Elsevier

Computers in Human Behavior

Volume 28, Issue 6, November 2012, Pages 2083-2090
Computers in Human Behavior

Texting while driving on automatic: Considering the frequency-independent side of habit

https://doi.org/10.1016/j.chb.2012.06.012Get rights and content

Abstract

This study tested the potential of the frequency-independent components of habit, or automaticity, to predict the rate of texting while driving. A survey of 441 college students at a large American university was conducted utilizing a frequency-independent version of the experimentally validated Self-Report Habit Index (SRHI; Orbell and Verplanken, 2010, Verplanken and Orbell, 2003). Controlling for gender, age, and driver confidence, analyses showed that automatic texting tendencies predicted both sending and reading texts while driving. The findings suggest that texting while driving behavior may be partially attributable to individuals doing so without awareness, control, attention, and intention regarding their own actions. The unique contribution of automaticity explained more variance than overall individual usage, and remained significant even after accounting for norms, attitudes, and perceived behavioral control. The results demonstrate the importance of distinguishing the level of automaticity from behavioral frequency in mobile communication research. Future applications and implications for research are discussed.

Highlights

► We examined texting while driving using a modified version of the SRHI. ► Automatic texting tendencies predict sending and reading of texts while driving. ► Texting automaticity represents a distinct construct from texting frequency. ► Changing unconscious mechanisms may help curb dangerous driving behavior.

Introduction

On the surface, the decision to engage in texting while simultaneously navigating rush hour traffic seems absurd. In addition to operating the vehicle’s interface, obeying travel laws, traversing traffic, and locating destinations, the texting individual is required to pinpoint and retrieve his or her mobile device, situate the current conversation, and devise an appropriately human message – placing lives not just in the hands of the driver, but in the fingers. It is no surprise then that the National Transportation Safety Board recently called on all remaining states in the US to forbid such behavior after examining specific cases of texting-based accidents (NTSB, 2011).

Despite increased bans and awareness, the phenomenon of texting while driving continues to escalate (Lowy, 2011). Yet at the same time, national surveys show most people favor driving bans (Strayer, Watson, & Drews, 2011), and people perceive this behavior to be very risky (Atchley, Atwood, & Boulton, 2011). Cell phone and text message distractors have been shown to inhibit individuals’ cognitive abilities, evidenced by lower performance on computerized true–false exercises (Smith, Isaak, Senette, & Abadie, 2011). Likewise, texting behind the wheel has been found to impair driving in simulated experiments (Drews, Yazdani, Godfrey, Cooper, & Strayer, 2009). In their review of cognitive distraction in motor vehicles (Strayer et al., 2011) argue that explaining the misalignments among safety, perceived risk, and behavior is essential both theoretically and for the purposes of elevating public policy and safety. This study takes a step in that direction by examining key predictors of texting while driving, while also addressing conceptual and methodological needs that are apparent in the extant research in this area.

Over the last few years, a series of studies have emerged that investigate the psychological predictors of mobile phone use while driving (Atchley et al., 2011, Feldman et al., 2011, Nemme and White, 2010, Walsh et al., 2008, White et al., 2010, Zhou et al., 2012, Zhou et al., 2009). Drawing on cognitive dissonance theory (Atchley et al., 2011) found that once young drivers make the decision to text, they then perceive the road conditions to be less dangerous. Participants claimed that they more frequently read than sent messages, and texted more for the purpose of coordination than relieving boredom. Feldman et al. (2011) investigated the link between mindfulness and texting while driving, and found them to be negatively related. In contrast to participants’ reports in the study by Atchley et al. (2011), they found that this relationship appeared to be mediated by motives to regulate emotions, such as anxiety, loneliness, and boredom. Zhou et al. (2012) recently examined the role of compensatory decisions, such as pulling to the side of the road or reminding the caller that the individual was driving. Participants reported that they were likely to use these strategies, and such behavior was most predicted by intentions to do so and perceived behavioral risk and control. These studies reveal a complex picture regarding texters’ motivations that plays out on a moment-to-moment basis and depends on intentions, risk perception, and personality differences.

Several studies have applied the widely used and validated theory of planned behavior to explain texting while driving (TPB; see Armitage and Conner, 1999, Armitage and Conner, 2001, Azjen, 1991). The basic model includes attitudes, subjective norms, and perceived behavioral control (PBC), which indirectly influence behavior by way of conscious intentions. Studies of mobile phone use support the validity of TPB as a framework for understanding this behavior (e.g., Nemme and White, 2010, Zhou et al., 2009, Zhou et al., 2012). Although somewhat different patterns have emerged for calling (Zhou et al., 2009), texting while driving results have shown that attitudes, more than subjective norm or PBC, significantly predict intentions to text and drive (Walsh et al., 2008). More recently, Nemme and White (2010) provided evidence for the role of robust social influence factors by adding moral and group norms to the model, each of which are significant predictors of texting while driving. Using a longitudinal design, the study also found that the control variable of past behavior was the strongest predictor of both intentions to text and drive and reported frequency of this behavior. Since frequent behaviors can lead to habitual processes, the authors noted the potential for habit to influence texting behavior while driving. Past behavioral frequency, however, does not differentiate between conscious and nonconscious decisions, which is vital when measuring habit (LaRose, 2010). Moreover, reported levels of past frequency do not take into account the defining characteristics of habitual behavior. Thus, the current study aims to investigate the role of habit in texting while driving with a focus on how (rather than how much) the behavior is carried out.

Habit has been identified to play a major role in a number of activities related to media, communications, information systems, and human–computer interaction research (LaRose, 2010, Limayem et al., 2007). Not surprisingly, it has also begun to gain the attention of mobile communication researchers. Employing a social cognitive framework, Peters (2009) found habit, rather than outcome expectations, to be the best predictor of mobile phone usage. Furthermore, Oulasvirta, Rattenbury, Ma, and Raita (2012) recently used logs, programmed into smartphones, to examine the habitual nature of smartphone behaviors. In doing so, the researchers identified the “checking habit” from sessions that were rapidly executed, repeated in an identical manner, and associated with the same cue. The most salient checking habit involved “touching” the home screen for one second. SMS messaging clients were the most used applications after the home screen and were also noted for their high level of habit-like behaviors. The researchers introduced the idea of checking habits as a “gateway” to other applications. In turn, an individual could begin a touching habit and notice an SMS cue unintentionally. Such checking habits represent a type of automatic behavior, or automaticity.

Automaticity can be understood as behavior that is triggered by situational cues and lacks control, awareness, intention, and attention (Bargh, Chen, & Burrows, 1996). In a series of studies on smoking behavior, Orbell and Verplanken (2010) showed that habit could be viewed as a form of “cue-contingent automaticity.” A texting cue, for instance, could manifest as a vibration, a “new message” symbol, a peripheral glance at a phone, an internal “alarm clock”, a specific context, or perhaps a mental state. Thus, the triggers can be either external or internal. In the case of more habitual behavior, reacting to these cues becomes automatized to the point that the actor may do so without even meaning to do it. Oulasvirta et al. (2012) argue that the conception of addictive smartphone usage—similar to Internet behavior (e.g., checking e-mail, see LaRose, Lin, & Eastin, 2003)—may simply be an exaggeration of habitual operations.

Present theories of habit highlight the advantages of looking at behaviors from a frequency-independent perspective (LaRose, 2010, Verplanken, 2006, Verplanken, 2010). In addition to delineating conscious and unconscious behaviors, recent research indicates there is individual variability in both the maximum automaticity and length of time that individuals’ habits take to peak (Lally, Van Jaarsveld, Potts, & Wardle, 2010). In the past, and in everyday usage, habits were and still are often equated with behaviors done regularly that are hard to give up (see Chatzisarantis & Hagger, 2007). Conversely, the construct of habitual behavior represents not just a linear relationship with past usage, but individual differences in automaticity. Frequent behaviors can be consciously performed in a reliable manner, and infrequent behaviors can be performed unconsciously.

This is particularly relevant for the area of mobile communication. Mobile phones have now become an ingrained element within society and are almost always at an arm’s reach. Paradoxically, they have become “taken for granted” and “forgotten” due to operational necessity (Ling, 2012). Hence, the current study considers whether automatic phone tendencies may be better represented along a continuum independent of frequency. Two mobile phone users, then, could use their devices at an equal rate, but differ in the degree to which they perform the behavior automatically. Consequently, in this study, we hypothesized (H1) that the frequency-independent side of habit, or automaticity, would be a positive predictor of texting while driving. Furthermore, we predicted (H2) that the measure for automaticity would predict the outcome variable (texting while driving), even when controlling for individual differences in the overall frequency of texting.

In his comprehensive review of media habits, LaRose (2010) highlights mobile phones as an important avenue for future research due to their presence in constantly shifting contexts. Mobile phone habits present an interesting case because their potential cues and associations are essentially unlimited. Thus, it may be that texting while driving is a behavior acted out despite one’s expressed (and best) interest. Habitual processes are known to guide behavior even when individuals possess intentions to alter such habits (see LaRose, 2010) and in times of conflicting motives (Neal, Wood, Wu, & Kurlander, 2011). Because of this, studies that exclusively use the theory of planned behavior variables may be insufficient in accounting for crucial aspects of the outcome. Therefore, we expected (H3) the relationship between texting automaticity and texting while driving to remain significant when accounting for other known conscious predictors of this behavior, including attitudes, norms, and PBC. This analytic structure helps to clarify the role of habit/automaticity when examined on its own and in relation to other key pieces of the puzzle already in place.

Section snippets

Sample and procedure

A total of 441 undergraduate students at a large university in the middle-eastern part of the US volunteered for this study to fulfill participation requirements for courses in Communication Studies as well as Psychology. While this convenience sample does not allow for generalizability, its characteristics are not unlike other studies laying the groundwork in this area (e.g. Feldman et al., 2011). Sixty-two percent of the participants were female and mean age was 18.43 (SD = 2.49). Participants

Results

The means, standard deviations, and component loadings for each item of the habitual texting measure are displayed in Table 1. Bivariate correlations for all study variables are presented in Table 2. As one would expect, high correlations were found between participants’ sending and reading behavior, both overall and in the driving context.

The hypotheses and corresponding results are organized with three sets of OLS regression analyses that allow for the role of habit (with a focus on

Discussion

Texting while driving is a large problem that has only garnered a small amount of psychological research so far. Much of that work has attempted to use the Theory of Planned Behavior (TPB) as a framework for investigating the predictive role of variables related to conscious decision-making. TPB offers the strength of being broadly applicable, which has been useful in identifying key components for explanations of many human behaviors, including texting while driving. At the same time, TPB

Limitations

One limitation of this study is its cross-sectional design, which does not provide empirical grounds for causal claims. That said, some of the flows of causality can be conceptualized in a theoretical sense. For example, it seems more elegant theoretically to argue that habit, as an overarching orientation toward texting, leads to texting behind the wheel rather than the reverse. Actually, it is quite plausible that they influence each other, but it seems that in this case texting while driving

Conclusions

Moving forward, the results of this foundational research call for a rerouted discussion of the texting while driving phenomenon. In laying out their notion of unconscious behavioral guidance systems, Bargh and Morsella (2010) illuminate the bias inherent in focusing on consciousness in human behavior. They even go so far as to state that much of behavior throughout history has been “zombie-like.” While we do not embrace the dystopian notion that mobile communication is turning people into

Acknowledgements

We would like to thank Sonya Dal Cin, Emily B. Falk, Ethan Kross, Rich Ling, Roger D. Klein, Jennifer Q. Morse, Kimberly Nolf, Brad Sanders, and Patricia Donley for the invaluable input and support that made this project succeed.

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