TAM2 vs. UTAUT and other models

Finally, in accordance to the previous section’s summary of that integrating approach (e.g., TRA, TAM, and TPB within the TAM2) and the more detailed view of the unexplored issues in technology acceptance research, this section presents a comparison between two widely accepted integrated models, the TAM2 and UTAUT, developed by Venkatesh &

Davis (2000) and Venkatesh et al., (2003) respectively. The two models share points of similarity in that both are based on the integrating approach and the paths are examined based on the cross-over effect (e.g., Venkatesh & Bala, 2008). Both models address acceptance as well as usage by excluding the concept of A and assume that perceived technological characteristics will directly influence the individuals’ BI to accept the technology under consideration. Social norms (i.e., subjective norms in the TAM2 and social influence in UTAUT) and voluntariness of use were re-entered in the models that were previously omitted or were considered to be a limitation of the TAM. Finally, the moderating impact of usage experience over social norms was also highlighted in both models.

Contrary to the similarities, the two models differ in that the TAM2 applies the integrating approach based on a stream of research which intends to examine the key determinants of user acceptance due to the PU (e.g., Davis et al., 1989), rather than incorporating them into

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the nomological framework; whereas UTAUT examines the additional constructs directly as part of the framework. Second, unlike UTAUT, the TAM2 does not incorporate the effect of the demographic variables (i.e., age and gender) that remain powerful moderators in information system acceptance research (e.g., Venkatesh & Morris, 2000). And finally, the TAM2 includes uni-dimensional constructs (i.e., singular in nature and cannot be broken into further dimensions), whereas UTAUT incorporates multidimensional constructs (i.e., constructs are developed by summing up more than one uni-dimensional construct).

From the perspective of effectiveness, both models remain strong in explanatory power in comparison with the earlier version, except providing a less parsimonious structure.

However, parsimony is not the only factor for the acceptance of a model. According to Taylor & Todd (1995b), models need to be evaluated in terms of their explanatory power as well as parsimony. Whereas Venkatesh et al. (2003), supporting Taylor & Todd, argued that parsimony is desirable to the extent that it facilitates understanding. By looking at the overall criticism of both models, the TAM2 and UTAUT, in sections (2.8 & 2.10) respectively, it is argued that the TAM2 is better than UTAUT. Despite the fact that UTAUT explained a higher variance in explaining BI (see Venkatesh et al.’s (2003) comparison of eight models where UTAUT was better than the rest) but it was only accounted due to moderating factors (Raaj & Schepers, 2006). Also, UTAUT’s approach to integrate multidimensional constructs from the pool of uni-dimensional constructs is not a valid approach to examining the effect of all the 41 independent variables for predicting intention (Bagozzi, 2007). Therefore, apart from its approach to integrating the constructs of interest to predict intended behaviour, the TAM2 is considered to be better than UTAUT. The comparative results from the explanatory power perspective are not presented in this section because, as far as the researcher is aware, there is no study that compares the results of these models simultaneously in one single study, except for Venkatesh et al., (2003). He found that TAM2 produced 38% variance in voluntary settings and 39% in mandatory settings, whereas UTAUT explained 52% to 77% variance in predicting behaviour intention. The findings of just Venkatesh et al., (2003) cannot be taken as evidence of generalisation to explain the higher explanatory power due to the chance of the researchers’ bias to support their own results compared with others.

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Author Purpose of Study Context/Sample/

methodology Moderators Variable Model

Variance/

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Huh et al., (2009) Comparison between TAM, TPB and DTPB

77

78

79

80

81

Igbaria et al. (1997) Revised TAM model

Use of computing

82

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Davis et al., (1989)

Development of TAM and comparison with TRA

Word processing

Student(107) Longitudinal Study

NA PEOU, PU, A, BI, SN

TRA BI=0.26

A=0.30 A
BI SN
BI

TAM BI=0.51 A=0.36 PU=0.05

PU
BI, PU
A PEOU
A,

PEOU
PU A
BI

Table 2.2: Comparison of various technology acceptance models based on constructs significance and explanatory power (R2)

PEOU=perceived ease of use, A=attitude=, BI= behavioural intention= B(U)= Behaviour (usage), PBC= perceived behavioural control, SN= subjective norms, EXP= experience, VOL= voluntariness, IMG= image, JR= job relevance, RD= result demonstrability, OQ= output quality, G= gender, AG= age, PE= performance expectancy, EE= effort expectancy, SI= social influence, FC= facilitation conditions, SE= self efficacy, CA= computer anxiety, COMP=

compatibility, PI= peer influence, SI= superior influence, RF= resource facilitation, TF= technology facilitation, TS= technical support, PII= personal innovativeness in IT, RA= relative advantages, CPL= computer playfulness, PEN=

perceived enjoyment, OBU= objective usability, PEC= perception of external control, COLX= complexity, NI= normative influence, INI= interpersonal influence, EIN= external influence, MAS= masculinity, FAM= femininity, AND=

androgynous, M=Men, W=Women, EDU=educational level, ORG=, organisational support, UT= user training, ECUS= end user computing support, SQ= system quality, PRU= perceived usage, VRU= variety of use, MS= management support, ICS= internal computing support, ICT= internal computing training, SU= system usage, ECS= external computing support, ECT= external computing training, PWN= PC owner ship, TSK= task characteristics, IN=

interpersonal norm, SCN= social norms, WSE= web self-efficacy, ISE= internet self-efficacy, TST= Technical support and training, SF= Social Factors, ITMS= Institutional factors top-management support, ILMS= institutional factors local-management support, OBS= observability, TRI= trialability, RSK= Risk, AI= Adoption intention, CO= Cost, II= interpersonal influence, EI= External influence, PP= Perceived playfulness, PC= Perceived controllability , ES= e-service satisfaction.

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