Cross-cultural inconsistent usage and outcomes of IT
Valeria Lamper
Student number: 11153172
University of Amsterdam Faculty of Science
Master Information Studies: Business Information Systems Supervisor: Dick Heinhuis
Abstract
In this study are the cross-cultural inconsistent usage and outcomes of IT investigated. More specifically, which individual cultural IT values account for differences in technology acceptance. Previous studies on the topic have shown that several assumptions were made which indicated that the conceptualization of culture should be based on the IT values of the individual. The cultural IT values of the construct IT Adaptability seemed to fit the required scope of this study, in addition to the UTAUT constructs Social Influence, Behavioral Intention and Behavioral Expectancy. While the cross-cultural inconsistencies have been studied by many scholars, is the perspective on the conceptualization of culture at the level of the individual by just some scholars discussed, and rarely applied, and additionally not yet researched in relation to Behavioral Expectancy. The results show that by incorporating IT Adaptability, more variance is explained of Behavioral Intention. Furthermore are three IT Cultural Dimensions identified which moderate the effect of Social Influence on Behavioral Intention, namely; Opportunistic Use, Extensive Use and Socializing Use. Finally, no support was found that the individuals’ IT culture affects Behavioral Expectancy.
1 Introduction
To support strategic globalization initiatives, multinationals develop and deploy GIS’s (Global Information Systems) organization wide across countries. GIS’s are developed to, among other things, align, standardize and optimize processes. To benefit from GIS’s organizations must ensure the intended acceptance and usage by employees. (Venkatesh et al. 2003) However, overall underutilization of IS’s remains a major problem (Venkatesh et al. 2017), and due to the globalization of business an extra level of complexity is added to GIS projects. (Parry et al. 2015)
Articles on low success rates of IT projects are recurrently published. Innotas (CIO from IDG 2013) revealed that 50% of the surveyed businesses in 2013 experienced IT project failures, and in 2015 these numbers increased to 55%. (CIO from IDG 2017) In the context of IT management and resource planning, Kevin Kern (CEO Innotas) says that organizations shifted from a hardware and operating system focus to a more application-centric focus, which increased the number of IT projects. Kern also suggests that IT departments have trouble in saying “no”, because of the risk of being seen as a less valuable business partner. “Project managers aren't just project-based, they're supervisors” says Kern. “They are managing solutions and applications, as well as managing the software developers, and there aren't enough developers, ever. So, project managers are being asked to take on so many responsibilities that their job descriptions get blurred” Kern says. (CIO from IDG 2013) In a different perspective Parry et al. (2015) argues that the most critical factors for success in GIS projects, are issues which pertain to culture rather than strategic management. Consistently is argued by Leidner and Kayworth (2006) that culture is oftentimes partially blamed when organizations experience failure.
Many cross-cultural studies have been conducted on inconsistent IT acceptance effects. (Straub et al. 2002, Myers & Tan 2002, Leidner & Kayworth 2006, Srite & Karahanna 2006, Kappos & Rivard 2008, Leidner 2010) Leidner & Kayworth (2006) state that cultural differences result in different usage and outcomes of IT, arguing that cultural values shape how individuals use IT. Additionally they state that cross-cultural inconsistent outcomes of IT usage, occur on all levels, and need to be understood in order to build knowledge on potential conflicts in IT management, IT development and IT implementation. In a similar understanding, Srite & Karahanna (2006) state that behaviors are influenced by espoused cultural values, and suggests that various
managerial interventions can be applied to alleviate resistance to system use. It is the role of the manager, according to Jackson and Philip (2010), to act in an ad-hoc and incremental manner; they emphasize the dynamic nature of the interaction between people and technology, and argue that ongoing attention is required due to unanticipated cultural and technological issues.
1.2 Problem statement and practical relevance
The inconsistent IT outcomes and its relation to culture have been discussed in many studies. Most scholars conceptualize culture at a national or organizational level and assume that the different outcomes of technology acceptance are inherent to national or organizational cultural characteristics. However, according toStraub et al. (2002) could this conceptualization of culture, be the cause of confusion in IS research, which could lead to invalid conclusions. This is consistent with the arguments of Myers and Tan (2002); they argue that the conceptualization of culture at a national level assumes that differences in culture are in a way aligned with territorial boundaries of a nation, and state that this is a problematic perspective. They discuss that this perspective ignores the fact that ethnic and cultural groups can exist across and within nations. Straub et al. (2002) argues that individuals are influenced by plethora of cultures, in which some are national and some are organizational, emphasizing that clear delineated boundaries should be avoided in conceptualizing culture in IS research. They discuss a separation between the individuals’ cultural values and the cultural values of a group, which directs the focus of this study to the individual.
In the context of continuous IT improvements, could be argued that boundaries at all levels are becoming less defining. Advanced IT and web technologies enable organizational structures to adapt to the less confining boundaries of time, location, individualization and organization (Afsarmanesh & Camarinha-Matos 2005). And considering the continuous globalization of business many companies are increasingly doing business beyond their national boundaries, which suggests that national boundaries are becoming less significant. (Myers & Tan 2002, Boode 2005) Additionally, we need to take into consideration that currently people from different nationalities with diverse cultural backgrounds work alongside in a firm, further emphasizing the need to revise the scope of culture in IS research to effectively build knowledge on potential conflicts in GIS projects. In this study is therefore argued that the conceptualization of culture needs to be at the level of the individual in order to build knowledge for possible managerial interventions.
1.2 Research Questions
In the context of continuous globalization of business; the pressing need to build a better understanding of different usage and outcomes of IT remains. Considering the previously mentioned less defining boundaries of culture, either at national or organizational level, is argued in this study that previous research is conducted with a too simplistic conceptualization of culture. The previously used conceptualization of culture and its possibly consequential confusion could explain some of the difficulties in developing generalizable theories; and by that the difficulties in acquiring knowledge from which possible directions for managerial interventions can be developed. In order to research the inconsistent usage and outcomes of IT, and in an attempt to avoid any confusion, is this study conducted at the level of the individual. Formulating the following research question: To what extent do individual cultural values account for different outcomes in technology acceptance? This study will answer the main research question as follows. First is in the literature review the current theoretical understanding of technology acceptance outlined, additionally is the current state of knowledge on culture in IS literature assessed. Based on previous literature are theories, measurements and perspectives evaluated. Second, a research model is developed, and hypotheses are formulated to measure the effect of individual cultural values on technology acceptance. After which the hypotheses are tested, the results are discussed, and a conclusion is presented.
1.3 Theoretical relevance
Culture is a challenging variable to research in the IS field, and throughout literature is stated that it has been troublesome to develop and refine theories. (Straub et al. 2002, Leidner and Kayworth 2006) Possible causes of the complexity of culture in IS research are recurrently discussed, such as; the divergent definitions of culture (Straub et al. 2002, Leidner & Kayworth 2006) and the difficulties in finding appropriate measures (Kaba & Osei-Bryson 2013; Leidner & Kayworth 2006). Oshlyansky et al. (2007) discusses the complexity of culture and its applicability to IT acceptance research, emphasizing that a better understanding is needed in order to build a foundation of empirical evidence. The influence of culture on technology acceptance has also been studied within various scientific fields (such as in; Decision Sciences; Leidner et al. 1999, Educational technology; Nistor et al. 2014, Strategic change; Parry et al. 2015, Economic and finance; Al-Qeisi & Hegazy 2015). The most commonly used technology acceptance models are; TAM (Technology Acceptance Model) developed by Davis (1989), TAM2 by Venkatesh and Davis (2000) and the UTAUT model (Unified Theory of Acceptance and Use of Technology) developed by Venkatesh et al. (2003). Whereas, the most commonly used concepts to describe culture or explain different technology acceptance outcomes, are the cultural dimensions, based on national cultural characteristics developed by Hofstede (1983). In appendix 1 is an overview depicted of more recent cross-cultural studies, which are based on national cultural characteristics.
Straub et al. (1997) proved the validity of TAM in predicting the American experience, as is consistent with other studies. However, they also found that TAM would not be able to predict the Japanese experience.
Whereas Al-Gahtani et al. (2007) and Im et al. (2011) proved the validity of most UTAUT constructs outside the borders of the United States. Both studies conclude that cultural differences caused inconsistent technology acceptance effects between the sampled countries, and found inconsistencies in technology acceptance concepts related to social norms or social influence. As stated by Al-Gahtani et al. (2007) “in contexts removed from Western nations, the impact of subjective norms on the individual and organizational acceptance of IT could vary markedly”. Al-Gahtani et al. (2007) explained the significant influence of social norms on the main determinant of system use in the context of national cultural dimensions (Hofstede 1983); being the high Power Distance and low Individualism nature of Saudi Arabia. Srite and Karahanna (2006) argue that numerous studies proved the importance of social norms as a determinant of acceptance behaviors. In their study they indicate that social norm has a stronger influence on the determinant of acceptance behavior for individuals who espouse feminine and high uncertainty avoidance cultural values (Hofstede 1983). And Venkatesh and Zhang (2010) concluded a significant difference on the effect of social influence, and attributed these inconsistencies to the cultural differences between the U.S and China. Consistently with these finding, can be derived from previous literature that the inconsistent usage and outcomes in IT seems to be closely related to the constructs; Social Norm (Straub et al. 2003, Srite & Karahanna 2006, Al-Gahtani et al. 2007, Delong & Fahey 2000, Keesing 1974, Nadler & Tushman 1988) and Social Influence (Im et al. 2011, Al-Qeisi & Hegazy 2015, Chow et al. 2000, Gold et al. 2001).
Leidner & Kayworth (2006) identified two streams in IS research; scholars that attributed the inconsistent technology acceptance outcomes, across countries, to national cultural characteristics, and scholars that attributed different technology acceptance outcomes, across organizations, to organizational cultural characteristics. They furthermore discuss that most studies in IS literature conceptualized culture as being persistent, uniform, and consistent across the organization or nation, furthermore arguing that previous studies generally treat culture as being homogenous. But most importantly, Straub et al. (2002) argues that most definitions of culture are based on the assumption that an individuals’ membership of a group, defines the nature of values they espouse. And that at the same time individuals are influenced by plethora of cultures, in which some are national and some are organizational, emphasizing the too simplistic conceptualization of culture in IS research.
1.4 Academic contributions
This study contributes to academic research in three ways. First, culture is conceptualized at the level of the individual, regardless of nationality or organization. A base is therefore created to develop directions for managerial interventions, and in doing so recognizing the divergent cultural backgrounds of individuals. Secondly, models and constructs used in previous research did not entail the Behavioral Expectancy construct from the UTAUT model (Venkatesh et al. 2017). This construct is not previously evaluated in research related to the impact of culture on technology acceptance. Third, a research model is presented, after which the moderating effect of the individuals’ IT values in technology acceptance is validated.
1.5 Research method
Due to the wide-ranging definitions of culture; the view is adopted that a single perspective is not sufficient enough to comprehend culture as a whole. Considering the complexity of the multi-layered facets of culture, this research resides at the individual level. And additionally, this research is conducted with a focus point at an organizational level.
In order to answer the research question a literature review is conducted to identify the current state of knowledge. The literature review consists of two parts; a review of technology acceptance theories where relevant constructs are identified, then is continued with the current state of knowledge on culture in IS literature, again identifying relevant constructs and measurements. After which, in chapter three, the development of the research model is discussed and the hypotheses are formulated. Data will be gathered by conducting a
questionnaire based on the developed research model. A quantitative questionnaire is used, which is spread to a selected target group within one company to acquire valid data. In the fourth chapter is the research methodology described, after which is continued with the conclusion and discussion in chapter five.
2 Literature review
In order to evaluate appropriate measures, and from thereon, to research the extent in which individual cultural values account for different outcomes in technology acceptance, technology acceptance theories are reviewed in paragraph 2.1. In paragraph 2.2 is the current state of knowledge of culture in IS literature discussed to evaluate appropriate cultural measures. After which the conclusion can be found in paragraph 2.3.
2.1 Technology acceptance theories
In the previous chapter is discussed that in IS research, the inconsistent usage and outcomes in IT seems to be closely related to Social Norm and Social Influence. In the evaluation of technology acceptance theories is therefore mainly focused on the main determinants of system use, and the concepts of Social Norm and Social Influence. In appendix 2 is an overview depicted of the main acceptance theories.
2.1.1 TRA, TPB, TAM and TAM2
TRA (Theory of Reasoned Action) is based on the theory of attitude and provides understanding in the relationship between attitudes and behaviors within human action (appendix 3, figure 2). In the TRA is BI (Behavioral Intention) the major determinant for actual behavior, in which BI is defined as “an individual’s subjective probability that he or she will perform a specified behavior.” BI is in the TRA explained through the constructs attitude towards an actual behavior and subjective norm. In which subjective norm is described as “the perceived social pressure to perform or not to perform the behavior.” Many studies confirm a high correlation of the three constructs, and subsequently a high correlation with actual behavior. (Sheppard et al. 1988) In a contradictory perspective is argued that due to circumstantial limitations BI does not consistently lead to actual behavior, and that therefore BI cannot be the sole determinant of actual behavior. (Norberg et al. 2007)
In the TPB (Theory of Planned Behavior) is a third construct added, namely perceived behavioral control (Ajzen 1991), in which perceived behavioral control refers to the degree to which a person believes that they control any given behavior (appendix 3, figure 3). In the TPB is argued that people are more likely to conduct a certain
behavior when they feel that they can enact them successfully. Both the TRA and the TPB incorporated the concept of social influence, in which social influence is measured by the evaluation and opinions of social groups.
In the TAM (Technology Acceptance Model) Davis (1986) discussed the first steps towards the development of a user technology acceptance model (appendix 3, figure 4). He defined the major motivational variables that mediate between system characteristics and actual system use by end-users in an organizational context, and furthermore, how these variables causally relate to one another and how user motivation prior to organizational implementation can be measured. Davis (1986) used the TRA (Fishbein 1967, Fishbein & Ajzen 1975) as a foundation for the TAM in which BI is the major determinant for actual system use. Perceived usefulness is defined as "the degree to which an individual believes that using a particular system would enhance his or her job performance." And Perceived ease of use is defined as "the degree to which an individual believes that using a particular system would be free of physical and mental effort." (Fishbein & Ajzen 1975) As in the TRA (Fishbein & Ajzen 1975; 1980) Venkatesh and Davis (2000) proposed the extension of TAM, namely TAM2 (Technology Acceptance Model 2). They incorporated theoretical constructs involving social influence processes; subjective norm, voluntariness, and image. And they added cognitive instrumental processes; job relevance, output quality, result demonstrability. SN (Social Norm) is a component adopted from the TRA, and in TAM2 is argued that it is the medium for the social influence processes (appendix 3, figure 5).
2.1.2 UTAUT
In the development of UTAUT (Unified Theory of Acceptance and Use of Technology) Venkatesh et al. (2003) created an empirical comparison of eight models; TRA (Fishbein 1967; Fishbein & Ajzen 1975), TAM (Davis 1989), Motivational Model (MM) (Davis et al. 1992; Vallerand 1997), Theory of Planned Behavior (TPB) (Ajzen 1991), Combined TAM and TPB (C-TAM-TPB) (Taylor & Todd 1995), Model of PC Utilization (MPCU) (Thompson et al 1991; Triandis 1977), Innovation Diffusion Theory (IDT) (Moore & Benbasat 1991) and Social Cognitive Theory (SCT) (Compeau & Higgins 1995; Bandura 1986). UTAUT is widely used in the scientific domain and explains BI through four constructs, namely; PE (Performance Expectancy), EE (Effort Expectancy), SI (Social Influence) and FC (Facilitating Conditions). In appendix 4 are the four constructs outlined. This overview provides; the definitions of the constructs, on which theories the constructs are based and the theoretical background and impact of the moderators (age, gender, experience and voluntary of use). The moderators are explained through gender roles, socialization processes and the susceptibility to external factors such as; social group’s influences and the opinion of others. SI is defined as “the degree to which an individual perceives that important others believe he or she should use the new system”. The impact on BI is driven by three mechanisms; compliance, internalization, and identification. (Venkatesh and Davis 2000; Warshaw 1980) Venkatesh et al (2017) recognized the limitations of BI, as being the sole determinant of system usage and revised the UTAUT model and incorporated the BE (Behavioral Expectancy) construct (appendix 3, figure 6). In this model is the variance of BE explained by two determinants (Social Influence and Facilitating Conditions) and four moderators (Gender, Age, Experience and Voluntary of use). BI is defined as “the degree to which a person has formulated conscious plans to perform or not perform some specified future behavior.” (Warshaw & Davis, 1985) Whereas BE is defined as “an individual’s self-reported subjective probability of his or her performing a specified behavior, based on his or her cognitive appraisal of volitional and non-volitional behavioral determinants.” (Warshaw & Davis, 1984) Argued is that the internally oriented set of mechanisms (PE and EE) influence BI and the externally oriented mechanism (FE) influence BE. SI would play a dual role as an internally and externally oriented mechanism which positively effects BI and BE. The positive effect of SI on BE reflects an external orientation which entails the influence of external (to the individual) referents regardless of the level of experience. Venkatesh et al. (2017) state that the influence of moderators on both SI paths differ, further reinforcing the differences in the construct SI as an internally versus externally oriented mechanism. The UTAUT model test results for BI, BE and system use show that 40% (direct effect) and 74% (direct effect and interaction terms) of the variance in BI is explained, 58% (direct effect) and 78% (direct effect and interaction terms) of the variance in BE is explained, and between 39% (direct effect) and 63% (direct effect and interaction terms) of the variance in system use is explained. SI explains 8% (direct effect) and 5% (direct effect and interaction terms) of the variance in BI, and SI explains 32% (direct effect) and 10% (direct effect and interaction terms) of the variance in BE. (Venkatesh et al. 2017)
2.2 Culture in IS literature
In the previous chapter is emphasized that cultural boundaries are becoming less significant. Additionally is in this study the view adopted that the conceptualization of culture should avoid the assumption that the cultural values of a group reflects the individuals’ espoused values, individuals are influenced by plethora of cultures, and therefore is in this part of the literature review mainly focused on theories that incorporate this perspective. 2.2.1 Perspective on culture
Culture as defined by Hofstede (1983) is “the collective programming of the mind which distinguishes the members of one category of people from another”. Whereas Straub et al. (2002) argues that most definitions of culture are based on the assumption that an individuals’ membership of a group, defines the nature of values they espouse. They argue that individuals are influenced by plethora of cultures and sub-culture-some ethnic, in which some are national and some are organizational. Straub et al. (2002) introduced the “virtual” onion metaphor. The layering of the onion implies that an individual’s culture is not a permanent and immutable set of relationships; a person would be a cultural composite of sub-cultures and elements, which he or she found to be important according to changing conditions. This is consistent with the arguments of Leidner and Kayworth (2006), they argue that potential problems can be created when the assumption is made that individual users within a given cultural group will respond in a consistent fashion, this perspective would not take into account possible individual differences that may lead to different behaviors.
2.2.2 Conceptualization of culture at the level of the individual
Based on the concept of the “virtual” onion (Straub et al. 2002); Walsh et al. (2010) conceptualized an IT culture layer which resides at the level of the individual. This layer is adapted to IS theories, and is based on needs and motivation theories; such as Perceived IT needs, Intrinsic IT motivation, Extrinsic IT motivation and IT amotivation. Walsh et al. (2010) state that user IT culture evolves simultaneously with the emergence and development of an individual’s IT needs. Walsh et al. (2016) continued with the findings of Walsh et al. (2010) by incorporating the users’ individual characteristics; personality (Devaraj et al. 2008; McElroy et al. 2007), IT values (Leidner & Kayworth 2006), and their IT culture (Walsh et al. 2010). They linked IT usage to an IT disposition that reflects the frame of mind of a user’s approach to IT. According to Walsh et al. (2016) have dispositional facets related to the use of IT been neglected in previous literature. Walsh et al. (2016) introduced the concept of Expectable Use, which in this study will further be referred to as IT Adaptability. Walsh et al. (2016) defined IT Adaptability as “the user’s disposition, or inclination, to use any IT (digital devices, software, etc.) pro-actively and in a self-determined fashion.” The dispositional facet of IT usage are user specific and includes some affective, cognitive and behavioral elements, however mainly induced by the individuals’ personality, previous IT experience, the surrounding context and the social groups that the individual belong to. IT Adaptability is measured by six ITC dimensions (IT Culture Dimensions); Fearful Use, Self-Indulging Use, Opportunistic Use, Extensive Use, Self-Enhancing Use and Socializing Use. In table 1 is an overview presented of the ITC dimensions and their description.
Table 1 ITC Dimensions
ITC Dimension Description
Fearful Use This ITC dimension is in literature referred to as ‘general computer anxiety’. Users are then often insufficient trained, while the user feels the need to comply, and fit in with their work group.
Self-Indulging Use This ITC dimension reflects the need to use IT for fulfillment and for power and prestige. Often combined with sharp ambition, and could lead to abusive behavior when combined with the users’ perception of some lack of recognition. Abusive is mentioned in the sense that a user could withhold others from information to be needed.
Opportunistic Use This ITC dimension involves extrinsic motivation. When there is a high score on this dimension, then IT is used to achieve tasks more efficiently.
Extensive Use This ITC dimension is applicable when IT use is considered essential and used in most aspects of life.
Self-Enhancing Use This ITC dimension incorporates the fulfillment of self-accomplishment needs. Users who score high on this dimension enhance their own development and feel the need to discover. Socializing Use This ITC dimension relates to affiliation needs. IT is used to keep in touch with others;
communicate to personal or work groups or in social networks.
IT Adaptability (appendix 5, figure 7) is conceptualized as a second-order formative construct, with six first-order reflective ITC Dimensions (Fearful Use, Indulging Use, Opportunistic Use, Extensive Use, Self-Enhancing Use and Socializing Use). Walsh et al. (2016) conducted an exploratory factor analysis to develop scales and eliminate redundant variables for the second-order formative construct IT Adaptability. PLS SEM was used to verify the measurement model of IT Adaptability. And all ITC Dimensions significantly explained the variance of IT Adaptability (p < .001); Fearful Use (22,5%), Self-Indulging Use (20,3%), Opportunistic Use (21,3%), Extensive Use (34,8%), Self-Enhancing Use (25,8%) and Socializing Use (15,8%). For the reflective constructs (ITC Dimensions), a factor analysis was conducted to confirm the item loadings, reliability and discriminant validity. They found that the items used for the six ITC Dimensions represent the same latent variable. Only one item of the ITC dimension, Self-Enhancing Use, showed a cross-loading slightly below the required value of .707, namely .706. As for the external validity, Walsh et al. (2016) discusses that the more IT-acculturated a user in general is (higher ITC index) the more self-determined their IT usage becomes (higher IT Adaptability). To confirm whether the ITC dimensions represent the concept of IT Adaptability, the two should be highly correlated, which is the case according to Walsh et al. (2016).
2.3 Conclusion
In order to research the extent in which individual cultural values account for different outcomes in technology acceptance, are several constructs and research perspectives discussed. From the UTAUT model (Venkatesh et al. 2017) are the constructs SI (Social Influence), BI (Behavioral Intention) and BE (Behavioral Expectancy) selected, due to the theoretical distinction between the two; being the internally formulated behavioral
commitment which reflects BI, and the external factors (in addition to BI’s internal factors), which reflects BE in estimating the probability of behavioral performance. And additionally, are the cultural measurements derived from the concept of IT Adaptability, which is measured through the ITC dimensions (Walsh et al. 2016). According to Walsh et al. (2016) have dispositional facets related to the use of IT been neglected in previous literature. Walsh et al. (2016) linked IT usage to an IT disposition that reflects the frame of mind of a user’s approach to IT, regardless of the affordances and strengths of specific software. Walsh et al. (2010) incorporated the users’ individual characteristics; personality (Devaraj et al. 2008; McElroy et al. 2007), IT values (Leidner & Kayworth 2006), and their IT culture (Walsh et al. 2010) to measure the internal believe structure towards IT, these measurements are depicted as the construct IT Adaptability, providing a direct link to the internal believe structure of UTAUT construct BI. Additionally is the influence of the UTAUT moderators (age, gender, experience and voluntary of use) on technology acceptance explained through gender roles, socialization processes and the susceptibility to external factors such as; social group’s influences and the opinion of others. Considering that according to Walsh et al. (2016) the aspects of socialization processes, and the sensitivity to the opinion of others, inherent are to the internal believe structure towards IT, can be argued that the incorporated socialization processes, can be perceived at different levels in research.
3 Research model and hypotheses development
The development of the research model and the corresponding hypotheses are divided in three sections. First, IT Adaptability is conceptualized as a second-order formative construct, with six first-order reflective ITC
constructs; IT Adaptability representsthe weighted sum of the ITC Dimensions and thus the construct cannot be expressed merely through one of them. In paragraph 3.1 are therefore first the relationships theorized at the level of the second-order formative construct, which entails the relationships between the selected UTAUT constructs and IT Adaptability. Second, the six first-order reflective ITC Dimensions represent the dispositional facet of IT usage, identifying specific internal characteristics, in which a deeper understanding can be examined. In paragraph 3.2 are therefore relationships theorized at the level of the first-order reflective constructs, which entails the relationships between the selected UTAUT constructs and the ITC Dimensions. After which the research model is aggregated and introduced in paragraph 3.3.
3.1 UTAUT and IT Adaptability
In this section are the relationships theorized at the level of the second-order formative construct (IT
Adaptability), after which hypotheses are formulated. First, Walsh et al. (2016) defined IT Adaptability as “the user’s disposition, or inclination, to use any IT (digital devices, software, etc.) pro-actively and in a self-determined fashion.” They linked IT usage to an IT disposition that reflectsthe frame of mind of a user’s approach to IT. According to Walsh et al. (2016) is this internal believe structure towards IT inherent to the aspects of; socialization processes, the sensitivity to the opinion of others, and the need for affiliation, from which a positive relationship can be suggested between the UTAUT construct SI and IT Adaptability. Similarly can be discussed that the individuals’ internal believe structure towards IT positively effects the degree to which a person formulates conscious plans to use a system, which reflects the connection to BI, and additionally the internal aspects of BE. Formulating the following hypotheses:
H1. Social Influence has a positive effect on IT Adaptability H2. Social Influence has a positive effect on Behavioral Intention
H2a. IT Adaptability mediates the effect of Social Influence on Behavioral Intention H3. Social Influence has a positive effect on Behavioral Expectancy
H3a IT Adaptability mediates the effect of Social Influence onBehavioral Expectancy H4. IT Adaptability has a positive effect on Behavioral Intention
H5. IT Adaptability has a positive effect on Behavioral Expectancy
3.2 UTAUT and ITC Dimensions
In this section are the relationships theorized at the level of the first-order reflective constructs (ITC dimensions), after which the hypotheses are formulated. This section is focused on the moderating effects of the ITC
dimensions. The specific internal characteristics which are reflected by the ITC dimensions provide the opportunity to build more in depth knowledge on the individuals’ IT values. Venkatesh et al. (2017) uses the moderators of the UTAUT model (age, gender, experience and voluntary of use) to explain different technology acceptance effects. Based on their findings, they suggest that individuals could be selected for specific
managerial interventions. Whereas Walsh et al. (2016) argues that age becomes less significant in determining how comfortable an individual is with IT, they state that ‘digital natives’ and ‘digital immigrants’ have started working side by side, and that both can appear not at ease with IT usage or both can appear comfortable with IT usage. Additionally, gender roles are perceived differently in various cultures, indicating that the espoused cultural values of individuals’, based on gender, varies. Expected is that more generalizable conclusions could be derived when the moderating effect of specific internal characteristics is examined. Formulating the following hypotheses:
H6. The IT Adaptability constructs moderate the effect of Social Influence on Behavioral Intention H6a. Fearful Use moderates the effect of Social Influence on Behavioral Intention
H6b. Self-Indulging Use moderates the effect of Social Influence on Behavioral Intention H6c. Opportunistic Use moderates the effect of Social Influence on Behavioral Intention H6d. Extensive Use moderates the effect of Social Influence on Behavioral Intention H6e. Self-Enhancing Use moderates the effect of Social Influence on Behavioral Intention H6f. Socializing Use moderates the effect of Social Influence on Behavioral Intention
H7. The IT Adaptability constructs moderate the effect of Social Influence on Behavioral Expectancy H7a. Fearful Use moderates the effect of Social Influence on Behavioral Expectancy
H7b. Self-Indulging Use moderates the effect of Social Influence on Behavioral Expectancy H7c. Opportunistic Use moderates the effect of Social Influence on Behavioral Expectancy H7d. Extensive Use moderates the effect of Social Influence on Behavioral Expectancy H7e. Self-Enhancing Use moderates the effect of Social Influence on Behavioral Expectancy H7f. Socializing Use moderates the effect of Social Influence on Behavioral Expectancy
3.3 Research model
In the previous two paragraphs are the two measurement levels applied in this study outlined. In the research model are these measurements aggregated. In Figure 3 is the research model depicted.
4 Research methodology
In this chapter is the research methodology outlined. First, in paragraph 4.1 are the measurements discussed. From thereon is in paragraph 4.2 the data sample outlined, after which is continued with the data analysis and the results.
4.1 Measurement
In order to test the proposed research model a quantitative questionnaire is conducted. The questionnaire is distributed within one company to users of one particular IT system, namely SAP. The organization in which this research is conducted operates on a global scale, in which GIS projects are continuously initiated. In the
development of the questionnaire are questions from prior research adopted in order to ensure content validity. There are two parts in the questionnaire; first part contains questions to measure the constructs SI, BI, BE and System Use (Venkatesh et al. 2008; Venkatesh et al. 2017), and the second part contains the cultural measures of the individuals’ IT values, namely the ITC Dimensions (Walsh et al. 2016). The measurements of the ITC Dimensions can be found in appendix 6, and the distributed questionnaire can be found in appendix 7. The total population to which the survey was sent consists of 90 employees, of which 56 responses were received. The following table gives an overview of the construct, scale and source.
Table 2 Construct sources
Construct Scale Source
Social Influence 7-point Likert Venkatesh et al. (2008) Behavioral Intention 7-point Likert Venkatesh et al. (2008) Behavioral Expectancy 7-point Likert Venkatesh et al. (2017) ITC Dimensions 7-point Likert Walsh et al. (2016)
4.2 Data sample
Questions based on the UTAUT moderators (gender, age group and experience) were included to measure the background of the respondents. And to measure the UTAUT construct System Use; three measurements (frequency, intensity and duration) from Venkatesh et al. (2008) were used. In this paragraph are these variables used to obtain an understanding of the data sample. This overview is provided in appendix 8.1. Most respondents are aged in the category ‘Between 21 and 30’ (32,1%), followed by the categories ‘Between 41 and 50’ (25%) and ‘Between 51 and 60’ (23,2%). More than half of the sample is male (58,9%), and most respondents have more than two years of experience with SAP (76,8%). Most respondents use SAP probably daily (37,5%), and most respondents consider their intensity of use above moderate (65%). The weekly average duration of use is 13,6 hours.
4.3 Data analysis and results
This paragraph is divided in several sections. All statistical measures are computed using SPSS, the SPSS outputs can be found in appendix 8.A sampling distribution for the mean should be very close to a normal distribution with a sample size of 30 or more (Saunders et al. 2009), therefore is continued with parametric measures.
4.3.1 Internal consistency analysis
The scales for the ITC index have been validated in previous studies (Von Stetten et al. 2011; Walsh, 2009, 2014; Walsh and Gettler-Summa, 2010), and therefore is in this study continued with a measure of internal consistency, namely Cronbach’s Alpha. In appendix 8.2 are the Cronbach Alpha values depicted for the second-order formative construct (IT Adaptability), and in appendix 8.3 are the Cronbach Alpha values depicted for the first-order reflective ITC Dimensions and the UTAUT constructs. The minimum Cronbach Alpha used for this thesis is .70, as is recommended by Nunnally (1978). IT Adaptability initially measured a Cronbach Alpha of .873. All UTAUT construct show acceptable values, and most ITC Dimensions as well. Two ITC Dimensions show a lower value than recommended; Self-Indulging Use (.538) and Self-Enhancing Use (.083). Walsh et al. (2016) highlighted from the Self-Enhancing Use construct the REV_SELFENHUSE2 item as well; this item will therefore be excluded in further measurements. After excluding the REV_SELFENHUSE2 item, the Cronbach
Alpha value depicts .664. Additionally, after the item was excluded from the IT Adaptability construct, the Cronbach Alpha was measured with a value of .887.
Table 3 Cronbach Alpha
Construct Cronbach Alpha
IT Adaptability .887 (*.873) Fearful Use .849 Self-Indulging Use .538 Opportunistic Use .881 Extensive Use .869 Self-Enhancing Use .662 (*.083) Socializing Use .942 Social Influence .700 Behavioral Intention .944 Behavioral Expectancy .878
4.3.2 Regression analysis IT Adaptability
A regression analysis was conducted to investigate total effects (H1, H2, H3, H4, and H5) and mediated, direct and indirect effects (H2a, H3a). To measure the proposed paths is a computational tool of Hayes, called
PROCESS, used. (Hayes 2013, 2015, 2018) From this tool is Model 4 used, which entails the regression analysis for mediation. (Hayes 2013, 2015) In table 4 are the results presented which relate to the level of the second-order formative construct, IT Adaptability. In the first following section are the results from the theorized paths towards Behavioral Intention depicted, and in the second section are the results discussed from the theorized paths towards Behavioral Expectancy.
Table 4 Regression analysis IT Adaptability
Hypothesis Total effect Direct
effect
Indirect effect
Results H1. Social Influence has a positive effect
on IT Adaptability
.211 * R2 = .0946
Supported H2. Social Influence has a positive effect
on Behavioral Intention .686 * R2 = .3669 .595 * R2 = .4284 .091 ** Supported H2a. IT Adaptability mediates the effect
of Social Influence on Behavioral Intention .686 * R2 = .3669 .595 * R2 = .4284 .091 ** Supported
H3. Social Influence has a positive effect on Behavioral Expectancy .125 R2 = .0323 .146 R2 = .0409 -.021 Not Supported H3a. IT Adaptability mediates the effect
of Social Influence on Behavioral Expectancy .125 R2 = .0323 .146 R2 = .0409 -.021 Not Supported
H4. IT Adaptability has a positive effect on Behavioral Intention
.6964 * R2 = .1783
Supported H5. IT Adaptability has a positive effect
on Behavioral Expectancy -.0334 R2 = .0011 Not Supported * Significance of p < .05 ** Significance of p < .001
Results indicate that Social Influence has a significant, total effect on IT Adaptability (b = .211, t(54) = 2.375, p = .021) which explained 9,46% of the variance in IT Adaptability. Additionally Social Influence also had a significant, total effect on Behavioral Intention (b = .686, t(54) = 5.594, p < .0001), in which 36,69% of the variance in Behavioral Intention was explained. When IT Adaptability (b = .595, t(53) = 4.815, p <.0001) was included in the equation as a mediator, the variance explained in Behavioral Intention increased to 42,84% (direct effect). In the original UTAUT model explained Social Influence 8% (direct effect) and 5% (direct effect and interaction terms) of the variance in Behavioral Intention. In this study should be considered that only IT Adaptability is included in the equation, and in the original UTAUT model were all the original constructs and moderators taken into account. From this study can be derived that Behavioral Intention is positively influenced
by Social Influence and that an individuals’ cultural IT values can be influenced through Social Influence, which in turn has a moderate positive effect on Behavioral Intention. Furthermore IT Adaptability has a significant, total effect on Behavioral Intention (b = .696, t(54) = 3.422, p = .0012), but not on Behavioral Expectancy. The computed outcomes of SPSS can be found in appendix 8.4.
From the analysis regarding the paths to Behavioral Expectancy can be derived that both Social Influence and IT Adaptability had no significant effect, subsequently can be argued that IT Adaptability had no mediating effect. Previous studies suggest that social influence affects behavior through compliance, internalization, and
identification (Kelman, 1958, 1961; Venkatesh & Davis, 2000; Venkatesh et al. 2017), suggested is that through Social Influence, Behavioral Expectancy is influenced through compliance (external aspect), and Behavioral Intention is influenced through internalization and identification (internal aspects). Considering that IT Adaptability measures the individuals’ internal believe structure towards IT can be suggested that IT Adaptability in general relates more to internalization and identification than compliance.
4.3.3 Regression analysis ITC Dimensions
To examine the moderating effect, a regression analysis was conducted by use of the PROCESS tool in SPSS. For this analysis is Model 1 used from Hayes (2013, 2015), which entails the regression analysis for moderation. In this section are the calculations computed for each ITC dimension separately to measure the conditional effects (simple slopes), which entails that the moderating effects are measured with regard to a low, average or high indication of the measured ITC Dimension. This analysis was performed using a bootstrapping of 1000 subsamples to identify the significance levels. The outcomes are considered significant if the overall model, the predictor and the interaction between the predictor and the moderator are significant (Hayes 2013, 2015). After this interpretation, are the conditional effects analyzed. In table 5 are the results depicted of the moderating effects of the ITC Dimensions between Social Influence and Behavioral Intention, followed by a section in which these results are discussed. In table 6 are the results depicted of the moderating effects of the ITC Dimensions between Social Influence and Behavioral Expectancy, again, followed by a section in which these results are discussed.
Table 5 ITC Dimension moderation analysis for Behavioral Intention
Hypothesis Significance
model
Conditional effect of moderator Results Low Average High
H6a. Fearful Use moderates the effect of Social Influence on Behavioral Intention
Not significant
- - - Not
supported H6b. Self-Indulging Use moderates the
effect of Social Influence on Behavioral Intention
Not significant
- - - Not
supported H6c. Opportunistic Use moderates the
effect of Social Influence on Behavioral Intention
Significant .8905 ** .6190 ** .3476 Supported
H6d. Extensive Use moderates the effect of Social Influence on Behavioral Intention
Significant .7249 ** .3950 ** .0651 Supported
H6e. Self-Enhancing Use moderates the effect of Social Influence on Behavioral Intention
Significant .8951 ** .5254 ** .1557 Supported
H6f. Socializing Use moderates the effect of Social Influence on Behavioral Intention
Significant .9242 ** .5828 ** .2413 Supported
* Significance of p < .05 ** Significance of p < .001
From the analysis at the level of the first-order reflective ITC Dimensions can be concluded that Opportunistic Use, Extensive Use, Self-Enhancing Use and Socializing Use moderate the effect of Social Influence on Behavioral Intention. The results for Self-Enhancing Use should be interpreted with caution; the calculated Cronbach Alpha value (.664) indicates that the internal consistency is not exclusively reliable. The results are presented with low, average and high conditional effects, meaning that the moderating effects are measured with regard to a low, average or high score on the ITC Dimension. From the results of all relevant ITC Dimensions
can be derived that when a low conditional effect is measured, the ITC Dimensions significantly increases the effect of Social Influence on Behavioral Intention. When an average conditional effect is measured, results still show a positive moderating effect. The moderating effect decreases with an increasing measured conditional effect, in such a way that when a high conditional effect is measured, the moderation becomes negligible and insignificant. Results show that Fearful Use and Self-Indulging Use do not moderate the effect between Social Influence and Behavioral Intention. This could be explained by the conceptualization of the two ITC
Dimensions. Walsh et al. (2016) describes that Fearful Use is often induced by the users’ need to comply, and as discussed in the previous paragraph; suggested is that Social Influence affects Behavioral Expectancy through compliance (external aspect), and Behavioral Intention through internalization and identification (internal aspects). The outlined external aspects on which Fearful Use is based could be the cause to why the results show that Fearful Use does not have a moderating effect. The results derived from SPSS can be found in appendix 8.5. Table 6 ITC Dimension moderation analysis for Behavioral Expectancy
Hypothesis Significance
model
Conditional effect of moderator Results Low Average High
H7a. Fearful Use moderates the effect of Social Influence on Behavioral
Expectancy
Not significant
- - - Not
supported H7b. Self-Indulging Use moderates the
effect of Social Influence on Behavioral Expectancy
Not significant
- - - Not
supported H7c. Opportunistic Use moderates the
effect of Social Influence on Behavioral Expectancy
Not significant
- - - Not
supported H7d. Extensive Use moderates the effect
of Social Influence on Behavioral Expectancy
Not significant
- - - Not
supported H7e. Self-Enhancing Use moderates the
effect of Social Influence on Behavioral Expectancy
Not significant
- - - Not
supported H7f. Socializing Use moderates the effect
of Social Influence on Behavioral Expectancy
Not significant
- - - Not
supported
From the results can be derived that none of the ITC Dimensions moderates the effect of Social Influence on Behavioral Expectancy. The results for Fearful Use in this analysis contradicts the previous reasoning
to
why Fearful Use did not moderate the effect on Behavioral Intention; the external aspects of Fearful Use should then have moderated the effect on Behavioral Expectancy. Venkatesh et al. (2017) explains that Social Influence was not operationalized with an explicit distinction between the internal orientation and external orientation. They presented theoretical arguments about the internal and external facets of these constructs; however, further research should be conducted to examine how specific normative and control beliefs tie into the internal and external facets.The results derived from SPSS can be found in appendix 8.6.5 Conclusion and Discussion
In this study is a model developed in order to answer the research question “To what extent do individual cultural values account for different outcomes in technology acceptance?” By use of the construct IT
Adaptability (Walsh et al. 2016), were IT values identified, which relied on individual cultural characteristics. This dispositional facet of IT usage is user specific and includes some affective, cognitive and behavioral elements, however mainly induced by the individuals’ personality, previous IT experience, the surrounding context and the social groups that the individual belong to. Furthermore, the technology acceptance constructs, which were in scope of this study, were derived from UTAUT (Venkatesh et al. 2017). This chapter discusses the conclusion, discussion, practical implications, limitations and possibilities for future research.
5.1 Conclusion
Overall underutilization of IS’s remains a major problem (Venkatesh et al. 2017), furthermore, are recurrently low success rates discussed of IT projects. Two aspects in previous cross-cultural studies were identified to be highly susceptible for confusion and subsequently for possible invalid conclusions. First, the inconsistent outcomes in technology acceptance in previous cross-cultural studies were mainly based on the assumption that the cultural characteristics of a groups, reflects the espoused cultural values of the individual. Secondly, the conceptualization of culture relied highly on clear delineated boundaries, either national or organizational boundaries, and by this ignoring the fact that ethnic and cultural groups can exist across and within nations. In this study is the view adopted that those assumptions should be avoided, and that due to the globalization of business, boundaries are increasingly becoming less significant. A conceptualization of culture at the level of the individual was therefore incorporated.
From the analysis at the level of thesecond-order formative construct, IT Adaptability, can be concluded that an individuals’ espoused IT culture can be influenced by the degree to which an individual perceives that important others believe he or she should use a new system.Furthermore can be concluded that an individuals’ IT culture contributes to behavioral intent, further explained as the degree to which a person has formulated conscious plans to perform or not perform some specified future behavior. The external mechanisms of behavior
expectation were not identified in this study, for which can be argued that further research is required. Venkatesh et al. (2017) outlined the need to conduct further research regarding the internal and external aspects of Social Influence, and how specific these aspects relate to normative and control beliefs. The major discrepancy found in this study is that Social Influence was not a predictor of Behavioral Expectancy, while it was according the findings of Venkatesh et al. (2017). This conclusion emphasizes the need to further examine the theoretical background of Behavioral Expectancy, and possibly refine the measures which are used for this construct. From the analysis at the level of the first-order reflective ITC Dimensions can be suggested that Opportunistic Use, Extensive Use, Self-Enhancing Use and Socializing Use moderate the effect of Social Influence on Behavioral Intention. However, the questionable internal consistency of Self-Enhancing Use could cause an invalid conclusion regarding this construct. For all relevant ITC Dimensions can be argued that when an individual has a low score on an ITC Dimension, he or she become more susceptible for social influences that affects the internal believe structure towards IT. This susceptibility becomes less with an increased score on an ITC Dimension, indicating that this susceptibility becomes less when an individual developed a more positive frame of mind in his or hers approach to IT. This study contributes to academic literature by identifying three specific individual cultural IT characteristics which affects the behavior intention, namely; Opportunistic Use, Extensive Use and Socializing Use. Consequently are also the cultural IT values identified which did not affect behavioral intention, namely; Fearful Use.
5.2 Managerial implications
The results of this study provides several implications for IT management. To know how behavior intention is influenced when it comes to managerial interventions is of importance to project management, and others involved during the implementation of an IT. First, a low indication of Opportunistic Use strongly moderates the effect of Social Influence on Behavioral Intention. This IT value involves extrinsic motivation, to increase this dimension; the individuals’ internal belief structure towards IT needs to recognize that use of the system helps him or her to achieve tasks more efficiently. Secondly, a low indication of Extensive Use strongly moderates the effect of Social Influence on Behavioral Intention. This ITC dimension is applicable when IT use is considered essential and used in most aspects of life. Individuals that have not espoused this belief are more susceptible for social influences. And third, a low indication of Socializing Use strongly moderates the effect of Social Influence on Behavioral Intention. This ITC dimension relates to affiliation needs. IT is used to keep in touch with others; communicate to personal or work groups or in social networks. Individuals could be recognized in to what extent they contribute or communicate via these channels; also this characteristic shows that the individual is more susceptible for social influences. Additionally, Fearful Use did not moderate the effect of Social Influence on Behavioral Intention. This ITC dimension is in literature referred to as ‘general computer anxiety’. Users are then often insufficient trained, while the user feels the need to comply, and fit in with their work group. Whether managerial interventions are based on addressing how efficiently a system works, or whether training is required could affect the outcome in behavioral intention. Subsequently can be argued that when the managerial intervention consist of making the individual more aware of how efficient a system works, while the individual generally experiences computer anxiety, could induce an opposite effect; due to that the user feels more pressure to comply, the intervention could have increased the anxiety towards system use. This reasoning is in line with the perception on the role of the manager according to Jackson and Philip (2010), they emphasized the dynamic nature of the interaction between people and technology, and argue that ongoing attention is required due to unanticipated cultural and technological issues.
5.4 Limitations
Several limitations can be considered for this study. The external aspects of Behavioral Expectancy could have been identified though other UTAUT constructs, which were not measured. The incorporation of all UTAUT constructs would have caused an extensive questionnaire, with the risk of receiving fewer responses than required. Second, the questionnaire was distributed within one company, and 76,8% of the respondents have more than two years of experience with the system. As throughout literature is stated that system use over time, attenuates the effect of Social Influence, due to that increasing experience provides a more instrumental basis for IT use. (Venkatesh et al. 2017)
5.5 Future research
For future research can several directions be discussed. First, the operationalization of Social Influence could be refined by identifying external aspects which are inherent to Behavioral Expectancy. This should be considered essential to further build knowledge for managerial interventions regarding technology acceptance. Secondly, experience over time could not be studied within the timeframe of this study. Since an individuals’ IT culture is influenced by plethora of cultures (Straub et al. 2002), and considering the dynamic nature of the interaction between people and technology (Jackson and Philip 2010), a valuable contribution would be to examine if there are certain tendencies in an individuals’ IT culture over time. Additionally, to examine which managerial intervention affects the identified ITC dimension over time. Third, according to Walsh et al. (2016) is the individuals’ IT culture influenced by external referents, but during the development of a system, would it be interesting to know to what extent system characteristics affects the ITC Dimensions. Additionally, to examine which characteristics should be avoided to create a system which is compliant with as much cultures (ITC Dimensions) as possible. And finally, this study was conducted in a mandatory setting; to build a wider understanding empirical evidence on this topic needs to be derived from research in a voluntary setting.
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