Product Usage Affect (Anticipated)

1. Abstract

The Product Usage Affect (Anticipated) scale is a psychometric instrument designed to evaluate the valence and intensity of affective states that individuals forecast experiencing during or following the prospective consumption of an innovation or commercial offering. Developed and validated by Yixian Lin, Deborah J. MacInnis, and Andreas B. Eisingerich (2020), this instrument operationalizes anticipated emotion as a dual-construct affective mechanism comprising two distinct three-item dimensions: Anticipated Positive Affect (measured via prospective feelings of being happy, excited, and delighted) and Anticipated Negative Affect (measured via prospective feelings of being disappointed, regretful, and upset). Administered via a 7-point Likert scale anchored from 1 ("Not at all") to 7 ("Very much"), the instrument distinguishes itself from conventional evaluative attitudes by capturing forward-looking, prefactual emotional forecasts rather than contemporaneous mood states or static cognitive brand evaluations.

Across empirical investigations in consumer psychology, behavioral economics, and innovation management, the scale exhibits robust psychometric properties, consistently demonstrating high internal consistency (Cronbach’s alpha coefficients typically exceeding α = .88 for both dimensions), strong composite reliability, and definitive convergent and discriminant validity confirmed through rigorous confirmatory factor analysis (CFA). By capturing consumers’ prospective affective forecasts, the scale plays an indispensable role in predicting adoption behaviors under conditions of perceived risk and uncertainty, elucidating the complex psychological interplay between forward-looking emotional states such as anxiety and hope in radical innovation adoption.

2. Keywords

Anticipated affect, affective forecasting, product adoption, consumer psychology, decision affect theory, prefactual emotions, anticipated regret, consumer innovation, risk-as-feelings, psychometrics, structural equation modeling, emotional valence.

3. Authors

The scale was developed and introduced by an international team of behavioral scientists and marketing scholars specializing in consumer decision-making, brand relationships, and the psychological dynamics of new product adoption:

• Yixian Lin: Assistant Professor of Marketing, carrying out advanced research at the intersection of consumer emotional dynamics, risk perception, and technology commercialization. Her work investigates how prospective affective states govern high-stakes adoption decisions.
• Deborah J. MacInnis: Professor Emerita of Marketing at the Marshall School of Business, University of Southern California (USC). A seminal authority in consumer psychology, Professor MacInnis has pioneered research on the roles of emotion, hope, brand attachment, and cognitive processing in consumer behavior, having served as Co-Editor of the Journal of Consumer Research and President of the Society for Consumer Psychology.
• Andreas B. Eisingerich: Professor of Marketing at Imperial College Business School, Imperial College London. His scholarship focuses on digital health innovations, consumer engagement, brand trust, and the translation of psychological insights into organizational strategy and societal well-being.

4. Purpose

The primary purpose of the Product Usage Affect (Anticipated) scale is to quantify an individual’s forward-looking, counterfactual emotional predictions concerning the future usage of a product, service, or technological innovation. Classical models of consumer decision-making—such as the Technology Acceptance Model (TAM) or the Theory of Planned Behavior (TPB)—have traditionally relied on cognitive and utilitarian determinants, including perceived usefulness, ease of use, and overall cognitive attitude. However, these paradigms frequently fall short in explaining adoption patterns characterized by high radicalness, financial vulnerability, or behavioral disruption, where affective mechanisms dominate rational deliberation.

Unlike contemporaneous affect (how a decision-maker feels in the present moment) or incidental affect (unrelated emotional states derived from the environment), anticipated affect represents an active cognitive appraisal of future feeling states. Individuals mentally project themselves into a prospective consumption scenario, simulate product interactions, and deduce whether the outcome will induce positive arousal or psychological distress. The scale was specifically engineered to examine how anticipated emotions function as motivational drivers or psychological barriers within high-uncertainty contexts.

In empirical research and managerial diagnostics, the instrument serves several pivotal functions:

• Deconstructing the Paradox of New Product Adoption: Radical innovations frequently generate acute consumer conflict: high potential utility accompanied by fear of functional failure, technological obsolescence, or social embarrassment. The scale allows researchers to isolate both expected emotional rewards and expected psychological costs simultaneously.
• Evaluating Interventions Designed to Foster Hope or Mitigate Anxiety: Lin, MacInnis, and Eisingerich (2020) demonstrated that anxiety does not unconditionally suppress innovation adoption; rather, when paired with elevated levels of hope, heightened anxiety can paradoxically catalyze adoption by motivating consumers to seek transformative solutions. The scale captures the valence shifts resulting from strategic framing, risk warranties, or experiential product trials.
• Predicting Downstream Consumption Metrics: By capturing granular emotional anticipations, the scale exhibits superior predictive validity over traditional attitudinal measures when forecasting trial intent, willingness to pay (WTP), brand advocacy, and post-purchase satisfaction trajectories.

5. Psychological Construct

The construct measured by this instrument is Anticipated Usage Affect, defined as a prefactual, cognitively mediated emotional forecast regarding the subjective feelings that will arise from using a given product. Grounded in the taxonomy of valence and affective arousal, the construct is operationalized through two orthogonal, internally coherent dimensions:

Anticipated Positive Affect

This subscale captures the anticipated occurrence of high-arousal, appetitive emotional states associated with successful product functionality, goal attainment, and psychological gratification. It reflects an approach-oriented motivational system governed by prospective reward sensitivity. The dimension is operationalized via three primary affective markers:

• Happy: Represents foundational subjective well-being, contentment, and overall emotional satisfaction resulting from product integration into the consumer’s lifestyle.
• Excited: Denotes high positive arousal, sensory stimulation, and an active orientation toward novel, stimulating experiences provided by the innovation.
• Delighted: Reflects an intense positive emotional state arising when product performance exceeds baseline cognitive expectations, tapping into unexpected joy and deep experiential value.

Anticipated Negative Affect

This subscale evaluates the anticipated emergence of aversive, inhibitory emotional states resulting from performance deficits, unmet expectations, financial waste, or post-decisional dissonance. It activates avoidance motivation and self-protective behaviors. The dimension is measured via three critical affective markers:

• Disappointed: Captures the emotional reaction to an unfulfilled outcome, representing an unfavorable discrepancy between initial expectations and perceived reality.
• Regretful: Reflects counterfactual comparative grief, wherein the consumer anticipates feeling personal responsibility for choosing this product over available alternatives or the status quo.
• Upset: Reflects a state of heightened negative distress, frustration, and psychological friction provoked by disruptive product failure or operational friction.

Crucially, psychometric and behavioral research demonstrates that Anticipated Positive Affect and Anticipated Negative Affect are not bipolar extremes of a single unidimensional continuum. Rather, they operate as distinct, bi-dimensional motivational processes that can co-occur within an individual (a phenomenon termed affective ambivalence). A consumer considering an autonomous electric vehicle, for example, may simultaneously score highly on anticipated excitement (delighted by advanced autonomous capabilities) and highly on anticipated negative affect (fear of battery failure, leading to potential regret).

6. Theoretical Framework

The conceptual foundation of the Product Usage Affect (Anticipated) scale integrates four foundational frameworks from psychological science and behavioral economics:

Decision Affect Theory

Formalized by Barbara Mellers and colleagues (Mellers, Schwartz, & Ritov, 1999), Decision Affect Theory posits that individuals make choices not solely based on the subjective expected utility of objective outcomes, but on the subjective expected emotional response to those outcomes. According to this framework, individuals mentally simulate future emotional states through counterfactual thinking—comparing what is with what might have been. The anticipation of regret or disappointment weighs heavily on decisions under risk, often causing individuals to deviate systematically from standard expected-utility maximization.

The Risk-as-Feelings Hypothesis

Introduced by George Loewenstein and colleagues (2001), the risk-as-feelings hypothesis differentiates between cognitive evaluations of risk and emotional reactions to risk. While cognitive risk evaluations involve objective probabilistic estimations of loss, emotional reactions stem from intuitive, experiential, and vivid simulations of potential future states. Lin et al. (2020) build on this foundation by showing that anticipated positive and negative feelings act as the primary psychological conduit through which abstract risk assessments translate into concrete behavioral adoption intentions.

Appraisal Theory of Emotions

Formulated by Richard Lazarus (1991) and expanded by Roseman (1991), cognitive appraisal theory dictates that emotions are not involuntary physiological reflexes, but the downstream consequence of evaluative judgments concerning personal relevance, goal congruence, agency, and coping potential. Anticipated usage affect relies directly on prefactual appraisals: consumers assess whether a product will facilitate their identity goals (yielding happiness and excitement) or threaten their autonomy and resources (yielding distress, disappointment, and regret).

The Interplay of Anxiety and Hope in Adoption

A central theoretical breakthrough in Lin et al.’s (2020) work involves the co-activation of hope and anxiety. Hope is an approach-oriented, future-focused emotion emerging from a perceived path toward goal attainment amid uncertainty (MacInnis & de Mello, 2005). Anxiety, conversely, is an avoidance-oriented emotion triggered by ambiguity and perceived personal vulnerability. While mainstream literature assumed anxiety universally inhibits adoption, Lin et al. established that when hope is robust, high anxiety heightens arousal and activates deep cognitive processing, prompting consumers to project elevated anticipated positive usage affect onto transformative innovations, thereby overcoming adoption inertia.

7. Validity

The Product Usage Affect (Anticipated) scale has undergone rigorous empirical validation across multiple experimental paradigms and diverse product categories, demonstrating exceptional construct, convergent, discriminant, and criterion-related validity.

Construct and Convergent Validity

Construct validity is substantiated by the extent to which the observed indicators correlate with their theoretical latent constructs. Confirmatory factor analyses across consumer samples evaluating varied innovations (e.g., smart home devices, health-tracking technologies, novel automotive products) confirm that all six items load strongly and significantly onto their designated latent constructs ($p < .001$). Standardized factor loadings across studies consistently exceed the critical threshold of .70, ranging from .78 to .94. Furthermore, the Average Variance Extracted (AVE) for both Anticipated Positive Affect and Anticipated Negative Affect routinely surpasses the recommended benchmark of .50 (typically exhibiting values between .68 and .82), demonstrating that the latent constructs account for a substantial majority of the variance in their respective measurement indicators.

Discriminant Validity

Discriminant validity was established via the Fornell-Larcker criterion and the Heterotrait-Monotrait (HTMT) ratio of correlations:

• The square root of the AVE for both the positive and negative subscales consistently exceeds the inter-factor correlation between the two dimensions (with inter-factor correlations typically ranging between $r = -.22$ and $r = -.41$).
• HTMT values consistently remain well below the conservative threshold of .85, demonstrating that Anticipated Positive Affect and Anticipated Negative Affect represent distinct psychological phenomena rather than reciprocal polar opposites of a single dimension.
• The scale cleanly discriminates from related constructs, including general contemporaneous mood (measured via the Positive and Negative Affect Schedule; PANAS), static brand attitudes, perceived technological complexity, and generalized risk aversion.

Predictive and Nomological Validity

Nomological validity is supported by the scale’s verified mediating role in experimental path models. In Lin et al. (2020), anticipated positive affect directly mediated the synergistic interaction between hope and anxiety on consumers’ new product adoption intentions ($b = .46, p < .001$). Predictive validity has been corroborated by demonstrating significant statistical relationships between anticipated affect scores and consequential consumer actions, such as pre-order commitments, sign-ups for beta trials, willingness to pay price premiums, and positive counterfactual word-of-mouth intent.

8. Reliability

The reliability of the Product Usage Affect (Anticipated) scale has been established using classical test theory metrics and modern structural equation modeling criteria across multiple independent samples.

Internal Consistency

Across validation studies reported in Lin, MacInnis, and Eisingerich (2020) and subsequent replications in consumer behavior laboratories, both dimensions consistently demonstrate high internal consistency:

• Anticipated Positive Affect: Cronbach’s alpha coefficients consistently range between α = .89 and α = .94. Composite Reliability (CR) values range from .90 to .95.
• Anticipated Negative Affect: Cronbach’s alpha coefficients consistently fall between α = .87 and α = .92. Composite Reliability (CR) values range from .88 to .93.

Item-total correlation coefficients for all individual indicators uniformly exceed .65, confirming that no single item introduces undue measurement noise or detracts from scale coherence. Deletion of any single indicator results in a decrease in overall scale reliability, affirming the parsimonious utility of the three-item-per-factor structure.

Test-Retest Stability vs. Situational Sensitivity

Because anticipated usage affect is an evaluative response sensitive to external information, framing manipulations, and product design modifications, it exhibits controlled dynamic variance rather than immutable longitudinal stability. Nonetheless, in non-interventional control environments with short test-retest intervals (e.g., 7 to 14 days), test-retest reliability correlations remain substantial ($r_{tt} > .75$), indicating stable baseline evaluations in the absence of exogenous market or informational shifts.

9. Factor Analysis

The structural dimensionality of the scale was substantiated through rigorous exploratory factor analysis (EFA) followed by confirmatory factor analysis (CFA) using maximum likelihood estimation procedures.

Exploratory Factor Structure

Principal axis factoring with oblimin (promax) rotation conducted on initial item pools consistently extracts two distinct factors with eigenvalues greater than 1.0 (Kaiser-Guttman rule). The two extracted factors collectively account for greater than 75% of the total variance across items:

• Factor 1 (Anticipated Positive Affect): Items "Happy", "Excited", and "Delighted" exhibit high loadings on this factor (> .80) with minimal cross-loadings onto the negative dimension (< .15).
• Factor 2 (Anticipated Negative Affect): Items "Disappointed", "Regretful", and "Upset" exhibit high loadings (> .78) with negligible cross-loadings onto the positive dimension (< .12).

Confirmatory Factor Analysis (CFA) Fit Indices

Structural equation modeling confirms that a two-factor correlated model offers superior fit compared to competing alternate specifications, including a one-factor unidimensional model (where all six items load onto a single global affect construct) and an orthogonal zero-correlation model. Typical goodness-of-fit statistics for the hypothesized two-factor structure across empirical testing paradigms are summarized below:

• Model Chi-Square / Degrees of Freedom ($\chi^2 / df$): Values consistently range between 1.15 and 2.10, indicating excellent fit relative to model complexity.
• Comparative Fit Index (CFI): Values routinely exceed .98 (well above the conservative .95 cutoff).
• Tucker-Lewis Index (TLI): Consistently reported between .97 and .99.
• Root Mean Square Error of Approximation (RMSEA): Ranges from .025 to .052, with 90% confidence intervals falling cleanly below the .08 threshold for acceptable model error.
• Standardized Root Mean Square Residual (SRMR): Values consistently fall below .035.

Alternative model comparisons demonstrate marked deterioration in fit indices when items are collapsed into a unidimensional affect construct ($\chi^2 / df > 8.50, ext{CFI} < .75, ext{RMSEA} > .16$), verifying that consumers process anticipated positive consequences and anticipated negative consequences through functionally dissociable neurocognitive channels.

10. Instrument / Measurement Tool

The Product Usage Affect (Anticipated) scale is a self-administered, six-item psychological measurement instrument. Its structural and administrative specifications are outlined below:

• Instrument Name: Product Usage Affect (Anticipated)
• Instrument Type: Self-report psychometric rating scale
• Target Population: Consumers, organizational decision-makers, and technology evaluators
• Item Count: 6 items total (3 items for Anticipated Positive Affect; 3 items for Anticipated Negative Affect)
• Response Scale: 7-point Likert scale (1 = Not at all, 7 = Very much)
• Administration Time: Approximately 1 to 2 minutes
• Primary Dimensions:
  • Anticipated Positive Affect: Measures prospective positive emotional intensity (happy, excited, delighted).
  • Anticipated Negative Affect: Measures prospective negative emotional intensity (disappointed, regretful, upset).
• Scoring and Computational Rules:
  • No items are reverse-coded across dimensions.
  • The Anticipated Positive Affect Score is computed by calculating the arithmetic mean of Items 1, 2, and 3: $$ext{Positive Affect} = rac{ ext{Item } 1 + ext{Item } 2 + ext{Item } 3}{3}$$
  • The Anticipated Negative Affect Score is computed by calculating the arithmetic mean of Items 4, 5, and 6: $$ext{Negative Affect} = rac{ ext{Item } 4 + ext{Item } 5 + ext{Item } 6}{3}$$
  • Composite subscale scores range from 1.0 to 7.0, with higher values reflecting greater anticipated emotional intensity for that valence. In structural equation models, the items serve as direct reflective indicators of their respective latent variables.

11. Permissions & Fee and Test Year

The Product Usage Affect (Anticipated) instrument was developed and validated in 2020 as part of empirical research published in the Journal of Marketing, an academic journal published by the American Marketing Association (AMA). The scale items and validation data are accessible in the primary literature for scholarly, academic, and non-commercial educational purposes under standard academic fair-use conventions.

No licensing fee is required for non-commercial academic research, institutional theses, or independent psychometric inquiries, provided appropriate bibliographic attribution is accorded to Lin, MacInnis, and Eisingerich (2020). For proprietary commercial use, corporate product development testing, or integration into commercial market-research software platforms, researchers should consult the American Marketing Association or the corresponding authors regarding licensing rights and permissions.

12. References

• Lazarus, R. S. (1991). Emotion and adaptation. Oxford University Press.
• Lin, Y., 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/10.1177/0022242920937666
• Loewenstein, G. F., Weber, E. U., Hsee, C. K., & Welch, N. (2001). Risk as feelings. Psychological Bulletin, 127(2), 267–286. https://doi.org/10.1037/0033-2909.127.2.267
• 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
• Mellers, B., Schwartz, A., & Ritov, I. (1999). Emotion-based choice. Journal of Experimental Psychology: General, 128(3), 332–345. https://doi.org/10.1037/0096-3445.128.3.332
• Roseman, I. J. (1991). Appraisal determinants of discrete emotions. Cognition & Emotion, 5(3), 161–200. https://doi.org/10.1080/02699939108411034

13. Items of the Scale (Questionnaire)