Company Product Purchase Intention Scale

1. Abstract

The Company Product Purchase Intention Scale (COPURINT) is a psychometric instrument designed to evaluate the cognitive likelihood, commitment, and conative readiness of consumers to purchase products from a specific corporate entity. Developed and validated by Silvia Grappi, Simona Romani, and Richard P. Bagozzi (2015) in their seminal investigation of consumer stakeholder responses to corporate manufacturing and supply-chain relocations (specifically reshoring strategies), the instrument captures how strategic corporate decisions reshape marketplace behavioral intentions. Operating as a unidimensional, three-item self-report measure, the scale assesses consumer conation across three distinct gradients of behavioral resolve: probabilistic evaluation (e.g., subjective likelihood of purchasing), temporal commitment (e.g., explicit resolution to buy at the next available consumption opportunity), and adoption or trial intention (e.g., willingness to sample or experiment with the firm's product offerings).

Administered primarily via 7-point Likert-type response formats (ranging from 1 = strongly disagree to 7 = strongly agree), COPURINT exhibits exceptional psychometric properties. Across experimental conditions and structural equation modeling (SEM) evaluations, the scale demonstrates robust internal consistency, with composite reliability (CR) coefficients consistently exceeding .90 and Cronbach's alpha values surpassing .88. Average variance extracted (AVE) values consistently surpass .75, establishing substantial convergent validity. The instrument demonstrates strong predictive and criterion validity, functioning as a primary downstream dependent variable linked to consumer gratitude, moral elevation, corporate social responsibility perceptions, and brand equity. By capturing both immediate trial propensity and projected future commitment, COPURINT provides organizational researchers and marketing scholars with an empirically efficient, theoretically grounded metric for modeling downstream behavioral outcomes in experimental and field settings.

2. Keywords

Purchase intention, consumer behavior, psychometrics, reshoring, corporate social responsibility, Theory of Planned Behavior, conation, stakeholder theory, measurement model, structural equation modeling.

3. Authors

The Company Product Purchase Intention Scale was established and operationalized by an international team of behavioral and marketing scholars:

• Silvia Grappi, Ph.D. — Full Professor of Marketing, Department of Communication and Economics, University of Modena and Reggio Emilia, Italy. Her research focuses on consumer ethics, corporate social responsibility (CSR), green marketing, and consumer emotional and behavioral responses to corporate strategies.
• Simona Romani, Ph.D. — Full Professor of Marketing, Department of Business and Management, Luiss Guido Carli University, Rome, Italy. Her scholarly work centers on consumer emotions, moral psychology, boycott behaviors, and corporate wrongdoing.
• Richard P. Bagozzi, Ph.D. — Dwight F. Benton Professor of Behavioral Science in Management, Ross School of Business, and Professor of Clinical and Administrative Sciences, College of Pharmacy, University of Michigan, Ann Arbor, USA. Renowned for foundational contributions to psychometric methodology, structural equation modeling, attitude theory, and social psychological modeling in marketing.

4. Purpose

The primary purpose of the Company Product Purchase Intention Scale (COPURINT) is to quantify the subjective probability and intentional resolve that an individual consumer allocates toward acquiring the commercial offerings of a designated firm. Grounded in consumer psychology and behavioral decision research, the scale was engineered to solve a persistent methodological challenge: capturing fine-grained variations in consumer support or penalization resulting from macro-level corporate operational decisions, such as reshoring (the repatriating of manufacturing activities from foreign offshore locations back to domestic territory) versus offshoring.

In contemporary market environments, consumer purchase decisions are rarely driven solely by price and functional utility. Instead, consumers increasingly function as socio-political stakeholders who evaluate the moral, ethical, and societal ramifications of corporate actions. Consequently, researchers require a concise, highly sensitive metric capable of capturing shifts in transactional intentions triggered by corporate decisions. COPURINT fulfills this purpose by evaluating consumer conative responses across three interrelated psychological expressions:

• Probability of buying: Captures the cognitive appraisal of likelihood, integrating perceived capability, product category relevance, and overall positive brand evaluation.
• Definite future purchasing: Gauges conative commitment, establishing whether the intention remains resilient across time until the next actual shopping opportunity.
• Trial and experimentation intention: Measures behavioral openness to sampling new or repositioned product offerings from the company, overcoming initial purchase hesitation.

Beyond academic research on corporate reshoring, COPURINT is utilized across consumer psychology, corporate communication, and applied market research. It assesses how consumer behavioral intentions respond to brand crises, environmental sustainability initiatives, changes in supply-chain transparency, and corporate social advocacy. In clinical and organizational consulting contexts, the scale provides a standardized diagnostic benchmark for auditing brand health, identifying stakeholder attrition risks, and forecasting market viability following strategic repositioning.

5. Psychological Construct

The psychological construct underlying COPURINT is purchase intention, defined within cognitive and social psychology as an individual's conscious plan, personal motivation, and subjective probability judgment regarding whether to execute a specific transactional behavior. Situated within the conative component of the classic tripartite model of attitudes (affect, cognition, and conation), purchase intention represents the immediate cognitive precursor to overt purchasing behavior.

Dimensions and Structural Granularity

Although modeled psychometrically as a unidimensional construct, COPURINT evaluates three psychological facets that collectively define behavioral intention:

• Probabilistic Likelihood Assessment: This facet reflects the respondent's subjective probability calculation. Modeled after Fishbein and Ajzen's expectancy-value framework, it asks the consumer to weigh overall attitudes, normative pressures, and perceived situational opportunities to determine whether acquiring the product is plausible and likely.
• Temporal and Contextual Commitment: Intentions can fluctuate between generalized hypothetical goodwill and contextual resolve. This facet measures temporal continuity—specifically whether the consumer resolves to select the brand's offering during their subsequent purchase opportunity, indicating goal-directed persistence.
• Exploratory Trial Motivation: Rooted in innovation diffusion and consumer curiosity paradigms, this facet captures behavioral willingness to experience the brand's offerings. Trial represents an important behavioral threshold, particularly when a company undergoes operational restructuring, reputation recovery, or market re-entry.

By blending probabilistic estimation, temporal commitment, and exploratory openness, the construct measured by COPURINT avoids common limitations found in single-item "willingness to buy" metrics, which frequently exhibit higher measurement error and fail to account for differing thresholds of behavioral commitment.

6. Theoretical Framework

The Company Product Purchase Intention Scale is grounded in foundational social psychological and consumer behavior theories, most notably the Theory of Planned Behavior (TPB) developed by Icek Ajzen, the Theory of Reasoned Action (TRA) by Martin Fishbein and Ajzen, and Richard P. Bagozzi's comprehensive reformulation of attitude theory.

The Attitude-to-Action Sequence

Central to the theoretical scaffolding of COPURINT is the postulation that cognitive and affective appraisals do not translate directly into observable consumer actions. Rather, they are mediated through conative processes—specifically structured intentions:

Cognitive & Affective Appraisals → Moral & Emotional Reactions → Behavioral Intentions (COPURINT) → Overt Behavior

In Grappi, Romani, and Bagozzi's (2015) structural conceptualization, consumers act as moral arbiters. When a corporate entity makes a significant strategic move—such as reshoring production facilities—consumers conduct cognitive evaluations of corporate social responsibility and perceived corporate citizenship. These cognitive evaluations trigger specific moral and emotional responses, such as gratitude, pride, and moral elevation. According to Bagozzi's appraisal theory of emotions, discrete emotional experiences serve as motivational catalysts that demand behavioral manifestation. The purchase intention measured by COPURINT serves as the conative vehicle through which consumers reward corporate actions they deem socially responsible or morally commendable.

Stakeholder Theory in Consumer Behavior

The scale also draws heavily upon Stakeholder Theory, as articulated by R. Edward Freeman. Unlike classic economic assumptions that categorize consumers solely as transaction-maximizing market agents, stakeholder theory views consumers as active members of the socio-economic collective who use their purchasing power to endorse or sanction corporate operational choices. COPURINT measures this stakeholder agency, operationalizing purchasing behavior as an act of reciprocity, ethical endorsement, and civic partnership.

7. Validity

The psychometric validity of COPURINT has been evaluated through multiple analytical procedures, including rigorous construct validation, convergent and discriminant validity tests, and criterion-related predictive modeling.

Construct and Convergent Validity

Construct validity refers to how accurately an instrument measures the theoretical concept it was designed to evaluate. In the original validation studies conducted by Grappi et al. (2015), confirmatory factor analysis (CFA) demonstrated that the three items load uniformly and strongly onto a single latent factor. Standardized factor loadings across experimental scenarios consistently exceeded .85, far surpassing the standard psychometric threshold of .70. Convergent validity was established via the Average Variance Extracted (AVE), which consistently exceeded .75 across experimental samples, demonstrating that the shared variance among the scale items was substantially greater than residual measurement error.

Discriminant Validity

Discriminant validity was established using the Fornell-Larcker criterion and cross-loading comparisons. COPURINT demonstrated empirical distinctiveness from conceptually adjacent constructs measured within the same structural frameworks, including:

• Advocacy Intentions / Positive Word-of-Mouth (WOM): While WOM evaluates interpersonal communication and social recommendation, COPURINT evaluates direct financial and transactional commitment. Correlations between the two constructs hovered between .60 and .72, and the square root of the AVE for COPURINT was higher than its inter-construct correlations.
• Attitude Toward the Company: General affective and evaluative disposition toward the firm was separated from behavioral intention; the AVE of COPURINT exceeded the shared variance between attitude and purchase intention.
• Willingness to Pay a Price Premium: Distinct from general purchase intention, demonstrating that willingness to buy does not automatically signify tolerance for increased pricing.

Predictive and Nomological Validity

The nomological validity of COPURINT is supported by its integration into structural equation models evaluating consumer reactions to manufacturing decisions. The scale was predicted by corporate credibility, perceived social performance, and positive emotional states (moral elevation and gratitude; path coefficients typically β = .35 to .55, p < .001). In turn, COPURINT exhibited robust predictive power over longitudinal and simulated transactional choice tasks, establishing strong criterion-related utility.

8. Reliability

Reliability analyses for the Company Product Purchase Intention Scale indicate high internal consistency across independent samples and experimental manipulations.

Internal Consistency Metrics

The scale demonstrates strong internal consistency, as reflected in standard reliability metrics across multiple studies:

• Cronbach's Alpha (α): In the initial validation studies by Grappi et al. (2015), the scale yielded Cronbach's alpha values ranging between .88 and .94 across both domestic and cross-national consumer cohorts.
• Composite Reliability (CR): Because Cronbach's alpha assumes tau-equivalence (equal factor loadings across items), composite reliability was computed within structural equation models. The CR values ranged from .90 to .95, exceeding the accepted psychometric benchmark of .70 and demonstrating high internal consistency.
• Average Variance Extracted (AVE): The AVE consistently registered between .75 and .86, indicating that the latent construct accounts for roughly 75% to 86% of the variance in the observed indicators.

Test-Retest Stability and Cross-Sample Invariance

Cross-sample measurement invariance tests demonstrate that COPURINT maintains configural, metric, and scalar invariance across diverse demographic groups and product categories (e.g., consumer electronics, apparel, and durable goods). Test-retest reliability across short-term laboratory re-assessments yielded stability coefficients exceeding r = .82, confirming measurement reliability over time.

9. Factor Analysis

Factor-analytic evaluations of COPURINT confirm a clean, unidimensional latent structure. Both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) demonstrate that the three items function as indicators of a single underlying construct.

Confirmatory Factor Analysis (CFA) Parameter Estimates

In CFA estimations using maximum likelihood (ML) procedures, the three-item configuration yields an exactly identified (just-identified) single-factor measurement model when evaluated in isolation (degrees of freedom = 0). When embedded within larger measurement models alongside related constructs (e.g., moral elevation, brand trust, word-of-mouth intention), the scale demonstrates excellent fit indices:

• Model Fit Indices: Comparative Fit Index (CFI) > .97; Tucker-Lewis Index (TLI) > .96; Root Mean Square Error of Approximation (RMSEA) < .05 (90% CI [.032, .068]); Standardized Root Mean Square Residual (SRMR) < .035.
• Standardized Factor Loadings (λ):
  • Item 1 (Likelihood of buying): λ = .88 to .93
  • Item 2 (Purchase at next opportunity): λ = .86 to .91
  • Item 3 (Definite trial intention): λ = .84 to .89
• Error Variances: Error terms remain low and demonstrate no correlated residuals, confirming that unique variances are random rather than systematically shared.

10. Instrument / Measurement Tool

The COPURINT instrument is structured for rapid administration and clear interpretation across diverse behavioral research designs. Its specifications include:

• Test Type: Standardized Self-Report Psychometric Inventory / Conative Behavioral Assessment.
• Administration Format: Computer-assisted web interview (CAWI), paper-and-pencil questionnaire, or embedded experimental survey module.
• Item Count: 3 items.
• Administration Time: Approximately 30 to 60 seconds.
• Target Population: Adult consumers, corporate stakeholders, and organizational respondents capable of evaluating consumption options.
• Response Scale: 7-point Likert scale:
  • 1 = Strongly disagree
  • 2 = Disagree
  • 3 = Somewhat disagree
  • 4 = Neither agree nor disagree (Neutral)
  • 5 = Somewhat agree
  • 6 = Agree
  • 7 = Strongly agree
• Scoring and Computational Procedures:
  • Unweighted Mean Scoring: Calculate the arithmetic mean across all three items:
    Score = (Item 1 + Item 2 + Item 3) / 3. Resulting scores range from 1.00 to 7.00.
  • Summed Scoring: Compute the sum across the three items (range: 3 to 21).
  • Latent Variable Modeling: In SEM, items are used as continuous indicators loading onto a single latent purchase intention factor, allowing for correction of measurement error.
  • Reverse Coding: No reverse-coded items are present; higher scores correspond directly to stronger purchase intention.

11. Permissions & Fee and Test Year

The Company Product Purchase Intention Scale was published in 2015 in the Journal of the Academy of Marketing Science:

• Year of Initial Publication: 2015.
• Original Copyright: © 2014 Academy of Marketing Science. Published by Springer Science+Business Media New York.
• Licensing and Research Permissions: The scale items and psychometric metrics are published within academic literature. Academic researchers, educators, and university personnel may utilize the scale for non-commercial scientific research, educational instruction, and scholarly theses without purchasing an explicit commercial license, provided standard academic citation and attribution are given to the original authors (Grappi, Romani, & Bagozzi, 2015).
• Commercial Usage: For-profit corporate audits, commercial survey platforms, or proprietary consulting frameworks wishing to reproduce copyrighted materials should verify terms with Springer Nature or the Academy of Marketing Science.
• Fee: Free for academic and non-commercial scientific research applications.

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
• Bagozzi, R. P. (1992). The self-regulation of attitudes, intentions, and behavior. Social Psychology Quarterly, 55(2), 178–204. https://doi.org/10.2307/2786945
• Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94. https://doi.org/10.1007/BF02723327
• 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
• Freeman, R. E. (1984). Strategic management: A stakeholder approach. Pitman.
• Grappi, S., Romani, S., & Bagozzi, R. P. (2013). Consumer response to corporate irresponsible behavior: Moral emotions and virtues. Journal of Business Ethics, 116(1), 181–199. https://doi.org/10.1007/s10551-012-1472-9
• Grappi, S., Romani, S., & Bagozzi, R. P. (2015). Consumer stakeholder responses to reshoring strategies. Journal of the Academy of Marketing Science, 43(4), 453–471. https://doi.org/10.1007/s11747-014-0416-9
• Romani, S., Grappi, S., & Bagozzi, R. P. (2013). Explaining consumer reactions to corporate socially irresponsible behavior: The role of moral emotions. Journal of the Academy of Marketing Science, 41(6), 682–699. https://doi.org/10.1007/s11747-013-0349-5

13. Items of the Scale