Abstract
The Customer Engagement and Sharing Behaviors–Model Inventory (CESBM), developed by Taylor, Carlson, Liao, and Rahman (2023), is an advanced psychometric instrument designed to capture consumer cognitive, affective, and behavioral dynamics within online brand communities (OBCs) on social media platforms such as Facebook. Departing from conventional paradigms that assume an exclusively positive linear relationship between consumer involvement and behavioral advocacy, the CESBM was conceptualized to evaluate a non-linear, negative quadratic relationship between customer engagement (CE) levels and sharing behavior intention (SBI). The inventory integrates validated constructs adapted from seminal empirical literature: customer engagement across five multidimensional facets (Enthusiasm, Attention, Absorption, Interaction, and Identification; adapted from So et al., 2016), environmental antecedents including Perceived Innovativeness (Carlson et al., 2019) and Perceived Interactivity (Jahn & Kunz, 2012), and downstream Sharing Behavior Intention (Labrecque, 2014).
The instrument operationalizes these dimensions using a 7-point Likert response format across 20 primary indicator items. Psychometric validation was conducted on an adult sample of Facebook OBC members in the United States ($N$, mean age = 39.34 years). Confirmatory factor analysis (CFA) affirmed strong structural fit for the multi-construct measurement model ($\chi^2/df = 1292.46/224$, CFI = 0.98, IFI = 0.98, RFI = 0.98, RMSEA = 0.10). Convergent validity was established with all Average Variance Extracted (AVE) values surpassing the 0.50 benchmark, while discriminant validity was verified using both the Fornell-Larcker criterion and the Heterotrait-Monotrait ratio of correlations (HTMT < 0.90). Composite reliability (CR) and Cronbach’s alpha coefficients for all subscales exceeded the standard 0.70 threshold. Assessment of common method variance via Harman’s single-factor test confirmed the distinct multidimensionality of the inventory. The CESBM serves as a vital diagnostic and predictive tool for consumer psychologists, digital marketing scholars, and community managers investigating optimal engagement thresholds and mitigating digital engagement burnout.
Keywords
Customer Engagement, Online Brand Communities, Sharing Behavior Intention, Perceived Innovativeness, Perceived Interactivity, Social Media Marketing, Nonlinear Consumer Behavior, Social Exchange Theory, Consumer Burnout, Psychometrics
Authors
The Customer Engagement and Sharing Behaviors–Model Inventory was developed and validated by a multidisciplinary team of scholars specializing in digital marketing, consumer psychology, and brand management:
- Alex Taylor, Ph.D. — Newcastle Business School, The University of Newcastle, Callaghan, NSW, Australia. Email: [email protected]
- Jamie Carlson, Ph.D. — Newcastle Business School, The University of Newcastle, Callaghan, NSW, Australia. Email: [email protected]
- Yi-Chuan Liao, Ph.D. (Corresponding Author) — Department of Business Administration, National Dong Hwa University, Hualien, Taiwan. Email: [email protected]
- Mohammad M. Rahman, Ph.D. — Department of Management, Marketing, and Entrepreneurship, John L. Grove College of Business, Shippensburg University, Shippensburg, PA, United States.
Purpose
The primary purpose of the Customer Engagement and Sharing Behaviors–Model Inventory (CESBM) is to quantify, diagnose, and model the psychological and behavioral mechanisms governing consumer participation within social media-hosted online brand communities (OBCs). For decades, consumer psychology and relationship marketing literature operated under the foundational assumption that customer engagement functions as a purely monotonic positive driver of desirable marketing outcomes. Marketers presumed that maximizing engagement indefinitely would yield proportional increases in brand advocacy, organic word-of-mouth, and content dissemination. The CESBM was specifically constructed to challenge this linear orthodoxy by empirically examining the inflection points where excessive engagement may yield diminishing, stagnant, or negative behavioral returns—specifically manifested as an inverted U-shaped or negative quadratic trajectory in sharing behavior intention (SBI).
In contemporary digital environments, consumers are subjected to relentless algorithmic feeds, real-time community prompts, and peer interactions. In this context, hyper-engagement can inadvertently transition into psychological over-saturation, cognitive overload, fatigue, or heightened possessiveness over community boundaries, which suppresses the motivation to broadcast content across personal networks. The CESBM provides the psychometric architecture necessary to detect this subtle behavioral tipping point. By isolating the distinct cognitive, emotional, and social facets of customer engagement, alongside environmental community perceptions (innovativeness and interactivity), the instrument allows researchers to pinpoint which dimensions drive viral sharing behaviors and which components contribute to behavioral stagnation when overstimulated.
Beyond academic theory testing, the CESBM has substantial utility for consumer behavior research, brand management, and applied platform analytics. In commercial settings, digital marketing strategists can deploy the inventory to benchmark community health, assess whether platform interface updates improve or degrade perceived interactivity, evaluate how perceived content innovativeness conditions consumer participation, and determine whether high-frequency community contributors are entering stages of engagement burnout. In behavioral research settings, the tool provides a standardized, psychometrically validated battery for experimental and longitudinal designs examining consumer-brand relationships, digital brand advocacy, and social media dynamics.
Psychological Construct
The CESBM is a multi-dimensional measurement model comprising eight distinct, inter-correlated constructs grouped into three overarching structural domains: Community Environment Perceptions, Customer Engagement Dimensions, and Downstream Behavioral Intentions. Below is an exhaustive theoretical and operational breakdown of each psychological construct included in the inventory:
1. Perceived Innovativeness
Rooted in innovation diffusion theory and consumer perception models (Carlson et al., 2019), Perceived Innovativeness captures the consumer’s subjective evaluation of an online brand community’s novelty, uniqueness, and technological or experiential creativity relative to rival brand platforms. Rather than measuring the objective technical attributes of a social media page, this construct assesses the degree to which a consumer perceives the community’s content, interactive features, and experiential format as cutting-edge and differentiated. High perceived innovativeness stimulates cognitive curiosity, validates the consumer’s choice of community membership, and elevates the perceived intrinsic value of the brand ecosystem.
2. Perceived Interactivity
Adapted from the digital media communication framework established by Jahn and Kunz (2012), Perceived Interactivity measures the perceived ease, efficacy, and responsiveness with which a consumer can converse and exchange information with the host brand within the OBC. This construct reflects reciprocal, synchronous communication capability. It measures whether the consumer feels the brand actively listens, responds to inquiries, and facilitates two-way dialogue, transforming a static informational broadcasting channel into an active, responsive relational exchange environment.
3. Customer Engagement (Multidimensional Core)
The central engine of the CESBM is the multidimensional Customer Engagement (CE) construct, synthesized through the operationalization developed by So et al. (2016). Customer engagement represents a consumer’s state of active, brand-related mental and behavioral investment within the OBC, delineated into five distinct sub-dimensions:
- Enthusiasm: Represents the consumer’s enduring affective drive, excitement, and emotional zeal toward the brand community. Unlike transient satisfaction, enthusiasm reflects a passionate attachment and intrinsic delight derived from belonging to the community.
- Attention: Represents the cognitive dimension of engagement, characterized by the allocation of conscious mental resources, focus, and vigilance toward the brand community’s activities, discussions, and updates. Consumers with high attention actively track brand developments and demonstrate sustained interest in brand-related information.
- Absorption: Represents a profound state of cognitive and emotional immersion or “flow” wherein the consumer experiences deep concentration, time distortion, and difficulty disengaging from community interactions. While absorption reflects high intrinsic gratification, extreme levels may lead to cognitive fatigue.
- Interaction: Represents the observable, behavioral manifestation of engagement within the OBC. It evaluates active social participation, exchanging viewpoints, networking with fellow consumers, and contributing UGC (user-generated content) rather than passively lurking.
- Identification: Grounded in Social Identity Theory, this dimension measures the consumer’s psychological incorporation of the brand community into their self-concept. High identification causes the consumer to perceive brand community triumphs as personal successes and external criticisms of the brand as personal affronts.
4. Sharing Behavior Intention (SBI)
Operationalized from Labrecque (2014), Sharing Behavior Intention constitutes the primary downstream behavioral outcome in the CESBM. It reflects the consumer’s conscious subjective probability and behavioral readiness to disseminate brand content across their broader personal social networks. SBI encompasses actions such as reposting/sharing community content onto their personal timeline, endorsing content via “likes,” and contributing public comments that expose the brand community to secondary external audiences.
Theoretical Framework
The conceptual architecture of the Customer Engagement and Sharing Behaviors–Model is anchored at the intersection of several foundational psychological and marketing theories: Social Exchange Theory (SET), Uses and Gratifications Theory (UGT), Social Identity Theory, and the Non-linear Inverted-U Hypothesis (akin to the Yerkes-Dodson principle of psychological arousal and behavioral performance).
Social Exchange Theory and Reciprocity Dynamics
Social Exchange Theory posits that human social relationships are sustained through subjective cost-benefit analyses and the anticipation of reciprocal rewards. In the context of the CESBM, community environment features (Perceived Innovativeness and Perceived Interactivity) act as environmental inputs and relational investments provided by the brand. When consumers perceive that a brand invests resources into creating an innovative, highly interactive community, the norm of reciprocity compels them to invest psychological and social capital back into the brand, manifesting as heightened cognitive, emotional, and social engagement.
Uses and Gratifications Theory (UGT)
UGT provides the motivational rationale explaining why individuals proactively select and engage with specific social media platforms. Within the CESBM, consumers engage with brand pages to satisfy cognitive needs (learning, information surveillance via Attention), hedonic needs (enjoyment, emotional stimulation via Enthusiasm and Absorption), and social integration needs (dialogue, community standing via Interaction and Identification). When these gratifications are fulfilled, consumers transition from passive recipients of marketing communication into active community members.
The Curvilinear Paradox: Negative Quadratic Dynamics
The defining theoretical contribution of the model formulated by Taylor et al. (2023) is its formal departure from linear behavioral modeling. Drawing upon cognitive overload and psychological reactance frameworks, the model proposes that customer engagement exhibits a curvilinear, negative quadratic ($\eta_{CE}^2 < 0$) relationship with outward sharing behavior intentions:
SBI = β0 + β1(CE) − β2(CE)2 + Γ(Controls/Covariates) + ε
At low-to-moderate levels of engagement, increasing consumer involvement stimulates external sharing, as consumers seek to display their brand affinities to their peers. However, past a critical threshold, hyper-engagement causes behavioral deceleration or decline. Highly absorbed and intensely identified consumers often treat the brand community as a private, exclusive sanctum. Consequently, external sharing is suppressed due to territoriality (protecting the community from dilution by outsiders), social fatigue (exhaustion from intensive internal interaction), or fear of social evaluation from personal network contacts who may not share their extreme brand enthusiasm. The CESBM formalizes this contingent curvilinear perspective, providing empirical support for the “too-much-of-a-good-thing” phenomenon in consumer brand interactions.
Validity
The psychometric integrity of the Customer Engagement and Sharing Behaviors–Model Inventory was confirmed through extensive construct, content, convergent, and discriminant validity analyses using structural equation modeling (SEM) and hierarchical regression.
Content Validity
To establish rigorous content validity, all survey items were adapted from mature, peer-reviewed scales published in top-tier marketing and consumer research journals (Carlson et al., 2019; Jahn & Kunz, 2012; Labrecque, 2014; So et al., 2016). Pre-testing procedures ensured that item wordings were adapted to the context of social media OBCs (Facebook brand pages). During measurement refinement, one indicator yielded a factor loading of 0.66, which fell slightly below the conservative 0.70 benchmark. To ensure measurement robustness, the authors conducted a sensitivity re-run of the structural hierarchical regression models excluding this item; the core empirical hypotheses and curvilinear parameters remained unchanged. However, following content validity best practices, the item was retained in the final model to preserve theoretical breadth and domain coverage across the engagement facets.
Convergent Validity
Convergent validity was evaluated by assessing standardized factor loadings, composite reliability (CR), and Average Variance Extracted (AVE). In the confirmatory factor model, latent factor loadings ($lambda$) were statistically significant ($p < 0.001$), with virtually all loadings exceeding the 0.70 threshold. In accordance with the criteria established by Fornell and Larcker (1981) and Bagozzi and Yi (1988), all constructs yielded AVE values exceeding the recommended 0.50 cutoff, confirming that each latent construct explains more than half of the variance in its observed indicators rather than measurement error.
Discriminant Validity
Discriminant validity was established via two rigorous psychometric approaches:
- Fornell-Larcker Criterion: The square root of the AVE for each latent construct was compared against the inter-construct correlation coefficients. For every construct, the square root of the AVE exceeded its highest correlation with any other construct in the model.
- Heterotrait-Monotrait (HTMT) Ratio of Correlations: Applying the modern criteria of Henseler, Ringle, and Sarstedt (2015), all HTMT values between constructs fell comfortably below the conservative 0.85 and standard 0.90 thresholds, demonstrating that each dimension captures a distinct empirical construct.
Predictive and Curvilinear Criterion Validity
Criterion validity was established through hierarchical polynomial regression analysis. The insertion of the squared customer engagement term ($CE^2$) resulted in a statistically significant change in variance explained ($\Delta R^2$), with a negative regression coefficient confirming the hypothesized inverted U-shaped relationship with Sharing Behavior Intention. This confirmed the predictive validity of the scale for non-linear consumer dynamics.
Reliability
The reliability of the CESBM inventory was confirmed through internal consistency measures evaluated across all subscales. The measurement properties complied with established psychometric standards outlined by Hair et al. (2006) and Netemeyer et al. (2003):
- Cronbach’s Alpha ($lpha$): All individual constructs and sub-dimensions demonstrated internal consistency coefficients exceeding the standard 0.70 benchmark, with several dimensions exceeding 0.85, indicating high inter-item correlation within each target domain.
- Composite Reliability (CR): To account for unequal factor loadings across indicators, Composite Reliability values were computed. All constructs produced CR values well above the 0.70 threshold recommended by Fornell and Larcker (1981), reflecting high construct-level internal reliability.
The table below summarizes the psychometric performance benchmarks established for the CESBM subscales:
| Construct Dimension | Item Count | Cronbach’s α | Composite Reliability (CR) | Average Variance Extracted (AVE) |
|---|---|---|---|---|
| Perceived Innovativeness | 3 | > 0.70 | > 0.70 | > 0.50 |
| Perceived Interactivity | 3 | > 0.70 | > 0.70 | > 0.50 |
| Customer Engagement (Overall) | 14 | > 0.85 | > 0.85 | > 0.50 |
| – Enthusiasm | 3 | > 0.70 | > 0.70 | > 0.50 |
| – Attention | 3 | > 0.70 | > 0.70 | > 0.50 |
| – Absorption | 3 | > 0.70 | > 0.70 | > 0.50 |
| – Interaction | 3 | > 0.70 | > 0.70 | > 0.50 |
| – Identification | 2 | > 0.70 | > 0.70 | > 0.50 |
| Sharing Behavior Intention (SBI) | 3 | > 0.70 | > 0.70 | > 0.50 |
Factor Analysis
The factorial structure and construct validity of the CESBM were tested using maximum-likelihood Confirmatory Factor Analysis (CFA) in AMOS/SPSS. The measurement model assessed whether the empirical data conformed to the theoretical multi-construct specification.
Confirmatory Factor Analysis (CFA) Fit Statistics
The CFA of the measurement model yielded strong fit metrics:
- Chi-Square / Degrees of Freedom Ratio ($\chi^2/df$): 1,292.46 / 224 ($pprox 5.77$). While $\chi^2$ is sensitive to larger sample sizes, relative fit indices confirmed good baseline fit.
- Comparative Fit Index (CFI): 0.98, demonstrating strong fit well above the standard 0.90 and conservative 0.95 criteria.
- Incremental Fit Index (IFI): 0.98, indicating substantial improvement over the baseline null model.
- Relative Fit Index (RFI): 0.98, reflecting high parsimonious data fitting.
- Root Mean Square Error of Approximation (RMSEA): 0.10, indicating acceptable model fit given the complex multi-indicator structure.
Factor Loadings and Parameter Estimates
Standardized factor loadings across the latent dimensions demonstrated substantial convergent validity. All item factor loadings were statistically significant at $p < 0.001$, with parameter estimates ranging from 0.70 to 0.92 (with only a single indicator loading at 0.66, which was retained for content representation). These results confirmed that the items served as reliable markers for their targeted latent constructs.
Common Method Variance (CMV) Assessment
Because cross-sectional self-report surveys are potentially susceptible to common method bias, the authors conducted Harman’s single-factor test as an exploratory and confirmatory diagnostic. In the unrotated single-factor CFA, all 20 indicator items were forced to load onto a single omnibus factor ($\chi^2 = 2009.49$ with $df = 230$). The single-factor model exhibited significantly worse fit compared to the hypothesized multi-factor model ($\Delta \chi^2 = 717.03, \Delta df = 6, p < 0.001$). Furthermore, no single factor accounted for greater than 40% of the total variance, demonstrating that common method variance did not compromise the validity of the data.
Instrument / Measurement Tool
The structural, operational, and administration specifications of the Customer Engagement and Sharing Behaviors–Model Inventory are detailed below:
- Instrument Name: Customer Engagement and Sharing Behaviors–Model Inventory (CESBM)
- Test Type: Standardized self-report psychometric measurement model / survey inventory
- Primary Target Population: Human adults (≥ 18 years of age) who are active members or followers of brand communities hosted on social media platforms (e.g., Facebook brand pages)
- Empirical Standardization Sample: Adult social media users located in the United States ($N$, mean age = 39.34 years; balanced gender representation across male and female respondents)
- Total Item Count: 20 core operational items
- Subscales / Dimensions (8 sub-dimensions):
- Perceived Innovativeness (3 items)
- Perceived Interactivity (3 items)
- Customer Engagement: Enthusiasm (3 items)
- Customer Engagement: Attention (3 items)
- Customer Engagement: Absorption (3 items)
- Customer Engagement: Interaction (3 items)
- Customer Engagement: Identification (2 items)
- Sharing Behavior Intention [SBI] (3 items)
- Response Format: 7-point Likert response scale ranging from 1 (“Strongly Disagree”) to 7 (“Strongly Agree”)
- Scoring and Computational Procedures:
- Dimension scores are calculated by averaging the response values of their respective items. Higher composite scores denote greater levels of that construct.
- An omnibus Customer Engagement (CE) composite score can be modeled as a second-order latent factor or calculated by averaging the scores across its five sub-dimensions (Enthusiasm, Attention, Absorption, Interaction, and Identification).
- To assess non-linear relationships with Sharing Behavior Intention (SBI), mean-center the continuous engagement score ($CE_{mc} = CE – \mu_{CE}$) and compute its quadratic term ($CE_{mc}^2$) prior to entering it into hierarchical polynomial regression or non-linear structural equation models.
- Administration Time: Approximately 5 to 8 minutes
Permissions & Fee and Test Year
The Customer Engagement and Sharing Behaviors–Model Inventory was developed and published in 2023 by Alex Taylor, Jamie Carlson, Yi-Chuan Liao, and Mohammad M. Rahman. The inventory is available free of charge for non-commercial academic research, pedagogical use, and scholarly investigation, provided that appropriate citation and scholarly attribution are given to the original authors and the Journal of Business Research.
Commercial utilization, monetization, or proprietary integration into corporate analytics platforms requires formal permissions from the authors and the copyright holder (Elsevier / Journal of Business Research). Researchers seeking correspondence or collaboration should contact the corresponding author, Dr. Yi-Chuan Liao, Department of Business Administration, National Dong Hwa University, Hualien, Taiwan (Email: [email protected]).
References
- Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94. https://doi.org/10.1007/BF02723327
- Carlson, J., Rahman, M., Voola, R., & De Vries, N. (2019). Customer engagement behaviours in social media: Capturing innovation opportunities. Journal of Services Marketing, 33(1), 83–94. https://doi.org/10.1108/JSM-02-2018-0059
- 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
- Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (6th ed.). Pearson Prentice Hall.
- Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
- Jahn, B., & Kunz, W. (2012). How to transform consumers into fans of your brand. Journal of Service Management, 23(3), 344–361. https://doi.org/10.1108/09564231211248444
- Labrecque, L. I. (2014). Fostering consumer–brand relationships in social media environments: The role of parasocial interaction. Journal of Interactive Marketing, 28(2), 134–148. https://doi.org/10.1016/j.intmar.2013.12.003
- McFarlin, D. B., & Sweeney, P. D. (1992). Distributive and procedural justice as predictors of satisfaction with personal and organizational outcomes. Academy of Management Journal, 35(3), 626–637. https://doi.org/10.2307/256489
- Netemeyer, R. G., Bearden, W. O., & Sharma, S. (2003). Scaling procedures: Issues and applications. SAGE Publications. https://doi.org/10.4135/9781412985772
- So, K. K. F., King, C., & Sparks, B. (2014). Customer engagement with tourism brands: Scale development and validation. Journal of Hospitality & Tourism Research, 38(3), 304–329. https://doi.org/10.1177/1096348012451456
- So, K. K. F., King, C., Sparks, B. A., & Wang, Y. (2016). The role of customer engagement in building consumer loyalty to tourism brands. Journal of Travel Research, 55(1), 64–78. https://doi.org/10.1177/0047287514541008
- Taylor, A., Carlson, J., Liao, Y.-C., & Rahman, M. M. (2023). Customer engagement and sharing behaviors: Toward a contingent curvilinear perspective. Journal of Business Research, 154, Article 113284. https://doi.org/10.1016/j.jbusres.2022.08.048
Items of the Scale
Instructions: Please indicate your level of agreement with each statement regarding your experience with this Online Brand Community (OBC) on Facebook. All items are evaluated on a 7-point Likert scale:
Perceived Innovativeness
- The online brand community (OBC) content is highly innovative compared with others that I have seen.
- The OBC delivers an innovative experience.
- The OBC offered a unique experience for customers that is different from those that other OBCs offer.
Perceived Interactivity
- I can get answers from the brand on this OBC.
- I can interact easily with the brand on this OBC.
- I can communicate effectively with the brand on this OBC.
Customer Engagement: Enthusiasm
- I love this OBC.
- I am enthusiastic about this OBC.
- I am passionate about this OBC.
Customer Engagement: Attention
- I pay a lot of attention to anything on this OBC.
- Anything related to this brand on the OBC grabs my attention.
- I like to learn more about this brand through the OBC.
Customer Engagement: Absorption
- When interacting with the OBC intensely, I feel happy.
- When interacting with the OBC, it is difficult to detach myself.
- When I am interacting with the OBC, I forget everything else around me.
Customer Engagement: Interaction
- I am an interacting member of this OBC community.
- I am an active member of this OBC community.
- I am a participating user of this OBC community.
Customer Engagement: Identification
- When someone criticizes this brand on the OBC, it feels like a personal insult.
- I am very interested in what others think about this OBC.
Sharing Behavior Intention (SBI)
- I will share this brand’s OBC content in the future.
- I intend to “like” this brand’s OBC content.
- I will comment on this brand’s OBC content in the future.