A dual-attitude model of system use: The effect of explicit and implicit attitudes
Introduction
The example above may resonate with many readers. Those familiar with mainstream information systems (IS) use research may also notice that the student’s explanation was somewhat oxymoron – she had no behavioral intentions to use social networking sites (“I never intend to use them”), and her self-reported attitude toward these websites was not very favorable (“they are not something I really enjoy with or benefit from”), but, nevertheless, she would be using them. If attitude toward using the system was weak, and behavioral intentions were dormant, what drove system use behavior in her case? This question represents a problem with traditional rationale-based behavior models which we seek to address in this study.
It is well-established that system use is driven by explicit and mostly-rational processes of which users are largely aware and which are measured through self-reports. In this study, we suggest that the focus on purely explicit sets of antecedents of system use portrays only a partial picture of reality, and that supplementing this view with other types of predictors of system use, specifically implicit and subconscious, can augment our understanding of system use phenomena. To this end, we introduce implicit attitude and theoretically explain that it has the potential to directly drive system use by bypassing and influencing the often-studied rational decision-making mechanisms. Applying this proposed dual-attitude structure (explicit and implicit attitudes) to the abovementioned student statement, it is possible that while the student held a moderate or neutral explicit attitude toward social networking sites (SNS), she had a very positive implicit attitude toward them, and that this implicit attitude was triggered upon a mere exposure to a browser and drove automatic responses in the form of system use behavior.
Implicit attitude differs from explicit attitude in the ways it is formed, stored, retrieved, and operates. Explicit attitude toward an IS is a deliberately developed psychological evaluation of the IS, of which users are largely aware and which they may clearly describe in self-reports. Explicit attitude is constructed by means of a thoughtful process; people deliberately access relevant information in their memory, develop an evaluation (favorable or not) of an object (e.g., an IS) within the current context, become aware of their attitude, and can clearly describe it. In contrast, implicit attitude is a stable evaluation of an IS that is formed a-priori, is stored in special fast-access memory, and is activated with little or no conscious effort in response to internal or external stimuli associated with the attitude object. Its key attributes include limited awareness, subconsciousness, processing efficiency, fast accessibility, lack of intentionality, little control, intuition, slow learning and unlearning, context independence, and temporal stability [6,7,23].
While explicit and implicit attitudes differ, both can affect behavior, including presumably IS use, albeit through different mechanisms. Explicit attitude influences behavior through the formation of behavioral intentions [80,81,94], whereas implicit attitude operates through two mechanisms. First, it directly triggers behavioral responses (e.g., system use) without producing behavioral intentions. Second, it promotes the habituation of behaviors by providing users with easy to access cue-behavior associations. Implicit attitude is particularly salient in routine and high-familiarity technology use settings because of users’ tendencies to save mental resources. Hence, it is important to extend the current IS research focus on explicit processes to include implicit ones; and this study seeks to make first strides in this direction.
Given the above-described nature of implicit attitude, one of the noteworthy challenges of accounting for it is its measurement. Explicit attitude is measured by directly soliciting responses from system users by means of surveys or interviews. In contrast, implicit attitude can only be measured indirectly, because it is usually beyond user awareness. In the present study, we measure implicit attitude by means of the Implicit Association Test (IAT) [37]. Explicit predictors of system use, namely explicit attitude and IS habit, as well as the outcomes of such predictors, i.e., behavioral intentions and system use, were captured through a survey of Facebook users. The results of Partial Least Squares (PLS) analyses lend support to the idea of dual attitude structures and to the hypothesized mutual, yet different, effects of explicit and implicit attitudes on system use. Consistent with prior IS research, it was found that explicit attitude has a positive effect on behavioral intentions, which in turn drive system use. The main contribution of this study is in extending this well-established view, and showing that implicit attitude can directly impact system use above and beyond the effect of explicit attitude and behavioral intentions; and that in addition, it can facilitate the development of IS habit, which moderates (suppresses) the effect of behavioral intentions on use.
Section snippets
Theoretical background
This study argues that IS user behavior can be driven not only by explicit attitude that users have toward an IS [80,81,94] but also by implicit attitude, of which users may be generally unaware. To advance this idea, this section first defines explicit and implicit attitudes and portrays the key differences between them. It then differentiates between implicit attitude and IS habit, presents the model of dual attitudes, and explains how implicit attitude can influence system use.
Hypotheses
As per the model of dual attitudes, implicit attitude is the first, default attitude that is activated at a mere exposure to a system-related stimulus [26,88], and it has the potential to directly drive behavior [55,64,65]. This phenomenon (termed the “attitude-behavior highway”) may be explained from the perspective of the Associative Memory Network Model of Implicit Attitude toward IS (Fig. 1), which adapts ideas from the connectionist [69,70], dual-processing memory [71],
Methodology
This study involves capturing (1) explicit and (2) implicit attitudes and their outcomes, in the routine and high-familiarity use setting of Facebook. The first subsection below describes the information technology artifacts (two are needed as explained later) that were used in this study. Because implicit attitude cannot be measured by means of self-reports, the second subsection describes a technique, the IAT, for capturing implicit attitude. The third subsection outlines the specific
Preliminary assessment
First, a Multivariate Analysis of Variance (MANOVA) test was applied to all constructs of the model with the order of tasks as a fixed factor. It indicated that the order had no significant effect on construct values (Wilks’ Lambda = 0.474, p = 0.35). Second, the potential effect of social desirability bias was examined. No statistically significant correlations were observed between social desirability scores and the model’s constructs, which indicated that social desirability bias did not
Discussion
This study hypothesized and empirically demonstrated that IS use, at least in familiar and routine use contexts, may be driven not only by explicit attitude but also by implicit attitude toward the system. For this, the model of dual attitudes [88] was adapted to the IS use context and tested with respect to a hedonic and habituated system (i.e., Facebook). The results support the proposed dual attitudes view and can be interpreted in two complementary ways.
First, considering Base Model 1 as a
Conclusion
This study demonstrated that IS users hold two types of attitudes: implicit, which can directly and through habituation affect system use, and explicit, which drives system use through the formation of behavioral intentions. As such, implicit attitude can trigger system use without having individuals to cognitively develop behavioral intentions, and IS users can engage the “attitude-behavior highway” when exposed to cues associated with the IS. Consequently, the model of dual attitudes enriches
Dr. Alexander Serenko is a Professor of Management Information Systems in the Faculty of Business Administration at Lakehead University, Canada and a Visiting Professor in the Faculty of Information at the University of Toronto, Canada. Dr. Serenko holds a Ph.D. in Management Information Systems from McMaster University. His research interests pertain to scientometrics, knowledge management, and technology addiction. Alexander has published more than 70 articles in refereed journals, including
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2022, Information and ManagementCitation Excerpt :In this case, implicit attitude would be only partially overridden, and the final behavior would be a product of both explicit and implicit attitudes. At some point, when motivation and opportunity are high, explicit attitude completely overrides implicit attitude, and the resulting behavior is driven by explicit attitude only, through behavioral intentions [208]. With respect to the present study, the key contribution of a model of dual attitudes [238] and the MODE model [75; 165] is that they emphasize the existence and the role of explicit and implicit attitudes in human behavior and explain how they may drive one's digital piracy behavior.
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2021, Information and ManagementCitation Excerpt :An implicit construct is a stable concept of an object “that is formed a-priori, is stored in special fast-access memory, and is activated with little or no conscious effort in response to internal or external stimuli” related to the object ([50], pp. 657–658). Because survey-based measures are explicit in their nature, SD bias can be mitigated using implicit constructs by indirectly measuring them [50]. Prior IS research used the implicit association test (IAT) as a technique for implicit construct measurement [50,51].
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2020, Computers in Human BehaviorCitation Excerpt :It should be noted that consciously including short periods of physical activity in one's daily life (e.g., starting with 15 min and then increasing to 30 or 45 min a day) contributes to having rewarding experiences (Haible et al., 2020; Richards et al., 2015; World Health Organization, 2010). The activation of implicit associations by means of relevant cues (e.g., seeing one's bicycle or running shoes) linked to positive experiences might foster engaging in physical activity (Serenko & Turel, 2019) and experiencing offline flow (Csikszentmihalyi, 1990). The more time spent on physical activity and the more offline flow is experienced during this activity, the less the need to escape into the online world to gain positive experiences, and the less the risk of developing addictive SMU tendencies.
Dr. Alexander Serenko is a Professor of Management Information Systems in the Faculty of Business Administration at Lakehead University, Canada and a Visiting Professor in the Faculty of Information at the University of Toronto, Canada. Dr. Serenko holds a Ph.D. in Management Information Systems from McMaster University. His research interests pertain to scientometrics, knowledge management, and technology addiction. Alexander has published more than 70 articles in refereed journals, including MIS Quarterly, European Journal of Information Systems, Information & Management, Communications of the ACM, and Journal of Knowledge Management. He has also won six Best Paper awards at Canadian and international conferences. In 2015, Dr. Serenko received the Distinguished Researcher Award which is the highest honor conferred by Lakehead University for research and scholarly activity.
Dr. Turel is a Professor of Information Systems and Decision Sciences at the College of Business and Economics, California State University, Fullerton, and a Scholar in Residence at the Decision Neuroscience Program, Department of Psychology at the University of Southern California (USC). His research interests include a broad range of behavioral, bio-physiological, and managerial issues in various information systems use contexts. He has published more than 90 articles in business, research methods, psychology, psychiatry, and medicine journals. Examples of business outlets include MIS Quarterly, Journal of MIS, MIT Sloan Management Review, European Journal of Information Systems, Information Systems Journal, and Communications of the ACM. Examples of psychology outlets include Journal of Psychiatric Research, Cognitive Affective and Behavioral Neuroscience, Substance Use and Misuse, Social Neuroscience, Progress in Neuropsychopharmacology & Biological Psychiatry, Scientific Reports, and Psychiatry Research: Neuroimaging.