An Overview Of Technology Acceptance Models Business Essay Example

and trust levels linked to mobile payment methods and identifies the main factors that impact consumer trust in these methods.

The research worker has formulated four aims in order to achieve the purpose. These aims are: 1) To critically review literature on the acceptance and credence of mobile payment in Nigeria. 2) To identify the main concepts influencing consumers' acceptance and use of mobile payment. This will involve conducting structured interviews with potential mobile payment consumers. 3) To empirically validate the research model regarding the acceptance and usage of mobile payment services in Nigeria.

The aim of this study is to develop recommendations for promoting the acceptance and credibility of mobile payment services in Nigeria. This will be achieved by drawing on existing literature and conducting fieldwork on the acceptance of mobile payment. The recommendations will be gathered from the perspectives of stakeholders such as banks, mobile operators, and consumers. Additionally, the research model used in this study will be constructed to answer the research questions raised earlier, which are based on theories about technology acceptance.

The aim of this survey is to assess the intention to use mobile payment instead of the actual utilization of mobile payment. Previous research has discovered a strong correlation between real behavior and intention (Davis, 1985; Ajzen and Fishbein, 1980). According to the UTAUT model, there are four factors that affect the adoption of mobile payment: performance expectance (PE), effort expectance (EE), social influence (SI), and facilitating conditions (FC). Therefore, Venkatesh et al. propose that these factors have a significant impact on determining the usage of mobile payment.

According to al. (2003), the hypotheses proposed were:

Conceptual foundations:

Mobile payment services are viewed as a

unique type of electronic payment, with different conceptualizations emphasizing the mobile device as the defining feature that distinguishes it from other payment methods. Some authors specifically highlight cell phones (e.g.

, Henkel 2002), while others include all mobile communicating devices (e.g., Zmijewska and Lawrence 2006). Looking at the map of mobile payments, all definitions refer to the transfer of monetary value. Variations can be observed in regards to the stages of the payment process that are considered to be part of mobile payment.

In the current survey, the research worker aims to examine mobile payment services and assess all transactions made with a mobile device for goods, services, and activities. However, the focus of this study is on consumers as users of mobile payment services, as the drivers for acceptance may vary in a B2B context. Another key term in this research is consumer acceptance, which refers to an individual's relatively stable cognitive and emotional perception towards something.

LITERATURE REVIEW OF TECHNOLOGY ACCEPTANCE MODELS

Introduction:

In this chapter, the research worker critically evaluates existing literature regarding the acceptance of new technology, specifically focusing on important factors that influence consumers' behavioral intentions towards accepting and using mobile payment. The chapter begins with an overview of various technology acceptance models to identify the different factors that impact the acceptance of new technology.

An overview of Technology Acceptance Models:

Information systems provide engineering to enhance organizational and individual performance (Cameron and Webster, 2005). However, new technologies cannot be effective unless they are accepted and used. The topic of users' acceptance of new technology has been extensively researched over the past three decades. This research has resulted in various technology acceptance theories and models that

predict and explain the influence of individual behavioral intentions on the acceptance and usage of new technology (Taylor and Todd, 1995a; Venkatesh and Davis, 2000; Chau and Hu, 2001; Venkatesh et al., 2003; Tetard and Collan, 2009; Lin and Chang, 2011). This study will consider widely-used technology acceptance models that have been iteratively improved and built upon each other. These models and theories include the theory of reasoned action (TRA), technology acceptance model (TAM), motivational model (MM), theory of planned behavior (TPB), combined technology acceptance model and theory of planned behavior (C-TAM-TPB), model of PC use (MPCU), innovation diffusion theory (IDT), social cognitive theory (SCT), integrated theory of acceptance and use of technology (UTAUT), technology task fit (TTF), and lazy user theory (LUT).

The Unified Theory of Acceptance and Use of Technology (UTAUT) Model, developed by Venkatesh et al. (2003), combines eight different theories into one integrated model of acceptance theory. This model specifically focuses on individuals' intentions and behaviors in relation to accepting and using new technology. The model consists of four constructs/variables (performance expectancy (PE), effort expectancy (EE), social influence (SI), and facilitating conditions (FC)) that influence individuals' behavioral intentions towards acceptance and usage of new technology, as well as four moderators (gender, age, experience, and voluntariness of usage). It can be argued that these models are widely used in the field of technology acceptance. However, it is clear that almost all of these models are based on the TRA model developed by Ajzen and Fishbein in 1980.

The TAM was used as the basis for the UTAUT model, along with other theories such as TRA, TPB, C-TAM-TPB, and IDT. However, Venkatesh et al. (2003)

developed the UTAUT model by incorporating various technology acceptance theories and their extensions. The UTAUT relies on eight theories that are derived from the TRA.

Reasoning behind the UTAUT Model:

The UTAUT model is chosen for this study due to its advantages. TAM predicts 30% of technology acceptance success, while TAM2 predicts 40%. UTAUT combines the 32 variables from existing eight models (TRA, TPB, TAM, MM, C-TPB-TAM, MPCU, IDT, and SCT) into four main outcome factors and four moderating factors.

The combination of independent variables and moderators has improved the predictive accuracy to 70%, a significant improvement compared to previous TAM model rates. After reviewing various acceptance theories in the field of technology, it is determined that the UTAUT model is the most suitable conceptual framework for this research. The UTAUT model integrates eight acceptance theories and their extensions, taking into account the most important factors that influence technology acceptance. Specifically, the UTAUT theory considers the factors that impact acceptance of new technology, with a focus on whether this acceptance is voluntary or mandatory (Yeow et al., 2008). To encourage users to accept and utilize new technology, various approaches can be implemented.

The UTAUT classifies these methods as optional and mandatory. It allows for the evaluation of whether users' trust and usage of mobile payment is voluntary or compulsory. The UTAUT considers concepts that vary between developing and developed countries. The acceptance of new technology is typically influenced by different factors that may vary across communities. In this study, the concepts impacting the adoption of mobile payment in developed countries might not necessarily be the same as those influencing its usage in developing countries. When comparing to other

technology acceptance models, the UTAUT accounts for 70% of the variance affecting new technology acceptance in diverse societies (Bandyopadhyay and Francastoro, 2007). The UTAUT has been applied across various disciplines (Hennington and Janz, 2007).

Therefore, the UTAUT theory is not solely focused on a specific sector and can be applied to the telecom and banking sectors to identify the main factors influencing consumers' behavioral intentions towards the acceptance and use of mobile payment. This model is widely recognized as a comprehensive and powerful model for evaluating technology acceptance.

Hennington and Janz (2007) emphasized that the UTAUT model is currently one of the most inclusive and powerful models for technology acceptance. Bandyopadhyay and Francastoro (2007) argued that the UTAUT model has been utilized in more participative organizational cultures, where individuals have the autonomy to make their own decisions regarding technology acceptance. This theory was originally developed in the context of a developed country (USA).

Furthermore, Lin and Anol (2008) and Yeow et al.

According to (2008), UTAUT is a comprehensive theoretical model that can be used to investigate the acceptance of new technology in various fields. Numerous studies have implemented the UTAUT model to explore the acceptance and usage decisions of technology in developing countries. For instance, Bandyopadhyay and Francastoro (2007) employed the UTAUT model to examine the cultural factors impacting user acceptance of information technology in India. The results revealed that performance expectancy (PE), effort expectancy (EE), and social influence (SI) played significant roles in influencing the prepayment metering system.

Lin and Anol ( 2008 ) conducted a study in Taiwan, applying the UTAUT theoretical model to analyze the phenomenon of learning online social support. The results indicated that all

the concepts outlined in the model were significant, except for the facilitating condition concept which was found to be insignificant. Similarly, Loke ( 2008 ) investigated the perceptions and experiences of merchants towards recognition card payments in Malaysia. The findings revealed that the most influential concepts in a merchant's decision-making process were performance expectancy and social influence, as outlined in the UTAUT model. Furthermore, Abdul-Rahman et al. ( 2011 ) examined the influencing factors for generic information systems using tablet personal computers and mobile communication in Malaysia, employing a modified version of the UTAUT model.

The study found that the anticipation of public presentations, anticipation of efforts, and information quality were important factors that influenced users' behavioral intentions towards the acceptance and use of technology. However, service quality was not found to have a significant relationship with users' behavioral intentions. In conclusion, the UTAUT model is a useful framework for understanding the acceptance and use of technology in various fields in developing countries. It is anticipated that this model may need to be adjusted to fit the specific needs of this study and the characteristics of the research participants. This adjustment may involve adding or removing certain factors to ensure its applicability in a developing country like Nigeria.

Unified Theory of Acceptance and Use of Technology (UTAUT):

The UTAUT framework combines eight different theories (TRA, TAM, MM, TPB, C-TAM-TPB, MPCU, IDT and SCT) into an integrated model focusing on users' behavioral intentions to accept and use new technology, as previously indicated. Venkatesh et al. (2003) explained that after reviewing and consolidating the eight different models, seven elements were identified that influence behavioral intentions and usage. These elements

include:

• Performance anticipation
• Effort anticipation
• Social influence
• Facilitating conditions
• Attitude toward using technology
• Anxiety
• Self-efficacy

Additionally, four moderators (age, gender, experience, and voluntariness of usage) were identified as impacting the main effects (Venkatesh et al., 2003; Hennington and Janz, 2007).
Ventakesh et al.

, (2003) conducted a study on the theoretical model and found that certain factors such as performance expectation, effort expectation, and social influence had a significant impact on usage behavior through behavioral intention. Facilitating conditions directly influenced usage behavior. However, the remaining three elements (attitude towards using technology, self-efficacy, and anxiety) did not have a significant effect on behavioral intention or usage behavior (Venkatesh et al., 2003; Mazman and Usluel, 2009).

Venkatesh et al., (2003) found that the attitude towards using technology was only important in relation to specific knowledge related to performance and effort expectations. Therefore, the attitude towards using technology has an impact on intention and usage behavior through performance and effort expectations. On the other hand, self-efficacy and anxiety do not have direct determinants as they are conceptualized from effort expectation as a perceived ease of use. Consequently, Venkatesh et al., (2003) introduced the Unified Theory of Acceptance and Use of Technology (UTAUT) (see Figure 1).

Modification of the UTAUT model:

The UTAUT model was developed in the USA, a developed country, as one of the most comprehensive, inclusive, and powerful technology acceptance models that can be applied in various scientific fields (Hennington and Janz, 2007).

To implement this theoretical account in an underdeveloped country like Nigeria, some changes are likely. This section examines the UTAUT factors and explores the possibility of adding or removing factors to create an integrated conceptual model that identifies the major factors affecting mobile payment

acceptance in Nigeria.

The UTAUT Moderators:

After reviewing the four moderators, the experience moderator was excluded from the model. This moderator was used in other contexts as a placeholder for users' technology experience, but it does not apply to Nigeria. Nigeria has seen an increase in computing skills and internet usage due to improved IT infrastructure and wider availability of computers and technology. If the experience moderator were included, it would not accurately reflect the users' experience since their experience is influenced by the country's educational level and IT infrastructure.

Furthermore, if the experience moderator were utilized, there would be two distinct groups: those before the educational system was changed and the IT infrastructure was improved, and those after those changes were implemented. Therefore, incorporating the degree of instruction as a moderator would better showcase the recent changes in education and IT infrastructure in the model. Nambisan and Wang (2000) and Nafziger (2006) also noted that education level greatly influences the acceptance and utilization of new technologies, with highly educated individuals being more likely to embrace them. Additionally, gender, age, and educational level are moderators specific to individual users, so they can be combined under a general moderator called the single moderator. The proposed modification to the original UTAUT model suggests replacing the independent variable "Effort Expectancy" with "Relevance". As stated by Thong et al., this change would align with the updated theoretical framework.

, (2004), Relevance is "the grade to which something is closely connected with the topic of concern or the state of affairs one is believing approximately." In this instance, it refers to 'the grade to which one believes that the introduced engineering services are necessary

in the public presentation of nomadic payment services.' Studies which support the importance of this concept in other contexts include Saracevic (2004); Nicholson (2004); Kwak et al., (2002) among others.

Social Influence:

By measuring the societal influence concept, it seems that it is a portion of civilization influence. Therefore, to analyze this concept more profoundly, the societal influence concept will be replaced by the civilization concept in order to make a comprehensive conceptual model of nomadic payment credence in Nigeria.

Several studies have shown that civilization plays a significant role in the acceptance and usage of advanced technologies. For example, Slowikowski and Jaratt (1997), Png et al. (2001), Twati and Gammack (2006), and Levy (2007) all demonstrated this impact. Venkatesh et al. (2003) found that age, gender, experience, and voluntariness of usage act as moderating factors in the relationship between societal influence (now referred to as civilization) and behavioral purpose.

Research indicates that older individuals and females are generally more aware of the feelings of others. Additionally, Chanasuc and Praneetopolgrang (2008) explained that the level of education moderates the relationship between culture and behavior, particularly among those with lower levels of education. Furthermore, in voluntary situations, cultural factors become less influential (Venkatesh et al., 2003).

Facilitating Conditions:

The facilitating conditions in the UTAUT theoretical model are the only concept that directly contributes to usage behavior, rather than behavioral intentions. According to Venkatesh et al., (2003), facilitating conditions do not have a direct influence on behavioral intentions. Additionally, when both performance expectation and effort expectation concepts are present, facilitating conditions become insignificant in predicting behavioral intentions. Liu et al., (2005) and Friertag and Berg (2008) argued that

facilitating conditions still influence behavioral intentions towards the acceptance and usage of new technology, even in the presence of performance expectation and effort expectation.

Ventakesh (1999) found that users rely on easing conditions and external control to gauge the perceived ease of use of information technology. Both support as a facilitating condition and external control were found to strongly influence the perceived ease of use. Building on this research, Ngai et al. (2007) extended the technology acceptance model (TAM) to include technical support as an independent variable in explaining WebCT. In doing so, the researchers replaced the term "easing condition" with "technical support".