Cart Abandonment (Concern about Order Costs) (CAOC)

1. Abstract

The Cart Abandonment (Concern about Order Costs) (CAOC) scale is a specialized psychometric instrument designed to assess a consumer’s propensity to discontinue an online purchase due to unexpected, auxiliary, or escalated price points disclosed at the final stages of electronic transactions. Developed by marketing scholars Monika Kukar-Kinney and Angeline G. Close in their seminal 2010 investigation into digital commerce behavior, the CAOC scale isolates the psychological and behavioral response to what is widely recognized in behavioral economics as “cost-shock” or drip pricing friction. While digital shopping carts fulfill dual roles—serving both as immediate transactional vehicles and as navigational holding spaces or organizational wish lists—the CAOC operates specifically within the transactional failure domain. It comprises a focused three-item unidimensional battery evaluated via a seven-point Likert scale, ranging from strongly disagree to strongly agree.

Psychometrically, the CAOC exhibits robust measurement properties. In its foundational validation, the scale demonstrated high internal consistency reliability, yielding a Cronbach’s alpha coefficient of 0.82 and a composite reliability exceeding conventional psychometric thresholds. Confirmatory factor analyses (CFA) across diverse consumer samples establish significant discriminant and convergent validity, showing that concern about order costs represents a distinct psychological construct separate from price-comparison search behavior, organizational cart use, or procrastination tendencies. Standardized factor loadings across all three indicators consistently exceed 0.70, accounting for substantial variance in actual and reported virtual shopping cart abandonment. By quantifying the acute sensitivity of digital shoppers to unanticipated fees such as shipping surcharges, localized sales taxes, and total transaction thresholds, the CAOC scale bridges foundational economic theories—notably mental accounting and prospect theory—with modern consumer decision journeys, offering empirical diagnostic utility for both academic behavioral researchers and digital marketing practitioners.

2. Keywords

Cart Abandonment, Concern about Order Costs, CAOC, E-Commerce Psychology, Drip Pricing, Partitioned Pricing, Online Consumer Behavior, Shopping Cart Abandonment, Mental Accounting, Transaction Utility, Psychometrics

3. Authors

The Cart Abandonment (Concern about Order Costs) scale was authored by:

• Monika Kukar-Kinney, Ph.D. — Professor of Marketing, Robins School of Business, University of Richmond, Richmond, Virginia, USA. Expert in consumer psychology, behavioral pricing, shopping cart dynamics, and compulsive buying behaviors.
• Angeline Close Scheinbaum (née Angeline G. Close), Ph.D. — Associate Professor and Endowed Research Fellow, Department of Marketing, College of Business, Clemson University (formerly at the Stan Richards School of Advertising & Public Relations, University of Texas at Austin), USA. Expert in consumer behavior, digital commerce, event marketing, and integrated brand promotion.

4. Purpose

The primary purpose of the Cart Abandonment (Concern about Order Costs) (CAOC) scale is to empirically capture, isolate, and quantify a consumer’s affective and behavioral inclination to terminate an online transaction when confronted with the full, final monetary cost of an order. In the ecosystem of electronic commerce, shopping cart abandonment represents a pervasive structural challenge, with commercial baseline estimates indicating that between 65% and 75% of initialized digital carts are vacated prior to final payment processing. Methodologically, however, cart abandonment is not a homogeneous psychological phenomenon; individuals abandon carts for a myriad of distinct cognitive, navigational, exploratory, and economic reasons.

Prior to the establishment of the CAOC scale by Kukar-Kinney and Close (2010), research largely treated cart abandonment as a binary, single-stage outcome variable, often conflating deliberate price-comparison strategies, hedonic browsing, and information-seeking behaviors with genuine transaction failure. The theoretical rationale underpinning the CAOC scale addresses this critical conflation. The instrument is engineered to diagnose what behavioral researchers term “cost-shock abandonment.” This dynamic emerges when a consumer successfully navigates product selection, forms an initial cognitive purchase commitment based on item-level focal prices, progresses deep into the checkout funnel, and subsequently encounters supplementary, unexpected charges—chief among them shipping and handling levies, regional sales taxes, processing fees, or an aggregate order total that breaches the buyer’s subjective reservation price.

In academic research, the CAOC scale enables scholars to map the exact structural paths through which pricing transparency, price partitioning, and checkout interface design interact with consumer loss aversion. By isolating cost concerns from alternative motivations—such as using the cart as an external memory aid or window-shopping device—the scale provides a granular lens for testing hypotheses related to pricing psychology, consumer perceived fairness, and transactional friction. For applied e-commerce analytics, consumer experience (CX) auditing, and marketing strategy, the CAOC offers an empirical baseline for diagnosing whether cart attrition is driven by merchant-specific surcharge policies versus broader product-level disinterest, providing actionable insights for designing checkout funnels, establishing free-shipping thresholds, and deploying dynamic retargeting interventions.

5. Psychological Construct

The CAOC scale operationalizes a specific psychological construct situated at the intersection of consumer decision-making, behavioral pricing, and affective reaction: late-stage order cost sensitivity. Unlike general price consciousness or chronic frugality—which represent enduring individual traits that govern an individual’s broad spending habits across contexts—the CAOC measures a context-specific, stimulus-reactive psychological state that manifests during the interactive checkout phase.

The construct encompasses three primary cognitive and emotional dimensions:

• Shipping Surcharge Reactance: The subjective cognitive friction and perceived unfairness elicited by logistical delivery fees. Consumers frequently compartmentalize product value and delivery value into separate psychological accounts. Consequently, an unexpected shipping fee is rarely interpreted as an equitable cost of transit; instead, it is experienced as a deadweight loss or a transactional penalty, inducing psychological reactance and prompting abrupt checkout termination.
• Tax and Auxiliary Fee Disruption: The cognitive dissonance caused by non-product price escalation. When mandatory localized sales taxes, service charges, or regulatory surcharges are added to the transaction subtotal, the perceived value proposition of the purchase degrades instantly. The consumer perceives a discrepancy between the advertised focal price and the actual out-of-pocket commitment, leading to feelings of merchant opportunism and perceived price unfairness.
• Aggregate Threshold Violation (Total Order Shock): The holistic psychological realization that the cumulative cost exceeds the consumer’s pre-established mental budget or spending ceiling. Even if each individual line item (product, tax, shipping) is ostensibly understood, the synthesized total produces an acute moment of cognitive evaluation wherein total negative utility outstrips the anticipated acquisition utility, resulting in immediate cart abandonment.

In practice, consider a consumer purchasing a pair of running shoes listed at $100. Over twenty minutes of digital navigation, the consumer constructs a mental anchor at this$100 price point. Upon advancing through the checkout gateway—after entering personal and shipping data—the interface presents a revised subtotal: $100 for the footwear,$15.50 for standard ground shipping, and $8.25 for municipal sales tax, resulting in an aggregate financial liability of$123.75. For an individual high in CAOC, this 23.75% price escalation breaks the subjective contract of the transaction. The abandonment response is not driven by a newfound desire to purchase the footwear elsewhere (comparison shopping), nor is it an intentional deferral for future budgeting; it is an acute, reactive refusal to complete the purchase due to negative transactional utility generated by late-disclosed, non-core expenses.

6. Theoretical Framework

The theoretical architecture of the Cart Abandonment (Concern about Order Costs) scale draws predominantly from behavioral economics, cognitive pricing paradigms, and decision theory. Four foundational frameworks provide the conceptual scaffolding for the scale:

1. Mental Accounting and Transaction Utility Theory

Developed by Nobel laureate Richard Thaler (1985, 1999), Mental Accounting Theory posits that economic actors do not treat money as purely fungible; rather, they categorize expenditures into discrete cognitive budgets. Thaler bifurcated the perceived value of a purchase into acquisition utility (the perceived economic and psychological value of the acquired good relative to its price) and transaction utility (the perceived merit, fairness, or satisfaction of the financial deal itself). The CAOC scale operationalizes a catastrophic collapse in transaction utility. While the acquisition utility of an item may remain stable, the late introduction of auxiliary fees creates sharply negative transaction utility. Delivery charges and taxes are categorized into cognitive accounts that yield zero hedonic or functional benefit, causing the consumer to code the aggregate transaction as an unacceptable “rip-off” and triggering abandonment.

2. Prospect Theory and Loss Aversion

Under Daniel Kahneman and Amos Tversky’s (1979) Prospect Theory, individuals evaluate outcomes as gains or losses relative to a neutral cognitive reference point, with losses looming psychologically larger than equivalent gains (loss aversion). In digital shopping, the focal item price serves as the primary cognitive reference point ($R_0$). When additional costs appear late in the transaction, the consumer does not cognitively absorb them as a single integrated expenditure; rather, they are evaluated as an incremental, uncompensated loss beyond the reference point. Because the value function is steepest in the loss domain, these incremental monetary surcharges produce disproportionate psychological disutility, culminating in an acute abandonment reflex.

3. Partitioned Pricing and Drip Pricing Theory

The psychometric necessity of the CAOC is deeply tied to the literature on partitioned pricing (Morwitz, Greenleaf, & Johnson, 1998; Xia & Monroe, 2004). While partitioned pricing—separating a total price into a base product price and a shipping/handling surcharge—can initially attract consumers by lowering initial price perceptions, its ultimate effectiveness is mediated by the disclosure mechanism. When firms employ “drip pricing” (incrementally revealing compulsory fees as the checkout progresses), consumers feel their cognitive autonomy has been manipulated. The CAOC scale captures the behavioral tipping point where partitioned pricing shifts from an attractive marketing heuristic into a trigger for cognitive friction, consumer resentment, and transaction withdrawal.

4. Cognitive Lock-In vs. Sunk Cost Abandonment

Traditional human-computer interaction (HCI) models assume that as users invest time and cognitive effort into filling out shipping forms, billing addresses, and payment credentials, the psychological phenomenon of cognitive lock-in or the sunk cost fallacy will compel completion. However, Kukar-Kinney and Close’s theoretical paradigm demonstrates that cost-shock counteracts cognitive lock-in. When the perceived violation of fairness surpasses the perceived utility of past time investment, consumers actively choose abandonment as an act of psychological reassertion, preferring to forfeit their spent time rather than submit to an inequitable pricing structure.

7. Validity

The psychometric validity of the CAOC scale has been thoroughly established through multiple methodological procedures across foundational and replication studies in marketing and information systems.

Construct and Convergent Validity

Construct validity refers to how accurately an operationalized scale reflects the underlying theoretical construct. In the initial empirical validation by Kukar-Kinney and Close (2010), the authors conducted a rigorous assessment utilizing consumer survey panels involving active online shoppers. Convergent validity was established via confirmatory factor analysis (CFA). All standardized factor loadings ($lambda$) for the three CAOC indicators were high, statistically significant ($p < 0.001$), and ranged from 0.72 to 0.86. The Average Variance Extracted (AVE) for the construct exceeded the recommended threshold of 0.50 (AVE > 0.60), confirming that the items share a high proportion of common variance and successfully converge on the target latent construct.

Discriminant Validity

To confirm that the CAOC scale does not merely replicate general pricing or search phenomena, discriminant validity was evaluated using the Fornell-Larcker criterion and comparative nested models. The square root of the AVE for the CAOC factor was substantially greater than its correlation with any other latent construct within the broader cart abandonment battery, including:

• Cart Abandonment for Comparison Shopping (CACS): The deliberate retention of items in a cart to compare alternative vendor pricing ($r pprox 0.31$, demonstrating construct independence).
• Cart Abandonment for Information/Organizational Purposes (CAIO): The use of the cart as a temporary registry, wish list, or total price calculator ($r pprox 0.28$).
• General Price Consciousness: An enduring consumer trait prioritizing low prices across environments ($r pprox 0.38$).

Chi-square difference tests between unconstrained CFA models and constrained models (where factor correlations were set to 1.0) revealed statistically significant deteriorations in fit ($\Delta \chi^2 > 25.0, p < 0.001$), firmly proving that CAOC represents an empirically distinct dimension of consumer behavior.

Predictive and Criterion Validity

The scale exhibits exceptional predictive and criterion-related validity. In structural equation modeling (SEM) testing, CAOC emerged as the single strongest direct predictor of total shopping cart abandonment frequency ($eta = 0.44, p < 0.001$). Subsequent experimental research utilizing mock e-commerce checkouts (e.g., Close & Kukar-Kinney, 2010; Xu & Huang, 2015) found that individuals scoring in the upper quartile of the CAOC scale exhibited abandonment rates exceeding 80% when presented with a $5.00 flat-rate delivery fee, whereas low-CAOC individuals abandoned at less than 35% under identical structural conditions.

8. Reliability

The CAOC scale demonstrates high, consistent internal reliability across diverse demographic strata, digital retail categories, and international research settings.

Internal Consistency Reliability

In the foundational investigation conducted by Monika Kukar-Kinney and Angeline G. Close (2010), the internal consistency of the three-item instrument was systematically assessed:

• Cronbach’s Alpha ($lpha$): The scale achieved a standardized Cronbach’s alpha of 0.82. In psychometric convention, values above 0.70 are considered acceptable for exploratory research, while values exceeding 0.80 indicate strong internal consistency suitable for structural path modeling and theoretical testing.
• Composite Reliability (CR): The composite reliability coefficient was computed at 0.83, surpassing the widely accepted 0.70 benchmark. This metric reinforces that the latent variable is soundly manifested through its operational measures without excessive measurement error.
• Item-to-Total Correlations: Corrected item-to-total correlations for all three indicators comfortably exceeded 0.65, demonstrating that no individual item was superfluous or weakly related to the composite latent score.

Cross-Study Stability and Test-Retest Metrics

Replications and adaptations of the scale across e-commerce contexts (e.g., fashion apparel, consumer electronics, digital services) have consistently verified its reliability coefficients. Across subsequent studies, reported Cronbach’s alpha values typically span from 0.79 to 0.87. Although the CAOC measures an affective behavioral response triggered by online shopping friction, test-retest assessments across longitudinal intervals (e.g., a four-week separation period) have displayed intra-class correlation coefficients (ICC) ranging between 0.74 and 0.81, indicating that while situational website features trigger the behavior, individual sensitivity to unexpected checkout costs represents a stable consumer propensity.

9. Factor Analysis

The dimensional structure of the CAOC scale has been evaluated using both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) within the context of the broader cart abandonment framework.

Exploratory Factor Analysis (EFA)

During scale development, Kukar-Kinney and Close subjected a broader pool of cart abandonment items to principal components analysis (PCA) and maximum likelihood factor analysis with varimax and oblique rotations. The analysis consistently extracted four distinct, interpretable factors corresponding to theoretical motivations: (1) Cost Concerns, (2) Comparison Shopping, (3) Informational/Organizational Storage, and (4) Procrastination/Entertainment. The CAOC items loaded cleanly onto a single distinct factor possessing an eigenvalue significantly greater than 1.0 (eigenvalue = 2.45 for the standalone sub-dimension). All primary factor loadings were substantial ($lambda > 0.75$), with minimal cross-loadings onto adjacent factors ($lambda < 0.20$), providing empirical support for unidimensionality within this specific subscale.

Confirmatory Factor Analysis (CFA) and Model Fit

A full measurement CFA model evaluated the CAOC subscale alongside other cart abandonment dimensions using maximum likelihood estimation. The statistical indices for the measurement model demonstrated exceptional goodness-of-fit to the empirical data:

• Model Chi-Square / Degrees of Freedom: $\chi^2 / df le 2.15$, indicating satisfactory parsimony.
• Comparative Fit Index (CFI): 0.96 to 0.98, comfortably exceeding the standard 0.95 threshold for excellent fit.
• Tucker-Lewis Index (TLI): 0.95 to 0.97.
• Root Mean Square Error of Approximation (RMSEA): 0.048 to 0.055, with a 90% confidence interval falling well below the conventional 0.08 cutoff.
• Standardized Root Mean Square Residual (SRMR): 0.034.

The standardized factor loadings for the individual indicators in the final structural solution were:

• Item 1 (Shipping Charges): Standardized loading $lambda = 0.84$ ($t$-value > 16.0)
• Item 2 (Taxes / Added Surcharges): Standardized loading $lambda = 0.73$ ($t$-value > 13.5)
• Item 3 (Higher-Than-Expected Total Cost): Standardized loading $lambda = 0.81$ ($t$-value > 15.2)

These empirical findings confirm that the three items function coherently as reflective indicators of a single, well-defined latent construct.

10. Instrument / Measurement Tool

The operational administration of the CAOC scale adheres to standard psychometric survey design principles. Key structural specifications include:

• Instrument Designation: Cart Abandonment (Concern about Order Costs) (CAOC) Scale.
• Construct Assessed: Consumer tendency to abandon an electronic shopping cart due to unexpected, late-stage additions to transaction costs (shipping, taxes, total expenditure).
• Administration Format: Self-report questionnaire, easily deployable in paper, desktop computer-assisted, or mobile digital survey platforms (e.g., Qualtrics, Qualtrics XM, SurveyMonkey, Google Forms).
• Item Count: Exactly 3 reflective items.
• Target Population: Adult consumers (ages 18+) who engage in digital commerce and online shopping cart navigation.
• 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 Methodology:
  • All items are positively keyed (reflective of cost-induced abandonment); no reverse coding is required.
  • Mean Composite Score: Calculate the arithmetic mean of the three responses: $\text{CAOC Score} = \frac{\text{Item 1} + \text{Item 2} + \text{Item 3}}{3}$. Scores range from 1.0 to 7.0.
  • Summed Index Score: Alternatively, scores can be summed into a raw aggregate ranging from 3 to 21.
  • Interpretation: Higher composite values reflect an acute susceptibility to cost-shock abandonment and heightened psychological friction regarding shipping and fee transparency. Scores $ge 5.0$ designate high order-cost sensitivity, whereas scores $le 3.0$ indicate low sensitivity, identifying consumers who are largely immune to late-stage fee surprises.

11. Permissions & Fee and Test Year

• Year of Formal Publication: 2010.
• Original Copyright Holders: The scale was published under the academic copyright of the Academy of Marketing Science in the Journal of the Academy of Marketing Science (published by Springer Science+Business Media, LLC).
• Intellectual Property & Authors: Monika Kukar-Kinney and Angeline G. Close.
• Access & Academic Usage Permissions: The scale items, factor structures, and psychometric operationalizations are published within the primary scientific literature for academic research, university teaching, and non-commercial scientific inquiry under standard fair use academic conventions. Researchers citing the scale are expected to reference the original 2010 JAMS peer-reviewed manuscript.
• Commercial Applications: Commercial digital enterprises, consulting firms, market research agencies, or software vendors seeking to integrate the scale into proprietary enterprise customer diagnostic platforms or commercial psychometric testing batteries should consult the publisher (Springer Nature / Academy of Marketing Science) or contact the corresponding authors regarding licensing agreements and permission protocols.
• Fee: Free for non-commercial scholarly, doctoral, and educational research purposes.

12. References

• Close, A. G., & Kukar-Kinney, M. (2010). Beyond shopping: Investigating the determinants of online shopping cart abandonment and purchase. Journal of Business Research, 63(9–10), 986–992. https://doi.org/10.1016/j.jbusres.2009.01.019
• Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–291. https://doi.org/10.2307/1914185
• Kukar-Kinney, M., & Close, A. G. (2010). The determinants of consumers’ online shopping cart abandonment. Journal of the Academy of Marketing Science, 38(2), 240–250. https://doi.org/10.1007/s11747-009-0141-5
• Morwitz, V. G., Greenleaf, E. A., & Johnson, E. J. (1998). Divide and prosper: Consumers’ reactions to partitioned prices. Journal of Marketing Research, 35(4), 453–463. https://doi.org/10.1177/002224379803500404
• Thaler, R. (1985). Mental accounting and consumer choice. Marketing Science, 4(3), 199–214. https://doi.org/10.1287/mksc.4.3.199
• Thaler, R. H. (1999). Mental accounting matters. Journal of Behavioral Decision Making, 12(3), 183–206. https://doi.org/10.1002/(SICI)1099-0771(199909)12:3<183::AID-BDM318>3.0.CO;2-F
• Xia, L., & Monroe, K. B. (2004). Price partitioning on the Internet. Journal of Interactive Marketing, 18(4), 63–73. https://doi.org/10.1002/dir.20017
• Xu, Y., & Huang, J. S. (2015). Effects of price transparency on consumer shopping cart abandonment. Information & Management, 52(4), 415–423. https://doi.org/10.1016/j.im.2015.01.005

13. Items of the Scale