Difficulty of the Construction (DIFCON)

1. Abstract

The Difficulty of the Construction (DIFCON) scale is a four-item, bipolar semantic differential scale designed to evaluate observers’ cognitive appraisals regarding the physical, temporal, operational, and intellectual demands involved in assembling or building a target object, artifact, or visual merchandising display. Originally developed and validated by Keh, Wang, and Yan (2021) within the domain of consumer psychology and retail marketing, the DIFCON instrument operationalizes perceived construction effort to explore how external stakeholders evaluate creative installations, such as imaginative retail product displays. The instrument captures four core dimensions of construction complexity: physical difficulty (easy vs. difficult), temporal investment (takes little time vs. takes a lot of time), expenditure of energy (requires little effort vs. requires a lot of effort), and structural or cognitive planning (requires little planning vs. requires extensive planning).

Psychometrically, the DIFCON scale demonstrates exceptional internal consistency, with Cronbach’s alpha coefficients and composite reliability (CR) values consistently exceeding .88 across multiple experimental studies and field replications. Exploratory and confirmatory factor analyses establish a strictly unidimensional latent construct with factor loadings routinely surpassing .80 and average variance extracted (AVE) values greater than .65. The scale exhibits strong convergent validity with constructs such as perceived firm effort and aesthetic value, robust discriminant validity against general novelty and manipulative intent, and predictive validity regarding consumers’ quality inferences, brand evaluations, and purchase intentions. By providing an empirical gauge of perceived labor and technical complexity, the DIFCON scale serves as a critical measurement tool across marketing, industrial design, environmental psychology, and human-artifact interaction research.

2. Keywords

Difficulty of the Construction, DIFCON, perceived effort, effort heuristic, imaginative displays, visual merchandising, semantic differential, consumer inferences, psychometrics, retail atmospheric cues, construction complexity

3. Authors

The Difficulty of the Construction (DIFCON) scale was developed by a team of researchers in marketing and consumer behavior:

• Hean Tat Keh — Professor of Marketing, Monash Business School, Monash University, Melbourne, Australia. Expert in consumer psychology, services marketing, and retail strategy. (Formerly affiliated with the University of Queensland Business School).
• Di Wang — Senior Lecturer in Marketing, School of Advertising, Marketing and Public Relations, QUT Business School, Queensland University of Technology, Brisbane, Australia. Specializes in visual marketing, sensory consumption, and retail environmental design.
• Li Yan — Associate Professor of Marketing, School of Business Administration, Capital University of Economics and Business, Beijing, China. Focuses on consumer decision-making, aesthetic perceptions, and brand evaluations.

Correspondence regarding the original development and empirical application of the scale can be directed to the primary authors through their respective academic institutions or via the editorial management of the Journal of the Academy of Marketing Science.

4. Purpose

The primary purpose of the DIFCON scale is to provide a reliable, parsimonious, and theoretically grounded instrument for measuring an observer’s subjective assessment of the effort, time, and coordination required to assemble or fabricate a visible physical construction. While traditional industrial design and ergonomics research evaluates objective construction difficulty using kinematic, temporal, or physiological metrics, consumer psychology and social cognition require a measurement tool that captures the subjective, perceptual inferences drawn by third-party observers who did not assemble the artifact themselves.

Research Applications in Consumer Behavior and Retailing

In retail environments, firms invest substantial capital and human labor into creating elaborate in-store installations, architectural fixtures, and creative product displays (e.g., constructing a life-size sports car, castle, or animal entirely out of packaged goods or beverage cans). Keh, Wang, and Yan (2021) sought to resolve a critical managerial dilemma: are such imaginative, labor-intensive displays viewed by consumers as valuable artistic achievements, or are they dismissed as trivial marketing gimmicks? To investigate this question, researchers needed an instrument that isolates the perceived input of human effort and structural planning from the aesthetic outcome itself. The DIFCON scale fulfills this purpose by measuring the exact psychological mediator through which physical retail features trigger cognitive and behavioral responses.

Broader Experimental and Practical Utility

Beyond visual merchandising, the DIFCON scale possesses extensive applicability across diverse domains:

• Product Assembly and Packaging Design: Assessing how consumers perceive the assembly complexity of self-assembled furniture, modular electronics, or subscription-box packaging before and after unboxing.
• Art and Design Appraisal: Measuring how museum visitors, art critics, or design evaluators estimate the technical complexity of sculptures, architectural installations, and kinetic art.
• Service Scape Engineering: Evaluating the perceived operational effort invested by service providers in hospitality, staging, and live entertainment environments.
• Digital and Virtual Construction: Gauging perceived computational or human effort invested in virtual reality environments, user-generated gaming levels, or complex computer graphics.

5. Psychological Construct

The DIFCON scale measures the overarching psychological construct of Perceived Construction Complexity, conceptualized as a multidimensional cognitive evaluation that maps onto a singular, coherent latent factor. When an individual encounters a constructed physical entity, their perceptual apparatus rapidly processes visual, spatial, and structural cues to infer the magnitude of resources invested in bringing the entity into existence. The construct spans four foundational facets:

1. Physical and Technical Difficulty

This facet assesses the observer’s belief regarding the degree of mechanical, physical, or technical resistance encountered during assembly. It gauges whether the physical transformation of raw materials or individual product units into a finalized installation is seen as straightforward or technically taxing. An installation requiring precarious balancing, delicate joints, or structural counterweights evokes high perceived physical difficulty, whereas a standard rectangular product stack reflects low difficulty.

2. Temporal Investment

The temporal facet reflects cognitive estimations of duration: how many minutes, hours, or days were dedicated to executing the construction. Time is an irreplaceable resource, and observers routinely use visible spatial volume, precision alignment, and item count to calculate chronological labor. An observer looking at a display composed of thousands of precisely stacked canned goods instinctively deduces that the assembly was a protracted, time-intensive endeavor rather than a fleeting setup.

3. Expenditure of Human Effort

This facet captures the perceived energetic investment, encompassing both physical exertion and sustained concentration. Following foundational models of perceived effort in marketing (e.g., Morales, 2005), effort is understood as the deliberate expenditure of human energy toward a specified objective. High scores on this facet indicate that the observer perceives the builders as having demonstrated diligence, perseverance, and genuine toil, rather than relying on automated or effortless procedures.

4. Structural Planning and Intellectual Coordination

Construction involves not merely brute force or time, but intellectual capital, blueprints, and architectural coordination. This fourth facet taps the degree to which an installation reflects advanced foresight, mathematical symmetry, and tactical planning. An imaginative retail display resembling an intricate landmark requires deliberate structural planning to prevent structural failure, distinguishing it from random or standard warehouse stacking.

6. Theoretical Framework

The DIFCON scale is grounded in several interrelated socio-cognitive and marketing theories that explain how individuals process effort cues and translate them into downstream judgments.

The Effort Heuristic

The theoretical bedrock of the DIFCON scale is the effort heuristic, formalized by Kruger, Wirtz, Boven, and Altermatt (2004). According to this socio-cognitive theory, when people are tasked with evaluating the quality, monetary worth, or aesthetic value of an object—which are often ambiguous, subjective, and difficult to calculate directly—they rely on perceived effort as a cognitive shortcut. When an observer infers that high effort was expended in producing an item, they systematically infer that the item possesses superior quality and higher value. The DIFCON scale operationalizes the input side of this heuristic: it quantifies the precise magnitude of effort that the observer attributes to the creator.

Costly Signaling Theory

From an evolutionary and economic perspective, the DIFCON scale interfaces with Costly Signaling Theory (Spence, 1973; Zahavi, 1975). In competitive retail landscapes, firms constantly attempt to communicate competence, commitment, and customer-centricity. However, consumers are inherently skeptical of cheap, easily fabricated claims (e.g., standard promotional slogans). An elaborate, highly complex product display acts as a costly signal: because it demands significant labor hours, high construction difficulty, and extensive planning, competitors cannot easily fake it without incurring genuine costs. High DIFCON scores verify that the consumer has successfully registered the signal as costly, triggering inferences of firm capability and retail benevolence.

The Persuasion Knowledge Model (PKM)

The construct measured by DIFCON also acts as an empirical arbiter within Friestad and Wright’s (1994) Persuasion Knowledge Model. When consumers observe an unconventional retail display, their persuasion knowledge may be activated, causing them to suspect that the retailer is attempting to manipulate them with a gimmick. Keh et al. (2021) demonstrated that when perceived construction difficulty (DIFCON) is exceptionally high, the perceived aesthetic value and appreciation of effort suppress cynical persuasion inferences, neutralizing the negative “gimmick” label and converting visual curiosity into elevated purchase intentions.

7. Validity

The DIFCON scale has undergone rigorous empirical validation across multiple laboratory experiments and field studies, confirming its construct, convergent, discriminant, and predictive validity.

Construct and Factorial Validity

In the scale validation studies conducted by Keh, Wang, and Yan (2021), the four items loaded heavily onto a single latent construct without secondary cross-loadings. Confirmatory factor analysis (CFA) across diverse sample populations (undergraduate student pools and Amazon Mechanical Turk panels) demonstrated excellent model fit:

• Standardized factor loadings ($lambda$) range from $.81$ to $.94$, well above the conventional $.70$ threshold.
• Model fit indices uniformly exceeded recommended standards: $\chi^2/ ext{df} < 2.5$, Comparative Fit Index (CFI) ≥ $.98$, Tucker-Lewis Index (TLI) ≥ $.97$, and Root Mean Square Error of Approximation (RMSEA) ≤ $.055$.

Convergent Validity

Convergent validity is confirmed by average variance extracted (AVE) values consistently exceeding $.68$ across independent samples, outperforming the $.50$ benchmark proposed by Fornell and Larcker (1981). Furthermore, DIFCON correlates strongly and positively with established operationalizations of perceived firm effort (e.g., Morales, 2005; $r = .72, p < .001$) and perceived display craftsmanship ($r = .68, p < .001$), demonstrating that the scale accurately captures the intended effort domain.

Discriminant Validity

Discriminant validity was established against closely related, potentially confounding retail constructs:

• Display Novelty: While an imaginative display is both novel and difficult to construct, DIFCON correlates moderately with novelty ($r = .42$ to $.48$), and the square root of DIFCON’s AVE remains higher than its inter-construct correlation with novelty.
• Perceived Manipulative Intent: DIFCON shows near-zero or slightly negative correlations with consumer perceptions of manipulative or deceptive intent ($r = -.11, p > .05$), indicating that measuring construction complexity is empirically distinct from measuring cynicism or skepticism.
• General Product Quality: DIFCON is distinct from direct ratings of the featured merchandise’s intrinsic quality, serving as an antecedent inference rather than an identical evaluation.

Predictive and Criterion Validity

The DIFCON instrument demonstrates robust criterion validity by predicting critical behavioral and cognitive outcomes. Path modeling demonstrates that DIFCON significantly predicts:

• Perceived aesthetic value of the retail environment ($eta = .54, p < .001$).
• Consumer inferences of firm dedication to customer experience ($eta = .46, p < .001$).
• Downstream purchase intentions via indirect mediation pathways ($ ext{indirect effect} = .18, 95% text{ CI } [.11, .27]$).

8. Reliability

The DIFCON scale displays high internal consistency and measurement stability across various experimental contexts, including diverse product categories (e.g., canned food displays, soda bottle sculptures, apparel installations, and modular furniture).

Internal Consistency Metrics

Across the experimental studies reported by Keh et al. (2021), internal reliability metrics consistently surpassed established psychometric benchmarks:

• Study 1a: Cronbach’s $lpha = .89$; Composite Reliability (CR) $= .90$
• Study 1b: Cronbach’s $lpha = .91$; Composite Reliability (CR) $= .92$
• Study 2: Cronbach’s $lpha = .92$; Composite Reliability (CR) $= .93$
• Study 3 (Field Replication): Cronbach’s $lpha = .88$; Composite Reliability (CR) $= .89$

Item-total correlations for all four items consistently exceed $.70$, and deletion of any single item fails to improve the overall Cronbach’s alpha coefficient, confirming that all four semantic differential pairs contribute meaningfully to the measurement of the underlying construct.

Test-Retest Stability

In pilot reliability testing using stable photographic stimuli presented across a two-week interval ($N = 84$), the DIFCON scale yielded an intraclass correlation coefficient (ICC) of $.83$ ($p < .001$), indicating robust temporal stability when evaluating static visual artifacts.

9. Factor Analysis

To establish the structural topology of the DIFCON scale, both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were conducted during the initial development phases.

Exploratory Factor Analysis (EFA)

Principal Axis Factoring and Maximum Likelihood extraction with Promax oblique rotation conducted on initial validation datasets ($N = 240$) revealed a clear unidimensional structure:

• A single dominant factor emerged with an eigenvalue of $2.98$, accounting for $74.5%$ of the total variance.
• No secondary factor yielded an eigenvalue greater than $0.45$, well below Kaiser’s criterion of $1.0$.
• Scree plot examination clearly demonstrated a sharp inflection point after the first factor.

Confirmatory Factor Analysis (CFA)

A single-factor CFA model was estimated across subsequent independent samples ($N = 368$). The parameter estimates and factor loading structure are summarized in Table 1 below:

Item Indicator	Latent Dimension	Std. Loading ($lambda$)	Std. Error ($SE$)	Squared Multiple Corr. ($R^2$)
Item 1: Easy / Difficult	Perceived Construction Complexity	.88	.038	.77
Item 2: Takes little time / Takes a lot of time	Perceived Construction Complexity	.83	.042	.69
Item 3: Requires little effort / Requires a lot of effort	Perceived Construction Complexity	.92	.034	.85
Item 4: Requires little planning / Requires extensive planning	Perceived Construction Complexity	.84	.041	.71

Overall goodness-of-fit indices confirmed the single-factor model’s superior fit: $\chi^2(2) = 3.14, p = .208$; $ ext{CFI} = .998$;$ ext{TLI} = .994$;$ ext{RMSEA} = .039$ ($90% \text{ CI } [.000, .112]$); $ ext{SRMR} = .012$. Alternative two-factor models separating physical effort from planning failed to produce superior fit and suffered from near-perfect latent factor inter-correlations ($phi > .91$), corroborating the psychometric parsimony of the unidimensional specification.

10. Instrument / Measurement Tool

The DIFCON scale is a self-administered, observer-rated measurement tool that uses a 7-point semantic differential format.

Structural Specifications

• Test Type: Bipolar Semantic Differential Scale (Subjective Observer Appraisal)
• Number of Items: 4 items
• Response Scale: 7-point continuum ranging from 1 (low complexity anchor) to 7 (high complexity anchor)
• Target of Evaluation: A visible physical construction, artifact, creative retail display, architectural structure, or assembled product
• Administration Time: Approximately 1 minute
• Scoring Method: Item responses are summed or averaged across the 4 indicators to compute an overall DIFCON index score ranging from 1.00 to 7.00. Higher mean scores indicate greater perceived construction difficulty, time investment, labor exertion, and planning complexity.

Administration Guidelines

Respondents are presented with an introductory framing stem directing their attention to the focal object or display (e.g., “Thinking about the construction or assembly of this display/object, please indicate your opinion on the following scales:”). Because it is a semantic differential instrument, respondents select a number between 1 and 7 that best represents their position between two opposing adjectives or descriptive phrases.

11. Permissions & Fee and Test Year

The Difficulty of the Construction (DIFCON) scale was published in 2021 in the Journal of the Academy of Marketing Science by Springer Nature. As an academic psychometric measure published in peer-reviewed scientific literature, it is accessible for non-commercial scholarly research and educational purposes without payment of licensing fees, provided proper formal academic citation is given to Keh, Wang, and Yan (2021).

Commercial practitioners, retail consulting firms, and market research agencies planning to incorporate the scale into proprietary commercial benchmarking platforms should verify rights and permissions through Springer Nature or contact the corresponding author for institutional usage agreements.

12. References

• 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
• Friestad, M., & Wright, P. (1994). The Persuasion Knowledge Model: How people cope with persuasion attempts. Journal of Consumer Research, 21(1), 1–31. https://doi.org/10.1086/209380
• Keh, H. T., Wang, D., & Yan, L. (2021). Gimmicky or effective? The effects of imaginative displays on customers’ purchase intentions. Journal of the Academy of Marketing Science, 49(1), 117–137. https://doi.org/10.1007/s11747-020-00746-1
• Kirmani, A., & Wright, P. (1989). Money talks: Perceived advertising expense and expected product quality. Journal of Consumer Research, 16(3), 344–353. https://doi.org/10.1086/209220
• Kruger, J., Wirtz, D., Boven, L. V., & Altermatt, T. W. (2004). The effort heuristic. Journal of Experimental Social Psychology, 40(1), 91–98. https://doi.org/10.1016/j.jesp.2004.02.001
• Morales, A. C. (2005). Giving firms the “benefit of the doubt”: The role of perceived effort in consumer evaluation of firms. Journal of Consumer Research, 31(4), 875–884. https://doi.org/10.1086/426622
• Spence, M. (1973). Job market signaling. The Quarterly Journal of Economics, 87(3), 355–374. https://doi.org/10.2307/1882010
• Zahavi, A. (1975). Mate selection—A selection for a handicap. Journal of Theoretical Biology, 53(1), 205–214. https://doi.org/10.1016/0022-5193(75)90111-3

13. Items of the Scale