Technology Acceptance Model – The Technology Acceptance Model (TAM) describes the acceptance of information systems by individuals. TAM states that technology acceptance is predicted by the user’s behavioral intentions, which depend on the technology’s usefulness and ease of use to accomplish a task.
The adoption and use of information technology can bring immediate and long-term benefits such as productivity, financial and time efficiency, and convenience at the organizational and individual levels (Foley Curley, 1984; Sarada, Barr, & McDonnell, 1988). The potential for technology to provide benefits has long motivated HRM research to examine individuals’ willingness to adopt innovative technologies (Davis, 1989). Technology adoption research became important with the rise of personal computers in the 1980s. However, a major obstacle to the development of PC adoption research is the lack of empirical insight into users’ reactions to information system performance. Prior to the development of TAM, there were various technical and organizational approaches aimed at developing research related to human resources (e.g. (Benbasat, Dexter & Todd, 1986; Robey & Farrow, 1982; Franz & Robey, 1986)). Research has emphasized the importance of factors such as user information system design and implementation (Robey & Farrow, 1982; Franz & Robey, 1986). Another stream of research supports practitioners’ emphasis on the development of information systems, particularly in evaluating and improving system design and specifications (Gould and Lewis, 1985; Good et al., 1986). These studies made extensive use of subjective perceptions of performance, but neglected to examine the quality of these measures. Consequently, correlations of these subjective measures were insufficient to confirm their internal and external validity (De Sanctis, 1983; Ginzberg, 1981; Schwewe, 1976; Srinivasan, 1985). Therefore, there is a need to develop reliable measures to investigate the attitudinal factors that mediate the relationship between IS characteristics and system use. The theory of reasoned action (TRA), developed by Ajzen and Fishbein (Ajzen, 2011), has been used to predict behavior in different domains. However, the general nature of TRA has led to much debate about the theoretical limitations of the model’s use in the IS field (Davis, Bagozzi & Warshaw, 1989; Bagozzi, 1981). The model did not account for variables specific to technology use. Therefore, researchers had to identify the main factors of technology and information system use. To address the limitations associated with the lack of theoretical models and scales for measuring technology acceptance, Davis (Davis, 1989) developed the Technology Acceptance Model (TAM) based on the TRA. The basic rationale of the model was that, from a technology use perspective, behavioral intention is related to specific beliefs about technology use rather than a general view of behavioral intention. The purpose of TAM was to provide a basis for examining multiple aspects of technology user behavior while maintaining a motivational perspective (Davis, 1989).
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Technology Acceptance Model
The primary goal of TAM was to shed light on underlying technology adoption processes to predict behavior and provide a theoretical explanation for successful technology implementation. The practical purpose of TAM was to inform practitioners of the steps to take before implementing a system. Several steps were taken to achieve the goals of the theory (Davis, 1989; Davis, 1993). Davis began developing a technology acceptance model by discussing the relationship between IS characteristics (externalities) and the actual use of the system. This model was based on the theory of reasoned behavior, which provided a psychological perspective on human behavior that was lacking in the emergency care literature at the time (Davis, 1989; Davis, 1993).
The Technology Acceptance Model
The second step is to identify and identify variables and validate measures that are highly correlated with system use. Based on previous empirical literature on human behavior and information systems management, multi-item scales were developed, tested, and validated in several studies for ease of use and perceived usefulness. Both studies are hypothesized to be key determinants of consumer adoption due to evidence from previous studies (eg, Johnson and Payne, 1985; Payne, 1982; Robey, 1979). (Johnson and Payne, 1985; Payne, 1982). For the use of an information system as defined by the trade-off between the usefulness of the system and the difficulty of use (Davis, 1989). Perceived usefulness is defined as an individual’s perception of improving the level of use of a particular technology; The concept of this construct derives from Bandura’s concept of outcome appraisal, which refers to the expectation of a positive outcome that motivates an individual’s behavior (Bandura, 1982). Perceived usefulness refers to the usability of the system. It was developed based on evidence supporting the effects of expectations on system use. (Robbie, 1979). Ease of use is defined as the degree to which a person perceives a particular system as easy to use (Davis, 1989). This construct refers to situation-based beliefs about how one might act for a future task (Davis, 1989; Bandura, 1982). Self-efficacy plays a predictable role in technology use decisions (Hill, Smith, & Mann, 1987). Furthermore, ease of use shared similarities with the factor of complexity identified as a barrier to innovation adoption in the innovation diffusion literature. It is defined as the difficulty for people to understand and use the innovation (Mahajan, 2010). Validity and reliability were assessed by examining self-reported IS use on two proposed factors from an organizational perspective. The developed scale showed excellent psychometric properties. This model was validated by confirming the significant relationship between perceived usefulness, ease of use, intention and usage behavior (Davis, 1989).
According to TAM, technology adoption is a three-step process in which external factors (system design features) lead to cognitive responses (ease of use and perceived usefulness), which lead to affective responses (attitudes/intentions to use the technology). consumer behavior (Davies, 1989; Davies, 1993). TAM refers to behavioral outcomes predicted by perceived ease of use, perceived usefulness, and behavioral intention (Figure 1). Ease of use and perceived usefulness predict positive outcomes of behavior and believe that the behavior does not require effort (Davis, 1989). According to research, this can be replaced by behavioral attitudes (Davis, 1993), which evaluate the potential consequences of behavior (Ajzen, 2011). The greater the emotional response, the greater the likelihood of the behavior. The effect of perceived usefulness on actual use can be direct, indicating the importance of this variable in predicting behavior. Although perceived ease of use does not directly affect consumption behavior, it supports the effect of perceived usefulness (Davis, 1993). The model suggests that if a program is expected to be easy to use, the user will find it useful and will be encouraged to adopt the technology (Davis, 1989; Davis, 1993).
Model development and measures of technology adoption have made important theoretical contributions and are of great practical importance. Using the model to examine the use of IS allowed us to assess users’ willingness to adopt multiple technologies (Hwang, 2005; Geffen, Karahanna & Straub, 2003; Araujo & Casais, 2020). Due to the lack of standardized subjective measures. The development of constructs with strong and significant correlations with usage behavior enabled us to understand the cognitive and affective factors that mediate the influence of system characteristics on technology adoption (Davis, 1989).
Given the established relationship between technology adoption and firm performance in organizations, the original TAM (e.g. (Goodhue & Thompson, 1995; Davis, Bagozzi & Warshaw, 1992)) has remained central to the research agenda of technology adoption studies. )). Although the widespread use of TAM confirms the robustness of the theory (about 40% average technology adoption), the authors of the model try to increase its predictive power. The basis for expanding the model is a limited understanding of the conditions underlying users’ perceptions of technology use. 0.6 (Venkets and Davies, 2000). However, there is a lack of evidence in the literature on factors affecting the perceived usefulness of technology.
Technology Acceptance Model
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