{"id":3532,"date":"2026-09-16T11:40:26","date_gmt":"2026-09-16T11:40:26","guid":{"rendered":"http:\/\/ashadocs.org\/scales\/behavioural-intention-general-bigen\/"},"modified":"2026-09-16T11:40:26","modified_gmt":"2026-09-16T11:40:26","slug":"behavioural-intention-general-bigen","status":"publish","type":"psych_scale","link":"https:\/\/ashadocs.org\/?psych_scale=behavioural-intention-general-bigen","title":{"rendered":"Behavioural Intention (General) (BIGEN)"},"content":{"rendered":"<h2>1. Abstract<\/h2>\n<p>The <strong>Behavioural Intention (General) (BIGEN)<\/strong> scale represents an established psychometric measurement framework designed to quantify an individual&#8217;s subjective probability, conative readiness, and stated likelihood of performing a designated future action. Rooted deeply within cognitive social psychology and applied behavioral science\u2014specifically the <a href=\"https:\/\/en.wikipedia.org\/wiki\/Theory_of_planned_behavior\" target=\"_blank\" rel=\"noopener noreferrer\">Theory of Planned Behavior (TPB)<\/a> and the <a href=\"https:\/\/en.wikipedia.org\/wiki\/Theory_of_reasoned_action\" target=\"_blank\" rel=\"noopener noreferrer\">Theory of Reasoned Action (TRA)<\/a>\u2014the instrument assesses behavioural intention as the primary proximal antecedent of overt behavior. The BIGEN architecture is operationalized as a curated pool of nine bipolar adjective pairs formatted across a multi-point semantic differential scale (typically administered using a 7-point response gradient). Across empirical literature in consumer research, organizational management, health-related interventions, and digital technology adoption, subsets of three to five items from this nine-item pool are routinely extracted to assess unified, unidimensional behavioral inclinations, such as purchase intention, adoption propensity, compliance behavior, and recommendation likelihood.<\/p>\n<p>Extensive psychometric evaluations consistently indicate that the BIGEN instrument demonstrates robust measurement properties. Internal consistency reliability is universally high across diverse contextual domains, with Cronbach&#8217;s alpha (<span class=\"arabpsy-math-inline\" data-latex=\"\u0007lpha\">$\u0007lpha$<\/span>) <span class=\"arabpsy-eq\" dir=\"ltr\">coefficients routinely exceeding .85 and frequently surpassing .92. Composite reliability values consistently eclipse standard benchmarks, reflecting minimal random measurement error. Confirmatory factor analytic investigations reliably confirm a single-factor, congenerically dominant structure characterized by standardized factor loadings ranging from .75 to .96, high average variance extracted (AVE &gt; .65), and strong invariance across demographic and cultural cohorts. Criterion, convergent, and predictive validities have been rigorously corroborated through meta-analytic modeling, demonstrating substantial correlations between BIGEN indices and subsequent observable actions<\/span> (<span class=\"arabpsy-math-inline\" data-latex=\"r \\approx .45\">$r \\approx .45$<\/span> to <span class=\"arabpsy-math-inline\" data-latex=\".60\">$.60$<\/span>). This article provides a definitive structural, psychometric, and operational analysis of the BIGEN scale, detailing its theoretical heritage, analytical validation metrics, practical administration parameters, and methodological considerations.<\/p>\n<h2>2. Keywords<\/h2>\n<p>Behavioural intention, Theory of Planned Behavior, Theory of Reasoned Action, semantic differential scale, psychometrics, consumer behavior, purchase intention, construct validity, predictive validity, confirmatory factor analysis<\/p>\n<h2>3. Authors<\/h2>\n<p>The <strong>Behavioural Intention (General) (BIGEN)<\/strong> scale does not derive from a single proprietary author or single static psychometric inventory; rather, it represents a standardized psychometric synthesis synthesized from the foundational measurement paradigms of <strong>Icek Ajzen<\/strong> (University of Massachusetts Amherst) and <strong>Martin Fishbein<\/strong> (University of Pennsylvania). Subsequent methodological aggregation, cataloging, and psychometric curation were pioneered by marketing and psychometrics scholar <strong>Gordon C. Bruner II<\/strong> (Southern Illinois University Carbondale) in the landmark compendium series <em>Marketing Scales Handbook<\/em>. Bruner systematically reviewed hundreds of empirical studies to isolate the nine most robust, recurring bipolar semantic differential pairs utilized across academic marketing, organizational psychology, and behavioral economics literature.<\/p>\n<h2>4. Purpose<\/h2>\n<p>The primary purpose of the Behavioural Intention (General) (BIGEN) instrument is to capture, quantify, and standardize the measurement of conative commitment\u2014specifically, an actor&#8217;s conscious plan, perceived likelihood, or deliberate decision to perform a concrete behavior under defined temporal, situational, and contextual parameters. In behavioral research, direct observation of future actions is frequently unfeasible, cost-prohibitive, ethically constrained, or chronologically delayed. Psychometric measurement of behavioral intentions bridges the diagnostic gap between internal psychological states (e.g., attitudes, beliefs, social norms, perceived behavioral control) and observable behavioral execution.<\/p>\n<p>In applied research settings, the BIGEN scale serves multiple diagnostic and prognostic purposes:<\/p>\n<ul>\n<li><strong>Predictive Behavioral Modeling:<\/strong> Evaluating consumer adoption of nascent innovations, prospective purchase patterns, brand switching, health-promoting regimens, or digital system utilization prior to market or program launch.<\/li>\n<li><strong>Structural Equation Modeling (SEM):<\/strong> Functioning as the principal endogenous dependent variable or mediating construct within complex causal networks testing sociological, psychological, or organizational hypotheses.<\/li>\n<li><strong>Intervention and Experimental Evaluation:<\/strong> Serving as a sensitive post-manipulation dependent metric in randomized controlled trials to assess the immediate impact of persuasive communications, advertising treatments, framing conditions, or policy modifications.<\/li>\n<li><strong>Benchmarking Across Behavioral Target Domains:<\/strong> Due to its deliberate semantic neutrality, BIGEN allows psychometricians to adapt the item stems to virtually any target action (e.g., &#8220;registering for a health screening,&#8221; &#8220;purchasing an electric vehicle,&#8221; &#8220;voting in an election,&#8221; or &#8220;adopting cloud software&#8221;) without modifying the underlying bipolar semantic adjective pairs.<\/li>\n<\/ul>\n<p>By providing a psychometrically stable, unidimensional continuum, BIGEN mitigates the severe error variance, idiosyncratic wording artifacts, and acquiescence biases often introduced when researchers write ad-hoc single-item Likert questions.<\/p>\n<h2>5. Psychological Construct<\/h2>\n<p>The psychological construct evaluated by BIGEN is <strong>behavioural intention<\/strong>. Within cognitive psychology, behavioral intention is conceptualized as the subjective probability that an individual will enact a given target behavior. It is the immediate cognitive representation of a person&#8217;s readiness to perform a behavior, encapsulating both the motivational magnitude (the intensity of effort an individual is willing to exert) and conative directionality (the affirmative decision to pursue versus avoid the action).<\/p>\n<p>The construct possesses several nuanced characteristics that differentiate it from adjacent psychological phenomena:<\/p>\n<h3>Distinction from Attitudes<\/h3>\n<p>While an <a href=\"https:\/\/en.wikipedia.org\/wiki\/Attitude_(psychology)\" target=\"_blank\" rel=\"noopener noreferrer\">attitude<\/a> reflects an individual&#8217;s positive or negative affective or evaluative appraisal of an object, concept, or action (e.g., &#8220;Exercising daily is beneficial&#8221;), an intention represents a personalized self-instruction to execute the action (e.g., &#8220;I intend to exercise daily&#8221;). Evaluative favorability is a necessary but often insufficient antecedent of intentional conation; individuals may hold positive attitudes toward actions they have no immediate intention of performing due to competing priorities, perceived friction, or situational constraints.<\/p>\n<h3>Distinction from Desires and Hopes<\/h3>\n<p>Unlike desires, wishes, or aspirations\u2014which can exist independently of perceived reality, feasibility, or genuine behavioral commitment (e.g., &#8220;I wish I could travel to Mars&#8221;)\u2014behavioural intention inherently incorporates an implicit subjective calculation of feasibility, subjective probability, and cognitive agency. An intention implies a commitment to marshal the required effort, allocate temporal resources, and overcome proximal barriers to realize the goal.<\/p>\n<h3>Unidimensional Nature<\/h3>\n<p>Across extensive structural validation literature, general behavioral intention operates as a strictly unidimensional construct. Although intention encompasses semantic nuances spanning probability (e.g., <em>improbable \/ probable<\/em>), personal certainty (e.g., <em>uncertain \/ certain<\/em>), and definitive readiness (e.g., <em>definitely will not \/ definitely will<\/em>), these indicators reflect a singular latent continuum representing the intensity of conative inclination toward the designated behavioral threshold.<\/p>\n<h2>6. Theoretical Framework<\/h2>\n<p>The theoretical architecture underpinning the BIGEN scale is anchored primarily in the <strong>Theory of Reasoned Action<\/strong> (Fishbein &amp; Ajzen, 1975) and its subsequent expansion, the <strong>Theory of Planned Behavior<\/strong> (Ajzen, 1985, 1991). These frameworks posit that human behavior is generally reasoned, goal-directed, and systematically informed by available cognitive information, social considerations, and perceived self-efficacy.<\/p>\n<p>According to the Theory of Planned Behavior, human action is guided by three distinct varieties of beliefs:<\/p>\n<ul>\n<li><strong>Behavioral Beliefs:<\/strong> Beliefs regarding the likely consequences of the behavior, producing a favorable or unfavorable <em>attitude toward the behavior<\/em>.<\/li>\n<li><strong>Normative Beliefs:<\/strong> Beliefs regarding the expectations of salient social referents, culminating in perceived social pressure or <em>subjective norm<\/em>.<\/li>\n<li><strong>Control Beliefs:<\/strong> Beliefs concerning the presence of operational factors that may facilitate or impede performance, yielding <em>perceived behavioral control<\/em>.<\/li>\n<\/ul>\n<p>These three components converge directly upon <strong>behavioural intention<\/strong>, which acts as the definitive critical mediator between latent sociocognitive evaluations and overt behavioral realization. In classical Fishbeinian psychometrics, behavioral intention is formulated mathematically as:<\/p>\n<p style=\"text-align:center; font-style:italic;\"><span class=\"arabpsy-eq\" dir=\"ltr\">B \u2248 I = (A<sub>B<\/sub>)w<sub>1<\/sub> + (SN)w<sub>2<\/sub> + (PBC)w<sub>3<\/sub><\/span><\/p>\n<p>Where behavioral performance (<span class=\"arabpsy-math-inline\" data-latex=\"B\">$B$<\/span>) is directly approximated by intention (<span class=\"arabpsy-math-inline\" data-latex=\"I\">$I$<\/span>), which itself is a weighted linear function of attitude toward the behavior (<span class=\"arabpsy-math-inline\" data-latex=\"A_B\">$A_B$<\/span>), subjective norms (<span class=\"arabpsy-math-inline\" data-latex=\"SN\">$SN$<\/span>), and perceived behavioral control (<span class=\"arabpsy-math-inline\" data-latex=\"PBC\">$PBC$<\/span>), scaled by empirical regression weights (<span class=\"arabpsy-math-inline\" data-latex=\"w\">$w$<\/span>).<\/p>\n<p>Foundational assumptions of this theoretical framework dictate that intention serves as the single best cross-sectional predictor of behavior, provided two conditions are fulfilled: (1) the measure of intention corresponds perfectly to the behavioral criterion in terms of Action, Target, Context, and Time (the <em>Principle of Compatibility<\/em> or <em>TACT principle<\/em>); and (2) the intention remains stable during the temporal interval separating measurement from overt behavioral execution.<\/p>\n<h2>7. Validity<\/h2>\n<p>The construct, convergent, discriminant, and predictive validity of the BIGEN pool has been rigorously documented across four decades of empirical behavioral literature, involving hundreds of thousands of research participants across international settings.<\/p>\n<h3>Content and Face Validity<\/h3>\n<p>Content validity of the BIGEN item pool is substantiated through its explicit operational alignment with Fishbein and Ajzen&#8217;s psychometric dictates. By employing standardized bipolar semantic endpoints directly indexing likelihood, probability, and personal conviction, the scale exhibits unambiguous face validity, reliably eliciting subjective conative probabilities without confounding the assessment with affective appraisal or cognitive utility.<\/p>\n<h3>Convergent Validity<\/h3>\n<p>Convergent validity is evidenced by strong, statistically significant correlations between subsets of the BIGEN scale and alternative intention operationalizations, including single-item purchase measures (e.g., the Juster 11-point Probability Scale), multi-item Likert-type measures (&#8220;I plan to&#8230;&#8221;, &#8220;I expect to&#8230;&#8221;), and continuous visual analogue scales. Structural equation modeling studies consistently document standardized factor loadings (<span class=\"arabpsy-math-inline\" data-latex=\"lambda\">$lambda$<\/span>) well above the conventional .70 threshold (typically <span class=\"arabpsy-math-inline\" data-latex=\".82 &lt; lambda &lt; .95\">$.82 &lt; lambda &lt; .95$<\/span>) and Average Variance Extracted (AVE) values consistently exceeding .70, comfortably eclipsing the standard .50 convergent cutoff proposed by Fornell and Larcker (1981).<\/p>\n<h3>Discriminant Validity<\/h3>\n<p>Discriminant validity has been exhaustively demonstrated against closely aligned constructs within the TPB network, including <em>Attitude Toward the Act<\/em>, <em>Subjective Norms<\/em>, <em>Perceived Behavioral Control<\/em>, and <em>Desire<\/em>. In formal CFA modeling, unconstrained multi-factor models exhibit superior fit relative to models constraining the correlation between intention and attitude to unity (<span class=\"arabpsy-math-inline\" data-latex=\"\\Delta\\chi^2, p &lt; .001\">$\\Delta\\chi^2, p &lt; .001$<\/span>). Furthermore, the square root of the BIGEN AVE consistently exceeds inter-construct latent correlations (<span class=\"arabpsy-math-inline\" data-latex=\"\\sqrt{\text{AVE}} &gt; r\">$\\sqrt{\text{AVE}} &gt; r$<\/span>), satisfying both the Fornell-Larcker criterion and the more rigorous <a href=\"https:\/\/en.wikipedia.org\/wiki\/Heterotrait-monotrait_ratio\" target=\"_blank\" rel=\"noopener noreferrer\">Heterotrait-Monotrait Ratio of Correlations (HTMT)<\/a> benchmark, which universally remains below .85.<\/p>\n<h3>Predictive and Criterion Validity<\/h3>\n<p>The predictive power of BIGEN toward actual downstream behavior represents one of the most thoroughly replicated phenomena in applied psychology. Landmark meta-analyses by Sheppard, Hartwick, and Warshaw (1988), Kim and Hunter (1993), and Sheeran (2002) demonstrate that intention-behavior correlation coefficients typically range from <span class=\"arabpsy-math-inline\" data-latex=\"r = .45\">$r = .45$<\/span> to <span class=\"arabpsy-math-inline\" data-latex=\"r = .62\">$r = .62$<\/span>. In Sheeran&#8217;s (2002) meta-analysis encompassing 422 studies (<span class=\"arabpsy-math-inline\" data-latex=\"N = 82,107\">$N = 82,107$<\/span>), intention accounted for approximately 28% of the variance in actual future behavior (<span class=\"arabpsy-math-inline\" data-latex=\"R^2 = .28\">$R^2 = .28$<\/span>), confirming the robust prognostic validity of intention measures when constructed in accordance with the compatibility principle.<\/p>\n<h2>8. Reliability<\/h2>\n<p>The internal consistency and temporal stability of the BIGEN scale pool have demonstrated exceptional psychometric robustness across a wide variety of administrative modalities, target populations, and experimental paradigms.<\/p>\n<h3>Internal Consistency Reliability<\/h3>\n<p>Whether researchers deploy a comprehensive nine-item operationalization or extract typical three-, four-, or five-item subsets from the pool, the scale yields consistently elevated reliability statistics:<\/p>\n<ul>\n<li><strong>Cronbach\u2019s Alpha (<span class=\"arabpsy-math-inline\" data-latex=\"\u0007lpha\">$\u0007lpha$<\/span>):<\/strong> Literature cataloged across marketing and behavioral disciplines reports Cronbach&#8217;s alpha values typically ranging between .88 and .97. In Bruner\u2019s meta-syntheses, the median reported alpha across applied purchase intention paradigms is approximately .93.<\/li>\n<li><strong>McDonald\u2019s Omega (<span class=\"arabpsy-math-inline\" data-latex=\"\\omega\">$\\omega$<\/span>):<\/strong> Modern psychometric evaluations utilizing McDonald\u2019s hierarchical and total omega consistently mirror these findings, demonstrating values routinely <span class=\"arabpsy-math-inline\" data-latex=\"ge .92\">$ge .92$<\/span>, confirming that the high alpha values are not merely an artifact of tau-equivalence assumptions.<\/li>\n<li><strong>Composite Reliability (CR):<\/strong> Structural equation modeling studies report composite reliability parameters consistently well above .90, verifying exceptional latent internal coherence with minimal residual error variance.<\/li>\n<\/ul>\n<h3>Test-Retest Stability<\/h3>\n<p>The temporal stability (test-retest reliability) of BIGEN is intrinsically tied to the stability of underlying cognitive beliefs and situational dynamics over time. Under controlled, short-term test-retest windows (e.g., 24 to 72 hours with no intervening persuasive or informational stimuli), test-retest correlation coefficients (<span class=\"arabpsy-math-inline\" data-latex=\"r_{tt}\">$r_{tt}$<\/span>) regularly exceed .85. Over extended periods (weeks to months), stability coefficients naturally attenuate (<span class=\"arabpsy-math-inline\" data-latex=\"r_{tt} \\approx .50 - .70\">$r_{tt} \\approx .50 &#8211; .70$<\/span>) due to dynamic changes in contextual affordances, life events, and spontaneous attitude updating, precisely mirroring theoretical expectations outlined by Ajzen.<\/p>\n<h2>9. Factor Analysis<\/h2>\n<p>Both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) have been extensively conducted on the BIGEN item pool to clarify its latent dimensional structure.<\/p>\n<h3>Exploratory Factor Analysis (EFA)<\/h3>\n<p><span class=\"arabpsy-eq\" dir=\"ltr\">Principal Axis Factoring and Principal Component Analyses universally yield a single dominant latent factor based on Kaiser\u2019s eigenvalue criterion (eigenvalue &gt; 1.0) and Cattell&#8217;s scree test evaluation. A single factor routinely explains between 70% and 88% of the total variance among the observed items. Communalities<\/span> (<span class=\"arabpsy-math-inline\" data-latex=\"h^2\">$h^2$<\/span>) across all nine adjective pairs consistently range from .68 to .92, indicating that negligible variance remains unaccounted for by the primary intention construct.<\/p>\n<h3>Confirmatory Factor Analysis (CFA)<\/h3>\n<p>When evaluated via CFA using Maximum Likelihood (ML) or Robust Maximum Likelihood (MLR) estimation, the unidimensional model demonstrates superior fit to empirical data across diverse samples. Standard model fit indices routinely fall within widely endorsed thresholds for good structural fit:<\/p>\n<ul>\n<li><strong>Comparative Fit Index (CFI):<\/strong> Routinely <span class=\"arabpsy-math-inline\" data-latex=\"ge .97\">$ge .97$<\/span> (often exceeding .99).<\/li>\n<li><strong>Tucker-Lewis Index (TLI):<\/strong> Routinely <span class=\"arabpsy-math-inline\" data-latex=\"ge .96\">$ge .96$<\/span>.<\/li>\n<li><strong>Standardized Root Mean Square Residual (SRMR):<\/strong> Routinely <span class=\"arabpsy-math-inline\" data-latex=\"le .035\">$le .035$<\/span>.<\/li>\n<li><strong>Root Mean Square Error of Approximation (RMSEA):<\/strong> Routinely <span class=\"arabpsy-math-inline\" data-latex=\"le .06\">$le .06$<\/span> with narrow 90% confidence intervals.<\/li>\n<\/ul>\n<p>Standardized factor loadings (<span class=\"arabpsy-math-inline\" data-latex=\"\\lambda_i\">$\\lambda_i$<\/span>) for individual adjective pairs consistently achieve statistical significance (<span class=\"arabpsy-math-inline\" data-latex=\"p &lt; .001\">$p &lt; .001$<\/span>) and high magnitudes. For example, pairs such as <em>unlikely \/ likely<\/em>, <em>improbable \/ probable<\/em>, and <em>definitely will not \/ definitely will<\/em> consistently show loadings exceeding .88, establishing them as exceptionally strong reflective indicators of the conative construct.<\/p>\n<h2>10. Instrument \/ Measurement Tool<\/h2>\n<p>The operational administration of the Behavioural Intention (General) scale is straightforward, highly versatile, and easily adapted for digital surveys, laboratory pencil-and-paper questionnaires, and mobile testing environments.<\/p>\n<ul>\n<li><strong>Instrument Type:<\/strong> Self-report conative psychometric battery utilizing semantic differential rating formatting.<\/li>\n<li><strong>Target Respondent:<\/strong> General adult or adolescent populations capable of understanding subjective probability estimations regarding specific behaviors.<\/li>\n<li><strong>Number of Items:<\/strong> A pool of 9 bipolar adjective pairs; empirical studies routinely administer subsets ranging from 3 to 5 pairs depending on survey length constraints.<\/li>\n<li><strong>Response Scale:<\/strong> Typically administered as a 7-point bipolar semantic differential scale (scored 1 to 7), though 5-point, 9-point, and 101-point continuous probability sliders are occasionally applied.<\/li>\n<li><strong>Stem Formulation:<\/strong> A standardized context-specific prompt formulated in accordance with the TACT principle (e.g., <em>&#8220;All things considered, how likely are you to [Target Action] within the next [Time Frame]?&#8221;<\/em>).<\/li>\n<li><strong>Scoring Procedure:<\/strong>\n<ul>\n<li><span class=\"arabpsy-eq\" dir=\"ltr\">Individual item responses are coded numerically such that higher values consistently represent a stronger intention to perform the target action (e.g., 1 = lowest intention, 7 = highest intention)<\/span>.<\/li>\n<li>If reverse-polarity pairs are introduced to detect careless responding, they are reverse-scored prior to aggregation.<\/li>\n<li>An overall Behavioural Intention score is calculated either as an arithmetic mean across items (scale score range: 1.00 to 7.00) or as a summed composite score (for a 3-item measure: range 3 to 21). In SEM applications, items are modeled as reflective observed indicators of a latent intention variable.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h2>11. Permissions &amp; Fee and Test Year<\/h2>\n<p>The foundational semantic differential items that comprise the Behavioural Intention (General) pool originated in academic publications dating back to Fishbein and Ajzen (1975), with systematic cataloging and psychometric aggregation completed by Dr. Gordon C. Bruner II in the 1990s through early 2000s in the <em>Marketing Scales Handbook<\/em> series.<\/p>\n<p>The general semantic adjective pairs themselves reside in the academic public domain and are widely considered open-access psychometric tools for scholarly, scientific, and educational research purposes without licensing fees. Researchers are expected to appropriately cite the original theoretical and compilation sources (e.g., Fishbein &amp; Ajzen, 1975; Ajzen, 1991; Bruner, 2009) in subsequent academic reports and publications. Commercial market research organizations utilizing specific compiled proprietary handbooks should verify publisher permissions or institutional database access through respective clearinghouses.<\/p>\n<h2>12. References<\/h2>\n<p>Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl &amp; J. Beckmann (Eds.), <em>Action control: From cognition to behavior<\/em> (pp. 11\u201339). Springer-Verlag. <a href=\"https:\/\/doi.org\/10.1007\/978-3-642-69746-3_2\" target=\"_blank\" rel=\"noopener noreferrer\">https:\/\/doi.org\/10.1007\/978-3-642-69746-3_2<\/a><\/p>\n<p>Ajzen, I. (1991). The theory of planned behavior. <em>Organizational Behavior and Human Decision Processes<\/em>, 50(2), 179\u2013211. <a href=\"https:\/\/doi.org\/10.1016\/0749-5978(91)90020-T\" target=\"_blank\" rel=\"noopener noreferrer\">https:\/\/doi.org\/10.1016\/0749-5978(91)90020-T<\/a><\/p>\n<p>Bruner, G. C., II. (2009). <em>Marketing scales handbook: A compilation of multi-item measures for consumer behavior &amp; advertising research<\/em> (Vol. 5). GCBII Productions.<\/p>\n<p>Fishbein, M., &amp; Ajzen, I. (1975). <em>Belief, attitude, intention, and behavior: An introduction to theory and research<\/em>. Addison-Wesley.<\/p>\n<p>Fornell, C., &amp; Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. <em>Journal of Marketing Research<\/em>, 18(1), 39\u201350. <a href=\"https:\/\/doi.org\/10.1177\/002224378101800104\" target=\"_blank\" rel=\"noopener noreferrer\">https:\/\/doi.org\/10.1177\/002224378101800104<\/a><\/p>\n<p>Kim, M. S., &amp; Hunter, J. E. (1993). Attitude-behavior relations: A meta-analysis of attitudinal relevance and topic. <em>Journal of Communication<\/em>, 43(1), 101\u2013142. <a href=\"https:\/\/doi.org\/10.1111\/j.1460-2466.1993.tb01251.x\" target=\"_blank\" rel=\"noopener noreferrer\">https:\/\/doi.org\/10.1111\/j.1460-2466.1993.tb01251.x<\/a><\/p>\n<p>Sheeran, P. (2002). Intention\u2014behavior relations: A conceptual and empirical review. <em>European Review of Social Psychology<\/em>, 12(1), 1\u201336. <a href=\"https:\/\/doi.org\/10.1080\/14792772143000003\" target=\"_blank\" rel=\"noopener noreferrer\">https:\/\/doi.org\/10.1080\/14792772143000003<\/a><\/p>\n<p>Sheppard, B. H., Hartwick, J., &amp; Warshaw, P. R. (1988). The theory of reasoned action: A meta-analysis of past research with recommendations for modifications and future research. <em>Journal of Consumer Research<\/em>, 15(3), 325\u2013343. <a href=\"https:\/\/doi.org\/10.1086\/209170\" target=\"_blank\" rel=\"noopener noreferrer\">https:\/\/doi.org\/10.1086\/209170<\/a><\/p>\n<h2>13. Items of the Scale<\/h2>\n<div class=\"wpt-scale-items-container\" style=\"direction:ltr; text-align:left; font-family:sans-serif;\">\n<p>The Behavioural Intention (General) (BIGEN) inventory comprises a standardized pool of nine semantic differential adjective pairs. In research administrations, these pairs are positioned at opposing endpoints of a multi-point scale (predominantly 7-point) following a clear behavioral stem specifying the target, action, context, and time horizon (e.g., <em>&#8220;Please indicate your intention to perform [Target Behavior] within [Specified Period]:&#8221;<\/em>).<\/p>\n<p>Below are the nine bipolar adjective pairs that constitute the complete BIGEN measurement pool:<\/p>\n<ol style=\"line-height: 2.0; font-size: 15px;\">\n<li>\n      <strong>Unlikely<\/strong><br \/>\n      <span style=\"margin: 0 10px; color: #666;\">[ 1 &mdash; 2 &mdash; 3 &mdash; 4 &mdash; 5 &mdash; 6 &mdash; 7 ]<\/span><br \/>\n      <strong>Likely<\/strong>\n    <\/li>\n<li>\n      <strong>Improbable<\/strong><br \/>\n      <span style=\"margin: 0 10px; color: #666;\">[ 1 &mdash; 2 &mdash; 3 &mdash; 4 &mdash; 5 &mdash; 6 &mdash; 7 ]<\/span><br \/>\n      <strong>Probable<\/strong>\n    <\/li>\n<li>\n      <strong>Impossible<\/strong><br \/>\n      <span style=\"margin: 0 10px; color: #666;\">[ 1 &mdash; 2 &mdash; 3 &mdash; 4 &mdash; 5 &mdash; 6 &mdash; 7 ]<\/span><br \/>\n      <strong>Possible<\/strong>\n    <\/li>\n<li>\n      <strong>Uncertain<\/strong><br \/>\n      <span style=\"margin: 0 10px; color: #666;\">[ 1 &mdash; 2 &mdash; 3 &mdash; 4 &mdash; 5 &mdash; 6 &mdash; 7 ]<\/span><br \/>\n      <strong>Certain<\/strong>\n    <\/li>\n<li>\n      <strong>Definitely will not<\/strong><br \/>\n      <span style=\"margin: 0 10px; color: #666;\">[ 1 &mdash; 2 &mdash; 3 &mdash; 4 &mdash; 5 &mdash; 6 &mdash; 7 ]<\/span><br \/>\n      <strong>Definitely will<\/strong>\n    <\/li>\n<li>\n      <strong>Non-existent<\/strong><br \/>\n      <span style=\"margin: 0 10px; color: #666;\">[ 1 &mdash; 2 &mdash; 3 &mdash; 4 &mdash; 5 &mdash; 6 &mdash; 7 ]<\/span><br \/>\n      <strong>Existent<\/strong>\n    <\/li>\n<li>\n      <strong>Doubtful<\/strong><br \/>\n      <span style=\"margin: 0 10px; color: #666;\">[ 1 &mdash; 2 &mdash; 3 &mdash; 4 &mdash; 5 &mdash; 6 &mdash; 7 ]<\/span><br \/>\n      <strong>Undoubtful \/ Clear<\/strong>\n    <\/li>\n<li>\n      <strong>No chance<\/strong><br \/>\n      <span style=\"margin: 0 10px; color: #666;\">[ 1 &mdash; 2 &mdash; 3 &mdash; 4 &mdash; 5 &mdash; 6 &mdash; 7 ]<\/span><br \/>\n      <strong>Every chance<\/strong>\n    <\/li>\n<li>\n      <strong>Would not consider<\/strong><br \/>\n      <span style=\"margin: 0 10px; color: #666;\">[ 1 &mdash; 2 &mdash; 3 &mdash; 4 &mdash; 5 &mdash; 6 &mdash; 7 ]<\/span><br \/>\n      <strong>Would definitely consider<\/strong>\n    <\/li>\n<\/ol>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>A comprehensive psychometric guide to the Behavioural Intention (General) (BIGEN) scale, examining its theoretical origin within the Theory of Planned Behavior, factor structure, reliability parameters, and semantic differential items.<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"closed","template":"","categories":[5788,2,79],"tags":[12771,12769,12770,2320,2376,208,8496],"class_list":["post-3532","psych_scale","type-psych_scale","status-publish","hentry","category-consumer-research","category-psychometrics","category-social-psychology","tag-attitude-behavior-relation","tag-behavioural-intention","tag-bigen","tag-psychometric-scales","tag-semantic-differential","tag-theory-of-planned-behavior","tag-theory-of-reasoned-action"],"_links":{"self":[{"href":"https:\/\/ashadocs.org\/index.php?rest_route=\/wp\/v2\/psych_scale\/3532","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ashadocs.org\/index.php?rest_route=\/wp\/v2\/psych_scale"}],"about":[{"href":"https:\/\/ashadocs.org\/index.php?rest_route=\/wp\/v2\/types\/psych_scale"}],"author":[{"embeddable":true,"href":"https:\/\/ashadocs.org\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/ashadocs.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=3532"}],"version-history":[{"count":0,"href":"https:\/\/ashadocs.org\/index.php?rest_route=\/wp\/v2\/psych_scale\/3532\/revisions"}],"wp:attachment":[{"href":"https:\/\/ashadocs.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3532"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ashadocs.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3532"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ashadocs.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3532"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}