Quimiorresistencia

With the assumption that cultural diversity is pervasive and can exert an effect on the predictably of technology acceptance behaviour, a number of researchers within the information system field have started to investigate and address the cultural issues which might cause the failure of IT acceptance across the cultures (e.g., Straub et al., 1997; Rose

& Straub, 1998; McCoy et al., 2005; Alsajjan & Dennis, 2010). In this line of research, the influence of Hofstede’s proposed dimensions of national culture is very common and observable (Ford et al., 2003). Therefore, in order to assess the impact of Hofstede’s cultural dimensions on the predictors of IT acceptance, this section categorises the discussion of the previous citations into three groups:

1. Citations which incorporated and examined the impact of Hofstede’s dimensions within the same culture.

2. Citations which incorporated and examined the impact of Hofstede’s dimensions across the cultures.

3. Citations which did not incorporate Hofstede’s dimensions but examined their results across the cultures with the help of Hofstede’s country-level cultural scores.

In the first group of citations, only a handful studies examined the impact of Hofstede’s cultural dimensions on an individual’s acceptance behaviour. In this group, researchers are specifically interested in examining an individual’s gender-based differences of technology acceptance behaviour in relation to Hofstede’s masculinity-femininity (MAS) dimension.

Out of this few, one of the key studies is Venkestesh et al. (2004), who examined the

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impact of gender in terms of MAS on the construct of TPB. Using Bem’s (1974) Sex Role Inventory (BSRI) scale to measure the individuals’ differences, Venkatesh et al. (2004) found that masculine-type individuals exhibited the same patterns as men in previous research; however, feminine-type individuals were different from women in that they were influenced only by SN and PBC. Even though Venkatesh et al. (2004) did not use Hofstede’s cultural dimensions directly due to their limited applicability at individual level, the results of the study were imperative in terms of highlighting cultural impact on acceptance behaviour.

The second group of citations which directly incorporated Hofstede’s cultural dimensions and examined them across the countries also includes a very limited number of studies within the information system domain. Out of many, a discussion of a few noteworthy studies (Parboteeah et al., 2005; Srite & Karahanna, 2006; Hasan & Dista, 1999; Pavlou &

Chai, 2002; McCoy et al., 2005) is presented due to their relevance to the present study. In this line of research, using an interpretive case study, Hasan & Dista (1999) examined four dimensions of Hofstede’s cultural theory (PD, IC, MAS, UA) within ten organisations across the Middle East, Africa and Australia. Focusing on the technology transfer outcome, authors found that: adoption of IT was slower in risk-hesitant countries i.e., with high UA (e.g., Middle East and Africa); adoption was higher in cultures where IT staff and mangers were in continuous sharing i.e., with low PD (e.g., Australia); specifically, the adoption of group-oriented applications was favoured by collectivist cultures (e.g., Middle East and Africa), and finally, patterns of IT adoptions were varied according to the level of masculinity (technology focused) vs. femininity (people and end-user focused). Hasan &

Dista’s results were quantitatively echoed by Parboteeah et al. (2005), who examined the impact of the three cultural dimensions (IC, UA, MAS) on the perception of usefulness (PU) to accept the technology. Covering 24 nations with a sample of 26,999, Parboteeah et al. (2005) found that there was a negative relationship between IC and UA on PU and a positive relationship between MAS and PU.

Apart from the direct impact of culture on the individual’s technology acceptance behaviour, fewer studies also examined the indirect impact (i.e., moderator). For instance, using the US and Uruguay sample, McCoy et al. (2005) examined the impact of the four cultural dimensions (IC, UA, MAS, PD) on the modified version of the TAM. The results revealed that culture played an inchoate moderating impact so that the impact of PBC on BI was strongly observed in the Uruguay sample as compared with the US sample.

However, contrary to expectations, the author did not find any significant difference

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between the relationships of PU, PEOU, and SN on BI in both countries (ibid). In a similar line of research, Srite & Karahanna (2006) examined the moderating impact of the four cultural dimensions (IC, UA, MAS, PD) between the PU, PEOU and SN on BI. Using 30 countries and a sample of 223, the authors found a negative moderating impact of MAS and PD between SN and BI, and a positive moderating impact of UA between SN and BI.

Similarly, Pavlou & Chai (2002) examined TPB in the US and China sample, and found that cultural dimensions (PD, IC, UA, and long-term orientation) played a significant moderating impact on the individual’s perception to accept the e-commerce behaviour.

Specifically, the model explained higher variance (77%) in Chinese culture (i.e., collectivist) compared with the US culture of 33% (i.e., individualist). Pavlou & Chai (2002) found that beliefs such as SN and SI were strongly observed in Chinese culture compared with the US, which in turn perceived higher importance of the PBC on behavioural intention. In comparison with Korean and US culture, Choe & Geistfeld (2004) examined the moderating impact of two cultural dimensions (IC and UA) on the individual’s behaviour to adopt the e-commerce behaviour. The authors found that the cultural dimension UA played a significant moderating impact, so that the impact of perceived risk (PR) was strongly observed only in Korean culture, whereas SE was only significant in US culture. The authors did not find any significant difference between the two countries based on the moderating impact of IC (ibid).

The third group of the citations, which includes a very large number of the studies, aims to examine the robustness of technology acceptance models across the context of two or more countries. The core objective behind these studies is that studies predicating technology acceptance behaviour have largely been conducted within North America and specifically within a single country, that is, the US (e.g., Venkatesh & Morris, 2000; Venkatesh et al., 2004). Therefore, their validity and reliability is questionable when re-examined outside the US (Straub et al., 1997; Abbasi et al., 2010). In this stream of the research, the replication of technology acceptance models is widely observed in a diversified context.

For instance, within e-commerce and e-service (e.g., Jarvenpaa et al., 1999; Seyal et al., 2004; Hsu & Chiu, 2004), in Internet-banking (e.g., Shish & Fang, 2004), in broadband Internet use and adoption (e.g., Oh et al., 2003; Choudrie & Lee, 2004; Khoumbati et al., 2007; Seyal et al., 2002, 2003), in healthcare (e.g., Wu et al., 2007), and in email and academic use (e.g., Struab et al., 1997; Hu et al., 2003). Predominantly, previous studies outside the US justified their results by discovering similarities and differences between the native country’s cultural indices proposed by Hofstede (1980), with the studies

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conducted in a US context. Surprisingly, most of these studies did not directly measure cultural dimensions (e.g., Straub et al., 1997), which leaves a gap in understanding individual-level differences of acceptance behaviour. Despite the fact that most of these studies found a significant difference between the models evaluated in a US and non-US context, their discussion and importance is not relevant to the present context of the study.

Reasonably, the present study not only aims to examine the inherent cultural biasness within models of technology acceptance behaviour but it also intends to examine the individual-based differences that lie within the same culture. Therefore, the present study only intends to focus on those studies which, directly or indirectly, incorporate Hofstede’s dimensions.

The only study relevant to the discussion in the third group of citations is Straub et al.

(1997). Arguably, Straub et al.’s (1997) study is one of the pioneering studies which explores inherent bias within the TAM and is one of the most widely cited studies within cross-cultural research (449 on Google Scholar to date). It is one of the most important studies that emphasises the need to incorporate Hofstede’s dimensions so that individual-level cultural differences may be examined towards predicting technology acceptance behaviour. Straub et al. (1997) examined the TAM in Japan, Switzerland and the US and found that the TAM produced similar variance (R²) in the explanatory power of behavioural usage in both the US and Switzerland (10%), but was very different in Japan (only 1%). Straub et al. (1997) justified these results with Hofstede’s dimensions and argued that Japanese culture tends towards greater power distance, collectivist sentiments and higher uncertainty avoidance, which may limit their Internet usage and disassociate from the intention to accept. Such results are justified because Davis, at the time of the TAM development (Davis, 1989), did not consider its un-biased reliability in cross-cultural settings.

2.16. Modification and adoption of Hofstede’s dimensions in the present