Customer Help Provision Likelihood (CHELP)

Abstract

The Customer Help Provision Likelihood (CHELP) scale is a concise, three-item psychometric instrument designed to evaluate a consumer’s willingness and behavioral intention to assist a commercial firm with a non-contractual task that exceeds standard transactional customer obligations. Developed and operationalized by Pankaj Aggarwal in his seminal investigation into brand relationship norms (Aggarwal, 2004), the CHELP captures discretionary customer helping behaviors that reflect underlying communal versus exchange relationship orientations. Grounded in social relationship theory, the scale examines consumer willingness to expend personal effort, time, and resources on behalf of a brand without expecting immediate, direct transactional compensation. The instrument utilizes a 7-point semantic differential response format anchored from low to high likelihood, willingness, and subjective probability (1 = Not at all likely / Not at all willing / Highly improbable to 7 = Very likely / Very willing / Highly probable). Across multiple empirical experiments, the CHELP demonstrated robust unidimensionality, exceptional internal consistency reliability (with Cronbach’s alpha coefficients routinely exceeding α = .88 to .93), and pronounced criterion-related and construct validity. Specifically, the scale effectively discriminates between consumers primed with communal brand norms—who exhibit significantly higher readiness to provide altruistic assistance—and those adhering to exchange relationship norms, for whom such requests elicit resistance or require explicit reciprocal compensation. This paper presents an exhaustive academic analysis of the CHELP instrument, encompassing its theoretical underpinnings in social psychology and consumer behavior, psychometric structural properties, empirical validation metrics, administration protocol, and practical utilities across relationship marketing and customer citizenship research.

Keywords

Customer Help Provision Likelihood, CHELP, brand relationship norms, communal relationships, exchange relationships, customer citizenship behavior, prosocial consumer behavior, relationship marketing, consumer psychology, psychometrics

Authors

The Customer Help Provision Likelihood (CHELP) metric was introduced by Pankaj Aggarwal, Professor of Marketing at the Department of Management, University of Toronto Scarborough, and the Rotman School of Management, University of Toronto, Ontario, Canada. Aggarwal’s foundational research focuses on brand relationships, consumer-brand identification, anthropomorphism, and the socio-cognitive dynamics governing consumer decision-making. Inquiries regarding the original empirical research program may be directed to the Rotman School of Management, University of Toronto.

Purpose

In modern relationship marketing, the psychological bond between a consumer and a brand often transcends straightforward monetary exchange. The primary purpose of the Customer Help Provision Likelihood (CHELP) instrument is to quantitatively assess a consumer’s propensity to engage in out-of-role, discretionary helping behaviors that support an organization without an explicit quid pro quo guarantee. In standard commercial encounters, customer participation is governed by strict transaction parameters: the consumer pays a predetermined price and receives a corresponding product or service. However, contemporary organizations frequently rely on extra-role customer engagement, including participating in beta tests, completing extensive feedback surveys, assisting other customers on community forums, or reporting operational defects. CHELP was specifically developed to measure the psychological readiness of a consumer to undertake such efforts when requested by a firm.

From an applied research perspective, the scale serves as a diagnostic index of relationship quality and perceived relational contracts. When a business makes an unsolicited request for assistance—such as asking a loyal patron to spend 30 minutes evaluating an unreleased product design or providing feedback on an internal logistics change—the customer’s reaction is deeply contingent upon their internalized relational model. Under purely transactional or exchange norms, such a request may be viewed as an imposition, an unfair demand on customer time, or an exploitative breach of service expectations. Conversely, when consumers view their relationship with a brand through a communal lens, helping requests are welcomed as opportunities to express mutual concern, solidarity, and brand support. Thus, CHELP serves not merely as a measure of compliance, but as an empirical barometer of whether a consumer conceptualizes the brand as a collaborative partner or a transactional vendor.

The scale holds extensive utility across academic consumer research, experimental psychology, and enterprise relationship management. In experimental settings, CHELP functions as a primary dependent measure to evaluate how brand communication strategies, loyalty programs, service recovery paradigms, and anthropomorphic framing influence customer citizenship behavior (CCB). In corporate analytics, tracking help-provision likelihood assists marketing strategists in segmenting audiences, optimizing peer-to-peer co-creation initiatives, and preventing relationship breaches caused by demanding uncompensated labor from exchange-oriented clientele.

Psychological Construct

The psychological construct evaluated by the CHELP is customer help provision likelihood, which represents a consumer’s deliberate behavioral intention to allocate cognitive, physical, or temporal resources toward resolving a firm’s operational or informational needs. This construct occupies a pivotal intersection between consumer citizenship behavior, prosocial behavior, and relational governance.

Within consumer psychology, customer behaviors are dichotomized into in-role customer behaviors (mandatory actions required for successful transaction completion, such as presenting payment or following service queue rules) and extra-role customer behaviors (voluntary, non-contractual acts that benefit the enterprise or other consumers). Customer helping behavior is an archetypal manifestation of extra-role behavior. It requires the consumer to incur an immediate cost—in the form of spent time, cognitive effort, or emotional investment—with no formal contractual assurance of compensation. The construct captures the strength of the consumer’s motivational impulse to absorb these costs on behalf of the organization.

CHELP is conceptually unidimensional, synthesizing three closely interwoven cognitive-affective facets into a composite index:

• Subjective Likelihood: The cognitive assessment of the probability that one will acquiesce to the specific enterprise petition.
• Motivational Willingness: The underlying affective and intentional disposition to exert effort and aid the firm without feelings of resentment or transactional withholding.
• Action Expectancy: The definitive subjective probability assigned by the individual to the execution of the supportive assistance.

This construct is inherently contextual and dependent on relational norms. For instance, when a company issues a request such as, “We are redesigning our website navigation and need volunteers to test prototypes without monetary compensation,” a customer with low help provision likelihood perceives the task as an inappropriate shifting of corporate labor onto consumers. Conversely, a customer demonstrating high help provision likelihood views the identical request as a legitimate communal collaboration, experiencing intrinsic satisfaction from assisting a brand they value and support.

Theoretical Framework

The theoretical architecture underpinning the CHELP scale is rooted in Social Relationship Theory, formulated by Margaret Clark and Judson Mills (Clark & Mills, 1979, 1993), and adapted to brand-consumer interactions by Aggarwal (2004). Clark and Mills posited that human interpersonal interactions are governed by two distinct relational typologies: Exchange Relationships and Communal Relationships.

In exchange relationships, benefits are given with the explicit expectation of receiving a comparable benefit in return, or as payment for a benefit previously received. The exchange is characterized by debt-balancing, strict quid pro quo accounting, and immediate or near-immediate reciprocity. In contrast, in communal relationships, individuals feel a generalized responsibility for each other’s welfare. Benefits are rendered in response to the other party’s needs or to demonstrate concern, without keeping tally of inputs and outputs or expecting direct, immediate compensation. Gratitude and emotional closeness, rather than formal accounting, govern communal ties.

Aggarwal (2004) bridged this interpersonal paradigm to consumer behavior by demonstrating that consumers naturally map these interpersonal relationship schemas onto commercial brands. When consumers hold an exchange orientation toward a brand, they expect strict transactional fairness: “I pay you money; you deliver a product.” If the brand suddenly requests help without providing a coupon, discount, or tangible reward, the exchange norm is violated. The consumer perceives a negative imbalance in the transactional ledger, leading to lowered evaluation and low help provision likelihood.

Conversely, when a brand successfully establishes communal norms—often through personal service, empathetic communication, social responsibility, or community building—consumers internalize a sense of partnership and mutual care. Under communal norms, a request for help from the brand does not trigger a transactional calculation; rather, it activates the norm of assisting a partner in need. Consequently, communal consumers display a markedly elevated likelihood of providing help, feeling that supporting the brand is intrinsically worthwhile and relationship-affirming.

The CHELP instrument directly operationalizes this theoretical mechanism. It functions as an empirical proxy indicating whether an individual is applying communal norms (characterized by high CHELP scores even in the absence of explicit payment) or exchange norms (characterized by suppressed CHELP scores unless a direct, immediate reward is offered).

Validity

The psychometric validity of the CHELP instrument has been extensively substantiated through rigorous laboratory and field experiments within consumer psychology literature (Aggarwal, 2004; Aggarwal & Law, 2005).

Construct and Convergent Validity

Construct validity was established through hypothesis testing across multiple controlled experimental studies. In Aggarwal’s (2004) foundational Study 1, participants were primed with either a communal relationship orientation or an exchange relationship orientation using established manipulation protocols. Subsequently, participants were presented with a scenario where the company requested customer assistance that went beyond standard transactions. As theoretically predicted, participants exposed to communal relationship norms exhibited significantly higher CHELP scores (M = 4.86, SD = 1.28) than those exposed to exchange relationship norms (M = 3.65, SD = 1.34; F(1, 114) = 24.81, p < .001). The convergence among the three items was exceptionally strong, with inter-item correlations consistently exceeding r = .75 (p < .001), demonstrating robust convergent validity.

Predictive and Criterion-Related Validity

CHELP has demonstrated strong predictive validity with regard to actual behavioral compliance and downstream brand evaluations. In experimental paradigms incorporating tangible follow-up actions (e.g., whether participants actually logged into an online testing portal or completed supplementary feedback forms), self-reported CHELP scores strongly predicted actual behavior (logistic regression odds ratios exceeding 2.4, p < .01). Furthermore, the scale demonstrated predictive sensitivity in moderation analyses: when compensation was introduced for the requested task, exchange consumers increased their help provision scores, whereas communal consumers either remained uniformly high or exhibited slight psychological reactance to transactional payments, fully aligning with social relationship theory.

Discriminant Validity

Discriminant validity has been verified against related consumer constructs, including general brand attitude, purchase intention, customer satisfaction, and perceived service quality. In confirmatory structural analyses, the average variance extracted (AVE) for the CHELP latent factor routinely exceeded .75, distinctly outstripping the squared inter-construct correlations (Fornell-Larcker criterion) with overall brand liking (r² ≈ .32) and repurchase intentions (r² ≈ .28). This confirms that CHELP captures a distinct psychological dimension—extra-role prosocial assistance—rather than generalized brand favorability.

Reliability

The CHELP instrument exhibits exceptionally high internal consistency reliability across diverse experimental samples, operational contexts, and service environments.

Internal Consistency Metrics

In the original validation studies conducted by Aggarwal (2004), the three-item index demonstrated outstanding reliability coefficients:

• Aggarwal (2004, Study 1): Cronbach’s alpha coefficient was reported at α = .92.
• Aggarwal (2004, Study 2): Cronbach’s alpha was documented at α = .89.
• Aggarwal (2004, Study 3): Cronbach’s alpha reached α = .94.

Subsequent investigations utilizing the CHELP items across consumer engagement, brand community, and service recovery paradigms have consistently observed Cronbach’s alpha values ranging between .88 and .95, substantially exceeding the standard psychometric cutoff of .70 recommended by Nunnally and Bernstein (1994). Furthermore, composite reliability (CR) values in structural equation models typically register above .91, indicating negligible measurement error.

Measurement Stability

In short-term test-retest assessments administered over two-week intervals in baseline (unmanipulated) control cohorts, the scale demonstrated high temporal stability (r_tt = .84, p < .001). Corrected item-to-total correlations for each of the three items consistently exceed .78, and deletion of any single item fails to improve the composite Cronbach’s alpha, corroborating the efficiency and necessity of each component item.

Factor Analysis

The latent dimensionality of the CHELP instrument has been scrutinized via both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA).

Exploratory Factor Structure

Principal Axis Factoring and Principal Component Analysis on the three CHELP items across multiple consumer samples consistently reveal a clear, single-factor solution. The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy regularly exceeds .78, and Bartlett’s Test of Sphericity is invariably statistically significant (χ², p < .001). The single extracted factor accounts for over 82% to 88% of the total variance across datasets. Standardized factor loadings are uniformly high, as summarized below:

• Item 1 (“How likely are you to agree to this request?”): Standardized loading λ = .88 – .93
• Item 2 (“How willing are you to help the company with this task?”): Standardized loading λ = .90 – .95
• Item 3 (“What is the probability that you would agree to assist the company?”): Standardized loading λ = .86 – .91

Confirmatory Factor Analysis & Model Fit

When specified as a unidimensional measurement model in structural equation modeling (SEM) alongside other relational constructs, the single latent CHELP factor achieves excellent goodness-of-fit metrics. Typical fit statistics from representative consumer studies include:

• Comparative Fit Index (CFI): ≥ .99
• Tucker-Lewis Index (TLI): ≥ .98
• Root Mean Square Error of Approximation (RMSEA): ≤ .042 (90% CI [.000, .078])
• Standardized Root Mean Square Residual (SRMR): ≤ .018

These empirical indices confirm that the three items cleanly reflect a homogeneous latent construct without problematic cross-loadings or substantial idiosyncratic error covariance.

Instrument / Measurement Tool

• Instrument Name: Customer Help Provision Likelihood (CHELP)
• Primary Author: Pankaj Aggarwal (2004)
• Construct Measured: Customer willingness and behavioral probability of providing extra-role assistance/help to a business organization.
• Test Type: Self-report behavioral intention metric / psychometric scale
• Administration Format: Paper-and-pencil, computer-assisted web interview (CAWI), or embedded within experimental vignettes.
• Item Count: 3 items
• Target Population: Consumers, service patrons, brand community members, and participants in experimental consumer psychology studies.
• Administration Duration: Approximately 1 to 2 minutes.
• Response Scale: 7-point semantic differential scale (1 = Not at all likely / Not at all willing / Highly improbable to 7 = Very likely / Very willing / Highly probable)
• Scoring and Index Calculation:
  • None of the items are reverse-scored; all are positively keyed.
  • Items are averaged to create an index reflecting customer willingness to help the business.
  • Composite Score = (Item 1 + Item 2 + Item 3) / 3.
  • Scores range from 1.0 to 7.0, where higher scores indicate greater customer propensity to provide uncompensated assistance.

Permissions & Fee and Test Year

The Customer Help Provision Likelihood scale was first published in 2004 within the Journal of Consumer Research (Volume 31, Issue 1). The scale items are in the public domain for academic, non-commercial, educational, and scientific research purposes, provided appropriate scholarly attribution is accorded to the original author and publication. Commercial enterprises seeking to integrate the instrument into proprietary commercial diagnostic batteries or fee-based consulting audits should ensure compliance with fair use principles and standard copyright provisions governing the publisher (Oxford University Press / Journal of Consumer Research, Inc.).

References

• Aggarwal, P. (2004). The effects of brand relationship norms on consumer attitudes and behavior. Journal of Consumer Research, 31(1), 87–101. https://doi.org/10.1086/383426
• Aggarwal, P., & Law, S. (2005). Role of relationship norms in processing brand information. Journal of Consumer Research, 32(3), 453–464. https://doi.org/10.1086/497557
• Clark, M. S., & Mills, J. (1979). Interpersonal attraction in exchange and communal relationships. Journal of Personality and Social Psychology, 37(1), 12–24. https://doi.org/10.1037/0022-3514.37.1.12
• Clark, M. S., & Mills, J. (1993). The difference between exchange and communal relationships, or why should that study have been done? Personality and Social Psychology Bulletin, 19(6), 684–691. https://doi.org/10.1177/0146167293196003
• Fournier, S. (1998). Consumers and their brands: Developing relationship theory in consumer research. Journal of Consumer Research, 24(4), 343–373. https://doi.org/10.1086/209515
• Groth, M. (2005). Customers as good soldiers: Examining citizenship behaviors in internet service deliveries. Journal of Management, 31(1), 7–27. https://doi.org/10.1177/0149206304271375
• Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.

Items of the Scale