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"Habits are response dispositions that are activated automatically by the context cues that co-occurred with responses during past performance" (Neal, Wood, & Quinn, 2006, p. 198).

This definition includes three components of habits (past behavior or performance, response automaticity, and contextual cues) that are explained below.

Past Behavior

Intuitively, one conceptual meaning of habit involves repeating past behavior. The frequency of a behavior has been found to predict its re-occurrence (Ajzen & Fishbein, 2005). The relationships between frequency of past and future behaviors are not mediated by planned behavior or reasoned action (Conner & Armitage, 1998; Ouellette & Wood, 1998).

In a meta-analysis, within a Theory of Planned Behavior (TPB) framework, Ouellette and Wood (1998) found the frequency of past behavior (within in constant contexts) reflects habit strength. In addition they found even in unstable contexts, past behavior may (besides other TPB variables) contribute to intentions leading to behaviors. In a study of student exercise behavior within a TPB framework, TPB variables accounted for 46% of the variance in later exercise behavior. The addition of past behavior to the model significantly increased the explained variance to 54% (Norman & Smith, 1995).

Some measures of habit strength exclusively assess the past frequency of a behavior (e.g., Triandis, 1980), as well as the frequency of engaging in a behavior, which has been found to be an adequate proxy for habit (Ouellette & Wood, 1998). However, measuring habit strength simply in terms of the tendency of frequent behaviors to predict subsequent behaviors has been criticized as circular logic that lacks explanatory value -- and is seen as incapable of detecting when habits are resisted (Jaccard & Blanton, 2005). This "habit is as habit does" operationalization sometimes is not accepted as illuminating (Jaccard & Blanton, 2005, p. 145). Some measures of habit strength (independent of behavior) are proposed as an alternative option (Bamberg, Ajzen, & Schmidt, 2003).


More discriminating conceptual and operational definitions of habit include automaticity. Automaticity in behavior is marked by efficiency, lack of awareness, unintentionality, and/or uncontrollability (Bargh, 1994). Although not fully consistent with Bargh's explication of automaticity, the inclusion of this concept in defining habits means the initiation and performance of a habit may not be conscious decisions and may be outside of a person's awareness.

Alternatively, performance is seen as automated from procedural memory (Wood   Neal, 2007). An example is when a family member automatically reaches for a salt shaker, consistently located in the middle of the table next to the napkins, to add salt to meat or vegetables before starting a meal.

Habits are "learned dispositions" (Wood & Neal, 2007, p. 844) or behaviors that, with enough repetition, become automatic. Habits are learned or developed in pursuit of a goal; such as adding salt to improve taste. As a behavior habituates and becomes automatic, the habit is no longer consciously mediated by the goal (Triandis, 1980; Wood & Neal, 2007). The behavior appears to be done without thinking or even intention. This is consistent with the idea (Ouellette & Wood, 1998) that habitual response involves "little deliberation or explicit intention to act" (Jaccard & Blanton, 2005, p. 144). In terms of a cognitive sequence, habitual behavior is based on goal intentions established in the past, and is performed at the stage of well-rehearsed implementation intentions (Gollwitzer, 1999).

Within this perspective, habits are functional (Verplanken & Orbell, 2003). Habits are useful to conserve cognitive resources, or keep us from having to "plan, consciously guide, and monitor every action…" (Neal, et al., 2006, p. 198).

Some theorized underlying cognitive processes include dual memory systems, with habitual response operating through an automatic system, and intention and other deliberative formulations operating through a reflective structure (Rothman, Sheeran, & Wood, 2009). The automatic memory system "slowly learns associations over an accumulated set of experiences" (Rothman, et al., 2009, p. S4). Specific associations initiate procedural memory in performance of the linked habit (Quinn, Pascoe, Wood, & Neal, 2010). For habits, contextual or circumstantial cues represent learned associations. Contextual cues are described below following a brief discussion of semi-automatic responses in more complex, multiple-step habits.

To backtrack momentarily, an obvious question concerns the level of complexity at which a behavior can be considered a "habit?" The steps and time involved in a behavior obviously affect how consciously it may be intended and performed. For example, it is easier to assume automaticity in the above example to add salt at the dining room table than in a more complex habit, such as to exercise at a local fitness facility -- since the later involves multiple behaviors and contexts that include: packing gear, driving there, walking to the locker, and so on. These more complicated behaviors are recognized, at least conceptually, as habitual behavioral patterns (Verplanken, Aarts, van Knippenberg, & Moonen, 1998) or semiautomatic response patterns (Bargh, 1989), marked by routinized (Aarts, Verplanken, & Knippenberg, 1998) sequences of actions within and across different contexts. Automaticity is postulated to operate more within each context or phase, with semiautomatic response patterns operating more between contexts or phases (Bargh, 1989). Other research (Verplanken, et al., 1998; Knussen, Yule, MacKenzie, & Wells, 2004) suggests as in simple habitual behavior, behavioral intention is not consciously formed in habitual behavioral patterns.

Habits have been described as a form of automaticity that is not dependent upon or motivated by goals (Bargh & Barndollar, 1996). Wood & Neal (2007) describe a type of goal-directed automaticity where the initiation of behavior includes more overt motivation of an individual's goals. Experiments by Neal, Wood, Labrecque & Lally (2011) found - for strong habits -contextual cues [not goals] elicited memories of the related habitual behavior as well as its actual performance. Conversely, habits of moderate strength are influenced by goals. Neal, Wood, Labrecque and Lally (2011) added some adults perceive a stronger role of goals, even for their most habitual behaviors. The varying degrees of automaticity between simple and more complex behaviors illustrate some of the boundary issues raised within habit research.

Contextual Cues

Examples of contextual cues include: performance location, preceding actions in a sequence, if other people are associated with the habit (Verplanken, et al., 1998; Wood & Neal, 2007), the specific time of day, and even a particular mood (Wood, Tam, & Witt, 2005). Returning to the example of a family member automatically reaching for salt at the dining room table, the initiating contextual cue may be sitting in the same dining room table chair once a meal seems ready.

When triggering cues shift across differing contexts, conceptualizations of habit that include context cues suggest the exercise of a habit is more conscious. For example, the habitual strength to add salt might be less eating at someone else's house because the initiating cue of sitting at a chair in a familiar dining room table is disrupted. Since the procedure suddenly is not automatic, some research suggests it takes deliberative thought to request: "please pass the salt."

In the operationalization of habit that includes context, evidence of current contextual cues similar to previous contextual cues exist to suggest automaticity has been triggered. Wood et al. (2005) studied changes in habits such as exercise, reading newspapers, and watching TV among students who transferred universities. Wood et al (2005) found habitual behavior patterns were more consistent across contexts when contextual cues were similar. [In the study, whether a student typically performed the behavior alone or with others and whether his or her roommate typically performed the behavior impacted consistent behaviors.] Wood et al. also found habit patterns changed when the contextual cues changed. Some interaction between context change and behavioral intention was found, but this alone could not explain the relationship between context change and habit change. Moreover, regardless of whether the context changed, non-habitual behavior was found to be guided by behavioral intentions.

Some research has not included context within an operational definition of habit. In these studies, automaticity alone is measured. For example, Bamberg et al. (2003) used a narrower conceptualization of habit in a study of the effect of introducing prepaid bus tickets on bus use by students. Within a TPB framework, they found that, prior to introducing prepaid bus tickets, past travel mode behavior improved prediction of later travel mode behavior, even after accounting for attitude, subjective norms, and perceived behavioral control. This relationship was re-measured after the introduction of a prepaid bus ticket. The researchers found the habit failed to mediate between past and later behavior, arguing that "choice of travel mode is largely a reasoned decision; …[and that] past travel mode choice contributes to the prediction of later behavior only if circumstances remain relatively stable" (Bamberg, et al., 2003, p. 176). Alternately, Wood and Neal (2007) argue the mediating role of travel mode habit disappeared in Bamberg et al.'s study because contextual cues changed with the addition of prepaid bus tickets.

Neal, Wood, Wu & Kurlander (2011) illustrated the role of contextual cues in procedural automaticity. A disruption of habitual response, initiated by a contextual cue, occurred because there was a change at the procedural level. For example, cued by the context of a movie theater, participants ate popcorn (even if it was stale), but stopped when right-handers were forced to use their left hand to eat (or vice versa).

Lack of Consensus View

The above discussion illustrates scholars disagree about the conceptualization of habits and the discussion notes some of the boundary issues that make it challenging to postulate a relationship between habits and complex behaviors.

Unsurprisingly, some scholars argue consistency between past and current behavior is better explained without adding the construct of habit. These arguments ultimately rest on beliefs that the definitions of habits have not been adequately discriminated from similar constructs (e.g., attitude). In turn, the addition of other candidate variables besides habit to theoretical models (such as the TPB) may substantially reduce the variance that otherwise would be erroneously attributed to habits (Bamberg, et al., 2003). Such other candidate variables to be added include, but are certainly not limited to, personal norms, anticipated regret, self-identity, or affect (Bamberg, et al., 2003). In addition, some scholars suggest there are alternative explanations that eclipse a need to assess habit as an intermediate variable. For example, the unexplained variance in models may be attributed to common method variance in the measurement of prior and later behaviors. Also, the consistency between past and future behavior may be attributed simply to temporal stability (Bamberg, et al., 2003).

Although there is no consensus about the operationalization of habits or its discrimination from some other variables, habit remains an intuitively appealing construct. "Habit" is a regular part of informal conversation and is used in academic publications, sometimes without definition (e.g., Bandura, 2004; Moreno et al., 2005; Schlumperger, 1985). Further explication of habit and associated measurement development could contribute to effective health communication interventions, discussed below within the Additional Commentary section. Even those who criticize the current operationalization of habit on methodological grounds recognize that "[i]t would be premature to conclude… that there is no role for habit in human action" (Bamberg, et al., 2003, p. 185).

Example Measures

Self-Report Habit Index (Verplanken & Orbell, 2003)

The Self-Report Habit Index (SRHI) (Verplanken & Orbell, 2003), provided below, is an instrument that measures self-reported perceptions of habit strength for an identified behavior. In addition to measuring prior behavior and automaticity, the SRHI measures identity expression as a component of habit. A self-identity component is added (e.g., question 9.) because "…habits are part of how we organize everyday life and thus might reflect a sense of identity or personal style" (Verplanken & Orbell, 2003, p. 1317). The article from which the SRHI was taken (Verplanken & Orbell, 2003) combines the role of contextual cues in eliciting habit's property of automaticity, but the instrument does not include questions about context. The reasons may include that context is not a direct property of habit, but rather an initiator of automacity; or that context is so widely defined that measuring the breadth of potential contextual cues is problematic.

Appendix A

The Self-Report Habit Index

(Verplanken & Orbell, 2003, p. 1329)

Behavior X is something . . .

  1. I do frequently.
  2. I do automatically.
  3. I do without having to consciously remember.
  4. that makes me feel weird if I do not do it.
  5. I do without thinking.
  6. that would require effort not to do it.
  7. that belongs to my (daily, weekly, monthly) routine.
  8. I start doing before I realize I'm doing it.
  9. I would find hard not to do.
  10. I have no need to think about doing.
  11. that's typically "me."
  12. I have been doing for a long time.

Note: The items are accompanied by response scales anchored by agree / disagree and preferably should contain five or more response categories. In the present studies, 7-point (Studies 1 and 2) and 1 l-point response scales (Studies 3 and 4) were used. Some items may have to be reworded in line with the behavior under study.

Other measures related to habit include those used in laboratory experimental research settings. Consistent with an assumption of automaticity, a fast-response measure (Jaccard & Blanton, 2005) has been used to measure habit by measuring cognitive accessibility. For example, travel mode habits were measured by asking respondents to choose as quickly as possible their preference (car, bus, bicycle, train, or walking) in response to 10 travel destinations and purposes (e.g., movie theater, summer excursion) (Verplanken, et al., 1998).


The SHRI was tested across four studies (Verplanken & Orbell, 2003). The instrument showed high internal reliabilities across all four studies, with alphas across test administrations of .89, .92, .89, .94, .95, .94, and .85. In Study 1, measurements separated by one week showed high test-retest reliability, .90 (p < ,001).


In Study 2, the SHRI showed strong and significant correlation with the fast-response measure described above (r = 58, p < .001). Study 3 correlated the SHRI with behavioral frequency measures, a proxy measure of habit. The SHRI showed consistent and significant correlation with frequency measures for three behaviors (r's = .74, .55, and .65, all p's < .001). In study 4, by comparing results to a dataset of personal habits, the SRHI was found to discriminate significantly between behaviors that differ in frequency. The SHRI score was significantly higher for 31 habits executed daily than for 31 habits executed weekly, t(72) = 2.31, p < .03.

Rationale for Selection

The SHRI was selected because it is generally applicable and has been tested for reliability and validity.

Additional Commentary

The literature generally suggests habitual behavior is well learned through repetition, and its performance is dependent on context stability. Habit is triggered by contextual cues and is at least semiautomatic in execution. To health communication researchers, these properties suggest behavior change strategies may have the capacity to develop or maintain therapeutic habits or prevent or stop unhealthy habits.

Quinn et al. (2010) tested four strategies to prevent the performance of unwanted habits: vigilant monitoring (thinking "don't do it" or carefully watching for slip ups); distraction; stimulus control (removing oneself from the triggering situation or removing opportunity to perform the behavior); and doing nothing. In addition, Quinn et. al. (2012, p. 499) examined these strategies in response to the related variable of temptations or "hot" stimuli that activate "visceral factors such as hunger, thirst, and sexual desire." Vigilant monitoring was the only strategy found to be effective in inhibiting strong habits. It worked "not by changing the strength of the habit memory trace but by heightening inhibitory, cognitive control processes" (Quinn, et al., 2010, p. 499). On the other hand, stimulus control was found to be effective in inhibiting strong affective impulses to temptations. In short, concentration worked best for habit and avoidance worked best for temptation.

With the wide availability of Web-enabled smartphones, health communication tools can be made available in real-time to assist users with vigilant monitoring, or contextual or procedural disruption in stopping unhealthy habits.

Similarly, such tools can be used to foster therapeutic habits surrounding exercise, eating, and other health behaviors. An example would be communicated cues that help individuals initiate routine exercise. The construct of habit facilitates assistive health communication interventions that go beyond more commonly used persuasive interventions.

Finally, the latter description focuses on the use of habit in health communication interventions aimed at health-related behaviors. An additional use of "habit" in health communication identifies target audience information and media habits in order to maximize message exposure in health campaigns (Donovan, 1995).


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