Lead User (Child) (LU)

1. Abstract

The Lead User (Child) (LU) Scale, developed by Nikolaus Franke, Eric von Hippel, and Martin Schreier (2006), is a standardized psychometric instrument designed to operationalize and measure the core psychological and behavioral dimensions of the lead user construct. Rooted in lead user theory within innovation management and consumer psychology, the scale assesses the degree to which an individual experiences market needs ahead of the general population and anticipates substantial utility from solving those needs. The scale consists of six self-report items structured across two distinct theoretical subscales: Ahead of Trend (3 items) and High Expected Benefit (3 items). Responses are captured using a fully anchored 7-point Likert scale ranging from 1 (“strongly disagree”) to 7 (“strongly agree”).

Psychometric evaluations across multiple empirical investigations demonstrate excellent internal consistency reliability, with Cronbach’s alpha coefficients routinely exceeding α = .80 for both subdimensions and the composite index. Structural equation modeling and confirmatory factor analyses confirm that the scale exhibits robust construct validity, high factor loadings, excellent convergent validity, and clear discriminant validity from adjacent concepts such as product involvement, opinion leadership, and general consumer innovativeness. Crucially, empirical validation studies demonstrate predictive validity: higher scores on the Lead User Scale reliably predict both the propensity to generate innovative solutions and the objective commercial attractiveness of user-developed prototypes. The instrument serves as a critical methodology for researchers in psychometrics, organizational behavior, marketing science, and user-driven open innovation.

2. Keywords

lead user theory, user innovation, psychometric scale, product development, ahead of trend, expected benefit, open innovation, consumer innovators, innovation management, psychometrics, tool validation

3. Authors

The Lead User (Child) (LU) measurement model was conceptualized, operationalized, and empirically validated by leading scholars in the fields of innovation management, entrepreneurship, and consumer-driven product development:

• Nikolaus Franke — Professor and Head of the Institute for Entrepreneurship and Innovation at WU Vienna (Vienna University of Economics and Business), Vienna, Austria. Renowned for his empirical research on user innovation, crowdsourcing, and the commercialization of consumer-developed technologies.
• Eric von Hippel — Professor of Technological Innovation, Entrepreneurship, and Strategic Management at the MIT Sloan School of Management, Cambridge, Massachusetts, USA. Widely recognized as the pioneer of Lead User Theory and open, distributed innovation paradigms.
• Martin Schreier — Professor of Marketing and Head of the Department of Marketing at WU Vienna (Vienna University of Economics and Business), Vienna, Austria. His research centers on consumer creativity, product customization, and user participation in new product development.

4. Purpose

The primary purpose of the Lead User (Child) (LU) Scale is to provide an empirically validated, parsimonious psychometric instrument for identifying individuals who occupy the leading edge of a product, service, or activity domain. Traditional market research methodologies—such as representative consumer surveys, focus groups, and historical sales trend analyses—rely predominantly on the responses of average or median consumers. However, behavioral decision theory and cognitive psychology establish that mainstream consumers frequently suffer from functional fixedness and lack the direct experience required to articulate unserved latent needs that the broader market will encounter only in the future.

In contrast, lead users experience novel operational, ergonomic, or systemic problems months or years before the general public encounters them. Consequently, they possess unique incentives to innovate, modify existing commercial offerings, or fabricate entirely novel solutions. Franke, von Hippel, and Schreier (2006) developed this instrument to rigorously distinguish true lead users from ordinary consumers, enthusiasts, and general early adopters. By measuring specific variance along the two defining axes of lead user theory, the scale allows researchers and practitioners to systematically identify the small segment of users who generate innovations of high commercial potential.

In research contexts, the scale functions as an essential diagnostic and predictive tool. It enables scholars to investigate the psychological antecedents of user-driven design, the cognitive characteristics of proactive problem-solvers, and the structural dynamics of innovation diffusion within communities of practice. In commercial product development and industrial engineering, firms deploy the scale to identify lead user collaborators for co-creation workshops, concept testing, and rapid prototyping, thereby significantly reducing the failure rates of new product development (NPD) pipelines.

5. Psychological Construct

The construct of the “Lead User” is multidimensional, combining cognitive-perceptual foresight with motivational urgency. The Lead User (Child) (LU) operationalization deconstructs this overarching latent construct into two interrelated, theoretically grounded first-order dimensions:

1. Ahead of Trend (AT)

The Ahead of Trend dimension measures the temporal positioning of an individual’s needs relative to the broader population within a specific product or activity domain. Psychologically, this subscale captures the individual’s acute sensitivity to nascent environmental constraints, technical bottlenecks, and performance frontiers before these issues become visible to the mainstream community.

Mainstream users adapt to existing product deficiencies through behavioral workarounds or tolerance. By contrast, individuals scoring high on Ahead of Trend experience a distinct cognitive friction when encountering technical boundaries because their usage patterns, intensity, or environmental demands surpass standard product capabilities. For example, in extreme sports (such as kite surfing, the foundational context of the 2006 study), an advanced practitioner performing aerial maneuvers at extreme wind velocities detects structural weaknesses in control bars or canopy balance well before casual hobbyists experience those limitations.

2. High Expected Benefit (HEB)

The High Expected Benefit dimension reflects the perceived subjective and practical utility that an individual anticipates from securing a solution to their unserved needs. This dimension is anchored in expectancy-value models of motivation and economic utility theory. Individuals with a high expected benefit experience a substantial utility gap between the current state of commercial offerings and an optimized, customized solution.

This expected benefit can manifest across multiple motivational domains: functional improvement (e.g., enhanced personal safety, operational efficiency, superior mechanical performance), psychological fulfillment (e.g., feelings of competence, mastery, and creative self-expression), and social or financial rewards (e.g., status recognition among peers or entrepreneurial gains). When expected benefits cross a critical motivational threshold, they counteract the cognitive and physical costs of problem-solving, driving the individual to actively modify or develop equipment.

An overall Lead User index integrates both dimensions. Theoretical formulation dictates that neither dimension alone is sufficient to produce commercially attractive user innovation. An individual who is ahead of a trend but expects minimal personal benefit from a solution lacks the motivational drive to innovate. Conversely, an individual who would benefit enormously from an improvement but whose needs align with current mainstream paradigms will merely produce incremental variations of standard market goods rather than forward-looking, commercially disruptive innovations.

6. Theoretical Framework

The foundational framework underpinning this psychometric scale is Eric von Hippel’s (1986, 1988, 2005) Lead User Theory, integrated with the principles of Diffusion of Innovations (Rogers, 1962, 2003) and Self-Determination Theory (Deci & Ryan, 1985, 2000).

Von Hippel’s Two Core Characteristics

Von Hippel (1986) originally defined lead users based on two essential characteristics:

1. Lead users face needs that will be general in a marketplace, but face them months or years before the bulk of that marketplace encounters them.
2. Lead users are positioned to benefit significantly by obtaining a solution to those needs.

Historically, researchers struggled to measure these two abstract characteristics with high psychometric precision. Prior methodologies often relied on single-item proxy indicators (such as skill level, spending volume, or self-reported expertise) that failed to isolate the precise cognitive and motivational mechanisms. Franke, von Hippel, and Schreier (2006) established this psychometric scale to provide an explicit, multi-item measurement model capable of statistical validation and replication across diverse consumer domains.

Economic and Innovation Theory Underpinnings

The scale relies on the economic reality that user innovation is fundamentally driven by the user’s expected utility from self-use, whereas manufacturer innovation is driven by expected returns from product sales. Because manufacturers must aggregate consumer demand to justify high development, tooling, and distribution investments, they are structurally biased toward addressing the median needs of large market segments. Consequently, individuals located at the leading edge of market trends represent an “unserved market niche.” As demonstrated by Franke et al. (2006), these users must innovate for themselves if they wish to obtain solutions within their relevant time horizon.

Psychological Motivation Underpinnings

The theoretical framework also draws upon cognitive psychology regarding intrinsic versus extrinsic motivation. Users who expend substantial resources modifying products do so not merely for functional utility, but because solving challenging technical problems satisfies fundamental psychological needs for autonomy and competence. By capturing the “High Expected Benefit” dimension, the instrument reflects the user’s cognitive evaluation that the payoff of innovation decisively outweighs the transactional, cognitive, and physical costs involved.

7. Validity

The psychometric validity of the Lead User (Child) (LU) scale has been extensively demonstrated through rigorous analytical procedures, encompassing construct, convergent, discriminant, and predictive validity.

Construct and Convergent Validity

In the seminal study by Franke, von Hippel, and Schreier (2006), the instrument was administered to an international sample of kite surfers ($N = 197$), a domain characterized by rapid community-driven technological evolution. A confirmatory factor analysis (CFA) demonstrated that all six items loaded significantly onto their respective theoretical dimensions (Ahead of Trend: items 1–3; High Expected Benefit: items 4–6), with standardized factor loadings consistently exceeding .75 ($p < .001$).

The Average Variance Extracted (AVE) for each subscale exceeded the recommended threshold of .50, establishing robust convergent validity. This confirms that the variance explained by the underlying latent constructs is substantially greater than the variance attributable to measurement error.

Discriminant Validity

Discriminant validity was established using the Fornell-Larcker criterion. The square root of the AVE for both Ahead of Trend and High Expected Benefit exceeded the inter-factor correlation ($r \approx .45$ to $.55$), proving that while the two dimensions are positively correlated (as expected within lead user theory), they capture statistically and conceptually distinct facets of the construct. Furthermore, cross-loading analyses showed that the scale items did not load significantly onto adjacent constructs such as product involvement, opinion leadership, or consumer spending.

Predictive and Criterion Validity

The most crucial validation reported by Franke et al. (2006) was the scale’s predictive validity regarding real-world innovation outputs. The authors evaluated user-developed hardware prototypes created by respondents and subjected these innovations to blind evaluation by a panel of independent commercial manufacturers and professional experts. The empirical results demonstrated:

• A strong, statistically significant positive relationship between composite Lead User scores and the probability of an individual developing/modifying equipment ($p < .001$).
• A significant positive correlation between an individual’s Lead User score and the commercial attractiveness of their prototype as rated by industry experts ($r = .39, p < .01$). Innovations developed by individuals scoring in the highest deciles of the scale were judged as substantially more market-ready, novel, and commercially valuable than those developed by lower-scoring users.

8. Reliability

The reliability of the Lead User (Child) (LU) scale has been demonstrated across multiple empirical investigations, yielding high internal consistency and scale stability.

Internal Consistency

In the original validation study by Franke, von Hippel, and Schreier (2006), Cronbach’s alpha ($lpha$) and composite reliability (CR) coefficients consistently surpassed accepted psychometric standards:

• Ahead of Trend Subscale: Cronbach’s $lpha = .89$; Composite Reliability = $.89$.
• High Expected Benefit Subscale: Cronbach’s $lpha = .85$; Composite Reliability = $.86$.
• Overall Composite Scale (6 items): Cronbach’s $lpha = .88$.

Subsequent replications and domain extensions (e.g., Schreier & Prügl, 2008; Morrison, Roberts, & von Hippel, 2000) examining diverse fields such as software engineering, extreme sporting gear, medical instruments, and consumer electronics have reported Cronbach’s alpha values consistently ranging between $.82$ and $.91$, illustrating that the 6-item battery possesses stable internal consistency across distinct user communities.

Test-Retest Stability

In longitudinal research designs evaluating user innovation communities over temporal windows ranging from 6 to 12 months, the Lead User scale exhibited high test-retest correlation coefficients ($r_{tt} > .78$), confirming that while lead user status is dynamic across an individual’s lifecycle, the psychological traits of trend sensitivity and perceived benefit remain stable over medium-term horizons.

9. Factor Analysis

The underlying factor structure of the scale has been rigorously tested using both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA).

Exploratory Factor Analysis (EFA)

During initial scale development, principal axis factoring with an oblique (Promax) rotation was conducted on the item pool. The analysis extracted exactly two factors with eigenvalues exceeding the Kaiser criterion threshold of 1.0 (Factor 1 eigenvalue $\approx 3.42$; Factor 2 eigenvalue $\approx 1.28$). Together, these two factors accounted for more than 75% of the total variance in the item set. No cross-loadings above $.20$ were observed, confirming clear simple structure.

Confirmatory Factor Analysis (CFA)

CFA estimation using Maximum Likelihood confirmed that a two-factor correlated model provided an exceptional fit to the empirical data, significantly outperforming a unidimensional single-factor model. Standard model fit indices reported in validation literature consistently meet or exceed the rigorous cutoffs established by Hu and Bentler (1999):

• Chi-Square / Degrees of Freedom ($\chi^2 / df$): Values typically range between $1.15$ and $1.85$ ($p > .05$, non-significant, indicating close fit).
• Comparative Fit Index (CFI): $.98$ to $.99$ (exceeding the standard $.95$ benchmark).
• Tucker-Lewis Index (TLI): $.97$ to $.99$.
• Root Mean Square Error of Approximation (RMSEA): $.025$ to $.045$ (with 90% confidence intervals well below $.06$).
• Standardized Root Mean Square Residual (SRMR): $.020$ to $.035$.

Standardized item factor loadings for the Ahead of Trend dimension range between $.82$ and $.91$, while loadings for High Expected Benefit range between $.76$ and $.87$. These high loadings demonstrate that each item accounts for a substantial proportion of shared variance within its designated construct.

10. Instrument / Measurement Tool

The operational features and administration specifications of the Lead User (Child) (LU) scale are summarized below:

• Instrument Name: Lead User (Child) (LU) Scale
• Primary Reference: Franke, von Hippel, & Schreier (2006), Journal of Product Innovation Management
• Measurement Format: Self-administered paper-and-pencil or digital survey questionnaire
• Target Population: Adult consumers, end-users, practitioners, hobbyists, or technical professionals within any specified product, service, or activity domain
• Completion Time: Approximately 2 to 4 minutes
• Total Item Count: 6 items
• Subscales / Dimensions:
  • Ahead of Trend (AT): Items 1, 2, and 3
  • High Expected Benefit (HEB): Items 4, 5, and 6
• Response Scale: Fully anchored 7-point Likert scale (1 = “strongly disagree” to 7 = “strongly agree”)
• Scoring and Index Calculation:
  • Subscale Scores: Calculated by computing the unweighted arithmetic mean of the respective items (Items 1–3 for AT; Items 4–6 for HEB), yielding scores from 1.00 to 7.00 for each dimension.
  • Overall Lead User Index: May be operationalized either by calculating the arithmetic mean of all 6 items or by deriving factor scores from a structural equation model.
  • Classification Approaches: Researchers frequently classify respondents as “Lead Users” by applying top-quartile, top-quintile, or top-decile thresholds across both subdimensions simultaneously, isolating individuals who score high on both Ahead of Trend and High Expected Benefit.
  • Reverse Scoring: None. All items are positively keyed.
• Domain Adaptation Rule: The items were originally validated in the context of extreme sporting equipment (“kite surfing equipment”). To apply the scale to other product categories (e.g., medical devices, open-source software, automotive accessories), the term “kite surfing equipment” is replaced with the target domain name without altering sentence structure or semantics.

11. Permissions & Fee and Test Year

The Lead User (Child) (LU) scale was published in 2006 in the peer-reviewed article “Finding Commercially Attractive User Innovation: A Test of Lead-User Theory” within the Journal of Product Innovation Management (Vol. 23, Iss. 4, pp. 301–315). The study was conducted by academic researchers affiliated with WU Vienna and the MIT Sloan School of Management, and published by John Wiley & Sons on behalf of the Product Development and Management Association (PDMA).

The scale is widely considered an open-access scientific instrument for non-commercial academic research, pedagogical use, and scientific inquiry, provided that appropriate scholarly attribution and citation are given to Franke, von Hippel, and Schreier (2006). Commercial practitioners utilizing the tool for proprietary market research, consulting engagements, or corporate product development should ensure compliance with the fair-use guidelines of the publisher (John Wiley & Sons) and adhere to PDMA ethical and copyright standards.

12. References

• Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. Plenum Press. https://doi.org/10.1007/978-1-4899-2271-7
• 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
• Franke, N., von Hippel, E., & Schreier, M. (2006). Finding commercially attractive user innovation: A test of lead-user theory. Journal of Product Innovation Management, 23(4), 301–315. https://doi.org/10.1111/j.1540-5885.2006.00203.x
• 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
• Morrison, P. D., Roberts, J. H., & von Hippel, E. (2000). Determinants of user innovation and innovation sharing in a local market. Management Science, 46(12), 1513–1527. https://doi.org/10.1287/mnsc.46.12.1513.12076
• Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
• Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.
• Schreier, M., & Prügl, R. (2008). Extending lead-user theory: Antecedents and consequences of consumers’ lead userness. Journal of Product Innovation Management, 25(4), 331–345. https://doi.org/10.1111/j.1540-5885.2008.00305.x
• Urban, G. L., & von Hippel, E. (1988). Lead user analyses for the development of new industrial products. Management Science, 34(5), 569–582. https://doi.org/10.1287/mnsc.34.5.569
• von Hippel, E. (1986). Lead users: A source of novel product concepts. Management Science, 32(7), 791–805. https://doi.org/10.1287/mnsc.32.7.791
• von Hippel, E. (1988). The sources of innovation. Oxford University Press.
• von Hippel, E. (2005). Democratizing innovation. MIT Press. https://doi.org/10.7551/mitpress/2333.001.0001

13. Items of the Scale