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Behavioral Intention


Behavioral intention (BI) is defined as a person's perceived likelihood or "subjective probability that he or she will engage in a given behavior" (Committee on Communication for Behavior Change in the 21st Century, 2002, p. 31).

BI is behavior-specific and operationalized by direct questions such as "I intend to [behavior]," with Likert scale response choices to measure relative strength of intention. Intention has been represented in measurement by other synonyms (e.g., "I plan to [behavior]") and is distinct from similar concepts such as desire and self-prediction (Armitage & Conner, 2001). Ajzen (1991) argued that BI reflects how hard a person is willing to try, and how motivated he or she is, to perform the behavior

In theory in which is it included, BI is the most proximate predictor of behavior (Ajzen, 1991), and behavior is ultimately the variable that most health communication interventions aim to influence. In this framework individual behaviors are in pursuit of a larger goal, such as better health or quitting smoking. BI has been found to have high predictive validity in relation to behavior (Committee on Communication for Behavior Change in the 21st Century: Improving the Health of Diverse Populations, 2002), indicating that respondents in general accurately rate their intention to perform the behavior in question. Meta-analyses reviewed, which included health behaviors, found from 19% to 38% of variance in behavior explained by BI (Armitage & Conner, 2001; Sheeran & Orbell, 1998; Sheppard, Jon, & Warshaw, 1988; Van den Putte, 1991).

Two main theories used in health communication that include BI are the Theory of Reasoned Action (TRA) (Fishbein & Ajzen, 1975)and the Theory of Planned Behavior (TPB) (Ajzen, 1991). In addition to BI, these two theories share the variables of attitude toward performing the behavior and subjective norms, which are perceptions of what important others think about the behavior. TPB also includes perceived behavioral control over successful performance of the behavior. Although BI is most proximate to behavior, for some behaviors it must be considered in conjunction with behavioral control as immediately antecedent to the behavior (Ajzen, 2002 (revised Jan 2006)). For example, in quitting addiction or dieting, BI may be strong but low perceived (and actual) behavioral control may make the BI-behavior link unreliable. This potential direct influence of perceived behavioral control on behavior (regardless of BI) is included in a general model of the determinants of behavior change (Committee on Communication for Behavior Change in the 21st Century: Improving the Health of Diverse Populations, 2002), although the related term of self-efficacy is used instead of perceived behavioral control.

A basic behavioral intention (I intend to exercise more) may be elaborated in terms of how, when, and other specifics (I intend to jog for 30 minutes at least four times per week….). The former has been labeled a goal intention and the latter an implementation intention (Gollwitzer & Brandstätter, 1997; Milne, Orbell, & Sheeran, 2002). Research has shown that specification in planning is associated with a greater likelihood to perform the behavior (Scholz, Schüz, Ziegelmann, Lippke, & Schwarzer, 2008).

Gollwitzer (1997) used implementation intention in a different way than specifying how, when, where, and so on. He used implementation intention as a plan of behavior in response to specific situations. An example would be an alcoholic's plan: "If I run into my old drinking buddies, I'll tell them I have to be somewhere and then call my AA sponsor." This type of implementation intention is grounded in self-regulation theory; whereas other types are grounded in motivation theory (Gollwitzer & Brandstätter, 1997).

Suggested Approach and Example Measures

A number of single question measures of BI were found in this review (e.g., Lipkus, Green, & Marcus, 2003; Paek, 2008). Internal reliability of a measurement instrument, based on correlation between items, cannot be assessed in such cases. Single-item measurement of BI (and other cognition-behavioral variables) has been criticized as an approach (Peter, 1979).

Five sets of questions concerning different types of health behavior are provided as examples.

In an instructional piece on constructing a TPB questionnaire, Ajzen (2002) urges multiple questions for BI in order to obtain reliable self-report. He stresses the particular importance of pilot-testing BI questions for psychometric performance, including because it is a behavior-specific variable. He goes on to provide an example of three questions about BI to perform a specified exercise activity. For each, he uses a seven-point Likert scale:

  • I intend to walk on a treadmill for at least 30 minutes each day in the forthcoming month. (extremely unlikely to extremely likely)
  • I will try to walk on a treadmill for at least 30 minutes each day in the forthcoming month. (definitely true to definitely false)
  • I plan try to walk on a treadmill for at least 30 minutes each day in the forthcoming month. (strongly disagree to strongly agree)

Note that, in addition to delineating implementation intentions in terms of frequency and duration, Ajzen uses different scale endpoints in each question.

To measure BI to quit smoking, Rise, Kovac, Kraft, and Moan (2008) used the following three questions (α = .97):

  • During the next 3-4 months:
    • I intend to quit smoking.
    • I expect to quit smoking.
    • I will try to quit smoking.

For each question, a 7-point scale was used with very unlikely and likely as endpoints. The TPB was the theoretical framework used in this study.

Wong and Capella (2009) used different behaviors, at different levels of progress, to represent BI toward the goal of quitting smoking (α = .84):

  • How likely is it that in the next 3 months you will:
    • Quit smoking completely and permanently
    • Reduce the number of cigarettes you smoke in a day
    • Talk to someone (friend, family member, spouse) about quitting smoking

In the same study, Wong and Capella measured the intention to seek help in quitting smoking. The two items were highly correlated (r = .83):

  • How likely is it that in the next 3 months you will:
    • Seek counseling/support to help you quit smoking
    • Enroll in a smoking cessation program if one were available to you at minimal cost and easy access

Both sets of questions used a four-point scale with definitely will not and definitely will as endpoints.

Using the TPB, Norman and Conner (1996) conducted a study in Britain on predicting a repeat attendance at a "health check" in which risk factors are assessed and health promotion advice provided. To measure BI, they used three questions, each with a 7-point response scale (α = .91):

  • If you had the opportunity, how likely is it that you would attend a health check at your doctor's surgery? 
  • I intend to attend a health check if offered the opportunity.
  • If I was offered a health check, I would try to attend.

Note that the first question is self-prediction which, though closely related, is a different concept than intention (Armitage & Conner, 2001).


The measures selected above showed good to excellent reliability, with alphas ranging from .83 to .97.

Rationale for selection

These measures were selected because they represent BI toward different health-related measures (exercise, quitting smoking, and health screening). As the main author of the TPB, including Ajzen's (2002) general instruction and example questions was seen as useful.

Additional Commentary

An important consideration in interpreting the BI-behavior link is the type of behavior involved. Behaviors with many steps may be more difficult to perform, include more opportunities to abandon, and often thus require greater strength of BI to perform. In addition to number of steps, some behaviors are more complex than others to perform. If a subject feels that the behavior is too complex to perform, his or her perceived behavioral control over it will likely be low (Ajzen, 1991).

Other reasons, in addition to number of steps and complexity, make some behaviors more difficult than others to perform. Addiction warrants special consideration. The behavior of quitting addiction to, for example, alcohol or drugs, is made more difficult by the additional physical component.

The context in which a behavior is performed is also important (Webb & Sheeran, 2006). For example, social norms of drinking at a party can make it more difficult not to drink as compared with other contexts. Having a work-site exercise facility and choice of time when to use it may increase perceived behavioral control and make sticking to an exercise plan easier.

Time between BI and behavior is also important. The correlation between BI and behavior becomes less reliable over time (Sheeran & Orbell, 1998).

Finally, as a proxy for willingness to try and motivation to perform a behavior, BI has useful potential application. Depending on type and context of a behavior, information about willingness and motivation may focus one intervention on the subset population with higher BI strength, and another intervention on influencing variables that affect BI (such as social norms and perceived behavioral control).


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