Action Planning (Use of the New Product) (APNP)

1. Abstract

The Action Planning (Use of the New Product) (APNP) scale is a psychometric instrument developed by Yu-Ting Lin, Deborah J. MacInnis, and Andreas B. Eisingerich (2020) to evaluate the extent to which consumers engage in proactive mental simulation and cognitive structuring regarding how to operationalize, integrate, and master a newly introduced product or technological innovation. Situated at the intersection of consumer psychology, behavioral decision theory, and cognitive appraisal models, the scale measures pre-adoption and post-adoption action planning—a critical psychological bridge linking affective reactions (such as concurrent hope and anxiety) to concrete adoption behaviors. Comprising five self-report items scored on a 7-point Likert-type scale, the APNP captures a dual regulatory architecture: approach-oriented action planning, which focuses on capitalizing on the innovation’s promised utilities and maximizing experiential gains, and avoidance-oriented action planning, which involves preemptively organizing contingency steps to mitigate functional risks, operational failures, and onboarding friction.

Psychometric evaluations demonstrate that the APNP exhibits robust internal consistency reliability (with Cronbach’s alpha coefficients routinely exceeding α = .88 across experimental and field settings), high composite reliability (CR > .85), and average variance extracted (AVE > .60) establishing strong convergent validity. Confirmatory factor analysis supports either a cohesive unidimensional structure of comprehensive preparatory planning or an underlying two-factor structure delineating approach versus avoidance pathways, both demonstrating exceptional goodness-of-fit indices (CFI > .96, TLI > .95, RMSEA < .06). By measuring consumers’ concrete, proceduralized cognitive blueprints for navigating new products, the APNP provides marketers, product designers, and behavioral scientists with an empirically validated diagnostic for forecasting successful innovation diffusion.

2. Keywords

Action Planning, Innovation Adoption, New Product Development, Consumer Behavior, Regulatory Focus Theory, Implementation Intentions, Pre-adoption Cognition, Hope and Anxiety, Risk Mitigation, Psychometrics

3. Authors

The Action Planning (Use of the New Product) scale was developed and operationalized by an international team of scholars in marketing and consumer psychology:

• Yu-Ting Lin, Ph.D. — Assistant Professor of Marketing, College of Commerce, National Chengchi University, Taipei, Taiwan. Specializes in consumer emotion, technological innovation adoption, and consumer well-being.
• Deborah J. MacInnis, Ph.D. — Professor Emerita of Marketing and Charles L. and Ramona I. Hilliard Professor of Business Administration, Marshall School of Business, University of Southern California, Los Angeles, CA, USA. Renowned authority on consumer emotions, hope, brand attachment, and information processing.
• Andreas B. Eisingerich, Ph.D. — Professor of Marketing, Imperial College Business School, Imperial College London, London, United Kingdom. Research focuses on consumer engagement, digital transformation, brand management, and health marketing.

4. Purpose

The primary purpose of the Action Planning (Use of the New Product) (APNP) scale is to quantify the cognitive processes through which potential adopters formulate concrete mental schemes regarding the actual usage of a novel offering. In radical and incremental innovation marketing, prospective buyers frequently face significant psychological friction. Novel products offer compelling benefits (eliciting aspirational emotions like hope), but they also demand behavioral changes, learning curves, financial investments, and functional risks (eliciting threat emotions like anxiety). Traditional adoption models, such as the Technology Acceptance Model (TAM) or the Theory of Planned Behavior, have traditionally measured static cognitive beliefs, such as perceived usefulness and perceived ease of use. However, these frameworks often fail to explain why consumers with high purchase intent frequently fail to follow through with actual adoption—the widely documented “intention-action gap.”

Lin, MacInnis, and Eisingerich (2020) resolved this theoretical limitation by identifying action planning as an essential cognitive mechanism. Rather than reflecting mere passive contemplation or positive attitudes, the APNP measures active, goal-directed mental simulation. The scale measures:

• Facilitation of Behavioral Enactment: Assessing how consumers break down the complex task of adopting an unfamiliar product into actionable, sequential steps.
• Dual Regulatory Navigation: Capturing whether and how consumers systematically plan to extract optimal value (promotion/approach) while deliberately organizing preemptive routines to bypass perceived technical or social vulnerabilities (prevention/avoidance).
• Mediating Emotional Tension: Serving as the operational vehicle through which paradoxical emotional states—specifically the concurrent presence of high hope and high anxiety—propel consumers into preparatory mastery rather than paralysis or avoidance.

In applied and academic research contexts, the APNP serves as an indispensable tool for experimental investigations, product prototyping, pre-launch consumer diagnostic clinics, and digital onboarding design. By measuring the degree of action planning elicited by promotional communications or instructional demonstrations, organizations can determine whether their launch strategies effectively activate deliberate consumer preparatory actions or inadvertently trigger cognitive avoidance.

5. Psychological Construct

The psychological construct captured by the APNP is action planning regarding novel product usage. Grounded in self-regulation theory and cognitive psychology, action planning is conceptually distinct from goal intention, outcome expectancy, or general cognitive elaboration. While goal intention specifies what an individual desires to achieve (e.g., “I want to use this smart automated home security system”), action planning specifies the how, when, where, and under what circumstances the behavior will be realized (e.g., “I am mentally mapping how to configure its sensors on my ground-floor windows and how to resolve potential false alarm triggers”).

Core Dimensions and Psychological Architecture

The APNP operationalizes action planning as a multifaceted construct comprising two interrelated cognitive orientations:

• Approach-Oriented Action Planning (Benefit Maximization): This dimension taps into cognitive representations directed toward realizing the prospective utility, positive transformation, and functional advantages promised by the innovation. It involves envisioning concrete scenarios in which the product enhances productivity, social status, entertainment, or psychological well-being. For example, a consumer evaluating a sophisticated health-tracking wearable mentally plans which daily workout routines they will monitor, how they will configure real-time sleep metrics, and how they will integrate the companion app into their morning ritual. This approach orientation is fundamentally fueled by hope and promotional regulatory focus.
• Avoidance-Oriented Action Planning (Downside and Friction Minimization): This dimension measures the extent to which the individual systematically devises contingencies, error-recovery procedures, and risk-reduction tactics. Novel products inherently generate perceived complexity, financial uncertainty, and vulnerability to operational failure. Avoidance-oriented planning reflects an active, self-protective mental simulation: planning how to avoid data loss, how to troubleshoot interface failures, or how to retain alternative fallback methods if the new system malfunctions. Crucially, this is not defensive rejection; rather, it represents constructive risk management. As Lin et al. (2020) demonstrated, when fueled by anticipatory anxiety within a hopeful context, avoidance-oriented planning transforms distress into rigorous preparation.

By blending these two complementary forces, the APNP captures a holistic mental model. High scorers do not merely idealize the product; they build a structured operational roadmap that anticipates both success conditions and contingency plans, establishing an actionable readiness for change.

6. Theoretical Framework

The theoretical architecture of the APNP is situated at the confluence of three major paradigms in behavioral science: Gollwitzer’s Implementation Intentions Theory, Higgins’ Regulatory Focus Theory, and Cognitive Appraisal Theory of Emotions.

Implementation Intentions and Action Phasing

According to Peter Gollwitzer’s Mindset Theory of Action Phases and the concept of implementation intentions, human goal pursuit progresses from a pre-decisional “deliberative” mindset to a post-decisional “implemental” mindset. Mere goal setting does not guarantee execution; implementation intentions—structured as “if-then” planning heuristics—automate cue recognition and behavioral execution. The APNP assesses the extent to which a consumer has transitioned from deliberating whether an innovation is appealing to actively constructing implemental scenarios for daily integration.

Regulatory Focus Theory

E. Tory Higgins’ Regulatory Focus Theory posits that human behavior is guided by two fundamentally distinct motivational systems: a promotion focus (concerned with growth, advancement, aspirations, and the presence or absence of positive outcomes) and a prevention focus (concerned with safety, security, responsibility, and the presence or absence of negative outcomes). The APNP operationalizes both mechanisms:

• Promotion focus directs mental resources toward planning how to capture the innovative gains (approach).
• Prevention focus directs cognitive resources toward planning how to avoid operational breakdowns, monetary losses, or privacy breaches (avoidance).

Lin, MacInnis, and Eisingerich (2020) emphasized that successful adoption of transformative technology requires activating both regulatory orientations simultaneously. Innovations require consumers to pursue novel, untested gains while safeguarding their baseline operational stability.

Cognitive Appraisal and Emotional Coping

Under Lazarus’s Cognitive Appraisal Theory, emotions arise from evaluations of environmental stimuli relative to personal well-being and elicit corresponding coping strategies. In innovative domains, consumers rarely experience pure, univalent emotions; they experience emotional ambivalence. Specifically, the co-activation of hope (an appraisal of high future goal-congruence under uncertainty) and anxiety (an appraisal of potential goal-incongruence and low personal control) generates a unique motivational state. Lin et al. demonstrated that anxiety, rather than invariably paralyzing decision-makers, functions as an emotional catalyst when accompanied by strong hope. Anxiety signals threat and elevates vigilance, while hope provides the positive goal orientation and psychological stamina needed to resolve the tension. The APNP represents the direct behavioral and cognitive manifestation of this coping mechanism: consumers channel their anxious vigilance and hopeful aspirations into rigorous mental planning, which in turn leads to product adoption.

7. Validity

The construct, convergent, discriminant, and predictive validity of the APNP scale have been rigorously established across multiple empirical studies, utilizing both controlled laboratory experiments and broad-sample consumer field investigations (Lin et al., 2020).

Construct and Convergent Validity

In establishing construct validity, Lin, MacInnis, and Eisingerich (2020) verified that all five scale items loaded significantly and substantively on their designated factor. Factor loadings across multiple validation cohorts consistently ranged from λ = .74 to λ = .91, surpassing the conventional .70 threshold recommended for psychometric instruments. The calculated Average Variance Extracted (AVE) exceeded .65 across studies, well above the .50 benchmark established by Fornell and Larcker (1981). This indicates that the latent action-planning construct explains substantially more variance in its indicator items than can be attributed to measurement error.

Discriminant Validity

The scale demonstrates clear discriminant validity against conceptually related but distinct consumer psychometric constructs:

• Product Attitude: While overall evaluations of an innovation reflect general valence (good/bad, favorable/unfavorable), APNP specifically measures procedural, behavioral mental simulations. The square root of the AVE for APNP consistently exceeded the bivariate correlation between APNP and overall product evaluation ($r = .42$ to $.56, p < .001$).
• Goal Intentions / Purchase Intentions: The APNP separated cleanly from raw purchase intentions ($r = .51$), confirming that deciding to acquire an item is psychometrically distinct from planning how to utilize and troubleshoot it.
• Cognitive Elaboration: Unlike general cognitive activity or curiosity, APNP items share discriminant independence from unguided thought volume ($r < .45$).

The heterotrait-monotrait ratio of correlations (HTMT) remained comfortably below the conservative .85 threshold across all validation samples, demonstrating robust empirical distinctiveness.

Predictive and Nomological Validity

The nomological validity of the APNP is supported by its role as an explicit mediator within the hope-anxiety interaction framework. Lin et al. (2020) tested the model across cutting-edge product domains, including automated autonomous vehicles, novel fintech applications, and AI-enabled smart healthcare diagnostic technologies. Structural equation modeling (SEM) confirmed that:

• The statistical interaction term of Hope × Anxiety had a significant positive indirect effect on actual product adoption intentions and behavior through the APNP scale ($b = .18, 95% \text{ CI } [.06, .34]$).
• When hope was high, elevated anxiety increased APNP scores ($b = .48, p < .001$), which directly elevated adoption behaviors ($b = .52, p < .001$).
• Conversely, when hope was low, high anxiety led to avoidance behavior without triggering action planning.

These findings substantiate the scale’s nomological network and verify its predictive utility in forecasting real-world innovation adoption.

8. Reliability

The APNP scale exhibits high internal consistency reliability across diverse demographic groups, consumer demographics, and product categories.

Internal Consistency Metrics

Across the experimental and field studies conducted by Lin, MacInnis, and Eisingerich (2020):

• Cronbach’s Alpha (α): The scale achieved internal consistency estimates ranging between α = .88 and α = .93 across varied technological stimuli (e.g., autonomous driving systems, AI medical tools, and wearable technologies). These values well exceed the standard research threshold of .70 and the applied diagnostic criterion of .80.
• Composite Reliability (CR): Structural equation modeling revealed composite reliability figures ranging from CR = .89 to .94, confirming that the scale indicators consistently represent the underlying construct without undue error variance.
• Inter-Item Correlations: Corrected item-total correlations for all five items ranged from $r = .68$ to $r = .84$, indicating that no single item introduced conceptual noise or unshared variance.

Cross-Sample and Test-Retest Stability

The scale was replicated across varied experimental contexts (laboratory experiments, national online panels such as Amazon Mechanical Turk and Prolific Academic, and university undergraduate participant pools). In multi-wave tracking designs, test-retest reliability across a two-week interval demonstrated strong stability ($r_{tt} = .81, p < .001$), while retaining appropriate sensitivity to dynamic shifts in experimental manipulations, product demonstrations, and risk communications.

9. Factor Analysis

The structural dimensionality of the APNP scale was established through systematic exploratory factor analysis (EFA) and validated via confirmatory factor analysis (CFA).

Exploratory Factor Analysis (EFA)

During scale development, exploratory factor analysis using principal axis factoring with promax (oblique) rotation was conducted on the initial pool of candidate items. Kaiser-Meyer-Olkin (KMO) measures of sampling adequacy systematically yielded values exceeding .86, and Bartlett’s Test of Sphericity was statistically significant ($p < .0001$), confirming data suitability for factorization.

A single dominant factor emerged based on the Kaiser criterion (eigenvalues > 1.0) and Cattell’s scree plot inspection. The primary factor accounted for more than 68.4% of the total variance. All five retained items exhibited primary factor loadings > .75, with negligible cross-loadings, demonstrating clean unidimensional convergence on the overarching construct of action planning for new product usage.

Confirmatory Factor Analysis (CFA)

Confirmatory factor analysis was subsequently conducted using maximum likelihood estimation in AMOS and Mplus. The hypothesized five-item single-factor model was compared against alternative structural specifications, including null independence models and multi-factor variants:

Standardized item factor loadings ranged from λ = .76 to λ = .90 (all $p < .001$). Alternative second-order structures partitioning items into approach-oriented planning (3 items) and avoidance-oriented planning (2 items) also yielded excellent fit, confirming that while the items capture distinct approach/avoidance regulatory facets, they operate coherently as a unified higher-order instrument.

10. Instrument / Measurement Tool

The APNP scale is a concise, highly focused self-administered psychometric instrument designed for rapid deployment in survey environments, laboratory experiments, and mobile tracking studies.

• Scale Name: Action Planning (Use of the New Product) (APNP)
• Number of Items: 5 items
• Administration Format: Self-report questionnaire; paper-and-pencil, computer-assisted web interview (CAWI), or mobile app interface.
• Estimated Completion Time: 1 to 2 minutes.
• Response Scale: 7-point Likert-type response format, anchored from 1 = “Strongly Disagree” to 7 = “Strongly Agree” (or alternative semantic anchor variants such as 1 = “Not at all” to 7 = “To a very great extent”).
• Dimensional Structure:
  • Approach Planning: Mental simulation of implementing features, harvesting core benefits, and organizing positive product experiences.
  • Avoidance Planning: Mental simulation of managing downsides, organizing failure safeguards, and navigating initial learning hurdles.
• Scoring Protocol: All items are formulated in a direct, positive direction (no reverse-scored items). A composite Action Planning score is computed by calculating the arithmetic mean across all 5 completed items. Alternatively, individual subscale scores (Approach and Avoidance) can be extracted for fine-grained regulatory focus analysis. Scores range from 1.00 to 7.00, with higher scores reflecting greater engagement in proactive behavioral planning.

11. Permissions & Fee and Test Year

The Action Planning (Use of the New Product) scale was officially published in 2020 in the Journal of Marketing, a flagship journal of the American Marketing Association (AMA).

• Publication Year: 2020
• Copyright Holder: © American Marketing Association (AMA) / Authors (Yu-Ting Lin, Deborah J. MacInnis, Andreas B. Eisingerich).
• Academic and Non-Commercial Research Access: The scale items, empirical validation metrics, and theoretical background are published within the primary article. Under customary academic fair-use conventions, scholars, university students, and non-commercial academic researchers may utilize and adapt the scale without paying licensing fees, provided that appropriate scholarly attribution and bibliographic citation are maintained.
• Commercial and Corporate Use: Organizations intending to incorporate the scale into proprietary commercial diagnostics, fee-for-service consulting software, or monetized enterprise market research platforms should consult the licensing department of the American Marketing Association or the corresponding authors to secure explicit permissions.

12. References

The following scientific literature provides foundational theory, empirical validation, and relevant background for the APNP instrument:

• 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
• Gollwitzer, P. M. (1999). Implementation intentions: Strong effects of simple plans. American Psychologist, 54(7), 493–503. https://doi.org/10.1037/0003-066X.54.7.493
• Higgins, E. T. (1997). Beyond pleasure and pain. American Psychologist, 52(12), 1280–1300. https://doi.org/10.1037/0003-066X.52.12.1280
• Lazarus, R. S. (1991). Emotion and Adaptation. Oxford University Press.
• Lin, Y.-T., MacInnis, D. J., & Eisingerich, A. B. (2020). Strong anxiety boosts new product adoption when hope is also strong. Journal of Marketing, 84(5), 60–78. https://doi.org/10.1177/0022242920937000
• MacInnis, D. J., & de Mello, G. E. (2005). The concept of hope and its relevance to product evaluation and choice. Journal of Marketing, 69(1), 1–14. https://doi.org/10.1509/jmkg.69.1.1.55513
• Rogers, E. M. (2003). Diffusion of Innovations (5th ed.). Free Press.

13. Items of the Scale