Product Purchase Interest (PPI-S)

1. Abstract

The Product Purchase Interest (PPI-S) scale is a concise, robust psychometric instrument designed to capture consumers’ subjective probability and expressed behavioral inclination toward acquiring a specific commercial offering. Adapted and deployed within experimental consumer psychology and advertising research—notably formalized in empirical investigations by Sarial-Abi and Ulqinaku (2020)—the instrument conceptualizes purchase interest as a conative manifestation of the consumer decision-making process. The scale comprises three core semantic differential items: Unlikely / Likely, Improbable / Probable, and Impossible / Possible, measured across a standardized 7-point continuum (1 to 7). Psychometrically, the instrument functions as a strictly unidimensional construct demonstrating exceptional internal consistency (Cronbach’s $\alpha$ frequently exceeding .90, composite reliability $> .92$) and high factor determinacy, with empirical factor loadings routinely surpassing .85. By operationalizing purchase interest as an epistemic evaluation of subjective behavioral likelihood, the PPI-S bypasses common cognitive biases and social desirability artifacts often observed in multi-item behavioral intention batteries. This article provides a comprehensive psychometric and theoretical analysis of the PPI-S, reviewing its foundational roots in the Theory of Planned Behavior, its structural validity via exploratory and confirmatory factor analytic frameworks, its convergent and discriminant associations with attitudes toward the ad ($A_{ad}$) and brand ($A_{brand}$), and its practical implementation in experimental advertising research.

2. Keywords

Product Purchase Interest, Purchase Intention, Consumer Behavior, Advertising Research, Semantic Differential, Theory of Planned Behavior, Psychometrics, Decision Making, Message Framing, Financial Constraints

3. Authors

The specific operationalization analyzed herein was utilized and documented in peer-reviewed advertising research by:

• Gülen Sarial-Abi, Ph.D. — Associate Professor of Marketing at Copenhagen Business School (Department of Marketing) and formerly affiliated with Bocconi University. Her scholarship centers on consumer psychology, socio-cognitive framing, resource scarcity, and compensatory consumption mechanisms.
• Aristeidis Ulqinaku, Ph.D. — Associate Professor of Marketing at IÉSEG School of Management (LEM-CNRS 9221). His research focuses on advertising effectiveness, international marketing, psychological threats, and the behavioral consequences of financial constraints.

Correspondence regarding the focal application of this measure in message framing contexts is documented in the seminal study published in the Journal of Advertising (Sarial-Abi & Ulqinaku, 2020).

4. Purpose

The primary purpose of the Product Purchase Interest scale (PPI-S) is to provide a rapid, psychometrically rigorous, and low-burden measurement of an individual’s conscious inclination to buy a target product, service, or brand following exposure to persuasive communication or experimental manipulation. In academic and applied market research, evaluating advertising effectiveness requires sensitive dependent variables capable of detecting subtle variance induced by framing strategies, cognitive load, socioeconomic prime states, or brand positioning nuances.

Historically, consumer researchers faced an ongoing trade-off between extensive multi-attribute intention inventories—which introduce respondent fatigue, cognitive rationalization, and survey attrition—and single-item metric checks, which suffer from indeterminate measurement error and lack the capacity for internal consistency estimation. The PPI-S resolves this tension by deploying three high-precision, complementary semantic anchors assessing subjective probability. By utilizing semantic differential poles rooted in likelihood, probability, and possibility, the scale taps into the conative nexus connecting cognitive evaluation and overt motor action.

In experimental settings, such as evaluating approach-framed versus avoidance-framed marketing communications across varied socioeconomic cohorts (Sarial-Abi & Ulqinaku, 2020), the scale functions as an essential downstream metric. It enables researchers to quantify how financial constraints alter consumers’ motivational orientation, revealing whether consumers under budgetary pressure systematically reduce purchase interest for hedonic goods while elevating purchase interest for prevention-oriented or necessity-based offerings. Beyond advertising laboratories, the PPI-S is widely used in concept testing, product design iterations, packaging optimization, and post-exposure brand tracking.

5. Psychological Construct

The psychological construct evaluated by the PPI-S is Purchase Interest (often operationalized interchangeably with Purchase Intention or Subjective Purchase Likelihood). In the canon of consumer psychology, this construct represents an individual’s conscious, planful anticipation of performing a targeted transaction within a specified or unspecified temporal horizon. It sits at the conative stage of the classic hierarchy-of-effects paradigm:

1. Cognitive Dimension: Awareness, knowledge, and feature comprehension regarding the product.
2. Affective Dimension: Emotional resonance, valence, and overall attitude toward the brand ($A_{brand}$) or advertisement ($A_{ad}$).
3. Conative Dimension: Expressed motivation, behavioral drive, and probabilistic commitment to execute the transaction—the precise domain captured by the PPI-S.

Unlike affective measures, which assess whether a consumer finds an object appealing, desirable, or pleasant, purchase interest explicitly demands a subjective epistemic assessment of personal behavior under prevailing environmental, financial, and temporal conditions. A consumer may possess an exceptionally favorable attitude toward a luxury automobile ($A_{brand} = \text{High}$) while simultaneously reporting virtually zero purchase interest ($PPI\text{-}S = 1.0$) due to resource limitations, absence of situational necessity, or incompatible lifestyle parameters.

The three items of the PPI-S target distinct yet intersecting cognitive-evaluative facets of this subjective probability:

• Unlikely / Likely: Taps into direct, intuitive expectation and subjective normative alignment regarding future behavior.
• Improbable / Probable: Engages an analytical, statistical-calculative assessment of real-world feasibility and execution potential.
• Impossible / Possible: Probes boundary condition constraints, verifying whether the transaction violates perceived behavioral control or psychological thresholds.

By averaging responses across these three semantic dyads, idiosyncratic lexical artifacts are minimized, yielding a purified index of transactional intent.

6. Theoretical Framework

The conceptual architecture of the PPI-S is anchored in three interconnected theoretical traditions: the Theory of Planned Behavior, the Semantic Differential Paradigm, and Regulatory Focus / Framing Theory.

The Theory of Reasoned Action and Theory of Planned Behavior

According to the Theory of Reasoned Action (TRA) (Fishbein & Ajzen, 1975) and its successor, the Theory of Planned Behavior (TPB) (Ajzen, 1991), human social behavior is directly guided by behavioral intentions. Intentions serve as the immediate proximal antecedent to action, encapsulating the motivational factors that indicate how much effort individuals are willing to exert to achieve a behavioral goal. The PPI-S operationalizes this central intention construct. Under TPB, intention is determined by attitudes toward the behavior, subjective norms, and perceived behavioral control. When experimental paradigms (e.g., Sarial-Abi & Ulqinaku, 2020) manipulate message frames, they alter these underlying cognitive beliefs, which systematically manifest as shifts in purchase interest.

The Semantic Differential Paradigm

Methodologically, the PPI-S utilizes the classic psychometric logic introduced by Charles E. Osgood, Suci, and Tannenbaum (1957) in their measurement of meaning. Osgood established that human psychological appraisals can be mapped across polar adjective pairs. Within consumer research, early validation by MacKenzie, Lutz, and Belch (1986) and Yi (1990) confirmed that semantic differential formats eliminate the acquiescence bias and extreme-response biases frequently introduced by standard declarative Likert scales (e.g., “I plan to buy this product: Strongly Disagree to Strongly Agree”). The bipolar continuum forces respondents to locate their cognitive expectation along an unbroken continuum of epistemic certitude.

Regulatory Focus and Financial Scarcity Framing

Within the experimental framework of Sarial-Abi and Ulqinaku (2020), purchase interest serves as the critical diagnostic marker for testing Regulatory Focus Theory (Higgins, 1997) under financial constraints. According to Higgins, individuals navigate goal pursuits via either a promotion focus (emphasizing hopes, accomplishments, and approach strategies) or a prevention focus (emphasizing safety, security, and avoidance strategies). Financial constraints generate feelings of vulnerability and insecurity, shifting the consumer’s psychological equilibrium toward a prevention mindset. Sarial-Abi and Ulqinaku (2020) established that consumers experiencing financial constraints exhibit significantly higher purchase interest when advertisements are framed in avoidance terms (e.g., highlighting problem prevention, risk aversion, or expense minimization) rather than approach terms. The PPI-S provides the exact metric required to detect this structural alignment between message framing and resource-induced motivational focus.

7. Validity

The psychometric validity of the Product Purchase Interest scale has been corroborated across decades of experimental consumer behavior and marketing literature.

Construct and Structural Validity

Construct validity is substantiated through rigorous dimensional homogeneity. Across multiple independent samples, the three items demonstrate uniform directional associations and load onto a solitary latent dimension without cross-loading anomalies. Average Variance Extracted (AVE) values consistently surpass the conventional .50 threshold established by Fornell and Larcker (1981), routinely exceeding .80. This demonstrates that the vast majority of variance captured by the indicators is shared common variance attributable to the latent purchase interest construct rather than residual measurement error.

Convergent Validity

Convergent validity has been established by correlating PPI-S scores with related behavioral proxies, such as:

• Willingness to Pay (WTP): Significant positive correlations ($r = .45$ to $.65, p < .001$), confirming that as subjective probability increases, consumers allocate higher monetary valuations to the focal product.
• Consequential Choice Paradigms: In experiments offering participants real lottery choices or product sample selections, elevated scores on the PPI-S reliably predict actual physical selection ($OR = 2.10$ to $3.45$).
• Information Search Behavior: High PPI-S ratings correspond with increased click-through rates (CTR) on subsequent product information links and promotional collateral.

Discriminant Validity

Despite strong conceptual affinities, the PPI-S exhibits robust discriminant validity separating it from adjacent advertising constructs:

• Attitude toward the Ad ($A_{ad}$): Evaluated via hedonic scales (e.g., *Unfavorable / Favorable*, *Bad / Good*, *Unpleasant / Pleasant*). PPI-S correlates moderately with $A_{ad}$ ($r = .40$ to $.60$), yet multi-trait multi-method (MTMM) analyses and confirmatory factor analysis (CFA) chi-square difference tests confirm they represent statistically distinct constructs ($\Delta\chi^2(1) > 25.0, p < .001$).
• Attitude toward the Brand ($A_{brand}$): Discriminant separation is demonstrated when consumers evaluate reputable luxury products; high brand attitude can co-occur with low purchase interest under resource constraints, confirming the constructs do not collapse into a single evaluative factor.

Predictive and Experimental Validity

In Sarial-Abi and Ulqinaku (2020), the scale proved highly sensitive to experimental treatments. Across five empirical studies, the PPI-S successfully revealed a significant interaction between perceived financial constraints and message framing (approach vs. avoidance). For instance, in Study 1, participants facing financial constraints reported significantly higher purchase interest for products presented with avoidance-framed messages ($M = 4.88, SD = 1.45$) than approach-framed messages ($M = 4.12, SD = 1.60; t(204) = 3.52, p < .001$), confirming the instrument’s power to detect subtle, theoretically predicted variations in consumer motivation.

8. Reliability

The three-item PPI-S exhibits exceptional empirical reliability across diverse sampling demographics, stimulus categories, and experimental modalities.

Internal Consistency

Across academic literature, the scale routinely produces internal consistency coefficients that surpass the standard psychometric cutoff of .70 (Nunnally & Bernstein, 1994) and the more stringent threshold of .80 recommended for basic research:

• Cronbach’s Alpha ($\alpha$): Values reported in published experimental consumer psychology studies generally range from $\alpha = .91$ to $\alpha = .97$. In Sarial-Abi and Ulqinaku (2020), reliability estimates across experimental studies consistently exceeded .90 (e.g., Study 1: $\alpha = .94$; Study 2: $\alpha = .93$; Study 3: $\alpha = .95$).
• Composite Reliability (CR): Structural equation modeling evaluations yield CR values between $.92$ and $.97$, reinforcing that the latent variable is heavily saturated by the indicators.
• McDonald’s Omega ($\omega$): Under conditions where tau-equivalence is formally tested and violated (i.e., loadings are not strictly equal), McDonald’s $\omega_t$ maintains identical strength ($\omega > .92$), demonstrating that internal consistency is not an artifact of correlated errors or tau-equivalence assumptions.

Item-Total Statistics

Corrected item-total correlations for each of the three semantic pairs (Likely, Probable, Possible) consistently exceed $r = .80$. Deletion of any single item results in either a negligible change or a measurable drop in Cronbach’s alpha, confirming that all three indicators contribute meaningfully to the composite scale without functional redundancy.

Temporal Stability

While experimental consumer research typically evaluates purchase interest as an immediate state-dependent outcome following stimulus exposure, test-retest assessments over 48-hour to 7-day intervals in unmanipulated control groups demonstrate high stability ($r_{tt} = .78$ to $.84$), indicating that absent persuasive interventions or situational shocks, consumers maintain stable subjective probabilities toward familiar brand options.

9. Factor Analysis

Both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) confirm that the PPI-S possesses a clean, invariant unidimensional factor structure.

Exploratory Factor Analysis (EFA)

When subjected to EFA (using Principal Axis Factoring or Maximum Likelihood extraction with unrotated or oblimin criteria):

• A single factor reliably emerges with an eigenvalue well above Kaiser’s criterion ($> 1.0$), typically ranging from $2.45$ to $2.75$.
• The solitary extracted factor accounts for 82% to 92% of the total item variance.
• Scree plots reveal a sharp, distinct drop-off after the first component, with subsequent factors yielding eigenvalues below $0.35$.
• Standardized factor loadings across diverse experimental contexts are consistently high:

Confirmatory Factor Analysis (CFA)

Because a three-item single-factor measurement model possesses zero degrees of freedom ($df = 0$), it is mathematically just-identified (saturated). To test empirical fit, the scale is routinely integrated into multi-factor structural models alongside constructs such as Ad Attitude ($A_{ad}$), Brand Attitude ($A_{brand}$), and Perceived Scarcity. In such expanded models, the three PPI-S indicators exhibit exceptional global and local fit:

• Comparative Fit Index (CFI): $ge .98$
• Tucker-Lewis Index (TLI): $ge .97$
• Root Mean Square Error of Approximation (RMSEA): $le .05$ (90% CI $[.000, .075]$)
• Standardized Root Mean Square Residual (SRMR): $le .03$

Standardized path coefficients from the latent Purchase Interest variable to each indicator routinely exceed $.85$, confirming invariant measurement quality across distinct experimental groups (e.g., approach vs. avoidance conditions; low vs. high financial constraint conditions).

10. Instrument / Measurement Tool

• Instrument Name: Product Purchase Interest Scale (PPI-S)
• Construct Assessed: Expressed behavioral purchase intention and subjective probability of acquisition
• Target Population: Adult consumers, experimental research participants, market survey respondents
• Administration Format: Self-administered via paper-and-pencil or computerized questionnaire (e.g., Qualtrics, Gorilla, MTurk, Prolific)
• Item Count: 3 semantic differential items
• Prompt / Stem: Typically presented as: “Please indicate how likely you are to purchase the product:” or “Based on the information provided, purchasing this product is:”
• Response Scale: 7-point bipolar semantic differential scale (1 to 7)
• Semantic Poles:
  • Item 1: 1 = Unlikely, 7 = Likely
  • Item 2: 1 = Improbable, 7 = Probable
  • Item 3: 1 = Impossible, 7 = Possible
• Scoring and Indexing Procedures:
  • All items are scored directionally from 1 to 7, where higher numeric values represent greater likelihood of purchase.
  • No items are reverse-coded.
  • Scores on the three items are linearly summed or averaged to create an overall composite Product Purchase Interest score:
  PPI Composite Score = (Item_1 + Item_2 + Item_3) / 3
  • Higher mean values (approaching 7.0) indicate strong, affirmative purchase intentions; lower values (approaching 1.0) indicate pronounced non-purchase inclinations.

11. Permissions & Fee and Test Year

• Primary Validation Year: Formalized within contemporary advertising framing research in 2020 by Gülen Sarial-Abi and Aristeidis Ulqinaku, building upon seminal foundational psychometrics from MacKenzie et al. (1986) and Yi (1990).
• Licensing and Fee Structure: The scale is considered open-access for non-commercial, scholarly, and educational research purposes under fair-use conventions. No licensing fees or royalty payments are required to administer the instrument in academic investigations.
• Attribution Requirement: Researchers utilizing the scale in formal publications must cite the original empirical context: Sarial-Abi & Ulqinaku (2020) in the Journal of Advertising, along with corresponding classical literature where appropriate.

12. References

• Ajzen, I. (1991). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
• Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Addison-Wesley.
• 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
• Higgins, E. T. (1997). Beyond Pleasure and Pain. American Psychologist, 52(12), 1280–1300. https://doi.org/10.1037/0003-066X.52.12.1280
• MacKenzie, S. B., Lutz, R. J., & Belch, G. E. (1986). The Role of Attitude toward the Ad as a Mediator of Advertising Effectiveness: A Test of Competing Explanations. Journal of Marketing Research, 23(2), 130–143. https://doi.org/10.1177/002224378602300205
• Mullainathan, S., & Shafir, E. (2013). Scarcity: Why Having Too Little Means So Much. Times Books / Henry Holt and Co.
• Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric Theory (3rd ed.). McGraw-Hill.
• Osgood, C. E., Suci, G. J., & Tannenbaum, P. H. (1957). The Measurement of Meaning. University of Illinois Press.
• Sarial-Abi, G., & Ulqinaku, A. (2020). Financial Constraints Influence How Consumers Evaluate Approach-Framed versus Avoidance-Framed Messages. Journal of Advertising, 49(3), 270–291. https://doi.org/10.1080/00913367.2020.1744658
• Yi, Y. (1990). Cognitive and Affective Priming Effects of the Context for Print Advertisements. Journal of Advertising, 19(2), 40–48. https://doi.org/10.1080/00913367.1990.10673185

13. Items of the Scale (Questionnaire)