Behavior is the variety of mannerisms and actions that individuals, systems, teams, and other artificial entities display in interactions and conjunctions with themselves and the environment. The environment in this case comprises other systems and entities surrounding the actor and the physical or non-living environment. From the standpoint of behavior informatics, behavior comprises an operation, an actor, interaction processes, and their qualities. The effectiveness of programs and designed models in diverse disciplines is highly dependent on understanding the behaviors of different actors related to the operations of these models. This meansthat an understanding of the relevant theories of behavior change is necessary to develop and implement interventions that can improve behavior according to desires of the designers of programs and models. This is because effective interventions ought to target both individuals, on one hand, and interpersonal, environmental, and organizational factors that influence and motivate behavior, on the other, to promote sustainable change in behavior according to the objectives of programs and their designers (World Bank, n.d., p. 1).
Some of the relevant theories in studying behavior include the “theory of reasoned action”, TRA, “theory of planned behavior”, TPB, “Technology Acceptance Model”, TAM, and “Decomposed Theory of Planned Behavior”, DTPB. In explaining behavior, none of these theories or conceptual frameworks, singly, is dominant or adequate in itself, necessitating the use of a combination of them to understand behavior and their motivations and influences. Some of the theories are also more relevant and useful than others in explanations of actors’ behavior in specific fields or disciplines, depending on the central elements of their frameworks and the structure of behavior in specific fields or disciplines.
Literature Review: the Theories
1. Theory of Reasoned Action
This theory represents one part of psychology’s persuasion model, with its use in the contexts of communication to influence understanding of persuasive messages and their effects on individuals. Advanced by Martin Fishbein and Icek Ajzen from a study of attitudesin 1967, it aims to describe the connection between outlooks (attitudes) and actions in the context of human action. Its orientation involves the attempt to predict individuals’ behavior in consideration of their establishedoutlooks and behavioral objectives. Essentially, the theory advances the idea that an individual’s choice to engage in specific behavior has basis on expected outcomes from its performance at the personal level. Serving to understand voluntary behavior among individuals, TRA’s ideas relate with personal motivation at a basic level to perform an action (Jasaragic, 2014, p. 1-2).
The theory’s argument is that the intention to perform a particular behavior serves as the precedent to actual performance. Known as behavioral intention, the intention to perform a particular behavior is the outcome of personal belief that performance of the action would influence a particular outcome, which the actor considers desirable or beneficial. The concept of behavioral intention is essential in understanding behavior within this theory’s framework because intentions have a foundation in attitudes towards behaviors and norms, from a subjective viewpoint. TRA’s suggestion is that stronger intentions, based on stronger attitudes towards norms and the desirability of behavior, influence increased likelihoods and efforts to perform a particular behavior. The theory’s developers, Fishbein and Ajzen, suggested two aspects that underlie intention: “subjective norms” and “attitudes”. Subjective norms present as perceived social pressures that the individual experiences to either perform or avoid a specific behavior, based on personal perceptions, while attituderepresentspersonal opinion about the positive or negative character, and hence desirability, of a behavior (Jasaragic, 2014, p. 4-5).Attitudes and subjective norms combine to influence and determine behavioral intention, which then causes performance of behavior.
The theory also proposes that three circumstancescan influence the link between behavioral intention and behavior. The first is that the degree of intention corresponds with the level of specificity, meaning that to predict the occurrence of a particular action, the behavioral intention has to be equally particular. Secondly, a stability of intentions in the intervening period between time of measurement and behavior performance is necessary, implying that the strength and substance of intention must remain the same between the time of measurement and performance of the action. The third condition concerns the extent to which execution of the intention applies under the individual’s voluntary control, implying that the individual always holds control over whether to execute the behavior (Hagger & Chatzisarantis, 2005, p. 22).
TRA has applied in many studies as a model to examine particular types of behavior, especially those related with high levels of risk and danger, such as deviant behaviors. Typically, in these studies, researchers have focused on intention as the central factor of influence in behavior, although some studies have taken the approach of measuring behavior objectively, without drawing associations with prior intentions, as a way of testing the theory (Jasaragic, 2014, p. 4-5; Hagger & Chatzisarantis, 2005, p. 22).
2. Theory of Planned Behavior
As a concept of studying behavior, TPB adopts the format of linking behavior and belief. Proposed by Icek Ajzen as an improvement on the predictive potential of TRA, discussed above, TPB includes the concept of “perceived behavioral control”. Proposing that the combination of attitudes towards behavior, perceived behavioral control, and subjective norms influence behavioral intentions and behavior, the theory has applied broadly in studies of relations among attitudes, beliefs, behaviors, and behavioral intentions (Pavlou & Fygenson, 2006, p. 117-119). Ajzen proposed the theory from that of reasoned action in 1985 to address the limitation of the older theory concerning the significance of circumstantial limitations in restricting the actualization of behavioral intentions into actual behavior (Armitage & Conner, 2001, p. 471-472). Introducing the “perceived behavior control” element, Ajzen extended TRA’s provisions to cover non-volitional actions in predicting behavioral intentions and actual behavior. The new concept of perceived behavioral control related with the self-efficacy theory’s arguments that expectations that form from motivation, feelings of frustrations, and repetitive performance of behaviors in the past determine current and future behavioral reactions and effects of attitudes and subjective norms. In this context, self-efficacy, as another theorist, Bandura, had argued, concerns conviction at a personal level about the possibility of executing successfully the behavior necessary to produce desired outcomes (Armitage & Conner, 2001, p. 471-474; Pavlou & Fygenson, 2006, p. 119).
TPB was a response to observations that behavior was not 100% under individual volition and control, leading to establishment of three considerations as the guiding principles for behavior. These are “behavioral beliefs” (beliefs about likely effects of an action), “normative beliefs” (about the expectations of others), and “control beliefs” (about circumstances and factors facilitating or impeding performance of behavior) (Armitage & Conner, 2001, p. 471-472).Behavioral beliefs generate favorable or unfavorable attitudes concerning a behavior, while normative beliefs influence perceived social pressure and subjective norms and control beliefs influence perceived behavioral control. Working together, the three elements influence a behavioral intention, with favorable attitudes, positive subjective norms, and greater perceived control influencing a strong personal intention to execute the behavior (Armitage & Conner, 2001, p. 471-472). The theory’s proposition is that perceived behavioral control affects actual behavior both directly and indirectly, based on behavioral intention.
Because of incorporation of the element of perceived behavioral control in TPB, the theory is stronger than TRA because it can cover the non-volitional behavior of people that the older theory could not explain. The theory has been vital in improving the predictability of intention in different fields, especially considering “social norm” as a vital variable (Armitage & Conner, 2001, p. 472).
3. Technology Acceptance Model
TAM represents a considerably different theory in understanding behavior compared to the other theories discussed above because of its orientation as an information systems theory. The theory presents as a model of the process in which potential users and the community accept and utilize an innovation.It proposes that when a new technology emerges in a society, decisions among users on how and when to utilize it are dependent on several factors. These comprise perceived worth of the innovation and views about the ease of application or use. Fred Davis defined the perceived usefulness factor as the extent to which a person or potential user of the technology believes that its use would strengthen and enhance (contribute value) personal performance in a role or job (Chuttur, 2009, p. 3; Park, 2009, p. 151-153). Perceived ease of use is the extent to which a potential user of new technology is confident or believes that use of the technology would influence reduced application of effort, in effect promoting efficiency and convenience in work or a personal role. As an extension of TRA, discussed above, TAM replaced the older theory’s attitude criteria with two measures targeting the approval of technology in a community: “ease of use” and “usefulness” (Chuttur, 2009, p. 2-3). While the two theories above feature strong behavioral elements, arguing that intentions to act among individuals would influence freedom and will to act with little limitations, TAM features the argument that many constraints, including limited freedom to behave in certain ways, apply in the real world.
New technologies are typically complex and challenging at the time of introduction to a society and potential users, while uncertainty in the minds of potential users as decision-makers in adopting new technology is also an important factor. This means that the formation of attitudes and intentions aimed at learning the use of technologies and accepting or rejecting them represents a vital factor for TAM. These attitudes and intentions are especially vital in influencing potential users’ focus on learning the use of new technologies as a foundation for their decisions and efforts at utilizing them. Attitudes towards use and intentions to utilize the technologies may lack conviction or be ill informed, especially after initial efforts to learn to utilize the technology evolveinto experienced knowledge of the utility and significance of the new technology.In proposing the theory in 1985, Davis’ hypothesis was that a potential user’s attitude towards an innovation is an essential determinant factor in the potential of the user to accept and actually use the technology, on one hand, or reject it, on the other (Chuttur, 2009, p. 2). Perceived ease of use has a direct influence on the element of perceived usefulness, with the two factors influencing the potential user’s attitude towards using the innovation. The two beliefs –“perceived ease of use” and “perceived usefulness”- are subject to the influence of the design characteristics of the innovation or new system (Chuttur, 2009, p. 2).
As a theoretical framework, TAM has become popular as an explanatory and predictive model in the use of new systems (innovations). Research projects that focus on the acceptance of technology among users have utilized the TAM model widely, although other researchers have modified it in their use to suit their own preferences by adding some variables and modifying relationships that the original model featured (Chuttur, 2009, p. 2).
4. Decomposed Theory of Planned Behavior
Among all the theories discussed above, I believe that DTPB is the most effective and useful theory in studying the influences of behavior. DTPB is a variation of TPB (discussed above) that adopts the format of breaking down normative, control, and attitudinal beliefs into sets that are measurable in a better way than the forms that the earlier theories utilize. Proponents of this theory argue that an effective and comprehensive understanding of relationships between belief structures and precursors of intention requires the breakdown (decomposition) of attitudinal beliefs. The theorists’ position is that the decomposed design of TPB demonstrates greater and more effective explanatory capacity than that of the original version and the TRA theory (Shih & Fang, 2004, p. 216).
DTPB breaks down the three primary antecedents of behavioral intention of TPB into a set of striking beliefs with a foundation on TAM (discussed above) and Innovation Diffusion Theory. Antecedents of attitude towards behavior constitute the three most consistent characteristics of innovation that the innovation diffusion theory defines: complexity, relative advantage, and compatibility (Moons & De Pelsmacker, 2015, p. 6215). Complexity is the extent to which potential users perceive an innovation as difficult to understand and use, while relative advantage is the extent to which potential users perceive an innovation as an improvement of already existing or available versions. Compatibility presents as the degree of perception of an innovation as aligning with experiences, desires, and existing values of potential users. “Observability”(extent to which an innovation’s outcomes are visible) and “triability” (degree to which experimentation with an innovation is possible) represent other factors, although these two apply mostly in later phases following introduction and availability of the innovation in the market (Moons & De Pelsmacker, 2015, p. 6215).
The DTPB model also decomposes the influence of the component of “subjective norm” into two principal reference clusters – peers and the mass media. The impact of these groups may be different, in terms of the direction of influence on potential users’ attitudes. Considerations of the influences of these groups reflect the reality in the society that influential reference groups and people of importance in people’s lives have power to stimulate people’s compliance in choices to perform certain behaviors (Moons & De Pelsmacker, 2015, p. 6215). The theoretical model breaks down the component of perceived behavioral control into the elements of “personal ability” (based on the notion of “self-efficacy” – belief among potential consumers in their ability to influence events affecting their lives) and “external source facilitators or constraints” (resources such as money, time, etc., and technological probabilities) (Moons & De Pelsmacker, 2015, p. 6216).
Armitage, C, & Conner, M 2001. “Efficacy of the Theory of Planned Behavior: a Meta-analytic Review.” British Journal of Social Psychology 40: 471-499. Retrieved from: https://www.researchgate.net/publication/227533335_Efficacy_of_the_Theory_of_Planned_Behavior_A_Meta-Analytic_Review
Chuttur, M 2009. “Overview of the Technology Acceptance Model: Origins, Developments, and Future Directions.” SproutsWorking Papers on Information Systems 9(37). Retrieved from: http://www.globelegislators.org/pdfjs/test/pdfs/TAMReview.pdf
Hagger, M, & Chatzisarantis, N 2005. The Social Psychology of Exercise and Sport. McGraw-Hill Education, London
Jasaragic, J 2014. “Theoretical Background for Knowledge-sharing Behavior: Review of Theory of Reasoned Action and Theory of Planned Action.” International Journal of Management Science and Business Administration. Retrieved from: http://researchleap.com/wp-content/uploads/2014/10/Theoretical-background-for-knowledge-sharing-behavior-Review-of-Theory-of-Reasoned-Action-and-Theory-of-Planned-behavoir.pdf
Moons, I, & De Pelsmacker, P 2015. “An extended Decomposed Theory of planned Behavior to predict the usage Intention of the Electric Car: a Multi-group Comparison.” Sustainability 7:6212-6245.
Park, S 2009. “An Analysis of the Technology Acceptance Model in understanding University students’ Behavioral Intention to use E-Learning.” Educational Technology and Society 12(3): 150-162. Retrieved from: http://www.ifets.info/journals/12_3/14.pdf
Pavlou, P, & Fygenson, M 2006. “Understanding and predicting Electronic Commerce Adoption: an Extension of the Theory of Planned Behavior.” MIS Quarterly 30(1): 115-143. Retrieved from: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2380168
Shih, Y, & Fang, K 2004. “The Use of a Decomposed theory of planned Behavior to study Internet Banking in Taiwan.” Internet Research 14(3): 213-223. Retrieved from: https://ir.nctu.edu.tw/bitstream/11536/27251/1/000222845900003.pdf
The World Bank n.d. “Theories of Behavior Change.” World Bank CommGap Article. Retrieved from: http://siteresources.worldbank.org/EXTGOVACC/Resources/BehaviorChangeweb.pdf