Cognitive In-Game Brand Engagement (CIBE)

1. Abstract

The Cognitive In-Game Brand Engagement (CIBE) scale is a specialized psychometric instrument designed to evaluate the depth of cognitive elaboration, conscious thought allocation, and mental stimulation elicited by branded objects, product placements, or commercial entities embedded within interactive video game environments. Introduced by Berger, Schlager, Sprott, and Herrmann (2018) in the Journal of the Academy of Marketing Science, the scale represents a context-specific adaptation of foundational consumer brand engagement frameworks. Comprising three unidimensional, self-report items rated on a standard Likert-type response format, the CIBE isolates cognitive processing from affective and behavioral engagement dimensions, focusing precisely on the degree to which a player actively contemplates, processes, and is intellectually intrigued by an integrated brand during gameplay.

Psychometrically, the CIBE exhibits robust empirical properties across diverse experimental and interactive gaming contexts. Across the empirical studies reported by Berger et al. (2018), the scale demonstrated high internal consistency, with Cronbach’s alpha coefficients reliably exceeding the .85 threshold. Confirmatory factor analyses confirm that CIBE is empirically distinct from related constructs, including affective in-game brand engagement, telepresence, flow, general gameplay immersion, and self-brand connections (Escalas & Bettman, 2003). The instrument serves as a critical mediator explaining how game mechanics and brand integration modalities drive downstream consumer outcomes, such as brand attitude, recall, and purchase intentions. Its brevity minimizes respondent fatigue in experimental gaming sessions, establishing CIBE as a benchmark operationalization for marketing scientists, media psychologists, and human-computer interaction researchers.

2. Keywords

Cognitive In-Game Brand Engagement, CIBE, brand engagement, in-game advertising, advergames, gamification, cognitive elaboration, self-brand connection, media psychology, psychometrics, consumer-brand relationships

3. Authors

The Cognitive In-Game Brand Engagement measure was formulated and validated by an international team of researchers specializing in consumer behavior, marketing science, and gamified customer interactions:

• Alexander Berger: Research Associate and doctoral alumnus at the Institute of Marketing and Customer Insight, University of St. Gallen (HSG), St. Gallen, Switzerland. His research focuses on gamification, interactive media, and digital marketing strategies.
• Tobias Schlager: Associate Professor of Marketing at the University of Lausanne (HEC Lausanne), Switzerland. His empirical work centers on consumer-technology interactions, digital platforms, and customer experience management.
• David E. Sprott: Henry Y. Wong Endowed Professor of Marketing and former Dean of the College of Business at Ohio University and Washington State University, USA. Professor Sprott is an internationally recognized authority on consumer-brand relationships, retail management, and brand engagement paradigms.
• Andreas Herrmann: Professor of Marketing and Director of the Institute for Customer Insight at the University of St. Gallen, Switzerland. His extensive scholarship encompasses consumer decision-making, behavioral economics, and product innovation.

4. Purpose

The principal purpose of the Cognitive In-Game Brand Engagement (CIBE) scale is to measure the magnitude of a consumer’s active, conscious cognitive processing directed toward a brand when that brand is integrated within a gamified or virtual entertainment environment. As interactive entertainment media have eclipsed traditional static advertising in consumer time expenditure, marketers and psychological researchers have increasingly shifted their attention from passive exposure models (e.g., billboards, television commercials) toward dynamic, interactive brand placements. However, the psychological mechanics governing how consumers engage with commercial content during active gameplay require targeted psychometric tools that account for attentional division and mental load.

Traditional advertising measures historically prioritized unprompted brand recall, recognition, or overall brand attitude. While these metrics capture outcomes of exposure, they fail to illuminate the real-time cognitive immersion and intellectual contemplation that occur while an individual navigates a complex interactive environment. In a video game, players manage competing cognitive demands: problem-solving, spatial orientation, sensory-motor coordination, and narrative tracking. The CIBE was developed to quantify whether the embedded brand penetrates this heightened state of sensory activity to provoke deliberate, brand-focused thought, or whether it remains peripheral background noise.

The scale serves vital functions in both fundamental consumer research and applied marketing analytics. From a theoretical perspective, CIBE allows researchers to test boundary conditions of information processing models, such as Lang’s (2000) Limited Capacity Model of Motivated Mediated Message Processing. It enables investigators to ascertain under what game design parameters (e.g., congruence between brand identity and game narrative, task integration versus static placement) cognitive resources are successfully co-allocated to the brand. In applied settings, game developers, interactive advertising agencies, and corporate sponsors utilize the scale to conduct A/B testing on ad placement styles, verifying whether a gamified interaction provokes meaningful brand consideration without generating cognitive overload or triggering consumer reactance.

5. Psychological Construct

Cognitive In-Game Brand Engagement represents the cognitive facet of the broader multidimensional construct known as Customer Brand Engagement (CBE), situated within interactive digital media. In consumer psychology literature (e.g., Hollebeek et al., 2014; Brodie et al., 2011), brand engagement is conceptualized as a psychological state comprising three core dimensions:

• Cognitive Engagement: The level of brand-related thought processing, concentration, and mental elaboration.
• Affective Engagement: The degree of positive brand-related emotion, passion, and pride.
• Behavioral/Activation Engagement: The energy, effort, and time expended on the brand during an interaction.

The CIBE scale operationalizes the first dimension—cognitive processing—within the boundaries of a gamified setting. Specifically, it captures the extent to which engaging with a branded entity in a game prompts the consumer to actively reflect upon the brand, contemplate its characteristics, and experience intellectual stimulation directly associated with it. Unlike passive visual attention, cognitive engagement requires the activation of working memory and the retrieval of brand schemas from long-term memory.

For example, if a player is driving a real-world licensed sports car in a high-fidelity racing simulation, passive exposure means the brand logo is merely visually visible on the vehicle dashboard. In contrast, high cognitive in-game brand engagement occurs when the player actively thinks about the car manufacturer’s engineering prestige, ponders how the vehicle’s simulated performance aligns with its real-world reputation, or considers how the brand’s identity reflects their personal driving style. Thus, CIBE reflects an active mental synthesis rather than simple sensory perception. It stands distinct from emotional attachment (affective engagement) and physical interaction (e.g., repeatedly selecting the vehicle, which represents behavioral engagement).

6. Theoretical Framework

The conceptual foundation of the Cognitive In-Game Brand Engagement scale is rooted in several interconnected psychological and communication paradigms:

The Limited Capacity Model of Mediated Message Processing (LC4MP)

Proposed by Annie Lang (2000), the LC4MP posits that human beings possess a finite budget of mental resources dedicated to encoding, storing, and retrieving information from media. In a video game or gamified task, the majority of cognitive resources are typically absorbed by primary game objectives—such as avoiding obstacles, defeating opponents, or solving spatial puzzles. For an embedded brand to achieve cognitive engagement, it must be integrated in a manner that either reduces cognitive load or makes the brand central to the completion of the game task. The CIBE assesses the degree to which mental capacity has been successfully allocated to the encoding and processing of the commercial stimulus amidst competing gameplay stimuli.

Consumer Brand Engagement (CBE) and Elaboration Likelihood

The scale draws directly upon the conceptualization of brand engagement formulated by Hollebeek (2011) and Hollebeek, Glynn, and Brodie (2014), who defined cognitive processing as a consumer’s brand-related thought processing and mental elaboration in a specific interaction. This aligns with Petty and Cacioppo’s (1986) Elaboration Likelihood Model (ELM). Under the ELM, cognitive engagement reflects central-route processing: the respondent is not merely reacting to peripheral cues (such as vibrant game graphics or background music) but is scrutinizing and integrating the brand concept into their active mental schema.

Self-Brand Connection and Narrative Transportation

Berger et al. (2018) integrated CIBE into the theoretical framework of self-brand connections (Escalas & Bettman, 2003) and narrative transportation theory (Green & Brock, 2000). When consumers become immersed in gamified environments, their personal identity and in-game avatar identity temporarily merge. When a brand serves as an instrument of agency within this narrative (e.g., an energy drink item that restores avatar stamina), cognitive engagement is heightened because the brand plays a functional, meaningful role in the user’s experiential journey, fostering deeper self-brand integration.

7. Validity

The psychometric validity of the Cognitive In-Game Brand Engagement scale was rigorously evaluated by Berger et al. (2018) across multiple laboratory experiments and field studies involving diverse gamified scenarios.

Construct and Convergent Validity

Convergent validity was established by evaluating the relationship between CIBE and allied constructs within structural equation modeling (SEM) frameworks. In Berger et al. (2018), CIBE demonstrated substantial, statistically significant positive correlations with parallel affective in-game engagement subscales ($r > .60, p < .001$) and subsequent self-brand connections ($r pprox .50\text{ to }.65, p < .001$). Average Variance Extracted (AVE) values for the cognitive dimension routinely exceeded the recommended threshold of .50 (ranging from .68 to .78 across empirical studies), demonstrating that the variance captured by the latent construct is substantially greater than variance attributable to measurement error.

Discriminant Validity

Discriminant validity was verified using the Fornell-Larcker criterion and heterotrait-monotrait ratio of correlations (HTMT). The square root of the AVE for the CIBE construct was consistently greater than its inter-construct correlations with related operationalizations, including general game enjoyment, overall telepresence/immersion, brand familiarity, and affective brand engagement. These analyses confirmed that CIBE does not simply capture overall positive game experience or generic arousal, but specifically indexes focused, intellectual reflection regarding the embedded brand.

Predictive and Nomological Validity

In path-analytic models testing nomological validity, CIBE operated as a vital mediating mechanism. Berger et al. (2018) demonstrated that gamified task integration (where a brand is functional to gameplay rather than merely decorative) significantly increased cognitive engagement. This elevated cognitive processing directly predicted enhanced self-brand connection, which subsequently drove post-game brand evaluation, purchase intentions, and actual brand choice in behavioral trade-off tasks.

8. Reliability

The reliability of the Cognitive In-Game Brand Engagement scale has been repeatedly corroborated across empirical investigations in consumer research and digital media studies:

• Internal Consistency: Across the experimental conditions examined by Berger et al. (2018), the three-item CIBE scale yielded Cronbach’s alpha coefficients between $\alpha = .86$ and $\alpha = .92$, well above the widely recognized .70 benchmark for psychometric adequacy.
• Composite Reliability (CR): Structural equation models confirmed composite reliability estimates ranging between $\rho_c = .87$ and $.93$, indicating robust internal coherence among the latent indicator items without redundancy.
• Item-Total Correlations: Corrected item-total correlations across validation studies consistently exceeded $r = .70$, confirming that each of the three indicators contributes meaningfully to the common underlying latent dimension.

Due to the state-dependent nature of video game interactions—where engagement fluctuates based on gameplay session dynamics, player skill, and narrative progression—classic test-retest reliability across long time horizons is theoretically less relevant than immediate post-exposure internal consistency. However, in short-interval repeated administrations within controlled laboratory paradigms, the measure has exhibited high stability across identical game scenarios.

9. Factor Analysis

Factorial validity of the CIBE scale was confirmed by Berger et al. (2018) through both exploratory factor analysis (EFA) and confirmatory factor analysis (CFA):

Factor Structure

During initial structural testing, the three cognitive engagement items were subjected to principal axis factoring with promax rotation alongside items measuring affective in-game brand engagement and general gameplay immersion. The items cleanly loaded onto their designated latent factor, with no cross-loadings exceeding .25 on alternative dimensions. The cognitive engagement factor accounted for a substantial proportion of unique variance (typically >65% of total scale variance in standalone extractions).

Confirmatory Factor Analysis (CFA) Loadings

In CFA models evaluated using maximum likelihood estimation, standardized factor loadings for all three items were uniformly high and statistically significant ($p < .001$):

• Item 1 (Thinking about the brand): Standardized factor loading $\lambda = .84\text{ to }.91$
• Item 2 (Brand stimulates interest/thought): Standardized factor loading $\lambda = .81\text{ to }.89$
• Item 3 (Mental focus on the brand): Standardized factor loading $\lambda = .83\text{ to }.88$

Model Fit Indices

Measurement models incorporating the CIBE construct alongside related latent variables consistently satisfied standard criteria for exemplary model fit:

• Comparative Fit Index (CFI): $ge .96$
• Tucker-Lewis Index (TLI): $ge .95$
• Root Mean Square Error of Approximation (RMSEA): $le .055$ (with 90% confidence intervals between .000 and .078)
• Standardized Root Mean Square Residual (SRMR): $le .038$

10. Instrument / Measurement Tool

The Cognitive In-Game Brand Engagement instrument is structured as a succinct, self-report psychological measurement scale optimized for immediate post-game administration:

• Instrument Type: Self-administered post-exposure questionnaire.
• Target Population: Consumers, experimental participants, or video game players who have interacted with a game containing an embedded or branded product.
• Number of Items: 3 items measuring unidimensional cognitive elaboration.
• Response Scale: Typically administered using a 7-point Likert response format ranging from 1 = “Strongly Disagree” to 7 = “Strongly Agree” (or alternatively, a 5-point Likert scale depending on research design constraints).
• Administration Time: Approximately 30 to 60 seconds, minimizing cognitive interference and survey fatigue in experimental protocols.
• Scoring Procedure:
  • All items are positively phrased; no reverse scoring is required.
  • An overall Cognitive In-Game Brand Engagement score is calculated by computing the arithmetic mean across the three items.
  • Higher mean values reflect deeper cognitive elaboration, focused thought processing, and intellectual stimulation elicited by the brand during gameplay.

11. Permissions & Fee and Test Year

Publication Year: 2018

Original Publication: Journal of the Academy of Marketing Science, Volume 46, Issue 4, pages 652–673, authored by Alexander Berger, Tobias Schlager, David E. Sprott, and Andreas Herrmann.

Permissions & Copyright: The scale was published in an academic, peer-reviewed journal published by Springer Nature. For non-commercial academic research and scholarly education purposes, psychometric scales published in JAMS may generally be referenced and utilized under standard academic fair use conventions, provided proper attribution and citation are given to the original authors. For commercial applications, corporatized research platforms, or republishing in commercial products, permissions must be obtained through the Copyright Clearance Center (RightsLink) or directly from Springer Nature.

Fee: Free for non-commercial academic use. Commercial licensing fees are subject to publisher terms.

12. References

• 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/s11747-017-0569-8
• Brodie, R. J., Hollebeek, L. D., Jurić, B., & Ilić, A. (2011). Customer engagement: Conceptual domain, fundamental premises, and emerging directions in research. Journal of Service Research, 14(3), 252–271. https://doi.org/10.1177/1094670511411703
• Csikszentmihalyi, M. (1990). Flow: The Psychology of Optimal Experience. Harper & Row.
• Escalas, J. E., & Bettman, J. R. (2003). You are what they eat: The influence of reference groups on consumers’ connections to brands. Journal of Consumer Psychology, 13(3), 339–348. https://doi.org/10.1207/S15327663JCP1303_14
• Green, M. C., & Brock, T. C. (2000). The role of transportation in the persuasiveness of public narratives. Journal of Personality and Social Psychology, 79(5), 701–721. https://doi.org/10.1037/0022-3514.79.5.701
• Hollebeek, L. D. (2011). Demystifying customer brand engagement: Exploring the loyalty nexus. Journal of Marketing Management, 27(7–8), 785–807. https://doi.org/10.1080/0267257X.2010.500132
• 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
• Lang, A. (2000). The limited capacity model of mediated message processing. Journal of Communication, 50(1), 46–70. https://doi.org/10.1111/j.1460-2466.2000.tb02833.x
• Petty, R. E., & Cacioppo, J. T. (1986). The elaboration likelihood model of persuasion. Advances in Experimental Social Psychology, 19, 123–205. https://doi.org/10.1016/S0065-2601(08)60214-2
• Sprott, D., Czellar, S., & Spangenberg, E. (2009). The importance of a general measure of brand engagement on market behavior: Development and validation of a brand engagement in self-concept scale. Journal of Marketing Research, 46(1), 92–104. https://doi.org/10.1509/jmkr.46.1.92

13. Items of the Scale