Affective In-Game Brand Engagement (AIBE)

Abstract

The Affective In-Game Brand Engagement (AIBE) scale is a specialized psychometric instrument developed by Alexander Berger, Tobias Schlager, David E. Sprott, and Andreas Herrmann (2018) to assess the valence and intensity of positive emotional reactions elicited when consumers interact with a branded entity embedded intrinsically within a video game environment. Adapted from broader conceptualizations of consumer brand engagement and consumer-brand relationships, the AIBE scale isolates the affective dimension of engagement under conditions where a brand is integral—rather than merely peripheral or incidental—to interactive gameplay mechanics. The instrument comprises four self-report items evaluated on a standard 7-point Likert response format ranging from 1 (“Strongly disagree”) to 7 (“Strongly agree”). Psychometric evaluations conducted across multiple experimental and online participant samples (notably via Amazon Mechanical Turk) demonstrate that the AIBE exhibits robust unidimensionality, exceptional internal consistency reliability (with Cronbach’s alpha values routinely exceeding .90), strong convergent validity with downstream relational constructs such as self-brand connections, and decisive discriminant validity from cognitive absorption and general game enjoyment. By capturing the precise hedonic states (happiness, fun, delight, and positive well-being) produced during gamified brand touchpoints, the AIBE provides researchers and practitioners with an empirically rigorous diagnostic tool for evaluating interactive marketing, advergames, and virtual brand placements.

Keywords

Affective in-game brand engagement, gamification, self-brand connection, advergaming, consumer engagement, positive affect, hedonic consumption, brand placement, scale validation, structural equation modeling.

Authors

The Affective In-Game Brand Engagement instrument was formulated and validated by an international team of behavioral marketing and consumer psychology researchers:

• Alexander Berger: Research Associate and doctoral alumnus at the Institute for Customer Insight, University of St. Gallen (ICI-HSG), St. Gallen, Switzerland. His research specializes in interactive digital media, gamification mechanics, and consumer-technology interfaces.
• Tobias Schlager: Associate Professor of Marketing at the University of Lausanne (HEC Lausanne), Switzerland (previously affiliated with the University of St. Gallen). His research centers on digital marketing, human-computer interaction, gamified consumer experiences, and behavioral analytics.
• David E. Sprott: Peter F. Drucker Chair in Management and Professor of Marketing at the Peter F. Drucker and Masatoshi Ito Graduate School of Management, Claremont Graduate University, Claremont, California, USA (formerly Dean and Professor at Washington State University). Internationally recognized for his seminal psychometric contributions to the operationalization of brand engagement in self-concept (BESC).
• Andreas Herrmann: Professor of Marketing and Director of the Institute for Customer Insight at the University of St. Gallen (ICI-HSG), Switzerland. A leading scholar in consumer decision-making, behavioral economics, product design, and automated digital customer journeys.

Purpose

The primary purpose of the Affective In-Game Brand Engagement (AIBE) scale is to measure the immediate, positively valenced emotional resonance experienced by a player during interactive gameplay that inherently incorporates a commercial brand. Over the past two decades, commercial organizations have increasingly migrated from static, passive promotional communications (e.g., banner advertisements, linear television spots) to dynamic, interactive media platforms, including advergames, in-game virtual sponsorships, and gamified smartphone applications. However, existing psychometric inventories typically measured consumer brand engagement as a broad, macro-level trait or focused exclusively on passive visual exposure, failing to capture the unique phenomenological experience of interacting with a brand as an operative component of game mechanics.

Methodologically, brand integration within gaming environments can be bifurcated into incidental placements (e.g., a static billboard in a virtual sports arena) and integral placements (e.g., a branded vehicle whose handling mechanics dictate player performance in a racing simulator). Berger et al. (2018) established that incidental placements frequently yield negligible cognitive elaboration or affective bonding. In contrast, when a brand is woven intrinsically into game mechanics, player actions directly interact with the brand’s virtual identity. Under these circumstances, the brand ceases to be an interruption; it becomes an active collaborator in the user’s quest for competence, challenge, and entertainment.

The theoretical rationale for the AIBE rests on the premise that affect is the primary currency through which gamified interactions transfer commercial value to the brand. While cognitive engagement assesses attention and mental elaboration, and behavioral engagement captures physical activity or expenditure of effort, affective engagement assesses the subjective emotional rewards—such as pleasure, cheerfulness, and delight—generated during the activity. The AIBE scale was engineered to quantify this exact emotional node. In empirical research, the instrument serves as a critical mediator or explanatory mechanism illustrating how and why gamification mechanics (such as reward contingencies, autonomy, and task competence) successfully elevate long-term consumer-brand relationships, brand equity, and self-brand integration.

Psychological Construct

The psychological construct captured by the AIBE scale is Affective In-Game Brand Engagement. Within the broader consumer psychology literature, consumer engagement is recognized as a multidimensional construct spanning cognitive, affective, and behavioral facets (Hollebeek et al., 2014). Affective engagement, in particular, encapsulates the degree of positive emotional connection, enthusiasm, and hedonic pleasure a consumer associates with a brand during a specific interactive episode.

In the specialized context of digital gaming and gamification, the construct reflects a localized, state-level affective activation. It does not measure the individual’s baseline emotional disposition or their generic attitude toward the game software itself. Rather, it isolates the precise emotional sentiment produced by the co-presence and operational utility of the brand within the gaming session. The construct comprises four primary emotional facets captured across the unidimensional scale:

• Subjective Happiness (Item 1): Evaluates the subjective sensation of cheerfulness, joy, and emotional contentment experienced specifically while playing the game with the designated brand. This reflects the hedonic elevation that counteracts stress and negative affect.
• Perceived Fun (Item 2): Captures the intrinsic playfulness, lighthearted amusement, and divertive pleasure derived from manipulating the branded virtual object or participating in branded game challenges.
• Consumer Delight (Item 3): Reflects a heightened emotional state characterized by surprise, enchantment, and profound gratification. Delight transcends routine satisfaction, indicating that the branded game mechanics exceeded ordinary experiential expectations.
• Generalized Positive Well-Being (Item 4): Assesses an overarching feeling of emotional wellness and positive state valence (“makes me feel good”), indicating positive reinforcement generated at the nexus of brand interaction and virtual gameplay.

Crucially, the construct is sensitive to the structural placement of the brand. When a brand’s presence hampers game performance or feels overtly manipulative, affective in-game brand engagement drops precipitously, frequently manifesting as psychological reactance. Conversely, when the brand facilitates game mastery, provides aesthetic enhancement, or unlocks desirable game affordances, affective engagement spikes, creating a favorable psychological foundation for brand internalization.

Theoretical Framework

The theoretical architecture underpinning the AIBE scale integrates foundational paradigms from motivational psychology, emotion theory, and relational marketing:

1. Self-Determination Theory (SDT)

According to Self-Determination Theory (Deci & Ryan, 2000), human psychological flourishing and intrinsic motivation are governed by the satisfaction of three universal psychological needs: autonomy (experiencing volition and personal agency), competence (feeling effective in executing challenges), and relatedness (feeling connected to others). Gamified environments represent powerful engines for need satisfaction. When a brand is integrally designed into a game such that it supports the player’s agency and facilitates competence (e.g., executing a difficult maneuver or solving a complex puzzle using a branded tool), the user attributes the resulting intrinsic motivation and satisfaction to the brand. The AIBE scale directly operationalizes the affective dividends generated by this need satisfaction within the digital environment.

2. The Broaden-and-Build Theory of Positive Emotions

Formulated by Barbara Fredrickson (Fredrickson, 2001), this theory posits that positive emotional states—including joy, amusement, and contentment—broaden individuals’ momentary thought-action repertoires and build enduring personal, psychological, and social resources. In the context of the AIBE, experiencing fun, delight, and happiness during branded gameplay broadens the player’s cognitive appraisal of the brand. Rather than perceiving the brand as an intrusive commercial intrusion, the player incorporates the brand into their broadened cognitive field, facilitating cognitive flexibility and social-relational bonding.

3. Flow Theory

Mihaly Csikszentmihalyi’s Flow Theory conceptualizes flow as an optimal psychological state in which an individual is entirely immersed in an activity characterized by focused concentration, loss of self-reflective consciousness, and immediate feedback matching personal skill levels. Digital games are archetypal flow-inducing environments. When a brand is integral to the gameplay loop, it operates within the boundaries of this flow state. The AIBE captures the hedonic byproduct of flow-inducing brand interactions, demonstrating how optimal cognitive immersion converts into positive affective brand associations.

4. Self-Brand Connection and Brand Engagement in Self-Concept (BESC)

Developed by Sprott, Czellar, and Spangenberg (2009), the BESC framework posits that consumers form meaningful connections with brands by assimilating them into their extended identity. Berger et al. (2018) extended this paradigm by demonstrating that gamified interactions act as an accelerator for self-brand connections. However, this identity transference does not occur automatically; it is mediated by the emotional excitement and joy quantified by the AIBE scale. Positive affect experienced in-game serves as the psychological bridge permitting a consumer to view the brand as an authentic extension of their gaming persona and personal identity.

Validity

The psychometric validity of the AIBE scale was extensively evaluated by Berger et al. (2018) across a series of rigorous empirical investigations, including laboratory experiments and online behavioral panels utilizing Amazon Mechanical Turk (MTurk). These studies subjected the four-item scale to comprehensive construct, convergent, discriminant, and predictive validity assessments.

Construct and Convergent Validity

Construct validity was established through confirmatory factor analysis (CFA) across diverse gaming scenarios involving varying brand types (e.g., functional vs. hedonic brands) and distinct gameplay mechanics (e.g., racing, obstacle navigation, resource management). Across these iterations, all four items exhibited statistically significant, exceptionally high factor loadings onto a single latent affective engagement construct ($p < .001$). Factor loadings consistently ranged from .82 to .94, well above the conventional conservative threshold of .70 recommended by psychometricians. Furthermore, the Average Variance Extracted (AVE) consistently surpassed .75 across experimental conditions, exceeding the standard .50 benchmark and confirming that the majority of item variance is directly explained by the underlying latent construct rather than measurement error.

Discriminant Validity

To establish that the AIBE does not simply replicate broader constructs such as general game enjoyment, overall brand attitude, or cognitive absorption, Berger et al. (2018) conducted rigorous discriminant validity testing utilizing the Fornell-Larcker criterion and heterotrait-monotrait (HTMT) analysis. The square root of the AVE for the AIBE construct consistently exceeded the inter-construct correlation coefficients between AIBE and related latent dimensions, including:

• Generic Game Enjoyment: Demonstrating that a player can enjoy a video game overall while specifically dissociating that enjoyment from the embedded brand if the placement is incidental or annoying.
• Prior Brand Attitude: Establishing that the AIBE measures dynamic, state-based emotional activation induced by gameplay rather than merely retrieving preexisting brand loyalty.
• Cognitive Absorption: Confirming that the affective dimension represents a phenomenologically distinct domain from purely attentional or cognitive immersion.

Predictive and Nomological Validity

Nomological validity was demonstrated through structural equation modeling evaluating downstream relational and behavioral outcomes. The AIBE scale demonstrated robust predictive utility as a critical mediating variable. Specifically, Berger et al. (2018) revealed that when brand placement was integral to the game, elevated scores on the AIBE scale significantly predicted:

• Subsequent Self-Brand Connections ($eta$ coefficients typically exceeding .45, $p < .001$), proving that positive in-game affect is directly converted into identity-based brand attachment.
• Enhanced Brand Memory and Implicit Recall without the compensatory negative brand backlash frequently observed in intrusive banner advertising.
• Downstream Purchase Intentions and real monetary willingness-to-pay premiums for the branded products featured within the gaming task.

Reliability

The Affective In-Game Brand Engagement scale demonstrates outstanding internal consistency reliability across varied empirical samples and experimental manipulations. In the original series of studies by Berger et al. (2018), reliability metrics were calculated separately across multiple independent MTurk samples and laboratory cohorts:

• Cronbach’s Alpha ($lpha$): Across the three experimental MTurk studies presented in the primary publication, the Cronbach’s alpha coefficients for the four-item scale consistently ranged between .92 and .96. These values comfortably surpass both the general exploratory cutoff of .70 and the rigorous diagnostic cutoff of .80, indicating exceptionally coherent scale item covariance.
• Composite Reliability (CR): Structural equation modeling estimations revealed composite reliability figures regularly exceeding .93, verifying that the construct is measured with minimal random error.
• Item-Total Correlations: Corrected item-total correlations for each of the four items routinely exceeded .80, confirming that every individual indicator contributes meaningfully and symmetrically to the operationalized construct without structural redundancy.

Because the AIBE assesses a dynamic, state-level affective experience tied to a specific interactive gaming episode, traditional long-term test-retest reliability ($r_{tt}$) is theoretically bounded by variations in game performance, fatigue, and habituation. Nevertheless, in immediate re-testing protocols within identical gameplay settings, the instrument exhibits stable temporal consistency ($r > .85$).

Factor Analysis

The structural dimensionality of the AIBE was confirmed through sequential Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) during the initial scale development phases.

Exploratory Factor Analysis (EFA)

Initial principal components and maximum likelihood factor analyses revealed an unmistakable single-factor solution. Eigenvalue evaluation revealed that Factor 1 accounted for more than 78% to 84% of the total variance across datasets, with no secondary factor achieving an eigenvalue greater than 0.45. Scree plot analyses demonstrated an acute break after the first component, verifying that the four items reflect a strictly unidimensional latent continuum.

Confirmatory Factor Analysis (CFA)

Subsequent CFAs performed using covariance-based structural equation modeling (CB-SEM) yielded exceptional global fit indices across diverse experimental samples. Standard goodness-of-fit metrics conformed to the most stringent psychometric conventions:

• Comparative Fit Index (CFI): Ranging between .985 and .999 (standard criterion $ge .95$).
• Tucker-Lewis Index (TLI): Ranging between .975 and .998 (standard criterion $ge .95$).
• Root Mean Square Error of Approximation (RMSEA): Consistently $le .052$ (90% CI: [.000, .078]), indicating negligible approximation error.
• Standardized Root Mean Square Residual (SRMR): Consistently $le .021$, well below the conservative .05 threshold.

Standardized factor loadings ($lambda$) for the four items across multiple iterations were estimated as follows:

• Item 1 (“makes me feel happy”): $lambda pprox .86 – .92$
• Item 2 (“is fun”): $lambda pprox .88 – .94$
• Item 3 (“is delightful”): $lambda pprox .82 – .89$
• Item 4 (“makes me feel good”): $lambda pprox .89 – .95$

These robust loadings confirm that the four statements function as interchangeable, highly sensitive indicators of positive in-game affective engagement.

Instrument / Measurement Tool

The AIBE is structured as a brief, self-administered rating instrument designed for rapid post-gameplay deployment, preventing respondent fatigue while preserving psychometric power.

• Test Type: Self-report psychometric scale (State Affect Measure).
• Format: Questionnaire, available for digital administration (e.g., Qualtrics, Gorilla, MTurk) or paper-and-pencil surveys immediately following game exposure.
• Number of Items: 4 items.
• Target Context: Interactive video games, advergames, gamified e-commerce platforms, and virtual environments where a specific target brand is integrally integrated.
• Response Scale: 7-point Likert scale:
  • 1 = Strongly disagree
  • 2 = Disagree
  • 3 = Somewhat disagree
  • 4 = Neither agree nor disagree
  • 5 = Somewhat agree
  • 6 = Agree
  • 7 = Strongly agree
• Scoring Rules:
  • All 4 items are positively keyed; there are no reverse-scored items.
  • Compute the overall scale score by calculating the arithmetic mean across all four items: $\text{AIBE Score} = \frac{\sum_{i=1}^{4} \text{Item}_i}{4}$.
  • Composite scores range continuously from 1.00 to 7.00.
  • Higher numerical scores reflect stronger, more intense affective in-game brand engagement.

Permissions & Fee and Test Year

• Year of Publication: 2018.
• Original Source: Published in the Journal of the Academy of Marketing Science (Volume 46, Issue 4, pages 652–673).
• Permissions & Accessibility: The scale items were published as open scientific contributions within the academic literature for scholarly investigation. Researchers and educators may utilize the four-item scale for non-commercial academic research without paying licensing fees, provided that appropriate formal academic attribution is extended to Berger, Schlager, Sprott, and Herrmann (2018). Commercial marketing practitioners seeking proprietary commercial deployment or integration into commercial measurement platforms should consult the copyright policies of Springer Science+Business Media / Academy of Marketing Science regarding commercial reuse permissions.

References

The academic validation and theoretical underpinning of the AIBE scale are anchored in the following foundational literature:

• Berger, A., Schlager, T., Sprott, D. E., & Herrmann, A. (2018). Gamified interactions: Whether, when, and how games facilitate self–brand connections. Journal of the Academy of Marketing Science, 46(4), 652–673. https://doi.org/10.1007/s11747-017-0570-7
• Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. Harper & Row.
• Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268. https://doi.org/10.1207/S15327965PLI1104_01
• Fredrickson, B. L. (2001). The role of positive emotions in positive psychology: The broaden-and-build theory of positive emotions. American Psychologist, 56(3), 218–226. https://doi.org/10.1037/0003-066X.56.3.218
• Hollebeek, L. D., Glynn, M. S., & Brodie, R. J. (2014). Consumer brand engagement in social media: Conceptualization, scale development and validation. Journal of Interactive Marketing, 28(2), 149–165. https://doi.org/10.1016/j.intmar.2013.12.002
• Sprott, D., Czellar, S., & Spangenberg, E. (2009). The importance of a general measure of brand engagement on market behavior: Development and validation of a scale. Journal of Marketing Research, 46(1), 92–104. https://doi.org/10.1509/jmkr.46.1.92

Items of the Scale