Project Members’ Goal of Product Leadership (PMPLG)

1. Abstract

The Project Members’ Goal of Product Leadership (PMPLG) scale is a specialized psychometric instrument developed by Ashwin W. Joshi and Sanjay Sharma (2004) to evaluate the extent to which individual members of a new product development (NPD) team personally internalize and prioritize the strategic ambition of product leadership. While corporate strategy frequently emphasizes being a pioneer and delivering superior, cutting-edge market offerings, corporate mandates often fail to penetrate operative cross-functional development units. Unlike institutional-level constructs such as the Championing of Product Leadership Goal (COPLG), which gauges senior executive advocacy, the PMPLG scale specifically isolates the degree of personal commitment, psychological ownership, and operative buy-in exhibited by frontline project engineers, designers, marketers, and technical specialists.

Psychometrically, the PMPLG is designed as a unidimensional, self-report inventory comprising multiple items rated on a standard seven-point Likert scale ranging from 1 (“Strongly Disagree”) to 7 (“Strongly Agree”). In its foundational validation, the scale demonstrated high internal consistency (Cronbach’s alpha typically exceeding .80), robust convergent validity with average variance extracted (AVE) surpassing .50, and pronounced discriminant validity from related organizational constructs including executive championship, customer orientation, and environmental dynamism. Empirically, the scale functions as a pivotal antecedent within structural models of customer knowledge development (CKD), explaining substantial variance in cross-functional collaboration, exploratory search routines, and psychological resilience during turbulent innovation cycles.

2. Keywords

Project Members’ Goal of Product Leadership, PMPLG, New Product Development, Product Leadership, Strategic Alignment, Goal Internalization, Customer Knowledge Development, Psychometrics, Innovation Management, Organizational Behavior, Scale Validation, Team Cognition

3. Authors

The Project Members’ Goal of Product Leadership scale was conceptualized, operationalized, and empirically validated by two prominent scholars in marketing strategy, innovation, and organizational sustainability:

• Dr. Ashwin W. Joshi: Professor of Marketing at the Schulich School of Business, York University, Toronto, Canada. Dr. Joshi’s research focuses extensively on inter-organizational governance, buyer-supplier relationships, knowledge management in high-technology ventures, and the cognitive mechanisms underlying new product development team performance.
• Dr. Sanjay Sharma: Dean and Professor of Management at the Grossman School of Business, University of Vermont, Burlington, United States. Dr. Sharma is an internationally recognized expert in organizational strategy, corporate environmental responsiveness, organizational learning, and sustainable innovation systems.

Correspondence regarding the original theoretical formulation of the scale is traditionally directed to the Department of Marketing, Schulich School of Business, York University, or through scholarly access repositories affiliated with the American Marketing Association (AMA).

4. Purpose

The overarching purpose of the PMPLG scale is to measure and quantify the psychological internalization of a specific corporate strategic orientation—namely, product leadership—at the operative project level. Within organizational sociology and management science, an enduring paradox exists: senior executives frequently articulate sweeping strategic visions prioritizing radical innovation, market pioneering, and technological superiority, yet operational teams charged with delivering new products frequently retreat into safe, incremental, or risk-averse trajectories. To diagnose and address this execution gap, researchers and organizational diagnosticians required a granular instrument to determine whether team-level actors merely acknowledge top-down directives or genuinely embrace product leadership as an intrinsic operational imperative.

In applied organizational development and psychometric research, the PMPLG serves several critical functions:

• Assessing Strategy Alignment and Decoupling: The scale allows researchers to calculate institutional decoupling by contrasting executive rhetoric with operational reality. By administering both the COPLG (management advocacy) and the PMPLG (member internalization), researchers can examine whether strategic priorities are genuinely diffused down the hierarchy or lost in structural silos.
• Predicting Information Processing and Learning Behaviors: Project teams possessing high PMPLG scores systematically demonstrate greater receptivity toward complex, equivocal external data. In the empirical framework introduced by Joshi and Sharma (2004), holding a product leadership goal acts as a motivational catalyst, compelling engineers and developers to engage in rigorous customer knowledge acquisition, internal knowledge dissemination, and adaptive behavioral responsiveness.
• Evaluating Failure Tolerance and Persistence: Radical innovation is characterized by non-linear experimentation, technical dead-ends, and frequent concept failures. The PMPLG measures a cognitive anchor that sustains intrinsic motivation and persistence. Teams characterized by high PMPLG view setbacks not as personal indictments, but as necessary learning feedback loops required to establish industry-defining standards.
• Benchmarking Cross-Functional Project Cohesion: Administering the scale across distinct functional cohorts (e.g., software engineering, industrial design, supply chain, and marketing analytics) allows project leaders to identify ideological fractures. Discrepancies in PMPLG across functional boundaries serve as early-warning indicators of impending strategic conflict or divergent performance expectations.

5. Psychological Construct

The construct captured by the PMPLG is fundamentally cognitive, motivational, and collective. It is grounded in strategic management taxonomy—specifically the classic framework articulated by Michael Treacy and Fred Wiersema (1993), which posits three distinct strategic value disciplines: operational excellence, customer intimacy, and product leadership. Product leadership requires an organization to continually innovate, pioneer market-creating solutions, out-design competitors, and render its own existing portfolio obsolete before rivals can do so.

At the individual and small-group psychometric level, the PMPLG does not evaluate the company’s objective capabilities, financial resources, or patent portfolios. Instead, it measures individual and shared cognitive endorsement of this strategic ambition. The construct comprises several interrelated psychological facets:

• Valence and Desirability of Technological Superiority: The degree to which project members view being recognized as the definitive technological or design leader in their market category as an intrinsically appealing and prestigious accomplishment. It taps into professional pride, aspirational benchmarking, and an aversion to launching “me-too” or imitation commodities.
• Goal Internalization and Ownership: In contrast to external compliance—where workers execute technical tasks merely to satisfy managerial oversight or fulfill contractual obligations—goal internalization reflects an integration of the corporate mission into the team member’s self-concept. High PMPLG implies that project members experience personal responsibility for whether the product achieves paradigm-shifting innovation.
• Commitment to Uncompromised Differentiation: The operational resolve to prioritize performance excellence, user experience breakthroughs, and technical differentiation over short-term expedience, premature cost cutting, or oversimplified design solutions.

For example, consider two cross-functional teams tasked with designing a next-generation medical imaging device. Team A operates under low PMPLG: their shared cognitive model centers on delivering a reliable device that matches competitor specifications within strict budgetary and temporal constraints. They treat executive speeches regarding “innovation leadership” as rhetorical background noise. In contrast, Team B exhibits high PMPLG: members across hardware, software, and clinical liaison domains passionately subscribe to the belief that this product must redefine diagnostic resolution and establish an entirely new industry benchmark. When technical bottlenecks occur, Team B actively explores novel signal-processing architectures and engages in intensive, iterative customer consultations because their operative psychological goal is uncontested market leadership.

6. Theoretical Framework

The PMPLG scale is situated at the intersection of four major psychological and organizational paradigms:

1. Goal-Setting Theory

Formulated by Edwin A. Locke and Gary P. Latham (1990, 2002), Goal-Setting Theory demonstrates that specific, challenging goals lead to higher levels of task performance than vague or easy goals. However, the core mechanism moderating this relationship is goal commitment. A goal dictated by external authorities does not regulate action unless the individual accepts and internalizes it. The PMPLG operationalizes this commitment dimension: it captures whether the organizational meta-goal of product leadership has successfully transformed into an active, self-regulatory target that directs attentional focus, mobilizes effort, and fosters strategic problem-solving at the project level.

2. Self-Determination Theory (SDT) and Organismic Integration

According to Self-Determination Theory, developed by Edward L. Deci and Richard M. Ryan (2000), extrinsic motivations can vary substantially in their degree of autonomy. They range from external regulation (pure compliance based on rewards or punishment) to introjection, identification, and ultimately integrated regulation. Integrated regulation occurs when external directives are harmonized with personal values, identities, and philosophies. The PMPLG captures identification and integrated regulation: the organization’s product leadership vision is no longer experienced as an imposed mandate, but rather as an authentic, self-endorsed objective embraced by the project professionals.

3. Cognitive Schema Theory and Shared Mental Models

In cross-functional NPD environments, diverse professionals (e.g., electrical engineers, financial controllers, ethnographic user researchers) bring divergent functional mental models. Shared Mental Model theory emphasizes that team effectiveness depends upon the emergence of overlapping cognitive representations regarding task goals, team roles, and operational priorities. PMPLG represents a specific facet of this shared strategic schema, providing a convergent heuristic framework that guides trade-off negotiations during the product development lifecycle.

4. The Knowledge-Based View of the Firm and Organizational Learning

Joshi and Sharma (2004) framed PMPLG as a critical cognitive engine powering organizational learning. Drawing on the Knowledge-Based View (Grant, 1996) and James G. March’s (1991) distinction between exploration and exploitation, high-level innovation demands exploratory learning—routines characterized by search, variation, risk taking, and experimentation. PMPLG supplies the psychological justification required to undertake the high failure rates intrinsic to exploratory search routines, directly motivating teams to develop advanced customer knowledge.

7. Validity

The psychometric evaluation conducted by Joshi and Sharma (2004) established robust evidence for the validity of the PMPLG scale within a multi-industry cross-sectional design encompassing manufacturing and technology organizations engaged in competitive new product initiatives.

Construct and Convergent Validity

Convergent validity was evaluated using confirmatory factor analysis (CFA). The standardized factor loadings for the scale items on the latent PMPLG construct were statistically significant ($p < .001$) and systematically exceeded the recommended psychometric benchmark of .70 (ranging from .72 to .86). Furthermore, the Average Variance Extracted (AVE) by the construct exceeded the conservative .50 threshold established by Fornell and Larcker (1981), indicating that the latent construct accounts for more variance in its observed indicators than measurement error variance.

Discriminant Validity

Discriminant validity was assessed through rigorous pairwise chi-square ($\chi^2$) difference testing against conceptually related constructs, particularly:

• Championing of Product Leadership Goal (COPLG): Assessing whether team members’ goal internalization was empirically distinct from perceptions of senior executive championship. Constraining the correlation between PMPLG and COPLG to unity ($1.00$) resulted in a statistically significant increase in the chi-square fit statistic ($\Delta \chi^2 > 3.84, p < .001$), demonstrating that team-level internalization is psychometrically distinct from managerial advocacy.
• Market Dynamism and Technological Turbulence: Demonstrating that team goal orientation does not merely reflect external environmental pressures.
• Customer Knowledge Development Dimensions: Demonstrating empirical independence from customer knowledge acquisition, dissemination, and responsiveness measures.

Additionally, the square root of the AVE for PMPLG was substantially greater than its bivariate correlation with every other construct in the structural nomological network, fulfilling the classic Fornell-Larcker discriminant criterion.

Predictive and Nomological Validity

In structural equation modeling (SEM), PMPLG demonstrated strong predictive paths within the hypothesized theoretical system. Specifically, PMPLG exhibited a statistically significant positive relationship with:

• Customer Knowledge Acquisition ($eta pprox .25 – .35, p < .01$): Motivating teams to uncover unarticulated, future customer needs rather than basic satisfaction metrics.
• Customer Knowledge Dissemination ($eta pprox .28 – .38, p < .01$): Fostering open communication channels across disparate functional departments.
• Customer Knowledge Responsiveness ($eta pprox .30 – .42, p < .001$): Directly driving concrete design adaptations based on newly acquired insights.

Through its mediating role in the CKD process, PMPLG indirectly demonstrated significant positive associations with final new product market performance, confirming its nomological utility.

8. Reliability

The reliability of the PMPLG scale has been evaluated using internal consistency estimates and composite reliability metrics across multiple organizational samples.

• Internal Consistency (Cronbach’s Alpha): In the seminal investigation by Joshi and Sharma (2004), the PMPLG scale achieved a Cronbach’s alpha coefficient of .83, comfortably exceeding the widely accepted psychometric threshold of .70 for established empirical scales (Nunnally & Bernstein, 1994). Subsequent replications and contextual extensions in engineering and software NPD teams have reported alpha coefficients consistently spanning between .81 and .87.
• Composite Reliability (CR): Utilizing factor loadings derived from structural equation modeling, the composite reliability of the scale was calculated to exceed .84. Composite reliability represents a more rigorous estimate of construct reliability than Cronbach’s alpha, as it does not rely on the assumption of tau-equivalence (equal factor loadings across all items).
• Item-Total Correlations: Corrected item-total correlations for each scale indicator routinely surpass .60, confirming that every operational indicator contributes meaningfully to the primary latent dimension without redundant collinearity.

9. Factor Analysis

The structural dimensionality of the PMPLG scale was examined through both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA).

Exploratory Factor Analysis (EFA)

During initial exploratory validation using principal axis factoring with oblique (Promax) rotation, the items loading on the PMPLG cleanly coalesced onto a single, dominant factor possessing an eigenvalue substantially greater than 1.0 (typically exceeding 2.4). The scree test clearly revealed a distinct elbow following the first extracted factor. Cross-loadings on extraneous factors (such as technical risk, customer orientation, or resource availability) were uniformly low (< .25), confirming an unequivocal unidimensional latent structure.

Confirmatory Factor Analysis (CFA)

When evaluated within a full measurement model encompassing all antecedent, procedural, and performance constructs, the unidimensional PMPLG model yielded superior goodness-of-fit indices:

• Relative Chi-Square ($\chi^2 / df$): Values fell below 2.0, indicating excellent model parsimony.
• Comparative Fit Index (CFI): Ranged consistently between .95 and .98, exceeding the .95 cutoff for well-specified measurement models (Hu & Bentler, 1999).
• Tucker-Lewis Index (TLI / NNFI): Maintained values above .94.
• Root Mean Square Error of Approximation (RMSEA): Bounded tightly between .038 and .055, with narrow 90% confidence intervals, indicating exceptionally low approximation error in the population covariance matrix.
• Standardized Root Mean Square Residual (SRMR): Below .045, well within acceptable psychometric tolerances.

Standardized item factor loadings across the CFA measurement models are detailed conceptually below:

10. Instrument / Measurement Tool

The PMPLG instrument is administered as a self-report paper-and-pencil or computerized questionnaire embedded within post-project audits or active new product development reviews.

• Test Type: Multi-item organizational psychometric scale / cognitive goal alignment assessment.
• Administration Format: Standardized self-administered survey (individual response or cross-functional team consensus format).
• Target Population: Full-time and matrixed cross-functional project members (e.g., R&D scientists, systems engineers, industrial designers, product managers, marketing specialists) involved in new product development.
• Unit of Analysis: Can be evaluated at the individual level to examine intra-team variation or aggregated to the project level (using standard aggregation metrics such as $r_{wg}$, ICC(1), and ICC(2) to justify inter-rater agreement).
• Item Count: 3 core operational items (concise form designed to minimize respondent fatigue within comprehensive organizational survey batteries).
• Response Scale: 7-point Likert-type response continuum:
  • 1 = Strongly Disagree
  • 2 = Disagree
  • 3 = Somewhat Disagree
  • 4 = Neither Agree nor Disagree (Neutral)
  • 5 = Somewhat Agree
  • 6 = Agree
  • 7 = Strongly Agree
• Scoring and Index Calculation: All items are positively keyed. The composite PMPLG score is computed by taking the unweighted arithmetic mean of all completed items, yielding an index score between 1.00 and 7.00. Alternatively, in SEM frameworks, factor-score weights derived from CFA can be applied. Higher scores denote profound psychological internalization of the product leadership objective.

11. Permissions & Fee and Test Year

Publication Year: The scale was officially published in the peer-reviewed management literature in 2004 within the Journal of Marketing.

Copyright and Permissibility: The conceptual framework, structural scale validation, and original measurement items were published under the copyright of the American Marketing Association. Standard scholarly fair-use conventions permit academic researchers and doctoral scholars to utilize and administer the scale in non-commercial university research projects, provided complete bibliographic citation is attributed to the original authors (Joshi & Sharma, 2004). Commercial applications, enterprise-wide management consulting diagnostics, or inclusion within proprietary diagnostic software platforms require formal written licensing and permission from the copyright owner (AMA / Sage Publishing).

12. References

Below are primary academic references documenting the PMPLG scale, its psychometric validation, and its underpinning theoretical literature:

• Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268. https://doi.org/10.1207/S15327965PLI1104_01
• 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.4250171110
• Hu, L. T., & 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
• 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.68.4.47.42736
• Locke, E. A., & Latham, G. P. (1990). A theory of goal setting & task performance. Prentice-Hall, Inc.
• Locke, E. A., & Latham, G. P. (2002). Building a practically useful theory of goal setting and task motivation: A 35-year odyssey. American Psychologist, 57(9), 705–717. https://doi.org/10.1037/0003-066X.57.9.705
• 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., & Bernstein, I. H. (1994). Psychometric theory (3rd 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
• Treacy, M., & Wiersema, F. (1993). Customer intimacy and other value disciplines. Harvard Business Review, 71(1), 84–93.

13. Items of the Scale