PLANNED BEHAVIOR VERSUS GOAL-DIRECTED AUTOMATICITY THE IMPACT OF ATTITUDE AND GENERAL HABIT ON ADOPTION AND NON-

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1 PLANNED BEHAVIOR VERSUS GOAL-DIRECTED AUTOMATICITY THE IMPACT OF ATTITUDE AND GENERAL HABIT ON ADOPTION AND NON- ADOPTION Completed Research Paper Julia Kroenung Goethe University Frankfurt Grueneburgplatz 1, Frankfurt Andreas Eckhardt Goethe University Frankfurt Grueneburgplatz 1, Frankfurt Steffen Bernius Goethe University Frankfurt Grueneburgplatz 1, Frankfurt Abstract "Planned behavior" is a widely-known paradigm used to predict individual behavior. Basing on a competing paradigm called goal-directed automaticity, research addressed the mutual support of both paradigms thus the importance of habits for the continued usage. A versus between both paradigms thus the inhibiting effect of habits on the adoption of a new system was thematized in 2012 by Polites and Karahanna. Within this research, both concepts are brought face to face by examining the results of an empirical survey of 378 County employees who were separated into adopter and non-adopter groups. Given this opposing behavioral outcome, the moderating effect of habit on the relationship between affect/cognition and intention was examined by means of a group-based regression approach. The results show opposing patterns for both groups which holds the implication that the directional impact of habit strength on this relationship is likewise opposing. Keywords: Habit, Automaticity, Affect, Cognition, Non-adoption Thirty Fourth International Conference on Information Systems, Milan

2 Human Behavior and IS Introduction As a means of realizing value from IT investments, research on the adoption and usage of information systems is often described as one of the most mature research streams in contemporary information systems literature (DeLone and McLean 1992; DeLone and McLean 2003; Hu et al. 1999; Taylor and Todd 1995). Numerous models delineating determinants of individual adoption have been developed, refined, and unified (Davis et al. 1989; Venkatesh and Brown 2001; Venkatesh and Davis 2000; Venkatesh et al. 2003) in order to explain IT usage behavior on the basis of beliefs and attitudes. Basing on theories like the theory of reasoned action (Fishbein, 1963; Fishbein and Ajzen 1975) and the theory of planned behavior (Ajzen 1991) these models focus on two central paradigms: First, the user s conscious evaluation (thus the formation of attitudes and beliefs) about using technology, and second the premise that IT usage is activated by an intention to use an IT artifact (Jasperson et al. 2005; Venkatesh et al. 2003). This evaluations-intention-usage relationship characterizes the vast majority of adoption models in IS research (Kim et al. 2005). Although conscious use and thus deliberate evaluations and intentions characterize many forms of IT use, contemporary research also raises the issue of automatic use which occurs outside conscious awareness and is mainly based on habit or automaticity (Jasperson et al. 2005; Limayem et al. 2007, Venkatesh et al. 2012). Especially, when considering behaviors in the same physical and social environment, research guided by the theories based on the attitude behavior relationship seems to ignore the aspect of the repetitive nature of these behaviors (Aarts et al. 1998). Research on the attitude behavior relationship rooting in the discipline of social psychology has addressed the impact of habit and automaticity on the relationship from two conceptual perspectives: Past behavior as an antecedent of attitude and thus indirectly predictive of behavior (Ajzen 1984; Eagly and Chaiken 1993; Eagly and Chaiken 2007), and habit as a behavioral determinant equivalent to behavioral intention (Triandis 1977; Triandis 1980). Recent research however, has focused on the interdependencies between habits and attitudes. Predominantly, the distinction between planned behavior which is mainly determined by attitudes and goal-directed automaticity that describes goal-directed behaviors as a result from learned sequences of acts is at interest (Verplanken and Aarts 2011). A trivial but important coherence in this respect was originally thematized by Triandis (1977) and adapted by various researchers (e.g. Ronis et al. 1989; Verplanken and Aarts 2011), is that attitude behavior correlations are stronger to the extent that habits are weaker and vice versa. In IS adoption literature, the conceptualization of user behaviors as planned or reasoned behavioral acts based on intentions, beliefs, and attitudes is widely spread (Venkatesh et al. 2003), whereas the role of habits, habitual behavior, and automaticity has rarely found attention. A review of technology acceptance literature conducted by Polites and Karahanna (2012) reveals only a few studies that lay focus on habits in the adoption context. Among those studies, the role of habit as a driver of sustained usage is at focus while research on the impact of habit on beliefs and intention is sparse (Polites and Karahanna 2012). Polites and Karahanna (2012) added the aspect of the inhibiting power of habit regarding the adoption of a new technology. However, research explicitly discussing the impact of habit on non-adoption is still nonexistent. Combining these findings with recent development in social psychology research on attitudes and habits, we explicitly aim at answering the following research questions: RQ1: On a theoretical basis, how do attitudes and habits interact in the determination of behavior dependent on the level of habitual strength? RQ2: What are pro- and counterintentional habits and what is their effective direction with respect to behavioral processing? Transferring the issues of habits and their interference with planned behavioral processing on the IS adoption context, research has indicated that it makes sense to distinguish habits by means of their effective direction on the endogenous variable (e.g. the inhibiting effect of incumbent system habit on the adoption of a new system but its positive effect on continued use of the incumbent system) (Polites 2005; Limayem et al. 2007; Polites and Karahanna 2012). There have also been studies supporting the split of attitude into affect and cognition based on theoretical considerations on attitudes in social psychology (i.e. Yang and Yoo 2004; Kroenung and Eckhardt 2011). Although affect and cognition towards a respective 2 Thirty Fourth International Conference on Information Systems, Milan 2013

3 Kroenung et al. Planned Behavior versus Goal-directed Automaticity behavior are likely to coincide, in some cases dissonances occur leading to the outcome that one of the two is not predictive for behavior (Eagly and Chaiken 1993). Given that adopter and non-adopter of a system can evidently be distinguished by their opposite values in the endogenous variable of IS usage, they should also display different or opposing effects in which habit influences their decision making in terms of affect/cognition and intentions. Therefore, the third research question is derived: RQ3: Do adopter and non-adopter differ in the extent to which their relationships between affect/cognition and intentions are moderated by habit strength? In order to address these questions, we aim to contribute to the field of IS adoption in the following ways: First, We discuss the different directional effects of habits and their impact on the processes of planned behavior. Second, we address the important aspect of habit strength within this coherence. And third, we analyze the moderating effect of habit on the variables of planned behavior by explicitly separating groups with adoption and non-adoption behaviors. Attitudes and habits as predictors of human behavior Ajzen (1991) states that explaining human behavior in all its complexity is a difficult task, which can be approached at many levels (Ajzen 1991, p. 179). Concepts like attitude and habit that refer to behavioral dispositions have been at focus in these attempts to predict and explain human behavior (see e.g. Ajzen 1988; Campbell 1963; Fishbein 1963; Sherman and Fazio 1983). Basing on these concepts, famous behavioral theories as the theories of reasoned action (Fishbein 1963), planned behavior (Ajzen 1988) or interpersonal behavior (Triandis 1977) have been developed and applied through a wide range of different research contexts during the past decades. The causal variables that determine behavior vary slightly across the models and theories as do the boundary conditions. Whereas attitudes represent a traditional and important determinant of human behavior in social psychology literature (see Eagly and Chaiken 1993; Eagly and Chaiken 2007; Glasman and Albarracín 2006), habit is often treated as a construct of marginal interest (Verplanken and Aarts 2011). Nonetheless, recent research has considered habit as boundary conditions of the validity of models of planned behavior, rational decision-making and deliberate action (Verplanken and Aarts 2011). In the following, we will review research on both attitudes and habits separately, and thematize their interplay with respect to the prediction of behavior in the forthcoming section. Attitudes a three-dimensional concept Because of the importance accorded to attitudes as causes of attitude-consistent behavior, the concept of attitudes has become a fundamental construct for the social sciences (Allport 1935). This is because human beings react to their environments in an evaluative manner (Albarracín et al. 2005). A holistic conceptual definition of attitude was provided by Eagly and Chaiken in They define attitude as a psychological tendency that is expressed by evaluating a particular entity with some degree of favor and disfavor (Eagly and Chaiken 1993, p. 1; see also Bohner and Dickel 2011). The core in this definition is that the authors explicitly emphasize that evaluation comprises all classes of attitudinal responding affect, cognition, and behavior. Affective evaluative responses consist of emotions, feelings, or moods that people experience, when they are face a certain attitude object (directly or indirectly). Cognitive evaluative responses represent beliefs, thoughts, or ideas about the attitude object. Evaluative responses of the behavioral class are overt actions that people perform with respect to the attitude object. In line with the concept that attitudinal responses can be categorized into three classes is the assumption that attitudes comprise three types of antecedents. The idea that affective, cognitive, and behavioral processes precede the development of attitudes has been proposed in numerous publications on attitudes (e.g. Breckler 1984; Triandis 1971; Zanna and Rempel 1988). The formation of attitudes on the basis of cognitive processes is inherent in most of the research done on attitudes (Ajzen 1991; Albarracín et al. 2005; Fishbein and Ajzen 1975; Eagly and Chaiken 1993). A cognitive learning process is assumed to happen, when people gain information about an attitude object, and consequently form beliefs. Thirty Fourth International Conference on Information Systems, Milan

4 Human Behavior and IS With reference to affective antecedents of attitude, Zajonc (1984) stated that preferences (e.g. evaluations) often arise immediately simultaneously with the first contact an individual has with the attitude object, without being mediated by cognitive activities, i.e. the immediate feeling of sympathy/antipathy toward another person at first sight. The assumption that attitudes form on the basis of past behavior is based upon the premise that people tend to infer attitudes that are consistent with their behaviors. While theories like cognitive dissonance (Festinger 1957) and the concept of attitudes as representations in memory (Bohner and Dickel 2011) outline that behavioral processes (e.g. direct or indirect behavioral experience with the attitude object) precede attitude change and reformation, the formation of an initial attitude represents a special case. Just as it seems quite intuitive that cognitive and/or affective processes antecede the development of an initial attitude, it is counterintuitive that behavior precedes the initial formation of attitude. As to this question, literature provides various viewpoints, too. Some authors (Zanna and Rempel 1988) claim that simple behavioral processes can precede initial attitudes. Others by contrast, hold the argument that an initial attitude is constructed on the spot and determined only by affect and/or cognition and moderating situational factors (Fazio 1995). The following Figure 1 illustrates the tripartite model of attitude formation and responding. Figure 1. Attitude formation and responding (Eagly and Chaiken 1993) Attitudes and the prediction of behavior Early research on attitudes in social psychology that tried to examine if a causal relationship between attitudes and behavior exists, unsurprisingly produced contradictory results as did the research attempting to find stringent discriminant validity between the three response classes. This reflects considerable variability in the degree to which attitudes influence behavior (Ajzen 2001; Glasman and Albarracín 2006). Consequently, a second generation of research was devoted to delineating the conditions, under which attitudes are likely to predict behavior. Two research streams in social psychology addressed this issue. One approach is mainly concerned with aspects of measurement and how improvements in the assessment of attitudes and behavior can enhance the prediction of behavior by attitudes (Bohner and Waenke 2002). The second approach, which builds the theoretical basis of this research, involves the search for personal, situational, and content variables that potentially moderate the relationship. In order to predict behavior on the basis of attitudinal evaluations with at least moderate precision, prominent theories like the theories of reasoned action (Fishbein 1963; Fishbein and Ajzen 1975), planned behavior (Ajzen 1991) and interpersonal behavior (Triandis 1971; 1977) were developed. Their applicability as been demonstrated in various content domains (Ajzen 2001), and thus little can be gained in demonstrating their applicability in particular domains. Another set of studies has compared these theories to models designed for application in specific domains. The results indicated that these general, content-free models perform better regarding the prediction of behavior by means of evaluation and beliefs. Far less intention has been spent on the question which factors moderate the attitude behavior consistency. Glasman and Albarracín (2006) provide a meta-analytical study on factors driving and inhibiting attitude behavior consistency. Recent research in social psychology has moved one step further focusing on the interplay between attitude and habit and its impact on attitude behavior consistency (Verplanken and Aarts 2011). 4 Thirty Fourth International Conference on Information Systems, Milan 2013

5 Kroenung et al. Planned Behavior versus Goal-directed Automaticity Attitudes in IS literature In IS literature user attitude represents an interesting and controversial case (Kroenung and Eckhardt 2011; Venkatesh et al. 2003; Yang and Yoo 2004; Zhang et al. 2008; Zhang and Sun 2009). Although, the construct of attitude was frequently used as determinant for the endogenous variables in the behavioral models of IS adoption research, studies yielded inconsistent results as to attitude s role of impact (Venkatesh et al. 2003; Zhang et al. 2008). Although the attitude was used to predict behavioral intention very well in some models/circumstances it remained insignificant in predicting behavioral intention in others (Venkatesh et al. 2003; Yang and Yoo 2004). The results of a scientometric study on attitudes over the last two decades in 14 IS journals yielded similar results: Up to 20% of attitude behavior relationships applied in IS adoption literature were insignificant (Kroenung and Eckhardt 2011). Some research approaches have addressed the issue of inconsistent attitude behavior relationships in IS research and suggested potential solutions in order to avoid insignificancies and increase the construct s predictive validity on behavioral intention. For instance, Yang and Yoo (2004) split up the construct into cognitive attitude and affective attitude and found cognitive attitude to be more predictive of behavioral intention to adopt a system. This result opposed the generally affectively scaled attitude construct in IS literature (Kroenung and Eckhardt 2011). Although with regards to the tripartite model of attitude this distinction into affect and cognition seems meaningful, the paper lacks explanation as to the reasons why cognitive attitude is more predictive of behavioral intention. Kroenung and Eckhardt (2011) introduced the tripartite model of attitude to IS literature and suggested a separation of affect and cognition dependent on the characteristics of the IT artifact. Both papers show evidence, that it makes sense to distinguish at least affect and cognition with reference to user attitude, but neglect the interplay of attitude and habit or the way that habit potentially impacts the attitude behavior relationship and thus the correlation between two and the third class of attitudinal responses. Another interesting paper that has to be mentioned in the context of this research is the work published by Titah and Barki (2009) on nonlinearities between attitude and subjective norms in information technology acceptance. The authors examined the relationship between the constructs attitude and subjective norm concerning technology acceptance and found, that attitude and subjective norm function as substitutes regarding technology acceptance of individuals. This finding underpins the approach we pursue within this research stating that attitude predicts adoption behavior to the extent that other variables (in case of this research habit) are weaker and vice versa. Thus, in the following we will thematize habits from a theoretical viewpoint and outline basic implications for the attitude behavior relationships in the IS literature. Habits Habits are defined as learned sequences of acts that have become automatic responses to specific cues, and are functional in obtaining certain goals or end-states (Verplanken and Aarts 2011, p. 104), and its formation has long been central in behavioral research (e.g. Hull 1943; Tolman 1932; Watson 1914). Many behaviors that social psychologists are interested in may become habits once they have been repeatedly and satisfactorily executed (Verplanken and Aarts 2011). Habit thus comprises a goal-directed kind of automaticity that may be consciously instigated (Bargh 1989). In the literature, analogous to the terms attitude and evaluation, habit and past behavior were used synonymously. The important difference is that past behaviors may turn into habit once it has been sufficiently and satisfactorily repeated (Ronis et al. 1989). Varying from simple actions to complex behavioral patterns, individuals tend to have numerous habits that are developed during a lifetime. Verplanken and Aarts (2011) define three characteristics of habits that we will briefly review within the next paragraphs. These are automaticity, dependence on situational constancy, and the functionality of habits. Since habits base upon past experiences, habits represent a distinct type of behavior that is related to forms of automaticity (Ouellette and Wood 1998). Bargh (1994; 1996) differentiated four characteristics that are related to automaticity: (Un)controllability, (un)intentionality, (lack of) awareness, and efficiency. Alike automaticity, habits can be described by these features. Accordingly, we would Thirty Fourth International Conference on Information Systems, Milan

6 Human Behavior and IS characterize most of our habits as behavioral sequence that are intentional, to a certain extent controllable, executed without much awareness, and efficient (Verplanken and Aarts 2011). Habits are intentional, in the sense that they refer to a specific goal, although their extent of intentionality decreases dependant on how often the behavior is executed. However the fact that habits are functional in achieving some goal makes them intentional to a certain degree (Verplanken and Aarts 2011). Even though some habits seem hard to break especially when they are long established, most are controllable at least to some extent. By conscious contemplation, we consider ourselves capable of overcoming most of our habits (Verplanken and Aarts 2011). The most prominent characteristics of automaticity are the lack of awareness and efficiency (Verplanken and Aarts 2011). Generally, we may not be aware of many behavioral options that are present in our everyday life. Without being aware of the routine choices and decisions we make, we experience a fluency of behavior. Likewise, habits are efficient in the sense that little mental effort is required to execute them (Verplanken and Aarts 2011). Although in the literature there is general agreement on the point that habits are automatic in the way that they can be performed quickly, with minimal attention and simultaneously with other activities, discussion occurred on the point that automatic implies nonvolitional (Ronis et al. 1989). In line with Bargh (1994; 1996) and Ouellette and Wood (1998), we hold the viewpoint that habits like many behaviorally established routines such as driving a car or exercise can be both volitional and automatic. The characteristic of situational constancy rises from the fact that habits develop in stable contexts (Verplanken and Aarts 2011). According to learning theory, social behavior is formed by building associations between environmental cues, individuals responses, and experienced consequences (e.g. Hull 1943; Tolman 1932; Watson 1914). Thus, an individual must encounter situations that are similar as to instigation cues, responses, and consequences (Verplanken and Aarts 2011). Habits develop by the systematic experience of rewarding consequences ( ) (Verplanken and Aarts 2011, p. 106), and have been studied profoundly in behavioral research (Hull 1943; Tolman 1932; Watson 1914). These rewarding characteristics of habits make them functional or instrumental from the perspective of the individual who develops them. Especially, when we think of habits that may be undesirable from the perspective of an outsider (government promoting public transport as an alternative for habitual car drivers) it is important to comprehend that from the individual s viewpoint, the reward and thus the achieved goal in driving the car is the feeling of comfort (Verplanken et al. 1994). An interesting example with respect to functionality of habits is the avoidance switching costs (Polites and Karahanna 2012). If a person sticks to habitual patterns, the avoidance of switching costs (i.e. learning of new habitual patterns) can also be regarded as functional. Thus we argue that habits are also functional when they serve the purpose of avoiding negative consequences (e.g. discomfort, effort). This issue has been recently addressed in IS adoption literature by Polites and Karahanna (2012). Using the concept of Status Quo Bias (Samuelson and Zeckhauser 1988) which describes the bias of individuals decisions towards maintaining the status quo, the authors develop the construct of individual level inertia that is defined as attachment to, and persistence of, existing behavioral patterns (i.e., the status quo), even if there are better alternatives or incentives to change (Polites and Karahanna 2012, p. 24). Thus, the concept of inertia describes the extent that habitual patterns prevent individuals to change their behavior. It is therefore somehow natural, that inertia increases in line with habit strength. Or, in other words, the more individuals are used to a certain behavior, the less is their willingness to change it. Within the next section we describe the effect that habit strength has upon individual behavior. Habit, automaticity, and behavior In explanations of behavior, that refer to daily routine, habits denote one s customary way of behaving. The claim that a certain behavior is performed because of habit provides understandable explanation for an act that might seem irrational (Ouellette and Wood 1998).A meta-analysis conducted by Ouellette and Wood (1998) indicated that behaviors that are performed on a daily or weekly basis in stable contexts, are much better predicted by past behavior than behaviors that are performed less frequently under unstable contexts. Beside the frequency of past behavior, habit strength is an important factor that has to be examined in the context of decision-making by means of habits (Ouellette and Wood 1998; Verplanken et al. 1997; Verplanken and Aarts 2011). Referring to goal-directed automaticity as a decision making process that is 6 Thirty Fourth International Conference on Information Systems, Milan 2013

7 Kroenung et al. Planned Behavior versus Goal-directed Automaticity initiated by the activation of a certain goal, Verplanken et al show that individuals with weak habits take four more or less conscious steps from goal activation to the final choice, whereas individuals with strong habits bypass these steps. The following Figure 2 outlines these differences between strong and weak habits in the process of making choices. Figure 2. Process model of making choices by individuals with weak and strong habits (Verplanken et al. 1997, p. 542) Traditionally, research on decision-making determined by habits follows two different approaches: The first is the focus on goal-directed automaticity (see Figure 2) as an opposing concept to planned behavior, and the second is the operationalization of habit as past behavior in models of predominantly basing on attitude-behavior consistency (Mittal 1988). The latter is based on the argument that habit reflects a repetitive performed behavior that results from repetition of deliberate decision-making by means of reasoned action or planned behavior (Beatty and Kahle 1988). Taking the example of repeated brand choice, Beatty and Kahle (1988) propose a model that integrates habit in the theory of reasoned action. The following Figure 3 illustrates how habit is incorporated in process of deliberate action. Figure 3. Conceptual Framework for the theory of reasoned action with the addition of habit (Beatty and Kahle 1988, p. 3) Relevant literature that considers habit as an explanatory construct is limited (Mittal 1988). Fishbein and Ajzen (1975) recognize habit as a mediating factor of the relationship between intention and behavior, but do not formally incorporate it in their model. Triandis by contrast proposed a model in which habit was modeled as a predictor of behavior (Triandis 1977). Triandis (1977) sees habit as equivalent to past behavior, but also added qualifiers as routinized and automatic (Mittal 1988).The weights of habit and intention vary on a range between 0 and 1, which implies that intention is predictive of behavior to the extent that habit is weaker. Or as Verplanken and Aarts (2011) put it, the antecedents of the theory of planned behavior predict future behavior to the extent that behavior is not habitual. Beside this interaction between habit and intention, another important aspect of Triandis s model is the explicit separation between affect and attitude. Although the attempt to incorporate affect as separate antecedent into a model has received support (e.g. Steg et al. 2001), the definition of attitude as cognitive evaluation contradicts the tripartite model of attitude that conceptually includes both cognitive and affective elements. Nevertheless, with respect to this research, Triandis s (1977) model is important in two senses. The first is the mentioned separation of affect and cognition, and the second the interaction between habit and Thirty Fourth International Conference on Information Systems, Milan

8 Human Behavior and IS intention is part of his model of the attitude-behavior relationship. The author assumes that when a behavior is new it is fully under control of attitudes and corresponding intentions, whereas when it has been sufficiently repeated the habit component gains importance (Verplanken and Aarts 2011). This interrelation of attitude and habits inherent in Triandis s (1977) model, is compatible with Fazio s (1990) MODE model of attitude-behavior relationships. According to Fazio (1990, 1995), and Fazio and Petty (2008) when a behavior is new, or an individual is motivated to deliberate about behavioral choices, behavior is very likely the result of a process described by the theories of reasoned action and planned behavior. Attitudes that express a tendency of favor or disfavor are formed on the bases of beliefs about the expected consequences (Ajzen 1991). If for some reason there is little or no motivation to deliberate, which is the case for habitual behaviors (and decreasing with increasing habitual strength), according to the MODE model behavior is denoted as a spontaneous attitude-to-behavior process (Fazio 1990; Fazio 1995; Verplanken and Aarts 2011). In this case, attitudes spontaneously guide behaviors to the extent that they are accessible in memory. Accessible attitudes might also impose selectivity in the perception of behavioral choices (e.g. Houston and Fazio 1989). Strongly developed habits show similar effect since they are also associated with limited and selective information processing (Verplanken and Aarts, 2011). Because of their functional nature and strict reference to specific actions in the past that have (perceived) positive consequences, many habits may be associated with positive attitudes toward the habitual response (Verplanken and Aarts 2011). However, under strong habit conditions there is little motivation or reason to deliberate. Thus the MODE model in this case would predict that attitudes are only involved in habitual behavior to the extent that they are accessible in memory (Verplanken et al. 1998). Moreover it can be expected that attitudes toward the habitual behavior are more accessible in memory than attitude towards alternative (non-habitual) behaviors (Verplanken and Aarts 2011). The reverse implication would be that habits strengthen the affective component in attitudes which has found to be more accessible that it s cognitive counterpart (Verplanken et al. 1998). Although this compatibility with Fazio s MODE model indicates that the impact of habit strength on the predictive power of attitude resembles the one of habit strength on intention that was formulated by Oulette and Wood (1998) and modeled by Triandis (1971, 1977), it has to be clarified that Triandis did not model this coherence between habit and attitude. A direct comparison between the effects of attitude and habit on the prediction of behavior was scrutinized by Verplanken et al. (1994). In their approach, the authors model attitude towards train and car-choice, as well as general car choice habit as direct determinants for car choice behavior. The results indicate that predictive power of attitude decreases to the extent that the predictive power of habit increases and vice versa (Verplanken et al. 1994). The results further show that dependent on the behavioral outcome (train or car use) general habit can have a supporting effect on the relationship between attitude and behavior or an inhibiting effect, which both are also highly dependent on the level of habit strength. This approach resembles the one conducted by Bagozzi (1981) who also conceptualized attitude and habit as determinants of future behavior and behavioral intention, with the constraint that habit is used synonymously with past behavior. In order to separate these two effects, the following Figures 4 displays the different ways of processing dependent on the pro- or counterintentional effect of habit on the attitude behavior relationship. Deliberate or cognitive processing as illustrated on the left side of Figure 4 represents the case when the directional effects of habits and attitudes correspond with one another leading to the same behavioral outcome. This case is referred to as prointentional habits. The decision making proceeds via attitude and its antecedents (affect, cognition and - if present - prior experience). Since the focus of this research explicitly lays on the interplay between attitude and habit, other behavioral antecedents as subjective norm and perceived behavioral control were disregarded. With respect to pure deliberate processing, attitudinal evaluations about behavioral outcomes are translated into the so called behavioral responding by the formation of a behavioral intention which then with a certain probability results in the corresponding behavior. So basically there are two major process steps, attitude formation and behavioral responding which are illustrated by means of the two dashed squares. By repeating this behavior more or less frequently, the celerity of these processes increases and a certain automatism (habit) develops (dashed arrow from Behavior to Experience). As a consequence, individuals engage in a behavior automatically without preceding evaluations of that behavior, unless some 8 Thirty Fourth International Conference on Information Systems, Milan 2013

9 Kroenung et al. Planned Behavior versus Goal-directed Automaticity circumstance (e.g. a change in environmental or situational variables) requires conscious thought (Petty and Cacioppo 1981; Ronis et al. 1989). Thus, with increasing habit strength the processing shifts from deliberate to spontaneous processing (lower part of the Figure). The Figure 4 thereby refers to a behavior that is neither entirely new nor fully automatic in order to outline two interrelations between on deliberate and spontaneous processing that has been described within the last view paragraphs. The first is that past behavior which represents a necessary precondition for habit development is inherent in the tripartite model of attitudes (Eagly and Chaiken 1993; Eagly and Chaiken 2007). Though in the long run, habits are expected take over as dominant predictor of behavior, the element of experience/past behavior also determines the formation of attitudes. The second refers to the accessibility of attitude as described in Fazio s (1990) MODE model. According to the model, attitudes that are accessible in memory guide behavior by means of spontaneous processing. This holds the implication for habitual behaviors that when an individual is confronted with behavior-related cues (e.g. a specific situation or environment), behavior is activated by quickly accessing the behavior-relevant attitude in memory (Verplanken and Aarts 2011). In the Figure 4 this coherence is depicted by means of the dashed arrow between habit and attitude formation and behavioral responding. Figure 4 (right side) also illustrates the case of two competing behaviors (e.g. the adoption of a new system (Behavior X) and the non-adoption of the system and continued use of an incumbent system (Behavior Y). The impact of incumbent system habit (Habit Y) on the variables of behavioral responding of Behavior X (Intention towards X and Behavior X) is termed counterintentional (Polites 2009) as is Habit Y in relation to Behavior X referred to as counterintentional habit, since it opposes the intended Behavior X. Verplanken and Aarts (2011) define these habits as counteracting attitudes and intention and explain that they often originate from short-term hedonistic motives like comfort or enjoyment or less effort compared to the adoption of a new system. With respect to behavioral processing the presence of two competing behaviors makes the behavioral prediction more complicated in relation to the case illustrated in Figure 4. The upper part of the Figure 4 has not changed except that we assume that a longterm motivation (adopting the new system) triggers attitude formation. Verplanken and Feas (1999) found that the interference or the counterintentional effect of a habit begins when this long term motivation conflicts with this short-term hedonism that determines Habit Y. This Habit Y also conflicts with the attitude to perform Behavior X, and has shown to negatively impact the variables of behavioral responding (i.e. Polites and Karahanna 2012, Verplanken et al. 1994) symbolized by the negative labeled arrow from Habit Y to the dashed square Behavioral Responding. This negative effect of counterintentional habits has also been related with the theoretical concepts of action slips (Betsch et al. 2004; Heckhausen and Beckmann 1990), innovation dissonance (Roger and Shoemaker 1971), behavioral lock-in or inertia (Barnes et al. 2004; Polites 2005; Polites and Karahanna 2012), and resistance to change (e.g. Hellriegel and Slocum 2003). The relationship between Habit Y and Behavior Y which is symbolized by the positive labeled arrow resembles this same relationship in Figure 4. However, whereas in Figure 4 the behavioral outcome does not change with an increase in habit strength, only the power of its determinants shifts from the upper to the lower part of the Figure (from deliberate to spontaneous processing). Habit strength is critical for the resulting behavioral outcome (Behavior X or Behavior Y). Thus, overcoming counterintentional habit with respect to the desired behavior may take much conscious effort. In particular, this holds true when the bad habit results in immediate short-term rewards (e.g. less effort) whereas the competing behavior provides long-term benefits (Ouellette and Wood 1998). The arrow in Figure 4 that is labeled with the question mark, refers to a relationship which has not been explicitly thematized in the IS context and will be extensively addressed within this research. Compared with Figure 4 and the prointentional effect of habit, where the MODE model postulates a bidirectional moderating effect between habit and the attitude behavior relationship, this very effect is questionable if habits and attitudes oppose one another. Thirty Fourth International Conference on Information Systems, Milan

10 Human Behavior and IS Figure 4 Deliberate vs. spontaneous processing The outlined difference between the intentional and counterintentional effect of habits can also be found in studies on habit in IS literature although a review of Polites and Karahanna (2012) revealed only a view studies that specifically focus on habit (e.g. Gefen 2003; Kim and Malhotra 2005; Kim et al. 2005, Limayem and Hirt 2003; Limayem et al. 2007; Wu and Kuo 2008). These studies referred to habit as positive determinant of continued and sustained use of a system prointentional habits. The role of counterintentional habits on IS adoption and usage has not been studied as often (Polites and Karahanna 2012). As to IS usage, counterintentional habits are of specific relevance in case of induced system change. Or, as Polites and Karahanna (2012) put it, when incumbent system habit inhibits the acceptance of a new system. As to this coherence, Polites (2005) proposes a conceptual model including the construct Counterintentional habit, which is hypothesized to have a negative effect on intention to use a new system and new system usage. Furthermore, Murray and Haubl (2007) have shown how repeated use of a particular system could lead to cognitive switching costs and lock-in in the sense that individuals prefer the present system over alternatives in the future. With reference to counterintentional habits in relation to usage behavior, the article provides valuable insights as to the inhibiting role of counterintentional habits (Polites and Karahanna 2012). Very recent research in the IS domain has picked up on this research and investigated how inertia as a result persistence of habit towards an incumbent system inhibits the adoption of a new system (Polites and Karahanna 2012). The authors empirically test effects of inertia on behavioral beliefs (Perceived Ease of Use, and Relative Advantage) and the intention to use a system. In addition they test and graphically illustrate how inertia moderates the relationship between subjective norm and intention to use new system. Beside their extensive review on habit and inertia in IS adoption literature basic contribution with reference to this research is that incumbent system habit inhibits the acceptance of a new technology, which directly relates to the effect of counterintentional habit on behavior as depicted in Figure 4. Venkatesh et al. (2012) by contrast did not hypothesize a specific nature (pro- or counterintentional) of habits in their UTAUT2 model, but modeled habit as direct determinant of intention and usage rather than either one of the two and thereby acknowledged its effect on both variables that represent behavioral responding. Basing on these findings, we draw the following conclusions. First the concept of habit has been addressed from two perspectives in IS adoption and usage literature. First, the positive impact of habit on continued and sustained usage. And less frequently, the negative effect of incumbent system habit on the adoption of a new system. Thus, with respect to the endogenous variable or target behavior adoption of a system prointentional and counterintentional habits have been thematized in IS literature. The effect of one habit 10 Thirty Fourth International Conference on Information Systems, Milan 2013

11 Kroenung et al. Planned Behavior versus Goal-directed Automaticity on the deliberate processing of two competing behaviors (adoption and non-adoption) has not been thematized nor has the moderating effect of habit on the attitude behavior relationship. Second, the issue of habit strength has been neglected in the IS context. Authors noted that planned behavior, predominantly determined by attitudinal beliefs, and goal-directed automaticity, predominantly determined by habits, are different types of behavior and should both be considered depending on the respective context (Kim et al. 2005), but have not explicitly focused on the impact of habit strength. Therefore, we define the following research gaps which we aim to address: First, the combined examination of the pro and counterintentional effect of habit on two competing behaviors; second, the impact of habit strength on the attitude-behavior relationship in the IS context. Within the next section, these gaps will be combined with the theoretical foundation of the precedent sections and research hypotheses will be formulated with reference to research questions introduced in the introduction. Research hypotheses Considering that in the context of IS adoption individual users of IS could react in different ways to a new technology. They may reject it completely, partially use its functions, actively resist it, unwillingly accept it, or embrace it fully. In the IS adoption literature, different terms are used to describe different aspects of an individual s decision not to use a certain technology (Laumer and Eckhardt 2011). Thereby, the two opposing behaviors are acceptance of and resistance to technologies. Acceptance is defined as the agreement to receive something offered or proposed and adoption as the choice to take up or follow an option (Spanes and Stevenson 2005). Resistance behaviors can be distinguished into rejection and nonadoption. Rejection refers to a user s conscious decision to avoid a technology, whereas non-adoption which implies the option of future use (Cenfetelli 2004).We focused our analysis and the examples in the preceeding section on adoption and non-adoption behavior for the following theoretical and practical reasons: First, if usage is considered to be the intended behavior, non-adoption as the decision not to use seems the more fitting opposite than actively avoiding a technology. At this point, the aspect if habits have already been developed or how strong they are is not important. As to analysis it is important to define the behaviors and the variables of deliberate and spontaneous processing with respect to the Figures 4 and 5. A second, more practical reason for that we focused on non-adoption instead of resistance is that we could neither exclude future usage nor was intention to avoid operationalized within the questionnaire. Although prior research indicates that intentions may be a weak proxy for adoption behavior (e.g. Bhattacherjee and Sanford 2006), we used it for the following reasons: First, the two groups had to be comparable in the endogenous variable in order to differentiate the directional effect of habit (pro and counterintentional). Adopters use the systems while non-adopters do not use it or even resist. By using behavioral intention as endogenous variable this inconsistency can be bypassed. Besides, research in social psychology has indicated that intention as variable can be accounted to the group of variables representing behavioral responding as depicted in the Figures 4 and 5 (see Eagly and Chaiken 1993). Second, referring to Triandis s (1977) model the interaction between habit and intention is a central part. Triandis assumes that when a behavior is new it is fully under control of attitudes and corresponding intentions, whereas when it has been sufficiently repeated the habit component gains importance (Verplanken and Aarts 2011). Bagozzi (1981) even modelled attitude and habit as determinants of future behavior and intention, and Verplanken et al. (1994) stressed the importance of attitude and habit as determinants of behavior and thematized their relationship with respect to different levels of habit strength and differing behavioral outcomes (car use and train use). Adding the differentiation of the attitudinal components affect and cognition, the concept of habit strength and the distinctive effects of habit on the competing behaviors adoption and non-adoption, implies that non-adopters should show different patterns of how their affect and cognition impacts behavioral intention dependent on the level of habit strength. Referring to Figure 4, the habit of non-adopters towards continuing not to use the system is assumed to have a counterintentional effect on the intention to adopt the system and prevents them to develop adoption intentions. With increasing habit strength this coherence should be strengthened and mitigated with decreasing habit strength. Thus, if this for non-adopters counterintentional habit is at a weak level, the influence of attitude on behavioral intention gains strength. Therefore the following hypothesis is derived: Thirty Fourth International Conference on Information Systems, Milan

12 Human Behavior and IS H1: Concerning non-adopters, the weaker habit, the stronger the relationship between attitude and behavioral intention. Referring to Figure 4, for adopters by contrast habit is assumed to have a positive (prointentional) effect on the attitude behavior relationship that in line with their intentions impact adoption behavior. The impact of attitude on behavioral intention should be stronger to the extent that habit is weaker and vice versa. The following hypothesis 2 describes this coherence: H2: Concerning adopters, the stronger habit, the stronger the relationship between attitude and behavioral intention. The third hypothesis addresses the coherence between habit strength and affect and cognition that is described in the second section. Relying on the MODE model, affect has been found to be more accessible than its cognitive counterpart. Thus, the effect of habit strength on the relationship between affect and intention should be stronger that the effect on the relationship between cognition and intention. This leads to the following hypothesis: H3: Habit strength has a stronger impact on the relationship between affect and intention than the relationship between cognition and intention. Within the following Figure 5, the research framework and variable setup are depicted: Figure 5. Research framework Methodology and Results We evaluate our research model by using data collected by a survey within a regional government authority in The general purpose of the study was to explain users acceptance of a newly implemented document management system, so we invited all registered users of the document management system to participate in our study. With more than 5,237 registered users the system s primary purpose is to facilitate the document workflow, exchange, and tracking in and between all federal state ministries. The registered users were invited using a paper-based questionnaire containing a letter of intent of the undersecretary of the federal state. In total 1,166 registered users answered our questionnaire accounting for a response rate of 22.3 per cent. We then sorted the sample by means of the usage variable and extracted those who checked 5 on the Likert scale which indicates that an individual has never used the system, and those who checked the values 1 or 2 (frequent or very frequent system use), in order to verify that the system has been truly adopted. This procedure resulted in 567 responses. Due to a restrictions on behalf of the regional government the questionnaire had to allow missing values, to avoid that anybody felt inconvenient by answering a question. Deleting these missing values in the target variables resulted in another 509 responses. To obtain the final sample, selected only those individuals, who reported either strong (values 4 and 5) or weak habits (values 1 and 2) on the semantic differential scale with reference to the prior paper-based process. This leads to the final sample of 378 individuals. An overview over the different groups and subgroups is given in the following Table Thirty Fourth International Conference on Information Systems, Milan 2013