Individualism-Collectivism

Individualism-collectivism (IC) refers to the extent to which one perceives the relationship between one’s self and the group of which one is a member (Hofstede, 1980). In individualist culture, people tend to be more self-conceived and prioritise their own interest above others (i.e., members in the same group or the organisation’s interest). Conversely,

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an individual in a collectivist culture believes that one’s self-identity is dependent upon the group’s identity. In other words, they are profoundly influenced by the group, and continue to prove their unquestioning loyalty by complying with all acceptable or/and non-acceptable norms and values (e.g., Srite & Karahanna, 2006; Hofstede & Hofstede, 2005).

As in individualist cultures, personal goals are more important than the collective, therefore individuals in these cultures are expected to be influenced by the attitude and behavioural beliefs. In contrast, in collectivist cultures where individuals’ decisions to accept something is based on the group’s decision, it is expected that individuals in these cultures will be influenced by the normative beliefs. The position of this argument is also consistent with the literature in information system. For instance, Bontempo & Rivero (1990) in meta-analysis, reported that an individualist’s behaviour is more closely related to the attitude and collectivists towards norms.

Further exploration can also be understood from the basic conceptualisation of the beliefs.

For instance, in terms of PU which is one’s subjective probability to view the usefulness of technology for self-interest can only be favoured to the individualistic. Rationally, within a collectivist society, subjective probability is related to groups (McCoy, 2002) which can favour the normative and control belief but not the belief PU (see also Parboteeah et al., 2005). From the perspective of PEOU and SE, which are mostly complementary concepts (e.g., Davis, 1989) and can be improved with the facilitation conditions (RF, TF), it will be more relevant to the collectivist culture. This rationale is consistent with Hofstede’s (1980) argument which posits that within the working environment, collectivism is associated with training, physical conditions and use of skills. Finally, from the perspective of normative beliefs and management support that are the perceptions of one’s decision based on others’ will be expected to be higher in collectivist culture, as one is highly compliant with priorities of group values. The rationales presented to conceptualise the individualism-collectivism are also supported in information system literature (e.g., McCoy et al., 2005; McCoy et al., 2007; Srite & Karahanna, 2006; Pavlou & Chai, 2002;

Choe & Geistfeld, 2004; Parboteeah et al., 2005; Alsajjan & Dennis, 2010).

Apart from the direct conceptualisation, the interrelated effect of individualism-collectivism can also be understood with the argument of Hofstede & Hofstede (2005).

Hofstede argued that cultures high in individualism are also correlated with the culture low on power distance, low on uncertainty avoidance, high on masculinity, men in gender, and finally, younger in age. Observing the discussion in relevant sections, specifically

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within gender and masculinity (see section 3.4.2), it is noticed that relations proposed in the present section are highly relevant within all groups. Thus, despite the clear rationale that behavioural beliefs PU and AT will be highly relevant to the individualist, and normative and control beliefs PEOU, SE, TF, RF, GS, IS, SN, and NAT will be relevant to collectivist cultures, still it is hypothesised on an exploratory basis that:

H16a: The influence of the predictors BI, PU, TF, RF, SE, IS, and GS towards BU is moderated by the cultural dimension IC, or (BI, PU, TF, RF, SE, IS, GS) X IC

BU

H16b: The influence of the predictors SN, PEOU, IS, and GS towards PU is moderated by the cultural dimension IC, or (SN, PEOU, IS, GS) X IC



PU

H16c: The influence of the predictors PU, PEOU, TF, RF, SE, AT, NAT, and SN towards BI is moderated by the cultural dimension IC, or (PU, PEOU, TF, RF, SE, AT, NAT, SN) X IC



BI.

H16d: The influence of the predictor SN towards PEOU is moderated by cultural dimension IC, or SN X IC

PEOU.

3.7.3. Power Distance

Power distance (PD) is the extent to which an inequality is accepted as normal in a given culture (Hofstede, 1980). In low PD cultures, individuals tend to be more egalitarian, and feel less dependent on their superiors or working colleagues. Consequently, freedom of equality diffuses the hierarchical structure within organisations and encourages individuals to take part in the decision-making process (Leidner & Kayworth, 2006). In contrast, within a culture high on PD, superiors and subordinates consider themselves unequal, and thus, the traditional centralised hierarchal system is observed (Hofstede & Hofstede, 2005).

In high PD cultures, superiors are expected to tell and direct, and in turn, the subordinate completes the tasks without measuring its merit or ethical values with the assumption that the ‘superior is always right’ (ibid). In simple words, both groups of PD (high and low) are distinct from each other on the basis of the compliance effect.

As in high PD, the compliance effect is higher, therefore individuals in these cultures are expected to be influenced by the normative, control and management support belief. In contrast, individuals in low PD are expected to be influenced by the behavioural beliefs, specifically with the PU. The argument is consistent with the concept of ‘social influence’

(also known normative beliefs (see Venkatesh et al., 2003)) which can be manifest through compliance, identification, and internationalisation effect (Kelman, 1958). According to Srite & Karahanna (2006), within compliance an individual tends to accept influence from another person/group with the aim of gaining a favourable advantage. In addition, from the perspective of hierarchy, the effect of compliance is noticed to be higher as it is directed by the more superior authority (ibid). Complementing Srite & Karahanna’s argument with concept of high on PD (individuals are more concerned about complying with a superior’s

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requirements and will fear disagreement), it is obvious that the effect of normative belief towards acceptance intention will be more relevant in cultures with higher PD.

From the perspective of attitude and behavioural belief, specifically in terms of PU, the effect is expected to be weaker within higher PD cultures. The rationale is consistent with the previous literature (e.g., Harris, 1997; Gunton, 1988; Panko, 1988) which suggests that technology is effective in conditions when it empowers the individuals to use it and decide for themselves how to use it. Obviously, when individuals are empowered to think on their own, they might start to depend upon their own skills (i.e. SE) and directly adopt the technologies by perceiving its likely importance in job performance (i.e. PU). In some cases, based on experience and familiarity with the technology, individuals start to suspect their superior’s skills and regret accepting their decisions directly (Leonard-Barton &

Deschamps, 1988). Such instrumental values are contradictory within a high PD culture where individuals are supposed to comply with their superior.

The rationales suggested above are also cited and validated within information systems acceptance literature. For instance, researchers (e.g., Srite & Karahanna, 2006; Pavlou &

Chai, 2002; Hasan & Dista, 1999) found that the impact of normative beliefs on BI were highly relevant within higher PD cultures only. Finally, from the perspective of the interrelated effect of higher PD with other dimensions – higher on collectivism, higher on feminine, women in gender, and older in age (Hofstede & Hofstede, 2005) – it is noticed that the relationships presented in this section are also consistent with the relevant dimensions (for more details, see the relevant sections). Therefore, despite the clear rationales that high PD positively affects normative beliefs and negatively affects behavioural belief, it is still hypothesised on an exploratory basis that:

H17a: The influence of the predictors BI, PU, TF, RF, SE, IS, and GS towards BU is moderated by the cultural dimension PD, or (BI, PU, TF, RF, SE, IS, GS) X PD

BU

H17b: The influence of the predictors SN, PEOU, IS, and GS towards PU is moderated by the cultural dimension PD, or (SN, PEOU, IS, GS) X PD



PU

H17c: The influence of the predictors PU, PEOU, TF, RF, SE, AT, NAT, and SN towards BI is moderated by the cultural dimension PD, or (PU, PEOU, TF, RF, SE, AT, NAT, SN) X PD



BI H17d: The influence of the predictor SN towards PEOU is moderated by cultural dimension PD, or SN

X PD

PEOU.

3.7.4. Uncertainty Avoidance

Uncertainty Avoidance (UA) is the extent to which a member of the culture feels uncomfortable with uncertain situations (Hofstede, 1980). According to Dorfman &

Howell (1988), it is the degree to which an individual favours structured over un-structured situations. In high UA cultures, individuals often feel threatened by uncertain conditions

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(Ford et al., 2003; Parboteeah et al., 2005). To avoid such feelings and achieve stability at work, members in these societies are heavily pushed to rely upon formal rules and pre-defined structures (Hofstede, 1980). By contrast, in low UA cultures, people feel autonomous, and methods and procedures are volatile as individuals find new ways to accomplish given tasks (Hofstede, 1980; Gunton, 1988). According to Dorfman & Howell (1980), in low UA cultures, people tend to be more tolerant and feel less anxiety when confronted with challenging or unpredictable problems.

The effect of UA in the technology acceptance domain is most clearly discernible on behavioural beliefs and normative beliefs towards behaviour intention. For instance, from the perspective of normative beliefs, the effect is expected to be stronger in situations with a higher level of uncertainty. The rationale is consistent with the study of Srite &

Karahanna (2006). According to this, the social environment can be an important source of information to reduce the level of uncertainty by determining the rules and their acceptability. More specifically, if peers or superiors in an environment share their own experience and perceptions, then individuals start to take them as evidence of reality (i.e., rule), which is socially desirable and acceptable (Karahanna, et al., 2005), and as a result, the effect of uncertainty is reduced.

Similar to normative beliefs, the effect of belief PEOU, control beliefs and management support beliefs are also expected to be stronger with respect to situations with a higher level of uncertainty. This argument is consistent with the level of anxiety and stress that is noted to be higher in highly uncertain situations (e.g., Dorfman & Howell, 1988; Hofstede

& Hofstede, 2005). According to social cogitative theory (SCT), anxieties and expectancies (self-efficacy and ease of use) are reciprocal to each other (Bandura, 1986).

Therefore, higher anxieties indirectly decrease the efficacy (one’s self-evaluation/confidence to complete targeted task) and decrease overall performance (Bandura, 1977; Rosen & Maguire, 1990; Brosan, 1998). To overcome such conditions and increase self-control, an individual tends to rely on facilitation conditions (resource and technology facilitations, and management support) and support from the social environment (e.g., Srite & Karahanna, 2006; Venkatesh & Morris, 2000; Hwang, 2005).

Thus, normative and control beliefs will be more important predictors of BI for individuals within high uncertainty avoidance cultures.

In contrast, the effect of belief PU is noticed to be weaker with respect to higher UA cultures (e.g., Parboteeah et al., 2005; Harris, 1997; Alsajjan & Dennis, 2010). The reason

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is obvious in that IT innovations inherently involve change and uncertainty in organisations (Yoon et al., 1995). Literature suggests that individuals in higher UA culture are more bound or habitual with pre-defined rules and standardised procedure (e.g., McCoy, 2002; Veiga et al., 2001). Consequently they are more likely to view change as negative and fail to perceive the usefulness of information technology in their work (Parboteeah et al., 2005; Ford et al., 2003).

In fact, previous research suggests that the usefulness of technology is associated with the freedom through which an individual is able to decide when and how the technology will be useful to accomplish targeted tasks (e.g., Cotterman & Kumar, 1989; Goodhue &

Thompson, 1995). According to Harrison & Rainer (1992) and Harris (1997), an individual who is less conformist to rules (low UA), social norms, and accepted work patterns is less likely to have feelings of anxiety towards technology, and therefore will be more likely to accept the information technology on their own through perceived advantages (PU). Apart from the given argument, the previous research reviewed by Ford et al. (2003), and literature (e.g., Parboteeah et al., 2005; Hasan & Dista, 1999; McCoy et al., 2007; Hwang, 2005) reported that countries that were higher on UA typically showed higher resistance towards new technological applications and relied more on traditions. Finally, from the perspective of the interrelated effect of higher UA with other dimensions – higher on collectivism, higher on feminine, women in gender, and older in age (Hofstede &

Hofstede, 2005) – it is noticed that the relationships presented in this section are also consistent with the relevant dimensions (for more details, see the relevant sections). Hence, despite the clear rationale that higher UA positively affects the relationship between normative and control beliefs, and negatively on perception of usefulness, still it is hypothesised on exploratory basis that:

H18a: The influence of the predictors BI, PU, TF, RF, SE, IS, and GS towards BU is moderated by the cultural dimension UA, or (BI, PU, TF, RF, SE, IS, GS) X UA



BU

H18b: The influence of the predictors SN, PEOU, IS, and GS towards PU is moderated by the cultural dimension UA, or (SN, PEOU, IS, GS) X UA

PU

H18c: The influence of the predictors PU, PEOU, TF, RF, SE, AT, NAT, and SN towards BI is moderated by the cultural dimension UA, or (PU, PEOU, TF, RF, SE, AT, NAT, SN) X UA

BI

H18d: The influence of the predictor SN towards PEOU is moderated by cultural dimension UA, or SN

X UA



PEOU

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Conclusion

This chapter has provided a theoretical framework based on the previous prominent theories and models in the technology acceptance research domain i.e., SCT, TAM, TAM2, TRA, DTPB, TTF and UTAUT. In addition, Bem’s BSRI and Hofstede’s cultural theory were also integrated to examine the moderating effect on direct relationships. Based on 13 direct determinants, a total of 12 hypotheses with 23 paths were proposed in the model. Specifically, for the direct relationship of behavioural beliefs (PEOU and PU) three hypotheses with four paths, for normative beliefs (PI and SI) two hypotheses with six paths, for control beliefs (TF, RF, and SE) two hypotheses with six paths, for task characteristics (AT and NAT) two hypotheses with two paths, for behavioural intention (BI) one hypothesis with one path, and finally, for management support (GS and IS) two hypotheses with four paths were presented.

After direct relationships, based on seven demographic and four cultural factors 6 hypotheses with 44 paths were proposed on an exploratory basis. Specifically, for the demographic variables (age, gender, education-level, organisation-type, academic position, voluntariness, and usage experience) two hypotheses with twenty-eight paths, and for cultural variables (PD, IC, MAS, and UA) four hypotheses with sixteen paths were presented. Dissecting the number of moderating relationships into a large number of paths only intends to examine the possible effect, not the causal effect at each level. In the next chapter, the proposed methodology and data analytical tools are being discussed to validate the paths presented in this chapter.

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Chapter 5

Research methodology

Introduction

The purpose of this chapter is to elucidate and justify the methodology and approach to collecting and analysing the data. Choosing a certain methodology, as well as methods for data collection and analysis, is tantamount to deciding an overall viewpoint that determines what the researcher will come to find. In this chapter, section (4.1) discusses why this research is positioned from the perspective of positivist epistemology and ontology.

Sections (4.2 and 4.3) discuss the two major paradigms in social science research, i.e., qualitative and quantitative research, with a justification of the selection of the quantitative research method of surveys for this study. Section (4.3) explains the overall research design, including the purpose of the study, type of investigation, extent of researcher interference, study settings, unit of analysis, and time horizon. Section (4.4) discusses the survey questionnaire development process, content, wording and layout criteria. Section (4.5) discusses the selection of the population and sample for the present research with the support of strong rationales and justifications. Sections (4.6 and 4.7) discuss the instrument development and scale adoption respectively. Finally, sections (4.8, 4.9, and 4.10) discuss the data collection techniques, pilot study, and data analysis technique with structural equation modelling (SEM) using PLS. Finally, section (4.11) considers the ethical issues with a summary in end.