier-buyer networks relative to purely internal knowledge. To operationalize this overarching orientation, the scale deconstructs organizational knowledge stores and strategic responses into several foundational constructs:

1. Knowledge Stores (Relational Cognitive Dimensions)

Adapted from the relational capital conceptualizations of Johnson et al. (2004), knowledge stores represent institutionalized informational and procedural memory derived from dyadic exchanges. These are decomposed into three dimensions:

• Interactional Knowledge: This construct captures an enterprise’s procedural and behavioral competence in managing the human, communicative, and diplomatic interfaces of business relationships. It manifests in internalized routines for bilateral negotiation, joint conflict resolution, cooperative program implementation, and structural alliance management. An enterprise high in interactional knowledge possesses sophisticated interpersonal and inter-firm socio-emotional capital, enabling seamless boundary-spanning communication and dispute mitigation when relational friction occurs.
• Functional Knowledge: This operational dimension reflects deep, process-oriented technical capabilities held jointly with partners. It encompasses mutual cost-reduction engineering, integrated manufacturing protocols, collaborative product design, shared quality management systems, and just-in-time (JIT) logistic integration. High functional knowledge signifies that an enterprise has deeply interwoven its physical and operational workflows with its external network, enabling joint operational problem-solving.
• Market Knowledge: This dimension taps into institutional and competitive environmental awareness gained through relational exposure. It assesses the depth of the firm’s grasp of statutory frameworks, changing commercial procurement conditions, regulatory mandates governing buyer-supplier exchanges, and the procurement strategies executed by competitor networks.

2. New Product Development (NPD) Strategy Constructs

Rooted in the organizational ambidexterity framework operationalized by Zhou and Wu (2010), this part of the instrument evaluates the strategic trajectory of innovation efforts, bifurcating organizational routines into exploration and exploitation:

• Exploration Strategy: Measures the extent to which an enterprise pursues variance-seeking, radical, and generative learning in its technological development. It reflects managerial commitment to acquiring manufacturing technologies and design skills entirely novel to the firm, developing product concepts previously unprecedented in the industry, acquiring unfamiliar managerial paradigms for innovation, and training R&D personnel in non-contiguous disciplinary areas where the firm holds no prior baseline experience.
• Exploitation Strategy: Measures efficiency-seeking, incremental, and convergent behavior in new product creation. This construct assesses the extent to which an enterprise upgrades, deepens, and leverages existing technological architectures. It captures investments focused on exploiting mature technical platforms, executing local problem searches that reside adjacent to familiar solutions, and incrementally improving product lines where the firm already possesses deep historical expertise.

3. Environmental and Governance Boundary Constructs

The scale integrates two critical moderators to anchor behavioral choices in objective reality:

• Technology Turbulence: Operationalized via Jaworski and Kohli’s (1993) environmental framework, this construct reflects the perceived volatility, unpredictability, and velocity of technological transformation within the firm’s operating sector, tracking the emergence of technological breakthroughs and the rapid obsolescence of baseline techniques.
• State Ownership: A structural governance variable measuring the degree of governmental equity participation in the enterprise, which theoretically introduces socio-political objectives, bureaucratic constraints, and asymmetric resource endowments that systematically shape how risk-laden innovation choices are made.

6. Theoretical Framework

The measurement instrument is anchored in the theoretical integration of three prominent frameworks within industrial psychology, organizational sociology, and strategic management: the Knowledge-Based View (KBV) of the Firm, Relational Exchange Theory, and the Theory of Organizational Ambidexterity.

The Knowledge-Based View (KBV) and Relational Capital

The Knowledge-Based View, pioneered by theorists such as Grant (1996) and Kogut and Zander (1992), posits that an enterprise’s competitive advantage and strategic posture stem primarily from its capacity to accumulate, integrate, and deploy tacit and explicit knowledge assets. The instrument expands this perspective by departing from the traditional inward-looking bias of KBV, incorporating principles of Relational Exchange Theory (Macneil, 1980; Dyer & Singh, 1998). It maintains that the most economically significant knowledge resides not merely within individual cognitive boundaries or isolated corporate R&D laboratories, but rather within dyadic relationships and inter-firm routines.

By conceptualizing knowledge as multi-layered (interactional, functional, and market-oriented), the scale treats knowledge stores as social artifacts generated through sustained joint action. Interactional knowledge provides the relational governance structures and psychological safety necessary to overcome opportunism; functional knowledge constitutes the shared technological grammar required for co-specialization; and market knowledge forms the external cognitive radar guiding strategic resource allocation.

Organizational Ambidexterity: Exploration vs. Exploitation

The behavioral outcomes measured by the instrument are rooted in March’s (1991) foundational taxonomy of organizational learning, which dichotomizes organizational adaptation into exploration and exploitation. Exploration entails discovery, variance generation, experimentation, risk-taking, and radical technological innovation. In contrast, exploitation entails refinement, execution, efficiency, variance reduction, and incremental technological iteration.

Subsequent psychometric and strategic developments (He & Wong, 2004; Zhou & Wu, 2010) established that these two behaviors represent distinct, non-mutually exclusive strategic orientations that make competing claims on corporate resources and cognitive attention. The theoretical framework of Wang et al.’s (2023) model posits that the specific emphasis an organization places across its relational knowledge stores creates a distinct cognitive lens that primes decision-makers to choose between exploration and exploitation. For example, high functional knowledge may induce path-dependency and lock-in, biasing the firm toward technological exploitation, whereas diverse interactional and market knowledge may expose the firm to disruptive ideas, spurring exploration.

Contingency Theory and Institutional Context

Finally, the scale incorporates Contingency Theory (Lawrence & Lorsch, 1967) and Institutional Economics. The framework rejects universalistic outcomes, postulating that the translation of relational knowledge into exploratory or exploitative new product strategies is moderated by contextual friction. Under high technology turbulence, prior knowledge stores risk rapid obsolescence, forcing firms to reconfigure their relational priorities toward radical exploration. Simultaneously, variations in state ownership introduce differing institutional logics: state-owned enterprises (SOEs) often experience soft budget constraints alongside bureaucratic risk-aversion, whereas private enterprises are governed by strict commercial market disciplines, radically altering how relational knowledge stores guide NPD strategy.

7. Validity

The validity of the measurement model was established by Wang et al. (2023) through rigorous methodological testing of construct, convergent, and discriminant validity across a representative empirical sample of Business-to-Business (B2B) manufacturing and technical enterprises operating in China.

Convergent Validity

Convergent validity evaluates the extent to which independent survey items designed to measure a specific psychological or organizational construct share a high proportion of common variance. In the validation study:

• All survey items exhibited positive and statistically significant standardized factor loadings on their designated latent constructs (all significant at p < 0.05, with the vast majority achieving significance at p < 0.001).
• Standardized item loadings across all primary scales predominantly surpassed the conventional 0.65 to 0.70 benchmark recommended by psychometric literature (Nunnally, 1978).
• The average variance extracted (AVE) was computed for each multi-item latent construct. All latent variables demonstrated AVE values exceeding or hovering cleanly around the 0.50 threshold, establishing that the variance explained by each underlying construct is greater than the variance attributable to measurement error.

Discriminant Validity

Discriminant validity confirms that operational dimensions designed to assess theoretically distinct constructs are not excessively correlated with one another. Wang et al. (2023) established discriminant validity utilizing the gold-standard Fornell and Larcker (1981) criterion and criteria outlined by Bagozzi, Yi, and Phillips (1991):

• The calculated AVE for every individual latent construct exceeded the squared correlation (shared variance) between that construct and any other construct within the structural model.
• Inter-construct correlations between interactional knowledge, functional knowledge, and market knowledge remained within ranges that precluded multi-collinearity, demonstrating that respondents perceived these knowledge repositories as conceptually independent facets of relational capital.
• Exploration and exploitation constructs displayed distinct factor alignment, confirming their empirical divergence in measuring contrasting strategic trajectories.

Common Method Bias (CMB) Checks

Because enterprise survey data often rely on senior key informants (e.g., CEOs, R&D Directors, Marketing VPs), systematic common method variance (Podsakoff et al., 2003) presents a potential validity threat. The authors instituted both procedural remedies (item randomization, anonymous guarantees) and empirical statistical controls. Specifically, a marker variable analysis was performed wherein the correlation matrix was adjusted by subtracting the lowest positive correlation observed between an unrelated marker variable and relative exploration in NPD (γ = 0.10, p > 0.10). Following this rigorous adjustment, the structural correlations remained statistically robust and unchanged, confirming that common method bias did not distort the scale’s empirical validity.

8. Reliability

The reliability of the instrument was evaluated through comprehensive assessments of internal consistency. Internal consistency indicates the degree to which items within a designated subscale measure the same underlying construct homogeneously without excessive item redundancy.

Composite Reliability

Recognizing the limitations of Cronbach’s alpha—namely its conservative assumption of tau-equivalence which assumes equal factor loadings across items—the scale developers prioritized Composite Reliability (CR), calculated via structural equation modeling parameters. The CR values across the multi-item subscales were found to fall between 0.73 and 0.87, comfortably surpassing the standard threshold of 0.70 established by Fornell and Larcker (1981) and Hair et al. (2010):

• Interactional Knowledge Subscale (4 items): Exhibited high composite reliability (CR ≥ 0.80), demonstrating that items evaluating inter-firm dispute management, bilateral negotiation, and cooperative program implementation hang together with high internal cohesion.
• Functional Knowledge Subscale (5 items): Achieved excellent composite reliability (CR ≥ 0.84), showing strong internal consistency across joint operational tasks, JIT collaboration, and co-development.
• Market Knowledge Subscale (3 items): Demonstrated acceptable composite reliability (CR ≥ 0.73), confirming consistent measurement of statutory, purchasing, and competitive dynamics.
• NPD Exploration Strategy Subscale (5 items): Yielded superior internal consistency (CR ≥ 0.85), indicating that statements measuring radical learning, disruptive capability building, and new technological paradigms capture a coherent latent behavioral continuum.
• NPD Exploitation Strategy Subscale (3 items): Exhibited robust internal consistency (CR ≥ 0.78), validating the stability of items tapping into existing process upgrades, mature technology exploitation, and proximal problem-solving.
• Technology Turbulence Subscale (3 items): Maintained reliable internal consistency (CR ≥ 0.79), capturing environmental unpredictability and pace of change.

These empirical metrics confirm that the instrument possesses high scale stability and low measurement error, making it suitable for both cross-sectional hypothesis testing and longitudinal organizational evaluations.

9. Factor Analysis

The factorial structure and underlying latent geometry of the instrument were validated using Confirmatory Factor Analysis (CFA) within a covariance-based Structural Equation Modeling (SEM) framework.

Model Fit and Parameter Estimates

To verify that the empirical data fit the theoretical six-factor latent structure (Interactional Knowledge, Functional Knowledge, Market Knowledge, Exploration, Exploitation, and Technology Turbulence), a full measurement CFA was estimated. The analysis yielded model fitness indices that satisfied widely accepted methodological criteria:

• Chi-Square to Degrees of Freedom Ratio: χ² = 397.49 with degrees of freedom df = 215 (p < 0.001). The χ²/df ratio equals approximately 1.85, comfortably below the upper threshold of 3.0 (and well within the conservative limit of 2.0) that denotes acceptable model parsimony and fit.
• Comparative Fit Index (CFI): Obtained a value of 0.91, surpassing the accepted minimum standard of 0.90 for complex enterprise-level multi-construct models (Bentler, 1990).
• Tucker-Lewis Index (TLI / Non-Normed Fit Index): Recorded at 0.90, establishing that the hypothesized model offers a 90% improvement in fit over a baseline null model of uncorrelated variables.
• Root Mean Square Error of Approximation (RMSEA): Measured at 0.07 (with an acceptable 90% confidence interval). This falls squarely within the established range of 0.05 to 0.08, indicating good approximation fit in the population (Browne & Cudeck, 1992).

Factor Loadings and Structural Integrity

Detailed examination of the factor solution demonstrated that each item loaded cleanly onto its specified target construct without cross-loadings that exceeded theoretical or statistical thresholds. Standardized factor loadings across items were statistically significant (p < 0.001) and generally clustered between 0.65 and 0.88. Competing nested factor models (such as collapsing the three relational knowledge stores into a single general knowledge factor, or merging exploration and exploitation into a single bipolar continuum) exhibited significant deterioration in χ² fit indices (Δχ² statistically significant at p < 0.001). These results validate the multi-dimensional architecture of the relational knowledge and NPD strategy construct space.

10. Instrument / Measurement Tool

The operational framework of the instrument is structured as follows:

• Instrument Type: Standardized Organizational Assessment Inventory / Key-Informant Survey Questionnaire.
• Administration Format: Self-administered electronic survey or standardized structured executive interview.
• Target Respondent: C-level executives, Chief Technology Officers (CTOs), R&D Directors, Chief Marketing Officers (CMOs), or Senior Product Managers with deep systemic visibility into corporate partnership history and technological R&D operations.
• Total Item Count: 24 operational items.
  • Knowledge Stores: 12 items (Interactional: 4 items; Functional: 5 items; Market: 3 items)
  • NPD Strategy: 8 items (Exploration: 5 items; Exploitation: 3 items)
  • Technology Turbulence: 3 items
  • State Ownership: 1 ordinal categorical item
• Response Formats:
  • Knowledge Stores Scale: 7-point Likert-type intensity scale ranging from 1 (“Very Little Knowledge”) to 7 (“Lots of Knowledge”).
  • NPD Strategy Scale (Exploration & Exploitation): 7-point Likert-type extent scale ranging from 1 (“Very Low”) to 7 (“Very High”).
  • Technology Turbulence Scale: 7-point Likert agreement scale ranging from 1 (“Strongly Disagree”) to 7 (“Strongly Agree”).
  • State Ownership: 6-point ordinal band scale (1 = No state ownership; 2 = <10%; 3 = 10%–20%; 4 = 20%–50%; 5 = 50%–80%; 6 = >80%).
• Scoring and Index Computation:
  • Subscale Scores: Computed by calculating the non-weighted arithmetic mean of the items within each sub-dimension. Higher scores indicate greater knowledge stock or strategic prioritization.
  • Relative Relational Knowledge Emphasis Index: Can be computed algebraically depending on the specific empirical design (e.g., calculating relative ratios or regression residual vectors contrasting interactional and functional knowledge against pure market intelligence).
  • Ambidexterity Indices: Can be modeled either as an orthogonal interaction term (Exploration × Exploitation) to gauge combined ambidexterity, or as an absolute difference score (|Exploration − Exploitation|) to gauge strategic balance.

11. Permissions & Fee and Test Year

• Test Year: 2023.
• Original Publication: Industrial Marketing Management, Volume 109, Pages 257–270.
• Intellectual Property & Copyright: The structural model, conceptual synthesis, and empirical validation are copyrighted © 2023 by Elsevier Inc. on behalf of the authors (Wang, Wei, Liu, Xia, & Zhao). Individual item stems are based on peer-reviewed academic adaptations of scales originally developed by Johnson et al. (2004), Zhou & Wu (2010), and Jaworski & Kohli (1993).
• Licensing and Usage Permissions: The instrument is accessible free of charge for non-commercial academic research, pedagogical investigations, and higher education teaching purposes. Academic researchers may replicate and administer the items providing full bibliographic attribution to the original authors and the publishing journal.
• Commercial Applications: Commercial deployment, proprietary consulting implementations, inclusion in fee-for-service enterprise diagnostic software, or corporate training platforms requires formal permission or licensing arrangements from the copyright holder (Elsevier) and the primary authors.

12. References

The academic validation and theoretical underpinning of this instrument draw upon the following peer-reviewed literature:

• Bagozzi, R. P., Yi, Y., & Phillips, L. W. (1991). Assessing construct validity in organizational research. Administrative Science Quarterly, 36(3), 421–458. https://doi.org/10.2307/2393203
• Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238–246. https://doi.org/10.1037/0033-2909.107.2.238
• Browne, M. W., & Cudeck, R. (1992). Alternative ways of assessing model fit. Sociological Methods & Research, 21(2), 230–258. https://doi.org/10.1177/0049124192021002005
• Dyer, J. H., & Singh, H. (1998). The relational view: Cooperative strategy and sources of interorganizational competitive advantage. Academy of Management Review, 23(4), 660–679. https://doi.org/10.5465/amr.1998.1255632
• 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
• Grant, R. M. (1996). Toward a knowledge-based theory of the firm. Strategic Management Journal, 17(S2), 109–122. https://doi.org/10.1002/smj.4250171104
• Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis: A global perspective (7th ed.). Pearson Education.
• He, Z. L., & Wong, P. K. (2004). Exploration vs. exploitation: An empirical test of the ambidexterity hypothesis. Organization Science, 15(4), 481–494. https://doi.org/10.1287/orsc.1040.0078
• Jaworski, B. J., & Kohli, A. K. (1993). Market orientation: Antecedents and consequences. Journal of Marketing, 57(3), 53–70. https://doi.org/10.1177/002224299305700304
• Johnson, J. L., Sohi, R. S., & Grewal, R. (2004). The role of relational knowledge stores in interfirm partnering. Journal of Marketing, 68(2), 21–36. https://doi.org/10.1509/jmkg.68.2.21.27786
• Kogut, B., & Zander, U. (1992). Knowledge of the firm, combinative capabilities, and the replication of technology. Organization Science, 3(3), 383–397. https://doi.org/10.1287/orsc.3.3.383
• Lawrence, P. R., & Lorsch, J. W. (1967). Differentiation and integration in complex organizations. Administrative Science Quarterly, 12(1), 1–47. https://www.jstor.org/stable/2391211
• Macneil, I. R. (1980). The new social contract: An inquiry into modern contractual relations. Yale University Press.
• March, J. G. (1991). Exploration and exploitation in organizational learning. Organization Science, 2(1), 71–87. https://doi.org/10.1287/orsc.2.1.71
• Nunnally, J. C. (1978). Psychometric theory (2nd ed.). McGraw-Hill.
• Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
• Wang, X., Wei, R., Liu, Y., Xia, H., & Zhao, Y. (2023). The effects of relational knowledge emphasis on new product development strategy. Industrial Marketing Management, 109, 257–270. https://doi.org/10.1016/j.indmarman.2023.02.005
• Zhou, K. Z., & Wu, F. (2010). Technological capability, strategic flexibility, and product innovation. Strategic Management Journal, 31(5), 547–561. https://doi.org/10.1002/smj.827

13. Items of the Scale