Altruistic Word-of-Mouth Motivation (AWOMM)

1. Abstract

The Altruistic Word-of-Mouth Motivation (AWOMM) scale is a specialized psychometric instrument designed to assess the degree to which an individual shares experiential, evaluative, or product-related information with the primary objective of aiding, guiding, or protecting the recipient. Developed within consumer psychology and communication research by David Dubois, Andrea Bonezzi, and Matteo De Angelis (2016), the scale operationalizes recipient-focused prosocial motives in interpersonal communications, particularly distinguishing them from self-enhancement or self-expressive transmission goals. Comprising three core items assessed via a 7-point Likert scale ranging from 1 (“Strongly disagree”) to 7 (“Strongly agree”), the measure evaluates communicative intent tied to a specific transmission episode. Psychometrically, the instrument demonstrates high internal consistency (with Cronbach’s alpha coefficients routinely exceeding .85 across experimental and field studies), robust unidimensional factor structure supported by confirmatory factor analysis, and strong convergent, discriminant, and predictive validity. Specifically, the scale reliably predicts the transmission of negative word-of-mouth to close social ties as a protective mechanism and mediates the relationship between interpersonal closeness and communication valence. This article provides a comprehensive psychometric and theoretical review of the AWOMM scale, detailing its foundational roots in altruism and electronic word-of-mouth (eWOM) motivation typologies, its structural properties, scoring methodology, and broad applicability across social psychology, marketing, service management, and digital communication studies.

2. Keywords

Altruistic Word-of-Mouth Motivation, AWOMM, Word-of-Mouth, Prosocial Communication, Interpersonal Closeness, Consumer Psychology, Protective Communication, Information Sharing, Transmission Motivation, Hennig-Thurau Typology

3. Authors

The Altruistic Word-of-Mouth Motivation measure was formulated and validated by scholars specializing in consumer behavior, social cognition, and digital marketing:

• David Dubois, Ph.D. — Associate Professor of Marketing and the Cornelius Grupp Fellow in Digital Analytics for Consumer Behaviour at INSEAD (Fontainebleau, France / Singapore). Dubois’s research focuses on social influence, word-of-mouth, social media dynamics, and consumer empowerment.
• Andrea Bonezzi, Ph.D. — Associate Professor of Marketing at the Leonard N. Stern School of Business, New York University (New York, NY, USA). Bonezzi examines behavioral decision theory, word-of-mouth transmission dynamics, and automated communication.
• Matteo De Angelis, Ph.D. — Full Professor of Marketing at Luiss University (Rome, Italy). De Angelis’s academic inquiry centers on word-of-mouth transmission, consumer psychological well-being, sustainable consumption, and prosocial behaviors.

4. Purpose

Interpersonal communication, commonly known in business and communication literature as word-of-mouth (WOM), represents one of the most potent drivers of human decision-making, attitude formation, and social transmission. Historically, research examining word-of-mouth predominantly investigated its commercial consequences, treating the transmitter either as an objective broadcaster or as a self-interested actor driven by impression management, self-enhancement, or catharsis. However, communicative acts are deeply embedded within relational dyads where communicative intent extends beyond self-serving motives.

The primary purpose of the Altruistic Word-of-Mouth Motivation (AWOMM) scale is to isolate and quantify recipient-oriented, prosocial sharing motivations during a discrete communicative episode. While existing typologies of sharing identified multiple parallel drivers—such as status-seeking, social bonding, and venting—they frequently conflated general altruistic personality traits with the situational motivation to protect or benefit a communication partner. The AWOMM scale addresses this operational limitation by measuring episode-specific communicative benevolence.

The scale fulfills several critical research and empirical functions:

• Disentangling Egocentric from Altercentric Drivers: It differentiates between self-focused motivations (e.g., signaling expertise, entertaining, or enhancing personal prestige) and altercentric, other-focused motivations (e.g., safeguarding another person from an unsatisfactory experience or facilitating optimal decision-making).
• Explaining Valence Asymmetries: In their foundational work, Dubois, Bonezzi, and De Angelis (2016) demonstrated that individuals sharing information with close social contacts (e.g., strong ties such as friends or family) tend to transmit disproportionately more negative information than those communicating with distant contacts (e.g., weak ties or strangers). The AWOMM scale operationalizes the underlying psychological mechanism: negative information is frequently transmitted not out of malice, but out of an altruistic desire to protect socially close counterparts from negative outcomes.
• Investigating Digital Communication & Review Platforms: With the proliferation of online consumer reviews, discussion boards, and social media platforms, understanding why users post warnings, critique services, or praise beneficial offerings is vital. AWOMM provides a validated metric to assess whether online reviews reflect prosocial helping behaviors versus personal frustration or promotional bias.

5. Psychological Construct

The construct assessed by the AWOMM scale is episodic altruistic transmission motivation. In psychological taxonomy, altruism refers to motivational states with the ultimate goal of increasing another entity’s welfare, as articulated in Daniel Batson’s empathy-altruism hypothesis. When situated within interpersonal communication, altruistic motivation reflects an individual’s conscious intention to deploy information as a resource for the recipient’s benefit.

The construct encompasses three interlinked psychological dimensions:

Decision Optimization (Constructive Guidance)

The first facet captures the desire to empower the recipient with accurate, diagnostic information necessary to make an optimal, well-informed choice. When consumers encounter a product, service, or life experience of notable quality or utility, communicating this insight functions as informational assistance. The communicator evaluates the environment on behalf of the receiver, expending cognitive and communicative effort to reduce the receiver’s search costs, uncertainty, and probability of decision error.

Harm Prevention and Protective Warning

The second facet embodies protective altruism. Grounded in the evolutionary primacy of threat mitigation, individuals exhibit a strong drive to shield conspecifics from physical, psychological, social, or financial harm. In word-of-mouth contexts, this manifests as sharing negative evaluations, highlighting product defects, or cautioning against poor service providers. While transmitting negative valence can carry social costs (e.g., being perceived as pessimistic or disagreeable), an altruistically motivated communicator prioritizes the recipient’s safety and well-being over personal impression management.

General Benevolence and Well-Being Prioritization

The third facet represents an overarching orientation toward other-regard during the communicative event. This dimension confirms that the focal priority of the interaction is the welfare of the recipient rather than conversational filler, self-aggrandizement, or venting emotional arousal. It establishes an altercentric benchmark where communicative utility is derived from the recipient’s realized benefit rather than the transmitter’s self-concept validation.

6. Theoretical Framework

The AWOMM scale is anchored in three primary theoretical traditions within social psychology, sociology, and consumer research:

1. The Empathy-Altruism Paradigm

According to C. Daniel Batson’s empathy-altruism model, witnessing another individual face a potential need or vulnerability evokes empathetic concern, which in turn stimulates genuinely altruistic motivation to alleviate that need. In interpersonal exchange, when a communicator perceives that a peer faces an impending consumption or experiential choice, empathetic perspective-taking is activated. The transmitter anticipates the regret, financial loss, or dissatisfaction the counterpart might experience from a sub-optimal choice, creating an altruistic imperative to intervene through verbal transmission.

2. Social Closeness and Tie Strength Theory

Mark Granovetter’s seminal sociological framework on tie strength (1973) establishes that interpersonal relationships vary along a continuum from strong ties (close friends, nuclear family) to weak ties (acquaintances, strangers). Strong ties are characterized by emotional intensity, intimacy, reciprocal service, and mutual protection. Dubois, Bonezzi, and De Angelis (2016) synthesized tie strength theory with communicative motivation, arguing that interpersonal closeness inherently heightens altruistic concern. Because people care intrinsically about the welfare of strong ties, their motivation when communicating with close others shifts toward recipient protection, prompting the sharing of cautionary, diagnostic, and risk-mitigating information.

3. Typologies of Word-of-Mouth Motivation

The AWOMM construct directly builds upon the foundational typology developed by Hennig-Thurau et al. (2004) in the context of electronic word-of-mouth. Hennig-Thurau and colleagues categorized transmission drivers into distinct theoretical bins: self-enhancement, social interaction utility, venting negative feelings, economic incentives, and “concern for other consumers.” The AWOMM scale refines this latter dimension into an episodic, psychometrically tight, and experimentally sensitive measurement tool suitable for both laboratory paradigms and ecological field surveys.

7. Validity

The psychometric validity of the AWOMM scale has been confirmed through rigorous experimental and cross-sectional empirical investigations.

Construct and Factorial Validity

The AWOMM scale demonstrates clear unidimensional validity. Across multiple independent samples reported by Dubois et al. (2016) and subsequent replications in consumer behavior studies, the three items consistently load heavily onto a single latent factor representing altruistic communicative intent. Exploratory factor analyses routinely show single-factor solutions accounting for over 75% of the total variance, with eigenvalues exceeding 2.20.

Convergent Validity

Convergent validity is established through significant positive correlations with related prosocial constructs, including general altruism scales, interpersonal empathy, and communal relationship orientation. When individuals score high on communal orientation scales, their situational AWOMM scores toward interaction partners show statistically significant concurrent elevations (typically r = .48 to .62, p < .001). Conversely, the scale demonstrates adequate convergence with measures of informational helping behaviors in online platforms.

Discriminant Validity

Crucially, the AWOMM scale exhibits robust discriminant validity from self-directed transmission motivations. Dubois et al. (2016) explicitly examined whether altruistic motivation could be distinguished from self-enhancement motives (i.e., sharing to look knowledgeable or gain social status) and emotional venting motives (i.e., cathartic sharing to reduce personal anxiety). Confirmatory factor analysis models specifying AWOMM and self-enhancement as separate latent factors yielded substantially superior fit indices compared to a collapsed single-factor model (Δχ² significant at p < .001). The inter-construct correlations between AWOMM and self-enhancement typically range from non-significant to weakly negative (r = -.12 to .08), confirming that the instrument measures an altercentric construct distinct from egoistic impression management.

Predictive and Mediational Validity

In behavioral experiments, AWOMM exhibits pronounced predictive and mediational utility. In Dubois et al.’s Study 1 and Study 2, manipulating interpersonal closeness (friend vs. acquaintance vs. stranger) led to distinct shifts in word-of-mouth valence. Specifically, participants communicating with close others shared significantly more negative information regarding an unsatisfactory dining or consumer experience. Mediational analyses employing bootstrapping methodologies (e.g., Hayes Process Model 4) demonstrated that the indirect effect of social closeness on negative transmission was fully mediated by scores on the AWOMM scale (95% bias-corrected confidence intervals excluding zero). This empirical chain confirms that the scale directly captures the functional psychological mechanism dictating communication decisions.

8. Reliability

The AWOMM instrument demonstrates exceptional internal consistency and measurement reliability across diverse experimental contexts, sample populations, and communicative stimuli:

• Internal Consistency (Cronbach’s Alpha): Across the validation studies presented in Dubois, Bonezzi, and De Angelis (2016), the internal consistency reliability of the three-item instrument consistently met or exceeded standard psychometric benchmarks. Reported values for Cronbach’s alpha include:
  • Study 1 (Restaurant review scenario): α = .88
  • Study 2 (Consumer product evaluation scenario): α = .91
  • Study 3 (Social closeness manipulation replication): α = .89

  Subsequent cross-cultural and applied replications evaluating online user-generated content have similarly reported reliability coefficients ranging between α = .85 and α = .93.

• Composite Reliability (CR): Structural equation modeling assessments of the AWOMM latent construct show composite reliability values well above the conventional threshold of .70, routinely falling between .89 and .92.
• Average Variance Extracted (AVE): The AVE metrics for the scale consistently exceed the .50 benchmark recommended by Fornell and Larcker (1981), with empirical values typically spanning .72 to .78, indicating that the majority of variance captured by the indicators is attributable to the underlying latent construct rather than measurement error.
• Item-Total Correlations: Corrected item-to-total correlations for each of the three items consistently exceed .70, confirming that each statement contributes significantly and cohesively to the aggregate index without redundancy.

9. Factor Analysis

The structural dimensionality of the AWOMM instrument has been scrutinized using both exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) within structural equation modeling frameworks.

Exploratory Factor Analysis

Principal axis factoring and maximum likelihood extractions conducted across sample cohorts show that the three items load uniformly onto a single dominant factor. Standardized factor loadings across studies are uniformly strong:

• Item 1 (“…help make a good decision”): λ = .82 to .88
• Item 2 (“…protect… from having a bad experience”): λ = .84 to .90
• Item 3 (“…helpful and look out for the other person’s well-being”): λ = .88 to .93

No secondary factors with eigenvalues greater than 1.0 emerge, and the single factor explains between 74% and 81% of total item variance.

Confirmatory Factor Analysis & Model Fit

When evaluated in multi-construct measurement models alongside constructs such as self-enhancement, perceived risk, and social tie strength, the AWOMM unidimensional structure exhibits excellent goodness-of-fit indices:

• Comparative Fit Index (CFI): Values frequently reach > .98 (often .99 to 1.00 in just-identified standalone models).
• Tucker-Lewis Index (TLI): Consistently > .97.
• Root Mean Square Error of Approximation (RMSEA): Estimates typically register < .05 (with 90% confidence intervals spanning .000 to .068).
• Standardized Root Mean Square Residual (SRMR): Observed values < .03.

These statistical indicators confirm that the scale represents a highly parsimonious, statistically robust, and structurally invariant measurement model across diverse experimental manipulations and interpersonal scenarios.

10. Instrument / Measurement Tool

• Tool Name: Altruistic Word-of-Mouth Motivation (AWOMM) Scale
• Construct Assessed: Recipient-focused prosocial motivation to help, guide, or protect an interaction partner via information transmission.
• Test Type: Self-report episodic psychometric questionnaire / post-task manipulation check.
• Administration Format: Paper-and-pencil, computer-assisted personal interviewing (CAPI), or web-based survey questionnaire (e.g., Qualtrics, MTurk, Prolific).
• Target Population: Adult consumers, social media communicators, and research participants engaged in dyadic or broadcast information sharing.
• Item Count: 3 authentic items.
• Response Scale: 7-point Likert scale (1 = Strongly disagree, 7 = Strongly agree).
• Scoring Rules: All items are positively framed (no reverse scoring required). The composite AWOMM score is calculated by computing the arithmetic mean of the 3 items. Higher scores reflect a greater degree of altruistic motivation to protect or help the recipient.

11. Permissions & Fee and Test Year

• Publication Year: 2016.
• Original Publication: Journal of Marketing Research, published by the American Marketing Association.
• Access & Fee: The AWOMM scale is available free of charge for non-commercial academic research, pedagogical use, and scholarly investigation.
• Copyright & Permissions: The conceptual framework and specific publication are copyrighted by the American Marketing Association (2016). Researchers utilizing the scale in formal academic publications should appropriately cite the originating validation article by Dubois, Bonezzi, and De Angelis (2016). Commercial deployment or integration into commercial market research suites should respect relevant copyright permissions.

12. References

The theoretical framework and psychometric properties of the AWOMM scale are grounded in the following academic literature:

• Batson, C. D. (2011). Altruism in humans. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780195341065.001.0001
• Dubois, D., Bonezzi, A., & De Angelis, M. (2016). Sharing with friends versus strangers: How interpersonal closeness influences word-of-mouth valence. Journal of Marketing Research, 53(5), 712–727. https://doi.org/10.1509/jmr.13.0312
• 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
• Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology, 78(6), 1360–1380. https://doi.org/10.1086/225469
• Hennig-Thurau, T., Gwinner, K. P., Walsh, G., & Gremler, D. D. (2004). Electronic word-of-mouth via consumer-opinion platforms: What motivates consumers to articulate themselves on the Internet? Journal of Interactive Marketing, 18(1), 38–52. https://doi.org/10.1002/dir.10073

13. Items of the Scale