**3. Literature review**

### **3.1. Related works on technology acceptance**

Davis [4] develops technology acceptance model (**Figure 1**) to determine factors that influence the acceptance of technology. Two most important individual beliefs about using information technology are perceived usefulness (PU) and perceived ease of use (PEOU) that are able to explain individual's intention to use the technology. Davis [4] concluded that

**Figure 1.** Technology acceptance model [4].

and wordings utilized as a part of this range has dependably been utilized conversely by specialists. Numerous conflicting reports and clashing discoveries from past investigations that consider influence have brought about modest number of research endeavors here. Nonetheless, inquiry about them has demonstrated that reflexes, social judgment, discernment, and conduct [1, 2] are impacted by influence, mind-set, and feeling that constitute the

In the information systems' (IS) area, client assessment or client acknowledgment of information technology (IT) is considered as a volitional conduct [3] and has been examined basically with an intellectual introduction [4–6]. Research in this area has dependably been vigorously affected by the insight state of mind conduct models, from Theory of Reasoned Action and the Theory of Planned Behavior [7]. Even though some works on affect, affectivity, playfulness, enjoyment, and emotion have been studied, the affective aspects are less central in most of these studies, with some exceptions, such as studies on aesthetics [8], computer playfulness [9], flow [10], and users' experiences in technology acceptance [11]. Therefore, if the roles of affect indeed play a role in technology acceptance, what aspect of study should be examined and in what relationships of role of affect toward other constructs in the technology accep-

Due to conflicting findings and inconsistent terminologies used in the research that considers affect, moods, emotions, and feelings, and the role of affect has been very much ignored by researchers in general. However, recent research has found that the inclusion of affective constructs is able to explain attitude and behavior more extensively in their models. Nevertheless, research that examines role of affect from the perception of the knowledge workers on the KS tools' characteristics in terms of features and functions to induce positive or negative affective (PA and NA) states is lacking. This study extends technology acceptance model (TAM) with PA and NA on perceived ease of use (PEOU), perceived usefulness (PU), and BI to predict the behavioral intention to use KS tools by knowledge workers in

Davis [4] develops technology acceptance model (**Figure 1**) to determine factors that influence the acceptance of technology. Two most important individual beliefs about using information technology are perceived usefulness (PU) and perceived ease of use (PEOU) that are able to explain individual's intention to use the technology. Davis [4] concluded that

major parts of individuals.

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tance model.

**2. Research gaps**

MSC-status organizations.

**3. Literature review**

**3.1. Related works on technology acceptance**

perceived usefulness was the strongest predictor to one's intention to use an information technology.

In TAM, the goal is to utilize the primary determinant of use to accept or not to accept a new tool. The intention to utilize is controlled by the individual's personality toward utilizing a specific tool. Perceived usefulness (PU) and perceived ease of use (PEOU) impact a person's state of mind toward utilizing a specific tool. Perceived usefulness (PU) is characterized as how much individuals trust that utilizing a specific tool would improve his or her task execution [4]. Perceived usefulness is the key determinant that emphatically influences users' convictions and expectation to utilize the innovation. Perceived ease of use (PEOU) is characterized as how much the user utilizes a specific tool, and it is free of effort [4]. Past research has demonstrated that perceived ease of use (PEOU) impacts aim in two ways: direct and indirect impact through usefulness of the tool [4]. As indicated by Davis [4], PEOU has no critical impact on behavioral expectation to utilize in light of the fact that PU intervened its impact. PEOU does not affect straightforwardly on user's behavioral goal since it affects behavioral expectation through PU.

Venkatesh and Davis [12] extended TAM by calling it TAM2 with social influence and cognitive processes on the Perceived Usefulness and intention usage (**Figure 2**). In TAM2, subjective norm [7] is hypothesized to have a direct effect on the intention of an individual to choose to perform a certain behavior even if he/she is not favorable toward that behavior, but due to other referents think he/she should; hence, the individual complies with these referents. In mandatory system usage settings, subjective norms were found to have direct effect on intention over PU and PEOU. The model posits voluntariness as a moderating variable to distinguish between mandatory versus voluntary. Nevertheless, subjective norms can influence intention indirectly through perceived usefulness that is called internalization. Therefore, according to TAM2, the direct compliance-based effect of subjective norm on intention over PU and PEOU will occur in mandatory but not voluntary system usage settings [12]. Job relevancy, output quality, and result demonstrability are determinants for cognitive instruments on PU.

**Figure 2.** Extended technology acceptance model (TAM 2) [12].

TAM2 proposes that individuals rely on the fit between their job and the performance outcomes of using the system. This will determine their perceived usefulness of the system based on the job relevancy. It was defined as an individual's perception regarding the degree to which the target system is applicable to his or her job. Output quality is the quality of the end result produced by the system to the individual. An individual will take into account on how well the system performs those tasks. If the system does not produce any desirable output to enhance individual performance, it is deemed to believe that the user acceptance rate will drop. Therefore, TAM2 theorizes that result demonstrability defined by Moore and Benbasat [13] as "tangibility of the results of using the innovation" will directly influence perceived usefulness. TAM3 [12] is an extension of TAM where anchors and adjustments are hypothesized to influence PEOU in the model. Anchors are the degree to have general beliefs about computers and its usage, whereas adjustments are the degree of belief that is shaped based on direct experience with the target technology. The results indicate that there are strong correlations for these variables to PEOU. The antecedents for perceived ease of use include computer self-efficacy, perceptions of external control, computer anxiety, computer playfulness, perceived enjoyment, and objective usability. Unified theory of acceptance and use of technology (UTAUT) was introduced by Venkatesh in 2003. UTAUT was developed through the consolidation of various construct of eight models applied to IS usage behavior. These eight models are TAM, TRA, TPB, motivational model, integration of TAM and TPB, PC utilization model, innovation diffusion theory, and social cognitive theory. Behavioral intention and usage behavior were the two dependent variables. On the other hand, eight independent variables include performance expectancy, effort expectancy, social influence, facilitating condition, gender, age, experience, and voluntariness of use. Three main constructs are the determinants of the intention to use and behavior usage (**Figure 3**): performance expectancy, effort expectancy, and social influence. Performance expectancy was the strongest predictor among the eight factors. UTAUT theorizes that social influence holds significance only in mandatory technology use of situations.

Affective Technology Acceptance Model: Extending Technology Acceptance Model with Positive... http://dx.doi.org/10.5772/intechopen.70351 151

**Figure 3.** The unified theory of Acceptance and Use of Technology [6].

### **3.2. Affect, mood, emotion, sentiment, and feeling**

TAM2 proposes that individuals rely on the fit between their job and the performance outcomes of using the system. This will determine their perceived usefulness of the system based on the job relevancy. It was defined as an individual's perception regarding the degree to which the target system is applicable to his or her job. Output quality is the quality of the end result produced by the system to the individual. An individual will take into account on how well the system performs those tasks. If the system does not produce any desirable output to enhance individual performance, it is deemed to believe that the user acceptance rate will drop. Therefore, TAM2 theorizes that result demonstrability defined by Moore and Benbasat [13] as "tangibility of the results of using the innovation" will directly influence perceived usefulness. TAM3 [12] is an extension of TAM where anchors and adjustments are hypothesized to influence PEOU in the model. Anchors are the degree to have general beliefs about computers and its usage, whereas adjustments are the degree of belief that is shaped based on direct experience with the target technology. The results indicate that there are strong correlations for these variables to PEOU. The antecedents for perceived ease of use include computer self-efficacy, perceptions of external control, computer anxiety, computer playfulness, perceived enjoyment, and objective usability. Unified theory of acceptance and use of technology (UTAUT) was introduced by Venkatesh in 2003. UTAUT was developed through the consolidation of various construct of eight models applied to IS usage behavior. These eight models are TAM, TRA, TPB, motivational model, integration of TAM and TPB, PC utilization model, innovation diffusion theory, and social cognitive theory. Behavioral intention and usage behavior were the two dependent variables. On the other hand, eight independent variables include performance expectancy, effort expectancy, social influence, facilitating condition, gender, age, experience, and voluntariness of use. Three main constructs are the determinants of the intention to use and behavior usage (**Figure 3**): performance expectancy, effort expectancy, and social influence. Performance expectancy was the strongest predictor among the eight factors. UTAUT theorizes that social influence holds significance only in mandatory

**Figure 2.** Extended technology acceptance model (TAM 2) [12].

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technology use of situations.

Every single sociology shares an interest to attempt, to clarify, and to foresee individual's behaviors, where these behaviors are impacted by subjective procedures. Most theories derived from behavioral often ignore role of affect factors. Role of affect refers to one's feeling or how an individual feels when performing tasks [14, 15]. Affect also refers to one's emotions, moods, and feelings, and they are used interchangeably [2, 16–18].

Dispositional affect is defined as a person's affective predisposition toward perceiving the world around him or herself either positively or negatively [17, 19]. It has strong influences on individual behavior [20, 21]. Many related information systems research uses different terms to represent the role of affect such as "anxiety" when using computers, "computer playfulness," "affect" toward computers, the influence of emotions toward users' attitudes, and use of specific IT [9, 22–24]. Mood is an intra-individual change, generally nonintentional which is not associated with explicit intentions to act [25, 26]. Lazarus [17] defined mood as an affective state that comes and goes depending on particular conditions. Mood is low intensity, diffuse feeling states that usually do not have a clear antecedent [27]. Mood can be characterized as relatively unstable short term intra-individual changes [28]. Mood can be evoked by both dispositional affect and emotions. Unlike emotions, people may not realize that they are experiencing a "mood" and may also not realize that mood is influencing their behavior [27]. Emotions differ from both dispositional affect and mood. Emotions have a clear cause or object, usually are shorter in duration and more focused and intense [29]. Emotions are more likely to change beliefs than mood [30, 31]. Emotions are more likely to disrupt activity [17]. It is also said as an intense feeling; a complex and usually strong subjective response that typically accompanied by physiological and behavioral changes in body [32]. Emotions can occur during the impact period (i.e., when the new information technology (IT) had been deployed and was being used). In this period, emotions are generated based on individuals' perceptions on the features of the new technology and on their usage of the new technology resources. Individuals will assess whether the technology constitutes a threat or an opportunity and how it can adapt into their daily tasks by changing their working behaviors [33]. Some specific emotion terms such as pleasure, arousal, and enjoyment are used to relate users' attitude toward actual use of a technology [6]. Feelings are sensations perceived by the sense of touch; an affective state of consciousness that resulted from emotions, sentiments, or desires. On the other hand, cognition arises on the human beings' perception toward using technologies [16, 34]. Behavioral aspect would be from the individual's reactions toward using the information technologies [11]. Emotional Intelligence is a variable with a multifactor individual difference [35] that meets the traditional standards of intelligence. Being emotionally intelligent involves being actively able to identify, understand, process, and influence one's own emotions and those of other to guide feeling, thinking, and action. Sentiments are valence appraisals of an object that involves evaluation of whether something is liked or disliked. These evaluations were evoked by phenomena. It can come from previous experience with the object or situation or through social learning [29]. Satisfaction has been the most widely studied sentiment. Most of the work conducted has focused on satisfaction at the individual level either because of workplace events or as a predictor of workplace outcomes [19].

Zhang and Li [36] examined the affects of emotional assessments of IT on IT utilized choices. Refer to Zhang and Li [36], two protest based full of feeling assessment builds: recognition on IT's ability to incite positive affect and impression of the IT's capacity to prompt negative affect able to influence. Their investigation demonstrated that positive affect and negative affect are particular ideas that affect perceived usefulness (PU), perceived ease of use (PEOU), and attitude toward utilizing IT tools. These impacts remain constant amid individuals in using and utilizing IT tools (ATT). Positive affect impacts PU, PEOU, and ATT, yet it turns out to be less critical to PU after some time, and positive affect just impacts PEOU; however, it turns out to be more vital to PEOU over the long run. Therefore, Zhang and Li [37] presumed that affect influence a key part in individuals' connections in using IT tools.

Loiacono and Djamasbi [15] also found that positive mood played a significant role in the adoption of a new technology. Their study looked at the effects of positive mood, and to understand how individual's characteristics affect an individual's cognition and behavior on the acceptance of a Decision Support System. The objective of their research is to investigate how affect can be a vital component for technology acceptance to make rational decision making. Based on Isen et al. [38], qualities of task characteristics impact one's certain state of mind particularly on tolerating another innovation, for example, Decision Support Systems (DSS) which requires subjective capacities to deal with troublesome/complex task. Association can control one's state of mind by encouraging positive temperament inside the association, and it can enhance association's results [16]. From their findings, Loiacono and Djamasbi [15] reported that positive mood could bring improvements in new technology acceptance.
