Tense Moods Make You Use Habitually Facebook: A New Framework for Habitual IT Use based on the Mechanisms of Mood Regulation

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1 Tense Moods Make You Use Habitually Facebook: A New Framework for Habitual IT Use based on the Mechanisms of Mood Regulation Saerom Lee College of Business Administration Seoul National University, Seoul, Korea rosee318@gmail.com Research-in-Progress Jungjoo Jahng Graduate School of Business Seoul National University, Seoul, Korea jahngj@snu.ac.kr ABSTRACT The Information Systems (IS) field has begun to focus on automatic behavior, which is not based on reason-based theory. In this study, we investigate habitual information technology (IT) use as an automatic behavior. We focus on the mood(s) that factor(s) into habitual behavior. People automatically regulate their mood positively when they recognize tense moods. We categorize moods into two groups: tense-energy and calm-tiredness. We started our investigation by identifying habitual behavior whenever people in tense moods automatically took action. This study provides a unified view of the process model associated with habitual behavior and mood regulation. According to the model, propositions are suggested. Keywords Habitual Behavior, Automatic Usage, Mood, Mood Regulation INTRODUCTION Late at night, a man/woman tired of his/her long daily schedule lies on his/her bed, thinking about his/her manager, who is very tough on him. He/she feels lonely and calm tonight. Yawning, he/she logs in to Facebook and looks at his Korean friends newest posts. Many researchers report that most social network service (SNS) users use their social networks at particular times of day, such as before leaving the office or late at night (Xu and Tan, 2012). Although we believe that we usually follow rational thinking and decision-making processes, the fact is that more than 40% of our behavior is automatic (Duhigg, 2012). Researchers have recently begun to focus on automatic use in order to explain error terms of actual behavior. Because no rational decision-making process can fully explain or predict behavior, they needed to discover a previously unexplained construct to predict human behavior (Lee and Jahng, 2012). Habits are some of the automatic behaviors learned by repeated action following a certain cue, such as goals or moods (Limayem and Hirt, 2003; Triandis, 1977, 1980; Verplanken and Aarts, 1999). Habits have been studied in various fields, such as neuroscience, psychology, education, etc. In the neuroscience field, researchers try to find out the brain region that drives automatic behavior, as well as any changes that may take place in the brain when habits are formed in certain situations (Graybiel, 2008). In psychology, researchers investigate psychological factors affecting habitual behavior, such as rewards, stress, stable contexts, etc. In the IS field, a habit is regarded as a past experience that could enhance the explanation of actual behavior. However, a habit is more than a past experience that works differently in the brain. In addition, a habit is formed in a stable context with certain cues, thereby driving habitual behavior. A habit is not easily broken once formed. In this regard, the field of consumer behavior has also paid attention to consumption habits. In the IS field, researchers are also interested in habits; they intend to use habits to explain inertia or the path dependency of organization toward certain IS. For a rich understanding of habitual behavior, researchers start to investigate an appropriate environment for forming a habit (Ouellette and Wood, 1998; Ronis, Yates, and Kirscht, 1989; Wood, Quinn, and Kashy, 2002). Generally speaking, a habit is usually formed in a stable context, such as certain times, places, etc (Polities and Karahana, Forthcoming). In addition, if the habit is formed with a cue, people automatically perform learned behavior whenever they encounter that cue. In this regard, it is necessary to identify cues in order to understand the mechanism(s) of habitual behavior. Therefore, this study determines Proceedings of the Nineteenth Americas Conference on Information Systems, Chicago, Illinois, August 15-17,

2 the factors that act as cues in certain situations. We focus particularly on moods, which are mental states. Today, many researchers place importance on moods and mood regulation (Larsen, 2000). Mood regulation makes people change their bad moods to good moods in order to avoid mental illness (Bradley, 1990). In the IS field, studies also provide diverse perspectives on subjective factors, such as effects or emotions. However, there are limited approaches to using mood as a driver of habitual behavior. In next section, we first explore the framework for IT usage from initial adoption to habitual IT usage. Second, we review the definition of habit as given by many researchers with previous experience in the IS field. Third, we summarize the definition and characteristics of mood and mood regulation. Fourth, this research provides a research framework that explains the mechanism(s) of habitual behavior via mood regulation. Finally, propositions that explain the mechanisms of habit are suggested, along with the theoretical and practical contributions of the present study. TECHNOLOGY USAGE IN IS FIELDS In the IS field, researchers have been studying IS usage. Most of this research was conducted based on reason-based processes, such as the theory of reasoned action, the theory of planned behavior, and the technology acceptance model, that specialize in explaining behavior toward new technology (Guinea and Markus, 2009; Lee and Jahng, 2012). After a decade, most people start to use ISs not only in their workplace, but everywhere else as well. At that moment, the important issue in the IS field is not an intention to use, but rather the satisfaction felt after the initial usage. Therefore, post-adoption become crucial to IS usage research and is determined by subjective variables, such as satisfaction and effects. In addition, postadoption studies expended their research on continuous IS use. Today, researchers in the IS field try to explain the error terms of the relationship between intention to use and actual behavior by turning their attention to unconscious behavior, such as habits. Although many researcher deal with habits as the moderating effects of continuous IS use (Limayem, Hirt, and Cheung, 2007), a habit is the result of behavior learned through repetition. If people repeatedly use ISs in certain situations, they will eventually be able to use ISs without effort. In this regard, a habit is conceived as the result of a cognitive process (Rosenstein and Grant, 1997), while the behavior is conducted without conscious thought. Therefore, we regard habits as the next step of continuous use. HABIT In this study, we regard habits as automatic actions, frequently repeated following a cue, to achieve a certain goal (Hull, 1943; Limayem and Hirt 2003; Triandis, 1977, 1980; Verplanken and Aarts, 1999). Habits are situational behavior sequences that are or become automatic, occurring without conscious thought (Triandis, 1980). A habit is a type of automatic action that lacks intentionality, awareness, and controllability, although its efficiency is high. People s neural energy tends to diminish when habits are formed that work without conscious thought. Once a habit is formed, it does not require much energy to follow since it is learned and imprinted in the brain (Duhigg, 2012). To reduce energy requirements for conscious processes, habits follow certain cues that make people behave automatically (Hull, 1943; Limayem and Hirt 2003; Triandis, 1977, 1980; Verplanken and Aarts, 1999). These cues can be times, places, specific situations, moods, goals, etc (Polities and Karahana, Forthcoming). For example, we usually turn on the computer in the morning when we arrive at work, or check use our mobile phones to check our after waking up. Although there is no real purpose to turning on the computer or checking when we do, time and place can be cues for habitual behavior. Those behaviors are learned through a learning process that diminishes behavioral drivers. According to Hull s learning theory, people recognize the need when their body systems lose balance (Hull, 1943). The need is then changed into a drive. He emphasizes that people reduce these drives by taking action. The result of this action could cause satisfaction that motivates us to learn the behavior again. With repetition, we make the behavior a habit that requires minimal brainpower. After forming the habit, we follow the habit as whenever we encounter its triggering cue. Researchers try to discover psychological factors, such as rewards or stress, that help people form habits (Verplanken and Aarts, 1999). HABITS IN IS RESEARCH In the IS field, researchers are starting to pay attention to habitual IS use. Lee and Jahng, (2012) summarized habit in IS fields (see Table 1). Habits in particular play an important role in explaining unobserved errors between the intension to use and actual usage. However, most studies regard habits as empty constructs without rich understandings of the habits themselves (Verplanken and Aarts, 1999). For example, Limayem and Hirt, (2003) identified how habits influence intention to use and actual behavior. Kim, Malhotra, and Narasimhan, (2005) closed the debate between habit/automaticity perspective Proceedings of the Nineteenth Americas Conference on Information Systems, Chicago, Illinois, August 15-17,

3 and the instant activation perspective by examining empirical data. They concluded that heavy IS users rarely evaluate systems. Other researchers also focused on the position of habits in IS research. Limayem et al., (2007) resolved the ambiguity of habits that are regarded as moderating or having a directing effect on intention to use. They conducted empirical research to show that habits have a moderating effect on intention to use. Most research fails to explain the habit itself. Researchers use questionnaires that ask about perceived habits. However, these questionnaires could not fully explain the factors that affect habitual behavior. In addition, there are limitations that could foul understandings about the mechanisms of habit formation. Moreover, only some research explains the characteristics of habits. Recently, researchers started to concentrate on behaviors done repeatedly once chosen. The researchers provide a framework that explains those behaviors, such as path dependency or status quo bias. A habit is a psychological perspective as a framework for explaining behaviors gained from past experience. We can find that the trend in IS research is to try to analyze IT usage behavior. Because people usually form habits for certain technologies, it is hard for them to change when they have to adopt new one (Polites and Karahanna, 2012). Reference Definition of Habit Research Model Role of Habit Methodology Jasperson, Carter, and Zmud, (2005) Lankton, Wilson, and Mao, (2010) Past behavior Learned actions that have become automatic responses to cues and are intended to obtain one or more goals Individual Cognition Model Extension Model of Limayem et al. (2007) Independent variable of Postadoptive intentions Use History: Habit becomes the dominant predictor of individual s post-adoptive behavior Independent variable to Continued IT use Identify additional antecedents of habit Limayem and Hirt (2003) The non-deliberate, automatically inculcated response that individuals may bring to IS usage Triandis Framework (Triandis 1977) Independent variables to actual usage behavior and affect Limayem et al. (2007) Learned sequences of acts that become automatic responses to specific situations, which may be functional in obtaining certain goals or end states Bhattacherjee s IS Continuance Model (Bhattacherjee 2001) Moderating variable between IS continuance intention and IS continuance usage Polites and Karahanna (2012) Learned sequences of acts that have become automatic responses to specific cues, and are functional in obtaining certain goals or endstates Technology Acceptance Model (TAM) Independent variables of Inertia Wilson, Mao, and Lankton, (2010) Learned sequences of acts that have become automatic responses to specific cues, and are functional in obtaining certain goals Unified Theory of Acceptance and Use of Technology (UTAUT) Independent variables of Intention and continued IT use frequency and performance expectancy, effort expectancy, and social influence which is independent variables of Intention Table 1. Summary of Prior IS Research on Habit (Lee and Jahng, 2012) Proceedings of the Nineteenth Americas Conference on Information Systems, Chicago, Illinois, August 15-17,

4 MOOD Mood is defined as diffuse affective states (Larsen, 2000). Mood can be distinguished with emotion (Parkinson, Totterdell, Briner, and Reynolds, 1996). Although mood and emotion are both felt by the person and can be expressed, they are significantly distinct (Larsen, 2000). Emotions are associated with rapid responses by bodily systems. Emotions persist shorter than mood but durations are longer than mood. Parkinson et al., (1996) pointed out that we can easily know when emotions start and finish. Furthermore, emotions typically have verifiable causes. However, although moods and emotions have common features, moods have distinguishing characteristics. Thayer, (1996) regards moods as something like a clinical thermometer, reflecting the internal and external events that affect us. Moods last longer and are less intense than emotions. In addition, moods build gradually, in that it is hard to identify their exact start and finish times. Moods are caused by both psychological and biological reasons. Scientists have recently asserted that our bodies and minds are not separate; moods are a window into the state of both (Thayer, 1996). In addition, it is hard to verify the causes of moods since they can be affected by many things. People usually have difficulty recognizing their moods, since moods persist for long periods and change very slowly. Therefore, it is possible that behavior caused by a mood is also a subconscious behavior. Most research divides moods into positive and negative (Watson and Tellegen, 1985). There are many measurements to verify positive and negative moods or depression. ession. In this research, we adopt the dimension suggested by Thayer, (2000), who prefers to divide moods into energy and tension. He categorizes moods into four states: calm-energy, calm-tiredness, tenseassociated with each mood, energy, and tense-tiredness. Figure 1 describes the taxonomy of moods and the adjectives following the categorization from Thayer, (2000). Above the Figure 1, there is arrow directing tense to calm mood which explains the way of mood regulation. Next section will introduce a mood regulation as a mechanism of changing tense to calm mood. Figure 1. Dimensions of mood MOOD REGULATION Mood researchers do not limit their investigations to describing concepts and distinguishing them from emotions. Mood experiences, such as valence, intensity, duration, and frequency, could play a crucial role in human behavior, especially when conditions are tense. To regulate subjective states, regulatory behaviors may involve changing the environment or some problems (Larsen, 2000). Researchers regard these behaviors as mood regulation. For example, to make us feel good, we usually drink coffee, talk with friends, or go for walks. Thayer, (1996) investigated human behaviors that are motivated by mood regulation to change bad moods to good moods. Osmond, Mullalm, and Bisbee, (1985) discovered that people prefer physical pain to depression if the physical pain could change their depressed mental state. In this regard, pleasure or neutral moods can be the result of behavior (Thayer, 1996). It is natural that we try to reach good moods and find pleasure. According to many researchers, people continuously sense their moods through cognitive processes. In addition, they unconsciously try to improve their bodies status. In this regard, Larsen, (2000) 0) summarized the mechanism of mood regulation. Scientists called these behaviors mood regulation. Mood regulation can be linked to Hull s theory. According to Hull s theory, people learn certain behaviors by reducing the drive. If the behavior is repeated again and again, a habit is Proceedings of the Nineteenth Americas Conference on Information Systems, Chicago, Illinois, August 15-17,

5 formed as a mental image. Mood regulation is associated with behaviors that reduce tense moods. Habitual behaviors are easily performed whenever their triggering cues are sensed. Many researchers assert that moods can alter the probability of specific actions, influence attention, etc (Cacioppo and Gardner, 1999). Kavanagh and Bower, (1985) empirically examined the notion that moods act like a filter for determining and evaluating whether a piece of information is valuable. RESEARCH FRAMEWORK Research framework tries to explain the factors that affect habitual IT use, focusing especially on moods as drivers. First, as seen in Figure 2, we developed a habitual behavior process with mood regulation. In the process, the model describes the relationship between mood and mood regulation. If people recognize that they are in a tense mood, they start to change their mood. At that time, most people do not notice that they take action to change their mental status. Meanwhile, there are some who cognitively change their mood with strategies. Therefore, automatic behavior does not always work the same way, depending habit strength. In the model, we describe the habit strength as a moderating effect. In this regard, mood regulation works as a mechanism of habitual behavior. Someone who has high levels of mood regulation, which is a willingness to change their mood, can easily change their mood. In addition, the relationship between mood regulation and habitual behavior can be changed by the strength of a habit. PROPOSITIONS Mood Regulation Figure 2. Framework for habitual behavior with mood regulation The first component of the habitual behavior process is mood. Many researchers have investigated the relationship between mood and behavior. Anderson, Collins, Schmitt, and Jacobvitz, (1996) identified the relationship between stress and addiction: more morbid behavior. As with tense-tiredness moods, loneliness was also studied in terms of the Internet being used to overcome lonely moods (Morahan-Martin and Schumacher, 2000; Scherer, 1997; Young and Rogers, 1998). Mastro, Eastin, and Tamborini, (2002) tested the relationship between unpleasant moods and Internet consumption. Actions that regulate mood may or may not be conscious (Thayer, 1996). Individuals differ in their abilities to cope with their moods (Eisenberg, Fabes, Guthrie, and Reiser, 2000); some people have strategies to cope with unpleasant moods, but others naturally keep their feelings more pleasant. We started our investigation with the assumption that people unconsciously compare their current mood with desired states and automatically take corrective action. If they feel tense, they employ mood regulation to ensure appropriate behavior. Thayer, (1996) asserted that even people who do not know or understand their moods take action aimed at calming down. Some people strategically take action to change their moods, but others automatically regulate their moods. Mood regulation is influenced by individual characteristics, such as gender or experience with certain mobile applications. We deal with these individual characteristics as the control variables of mood regulation. In this regard, we can draw two propositions, given below: Proceedings of the Nineteenth Americas Conference on Information Systems, Chicago, Illinois, August 15-17,

6 Proposition 1A: A tense mood make people take action to try to become calm; people with higher tension levels regulate mood more strongly than those in less-tense moods. Proposition 1B: Individual characteristics (e.g., gender, age, etc.) exert different influences on mood regulation mechanisms. Habitual Behavior The second step of the process model is associated with habitual behavior. In this step, habit strength is a moderating variable between mood regulation and habitual behavior, such as the level of habit strength influence on a man/woman who wants to regulate his/her mood. If it is a strong habit for him/her to use Facebook whenever he/she feels lonely or depressed, he/she will prefer to log in to Facebook even if he/she can access other applications. In the IS field, we can easily find habitual IT use aimed at changing moods. It is common for a person to use various smart-phone applications whenever they are in a bad mood (Thayer, 2000). Music in particular is very widely used to help ease bad moods. Socialization is another behavior meant to change bad moods. Talking and being with others reduces tension and directly enhances activities. According to Thayer, (2000) research, most researchers assume that the main benefit of social interaction is to relieve tension. In contrast, we can easily see that a lack of social interaction prohibits users from forming habits. To reduce the occurrence of unpredictable situations, we will not consider physical surroundings that could protect habitual behavior. Proposition 2A: Mood regulation influences habitual behavior. Proposition 2B: People with high habit strength will more automatically follow their habits in an effort to change their mood than will people with weak habits. Habit Strength Just as habits are formed by following the learning theory, there are various levels of habit strength (Hull, 1943). Task characteristics, such as complexity or importance, heavily influence habit strength. Lankton, et al., (2010) also identified factors that affect habit formation. They focused on task characteristics, such as task importance and task complexity. Since task complexity refers to perceived triggers in certain tasks, people should consciously behave in order to achieve their goals. For example, if a task requires much new consideration, the new triggers in the task make people rethink their behavior. In addition, habit strength is also associated with behavioral satisfaction. Once people who adopt a technology believe that that technology can provide an expected outcome, they use the technology whenever they need to accomplish a certain goal, with less evaluation being done than during the initial adoption (Kim et al., 2005). When a behavior perfectly reduces a drive, satisfaction levels will be rise. Therefore, we can conclude that task complexity and satisfaction are important factors affecting habit strength. Other important factors that could influence habit strength are psychological factors such as reward and stress. Duhigg, (2012) emphasizes the role of rewards in forming habits. He describes various cases associated with rewards for habitual behavior. For example, a man/woman who has bad habit such as spending too much time playing online games or drinking alcohol could change his/her behavior by recognizing the problem and replacing the bad habit with a reward. We give four propositions below: Proposition 3A: If a task is easy to learn and repeat, the resulting habit strength is greater than it would be if the task was complex. Proposition 3B: If a task is not important, the resulting habit strength is greater than it would be if the task was important. Proposition 3C: If the satisfaction level is high, the habit formed is stronger than the habit formed when the satisfaction level is low. Proposition 3D: Psychological factors, such as rewards, heavily affect habit strength. CONLUTION AND FUTURE RESEARCH This research describes the mechanism of habitual behavior with mood regulation. We provide rich understanding of mood suggested by Thayer, (1996) who categorizes the mood into four dimensions. Mood regulation draws automatic behavior if habit is formed to change the state of mood. This study could provide explanation of habitual behavior, for example, most of users of SNS is activated certain time. This approach has various theoretical and practical contributions. First, from Proceedings of the Nineteenth Americas Conference on Information Systems, Chicago, Illinois, August 15-17,

7 theoretical perspective, fallowing the Hull s learning theory, this study explains the holes between habitual behavior and mood as a drive. We provide process model of habitual behavior which reduce the tense mood by using technology automatically. Our findings can provide useful information to practitioners on how to effectively lead the users to active their habits. Further improvement of this study will examine the process model empirically. Proceedings of the Nineteenth Americas Conference on Information Systems, Chicago, Illinois, August 15-17,

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