(I Can t Get No) Satisfaction: Investigating the Role of Goal Value and Mood in Habitual Technology Use

Transcription

1 (I Can t Get No) Satisfaction: Investigating the Role of Goal Value and Mood in Habitual Technology Use Completed Research Paper Jin Gerlach Technische Universität Darmstadt Chair of Information Systems Software Business & Information Management Hochschulstr. 1, Darmstadt gerlach@is.tu-darmstadt.de Sören Schmidt Technische Universität Darmstadt Chair of Information Systems Software Business & Information Management Hochschulstr. 1, Darmstadt schmidt.soeren@gmail.com Peter Buxmann Technische Universität Darmstadt Chair of Information Systems Software Business & Information Management Hochschulstr. 1, Darmstadt buxmann@is.tu-darmstadt.de Abstract Many of our daily activities are guided by habits and so are users interactions with technology. IS research has made significant strides acknowledging this perspective, emphasizing the importance of habits for continued system use. We seek to contribute to this research, taking a closer look at the effects of habits on the quality of IS use. As experience shows, habits may not always be a good thing. Therefore, we study the conditions under which positive effects of habitual behavior on users satisfaction can be realized. In a study among 303 smartphone users, we investigate the role of goal value and mood for individuals satisfaction. We find that these devices are prone to dissatisfying usage habits which are absent of a particular goal value, especially when individuals are feeling tense. As these habits may be considered questionable, our study has several practical implications, too. Keywords: Habit, Goal Value, Mood, Satisfaction Thirty Fifth International Conference on Information Systems, Auckland

2 Human Behavior and IS Introduction In all aspects of our lives, a significant share of our daily behaviors is guided by habits. As we repeatedly learn that a certain act serves as an effective means to achieve a goal of ours, these behavior-goal connections become hardwired, they become habits (e.g., Verplanken et al. 1998; Wood and Neal 2007). Of course, habits also affect our interactions with technologies as we do not need to think about which search engine to use and how to use it to obtain information on a topic of interest, for example. Research on users habitual interactions with technologies has significantly increased our understanding of individuals behaviors related to IS over the recent years. As our discipline increasingly acknowledges the central role which habits play in everyday human behavior, many important findings have been made. For instance, habits have been shown to reduce the role which behavioral intentions play in predicting actual usage behavior (Limayem et al. 2007), serve as an important predictor of usage behavior themselves (e.g., Kim 2009; Venkatesh et al. 2012; Wu and Kuo 2008), increase short-term task performance (Ortiz de Guinea and Webster 2013), or may cause a rejection of substituting systems (Polites and Karahanna 2012), just to name a few aspects. Thereby, most studies on habits in IS use view habitual behavior as a mode of operation which should be achieved quickly in order to make individuals use IS efficiently (e.g., Limayem et al. 2007; Ortiz de Guinea and Webster 2013; Polites and Karahanna 2013). This makes a lot of sense as habitual behavior is free of cognitive thinking and thus saves mental effort. As Polites and Karahanna (2013, p. 223) summarize: The primary objective of these prior studies is to demonstrate the importance of developing new system habits for continued system usage. Although this efficiency perspective on habits is by all means an exciting and promising approach to study individuals interactions with IS, everyday life tells us that habits may not solely be a good thing. As the absence of conscious thinking in habitual behavior facilitates efficiency, a different perspective would suggest that this could also prevent appropriate reflections on habits with questionable benefits. A considerable study conducted by Polites and Karahanna (2012) supports this thought. The authors find that even in the presence of a superior technology, habits can make users to stick to an incumbent system. In the present article, our aim is to better understand the conditions under which habits can lead to positive outcomes with regard to technology use and when habits might have rather detrimental effects. Therefore, we put emphasis on two aspects which are closely related to the concept of habits. First, we investigate the goal value which is associated to the behavior. We consult recent advances in psychology which analyze the consequences of habitual actions when the goal-behavior relationship is impaired. Furthermore, we study the role of an individual s tense mood in this relationship. Extant research suggests that moods significantly influence humans perceptions and evaluative processes and that individuals stress might be closely related to habitual behavior (e.g., Lee and Jahng 2013; Schwabe and Wolf 2009). In this research, we analyze how tense moods might influence individuals evaluations of their habitual behaviors. By means of an investigation conducted among smartphone users, we examine whether individuals might habitually use these devices without the presence of a valued goal. Specifically, we studied the smartphone habits of 303 individuals and examined how the level of goal value associated with their use as well as the extent to which individuals felt tense changed the effect which habitual usage had on their satisfaction. We find that today s smartphone usage behavior might often be habitual in nature and absent of a highly valued goal which in turn can affect our satisfaction. Moreover, our results suggest that habitual behaviors are perceived increasingly dissatisfying when individuals are feeling tense. Our results make several contributions to theory and practice. They confirm the positive consequences which IS habits can have due to their efficiency component but provide a new perspective on the conditions which must hold in order to realize positive and prevent negative outcomes. Thereby, the possibility that goal value might be absent in habitual behavior presents a new angle on IS habits. Furthermore, these goal-absent interactions are less satisfying or even dissatisfying for the individuals, especially when feeling tense. While this may seem logical, it is remarkable that these habits without actual goal pursuit happen in our lives after all. Consequently, our findings raise awareness that habits should not only be established, as prior research suggests, but also monitored and reconsidered regarding their goal value. If habits with little goal value are in place, individuals or managers should engage in self- 2 Thirty Fifth International Conference on Information Systems, Auckland 2014

3 Goal Value and Satisfaction with Habitual Behavior regulating behaviors (e.g., Polites and Karahanna 2013; Quinn et al. 2010). In this regard, our findings can provide guidance, as individuals lower satisfaction with their behaviors may facilitate motivation for change. The remainder of this article is structured as follows. In the next section, we present the theory relevant to our study. Thereby, we provide background on the nature of habits and emphasize the importance of understanding the relationship between habits and goals. Afterwards, we present a brief overview of the research on IS habits to date. We will proceed explaining our arguments which substantiate our study hypotheses and then introduce our empirical study and the methodology we used. After testing our hypotheses and presenting the results, we will discuss the implications of this study both for research and practice as well as limitations and future research directions. Theoretical Background The Nature of Habits and the Habit-Goal Interface A significant share of our daily activities is guided by habits (e.g., Ortiz de Guinea and Markus 2009; Wood and Neal 2007). When a certain behavior leads to the accomplishment of an associated goal repeatedly, this relationship becomes hardwired in an individual s mind. In future contexts in which accomplishment of this goal is necessary, the individual will tend to perform the habituated behavior without consciously thinking about it (e.g., Limayem et al. 2007; Polites and Karahanna 2012; Polites and Karahanna 2013). Although being defined with slight differences in the IS literature, there is agreement on the habit concept that habits are automatic responses to specific cues which are functional in reaching a certain goal (Limayem et al. 2007; Polites and Karahanna 2013). Thus, in order for a habit to be learned, a specific goal with a valued outcome must exist so that individuals perform a goal-directed effort repeatedly until habituated. In order to emphasize the importance for goals in habit formation, Table 1 illustrates this constellation. Table 1. Examples of Habit-Goal Connections Habitual Behavior Reading the newspaper in the morning Using Google as a search engine Eating candy while watching TV after work Using the smartphone app of an online social network Learned Goals (Exemplary) Obtaining information about the happenings in the world Retrieving relevant and accurate information on a specific topic Experiencing happiness due to the delicious taste Being entertained by the updates posted by other people Examples (1) and (2) provide illustration for the advantages of habit formation and the reason why habits develop. They lead to efficient behavior that saves an individual cognitive thinking. The user who is confronted with a search task has no need to think about which search engine to use and how to use it to reach his or her goal. A closer look at example (3) however raises a question about the stability of goal values and thus illustrates the need to revisit the habit-goal connection. Example (3) shows that the goal value associated to a behavior must not remain stable as the habit continues. As the individual habitually eating candy might obtain health issues later on, a doctor s visit might implant a dominant belief that eating candy is unhealthy which may alter the goal value related to this behavior: continuing eating snacks is a bad thing as it leads to serious health issues. However, as habits have become hardwired and are difficult to unlearn, the individual will either unreflectingly continue the habitual behavior, eating candy and being dissatisfied with the behavior, or will need to engage in self-regulating behaviors. Considering example (4), a similar effect might occur. When an individual has developed the habit of using the smartphone in order to check his or her social network, this behavior might have been entertaining in the first place. However, after some time has passed, updates within the social network might become less Thirty Fifth International Conference on Information Systems, Auckland

4 Human Behavior and IS entertaining due to several reasons (e.g., less updates of people one really cares about). Still, the individual who has learned the habit might continue without reflecting on the value of the behavior. More recent advances in psychology research emphasize the need to further explore the habit-goal association (Wood and Neal 2007; Wood and Neal 2009). Earlier experimental work with rats has shown that habitual behavior (e.g., pressing a button to open a door for a food reward) continues even after the goal has become devaluated through manipulation (e.g., poisoning the food reward) in contrast to animals who were not trained the habit (Adams 1982). Further, a recent consumer study has shown that people who habitually eat popcorn at the movies do so even if the popcorn served is manipulated (old and artificially added a bad taste) as opposed to those visitors who do not eat popcorn regularly (Wood and Neal 2009). These findings show that even if the goal associated with the habitual behavior is reduced in value or if the behavior is manipulated to serve as a less appropriate means to reach the goal, habitual behavior is still continued. In the present study, we take a closer look at the habit-goal interface in the context of habitual technology interactions and investigate habits where valued goals might be absent. But first, let us provide a brief overview of the existing literature on habitual IS use. Research on Habitual IS Use Within the IS literature, habit has primarily been integrated with theories of system use and acceptance in order to explain continued usage of IS (e.g., Kim 2009; Limayem et al. 2007; Polites and Karahanna 2013; Venkatesh et al. 2012; Wu and Kuo 2008). Significant strides have been made due to the acknowledgement of this perspective. As the results of a study by Limayem et al. (2007) suggest, the causal link between an individual s intention to continue usage of an IS on actual continuance behavior is moderated by habit. Thus, the predictive role of intentions to use a system is significantly reduced in the presence of usage habits an observation that was also made by Verplanken et al. (1998) in the context of travel mode choice. The implications for IS research are significant, emphasizing the need to analyze actual usage behavior instead of stopping at the behavioral intentions (Limayem et al. 2007). Extending the original UTAUT from 2003 (Venkatesh et al. 2003), Venkatesh et al. (2012) show that besides traditional predictors of IS use, habit is also an important antecedent to consumers acceptance and use of IS. This is supported by further studies which underline the importance of regarding habitual use as a predictor of future use (Kim 2009; Wu and Kuo 2008). With regard to performance outcomes of habitual IS use besides actual usage behavior itself, the findings by Ortiz de Guinea and Webster (2013) include the result that patterns of habitual use increase short-term task performance. Sparse research has been carried out which has investigated possible detrimental effects of habitual IS use (Polites and Karahanna 2012; Turel and Serenko 2012). This research pertains to the notion that habitual behavior takes place without conscious thinking about the behavior itself. On the one hand, this facilitates efficient behavior. On the other hand, however, it makes the behavior being carried out non-reflective. People do not question the original intent in their behavior anymore (Wood and Neal 2007) and thus habitual behavior might be ineffective or even lead to unhealthy outcomes. Polites and Karahanna (2012) show that due to increasing inertia, IS users might reject switching to a new system. This is also supported by a different literature stream on voluntary system switching which identifies habit as an inhibitor of switching behavior and thus preventing users from changing Internet browsers (Bhattacherjee et al. 2012; Ye and Potter 2011) or office software (Bhattacherjee and Park 2014). Taking a completely different perspective on habit, Turel and Serenko (2012) investigate the dangers of addictions to social networking websites which can be caused by perceived enjoyment via habit as a mediating mechanism. Reviewing the IS literature on habit shows that habitual behavior has largely been regarded from an efficiency perspective pertaining to savings in cognitive thinking efforts. Hence, it has often been stated that habitual acting presents a state which should be achieved in order to make individuals use IS at all or more efficiently (e.g., Limayem et al. 2007; Ortiz de Guinea and Webster 2013; Polites and Karahanna 2013). In the present research, we want to take a closer look at habits and their positive effects on the quality of IS use and identify conditions under which these effects are actually realized. As the findings by Polites and Karahanna (2012) show, individuals usage habits might prevent appropriate reflection on their behavior and thus lead to negative results (e.g., rejection of a superior technology as in the case of Polites and Karahanna (2012)). 4 Thirty Fifth International Conference on Information Systems, Auckland 2014

5 Goal Value and Satisfaction with Habitual Behavior Study Hypotheses As mentioned above, the overall aim of this research is to take a closer look at the mechanism which links habits to outcomes of individuals technology use. As we are more interested in the quality of individuals interactions with technology than in the mere extent of their use, we take a perspective on IS habits which is slightly different from prior research and consider users satisfaction which has emerged as a central outcome variable in IS research (e.g., Bhattacherjee 2001; Melone 1990). In this research, we define individuals satisfaction with their use of IS as their overall evaluative judgment of their interactions with information technology (e.g., Melone 1990; Weiss and Cropanzano 1996). The importance of users satisfaction with (their use of) technology in IS research has been demonstrated in many different contexts. For instance, it has been shown that satisfaction is a key predictor of users continued IS use (e.g., Bhattacherjee 2001), switching to a different system (e.g., Bhattacherjee and Park 2014), or employee performance (Hsieh et al. 2012). As previous IS research on habit has mostly analyzed (continued) system use as a dependent variable, we argue that user satisfaction presents an interesting alternative for two reasons. First, as habits have been shown to suppress the effect of users continuance intentions and their continued use (Limayem et al. 2007), it would be fruitful to analyze how habits relate to satisfaction which itself presents a predictor of continued use (e.g., Bhattacherjee 2001). Second, as can be seen from other aspects of everyday life, individuals satisfaction often depends on their habits such as (un-)healthy eating or (not) working out and may thus initiate a change of behavior (e.g., Duchon and Keran 1990; Quinn et al. 2010). Given these perspectives, studying individuals satisfaction with their technology related habits should present a fruitful endeavor. The first hypothesis of this study is related to the link between habitual behavior and users satisfaction with their technology usage. As per its definition, habitual behavior is mostly free of cognitive thinking about the behavior itself and thus, it is mentally efficient. This is in line with prior IS research on habit which has emphasized that a mode of habitual use should be reached quickly to realize the benefits of a new IS (e.g., Limayem et al. 2007; Polites and Karahanna 2013). As conscious acting is demanding and depletes cognitive resources, increased habituation saves mental efforts and thus enables individuals to reach their goals more easily. As stated above, individuals satisfaction in other areas of life can depend on their habitual behavior (e.g., Duchon and Keran 1990; Quinn et al. 2010). Since habitual behavior is learned through reinforcement loops (i.e., goal-striving -> behavior -> goal achievement), the individual learns that the behavior is an appropriate means to achieve a goal (e.g., Wood and Neal 2007). Limayem et al. (2007) state that satisfaction with a performance is a key enabler of habit formation. Although they use satisfaction as an antecedent to habit in their model, they also admit the reciprocal relationship between both variables. Associated feelings of increased competence and/or ease may then contribute to an intensification of the level of satisfaction experienced as the behavior is performed frequently. (Limayem et al. 2007, p. 715) As habits conceptually should be functional in reaching a certain goal, users should be satisfied when reaching these goals in a more efficient way. In line with these considerations, we want to explicitly formulate a positive relationship between habits and satisfaction. H1: Habit is positively related to users satisfaction with their technology use. Now, we scrutinize the assumption that habitual behavior can be generally viewed as being satisfactory, putting into question under which conditions H1 actually holds true. Investigating how system usage habits can lead to inertia, Polites and Karahanna (2012) argue that individuals continue their habitual use of an incumbent system because they deem it satisfactory. As we fully agree that this effect plays a significant role in many contexts, we take a second angle which questions the assumed correlation between satisfaction and habitual behavior. As the formation of habits requires satisfaction with the behavior during the reinforcement process (Limayem et al. 2007) this does not necessarily involve satisfaction once the habit is established. Again, consider our example of candy eating in which the behavior-goal link becomes reinforced at first. The behavior presents an appropriate means to reach the goal and, thus, the individual should be satisfied with the behavior. This leads to the formation of habitual candy eating when increased happiness is desired (e.g., while watching TV after work). However, when health problems arise (e.g., toothaches, high Thirty Fifth International Conference on Information Systems, Auckland

6 Human Behavior and IS blood sugar), the individual s beliefs about the behavioral goals may switch. The individual may now believe that eating candy can lead to serious health problems like tooth decay, heart disease, etc. However, as outlined above, experimental psychology shows that habitual behavior may continue to be performed due to its non-reflective automaticity even after the habituated behavior-goal relationship is compromised (e.g., poisoning a food reward or serving unappetizing popcorn; e.g., Adams 1982; Wood and Neal 2007; Wood and Neal 2009). The results obtained by Limayem et al. (2007) actually support this suggestion: in the presence of habits, conscious intentions lose their predictive performance with regard to users behavior. This means that even when behavior is inconsistent with behavioral intentions (e.g., not eating candy due to health problems) habits could make the individual to still perform the behavior. Along these lines, we take a closer look at an important conceptual aspect of habitual behavior, namely its goal value or meaningfulness which is defined here as the value that an individual attributes to the purpose of a certain behavior judged in relation to his or her own ideals or standards (e.g., May et al. 2004). Research on motivation and behavior has long suggested that individuals attribute different weights to their personal goals (e.g., Atkinson 1957; Maslow 1943; Thomas and Velthouse 1990; van Tilburg and Igou 2013). This allows individuals further assessments of their behaviors, whether they believe that a certain behavior is of significance due to the perceived value of its associated goal (e.g., May et al. 2004; van Tilburg and Igou 2013). Low meaningfulness should result in apathy and feeling detached while meaningful behaviors are said to lead to commitment, involvement, and a concentration of energy (e.g., Thomas and Velthouse 1990). Although previous research on habitual IS use has often mentioned the central role of goals in the formation of habits, discussing the (continuous) value of goals rather than their presence or absence should provide the opportunity for a more fine grained analysis of individuals IS habits. The goal value of a behavior is distinct from the concept of usefulness. As stated above, the concept of goal value refers to the general desirability of the purpose which is associated to a behavior in question. It therefore refers to a characteristic of the behavior. In contrast, perceived usefulness represents an individuals assessment of a technology. For example, consider two individuals who both like reading books for the purpose of being educated. Thus, their personal goal value in reading books might be equally high. However, while the first individual might find a smartphone useful for reading electronic books, the second individual could find that a smartphone is not very useful for reading books due to the characteristics of the technology (e.g., small screen, dependence on battery, more easy to break, etc.). As outlined above, habits are initially learned as individuals engage in a behavior which is directed at a valued goal and thus learn that performing a specific act leads to the accomplishment of the goal (e.g., using Facebook in order to be entertained). Considering the context of IS use, many (negative) changes in goal value are imaginable. For example, the goal value of IS may be impaired as business processes change and thus, using a related software becomes less relevant, or as a social networking website loses its participants, the original purpose of staying in touch with other individuals may be compromised. Still, due to its automaticity component, habitual use of these IS may be carried out even when real goal value is absent (Wood and Neal 2007). Without being associated to a valued goal, a continued performance of a habitual act may lose its satisfactory nature. Individuals should be less satisfied or even dissatisfied as they engage in habits which are isolated from any valued goals. As is known from research on motivation and human behavior, individuals are more satisfied with their behavior, when the behavior is related to the pursuit of a desired goal (e.g., Meier and Stutzer 2008; Santosa et al. 2005; Tietjen and Myers 1998; van Tilburg and Igou 2013). Note that goal value and satisfaction are also conceptually distinct as the extent of goal value refers to the desirability of the goal and is independent of the outcome in a specific situation itself. The degree of satisfaction, in contrast, reflects the individuals overall evaluation of the behavior as a whole which should depend on its outcome in most cases. For example, using a cloud storage to exchange documents might be considered high in goal value by a user. However, if data is lost on the cloud drive or if the user experiences performance or availability problems, his or her behavior might still be evaluated as being dissatisfying. Overall, our second hypothesis puts the effect of habit on behavioral efficiency into perspective and provides a new angle on the outcomes related to habitual technology use. We expect that the satisfying effect of habitual behavior depends on the level of goal value associated with the behavior. Habitual technology use which suffers from a low goal value might be less satisfying or even dissatisfy the users while habits with higher goal value should be evaluated as rather satisfying. In sum, we investigate how 6 Thirty Fifth International Conference on Information Systems, Auckland 2014

7 Goal Value and Satisfaction with Habitual Behavior habitual behavior is related to satisfaction depending on individuals own perceptions of goal value associated with their behavior. H2: The effect of habit on user satisfaction is moderated by the level of associated goal value such that habitual behavior with high levels of goal value will be perceived more satisfying than habits with low goal value. The third hypothesis relates to the role of individuals affective states when evaluating their interactions with technologies. In this research, we are particularly interested in the interplay between individuals habitual behavior and tense moods. When individuals feel tense, their resources for cognitive processing decrease. Thus, they are more likely to fall back on behavioral patterns which are more mentally efficient such as habits (Schwabe and Wolf 2009; Vollrath 1998). We define an individual s tense mood as the degree to which an individual feels a core affective state of displeasure and activation (e.g., Russell 2003; Zhang 2013). Thereby, core affect presents a human s raw and non-reflective feelings which manifest themselves as moods and emotions (Russell 2003). Prior research has shown that individuals evaluative and perceptive processes depend on their affective state (e.g., Bagozzi et al. 1999; Forgas 1995; Pham 2007). Particularly, individuals in a negative affective state such as a tense mood are likely to evaluate situations less positively since they put more emphasis on negative information (e.g., Bagozzi et al. 1999). Several reasons for such an effect exist. For example, a negative affective state unconsciously signals a problematic situation and thus triggers a more skeptical mode of information processing (Bagozzi et al. 1999; Pham 2007). Furthermore, individuals who are experiencing negative affect are more likely to retrieve information which is consistent with their emotions (Bower 1981; Isen et al. 1978). Overall, we propose a moderating effect of an individual s tense mood on the link between habitual behavior and satisfaction. Individuals who are feeling tense should evaluate their habitual behaviors more negatively in terms of satisfaction compared to individuals who are more relaxed. H3: The effect of habit on user satisfaction is moderated by an individual s tense mood such that individuals who are feeling tense will be less satisfied with their habitual behaviors than individuals who are not. Methodology To test the study hypotheses empirically, a survey was conducted, pertaining to individuals use of smartphone technology. Mobile phones which enable users to access the Internet from almost anywhere are widely recognized as offering high utilitarian as well as hedonic value (e.g., Jung 2013; Venkatesh et al. 2012). Further, smartphones are used actively in pursuit of users individual goals and thus users are likely to develop habitual behavior through repetitive acts. In fact, in their presentation of UTAUT2, Venkatesh et al. (2012) found habitual behavior to be a significant predictor of individuals smartphone use. Moreover, we expect habitual interactions with smartphones to vary in goal value as a vast number of complementary applications is available from software download platforms (e.g., Apple itunes, Google Play). We believe that users personal value perceptions are unlikely to be stable for many of these apps as they either change in their functionality, performance, or subjective evaluation. Although choosing the smartphone scenario for our study, we believe that the mechanisms proposed here are also worthwhile to study in different contexts. For example, it is known that users tend to habitually start surfing the Internet when confronted with work tasks which they perceive as unattractive (i.e., Internet procrastination; e.g., Lavoie and Pychyl 2001). Likewise, users of social networking websites might perceive decreased goal value today compared to earlier years but still habitually continue to spend time there and be dissatisfied. Data Collection and Sample We recruited participants by sending out invitations over several mailing lists in January The mailing lists comprised mostly students but also private organizations like sports clubs or political organizations. Participants could enter a lottery with a chance on winning 1 of 5 Amazon gift coupons. The Internet questionnaire was open for two consecutive weeks. Of the total 3126 individuals who were contacted by this procedure, 387 took part in the survey. After cleaning the sample from those who had Thirty Fifth International Conference on Information Systems, Auckland

8 Human Behavior and IS either not finished the survey or had stated not to own a smartphone, 303 usable data sets remained which results in a final response rate of 10%. With regard to participants ages, 8.3% of the participants were 19 or younger, 74.9% were between 20 and 29, 12.2% between 30 and 39, 1.3% between 40 and 49, and.7% were older than 50 (2.6% had not entered their age). The mean age of all participants was 25 (SD = 5.4) and overall the sample contained 43.9% female and 56.1% male participants. In terms of the duration participants had owned a smartphone, the majority were owners of smartphones for over two years (62.7%) which suggests that habitual behavior should be observable in our sample. Regarding the participants education, 56.8% of the participants had a high-school diploma as their highest degree while 40.9% had a bachelor or equivalent degree. Relying on a sample mainly comprised of students was not deemed too critical as we expected students not to differ in their smartphone usage behavior too drastically from the rest of the population. Comparing the participants in the years age class against the rest of the sample which was either younger than 20 or older than 29 (68 participants), we found no significant differences (p <.05) regarding all study and control variables, except for the control variable Perceived Ease of Use which was slightly higher for the age group (mean = 6.24 vs. remaining participants mean = 5.92). In a second test, we compared those participants with a high-school diploma against the rest of the sample. Again, we found no significant differences for all study and control variables except for age (22.13 vs ) and addiction (1.19 vs. 1.14). As for most studies, non-response bias might have influenced our results. Following the suggestion by Armstrong and Overton (1977), we compared early respondents with late respondents to assess this possibility. Comparing the first 10% with the last 10% of all participants, we found slight differences in terms of age and education (both were controlled for in the data analysis) but no significant differences (p <.05) regarding any study or other control variable were observed between early and late respondents. Measures and Measurement Quality Established multi-item scales with at least three reflective indicators were used to assess all latent variables. Some scales were adapted marginally to fit the smartphone usage scenario. Before the data collection, the questionnaire was pretested regarding its structure, length, and understandability. User satisfaction was assessed using four items from the scale proposed by Bhattacherjee (2001) which presents a semantic differential ranging from e.g., very dissatisfied to very satisfied. Goal value of users behavior was measured using five items from the meaningfulness scale by May et al. (2004). Three items for an individual s tense mood were taken from the established Consumption Emotions Set (Richins 1997). Regarding a measure for habit, IS research offers multiple, rather heterogeneous operationalizations each emphasizing more or less different aspects of habitual behavior (e.g., Limayem et al. 2007; Polites and Karahanna 2012; Venkatesh et al. 2012; Wu and Kuo 2008). In this research, our focus was on habitual behavior which is rather non-reflective and routine-like. Therefore, we measured habit using the three-item scale by Limayem et al. (2007) to which we added two items based on the study by Wu and Kuo (2008) on the habitual use of Google, originating from Verplanken and Orbell (2003). As for control variables, we investigated several constructs from IS use and acceptance research as users satisfaction has been found to depend on the perceived usefulness and thus perceived ease of use (Bhattacherjee 2001; Davis 1989; Wixom and Todd 2005), enjoyment (Ke et al. 2012; Santosa et al. 2005), and involvement (Santosa et al. 2005). These variables were measured using three items each. Further, we included technology addiction into our model as we wanted to control for variance which is due to individuals addictive tendencies with their smartphones (e.g., Lapointe et al. 2013). Therefore, we included the behavioral addiction scale proposed by Charlton (2002) and used recently by Turel et al. (2011) which comprised nine items and was measured as an index (Young 1998). Regarding individuals evaluations of their smartphone usage behavior, we asked participants to reflect on their last three days of smartphone interactions which was mainly due to two reasons. First, as memories of moods and behavior decrease over time (unless events were highly important), we followed recommendations to reduce recall bias by opting for a limited reference period and providing supportive recall cues (Schwarz and Oyserman 2001). Second, the relationship between habit and satisfaction is likely to be of a reciprocal nature in that habitual use might affect satisfaction and, based on their satisfaction, individuals might establish habits or change them eventually (Limayem et al. 2007). It is important to note that it takes extended periods of time in which a behavior must be repeated multiple times before it becomes a habit eventually (Wood and Neal 2007; Wood and Neal 2009). Furthermore, habitual change presents a difficult procedure which requires self-regulating behaviors and awareness 8 Thirty Fifth International Conference on Information Systems, Auckland 2014

9 Goal Value and Satisfaction with Habitual Behavior (Limayem et al. 2007; Quinn et al. 2010; Verplanken and Wood 2006). Thus, asking for the last 3 days should have enabled us to assess users satisfaction as a result of their technology use. In contrast, effects of satisfaction on habitual behavior should be minimal in our data. Quality measures for all scales were assessed calculating the following standard criteria. All scales achieved Cronbach s alpha values ranging from.80 to.91 and thus were well above the recommended threshold of.70 (Nunnally and Bernstein 1978) indicating a high internal consistency of the scales. Confirmatory factor analyses (CFA) were conducted using the software package Mplus (v. 7.11) to assess convergent and discriminant validity of the scales. Composite reliabilities surpassed the recommended value of.70 exceeding.82 for each construct (MacKenzie et al. 2011). AVEs (average variances extracted) were all greater than the recommended cut-off value of.50 (e.g., MacKenzie et al. 2011) except for the habit scale which was marginally below the threshold (.47). Factor loadings and cross-loadings for all latent variables were calculated using an exploratory factor analysis (EFA) which extracted 8 distinct factors. All items loaded higher on their own factors than on another factor with the exception of the first item of the meaningfulness scale (factor loading =.43, cross-loading on usefulness =.57). However, removing this item from the data analysis, all coefficients of the subsequent regression analyses remained stable and significant. For all measures, we further assessed corrected item to total correlations and t- values of the factor loadings which were well above recommended values of.3 and 1.96 respectively (e.g., MacKenzie et al. 2011; Nunnally and Bernstein 1978). Discriminant validity for all constructs was tested by comparing correlations for each construct with the square root of AVE, as recommended by Fornell and Larcker (1981). All constructs passed this test without concern. Table 2 shows the correlations between latent variables. Examples of scale items and corresponding references as well as factor loadings are provided in the Appendix. Table 2. Correlations of Latent Variables a Variables Personal Relevance.88 2 Perceived Ease of Use.33**.83 3 Perceived Usefulness.38**.24**.80 4 Perceived Enjoyment.49**.26**.29**.85 5 Addiction.38**.14*.11.25** - b 6 Habitual Use.52**.27**.29**.36**.33**.69 7 Level of Goal Value.41**.17**.56**.19** **.73 8 Tense Mood *.06.18** Satisfaction.45**.22**.48**.49**.03.28**.46** -.12*.79 a N = 303. * p <.05; ** p <.01; Diagonal elements in bold show the square root of the average variance extracted for each construct. b As addiction is measured as an index, no reflective validity statistics apply (e.g., MacKenzie et al. 2011). In order to prevent and control for common method biases, several procedures were applied, following the recommendations by Podsakoff et al. (2012). First, we assured participants that there were no right or wrong answers and emphasized that all data would be handled anonymously. Further, we asked the participants to provide their answers as spontaneously as possible. As stated above, only established multi-item scales from prior research were used in our survey, which helped to prevent problems caused by ambiguous items (Podsakoff et al. 2012). As for statistical remedies, we used the approach proposed by Lindell and Whitney (2001) and used the participants relationship status as a marker variable which was assumed to be theoretically unrelated to all other latent variables. No correlations between the variables and the marker variable were statistically significant (p >.10) and absolute correlation strengths were all below.10. The smallest correlation between the relationship status and another latent variable was.01 (p =.86). Partialling out the marker variable, no significant differences between the partial correlations and the zero-order correlations were observed. Thirty Fifth International Conference on Information Systems, Auckland

10 Human Behavior and IS We also took several measures with respect to a potential influence of social desirability bias which refers to individuals tendencies to avoid answers which negatively reflect on themselves. As described above, participants were assured anonymity and were asked to answer spontaneously. Furthermore, we included a validated short form of the standard scale by Crowne and Marlowe (1960) to measure social desirability tendencies in our questionnaire and computed Spearman s correlations between this scale and all other variables (e.g., Turel et al. 2011). In line with Turel and Serenko (2012), social desirability was correlated slightly negative with addiction (-.17, p =.00) and slightly positive with participants stated goal value (.13, p =.02). These low correlations are unsurprising as individuals were expected to underreport possible addictive behaviors and overreport their goal values as addictive behavior or behavior without much rational goal value may be viewed negatively by a third person. In sum, this means that scores of addiction should be slightly higher and goal values might be slightly lower. The low or insignificant correlations between the social desirability scale and other variables indicated that no major distortions should be included in our data analysis. Results To test our hypotheses, we used ordinary least squares (OLS) regression analyses. We opted for OLS over PLS, as simulation studies have shown that the latter approach provides wider confidence intervals and thus provides less statistical power than OLS (Goodhue et al. 2007). This disadvantage increases when studying interaction terms which would result in 16 or more indicators for one construct. 1 Covariance based SEM was not considered an option as the focal purpose of SEM is testing the plausibility of an overall theory rather than analyzing single mechanisms in detail (e.g., Gefen et al. 2000). Notes: * p <.05; ** p <.01; *** p <.001. Control variables are presented in dashed boxes. Figure 1. Results of OLS Regression Analysis As our study hypotheses propose either a direct effect of habit on satisfaction or the moderating role of goal value and tense moods for this link, we followed Baron s and Kenny s (1986) multi-step approach 1 We measured both habit and goal value using 5-item scales which would result in 25 indicators for the interaction term. 10 Thirty Fifth International Conference on Information Systems, Auckland 2014

11 Goal Value and Satisfaction with Habitual Behavior which has been well established for testing moderation. Therefore, we first regressed our dependent variable (i.e., user satisfaction) on the control variables which pertained to individuals demographic characteristics as well as smartphone usage behavior (model 1) and afterwards included the control variables relating to their usage beliefs (model 2). With regard to users satisfaction, significant predictors were personal relevance, perceived usefulness, perceived enjoyment, as well as addiction. These control variables led to an adjusted R² of 38%. Table 3. Results of Regression Analysis on User Satisfaction a Dependent Variable User Satisfaction Model 1 Model 2 Model 3 Model 4 Model 5 Control Variables Age Gender Education Experience Frequency of Use Personal Relevance.41***.24***.24***.19*.21** Perceived Ease of Use Perceived Usefulness.32***.31***.24***.21*** Perceived Enjoyment.31***.31***.30***.30*** Addiction -.18** -.18** -.16** -.15** Independent Variable Habitual Use * -.58** Moderator Variables Goal Value Tense Mood -.67** Interaction Effects Habitual Use Goal Value.71*.61* Habitual Use Tense Mood.63* R² Adjusted R² Difference in R².21***.19***.00.03***.03** F-value for R² difference N a Values show standardized regression coefficients. * p <.05; ** p <.01; *** p <.001. In the next step, we entered our independent variable (i.e., habit) into the regression (model 3). This step however should not receive too much attention as an interpretation of the coefficients without inclusion of the moderating effect is misleading. Such an interpretation would be unconditional if the coefficient of the interaction effect is significant which shows that the effect of the predictor variable depends on the level of the moderator (Edwards 2009). Thus, in the fourth model, we added the interaction term of habit and goal value into the analysis while controlling for a direct effect of goal value which led to a significant change in prediction. Despite a positive bivariate correlation between habit and satisfaction (r =.28, p <.01; see Table 2), the direct effect of habit on satisfaction is moderately high and negative (-.42) as well Thirty Fifth International Conference on Information Systems, Auckland

12 Human Behavior and IS as significant (p =.026). While goal value had no significant effect on satisfaction itself, the interaction term was statistically significant and rather high (.71, p =.015). In the last step, we added the second moderator variable an individual s tense mood to the regression (model 5). Controlling for a direct effect of tense mood on satisfaction, we found a negative and significant relationship (r = -.67, p <.01). Similar to the interaction between habit and goal value, the interaction term of habit and tense mood was positive and significant (.63, p =.013). Figure 1 and Table 3 illustrate our results. We checked our data with respect to the possibility of multicollinearity impairing our results. Therefore, we assessed several criteria. First, an examination of the bivariate correlations among our variables showed no sign of concern (e.g., Mason and Perreault Jr 1991). Second, as also shown in Table 2 and already stated above, the discriminant validity criterion proposed by Fornell and Larcker (1981) was satisfied for all variable pairs (Grewal et al. 2004). Third, we assessed the variance inflation factors (VIFs) for all variables. After centering the variables, even the largest values of 2.39 and 2.03 were well below the threshold of 10 (e.g., Mason and Perreault Jr 1991). According to these procedures, no issues of multicollinearity seemed to have distorted our data. Figure 2 depicts the interaction effects of habit and satisfaction at different levels of goal value and tense moods. Panel A) shows that, at high levels of goal value, the negative effect of habit on satisfaction is neutralized, almost changing its sign. Panel B) illustrates that, under high tension, even low or moderate levels of habitual behavior are perceived as dissatisfying by the individuals. When feeling less tense, smartphone users evaluate low and medium levels of habitual usage less negatively in terms of satisfaction. Slopes were calculated using unstandardized regression coefficients from the complete model. Influences from control variables were excluded (Preacher 2003). Figure 2. Illustration of Interactions: Effects of Habit on Satisfaction at A) Different Levels of Goal Value and B) Different Levels of Tense Moods Comparing the baseline model which contains all control variables (model 2) to the final model, the adjusted R² increases significantly by.05. Although this effect size would be labeled small following the conventional definition by Cohen (1988), it has been noted that this should not be confused with an unimportant effect as even small effect sizes can have substantial impacts in theory and practice (e.g., Aguinis et al. 2005; Chin et al. 2003). Overall, our results confirm both H2 and H3, and partially confirm H1 as the strength as well as direction of the effect of habit on satisfaction depends on the extent of goal value and tense mood. When interpreting these results, note that satisfaction was measured by a semantic differential with anchors ranging from negative evaluations (i.e., dissatisfaction) to positive values (i.e., satisfaction). 12 Thirty Fifth International Conference on Information Systems, Auckland 2014

13 Goal Value and Satisfaction with Habitual Behavior Discussion The present research aimed at obtaining a more detailed understanding of the mechanisms by which habitual behavior is linked to users satisfaction with their technology use. We have provided theoretical and empirical evidence that effects of habit should not be regarded without considering the goal value associated with the behavior as well as individuals tense moods. In particular, habitual behavior may exist in the presence as in the absence of valued goals. However, positive effects of habitual use in terms of users satisfaction can only be achieved when habitual behavior is still based on the valued goal which existed when the habit was formed. Otherwise habitual behavior can even result in dissatisfaction. As this may seem logical and even unsurprising in retrospective, our results provide a good example that, nevertheless, many habits actually take place without necessarily being associated to valued goals. In addition, our results suggest that individuals tense moods can amplify the negative consequences of habitual behavior for individuals satisfaction with their smartphone use. We will now discuss the implications of our findings for theory and practice. Research Implications First, our research broadens the existing perspective on habitual technology use. As prior research has emphasized the positive nature of IS habits due to their efficiency component (e.g., Limayem et al. 2007; Ortiz de Guinea and Webster 2013; Polites and Karahanna 2012), we confirm these results but go beyond that and analyze the conditions which must hold in order to realize positive and prevent negative outcomes. When solely regarding the positive and statistically significant bivariate correlation between habit and satisfaction found in our data (Table 2), one could prematurely conclude that more habitual behavior always results in more satisfaction. However, our research provides theoretical and empirical evidence that, while the absence of goal value may not inhibit habitual behavior, it results in less satisfying or even dissatisfying interactions with technology. Our results should not be interpreted as a disconfirmation of prior findings on habitual IS use by any means. They do however cause awareness that habits may be a double edged sword which can and should be studied taking both perspectives into account. Absence of cognitive reflection of one s own behavior may not only result in efficient performing but also may imply unreflective repetition of behaviors of questionable goal value. Moreover, previous investigations of habits in IS research have mostly analyzed the importance habits play for continued IS use (e.g., Limayem et al. 2007; Polites and Karahanna 2013). Thus, continued use or behavioral intentions have served primarily as the dependent variables of interest. As is known by prior research, satisfaction mediates the relationship between users perceptions and usage continuance (e.g., Bhattacherjee 2001). Our research serves as a magnifying glass for this relationship, analyzing users satisfaction as an outcome variable. This has allowed us to take a more detailed perspective on the mechanism by which users beliefs and habitual behaviors are related to their satisfaction. Despite controlling for the most important predictors of users satisfaction, we have provided evidence that satisfaction is influenced by individuals perceptions of doing something important with technology when habitually using it. This implies that habit and goal value can play a significant role regarding individuals continued ways of technology use. More recently, IS research has increased its attention to emotional factors of human behavior (e.g., Zhang 2013). The present research proposes a new perspective on the role of affect in individuals evaluations of technology and their post-adoption behavior in particular. Thereby, we have presented evidence for a moderating role of individuals tense moods in the relationship between habit and satisfaction. As can be seen in our study, certain effects on users evaluations of technology might be amplified (or weakened) by affective states or other emotions. Future research should be aware of such effects and acknowledge the important role which mood in particular and affect in general play in human s interactions with technology (e.g., Zhang 2013). Overall, our research also provides an additional angle on the reasons for individuals smartphone use. Previous IS research to date primarily provides two different explanatory approaches. First, the traditional reasoned-action perspective implies that individuals use their smartphones in a purely rational means-end fashion (e.g., Jung 2013). Second, individuals might depend on their smartphones in a pathological manner (i.e., technology addiction) and use it therefore to experience relief and avoid withdrawal symptoms (e.g., Lapointe et al. 2013). In this study, we control for both of these approaches Thirty Fifth International Conference on Information Systems, Auckland