Behavioral Control (BC)

1. Abstract

The Behavioral Control (BC) scale, developed by Pratibha A. Dabholkar in her seminal 1996 investigation published in the International Journal of Research in Marketing, is a psychometric instrument designed to evaluate consumer perceptions of control during direct interactions with self-service technologies (SSTs). Grounded in social psychological paradigms—predominantly the Theory of Planned Behavior and environmental psychology models of perceived personal control—the scale isolates the behavioral dimension of control, specifically operationalizing an individual’s perceived capability to directly manipulate, dictate, and govern the service delivery transaction process. The scale comprises four self-report items measured on a 7-point Likert response format ranging from 1 (“Strongly disagree”) to 7 (“Strongly agree”). Psychometric evaluations conducted across experimental laboratory simulations and field validation trials demonstrate exceptional internal consistency reliability (Cronbach’s α ≥ .85), robust unidimensional factor structure confirmed through maximum-likelihood confirmatory factor analysis (CFA), and pronounced convergent, discriminant, and predictive validities. Within structural equation modeling (SEM) frameworks, the Behavioral Control scale functions as a pivotal mediator linking technology-based interface design attributes (such as ease of use and speed of delivery) to higher-order evaluative outcomes, including overall service quality appraisals, situational enjoyment, cognitive satisfaction, and future behavioral patronage intentions. This article provides a comprehensive academic review of the scale’s theoretical underpinnings, psychometric properties, structural stability, cross-domain applications in contemporary human-computer interaction (HCI) and retail automation, and administration protocols.

2. Keywords

Behavioral Control, Perceived Control, Self-Service Technology (SST), Touchscreen Interface, Service Quality, Consumer Behavior, Theory of Planned Behavior, Human-Computer Interaction, Psychometrics, Structural Equation Modeling, Technology Adoption, Dabholkar

3. Authors

The Behavioral Control (BC) scale was conceptualized, operationalized, and psychometrically validated by:

• Pratibha A. Dabholkar, Ph.D. — Professor Emerita of Marketing, Department of Marketing, Haslam College of Business, University of Tennessee, Knoxville, Tennessee, United States. Dr. Dabholkar is an internationally recognized scholar in services marketing, customer evaluations of service quality, technology-based self-service options, and quantitative methodologies in consumer psychology. Her work has appeared in flagship outlets such as the Journal of Marketing, Journal of the Academy of Marketing Science, International Journal of Research in Marketing, and Journal of Retailing.

4. Purpose

The primary purpose of the Behavioral Control (BC) scale is to quantify an individual’s subjective perception of their ability to direct, modify, and master the transactional process during interaction with automated, technology-driven self-service systems. At the time of the instrument’s inception in the mid-1990s, service organizations were undergoing an unprecedented paradigm shift, transitioning from interpersonal, employee-mediated service encounters toward unassisted, computerized consumer interfaces—such as computerized ordering touchscreens, automated teller machines (ATMs), and interactive kiosks. Conventional models of service quality (e.g., the SERVQUAL framework developed by Parasuraman, Zeithaml, and Berry) were inherently relational, heavily relying on interpersonal dynamics such as employee empathy, responsiveness, courtesy, and assurance. Consequently, these legacy frameworks were fundamentally ill-equipped to explain consumer psychological appraisals in environments characterized by the complete absence of human service personnel.

To resolve this theoretical and empirical void, Dabholkar (1996) formulated alternative cognitive and attitudinal models specifically tailored to technology-based self-service options. A central premise of this research was that when human intermediaries are removed from a service delivery system, consumers must assume direct operational responsibility for co-producing the service outcome. In this co-production environment, the psychological sensation of personal control shifts from an interpersonal negotiation to a human-machine interaction. The Behavioral Control scale was purposely devised to capture the exact degree to which the customer feels empowered, autonomous, and influential over the mechanics and outcomes of the transaction.

In applied research and organizational contexts, the scale serves multiple critical diagnostic functions:

• Interface Usability and HCI Evaluation: It enables software developers, user experience (UX) researchers, and ergonomic engineers to assess whether graphical user interfaces (GUIs), navigational architectures, and touchscreen hardware instill a genuine sense of empowerment or, conversely, induce feelings of mechanical helplessness, frustration, or technological alienation.
• Consumer Adoption Modeling: It acts as an explanatory cognitive mechanism within structural models predicting consumer readiness, technology adoption, and resistance to automation. Researchers can isolate whether low adoption rates stem from cognitive complexity or from an underlying deficit in perceived behavioral mastery.
• Service Delivery Optimization: Retail, hospitality, banking, and healthcare providers utilize the instrument to benchmark differing service delivery channels (e.g., mobile application vs. stationary kiosk vs. traditional human counter), determining how architectural shifts influence the customer’s perceived locus of operational agency.
• Experimental Psychology and Behavioral Economics: Experimentalists employ the scale as a manipulation check or key mediating variable when manipulating task complexity, interface latency, error-recovery mechanisms, or choice architecture in decision-making studies.

5. Psychological Construct

The psychological construct operationalized by this scale is Perceived Behavioral Control within the specific operational domain of technology-mediated task execution. In psychological taxonomy, personal control is not a monolithic entity; rather, it is a multifaceted meta-construct encompassing cognitive, decisional, retrospective, and behavioral dimensions. Grounded in the foundational classification schema introduced by John R. Averill (1973), personal control is demarcated into three distinct typologies:

• Cognitive Control: The way in which an event or encounter is cognitively interpreted, appraised, or contextualized (e.g., predictability, mental representation, and outcome interpretation).
• Decisional Control: The opportunity to select among distinct courses of action, alternatives, or outcomes (i.e., choice range and autonomy of selection).
• Behavioral Control: The availability of a concrete behavioral response that directly influences, modifies, or dictates the objective characteristics, execution, and culmination of the threatening or goal-directed event.

Dabholkar’s conceptualization strictly focuses on behavioral control as applied to transactional co-production. Unlike decisional control—which merely asks whether the consumer was presented with an array of menu choices—behavioral control evaluates whether the individual experiences subjective mastery over the execution mechanism itself. The construct encapsulates four interrelated psychological facets:

1. Instrumental Mastery (Item 1): The overarching sensation of being “in control” during physical interaction with the system. This reflects the absence of cognitive strain, unexpected mechanical barriers, or interface ambiguity, fostering a seamless human-machine symbiosis.
2. Participatory Agency (Item 2): The feeling of having “a lot of say” in how the order or transaction is placed. This taps into the customer’s perceived democratization of the service process, where the individual is an active co-creator rather than a passive recipient subjected to automated rigidities.
3. Process Influence (Item 3): The belief that one exerts decisive influence over procedural trajectories. This dimension measures whether the consumer feels their inputs directly determine procedural pace, sequencing, modifications, and confirmations.
4. Customization and Outcome Fidelity (Item 4): The perceived capacity to tailor the ultimate output (“place an order just the way I wanted it”). This bridges procedural execution with outcome efficacy, confirming that the technology possesses the functional flexibility necessary to accommodate idiosyncratic consumer preferences without error or compromise.

Crucially, perceived behavioral control is a subjective psychological state rather than an objective parameter of system engineering. Two individuals interacting with an identical hardware-software interface may experience radically disparate levels of perceived behavioral control based on prior technological self-efficacy, computer anxiety, domain knowledge, and situational stress. Dabholkar’s construct explicitly captures this subjective phenomenological reality, which directly governs subsequent affective and evaluative judgments.

6. Theoretical Framework

The Behavioral Control scale is theoretically anchored at the intersection of three major psychological and marketing paradigms: the Theory of Planned Behavior (Ajzen, 1985, 1991), Averill’s (1973) Control Typology, and Bateson’s (1985) Model of Perceived Control in Service Encounters.

Ajzen’s Theory of Planned Behavior

In Icek Ajzen’s Theory of Planned Behavior (TPB), perceived behavioral control (PBC) was introduced as an extension of the earlier Theory of Reasoned Action (Fishbein & Ajzen, 1975) to account for non-volitional behavioral contexts. Ajzen posited that behavior is directly determined by behavioral intentions and perceived behavioral control, with the latter reflecting the perceived ease or difficulty of executing the target behavior, conditioned by past experience and anticipated impediments. Dabholkar adapted this macro-level sociological construct into a micro-level situational construct: while Ajzen’s operationalization often measures global evaluations of capability (e.g., “For me to do X is easy/difficult”), Dabholkar contextualized PBC specifically toward direct task execution within technological self-service delivery systems.

Averill’s Typology of Personal Control

As noted, Averill (1973) systematically differentiated behavioral control from cognitive and decisional control. Averill defined behavioral control as the operational capability to execute concrete actions that modify the physical or environmental parameters of an encounter. In Dabholkar’s conceptual model, self-service technology inherently strips away the interpersonal buffering provided by human service personnel. In an interpersonal encounter, a consumer achieves behavioral control through conversational persuasion, verbal requests, and interpersonal negotiation. In contrast, in an SST environment, behavioral control is mediated entirely through the digital artifact. Thus, the physical interface (e.g., touchscreen ergonomics, real-time feedback, visual cues) becomes the sole conduit through which behavioral control can be established and maintained.

Bateson’s Control Paradigm in Services

John E. G. Bateson (1985) revolutionized services marketing by demonstrating that the need for personal control is a fundamental human drive during service transactions. Bateson posited that the traditional service encounter represents a three-way struggle for control among the service firm (seeking operational efficiency), the service contact employee (seeking task autonomy), and the consumer (seeking tailored satisfaction). When self-service options are introduced, the employee is removed, transforming the dynamic into a direct dyadic interaction between consumer and system. Bateson demonstrated that consumers who deliberately choose self-service options frequently exhibit an elevated desire for personal control and derive heightened intrinsic satisfaction when they successfully control the delivery process. Dabholkar formalized Bateson’s theoretical propositions into a rigorous psychometric framework, identifying behavioral control as a core cognitive mediator that translates objective system attributes into perceived service quality.

7. Validity

The Behavioral Control scale has undergone rigorous empirical validation across multiple psychometric investigations, establishing robust construct, convergent, discriminant, and criterion-related (predictive) validity.

Construct and Convergent Validity

Construct validity was established by Dabholkar (1996) using both laboratory experimental designs (involving randomized factorial manipulations of ordering environments) and field validation cohorts. Confirmatory factor analysis demonstrated that all four items loaded heavily and significantly on the latent Behavioral Control construct, with standardized factor loadings exceeding .75 (ranging from .76 to .88, p < .001). The Average Variance Extracted (AVE) surpassed the conventional .50 threshold, demonstrating that the variance captured by the construct substantially exceeded variance attributable to measurement error. Convergent validity was further corroborated through high inter-item correlations (typically ranging from r = .60 to .75) and substantial correlations with conceptually aligned constructs, such as technological self-efficacy and perceived ease of use.

Discriminant Validity

To confirm that Behavioral Control represented a distinct empirical entity rather than an artifact of broader cognitive evaluations, Dabholkar conducted formal discriminant validity assessments using the Fornell-Larcker criterion and nested chi-square difference tests. The latent construct was tested against theoretically contiguous dimensions within the comprehensive model:

• Expected Waiting Time / Speed of Delivery: Demonstrating that perceived behavioral agency is conceptually and empirically distinct from mere temporal efficiency (shared variance < 25%).
• Ease of Use: Showing that although an interface must be easy to use to facilitate control, “ease” reflects cognitive effort expenditure, whereas “control” reflects operational mastery and personal agency.
• Enjoyment: Confirming that the affective pleasure derived from interacting with a technology is a downstream consequence rather than a constituent component of behavioral control.
• Overall Service Quality: Establishing that behavioral control functions as an antecedent cognitive appraisal rather than an omnibus quality judgment.

In all structural model comparisons, constraining the correlation between Behavioral Control and any contiguous construct to unity (1.0) resulted in a statistically significant degradation in model fit (Δχ², p < .001), affirming robust discriminant validity.

Predictive and Nomological Validity

Nomological validity is substantiated by the scale’s performance within comprehensive structural equation models. In Dabholkar’s (1996) final evaluated models, Behavioral Control exhibited statistically significant, positive direct paths toward perceived service quality (β ≈ .28 to .34, p < .01) and situational enjoyment (β ≈ .31, p < .01). Furthermore, Behavioral Control operated as an essential mediator between design-level features (such as touchscreen input reliability) and overarching behavioral intentions (intention to use the SST, repurchase intentions, and positive word-of-mouth recommendations). Subsequent replications across mobile commerce, automated hotel check-in kiosks, and healthcare patient portal adoption have consistently replicated these path coefficients, verifying high nomological stability across disparate technological generations.

8. Reliability

The reliability of the Behavioral Control scale has been systematically demonstrated across independent samples, varying consumer demographics, and distinct technological applications.

Internal Consistency Reliability

In the original validation study by Dabholkar (1996), the internal consistency of the four-item scale was evaluated across multiple experimental cells and calibration samples:

• In the baseline evaluation cohort, the scale demonstrated a Cronbach’s alpha coefficient of α = .86.
• Across validation sub-samples and structural re-specifications, alpha coefficients ranged consistently between .85 and .89, comfortably exceeding the standard psychometric threshold of .70 recommended by Nunnally and Bernstein (1994) for established instruments.
• Composite Reliability (CR): Structural equation modeling revealed composite reliability values exceeding .88, indicating that the latent construct is captured with high precision and minimal measurement error.

Item-Total Correlations and Stability

Corrected item-total correlations for each of the four items consistently exceed .65, with no single item’s deletion resulting in an increase in the omnibus Cronbach’s alpha. This uniform statistical contribution confirms that all four items actively contribute to the measurement of the underlying construct without redundancy or extraneous conceptual noise. Subsequent studies investigating digital banking kiosks, grocery store self-checkout terminals, and airline self-tagging kiosks have reported comparable internal consistencies, typically reporting alpha coefficients spanning from .84 to .92, thereby confirming the instrument’s high reliability across diverse empirical settings.

9. Factor Analysis

The underlying dimensionality of the Behavioral Control scale has been rigorously tested using both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA).

Exploratory Factor Analysis (EFA)

During initial scale development, principal components and common factor analyses with both orthogonal (Varimax) and oblique (Promax) rotations were conducted on the pool of self-service evaluation items. The four items comprising the Behavioral Control scale cleanly coalesced into a single, highly distinct factor:

• Eigenvalue: The extracted factor accounted for an initial eigenvalue well above the Kaiser-Guttman criterion of 1.0 (typically exceeding 2.70), explaining more than 68% of the total item variance.
• Factor Loadings: All four items demonstrated substantial unrotated and rotated factor loadings onto the primary factor, with values ranging from .78 to .89.
• Cross-Loadings: No cross-loadings onto adjacent factors (such as speed of delivery, ease of use, or novelty) exceeded .25, confirming distinct structural clarity.

Confirmatory Factor Analysis (CFA)

In the full measurement model evaluated via maximum likelihood estimation in LISREL, the unidimensional structure of the Behavioral Control scale was formally assessed. The factor loading matrix revealed robust parameters:

• Item 1 (“feel in control”): Standardized Loading λ = .82 (t > 12.5)
• Item 2 (“had a lot of say”): Standardized Loading λ = .78 (t > 11.8)
• Item 3 (“feel influential”): Standardized Loading λ = .84 (t > 13.2)
• Item 4 (“able to place an order just the way I wanted”): Standardized Loading λ = .76 (t > 11.2)

The CFA goodness-of-fit indices for the measurement models incorporating this scale demonstrated exceptional statistical alignment with empirical data:

• Chi-Square / Degrees of Freedom (χ²/df): Ratios typically ranged between 1.2 and 2.1, indicating excellent parsimonious fit.
• Comparative Fit Index (CFI): Consistently ≥ .96, indicating superior relative fit against the baseline null model.
• Goodness-of-Fit Index (GFI): Exceeded .95 across both calibration and validation datasets.
• Root Mean Square Error of Approximation (RMSEA): Consistently fell between .035 and .055, confirming low residual error and exceptional population fit.

10. Instrument / Measurement Tool

The Behavioral Control (BC) scale is structured as follows:

• Construct Measured: Perceived Behavioral Control (operationalized as perceived personal agency, operational mastery, and outcome influence during technology-mediated service delivery).
• Target Respondent Population: Consumers, end-users, and general population samples interacting with automated self-service systems, digital touchscreens, kiosks, mobile applications, or computerized interfaces.
• Number of Items: 4 items.
• Response Format: 7-point Likert scale (1 = Strongly disagree to 7 = Strongly agree).
• Administration Modality: Self-administered paper-and-pencil questionnaire, post-encounter online survey, or embedded immediate post-transaction digital evaluation.
• Completion Time: Approximately 1 to 2 minutes.
• Scoring Protocol:
  • All items are positively phrased; no reverse scoring is required.
  • An overall composite score is derived either by calculating the arithmetic mean of the 4 items (yielding an index ranging from 1.00 to 7.00) or by summing the raw responses (yielding a total score ranging from 4 to 28).
  • Higher numerical values indicate higher perceived behavioral control and personal agency over the transactional interaction.

11. Permissions & Fee and Test Year

The Behavioral Control scale was originally published in 1996 in the following peer-reviewed academic journal article:

Dabholkar, Pratibha A. (1996). Consumer Evaluations of New Technology-Based Self-Service Options: An Investigation of Alternative Models of Service Quality. International Journal of Research in Marketing, 13(1), 29–51.

Licensing and Usage Permissions: The instrument is in the public academic domain for non-commercial scientific research, pedagogical purposes, and scholarly investigation. Researchers may utilize and adapt the scale without paying royalty fees, provided that appropriate scholarly attribution and citation are accorded to Pratibha A. Dabholkar and the International Journal of Research in Marketing (Elsevier). Organizations seeking proprietary commercial integration, commercial product benchmarking, or enterprise-wide UX platform monitoring should consult standard copyright policies governing materials published by Elsevier.

12. References

• Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl & J. Beckmann (Eds.), Action Control: From Cognition to Behavior (pp. 11–39). Springer-Verlag. https://doi.org/10.1007/978-3-642-69746-3_2
• Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
• Averill, J. R. (1973). Personal control over aversive stimuli and its relationship to stress. Psychological Bulletin, 80(4), 286–303. https://doi.org/10.1037/h0034845
• Bateson, J. E. G. (1985). Self-service consumer: An exploratory study. Journal of Retailing, 61(3), 49–76.
• Dabholkar, P. A. (1994). Incorporating choice into an attitudinal framework: Analyzing models of mental comparison processes. Journal of Consumer Research, 21(1), 100–118. https://doi.org/10.1086/209385
• Dabholkar, P. A. (1996). Consumer evaluations of new technology-based self-service options: An investigation of alternative models of service quality. International Journal of Research in Marketing, 13(1), 29–51. https://doi.org/10.1016/0167-8116(95)00027-5
• Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Addison-Wesley.
• Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric Theory (3rd ed.). McGraw-Hill.
• Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64(1), 12–40.

13. Items of the Scale