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Abstract-This paper focuses on the importance of positioning 'culture' as one of the vital factors that influences the adoption of ICT. The analysis of various models has made it to believe that cultural factors were not given importance to the development of adoption models. But it is understood that the national culture influences the actual behavior and it can provide additional explanatory power in explaining the variation of the behavior towards adopting a technology.
Keywords - Culture; information & communiction technology (ICT); adoption model
The growth of information and communication technologies (ICT) has been phenomenal and it immensely influences our day to day life as well. Individual acceptance of information and communication technology has been a central and recurrent theme in information systems research for more than two decades . The rate at which the new technology is adopted and incorporated into the organizational process is considered to be a major factor in driving the pace of economic growth. Understanding ICT acceptance is important because the expected benefits of its usage such as gains in efficiency, effectiveness or productivity can not be realized if individual user does not accept the system.
Generally culture has been extensively studied in the anthropology and management disciplines but behavioral models utilized to study the adoption of technology do not include the national culture. The aim of the paper is to analyze the appropriateness of the existing models and to propose an ICT adoption model.
empirical studies on technology acceptance
There are a number of empirical studies undertaken to investigate the key factors determining the individual acceptance of technologies. Out of these empirical studies, a number of research models have emerged such as Theory of Reasoned Action (TRA), Technology Acceptance Model (TAM), Theory of Planned Behavior (TPB), Motivational Model (MM), Combined TAM and TAB (C-TAM-TPB), Model of PC Utilization (MPCU), Social Cognitive Theory (SCT) and Innovation Diffusion Theory (IDT).
TAM is arguably the most widely used model. This model developed by Davis  to explain computer usage behavior. This model was derived from Fishbein and Ajzen's  theory of reasoned action (TRA). TRA demonstrates that a person's performance of a specified behavior is determined by his or her behavioral intention (BI) to perform the behavior, and BI is jointly determined by the person's attitude (A) and subjective norm (SN) concerning the behavior in question. Attitude (A) is defined as an individual's positive or negative feelings about performing the target behavior and the subjective norm is referred to the person's perception that most people who are important to him/her think that he/she should or should not perform the behavior in question. According to the TRA, beliefs influence attitudes, which in turn lead to intentions, which then generate behaviors.
TAM adapted this belief-attitude-intention-behavior relationship to measure user acceptance of IT. TAM (figure 1) proposes two particular beliefs, perceived usefulness and perceived ease of use that are the two primary drivers that determine technology acceptance. He defined the former as "the degree to which a person believes that using a particular system would enhance his/her job performance" and the latter as "the degree to which a person believes that using a particular system would be free of effort" [2, 14]. According this model, both perceived usefulness (U) and perceived ease of use (E) influence the attitude of individuals towards the use of a particular technology, while attitude (A) and perceived usefulness (U) predict the individual's behavioral intention (BI) to use the technology. Perceived usefulness is also influenced by perceived ease of use (E). Perceived ease of use (E) can indirectly affect the acceptance of technology through perceived usefulness (U), while behavioral intention (BI) is also linked to subsequent adoption behavior. TAM also suggests that external variable intervene indirectly, influencing both perceived usefulness (U) and perceived ease of use (E) . As a result of its week correlation with both behavioral intention (BI) and perceived usefulness (U), Attitude (A) was subsequently omitted from the model by Davis et al. .
Perceived Usefulness (U)
Perceived Ease of Use (E)
Behavioral Intention to Use (BI)
Attitude towards Use (A)
Figure 1. Technology Acceptance Model (TAM)
The Theory of Planned Behavior (TPB) [5, 6] is an extension of the TRA which was related to voluntary behavior. Behavior is not 100% voluntary and under control. Because of the limitations of TRA in dealing with behaviors over which people have incomplete volitional control, a third independent determinant of intention, perceived behavior control (PBC) was introduced, and with this addition the theory was called the theory of planed behavior (TPB).
TPB is a theory that predicts deliberate and planned behavior and TPB is considered to be more general than TRA . Cooper and Richardson  compared TAM and TPB found that both TAM and TPB predicted intention to use an information system (IS) quite well, with TAM having a slight empirical advantage.
Refinement of technology acceptance models
Researchers believe that technology acceptance is more complex and have investigated a number of variables that influence acceptance behaviors. Individual user can accept or reject the system and understanding why they behave so has proven to be one of the most challenging tasks for information system researchers . Among number of models that try to exhibit the process of user acceptance of information system, the technology acceptance model (TAM) is one the most cited theoretical frameworks.
Legris et al., , in a critical review and meta-analysis of the technology acceptance model concluded: "TAM is a useful model, but has to be integrated into a broader one, which would include variables related to both human and social change processes, and to the adoption of the innovation model." Benbasat and Barki  argue that the independent attempts by several researchers to expand TAM has created a state of theoretical chaos and confusion, in which it is not clear which version of TAM is the commonly accepted one. Lee et al.,  noted that the intense research focused on TAM seems to have diverted researchers' attention away from more relevant research.
Venkatesh et al.,  noted that most of the studies had been conducted in voluntary contexts, while use of information systems in real-life organizations is, to most extent, mandatory and they have identified a number of limitations of the previous studies and formulated a unified model having empirically compared the prominent eight models and their extensions. The unified model is called the Unified Theory of Acceptance and Use of technology (UTAUT). This model that comprises all the variables found in the eight existing models and a selected subset of additional constructs and was validated using both existing data, from the previous TAM studies, and data obtained in two new surveys.
The UTAUT aims to explain user intentions to use an IS and subsequent usage behavior. The model is presented in figure 2. The theory was formulated with four core determinants of intention and usage, and four moderators of key relationships. Three core determinants (performance expectancy, effect expectancy, and social influence) determine the behavioral intention to use technology and the other core variable (facilitating condition) directly determines the usage behavior. The constructs, definitions of the constructs and root constructs of UTAUT have been shown in Table I.
Figure 2. Unified Theory of Acceptance and Use of Technology (UTAUT)
Voluntariness of use
Gender, age, experience, and voluntariness of use are posited to mediate the impact of the four key constructs on usage intention and behavior. Gender and Age influence performance expectance, effort expectance, and social influence. Age and experience moderate the facilitating conditions. Experience moderates effort expectancy, social influence, and facilitating conditions. Voluntariness of use moderates the effect of social influence in UTAUT .
Table 1 Definitions of constructs of UTAUT
(Adapted from Venkatesh et. al., 2003)
Constructs of UTAUT
Constructs in other models
The degree to which an individual believes that using the system will help him or her to attain gains in job performance
Perceived usefulness (TAM)
Extrinsic motivation (MM)
Relative advantage (IDT)
Outcome expectations (SCT)
The degree of ease associated with the use of the system
Perceived ease of use (TAM)
Ease of use (IDT)
The degree to which an individual perceives that important others believe that he or she should use the system
Subjective norm (TRA, TAM, C-TAM-TPB)
Social factors (MPCU)
The degree to which an individual believes that an organizational and technical infrastructure exists to support use of the system
Perceived behavioural control (TBP, C-TAM-TBP)
Facilitating conditions (MPCU)
From a theoretical perspective, UTAUT  provides a refined view of how the determinants of intention and behavior evolve over time. An extensive review of past studies [14; 15] has shown that the UTAUT model can explain up to 70% of the variance in usage intention  which is an extremely a higher prediction ability (R2) and a major improvement. This is relatively a new model that has been validated by Venkatesh et al.  based on historical data from previous adoption researches and data obtained from two new surveys. By generating a significantly higher percentage of technology innovation success, the UTAUT is ranked to be a superior model than others.
National culture and technology acceptance
We live and work within a certain cultural environment and the culture can influence actual behavior and enhance the adoption and use of ICT. Moghadam, & Assar  suggests from the evidence of the literature and empirical studies that the country's national culture is the important factor limiting the adoption of ICT. Differences in national cultures have been found to explain some variations in perceptions and adoption of information technologies [17; 18; 19].
It can be noted that the most of the studies were conducted in the United States and in Canada and when it was tested especially in Switzerland, Japan, Arab countries and Hong Kong, the models have been found to be less predictive [20; 19]. This is because of the cultural differences in these countries. Many studies have determined that culture has a strong contextual influence on whether and how individuals, organizations and societies employ ICT . According to Meso et al.,  significance of cultural factors in the developing countries even becomes deeper. Therefore culture is suggested as important factor in explaining IT usage behavior  and Leidner & Kayworth  proposed that studies need to move beyond trying to use cultural values to predict IT adoption.
Culture has been defined according to several perspectives but the most common definition of culture is from Hofstede view. Hofstede  has provided an in-depth discussion on culture and the various dimension of culture which have been widely accepted and used in a variety of disciplines [25; 26] and validated directly or indirectly by many other researches in different settings . Hofstede's (24, p. 5) view of culture is that it is "learned, not inherited. It derives from one's social environment, not from one's genes. The collective programming of the mind that distinguishes the members of one group or category of people from another." He argues that culturally everyone belongs simultaneously to several different kinds of groups and is variously influenced by different layers of mental programming within themselves. The dimensions of the culture are Power Distance, Individualism/Collectivism, Uncertainty Avoidance and Masculinity/Femininity.
Power distance (PD)
Power distance is "a measure of the interpersonal power or influence between (a superior) and (a subordinate) as perceived by the (subordinate)" (Hofstede, 24, p.71). The power distance dimension refers to the inequality of the distribution of power in a country. Large PD-cultures are hierarchical and authoritarian whereas small PD-cultures demonstrate flat organization and value participation. Therefore it is assumed that countries with a high PD score will have a lower rate of ICT adoption than countries with a low PD score.
Uncertainty avoidance (UA)
Uncertainty avoidance describes the extent to which individuals feel threatened by uncertain or unknown situation. Hofstede (24, p. 83) defines uncertainty avoidance as "The degree to which members of a society feel uncomfortable with uncertainty and ambiguity". Adoption of a new technology involves risk and uncertainty. Strong UA-cultures are characterized by little risk taking, minimal innovation, stability, conservative, and thorough planning. Weak UA-cultures are innovative and creative, and tolerant of differences in views and behavior. Therefore it is assumed that countries with a high UA score have a lower rate of ICT adoption than countries with a low UA score.
Individualism vs. Collectivism describes the interactions between individuals and the group to which the individual belongs. People in individualistic countries are more concerned by themselves while people in collectivistic countries conform more readily to the norms of the group. Individuals in individualistic countries feel free to express their own views. Therefore it is assumed that countries with high individualistic culture score to have a higher rate of ICT adoption than countries with collectivistic culture.
Masculinity/Femininity which focuses on the differences between social roles attributed to men and women and expected behavior of the two sexes. Masculine cultures focus on achievements and success. Feminine cultures are characterized by solidarity, equality, consensus seeking and concern about social relationships. According to Hofstede , organizations in masculine cultures focus on rewards, recognition, training and improvement of the individual. These are characteristics common to innovative organizations. Therefore, it is assumed that countries with a high masculinity culture score to have a higher rate of ICT adoption.
The UTAUT - and its predecessors leave out culture as a key antecedent to technology acceptance . Zakour  suggested that individuals were conditioned by their culture, so the impact of cultural factors on the usage behavior should be considered when studying technology acceptance (such as TAM) in countries outside the U.S. Hofstede  stated that culture, shapes individual value and affects behavior and was seen to be different across nations or continents. People may behave differently depending on their culture. Any research model that is to be applied in a multi-cultural context needs to be evaluated by making theoretical connection between the adoption model and national culture constructs.
Figure 2. Proposed Model
The construct 'social influences' in the UTAUT model represents societal pressure on users to involve in a certain behavior but individual behavior varies by culture and the culture will provide additional explanatory power in explaining the variation of user' intention to use a technology . Therefore an ICT adoption model (figure 2) integrating cultural factors into the refined model (UTAUT) is proposed to be evaluated in the later study.
It is understood the technology adoption behavior is more complex and researchers have investigated a number of variables that influence the individual adoption behavior. The models emerged out of the empirical studies left out culture as a key antecedent to technology adoption but culture influences the actual behavior and it can provide additional explanatory power in explaining the variation of the behavior towards adopting a technology. This paper proposes a model incorporating the cultural factors into the Unified Theory of Acceptance and Use of Technology (UTAUT) and to be evaluated by an empirical study in the future.